




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 9(1) (2024), 32-41 

 

32 

-  

     MULTIDISCIPLINARY SCIENTIFIC RESEARCH 

 
        BJMSR VOL 9 NO 1 (2024)  P-ISSN 2687-850X  E-ISSN 2687-8518 

 
          Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                            

THE FINANCIAL DEVELOPMENT, INSTITUTIONS, AND 

POVERTY REDUCTION: EMPIRICAL EVIDENCE FROM SOUTH 

ASIAN COUNTRIES             

     
  Shamim Ara Labony (a)    Md. Shariful Haque (b)   Farzana Rahman (c)1   A. M. Shahabuddin (d)    Tania Sultana (e)   

    
(a) Research Fellow, Center for Research on Islamic Management and Business, Bangladesh; E-mail: shamimaralabony@gmail.com 
(b) Associate Professor, Department of Economics & Banking, International Islamic University Chittagong, Bangladesh; E-mail: sharif@iiuc.ac.bd 
(c) Research Associate, Center for Research on Islamic Management and Business, Bangladesh; E-mail: munnifarzana19@gmail.com 
(d) Associate Professor, Department of Business Administration, International Islamic University Chittagong, Bangladesh; E-mail: ams_iiuc@yahoo.com 
(e) Lecturer, Department of Business Administration, International Islamic University Chittagong, Bangladesh; E-mail: tania@iiuc.ac.bd 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 6th February 2024 

Reviewed & Revised: 6th February 

to 22nd April 2024 
Accepted: 23rd April 2024 

Published: 1st May 2024 

 
Keywords: 

 
Financial Development, Institutions Quality, 

Poverty, South Asia, Fixed Effects, Random 

Effects, Hausman Test 

 
JEL Classification Codes: 

 

      O16, I32, O53, C23 

  

      Peer-Review Model:  

 

      External peer-review was done through  

      double-blind method.        

 
A B S T R A C T 

 

Fighting poverty is one of the most critical targets of development plans and initiatives. In the pursuit of 

lasting growth, emerging nations now face the most challenging issue of eliminating poverty, which 

remains one of the most significant challenges addressing humanity nowadays. The study explores the 

relationships between the institutional quality, financial development, and poverty-fighting initiatives of 

South Asian states. It goes beyond the potential bias in earlier studies caused by omitting variables by 

considering the impact of the interaction between the financial sector and institutional framework. The 

fixed effects models with STATA15 are employed in this study from 2000 to 2019. This study's analysis 

uses panel data and secondary sources to conduct the inquiry with a sample of 7 South Asian economies 
such as Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. This comprehensive 

compilation of annual data was done with consultation from the International Monetary Fund (IMF) 

and the World Bank Development Indicators (WDI). The study results show that a 1% increase in 

financial development is associated with a 39.88% decrease in poverty, which is statistically significant 

and favourable. It also reveals that institutional quality plays a vital role in poverty reduction in South 

Asia, with a 1% increase in institutional quality leading to a 2.61% increase in poverty. Besides, a 1% 

increase in GDP per capita growth correlates with a 0.12% decrease in poverty. The study's findings 
provide significant insights into poverty reduction by considering the relationship between institutional 

challenges and financial development through a flexible, functional structure in South Asian countries.   

 
 

© 2024 by the authors. Licensee CRIBFB, U.S.A. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0/).  

            

 

INTRODUCTION 

Institutions and financial development are essential to economic growth and alleviating poverty in emerging or developing 

nations. According to Milgrom et al. (1990), institutions are the boundaries people have established to govern social 

interaction or are the community's norms. In contemporary academic investigations on economic growth, the significance 

of financial development and institutions being separate and essential elements of economic growth is frequently stressed. 

The poverty reduction strategy will be prioritized in emerging nations over the growth model. This is because while 

economic progress fosters growth, the conditions of the impoverished are not necessarily improved by it (Abraham & 

Ahmed, 2011). Appiah-Otoo and Song (2021) state that financial development has the following effects on poverty. First, 

financial development reduces information asymmetry and high borrowing costs, facilitating credit for the impoverished. 

To increase access to financial services, create jobs, raise household incomes, and reduce poverty, financial development 

also assists people in need in launching microenterprises using their borrowed or savings capital. In summary, financial 

development promotes trade facilitation, corporate control, risk management, innovation, and resource allocation for 

investment projects, all of which indirectly positively impact poverty (trickle-down theory). 

                                                      
1Corresponding author: ORCID ID: 0000-0003-3215-3143 
© 2024 by the authors. Hosting by CRIBFB. Peer review under the responsibility of CRIBFB, U.S.A.  

https://doi.org/10.46281/bjmsr.v9i1.2207 

  
To cite this article: Labony, S. A., Haque, M. S., Rahman, F., Shahabuddin, A. M., & Sultana, T. (2024). THE FINANCIAL DEVELOPMENT, 

INSTITUTIONS, AND POVERTY REDUCTION: EMPIRICAL EVIDENCE FROM SOUTH ASIAN COUNTRIES. Bangladesh Journal of 

Multidisciplinary Scientific Research, 9(1), 32-41. https://doi.org/10.46281/bjmsr.v9i1.2207 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/bjmsr.v9i1.2207
https://orcid.org/0009-0007-0594-0340
https://orcid.org/0000-0002-2692-3634
https://orcid.org/0000-0003-3215-3143
https://orcid.org/0000-0002-3471-4445
https://orcid.org/0000-0002-0540-5854


Labony et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(1) (2024), 32-41 

 

33 

South Asia's poverty rate decreased significantly from 94.60% to 82.20% between 2002 and 2019. Over the given 

period, there has been a notable decline of 12.40 percent. However, a counterargument claims that the wealthy and powerful 

gain the most from changes to the financial system. Concerns about how financial development would primarily benefit 

affluent individuals or institutions arise from this point of view. The differing perspectives presented by Lee et al. (2020) 

reflect the ongoing debate over the benefits of financial growth for distributing capital and its capacity to improve the lives 

of those who are economically disadvantaged in society. The emergence of risky social groupings, disputes, stigmatization, 

low living standards, financial difficulties, lack of freedom, anxiety about the future, unemployment, inadequate education, 

and bad diet are all linked to this sharp increase in poverty (Appiah-Otoo & Song, 2021). Because of this, governments still 

consider it extremely important to end poverty in all its forms (Acheampong et al., 2021).  

The study examines the connections between South Asian states' efforts to combat poverty and their institutional 

quality and financial development. For this purpose, panel data and secondary sources from 2000 to 2019 are used to 

investigate a sample of seven South Asian economies using fixed-effects models with STATA15.  

The study is organized methodically. Section 2 offers an overview of recent academic research on the subject. 

Section 3 explains the econometric techniques and describes the data sources used. Section 5 offers a comprehensive 

discussion of the study's findings, while Section 4 presents the study's analysis and outcomes. Section 5 provides a succinct 

overview of the findings and policy suggestions and acknowledges future research. 

 

LITERATURE REVIEW 

The countries in the South Asian continent have made significant progress in the last ten years in developing their financial 

systems compared to other regions. However, there is still much room for improvement. On the other hand, in many South 

Asian countries, financial progress has stagnated since the early 1980s. From the perspective of the depth of its financial 

markets and institutional improvement, the region lags, except in its middle-income nations. Financial development has 

been highly beneficial to the impressment and deployment of financial capital or resources, the execution of growth-

promoting and stabilization regulations, and all of these processes (Sahay et al., 2015). To accelerate the financial 

development of South Asian nations, robust institutional and legal frameworks, effective corporate governance, and 

increased information transparency are necessary. In order to create an atmosphere that encourages the financial industry's 

growth, it is necessary to reinforce legal frameworks, protect the interests of minority shareholders, uphold contract 

enforcement, and preserve judicial independence. To help close the gap with the targeted benchmark, regional financial 

development strategies can also be brought into compliance with international standards. 

Poverty is defined as not having enough money to live a decent life, which also includes having access to poor 

food, shelter, and healthcare, as well as being unemployed and marginalized in society. There is a link between economic 

progress and the objective of eradicating poverty, even though it does not guarantee it (Donou-Adonsou & Sylwester, 2016). 

One element that leads to inequality is uneven development, which is also a byproduct of development. Financial 

development is considered a direct or indirect tool in the battle against poverty because it removes market flaws that prohibit 

individuals in need of assistance from receiving credit (Jalilian & Kirkpatrick, 2002). Policies that boost financial 

development and enhance the financial services accessible to the underprivileged can positively impact their income and 

eventually contribute to eradicating poverty. Keho (2017) found evidence of a long-lasting link between financial 

development, economic progress, and poverty eradication in several African countries. The impact of Pakistan's financial 

growth on poverty reduction initiatives was examined by Ayinde and Yinusa (2016), who found that consumption per capita, 

a measure of poverty, was impacted. Nonetheless, poverty and domestic bank holdings had no appreciable long-term 

relationship. Chakroun et al. (2020) discovered that augmenting the accessibility of funds and deposit alternatives had 

diminished poverty in emerging countries, as opposed to loans. In addition, Appiah-Otoo et al. (2022) stated that although 

finance has a favourable impact on reducing poverty, this effect is mitigated by poor institutional quality. Furthermore, 

Aracil et al. (2022) found that institutional deterioration in developing nations fails to stimulate economic performance and 

poverty reduction. 

Distributing resources and fostering economic progress depends heavily on the financial industry. Innovations, 

improvements to current establishments, and modifications to organizational structures that lessen information asymmetry, 

promote competition, raise market completeness, and cut transaction costs are all included in financial development (Graff, 

2003). Economic growth is the fruit of its promotion of efficient use of resources, productivity, and investment. According 

to Greenwood et al. (2013), market size has less effect on economic growth than banking sector efficiency and 

competitiveness. Advances in human rights and political rights magnify the benefits of South Asia's economic expansion. 

Mohammadi-Sartang et al. (2023) examined 469 companies stated on the Tehran Stock Exchange and explored a robust as 

well as favourable relationship between financial market returns as well as governance indicators; specifically, ownership 

concentration and board size hurt stock returns, while institutional ownership, ownership structure, and board independence 

have a positive influence. Remarkably, institutional systems in developed countries are relatively stable and seldom alter. 

That being said, the rapid expansion of some highly economizing states has resulted in a rapid transformation of South 

Asian institutions. Emerging countries' fast-paced development phase makes institutional quality crucial to promoting 

positive outcomes from their budding finance sector. Different states have different financial and institutional development 

levels, and these differences correlate with minimum institutional quality standards (Law et al., 2013). 

A deficiency exists in the existing literature about studies that evaluate the relationship and influence among 

financial development, institutional quality, and poverty mitigation in South Asian countries. In this field, there is a 

significant research vacuum that the current study aims to close. Through analysing the association between financial 

development, institutional quality, and poverty reduction in South Asian countries, this study focuses on filling this research 

gap and enhancing our understanding of the intricate dynamics of reducing poverty in the region. 



Labony et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(1) (2024), 32-41 

 

34 

The following are the hypotheses that this study takes into account. 

 

H1: Poverty is negatively impacted by financial development 

H2: Institutional quality has a negative influence on poverty 

H3: Economic development and poverty are negatively correlated 

H4: Inflation has an adverse association with poverty 

H5: There is a negative link between trade openness and poverty 

 
This study's conceptual framework is provided below in Figure 1. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1. Conceptual Framework 

 

MATERIALS AND METHODS 

The sample was selected considering data availability and includes seven South Asian economies. The economies covered 

include those of Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka; taken as a whole, they show a 

range of economic conditions in the South Asian region. This research uses a meticulously balanced panel dataset that spans 

seven countries and is monitored for two decades. The World Bank Development Indicators (WDI) and the International 

Monetary Fund (IMF) were consulted for this meticulous compilation of annual data spanning 2000 through 2019. The 

variables that are taken into consideration are divided into the categories of independent and dependent. The independent 

variables are financial development (FD), institutional quality (INST), economic development (GDPPC), inflation (INF), 

and trade openness (TOP). Besides, the dependent variable is poverty (POV). 

 

Model Specification 
The principal purpose of this investigation is to evaluate the long-term impacts of financial development and institutions on 

alleviating poverty in a selected group of South Asian countries. In order to achieve these objectives, an econometric model 

has been empirically developed as follows: 

  
𝑷𝑶𝑽𝒊𝒕= 𝜷𝟎 + 𝜷𝟏𝑭𝑫𝒊𝒕 + 𝜷𝟐𝑰𝑵𝑺𝑻𝒊𝒕 + 𝜷𝟑𝑮𝑫𝑷𝑷𝑪𝒊𝒕 + 𝜷𝟒𝑰𝑵𝑭𝒊𝒕 + 𝜷𝟓𝑻𝑶𝑷𝒊𝒕 + 𝜺𝒊𝒕 

Financial Development 

Independent Variables Dependent Variables 

Institutional Quality 

Economic Development 

Inflation 

Trade Openness 

Poverty 

H2 (-) 

H3 (-) 

H4 (-) 

H5 (-) 

H1 (-) 



Labony et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(1) (2024), 32-41 

 

35 

Where, 

POV         = Poverty  

FD            = Financial development  

INST         = Institutional quality 

GDPPC     = GDP per capita growth 

TOP          = Trade openness  

𝜀𝑖𝑡            = error term  

𝑖              = the total number of individuals or cross-sections  

𝑡              = the number of periods  

 
Model Selection Criterion and Methods 
In contrast to cross-sectional samples and time series, the panel data model offers several advantages. In particular, when 

the time series for each cross-section is short, investigating panel data models with several degrees of freedom produces 

more consistent and dependable results Maliki and Benghalem (2019). As opposed to other data types, panel data considers 

individual heterogeneities. According to Semykina and Wooldridge (2010), there are many different types of panel data 

models, including fixed effect models, random effect models, between estimators, within estimators, dummy variable 

estimators, first differencing estimators, feasible generalized least squares (FGLS), feasible ordinary least squares (OLS), 

Monte Carlo approaches, and many others. The data set in this study was first subjected to pre-regression analysis, which 

included tests for residual normality, correlation analyses, and descriptive statistics analyses. The data's average, maximum, 

minimum, and standard deviation were investigated using descriptive statistics. Correlation analysis was employed to 

evaluate the link between the factors and search for any possible collinearity between the significant variables. The 

estimators were then tested by Samargandi et al. (2015) using the fixed and random effects models. 

 
Fixed-Effects Model 

The fixed effects model serves as a means to combine cross-sectional and time-series data, which is particularly useful when 

focusing on specific groups of countries. This model's intercept is presumed to represent all individual variations adequately. 

It allows each parameter to vary across individuals and periods. The disturbance term in this model consists of two 

components: a residual term that varies across time and individuals (in this context, nations) and an individual-specific 

effect. However, it is essential to note that the fixed effects model cannot accommodate variables that remain constant for 

each cross-section over time. The core idea behind fixed effects is that distinct intercepts can explain individual variations 

across different groups. Estimating the fixed effects model with varying intercepts among individuals often employs the 

dummy variable technique, termed the "Least Squares Dummy Variable approach" (LSDV). For panel data with fixed 

effects, the regression equation is structured accordingly:  

 

𝒀𝒊𝒕 = 𝜶𝒊 + 𝜷𝟏𝑿𝒊𝒕 + 𝜺𝒊𝒕 

Here, 

For i= 1, 2, ..., N and t= 1, 2, ..., T 

 

Where N= the total number of individuals or cross section and T= the number of periods. 

The constant slope coefficient holds for every individual and instance in time. Instead of varying based on the individuals' 

periods, the intercept 𝛼𝑖 fluctuates based on the country.  

 

Random Effects Model 

The error components model, also called the random effects model, is suitable for a random selection of' N' individuals from 

a large population. Because the sample population was selected randomly, the intercept is presumed to reflect individual 

variations and should be regarded as random variables rather than fixed numbers. The random effects model incorporates a 

random variable denoted by & that varies across cross-sections but remains consistent over time. The disparities in intercepts 

are accounted for by the error terms specific to each individual or entity. One benefit of the random effects model is its 

capability to reduce heteroscedasticity. It employs the concepts of maximum likelihood estimation or generic least squares. 

Software packages for statistics sometimes offer programs for implementing models with random effects. The equation for 

panel data regression in the random effects model is following: 

 

𝒀𝒊𝒕 = 𝜶𝒊 + 𝜷𝟏𝑿𝒊𝒕 + 𝝁𝒊 + 𝜺𝒊𝒕 

Where, 

N= total number of individuals or cross-section  

T= the number of periods  

𝑒𝑖𝑡 = the residual term encompassing both cross-sectional and time-series components 

𝜇𝑖 = the residual specific to each observation, which remains constant across all periods. 

These variables are defined for i = 1, 2, ..., N and t = 1, 2, ..., T. 

 
Hausman Test 

The most successful fixed effect and random effect strategies are compared using the Hausman test for the homogeneity of 

the Unobserved Error Component (Law & Habibullah, 2009). In light of this, the null hypothesis for the Hausman test is: 



Labony et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(1) (2024), 32-41 

 

36 

𝑯𝟎 ∶  �̂�𝑹𝑬 = �̂�𝑭𝑬 

 

Where the value of the coefficient indices for the explanatory factors that vary with time, but not the time variables, are 

denoted by �̂�𝑅𝐸  and �̂�𝐹𝐸 .  

RESULTS 

Descriptive Analysis 
Every variable's mean, standard deviation and other pertinent statistics are displayed to ensure a clear and accurate 

representation of the variables.  

 

Table 2. The results of the descriptive analysis 

 
Variables  No. of observation  Mean value Standard error Minimum value  Maximum value 

POV 140     13.57429     12.35062                   0 46.7 

FD 140     0.2307857     0.1009742         0.07          0.49 

INST 140           8.256929           1.58921           5 11.08 

GDPPC 140     5.623571     3.683692       -13.1        26.1 

INF 140     6.181429     3.401218              -2.8        19.6 

TOP 140  73.58571     58.87852        24.7       259.9 

[Note: poverty = POV, financial development = FD, institutional quality = INST, economic development = GDPPC, inflation = INF, and trade openness 

= TOP]     Source: Calculations by the authors using STATA15 

 

Table 2 represents the results of descriptive analysis. The average value of poverty is 13.57, while the standard 

deviation value is 12.35, varying from 0 to 46.7. The minimum and maximum estimates for the financial development are 

0.07 and 0.49, respectively, whereas the mean estimate is 0.23. The maximum value of institution quality is 11.08, with an 

average of 8.25. Economic development has a standard deviation of 3.68, with a mean value of 5.62. It ranges from -13.1 

to 26.1. In addition, inflation and trade openness have average values of 6.18 and 73.58, respectively. 

 

Correlation Analysis  
A statistical technique known as correlation, part of bivariate analysis, measures the strength and direction of the relationship 

between two variables. The correlation coefficient ranges from +1 to -1, indicating the degree of association between the 

variables.  

 

Table 3. Results of correlation analysis 

 
 POV FD INST GDPPC INF TOP 

POV 1.0000      

FD 0.3767 1.0000     

INST 0.2913   -0.2048 1.0000    

GDPPC -0.0360    0.0829    0.0148 1.0000   

INF -0.0702    0.0956   -0.2402   -0.0924 1.0000  

TOP -0.4225   -0.3947    0.1466    0.0597 -0.2509 1.0000 

[Note: poverty = POV, financial development = FD, institutional quality = INST, economic development = GDPPC, inflation = INF, and trade openness 
= TOP]     Source: Calculations by the authors using STATA15 

       
Table 3 depicts the results of the correlation matrix. The outcomes show a favourable association of poverty with 

financial development, which is 0.37. Besides, poverty and institutional quality are positively correlated with a magnitude 

of 0.29. In addition, economic development is negatively associated with poverty, which is -0.03. Inflation and trade 

openness hurt poverty, which are -0.07 and -0.4225, respectively. Thus, it is clear that poverty has a negative association 

with per capita economic growth, inflation, and trade openness. In contrast, poverty positively affects financial development 

and institutional quality. 

 

Multicollinearity Test 
In a multivariate regression analysis, multicollinearity results if two or more independent variables are linearly correlated. 

That means there is a direct correlation between them, which leaves the parameter coefficients unknown and inflates the 

standard error of the calculated coefficients. 

 
Table 4. The results of the multicollinearity test 

 
Particulars Variance Inflation Factor (VIF) 1/Variance Inflation Factor (VIF)   

GDPPC 1.26     0.792938 

FD 1.23     0.809853 

INF 1.13     0.888141 

INST 1.10     0.908448 

TOP 1.02     0.976620 

Mean VIF 1.15  

[Note: economic development = GDPPC, financial development = FD, inflation = INF, institutional quality = INST, and trade openness = TOP] 
Source: Calculations by the authors using STATA15 



Labony et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(1) (2024), 32-41 

 

37 

The mean Variance Inflation Factor (VIF) and its reciprocal can be used to determine whether multicollinearity is 

present (Gujarati & Porter, 2003). The data is considered free of multicollinearity issues if the mean VIF is less than 10. A 

VIF test was conducted for every explanatory variable in this study. The findings indicated that every VIF value was less 

than 10. Consequently, it is possible to use Gujarati's criterion to clearly say that these data do not exhibit the 

multicollinearity problem. 

 

Normality Test 
The Normality test is employed to ascertain if the residuals maintain the normal distribution. The outcomes of the normality 

test are displayed in Figure 2 below. 

 

 
Figure 2. Outcomes of normality test 

Source: STATA15 

 

In this instance, the probability value is 0.0137, and the Jarque-Bera statistic is 8.583. The determination is made 

based on whether the appropriate p-value of the Jarque-Bera statistic exceeds 5%; if it does, the null hypothesis cannot be 

rejected; otherwise, it would be rejected. With Jarque-Bera's probability value being 0.0137, the null hypothesis is rejected 

at a significance level of 5%, as depicted in the preceding analysis. Consequently, the residuals of this model do not exhibit 

a normal distribution. However, Werdati et al. (2020) proposed that in large samples, typically consisting of more than 100 

observations, the normality assumption may be relaxed, and its impact may be insignificant. 

 

Fixed Effect Model 
The study suggests that factors within the cross sections might affect or bias the predictor or outcome variables when 

utilizing the fixed effect (FE) estimator, necessitating its consideration. This implies an association between an entity's error 

term and predictor variables. The fixed effect (FE) approach removes these time-invariant properties from the equation to 

determine the full impact of the predictor variables. In such a model specification, ordinary least squares regression 

estimation typically provides the best linear unbiased estimates because, in general terms, the null hypotheses of the tests 

indicate the absence of fixed effects (Greene, 2008).  

 

Table 5. The results of the fixed effect (within) regression 

 
R – sq: 

within = 0.2628 

between = 0.2316 

overall = 0.0002 

No. of observation = 140 

No. of Groups = 7 

Observation per group: 

minimum = 20 
average = 20.0 

maximum = 20 

Corr (u_i, xb) = -0.5469 F (5, 128) = 9.13 

Prob > F = 0.0000 

Dependent Variable: Poverty (POV) 

Explanatory 

Variables 

Coefficient Std. Error t P > | t | [95% Confidence Interval] 

FD -39.87724 18.9237 -2.11 0.037 -77.32102 -2.433463 

INST 2.610134 0.4763938 5.48 0.000 1.667507 3.55276 

GDPPC -0.1225742 0.1902936 -0.64 0.521 -0.4991026 0.2539542 

INF -0.3651488 0.2213858 -1.65 0.102 -0.8031985 0.0729008 

TOP -0.0117985 0.0407636 -0.29 0.773 -0.092463 0.0688594 

-CONS 5.04034 6.70117 0.75 0.453 -8.21907 18.29975 

sigma_u 

sigma_e 

rho 

12.517296 
7.9155184 

0.71434318 

F test that all u_i = 0: F (6, 128) = 12.36 Prob > F = 0.0000 

[Note: poverty = POV, financial development = FD, institutional quality = INST, economic development = GDPPC, inflation = INF, and trade openness 
= TOP]     Source: Calculations by the authors using STATA15 

 



Labony et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(1) (2024), 32-41 

 

38 

The fixed effect regression results are shown in Table 05. Financial progress is deemed considerable at the 1% 

level, while institutional quality is at the 5% level. Furthermore, these variables are considered unimportant because the p-

values for trade openness, inflation, and economic progress are higher than their significance level. Trade openness, 

inflation, financial development, and economic growth hurt poverty, demonstrating that these factors also positively impact 

it. Conversely, there is evidence that poverty positively correlates with institutional quality, suggesting that poverty is 

exacerbated by institutional quality.  

 

Random Effect Model 
In the random effects model, variations between entities are presumed to be random and unrelated to the predictor or 

independent variables included in the model.  

 

Table 6. Results of the random effects of GLS regression 

 
R – sq: 

within = 0.1582 

between = 0.7279 

overall = 0.4027 

No. of observation = 140 

No. of groups = 7 

Observation per group: 

minimum = 20 

average = 20.0 
maximum = 20 

Corr (u_i, x) = 0 (assumed)  Wald chi2(5) = 90.33 

Prob > Chi2 = 0.0000 

Dependent Variable: Poverty (POV) 

Explanatory 

Variables 

Coefficient Std. Error t P > | t | [95% Confidence Interval] 

FD 39.47344 9.074651 4.35 0.000 21.68746 57.25943 

INST 3.01142 0.5443923 5.53 0.000 1.944431 4.07841 

GDPPC -0.1876591 0.2265151 -0.83 0.407 -0.6316206 0.2563024 

INF -0.3900013 0.2572574 -1.52 0.130 -0.8942165 0.1142139 

TOP -0.0787779 0.0157278 -5.01 0.000 -0.1096037 -0.0479521 

-CONS -11.1377 6.2404 -1.78 0.074 -23.36866 1.093263 

sigma_u 

sigma_e 

rho 

0 
7.9155184 

0 

[Note: poverty = POV, financial development = FD, institutional quality = INST, economic development = GDPPC, inflation = INF, and trade openness 

= TOP]     Source: Calculations by the authors using STATA15 

 

Table 06 shows the outcomes of the GLS regression's random effects. It is determined that, at the 1% level, trade 

openness, institutional development, and financial development are significant. Furthermore, since the p-values exceed their 

significance level, inflation and economic development are determined to be unimportant. Poverty is negatively impacted 

by economic progress, inflation, and trade openness, suggesting that these factors also have a reducing effect on poverty. 

Conversely, research shows that institutional quality and financial development benefit poverty, suggesting that these factors 

worsen poverty.  

 

Hausman Test 
The Hausman test was utilized to ascertain the most suitable and reliable model when employing panel data estimation, 

considering both fixed-effect and random-effect estimators. The fixed-effect estimator considers time-invariant 

characteristics present across multiple nations. Consequently, the study must consider the possibility that anything internal 

to the entity could contribute to the bias of the explanatory variables while performing the fixed-effect (FE) test.  

 

Table 7. Results of the Hausman test 

 
Dependent Variable: Poverty (POV) 

Explanatory 

Variables 

Coefficients (b – B) 

Difference 

sqrt (diag (v_b – v_B)) 

S.E. (b) 

Fe 

(B) 

re 

FD -39.87724 39.47344 -79.35068 16.60594 

INST 2.610134 3.01142 -0.4012868 0.00 

GDPPC -0.1225742 -0.1876591 0.0650848 0.00 

INF -0.3651488 -0.3900013 0.0248525 0.00 

TOP -0.0177985 -0.0787779 0.0669794 0.0376073 

Test: Ho: difference in coefficients, not systematic 

chi2 (5) = 24.18 

Prob > chi2 = 0.0002 

[Note: financial development = FD, institutional quality = INST, economic development = GDPPC, inflation = INF, and trade openness = TOP] 

Source: Calculations by the authors using STATA15 

 

Based on the available data, the chi-square test yielded a p-value of 0.0002, indicating significance below the 

conventional threshold of 0.05 and leading to the rejection of the null hypothesis. Thus, the fixed effect model appears 

appropriate. This model assumes that observed differences or relationships in the data are specific to the entities or groups 

being studied rather than random fluctuations.  



Labony et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(1) (2024), 32-41 

 

39 

DISCUSSIONS 

In the context of certain South Asian countries, the results of the fixed effect model demonstrate that financial development 

(FD) and institutional quality (INST) exhibit negative and positive effects on poverty (POV), respectively. Notably, a 1% 

increase in financial development is associated with a 39.88% decrease in poverty, contrary to findings by Dandume (2014). 

Additionally, institutional quality plays a significant role in poverty in South Asia, with a 1% increase leading to a 2.61% 

increase in poverty. This finding aligns with assertions by Aracil et al. (2022) that institutional deterioration in developing 

nations fails to stimulate economic performance and, consequently, poverty reduction. Although the impact of GDP per 

capita growth (GDPPPC) is modest, it is pro-poor, as indicated by its opposing sign. Furthermore, a 1% increase in GDP 

per capita growth correlates with a 0.12% decrease in poverty. Poverty is lessened in part by the acceleration of economic 

growth. The findings drawn by Egena et al. (2014) align with this. Inflation (INF) and trade openness (TOP) are likewise 

unimportant. Trade openness benefits the poor, as evidenced by its negative value, which indicates that it reduces poverty 

in South Asia. This was not wholly unexpected since trade liberalization frequently lowers poverty in countries with 

powerful institutions, solid financial sectors, and high levels of education. 

A summary of the empirical findings of this investigation is depicted in the following table 08. 

 

Table 8. Significant findings of this study 

 
Proposed Hypotheses Major Findings Support the Proposed Hypotheses or Not Literature 

H1: Poverty is negatively 

impacted by financial 

development. 

Financial development  
has a negative and  

significant influence 

 on poverty. 

Supported Opposite to the 
findings by 

Dandume (2014) 

H2: Institutional quality has a 

negative influence on poverty. 

Poverty is positively  

impacted by the  

institutional quality  
Moreover, that is 

significant. 

Not supported Similar to 

assertions by 

Aracil et al.(2022) 

H3: Economic development 

and poverty are negatively 

correlated. 

The relationship  

between economic  

development with 

 poverty is negative  

but insignificant. 

Supported Similar to the 

findings drawn by 

Egena et al. (2014), 

they align with 

this. 

H4: Inflation has an adverse 

association with poverty. 

There is a negative  

and insignificant  

influence of inflation  
on poverty. 

Supported  

 

Likewise 
unimportant 

H5: There is a negative link 

between trade openness and 

poverty. 

Poverty is negatively  

associated with trade  

openness and that is 
 insignificant. 

Supported 

Source: Synthesis of the authors 

 
CONCLUSIONS 

Increasing financial development is essential to advancing development since it offers several benefits for reducing poverty 

and promoting wealth. Thus, it is essential to look into the factors that influence financial development throughout the South 

Asian region to assess progress and mitigate poverty. This research examines how financial development influences poverty 

levels across several South Asian nations while also considering how the association between the financial sector and 

institutional framework may affect results by potentially biasing omitted variables. Through the use of fixed effects and 

random effects models, the study investigates these relationships. The main findings of this empirical study can be 

summarized as follows: Firstly, it was found that poverty is negatively influenced by advancements in financial 

development, economic development, inflation, and trade openness, indicating that higher financial improvement, economic 

development, inflation, and trade openness reduce poverty in South Asian countries, with this relationship being statistically 

significant. It is also evident from the estimate that poverty is significantly increased if institutional quality increases.  

The study's empirical findings significantly impact theoretical understanding and practical policymaking. To combat poverty 

effectively, policymakers should prioritize initiatives that bolster financial institutions, promote robust economic growth, 

and facilitate trade liberalization. Strengthening institutional frameworks can enhance the benefits of economic development 

for the marginalized. Policymakers should focus on reforms that strengthen governance, uphold the rule of law, and mitigate 

political instability. Supporting economic openness, skill training, and human capital development is crucial. Prioritizing 

policies that enhance labour market flexibility can facilitate resource reallocation, stimulating growth and poverty reduction. 

Future research should utilize multidimensional poverty indicators, expand analysis to more South Asian nations, and 

conduct longitudinal studies to assess policy efficacy over time. Overall, empirical data from future studies can inform 

evidence-based policymaking and enhance the success of poverty reduction initiatives in South Asia. 

 
 

Author Contributions: Conceptualization, S.A.L, M.S.H., F.R., and A.M.S.; Methodology, S.A.L., M.S.H., and F.R.; Software, S.A.L., and M.S.H.; 
Validation, S.A.L., M.S.H., F.R., A.M.S., and T.S.; Formal Analysis, S.A.L, M.S.H., and F.R; Resources, S.A.L, F.R., A.M.S., and T.S.; Data Curation, 

S.A.L., M.S.H., A.M.S., and T.S.; Writing-Original Draft Preparation, S.A.L., M.S.H., F.R., and T.S.; Writing-Review & Editing, M.S.H., and F.R; 

Visualization, S.A.L., M.S.H., F.R., AM.S., and T.S.; Supervision, M.S.H., A.M.S., and T.S.; Project Administration, S.A.L.; Funding Acquisition, S.A.L., 
M.S.H., F.R., AM.S., and T.S. Author has read and agreed to the published version of the manuscript. 



Labony et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(1) (2024), 32-41 

 

40 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 

groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgments: Not Applicable. 
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 
Conflicts of Interest: The authors declare no conflict of interest.           

                                                                                                                                                                                                                         

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