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: cristian.incaltarau@uaic.ro; gcpas@uaic.ro

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!