The Microeconomic
Implications of Labor
Regulations: Cross-Country
Evidence from Within the Firm
Francine Lafontaine
Jagadeesh Sivadasan
Ross School of Business, University of Michigan
AEA Meetings, Jan 2007
(Updated Jan 2009)
Introduction



Goal: Assess the effects of regulations that create
rigidities in the labor market
Important and controversial question: a number of papers
have considered the effects of such regulations at the
macro level (e.g. Botero et al (2004), Lazear (1990))
We focus on the very micro level, using data from a
single fast-food chain with operations in more than 40
countries around the world:


most importantly, we quantify the effect of the regulations on firm
labor adjustment decisions
also examine effect on extent of operations
2
Introduction

Important advantages of our empirical setting:
 Fundamentally
same production technology
 Same output (as close as can get)
 Labor intensive - labor issues really matter
 We have very detailed data: weekly revenues and
costs information for each outlet outside the US (more
than 2500 of them, over 4 years, in 43 countries)
3
Introduction

Important advantages of our empirical setting:
 The
high frequency of our data has two main
advantages:
 Gets around problems with annual data, which can hide
a lot of within year turnover (Hamermesh and
Pfann (1996))
 Allows us to adopt estimation strategies with lots of
controls (i.e. fixed effects at the outlet, outlet-year, or
outlet-year-season)
4
Introduction

We focus on questions that are particularly suited
to our data
paper – addresses the effect of regulation
on productivity and labor demand
 In some sense, more direct place to look for effects
 But in reality, theory ambiguous on these effects,
whereas clear predictions on hysteresis and effect on
responsiveness to output changes
 And our data – labor costs, not labor levels =>
potential biases due to “poor” measure of wages
 Companion
5
Introduction – Preview of Results

We find strong evidence that in countries with
more rigid labor laws:
 Outlets’
choices of labor levels are less responsive,
from period to period, to changes in revenues, and
 there is more hysteresis in labor levels, that is labor in
one period is more related to previous period labor

We also find some evidence that in such
countries:
 the
Company enters later and operates fewer outlets - and uses “local” franchising more as well
6
Organization of this Talk





Basic Model and Predictions
A Note on Dynamics
Data and Definition of Variables
Results for Labor Adjustment – Botero & GCS
Key Identification Issues
 Contrasting
 IV


with Materials Adjustment Results
Results
Results on Company’s Extent of Operations
Conclusions
7
Basic Model


Draws on Heckman and Pagés (2003), who drew on
Holt, Modigliani, Muth and Simon (1960)
Given our weekly data, we take capital as given (or
contributing to the Hicks neutral productivity term), and
write a 2 input Cobb-Douglas production process with
labor and materials

Yt  t Lt M t

where Yt is the quantity of output produced by the firm
in period t, Lt is labor, and Mt represents materials used
8
Basic Model

Assume iso-elastic demand curve:
Pt  Qt1/ 

where Pt is the price per unit output in period t, 
represents demand shifters and  is the price elasticity of
demand
The firm’s profit to be maximized is
 t  Pt Qt  Wt Lt  St M t
where Wt is the wage rate faced by the firm in period t,
and St is the per unit cost of materials
9
Basic Model


Each week, manager chooses labor and materials
so FOCs for these are binding
Assume horizontal labor and materials supply in
each local market (each outlet buys little, and
even as a group they are a tiny part of the market)
=> obtain optimal labor and materials demand function
in terms of the primitives (prices, demand &
production function parameters)
=> can write total labor and material cost equations
conditional on output
10
Basic Model

These input demand equations are
b  log  '  rt
*
f t  log  '  rt
*
t
where we use bt to denote log(WtLt) and ft to mean
log(StMt), and


1
1
 '   1 


 and

 '   1 

.

These equations then describe equilibrium input costs in
the absence of adjustment costs
11
Basic Model

Now suppose that there are costs to adjusting labor.
First, let the cost of being off the static optimum be
quadratic in log labor
cto   o (bt*  bt )2

where o > 0. Second, suppose that the cost of changing
labor levels from one period to the next are given by
cta   a (bt  bt 1 ) 2

where we expect a to be positive and increasing in the
rigidity of labor regulations
12
Basic Model

Each outlet minimizes the sum of these costs. This
yields optimal labor choice
bit  (1 -  )b   bi ,t 1
j
*
it
j
where outlet i is in country j, and  j 

 aj
 o
j
a
.
The optimal labor cost equation above can be rewritten
as
j
j
j
bit  (1 -  )rit   bi ,t 1  (1 -  )log  '
13
Basic Model

Taking, as a first approximation,
 j  a0  a1 j
we get the following econometric specification for the
labor costs of outlet i in country j at time t:
bit  (1  a0  a1 )rit  (a0  a1 )bi ,t 1  (1  a0  a1 )log  '
j
j
j
 rit  bi ,t 1   r rit   b bi ,t 1  is   it
j
j
where τ j is the index of labor regulation, and is stands
for store, store-year, or store-season-year fixed effects.
14
Basic Model


In this regression, we expect r to be negative and b to
be positive.
In other words, our simple model yields two principal
implications that we bring to data:



Labor costs should be less responsive to changes in revenues
in countries where regulations are more stringent
Labor costs at time t should be more dependent on labor costs
at time t-1 in countries with more stringent laws (hysteresis)
These predictions are intuitive, and the latter has been
tested in a number of studies of the effect of regulation
on labor demand (see survey in Heckman and Pagés,
2004).
15
Note on Dynamics




Our two testable implications are derived from a very simple model
Heckman and Pagés (2004) express concern that the labor hysteresis
prediction may not arise in a more general dynamic model
We solved a more general dynamic stochastic programming model:
Two state variables are current productivity and last period labor.
We solve numerically for four scenarios:


with both symmetric (quadratic) and asymmetric (i.e. severance pay
only) adjustment costs. and
for iid as well as persistent demand /productivity shocks processes
16
Note on Dynamics

To approximate our actual data, using optimal policy functions, we ran regressions
on simulated behavior of 75 outlets for 104 periods across 45 regimes
Zero adjustment costs
Log (Lagged labor cost)
Log (Revenue)
Adj. cost X Log (Lagged labor cost)
Adj. cost X Log (Revenue)
Constant
Fixed Effects
Observations
Adjusted R-squared
Number of clusters
IID
shocks
Persistent
shocks
Symmetric quadratic
adjustment costs
IID
Persistent
shocks
shocks
0.0000
[0.0001]
0.984***
[0.0001]
0.0006***
[0.0001]
0.984***
[0.0002]
0.657***
[0.039]
0.198***
[0.022]
0.574***
[0.015]
0.264***
[0.018]
0.874***
[0.043]
0.190***
[0.030]
0.251***
[0.017]
0.288***
[0.020]
0.117***
[0.008]
0.398***
[0.026]
0.146***
[0.008]
0.677***
[0.023]
0.0001
[0.0002]
-0.0001
[0.0004]
-0.0004
[0.0003]
0.0001
[0.0006]
1.130***
[0.151]
-0.540***
[0.111]
0.690***
[0.090]
-0.602***
[0.089]
1.856***
[0.104]
-0.885***
[0.118]
0.398***
[0.032]
-1.083***
[0.073]
0.253***
[0.020]
-1.344***
[0.067]
0.421***
[0.031]
-0.971***
[0.089]
-1.529***
[0.0001]
-1.528***
[0.0002]
-0.555***
[0.072]
-0.688***
[0.022]
-0.181**
[0.072]
-1.267***
[0.023]
-1.470***
[0.021]
-1.337***
[0.005]
Outlet-year- Outlet-yearseason
season
351000
0.999
45
351000
0.999
45
Asymmetric linear
adjustment costs
IID
Persistent
shocks
shocks
Outlet-year- Outlet-year- Outlet-year- Outlet-yearseason
season
season
season
351000
0.801
45
351000
0.932
45
351000
0.782
45
351000
0.929
45
Fixed (lump-sum)
adjustment costs
IID
Persistent
shocks
shocks
Outlet-year- Outlet-yearseason
season
351000
0.852
45
351000
0.90
45
17
The Data



Mostly from internal firm records
Cover over 2500 outlets in more than 40 countries
worldwide, weekly from 2000 to 2003
Data on
 Revenues
per week
 Total labor costs per week
 Total materials costs per week
 Number of items (standardized notion of output)
18
The Data

We measure the rigidity of the labor regulations
in each country using the Botero et al (2004)
index (see appendix in paper for details)
advantage – computed similarly across
countries
 Main disadvantage – laws may not be enforced as
strongly everywhere
 Main

We verify our results using an index of hiring and
firing flexibility from the Global Competitiveness
Survey (2002) of business executives
19
Index of labor regulation (Botero, et al)
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
RUS
KAZ
FRA
NLD
NOR
UKR
POL
DOM
VNM
BEL
HRV
LKA
KOR
THA
BOL
AUS
IRL
SGP
UGY
USA
HKG
ZMB
0
20
.8
Russian
Federation
Tunisia
Portugal
Germany
Zimbabwe
0
.2
.4
.6
Spain
France
Sweden
Slovenia
Netherlands
Latvia Finland
Jordan
Norway
Indonesia
Ukraine Slovakia
Venezuela
Italy
Poland
Panama
Lithuania
Dominican Republic
Mexico
DenmarkBrazil
Vietnam
Czech Republic
Bulgaria
Greece
AustriaCroatiaBelgium
Chile Philippines
Sri Lanka
Peru
Taiwan
Switzerland
South
Korea
India
China
Thailand
Turkey
Ecuador
Hungary
Bolivia
Australia
Colombia Argentina
Ireland
South Africa
Singapore
Israel
Great Britain
Canada Morocco
United States
Malaysia
Hong Kong
Jamaica
New
ZealandJapan
.2
.4
.6
.8
1
Hiring/firing inflexibility (2002, Global Competitiveness Report)
Fitted (OLS)
Fitted (GLS, Root-N weight)
21
Panel Characteristics (baseline sample)
Year
2000
2001
2002
2003
Total
Number of
observations
80,429
85,113
74,201
82,305
322,048
Number of
outlets
1,721
1,828
2,147
1,938
2,526
Number of
countries
39
37
38
37
43
22
Descriptive Statistics
Panel B: Summary statistics (variables in logs)
Variable
N
Log (Labor cost)
322,048
Log (Revenue)
322,048
Log (Material cost)
318,749
Mean
SD
P25
Median
P75
Min
Max
7.19
8.84
7.72
0.85
0.69
0.66
6.71
8.46
7.37
7.27
8.90
7.78
7.78
9.32
8.16
-5.05
2.85
-4.87
10.25
11.50
10.94
Panel C: Summary statistics (variables in levels)
Variable
N
Mean
SD
P25
Median
P75
Min
Max
1,391.21
5,329.17
1,626.86
0.16
819.84
4,730.58
1,590.36
-0.15
1,434.39
7,332.80
2,394.45
0.03
2,390.97
11,156.53
3,481.43
0.16
0.01
17.30
0.01
-0.25
28,219
98,668
56,580
0.42
Labor cost
Revenue
Material cost
Index of labor regulation
322,048
322,048
318,749
322,048
1,798.57
8,485.44
2,706.78
0.00
23
1
.8
.6
.4
.2
0
0
.2
.4
.6
.8
1
Correlation between changes in input costs and revenue
.2
.4
.6
.8
Index of labor regulation (Botero, et al)
.2
.4
.6
.8
1
Hiring/firing inflexibility (2002, Global Competitiveness Report)
Correlation between changes in labor cost and revenue
Correlation between changes in labor cost and revenue
Fitted (GLS, Root-N weight)
Fitted (GLS, Root-N weight)
Correlation between changes in material cost and revenue
Correlation between changes in material cost and revenue
Fitted (GLS, Root-N weight)
Fitted (GLS, Root-N weight)
24
Table 4: Baseline Results
(4)
(5)
(6)
0.501***
[0.049]
0.341***
[0.040]
1.013***
[0.291]
-0.570***
[0.144]
0.623***
[0.227]
0.348***
[0.036]
0.360***
[0.035]
0.909***
[0.223]
-0.488***
[0.101]
1.550***
[0.310]
0.203***
[0.033]
0.391***
[0.036]
0.687***
[0.203]
-0.414***
[0.106]
2.305***
[0.358]
Fixed Effects
Outlet
Outlet-year
Outlet-yearseason
Observations
Adjusted R-squared
Number of clusters
322,048
0.945
43
322,048
0.952
43
322,048
0.959
43
Log (Lagged labor cost)
Log (Revenue)
Regulation X Log (Lagged labor cost)
Regulation X Log (Revenue)
Constant
25
Table 4: Baseline Results
Effect of a one standard deviation (0.85) increase in Log (Lagged Labor) in percentage terms
At Regulation = mean (0.00)
42.59
29.58
17.26
At Regulation = mean + sd (= 0.16)
56.36
41.94
26.60
Impact of increase in Regulation
13.78
12.36
9.34
Effect of a one standard deviation (0.69) increase in Log (Revenue) in percentage terms
At Regulation = mean (0.00)
23.53
24.84
26.98
At Regulation = mean + sd (= 0.16)
17.24
19.45
22.41
Impact of increase in Regulation
-6.29
-5.39
-4.57
26
Table 5: GCS Index Results
(1)
(2)
(3)
0.530***
[0.053]
0.355***
[0.041]
0.988***
[0.332]
-0.734***
[0.221]
0.244
[0.240]
0.371***
[0.041]
0.383***
[0.036]
0.914***
[0.291]
-0.714***
[0.217]
1.136***
[0.316]
0.220***
[0.034]
0.418***
[0.038]
0.710***
[0.235]
-0.629**
[0.262]
1.909***
[0.363]
Fixed Effects
Outlet
Outlet-year
Outletyear-season
Observations
Adjusted R-squared
Number of clusters
338,660
0.948
48
338,660
0.955
48
338,660
0.961
48
Log (Lagged labor cost)
Log (Revenue)
Inflexibility X Log (Lagged labor cost)
Inflexibility X Log (Revenue)
Constant
27
Table 5: GCS Index Results
Effect of a one standard deviation (0.85) increase in Log (Lagged Labor) in percentage terms
At Inflexibility = mean (0.00)
45.05
31.53
18.70
At Inflexibility = mean + sd (= 0.13)
55.97
41.63
26.55
Impact of increase in Inflexibility
10.92
10.10
7.85
Effect of a one standard deviation (0.69) increase in Log (Revenue) in percentage terms
At Inflexibility = mean (0.00)
24.50
26.43
28.84
At Inflexibility = mean + sd (= 0.13)
17.91
20.02
23.20
Impact of increase in Inflexibility
-6.58
-6.40
-5.64
28
Key Identification Issues

As per our model above, the error term is:
 

1
eit  (1  a0  a1 )log   it 1   
  it  
Thus the error term includes omitted supply-side
parameter it (output elasticity with respect to labor) and
demand side parameter it (elasticity of demand)
Our store or store-year-season fixed effects control
implicitly for the differences in τ j across countries, and
for these supply and demand parameters insofar as they
are fixed within a store or store-year or store-year-season
j


29
Key Identification Issues

Unanticipated demand and productivity shocks can also
add to the error term


if labor is set early, then a high (low) demand shock shows up as
too little (much) labor conditional on output
Since we are interested in the coefficient on lagged labor
or revenue interacted with regulation, a bias arises only
when:


the omitted demand and supply parameters vary within storeyear-seasons, and are correlated with lagged labor/revenue in a
different way across different regulation regimes
prediction errors (with regard to demand and productivity
shocks) are correlated with levels of regulation
30
Key Identification Issues

We address these potential identification issues in two different ways:

using the material demand equation as a control
 with instrumental variables

The material demand equation is a good control because:

omitted demand and supply parameters are the same for the material
as for the labor demand equation
 prediction error also would bias the material demand specification in a
similar way

IV approach

draws from traditional approaches in the literature (Blundell and Bond
1998)
 uses lagged endogenous variables that are uncorrelated with
unpredicted component of current demand and productivity shocks
 we have more instruments than endogenous variables, so we can
perform an overidentification test – good results!
31
Table 6: Material Costs Results (Botero et al. Index)
(4)
(5)
(6)
0.159***
[0.038]
0.852***
[0.027]
-0.211
[0.201]
-0.020
[0.138]
-1.032***
[0.084]
0.112***
[0.036]
0.901***
[0.020]
-0.168
[0.197]
-0.004
[0.093]
-1.102***
[0.127]
0.033*
[0.019]
0.942***
[0.008]
-0.089
[0.125]
-0.075*
[0.043]
-0.856***
[0.132]
Fixed Effects
Outlet
Outlet-year
Outletyear-season
Observations
Adjusted R-squared
Number of clusters
362,711
0.947
43
362,711
0.953
43
362,711
0.960
43
Log (Lagged materials cost)
Log (Revenue)
Regulation X Log (Lagged materials cost)
Regulation X Log (Revenue)
Constant
32
Table 6: Material Costs Results (Botero et al. Index)
Effect of a one standard deviation (0.66) increase in Log (Lagged Materials cost)
At Regulation = mean (0.00)
At Regulation = mean + sd (=0.16)
Impact of increase in Regulation
10.49
7.39
2.18
8.27
5.62
1.24
-2.23
-1.77
-0.94
Effect of a one standard deviation (0.69) increase in Log (Revenue)
At Regulation = mean (0.00)
58.79
62.17
65.00
At Regulation = mean + sd (=0.16)
58.57
62.12
64.17
Impact of increase in Regulation
-0.22
-0.04
-0.83
33
Table 7: Robustness check: OECD Sample and Interaction Terms
(1)
OECD
Log (Lagged labor cost)
Log (Revenue)
Regulation X Log (Lagged labor cost)
Regulation X Log (Revenue)
(2)
(3)
(4)
(5)
0.174***
[0.030]
0.461***
[0.036]
1.709***
[0.325]
-1.085***
[0.276]
-0.052
[0.063]
0.639***
[0.065]
0.204
[0.333]
0.726***
[0.205]
0.165
[0.176]
0.645***
[0.171]
0.428***
0.331**
0.380*
0.687**
0.700***
[0.141]
[0.161]
[0.200]
[0.299]
[0.238]
-0.354*
-0.069
-0.225
-0.584***
-0.543***
[0.183]
[0.108]
-0.158***
[0.152]
[0.152]
[0.141]
GDP X Log (Lagged labor cost)
[0.033]
GDP X Log (Revenue)
0.156***
[0.030]
Entry barriers X Log (Lagged labor cost)
0.081***
[0.021]
Entry barriers X Log (Revenue)
-0.078***
[0.025]
Wage flexibility X Log (Lagged labor cost)
-0.0002
[0.064]
Wage flexibility X Log (Revenue)
-0.063
[0.038]
Labor relations X Log (Lagged labor cost)
0.008
[0.040]
Labor relations X Log (Revenue)
-0.053
[0.032]
Fixed effects
Constant
Observations
Outletyearseason
1.996***
[0.314]
236,291
Outletyearseason
Outletyearseason
Outletyearseason
Outletyearseason
2.197***
[0.321]
322,048
2.273***
[0.296]
265,842
2.238***
[0.344]
321,569
2.291***
[0.321]
321,569
34
Table 8: Robustness check: DID comparison of top and bottom decile of change in Index
of Inflexibility between 2002 and 2004
LABOR
(1)
Log (Lagged input cost)
Log (Revenue)
Year 2003
Year 2003 X Log (Lagged input cost)
Year 2003 X Log (Revenue)
DInf_p90 X Year 2003
DInf_p90 X Log (Lagged input cost)
DInf_p90 X Log (Revenue)
DInf_p90 X Year 2003 X Log (Lagged input cost)
DInf_p90 X Year 2003 X Log (Revenue)
DGDPGR X Year 2003
DGDPGR X Log (Lagged input cost)
DGDPGR X Log (Revenue)
DGDPGR X Year 2003 X Log (Lagged input cost)
DGDPGR X Year 2003 X Log (Revenue)
Constant
Fixed effects
(2)
0.570***
[0.032]
0.152***
[0.038]
-0.474*
[0.280]
-0.328***
[0.040]
0.321***
[0.037]
1.435***
[0.494]
-0.143
[0.112]
0.196
[0.171]
0.519***
[0.127]
-0.634***
[0.105]
15.56**
[6.029]
-1.970***
[0.660]
1.403
[1.378]
1.881**
[0.857]
-3.569***
[0.823]
1.233***
[0.288]
0.358***
[0.057]
0.198***
[0.053]
-0.179
[0.191]
0.083
[0.202]
0.432**
[0.198]
-0.722***
[0.211]
Outlet
MATERIALS
(3)
(4)
-0.480
[1.073]
0.341
[1.654]
0.647
[1.101]
-4.088**
[1.780]
1.968***
[0.252]
0.157***
[0.022]
0.897***
[0.023]
-0.138
[0.122]
-0.034
[0.023]
0.048*
[0.025]
1.107***
[0.164]
0.104***
[0.037]
-0.091**
[0.045]
0.001
[0.053]
-0.125**
[0.057]
9.037***
[1.803]
1.555***
[0.303]
-1.110***
[0.350]
-2.257***
[0.360]
0.806**
[0.362]
-1.250***
[0.132]
1.061***
[0.256]
-0.135
[0.320]
-2.088***
[0.304]
0.181
[0.375]
-0.941***
[0.086]
Outlet-
Outlet
Outlet-
-0.233***
[0.059]
0.328***
[0.067]
0.055***
[0.016]
0.918***
[0.021]
0.008
[0.018]
0.032
[0.022]
0.103***
[0.033]
0.009
[0.044]
-0.077*
[0.044]
-0.078
[0.060]
35
Table 8: Robustness check: Case study of labor reform in South Korea (1996-98)
Log (Lagged input cost)
Log (Revenue)
POST_REFORM X Log (Lagged input cost)
POST_REFORM X Log (Revenue)
BEFORE-AFTER
LABOR
MATERIALS
(1)
(2)
(3)
(4)
0.765*** 0.270***
0.332***
0.122***
[0.049]
[0.058]
[0.039]
[0.024]
0.206*** 0.160***
0.717***
0.822***
[0.034]
[0.041]
[0.034]
[0.026]
-0.404***
-0.129**
-0.024
-0.018
[0.053]
[0.064]
[0.044]
[0.029]
0.335*** 0.574***
0.033
0.040
[0.043]
[0.049]
[0.037]
[0.035]
Constant
-0.127
[0.096]
-1.242***
[0.100]
Fixed Effects
Outlet
Observations
Adjusted R-squared
Number of clusters
15,071
0.854
152
0.515***
[0.145]
Outletyearseason
15,071
0.894
152
-0.710***
[0.137]
Outletyearseason
15,099
0.963
152
D_KOREA X Log (Lagged input cost)
D_KOREA X Log (Revenue)
D_KOREA X POST_REFORM X Log (Lagged input cost)
D_KOREA X POST_REFORM X Log (Revenue)
Outlet
15,099
0.944
152
36
Table 8: Robustness check: Case study of labor reform in South Korea (1996-98)
Log (Lagged input cost)
Log (Revenue)
POST_REFORM X Log (Lagged input cost)
DIFFERENCE-IN-DIFFERENCES
LABOR
MATERIALS
(5)
(6)
(7)
(8)
0.272***
0.084***
0.074***
0.023***
[0.021]
[0.016]
[0.009]
[0.007]
0.460***
0.549***
0.958***
0.965***
[0.013]
[0.013]
[0.009]
[0.010]
0.190***
0.090***
0.054***
0.018
[0.015]
[0.021]
[0.014]
[0.012]
-0.167***
-0.152***
-0.043***
0.005
[0.013]
0.493***
[0.054]
-0.254***
[0.036]
[0.014]
0.186***
[0.060]
-0.389***
[0.043]
[0.012]
0.257***
[0.040]
-0.241***
[0.035]
[0.013]
0.099***
[0.024]
-0.143***
[0.028]
-0.594***
-0.219***
-0.078*
-0.036
[0.055]
[0.067]
[0.046]
[0.031]
D_KOREA X POST_REFORM X Log (Revenue)
0.502***
0.726***
0.075*
0.035
Constant
[0.045]
1.156***
[0.105]
[0.050]
2.158***
[0.085]
[0.038]
-1.453***
[0.053]
[0.038]
-1.153***
[0.053]
Fixed Effects
Outlet
Outletyearseason
Outlet
Outletyearseason
Observations
Adjusted R-squared
Number of clusters
71,273
0.977
592
71,273
0.984
592
71,200
0.971
592
71,200
0.980
592
POST_REFORM X Log (Revenue)
D_KOREA X Log (Lagged input cost)
D_KOREA X Log (Revenue)
D_KOREA X POST_REFORM X Log (Lagged input cost)
37
Impulse response functions based on VAR analysis
38
Implied Rigidity Estimates




The underlying structural parameters  o and  a are not
simultaneously identified.
However  j  a0  a1 j is identified
Model implies following relationship between optimal
adjustment and actual adjustment of labor
More regulation => higher  j => greater dampening of
adjustment
39
Table 11: Estimates of Dampening Factor
(change in labor costs in the absence of adjustment costs / actual change in labor costs)
Estimate of a0
Estimate of a1
Dampening factor estimate
Regulation
P25
P75
Change
(percent)
Panel 1: Using results from Column 6 of Table 4
Coefficient on Log (Lagged labor cost):
0.203
Coefficient on Regulation X Lagged labor cost:
0.687
0.900 0.687
23.7
1 - Coefficient on Log (Revenue):
0.609
- (Coefficient on Regulation X Revenue):
0.414
0.453 0.325
28.3
Average of above:
0.406
Average of above:
0.550
0.677 0.506
25.2
Panel 2: Using results from Column 1 of Table 7 – OECD only
Coefficient on Log (Lagged labor cost):
0.174
Coefficient on Regulation X Lagged labor cost:
0.428
0.890 0.749
15.9
1 - Coefficient on Log (Revenue):
0.539
- (Coefficient on Regulation X Revenue):
0.354
0.514 0.397
22.7
Average of above:
0.356
Average of above:
0.391
0.702 0.573
18.4
40
A Look at the Firm’s Expansion


If more rigid labor regulations imply that individual
outlets cannot adjust labor as much as they otherwise
would, then all else the same, outlets in these markets will
be less profitable.
This suggests the firm should



Enter later
Expand less rapidly
Franchise more ?
in highly regulated markets
41
Labor Regulation and International Expansion: Time to Entry
Whole Sample
Log(GDP/capita in USD)
Log(Population)
Log(Distance to USA in
kms)
Regulation (Botero et al)
-0.31**
[0.07]
-0.19**
[0.07]
0.28
[0.19]
1.10*
[0.44]
Regulation (GCS)
Constant
Observations
R-squared
Number of Clusters
5.09*
[2.01]
6906
0.42
37
-0.22**
[0.08]
-0.11
[0.06]
0.26
[0.18]
-0.45
[0.65]
3.80*
[1.61]
8111
0.23
43
Country = Observation
(end of 2001)
-0.32**
-0.22**
[0.07]
[0.08]
-0.18*
-0.10
[0.07]
[0.07]
0.24
0.23
[0.21]
[0.19]
1.14*
[0.45]
-0.44
[0.70]
5.36*
3.95*
[2.14]
[1.72]
34
40
0.45
0.24
42
Labor Regulation and International Expansion: Number of Outlets
Whole Sample
Log(GDP in USD)
Log(Population)
Log(Distance to USA in
kms)
Regulation (Botero et al)
0.59**
[0.13]
0.63**
[0.17]
-0.45+
[0.26]
-2.02*
[0.83]
Regulation (GCS)
Constant
Observations
R-squared
Number of clusters
-8.25*
[3.56]
7423
0.41
41
0.57**
[0.12]
0.54**
[0.15]
-0.40
[0.27]
-0.37
[1.47]
-7.54**
[2.81]
8628
0.38
47
Country = Observation
(end of 2001)
0.51**
0.50**
[0.18]
[0.16]
0.58**
0.53**
[0.20]
[0.18]
-0.38
-0.30
[0.32]
[0.32]
-2.60*
[1.07]
-1.99
[2.01]
-7.10
-6.83+
[4.64]
[3.51]
38
44
0.34
0.30
43
Labor Regulation and International Expansion: Use of Franchising
Whole Sample
Log(GDP in USD)
Log(Population)
Log(Distance to USA in
kms)
Regulation (Botero)
0.15
[0.06]*
-0.001
[0.06]
-0.19
[0.06]**
0.12
[0.37]
Regulation (GCS)
Constant
Observations
R-squared
Number of clusters
0.59
[1.53]
2852
0.35
30
0.12
[0.05]*
-0.01
[0.04]
-0.28
[0.07]**
0.82
[0.40]+
1.43
[1.12]
3372
0.29
36
Country = Observation
(end of 2002)
0.18
0.14
[0.06]*
[0.05]**
0.03
0.01
[0.06]
[0.04]
-0.17
-0.27
[0.08]*
[0.08]**
0.37
[0.38]
0.91
[0.40]*
-0.35
0.76
[1.68]
[1.30]
29
33
0.38
0.34
29
33
44
Conclusion


Using weekly data from outlets of a multinational
fast-food chain, we have shown evidence of a
statistically and economically important effect of
labor regulations on labor decisions at the micro
level
To our knowledge this is the first time that effects
of such policies are documented in a crosscountry context at such a micro level
45
Conclusion

Specifically, using our most conservative
estimates, we find that an increase of one standard
deviation in the labor regulation rigidity index
 reduces
the response of labor cost to a one standard
deviation increase in output (revenue) by about 4.4
percentage points (from 26.4 per cent to 22.0
percent)
 increases the response of labor cost to a one
standard deviation increase in lagged labor cost by
about 9.6 percentage points (from 17.0 per cent to
26.6 per cent)
46
Conclusion

We have also shown that
 results
are similar whether we use the Botero et al.
index, or an alternative measure of labor regulation
from the Global Competitiveness Survey
 The effects do not hold for material costs, confirming
that they are not spurious – the increased rigidity in
labor is not driven by omitted variables that are also
likely to affect other variable costs
 The effects are even stronger when we estimate
using an IV approach
47
Conclusion

Consistent with the impact on adjustment
behavior, we also find that the Company
 delayed
entry, and
 operates fewer outlets
 and its partners rely on franchising more
in countries with more rigid labor laws
48
Conclusion



So we have shown that labor levels are more
persistent in countries that enact more rigid labor
laws, an effect these policies are meant to achieve
However, increases (responses to positive shocks)
as well as decreases in labor levels are affected
Consistent with our earlier findings that outlet
level labor demand was lower in more heavily
regulated markets, our results on timing of entry
and level of operations of the Company across
markets suggest that easing these laws would
increase employment and output in this industry
49
Other paper (Lafontaine and Sivadasan, “Within-firm
Labor Productivity across Countries: A case study):
Quantifying the Labor Demand effect



We find coefficients for regulation of about -0.4
=> an increase in labor regulation (Botero Index)
from its 25th percentile to its 75th percentile value
reduces labor per outlet by 0. 4 * 0.31 = 12%
Alternatively, a one standard deviation increase in
the labor regulation rigidity index leads to a
reduction in conditional labor demand of 6.4 per
cent
50
Interpreting the Expansion Effects


From column 1 (part a): an increase in labor
regulation by one standard deviation increases the
time to entry by 1.7 years on a mean of 8.9 years
(so not quite 20% increase)
From column 1 (part b): a one standard deviation
increase in the index of labor regulation reduces
the number of stores by about 3.2 relative to a
mean of 12.06 outlets (a >25% reduction)
51
Interpreting the Expansion Effects


We also find some evidence that the franchisor
and its partners rely on franchising more the more
highly regulated the labor market is
Our analyses here are based on more limited
information, however, and we have yet to verify
robustness
52