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.