The effect of education on migration: Evidence from school reform

Munich Personal RePEc Archive

The effect of education on migration:

Evidence from school reform

Böckerman, Petri and Haapanen, Mika

21 December 2010

Online at https://mpra.ub.uni-muenchen.de/27629/

MPRA Paper No. 27629, posted 23 Dec 2010 21:26 UTC

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Table 1 The estimated marginal effects of the availability of polytechnic education on migration probability (sample of matriculated, 3-year follow-up period)

Model specification LPM AME from probit

(A) No controls 0.009*** (0.001) 0.009*** (0.001)

(B) Matriculation year dummies 0.009*** (0.001) 0.007*** (0.001) (C) Matriculation year and region dummies 0.003*** (0.001) 0.000 (0.001) (D) = (C) + Extensive set of controls 0.003*** (0.001) 0.001 (0.001) Notes: Sample: Individuals are observed during the matriculation year and the following two years.

Number of observations is 81,630 in all estimations. Dependent variable: NUTS3 migration dur- ing the current year. Explanatory variable of interest: Number of 1^st year polytechnic students di- vided by the number of 19-24-year-olds in the NUTS3 region. The set of controls are defined in Appendix, Table A1. * (**, ***) = statistically significant marginal effect at the 0.10 (0.05, 0.01) level. Robust standard errors reported in parentheses allow for clustering at the matriculation year and regional level. LPM = Linear probability model, AME = average marginal effect is computed as average over all observations.

Table 2 The estimated marginal effects of the availability of polytechnic education on migration probability (sample of matriculated, extensive follow-up pe- riod)

Model specification

Availability during the

matriculation year Three-year average in availability

LPM AME from

probit LPM AME from

probit

(A) No controls 0.006*** 0.006*** 0.006*** 0.006***

(B) Matriculation year dummies 0.008*** 0.007*** 0.008*** 0.007***

(C) Matriculation year and region dum-

mies 0.001*** 0.000 -0.000 -0.001

(D) = (C) + Extensive set of controls 0.002*** 0.000 -0.000 -0.000 Notes: Sample: Individuals are observed during the matriculation year and all the following available

years. Number of observations is 272,430 in all estimations. Dependent variable: NUTS3 migra- tion during the current year. Explanatory variable of interest: Number of 1^st year polytechnic stu- dents divided by the number of 19-24-year-olds in the NUTS3 region. The set of controls are de- fined in Appendix, Table A1. * (**, ***) = statistically significant marginal effect at the 0.10 (0.05, 0.01) level. Significance levels are based on robust standard errors that allow for clustering at the matriculation year and regional level. LPM = Linear probability model, AME = average marginal effect is computed as average over all observations.

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Table 3 The estimated effects of the instrumental variables on education (OLS, first stage of IV estimates, sample of graduates)

Variable Full sample,

using years of schooling

Vocational college vs. sec- ondary degree

Polyt. vs.

vocational college degree

Master’s vs.

polytechnic degree Availability of polytechnic

education 0.004 -0.014*** 0.021*** 0.001

(0.013) (0.005) (0.005) (0.003)

Matriculation results 0.620*** 0.058*** 0.035*** 0.164***

(0.012) (0.006) (0.005) (0.007)

Matriculation results not

missing -2.740*** -0.237*** -0.156*** -0.815***

(0.118) (0.047) (0.038) (0.051)

Diagnostics

Joint significance of the

instruments (F-test) 884,834

(p < 0.001) 34.315

(p < 0.001) 20.063

(p < 0.001) 183.868 (p < 0.001) Number of observations 102,583 63,861 46,453 38,722

Notes: Sample: Individuals are observed during the graduation year from specialized education and the following years. Dependent variable: Years of schooling or the level of education (binary vari- able). * (**, ***) = statistically significant marginal effect at the 0.10 (0.05, 0.01) level. Robust standard errors are reported in parentheses that allow for clustering at the matriculation year and regional level. OLS = Ordinary least squares. See Appendix, Table A1 for the set of control vari- ables (incl. matriculation region and year dummies) and definitions of the instrumental variables (availability of polytechnic education is measured during the matriculation).

Table 4 The estimated effects of education on migration propensity (sample of graduates)

Model Full sample,

using years of schooling

Vocational college vs.

secondary de- gree

Polyt. vs.

vocational college degree

Master’s vs.

polytechnic degree

OLS estimates 0.008*** 0.004** 0.006* 0.026***

(0.001) (0.002) (0.003) (0.004)

IV estimates 0.012*** 0.090*** 0.085** -0.001

(0.001) (0.022) (0.036) (0.010)

LIML estimates 0.012*** 0.091*** 0.085** -0.001

(0.001) (0.022) (0.036) (0.010)

Diagnostics for IV Test for exogeneity of the

educ. var. (F-test) 7. 608

(p = 0.006) 21.767

(p < 0.001) 5.250

(p = 0.023) 9.413 (p = 0.002) Overidentifying restric-

tions 0.318

(p = 0.853) 8.401

(p = 0.015) 0.152

(p = 0.927) 0.601 (p = 0.740)

Average migration rate 0.075 0.062 0.072 0.096

Number of observations 102,583 63,861 46,453 38,722

Notes: Sample: Individuals are observed during the graduation year from specialized education and the following years. Dependent variable: NUTS3 migration during a year. * (**, ***) = statistically significant marginal effect at the 0.10 (0.05, 0.01) level. Robust standard errors are reported in parentheses. OLS = Ordinary least squares, IV = instrumental variables. The instruments for the level of education are: availability of polytechnic education during matriculation, matriculation result and matriculation result not missing (see Table A1 for definitions). LIML = Limited in- formation maximum likelihood. See Appendix, Table A1 for the set of control variables (incl.

matriculation region and year dummies).

";

Figure 1 New polytechnic and university students in Finland 1990–2008. Source:

AMKOTA and KOTA databases

010000200003000040000Number of 1st-year students

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Polytechnic students

University students

!

Figure 2 First-year polytechnic students per 19-24-year-olds in 1992–2008 (lines represent NUTS3 regions). Source: AMKOTA database & Statistics Fin- land, Population statistics

05101520Students per hundreds of 19-24-year-olds

1992 1994 1996 1998 2000 2002 2004 2006 2008

"

Figure 3 Number of graduates from specialized education (only 1988–2001 matri- culated who graduate at an age under 35). Source: own sample data

0200400600800Number of graduates

1988 1990 1992 1994 1996 1998 2000 2002

Lowest level tertiary education Polytechnic degree

Bachelor's degree Higher-degree level tertiary education Doctorate or equivalent level tertiary

77 :

Table A1 Description of covariates and their mean values for three samples

Covariate Description (1) (2) (3)

Dependent variables

Migrate 1 if the NUTS3 region of residence is different from

previous year, 0 otherwise 0.078 0.074 0.075

Yrs of school. Years of schooling (12, 13, 14.5, 15.5 or 17.5) 12.050 13.280 14.900 Secondary

degree 1 if person has a specialized higher secondary level de- gree after matriculation (13) or nothing (12 years of schooling), 0 otherwise

0.991 0.710 0.306

Vocational

college degree 1 if person has a vocational college degree (14.5 years), 0

otherwise 0.009 0.121 0.316

Polytechnic

degree 1 if person has a polytechnic degree (15.5 years), 0 oth-

erwise 0.000 0.051 0.136

Bachelor’s

degree 1 if person has a bachelor’s degree from a university

(15.5 years), 0 otherwise 0.000 0.032 0.015

Master’s de-

gree 1 if person has a master’s degree from a university (17.5

years), 0 otherwise 0.000 0.091 0.241

Instrumental variables Availability of polyt. educati- on

Number of 1^st year polytechnic students divided by the hundreds of 19-24-year olds in the NUTS3 region during matriculation year (three year averages in parentheses).

4.426

(4.955) 3.111

(3.711) 1.929 (2.573) Matricul. result General grade from matriculation exam. Range from 1

(worst grade) to 6 (best grade). 0 if grade is missing 3.904 3.786 3.575 Matr. result not

missing 1 if matriculation result is not missing, 0 otherwise 0.926 0.892 0.860 Control variables

Age Age in years 18.156 18.157 19.144

Female 1 if female, 0 if male 0.576 0.576 0.668

Swedish 1 if person belongs to the Swedish minority, 0 otherwise 0.050 0.050 0.046 Married 1 if married or cohabiting, 0 otherwise 0.023 0.020 0.020 Sp. empl. 1 if spouse is employed, 0 otherwise 0.008 0.006 0.005 Sp. educ. Spouse’s level of education (0 if no spouse, 1 if compre-

hensive educ.,…, 5 if higher tertiary educ.) 0.038 0.033 0.032

Sp. income Annual income of spouse, 10 000 € 0.014 0.013 0.014

Children 1 if children under 18 years in the family, 0 otherwise 0.002 0.002 0.002 Earnings Annual earnings subject to state taxation, 10 000 € 0.162 0.161 0.157 Rural 1 if living in an rural municipality (based the degree of

urbanisation and on the population of the largest urban

settlement; see Statistics Finland 2001), 0 otherwise 0.236 0.240 0.271 Semiurban 1 if living in a semiurban municipality, 0 otherwise (see

above; reference is “urban” municipality) 0.174 0.172 0.178 Unempl. Rate Unemployment rate in travel-to-work area, % 14.568 13.396 12.304 Amenities Percentage of the service sector workers in the NUTS4

region 56.995 55.965 54.196

Region of birth 1 if living in the region of birth, 0 otherwise 0.811 0.805 0.811

Number of observations 81,630 272,430 102,583

Notes: Control variables are measured on a year before an individual matriculates. Educational variables after matriculation refer to the first specialized degree. Sample includes: (1) Observations from the matriculation year and the following two years; (2) All possible observations after matriculation; (3) All possible observations after graduation from specialized education after matriculation.

Figure A1 Capital Helsinki and NUTS3 regions in Finland. Note: Itä-Uusimaa is merged with Uusimaa in the analysis and Åland (“Ahvenanmaa” in Fin- nish) is excluded from it

Helsinki