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THE EFFECTS OF REMITTANCES ON ECONOMIC GROWTH IN SUB-SAHARAN AFRICA
BY
LEARNMORE MUCHEMWA
SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS OF THE
DEGREE OF
MASTER OF COMMERCE
IN ECONOMIC DEVELOPMENT AND POLICY ISSUES
IN THE
DEPARTMENT OF ECONOMICS AND ECONOMETRICS
AT THE
UNIVERSITY OF JOHANNESBURG
Supervisor: Prof P.F. Blaauw
Co-Supervisor: Prof F. Tregenna
2
DECLARATION
I
LEARNMORE MUCHEMWA
Declare that
THE EFFECTS OF REMITTANCES ON ECONOMIC GROWTH IN SUB-SAHARAN AFRICA
Is my own work, that all sources used or quoted have been indicated and
acknowledged by means of complete references, and that this research was not
previously submitted by me for a degree at another University.
Learnmore Muchemwa
3
ABSTRACT
The subject of the growth effects of remittances is characterised by different and conflicting
perspectives. While migration optimists believe in positive growth effects of remittances,
migration pessimists, on the other hand, challenge this position and claim that remittances
have either a negative or statistically insignificant effect on economic growth. Those for
remittances argue that remittances have a positive effect on economic growth mainly through
subsequent increases in investment capital and human capital. Migration pessimists, however,
stress that remittances negatively impact economic growth, mainly, because of inflationary
pressures and moral hazards that result in reduced labour supply. Given such contrasting
literature, this study makes an attempt to contribute to the existing literature by assessing the
growth-effects of remittances in twenty-nine Sub-Saharan Africa countries over the period
1980-2008. The Arellano-Bover/Blundell-Bond GMM one-step estimator is used in the
assessment. Empirical results from the study reveal evidence supporting for statistically
significant positive growth effects of remittances in Sub-Saharan Africa. The study further
reveals that these positive growth effects of remittances in Sub-Saharan Africa happen
through the human capital channel. Even when heterogeneity of sub-regions is taken into
account, there is still evidence showing positive growth effects of remittances in Sub-Saharan
Africa. Results, however, reveal that in West Africa, remittances have a low positive effect
on economic growth.
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ACKNOWLEDGEMENTS
First and foremost, I thank the Almighty God for equipping me with the strength to carry out
this study. I would also like to appreciate and thank my two academic supervisors, Professor
Derick Blaauw and Professor Fiona Tregenna for their knowledge input, support and
guidance throughout the stages of this study. This academic piece is what it is today because
of you.
My sincere gratitude also goes to my other lecturers, Mr Arnold Wentzel, Professor Alain
Kabundi and Professor Stephen Gelb for equipping me with the knowledge that has been a
solid base for this academic piece.
I also want to thank my family for the financial and emotional support. Last but not least, I
would like to thank all my colleagues for all the constructive comments and support.
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TABLE OF CONTENTS
CHAPTER ONE ................................................................................................................................. 10
INTRODUCTORY BACKGROUND .................................................................................................. 10
1.1 Background ..................................................................................................................................... 10
1.2 Research problem/question ............................................................................................................. 11
1.3 Hypotheses of the study .................................................................................................................. 12
1.4 Significance of the research ............................................................................................................ 12
1.5 Structure of the Study ..................................................................................................................... 14
CHAPTER TWO ................................................................................................................................ 15
LITERATURE REVIEW ..................................................................................................................... 15
2.1 Introduction ..................................................................................................................................... 15
2.2 Background literature on remittances ............................................................................................. 16
2.2.1 Theories and behavioural patterns of remittances ........................................................................ 17
2.2.2 Relating globalisation to labour migration and remittances ........................................................ 19
2.2.3 Growth effects of conventional external capital flows ................................................................ 20
2.3 Growth effects of remittances ......................................................................................................... 21
2.3.1 Growth effects of remittances through domestic savings and investment ................................... 23
2.3.2 Growth effects of remittances through consumption ................................................................... 24
2.3.3 Growth effects of remittances through the Human Capital Investments channel ........................ 25
2.3.4 Growth effects of remittances through labour supply .................................................................. 26
2.3.5 Growth effects of remittances through exchange rate and export performance .......................... 27
2.3.6 Growth effects of remittances through extra demand .................................................................. 27
2.3.7 The growth effects of remittances through financial development .............................................. 28
2.3.8 Conflicting literature on the growth effects of remittances ......................................................... 29
2.4 Empirical evidence from earlier studies.......................................................................................... 32
2.5 Conclusion ...................................................................................................................................... 36
CHAPTER THREE ............................................................................................................................ 37
CONTEXTUAL BACKGROUND ...................................................................................................... 37
3.1 Introduction ..................................................................................................................................... 37
3.2 The Sub-Saharan African context ................................................................................................... 37
3.2.1 SADC and the imbalanced labour migration flows ..................................................................... 39
3.3 Aggregate trends of remittance inflows in Sub-Saharan Africa ..................................................... 41
3.4 Growth patterns in developing regions ........................................................................................... 44
3.4 Conclusion ...................................................................................................................................... 46
CHAPTER 4 ........................................................................................................................................ 47
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RESEARCH METHODOLOGY.......................................................................................................... 47
4.1 Introduction ..................................................................................................................................... 47
4.2 Research design .............................................................................................................................. 47
4.3 Model specification ......................................................................................................................... 49
4.3.1 A priori expectations .................................................................................................................... 51
4.3.2 Data sources ................................................................................................................................. 52
4.3.3 Comparative analysis ................................................................................................................... 52
4.4 Methods of estimation..................................................................................................................... 53
4.4.1 System dynamic panel data estimation ........................................................................................ 56
4.4.2 Diagnostic tests ............................................................................................................................ 57
4.5 Conclusion ...................................................................................................................................... 58
CHAPTER 5 ........................................................................................................................................ 59
EMPIRICAL ANALYSIS .................................................................................................................... 59
5.1 Introduction ..................................................................................................................................... 59
5.2 Data description .............................................................................................................................. 59
5.4 Model results ................................................................................................................................... 66
5.4.1 Primary model estimations .......................................................................................................... 66
5.4.2 Baseline model results ................................................................................................................. 68
5.4.3 Supplementary regressions .......................................................................................................... 70
5.4.4 Sub-regional analysis ................................................................................................................... 73
5.5 Diagnostic tests results .................................................................................................................... 75
5.6 Conclusion ...................................................................................................................................... 76
CHAPTER 6 ........................................................................................................................................ 78
CONCLUSION ..................................................................................................................................... 78
6.1 Introduction ..................................................................................................................................... 78
6.2 Research findings ............................................................................................................................ 78
6.3 Policy recommendations ................................................................................................................. 81
BIBLIOGRAPHY ............................................................................................................................... 84
APPENDIX .......................................................................................................................................... 91
Appendix 1: List of Sub-Saharan African countries used in the study ................................................. 91
Appendix 2: Estimations for the entire panel (n=29, t=29) .................................................................. 92
Appendix 3: Baseline model estimations (n=29, t=6) .......................................................................... 93
Appendix 4: Growth model estimations using remittances as a share of GDP (n=29, t=6) ................. 94
Appendix 5: Growth model estimations: without outliers (n=24, t=6) ................................................. 95
Appendix 6: Growth model estimations without the investment variable (n=29, t=6) ......................... 96
Appendix 7: Growth model estimations without the enrolment variable (n=29, t=6) .......................... 97
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Appendix 9: The Sargan test of over identifying restrictions ............................................................... 99
Appendix 10: Test for autocorrelation .................................................................................................. 99
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LIST OF TABLES
Table 2.1 Channels under which remittances effect economic growth ...................................... 22
Table 2.2 Summary of contrasting literature on growth effects of remittances ......................... 30
Table 2.3 Summary of previous empirical findings ...................................................................... 35
Table 3.1 Income groups of different countries in Sub-Saharan Africa ..................................... 38
Table 5.1 Descriptive statistics summary ....................................................................................... 60
Table 5.2 Correlation matrix ............................................................................................................. 61
Table 5.3 Estimations for the entire panel (n=29, t=29) ............................................................... 67
Table 5.4 Baseline model estimations (n=29, t=6) ........................................................................ 68
Table 5.5 Growth model estimations using remittances as a share of GDP (n=29, t=6) .......... 69
Table 5.6 Growth model estimations: without outliers (n=24, t=6) ............................................ 70
Table 5.7 Growth model estimations without the investment variable (n=29, t=6) .................. 71
Table 5.8 Growth model estimations without the enrolment variable (n=29, t=6) ................... 72
Table 5.9 Growth model estimations with dummy variables for West and Southern Africa..74
LIST OF FIGURES
Figure 3.1 Remittances, Foreign Direct Investment and Official Development Assistance inflows to Developing Economies ......................................................................................................................... 41
Figure 3.2 Distribution of Remittance, ODI and FDI inflows in Sub-Saharan Africa ......................... 43
Figure 3.3 Growth patterns in developing regions ................................................................................ 45
Figure 5.1 Relationship between remittances and growth .................................................................... 62
Figure 5.2 Scatter plot illustrating the relationship between growth and remittances .......................... 64
Figure 5.3 Correlation between averaged remittances and averaged growth ....................................... 65
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ABBREVIATIONS
2 SLS Two Stage Least Squares
FDI Foreign Direct Investment
FE Fixed Effects
GDP Gross Domestic Product
GMM Generalised Methods of Moments
GNP Gross National Product
IMF International Monetary Fund
LSDV Least Squares Dummy Variable
NELM New Economics of Labour Migration
ODA Official Development Assistance
OECD Organisation for Economic Cooperation and Development
OLS Ordinary Least Squares
RE Random Effects
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CHAPTER ONE
INTRODUCTORY BACKGROUND
1.1 Background
Low income levels are one of the greatest economic challenges facing a majority of countries
in Sub-Saharan Africa (SSA). Data from the World Bank Development Indicators indicate
that close to 85 per cent of countries within Sub-Saharan Africa are classified under the low
and lower middle income country categories (World Bank, 2010). Such low level income
classifications of the majority of countries in Sub-Saharan Africa reveal the challenge of
limited economic development currently holding in the region at large. Supporting the notion
of limited economic development within the region, Garner (2006:3) describes Sub-Saharan
Africa as a region popular for underdevelopment and “...non-existent economic growth”.
Sub-Saharan Africa as a region has failed to move at par with some other developing regions
regarding overall economic growth levels. In fact, in Sub-Saharan Africa, low levels of GDP
can be traced as one of the lead causes of poverty. The United Nations (2009:15) reports how
the share of people living in poverty in Sub-Saharan Africa as a whole more than doubled
between 1981 and 2005.
Because of poor economic performance and extensive poverty coupled with other factors like
excessive population growth and unstable political factors, many people in Sub-Saharan
Africa have resorted to international migration (Adepoju, 2008:5). Towards the end of the
twentieth century the subject on international migration has attracted much attention from
economic scholars. Scholars such as Wong and Yip (1999), Hague and Kim (1995), Vidal
(1998), Mountford (1997), and Stark, Helmenstein and Prskawetz (1997) led studies that
examined the relationship between migration and economic growth. In most of these studies,
the issue of migrant’s remittances emerges as one particular area that dominates the migration
discourse.
Since remittances are a major feature of international migration, it is critical to assess their
impact on economic activity in Sub-Saharan Africa, a region dominated by excessive
migration and low levels of relative economic growth. One way of doing this is to assess the
effects of remittances on economic growth in recipient countries in Sub-Saharan Africa. It
remains to be established whether remittances are a solution to the challenge of low levels of
economic growth persisting in most countries within Sub-Saharan Africa.
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1.2 Research problem/question
The literature on how remittances impact economic growth of recipient countries is
conflicting. Some scholars believe that migrant remittances have positive growth effects in
recipient economies (Pradhan, Upadhyay & Upadhyaya, 2008; Fayissa & Nsiah, 2010b; de
Haas, 2005; Dos Santos & Vinay, 2003) while other scholars highlight the negative growth
effects of remittances (Chami, Fullenkamp & Jahjah, 2003; Karagoz, 2009). The latter argue
that remittances do not result in positive economic growth since the two variables are
negatively correlated. Adding to the debate, there are also scholars who claim that
remittances have no impact on economic growth of recipient countries (Barajas, Chami,
Fullenkamp, Gapen & Montiel, 2009; Rao & Hassan, 2011). For these scholars, there is no
causal relationship between remittances and economic growth of developing economies. All
these conflicting empirical findings on the growth effects of remittances are, to some extent,
informed by the available theoretical literature conversing on the channels through which
remittances impact economic growth.
There are a lot of different perspectives in both the empirical and theoretical literature on the
growth effects of remittances. Available theoretical literature on the growth effects of
remittances can be categorised into two main schools of thought. The two schools of thought
include the “migration optimists” and the “migration pessimists”.
Migration optimists argue for positive growth effects of remittances. They demonstrate the
positive indirect growth effects of remittances through economic channels such as increased
savings, investment capital, human capital investments, extra employment and the overall
multiplier effects of consumption on aggregate demand and output (Adenutsi, 2010:34;
Balde, 2010:17). Unlike the migration optimists, migration pessimists argue against the
positive growth effects of remittances.
According to the migration pessimists, remittances have either negative growth effects or
zero impact on economic growth. They argue that remittances are mostly used for
consumption instead of productive investments as argued by the migration optimists (de
Haas, 2007:5). They also argue that remittances create moral hazard problems which reduce
labour supply in recipient economies (Chami et al., 2003:5). Migration pessimists also
indicate that remittances have negative growth effects also as a result of reduced human
capital investments (Chami et al., 2003:5) and inflationary pressures (Amuedo-Dorantes &
Pozo, 2004:1408). Both the two schools of thought use the same channels namely;
12
consumption, human capital investment and labour supply, to highlight their contrasting
evidence on the growth effects of remittances.
As a result of the contested literature, it is difficult for one to conclude on the growth effects
of remittances in a region like Sub-Saharan Africa. There is a need to examine the growth
effects of remittances and answer the research question that follows:
How do remittances impact economic growth of recipient countries in Sub-Saharan Africa?
1.3 Hypotheses of the study
A negative and statistically significant co-efficient of remittances indicates that remittances
have a negative impact on economic growth. An increase in remittances will result in lower
economic growth.
A positive and statistically significant co-efficient of remittances implies that remittances
have positive growth effects. An increase in the volumes of remittances will result in
increased economic growth.
A statistically insignificant co-efficient of remittances implies that remittances do not have
significant direct growth effects. In other words, either the positive or negative impact of
remittances on economic growth will be low.
1.4 Significance of the research
Over the past years there has been an increase in the number of studies conversing on the
subject of remittances. Most of the studies focused entirely on the growth effects of
remittances in developing economies in general. Very little has been done on remittances in
Sub-Saharan Africa or its sub-regions in particular. Campbell and Kandala (2011:130) note
the scarcity of studies examining the macroeconomic effects of remittances in Sub-Saharan
Africa.
There is a need for a study that distinguishes Sub-Saharan Africa from the rest of the
developing world in assessing the growth effects of remittances. Sub-Saharan Africa is a
region that is unique in its migration and remittance inflow patterns hence the need for a
13
study that takes such heterogeneity into consideration (Gupta, Pattillo & Wagh, 2008:105;
Adenutsi, 2010:33). Most of the available literature on remittances in Sub-Saharan Africa is
not entirely focused on the growth effects of remittances. Gupta et al. (2008) led a study that
assessed the impact of remittances on poverty and financial development in Sub-Saharan
Africa. Anyanwu and Erhijakpor (2010) analysed the overall effects of remittances on
poverty in Africa while Fayissa and Nsiah (2010b) assessed the impact of remittances on
economic growth and development in Africa. Salisu (2005) looked at the determinants of
remittances in Sub-Saharan Africa. Singh, Haacker, Lee and Goff (2010) not only looked at
the determinants of remittances, instead, they went on further to establish the macroeconomic
role of remittances in Sub-Saharan Africa. This study comes in to complement the few-
available research studies on the growth effects of remittances in Sub-Saharan Africa. In
examining the relationship between remittances and economic growth in Sub-Saharan Africa,
the study also incorporates results from two Sub-Saharan Africa sub-regions. This
methodology enables an analysis of the growth effects of remittances in both Sub-Saharan
Africa, and two of its sub-regions are utilised in order to cover some of the prevailing
literature gaps on the subject of remittances and economic growth in the region.
Besides being specific to the Sub-Saharan Africa context, the study employs a macro-
economic approach to the assessment of remittances. Focal interest is on the relationship
between remittances and economic growth of twenty-nine Sub-Saharan African countries.
Some studies such as the one by Lucas and Stark (1985) are micro-economic in approach.
They seek to understand the behavioural patterns of remittances at household level. Much
attention is placed on the motivations and spending patterns around remittances.
Understanding the mechanics of remittances is an important aspect that allows for informed
assessments of the macro-economic effects of such remittances. This research, therefore,
complements all the other studies on remittances that are micro-economic in focus. It helps to
shed light on how remittances affect macro-economic variables such as economic growth.
In order to try and come up with justified and unbiased results on the growth effects of
remittances in recipient countries in Sub-Saharan Africa, this study employs modern panel
data estimation methods. Such modern panel data estimation tools and methodology
distinguish it from prior studies that had challenges in handling endogeneity between
remittances and economic growth, a feature which resulted in most of the studies coming up
with questionable results due to misspecified models.
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1.5 Structure of the study
Having introduced the research, the rest of the study is presented as follows: Chapter 2
outlines the literature on the growth effects of remittances; Chapter 3 presents the contextual
background information on remittances in Sub-Saharan Africa. Chapter 4 gives a presentation
of the research methodology and model specification while Chapter 5 reports on the results
and Chapter 6 sums up the study with the conclusion and policy recommendations.
15
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter outlines the different theoretical and empirical discussions on remittances
covered in the migration literature. The subject on remittances remains one of the most
researched about areas in migration economics. In many of the studies, the motivation has
been that of trying to understand remittances as a welfare-enhancing tool, that is, whether
remittances as a private source of capital can be of significance in improving the lives of
individuals in different parts of the world. It is against this background that this study
explores how the available literature can be of significance in helping us assess the
contribution of remittances in enhancing human welfare.
In order for one to be able to model a comprehensive assessment of how remittances can
impact economic growth, it is critical that the study looks into the theory of growth as
presented in the literature. The relationships between capital, investment, savings,
consumption and growth will constitute a vital framework for this study. As will be
elaborated in the forthcoming subsections, there are diverse opinions within migration
literature when it comes to the question of whether remittances are mainly invested or
consumed. The conventional approach presents remittances as positively impacting economic
growth only if they are invested. If remittances are only used for consumption then their
potential to positively impact economic growth becomes questionable. There are circles
however that argue that remittances can still boost economic growth even if they are used for
consumption. Such an argument signifies how the literature is highly contested. It is against
this background of contrasting literature that this chapter will scrutinise the available
literature and relate it to the Sub-Saharan African context in an effort to fill the gaps on how
remittances and economic growth are related.
The chapter begins by presenting the different definitions available for remittances. The
chapter goes on to discuss the theories explaining for the different behavioural patterns of
remittances. The discussion on the behavioural patterns of remittances is then followed by
another discussion relating globalisation to labour migration and remittances. This is then
followed by a brief theoretical outline of the growth effects of other external sources of
16
capital such as Foreign Direct Investment (FDI) and foreign aid. The rest of the chapter is
predominantly focused on the growth effects of remittances.
2.2 Background literature on remittances
Migration literature defines remittances in two different ways. According to Rajan (2006:3)
the narrowest definition of remittances is restricted to the transfer of funds made by labour
migrants to their countries of origin (Karagoz, 2009:1892; Jongwanich, 2007:2). The funds
are normally presented in the migration literature as being sent to families or relatives back
home. There is however a more broad or formal definition of remittances derived from the
IMF Balance of Payments Yearbook that incorporates “compensation of employees” and
“migrants’ transfers” to workers’ remittances (Rajan, 2006:3; Jongwanich, 2007:2;
Salomone, 2006:2). In the IMF statistics, compensation of employees is accounted for in the
income component. Migrants’ transfers are included in the capital transfers while workers’
remittances form part of the current transfers (Jongwanich, 2007:2; Salomone, 2006:2). This
study makes use of the much broader definition derived from the IMF Balance of Payments
Yearbook.
As noted by Jongwanich (2007:2), there are quite a number of limitations present in
accounting for remittances in different countries around the world. The derivations and
definitions of remittances tend to differ amongst countries. There are different channels that
migrants may make use of when sending remittances to their countries of origin. Large
amounts of remittances are not captured simply because they are sent through informal
channels while those sent through the formal channels might tend to be understated as a result
of some weaknesses in the data collection systems (Jongwanich, 2007:2). These limitations
can to some extent distort the conclusions normally drawn in most remittances studies
especially those with a macro-economic orientation.
Having looked at how remittances are defined and some of the limitations in the data
computations, it is critical that we look at the determinants of the levels of migrants’
remittance flows. The OECD (2006:145) mentions two important factors explaining for the
level of migrants’ remittance flows. The first factor is the individual migrant’s income and
savings which influence the amount of money he/she might be able to remit back home. The
other factor involves the residence status of the migrant in the host country and his/her
17
intentions with regard to whether he/she is going to be in the host country temporarily or
permanently. The OECD (2006:145) highlights that network effects and the welfare of the
migrant’s family left in the home country are other significant factors that explain for the
level of remittances flow.
One of the factors responsible for the level of migrants’ remittance flows is the issue of
motivation to remit (OECD, 2006:145; de Haas, 2007:7; Vargas-Silva, 2008:292; Brown,
2006:62; Fayissa & Nsiah, 2010b:94; Chami et al., 2003:3; Schiopu & Siegfried, 2006:8).
What motivates migrants to remit is an important subject especially when trying to assess
how remittances impact economic growth. In order to answer the question on the relationship
between economic growth and remittances it is important that one looks at the objectives of
the migrants who are remitting the funds back home. Chami et al. (2003:3) indicate that, in
order for remittances to positively impact development, there is a need for people to
understand the behavioural patterns of such remittances. Such behavioural patterns are drawn
from what motivates migrants to remit funds back home. These motives have been turned
into microeconomic theories which help explain for the flow of remittances.
2.2.1 Theories and behavioural patterns of remittances
The literature on remittances identifies three theories to explain the flow of remittances.
These are; Pure Altruism, Pure Self Interest and Tempered Altruism which is also referred to
as Enlightened Self Interest. Most discussions in the literature are centred on the first two of
them. These theories illustrate that remittances are sent mainly as a result of pure altruistic
and self-interest motives (de Haas, 2007:7; Schiopu & Siegfried, 2006:8; Hagen-Zanker &
Siegel, 2007:4; Lucas & Stark, 1985:902). The literature on remittances is however
conflicting when it comes to the question of determining which of the two motives listed
above better explains the increased flow of remittances. Both altruistic and self-interest
motives have been argued to be factors responsible for the increased or decreased flow of
remittances. Sayan (2006:5) challenges the idea of viewing altruism as the only motive for
remitting. He asserts that “remitting is a multifaceted behaviour” hence it involves many
other explanations besides altruistic and self-interest motives. This study will only discuss the
two main theories used to explain for remittances. These two theories are crucial in providing
the framework of knowledge explaining for the different growth-remittance inflow patterns in
Sub-Saharan African countries.
18
The Pure Altruism theory highlights that migrants remit money back home in concern of the
welfare of the remaining family members (Hagen-Zanker & Siegel, 2007:5; OECD,
2006:145). Chami et al. (2003:4) report that in this model, the migrant’s utility is derived
from that of his/her family back home. The migrant is rather satisfied when the welfare of his
family back home is better off (OECD, 2006:145). This implies that the migrant is motivated
to remit more funds to his family when there are unfavourable economic conditions holding
in the home country. The theory observes that remittances are “compensatory transfers” since
they increase when the migrant’s home country is faced with economic disruptions such as
droughts and a financial crisis (Chami et al., 2003:4). In order for the migrant to remit more
funds, the economic disruptions or “bad luck”, a term used by Chami et al. (2003:4), must be
creating a shortfall for the remaining family. As a result, the compensatory nature of
remittances under the Pure Altruism model implies that remittances are countercyclical, that
is, they increase during times when there is deterioration in economic conditions in the
business cycle (Vargas-Silva, 2008:292; Chami et al., 2003:4). The Bank of Uganda (2007)
emphasises that altruistic remittances can be countercyclical to GDP patterns possibly
because migrants tend to remit more during periods of economic disturbances in order for
their families in the home country to smoothen their consumption. Also commenting on
behavioural patterns of remittances under a Pure Altruism model, Brown (2006:63) suggests
that there is an inverse relationship between the volumes of remittances and economic
conditions holding in the home country. Under this model, favourable economic conditions in
the home country would imply a reduction in the volume of remittance inflows.
The Pure Self Interest theory is modelled around the argument that remittances are not always
countercyclical. There are some instances or contexts where volumes of remittances reduce
following poor economic conditions in the recipient country. In such a case, there is no
inverse relationship between volumes of remittances and the economic performance of the
home country as postulated by Brown (2006:63). In fact, there might be a positive correlation
between volumes of remittances and economic performance of the home country where bad
economic conditions may result in low volumes of remittances. Such behavioural patterns
have led to the formulation of the Pure Self Interest theory. Lucas and Stark (1985:904) claim
that migrants’ self-interest can be one other motive for remittances. In this context, migrants
remit money in order for them to invest or inherit in assets back home and also for them to
return home with dignity (Hagen-Zanker & Siegel, 2007:5; OECD, 2006:146). When there is
deterioration in economic performance of the home country, migrants are most likely to remit
19
less since the situation will have a negative impact on both investible and inheritable assets.
There is most likely to be an increase in the volumes of remittances if the home economy is
undergoing a favourable spell.
It is however significant to note that both the Pure Altruism and Pure Self-Interest models do
not account for all the reasons that motivate migrants to remit to their home countries (Lucas
& Stark, 1985:904). There are some other different behavioural patterns of remittances which
can be influenced by other factors not explicitly highlighted in the literature. Factors such as
the relationship between the migrant and the remittances’ recipient, distance between host
and recipient country and the costs involved in remitting income back home can influence the
patterns of remittance inflows. Channels through which migrants remit their income back
home have some influence on the overall cost of remittance transmission.
2.2.2 Relating globalisation to labour migration and remittances
Globalisation has been widely used to explain increased global volumes in the flow of labour
migration (Trimikliniotis, Gordon & Zondo, 2008:1324). A clear definition of globalisation
can help one to derive the relationship between globalisation and labour migration. Martens
and Raza (2010:280) define globalisation as the increased “international movement of goods
and services, financial capital, information and people”. From this definition, the increased
international movement of people can to some extent be equated to labour migration. It can
thus be noted that labour migration is a function of globalisation since it results in an
increased demand for skilled labour internationally. The need for skilled labour results in
many countries importing labour. There is indeed a causal link that exists between
globalisation and labour migration. The increased flow of goods and capital around the world
as a result of globalisation has also resulted in increased volumes of labour migration
(Molina, 2007:1). Molina (2007:1) furthermore highlights that the increased labour migration
volumes coming out of globalisation have resulted in increased remittance flows around the
world. Brown (2006:55) reaffirms this argument by asserting how remittances have
increasingly become a reference point of globalisation. To date, remittances have become a
vital source of capital for many developing countries. Acosta, Lartey and Mandelman
(2009:102) claim that over the past years the volumes of remittance flows into developing
countries have surpassed those of other external capital flows like official aid.
20
Not only is globalisation resulting in increased remittance inflows, one can also argue that
external capital flows like aid and foreign direct investment are a result of globalisation. It is
imperative to briefly assess the growth effects of foreign direct investment (FDI) and foreign
aid since these are the more common sources of external capital for developing countries.
2.2.3 Growth effects of conventional external capital flows
FDI can positively impact economic growth of recipient countries (Yasin, 2005:23;
Borensztein, De Gregorio & Lee, 1998:115). It is believed that FDI, a source of external
capital which is mainly associated with multinational corporations, results in capital
accumulation and the transfers of technological advancement coupled with managerial
expertise (Yasin, 2005:24; Alfaro, 2003:2; Borensztein et al., 1998:115). Recent growth
literature argues that technological diffusion is an important element explaining economic
growth and development in modern day economies (Borensztein et al., 1998:115). If
developing economies are to grow, there is a need for them to “catch-up” with developed
countries in terms of technological advancement. It is then through FDI that developing
nations have access to such technology. While de Mello (1999:135) highlights that the main
channel under which FDI contributes to positive economic growth is that of increment in
physical capital in developing nations, Borensztein et al. (1998:118) dispute this notion and
emphasise that stimulation of technological progress happens to be the core channel. It is also
believed that FDI has some positive indirect effects on human capital development which
also bear some overall positive impact on the economic development of developing nations
(Alfaro, 2003:2; de Mello, 1999:135).
Foreign aid, portfolio investment and private debt are some other important conventional
sources of external capital mostly used by developing countries. Traditional growth theories
like the 2-gap model argued that the savings constraint is one of the main barriers to
economic growth (Iqbal, 1995:1119). Foreign aid can thus be used to offset this constraint
and supply much needed capital in developing nations (Ali & Isse, 2006:242). Molina
(2007:69) argues that foreign aid is normally disbursed to countries with limited gross
national savings. By so doing, foreign aid can trigger long-run economic growth as observed
by Minoiu and Reddy (2010:27). Asteriou (2008) supports the argument of a positive
relationship between foreign aid and long-run economic growth. The main attraction in the
foreign aid-economic growth literature is that aid helps increase stock of physical and human
21
capital in developing countries (Burke & Esfahani, 2006:351). It can thus be employed as a
liquidity mechanism that allows for developing nations to have operating capital that
calibrates huge developmental projects in developing economies.
Having reviewed the growth effects of foreign direct investment and foreign aid, it is now
important for the study to focus more on growth effects of remittances as they are presented
in the literature.
2.3 Growth effects of remittances
Having looked at the various channels through which other external sources of capital like
foreign direct investment and official development assistance impact economic growth, it is
now necessary to consider the different macro-economic growth effects of remittances as
discussed in the literature. The literature on remittances is filled with conflicting views when
it comes to the question of how remittances directly or indirectly impact economic growth of
developing economies. A lot has been said and argued about the contribution of remittances
to economic growth of developing countries but to date there isn’t any consensus in the
literature regarding this discussion1.
First, the study outlines the main channels under which remittances effect economic growth
as presented in the literature. These can be presented as follows:
1 Refer table 2.2 and table 2.3 of this study.
22
Table 2.1 Channels under which remittances effect economic growth
Channel
Literature
Domestic savings and investment
Brown (2006:61); Gupta et al. (2008:105); Adenutsi (2010:34); Catrinescu, Leon-Ledesma, Piracha and Quillin (2009:81); Balde (2010:17); de Haas (2007:14); Ratha (2003:157); Karagoz (2009:1898); Drinkwater, Levine and Lotti (2003:1)
Consumption
Human capital investments
Barajas et al. (2009:6); de Haas (2007:6); Brown (2006:65)
Labour supply Brown (2006:67); Barajas et al. (2009:6); Adenutsi (2010:36); Acosta, Lartey & Mandelman (2009:104); Pradhan et al. (2008:498); Chami et al. (2003:5)
Exchange rate and export performance Barajas et al. (2009:8); Gupta et al. (2008:105); Acosta, Lartey & Mandelman (2009:114); Catrinescu et al. (2009:81); Acosta, Baerg and Mandelman (2009:2); Amuedo-Dorantes and Pozo (2004:1408); Pradhan et al. (2008:498); Karagoz (2009:1899); OECD (2006:156)
Extra demand
OECD (2006:156); Catrinescu et al. (2009:81)
Financial development
Guiliano and Ruiz-Arranz (2009:147); Bettin and Zazzaro (2009:2); Gupta et al. (2008:104); Rao and Hassan (2011:701); Guiliano and Ruiz-Arranz (2009:144); Acosta, Baerg and Mandelman (2009:3)
Migration literature is filled with many conflicting arguments when it comes to the subject of
how the channels listed in Table 2.1 influence the growth effects of remittances. The
literature is also not clear how the mentioned channels contribute to the growth effects of
remittances. The following sub-sections present a detailed discussion on how each of the
identified channels directly or indirectly influences the growth-effects of remittances in
developing countries.
23
2.3.1 Growth effects of remittances through domestic savings and investment
Like most private capital flows, remittances can have significant macroeconomic effects on
growth in developing economies (Fayissa & Nsiah, 2010b). Investment is one of the channels
through which remittances directly or indirectly impact economic growth. Some economists
believe that remittances play a similar role like other external sources of capital such as
foreign aid and foreign direct investment in boosting national savings in developing
economies (Molina, 2007:69; Barajas et al., 2009:4).
Adenutsi (2010:34) stresses that developmental migration optimists argue that remittances
can have a positive impact on economic growth of developing economies as a result of
investment capital coming out from the remitted funds. In other words, remittances are
believed to provide the much needed capital for households which they can use for savings
and also to finance investments (Catrinescu et al., 2009:81). Ratha (2003:157) observes that
when remittances are channelled to countries that have healthy and sound economic policies,
most of them can be invested in productive projects.
When remittances are saved or productively invested, they can have an indirect positive
impact on economic growth of the recipient economy (Balde, 2010:17). In his argument,
Balde (2010:17) observes that remittances alone do not have a direct positive effect on
economic growth but they do have an indirect positive effect that is channelled through the
generation of savings and investments. Ratha (2003:164) notes that when remittances are
invested in developing economies, a higher output growth can be realised in that particular
economy. Karagoz (2009:1898) supports the argument that the most significant channel
under which remittances can have a positive effect on economic growth in developing
countries is through savings and investment.
Drinkwater et al. (2003:2) further discuss the manner in which the growth effects of
remittances operate. It is believed that remittances help ease credit constraints that happen to
be the most common challenge associated with private enterprises in many developing
countries. Drinkwater et al. (2003:2) argue that remittances can have similar growth effects
like those of foreign direct investment when it comes to easing such financial constraints
holding at national level. Many private enterprises in developing economies operate at
limited production levels due to the investment challenges they encounter. Such challenges
can be addressed by remittance flows provided the country’s financial system is effective in
allocating financial resources to where they are needed most.
24
2.3.2 Growth effects of remittances through consumption
While developmental migration optimists argue that remittances can have a positive growth
effect through increased savings and investments, migration pessimists refute such a notion
and argue that remittances are rarely used for productive investments but are rather usually
allocated for consumption (de Haas, 2007:5). Brown (2006:61) supports the claim that
remittances are usually used for consumption by reporting how huge shares of remitted
income are usually used for food.
Barajas et al. (2009:6) make use of two arguments to elaborate on why a larger chunk of
remitted income is used for consumption instead of being saved or invested as argued by
developmental migration optimists. The first argument claims that, since remittances are
compensatory in nature, they are more likely to be channelled or directed towards families
with a higher propensity to consume as compared to families with a higher propensity to
invest or save. Secondly, they maintain that due to the perceived permanency of remittances
by receiving households, there is likely to be a moral hazard problem where the receiving
households are more likely to use the income from remittances for consumption and not for
productive investments. As a result, a larger share of remittances might then be used for
consumption alone, a situation which is argued as having a negative effect on economic
growth of developing countries (de Haas, 2007:14). Also in support of the argument that a
greater share of remittances is mainly used to finance consumption is the OECD (2006:154)
which claims that remittances like any other sources of income are likely to be spent basing
on the hierarchy of needs. According to the hierarchy of needs, consumption is at the initial
stages and dominates a bigger share hence most households are likely to spend their income
on consumption as compared to investment.
While it has been widely claimed that remittances are mainly used for consumption and not
for productive investments, recent evidence suggests that even when used for consumption,
remittances can still result in some positive growth effects due to the multiplier effects on
aggregate demand and output in the entire economy (Brown, 2006:65; Gupta et al., 2008:105;
Pradhan et al., 2008:498; Ratha, 2003:164; Ajayi, Ijaiya, Ijaiya, Bello, Ijaiya & Adeyemi,
2009:79). This evidence further complicates the subject of growth effects of remittances
especially when related to the issue of savings and consumption.
The argument that remittances will always positively impact economic growth of a recipient
country even when they are used to finance consumption is mainly used by the
25
developmental migration optimists to refute the migration pessimists’ argument that
concludes on negative growth effects of remittances due to consumption. Brown (2006:65)
makes use of the Keynesian multiplier effects to explain the positive indirect growth effects
that can be realised from remittances. It is believed that the increased consumption which
implies increased spending may generate an increased demand which results in increased
production. Barajas et al. (2009:3) support the above argument by referring to the case study
of Pakistan and Mexico where evidence of the existence of such multiplier effects within the
two economies was established. In trying to consolidate the whole argument, Ajayi et al.
(2009:079) observe that when remittances are invested they result in output growth while if
they are used for consumption, they can still still positively impact economic growth of
recipient countries through the multiplier effects on aggregate demand and output.
Having looked at the relationship between remittances and economic growth that is
influenced by consumption, the next sub-section looks at human capital investments. The
belief that a bigger percentage of income from remittances is used to help fund education of
the remaining recipient family members is common in migration literature. The next sub-
section explains how human capital investments can be a channel through which remittances
indirectly effect economic growth of the recipient economy.
2.3.3 Growth effects of remittances through the Human Capital Investments channel
Migrants’ remittances might impact economic growth through human capital investments.
Remittances boost human capital investments when they are used to finance the education
and health of recipients. According to the human capital theory, more human capital
investments result in positive economic growth for a country in the long-run (Olaniyan &
Okemakinde, 2008:158).
Barajas et al. (2009:6) mentions that remittances help boost human capital investments which
are critical in economic growth through financing formal schooling of the receiving
households. They however observe that positive economic growth would only be possible
when those who have received the formal education are employed in the labour markets of
that particular country. In other words, remittances can indirectly contribute to positive
economic growth in a recipient country when individuals who receive the formal education
financed through remittances supply their labour to the local labour markets.
26
According to de Haas (2007:6), remittances can either have a promoting or discouraging
influence on the education of remaining citizens. Scholars following New Economics of
Labour Migration (NELM), a hypothesis developed within the American research context
responding to both the migration optimists and pessimists, do believe that remittances impact
positively on education levels (de Haas, 2007:6). Their argument is motivated by the
assumption that income from migrants will mainly be used to finance the remaining family
members’ education. Remittances are here perceived as a means of financing development
through education in many developing countries.
Remittances can help boost human capital in recipient countries through financing the health
care of recipients. When remittances are used to finance health care in recipient countries as
highlighted by Brown (2006:65), this can have positive impacts on human capital
development which in the long run leads to positive economic growth. It is however
important to determine whether a greater share of remitted income is used for education and
health care in developing countries. Not many studies have revealed and supported the
argument that remittances are being used to finance education and health care in developing
countries.
2.3.4 Growth effects of remittances through labour supply
While it is universally acknowledged that labour plays a vital role in economic growth, it is
critical to assess the growth effects of remittances through the labour supply and effort
channel. The argument that remittances result in some moral hazard problems was first raised
by Chami et al. (2003). Chami et al. (2003:5) argue that due to information asymmetry
holding between the migrant and the remittances recipient household, moral hazards through
reduced labour supply by the members of the recipient households are likely to negatively
impact economic growth of the recipient economy. Many scholars support this claim and
make use of different terms to describe the moral hazard problem emanating from
remittances. Adenutsi (2010:36) supports the moral hazard argument and refers to the
reduced labour supply by the recipient household members as “voluntary unemployment”
resulting from increased remittance flows. Pradhan et al. (2008:498) use the term “idleness”
to explain the moral hazard problem, a situation where recipients of remittances either reduce
their labour effort or completely refrain from working. It is believed that the recipient
households may use the remittances for increased leisure while reducing their labour supply
27
(Karagoz, 2009:1899; Barajas et al., 2009:7). Acosta,Lartey and Mandelman (2009:104)
reveal how the moral hazard problem negatively affects economic growth in the recipient
economy. They argue that reduced labour supply results in higher wages which results in
increased production costs. In the long run, such production costs may have an adverse effect
on output. This argument is related with the discussion on the Dutch-disease negative effects
of remittances explained through exchange rate appreciation and export performance.
2.3.5 Growth effects of remittances through exchange rate and export performance
According to Gupta et al. (2008:105) the Dutch disease effects of remittances is a subject that
is strongly contested in migration literature. Acosta, Baerg and Mandelman (2009:2) identify
the Dutch disease as an “...upward pressure on the real exchange rate...” that is caused by an
influx of capital inflows. Such capital inflows may result from external sources of capital
such as aid or from huge incomes realised from trade in natural resources (Acosta, Baerg &
Mandelman, 2009:2). It is believed that an influx of remittances may result in real exchange
rate appreciation which has a negative effect on export performance in the long run (Karagoz,
2009:1899; OECD, 2006:157; Amuendo-Dorantes & Pozo, 2004:1414; Catrinescu et al.,
2009:81). Pradhan et al. (2008:498) further argue that low export performance resulting from
real exchange appreciation can have some negative effects on economic growth of the
remittance recipient economy. According to Catrinescu et al. (2009:81) low export
performance resulting from real exchange rate appreciation caused by remittances has an
adverse effect on output. Generally the adverse effects of low export performance on output
tend to negatively impact employment. Such negative employment effects can result in
negative growth effects in an economy.
2.3.6 Growth effects of remittances through extra demand
It is not always the case that remittances will result in negative growth effects due to low
export performance which has adverse effects on output. In some cases remittances generate
an extra demand in the economy which can either have positive or negative growth effects
(OECD, 2006:156). The negative growth effects may be as a result of the inflationary
pressures arising from extra demand while positive growth effects happen through positive
employment effects. The thesis of positive employment arising from extra demand generated
28
by remittances is in contrast to the position of Catrinescu et al. (2009:81) who conclude about
the negative growth effects of remittances caused by negative employment effects. According
to Catrinescu et al. (2009:81) the negative employment effects result from low output caused
by low export performance.
It then remains to be identified whether remittances have positive or negative employment
effects. Commenting on the subject of extra demand, the OECD (2006:156) concludes that
the flexibility of an economy’s reaction to extra demand is essential in establishing whether
remittances will have positive employment effects or negative inflationary pressures.
2.3.7 The growth effects of remittances through financial development
Having studied other potential channels through which remittances may impact economic
growth, it is critical for the study to assess the financial development channel. It is reported
that financial development within an economy can aid economic growth (Giuliano & Ruiz-
Arranz, 2009:147). Migration literature has always perceived financial development as a
prerequisite for positive growth effects of remittances. Bettin and Zazzaro (2009:2) argue that
in order for remittances to have positive economic growth effects, there must be a well-
developed financial system. According to Giuliano and Ruiz-Arranz (2009:144), well
developed financial systems can be important resource allocation mechanisms that help
distribute remittances to projects most vital for an economy’s growth. This observation is also
supported by Acosta, Baerg and Mandelman (2009:3) who insist that well developed
financial systems are critical in channelling remittances into important investment projects
within an economy. From this perspective, financial systems are viewed as the necessary
economic transformers that help in transforming remittances into important investment
capital needed for economic growth.
There have been some changes in how scholars perceive the relationship between
remittances, financial development and growth. Financial development is now perceived as a
channel influencing the growth effects of remittances. Financial development has always
been believed to be one of the requirements for positive growth effects of remittances. Recent
studies however reveal that remittances can in fact play a significant role in developing
financial systems. Rao and Hassan (2011:701) demonstrate that remittances have positive
indirect growth effects since they aid and speed up financial development in recipient
29
countries. Remittances help develop the financial system of a particular economy. Gupta et
al. (2008:104) also observe that remittances promote financial development. Remittances can
have a positive impact on growth if they have a significant positive effect on the development
of the recipient country’s financial system.
2.3.8 Conflicting literature on the growth effects of remittances
Table 2.2 summarises some of the major contrasts evident in the literature. It presents the
different perspectives drawn from both the Migration Pessimist and Optimist schools of
thought.
30
Table 2.2 Summary of contrasting literature on growth effects of remittances
Channel Contrasting Literature Optimists Pessimists
Investment vs. Consumption Remittances positively impact economic growth of recipient economies through investment capital. Migration optimists believe that a larger proportion of remittances are saved and the savings are used to finance investments (Catrinescu et al., 2009:81). Even when remittances are used to finance consumption, they can still positively impact economic growth in recipient economies through multiplier effects on aggregate demand and output (Brown, 2006:65; Gupta et al., 2008:105; Pradhan et al., 2008:498; Ratha, 2003:164).
Remittances are rarely used for productive investments. In fact, migration pessimists argue that remittances are mainly used for consumption which has negative effects on economic growth of recipient economies mainly due to inflationary pressures (de Haas, 2007:14).
Human capital investment Remittances positively impact economic growth of recipient economies through human capital investments. The theorists argue that a bigger share of remittances is used for education and health which result in the creation of human capital in recipient economies (Barajas et al., 2009:6).
Remittances negatively impact economic growth as a result of the adverse effects they have on human capital creation. Migration pessimists argue that remittances can be a disincentive for enrolment in education for recipient households (Chami et al., 2003:5).
Labour supply In the long run remittances result in the increase of skilled employees within the recipient country. This is mainly because remittances are used to finance the education of remaining household members (Barajas et al., 2009:6).
Remittances negatively impact economic growth of recipient economies as a result of the moral hazard problem. In this argument, migration pessimists believe remittances result in a reduction in labour supply. In this regard, remittances are portrayed as motivating recipient household members not to work (Chami et al., 2003:5)
Extra demand Creates extra employment (OECD, 2006:56) Causes inflationary pressures that have a long run negative impact on employment (Catrinescu et al., 2009:81)
31
Table 2.2 outlines how growth effects of remittances as presented in different circles tend to
conflict with each other. The biggest and most contested issue pertains to whether remittances
are saved or consumed. Migration optimists believe that remittances are saved and generate
important capital for development in an economy. Migration pessimists refute such thinking
and argue that remittances are mainly used to finance consumption hence have limited or no
bearing on economic growth (de Haas, 2007:14).
A further complication in the debate on the growth effects of remittances is the question on
whether remittances used for consumption have a bearing on economic growth. The school of
thought that perceives remittances as an important source of development capital argue that
remittances used for consumption can still result in increased economic growth as a result of
multiplier effects on aggregate demand which have a positive bearing on total output (Brown,
2006:65; Gupta et al., 2008:105; Pradhan et al., 2008:498; Ratha, 2003:164).
The subject of whether remittances create human capital or act as a disincentive in the
creation of human capital is also strongly debated in the literature. Remittance optimists
believe that remittances provide essential capital that boosts the education of the remaining
household members (Barajas et al., 2009:6). Such attainment of education is perceived as a
significant human capital investment that has a positive long run effect on economic growth
of the recipient economy. This line of thinking is however dismissed by remittance pessimists
who suggest that remittances can be a disincentive for education attainment and completion.
For them, remittances have an adverse effect on human capital investments that negatively
impact economic growth (Chami et al., 2003:5).
Remittance pessimists also point to the moral hazard problem of remittances that negatively
affect labour supply. In this scenario, remittances are believed to be a disincentive to work
(Chami et al., 2003:5). Another conflicted issue regards the subject of whether the extra
demand generated by remittances increase or lower employment. Those in support of
remittances believe that such extra demand can create extra employment (OECD, 2006:56)
while those against remittances argue that the extra demand causes inflationary pressures that
have negative employment effects in the long run (Catrinescu et al., 2009:81).
One cannot solely rely on the available theoretical literature to answer the research question
on how remittances impact economic growth since the literature is highly contested. As a
result, it is important to try and make use of available empirical evidence. The next sub-
32
section assesses some of the empirical evidence drawn from earlier studies examining the
relationship between remittances and economic growth.
2.4 Empirical evidence from earlier studies
Quite a number of empirical research studies have been done on the subject of remittances
and economic growth in the developing world. As will be highlighted in this section, there
are conflicting results that have been revealed in different studies assessing the growth effects
of remittances. There is no consensus amongst scholars with regard to the findings on the
effects of remittances on economic growth in recipient countries.
Besides looking at economic growth in particular, some studies have tried to establish the
relationship between remittances and other macroeconomic variables such as investment, real
exchange rate and export performance. Balde (2010) uses a sample of 34 Sub-Saharan
African countries from 1980 to 2004 to evaluate the effectiveness of remittances and foreign
aid in promoting savings and investments. The results from the 2 Stage Least Squares
estimation model used in the study indicate that both foreign aid and remittances bear some
positive influence in the promotion of savings and investment in the Sub-Saharan African
economies. It was however established that remittances are more effective than foreign aid
when it comes to promoting savings and investment in the 34 countries. From this study, one
can argue that if remittances have some positive effect in promoting savings and investments,
they can then be expected to also have some positive indirect influence on economic growth
of recipient countries. Such a conclusion needs to be compared with findings from two
studies that looked at the Dutch disease effects of remittances.
A panel study of 109 developing countries led by Acosta, Lartey and Mandelman (2009) over
the period of 1990 to 2003 shows that remittances exert some appreciation pressures on the
real exchange rate. The results from this study are to some extent similar to the results
reached by Amuedo-Dorantes and Pozo (2004) in their panel study of 13 Latin American and
Caribbean countries. Results from this study by Amuedo-Dorantes and Pozo (2004) reveal
that remittances may lower the export performance or international competitiveness of a
country due to currency appreciation in real terms. This can then have some adverse effects
on economic growth of the recipient economy under consideration. This conclusion
contradicts the findings from the study by Balde (2010). As a result, the study considers other
33
empirical evidence from studies that focused primarily on the relationship between
remittances and economic growth of developing economies in general.
The empirical literature on studies focusing primarily on the relationship between remittances
and economic growth is also highly contested. While Fayissa and Nsiah (2010a), Fayissa and
Nsiah (2010b), Giuliano and Ruiz-Arranz (2009), Catrinescu et al. (2009), and Pradhan et al.
(2008) show a positive relationship between remittances and economic growth, Chami et al.
(2003), Barajas et al. (2009), Karagoz (2009), and Rao and Hassan (2011) tend to reveal a
negative relationship between remittances and economic growth.
Amongst some of the studies concluding a positive relationship between remittances and
economic growth are Pradhan et al. (2008) who used a panel data econometric model of 39
developing countries from 1980 to 2004. Both the Fixed Effects and Random Effects
methods of estimation revealed a low positive relationship between remittances and
economic growth. One significant problem that can be raised from this study is the failure of
the study to account for the endogeneity problem. The study did not employ other alternative
modelling techniques to solve for endogeneity. If not taken into consideration, the
endogeneity problem can have some adverse effects on the overall research findings.
Using a panel of 100 developing countries and data ranging from 1975 to 2002, dynamic
GMM estimations by Giuliano and Ruiz-Arranz (2009) also show a positive relationship
between remittances and economic growth. However, the findings show that the positive
relationship holds in countries that have poor or undeveloped financial systems. These
findings are in line with those from a study done by Fayissa and Nsiah (2010b) that studied
the relationship between remittances and economic growth in 36 African countries from 1980
to 2004. The Fixed Effects, Random Effects and the Arellano-Bond dynamic GMM methods
of estimation showed a positive impact of remittances on economic growth of countries with
less developed financial systems.
A study by Catrinescu et al. (2009), making use of dynamic GMM estimations, also provides
support for a positive impact of remittances on economic growth. The study made use of 162
countries over a period of 32 years and it was established that remittances can positively
impact long term economic growth of a recipient country provided there are healthy political
and economic institutions in those countries.
34
Using data from 1970 to 1998, Chami et al. (2003) used a panel model of 113 developing
countries to test whether remittances are correlated with GDP growth. The Instrumental
variable method of estimation employed in the study revealed a negative relationship between
remittances and economic growth. The study shows that moral hazards resulting from
remittances tend to lower employment supply, hence the adverse effects on economic growth.
Using Ordinary Least Squares (OLS) and Fixed Effects models, Barajas et al. (2009) found
that remittances do not impact economic growth. They made use of a panel study comprising
of 84 countries with data from 1970 to 2004. The study refers to the lack of evidence on
remittance-led growth in any developing economy. The study provides evidence of cases
where remittances have instead retarded the economic growth of some countries. Barajas et
al. (2009) highlight that misspecifications of the model and the failure to deal with the
endogeneity problem can be one major reason why some previous studies revealed findings
in support of the positive growth effects of remittances.
Also supporting the above observations are results from a study by Rao and Hassan (2011).
Making use of a panel of 40 countries and data from 1960 to 2007, the study by both the
conventional estimation methods (OLS, Fixed Effects and Random Effects) and the dynamic
GMM estimations find that remittances do not have a direct positive impact on economic
growth as argued by some other scholars.
Like the theoretical literature, empirical literature is also highly contested. There are different
and contradicting findings regarding the growth effects of remittances coming out from the
studies. This makes it difficult to draw conclusions on how remittances impact economic
growth of recipient countries. Table 2.3 gives a summary of the contradicting empirical
findings on the growth effects of remittances.
35
Table 2.3 Summary of previous empirical findings
Study Aim Methodology Conclusion
Fayissa and Nsiah (2010b)
To investigate the effects of remittances on growth in 36 African countries between the period of 1980 and 2004.
The study employed both the Fixed and Random effects estimation models. In order to account for endogeneity, the study also used the Arellano-Bond dynamic GMM estimation model.
Remittances have a positive and statistically significant effect on economic growth.
Fayissa and Nsiah (2010a)
To assess the impact of remittances on economic growth in 18 Latin American countries from 1980-2005.
The study used Fixed and Random effects estimations. It also made use of the Arellano-Bond dynamic GMM estimations in order to account for the endogeneity problem.
Remittances have a positive and significant effect on economic growth.
Pradhan et al. (2008) To investigate the impact of remittances in 39 developing countries from the period 1980-2004.
A standard growth model was using both Fixed and Random effects estimations.
Remittances have a positive impact on economic growth.
Giuliano and Ruiz-Arranz (2009)
To analyse the significance of remittances in the promotion of economic growth in 100 developing countries between 1975 and 2002.
GMM estimation model. The significance of remittances in the promotion of economic growth is evident in countries with undeveloped financial systems. In countries with developed financial systems, the significance of remittances in promoting economic growth is limited.
Bettin and Zazzaro (2009)
To assess the impact of remittances on economic growth of 66 developing countries from 1991-2005
GMM and SGMM estimation model. Remittances have a positive impact on economic growth only in countries with a developed banking system.
Catrinescu et al. (2009) To fill in empirical gaps in the literature that look at the relationship of remittances and economic growth. The study assessed the impact of remittances on economic growth in 162 countries over a period of 32 years.
Dynamic GMM panel estimations Remittances positively impact long-term economic growth in economies with health economic and political institutions.
Singh et al. (2010) To investigate the determinants and the macroeconomic role of remittances in 36 Sub-Saharan Africa countries between 1990 and 2008.
Makes use of the Fixed Effect Two-Stage Least Squares (FE 2SLS)
Remittances have negative growth effects.
Chami et al. (2003) To analyse the impact of remittances on economic growth in 113 countries over the years 1970 to 1998.
Makes use of Fixed and Random effects estimation. They also employed instrumental variable estimation.
Remittances have a negative effect on economic growth mainly as a result of the moral hazard problem. The moral hazards tend have some adverse effects on labour supply.
Barajas et al. (2009) To analyse the impact of remittances on economic growth in 84 countries over the period 1970 to 2004.
Makes use of the OLS IV and Fixed Effects IV estimation methods.
Remittances have no impact on growth.
Rao and Hassan (2011) To assess the impact of remittances in 40 countries over the period 1960 to 2007.
Made use of OLS, Fixed and Random effects estimation methods. Also made use of the system GMM method.
Remittances do not have any significant impact on economic growth.
36
2.5 Conclusion
The literature on the relationship between remittances and economic growth of recipient
countries is not conclusive in answering the research question about the impact of remittances
on economic growth in recipient countries. The literature has helped in identifying the
motivations for remittances and has also been significant in highlighting the growth effects of
other external sources of capital. The only drawback here is the inconclusive literature on the
growth effects of remittances in developing countries which makes it difficult to answer the
research question. This comes as a result of the conflicting theoretical and literature on how
remittances impact economic growth of recipient countries. While migration optimists
perceive that remittances have a positive effect on economic growth through increased
physical capital and human capital investments, migration pessimists share a different
perspective. They argue that remittances have a negative impact on economic growth due to
increased consumption which has inflationary effects and moral hazards that result in reduced
labour supply and falling enrolment in education. Even the available empirical evidence is
highly conflicted, some studies conclude on positive growth effects of remittances (Fayissa &
Nsiah, 2010a; Fayissa & Nsiah, 2010b; Pradhan et al., 2008), some on negative growth
effects of remittances (Singh et al., 2010; Chami et al., 2003) while some maintain that
remittances have no impact on economic growth (Barajas et al., 2009; Rao & Hassan, 2011).
There is need for further empirical research on this subject.
Having looked at both the theoretical and empirical literature on growth effects of
remittances, the next chapter provides further background information on Sub-Saharan Africa
and also discusses the trend behaviour of remittances and economic growth in Sub-Saharan
Africa.
37
CHAPTER THREE
CONTEXTUAL BACKGROUND
3.1 Introduction
This chapter gives a brief contextual background on the subject of remittances and economic
growth in Sub-Saharan Africa. It explores the context of migration within Sub-Saharan
Africa and also outlines some of the major economic imbalances evident in one of Sub-
Saharan Africa’s sub-regions. Most of the economic imbalances discussed are believed to be
some of the lead causes of migration within Sub-Saharan Africa. A discussion on the
performance of remittances in relation to other external sources of capital follows the
exploration of the context of migration within the region. The pattern of remittance inflows in
Sub-Saharan Africa is compared to the behavioural patterns of remittance, official
development assistance and foreign direct investment inflows in other developing regions.
The chapter illustrates how the aggregate trends of remittance inflows within Sub-Saharan
Africa mismatch those of other developing regions such as Latin America and Eastern Asia.
The chapter also compares the growth trends of Sub-Saharan Africa with those of other
developing regions.
3.2 The Sub-Saharan African context
There are quite a number of studies which have investigated issues of growth, development
and poverty in Sub-Saharan Africa (Adepoju, 2008; Adenutsi, 2010; Gupta et al., 2008;
Yasin, 2005). The studies have assessed the role of external sources of capital such as
remittances in addressing macro-economic challenges such as poverty and low growth.
According to the United Nations (2009:15), Sub-Saharan Africa is a region well known for
low levels of economic growth and high poverty levels. Data from the World Bank (2010)
shows that 85 per cent of countries in Sub-Saharan Africa are categorised within the low and
lower middle income groups. Table 3.1 details the different income classes of countries
within the region.
38
Table 3.1 Income groups of different countries in Sub-Saharan Africa
INCOME CATEGORY
COUNTRIES2
Low Income Burundi; Benin; Burkina Faso; Central
African Republic; Cape Verde; Eritrea;
Ethiopia; Ghana; Guinea; Gambia; Guinea-
Bissau; Kenya; Liberia; Madagascar; Mali;
Mozambique; Mauritania; Malawi; Niger;
Rwanda; Sierra Leone; Somalia; Chad;
Togo; Tanzania; Uganda; Democratic
Republic of the Congo; Zambia; Zimbabwe
Lower Middle Income Angola; Côte d'Ivoire; Cameroon; Republic
of Congo; Cape Verde; Lesotho; Nigeria;
Sudan; Senegal; São Tomé and Principe;
Swaziland
Upper Middle Income Botswana; Gabon; Mauritius; Mayotte;
Namibia; Seychelles; South Africa
High Income: non OECD Equatorial Guinea
Source: World Bank (2010)
From Table 3.1 above, it is evident that the problem of low income levels is an issue in Sub-
Saharan Africa. Less than 20 per cent of countries within Sub-Saharan Africa can be
classified under the upper middle and high income groups. Even in the case of Equatorial
Guinea, the only Sub-Saharan Africa country categorised in the ‘High Income: non OECD’,
it should be noted that the country cannot be held in any highly developed community. The
oil income from the country does not translate into development, income distribution is rather
skewed and poverty is still rampant. According to Adepoju (2008:5), low economic growth
coupled with other challenges such as poverty, political instability, increased population
2 Countries considered for this study are in italics.
39
growth and environmental degradation are some of the major causes of increased migration
in Sub-Saharan Africa.
Migration in Sub-Saharan Africa is not a new phenomenon. Countries such as Ghana, Kenya,
Gabon and South Africa are believed to have been traditional destinations for migrants in
West, East, Central and Southern Africa respectively (Adepoju, 2008:6). Many other
migrants from the region have opted for the Gulf region, Europe and North America
(International Organisation for Migration, 2003:218). Lately there has been a decline in the
number of Sub-Saharan African migrants opting for destinations in the Gulf region, Europe
and North Africa. Many skilled migrants are considering South Africa, Botswana, Namibia
and Gabon as alternative destination countries (Adepoju, 2008:9). As can be seen from the
literature, the SADC region is increasingly becoming a favoured destination for many
migrants in the region. Although the SADC region is increasingly becoming popular amongst
many migrants; it has its own particular economic challenges. There are uneven economic
patterns in this particular sub-region. The next sub-section presents a special case study of
SADC, a sub-region that harbours some of Sub-Saharan Africa’s economic power houses
such as South Africa, Botswana, Namibia and Mauritius. It also happens to be the sub-region
that has some of the region’s top remittance receiving countries, for instance, Lesotho and
Zimbabwe.
3.2.1 SADC and the imbalanced labour migration flows
The national income categories from the World Bank’s (2010) World Development
Indicators show that the SADC region is constituted of diverse economies. Seven of the
region’s countries are classified as low income, three fall in the lower middle income
category while five countries are in the upper middle income class. The World Bank (2010)
also classifies five of the region’s countries as highly indebted poor countries. It is against
this background of regional economic diversity that South Africa leads the region in terms of
economic activity (Kabundi & Loots, 2007:738). Khamfula and Huizinga (2004:710) observe
that South Africa accounts for close to three quarters of the region’s total GNP and has
significant dominance in intra-regional trade where it accounts for 70 per cent of regional
trade activities. Kabundi and Loots (2007:739) support the above claim about South Africa’s
regional dominance by reporting on how the country attracted approximately 71 per cent of
the region’s gross domestic investment.
40
SADC (2005:72) outlines that economic problems evident in most of the countries in the
region such as low per capita income, high budget deficits, low savings and investment rates
and increasing external debt burdens significantly contribute to falling social profiles. Such
economic challenges have resulted in increased poverty levels and falling human
development indices.
The lack of democratic institutions in some SADC countries has seen some severe violations
of human rights. In some broader contexts, such violations have resulted in political
instability. Political unrest coupled with some economic and social challenges amongst
countries has seen many people resorting to migration (Adepoju, 2008:5).
The migration patterns in the SADC region can be argued to be an imbalanced process, with
many migrants flocking to some few destination countries such as South Africa
(Trimikliniotis et al., 2008:1325). Trimikliniotis et al. (2008:1336) believe that uneven
development within the SADC region accounts for the imbalanced migration patterns. This
imbalanced migration results from the fact that South Africa is the region’s largest economy
hence migrants from other countries in the region are attracted to the perceived economic
opportunities (Jefferis, 2007:91). Most of these countries in the region have undiversified
economies which rely solely on primary exports while on the other hand, South Africa is
industrialised and economically well diversified (Jefferis, 2007:92). Such economic
diversification entails more employment opportunities which can be an attracting force to the
region’s migrant workers.
Though South Africa can be said to be the SADC region’s major migration destination
country, some scholars also include countries like Botswana and Namibia as other leading
destination countries for most migrants in the region (Oucho, 2002:26; Appleton, Sives and
Morgan, 2006).
Even though South Africa may not be the sole destination country, the argument of
imbalanced labour migration within the SADC region seems to be valid still since the
migration flows in the region involve a few destination countries and a lot of source
countries. Such economic imbalances prevalent in most sub-regions within Sub-Saharan
Africa seem to be reflected in the region’s overall remittances inflow patterns. The next sub-
section illustrates how the overall pattern of remittance inflows in Sub-Saharan Africa
mismatches that of other developing regions.
41
3.3 Aggregate trends of remittance inflows in Sub-Saharan Africa
The role of capital in development is clearly highlighted in the economic literature. Both
physical and human components of capital play a critical role in the computations of a
country’s total output. In trying to understand how remittances impact economic growth of
the recipient countries, this study compares the volume of remittance inflows relative to those
of other external sources of capital in developing countries. Foreign Direct Investment (FDI)
and Official aid are the orthodox external sources of capital that most developing countries
make use of. Lately, the role of remittances as a significant source of external capital in
developing countries has been widely explored in the development finance literature (Bank of
Uganda, 2007; Barajas et al., 2009:1; Ratha, 2003:157). Data from the World Bank (2009)
and World Bank (2010) clearly illustrates how remittances have become an important
external source of finance in developing countries. Figure 3.1 highlights the aggregate trends
of remittance inflows relative to those of some other sources of external capital in developing
economies, that is, foreign direct investment and official development assistance, over the
period 1980-2008.
Figure 3.1 Remittances, Foreign Direct Investment and Official Development Assistance
inflows to Developing Economies
Source: World Bank (2009 & 2010)
-
100,000
200,000
300,000
400,000
500,000
600,000
700,000
Am
ou
nt
( C
urr
en
t U
S $
Mil
lio
n)
Year
Remittances
FDI
ODA
42
Figure 3.1 illustrates that remittance flows to developing countries have been rising since
1990. As depicted in Figure 3.1, it is believed that global flows of remittances to developing
countries now surpass those of official development assistance (Brown, 2006:56; Giuliano &
Ruiz-Arranz, 2009:144). Adenutsi (2010:32) reports that there was a 300 per cent increase in
the flow of remittances to developing countries between 1995 and 2005. He goes on further
to demonstrate how remittances to developing countries doubled the volumes of official aid
and remained slightly below the total value of foreign direct investment to the developing
region.
Gupta et al. (2008:105) however note that the above trend excludes Sub-Saharan Africa
where the volumes of aid still out-match remittance receipts in the region. Gupta et al.
(2008:105) highlight that since 2000, there was a 13 per cent increase in aid flows to Sub-
Saharan Africa as compared to a 10 percentage increase in remittance inflows to the region
for that same particular period. Adenutsi (2010:33) supports the above claim by insisting that
the mean absolute value of total remittance flows to the Sub-Saharan Africa region do not
even account for a third of the mean absolute value Official Development Assistance (ODA)
flows to the region. Data from the World Bank (2009) and World Bank (2010) illustrate a
picture which supports the claim that remittances to Sub-Saharan Africa have not been at par
with the general trend manifesting in other developing regions. Figure 3.2 shows that the
volumes of remittance inflows to Sub-Saharan Africa still remain below that of other sources
of external capital like official development assistance and foreign direct investment.
43
Figure 3.2 Distribution of Remittance, ODI and FDI inflows in Sub-Saharan Africa
Source: World Bank (2009 & 2010)
The distribution of remittances, ODA and FDI in Sub-Saharan Africa as depicted in Figure
3.2 is the opposite of that manifesting in Latin America. Figure 3.2 shows that the volume of
remittance inflows in Sub-Saharan Africa is less than ODA and FDI. In Latin America, not
only have remittance flows surpassed ODA volumes, they have even replaced FDI as the
leading source of external financing (Vargas-Silva, 2008:290). Countries like Mexico have
remittances as their major source of external capital as compared to foreign aid (Vargas-
Silva, 2008:291). Available literature on remittances clearly explains why remittances are
becoming a leading source of external capital for developing countries.
Brown (2006), Gupta et al. (2008), Ratha (2003) and others explain why remittances have
emerged as an important source of external capital for developing countries. The most
important reason pertains to the issue of remittances being less volatile and stable (Bank of
Uganda, 2007; Ajayi et al., 2009:79; Brown, 2006:60; Gupta et al., 2008:105). It is believed
that remittances are less volatile and stable when compared to other private capital flows like
official aid and FDI. Altruistic remittances tend to be stable and less volatile since they are
pro-cyclical (Bank of Uganda, 2007). Altruistic remittance receipts tend to increase during
unfavourable economic periods in the home country. While FDI flows normally tend to
$0
$5,000,000,000
$10,000,000,000
$15,000,000,000
$20,000,000,000
$25,000,000,000
$30,000,000,000
$35,000,000,000
$40,000,000,000
$45,000,000,000
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
Am
ount
(C
urre
nt U
S $)
Year
Remittances
ODA
FDI
44
decrease in high risk developing countries, remittance flows normally tend to take the
opposite direction (Ratha, 2003:163). As a result of remittances being less volatile and more
stable, most developing countries have resorted to promoting remittance inflows from their
emigrants.
Unlike other sources of external capital like loans, remittances do not need to be serviced in
the future (Bank of Uganda, 2007; Brown, 2006:56). The fact that there are no premiums on
remittances to be paid out by the recipient countries in the future makes remittances a stable
and less volatile source of external financing.
3.4 Growth patterns in developing regions
Having looked at the aggregate trends of remittance inflows in Sub-Saharan Africa, the study
goes on forward to examine the different economic growth patterns in developing regions
around the world. The aim here is to compare the overall pattern of annual growth in Sub-
Saharan Africa relative to that of other developing regions over the period 1980-2008.
Three developing regions are included in the assessment and these include East Asia &
Pacific, Latin America & Caribbean, and Middle East & North Africa. Data from the World
Bank (2010) shows that these four developing regions have experienced different growth
patterns over the past three decades. Figure 3.3 illustrates the distribution of growth in the
four developing regions over the period 1980-2008.
45
Figure 3.3 Growth patterns in developing regions
Source: World Bank (2010)
Figure 3.3 illustrates that over the period 1980-2008, Sub-Saharan Africa experienced a series
of both declining and rising growth trends. The periods 1980-1983 and 1988-1992 are
amongst some of the periods when Sub-Saharan Africa experienced severe falls in economic
growth. The periods 1983-1988 and 1992-1996 saw some major rise in economic growth
within the region. In 2007 Sub-Saharan Africa reached its maximum economic growth rate of
6.5 per cent.
Figure 3.3 also shows over the period 1980-2008, economic growth trends in Sub-Saharan
remained less volatile as compared to those of Latin America & Caribbean, and Middle East
& North Africa. Even though the growth trends in Sub-Saharan Africa remained less volatile
as compared to those of other developing regions, it must however be noted that the levels of
economic growth in Sub-Saharan Africa and Latin America & Caribbean were relatively low
as compared to those of East Asia & Pacific. While Sub-Saharan Africa had a maximum
economic growth rate of 6.5 per cent in 2007, East Asia & Pacific recorded an economic
growth rate of 12.3 per cent for that same year. From 2004 onwards, Sub-Saharan Africa has
-4
-2
0
2
4
6
8
10
12
14
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
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98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
Re
al G
row
th (
%)
Year
East Asia & Pacific Latin America & Caribbean
Middle East & North Africa Sub-Saharan Africa
46
however recorded growth rates that are slightly above those of Middle East & North Africa,
and Latin America & Caribbean.
Figure 3.3 also shows that from 2000 onwards, the growth pattern in Sub-Saharan Africa was
almost similar with that of Middle East & North Africa. From 2003 onwards the growth
patterns in Sub-Saharan Africa, Middle East & North Africa, and Latin America & Caribbean
were almost similar.
3.5 Conclusion
This chapter looked at how economic factors like low economic growth and poverty together
with geo-political factors like political instability, increased population growth and
environmental degradation have influenced increased migration in Sub-Saharan Africa. The
chapter also made use of available data to present the simultaneous increase in volumes of
remittance inflows to developing countries over the past few decades. Evidence from the
chapter highlights that remittances have become a significant external source of capital in
many developing countries. Remittances to developing regions, except for Sub-Saharan
Africa, have been on the rise since 1990. Evidence from the chapter further reveals that the
volumes of remittance inflows to developing regions, again with the exception of Sub-
Saharan Africa, now surpass that of Official Development Assistance, an external source of
capital that used to dominate developing regions. Volumes of remittance inflows to Sub-
Saharan Africa still remain below that of other sources of capital like FDI and Official
Development Assistance.
Although the trend pattern of remittance vis-a-vis that of other conventional external sources
of capital in Sub-Saharan Africa seems to contrast that of other developing regions, the
increase in the sums of remittance inflows to the region must not be overlooked. It is against
this background that this study aims at looking at the growth effects of such increases in
remittances. The chapter also illustrated how the relative levels of economic growth in Sub-
Saharan Africa remain low as compared to those of other developing regions like East Asia
and the Pacific. Having looked at the distribution of remittances inflows and growth in Sub-
Saharan Africa, the following chapter outlines the methodology to be followed in assessing
the growth effects of remittances in this study.
47
CHAPTER 4
RESEARCH METHODOLOGY
4.1 Introduction
This chapter presents the research methodology to be used in this study and all the other
relevant information pertaining to how the study was structured and conducted. It gives some
background information on the research design and the model specification. Although the
model is built on the existing literature, it incorporates other empirical means that are of
significance in filling in the gaps within the remittances literature. It provides the
methodological framework that makes it possible to answer the research questions on the
growth effects of remittances in Sub-Saharan Africa.
4.2 Research design
A quantitative research design in the form of an econometric model is employed for this
study. Panel data is used to assess the impact of remittances on economic growth of recipient
countries in Sub-Saharan Africa.
Previous researchers investigating the growth effects of remittances in developing countries,
for example, Fayissa and Nsiah (2010b), Chami et al. (2003) and Pradhan et al. (2008) also
made use of econometric modelling. There are many reasons why different studies make use
of an econometric research design when assessing the growth effects of remittances.
Econometric models highlight whether there is a relationship between remittances and
economic growth of the recipient economies. This approach also makes it possible for the
researcher to examine whether the relationship between remittances and economic growth is
statistically significant or insignificant. Besides showing the statistical significance of the
relationship between the economic variables of interest, the econometric research method
also identifies whether the relationship is negative or positive. Furthermore, this research
design can quantify the degree of positivity or negativity of the relationship between
remittances and economic growth. One can make use of the reported coefficients to estimate
how a particular change in the explanatory variables affects the dependent variable. Such
detailed empirical analysis is critical for answering the research questions of this study hence
the major motivation for employing the methodology.
48
4.2.1 Panel structure
Scholars examining the growth effects of remittances often use panel models as compared to
time series and cross-sectional data models. Scholars including Fayissa and Nsiah (2010a),
Fayissa and Nsiah (2010b), Barajas et al. (2009), Rao and Hassan (2011), Giuliano and Ruiz-
Arranz (2009), Bettin and Zazzaro (2009), Catrinescu et al. (2009), Acosta, Lartey and
Mandelman (2009) and Pradhan et al. (2008) are some of the recent scholars who used panel
modelling in assessing the growth effects of remittances. Oda (2004:222) highlights some of
the reasons why many scholars prefer the use of panel data. Firstly, the model gains more
degrees of freedom. Secondly, panel data prevents the risk of realising biased results due to
unobserved country-specific effects that may be correlated with the explanatory variables
included in the model. The last reason is that panel data makes it possible to investigate
dynamic effects, a feature that is not possible when only using cross-sectional data.
This study makes use of panel data consisting of twenty-nine developing economies in Sub-
Saharan Africa analysed over a period of twenty-nine years, that is, from 1980 to 2008.
Following the approach implemented in studies by Pradhan et al. (2008), Jongwanich (2007)
and Oda (2004), the study’s baseline model comprises of averaged time periods. Data is
presented in averaged five-year time periods and a four-year time period for the last sub-set.
This results in a panel with a t-component of six. According to Oda (2004) averaging is
essential in order to circumvent the influence of short-run fluctuations provoked by business
cycles on estimation. Also data averaging helps to solve for inconsistency caused by data
gaps.
Although the study makes use of averaged time periods, primary model results for a model
with a t-component of twenty-nine are also reported. This is part of an exercise where the
study begins by assessing and reporting regression results for the whole panel. In this part,
the study ignores the influence of short-run fluctuations provoked by business cycles and
reports the relationship between remittances and economic growth.
Having specified the panel structure, it is, however, important to clarify the basis of selection
and lead reasons why twenty-nine countries out of forty-eight Sub-Saharan countries were
shortlisted for this study. Nineteen Sub-Saharan Africa countries were dropped from this
study mainly due to missing data for critical variables such as remittances and secondary
school enrolment. The countries shortlisted in the panel had sufficient data needed for this
study. Although removing countries with missing data at random can result in increased
49
standard errors, this does not necessarily imply that the model will result in biased estimates
(Durlauf, Johnson & Temple, 2004:124). However, it would have been interesting to have as
part of the panel, leading remittance recipient economies such as Zimbabwe which was
dropped because of the many data gaps.
The same approach used to select the twenty-nine countries for the panel was also used for
the timeframe. The period with fewer data gaps for most of the twenty-nine Sub-Saharan
African countries was considered for this study. It seems that data availability for most of the
countries falls within the period 1980 to 2008. As a result, the study had to use this timeframe
to assess the growth effects of remittances in Sub-Saharan Africa.
One major limitation of dropping countries without data and shortening the time period based
on data availability is that of reducing the degrees of freedom. This can be pointed out as an
unavoidable limitation of this study.
4.3 Model specification
The model used in this study is an extension of the neoclassical growth model. The study
makes use of the augmented Solow model as discussed in Bond, Hoeffler and Temple
(2001:15). The model used in this study can be specified as:
��� = �� + ��,�͂͂͂͂͂ + � ������ + ����� + �������� + ������� + ������� +
���� ��� + �!"���� + #�� (1)
To include the lag of growth as another explanatory variable, the model can be extended and
specified as follows:
��� = �� + ���,�$ + � �,�͂͂͂͂͂ + �������� + ����� + �������� + ������� + ������� +
�!�� ��� + �%"���� + #�� (2)
&'( is the dependent variable, economic growth. This study uses per capita real GDP growth
as the measure for economic growth.
i: countries; (Country: 1, 2, ..., 29)
50
t: periods; (Period: 1, 2, ..., 6)3
)* is the constant,
&',($+ is the lag of growth.
,',(͂ is the initial income. In this study, initial income is presented by the logarithm of real per
capita GDP at the beginning of each sub-period. The variable controls for convergence4.
-./0'( is the logarithm of real per capita remittance5.
1'( represents investment. The study uses the logarithm of real investment as a share of GDP
to represent investment.
2345'( represents human capital. This study uses the logarithm of total percentage in gross
secondary education enrolment as a proxy for human capital.
136'( is inflation, which is included as an indicator of macroeconomic stability. Inflation is
expressed in its natural logarithmic form and data is presented as annual averages.
78.3'( is openness, expressed as the percentage of the total value of exports and imports as a
share of GDP. The variable is included to capture the effect of trade policy on growth. Data
for openness is entered in its logarithmic form.
9:;0'( is the logarithm of government consumption. It is expressed using data for general
government final consumption expenditure as a percentage of GDP.
<:8'( is the logarithm of population growth. In this context, population growth is included as
a proxy for labour supply.
='( is the error term.
The model has got one dependent variable and eight explanatory variables. Remittances
(Remt) is the variable of interest while initial income, investment and enrolment are the
control variables. Population growth, openness, government consumption and inflation are a
set of control variables that are believed to have an effect on economic growth. To solve for
3 For the primary model where data is not averaged, t represents years; (Year: 1, 2, …, 29).
4 While 0͂ in the baseline model is the first year of each period, in the primary model where data is not
averaged, 0͂ is the real income at the beginning of the year. 5 Chapter 5 also presents results for a model run using data for remittances expressed as a share of GDP.
51
the skewness of data and also for the purpose of ease of interpretation, all explanatory
variables in the model are expressed in their natural logarithmic form.
4.3.1 A priori expectations
According to Pradhan et al. (2008) the initial income variable which controls for convergence
is expected to have a negative coefficient because of the diminishing returns to capital.
Neoclassical economics highlights how countries starting with low income per capita are
anticipated to catch up with the ones that were already on a higher level of development.
According to neo-classical growth theories, the coefficients for investment and enrolment are
expected to have a positive sign (Fayissa & Nsiah, 2010a, Jongwanich, 2007:8). An increase
in enrolment results in the growth in human capital which has an ultimate positive effect on
overall economic growth. The same applies to investment, where an increase in investment
has some direct positive effect on output and overall economic growth.
The coefficient for population growth is expected to have a positive sign due to the increased
availability of labour. Increased population growth tends to result in increased labour supply
which has a positive effect on ultimate economic growth. In the case of openness, a positive
coefficient is expected. This is mainly because openness results in increased competition and
innovation, access to larger markets and a reduction in rent seeking activity motivated by
trade restriction (Jongwanich, 2007:8).
Both coefficients for inflation and government consumption are expected to be negative
(Jongwanich, 2007:8). Higher inflation tends to have detrimental effects on the level of
savings. It negatively affects private investment thereby negatively impacting economic
growth. Government consumption as a measure of government’s spending in non-productive
investments tends to negatively impact economic growth. Such spending is sometimes
associated with the crowding out effect which has negative effects on financial development
and growth.
52
4.3.2 Data sources
Data for remittances was drawn from the World Bank indicators on remittances published in
2009. Data for initial income, per capita income growth, population growth, trade openness,
enrolment in secondary education and government consumption was drawn from the World
Development Indicators and Global Development Finance (2010) published by the World
Bank. Data for investment and inflation was accessed from the IMF’s World Economic
Outlook Database (2011).
4.3.3 Comparative analysis
Different exploratory techniques are used in this study to help bring an understanding of the
real growth effects of remittances. The study compares results from a baseline model run
using period averaged data (with a t-component of six) and the other one that makes use of all
the yearly data without averaging (t-component of 29).
Although it might be interesting to assess the growth effects of remittances at sub-regional
level for comparative purposes, it is unfeasible for this study to model separate regressions
for the four Sub-Saharan Africa sub-regions. But it is still critical for the study to take into
account the heterogeneity of the sub-regions when assessing the growth effects of remittances
in Sub-Saharan Africa. There is a need to consider some important features or characteristics
of remittances that are unique amongst the different sub-regions. In order to account for
heterogeneity of sub-regions, the study makes use of regional dummies to distinguish
between these sub-regions. The study extends the model by way of including regional
dummies for Western and Southern Africa. Regional dummies for Western Africa and
Southern Africa sub-regions are included since a lot of countries in the panel are drawn from
these two sub-regions. Results from the growth model with dummies for Western Africa and
Southern Africa sub-regions will then be compared with those of the baseline model. The
model with the regional dummy variables can be presented as follows:
��� = �� + ���,�$ + � �,�͂͂͂͂͂ + �������� + ��>?@A��B + ��C?@A��B + ����� +
�������� + �!����� + �%����� + ���� ��� + �"���� + #�� (3)
53
Where DEFGHHI is a variable that captures the effects of remittances in Southern Africa
alone. This variable is a product of the Southern Africa sub-regional dummy multiplied by
the variable of interest, remittances.
JEFGHHI is a variable that captures the effects of remittances in the Western Africa sub-
region alone. This variable is a product of the West Africa sub-regional dummy multiplied by
the variable of interest, remittances.
Computing sub-regional dummies allows for the study to assess the effect of remittances in a
particular sub-region of interest with respect to the rest of Sub-Saharan Africa.
The study also explores the channels through which remittances impact growth as discussed
in the literature. Since channels like investment and human capital have been identified as
some of the channels under which remittances impact economic growth, one might argue that
having the same variables as part of the growth regression model may weaken the possible
contribution of remittances on overall economic growth. As a result, the study investigates
scenarios where one of two important control variables, enrolment and investment, are
dropped in the model. The idea here is to assess any significant changes in the relationship
between remittances and growth when one of the above variables is either included or
excluded in the model. Remittances are likely to have a statistically significant positive effect
on growth when both investment and enrolment or one of the variables is not included in the
model. It is however inappropriate to run a regression without both investment and enrolment
as this would result in a misspecified model.
4.4 Methods of estimation
There are different types of panel data models which include; pooled regression model
(Ordinary Least Squares), Fixed Effects, Random Effects and the Generalised Methods of
Moments (GMM) (Cakir, 2008:45). There are different motivations available to support the
use of a particular method of estimation. Below are conditions that need to be followed when
employing a particular estimation method.
The OLS method of estimation needs to satisfy the following assumptions. The regression
model must be linear in parameters. OLS can still be applicable even in a situation where the
variables are non-linear but the parameters are linear. The observations included in the panel
54
must be greater than the number of parameters to be estimated in the model. There must not
be any multicollinearity amongst the included explanatory variables. Other assumptions
include zero covariance between the explanatory variables and the error term, the mean value
of the error term must be equal to zero, explanatory variables must be independent of the
error term, constant variance of the error term, no autocorrelation between any included
explanatory variables and its disturbances and lastly the disturbance must be normally
distributed. All these given assumptions are critical when interpreting OLS estimations
(Gujarati, 1995:59).
One limitation of using the OLS method of estimation in a dynamic growth model like this is
that the method does not account for country specific effects. In order to account for such
country specific effects, it is advisable that one makes use of the Least Squares Dummy
Variable (LSDV) method of estimation. The use of dummies can help to capture the
qualitative and unobserved effects in a model (Cakir, 2008:45).
In some cases the LSDV method of estimation might not be preferable and one may opt for
the Fixed Effects method of estimation. There are a number of advantages for using the
Fixed Effects method of estimation. One of the key advantages has to do with the problem of
biased results as a result of unobserved heterogeneity. According to Durlauf et al. (2004:105),
the use of the Fixed Effects method of estimation might solve for the bias in estimations due
to omitted variables that are correlated with the included explanatory variables. In other
words, the Fixed Effects method removes all the omitted variables that are constant over time
hence solving for the possible bias that would have resulted if the omitted variables are
correlated to the included explanatory variables. The Fixed Effects method is sometimes
preferred over the Random Effects method. It is difficult to use the Random Effects model
since the included explanatory variables need to be independent of the specific effects. When
the included explanatory variables are correlated with the specific effects, the Fixed Effects
method will result in unbiased estimations while the Random effects will most likely result in
biased estimations (Durlauf et al., 2004:105).
One of the greatest challenges involved in growth modelling is the endogeneity problem. This
problem happens when there is a bi-directional causality relationship between the dependent
variable and its regressors. This problem often results in misspecified models which lead to
biased estimations. In this case, the endogeneity problem arises as a result of bi-directional
causality between economic growth and remittances. As a result, different growth scholars
55
have proposed different remedies for the endogeneity problem. The use of instrumental
variables can solve for the endogeneity problem (Chami et al., 2003:19). Oda (2004:223)
makes use of lagged variables to mitigate the endogeneity problem. Jongwanich (2007:12)
makes use of the Generalised Method of Moments (GMM) to solve for the endogeneity
problem. The main motivation for the wide use of GMM as a method of estimation is centred
on the ability of this method to deal with biases resulting from measurement error and the
endogeneity problem (Durlauf et al., 2004:111). Another major motivation for using GMM
estimators in the empirical growth research context revolves around the possibility of
obtaining consistent parameter estimates even when there is some measurement error and
endogenous explanatory variables are present (Bond, Hoeffler & Temple, 2001:14). GMM
makes use of the first-difference to eliminate country specific effects and it makes use of the
lags as instrument variables to solve for any bias resulting from the endogeneity problem.
This study makes use of the dynamic GMM method to estimate the impact of remittances in
Sub-Saharan Africa. Previous studies assessing the growth effects of remittances (Fayissa &
Nsiah, 2010a; Fayissa & Nsiah, 2010b; Giuliano & Ruiz-Arranz, 2009; Bettin & Zazzaro,
2009; Catrinescu et al., 2009; Rao & Hassan, 2011) also utilised the same method. The
Arellano-Bover Blundell-Bond linear GMM estimators will be used in particular. Besides
solving for endogeneity, one other motivation for making use of GMM dynamic estimations
is that of trying to avoid coming up with estimations that are either biased downwards or
upwards. In models where there is a small T and a bigger N, the Fixed Effects method
denoted above as Within-group (WG) tend to be biased downwards while OLS tends to be
biased upwards (Bond, 2002:5). GMM gives coefficients that are in-between the two biased
methods of estimation.
It must however be noted that the main reason why the GMM estimation method has been
preferred for this study has to do with the method being a straightforward remedy to the
endogeneity problem. In this context, the endogeneity problem is most likely to be an issue
between remittances and economic growth. The bi-directional causality between remittances
and economic growth needs to be addressed. The level of economic growth might have some
influence on the volume of remittance inflows. Low economic growth in a recipient country
may force migrants to remit more as postulated in the Pure Altruism theory. As a result,
remittances automatically becomes the dependent variable here with economic growth being
one of the explanatory variables explaining for the change in volume of remittance inflows. It
then becomes difficult to model a growth regression where you have remittances as one of
56
the explanatory variables since the relationship between the two variables is two-way. There
is some feedback which makes it difficult to determine the direction of causality.
The GMM estimation method also helps to solve for the endogeneity problem between
economic growth and other variables included in the model. Bi-directional causality is likely
to be present between economic growth and variables such as inflation, government
consumption and investment. While both inflation, government consumption and investment
might have an effect on economic growth, it is also interesting to note how the level of
economic growth has an effect on these three variables too. Growth levels have an effect on
overall money demand, exchange rate and the rate of imports and remittance inflows. It is
through these different channels that growth is likely to impact inflation. The rate of
government consumption is also dependent on the growth levels. Investment rate is most
likely to be high in countries that have high or moderate growth rates. Economic problems
that result in low economic growth such as famine and war are also likely to negatively
impact investment rate in a particular country. As a result, remittance, investment, inflation
and government consumption variables are expressed as endogenous variables in the model.
The GMM method of estimation will then have to solve for the endogeneity problem.
4.4.1 System dynamic panel data estimation
This study makes use of the Arellano-Bover/Blundell-Bond GMM one-step estimator to
assess the impact of remittances on economic growth of recipient economies in Sub-Saharan
Africa. This linear GMM estimator is mostly appropriate for models with small a T and a
large N, that is, dynamic models with few time periods and many individuals (Roodman,
2006:1). Roodman (2006:1) discusses many other conditions that the Arellano-Bover GMM
estimator is designed for. The estimator is appropriate for models with independent variables
that are not strictly exogenous; where there are fixed individual effects; a linear functional
relationship; a dynamic dependant variable; and finally a situation where there is
heteroskedasticity and autocorrelation within units but not across them.
In this study, the panel structure constituted of a small T and a larger N motivated for the use
of the Arrellano-Bover/Blundell-Bond GMM estimators. The system GMM estimator used in
the study incorporates the lags of the dependent variable. This increases the number of
explanatory variables in the model from eight to nine.
57
It is also critical that the study makes use of some diagnostic tests to check and determine the
correct specification of the model. The next sub-section documents how this study’s
diagnostic tests are modelled.
4.4.2 Diagnostic tests
The Sargan test is employed in the study to test for over identifying restrictions. This test is
significant when it comes to assessing the validity of instruments included in the model. It
helps identify the correct number of restrictions to be included in the growth model. It can
also be useful in locating the relevance of the included regressors.
The study also makes use of the Arellano-Bond test for autocorrelation. The test for AR (1) is
applied to the differenced residuals while the test for AR (2) detects autocorrelation in levels.
The test has a null hypothesis of no autocorrelation.
A correlation matrix with all the included explanatory variables is used to check for
multicollinearity. Multicollinearity is a problem that arises when either all or some of the
included regressors are highly correlated with each other (Koop, 2009:100). As a result, the
model encounters some difficulty in identifying the actual explanatory variable influencing
the dependent variable.
The model makes use of robust standard errors instead of the default GMM standard errors.
According to Mileva (2007:7) robust standard errors are consistent with panel-specific
autocorrelation and heteroskedasticity in one-step estimation. The major challenge with
heteroskedasticity is that of model misinterpretation. It results in models with incorrect p-
values and t-stats which ultimately lead to false conclusions.
Having discussed about multicollinearity, autocorrelation and heteroskedasticity, it is also
important to check the R² tests. R² tests are important in determining the ‘goodness of fit’ of
the regression line. It is, however, unfortunate to note that the modelling technique employed
in this study does not show the R² tests.
It is also essential that the study checks for the distribution pattern of the included variables
for the different countries. The presence of outliers can result in model misspecification. A
country with variables that do not match the general distribution of all other countries
included in the panel needs to be dropped off. Distribution graphs can be a useful technique
58
making it possible to spot such outliers in the model. The study makes use of a scatter plot
and individual countries’ graphs showcasing the distribution of logged remittances and
economic growth, to check for outliers.
No panel unit root tests are performed since the use of logs and ratios, for most of the
variables, done in this study makes non-stationary series into a stationary series. Also, the
GMM estimator employed in this study makes use of the first-difference hence there is no
need to perform panel unit root tests since the series is already stationary.
4.5 Conclusion
This chapter presented the methodology to be followed in assessing the growth effects of
remittances in twenty-nine Sub-Saharan African countries. As part of the methodology, a
system GMM one-step estimator will be utilised in this econometric model which is an
extension of the neoclassical Solow growth model. The data for the main growth model
constitutes of five year period averages over the period 1980 to 2008.
Having presented the methodology to be followed in the study, the next chapter presents and
discusses the results. It attempts to answer the research question on the impact of remittances
on economic growth of recipient countries in Sub-Saharan Africa.
59
CHAPTER 5
EMPIRICAL ANALYSIS
5.1 Introduction
This chapter reveals the research study’s major findings on the growth effects of remittances
in recipient economies in Sub-Saharan Africa over the period 1980 to 2008. Results from the
baseline model (regressions run using averaged data) are compared with those run using all
the annual data available in the panel. Results from a model incorporating dummy variables
for two Sub-Saharan Africa sub-regions, West and Southern Africa, are also compared with
those of the baseline model. The chapter also reports the major findings emerging from the
exploratory regressions which analyse the growth effects of remittances through the channels
discussed in the literature. The chapter concludes by outlining the results for the different
diagnostic tests carried out in the study.
5.2 Data description
By way of background, Table 5.1 summarises the basic descriptive statistics of variables
included in the model. In the table, all data descriptions for the dependent variables, Remt, Y,
I, Pop, Enrl, Inf, Opn and Govt, are in natural logarithmic form.
The table illustrates the distribution of data for the dependent and explanatory variables. It
shows the mean, standard deviation, the range and the individual observations of the nine
variables included in the growth model.
Table 5.1 also shows that data for Growth, Remittances (Remt), Inflation (Inf), Investment
(I), Openness (Opn), Initial income (Y), Population growth (Pop) and Enrolment (Enrl) is
available for all the twenty-nine Sub-Saharan African countries included in the panel. This is
demonstrated by an ‘n=29’ present in all the mentioned variables.
60
Table 5.1 Descriptive statistics summary
Variable
Mean
Std. Dev
Min
Max
Observation
Growth overall 1.0675 3.2033 -7.9640 10.8450 N 171
between 1.5576 -2.3027 4.9139 n 29
within 2.8050 -8.9061 10.4810 T-bar 5.8966
Remt overall 1.7868 1.9191 -2.6555 5.6712 N 158
between 1.8744 -2.4645 5.2890 n 29
within 0.6765 -0.2577 3.6774 T-bar 5.4483
Y overall 6.0865 0.9323 4.7446 8.3627 N 168
between 0.9230 4.8426 8.0703 n 29
within 0.1689 5.4271 6.5971 T-bar 5.7931
I overall 2.8738 0.5265 -0.1091 4.1638 N 172
between 0.3983 2.0088 3.7385 n 29
within 0.3490 0.5331 3.8453 T-bar 5.9310
Pop overall 0.8956 0.3891 -1.2839 1.7725 N 172
between 0.2724 -0.0363 1.2817 n 29
within 0.2820 -0.9629 1.5756 T-bar 5.9310
Enrl overall 3.0706 0.7913 0.8774 4.5448 N 155
between 0.7130 1.4440 4.3503 n 29
within 0.3765 2.0100 4.3120 T-bar 5.3448
Inf overall 2.1926 0.9906 -1.3767 4.5155 N 170
between 0.6961 0.9415 3.3502 n 29
within 0.7108 -0.2938 3.7027 T-bar 5.8620
Opn overall 4.1520 0.5298 2.4051 5.2663 N 165
between 0.4837 3.0983 5.0579 n 29
within 0.2172 3.4589 4.8482 T-bar 5.6897
Govt overall 2.6619 0.3952 1.6105 3.7035 N 169
between 0.3343 2.0751 3.4380 n 29
within 0.2154 2.1578 3.4861 T-bar 5.8276
61
From the distribution of data shown in Table 5.1, it is important to further explore how the
explanatory variables correlate with each other. This can be achieved by making use of a
correlation matrix. A correlation matrix is significant for testing for multicollinearity among
the included regressors.
Table 5.2 Correlation matrix
Remt Govt Opn Inf Enrl Pop I Y
Remt 1.0000 Govt 0.4593 1.0000 Opn 0.5732 0.4945 1.0000 Inf -0.1803 -0.0908 -0.1415 1.0000 Enrl 0.3185 0.2551 0.4583 0.0597 1.0000 Pop -0.3897 -0.1525 -0.2539 -0.0673 0.2896 1.0000 I 0.3230 0.2534 0.3219 -0.1195 0.1389 -0.3631 1.0000 Y 0.4545 0.3848 0.4499 -0.0687 0.6875 -0.3488 0.2508 1.0000
Besides having no error message from Stata statistical software reporting a highly singular
matrix, a further test for multicollinearity shows that none amongst the included explanatory
variables is highly correlated with each other. Table 5.2 outlines the results from the
correlation matrix. From the correlation matrix, there is no evidence of explanatory variables
that are highly correlated with each other.
5.3 Growth-remittances behavioural analysis
After looking at the general descriptive statistics, the next sub-section graphically illustrates
the relationship between the two variables of interest, growth and remittances. Figure 5.1
illustrates the relationship between remittances and growth in the twenty-nine Sub-Saharan
African countries considered for this study.
62
Figure 5.1 Relationship between remittances and growth6
Source: World Bank (2009 & 2010)
6 Both remittances and growth are expressed in percentages. The time period runs from 1980 to 2008. Each
time period is made up of five years except for the 6th
time period consisting of four years.
-10
-50
510
-10
-50
510
-10
-50
510
-10
-50
510
-10
-50
510
0 2 4 6
0 2 4 6 0 2 4 6 0 2 4 6 0 2 4 6 0 2 4 6
Botswana Burkina Faso Cameroon Cape Verde Congo Rep Cote d' Ivoire
Ethiopia Gambia Ghana Guinea Guinea-Bissau Kenya
Lesotho Madagascar Malawi Mali Mauritania Mauritius
Mozambique Namibia Niger Rwanda Senegal Sierra-Leone
South Africa Sudan Swaziland Tanzania Togo
Growth Remt
Period
Graphs by Country
63
Figure 5.1 illustrates the relationship between logged remittances and economic growth. As
can be seen from the graph, while per capita remittances seem to be on a rising trend,
economic growth remains relatively low and volatile in most of the countries. It is however
interesting to note that from period four onwards most of the countries in Sub-Saharan Africa
seem to have experienced a rising trend in economic growth.
While many countries, for example Swaziland, Congo Republic, Cote d’ Ivoire, Gambia,
Lesotho, Mozambique and Niger seem to have constant remittance flows regardless of the
changing levels of growth, Figure 5.1 shows that in Sudan an increase in economic growth is
associated with a simultaneous increase in remittance inflows. There is a positive relationship
between remittances and economic growth. Such a positive relationship is in line with the
Pure Self Interest theory. According to this theory, migrants are most likely to remit back
home when there are favourable conditions that allow them to invest.
The dissimilar behavioural trends of remittances and growth in the different countries
illustrated in Figure 5.1 to some extent demonstrate the heterogeneity of the countries
included in the panel. Because of such heterogeneity evident in the different patterns shown
in Figure 5.1, there is a need to refer to some other correlation plots in order to determine the
positivity or negativity of the growth-remittances relationship in Sub-Saharan Africa. As a
result, the graphs in Figure 5.1 need only to be used as a guide to showcase the different
behavioural trends of remittances and economic growth in the different countries. To further
understand the relationship between remittances and economic growth in the twenty-nine
countries, the study makes use of a graph that sums the correlation between the remittances
and economic growth in all the twenty-nine countries.
The following figures are scatter plots illustrating the correlation between averaged per capita
remittances and averaged per capita growth in the twenty-nine Sub-Saharan countries under
study from 1980 to 2008.
64
Figure 5.2 Scatter plot illustrating the relationship between growth and remittances7
Source: World Bank (2009 & 2010)
7 Growth is in percentages and Remittances per capita are expressed in their natural logarithmic form (where Remittances are expressed in US Dollars (US$)).
-10
-5
0
5
10
Gro
wth
-2 0 2 4 6 Remittances per capita
65
Figure 5.3 Correlation between averaged remittances and averaged growth8
Source: World Bank (2009 & 2010) 8 Growth is in percentages and Remittances per capita are expressed in US Dollars (US$). Data for Growth and Remittances per capita is expressed as averages for the
entire time period, 1980-2008.
Botswana
Burkina Faso
Cameroon
Cape Verde
Congo Rep
Cote d' Ivoire
Ethiopia
Gambia
GhanaGuinea
Guinea Bissau
Kenya
Lesotho
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Rwanda
SenegalSierra-Leone
South Africa
Sudan
Swaziland
Tanzania
Togo
-2
0
2
4
6G
row
th
0 50 100 150 200Remittances per capita
66
The scatter plot in Figure 5.2 shows that there is a positive relationship between remittances
and economic growth in the twenty-nine Sub-Saharan Africa countries. Figure 5.3 also
illustrates a positive correlation between averaged growth and averaged remittances. Figure
5.3 also allows for the visual inspection method of identifying outliers. Botswana, Swaziland,
Mauritius, Cape Verde and Lesotho are the major outliers in the panel. It is imperative that
the study reports and compares the effect of remittances on economic growth when five of
these countries are excluded in the panel.
With reference to Figure 5.3, it is, to some extent, appropriate to do a sub-regional analysis of
the countries included in the panel. Most of the countries in Western, Eastern and Central
Africa sub-regions are characterised by relatively low per capita remittance inflows and either
low or negative per capita growth. In the Southern Africa sub-region, quite a number of
countries have moderate per capita growth figures. The southern Africa sub-region is also
characterised by a fair amount of countries with high per capita remittances.
However, the real relationship between economic growth and remittances cannot be
concluded basing on the given scatter plots. There is a need to make use of an analytic tool
that controls for possible causality between economic growth and remittances. There is also a
need to test the statistical significance of the relationship between the two variables. In order
to conclude on the actual relationship between remittances and economic growth, the next
sub-sections report the results obtained from the Arellano-Bover/Blundell Bond Generalised
Methods of Moments (GMM) one-step estimator method.
5.4 Model results
This section reports on the different estimates observed from the Arellano-Bover/Blundell
Bond Generalised Methods of Moments (GMM) one-step estimator. The section examines
results observed from the different models. Detailed results for the primary and baseline
models are given. Results from the various exploratory exercises are also outlined in detail.
5.4.1 Primary model estimations
First to begin with, are estimates from a regression on a growth model that makes use of all
the data available without making use of averages. In this model, the time component is not
67
in the form of averaged periods but takes into account all the data for all the individual years,
that is, from 1980 to 2008. The t-component for the panel equals to twenty-nine while the n-
component equals twenty-nine.
Table 5.3 Estimations for the entire panel (n=29, t=29)
Growth
Coef.
Robust Std. Err.
z
P>│z│
Growth L1. -0.6774 0.0668 -1.01 0.311 Remt 0.0579 0.2187 0.26 0.791 Y -1.1067 0.6787 -1.63 0.103 I 1.7604** 0.8491 2.07 0.038 Enrl 1.5173** 0.6804 2.23 0.026 Inf 0.3244 0.3076 1.05 0.292 Opn 1.4591 1.3163 1.11 0.268 Govt -2.6242 1.7077 -1.54 0.124 Pop -1.1723 1.2817 -0.91 0.360 Note: (*,**,***) denotes statistical significance at the 10%, 5%, 1% level, respectively.
Source: Stata 11 estimations
Results in Table 5.3 show no evidence of a statistically significant relationship between
remittances and economic growth in Sub-Saharan Africa. The results reveal that remittances
(Remt) have a positive and statistically insignificant effect on economic growth.
Results in Table 5.3 also reveal that enrolment (Enrl) and investment (I) have a positive and
statistically significant effect on economic growth. Inflation (Inf) and openness (Opn) have
positive and statistically insignificant effect on economic growth. The lag of growth (Growth
L1.), initial income (Y), government consumption (Govt) and population growth (Pop) have
negative and statistically insignificant effects on growth.
It can thus be concluded that, without controlling for the influence of short-run fluctuations
provoked by business cycles, there is no evidence supporting either a positive or negative
statistically significant effect of remittances on economic growth in the twenty-nine Sub-
Saharan Africa countries. In the next sub-sections, the study assesses the impact of
remittances on growth making use of averaged data to control for the short-run fluctuations.
68
5.4.2 Baseline model results
This sub-section reports the regression estimates for a growth model run using averaged
periods, where t=6 and n=29. In this sub-section, the study uses two different ways to present
remittances in the model. The study uses per capita remittances and remittances as a share of
GDP. The aim here is to assess whether the way we present remittances in a model has an
effect on the overall growth model.
In Table 5.4, the remittances variable is computed using per capita remittances.
Table 5.4 Baseline model estimations (n=29, t=6)
Growth
Coef.
Robust Std. Err.
z
P>│z│
Growth L1. -0.2255*** 0.0715 -3.15 0.002 Remt 0.3454 0.4049 0.85 0.394 Y -1.9281** 0.7897 -2.44 0.015 I 2.0526*** 0.6050 3.39 0.001 Enrl 1.7255* 0.9072 1.90 0.057 Inf -0.1573 0.2735 -0.57 0.565 Opn -0.1892 1.3866 -0.14 0.891 Govt 0.8816 1.2757 0.69 0.490 Pop 0.7846 0.8626 0.91 0.363 Note: (*,**,***) denotes statistical significance at the 10%, 5%, 1% level, respectively.
Source: Stata 11 estimations
Even when the study controls for the influence of short-run fluctuations provoked by business
cycles, results in Table 5.4 show no evidence supporting the fact that remittances have an
effect on economic growth. The results in Table 5.4 are similar with those revealed in the
primary model (refer Table 5.3) where remittances (Remt) have a positive and statistically
insignificant effect on economic growth.
Again, enrolment (Enrl) and investment (I) have a positive and statistically significant effect
on growth. Initial income (Y) is, once more, reported as having a negative and statistically
significant effect on growth. The lag of growth (Growth L1.) is also reported as having a
negative and statistically significant effect on growth.
69
Government consumption (Govt) and population growth (Pop) have a positive and
statistically insignificant effect on economic growth. Inflation (Inf) and openness (Opn) have
a negative and statistically insignificant effect on economic growth.
From Table 5.4, model estimates for initial income (Y), investment (I), enrolment (Enrl),
population growth (Pop) and inflation (Inf) variables came up as predicted9 while the signs
for government consumption (Govt) and openness (Opn) coefficients did not match
expectations. Positive signs were expected for the coefficients of investment, enrolment,
population growth and openness. Negative signs were expected for variables such as initial
income, government consumption and inflation.
Table 5.5 Growth model estimations using remittances as a share of GDP (n=29, t=6)
Growth
Coef.
Robust Std. Err.
z
P>│z│
Growth L1. -0.2019*** 0.0761 -2.65 0.008 Remt 0.2630 0.4078 0.64 0.519 Y -1.5311* 0.8334 -1.84 0.066 I 1.9617*** 0.6376 3.08 0.002 Enrl 1.7424* 0.9101 1.91 0.056 Inf -0.1789 0.3142 -0.57 0.569 Opn -0.4828 1.4370 -0.34 0.737 Govt 0.7988 1.2582 0.63 0.525 Pop 0.5983 0.8236 0.73 0.468 Note: (*,**,***) denotes statistical significance at the 10%, 5%, 1% level, respectively.
Source: Stata 11 estimations
Table 5.5 shows growth model results when the remittances variable is computed using
remittances as a share of GDP. Results in Table 5.5 still show that remittances (Remt) have a
positive and statistically insignificant effect on economic growth. Computing the variable
remittances in different ways has no significant effect on the overall impact of remittances on
economic growth.
Remittances seem to have no statistically significant effect on economic growth as so far
revealed in the results. In the next sub-section, the study assesses the impact of remittances 9 See Section 4.3.1 (A priori expectations)
70
on economic growth when outliers are excluded from the panel. The objective here is to
assess whether there will be any evidence to support that remittances have an impact on
economic growth. The next sub-sections also assess the effect of remittances on growth when
one of the channels, argued in the literature as having an effect on the growth effect of
remittances, is dropped.
5.4.3 Supplementary regressions
This sub-section begins by assessing the effect of remittances on economic growth when
outliers are excluded in the panel. Based on the scatter plot in Figure 5.3, the study takes out
five countries, presented as outliers, from the panel. These five countries are Botswana,
Swaziland, Mauritius, Cape Verde and Lesotho. Table 5.6 shows the growth model results
when the five countries are excluded from the panel.
Table 5.6 Growth model estimations: without outliers (n=24, t=6)
Growth
Coef.
Robust Std. Err.
z
P>│z│
Growth L1. -0.2520*** 0.0731 -3.45 0.001 Remt 0.3726 0.4582 0.81 0.416 Y -2.4380*** 0.9171 -2.66 0.008 I 2.6009*** 0.7737 3.36 0.001 Enrl 2.3738*** 0.8499 2.79 0.005 Inf -0.3129 0.2358 -1.33 0.185 Opn -0.4213 1.3561 -0.31 0.756 Govt 0.6577 1.1562 0.57 0.569 Pop 1.5228 1.0356 1.47 0.141 Note: (*,**,***) denotes statistical significance at the 10%, 5%, 1% level, respectively.
Source: Stata 11 estimations
Results in Table 5.6 show no evidence of remittances having a statistically significant effect
on economic growth, even when outliers are taken out of the panel. Remittances (Remt) still
remain positive and statistically insignificant.
71
Investment (I) and Enrolment (Enrl) have a positive and statistically significant effect on
economic growth. The lag of growth (Growth L1.) and initial income (Y) have a negative and
statistically significant effect on economic growth. Similar to the baseline model results,
government consumption (Govt), inflation (Inf), openness (Opn) and population growth
(Pop) have a statistically insignificant effect on economic growth.
Even though the growth effect of remittances still remains statistically insignificant, there is a
need to assess the effect when either the investment or the enrolment variable is dropped in
the model. It is possible that having the variable of remittances together with those of
investment and enrolment in the same model may distort the real growth effects of
remittances since these variables are reportedly the major channels through which
remittances affect economic growth. The effect of remittances on growth might be captured
in these other variables resulting in biased results being reported for remittances.
To try and reveal the ‘true’ effect of remittances on economic growth, the study engages in
some experimental exercises where one of these major channels is dropped from the model.
The argument here is that either the human capital or investment variable tends to absorb the
growth effects of remittances in the model. Thus, in order to establish the real growth effects
of remittances, either the human capital or investment variable needs to be dropped in the
model since these happen to be the main channels through which remittances impact
economic growth.
Table 5.7 Growth model estimations without the investment variable (n=29, t=6)
Growth
Coef.
Robust Std. Err.
z
P>│z│
Growth L1. -0.19** 0.0807 -2.35 0.019 Remt 0.0561 0.4624 0.12 0.904 Y -1.7531* 1.0059 -1.74 0.081 Enrl 2.3818** 1.2066 1.97 0.048 Inf -0.0258 0.3064 -0.08 0.933 Opn 0.0270 1.7412 0.02 0.988 Govt 1.7342 1.4487 1.20 0.231 Pop 0.9041 1.0643 0.85 0.396 Note: (*,**,***) denotes statistical significance at the 10%, 5%, 1% level, respectively.
Source: Stata 11 estimations
72
Table 5.7 shows estimates for a growth model when investment, one of the major channels
through which remittances may indirectly impact economic growth, is dropped from the
model. The results show no evidence of remittances affecting economic growth through
investment. Even when the study takes out investment from the model, assuming that the
growth effects of remittances happen through the investment channel, the results still show
that remittances (Remt) have a positive and statistically insignificant effect on remittances.
Estimates for the other variables appear similar to those reported in the baseline model in
Table 5.4 except for openness (Opn) which has now a positive but statistically insignificant
effect on growth. Government consumption (Govt) and population growth (Pop) retain the
same positive and statistically insignificant effect on economic growth. The coefficient of
enrolment still remains positive and statistically significant. Inflation has a negative and
statistically insignificant effect on economic growth. The coefficient for the lag of growth and
initial income still remains negative and statistically significant.
Having looked at the estimates of the growth model which excludes investment as one of the
control variables, the next step is to drop the human capital component. In this exploratory
exercise, the study drops the enrolment variable which is the proxy for human capital in the
model. The idea here is to observe whether the effect of remittances on economic growth will
change. Table 5.8 presents the growth model results when the enrolment variable is dropped.
Table 5.8 Growth model estimations without the enrolment variable (n=29, t=6)
Growth
Coef.
Robust Std. Err.
z
P>│z│
Growth L1. -0.2294*** 0.0679 -3.38 0.001 Remt 0.5896* 0.3278 1.80 0.072 Y -1.9758*** 0.7615 -2.59 0.009 I 3.5530*** 0.7187 4.94 0.000 Inf -0.4680 0.3595 -1.30 0.193 Opn 0.8322 1.1250 0.74 0.459 Govt -0.1513 1.3309 -0.11 0.909 Pop 0.7944 1.1012 0.72 0.471 Note: (*,**,***) denotes statistical significance at the 10%, 5%, 1% level, respectively.
Source: Stata 11 estimations
73
Table 5.8 shows evidence of remittances positively affecting economic growth through
human capital. When the study takes out enrolment, the proxy for human capital, from the
model, remittances (Remt) are for the first time reported as having a positive and statistically
significant effect on economic growth. Remittances (Remt) have a positive coefficient of
0.5896 which is statistically significant at ten per cent level. This shows that an increase in
remittances results in increased economic growth in Sub-Saharan Africa. Such a positive
effect of remittances on economic growth is mainly through the human capital channel. In the
previous results, remittances have been reported as having a statistically insignificant positive
effect on economic growth. In this context, having dropped the human capital variable,
remittances are for the first time reported as having a statistically significant positive effect
on economic growth. This implies that having the enrolment variable, a proxy of human
capital, included in the model eats up the ‘true’ growth effects of remittances. The actual
growth effects of remittances can only be established when the enrolment variable is dropped
from the model.
Investment (I) has a positive and statistically significant effect on growth. When enrolment is
excluded from the model, a higher coefficient for investment is realised. Table 5.8 shows a
higher coefficient for investment of 3.5530. Initial income (Y) and the lag of growth (Growth
L1.) still remain negative and statistically significant. Inflation (Inf) and government
consumption (Govt) have a negative and statistically insignificant effect on economic growth.
Openness (Opn) and population growth have a positive and a statistically insignificant effect
on economic growth.
It is interesting to note that the signs for all the coefficients reported in Table 5.8 came out as
postulated in the literature. Positive coefficients were expected for investment (I), openness
(Opn) and population growth. Negative coefficients were also expected for initial income
(Y), government consumption (Govt) and inflation (Inf).
5.4.4 Sub-regional analysis
Having explored the growth effects of remittances through the possible channels discussed in
the literature, it is important that the study also examines the remittances-growth relationship
based on area of coverage. There is a need to relax the assumption that Sub-Saharan Africa
sub-regions are homogenous. Because of the heterogeneity of countries and sub-regions as
74
revealed in the scatter plot in Figure 5.3, it is imperative that the study distinguish Sub-
Saharan African sub-regions by use of dummies.
Making use of a sub-regional dummy to distinguish the a particular sub-region from the rest
of the Sub-Saharan Africa region, the test for the effect of remittances on economic growth is
done for Western Africa and Southern Africa. The results are then compared with those of
Sub-Saharan Africa as a whole.
Table 5.9 shows results for the growth effects of remittances when dummies for the Western
Africa and Southern Africa sub-regions are included in the model.
Table 5.9 Growth model estimations with dummy variables for West and Southern
Africa sub-regions (n=29, t=6)
Growth
Coef.
Robust Std. Err.
z
P>│z│
Growth L1. -0.2555*** 0.0605 -4.22 0.000 Remt 0.7102** 0.3263 2.18 0.029 SAMNOOP -0.0075 0.0107 -0.69 0.488 WAMNOOP -0.0311*** 0.0118 -2.63 0.008 Y -1.6322** 0.7237 -2.26 0.024 I 2.5183*** 0.5775 4.36 0.000 Enrl 1.9570*** 0.7354 2.66 0.008 Inf -0.4599** 0.2161 -2.13 0.033 Opn -0.7339 1.2361 -0.59 0.553 Govt 0.1214 1.1277 0.11 0.914 Pop 1.2905* 0.6884 1.87 0.061 Note: (*,**,***) denotes statistical significance at the 10%, 5%, 1% level, respectively.
Source: Stata 11 estimations
When heterogeneity of sub-regions is taken into account, there is evidence revealing positive
growth effects of remittances in the whole of Sub-Saharan Africa. From Table 5.9,
remittances (Remt) have a positive and statistically significant coefficient of 0.71. The
coefficient is statistically significant at the five per cent level. These results illustrate the
importance of taking the heterogeneity of units, sub-regions, into account. When
heterogeneity of sub-regions is not accounted for, results in the baseline model show no
75
evidence of any positive and statistically significant effects of remittances on economic
growth in Sub-Saharan Africa. But, however, having included dummy variables to
distinguish between sub-regions, a positive and statistically significant effect of remittances
on economic growth is reported. This shows evidence that countries and sub-regions in Sub-
Saharan Africa are not homogenous when it comes to the impact of remittances on economic
growth.
Results in Table 5.9 also show negative estimated coefficients for the regional dummies of
remittances in West and Southern Africa. The negative and statistically significant West
Africa dummy variable coefficient shows evidence that remittances have a less positive effect
on economic growth in West Africa. Adding the West Africa dummy coefficient (-0.0311)
together with the remittances coefficient for Sub-Saharan Africa (0.7102) gives a low
remittances coefficient for West Africa of 0.6791. This shows that the positive effect of
remittances on economic growth in West Africa is low when compared to that of Sub-
Saharan Africa as a whole. The West African sub-region is not homogenous with other sub-
regions included in the panel. The statistically insignificant Southern Africa dummy variable
coefficient shows no evidence of a difference between Southern Africa and the rest of the
Sub-Saharan Africa region.
Inflation (Inf) is for the first time reported as having a negative and statistically significant
effect on economic growth. The lag of growth (Growth L1.) and initial income (Y) also have
a negative and statistically significant effect on economic growth. Investment (I), enrolment
(Enrl) and population growth (Pop) have a positive and statistically significant effect on
economic growth. Government consumption (Govt) and openness (Opn) are reported as
having a statistically insignificant effect on economic growth.
5.5 Diagnostic tests results
This sub-section reveals the results for the different tests done on the main growth model
used in this study. The results are for the diagnostic tests done for over identifying restrictions
and autocorrelation.
The Sargan test of over identifying restrictions shows that all the instruments used in the
growth model are valid. With a p-value of 0.0233 the study accepts the null hypothesis that
over identifying restrictions are valid. Appendix 9 shows the Sargan test run for this study.
76
The test for zero autocorrelation in first–differenced errors shown in Appendix 10 reveals that
there is no autocorrelation. With a p-value of 0.0205 in AR (1) the study accepts the null
hypothesis that there is no autocorrelation in the first differenced errors.
Results from the diagnostic tests run for this study do not reveal evidence of misspecification
in the growth model. Results from the diagnostic tests, however, reveal that the underlying
assumptions for a GMM model were taken into consideration.
5.6 Conclusion
Results revealed in this chapter gave evidence that remittances have a positive impact on
economic growth in Sub-Saharan Africa. Although the baseline model results show a positive
and statistically insignificant effect of remittances on growth, results from the supplementary
models reveal a different outcome. When heterogeneity of sub-regions is taken into account
through the use of sub-regional dummies, there is evidence revealing positive and statistically
significant growth effects of remittances in Sub-Saharan Africa. There is also evidence of
remittances positively affecting economic growth through the human capital channel. When
the enrolment variable, a proxy of human capital, is excluded from the model, results show
that remittances have a positive and statistically significant effect on economic growth. There
is, however, no evidence to support remittances affecting economic growth through the
investment channel. When the investment variable is excluded in the model, remittances have
a positive but statistically insignificant effect on economic growth.
Having the human capital and the investment variable included in the growth model together
with the remittances variable may result in distorted results on the growth effects of
remittances. This might be one possible reason why some previous studies reported on
remittances having a statistically insignificant effect on growth. It is there recommended that
studies interested in assessing the growth effects of remittances take into consideration the
possible bias that might result on the outcome due to either the investment or human capital
variable absorbing the actual effect of the remittances variable on economic growth.
One possible reason why studies like Barajas et al. (2009) and Rao and Hassan (2011) report
on remittances having no statistically significant impact on economic growth might be as a
result of not accounting for the heterogeneity of sub-regions and countries when assessing the
growth effects of remittances. In this study, when heterogeneity of sub-regions is not taken
77
into account, remittances are reported as having no impact on economic growth. However,
when the study relaxes the homogeneity of sub-regions assumption and account for
heterogeneity, remittances are found to have a positive impact on economic growth. Such
positive impact of remittances on economic growth varies across sub-regions, with
remittances in West Africa reported as having a less positive effect on growth.
Having looked at the results, the next chapter concludes the whole study. It touches on the
major findings emerging from this study. It also raises a few policy recommendations and
flags potential areas for future research on the remittances discourse.
78
CHAPTER 6
CONCLUSION
6.1 Introduction
This chapter concludes the study. It reiterates the research question and the findings drawn
from this study. The chapter goes on to give a detailed discussion on possible implications of
the research findings. The study also raises some tentative policy recommendations on how to
make remittances a more effective tool for promoting economic growth in the region. The
last section of the chapter concludes the entire study by proposing potential areas for future
research on the subject of remittances and economic growth.
6.2 Research findings
The relationship between remittances and economic growth is filled with contrasting
empirical evidence10. Migration optimists and migration pessimists show conflicting evidence
on how remittances impact economic growth. While migration optimists would argue that
remittances have a positive effect on economic growth through increased physical capital and
human capital investments (Catrinescu et al., 2009:81; Barajas et al., 2009:6), migration
pessimists are of a different perception. They argue that remittances have a negative impact
on economic growth due to increased consumption which has inflationary effects and moral
hazards that result in reduced labour supply and falling enrolment in education (Chami et al.,
2003:5).
Such discrepancies in the remittances literature motivated this research study to explore the
growth effects of remittances in Sub-Saharan Africa. Using the Arellano-Bover/Blundell-
Bond GMM one-step estimator, the study examined the effects of remittances in twenty-nine
Sub-Saharan African countries over the period 1980 to 2008.
Data for averaged per capita remittances and per capita growth in the twenty-nine Sub-
Saharan African countries over the period 1980 to 2008 revealed that the two economic
variables are positively correlated. The combined scatter plot for the twenty-nine countries
revealed some positive correlation between remittances and economic growth.
10
See table 2.3 in Chapter 2 of this study.
79
Results from the study show no evidence highlighting the significance of employing period
averaged data when running growth models. Though it is important to make use of period
averaged data in order to control for the influence of short-run fluctuations provoked by
business cycles, almost similar results were obtained for the primary and baseline models.
This study’s primary growth model made no use of period averaged data while the baseline
model made use of period averaged data. These two models, however, yielded almost similar
results with remittances being positive and statistically insignificant in both cases.
There is evidence from the study revealing that the manner in which the remittances variable
is modelled has no significant impact on the overall outcome of the growth model. Modelling
the remittances variable using ‘per capita remittances’ or ‘remittances as a share of GDP’
yielded almost similar results. In both scenarios, the results show that remittances have a
positive and statistically insignificant effect on economic growth.
Results from the study’s baseline model revealed that remittances in Sub-Saharan Africa have
a positive and statistically insignificant effect on economic growth. Evidence from this study
does not point to a negative relationship between remittances and economic growth as
presumed and uncovered in some studies. There is, however, evidence from the study which
shows that remittances positively impact economic growth in Sub-Saharan Africa through the
human capital channel. Remittances have a positive and statistically significant effect on
economic growth when enrolment, the proxy of human capital, is excluded from the model.
Results also show no evidence of remittances affecting economic growth in Sub-Saharan
Africa through investments. Though positive, remittances remain statistically insignificant
when the investment variable is excluded from the model.
This study reveals evidence that when the heterogeneity of sub-regions is taken into account,
through the use of dummies, remittances have a statistically significant positive impact on
economic growth in Sub-Saharan Africa as a whole. There is also evidence showing
differences in growth effects of remittances in West Africa and the entire Sub-Saharan
Africa. In West Africa, though, remittances have a positive and statistically significant effect
on economic growth, the effect is, however, less than that reported for the entire Sub-Saharan
Africa region. For Southern Africa, results show no evidence of any difference between the
growth effects of remittances in the sub-region and those for Sub-Saharan Africa as a whole.
80
The study reveals that investment and human capital, two of the variables identified in the
literature as major channels through which remittances indirectly impact growth, have a
positive and statistically significant effect on economic growth. Enrolment in secondary
education which is used as a proxy for human capital investment together with investment
reported positive and statistically significant coefficients.
The study shows a negative and statistically significant effect of the lag of economic growth
and initial income on economic growth in Sub-Saharan Africa. Baseline model results show
that government consumption and population growth have a positive but statistically
insignificant effect on economic growth. Baseline model results also show that inflation and
openness have negative and statistically insignificant effect on economic growth in Sub-
Saharan Africa.
6.2.1 Implications of the findings
Though migration results in the loss of economic activity due to falling skill levels, there is,
however, an offset growth through remittances. Remittances have a positive impact on
economic growth in Sub-Saharan Africa. Increased remittance inflows may result in high
levels of economic growth in Sub-Saharan Africa. The positive impact of remittances on
economic growth in Sub-Saharan Africa happens mainly through the human capital economic
channel. When remittances are used for human capital investments in the region, positive and
increased economic growth can be realised. There is a possibility that most households
receiving remittances in Sub-Saharan Africa are utilising them for education. When
enrolment in primary, secondary and tertiary education is increasing, there is a simultaneous
rise in the quality of labour force available for in a particular country. Quality labour force
implies improved production which bears a positive impact on overall economic growth.
The lack of evidence to support the positive growth effects of remittances through
investments might imply that recipient households in Sub-Saharan Africa rarely use
remittances as investment capital. Remittances are being mainly used for consumption and
education support. This might as well imply that there are few entrepreneurial activities
taking place within most of the remittances recipient households. This could be moral hazard
problem resulting from too much reliance on remittance income. Remittance recipient
81
households might end up absconding from any entrepreneurial ventures that they might
engage in due to reliant and constant remittance income.
The results from the study also show the heterogeneity of sub-regions in Sub-Saharan Africa.
Remittances do not result in the same growth effects in different sub-regions. There is
evidence from the study revealing that remittances have low positive growth effects in the
West African sub-region. This implies that there are many other factors to consider before
making conclusions about the growth effects of remittances. These can be geographical,
cultural, religious or political factors. Policies differ, what works for Southern Africa might
not be effective and appropriate for West Africa. But it must, however, be noted that
remittances positively impact economic growth in Sub-Saharan Africa.
Though it is complicated to model or measure, the study needs not to overlook the possible
contribution of in-kind remittances in enhancing economic growth in Sub-Saharan Africa. In-
kind remittances to developing countries are in most cases left unrecorded hence assessments
on the growth effects of remittances do not reflect the ‘true’ result. This study concludes on
positive growth effects of remittances without taking into account the contribution of in-kind
remittances. It can thus be further argued that when in-kind remittances are modelled in, there
should be a much greater positive effect of remittances on economic growth in Sub-Saharan
Africa.
6.3 Policy recommendations
Remittances are an important source of external capital that can help boost both economic
and social development in Sub-Saharan Africa. Countries need to consider adopting
institutions that help in amplifying the growth effects of remittances into sustainable
development. The entry point is to first come up with policies that make it cheap, easy and
safe to receive remittances. Countries must establish efficient and effective formal channels
for sending and receiving remittances. If a country wants to boost development through
investment, policy makers may consider reforming their financial sector policies. The
financial sector can be granted the obligation to influence the formal transmission of
remittances. Favourable cost structures for remitting money through financial sector
regulation can be considered. Where possible, the government can provide incentives that
make remitting money using formal channels cheaper as compared to the informal ones.
82
Increased positive growth effects of remittances are most likely to occur when remittances
are transmitted in formal channels. That way, banks and other traditional financial institutions
will have access and control to this source of investment capital.
Since evidence from the study reveals that remittances positively impact economic growth
through human capital investments such as education, there is a need for policies educating
and encouraging remittance recipient households to continue investing in education.
Education policies advocating for the continued and increased enrolment of household
members into primary, secondary and tertiary education need to be adopted. Even the
importance of health care needs also to be taught to the people.
Besides promoting the use of remittances in funding education, financial sector policies need
also to be complemented with other policies advocating for the increased use of remittances
in funding entrepreneurial activities in the economy. There is a need for an effective and
efficient distribution system, that is, a vibrant financial system that can mobilise remittance
funds and distribute them as investment capital to unfunded entrepreneurs. There must be a
way to influence remittance recipient households to save their income so that the proceeds
can be distributed to critical sectors of the economy.
Even when remittances are used for consumption, there is a need for policies that protect
local industries. Remittances are likely to have a positive growth effect for a particular
country when they are used to acquire locally produced products.
6.4 Future research
When heterogeneity of Sub-regions in Sub-Saharan Africa is not taken into account, results
report on statistically insignificant growth-effects of remittances. There is, however, evidence
from this study showing heterogeneity of sub-regions in Sub-Saharan Africa with remittances
in West Africa reported as having a less positive effect on economic growth as compared to
other sub-regions. Again, when the study makes use of dummy variables to distinguish sub-
regions, remittances are reported as having a positive and statistically significant impact on
economic growth in Sub-Saharan Africa. Since there is heterogeneity between sub-regions
that needs to be considered in order to come up with ‘true’ growth effects of remittances, it
might as well be interesting to extend the exercise and undergo a country-specific assessment
83
of the growth effects of remittances. That way, there will be more empirical evidence to
supplement this study’s findings.
84
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APPENDIX
Appendix 1: List of Sub-Saharan African countries used in the study
Country Sub-Region
Cameroon Central Africa Congo Rep Central Africa Rwanda Central Africa Ethiopia Eastern Africa Kenya Eastern Africa Sudan Eastern Africa Tanzania Eastern Africa Botswana Southern Africa Lesotho Southern Africa Madagascar Southern Africa Malawi Southern Africa Mauritius Southern Africa Mozambique Southern Africa Namibia Southern Africa South Africa Southern Africa Swaziland Southern Africa Burkina Faso Western Africa Cape Verde Western Africa Cote d’Ivoire Western Africa Gambia Western Africa Ghana Western Africa Guinea Western Africa Guinea-Bissau Western Africa Mali Western Africa Mauritania Western Africa Niger Western Africa Senegal Western Africa Sierra-Leone Western Africa Togo Western Africa
92
Appendix 2: Estimations for the entire panel (n=29, t=29)
....
GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLLDDDD....ggggrrrroooowwwwtttthhhh LLLLDDDD....IIII LLLLDDDD....IIIInnnnffff LLLLDDDD....GGGGoooovvvvtttt LLLLDDDD....RRRReeeemmmmttttIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr lllleeeevvvveeeellll eeeeqqqquuuuaaaattttiiiioooonnnn SSSSttttaaaannnnddddaaaarrrrdddd:::: DDDD....EEEEnnnnrrrrllll DDDD....OOOOppppnnnn DDDD....YYYY DDDD....PPPPoooopppp GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLL((((2222////....))))....ggggrrrroooowwwwtttthhhh LLLL((((2222////....))))....IIII LLLL((((2222////....))))....IIIInnnnffff LLLL((((2222////....))))....GGGGoooovvvvtttt LLLL((((2222////....))))....RRRReeeemmmmttttIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr ddddiiiiffffffffeeeerrrreeeennnncccceeeedddd eeeeqqqquuuuaaaattttiiiioooonnnn PPPPoooopppp ----1111....111177772222333333336666 1111....222288881111777733333333 ----0000....99991111 0000....333366660000 ----3333....666688884444444488887777 1111....333333339999888811116666 YYYY ----1111....111100006666777700006666 ....6666777788886666888844444444 ----1111....66663333 0000....111100003333 ----2222....444433336666999900003333 ....2222222233334444999911112222 OOOOppppnnnn 1111....444455559999000066667777 1111....333311116666333344449999 1111....11111111 0000....222266668888 ----1111....111122220000999922229999 4444....000033339999000066663333 EEEEnnnnrrrrllll 1111....555511117777222266668888 ....6666888800003333888899996666 2222....22223333 0000....000022226666 ....1111888833337777222299993333 2222....888855550000888800008888 RRRReeeemmmmtttt ....0000555577778888999988886666 ....2222111188886666666688887777 0000....22226666 0000....777799991111 ----....3333777700006666888844441111 ....4444888866664444888811113333 GGGGoooovvvvtttt ----2222....666622224444111155553333 1111....777700007777777733336666 ----1111....55554444 0000....111122224444 ----5555....999977771111222255555555 ....7777222222229999444488886666 IIIInnnnffff ....3333222244444444000033339999 ....3333000077776666333322223333 1111....00005555 0000....222299992222 ----....2222777788885555444444443333 ....999922227777333355552222 IIII 1111....777766660000333355551111 ....8888444499991111333388885555 2222....00007777 0000....000033338888 ....0000999966660000777700006666 3333....444422224444666633332222 LLLL1111.... ----....0000666677777777444499996666 ....0000666666668888444411114444 ----1111....00001111 0000....333311111111 ----....1111999988887777555566663333 ....000066663333222255557777 ggggrrrroooowwwwtttthhhh ggggrrrroooowwwwtttthhhh CCCCooooeeeeffff.... SSSSttttdddd.... EEEErrrrrrrr.... zzzz PPPP>>>>||||zzzz|||| [[[[99995555%%%% CCCCoooonnnnffff.... IIIInnnntttteeeerrrrvvvvaaaallll]]]] RRRRoooobbbbuuuusssstttt OOOOnnnneeee----sssstttteeeepppp rrrreeeessssuuuullllttttssss PPPPrrrroooobbbb >>>> cccchhhhiiii2222 ==== 0000....0000000000000000NNNNuuuummmmbbbbeeeerrrr ooooffff iiiinnnnssssttttrrrruuuummmmeeeennnnttttssss ==== 333322226666 WWWWaaaalllldddd cccchhhhiiii2222((((9999)))) ==== 55557777....88886666
mmmmaaaaxxxx ==== 11119999 aaaavvvvgggg ==== 11110000....88882222777755559999 OOOObbbbssss ppppeeeerrrr ggggrrrroooouuuupppp:::: mmmmiiiinnnn ==== 1111TTTTiiiimmmmeeee vvvvaaaarrrriiiiaaaabbbblllleeee:::: yyyyeeeeaaaarrrrGGGGrrrroooouuuupppp vvvvaaaarrrriiiiaaaabbbblllleeee:::: ccccoooouuuunnnnttttrrrryyyy NNNNuuuummmmbbbbeeeerrrr ooooffff ggggrrrroooouuuuppppssss ==== 22229999SSSSyyyysssstttteeeemmmm ddddyyyynnnnaaaammmmiiiicccc ppppaaaannnneeeellll----ddddaaaattttaaaa eeeessssttttiiiimmmmaaaattttiiiioooonnnn NNNNuuuummmmbbbbeeeerrrr ooooffff oooobbbbssss ==== 333311114444
.... xxxxttttddddppppddddssssyyyyssss ggggrrrroooowwwwtttthhhh EEEEnnnnrrrrllll OOOOppppnnnn YYYY PPPPoooopppp,,,, nnnnooooccccoooonnnnssssttttaaaannnntttt llllaaaaggggssss((((1111)))) eeeennnnddddoooogggg((((IIII IIIInnnnffff GGGGoooovvvvtttt RRRReeeemmmmtttt)))) vvvvcccceeee((((rrrroooobbbbuuuusssstttt)))) aaaarrrrtttteeeessssttttssss((((2222))))
93
Appendix 3: Baseline model estimations (n=29, t=6)
....
GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLLDDDD....ggggrrrroooowwwwtttthhhh LLLLDDDD....IIII LLLLDDDD....IIIInnnnffff LLLLDDDD....GGGGoooovvvvtttt LLLLDDDD....RRRReeeemmmmttttIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr lllleeeevvvveeeellll eeeeqqqquuuuaaaattttiiiioooonnnn SSSSttttaaaannnnddddaaaarrrrdddd:::: DDDD....EEEEnnnnrrrrllll DDDD....OOOOppppnnnn DDDD....YYYY DDDD....PPPPoooopppp GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLL((((2222////....))))....ggggrrrroooowwwwtttthhhh LLLL((((2222////....))))....IIII LLLL((((2222////....))))....IIIInnnnffff LLLL((((2222////....))))....GGGGoooovvvvtttt LLLL((((2222////....))))....RRRReeeemmmmttttIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr ddddiiiiffffffffeeeerrrreeeennnncccceeeedddd eeeeqqqquuuuaaaattttiiiioooonnnn PPPPoooopppp ....7777888844446666333366666666 ....8888666622225555777700002222 0000....99991111 0000....333366663333 ----....99990000555599997777 2222....444477775555222244443333 YYYY ----1111....999922228888111111116666 ....7777888899997777111199993333 ----2222....44444444 0000....000011115555 ----3333....444477775555999933337777 ----....3333888800002222999944444444 OOOOppppnnnn ----....1111888899992222222233339999 1111....333388886666666644446666 ----0000....11114444 0000....888899991111 ----2222....999900007777 2222....555522228888555555552222 EEEEnnnnrrrrllll 1111....777722225555555533336666 ....9999000077771111555577774444 1111....99990000 0000....000055557777 ----....0000555522224444555599996666 3333....555500003333555533332222 RRRReeeemmmmtttt ....3333444455554444111188884444 ....4444000044448888555511118888 0000....88885555 0000....333399994444 ----....4444444488880000777766666666 1111....111133338888999911113333 GGGGoooovvvvtttt ....8888888811115555999999994444 1111....222277775555666655557777 0000....66669999 0000....444499990000 ----1111....666611118888666644443333 3333....333388881111888844442222 IIIInnnnffff ----....1111555577772222666677775555 ....2222777733335555222244446666 ----0000....55557777 0000....555566665555 ----....666699993333333366666666 ....3333777788888888333300009999 IIII 2222....000055552222666600001111 ....6666000044449999666644444444 3333....33339999 0000....000000001111 ....8888666666668888999922223333 3333....222233338888333300009999 LLLL1111.... ----....2222222255554444888800007777 ....0000777711114444999911113333 ----3333....11115555 0000....000000002222 ----....3333666655556666000011111111 ----....0000888855553333666600003333 ggggrrrroooowwwwtttthhhh ggggrrrroooowwwwtttthhhh CCCCooooeeeeffff.... SSSSttttdddd.... EEEErrrrrrrr.... zzzz PPPP>>>>||||zzzz|||| [[[[99995555%%%% CCCCoooonnnnffff.... IIIInnnntttteeeerrrrvvvvaaaallll]]]] RRRRoooobbbbuuuusssstttt OOOOnnnneeee----sssstttteeeepppp rrrreeeessssuuuullllttttssss PPPPrrrroooobbbb >>>> cccchhhhiiii2222 ==== 0000....0000000000000000NNNNuuuummmmbbbbeeeerrrr ooooffff iiiinnnnssssttttrrrruuuummmmeeeennnnttttssss ==== 77774444 WWWWaaaalllldddd cccchhhhiiii2222((((9999)))) ==== 66660000....22222222
mmmmaaaaxxxx ==== 5555 aaaavvvvgggg ==== 3333....888899996666555555552222 OOOObbbbssss ppppeeeerrrr ggggrrrroooouuuupppp:::: mmmmiiiinnnn ==== 1111TTTTiiiimmmmeeee vvvvaaaarrrriiiiaaaabbbblllleeee:::: ppppeeeerrrriiiiooooddddGGGGrrrroooouuuupppp vvvvaaaarrrriiiiaaaabbbblllleeee:::: ccccoooouuuunnnnttttrrrryyyy NNNNuuuummmmbbbbeeeerrrr ooooffff ggggrrrroooouuuuppppssss ==== 22229999SSSSyyyysssstttteeeemmmm ddddyyyynnnnaaaammmmiiiicccc ppppaaaannnneeeellll----ddddaaaattttaaaa eeeessssttttiiiimmmmaaaattttiiiioooonnnn NNNNuuuummmmbbbbeeeerrrr ooooffff oooobbbbssss ==== 111111113333
.... xxxxttttddddppppddddssssyyyyssss ggggrrrroooowwwwtttthhhh EEEEnnnnrrrrllll OOOOppppnnnn YYYY PPPPoooopppp,,,, nnnnooooccccoooonnnnssssttttaaaannnntttt llllaaaaggggssss((((1111)))) eeeennnnddddoooogggg((((IIII IIIInnnnffff GGGGoooovvvvtttt RRRReeeemmmmtttt)))) vvvvcccceeee((((rrrroooobbbbuuuusssstttt)))) aaaarrrrtttteeeessssttttssss((((2222))))
94
Appendix 4: Growth model estimations using remittances as a share of GDP (n=29, t=6)
GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLLDDDD....ggggrrrroooowwwwtttthhhh LLLLDDDD....IIII LLLLDDDD....IIIInnnnffff LLLLDDDD....GGGGoooovvvvtttt LLLLDDDD....RRRReeeemmmmIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr lllleeeevvvveeeellll eeeeqqqquuuuaaaattttiiiioooonnnn SSSSttttaaaannnnddddaaaarrrrdddd:::: DDDD....EEEEnnnnrrrrllll DDDD....OOOOppppnnnn DDDD....YYYY DDDD....PPPPoooopppp GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLL((((2222////....))))....ggggrrrroooowwwwtttthhhh LLLL((((2222////....))))....IIII LLLL((((2222////....))))....IIIInnnnffff LLLL((((2222////....))))....GGGGoooovvvvtttt LLLL((((2222////....))))....RRRReeeemmmmIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr ddddiiiiffffffffeeeerrrreeeennnncccceeeedddd eeeeqqqquuuuaaaattttiiiioooonnnn PPPPoooopppp ....5555999988883333000099991111 ....8888222233336666111133333333 0000....77773333 0000....444466668888 ----1111....000011115555999944443333 2222....222211112222555566661111 YYYY ----1111....555533331111000099996666 ....8888333333333333777755551111 ----1111....88884444 0000....000066666666 ----3333....111166664444444488881111 ....1111000022222222888899995555 OOOOppppnnnn ----....4444888822228888333311115555 1111....444433336666999955551111 ----0000....33334444 0000....777733337777 ----3333....222299999999222200004444 2222....333333333333555544441111 EEEEnnnnrrrrllll 1111....77774444222244443333 ....9999111100001111444488883333 1111....99991111 0000....000055556666 ----....0000444411114444222277774444 3333....555522226666222288888888 RRRReeeemmmm ....2222666622229999777722226666 ....4444000077777777999999998888 0000....66664444 0000....555511119999 ----....5555333366663333000000004444 1111....000066662222222244446666 GGGGoooovvvvtttt ....7777999988887777999977776666 1111....222255558888111155553333 0000....66663333 0000....555522225555 ----1111....666666667777111133337777 3333....222266664444777733332222 IIIInnnnffff ----....1111777788889999333355558888 ....333311114444111188885555 ----0000....55557777 0000....555566669999 ----....777799994444777722227777 ....4444333366668888555555554444 IIII 1111....999966661111777744446666 ....6666333377775555666644447777 3333....00008888 0000....000000002222 ....7777111122221111444422225555 3333....22221111111133335555 LLLL1111.... ----....2222000011118888999977774444 ....0000777766660000999944444444 ----2222....66665555 0000....000000008888 ----....3333555511110000333399997777 ----....0000555522227777555555552222 ggggrrrroooowwwwtttthhhh ggggrrrroooowwwwtttthhhh CCCCooooeeeeffff.... SSSSttttdddd.... EEEErrrrrrrr.... zzzz PPPP>>>>||||zzzz|||| [[[[99995555%%%% CCCCoooonnnnffff.... IIIInnnntttteeeerrrrvvvvaaaallll]]]] RRRRoooobbbbuuuusssstttt OOOOnnnneeee----sssstttteeeepppp rrrreeeessssuuuullllttttssss PPPPrrrroooobbbb >>>> cccchhhhiiii2222 ==== 0000....0000000000000000NNNNuuuummmmbbbbeeeerrrr ooooffff iiiinnnnssssttttrrrruuuummmmeeeennnnttttssss ==== 77774444 WWWWaaaalllldddd cccchhhhiiii2222((((9999)))) ==== 55559999....88885555
mmmmaaaaxxxx ==== 5555 aaaavvvvgggg ==== 3333....888899996666555555552222 OOOObbbbssss ppppeeeerrrr ggggrrrroooouuuupppp:::: mmmmiiiinnnn ==== 1111TTTTiiiimmmmeeee vvvvaaaarrrriiiiaaaabbbblllleeee:::: ppppeeeerrrriiiiooooddddGGGGrrrroooouuuupppp vvvvaaaarrrriiiiaaaabbbblllleeee:::: ccccoooouuuunnnnttttrrrryyyy NNNNuuuummmmbbbbeeeerrrr ooooffff ggggrrrroooouuuuppppssss ==== 22229999SSSSyyyysssstttteeeemmmm ddddyyyynnnnaaaammmmiiiicccc ppppaaaannnneeeellll----ddddaaaattttaaaa eeeessssttttiiiimmmmaaaattttiiiioooonnnn NNNNuuuummmmbbbbeeeerrrr ooooffff oooobbbbssss ==== 111111113333
.... xxxxttttddddppppddddssssyyyyssss ggggrrrroooowwwwtttthhhh EEEEnnnnrrrrllll OOOOppppnnnn YYYY PPPPoooopppp,,,, nnnnooooccccoooonnnnssssttttaaaannnntttt llllaaaaggggssss((((1111)))) eeeennnnddddoooogggg((((IIII IIIInnnnffff GGGGoooovvvvtttt RRRReeeemmmm)))) vvvvcccceeee((((rrrroooobbbbuuuusssstttt)))) aaaarrrrtttteeeessssttttssss((((2222))))
95
Appendix 5: Growth model estimations: without outliers (n=24, t=6)
....
GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLLDDDD....ggggrrrroooowwwwtttthhhh LLLLDDDD....IIII LLLLDDDD....IIIInnnnffff LLLLDDDD....GGGGoooovvvvtttt LLLLDDDD....RRRReeeemmmmIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr lllleeeevvvveeeellll eeeeqqqquuuuaaaattttiiiioooonnnn SSSSttttaaaannnnddddaaaarrrrdddd:::: DDDD....EEEEnnnnrrrrllll DDDD....OOOOppppnnnn DDDD....YYYY DDDD....PPPPoooopppp GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLL((((2222////....))))....ggggrrrroooowwwwtttthhhh LLLL((((2222////....))))....IIII LLLL((((2222////....))))....IIIInnnnffff LLLL((((2222////....))))....GGGGoooovvvvtttt LLLL((((2222////....))))....RRRReeeemmmmIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr ddddiiiiffffffffeeeerrrreeeennnncccceeeedddd eeeeqqqquuuuaaaattttiiiioooonnnn PPPPoooopppp 1111....5555222222228888 1111....00003333555566662222 1111....44447777 0000....111144441111 ----....5555000066669999777777774444 3333....555555552222555577778888 YYYY ----2222....444433338888000000001111 ....9999111177771111444411113333 ----2222....66666666 0000....000000008888 ----4444....222233335555555566665555 ----....6666444400004444333366668888 OOOOppppnnnn ----....4444222211112222777700009999 1111....33335555666600005555 ----0000....33331111 0000....777755556666 ----3333....000077779999000088881111 2222....222233336666555533339999 EEEEnnnnrrrrllll 2222....333377773333888844449999 ....8888444499999999000033332222 2222....77779999 0000....000000005555 ....7777000088880000666699992222 4444....000033339999666622229999 RRRReeeemmmm ....33337777222266662222 ....4444555588882222333366669999 0000....88881111 0000....444411116666 ----....5555222255555555000077779999 1111....222277770000777744448888 GGGGoooovvvvtttt ....6666555577776666777744442222 1111....111155556666111155556666 0000....55557777 0000....555566669999 ----1111....66660000888833335555 2222....999922223333666699998888 IIIInnnnffff ----....3333111122229999000033337777 ....2222333355558888333333337777 ----1111....33333333 0000....111188885555 ----....7777777755551111222299992222 ....1111444499993333222211119999 IIII 2222....66660000000088888888 ....7777777733337777000033332222 3333....33336666 0000....000000001111 1111....00008888444444445555 4444....111111117777333311111111 LLLL1111.... ----....2222555522220000000055557777 ....0000777733331111333311111111 ----3333....44445555 0000....000000001111 ----....3333999955553333333399999999 ----....1111000088886666777711114444 ggggrrrroooowwwwtttthhhh ggggrrrroooowwwwtttthhhh CCCCooooeeeeffff.... SSSSttttdddd.... EEEErrrrrrrr.... zzzz PPPP>>>>||||zzzz|||| [[[[99995555%%%% CCCCoooonnnnffff.... IIIInnnntttteeeerrrrvvvvaaaallll]]]] RRRRoooobbbbuuuusssstttt OOOOnnnneeee----sssstttteeeepppp rrrreeeessssuuuullllttttssss PPPPrrrroooobbbb >>>> cccchhhhiiii2222 ==== 0000....0000000000000000NNNNuuuummmmbbbbeeeerrrr ooooffff iiiinnnnssssttttrrrruuuummmmeeeennnnttttssss ==== 77771111 WWWWaaaalllldddd cccchhhhiiii2222((((9999)))) ==== 55554444....77774444
mmmmaaaaxxxx ==== 5555 aaaavvvvgggg ==== 3333....777799991111666666667777 OOOObbbbssss ppppeeeerrrr ggggrrrroooouuuupppp:::: mmmmiiiinnnn ==== 1111TTTTiiiimmmmeeee vvvvaaaarrrriiiiaaaabbbblllleeee:::: ppppeeeerrrriiiiooooddddGGGGrrrroooouuuupppp vvvvaaaarrrriiiiaaaabbbblllleeee:::: ccccoooouuuunnnnttttrrrryyyy NNNNuuuummmmbbbbeeeerrrr ooooffff ggggrrrroooouuuuppppssss ==== 22224444SSSSyyyysssstttteeeemmmm ddddyyyynnnnaaaammmmiiiicccc ppppaaaannnneeeellll----ddddaaaattttaaaa eeeessssttttiiiimmmmaaaattttiiiioooonnnn NNNNuuuummmmbbbbeeeerrrr ooooffff oooobbbbssss ==== 99991111
.... xxxxttttddddppppddddssssyyyyssss ggggrrrroooowwwwtttthhhh EEEEnnnnrrrrllll OOOOppppnnnn YYYY PPPPoooopppp,,,, nnnnooooccccoooonnnnssssttttaaaannnntttt llllaaaaggggssss((((1111)))) eeeennnnddddoooogggg((((IIII IIIInnnnffff GGGGoooovvvvtttt RRRReeeemmmm)))) vvvvcccceeee((((rrrroooobbbbuuuusssstttt)))) aaaarrrrtttteeeessssttttssss((((2222))))
96
Appendix 6: Growth model estimations without the investment variable (n=29, t=6)
GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLLDDDD....ggggrrrroooowwwwtttthhhh LLLLDDDD....IIIInnnnffff LLLLDDDD....GGGGoooovvvvtttt LLLLDDDD....RRRReeeemmmmttttIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr lllleeeevvvveeeellll eeeeqqqquuuuaaaattttiiiioooonnnn SSSSttttaaaannnnddddaaaarrrrdddd:::: DDDD....EEEEnnnnrrrrllll DDDD....OOOOppppnnnn DDDD....YYYY DDDD....PPPPoooopppp GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLL((((2222////....))))....ggggrrrroooowwwwtttthhhh LLLL((((2222////....))))....IIIInnnnffff LLLL((((2222////....))))....GGGGoooovvvvtttt LLLL((((2222////....))))....RRRReeeemmmmttttIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr ddddiiiiffffffffeeeerrrreeeennnncccceeeedddd eeeeqqqquuuuaaaattttiiiioooonnnn PPPPoooopppp ....9999000044441111111133339999 1111....000066664444222299993333 0000....88885555 0000....333399996666 ----1111....111188881111888866663333 2222....99999999000000009999 YYYY ----1111....777755553333111111115555 1111....000000005555888888886666 ----1111....77774444 0000....000088881111 ----3333....777722224444666611116666 ....222211118888333388886666 OOOOppppnnnn ....0000222266669999666688887777 1111....777744441111222244445555 0000....00002222 0000....999988888888 ----3333....333388885555888800008888 3333....444433339999777744446666 EEEEnnnnrrrrllll 2222....333388881111777766667777 1111....222200006666666644443333 1111....99997777 0000....000044448888 ....0000111166667777888899991111 4444....777744446666777744444444 RRRReeeemmmmtttt ....0000555566660000555544446666 ....4444666622223333777722226666 0000....11112222 0000....999900004444 ----....888855550000111177779999 ....9999666622222222888888882222 GGGGoooovvvvtttt 1111....777733334444111199992222 1111....444444448888666699999999 1111....22220000 0000....222233331111 ----1111....111100005555222200006666 4444....555577773333555599991111 IIIInnnnffff ----....0000222255557777888822229999 ....3333000066664444000033335555 ----0000....00008888 0000....999933333333 ----....6666222266663333222222228888 ....555577774444777755557777 LLLL1111.... ----....1111888899999999666600006666 ....0000888800007777222211119999 ----2222....33335555 0000....000011119999 ----....3333444488881111777722225555 ----....0000333311117777444488886666 ggggrrrroooowwwwtttthhhh ggggrrrroooowwwwtttthhhh CCCCooooeeeeffff.... SSSSttttdddd.... EEEErrrrrrrr.... zzzz PPPP>>>>||||zzzz|||| [[[[99995555%%%% CCCCoooonnnnffff.... IIIInnnntttteeeerrrrvvvvaaaallll]]]] RRRRoooobbbbuuuusssstttt OOOOnnnneeee----sssstttteeeepppp rrrreeeessssuuuullllttttssss PPPPrrrroooobbbb >>>> cccchhhhiiii2222 ==== 0000....0000000000005555NNNNuuuummmmbbbbeeeerrrr ooooffff iiiinnnnssssttttrrrruuuummmmeeeennnnttttssss ==== 66660000 WWWWaaaalllldddd cccchhhhiiii2222((((8888)))) ==== 22227777....99990000
mmmmaaaaxxxx ==== 5555 aaaavvvvgggg ==== 3333....888899996666555555552222 OOOObbbbssss ppppeeeerrrr ggggrrrroooouuuupppp:::: mmmmiiiinnnn ==== 1111TTTTiiiimmmmeeee vvvvaaaarrrriiiiaaaabbbblllleeee:::: ppppeeeerrrriiiiooooddddGGGGrrrroooouuuupppp vvvvaaaarrrriiiiaaaabbbblllleeee:::: ccccoooouuuunnnnttttrrrryyyy NNNNuuuummmmbbbbeeeerrrr ooooffff ggggrrrroooouuuuppppssss ==== 22229999SSSSyyyysssstttteeeemmmm ddddyyyynnnnaaaammmmiiiicccc ppppaaaannnneeeellll----ddddaaaattttaaaa eeeessssttttiiiimmmmaaaattttiiiioooonnnn NNNNuuuummmmbbbbeeeerrrr ooooffff oooobbbbssss ==== 111111113333
.... xxxxttttddddppppddddssssyyyyssss ggggrrrroooowwwwtttthhhh EEEEnnnnrrrrllll OOOOppppnnnn YYYY PPPPoooopppp,,,, nnnnooooccccoooonnnnssssttttaaaannnntttt llllaaaaggggssss((((1111)))) eeeennnnddddoooogggg((((IIIInnnnffff GGGGoooovvvvtttt RRRReeeemmmmtttt)))) vvvvcccceeee((((rrrroooobbbbuuuusssstttt)))) aaaarrrrtttteeeessssttttssss((((2222))))
97
Appendix 7: Growth model estimations without the enrolment variable (n=29, t=6)
....
GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLLDDDD....ggggrrrroooowwwwtttthhhh LLLLDDDD....IIIInnnnffff LLLLDDDD....GGGGoooovvvvtttt LLLLDDDD....RRRReeeemmmmtttt LLLLDDDD....IIIIIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr lllleeeevvvveeeellll eeeeqqqquuuuaaaattttiiiioooonnnn SSSSttttaaaannnnddddaaaarrrrdddd:::: DDDD....OOOOppppnnnn DDDD....YYYY DDDD....PPPPoooopppp GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLL((((2222////....))))....ggggrrrroooowwwwtttthhhh LLLL((((2222////....))))....IIIInnnnffff LLLL((((2222////....))))....GGGGoooovvvvtttt LLLL((((2222////....))))....RRRReeeemmmmtttt LLLL((((2222////....))))....IIIIIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr ddddiiiiffffffffeeeerrrreeeennnncccceeeedddd eeeeqqqquuuuaaaattttiiiioooonnnn PPPPoooopppp ....7777999944444444222288889999 1111....111100001111111155556666 0000....77772222 0000....444477771111 ----1111....333366663333777799997777 2222....999955552222666655555555 YYYY ----1111....999977775555888800007777 ....77776666111144445555 ----2222....55559999 0000....000000009999 ----3333....444466668888222222221111 ----....4444888833333333999922221111 OOOOppppnnnn ....8888333322222222000066667777 1111....111122225555000011111111 0000....77774444 0000....444455559999 ----1111....333377772222777777775555 3333....000033337777111188888888 IIII 3333....555555552222999988885555 ....7777111188886666555599994444 4444....99994444 0000....000000000000 2222....111144444444444433338888 4444....999966661111555533332222 RRRReeeemmmmtttt ....5555888899996666333333331111 ....3333222277778888444433334444 1111....88880000 0000....000077772222 ----....0000555522229999222288882222 1111....222233332222111199994444 GGGGoooovvvvtttt ----....1111555511113333000033333333 1111....333333330000888877775555 ----0000....11111111 0000....999900009999 ----2222....77775555999977777777 2222....444455557777111166663333 IIIInnnnffff ----....4444666688880000222211118888 ....333355559999444488881111 ----1111....33330000 0000....111199993333 ----1111....111177772222555599992222 ....222233336666555544448888 LLLL1111.... ----....2222222299993333666699991111 ....0000666677778888555533331111 ----3333....33338888 0000....000000001111 ----....3333666622223333555588888888 ----....0000999966663333777799994444 ggggrrrroooowwwwtttthhhh ggggrrrroooowwwwtttthhhh CCCCooooeeeeffff.... SSSSttttdddd.... EEEErrrrrrrr.... zzzz PPPP>>>>||||zzzz|||| [[[[99995555%%%% CCCCoooonnnnffff.... IIIInnnntttteeeerrrrvvvvaaaallll]]]] RRRRoooobbbbuuuusssstttt OOOOnnnneeee----sssstttteeeepppp rrrreeeessssuuuullllttttssss PPPPrrrroooobbbb >>>> cccchhhhiiii2222 ==== 0000....0000000000000000NNNNuuuummmmbbbbeeeerrrr ooooffff iiiinnnnssssttttrrrruuuummmmeeeennnnttttssss ==== 77773333 WWWWaaaalllldddd cccchhhhiiii2222((((8888)))) ==== 66660000....66668888
mmmmaaaaxxxx ==== 5555 aaaavvvvgggg ==== 4444....33337777999933331111 OOOObbbbssss ppppeeeerrrr ggggrrrroooouuuupppp:::: mmmmiiiinnnn ==== 3333TTTTiiiimmmmeeee vvvvaaaarrrriiiiaaaabbbblllleeee:::: ppppeeeerrrriiiiooooddddGGGGrrrroooouuuupppp vvvvaaaarrrriiiiaaaabbbblllleeee:::: ccccoooouuuunnnnttttrrrryyyy NNNNuuuummmmbbbbeeeerrrr ooooffff ggggrrrroooouuuuppppssss ==== 22229999SSSSyyyysssstttteeeemmmm ddddyyyynnnnaaaammmmiiiicccc ppppaaaannnneeeellll----ddddaaaattttaaaa eeeessssttttiiiimmmmaaaattttiiiioooonnnn NNNNuuuummmmbbbbeeeerrrr ooooffff oooobbbbssss ==== 111122227777
.... xxxxttttddddppppddddssssyyyyssss ggggrrrroooowwwwtttthhhh OOOOppppnnnn YYYY PPPPoooopppp,,,, nnnnooooccccoooonnnnssssttttaaaannnntttt llllaaaaggggssss((((1111)))) eeeennnnddddoooogggg((((IIIInnnnffff GGGGoooovvvvtttt RRRReeeemmmmtttt IIII)))) vvvvcccceeee((((rrrroooobbbbuuuusssstttt)))) aaaarrrrtttteeeessssttttssss((((2222))))
98
Appendix 8: Growth model estimations with dummy variables for West and Southern Africa sub-regions (n=29, t=6)
....
GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLLDDDD....ggggrrrroooowwwwtttthhhh LLLLDDDD....SSSSAAAA____DDDDRRRR LLLLDDDD....WWWWAAAA____DDDDRRRR LLLLDDDD....IIII LLLLDDDD....IIIInnnnffff LLLLDDDD....GGGGoooovvvvtttt LLLLDDDD....RRRReeeemmmmttttIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr lllleeeevvvveeeellll eeeeqqqquuuuaaaattttiiiioooonnnn SSSSttttaaaannnnddddaaaarrrrdddd:::: DDDD....EEEEnnnnrrrrllll DDDD....OOOOppppnnnn DDDD....YYYY DDDD....PPPPoooopppp GGGGMMMMMMMM----ttttyyyyppppeeee:::: LLLL((((2222////....))))....ggggrrrroooowwwwtttthhhh LLLL((((2222////....))))....SSSSAAAA____DDDDRRRR LLLL((((2222////....))))....WWWWAAAA____DDDDRRRR LLLL((((2222////....))))....IIII LLLL((((2222////....))))....IIIInnnnffff LLLL((((2222////....))))....GGGGoooovvvvtttt LLLL((((2222////....))))....RRRReeeemmmmttttIIIInnnnssssttttrrrruuuummmmeeeennnnttttssss ffffoooorrrr ddddiiiiffffffffeeeerrrreeeennnncccceeeedddd eeeeqqqquuuuaaaattttiiiioooonnnn PPPPoooopppp 1111....222299990000555511116666 ....6666888888883333999966667777 1111....88887777 0000....000066661111 ----....0000555588887777111166664444 2222....666633339999777744449999 YYYY ----1111....666633332222111177778888 ....7777222233336666666611112222 ----2222....22226666 0000....000022224444 ----3333....000055550000555522228888 ----....2222111133338888222277777777 OOOOppppnnnn ----....7777333333339999000011114444 1111....222233336666111133336666 ----0000....55559999 0000....555555553333 ----3333....111155556666666688883333 1111....66668888888888888888 EEEEnnnnrrrrllll 1111....999955556666999999991111 ....7777333355553333888877779999 2222....66666666 0000....000000008888 ....555511115555666655557777 3333....333399998888333322225555 RRRReeeemmmmtttt ....7777111100001111888866667777 ....3333222266662222555544447777 2222....11118888 0000....000022229999 ....0000777700007777333399992222 1111....333344449999666633334444 GGGGoooovvvvtttt ....1111222211113333888833339999 1111....111122227777777733334444 0000....11111111 0000....999911114444 ----2222....000088888888999933334444 2222....333333331111777700002222 IIIInnnnffff ----....4444555599998888666655559999 ....2222111166660000777711112222 ----2222....11113333 0000....000033333333 ----....8888888833333333555577777777 ----....0000333366663333777744441111 IIII 2222....555511118888222277779999 ....5555777777774444555500007777 4444....33336666 0000....000000000000 1111....333388886666444499997777 3333....666655550000000066662222 WWWWAAAA____DDDDRRRR ----....0000333311111111333377774444 ....0000111111118888111177775555 ----2222....66663333 0000....000000008888 ----....0000555544442222999999992222 ----....0000000077779999777755556666 SSSSAAAA____DDDDRRRR ----....0000000077774444555555553333 ....0000111100007777333388887777 ----0000....66669999 0000....444488888888 ----....0000222288885555000022228888 ....0000111133335555999922222222 LLLL1111.... ----....2222555555555555222244445555 ....0000666600005555111144449999 ----4444....22222222 0000....000000000000 ----....3333777744441111333311114444 ----....1111333366669999111177775555 ggggrrrroooowwwwtttthhhh ggggrrrroooowwwwtttthhhh CCCCooooeeeeffff.... SSSSttttdddd.... EEEErrrrrrrr.... zzzz PPPP>>>>||||zzzz|||| [[[[99995555%%%% CCCCoooonnnnffff.... IIIInnnntttteeeerrrrvvvvaaaallll]]]] RRRRoooobbbbuuuusssstttt OOOOnnnneeee----sssstttteeeepppp rrrreeeessssuuuullllttttssss PPPPrrrroooobbbb >>>> cccchhhhiiii2222 ==== 0000....0000000000000000NNNNuuuummmmbbbbeeeerrrr ooooffff iiiinnnnssssttttrrrruuuummmmeeeennnnttttssss ==== 99995555 WWWWaaaalllldddd cccchhhhiiii2222((((11111111)))) ==== 77771111....99998888
mmmmaaaaxxxx ==== 5555 aaaavvvvgggg ==== 3333....888899996666555555552222 OOOObbbbssss ppppeeeerrrr ggggrrrroooouuuupppp:::: mmmmiiiinnnn ==== 1111TTTTiiiimmmmeeee vvvvaaaarrrriiiiaaaabbbblllleeee:::: ppppeeeerrrriiiiooooddddGGGGrrrroooouuuupppp vvvvaaaarrrriiiiaaaabbbblllleeee:::: ccccoooouuuunnnnttttrrrryyyy NNNNuuuummmmbbbbeeeerrrr ooooffff ggggrrrroooouuuuppppssss ==== 22229999SSSSyyyysssstttteeeemmmm ddddyyyynnnnaaaammmmiiiicccc ppppaaaannnneeeellll----ddddaaaattttaaaa eeeessssttttiiiimmmmaaaattttiiiioooonnnn NNNNuuuummmmbbbbeeeerrrr ooooffff oooobbbbssss ==== 111111113333
.... xxxxttttddddppppddddssssyyyyssss ggggrrrroooowwwwtttthhhh EEEEnnnnrrrrllll OOOOppppnnnn YYYY PPPPoooopppp,,,, nnnnooooccccoooonnnnssssttttaaaannnntttt llllaaaaggggssss((((1111)))) eeeennnnddddoooogggg((((SSSSAAAA____DDDDRRRR WWWWAAAA____DDDDRRRR IIII IIIInnnnffff GGGGoooovvvvtttt RRRReeeemmmmtttt)))) vvvvcccceeee((((rrrroooobbbbuuuusssstttt)))) aaaarrrrtttteeeessssttttssss((((2222))))
99
Appendix 9: The Sargan test of over identifying restrictions
Appendix 10: Test for autocorrelation
....
PPPPrrrroooobbbb >>>> cccchhhhiiii2222 ==== 0000....0000222233333333 cccchhhhiiii2222((((66666666)))) ==== 99990000....777788881111
HHHH0000:::: oooovvvveeeerrrriiiiddddeeeennnnttttiiiiffffyyyyiiiinnnngggg rrrreeeessssttttrrrriiiiccccttttiiiioooonnnnssss aaaarrrreeee vvvvaaaalllliiiiddddSSSSaaaarrrrggggaaaannnn tttteeeesssstttt ooooffff oooovvvveeeerrrriiiiddddeeeennnnttttiiiiffffyyyyiiiinnnngggg rrrreeeessssttttrrrriiiiccccttttiiiioooonnnnssss.... eeeessssttttaaaatttt ssssaaaarrrrggggaaaannnn
....
HHHH0000:::: nnnnoooo aaaauuuuttttooooccccoooorrrrrrrreeeellllaaaattttiiiioooonnnn 2222 ----....22226666222288887777 0000....7777999922226666 1111 ----2222....3333111177776666 0000....0000222200005555 OOOOrrrrddddeeeerrrr zzzz PPPPrrrroooobbbb >>>> zzzz AAAArrrreeeellllllllaaaannnnoooo----BBBBoooonnnndddd tttteeeesssstttt ffffoooorrrr zzzzeeeerrrroooo aaaauuuuttttooooccccoooorrrrrrrreeeellllaaaattttiiiioooonnnn iiiinnnn ffffiiiirrrrsssstttt----ddddiiiiffffffffeeeerrrreeeennnncccceeeedddd eeeerrrrrrrroooorrrrssss
aaaarrrrtttteeeessssttttssss nnnnooootttt ccccoooommmmppppuuuutttteeeedddd ffffoooorrrr oooonnnneeee----sssstttteeeepppp ssssyyyysssstttteeeemmmm eeeessssttttiiiimmmmaaaattttoooorrrr wwwwiiiitttthhhh vvvvcccceeee((((ggggmmmmmmmm)))).... eeeessssttttaaaatttt aaaabbbboooonnnndddd
