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EERC Research Grants: Knowledge without Borders, Opportunities without Limits by Olga Kupets

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Page 1: Kupets eerc research grants and their opportunities

EERC Research Grants: Knowledge without Borders, Opportunities without Limits

by Olga Kupets

Page 2: Kupets eerc research grants and their opportunities

Outline

Research grant 1 (2002-2004)

Research grant 2 (2009-2011)

Conclusions and acknowledgements

2

Page 3: Kupets eerc research grants and their opportunities

Determinants of unemployment

duration in Ukraine

Research grant 1 (2002-2004)

Published in JCE, Vol. 34, 2006, pp. 228–247

Page 4: Kupets eerc research grants and their opportunities
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Questions and hypotheses Research questions

What are the factors that determine the probability of leaving

unemployment in Ukraine?

Does the Ukrainian unemployment insurance system

discourage quick exits to employment or some other factors

come into effect instead?

Hypotheses

No effect of unemployment benefits

Disincentive effect of alternative sources of subsistence,

especially income from casual work activities and

subsistence farming

Negative duration dependence after some period

5

Page 6: Kupets eerc research grants and their opportunities

Theoretical model: Job Search Theory (Mortensen, 1970)

Expected completed duration of unemployment spell λ(t)

depends on the probability of receiving a job offer ξ(t) and

the probability then of accepting this job offer θ(t) :

λ(t) = ξ(t) θ(t)

λ(t) = λ (X(t), t),

where X is a vector of explanatory variables which can vary

with unemployment duration t and

)(

)(

Δ

)ΔPr(lim)(Δ tS

tftTtTttλ

0

6

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Econometric model

Cox proportional hazards model (Cox, 1972)

where xi is the set of explanatory variables for individual i,

β is a vector of parameters to be estimated, and λ0(t) is

the baseline hazard at time t, which is allowed to be

nonparametric.

An independent competing risks model, with two risks -

exits to employment and exits to inactivity.

),)(exp()(=)( βtxtλtλ ioi

7

Page 8: Kupets eerc research grants and their opportunities

Ukrainian Longitudinal Monitoring Survey (ULMS) – 2003:

2122 unemployment spells between January 1998 –

December 2003 (1799 individuals)

Individual characteristics (gender, age, education,

marital status, number of children under 15)

Sources of subsistence during unemployment

(unemployment benefits, casual activities and

subsistence farming, household income, public

transfers, other)

Labor market history

Local labor market characteristics (regional

unemployment rate, type of settlement)

Data

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Results

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Results

.01

.01

5.0

2.0

25

Sm

oo

thed

haz

ard

fu

nct

ion

0 6 12 18 24 30 36 42 48 54 60Months

Estimated baseline hazard function for exits to employment

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BRAIN GAIN OR BRAIN WASTE?Performance of returning labor migrants

in the Ukrainian labor market

Research grant 2 (2009-2011)

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Questions and hypotheses

Research questions

Who are return labor migrants and what are the factors

determining their decision to return?

What are the activities migrants choose after their return?

Does migration experience matter? What is its impact?

Main hypothesis

Past migration experience has a negative impact on

employment outcomes of return migrants forcing them

to choose activities in the non-farm informal sector or

agriculture more often than in the non-farm formal sector

(in view of both human capital/ individual perspective and

structural/macro perspective)

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Data: sample of migrants

I. Modular population survey on labor migration issues

Fieldwork: May 26 - June 15, 2008

Target population: Ukrainian individuals of working age (women

from 15 to 54 years and men from 15 to 59 years) who worked

abroad at least once during January 2005-June 2008

Number of surveyed individuals of working age: 48 thds. persons

Of them, number of those who at least once were abroad for

employment reason since 2005 (labor migrants): 1,381 persons

(or 1,476 thds. persons if weighted). Regular cross-border

movements of people living near the state border are not

included!

After dropping observations based on the HBS sample with no

necessary data points, we have information on 667 current

migrants and 381 return migrants (357 with LFS information)

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Data: sample of non-migrants

II. Main Labor Force Survey Data

Initial sample of individuals aged 15-70 years: 47,527 persons

Individuals of working age: 36,366 persons

Sample size after dropping June observations for those

individuals who were surveyed both in May and June: 24,879

persons

Final sample of non-migrants (some of them might be returning

migrants, but they returned before 2005) after exclusion of

observations for individuals whose main place of work during the

reference week was located abroad: 24,675 persons

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Empirical approach: MNP with sample selection and bootstraping

Each individual is making a choice among the following alternatives:

1. Non-employment (U, OLF) – N

2. Employment in agriculture (A)

3. Non-farm employment in the formal sector (F)

4. Non-farm employment in the informal sector (I)

The indirect utility of choosing labor force status j by individual i is

Uij = β 'j xi + εij, (1)

where xi is a vector of characteristics which are likely to affect the choice of the labor force status, βj is a vector of choice-specific parameters, and εij are i.i.d. disturbances.

Multinomial probit model with generalized residual from the sample selection probit and bootstraping procedure.

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Results

Typology of return migrants in Ukraine:

“Return of retirement” – 17 individuals (or 4.8% of all return

migrants in the sample);

“Return of innovation” – 9 individuals (or 2.5%);

“Return of conservatism” – 134 individuals (37.5%);

Some of them might be also potential innovators but they had to

readjust their expectations to local realities in Ukraine and,

therefore, have largely failed to have visible impact on their origin

societies after return.

“Return of failure” – 197 individuals (55.2%).

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Results

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Conclusions and

acknowledgements

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Acknowledgements

EERC experts and friends

Hartmut Lehmann, Rostislav Kapeliushnikov, Irina

Denisova, Klara Sabirianova, John Earle, Reuben

Gronau, Michael Beenstock, Michel Sollogoub, Anna

Lukyanova, Inna Maltseva, Sergey Arzhenovsky, Anna

Mishura, Inna Blam, Sergey Kokovin

Roy Gardner, Irina Murtazashvili, Diana Weinhold,

Tom Coupe, James Leitzel, Olena Nizalova,

Aleksandra Burdyak, Tatiana Karabchuk

Eric Livny, Lyubov Belikova, Natalia Bystrytska, Irina

Sobetska AND MANY OTHERS

Page 21: Kupets eerc research grants and their opportunities

Many thanks to EERC for the opportunities

to LEARN, GROW and

SEE THE WORLD!

Olga Kupets

EERC/KSE Alumni (2000)

[email protected]