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“Assessing Migratory Vulnerability and Emigration Potential of the Ukrainians Regions:

Demo-Socio-Economic Approach”

Dr. Volodymyr Anderson

Head of Laboratory for Regional Studies and GIS

Department of Economic and Social Geography

Odessa I.I.Mechnikov National University, Odessa, Ukraine

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Problem definition

• Ukraine as a newly independent nation and transitional economy is facing with a critical demographic situation, where the labor migration factor plays a key role;

• approximately 4% of all world migrants are affiliated with Ukraine;

• by expert estimations, the share of the population working abroad reaches 10% in Ukraine with the five million Ukrainian migrant workers;

• the amount of money transfers to the motherland from Ukrainian emigrant workers is estimating as 8 billion USD per year (for comparison: the 2007 Ukrainian budgetary revenues were fixed as 30 billion USD);

• till now the problem has not been studied enough in spatial context to define spatial patterns/dependences and regional differences in transnational labor migration over the Ukraine;

• we propose to study the problem within the intersection of four independent aspects (conceptual axes): 1) types of development (demographic, economic, and socio-cultural); 2) types of migration (there are 3 independent aspects can be found out: emigration/immigration, periodic labor migration/permanent labor migration, and legal/illegal or forced migration); 3) regional dimension (there are a set of macro- mezo- and microregions can be defined and examined); 4) policymaking dimension.

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The role and main flows of transnational labor migration in Ukraine

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Conceptual axes (logical matrix) of the proposed research

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Four Ukrainian macro-regions demonstrating different patterns of transnational labor migration

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Two types of Ukrainian border regions demonstrating different patterns of transnational labor migration

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Five types of Ukrainian mezo-regions by level of economic development

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Five types of Ukrainian mezo-regions by level of labor market development

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Five types of Ukrainian mezo-regions by level of employment

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Seven types of Ukrainian rural micro-regions (by level of rurality)

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Spatial regression model: general description

The general purpose of standard liner regression analysis is to find a (linear) relationship between a dependent variable and a set of explanatory variables:

y = Xb + eAssumption about the random error of the regression equation need to be made:a) The random error has mean zero;b) The random error terms are uncorrelated and have a common variance (homoskedastic);c) The random error term follows a normal distribution.

In case of spatial variables a spatial dependence effect occurs and these assumptions may not bealways satisfied in practice. With spatial lag in regression, the assumption of uncorrelated error terms is violated.In addition, the assumption of independent observation is also violated.As a result, the estimates are biased and inefficient. Spatial lag is suggestive of a possible diffusionprocess – events in one place predict an increased likelihood of similar events in neighboring places.

Source: Brunsdon, C., A.S. Fotheringham, and M. Charlton, 1999. Some notes on parametricsignificance tests for geographically weighted regression. Journal of Regional Science39(3): 497-524.

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Structure of spatial regression model: a set of dependent variables

(eight different types of migration derived from “conceptual axes” of the project)

Three basic dichotomous spatial variables:

1. Labor out-migration v. Labor in-migration

2. Labor periodic migration v. Labor permanent migration

3. Labor legal migration v. Labor illegal migration

Eight derivative spatial variables to be examined:

• Legal periodic labor out-migration

• Illegal periodic labor out-migration

• Legal periodic labor in-migration

• Illegal periodic labor in-migration

• Legal permanent labor out-migration

• Illegal permanent labor out-migration

• Legal permanent labor in-migration

• Illegal permanent labor out-migration

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Additional migratory variables to be examined:

1. Urban migration

2. Rural migration

3. Structure of natural and migratory population movement

4. Border crossing: departure from Ukraine

5. Border crossing: entry to Ukraine

6. Migration between Ukraine and world countries

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Example: Cartographic representation of spatial DEPENT variable “Migration rate and dynamics by Ukrainian regions”

Source: Ukraine in Maps. Kyiv-Budapest, 2008

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Structure of spatial regression model: a set of spatial independent (explanatory) ECONOMIC variables:

1. Regional gross value added2. Employment in regional economy (by sectors)3. Regional socioeconomic development index4. Level of privatization5. Joint ventures6. Entrepreneurship activity7. Market infrastructure8. Science and innovation activities9. Scientific manpower10. Industrial development of territory11. Transformation of sectoral structure of industry12. Agricultural land use13. Agricultural gross output

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Example: Cartographic representation of spatial explanatory ECONOMIC variable

“Gross value added by Ukrainian regions”Source: Ukraine in Maps. Kyiv-Budapest, 2008

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Example: Cartographic representation of spatial explanatory ECONOMIC variable

“Employment of the population by Ukrainian regions”Source: Ukraine in Maps. Kyiv-Budapest, 2008

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Structure of spatial regression model: a set of spatial independent (explanatory) DEMOGRAPHIC variables:

1. Birth rate (urban population)2. Birth rate (rural population)3. Death rate (urban population)4. Death rate (rural population)5. Infant mortality6. Death rate by main death causes7. Natural increase (urban and rural)8. Life span9. Population vitality10. Family composition of population11. Sex and age composition of population12. Population aged 15-59 years (sex and age composition)13. Population aged 60 years and older14. National composition of population and ethnic distribution15. Distribution of ethnic groups

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Example: Cartographic representation of spatial explanatory DEMORGAPHIC variable “Natural population change by Ukrainian regions”

Source: Ukraine in Maps. Kyiv-Budapest, 2008

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Example: Cartographic representation of spatial explanatory DEMORGAPHIC variable “Ethnic composition of population by Ukrainian regions”

Source: Ukraine in Maps. Kyiv-Budapest, 2008

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Structure of spatial regression model: a set of spatial independent (explanatory) SOCIAL variables:

1. Human development index

2. Financing of human development

3. Sources of income of population

4. Population income from private households

5. Money income of population

6. Wages and salaries

7. Structure of households money income

8. Distribution of money income

9. Population welfare standards

10. Population poverty

11. Primary morbidity of population

12. General morbidity of population

13. Population morbidity: HIV incidence

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Example: Cartographic representation of spatial explanatory SOCIAL variable “Average personal income by administrative rayons of Ukraine”

Source: Ukraine in Maps. Kyiv-Budapest, 2008

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Structure of spatial regression model: a set of spatial independent (explanatory) variables:

LABOR MARKET INDEXES

1. Population employment

2. Population employment by types of economic activity

3. Population employment (domiciliary)

4. Demographic pressure for population of active working age

5. Level of unemployment

6. Labor market development index

7. The unemployed with higher education

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Example: Cartographic representation of spatial explanatory LABOR MARKET variable “Unemployment by administrative rayons of Ukraine”

Source: Ukraine in Maps. Kyiv-Budapest, 2008

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Additional spatial analysis models to be used for labor migratory data exploration in spatial dimension during the project

• Spatial econometrics (James P. LeSage, 1999) – to discover spatial autocorrelation effects in labor migratory data (by Ukrainian regions and rayons)

• Interactive exploratory spatial data analysis (Luc Anselin, 1999) – to discover spatial associations and patterns in spatial labor migratory data (by regions and rayons)

• Visual spatial data mining (Pinfu Yang; Tong Zhang, 2005) – to discover and explain spatial irregularities in labor migratory data when variables are measured in nominal (qualitative) scale

• Spatial cross-table analysis (Ed Lindsey, 2008) – to discover local spatial dependences in labor migratory data measured in nominal scale (in case of small spatial sample sets)

• Fisher criterion (Marco Loog, 2006) – to overcome the small sample problem in spatial analysis of labor migratory data measured in dichotomous nominal scale

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Eight regional surveying case studies (to be defined after spatial regression analysis)

1. Region were legal periodic labor out-migration maximized

2. Region were illegal periodic labor out-migration maximized

3. Region were legal periodic labor in-migration maximized

4. Region were illegal periodic labor in-migration maximized

5. Region were legal permanent labor out-migration maximized

6. Region were illegal permanent labor out-migration maximized

7. Region were legal permanent labor in-migration maximized

8. Region were illegal permanent labor out-migration maximized

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Regional complex surveying case study(Ukraine-Transnistria-Moldova-Romania transborder region)

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Thank you for attention!