Migration and regional convergence in Romania. A spatial ... · Migration and regional convergence...
Transcript of Migration and regional convergence in Romania. A spatial ... · Migration and regional convergence...
Migration and regional convergence in
Romania. A spatial dynamic panel analysis
between 1997 and 2015
Cristian INCALTARAU & Gabriela PASCARIU
Centre for European Studies, Alexandru Ioan Cuza University of Iasi,
e-mail: [email protected]; [email protected]
RSA Central & Eastern Europe Conference 2017, Cluj-Napoca, Romania
Contents
1. Theoretical mechanisms explaining migration impact on
convergence and empirical evidences
2. Research questions
3. Econometric strategy
4. The patterns of internal migration and regional disparities in
Romania
5. Estimation results
6. Conclusions
1. Mechanisms explaining migration impact on convergence
Migration
Convergence
Increasing occurrence
of agglomeration
economies
- Decrease the labour/capital ratio in
less developed emigration regions
- Increase the labour/capital ratio in
more developed immigration regions(e.g. Barro and Sala-I-Martin, 2004)
Migration inflows generate an
expansionary effect on output,
employment and income in
destination regions.
Divergence
Divergence
Especially during the early stages, migration is a
selective process and it drains people with high
human endowment from the poorer origin areas.
Neoclassical quantitative effect
Composition effect
(e.g. Shioji, 2001; Fratesi and Riggi, 2007)
(e.g. Krugman, 1991; Baldwin, 1999)
Aggolemaration effect
Some empirical evidences on the GDP per capita convergence impact of migration
Country/Period Study Net effectSpain (1995–2002) Maza (2006) +
Norway (1980–2000) Østbye and Westerlund (2007) –
Sweden (1980–2000) Østbye and Westerlund (2007) +
Turkey (1975–2000) Kırdar and Saracoğlu (2008) Strong +
Spain (1996–2005) Hierro and Maza (2010) Weak +
Japan (1960–1990) Shioji (2001) Weak –
Poland (1995–2006) Wolszczak-Derlacz (2009) No (internal)
– (international)
Romania (2004–2009) Bunea (2011) No
EU(27) (2000–2007) Huber and Tondl (2012) –
Italy (1980–2001) Fratesi and Peroco (2014) –
Indonesia (1975–2005) Vidyattama (2014) No
Croatia (2000-2011) Borozan, 2015 –
Russia (1995-2010) Vakulenko (2016) No
Germany (1995-2010) Kubis and Schneider (2016) –
2. Research questions
1. Growth effect – Did migration support regional growth in Romania?
2. Convergence effect – Did migration reduce or enlarge regional income gaps?
3.Composition effect – How does the impact of migration change when controlling
for human capital endowments? Does quantity or composition effect prevail?
4.Network effect – Does the impact of migration enlarge or reduce when controlling
for spatial dependence?
3. Econometric strategyOur analysis is based on the human capital augmented version of the Solow model (Mankiw et al. 1992) which is usually expressed as the following growth model (e.g. Bond et al., 2001; Østbye and Westerlund, 2007; Huber and Tondl, 2012; Kubis and Schneider, 2016):
𝛥𝑦𝑗,𝑡 = 𝛼 + (𝛽 − 1) 𝑦𝑖,𝑡−1+𝛾𝑚𝑖𝑔𝑖,𝑡′ + 𝛿ℎ𝑐𝑖,𝑡 + 𝜃𝑥𝑖,𝑡 + 𝜂𝑖 + 𝜇𝑡 +𝑣𝑖,𝑡 (1)
where:
𝒚𝒋,𝒕 is log of real GDP per capita
𝒎𝒊𝒈𝒊,𝒋 are migration rates
𝒉𝒄𝒊,𝒋 is human capital endowments
𝒙𝒊,𝒋 are the control variables for: labour force, investment and economic structure
𝜼𝒊 is the unobserved region specific effects, 𝝁𝒕 the unobserved time effects and 𝒗𝒊,𝒕 the error term.
Spatial dependence structure
Moran’s I and Geary's C statistics
(z-scores) for lnY
H0: no spatial autocorrelation
Year Moran's i Geary's C
z p-value* z p-value*
1997 2.766 0.003 -1.851 0.032
1998 2.333 0.010 -1.553 0.060
1999 3.101 0.001 -2.819 0.002
2000 3.294 0.000 -1.986 0.023
2001 3.52 0.000 -2.156 0.016
2002 3.43 0.000 -1.236 0.108
2003 4.158 0.000 -1.719 0.043
2004 3.328 0.000 -1.388 0.083
2005 3.979 0.000 -1.774 0.038
2006 4.774 0.000 -2.286 0.011
2007 4.357 0.000 -1.782 0.037
2008 4.801 0.000 -2.226 0.013
2009 4.757 0.000 -2.154 0.016
2010 5.145 0.000 -1.824 0.034
2011 4.953 0.000 -1.96 0.025
2012 5.294 0.000 -2.306 0.011
2013 5.404 0.000 -2.185 0.014
2014 5.483 0.000 -2.283 0.011
2015 4.656 0.000 -1.883 0.030
1997 4.844 0.000 -2.043 0.021
1998 4.94 0.000 -1.808 0.035
1999 4.841 0.000 -1.748 0.040
Source: Author’s estimations using Stata 13
3. Econometric strategyThe following time - space dynamic panel is estimated using system GMM (Arellano
and Bover, 1995; Blundell and Bond, 1998):
𝑦𝑖,𝑡 = 𝛼 + 𝛽 𝑦𝑖,𝑡−1+ 𝑗=1𝑅 𝜔𝑖,𝑗𝑦𝑗,𝑡 + 𝛾𝑚𝑖𝑔𝑖,𝑡
′ + 𝛿ℎ𝑐𝑖,𝑡 + 𝜃𝑥𝑖,𝑡 + 𝜂𝑖 + 𝜇𝑡 +𝑣𝑖,𝑡 ,
with the convergence speed b=-ln(𝛽)/T
where:
𝝎𝒊,𝒋 is an element of the R×R spatial weights matrix Ω based on the inverse travel distance between i and j regional capitals (In order to check for robustness another weighting scheme was also used taking value 1 if regions i and j are within 2 hour of travel time or 0 otherwise).
With notations already described in the previous slide, see equation [1] notations.
Estimation strengths of system GMM time space dynamic model
a) The model controls for the human capital endowment of regions as
being a fundamental factor in the disequilibrium approach.
b) Accounts for migration simultaneity, as the changes in regional growth
can cause significant changes in migration flows.
c) Forth, it accounts for the heterogeneity and dependence of growth
rates between spatially related units (Bouayad-Agha & Védrine, 2010;
Kubis & Schneider, 2015; Kukenova & Monteiro).
d) Accounts for unobserved time and region specific effect, but also
econometric problems such as measurement errors and weak
instruments (Bond et al. 2001; Roodman, 2009).
Variables Description
Migration internal migration, International migration, Emigration and
Immigration are migration rates (per 1000 pop. aged 15-64 years in
t-1)
Human capital 1 Share of students in pop. aged 19-23 years
Human capital 2 Doctors per 1000 pop.
Investment (1997-2015) Highway density - the length of highways available divided by area
of county
Investment (2005-2015) Share of net investment of local units (industry, construction, trade
and other services) to GDP
Labour force Age dependency – share of young and old to active population
Economic structure Share of employment in agriculture, forestry and fishing
4. Data description
Figure 1. Romanian gross national and international migration rates during 1997-2014
Note: The study relies only on
permanent migration flows (leading
to a change in domcile).
Gross internal/international
migration cumulates both
immigration and emigration flows.
Source: own representation using
data from Romanian National
Institute of Statistics
Figure 1. Economic growth and migration rates by Romanian NUTS3, 1997-2015
(a) Average annual GDP per capita growth and
(b) average annual net migration growth rate (per 1000 population aged 15-64 years)
(a) (b)Note: Bucuresti outlier was excluded from the first map (a), while Ilfov was excluded from the second map (b);
net migration rates enclose both internal and external flows; net migration unit: per 1000 population aged 15-64 years.
Source: own representation using data from Romanian National Institute of Statistics. Made with Philcarto * http://philcarto.free.fr
Alba
Arad
Arges
Bacau
BihorBistrita-Nasaud
Botosani
Braila
Brasov
BuzauCalarasiCaras-Severin
Cluj
Constanta
Covasna
Dambovita
Dolj
Galati
Giurgiu
Gorj
Harghita
Hunedoara
Ialomita
Iasi
Ilfov
Maramures
Mehedinti
Mures
Neamt
Olt
Prahova
Salaj
Satu Mare
Sibiu
Suceava Teleorman
Timis
Tulcea
ValceaVaslui
Vrancea
.01
.02
.03
.04
.05
.06
GD
P p
er
ca
pita
gro
wth
ra
te, 19
97-2
01
5
5.2 5.4 5.6 5.8 6 6.2Log of 1997 GDP per capita
Figure 2. Romanian regional unconditional β-Convergence Figure 3. σ convergence - dispersion of GDP per capita across
Romanian NUTS3 regions, 1997-2015 (the cross-sectional
standard deviation of the log of per capita GDP)a) Absolute β- convergence applies when the poorer economies
are growing faster than the rich ones and they all tend to converge
to the same stationary level of real income per capita in the long
run (Barro and Sala-i-Martin, 2004)
b) σ convergence, occurs when there is a decrease in income per
capita dispersion between regions (Barro and Sala-i-Martin, 2004).
1997-2015 Absolute β Convergence No human capital Human capital Human capital and spatial lag
LSDVC System GMM No migration Net migration Gross migration No migration Net migration Gross migration No migration Net migration Gross migration
lnYt-1 1.134*** 1.012*** 0.975*** 0.923*** 0.894*** 0.961*** 0.861*** 0.865*** 0.872*** 0.809*** 0.844***
(0.0212) (0.00915) (0.0362) (0.0435) (0.0657) (0.0449) (0.0666) (0.100) (0.0738) (0.0859) (0.0876)
W1y_lnYt-1 0.341* 0.348** 0.299**
(0.195) (0.168) (0.140)
Net intern 0.000908 0.00245 0.00189
(0.000837) (0.00172) (0.00185)
Net extern -0.00246* -0.00476* -0.00400
(0.00144) (0.00255) (0.00249)
Outflows (int) 0.00234 0.00237 0.00225
(0.00229) (0.00211) (0.00191)
Outflows (ext.) 0.0424 0.0362 0.0169
(0.0270) (0.0236) (0.0211)
Inflows (int) 0.00248 0.00376 0.00245
(0.00180) (0.00278) (0.00256)
Inflows (ext.) -0.00421* -0.00604* -0.00371
(0.00228) (0.00333) (0.00299)
Students 0.00850 0.0280* 0.0512* 0.0369** 0.0535** 0.0626**
(0.00948) (0.0144) (0.0287) (0.0171) (0.0207) (0.0245)
Doctors 0.00272 0.00527 -0.00686 0.00563 0.00649 -0.00216
(0.00380) (0.00412) (0.00828) (0.00559) (0.00520) (0.00709)
Highway density 0.00298 0.00617** 0.00242 0.00275 0.00619* -0.00111 -0.000443 0.00113 -0.00474
(0.00264) (0.00299) (0.00404) (0.00291) (0.00312) (0.00374) (0.00199) (0.00310) (0.00350)
Agriculture share -0.140* -0.210** -0.177 -0.136 -0.223** -0.178 -0.194** -0.240** -0.195
(0.0811) (0.0827) (0.133) (0.0830) (0.0964) (0.152) (0.0780) (0.0917) (0.117)
Age depend. 0.0893 0.0406 0.00560 0.0728 -0.0354 -0.0526 -0.0211 -0.0938 -0.0849
(0.0761) (0.0834) (0.114) (0.0823) (0.108) (0.147) (0.135) (0.155) (0.140)
b -0.66% -0.06% 0.13% 0.42% 0.59% 0.21% 0.79% 0.76% 0.72% 1.12% 0.89%
N 756 798 798 798 798 798 798 798 798 798 798
N_g 42 42 42 42 42 42 42 42 42 42 42
Instruments 26 28 40 52 30 42 54 36 48 60
ar2p 0.002 0.001 0.001 0.002 0.001 0.001 0.001 0.000 0.001 0.001
ar3p 0.130 0.142 0.157 0.237 0.244 0.336 0.372 0.269 0.344 0.333
Hansen test 0.393 0.646 0.438 0.726 0.643 0.558 0.886 0.929 0.904 0.959
Sargan test 0.636 0.631 0.026 0.079 0.631 0.042 0.191 0.922 0.102 0.137
Notes: Robust standard errors
are given in parentheses.
Significance levels: * p < 0.1, **
p < 0.05, *** p < 0.01 The
model is estimated including
time-specific effects, but the
coefficents of the other
explanatory variables are not
displayed in this
table..Instruments were
collapsed and a 3 to 7 lag limit
was set. Lagged dependent, and
migration variables were treated
as endogenous, while tertiary
enrolment, doctors, age
dependency, highway density,
share of employment in
agriculture and time dummies
were treated as strictly
exogenous. Estimations are in
orthogonal deviations and being
performed using the Roodman’s
xtabond2 package in Stata
(Roodman, 2009). Instruments
tests results are also displayed in
the last part of the table (weakiv
command in Stata).
Estimation results: System GMM estimation of migration impact on regional growth in Romania, NUTS3, 1997-2015
2005-2015 Absolute β Convergence No human capital Human capital Human capital and spatial lag
LSDVC System GMM No migration Net migration Gross migration No migration Net migration Gross migration No migration Net migration Gross migration
lnYt-1 1.488*** 1.004*** 0.916*** 0.884*** 0.884*** 0.889*** 0.841*** 0.842*** 0.837*** 0.828*** 0.819***
(0.00269) (0.0128) (0.0589) (0.0646) (0.0683) (0.0663) (0.0719) (0.0737) (0.0892) (0.0915) (0.0924)
W1y_lnYt-1 0.306 0.171 0.168
(0.185) (0.184) (0.202)
Net intern 0.00152 0.00252** 0.00202
(0.00117) (0.00123) (0.00124)
Net extern -0.00229 -0.00491** -0.00394*
(0.00167) (0.00211) (0.00207)
Outflows (int) -0.000203 -0.00141 -0.00107
(0.00172) (0.00195) (0.00180)
Outflows (ext.) 0.00561 0.00916 0.00847
(0.0114) (0.00961) (0.00949)
Inflows (int) 0.00195 0.00291* 0.00250
(0.00153) (0.00160) (0.00163)
Inflows (ext.) -0.00293 -0.00533** -0.00450*
(0.00174) (0.00236) (0.00246)
Students 0.0330* 0.0548*** 0.0569** 0.0497** 0.0601*** 0.0627***
(0.0172) (0.0197) (0.0212) (0.0205) (0.0206) (0.0208)
Doctors 0.00258 0.00249 -0.0000863 0.00489 0.00326 0.00136
(0.00613) (0.00693) (0.00697) (0.00754) (0.00741) (0.00782)
Net investment 0.101 0.0810 0.0536 0.113 0.0622 0.0390 0.0433 0.0248 0.0103
(0.0659) (0.0525) (0.0536) (0.0756) (0.0581) (0.0705) (0.0660) (0.0592) (0.0675)
Agriculture share -0.292* -0.345** -0.340** -0.258* -0.276** -0.281* -0.267* -0.266* -0.286*
(0.151) (0.150) (0.153) (0.139) (0.136) (0.151) (0.151) (0.144) (0.156)
Age depend. 0.171* 0.162 0.146 0.130 0.0772 0.0761 0.0228 0.0296 0.0280
(0.0998) (0.125) (0.105) (0.101) (0.143) (0.134) (0.135) (0.151) (0.147)
b -3.61% -0.04% 0.80% 1.12% 1.12% 1.07% 1.57% 1.56% 1.62% 1.72% 1.82%
Observations 420 462 462 462 462 462 462 462 462 462 462
Groups 42 42 42 42 42 42 42 42 42 42 42
Instruments 19 21 35 49 23 37 51 30 44 58
ar1p 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
ar2p 0.019 0.053 0.069 0.074 0.050 0.075 0.077 0.056 0.074 0.079
Hansen test 0.460 0.310 0.423 0.841 0.312 0.513 0.914 0.174 0.808 0.995
Sargan test 0.628 0.190 0.130 0.236 0.180 0.162 0.312 0.352 0.273 0.471
Notes: Robust standard errors
are given in parentheses.
Significance levels: * p < 0.1, **
p < 0.05, *** p < 0.01 The
model is estimated including
time-specific effects, but the
coefficents of the other
explanatory variables are not
displayed in this
table..Instruments were
collapsed and a 2 to 7 lag limit
was set. Lagged dependent, and
migration variables were treated
as endogenous, while tertiary
enrolment, doctors, age
dependency, highway density,
share of employment in
agriculture and time dummies
were treated as strictly
exogenous. Estimations are in
orthogonal deviations and being
performed using the Roodman’s
xtabond2 package in Stata
(Roodman, 2009). Instruments
tests results are also displayed in
the last part of the table (weakiv
command in Stata).
Estimation results: System GMM estimation of migration impact on regional growth in Romania, NUTS3, 2005-2015
Conclusions
1)Overall, regional disparities have increased over 1997-2015 period (as indicated by both unconditional β
and σ convergence). When controlling for labour, investments and economic structure, a mildly
conditional convergence process of 0.13% per year was found.
2)Internal and international migration flows work differently on economic growth. While internal migration
flows support growth, international flows reduce growth.
3)
When controlling for human capital, the regional convergence speed increased, indicating that human
capital accumulation is particularly high in rich places (agglomeration economies); also adding
migration increases convergence speed even more, showing that the composition effect is one of the
mechanisms that migration uses for deepening regional balances.
4)
Controlling for spatial dependence reduces the impact of migration, suggesting the existence of a network
effect between neighboring regions; regional convergence speed also increases when the spatial lag was
included revealing the existence of spill-over effects on growth within spatially related regions.
5)The polarisation of Romanian economy is increasing, while Romanian authorities are facing a difficult trade-
off between efficiency and equity. Economic incentives for raising human capital and investment stock are
required in order support growth in emigration regions.
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Thank you!