kupets eerc research grants and their opportunities
TRANSCRIPT
EERC Research Grants: Knowledge without Borders, Opportunities without Limits
by Olga Kupets
Outline
Research grant 1 (2002-2004)
Research grant 2 (2009-2011)
Conclusions and acknowledgements
2
Determinants of unemployment
duration in Ukraine
Research grant 1 (2002-2004)
Published in JCE, Vol. 34, 2006, pp. 228–247
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
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
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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
′
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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
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
Many thanks to EERC for the opportunities
to LEARN, GROW and
SEE THE WORLD!
Olga Kupets
EERC/KSE Alumni (2000)