EFFECTS OF MOBILE MONEY ON FARMERS AND SMALL BUSINESS MEN
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Mobile money services can contribute to welfare gains in smallholder farm households. Previous research showed that one important pathway is through higher remittances received from relatives and friends. Here, the role of other impact pathways is examined, especially focusing on agricultural marketing and off-farm economic activities. Regression models show that the adoption of Mobile money technology has contributed to higher household incomes and consumption levels. Off-farm income gains are identified to be an important pathway in Buea. Typical off-farm income sources in small businesses are trade, transport. Since it is risky to move around with money, farmers in Buea benefit from savings and money transfer opportunities through Mobile money. Mobile money services help reduce cash constraints and facilitate transactions with buyers from outside Buea. In conclusion, Mobile money can contribute to rural development through various important pathways. Analysis of adoption patterns suggests that Mobile money services are good ways through which farmers can use to increase their performance in their agro sector.
Using of mobile phone technologies has rapidly increased in many developing countries since the late-1990s right up to date. This has contributed to economic growth and poverty reduction, especially in Cameroon precisely Buea where mobile phones have helped households to access better market information and fetch higher prices for their products. In addition to the direct positive effects of mobile phones on people’s lives, their widespread use has also facilitated the adoption of other mobile technologies. One important example is mobile money. Mobile money services enable the electronic transfer of money via mobile phones. This reduces transaction costs for the payment of bills, and making remittances; enhancing rural banking and financial inclusion. Recipients of these electronic transfers can either save the money on their mobile account or collect it in cash from a mobile money service center which have been spread all around Buea. Mobile money services are particularly attractive for people with limited access to the traditional banking system. The recent spread of this technology was particularly rapid in sub-Saharan Africa (Suri et al., 2012; Jack et al., 2013). Mobile money could revolutionize the nature of market transactions and private transfers for the previously unbanked, but so far relatively little is known about the real effects in developing countries (Nakasone et al., 2014). Especially for smallholder farmers, the knowledge about MM effects is thin. These studies confirm that Mobile money has positive effects on income, consumption, and food security. However, the pathways through which Mobile affects these welfare outcomes remain understudied.
1.2 Background of mobile money
History of mobile money
Mobile money was initially made popular by Safaricom and Vodafone’s M-Pesa (“M” for “mobile”, “pesa” for “money” in Swahili) in Kenya, which started in 2007. The M-Pesa application is installed on the SIM cards of customers and works on all handset brands. It is free to register and the user does not need to have a bank account. Safaricom receives fees for withdrawals and transfers, but keeps deposits into the mobile wallets free. The transfer service was quickly picked up for use as an informal savings account system and electronic
payment mechanism for bills, goods and services. With M-Pesa, Kenya is at the forefront of the mobile money revolution: the number of agents across the country increased by 40 percent in 2013. It is now estimated that 24.8 million subscribers use mobile money services, like M-Pesa, in Kenya (Communication Commission of Kenya, 2013). According to the Pew Research Center’s 2013 survey report, the number of Kenyans using mobile wallets to make or receive payments is higher than any of the other 24 countries surveyed: 50 per cent of the Kenyan adult population uses mobile money services (Pew Research Center, 2013). Mobile money services have spread rapidly in many developing countries. However, only a handful of these initiatives have reached a sustainable scale, in particular, GCASH and Smart Money in the Philippines; Wizzit, MTN Mobile Money and FNB in South Africa; MTN Mobile Money in Uganda; Vodacom M-PESA and Airtel in Tanzania; Celpay Holdings in Zambia and MTN Mobile Money, Orange Money in Côte d’Ivoire. The Philippines was one of the earliest adopters of mobile money services when SMART Communications launched SMART Money in 2001. The service, which uses SIM Tool-Kits, enables customers to buy airtime, send and receive
money domestically and internationally via mobile, and pay for goods using a card. In 2004, Globe Telecom launched GCASH. This service provides a cashless method for facilitating money remittances, settle loans, disburse salaries or commissions and pay bills, products and services via text message. In South Africa, MTN Mobile Money was launched in 2005 as a joint venture between the country’s second largest network operator MTN and a large
commercial bank, Standard Bank.
In Uganda, MTN was the first operator to launch mobile money services in 2009 and remains, by far, the market leader (Intermedia, 2012). By law, each mobile money provider has to partner with a bank. However, users do not need a bank account to use mobile money services. In Tanzania, Airtel was the first mobile network operator to introduce a Phone to-phone airtime credit transfer service, “Me2U,” in 2005 (Intermedia, 2013). Airtel partners with Citigroup and Standard Chartered Bank to provide Mobile Money services, including bill payments, payments for goods and services, phone-to-phone and phone-to-bank money transfers, and mobile wallets. In 2008, Vodacom Tanzania launched the second East African implementation of
the Vodafone m-money transfer platform, M-Pesa. Finally, in Côte d’Ivoire two mobile operators, Orange and MTN, are competing head to head in the mobile money market (CGAP, 2012). Orange Money was launched in 2008 by Orange in partnership with BICICI (BNP Paribas), and MTN Mobile Money was launched in 2009 by MTN in partnership with SGBCI
A quick overview in figures In 2008, a Ugandan software developer named Ronald Egesa of Mobitrix Uganda Ltd was reported by the leading newspapers to have developed the country’s first mobile phone bank that he called Smart Cash It was reported to be a network independent service. According to Media Intelligence’s tracked data on the Cameroon Remittance Market, MM was introduced in 2008, by Express Union. After the launch of Express Union Mobile, MTN Cameroon launched MTN Mobile Money in 2010. A year later, Orange Cameroun joined the digital market with Orange Money in 2011. Société Générale Cameroun (SGC) later launched Monifone in 2012.
As at now, there were about 6.8 million MM subscribers and close to 1.5 million active users in Cameroon. MTN Mobile Money by the end of June 2016 registered 2.4 million subscribers with about 220 000 active users whereas by December 2015, Orange Money registered 2.2 million subscribers and 200 000 active users and Express Union Mobile registered 500 000 subscribers and 300 000 actives. accounts.
As at end September 2016, the number of subscribers to the MTN Mobile Money service in Cameroon reached 2.7 million, an increase of 13.2% compared to the 2nd Quarter of 2016. In absolute value, this statistic reveals that 300,000 new clients joined this service in Cameroon, since there were only 2.4 million registered as at end June 2016 (Business in Cameroon 2016). By the close of 2015, MTN Mobile Money opened 2500 Cash In/Out Points, Orange Cameroun had 1700 Pay-Out Points while Express Union served its customers from its 650 POS. Presently, the Mobile Money service in Cameroon is at its Growth Stage and is being introduced to new business sectors including taxation, insurance, etc… serving a wider range of payment (utilities, transportation, education, E-commerce, hospital bills) and disbursement (salary, pension, tax, insurance, remittance, cash deposit, A2A) services. However, the active use of MM, in Cameroon, is still slow given that its providers still face tough challenges implementing the cashless culture and boosting the customer experience in order to develop MM from Growth to Maturity in Cameroon.
Mindful of the aforementioned operational MM services in Cameroon, the next daunting task is for the providers to determine the best strategies to effectively implement MM and ensure the active use of the said services.
One pathway that most studies mention is remittances which is a non-commercial transfer of money by foreign worker or citizens with familiar ties abroad for household income). More remittances received from relatives and friends increase household incomes directly; indirect effects can occur because remittances also act as a kind of insurance (Jack and Suri, 2014; Munyegera and Matsumoto, 2016). Wider effects for other economic activities of farm households have hardly been studied. One exception is Kikulwe et al. (2014) who showed that MM has increased the use of agricultural inputs and levels of commercialization in the Kenyan small farm sector.
We add to this literature by further analyzing how the adoption of MM technology affects the economic activities of smallholder households, including both farm and off-farm activities. To our knowledge, impacts of MM on off-farm income of smallholder farmers have not been analyzed beyond the question of remittances. We hypothesize that the new options for savings as well as for transferring money between business partners may especially encourage self-employed activities and thus increase off-farm income. Through similar mechanisms, agricultural incomes may increase as well. Here, we are particularly interested to see whether MM allows farmers to access high-value markets where better prices can be obtained. For the empirical analysis, we use panel data collected from farmers in Buea. Buea is of interest not only because many of the poor are smallholder farmers, but also because MM technology has been rapidly adopted there in recent years.
Mobile Money Development Strategies
Business Model: MM operators, in Cameroon, use two business models to provide their services. They include: B2C (serve customers directly at their POS) and B2B (serve customers indirectly via banks or other third-party partners). Express Union Mobile uses the B2C model while MTN Mobile Money and Orange Money both implement the B2C and B2B models.
Mobile Money Services: MM operators in Cameroon provide about 15 live services. The said services, according the Digital Payments Ecosystem designed by GSMA’s Mobile Money Program (2015), have been classified into three main groups namely: Primarily Payments: C2B/B2B (school fees, hospital bills, utility & media bills, E-commerce and retail service payments), Primarily Disbursements: B2C (NGO subsidies and remittance payments) and both Payments & Disbursements: P2G/ B2B/ C2B (salary, taxation and insurance payments).
Mobile Money Subscribers: The strategies implemented by the Mobile Money providers to get new subscribers and maintain current ones include: launching free subscription campaigns, providing fast/user-friendly services via mobile phones and websites, offering up to 100% bonus on Voice Calls for every airtime purchase, providing remittance services at very low costs (Starting from FCFA 10), increasing the number of Cash In/Out Points, offering discounts for the purchase of digital devices including Smartphones, touchpads and Internet modems, providing efficient time management and ensuring cash security through instant mobile-payment services (bills, tuition fees, ticketing, insurance premium, etc.).
Market Coverage: Although the growth of Mobile Money in Cameroon mainly depends on its healthy provision, it also relies on an extensive market coverage strategy. In this connection, the MM providers have adopted 3 key strategies to increase their market proximity and serve as many customers as possible. These strategies include: opening Points of Sale, awarding distribution licenses to third-parties and signing partnerships with other players such as: banks, insurance companies, travel agencies, filling stations, super markets, Microfinance Institutions. The treatment variable in all models is MM use, which is defined as a dummy that takes a value of one if at least one household member had a MM money account and had used MM services in the respective year. In almost all adopting households, the household head is a MM user, even though other household members may have their own MM account as well.
Household welfare is measured in terms of two indicators, namely household income and per capita consumption. Household income is the combined farm and off-farm income obtained over a period of one year. Farm income includes the value of all farm produce – either sold or kept for household consumption – minus production costs. Off-farm income includes salaries, wages, and pensions of all household members, land rents and capital earnings, as well as any net profit (revenue minus cost) from non-agricultural businesses. Remittances are also included as an off-farm income source. The other welfare measure – per capita consumption – measures the value of all food and non-food goods and services consumed in the household divided by the number of persons living in the household. Food consumption data were collected through a seven-day food recall. For most non-food items, monthly expenditures were recorded. For the analysis, we converted all expenditure data to a daily basis.
Remittances and other off-farm incomes are used as intermediate outcome variables. Remittances refer to money received during the respective year from any relatives or friends not living in the same household. This can be through MM services or through any other mechanism. To differentiate between different types of off-farm income, we calculate offfarm income with and without remittances included.
To evaluate agricultural marketing pathways, we look at the proportion of coffee that is sold as shelled green beans. As explained above, selling shelled coffee requires drying and processing and allows farmers to enter higher-value markets. Furthermore, we use the average coffee price received by farmers in the respective year as another intermediate outcome variable. As farmers sold their coffee in various forms (e.g., red cherries, dried cherries, green beans), the prices reported are not directly comparable. For instance, 5 kg of red cherries or 2 kg of dried cherries will typically result in only 1 kg of shelled green beans. To make prices comparable, we used appropriate weight conversion factors. This does not account for the actual cost of processing, which is mainly the opportunity cost of time. However, during the survey many farmers told us that the cost is less of an issue. The main reasons mentioned for not selling more coffee in higher-value form were pressing consumption needs such as payments for medical care, school fees, food, or fuel.
All monetary values are expressed in Ugandan shillings ((UGX): 1 US$ = 2,690 UGX). To account for inflation and make monetary values comparable for the two survey rounds, 2012 data were adjusted to 2015 using the official consumer price index (UBOS, 2015).
For most of the regression models, the same vector of covariates is used, even though – depending on the particular outcome – individual variables are sometimes added. The vector of covariates includes household characteristics, such as education, age, and gender of the household head, farm characteristics, such as land owned and the value of other productive assets, and spatial characteristics, such as distance to the next tarmac road and a district dummy.
The use of MM is closely correlated with the use of mobile phones. As mobile phones can also affect the welfare of households through channels other than MM, it is important to control for mobile phone use in order not to overestimate the MM treatment effect. Furthermore, cooperative membership and farmer participation in certification schemes for sustainability standards, such as Fairtrade or Organic, can influence farm household welfare (Chiputwa and Qaim, 2016). We include dummies for mobile phone use and sustainability certification into all impact models. Cooperative membership is closely correlated with certification in the study region, so we do not include both to avoid issues of collinearity. Use of mobile phones and participation in certification schemes are time-variant and may proxy for the farmers’ openness to technical and institutional innovations more generally. Thus, including these variables will also reduce any possible bias through unobserved time-variant heterogeneity.