Doctoral Dissertation
Essays on Information and Communication Technology, Poverty,
Environment, and Corruption

N’DRI LASME GNAGNE MATHIEU

Graduate School for International Development and Cooperation
Hiroshima University

September 2020

Essays on Information and Communication Technology, Poverty,
Environment, and Corruption

D 171138
N’DRI LASME GNAGNE MATHIEUA Dissertation Submitted to
the Graduate School for International Development and Cooperationof Hiroshima University in Partial Fulfillmentof the Requirement for the Degree ofDoctor of Philosophy
September 2020

i

Acknowledgement
Firstly, I would like to express my deepest gratitude to Prof. Kakinaka Makoto my supervisor
who allowed me to start this PhD program at Hiroshima University for his great humility andcontinuous support throughout all these years outside my home country. I am voiceless infront such mixture of talent, hardworking and humility. Thank you, Sensei, for being myadvisor and mentor.

Besides my main advisor, I would like to express my gratitude to the rest of mydissertation committee: Prof. Ichihashi Masaru, Associate Prof. Goto Daisaku, Associate Prof.
Takahashi Shingo, and Associate Prof. Lin Ching-Yang for their insightful comments andcontribution to improve this work.

I thank the Japanese government for the Monbukagakusho scholarship which helped
me to study in Japan.

I do not want to forget all those anonymous contributors to this success. I am grateful.

Lastly, I would like to thank my family, particularly Dr. Sess Gnagne Antoine fortrusting and investing in the building of my human capital.

ii

Summary of the Dissertation
The Sustainable Development Goals (SDGs), also known as the global goals, were adopted by
all United Nations member states in 2015 as a universal call to action to end poverty, protect
the planet and ensure that all people enjoy peace and prosperity by 2030. These goals are 17
and integrated so that action in one area will affect outcomes in others, and that development
must balance social, economic and environmental sustainability. Through the pledge to leave
no one behind, countries have committed to fast-track progress for those furthest behind first.
That is why the SDGs are designed to bring the world to several life-changing ‘zeros’, including
zero poverty, hunger, Acquired immunodeficiency syndrome (AIDS) and discrimination against
women and girls. Everyone is needed to reach these ambitious targets. The creativity, knowhow,technology and financial resources from all of society is necessary to achieve the SDGs in every
context, so that the need for partnerships to reach these goals which is the goal number 17 has
become the most important among them. The SDG 17 is a call for all contributions atinternational, regional , national, community, and individual level in term of cooperation( leading coherent policy development (Target 17.14)), assistance ( supporting capacity building
in developing countries (17.9)), or improving access to sustainable technologies and technology
development in emerging economies (17.7), etc. through academic research to make the first 16
goals realizable.
Given the importance of academic research align with the SDG 17, our individual contribution
through this doctoral dissertation is to analyze the contribution of Information andCommunication Technology (ICT) to reach the SDGs. To do so, we targeted three importantones among them namely SDG 1 (no poverty), SDG 13 (climate action), and SDG 16 (peace,justice, and strong institutions). The important interrogations which will legitimate our endeavor
are threefold: (i) why is ICT important to reduce poverty? (ii) why is ICT important to mitigate
climate change? (iii) how can ICT contribute to strengthening institutions?
Economic growth is achieved with technical progress which allows many scholars to work on
the relationship between ICT and Poverty (Jack & Suri, 2014). Among various types of ICT,
mobile money has attractive for the promotion of financial inclusion which is now recognized
as a crucial factor for economic growth. Our first essay examines the effect of financial inclusion
and mobile money on poverty in Burkina Faso where mobile money has not prevailed yet.
There are two opposite relationships between ICT and the environment namely the unfavorable
effects: ICT is positively associated with CO2 due to the life cycle of ICT production and thefavorable effects: ICT is negatively associated with CO2 due to green technology. Our second
essay analyzes the overall relationship in developing countries.
Corruption is recognized to harm economic growth by weakening institutions. Some studies
argue that ICT use mitigates corruption by improving institutional efficiency and transparency(Kanyam et al., 2017), so our third essay examines the relationship between ICT, corruption,and military expenditures by using country level panel data in the Sub-Saharan Africa region
which is regarded as the most corrupt region in the world (Willet, 2009). Our dissertation uses
different identification strategies with different indicators or measures of ICT to show theempirical analysis between ICT and the selected three SDGs.
From the results of the empirical analysis, we can notice that ICT reduces poverty, CO2
emissions, and corrupted military expenses for the sake of developing countries.
The first chapter is the general introduction. The second one is related to financial inclusion,mobile money, and individual welfare: The case of Burkina Faso. The third chapter deals withICT and environmental sustainability: Any differences in developing countries? The fourth
chapter explores the nexus between corruption, ICT and military expenditure in Sub-SaharanAfrica, and the fifth chapter is the general conclusion.

iii

Table of Contents
Acknowledgement........................................................................................................................ i
Summary of the Dissertation.......................................................................................................ii
Table of Contents ...................................................................................................................... iii
List of tables ..........................................................................................................................vii
List of figures ...................................................................................................................... viii
Chapter 1: Introduction ............................................................................................................... 1
Chapter 2: Financial inclusion, mobile money, and individual welfare: The case of Burkina
Faso ............................................................................................................................................. 3
2.1 Introduction ....................................................................................................................... 3
2.2 Literature review ............................................................................................................... 6
2.2.1 Financial inclusion and poverty reduction ..................................................................... 6
2.2.2 Mobile money and poverty reduction............................................................................. 8
2.3 Financial inclusion and mobile money in Burkina Faso ................................................. 10
2.4 Financial inclusion and poverty reduction ...................................................................... 11
2.4.1 Methodology ................................................................................................................ 13
Table 2.1. Description of variables. ...................................................................................... 17
2.4.2 Results .......................................................................................................................... 17
Table 2.2. Descriptive statistics ............................................................................................. 18
Table 2.3. Logistic regression ............................................................................................... 19
Table 2.4. Balancing property ............................................................................................... 20
Table 2.5. ATTs of financial inclusion.................................................................................. 20

iv

2.5 Incorporating the role of mobile money .......................................................................... 21
Table 2.6. Descriptive statistics ............................................................................................. 23
Figure 2.1. Incorporating mobile money use......................................................................... 25
Table 2.7. Logistic regression ............................................................................................... 26
Table 2.8. ATTs of financial inclusion and mobile money ................................................... 27
Table 2.9. ATTs of multivalued treatments........................................................................... 28
2.6 Conclusion ....................................................................................................................... 30
2.7 Appendix ...................................................................................................................... 31
Table 2.A1. OLS results ........................................................................................................ 31
Table 2.A2. ATTs using Kernel matching and 2-nearest neighbor matching estimations. .. 32
Table 2.A3. OLS results ........................................................................................................ 32
Table 2.A4. ATTs using Kernel matching and 2-nearest neighbor matching estimations. .. 33
Table 2.A5. Balancing property ............................................................................................ 33
Table 2.A6. Balancing property ............................................................................................ 34
Table 2.A7. Balancing property ............................................................................................ 34
Chapter 3:

ICT and environmental sustainability: Any differences in developing countries?

................................................................................................................................................... 35
3.1 Introduction ..................................................................................................................... 35
Table 3.1. Summary of literature review ............................................................................... 37
3.2 Methodology and data ..................................................................................................... 40
3.2.1 Model specification ...................................................................................................... 40
3.2.2 Data .............................................................................................................................. 42

v

Table 3.2. List of sample countries ....................................................................................... 43
Table 3.3. Variable definitions .............................................................................................. 44
Table 3.4. Descriptive statistics ............................................................................................. 44
Table 3.5. Correlation matrix ................................................................................................ 45
3.3 Results and discussion ..................................................................................................... 45
3.3.1 Panel stationarity tests .................................................................................................. 45
Table 3.6. Panel unit root tests (1990 - 2014) ....................................................................... 47
3.3.2 Panel cointegration tests ............................................................................................... 48
Table 3.7. Pedroni panel cointegration tests (1990-2014)..................................................... 49
Table 3.8. Kao panel cointegration tests (1990-2014) .......................................................... 50
3.3.3 Long- and short-run estimates ...................................................................................... 50
Table 3.9. Pooled mean group with dynamic autoregressive distributed lag: PMG-ARDL(1,1,1,1,1) .............................................................................................................................. 51
3.3.4 Dumitrescu and Hurlin panel causality tests ................................................................ 53
Table 3.10. Dumitrescu and Hurlin panel causality test ........................................................ 56
3.3.5 Robustness checks ........................................................................................................ 57
3.4 Conclusion ....................................................................................................................... 58
3.5 Appendix ......................................................................................................................... 60
Table 3.A1. The definitions of additional variables .............................................................. 60
Table 3.A2. Descriptive statistics of additional variables ..................................................... 60
Table 3.A3. Panel unit root tests for additional variables (1990-2014) ................................ 61
Table 3.A4. Pedroni panel cointegration tests (1990-2014) .................................................. 62

vi

Table 3.A5. Kao panel cointegration tests (1990-2014)........................................................ 63
Table 3.A6. Pooled mean group with dynamic autoregressive distributed lag: PMG-ARDL(1,1,1,1,1) .............................................................................................................................. 63
Table 3.A7. Dumitrescu and Hurlin panel causality test ....................................................... 64
Chapter 4: Corruption, ICT and military expenditure in Sub-Saharan Africa. ......................... 65
4.1 Introduction ..................................................................................................................... 65
4.2 Literature review ............................................................................................................. 68
4.2.1 Corruption and military expenditure ............................................................................ 68
4.2.2 ICT and military expenditure ....................................................................................... 69
4.3 Methodology and data ..................................................................................................... 70
4.3.1 Data .............................................................................................................................. 70
Table 4.1. List of countries included in the analysis ............................................................. 72
Table 4.2. Variable definitions. ............................................................................................. 73
Table 4.3. Summary statistics of the variables included in the study.................................... 74
Table 4.4. Correlation matrix ................................................................................................ 74
4.3.2 Model specification ...................................................................................................... 74
4.4 Results and discussion ..................................................................................................... 76
Table 4.5. Estimation results of the two-step system GMM with orthogonal deviation. ...... 78
4.5 Conclusion ....................................................................................................................... 79
Chapter 5: Conclusion ............................................................................................................... 81
References ............................................................................................................................. 82

vii

List of tables
Table 2-1. Description of variables ........................................................................................... 17
Table 2-2. Descriptive statistics ................................................................................................ 18
Table 2-3. Logistic regression ................................................................................................... 19
Table 2-4. Balancing property................................................................................................... 20
Table 2-5. ATTs of financial inclusion ..................................................................................... 20
Table 2-6. Descriptive statistics ................................................................................................ 23
Table 2-7. Logistic regression ................................................................................................... 26
Table 2-8. ATTs of financial inclusion and mobile money ...................................................... 27
Table 2-9. ATTs of multivalued treatments .............................................................................. 28
Table 2-A1. OLS results ........................................................................................................... 31
Table 2-A2. ATTs using Kernel matching and 2-nearest neighbor matching estimations…...32
Table 2-A3. OLS results ........................................................................................................... 32
Table 2-A4. ATTs using Kernel matching and 2-nearest neighbor matching estimations ....... 33
Table 2-A5. Balancing property: Subsample 1: Financial inclusion without mobile money vsfinancial exclusion .................................................................................................................... 33
Table 2-A6. Balancing property: Subsample 2: Financial inclusion with mobile money vsfinancial exclusion .................................................................................................................... 34
Table 2-A7. Balancing property: Subsample 3: Financial inclusion with mobile money vsfinancial inclusion without mobile money ................................................................................ 34
Table 3-1. Summary of literature review .................................................................................. 37
Table 3-2. List of sample countries ........................................................................................... 43
Table 3-3. Variable definitions ................................................................................................. 44
Table 3-4. Descriptive statistics ................................................................................................ 44
Table 3-5. Correlation matrix .................................................................................................... 45
Table 3-6. Panel unit root tests (1990 - 2014) ............................................................................... 47
Table 3-7. Pedroni panel cointegration tests (1990-2014) ........................................................ 49
Table 3-8. Kao panel cointegration tests (1990-2014) .............................................................. 50
Table 3-9. Pooled mean group with dynamic autoregressive distributed lag: PMG-ARDL(1,1,1,1,1) .................................................................................................................................. 51
Table 3-10. Dumitrescu and Hurlin panel causality test ........................................................... 56
Table 3-A1. The definitions of additional variables ................................................................. 60
Table 3-A2. Descriptive statistics of additional variables ........................................................ 60

viii

Table 3-A3. Panel unit root tests for additional variables (1990-2014) .................................... 61
Table 3-A4. Pedroni panel cointegration tests (1990-2014) ..................................................... 62
Table 3-A5. Kao panel cointegration tests (1990-2014) ........................................................... 63
Table 3-A6. Pooled mean group with dynamic autoregressive distributed lag: PMG-ARDL(1,1,1,1,1) .................................................................................................................................. 63
Table 3-A7. Dumitrescu and Hurlin panel causality test .......................................................... 64
Table 4-1. List of countries included in the analysis) ............................................................... 72
Table 4-2. Variable definitions ................................................................................................. 73
Table 4-3. Summary statistics of the variables included in the study................................... 73

Table 4-4. Correlation matrix .................................................................................................... 74
Table 4-5. Estimation results of the two-step system GMM with orthogonal deviation .......... 79

List of figures
Figure 2-1. Incorporating mobile money use ............................................................................ 25

1

Chapter 1: Introduction
The Sustainable Development Goals (SDGs), also known as the global goals, wereadopted by all United Nations member states in 2015 as a universal call to action to end
poverty, protect the planet and ensure that all people enjoy peace and prosperity by 2030.
These goals are 17 and integrated so that action in one area will affect outcomes in others,
and that development must balance social, economic and environmental sustainability.
Through the pledge to leave no one behind, countries have committed to fast-track progress
for those furthest behind first. That is why the SDGs are designed to bring the world toseveral life-changing ‘zeros’, including zero poverty, hunger, Acquired immunodeficiency
syndrome (AIDS) and discrimination against women and girls. Everyone is needed to reachthese ambitious targets. The creativity, knowhow, technology and financial resources from allof society is necessary to achieve the SDGs in every context, so that the need for partnerships
to reach these goals which is the goal number 17 has become the most important among
them. The SDG 17 is a call for all contributions at international, regional , national,
community, and individual level in term of cooperation ( leading coherent policy
development (Target 17.14)), assistance ( supporting capacity building in developing
countries (17.9)), or improving access to sustainable technologies and technology
development in emerging economies (17.7), etc. through academic research to make the first
16 goals realizable.
Given the importance of academic research align with the SDG 17, our individual
contribution through this doctoral dissertation is to analyze the contribution of Information
and Communication Technology (ICT) to reach the SDGs. To do so, we targeted threeimportant ones among them namely SDG 1 (no poverty), SDG 13 (climate action), and SDG
16 (peace, justice, and strong institutions). The important interrogations which will legitimate
our endeavor are threefold: (i) why is ICT important to reduce poverty? (ii) why is ICT
important to mitigate climate change? (iii) how can ICT contribute to strengthening
institutions?
Economic growth is achieved with technical progress which allows many scholars towork on the relationship between ICT and Poverty (Jack & Suri, 2014). Among various types
of ICT, mobile money has attractive for the promotion of financial inclusion which is now
recognized as a crucial factor for economic growth. Our first essay examines the effect offinancial inclusion and mobile money on poverty in Burkina Faso where mobile money has
not prevailed yet.

2
There are two opposite relationships between ICT and the environment namely theunfavorable effects: ICT is positively associated with CO2 due to the life cycle of ICT
production and the favorable effects: ICT is negatively associated with CO2 due to green
technology. Our second essay analyzes the overall relationship in developing countries.
Corruption is recognized to harm economic growth by weakening institutions. Somestudies argue that ICT use mitigates corruption by improving institutional efficiency andtransparency (Kanyam et al., 2017), so our third essay examines the relationship between ICT,
corruption, and military expenditures by using country level panel data in the Sub-SaharanAfrica region which is regarded as the most corrupt region in the world (Willet, 2009). Ourdissertation uses divers identification strategies with different indicators or measures of ICT toshow the empirical analysis between ICT and the selected three SDGs. The dissertation isorganized as follows.
Chapter 1: Introduction
Chapter 2: Financial inclusion, mobile money, and individual welfare: The case of Burkina
Faso.
Chapter 3: ICT and environmental sustainability: Any differences in developing countries?
Chapter 4: Corruption, ICT and military expenditure in Sub-Saharan Africa.
Chapter 5: Conclusion.

3

Chapter 2: Financial inclusion, mobile money, and individual welfare:
The case of Burkina Faso
2.1 Introduction
The promotion of financial inclusion has been emphasized, particularly for poor people.
Financial accessibility is crucial for economic growth. 1 Demirgüç-Kunt & Klapper (2012)suggest an integral role of financial inclusion in poverty reduction by facilitating saving andborrowing, which empowers poor individuals to help smooth consumption and insure
themselves against vulnerabilities in their lives. However, the unfulfilled demand for financing
is still substantial around the world, so that the lack of financial inclusion remains a far-reachingproblem even though microcredit institutions have prevailed in many developing countries(Chaia et al., 2009). Indeed, adults without access to the banking system amount to 1.7 billion
in 2017, representing almost 40 percent of adults worldwide (Grohmann et al., 2018).
Traditional financial institutions, such as banks and nonbank financial institutions, have failed
to provide enough financial services or financial access for low-income people who needfinancial credit in developing countries (Diniz et al., 2012).
The emergence of mobile money or mobile financial services is expected to resolve issues
related to the difficulty in financial access to traditional financial institutions and to promote
the financial inclusion of poor people in developing countries. Several studies, such as Jackand Suri (2014) and Munyegera and Matsumoto (2016), suggest that mobile money can be thebest tool for individual financial inclusion because it allows individuals, especially among thefinancially excluded rural communities in many developing countries, to transfer purchasing
power by simple Short Messaging Service (SMS) technology with the low cost of sending
money across vast distances. Recently, mobile money, or rather interoperability, which is theassociation of mobile money and external parties such as traditional financial services, hasprevailed due to rapid progress in telecommunication technology and the regulatory efforts offinancial regulators. However, the penetration rates of mobile money differ substantially amongdeveloping countries. In fact, some of the least-developed countries in Africa still have a low
penetration of mobile money due in part to deficiencies in the telecommunication infrastructure.
For example, only 16 percent of adults are registered with mobile money services in Burkina

See, e.g., King and Levine (1993), Rajan and Zingales (1998), Beck et al. (2000), Levine et al. (2000), Khan
(2001), Claessens (2006), Demirguc-Kunt et al. (2008), and Ghosh (2016).
1

4
Faso. Given these arguments, this study attempts to evaluate the roles of financial inclusion
and mobile money in improving individual welfare and contributing to poverty reduction in alandlocked sub-Saharan country, Burkina Faso, one of the poorest countries in the world.
Many studies have discussed the linkage of financial inclusion with welfare andpoverty. Demirguc-Kunt et al. (2017) show that financial inclusion alleviates poverty andinequality through investment in the future, consumption smoothing, and financial risks
management. Burgess and Pande (2005) show that a state-led rural branch expansion isassociated with poverty reduction in India. Bruhn and Love (2014) state that more access tofinancial services promotes income growth for low-income people by enabling informal
business holders to maintain their businesses functional and thus creates an overall increase inemployment. Moreover, Brune et al. (2016) assess that offering financial access to individual
savings accounts not only increases banking transactions but also improves measures ofhousehold welfares, such as investments in inputs and subsequent agricultural yields, profits,
and expenditures. Furthermore, Klapper et al. (2016) sum up the advantages of financial
inclusion by showing how it can help achieve Sustainable Development Goals (SDGs).
At the same time, there has been a growing literature on the effects of mobile
money on poverty, particularly in the context of poor countries. The report of the Global System
for Mobile communications Association (GSMA) (2017a) states that mobile money contributes
to 11 of the 17 United Nations SDGs, decreasing inequality by enabling households to lift
themselves out of poverty and empowering underserved segments of the population. Suri andJack (2016) find that access to the Kenyan mobile money system M-PESA2 increased per
capita consumption and lifted 194,000 households, or two percent of households, out of poverty.
In addition, Munyegera and Matsumoto (2016) reveal a positive effect of mobile money accesson household welfare in Uganda. Moreover, Riley (2018) finds that after a village-level rainfall
shock, users of mobile money could prevent a drop in their consumption in Tanzania.
Furthermore, Danquah and Iddrisu (2018) show that mobile money access leads to higher sales
revenues for nonfarm enterprises and households and improves the chances of not being inpoverty in Ghana.
This paper makes three main contributions to the existing literature. First, weestimate the effects of mobile money use on an individual’s poverty indicators of nutrition,
health, and education, which would reflect basic needs as proxies of welfare, rather thanmonetary poverty measures such as income and savings, which are known as one-dimensional

2

M-PESA: “M” is for mobile and “PESA” means money in Swahili (the local language of Kenya).

5poverty (Batana et al., 2013). Second, we analyze the role of the combination of financial
inclusion and mobile money in determining poverty reduction at the individual level, unlike
other studies that focus separately on financial inclusion and mobile money (Munyegera &Matsumoto, 2016; Riley, 2018; Danquah & Iddrisu, 2018). Third, our study fills the gap in
empirical studies on mobile money and poverty reduction in low-income countries, such as
members of the West African Economic and Monetary Union (WAEMU), 3 where mobile
money is at an early stage, and the penetration rate is low. Although some studies evaluate theeffects of mobile money in Africa (Munyegera & Matsumoto, 2016; Riley, 2018), most of theirtargeted countries are at a more matured stage of mobile money systems in the East African
Community (EAC), 4which differ from the financial conditions of WAEMU members in termsof mobile money penetration. One of the closest studies related to our work is Ky andRugemintwari (2015). However, their study on Burkina Faso contexts examines therelationship between mobile money and savings without taking poverty reduction into account.
Thus, examining the case of a low-income country with low mobile money penetration,
Burkina Faso, would enable us to discuss the role of financial inclusion and mobile money atthe early stage of mobile money systems.
This study evaluates the effects of financial inclusion and mobile money use on an individual’s
poverty status using data on 5,066 individuals in Burkina Faso taken from the Finscope
Consumer Survey (2016). The survey primarily aims to describe the levels of financial
inclusion, to describe the landscape of financial access, to identify the drivers of and barriers
to financial access, to stimulate evidence-based dialogue and to create a baseline for financial
inclusion in the country. The survey data include three measures of multidimensional poverty
status: (i) nutrition, (ii) healthcare, and (iii) education, which are among the main Millennium
Development Goals (MDGs) agreed upon by 189 heads of state in 2000; they are known today
as the SDGs goals and have been designated as basic needs by Beck et al. (2007). Moreover,
our three measures are widely used in comparative analysis in Sub-Saharan Africa (SSA) for
practical, theoretical, and methodological reasons (Batana, 2013).
One critical challenge is that our treatment variable representing individuals’ choice of financial
inclusion and mobile money use is not random and may have possible relationships with their

The WAEMU comprises Benin, Burkina Faso, Cote d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo.
They use FCFA as currency, which is pegged on the euro. 1€≈656 FCFA, and they are listed in the worst ranked
countries based on the United Nations Development Programme (UNDP)’s 2016 Human Development Index;
Burkina Faso ranks 185th out of 188 countries, and Niger ranks 187 th.
4
The EAC comprises six countries in the African Great Lakes region in eastern Africa: Burundi, Kenya,
Rwanda, South Sudan, Tanzania, and Uganda. In the EAC, over 40 percent of the adult population use mobile
money on an active basis (90-day) (GSMA, 2017a).
3

6characteristics, which would cause selection biases. To mitigate such an endogeneity problem,
this study estimates the treatment effects by applying two matching methodologies: propensity
score matching (PSM) and inverse-probability-weighted regression adjustment (IPWRA). The
main results reveal that financial inclusion reduces poverty at the individual level. More
importantly, once individuals access financial services through mobile money, such favorable
effects on poverty alleviation become more substantial, so that policy makers in low-income
countries, such as Burkina Faso, should emphasize the promotion of mobile money in theirfinancial inclusion programs to enhance individual welfare. Our results also support the‘interoperability’ of financial services and mobile money to encourage financial institutions toprovide swift financial transactions for people anywhere, which would engender a deep senseof financial inclusion (GSMA, 2017b; Peric et al., 2018). In addition, financial regulators
should create a sound environment for the prevalence of mobile money through financial
regulations with supply- and demand-side considerations, as suggested by the International
Monetary Fund (IMF) (2019).
The rest of the paper is organized as follows. In Section 2, we provide a selective review of theliterature on financial inclusion, mobile money, and poverty reduction. Section 3 explains theoverview of financial conditions and poverty in Burkina Faso. Section 4 deals with theempirical analysis of financial inclusion and poverty reduction, and Section 5 incorporates therole of mobile money in our empirical analysis. Section 6 concludes with some policy
discussions.

2.2 Literature review
2.2.1 Financial inclusion and poverty reduction
The primary objective of financial inclusion is the pursuit of making financial services
accessible at affordable costs to all individuals and businesses (Diniz et al., 2012), and it isexpected to be a key driving force in reducing poverty and boosting prosperity (Beck et al.,2007; Han & Melecky, 2013; Bruhn & Love, 2014). The importance of financial inclusion was
first recognized by the United Nations Capital Development Fund (UNCDF) in the late 1990s
through the support of microcredit institutions including poor people in financial systems. Thishas been widely acknowledged as a crucial agenda to be achieved in many developing countries.

7
Many studies have shown that financial inclusion by traditional financial institutions(banks and nonbanks) helps to decrease rural poverty and improve various economic andfinancial conditions, such as credit, employment, insurance, and savings (Sapienza, 2004;
Kaboski & Townsend, 2011, 2012; Bruhn & Love, 2014; Cai et al., 2015; Dupas & Robinson,2013). Sapienza (2004), using a panel data during the period 1991 to 1995, shows that stateowned banks help firms located in depressed areas, which mitigates rural poverty in Italy.
Kaboski and Townsend (2011, 2012) describe the favorable impacts of microcredit loans in thecontext of Thailand’s Million Baht Village Fund Program, where every village received 1
million Baht (about $24,000) to initiate a village bank that provided funds available to villagers.
Bruhn and Love (2014) find that reaching low-income individuals by financial access, withover 800 branches opening almost overnight in 2002, has a considerable positive effect on
economic activity through the labor market (employment) in the case of Banco Azteca in
Mexico. Cai et al. (2015) conduct field experiments in China and show that promoting the
adoption of insurance significantly increases farmers’ sow production, and this effect seems topersist in the long run. Dupas and Robinson (2013) show that having access to savings accounts
encourages women to economize and to increase the size of their business in Kenya.
On the other hand, some studies fail to show clear evidence supporting the favorable
effect of financial inclusion on poverty reduction. In summarized studies in Uganda, Malawi,
and Chile, Dupas et al. (2016) find no evidence that reaching basic bank accounts for the rural
poor results in improving welfare or reducing poverty. Their study comments that accounts notgeared to particular needs and expensive costs due to the use of accounts are the main
hindrances to overall increases in savings or to improvements in developmental outcomes suchas consumption, schooling, and health. Banerjee et al. (2015a) summarize results across studies
and mention unclear evidence of financial inclusion by revealing a merely positive, but nottransformative, effect of microcredit on poverty reduction under different specifications in
Europe with the case of Bosnia and Herzegovina (Augsburg et al., 2015), Africa with the case8of Ethiopia (Tarozzi et al., 2015), and Morocco (Crepon et al., 2015), Asia with the case ofIndia (Banerjee et al., 2015b) and Mongolia (Attanasio et al., 2015), and America with the caseof Mexico (Angelucci et al., 2015). They generally suggest that though businesses can obtainprofit from loans, it is less clear that this profit is translated into developmental impacts, suchas increased incomes and broader welfare benefits for individuals. Such unfavorable evidence
is consistent with the argument of Demirguc-Kunt et al. (2015), who claim that over 40 percent
of adults worldwide remain financially excluded due to various barriers, such as physicalaccess, affordability, and eligibility. Owing to the limited traditional financial access,particularly in rural areas, associated with low infrastructure levels in developing countries, it
has widely been acknowledged that financial institutions should make use of advanced
telecommunication technology, such as mobile phones, to enhance financial accessibility,
especially for poor people (Claessens, 2006; Mas & Kumar, 2008; He et al., 2017).

2.2.2 Mobile money and poverty reduction
Mobile money is a financial innovation through Short Message Service (SMS) technology witha commission system to remunerate the providers of the different services (Jack & Suri, 2014).
Through a mobile phone, mobile money enables people to use various financial services,
including (1) person-to-person transfer of funds, including domestic and international
remittances, (2) person-to-business payments for sales and purchases of goods and services,and (3) mobile banking, through which customers can access their bank accounts to pay bills
or deposit and withdraw funds (Dolan, 2009; Riley, 2018). On the one hand, several studies
have examined the effects of mobile money on welfare in the context of poor countries. Amongthem, GSMA (2017a) states that mobile money contributes to eleven (including the threeproxies in this study) of the seventeen UN SDGs by enabling households to lift poor people
out of poverty and by empowering underserved segments of the population. Suri and Jack

9(2016) show that mobile money enables two percent of households to escape from extreme
poverty and its impacts are more pronounced for female-headed households in Kenya.
Munyegera and Matsumoto (2016) also find a positive effect of mobile money access onhousehold welfare, measured by real per capital consumption, in Uganda using a combinationof household fixed effects, instrumental variables, and matching methods. Riley (2018) reveals
that after a village-level rainfall shock in Tanzania, only users of mobile money can prevent a
drop in their consumption by applying difference-in-difference specification. Danquah andIddrisu (2018) find that mobile money improves the chances of not being in poverty by leading
to higher sales and revenues for nonfarm enterprises and households in Ghana.
On the other hand, a few studies have cast doubt on the positive effects of mobile moneyon poverty reduction by revealing some risks stemming from the use of mobile money, which
may diminish the positive effects and renew the debate around this relationship for researchersat the Consultative Group to Assist the Poor (CGAP) (2017). There is a growing interest in
possible win-to-win collaboration or interoperability between mobile money and external
parties, such as bank and nonbank services. Indeed, mobile money covers two distinct
industries (telecommunication and banking) with separate business models, so that thedevelopment of the necessary cross-sectoral partnership must include bridging cultures andregulations (World Bank, 2012). Such complex social and business forms could be difficult,and often risky, to manage for both providers and users. In addition, profitability in this industry
needs a large scale, and business operators are faced with the trade-off between higher costs torecoup their investments and lower costs to build a mass market with a large scale (Mas &Radcliffe, 2010). Moreover, the Consultative Group to Assist the Poor (CGAP) (2017) reveals
that mobile financial services could become a conduit for fraud and other criminal activities,
as explained in more detail in a document related to financial crimes on mobile money (Chatain
et al., 2011). Furthermore, Raphael (2016) reveals the risks and barriers for mobile money users
in mobile money transactions (MMT) and the frequencies of their incidences using primary

10
data collected in a survey conducted in Ilala district, Tanzania.

2.3 Financial inclusion and mobile money in Burkina Faso
Financial inclusion is expected to play an integral role in reducing poverty and insuring poor
people against several vulnerabilities in their lives (Demirgüç-Kunt & Klapper, 2012). Burkina
Faso is a landlocked Sahel country between the Sahara desert and the Gulf of Guinea andshares borders with six nations (Mali, Niger, Benin, Togo, Ghana, and Côte d’Ivoire). In the
country, a large portion of people remains financially excluded, despite ongoing efforts by the
new government. Indeed, the country’s report no. 19/16 of the International Monetary Fund in
2019 on Burkina Faso states that less than 25 percent of the population owns an account at
financial institutions, and among them, less than 10 percent have accessed loans from financial
institutions. In addition, the 2019 World Bank’s Doing Business Report argues that access tocredit in Burkina Faso is difficult and broadly comparable to its WAEMU peers. Financial
accessibility is generally constrained in rural areas, particularly for lower-income women.
There are also substantial informational and collateral barriers to affordable credit for small
and medium sized enterprises (SMEs), which also hampers private sector-led development indealing with poverty reduction. Although the government prioritizes the development of
microfinance institutions, microfinance remains relatively underdeveloped due to a lack of
information technology infrastructure, a shortage of scale economies, and fragility in business
operations. Thus, the prevalence of mobile money is more imminently needed in Burkina Faso
to alleviate poverty.
Mobile money use started increasing in Burkina Faso at the same time as its
WAEMU peers, ever since instruction N0 01/2006/SP DU 31 JUIL. 2006 (Banque Centrale desEtats de l’Afrique de l’Ouest (BCEAO), 2006), which enables a nonfinancial entity (mostly
telecommunication companies) to issue mobile money services in the WAEMU under theagreement of BCEAO. The two pioneers of mobile money in Burkina Faso are Airtel and11Telmob. Both are telecommunication companies, and they launched mobile money services in2012 and 2013, respectively. Airtel money was launched further to a partnership between Airtel
Burkina and Ecobank Bank in 2012 and later became Orange money. In 2013, a partnership
between Telmob and BICIA-Burkina (a subsidiary of BNP Paribas) gave rise to mobicash,
which is the second mobile payment service (money transfers and bill payments) in Burkina
Faso. Currently, the evolution of the mobile payments market is impressive, such that they
compete with traditional banks at some locations. In addition, BCEAO (2016) revealed that theflow of mobile money transactions in 2016 in Burkina Faso reached 2,415 billion FCFA($4,488,847,584 USD) but still recognized that it still has a huge potential of growth, which
can be seen in the economic literature. Indeed, according to Mothobi and Grzybowski (2017),
mobile phone users who live in areas with poor infrastructure tend to rely on mobile phones tomake financial transactions than individuals living in areas with better infrastructure. Ky andRugemintwari (2015) find that using mobile money services increases the ability of individuals
to save for health emergencies in the case of Burkina Faso, but they did not rely directly on
poverty reduction in their study.

2.4 Financial inclusion and poverty reduction
This study first assesses the effects of financial inclusion on poverty status at the individuallevel and then evaluates the role of mobile money in accelerating financial inclusion in a later
section. We took the data at the individual level in Burkina Faso from the Finscope Consumer
Survey (2016). The survey primarily aims to describe the degrees of financial inclusion, to
describe the landscape of financial access, to identify the drivers of and barriers to financial
access, to stimulate evidence-based dialogue, and to create a baseline for financial inclusion in
the country. The survey targeted adults who are 15 years old or older in a comprehensive listingof 675 enumeration areas within the 13 regions of Burkina Faso with 85,513 eligible

12households, and face-to-face interviews were conducted by an international study group during
the period from May 2016 to September 2016. The sample was randomly drawn by the Institut
National de la Statistique et de la Démographie (INSD). The sampling method broadly
resembled a stratified multistage random sampling with confirmation of quality control anddata validation.5 After removing observations with missing variables, our sample comprises
5,066 individuals, which includes demographic, socioeconomic, and geographic characteristics.
Monetary-based poverty measures (such as income) have widely been used to examine
the poverty status. However, several studies have emphasized the argument that in addition tothese money-metric poverty measures, other dimensions of poverty, such as education andhealth conditions, should be incorporated to examine the poverty status. In fact, obtaining the
precise information of income and expenditures are often difficult in developing countries,
especially in Sub-Saharan Africa which is generally regarded as having the highest levels ofpoverty and extreme poverty (Batana, 2013). Moreover, monetary-based poverty measures do
not capture all forms of deprivation. For example, although South Asian countries have reduced
monetary-based poverty at an impressive pace over the past decade, the region lags in the nonmonetary dimensions of the poverty status (World Bank, 2018). Accounting for themultidimensional concept of poverty (Sen, 1976), this study focuses on three outcome variables
related to poverty status: (i) lack of nutrition (skipped a meal because of lack of food, LON),(ii) lack of healthcare (stayed without medical treatment or medicine because of lack of money,
LOH), and (iii) lack of education (not been able to send children to school because of lack ofmoney, LOE), using a four-point Likert-type scale (1 = never; 2 = rarely; 3 = sometimes; 4 =often). Financial inclusion and exclusion at the individual level are captured by the dummy
variable (FI), which equals one if an individual uses financial access provided by financial
institutions (banks or nonbank financial institutions) and zero otherwise. Our specification

5

See the Finscope Consumer Survey (2016) for the details.

13
implies that individuals with FI = 1 are financially included, while those with FI = 0 are
financially excluded.

2.4.1 Methodology
This study first measures the effects of financial inclusion on individual poverty status to verify
the conventional wisdom that financial inclusion improves welfare in the case of Burkina Faso.
The reasons for using financial services could reflect individuals’ characteristics related to theirlevel of poverty status, so that linear regression models may be biased due to potential
endogeneity problems.6 Past literature suggests various methods to address such a selection
bias issue. Among them, many studies employ instrumental variables (IVs). However, finding
valid instruments is a challenge for many empirical studies. To mitigate potential endogeneity,
our study uses matching methods. Our analysis is based on the idea that the use of financial
services or financial inclusion represents a treatment, where individuals using financial services
comprise the treatment group, while nonusers represent the counterfactual group (control
group). Our measure of interest is the average treatment effect on the treated (ATT).7

6

The standard ordinary least squares (OLS) estimation of the model with poverty status as the outcome and thedecision to use financial inclusion as the independent variable creates the issue of self-selection bias, because thedecision could be affected by unobservable characteristics, such as an individual’s knowledge, that are alreadypart of the error term. The literature applies several empirical procedures to fix the selection bias. Among them,
many studies employ the instrumental variables (IV) method.
7

Following Imbens and Wooldridge (2009), the ATT is described as ATT = E[Y1 |D = 1] − E[Y0 |D = 1], where

D is the financial inclusion dummy; Y1 and Y0 are potential outcomes of the users and nonusers (twocounterfactual situations), respectively; Y0 |D = 1 is the value of the outcome of our interest that would havebeen observed if the individual had not chosen to use financial services (counterfactual outcome); and Y1 |D = 1
is the value of the outcome that is actually observed for the same individual. A fundamental problem is thedifficulty in estimating the ATT because the counterfactual outcome is the unobservable value of E[Y0 |D = 1].
When an individual’s choice of financial inclusion is random, the average outcome of individuals not exposed totreatment, E[Y0 |D = 1] is an adequate substitute, meaning that the ATT can be obtained from the differences inthe sample means of the outcome variable between the treatment and the control groups. However, the choice offinancial inclusion can be endogenous.

14
In nonexperimental analysis, the treatment is nonrandom (De Janvry et al., 2010;
Heckman & Vytlacil, 2007). In the case of nonrandomized assignments, observed andunobserved backgrounds of individuals may influence treatments as well as dependent
variables so that selection bias can be persistent. The concept of matching methods is to imitate
randomization regarding the assignment of the treatment. The unobserved counterfactual
dependent variable is imputed by matching the treated individuals with untreated individuals
that are as close as possible regarding all pretreatment characteristics. The estimate of the ATT
is described as follows:
ATT(x) = E[Y1 |D = 1, X = x] − E[Y0 |D = 0, X = x],where x is a set of relevant pretreatment characteristics, E[Y1|D = 1, X = x] is the expected
outcome for the units that received treatment, and E[Y0 |D = 0, X = x] is the expected
outcome for the treated units’ best matches. This study first estimates the ATT by applying thepropensity score matching (PSM) method, which could mitigate selection bias issues. Once thetreated units are matched, the PSM assumes no systematic differences in unobservablecharacteristics between treated and untreated units. Given the estimated propensity scores P(x)under the main assumptions, i.e., conditional independence, the independent and identically
distributed observations, and the common support assumptions, the ATT can be computed asfollows:8
ATT = E[Y1|D = 1, P(x)] − E[Y0 |D = 0, P(x)].
In the matching process, a sufficient overlap is assumed to exist between the control andAs underlined in several studies, such as Rosenbaum and Rubin (1985) and Heinrich et al. (2010), PSM
approaches work under the following assumptions. The first assumption is the conditional independence
assumption (CIA) or confoundedness. This assumption states that no unobservable variable affects both thelikelihood of treatment and the outcome of interest after conditioning on covariates. CIA is the strong
assumption and does not consider any unobservable differences. The second assumption is the independent andidentically distributed observations assumption, which requests the potential outcomes and treatment status ofeach unit to be independent of the potential outcomes and treatment status of all other units in the sample. The
third assumption is the common support or overlap requirement, which suggests that every observation comeswith a positive probability of being both treated and controlled. In addition, the PSM should satisfy thebalancing property, i.e., the mean value of covariates between treatment and control groups should be similar
after matching.
8

15
treatment groups (i.e., common support). Furthermore, even when the overlap assumption issatisfied, there may be a large gap between the propensity scores of the two closest individuals
available for match, leading to poor matches. To avoid this situation, this study uses the caliper
restriction (Caliendo & Kopeinig, 2008), which imposes a threshold for the maximum distance
between matched units. If the distance is above this threshold, the treated observation is
dropped to avoid obtaining biased estimates. In this study, common practice is applied with acaliper of 0.05.
However, the ATT estimated from PSM can still suffer from biased results in thepresence of misspecification in the propensity score model (Robins et al., 2007; Wooldridge,
2007, 2010).
One potential remedy for such a problem is to apply inverse-probability-weighted regression
adjustment (IPWRA) estimation methods (Imbens & Wooldridge, 2009). IPWRA estimators
use weighted regression coefficients to calculate averages of treatment-level predicted
outcomes, where the weights are the estimated inverse probabilities of treatment.9 Unlike PSM,
IPWRA has a double robust property that ensures consistent results, as it allows the outcome
and the treatment models to account for misspecification. PSM will provide inconsistentestimates if the treatment model is mis-specified. Moreover, with IPWRA, if the treatment
model is misspecified, the estimates of the treatment effect can still be consistent if the outcome
model is not misspecified. In addition, if the treatment model is not misspecified, IPWRA canalso provide consistent estimates even when the outcome model is misspecified. That is why
IPWRA estimates are consistent in the presence of misspecification in the treatment or outcome
model, but not both (Imbens & Wooldridge, 2009; Wooldridge, 2010).10 To estimate treatment

The use of IPWRA also requires several assumptions, such as the conditional independence, the independentand identically distributed observations, and the overlap assumptions.
10
In addition to the misspecification issue, IPWRA improves on PSM in two ways. The first one is theinclusion of controls for the observation’s baseline characteristics in the outcome model. Both IPWRA and PSM
must satisfy the conditional independence assumption, which states that no unobservable variable affects boththe likelihood of treatment and the outcome of interest after conditioning on covariates. Since IPWRA includes
more covariates in the outcome model than PSM, which includes only the covariates in the treatment model, this916
effects using IPWRA, we start by estimating the parameters of the treatment model and derive
inverse-probability weights. By using the estimated inverse-probability weights, we fit
weighted regression outcome models for each treatment level and obtain the treatment-specificpredicted outcomes for each subject. At the end, we compute the means of the treatmentspecific predicted outcomes so that the contrasts of these averages provide the estimates of theaverage treatment effects.
This study evaluates the effects of financial inclusion on three measures of anindividual’s poverty status related to lack of nutrition, healthcare, and education (LON, LOH,
and LOE). To construct a control group of untreated units that is as similar as possible to thetreatment group, we need to select appropriate covariates representing the pretreatment an
individual’s characteristics. Following the studies by Ogutu et al. (2014) and Barnett et al.
(2019), we select relevant pretreatment individual characteristics, including the age of the
respondent in years (AGE), a gender dummy (FEMALE) that is equal to one if the respondentis female and zero otherwise, the family size or the number of household members (HSIZE), a
distance dummy (TTM) that indicates the time to reach the market by using a six-point Likerttype scale (1=less than 10 minutes; 2=11-20 minutes; 3=21-30 minutes; 4=31-60 minutes; 5=61
minutes-2 hours; 6= more than 5 hours), and a mobile phone dummy (MP) that equals one if
the respondent owns a mobile phone and zero otherwise. We also control for the primaryeducation attendance of each respondent by including a dummy (PEDUC) which equals one
when the respondent attended primary education and zero otherwise, the land (hectares) owned
by each respondent ( LSIZE ) and an area dummy ( RURAL ) which equals one when theassumption is more likely to hold with IPWRA than with PSM. The second improvement is that, unlike PSM,
which compares each treatment observation to control observations that have a similar likelihood of being
treated in a restrictive way, IPWRA implicitly compares every unit to every other unit while placing higher
weights on observations that have a similar likelihood of being treated and lower weights on observations thatare dissimilar.

17
respondent lives in a rural area and zero otherwise.11

Table 2.1 displays the descriptions ofvariables used in this study.
Table 2.1. Description of variables.
variables

Description

Dependent
variables
LON

Lack of nutrition (skipped a meal because you did not have food (1= Never/ 2 = Rarely/ 3 = Sometimes/ 4 =Often))

LOH

Lack of healthcare (stayed without medical treatment or medicine because you did not have money (1= Never/
2 = Rarely/ 3 = Sometimes/ 4 = Often))

LOE

Lack of education (not been able to send children to school because of lack of money for transport or uniform
or other school costs (1= Never/ 2 = Rarely/ 3 = Sometimes/ 4 = Often))

TreatmentvariablesFI

Use of financial services (1 for formal financial access and 0 for otherwise)MM

Use of mobile money (1 for mobile money use and 0 for otherwise

Covariates
AGE

Age of respondent (years)

FEMALE

Dummy for gender of respondent (1=female/ 0=male)

HSIZEHousehold size

TTM

Dummy for Time to Market (1= "Less than 10 minutes"/ 2= "11 -20 minutes"/ 3= "21 - 30 minutes"/ 4= "31 60 minutes"/ 5= "61 minutes - 2 hours"/ 6= "More than 5 hours")

MP

Dummy for Mobile Phone (1= own a Mobile Phone; 0= Otherwise)

PEDUC

Dummy for Primary Education (primary school (1= No formal schooling or Preschool or Primary/ 0 =Otherwise))

LSIZE

Land size (hectares)

RURAL

Dummy for area (1 = Rural; 0 = Otherwise)

2.4.2 Results
Table 2.2 shows the summary statistics of the variables. The sampled individuals are divided
into the treatment (financial inclusion) group with 1,148 individuals (29.8 percent) and thecontrol (financial exclusion) group with 2,703 individuals (70.2 percent). There are significant

In Burkina Faso, most of the total adult population (76%) live in rural areas. The definition of the rural area isdecided by the Institut National de la Statistique et de la Démographie (INSD. The rural area is an area where
there is less than 5,000 people living further than 2 km from a rood in good or fair condition (World Bank,2019).
1118
differences in means for poverty status measures and pretreatment variables between
financially included and excluded groups. Financially excluded individuals tend to be poorer
than financially included individuals in terms of nutrition, healthcare, and education. The
process in this study begins with the estimation of propensity scores for the treatment variable
by applying a logistic regression model, where the probability of financial inclusion isregressed on our individual characteristics.

Table 2.2. Descriptive statistics
Whole sample

Financial inclusion

Financial exclusion

Mean diff

(1)

(2)

(3)

(3)-(2)

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev

Mean

LON

1.8886

1.0692

1.72471.01301.9582

1.08490.2334***LOH

1.84891.0482

1.6881

0.9732

1.9171

1.07140.2290***LOE

1.6964

1.05661.51740.91711.77251.10190.2550***FI

0.29810.4575

-

-

-

-

-

MM

0.18980.3922

-

-

-

-

-

AGE

34.8169

14.840737.3850

14.5673

33.7262

14.8240-3.6588***

FEMALE

0.48270.49980.36060.48040.5346

0.49890.1740***HSIZE

8.84374.75469.43475.01148.59274.6193-0.8420***TTM

2.40251.09592.35451.09082.42291.09770.0683*MP

0.82500.38000.88410.32020.79980.4002-0.0843***

PEDUC

0.88730.31630.8632

0.34370.89750.30330.0343***LSIZE

5.03214.68236.02336.70154.6111

3.4030-1.4121***RURAL

0.91820.27410.86150.3456

0.94230.23320.0808***

No of obs.

3,851

1,148

2,703

Note: ***, **, and * denote the significance at the 1%, 5%, and 10% levels, respectively.

Table 2.3 displays the estimated results of the logistic regression. The results confirm thatfemale, young, and less educated people are more likely to be financially excluded. In addition,
people in rural regions tend to be financially excluded. Moreover, people without assets, suchas a mobile phone and land, are more likely to be financially excluded. In the matching process,sufficient overlap exists between the control and treatment groups, i.e., common support(Caliendo & Kopeinig, 2008). In addition, large gaps may exist between the propensity scoresof the closest individuals available for match, which leads to poor matches. To mitigate this

19
issue, we implement a restriction of a 0.05 caliper. Moreover, the density distribution of thepropensity scores in the treatment and control groups confirms that differences in the density
distributions prior to the matching have been removed.

Table 2.3. Logistic regression
AGE
FEMALE
HSIZE
TTM
MP
PEDUC
LSIZE
RURAL
Constant
No. of obs.
LR chi2(8)Prob>chi2
Pseudo R-squared
log likelihood

Coefficient
0.0182***(0.0026)-0.6640***(0.0762)
0.0110(0.0082)-0.0162(0.0342)0.6361***(0.1090)-0.3081**(0.1218)0.0701***(0.0096)-1.0787***(0.1256)-0.9168***(0.2100)
3,851
338.670.00000.0722-2176.8834

Notes: (1) Standard errors are in parentheses. (2) ***, **, and * denote the significance at the 1%, 5%, and 10% levels,
respectively

The balancing property in Table 2.4 shows that the p-values related to the differences in meansof covariates between the two groups after matching are insignificant, indicating that ourmatching achieves appropriate balancing properties.

20
Table 2.4. Balancing property
Mean

Bias

P-value

TreatedControlreduction

AGE

37.38533.7260.000

FEMALE

0.36060.5346

0.000

HSIZE

9.43478.59270.000

TTM

2.35452.42290.077

MP0.88410.7998

0.000

PEDUC

0.86320.8975

0.002

LSIZE

6.02334.6112

0.000

RURAL

0.86150.94230.000

AGE

36.96836.647

91.2

0.632

FEMALE

0.38020.3840

97.8

0.858

HSIZE

9.1906

9.1943

99.6

0.986

TTM

2.37922.3943

77.9

0.751

MP

0.87550.8792

95.5

0.791

PEDUC

0.88020.8651

56.0

0.297

LSIZE

5.38075.3605

98.6

0.918

RURAL

0.89810.8840

82.5

0.296

Before matching

After matching

Table 2.5 displays the estimated ATTs of financial inclusion on our three proxies ofindividual welfare related to lack of nutrition, healthcare, and education (LON, LOH, and LOE)
under the PSM and IPWRA frameworks. The results present clear evidence supporting that
financial inclusion guided by financial services induces poverty reduction at the individuallevel. Once an individual is financially included, the three measures of poverty status (LON,
LOH, and LOE) are reduced by 0.16-0.20, 0.17-0.24, and 0.24-0.30 points, respectively.

Table 2.5. ATTs of financial inclusion

LON
LOH
LOE

PSM

IPWRA

-0.1635***-0.2032***(0.0449)(0.0394)-0.1673***-0.2354***(0.0435)(0.0395)-0.2401***-0.3018***(0.0433)(0.0391)

Notes: (1) Standard errors are in parentheses. (2) ***, **, and * denote the significance at the 1%, 5%, and 10% levels, respectively.

21

For robustness checks, we also conduct ordinary least squares (OLS) regressions (Table 2.A1
in the appendix). The coefficients of LON, LOH, and LOE are significantly negative, which
support the PSM and IPWRA results.12 Our results in the case of Burkina Faso are consistent
with the conventional relationship between financial inclusion and poverty reduction (Burgess& Pande, 2005; Demirguc-Kunt et al., 2017). Enabling people to access financial services
would help achieve poverty reduction, which is emphasized in the Sustainable Development
Goals (SDGs). Since limited capital or credit is the main hindrance for entrepreneurs to accessinputs and high-end markets for their output (Okello, 2010) and for households to smoothconsumption (Demirgüç-Kunt & Klapper, 2012), financial services could help eliminate
extreme poverty. Deepening financial inclusion can induce favorable welfare effects that
extend beyond benefits in the financial realm to the real economy (Grohmann et al., 2018).

2.5 Incorporating the role of mobile money
This section extends the analysis to evaluate how an individual’s welfare is affected by the
introduction of mobile money. In the previous section, we consider two categories based onwhether or not an individual has financial accounts in financial institutions (FI): financially
included individuals (treatment group) (FI = 1) and financially excluded individuals (control
group) (FI = 0).

A mobile money user is captured by the dummy variable (MM) which equals

one if an individual uses mobile money and zero otherwise. To evaluate the role of mobile
money, we further divide financially included individuals (financial inclusion) into two groups
based on the usage of mobile money, and our whole sample now comprises three groups: (i)Recently, matching techniques have moved away from the PSM towards other matching techniques. Kingand Nielsen (2019) emphasize the weakness of the PSM, which often suffers from increasing imbalance,
inefficiency, model dependence, and bias, and they suggest other matching methods, such as Mahalanobis
Distance Matching (MDM) and Coarsened Exact Matching (CEM), which approximate a fully blocked
experimental design. Thus, in addition to the PSM, we also conduct the robustness checks by applying twoalternative matching methods, (i) kernel matching and (ii) 2-nearest neighbor matching. Table 2.A2 in the
appendix shows the estimated ATTs of these two methods, which confirm the results of the PSM and IPWRA.
12

22
financially included individuals without mobile money (financial inclusion without mobile
money) ( FI = 1 and MM = 0 ), (ii) financially included individuals with mobile money(financial inclusion with mobile money) (FI = 1 and MM = 1), and (iii) financially excluded
individuals (financial exclusion) (FI = 0 and MM = 0). In the data set, there exist a small
number of individuals who do not have financial accounts in financial institutions but who use
mobile money (27 out of 3,878). This case is extremely rare because people need to open
financial accounts in financial institutions to use mobile money. Thus, we exclude such samples
from our data set. Table 6 shows the descriptive statistics for our whole sample and each of thethree groups. Among 1,148 financially included individuals, there are 731 who used mobile
money (63.68 percent) and 417 who did not use mobile money (36.32 percent). We observethat the differences in means of the three poverty statuses appear to be substantial among thethree groups.

23

Table 2.6. Descriptive statistics
Whole sample(1)

Financial inclusion

Financial inclusion

Without mobile money

With mobile money

(2)

(3)

Financial exclusion

Mean diff(4)-(2)(4)-(3)(4)

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev

Mean

LON

1.8886

1.06921.8201

1.05331.67030.98591.9582

1.08490.1380**LOH

1.84891.0482

1.7746

0.99371.63880.95851.9171

1.07140.1425***0.2783***LOE

1.6964

1.05661.64991.01771.44190.84581.77251.10190.1226**0.3306***FI

0.29810.4575

-

-

-

-

-

-

-

-

AGE

34.8169

14.840737.6834

15.188737.2148

14.2084

33.7262

14.8240-3.9572***-3.4885***

FEMALE

0.48270.49980.42450.49480.3242

0.46840.5346

0.49890.1101***0.2104***HSIZE

8.84374.75469.02884.56629.6662

5.23748.59274.6193-0.4361*-1.0735***TTM

2.40251.09592.49881.15222.2722

1.04612.42291.09770.07590.1506***MP

0.82500.38000.82970.37630.91520.27880.79980.4002-0.0299

0.1153***PEDUC

0.88730.38000.91610.27760.83310.37310.89750.3033-0.01850.0644***LSIZE

5.03214.68236.04606.68396.01036.71604.6111

3.4030-1.4348***-1.3992***

RURAL

0.91820.27410.91130.28470.83310.3731

0.94230.23320.0310**0.1092***

No of obs.

3,851

417

731

2,703

Note: ***, **, and * denote the significance at the 1%, 5%, and 10% levels, respectively.

To evaluate the effects of mobile money, this study applies two matching methods (PSM
and IPWRA) after the satisfaction of the balancing properties of our covariates over each of
the following three subsamples separately.13 The first subsample (subsample 1) consists of (i)financially included individuals without mobile money (financial inclusion without mobile
money) and (iii) financially excluded individuals (financial exclusion), where the treatment andcontrol groups comprise financial inclusion without mobile money and financial exclusion,respectively. Subsample 1 allows us to examine how financially excluded individuals improvetheir poverty status by accessing financial services without mobile money. The second
subsample (subsample 2) consists of (ii) financially included individuals with mobile money(financial inclusion with mobile money) and (iii) financially excluded individuals (financial
exclusion), where the treatment and control groups comprise financial inclusion with mobile

The results of the balancing properties regarding each binary treatment variable are displayed in Tables 2.A5,
2.A6, and 2.A7 in the appendix and show how best ours matching methods reduce controls variables bias after
matching.
130.2879***24
money and financial exclusion, respectively. Subsample 2 enables us to evaluate how
financially excluded individuals enhance their poverty status by accessing financial services
with mobile money. The third subsample (subsample 3) consists of (ii) financially included
individuals with mobile money (financial inclusion with mobile money) and (i) financially
included individuals without mobile money (financial inclusion without mobile money), where
the treatment and control groups comprise financial inclusion with and without mobile money,
respectively. Subsample 3 allows us to evaluate how financially included, but without mobile
money, individuals enhance their poverty status by using mobile money, i.e., the value addedof mobile money for individuals who have already accessed financial services. Figure 2.1
presents the three groups (financially excluded individuals, financial inclusion without mobile
money, and financially included individuals with mobile money) and three subsamples(subsamples 1, 2, and 3). For each of the three subsample analyses, we use the same
pretreatment variables or covariates as those in the previous section.

25
Figure 2.1. Incorporating mobile money use(iii)
Financially excluded individuals(FI=0 and MM=0)(i)
Financially included individuals
without mobile money(FI=1 and MM=0)

Treatment group
Subsample 1(ii)
Financially included individuals
with mobile money(FI=1 and MM=1)

Control group(i) Financially included individuals (iii) Financially excluded individuals
without mobile money

Subsample 2(ii) Financially included individuals (iii) Financially excluded individuals
with mobile money

Subsample 3(ii) Financially included individuals (i) Financially included individuals
with mobile money

without mobile money

Table 2.7 shows the estimated results of logistic regressions, which enable us to obtainpropensity scores of the PSM method for each subsample analysis. The estimated results
generally coincide with the findings in the previous case of the full sample, where the treatment
and control groups comprise financially included individuals and financially excluded
individuals, respectively.

26
Table 2.7. Logistic regression
Subsample 1
Financial

Subsample 2
inclusion

Financial

Subsample 3
inclusion

Financialinclusion

without mobile moneywith mobile money vswith mobile money vsvs financial exclusion

financial exclusion

financialinclusion

without mobile money
AGE

0.0161***0.0195***0.0008(0.0036)(0.0031)(0.0046)-0.4208***-0.8086***-0.4655***(0.1098)(0.0927)(0.1337)-0.0106

0.0245***0.0410***(0.0124)(0.0094)(0.0149)

0.0812*-0.0791*-0.1781***(0.0484)(0.0419)(0.0581)0.2383*0.9905***0.7492***(0.1435)(0.1476)(0.1924)

0.1231-0.5041***-0.6590***(0.2050)(0.1376)(0.2169)

0.0747***0.0629***-0.0110(0.0121)(0.0110)(0.0104)-0.6206***-1.2530***-0.6265***(0.1964)(0.1397)(0.2097)-2.4633***-1.3122***1.3443***(0.3246)(0.2503)(0.3626)

No. of obs.

3,120

3,434

1,148

LR chi2(8)90.51352.23

71.70

Prob>chi2

0.00000.00000.0000Pseudo R-squared

0.03690.09910.0477log likelihood-1181.7611-1601.7893-716.3887

FEMALE
HSIZE
TTM
MP
PEDUC
LSIZE
RURAL
Constant

Notes: (1) Standard errors are in parentheses. (2) ***, **, and * denote the significance at the 1%, 5%, and 10% levels, respectively

Table 2.8 presents the estimated results of the ATTs based on the PSM and IPWRA
methods for each of the three subsamples. We also conduct OLS regressions (Table 2.A3 in the
appendix), which support the PSM and IPWRA results.14 The first two columns correspond to
the first subsample, where the treatment and control groups comprise financial inclusion
without mobile money and financial exclusion, respectively; the next two columns correspondto the second subsample, where the treatment and control groups comprise financial inclusion
with mobile money and financial exclusion, respectively; and the last two columns correspond

Similar to the previous discussions about the weakness of the PSM in footnote 12, we also conduct therobustness checks by applying two alternative matching methods, (i) kernel matching and (ii) 2-nearest neighbormatching, for each of the three subsamples. Table A4 in the appendix confirms that the estimated ATTs are
consistent with those of the PSM and IPWRA.
14

27
to the third subsample, where the treatment and control groups comprise financial inclusion
with and without mobile money, respectively.
Table 2.8. ATTs of financial inclusion and mobile money
Subsample 1

Subsample 2

Subsample 3

Financial inclusion without mobile

Financial inclusion with mobile money

Financial inclusion with mobile money

money vs financial exclusion

vs financial exclusion

vs financial inclusion without mobile

without mobile money

without mobile money

money

PSM

IPWRA

PSM

IPWRA

PSM

IPWRA

-0.0757-0.1358**-0.2586***-0.2413***-0.1328*-0.1088*(0.0739)(0.0569)(0.0562)(0.0459)(0.0745)(0.0647)LOH-0.1373*-0.1648***-0.2535***-0.2765***-0.1432**-0.1013(0.0726)(0.0544)(0.0547)(0.0465)(0.0703)(0.0629)LOE-0.1425*-0.1584***-0.3759***-0.3845***-0.2187***-0.2183***(0.0734)(0.0554)(0.0538)(0.0447)(0.0678)(0.0622)LON

Notes: (1) Standard errors are in parentheses. (2) ***, **, and * denote the significance at the 1%, 5%, and 10% levels, respectively.

Concerning the first subsample analysis, the estimated results reveal that financial
inclusion, even without mobile money use, reduces our three measures of poverty status: lackof nutrition, healthcare, and education. For the second subsample analysis, the results showthat financial inclusion with mobile money also reduces our three measures of poverty status.
Importantly, the estimated ATTs are larger than those in the first subsample analysis, whichsuggests that the favorable effects of financial inclusion on an individual’s welfare would be
intensified by the usage of mobile money. The finding in the second subsample analysis canalso be verified by the third subsample analysis showing the positive effects of mobile moneyuse for the sample restricted to financially included individuals.15

For the robustness check, we also estimate multivalued treatment effects (ATTs) by applying the IPWRA
method. In this specification, we divide all individuals into three groups: (i) financial exclusion, (ii) financial
inclusion without mobile money, and (iii) financial inclusion with mobile money. Table 2.9 shows the estimatedresults, which generally support the baseline findings shown in Table 2.8.
15

28

Table 2.9. ATTs of multivalued treatments
IPWRA
LON

LOH

LOE

Financial inclusion without mobile money vs financial-0.1351**

exclusion(0.0568)

Financial inclusion with mobile money vs financial-0.2720***

exclusion(0.0486)

Financial inclusion without mobile money vs financial-0.1643***

exclusion(0.0544)

Financial inclusion with mobile money vs financial-0.3175***

exclusion(0.0473)

Financial inclusion without mobile money vs financial-0.1580***

exclusion(0.0554)

Financial inclusion with mobile money vs financial-0.3613***

exclusion(0.0431)

Notes: (1) Standard errors are in parentheses. (2) ***, **, and * denote the significance at the 1%, 5%, and 10%
levels, respectively.

Our results provide clear evidence supporting the important role of mobile money inenhancing the positive effects of financial inclusion in Burkina Faso. The ‘interoperability’ offinancial services and mobile money has been emphasized to enable financial institutions toprovide swift financial transactions for people anywhere (GSMA, 2017b; Peric et al., 2018).
Such convenient functions can be beneficial for people in rural areas, people who are far frombranches of financial institutions, and people who often face the difficulty of accessing
financial services. More specifically, our analysis confirms that the prevalence of financial
inclusion through mobile money improves the welfare status related to nutrition, healthcare,and education for poor people, which helps achieve some of the seventeen goals of the SDGs
(GSMA, 2017a). First, the improvement in nutrition status is related to zero hunger in Goal 2.
Easy access to financial services through mobile money helps small-sized agricultural farmers,
particularly in rural regions, increase crop productivity (Ogutu et al., 2014). This can inevitably
curb hunger and improve the nutrition status of poor farmers in Burkina Faso, whereapproximately 672,000 children under 5 years old (representing 21 percent of children under 529years old), suffer from chronic malnutrition (stunting or low height-for-age), and 10 percent ofthem suffer from acute malnutrition (United States Agency for International Development(USAID), 2018).
Second, the results showing the favorable effects on healthcare status support good
health and wellbeing in Goal 3 of the SDGs. Communicable diseases have continued to be theprimary cause of morbidity and mortality in Burkina Faso, with malaria being the largest
contributor to mortality for children under 5 years of age (USAID, 2018). Financial services
through mobile phones improves individuals’ ability to successfully manage their own healthand that of their family by tracking medical expenses, saving income, receiving remittances intimes of external shocks, and purchasing health insurance. Third, the improvement in education
status as the effect of financial inclusion would be consistent partly with the quality ofeducation in Goal 4 of the SDGs. Currently, mobile money providers often work with schools
as well as universities, either directly or through government authorities, to digitize thepayments of various fees, including registration fees, tuition fees, and examination fees, fromstudents, and they also digitize salary payments to school teachers and staff. Such advanced
technology would help achieve access to education for children in Burkina Faso, where a thirdof school-age children (around one million girls and boys) do not have access to education, andwhere 70 percent of the adult population is illiterate (Swiss agency for Development andCooperation, 2016). In sum, the interoperability of financial services and mobile money could
be an efficient means of improving conditions related to nutrition, healthcare, and education,
which are crucial nonmonetary elements of an individual’s welfare. Our results in the case of
Burkina Faso coincide with the argument of Suri and Jack (2016), who find that interoperability
is a key driver in mitigating poverty in Kenya, as the introduction of the mobile money system(M-PESA) has increased per capita consumption levels and lifted 194,000 households, or 2
percent of households, out of poverty (Jenkis, 2008).

30

2.6 Conclusion
Financial inclusion is one of the important agendas for less developed countries to solve
poverty issues and achieve the SDGs. The recent prevalence of mobile money services has
been expected to promote financial inclusion for poor people. This study has evaluated how
financial inclusion and mobile money in the context of their interoperability help reducepoverty and improve individuals’ welfare in the case of a least-developed country, Burkina
Faso, where the penetration rate of mobile money is relatively low compared to otherdeveloping countries. In particular, we have focused on nonmonetary poverty indicators ofnutrition, healthcare, and education. Our three poverty-related indicators have been targeted toachieve poverty reduction in Burkina Faso. In fact, the proportion of households with poor or
limited food consumption increased nationally from 26 percent in 2008 to 32 percent in 2012.
Food consumption quality dropped significantly among urban households, with 30 percent
exhibiting poor or limited food consumption compared to 12 percent in 2008 (World Food
Programme, 2014). The government has emphasized the improvement of people’s nutrition
status since 2016 under the National Food and Nutrition Security Policy (PNSAN) (Murphy etal., 2017). In addition, 79 percent of women reported at least one problem in accessing
healthcare, 72 percent of women reported a lack of money to pay for services as a barrier, 44
percent cited distance to the health center as a deterrent, and only 23 percent of women wereliterate in Burkina Faso (Institut National de la Statistique et de la Démographie (INSD) &Inner City Fund (ICF) International, 2012), so the government has also targeted theimprovement of healthcare and education by establishing the General Directorate of Health
Information and Statistics (DGISS) and the Programme Sectoriel de l’Education et de laFormation (PSEF) (Zida et al., 2017). The estimated results of the matching methods havepresented the favorable effects of financial inclusion on poverty reduction in terms ofindividuals’ nonmonetary welfares (nutrition, healthcare, and education). More importantly,
once financial services are provided through mobile money, such favorable effects become31more substantial. Our analysis has revealed the crucial role of the interoperability of financial
services and mobile money, such that financial and telecommunication regulators should createa sound environment for the prevalence of mobile money, as suggested by Suárez (2016).

2.7 Appendix
Table 2.A1. OLS results
Full sample
analysis
Treatment
AGE
FEMALE
HSIZE

LON

LOH

LOE

-0.2016***-0.2262***-0.2791***(0.0377)(0.0365)(0.0358)

0.0063***0.0070***0.0050***(0.0012)(0.0013)(0.0013)-0.0156-0.0188-0.0250(0.0348)(0.0342)(0.0347)0.0105***0.0150***0.0282***(0.0039)(0.0037)(0.0038)-0.0449***-0.0218-0.0809***(0.0157)(0.0156)(0.0159)- 0.4727***- 0.3621***-(0.0489)(0.0475)

0.2974***0.07880.0956*

0.0951*(0.0550)(0.0529)(0.0524)-0.0209***-0.0158***-0.0047(0.0061)(0.0047)(0.0036)-0.0304-0.1264*-0.0866(0.0649)(0.0646)(0.0632)2.2035***2.0091***1.8256***(0.1037)(0.1013)(0.1006)

No. of obs.

3,851

3,851

3,851

R-squared

0.06100.05020.0494TTM
MP(0.0483)
PEDUC
LSIZE
RURAL
Constant

Notes: (1) Treatment equals one for financially included individuals and zero for financially excluded individuals in the full sample analysis.
(2) Robust standard errors are in parentheses. (3) ***, **, and * denote the significance at the 1%, 5%, and 10% levels, respectively.

32

Table 2.A2. ATTs using Kernel matching and 2-nearest neighbor matching estimations.
Whole sample
Financial inclusion
LON

Kernel matching

2-nearest neighbor matching-0.1990***-0.1995***(0.0400)(0.0479)

LOH-0.2216***-0.2461***(0.0389)(0.0473)LOE-0.2958***-0.3193***(0.0383)(0.0483)

Notes: (1) Standard errors are in parentheses. (2) ***, **, and * denote the significance at the 1%, 5%, and 10% levels, respectively

Table 2.A3. OLS results

Treatment
AGE
FEMALE
HSIZE
TTM

Subsample

Subsample

Subsample

analysis 1analysis 2

analysis 3

LON

LOH

LOE

LON

LOH

LOE

LON

LOH

LOE

-0.1136**-0.1437***-0.1444***-0.2525***-0.2753***-0.3640***-0.1317**-0.1319**-0.2092***(0.0558)(0.0534)(0.0546)(0.0436)(0.0424)(0.0394)(0.0639)(0.0608)(0.0588)0.0060***0.0067***0.0055***0.0073***0.0076***0.0049***0.0038*0.0062***0.0042**(0.0014)(0.0014)(0.0014)(0.0013)(0.0014)(0.0014)(0.0022)(0.0022)(0.0020)-0.0344-0.0264-0.0510-0.0325-0.0564-0.0675*

0.05540.08590.1423**(0.0384)(0.0379)(0.0393)(0.0368)(0.0362)(0.0367)(0.0661)(0.0629)(0.0590)0.0081*0.0126***0.0280***0.0109***0.0160***0.0282***0.0205***0.0224***0.0328***(0.0043)(0.0042)(0.0046)(0.0042)(0.0040)(0.0040)(0.0071)(0.0066)(0.0061)--0.0269-0.1043***--0.0176-0.0824***-0.0323-0.0279-0.0256

0.0480***(0.0176)(0.0182)

0.0487***(0.0168)(0.0171)(0.0274)(0.0267)(0.0248)(0.0176)MP(0.0167)

-

-

-

-

-

-

-

-

-0.2005**

0.4587***0.3662***0.2879***0.4819***0.3535***0.3096***

0.4145***0.3138***(0.0942)(0.0520)(0.0504)(0.0522)(0.0517)(0.0506)(0.0516)(0.1070)(0.1001)

0.1213*0.1271**

0.09510.05580.08840.1025*0.00940.0174***0.0235(0.0638)(0.0625)(0.0639)(0.0576)(0.0554)(0.0552)(0.0939)(0.0866)(0.0788)-0.0284***-0.0181***-0.0035-0.0234***-0.0202***-0.0077**-0.0133*-0.0102*-0.0044(0.0079)(0.0068)(0.0052)(0.0067)(0.0050)(0.0037)(0.0074)(0.0054)(0.0039)-0.0048-0.0986-0.0566-0.0466-0.1234*-0.1133*-0.0590-0.1915**-0.1241(0.0842)(0.0832)(0.0818)(0.0696)(0.0691)(0.0683)(0.0906)(0.0913)(0.0556)

2.2127***2.0191***

1.8385***2.2398***2.0077***1.8972***2.0168***1.8529***1.4832***(0.1251)(0.1205)(0.1226)(0.1094)(0.1078)(0.1066)(0.1868)(0.1808)(0.1635)

No. of obs.

3,120

3,120

3,120

3,434

3,434

3,434

1,148

1,148

1,148

R-squared

0.05820.04300.04090.06830.0556

0.05590.03840.04240.0566

PEDUC
LSIZE
RURAL
Constant

Notes: (1) Treatment equals one for financially included individuals without mobile money and zero for financially excluded individuals inthe subsample analysis 1. (2) Treatment equals one for financially included individuals with mobile money and zero for financially excluded
individuals in the subsample analysis 2. (3) Treatment equals one for financially included individuals with mobile money and zero forfinancially included individuals without mobile money in the subsample analysis 3. (4) Robust standard errors are in parentheses. (5) ***, **,and * denote the significance at the 1%, 5%, and 10% levels, respectively.

33
Table 2.A4. ATTs using Kernel matching and 2-nearest neighbor matching estimations.
Subsample 1
FinancialinclusionmobileSubsample 3

Financial inclusion with mobile money vsFinancial inclusion with mobile money vsmoney vs financial exclusion without

financial exclusion

financial inclusion

mobile money

without mobile money

Kernel matching

without

Subsample 22-nearest neighbor

Kernel matching

matching
LON
LOH
LOE

without mobile money
2-nearest neighbor

Kernel matching

matching

2-nearest
matching-0.1332**-0.0915-0.2371***-0.2312***-0.1117-0.0814(0.0568)(0.0674)(0.0464)(0.0569)(0.0686)(0.0719)-0.1619***-0.1691**-0.2603***-0.2629***-0.0988-0.0205(0.0540)(0.0679)(0.0454)(0.0544)(0.0653)(0.0674)-0.1506***-0.2040***-0.3725***-0.3951***-0.2208***-0.1990***(0.0555)(0.0712)(0.0428)(0.0517)(0.0643)(0.0680)

Notes: (1) Standard errors are in parentheses. (2) ***, **, and * denote the significance at the 1%, 5%, and 10% levels, respectively

Table 2.A5. Balancing property
Subsample 1: Financial inclusion without mobile money vs financial exclusion
Mean

Bias

P-value

TreatedControlreduction

AGE

37.68333.7260.000

FEMALE

0.42440.5346

0.000

HSIZE

9.02888.59270.072

TTM

2.49882.42290.192

MP0.82970.7998

0.153

PEDUC

0.91610.8975

0.240

LSIZE

6.046

4.6112

0.000

RURAL

0.91130.94230.014

AGE

37.71537.877

95.9

0.884

FEMALE

0.42270.4227

100.0

1.000

HSIZE

8.9179

8.715

53.5

0.500

TTM

2.49522.4928

96.8

0.976

MP

0.83850.8382

67.7

0.710

PEDUC

0.91550.9082

60.9

0.713

LSIZE

5.74445.6289

91.9

0.754

RURAL

0.91060.9034

76.6

0.720

Before matching

After matchingneighbor

34
Table 2.A6. Balancing property
Subsample 2: Financial inclusion with mobile money vs financial exclusion
Mean

Bias

P-value

TreatedControlreduction

AGE

37.21533.7260.000

FEMALE

0.32420.5346

0.000

HSIZE

9.66628.59270.000

TTM

2.27222.42290.001

MP0.91520.7998

0.000

PEDUC

0.83310.8975

0.000

LSIZE

6.01034.6112

0.000

RURAL

0.8331

0.94230.000

AGE

36.93336.339

83.0

0.483

FEMALE

0.34160.3504

95.8

0.734

HSIZE

9.51249.7942

73.8

0.325

TTM

2.292

2.3036

92.2

0.838

MP

0.90950.9080

98.7

0.925

PEDUC

0.85250.8467

90.9

0.763

LSIZE

5.55195.5454

99.5

0.980

RURAL

0.87010.8496

81.3

0.276

Before matching

After matching

Table 2.A7. Balancing property
Subsample 3: Financial inclusion with mobile money vs financial inclusion without mobile money
Mean

Bias

P-value

TreatedControlreduction

AGE

37.21533.6830.600

FEMALE

0.32420.4245

0.001

HSIZE

9.66629.02880.038

TTM

2.27222.49880.001

MP0.91520.8297

0.000

PEDUC

0.83310.9161

0.000

LSIZE

6.0103

6.046

0.931

RURAL

0.83310.9113

0.000

AGE

37.32

37.877

50.0

0.822

FEMALE

0.40880.4227

55.8

0.216

HSIZE

9.1536

8.715

32.2

0.175

TTM

2.42712.4928

31.0

0.052

MP

0.87760.8382

81.7

0.521

PEDUC

0.90880.9082

52.9

0.036

LSIZE

6.11635.6289-152.6

0.857

RURAL

0.90360.9034

50.0

0.042

Before matching

After matching

35

Chapter 3: ICT and environmental sustainability: Any differences indeveloping countries?
3.1 Introduction
Information and communication technology (ICT) is an important contributor to economic
growth (Hoffert et al., 2002; Middlemist and Hitt, 1981). During the last decades, ICT has
brought about substantial changes in a global economy by increasing productivity, promoting
global supply chains, and uplifting economic growth (OECD, 2000). The swift decline in theprice of ICT equipment has caused substitution of ICT equipment for other forms of capital,
reducing the production expenses. This substitution has also facilitated investors to increase
ICT investments and restructure the corresponding economic activities (Jorgenson and Stiroh,1999).
Many empirical studies have shown a significantly positive effect of ICT capital on
output growth (O’Mahony and Vecchi, 2005, for the UK; Dimelis and Papaioannou, 2011, for
the US). The relationship between ICT and economic growth in the US reveals economicallysignificant contributions of ICT capital to economic growth after the mid-1990s (Jorgenson
and Stiroh, 2000). In addition, ICT has a potential to help reduce poverty, increase productivity,and boost economic performance in emerging and developing countries (Sanz and Hellström,2011). The impact of ICT is more substantial in developing countries than in developed
countries (Dimelis and Papaioannou, 2010).
Responsible consumption and production are highlighted as one of the 17 sustainable
development goals (SDGs) set by the United Nation (United Nations, 2015). The ICT sector
has become recognized as ever more significant in ensuring sustainable development (Klimova
et al., 2016). Since the 2000s, ICT has become an essential player in the journey toward a low
carbon economy. The “green ICT” initiative targets to reduce the unfavorable effect of ICT on

36
the environment. Green ICT represents the efficiency and effectiveness of this sector withminimal or no impact on the environment (Askarzai, 2011). ICT is an important tool to reducecarbon dioxide (CO2) emissions by building smarter cities, transportation systems, electricalgrids, and industrial processes (Danish et al., 2017).
Despite the Kyoto Protocol and the Paris Agreement, CO2 emissions are still increasing.
Several studies focus on the relationship between ICT and CO2 emissions. ICT products andservices cause environmental degradation (Arushanyan et al., 2014; Malmodin et al., 2010),
but they also have a large potential for reducing environmental degradation by substituting ICT
products for environmentally unfriendly products and making production processes more
efficient (Erdmann and Hilty, 2010). Table 1 summarizes several studies on unfavorable andfavorable effects of ICT on the environment.
Mingay (2007) argues that the ICT sector is estimated to produce 2% of global GHG
emissions. Danish et al. (2017) confirm that ICT products increase CO2 emissions throughout
their life cycle (production, use, and disposal). Dedrick (2010) and Molla et al. (2009)
emphasize the emergency to mitigate the unfavorable effects of ICT on the environment. Amri(2018) examines the linkage between CO2 emissions and ICT in Tunisia and fails to show thefavorable effect of ICT on the environment. The darker and more ominous side of the ICT
industry is its exponentially growing energy consumption. As our reliance on ICT devices andservices grows rapidly, so does our need for energy in manufacturing and electricity industries
to power these devices. The generation of the much-needed energy to make and operate all the
ICT devices on the market today is a crucial cause towards the creation of carbon dioxide, aleading Green House Gas (GHG), as well as other global warming pollutants (Belkhir andElmeligi, 2018). Overall energy consumption of ICT suggests that in 2007, the ICT sector
produced 1.3% of global GHG emissions with the corresponding global electricity
consumption of 3.9% (Malmodin et al., 2010). A forecast indicates that the ICT sector

37
expansion will cause the carbon footprint to reach 1.1 Gt by year 2020 (Malmodin et al., 2013).

Table 3.1. Summary of literature review
Authors
PeriodStudy area
Variables
Method.
I)
Studies focusing on the unfavourable effect of ICT on the environment.
CO2, total factor
1975productivity, ICT, trade,
ARDL withAmri (2018)
Tunisia2014
energy consumption, andbreak point.
financial development.

Mingay (2007)

20082009

ITorganizations

CO2, ICT, and GHG.

Compilationof many
studies.

Estimation
approach
GeSI (2008)
CO2, ICT, and GHG“cradle-tograve”
II)
Studies displaying the favourable effects of ICT on the environment.
Nine
members
from theCoLee and
Association
CO2, ICT, and economic
integrating
Brahmasrene
1991of Southeast
growthregression(2014)
2009
Asianestimation
Nations(ASEAN).
20072020

Zhang and Liu(2015)

Asongu (2018)

Aldakhil et al.
(2019)

2000–
2010

20002012

19752016

ICT industry(Worldwide)

China

44 SubSaharanAfricancountries

South Asia

Population, ICT industry,
Urbanization, GDP per
capital, Industrial
structure, total CO2
emission, and Energy
Intensity.

Interpretations
Insignificant impact of
ICT on CO2 emissions.
ICT sector has been
estimated to produce
2% of global
greenhouse gas
emissions.
Global greenhouse gas
will increase to an
estimated of 2.8% by
2020.

ICT shows significant
favourable relationshipwith environment.

STIRPATICT industry
contributes to reducing
China’s CO2
emissions.

CO2 per capita,
Educational quality,internet, mobile phones,
GDP growth, population
growth, Foreign
investment, trade
openness and regulation
quality

GMM

ICT can be employed
to dampen thepotentially favourable
effect on environment.

ICT, R&D, FDI, Trade,
GDP per capita, Energy
efficiency, agricultural
technology

RobustLeast
Squares
Regression(RLS)

ICT applications usedin diversified
economic restructuring
are helpful in reducing
high-mass carbonfossil emissions toachieve environmental
sustainability in South
Asia.

38
On the other hand, ICT is expected to be a possible solution to many environmental
problems (Higón et al., 2017). ICT broadens the opportunities to reduce the human impact onnature. For example, e-commerce, tele-working, and video conferencing have reduced theworldwide travelling of both people and goods and hence the consumption of petroleum andthe emission of greenhouse gases (Yi and Thomas, 2007). Weber et al. (2008) estimate thatcompared to traditional retails, e-commerce has approximately 30% lower energy consumption
and GHG emissions. If air travel is replaced for teleconferencing by 10% within the next 10
years in the United States, approximately 200 million tons of GHG abatement could beachieved. Shifting all newspaper subscriptions from paper to online has the potential to reduce
57.4 million tons of CO2 emissions over the next decade. Furthermore, Lee and Brahmasrene(2014) examine the relationships among ICT, CO2 emissions, and economic growth from apanel of ASEAN during the period from 1991 to 2009 and find a significantly favorable effectof ICT on economic growth and CO2 emissions. Concerning regional differences in China,
Zhang and Liu (2015) consider the impact of the ICT industry on CO2 emission using the
Stochastic Impacts by Regression on Population, Affluence, and Technology (STRIPAT) modelwith provincial data. Their finding concludes that the ICT industry mitigates the problem of
CO2 emission in China. In addition, Asongu (2018) analyzes the nexus between ICT, openness,
and CO2 emission in Africa, and the empirical findings based on the generalized method of
moments (GMM) approach suggest that ICT can be a useful tool to reduce the globalization
driven CO2 emissions.
Given the arguments related to favorable and unfavorable effects of ICT on global
warming and environmental sustainability, this study intends to find out which effect of ICT
on the environment is dominant in the case of developing countries. This study focuses ondeveloping countries, which provides an empirical contribution to curb the scarcity of
empirical evidences on the relationship between ICT and environment. Following Amri (2018)
among others, we use the sum of mobile and fixed telephone subscription data per 100 people
as a suitable proxy for ICT, since they are common, less costly, and less polluting among ICTs
products in developing countries (Cheng et al., 2013). Moreover, this study applies a panel
pooled mean group autoregressive distributive lag (PMG-ARDL) analysis, known as a modern
econometric method (Alola et al., 2019), to a comprehensive panel data for 58 developing

39
countries in the world during the period of 1990-2014. The sample countries are classified into
two groups: (i) relatively low-income group (GNI per capita of $1025 or less) and (ii) relatively
high-income group (middle- income countries 16 ), which will enable us to capture howdevelopment stages affects the long-run relationships among variables.
The original contributions of our study are three-fold. Firstly, our study presents thatthe long-run relationship between CO2 emissions and ICT depends on countries' income level,i.e., the development stage. In fact, CO2 emissions are negatively and significantly associatedwith ICT for “relatively low-income” developing countries, while this relationship is less clear(negative but not significant) for “relatively high-income” developing countries. Secondly, ourresults support the favorable effect of ICT on the environment under the ‘greening through ICT’argument and present ICT as a powerful tool to reduce CO2 emissions especially at the earlystages of economic development. Thirdly, our finding is a good news for developing countries
in general and particularly for the relatively low-income ones, which mostly complain about
the expensive costs of renewable energy sources. To enjoy the advantages in the context of ICT
in developing countries, decision makers can encourage the population to use ICT products bypromoting various policies, for instance, facilitating foreign investment in ICT sectors.
The remainder of this study is organized as follows. Section 2 describes data andmethodology used in this study. In section 3, we present the empirical results of our analysis,
including panel unit root tests, panel cointegration tests, the long-run estimates under PMGARDL method, and the Dumitrescu and Hurlin (2012) causality test, and we discuss some
implications derived from our empirical results. Final section 4 provides the conclusion.

According to the World Bank (2015), lower middle-income countries are identified with a GNI per capita
between $1026 and $4035 and the upper middle-income countries with a GNI per capita between $4036 and$12,475.
1640

3.2 Methodology and data
3.2.1 Model specification
This study attempts to discuss how ICT relates to CO2 emissions in developing countries. To
identify the relationship between ICT and CO2 emissions, controlling for other explanatory
variables, we consider the following empirical model:
lnCO2PCi,t = α0 + α1 ICTi,t + ∑k βk xk,i,t + μi + εit ,(1)

where lnCO2PCi,t is the log of CO2 emissions per capita in country i at year t; ICTi,t is themeasure of the level of ICT; xk,i,t ’s are other control variables which are expected to relate to
CO2 emissions; μi is the country-specific fixed effects; and εit is the error term. As a proxy
for the level of ICT, we use the sum of mobile and fixed telephone subscriptions data per 100
people. In the ICT related literature, mobile and fixed telephone subscriptions are commonlyused to measure the ICT access in developing countries (Amri, 2018; Amri et al., 2019; Higónet al., 2017; ITU, 2017).
As other control variables, we include the log of total energy consumption per capita(lnTECPCi,t ), and renewable energy penetration (RREPi,t ), which is measured by the ratio ofrenewable energy consumption to total energy consumption penetration. Total energy
consumption and renewable energy penetration are crucial factors to determine CO2 emissions(Balsalobre-Lorente et al., 2018). In addition, the model includes the log of real GDP per capita(lnRGDPPCi,t ), to control for a country's income level, which is related to the well-known
environmental Kuznets curve (EKC) argument. The EKC argument claims that the incomeemission relationship depends on a country’s income level, which suggests the important roleof the development stages. To account for different income-emission relationships, this study
classifies our sample of developing countries into two country groups: (i) relatively lowincome developing countries and (ii) relatively high-income developing countries. By doing

41so, we can discuss the links of development stages with the relationships among the variables
and their possible differences between the two groups.
To estimate the short- and long-run associations of CO2 emissions with ICT and othervariables for each of the two income groups, this study employs a panel ARDL framework that
includes the lags of both dependent and independent variables in Eq. (1):pqlnCO2PCi,t = ∑j=1 δi,j lnCO2PCi,t−j + ∑j=0 Xi,t−j φi,j + μi + εit ,(2)where Xi,t−j is a vector of the independent variables (ICT , lnTECPC , lnRGDPPC, RREP )with equal lags across individual countries; p is the lags of the dependent variable; q is thelags of the independent variables; μi is the country fixed effects; and εit is the error term.
The panel ARDL model allows for different coefficients across countries. Assuming theexistence of cointegration among the variables in Eq. (2), the error term would follow theprocess that is integrated of order zero. In this case, countries have the long-run equilibrium
relationship among the variables, and the time paths of the variables reflect the deviation fromtheir long-run equilibrium. The re-parametrizing model turns into an error correction form,
where the short-run adjustment can be explained by the deviation from the long-run
equilibrium:
p−1

q−1

∆lnCO2PCi,t = ϕi ECTi,t + ∑j=1 δ∗i,j ∆lnCO2PCi,t−j + ∑j=0 ∆Xi,t−j φ∗i,j + εit ,(3)

where ∆ is the difference operator, and ECTi,t = lnCO2PCi,t−1 − Xi,t θi is the error correction
term (ECT). The first part ϕi ECTi,t captures the convergence speed, and the latter part depictspthe short-run dynamics. The parameter ϕi = −(1 − ∑j=1 δi,j ) is the coefficient of the shortrun adjustment or the group specific speed of adjustment, and the parameter θi =q∗−(∑j=0 φi,j )/ϕi is the long-run coefficients. The parameters δ∗i,j and φi,j
are the short run
p

dynamic coefficients of the dependent and independent variables, where δ∗i,j = − ∑d=j+1 δi,dq∗
and φi,j= − ∑d=j+1 φi,d . It should be noticed that the coefficient of ECT is significantly

42
negative, i.e., ϕi < 0.
The panel ARDL technique has been applied recently to empirical works in various
contexts (Mensah et al., 2019; Alola et al., 2019; Essandoh et al., 2020). Pesaran and Smith(1995) and Pesaran et al. (1999) suggest that a panel ARDL model is more appropriate, since
it can be applied even when the variables follow different orders of integration (I (0) or/and I(1) but certainly not I (2)) or a mixture of both. The panel ARDL model has advantages overother dynamic panel methods, such as the fixed effects and the Generalized Methods ofMoment (GMM) estimators proposed by Anderson and Hsiao (1982, 1981), Arellano (1989),
and Arellano and Bover (1995), which may produce inconsistent estimates of the average valueof the parameters unless the coefficients are identical across countries (da Silva et al., 2018).
In addition, the panel ARDL can mitigate some endogeneity issues and simultaneously estimate
both short-run and long-run parameters in a single fitted model. Moreover, the panel ARDL
model can be estimated by employing the mean group (MG) estimator, which estimates theparameters for each country and then averages for the group. Assuming the homogeneous longrun coefficients across countries, the PMG estimators are more efficient, allowing the shortrun coefficients to vary across countries but with the homogenous long-run coefficients(Pesaran and Smith, 1995; Pesaran et al., 1999). In this study, we conduct the Hausman test to
confirm that the PMG estimator is more adequate than the MG estimator.

3.2.2 Data
In this study, we use panel data of 58 developing countries during the period from 1990 to 2014.
Based on the World Bank’s income classification, all sample countries are divided into two
income groups: “relatively low-income” and “relatively high-income” developing countries(Table 3.2). For the definitions of the variables (Table 3.3), CO2 emission per capita is measured

43in kilotons of oil equivalent (ktoe), and total energy consumption per capita is captured by totalenergy use per capita in kilotons of oil equivalent (ktoe). Real GDP per capita is measured by
GDP per capita in constant 2010 US dollars. Renewable energy penetration is measured by theratio of renewable energy to total energy consumption (Bekun et al., 2019). The level of ICT
is measured by the sum of mobile and fixed telephone subscription data per 100 people (Amri,2018). The data of all variables are taken from the World Bank's World Development Indicators(WDI) database. Table 3.4 and table 3.5 present the summary statistics and the correlation
matrix of the variables used in our analysis, respectively. The average measure of ICT in
relatively high-income developing countries is larger than that in relatively low-income
developing countries. In addition, the simple correlation analysis shows that ICT is positively
correlated with the log of CO2 emission per capita, irrespective of the income groups. As in
most of the time series literature, this study first conducts panel unit root tests and panel
cointegration tests. Then we evaluate the long-run relationships among variables with the shortrun dynamics by applying the panel ARDL model. Finally, we apply the Granger causality testof Dumitrescu and Hurlin (2012) to identify the directional relationships among the variables,i.e., whether one time series is useful in forecasting another.
Table 3.2. List of sample countries
Relatively low-income developing countries
Benin
GuatemalaBangladesh
Haiti
Bolivia
HondurasCôte d'Ivoire
India
Cameroon
Indonesia
Egypt, Arab Rep.
Iraq
El Salvador
Kenya
Ghana
Sri Lanka
Relatively high-income developing countries
Albania
Colombia
Algeria
Costa Rica
Argentina
Cuba
Botswana
Dominican Republic
BrazilEcuadorBulgaria
Gabon
Chile
Iran, Islamic Rep.
China
JamaicaMongolia
MoroccoMozambique
Nepal
Nicaragua
Nigeria
Pakistan
Paraguay

Philippines
SenegalSudan
Tanzania
Togo

JordanLebanonMexicoMauritius
Malaysia
PanamaPeru
RomaniaSouth Africa
ThailandTunisia
TurkeyUruguay44

Table 3.3. Variable definitions
Variable
CO2 per capita

Variable
lnCO2PC

Variables definitions (measurements)

Logarithm

CO2 emissions per capita in kilotons of oil
equivalent (ktoe)
Total energy use in kilotons of oil equivalent(ktoe)

yes

Total energy
consumption per capita

lnTECPC

Real GDP per capita

lnRGDPPCReal GDP per capita (constant 2010 US dollars)

yes

RREP

Ratio of renewable energy on total energy
consumption

no

ICT

Sum of mobile and fixed telephone subscription
data per 100 people (ratio)noRatio of renewableenergy penetration
Information
Communication
Technology

yesTable 3.4. Descriptive statistics
Variables
Relatively low-income developing countries
lnCO2PC
lnTECPC
lnRGDPPC
RREP
ICT
Relatively high-income developing countries
lnCO2PC
lnTECPC
lnRGDPPC
RREP
ICT

Obs.

Mean

Std. Dev.

Min.

Max.

725
725
725
725
725

-0.6169
6.15757.145557.15290.2946

0.99300.44550.654426.7035
0.3860-3.39454.77835.10560.32400.00212.59887.53168.614095.1776
1.6119

725
725
725
725
725

1.05697.04508.595123.0425
0.55200.5381
0.46260.475817.31540.5108-0.7126
5.9522
6.59190.06860.00582.30058.08259.586188.0958
1.9726

45

Table 3.5. Correlation matrix
lnCO2PC
lnTECPC
Relatively low-income developing countries
lnCO2PC
1.0000t-Statistic
Prob.
lnTECPC
0.7115***1.0000t-Statistic
27.2253Prob.
0.0000lnRGDPPC
0.7824***0.6489***t-Statistic
33.7770
22.9318Prob.
0.00000.0000RREP-0.8844*** -0.4847***t-Statistic-50.9562-14.9011
Prob.
0.00000.0000ICT
0.3574***0.3127***t-Statistic
10.28938.8533Prob.
0.00000.0000Relatively high-income developing countries
lnCO2PC
1.0000t-Statistic
Prob.
lnTECPC
0.9086***1.0000t-Statistic
58.5120Prob.
0.0000lnRGDPPC
0.3328***0.4904***t-Statistic
9.4907
15.1296Prob.
0.00000.0000RREP-0.4915*** -0.2538***
t-Statistic-15.1764-7.0557Prob.
0.00000.0000ICT
0.2663***0.3590***t-Statistic
7.4287
10.3419Prob.
0.00000.0000lnRGDPPC

REP

ICT1.0000-0.5993***-20.1303
0.00000.452013.6241***0.00001.0000-0.3020***-8.5187
0.00001.0000-1.00000.1588***4.32410.00000.4929***15.23250.00001.0000-0.0882**-2.3798
0.01761.0000-Notes: *, **, and *** represent the 10%, 5%, and 1% significance levels, respectively

3.3 Results and discussion
3.3.1 Panel stationarity tests
With the panel data, we need to check the stationarity of the variables to ensure that theyfluctuate around a constant mean, since running regression models with nonstationary variables
often leads to unreliable results. To check the stationarity of the variables, this study conducts
five types of panel unit root tests: (i) Levin-Lin- Chu (Levin et al., 2002); (ii) Breitung(Breitung, 2005; Breitung and Das, 2005); (iii) Im-Pesaran-Shin (Im et al., 2003); (iv) Fisher-46
ADF (Maddala and Wu, 1999); and (v) Fisher-PP (Choi, 2001) tests. All the tests employ a null
hypothesis that all the panels contain a unit root. Table 3.6 presents the results of the panel unit
root tests (level and first difference) on the variables used in this study (lnCO2PC, lnTECPC,lnRGDPPC, RREP, and ICT) for each income group. The test results confirm that for each
income group, all variables are integrated of order either zero or one, i.e., I(0) or I(1), which
satisfies the requirement for applying the panel ARDL analysis.

47

Table 3.6. Panel unit root tests (1990 - 2014)
Null: unit root
Levin, Lin & Chu
Breitung
Im, Pesaran and Shin
ADF-Fisher
PP-Fisher
Relatively low-income developing countries
lnCO2PC
0.2741[0.608]2.6811[0.996]-0.0107[0.496]63.2932[0.295]58.9177[0.442]ΔlnCO2PC-16.1773***[0.000]-8.1009***[0.000]-17.1443***[0.000]333.719***[0.000]725.665***[0.000]lnTECPC-0.6821[0.248]3.9942[1.000]-0.0680[0.473]67.7097[0.180]66.1958[0.215]ΔlnTECPC-16.8787***[0.000]-9.0239***[0.000]-15.9658***[0.000]314.860***[0.000]804.696***[0.000]lnRGDPPC-2.5319***[0.006]3.7127[1.000]-0.8155[0.207]98.7347***[0.001]78.0274**[0.041]ΔlnRGDPPC -14.4934***[0.000]-8.2036***[0.000]-13.9471***[0.000]289.336***[0.000]299.753***[0.000]RREP-1.1121[0.133]-0.0947[0.462]-0.2165[0.414]63.7203[0.282]56.9150[0.516]
ΔRREP-17.1219***[0.000]-10.9220***[0.000]-16.7841***[0.000]329.233***[0.000]510.575***[0.000]ICT
1.8636[0.969]13.3999[1.000]5.1877[1.000]76.3120*[0.054]9.0821[1.000]ΔICT
1.2480[0.894]3.4580[1.000]-4.2883***[0.000]113.473***[0.000]67.4393[0.186]
Relatively high-income developing countries
lnCO2PC-2.5706***[0.005]0.0407[0.516]-2.5959***[0.005]88.0865***[0.007]84.1825**[0.014]ΔlnCO2PC-18.1730***[0.000]-13.1437***[0.000]-18.8901***[0.000]372.550***[0.000]556.019***[0.000]lnTECPC-1.4105*[0.079]0.4741[0.682]-1.5408*[0.062]84.5783**[0.013]82.1570**[0.020]ΔlnTECPC-16.8268***[0.000]-9.5852***[0.000]-17.2304***[0.000]334.383***[0.000]528.869***[0.000]lnRGDPPC-1.6562**[0.049]1.0808[0.860]-3.3072***[0.000]104.019***[0.000]90.4040***[0.004]ΔlnRGDPPC -11.8146***[0.000]-6.6594***[0.000]-11.0120***[0.000]224.370***[0.000]608.102***[0.000]RREP-2.3748***[0.009]0.3406[0.633]-1.2161[0.112]63.6946[0.283]62.2625[0.327]
ΔRREP-16.6073***[0.000]-10.4363***[0.000]-15.9469***[0.000]312.377[0.000]726.224***[0.000]ICT-2.5639***[0.005]8.0804[1.00]
0.7120[0.762]52.0950[0.693]28.3499[1.000]ΔICT
0.0072[0.503]
1.9198[0.973]-4.0402***[0.000]127.634***[0.000]91.5258***[0.003]
Notes: (1) Figures in the parenthesis indicate p-values. (1) Optimal lag lengths are determined by Schwarz Info Criterion (SIC). (3) Individual intercept and individual linear
trend are included. (4) *, **, and *** represent the 10%, 5%, and 1% significance levels, respectively.

48

3.3.2 Panel cointegration tests
Although cointegration of the variables is not strictly required, if it exists, the panel ARDL
model has an error correction model interpretation and there is a strong evidence that the longrun estimates are common across all countries, supporting the application of the PMG
estimators. This study applies two panel cointegration tests developed by Pedroni (2004, 1999)
and Kao (1999), which extend the two-step residual-based cointegration tests of Engle andGranger (1987). Both tests have the null hypothesis of no cointegration and allow the panelspecific cointegrating vectors and the AR coefficients in the auxiliary regression to vary overpanels with heterogeneous intercepts and trend coefficients across cross-sections. Table 3.7
presents the results of the Pedroni cointegration tests. For each income group, six test statistics
are statistically significant, although some test statistics show less significance. Therefore, thePedroni panel cointegration tests indicate that the null of no cointegration can be rejected, sothat there exists a long-term relationship among lnCO2PC, lnTECPC, lnRGDPPC, RREP, and
ICT. As another method, the panel cointegration tests proposed by Kao (1999) take a similar
approach as the Pedroni tests but requires cross-section specific intercepts and homogeneous
coefficients on the regressors in the first stage estimation. Tables 3.8 shows the results of theKao cointegration tests, which coincides with the results of the Pedroni panel cointegration
tests.

49

Table 3.7. Pedroni panel cointegration tests (1990-2014)

Relatively low-income developing countries
Pedroni (1999)

Pedroni (2004) Weighted Statistic

Relatively high-income developing countries
Pedroni (1999)

Within-dimension (panel statistics)
Test
Statistic

Prob.

Panel v-Statistic
Panel rho-Statistic
Panel PP-Statistic
Panel ADF-Statistic
Panel v-Statistic
Panel rho-Statistic
Panel PP-Statistic
Panel ADF-Statistic

0.90420.76770.00000.00000.99880.59230.00000.0000-1.3059
0.7312-6.1706***-6.0676***-3.04310.2336-8.9530***-9.1069***

Between-dimension (individual statistics)
Test
Statistic

Prob.

Group rho-Statistic
Group PP-Statistic
Group ADF-Statistic

0.99820.00000.00002.9187-6.6668***-6.6760***

Panel v-Statistic-2.9308
0.9983
Group rho-Statistic
1.89830.9712
Panel rho-Statistic-0.5546
0.2896
Group PP-Statistic-16.7476***0.0000Panel PP-Statistic-13.6707***0.0000Group ADF-Statistic-8.4060***
0.0000Panel ADF-Statistic-7.8420***
0.000
Pedroni (2004) Weighted Statistic
Panel v-Statistic-3.46560.9997
Panel rho-Statistic
0.70000.7581Panel PP-Statistic-11.7356***0.0000Panel ADF-Statistic-6.8674***0.0000Notes: Optimal lag lengths are determined by Schwarz Info Criterion (SIC). Individual intercept and individual linear trend are included in the test regressions. *, **, and ***represent the 10%, 5% and1% significance levels, respectively.

50

Table 3.8. Kao panel cointegration tests (1990-2014)
Statistic
Prob.
Relatively low-income developing countries
ADF-7.0010*** 0.0000
Residual variance0.0143
HAC variance0.0124
Relatively high-income developing countries
ADF-3.5090*** 0.0002
Residual variance0.0054
HAC variance0.0031
Notes: *, **, and *** represent the 10%, 5% and 1% significance levels, respectively.

3.3.3 Long- and short-run estimates
Table 3.9 presents the results of the PMG-ARDL model for the groups of relatively highincome and low-income developing countries. The Hausman test results fail to reject the nullof long-run cross-section parameter homogeneity, which supports that the PMG estimator ismore appropriate than the MG estimator. The short-run estimates vary across countries, so thatthe mean group may not provide a good accuracy on the differences between countries. In
addition, the estimated coefficients of the error correction term (ECT), which indicate theconvergence speed, are significantly negative at the values of -0.4814 and -0.3700 for relatively
high-income and low-income developing countries, respectively. These results suggest that thehalf-life values are approximately 1.1 years for relatively high-income developing countries
and approximately 1.5 years for relatively low-income developing countries.17

17The half-life value indicates the length of time after a shock before the deviation shrinks to half of its

impact (Chari et al., 2000). The half-life is calculated as ln(0.5)/log(1 + ϕ).

51

Table 3.9. Pooled mean group with dynamic autoregressive distributed lag: PMG-ARDL (1,1,1,1,1)
Relatively high-income Relatively low-income
Developing countries
Developing countries

Long-run equation
lnTECPC
1.0202***(0.0000)
lnRGDPPC-0.0609***(0.0084)
RREP-0.0097***(0.0000)
ICT-0.0053(0.4934)
Short-run equation
ECT(-1)-0.4814***(0.0000)
lnTECPC
0.3324***(0.0002)lnRGDPPC
0.1439**(0.0123)
RREP
0.0038(0.8161)ICT
0.0427(0.5322)
Constant-2.5852***(0.0000)
Hausman
0.97(0.9148)
No of obs.
696

0.4951***(0.0000)
0.8697***(0.0000)-0.0090***(0.0000)-0.1626***(0.0000)-0.3700***(0.0000)
0.6996***(0.0041)0.1323(0.4444)-0.0205***(0.0002)0.0615(0.5896)-3.4144***(0.0000)
3.41(0.4924)
696

Notes: (1) *, **, and *** represent the 10%, 5% and 1% significance, respectively. (2) The dependent variable is lnCO2P.

The panel PMG-ARDL analysis shows a clear difference in the long-run ICT-emissions
relationship between the two income groups. The coefficient of ICT is significantly negative
for relatively low-income developing countries, while it is insignificant for relatively highincome developing countries. Given the arguments of the favorable and unfavorable effects of
ICT on the environment, CO2 emission in this study, our results suggest that the favorable effectof ICT dominates the unfavorable one in relatively low-income developing countries, while
the favorable and unfavorable effects of ICT are balanced in relatively high-income developing
countries. In relatively low-income developing countries, a 1 percent increase in the ICT
penetration rate (mobile and fixed phone penetration) is associated with a 0.16 percent decline

52
in CO2 emission per capita. This result is in contrast to the finding of the Tunisia case in Amri(2018) but is consistent with the finding of the Africa case in Asongu (2018). The introductionof ICT enables the economy to replace traditional and inefficient technology for advanced
technology, including green ICT, and to mitigate the environmental degradation. This favorable
effect of ICT on CO2 emissions is more substantial for least-developed countries where ICT isless prevalent. On the other hand, the installment of ICT is often accompanied with moredemand for energy due to its life cycle of production, use, and disposal, which would increase
CO2 emissions and induce the environmental degradation (Danish et al., 2017). However, thisunfavorable effect of ICT may be relatively small for least-developed countries, where theenergy demand associated with ICT is not so large. These arguments support that the favorable
ICT effect, or the green ICT, plays a dominant role in determining CO2 emissions in relatively
low-income developing countries.
Concerning the long-run relationship of CO2 emissions with other explanatory
variables (lnRGDPPC, lnTECPC, and RREP), the ARDL analysis first reveals the positive andnegative relationships between CO2 emissions and real GDP per capita for relatively lowincome developing countries and relatively high-income developing countries, respectively. A
1 percent increase in real GDP per capita is associated with a 0.06 percent decrease in CO 2
emission per capita in relatively high-income developing countries. On the other hand, a 1
percent increase in real GDP per capita is associated with a 0.87 percent increase in CO2emissions in relatively low-income developing countries. These findings are consistent with
the EKC hypothesis that postulates an inverted-U-shaped relationship between pollutions andper capita income. Second, our results identify the unfavorable impact of per capita energy
consumption on the environment. This unfavorable impact is more substantial for relatively
high-income developing countries. A 1 percent increase in per capita energy consumption isassociated with a 1.02 percent increase and a 0.50 percent increase in CO2 emissions per capita

53
for relatively high-income and relatively low-income developing countries, respectively. Third,
our analysis also finds the negative relationship between renewable energy penetration andCO2 emissions for both country groups. When a country increases the share of renewableenergy by 1 percent, CO2 emissions would be decreased by approximately 1 percent for bothcountry groups.
The ARDL analysis also provides several results of the short-run dynamics. First,
similar to the long-run relationship, energy consumption per capita and CO2 emissions per
capita have a positive relationship in the short-run for both country groups. Second, the shortrun relationship between real GDP per capita and CO2 emissions per capita is significantly
positive for relatively high-income developing countries, which is in contrast to the result of
their negative long-run relationship. On the other hand, there is no clear short-run relationshipbetween real GDP per capita and CO2 emissions per capita for relatively low-income
developing countries. Third, our results reveal that relatively low-income developing countries
experience the negative short-run relationship between renewable energy penetration and CO2
emissions per capita, while relatively high-income developing countries have the less clear
short-run relationship.

3.3.4 Dumitrescu and Hurlin panel causality tests
This subsection employs the panel Granger causality test, initiated by Dumitrescu and Hurlin(2012), to evaluate the direction of the relationship between variables used in the model. The
standard Granger causality test can be applied to the panel data, assuming the homogenous
panel with identical intercepts and slope coefficients (Granger, 1969). Recent studies such as
Koçak and Şarkgüneşi (2017), Bekun et al. (2019), and Essandoh et al. (2020) have applied theDumitrescu-Hurlin panel causality test, which is based on the heterogeneous assumption, toevaluate Granger causality in various energy and environmentally related fields. The

54
Dumitrescu-Hurlin panel causality test accounts for two-dimensional heterogeneities: theheterogeneity of the regression model for Granger causality test and the heterogeneity of thecausality relationship. Given the fact that countries are generally heterogeneous in terms of
their energy and ICT use patterns, this study also applies the heterogeneous Dumitrescu-Hurlin
panel causality test. Specifically, Dumitrescu and Hurlin (2012) propose the following linear
model:(k)(k)

yi,t = αi + ∑Kk=1 γi yi,t−k + ∑Kk=1 βi xi,t−k + εi,t ,where x and y are two stationary variables for country i in year t. The model allows slope(k)and lag parameters, βi(k)and γi , to vary across cross-sections but are assumed to be fixedover time. The null hypothesis is no causal relationship for any cross-section of the panel,
known as the homogeneous noncausality hypothesis (H0 : βi = 0 for any i = 1, 2, ⋯ , N), andthe alternative hypothesis is the existence of a causal relationship in at least one cross-section
unit. The individual Wald statistics are calculated for each cross-section, and then the test
statistic for the panel is calculated by taking the average of all individual Wald statistics
1

HNC(Dumitrescu and Hurlin, 2012), WN,T
= N ∑N
i=1 Wi,T , where Wi,T is the individual Waldstatistic for country i in year T , and N is the number of countries. 18 To investigate theGranger causality, all variables must be stationary. Thus, we use the first differences ofvariables (lnCO2PC, lnTECPC, lnRGDPPC, RREP, and ICT).
Table 3.10 displays the results of Dumitrescu-Hurlin panel causality tests. It isobserved that there are some differences in Granger’s causality between relatively low-income

18

HNC
Given the test statistic WN,T
, Dumitrescu and Hurlin (2012) derive its limiting distribution and show that an

HNC
alternative test statistic, ZN,T
= √

N
2K

HNC
( WN,T− K ), converges to the normal distribution N(0,1), where K is

HNC
the number of lags. For the practical use, WN,T
is recommended if the time dimension is lower than the crossHNC
section one, while ZN,T is recommended if the time dimension is higher than the cross-section one(Dumitrescu and Hurlin, 2012).

55
developing countries and relatively high-income developing countries. For the group of
relatively high-income developing countries, the test results show several causal relationships.
First, one-way Granger causal relationships exist from ICT to lnCO2PC, lnTECPC, and RREP,
which supports the significant role of ICT in predicting the variation of all variables except for
lnRGDPPC. Second, the analysis also presents one-way Granger causal relationships fromlnRGDPPC to lnTECPC and from RREP to lnRGDPPC. Real GDP per capita is an important
predictor for total energy consumption per capita, and renewable energy penetration is an
important predictor for real GDP per capita. On the other hand, the test results for the group of
relatively low-income developing countries also reveal several causal relationships. First, it isobserved that there are one-way Granger causal relationships from ICT to lnCO2PC and fromlnTECPC to ICT. Total energy consumption per capita is an effective predictor for ICT, which
in turn is an effective predictor for CO2 emissions per capita. Second, the results also present a
one-way Granger causality from RREP to lnCO2PC, which is consistent with the conventional
argument that renewable energy use helps reduce CO2 emissions. Third, the analysis observesa two-way or reciprocal Granger causality between ICT and RREP. This two-way relationshipcould be justified by the argument of a virtuous cycle that the prevalence of ICT is one
important determinant of renewable energy production, which could also increase the demandfor ICT products. Accounting for the causality from RREP to lnCO2PC, sound management of
ICT policy is a possible remedy for environmental sustainability through green ICT.

56

Table 3.10. Dumitrescu and Hurlin panel causality test
Null Hypothesis

Relatively low-income developing countries
Relatively high-income developing countries
W-Stat.
Prob.
Causality
W-StatProb
Causality
DlnTECPC does not homogeneously cause DlnCO2PC
1.39000.38040.94550.6026
DlnCO2PC does not homogeneously cause DlnTECPC
1.2966
0.55981.54690.1706
DlnRGDPPC does not homogeneously cause DlnCO2PC
0.9316
0.57251.30820.5353
DlnCO2PC does not homogeneously cause DlnRGDPPC
1.26560.62711.01730.7680DRREP does not homogeneously cause DlnCO2PC
1.6795*0.0739RREP → lnCO2PC
1.42000.3313
DlnCO2PC does not homogeneously cause DRREP
1.16400.86780.84820.4084
DICT does not homogeneously cause DlnCO2PC
3.0601***0.0000
ICT → lnCO2PC
0.5175*
0.0619 ICT → lnCO2PC
DlnCO2PC does not homogeneously cause DICT
0.94820.60841.04020.8236
DlnRGDPPC does not homogeneously cause DlnTECPC
1.28880.5764
1.7991**0.0305 lnRGDPPC → lnTECPC
DlnTECPC does not homogeneously cause DlnRGDPPC
1.07520.91011.29670.5594
DRREP does not homogeneously cause DlnTECPC
1.46850.26101.12590.9628
DlnTECPC does not homogeneously cause DRREP
1.20940.75710.92790.5645
DICT does not homogeneously cause DlnTECPC
1.32970.49190.5177*0.0620 ICT → lnTECPC
DlnTECPC does not homogeneously cause DICT
2.3711***0.0001lnTECPC →ICT
1.2794
0.5966DRREP does not homogeneously cause DlnRGDPPC
0.89590.4986
1.8712**0.0168 RREP → lnRGDPPC
DlnRGDPPC does not homogeneously cause DRREP
1.03370.8076
1.11910.9800
DICT does not homogeneously cause DlnRGDPPC
1.53500.18250.61380.1178DlnRGDPPC does not homogeneously cause DICT
1.59440.12850.79490.3200
DICT does not homogeneously cause DRREP
2.4962***0.00002.0482***0.0032 ICT → RREPDREP does not homogeneously cause DICT
2.01788*** 0.0043
ICT ←→RREP
0.89380.4943
Note: ***, ** and * means statistical rejection level. While → and ↔ represent one-way causality and bi-directional causality, respectively.

57

3.3.5 Robustness checks
To confirm the empirical validity of our estimated results in the previous subsections, we
conduct a robustness check by including two additional variables that are expected to relate to
CO2 emissions into the baseline model. The first variable is urbanization (URBAN), and thesecond is trade openness (TRADE). Many studies have discussed the role of international tradein determining the environment, and recent focus has been on the principles of carbon emission
responsibility in international trade from different perspectives (Dogan and Seker, 2016; Kimet al., 2018; Long et al., 2018; Essandoh et al., 2020). In addition, many works have existed on
the relationship between urbanization and CO2 emissions, given the arguments that changes inurbanization could affect patterns of energy use and CO2 emissions (Poumanyvong and Kaneko,
2010; Sadorsky, 2014).
The results of the extended model as a robustness check are generally consistent withthe findings of our baseline analysis (Tables 3.A1 to 3.A7 in the appendix). The long-run
relationship between CO2 emissions and ICT depends on countries’ income level, i.e., thedevelopment stage. In the long run, the prevalence of ICT is associated with the reduction of
CO2 emissions in relatively low-income developing countries, while no clear relationship exists
between ICT and CO2 emissions in relatively high-income developing countries. Moreover, theestimations of the extended model show some evidences supportive of clear differences in thelong-run relationships between the two groups. First, CO2 emissions per capita is positively
associated with trade openness only for relatively high-income developing countries. Second,
CO2 emissions per capita is negatively associated with urbanization for relatively high-income
developing countries, while it is positively associated with urbanization for relatively lowincome developing countries.

58

3.4 Conclusion
This research work has aimed to examine the relationship between CO2 emissions and ICT andhow the relationship is affected by development stages. We have employed a panel ARDL
analysis with PMG estimators to a panel of 58 developing countries, which are divided into
two income groups (relatively low- and relatively high-income developing countries), during
the sample period from 1990 to 2014. Our analysis has revealed that the long-run relationshipbetween CO2 emissions and ICT differs, depending on a country’s development stage. The
prevalence of ICT is associated with the low level of CO2 emissions in relatively low-income
developing countries, but ICT and CO2 emissions have no clear relationship in relatively highincome developing countries. In addition, the estimated results have confirmed the argumentof the environmental Kuznets curve (EKC) that the income level, captured by real GDP per
capita, is positively associated with CO2 emissions at the earlier stage development, while it isnegatively associated with CO2 emissions at the later stage development.
Our results could provide important policy implications about ICT and environmental
sustainability in developing countries. Less-developed countries at the earlier stage ofdevelopment tend to prioritize economic growth or poverty reduction over the environmental
issues and to use a large amount of nonrenewable energy due to the high cost of clean orrenewable energy, which would consequently intensify the environmental degradation withlarge CO2 emissions. Since our results suggest the favorable effect of ICT on the environment,
ICT promotion can be a powerful tool to fight such environmental degradation, particularly forless-developed countries.
Developing countries, particularly least-developed countries, with the substantial useof ICT need to emphasize “greening ICT” strands. Policymakers should implement various
ICT policies toward the prevalence of ICT products and services, such as attracting foreign
direct investment (FDI) with advanced and green ICT from developed countries. Another59
potential benefit of ICT for developing countries is that advanced technology reduces energy
use and improve energy efficiency. From an economic point of view, building infrastructure to
produce eco-friendly renewable energy is expensive for most developing countries. As analternative solution, ICT can guide these countries to achieve their climate targets by the
reduction in energy use. For instance, in the transportation sector, introducing ICT based
intelligent operation ensures more resource-efficient operation and reduced-physicaltransportation. By optimizing the performance of energy-using systems, ICT helps conserve
energy and reduce fossil-fuel use, which enables sustainable development in least-developing

60

3.5 Appendix
Table 3.A1. The definitions of additional variables
Variable

Variable

Urbanization

URBAN

Trade openness

TRADE

Variables definitions (measurements)
The percentage of the urban population in the
total population
Imports plus exports of goods and services (%of GDP)

Table 3.A2. Descriptive statistics of additional variables
Variables
Relatively low-income developing countries
URBAN
TRADE
Relatively high-income developing countries
URBAN
TRADE

Obs.

Mean

Std. Dev.

Min.

Max.

725
725

0.41230.62030.14150.25990.08850.0002

0.6976
1.5423725
725

0.65970.73070.15350.38200.26440.13750.94942.204061

Table 3.A3. Panel unit root tests for additional variables (1990-2014)
Null: unit root
Levin, Lin & Chu
Breitung
Im, Pesaran and Shin
ADF - Fisher
PP - Fisher
Relatively low-income developing countries
URBAN-6.4952***[0.000]-2.6235***[0.004]-1.5259*[0.063]253.409***[0.000]189.305***
ΔURBAN-6.1594***[0.000]-6.5317***[0.000]-2.1671**[0.015]311.854***[0.000]77.5900**
TRADE-2.3774***[0.009]-0.8883[0.187]-1.4467*[0.074]77.2320**[0.046]87.8969***
ΔTRADE-22.1606***[0.000]-13.6814***[0.000]-20.3147***[0.000]467.543***[0.000]1354.94***
Relatively high-income developing countries
URBAN
3.0774[0.999]3.3953[1.000]0.2661[0.605]117.703***[0.000]63.9496
ΔURBAN
2.2699[0.988]1.6731[0.953]-1.3639*[0.086]97.4551***[0.001]316.020***
TRADE-4.3577***[0.000]-1.2945*[0.098]-4.0101***[0.000]111.523***[0.000]91.4409***
ΔTRADE-19.2031***[0.000]-12.2570***[0.000]-17.3807***[0.000]334.266***[0.000]471.163***
Notes: (1) Figures in the parenthesis indicate p-values. (1) Optimal lag lengths are determined by Schwarz Info Criterion (SIC).
(3) Individual intercept and individual linear trend are included. (4) *, **, and *** represent the 10%, 5%, and 1% significance levels, respectively.

[0.000][0.044][0.007][0.000][0.276][0.000][0.003][0.000]62
Table 3.A4. Pedroni panel cointegration tests (1990-2014)
Within-dimension (panel statistics)
Between-dimension (individual statistics)
Test
Statistic
Prob.
Test
Statistic
Prob.
Relatively low-income developing countries
Pedroni (1999)
Panel v-Statistic-2.3845
0.9914
Group rho-Statistic
5.53551.000
Panel rho-Statistic
3.46340.9997
Group PP-Statistic-14.3201***
0.0000Panel PP-Statistic-7.1872***
0.0000Group ADF-Statistic-8.9739***0.0000Panel ADF-Statistic-5.0381***0.0000Pedroni (2004) Weighted Statistic
Panel v-Statistic-5.5226
1.0000Panel rho-Statistic
3.80010.9999Panel PP-Statistic-12.4858***
0.0000Panel ADF-Statistic--9.5535***0.0000Relatively high-income developing countries
Pedroni (1999)
Panel v-Statistic-4.261301
1.0000Group rho-Statistic
5.09101.0000Panel rho-Statistic
2.63230.9958Group PP-Statistic-22.3647***0.0000Panel PP-Statistic-16.1786***0.0000Group ADF-Statistic-10.3463***0.0000Panel ADF-Statistic-12.7960***
0.0000Pedroni (2004) Weighted Statistic
Panel v-Statistic-6.37421.0000Panel rho-Statistic
3.98421.0000Panel PP-Statistic-15.2395***
0.0000Panel ADF-Statistic-9.6394***0.0000Notes: (1) Optimal lag lengths are determined by Schwarz Info Criterion (SIC). (2) Individual intercept and individual linear trend are included in the test regressions. (3) *, **, and*** represent the 10%, 5% and1% significance, respectively.

63

Table 3.A5. Kao panel cointegration tests (1990-2014)
Statistic
Prob.
Relatively low-income developing countries
ADF-7.1164***0.0000Residual variance0.0142
HAC variance0.0122
Relatively high-income developing countries
ADF-3.6887***
0.0001
Residual variance0.0054
HAC variance0.0040Notes: *, **, and *** represent the 10%, 5% and 1% significance, respectively.

Table 3.A6. Pooled mean group with dynamic autoregressive distributed lag: PMG-ARDL (1,1,1,1,1)

Long-run equation
lnTECPC
lnRGDPPC
RREP
ICT
URBAN
TRADE
Short-run equation
ECT(-1)

Relatively high-income
developing countries

Relatively low-income
developing countries

1.0232***(0.0000)-0.0208(0.3138)-0.0121***(0.0000)-0.0083(0.3093)-0.5975***(0.0000)
0.0687***(0.0090)0.6291***(0.0000)
0.2264***(0.0000)-0.0135***(0.0000)-0.0491***(0.0024)0.0089**(0.0103)-0.0003(0.4451)-0.5763***--0.4836***(0.0000)(0.0000)
lnTECPC
0.2519**0.6115***(0.0154)(0.0080)
lnRGDPPC
0.06290.1870(0.4797)(0.3198)
RREP
0.0053-0.0143**(0.7540)(0.0162)
ICT-0.0974
0.0415(0.2087)(0.7570)
URBAN-5.3383
0.1207(0.3194)(0.5422)TRADE-0.0518*0.0011**(0.0971)(0.0308)
Constant-3.0786***-2.7350***(0.0000)(0.0000)
Hausman
1.15
2.40(0.9794)(0.8797)
No of obs.
696
696
Notes: (1) *, **, and *** represent the 10%, 5% and 1% significance, respectively. (2) The dependent variable is
lnCO2PC.

64

Table 3.A7. Dumitrescu and Hurlin panel causality test
Null Hypothesis
DlnTECPC does not homogeneously cause DlnCO2PC
DlnCO2PC does not homogeneously cause DlnTECPCDlnRGDPPC does not homogeneously cause DlnCO2PC
DlnCO2PC does not homogeneously cause DlnRGDPPCDRREP does not homogeneously cause DlnCO2PC
DlnCO2PC does not homogeneously cause DRREPDICT does not homogeneously cause DlnCO2PC
DlnCO2PC does not homogeneously cause DICTDURBAN does not homogeneously cause DlnCO2PC
DlnCO2PC does not homogeneously cause DURBANDTRADE does not homogeneously cause DlnCO2PC
DlnCO2PC does not homogeneously cause DTRADE
DlnRGDPPC does not homogeneously cause DlnTECPC
DlnTECPC does not homogeneously cause DlnRGDPPCDRREP does not homogeneously cause DlnTECPC
DlnTECPC does not homogeneously cause DRREPDICT does not homogeneously cause DlnTECPC
DlnTECPC does not homogeneously cause DICTDURBAN does not homogeneously cause DlnTECPC
DlnTECPC does not homogeneously cause DURBANDTRADE does not homogeneously cause DlnTECPC
DlnTECPC does not homogeneously cause DTRADE
DRREP does not homogeneously cause DlnRGDPPC
DlnRGDPPC does not homogeneously cause DRREPDICT does not homogeneously cause DlnRGDPPC
DlnRGDPPC does not homogeneously cause DICTDURBAN does not homogeneously cause DlnRGDPPC
DlnRGDPPC does not homogeneously cause DURBANDTRADE does not homogeneously cause DlnRGDPPC
DlnRGDPPC does not homogeneously cause DTRADE
DICT does not homogeneously cause DRREP
DRREP does not homogeneously cause DICTDURBAN does not homogeneously cause DRREP
DRREP does not homogeneously cause DURBANDTRADE does not homogeneously cause DRREP
DRREP does not homogeneously cause DTRADE
DURBAN does not homogeneously cause DICTDICT does not homogeneously cause DURBANDTRADE does not homogeneously cause DICTDICT does not homogeneously cause DTRADEDTRADE does not homogeneously cause DURBAN
DURBAN does not homogeneously cause DTRADE

Low-income countries
W-Stat.
Prob.
1.39000.3804
1.2966
0.55980.9316
0.57251.26560.62711.6795*0.07391.16405
0.86783.0601***0.00000.94820.6084
1.04710.84052.1927***0.00071.20250.77370.92350.5552
1.28880.5764
1.07520.9101
1.46850.26101.20940.75711.32970.49192.3711***0.0001
1.18350.82001.8715**0.01681.49860.22300.4538**0.03870.89590.49861.03370.80761.53500.18251.59440.12852.0610***0.00281.08800.94211.36590.42291.12050.97652.4962***0.00002.0179***0.0043
1.7620**0.04071.28960.5746
0.91830.54420.98540.69252.8051***0.00000.658700.15480.9594
0.6332
1.37330.40961.9802***0.0063
1.05610.8625

Causality

RREP→ lnCO2PC
ICT →lnCO2PC

lnCO2PC →URBAN

lnTECPC →ICTlnTECPC →URBAN

lnTECPC→ TRADEURBAN→ lnRGDPPCICT ←→RREP
URBAN→RREP

URBAN →ICTTRADE →

URBAN

Middle-income countries
W-StatProb.
0.94550.6026
1.54690.17061.30820.53531.01730.76801.42000.33130.84820.40840.5175*
0.06191.04020.8236
0.97400.66640.76430.2754
1.27660.60271.0891
0.94471.7991**0.03051.29670.55941.12590.96280.92790.56450.5177*0.06201.2794
0.59661.38590.3874
1.34150.46871.9211**0.01091.44620.29191.8712**0.01681.11910.98000.61380.11780.79490.32000.76900.28191.7947**0.03161.32370.5037
2.1972***0.00062.0482***0.00320.89380.4943
0.97620.67131.01060.75193.5001***0.00001.9513***0.0082
1.55660.16123.2755***0.00000.92300.5542
1.041030.82551.38800.38391.8166**
0.0265

Causality

ICT → lnCO2PC

lnRGDPPC → lnTECPCICT → lnTECPCTRADE → lnTECPCRREP → lnRGDPPC

lnRGDPPC → URBAN
lnRGDPPC → TRADEICT → RREPTRADE ←→ RREPICT → URBAN

URBAN → TRADE

Note: ***, ** and * means statistical rejection level. While → and ↔ represent one-way causality and bi-directional causality respectively.

65

Chapter 4: Corruption, ICT and military expenditure in Sub-SaharanAfrica.
4.1 Introduction
National security is listed among the priorities of nations (Gupta et al., 2001), but there is notransparency in the military department. This department is associated with the occurrence of plenty
corruption cases for secret defense purpose. Governments are typically the sole providers of defense
services and the opacity of defense information make certain aspects of defense provision subjected tocorruption. In fact, regulations typically confer power on the officials in charge of authorizing contracts
so the secrecy surrounding defense outlays gives rise to corruption, particularly in the procurement ofmilitary equipment. Furthermore, defense contracts are often excluded from freedom of information
legislation, where available; and are also often drawn in secrecy and under considerable discretionary
power by the authorities. Moreover, administrative procedures in military spending may not be closely
monitored by tax and customs administration authorities and defense contracts may not be liable tostandard budget oversight such as auditing and legislative approval.
Corruption is a major concern for developing countries as it can have a seriously damaging
effect on development and welfare through the weakening of the institutions (d’Algostino et al, 2016).
Moreover, one scholar observes that corruption ‘‘is not markedly worse than in many other parts ofthe developing and the former communist world, yet corruption in Africa is universally perceived, byexternal observers and by local reformers alike, as being ‘catastrophic’ in its impact on development”(Szeftel, 2000). In fact, Sub-Saharan Africa countries are listed among the most corrupted ones in theworld and this phenomenon occurs mostly in the states where the military are in charge and speciallyin the military expenditure. For instance, in the Congo (Zaire) late General Mobute Sese Seko presidentof the state was not free from corruption allegation as he was accused of hiring a concord jet for
shopping trip and built a palace fitted with a nuclear garget (Momoh, 2015). In Nigeria, the late military

66
junta General Sani Abacha was accused to have looted billions of dollars in which former Head ofState General Abdulsalami Abubakar regime recovered about US $ 750 million while former President
Olusegun Obasanjo administration convinced the Swiss banking authorities to freeze more than
US$600 million in late General Sani Abacha deposit and return nearly US$ 140 million to the Nigerian
government (Momoh, 2013). In the same vein, there was an allegation of bribery involving the role ofBAE systems and other weapons firms in the South Africa’s biggest arm deal (the sale of Hawk andGripen Warplanes for 1.66 billion pounces) (Momoh, 2015).
To deal with the endemic nature of corruption in Sub-Saharan Africa at the institutional levels
especially in the military expenditure, head of states have promulgated the control of corruption
through the implementation of anti-corruption laws (administrative reform, law enforcement, social
capital) (Shim and Eom, 2009). Unfortunately, these anti-corruption laws faced tremendous internaland external challenges. The internal ones are classified as political, economic, socio-cultural,technological and environmental factors such as discretionary anti-corruption laws which grant
immunities to some political leaders by making the procedure of prosecuting such leader when foundguilty of corruption charges difficult Momoh (2013) and the external ones such as money derived fromserious crime like financing terrorism, make the anti-corruption laws ineffective to reduce the military
expenditure.
To overcome the ineffectiveness of the anti-corruption laws, appears the Information andCommunication Technology (ICT). In fact, the literature on ICT supports the argument that ICT plays
an important role in public management reform (Asgarkhani,2005) by delivering better quality public
services with less waiting time and cost (Breen, 2000), helping citizens find jobs and better public
services (Brueckner, 2005), facilitating public engagement (Goodwin, 2005), and helping community
development (Hammerman, 2005). Ultimately, it can be argued that ICT enhances public
productivity (Yang and Rho, 2007), supports good governance (Basu, 2004), and improves
government accountability (Rose, 2004; Wong and Welch, 2004; Yang and Rho, 2007). As a resume,

67
ICT offers remarkable opportunities for promoting good governance, increasing transparency, andreducing corruption (Kanyam et al., 2017), so it is expected to reduce the misuse of the military
expenditure. In fact, most studies are related to corruption and military expenditures (Gupta et al.,2001); corruption and growth in Africa (D’Agostino et al., 2016); government spending, corruption,and growth (D’Agostino et al., 2016); ICT and corruption (Kanyam et al., 2017). Few studies haveattempted to look at the contribution of ICT to reduce the misuse of military expenses. Our main
objective is to examine how ICT as a policy variable is associated with the relationship between control
of corruption and military expenditure in Sub-Saharan Africa. To reach that objective we formulated
one hypothesis which states that the interaction of the control of corruption (traditional anti-corruption
factors) and the use of ICT is negatively correlated with the military expenditure. To test our hypothesis,
we use a panel data of 48 Sub-Saharan Africa countries from 2003 to 2015 and apply Arellano andBond GMM estimation as identification strategy the main result is that the relationship between control
of corruption and military expenditure depends on the level of ICT. When ICT level is low, there isless clear relationship between control of corruption and military expenditure. However, when ICT
prevails, there is negative relationship between control of corruption and military expenditure.
Policymakers should use ICT as a key policy variable to fight corruption in the defense sector,
especially for the military expenditure through good regulation of telecommunication sector,
encouraging foreign investments in ICT sector, educating and encouraging the population to be
familiar to ICT tools especially internet.
The remainder of this study is organized as follows. In section 2, we provide a selective reviewof the literature on corruption, ICT, and military expenditure. Section 3 describes data andmethodology used in this study. In section 4, we present the empirical results of our analysis, and we
discuss some implications derived from our empirical results. Final section 5 provides the conclusion.

68

4.2 Literature review
4.2.1 Corruption and military expenditure
The complex and clandestine nature of corruption makes it difficult to observe, measure, and to
estimate its extent precisely. While there is no direct mechanism for measuring corruption, researchers,
scholars, and development practitioners have deduced several indirect ways of getting information
about its prevalence in a country or institution (Tanzi,1998). Over time, research has shown thatpeople’s perceptions offer a reliable estimate of the nature and scope of corruption in a given country.
The most preferred and widely disseminated measures utilized by scholars and policy makers are thecorruption perception index (CPI) presented by Transparency International and the ‘‘control ofcorruption” (CoC) by the World Bank. The CPI is a composite index including opinion survey data
which captures the informed views of independent institutions specializing in governance and businessclimate analysis, country experts, business people, global analysts, and experts who are residents ofthe evaluated countries (Svensson, 2005; Transparency International, 2013). The CoC index, on theother hand, captures the ‘‘perception of the extent to which public power is exercised for private gain,
including both petty and grand forms of corruption, as well as ‘capture’ of the state by elites and privateinterest.” It draws on surveys from households and firms, commercial business information providers,
non-governmental organizations, and public sector organizations (World Bank, 2010). Even though
the two measures are highly correlated (Tanzi,1998), the CoC indicator is much broader than the CPI(Aidt,Dutta, & Sena, 2008) and it is the one used in this study. It incorporates a variety of aspects ofcorruption ranging from the frequency with which firms make additional payments to get things done,to the effects of corruption on the business environment, to measuring grand corruption in the politicalarena (Olken & Pande, 2011). Gupta et al. (2001) distinguished the supply side and the demand sideof corruption related to the military spending. by using a panel data from four different sources for up
to 120 countries during 1985–1998. Their study reveals that corruption is associated with higher
military spending as a share of both GDP and total government spending, as well as with arms

69
procurement in relation to GDP and total government spending. Langlotz and Potrafke (2019)confirmed that corrupt recipients of aid are likely to spend a higher share of their GDP on military
expenditure than less corrupt countries by using instrumental variable (IV) approach to a dataset that
includes new data on military expenditure for 124 recipient countries over the 1975–2012 period.
d’Algostino et al. (2016) by using Arellano and Bond GMM estimation to a comprehensive panel of
106 countries found that corruption and military spending have strong negative impacts on economic
growth. In the case of Africa d’Algostino et al. (2016) by using the same identification strategy
confirmed the negative effect of corruption and military spending on growth, but also showed that
corruption interacts with military burden, through indirect and complementary effects, to further
increase its negative effect.

4.2.2 ICT and military expenditure
ICT can reduce unnecessary interventions by public employees that engender the abuse of power andhelp to monitor and reveal public employees’ behaviors at a low cost through audit. ICT also
contributes by transparently providing information to the public and can build social capital byincreasing interactions among individuals. Foremost, ICT helps to prevent public employees’ corrupt
behavior by transparently providing information about governmental policymaking and service
delivery processes to the public. The public can access governmental websites and download
information as they desire, keeping track of governmental policymaking processes through the Internet.
In this approach, basic governmental operations are not altered, and relatively simple information isdelivered to information seekers. This approach can prevent corrupt behavior of public workers bysystematically reducing their arbitrary behavior in a centralized system as well as when publicorganizations are decentralized (Zuurmond, 2005), which enables horizontal networks among different
agencies easily. As a result, public service delivery becomes more accessible to the public, and thus
government workers will feel that they are more likely to be exposed if they decide to pursue corrupt
behavior. Some countries have adopted e-government initiatives as an anticorruption solution, and70
successful cases have been reported from several countries, including South Korea, India, Russia,
Argentina, and Chile (Bhatnagar, 2001a, 2001b; Chawla and Bhatnagar, 2001; Im, 2001; Shim andEom, 2008). Several researchers have argued that e-government approaches have greater potential tosucceed when they are integrated with an e-participation approach (Bruszt et al., 2005; Saxena, 2005).
The Organization for Economic Co-operation and Development (OECD, 2001) argues that democratic
participation processes should reflect the policy agenda of citizens, and that ICT enhances government
responsiveness by supporting the citizen participation process with e-participation applications. As
government service delivery is essentially a monopoly, an information delivery-oriented e-government
initiative could be perceived as a digital mandate (Tan et al., 2005) that might prevent citizens frombeing active users of e-government services. In this context, the e-participation approach is importantin that it gives the public an opportunity to observe and supervise policy implementation andparticipate in the government policy-making processes (Saxena, 2005). By strengthening
representative democracy, e-participation prevents small interest groups controlled by political elites
from dominating public policy (Macintosh, 2002). This approach is better described as ‘e-governance’
compared to ‘e-government’, in that it aims to transform the policy decision-making process into a‘citizen-centric, cooperative, and seamless but polycentric modern governance ‘(Saxena, 2005).

4.3 Methodology and data
4.3.1 Data
This study utilizes a balanced panel data set that consists of 48 countries in SSA (Table 4.1) spanningfrom 2003 to 2015 to estimate the effect of ICT as a policy variable in the relationship between thecontrol of corruption and military expenditure. We use the corruption and transparency index (control
of corruption, CoC) from the World Bank’s World Governance Indicators (WGIs) (Kaufmann, Kraay,
and Mastruzzi, 2011). The index ranges on a scale from -2.5 to 2.5, where a more negative or lower
score indicates a greater level of corruption. Internet adoption is measured by the number of internet
users (people with access to the worldwide computer network) per 100 persons. The military

71
expenditure data from Stockholm International Peace Research Institute (SIPRI) are derived from theNorth Atlantic Treaty Organization (NATO) definition, which includes all current and capital
expenditures on the armed forces, including peacekeeping forces; defense ministries and othergovernment agencies engaged in defense projects; paramilitary forces, if these are judged to be trainedand equipped for military operations; and military space activities. The control variables include sevennon-dummy variables (lagged dependent variable, trade, net ODA received, log of real GDP, log ofreal GDP per capita, political stability and total natural resource rents) and one dummy variable(democracy-dictatorship regime). These control variables have been substantially documented in theliterature on military expenditure (Maizels and Nissanke,1986; Gupta et al., 2001; d’Agostino et al.,2012, 2016a, 2016b; Langlotz and Potrafke, 2019 and Bjørnskov and Rode, 2020). The data of all
variables are taken from the World Bank's World Development Indicators (WDI) database except thedemocracy-dictatorship dummy variable which from Bjørnskov and Rode (2020). Details about thevariables and data sources are provided in Table 4.2. Table 4.3 displays the descriptive statistics,
including variable mean, standard deviation, and minimum and maximum values. The average military
expenditure in percentage of GDP is 1.73, while the average of corruption perception index andinternet users per 100 people are about 0.64 and 7.7, respectively. Table 4.4 presents the correlation
matrix (used to check for potential multicollinearity) for all variables in the study. The control ofcorruption, internet adoption, net ODA received, real GDP and political stability are negatively
correlated with military expenditure while the variables trade, real GDP per capita, total natural
resources rents and democray-dictatorship regime dummy variable are positively correlated withmilitary expenditure. To mitigate the bias associated with the fact that the variables may not be
normally distributed, we use an estimation technique other than Ordinary Least Squares.

72
Table 4.1. List of countries included in the analysis

AngolaBenin
Botswana
Burkina Faso
BurundiCabo Verde
Cameroon
Central African Republic
Chad
Comoros
Congo, Dem. Rep.
Congo, Rep.
Cote d’Ivoire
Djibouti
Equatorial Guinea
Eritrea

Eswatini
Ethiopia
Gabon
The Gambia
Ghana
GuineaGuinea-Bissau
Kenya
LesothoLiberia
Madagascar
MalawiMali
Mauritania
Mauritius
Mozambique

Namibia
Niger
Nigeria
Rwanda
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
SomaliaSouth Africa
Sudan
Tanzania
Togo
UgandaZambiaZimbabwe

73
Table 4.2. Variable definitions.
Variables
Military Expenditure

Signs
MILEXP

Variables definitions (measurements)

Sources

Military Expenditure (% of GDP)

World Bank
(WDI)

Reflects perceptions of the extent to which public power is
exercised for private gain, including
both petty and grand forms of corruption, as well as‘‘capture” of the state by elites and privateinterests. Estimate (ranges from approximately -2.5 (mostcorrupt) to 2.5 (least corrupt).
Includes subscribers who pay for internet access (dial-up,
leased line, and fixed broadband) andpeople with access to the worldwide computer network
without paying directly, either as themember of a household or from work or school. The
indicator is derived by dividing the numberof internet users by total population and multiplying by 100.
Trade as a percentage of GDP. Trade is the sum of exports
and imports of goods and services
measured as a share of the gross domestic product
Net ODA received (% of GNI) agencies of the members of
the Development Assistance Committee (DAC), by
multilateral institutions, and by non-DAC countries topromote economic development and welfare in countries andterritories in the DAC list of ODA recipients. It includes
loans with a grant element of at least 25 percent (calculated
at a rate of discount of 10 percent).

World Bank
(WGI)Control of corruption

CORRUPTIONInternet users (per 100
people)INTERNET

Trade

TRADE

Net ODA received (% ofGNI)

NET_ODAReal GDP

RGDP

GDP (constant 2010 US$)World Bank
(WDI)

Real GDP per capita

RGDPPCGDP per capita (constant 2010 US$)World Bank
(WDI)

Political Stability

POLSTAB

Total natural resources
rents (% of GDP)

TNATRESSRENT

DD regime

DDREGIME

Political Stability and Absence of Violence/Terrorism
measures perceptions of the likelihood of political instability
and/or politically motivated violence, including terrorism.
Estimate gives the country's score on the aggregate indicator,
in units of a standard normal distribution, i.e. ranging fromapproximately -2.5 to 2.5.
Total natural resources rents are the sum of oil rents, natural
gas rents, coal rents (hard and soft), mineral rents, and forest
rents.
Regime category dummy variable, following Democracy
Dictatorship dataset including colonies.
“0 for Parliamentary democracy”; “1 for Mixed democratic”;“2 for Presidential democracy”; “3 for Civilian dictatorship”;“4 for Military dictatorship” and “5 for Royal dictatorship”.

World Bank
(WDI)World Bank
(WDI)World Bank
(WDI)World Bank(WGI)World Bank
(WGI)

Bjørnskov
andRode (2020)

74
Table 4.3. Summary statistics of the variables included in the study.
Variables
MILEXP
CORRUPTIONINTERNETTRADE
NET_ODA
RGDP
RGDPPCPOLSTABTNATRESSRENT
DDREGIME

Obs.
509
624
612
573
600
595
595
624
595
611

Mean
1.7283-0.6401
7.702477.1600
9.36812.83e+102303.421-0.5283
12.88672.7119Std. Dev.
1.43130.619310.1991
39.2225
9.76607.24e+103342.82
0.929012.3344
1.1201Min.
0.1456-1.8687
0.031019.1008-0.2509
1.33e+08
194.8731-3.31490.00110Max.
20.8657
1.216754.2596311.3541
92.1415
4.64e+1120512.94
1.200259.6196
5

RGD
PPC

POL
STAB

1.00000.1929-0.1806-0.0079

1.00000.4691-0.0092

1.0000-0.30141.0000-0.0259-0.0644-0.30800.2602

Table 4.4. Correlation matrix

MILEXP
CORRUPTIONINTERNETTRADE
NET_ODA
RGDP
RGDPPCPOLSTABTNATRESSRENT
DDREGIME

MIL
EXP

CORRUP
TION

INTER
NET

1.0000-0.1969-0.15960.0328-0.1147-0.1255
0.0197-0.1758

1.00000.44030.2987-0.1102-0.0137
0.39090.73371.00000.3137-0.30190.2421
0.58320.33911.0000-0.0076-0.2033
0.48140.36071.0000-0.2547-0.4042-0.18270.3331-0.5163-0.28190.11680.12790.2722-0.3591-0.1755-0.0268-0.0537

TRADE

NET_
ODA

RGDP

TNAT
RESS
RENT

DD
REGIME

1.00004.3.2 Model specification
The Generalised Method of Moments (GMM) estimation approach is adopted for the following four
reasons. First, the number of countries or cross-sections (N equals 48) is substantially higher than the
periodicity per cross-section (T equals 13). Second, given that the GMM estimation technique isconsistent with a panel data structure, cross country variations are not eliminated in the estimations.
Third, the system estimator considers inherent biases in the difference estimator. Fourth, the estimation
procedure accounts for endogeneity by controlling for simultaneity in the explanatory variables using
an instrumentation process. Moreover, usage of time-invariant omitted variables (or time fixed effects)
also helps to mitigate the consequences of endogeneity bias. In accordance with Bond et al. (2001),
the system GMM estimator (see Arellano and Bover, 1995; Blundell and Bond, 1998) has better

75
estimation properties than the difference estimator (see Arellano and Bond, 1991). In this study, we
opt for the Roodman (2009a, 2009b) extension of Arellano and Bover (1995) because it has been
documented to restrict the proliferation of instruments and control for dependence among crosssections (see Baltagi, 2008; Boateng et al., 2016; Love and Zicchino, 2006). Hence, the extended
estimation procedure adopts forward orthogonal deviations as opposed to first differences. A two-stepprocedure is adopted instead of a one-step approach because it addresses concerns of heteroscedasticity
given that the one step procedure only controls for homoscedasticity. The following equations in level(1) and first difference in (2) summarize the standard system GMM estimation procedure.
𝑀𝑖,𝑡 = α0 + α1 𝑀𝑖,𝑡−𝜏 + α2 𝐶𝑖,𝑡 + α3 𝐼𝑖,𝑡 + α3 𝐶𝐼𝑖,𝑡 + ∑5ℎ=1 𝛿ℎ 𝑊ℎ,𝑖,𝑡−𝜏 + η𝑖 + ξ𝑡 + ε𝑖,𝑡𝑀𝑖,𝑡-𝑀𝑖,𝑡−𝜏=(1)

α1 (𝑀𝑖,𝑡−𝜏 − 𝑀𝑖,𝑡−2𝜏 ) + α2 (𝐶𝑖,𝑡 − 𝐶𝑖,𝑡−𝜏 ) + 𝛼3 (𝐼𝑖,𝑡 − 𝐼𝑖,𝑡−𝜏 ) + 𝛼4 (𝐶𝐼𝑖,𝑡 −𝐶𝐼𝑖,𝑡−𝜏 ) + ∑5ℎ=1 𝛿ℎ (𝑊ℎ,𝑖,𝑡−𝜏 − 𝑊ℎ,𝑖,𝑡−2𝜏 ) + (ξ𝑡 − ξ𝑡−𝜏 ) +ε𝑖,𝑡−𝜏 )(2)

where, 𝑀𝑖,𝑡 is military expenditure in country i at period t, α0 is a constant, C is the control ofcorruption, I represents internet adoption, CI is the interaction between control of corruption andinternet adoption, W is the vector of control variables (trade, net ODA received, real GDP, real GDP
per capita , political stability, total natural resources rents and democracy-dictatorship regime ), τ
represents the coefficient of auto-regression, ξ𝑡 is the time-specific constant, η𝑖 is the countryspecific effect and ε𝑖,𝑡 the error term.
It is appropriate to devote space to discussing identification properties and exclusion
restrictions in the GMM specification. All independent indicators are acknowledged as predeterminedor are suspected to be endogenous. Additionally, exclusively time-invariant variables or years are
considered to be strictly exogenous (also Asongu and Nwachukwu, 2016b; Boateng et al., 2016). The
intuition for the consideration builds on the fact that it is not likely for the time-invariant variables tobecome endogenous after a first difference (Roodman, 2009b).
In the light of above emphasis, the time-invariant variables impact on the outcome variable
exclusively through the predetermined variables. Furthermore, the statistical relevance of the exclusion

76
restriction is investigated with the Difference in Hansen Test (DHT) for instrument exogeneity.
Accordingly, the null hypothesis of the DHT should not be rejected for the time-invariant indicators to
explain the military expenditure variable exclusively through the suspected endogenous variables.
Hence, in the findings that are reported in Section 3, the assumption of exclusion restriction is validated
if the alternative hypothesis of the DHT related to instrumental variables (IV) (year, eq(diff)) is not
accepted. This is broadly in accordance with the standard IV procedure in which, a rejection of the null
hypothesis of the Sargan Overidentifying Restrictions (OIR) test is an indication that the instruments
affect the military expenditure variable beyond the suggested predetermined variable channels (seeAsongu and Nwachukwu, 2016c; Beck et al., 2003).

4.4 Results and discussion
Table 4.5 shows the estimation results of the two-step system GMM with orthogonal deviation. Column (1)and (2) are identified as preliminary specifications include the control of the corruption, ICT, lagged
military expenditure and the full set of controls without total natural resources rents and democracydictatorship regime. The third specification in Column (3) named interaction effect, controls for thesame covariates but add the interaction term between the control of corruption and ICT among theregressors. Finally, the specifications in column (4) and (5) check the robustness of the third (main)
specification by incorporating additional control variables (total natural resources rents anddemocracy-dictatorship regime). A Sargan OIR, test, a Hansen OIR test, a Difference in Hansen Testfor exogeneity of instruments ‘subsets are performed for all specifications. They confirm the validityof the instruments used with the respect of the rule of Thumb about the proliferation of instruments
which states that the number of instruments should be lower or equal to the number of groups, here
countries. The effect of the prior level of military expenditure is positive across all models andstatistically significant in all specifications except for the fifth specification. Therefore, military
expenditure seems to be persistent and have inertia. The result from the columns (1) and (2) show that77
the control of corruption namely the traditional anti-corruption factors in Sub-Saharan Africa and ICT
namely the internet adoption are not significantly associated with the military expenditure. Theinteraction between the control of corruption and internet adoption (column (3)) is negative andstatistically significant, so the marginal effect of this interaction reduces significatively at 1% level ofsignificance the military expenditure. From this result it appears clearly that when ICT prevails,
military expenditure is negatively associated with control of corruption, so internet use in association
with the control of corruption is a powerful tool to reduce military expenditure in Subsaharan Africancountries. This result remains robust even when we include total natural resources rents anddemocracy-dictatorship regime dummy variable in the specifications (4) and (5). In fact, in SSA, many
African politician has made politics in Africa a means to an end and not an end itself so that African
states institutions are weak as most of these institutions lack the capacity to provide basic social services forthe citizens rather they are “inverted” as they tend to “look inward” rather than “outward” in their administrationof social services. Furthermore, some SSA countries grant certain immunities to some political leaders bymaking the procedure of prosecuting such leader when found guilty of corruption charges difficult, so that thetraditional anti-corruption laws are ineffective. One effective way to correct this misuse of a public or private
position for direct or indirect personal gain in the military expenditure is to reveal these dishonest acts bybringing transparency back in these institutions through ICT so that the law will be respected (NATO, 2010).
For instance, by doing so the UK Department for International Development (DFID) shed light on

several frauds that occurred in Sub-Saharan Africa (Willet, 2009). Furthermore, some countries haveadopted e-government initiatives as an anticorruption solution, and successful cases have beenreported from several countries, including South Korea, India, Russia, Argentina, and Chile (Bhatnagar,
2001a, 2001b; Chawla and Bhatnagar, 2001; Im, 2001; Shim and Eom, 2008), and recently in the caseof Niger ( Mondafrique, 2020).

78
Table 4.5. Estimation results of the two-step system GMM with orthogonal deviation.

Constant

(1)

(2)

(3)

(4)

(5)

-41.1666--5.1414--(0.552)MILEXP (-1)
CORRUPTIONINTERNETINTERNET×(0.932)0.4260**0.3706**

0.4114***0.4033**0.3627(0.034)(0.037)(0.009)(0.027)(0.291)0.1586-0.9135-0.1626-0.2181-0.3693(0.927)(0.378)(0.879)(0.822)(0.737)-0.01800.02470.01890.0118(0.546)(0.402)(0.485)(0.652)--0.0381***-0.0385**-0.0336*(0.010)(0.027)(0.089)-CORRUPTIONTRADE
NET_ODALog (RGDP)
Log (RGDPPC)POLSTABTNATRESSRENT-0.0093-0.0068-0.0124**-0.0127-0.0105(0.211)(0.119)(0.032)(0.106)(0.301)0.0016-0.0063-0.0110-0.0098-0.0072(0.890)(0.389)(0.139)(0.270)(0.316)2.4791-0.00960.4541

0.1134-0.0842(0.485)(0.996)(0.854)(0.964)(0.978)-2.0905-0.8190-0.6210-0.3626-0.2640(0.356)(0.568)(0.638)(0.739)(0.857)-0.2323-0.1313-0.2754-0.3163-0.2272(0.500)(0.717)(0.246)(0.264)(0.583)---0.00130.0019(0.972)(0.961)
0.0944

DDREGIME

-

-

-

-

AR(1)

(0.087)(0.219)(0.152)(0.223)(0.515)AR(2)(0.156)(0.134)(0.170)(0.183)(0.152)

Sargan OIR(0.177)(0.062)(0.109)(0.081)(0.062)

Hansen OIR(0.201)(0.218)(0.775)(0.737)(0.750)

H excluding group(0.170)(0.508)(0.685)(0.653)(0.595)

Dif (null, H = exogenous)(0.440)(0.042)(0.702)(0.642)(0.876)

H excluding group(0.165)(0.293)(0.145)--

Dif (null, H = exogenous)(0.292)(0.225)(0.873)0.895-

Wald χ2

67.70***

62.92***

116.76***243.42***

170.18***Instruments

35

35

35

35

35

Countries

43

43

43

43

42

Observations

431

424

424

424

412

(0.699)DHT for instruments(a)Instruments in levels(b) IV (years, eq (diff))

79

4.5 Conclusion
Much effort has been exerted by many governments, development practitioners, and other stakeholders
to fight the secrecy on military expenditure in Sub-Saharan Africa since this region is regarded as themost corrupt in the world (Willet, 2009), especially in recent years, but this battle is still a challenge.
With the advent of the internet use, policy reformers from governments and many anti-corruption
organizations have recently encouraged the use of ICT systems (here internet) to bring transparency
in military expenditure (NATO, 2010). This study contributes to the existing literature by empiricallyinvestigating the effect of internet adoption on the relationship between the control of corruption andmilitary expenditure by utilizing a large panel data set. The empirical findings suggest that therelationship between control of corruption and military expenditure depends on the level of ICT. When
ICT level is low, there is less clear relationship between control of corruption and military expenditure.
However, when ICT prevails, there is negative relationship between control of corruption and military
expenditure, so that using the internet within a supportive and regulatory environment could help deter
misuse of military allocation. Policymakers should emphasize on the promotion of building integrity
to reduce corruption in defense through the implementation of e-governance via internet adoption andrise the marginal cost of corruption in military expenditure much higher than the marginal benefits sothat people will refrain from dishonest acts. Furthermore, policymakers should create a soundenvironment for the prevalence of ICT such as good regulation of telecommunication sector,
encouraging foreign investments in ICT sector, educating and encouraging the population to be
familiar to ICT tools especially internet. On a cautious note, it is also important to balance this finding
with the fact that regulating the internet could restrict the flow of information which could suppress
people from being communicative and expressive, and thus changing the way information is dealt withover the internet. From a policy perspective, by examining the influence of internet penetration on
military expenditure, our study helps practitioners and policy makers to better understand the role ofinternet as an instrument for raising the level of transparency in the military department. Our results

80suggest that there is a potential external benefit of associating internet penetration to the control ofcorruption in order to reduce corruption in military expenditure. However, we remain cautious about
any assertions of a causal linkage between the interaction term and military expenditure since ouranalysis rely on the marginal effect, so the need to find a net effect of our interaction term on military
expenditure is laudable.

81

Chapter 5: Conclusion
SDGs constitute an important agenda for the international community, especially for developing
countries since they are the most concerned and vulnerable to underdevelopment. To help them achievethese interrelated goals, this dissertation focuses on three important SDGs goals for developing
countries namely SDG 1 (no poverty), SDG 13 (climate action), and SDG 16 (peace, justice and strong
institutions. In fact, developing countries face a real challenge to reach these goals which maintainthem in the trap of non-sustainability in the pursue of their development agenda. To conduct thisempirical analysis, we focus on different measures and services of Information and Communication
technology (ICT) in which these countries have a real potential of growth such as fixed telephone,mobile cellular penetration, internet use, and mobile money service by using different identification
strategies, samples and scope. From the results it appears clearly that financial inclusion and mobile
money in the context of their interoperability help reduce poverty and improve individuals’ welfare in
the case of a least-developed country, Burkina Faso, where the penetration rate of mobile money isrelatively low compared to other developing countries. Furthermore, we found that the long-run
relationship between CO2 emissions and ICT differs, depending on a country’s development stage. The
prevalence of ICT is associated with the low level of CO2 emissions in relatively low-income
developing countries, but ICT and CO2 emissions have no clear relationship in relatively high-income
developing countries. Moreover, our study reveals that the control of corruption and internet adoption
fail to reduce military expenditure but the interaction effect of both are statistically significant on the
reduction of military expenditure, so that using the internet within a supportive and regulatoryenvironment could help deter misuse of military allocation. This dissertation could provide important
policy implications for developing countries to face their big challenges in order to reach the SDGs by2030. In fact, policymakers could leverage on ICT to reach their SDGs goals by facilitating a good
ecosystem to the growth of ICT through good regulation of the sector which will encourage foreign
investments in ICT sector and stimulate the trust of the population to use ICT products. We cannot
close this chapter without mentioning some limits of our study. In fact, based on the availability ofdata we use in our analysis some old measures of ICT which are possible to bias the accuracy of ourpolicy recommendations, so we suggest that futures researches should focus on more contemporary
measures of ICT in developing countries to do their empirical analysis.

82

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