Mobile Telephony, Financial Inclusion and Inclusive Growth 1

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1 Mobile Telephony, Financial Inclusion and Inclusive Growth 1 Joshua Yindenaba Abor Department of Finance, University of Ghana Business School, Legon joshabor@ug.edu.gh Mohammed Amidu Department of Accounting, University of Ghana Business School, Legon. mamidu@ug.edu.gh Haruna Issahaku Department of Economics and Entrepreneurship Development, Faculty of Integrated Development Studies, University for Development Studies, Ghana. iharuna@uds.edu.gh Abstract The paper employs the multipurpose nature of mobile telephony to investigate its welfare implications using a large sample of Ghanaian households. We use seemingly unrelated probit and instrumental variable procedure to test for two related issues: First, we investigate whether mobile telephony promotes pro-poor development by helping households to efficiently allocate consumption and navigate out of poverty. Second, we find out whether access to a broad array of financial services enhances the capacity of households to live worthwhile lives. The results of the study show that mobile penetration and financial inclusion significantly reduce the probability of a household becoming poor and increase per capita household consumption of food and non-food items. Our results show that the welfare benefits of mobile telephony and financial inclusion are not more pronounced in femaleheaded households. These insights should serve as useful guide for government and other stakeholders who are looking for ways to improve livelihoods. 1 Joshua Y. Abor and Mohammed Amidu would like to thank the DFID-ESRC for funding this research which is part of DFID-ESRC Research Project Ref ES/NO13344/1: Delivering Inclusive Financial Development & Growth. 1

2 1. Introduction Literature shows that the number of mobile subscribers in 2002 exceeded the number of fixed line subscribers worldwide. This ushered in a new dawn in the history of telecommunications, which enthroned the mobile phone as the king of voice communication (Srivastava, 2005). In Africa, about 46% of the population subscribe to one mobile service or another as at the end of 2015 (GSMA, 2016). According to Ghana s National Communication Authority (NCA, 2016), mobile voice and mobile data penetration in Ghana stood at % and 69.43% receptively as at the end of October The question is, has this phenomenal growth in digital communication delivered shared prosperity to Ghanaian households, given that 24% are poor? To answer this question, this study examines whether mobile technology has improved the ability of households to increase their consumption of durable and nondurable goods and escape poverty. Mobile phone usage is nebulous it can be used to either good or bad effect. On one hand, mobile technology may help engender inclusive development (Asongu & Nwachukwu, 2016; Beuermann, McKelvey, & Vakis, 2012), reduce inequality (Asongu, 2015), increase income (Wijeratne & Silva, 2014), facilitate cooperation and information sharing (Sreekumar, 2011), among others. On the other hand, mobile usage can increase the number of road accidents (Mohammadi, 2009), reduce concentration during meetings and learning, dilute culture, increase safety concerns and fraud, and lead to game addiction (Srivastava, 2005), among others. The nebulous nature of mobile phones means that their effects, either negative or positive, will be context and individual specific. This justifies this country case study, which presents the first evidence of the effects of mobile penetration on household welfare in Ghana. The analysis is extended to include the role of financial inclusion in inclusive development. Previous studies have mostly looked at either the effects of financial inclusion on inclusive growth (see Adomako, Danso, & Ofori Damoah, 2016; Arora, 2012; Ghosh, 2016) or the effect of mobile technology on inclusive growth (see Asongu, 2015; Asongu & Nwachukwu, 2016; Beuermann et al., 2012), but not both. We contribute to the literature by studying the two together. This will help us to understand whether the positive link found by previous studies between financial inclusion and inclusive growth will still pertain, when we capture the effect of the digital economy and vice versa. Our study makes a number of unique contributions to the literature. We present the first evidence of the effect of mobile penetration on inclusive growth within the Ghanaian context. Second, in the context of the global literature, we depart from the extant literature, which often models the growth impact of finance and mobiles separately, by simultaneously examining the effects of mobile telephony and financial inclusion on pro-poor growth. Thirdly, we explore whether there are gender and location premia associated with the propoor effects of mobile penetration and financial inclusion. This will tell whether these two policy variables can be harnessed to bridge the gender developmental gap and the rural-urban poverty gap. We posit that mobile technology helps households to minimise cost (of transactions and of information) and maximize their utility (that is, efficiently increase per capita consumption and optimally exit poverty). Similarly, financial inclusion facilitates payments and reduces information asymmetries and thereby helps households to become prosperous. We test these 2

3 propositions using the most recent Ghana Living Standard Survey data (GLSS 6) compiled by the Ghana Statistical Service between 2012 and The data covers a total of 16,772 households randomly selected from across the country. We measure inclusive growth using two variables, poverty status of the household and household consumption expenditure per capita. Mobile penetration is measured as the number of people owning a mobile phone and the number of people actually using a mobile network. Financial inclusion is proxied by three indicators: account ownership, access to credit and insurance penetration. Because poverty status and mobile penetration are both dummies, we employ the seemingly unrelated (SURE) probit to effectively deal with the endogenous nature of the two variables. In the case of the consumption expenditure model, the continuous nature of consumption expenditure per capita allows us to use instrumental variable method to good effect. The results reveal that both mobile penetration and financial inclusion reduce the incidence of poverty, while increasing per capita household consumption of food and non-food items. Further analysis showed that these effects are not more pronounced in households headed by females than in those headed by males. The results mean that the two variables can be deployed effectively by policy makers and development agencies and institutions to promote inclusive growth. The rest of the paper is structured as follows. Section two discusses the relevant literature. Section three espouses the empirical strategy. Section four presents results and analysis, while section five concludes. 2. Literature review In this section, we review literature on the positive and negative effects of mobile phone usage. Mobile phone has both positive and negative implications. Asongu (2015) provides evidence of mobile penetration reducing inequality in Africa. Similarly, Asongu and Nwachukwu (2016) find that mobile phone diffusion enhances inclusive development in Sub- Saharan Africa. Again, they reveal that institutions enhance the positive effect of mobile phones on inclusive growth. Studies in rural Peru further show that mobile coverage increased household consumption per capita and by so doing reduced both extreme and absolute poverty (Beuermann et al., 2012). This is because mobile phones reduce information search cost and enable villagers to have access to weather, market and price information, all of which enable production and productivity. There is additional evidence of mobile phone improving economic growth in Indian States (Ghosh, 2016). Mobile phones are found to advance literacy, improve jobs, access to health services and enhanced social networking among people at the bottom of the pyramid in India (Londhe, Radhakrishnan, & Divekar, 2014). In an intervention for Sri Lankan mushroom farmers led by the University of Ruhana, farmers were provided with mobile phones with interactive voice messages (IVM) that relayed scientific knowledge to them. The results from the intervention showed improved income of participating farmers arising from their receipt of technical knowhow timeously and accurately made possible by the IVM (Wijeratne & Silva, 2014). It may be argued that the welfare benefits derived from mobiles is transient, giving the cost involved in owning and maintaining a phone as well as perceived negative effects. In a recent study on M-PESA in Kenya, Suri and Jack (2016) show that mobile money has significant and favourable long-run impact on both household consumption and poverty. The impact was found to be larger for 3

4 households headed by females. There is evidence of mobile phones enabling cooperation and information sharing among fishers in Kerala, India, who share information amongst themselves regarding rescue, potential bumper locations and safety (Sreekumar, 2011). Mobile phones are further used to send and receive remittances (Ojong, 2016), which could transfer funds for investments in inclusive development promoting ventures. Bailard (2009) investigates how mobile phone usage affects corruption in a sample of African countries and finds that mobile phone diffusion is negatively correlated with corruption perception in the sample countries. This is because, by ensuring that information and communication are decentralised, mobile phones reduce the avenues for corruption, and increase the ability to detect and punish corruption (Bailard, 2009). While the above positive effects are welcome, it is imperative not to lose sight of the flipside of mobile technology. Srivastava (2005) observes that the mobile phone has transitioned from an obvious technological object to a social object. Mobile phone increases the invasion of privacy, the dilution of cultural values and game addiction. It has been reported in Korean and Japan that some children play games for 24 hours non-stop (Srivastava, 2005). Mobile phone usage can distract the attention of users during work hours, meetings, lectures and driving. Evidence from Iran shows that some drivers use their phones while driving, thereby increasing the risk of accidents (Mohammadi, 2009). The impersonal nature of mobile communication makes it susceptible to fraudulent use. Callers sometimes mutilate their voices to avoid detection, make pretentious calls and give misrepresentations with the aim to deceive and defraud. Turning to the link between financial inclusion and inclusive growth, from a sample of Ghanaian firms, Adomako et al. (2016) and Osei-Assibey (2013) find that access to finance enhances firm growth and productivity respectively. In a related study in India, Ghosh (2016) established a positive relationship between financial penetration and growth per capita in several states. The ability for financial inclusion to advance human capital development has also been established in the literature. Arora (2012) reveals a positive correlation between access to banking services and educational attainment but a negative relationship between financial depth and pupil-teacher ratio in a sample of 21 Asian countries. Other studies have looked at the impact of financial inclusion on growth from the gender dimension. For instance, Swamy (2014) provides insights from India that, financial inclusion increased income but this increment was found to be larger among females (8.40%) than males (3.97%). On the contrary, Nanziri (2016) did not find any gender gap in the welfare effects of financial inclusion in South Africa. There is also evidence to the effect that there is less tax evasion in countries where inclusive finance is prevalent (Ahamed, 2016). There are sound theoretical basis for expecting financial inclusion to promote inclusive development. Through its ability to lower transaction cost and reduce information asymmetries, finance first of all, facilitates the efficient allocation of resources to growth enhancing sectors and projects. Secondly, it helps in the mobilisation of savings for investment; and thirdly, it provides avenues for risk sharing and mitigation. 4

5 3. Evaluating methodology 3.1 Econometric Model Intuitively, we expect that individuals who are not poor will have a better chance of owning mobile phone than the poor. This means that if we are not cautious with the modelling we may attribute the alleviation of poverty among mobile users wrongly. It may be that it is the ownership of the model phone that helped the household to move out of poverty, or that it is because the household is already well to do that is why it has a mobile phone. This is the usual endogeneity question that often arises in studies of this nature. Due to this reason we model the decision to own and use a mobile phone and the poverty status of the household simultaneously. Mobile penetration is proxied by two variables: ownership of mobile phone which takes the value 1 if household owns a phone and 0 otherwise, and mobile phone usage which takes the value 1 if household uses a phone and 0 otherwise. Our measure of inclusive growth, poverty status, is labelled 1 if the household is non-poor and 0 if household is poor. Due to the fact that both mobile penetration and inclusive growth are dichotomous, based on Greene (2003), we specify the following latent simultaneous model: y i1 = π 1x i1 + φ 1 y i2 + ε i1, y i1 = 1(y i1 > 0) (1) y i2 = π 2x i2 + φ 2 y i1 + ε i2, y i2 = 1(y i2 > 0) (2) Where ( ε 1 ε ) ~N [( ) (1 ρ ρ 1 )] That is, the two error terms are jointly determined with zero mean, constant variance and correlation, ρ. If the two equations are unrelated ρ = 0, which will imply that we do not have to estimate the two equations jointly. Therefore, we test the null hypothesis that ρ = 0. y 1 is a latent variable representing the unobservable latent variable, mobile phone penetration. y 1 is the observed decision to own mobile phone. y 1 takes the value 1 if the household owns a mobile phone and zero otherwise. y 2 is the unobservable welfare status of the household, while y 2 is the measured poverty status of the household. y 1 takes the value 1 if the household is nonpoor and 0 otherwise. x 1 is a vector of exogenous variables measuring household and community characteristics which explain the decision to own or use a mobile phone or otherwise, x 2 is a vector of exogenous variables comprising household and community variables which explain the welfare status of the household. The π s and φ s are vectors of parameters. Because equations (1) and (2) contain endogenous regressors, we cannot use the usual probit model to derive unbiased estimates. For this reason we estimate equations (1) and (2) using seemingly unrelated (SURE) probit procedure. The model is based on the intuition that the poverty status of the household is conditional on the decision of the household to own or use a mobile phone and vice versa. The SURE probit addresses unobserved heterogeneity, endogeneity and correlation allowing for efficient and unbiased estimates to be obtained. In the case where our measure of inclusive growth is household per capita consumption expenditure, which is a continuous variable, we specify and estimate the following model: 5

6 Y i = β o + β 1 Z i + β 2 τ i + δ X i + ε i (3) Where Y i is household expenditure per capita which is a proxy for inclusive growth, Z i is a measure of mobile phone penetration and τ i is a measure of financial inclusion for household i, X i is a vector of exogenous regressors consisting of household characteristics and community infrastructure variables. Mobile penetration is measured by two variables: phone ownership (OWN_PHONE) and phone usage (USE_PHONE). Financial inclusion is proxied by three dummy variables: account ownership, access to credit and insurance penetration. All variables and their definitions are presented in Table 1. Because of the endogenous relationship between expenditure per capita and mobile penetration, using OLS to estimate equation (3) may produce biased results. Therefore, we estimate equation 3 using two stage least squares to address endogeneity. We estimate an exactly identified model using distance to the nearest telecommunication network to the household as an instrument. Our assumption is that the distance to the nearest telecommunication network will directly affect the ownership and use of phone by the household but not household consumption. We employ Woolridge s robust regressions and regression based method to test for the existence of endogeneity. In both cases, the null hypothesis is that the there is no endogeneity. 3.2 Data The Ghana Living Standard Survey round 6 data (GLSS 6) obtained from the Ghana Statistical Service is the data used for this study. The data comprises a total of 16,772 households randomly selected from households across the country. It is the richest and most comprehensive household level data for the country, beside the census data. The variables extracted from the database and used for the study are presented in Table 1. Table 1. Variables and their definitions Variable Definition Measurement Inclusive Growth Variables POOR Poor or non-poor status 1=if non-poor; 0= if poor REAL Real per capita annual food and nonfood Ghana cedis expenditure, regionally deflated Mobile Phone Variables OWN_PHONE Ownership of at least one mobile phone 1= if yes; 0=otherwise USE_PHONE Does household use mobile phone 1= if yes; 0=otherwise network? Financial Inclusion Variables BANK_ACCT Ownership of bank account 1= if yes; 0=otherwise CRED_ACCESS Application for loan in the past 12 1= if yes; 0=otherwise months INSURANCE Ownership of insurance policy 1= if yes; 0=otherwise Household Characteristics HHAGE Age of household head Years HHSEX Gender of household head 1= if male; 0=otherwise 6

7 EDYEARS Household head s educational Years of schooling attainment HHEMSTAT Household head s employment status 1= if employed; 0=otherwise HHEMTYPE Employment type of household head 1= if salary or wage worker; 0=otherwise HHLOC Locality of household 1= if rural; 0=otherwise HHSIZE Household size Number of persons LANDSIZE Land size owned by household Hectares INC_RMT Amount of income received through Ghana cedis remittances Community Infrastructure MTRD There is motorable road to community 1= if yes; 0=otherwise COMMBANK Nearest bank to community Kilometres COMMMKT Permanent daily community market 1= if yes; 0=otherwise PTPASS Public transport pass through 1= if yes; 0=otherwise community COMMELECT Community has electricity 1= if yes; 0=otherwise COMMPTOF Distance to nearest post office Kilometres 4. Empirical results 4.1 Descriptive statistics From the descriptive statistics in Table 2, the average real annual household expenditure is GH 1,112 (US$265 2 ). This is above the 2013 extreme poverty line of GH per annum but below the 2013 absolute poverty line of GH 1,314 per annum. At the international level, this falls below international poverty line of US$693.5 per annum (US$1.9 per day 3 ). This means that a significant number of households in Ghana have yet to share in the growth that has occurred over the past decades. In terms of categorisation into poor and non-poor households, about 76% of households is non-poor with the remaining 24% being poor. Table 2. Descriptive Statistics Variable Obs Mean Std. Dev. Min Max Continuous variables REAL EDYEARS HHAGE HHSIZE LANDSIZE INC_RMT COMMBANK FONNETW Categorical variables Variable Percentage 2 The exchange rate is 4.2GHC/USD 3 This international poverty line set by the World Bank in

8 (Yes) POOR OWN_PHONE USE_PHONE BANK_ACCT CRED_ACCESS 8.98 INSURANCE HHEMSTAT HHSEX HHLOC HHEMTYPE MTRD COMMMKT 3.41 PTPASS COMMELECT COMMPTOF 1.19 Inclusive growth is measured by two variables: POOR is a dummy for poverty status of household labelled 1 if household is nonpoor and 0 otherwise, REAL is household consumption per capita, mobile penetration is proxied by two variables: phone ownership (OWN_PHONE) labelled 1 if household owns at least one mobile phone and 0 otherwise, and phone usage (USE_PHONE) labelled 1 if household uses phone network and 0 otherwise. Financial inclusion is measured by three variables: account ownership (BANK_ACCT) which takes the value 1 if household owns account and 0 otherwise, access to credit (CREDIT_ACCESS) which takes the value 1 if household has access to credit and 0 otherwise, and insurance penetration (INSURANCE) which is labelled 1 if household has insurance policy and 0 otherwise. EDYEARS number of years of schooling by household head, HHAGE is age of household head, HHSEX is gender of household head labelled 1 if male ad zero otherwise, HHLOC is location of household labelled 1 if rural and 0 if urban, HHSIZE is household size, HHEMSTAT is employment which is labelled 1 if household head is employed and 0 otherwise, HHEMTYPE is employment type which is labelled 1 if household head is a salaried worker and 0 otherwise, LANDSIZE hectares of land owned by household, INC_RMT is remittances income, COMMBANK distance to the nearest commercial bank in km, COMMPTOF is distance to the nearest post office in km, FONNETW is the distance to the nearest telecommunication network, MTRD is motorable road and labelled 1 if community has motorable road and zero otherwise, COMMMKT is daily community marked with label 1 if community has a daily market and 0 otherwise, PTPASS is public transport with value 1 if public transport passes through community and 0 otherwise, COMMELECT is electricity and labelled 1 if community has electricity and 0 otherwise. Mobile phone ownership and usage is taking root in Ghana. Majority (76%) of households own at least one mobile phone while an even greater majority (95%) use it for various purposes. Financial inclusion is still a big hurdle for most households with less than half of households having access to each of accounts, formal credit and insurance. Only 42% of households have a bank account, about 9% have access to formal credit and 26% have an insurance policy. About 97% of households are employed with only 23% employed in the formal sector; 72% of households are male-headed with the average head being middle-aged (46 years). A significant number of households have access to basic community infrastructure, which infrastructure is vital for viable household enterprises. Majority (56%) of households are located in rural areas, have access to motorable roads and electricity (74%), do not have daily community markets (only 3% have) and post office (only 1% have). 4.2 Effects of Mobile Telephony and Financial Inclusion on Poverty Table 3 reports the SURE probit estimates for mobile phone penetration and poverty status equations with ownership of bank account as the measure of financial inclusion. Table 4 and Table 5 replicate Table 3, the only difference is that access to credit and insurance penetration are used respectively as measures of financial inclusion in Table 4 and Table 5. In all three 8

9 tables, the Wald statistics show that the explanatory variables jointly and significantly explain mobile penetration as well as the poverty status of households. The log likelihood test is significant at 1%, which shows that the two equations (mobile telephony and poverty status equations) are indeed dependent and should be estimated simultaneously. The estimates for rho are negative implying that the unobserved factors that affect mobile technology and inclusive growth are negatively correlated. Generally, the diagnostic tests show that we have correctly specified the models. Largely, the control variables have the expected sign. From Table 3, mobile telephony and poverty status are bi-causal further justifying the simultaneous estimations. At 1% level, both the ownership and use of mobile phone increases the probability of a household exiting poverty with marginal effects ranging from (mobile ownership) to (mobile usage). As expected, the marginal effects show that mobile usage has a larger effect on welfare than mobile phone ownership. These results are confirmed in Tables 4-5, where different measures of financial inclusion are employed. There are several reasons why mobile penetration will lead to greater participation in growth. Mobile phones help in effective communication by reducing both the cost and time involved in communication. The time and money saved can be invested in welfare enhancing activities. Households can also send and receive money via their mobile phones. This will further promote both household consumption and investment. For farmers, mobile phones will help them get back to extension officers and other experts for clarifications to help them improve their farming activities. Mobile phone users have access to information related to entrepreneurship, weather, prices and markets, which are vital for enhancing household socioeconomic activities. Also, because mobile technology increases citizen vigilance, it reduces private expropriation of societal resources and by so doing may reduce poverty. The results of this study are consistent with the findings of Suri and Jack (2016) that, mobile money reduces poverty in the long-run. The welfare status of households significantly affects the ownership and usage of mobile phones at 1% in Tables 3-5. The coefficients are positive, implying that non-poor households have a higher probability of owning a mobile phone. Mobile phones come in various makes and prices, thus some minimum amount is required to purchase a mobile phone. From Table 3, the marginal effect of inclusive growth is for phone ownership and for phone usage. Similar results are found in Tables 4 and 5. These results mean that some poor households may be unable to own mobile phone because they lack the purchasing power. Households with very limited resources will prioritise spending their resources to resolve food security and health problems rather than buy a mobile phone. On the otherhand, some well to do families use mobile phones as a status symbol and hence will buy the latest mobile phone in the market. There are some individuals who even own multiple phones. From Tables 3-5, the measures of financial inclusion namely, ownership of account, access to credit and insurance have positive and significant relationship (at 1%) with the inclusive growth measure. The intuition is that if households have access to a broad range of financial services they can exchange and save financial resources more timeously and efficiently allocate resources better, have access to finance for investments and manage risk better. All these can improve the ability of households to earn more income and for that matter contribute to and as well as benefit from growth. Financial inclusion enables firms and households to mobilise resources timeously, which resources are invested to foster productivity. Households with access to financial services can finance the educational needs of the household, which will lead to an improvement in the quality of human capital. These findings are in tandem with the findings of Adomako et al. (2016) and Arora (2012). 9

10 Table 3. SURE Probit Model Estimates for Mobile Telephony and Inclusive Growth (Poverty Status) (1) (2) (7) (8) dy/dx VARIABLES OWN_PHONE dy/dx POOR dy/dx USE_PHONE dy/dx POOR OWN_PHONE 1.761*** (0.0230) USE_PHONE 1.969*** (0.0362) POOR 1.558*** *** (0.0220) (0.0314) BANK_ACCT 0.234*** *** (0.0255) (0.0289) lhhage *** *** (0.0323) (0.0340) (0.0538) (0.0404) HHLOC *** *** (0.0250) (0.0289) HHSIZE *** *** ( ) ( ) EDYEARS *** * *** ( ) ( ) ( ) ( ) HHEMTYPPE ** *** (0.0335) (0.0354) HHSEX 0.287*** *** *** *** (0.0249) (0.0266) (0.0369) (0.0318) LANDSIZE *** *** ( ) ( ) INC_RMT 5.60e-05*** 6.73e-05*** e e *** (1.72e-05) (2.06e-05) (1.58e-05) (2.69e-05) MTRD *** *** (0.0283) (0.0387) COMMBANK ( ) ( ) COMMMKT (0.0616) (0.0473) PTPASS 0.160*** *** (0.0238) (0.0290) COMMELECT * (0.0250) (0.0257) (0.0345) (0.0289) COMMPTOF * *** (0.0875) (0.112) 10

11 FONNETW *** ( ) ( ) HHEMSTAT 0.323*** (0.0533) (0.113) RADIO 0.163*** *** (0.0194) (0.0329) TV 0.598*** *** (0.0260) (0.0447) Constant ** *** *** (0.143) (0.142) (0.248) (0.165) Diagnostics Observations 16,772 16,772 15,181 15,181 Rho (ρ) (0.0005) (7.81e-10) Wald *** *** Log likelihood Likel. ratio test of ρ χ 2 (1) *** *** SURE probit estimates for determinants of phone penetration and inclusive growth (poverty status). POOR is a dummy for poverty status of household labelled 1 if household is nonpoor and 0 otherwise, mobile penetration is proxied by two variables: phone ownership (OWN_PHONE) labelled 1 if household owns at least one mobile phone and 0 otherwise, and phone usage (USE_PHONE) labelled 1 if household uses phone network and 0 otherwise. Financial inclusion is measured by account ownership (BANK_ACCT) which takes the value 1 if household owns account and 0 otherwise. EDYEARS number of years of schooling by household head, lhhage is age of household head, HHSEX is gender of household head labelled 1 if male ad zero otherwise, HHLOC is location of household labelled 1 if rural and 0 if urban, HHSIZE is household size, HHEMSTAT is employment which is labelled 1 if household head is employed and 0 otherwise, HHEMTYPE is employment type which is labelled 1 if household head is a salaried worker and 0 otherwise, LANDSIZE hectares of land owned by household, INC_RMT is remittances income, COMMBANK distance to the nearest commercial bank in km, COMMPTOF is distance to the nearest post office in km, MTRD is motorable road and labelled 1 if community has motorable road and zero otherwise, COMMMKT is daily community marked with label 1 if community has a daily market and 0 otherwise, PTPASS is public transport with value 1 if public transport passes through community and 0 otherwise, COMMELECT is electricity and labelled 1 if community has electricity and 0 otherwise. Robust Standard errors in parentheses. ***, **, * imply 1%, 5%, 10% significance respectively. The diagnostics tests are (1) number of observations, (2) Rho which tests for correlation between errors, (3) Wald tests of model validity, and (4) log likelihood ratio test of model correlations. dy/dx is marginal effect. 11

12 Table 4. SURE Probit Model Estimates for Mobile Telephony and Inclusive Growth (5) (6) (7) (8) VARIABLES OWN_PHONE dy/dx POOR dy/dx USE_CPHONE y/dx POOR dy/dx OWN_PHONE 1.800*** (0.0198) USE_PHONE 1.977*** (0.0256) CRED_ACCESS 0.158*** *** (0.0320) (0.0483) POOR 1.581*** *** (0.0173) (0.0229) lhhage *** *** (0.0322) (0.0343) (0.0442) (0.0380) HHLOC *** *** (0.0237) (0.0280) HHSIZE *** *** ( ) ( ) EDYEARS *** *** ( ) ( ) ( ) ( ) HHEMTYPPE 0.106*** *** (0.0340) (0.0351) HHSEX 0.285*** *** *** *** (0.0255) (0.0250) (0.0399) (0.0313) LANDSIZE *** *** ( ) ( ) INC_RMT 5.58e-05*** e-05*** e e *** (1.78e-05) (2.16e-05) (1.54e-05) (2.56e-05) MTRD *** ** (0.0284) (0.0366) COMMBANK ( ) ( ) COMMMKT (0.0781) (0.0798) PTPASS 0.167*** *** (0.0260) (0.0277) COMMELECT * (0.0242) (0.0235) (0.0365) (0.0285) COMMPTOF ***

13 (0.119) (0.122) FONNETW *** e e-06 ( ) ( ) HHEMSTAT 0.331*** * (0.0339) (0.0514) RADIO 0.162*** *** (0.0215) (0.0188) TV 0.599*** *** (0.0260) (0.0420) Constant ** *** *** (0.130) (0.142) (389.1) (0.189) (0.149) (488.8) Diagnostic tests Observations 16,772 16,772 16,772 15,181 15,181 15,181 Rho -1-1 (2.81e-10) (4.99e-10) Wald *** *** Log likelihood Likel. ratio test of ρ χ 2 (1) *** *** SURE probit estimates for determinants of phone penetration and inclusive growth (poverty status). POOR is a dummy for poverty status of household labelled 1 if household is nonpoor and 0 otherwise, mobile penetration is proxied by two variables: phone ownership (OWN_PHONE) labelled 1 if household owns at least one mobile phone and 0 otherwise, and phone usage (USE_PHONE) labelled 1 if household uses phone network and 0 otherwise. Financial inclusion is measured by access to credit (CREDIT_ACCESS) which takes the value 1 if household has access to credit and 0 otherwise. EDYEARS number of years of schooling by household head, lhhage is age of household head in logs, HHSEX is gender of household head labelled 1 if male ad zero otherwise, HHLOC is location of household labelled 1 if rural and 0 if urban, HHSIZE is household size, HHEMSTAT is employment which is labelled 1 if household head is employed and 0 otherwise, HHEMTYPE is employment type which is labelled 1 if household head is a salaried worker and 0 otherwise, LANDSIZE hectares of land owned by household, INC_RMT is remittances income, COMMBANK distance to the nearest commercial bank in km, COMMPTOF is distance to the nearest post office in km, MTRD is motorable road and labelled 1 if community has motorable road and zero otherwise, COMMMKT is daily community marked with label 1 if community has a daily market and 0 otherwise, PTPASS is public transport with value 1 if public transport passes through community and 0 otherwise, COMMELECT is electricity and labelled 1 if community has electricity and 0 otherwise. Robust Standard errors in parentheses. ***, **, * imply 1%, 5%, 10% significance respectively. The diagnostics tests are (1) number of observations, (2) Rho which tests for correlation between errors, (3) Wald tests of model validity, and (4) log likelihood ratio test of model correlations. dy/dx is marginal effect. 13

14 Table 5. SURE Probit Model Estimates for Mobile Telephony and Inclusive Growth (9) (10) (11) (12) VARIABLES OWN_PHONE dy/dx POOR dy/dx USE_PHONE dy/dx POOR dy/dx OWN_PHONE 1.790*** (0.0166) USE_PHONE 1.973*** (0.0301) POOR 1.569*** *** (0.0146) (0.0300) INSURANCE 0.147*** *** (0.0234) (0.0307) lhhage *** *** (0.0320) (0.0321) (0.0502) (0.0401) HHLOC *** *** (0.0177) (0.0280) HHSIZE *** *** ( ) ( ) EDYEARS *** ** *** ( ) ( ) ( ) ( ) HHEMTYPPE *** *** (0.0333) (0.0360) HHSEX 0.284*** *** *** *** (0.0229) (0.0234) (0.0374) (0.0300) LANDSIZE *** *** ( ) ( ) INC_RMT 5.60e-05*** e-05*** e e *** (1.80e-05) (2.17e-05) (1.52e-05) (2.59e-05) MTRD *** ** (0.0260) (0.0372) COMMBANK e ( ) ( ) COMMMKT (0.0756) (0.0714) PTPASS 0.166*** *** ( ) (0.0336) COMMELECT (0.0245) (0.0243) (0.0452) (0.0296) COMMPTOF ** ***

15 (0.0776) (0.0286) FONNETW *** ( ) ( ) HHEMSTAT 0.326*** (0.0657) (0.113) RADIO 0.163*** *** (0.0104) (0.0357) TV 0.600*** *** (0.0200) (0.0369) Constant ** *** *** (0.148) (0.131) (0.231) (0.168) Diagnostics Observations 16,772 16,772 16,772 15,181 15,181 15,181 Rho -1-1 (3.21e-10) (2.23e-09) Wald *** *** Log likelihood Likel. ratio test of ρ χ 2 (1) *** *** SURE probit estimates for determinants of phone penetration and inclusive growth (poverty status). POOR is a dummy for poverty status of household labelled 1 if household is nonpoor and 0 otherwise, mobile penetration is proxied by two variables: phone ownership (OWN_PHONE) labelled 1 if household owns at least one mobile phone and 0 otherwise, and phone usage (USE_PHONE) labelled 1 if household uses phone network and 0 otherwise. Financial inclusion is measured by insurance penetration (INSURANCE) which is labelled 1 if household has insurance policy and 0 otherwise. EDYEARS number of years of schooling by household head, lhhage is age of household head, HHSEX is gender of household head labelled 1 if male ad zero otherwise, HHLOC is location of household labelled 1 if rural and 0 if urban, HHSIZE is household size in logs, HHEMSTAT is employment which is labelled 1 if household head is employed and 0 otherwise, HHEMTYPE is employment type which is labelled 1 if household head is a salaried worker and 0 otherwise, LANDSIZE hectares of land owned by household, INC_RMT is remittances income, COMMBANK distance to the nearest commercial bank in km, COMMPTOF is distance to the nearest post office in km, MTRD is motorable road and labelled 1 if community has motorable road and zero otherwise, COMMMKT is daily community marked with label 1 if community has a daily market and 0 otherwise, PTPASS is public transport with value 1 if public transport passes through community and 0 otherwise, COMMELECT is electricity and labelled 1 if community has electricity and 0 otherwise. Robust Standard errors in parentheses. ***, **, * imply 1%, 5%, 10% significance respectively. The diagnostics tests are (1) number of observations, (2) Rho which tests for correlation between errors, (3) Wald tests of model validity, and (4) log likelihood ratio test of model correlations. dy/dx is marginal effect. 15

16 4.3 Effects of Mobile Telephony and Financial inclusion on Real Per Capita Consumption Table 6 shows robust least squares estimates of the effects of mobile phones and financial inclusion on real per capita household consumption expenditure. The discussion is structured in two main ways. First, the effects of mobile phones on per capita consumption expenditure and second, the influence of financial inclusion on per capita consumption. Mobile phone ownership and usage are positively related to real per capita consumption expenditure and is significant at 1% in all the respective models. The coefficients are also economically large with the least marginal effect being for mobile ownership and for mobile usage. This means that the consumption per capita of mobile owners and users is at least 48% [(exp(0.393)-1)*100] higher than non-users. The explanation is that mobile phone enhances users access to information, enables information sharing, facilitates learning, opens up opportunities for a variety of choices, increases access to a range of financial services and products. By performing these roles, mobile phone increases productivity and the income earning opportunities of households and through that spurs consumption. All the financial inclusion variables are positively related to real per capita consumption expenditure and are all significant at 1% in all the models they appear. The marginal effects are also economically meaningful. When households have access to financial services, they can take advantage and invest in positive NPV projects, make and receive payments with ease and at reduced costs, and invest in building human capital. All of these lead to an increase in the income earning abilities of households and enable them to consume a wide variety of durable and non-durable commodities. VARIABLES Table 6. Baseline OLS Estimates: Mobile Phone, Financial Inclusion and Real Per Capita Consumption (13) (14) (15) (16) (17) (18) Log REAL Log REAL Log REAL Log REAL Log REAL Log REAL OWN_PHONE 0.545*** 0.597*** 0.594*** (0.0157) (0.0159) (0.0158) USE_PHONE 0.393*** 0.430*** 0.438*** (0.0305) (0.0311) (0.0310) BANK_ACCT 0.363*** 0.397*** (0.0131) (0.0135) CRED_ACCESS 0.219*** 0.235*** (0.0192) (0.0199) INSURANCE 0.204*** 0.213*** (0.0132) (0.0138) lhhage *** *** (0.0182) (0.0191) (0.0185) (0.0195) (0.0187) (0.0196) HHSIZE *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) EDYEARS *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) HHEMTYPPE *** *** 0.144*** 0.149*** 0.116*** 0.120*** (0.0145) (0.0150) (0.0147) (0.0153) (0.0148) (0.0153) HHSEX *** *** *** *** *** *** 16

17 (0.0139) (0.0145) (0.0141) (0.0148) (0.0141) (0.0148) LANDSIZE ** ** ** ** ** ** ( ) ( ) ( ) ( ) ( ) ( ) INC_RMT 2.80e-05*** 3.02e-05*** 3.05e-05*** 3.32e-05*** 2.83e-05*** 3.10e-05*** (9.99e-06) (1.05e-05) (1.03e-05) (1.09e-05) (9.97e-06) (1.06e-05) COMMBANK *** ** ** *** * ( ) ( ) ( ) ( ) ( ) ( ) MTRD * (0.0203) (0.0216) (0.0206) (0.0220) (0.0205) (0.0219) COMMMKT ** ** ** (0.0328) (0.0352) (0.0333) (0.0362) (0.0333) (0.0361) PTPASS 0.199*** 0.176*** 0.215*** 0.195*** 0.214*** 0.194*** (0.0160) (0.0168) (0.0163) (0.0172) (0.0162) (0.0171) COMMELECT *** ** *** ** ** ** (0.0142) (0.0150) (0.0144) (0.0153) (0.0144) (0.0153) COMMPTOF * ** ** (0.0592) (0.0660) (0.0602) (0.0673) (0.0602) (0.0672) HHLOC *** *** *** *** *** *** (0.0128) (0.0134) (0.0130) (0.0136) (0.0129) (0.0136) Constant 5.980*** 6.163*** 5.987*** 6.200*** 6.089*** 6.293*** (0.0760) (0.0831) (0.0771) (0.0848) (0.0775) (0.0850) Diagnostics Observations 16,772 15,181 16,772 15,181 16,772 15,181 R-squared F-statistic *** *** *** *** *** *** OLS estimates of effects of mobile penetration and financial inclusion of household per capita consumption expenditure (inclusive growth). REAL, the dependent variable, is household consumption per capita, mobile penetration is proxied by two variables: phone ownership (OWN_PHONE) labelled 1 if household owns at least one mobile phone and 0 otherwise, and phone usage (USE_PHONE) labelled 1 if household uses phone network and 0 otherwise. Financial inclusion is measured by three variables: account ownership (BANK_ACCT) which takes the value 1 if household owns account and 0 otherwise, access to credit (CREDIT_ACCESS) which takes the value 1 if household has access to credit and 0 otherwise, and insurance penetration (INSURANCE) which is labelled 1 if household has insurance policy and 0 otherwise. EDYEARS number of years of schooling by household head, HHAGE is age of household head, HHSEX is gender of household head labelled 1 if male ad zero otherwise, HHLOC is location of household labelled 1 if rural and 0 if urban, HHSIZE is household size, HHEMSTAT is employment which is labelled 1 if household head is employed and 0 otherwise, HHEMTYPE is employment type which is labelled 1 if household head is a salaried worker and 0 otherwise, LANDSIZE hectares of land owned by household, INC_RMT is remittances income, COMMBANK distance to the nearest commercial bank in km, COMMPTOF is distance to the nearest post office in km, MTRD is motorable road and labelled 1 if community has motorable road and zero otherwise, COMMMKT is daily community marked with label 1 if community has a daily market and 0 otherwise, PTPASS is public transport with value 1 if public transport passes through community and 0 otherwise, COMMELECT is electricity and labelled 1 if community has electricity and 0 otherwise. The diagnostic tests are (1) number of observations, (2) R-squared test of model explanatory power and (3) F-statistic test of joint significance of parameters. Robust standard errors in parentheses ***, **, * implies 1%, 5% and 10% significance respectively. From the baseline results, we have found that both mobile penetration and financial inclusion have statistical and economically significant positive effects on household per capita consumption. This means that household consumption is highly responsive to mobile telephony and financial inclusion. 4.4 Effects of mobile telephony and financial inclusion on real per capita consumption 2SLS We present in Table 7 the robustness checks using instrumental variable procedure to account for endogeneity. As was done with the baseline results, we proceed with the discussion by first focusing on the mobile phone-inclusive growth nexus and second, the financial inclusion-inclusive growth (real per capita consumption) link. Consistent with the baseline 17

18 OLS results, mobile phone ownership and usage are positively correlated with real per capita household consumption expenditure in the respective models they appear. The coefficients are even larger and are statistically significant at 1% level. All the measures of financial inclusion possess the expected positive signs. However, account ownership and access to credit are only significant in models where mobile usage is used as the measure of mobile phone penetration. On the other hand, the effect of access to insurance on welfare is positive and significant for both measures of mobile penetration. Table 7. 2SLS Results (19) (20) (21) (22) (23) (24) VARIABLES Log REAL Log REAL Log REAL Log REAL Log REAL Log REAL OWN_PHONE 2.691*** 2.649*** 2.577*** (0.632) (0.673) (0.671) USE_PHONE 4.786*** 4.068*** 4.227*** (0.874) (0.857) (0.860) BANK_ACCT *** (0.0913) (0.0305) CRED_ACCESS *** (0.0554) (0.0296) INSURANCE 0.111*** 0.215*** (0.0365) (0.0193) lhhage 0.400*** 0.179*** 0.392*** 0.151*** 0.364*** 0.124*** (0.104) (0.0443) (0.111) (0.0419) (0.116) (0.0426) HHSIZE *** *** *** *** *** *** (0.0150) ( ) (0.0165) ( ) (0.0166) ( ) EDYEARS *** *** *** ( ) ( ) (0.0128) ( ) (0.0125) ( ) HHEMTYPPE *** *** *** 0.113*** *** *** (0.0202) (0.0206) (0.0248) (0.0205) (0.0224) (0.0210) HHSEX *** *** *** *** *** ** (0.0261) (0.0246) (0.0302) (0.0224) (0.0297) (0.0227) LANDSIZE * * ** ** * * ( ) ( ) ( ) ( ) ( ) ( ) INC_RMT 1.04e e-05*** 1.13e e-05*** 1.08e e-05*** (7.97e-06) (1.01e-05) (8.81e-06) (1.04e-05) (8.49e-06) (1.01e-05) COMMBANK *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) MTRD ** ** ** (0.0303) (0.0405) (0.0302) (0.0366) (0.0295) (0.0372) COMMMKT *** *** *** (0.0569) (0.0523) (0.0570) (0.0497) (0.0562) (0.0503) PTPASS 0.173*** 0.149*** 0.176*** 0.168*** 0.176*** 0.165*** (0.0245) (0.0294) (0.0261) (0.0269) (0.0257) (0.0276) COMMELECT *** *** *** (0.0218) (0.0266) (0.0217) (0.0246) (0.0210) (0.0252) COMMPTOF ** ** * ** (0.0909) (0.0872) (0.0891) (0.0816) (0.0881) (0.0831) HHLOC *** *** *** *** *** *** (0.0740) (0.0325) (0.0927) (0.0341) (0.0890) (0.0340) Constant 3.551*** 1.611* 3.610*** 2.410*** 3.746*** 2.345*** (0.721) (0.912) (0.784) (0.898) (0.798) (0.902) 18

19 Diagnostics Observations 16,772 15,167 16,772 15,167 16,772 15,167 Wald chi *** *** *** *** *** Woolridge Rob *** *** *** *** *** *** Rob. Reg *** *** *** *** *** *** 2SLS estimates of effects of mobile penetration and financial inclusion of household per capita consumption expenditure (inclusive growth). REAL, the dependent variable, is household consumption expenditure per capita in logs. Mobile penetration is proxied by two variables: phone ownership (OWN_PHONE) labelled 1 if household owns at least one mobile phone and 0 otherwise, and phone usage (USE_PHONE) labelled 1 if household uses phone network and 0 otherwise. Financial inclusion is measured by three variables: account ownership (BANK_ACCT) which takes the value 1 if household owns account and 0 otherwise, access to credit (CREDIT_ACCESS) which takes the value 1 if household has access to credit and 0 otherwise, and insurance penetration (INSURANCE) which is labelled 1 if household has insurance policy and 0 otherwise. EDYEARS number of years of schooling by household head, lhhage is age of household head in logs, HHSEX is gender of household head labelled 1 if male ad zero otherwise, HHLOC is location of household labelled 1 if rural and 0 if urban, HHSIZE is household size, HHEMSTAT is employment which is labelled 1 if household head is employed and 0 otherwise, HHEMTYPE is employment type which is labelled 1 if household head is a salaried worker and 0 otherwise, LANDSIZE hectares of land owned by household, INC_RMT is remittances income, COMMBANK distance to the nearest commercial bank in km, COMMPTOF is distance to the nearest post office in km, MTRD is motorable road and labelled 1 if community has motorable road and zero otherwise, COMMMKT is daily community marked with label 1 if community has a daily market and 0 otherwise, PTPASS is public transport with value 1 if public transport passes through community and 0 otherwise, COMMELECT is electricity and labelled 1 if community has electricity and 0 otherwise. The diagnostic tests are (1) number of observations, (2) R-squared test of model explanatory power and (3) Wald test of joint significance of parameters, (4) Woolridge Robust Regressions test of endogeneity with the null hypothesis of no endogeneity (4) Robust regression test of endogeneity with null hypothesis of no endogeneity. Robust standard errors in parentheses ***, **, * implies 1%, 5% and 10% significance respectively. Comparing the baseline and the 2SLS results, the clear findings are that, first, mobile phone ownership increases the ability of a household to increase the consumption of food and nonfood items to improve their welfare. Second, the positive effect of financial inclusion on household welfare is significant when the household uses mobile phone technology. There are a number of explanations regarding why mobile penetration will enhance household consumption and welfare. With mobiles, a household can easily make a call in moments of shocks to receive assistance. Also, during health and financial emergencies, households can easily send and withdraw funds through their mobile phones to meet their pressing needs. Again, households save a lot of travel time by simply using the mobile phone and the time saved can be invested in alternative income generating activities. Furthermore, the advent of mobile phone has brought jobs to many household members in the form of mobile money agents, phone call centre workers, phone sales agents, recharge card sales agents, mobile phone repairers, among others. These jobs provide significant amounts of income to boost household consumption. Therefore, with the help of mobile phones, household are able to deal with emergencies better, have access to alternative job opportunities, and increase their health, education and other household welfare enhancing expenditures. These findings find support in Wijeratne and Silva (2014), Suri and Jack (2016) and Asongu and Nwachukwu (2016). 4.5 Further robustness Exploring the gender and locality dimensions In this section, the aim is to investigate whether mobile technology and financial inclusion can bridge the development gap in gender and locality. To assess the gender gap we extracted female-headed households from the sample and estimated the correlates of poverty status and per capita consumption expenditure. The SURE probit estimates and 2SLS are presented in appendix Tables A.1 and A.2 respectively. From appendix Table A.1, the coefficient for mobile ownership is positive and significant with a marginal effect of which is quite smaller than the marginal effect of the full sample (0.5955). Similar results pertain to mobile 19

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