Download population and economic development in ethiopia: vecm approach

POPULATION AND ECONOMIC DEVELOPMENT IN ETHIOPIA:
VECM APPROACH
Abstract
1
1
2
Gidisa Lachisa and 2H/Gebriel Yirdaw
Lecture, Addis Ababa University School of Commerce, e-mail: [email protected]
Lecture, Addis Ababa University School of Commerce, e-mail: [email protected]
In least developing countries, uncontrolled population growth adversely affects economic
development and favorable economic development induces population. The mixed empirical
results with regards to Granger causality between population and economic development inspired
this study to venture into the area with respect to the Ethiopian context. The study employed
vector error correction model (VECM) and investigated the causal relationship between
population and economic development using World Bank dataset for the period from 1981 to
2012. The results are consistent with some previous research findings: there exists unidirectional
long run causal relationship from economic development to population but, no long run causal
relationship exists from population to economic development. The short run causality tests further
suggest that population Granger-causes economic development; however, there is no reverse
short run causality form economic development to population.
Keywords: Population; Economic Development; VECM
Table of Contents
1. Introduction.............................................................................................................................................2
2. Economic Development and Population .................................................................................................6
2.1. Economic Development ....................................................................................................................6
2.2. Population Growth and Economic Development: Empirical ............................................................7
3. Data and Model Specification ...............................................................................................................10
4. Findings and Discussion ........................................................................................................................12
4.1. Trend of Birth, Death and Population Growth in Ethiopia .............................................................12
4.2. Economic Growth and Population..................................................................................................15
4.3. Results from Econometric Analysis ................................................................................................17
5. Conclusion and Policy Implications .......................................................................................................21
References ...............................................................................................................................................234
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1. Introduction
The impact of population on economic growth has been under discussion following the seminal
work of Malthus in 1798. The debate on this issue centers on the question as to whether or not
population growth promotes or retards economic growth. Some economists including, Coale and
Hoover (1958) argue that population growth diverts resources from savings and capital
accumulation to current consumption of goods and services. Similarly, McNicoll (1984), Hammer
(1986) and Kelley (1988) suggest that population growth redirects resources from education and
health services to current consumption.
The diversion of resources from capital accumulation, education, and health services will inhibit
economic growth, Ceteris paribus. This has serious consequences especially for less developed
countries (LDCs) because population growth in the face of lagging economic growth creates a
phenomenon known as “population trap” or low-level equilibrium trap (Pearce, 1983). James
(2002) blames rapid population growth and urbanization for unsustainable economic
development, the socio-cultural and ecological problems and the failures of political systems
among Sub-Sahara African countries.
For several years economists have often neglected the impact of fundamental demographic
processes on economic growth. But, Bloom and Canning (2001) are among the few who explore
the effect of the demographic transition on economic growth.
More recently, Dyson (2010) claims that mortality decline aids economic growth and hence leads
to an increase in the standard of living. As people live longer, they tend to think more about the
future and are more likely to take risk and innovate. For instance, Bloom and Canning (2001) and
Kalemli-Ozcan (2002) find evidence in developing countries that mortality decline has the
tendency to raise educational attainment and savings rates and thus to increase investment in both
physical and human capital. Strauss and Thomas (1998), also, showed that, mortality decline is
also accompanied by health gains that in turn enhance people’s economic productivity.
In addition to mortality decline, Dyson (2010) has identified population growth, fertility and agestructural change as well as urban growth/urbanization as demographic factors that affect
economic growth or development.
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In the early of twenty-first century, the world population had fluctuated around 6 billion, in which
developing countries contributed to 80% of the total amount and mostly occur in Asian and SubSaharan countries. The fact is population growth and economic growth always has a close
relationship. Over periods, the arguments about positive and negative effects of population on
economic development are still complicated and controversial problems for most of the
economists.
Thomas R Malthus, in his model (1826), he stated that the population growth can reduce the
output per capita because population increases at a geometrical rate while production rises at an
arithmetic rate so that output growth rate cannot keep the same pace. Unlike Malthus, Rober M.
Solow (1956) focused on the term “population growth rate” instead of the “population level”.
He stated that an increase in the population growth rate can decline the capital per worker as well
as the steady-state output per worker. As a result, higher population growth can bring the
detriment to the productivity and economic growth.
However, there are also some optimist views stated that population growth can make a positive
impact on economic growth. An example is Ahlburg (1998). He believed that larger population
can lead to “technology pushed” and “demand pulled” which means higher population growth
can increase the needs for goods and boost the technological development. Therefore, it can
increase the labor productivity, income per capita and living conditions.
The population growth rate affects both the consumption and the productivity of a country’s
economy. One more person can increase not only one pair of hands for labor but also one mouth
for consumption. Especially in Least Developing Countries, where the population growth is
developing more and more drastically, the economic growth therefore also changes critically over
periods. Up to now, the debates about whether population growth is beneficial or detrimental to
economic growth still have been discussed. As a result, it is significant to take into account the
effects of population growth on economic growth in these countries, which focus on per-capita
term specifically.
Researchers, policymakers, governments, civil society and social Medias are expressing their
concern over the effects of an ever increasing number of people on the planet and the greater
demand on food, healthcare, education and jobs.
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According to the report released by UNFPA (2011) most of the population growth is occurring in
developing countries and it is these countries which are affected most by the growth. Ethiopia is
featured in the report and by 2050 Ethiopia’s population is projected to reach 174 million to
become the 9th largest country in the world.
Ethiopia is one of the Sub-Saharan Africa fast growing countries in the last eight years. In recent
years, however, Ethiopia has become one of the fastest-growing non-oil economies in Africa,
with double-digit average growth at just over 10 percent over the last six years. Since 2003/4, the
country has experienced strong economic growth measured in terms of GDP. However, economic
growth in Ethiopia discussed without considering the consequences of population growth.
Measuring economic growth in Ethiopia concerned only on the causes not on measuring the
effects of economic growth. Economic growth in Ethiopia should be measured in terms of a
sustained increase in GDP per capita over time. GDP per capita is used when economic growth
focuses on standard of living. It shows the level of goods and services that, on average,
individuals purchase or otherwise gain access to. This type of measuring economic growth at least
takes into account the effects of population growth in the country.
We know that Ethiopia’s 85 million populations is growing at 2.5 % resulting in 2 million people
added per year. We know that households which have higher consumption requirements and
lower economic support ratio are growing by a constant average growth rate of 2,6% in rural
areas requiring more land for production. The absolute size of the national labour force is
estimated more than 30 million, with an annual average increase of 1.3 per cent. Urbanization, as
a result of rural-urban migration, is growing at higher speed (4.3%) affecting livelihood positions
and infrastructure development (Tsegaye, 2011).
Rapid population growth tends to depress savings per capita and retards growth of physical
capital per worker. The need for social infrastructure is also broadened and public expenditures
must be absorbed in providing the need for a larger population rather than in providing directly
productive assets.
Population pressure is likely to intensify the foreign exchange constraints by placing more
pressure on the balance of payment. The need to import food will require the development of new
industries for export expansion and/or import substitution.
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The rapid increase in school-age population and the expanding number of labor force entrants
puts ever-greater pressure on educational and training facilities and retards improvement in the
quality of education, which is a problem in developing economies.
A high rate of population growth not only has an adverse impact on improvement in food
supplies, but also intensifies the constraints on development of savings, foreign exchange, and
human resources. Also, too dense a population aggravates the problem of improving the health of
the population and intensifies pressure on employment and the amount of investment available
per labor market entrant (Martin 2009).
In the academic research community, compared to the magnitude of the problem, in Ethiopia,
there were few researches specifically addressing population change, dynamics and consequences.
The few studies conducted dealt with mainly the impact of population growth on the environment,
as a cause to land fragmentation and change in livelihood strategies.
In the past decade attempts have been made to show the major impacts of population growth on
development. However, there are signs which show that the population growth problematic is not
yet taken sufficiently in accounting development policy and planning. In Ethiopia the impact of
population growth is not yet concretely operationalized at the level of development policy can be
seen from the current discussion on economic growth or development.
Population growth is a key factor to the growth and development of any country. And with
continued divergence of opinions regarding the consequences of population growth on economic
development, this study thus serve as a necessary contribution to knowledge offering information
regarding its impact in economic growth of Ethiopia. The study also serves as a resource material
to policy makers and scholars by providing the relevant information regarding the issue. The
results of this study is to the policy makers by giving them useful information on various
explanatory variables that may be targeted in the evaluation of policy changes and the provisions
of new policies in order to enhance the desired level of economic growth. Moreover the study
provides useful information to the private and the public agencies in designing projects and
programmes that can assist in bringing a balance between growth in population and growth in the
economy.
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Therefore, the objective of this study is to analyze the causal relationship between population and
economic development by drawing a time series dataset from the World Bank, taking data from
1981 to 2011. Moreover, the study further tests the following hypotheses: Does population
Granger causes Economic development? Does Economic development Granger causes
population?
Given the big scope on the topic of population, this study is narrowed down only to examine the
causal relationship between population and economic development in Ethiopia. Thus, the impact
of other dimensions of population which include population density, size structure and other
factors on economic development has left out for further study.
The rest of the paper is organized as follows. In the next section, a selected review of the
theoretical and empirical literatures on the economic development and population growth is
given. This is followed by the formulation of an econometric model to be estimated. The results
of the study are reported in the subsequent section. Accordingly, the descriptive analysis is
presented first and the econometric analysis presented next. The final section gives concluding
remarks as well as policy recommendations.
2. Economic Development and Population
2.1. Economic Development
The close link between economic development and economic growth is simultaneously a matter
of importance as well as a source of considerable confusion. The idea behind measuring aggregate
output of the country in terms of GDP is to show the total economic activity of the country. GDP
measures the market value of final goods and services produced by a country in a given year
(Mankiw, 2010). It is commonly observed that countries with higher GDP are better-off than
countries with lower GDP. If GDP grows so will be business and jobs as economic growth is
manifested by an increase in a nation’s capacity to produce goods and services. That is, economic
growth is the increase in the value of goods and services produced by an economy (Stanlake and
Grant, 1995).
On the other hand, economic development is any effect or undertaking which aids in the growth
of the economy. That is, it is the “process” of developing and maintaining “suitable economic,
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social and political environment” in which “balanced growth” may be realized increasing the
wealth of the community. Economic development refers to the multidimensional process of
reorganization and reorientation of the entire economic system for improving living standard.
It is something beyond economic growth. Economic growth indicates the change in the quantities
of goods and services. But economic development shows the change in quantity of output and at
the same time change in the living standard. Economic growth is one component of economic
development. The major indictors of economic development include increase in productivity,
decline in social inequality, change in attitude and institution and increase in modern knowledge.
However, using the aforementioned measures of economic developments are difficult to capture,
it is possible to opt measuring real economic output and per capital income are proxies for
economic development as indicated by Todaro and Smith (2012).
2.2. Population Growth and Economic Development: Empirical
The relationship between population growth and economic development theoretically examined
by different researchers. For example, Bucci and La Torre (2007) used a two-sector endogenous
growth model. They pointed out that population growth may have a negative or ambiguous effect
on a country’s economic development. In other words, when physical capital and human capital
are substitute, the population growth has a negative impact on the economic development. On the
other hand, when physical capital and human capital are complementary, the effect of population
growth becomes ambiguous.
Furthermore, Turnemaine (2007) in order to analyze the relationship between population growth
and per capita growth developed a model in which technical progress, human capital and
population growth interact endogenously. The researcher concluded that population growth can
have either a positive or a negative impact on the economic development. The outcome depends
on the relative contribution of population and human capital to the economic development.
There is also scholars and policy advocates who assume that large populations will stimulate
technological change and productivity. The most famous advocate of this position is the late
Julian Simon, whose book, The Ultimate Resource (1981) argued that each person was a potential
source of ingenuity and creativity. Societies with larger populations would be more likely to
develop because of their larger number of potential scientists, inventors, and creative minds.
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The long run benefits of population growth that links to economic development of least developed
countries are on the positive balance, contrary to conventional wisdom, as it has a negative effect
on living standards in the short run due to diminishing returns and the temporary burden it
imposes on the society at large, it has positive effects on living standards in the long run due to
advancement of knowledge and economies of scale. However, when compared to constant-size
population, moderate population growth improves standards of livings both in more developed
and in less developed countries. In the long run, a growing population tends to advance
knowledge, which, in turn, increases productivity and output at a higher rate than that of
population growth. Nevertheless, a country's optimal policy regarding population growth depends
on the weight given to future periods relative to the present (Simon , 1977).
It appears that what matters for economic growth is the accumulation of human capital (educated,
skilled and healthy population) (Orbeta AC 1992; Rosenzweig MR 1990; Strulik H 2005).
With regard to human capital implications, populations experiencing rapid population growth
demonstrate particular characteristics in terms of composition. They have distinct age structures
where approximately 40% of the population is less than 15 years of age. In the later stages of
demographic transition, the size of the working-age group increases (McNicoll G 1984). It is
estimated that the size of young-age populations will continue to increase in sub-Saharan Africa,
South and West Asia and the Arab region until 2015 (United Nations Population Division 2005).
Limited schooling and employment opportunities force migration and change the size and
composition of cities.. Rapid urbanization without planning puts greater financial and physical
restraints on development variables like education, health and social services if these services
cannot keep pace with increasing demand. Even in the developed-country cities, population
growth has been reported to increase costs of providing public services as well as to reduce
service levels.
The interaction between population growth and economic development has been quantitatively
studied by different scholars; the majority of these academic investigations have used crosssection regression to analyze the relations between the two variables (Ahlburg 1996; Easterlin
1967; Kelley and Schmidt 1996; Kuznets 1967; Simon 1992; Thirlwall 1972). Some of these
studies did detect a statistically significant relationship between population growth and economic
development. However, the cross-section regression analyses employed in these studies do not
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allow reaching a conclusive opinion as the results were contradictory. As a result, the research
studies on the relationship between per capita income and population growth that employed crosssection regression analyses tended to suffer from the problem of heteroskedasticity.
A lack of adequate data sets posed a serious obstruction for conducting time-series regression
analyses of the relationship between population growth and economic development.
However, since the end of the 1990s, reliable time-series data sets extensive enough to allow
conducting time-series regress on analysis to examine the long-run relationship between
population and economic development have been available.
Fortunately the availability of high-quality data sets has encouraged further research on the topic
that employed standard econometric tools for time-series data, such as unit roots test, Johansen co
integration test (1988), Granger causality test (Granger 1969). For instance, Dawson and Tiffin
(1998), Thornton (2001) used time-series data to analyze a long-run relationship between
population growth and economic development in India, Argentina, Brazil, Chile, Colombia,
Mexico, Peru, and Venezuela. The study employed the augmented Dickey-Fuller (ADF) unit root
test and Johansen co integration test to analyze the relationships between the two variables.
However, no long-run relation between population and real per capita GDP appear to exist; hence,
population growth neither causes growth of per capita GDP nor is caused by it.
Moreover, Furuoka (2005) take Malaysia as a case study to examine the relationship between
population growth and per capita Gross Domestic Product. The study used Johansen co
integration test and the Error Correction model (ECM) and concluded that there existed a longrun equilibrium relationship between the population growth and per capita Gross Domestic
Product (GDP) growth.
Although it is clear that populations with higher economic development have lower fertility
levels, and thus stable population sizes, the evidence with respect to the effect of population
growth on the economic growth and development is less straightforward. Studies reported
conflicting results: either negative (Klasen and Lawson 2007, Ahituv A 2001; Kelley AC &
Schmidt RM 1995, Thirlwal 1972), or positive (Crook N ,1997) effects of population growth on
economic growth. The direction and size of the effect vary from country to country according to
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which stage of the demographic transition the country is at and its related characteristics such as
the political and economic context (Barlow R 1994; Kelley AC 1988).
For example, an analysis of 45 countries found a greater positive effect of declining fertility on
economic growth for least developed countries and those with higher initial fertility levels
(Eastwood R & Lipton M, 2001) and where as the analysis of 86 countries showed increased
economic growth with declining population growth, fertility and mortality(Kelley AC & Schmidt
RM 2001).
3. Data and Model Specification
This study analyzes the causal relationship between population and economic development in
Ethiopia using a time series data covering the year between 1981 and 2011. As indicated by
Todaro and Smith (2012), real economic output and per capital income are proxies for economic
development. In this study, however, real gross domestic product is used to proxy for economic
development. The data series for the two variables are obtained from World Bank World
Development Indicators database.
Considering these the empirical models to be estimated in this study are
𝑙𝑅𝐺𝐷𝑃𝑡 = 𝛼0 + 𝛼1 𝑙𝑃𝑜𝑝𝑡 + ℇ𝑡 …..………………………………………..…...…. (1)
𝑙𝑃𝑜𝑝𝑡 = 𝛽0 + 𝛽1 𝑙𝑅𝐺𝐷𝑃𝑡 + ℇ𝑡 …..……………………………………..……...…. (2)
Where lRGDP is the logarithm of real GDP, lPop is the logarithm of Population, α’s and β’s
represent parameter estimates, and ℰt is the error term.
The models set here consider a causation effect between population and economic development at
point in time. However, as indicated by Gebhard K. and JürgenW. (2007), everything depends on
everything else not at point in time as well overtime. This in addition helps to describe the
dynamic evolution of a number of variables from their common history. It is, therefore, worth to
consider real GDP and Population as endogenous. To capture this Vector Autoregressive (VAR)
model is appropriate.
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Specifically, the VAR model in this study has the following structure:
𝑙𝑅𝐺𝐷𝑃𝑡 = 𝛼0 + 𝛼1 𝑙𝑅𝐺𝐷𝑃𝑡−1 + … + 𝛼𝑘 𝑙𝑅𝐺𝐷𝑃𝑡−𝑘 + 𝛽1 𝑙𝑃𝑜𝑝𝑡−1 + … + 𝛽4 𝑙𝑃𝑜𝑝𝑡−𝑘 + ℇ𝑡 … (3)
𝑙𝑃𝑜𝑝𝑡 = 𝛼0 + 𝛼1 𝑙𝑅𝐺𝐷𝑃𝑡−1 + … + 𝛼𝑘 𝑙𝑅𝐺𝐷𝑃𝑡−𝑘 + 𝛽1 𝑙𝑃𝑜𝑝𝑡−1 + … + 𝛽4 𝑙𝑃𝑜𝑝𝑡−𝑘 + ℇ𝑡 …… (4)
In this K- dimensional stochastic process α’s and β’s are (nxk) and (mxk) matrix of the parameters
respectively, ε is the error term which is assumed iid(0, δ2), and k the lag length of the
endogenous variables.
This model and empirical work based on time series data assumes that the underling time series is
stationary. Therefore, a test of stationarity that has become widely popular over the past several
years which is a unit root test must be considered to get a robust result (Gujarati, 2006). However,
most time series macroeconomic variables are nonstationary at level. In this case to get a robust
result test for cointegration is necessary.
The issue of cointegration applies when two series (the dependent and independent variables) are
stationary at first difference I(1), but a linear combination of them is stationary at level I(0); in
this case, the regression of one on the other is not spurious, but instead tells us something about
the long-run relationship between them (Wooldridge, 2000).
In addition to learning about a potential long-run relationship between two series, the concept of
cointegration enriches the kinds of dynamic models at our disposal. If the endogenous variable Yt
and explanatory variable(s) Xt are integrated of I(1) processes and are cointegrated a vector error
correction model allows us to study the short-run dynamics in the relationship between Y and X
(Wooldridge, 2000).
The Johansen cointegration test can be used to test for cointegration and to determine the number
of cointegrating vectors once the VAR model is estimated. In the process, since the Johansen test
is sensitive to the nature to the series included in the model and the lag length, the optimal lag
length should be determined based on different lag selection criteria.
Based on the identified numbers of cointegrating vectors a Vector Error Correction Model
(VECM) should be set. The VECM captures the adjustment toward the long run equilibrium and
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the short run causality between variables. Therefore, the vector error correction models for this
particular study are specified as follows:
𝛥𝑙𝑅𝐺𝐷𝑃𝑡 = 𝛼0 + 𝛼1 𝛥𝑙𝑅𝐺𝐷𝑃𝑡−1 + … + 𝛼𝑘 𝛥𝑙𝑅𝐺𝐷𝑃𝑡−𝑘 + 𝛽1 𝛥𝑙𝑃𝑜𝑝𝑡−1 + … + 𝛽4 𝛥𝑙𝑃𝑜𝑝𝑡−𝑘 +
𝛿𝐸𝐶𝑡−1 + ℇ𝑡 ……………………………………………………………………………..…….. (5)
𝛥𝑙𝑃𝑜𝑝𝑡 = 𝛼0 + 𝛼1 𝛥𝑙𝑅𝐺𝐷𝑃𝑡−1 + … + 𝛼𝑘 𝛥𝑙𝑅𝐺𝐷𝑃𝑡−𝑘 + 𝛽1 𝛥𝑙𝑃𝑜𝑝𝑡−1 + … + 𝛥𝛽4 𝑙𝑃𝑜𝑝𝑡−𝑘 +
𝛿𝐸𝐶𝑡−1 + ℇ𝑡 ……………………………………………………………………………………. (6)
Where: Δ stands for the first differences of the matrix of the two variables considered, 𝐸𝐶𝑡−1 is
representing the vector error correction term. k is the optimum lag length determined by the lag
selection criteria.
4. Findings and Discussion
4.1. Trend of Birth, Death and Population Growth in Ethiopia
The natural increase in population size of a given country depends on the crude birth rate and
death of that country. The crude birth rate is the number of children born alive each year per 1,000
Population. On the other hand, death rate is the number of deaths each year per 1,000 Population.
The difference between the two represents the rate of population growth if we control for
immigration and emigration. The birth rate of Ethiopia was around 47.8 in 1961 which in 2011
reduced to only 34.1. Though this figure indicates a reduction, it doesn’t signify
fall in the
addition to the total population in absolute terms as population is growing progressively. Looking
at the death rate of the country, there is considerable reduction to 8.1 in 2011 from a 24.4 in 1961.
This is a more than half reduction in death rate which could be due to the modern medication and
people’s awareness about health.
From the birth rate and death rate that the country is experiencing one can perceive a sizeable
difference between the two which would result in a continuous population growth. As can be
observed from Figure 1 below, there is no tendency for the difference between birth and death
rate to converge.
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Figure 1. Trend of Birth Rate and Death Rate per 1,000 Population (1961 – 2011)
50
50
40
40
30
30
20
20
10
10
0
0
1970
1980
BIRTH
1990
2000
2010
DEATH
Source: Authors computations based on WB World development indicator database.
As the trend shows though there was a continuous reduction in death rate from 1961 to 2011, the
birth rate began to reduce only since mid of 1980’s. The trend depicts significant difference
between birth rate and death rate which adds more on the size to the population of the country.
This can also be evidenced by the deviation in an average birth rate of around 45.6 and death rate
of around 17.9 per 1,000 Population for the time period covered. This significant difference is an
implication of alarmingly growing population in the country.
As shown in Figure 2 below, there was a momentous population growth in the country. In 1961
the total population of Ethiopia was around 22 million and in 2011 become around 89 million.
This indicates a more than fourfold increment in the total population during an average life
expectancy of one Ethiopian citizen or during one generation time.
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Figure 2. Trend of Total Population (1961 – 2011)
9.0E+07
9.0E+07
8.0E+07
8.0E+07
7.0E+07
7.0E+07
6.0E+07
6.0E+07
5.0E+07
5.0E+07
4.0E+07
4.0E+07
3.0E+07
3.0E+07
2.0E+07
2.0E+07
1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011
POP
Source: Authors computations based on WB World development indicator database.
This result is consistent with the trend of birth rate and death rate gap which is the source of
population growth. Population growth rate in 1961 was 2.32% and now (2011) is still higher than
the former and is around 2.6%. Here it is possible to envisage how much 2.32% of 22 million and
2.6% of 89 million. The latter in absolute term adds to the total population by more than 4.5 times
the former. This indicates a booming population size of the country.
For the time period considered on average the population was growing at an average rate of
around 2.74% which is higher than the 1961 and 2011 case considered above. The lowest
population growth rate in the country was recorded between the years 1976 to 1980 with an
average annual growth rate of 1.58%. Contrary to this, the highest population growth rate was
recorded between the time periods of 1990 to 1994 with an average rate of around 3.46%.
The other way to see how population growth is noteworthy is through looking at the doubling
time (the numbers of years required for the population to double). Table 1 below depicts the
doubling time.
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Table 1. Population Growth and Doubling Time (1961 – 2011).
Year
1961
1988
2011
Total Population
Average Annual Growth
Doubling Time (In years)
22671134
44916621
89393063
2.32
2.53
2.99
28
23
Source: Authors computations based on WB World development indicator database.
Considering 1961 as starting point, the population to double from 22 million to 44 million
required around 28 years. This is just an increment in population size by around 22 million. This
to happen an annual average (1961 – 1988) population growth was 2.53%. However, the
population to double from 44 million to 88 million required only less than 23 years with an annual
average growth rate of 2.99%. Again this is consistent with the previous findings and an indicator
of thriving population growth in the country.
4.2. Economic Growth and Population
In the section that precedes the result indicates unremitting and persistently increasing total
population in Ethiopia. At this point one might question whether this is a problem or not.
Actually, when one deals about population it is not the number that matters however the welfare
of the society. Todaro and Smith (2003) indicated that the debate over the seriousness of the
consequences of rapid population growth is not yet solved. That is, there are arguments for and
against the premise that the consequences of rapid population growth lead to serious development
problems. Before we go to the econometric analysis let’s consider the trend relationship between
economic growth and population.
Figure 3 below shows the relationship between real GDP and population growth. The welfare of
the economy as can be measured by per capita income can be observed from the difference
between the former and latter. As the trend shows there was constant population growth rate
between the years 1981 to 2011. However, the real GDP growth rate that the countries
experienced over the same time period shows fluctuation with both positive and negative growth.
In 1983 and 1984 the country registered a significant slump in economic growth which could be
due to the drought that prevailed in the country. The other significant fall in economic growth was
between 1990 and 1991. This is a time period where there was war between Ethiopia and Eritrea.
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Figure 3. Real GDP Growth and Population Growth Trend (1981 – 2011)
15
15
10
10
5
5
0
0
-5
-5
-10
-10
-15
-15
1985
1990
1995
POPG
2000
2005
2010
RGDPG
Source: Authors computations based on WB World development indicator database.
A clear and somewhat continual positive economic growth which outweighs the population
growth was observed since 2004 and is under this circumstance that the country per capita income
can grow. Here one might be interested to see the causal relationship between economic growth
and population.
As indicated in Figure 4 below, with given fluctuations, on average there was a continuous
growth in the logarithm of real GDP of the country, particularly with a considerable growth since
2003. On the other hand, the logarithm of population looks stably growing overtime indicating
existence of positive relationship between economic growth and population. However, based on
this visual observation nothing can be concluded about the causal relationship between the two
variables. This, therefore, requires an econometric investigation.
Page | 16
Figure 4. Trend of the logarithm of Real GDP and Population (1981 – 2011)
24
24
23
23
22
22
21
21
20
20
19
19
18
18
17
17
1985
1990
1995
LRGDP
2000
2005
2010
LPOP
Source: Authors computations based on WB World development indicator database.
4.3. Results from Econometric Analysis
As indicated under model specification a time series econometric analysis to give a robust result
requires stationary variables. The most commonly used test of stationarity(non-stationarity) is the
Augmented Dickey Fuller (ADF) unit root test. The result of the unit root test is presented in
Table 3 below. The result shows that we fail to reject the null hypothesis of unit root for all
variables at level signifying non-stationarity of the variables. However, differencing the variables
resulted in rejection of the null hypothesis and implies all variables are stationary at first
difference. As shown in Figure 4 above, the logarithm of population involves a time drift, as a
result a unit root test with constant and trend is appropriate.
Page | 17
Table 2: Augmented Dickey Fuller test for Unit Root
Variables
LOGPOP
LOGRGDP
Level
Constant
Constant
without Trend
with Trend
Constant
without Trend
First Difference
Constant
with Trend
-4.040248*
-2.821555
-2.232352
-4.514806*
2.036756
-0.648143
-4.153126*
-4.838961*
Note: * represents significance at 1 percent level.
Since the variables considered are stationary at least at first difference I(1), the test for
cointegration is required to have valid estimate with non-stationary series. This is possible if the
linear combination of non-stationary series is stationary.
Considering the possibility of endogenity of macroeconomic variables, the Johansen cointegration
test is used to test for cointegration and to determine the number of cointegrating vectors once the
VAR model is estimated. In addition, since the Johansen test is sensitive to the lag length
included in the model, in this study based on the different selection criteria lag order of four is
appropriate for the model. Accounting for this, two types of cointegration tests are given in Table
3 and Table 4 below with the null hypothesis of no co-integrating vector.
Table 3: Cointegration Rank Test (Trace)
Hyp. No. of CE(s)
Eigenvalue
Trace Statistic
None *
0.659470
28.39308
At most 1
0.014681
0.384545
* Denotes rejection of the hypothesis at the 0.05 level
0.05 Critical Value
Prob.**
15.49471
3.841466
0.0003
0.5352
Table 4: Cointegration Rank Test (Maximum Eigenvalue)
Hyp. No. of CE(s)
Eigenvalue
Max-Eigen Statistic
None *
0.659470
28.00853
At most 1
0.014681
0.384545
* Denotes rejection of the hypothesis at the 0.05 level
0.05 Critical Value
Prob.**
14.26460
3.841466
0.0002
0.5352
The result from the above two tests indicates rejection of the null hypothesis of no cointegrating
vector. On the other hand, both tests fail to reject the null hypothesis of a unique cointegrating
vector against the alternative of two cointegrating vectors. This indicates that the model has single
cointergating vector.
Page | 18
In a time series analysis OLS regression to result unbiased, consistent and efficient outcomes
there are conditions pre-imposed. These include normality, hetroskedasticity and serial correlation
tests. Therefore, to get a valid result in this study these diagnostic tests are made. The Jarque-Bera
test of normality of the error term from the VECM indicates that the error terms are normally
distributed. In the dynamic form of hetroskedasticity where lagged dependent variable is
considered as explanatory variable, Engle suggested what is called autoregressive conditional
hetroskedasticity (ARCH) model. The test result in this particular study rejects the null hypothesis
of no ARCH effect meaning there is no autoregressive conditional hetroskedasticity problem.
The other test, serial correlation occurs in time-series studies when the errors associated with a
given time period carry over into future time periods. A popular test for serial correlation is the
Durbin-Watson (DW) statistic. As indicated by Pindyck & Rubinfeld (1998), the DW statistic will
lie in the 0-4 range, with a value near two indicating no first-order serial correlation. The test
result from this study is also consistent with this assertion. In addition to this, VER Residual
Serial Correlation LM Test fails to reject the null hypothesis of no serial correlation at lag order of
four.
The short run and long run bidirectional causalities between real GDP and population growth is
analyzed using VECM, the advantage of testing causality using VECM over direct Granger
causality test is that the former enables analyze both the long run and short-run dynamics of the
economic growth and population growth. The log run relationship is summarized in Table 5
below.
Table 5: Estimation result of the Cointegration Equation (VAR model)
Cointegrating Eq:
CointEq1
CointEq2
LOGRGDP(-1)
1.000000
-0.055770
LOGPOP(-1)
-17.93073 *
1.000000
C
129.6042
-7.228047
Note: * represents significance at 1 percent level.
The long run causal relationship as indicated in the above table prevails that there exist significant
and negative causation of population on economic development. The result found is in line with
the Solo growth model (1956) where population growth adversely affects economic growth in the
Page | 19
long run as more people become idle due to age or other factors then these people would
definitely have negative impacts on economic growth. However, there is no statistically
significant causation of economic development on population.
Since the economic growth and population are co-integrated with each other as indicated in Table
3 and Table 4; therefore, the long run causality is estimated by the negative signed and significant
value of the coefficient of the one period lagged error-correction term ECt-1. Accordingly if the
estimated result shows the estimated lagged error correction term is negative and significant it
suggests that the error correction is happening in the model. The short run dynamics and
relationship of economic development and population is presented in Table 6 below. From the
first model where economic development is considered as dependent variable the coefficient of
feedback coefficient ECt-1 (Error Correction term)) is -0.011, but it is found statistically
insignificant, suggesting that there is no disequilibrium in previous year that to be corrected in the
current year.
Table 6: Estimation result of VECM
Dependent Variable: ΔLOGRGDP
Regressors
Coefficient
t-Statistic
ECt-1
-0.011045
ΔLOGRGDP(-1)
Dependent Variable: ΔLOGPOP
Prob.
Coefficient
t-Statistic
Prob.
-1.110713
0.2831
-0.001633
-5.436830
0.0001
0.058984
0.307655
0.7623
5.78E-05
0.178887
0.8603
ΔLOGRGDP(-2)
-0.526170
-2.793198
0.0130
0.000526
1.657931
0.1168
ΔLOGRGDP(-3)
0.084249
0.468911
0.6455
0.000529
1.747107
0.0998
ΔLOGRGDP(-4)
-0.354834
-1.877278
0.0788
0.000606
1.904420
0.0750
ΔLOGPOP(-1)
41.87137
0.369633
0.7165
2.158282
11.30991
0.0000
ΔLOGPOP(-2)
-182.1961
-0.686559
0.5022
-1.698798
-3.799951
0.0016
ΔLOGPOP(-3)
227.3610
0.958938
0.3519
0.417491
1.045247
0.3114
ΔLOGPOP(-4)
-93.52634
-1.225816
0.2380
0.028483
0.221604
0.8274
C
0.118759
0.799864
0.4355
0.001204
4.813323
0.0002
However, considering population as dependent variable depicts that the error correction term
(ECt-1) is negative and statistically significant at one percent level of significance. The coefficient
-0.0016 indicates disequilibrium in the previous year adjusts by around 0.16 percent per annum to
the long run equilibrium.
Page | 20
The impact of lagged population considered independently has no significant effect on the real
GDP as a one period increment in population (new birth) has no contribution to the economy of
the country. However, the cumulative effect of population on real GDP as tested using the well
known Wald test on the estimated coefficients as presented in Table 7 below indicates that in the
short run population has significant impact on real GDP. This result is in support of the
proposition that population Granger causes real GDP. The different empirical studies (Klasen and
Lawson 2007, Ahituv A 2001; Kelley AC & Schmidt RM 1995, Thirlwal 1973, Furuoka 2005)
are also in supports of the above aforementioned result. Contrary to this, in the short run real GDP
has no causation effect on population.
Table 6: Wald Test
ΔLOGPOP  ΔLOGRGDP
Test Statistic
Value
df
Probability
ΔLOGRGDP  ΔLOGPOP
Value
df
Probability
F-statistic
2.496035
(4, 16)
0.0842
2.038218
(4, 16)
0.1372
Chi-square
9.984139
4
0.0407
8.152872
4
0.0861
5. Conclusion and Policy Implications
The study is based on a time series data covering a time period of 1981 to 2012 and analyzed the
bi-causal relationship between population and economic development (Real GDP is used as a
proxy). Both descriptive and econometric methods of analysis were employed.
The descriptive analysis shows existence of high population growth in Ethiopia. This is evidenced
by persistent and large gap between birth rate and death rate where the birth rate is on average
was greater by around 27 per 1,000 Population. In addition, the doubling time of the population is
declining. Based on the trend of population and economic growth while there exist a persistence
increment in population size and real GDP, there is slight fluctuation in the real GDP overtime.
To get a robust econometric analysis, the study employed different tests such as unit root,
cointegration, and Granger causality tests. The unit root test indicates that the variables
considered are stationary at first difference. The cointegration test using Johansen test (trace and
maximum eignvalue tests) reveals that population and real GDP drifts together in the long run.
Page | 21
Based on this, a VECM with an optimal lag length of four were estimated considering real GDP
and Population as dependent variables in two different models. This model is valid in terms of the
normality, homosekedasticity, and no serial correlation of the residual term.
According to the estimation result, though population Granger causes real GDP in the long run,
there is no Granger causality from real GDP to population. The short run result is also consistent
with the long run result in the sense that jointly coefficients of lagged population (as indicated by
Wald test) cause real GDP, where the reverse scenario is not valid. On the other hand, the short
run speed of adjustment as measured by the coefficient of error correction term dictate that a
previous year shock (disequilibrium) adjusts by 0.16 percent per annum in the model where
population is the dependent variable.
Therefore, considering the significant and negative impact of population on economic
development, the concerned body has to device sound population policy that reduces the growth
of population so as to reap the fruit of blossom economy.
Page | 22
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