Does financial literacy inevitably lead to access to finance services? Evidence from rural Ghana

Twumasi, Martinson Ankrah; Jiang, Yuansheng; Wang, Pengcheng; ding, Zhao; Frempong, Lady Nadia; Acheampong, Monica Owusu

doi:10.1590/0103-8478cr20210112

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AGRIBUSINESS • Cienc. Rural 52 (3) • 2022 • https://doi.org/10.1590/0103-8478cr20210112 copy

Does financial literacy inevitably lead to access to finance services? Evidence from rural Ghana

A educação financeira leva inevitavelmente ao acesso a serviços financeiros? Provas da zona rural de Gana

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

This study examined the determinants of financial literacy (FL) and its impact on access to financial services (AFS), using data collected from rural Ghana. A two-stage residual inclusion model is utilized to address the selection bias issue. The results showed that FL is affected by household heads’ age, gender, education, asset ownership, homeownership, and economics education. The results revealed that FL is significant and positively related to AFS, but its square shows an inverse relation with saving mobilization. This indicated a non-linear relationship between FL and AFS. Moreover, we find that FL has a larger AFS impact for households with high-income and male household heads relative to their counterparts. The study recommended that the government can initiate the creation of a rural committee to educate rural residents on financial issues through radio broadcasting and meetings. Our findings highlighted the importance of FL on AFS in enhancing the welfare of rural households.

Key words:
access to financial services; financial literacy; two-stage residual inclusion model; Ghana JEL codes: D91; C21; C26; D12

Authorship

Martinson Ankrah Twumasi

College of Economics, Sichuan Agricultural University, Chengdu, China.Sichuan Agricultural UniversityChinaChengdu, ChinaCollege of Economics, Sichuan Agricultural University, Chengdu, China.

https://orcid.org/0000-0001-8703-7102

Yuansheng Jiang ^*E-mail: yjiang@sicau.edu.cn. *Corresponding author

College of Economics, Sichuan Agricultural University, Chengdu, China.Sichuan Agricultural UniversityChinaChengdu, ChinaCollege of Economics, Sichuan Agricultural University, Chengdu, China.

https://orcid.org/0000-0003-3033-3693

Pengcheng Wang

College of Economics and Management, China Agricultural University, Beijing, China.China Agricultural UniversityChinaBeijing, ChinaCollege of Economics and Management, China Agricultural University, Beijing, China.

https://orcid.org/0000-0001-7335-0827

Zhao ding

College of Economics, Sichuan Agricultural University, Chengdu, China.Sichuan Agricultural UniversityChinaChengdu, ChinaCollege of Economics, Sichuan Agricultural University, Chengdu, China.

Lady Nadia Frempong

College of Management, Sichuan Agricultural University, Chengdu, China.Sichuan Agricultural UniversityChinaChengdu, ChinaCollege of Management, Sichuan Agricultural University, Chengdu, China.

https://orcid.org/0000-0002-5123-9578

Monica Owusu Acheampong

School of Management, University of Cape Coast, Cape Coast, Ghana.University of Cape CoastGhanaCape Coast, GhanaSchool of Management, University of Cape Coast, Cape Coast, Ghana.

https://orcid.org/0000-0001-7421-6160

E-mail: yjiang@sicau.edu.cn. ^*Corresponding author

Editors

Leandro Souza da Silva (0000-0002-1636-6643)

Daniel Arruda Coronel (0000-0003-0264-6502)

DECLARATION OF CONFLICT OF INTEREST

We have no conflict of interest to declare.

AUTHORS’ CONTRIBUTIONS

All authors have equal contributions.

SCIMAGO INSTITUTIONS RANKINGS

College of Economics, Sichuan Agricultural University, Chengdu, China.Sichuan Agricultural UniversityChinaChengdu, ChinaCollege of Economics, Sichuan Agricultural University, Chengdu, China.

College of Management, Sichuan Agricultural University, Chengdu, China.Sichuan Agricultural UniversityChinaChengdu, ChinaCollege of Management, Sichuan Agricultural University, Chengdu, China.

School of Management, University of Cape Coast, Cape Coast, Ghana.University of Cape CoastGhanaCape Coast, GhanaSchool of Management, University of Cape Coast, Cape Coast, Ghana.

Figures | Tables

Figures (2)
Tables (8)

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Figure 1
Distribution of AFS percentage by household income level status and gender Forward all figures .tiff with at least 300 dpi.

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Figure 2
Distribution of average NAFS by household income level status and gender.

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Table 1
Socioeconomic, Demographic and other characteristics of the respondents.

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Table 2
Financial literate and illiterate mean differences of selected variables.

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Table 3
Problem Confrontation Index (PCI) computation.

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Table 4
2SRI model estimate for the effect of financial literacy on access to financial services.

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Table 5
Impact of financial literacy on AFS and NAFS by household income level and gender composition.

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Table 6
Robustness checks results.

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Table 7
Financial literacy questions and responses.

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Table 8
IV validity test (Pearson correlation coeﬃcient analysis).

Figure 1 Distribution of AFS percentage by household income level status and gender Forward all figures .tiff with at least 300 dpi.

Figure 2 Distribution of average NAFS by household income level status and gender.

Table 1 Socioeconomic, Demographic and other characteristics of the respondents.

Variables	Description	Mean	Std. Dev.
AFS	1 if the household has access to at least one of the three financial services, 0 otherwise	0.47	0.50
Number of AFS	The number of financial services accessed by a respondent	1.42	0.97
Fiancial literacy	1 if the respondent is financially literate (whether the respondent score is above the median financial literacy score), 0 otherwise	0.33	0.42
Financial literacy(FL)	Financial literacy score	2.63	1.79
Age	Age of household head (years)	41.72	12.21
Gender	1 if household head is male, 0 otherwise	0.69	0.46
Marital status	1 if respondent is married; 0 otherwise	0.71	0.80
Education	1 if the respondent has high school or higher education, 0 otherwise	0.27	0.49
Household size	Number of members in a household (persons)	6.61	3.21
Number of children	Number of children (less than 18years) in a household	3.59	1.49
Asset ownership	1 if the respondent owns a TV/radio, 0 otherwise	0.78	0.53
Homeownership	1 if the respondent owns a house, 0 otherwise	0.39	0.49
Extention services	1 if the respondent has access to estension services; 0 otherwise	0.41	0.47
Distance	Distance from the respondent house to the nearest financial institution (kilometers (km))	2.14	0.81
Economics education	1 if the respondent knows someone (relative/friend) with economics education ; 0 otherwise	0.24	0.41

Source: survey results, 2020

Table 2 Financial literate and illiterate mean differences of selected variables.

Variables	Literate	Illiterate	Mean differences
AFS	0.56 (0.46)	0.38 (0.45)	0.18^***
NAFS	1.73 (0.83)	1.11 (0.91)	0.62^**
Age	38.32 (17.23)	45.12 (18.61)	-6.80^*
Gender	0.76 (0.51)	0.62 (0.49)	0.14^*
Marital status	0.74 (0.49)	0.68 (0.48)	0.06^*
Education	0.39 (0.42)	0.15 (0.36)	0.24^***
Household size	6.12 (3.69)	7.10 (3.52)	-0.98
Number of children	3.84 (1.06)	3.34 (0.98)	0.50
Asset ownership	0.82 (0.56)	0.74 (0.52)	0.08^*
Home ownership	0.46 (0.49)	0.32 (0.44)	0.14^*
Extension services	0.45 (0.32)	0.37 (0.40)	0.08
Distance	1.46 (0.88)	2.82 (1.03)	-1.36^**
Economics education	0.31 (0.48)	0.17 (0.39)	0.14^**

Source: survey results, 2020. ^*, ^**, and ^*** represent statistical significance at 10%, 5% and 1% alpha levels, respectively. All numbers in parentheses are robust standard errors.

Table 3 Problem Confrontation Index (PCI) computation.

	----------Extent of problem confrontation---------					Rank
Problem	High (3)	Medium (2)	Low (1)	Not at all (0)	PCI	Order
Distance to nearest FI is far	97	194	227	54	906	6
Lack of trust	273	94	205	0	1212	3
Religious reason	120	241	211	0	1053	5
Insufficient money	219	279	75	0	1288	2
A family member has one	77	156	256	83	799	7
Lack of documentations	129	374	69	0	1204	4
Do not understand FIs terminologies	233	301	38	0	1339	1
Risk rationing	85	107	289	91	758	8

Source: Survey results, 2020.

Table 4 2SRI model estimate for the effect of financial literacy on access to financial services.

Variables	First stage	Second stage
	Financial Literacy (0/1)	Access to financial services (AFS)	Number of financial services accessed (NAFS)
Financial Literacy		0.571 (0.071)^***	0.066 (0.035)^***
Financial Literacy²		-1.064 (0.048)^***	-0.547 (0.042)^***
Age	-0.007 (0.002)^*	-0.184 (0.055)^*	-0.051 (0.031)
Gender	0.012 (0.007)^**	0.035 (0.019)^**	0.145 (0.063)^**
Marital status	0.007 (0.002)	0.064 (0.1152)	0.024 (0.019)
Education	0.013 (0.002)^**	0.303 (0.184)^**	0.127 (0.051)^**
Household size	-0.028 (0.005)	-0.198 (0.127)^**	-0.071 (0.032)
Number of children	0.156 (0.097)	0.002 (0.000)	0.016 (0.002)
Asset ownership	0.029 (0.013)^*	0.088 (0.021)	0.022 (0.016)
Home ownership	0.057 (0.014)^**	0.111 (0.036)^**	0.004 (0.000)^**
Extension services	0.044 (0.019)	0.022 (0.017)^*	0.005 (0.004)
Distance	-0.051 (0.027)	-0.090 (0.057)^***	-0.059 (0.013)^*
Economic education	0.037 (0.012)^**
Residuals		0.012 (0.003)^*	0.124 (0.037)^*
Log-pseudo likelihood	-1387.765	-967.834	-2691.441
Regional dummies	Yes	Yes	Yes
Observation	572	572	572

Source: survey results, 2020. ^*, ^**, and ^*** represent statistical significance at 10%, 5% and 1% alpha levels, respectively. All numbers in parentheses are robust standard errors.

Table 5 Impact of financial literacy on AFS and NAFS by household income level and gender composition.

	---------Second stage of AFS (Probit model)---------			------------Second stage of NAFS (Poisson model)------------
Variable	Male	Female	High-income household	Low-income household	Male	Female	High-income household	Low-income household
Financial Literacy	0.031 (0.012)^*	0.012 (0.007)*	0.121 (0.090)^**	0.018 (0.015)	0.072 (0.034 )^**	0.028 (0.013)^*	0.078 (0.031)^*	0.021 (0.009)
Financial Literacy²	-0.073 (0.019)^*	-1.082 (0.147)^**	0.313 (0.031)	-0.034 (0.011)	-0.056 (0.024)^*	-0.005 (0.001)^**	0.029 (0.008)	-0.016 (0.007)
Age	-0.074 (0.027)^***	-0.021 (0.015)	0.028 (0.014)^**	-0.116 (0.035)	-0.001^** (0.000)	-0.016 (0.003)	-0.033^* (0.017)	-0.014^** (0.005)
Gender			0.046 (0.018)^*	0.014 (0.012)^*			-0.014^*** (0.009)	-0.011^* (0.005)
Marital status	0.009 (0.001)^*	0.063 (0.094)	0.117 (0.012)^*	0.031 (0.015)	-0.052 (0.014)	0.033 (0.016)	0.047 (0.007)	0.028 (0.019)
Education	0.032 (0.018)^**	-0.047 (0.012)^*	0.042 (0.051)^**	0.005 (0.001)^*	-0.021 (0.019)	0.009 (0.003)	-0.039^* (0.019)	-0.034 (0.011)
Household size	-0.131 (0.053)	-0.053 (0.018)^*	0.089 (0.034)^*	-0.043 (0.021)^**	-0.004^** (0.002)	-0.025^** (0.013)	-0.029^* (0.018)	-0.011^** (0.007)
Number of children	-0.116 (0.053)	-0.632 (0.242)^*	0.049 (0.015)	-0.071 (0.045)	-0.049^* (0.018)	-0.027^** (0.015)	-0.010^* (0.002)	-0.003 (0.001)
Asset ownership	-0.146 (0.075)	-1.380 (0.183)^*	-0.055 (0.019)^*	-0.031 (0.014)^**	-0.033^* (0.019)	-0.018^* (0.012)	-0.037^* (0.019)	-0.015 (0.007)
Home ownership	0.153 (0.055)^**	0.018 (0.006)^*	0.013 (0.109)^***	0.031 (0.007)^*	-0.057^** (0.024)	-0.022^* (0.010)	-0.021^** (0.011)	-0.013^* (0.005)
Extension services	0.018 (0.007)^*	0.011 (0.004)	0.017 (0.004)^*	0.061 (1.098)	-0.032^* (0.009)	-0.011 (0.0172)	-0.037^* (0.0329)	-0.015^** (0.0137)
Distance	0.023 (0.011)^**	0.066 (0.026)	0.016 (0.002)	0.054 (0.024)	0.076^* (0.021)	0.011 (0.004)	-0.023^** (0.011)	-0.015^** (0.013)
Residual	0.638 (0.159)^**	0.153 (0.680)^*	0.442 (0.062)^***	0.091 (0.182)^*	-0.054 (0.018)	0.131 (0.077)	-0.089^* (0.042)	-0.062^* (0.019)
Log-pseudo likelihood	-1387.765	-967.834	-2691.441	-554.789	-1101.991	-670.142	-778.092	-2729.802
Regional dummies	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observation	395	177	274	298	395	177	274	298

Source: survey results, 2020. Asterisks ^*. ^** and ^*** represent significant levels at 10%, 5% and 1% respectively. All numbers in parentheses are robust standard errors. Reference region is Northern.

Table 6 Robustness checks results.

Variables	AFS (IV Probit)	NAFS (IVPoisson)
FL	0.376 (1.018)^***	0.085 (0.026)^***
FL²	-0.521 (0.211)^***	-1.149 (1.071)^***
Control variables	Yes	Yes
Regional dummies	Yes	Yes
Instrumental variables	Yes	Yes
Wald 𝑋 2	49.62^***	76.17^***
Observation	572	572

Source: Survey results, 2020 ^*** represents statistical significance at 1% alpha levels respectively. All numbers in parentheses are robust standard errors. Reference region is Northern

Table 7 Financial literacy questions and responses.

Question	Answers
Suppose you had $100 in your savings account with a 2% annual interest. After 5 years, how much will you have in this account if you leave your money to gain interest? (Interest rate)	(a) more than $102 (b) exactly $102 (c) less than $102 (d)I don’t know
Your savings account has a 1% annual interest and the annual inflation is 2%. After a year, will you be able to buy more than today using the money saved in this account? (Inflation)	(a) Yes (b)No (c)I don’t know
Is the following statement true or false? “Buying a single company stock usually provides a safer return than a stock mutual fund.” (Risk diversification)	(a) True (b) False (c)I don't know
You want to borrow GH¢500 from a money lender (M1). He says that you can get it but you must pay him GH¢600 in a month. Another money lender (M2) says you have to pay GH¢600 plus 15% in a month. Which one is better? (Borrowing)	(a)M1 (b)M2 (c)I don't know
Assume a friend inherits GH¢10,000 today and his sibling inherits GH¢10,000 3 years from now. Who is richer because of the inheritance? (Time value of money)	(a) My friend (b) His sibling (c) They are equally rich (d)I don’t know
Suppose that in the year 2010, your income has doubled and prices of all goods have doubled too. In 2010, how much will you be able to buy with your income? (Money illusion)	(a) More than today (b) The same (c) Less than today (d)I don’t know
A second hand farm machinery is more expensive to insure than a brand new one? (insurance)	(a) True (b) False (d)I don’t know

Table 8 IV validity test (Pearson correlation coeﬃcient analysis).

Variables	Correlation coefficients	P-Value
AFS	0.257	0.338
NAFS	1.056	0.592
FL	0.088	0.015

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Universidade Federal de Santa Maria Universidade Federal de Santa Maria, Centro de Ciências Rurais , 97105-900 Santa Maria RS Brazil , Tel.: +55 55 3220-8698 , Fax: +55 55 3220-8695 - Santa Maria - RS - Brazil
E-mail: cienciarural@mail.ufsm.br

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[1] E-mail: yjiang@sicau.edu.cn. ^*Corresponding author