1 “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

2 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.

3 The role and main flows of transnational labor migration in Ukraine

4 Conceptual axes (logical matrix) of the proposed research

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

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

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

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

9 Five types of Ukrainian mezo-regions by level of employment

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

11 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 + e Assumption 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 be always 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 diffusion process – events in one place predict an increased likelihood of similar events in neighboring places. Source: Brunsdon, C., A.S. Fotheringham, and M. Charlton, Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science 39(3):

12 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

13 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

14 Example: Cartographic representation of spatial DEPENT variable “Migration rate and dynamics by Ukrainian regions” Source: Ukraine in Maps. Kyiv-Budapest, 2008

15 Structure of spatial regression model: a set of spatial independent (explanatory) ECONOMIC variables: 1.Regional gross value added 2.Employment in regional economy (by sectors) 3.Regional socioeconomic development index 4.Level of privatization 5.Joint ventures 6.Entrepreneurship activity 7.Market infrastructure 8.Science and innovation activities 9.Scientific manpower 10.Industrial development of territory 11.Transformation of sectoral structure of industry 12.Agricultural land use 13.Agricultural gross output

16 Example: Cartographic representation of spatial explanatory ECONOMIC variable “Gross value added by Ukrainian regions” Source: Ukraine in Maps. Kyiv-Budapest, 2008

17 Example: Cartographic representation of spatial explanatory ECONOMIC variable “Employment of the population by Ukrainian regions” Source: Ukraine in Maps. Kyiv-Budapest, 2008

18 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 mortality 6.Death rate by main death causes 7.Natural increase (urban and rural) 8.Life span 9.Population vitality 10.Family composition of population 11.Sex and age composition of population 12.Population aged years (sex and age composition) 13.Population aged 60 years and older 14.National composition of population and ethnic distribution 15.Distribution of ethnic groups

19 Example: Cartographic representation of spatial explanatory DEMORGAPHIC variable “Natural population change by Ukrainian regions” Source: Ukraine in Maps. Kyiv-Budapest, 2008

20 Example: Cartographic representation of spatial explanatory DEMORGAPHIC variable “Ethnic composition of population by Ukrainian regions” Source: Ukraine in Maps. Kyiv-Budapest, 2008

21 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

22 Example: Cartographic representation of spatial explanatory SOCIAL variable “Average personal income by administrative rayons of Ukraine” Source: Ukraine in Maps. Kyiv-Budapest, 2008

23 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

24 Example: Cartographic representation of spatial explanatory LABOR MARKET variable “Unemployment by administrative rayons of Ukraine” Source: Ukraine in Maps. Kyiv-Budapest, 2008

25 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

26 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

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

28 Thank you for attention!