Journal of Applied Economics and Business Studies, Volume. 4, Issue 4 (2020) 187-214 https://doi.org/10.34260/jaebs.449 

187 

 

Journal of Applied Economics 
and Business Studies (JAEBS) 

Journal homepage: https://pepri.edu.pk/jaebs 

ISSN (Print): 2523-2614 

   ISSN (Online) 2663-693X 

 

 

Contribution of Spatial Concentration in 

Exaggerating Inequalities: Across Various Urban 

Regions of Pakistan 

 

Uzma Tabassum1*, Shaista Alam2 and Ambreen Fatima3 
1 PhD Scholar and Research Assistant, Applied Economics Research Centre (AERC), University of Karachi 
2 Senior Research Economist, Applied Economics Research Centre (AERC), University of Karachi 
3Senior Research Economist, Applied Economics Research Centre (AERC), University of Karachi 
 

ABSTRACT 

Industrial agglomeration and inequalities are much of the concerns in recent 
literature. Pakistan, being a developing country, is also restricted by 
resource availability to treat all regions equally with respect to investment 
and development. As a result, regions with growing agglomeration 
experience higher income levels relative to other regions. To investigate this 
empirically this study employed propensity score matching (PSM) across 
urban regions in Pakistan using Labour Force Survey data 2017-18. For 
agglomeration regional herfindahl indices were estimated and regions with 
above average index value along with having positive index growth were 
considered as treated or agglomerated regions. The positive and significant 
coefficient of regions with treatment signifies that regions with 
agglomeration were found to have higher relatively income. Hence 
introducing industrial concentration in untreated regions would be effective 
in reducing inequalities rather tackling them by reducing agglomeration in 
agglomerated regions. 

 Keywords  

Inequality, 

Agglomeration, 

Propensity 

Score 

Matching, 

Urban Regions, 

Herfindahl 

Index. 

JEL 

Classification 

C31;  D30; D3; 

O14; O15;  

1. Introduction 

Over the last few epochs world trends draw attention to two apparent traits in 

economic growth. One is rising inequalities and the other is increasing geographical 

concentration of economic activities across regions. Various strands of the theory 

suggest that openness of region and removal of barriers in free mobility of factor will 

probably result in a higher level of concentration of industries and as a result greater 

specialization of the region. This might have repercussions in growing income 

disparities across regions. Such growth is also observable in Pakistan where unbalanced 

 
*
uzma_tbsm@hotmail.com 

 The work is the part of PhD thesis under process. 



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

188 

growth strategy is pervaded because of resource constraints (Tabassum, 

2016).Therefore investments and economic activities concentrate in some regions of the 

country that amplifies inequalities across regions, as the exogenous characteristics of 

regions, skill composition of the workforce and agglomeration economies attributed to 

productivity differences that in turn assumed to be reflected in income distribution. 

Given that the distributions of industries are not uniform across regions in an effort to 

specifically elicit the effect of agglomeration on spatial disparities it is important to 

know that how the concentration of industries affect the growth process of a particular 

region. What is the role of urbanization and localization economies in provoking spatial 

disparities? How these spatial disparities are connected with inequalities in the income 

distribution of individuals living in that region? 

The various regions would be different in sizes with respect to their physical and 

economic structure, depending on the agglomeration behaviour.  The swiftness and 

assortment of industrial agglomeration in a region describe the economic conditions of 

that region. On the theoretical side, the concept of Agglomeration economies define the 

process of clustering of firms in close proximity to one another to exploit the benefits 

of being together within intension to minimize per-unit cost of production or achieved 

economies of scale (UN-HABITAT, 2011).  The concept was first acknowledged by 

Weber (1909) while the detailed explanation of the sources of these economies was 

given by Marshall (1890). Marshall (1890) provided three bases through which 

economies of scale can be achieved. According to him agglomeration enables firms to 

economize on their unit cost by sharing knowledge regarding efficient production 

technologies and management strategies. Further Firms may agglomerate to share 

supply of intermediate inputs. The demand for intermediate input is greater when there 

are many firms located at the same place the average cost of providing such intermediate 

inputs to each firm will be low as high demand enable the producer of intermediate input 

to achieve scale economies. Lastly Sharing labour pool benefit firm when the variation 

in firm product demand is greater than the variation in industry level demand. The 

labour Fired from the firm that is facing low product demand can be hired by a firm that 

is facing high product demand. For doing so firm have to bear no cost for searching and 

training labour  thus declining unit cost for two reasons: one by firing unwanted labours 

by  a firm facing low product demand. And secondly by reducing search and training 

cost by high product demand firm. Duranton & Puga (2004) also describe all these as a 

process of learning, sharing and matching. 

In a nutshell productivity and income growth of region positively affect by 

agglomeration but these positive effects may come from urbanization economies or 

localization economies. In localization economies firms that belong to a single industry 

cluster together to exploit agglomeration benefits thus they specialize in that particular 

industry production. While in urbanization economies firms that belong to different 

industries cluster together to exploit agglomeration benefits that results diversified 



Journal of Applied Economics and Business Studies, Volume. 4, Issue 4 (2020) 187-214 https://doi.org/10.34260/jaebs.449 

189 

production. In viewpoint of efficiency spatial inequalities may have positive and 

negative effects. Inequalities resulting from regional specialization based on 

localization economies may be beneficial as it increases productivity but if these are 

caused by external economies or urbanization economies then they may not be optimal 

(Kim, 2008). Spatial inequalities caused by unrestrained population concentration in 

large primate cities may inflict many social ills in society. Thus spatial inequalities 

possibly are undesirable for society if it provoke social inequalities across the region.  

This research seeks to shed some light on the process of agglomeration and its effect 

on the income of region that experiencing these agglomerations. The literature focusing 

spatial disparities between urban regions based on agglomeration are rather scarce in 

the context of Pakistan. A recent set of empirical literature both nationally and 

internationally majorly investigated the determinants of income inequalities. These 

studies point out socio-economic, political and open economy variables to contribute in 

influencing inequalities.  Disaggregating these determinants includes individual and 

region specific variables such as education, health, gender etc. for the former and 

industrial clustering, policies and public service provision, agglomeration patterns etc. 

for the later (Sial et al, 2018., Fambon, 2017., Naseer & Ahmed, 2016., Burki & Khan, 

2012., Davtyan; 2014., Jamal, 2006). Extensive research is already available 

domestically on the aspects explaining prevalence of regional inequalities but it largely 

focuses on socio-economic determinants of inequalities while the impact of location 

specific industrial clustering on embellishing inequalities has mainly overlooked. This 

research will evaluate the contribution of spatial concentration in inflating inequalities 

across various regions by exploring the likelihood of prosperous region become more 

prosperous, as heterogeneity across industrial expansion and in labour demand frames 

inequalities among individuals. 

The empirical assessment is based on survey data conducted by the Pakistan Bureau 

of Statistics (PBS). In the study large cities and other urban areas have taken as spatial 

units. Because some urban regions were not included in past years survey so they left 

intentionally from recent sets of survey to avoid inconsistency in data overtime. For 

estimating the agglomeration impact, this study used the Labour Force Survey (LFS) 

for the period of 2017-18. Further to identify the urban regions that are experiencing 

persistent inter-temporal spatial concentration in the case of Pakistan, the Herfindahl 

indices are computed for the years 2010 and 2018. The regions that have above average 

level of agglomeration in a most recent period and positive growth from the base year 

marked as treated regions. The study used non-parametric Propensity Score Matching 

(PSM) techniques to overcome the problem of selection bias arise due to the selection 

of regions who received treatment-spatial agglomeration, particularly in non-

experimental designs. This research is a value addition to existing literature of Pakistan 

as it is the first study that employed propensity score matching to study agglomeration 

linkages with spatial inequalities arises from the unbalanced growth of regions. Further, 

it also contributes in empirical literature by estimating Herfindahl index for spatial 



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

190 

concentration at regional level over time which is used as a base for identification of 

treated and untreated urban regions.        

The rest of the paper is proceed by highlighting empirical literature done nationally 

and internationally, followed by the review of theoretical model, section four sheds light 

on the estimation technique and data employed, section five discusses the results and 

final section wrap up finding and expected policy implications. 

2. Review of Literature  

Spatial concentration of industries is placed among the highest feature of economic 

activity in both developed and seeking to be developed nations.  A vast existing 

empirical literature suggest that industries are unevenly distributed across spatial units 

because of difference in transportation cost, immediacy to market, presence of skilled 

and cheap worked force and natural locational advantage etc. Thus industries 

agglomerate in a few regions more than others that are comparatively less attractive 

(Ellison & Glaser; 1997, Maurel & Sedilot, 1999 and Abid & Mushtaq, 2011). 

Geographic concentration and regional specialization of industries were seen by many 

researchers as to be two sides of the same coin. Specialization of region and 

concentration of industries in specific location were defined using the analogous 

production structures as base so as to reflect the same reality which is evident from 

various studies in the existing literature of regional economics (Aiginger, 1999).  

It was since 19th and 20th century that researchers were modeling regional 

specialization and geographical concentration rooting from trade theory and location 

theory respectively and conducting empirical studies on them. The literature about 

regional concentration probably commence with Krugman (1991). Krugman (1991) was 

the one which estimated Gini coefficient to capture concentration in various regions by 

comparing the regional market shares for a single industry with manufacturing’s 

employment structure. Setting a traditionally localized industry (automotive industry) 

as a benchmark he found that large numbers of industries were localized though most 

of these were not cutting-edge and that industries related to high technology sectors such 

as textile, were highly clustered. However estimated concentration of such industries 

was not solely depends on technological spillover and what he found was biased in terms 

of the data he got. Aiginger & Rossi-Hansberg (2006) deliberated regional 

specialization and geographical concentration as the two sides of the coin and theses 

two could be calculated from the same matrix showing industries in rows and 

geographical regions in columns. For the estimation of specialization columns are to be 

measured while for that of concentration rows are to be considered. Aiginger & Davies 

(2004) using a mathematical model pointed out that if inequalities across columns vary 

so does it varies across rows. 

The pragmatic evidence on the relationship between agglomeration economies and 

regional inequalities in developed nations may have immense importance. Geppert et al 



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191 

(2008) explored this connection in the context of West Germany using data from 1980 

to 2000 and suggest that industrial agglomeration can be a considerable contributor to 

regional income differentiation.  They find that the number of regions that are already 

rich with large agglomeration of industries has succeeded in further distancing 

themselves from the others. Thus, concentration of industries has sizeable contribution 

to spatial income differentiation. Larger agglomerated regions such as Munich, 

Hamburg and Frankfurt are leading among the richer regions that are becoming richer 

and showing a sign of urbanization economies. Though, the analysis does not ascertain 

the particular nature of causal mechanism that could be underlying this relationship. 

Kim (2008) surveyed the theoretical and empirical literature about spatial and 

regional inequalities. The survey of literature underscores that endowment of resources, 

proximity to port and increasing returns attributable to concentration of human 

interaction increases spatial inequalities. All these are beneficial for region to enhance 

their productivity therefore these regions grow relatively with faster rate of growth. 

However congestion cost and excessive urban concentration may be harmful. Thus, in 

equilibrium there is an optimal level of spatial disparities to combat with inefficient 

level of agglomeration. Midelfart (2004) examined the role played by agglomeration of 

economic activities, as measured by density of employment, and composition of labour 

force skills in explaining regional income difference in the Norwegian economy 

especially focusing on Norway's manufacturing sector. Using data for the sub-regional 

NUTS 4 spatial units of Norway for two time period 1989 &1999 the study concluded 

that high income seems to be an upshot of agglomerated economic activities in contrast 

to differences in skill composition of labor force. Two different measures of income are 

considered as a proxy to measure Income disparities, per hour GDP of employees and 

mean hourly earnings. Considering GDP per hour the regional inequalities are found 

significant and much more distinct relative to earnings per hour. Further disparities 

shows widening trend while considering GDP per hour in contrast to the result of 

average hourly earnings which shows reduction in regional disparities for same time 

span. 

Regional specialization and the geographic concentration patterns of manufacturing 

industries as measured by dissimilarity index in Bulgaria, Estonia, Hungary, Romania 

and Slovenia had changed during 1990-1999 (Traistaru et al., 2002). They found that in 

Bulgaria and Romania regional specialization had increased with no significant changes 

in Estonia, Hungary and Slovenia.  Further they found that large economies of scale, 

high technology and high wages were associated with highly concentrated industries 

therefore agglomeration of these industries foster the growth of these region in which 

they clustered. On the other hand industries with low technology and low wages were 

more likely to be dispersed spatially. 

Glaeser et al (1992) and Henderson et al (1995) have made decisive contributions to 

the empirical literature focus on urban area-city. They estimate the effect of city 



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

192 

employment growth on specialization and diversification index. The specialization 

index is measured as a proportion of industry employment to the total employment of 

city. The diversity index computed by Glaeser et al (1992) is calculate by taking the 

employment share of five largest industries in the city while Henderson et al (1995) 

measured diversity index as  employment share of all industry in the city  aggregate 

employment.  Glaeser et al (1992) also include the inverse of plant average size as a 

competition index. The result of Gleaeser's model shows that city growth is positively 

associated with diversity index and negatively by specialization and competition index. 

Along with specialization and diversification index Henderson et al (1995) also used 

city employment in all other industries as an additional urbanization variable and found 

positive impact of both specialization and overall employment on the growth of 

employment. Focusing on the role of agglomeration of industries in specific regions as 

pedestal of productivity effects and developmental impulses Fan & Scott (2003) 

investigates the interconnections between economic growth and geography in the 

developing countries of East Asia. Findings indicate that agglomeration and 

productivity have strong association in Chinese industries, especially electronics, 

computer and garments industries since the public policies in china follows economic 

liberalization.  

The scrutiny of empirical literature done in Pakistan indicates that, the literature on 

agglomeration is usually focuses on the extent with which industries agglomerate in a 

particular region and the effect of agglomeration on the efficiency of similar and 

dissimilar firms in a local system. Burki & Khan (2011) measure agglomeration of 

industries using data taken from Census of Manufacturing Industries (CMI). As per 

results the agglomeration is pervasive in Pakistani industries, the 35.3% industries are 

profoundly agglomerated. While, 38.2% industries are somewhat less agglomerated and 

26.5% Pakistani industries are dispersed. District population, increase in road density, 

skilled worked force are found significant contributor in promoting spatial 

concentration of industries. Chaudhry & Haroon (2015) evaluate the economic impact 

of new firm in the market using Punjab’s Directory of Industries for the year 2002, 2006 

and 2010. The finding indicates that new firm entry has noteworthy effect on socio-

economic outcome. Some of these outcomes are immediate while some outcomes 

become visible with lag. They recommended that policy makers should be familiar with 

nature of firm as impacts of new firm differ with the type of firms that affirms the need 

of specific industrial policy. 

Azhar & Adil (2019) explore the effect of agglomeration on social inclusion & firm 

efficiency for Punjab districts. Social inclusion is made of mortality rate, antenatal care, 

improved water & sanitation which then compressed into a single variable using factor 

component analysis. The results demonstrate that agglomeration is positively associated 

with firm efficiency and also it has significant association with social-inclusion. The 

districts with higher agglomeration also experience higher social inclusion. In 

developed nations the empirical evidence on spatial inequalities and agglomeration may 



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193 

contain immense importance, the pattern of agglomeration and its impact on spatial 

inequalities in developing nations like Pakistan is crucial to study. But despite the 

obvious policy concerns, there has been no empirical work found that explores the role 

of agglomeration in defining the income level of that region where these agglomerations 

take place. This study tries to fill the gap that is evident in the literature. 

3.  The Review of Selected Theoretical Model 

In the theoretical literature the most cited work is the "New Economic Geography 

(NEG)" model put forward by Krugman (1991). Researchers have based their empirical 

findings on the theoretical arguments placed by New Economic Geography (NEG) 

model. According to NEG model spatial inequality is the net effect of two contrasting 

forces, one is centripetal force and the other is centrifugal force. Centripetal forces 

determine agglomeration or concentration of economic activities that is caused by 

imperfect competition, increasing returns to scale and mobility of factor. On the other 

side centrifugal forces such as congestion, high transportation cost, restricted mobility 

of factor encourages dispersion of economic activities.  When the cost of supplying 

goods at far off places would is high then firm would be dispersed around markets that 

are immobile. In contrast when transportation cost is low immobile market can easily 

get access to distinct places that encourage firms to agglomerate at particular place 

gaining advantages of economies of scale and externalities.  The region that 

experiencing increasing agglomeration also come up with higher economic activities 

which results in spatial inequalities showing the positive inter relation (Curve AA). At 

certain level higher agglomeration will results in increase congestion and negative 

externalities offsetting the positive gain of agglomeration. Consequently a new 

relationship established between agglomeration and spatial inequality that is opposite to 

the previous one (Curve RR). 

             Figure-1:  Nexus between income growth and regional inequalities 

 
 Source: Gardiner et al. 2010. 

Spatial agglomeration of economic activities positively contributes in the economic 

growth of nation through localized positive spillovers. Agglomeration increase 

productivities and real output via labour pooling and knowledge spill over as shown 



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

194 

from the curve SS. The equilibrium level of agglomeration, inequalities and growth 

comes from the intersection of two curves RR and AA. There is a tradeoff between 

national growth and spatial inequalities produce from agglomeration. The effective 

intervention of government that break up agglomeration shift RR curve to RR1 and 

reduces spatial inequality by the transfer of resources to poorer regions but also erase 

the spillover effects that results in decreasing national growth. The income of region in 

which economic activities clustered grow relatively at a faster rate therefore a similar 

trade-off became visible within regions. Therefore on the basis of NEG model it is 

expected that the regions that are experiencing agglomeration may undergo high income 

level relative to other regions. 

4. Estimation Techniques 

First the Herfindahl indices will be computed to measure the inter-temporal spatial 

concentration of sector. Using these indices values study will identifies regions that 

experience higher than average initial level of concentration and positive growth of 

spatial concentration.  

4.1 Measurement of Agglomeration  

Herfindahl index of spatial concentration is calculated as the sum of the regional 

shares in national employment in the particular industry both by industry and regions. 

Symbolically, 

𝐒𝐂𝐑𝐣
𝐜 =  ∑𝐢𝐂𝐑𝐢𝐣

𝐜𝟐  

Where: 

𝑪𝑹𝒊𝒋
𝒄  = The concentration ratio: The weight of employment in the sector i from the city 

j in the overall employment of the sector 

𝑪𝑹𝒊𝒋
𝒄 =

𝑬𝒊𝒋

𝑬𝒊
=  

𝑬𝒊𝒋
∑ 𝑬𝒊𝒋𝒊

 

The above index ranges from 0 to 1. The more a region’s Herfindahl index is closer 

to 0 the less spatially concentrated the region is, and the more it is away from 0 or closer 

to 1 the more it is said to be spatially concentrated. 

4.2 Measuring the linkage between spatial concentration and income  

To estimate causal treatment effects of agglomeration in exaggerating income 

inequalities across various regions Propensity-Score Matching (PSM) technique is used. 

The PSM technique was initially presented in the pioneering work of Rosenbaum and 

Robin (1983a, 1983b, 1985).  Propensity score matching was the first time applied in 

the field of macroeconomics, to find the effects of currency unions on trade flow. 

Afterward, many economists used this technique to evaluate the causal effects in non 

experimental research. 



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Propensity Score Matching (PSM) 

In an experimental research randomization is assumed as there is an equal 

distribution of group members' characteristics for both treated and control group via 

random sampling and random assignments. Thus randomization limits potential 

selection bias therefore the only interest is to estimate cause of an effect. The 

observational studies lack randomization as conclusions are drawn based on data that is 

collected without random assignment therefore the results become inconsistent and 

biased.  In such condition Propensity Score Matching provides the most accurate 

estimates of the impact. 

The study employed Propensity Score Matching (PSM) to analyze the effect of 

agglomeration of industries on the regional income in Pakistan. For the analysis purpose 

regions that are experiencing increasing growth of agglomeration are treated as 

“Treatment Group” while the regions where agglomeration is not persistent are 

considered as “Control group or Counterfactuals.”  As we have N units (subjects or 

Regions). C denotes a treatment condition and Y as a potential outcome. For each unit 

i (i=1.......41), Ci =1 indicates that the subject unit is belongs to the treatment group with 

subsequent potential outcome O1i and Ci =0 shows that the subject i is in counterfactual 

group with potential outcome O0i. To analyze causal effects in counterfactual 

framework the core interest is to find treatment effect for each unit i defined as  

                                                     ∆i= O1i - O0i        1 

For each unit i, Y1i and Y0i are not discernible at the same time because the same 

unit cannot simultaneously be in both the treated and untreated groups. Alternatively, 

Average Treatment Effects (ATE) can be estimated  

ATE = E (O1 – O0) = E (O1) –E (O0)                               2 

The second frequently estimated effects are Average Treatment on Treated (ATT). 

It is the average effect of treatment on the units which are treated defined as  

ATT = (O1 - O0 | C=1) = E (O1/C = 1)] - E (O0/C = 1)                           3 

The ATE is the difference between average outcomes of treated group and the 

average outcome of untreated group, no one of which is fully observe. The estimation 

of O1 requires average over the outcome of treatment for treated group (O1|C = 1) which 

are observed as well as average over the outcome of treatment for the untreated group 

(O1|C = 0), the unobserved counterfactual. In a similar way to estimate O0 average over 

the outcome of non-treatment for untreated group (O0|C = 0) and average over the 

outcome of non-treatment for treated group (O0|C = 1) required, from which (O0|C = 1) 

is also an unobserved counterfactual.  

      ATE= E (O1 - O0) = E (O1) - E (O0)                                      4 



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

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= [Pr (C =1) E (O1|C = 1)] +[ Pr (C =0) E(O1|C = 0)] - [Pr (C =1) E(O0|C = 1)] +  

[ Pr (C =0) E(O0|C = 0)]                                         5 

= Pr(C=1) [E (O1|C = 1) - (E (O0|C = 1)] + Pr (C = 0) [E (O1|C = 0) - E (O0|C = 0)]   6   

Here Pr (C=1) is the probability that unit is treated and Pr (C = 0) is the probability 

that unit is not treated. The first part of the equation-6 is just the proportion of units 

receiving the treatment multiply by the Average Treatment Effect on treatment. It is 

very likely in the absence of randomization that subjects' background characteristics in 

the two groups are significantly different which may also influence potential outcome 

so the present study cannot directly assess the effect of treatment without controlling for 

other influential factors. The unbalanced distribution influential factor between two 

groups may create selection which creates biased ATE. Hence, to   solve the selection 

bias problem and evaluate the treatment effect matching adopts two postulations. 

Conditional Independence Assumption (CIA) 

Conditional on influential factors X, C and (O1 & O0) are independent. X is the 

vector of observed cofounders. To estimate ATT the Conditional Independence 

Assumption (CIA) states  

  O0⊥ C | X or O0⊥ C | Pr(X)     7 

Assumption states that there is no association among treatment and the potential 

outcome that arise without treatment, conditional on observable characteristics or the 

propensity score (Rosenbaum and Rubin; 1983). Further for estimating ATE the CIA 

states that conditional on observable characteristic or propensity score, treatment is 

independent of both treated and untreated outcomes because for the estimation of ATE 

treated units are required to serve as a counterfactuals for untreated units. 

O1, O0 C | X    or    O1, O0 C | Pr(X)                         8 

Common Support Assumption (CSA) 

A second important assumption besides independence required in matching method 

is the common support or overlap condition. It precludes the occurrence of perfect 

predictability of C conditional on X.  It ensures that units with the similar values of X 

possess a positive probability of being both in treated and untreated (Heckman et al; 

1999). Furthermore, the common support assumption makes sure that these propensity 

scores must fall between 0 and 1.  

 0 <Pr(C = 1 | X) < 1 9 

 

The common support assumption in strict sense can be written as 

0 < c <Pr(C = 1 | X) <1-c < 1            10 



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In a nutshell propensity score analysis set forth by Rosenbaum and Rubin (1983) 

ensures to reduce selection biased by balancing the distribution of observed influential 

characteristics (cofactors) between two groups; treated and control. Therefore PSM 

allow one to get unbiased estimates from observational analysis. PSM balances the 

allocation of observed cofactors among treated and untreated group based on 

resemblance of their predicted probabilities of having treatment; Propensity Scores. The 

PSM estimates mean effects without taking any arbitrary assumptions related to 

functional form and error distribution and not require parametric modeling relating 

subject to outcome.  

Estimating the Propensity Score  

For practical implementation of the propensity score two actions are required to take, 

one is about the model to be used for the estimation, and the other is about selection of 

variables that include in the model. For dichotomous dependent variable or binary 

treatment both logit and probit models set forth similar bounded results as compare to 

linear probability model that gives predictions exterior to [0,1] bounds. While deciding 

the variable to be included for estimating of propensity score it must be kept in mind 

that propensity score balances the covariate across treatment status.  Thus, executing 

matching necessitate selection of covariates X that plausibly satisfy this condition. 

Omitting essential covariates can critically raises bias in resulting estimates (Heckman 

et al., 1997). Only those covariates should be included that simultaneously influence the 

treatment decision and the outcome variable. Further It should also be ensures that 

variables included in the model should not be affected by treatment or the anticipation 

of it. Heckman et al (1999) further indicate that the data for both groups should originate 

from the same resource e.g. from same questionnaire. The too good data is also not 

appropriate because if propensity score is either 0 or 1 for some values of X then 

conditional on those X values, matching would not be possible. These characteristics 

either never or also receive treatment. So, the common support condition not be realized 

and matches cannot be carried out (Kahn and Tamer; 2010). Randomization is needed 

to guarantee that unit with indistinguishable characteristics can be observed in two 

groups (Heckman et al., 1998). 

Selection of Matching Algorithm 

A variety of different strategies can be use to match units on their propensity scores. 

These method are summarize in figure-2 

 

 

 

Figure-2 Ways to perform matching 



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

198 

 

 

 

 

 

 

         

 

        

 

        

 Source: Author visualization 

The commonly used method of propensity score matching is nearest neighbor 

matching (Rosenbaum & Rubin; 1985) that matches each unit "i" in the treatment group 

with a unit "j" in untreated group with closets absolute distance among their propensities 

expressed as  

 Ɗ (i,j) = min{|Ƥ(Xi- Ƥ(Xj)|} 11 

Alternatively, Caliper matching matches each unit "i" in the treatment group with 

"j" in untreated group within a particular caliper band "b" (Cochran & Rubin, 1973), 

stated as  

 Ɗ (i,j) = min{|Ƥ(Xi-Ƥ(Xj)| <b} 12 

Rosenbaum and Rubin (1985) suggested that the caliper band ought to be less than 

or equivalent to 0.25 of the Standard deviation of propensity score. Later Austin (2011) 

suggested that the optimal value of b should be 0.20. Dehejia & Wahba (2002) proposed 

the idea of radius matching which matches each unit "i" in the treated group with 

multiple units in untreated group within a particular band 

Ɗ (i,j) = min{|Ƥ(Xi- Ƥ(Xj)| < b}                  13 

In the strategy of stratification matching or interval matching the common support 

of propensity score is alienated into a set of interval. The impact within each 

intermission is measured by computing the mean difference in potential outcomes 

between treated and untreated (Rosenbaum and Rubin; 1983). Under the normality the 

use of five intervals are often enough to remove 95% of the biasness associated with 

covariates (Cochrane and Chambers; 1965 and Imbens; 2004).Kernel matching and 

local linear matching are non-parametric matching estimators. In kernel matching 

weighted averages of all units in the control group are utilized to create the ten 

counterfactual outcomes. Weights rely on the extent with which each treated unit is 

farther from the untreated group and the accomplice observation for which the 

counterfactual is estimated.  

Different Matching 

Algorithms 

Nearest 

Neighbor (NN) 

Caliper and 

Radius 

Stratification 

and Interval 

Kernel and 

Local Linear 



Journal of Applied Economics and Business Studies, Volume. 4, Issue 4 (2020) 187-214 https://doi.org/10.34260/jaebs.449 

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This study employ the most commonly used matching strategy Nearest Neighbor 

(NN) matching. Differences are attained after matching the entire treated unit with 

control units and come up with ATT by averaging these differences. This kind of 

matching can be performed with or without replacement. When the matching is done 

with replacement (one nearest neighbor) it curtails biasness but enlarge the variance. In 

contrast, matching with more nearest neighbors inflate biasness but reduces the 

variability. The general formula of ATT is  

        ATT = (O1 - O0 | C=1) = E (O1|C = 1)] - E (O0|C = 1)  14 

Using Nearest Neighbor (NN) matching ATT is estimated as  

    ATTNN =
1

𝑁𝐶
∑ [𝑂𝑖

𝑜𝑏𝑠
− ∑ 𝑊𝑖𝑗𝑗∊𝐶(𝑖)𝑀 𝑂𝑖𝑗

𝑜𝑏𝑠
]i:wi=1                            15 

=
1

𝑁𝐶
∑ 𝑂𝑖

𝑜𝑏𝑠
𝑖:𝑤𝑖=1 −

1

𝑁𝑇
∑ 𝑊𝑗𝑗∊𝐶(𝑖)𝑀 𝑂𝑖𝑗

𝑜𝑏𝑠
                                     16 

NC indicate the no. of observations in the treated group 

Ni
U indicate the no. of controls or untreated matched with treated observations i 

Wij = 
1

𝑁𝑈
if j is a control unit of i and 0 otherwise and Wj = ∑iWij 

C (i)M indicate the set of first M matches for unit i 

5. Empirical Findings  

Regions experiencing positive growth of agglomeration and above-average level of 

agglomeration in most recent period 2017-18 are listed in Table-1.The results are 

obtained using LFS data for the period 2010 to 2018. For this time span only eight of 

the 40 urban regions fulfilled both criteria necessary to qualify as a ‘Treated regions. 
 

Table-1: Summary statistics of Herfindahl-Hirschman Index (HHI) 

 



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

200 

 

    Source: Authors’ own Calculation based on Herfindahl index 
 

After identifying the treated and untreated regions the second step is to estimate 

causal treatment effects of agglomeration on regions income that responsible to 

exaggerate inequalities across treated and untreated regions. As stated earlier for this 

purpose Propensity-Score Matching (PSM) technique is used.  

Based on the hypothesis to be tested, Number of covariates appears to be critical for 

this research. These covariates include the demographics of the household resides in the 

regions which are spatially concentrated, the knowledge and education variables. In 

addition with other categorical variables such as migrant or non migrant household, 

occupation and non categorical labour market variables such as female participation in 

the labour market, no of work hours etc all these variables expected to influence the 

average income of the household.  

The comparison of covariates across treated and untreated subject can be done by 

comparing the mean or median of continuous variables and the allocation of categorical 



Journal of Applied Economics and Business Studies, Volume. 4, Issue 4 (2020) 187-214 https://doi.org/10.34260/jaebs.449 

201 

variables among these two groups. For this purpose the standardized difference (SD) 

can be used. The standardized difference evaluates the variation in means in units of the 

pooled standard deviation (Flury & Riedwyl; 1986). For continuous variable the SD is 

measured as: 

𝑆𝐷𝑐𝑜𝑛 =
�̅�𝑡𝑟𝑒𝑎𝑡𝑒𝑑 − �̅�𝑐𝑜𝑛𝑡𝑟𝑜𝑙

√
𝑆𝑡𝑟𝑒𝑎𝑡𝑒𝑑

2 +𝑆𝑐𝑜𝑛𝑡𝑟𝑜𝑙
2

2

 

Where �̅�𝑡𝑟𝑒𝑎𝑡𝑒𝑑&�̅�𝑐𝑜𝑛𝑡𝑟𝑜𝑙  are the sample mean of continuous variables in treated 
and untreated groups respectively, 𝑆𝑡𝑟𝑒𝑎𝑡𝑒𝑑

2 &𝑆𝑐𝑜𝑛𝑡𝑟𝑜𝑙
2  are the sample variance in both 

groups. For dichotomous variables SD is measured as: 

𝑆𝐷𝑑𝑖𝑐ℎ𝑜 =
Ƥ̂𝑡𝑟𝑒𝑎𝑡𝑒𝑑 − Ƥ̂𝑐𝑜𝑛𝑡𝑟𝑜𝑙

√
Ƥ̂𝑡𝑟𝑒𝑎𝑡𝑒𝑑∗(1−Ƥ̂𝑡𝑟𝑒𝑎𝑡𝑒𝑑 )+Ƥ̂𝑐𝑜𝑛𝑡𝑟𝑜𝑙∗(1−Ƥ̂𝑐𝑜𝑛𝑡𝑟𝑜𝑙)

2

 

Where Ƥ̂𝑡𝑟𝑒𝑎𝑡𝑒𝑑&Ƥ̂𝑐𝑜𝑛𝑡𝑟𝑜𝑙 are the occurrence of dichotomous variable in treated and 
untreated group. SMD is not influenced by sample size and allow comparison of the 

variables measured in different units. Less than 0.1 value of SMD indicates aim 

perceptible difference in mean or prevalence of variables between two groups (Normand 

et al; 2001). The below table shows the "Standardized Difference (SD)" - the difference 

in term of Standard error of some of demographic variables  in treated and untreated 

group. The result shows that the treated and untreated differ by about -0.24 SD in 

dependency ratio, dependency ratio is higher in the untreated regions than the regions 

that are experiencing concentration. Education and age of highest earner in treated and 

untreated differ by 0.12 SD while the age of head of household is more similar in treated 

and untreated as SD is about 0.08. Significant difference appears in average years of 

schooling that is about 0.37 SD.  

Table-2 Standardized Differences in Selected demographic Variables 

 Mean in treated Mean in Untreated Standardised diff. 

Dependency Ratio 2.87 3.39 -0.24 

Average Education 8.25 7.15 0.37 

Education of Highest Earner 5.9 5.45 0.12 

Age of highest earner  37.25 35.84 0.12 

Age of HOH 48.25 47.17 0.08 

Source: Authors calculations based on LFS 2017-18 

The standardized difference in selected binary variable such as gender of head of 

household and gender of highest earner is quite negligible but the proportion of migrant 

household  in treated group is quite differ than comparative group as the value of SD is 

about 0.26.  

 



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

202 

Table-3 Standardized Differences in Selected dichotomous Variables 

 Proportion in 

treated 

Proportion in 

Untreated 

Standardised 

diff. 

Migration  0.20 0.11 0.26 

Gender of HOH (Male) 0.91 0.94 -0.09 

Gender of Highest Earner 

(Male)  
0.90 0.92 -0.09 

Source: Authors calculations based on LFS 2017-18 

 

The standardized difference in labour market related variables shows that 

percentage of females participate in labour market both in treated and untreated group 

differ by about -0.21 SD. Female in untreated group participate more in labour market 

than the female resides in spatially agglomerated regions. The individuals belongs to 

the household located in treated regions are technically more sound and the standardized 

difference is about 0.32 between treated and untreated group.   The average weekly 

hours work is differ by 0.4 SD, households resides in spatially concentrated regions 

weekly works more hours than the comparative group. There is a negligible difference 

(0.06 SD) in working age population between treated and untreated regions. 
 

Table-4 Standardized Differences in Labour Market Variables 

 Mean in 

treated 

Mean in 

Untreated 

Standardised 

diff. 

Female participation Ratio 0.14 0.21 -0.218 

% of Technically trained working 

age individuals in HH 
0.26 0.17 0.322 

Working age population   3.84 3.71 0.062 

Average weekly hours works 51.26 45.81 0.401 

Source: Authors calculations based on LFS 2017-18      

The figure-3 demonstrates the outcome variable-average income of the household from 

the districts where spatial concentration is observed during the considered time span and 

the average income of the household from the districts that are spatially disbursed. It is 

visible from the figure that the households living in those urban regions which are 

experiencing agglomeration enjoys high level of per-capita income in contrast to those 

household which are living in regions where concentration is not high and persistent. 

 

 

Figure-3: Average income of household in treated and untreated region 



Journal of Applied Economics and Business Studies, Volume. 4, Issue 4 (2020) 187-214 https://doi.org/10.34260/jaebs.449 

203 

 
                    Source: Authors visualization based on LFS 2017-18      

Next Figure also shows significant difference in average income of household 

resides in spatially concentrated urban regions and the regions where such concentration 

is not quite visible. The largest part of the data points are either overlap the diagonal or 

to the left of the diagonal that shows that the values of the distribution on the y-axis are 

higher than those of the distribution on the x-axis means income of the household reside 

in agglomerated region is much higher than the income of the households that reside in 

the regions which does not fulfill the criteria to be called agglomerated. It can also be 

observed that income inequality is somewhat stronger for higher income but it is quite 

weak for lower income. 

Figure -4: Quantile-Quantile Plot of income distribution 

 
        Source: Authors visualization based on LFS 2017-18      

 

Estimation of Propensity Score 

The estimates of probit models are used to estimates the propensity scores based on 

which the matching is done afterward. The probit model provides the probabilities or 

propensities to calculate the average treatment effect of spatial concentration on the 

average income of household resides in these regions. 

0

10
00

00
20

00
00

30
00

00
40

00
00

m
ea

n 
of

 In
co

m
e

0 1

0

50
00

00
0

1.
00

e+
07

1.
50

e+
07

in
co

m
e,

 a
gg

_T
 =

= 
1

0 1000000 2000000 3000000 4000000
income, agg_T == 0

Quantile-Quantile Plot



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

204 

To estimates the propensities the dichotomous treatment variable -spatial 
concentration run on the covariates selected however, the estimates of the probit model 
are just to predict the propensities and hence not provide any meaning full explanation.  
The study has first estimated probability of receiving treatment by regressing binary 
variable agglomeration equals 1 if regions spatial concentration is above average in base 
period and with positive growth and 0 otherwise as a function of household characteristics 
including average education, age & gender of head of household, age and education of 
highest earner, dependency ratio, Female participation Ratio,  Percentage of technically 
trained working age individuals in household, Average weekly hours works and 
Provincial and occupational dummies. The probability of receiving treatment is positively 
correlated with Average schooling, age of highest earner and head of household, migrant 
household, male headed household, no of working age individuals in household, Average 
weekly hours works, Percentage of Technically trained working age individuals in 
household and negatively correlated with age of head of household, education of highest 
earner, dependency ratio and female participation rate.  Almost all variables are 
significant, except some occupational and regional dummies. The results as stated above 
are annexed (see Table A1). Overall, the model is statistically fit on the basis of the 
likelihood chi-square. For the estimation of PS, 18,722 observations that fall within the 
common support region [.002032, .8526713] have been used (see Annex Table-A2). 

Common Support or Overlap Condition 

It is essential to test out the common support region for the both treated and untreated 

group. If the distribution does not overlap it would be problematic but using the 

minimum and maximum comparison the problem could be tackled.  To do this all those 

observation that has propensity score smaller than minimum or larger than the maximum 

would be deleted. Hence all those observations are withdrawn from the analysis that 

comes into view outside the common support region.  Table-5 shows the number of 

observation removed from the analysis on the basis of nearest neighbor matching. The 

number of removed observation is quite low in this case only 3 observations have been 

removed from the analysis that appeared off support. 

Table 5: Common Support Condition for Nearest Neighbor 

psmatch2 Treatment 

Assignment 

psmatch2 common  

off support on support Total 

Untreated 0 14,036 14,036 

Treated 3 4,683 4,686 

Total 3 18,719 18,722 

Source: Author's estimations based on LFS 2017-18. 

A description of the density distribution of propensity score can be seen in figure-1. 

The upper part of the figure shows propensity score distribution for treated, whereas the 

below part of the figure refers to the treated. The symmetric distribution of the 

covariates after the matching indicates the overlapping of the treated and non-treated 

groups and hence it is concluded that the common support condition is satisfied. 



Journal of Applied Economics and Business Studies, Volume. 4, Issue 4 (2020) 187-214 https://doi.org/10.34260/jaebs.449 

205 

Figure-5: Distribution of Propensity Score to test Quality of Matching among 

Treated and Untreated Groups 

 
      Source: Author's estimations based on LFS 2017-18. 

Effects of Spatial Concentration on Average Income: 

This research work has empirically tested that concentration of industries at 

particular region has positive impact on the livelihood of household resides in these 

regions. The role of spatial concentration in raising livelihood by positively affect 

income of the household is proved valid. The empirical result of Average treatment effect 

of the spatial concentration on income level of household is reported in table-6. 

Statistically significant and positive coefficient indicated that the one percent increase in 

spatial concentration raises income level of the households that reside in these urban 

districts of Pakistan by 346854. 

Table-6: Average Treatment Effects of Agglomeration on Income 

Matched/Unmatched Treated Controls Difference T-stat 

Unmatched 12.75 12.38 .374 27.07 

ATT 12.76 12.55 .203 9.09 

Source: Author's estimations based on LFS 2017-18. 

The positive and statistically significant value of average treatment effect of 

unmatched group (ATT) shows that on average, income of household increased by 

[(e0.374– 1)*100 =45.4 %] for 2017-18. Further, the positive and statistically significant 

value of the average treatment effect on treated (matched ATT) shows that income of 

household increased by [(e0.20335– 1) = 22.55%].  

0 .2 .4 .6 .8
Propensity Score

Untreated Treated: On support

Treated: Off support



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

206 

Quality of Matching 

The study carried out t-tests to evaluate either difference between treated and non-

treated groups are statistically significant or not. Once the balance is attained with PSM, t-

statistic turns out to be statistically insignificant because there is negligible difference 

between treated and non-treated. In this regards, the covariates’ variance ratios of the treated 

over the untreated measures are also estimated. The ratio should be equal to one if there is 

perfect balance across samples. All these measures provide portent of whether particular 

covariates are balanced across treated and non-treated groups. The results indicate that 

before matching most of the variables have demonstrated statistically significant differences 

but after matching most of the covariates found balanced and no significant statistical 

differences have been found1.Further the Table-7 indicates that the bias percentage for each 

of the covariate is also reduced significantly after applying matching. 

Table-7 Covariates Balancing Test (PS-test) 

Variable 
Unmatched/ Mean %Reduction t-test 

V(T)/V(C1) Matched 
  Treated Control %bias Bias T 

Female participation Rate U 0.138 0.213 -21.8 74 -30.26 0.63* 
M 0.258 0.239 5.7 2.58 1.03 

Gender of the Head – Male U 1.084 1.059 9.8 77.3 16.08 1.39* 
M 1.077 1.071 2.2 1.06 1.07* 

Working age members in 

household 

U 3.841 3.713 6.2 
-70.7 

9.35 0.94* 

M 4.472 4.254 10.6 5.23 1.12* 

Education of the Highest 

Earner 

U 5.631 4.897 19.5 
93.1 

23.33 1.09* 

M 5.731 5.781 -1.3 -0.66 1.08* 

Age of the Highest Earner U 37.07 35.59 12.6 97.8 15.07 1.06* 
M 36.27 36.30 -0.3 -0.12 0.95 

Technical/Vocational 

training 

U 0.253 0.166 30.9 
99.4 

50.06 1.32* 

M 0.285 0.285 0.2 0.09 1.05 

Average  hours work U 52.061 47.177 42.8 96.2 62.69 0.95* 
M 49.668 49.851 -1.6 -0.86 0.79* 

Average schooling U 8.203 6.806 48.5 99 71.9 1 
M 8.263 8.278 -0.5 -0.23 1.01 

Migrant HOH U 1.166 1.089 33 99.3 56.51 1.68* 
M 1.178 1.179 -0.2 -0.1 1.05 

Dependency ratio  U 3.189 3.448 -11.5 45.1 -15.82 0.67* 
M 2.091 2.234 -6.3 -4.26 0.94* 

Age of HOH U 47.15 45.826 10.3 70.3 15.62 0.95 
M 48.863 48.47 3.1 1.52 1.03 

*if variance ratio outside [0.98; 1.02] for U and [0.94; 1.06] for M 
Source: Author estimation using LFS-2017-18 

The absolute percentage bias for treatment and the control group before and after 

the matching are presented in the table-8. It can be observed from the table that the 

absolute mean bias has reduced from 18.8 to 1.6 percent and absolute median bias 

reduced from 9.2 to 1.2 percent for the year 2017-18 after performing the PSM. Also, 

 
1occupational and provincial dummies were included in pstest and also found statistically insignificant 



Journal of Applied Economics and Business Studies, Volume. 4, Issue 4 (2020) 187-214 https://doi.org/10.34260/jaebs.449 

207 

after matching Pseudo R2 is reduced significantly from 0.21 to 0.003. Insignificant p-

value of likelihood ratio after matching implies that estimation of the propensity score 

matching is efficient regarding balancing the distribution of covariates between two 

groups. So, on the basis of the measures presented it can be concluded the (Nearest 

Neighbor) Propensity score matching is an efficient method to construct a similar 

control group and to estimate the average treatment effect. 

Table-8 Average percentage bias 

Sample Ps R2 LR chi2 p>chi2 Mean Bias Med Bias 

Unmatched 0.215    3143.96 0.000 18.8        9.2 

Matched 0.003 43.05 0.555 1.6 1.2 

Source: Author estimation using LFS-2017-18 

The graph indicates how individual variables balance after matching. The x-axis 

displays the standardized bias, which is the percentage difference of the sample means 

in the treated and non-treated as a percentage of the square root of the average of the 

sample variances in the treated and non-treated groups (Rosenbaum and Rubin; 1985) 

Figure-6 Standardized bias across covariates 

 
  Source: Author estimation using LFS-2017-18 

It can be visualized from figure that the unmatched sample exhibit large imbalances 

with standardized bias being present across many of covariates but once the matching 

is done the standardized differences diminished significantly. Further the same results 

can also be seen through the histogram. It can be observed that the standardized 

-100 -50 0 50
Standardized % bias across covariates

Unmatched

Matched



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

208 

percentage bias has reduced significantly after the matching. Comparing the upper panel 

which shows the percentage bias in unmatched (non-treated group) with the second 

panel reveals significant reduction in percentage bias after the matching. 

Figure-7 Histogram of Standardized % Bias 

 
  Source: Author estimation using LFS-2017-18 

6. Conclusion and Policy Implications 

The purpose of this research is to explore the variation of spatial concentration 

/agglomeration across urban districts of Pakistan and analyses the effect of this 

concentration on the prevalence of income inequalities. The basic view is that spatial 

concentration amplifies inequalities across regions, because of the differences in 

productivity. The empirical literatures on the spatial disparities between urban regions 

based on agglomeration are rather scarce. This study attempts to start bridging this gap 

by using survey data conducted by Pakistan Bureau of Statistics (PBS). The empirical 

results and the evidences provided clearly indicate that distribution of income is 

significantly affected by spatial concentration supports the viewpoint of New Economic 

Geography (NEG) model. As per New Economic Geography (NEG) model the 

economic activities will be higher in the region of increased agglomeration which results 

in spatial inequalities showing the positive inter relation. To reveal results this study 

used two steps methodology. First the Herfindahl indices have computed, using Labour 

Force Survey (LFS) for the year 2010 to 2018, to measure the inter-temporal spatial 

concentration of industries. Using the values of indices urban regions are identified that 

have positive and above average level of concentration. Four large cities, Gujranwala, 

Sialkot, Lahore, Multan and four other urban areas Rawalpindi, Faisalabad, Gujranwala 

0

.0
5

.1
.1

5

D
en

si
ty

-80 -60 -40 -20 0 20 40 60 80
Standardized % bias across covariates

Unmatched
0

.0
5

.1
.1

5

D
en

si
ty

-80 -60 -40 -20 0 20 40 60 80
Standardized % bias across covariates

Matched



Journal of Applied Economics and Business Studies, Volume. 4, Issue 4 (2020) 187-214 https://doi.org/10.34260/jaebs.449 

209 

and DG Khan fulfilled both criteria. After identifying the agglomerated regions the 

second step was to estimate causal treatment effects of agglomeration on regions income 

that responsible to exaggerate inequalities across agglomerated and non agglomerated 

regions. Propensity-Score Matching (PSM) was applied for this purpose. For the 

analysis eight regions that are experiencing increasing growth of agglomeration was 

considered as "Treated Group" while the regions where agglomeration appeared non 

persistent was considered as “Control Group.” The statistically significant coefficient of 

average treatment effect on treated (ATT) with positive sign   indicated that the one 

percent increase in spatial concentration increase income level of the households that 

reside in these urban districts of Pakistan by 346854. Further no significant statistical 

differences have been found after matching and most of the covariates found balanced 

as per ps-test results. After performing PSM absolute percentage bias also decrease 

significantly. The absolute mean bias has reduced from 18.8 to 1.6 percent and absolute 

median bias reduced from 9.2 to 1.2. In a nut shell, it can be concluded that 

agglomeration is important determinant of regional variations in income. The reported 

findings have significant implications for regional policy. It means that obstructing the 

agglomeration of industries with the intention of encouraging equality may hurts 

productivity. In order to lessen spatial income inequalities focusing on the development 

of regional agglomeration of activities, rather than dispersion of resources to every 

single region, seems to be the correct way to carry on. The effective policies to promote 

agglomeration are needed for growing urban population that has the potential to turn 

out to be an effective part of economic growth. Pakistan fronting many challenges; Key 

to this is the provision of employment to growing labour force and create vibrant 

industrial sector that can provide variety of jobs requiring skills of different intensity. 

In the absences of such opportunities the standard of living for those that are affected 

abated which may lead social unrest. There is also need to improve infrastructure in 

order to promote agglomeration and greater inclusion and equity.  

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Journal of Applied Economics and Business Studies, Volume. 4, Issue 4 (2020) 187-214 https://doi.org/10.34260/jaebs.449 

213 

Annexure 

 

Table A1: Estimates of Probit Model (If Agglomeration=1) 

Standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 
Source: Author's estimations based on LFS 2017-18 

 

Variables  Coefficient 

Average schooling      0.075*** 

(0.00517) 
Age of highest earner    0.009*** 

(0.00092) 

Education of highest earner    -0.012*** 

(0.0044) 

Age of head of household     0.005*** 

(0.00093) 

Dependency Ratio 0.0184** 

(0.00802) 

Migrant household    0.394*** 

(0.0440) 

Technical/Vocational training  0.266*** 

(0.0410) 

Working age members in household   0.0224*** 

(0.00589) 

Gender of HOH 0.298*** 

(0.0445) 

Work hours  0.0099*** 

(0.00118) 

Female Participation  Rate -0.282*** 

(0.0351) 

Constant -1.617*** 

(0.246) 

Occupational dummies Most of the dummies appears 

statistically significant 

Provincial dummies  

Observations 18,722 

LR chi2(46)       3143.96*** 

Pseudo R2 0.215 



Uzma Tabassum, Shaista Alam and Ambreen Fatima 

214 

 

Table A-2: Distribution of Estimated Propensity Score-Livelihood 

Percentiles Smallest 
  

1% 0.007168 0.002032 
  

5% 0.014106 0.0023592 
  

10% 0.021964 0.0025063 Obs 18722 

25% 0.106504 0.0025063 Sum of Wgt. 18722 

50% 0.251298 
 

Mean 0.25002 

  
 

Largest Std. Dev. 0.167869 

75% 0.36499 0.8400924 
  

90% 0.466755 0.8439572 Variance 0.02818 

95% 0.536866 0.8474239 Skewness 0.349559 

99% 0.678517 0.8526713 Kurtosis 2.591341 

95% 0.536866 0.8474239 Skewness 0.349559 

99% 0.678517 0.8526713 Kurtosis 2.591341 

Note: The region of common support is [.002032, .8526713] 

Source: Author's estimations based on LFS 2017-18