Patrick Mbouombouo Mfossa
email: pmfossa91@gmail.com

PhD Candidate

Mobile Money-Driven Financial Inclusion and Financial Resilience in Sub-Saharan Africa: Insights from Cameroon

Keywords: financial inclusion, financial resilience, mobile money, endogenous treatment

While there has been increasing interest in the economic effects of mobile money in Sub-Saharan Africa, there is little empirical literature on the role of mobile money in the nexus between financial inclusion and financial resilience. This paper uses the 2017 Global Findex 1,000 representative sample collected in Cameroon to examine how mobile money affects people's ability to face negative shocks by coming up with an emergency fund in due time. Our results indicate that access to this financial inclusion tool increases the average ability of being resilient during an economic emergency, but the magnitude of this effect depends on whether the treatment-effects model implemented controls for the endogeneity of mobile money adoption or not. Whereas the average resilience ability if no one in the treated and the overall population had access to a mobile money account is considerably higher when disregarding endogeneity (0.47 vs. 0.37 and 0.62 vs. 0.09 respectively), our results demonstrate that the increase in financial resilience ability due to mobile money adoption is higher when controlling for endogeneity (0.74 vs. 0.053 and 0.60 vs. 0.07 respectively). Thus, disregarding endogeneity tend to underestimate the positive effect of this digital financial inclusion tool.

Introduction

The importance of an inclusive financial sector is widely known in the international development community and is perceived with priority in many countries [START_REF] Belayeth | Does Financial Inclusion Increase Financial Resilience? Evidence from Bangladesh[END_REF]. In one of its communiqué, the G20 agreed to take the financial inclusion agenda forward and to assist countries, policymakers and stakeholders in focusing global efforts on measuring and sustainably tracking progress on access to financial services globally [START_REF] Allen | The Foundations of Financial Inclusion: Understanding Ownership and Use of Formal Accounts[END_REF]. Because financial exclusion captures a situation where there are individuals whose marginal benefit from accessing and using financial services exceeds the marginal cost, but who are excluded by barriers, many developing countries have publicly committed to promoting financial inclusion by establishing a National Financial Inclusion Strategy [START_REF] Tomilova | Financial Inclusion + Stability, Integrity, and Protection (I-SIP): Policy Making for an Inclusive Financial System[END_REF]. These comprehensive public documents -that present a strategy developed at the national level to systematically accelerate the level of financial inclusion, have gained a great deal of traction in recent years and are becoming an increasingly common policy approach of many developing countries (AFI, 2015).

With financial inclusion, policy-makers hope that access to formal financial services will allow poor and low-income households in low and middleincome countries to enhance their welfare, grasp opportunities, mitigate shocks and ultimately escape poverty. In Sub-Saharan Africa (SSA), the advent and rapid adoption of mobile money in both urban and rural areas, as well as the widespread availability of mobile phones1 among the unbanked make mobile money-driven financial inclusion a significant component of these strategies 2 .

The 2017 Global Findex report reveals that Sub-Saharan Africa is the only region in the world where the share of adults with a mobile money account exceeds 10%. According to this report, within this region, the number of adults with a financial institution account has risen by a modest 4 percentage point since 2014, while the share with a mobile money account has grown roughly twice as fast -increasing by 9 percentage point. As highlighted in Figure 1 below, this increase differs widely across countries. In Cameroon for instance, the percentage of adults with a mobile money account has increased by 17.7 percentage point between 2014 and 2017. 2013) argue that mobile money services form a crucial link to the formal economy in developing countries for consumers by increasing levels of financial inclusion and overcoming inefficiencies of the financial infrastructure system. But, as the World Bank has noted in its 2014 report on global financial development, financial inclusion does not mean increasing access for the sake of access (World Bank, 2014). This brings us to the crucial question as to whether financial inclusion promotes people's financial resilience ability. Defined by Global Findex as the ability to come up with an emergency fund of 1/20 of GNI per capita in local currency within a short period to face adverse shocks3 , financial resilience also refers to the ability to maintain spending and living standards during an economic emergency [START_REF] Klapper | Get out, stay out: how financial resilience helps end poverty[END_REF]. Consistent with the two previous definitions, [START_REF] Marjolin | Why is financial stress increasing? Financial Resilience in Australia 2016 -Part 1, Centre for Social Impact[END_REF] go on to argue that financial resilience is ultimately about people's ability to access and draw on internal capabilities and appropriate, acceptable and accessible external resources and supports in times of financial adversity. According to the World Bank, inclusive financial systems provide individuals with greater access to resources to meet their financial needs such as saving for retirement, investing in education, capitalizing on business opportunities or confronting shocks (World Bank, 2014).

While acknowledging its importance, [START_REF] Demirgüç-Kunt | The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution[END_REF] remind us that financial inclusion is not an end in itself but rather a means to an end. These authors argue that when people have a safe place to save money as well as access to credit when needed, they are better able to manage financial risk. Furthermore, [START_REF] Buckland | Building financial resilience: Do credit and finance schemes serve or impoverish vulnerable people?[END_REF] goes so far as to suggest that finances need to be constructed to build financial resilience, not the other way around. Therefore, mobile money-driven financial inclusion needs to improve people's ability to face adverse shocks significantly. The quantitative empirical work on mobile money-driven financial inclusion falls into two categories. Studies which assess the determinants of mobile money adoption where proxies for financial inclusion via mobile money (account ownership and/or account usage) are the dependent variables [START_REF] Allen | The Foundations of Financial Inclusion: Understanding Ownership and Use of Formal Accounts[END_REF][START_REF] Wale | Account ownership and use of financial services among individuals: Evidence from selected Sub-Saharan African economies[END_REF][START_REF] Chikalipah | What determines financial inclusion in Sub-Saharan Africa[END_REF] and studies of the effects of mobile money-driven financial inclusion on microeconomic outcomes, where access to and/or usage of mobile money is not the dependent variable. Examples of the latter include whether mobile money promotes improved risk-sharing, food security, consumption, business profitability, saving, effective use of cash transfers, payments and microbusiness investment for low-income people [START_REF] Apiors | Mobile money, individuals' payments, remittances, and investments: evidence from the Ashanti region, Ghana[END_REF][START_REF] Jack | Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution[END_REF][START_REF] Riley | Mobile Money and Risk Sharing Against Aggregate Shocks[END_REF]Suri et al., 2012;[START_REF] Blumenstock | Promises and Pitfalls of Mobile Money in Afghanistan: Evidence from a Randomized Control Trial[END_REF][START_REF] Suri | The Long-Run Poverty and Gender Impacts of Mobile Money[END_REF][START_REF] Morawczynski | Exploring the Usage and Impact of 'Transformational' Mobile Financial Services: The Case of M-Pesa in Kenya[END_REF][START_REF] Morawczynski | Poor People Using Mobile Financial Services: Observations on Customer Usage and Impact from M-Pesa[END_REF][START_REF] Munyegera | Mobile Money, Remittances, and Household Welfare: Panel Evidence from Rural Uganda[END_REF]2016a;[START_REF] Aker | Payment Mechanisms and Anti-Poverty Programs: Evidence from a Mobile Money Cash Transfer Experiment in Niger[END_REF].

Previous studies tackling the question of whether mobile money-driven financial inclusion increases financial resilience in SSA, have mostly focused on how mobile money account ownership/use affects people's consumption when they experience a negative income shock and people's response to health shocks. Looking at the impacts of mobile money in Kenya, [START_REF] Jack | Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution[END_REF] found that households with M-PESA are better able to smooth risks, and their consumption is less sensitive to shocks. In a complementary work, Suri et al. (2012) look specifically at how M-PESA affects people's response to health shocks. They find that M-PESA users are able to spend more on medical expenses in the event of a health shock while also increasing expenses on food and maintaining their education expenditure. Nonuser households or households far from agents are unable to increase expenditure on food after the shock, decrease their nonfood subsistence expenditure, and might pull children out of school to finance health care costs. [START_REF] Riley | Mobile Money and Risk Sharing Against Aggregate Shocks[END_REF] examined the impact of mobile money on consumption after a rainfall shock such as flood or drought in Tanzania and found that after a villagelevel rainfall shock, it is only users of mobile money who are able to prevent a drop in their consumption. Despite this growing body of studies, Jana Hamdan's interesting review of the recent literature on the impact of mobile money in developing countries [START_REF] Hamdan | The impact of mobile money developing countries[END_REF] reveals that very little is known as to how mobile money affects people's ability to face negative shocks by coming up with an emergency fund. Additionally, previous studies have mostly focused on advanced mobile money markets such as Kenya, Uganda, and Tanzania. Whether mobile money-driven financial inclusion promotes people's financial resilience ability in an emerging mobile money market such as that of Cameroon is not well understood.

Therefore, the objective of this study is to increase our understanding of the role of mobile money in the financial inclusion-financial resilience nexus in Sub-Saharan Africa by focusing on the case of Cameroon. It draws on and extends the financialization of the everyday theory [START_REF] Roy | Poverty Capital[END_REF][START_REF] Van Der Zwan | Making Sense of Financialization[END_REF], and tries to look at how access to mobile money affects people's ability to face negative shocks by coming up with an emergency fund in due time.

The everyday financialization is an important lens to assess financial inclusion interventions because it focuses on the vulnerabilities of ordinary people, people who only recently have been brought into the financial world [START_REF] Buckland | Building financial resilience: Do credit and finance schemes serve or impoverish vulnerable people?[END_REF]. [START_REF] Van Der Zwan | Making Sense of Financialization[END_REF] argues that the financialization of the everyday has facilitated the decline of the welfare state that previously provided "cradle to grave" services and has linked vulnerable people with capital markets in order to enable them to protect themselves from life's uncertainties. The author also argues that innovation and extension of information technology have enabled finances to reach the everyday level. One of the most exciting innovations in this regard is mobile money technology [START_REF] Klapper | Get out, stay out: how financial resilience helps end poverty[END_REF]. The everyday styled financialization, like that associated with Bangladesh-inspired microcredit or more recently with digital loans accessed and delivered through mobile phones, democratizes capital and, by extension, human well-being and development [START_REF] Roy | Poverty Capital[END_REF]. But since financialization of the everyday theory does not provide a framework to assess the impact of the increase of finances in daily life on human well-being, what is needed for this is a theory of human well-being.

Following [START_REF] Buckland | Building financial resilience: Do credit and finance schemes serve or impoverish vulnerable people?[END_REF], this study extends the everyday financialization theory by drawing on the human capabilities approach of [START_REF] Sen | Development as freedom[END_REF] and [START_REF] Nussbaum | Frontiers of Justice: Disability, Nationality, Species Membership[END_REF]. Financialization proponents may argue that more finances in daily life -through the expanding availability of financial products and the growing demand for them -is better, but scholars of human well-being understand that financialization is only a means but that the end is human improvement. As [START_REF] Sen | Development as freedom[END_REF] and [START_REF] Nussbaum | Frontiers of Justice: Disability, Nationality, Species Membership[END_REF] go on to argue that improved well-being is about removing obstacles that constrain people from achieving their capabilities, [START_REF] Klapper | Get out, stay out: how financial resilience helps end poverty[END_REF] reminds us that alongside the global poor is a much larger group of people who are just an emergency away from the poverty line. For them, the importance of an inclusive financial system is closely related to its ability to ensure that they get more of the money that they need when they need it. That is, when they are facing an economic emergency that can push them and their families into destitution.

In terms of political economic theory, this study is rooted in a reform market approach [START_REF] Buckland | Building financial resilience: Do credit and finance schemes serve or impoverish vulnerable people?[END_REF]. That is to say, given that markets are currently the most prominent way to organize the economy but often fail to deliver on important human outcomes, a strong state and civil society response is needed. Financial exclusion falls within this configuration since it captures a situation where there are individuals whose marginal benefit from accessing and using financial services exceeds the marginal cost, but who are excluded by barriers (Allen et al. 2012). When market failure occurs, governments often step in to directly or indirectly provide services, or to change the behavior of businesses and individuals through regulation. Mobile moneydriven financial inclusion interventions can therefore be seen as a reformist process driven primary by the state and inter-state actors that seek to bring financially excluded people into the formal system to foster their financial well-being.

The rest of the paper is structured as follows. In the next section we provide insights on mobile money services and distribution channels in Cameroon. In Section 3, we provide an in depth description of how our two concepts of interest (financial resilience ability and mobile money-driven financial inclusion) were computed and follow this with a discussion of our empirical framework in Section 4. In Section 5 we present our results, and we conclude in Section 6.

Mobile Money Services and Distribution Channels in Cameroon

The mobile money revolution that started in Kenya in 2007 with the launch of M-PESA is now spreading worldwide, and Cameroon is no exception. In December 2009, the country's leading money transfer operator -Express Union, launched the first mobile money service in the country. About 10 years later, Cameroon has five active mobile money providers who compete nationwide. While banks and some microfinance institutions (MFIs) can directly request an authorization from the central bank authority to issue electronic money via cellphone (mobile money), Telco operators can only offer financial services by partnering with a commercial bank. Telco e-money providers in the CEMAC are therefore required to make reference to the name and logo of their partnering bank in any advertising concerning e-money activities. For instance, Orange must mention the name and logo of BICEC when advertising their mobile money service, while MTN is requested to mention the name and logo of Afriland First Bank.

Like in any other market, money transfer facilities are the most common service offered by mobile money providers alongside airtime pop-up. Coming in various types, these service enable people to transfer resources across a wider and more diverse network within the country and even above. In a typical P2P mobile money transfer, the amount is instantly transferred from the sender's account to the receiver's account and a short SMS is sent to both parties, indicating the amount of money transferred, the transaction reference identity as well as the new balance. In addition to using mobile money account to top up airtime, providers offer a wide range of payment facilities. Account holders can pay utility bills, products and services for selected providers as well as tuition fees. These various payment facilities make life simple as it enables consumers to save time and money.

Table 1: Mobile money providers and services

Despite the fact that mobile money providers in Cameroon are yet to offer interest-earning savings, they do offer a default solution to consumers that consists of storing wealth by depositing money in their mobile money accounts. Defining savings as simply having a balance of funds in one's mobile money account, the large agent network in both urban and rural areas makes it easy for account holders to save at their best convenience and to withdraw their savings whenever needed -provided that the regulatory ceilings are respected.

Some mobile money providers enable account holders to perform insurance-related operations right from their mobile phones. Such operations include insurance subscription, payment of the premium, and perception of damages. Orange Money (OM) account holders even perform their ROSCA/Savings Group activities -provided that the Association is duly registered and has opened and has fulfill Orange Cameroon requirements. Furthermore, customers using OM to manage small lucrative or fundraising activities in their local communities for friendly / family manifestations (event, tontines), can take advantage of Orange's small business partner offer.

While with a classic OM account there is a maximum number of transfers and withdrawals per period (5/day; 15/week; 40/month), the small business partner feature makes it possible for account holders to bypass this restriction at a special pricing.

Mobile money rely on widespread availability of mobile phones and networks of agents to allow people to access financial services right from their cellphones. CGAP's CEO Greta Bull reminds us that technology and distribution are one of the four factors that are changing the landscape for financial inclusion. To access digital financial services, access to a mobile connection is important, but it is equally important to be able to convert cash to digital money and, at least for now, back into cash again. So mobile phones have been important in places like Kenya, but the real game changer has been the emergence of large and well-functioning agent networks. Until accounts are more widely available or people are willing to accept the leap into purely digital money, agents will remain a fact of life.

Cameroon has a relative well-functioning agent network made of local shops, MFIs, petrol stations, restaurants, mobile money kiosks and money transfer operators (MTOs). While some MFIs offer their own money transfer service, they have also partnered with mobile money providers to enable people to perform mobile money transactions in their branches. The Case of ACEP Cameroon offering both Orange money and MTN MoMo services is very relevant as this MFI has its own money transfer service -ACEP Cash. The case of Express Union too is of interest as this MTO offers both its mobile money service -EU Mobile Money -as well as that of Société Générale Cameroon-YUP. Other MTOs such as Express Exchange offers both Orange money and MTN MoMo services in addition to their own basic services.

These large distribution networks are the backbone of the mobile money technology as agents perform crucial tasks such as onboarding, supporting and educating thousands of customers on a daily basis.

They also enable account holders to convert physical cash to digital value (cash-in) and, at least for now, back into cash again (cash-out). During the laps of time separating the cash-in and cash-out phases, the interface customers use to initiate transfers and payments directly on their mobile handsets (also known as the technical access channel), plays a crucial role too (GSMA, 2015). In Cameroon, while for some mobile money providers such as YUP, consumers can only access their services via an interface that displays solely on smartphones, others manage to enable both consumers with basic feature phones and smartphones to access their services.

Computing financial resilience ability and mobile money-driven financial inclusion

To better understand how financially resilient people around the world are to unexpected shocks, the 2017 Global Findex survey asked respondents whether or not it would be possible to come up with an amount equal to 1/20 of gross national income (GNI) per capita in local currency within the next month. Thus, to compute financial resilience ability, we use the question: "Now, imagine that you have an Not surprisingly, there is enormous variation in the financial resilience ability between individuals in the highest within-country income quintile (16.8%) and those in the lowest income quintile (4.5%). The ability to face an adverse shocks by coming up with an emergency fund seems to increase sharply with employment status. The share of adults in the workforce who say they could cover an emergency expense (36.8%) is three times higher than the share of those out of workforce who said so (11.7%). Adults between the ages of 25 and 64 are more likely to report that they could cover an emergency expense in due time. Even though the share of men who say they could come up with an emergency fund is higher than the share of women, the financial resilience gender gap is not that important here as compared to other countries.

In this study, the concept of mobile money-driven financial inclusion is used to emphasize the crucial role of mobile money in the process by which financially excluded people in Sub-Saharan Africa become integrated into the mainstream financial system for all of their financial service needs. The importance of mobile money can be captured by simply highlighting the number of times this word appears in the national financial inclusion strategies documents of countries who have established one. It can also be captured by looking at the share of adults with a mobile money account as compared to that of those with a financial institution account. To compute mobile money account ownership, we rely on the question: "An account can be used to save money, to make or receive payments, or to receive wages or financial help. Do you, either by yourself or together with someone else, currently have an account at a bank or another type of formal financial institution? Yes or no?"

At the country level, answers to this question where recorded in such a way that one could clearly distinguish mobile money accounts from financial institution accounts. On average, 19.5 percent of adults in our sample report having a mobile money 48.5% 51.5%

account and for 9% of the overall sample, mobile money is their only account. Not surprisingly, there is some kind of variation in account penetration between individuals in the high within-country income quintile (8.7%) and those in the lowest income quintile (1%). Mobile money account ownership increases sharply with the employment status. Individuals in the workforce (16%) are almost five times as likely to own a mobile money account as individuals who are out of workforce (3.5%). Furthermore, we find that in our sample, 10.2 percent of men report having a mobile money account, compared with 9.3 percent of women. Adults with a secondary education and more (15.2%) are, on average, almost four time as likely to have a mobile money account as those with a primary education or less (4.4%). Finally, adults between the ages of 25 and 64 are more likely to report having a mobile money account (13.6%) than younger adults (5.8%) and those aged 65 and over (0.2%).

Cameroon therefore offers an interesting opportunity to better understand how this financial inclusion tool which has gained a great deal of traction affects the financial resilience ability of individuals living in emerging mobile money markets.

Methods

In this study, we use the 2017 Global Findex survey, and we focus on the 1,000 representative sample randomly collected from the civilian and noninstitutionalized population aged 15 and above in Cameroon from February 21 to March 7, 2017. To examine the role of mobile money in the nexus between financial inclusion and financial resilience 4 The presence of selec²tion-bias is verified by checking if there is a significant difference between the treated and the controlled groups. Results of the two-sample t-test are presented later on.

in Cameroon, we, first of all, rely on the following probit model:

Yi = a + bMi + cIi′ + dFi′ + ui

Where: Yi, a dichotomous outcome variable capturing financial resilience ability as defined earlier in this paper. Mi is a dummy variable indicating whether or not respondent i have a mobile money account. Ii′ is a vector representing a set of variables describing each respondent i, precisely his gender, age, educational attainment, employment status, income status and dummy variable that takes the value 1 if the respondent mentioned that he holds a financial institution account (see annex 1). Fi′ is a vector representing respondents' savings and borrowing behaviors and ui, the error term which is assumed to follow a Normal distribution.

We examined whether parameter b is statistically significantly different from zero. Since individuals may self-select 4 themselves as owning a mobile money account, variable M may be endogenous. Account holders may be people who naturally have an affinity toward adopting new products quickly. Furthermore, it may be the case that not all the variables common to both mobile money adoption and financial resilience ability are observables. For example, participants with mobile money account may be people who are more kind to maintaining informal social networks from which they can draw upon during adverse events, or may be more prompt to pledging future labor in return for advance wages when times are tough.

Running an IV regression would have been a good approach to account for the endogeneity of mobile money adoption, but we did not find a good instrument in the Global Findex dataset. As a result, we employed a treatment-effects model that controls for endogeneity using a control-function approach5 [START_REF] Wooldridge | Econometric analysis of cross section and panel data[END_REF]. Endogenous treatment-effects models are appropriate whenever the conditional independence assumption is violated -due to the fact that not all variables that affect both treatment assignment and outcomes are observables.

The treatment-effects model adopted in this study is estimated with Stata 15 through eteffects commands.

The "aequations" option is included in the eteffects syntax to report the coefficients of the treatments and outcome models. Like any other treatment-effects model, estimators implemented in eteffects properly deal with the fundamental appraisal problem that arises from the impossibility of observing what would have happened to a given person in a situation where they have access to mobile money and in the situation where they do not. But, unlike standard treatment-effects models, eteffects regressions control for endogeneity and can thus be used to consistently estimate the average treatment effect (ATE), the average treatment effect on the treated (ATET) as well as the potential-outcome means (POMs) under observable and unobservable selection.

The endogenous treatment-effects model is given by:

Where the subscript i denotes the individual level observations, 𝑦𝑦 𝑖𝑖1 is the potential outcome of receiving the treatment, 𝑦𝑦 𝑖𝑖0 is the potential outcome when the treatment is not received, 𝑡𝑡 𝑖𝑖 is the observed binary treatment, and 𝑦𝑦 𝑖𝑖 is the observed outcome. Each one of the potential outcomes is determined by its expected value conditional on a set of regressors 𝑋𝑋 𝑖𝑖 and an unobserved random component 𝜀𝜀 𝑖𝑖𝑖𝑖 , for 𝑗𝑗 ∈ {0,1}. Similarly, the treatment is given by its expectation conditional on a set of regressors 𝑍𝑍 𝑖𝑖 , which does not need to differ from 𝑋𝑋 𝑖𝑖 and an unobserved component 𝑣𝑣 𝑖𝑖 (see annex 1).

While equations ( 1)-( 5) describe the parametric treatment-effects model, equation ( 6) adds endogeneity to the framework. It states that the unobservables in the potential-outcome equations are correlated to treatment status. Equations (3), ( 5), and ( 6) are the basis of the control-function estimator implemented by eteffects. Equation ( 5) states that the unobserved components in the potential outcome are independent of 𝑧𝑧 𝑖𝑖 . As a result, the correlation between 𝑡𝑡 𝑖𝑖 and the unobserved components must be equivalent to the correlation between 𝜀𝜀 𝑖𝑖𝑖𝑖 and 𝑣𝑣 𝑖𝑖 . Equation ( 3) is fitted using a probit estimator and 𝑣𝑣 𝚤𝚤 � is obtained as the difference between the treatment and our estimate of 𝐸𝐸(𝑡𝑡 𝑖𝑖 |𝑍𝑍 𝑖𝑖 ) and use the statistic to compute an estimate of 𝐸𝐸�𝑦𝑦 𝑖𝑖𝑖𝑖 |𝑥𝑥 𝑖𝑖 , 𝑣𝑣 𝑖𝑖 , 𝑡𝑡 𝑖𝑖 � 𝑓𝑓𝑓𝑓𝑓𝑓 𝑗𝑗 ∈ {0,1}. Since our outcome model is set to be linear, we can write it as follow:

The parameters of equations ( 3) and ( 7), and the ATE, ATET are estimated using the generalized method of moments (GMM). The moment equations used in GMM are the sample analogs of 𝐸𝐸{𝑤𝑤 𝑖𝑖 ′ 𝜖𝜖 𝑖𝑖 (𝜃𝜃)} = 0, where 𝑤𝑤 𝑖𝑖 are the instrument, 𝜖𝜖 𝑖𝑖 (𝜃𝜃) are residuals, and 𝜃𝜃 the parameters of the model. The moment conditions in the GMM estimation are given by: Where 𝑣𝑣 𝚤𝚤 � = 𝑡𝑡 𝑖𝑖 -Ф(𝑧𝑧 𝑖𝑖 ′ 𝜋𝜋 �), n, the number of observations, and 𝛽𝛽 11 � , 𝛽𝛽 10 � , 𝛽𝛽 21 � , 𝛽𝛽 20 � ,𝜋𝜋, � 𝐴𝐴𝐴𝐴𝐸𝐸 � 𝐴𝐴𝐴𝐴𝐸𝐸 � 𝐴𝐴 𝑃𝑃𝑃𝑃𝑃𝑃0 � are the parameters.

Results

Before highlighting regression results, we provide some descriptive statistics as well as a comparison of our treatment and control groups in terms of the independent variables used in the econometric analysis.

1 𝑛𝑛 � 𝑥𝑥 𝑖𝑖 ′ (𝑦𝑦 𝑖𝑖 -𝑥𝑥 𝑖𝑖 ′ 𝑛𝑛 𝑖𝑖=1 𝛽𝛽 1𝚥𝚥 � + 𝑣𝑣 𝚤𝚤 � 𝛽𝛽 2𝚥𝚥 � )𝑡𝑡 𝑖𝑖 = 0 1 𝑛𝑛 � 𝑥𝑥 𝑖𝑖 ′ (𝑦𝑦 𝑖𝑖 -𝑥𝑥 𝑖𝑖 ′ 𝑛𝑛 𝑖𝑖=1 𝛽𝛽 1𝚥𝚥 � + 𝑣𝑣 𝚤𝚤 � 𝛽𝛽 2𝚥𝚥 � )(1 -𝑡𝑡 𝑖𝑖 ) = 0 1 𝑛𝑛 � 𝑧𝑧 𝑖𝑖 ′ �𝑡𝑡 𝑖𝑖 Ø(𝑧𝑧 𝑖𝑖 ′ 𝜋𝜋 �) Ф(𝑧𝑧 𝑖𝑖 ′ 𝜋𝜋 �) -(1 -𝑡𝑡 𝑖𝑖 ) Ø(𝑧𝑧 𝑖𝑖 ′ 𝜋𝜋 �) 1 -Ф(𝑧𝑧 𝑖𝑖 ′ 𝜋𝜋 �) � 𝑛𝑛 𝑖𝑖=1 = 0 1 𝑛𝑛 ��(𝑥𝑥 𝑖𝑖 ′ 𝛽𝛽 10 + 𝑣𝑣 𝚤𝚤 � 𝛽𝛽 20 ) � -𝑃𝑃𝑃𝑃𝑃𝑃0 � � 𝑛𝑛 𝑖𝑖=1 = 0 1 𝑛𝑛 ��(𝑥𝑥 𝑖𝑖 ′ 𝛽𝛽 11 + 𝑣𝑣 𝚤𝚤 � 𝛽𝛽 21 ) � -𝑃𝑃𝑃𝑃𝑃𝑃0 � -𝐴𝐴𝐴𝐴𝐸𝐸 � � 𝑛𝑛 𝑖𝑖=1 = 0 1 𝑛𝑛 � �(𝑥𝑥 𝑖𝑖 ′ 𝛽𝛽 11 + 𝑣𝑣 𝚤𝚤 � 𝛽𝛽 21 ) � 𝑛𝑛 𝑛𝑛 𝑡𝑡 -𝑃𝑃𝑃𝑃𝑃𝑃0 � 𝑛𝑛 𝑛𝑛 𝑡𝑡 -𝐴𝐴𝐴𝐴𝐸𝐸𝐴𝐴 � � 𝑛𝑛 𝑖𝑖=1 = 0 (8) (9) (10) (11) (12) (13) (1) (6)
(5) (4)

(3)

(2) -

𝑦𝑦 𝑖𝑖0 = 𝐸𝐸(𝑦𝑦 𝑖𝑖0 |𝑋𝑋 𝑖𝑖 ) + 𝜀𝜀 𝑖𝑖0 𝑦𝑦 𝑖𝑖1 = 𝐸𝐸(𝑦𝑦 𝑖𝑖1 |𝑋𝑋 𝑖𝑖 ) + 𝜀𝜀 𝑖𝑖1 𝑡𝑡 𝑖𝑖 = 𝐸𝐸(𝑡𝑡 𝑖𝑖 |𝑍𝑍 𝑖𝑖 ) + 𝑣𝑣 𝑖𝑖 𝑦𝑦 𝑖𝑖 = 𝑡𝑡 𝑖𝑖 𝑦𝑦 𝑖𝑖1 + (1 -𝑡𝑡 𝑖𝑖 )𝑦𝑦 𝑖𝑖0 𝐸𝐸�𝜀𝜀 𝑖𝑖𝑖𝑖 |𝑥𝑥 𝑖𝑖 , 𝑧𝑧 𝑖𝑖 � = 𝐸𝐸�𝜀𝜀 𝑖𝑖𝑖𝑖 |𝑧𝑧 𝑖𝑖 � = 𝐸𝐸�𝜀𝜀 𝑖𝑖𝑖𝑖 |𝑥𝑥 𝑖𝑖 � = 0 𝐸𝐸�𝜀𝜀 𝑖𝑖𝑖𝑖 |𝑡𝑡� ≠ 0 𝑓𝑓𝑓𝑓𝑓𝑓 𝑗𝑗 ∈ {0,1} (

Results of the probit regression

The results from the basic probit regression (which includes mobile money account ownership as the only regressor), as well as those from the full regression (where I' and F' are controlled for), show that the coefficient for mobile money is positive and significant at 1% level (see annex 2).

Furthermore, the average marginal effect of mobile money account ownership from the full probit reveals that a 1% increase in mobile money adoption leads to a 16.5 percentage point increase in the likelihood of being financially resilient (p = 0.000, CI95% = 0.075 to 0.256). The results also demonstrate that better saving patterns indicate more financial resilience as respondents who set aside money in the past were more likely to be financially resilient than those who do not save. Unlike saving patterns, to have borrowed money from any source and for any reason in the past 12 months tend to decrease the likelihood of being financially resilient. Age squared was introduced as an explanatory variable alongside age to capture the effect at differing ages, rather than assuming the impact is linear for all ages. Our results suggest that while the impact of age on the likelihood of being financially resilient is positive and significant, this effect is lessoned as people get older.

Surprisingly, despite the presumption that female respondents would find it more challenging to come up with an emergency fund within a short period, gender has no significant effect on financial resilience ability. Both the regression coefficient and the average marginal effect for this variable are negative, but they are not significant even at 10% level. Our results also show that being in the workforce turn out to reduce the likelihood of being financially resilient by 1.14 percentage point, but this effect is not significant. Since for a given country, the workforce is often defined as the people engaged in or available for work, the negative impact of this variable on the probability of being financially resilient may be due to the preeminence of unemployed people who are actively seeking for a paying work on those who are employed.

-

Results of the treatment-effects regressions

After conditioning on a set of observable covariates in the models for treatment assignment and the potential outcomes while allowing some remaining unobservable components to affect both the treatment assignment and the potential outcomes following the model specified in the previous section (see annex 3), results obtained reveal that when no one in the population has access to mobile money, the average ability of being financially resilient is 0.37. This average financial resilience ability (ATE) would have been 0.75 higher if all individuals adopted mobile money than if none of them did. The results also suggest that among mobile money adopters, the average ability of being financially resilient would have been 0.099 if none of these individuals owned a mobile money account. For the population of individuals who have access to mobile money, the average resilience ability is 0.601 higher than if none of these individuals owned a mobile money account.

As highlighted in the previous section, we included the aequations option to report the coefficients of the treatment and outcome models. While the TME1 section of the output displays the coefficients for the 0.05, the null hypothesis (Ho: diff = 0) is rejected leading to the conclusion that there is a difference between sample means of the variable of interest (Ha: diff != 0).

treatment model, the OM0 and OM1 sections display coefficients of the outcome model for the untreated and treated groups respectively (see annex 3). The results attest that better saving patterns significantly increase the likelihood of being resilient during an economic emergency for both mobile money account holders and non-holders. Just like with the full probit regression, to have borrowed money from any source and for any reason in the past tend to decrease the likelihood of being financially resilient for both account holders and non-holders. This result can be explained by the fact borrowing to finance current needs or to face current shocks leads to an increase in debt-to-income ratio with detrimental effects on future indebtedness capacity. The coefficient of the outcome model related to gender reveals that in times of financial adversity, women will find it difficult to come up with an emergency fund -even though this coefficient is only significant for those without access to mobile money.

The control-function approach used in this study estimates the correlation between the unobservables of the treatment assignment and potential outcome models. If there is no correlation, then there is no endogeneity and estimating the average treatmenteffects using propensity score matching, nearest neighbor matching, or inverse probability weighted regression adjustment would have been more accurate. We therefore perform a Wald test to determine whether the estimated relationship between treatment-assignment and the potential outcome models are different from zero using Stata estat endogenous post-estimation command. Result reveals that the unobservable factors that affect mobile money adoption also mediate financial resilience ability.

[chi2 (2) = 6.17; Prob ˃ chi2 = 0.0458]

We then compared the endogenous treatment-effects with those obtained if we ignore the endogeneity of mobile money adoption. Despite the fact that different techniques were used -Propensity score matching (PSM), Nearest neighbor matching (NNM), and Inverse probability weighted regression adjustment (IPWRA) -, below we only highlight the ATE and ATET obtained using the IPWRA estimator 7 . Results of the other techniques are displayed in annex 3.

As shown in Table 4, the average treatment-effects obtained after conditioning on a set of observable 7 The IPWRA estimator is a doubly robust estimator that combines the outcome modeling strategy of the regression adjustment (RA) estimator and the treatment modeling strategy of covariates in the models for treatment assignment and the potential outcomes, while ignoring the endogeneity of the mobile money adoption assignment differ considerably from those reported in Table 3. Comparing results reported in these two tables, one can notice that both the average resilience ability when no one in the population have access to mobile money and the average resilience ability of the treated individuals if none of them owned a mobile money account are considerably higher when ignoring the presence of unobservables factors (0.47 vs. 0.37 and 0.62 vs. 0.09 respectively).

The comparison also reveals that the increase in the resilience ability due to mobile money adoption in the overall population as well as in the treated population is significantly higher when controlling for endogeneity (0.74 vs. 0.053 and 0.60 vs. 0.07 respectively). Thus, disregarding the endogeneity of mobile money adoption by ignoring the presence of unobservables tend to underestimate the positive effect of this digital financial inclusion tool.

Discussion and Concluding Remarks

The importance of financial inclusion is widely known in the international development community and is perceived with priority in many countries. In SSA countries, the advent and rapid adoption of mobile money, as well as the widespread availability of mobile phones among the unbanked, make mobile money-driven financial inclusion a significant component of National Financial Inclusion Strategies. While several studies have focused on mobile money in recent years, very few have examined the role of mobile money in the nexus the inverse probability weighted (IPW) estimator. Although IPWRA requires us to build two models, this estimator has a remarkable property as the researcher only needs to specify one of the two models correctly.

between financial inclusion and financial resilience.

Consequently, this study aimed to test the hypothesis according to which access to mobile money increases the likelihood of being resilient during an economic emergency.

In summary, results from our econometric analyses show that access to mobile money increases the average ability of being financially resilient -that is the ability to maintain spending and living standard during an economic emergency [START_REF] Klapper | Get out, stay out: how financial resilience helps end poverty[END_REF]. This positive effect of mobile money is even higher when controlling for unobservable factors that jointly affect mobile money adoption and financial resilience ability.

These results are in line with some studies demonstrating that financial inclusion increases financial resilience [START_REF] Belayeth | Does Financial Inclusion Increase Financial Resilience? Evidence from Bangladesh[END_REF][START_REF] Buckland | Building financial resilience: Do credit and finance schemes serve or impoverish vulnerable people?[END_REF][START_REF] Riley | Mobile Money and Risk Sharing Against Aggregate Shocks[END_REF]Suri et al., 2012;[START_REF] Ramji | Financial inclusion in Gulbarga: Finding usage in access[END_REF][START_REF] Jack | Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution[END_REF]. For instance, Suri et al. (2012) looked at how mobile money affects people's response to health shocks in Kenya and found that M-PESA users are able to spend more on medical expenses in the event of a health shock while also increasing expenses on food and maintaining their education expenditure. Nonuser households or households far from agents are unable to increase expenditure on food after the shock and decrease their nonfood subsistence expenditure. [START_REF] Jack | Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution[END_REF] found that non-M-PESA users see a 7-10 percent reduction in consumption in the event of a negative shock, while M-PESA users see a smaller reduction in consumption that is statistically indistinguishable from zero. They concluded that households with M-PESA are better able to smooth risks, and their consumption is less sensitive to shocks.

Our results also support the view of some scholars arguing that savings helps to decrease vulnerability through a protective function (accumulating savings to use in the event of a shock) and a promotive function (accumulating assets to reduce the likelihood that a shock will take place). In their paper "assessing the insurance role of micro-savings" Hulme et al. ( 2009) reported that those who save with formal or informal savings mechanisms are more likely to rely on savings in the event of an income shock, rather than reducing consumption, selling assets, borrowing, or increasing employment. Over half of adults in Sub-Saharan Africa save money but relatively few use formal methods such as banks or microfinance institutions to do so. In Cameroon for instance, far more adults use rotating savings and credit associations as well as what Global Findex categorizes as "other methods" which may entail saving cash at home, or buying livestock, jewelry, or real estate [START_REF] Klapper | Savings, credit and financial resilience in Sub-Saharan Africa[END_REF].

According to the 2017 Finscope consumer survey, some mobile money adopters often rely on their account to put aside money on a regular basis. Formal financial inclusion starts with having a bank or mobile money account. This foundation of financial inclusion is crucial as it may lead to noteworthy increases in savings and financial resilience.

Based on these results and the widespread availability of mobile phones among the unbanked in Cameroon -both in urban and rural areas-, we recommend that appropriate policies should be designed in other to reduce barriers to mobile money adoption. Computed data from the 2017 Global Findex reveals several barriers to having a mobile money or a bank account in Cameroon such as lack of documentation, distance, or lack of trust. Amongst the unbanked, about 80% reported that they perceive not having enough money as a barrier to having an account. Policy-makers should also concentrate on the importance of financial resilience for sustainable development and the ways that inclusive financial technology -such as mobile money -can get families more of the money that they need and when they need it. By significantly lowering the cost of moving money through social networks, the mobile money technology is showing the way. As results reveal that better saving patterns lead to an increase in the likelihood of being resilient in times of financial adversity, policymakers and mobile money providers in Cameroon should focus on developing interest-earning mobile money based savings products -like MoKash in Uganda that allows users to save as little as UGX 50 (about 10 FCFA) and enables them to schedule savings to happen automatically on a daily, weekly or monthly basis.

Although our results provide some evidence in favor of mobile money-driven financial inclusion interventions in Cameroon with regards to their effects on people's financial resilience, this study presents some limits. First of all, we did not control for inactive mobile money accounts since we had no available data to capture account dormancy. This could have been another factor influencing people's ability to face adverse shocks by coming up with an appropriate emergency fund within a short period.

While the section related to MOBILE and INTERNET of the 2017 Global Findex questionnaire -more precisely the question labeled as Fin13 -asked respondents about their mobile money accounts usage patterns, data related to this question where not reported in the individual-level dataset for Cameroon.

Focusing on mobile money access rather than usage may be an unpopular position as account dormancy is today's bogeyman of financial inclusion. While everyone knows the limits of looking only at access as a measure of financial inclusion [START_REF] Soursourian | How useful is usage in measuring financial inclusion impact? CGAP Blog[END_REF], access remains an essential part of the financial inclusion equation as well as usage.

Following [START_REF] Soursourian | How useful is usage in measuring financial inclusion impact? CGAP Blog[END_REF], one could argue that the role of financial inclusion should be to expand the set of options available to people, so they are better positioned to achieve their goals. One such goal may be the willingness of staying out of poverty by relying on a mobile money account to access and draw on acceptable and accessible external resources and support in times of financial adversity.

It should have been important to also run an IV regression to account for the endogeneity of mobile money adoption and compare the results with those obtained by running an endogenous treatment effects model, but no valid instrument variable was found in the Global Findex dataset.

The study did not look at resilience strategies. Shifting the discussion from whether people are financially resilient to how they manage to achieve it may be an interesting starting point for future research. As the 2017 Findex pointed out, people use a wide range of strategies to come up with an emergency fund in due time. This includes selfresilience strategies (savings, money from working and sale of assets) but also resilience strategies that involve relying on others (family or friends, and borrowing). Understanding how access to mobile money affects the choice of these financial resilience strategies is critical to designing effective public policies and appropriate mobile money products that offer a wide range of accurate facilities to consumers. Notes: NNM does not use a formal model for either the outcome or the treatment status, but this flexibility comes at a price. When matching on more than one continuous covariate, the NNM estimator must be augmented with a bias-correction term.

ANNEXES

Notes: PSM does not require bias correction, because it uses a model for the treatment. If the treatment model is reasonably well specified, PSM will perform at least as well as NNM.

Figure 1 :

 1 Figure 1: Adults with a mobile money account, SSA

  emergency and you need to pay [1/20 of GNI per capita in local currency]. Is it possible or not possible that you could come up with [1/20 of GNI per capita in local currency] within the NEXT MONTH?"On average, 48.5 percent of adults in the sample reported that it is possible to come up with such an amount in due time.

Figure 2 :

 2 Figure 2: Financial resilience ability, Cameroon

Figure 3 :

 3 Figure 3: Financial resilience abilities, by individual characteristics

Figure 4 :

 4 Figure 4: Mobile money account ownership, by individual characteristics

Figure 5 :

 5 Figure 5: Barriers to financial inclusion, Cameroon

Table 2 :

 2 Descriptive statistics

SD = standard deviation.

Table 3 :

 3 Endogenous treatment-effects estimation

			Robust	[95%
	Fin-resilience	Coef.	Std.	Conf.
			Err.	Interval]
	ATE mob_account (1 vs. 0)	0.746 **	0.32222	0.11523 1.37831
	POmean mob_account (0)	0.372***	0.05408	0.26629 0.47830
	ATET mob_account (1 vs. 0)	0.601**	0.26223	0.08746 1.11540
	POmean mob_account (0)	0.099	0.26146	-0.41286 0.61205

Note:* = 1%, ** = 5%, * = 10% of significance. Outcome model: Linear. Treatment model: Probit. Number of observations: 992.

Table 4 :

 4 teffects ipwra estimation

			Robust	[95%
	Fin-resilience	Coef.	Std.	Conf.
			Err.	Interval]
	ATE mob_account (1 vs. 0)	0.053	0.04012	-0.02467 0.13260
	POmean mob_account (0)	0.475***	0.01765	0.44039 0.50957
	ATET mob_account (1 vs. 0)	0.076*	0.04102	-0.00377 0.15705
	POmean mob_account (0)	0.624***	0.02796	0.56958 0.67920

Note:* = 1%, ** = 5%, * = 10% of significance. Outcome model: Linear. Treatment model: Probit. Number of observations: 992.

Annex 1: Dependent and independent variables

  Probit regressions. Standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10%. Dependent variable is a dummy equal to 1 if the respondent reported that he/she was able to come up with an emergency fund of 1/20 of GNI per capita in local currency within the next month. Coefficients are average marginal effects (dy/dx) and indicate the increase or decrease in percentage points of the likelihood of being financially resilient. The numbers in the parentheses in the Wald chi2 row indicate the degree of freedom of the Chi-square distribution.

	Annex 2: Probit regression outcome: --financial resilience ability Annex 3: Full treatment-effects model estimations Endogenous treatment-effects estimation Number of obs = Treatment-effects estimation Number of obs Treatment-effects estimation Number of obs	= =	992	992 992
	Outcome model : linear Estimator : IPW regression adjustment Estimator : IPW regression adjustment
	Basic Treatment model: probit Outcome model : linear Outcome model : linear	Basic	Basic	Basic regression	Basic regression
	Variables Dependent variable Financial resilience Dummy equal 1 if the respondent is able to come up with an emergency fund of Description 1/20 of GNI per capita in local currency within the next month. Mob_ account Dummy equal 1 if respondent reported to currently have a mobile money account Female Dummy that takes the value 1 if the respondent is female and 0 otherwise Age Age of the respondent in years Age square Age of the respondent in years, squared Poorest 20% Dummy that takes the value 1 if the respondent falls in the lowest income quintile and 0 otherwise. Income quintiles are based on the incomes of the respondents in a country. (0.000076)*** Age squared -0.0002103 (0.04239)*** (0.04447) (0.04463) Female -0.0510816 (0.03608) (0.00622)*** Age .0104457 .0227772 0.46 0.647 -.0341968 .0550882 chi2 value (p-value) -42.9713 (0.000) 15.6509 (0.000) --(0.000) (0.000077)** -0.0001831 26.7784 (0.03517) (0.00622)*** Age 0.0182789 Female -.0373748 .1009062 -0.37 0.711 -.2351473 .1603976 Age .0169213 .0055647 3.04 0.002 .0060147 .0278279 0.0160738 fin_account .5437075 .1098537 4.95 0.000 .3283981 .7590168 Age .0104457 .0227772 0.46 0.647 -.0341968 Female -.088017 .0340465 -2.59 0.010 -.154747 -.021287 Female -.0497418 .043242 -1.15 0.250 -.1344947 .035011 .0550882 TME1 Female -.0373748 .1009062 -0.37 0.711 -.2351473 .1603976 OME0 OME0 -0.0534343 0 .3722998 .0540862 6.88 0.000 .2662928 .4783068 fin_account .5437075 .1098537 4.95 0.000 .3283981 .7590168 Individual characteristics variables TME1 0 .4749867 .0176491 26.91 0.000 .4403951 .5095782 0 .6243935 .0279646 22.33 0.000 .5695839 .6792031 mob_account (0.04378) POmean mob_account mob_account Fin_account 0.1159214 0.0460892 0.0517315 -0.0071122 0 .0995949 .2614649 0.38 0.703 -.4128668 .6120566 POmean POmean Independent variables : Socio-economic characteristics regression (mob_account ownership) regression + Financial institution account control regression + Fin_account + Individual characteristics + Fin_account + Financial behavior controls + Financial behavior Mob_account 0.2634834 0.2461506 0.1961862 0.2151827 0.1659531 (0.02806)*** (0.03301)*** (0.04331)*** (0.03884)*** (0.04611)*** (1 vs 0) .74677 .3222202 2.32 0.020 .1152301 1.37831 mob_account Other Financial inclusion variable mob_account POmean (1 vs 0) .0539656 .0401243 1.34 0.179 -.0246766 .1326078 (1 vs 0) .0766374 .0410288 1.87 0.062 -.0037775 .1570523 ATE Fin_resilience Coef. Std. Err. z P>|z| [95% Conf. Interval] (1 vs 0) .6014361 .2622351 2.29 0.022 .0874648 mob_account mob_account 1.115407 ATET ATE mob_account Robust ATET Fin_resilience Coef. Std. Err. z P>|z| Fin_resilience Coef. Std. Err. z P>|z| [95% Conf. Interval] [95% Conf. Interval] characteristics Treatment model: probit Outcome model : linear Fin_resilience Coef. Std. Err. z P>|z| Robust Robust [95% Conf. Interval] indiv. Endogenous treatment-effects estimation Number of obs = 992 Robust + Fin_account + Treatment model: probit Treatment model: probit
	Second 20% Middle 20% OME0 Tertiary educ Second educ emp_in Dummy that takes the value 1 if the respondent falls in the second lowest income Dummy that takes the value 1 if the respondent falls in the middle income quintile and 0 otherwise. (0.10190) _cons -.0853115 .4822846 -0.18 0.860 -1.030572 .8599489 1.2276 (0.268) (0.08854)** -0.1632527 -0.1955951 .2711629 .1226619 2.21 0.027 .03075 .5115759 quintile and 0 otherwise. 11.4586 (0.001) -0.2758911 -0.2975064 Primary educ (0.12031)** (0.11731)** inc_qfourth20 -.2212174 .1316388 -1.68 0.093 -.4792247 .0367899
	Fourth 20% Poorest 20% Female Dummy that takes the value 1 if the respondent falls in the second highest income quintile and 0 otherwise. (0.05815) Age .0142019 .005981 2.37 0.018 .0024795 .0259244 5.4663 (0.019) (0.05468)* -0.07701 -0.1041789 -.088472 .0361337 -2.45 0.014 -.1592927 -.0176512
	Richest 20% Emp_in inc_qmiddle20 Dummy that takes the value 1 if the respondent falls in the highest income quintile and 0 otherwise. Dummy that takes the value 1 if the respondent is in the workforce and 0 otherwise. (0.05611) -.0603555 .0646153 -0.93 0.350 -.186999 .0662881 36.1840 (0.000) (0.004) (0.05544) Fourth 20% 0.0514182 0.0275984 inc_qsecond20 -.0811092 .0733205 -1.11 0.269 -.2248146 .0625963 8.3574 (0.05701) inc_qpoorest20 -.1670002 .0740695 -2.25 0.024 -.3121736 -.0218267 Middle 20% Secondaryeduc -.2846867 .1280638 -2.22 0.026 -.5356872 -.0336862 (0.05583) Primaryeduc -.3172076 .1497062 -2.12 0.034 -.6106264 -.0237889 Second 20% -0.0271852 -0.0401796 Age2 -.0001517 .0000733 -2.07 0.039 -.0002953 -7.98e-06
	Independent variables : Individuals' Socio-economic characteristics Primary educ Dummy that takes the value 1 if the respondent completed elementary education or less and 0 otherwise. (0.05419)*** -0.0114198 (0.04076) _cons .4689074 .2082574 2.25 0.024 .0607304 .8770844 44.7976 (0.000) _cons .4689074 .2082574 2.25 0.024 .0607304 .8770844 (0.04001) Borrowed -.0586626 .0347051 -1.69 0.091 -.1266833 .0093582 Borrowed -.0586626 .0347051 -1.69 0.091 -.1266833 .0093582 -0.0474452 Saved .2020317 .0372048 5.43 0.000 .1291115 .2749518 Saved .2020317 .0372048 5.43 0.000 .1291115 .2749518 (0.05566)** emp_in -.0681676 .0415785 -1.64 0.101 -.14966 .0133248 emp_in -.0681676 .0415785 -1.64 0.101 -.14966 .0133248 Richest 20% Emp_in 0.146019 0.1184212 inc_qfourth20 -.0709653 .0606978 -1.17 0.242 -.1899308 .0480001 inc_qfourth20 -.0709653 .0606978 -1.17 0.242 -.1899308 .0480001
	Secondary educ Financial behavior variables Dummy that takes the value 1 if the respondent completed secondary education and some education beyond secondary education and 0 otherwise. Tertiary educ Dummy that takes the value 1 if the respondent completed years of education beyond secondary school and 0 otherwise. Fin_ Account Dummy equal to 1 if respondent reported to currently have a bank account at a formal financial institution-a bank, credit union, cooperative, post office, or microfinance institution. Independent variables : Savings and Credit patterns N 1000 1000 992 1000 Wald chi2 36.73 (1) 42.94 (2) 87.77 (12) 66.78 (4) emp_in .1744464 .0970947 1.80 0.072 -.0158557 inc_qfourth20 .2424776 .0948211 2.56 0.011 .0566317 inc_qfourth20 .2424776 .0948211 2.56 0.011 .0566317 inc_qmiddle20 .0201786 .1158632 0.17 0.862 -.2069091 inc_qmiddle20 .0201786 .1158632 0.17 0.862 -.2069091 inc_qsecond20 -.0291098 .1593322 -0.18 0.855 -.3413952 (0.01850)*** (0.01852)*** (0.01795)*** (0.01813)*** inc_qsecond20 -.0291098 .1593322 -0.18 0.855 -.3413952 inc_qpoorest20 .3660789 .1610275 2.27 0.023 .0504709 Saved 0.2213851 (0.04163)*** Borrowed -0.0644657 (0.03738)* Constant 0.4653774 0.4653606 0.4605294 0.4656437 inc_qpoorest20 .3660789 .1610275 2.27 0.023 .0504709 Secondaryeduc .1896068 .175939 1.08 0.281 -.1552273 Secondaryeduc .1896068 .175939 1.08 0.281 -.1552273 Primaryeduc .0527144 .230218 0.23 0.819 -.3985046 Primaryeduc .0527144 .230218 0.23 0.819 -.3985046 Age2 .0003111 .0001324 2.35 0.019 .0000516 Age2 .0003111 .0001324 2.35 0.019 .0000516 Age -.021035 .0112013 -1.88 0.060 -.0429891 Age -.021035 .0112013 -1.88 0.060 -.0429891 Female -.0057213 .06371 -0.09 0.928 -.1305907 Female -.0057213 .06371 -0.09 0.928 -.1305907 OME1 OME1	32.3977 (0.000) 11.5774 (0.001) .3647485 .4283236 106.20 (14) .4283236 .2472663 992 .2472663 .2831755 (0.000) (0.01758)*** .2831755 .6816869 54.5055 (0.03710) .6816869 .5344409 0.4608576 .5344409 .5039333 .5039333 .0005706 .0005706 -0.048382 .0009192 .0009192 (0.04238)*** .119148 .119148 0.2083274
	Saved Borrowed TEOM0 TEOM0 Pseudo R2 Borrowed past 12 months using a formal/informal financial method and 0 otherwise. together with someone else, from any source for any reason in the past 12 months and 0 otherwise. _cons -.5301472 .2672609 -1.98 0.047 -1.053969 -.0063255 (0.000) 2.0159 (0.156) _cons .9480331 .3048443 3.11 0.002 .3505493 1.545517 Dummy equal to 1 if respondent reported to have borrowed money alone or 0.0340 0.0417 0.0858 0.0704 _cons .9480331 .3048443 3.11 0.002 .3505493 -.0004064 .0656991 -0.01 0.995 -.1291743 .1283615 1.545517 0.1107 Borrowed -.0004064 .0656991 -0.01 0.995 -.1291743 Saved .1880788 .0877284 2.14 0.032 .0161343 .3600234 .1283615 Dummy equal to 1 if respondent reported to have saved or set aside money in the 77.8099 Prob > chi2 0.0000 0.0000 0.0000 0.0000 0.0000 Saved .1880788 .0877284 2.14 0.032 .0161343 .3600234 emp_in .1744464 .0970947 1.80 0.072 -.0158557 .3647485
	TEOM1	_cons	-.5301472		.2672609	-1.98	0.047	-1.053969	-.0063255
		_cons	-.6744041	.3922489	-1.72	0.086	-1.443198	.0943896
	TEOM1
		_cons	-.6744041		.3922489	-1.72	0.086	-1.443198	.0943896

Note: The Pearson's chi-squared tests performed on a set of two-way tables in order to measure association between the dependent variable and each independent variable are reported in the last column. The results of the chi-squared tests seems to legitimate the use of a probit regression technique because of their ability to control for many variables simultaneously.

Note:

According to GSMA (2018), mobile phone adoption in the region has grown rapidly in recent years; overall subscriber penetration reached 44% in

2017, up from just 25% at the start of this decade.

We downloaded the National Financial Inclusion Strategies of some SSA countries from the World Bank's National Financial Inclusion Strategies Resource Center and performed a basic content analysis by highlighting the number of times "mobile money" or related expression was used.

In 2017, Cameroon's GNI per capita in constant LCU was 637252 FCFA. 1/20 of such an amount is therefore 31862 FCFA.

This method controls for endogeneity by including the residuals from the treatment-assignment model as a regressor in the models for the potential outcomes.

The Stata command ttest tests that an independent variable has the same mean within the two groups (in this case our treatment and control groups). diff = mean(0) -mean(1); if Pr(|T| > |t|) is ≤


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Victor Ramos

NetANPI: a network selection mechanism for LTE traffic offloading based on the Analytic Network Process

Keywords: Traffic Offloading, MADM Mechanisms, LTE (Long Term Evolution)

Traffic offloading in Long Term Evolution (LTE) networks is a key process in cellular networks so a mobile device chooses one of the available femtocells. In the last few years, several network selection methods have been proposed based on multiple attribute decision making (MADM) techniques for traffic offloading in LTE. The Analytic Network Process (ANP) is an MADM method that has been barely studied for network selection in cellular networks; ANP configures the decision making problem as a network of attributes in order to derive priority scales of individual judgments. We propose in this paper NetANPI (Network selection ANP-based mechanism with Ideal network comparison), which is an ANP decision making mechanism for traffic offloading in LTE. We show its effectiveness by contrasting it with MADM methods proposed in the literature as AHP, ELECTRE, TOPSIS, GRA, and SAW. We also introduce a mechanism to rank the MADM algorithm using utility theory. Our results show that NetANPI outperforms the most used MADM mechanism for the interactive and conversational traffic classes.

I. INTRODUCTION

One of the most relevant goals of LTE cellular networks is to provide QoS (Quality of Service) to a large number of users, while at the same time offering a wide coverage. Such goal is difficult to achieve due to physical reasons in a cellular cell, since as the number of users grows the achievable throughput per user decreases. One of the most popular techniques to solve such problem is traffic offloading via femtocells, which consists in deriving traffic from a macrocell with a wide coverage range (hundreds of meters) to femtocells. In contrast, such femtocells have a reduced coverage range (tens of meters) but they can provide good QoS parameters to a small number of users.

In the near future, it is expected that the number of femtocells will considerably grow, which will naturally result in frequent coverage overlapping. As a consequence, it is of key importance to account with efficient handover mechanisms, also called network selection mechanisms, so the mobile device may choose one of the available femtocells. Multiple Attribute Decision Making (MADM) mechanisms have been widely used to solve the network selection problem. Such mechanisms allow to choose one of the available alternatives according to multiple, and perhaps, conflicting criteria as their input [START_REF] Wang | Mathematical modeling for network selection in heterogeneous wireless networks -a tutorial[END_REF].

The Analytic Network Process (ANP) is an MADM mechanism that has been very little explored to solve the network selection problem. ANP allows to see the scenario as a network of attributes, where nodes may be an alternative, a criterion, a sub-criterion, or even groups of criteria. ANP compares each network node with the other nodes connected to it via a pairwise comparison, building a super-matrix. The final result is a ranking vector of alternatives, which results in a very efficient method to choose a network, as we will see. Thus, in this paper we propose an ANP-based method for network selection that we call NetANPI.

NetANPI characterizes each alternative (femtocell) by four different performance parameters: Packet Loss (PL), Average Throughput (AT), Delay per Packet (DP), and Network Occupancy representing the available Resource Blocks (RB) in a femtocell. Besides comparing among different alternatives, NetANPI also allows to compare each network with an ideal case. Based on the four traffic classes defined by the 3GPP TS 23.107 standard (streaming, background, conversational, and interactive), NetANPI allows for user heterogeneity with different QoS requirements.

To evaluate the performance of NetANPI, we run numerical simulations comparing NetANPI with the most used MADM mechanisms for network selection found in the literature. Then, we introduce a method to rank MADM methods, which is based on utility theory. Our results show that NetANPI outperforms the other methods for the cases of interactive and conversational traffic classes. This paper is organized as follows, Section II presents the background and related work. Section III describes our proposed NetANPI mechanism for network selection. Section IV details how we use a utility theoretical network selection algorithm to rank different MADM algorithms. Section V presents the performance comparison and results, and finally Section VI draws some concluding remarks.

II. BACKGROUND AND RELATED WORK

In this section, we describe how traffic offloading is done in LTE networks and how it applies to the network selection problem. We also describe briefly some related work on network selection.

A. Traffic Offloading in LTE

With the vast increase of mobile applications, traffic over cellular networks has grown exponentially in the last few years [START_REF] Aijaz | A survey on mobile data offloading: technical and business perspectives[END_REF]. Such phenomenon comes along with a corresponding increase in network congestion, which directly impacts the QoS provided to users. Traffic offloading via femtocells has become one of the most used techniques to solve such problem. This technique refers to offloading the traffic from a macrocell to femtocells. In LTE networks, a macrocell is an eNB (evolved Node B) with a wide coverage range and femtocells are indoor base stations with a low coverage range. In this way, the QoS provided to users may be kept within acceptable levels since a femtocell supports a small number of users with good QoS parameters.

Figure 1 illustrates the LTE femtocell/macrocell network architecture. There are two subsystems, the Core Network or Evolved Packet Core (EPC), and the Radio Access Network (RAN). Each femtocell is connected to the EPC by a broadband/Internet network and the macrocell has a direct link to the Mobility Management Entity (MME) in the EPC. Our main interest in this work is on the RAN since macrocell and femtocells are located in this subsystem. As we can observe, the macrocell coverage overlaps with that of femtocells, and there is a large number of overlapped femtocells, thus the traffic offloading technique is needed. There are two ways to perform traffic offloading: the user-centric approach and the network-centric approach. In the former, the user makes the decision and in the latter it is the network that makes it. In this paper, we follow the first approach. Thus, the network selection procedure is performed by the user to handoff from the macrocell to one of the available femtocells. Network selection in this context consists in choosing the best femtocell based on its performance parameters.

B. Related work on MADM network selection mechanisms

Extensive research has been done on network selection mechanisms. The main efforts have been done on vertical handoff between wireless networks, where a diverse set of mathematical approaches to model this problem have been proposed. Among the most important ones we may find utility theory, multiple attribute decision making (MADM), fuzzy logic, combinatorial optimization, Markov chain models, and recently game theory [START_REF] Wang | Mathematical modeling for network selection in heterogeneous wireless networks -a tutorial[END_REF][START_REF] Yan | A survey of vertical handover decision algorithms in fourth generation heterogeneous wireless networks[END_REF][START_REF] Kassar | An overview of vertical handover decision strategies in heterogeneous wireless networks[END_REF]. One of the contributions applying MADM methods to the network selection problem is that of Stevens-Navarro and Wong in [START_REF] Stevens-Navarro | Comparison between vertical handoff decision algorithms for heterogeneous wireless networks[END_REF]. They compare four different mechanisms namely MEW, SAW, TOPSIS and GRA, to conclude that GRA shows better performance regarding the background traffic class. In [START_REF] Vaca-Ramirez | A vertical handoff decision algorithm which considers the uncertainty during the decision making process[END_REF], Vaca and Ramos propose an algorithm considering the uncertainty during the decision making process. In [START_REF] Ramirez-Perez | On the effectiveness of multi-criteria decision mechanisms for vertical handoff[END_REF], Ramirez and Ramos present a hierarchical decision scheme based on the Analytic Hierarchy Process (AHP). Later in [START_REF]A QoS hierarchical decision scheme for vertical handoff[END_REF], the same authors present a strong comparison between various MADM algorithms showing their advantages and disadvantages for network selection. An evolution of the AHP method is the ANP general framework presented in [START_REF] Saaty | Fundamentals of the analytic network process dependence and feedback in decision-making with a single network[END_REF]. We may find in the literature very few contributions applying ANP to the network selection problem. In this paper, we show that using ANP may be a very attractive solution for that purpose since it allows to see the scenario as a network of attributes; such capability has been barely studied in previous work regarding ANP [START_REF] Lahby | An intelligent network selection strategy based on MADM methods in heterogeneous networks[END_REF][START_REF] Datta | ANP based vertical handover algorithm for vehicular communication[END_REF].

III. ANP AND THE NETWORK SELECTION PROBLEM

The Analytic Network Process is a general framework to derive relative priority scales of individual judgments, or absolute numbers from actual measurements. Such individual judgments represent the relative influence of one or more elements over the other(s) in a pairwise comparison with respect to an underlying control criterion [START_REF] Saaty | Fundamentals of the analytic network process dependence and feedback in decision-making with a single network[END_REF]. ANP configures the problem of decision making as a network of attributes, where nodes may be an alternative, a criterion, a sub-criterion, or even criteria groups. Each node is contrasted with the nodes connected to it through a pairwise comparison to build a super-matrix, where the final result is a ranking vector of the networks [START_REF] Saaty | Fundamentals of the analytic network process dependence and feedback in decision-making with a single network[END_REF].

In this paper we propose NetANPI, which is an ANP mechanism considering the influence of the alternatives on criteria. Besides comparing among different network alternatives, NetANPI also allows to compare each network with itself. This fact may sound weird because it is difficult to say that a network exhibits better throughput than latency, for example, but it may be achieved if the network is compared with an ideal one. This comparison includes a new ranking in the decision making mechanism in order to make it more robust. To achieve such evaluation, we adapt a well know ELECTRE (ELimination Et Choix Traduisant la REalité) version presented in [START_REF]A QoS hierarchical decision scheme for vertical handoff[END_REF] that takes into account the comparison of networks with an ideal network. In Table I, we list the parameters to carry out this comparison. Unlike other MADM methods, ANP is able to build groups of criteria. NetANPI groups three QoS parameters into one cluster, while occupancy is considered separately. This is because both clusters have a different nature. On one hand, the former refers to the link's quality perceived by the user at the instant when the decision is made. On the other hand, the latter refers to the A diagram for NetANPI is presented in Figure 2; as we can see, there is a preference relation among the options (available networks) and the QoS criteria, that is how we graphically represent the comparison of each network with itself.

AT DP PL

QoS

Best network

Occupancy

N 1 N 2 N n . . .

A. Selection criteria and application classes

We consider the four different traffic classes defined in the TS 23.107 3GPP standard, each with its corresponding QoS requirements. Such characterization is presented in Table II. We represent each candidate network by three different QoS parameters along with network occupancy.

Network Occupancy (NO) is defined as the percent of Resource Blocks (RB) available in each network at the instant when the decision is made. The Average Throughput (AT) is the achievable average throughput per user in the network, while the Delay per Packet (DP) represents the end-to-end delay. Finally, Packet Loss (PL) is defined as the packet loss rate in the network.

B. Pairwise comparison of NetANPI

As we have described, the structure of NetANPI is composed of two types of criteria: network occupancy and the

Conversational

Streaming Background Interactive AT DP PL AT DP PL AT DP PL AT DP PL AT 1

1 4 1 1 2 2 1 1 1 1 1 6 1 DP 4 1 4 1 2 1 1 1 1 1 6 1 6 PL 1 1 4 1 1 2 1 1 1 1 1 1 1 6 1
QoS parameters. This is why we need to make two pairwise comparisons for each traffic class, one for the QoS cluster and the other one for QoS and occupancy. In Table III, we present the first comparison matrices. In the second comparison, we choose a relation of two-to-one.

Weights selection is purely subjective; however, it is based on the QoS requirements for each application class defined in the TS 23.107 3GPP standard. For instance, the interactive class is very restrictive regarding delay; that is why the relation between such criterion and the rest is of six-toone. A similar case occurs for the case of the conversational class, but in this case the relation is of four-to-one since this class is less restrictive than the previous one. The converse case is the background traffic class since in this case there is no preference among criteria. Finally, we generate the corresponding super-matrix from priority vectors, which are obtained by analyzing the eigenvalues from both, the pairwise comparison and the comparison with an ideal network. The structure of the super-matrix is shown in Figure 3.

Each box of the supermatrix in Figure 3 corresponds to an eigenvalue analysis made according to the pairwise comparison described above. For example, the green boxes represent the priority vector of the QoS eigenvalue analysis made for each alternative. The final priority vector will appear in the red boxes once Equation ( 1) is applied. This procedure is similar to obtaining the steady state of a Markov Chain, i.e.:

W lim = lim k→∞ W k , ( 1
)
where W is the supermatrix.

IV. UTILITY THEORY FOR NETWORK SELECTION

Utility theory was developed by Von Newman and Morgenstern [START_REF] Nguyen-Vuong | On utility models for access network selection in wireless heterogeneous networks[END_REF]. It has been used very often in microeconomics to characterize the ability of a good or service to satisfy a human need. In this context, utility theory normally uses utility functions to express numerically such level of satisfaction. Different users may have different levels of satisfaction for the same good or service depending on their corresponding 2015 36th IEEE Sarnoff Symposium In our context, users with different preferences are replaced by different classes of applications with different requirements for each QoS parameter. There are two types of utility functions, single-criterion and multiple-criterion. In this paper, we consider the sigmoidal utility functions presented in [START_REF] Nguyen-Vuong | On utility models for access network selection in wireless heterogeneous networks[END_REF] for an upward x criterion such that x α ≤ x ≤ x β < ∞; they are defined as follows:

u(x) =                                  0 if x < x α x-xα xm-xα ζ 1+ x-xα xm-xα ζ if x α ≤ x ≤ x m 1 - x β -x x β -xm γ 1+ x β -x x β -xm γ if x m < x ≤ x β 1 if x > x β . (2)
Where γ and ζ are tuned steepness parameters, which express the elasticity of the criterion; they are defined by:

γ = ζ(x β -x m ) x m -x α ζ ≥ max{ 2(x m -x α ) x β -x m , 2}. (3)
A large value of ζ models an elastic application; i.e., a small variation would affect drastically the application's performance. Regarding the downward parameters, the utility function is 1 -u(x).

We define a different utility function for each parameter and each class of application (see Table IV for details). The decision is made as follows: we evaluate the utility function over each network parameter, then we add the corresponding utilities obtained in each network. These values form a ranking vector of the networks, the largest value in this vector indicates the decision point that will be used to compare the MADM mechanisms.

The values of x α and x β characterize different types of applications for LTE networks. The variable ζ is chosen according to the TS 23.107 3GPP standard, and assumes different values according to the class of application [START_REF] Elnashar | Design Deployment And Performance Of 4G LTE Networks[END_REF]. The interactive class is inelastic for the delay parameter, the realtime voice and video applications are inelastic regarding their demand for bandwidth. Browsing and e-mail are perfectly elastic on delay requirements [START_REF] Shenker | Fundamental design issues for the future Internet[END_REF][START_REF] Dasilva | Pricing for QoS-enabled networks: A survey[END_REF]. This is why the interactive class takes ζ = 12, while for the background class there is no inelastic parameter, and so all the values for ζ = 3.

V. PERFORMANCE ANALYSIS

We present in this section the simulation scenario and describe the results we obtain.

A. Simulation scenario

Our aim in this work is to compare the most cited MADM mechanisms in the literature for the network selection problem with our NetANPI mechanism so as to show the effectiveness of our proposal. To do so, we implement numerically the following mechanisms: NetANPI, AHP, ANP (not contrasting it with an ideal network), TOPSIS, ELECTRE, SAW, and GRA. The simulations are run for a set from 2 to 20 different network alternatives. We execute 300,000 simulations for each decision making (see Table V), where the network parameters are generated from a normal distribution characterized by µ and σ indicated in Table VI and chosen in a way that corresponds to standard values for LTE networks.

Since all the MADM mechanisms evaluated consider subjectivity in the decision process, determining which of them is the best for a particular type of application is not straightforward. Thus, we propose a way to rank the evaluated mechanisms that is based on utility theory. Basically, we implement the sorting utility algorithm proposed in [START_REF] Nguyen-Vuong | On utility models for access network selection in wireless heterogeneous networks[END_REF] and described in Section IV. Then, we compute the euclidean distance in Equation ( 4) between the points selected by the MADM methods with the point selected by the utility theory sorting mechanism. The method with the largest euclidean distance is the worst one for each case. Therefore, the euclidean distance is given by:

d E (P, Q) = n i=1 (p i -q i ) 2 , (4)
where P = (p 1 , p 2 , . . . , p n ) and Q = (q 1 , q 2 , . . . , q n ) are the euclidean points in the n-dimensional euclidean space.

B. Results

Figure 4 shows plots representing the most relevant parameters for each class of application: delay for the conversational and interactive classes, and throughput for the streaming and background classes. For each plot, we consider 95% The comparisons we make consider only one parameter at a time and they depend on the subjectivity when assigning weights. Thus, as we said above, in order to reduce such subjectivity we compare the euclidean distance with our utility theoretical method. Thus, we see in Figure 5 that NetANPI exhibits the best performance for the interactive and conversational application classes. For the streaming and background classes, the baseline ANP mechanism shows the best performance. As we may see, AHP, ANP, and NetANPI improve their performance as the number of options increases. Besides, in all cases the distance increases proportionately along with the number of options.

VI. CONCLUSIONS In this paper we proposed NetANPI, which is an MADM mechanism based on the Analytic Network Process taking into account the contrast of available networks with their ideal counterpart. We also proposed a mechanism to rank different network selection methods on a utility theoretical way. We executed numerical simulations to compare the most cited MADM mechanisms in the literature in order to show the effectiveness of our proposed NetANPI method when selecting an available network. NetANPI showed the best performance on delay among the other mechanisms regarding the conversational and interactive traffic classes. Equivalently, our utility theoretical approach confirmed that NetANPI is very well suited for such type of application classes.

Figure 1 .

 1 Figure 1. LTE femtocell/macrocell network architecture.

Figure 2 .

 2 Figure 2. Diagram of NetANPI.

Figure 3 .

 3 Figure 3. Supermatrix of NetANPI

Figure 4 .

 4 Figure 4. Performance comparison.

Figure 5 .

 5 Figure 5. Distance comparison.

Table I COMPARISON

 I WITH IDEAL NETWORK PARAMETERS.

			Conversational	Streaming	Background	Interactive
			Ideal	Weight	Ideal	Weight	Ideal	Weight	Ideal	Weight
		AT 200 Kb/s	0.175	3 Mb/s	0.45	1 Mb/s	0.33	500 Kb/s	0.15
		DP	100 ms	0.65	300 ms	0.30	300 ms	0.33	75 ms	0.75
		PL	10 -2 %	0.175	10 -2 %	0.25	10 -6 %	0.33	10 -4 %	0.15
		Table II
	3GPP TS 23.107 APPLICATION CLASSES.
	Traffic Class Example	QoS Requirements
	Conversational	VoIP	Low delay, preserve delay
			variation.
	Streaming	Video	Minimum throughput
		streaming	and delay levels
	Interactive	Gaming	Very low level of delay,
			preserve time variation.
	Background	e-mail	Preserve payload content
	number of users with respect to the total network capacity,
	such occupancy is used to predict if the network handover
	may cause congestion or not.

Table V SIMULATION

 V PARAMETERS.

	Parameter	Value
	Number of alternatives at	2, 3, . . . , 20
	each decision point
	Number simulation per	300, 000
	decision point
	Reference decision method Sigmoidal utility function
	MADM Mechanisms	NetANPI, AHP,
		ANP, GRA, SAW
		TOPSIS, ELECTRE
	Selection criteria	NO, AT, PD, PL

Table VI NETWORK

 VI PARAMETERS.

	Parameter Minimum value Maximum value
		(µ -2σ)	(µ + 2σ)
	NO	0%	100%
	AT	1 b/s	15 Mb/s
	PD	1 µs	350 ms
	PL	10 -7 %	20%
	confidence intervals. On one hand, we see how NetANPI
	outperforms the other mechanisms on delay regarding the
	conversational and interactive application classes. On the other
	hand, regarding the streaming and background application
	classes, ELECTRE outperforms the other mechanisms fol-
	lowed closely by NetANPI. These results suggest that Ne-
	tANPI is an excellent candidate to be used as a mechanism
	for network selection.

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