POPULATION AND ECONOMIC DEVELOPMENT IN ETHIOPIA: VECM APPROACH

Transcription

1 POPULATION AND ECONOMIC DEVELOPMENT IN ETHIOPIA: VECM APPROACH Abstract 1 Gidisa Lachisa and 2 H/Gebriel Yirdaw 1 Lecture, Addis Ababa University School of Commerce, [email protected] 2 Lecture, Addis Ababa University School of Commerce, [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 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

2 Table of Contents 1. Introduction Economic Development and Population Economic Development Population Growth and Economic Development: Empirical Data and Model Specification Findings and Discussion Trend of Birth, Death and Population Growth in Ethiopia Economic Growth and Population Results from Econometric Analysis Conclusion and Policy Implications References Page 1

3 1. Introduction The impact of population on economic growth has been under discussion following the seminal work of Malthus in 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. Page 2

4 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 Sub- Saharan 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. Page 3

5 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 9 th 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. Page 4

6 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. Page 5

7 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 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, Page 6

8 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) 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. Page 7

9 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 Page 8

10 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 Page 9

11 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 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 lrgdp t = α 0 + α 1 lpop t + ℇ t (1) lpop t = β 0 + β 1 lrgdp t + ℇ t (2) Where lrgdp is the logarithm of real GDP, lpop is the logarithm of Population, α s and β s represent parameter estimates, and E t 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. Page 10

12 Specifically, the VAR model in this study has the following structure: lrgdp t = α 0 + α 1 lrgdp t α k lrgdp t k + β 1 lpop t β 4 lpop t k + ℇ t (3) lpop t = α 0 + α 1 lrgdp t α k lrgdp t k + β 1 lpop t β 4 lpop t k + ℇ t (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, δ 2 ), 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 Y t and explanatory variable(s) X t 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 Page 11

13 the short run causality between variables. Therefore, the vector error correction models for this particular study are specified as follows: ΔlRGDP t = α 0 + α 1 ΔlRGDP t α k ΔlRGDP t k + β 1 ΔlPop t β 4 ΔlPop t k + δec t 1 + ℇ t.... (5) ΔlPop t = α 0 + α 1 ΔlRGDP t α k ΔlRGDP t k + β 1 ΔlPop t Δβ 4 lpop t k + δec t 1 + ℇ t. (6) Where: Δ stands for the first differences of the matrix of the two variables considered, EC t 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 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 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. Page 12

14 Figure 1. Trend of Birth Rate and Death Rate per 1,000 Population ( ) BIRTH 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. Page 13

15 Figure 2. Trend of Total Population ( ) 9.0E E E E E E E E E E E E E E E E 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. Page 14

16 Table 1. Population Growth and Doubling Time ( ). Year Total Population Average Annual Growth Doubling Time (In years) 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 ( ) 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 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 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 This is a time period where there was war between Ethiopia and Eritrea. Page 15

17 Figure 3. Real GDP Growth and Population Growth Trend ( ) POPG 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 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

18 Figure 4. Trend of the logarithm of Real GDP and Population ( ) LRGDP LPOP Source: Authors computations based on WB World development indicator database 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

19 Table 2: Augmented Dickey Fuller test for Unit Root Variables Level Constant without Trend Constant with Trend Constant without Trend First Difference Constant with Trend LOGPOP * * LOGRGDP * * 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 0.05 Critical Value Prob.** None * At most * Denotes rejection of the hypothesis at the 0.05 level Table 4: Cointegration Rank Test (Maximum Eigenvalue) Hyp. No. of CE(s) Eigenvalue Max-Eigen Statistic 0.05 Critical Value Prob.** None * At most * Denotes rejection of the hypothesis at the 0.05 level 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

20 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) LOGPOP(-1) * C 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

21 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 EC t-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 EC t-1 (Error Correction term)) is , 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 Dependent Variable: ΔLOGPOP Regressors Coefficient t-statistic Prob. Coefficient t-statistic Prob. EC t ΔLOGRGDP(-1) E ΔLOGRGDP(-2) ΔLOGRGDP(-3) ΔLOGRGDP(-4) ΔLOGPOP(-1) ΔLOGPOP(-2) ΔLOGPOP(-3) ΔLOGPOP(-4) C However, considering population as dependent variable depicts that the error correction term (EC t-1 ) is negative and statistically significant at one percent level of significance. The coefficient indicates disequilibrium in the previous year adjusts by around 0.16 percent per annum to the long run equilibrium. Page 20

22 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 ΔLOGRGDP ΔLOGPOP Test Statistic Value df Probability Value df Probability F-statistic (4, 16) (4, 16) Chi-square 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

23 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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