IS LABOR POOLING FOUNDATION FOR
AGGLOMERATION?
APPLIED ECONOMICS
RESEARCH CENTRE
PRESENTED BY
FARAH ATIQ (BATCH 2018-19)
Supervisor:
Dr. Faisal Sultan Qadri
Co-Supervisor:
Dr. Ambreen Fatima
INTRODUCTION
This study focuses on a potential source of agglomeration economies
to which Alfred Marshall (1890) devoted particular attention: labor market
pooling. While there are various interpretations of labor market pooling as
a source of agglomeration economies, Marshall emphasized that “a localized
industry gains a great advantage from the fact that it offers a constant
market for skill” (Marshall 1890, 271).
WHAT IS INDUSTRIAL AGGLOMERATION?
It refers to high concentration of industrial activities in an area because industries
may enjoy both internal and external economies when they cluster together
(agglomerate).
WHAT IS LABOR POOLING
It refers access to the pool of workers, spatially concentrated which helps to
reduce the cost.
INTRODUCTION
The productivity advantages of cities and urban clusters with a high density of firms and workers have been
perceived for a long time, and already received the attention of Smith (1776) and Marshall (1890). However
these studies didn’t clarify mechanism.
We may perceive some advantages from having a large local labor market, but what is the precise channel
through which such advantages operate?
Is it because a larger labor market improves matching between employers and employees?
Or is it because large concentrations of employment iron out idiosyncratic shocks and improve
establishments’ ability to adapt their employment to good and bad times?
Or perhaps because larger markets allow workers to specialize in a narrower set of activities and improve
their performance?
To answer such questions we need good models that formalize the microeconomic foundations of urban
agglomeration economies, as well as detailed empirical work able to identify and quantify the precise
mechanisms. This is an area where there has also been much recent progress. However, as we discussed in
detail below, there are substantive open questions that need to be discussed
INTRODUCTION
Early work by Marshall (1920) classified the three principal benefits available to firms that
choose to locate in a geographically concentrated area into:
(i)
labor pooling
(ii)
knowledge spillovers, and
(iii) availability of specialized inputs, all of which give entrepreneurs the incentive to locate in
concentrated areas.
Over the past 30 years, urban economists have been rather successful at documenting and
quantifying these advantages.
Krugman (1991) formalizes this reasoning by considering a series of sectors where
establishments experience idiosyncratic shocks.
Estimates shows that productivity increases with the increase in the size of an agglomeration,
range between 2 and 8 percent (Rosenthal and Strange, 2004).
INTRODUCTION
Overman and Puga (2010) looked at the mechanism empirically. They measure the likely
importance of labor pooling by calculating the fluctuations in employment of individual
establishments relative to their sector and averaging by sector. They finds that sectors whose
establishments experience more idiosyncratic volatility are more spatially concentrated, even
after controlling for a range of other industry characteristics.
Ellison et al. (2010) found out that industries with similar labor mixes tend to co-agglomerate,
which is indicative of labor pooling - also bring sectors together.
The Ellison and Glaesar index has been computed for manufacturing industries in Pakistan by
Burki and Khan (2010) and shows that there is a considerable industry level variation in the
extent of concentration exhibited by the firms.
Gardezi (2009) measure industry specific agglomeration by using geographical distances
between pairs of firms and identified importance of labor pooling as cause of it.
SIGNIFICANCE/RATIONAL OF THE STUDY
•In the process of development, particularly the take off stage of an economy (crucial
stage), largely depends on the industrial base of country. Industries spatially
concentrate to minimize cost of production. According to economic survey of Pakistan
(2018-19) the industrial sector consist of 20.3% of GDP. It shows the degree of
attention this sector need.
•This study is trying to trace the contribution of labor pooling as a source of industrial
agglomeration and spatial concentration of industrie in the context of Pakistan.
Reason for focusing Pakistan in this study is simple, Pakistan is a labor abundant
country. It is beneficial if this abundance can be intentionally utilized for the arrival
of new firms to form economic zones and lead country towards efficient utilization of
resources.
SIGNIFICANCE/RATIONALE OF THE STUDY
In the recent few decades, due to political and economic instability there is a shift in
urbanization trend, and performance of manufacturing sector has gone down. Economic survey
2018-19 highlighted that the growth rate of manufacturing sector was 5.43% in 2017-18,
however, it has the negative growth in 2018-19 (-0.27). Moreover, the overall industrial sector
growth is 1.4% as compare to 4.29% last year.
Many studies have calculated agglomeration index in Pakistan. Though, due to limited data
availability Studies mostly covered Punjab area.
Recently, research conducted by Tabbsum et al. (2018) using data from labour force survey
(LFS) for the years 2005-06, 2009-10 and 2012-13 conducted study at city level.
Its results are based on Herfindahl index for spatial concentration by region revealed that
among the cities considered Karachi has the highest value as it is the only port city in Pakistan.
SIGNIFICANCE/RATIONALE OF THE STUDY
 Further Industries are increasingly clustering in Lahore on account of having a well equipped
infrastructure. Spatial concentration Index indicated that in Pakistan basic metal industries
tends to concentrate spatially while that of textile industry and food, beverages & tobacco
industry are getting dispersed by location.
Cities offer a variety of services and facilities to the industries that small rural areas often
fail to provide. These services might include availability of large percentage of skilled labor
force, large pool of workers with varied skills. Hence There is need to assess the Marshal’s
claim of labor pooling as source of agglomeration.
The report prepared by AERC titled “City Dynamics in Pakistan- 2015” presented the
agglomeration and urban concentration fact and figures. According to the report, Karachi city
is far higher than the rest of the cities in terms of population and other economic indicators.
Moreover, there is skill deficit in all the large cities of Pakistan.
POPULATION IN MAJOR URBAN CITIES OF PAKISTAN (1998-2011)
Source: City Dynamics in Pakistan- 2015
LABOUR FORCE BY SKILL LEVEL IN LARGE CITIES
Source: City Dynamics in Pakistan- 2015
HERFENDAHL INDEX FOR SPECIALIZATION
Source: City Dynamics in Pakistan- 2015
HERFINDAHL INDEX FOR SPATIAL CONCENTRATION BY CITY
Source: City Dynamics in Pakistan- 2015
OBJECTIVE OF THE STUDY
GENERAL:
The objective of this study is to contribute to the existing research by providing new pragmatic
results about concentration of industries and firm due to labor pooling in fourteen major cities,
Karachi, Lahore, Islamabad, Faisalabad, Rawalpindi, Multan, Gujranwala, Sargodha, Sialkot,
Bahawalpur, Hyderabad, Sukkur, Peshawar, and Quetta, of Pakistan.
SPECIFIC:
To calculate city level index of concentration for manufacturing sector of Pakistan using recent
datasets of 2014-15 and 2017-18.
To calculate city level index of labor pooling for manufacturing sector of Pakistan.
To assess demographic, labor market characteristics, that affect the industrial concentration in
the cities.
To assess, whether the industrial agglomeration in manufacturing sector in these 14 major cities
is due to the Labor Pooling.
HYPOTHESIS
This study focuses on the causes of industrial agglomeration hence study is based on
the hypothesis that:
Whether labor pooling causes the industrial agglomeration particularly in
manufacturing sector.
 To what extendt the arrival of new firm in manufacturing sector contribute to
industrial agglomeration particularly in manufacturing sector.
 How much the demographic, labor market characteristics of a particular place such
as a city contribute in the extendt of agglomeration in it.
THEORATICAL BACKGROUND
This section provides the microeconomic foundations for labor pooling as foundation for
agglomeration through a simple model. It is built on the work of Krugman (1991) which later
followed by the Overman and Puga (2009) and Gardezi (2013).
1. Setup
The theoretical building of this framework begin by considering the number of industries
indexed as j = 1,…, J. In every industry there is particular number of firms sub-indexed i = 1,
.., n, and each of them have a pool of workers with specific skills and expertise. After located
at particular place each establishment go through a particular shock𝟄𝒊 . Productivity shock that
experience by each firm is heterogynous in nature i.e. the productivity shock will be
establishment specific as well as has no correlation across firm. Establishment decides the
quantity of labor it chooses to hire from the locally available labor pool. Suppose if the level
of employment chooses by establishment isli,and𝞴 shows intensity of decreasing return to scale
of a firm’s production function, the profit will be as following:
1
2
πi = [β + ϵi ]li − λl2i − wl2i (1)
CONTINUE…
2. Wages
As in work of Krugman (1991) the local wage is considered as given by firms. Hence,
the firm after undertaking shocks set its quantity of workers where, the marginal
productivity of worker will be equal to wages. The demand for labor in firm i is:
li =
β+ϵi −w
λ
(2)
Aggregated supply of hours of the work by labors is symbolized as Li,j in a given city
and sector.Equilibrium in labor market together form in equation (3) as follows:
Li =
n
i=1 li
=
β+ n
i=1 ϵi −w
λ
(3)
CONTINUE…
Equilibrium wage can be determined by above equations:
L
1
w = β − λN + N
n
i=1 ϵi
(4)
Thus, the expected wage is:
L
𝐸(w) = β − λ N
(5)
3. Profits
Solving equation (1) by putting equation (2) in it leads to:
𝜋𝑖 =
[β+ϵi −w]2
2λ
(6)
Firms adjust their production in response to productivity shocks therefore; firm’s profit is a convex
function of production shocks and wages. Incorporating expectation in profit function yields:
𝐸(𝜋𝑖 ) =
[β−E(w)]2 + var[ϵi −w]
2λ
(7)
CONTINUE…
In equation (7), Substitute equation (5) and var [𝟄i ,–w] = var [𝟄i] + var [w] – 2cov[𝟄i, w], which
results in
𝐸(𝜋𝑖 ) =
λ L 2
2 N
+
var w +var ϵ𝑖 −2 cov[ϵi ,w]
2λ
(8)
When there is no productivity shock, firm’s profit can represented by the first term of the right hand
side of the equation. The increase in labor to firm ratio increases the expected profit of a firm
because from equation (5), it can be seen that increase in this ratio reduces expected wages.
Moreover, second term of the right hand side part of equation captures the effect of labor
pooling. It reflects the expected profit is likely to increase with increase in productivity shock’s
variance and local wage’s variance however decreases with increase in productivity shock and
wage’s covariance. This can be explained as, if firms increase its production due to productivity
shock but simultaneously it also increases the wages or vice versa in negative productivity shocks,
this will reduce firm’s expected profit. Explanation above provides the micro level base for
framework of labor pooling as foundation for agglomeration. So firms that faces the productivity
shocks which affect the local wage prefers to agglomerate with other firm to lessen the impact of
wage fluctuation.
CONTINUE
4. Relocation of Firms and Workers
Overman and Puga (2009) modeled production and relocation in dynamic setup. Initially firms
explicitly choose the location along with number of workers. At equilibrium neither nether firm
nor worker has incentive in relocation because wages are equalized. The labor per firm ratio
is same across location. Furthermore, the relocation of a worker does not affect the wages or
profit; though, the wages at origin and final location can get affected if a firm decides to
relocate. Afterwards, firms face the productivity shock ϵi which creates the deviation in
profitability. The destination at which firm decides to relocate, the worker to firm ratio
decreases over there. This will put upward pressure on expected wages and in result expected
to decline profit. Moreover, if there is large number of firms at destination location,
productivity shock will not largely affect local wages. Because of this firms are able to lighten
down the shocks and gain higher expected profit. If industry’s shocks are more heterogeneous
in nature, it will tend to agglomerate more to gain more expected profit.
INTERNATIONAL REVIEW
Study Author
Year
Title
Labor pooling as
an agglomeration
Edilberto Tiago
factor: Evidence
de Almeida,
2018 from the Brazilian
Roberta de
Northeast in the
Moraes Rocha
2002–2014
period
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
This study evaluates importance of
labor pooling to explain industrial
agglomeration in the Northeast of
Brazil, employing firm-level
microdata. For this purpose, they
applied regression models in which
they regressed the Ellison and
Glaeser (1997) index as a function
of a proxy for labor pooling, to
capture exogenous shocks in the
labor market while controlling for
observed sector characteristics that
vary in time and sector fixed
effects.
The results of this study are consistent
with a reduction in the level of industrial
concentration in the period from 2002
to 2014. For labor pooling, as
predicted, the role of the labor pooling
variable is positive and significant. Thus,
industries where, on average, plants
face more idiosyncratic shocks relative
to their industry are more spatially
concentrated.
Study Author
Selcen
Öztürk,
Dilek Kılıç
Year
Title
Do Firms Benefit
From
Agglomeration? A
Productivity
2016
Analysis for
Turkish
Manufacturing
Industry
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
This study was the first attempt to
investigate such relationship for
Turkish manufacturing industry by
accounting for the two way
relationship between agglomeration
and productivity employing a
dynamic system GMM model and
by using proper proxies for both
agglomeration and productivity
The results of this suggest a negative
relationship between productivity and
agglomeration, which supports the
negative externalities hypothesis. The
question remains why some firms would
still choose to locate or remain in the
agglomerated regions even though
there is persistent evidence on negative
externalities. As an answer to this
question, it can be argued that in order
to take advantage of the
agglomeration economies, such as
vertical linkages, access to markets or
other several benefits of locating in
dense areas might not offset the
associated costs
Study Author
Year
Title
What Causes
Glenn Ellison,
Industry
Edward L.
Agglomeration?
Glaeser and 2014
Evidence from CoWilliam R.
agglomeration
Kerr
Patterns
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
They note that these theories make
different predictions about which
pairs of industries should be coagglomerated. This paper focus
Marshall’s three theories of industry
agglomeration to each industry
pair: (1) agglomeration saves
Their findings suggest that input-output
transport costs by proximity to input
dependencies are the most important
suppliers or final consumers, (2)
factor, followed by labor pooling.
agglomeration allows for labor
market pooling, and (3)
agglomeration facilitates
intellectual spillovers. With
longitudinal data, this paper test
which part is more relevant
empirically.
Study Author
Year
Title
Monica
Marshallian
Andini ,
labour market
Guido de
pooling:
Blasio , Gilles 2013
Evidence from
Duranton
Italy
,William C.
Strange
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
This paper employs a unique
Italian data source to take a
comprehensive approach to
labour market pooling. It
jointly considers many different
aspects of the agglomeration
— labour market relationship,
including turnover,
learning, matching, and hold up.
It also considers labour market
pooling from the perspective of
both workers
and firms and across a range of
industries.
Their findings shows some support for
theories of labour market
pooling, but the support is weak.
Specifically, there is a general positive
relationship of turnover to local
population density, which is consistent
with theories of agglomeration and
uncertainty. There is also evidence
of on-the-job learning that is consistent
with theories of labour pooling, labour
poaching, and hold up. In addition,
the paper provides evidence consistent
with agglomeration improving job
matches.
Study Author
Year
Title
Kristian
The Determinants
Behrens, Mark
Brown and 2013 of Agglomeration
Redux
Th´eophile
Bougna
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
They study the determinants of
They find that between 1990 and
agglomeration of Canadian
2009, industry localization has
manufacturing industries from
persistently fallen. The average degree
1990 to 2009. In so doing, they
of localization decreased by 36%
revisit the seminal contribution by within 10km, by 22.6% within 100km,
Rosenthal and Strange 2001,
and by 11.3% within 500km. Declining
The determinants of agglomeration“ localization is associated with import
using a long panel and
competition, particularly from
continuous measures of localization.
low wage countries, increasing
They pay particular attention to the transportation costs and the spreading
role of transportation
out of upstream input suppliers and
costs constructed using extensive downstream demand for intermediate
Canadian trucking micro data
inputs. While they find strong evidence
international trade expoof trade-driven changes in localization,
sure, and input sharing constructed they find less evidence for knowledge
using micro-geographic location spillovers and labour market pooling as
patterns of plants.
drivers in changes in localization.
Study Author
Year
Title
Geographical
Agglomeration of
Chinese
Zhang Hua & 2013
Manufacturing
Zheng Wei
Industries
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
The results indicate that within-county
Applying the agglomeration index
spillovers are stronger than nearbyproposed by Maurel and Sedillot.
county spillovers. and within-prefecture
they shows that the most
spillovers are stronger than nearby
agglomerated industries are
prefecture spillovers. Localized
resource-intensive, capital and
spillovers are still quite substantial at a
technology intensive while industries
range beyond that of counties.
demanding localized inputs or
Comparing the agglomeration index of
serving localized markets or
Chinese manufacturing industries in
favored by local governments are
1996, 200 I and 2004, agglomeration
fairly dispersed. The more
seems to be a general tendency. Results
disaggregated industries are more
also indicate that some industries
spatially agglomerated. At the finer
have experienced remarkable changes
spatial scale, industries are more
in their levels of agglomeration
dispersed.
in the period 1996-2004.
Study Author
Year
Title
Jordi JofreThe mechanisms
Monseny,
of agglomeration:
Raquel
Evidence from the
Marín-López, 2011 effect of interindustry relations
Elisabet
on the location of
Viladecansnew firms
Marsal
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
This paper explores the relative
importance of each of Marshall’s
agglomeration mechanisms by
examining the location of new
manufacturing firms in Spain. In
particular, they estimate the count The evidence shows the presence of all
of new firms by industry and
three agglomeration mechanisms,
location as a function of (prealthough their incidence differs
determined) local employment
depending on the geographical scale
levels in industries that: 1) use
of
similar workers (labor market
the analysis.
pooling); 2) have a customer
supplier
relationship (input sharing); and 3)
use similar technologies
(knowledge spillovers).
Study Author Year
Title
Labor Pooling as a
Henry G.
Source of
201
Overman and
Agglomeration: An
0
Diego Puga
Empirical
Investigation
Type of Study
Key Finding
Recommendation/Conclusion
Quantitative:
Empirical
Estimation
They extended Krugman (1991)
approach to derive the specific
prediction that industries facing
stronger idiosyncratic shocks will
exhibit a greater tendency to
agglomerate and that
agglomeration will be associated
with worker turnover. Particularly
they took plant-level data for the
United Kingdom and find evidence
of labor-market pooling as a
micro-determinant of
agglomeration.
Study determined significant
coefficient of the pooling (measure)
calculated using plants relative to UK
manufacturing as a whole is purely
driven by plants experiencing
idiosyncratic shocks relative to their
industry (“plant to sector”). Industries
that tend to experience idiosyncratic
results relative to manufacturing as a
whole (“sector to United Kingdom”) do
not tend to be more geographically
concentrated.
Study Author Year
Title
THE MAGNITUDE
201 AND CAUSES OF
Diego Puga
0 AGGLOMERATION
ECONOMIES
Type of Study
Qualitative:
Theoretical
Key Finding
Recommendation/Conclusion
The results shows despite the broad
This paper with the help of past
agreement on the magnitude of
studies, discussed substantial
agglomeration economies at the urban
evidence of agglomeration
level, the literature has been far less
economies based on three
successful at distinguishing between the
approaches. First, on a clustering possible sources. On the theoretical
of production beyond what can be
side, there are good models of
explained by chance or
agglomeration through sharing and
comparative advantage. Second,
matching, but not a deep enough
on spatial patterns in wages and theoretical understanding of learning
rents. Third, on systematic
in cities. On the empirical side,
variations in productivity with the evidence of matching as a source of
urban environment.
agglomeration is perhaps most
needed.
Study Author
PierrePhilippe
Combes,
Gilles
Duranton
Year
Title
Labour Pooling,
Labour Poaching,
2006
and Spatial
Clustering
Type of Study
Qualitative
Key Finding
Recommendation/Conclusion
When firms cluster in the same
local labour market, they face a
trade-off between the benefits of
labour pooling and the costs of
labour poaching. They explore this
tradeoff in a duopoly game.
The results show that (because of
Depending on market size, on the
tradeoff in duopoly game) codegree of horizontal
location, although it is always efficient
differentiation between goods, in our framework, is not in general the
and on worker heterogeneity in non-cooperative equilibrium outcome.
terms of knowledge transfer cost,
we characterize the strategic
choices of firms regarding
locations, wages, poaching and
prices.
Study Author
Courtney
LaFountain
Year
Title
Where do firms
locate? Testing
2005
competing
models of
agglomeration
Type of Study
Quantitative
Key Finding
Recommendation/Conclusion
Results shows the evidence that data
for the textiles, paper,
chemicals, petroleum and coal,
electronics, and instruments industries
are consistent with the predictions of
This paper is an attempt to gain
the natural advantage model. For
insight into why firms in different firms in these industries, proximity to
industries locate in
industry-specific inputs is of primary
different places. This will, in turn, importance when deciding where to
contribute to our understanding of
locate. Moreover, firms in
different causes of
miscellaneous manufacturing
Agglomeration.
industries prefer to locate in places
where people are employed
in a wide variety of different
industries, so they will benefit from
positive externalities.
Study Author
Stuart S.
Rosenthal,
William C.
Strange
Year
2003
Title
Geography,
Industrial
Organization,
and
Agglomeration
Type of Study
Key Finding
Recommendation/Conclusion
Quantitative
This study use a unique and rich
database in conjunction with
mapping software to measure the
geographic extent and nature of
agglomerative externalities.
They test for the existence of
organizational agglomeration
economies of the kind studied
qualitatively by Saxenian (1994).
This is a potentially important
source of
increasing returns that previous
empirical work has not considered.
Results indicate that localization
economies
attenuate rapidly and that industrial
organization affects the benefits of
agglomeration. Results also indicate
that industrial structure and corporate
organization affect the
benefits that arise from clustering
within a given industry.
Study Author
Stuart S.
Rosenthal,
William C.
Strange
Year
2001
Title
The Determinants
of Agglomeration
Type of Study
Quantitative
Key Finding
Recommendation/Conclusion
Results indicate that proxies for labor
This paper examines the micro
market pooling have the most robust
foundations of agglomeration
effect,
economies for U.S.
positively influencing agglomeration
manufacturing industries. Using
at all levels of geography. Proxies for
industries as observations, we
knowledge
regress the Ellison Gleaser
spillovers, in contrast, positively affect
measure of
agglomeration only at the zipcode
spatial concentration on industry
level. Reliance
characteristics that proxy for the
on manufactured inputs or natural
presence of knowledge
resources positively affects
spillovers, labor market pooling,
agglomeration at the state
input sharing, product shipping
level but has little effect on
costs, and natural
agglomeration at lower levels of
advantage. The analysis is
geography. The same is
conducted separately at the
true for the perishability of output, a
zipcode, county, and state levels.
proxy for product shipping costs
Study Author
GLENN ELLISON
AND EDWARD L.
GLAESER*
Year
Title
The Geographic
Concentration of
Industry: Does
1999
Natural Advantage
Explain
Agglomeration
Type of Study
Key Finding
Recommendation/Conclusion
Quantitative:
Empirical
Estimation
This study finds the sensitivity of
location decisions to the cost of a
particular input to be related to
the intensity with which the industry
uses the input. The finding shows
that virtually all industries are at
least slightly agglomerated is
apparently fairly robust to the
introduction of measures of cost
advantages.
In this study, it is observed that
least half of observed
geographic concentration is due
to natural advantages.
Simultaneously, there remain a
large number of highly
concentrated industries where it
seems that agglomeration must
be explained by localized
intraindustry spillovers.
Study Author
D P Angel
Year
Title
High-technology
agglomeration
and the labor
1990
market:
the case of Silicon
Valley
Type of Study
Case Study
Key Finding
Recommendation/Conclusion
In this paper the pattern of labormarket activity associated with
In his conclusion he pointed out that
major high-technology
Recent assessments of US
agglomerations within the USA are competitiveness in high-technology
examined, drawing upon the results sectors of production have been critical
of a mailed questionnaire survey of
of the entrepreneurial pattern of
firms in the semiconductor industry. industrialization observed in Silicon
The analysis is focused upon the
Valley, depicting the region as a
cluster of specialized semiconductor
fragmented collection of small
firms in Silicon Valley, to determine producers each pursuing technological
the contribution of local laborand commercial advantage
market processes to the growth and independently, with little coordination
development of this highor communication among firms.
technology production complex.
NATIONAL REVIEW
Study Author
Year
Title
Urban
Development and
Khalida
Industrial
Mahmood,
Clustering in
Razzaq Ahmed 2016
Pakistan: A Study
and Nighat
Based on
Bilgrami-Jaffer
Geographical
Perspective
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
This study reveals that the urban
growth significantly serves as strong
It is concluded that Geo-spatial
sources of economic and
agglomeration comes out as an
employment opportunities for a
outcome of industrial clusters supported
large number of population and
by related infrastructure and relevant
local as well as migrated from other
industrial development. The economic
areas. The unchecked influx of
benefits of urban clusters encompass a
working migrants from the interior
large number of people in a
of the country becomes the reason
developing society benefiting not only
of emerging shanty towns in urban
the local economy but also to all
centers like Karachi. The results
population living in the far off interior
reveal the importance of spatial
of the country. These clusters also serve
interaction, population potential,
as catalyst which distributes these
manufacturing value added and
benefits to the neighborhood nodes and
urban population which are all the
areas also.
variables of agglomeration and
industrial clustering..
Study Author
Maryiam
Haroon and
Azam
Chaudhry
Year
Title
Where Do New
Firms Locate? The
Effects of
Agglomeration on
2014 the Formation
and Scale of
Operations of
New Firms in
Punjab
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
This paper use developing country
data to estimate the impact of
localization (the benefits accruing
Their findings reveal that
to firms that choose to locate in a
agglomeration measured through
specific region within a specific
density of employment has a
industry) and urbanization (the
significant impact on the formation of
benefits accruing to firms located
new firms and on their scale of
close to each other regardless of
operations in Punjab.
the type of industry to which they
belong) on new firms’ formation
and scale of operations.
Study Author
Najam uz Zehra
Gardezi
Year
Title
Labor Pooling as a
Determinant of
2013
Industrial
Agglomeration
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
This paper constructs a distancebased measure of agglomeration
to verify the existence of
localization economies
The first is the potential for labor
particularly in Punjab. The
pooling that may arise for firms
industry-level measure of
that need to alter their
concentration—is regressed on a employment levels, i.e., those
number of industry characteristics firms that are more susceptible to
that measure the presence of productivity shocks. The second is
positive externalities. In particular, input sharing whereby firms can
a measure of each industry’s have the advantage of attracting
potential for labor pooling is used the production of specialized
to determine whether firms that
inputs.
experience greater fluctuations in
employment are likely to be more
concentrated.
Study Author
Year
Abid A. Burki
2012
and Mushtaq
A.Khan,
Title
Agglomeration
Economies and
their Effects on
Technical
Inefficiency of
Manufacturing
Firms
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
Their empirical results show a significant
impact of the agglomeration index in
The findings of this paper are that
decreasing technical inefficiency
geographic concentration of
of the firms. The null hypothesis that
manufacturing industries was
industry agglomeration index was not
widespread in Pakistan, but it was
related to technical inefficiency was
declining over time. They find that
strongly rejected. In other words,
the size of district level population,
firms from agglomerated industries
increase in district road density, and
faced more favorable exogenous
increase in district level pool of
operating conditions. In general, any
technically trained workers all
further increase in localization was
helped in promoting agglomeration
counterproductive for the firms in our
of manufacturing industries.
data set; however, this effect was
not uniform across all industries.
Study Author
Year
Title
Spatial Inequality
and Geographic
Abid A. Burki,
Concentration of
Mushtaq A. 2010
Manufacturing
Khan
Industries in
Pakistan
Type of Study
Quantitative:
Empirical
Estimation
Key Finding
Recommendation/Conclusion
The study concluded that the most
highly concentrated districts in largeThis paper examines what factors scale manufacturing employment are
cause agglomeration of
clustered around metropolitan cities of
manufacturing industries in
Karachi and Lahore, and their
Pakistan and what is the nature of surrounding districts. It also find that
scale economies. It also explores
agglomeration of 3-digit
whether manufacturing industries manufacturing industries is widespread
are agglomerated, and if so, which where only a small proportion fall in
ones. Moreover, it also presents
the category of low concentration
evidence on how geographic
industries. It also discover that the size
concentration emerges from the of district level population, increase in
dynamic process over time. It takes road density, and increase in the pool
districts as spatial units, which
of trained workers promote
represent the third-level of
agglomeration of manufacturing
administrative jurisdiction after
industries. It finally concluded that in
provinces and administrative
Pakistan, localization economies or
divisions.
within-industry externalities are
.
important, which shows that there is
much less role for technological
spillovers and inter-industry learning.
CONTRIBUTION
This study will provide recent estimates on the industrial concentration for 14 major
cities as classified by LFS.
The modified measure for labor pooling this study use, has never been used before.
“City Dynamics in Pakistan- 2015”, a report published by AERC which is the only
study assess the industrial concentration of major cities through LFS (data till 201011), now this study will take forward that work and evaluate the strength of labor
pooling in industrial concentration using most recent data available on LFS.
METHODOLOGY
This study will use data from Pakistan Labor Force Survey 2014-15 and 2017-18.
The study will use city level 4-digit data by Labor Force Survey. The study will include major urban
cities specified by LFS i.eKarachi, Lahore, Islamabad, Faisalabad, Rawalpindi, Multan, Gujranwala,
Sargodha, Sialkot, Bahawalpur, Hyderabad, Sukkur, Peshawar, and Quetta of Pakistan.
 Model
𝐴𝑠𝑖𝑡 = 𝛽0 + 𝛽1 𝐿𝑝𝑖𝑡 + 𝛽2 𝐴𝑟𝑖𝑡 + 𝛽3 𝑆𝑒𝑖𝑡 + 𝜀𝑖𝑡
𝐴𝑠𝑖𝑡 shows the Ellison-Glaeser index of concentration for industry i at time t. Where
i=1,2,3,4…n and t = 1,2,3,4…n.
Lp is the potential for Labor Pooling in the industry i,
Ar is the arrival of employer in particular industry,
Se is the vector of demographic, labor market characteristics in a city,
𝜀𝑖𝑡 is the error term in model expected to be random
MEASUREMENT OF VARIABLE
Symbol
As
Lp
Ar
Se
Variable
Measurement
As is a measure of
spatial concentration Let sa be the share of sector’s employment
for sector s. Ellison- that is in area a and xa be the share of total
Glaeser index of manufacturing employment that is in area a.
(with the help of Herfindahl index)
geographical
concentration
Lp is a measure of the The difference between percentage change
potential for labor in firm employment and percentage change
pooling in the sector.
in industry’s employment.
The change in total number of new employer
in industry i city c,
The average age of the population, the male
demographic, labor
percentage of the population, average
market
income, the percentage of the population
characteristics, that with primary education, the percentage of
the population with secondary education,
affect the location
and the percentage of the population with
decision of firms
tertiary education.
Firm arrival
𝐴𝑠 =
𝐹𝑠−(1− 𝑎 𝑥 𝑎2 ) 𝐻𝑠
1 − 𝑎 𝑥𝑎2 (1 − 𝐻𝑠 )
𝐹𝑠 =
𝑠𝑎 − 𝑥𝑎
2
𝑎
𝑧𝑖2
𝐻𝑠 =
𝑖
𝐿𝑝 =
𝑖
% △ 𝐸𝑓 − % △ 𝐸𝑖
𝑁𝑖
𝐴𝑟 = (𝑀𝑡 − 𝑀𝑡−1 )
EMPIRICAL WORK
The data under observation is short panel data having too many entities but few
periods.
The time period involved are 2014-15 and 2017-18
The panel data model will be fixed effect model because the intercept vary across
group/time.
The model will be estimated through OLS and fixed/random effect depending upon
the results oh Hausman test.
The proposed method through which fixed effect model will estimate is Least Square
Dummy Variable (LSDV) regression.
EXPECTED RESULTS
From the previous studies that has conducted so far, the expected results are
The increase in potential for labor pooling in an industry expected to increase
geographic concentration of an industry. (Overman and Puga, 2010)
The variable arrival of employer expected to be positively related with industrial
concentration (Haroon and Azam Chaudhry, 2014). However it can be other way
round.
Industries expected to concentrate more in areas where demographic, labor market
condition are better than other or in line with the industries requirement.