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Financial inclusion, financial technology, and economic development: a composite index approach

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Abstract

Financial inclusion is recognized by policy makers as one of the main tools of promoting household income and economic development. Recently, increasing attention has been focused on proposing reliable indicators to quantify financial inclusion by country. In this research, we adopt a composite index approach for that purpose. The main distinguishing feature of our empirical exercise is its data-driven spirit; in particular, we make very few assumptions about the nature of the composite index. Moreover, we define financial inclusion from three main dimensions making use of both demand and supply side data and recognize that financial technology and digital finance are playing an increasing role in boosting financial inclusion. Next, we analyze financial inclusion changes over time by distinguishing between catching-up and environment change effects. The latter allows us to verify whether policy makers have succeeded in creating an environment that has fostered financial inclusion and quantify the scope for policy interventions. Finally, we take the heterogeneity between countries into consideration by partitioning countries into income per capita categories. Our empirical exercise reveals important patterns useful in understanding financial inclusion differences and designing future policy implementations.

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Notes

  1. Financial inclusion is defined as the availability and equality of opportunities to access financial services with the aim of providing affordable and sustainable financial services to unbanked and underbanked individuals (Nanda and Kaur 2016).

  2. ‘By 2020, adults who currently are not part of the formal financial system are able to have access to a transaction account to store money, send and receive payments as the basic building block to manage their financial lives.’ (World Bank 2017b).

  3. The definition of ‘account’ is ‘the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months.’ A ‘mobile money account’ is defined as ‘the percentage of respondents who report personally using a mobile money service in the past 12 months.’ When ‘account’ and ‘mobile money account’ are both considered in one dimension, an overlap might occur.

  4. The definition of ‘financial institution account’ is ‘the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution.’

  5. An example of how the World Bank defines countries: ‘For the current 2020 fiscal year, low-income economies are defined as those with a GNI per capita, calculated using the World Bank Atlas method, of $1025 or less in 2018; lower middle-income economies are those with a GNI per capita between $1026 and $3995; upper middle-income economies are those with a GNI per capita between $3996 and $12,375; high-income economies are those with a GNI per capita of $12,376 or more’ (World Bank 2020).

  6. Their final measure does not include quality due to lack of data.

  7. An alternative is to use a geometric weighting scheme. In general, the (weighted) arithmetic average is more popular for practical work. A formal reason is that it suffices that one dimension equals zero to make the FI index also zero. In our context, the arithmetic average is also preferable since, as explained below, it allows us to endogenously compute the weights. Nevertheless, it should be acknowledged that one advantage of the geometric weighting scheme is that it reduces the compensability issue (that is, greater indicators compensate for lower indicators). Indeed, in general, the arithmetic weighting procedure implies proportional compensability.

  8. We add the following regularity conditions for the weights and sub-indexes:

    $$\begin{aligned}&0 \le \omega _{kt}^{AC} \le 1; 0 \le \omega _{kt}^{AV} \le 1; 0 \le \omega _{kt}^{US} \le 1, \end{aligned}$$
    (5)
    $$\begin{aligned}&\omega _{kt}^{AC}+ \omega _{kt}^{AV} + \omega _{kt}^{US}=1, \end{aligned}$$
    (6)
    $$\begin{aligned}&0 \le \textit{AC}_{kt} \le 100; 0 \le \textit{AV}_{kt} \le 100, 0 \le \textit{US}_{kt} \le 100. \end{aligned}$$
    (7)

    The regularity conditions (2) to (4) ensure that the FI index lies in a natural interval; here, between 0 and 100. To ensure this holds, we constrict the three sub-indexes to also between 0 and 100, and the weights to sum to unity and lie in the [0, 1] interval.

  9. We note that ‘the term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics’ (World Bank, https://datahelpdesk.worldbank.org/knowledgebase/articles/906519).

  10. Grohmann et al. (2018) define the access of finance by using the proportion of the population that has a formal bank account, including mobile money accounts and the proportion of adults that has a debit card.

  11. We treat ‘financial institution account’ and ‘mobile money account’ as two indicators. The aim is to assess the effect of mobile money access on the FI level; therefore, we do not use the ‘account’ indicator, which is defined as the percentage of adults who have an account at a financial institution or using mobile money service.

  12. Secure Internet servers is the number of distinct, publicly trusted TLS/SSL certificates (IMF database). Data are originally from Netcraft Secure Server Survey (http://www.netcraft.com/). Adult population estimates, from the World Bank’s World Development Indicators (WDI) dataset, are used to rescale the IMF-FAS raw data for secure Internet servers per 1 million people.

  13. Domestic credit to the private sector refers to financial resources provided to the private sector by financial corporations, such as through loans, purchases of non-equity securities, and trade credits and other accounts receivable, that establish a claim for repayment.

  14. This representation dates at least to Diewert (1980), who uses it in a production context.

  15. We thank an anonymous referee for the idea of including world maps.

  16. H\(_0\): the 2011 and 2017 distributions are equal; H\(_1\): 2017 distribution is larger than the 2011 distribution.

  17. Another option would be to categorize countries by region. While this partitioning seems natural, it lacks economic intuition and has not been reported in the previous work.

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Authors and Affiliations

  1. International Business School Suzhou, Xi’an Jiaotong-Liverpool University, 111 Ren’ai Rd, Suzhou, Jiangsu, 215123, China

    Fan Liu

  2. HEC Liège, Université de Liège, B31 - Blvd du Rectorat 7, 4000, Liège, Belgium

    Barnabé Walheer

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  1. Fan Liu
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We thank participants of the XVI European Workhop on Efficiency and Productivity Analysis held in London for helpful comments. We also thank anonymous referees and editor Bertrand Candelon for insightful suggestions.

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See Table 10.

Table 10 Income categories
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Liu, F., Walheer, B. Financial inclusion, financial technology, and economic development: a composite index approach. Empir Econ 63, 1457–1487 (2022). https://doi.org/10.1007/s00181-021-02178-1

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