THE IMPACT OF LOCATION ON
FIRM PRODUCTIVITY IN
MOROCCAN MANUFACTURING
Fraser Thompson (Oxford
University) and Taye Mengistae
(World Bank)
THE BIG QUESTION
 Explaining
the regional variation in
economic performance across the
globe.
WHAT ARE THE ALTERNATIVE
EXPLANATIONS?
1.
Natural geography
•
Time-invariant effects of the local environment on firm performance
(e.g. a region’s proneness to drought, transport costs, etc)
•
McNeill (1991); Diamond (1997); Gallup Sachs & Mellinger (2000).
2.
Agglomeration Externalities
•
The benefits that firms extract from the local industrial structure,
thought of in terms of the levels of competition, specialization and
industrial diversity.
•
Jacobs (1969)
3.
Institutions
•
The accumulated effect of the local business environment on firm
performance, including regulation, infrastructure and the provision of
key inputs (e.g. finance, skilled labour)
•
North, Summerhill & Weingast (2000); Knack & Keefer (1995); Hall &
Jones (1999)
WEAKNESSES WITH THE
EXISTING EMPIRICAL EVIDENCE?
1.
Measurement error in evaluating institutional quality
•
•
•
2.
Small sample size leading to non-robust results
•
•
3.
Overly qualitative
Overly broad (e.g. index of anti-diversion policies)
Lack of policy implications
Lack of sufficient countries to pool in sample leads to a small number
of observations and non-robust results.
Furthermore, country-level aggregation can obscure important firmlevel effects (Manski, 1995).
Lack of sub-national focus
•
Using national-level data assumes that these location factors are
constant within countries, which seems unlikely.
OUR FOCUS

Using Moroccan firm-level data, we identify
and estimate the influences of geography,
agglomeration externalities and institutions
on firm productivity.
OUR CONTRIBUTION
1.
2.
3.
4.
More refined measures of institutions and
agglomeration externalities
First simultaneous test of their respective
influences on firm productivity
Policy-relevant evidence for a country at a critical
juncture (e.g. Association Agreement with the EU
and FTA with US)
Improvements to existing empirical methodology
–
–
Productivity Estimates
Location Endogeneity
TWO SOURCES OF DATA
1.
Census of Manufacturers (1994-2001)
–
2.
Contains the following data: sales, production, export,
investment, sector, number of permanent and temporary
workers, year of establishment, legal status and location.
Investment Climate Assessment (2003)
–
–
Detailed business environment information for firms in six
locations: Grand Casablanca; Tanger-Tetouan; Rabat;
Fes; Settat; and Nador.
Only available for 2003, but we assume that the
investment climate is fixed in the short-term (2001-2003).
AGGLOMERATION EXTERNALITIES
THREE THEORIES EXIST FOR HOW INDUSTRIAL
STRUCTURE AFFECTS FIRM PERFORMANCE
MAR
PORTER
JACOBS
Specialisation
+
+
-
Diversity
-
-
+
Competition
-
+
+
AGGLOMERATION EXTERNALITY
VARIABLES
1.
Total workers in location-sector (specialisation)
2.
Total workers in location (diversity)
3.
Diversity HHI using sector employment shares
Dit 
1
 List


sist  Lit



2
4.
Number of firms in the location-sector
5.
Firm Market Share
6.
Competition HHI
Cist2 
1
 Qnt 



nist Qist 
2
GEOGRAPHY
GEOGRAPHY



Province-level dummy variables
66 provinces in entire country
We have investment climate data for 21
provinces
INSTITUTIONS
4 CRITERIA FOR SELECTING
INSTITUTIONAL VARIABLES
1.
Important Constraints on Firm Performance
2.
Exhibit Regional Variation
3.
Not vague or imprecise
4.
Capture all aspects of the investment climate
Co
Ac st o
ce f fi
Ac
ss n a
ce
to nci
ss
fin ng
An t
tic o in T anc
om d ax e
pe u st r at
tit rial es
ive la
pr nd
a
Ta cti
Co x ce
un a dm
Le ter in
ga fei
t
W l sy ing
M o rk ste
ac e
m
ro r S
k
i
Cu nst ills
st ab i
om lit
y
La s re
bo g.
Co r re
rru g.
E
Bu le ptio
s. ctr n
Li ici
ce ty
ns
in
Un g
Tr ion
Te
an s
le
sp
co
o
m
m C rt
un r im
ic
at e
io
ns
PERCENTAGE OF FIRMS WHO CONSIDER
CONSTRAINT TO BE “MAJOR” OR VERY “SEVERE”
90
80
70
60
50
40
30
20
10
0
INSTITUTIONAL VARIABLES
“Perception” Measures
1.
2.
3.
4.
5.
6.
7.
8.
Electricity
Telecommunications
Transport
Corruption
Tax Administration
Skill Shortages
Legal System
Access to Credit
“Quantitative” Measures
1.
2.
3.
4.
5.
6.
7.
Financial Delay (months)
Construction Permit Delay
(days)
Time per week dealing with
bureaucracy (%)
Generator (1 if yes)
Discount on total sales from
transport delays (%)
Delay in filling technical
vacancy (days)
Workforce with less than
primary education (%)
REGIONAL VARIATION IN
“PERCEPTION” INDICATORS
Region
Electricity
Telecommunications
Transport
Corruption
Tax
Administration
Skill
Shortages
Legal System
Ben Slimane
1.75
2.03
1.67
1.21
1.27
1.55
Berkane
1.00
1.00
1.00
3.00
1.00
1.00
Casablanca
1.61
1.35
1.67
1.95
2.82
2.40
Fes
2.05
1.31
1.81
2.95
2.59
2.58
Jrada
1.00
1.00
4.00
3.00
1.00
1.00
Khemisset
1.83
1.17
2.17
1.51
1.51
2.42
Larache
1.00
1.00
1.00
1.00
1.48
2.68
Mediouna
2.00
1.95
1.50
1.32
2.03
1.47
Mohammedia
1.61
1.70
1.75
2.05
3.01
1.88
Nador
2.16
1.00
2.28
1.72
2.80
1.89
Nouaceur
1.88
1.30
1.88
1.48
2.09
2.09
Oujda-Angad
1.34
1.24
2.57
2.74
3.01
2.56
Rabat
1.28
1.05
1.19
2.09
3.28
2.43
Sale
1.72
1.17
1.82
3.06
3.61
2.62
Sefrou
1.00
1.00
1.00
2.00
1.00
1.00
Settat
1.17
1.07
1.27
1.51
1.69
2.44
Skhirate-Temara
2.46
1.95
1.50
1.49
3.26
2.45
Tanger-Assilah
2.66
1.91
2.03
1.79
3.37
2.46
Taourirt
4.00
1.00
4.00
2.00
4.00
1.00
Tetouan
1.52
1.35
1.91
2.45
3.16
3.25
Moulay Yacoub
1.00
1.00
2.00
3.00
3.00
1.00
Source: Moroccan Investment Climate Assessment (World Bank). Perception Indices are constructed such that: 1
constraint; 3 - medium constraint; 4 - major constraint; 5 - very severe constraint.
Access to
Credit
1.21
1.00
2.51
2.88
2.00
2.08
1.00
2.13
2.84
1.77
2.73
3.76
3.56
2.74
1.00
2.99
2.72
2.32
5.00
3.14
5.00
- no constraint;
3.03
5.00
4.07
3.96
1.00
4.17
3.68
3.34
3.12
3.47
3.64
3.14
3.14
4.21
4.00
3.47
4.21
4.29
5.00
2.95
1.00
2 - minor
REGIONAL VARIATION IN “HARD”
INDICATORS
Discount on
Time Per Week
Generator
Delay in filling Workforce with
Financial Delay
Sales from
Region
Dealing with
(dummy = 1 if
Technical
less than primary
(months)
Transport Delays
Bureaucracy (%) have generator)
Vacancy (weeks)
educ. (%)
(%)
Ben Slimane
2
20.47
3.05%
0.00
0.00%
1.22
21.04%
Berkane
2
90
10.00%
0.00
0.00%
53
73.00%
Casablanca
1.8
38.59
7.28%
0.16
0.23%
3.27
26.04%
Fes
2.38
51.94
35.57%
0.15
0.37%
4.93
52.35%
Khemisset
3
16.19
8.53%
0.83
7.50%
4.67
40.42%
Larache
3
25.72
1.00%
0.00
0.00%
6
73.17%
Mediouna
1.29
51.7
6.88%
0.00
0.00%
2.31
24.95%
Mohammedia
1.79
37.82
12.96%
0.27
0.08%
3.22
35.00%
Nador
2.54
48.93
13.79%
0.35
0.03%
10.37
20.27%
Nouaceur
1.35
30.38
0.70%
0.05
0.00%
5.41
17.96%
Oujda-Angad
0.87
33.12
15.58%
0.26
0.53%
5.3
19.60%
Rabat
1.49
37.6
16.13%
0.14
0.00%
1.89
50.12%
Sale
2.93
35.8
26.65%
0.04
1.11%
2.28
59.71%
Settat
1
24.88
0.80%
0.37
0.00%
7.11
22.93%
Skhirate-Temara
2.91
33.13
16.63%
0.40
0.00%
3.26
22.57%
Tanger-Assilah
2.75
43.3
3.75%
0.17
1.11%
5.94
75.35%
Taourirt
3
30
30.00%
1.00
2.00%
5
10.00%
Tetouan
1.1
30.59
0.89%
0.17
1.86%
2.16
49.64%
Source: Moroccan Investment Climate Assessment (World Bank)
Construction
Permit Delay
(days)
ENSURING CONSISTENT ESTIMATES FOR
INVESTMENT CLIMATE VARIABLES






This analysis assumes that the investment climate indicators are
exogenous determinants of firm performance.
However, the performance of firms may actually influence their
investment climate constraints.
E.G. High-performing firms (possibly engaged in a leading technology
field) are more likely to experience difficulties in finding suitably
qualified employees.
Solution: use averages of the investment climate data across firms in a
given location-sector.
This reverse-causality is likely to be mitigated to the extent that
individual firm performance is unable to influence regional averages of
these investment climate indicators.
Not only does this ease the endogeneity problem, it also substantially
increases the sample size by including firms which do not have
investment climate information, but which are located in the same
location-sector for firms which we do have information available.
SECTOR TFP ESTIMATES
Firm-Level Revenue (log)
Firm Characteristics (Marginal Effects)
Capital Stock (log) b
Firm-level employment (log)
Firm Age (log)
Firm Age (log squared)
Share of Casual Employment
b
a
Food & Tobacco Textiles Apparel & Footwear Machinery & Equip Wood & Furniture Paper & Printing Chemicals, Rubber, etc
0.381
.434
.355
.149
.425
.203
.371
(3.76)***
(1.20)
(1.67)*
(2.55)**
(2.34)**
(3.41)***
(0.04)
0.888
.881
.610
1.115
.743
.793
.731
(3.98)***
(2.22)**
(2.44)**
(0.79)
(1.50)
(1.06)
(2.78)***
.040
-.632
.191
-.502
-1.131
-.097
.136
(0.35)
(0.58)
(0.58)
(0.82)
(1.28)
(0.11)
(1.30)
.062
.190
.023
.190
.277
.034
.005
(1.66)*
(0.97)
(0.25)
(1.59)
(1.45)
(0.23)
(0.18)
-1.085
.114
-.734
.024
-.451
-.223
-.697
(2.40)**
(0.10)
(1.08)
(0.04)
(0.97)
(0.31)
(1.32)
Intercept
Included
Included
Included
Included
Included
Included
Included
Year Dummy Variables
Included
Included
Included
Included
Included
Included
Included
F test (p value)
67.34 (0.00) 10.44 (0.00)
76.90 (0.00)
46.50 (0.00)
18.25 (0.00)
36.89 (0.00)
133.99 (0.00)
Quasi-concavity (proportion)
1.0000
0.9871
0.9743
0.9968
0.9946
0.8548
0.9987
Monotonicity (proportion)
1.0000
0.9843
0.9924
0.9885
0.9984
0.9378
0.9963
Sargan / Hansen test (p value) c
114.10 (0.51) 71.94 (0.58)
117.03 (0.43)
127.96 (0.18)
63.28 (1.00)
71.72 (0.55)
111.46 (0.63)
Number of observations
4825
2142
4442
4017
1573
1679
3542
Standard errors have been computed using the White adjustment for heteroskedasticity. Significance at the 1%, 5% and 10% levels is indicated by *, ** and *** respectively. The instrument set
for the differenced equations consists of levels of predicted capital stock and firm employment in periods t-3 to t-7. The instrument set for the differenced equation includes the same variables
differenced one period earlier. Firm age, firm age squared and location dummy variables are used as additional instruments.
a The marginal effects for the translog functions are evaluated at their sample means.
b The t statistics relate to the linear effect of these variables only.
c
Tests for the exogeneity of instruments
SIGNIFICANT VARIABLES
(“PERCEPTION” INDICATORS)

Location Fixed Effects:
1.
2.

Agglomeration Externalities:
1.

Positive: Mohammedia; Nador; Nouaceur; Rabat; Settat;
Skhirate-Temara.
Negative: Khemisset; Moulay Jacoub.
Positive: Market Share and Number of Firms in Location-Sector
Business Environment:
1.
2.
Positive: Electricity (endogenous???)
Negative: Telecommunications; Tax Administration; Legal
System; Access to Credit.
SIGNIFICANT VARIABLES
(“QUANTITATIVE INDICATORS”)

Location Fixed Effects:
1.
2.

Agglomeration Externalities:
1.

Positive: Fes; Mohammedia; Nador; Sale; Skhirate-Temara;
Tanger-Assilah.
Negative: Ben Slimane;
Positive: Market Share and Number of Firms in Location-Sector
Business Environment:
1.
Negative: Financial Delay; Construction Permit; Time Per Week;
Discount on Sales from Transport Delays; Delay in filling technical
vacancies; Workforce with less than primary education.
OTHER RESULTS


The location effects appear generally more cogent
for limited liability firms than for corporations.
The effect of the local business environment also
appears to be generally stronger for non-exporters
compared to exporters. However technical skill
vacancies appear particularly crippling for exporters,
which is possibly reflective of the higher skill
demands of exporters.
LOCATION EFFECTS ON TFP (FOOD,
TOBACCO & BEVERAGES)
10.00%
-20.00%
-30.00%
-40.00%
Location
t
h
ur
ir
Ta
o
ila
-A
ss
tta
t
Se
ge
r
Ta
n
da
-
An
ga
d
r
ad
o
N
O
uj
Fe
oh
s
am
m
ed
ia
M
ca
bl
an
as
a
n
-10.00%
C
Sl
im
an
e
0.00%
Be
% of Total Regional TFP Explained
20.00%
Agglomeration Externalities
Location Fixed Effect
Business Environment
LOCATION EFFECTS ON TFP
(APPAREL, LEATHER & FOOTWEAR)
40.00%
20.00%
10.00%
Agglomeration Externalities
0.00%
Location Fixed Effect
B
e
C rk
a s an
ab e
la
nc
a
K F
he es
m
is
se
M La r t
oh ac
a m he
m
N ed
o u ia
ac
eu
R r
ab
at
S
S
al
kh
e
S
ir a
e
t
t
Ta e-T tat
ng em
er a
-A ra
ss
ila
h
% of Total Regional TFP Explained
30.00%
-10.00%
-20.00%
-30.00%
-40.00%
Location
Business Environment
LOCATION EFFECTS ON TFP
(CHEMICALS, RUBBER & PLASTICS)
15.00%
5.00%
S
-10.00%
S
kh
et
ir a
ta
te
t
-T
Ta
e
ng ma
ra
er
-A
ss
ila
h
al
e
S
-5.00%
R
ab
at
0.00%
C
as
ab
la
M
nc
oh
am a
m
ed
ia
N
ou
ac
O
e
uj
d a ur
-A
ng
ad
% of Total Regional TFP Explained
10.00%
-15.00%
-20.00%
-25.00%
-30.00%
-35.00%
Location
Agglomeration Externalities
Location Fixed Effect
Business Environment
CONCLUSION / POLICY IMPLICATIONS


The paper supports many previous arguments of the
predominant importance of institutions and business
environment constraints over other location effects in
influencing firm performance.
If the location fixed effects can be considered the
relative intransient impacts on firm productivity, then
the small size of these effects compared to those
arising from the business environment suggest that
historical obstacles can be overcome through
appropriate policy responses.