Financial inclusion and risk exposure among different income groups: the impact of COVID-19 pandemic

Monsurat Ayojimi Salami, Adel M. Sarea, and M. Kabir Hassan

3.1 Introduction

In 2009, no less than three billion of the world population lacked access to formal financial sendees (Chibba, 2009) such as bank accounts, credit, as well as insurance, and much more. Financial inclusion has been recognized globally and especially in Africa since 2000 (Ain et al., 2020; Nizam et al., 2020). As part of efforts to reduce poverty and narrow inequality gaps, several individuals and organizations have donated generously to financial inclusion programmes (Chibba, 2009), which has given rise to the development of several foundations aiming to alleviate poverty. However, when the global COVID-19 pandemic emerged, even developed countries were hit hard, and currently, it is the topmost challenge facing the global economy. Therefore, the ability to abide by the global standard operating procedure (SOP) for combating the COVID-19 pandemic is ofless priority in developing countries as poverty is their topmost challenge. As a result, the International Monetary Fund (IMF) and World Bank advised giving developing countries sufficient cash injections to ease the impact of the COVID-19 pandemic, as they have no safety net to stop them from falling into deeper poverty (OXFAM, 2020).

With current conflicting priorities between developing countries and the rest of the world, examining financial inclusion in the context of the COVID-19 pandemic is currently essential. Financial inclusion is a means to tackle poverty by reducing inequality (Omar & Inaba, 2020) and addressing millennium development goals (Chibba, 2009). Through this, the financially excluded population is expected to reduce drastically. However, the COVID-19 pandemic is currently a pressing global issue with the potential to increase the poverty level and further widen inequality gaps globally. This is because most financial inclusion aids are from wealthy economies and most of those countries are facing economic challenges. About 52 donor countries were reported to have contributed about USD $82 billion, while low-income and lower-middle-income countries need an additional USD$ 2.4 trillion yearly for sustainable development goals (SDGs) (OXFAM, 2019). Although studies have established the potential of financial inclusion to reduce poverty (Ghosh & Sahu, 2020; Melubo & Musau, 2020) and even promote pro poor growth which are national policies to stimulate economic

Financial inclusion and risk exposure 41 growth for the benefit of poor people (Waghmare, 2020), this has led to several meetings and conferences to find a breakthrough for the poverty issue through financial inclusion (Chibba, 2009; Fowowe, 2020). As a result, a series of conferences were held in developed countries in 2007 on financial inclusion including the global conference titled “Next Generation Access to Finance: Gaining Scale and Reducing Costs with Technology and Credit Scoring” held in Washington, DC, in September 2007, and “Making Finance Work for the Poor” held in the UK (Chibba, 2009; Nicole, 2008). Financial inclusion is a means of promoting resilience and at the same time preventing poverty traps (Fowowe, 2020). Still, there is no significant improvement in the poverty level of the billions of low-income families. However, Kenya has made a significant difference as about 82.9% of the population in financially inclusive (Arthur et al., 2020; Melubo & Musau, 2020). The question here is how efficient and effective are those strategies and policies on financial inclusion if financial inclusion introduced in the last two decades does not work efficiently?

As a result, the current study aims to firstly, examine and quantify different financial inclusion indicators commonly employed at the country level across different income groups. Secondly, the study will investigate the income group that is most vulnerable to risk. Thirdly, this study will explore and identify the targeted income group for financial inclusion across 121 countries. Fourthly, this study will make recommendations to policymakers based on the empirical findings of this study.

Briefly, the findings of this study are that few financial inclusion indicator variables are statistically significant, and they show that country-level financial inclusion is in favour of the lower-middle-income group than any other income groups examined. To be more precise, the national financial inclusion strategy seems to be broad enough to accommodate both low-income, low-middle-income, and upper-middle-income groups. The general financial inclusion strategy favours mainly the middle-income groups, while the microfinance strategy seems to have been designed for the lower-middle-income group. Similarly, financial inclusion policy indicators such as the requirement policy and priority lending policy tend to favour the lower-middle-income group.

3.2 Literature review

Chibba (2009) references the 2008 global financial crisis as well as the food and energy crisis to stress the importance of financial inclusion for developing countries. Sarma (2008) developed an index for financial inclusion, having argued that financial inclusion research is facing a lack of comprehensive studies. Similarly, Ghosh and Sahu (2020) developed an index for comparing the degree of financial inclusion achievement of 26 Asian countries between 2013 and 2017 using a weighted arithmetic mean and concluded a significant difference between the income groups. In addition, Fauzan et al. (2020) developed a financial inclusion index of 33 provinces in Indonesia and concluded that poverty negatively and significantly affects financial inclusion.

Fowowe (2020) conducted a study on the effect of financial inclusion on household agricultural productivity in Nigeria and concluded that financial inclusion enhances agricultural productivity in Nigeria. Prasetiyani and Vikaliana (2020) revealed that the national vision for financial inclusion in Indonesia is to develop a financial system that would be accessible to all levels of society through which economic growth would be encouraged, and income redistributed to alleviate poverty. Mengesha et al. (2020) conducted a study on the relationship between the education level and financial literacy of households in the Jimma zone of Ethiopia using Analysis of Variance (ANOVA). They concluded that there is a positive and significant mean difference across different education levels.

Brahim and Kamalu (2020) examined the financial inclusion of 30 samples of Islamic banks in Organisation of Islamic Cooperation (OIC) member countries using panel analysis for the data from 2013 to 2018 using a system generalized method of moments (system GMM), panel cointegration, and panel causality test to establish that Islamic banks significantly affect the level of financial inclusion in OIC countries. However, Brahim and Kamalu’s (2020) findings committed an overstating error by including the result of the coefficient of interaction with the marginal effect of interaction. To avoid understating or overstating the interpretation of interaction model, both the coefficient of interaction and marginal effect need to be statistically significant and reported (Kingsley et al., 2017).

In making a comparison between developed and developing countries, Rodrigue and Roger (2020) narrow the main causes of inequality of access to finance and banking sectors. Rodrigue and Roger (2020) established a direct relationship between financial inclusion and remittances using GMM for panel data of 47 countries in Sub-Saharan Africa for the period from 2004 to 2014. The authors reveal that regular remittance to the poor may enable the bank sector to grant credit to low-income people to start an economic activity. Similarly, Arthur et al. (2020) also reveal a positive and statistically significant relationship between remittances to Kenya and financial inclusion.

3.3 Methodology

Data about financial inclusion strategies and policies and risk exposure among different income groups were obtained from the official website of the World Bank (http://worldbank.org) for 124 countries; there are three points of missing data due to unspecified income groups. Therefore, a total of 121 countries’ data income groups were finally analyzed. Since the data used in this study focuses on the country level, the database provided categorical data; hence financial inclusion variables are used as independent variables while income groups are the dependent variables. The data obtained were classified into five parts: first, financial inclusion strategies, which are national financial strategy (NFS), general financial strategy (GFS), national development strategy (NDS), microfinance strategy' (MFS), and financial literacy strategy (FLS); secondly, financial inclusion policy, which is the requirement policy, priority lending policy, tax incentive policy, deposit-taking institutions’ policy, encouraging policy, and no policy implementation; thirdly, type of risk assessed, which are operational risk, customer risk, and other risk; fourthly, the targeted group for financial inclusion is examined among different income groups; and fifthly, the income groups that are categorized into high-income, upper-medium-income, lower-medium-income, and low- income groups.

The analysis approaches employed are descriptive statistics, one-way ANOVA, and post hoc test. Descriptive statistics provide information about frequencies and percentages of the income groups as well as mean and standard deviation (SD) of the financial inclusion dependent variable indicators. ANOVA is used to explore the differences among financial inclusion indicators across the income groups and the post hoc test identifies the income groups with a mean difference. Seamer and Melia (2015) and Mengesha et al. (2020) used ANOVA to examine the significance of the mean difference. Similarly, Nilsson (2009) used both ANOVA and the post hoc test to quantify the mean difference and specific group for the mean difference.

3.4 Empirical findings

This study provides descriptive statistics results of income groups in relation to frequency and percentage, followed by the mean and standard deviation of financial inclusion indicator variables. Similarly, a correlation matrix of financial inclusion indicator variables is examined following the ANOVA and post hoc test results. In the end, the percentage of the population living below the poverty level in selected countries and total cases of the population infected with COVID-19 across countries are presented.

Table 3.1 presents the descriptive statistics of the income groups in 121 counties in six areas namely Europe and Central Asia, East Asia and the Pacific, Latin America and the Caribbean, the Middle East and North Africa, South Asia, and Sub-Saharan Africa. As can be observed in Table 3.1, low-income earners constitute the smallest percentage, while high-income earners constitute the highest percentage. It is evident that about 67.8% are facing different issues of financial exclusion. This indicates that financial inclusion needs urgent and special attention, most especially during the current COVID-19 pandemic.

Hence, it is important to quantify the strategies and policies employed at the country level for financial inclusion. Table 3.2 presents statistical evidence of the

Table 3.1 Descriptive statistics of income groups

Income groups

Frequency

Percentage

Low-income

11

9.1%

Lower-middle income

34

28.1%

Upper-middle income

37

30.6%

High-income

39

32.2%

Table 3.2 Financial inclusion strategy

Financial inclusion strategy indicators

Income groups

Mean

SD

National financial strategy

Low-income

0.8162

0.4045

Lower-middle income

0.7353

0.4478

Upper-middle income

0.5946

0.4977

High-income

0.1538

0.3655

General financial strategy

Low-income

0.2727

0.4671

Lower-middle income

0.5000

0.5075

Upper-middle income

0.4595

0.5052

High-income

0.1795

0.3888

National development strategy

Low-income

0.2727

0.4671

Lower-middle income

0.4118

0.4996

Upper-middle income

0.4054

0.4977

High-income

0.1795

0.3888

Microfinance strategy

Low-income

0.3636

0.5045

Lower-middle income

0.4412

0.5040

Upper-middle income

0.1622

0.3737

High-income

0.2051

0.4091

Financial literacy strategy'

Low-income

0.4545

0.5222

Lower-middle income

0.5000

0.5075

Upper-middle income

0.6216

0.4917

High-income

0.6410

0.4860

Source: Author’s own

financial inclusion strategies employed in 121 countries across different income groups.

In Table 3.2, the national financial strategy on financial inclusion is more in favour of the low-income group (mean = 0.8162, SD = 0.4045) than the high-income group (mean = 0.1535, SD = 0.3655). The next highest mean is for the lower-medium income group (mean = 0.7353, SD = 0.4478) followed by the upper-middle-income group (mean = 0.5946, SD = 0.4977).

However, statistical evidence shows that the national financial strategy on financial inclusion is more in favour of middle-income groups with 58.7% as both lower-middle-income (28.1%) and upper-middle-income groups (30.6%) as shown in Table 3.1. The lower-middle-income group scored the highest (mean = 0.5000, SD = 0.5075) followed by the upper-middle-income group (mean = 0.4595, SD = 0.5052).

In the same vein, statistical evidence shows that the national development strategies developed by countries are designed in favour of middle-income groups. The lower-middle-income group scored the highest (mean = 0.4118, SD = 0.4996) followed by the upper-middle-income group (mean = 0.4054, SD = 0.4977). While the high-income group (mean = 0.1795, SD = 0.3888) scored the lowest among the groups. This indicates that at the country' level, both

Financial inclusion and risk exposure 45 the general financial strategy and national development strategy are designed in favour of middle-income groups.

Based on statistical evidence for microfinance strategy, both low-income (mean = 0.3636, SD = 0.5045) and lower-middle-income (mean = 0.4412, SD = 0.5040) groups have a high mean score. This implies that both low-income and lower-middle-income groups are the focus of microfinance strategies.

The empirical findings on financial literacy strategy show that the high-income group (mean = 0.6410, SD = 0.4860) is more financially literate followed by the upper-middle-income group (mean = 0.6216, SD = 0.4917), while the low-income group (mean = 0.4545, SD = 0.5222) has the least financial literacy.

In addition, a summary of the financial inclusion strategy is presented in Table 3.3.

From the findings presented in Table 3.3, it can be inferred that both the national financial strategy and microfinance strategy are designed as financial inclusion strategies in favour of low-income and lower-middle-income groups. Similarly, the general financial strategy and national development strategy are in favour of middle-income groups, that is, lower-middle-income groups and upper-middle-income groups; upper-middle-income and high-income groups are financially literate.

Having grouped the financial inclusion strategy and their most focused groups, we proceeded with the financial inclusion policy, which is presented in Table 3.4.

Table 3.4 presents the report on financial inclusion policy. The mean score of the lower-middle-income group (mean = 0.5000, SD = 0.5075) is the highest followed by the upper-middle-income group (mean = 0.2162, SD = 0.4173), while the lowest-scoring group for requirement policy is the low-income group (mean =0.0909, SD = 0.3015).

Similarly, middle-income groups are the focus of priority lending. The lower-middle-income group ranked high (mean = 0.5588, SD = 0.5040) followed by the upper-middle-income (mean = 0.4054, SD = 0.4977) group while the

Table 3.3 Summary of financial inclusion strategy and their associated groups

Financial Inclusion Strategy Indicators

Income Groups Favoured

National financial strategy

Low-income group

Lower-middle-income group

Microfinance strategy

Low-income group

Lower-middle-income group

General financial strategy

Lower-middle income group

Upper-middle-income group

National development strategy

Lower-middle-income group

Upper-middle-income group

Financial literacy strategy

High income

Upper-middle-income group

Table 3.4 Financial inclusion policy

Financial Inclusion Policy Indicators

Income Groups

Mean

SD

Requirement policy

Low income

0.0909

0.3015

Lower-middle income

0.5000

0.5075

Upper-middle income

0.2162

0.4173

High income

0.1026

0.3074

Priority lending policy

Low income

0.2727

0.4671

Lower-middle income

0.5588

0.5040

Upper-middle income

0.4054

0.4977

High income

0.2564

0.4424

Tax incentive, savings schemes

Low income

0.0909

0.3015

policy

Lower-middle income

0.1765

0.3870

Upper-middle income

0.2432

0.4350

High income

0.3590

0.4860

Deposit-taking Institutions Policy

Low income

0.5455

0.5222

Lower-middle income

0.5294

0.5066

Upper-middle income

0.4324

0.5023

High income

0.6667

0.4776

Encouraging policy

Low income

0.5455

0.5000

Lower-middle income

0.7647

0.4306

Upper-middle income

0.7568

0.4350

High income

0.5641

0.5024

None policy implementation

Low income

0.0909

0.3015

Lower-middle income

0.0588

0.2388

Upper-middle income

0.0811

0.2767

High income

0.1538

0.3001

Note: SD represents Standard deviation.

Source: Author’s own

high-income group scored the lowest. This shows the existence of financially excluded population in countries. However, this will not be successful in isolation and needs to be supported by access to finance strategies for low-income groups.

However, a tax incentive and saving schemes policy is in favour of high-income groups (mean = 0.3590, SD = 0.4860) followed by the upper-middle-income group (mean = 0.2432, SD = 0.4350), while the low-income group (mean = 0.0909, SD = 0.3015) is the least favoured. It is obvious that both high-income and upper-middle-income groups are at better financial capacity compared with the low-income or lower-middle-income groups.

Furthermore, on deposit-taking institutions policy, high-income groups score the highest mean (mean = 0.6667, SD = 0.4776) followed by the low-income group (mean = 0.5455, SD = 0.5222), while the upper-middle-income group (mean = 0.4324, SD = 0.5023) is the least favoured.

The encouragement policy could be inferred as favouring the middle-income group. The lower-middle-income group (mean = 0.7647, SD = 0.4306) has the highest mean followed by the upper-middle-income group (mean = 0.7568, SD = 0.4350), while the lowest mean is that of the high-income group (mean = 0.5641, SD = 0.5024). This implies that income groups above low income benefit from the encouragement policy.

For situations where a financial inclusion policy is not implemented, the high-income (mean = 0.1538, SD = 0.3001) and low-income (mean = 0.0909, SD = 0.3015) groups have the highest mean among all groups examined. This because the two groups are at the extreme ends of financial inclusion. If no financial inclusion policy is implemented, the high-income group is already financially comfortable while the low-income group has already been excluded from formal financial services. Therefore, they are not really experiencing a lack of financial inclusion policy. However, middle-income groups are the focus of financial inclusion policy, and they already understand the implication of a lack of a financial inclusion policy. Hence, the lower-middle-income (mean = 0.0588, SD = 0.2388) and upper-middle-income (mean = 0.0811, SD = 0.2767) groups are highly affected.

In summary, middle-income groups, that is, both lower- and upper-middle-income groups score high in requirement policy, priority lending policy, and encouraging policy, while the high-income group score the highest in deposittaking institution policy and no policy implementation. This implies that the high-income group is not affected by either deposit policy or lack of financial inclusion policy while middle-income groups are.

Even if a financial inclusion strategy and financial inclusion policy were made available, access to finance is highly essential. Table 3.5 presents a statistical report on access to finance across the four income groups.

Table 3.5 reveals the access of each income group. The mean score for the lower-middle-income group (mean = 1.0294, SD = 0.7972) is the highest followed by the upper-middle-income group while the low-income group is the least quantifiable for finance in the group. This finding consistently supports the financial exclusion of the less privileged group.

To avoid a premature conclusion, we proceeded further with the identification of a targeted group for financial inclusion and the report is presented in Table 3.6. The low-income group (mean = 1.0000, SD = 0.0000) is the most targeted group for financial inclusion followed by the lower-middle-income group

Table 3.5 Access to finance

Income Groups

Mean

SD

Access to finance

low income

0.8182

0.8739

lower-middle income

1.0294

0.7972

upper-middle income

1.0000

0.8498

high income

0.8462

0.8441

Table 3.6 Specific targeted group for financial inclusion

Income Groups

Mean

SD

Specific group

Low income

1.0000

0.0000

target

Lower-middle income

0.8000

0.4218

Upper-middle income

0.5714

0.5136

High income

0.3333

0.5774

Source: Author’s own

(mean = 0.8000, SD = 0.4218) while the least targeted group is the high-income group (mean = 0.3333, SD = 0.5774).

Interestingly, the findings in Table 3.5 and Table 3.6 reflect the reality of financial inclusion, as the middle-income groups are the focus of access to finance while the low-income group has the least focus; financial inclusion is mainly designed for financially excluded populations, which could be a combination of low-income and lower-middle-income groups. This argument is consistently supported by OXFAM (2019) in that both low-income and lower-middle-income groups are financially excluded populations that need global attention. Therefore, how could country-level financial inclusion be effective and efficient if low-income groups are excluded from access to formal financial services mainly designed for them?

Similarly, we could not underrate the series of risks that might be associated with financial inclusion. This study examines some types of risk, such as operational risk, customer risk, other risks, and total risk, as a combination of individual risks examined. The report on risk is presented in Table 3.7.

The report in Table 3.7 indicates that the most vulnerable group to risk is the lower-middle-income group as it has the highest standard deviation for operational risk, other risks, and total risk of0.3051,0.3456, and 0.7184, respectively. The upper-middle-income group is in the second-highest risk position in customer risk (SD = 0.4443) and other risks (SD = 0.3078), the high-income group is in the second-highest risk position in operational risk (SD = 0.2582) and total risk (SD = 0.6217). However, the low-income group is the least vulnerable to risk as it is the second-highest risk in customer risk only.

Since this finding is novel and there is no specific benchmark for quantifying the severity of those risks found in other research studies, we therefore proceeded with examining the homogeneity of variance and the report is presented in Table 3.8. Although Sarma (2008) developed a financial inclusion index with a given range of 0< Wj < 1 and interpreted it as the higher the weight (ipj, the better the country in financial inclusion, this finding may not be applicable here because this study focuses on income groups and using such an index is subjective in nature due to a lack of significance test to predict how low is low and how large is large.

As observed in Table 3.8, homogeneity of variance is statistically significant for most of the indicators for financial inclusion, as examined in this study, except for financial literacy strategy, access to finance, and operational risk.

Table 3.7 Type of risk assessed

Risk Associated

Income Groups

Mean

SD

Operational risk

Low income

1.0000

0.0000

Lower-middle income

0.9000

0.3051

Upper-middle income

0.9500

0.2236

High income

0.9333

0.2582

Consumer risk

Low income

0.7778

0.4410

Lower-middle income

0.8667

0.3458

Upper-middle income

0.7500

0.4443

High income

0.9333

0.2582

Other risk

Low income

1.0000

0.0000

Lower-middle income

0.8667

0.3456

Upper-middle income

0.9000

0.3078

High income

0.9333

0.2582

Total risk

Low income

2.7778

0.4410

Lower-middle income

2.6333

0.7184

Upper-middle income

2.8000

0.4140

High income

2.6757

0.6217

Note: SD, standard deviation. Source: Author’s own

Table 3.8 Test of homogeneity of variance

Financial Inclusion Indicators

Mean Coefficient

Significance

National financial strategy

8.0200“

0.0000

General financial strategy

14.3130“

0.0000

National development strategy

10.3650“

0.0000

Microfinance strategy

8.4460“

0.0000

Financial literacy strategy

1.0720

0.3640

Access to finance

0.4320

0.7300

Requirement policy

16.7120“

0.0000

Priority lending policy

4.6090 b

0.0040

Tax incentive, savings schemes policy

7.3230“

0.0000

Deposit-taking institutions policy

2.2300e

0.0880

Encouraging policy

5.1330b

0.0020

No policy implementation

2.7280b

0.0470

Specific group target

14.0100“

0.0000

Operational risk

1.8190

0.1520

Consumer risk

3.6960b

0.0160

Other risk

2.5410'

0.0630

Total risk

2.1810'

0.0980

T% significance level b5% significance level 40% significance level Source: Author’s own

We further proceed with examining the correlation between indicators for independent variables, and the result is presented in Table 3.9.

3.4.1 Correlation

Based on the correlation matrix result presented in Table 3.9, the magnitude of the relationship among the indicators is denoted by the coefficient of correlation. Most of the correlation coefficients are statistically significant, and none of the correlation coefficients is higher than the threshold correlation value. This implies that the financial inclusion indicators are within the range of low to moderate correlation. This indicates no evidence of multi-collinearity among the financial inclusion independent variable indicators. While some correlation coefficients are not statistically significant, this is consistent with the report made in the study by Nizam et al. (2020).

3.5 ANOVA result

Despite the mean differences reported in the earlier tables, the significance of the mean differences of financial inclusion is explored using ANOVA. Table 3.10 shows that there is a statistically significant difference in the mean of national financial strategy and income groups F(3,117) = 13.990, p <0.01. There is a statistically significant difference between the mean of general financial strategy and income groups F(3,117) = 3.632, p < 0.05. Similarly, there is a statistically significant mean difference between microfinance strategy’ and income groups F(3,117) = 2.947, p < 0.05. This indicates that there is a statistically significant difference in the mean of national finance strategy, general finance strategy, and microfinance strategy in at least one of the income groups.

Furthermore, there is a statistically significant difference between the mean of requirement policy and income groups F(3, 117) = 6.665, p < 0.01. In addition, there is a statistically significant mean difference in priority lending policy and income groups, F(3,l 17) = 2.644, p < 0.1. This implies that the mean difference in requirement policy and priority lending policy is statistically significantly different in at least one of the income groups.

The ANOVA shows significant differences between some means of financial inclusion indicators. The mean difference of five financial inclusion indicators are statistically significant; three of the indicators are related to financial inclusion strategy and two indicators are related to financial inclusion policy. Moreover, we proceed with the post hoc test to identify the income groups of which the mean for financial inclusion indicators are different. The results of the post hoc test are presented in Table 3.11.

Noting that the mean differences of other financial inclusion indicators, such as access to finance, associated risks, and the rest of the financial inclusion strategies and policies, are not statistically significantly different. The implication of this is that there is no significant difference in treatment across the income groups for those financial inclusion indicators. The indication is irrespective of income

Table 3.9 Pearson’s correlation matrix result

FI

NFS

GFS

NDS

MFS

FLS

ATF

PR

PLP

TIP

D-TIIP

EP

NP

STG

Risk-O

Risk-

Other

Risk-C

Risk-

Total

NTS

1

GFS

0.2730“

1

NDS

0.2150k

0.4640"

1

MFS

0.3370"

0.3420"

0.3390"

I

FLS

0.0950

0.3470"

0.199 O'*

0.1130

I

ATF

0.2740"

0.1540*

0.0870

0.1910'*

0.3290"

1

PR

0.2250"

0.2790"

0.2660»*

0.2990'*

0.8140'*

0.169*

1

PLP

0.3030"

0.1360

0.2940»*

0.2 0701*

0.1710*

0.257»*

0.2960»*

1

TIP

0.0290

0.2 0001*

0.185 O'*

0.1710*

0.29801*

0.259k

0.0770

0.2960k

1

D-TIP

■0.0010

0.1240

0.1370

0.22601*

0.0860

0.145

0.219 O'*

0.1870k

0.181**

I

EP

0.23401*

0.1740«

0.291 O'*

0.19101*

0.0860

0.118

0.1570«

0.26601*

0.117

0.3150"

I

NP

■0.1590*

■0.0570

-0.1320

■0.1570*

■0.1550*

■0.150*

■0.2020'*

■0.2790'*

-0.2021*

■0.3870"

■0.5080"

1

STG

0.0001"

0.0000

0.0540

0.1470

0.1090

■0.146

0.1330

0.1290

0.032

■0.1470

■0.1330

0.0350

1

Risk-O

0.3200"

■0.0890

0.1190

0.0590

■0.1250

0.115

■0.0400

0.0320

0.038

0.1730

■0.0660

0.0850

0.0001"

1

Risk-Other

0.0130

0.0040

■0.0050

0.0120

0.1800

0.094

0.0290

0.1120

■0.016

0.1530

0.0770

■0.0560

0.40801*

0.0990

1

Risk-C

0.0810

0.1610

-0.0690

0.0500

0.2030

0.023

0.1500

■0.0340

0.013

0.24001*

0.0940

-0.23601*

0.0550

0.1760

0.2360»*

I

Risk-Total

0.1840

0.0620

0.0050

0.0590

0.1550

0.105

0.0870

0.0450

0.015

0.2860k

0.0660

■0.1330

0.2040

0.5570"

0.6540"

0.7800"

1

  • 41% significance level.
  • *5% significance level.
  • *10% significance level.

NFS, national financial strategy; GFS, general financial strategy; NDS, national development strategy; MFS, microfinance strategy; FLS, financial literacy strategy; ATF, access to finance; PR, policy requirement; PLP, priority lending policy; TIP, tax incentive policy; D TIP, deposit-taking institutions policy; EP, encouraging policy; NP, no policy implementation; STG, specific target group; Risk-O, operational risk; Risk-Other, other risk; Risk-C, customer risk; Risk-T, total risk.

Source: Author’s own

Financial inclusion and risk e:

Table 3.10 T-tcst result for Financial Inclusion Indicator

Financial Inclusion Indicators

Sum of Squares

Mean

Square

F

Significant level

National financial strategy

7.9820

3

2.6610

13.990

0.0000

General financial strategy

2.3850

3

0.7950

3.6320

0.0150

National development strategy

1.3500

3

0.4500

2.1000

0.1040

Microfinance strategy

1.6860

3

0.5620

2.9470

0.0360

Financial literacy strategy'

0.6000

3

0.2000

0.8090

0.4910

Access to finance

0.9110

3

0.3040

0.4350

0.7280

Requirement policy

3.2930

3

1.0980

6.6650

0.0000

Priority lending policy

1.8250

3

0.6080

2.6440

0.0520

Tax incentive, savings schemes policy

0.9270

3

0.3090

1.6700

0.1770

Deposit-taking institutions policy

1.0540

3

0.3510

1.4210

0.2400

Encouraging policy

1.1840

3

0.3950

1.8300

0.1460

No policy implementation

0.1850

3

0.0620

0.6780

0.5670

Specific group target

1.2740

3

0.4250

2.1630

0.1140

Operational risk

0.0790

3

0.0260

0.4010

0.7520

Consumer risk

0.3480

3

0.1160

0.8380

0.4780

Other risk

0.1380

3

0.0460

0.5190

0.6710

Total risk

0.4940

3

0.1650

0.4160

0.7420

df = degree of freedom

Note: between groups degree of freedom = 3, and within groups the degree of freedom =117, therefore F(3,117).

Source: Author’s own

Table 3.11 Tukey post hoc test

Financial Inclusion Indicators

Income

Group

Income

Group

Mean Difference

Significant level

National financial strategy

Low

High

0.6643

0.0000

Lower

0.5815

0.0000

Upper

0.4408

0.0000

General financial strategy

Lower

High

0.3205

0.0220

Upper

0.2800

0.0500

Microfinance strategy'

Lower

Upper

0.2790

0.0400

Requirement policy'

Lower

Low

0.4091

0.0220

Upper

0.2838

0.0200

High

0.3974

0.0000

Priority' lending policy'

Lower

High

0.3024

0.0410

Note that the result of the post hoc test presented in Table 3.11 is based on Tukey HSD, which is also consistent with the Bonferroni test result.

Source: Author’s own

Financial inclusion and risk exposure 53 groups, and the treatment is the same. For example, the low-income group is placed on the same scale as the high-income group for access to finance, tax incentives, saving scheme policy, deposit-taking institution policy, and encouraging policy since the mean difference is not statistically significant. The statistical insignificance of financial literacy among the income groups is inconsistent with the findings by Mengesha et al. (2020).

Table 3.11 shows that those financial inclusion indicators with a statistically significant mean difference are in favour of low-income to middle-income groups, and they are within the scope of financial inclusion strategy and policy. For national financial strategy, low-income and middle-income groups have a statistically significant mean difference greater than the high-income group. The findings also show that national financial strategy is designed to favour the low-income group over the rest of the income groups. The low-income group has the highest mean difference (MD) = 0.6643, p < 0.01, followed by the lower-middle-income group MD = 0.5815, p < 0.01, and the lowest mean difference is the upper-middle-income group MD = 0.4408, p < 0.01.

For general financial strategy, the middle-income groups have a greater and statistically significant mean difference than that of the high-income group. This implies that the general financial strategy is designed to favour middle-income rather than low-income or high-income groups. It is found that the lower-mid-dle-income group (MD = 0.3205, p < 0.05) has a greater mean difference than the upper-middle-income group (MD = 0.2800, p < 0.05).

The mean difference of microfinance strategy (MD = 0.2790, p < 0.05) is greater and statistically significant in favour of the lower-income group than that of the upper-middle-income group. This indicates that microfinance strategies across 121 countries were in favour of the lower-middle-income group than the upper-middle-income group. However, Chibba (2009) argued that microfinance alone is not sufficient to cater for financial inclusion as a whole.

On financial inclusion policy, a statistically significant mean difference is found in requirement policy and priority lending indicators in different income groups. There is a statistically significant greater mean difference in favour of the lower-middle-income group on requirement policy than other income groups: the low-income group scored MD = 0.4091, p < 0.05, the upper-middle-income group scored MD = 0.2838, p < 0.05) and the high-income group scored MD = 0.3970, p < 0.01. Similarly, the mean difference in priority lending policy (MD = 0.3024, p < 0.05) is significantly in favour of the lower-middle-income group than the high-income group. This finding is inconsistent with the national vision of Indonesia to achieve financial inclusion (Prasetiyani & Vikaliana, 2020).

Through these findings, an interesting inference can be made that the lower-middle-income group has the most focus of financial inclusion strategies globally followed by the upper-middle-income group, while the low-income group is the least favoured. Similarly, the lower-middle-income group is the focus of financial inclusion policy. This indicates that global financial inclusion has a greater preference for the lower-middle-income group than the upper-middle-income and low-income groups. This finding further emphasizes that financial inclusion is not designed for extremely low poverty levels and populations but rather relatively lower poverty levels and populations. Similarly, country-level financial inclusion indicators have not reduced the negative impact of urbanization and poverty by focusing mainly on the lower-middle-income group. Trivedi (2020) described financial inclusion as a pathway to normalizing the negative effect of urbanization as well as the mitigation of poverty. As claimed by Rodrigue and Roger (2020), the inequality between developed and developing countries is due to access to finance and the banking sector of the rich countries. Therefore, the mean difference in access to finance needs to be statistically significant for at least low-income and lower-middle-income groups for country-level financial inclusion indicators to be appropriate measures to eradicate poverty and support an efficient economy.

3.6 Implication of the COVID-19 pandemic on countrylevel financial inclusion

Country-level financial inclusion is more focused on the lower-middle-income group. However, the expectation about country-level financial inclusion is to be designed in such a way to address the poverty level of the grassroots population. Financial inclusion is to include the financially excluded population even to the bottom of the population pyramid (Prasetiyani & Vikaliana, 2020). Without that, several campaigns on SDGs may be far from achieving in the near future. This is because the COVID-19 pandemic has obstructed the economy and created more challenges to the global economy, which may increase the poverty level and even widen the inequality gap much more. Prior to the COVID-19 pandemic, several countries had committed to reducing inequality - the commitment to reducing inequality (CRI) (OXFAM, 2017). The current challenge is if the COVID-19 pandemic will prevent CRI.

Sarma (2008) states that even a certain proportion of developed countries’ populations are financially excluded. Sarma (2008) further reveals that two decades ago, the policy on financial inclusion has been implemented in the USA. Still, more than 40 million people are documented as living below the poverty level, with about 18.5 million living in deep poverty (UNHCR, 2017). This finding is consistent with the OXFAM (2017) report that among wealthy economies, the USA is performing very badly on the CRI index. The aftermath of this current COVID-19 pandemic is that global poverty levels may be on the rise again, as an additional half a billion of the global population could be pushed into poverty and this could set back the fight against poverty for about three more decades, especially in Sub-Saharan Africa, the Middle East, and North Africa. This is because millions are losing their jobs globally; about half of all jobs in Africa could be lost in the aftermath of the COVID-19 pandemic (OXFAM, 2020).

3.7 Conclusion

Few of the several financial inclusion indicators examined in this study, such as national financial strategy, general financial strategy, microfinance strategy,

Financial inclusion and risk exposure 55 requirement policy, and priority lending policy, are statistically significantly designed in favour of the lower-middle-income group. However, all other financial inclusion strategies and policies, such as national development strategy, financial literacy strategy, tax incentive, savings schemes policy, deposit-taking institutions policy, encouraging policy and no policy implementation, are not statistically significantly different. In addition, the mean difference in access to finance is not statistically significant across the income groups. This implies that all income groups are exposed to similar treatment on access to finance and all other indicators are not statistically different in the mean. The argument here is despite that the microfinance strategy and priority lending policy are statistically significant, as long as the mean difference on access to finance is statistically not significant, even the lower-middle-income group may need to compete with other high-income groups for finance. This will create challenges for the lower-middle-income group as well.

It is obvious that financial exclusion and the COVID-19 pandemic are pressing global issues, although their severity varies drastically across the globe. Africa has a huge proportion of a financially excluded population and is severely affected by the impact of the COVID-19 pandemic (OXFAM, 2020). Furthermore, OXFAM (2020) advised the IMF and World Bank to extend cash injections to developing countries to prevent a setback of about three decades in poverty eradication programmes. OXFAM even stressed that with about USD $82 billion donated by about 52 donor countries in December 2019 when the COVID-19 pandemic was not declared as a pandemic, still an additional USD $2.5 trillion is required on a yearly basis to make developing countries meet the sustainable development goals (SDG). In addition, Chibba (2009) references a report of the Consultative Group to Assist the Poor made in 2006 that two billion people of the three billion poor and financially excluded population live in developing countries; hence special attention is required for African regarding financial inclusion.

References

Ain, N. ul, Sabir, S., & Asghar, N. (2020). Financial inclusion and economic growth : Empirical evidence from selected developing economies. Review of Economics and Development Studies, 6(1), 179-203.

Arthur, E. K., Musau, S. M., & Wanjohi, F. M. (2020). Diaspora remittances and financial inclusion in Kenya. European Journal of Business and Management Research, 5(2), 110. doi: 10.24018/ejbmr.2020.5.2.289.

Brahim, W. H. B. W., & Kamalu, K. (2020). How does the Islamic banking development spur financial inclusion In OIC member countries? Asian People Journal, 3(1), 170-185.

Central Intelligence Agency (CIA). (2016). The World Factbook 2016. Retrieved from https://www.cia.gov/libraty/publications/download/download-2016/index. html

Chibba, M. (2009). Financial inclusion, poverty reduction and the millennium development goals. European Journal of Development Research, 21(2), 213-230. doi: 10.1057/ejdr.2008.17.

Fauzan, I. F., Firdaus, M., & Sahara, S. (2020). Regional financial inclusion and poverty: Evidence from Indonesia. Economic Journal of Emerging Markets, 12( 1), 25-38. doi: 10.20885/ejem.voll2.issl.art3.

Fowowe, B. (2020). The effects of financial inclusion on agricultural productivity in Nigeria. Journal of Economics and Development, 1, 1-19. doi: 10.1108/ JED-11-2019-0059.

Ghosh, S., 8c Sahu, T. N. (2020). How financially inclusive the Asian’s are? An empirical analysis. Rajagiri Management Journal, 14(1), 49-69.

Kingsley, A. F., Noordewier, T. G., 8c Bergh, R. G. V. (2017). Overstating and understating interaction results in international business research. Journal of World Business, 52(2), 286-295. doi: 10.1016/j.jwb.2016.12.010.

Melubo, K. D., 8c Musau, S. (2020). Digital banking and financial inclusion ofwomen enterprises in Naro County, KENYA. International Journal of Current Aspects in Finance, Banking and Accounting, 2(1), 28-41. doi: 10.35942/ijcfa.v2il.l04.

Mengesha, T., Timbula, M. A., Mekonnen, Y., 8c Kebede, M.(2020). Financial literacy and its determinants among households in Jimma zone. International Journal of Commerce and Finance, 6(1), 102-112.

Nicole Pasricha (2008). Next generation access to finance: Gaining scale and reducing costs with technology and credit scoring. Enterprise Development Microfinance, 19(1), 59-68.

Nilsson, J. (2009). Segmenting socially responsible mutual fund investors responsibility. International Journal of Bank Marketing, 27(1), 5-31. doi: 10.1108/02652320910928218.

Nizam, R., Karim, Z. A., Abdul Rahman, A., 8c Sarmidi, T.(2020). Financial inclusiveness and economic growth : New evidence using a threshold regression analysis. Economic Research-Ekonomska Istrazivanja. 33(1), 1465-1484. doi: 10.1080/1331677X.2020.1748508.

Omar, A., 8c Inaba, K. (2020). Does financial inclusion reduce poverty and income inequality in developing countries ? A panel data analysis. Journal of Economic Structures, 9(37), 125. doi: 10.1186/s40008-020-00214-4.

Oxford Committee for Famine Relief (OXFAM). (2017). The commitment to reducing inequality index. Development Finance International and OXFAM Research Report, 132.

Oxford Committee for Famine Relief (OXFAM). (2019). World Bank donor countries approve $82 billion package to fight extreme poverty: Oxfam reaction. Retrieved from https://www.oxfam.org/en/press-releases/world-bank-donor-countries -approve-82-billion-package-fight-extreme-poverty-oxfam.

Oxford Committee for Famine Relief (OXFAM). (2020). Half a billion people could be pushed into poverty by coronavirus, warns Oxfam. Retrieved from https:// www.oxfam.org/en/press-releases/half-billion-people-could-be-pushed-poverty -coronavirus-warns-oxfam.

Prasetiyani, E., 8c Vikaliana, R. (2020). Construction of financial inclusion behaviour models in the implementation of fintech at fishermen SMEs Carita, Purwaarta. Journal of Management and Leadership, 3( 1), 43-54.

Rodrigue, N. Y., 8c Roger, N. (2020). Remittances, financial inclusion and income inequality in Africa remittances, financial inclusion and income inequality in Africa. Munich Personal RePEc Archive. 1, 1-23.

Financial inclusion and risk exposure 57

Sarma, M. (2008). Index of financial inclusion, New Delhi: Indian council for research on international economics relations. Retrieved from https://www.icrier.org/pd f/Working_Paper_215 .pdf.

Seamer, M., & Melia, A. (2015). Remunerating non-executive directors with stock options: Who is ignoring the regulator? Accounting Research Journal, 28(3), 251-267. doi: 10.1108/ARJ-12-2013-0092.

Trivedi, A. S. (2020). Accessibility of the banking facilities under financial inclusion by urban women of Ahmedabad district. International Journal of Research and Analytical Reviews (IJRAR), 7(2), 203-214.

United Nations Human Rights (UNHR). (2017). Statement on visit to the USA, by Professor Philip Alston, United Nations Special Rapporteur on extreme poverty and human rights. Received from https://www.ohchr.org/EN/NewsEvents/ Pages/DisplayNews.aspx?NewsID=22533.

Waghmare, S. V. (2020). An overview of financial inclusion in the Indian economic scenario routes of financial inclusion in India. An Annual Interdisciplinary Journal of History, 6(6), 528-536.

 
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