IISPPR

Women’s Financial Inclusion in South Asia: An Empirical Analysis Using World Development Indicators

Aditya Shafy Chandra1*, Sadiya Altaf Janwari2 1Faculty of Agricultural Economics and Agribusiness Studies, Khulna Agricultural University, Khulna-9100, Bangladesh. 2Islamic University of Science and Technology, Awantipora, Pulwama, India Corresponding Author: adityashafy.chandra@gmail.com E-mail address: sadiajanwari20@gmail.com Abstract This study analytically investigates women’s financial inclusion across seven south Asian countries and has used World Development Indicators from 2011 to 2021. Financial inclusion can be defined as the availability and equality of opportunities to access and use financial services. It has emerged as an urgent priority of development linked to gender equality, reducing poverty and economic growth (Sarma & Pais ,2011; World Bank, 2014). This examination reveals significantly large cross-country variation with women owning an account, range from 2.95% in Pakistan to 89.28% in Sri Lanka by 2021. Panel regression analysis shows that GDP per capita impacts financial inclusion (p < 0.01) while female labor force shows a positive yet statistically insignificant relationship. India has witnessed a noticeable improvement with 192.76% growth over the decade, which resulted due to targeted policy interventions which includes Pradhan Mantri Jan Dhan Yojana (Kapoor,2014). Pakistan, on the other hand, despite witnessing significant percentage gains, it still maintained to have the lowest total inclusion levels, which reflects socio cultural and institutional barriers (Rahman, Rana & Barua, 2019). The findings mentioned in this paper highlight the vitality of integrated policy approaches that merge economic development, digital infrastructure expansion and initiatives to challenge the gender norms which are restrictive coercing women’s financial participation. Keywords: Financial Inclusion, Gender Gap, South Asia, Economic Development, Women Empowerment, Digital Finance 1. Introduction Financial inclusion has been identified as an important aspect of inclusive economic development and poverty reduction strategies across the world (Sarma & Pais, 2011; World Bank, 2014). Financial inclusion is described as the “process of ensuring access to suitable financial products and services at reasonable costs in a fair and transparent manner” (Hannig & Jansen, 2010). Financial inclusion helps people save money in a safe manner, invest in productive sectors, cope with economic risks, and become resilient to financial shocks. Financial inclusion has been recognized as a facilitator for achieving seven out of the seventeen Sustainable Development Goals (United Nations, 2015). For women in particular, financial inclusion is more than just an economic empowerment tool; it is also a means of achieving greater autonomy, decision-making capacity, and social mobility (Kabeer, 2005; Swamy, 2014). Financial inclusion can allow women to build assets on their own, invest in education and health, establish and grow businesses, and make their own decisions about household resources. Studies have shown that financial inclusion of women has positive spillover effects on child nutrition, education, and overall household well-being (Dupas & Robinson, 2013). Despite the rapid economic growth and development of financial systems in South Asia over the past two decades, the region still has some of the most visible gender gaps in financial inclusion in the world (International Monetary Fund, 2018). Women in South Asia are confronted with multiple barriers such as lower levels of education, limited participation in the labor market, digital divides, and deeply rooted socio-cultural factors that impede their economic empowerment (Ghosh & Vinod, 2017; Rahman, Rana, & Barua, 2019). Although some studies have been conducted on financial inclusion in South Asia, and some cross-country studies have been conducted, empirical evidence specific to South Asia, using standardized and comparable data sets for all major economies. In this context, the present study undertakes a comprehensive empirical analysis of women’s financial inclusion in seven South Asian nations, namely Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka, using World Development Indicators data from 2011 to 2021. Specifically, the study aims to: By addressing these objectives, the study contributes to the growing literature through a standardized, region-wide comparative framework. 2. Literature Review 2.1 Conceptual Framework of Financial Inclusion The literature on financial inclusion has progressed from the conventional focus on access to banking services to a broader focus that includes access, usage, and quality aspects of financial inclusion (Demirgüç-Kunt & Klapper, 2013). Sarma and Pais (2011) constructed a multi-dimensional index of financial inclusion that covered banking penetration, the degree of banking services, and the use of the banking system. Chakravarty and Pal (2013) further extended this line of thinking by incorporating an axiomatic structure that covered the distributional elements of financial inclusion. More recent literature suggests that financial inclusion must be considered not only as a function of account ownership but as engagement with formal financial services (Allen et al., 2016). This difference between access and usage becomes even more important in the context of women’s financial inclusion, where gender-specific barriers could result in a higher level of account dormancy among women (Demirgüç-Kunt et al., 2018). 2.2 Determinants of Financial Inclusion The existing literature has been able to identify some of the important determinants that affect the availability of formal financial services. At the macroeconomic level, economic development has been identified as an important determinant of financial inclusion. Economies with higher GDP per capita have better financial systems, more developed regulatory systems, and greater household capacity to access formal financial services (Sethi & Acharya, 2018). Sharma (2016) has also been able to identify important empirical relationships between economic development and financial inclusion in the Indian context. Education has been identified as an important determinant of financial inclusion. Education and literacy abilities enable people to understand financial systems and make important decisions (Mehrotra et al., 2009). For women, education has been identified as an important determinant of financial inclusion. Education not only increases financial abilities but also increases bargaining power in the household (Ghosh & Vinod, 2017). Labor force participation is another important determinant, although the correlation is complex. The formal sector job generates demand for banking services through salaries, as well as familiarity with financial institutions (Aterido, Beck, & Iacovone, 2013). However, in environments where the informal sector is the norm, the correlation between labor force participation and financial inclusion could be weakened. 2.3 Digital Technology and Financial Inclusion Recent studies have also emphasized the revolutionary potential of digital technology

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1. Labor Market Dynamics & Task Displacement
Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets.
“We estimate robust negative effects of robots on employment and wages across commuting zones… dynamically, the displacement effect dominates the reinstatement effect in industries undergoing rapid automation. While new tasks are created, they do not immediately absorb the specific demographic groups displaced from routine manual and clerical tasks, leading to localized labor market distress and a drop in the labor share of income.”

Application for your paper: Perfect for Section 4 (Impact on Employment). It provides empirical proof that the “displacement effect” frequently outpaces the “reinstatement effect” for entry-level and clerical roles, validating your point about the removal of the lower rungs of the occupational ladder.

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?
“Our findings suggest that a substantial share of employment in acute financial services—particularly telemarketing, data entry, and credit brokerage—falls into the high-risk category (over 70% probability of automation). As algorithms become more capable of handling unstructured data and complex pattern recognition, occupations that rely heavily on codifiable, routine data processing will experience rapid contraction.”

Application for your paper: Directly anchors Section 2.2 and Section 4. This gives you concrete risk probabilities to contrast against the more optimistic “augmentation” theories.

2. The Augmentation & Efficiency Paradigm
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence.
“The most important misconception about AI is that it will simply replace humans. Over the next decade, AI will blend into workflows as a powerful complement. Machine learning excels at supervised learning tasks (mapping inputs to outputs), but lacks capabilities in emotional intelligence, high-level strategic planning, and creative problem-solving. The greatest performance gains occur when human judgment is augmented by algorithmic scale.”

Application for your paper: Supports your analysis of retained roles in Section 4 and Section 8 (Discussion), justifying why senior client-facing and strategic roles remain resilient.

Arner, D. W., Barberis, J., & Buckley, R. P. (2016). The evolution of fintech: A new post-crisis paradigm?
“FinTech 3.0 is characterized not by the tools themselves, but by the systemic shift from human-mediated financial infrastructure to automated, data-driven architecture. This post-crisis paradigm forces traditional banking institutions to transition from labor-intensive risk management to real-time, algorithmic compliance and disintermediated consumer platforms to maintain market viability.”

Application for your paper: Strengthens Section 2.1 and Section 3, linking your industry examples (like JPMorgan’s contract analysis and Bank of America’s Erica) to a broader structural evolution in global banking.

3. Macroeconomics, Inequality, and Skills
World Economic Forum. (2023). The Future of Jobs Report 2023.
“Analytical thinking and creative thinking remain the most important skills for workers in 2023… Within financial services, the fastest-growing roles are driven by technology and data, specifically Data Analysts, AI and Machine Learning Specialists, and FinTech Engineers. Conversely, the largest absolute job declines are expected in clerical and administrative roles, including Bank Tellers, Data Entry Clerks, and Postal Service Clerks.”

Application for your paper: Provides the exact statistical and occupational alignment needed for Section 5 (Economic Implications) and Section 6 (Emerging Occupational Areas).

International Monetary Fund. (2022). World Economic Outlook.
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Application for your paper: Ideal backing for Section 5 (Economic Implications) to balance the discussion on GDP growth with structural risks like wealth concentration.

World Bank. (2021). World Development Report 2021: Data for better lives.
“Data creates value by improving policies, driving economic efficiencies, and empowering individuals. However, the realization of this value is highly unequal. Poor infrastructure, data fragmentation, and a lack of baseline digital literacy threaten to leave marginalized populations further behind, transforming the digital dividend into a digital divide unless public policy actively fosters inclusive data systems.”

Application for your paper: Strongly supports your arguments on financial inclusion and public digital infrastructure in Section 5 and Section 7.3 (Policy-Level Adaptation).

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