research paper on financial development

Financial development, human capital and its impact on economic growth of emerging countries

Asian Journal of Economics and Banking

ISSN : 2615-9821

Article publication date: 14 December 2020

Issue publication date: 27 April 2021

This paper aims to investigate the critical aspect of financial development, human capital and their interactive term on economic growth from the perspective of emerging economies.

Design/methodology/approach

Data set ranged from 2002 to 2017 of 83 emerging countries used in this research and collected from world development indicators of the World Bank. The two-step system generalized method of moments is used to conduct this research within the endogenous growth model while controlling time and country-specific effects.

The findings of the study indicate that financial development has a positive and significant effect on economic growth. In emerging countries, human capital also has a positive impact on economic growth. Financial development and human capital interactively affect economic growth for emerging economies positively and significantly.

Research limitations/implications

The data set is limited to 83 emerging countries of the world. The time period for the study is 2002 to 2017.

Originality/value

This research contributes to the existing literature on human capital, financial development and economic growth. Limited research has been conducted on the impact of financial development and human capital on economic growth.

Financial development
Economic growth
Emerging countries
Human capital

Sarwar, A. , Khan, M.A. , Sarwar, Z. and Khan, W. (2021), "Financial development, human capital and its impact on economic growth of emerging countries", Asian Journal of Economics and Banking , Vol. 5 No. 1, pp. 86-100. https://doi.org/10.1108/AJEB-06-2020-0015

Emerald Publishing Limited

Published in Asian Journal of Economics and Banking . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

World Bank Report (2012) , the financial sector is a legal system, regulatory institutes, instruments and markets that enable transactions to be carried out by credit extension. The financial sector’s development aims to overcome the costs involved in the financial system. This process of reducing the information costs acquisition, contract enforcement and transactions led to intermediaries’ appearance, financial contracts and markets. Moreover, the report suggested that these financial institution systems perform and play a more important role in developing economic growth. Well-developed financial institutions in a country can perform better over a long time as they tend to grow faster due to the causal effect of financial development’s contribution to growth.

According to Levine (1997) , a well-developed financial institution is a key to the economic growth of the country as it acts to reduce the risk/uncertainty through well-organized risk management processes, effective sharing and utilization of saving by lowering the cost of transaction and access to financial institutions, monitoring transactions through proper regulatory bodies to promoting efficient market and comfort in trade by exchanging of goods, services, knowledge, technology and innovation. According to Ibrahim (2018) , the recent constant growth rates of world development indicators (WDIs) practiced in different regions partially stimulate financial deepening, with the financial sector’s development playing an important role in growth. In general, the role of finance in facilitating investment productivity and growth models of economics has been expanded to provide a theoretical basis for examining the relationship between financial sector development and economic growth. The growth in the economy can be facilitated via the financial system by increasing human and physical capital by assigning money to the utmost fruitful activities and sinking the cost of the resources used in saving and investment ( Montiel, 2011 ).

Besides financial development, the role of human capital is also essential in the growth process. Some countries have less stock of human capital, while some have a high supply of human capital. So due to this, the impact of financial development may not be the same for all countries. Barro and Sala-i-Martin (1999) stated that if capital is generally defined as human capital, it relaxes the limitations of declining returns and contributing to per capita long-term growth even in the lack of exogenous technological advancements. Barro and Lee (1996) investigated the human capital by using education and life expectancy proxy on economic growth and found that it affects economic growth. Blundell et al. (1999) revealed that the growth rate highly depends on human capital accumulation and innovation as the stock of human capital and education level influences labor productivity.

Countries with high-quality human capital stocks can benefit more from the financial sector, as many scientists, researchers, doctors, accountants and financial analysts in these countries can make efficient and effective choices among different alternates. They are more efficient and effective in using opportunities and resources and can also innovate better to support the financial sector growth. These are all essential to promote growth in the economy. Many studies have been researched the impact of financial development on economic growth ( Acaravci et al. , 2009 ; Eita and Jordaan, 2010 ; Levine, 2005 ; Rousseau, 2003 ). Many previous studies claimed that financial development significantly impacts economic growth in developing economies ( Acaravci et al. , 2009 ; Khan and Senhadji, 2003 ; Khan et al. , 2020 ; Rousseau and Sylla, 2005 ).

But in contrast, some studies also claim that government intervention and restriction in financial sectors negatively affect as restriction causes the problem to the economic development and adverse this relationship to real growth ( Boyreau-Debray, 2003 ; Fry, 1980 ; Lucas, 1988 ). On the other hand, many studies have also researched the effect of human capital on economic growth ( Barro and Lee, 1996 ; Blundell et al. , 1999 ; Lucas, 1988 ). They ignore the human capital and financial development interactive term on economic growth. There is less study on the human capital and financial development interactive term on economic growth. Kendall (2012) researched both human capital and financial development on economic growth in India’s sub-national economy. Hakeem (2010) and Ibrahim (2018) conducted the same research in Sub-Saharan Africa. Munir and Arshad (2018) mentioned two foremost difficulties that most developing economies face: achieving high growth in the economy and keeping economic development at the highest rate.

The primary aim of this study is to evaluate the interaction role of financial development and human capital on economic growth in the context of emerging countries. This study extends the existing literature in the following ways: first, there are limited studies on the simultaneous impact of financial development and human capital on economic growth. This study evaluates the interactive effect of financial development and human capital on economic growth in developing economies. Second, this study investigates the marginal impact of human capital and financial development on economic growth in developing countries. Finally, this study investigates the econometric relationship between financial development, human capital and economic growth. This study is important for policymakers and researchers in modeling the stability of the human capital and financial sector development in emerging economies in the future.

Literature review

This portion outlines the theoretical implications and related research in developed and developing economies. It evaluates a specific research section on the relationship between financial development, human capital and economic growth. Such understanding is critical and essential for the developing economies to carry out an empirical study on the relationship between financial development, human capital and economic growth.

Theoretical background

The basic principle of the endogenous growth theory is that capital stock increases (all these physical and human resources) create beneficial externalities that raise productivity. If the spillover effects are high, these will deter declining returns on investment. The consequences for growth are similar to those observed when separately examining technical development and human capital, but assuming that there are diminishing returns to human capital in the production of final output and education. Fischer (1991) stated that the interaction effect of financial development and human capital on economic growth fabricates certain appealing repercussions for the transitional dynamics. However, Young (1995) observed that although long-term growth is induced by independent-scale changes in the product quality, and therefore does not show nonlinearities, human capital is the long-term impact of growth rate on the economy. Recognizing that the economy’s growth rate is largely dictated by the potential to deliver human capital, human capital accumulation determines investment opportunities.

A deep-rooted financial system is an essential part of human resource development ( Diamond and Dybvig, 1983 ). Although recognized in the established theoretical literature, the relationship between financial growth and human capital remains less discussed at the empirical level. The literature shows that people with better education are less risk-averse, have high knowledge and are high savers. Improving education rates like adult education, thus, offer new opportunities for empowerment for people. Training also enables individuals to switch from informal to formal sector opportunities allowing them to access formal financial services. Development of the financial sector through credit channels often provides for the accumulation of human capital and influences economic growth. The consequence, then, is both ways.

Financial growth and good human capital endowment will promote greater use of the borrowed funds than individual savers. This may also increase management performance by fostering competition by successfully taking over or attempting to take over. Demirgüç-Kunt and Maksimovic (2005) argue that financial development and human capital allow specific entrepreneurs to engage in creative activity that impacted growth through productivity enhancement and viewed the financial and human capital environment as an important role in mitigating the effect of external shocks on domestic economies. They conclude that financial structures without the requisite institutional growth, human growth, educational achievement have led rather than mitigation to poor handling or even amplification of the danger. Such relationships provide the theoretical basis for the present study.

Empirical literature

Human capital has been articulated differently in different studies. They include human capital as health, education, Knowledge, migration, training and other factors investment in labor that can enhance labor productivity to contribute to the gross domestic product (GDP) of the country, as discussed in the previous literature. In the past two decades of twenty centuries, human capital has been dominated in growth literature with the great appearance of endogenous growth theory presented by Lucas (1988) and Romer (1986) as they contend in oppose to previous neo-classical growth theory. They said that if capital is efficiently allocated to the human capital, the return can be getting back in the shape of a stable return to scale despite diminishing and the low return to scale. Romer (1986) specified a long-term economic growth model in which human education capital includes an input to the production, which increases marginal production and growth over the long run. He further reasons that a country with a large size of human capital may grow much quicker than a country with a small human capital size.

Munir and Arshad (2018) practice the endogenous growth model to find the impact of stock of human capital and real physical capital to investigate the long-term and short-term effects on Pakistan’s economic growth. The research findings follow the endogenous growth model, suggesting that GDP per labor increases with accumulation factors of human capital and real physical capital as accumulation factors increase employment rate level, per capita income, labor productivity and economic growth sources. Rosendo Silva et al. (2018) investigated human capital on economic growth. Results show that better health also has a strong significant and positive impact on economic growth because the healthy worker can improve labor productivity more. Li and Liang (2010) practice human capital in East Asia, and results show that both stocks of health and education have a positive correlation to growth. Still, the stock of health capital is highly significant to growth than the stock of education capital. Neeliah and Seetanah (2016) study the positive relationship between human capital and economic growth in both the short run and long run. The study stated that there is a bi-directional association between human capital and growth. The main conclusion suggested that any shock to the development of human capital can destroy growth, so policy-making must pay attention to human capital.

Knowles et al. (2002) practice a neo-classical growth model approach, which included female and male human capital education separately. The research results show that female human capital is more important than male human capital in boosting labor productivity. Similarly, Sehrawat and Giri (2017) also examine female human capital and male human capital separately on India’s economic growth. The statistical results disclose that in both the short and long run, female human capital is statistically significant and positive to the development and increases labor productivity. However, male human capital is positive but unexpectedly insignificant to the growth. The study noted in long-run causal relationship of growth variable with physical capital, male and female human capital.

The early study of King and Levine (1993) presented a cross-country analysis based on Schumpeter’s view that the financial institution system can encourage growth in the economy. The level of financial development with various measures predicts strong relation with real GDP per capita. Levine (2005) evaluated and encountered the linkage between the system of financial operation and the economy’s growth. Evidence suggested that both the financial market and intermediary institutions are important for growth in a financial system. Moreover, the study proposed that well developed financial system comfort and illuminate constraint of external financing that firms may face in a way to economic growth.

Nyasha and Odhiambo (2015) conducted a review paper to highlight the empirical and theoretical relationship of bank-based and market-based financial development on growth in the economy of both developed and developing countries. They concluded that casualty relationship direction highly depends on the countries’ various specific characteristics, methodology, data sets and different factors used by the study. According to Jalles (2016) , there is a growing interest in the financial institution’s importance and are quality in the development process. Corruption is the main obstacle in economic development and lower corruption or better high-quality establishment enhancing financial development, and thus enhancing growth. Phiri (2015) claimed that there is an asymmetric relationship between financial development and growth. Banking activity proved a key factor for growth, while growth in the economy was confirmed as a lashing force behind the stock market development. Shahid et al. (2015) also specified that financial development has a significant and positive connection to economic growth.

The effects of financial development and growth in the SAARC nations have been studied by Sehrawat and Giri (2016) and the long-term connection of economic and economic growth has been explored. Sehrawat and Giri (2015) , long-term relationships in India’s economic and economic development, are also found. The impact of financial development in emerging economies and using the endogenous growth model is further studied by Masoud and Hardaker (2012) . It is investigated that the development of financial development is essential to growth and that the connection between stock market development and financial growth is stable in the long term.

There is growing concern about the relationship with economic growth in the human capital and financial development interactive term. The human capital and financial development growth in Sub-Saharan Africa has been examined by Ibrahim (2018) in the latest research. He said human capital and financial development boost economic growth in the short and long-term. The combined effect of human capital and financial development has suggested that financial development primarily stimulates growth with strong human capital quality. Better accumulation of human capital leads to innovation and adaptation of new technologies to promote global economic growth. Hakeem (2010) , the stock of physical capital and human capital is compulsory for growth. Due to financial under-development, the study did not find any strong effect of economic development on growth. However, the combined impact of human capital and financial development is key to accelerate the growth and nonappearance of anyone who can affect and reduce development speed in the Sub-Saharan Africa region. Evans et al. (2002) also claim positive and significant interaction of human capital and financial development toward the economy’s growth and ignorance can mislead as both are of the same importance to growth.

Is there any combined impact of human capital and financial development on economic growth in emerging countries?

Methodology

Data and preliminary findings.

This study constructed a set of panel data of 83 emerging economies from 2002–2017. The selected time interval and the number of countries were only based on the availability of data. Data related to all the variables used in this research was collected from WDIs, listed on the World Bank website. The study used two financial development indicators; domestic credit provided by the financial sector (DCfs) and domestic credit to private sectors (DCps), and two human capital indicators; secondary school enrollment (SSE) and primary pupil-teacher ratio (PPTR). DCps refers to financial resources provided by financial corporations to the private sector as a percentage of GDP such as via loans, non-equity securities purchases, commercial credits and other receivable accounts. While, DCfs includes all gross credit to different sectors as a percentage of GDP, except for net central government credit. SSE ratio is the ratio of total enrollment, irrespective of age, to the age group population in a percentage that corresponds officially to the educational level shown. SSE concludes the basic education that started at the primary level and is intended to lay the basis for permanent learning and human development. At the same time, the PPTR is the average number of pupils per primary school teacher in a percentage. In this research, we use real GDP per capita as an indicator of economic growth taken as the constant prices of the year 2010 in the US dollar amount in line with standard literature.

This research uses five control variables, namely, general government expenditure, inflation, labor force, trade openness and fixed capital formation. These variables are developed based on the growth theory of neo-classical. Government general expenditure measures the size of government and is projected to influence economic growth negatively. Inflation relates to the consumer price index, representing an annual shift in the cost for the average user of services and products. Inflation is used as the macroeconomic proxy of (in)stability and is expected to influence the economy’s growth negatively. Trade openness relates to the number of products and services as a percentage share of GDP exports and imports and is anticipated to impact economic growth positively. The labor force’s participation rate is the proportion (percentage) of the population 15 to 64 years of age who are economically active and is expected to positively influence the economy’s growth. While the gross capital formation relates to the cost of additions to the economy’s fixed assets plus net inventory changes as a proportion of GDP and is anticipated to have a positive effect on the economy’s development.

Specification of the model

In this study, to assess the impact of human capital and financial development on economic growth in emerging nations, we use Ibrahim (2018) ’s the endogenous model. This study uses SSE and PPTR variables as the stock of human capital and uses DCps and DCfs variables as financial development indicators: Δ l n y i t = δ + ρ l n y i t - 1 + α 2 l i t + α 3 p k i t + α 4 h k i t + α 5 f d i t + α 6 ( h k i t × f d i t ) + α 7 q i t + τ i + ϑ t + ε i t

y it = Real GDP per capita in the country i at time t

l it = Labor force in the country i at time t

pk it = Stock of physical capital in the country i at time t

hk it = Stock of human capital in the country i at time t

fd it = Financial development indicators in the country i at time t

q it = Government expenditure, inflation, trade openness in the country i at time t

τ i = Time effect in the country i

ϑ t = Country fixed effect at time t

ε it = Error term in the country i at time t .

The direct impact of human capital and financial development is examined based on α 4 and α 5, while the indirect effect of an interactive term is evaluated based on α 6 . As we rely on prior studies, we expect the direct impact of human capital and financial development α 4 , α 5 > 0. However, the PPTR is expected to negatively influence growth as learning and teaching must be efficient and effective if the ratio is low. On the other hand, the impact of an interactive term of both human capital and financial development is expected α 6 > 0.

The research used the two-step system generalized method of moments (GMM), the dynamic panel estimate, to determine the impact of human capital and financial development on economic growth in emerging nations. Meanwhile, Hansen (1982) presented the two-step system GMM; the system GMM has become a valuable estimation procedure in many fields of finance and applied economics. It can be viewed as a generalization of various other estimates, i.e. maximum likelihood and ordinary least square. System GMM is much more versatile. It uses assumptions about the extra moment conditions by using the lagged value of an independent and dependent variable as valid instruments in the model and levels of lagged for endogenous variables in the model. It is, therefore, less probable to be incorrectly specified. The system GMM is a suitable method to make unbiased and consistent estimates based on the system regression in variations with the regression level. Blundell and Bond (1998) , system GMM which considering the valid tools on even back of extremely persistent variables, is preferable to the GMM of first difference. However, the effectiveness and consistency of the system GMM technique depend on the validity of test tools as examined by the serial AR1 or AR2 correlation test and by the Hansen exogeneity test for overstated limitations.

Descriptive analysis

The total observations for real GDP are 1,328, with the mean value of the 3,443.437and having the standard deviation value of 2,952.97. The total observations for the government’s general expenditures are 1,278, with a mean value of 15.224 and having a standard deviation of 6.532. The inflation rate has a mean value of 6.28 with a total observation of 1,292 and has a standard deviation value of 6.47. The total number of observations for trade openness is 1,306, with a mean value of 79.11 and a standard deviation of 32.111. The total number of observations of the labor force is 1,328, with its mean value is 66.144 and its standard deviation value is 10.502. The number of observations for the variable of physical capital is 1,276, with a mean value of 24.075, while the standard deviation of 8.778.

The SSE and PPTR are the representative variables of the human capital. The SSE’s mean value is 67.57, with a total observation of 1,007 and has a standard deviation of 26.191. While on the other hand, the PPTR has a mean value of 30.292, with several observations of 1,024 and having a standard deviation of 12.69.DCps and DCfs are the representative variables of financial development. The mean value of the DCfs is 45.87, which indicates that financial sectors provide 45.87% of the GDP as a domestic credit. While on the other hand, the mean value of the DCps is 35.799, which indicates that almost 35.80% of credit provided by financial sectors in the form of domestic credit is allocated to private sectors. The standard deviation values of the DCps and DCfs are 27.582 and 38.301 correspondingly ( Table 1 ).

The correlation analysis of the variables allows the researchers to identify the correlation between the different variables that potentially affect the investigation’s independent variable contribution. However, the correlation analysis results shown in Table 2 , no variable presents a larger correlation that may affect the analysis results of this study.

Table 2 shows a significant and positive correlation between real GDP and SSE, the relationship between the real GDP and the PPTR as expected, which is negative and significant according to the suggested hypothesis. The correlation between real GDP and the two variables, i.e. DCps and DCfs, is positive and statistically significant, consistent with the hypothesis proposed.

The relationship between a dependent variable and control variables in this research is also according to the study expectations. It is positive and significant that real GDP is linked to per capita and capital formation, trade openness and government expenditure. There are adverse and significant inter-relation of labor and real GDP per capita while also negative, statistically significant interrelationship of inflation and real GDP. The correlation matrix generally shows a stable and not so preeminent correlation between all of these variables that may impact the analysis of this research.

We examine human capital and financial development and their interactive term through a two-step system GMM in panel data estimation in 83 emerging countries. In the model, we use a lag value of real GDP, financial development indicators, human capital indicators, inflation, physical capital, labor force, general government expenditure and trade openness variables consistent with standard literature. We include country and time effect in our estimation to deal with time associated shocks and heterogeneity of the country in growth. We estimate four different model combinations by introducing the different indicators of human capital and financial development in the model, and the results are presented in Table 3 .

First, begin with discussing estimated models’ fitness, we get p -values (0.0000) of Wald chi 2 for all models, indicating that models are well specified and jointly significant. The Hansen test for over-identifying restrictions shows that the used instruments are valid, and no hypotheses can be rejected. The AR 2 examination of autocorrelation reveals that there is no serial correlation among the variables.

The coefficient of the lag.1real GDP growth per capita is negative and in line with standard growth literature, implying a conditional convergence ( Barro, 1991 ; Ibrahim, 2018 ; Mankiw et al. , 1992 ). The results indicate that emerging countries are converging to their stable per capita growth, and over time, they will ultimately converge to a common growth rate in the economy. The convergence rate provided by the lagged coefficients improves in all models as we track other independent variables indicating that the region’s growth perspective effectively supports the hypothesis.

In model 1, the coefficient value of the human capital variable SSE is 0.0205 (positive) and significant to growth, indicating that human capital increases economic growth. This inline with previous studies like ( Barro, 2001 ; Bosworth and Collins, 2003 ; Hakeem, 2010 ; Mankiw et al. , 1992 ). The coefficient value of financial development variable DCfs is 0.0395 (positive) and significant to growth, indicating that financial capital increases economic growth. This is in line with previous studies( Ibrahim, 2018 ; Levine, 2005 ; Schumpeter, 1911 ). The interaction term results indicate that the combined effect of SSE and DCfs increases the growth by 0.0140%, which shows that the interaction term of human capital and financial development has a positive and significant influence on the economic growth at a 1% significance level. These are similar to previous studies ( Ibrahim, 2018 ; Evans et al. , 2002 ; Hakeem, 2010 ).

The Model 1 control variables’ findings indicate that the workforce is positive but insignificant for economic growth. Fixed capital formation influences financial development significantly and positively. While trade openness, inflation and overall government expenditure impact development are negatively and statistically significant.

In model 2, the coefficient value of the human capital variable SSE is 0.0127, positive and significant to growth, indicating that human capital increases economic growth. The coefficient value of financial development variable DCps is 0.0227, positive and significant to growth, indicating that financial development increases economic growth. The interaction term results also suggest that the combined effect of SSE and DCps increases the growth by 0.0793%, which shows that the interaction term of human capital and financial development has a positive and significant influence on the economic growth at a 1% significance level.

The Model 2 control variables’ findings also indicate that fixed capital formation has a positive and significant impact on financial development. The labor force has a negative but insignificant influence on growth. While trade openness, inflation and general government expenditure have a negative and significant influence on growth.

In model 3, the analysis was done to illuster the interaction effect of human capital (primary pupil-teacher ratio) and financial development (domestic credit) on the economic development in emerging countries. In model 3, the coefficient value of the human capital variable (primary pupil-teacher ratio) is −0.1114 (negative) and significant to growth, indicating that economic growth increases with decreasing primary pupil-teacher ratio as expected. Here human capital also has a positive and considerable influence on growth. The coefficient value of the financial development variable domestic credit is 0.1153 (positive) and significant to growth, indicating that financial development increases economic growth. The interaction term value suggests that the combined effect of the primary pupil-teacher ratio and domestic credit increases the growth by 0.0330%, which implies that the combined impact of human capital and financial development has a positive and significant influence on economic growth. The above analysis interprets that solely human capital and financial development enhance economic growth, but their combined effect boosts the economic growth of developing economies. The findings for Model 3 control variables show that the labor force, fixed capital formation and trade openness positively and significantly impact the growth. However, inflation and government general expenditure have a negative and significant impact on economic growth.

In model 4, the human capital variable PPTR coefficient is −0.0245 (negative) as expected, which indicates an increase of economic growth with the decrease of PPTR. In this respect, human capital influences growth positively and significantly. The financial development’s variable DCps coefficient value is 0.0470 and positive for growth, which indicates that financial development is increasing economic growth. The interaction term’s findings show that the combined impact of PPTR and DCps improves growth to 0.0101%, demonstrating that human capital and financial development have a positive and significant effect on the growth.

The findings of the control variables of Model 4 also indicate that the labor force, fixed capital formation and trade openness have a positive and significant impact on growth while inflation and government general expenditure have a negative and significant impact on economic growth.

Conclusion and recommendations

This study investigates the key aspects of human capital, financial development and interactive term in emerging countries. This research focuses on all emerging countries from which 83 economies have been selected based on data availability. This study uses panel data analysis for 2002 and 2017 and collected secondary data from WDIs. In this research, we use SSE and PPTR as human capital proxy and DCfs, as well as DCps variables as financial development indicators. The study uses growth rates of real GDP per capita of US dollars 2010’s constant prices to measure economic growth. It uses descriptive analysis, correlation analysis and a two-step system GMM method.

The main findings of this study are that human capital positively affects economic growth. This inline with previous studies like ( Barro, 2001 ; Bosworth and Collins, 2003 ; Hakeem, 2010 ; Mankiw et al. , 1992 ). SSE is positive to economic growth in model combinations 1 and 2. While PPTR is negative as expected to economic growth in model combinations 3 and 4. These results indicate that human capital increases economic growth in emerging countries. Besides, financial development has a statistically significant and positive impact on growth. This corresponds to earlier studies ( Ibrahim, 2018 ; Levine, 2005 ; Schumpeter, 1911 ). As DCfs is positive to economic growth in model combination 1 and 3. DCps is also positive to economic growth in model combination 2 and 4. These results indicate that financial development increases economic growth in emerging countries.

This study also explored the interactive term of human capital and financial development. The results indicate a positive and significant impact of the interaction term on economic growth in all model combinations. This is in line with previous studies ( Ibrahim, 2018 ; Evans et al. , 2002 ; Hakeem, 2010 ). In a nutshell, human capital and financial development are twins needed to accelerate growth in emerging countries. Hence, neglect of either could affect the pace of development in the states.

So, emerging countries should invest in human capital and focus on financial development. The results of this research show that human capital and financial development increase economic growth in emerging economies. They should increase access to education by increasing the number of schools across different regions and ensure the supply of highly qualified teachers. They should focus on the financial system and their functions to get the benefits from it. Policymakers in emerging countries should concentrate on this while making and implementing the country’s economic policies.

Limitation and future study of research

The data set is limited to 83 emerging countries of the world. The time period for the study is 2002 to 2017. Future studies can be done by increasing the time period of the study or to a specific region of the world. More variables can be added for more deep studies, and comparative analysis can be done among different countries.

Descriptive statistics of variables

Correlation matrix

Shows significance level at 1%,

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A Systems View Across Time and Space

Open access
Published: 05 December 2021

Financial inclusion and development in the least developed countries in Asia and Africa

Antonella Francesca Cicchiello ORCID: orcid.org/0000-0003-3367-1620 1 ,
Amirreza Kazemikhasragh 2 ,
Stefano Monferrá 1 &
Alicia Girón 3

Journal of Innovation and Entrepreneurship volume 10 , Article number: 49 ( 2021 ) Cite this article

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The purpose of this paper is to investigate the relationship between the financial inclusion index and development variables in the least developed countries in Asia and Africa by using annual data of 42 countries for the period 2000–2019. The pooled panel regression and panel data analysis technique are used to explore this relationship. The empirical finding indicates that economic growth leads to financial inclusion. Unemployment and literacy rates are among the factors contributing to financial inclusion, and it is observed that women are more vulnerable than men are to lack financial inclusion. In less developed countries, the economy relies heavily on agriculture, and people are less financially inclusive when they live in rural areas of these countries. Also, pay inequality reduces financial inclusion rates and has a negative impact on development. The low financial inclusion rate reduces the levels of development in these countries. The results of this study can lead to the development and empowerment of vulnerable groups in the studied countries. In order to improve the conditions for development, policymakers should consider policies that enhance literacy, eliminate gender inequality and increase pay equality.

Introduction

Financial inclusion (FI) can be defined as the process ensuring that individuals, households and businesses in a community have adequate access to formal financial services and products such as transactions, credit cards, payments, savings and insurance, and that these are delivered in a sustainable way (Singh & Singh Kondan, 2011 ).

Over the last years, financial inclusion has become one of the most critical issues in the area of monetary policy. Various international conferences, including the conference that the United Nations sponsored in 2019, emphasised the need to provide an adequate level of financial inclusion in the least developed countries, without which individuals and companies are unable to fully participate in the national economy.

Growing evidence shows that inclusive financial markets reduce rates of poverty and inequality by allowing individuals and households to manage consumption and payments, receive bank loans, have insurance coverage (Mader, 2018 ). Furthermore, financial inclusion promotes the birth of new innovative companies and the expansion of existing ones, creating jobs that contribute to national savings (Ajide, 2020 ). Finally, financial inclusion strengthens the economic empowerment and active participation in the financial system of youth, women and other groups of people previously excluded (Hendriks, 2019 ; Siddik, 2017 ). Agyemang-Badu et al. ( 2018 ), for example, reveal that financial inclusion reduces poverty and income inequality in Africa, and thus they recommend implementing policies and programs to strengthen the formal financial inclusion of the poor. In a recent study, Koomson et al. ( 2020 ) find evidence that an increase in financial inclusion reduce the poverty of Ghanaian households, especially of those headed by women, and prevent their exposure to future poverty.

It is against this background that governments and international bodies in Africa and Asia have started promoting financial inclusion. In particular, they adopted new mechanisms, strategies and policies aimed at achieving inclusive development and improve financial services to underserved individuals and companies (Chinoda & Kwenda, 2019 ; Gretta, 2017 ; Loukoianova et al., 2018 ).

In 2019 the African Development Bank (AfDB) launched the Africa Digital Financial Inclusion Facility (ADFI), an innovative financing facility designed to accelerate digital financial inclusion across Africa and ensure access to the formal economy to millions of Africans. Similarly, the Government of India developed a biometric ID system called “Aadhaar” made to increases access to formal financial services for consumers and reduces costs for providers (Banerjee, 2016 ).

Despite the efforts made, financial inclusion remains a key challenge in the Asian and African regions where the benefits of the digital age are not being shared equally and important access gaps persist between men and women, poorer and richer households and rural and urban populations (Demirgüç-Kunt et al., 2018 ). As a consequence, many countries in these regions register very high exclusion rates when compared to other countries (Abubakar et al., 2020 ; Le, Dang, et al., 2019 ). Nigeria, for example, still has a dismal position of 68% exclusion rate even after 4 years of the implementation of its strategy for financial inclusion of 2012. More than one billion people within developing Asia have no access to formal financial services, such as bank accounts (only 27% of adults have an account in a formal financial institution) (Le, Chuc, et al., 2019 ).

Financial exclusion remains a widespread obstacle. Conroy ( 2005 ) and Gloukoviezoff ( 2007 ) defined financial exclusion as the deprivation of access to the financial system for certain community groups. Governments such as the Indian one, have enacted laws to provide access to financial services for all and to provide appropriate credit to vulnerable groups in the lower-income quintiles. Nevertheless, some groups may still be denied access to financial services due to omnipresent factors such as social and ethnic discrimination. Last, populations in rural areas are be considered too costly for financial institutions.

Innovations in banking and financial systems are essential to increase the level of financial inclusion, enhance prosperity and reduce poverty in the least developed Asian and African countries.

The importance and need for inclusive financial systems in developing countries motivate our study. Using annual data from 42 least developed Asian and African countries for the period 2000–2019, we investigate whether development leads to an all‐inclusive financial system. In particular, this study aims to examine the relationship between financial inclusion and development by empirically identifying country-specific factors that are associated with the level of financial inclusion. For this purpose, following Sarma and Pais ( 2011 ) we calculate the financial inclusion index (FII) for each country analysed. Then, we use the pooled panel regression and panel data analysis technique to measure the relationship between the relevant variables and the financial inclusion index. Finally, we present the results of empirical analysis to determine country-specific factors associated with the level of financial inclusion. Overall, the results show that literacy, urbanisation, and unemployment are significantly associated with financial inclusion. Income inequality is another important factor.

The relationship between financial inclusion and development has been an ongoing debate in developing countries. However, this issue has been neglected in the least developed Asian and African countries where there is little or no evidence to support this relationship. This study adds to the existent literature on financial inclusion in different ways. First, it contributes to empirical evidence and to the understanding of the determinants of financial inclusion and its impact on economic growth focusing on the least developed countries in the Asian and African regions. Though a number of researchers have delved into issues related to development and financial inclusion, an essential gap exists in the literature regarding the least developed countries in these regions. Second, our analysis contributes to the literature on gender discrimination by analysing the impact of gender and related factors on financial inclusion and economic growth in the countries under study. Third, this study analyses all major relationships between variables using pooled panel regression and panel data analysis technique to properly process endogeneity associated with financial inclusion.

The rest of this study is organised as follows. “ Literature Review ” Section reviews the related literature on financial inclusion. “ Methods ” Section describes data, model and methodology. “ Results and Discussion ” Section presents and discusses the empirical results. “ Conclusions ” Section concludes the study.

Literature review

Financial inclusion and development.

The research on this topic has defined financial inclusion and financial exclusion in various contexts, including inclusion or exclusion from social activities. According to Marshall’s ( 2004 ), Wilson’s ( 2012 ) and Buckland’s ( 2012 ) studies, financial exclusion is defined as a lack of access to financial services. Exclusion can happen in many forms and depends on circumstances such as geographical location, the cost of services and a lack of proper information and education about the benefits of financial services. Financial exclusion stems from a dearth of access to formal financial services for individuals or groups in a community for multiple, potentially discriminatory reasons (Sinclair, 2001 ).

A well-developed and appropriate financial system is essential for economic growth and it can serve as a means of attracting the investment needed to drive a country’s development.

Development, in turn, can increase the breadth of financial services and the financial system. Policymakers can facilitate financial services by making changes to existing laws. An undeveloped financial system can be costly for individuals planning to use financial services (Beck & De La Torre, 2006 ), and the consequences of underdevelopment include financial exclusion of groups in society and reductions in economic growth. A less developed financial system offers lower-quality services to customers, puts into question the economic justification for investing in new businesses and deprives vulnerable groups, such as those from lower-income brackets, of economic benefits (Edwards, 2017 ; Servon & Kaestner, 2008 ).

Many studies have recently been published on financial inclusion in which the authors clearly define the importance of this subject. However, the crucial missing point is the standard measurement of the global index by scientists and policymakers for understanding the financial inclusion rate for each community or country. Different models have been used to measure the global index, but it is necessary that experts in this field reach a consensus on a model.

Developing countries are considering policies to create appropriate job opportunities, reduce gender discrimination and increase literacy. For example, Atkinson and Messy ( 2013 ) believe that methods and policies for a fiscal strategy can lead to reducing discrimination in the area of finance. This requires changes in policy structure at the financial level. The result of anti-discrimination policies can help vulnerable groups and promote economic development.

Researchers employed different econometric techniques to measure financial inclusion with different databases. For example, Grohmann et al. ( 2018 ) and Wang and Guan ( 2017 ) used data from different countries and showed that formal financial services were more inclusive of families headed by men. Researchers have been trying to measure the inclusion index using different techniques so that they can compare across countries. Sarma and Pais ( 2011 ) have estimated the financial inclusion index by using World Bank data and applied an econometric approach to combine data to create a financial inclusion index. Just as we used this method in our study, other researchers (e.g., Dienillah et al., 2018 ) have used Sarma’s method to calculate the financial inclusion index. Wang and Guan ( 2017 ) used this method to estimate the rate of financial inclusion in more than eighty countries, allowing for a comparison of the results between developed and developing countries.

Sarma ( 2012 ), in another study, used data from the World Bank database named the Global Financial Inclusion (Global Findex), to demonstrate the positive and significant relationship between development and financial inclusion. The study revealed a close relationship between the Human Development Index and the financial inclusion index; Sarma compared social factors such as income, literacy and urbanization to prove that the development of the financial and banking sectors is directly related to financial inclusion.

Demirgüç-Kunt and Klapper ( 2013 ) show that income and education are two crucial variables for accessing financial services. Kairiza et al., ( 2017 ) also confirms this and proposes a positive and significant relationship between financial inclusion and variables such as literacy, population and income. Furthermore, Kumar ( 2012 ) showed that poverty had fallen sharply in Indian cities where customers were provided greater access to financial and banking services. Park and Mercado ( 2015 ) similarly illustrate that changes in the regulation of the financial system led to a decrease in inequality and promote banking and financial stability. Jabir et al. ( 2017 ) reveal that financial inclusion dramatically reduced poverty among low-income households in sub-Saharan African countries by providing net wealth and greater social benefits.

In their study, Adeola and Evans ( 2017 ) prove how financial inclusion, in terms of financial access and financial usage, can help to drive economic diversification in Nigeria. According to the authors, financial inclusion can help Nigeria to build shared prosperity and abolish extreme poverty. Kim et al. ( 2018 ) find that financial inclusion has a positive effect on economic growth in the Organization of Islamic Cooperation (OIC) countries.

Using firm-level data from 79 emerging and developing countries, Chauvet and Jacolin ( 2017 ) analyse the impact of financial inclusion and bank competition on firm performance. The authors reveal that financial inclusion has a positive impact on firm growth, especially when bank markets are less concentrated. They also find that more competitive banks favour firm growth only when the levels of financial inclusion are high.

Le, Chuc, et al. ( 2019 ) investigate the impact of financial inclusion on financial efficiency and sustainability across 31 Asian countries. The authors reveal a negative impact of financial inclusion on financial efficiency but a positive impact on financial sustainability.

Based on a sample of 62 countries over the period from 2001 to 2012, Rizwan and Bruneau ( 2019 ) investigate the role of information and communication technologies (ICT) in extending financial inclusion and reducing poverty and income inequality. According to the authors' results, ICTs boost financial inclusion, accelerate the economic growth and reduce poverty and inequality.

In a recent study, Ajide ( 2020 ) reveals that financial inclusion also has a significant and positive effect on entrepreneurship in Africa.

Abubakar et al. ( 2020 ) identify financial inclusion as one of the growth-enhancing factors for developing countries and state that an inclusion faster than the rate of population growth would produce a better financial inclusion index and truly accelerated the economic growth of Nigeria. Using a sample of 53 developing countries between 2004 and 2017, Ouechtati ( 2020 ) empirically examines the effect of financial inclusion on poverty and income inequality. The author finds evidence that financial inclusion contributes to reducing poverty and income inequality by increasing the availability of credit and access to deposit accounts at commercial banks. Omar and Inaba ( 2020 ) find similar results in 116 developing countries in Asian, African, and Latin American and the Caribbean regions.

This research forms the core of the field of measuring financial inclusion and its relationship to development-related variables. Few prior studies examined the relationship between development and financial inclusion, but the above studies provide a broad view of relevant results and methodologies. Consistent with the previous literature review and its findings, this paper attempts to investigate financial inclusion and economic development in the selected countries through the use of development variables and to find the relationship between financial inclusion and income inequality.

Hence, we address the following research questions: First, what are the crucial factors that affect the level of financial inclusion in least developed countries? Second, does financial inclusion reduce unemployment and income inequality in least developed countries? Third, does financial inclusion increase literacy and rural population growth in least developed countries? Fourth, are there any conditions under which financial inclusion can play a more effective role in reducing inequality in wealth distribution and increasing the GDP in least developed countries?

This research differs from other studies in econometric techniques and case studies.

This study uses data collected from the following databases: The World Bank, the International Labour Organization (ILO), the International Monetary Fund (IMF) and the United Nations. The data sequence comprises annual data from 2000 to the end of 2019. We focused on the variables used in Sarma and Pais ( 2011 ), e.g., GDP, literacy rates, unemployment rates and Gini coefficients. We then used Stata to analyse the data. The panel analysis was used to determine the relationship between financial inclusion and development in the selected countries. Footnote 1

In line with the literature studied, we focused on important variables including GDP, literacy rate, the literacy rate for men, literacy rate for women, unemployment rate for men, unemployment rate for women, Gini coefficients as well as financial inclusion index. We created the financial inclusion index through the variables used in Sarma and Pais ( 2011 ).

The financial inclusion index for each country is calculated through a principal component analysis (PCA), using the relevant variables such as access to banking services, the number of bank branches, access to credit through the formal financial system and allocated credit to the private sector through the banking system. The choice of a PCA is advantageous for the creation of the index because this methodology creates a cumulative relationship between the variables (Naik, 2017 ), which establishes the representative index of financial inclusion. We estimated the total variance clarified by the principal components for each country. We also selected the value of the eigenvalue where it was calculated to be more than one; we removed other components with a value of less than one, establishing the preliminary eigenvalues linked with appropriate components, and then computed the financial inclusion.

Meanwhile, we took the eigenvalue greater than 1; we cut only the principal components lower than 1, and the components clarified the precise percentage of the entire difference restricted in all variables. The other components are not reflected, and subsequently, their marginal evidence is moderately unimportant. These principles were then used as the bulks to calculate the PCA. For occurrence, the first principal component. The financial inclusion index built on the original eigenvalues related to relevant components; we computed the financial inclusion index for each country separately from the complex module.

Table 1 provides descriptive statistics for all used variables. We used the literacy variable to explain the relationship between financial inclusion and literacy, which Trudell ( 2009 ) reports is one of the most important factors in development. The table below shows that the average literacy rate in men is much higher than in women, and more than half of the population is illiterate. Another variable is economic growth, which is one of the important variables for this concept (Litchfield, 1999 ). The population growth is high in less developed countries, and the impact of population growth on the level of per capita production depends on the pattern of population growth and on institutional plans (Simon, 2019 ). There are some studies on financial inclusion and income inequality: Abdulkarim and Ali ( 2019 ), for example, show that income inequality has a profound impact on financial inclusion. As income inequality increases, the degree of financial inclusion decreases, and this impedes development. The table below shows that the average inequality in less developed countries is higher than the global average. In the data used, the average unemployment rate for men is lower than for women, and the average financial inclusion index is close to zero.

With Sarma and Pais ( 2011 ) research in mind and in order to analyse and determine the relationship between financial inclusion and development in the least developed countries in Asia and Africa, we applied the following models:

Model 1: ${FII}_{it}=\alpha +{\beta }_{1}{GDP}_{it}+{\beta }_{2}{LIT}_{it}+{\beta }_{3}{RURP}_{it}+{\beta }_{4}{UNEMM}_{it}+{\beta }_{5}{UNEMF}_{it}+ {\beta }_{6}{GINC}_{it}+{\tau }_{t}+{\varepsilon }_{it}$

Model 2: ${GDP}_{it}=\alpha +{\beta }_{1}{FII}_{it}+{\beta }_{2}{LIT}_{it}+{\beta }_{3}{RURP}_{it}+{\beta }_{4}{UNEMM}_{it}+{\beta }_{5}{UNEMF}_{it}+{\beta }_{6}{GINC}_{it}+{\tau }_{t}+{\varepsilon }_{it}$

$FII$ represents the financial inclusion index in the period t , ${GDP}_{it}$ represents the GDP, $RURP$ represents the total population in the rural area, $UNEM$ represents the unemployment rate, $GINC$ represents the Gini coefficients, $LIT$ represents literacy and ${\varepsilon }_{i}$ represents the error term.

The error term is included in the model to characterize the unobserved time aspects that are different between countries but fixed within countries over time.

As mentioned previously, observations from the literature have illustrated that inclusive policies lead economic to development. We employed the pooled panel regression and panel data analysis technique to examine the impact of our hypothesis during the chosen period (2000–2019) in the selected countries. Later, we run the panel data analysis technique to approve our results from the pooled panel regression. Panel data analysis has advantages such as including more degrees of freedom than single cross-section or time-series and more sample variability than cross-sectional data. Additionally, it has a great capacity for capturing the complexity of variables interacting with each other (Biørn, 2016 ).

We assumed that there is an effect between variables in the selected countries, so we wanted to use the panel data analysis technique. In this technique, there is either a fixed-effect or a random effect between the independent variables and the dependent variable. The fixed effects model produces a constant estimate, but in contrast, the Hausman test determines an appropriate model. Panel data makes it possible to analyse the series of times and different countries. Therefore, estimating panel data increases estimation efficiency by analysing a large number of data, increasing the degree of freedom and reducing the collinearity between variables. Another advantage of this technique is that it allows for the analysis of data by using many variables at the same time in a time series and selected countries (Petersen, 2004 ).

Results and discussion

We first used pooled panel regression to find the relationship between the financial inclusion index and the underlying economic and social variables to better understand development. The indicators that we tried to identify include the following: economic growth, literacy rates, unemployment rates, income inequality (Gini coefficient) and rural population growth.

Table 2 represents the results of the pooled panel regression, which show that GDP has a direct and significant relationship with the financial inclusion index. This means that, by increasing gross domestic product, the financial sector provides more financial services to more individuals and groups in society. The results indicate a significant relationship between the financial inclusion index and literacy rates and educational attainment, and there was a clear, negative relationship between the index and rural populations. The Gini coefficient shows that increasing equality in wealth distribution can promote financial inclusion.

Table 3 shows the results of the main panel, specifically, the results of fixed effects and the consequences of random effects. The first column shows the main variables. As detailed in the methodology section, we used a PCA to create the financial inclusion index. The results of the random effects model indicate an increase in the financial inclusion of GDP. The results of the fixed effects are also similar. In empirical analyses, it is essential to choose between random and fixed effects (Bartels, 2008 ), and to do so, we employed the Hausman test, with the results suggesting that a fixed effects estimator was most appropriate.

The results show that financial inclusion can increase rapidly as GDP increases, also, GDP growth can increase the level of financial inclusion. For example, by increasing only one percent of GDP, we can see an increase of more than two times in the financial inclusion index. However, the results show that applying the inclusive policy can increase economic growth. These results corroborate the results of previous studies that demonstrated a significant relationship between these variables. As observed in the panel regression, these two variables have a positive and meaningful relationship with each other. We run the robustness check to control coefficient estimates’ behaviour when adding country (Neumayer & Plümper, 2017 ). Our findings show that the coefficients do not change much.

The results in Table 3 also show that the variable literacy rate is significant. The factor of literacy on financial inclusion for women is lower than for men; this shows that women in the studied countries are less financially inclusive than men. Table 3 indicates that the total literacy has a coefficient equal to 0.26. A 1% increase in the literacy variable can thus lead to a 26% increase in the financial inclusion index. The literacy coefficient is only 14% for men and 12% for women; this proves once more that women in less developed countries in Asia and Africa experience less financial inclusion than men do. The results suggest that the development of education needs to increase equally for men and women, the results of Panel B confirm that gender equality in education can increase economic growth. The unemployment rate is another variable that supports this conclusion, with the increase in the unemployment rate for men who less financial exclusive than women. Women were found to more likely experience financial exclusion when losing their jobs; this may be due to a variety of reasons, including racial discrimination. All the above patterns prove that gender is essential in the financial inclusion index. The same as results for pooled panel regression, we run the robustness check and the coefficients do not change much.

Our estimations also identified that increase in rural population size reduces financial inclusion. Because people living outside cities have less access to financial institutions, this poses a problem in the studied countries, where agriculture is an important pillar of the economy and where more financial inclusion is essential for agricultural development. Finally, as expected, an increase in the Gini coefficient will lead to a rise in the pay gap, and this will lead to a decrease in financial inclusion.

Conclusions

Despite the progress made in recent years around the world, financial inclusion is still a critical issue. About 1.7 billion adults do not have an account with a formal financial institution or a mobile money provider (Demirgüç-Kunt et al., 2018 ). Most of the financially excluded population is in developing countries.

The vulnerable groups in society are more likely to be excluded from the financial system (Carbo et al., 2005 ; McKillop and Wilson, 2007 ; Wilson, 2012 ), and the financial deprivation is higher in rural areas. The fair distribution of wealth in society has a direct relationship with financial inclusion, and research shows that a low degree of financial inclusion leads to social exclusion and, consequently, to less development.

This study empirically examines the relationship between financial inclusion and development by identifying country-specific factors that are associated with the level of financial inclusion in 42 least developed Asian and African countries for the period 2000–2019. A pooled panel regression and a panel data analysis technique are used to measure the relationship between the relevant variables and the financial inclusion index (FII). Also, we performed robustness check by country for pooled panel regression and panel data analysis.

The analysis of this study shows that the increase in GDP is a prominent indicator of financial inclusion. Inequality in wealth distribution can lead to financial exclusion and naturally affect economic growth. The literature in this field identifies a positive correlation between financial inclusion and human development (Thorat, 2006 ), and as the results of this study show, economic growth is indeed an essential factor in increasing financial inclusion.

Increased educational attainment leads to greater access to financial services, and the results show that gender-based discrimination affects less developed countries. Among the studied variables, as pay inequality and low access to financial services rise, financial inclusion decreases. The introduction of incentives to improve literacy, eliminate gender inequality and increase pay equity will enhance the conditions of development.

Finally, these findings emphasise that financial inclusion is, in fact, a reflection of the widespread involvement of all different groups in the society, the equal distribution of wealth, and the increasing level of literacy in all different groups of society.

Overall, the empirical findings of this research can be of particular interest for policymakers and other regulators to define impactful policies promoting financial inclusion in the least developed countries in Asia and Africa by ensuring the establishment of the right financial services and tools and the removal of cultural and economic barriers. Least developed countries can empower youth, women and other vulnerable groups traditionally marginalised by changing their financial policies and designing new incentives to increase financial participation.

Furthermore, this research contributes to providing statistical and economical validity to the Africa Digital Financial Inclusion Facility (ADFI), launched in 2019 by the African Development Bank (AfDB) and aimed at accelerating digital financial inclusion across Africa and ensure access to the formal economy to millions of Africans.

Going forward, future studies could expand the experimental setting of our study by including other socio-economic and cultural factors, and investigate whether our results continue to hold in different contexts, particularly in developing countries.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the Global Findex database.

Least developed countries in Asia: Afghanistan, Bangladesh, Bhutan, Cambodia, East Timor, Lao PDR, Myanmar, Nepal and Yemen. Least developed countries in Africa: Angola, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Comoros, Democratic Republic of Congo, Djibouti, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Niger, Rwanda, São Tomé and Príncipe, Senegal, Sierra Leone, Somalia, South Sudan, Sudan, Tanzania, Togo, Uganda and Zambia. We excluded Somalia from our sample due to the lack of data in our database.

Abdulkarim, F. M., & Ali, H. S. (2019). Financial inclusions, financial stability, and income inequality in OIC countries: a GMM and quantile regression application. Journal of Islamic Monetary Economics and Finance, 5 (2), 419–438.

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A Bibliometric Analysis of Employee Withdrawal Behaviour: Current Status, Development, and Future Research Directions

Published: 20 May 2024

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Garima Pancholi 1 ,
Abhineet Saxena 1 ,
Amit Jain 2 &
Mita Mehta 3

The pandemic has left us a legacy of “The Great Resignation,” i.e., an aberrant workforce withdrawal. Employees’ commitment and loyalty have undergone a transformative shift in recent years. Organizations are now focusing on building employee resilience through engagement. Organizations need to develop fresh approaches to understand the factors of employee withdrawal behaviour (EWB). Through a systematic review methodology using a bibliographic database over the past 30 years time, the present study seeks to provide a clear understanding of the trends in EWB by identifying its dynamics and suggesting future research directions. We reviewed 400 documents collected from the Scopus database. A bibliometric study was conducted on 367 articles from 1992 to 2022 using a VOS viewer. This paper provides insights about publication productivity over the years, productive countries, journals, authors, cited reports, keywords, and cluster analysis. The findings of this research paper give a roadmap for further study in this domain. This article adds value to an organizational study by focusing on predictors of employee withdrawal behaviour, identified through scientific bibliometric analysis. Research on employee withdrawal behaviour across different countries is shown in this article. Collaboration among authors from other countries will be impactful since such collaborations are lacking.

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The data set used for this bibliometric research is from the Scopus database, and any other author can access it openly.

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All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Ms. Garima Pancholi, Dr. Abhineet Saxena, Prof. (Dr.) Amit Jain, and Dr. Mita Mehta. Ms. Garima Pancholi wrote the first draft of the manuscript, and all authors commented on previous versions. All authors read and approved the final manuscript.

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Pancholi, G., Saxena, A., Jain, A. et al. A Bibliometric Analysis of Employee Withdrawal Behaviour: Current Status, Development, and Future Research Directions. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02090-w

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Attracting investment in key industries

Making Australians the beneficiaries of change

A Future Made in Australia is about creating new jobs and opportunities for every part of our country by maximising the economic and industrial benefits of the move to net zero and securing Australia’s place in a changing global economic and strategic landscape.

The Government’s $22.7 billion Future Made in Australia package will help facilitate the private sector investment required for Australia to be an indispensable part of the global economy.

For more information refer to the Future Made in Australia fact sheet [PDF 438KB]

Better deploying capital in priority areas

The Future Made in Australia package will realise Australia’s potential to become a renewable energy superpower, value‑add to our resources and strengthen economic security by better attracting and enabling investment in priority areas. The Government will create a Future Made in Australia Act and establish a National Interest Framework that identifies priority industries and ensures investments associated with them are responsible and targeted.

The Framework will have a focus on industries that contribute to the net zero transformation where Australia has a comparative advantage, and where Australia has national interest imperatives related to economic resilience and security.

Strengthening and streamlining approvals

This Budget provides a faster pathway to better decisions on environmental, energy, planning, cultural heritage and foreign investment approvals.

This includes:

$134.2 million to better prioritise approvals for renewable energy projects of national significance, and support faster decisions on environment, cultural heritage and planning approvals.
Working with the states and territories through the Energy and Climate Change Ministerial Council to accelerate electricity grid connections.
$20.7 million to improve engagement with communities impacted by the energy transition and accelerate the delivery of key energy projects.
$15.7 million to strengthen scrutiny of high‑risk foreign investment proposals, enhance monitoring and enforcement activities and support faster decisions.

The Government will also encourage foreign investment by providing refunds of 75 per cent of application fees for unsuccessful competitive bids.

Promoting sustainable finance

The Government is committing $17.3 million to mobilise private sector investment in sustainable activities. This includes extending Australia’s sustainable finance taxonomy to the agriculture sector and developing a labelling regime for financial products marketed as sustainable.

The Government will also examine opportunities to improve data quality and provide $1.3 million to develop and issue guidance for best practice transition plans.

Making Australia a renewable energy superpower

Powering australia with cheaper, cleaner, more reliable energy.

Australia’s potential to produce abundant renewable energy is a powerful source of comparative advantage. To realise this, the Government is unlocking more than $65 billion of investment in renewable capacity through the Capacity Investment Scheme by 2030.

This Budget helps Australians benefit from cheaper, cleaner energy sooner by investing $27.7 million to integrate consumer energy resources like batteries and solar into the grid.

The New Vehicle Efficiency Standard will save Australians around $95 billion at the bowser by 2050 and reduce transport emissions.

Unlocking investment in net zero industries and jobs

This Budget accelerates growth of new industries by establishing the $1.7 billion Future Made in Australia Innovation Fund and delivering a 10‑year extension of funding to the Australian Renewable Energy Agency. It also delivers the $44.4 million Energy Industry Jobs Plan and $134.2 million for skills and employment support in key regions.

The Future Made in Australia package establishes time‑limited incentives to invest in new industries. The Hydrogen Production Tax Incentive will make Australia’s pipeline of hydrogen projects commercial sooner, at an estimated cost of $6.7 billion over the decade. This Budget also expands the Hydrogen Headstart program by $1.3 billion.

Boosting demand for Australia’s green exports

The Government is making it easier for businesses and trading partners to source low‑emissions products by building better markets and product standards for green products.

This Budget provides $32.2 million to fast‑track the initial phase of the Guarantee of Origin scheme, focused on renewable hydrogen, and bring forward the expansion of the scheme to accredit the emissions content of green metals and low‑carbon liquid fuels. The Government is also working closely with trading partners to identify opportunities to drive greater supply chain transparency and better market recognition of high environmental, social and governance standards in the critical minerals sector.

Realising the opportunities of the net zero transformation

Australia is committed to reaching net zero greenhouse gas emissions by 2050 and is developing six sector plans covering:

electricity and energy
agriculture and land
the built environment.

This Budget continues investment in effective emissions abatement, including through $63.8 million to support emissions reduction efforts in the agriculture and land sector.

The Government is also investing $399 million to establish the Net Zero Economy Authority and support the economy‑wide net zero transformation. This Budget also invests an additional $48 million in reforms to the Australian Carbon Credit Unit scheme and $20.7 million to improve community engagement.

Strengthening resources and economic security

Backing a strong resources sector.

The Government is investing $8.8 billion over the decade to add more value to our resources and strengthen critical minerals supply chains. This Budget establishes a production tax incentive for processing and refining critical minerals at an estimated cost of $7 billion over the decade. It commits up to $1.2 billion in strategic critical minerals projects through the Critical Minerals Facility and the Northern Australia Infrastructure Facility, and pre‑feasibility studies for common user precincts.

This is in addition to $566.1 million to support Geoscience Australia to map all of Australia’s critical minerals, strategic materials, groundwater and other resources essential for the transition to net zero.

Manufacturing clean energy technologies

The Government is committing $1.5 billion to manufacturing clean energy technologies, including the $1 billion Solar Sunshot and $523.2 million Battery Breakthrough Initiative. These investments will be delivered by ARENA.

Strengthening supply chains

To support the delivery of the 82 per cent renewable energy target, the Government has formed the National Renewable Energy Supply Chain Action Plan with states and territories. The Government will invest an additional $14.3 million working with trade partners to support global rules on unfair trade practices and to negotiate benchmarks for trade in high quality critical minerals.

Digital, science and innovation

Investing in new technologies and capabilities.

The Government is investing $466.4 million to partner with PsiQuantum and the Queensland Government to build the world’s first commercial‑scale quantum computer in Brisbane.

The Government will undertake a strategic examination of Australia’s research and development (R&D) system with $38.2 million invested in a range of science, technology, engineering, and maths programs.

The Government is providing $448.7 million to partner with the United States in the Landsat Next satellite program to provide access to critical data to monitor the earth’s climate, agricultural production, and natural disasters.

Modernising and digitising industries

This Budget commits $288.1 million to support Australia’s Digital ID System. A National Robotics Strategy will also be released to promote the responsible production and adoption of robotics and automation technologies for advanced manufacturing in Australia.

Reforming tertiary education

The Government is committing $1.6 billion over 5 years, and an additional $2.7 billion from 2028–29 to 2034–35 to reform the tertiary education system and deliver Australia's future workforce.

This includes $1.1 billion for reforms to university funding and tertiary system governance.

Over $500 million will be provided for skills and training in priority industries and to support women’s participation in these sectors.

The Government will set a tertiary attainment target of 80 per cent of the working‑age population by 2050.

Supporting students on placements

The Government will establish Commonwealth Prac Payments (CPP) for students undertaking mandatory placements. From 1 July 2025, the payment will provide more than 73,000 eligible students, including teachers, nurses, midwives and social workers with $319.50 per week during their placements.

Felicity is a full‑time student receiving Youth Allowance, living by herself. She is studying a Bachelor of Nursing and must stop paid work during her mandatory prac placement. During her prac, Felicity receives $712.05 per week from the Government including: $319.50 of CPP, $285.55 of Youth Allowance (YA), $103.50 of Commonwealth Rent Assistance (CRA) and $3.50 of Energy Supplement.

Felicity receives $351.55 a week more than she would have in 2023 before indexation and the changes to YA, CRA and CPP in the current and 2023–24 Budget

Broadening access to university

From January 2026, needs‑based funding will provide per student funding contributions for under‑represented students. The Government will also provide $350.3 million to fully fund university enabling courses and increase pathways for prospective students to university.

Skills pipeline for priority industries

Skills and training for Future Made in Australia industries

The Government will expand eligibility to the New Energy Apprenticeships Program to include work in the clean energy sector, including in construction and advanced manufacturing. This will provide access to $10,000 incentive payments and support our target of 10,000 new energy apprentices.

The Government will commit $30 million to turbocharge the VET teaching workforce for clean energy courses and $50 million to upgrade and expand clean energy training facilities.

The Government will invest $55.6 million to establish the Building Women’s Careers program to support women’s participation in key industries including clean energy and advanced manufacturing.

Supporting apprentices and building the construction workforce

The $5,000 support payments to apprentices in priority occupations will be maintained for another 12 months to 1 July 2025, up from $3,000 in the absence of any changes. Employers of these apprentices will receive a $5,000 hiring incentive, up from $4,000 in the absence of changes. This will provide certainty to apprentices while the Strategic Review of the Apprenticeship Incentive System is underway.

The Government will also invest $88.8 million to deliver 20,000 new fee‑free TAFE places including pre‑apprenticeships in courses relevant to the construction sector. The Government will provide $1.8 million to deliver streamlined skills assessments for around 1,900 migrants from comparable countries to work in Australia’s housing construction industry.

Strengthening our defence industry capability

An integrated and focused approach to defending Australia

The Government is investing an additional $50.3 billion over ten years to implement the 2024 National Defence Strategy to meet Australia’s strategic needs.

Overall funding for Defence will reach $765 billion over the decade. Defence’s Integrated Investment Program has been rebuilt to create a focused Australian Defence Force, accelerate delivery of priority capabilities, and provide certainty to grow Australia’s defence industry. This includes funding for the Royal Australian Navy’s surface combatant fleet and establishing a guided weapons and explosive ordnance manufacturing capability earlier.

The Government is reforming Defence’s budget to support the National Defence Strategy and delivery of priority capabilities.

Developing defence industry and skills

Industry development grants funding of $165.7 million will also help businesses to scale up and deliver the Sovereign Defence Industrial Priorities, which include continuous naval shipbuilding and sustainment, and development and integration of autonomous systems.

The Government is providing $101.8 million to attract and retain the skilled industrial workforce to support Australian shipbuilding and delivery of conventionally armed, nuclear powered submarines. This includes a pilot apprenticeship program in shipbuilding trades and technologies.

Investing in civil maritime capabilities

The Government is providing $123.8 million to maintain and enhance civil maritime security capabilities. This includes $71.2 million to increase the Australian Border Force’s on‑water response and aerial surveillance capabilities.

Securing Australia’s place in the world

Strengthening relationships and simplifying trade

A stable, prosperous and resilient Pacific region

The Government is delivering over $2 billion in development assistance to the Pacific in 2024–25. This includes the Australia‑Tuvalu Falepili Union.

Investing in our relationship with Southeast Asia

Following the launch of Australia’s Southeast Asia Economic Strategy to 2040, the Government is committing $505.9 million to deepen ties with the region.

Australia recently celebrated 50 years of partnership with the Association of Southeast Asian Nations (ASEAN). At the ASEAN‑Australia Special Summit, the Government announced a range of new and expanded initiatives, including a $2 billion Southeast Asia Investment Financing Facility to boost Australian trade and investment.

Simplifying trade

The Government will abolish 457 nuisance tariffs from 1 July 2024, streamlining $8.5 billion in annual trade and eliminating tariffs on goods such as toothbrushes, fridges, dishwashers, clothing and sanitary products.

The Government will provide $29.9 million to coordinate trade simplification and deliver the Digital Trade Accelerator program, and $10.9 million to enhance the Go Global Toolkit to support exporters.

The Government is expanding the Australia‑India Business Exchange, diversifying trade and helping more Australian businesses build commercial ties with India and across South Asia. There will be $2 million to support Australian agricultural exporters entering the Chinese markets.

Support for small businesses

Helping small businesses

This Budget’s Small Business Statement reaffirms the Government’s commitment to deliver a better deal for small businesses, with $641.4 million in targeted support.

For more information refer to the small business fact sheet [PDF 0.98MB]

Improving cash flow

The Government is providing $290 million to extend the $20,000 instant asset write‑off for 12 months. There will be $25.3 million to improve payment times to small businesses and $23.3 million to increase eInvoicing adoption.

Easing cost pressures and reducing the administrative burden

This Budget provides $3.5 billion of energy bill relief, including rebates of $325 to around one million small businesses.

The Government is reducing the administrative burden for small business by abolishing 457 nuisance tariffs and delivering $10 million to provide additional support for small business employers administering the Paid Parental Leave scheme.

Supporting confidence and resilience in the small business sector

This Budget invests a further $10.8 million in tailored, free and confidential financial and mental wellbeing supports for small business owners.

The Government is providing $20.5 million to the Fair Work Ombudsman to help small businesses understand and comply with recent workplace relations changes.

There will be $3 million to implement the Government’s response to the Review of the Franchising Code of Conduct, including remaking and enhancing the Code, and an additional $2.6 million to support more small businesses through alternative dispute resolution.

A more resilient Australia

Preparing for the future

The Government is preparing Australia for future droughts and heightened risk of natural disasters.

Disaster resilience and preparedness

The Government will provide $138.7 million to improve Australia’s response and resilience to natural hazards and disasters. Support includes: funding for the National Emergency Management Agency to supply communities with vital goods, equipment, and temporary accommodation during an emergency, aerial firefighting capability, and mental health support. This is in addition to the $11.4 billion previously committed for Disaster Recovery Funding Arrangements for the states and territories.

The Government is establishing a pilot program for Australia’s Strategic Fleet. These vessels will improve Australia’s capacity to respond and support communities and supply chains during crises.

Preparing for drought and climate change

This Budget provides $174.6 million from the National Water Grid Fund to deliver new water infrastructure projects that will enhance water security, boost agricultural production and help drought proof regional communities.

The Government will provide $519.1 million from its Future Drought Fund to help farmers and rural communities manage the impacts of climate change and prepare for future droughts.

This investment will build the drought resilience of more farmers like Victorian cropper Ed Rickard.

The Fund supported Ed in developing a better farm business plan, which identified his need for weather stations and soil moisture probes. It also helped him implement a succession plan that ensured his farm’s long-term viability.

Demand-side and Supply-side Constraints in the Market for Financial Advice

In this review, we argue that access to financial advice and the quality of this advice is shaped by a broad array of demand-side and supply-side constraints. While the literature has predominantly focused on conflicts of interest between advisors and clients, we highlight that the transaction costs of providing advice, mistaken beliefs on the demand side or supply side, and other factors can have equally detrimental effects on the quality and access to advice. Moreover, these factors affect how researchers should assess the impact of financial advice across heterogeneous groups of households. While households with low levels of financial literacy are more likely to benefit from advice—potentially including conflicted advice—they are also the least likely to detect misconduct, and perhaps the least likely to understand the value of paying for advice. Regulators should consider not only how regulation changes the quality of advice, but also the fraction of households who are able to receive it and how different groups would have invested without any advice. Financial innovation has the potential to provide customized advice at low cost, but also to embed conflicts of interest in algorithms that are opaque to households and regulators.

Jonathan Reuter is affiliated with Boston College and NBER. Antoinette Schoar is affiliated with MIT Sloan, ideas42 and NBER. The authors thank Roman Inderst (editor) for helpful comments and Xin Xiong for helpful research assistance. Neither author has any funding or material and relevant financial relationships to disclose. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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JEL Classification

Cost of living help and a future made in Australia

Attracting investment in key industries

Better deploying capital in priority areas

Strengthening and streamlining approvals

Promoting sustainable finance

Making Australia a renewable energy superpower

Unlocking investment in net zero industries and jobs

Boosting demand for Australia’s green exports

Realising the opportunities of the net zero transformation

Strengthening resources and economic security

Manufacturing clean energy technologies

Strengthening supply chains