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Asian Finance & Banking Review; Vol. 4, No. 2; 2020 
ISSN 2576-1161    E-ISSN 2576-1188 

Published by CRIBFB, USA 

 

     8 
 

 

An Inferential Study on the Profitability Determinants of the Cement Industry in 
Bangladesh 

 
 

Sonia Rezina 
Assistant Professor 
School of Business 
Uttara University 
Dhaka, Bangladesh 

E-mail:rezina.sonia@gmail.com 
 

Aysha Ashraf 
Assistant Professor in Finance 
Asian University of Bangladesh 

Dhaka, Bangladesh 
E-mail:ayshadu94@gmail.com 

 
Md. Atiqullah Khan 

Independent Researcher 
Dhaka, Bangladesh 

E-mail:writetoatiq@gmail.com 
 
 
Received: June 29, 2020       Accepted: July 23, 2020            Online Published: July 30, 2020 
 
doi: 10.46281/asfbr.v4i2.684     URL: https://doi.org/10.46281/asfbr.v4i2.684 

 
 
Abstract 
This paper examines the impacts of firm-specific and macroeconomic factors in determining the profitability of the cement 
industry in Bangladesh. This study took stock exchange listed all cement companies of Bangladesh as samples and covered the 
period of 2000–2018. Return on Assets (ROA) was chosen as the dependent variable and firm size, expense to revenue ratio, 
leverage, age, inflation rate, GDP growth rate, and real interest rate were chosen as independent variables where the first four are 
firm-specific and the other three are macroeconomic factors. This study considered ROA as the profitability measurement of the 
firms. The study found that leverage, GDP growth rate, and real interest rate have significant impacts on the profitability. Firm 
size, age, GDP growth rate, and real interest rate have a positive impact whereas expenses to revenue ratio, leverage, and inflation 
have a negative impact on the profitability of the firms under the cement industry.  

Keywords: Firm Variables, Macroeconomic Variables, Cement Industry, Profitability, Bangladesh.     

1. Introduction 
Cement industry is said to have a fundamental contribution to the infrastructural development of any country. In the world of 
the cement market, Bangladesh's current position is fortieth but the country has huge potential in the days ahead as 14 
companies are exporting cement into different countries and the market is on growth where until the first half of the 1990s, 
around 95% of total demand for cement in Bangladesh was covered by imports (Tuhin, 2019).  

In the last 7 years, the cement industry of Bangladesh grew at almost 11.5% CAGR (Compound Annual Growth 
Rate) due to the doubling of demand from 14.5 million metric tons per year to almost 31 million metric tons per year with the 
cooperation of the government, several multinational manufacturers and local entrepreneurs entered the industry in the late 
1990s (EBL Securities Ltd., 2019).  

At present, Bangladesh cement industry is operated by the leading global performers like Lafarge (France), Holcim 
(Switzerland) along with 30 other local and Multinational manufacturers (Cement in Bangladesh: Building a Concrete Future – 
Light Castle Partners, 2020). Dhaka Stock Exchange (DSE) is currently enlisted with seven cement companies (Company 
Listing by Industry, 2020). 

https://doi.org/10.46281/asfbr.v4i2.684
https://doi.org/10.46281/asfbr.v4i2.684


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With a decent growth rate on average, there was a comprehensive advancement in Bangladesh in the last couple of 
years and the country is expecting to maintain growth above 7% in the years ahead (The Daily Star, 2019).  

According to a recent forecast by HSBC, Bangladesh will be the biggest mover in the global GDP product rankings in 
2030 and its economy may jump 16 notches and that will make the country become the 26th largest economy in the world 
(Rahman, 2018). Up to the year 2030, the country is also expected to have the highest yearly real GDP growth of 7.1 percent 
and the economy size to be expanded by 2.5 times from USD 300 Billion to USD 700 Billion in the interim (Rahman, 2018). 

Bangladesh is facing a massive requirement for basic infrastructure, housing, and services to support its tremendous 
economic activities, which has put the market in high demand for cement. In recent years; cement demand is massively increasing 
to contribute the megaprojects.  

Bangladesh has undertaken some large infrastructure projects to upkeep its huge economic growth. 7 mega 
infrastructure projects including bridges, rail lines, power plants, and a metro rail have been allotted with more than USD 3.5 
billion in FY2019 (Cement in Bangladesh: Building a Concrete Future – Light Castle Partners, 2020).  

Budget allocation to transportation and communication system has faced immense growth over time and therefore, the 
contribution of the development projects of the government towards the demand for cement has been identified as the highest in 
recent years overtaking the contribution of individual home-builders. Additionally, an export target of USD 10.50 million has 
been set by the Export Promotion Bureau (EPB) for the FY 2019-20 (Bangladesh revises export target for cement in FY20, 
2019).  

Because of the swift momentum of industrialization, urbanization, large-scale infrastructural and government 
development projects, and also the construction of several residential and commercial structures; there occurred remarkable 
growth in market demand. This trend is anticipated to be sustained in the upcoming years. Profitability is a measure of firms’ 
performance. Thus the factors influencing the profitability of the firms under cement industry is a topic of investigation.  

A firm’s performance and success are measured by its profitability. Thus a manager's prime objective is to maximize 
the profits of a firm. Stakeholders’ value and investors’ value are also maximized by profitability. Researchers have conducted 
numerous studies on profitability and its major determinants. The past literature study mainly considered the firm-specific and 
macroeconomic determinants of profitability. 

Agustinus and Rachmadi (2008) reviewed the factors influencing the corporate performance of Indonesia before and 
after the 1997 financial crisis. Employing Ordinary Least Square (OLS), an empirical analysis covering the period of 1994-2004 
of the Jakarta Stock Exchange (JSX) consisting of panel data of the 238 listed companies showed a greater influence of macro-
factors than firm-specific factors on company performance due to the 1997 great crisis. Moreover, the study also revealed a 
positive relationship exists between firm size and profitability.  

Hassan and Muniyat (2019) focused on a study that takes a random sample of five listed pharmaceutical companies 
and covers the period 2008–2017. The research method for the study is a panel analysis where ROA (Return on assets) was 
used as a measure of profitability. They found that expense to revenue, and company sizes have a significant correlation with 
profitability which was negative. Besides, they found a significant correlation between GDP growth and profitability which was 
positive. Additionally, inflation rate, and profitability have a significant negative relationship.  

This paper thoroughly studied the effects of firm-specific and macroeconomic factors on the profitability of the 
cement industry in Bangladesh. Firm size, the expense to revenue, leverage, and age were considered as a set of firm-specific 
determinants related to profitability. GDP growth rate and real interest rate were considered as the other set of macroeconomic 
variables which discuss profitability in the macroeconomic environment within which the cement industry operates. 

1.1Objectives of the Research 
 To identify the most relevant factors influencing the cement industry’s profitability in Bangladesh. 
 To show the influence of different firm level and macroeconomic level factors on Return on Assets (ROA) of the 

cement industry in Bangladesh. 

 To determine the association between firm-specific and macroeconomic factors on the profitability of the cement 
industry in Bangladesh. 

2. Review of Literature 
According to Aparna (2015), the efficiency of fund utilization by a firm is indicated by its high profit. Bashar and Islam (2014) 
elaborated that, profitability indicates the capacity of a firm to gain profit after deducting the operating expenses, interest on 
debt, and taxes from the sales revenue. 

Pratheepan (2014) mentioned that, for an export-oriented country, determinants of profitability are the essential 
elements for economic survival. He discovered a significant positive relationship between firm size and profitability and a 
negative relationship with leverage but that was not statistically significant in his study of Sri Lankan manufacturing companies. 
On the other hand, Aparna (2015) showed a negative relationship between profitability and firm size, leverage, and productivity 
in the case of the Steel Authority of India (SAIL).In their study of Philippine banks, Sufian and Chong (2008) found that a 



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bank’s profitability is negatively affected by the size, credit risk, and expense preference behavior, while positively affected by 
non-interest income and capitalization. 

Al-Jafari and Samman (2015) found a positive association of firm size, fixed assets, growth, and working capital on 
profitability which was significant and a negative association between profitability and financial leverage for the industrial firms 
in Oman. Maroa and Kioko (2016) found that there exists significant positive relationship between liquidity, firm size, and 
profitability whereas an insignificant positive relationship is experienced between leverage and profitability. 

Sivathaasan, Tharanika, Sinthuja, and Hanitha (2013) looked into the relationship of capital structure, firm size, 
working capital, growth rate, and non-debt tax shield with the profitability of the Sri Lankan manufacturing companies. They 
found that profitability is significantly related to capital structure and non-tax shield, whereas it has no significant relationship 
with firm size. 

A study of Malaysian construction companies conducted by Zaid, Wan Mud, and Zulqernain (2014) came with a 
result of a positive association among firm size, liquidity on the profitability which was significant. 

After analyzing the foreign and local banks in Ghana, Gyamerah and Amoah (2015) found that cost management and 
profitability have a significant negative relationship while bank size and credit risk have a significant positive relationship with 
profitability. Additionally, the study by Prempeh, Sekyere, and Amponsah Addy (2018) on the manufacturing firms of the said 
country stated that leverage and interest rates have a negative relationship with profitability while liquidity and firm size have a 
significantly positive relationship with profitability. Moreover, tangibility and GDP have shown no significant relationship with 
profitability.  

Egbunike and Okerekeoti (2018) explored that, in the case of Nigerian manufacturing firms, firm size, leverage, and 
liquidity have a positive relationship with profitability which was significant. Furthermore, after studying the macroeconomic 
variables, they found a positive correlation between GDP growth and profitability, but a negative correlation between inflation 
rate and profitability, both of which were significant. The researchers didn’t find any significant impact on interest rate and 
exchange rate. Highlighting the Nigerian economy, another study by Ehi-Oshio, Adeyemi, and Enofe (2013) found that, firm 
size and financial leverage have a positive relationship with corporate profitability. 

Prasetyantoko and Rachmadi (2008) disclosed a bigger impact of macro-factors than firm-level factors on the 
corporate performance of Indonesian firms, probably due to the 1997 great crisis. Additionally, this study discovered a positive 
relationship between firm size and profitability. Khan, Shamim, and Goyal (2008) explored that firm size and growth have a 
positive association and leverage has a negative association with profitability in the Indian telecom industry.  

Vatavu (2014) examined that tangibility, business risk, inflation rate, financial crisis, and the level of taxation are 
negatively related to profitability, and liquidity is positively related to the same in the context of Romanian companies. 

Age, as a firm-specific factor, got attention from many researchers, such as (Coad, Segarra-Blasco, & Teruel, 2010; 
Hirsch & Hartmann, 2014; Pervan & Curak, 2016), identified a signal of a decline in profitability as the firm's age increases. On 
the contrary, Majumdar (1997) found that a firm's size and age are positively related to its productivity and profitability. 

Pervan, Pervan, and Curak (2019) came up with a positive relationship of age and inflation rate with the profitability 
of the Croatian manufacturing industry. Some of the few pioneers to study on the firm performance based on firm-specific and 
industry-specific effects are Schmalensee (1985), Hansen and Wernefelt (1989), and Mauri and Michaels (1998). These 
subjects have also been examined by Akben-Selcuk (2016), Mirza and Javed (2013), Dogan (2013), Tailab (2014), Al-Jafari 
and Samman (2015), and Batra and Kalia (2016) in the recent years. 

Researchers conducted various studies to identify the association of firm size with profitability. Some of them are 
Fukao (2006), Asimakopoulos, Samitas and Papadogonas (2009), Nunes, Serrasqueiro, & Sequeira (2009), Stierwald (2010), 
Yazdanfar (2013), Zaid et al. (2014), and Pratheepan (2014). 

In their Pecking Order Theory, Myers and Majluf (1984) explained that leverage is inversely related to profitability. 
This view has also been confirmed by Kester (1986), Titman and Wessels (1988), Rajan and Zingales (1995), Samarakoon 
(1999), Booth, Aivazian, Demirgusc-Kunt, and Maksimovic (2001), and Al-Jafari and Samman (2015). On the contrary, 
Ifeduni and Charlse (2018) found higher profitability because of higher gearing (debt).Expense to revenue ratio has significant 
negative impact on profitability found by Burja (2011).  

In contrast, the trade-off, free cash flow (Jensen, 1986), signaling, and agency theories, showed the existence of a 
positive relationship between leverage and profitability, which was later been confirmed by the study of Sivathaasan et al. (2013).  

In the paper of Nanda and Panda (2018), it has been found that corporate profitability is mostly affected by various 
firm-level elements, namely firm size, liquidity, leverage, etc. rather than the macroeconomic indicator. After researching the 
Indian corporate sector from the circumstance of the pre and post-crisis performances, they found a positive association between 
firm size and profitability. The findings were similar to the studies conducted by Nunes et al. (2009), Asimakopoulos et al. 
(2009), Stierwald (2010), Yazdanfar (2013), and Pratheepan (2014). This was said to be the result of economies of scale. This 
study also found a negative relationship between leverage and profitability which was significant, like the study conducted by 
Nunes et al. (2009). 



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Demir (2007) showed that the real interest rate has a significant negative impact on the profitability of the 
manufacturing industry. But Alper and Anbar (2011) found that the real interest rate affects the performance of banks 
positively.  
All of the above findings and pieces of evidence are the perspective from foreign countries, whereas, we have found very few 
studies focusing on the above-mentioned aspect from the context of manufacturing companies from Bangladesh and the cement 
industry to be specific.  

Nevertheless, this study addressed various factors which seemed to have a substantial influence on profitability; and 
also considered the fact that, due to inefficient market and change of time, risk factor also changes. Moreover, certain factors 
affect the profitability of manufacturing companies of Bangladesh in particular, such as the size of the firm, the expense to 
revenue ratio, maintaining leverage in capital structure, and learning effect over the age of business operations as firm-specific 
factors and inflation, GDP growth rate, and real interest rate as macroeconomic factors. Therefore, the research was necessary to 
reveal the above factors. The paper has presented the impact of control variables on the dependent variable (ROA). In addition 
to that, it also exhibited the coefficient of determination among seven independent variables on the dependent variable. This 
study has been attempted to report this gap. 

3. Research Methodology 
3.1 Data and Variables 
All the companies listed under the cement industry on DSE (Dhaka Stock Exchange) have been chosen for conducting this 
study. The data period for conducting the study has been chosen for the last 19 years (2000-2018) and the total no. of 
observations for the study is 106. The dataset is an unbalanced panel dataset as some companies listed on the stock exchange in 
later years. Macroeconomic data were collected from World Bank portal (The World Bank Data, 2020). Firm-specific data were 
collected from the annual reports of the companies. 

3.2 Dependent Variable 
 ROA: ROA (Returns on Assets) has been chosen in this study as the dependent variable for measuring profitability. 

According to conducted studies, ROA is a better estimation of profitability as it also considers financial leverage. 
Through ROA of a company, the net income to the total assets of that business firm for a certain period can be easily 
understood. Because of that reason, each business prefers to secure a higher ROA as an implication of better efficiency 
of using the assets of the company. ROA (Return on Assets) has been calculated as net income divided by total assets. 

 
3.3 Independent Variables 
3.3.1 Firm-Specific Factors 

 Firm Size: The natural log of total assets of each company has been chosen as one of the firm-specific independent 
variables. The percentage change in assets gives a clear view of understanding the impact and previous studies followed 
a similar approach too. The size of a firm creates a huge impact on the profitability of the firm. Because of higher size, 
a company gets more benefits to maximize its profit.  

 Expense to Revenue Ratio: The expense to revenue ratio of a firm indicates the overall health of a business firm. A 
lower expense to revenue ratio indicates more profit potentiality of that business firm. Thus lower expense to revenue 
ratio is desired because it indicates that the expenses are minimized in comparison with the revenues.  

 Leverage: Leverage indicates the usage of the total debt of a firm to its market value or the book value of the assets. 
The higher interest burden is created when a company absorbs more leveraged debt. Leverage creates an active impact 
on the profitability of the business firm by using both equity and debt financing in the capital structure.  

 Age: The age of a firm positively impacts on its operations because of higher efficiency in managing costs and 
increasing revenues. Thus the age of the business firm is supposed to impact the firm's profitability positively. The 
year of commencement of business operations has been chosen as the beginning year of business. Thus age has been 
calculated from the year of commencement of business operations. The natural log of the age of each business firm has 
been taken as an independent variable for the study. 

 
3.3.2 Macroeconomic Factors 

 Inflation: Inflation creates an influence on all aspects of the economy. The business activities of any business firm are 
impacted by inflation. Inflation causes an increase in interest rate when there is a sharp rise in the inflation rate. Thus 
the profitability of the business firm is negatively impacted.  

 GDP Growth Rate: The economic growth of any economy can be understood by the GDP growth rate. It provides an 
understanding of the market potential from the macro aspect. GDP growth rate creates an active influence on the 
profitability of the business firm.  



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 Real Interest Rate: The deduction of the inflation rate from the nominal interest rate indicates the real interest rate. It 
is one kind of measurement to understand the positive change in the purchasing power of the lender while receiving 
the money from borrowers' repayment of interest and loan. Higher real interest rate increases the cost of borrowing of 
the business firms which reduces the profitability of the firms.  

 
3.3.3 Operational Definition 
Table 1. List of Operational Variables 
 

Variable Notation Definition 

Dependent Variable    

Return on Assets  ROA Net Income / Total Asset 

      

Independent Variables     

Firm Size LN Firm Size Natural Log of Total Asset 

Expense to Revenue Expense to Revenue Operating Expense / Sales Revenue 

Leverage Leverage Total Liability/ Total Asset 

Age LN Age Natural Log of Age 

Inflation Inflation (CPIt - CPIt-1)/ CPIt-1 

GDP Growth Rate GDP Growth Rate (GDPt - GDPt-1)/GDPt-1 

Real Interest Rate Real Interest Rate Nominal Interest Rate – Inflation Rate 

 
3.4 The Hypotheses 
The hypotheses, which has been assumed for the seven independent variables. If the variable is statistically significant, we will 
accept the hypothesis for this variable. The coefficient sign for each variable indicates our expectation. Again, this hypothesis can 
be accepted partially if the variable is mildly insignificant with an expected coefficient. For any case out of these, the hypothesis 
will be rejected. 
 
Table 2. List of Hypotheses 
 

Hypothesis Description 

H1 Profitability is positively and significantly correlated to firm size 

H2 Profitability is negatively and significantly correlated to expense to revenue ratio 

H3 Profitability is negatively and significantly correlated to leverage 

H4 Profitability is positively and significantly correlated to the age of a firm 

H5 Profitability is negatively and significantly correlated to inflation 

H6 Profitability is positively and significantly correlated to GDP growth rate 

H7 Profitability is negatively and significantly correlated to the real interest rate 

 
3.5 Model of the Study 
To understand the relationship between the dependent variable and the independent variables, the study was attempted. The 
model of this study is given below: 

ROA= β0 + β1*LN Firm Size + β2*Expense to Revenue + β3*Leverage + β4*LN Age + β5*Inflation + β6*GDP Growth 

Rate + β7*Real Interest Rate + εit         (1) 
 

The ROA (Return on Assets) in the above-mentioned equation indicates the profitability; β0 indicates the constant of 

this model. β1, β2... β9 are the coefficients to be estimated according to the variables and εi indicates the error term of this 
equation and the t indicates the specific period.  
 



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4. Empirical Results and Discussion 
4.1 Descriptive Statistics 
The descriptive statistics that include average, minimum, maximum, and standard deviation of all variables is represented in the 
following table:  
 
Table 3. Descriptive Statistics 
 

Variable Mean St. Dev. Minimum Maximum 

ROA 5.74% 0.17 -46% 160% 

Firm Size (in BDT million) 4193 4157 17 19714 

LN Firm Size 21.24 1.92 16.66 23.7 

Expense to Revenue 7.67% 0.13 1.00% 131% 

Leverage 0.58 0.21 0.17 0.98 

Age (in years) 21.09 12.97 2 55 

LN Age 2.87 0.62 0.69 4.01 

Inflation 6.38% 0.02 2.01% 11.40% 

GDP Growth Rate 6.05% 0.01 3.83% 7.86% 

Real Interest Rate 5.78% 0.02 3.07% 9.26% 

 
4.2 Correlation 
The level of correlation among the variables used in multivariate regression analysis is shown in Table 4. The correlation 
between the variables is not so strong which suggests that there exists no multicollinearity problem among the variables.  
 
Table 4. Correlation Matrix 
 

Variable [1] [2] [3] [4] [5] [6] [7] [8] 

ROA 1.00               

LN Firm Size 0.05 1.00             

Expense to Revenue -0.07 -0.28 1.00           

Leverage -0.25 -0.06 0.05 1.00         

LN Age -0.03 -0.01 -0.11 0.33 1.00       

Inflation -0.25 -0.03 0.00 0.12 0.18 1.00     

GDP Growth Rate -0.03 0.13 0.14 0.02 0.32 0.28 1.00   

Real Interest Rate 0.24 -0.12 -0.10 -0.10 -0.36 -0.43 -0.72 1.00 

4.3 Multicollinearity 
The VIF (Variance Inflation Factor) is a great measurement tool for multicollinearity among the variables. The mean VIF of the 
test shown in Table 5 is 1.54, which indicates that multicollinearity is not a problem for the dataset of this study.  
 
Table 5. Multicollinearity 
 

Variable VIF 1/VIF 

LN Firm Size 1.15 0.87 

Expense to Revenue 1.19 0.84 

Leverage 1.15 0.87 

LN Age 1.36 0.74 

Inflation 1.26 0.79 



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GDP Growth Rate 2.21 0.45 

Real Interest Rate 2.48 0.40 

Mean VIF 1.54   

4.4 Methods for Data Analysis 
The dataset for our model is a panel dataset. Between the fixed effects model and the random-effects model, we will analyze 
which model is appropriate for our study by conducting the Hausman test. Stata/MP 13 software is used for conducting and 
specifying this study. The acceptance level of significance is 1%, 5%, and 10% based on the outcome.  

4.5 Results and Discussions 
For deciding the appropriate model between the fixed effects model and the random-effects model, the Hausman test has been 
applied.  
 
Table 6. Hypothesis for the Hausman Test 
 

Hypothesis Description 

Null Random effects model is appropriate 

Alternative Random effects model is not appropriate 

 
The output of the Hausman test is given in the table below:  
 
Table 7. Hausman Test 
 

Results  For Hausman Test 

Chi-Square Statistic 2.6 

Chi-Square deg. f. 7 

Prob. 0.919 

 
From the result of the Hausman test, the null hypothesis cannot be rejected. Thus the estimated model for this study 

will be based on random effects.  
 

Table 8. Estimation Using Random Effects 
 

Variable Coefficients Std. Err. z-statistic p-value 

Constant -0.3988 0.31 -1.27 0.205 

LN Firm Size 0.0027 0.01 0.32 0.753 

Expense to Revenue -0.0457 0.13 -0.36 0.718 

Leverage -0.1870 0.08 -2.38 0.017** 

LN Age 0.0350 0.03 1.19 0.234 

Inflation -1.2634 0.83 -1.52 0.129 

GDP Growth Rate 4.3450 2.44 1.78 0.075* 

Real Interest Rate 4.0040 1.57 2.55 0.010*** 

Note. ***significant at 1%, **significant at 5%, *significant at 10%. 
 
From the output given above, it is found that real interest rate, leverage, and GDP growth rate are statistically 

significant at 1%, 5%, and 10% level of significance consecutively. For firm-specific factors, firm size and age have a positive 
impact on ROA where expense-to-revenue and leverage have negative impacts on ROA. For macroeconomic variables; we have 
found that ROA is influenced by real interest rate and GDP growth rate positively and by inflation negatively. The R-Squared 



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value is within 18.09%, between 38.82%, and overall 17.69%. The p-value of goodness of fit test (chi-square) is 0.0037 which 
is less than 0.05 indicates the model is strong.   

 
Table 9. Decisions of Hypotheses Acceptance or Rejection 
 

Hypothesis Variable Obtained 
Significance 

Expected Impact Obtained Impact Result 

H1 LN Firm Size Insignificant Positive Positive Accepted 

H2 Expense to Revenue Insignificant Negative Negative Accepted 

H3 Leverage Significant Negative Negative Accepted 

H4 LN Age Insignificant Positive Positive Accepted 

H5 Inflation Insignificant Negative Negative Accepted 

H6 GDP Growth Rate Significant Positive Positive Accepted 

H7 Real Interest Rate Significant Negative Positive Rejected 

 

Through conducting the study, we found that firm size, age, real interest rate, and GDP growth rate have positive 
impacts on profitability. Expense-to-revenue, leverage, and inflation have negative impacts on profitability. The firm size helps 
the firm to increase its profitability by expanding its capacity more efficiently. The findings of the study show positive impact on 
profitability supporting Prasetyantoko and Rachmadi (2008), Khan et al. (2018), Ehi-Oshio et al. (2013), Zaid et al. (2014), 
and Al-Jafari and Samman (2015) but varying from Goddard, Tavakoli, and Wilson (2005) and Aparna (2015). The higher 
expense to revenue ratio of the firm indicates lower profitability. Expense to revenue ratio has negative impact on profitability 
partially supporting (Burja, 2011). Leverage in this study is calculated by dividing total liabilities to the total assets of the firm. 
The result show significant negative impact on profitability as higher liability in a business firm reduces profitability supporting 
Khan et al. (2018), Aparna (2015), Prempeh et al. (2018)  but varying from Ehi-Oshio et al. (2013), Maroa and Kioko (2016), 
and Egbunike and Okerekeoti (2018).  

Business firm learns how to minimize costs and maximize profits with time. Thus, the age of the firm creates an active 
impact on profitability. The positive impact of age on profitability supports Majumdar (1997) but varies from Coad et al. 
(2010), Hirsch and Hartmann (2014), and Pervan & Curak (2016). Higher inflation causes an increase in the nominal interest 
rate and causes an increase in the cost of borrowing. Thus, the findings also indicate a negative impact on profitability as 
inflation leads to a reduction on the profitability of the firms. The result supports Vatavu (2014) and Egbunike and Okerekeoti 
(2018) but varies from Pervan et al. (2019) When an economy grows as per GDP, it creates more opportunities for the firms to 
make more profits by expanding the market demand of the products through infrastructure and development projects. Thus the 
impact of GDP growth rate is significantly positive on profitability of the cement industry supporting Egbunike and Okerekeoti 
(2018), Hassan and Muniyat (2019). Real interest has significant positive impact on profitability supporting Demir (2007) but 
varying Alper and Anbar (2011). 

 
5. Conclusion  
After conducting the study through secondary data, the findings revealed that leverage as a firm-specific variable and GDP 
growth rate and real interest rate as macroeconomic variables have significant impact on the profitability. The results show that 
firm size, age, GDP growth rate, and real interest rate have positive impact while expense to revenue ratio, leverage, and inflation 
have negative impact on the profitability of the firms under cement industry of Bangladesh. Thus after conducting a thorough 
analysis of the factors influencing the profitability of the said industry, the study found a significant relationship regarding both 
firm-specific and macro-economic factors. The cement industry of Bangladesh has become one of the fastest growing sectors in 
recent years. Further research may be conducted to measure the impacts of firm-specific and macroeconomic factors in 
determining the profitability for other industries too. In this study, only listed companies under the stock exchanges have been 
considered. Hence, all cement companies in Bangladesh can be considered for further study by other researchers. 

6. Recommendations 
By considering the statistical significance, managers and policymakers of the cement industry need to take essential actions 
regarding the enhancement of financial performance. As in the recent years, multinational cement companies are expanding their 
capacity despite having unutilized facilities in anticipation of increasing demands, the government should take necessary steps to 
promote development in urbanization, real estate development, and government projects by local cement companies. Business 
firms under cement industry should prefer equity financing instead of debt financing. Business firms should design their sales 



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forecasts according to the GDP growth rate movement and the real interest rate in the economy as both factors have significant 
positive impact on profitability. New technologies need to be adopted to improve the operational efficiencies of the firms. 

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Appendix A 
 
List of All the Listed Cement Companies on DSE and Chosen Data Period for the Study 

 

Companies Year of Commencement  Data Period 

1. Aramit Cement Limited 1963 2000-2018 

2. Confidence Cement Ltd. 1991 2000-2018 

3. Heidelberg Cement Bangladesh Ltd. 1998 2000-2018 

4. LafargeHolcim Bangladesh Limited 1997 2006-2018 

5. M I Cement Factory Limited 1994 2009-2018 

6. Maghna Cement Mills Ltd. 1992 2000-2018 

7. Premier Cement Mills Limited 2001 2012-2018 

 
Appendix B 
 
Data set for the Study 

 
S.L 
No. 

Company Year ROA LN 
Age 

LN Firm 
Size 

Expense to 
Revenue 

Leverage Inflation GDP 
Growth 

Real Int. 
Rate 

1 1 2000 1.60 3.61 19.80 0.056 0.509 0.022 0.053 0.090 

2 1 2001 0.05 3.64 19.91 0.022 0.543 0.020 0.051 0.093 

3 1 2002 -0.11 3.66 19.86 0.040 0.616 0.033 0.038 0.084 

4 1 2003 -0.14 3.69 19.80 0.063 0.720 0.057 0.047 0.059 

5 1 2004 -0.46 3.71 19.70 0.074 0.891 0.076 0.052 0.056 

6 1 2005 -0.06 3.74 19.87 0.027 0.980 0.070 0.065 0.058 

7 1 2006 0.01 3.76 20.26 0.026 0.603 0.068 0.067 0.055 

8 1 2007 0.03 3.78 20.07 0.031 0.953 0.091 0.071 0.058 

9 1 2008 0.00 3.81 20.30 0.035 0.954 0.089 0.060 0.047 

10 1 2009 0.08 3.83 20.39 0.038 0.907 0.054 0.050 0.061 

11 1 2010 0.08 3.85 20.70 0.044 0.865 0.081 0.056 0.047 

12 1 2011 0.04 3.87 20.97 0.051 0.858 0.114 0.065 0.051 

13 1 2012 0.03 3.89 21.16 0.047 0.850 0.062 0.065 0.053 

14 1 2013 0.03 3.91 21.18 0.063 0.827 0.075 0.060 0.060 



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15 1 2014 0.00 3.93 21.35 0.075 0.719 0.070 0.061 0.069 

16 1 2015 0.00 3.95 21.49 0.078 0.765 0.062 0.066 0.055 

17 1 2016 0.01 3.97 21.68 0.072 0.813 0.055 0.071 0.034 

18 1 2017 -0.03 3.99 21.93 0.093 0.887 0.057 0.073 0.031 

19 1 2018 0.05 4.01 20.84 0.168 0.168 0.055 0.079 0.038 

20 2 2000 0.19 2.20 20.45 0.051 0.223 0.022 0.053 0.090 

21 2 2001 0.16 2.30 20.72 0.045 0.305 0.020 0.051 0.093 

22 2 2002 -0.02 2.40 21.22 0.080 0.267 0.033 0.038 0.084 

23 2 2003 0.01 2.48 21.14 0.062 0.622 0.057 0.047 0.059 

24 2 2004 -0.03 2.56 20.66 0.071 0.351 0.076 0.052 0.056 

25 2 2005 0.02 2.64 20.78 0.038 0.412 0.070 0.065 0.058 

26 2 2006 0.03 2.71 20.97 0.031 0.270 0.068 0.067 0.055 

27 2 2007 0.05 2.77 20.82 0.031 0.377 0.091 0.071 0.058 

28 2 2008 -0.02 2.83 20.87 0.030 0.456 0.089 0.060 0.047 

29 2 2009 0.06 2.89 21.57 0.036 0.196 0.054 0.050 0.061 

30 2 2010 0.07 2.94 21.90 0.036 0.195 0.081 0.056 0.047 

31 2 2011 0.05 3.00 22.04 0.031 0.327 0.114 0.065 0.051 

32 2 2012 0.07 3.04 22.13 0.029 0.389 0.062 0.065 0.053 

33 2 2013 0.08 3.09 22.25 0.035 0.359 0.075 0.060 0.060 

34 2 2014 0.04 3.14 22.34 0.039 0.431 0.070 0.061 0.069 

35 2 2015 0.06 3.18 22.44 0.045 0.443 0.062 0.066 0.055 

36 2 2016 0.07 3.22 22.55 0.093 0.468 0.055 0.071 0.034 

37 2 2017 0.06 3.26 22.77 0.131 0.510 0.057 0.073 0.031 

38 2 2018 0.05 3.30 22.83 0.077 0.500 0.055 0.079 0.038 

39 3 2000 0.12 0.69 21.28 0.069 0.412 0.022 0.053 0.090 

40 3 2001 0.12 1.10 21.30 0.064 0.351 0.020 0.051 0.093 

41 3 2002 0.03 1.39 21.26 0.097 0.308 0.033 0.038 0.084 

42 3 2003 -0.01 1.61 22.13 0.105 0.583 0.057 0.047 0.059 

43 3 2004 0.00 1.79 22.14 0.098 0.589 0.076 0.052 0.056 

44 3 2005 0.03 1.95 22.19 0.083 0.574 0.070 0.065 0.058 

45 3 2006 0.13 2.08 22.14 0.052 0.439 0.068 0.067 0.055 

46 3 2007 0.18 2.20 22.36 0.057 0.446 0.091 0.071 0.058 

47 3 2008 0.10 2.30 22.49 0.053 0.438 0.089 0.060 0.047 

48 3 2009 0.14 2.40 22.52 0.053 0.424 0.054 0.050 0.061 

49 3 2010 0.14 2.48 22.69 0.060 0.338 0.081 0.056 0.047 

50 3 2011 0.09 2.56 22.80 0.058 0.343 0.114 0.065 0.051 

51 3 2012 0.14 2.64 22.94 0.051 0.314 0.062 0.065 0.053 

52 3 2013 0.14 2.71 23.10 0.067 0.301 0.075 0.060 0.060 

53 3 2014 0.12 2.77 23.04 0.069 0.359 0.070 0.061 0.069 

54 3 2015 0.14 2.83 23.00 0.081 0.409 0.062 0.066 0.055 

55 3 2016 0.15 2.89 23.04 0.087 0.451 0.055 0.071 0.034 



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56 3 2017 0.09 2.94 22.89 0.099 0.462 0.057 0.073 0.031 

57 3 2018 0.09 3.00 22.74 0.501 0.515 0.055 0.079 0.038 

58 4 2006 -0.05 2.20 16.66 1.315 0.750 0.068 0.067 0.055 

59 4 2007 -0.06 2.30 16.69 0.222 0.816 0.091 0.071 0.058 

60 4 2008 0.01 2.40 16.70 0.079 0.808 0.089 0.060 0.047 

61 4 2009 0.06 2.48 16.67 0.067 0.744 0.054 0.050 0.061 

62 4 2010 -0.09 2.56 16.70 0.299 0.845 0.081 0.056 0.047 

63 4 2011 -0.12 2.64 16.74 0.073 0.652 0.114 0.065 0.051 

64 4 2012 0.14 2.71 16.72 0.078 0.557 0.062 0.065 0.053 

65 4 2013 0.13 2.77 16.76 0.094 0.420 0.075 0.060 0.060 

66 4 2014 0.14 2.83 16.80 0.080 0.331 0.070 0.061 0.069 

67 4 2015 0.08 2.89 16.85 0.094 0.306 0.062 0.066 0.055 

68 4 2016 0.07 2.94 16.86 0.008 0.267 0.055 0.071 0.034 

69 4 2017 0.02 3.00 16.89 0.120 0.291 0.057 0.073 0.031 

70 4 2018 0.03 3.04 17.11 0.105 0.421 0.055 0.079 0.038 

71 5 2009 0.12 2.71 21.14 0.024 0.513 0.054 0.050 0.061 

72 5 2010 0.15 2.77 21.55 0.056 0.415 0.081 0.056 0.047 

73 5 2011 0.06 2.83 22.67 0.055 0.282 0.114 0.065 0.051 

74 5 2012 0.06 2.89 23.02 0.042 0.452 0.062 0.065 0.053 

75 5 2013 0.07 2.94 23.00 0.046 0.428 0.075 0.060 0.060 

76 5 2014 0.06 3.00 23.15 0.043 0.500 0.070 0.061 0.069 

77 5 2015 0.05 3.04 23.21 0.055 0.513 0.062 0.066 0.055 

78 5 2016 0.05 3.09 23.37 0.056 0.525 0.055 0.071 0.034 

79 5 2017 0.04 3.14 23.61 0.065 0.604 0.057 0.073 0.031 

80 5 2018 0.02 3.18 23.70 0.063 0.639 0.055 0.079 0.038 

81 6 2000 0.08 2.08 21.37 0.047 0.800 0.022 0.053 0.090 

82 6 2001 0.08 2.20 21.56 0.032 0.794 0.020 0.051 0.093 

83 6 2002 0.03 2.30 21.66 0.035 0.801 0.033 0.038 0.084 

84 6 2003 0.01 2.40 21.61 0.018 0.777 0.057 0.047 0.059 

85 6 2004 0.01 2.48 21.59 0.020 0.738 0.076 0.052 0.056 

86 6 2005 0.03 2.56 21.67 0.016 0.740 0.070 0.065 0.058 

87 6 2006 0.02 2.64 21.59 0.021 0.778 0.068 0.067 0.055 

88 6 2007 0.04 2.71 21.78 0.023 0.784 0.091 0.071 0.058 

89 6 2008 0.01 2.77 21.79 0.028 0.803 0.089 0.060 0.047 

90 6 2009 0.04 2.83 21.92 0.038 0.797 0.054 0.050 0.061 

91 6 2010 0.01 2.89 22.01 0.035 0.815 0.081 0.056 0.047 

92 6 2011 0.02 2.94 22.14 0.045 0.836 0.114 0.065 0.051 

93 6 2012 0.03 3.00 22.15 0.051 0.818 0.062 0.065 0.053 

94 6 2013 0.03 3.04 22.15 0.042 0.821 0.075 0.060 0.060 

95 6 2014 0.02 3.09 22.09 0.047 0.793 0.070 0.061 0.069 

96 6 2015 0.02 3.14 22.07 0.062 0.811 0.062 0.066 0.055 



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97 6 2016 0.01 3.18 22.24 0.052 0.826 0.055 0.071 0.034 

98 6 2017 0.01 3.22 22.27 0.064 0.824 0.057 0.073 0.031 

99 6 2018 0.01 3.26 22.65 0.063 0.875 0.055 0.079 0.038 

100 7 2012 0.03 2.40 22.61 0.047 0.658 0.062 0.065 0.053 

101 7 2013 0.06 2.48 22.86 0.017 0.621 0.075 0.060 0.060 

102 7 2014 0.05 2.56 23.01 0.050 0.663 0.070 0.061 0.069 

103 7 2015 0.04 2.64 23.03 0.055 0.663 0.062 0.066 0.055 

104 7 2016 0.06 2.71 23.11 0.066 0.613 0.055 0.071 0.034 

105 7 2017 0.05 2.77 23.07 0.070 0.586 0.057 0.073 0.031 

106 7 2018 0.07 2.83 23.33 0.052 0.664 0.055 0.079 0.038 

 

 
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