AN ANALYSIS OF MIGRATION FROM
THE CAUCASUS TO RUSSIA
BY
CHRISTOPHER DALE HABROCK, B.S., M.A.
A thesis submitted to the Graduate School
in partial fulfillment of the requirements
for the degree of
Master of Applied Geography
Major Subject: Geography
Minor Subject: Intelligence and Security Studies
New Mexico State University
Las Cruces, New Mexico
December 2013
“An analysis of migration from the Caucasus to Russia,” a thesis prepared by
Christopher Dale Habrock in partial fulfillment of the requirements for the degree,
Master of Applied Geography, has been approved and accepted by the following:
Loui Reyes
Dean of the Graduate School
_____________________________________________________________________
Michael N. DeMers
Chair of the Examining Committee
_____________________________________________________________________
Date
Committee in Charge:
Dr. Michael N. DeMers
Dr. John B. Wright
Dr. Yosef Lapid
ii
ACKNOWLEDGMENTS
I would like to thank the NMSU Geography Department for their support and
development throughout my graduate program. Much appreciation is also afforded to
my committee. Specifically, Dr. DeMers, thank you on many levels. You have
provided much insight into the GIS profession, waited patiently as I thought through
my ideas, but required timely progress during the GIS Capstone course, which was
very helpful. I am grateful for the many discussions at the personal level that made
school that much more enjoyable (KU basketball, NFL, and others). Additionally,
your willingness to help me with procedures and paperwork while I was physically
away from campus made graduating possible, for without it I would have had a much
more difficult and stressful time. Dr. Wright, your research design course and
separate meetings with you helped me to focus on a topic that was both feasible and
of personal interest. Research is much more enjoyable when the subject is close to
one’s own area of interest and I am fortunate that you helped me decide upon a topic
that aligns with area of concentration and passion. Dr. Lapid, your courses throughout
the minor program added enrichment and breadth to my studies. I thoroughly enjoyed
your lectures while also satisfying my appetite for current events and international
issues. Dr. Brown, thank you for introducing me to spatial statistics, I am now more
interested than ever in learning as much as possible about them (I am not sure this is
something to be thankful for though…joking). Though I have never met them, many
thanks are in place to Drs. Mikhail Blinnikov and Jeffrey S. Torguson at St. Cloud
State University and Dr. Robert J. Keiser at University of Wisconsin-Madison. I had a
iii
difficult time obtaining a shapefile of Russia; each one that I found seemed to “split
into two” whenever I resized them. They solved my problem by allowing me to use
theirs; without it, I would not have been able to complete this work. Lastly, thanks is
perhaps most appropriately afforded to Xuan Nguyen for the day-to-day support
given, laughter to keep the days light-hearted during periods of stress, and for always
being up for watching movies and anything else during times that I wanted a break
from school.
iv
VITA
June 18, 1982
Born in Overland Park, Kansas
2000
Graduated from Gardner-Edgerton High School,
Gardner, Kansas
2006
Graduated from Emporia State University with a
Bachelor of Science in Social Sciences
2008-2009
Teaching Assistant, Department of Social Sciences,
Emporia State University
2010
Graduated from Emporia State University with a
Master of Arts in History (world)
2010-Present
Adjunct Instructor, Geography, Allen County
Community College
2011-2012
Teaching Assistant, Department of Geography, New
Mexico State University
2012
Research Assistant, Educational Management and
Development, New Mexico State University
Professional and Honorary Societies
Phi Alpha Theta, National History Honor Society
Phi Kappa Phi, Honor Society
Papers and Presentations
Paper: Habrock, Christopher. “Caucasus Migration Patterns into Russia.”
Presentation, New Mexico State University 2013 Graduate Research and Arts
Symposium, Las Cruces, NM, March 13, 2013.
Paper: Habrock, Christopher. “Pragmatics Supporting Banal Nationalism in Russia:
An Analysis of Postage Stamps.” Presentation, New Mexico State University
2012 Graduate Research and Arts Symposium, Las Cruces, NM, March 13,
2012.
v
Thesis: Habrock, Christopher. A Mass Exodus: The Refugee Experience from
Vietnam to Wichita, Kansas. Thesis published by ProQuest. September, 2011.
Poster: Habrock, Christopher. “Saint Petersburg State University: Center of Russian
Language and Culture.” Poster presented at the Annual Study Abroad Poster
Session, Emporia, KS, November, 2009.
Poster: Nguyen, Xuan, and Christopher Habrock. “Islam and the Middle East.” Poster
presented at the 25th Annual Research and Creative Activity Forum, Emporia,
KS, April, 2009.
Field of Study
Major Field: Geography
Minor Field: Intelligence and Security Studies
vi
ABSTRACT
AN ANALYSIS OF MIGRATION FROM
THE CAUCASUS TO RUSSIA
BY
CHRISTOPHER DALE HABROCK, B.S., M.A.
Master of Applied Geography
New Mexico State University
Las Cruces, New Mexico, 2013
Dr. Michael N. DeMers, Chair
Russia’s population is shrinking. It recently joined the World Trade
Organization and likely will continue its economic growth. However, with a declining
population and one that is aging considerably, a labor void is a real concern and must
be addressed. Russia has the second largest immigrant stock in the world, second only
to that of the United States. In 2009, more than 93 percent of total immigration to
Russia came from former Soviet countries. Immigration from the former Soviet
countries contributes many economic migrants who seek better wages than possibly
earned in their respective origin countries. A problem posed for many immigrants is
vii
the rise of Russian nationalism and the perpetual fear—real or imagined—that many
live in within Russia. The challenge becomes one of avoiding feeding populist frenzy
through liberalized immigration policies versus filling the domestic labor shortage
with immigrant workers. Russia’s desire to regain superpower status hinges upon
improving its economic development; yet, its population is declining, forcing the
government to decide how best to manage migrant workers without restricting the
country’s economic growth.
I examined three neighboring countries from the Caucasus (Armenia,
Azerbaijan, and Georgia) for general migration patterns within Russia at the federal
subject level. Armenian net-migration was concentrated primarily in the southwestern
portion of Russia while Moscow and Kemerovo Oblasts were outliers to this pattern.
Azerbaijani net-migration was the most evenly dispersed, the highest concentration
was in Tyumen Oblast. Georgian net-migration demonstrated a dispersed pattern but
its overall net-migration total was considerably lower than that of Armenia and
Azerbaijan. More telling is that more Georgians emigrated to Krasnodar Kray than to
any other federal subject. North Ossetia was thought to receive the largest number of
net-migrants due to Georgia’s contentious history with Russia along their shared
border that witnessed a short war in August 2008.
I performed linear regression to test the strength of regional gross domestic
product purchasing power parity (PPP) as a predictor of net-migration. Results
indicated that it was a weak predictor for Armenia (r2 = 0.027), though stronger for
Azerbaijan (r 2 = 0.276). I was unable to perform regression for Georgia due to a
viii
failure to meet all preliminary assumptions for parametric testing. Furthermore,
geographically weighted regression (GWR) was useful for developing a stronger
regression model for Azerbaijan (r2 = 0.555).
ix
TABLE OF CONTENTS
LIST OF TABLES ................................................................................................... xi
LIST OF FIGURES .................................................................................................xii
DATA ON COMPACT DISC ................................................................................xiii
LIST OF ABBREVIATIONS ................................................................................. xiv
INTRODUCTION ..................................................................................................... 1
LITERATURE REVIEW .......................................................................................... 7
METHODS ............................................................................................................. 24
Study Area ........................................................................................................... 24
Data ..................................................................................................................... 25
Qualitative Analysis ............................................................................................. 25
Quantitative Analysis ........................................................................................... 26
RESULTS ............................................................................................................... 30
Qualitative ........................................................................................................... 30
Quantitative ......................................................................................................... 37
CONCLUSIONS ..................................................................................................... 49
DISCUSSION ......................................................................................................... 53
REFERENCES ........................................................................................................ 58
APPENDIX A. Map (Total net-migration rate, 2009) .............................................. 63
APPENDIX B. Map (Armenian net-migration rate, 2009) ....................................... 64
APPENDIX C. Map (Azerbaijani net-migration rate, 2009) ..................................... 65
APPENDIX D. Map (Georgian net-migration rate, 2009) ........................................ 66
APPENDIX E. Federal Subject Data ....................................................................... 67
x
LIST OF TABLES
Table 1. Comparison of U.S. and Russia Populations, 2005 and 2010 (World Bank
2011) ................................................................................................................ 30
Table 2. Shapiro-Wilk Test (Armenia) ..................................................................... 39
Table 3. Levene’s Test (Armenia) ............................................................................ 39
Table 4. Correlation (Armenia and PPP) .................................................................. 41
Table 5. Regression (Armenia and PPP) .................................................................. 41
Table 6. Shapiro-Wilk Test (Azerbaijan) ................................................................. 42
Table 7. Levene’s Test (Azerbaijan) ........................................................................ 43
Table 8. Correlation (Azerbaijan and PPP)............................................................... 44
Table 9. Regression (Azerbaijan and PPP) ............................................................... 44
Table 10. Shapiro-Wilk Test (Georgia) .................................................................... 45
Table 11. Correlation (Georgia and PPP) ................................................................. 47
xi
LIST OF FIGURES
Figure 1. Former USSR (Rubenstein 2002) ................................................................ 9
Figure 2. Caucasus Region (Caucasus Regions Map) ............................................... 17
Figure 3. Russia and Caucasus Countries (Armenia, Azerbaijan, and Georgia) ........ 24
Figure 4. Total Armenia, Azerbaijan, and Georgia Net-Migration Percentage of
Population, 2009 .............................................................................................. 31
Figure 5. Armenian Net-Migration 2009 .................................................................. 34
Figure 6. Azerbaijan Net-Migration 2009 ................................................................ 35
Figure 7. 2009 Georgian Net-Migration ................................................................... 37
Figure 8. Scatterplot (Armenia and PPP).................................................................. 40
Figure 9. Scatterplot (Armenia and PPP).................................................................. 43
Figure 10. Scatterplot (Georgia and PPP) ................................................................. 46
xii
DATA ON COMPACT DISC
Maps
1. Total Net-Migration Rate of Armenia, Azerbaijan, and Georgia
(filename: ARAZGR_mapdata_finalthesis.mxd)
2. Armenia Net-Migration Rate
(filename: ARMENIA_mapdata_finalthesis.mxd)
3. Azerbaijan Net-Migration Rate
(filename: AZERBAIJAN_mapdata_finalthesis.mxd)
4. Georgia Net-Migration Rate
(filename: GEORGIA_mapdata_finalthesis.mxd)
Shapefile and Data
1. Russia shapefile with attribute data
(filename: Russiadata.shp)
xiii
LIST OF ABBREVIATIONS
CIS = Commonwealth of Independent States
G-20 = Group of Twenty
GDP = Gross Domestic Product
GNI = Gross National Income
GWR = Geographically Weighted Regression
OLS = Ordinary Least Squares
PPP = Purchasing Power Parity
U.S. = United States
USSR = Union of Soviet Socialist Republics
WTO = World Trade Organization
xiv
INTRODUCTION
Russian nationalism is a growing issue whereby ethnic tension and hostilities
are directed toward non-ethnic Russians, specifically non-white immigrants who are
primarily migrant workers. Russian nationalist organizations often focus on racial,
ethnic, and religious differences as the cause(s) to many of their social and/or
economic issues ranging from unemployment, low pay, alcoholism, crime,
corruption, and other issues of concern. Of particular interest are that over 93 percent
of net-migration into Russia during 2009 came from states that broke away from the
former Soviet Union after its dissolution in 1991, and net-migration of citizens from
the Commonwealth of Independent States (CIS) states witnessed a six-fold increase
from 2006 to 2007. In 2009, net-migration of citizens from the CIS countries into
Russia was fifteen times greater than those from all other non-CIS countries (50,532
versus 3,299) (Federal State Statistics Service (Rosstat) 2010).
Prior to the dissolution of the Union of Soviet Socialist Republics (USSR),
international migration rates had stabilized. Once the USSR broke off into what
would become the CIS, migration increased due to reclassification of former internal
USSR citizenship to one of the newly created countries. As political and economic
realities in the newly created countries set in, economic migrants began to see Russia
as an attractive and familiar destination. Undoubtedly, Russia is significant because it
receives more international migrants than any other country besides the United States
(United Nations 2008). However, there is a distinct set of features that characterizes
Russian immigration from that of other leading receiving countries such as the United
1
States and Germany. Immigration in the United States and Germany is more
diversified in terms of migrant nationalities whereas in Russia, as aforementioned, 93
percent of migrants came from former Soviet states. This tells quite a bit about the
stock of migrants and fits the historical and regional narrative of the former Soviet
States in relation to immigration in Russia: historical ties (nearly seven decades of
communism), geographic proximity (largely neighboring states) and visa-free border
crossing, similar transportation infrastructures (public transportation and very little
automobile ownership), and psychological ease regarding language (Russian) and
shared territory (Ivakhnyuk 2006). This trend is unlikely to change in the near future.
Russia was integrated into the World Trade Organization (WTO) in August
2012 and its economy is among the largest of the emerging economies in the world. It
is a member of the Group of Twenty (G-20), which is comprised of the major
industrial economies representing approximately 90 percent of global GDP (Gross
Domestic Product) and 80 percent of international trade. Current limitations to
Russian economic growth hinge upon diversifying economic output while
simultaneously struggling to tackle problems with its labor supply and demographics.
Russia’s economy is largely commodity driven (petroleum, natural gas, and extractive
metals), thus exposing it to cyclical swings of boom and bust. Its GDP ranks seventh
in the world; 37.6 percent of its composition comes from industrial activity while
comparatively, U.S. industrial composition is 19.1 percent (Central Intelligence
Agency 2013). For the first time in history Russia has assumed the presidency of the
G-20 and will host the annual summit in September 2013 (G20 2013). Clearly, its
2
presence signifies its status as a relevant part to the world economy in addition to its
weight and influence in geopolitical and international affairs.
Demographically, Russia has a negative population growth rate in addition to
an aging population. In 1990, its total population mean was 34.9 years old, 37.1 in
2000, and 38.8 in 2009 (Federal State Statistics Service (Rosstat) 2010). By 2040,
Russia’s median age is estimated at 44.9. “Recent attention has focused on Russia’s
re-emergence as a global political force, fuelled by resource-driven economic growth.
But simultaneous population decline and ageing will become a serious drag on
economic growth, with the State budget burdened by increased costs for pensions and
health care for the elderly” (Heleniak 2013). Thus, it is plausible that Russia’s
demographic crisis poses a challenge to its economic sustainability and growth unless
a viable solution to its aging labor pool is addressed. Until its domestic natural
population growth turns positive for the long-term, Russia’s workforce must be
complemented and filled from elsewhere. Without immigration, Russia’s economic
growth will likely be constricted by the capacity of its declining working-age
population and labor pool. Migrant workers are vital to its growth and economic
security, and by extension, to the U.S. as it seeks to strengthen ties and engage in
greater international trade. Thus, an analysis of net-migration in Russia is necessary
to uncover the scale, scope, and distribution of immigration.
The primary objective to this research is to gain a greater understanding of
immigration in Russia. In 2009, more than 25 percent (63,339 of 247,499) of all netmigration to Russia came from the Caucasus countries of Armenia, Azerbaijan, and
3
Georgia (Federal State Statistics Service (Rosstat) 2010). This region was selected
because it is relatively small and is physically bordered by a sea on either side,
numbers just three countries yet makes up over a quarter of net-migration, and shares
Russia’s southern-most border (other major migrant groups come from Central Asia).
Mapping net-migration of citizens into the eighty-three federal subjects
(administrative regions) of Russia will provide a better depiction of the distribution of
who is moving where and at what quantities. The last component of this research
involves testing whether a statistically significant relationship between the economic
indicator of regional gross domestic product purchasing power parity (hereafter
referred to as PPP) exists with net-migration of each of the three Caucasian (from the
Caucasus, not the Caucasian race) countries at the Russian federal subject level.
The anticipated results of this research seek to support four hypotheses.
Hypothesis 1: net-migration is likely to be higher in places of high construction and
restoration activity such as Moscow and St. Petersburg (a function of economic
development). Hypothesis 2: net-migration is likely to be high in Yekaterinburg and
Novosibirsk. Hypothesis 3: net-migration is likely to be high in southwestern Russia
near the Caucasus. This area has been contested by the Chechen independence
movement and various wars fought in the region over the past two decades.
Hypothesis 4: PPP is expected to positively influence net-migration but the strength
may vary for each origin country. In other words, it is hypothesized that the economic
variable in question can be used to account for more net-migration variance in one
country of origin than another, thus supporting the premise that net-migration from
4
the Armenia, Azerbaijan, and Georgia is not “one and the same” but rather each is
unique despite their geographic juxtaposition of sharing borders with each other along
with a similar historical narrative as members of the former Soviet Union (i.e. sharing
both physical and cultural attributes). Of the three countries examined, Armenian
migration might be the most evenly dispersed due to its unusually high amount of
remittances and seemingly lack of economic opportunity, thus supporting economic
migrants motivated by monetary gain; Azerbaijani migration is believed to
concentrate near large cities regardless of location within Russia because it is a major
transport hub for petroleum and natural gas with more work opportunities than that of
Armenia or Georgia; Georgian migration is thought to be more southwest oriented
due to seeking refuge just far enough away from local violence, with St. Petersburg
and Moscow being outliers.
This research contributes to the larger discussion of immigration patterns of
Russia. To this researcher’s knowledge, no attempts at analyzing net-migration from
the Caucasus into individual Russian subjects have been performed. Many have
studied immigration from one country to another and looked for predictors, both
within single and multiple regression models, but PPP has not been tested as a sole
predictor of Caucasian net-migration to Russia. This type of research is most
familiarly performed in the U.S. by the Census Bureau. Moreover, many international
organizations such as the World Bank, International Monetary Fund, and the United
Nations Development Programme are just a few examples of who may benefit from
this by being able to better manage migration, development funds, education
5
initiatives, and myriad other uses that either rely on accurate, localized knowledge
and/or are involved with appropriating resources based upon immigration and
demographic change(s).
6
LITERATURE REVIEW
When studying the geography of human migration it is almost impossible to
study migratory factors of immigration and emigration for any location without
knowing its history and current situation. Looking beyond facts and figures by
supplementing them within the historical narrative and context is a significant
requirement to understanding regional patterns. Quite often, population movement
and migration are either the result of push factors residing in origin states (sending) or
pull factors in destination states (receiving). The conventional thinking to push-pull
theories of migration is that political oppression, sustained violence, and economic
hardship are the primary reasons people decide to leave a country (Portes and Borocz
1989). While this makes logical sense for most cases, Russian immigration following
the collapse of the Soviet Union in 1991 acquires another dimension—forced
relocation of individuals and their descendants from the Russian state back to their
ancestral, newly independent countries. The following provides a brief history of
Russia, the Soviet Union, and events that shaped the regional migration landscape.
Tsarist Russia gave way to the Bolshevik Revolution in 1917, led by Vladimir
Lenin (Service 2003). This ushered in a new era that transformed Russia from a
monarchy to a socialist state. By 1922, the Communist Party ruled the Union of
Soviet Socialist Republics (USSR) and implemented highly centralized planning, topdown economic policies, and an expansionist desire until its dissolution in 1991
(Service 2003). For most of the Soviet Union’s first few decades, it was well behind
Britain and the United States in production and industrial output. World War II (the
7
Great Patriotic War, as Russians refer to it), forced the Soviet Union into rapid
industrialization to support the war effort. It is at this juncture that many of the
population problems in modern Russia originated.
The USSR lost between twenty-six and twenty-seven million people
(estimates range from 10 to 20 percent) as a result of World War II. This drastically
transformed Russian demography by altering the sex ratio, leaving the 20-29 year-old
age demographic with approximately 50 percent more women than men (Ellman and
Maksudov 1994). This meant that many women were left without male partners,
leaving a gap in potential population sustainability. If Russia’s pre-war birthrate was
maintained, and is assumed to have continued, a hypothetical population loss of
thirty-five million resulted from the Great Patriotic War (Ellman and Maksudov
1994). Secondly, following the temporary post-war increase in births, those born in
the late 1940s up until 1950 are now at least sixty years old. While that is not an aged
population by developed nations’ standards, this is the paradox that is Russia—life
expectancy for males actually decreased after the demise of the Soviet Union. Male
life expectancy decreased from sixty-three years in 1990 to fifty-eight in 2000; it is
now increasing and as of 2009 it was sixty-two (World Health Organization 2011).
By those measures, the decrease of natural population due to the sex ratio, when
combined with an aging population that is already receiving pensions, has reduced the
overall number of working-age people. If only one absolute fact could be stated
regarding the collapse of the Soviet Union it is that Russia’s population has been
8
shrinking since the beginning of its transition from communism to democracy in
1992. This demographic problem poses one of the greatest risks to the Russian state.
Figure 1 shows the territorial expanse of the former Soviet Union.
Geographically, it occupied the area of present-day Armenia, Azerbaijan, Belarus,
Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Russia,
Tajikistan, Turkmenistan, Ukraine, and Uzbekistan.
Figure 1. Former USSR (Rubenstein 2002)
These fifteen states represent the former Soviet Union and are often referred
to as ex-Soviet states. Politically, all of them except the Baltic states of Estonia,
Latvia, and Lithuania had formed the Commonwealth of Independent States by 1993;
9
though it is more of a nominal and symbolic organization with little real supranational
authority (Center for Nonproliferation Studies 2007).
Culturally, there are hundreds of different ethnicities residing within this
territorial expanse. No single reason for the dissolution of the Soviet Union has been
agreed upon. Many argue that it was due to imperial overstretch and ethnic tension in
the remote territories far removed from Moscow, which sowed the seeds for national
sovereignty and political determinism. Such beliefs are consistent with the idea of
shatterbelts and ethno-political tension. Rogers Brubaker states the collapse of the
Soviet Union began with its inception. A stark difference of the Soviet Union from
that of other multi-ethnic states is the institutionalization of multiple nations and
nationalities. The USSR “established nationhood and nationality as fundamental
social categories sharply distinct from the overarching categories of statehood and
citizenship. In doing so, it prepared the way for its own demise” (Brubaker 1996).
During the first few decades of the USSR, the state sought to increase its
territorial expanse by forcibly moving people to occupy other regions of the USSR
and impose Russification. This form of colonization consisted of the educated,
professional, and administrative members of society (Polian 2004). The goal was to
occupy, develop, and reap the benefits of natural resources, strategic locations, and
industrial development. Furthermore, others were forcibly moved to spatially allocate
and distribute the population more evenly throughout Russia and into Siberia and the
Far East Region. Such political aspirations and economic desire are what sent many
ethnic Russians to the “sister republics” (Iontsev, Ivakhnyuk, and Soboleva 2010).
10
Russian outbound population movement across the Soviet Union was not a permanent
phenomenon. Joseph Stalin’s death in 1953 marked the end of a dictatorship marred
by multiple economic five-year plans, famine, purges, and political imprisonment,
most notoriously known as the gulags. Beginning in the late 1960s, many ethnic
Russians who were forcibly relocated to other areas within the Soviet Union were
now migrating back to their homeland. Much of this was fueled by economic
incentives and outlasted the duration of the Soviet Union (Iontsev, Ivakhnyuk, and
Soboleva 2010). The 1980s witnessed the Soviet Union going broke, policies of
glasnost (“openness”) and perestroika (“restructuring”), and a regional mood toward
independence and ethnic homogeneity. Combined, these factors and more all
contributed to the demise and dissolution of the USSR at the end of 1991, ushering in
a new era.
The 1990s are often referred to as the transition decade in Russia. The
previous seven decades under communist rule left the newly created Russian
Federation disorganized while faced with many challenges. Among them, while
wholly inconclusive, were fiscal mismanagement, a general lack of comprehension of
a market-oriented economy, inefficiency across all levels of government, and a
somber and bleak outlook for many Russians. Indicative of departing from a
centrally-planned economy and the security of state employment to a market-oriented
and capitalist economy, the new era left many without work and without the slightest
understanding of how to operate in a completely different economic system where
efficiency and the laws of supply-and-demand were a function of employment and
11
security. Questions regarding which government institutions remained, which were
non-existent, and the outlook for those in limbo remained. The lack of an organized
government restructuring meant that many who worked in the public sector were
unsure of their employment status, their next paycheck, and their future. Previous
energy, mining, farming, and other natural resources industries changed ownership
from that of the state to individuals, often purchased at undervalued prices. Making
matters worse in the new “Wild West” climate were that regulatory and
administrative policies proved difficult to enforce due to governmental restructuring
and instability. These challenges and relaxed enforcement were also beneficial to
many former Soviet citizens who wished to relocate.
During the early years of transition, visa-free migration within the CIS was
freely permitted. Immigration of CIS citizens to Russia during 1992-2007 far
exceeded that of all other countries (7,466,000 versus 88,800); levels of CIS
immigration to total immigration from 1992-1999 was over 99 percent and for 20002007 they remained over 92 percent (Iontsev, Ivakhnyuk, and Soboleva 2010). At
first glance, it might seem that many people desired to break away from Soviet
control and out from underneath Moscow’s influence. What then accounts for the
over seven million CIS citizens who moved back to Russia immediately following the
collapse and enduring through 2007 (Iontsev, Ivakhnyuk, and Soboleva 2010)?
Physical and geographic distance may have been a barrier for many people
who were forcibly relocated but their location within the CIS states once the Soviet
Union dissolved did not keep them there. The ease of movement in a seemingly
12
transparent border, due in part to the disarray of the transition and also the relative
ease at which people could cross CIS political borders, allowed for such high levels
of net-migration into Russia (Ivakhnyuk 2006). Many still retained ties to Russia and
the arbitrary imposition that they were on the other side of the border when the border
was created would not prevent them from reestablishing their familial, professional,
and emotional ties in Russia. The collective psyche of these migrants was rooted in
Russian culture, language, and familiarity of their homeland and the clear migration
vector from the ex-Soviet states to Russia was prevalent (Ivakhnyuk 2006; Iontsev,
Ivakhnyuk, and Soboleva 2010).
Other factors motivating CIS citizens’ migration to Russia concern economic
disparities and are easily recognized. Not all immigration to Russia is permanent.
There are numerous temporary migrants operating under work permits who only
desire to make money to send back to their families. Most of these are young males
who have few options other than leaving everything they knew behind to provide for
those who remain home. This is typically measured by remittances, which is defined
by International Monetary Fund’s (IMF) Balance of Payments manual as financial
transfers from those listed as residents in a host country to others in their origin
country (Ratha, Mohapatra, and Silwal 2009). This is a good instrument for analyzing
financial transfer but it does not account for total transfers because it is difficult to
measure illegal and quasi-illegal migrant workers, especially those that work in the
gray (irregular) and black (illegal) markets. National GDP disparities between Russia
and CIS states are very wide and the mean household incomes also reflect this same
13
pattern. Moldova, Tajikistan, and Armenia are among the top countries in the world
regarding remittances as a share of GDP, most of them coming from Russia (Heleniak
2008). The loss of money recirculation does not help the local economies—for the
migrant workers and Russians alike—but rather decreases both total money supply
and tax revenue from illegal and quasi-legal migrants. As of 2008, the amount of
remittances in Moldova and Tajikistan exceed 25 percent of their GDP (Ratha,
Mohapatra, and Silwal 2009).
From 1992 until 2002, CIS citizens could travel visa-free to Russia. However,
in 2002 Russia ceased to abide by the Bishkek Agreement of allowing visa-free travel
and instead opted to use its political and economic leverage to negotiate bilateral
policies individually with members of the CIS (Heleniak 2008). This resulted in a
decrease of documented immigration and workers. Yet, the declining population
crisis forced Russia to reexamine its policy of restrictive immigration in effort to
better manage immigration, bolster demographics, and improve the labor market.
Various domestic policies aimed at increasing Russian birth and fertility rates,
average lifespan, and overall health were discussed and implemented. In May 2006,
then-Russian President Vladimir Putin addressed the Russian Parliament to call for
directed action and measures to alleviate Russia’s most acute problem—demographic
crisis and population decline (Chivers 2006). A shrinking economy could lead to less
funding for pensioners, which would prove detrimental to Russia’s aging population.
The need to curb declining population is not easy to solve nor are its alleviations
popular to implement. Russian nationalism, ethnic tension toward non-Russians, and
14
violence has been on the rise over the past decade. The two wars in Chechnya,
terrorist attacks in Moscow and other cities, and general xenophobia toward
Caucasian, Muslim, and non-Slavic peoples have spurred extreme right, neo-Nazism,
and nationalist movements (focus Migration 2010). The delicate balance between
economic development’s labor needs and overtly populist and negative immigration
sentiment typify the general mood and media focus in Russia (Ivakhnyuk 2009).
The reality that immigration to Russia is not going to go away—whether
illegal or not—requires acknowledging fundamental challenges: Russia needs
workers and its overall and working-age populations are declining. In 2007, new
legislation allowed for easier access to obtaining work permits for CIS citizens. Part
of the legislation was to bring in more workers but with an overall attempt to
document, manage, and more successfully control who is coming in and where they
are going (Heleniak 2008). Another benefit to the revised policy is to help provide
safety and security for those who do migrate to Russia. Under the previous version of
the law, many immigrants feared being caught, arrested, and/or deported; therefore
authorities did not know where to allocate support. The effect of easier access to
working permits was immediate: the number of CIS citizens immigrating to Russia
skyrocketed from 8,722 in 2006 to 55,367 in 2007 (Federal State Statistics Service
(Rosstat) 2010).
This recent pattern of largely increased immigration of CIS citizens to Russia
has not gone unnoticed. Russian nationalism has been on the rise for quite some time.
While the problem was there before, the Great Recession and dramatic decline in oil
15
prices from $143 per barrel in early July 2008 to just over $35 per barrel in late
December 2008 did not help matters as the economy turned downward (United States
Energy Information Agency 2013). As with many economies that are heavily reliant
on commodities, such as Russia is with oil, they tend to lack overall stability and are
more sensitive to price shocks and volatile markets. In this instance, that means less
revenue, tightening of spending, lower foreign direct investment, and slow job
creation, if any. The combination of high unemployment, low wages, high
immigration among non-majority ethnicities and race carry the propensity for ethnic
tension and increased nationalist sentiment. A growing resentment of immigrants is
on the rise. Some of the reasons are pointedly stated to incite political incitement and
mobilization. Statements claiming that immigrants will work for lower wages and
with less workplace safety undermine Russian workers. The desire to determine and
control one’s ancestral homeland is one of the most powerful factors fueling
nationalism (Kaiser 1994).
A leading precursor leading to instability and expanded ethnic unrest began in
1992 when Chechnya sought independence from Russia. Chechnya lies within
Russian territorial control but has undergone a separatist movement for nearly two
decades. Two wars have since followed and many observers state that violence,
unemployment, corruption, and clan rivalry have spilled over into neighboring
Ingushetia and Dagestan (Bakke et al. 2009). The entire region lies in the North
Caucasus in Russia’s South District; the affected region lies just north of Georgia,
16
where the Caucasus Mountains serve as a natural and political border between the
southern Russian region and its Georgian counterpart (see Figure 2).
Figure 2. Caucasus Region (Caucasus Regions Map)
The recent August 2008 war between Russia and Georgia displaced many
South Ossetians as they migrated to North Ossetia. Contrary to popular perception,
ethnicity alone does not explain ethnic tension or attitudes amongst various ethnic
groups. The ethnicity is rather just a nominal form of self-identification while true
factors affecting and exacerbating ethnic tension reside along separatist, religious, and
17
trust stemming from decades of conflict and inter-group cultural differences. Thus,
the dilemma posed in the Caucasus region is not attributable by the existence of
ethnic differences leading to tension but rather that tension and regional conditions
cause people to turn inward for in-group identification and the desire to seek
sameness (Bakke et al. 2009). Despite being the second most densely populated
district in Russia (the Central District is most dense and contains Moscow), it is the
poorest of the seven districts and is only 58 percent urbanized. As Mikhail Blinnikov
(Blinnikov 2011), associate professor at St. Cloud State University put it, the
Southern Federal District, “if we were to seek an analogous region in the United
States based on economic and social characteristics, this would be also in the South
(Alabama, Mississippi, Louisiana)”.1
As mentioned earlier, over 92 percent of all immigration to Russia from 19922007 originated from those residing in the CIS. A large group of them are from the
North Caucasus region that has been war-torn and engaged in a political fight with
Moscow for quite some time. This does not bode well for alleviating ethnic tension in
the region, certainly least throughout Russia and in Moscow and St. Petersburg where
larger numbers of nationalists reside. Yet, will more jobs and decreased levels of
unemployment put this issue to rest?
1
Blinnikov’s work was published in 2011 but in 2010 the Southern Federal District
lost the southernmost portion (the Northern Caucasus region with associated federal
subjects) to a newly created North Caucasian Federal District. If making the same
comparative statement today it would apply to the newly created North Caucasian
Federal District and not the quoted Southern Federal District (this is likely due to the
lag time between initial research and publication.
18
In 2008, Russia’s Institute for Regional Policy stated that more than 1,400
projects each valued over $100,000,000 were being surveyed as investment projects
and could create over 3.2 million jobs by 2020; when combined with smaller-scale
jobs they would create up to 7 million jobs. Yet, by that time over 14 million of the
working age population will have declined (Ioffe and Zayonchkovskaya 2010).
Clearly, there will be an abundance of labor that, without immigration, will restrict
Russia’s economic potential, growth, and overall development.
The continued perils that plague Russia’s population crisis also open the door
for immigration. Today, Russia ranks as having the second largest immigrant stock,
while the United States ranks first. Many organizations do not see a turnaround
toward a positive population increase anytime in the near future. Russia’s ascension
to the World Trade Organization (WTO) is likely to increase its economic activity.
Yet, the implications that it has on the region—both as a whole and between Russia
and CIS members—concern many geographers, demographers, economists,
financiers, political analysts, and government officials both at the domestic and
international levels.
Attempts to model current and future net-migration within Russia are useful
for those concerned with the region. Based upon current economic data such as
regional GDP at the federal subject level, heavy industry, and proximity to the
Caucasus, I hypothesize the following three questions that can be addressed through
analyzing net-migration rates: 1) net-migration is likely to be higher in Moscow and
St. Petersburg, 2) net-migration is likely to be high in Yekaterinburg and Novosibirsk,
19
and 3) net-migration is likely to be high in southwestern Russia near the Caucasus. I
also posit a fourth hypothesis that PPP is expected to positively influence netmigration at the federal subject level and expect the results to vary based on each of
the three Caucasus countries examined (Armenia, Azerbaijan, and Georgia).
A leading researcher of economic theory of labor markets and immigration,
George J. Borjas, Professor of Public Policy, John F. Kennedy School of
Government, Harvard University, outlines several impacts and limitations to similar
studies. Using advanced econometric formulae in economic, labor market, and
immigration research, Borjas discusses implications to modeling spatial and temporal
data. The impact of aggregated data at administrative levels affects many spatial
studies; even more so if used in time-series and longitudinal study designs as data are
further distorted. Borjas’ research of immigration rates in the United States
acknowledges the modifiable areal unit problem’s impact on results. Data aggregated
at political and administrative boundaries is subject to bias; results vary depending on
how boundaries are drawn and subjects are calculated (Borjas 1999). Another
compounding impact involves data in a time-series. In advanced econometric models
where labor market and immigration are studied, several variables related to
immigrant stock, immigrant ratios of population in research subjects (aggregated
zones or areal units), and year-over-year change have been modeled. In a single-year
study, comparing subjects is appropriate. However, when used in year-over-year
studies while including changes in immigration ratios, it is more difficult to model.
This changing dynamic of aggregated data at levels artificially drawn presents many
20
challenges and theoretical concerns to modeling immigration; the same applies when
economic data are included. From a methodological standpoint, portraying
immigration, labor market, and economic data into accurate information is one of the
most debated topics in immigration, public policy, and economic literature (Borjas
1999).
The gravity model of trade involves using origin and destination countries’
GDP, the physical distance between locations, associated variables including the cost
of relocating, and cultural factors (language). Using data for sixteen countries in the
Organisation for Economic Co-operation and Development, GDP data for both origin
and destination countries was the most accurate indicator to migration resulting in a
gravity model regression of 0.691 at a significance level of 0.05 (Lewer and Van den
Berg 2008).
Theodore Gerber of Arizona State University addresses immigration
characteristics and challenges following the Soviet collapse. Since 1991, Russia
experienced decreased fertility, increased mortality, and a negative natural increase in
the population, all which contribute to its population decline (Gerber 2000). None of
my four hypotheses suggest net-migration rates to be higher than average in Russia’s
northern and eastern federal subjects due to low population and development. During
Soviet times, migration within the Soviet Union was disassociated with economic
activity and labor market conditions due to near uniform full employment. Following
the break-up of the USSR, migration began to respond to places of improving
economic conditions (Gerber 2000). Lewer and Van den Berg’s use of GDP
21
comparison in the gravity model, coupled with Gerber’s observation that migration is
responding to improved economic performance within Russia, supports the premise
that other relationships between migration and economic indicators may exist.
Distance between origin and destination countries is included in many
migration studies. Using distance, land versus air/water costs, and familiarity with
language and culture, Anna Maria Mayda of the Department of Economics and
School of Foreign Service at Georgetown University claims “improvements in the
mean income opportunities in the destination country significantly increase the size of
emigration rates” (Mayda 2007). Of the three major variables used (distance, cultural,
and demographics), Mayda’s study concludes that cultural was not a statistically
significant variable to migration. While language and similar cultures can make the
migration transition easier for migrants, it is not a significant factor in choosing a
destination. Distance is an important consideration, especially land. This does not
poses much of an impact to my study because the origin countries are Armenia,
Azerbaijan, and Georgia and the destination country is Russia; the Caucasus region is
adjacently south of Russia’s border with both Azerbaijan and Georgia sharing a
border with Russia. Mayda also states that the pull-factor of GDP per worker in
destination countries is more significant and better explains migration than the pushfactor of GDP per worker in origin countries (Mayda 2007). This directional
migration supports the rationale behind my fourth hypothesis: PPP influences netmigration at the federal subject level in Russia.
22
Lewer and Van den Berg used the gravity model of trade to assess which
variables better explain migration patterns. They concluded that the difference in
GDP between origin and destination countries is the most significant. Gerber’s
research reiterates associated demographic rates and characteristics of a declining
population in Russia. Mayda states that pull-factors regarding economic performance
in potential destination countries are more effective at explaining migration than
push-factors. In other words, migrants will not necessarily move from one
economically challenged environment to another; rather, they follow the money.
23
METHODS
Study Area
The focus of the study area consists of Russia and the Caucasus countries of
Armenia, Azerbaijan, and Georgia. More specifically, net-migration quantities from
each of the Caucasus countries are examined at the federal subject level within Russia
(there are eighty-three federal subjects). Figure 3 shows the proximity and
juxtaposition of the three Caucasus countries to Russia, all of which were included in
the former Soviet Union.
Figure 3. Russia and Caucasus Countries (Armenia, Azerbaijan, and Georgia)
The purpose of this research is to obtain a better understanding of the amount
and destination of net-migration patterns of those from the Caucasus into Russia. The
24
level of analysis is focused on the Russian federal administrative subjects. This
allows for the identification of net-migration across all of Russia.
Data
Multiple software programs were used in this research. First and foremost,
ESRI’s ArcGIS 10.0 was used to open the Russia shapefile. Attribute data (i.e.
population, economic indicators, net-migration, and other variables) were entered into
the shapefile. Specific data required for this study are 2009 net-migration amounts for
all eighty-three federal subjects. These figures represent net-migration from each of
Armenia, Azerbaijan, and Georgia at the federal subject level. This is crucial to
mapping migration, inspecting patterns, providing additional insight and
understanding of the data by means of visual inspection, and for subsequent analysis.
Much of this is found in the Demographic Yearbook of Russia 2010. IBM SPSS
Statistics 20.0 is used for performing regression analyses. ArcGIS 10.0 is also
required to perform GWR.
Qualitative Analysis
The first steps to developing a comprehensive understanding of human
migration patterns in Russia and the Caucasus region involve researching current and
former immigration legislation, obtaining census information in Russia, and
developing a backstory for its current demographic crisis. The next step is to mine
Russian census reports and extract useful, meaningful data that can be tied to
locations. Country level data are important to the narrative and also in placing
migration rates into context when looking at the effects of policy changes and
25
economic events. However, this alone is not nearly enough. A further disaggregation
of data needs to be present in order for intra-Russia migration analysis to occur at the
federal subject level. Once the data are collected a spreadsheet will be created for
later inclusion into the Russia shapefile’s geodatabase in ArcGIS 10.0. Net-migration
rates will be determined by dividing the raw number of net-migrants in a federal
subject by the number of native citizens residing there. The results will be classified
into five statistical natural breaks. Each federal subject is then color-coded to either of
the five classification ranges. Presenting data in this format permits visualization of
data and possible pattern recognition. Once performed for each of the three Caucasus
countries, attempts to answer the first three hypotheses regarding where higher levels
of net-migration rates are present and distribution will be analyzed. This methodology
is partially qualitative because it does not fully rely on inferential statistical methods
but involves examining raw figure data and presenting outcomes of comparisons
involved.
Quantitative Analysis
I will use net-migration levels—total and disaggregated—to represent the
movement of people. The majority of immigrants into Russia are from the CIS, many
from the Caucasus region. PPP will be used to test for statistical significance values
that explain net-migration (United Nations Development Program 2011).
Much of the work is performed using Microsoft Excel for building a
comprehensive spreadsheet. For spatial representations and analysis, a basic shapefile
of Russia with the eighty-three federal subject administrative boundaries is required.
26
Migration, population, and economic data will then be attributed to each federal
district—or administration—zone, respectively (see Appendix E). SPSS will be used
to determine whether data for each test are parametric. The data are independent of
one another, meeting one of three assumptions for parametric testing. The dependent
variable (outcome) is net-migration and the independent variable is PPP (predictor).
The next step involves testing the data for normality using the Shapiro-Wilk
test (Field 2005). If this assumption is met, Levene’s test will be used for determining
whether data meet the assumption of homogeneity of variance. If data meet all
assumptions then parametric analyses of Pearson’s correlation and linear regression
will be used. If data did not meet assumptions then non-parametric analysis is
required; therefore only a Spearman’s correlation will be performed. Once complete
and the data are in usable form and input into ArcMap, spatial statistics tools can be
performed.
Initially, autocorrelation using Moran’s I was going to be performed to look
for clustering or dispersion. Upon further thought, this was dropped due to the
extremely large area Russia covers and that the net-migration levels are shown in
polygon features—some rather small, others rather large—and the resulting analysis
would not be too meaningful given that net-migration is attributed to variously shaped
features and not individual points of incidence. The population distribution within
Russia is west-oriented, specifically with respect to Moscow and St. Petersburg. Netmigration is likely to exhibit a similar pattern as Russia’s population is rather sparse
given its large territory. Thus, this measure is not the best tool for the scale of this
27
research. A better tool is to use linear regression to test the strength of relationships
between explanatory (independent) and dependent variables. In this research’s case,
the dependent variable is net-migration and the explanatory variable to be tested is
PPP. Ordinary Least Squares (OLS) is a statistic that uses linear regression to
examine the degree of causation [or effect] an explanatory variable has on a
dependent variable. For this research, PPP could be tested for its predictive ability on
net-migration. Doing so requires that data for all eighty-three federal subjects are able
to be found and each subject is calculated in the same manner since this is a global
model. Unfortunately, data are missing for several federal subjects (only data for
seventy-nine of the eighty-three subjects are complete and will be used for all tests).
Performing a global model on less-than-complete datasets is problematic because if
there are any predictive indicators that are statistically significant then the model
could change if trying to model all of Russia in the future should data become
available or found later. Fortunately, there is a spatial statistical tool growing in
popularity and well-known to geographers—Geographically Weighted Regression
(GWR), which helps reduce typical regression model errors by calculating each
observation based on individual geographic weights such as size, location, and
proximity rather than the standard approach of using solely observation data without
accounting for geographic features. This study involves geographic data of people
across a landscape; therefore, GWR can strengthen regression model accuracy by
including spatial elements. Recall that hypotheses one, two, and three involve
determining where higher net-migration rates of Armenia, Azerbaijan, and Georgia
28
are found within Russia. Hypothesis four requires statistical methods of regression
analyses: PPP is expected to positively influence net-migration at the federal subject
level and the results from each of the three Caucasus countries examined are expected
to vary.
Besides the aforementioned reasons associated with the level of analysis this
study undertakes, none of the regression tools have been able treat data at the local
level. GWR uses linear regression, just as OLS does, but has the added value of
independently performing a linear regression for each feature in the data. Thus, the
results become much more spatial because each feature [or observation] is measured
independently and locally rather than wholly and globally.
29
RESULTS
Qualitative
Net-migration from the Caucasus to Russia demonstrates similar
circumstances but with stark differences. Armenia, Azerbaijan, and Georgia each
possess very low Gross-National Income per capita. They also experience significant
levels of out-migration (emigration) with respect to their overall population. While
these general characteristics are shared among the three neighboring Caucasian
countries, resulting net-migration distributions and levels vary across space and
concentration. As mentioned previously, the United States (U.S.) and Russia have the
largest migrant stocks in the world, respectively. For a familiar contextual
comparison, see Table 1. From 2005 to 2010, U.S. population increased by 13.8
million people whereas in Russia it decreased by 1.2 million. The most telling aspect
is that Russia’s population continues to decrease and its ratio of migrants to total
population continues to increase.
Table 1. Comparison of U.S. and Russia Populations, 2005 and 2010 (World
Bank 2011)
Pop.
Migrants
U.S.
U.S.
Russia
2005
295,516,599
143,150,000
39,266,451
12,079,626
13.29%
8.44%
2010
309,349,689
141,920,000
42,813,281
12,270,388
13.84%
8.65%
30
Russia
% Migrants of Pop.
U.S.
Russia
Figure 4 displays the aggregated net-migration of Armenia, Azerbaijan, and
Georgia to Russia as a ratio of the total population. For 2009, there averaged 748 netmigrants per each of eighty-three federal subjects.
Figure 4. Total Armenia, Azerbaijan, and Georgia Net-Migration Percentage of
Population, 2009
During 2009, aggregated net-migration expressed as a percentage of
population produced mixed results:
Hypothesis 1: net-migration is likely to be higher in places of high
construction and restoration activity such as Moscow and St. Petersburg (a function
of economic development). The total number of net-migrants is higher in Moscow
Oblast (not Moscow City). Moscow Oblast had the greatest number of net-migrants
in 2009 (3,766). Moscow City had 2,775 net-migrants. When combined as a region
31
they account for 6,521. This partly supports this hypothesis. However, as a ratio of
net-migration to population, both Moscow City and Moscow Oblast failed to support
this hypothesis (their net-migration ratios to population are not in the highest
classification range of natural breaks).
The total number of net-migrants is higher in St. Petersburg City (1,132); the
surrounding Leningrad Oblast had 968 net-migrants, totaling 2,100 for the region.
This falls short of Moscow City and Moscow Oblast. Seventeen federal subjects
received more net-migrants than St. Petersburg. If combining St. Petersburg City and
Leningrad Oblast, eight federal subjects had more net-migrants. The net-migration
ratios to population for this region also failed to support this hypothesis due to
relatively average migration rates and falling in the middle classification range. Thus,
regarding St. Petersburg, this hypothesis is not supported.
Hypothesis 2: net-migration is likely to be high in Yekaterinburg and
Novosibirsk. Yekaterinburg resides in Sverdlovsk Oblast and Novosibirsk is in
Novosibirsk Oblast. Both total net-migrants and net-migration ratios of population are
much lower than expected. Sverdlovsk Oblast’s (Yekaterinburg) net-migration rate is
in the second of five natural breaks classification ranges. This indicates its netmigration rate is lower than the average. Novosibirsk Oblast’s net-migration rate is in
the third classification range (average). This hypothesis is not supported.
Hypothesis 3: net-migration is likely to be high in southwestern Russia near
the Caucasus. Results are varied for this region. The highest rate of net-migration in
this study for all of Russia is present in southwestern Russia in the federal subject
32
Republic of North Ossetia-Alania, which is just north of the Russia-Georgia border.
However, of the three federal subjects bordering it, two have net-migration rates in
the lowest classification range with the other in the second lowest range. Krasnodar
Kray has the second largest number of net-migrants of all federal subjects (3,655,
second only to Moscow Oblast’s 3,766). It also has a higher than-average netmigration rate (is just outside of being in the top range). The only other federal
subject in this region with higher than average net-migration rates is the Republic of
Adygea. It resides fully within the Krasnodar Kray federal subject and is in the top
classification range. Overall, this hypothesis possesses large numbers of net-migrants
because of Krasnodar Kray’s status of receiving the second greatest amount of netmigrants but it does not support the hypothesis when viewed by net-migration rates;
this hypothesis is not supported.
The biggest surprise, which was not explicitly hypothesized, was that of the
second highest amount of net-migration—Krasnodar Kray in the North Caucasus. Its
population is 5.1 million and has 3,655 net-migrants, only 111 less than Moscow
Oblast. Aggregated net-migration rates were used for hypothesis testing. Countryspecific information of the Caucasus countries is also important to this study.
Armenia is a landlocked country in the Caucasus region. It has the lowest population
of the three Caucasus countries examined in this research at 3.1 million people with a
0.0 percent population growth rate (World Bank 2011, Armenia). For the purpose of
comparison U.S. GNI per capita is $47,240 (all figures in USD), global GNI per
capita is $8,740.50, and Armenia’s is less than half of that at just $3,100. Among the
33
three countries, Armenia’s emigration was the lowest with 870,200 migrants leaving
the country, which is just over 28 percent of its total population (World Bank 2011:
Armenia, United States, World). Armenian migration to Russia accounts for 32,782
(approximately 3.8 percent). Except for Krasnoyarsk Kray in central Russia, much of
Armenian immigration in Russia is distributed across the western portion of European
Russia. Higher rates of net-migration occur in federal subjects east, west, and north of
Moscow Oblast; in the Republic of Adygea (fully within Krasnodar Kray in
southwest Russia); in Orenburg Oblast south of Samara; and east of Novosibirsk in
Kemerovo Oblast (see Figure 5). Moscow and Krasnodar are sensible destinations but
Kemerovo Oblast was a surprise given that it is in western Siberia and Novosibirsk is
just west of it. Further investigation of the latter revealed it to have one of the largest
coal basins in the world with heavy mining and industry.
Figure 5. Armenian Net-Migration 2009
34
Azerbaijan is the largest country in the Caucasus region. Its population is
greater than Armenia’s and Georgia’s combined at 8.8 million people. Its population
growth rate is 1.0 percent, which is the only positive rate in the Caucasus region
(World Bank 2011, Azerbaijan). Azerbaijan’s GNI per capita is $4,840, the largest in
the Caucasus. Among the three countries, Azerbaijan’s emigration was the highest
with 1,432,600 migrants leaving the country, which is just over 16 percent of its total
population (World Bank 2011, Azerbaijan). Azerbaijani migration to Russia accounts
for 22,528 (approximately 1.6 percent of total emigration).
The central portion of Russia received many net-migrants. Net-migration rates
also support this as indicated in their classification in the highest natural break range
(see Figure 6). The top three federal subjects all received more than one thousand netmigrants in 2009, with 1,640 migrants being the greatest amount.
Figure 6. Azerbaijan Net-Migration 2009
35
After dissolution of the USSR in 1991, many migrants from Georgia to the
Russian Federation were ethnic Russians repatriating back to their ancestral and
familial ties after previous forced migration under Soviet rule. As the economy
transitioned throughout the following decade, many native Georgians gradually began
to migrate to Russia, primarily for economic reasons. The shared border between
Georgia and Russia, combined with conflict alongside it, led to Russia tightening its
immigration and visa policies (International Organization for Migration 2008). In
2002, Russia began requiring visas for Georgians; visa-free travel to Russia was still
permitted for all other CIS member states.
Georgia’s population is 4.3 million, approximately one million more than
Armenia (3.3 million) but less than half that of Azerbaijan (8.8 million). Its
population growth rate is -1.2 percent (World Bank 2011, Georgia). Georgia’s GNI
per capita is $2,530, making it the lowest in the Caucasus. For comparison, Georgia’s
GNI per capita is just 27 percent of Russia’s. Georgia experienced 1,057,700
migrants leaving the country, which is just over 25 percent of its total population
(World Bank 2011, Georgia). Georgian migrants to Russia account for 6,733,
(approximately 0.01 percent of total emigration).
Krasnodar Kray received the greatest number of net-migrants at 1,269,
equating to the second highest net-migration rate for Georgian migrants. The highest
net-migration rate occurred in the Republic of North Ossetia-Alania. For comparison,
it had sixteen fewer migrants than the federal subject with the second largest number
of net-migrants—Moscow City (692). Georgian net-migration is less concentrated
36
than Armenian and Azerbaijani rates. There are just two federal subjects have netmigration rates above the middle classification range (see Figure 7).
Figure 7. 2009 Georgian Net-Migration
Krasnodar is telling because it received more Georgian migrants than all other
Russian federal subjects. North Ossetia-Alania is significant as well and is possibly
explained by the division of Ossetian people after the collapse of the Soviet Union;
North Ossetia remained in Russia while South Ossetia is a contentious region in
Georgia.
Quantitative
Searching for explanatory predictors to net-migration is the most involved
portion of this study and requires performing multiple statistical tests of regression
analyses. Hypothesis 4 posits that PPP is expected to positively influence net37
migration but the strength may vary for each origin country. In other words, it is
hypothesized that the economic variable in question (PPP) can be used to account for
more net-migration variance in one country of origin than another, thus supporting
the premise that net-migration from Armenia, Azerbaijan, and Georgia is not “one
and the same” but rather each is unique despite their geographic juxtaposition of
sharing borders with each other along with a similar historical narrative as members
of the former Soviet Union (i.e. sharing both physical and cultural attributes). As
expected, results varied for each of the three Caucasus countries: Armenia,
Azerbaijan, and Georgia.
Both Armenia and Azerbaijan passed all preliminary statistical assumptions
required for further parametric analyses. Resulting statistics and figures for the
Shapiro-Wilk test, Levene’s test, scatterplot of variables, Pearson’s correlation, and
the coefficient of determination (r-squared) are presented for each of them. However,
data for Georgia did not meet all preliminary statistical assumptions. The data were
not indicative of a normal distribution and therefore violated data requirements for
parametric analysis (Shapiro-Wilk test rejected the null hypothesis that data are
normally distributed). Only a scatter plot of variables and the non-parametric analysis
of Spearman’s rho are presented.
Data for Armenia are not significantly different from a normal distribution. A
significance value of p ≤ .05 means the null hypothesis that data are normally
distributed must be rejected. Since Net-Migration Armenia is p = 0.351 the null-
38
hypothesis cannot be rejected and the data are assumed to be normally distributed.
This is the desired result for this study (see Table 2).
Table 2. Shapiro-Wilk Test (Armenia)
Net-Migration Armenia
PPP
12931
Shapiro-Wilk
Statistic df Sig.
0.889 3 0.351
Homogeneity of variance must also be met for parametric analyses to be
performed. One way this can be accomplished is using Levene’s test. Similar to the
Shapiro-Wilk test, in order to continue with parametric testing the statistical
significance [or p-value] of data cannot be p ≤ .05. If the p-value is greater than or
equal to 0.05 then data meet the assumption that variance among variables are not
statistically significantly different and are homoscedastic.
Levene’s test of Net-Migration Armenia resulted in p = 0.997, thus the null
hypothesis is accepted and there are no statistically significant differences in variance.
This is the desired result, meaning further hypothesis testing and analysis regarding
Armenia can now be performed (see Table 3).
Table 3. Levene’s Test (Armenia)
Levene's Test of Equality of Error Variances
Dependent Variable: Net-Migration Armenia
F
df1
df2
Sig.
0.154
76
2
0.997
39
Figure 8 displays the plotted locations for all observations (N = 79). The
scatterplot is useful for visualizing how the variables correlate but does not indicate
strength, direction, or statistical significance.
Figure 8. Scatterplot (Armenia and PPP)
Pearson’s correlation coefficient between the two variables is r = 0.164. It is
important to point out that the resultant correlation coefficient between Armenian netmigration and PPP is not statistically significant. Therefore, despite there being a
40
slightly positive correlation, it is considerably weak and not at any statistically
significant confidence interval (see Table 4).
Table 4. Correlation (Armenia and PPP)
Correlations
PPP
Net-Migration Armenia
Pearson Correlation
Sig. (1-tailed)
N
Pearson Correlation
Sig. (1-tailed)
N
PPP
1
79
0.164
0.074
79
Net-Migration Armenia
0.164
0.074
79
1
79
A line-of-best-fit was obtained for linear regression (y = 263.927 + 0.01x).
Table 5 states that the coefficient of determination indicates that the model can only
explain 2.7 percent of variance within the data (r2 = 0.027).
Table 5. Regression (Armenia and PPP)
Net-Migration Armenia
Adjusted
Std. Error of
R
R Square R Square the Estimate
0.164
0.027
0.014
487.778
Predictors: (Constant), PPP
GWR is a method of performing a local regression for every feature
(observation in traditional aspatial and non-spatial statistics) in a dataset (ESRI 2012).
It is available in the Spatial Statistics Toolbox in ArcMap. After performing GWR the
residuals must be tested for clustering by using another tool in the Spatial Statistics
41
Toolbox—Spatial Autocorrelation (Moran’s I). If the residuals are random then the
model is usable; if they indicate statistically significant dispersion or clustering then
the GWR model is considered misspecified and further improvement is required since
it significantly over- or under-predicts values for the independent variable being
modeled (predicted).
Net-Migration Armenia resulted in a GWR of r2 = 0.124. This local method is
better at explaining model variance than global method (r 2 = 0.027). Yet, the
Autocorrelation (Moran’s I) tool revealed residuals in the data to be clustered (z-score
= 3.528, p-value = 0.000). The GWR model is misspecified and additional predictive
variables are needed to enhance model strength.
Recall that assumptions must be met for parametric testing. Data for
Azerbaijan are not significantly different from a normal distribution (see Table 6).
Since Net-Migration Azerbaijan is p = 0.754 the data are assumed to be normally
distributed.
Table 6. Shapiro-Wilk Test (Azerbaijan)
Net-Migration Azerbaijan
PPP
12931
Shapiro-Wilk
Statistic Df Sig.
0.984
3 0.754
Recall that Levene’s test is used to ensure that the assumption of homogeneity
of variance is also met. Net-Migration Azerbaijan’s Levene’s test results are p =
1.000, therefore the null hypothesis that the variance of data are the same is accepted
42
and there are no statistically significant differences in variability (see Table 7). Figure
9 displays the plotted locations for all observations (N = 79).
Table 7. Levene’s Test (Azerbaijan)
Levene's Test of Equality of Error Variances
Dependent Variable: Net-Migration Azerbaijan
F
df1
df2
Sig.
0.079
76
2
1.000
Figure 9. Scatterplot (Armenia and PPP)
43
Pearson’s correlation coefficient between the two variables is r = 0.525,
significant at the 0.01 level (see: Table 8).
Table 8. Correlation (Azerbaijan and PPP)
Correlations
PPP
Pearson Correlation
1
Sig. (1-tailed)
N
79
Net-Migration Azerbaijan Pearson Correlation 0.525**
Sig. (1-tailed)
0.000
N
79
**. Correlation is significant at the 0.01 level (1-tailed).
PPP
Net-Migration Azerbaijan
0.525**
0.000
79
1
79
A line-of-best-fit was obtained for linear regression (y = -14.967 + 0.019x).
Table 9 states that the coefficient of determination indicates that the model can
explain 27.6 percent of variance within the data (r 2 = 0.276). The results indicate that
although the Pearson Correlation of r = 0.525 at the 0.01 confidence interval
indicates the variables co-vary with one another more frequently than not, PPP’s
explanatory power to predict net-migration in Azerbaijan is not very strong given r 2
= 0.276, though it is much more explanatory than Armenia’s r2 = 0.027.
Table 9. Regression (Azerbaijan and PPP)
Net-Migration Azerbaijan
Adjusted
Std. Error of
R
R Square R Square the Estimate
0.525
0.276
0.266
253.339
Predictors: (Constant), PPP
44
Net-Migration Azerbaijan resulted in a GWR of r 2 = 0.555. This local method
is better at explaining model variance than the global regression (r2 = 0.276).
Furthermore, the Autocorrelation (Moran’s I) tool revealed the data as random (zscore = -1.141, p-value = 0.254). The GWR model fits the required assumptions and
the residuals are not statistically dispersed or clustered; indicating that PPP is useful
as an explanatory variable to predict Armenian net-migration to Russia.
Unlike Armenia and Azerbaijan, data for Georgia are significantly different
from a normal distribution (see Table 10). Therefore, the normality assumption is not
met and parametric analyses cannot be performed.
Table 10. Shapiro-Wilk Test (Georgia)
Net-Migration Georgia
PPP
12931
Shapiro-Wilk
Statistic df Sig.
0.761 3 0.023
Pearson’s correlation cannot be used because the data for Georgia do not meet
all assumptions for parametric tests. The data are not assumed to be normally
distributed because the Shapiro-Wilk test resulted in p = 0.023. In other words, the
null hypothesis that data are normally distributed must be rejected and therefore
parametric tests with these data should not be performed. There are other methods to
measure regression but due to the unique characteristics of the Georgian netmigration and PPP data a different statistical test must be used. Visual inspection of
data and scatterplot analysis is worthwhile but it is not a reliable indicator of
45
correlation or normal distribution. When visually inspecting the scatterplot of data for
Georgia, there appear to be more outlying points that help make sense of why the data
failed to meet the Shapiro-Wilk test for normality (Figure 10 displays the plotted
locations for all observations (N = 79)).
Figure 10. Scatterplot (Georgia and PPP)
Spearman’s rho obtains Pearson’s correlation coefficient by using rank data.
The Georgia net-migration data does not meet all assumptions for parametric testing.
Though not ideal, Spearman’s rho is an alternative statistical method that is typically
46
used with non-parametric data. In the case of Georgian net-migration, rs = 0.27,
significant at the 0.01 level (see Table 11).
Table 11. Correlation (Georgia and PPP)
Correlations
Spearman's
rho
PPP
Correlation Coefficient
Sig. (1-tailed)
N
Net-Migration Georgia Correlation Coefficient
Sig. (1-tailed)
N
**. Correlation is significant at the 0.01 level (1-tailed).
PPP
1.000
79
0.270**
0.008
79
Net-Migration Georgia
0.270**
0.008
79
1.000
79
Neither linear regression nor GWR were performed on Georgian data because
they did not meet all assumptions required for parametric testing. The data are treated
as categorical and rank rather than interval and ratio; therefore only non-parametric
tests are available, as indicated with Spearman’s rho. The rs = 0.27 is not considered a
strong correlation of PPP and net-migration in Georgia.
Hypothesis 4 produced mixed results. Regarding Armenia, I failed to reject
the null-hypothesis that PPP can be used to predict net-migration. The GWR model
also failed to reject the null-hypothesis because the data appear clustered, indicating
that GWR is not an accurate method with the Armenia dataset. Regarding Azerbaijan,
I failed to reject the null-hypothesis that PPP can be used to predict net-migration.
However, using GWR on the Azerbaijan dataset produced r2 = 0.555 and is the
strongest predictive value and the only predictive value greater than r2 = 0.5.
47
Therefore, as hypothesized, PPP can be used as an explanatory variable to
Azerbaijani net-migration when modeled with GWR. Regarding Georgia, regression
tests could not be performed due to the inability of data to meet preliminary statistical
assumptions. Subsequently, with this data set, I failed to reject the null-hypothesis
that PPP can be used as an explanatory variable to Georgian net-migration. Regarding
the hypothesis that data indicate that Armenia, Azerbaijan, and Georgia are not “one
and the same” but rather each is unique despite their geographic juxtaposition of
sharing borders with each other along with a similar historical narrative as members
of the former Soviet Union (i.e. sharing both physical and cultural attributes), I
support this hypothesis based on the varied degrees of strength (Armenia r2 = 0.027,
Azerbaijan r2 = 0.276, and no testing for Georgia due to failing to meet parametric
data assumptions) associated with each models and that GWR can be used to model
PPP as an explanatory variable to Azerbaijani net-migration but none of the standard
regression models can be used.
48
CONCLUSIONS
After performing both qualitative and quantitative analyses of Caucasian netmigration into Russia, many conclusions can be drawn. First, total net-migration is
more widespread than initially hypothesized. The Krasnodar Kray area, which
contains the resort city Sochi on the Black Sea and with a sub-tropical climate, is the
most surprising and perhaps overlooked prior to this research. It is also home to the
2014 Winter Olympic Games. It would be interesting to determine if the netmigration rate in Krasnodar Kray rose after the announcement that Russia was
hosting the games.
Armenian net-migration is least correlated with PPP than Azerbaijan and
Georgia. For this reason, it is the more challenging of the three Caucasian countries to
make sense of where to begin with assessing net-migration to Russia. Azerbaijan is
correlated with PPP more than the other countries. Also, its regression results indicate
the ability to explain approximately 27 percent of net-migration by using PPP, alone.
Georgian data present more challenges than the other datasets because they do not
meet assumptions required for parametric testing and, resultantly, less robust statistics
can be drawn for Georgian net-migration.
Azerbaijani net-migration is more dispersed than Armenian and Georgian netmigration. Economic attractiveness for migrant workers tends to be much more the
case for it than the other countries. The net-migration rates, viewed in terms of natural
breaks, visually appear to be more dispersed and found in federal subjects in multiple
regions of Russia: southwestern Russia, federal subjects near Moscow Oblast, and
49
also in the Urals region in central Russia. Azerbaijani net-migration is better
predicted by PPP than all other data exhibit, indicating that the pull-factor of
economic attractiveness is a more powerful incentive for migrants.
Georgians share a close cultural link with the Russian federal subject North
Ossetia. It was a region divided after the USSR broke up in 1991. With it, so too were
the Ossetian people. It is evident that many ties remain between the two despite the
political border.
As expected, Moscow [and Moscow Oblast] attracts large numbers of
migrants. It is a top destination for Armenian and Georgian migrants but is relatively
uncharacteristic for Azerbaijanis. This is further testament to Azerbaijani migrants
exhibiting a more dispersed pattern as they “follow the money” much more so than
Armenians and Georgians.
It appears that migration is the result of pull factors rather than push factors.
Cultural ties, proximity, shared language, visa-free border crossing, and economic
gain are largely believed to be reasons Russia attracts migrants from the former
Soviet republics in the Caucasus (Ivakhnyuk 2006; Iontsev, Ivakhnyuk, and Soboleva
2010), although Anna Mayda states that language and cultural ties are not significant
pull factors (Mayda 2007). Given their respective GNI per capita levels, migrants
from any of the three countries stand a much more likely chance of earning better
wages in Russia than in their home countries.
The extremely low number of Georgian migrants that went to Russia is
telling, perhaps indicative of many factors: excluding Georgia from visa-free
50
migration status, tension in the region, and/or the recent Russia-Georgia War in
August 2008. Federal subjects that also reside along the Russia-Georgia border have
low numbers of net-migrants, possibly due to violence and instability in the affected
republics of Dagestan and Chechnya.
Regression results indicate that PPP is not a strong predictor of Russian netmigration. Only 2.7 percent of model variance explains Armenian net-migration into
the Russia. GWR was not a good model to employ with Armenian data because the
residuals were found to be significantly clustered; therefore the model was
misspecified because it could statistically model PPP as a predictive variable to
Armenian net-migration in Russia. Azerbaijani net-migration offers a stronger model
by explaining 27.6 percent of variance. When GWR was performed on Azerbaijani
data the model strength improved to r2 = 0.555. The residuals were tested and proved
to be randomly distributed so GWR is a useful tool in analyzing net-migration from
Azerbaijan to Russia. For this study, GWR could on be used with Azerbaijan data due
to data assumptions, requirements, and subsequent residual testing. Using GWR
improved the Azerbaijani model of using PPP as an explanatory variable of netmigration Georgian net-migration data failed to meet all assumptions for parametric
testing so only a Spearman’s rho could be used to describe the relationship between
net-migration and PPP (rs = 0.27**).
In conclusion, the number of net-migrants was greatest in Moscow Oblast,
with Krasnodar Kray a close second. This supports the hypothesis that more migrants
reside in the largest and most economically developed region near Moscow but St.
51
Petersburg failed to support this hypothesis due to Krasnodar Kray receiving the
second highest amount of net-migrants. Yekaterinburg and Novosibirsk were
hypothesized to receive a large number of net-migrants but ranked lower than
expected in the number of net-migrants or net-migration rates compared to all other
federal subjects in Russia. I expected the southwestern region in Russia to receive a
large number of net-migrants and have some of the highest rates of net-migration.
This did not occur except for Krasnodar Kray but then this was a surprise because it is
largely a resort and port city that will host the 2014 Winter Olympic Games. This was
not accounted for when forming hypotheses and instead the notion prior to this
research focused on the volatility and violence in this region and failed to recognize
the economic boom in Krasnodar Kray due the 2014 Winter Olympics in Sochi,
Russia. With the exception of GWR on Azerbaijani data, which indicated that 55
percent of net-migration could be explained by PPP, multiple statistical tests using
PPP as an explanatory variable to net-migration all failed to reject the nullhypotheses.
52
DISCUSSION
This research developed a greater understanding of regional variations,
economic concentrations, and migrant destinations within the country and the “near
abroad” former Soviet states, specifically those from the Caucasus countries of
Armenia, Azerbaijan, and Georgia. Prior to conducting this research, I believed the
highest numbers of net-migrants and net-migration rates would be along the RussiaGeorgia border (fleeing violence and relocating with family) and only in the cities of
Moscow, St. Petersburg, Yekaterinburg, and Novosibirsk (economic attractiveness).
This was not fully the case and I now have much more accurate, detailed, and
meaningful knowledge of net-migration from the Caucasus countries to Russia.
Some significant issues did arise when performing this analysis. First, the
scale at which to examine data is problematical. Russia is the largest country in the
world by area so the question was whether to focus on a specific issue(s) at a small
scale (an individual region) or to perform less specified analyses at a large scale. I
chose the latter for this research and it invariably became the focus of this research.
Gaining an understanding of migration patterns throughout Russia as well as learning
more about its administrative divisions was paramount.
I foresaw data collection being an issue prior to beginning this study due to
the changing nature and accountability of migration reporting throughout Russia.
Obtaining data, despite possible inconsistent reporting standards, was the primary
concern. The secondary concern I had was the amount of data I could find in English.
Thankfully, much of the data used were in both Russian and English and where
53
English was not used it was only minimal so I was able to comprehend and clarify
when needed. After looking for quite some time for any form of a recent Russian
census I finally found the Demographic Yearbook 2010, which is where most of the
raw numbers regarding migration, net-migration, and individual observations at the
Russian federal subject level were found. This was like finding a gold mine because
most of the data were recent (2009) and could be used for the level of analysis I was
interested in researching. It formed the basis for building the spreadsheet and adding
attribute data to the Russia shapefile’s geodatabase. The other variable needed was
PPP and was obtained from the International Monetary Fund.
Constructing the spreadsheet for future database creation was very timeconsuming. There are eighty-three federal subjects in Russia. Each one required
manually entering data across multiple attributes: PPP, net-migration for each of three
countries into each federal subject, and others that were not included in results but
were initially explored such as HDI (human-development index), population for 2008
and 2009, and income-index). This meant that 664 data figures were manually entered
into an Excel spreadsheet. Once this task was complete, map-making, spatial statistics
with ArcMap, and traditional statistics with SPSS could begin.
When creating choropleth maps for net-migration for each of the three
Caucasian countries into Russia, a problem quickly ensued—how to classify. I
wanted to communicate net-migration flows as easily and simply as possible but it
turned out that a universal method was impossible if simplicity were maintained. For
example, should net-migration levels be displayed in five classification ranges for
54
each map? If so, that meant the ranges within each class were different if using
natural breaks found in the data. Another approach could be to use manual intervals
to classify percent ranges but given the nature of net-migration as a ratio of
population, the resulting percentages would all fall into the first classification range.
Standard deviations might communicate patterns but it is not easily intelligible for
many readers and does nothing to state absolute amounts of migrants. Additionally,
standard deviation values would mean one thing for a map of Georgian net-migration
and quite another for that of Azerbaijan. I ended up using natural breaks so that there
were five classification ranges for each of Armenia, Azerbaijan, and Georgia datasets.
The old question of “which came first, the chicken or the egg?” surfaced with
this research. The net-migration levels used were for 2009. However, I am using PPP
as a predictor in my regression testing. Theoretical considerations concerned whether
the data for 2009 came at the end of 2008 or at the end of 2009. If the former, it might
be more accurate; if the latter, the 2008 economic crisis likely affected net-migration
because the net-effect of slowed economic growth would certainly decrease the need
for labor. In the end, I had to go with whatever data were available and since it was
from a still developing reporting structure in Russia it was all I could do.
The quantitative portion was initially thought to be based just on GWR and
only as a “throw in” to the overall research. However, it quickly became the focus of
my time and I realized that I had much more to learn about statistics than perhaps I
envisioned. An issue I had with this research was that no all data met assumptions for
simple linear regression. Transforming the data to appear “visually” normally
55
distributed appeared to be a good idea but when I ran the regression analysis on it the
results were non-sensible with respect to the original, untransformed data. I quickly
discarded this from my analysis and results because I was not satisfied with the
results and was not fully confident in how to best transform all data for the model
design.
A final consideration I have with the nature of this research is language. Most
of the data was obtained by searching online. Most internet search results were in
English, some were in Russian but this is very understandable given I primarily used
search terms in English on the U.S. Google domain. Yandex.ru is a huge company in
Russia that functions much like Google from internet searches to maps to e-mail,
among much more. Had I been more proficient in Russian and knew industry specific
search terms then different data may have been uncovered and used for this project.
Fortunately, many demographers of Russia publish in English, including a few from
Russian universities that I have contacted.
Upon completion of this research, I am left with many more questions and
interests than originally anticipated. Azerbaijani net-migration data appears to be in
usable form for developing a predictive model. The overlap of geography and
economics in this study is evident, but I believe model improvement requires
advanced econometric formulae that include other variables and statistical measures.
Conducting this type of study and analysis requires outside support by those with
more specific and advanced training in applied economics. Additional model inputs
could be categorical data obtained from questionnaires.
56
Overall, I learned quite much from this project but more than anything else, I
improved my ability to design, build, and manipulate data from start to finish in the
ArcGIS environment. Geodatabase creation, geoprocessing, layer formation, map
creation, spatial statistics, and more were greatly enhanced as a result of this analysis
and the fact that I thoroughly enjoy the subject matter (Russia and surrounding
regions) made conducting this research much more interesting. Lastly, my
comprehension of statistics as a whole improved with respect to selecting appropriate
statistical measures, data exploration, model design, data validation, and
interpretation.
57
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World Bank. 2011. Migration and Remittances Unit. Compiled by Dilip Ratha, S.
Mohapatra, and A. Silwal. Accessed 27 February, 2013.
http://siteresources.worldbank.org/INTPROSPECTS/Resources/3349341199807908806/RussianFederation.pdf.
World Bank. 2011. Migration and Remittances Unit. Compiled by Dilip Ratha, S.
Mohapatra, and A. Silwal. Accessed 27 February, 2013.
http://siteresources.worldbank.org/INTPROSPECTS/Resources/3349341199807908806/UnitedStates.pdf.
World Bank. 2011. Migration and Remittances Unit. Compiled by Dilip Ratha, S.
Mohapatra, and A. Silwal. Accessed 27 February, 2013.
http://siteresources.worldbank.org/INTPROSPECTS/Resources/3349341199807908806/World.pdf.
62
APPENDIX A. Map (Total net-migration rate, 2009)
63
APPENDIX B. Map (Armenian net-migration rate, 2009)
64
APPENDIX C. Map (Azerbaijani net-migration rate, 2009)
65
APPENDIX D. Map (Georgian net-migration rate, 2009)
66
APPENDIX E. Federal Subject Data
Administrative Regions and Associated Variables for Russia, 2009
Russian Federal
Subject
Adygea, Republic of
Altai Kray
Altai Republic
Amur Oblast
Arkhangelsk Oblast
Astrakhan Oblast
Bashkortostan Republic
Belgorod Oblast
Bryansk Oblast
Buryatia, Republic of
Chechen Republic
Chelyabinsk Oblast
Chukotka Autonomous
Okrug
Chuvash Republic
Dagestan, Republic of
Ingushetia, Republic of
Irkutsk Oblast
Ivanovo Oblast
Jewish Autonomous
Oblast
Kabardino-Balkar
Republic
Kaliningrad Oblast
Kalmykia, Republic of
Kaluga Oblast
Kamchatka Kray
Karachay-Cherkess
Republic
Karelia, Republic of
Kemerovo Oblast
Khabarovsk Kray
Khakassia, Republic of
Kirov Oblast
Komi Republic
Kostroma Oblast
Krasnodar Kray
NetMigration
Armenia
308
167
29
67
31
235
687
358
194
28
0
424
NetMigration
Azerbaijan
56
139
6
25
170
486
400
154
84
42
0
282
NetMigration
Georgia
59
11
2
6
6
40
84
55
18
5
0
167
Pop.
443,000
2,497,000
209,000
864,000
1,262,000
1,005,000
4,057,000
1,525,000
1,300,000
961,000
1,239,000
3,508,000
Regional
GDP
(PPP) in
US$
8,583
10,295
7,520
13,115
19,310
12,610
15,797
19,569
9,345
11,148
5,023
15,098
0
141
9
1
207
498
0
116
185
1
101
406
-1
13
2
0
14
46
50,000
1,279,000
2,712,000
508,000
2,505,000
1,073,000
39,220
10,971
9,337
3,494
15,987
7,425
18
20
-1
185,000
9,849
53
250
23
600
69
63
191
24
220
153
10
17
3
69
-1
892,000
937,000
284,000
1,003,000
344,000
7,666
14,136
14,500
12,931
12,931
67
172
1,882
197
26
76
38
139
2,140
17
179
916
140
35
116
159
107
246
13
70
25
24
3
11
1
11
1,269
427,000
687,000
2,822,000
1,402,000
538,000
1,401,000
959,000
692,000
5,142,000
8,669
12,931
18,721
12,320
13,680
9,634
22,335
10,941
13,899
67
APPENDIX E. (continued)
Administrative Regions and Associated Variables for Russia, 2009
Russian Federal
Subject
Krasnoyarsk Kray
Kurgan Oblast
Kursk Oblast
Lipetsk Oblast
Magadan Oblast
Mari El Republic
Mordovia, Republic of
Moscow City
Moscow Oblast
Murmansk Oblast
Nizhny Novgorod
Oblast
North Ossetia-Alania,
Republic of
Novgorod Oblast
Novosibirsk Oblast
Omsk Oblast
Orenburg Oblast
Oryol Oblast
Penza Oblast
Perm Kray
Primorsky Kray
Pskov Oblast
Rostov Oblast
Ryazan Oblast
Saint Petersburg
Sakha (Yakutia)
Republic
Sakhalin Oblast
Samara Oblast
Saratov Oblast
Smolensk Oblast
Sverdlovsk Oblast
Tambov Oblast
Tatarstan, Republic of
NetMigration
Armenia
949
83
496
305
2
202
127
1,218
2,399
139
NetMigration
Azerbaijan
1,054
230
111
105
48
124
57
865
838
442
NetMigration
Georgia
130
9
13
57
6
28
15
692
529
12
Pop.
2,890,000
953,000
1,156,000
1,163,000
163,000
700,000
833,000
10,509,000
6,713,000
843,000
Regional
GDP
(PPP) in
US$
20,779
10,833
12,860
17,902
16,748
10,265
11,394
40,805
17,255
15,555
1,227
892
49
3,341,000
14,709
212
237
543
212
1,132
222
333
114
385
138
1,489
438
488
46
339
265
105
770
88
176
149
169
56
507
196
504
676
11
67
35
56
26
28
23
1
6
231
85
140
702,000
646,000
2,640,000
2,014,000
2,112,000
817,000
1,380,000
2,708,000
1,988,000
696,000
4,242,000
1,158,000
4,582,000
9,343
16,397
13,383
16,213
19,507
11,214
10,764
16,642
12,574
9,877
11,302
11,510
25,277
61
26
1,254
1,125
261
473
386
523
32
46
553
710
204
437
118
834
2
6
239
108
16
100
140
122
950,000
514,000
3,171,000
2,573,000
974,000
4,395,000
1,097,000
3,769,000
21,159
43,462
14,520
12,812
11,845
15,811
11,469
23,290
68
APPENDIX E. (continued)
Administrative Regions and Associated Variables for Russia, 2009
Russian Federal
Subject
Tula Oblast
Tuva Republic
Tver Oblast
Tyumen Oblast
Udmurt Republic
Ulyanovsk Oblast
Vladimir Oblast
Volgograd Oblast
Vologda Oblast
Voronezh Oblast
Yaroslavl Oblast
Zabaykalsky Kray
TOTAL
NetMigration
Armenia
652
12
976
668
35
267
375
825
117
552
715
233
31,760
NetMigration
Azerbaijan
384
5
509
1,640
63
291
171
362
78
251
276
127
20,466
69
NetMigration
Georgia
100
0
93
150
17
63
59
73
7
49
127
48
6,495
Pop.
1,553,000
314,000
1,369,000
3,399,000
1,529,000
1,305,000
1,440,000
2,599,000
1,218,000
2,270,000
1,310,000
1,117,000
136,527,000
Regional
GDP
(PPP) in
US$
12,671
7,578
12,228
57,175
15,290
11,794
11,666
14,327
13,200
11,036
14,760
11,926
1,150,117