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American Journal of  Economics and 
Business Innovation (AJEBI)

The Impact of  Export and Diversification on Firm Performance: Evidence from Pakistan 
Khawaja Abdul Haq1*, Shahzad Munir1, Sohail Raza1 

Volume 1 Issue 3, Year 2022
ISSN: 2831-5588 (Online)

DOI: https://doi.org/10.54536/ajebi.v1i3.538
https://journals.e-palli.com/home/index.php/ajebi

Article Information ABSTRACT

Received: September 01, 2022

Accepted: September 22, 2022

Published: September 24, 2022

The pursuit of  internationalization is an important element for a variety of  emerging and 
transitioning economies in their ongoing reform process, following exhibits of  export-led 
development stories in nations such as China, Singapore, and South Korea. Moreover, firms 
play a major role in the country’s economy; hence, this study empirically analyzes the impact 
of  export and diversification on the performance of  firms in Pakistan. This paper utilizes 
World Bank enterprise data for Pakistani firms across the whole country for the years 2007 
and 2013, it is a panel data comprising 454 firms. The dependent variable for this study is 
sales per employee that we use as our performance measure and the independent variables 
are Export in terms of  Export intensity and Geographic diversification in terms of  an 
inverse measure named export concentration calculated by the Herfindahl-Hirschman in-
dex (HHI). For analysis, this paper utilizes Panel EGLS (estimated generalized least square) 
and the estimation suggests that exports and diversification both positively and significantly 
correlated in their respective sample. Nonetheless, there is an endogeneity issue with Panel 
EGLS and to counter that two-stage Panel EGLS is used with “average days to clear cus-
toms” as an instrumental variable (IV). Results show a positive association of  exports with 
firm performance again. However, the diversification variable proved to be exogenous as 
per the exogeneity bias test, so its IV results are not robust. Thus, the findings indicate that 
Pakistani firms perform better if  they export, thus supporting the learning by exporting 
hypothesis, in addition to their exports being geographically diversified.

Keywords
Export, Diversification, 
Sales Performance, Estimated 
Generalized Least Square

1 Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, 422 South Siming Road, Xiamen, 361005, China
* Corresponding author’s e-mail: kahaq100@gmail.com

INTRODUCTION 
The process of  internationalization of  business and 
trade is influenced by enterprises ‘ growth requirements. 
Internationalization is the entirety of  cross-border 
activities whether they be assets, goods, or services 
manufactured or traded within the same institution or 
amongst independent economic agents (Dunning, 1993). 
The internationalization determinants have a very diverse 
list; one of  the main entry-level determinants is Exports, 
which are widely recognized in developing countries as 
the prerequisite for economic growth. In an analysis 
of  the miracle of  East Asia, for example, the World 
(1993) found out that export-oriented economic policies 
play an important role in the region’s rapid economic 
growth. When it comes to firms, the level of  exporter 
has a significant impact on the performance and the 
effects of  learning by exporting are more important 
for established exporters than new exporters(Kraay, 
1999). Another key determinant of  internationalization 
is diversification, which can apply to both product and 
geographical location. Diversification of  the products is 
achieved by modifying or extending the current export 
basket of  goods. Meanwhile, Geographic diversification 
can be seen as another global strategy for diversification 
and may be characterized as expansion Bank across the 
boundaries of  foreign countries and regions into several 
markets or geographic locations (Hill et al., 1992). The 
developing world could potentially gain a more reliable 
income flow by diversifying export strategies than 
focusing only on a few markets or goods. According to 

the literature, the level of  diversification also contributes 
to firm performance.
Moreover, with increasing economic activity around the 
world and the elimination of  the major obstacles that 
have long limited businesses to the global marketplace, 
many firms can achieve significant growth opportunities 
by gaining international expertise, product development 
strategies, and technological spillovers from foreign trade. 
However, all these benefits come with various challenges, 
and due to that literature for firms that internationalize 
has mixed findings. Researchers have extensively studied 
the impact of  internationalization on both micro and 
macro level, yet, there is comparatively limited research 
for Pakistan in those aspects. In light of  all this detail, 
this paper particularly focuses on analyzing the impact 
of  Exports and Geographic diversification on the sales 
performance of  Pakistan’s firms. First, it deals with the 
concept of  learning-by-exporting (LBE), which means 
that exporting firms learn from their foreign activities 
which results in better performance compared to the non-
exporters. Nonetheless, empirical research supporting the 
LBE hypothesis is not yet conclusive. Second, this paper 
also contributes to the Diversification vs. Concentration 
debate, where there is mixed empirical evidence of  
better performance by choosing either one of  these two 
paths by firms. Third, it also conceptually differentiates 
between two definitions, as the export intensity was used 
as a measure of  diversifications (Contractor et al., 2007; 
Geringer et al., 2000; Reeb et al., 1998) as foreign sales 
over total sales proportion, it generally does not mean the 

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company has diversified exports across several nations. 
By contrast, a vast number of  highly diversified firms 
can still focus on the domestic market while their export 
intensity is low. Thus, the diversification of  geographical 
exports and the intensity of  exports are completely 
different concepts.
This paper use World Bank enterprise data for Pakistan’s 
firms across the whole country for the year 2007 and 
2013. It is a survey panel data comprising 454 firms. The 
dependent variable for this study is sales per employee, 
the independent variables are Export in terms of  export 
intensity, and Geographic Diversification in terms of  an 
inverse measure called export concentration calculated by 
the Herfindahl-Hirschman Index (HHI). For our analysis, 
this paper utilizes Panel EGLS (estimated generalized 
least square) and the estimation suggests that exports 
and diversification both positively and significantly 
correlated in their respective sample. Nonetheless, there 
is an endogeneity issue with Panel EGLS as per the 
exogeneity bias test, and to counter that two-stage Panel 
EGLS is utilized with Average days to clear customs as 
an instrumental variable. Results again show a positive 
and significant association of  Exports with firm 
performance, thus, supporting the learning-by-exporting 
hypothesis. However, the Diversification variable proved 
to be exogenous so its IV results are not robust.
The remainder of  this paper is organized in the following 
way. 

section 1.1 deals with Pakistan’s background in Exports. 
section 2 summarizes past literature and empirical research 
that has been conducted to study Internalization in the 
context of  export and diversification. Section 3 describes 
the dataset and its source for modeling the hypothesis. 
Section 4 explains the methodology used for evaluating 
the hypothesis. The empirical findings on the impact 
of  Export and Diversification on firm performance are 
presented in section 5 and are concluded and discussed 
for future research works in Chapter 6 later 

Background
In the country’s economy, exports are one of  the major 
growth factors and play a significant role. In the last 
decade, Pakistan has struggled together to liberalize its 
global trade with little or no success. Pakistan has recently 
ranked 68th in the world’s largest exporting country with 
a share of  0.11% in global exports . In Figure 1, exports 
used to make up about 13 percent of  Pakistan’s GDP 
in 2007 can be seen; recently they have been reduced 
to 8 percent in 2017. Pakistan’s export performance is 
influenced by several factors. The conflict between its 
economic and monetary policies is the most significant, 
as almost 45 products have lost their competitiveness 
on the global market since 2013 . In addition, Pakistan’s 
currency rise affects competitiveness with competitors 
like India and Bangladesh. The import tariff  on export 
products has increased production costs more .

Figure 1: Pakistan Exports of  goods and services (percentage of  GDP)
Source: World Bank national accounts data, and OECD National Accounts data files.

Figure 1: Pakistan Exports of  goods and services (percentage of  GDP)
Source: World Bank national accounts data, and OECD National Accounts data files.

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Comparing Figure 1 and Figure 2, given a steady fall in 
export contributions to GDP, the export value (based 
on current US dollars) saw its vicissitudes. From 2007 to 
2011, we can see a U curve in Figure 1, which is 2008-
2009 reflects a decline that represents the US crisis in 
which several nations, including Pakistan, have been 

impacted by the recession as their largest exporting 
partner in the United States. In 2011, exports rose before 
they again fell in 2012 as a result of  the decline in world 
trade due to Europe’s sovereign debt crises and increasing 
oil prices. The domestic energy shortage led to the poor 
performance of  exports, too . 

Figure 3: Pakistan’s Top 50% of  Export Share in 2017
Source: WITS (World Integrated Trade Solution), a subsidiary of  World Bank

The diversification of  export markets and goods is a 
significant irritant in raising export revenues. At present, 
the majority of  exports come from the textiles and 
clothing sectors, which account for around 60 percent . 
The export destinations of  Pakistan are largely focused 
on a few countries. Per figure 3, the top 50 percent 
of  Pakistan’s exports go to the United States, China, 
Afghanistan, the UK, Germany, and UAE. Among these 
counties, the United States accounts for the largest 17% 
and the United Arab Emirates the lowest at 4% for 2017. 
This pattern is not only seen in recent data but can also be 
seen in previous years. Regional market trading capacity 
remains still under-exploited.

LITERATURE REVIEW
Many empirical research papers have shown the higher 
performance of  exporting firms compared to the non-
exporters. As foreign buyers may provide exporters 
with technical assistance in increasing their production 
efficiency (Grossman & Helpman, 1991). High-quality 
standards could give businesses more incentive the 
upgrading production technologies in global markets than 
in domestic markets (Verhoogen, 2008). Participation in 
exports can contribute to a quicker understanding of  new 
product market potential (Maurin et al., 2002). Exports 
could enhance capacity utilization by increasing sales, 
thereby reducing the exposure of  businesses to periodic 
domestic downturns (World Bank, 1993).
Sohl et al. (2020) investigated the impact of  diversification 
on the firm performance by employing firm level data 
of  five Asian economies and found that there exists a 
direct positive relationship between diversification and 
firm revenue increment. On the other hand, Gnangnon 
(2021) studied the effect of  export on the firm revenue 
using the corss-section data at firm level of  developing 
economies and found that export plays an important role 
in increasing firm revenue. Niemenen (2020) found that 

the multidimensional aspect of  diversification has played 
a significant role in boosting the firm revenue directly. 
Varieties of  channels have been established by research 
through which exports will impact the performance 
of  a firm, though there are only two theories that are 
commonly used, they are referred to as Self  Selection (SS) 
and Learning-by-Exporting (LBE). SS is characterized as 
highly productive firms that are more prone to becoming 
exporters, as only those with low marginal costs have 
enough profits to meet global market entry fixed costs. 
On the other hand, LBE refers to firms that perform 
better after entering international markets. Industries 
entering international markets gain greater knowledge 
via the demands of  consumers from abroad, adopt new 
manufacturing techniques with higher capacity usage and 
thus boost performance in international markets. Other 
studies such as (Kraay, 1999) and (Dunning, 1993) found 
that established exporters and level of  export intensity 
play a bigger role in LBE rather than just being part of  it. 
This paper, in particular, focuses on the LBE hypotheses, 
this topic is being extensively covered in the past but its 
results are not yet conclusive along with Pakistan not 
being part of  past studies.  
(Castellani 2002) Used sample data of  over 2000 Italian 
manufacturing companies and estimated regressions of  
labor growth rate on export. His findings concluded that 
when the export was used in terms of  export intensity 
(share of  the international sale in total sale); it has a 
significant and positive impact on firm productivity 
growth. However, when the export was used as a dummy 
variable the firm’s productivity was not affected. The key 
point, however, is that the learning results are only above 
a specific level of  international engagement. The paper’s 
analysis, in particular, highlights the key conclusion that 
the learning effects exist just above a certain specific level 
of  export intensity. In another study, in his panel of  2105 
Chinese industry firms between 1988 and 1992, Kraay 

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(1999) investigated how businesses benefited from learning 
by exporting. He observed that past exports contribute 
to substantial enhancements in business efficiency by 
controlling past performance and unobserved enterprise 
characteristics. Surprisingly, these learning effects among 
existing exporters were most prominent. The learning 
results are insignificant and often negative for new 
entries to export markets. Greenaway and Kneller (2008) 
evaluated the export starters with non-exporters with UK 
firms’ data (1988-2002) and found that businesses earn 
substantial productivity improvements during the year 
they join, in contrast with similar firms that do not. They 
also find that the possibility to further improve in coming 
years is to raise the proportion of  export sales in a firm’s 
overall sales, thus conforming to the LBE hypothesis.  
Nonetheless, some studies indicate no confirmation 
of  the LBE hypothesis. Data from over 30,000 Indian 
manufacturing firms were used by Haidar (2012) to 
examine the relationship between productivity and 
participation in the export market throughout the 
1991-2004 period. His estimates for the study indicate 
no improvement in productivity from entering export 
markets. The paper’s key finding was that the most 
successful companies choose the path of  becoming an 
exporter, however, learning by exporting is in no way to 
speed up India’s manufacturing growth. Greenaway et al. 
(2005) study an empirical analysis of  Swedish production 
exporters and non-exporters. They used panel data set of  
3570 firms that span from 1980 to 1997. They found that 
exporters and non-exporters had interestingly identical 
performance traits.  They did not find evidence of  
learning by export possibly due to the fact of  Sweden’s 
highly open economy. Bernard & Wagner (1997) German 
industry panel data covered the period from 1978 to 
1992; their findings show that exporting could lead to 
success if  competition is more severe internationally 
than in the German domestic market.  They found no 
evidence of  the LBE hypothesis and indicated that firms 
are already productive three years before they enter the 
export market.
Firms wanting potential competitive advantage diversify 
geographically, as they can achieve economies of  scale 
and scope. Geographical diversification raises prospective 
customers and distributors, provides resource access, 
promotes the stabilization of  cash flow, and thus 
decreases overall risk (Rugman & Verbeke, 2004). In 
reaction to expected and unanticipated downward shifts 
in domestic or international demand, the company 
can quickly shift revenue and investment in markets 
where it anticipates higher performance (Lee & Song, 
2012). However, geographic diversification has also 
its disadvantages. In the early stage, companies have 
different environments to cope with in terms of  cultural 
working environments, regulations, capital markets, and 
goods, which could raise management and planning 
costs (Hennart, 2007). Throughout the macro-economic 
downturn, maintaining global operations and decision-
making can lead to additional coordinating costs and 

undermining productivity under a high point of  external 
uncertainty (Hill et al., 1992).
Bühner (1987) used data from the top 300 firms in 
Germany from 1966 through 1981. The author used 
the Herfindahl index to measure export diversification. 
By applying the generalized least square method and 
using accounting and market performance measures he 
concluded that diversification is positively correlated with 
firm performance. Furthermore, Riahi-Belkaoui (1996) 
showed a positive relation between firm performance and 
diversification by using a sample of  French companies 
in 1990. Return on assent was used as a performance 
measure and FSTS (Foreign sales/Total sales) was utilized 
for diversification. 
Geographical diversification has an ambiguous impact 
on firm value. Bodnar et al. (1997) found that firm value 
increased in geographical diversification; similarly, Denis 
(2002) found that geographical diversity decreases the 
firm value. An interesting thing is that the two studies have 
used the same sampling period and methodology though 
the findings are considerably different. While, Denis 
et al. (2002) used FSTS as a geographic diversification 
measure, Bodnar et al. (1997) used this sample as well as 
companies reporting on overseas taxes. Therefore, more 
internationally diversified companies are included in the 
Bodnar (1999) study. This disparity may account for the 
different outcomes. Later Kim (2008) used the same 
sample and has consistent results with Denis et al.(2002). 
He suggested that the cost of  diversification outweigh the 
benefits. 
From 1977 to 1993, Geringer et al. (2000) analyzed 
Japanese multinationals ‘ performance relationship 
with geographic diversification. In multiple regression 
models, pooled cross-section time series data was used. 
With several periods, he shows the connections between 
diversification and performance changed. In general, he 
did not identify strong interactive effects on diversity with 
firm performance. 

Data Analysis 
Sampling data and collection
The objective of  this study is to examine the impact of  
exports and their geographic diversification on Pakistan’s 
firm performance. This paper is based on a survey of  454 
enterprises across the country engaged in International 
and national business. This is a panel dataset for the years 
2007 and 2013, and firms surveyed in these years are a 
complete match denoted by the ID number given to them 
initially. Since the data used yearly panel data from the 
years 2007 and 2013, the total number of  observations 
for this study is 908 in total. This study uses 2007 and 
2013 cross-sectional data set as the data after 2013 which 
will better understand the performance of  the Pakistani 
firms before and after the financial crisis.
With respect to data source, this study obtains it from 
the World Bank’s 2007 and 2013 surveys of  Pakistani 
enterprises. This particular dataset contains all the main 
independent variables and control variables.

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Description of  Variables
For this study, Firm performance (sales per capita) is the 
dependent variable. It is total sales divided by the total 
number of  employees of  a firm to measure performance. 
It can also be denoted as labor productivity according to 
some studies. Usually when calculating the performance 
of  a firm most popular measure is Total factor productivity 
(TFP) but due to the limitations in the data, I went with 
the second most common measure of  firm performance 
denoted below.
Firm Performance = (Total Sales)/(Total Number of  
Employees)
My main independent variables are:
1. Percentage of  direct exports among total sales (Export 

Intensity).
2. Diversification of  export destination calculated by the 

Herfindahl-Hirschmann Index:                  
HHI=∑n

(i=1)si
2     i=1,2,….,n

Where s_i is the percentage export to a particular region 
and n is the number of  regions. It is an inverse indicator 
of  export diversification, based on the Herfindahl-
Hirschman index, a concentration ratio. Values ranges 
from 0 to 10,000 on this ratio, values near 0 are more 
export diversified and the value of  10,000 is a perfect 
concentration of  the exports. When the HHI values 
are below 1500, exports can be labeled diversified. 
Concentrated exports can also be split between the 
moderate (1500 <= HHI < 2500) and high (HHI 
>=2500) sub-categories.
Regarding the control variable, this study uses firm-
specific factors that can affect a firm’s performance. 
There are ten control variables: 
1. Labor cost is defined by the total cost of  labor divided 

by the total number of  employees. Labor cost=(Total 
cost of  labor)/(total number of  employess).
2. The number of  employees is the total full-time 

employees of  a firm in a given year.
3. Age of  firm 
4. Operation hours are the total hours of  operation of  the 

firm in a given year.
5. Employee education is the average number of  years 

of  education of  a typical production worker.
6. Capacity Utilization is the establishment’s output 

produced as a proportion of  the maximum output 
possible if  using all the resources available. 
7. Manager experience is the years of  experience top 

managers have in the firm.
8. Training is a dummy variable, where 1 is if  employees 

received any kind of  training from the firm and 0 means 
if  they did not receive any training.
9. Power outages are a dummy variable, where 1 is if  the 

firm had power outages during the year and 0 means if  
there were not any power outages.  
10. Foreign ownership is the percentage of  ownership of  

the firm by a foreign individual, company, or organization.
As our model is prone to endogeneity, we are using 
Average days taken to clear customs at exporter exit point 
(airport or seaport) as an instrumental variable. 

Summary Statistics and correlations
Table 1 reports the summary of  descriptive statistics 
for all variables used in this thesis measured yearly. The 
observation period belongs between the years 2007 and 
2013, with 908 observations. The table contains the mean, 
standard deviation, minimum, maximum values, and several 
observations of  each variable. Some control variables, e.g., 
Training and Power outages are dummy variables.
Table 2 presents the correlations between all explanatory 
variables used in this study to check for multicollinearity. 
Regarding a rule of  thumb, the absolute values of  
correlations among independent variables should not be 
higher than 0.8. The high correlation among independent 
variables will lead to a multicollinearity issue. As we can 
see from table 2, all the absolute values of  correlation 
coefficients are not higher than 0.8. Therefore, there is no 
multicollinearity issue for this study. Later, in the empirical results 
section, I have included another multicollinearity test known as 
the Variance inflation factor (VIF) for more reliability.

Table 1: Summary statistics
 Variable(s) Mean Max Min SD N
Firm Performance (Logarithm) 5.836 9.012 0.699 0.722 797
Exports (Percentage) 11.650 100 0 29.850 859
Export concentration (Logarithm) 3.710 4 3.097 0.190 148
Labor Cost (Logarithm) 4.847 7.716 0.398 0.591 783
Number of  employees (Logarithm) 1.376 3.740 0 0.629 904
Employee Education (Years) 4.978 20 1 8.524 809
Firm Age (Years) 22.644 101 1 13.443 894
Operation hours (Logarithm) 3.491 3.941 2.318 0.185 777
Capacity Utilization (Percentage) 73.922 100 3 20.542 752
Manager Experience (Years) 19.581 56 2 10.523 887
Training (Dummy) 0.163 1 0 0.369 892
Power Outage (Dummy) 0.917 1 0 0.276 907
Foreign Ownership (Percentage) 1.636 100 0 8.709 899
Average custom days (Instrument variable) 4.542 60 0 9.916 844
Note: See section 3.1 for the data sources. The variable measurements are provided in section 3.2. The units of  observations are in 
parentheses

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Table 2: Correlation Matrix
 Ex EC LC No. of  

E
EE FA OH CU ME T PO FO ACD

Ex 1             
EC 0.1577 1            
LC -0.0119 0.0970 1           
No. 
of  E

-0.1487 0.0934 0.0185 1          

EE 0.0754 0.1580 0.2644 0.1577 1         
FA -0.0440 -0.0295 -0.0429 0.1218 -0.0174 1        
OH -0.1425 0.0443 0.0550 0.0633 -0.0134 0.0083 1       
CU -0.0187 0.0434 0.1620 0.2313 0.1173 0.0785 0.3018 1      
ME -0.1114 -0.2487 -0.0152 0.0247 -0.1685 0.2861 -0.1032 0.0844 1     
T -0.3240 0.1518 0.0893 0.5108 -0.0250 0.1047 -0.0649 0.0875 -0.0271 1    
PO 0.0089 0.0402 -0.0135 -0.2772 0.0441 -0.0926 -0.0006 -0.1165 0.0070 -0.1548 1   
FO -0.0865 0.0206 0.0888 0.1647 -0.0215 0.0757 0.1137 0.1788 0.0011 0.0915 -0.4037 1  
ACD -0.1929 -0.087 0.0451 -0.0075 -0.0377 -0.0758 0.0406 -0.0551 -0.0855 -0.0580 0.0450 0.1071 1
Note: The table reports the correlations among all the explanatory variables used in this paper. Ex denotes Exports, EC refers to 
Export concentration, LC means labor cost, No. of  E denotes Number of  employees, EE refers to Employee education, FA means 
firm age, Oh denotes operation hours, CU refers to capacity utilization, ME refers to manager experience, T is denoted by Training, 
Po means Power outage, FO denotes Foreign ownership and ACD means average days taken to clear customs

METHODOLOGY
Estimated Generalized Least Square (EGLS)
The problem of  hetroskadescity is frequent in panel data. 
Hetroskadescity means that when the variance of  error 
terms varies over the observation. In that case, although 
least square estimates remain unbiased the standard error 
of  estimators is wrong making statistical inference not 
reliable and providing misleading results. To overcome 
this problem, we need to make data homoskedastic 
using an appropriate transformation and then apply 
the least square estimation technique to estimate the 
unknown parameters; the resulting estimates are known 
as estimated generalized least square (EGLS).  Therefore, 
this paper applied this methodology to find the impact of  
exports and diversification on firm performance and they 
are estimated in the following models:
Log(Firm Performance)it= β0+β1Exportsit+γk Xit

’+αl 
Yit

’+uit  ...................   (1)
Log(FirmPerformance)it=β0+β1Exportst Concentration+δl 

Zi
t’+uit  ........................ (2)

In equation (1), Log has denoted as base 10 logarithm, 
Firm performanceit denotes total sales divided by total 
employees of  firm i at time t. Exports denotes the export 
intensity of  firm i  and time t. X’are the labor-specific 
control variables of  firm i and time t which include labor 
cost, number of  employees, and average education of  
employees. Y’are firm-specific control variables, which 
consist of  firm age, operational hours, capacity utilization, 
top manager experience, training, power outage, and form 
of  ownership. γ and α are the vectors of  the coefficients 
of  control variables. uit denotes the error term. β1 is the 
coefficient used to estimate the impact of  export on firm 
performance.
In equation (2), Log has denoted as base 10 logarithm, 
(Firm Performance)it denotes total sales divided by total 
employees of  firm i at time t. Export Concentration 

denotes the concentration of  export destinations of  firm 
i  at time t. Z’are control variables which consist of  firm 
age, operational hours, capacity utilization, power outage, 
and form of  ownership. δ is the vector of  the control 
variables coefficients. uit marks the error term. β1 is the 
coefficient used to estimate the impact of  diversification 
on firm performance using an inverse measure of  export 
concentration.

Two Stage Estimated Generalized Least Square (2S 
EGLS)
The EGLS estimation results in equations (1) and (2) are 
prone to endogeneity. As export is correlated with firm 
performance and firm performance is correlated with 
export. Due to these circumstances, there is a chance of  
reverse causality. The solution to this problem is to utilize 
two-stage instrumental variable panel data EGLS (2SLS 
EGLS). The instrumental variable used is the average 
number of  days to clear customs. Thus, the following 
model is used for the estimates. 
Exportit=β0+β1Avg Customit+uit  .............   (3)
Log(Firm Performance)it= β0+β1 (Exports)it+γk Xit’+αl 

Yit’+uit.................. (4)
In equation (3), export was estimated by using an 
instrumental variable (average custom days), later, firm 
performance is regressed on the estimated exports in 
equation (4) where the log is denoted as base 10 logarithm, 
(Firm Performance)it denotes total sales divided by total 
employees of  firm i at time t. Estimated Exports denotes 
the export intensity of  firm i  and time t. X’are the labor-
specific control variables of  firm i and time t which 
include labor cost and average education of  employees. 
Y’are firm-specific control variables, which consist of  
firm age, operational hours, capacity utilization, top 
manager experience, training, power outage, and form 
of  ownership. γ and α are the vectors of  the coefficients 
of  control variables. uit denotes the error term. β1 is the 

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coefficient used to evaluate the impact of  estimated 
export on firm performance.
Export Concentrationit= β0+β1 AvgCustomit+uit                                                       

..................(5)
Log(FirmPerformance)it=β0+β1(Export Concentration) it+δl 

Zit’+uit    ................... (6)
In equation (5), export concentration was estimated by 
using an instrumental variable (average custom days), 
later, firm performance is regressed on the estimated 
diversification in equation (6) where the log is denoted as 
base 10 logarithm, (Firm Performance)it denotes total sales 
divided by total employees of  firm i at time t. Estimated 
export concetration denotes the concentration of  export 
destinations of  firm i  at time t. Z’are control variables 
which consist of  firm age, operational hours, capacity 
utilization, power outage, and form of  ownership. δ is the 
vector of  the control variables coefficients. uit marks the 

error term. β1 is the coefficient used to evaluate the impact 
of  estimated diversification on firm performance using 
an inverse measure of  estimated export concentration.

RESULTS AND DISCUSSION
This section describes the empirical findings and 
discussion from panel EGLS and two-stage EGLS 
methods with correlation matrices and covariates. The 
last section of  the thesis discusses the conclusion of  this 
overall study.

Impact of  Export on Firm Performance (EGLS & 
2S EGLS)
Table 3 shows the effect of  export from Pakistan 
on firms, performances (sales performance). Sales 
performance is in its logarithmic form, Labor cost is in 
logarithmic form, the number of  employees, Employees, 

Table 3: Impact of  Export on Firm’s sales performance
Dependent Variable: Sales Performance: Full Sample

(1) (2) (3) (4) (5) (6)
Exports 0.004*** 0.001 0.002*** 0.035*** 0.039*** 0.033***

(0.0005) (0.0004) (0.0004) (0.007) (0.004) (0.007)
Labor Cost 0.540*** 0.527*** 0.537*** 0.544***

(0.0126) (0.018) (0.057) (0.071)
Number of  Employees 0.124*** 0.048*** 0.012

(0.009) (0.012) (0.073)
Employees Education 0.005 0.003 -0.001* 0.0005

(0.003) (0.003) (0.002) (0.003)
Firm Age 0.010*** -0.003

(0.001) (0.006)
Operation Hours     0.814*** 1.166***

(0.055) (0.379)
Capacity Utilization     0.002*** 0.005*

(0.0004) (0.003)
Manager’s Experience 0.002 0.039***

(0.001) (0.014)
Training 0.228*** 0.813***

(0.076) (0.401)
Power Outage -0.711*** -0.109

(0.052) (0.237)
Foreign Ownership 0.003 0.010

(0.003) (0.010)
Constant 13.447*** 6.995*** 1.048** 11.317*** 4.819*** -5.537

(0.008) (0.147) (0.452) (0.406) (0.938) (3.574)
Observations 729 678 626 722 673 621
R-squared 0.062768 0.760369 0.81705 0.60626 0.702881 0.779813
Adjusted R-squared 0.061479 0.758944 0.813772 0.59689 0.692270 0.758642
F-statistic 48.68844 533.8698 249.2823 18.968 37.90427 17.1600
Prob(F-statistic) 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
“Note:  *** 1%; ** 5%; * 10%”. The columns labeled (1), (2), and (3) are the estimation results of  Panel EGLS techniques while 
(4), (5), and (6) are the Panel 2SLS EGLS estimation results, using the Average number of  days to clear custom as the Instrument.

is in logarithmic form, and the Operation Hours are in 
its logarithmic form. Every other variable is in its natural 
magnitude levels. In Table 3, column (1) to column (3) 
shows the baseline panel data model EGLS estimation 
results while column (4) to column (6) shows the Two 
Stage Least squares panel data model EGLS estimation 
results. In column (1) we present the simple panel data 

EGLS estimation of  the impact of  export on a firm’s 
performance which is significantly and positively 
correlated with the firm’s performance. However, the 
adjusted coefficient of  determination is only about 6.15% 
of  the variations in the firm’s performance. In column 
(2) we control for employee characteristics like the cost 
of  labor, the number of  employees, and the average 

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employee’s education. The result shows the insignificant 
and positive impact of  export on a firm’s performance. 
In column (3), we further control for the firm’s specific 
characteristics such as the age of  the firm, operational 
hours, capacity utilization, manager’s experiences, 
training, a form of  ownership, and power outage. The 
results show that a unit change in export leads to 0.2% 
in the firm’s performance. The adjusted coefficient 
of  determination improves to 81.38%. More so, the 
F-statistics test result on the joint significant hypothesis 
on all the explanatory variables is statistically significant in 
explaining the variations in the firm’s performance.
The panel data EGLS estimation results presented in 
columns (1) to (3) are prone to endogeneity bias due to 
the use of  export as an explanatory variable. As export 
explains a firm’s sales and firm’s (sale) performance also 
explains with export. As such, there are chances of  reverse 
causality and the presence of  reverse causality bias in the 
estimates of  the panel data model EGLS estimation. One 
of  the ways to correct this bias in the estimation result is 
to adopt the Two Stage Instrument Variable panel data 
EGLS (2S EGLS) estimation. The Instrumental variable 
adopted in this two-stage EGLS analysis is the average 
number of  days it takes from when consignments get to 
the right point of  departure to the time it clear at the 

custom. The 2SLS EGLS results are presented in Table 
3, column (4) to column (6). In column (4) we present 
the simple panel data model 2SLS EGLS estimation 
result. The results show that the effect of  export on a 
firm’s performance is around 3.5%. In column (5) we 
control for employee characteristics like the cost of  
labor and the average employee’s education. The result 
shows a significant and positive 3.9% impact of  export 
on a firm’s performance. In column (6), we further 
control for the firm’s specific characteristics such as the 
age of  the firm, operational hours, capacity utilization, 
manager’s experiences, training, a form of  ownership, 
and power outage. The results show that a unit change 
in export leads to a 3.3% in the firm’s performance. 
The adjusted coefficient of  determination improves to 
77.98%. More so, the F-statistics test result on the joint 
significant hypothesis on all the explanatory variables 
is statistically significant in explaining the variations 
in the firm’s performance. Therefore, the basic panel 
data model EGLS estimation underestimates the effect 
of  export on the performance of  the firms to be 0.2% 
instead of  3.3% as shown by the 2SLS EGLS estimation 
techniques, after controlling for employees’ specific 
and firm’s specific characteristics. Table 3.1 shows the 
Multicollinearity test results of  the estimated equations 

Table 3.1: Variance Inflation Factor for estimation results in Table 3
Covariates (2) (3) (5) (6)
Exports 1.565 4.749 1.358 7.834
Labor Cost 1.207 1.414 1.139 1.971
Employees 1.581 7.373 1.482
Employees Education 1.136 2.011 1.124 1.458
Firm Age 2.730 2.33
Operation Hours 2.808 5.682
Capacity Utilization 1.643 1.291
Manager’s Experience 4.431 5.733
Training 1.082 4.552
Power Outage 1.468 1.830
Foreign Ownership 1.199 1.467

in Table 3 corresponding to the exact column numbers 
in Table 3 (i.e. 2, 3, 5, and 6) using the Variance Inflation 
Factor (VIF). One could as well use the Tolerance to judge 
the collinearity in the estimated equations given its inverse 
relationship with the VIF estimates. The null hypothesis 
is that there is no multicollinearity and the decision rule is 
to reject the null hypothesis when the VIF estimates are 
greater than ten. The results presented in Table 3.1 shows 

that there is no VIF estimate that is greater than ten, 
therefore, we conclude that there is no multicollinearity 
in our estimated equations. 
The second objective of  this paper is to investigate the 
impact of  diversification on the (sales) performance 
of  the firm in Pakistan. For this reason, we present the 
EGLS estimation results in Table 4.

Table 4: Impact of  Diversification on Firm’s sales performance
Dependent Variable: Sales Performance: Export Sample

(1) (2) (3) (4)
Export Concentration -0.465*** -0.467*** 7.628*** 6.686*

(0.063) (0.125) (-3.453) (3.942)
Firm Age 0.018*** 0.022 1.458

(0.003) (0.010)
Operation Hours 1.159*** 5.682

(0.068) (0.635)
Capacity Utilization 0.020*** 5.733

(0.002) (0.009)

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Power Outage -0.535* -2.207** 1.830
(0.317) (0.919)

Foreign Ownership 0.0114 -0.014
(0.008) (0.018)

Constant 17.622*** 6.856*** -50.369* -49.926
(0.527) (1.368) (28.414) (36.686)

Observations 122 119 115 114
R-squared 0.314798 0.83171 0.303463 0.75291
Adjusted R-squared 0.309088 0.8227 0.314998 0.73906
F-statistic 55.13084 92.2538 6.741498 20.1211
Prob(F-statistic) 0.000000 0.000000 0.010671 0.000000
Note:  *** 1%; ** 5%; * 10%”. The columns labeled (1) and (2) are the estimation results of  Panel EGLS techniques while (3) and 
(5) are the Panel 2SLS EGLS estimation results, using the Average number of  days to clear custom as the Instrument.

Impact of  Diversification on Firm Performance 
(EGLS & 2S EGLS) 
Table 4 shows the effect of  diversification (using an inverse 
measure of  export concentration) on Pakistan firms’ 
performances (sales performance). Sales performance 
is in its logarithmic form, Export Concentration is in 
its logarithmic form, and Operation Hours are in its 
logarithmic form. Every other variable is in its natural 
magnitude levels. In Table 4, columns (1) and column 
(2) show the baseline panel data model EGLS estimation 
results while columns (3) to column (4) show the Two 
Stage Least squares panel data model EGLS estimation 
results for the impact of  diversification on firm’s 
performance. In column (1), we present the simple panel 
data EGLS estimation of  the impact of  diversification 
on a firm’s performance using Herfindahl–Hirschman 
Index which is significantly and negatively correlated with 
a firm’s performance. However, the adjusted coefficient 
of  determination is only about 30.91% of  the variations 
in the firm’s performance. In column (2) we control 
for the firm’s specific characteristics such as the age of  
the firm, operational hours, capacity utilization, power 
outage, and form of  ownership. The results show that 
a percentage change in export concentration leads to an 
inverse 0.47% percentage change in a firm’s performance. 
The adjusted coefficient of  determination improves to 
82.27%. More so, the F-statistics test result on the joint 
significant hypothesis on all the explanatory variables is 
statistically significant in explaining the variations in the 
firm’s performance.
The panel data EGLS estimation results presented in 
columns (1) and column (2) may have endogeneity bias 
due to the use of  Export Concentration as an explanatory 
variable. Arguably, export concentration explains a firm’s 
sales performance and the firm’s (sale) performance also 
explains export concentration. As such there are chances 
of  reverse causality and the presence of  reverse causality 
bias in the estimates of  the panel data model EGLS 
estimation. One of  the ways to correct this bias in the 
estimation result is to adopt the Two Stage Instrument 
Variable panel data EGLS (2S EGLS) estimation. The 
Instrumental variable adopted in this two-stage EGLS 
analysis is the average number of  days it takes from when 
consignments get to the right point of  departure to the 
time it clear at the custom. 

The two-stage EGLS results are presented in Table 4, 
column (3) to column (4). In column (3) we present the 
simple panel data model two-stage EGLS estimation 
result. The results show that the effect of  export 
concentration on a firm’s performance is around 7.6%. 
In column (4), we further control for the firm’s specific 
characteristics such as the age of  the firm, operational 
hours, capacity utilization, form of  ownership, and power 
outage. 
The results show that a percentage change in export 
concentration leads to a 6.69% in the firm’s performance. 
The adjusted coefficient of  determination improves to 
73.91%. More so, the F-statistics test result on the joint 
significant hypothesis on all the explanatory variables 
is statistically significant in explaining the variations in 
the firm’s performance. Therefore, the basic panel data 
model EGLS estimation underestimates the effect of  
export concentration on the performance of  the firms 
to be negative and 0.47% instead of  positive and 6.69% 
as shown by the two-stage EGLS estimation techniques, 

Table 4.1: Variance Inflation Factor for estimation 
results in Table 4
Dependent Variable: Sales Performance: Export Sample
Covariates (2) (4)
Export Concentration 1.227 7.571
Firm Age 1.036 1.178
Operation Hours 1.160 5.022
Capacity Utilization 1.004 6.626
Power Outage 1.030 2.485
Foreign Ownership 1.086 1.246

after controlling for the firm’s specific characteristics.
Table 4.1 shows the multicollinearity test results of  the 
estimated equations in Table 4 corresponding to the exact 
column numbers in Table 3 (i.e. 2, and 4) using the Variance 
Inflation Factor (VIF). One could also use the Tolerance 
to judge the collinearity in the estimated equations given 
its inverse relationship with the VIF estimates. The null 
hypothesis is that there is no multicollinearity and the 
decision rule is to reject the null hypothesis when the 
VIF estimates are greater than ten. The results presented 
in Table 4.1 shows that there is no VIF estimate that is 
greater than ten, therefore, we conclude that there is no 
multicollinearity in our estimated equations. In other 
words, our estimation result is robust. Intuitively, export 

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Table 5: Exogeneity Bias Test Results
Covariates Statistic P-value Conclusion
Export 1.721463 0.0857 Endogenous 

Variable
Export 
Concentration

1.082383 0.2814 Exogenous 
Variable

values are perceived as endogenous variables. However, 
we can also rigorously establish whether a variable 
perceived to be exogenous is truly endogenous using the 
exogeneity bias test. In Table 5, the test results on the 
null hypothesis that Export and Export concentration 
variables are individually exogenous are presented. 
Based on the test statistics and/or probability values, we 
reject the null hypothesis that Export is exogenous and 
conclude that Export is endogenous at a 10% level of  
significance. Therefore, the instrumental variable panel 
data result is robust and valid. Similarly, the endogeneity 
test was performed for Export concentration and we fail 
to reject the null hypothesis at a 10% level of  significance 
and therefore conclude that export concentration is 
exogenous. As such, the panel data EGLS results are 
robust and intuitive and we draw our conclusions from 
this result. 
In short, Export is positively and significantly 
correlated with the firm’s performance, whereas Export 
concentration is negatively significant with the firm 
performance. On this note, Pakistanis firms that export 
performs better than the firms that serve domestically. 
The research results here completely support the theory 
of  learning-by-exporting. On the other hand, geographic 
diversification also plays a part in the firm favor, as the 
firm could achieve economies of  scale and scope with 
stable cash flow and lower risk. 

CONCLUSION
This paper empirically analyzes the impact of  exports and 
diversification on firm performance in Pakistan by using 
exports in terms of  export intensity and diversification in 
terms of  export concentration as key main independent 
variables. This paper use World Bank enterprise data for 
Pakistan’s firms across the whole country for the year 
2007 and 2013. It is a survey panel data comprising 454 
firms with a total of  908 observations. I investigated 
the relationship between export and diversification on 
firm performance using the Panel estimated generalized 
least square (EGLS) method and two-stage EGLS 
models.  The outcome from Panel EGLS suggests that 
exports are significant and positively correlated with 
firm performance, later on, the export sample Export 
concentration resulted in a negative and significant 
association with the firm performance.
Similar to Sohl et al. (2020) Gnangnon (2021), this 
study finds that with more exposure to export and 
diversification, the firms in Pakistan have better revenues 
than the local domestic concentrated firms. This indicates 
that with export and diversification, the firm could 
have better revenue earnings which leads to better firm 

performance. On the other hand, similar to Niemenen 
(2020) diversifying their export, successful export 
development programs, such as the funding of  local 
and international trade fairs, the distribution of  reliable 
information about global markets, and export finance and 
credit insurance may help in favor of  this initiative.
However, Panel EGLS cannot eliminate the endogeneity 
problem, and as per the exogeneity bias test Exports are 
found to be endogenous, whereas export concentration 
concludes to be exogenous.  Therefore, we used Two-
Stage Panel EGLS to solve this issue. we used Average 
days to clear customs as an instrumental variable and 
the result for this model was the same for the exports as 
it was again positive and significant with its association 
with firm performance confirming the LBE hypothesis. 
Nevertheless, there was a change in Export concentration, 
as its association became negative to positively significant 
but as this variable is not endogenous, those results are 
not intuitive. 
Such findings for this study may lead to policy impacts 
and the patterns of  results suggest that businesses that 
export have performed better compared to the ones 
that serve domestically. Secondly, firms that diversify 
exports have a positive impact on performance. Thus, to 
enable firms to enter the export market and to consider 
diversifying their export, successful export development 
programs, such as the funding of  local and international 
trade fairs, the distribution of  reliable information about 
global markets, and export finance and credit insurance 
may help in favor of  this initiative.
Nevertheless, there are limitations to this study since the 
sample is only limited to two years 2007 and 2013, which 
are the latest available from the World Bank. Moreover, 
the sample is only for 454 private firms in Pakistan. 
The survey of  companies in other countries might give 
different results due to the difference in their rules and 
law. In addition, for future studies, it would be better if  
there were a larger sample size for Pakistani firms. 

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