Migration and the Labour Market: Data and Intro to … · Contents of today’s meeting 1...

Migration and the Labour Market:Data and Intro to STATA

Prof. Dr. Herbert Brucker

Otto-Friedrich-University of Bamberg

Projectseminar, Meeting May 27 and June 9, 2010

Herbert Brucker Projectseminar

Contents of today’s meeting

1 Repetition of last meeting

Repetition of Borjas (QJE 2003) and Ottaviano/Peri (NBER2006) structural modelA model with imperfect labour markets

2 Outline of the tasks ahead

3 An introduction to the data set

4 STATA: a primer


Teil I

Repetition of the last meeting


Repetition of last meeting

Borjas’(QJE 2003) national level regression model

Borjas’ (QJE 2003) production function approach

Ottaviano/Peri (NBER 2006) extension

A model with imperfect labour markets (Brucker/Jahn, SJE2010)


Teil II

Outline of workplan


What do we have to do?

1 Review of fundamental contributions in the literature (done?)

2 Getting familiar with the data and data handling with STATA

3 Providing descriptive statistics and making graphs

4 Running the Borjas (QJE 2003) national regression modelwith STATA

5 Getting familiar with the production function approach

6 Estimation of the parameters of the nested CES productionfunction following Borjas (QJE 2003) and Ottaviano/Peri(NBER 2003)

7 Simulation of migration effects using the estimated parameters

8 Presenting the findings

9 Writing the paper


Presentation of the data setDescriptive results

Teil III

Sketch of dataset



A nice data set ...

IAB Employment Sample (IABS)

2 % sample of all employees and unemployed derived fromsocial security recordsprecise information on wages and unemployment spellswe use the 1980 - 2004 period (25 time series observations)we restrict sample to Western Germany (without Berlin)



... with a lot of problems

Foreigners covered only by citizenship, not immigrants bycountry of birth

Many immigrants not covered, distortions though (i)naturalizations and (ii) 2nd and 3rd generation migrants

Wages censored at threshold level of contributions to pensionsystem

Only daily wage information (problem: part-time workers)

Incomplete education information (17% of our cases have noinformation)



How did we address the problems

Identification of foreignersWe treat all individuals as foreigners if they are once reportedas foreign nationals (to control for naturalizations)Identification of ethnic Germans (SSpataussiedler”) byprogramme participation (e.g. special language classes)We do not consider immigration from Eastern Germany(treated as German nationals)

Censored wage information: 5,800 Euro income ceiling (3 % oflabour force)

Imputation of wages above ceiling using the Buttner/Rassler(2008) heteroscedastic single imputation approach

Imputation of missing education information (17 per cent) byusing the Fitzenberger et al. (2005) approach

We exclude all part-time employees due to missing hourlywage information



Share of foreigners in labour force and employment

Abbildung: Share of foreign labor force and workers



Foreigner shares by education group

education 1980 1990 2000 2004

no vocational degree 0.240 0.279 0.394 0.388vocational degree 0.051 0.064 0.106 0.113high school (Abitur)+ vocational degree 0.065 0.058 0.078 0.082university degree 0.071 0.061 0.063 0.071


BasicsData managementData description

Intro to regressions

Teil IV

STATA: A Primer




Basics

STATA consists of a main menue and different editors andviewers

Three types of files:

Do files: Do-Files can be used to run all commands and savethem (very useful, you can always repeat what you have donein the last session)

Data files: e.g. wagcurve.dta

Log files: Report all commands and results of your session(not necessary, but perhaps useful)




Do Files

To open a Do File or create a new Do file: Syntax: doedit

Choose path where you work and your data are:Syntax: cd c: mig

Type your commands into the Do file

Run selected lines of the Do file (Run command in the Do filemenue)

Do entire Do File (Do command in the Do file menue)

Save your Do File after end of session

Hint: It is helpful to describe what you have done. You can dothis by placing words or sentences between stars,e.g. *** Creating Dummy Variables ***




Data management (I)

Getting started: open the data editor. Syntax: edit

A simple way to load data from EXCEL tables: copy allvariables (incl. labels) by control c and insert them in dataeditor by control v (there exist more sophisticated ways)

Preserve and close data editor

Load existing data sets. Syntax: Use c: mig wagecurve.dta,clear

Use command: loads datasetclear command: replaces old data in memory




Data management (II)

Creating new variables

Syntax: gen lnwage = log(wage) (Generates log of a variable)

Syntax: gen V1*V2 (Generates product of two variables)

Creating Dummy variables

Syntax: gen Ded1 = 0Generates Variable where all values are zero

Syntax: replace Ded1 = 1 if ed == 1Replaces all zero values with 1 if ed index has values of 1

The if command (condition of something)

Syntax: if x1 == 1 or ifx2 < 5




Data management (III)

Other useful commands

Syntax: rename oldvarname newvarname (renames variables)

Syntax: drop var (deletes variable from dataset)

Syntax: sort year (sorts dataset by a variable, e.g. a timeindex)

Numeric and string variables: All variables are either numeric(e.g. 512) or string (e.g. alpha)

How to deal with string variables?

Syntax: replace Ded1 = 1 if ed == novocational”

I.e. use novocationalınstead of a numeric value




Data description

Browse your data

Syntax: list year var1, var2, var3 ... varN

Produce summary statistics: observations, mean, standarddeviation, minimum, maximum

Syntax: sum year var1, var2, var3 ... varN




Graphics

Line Graphs

Syntax: graph twoway line var1 var 2 year

Bars

Syntax: graph twoway bar var1 var 2 year

Scatter Plots

Syntax: graph scatter line var1 var 2 year




Simple regressions

Simple ordinary least-squared estimation:

Syntax: regress var1 var2 var3 varX

Least-squared Dummy or fixed effects estimation

Syntax: regress var1, var2, var3 ... varX, D1, D2, D3 ... DN




Pooled or panel regressions

Organise your data set as a panel data set:

Syntax: tsset group timevar

Group variable: Index for each group in data set

Time variable: Index for each time period in data set (e.g.year)

Fixed-effects regression:

Syntax: xtreg var1, var2, var3 ... varX, fe

Random-effects regression (default):

Syntax: xtreg var1, var2, var3 ... varX, re




Recall the Borjas (QJE 2003) regression

Definition of labour supply shock:

mijt = Mijt/(Mijt + Nijt) (1)

Estimation equation:

yijt = θmijt+δi+xj+πt+(δi×xj)+(δi×πt)+(xj×πt)+eijt (2)

yijt = wage or unemployment rate in logarithms; Mijt =immigrants; Nijt = natives; δi = vector of fixed effects foreach education group; xj = vector of fixed effects for eachexperience group; πt = vector of fixed time effects; eijt =error term; i = 1...4 = index for education group; j = 1...8index of experience group




What do we have to do?

Create the migration variable

Create logs of the wage variable

Create the education, experience and time dummies

Create the interaction dummies (a lot of work)

Run the regression


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