CIRCLE, Lund University, Sweden
CENTER FOR INNOVATION, RESEARCH AND
COMPETENCE
IN THE LEARNING ECONOMY
Longitudinal Data Analysis
–
methods and applications in Innovation Studies
Martin Andersson
CIRCLE, Lund university
CIRCLE, Lund University, Sweden
OUTLINE
•
Part I:
WHY?

identification problems in Innovation Studies and
social sciences more broadly
•
Part II:
WHAT?

introducing panel data analysis
•
Part II:
HOW?

lab session on panel data
CIRCLE, Lund University, Sweden
Part I
Identification problems in Innovation
Studies and social sciences more broadly
CIRCLE, Lund University, Sweden
Identification
•
Main goal in regression analysis is often
to learn about causal
relationships from micro

data
capturing
non

experimental
economic
behavior
.
–
Studies ask "
treatment effect
" questions of the form: what is the
effect of X on Y?
•
What
is the
effect
of R&D investment on a
firm’s
productivity
?
•
Does the
the
local
milieu
of a
firm
affects
its
innovativeness
?
•
What
is the
effect
of general
purpose
technologies
in
growth
?
•
What
is the
effect
of a
a
local
university
on new
firm
formation?
•
Does
entrepreneurship
influence
regional
economic
growth
?
CIRCLE, Lund University, Sweden
Identification
•
Does the
estimated

parameter
reflect
a
causal
effect
of
X
on
Y?
•
How
to ”
isolate
” the
effect
of X on Y?
i
i
i
i
X
Y
CIRCLE, Lund University, Sweden
Identification
•
Identification is closely linked to consistency:
–
is the ”true” parameter
–
is our estimate
–
Ideally, we choose a model (right technique, right
variables and right assumptions) whichs means that
is consistent, such that it converges to when
N
is
large
–
This is essentially what identification is all about
true
ˆ
true
ˆ
CIRCLE, Lund University, Sweden
Identification
•
Manski (2003): the selection problem
”
The researcher wants to compare the outcomes that people would experience if
they were to receive alternative treatments. However, treatments are mutually
exclusive. At most, the researcher can observe the outcome that each person
experiences under the treatment that this person actually receives. The
researcher cannot observe the outcomes that people would have experienced
under other treatments. These other outcomes are counterfactual. Hence, data
on treatments and outcomes cannot by themselves reveal treatment effects.”
•
IDEAL: “treated” individuals selected randomly
•
When is random treatment selection appropriate?
–
in the analysis of data from classical randomized experiments. This is the
main reason why randomized experiments are valued so highly.
The assumption of random treatment selection is usually suspect in non

experimental settings, where observed treatments may be self

selected or
otherwise chosen purposefully.
CIRCLE, Lund University, Sweden
Identification
•
Example
1.1 in
Jurada
(2007):
–
Suppose that you are interested in the effect of military
service on subsequent earnings. You can look at the mean
difference in the outcome between veterans and non

vets.
•
…. but, inside this number hides not only a causal effect of
the service, but also the composition of other causal
variables in each group, both observed and unobserved.
•
Are there variables that affect both participation in the
program and the outcome? Are the vets earning more
because of the military service or are the high

earners more
likely to enroll in the army?
CIRCLE, Lund University, Sweden
Identification
•
One solution is to
control
for
factors
that
may
drive
selection
.
–
Typical procedure in most empirical papers
»
we are interested in
x
but to isolate its effect we control for
z
.
–
when is controlling for observable factors enough to identify a causal effect ?
=> when is “selection on observables plausible”?
•
When is it plausible that conditional on Z, assignment to treatment is “ideal”,
i.e. as good as random?
If applicants to a college are screened based on Z, but conditional on passing the
Z test, they are accepted based on a random draw.
IMPORTANT TO THINK ABOUT THE DATA GENERATING PROCESS (DGP)
CIRCLE, Lund University, Sweden
Identification
•
An
issue
of
”
selection
”
vs.
”
learning
”
•
Applies
to
several
different
topics
in Innovation Studies
–
Roles
of
selection
and
learning
is
typically
of great
conceptual
interest
and
policy relevant
–
usually
we
think
of ”
learning
” as
reflecting
a
causal
effect
:
–
Three
examples
from the
literature
:
•
Persistence
of Innovation
•
Exporting
and
productivity
•
Urban
Wage
Premium
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Example
1:
Persistence
of Innovation
•
Bettina Peters:
–
Persistence of Innovation:
stylised
facts and panel data evidence,
Journal of Technology Transfer
, 2009
•
German
manufacturing
and services
firms
1994

2002:
–
Is innovation persistent? =>
Yes
!
–
What
drives this?
CIRCLE, Lund University, Sweden
Example
1:
Persistence
of Innovation
•
1: “True” state dependence.
–
a causal behavioral effect:
the decision to innovate in one period in
itself enhances the probability to innovate in the subsequent period
.
•
(
i
) success breeds success (Mansfield 1968)
•
(ii) innovations involve dynamic increasing returns (Nelson and
Winter 1982 and
Malerba
and
Orsenigo
1993)
•
(iii) sunk costs in R&D investments (Sutton 1991)
CIRCLE, Lund University, Sweden
Example
1:
Persistence
of Innovation
•
2: Selection on time

invariant characteristics
–
Innovating firms may have characteristics which make them
particularly ”innovation

prone”
–
If these characteristics themselves show persistence over time,
they will induce persistence in innovation behavior.
–
If these are not appropriately controlled for, past innovation may
appear to affect future innovation merely because it picks up the
effect of the persistent characteristics.
–
In contrast to true state dependence this phenomenon is therefore
called
spurious state dependence
CIRCLE, Lund University, Sweden
Example
2:
Exporting
and
Productivity
•
Stylized fact that exporters are more productive
–
In Sweden, persistent exporters are about 20 % more productive than are non

exporters (
Andersson
et al 2008)
–
Why?
–
Learning

by

exporting
:
•
Causal effect from exporting on productivity
–
Knowledge accumulation through interaction with foreign customers may stimulate
innovation and productivity
–
Export markets more competitive and stimulate reduction of X

inefficiencies and adoption
of ‘best

practice’ routines
–
Self

selection:
•
Exports associated with entry costs, implying productivity thresholds that only more
productive firms can overcome (Bernard and Jensen 2004, Greenaway and Kneller
2007, Wagner 2007)
Simply
analzying
the
relationship
between
exports and
productivity
withouth
further
controls
and/or
study
of time
sequences
(ex post // ex ante)
tells
us
nothing
about
the
relevance
of the different
explanations
.
CIRCLE, Lund University, Sweden
Example
3: Urban
Productivity
Premium
•
Wages (and productivity) generally higher in larger regions
Figure
1
.
The relationship between mean wages (log) and accessibility to total
e
conomic activity
(log) acr
oss
Swedish municipalities in 2008.
Log access
ibility to wages
Log mean wage
CIRCLE, Lund University, Sweden
Example
3: Urban
Productivity
Premium
–
Selection
•
the “best” and the “brightest” move to the cities
–
Learning
•
Causal effect “from the environment” on productivity
–
operating
in a
dense
agglomeration stimulate a
worker’s productivity
CIRCLE, Lund University, Sweden
Example
3: Urban
Productivity
Premium
Alfred Marshall on
selection
in 1890:
”In almost all countries there is constant migration towards the
towns. The large towns and especially London absorb the very best
blood from all the rest of England: the most enterprising, the most
highly gifted, those with the highest physique and the strongest
characters go there to find scope for their abilities”
CIRCLE, Lund University, Sweden
Example
3: Urban
Productivity
Premium
•
Learning relates to
‘pure’ agglomeration effects and is
conceptually rooted in the literature on agglomeration economies
and localized human capital spillovers (Rauch 1993,
Glaeser
2008
).
•
Agglomerations as “innovation environments” (
Glaeser
1999)
CIRCLE, Lund University, Sweden
Example
3: Urban
Productivity
Premium
•
Big
literature
focused
on
untangling
the relative
roles
of
selection
and
learning
in
explaining
the UWP.
–
Risk of
overestimating
learning
(
causal
effect
from agglomeration
on
productivity
)
if
not
appropriately
controlling
for
selection
CIRCLE, Lund University, Sweden
Identification
•
Thinking
in terms of ”
selection
” and ”
learning
”
important
for
identification
•
but
,
•
…
selection
and
learning
effects
are, at
least
conceptually
,
seldom
mutually
exclusive
•
and,
•
their
relative
roles
often
bear
on
theory
as
well
as policy
CIRCLE, Lund University, Sweden
Identification
•
Example
UWP:
•
Theory and conceptualizations:
–
if selection is the dominant source of the city productivity premium, then
theory should focus on why cities attract more productive workers rather
than why cities are more productive (
Glaeser
and
Maré
2001)
•
Policy
–
learning effects provides support for policies stimulating the growth of large
city agglomerations.
CIRCLE, Lund University, Sweden
Identification
•
Example
persistence
of innovation:
•
Theory and conceptualizations:
–
Endogenous
growth models:
•
Romer
(1990
)
assumes that innovation
behaviour
is persistent at
the firm
level to a very large
extent.
•
Aghion
and
Howitt
(1982) suggest that
the process of creative destruction
leads to a
perpetual renewal
of
innovators.
–
Empirical knowledge
about the dynamics in firms’ innovation
behaviour
is a tool to assess
different endogenous
growth
models
CIRCLE, Lund University, Sweden
Identification
•
Policy
•
If
innovation is state dependent, innovation
–
stimulating policy measures such as
government support
programmes
are supposed to have a more profound effect
because they do not only affect current innovation activities but are also likely to
induce a permanent change in
favour
of innovation.
•
If
, on the other hand, individual heterogeneity induces persistent
behaviour
,
support
programmes
are unlikely to have long
–
lasting effects and policy should
concentrate more on measures which have the potential to improve innovation
–
relevant firm
–
specific factors.
CIRCLE, Lund University, Sweden
Identification
•
SUMMARY:
•
For identification of the effect of X on Y, accounting for selection
is imperative.
•
Selection vs. learning a key issue in many lines of inquiry in
Innovation Studies, as well as in the social sciences more
broadly
•
But how to account for selection?
CIRCLE, Lund University, Sweden
Identification
•
Selection on observables:
–
Estimate
effect of X on Y, while controlling for observable
characteristics of the ”observational units”, such as individuals or
firms.
•
Firms:
productiviy, employment size, location, capital stock, human
capital, industry affliation ownership structure
•
Individuals:
age, gender, education, place of residence, tenure, etc.
•
NOTE: one reason for the growing popularity of using micro

level datasets on
individuals and firms is the potential for accounting for selection on observables
CIRCLE, Lund University, Sweden
Identification
•
Problem!
–
We do not observe all relevant attributes of firms and
individuals that may be of importance in explaining the
phenomena we are interested in.
•
Firms:
managerial abilities, organizational routines,
attitudes towards risk, technological opportunities, etc
•
Individuals:
IQ, skills, creativity, risk attitudes and all other
sorts of innate abilities
–
What to do?
CIRCLE, Lund University, Sweden
Identification
•
One option is selection on “
unobservables
”
•
We can do this with panel data.
•
Many researchers maintain that the main advantage of panel
data is that one can get rid of unobserved heterogeneity,
since unobserved heterogeneity is
considered as ‘
the
‘
problem
of non

experimental
research.
CIRCLE, Lund University, Sweden
Part II
Introducing panel data analysis
CIRCLE, Lund University, Sweden
What is panel data?
•
Panel data are a form of longitudinal data,
involving
regularly
repeated observations on the same
individuals
•
Individuals
may be people, households, firms, areas,
etc
•
Repeated
observations
over time
•
repeated cross

sectional time

series
CIRCLE, Lund University, Sweden
Typical data structure
Id
Year
y
x
z
1
t1
y11
x11
z11
1
t2
y12
x12
z12
2
t1
y21
x21
z21
2
t2
y22
x22
z22
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
N
t1
yN1
xN1
zN1
N
t2
yN2
xN2
zN2
CIRCLE, Lund University, Sweden
Terminology in panel data applications
•
A
balanced panel
has the same number of time observations (
T
) for
each of the
n
individuals
•
An
unbalanced panel
has different numbers of time
observations
(
Ti
) on each
individual
•
A
compact panel
covers only consecutive time periods for
each individual
–
there are no “gaps”
•
Attrition
is the process of drop

out of individuals from the
panel, leading
to an
unbalanced (and possibly non

compact) panel
•
A
short panel
has a large number of individuals but few
time observations
on
each
•
A
long panel
has a long run of time observations on
each individual
, permitting
separate
time

series analysis for each
CIRCLE, Lund University, Sweden
Benefits of panel data
•
They are more informative (more variability, less
collinearity
, more
degrees
of freedom
), estimates are more efficient.
•
They
allow to study individual
dynamics
•
Some
phenomena are inherently longitudinal (
e.g.
poverty
persistence;
unstable employment
)
•
The ability to make causal inference is enhanced
by
temporal
ordering
•
They
allow to control for individual unobserved heterogeneity
CIRCLE, Lund University, Sweden
A note on casual inference and panel data
•
This example is based on Brüderl (2005)
•
Let
i
denote an individual,
t
time,
T
treatment and
C
non

treatment.
Y
is the
outcome we are interested in.
•
Optimal identification is: => impossible! (clones not available)
•
Cross

sectional data: => compare treated with untreated
•
This only provides the
“true
causal
effect”
if
the assumption of unit homogeneity
(no
unobserved heterogeneity) holds. Requires ’perfect’ controls
•
With panel data:
=> ’within estimation’
•
We observe the same indiviual before and after treatment.
Unit
homogeneity here is needed only in an intrapersonal
sense!!
C
t
i
T
t
i
Y
Y
0
,
0
,
C
t
j
T
t
i
Y
Y
0
,
0
,
C
t
i
T
t
i
Y
Y
0
,
1
,
CIRCLE, Lund University, Sweden
The issue with not accounting for
unobserved ability
•
We want to analyze the effect of human capital
x
on a firm
i
’s innovation output,
y
.
•
We have panel data and set up the following model:
–
Despite panel data, all issues of selection is at work
here as well.
–
It may be the managerial ability of the firms that
matters. The effect of
x
on
y
may be biased because
more high

ability managers tend to recruit more human
capital. (
omitted variable bias
)
it
it
it
x
y
CIRCLE, Lund University, Sweden
The issue with not accounting for
unobserved ability
•
Unobserved heterogeneity, such as managerial ability, end
up in the error term .
•
But if high

ability managers hire more human capital, then
this means that and are correlated.
–
This violates the assumption of exogenity
•
Endogeneity
(X

variable correlates with
the error term)
results
in biased regression estimates.
–
Endogeneity
can be
a
consequence
of
unobserved
heterogeneity.
it
it
x
it
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How panel data can take care of
unobserved heterogeneity
•
Panel
data
in itself do
not remedy the problem of
unobserved
heterogeneity
–
but one can apply techniques using panel data that do
that.
•
Within transformation does the trick.
•
Panel data:
–
Variance across individuals (between variance)
–
Variance within individuals over time (within variance)
CIRCLE, Lund University, Sweden
How panel data can take care of
unobserved heterogeneity
•
Suppose the managerial ability of each firm
i
is time

invariant.
–
Denote this by (firm

specific fixed effects)
–
A model including (unobserved) managerial ability would read:
(1)
–
Take the average value of each
i
:
(2)
We have ”taken away” the time dimension and have a cross

section with
average values of the time periods. (between variation)
it
i
it
it
x
y
i
i
i
i
i
x
y
CIRCLE, Lund University, Sweden
How panel data can take care of
unobserved heterogeneity
•
Now substract the second equation from the first:
•
is gone!
•
Why? => it is constant over time, so its mean value over
the periods for each
i
is the same:
•
Time

constant unobserved
heterogeneity is
no longer a
problem
i
it
i
it
i
it
x
x
y
y
)
(
0
i
i
i
i
i
CIRCLE, Lund University, Sweden
How panel data can take care of
unobserved heterogeneity
•
Within transformation means that the data is "time

demeaned".
•
Only
the within variation
is left
, because we subtract the
between variation.
•
The within

transformation made possible by panel data
allows researchers to account for time

invariant
unobserved heterogeneity
–
Better identification
CIRCLE, Lund University, Sweden
Is unobserved heterogeneity empirically
important?
•
Yes!
–
in many research papers the magnitude of the estimated
effects of
x
on
y
depends to large extent on whether one
accounts for unobserved heterogeneity or not
•
Example:
–
Andersson, Klaesson and Larsson (2012),
–
”Selection and Learning of Workers in Cities”
CIRCLE, Lund University, Sweden
Is unobserved heterogeneity empirically
important?
•
Main question:
–
How important is selection and learning, repectively, in
explaining the urban wage premium?
–
Panel
data of private sector workers 2001

2010
CIRCLE, Lund University, Sweden
Identification of selection
•
Workers are heterogeneous:
–
eduction, age, gender:
observed
–
innate ’abilities’:
unobserved
•
Suppose wages are10% higher in cities
–
how much of this is due to workers in cities being better
educated and older?
–
how much of the wage diffence remains
after
controlling
education and age?
•
if
selection is important, we should observe that the
wage premium drops as we account for worker
heterogeneity
.
CIRCLE, Lund University, Sweden
Identification of selection
We
run different models and test how sensitve the
city wage
premium
is to observed and unobserved worker heterogeneity
CIRCLE, Lund University, Sweden
Identificatin of learning
•
1: indirectly quantified while accounting for selection
–
remainder wage gap after controlling for spatial sorting of
workers
•
2: identification of workers that move from urban to rural regions.
–
faster human capital accumulation in cities => the
advantages of having worked in a larger dense city should
remain while moving away.
–
we estimate the wage premium for workers that move
away from dense agglomerations and test if their wage
drops or remains upon moving.
CIRCLE, Lund University, Sweden
Wages, education levels and skills in the
Swedish economic geography
Table 3.
Key figures divided by fraction of non

routine work tasks.
Jo
b type
Mean wage
(EUR)
Graduate
share
Mean
experience
Metropolitan
share
All types of professions
High fraction non

routine tasks
29 698
36 683
15%
28%
22
23
27%
36%
Low fraction non

routine tasks
23 088
3%
21
19%
Note:
Graduate share is the fraction of
workers with a university education of at least three years. Metropolitan
share is the fraction of workers that work in three biggest labor market regions: Stockholm, Göteborg and
Malmö. Wages converted to EUR using the 2008 exchange rate between SEK and
EUR of 9.68. High
(low) fraction non

routine jobs are those with fraction non

routine tasks above (below) the mean fraction
across all occupations (see Table 2).
CIRCLE, Lund University, Sweden
Wages, education levels and skills in the
Swedish economic geography
Table 4
. Mean wages (2008) and unadjusted wage gap between metropolitan and non

metropolitan workers.
Job type
Metropolitan
wag
e (EUR)
Non metropolitan
wage (EUR)
Wage
differential
All types of professions
34 417
27 926
23%
High fraction non

routine tasks
41 024
34 245
20%
Low fraction non

routine tasks
22 634
23 195

2%
Note:
The metropolitan areas are defined as the three bi
ggest labor market regions: Stockholm, Göteborg and
Malmö. Wages converted to EUR using the 2008 exchange rate between SEK and EUR of 9.68.
High
(low) fraction non

routine jobs are those
with fraction non

routine tasks
above (below) the mean fraction
acros
s all occupations (see Table 2).
How much of the wage premium in larger cities is due to
selection?
CIRCLE, Lund University, Sweden
Empirical model
...
ln
ln
ln
ln
3
2
1
E
rt
R
rt
M
rt
irt
De
De
De
w
(3)
irt
TR
tR
R
t
tR
T
t
t
t
R
R
R
D
D
D
D
γ
Z
1
1
81
1
...
De = market potential measures
(Harris 1954)
In red:
time effects, regional effects, region

specific ”shocks”
In green:
worker characteristics
CIRCLE, Lund University, Sweden
Table 6.
The relationship between spatial economic density and wages
, all private sector workers.
Raw OLS
Mincerian
OLS
Full OLS
Raw with
worker FE
Full with
worker F
E
Municipal density
(log)
0.0326***
0.0218***
0.0205***
0.00773***
0.00538***
(0.00322)
(0.00224)
(0.00123)
(0.000242)
(0.000242)
Regional density
(log)
0.0335***
0.0218***
0.0195***
0.00790***
0.00522***
(0.00777)
(0.00425)
(0.00641)
(0.000518)
(0.0
00514)
Extra

regional
density (log)

0.0323*

0.0221

0.0248***

0.0127***

0.00797***
(0.0185)
(0.0139)
(0.00788)
(0.000679)
(0.000674)
Years of schooling
0.0930***
0.0823***
0.117***
(0.00468)
(0.00300)
(0.0190)
Experience
0.0503***
0.0408***
0.0587***
(0.00326)
(0.00279)
(0.0190)
Experience^2

0.000781***

0.000635***

0.000745***
(5.42e

05)
(4.64e

05)
(2.84e

06)
Immigrant (dummy)

0.136***

0.108***
(0.00737)
(0.00409)
Male (dummy)
0.351***
0.330***
(0.00947)
(0.0
0425)
Tenure
0.0176***

0.0109***
(0.000458)
(0.000127)
Number of prior
employees

0.0120***

0.0189***
(0.00286)
(0.000176)
New occupation
(dummy)

0.0930***

0.0268***
(0.00222)
(0.000366)
Employer size (log)
0.0257***
0.
0183***
(0.00225)
(0.000175)
Year dummies
Yes
Yes
Yes
Yes
Yes
Region dummies
Yes
Yes
Yes
Yes
Yes
Region*Year effects
Yes
Yes
Yes
Yes
Yes
Education type
dummies
No
Yes
Yes
No
Yes
Industry dummies
No
No
Yes
No
Yes
Observations
12,367,700
12,367,70
0
12,367,700
12,367,700
12,367,700
Individuals
2,681,164
2,681,164
2,681,164
2,681,164
2,681,164
R

squared
0.031
0.248
0.288
0.059
0.078
Note:
The table reports
estimates of wage

density elasticities for private sector workers in Sweden 2002

2008.
Raw r
efers to the
wage equation in
e
quation (3)
without any further controls
.
The Mincerian model adds
years of schooling, experience and its squared value as well as dummies for immigrant
s
,
males
and
education specialization. The full specification further add
s variables reflecting labor market status and
employer characteristics of each worker. OLS refers to the pooled OLS estimator and FE to a panel
estimator
with worker fixed effects
. All v
ariables are defined in Table 1.
The full FE model excludes
immigrant
and sex dummies as these reflect time

invariant worker characteristics.
All models include
year and region dummies as well as
region

year dummies, where the latter account for any region

specific
time

varying shocks shared by all workers in the same local
labor market region
.
The dependent variable
is the nat
ural logarithm of wage earnings
. Robust standard errors are presented in brackets.
*** p<0.01,
** p<0.05, * p<0.1
.
CIRCLE, Lund University, Sweden
Is unobserved heterogeneity empirically
important?
•
The wage

density elasticity drops from 3.3% to 0.8% when
accounting for worker fixed effects (within transformation)!!
•
Selection on observables relatively unimportant.
CIRCLE, Lund University, Sweden
Table 7.
The relationship between spatial econ
omic density and wages for workers with occupations associated
with high fractions of non

routine job tasks.
Raw OLS
Mincerian
OLS
Full OLS
Raw with
worker FE
Full with
worker FE
Municipal density
(log)
0.0317***
0.0253***
0.0250***
0.00810***
0.00655***
(0.00206)
(0.00260)
(0.00141)
(0.000345)
(0.000346)
Regional density
(log)
0.0364***
0.0263***
0.0240***
0.00868***
0.00618***
(0.00734)
(0.00523)
(0.00829)
(0.000772)
(0.000769)
Extra

regional
density (log)

0.0271

0.0231

0.0253**

0.0124***

0.0
0834***
(0.0232)
(0.0183)
(0.0114)
(0.00105)
(0.00105)
Years of schooling
0.0797***
0.0766***
0.133*
(0.00312)
(0.00289)
(0.0691)
Experience
0.0556***
0.0513***
0.0926
(0.00204)
(0.00185)
(0.0691)
Experience^2

0.000862***

0.000794***

0.000737***
(3.95e

05)
(3.54e

05)
(4.05e

06)
Immigrant (dummy)

0.0419***

0.0371***
(0.00285)
(0.00260)
Male (dummy)
0.353***
0.351***
(0.00319)
(0.00306)
Tenure
0.0105***

0.00644***
(0.000400)
(0.000172)
Number of prior
e
mployees
0.000754

0.00741***
(0.000741)
(0.000237)
New occupation
(dummy)

0.0703***

0.0151***
(0.00209)
(0.000502)
Employer size (log)
0.0259***
0.0149***
(0.00314)
(0.000236)
Year dummies
Yes
Yes
Yes
Yes
Yes
Region dummies
Y
es
Yes
Yes
Yes
Yes
Region*Year effects
Yes
Yes
Yes
Yes
Yes
Education type
dummies
No
Yes
Yes
No
Yes
Industry dummies
No
No
Yes
No
Yes
Observations
5,986,454
5,986,454
5,986,454
5,986,454
5,986,454
Individuals
0.038
0.258
0.280
0.061
0.074
R

squared
1
,388,166
1,388,166
1,388,166
1,388,166
1,388,166
Note:
The table reports estimates of wage

density elasticities for private sector workers in Sweden 2002

2008
with occupations associated with high fractions of non

routine job tasks (see Table 2).
Raw refe
rs to the
wage equation in equation (3) without any further controls. The Mincerian model adds years of schooling,
experience and its squared value as well as dummies for immigrant
s
,
males
and education specialization.
The full specification further adds v
ariables reflecting labor market status and employer characteristics of
each worker. OLS refers to the pooled OLS estimator and FE
to a panel estimator with worker fixed
effects
. All variables are defined in Table 1. The full FE model excludes immigrant an
d sex dummies as
these reflect time

invariant worker characteristics. All models include
year and region dummies as well as
region

year dummies, where the latter account for any region

specific time

varying shocks shared by all
workers in the same local la
bor market region
.
The dependent variable is the natural logarithm of wage
earnings. Robust standard errors are presented in brackets.
*** p<0.01, ** p<0.05, * p<0.1
Non

routine
workers
CIRCLE, Lund University, Sweden
Table 9.
Wage premium for workers moving away from a metropolitan region to
the rest of the country, by
fraction of non

routine job tasks.
All private sector
workers
High fraction non

routine tasks
Low fraction non

routine tasks
Dummy
for
moving away
from metropolitan region
0.00286 ***
(0.0014)
0.01085***
(0.0020)

0.00055
(0.
0021)
Model
Full with worker fixed
effects
Full with worker fixed
effects
Full with worker fixed
effects
Note:
The table reports the coefficient estimate of a dummy variable reflecting a move from
any of Sweden’s
three metropolitan labor market regions (
Stockholm, Göteborg and Malmö)
to anywhere else in Sweden
.
The underlying model is a panel estimator with worker fix effects including the full set of additional
control variables report
ed
in
the ‘Full with worker FE’ specification in Tables 6

7.
Complete
estimation
results are obtained from the authors upon request.
The dependent variable is the natural logarithm of
wage earnings. Robust standard errors are presented in brackets.
*** p<0.01, ** p<0.05, * p<0.1
RESULTS FOR ”MOVERS” (from urban to rural)
CIRCLE, Lund University, Sweden
CONCLUSIONS
•
Who
you are and the kind of job you have are more
important than where you live in explaining spatial wage
disparities.
–
The
main reason why workers in denser regions earn more
is simply that they are different from the workers in more
rural regions.
•
Learning
effects (or pure agglomeration economies) are
not zero but are quantitatively of smaller importance than
spatial sorting.
CIRCLE, Lund University, Sweden
Some issues
•
Identification of parameters:
–
In the FE

model, parameters are identified from changes in
indiviuals over time.
–
Time

invariant variables cannot be estimated (gender, race,
education etc......)
–
Effects of dummy variables is only identified based on those
individuals that change status over time (e.g. from 0 to 1, or 1 to 0)
–
There
must be some variation in
the variable of interest. Otherwise
,
we cannot estimate
its effect
. This is
potentially a
problem, if only a
few observations show a change
in a variable.
CIRCLE, Lund University, Sweden
An example
•
Suppose you estimate the effect of a location in a high

tech
cluster on firm innovation.
–
We have a dummy which is 1 for firms in a cluster, and 0 otherwise
–
Model 1 is a cross

section:
•
Here the is identified from differences between firms ’inside’ and
’outside’ a high

tech cluster
–
Model 2 is a an FE

model (within transformation):
•
Here the is identified from firms that move into (or out from) a high

tech
cluster. Captures instantaneous effect on
y
from moving into a cluster.
i
i
i
i
Cluster
x
y
2
1
2
it
i
it
it
it
Cluster
x
y
2
1
2
CIRCLE, Lund University, Sweden
Some issues continued
•
Fixed and random effects:
–
An alternative to FE is Random

effects (RE).
–
RE exploits both within and between variation. But, it relies on the
restrictive assumption that the unobserved individual effects are
uncorrelated with the explanatory variable (x). The FE

model do
not.
–
The RE

estimator, however, provides estimates for time

constant
covariates. Many
researchers want to report effects
of sex, race, etc. Therefore,
they choose
the RE

estimator
over the FE

estimator.
CIRCLE, Lund University, Sweden
Panel data estimation in practice
•
Most statistical packages, such as STATA or LIMDEP,
have built

in meny

based procedures for panel data
analysis
•
Next class: panel data analysis in practice using STATA
–
declaring data to be panel
–
describing datasets
–
descriptive statistics
–
estimation of FE, RE and BE models
–
interpretation of coefficients
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