Migration and the Labour Market: Data and Intro to STATA

Transcription

1 Migration and the Labour Market: Data and Intro to STATA Prof. Dr. Otto-Friedrich-University of Bamberg, Meeting May 27 and June 9, 2010

2 Contents of today s meeting 1 Repetition of last meeting Repetition of Borjas (QJE 2003) and Ottaviano/Peri (NBER 2006) structural model A model with imperfect labour markets 2 Outline of the tasks ahead 3 An introduction to the data set 4 STATA: a primer

3 Teil I Repetition of the last meeting

4 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 (Brücker/Jahn, SJE 2010)

5 Teil II Outline of workplan

6 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 model with STATA 5 Getting familiar with the production function approach 6 Estimation of the parameters of the nested CES production function 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

7 Presentation of the data set Descriptive results Teil III Sketch of dataset

8 A nice data set... Presentation of the data set Descriptive results IAB Employment Sample (IABS) 2 % sample of all employees and unemployed derived from social security records precise information on wages and unemployment spells we use the period (25 time series observations) we restrict sample to Western Germany (without Berlin)

9 Presentation of the data set Descriptive results... with a lot of problems Foreigners covered only by citizenship, not immigrants by country 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 pension system Only daily wage information (problem: part-time workers) Incomplete education information (17% of our cases have no information)

10 Presentation of the data set Descriptive results How did we address the problems Identification of foreigners We treat all individuals as foreigners if they are once reported as foreign nationals (to control for naturalizations) Identification of ethnic Germans (SSpätaussiedler ) by programme 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 % of labour force) Imputation of wages above ceiling using the Büttner/Rässler (2008) heteroscedastic single imputation approach Imputation of missing education information (17 per cent) by using the Fitzenberger et al. (2005) approach We exclude all part-time employees due to missing hourly wage information

11 Presentation of the data set Descriptive results Share of foreigners in labour force and employment

12 Presentation of the data set Descriptive results Foreigner shares by education group education no vocational degree vocational degree high school (Abitur) + vocational degree university degree

13 Data management Intro to regressions Teil IV STATA: A Primer

14 Data management Intro to regressions STATA consists of a main menue and different editors and viewers Three types of files: Do files: Do-Files can be used to run all commands and save them (very useful, you can always repeat what you have done in the last session) Data files: e.g. wagcurve.dta Log files: Report all commands and results of your session (not necessary, but perhaps useful)

15 Do Files Data management Intro to regressions 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 file menue) 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 do this by placing words or sentences between stars, e.g. *** Creating Dummy Variables ***

16 Data management Intro to regressions Data management (I) Getting started: open the data editor. Syntax: edit A simple way to load data from EXCEL tables: copy all variables (incl. labels) by control c and insert them in data editor 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 dataset clear command: replaces old data in memory

17 Data management Intro to regressions 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 = 0 Generates Variable where all values are zero Syntax: replace Ded1 = 1 if ed == 1 Replaces 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

18 Data management Intro to regressions 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 time index) 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 novocationalïnstead of a numeric value

19 Data management Intro to regressions Browse your data Syntax: list year var1, var2, var3... varn Produce summary statistics: observations, mean, standard deviation, minimum, maximum Syntax: sum year var1, var2, var3... varn

20 Graphics Data management Intro to regressions 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

21 Data management Intro to regressions 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

22 Data management Intro to regressions 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

23 Data management Intro to regressions Recall the Borjas (QJE 2003) regression Definition of labour supply shock: Estimation equation: m ijt = M ijt /(M ijt + N ijt ) (1) y ijt = θm ijt +δ i +x j +π t +(δ i x j )+(δ i π t )+(x j π t )+e ijt (2) y ijt = wage or unemployment rate in logarithms; M ijt = immigrants; N ijt = natives; δ i = vector of fixed effects for each education group; x j = vector of fixed effects for each experience group; π t = vector of fixed time effects; e ijt = error term; i = = index for education group; j = index of experience group

24 Data management Intro to regressions 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