Panel Data Analysis of Population Growth and It Implication on
Economic Growth of Developing Countries
LABARAN HAMZA, Department of Economics, SRM University, Chennai, India. Email: labranthamza@yahoo.com
_____________________________________________________________________
Abstract
Economists have often neglected the impact of fundamental demographic processes on economic growth. Bloom and Canning (2001) are among the few who explore the effect of the demographic transition on economic growth. As such the study investigated the effects of population dynamic on the economic growth of thirty developing countries from Africa, Asia, and Latin America illustrating both orthodox and heterodox theories for the period of fourteen years by specifically determining the effects of birth rate, death rate, and migration on the economic growth. To this aim, it is possible to compare the population growth of each member and the whole group using paned data co-integration and causality. The study used annual secondary data on birth rate, death rate, as well as migration from World Development Indicators (WDI), and World Bank. The data were analysed using Linear Panel Data Regression Analysis of Pooled, Fixed Effect (LSDV), and Random Effect (ECM) econometrics techniques. The results showed that population growth and dynamism has a significant impact on economic growth of developing countries. As such, fiscal and monetary policy tools for the last three decades should not be seen as the utmost instruments of achieving target growth in developing countries, but should be combined with certain population policy instruments in stimulating economic growth especially in the higher birth rate economies such as Niger, Angola, Iraq, etc.
1. Introduction
The implications of population either in terms of size or composition for change, development and the quality of life has long and widely be debated by economists, demographer and some other concerned disciplines. Theoretically, there are three alternative views regarding population-economic growth nexus (Hudson, 1988; and Blanchet, 1991). “Population pessimists” - a popular view which belongs to the Malthusian or Orthodox school- believe that rapid population growth is problematic because it tends to overwhelm any induced response by technological progress and capital accumulation (Hoover, 1958; Ehrlich, 1968; and Coale and Hoover, 1958). “Population optimists” on the other hand are of the view that rapid population growth allows economies of scale to be captured and promotes technological and institutional innovation (Boserup E, 1981; Simon, 1981; Kuznets, 1967). Later research defeats both views as “population neutralists” contend that population growth in isolation from other factors has neither a significant positive nor a significant negative impact on economic growth (Bloom and Freeman, 1986; Kelley, 1988). However, going by what is bedevilling the developing countries today there is no doubt that Malthus has been vindicated because among other implications, rapid population growth will require that government spend more on provision of education, security, health, shelter and other social facilities. The fact that the different theories predict different causal mechanisms shows that there are gaps yet to be filled with empirical evidence. Therefore, the understanding of population-growth nexus has remained one of the oldest problems in economics.
While some developing countries of the world had fertility and mortality rates that were lower than those in developed nations, some including many in sub-Saharan Africa, were stuck in high fertility/low-growth traps, and others such as Niger, Angola etc. had hit speed bumps on the road toward longer, healthier lives (Mantu, 2001). In most of the poor developing countries, a sharp drop in death rates has not been accompanied by a corresponding fall in birth rates. The explosive growth of the human population in the world in the nineteenth and twentieth century was the result of a historically unprecedented decline in the rate of mortality, and a relatively stable fertility rate. The East Asian nations are among the nations of the world that have experienced the greatest success in "reaping" the demographic dividend produced by reduced mortality and fertility rates as a result of strong policy environment. Latin America has undergone a fairly sharp demographic transition, but because of a weak policy environment it has not been able to capitalize on it. The demographic transitions in South, Central and Southeast Asia started later and have been less pronounced than that in East Asia. The Middle East and North Africa are still in the early phases of the demographic transition, while indeed many parts of sub-Saharan Africa have
seen almost no decrease in traditionally high fertility rates (Rand Population Matters project, 2002).
The first part of this research aims to provide a summary of the alternative theoretical frameworks that deal with population and its impact on economic growth of developing countries. The latter parts present an econometric analysis of population and economic growth in the thirty developing countries from the cross-section of Asia, Africa, and Latin America for the period of fourteen years.
Unlike in the previous researches where pure time series econometrics was used in the study of short and long-run impact of population growth parameters on economic growth. The present research resort to Panel Data econometrics analysis, so that both the time and space dimensions and effect are captured between the subjects of the study by means of traditional and new theoretical perspectives of panel data.
This study therefore aims to measure the impact of the population on the economic growth of developing countries and see how these variables move together both in the short and long-run. To this end, it is possible to compare these impacts for each member and the whole group using panel data co-integration method.
2. Literature Review
While Dyson (2010) contends that mortality decline is the chief cause of economic development, McKeown (1976) argues that the direction of causality should be reversed, i.e., it is the improvement in the standard of living that results in lower death rates. Easterlin (1996) and Schofield and Reher (1991) also show that the dire living conditions that came with the industrial revolution and modern economic growth in cities of Europe during the nineteenth century might have raised mortality rates. On the other hand, evidence from contemporary developing economies tends to show that it is mortality decline that leads to economic growth, as it increases investment in both physical and human capital via increased savings rates and education (see, for instance Bloom and Canning (2001) and Kalemli-Ozcan (2002). Furthermore, mortality tends to fall as a result of declines in death rates from infectious diseases. Declines in these diseases tend to bring about an improvement in the nutritional status of children which in turn leads to a fitter future labour force. In fact, Strauss and Thomas (1998) show that healthier workers tend to be more productive. In pre-transitional societies, relatively rapid population growth almost always resulted in a fall in the standard of living due to the rather severe limits to the technical progress in agriculture or to the fixed supply of land, as pointed out by Malthus (1798; 1830 [1970]). This prompts Clark (2007) to state that income levels before the nineteenth century could not escape the Malthusian equilibrium due to the very low rate of technological advance in all economies.
Some theories suggest that more rapid population growth should be bad for economic performance because with a larger population each worker will have less productive factors, both non-accumulated and accumulated, to work with. Other theories suggest that greater population growth will lead to greater productivity either by inducing innovation, producing innovation, or through creating greater economies of scale, specialization or agglomeration (Boserup, 1981, Simon, 1992, Kremer, 1993). Robert Cassen's (1994) recent summary of the state of the art in research on Population and development, states nicely the conventional wisdom of contrasting negative factor accumulation effects versus possibly positive productivity effects: What about the effect of population on per capita income? Here simple economics suggests that the effect is probably negative. Unless population exerts a strong positive influence on capital formation and the suggestion that it does is a minority opinion-the more people opinion-there are, and opinion-the less capital opinion-there is per person; as a result even though total output may be larger with a bigger population, output per person is smaller. There are however, three arguments against this: larger population may generate economies of scale; they may induce favourable technological change; and when population is growing, the average age of the labour force will be younger, which may have beneficial productivity effects. The fact that the different theories predict a different causal mechanism shows that there is a gap yet to be filled with empirical evidence across countries. Between 1950 and 1995, the world's population grew from 2.5 billion to 5.7 billion people, and is expected to grow by another 4 billion people over the next 50 years. There has been a long-standing debate on the effects that such population growth can have on economic development and growth of countries. This debate is generally couched in the distinctions made by ‘population optimists' and by ‘population pessimists'. Population optimists believe that increases in population increase the incentives for the invention of new technologies and the diffusion of existing ones (Boserup 1981). They also point out that larger population allow for economies of scale both in production and in consumption (Kuznets 1966, Simon 1977). Population pessimists, on the other hand, believe that the burden placed on the resources of an economy by an increasing population is a hindrance to economic development. The original ‘Malthusian' perspective focused on agricultural resource constraints, while later economic models were based on the capital to labour ratio: increases in population meant that there was less capital per person, thereby reducing the productivity of labour, such as in the neoclassical model discussed above. Empirical studies, which have used cross-country data to try and evaluate these claims, however found little evidence to support either argument. Once the effects of initial income, education, and other determinants of growth are taken into account, population growth is found to have a negligible effect on growth of GDP (Bloom and Freeman 1986). This gave rise to the "population neutralist" or "revisionist" perspective,
which held that demography, was not a significant factor in the economic growth process. This view was in part responsible for the tenuous position population variables have recently occupied in studies of economic growth. More recent research, however, has pointed out that it is not sufficient to take into account simply the growth in population when attempting to evaluate the role-played by demography, as demographic effects are significantly more complex. Kelley and Schmidt (1995) show that the composition of population growth is an important factor. For example, if population growth occurs mainly through mortality declines that affect infants and children disproportionately (as is well known to be the case in high mortality populations), the effect on age structure will be different than if population growth occurs due to migration, which generally selects for working age people.
In all the foregoing studies, no attempt was made to analyse the impact of population changes, especially demographic behaviour on economic growth at panel, and the resultant implications on growth.
3. Methodology Overview
According to the nature of the data, which are a mixture (combination) of the independent and dependent variables related to developing countries in the years from 1985 to 2014, model estimation was done based on panel co-integration method which is a new approach in econometrics and the traditional approaches that’s fixed and random effect. Panel data is a method to integrate time series and cross-section data where due to corporate (joint) consideration of the variable changes in each cross-section and time, all available data are used. Nowadays, in addition to the traditional attitude of panel data econometrics, there exist new insights in this regard.
3.1. New Insights in Panel Econometrics
Panel Time Series (PTS) or unstable (non-stationary) panel econometrics was proposed as an important factor in the economic development with a new approach in the early '90s. Although there are not many texts on this issue and the concepts used in this method are theoretical and there is not much evidence in this regard, using this method is much simpler than the traditional one. In this method, the panel data are firstly divided from the viewpoint of priority of significance in time or sections and then the applicable models are considered for each one.
From the viewpoint of new theory of macro panels, two issues are raised:
i). Mixed regressions (Pooled) in which the parameter homogeneity is rejected. In other words, the regression is heterogeneous (Pesaran & Smith, 1995).
• Time series fully revised estimate techniques have been added to the fixed effect and random effect methods which are used in within-group dependence, autocorrelation and heterosk elasticity in interruption terms.
• There exist several tests for unit root test and panel co-integration test which are addressed in the following sections.
3.2. Old Views on Panel Econometrics
In this attitude, according to the main model of the panel data, the model and its estimators are recognized (identified) using the various tests. At the end, if the regression slope(gradient) is fixed (constant) at each section and the fixed terms changes from one section to another or in other words if time effect is not significant and just there is a significant difference between various sections, and coefficients of the sections do not change with time changes, the model is fixed effect. While, in the unilateral (one-way) fixed effect model, the slopes are constant but the constant terms are different in different times and in fixed (constant) two-way effect model, the slope of the functions is constant in each section but the constant term (abscissa) will vary with time and with the section. Seemingly unrelated regression (SUR) is another type of fixed effect model in which the constant terms and the slope of the regressions are different. But if the variables have been selected randomly, and there is no correlation between explanatory variables and correlation errors, to obtain efficient and consistent estimates, the random effects model can be used.
However, the purpose and objective of this research is to determine how the demographic structure and the economics growth of developing countries are related and affect one another in the short or long-run. As such relevant variables that go into the model are: Real Gross domestic Product (RGDP) which indicates a country’s economic performance over time, the birth rate, death rate both in thousand per persons, and the net migration. Note that variables which were not in rate were logged.
3.3. Data Sources
The study used annual secondary data of real GDP, birth rate, death rate, and net migration from World Development Indicators (WDI); World Bank, Annual Abstract of Statistics by National Bureau of Statistics(NBS). The period of the study was 2001 to 2014.
3.4. Model Specifications
The model functional form can be express as
LGDPit=α+β1BRit+β2DRit+β3NMigit+Uit …(1)
Where L= Logarithm
RGDPit = Real GDP
α= intercept
DRit = Death rate in thousand
NMigit = Net Migration
Uit = Panel error term
A panel data regression differs from a regular time-series or cross-section regression in that it has a double subscript on its variables, i.e.
i= 1, 2, 3 …, 30 Panel Identification (Countries) t= 1, 2, 3… 30 Time period
3.5. Justification for the Variable Selected
A higher GDP (Per Capita) will result in a higher birth rate, and a lower death rate. Background Information GDP - "Gross Domestic Product" - Total market value of all finished goods and services produced in a year, as well as investments, government spending, and exports minus imports. GDP (Per Capita) - Gross Domestic Product divided by Population. Birth Rate is the average number of births per 1000 persons. Death rate is the average number of deaths per 1000 persons, whereas bilateral causality existed between GDP and migration.
4. Empirical Results
This chapter systematically tests whether the birth rate, death rate, and migration has a negative effect on economic growth. We first provide the basic results and then conduct some diagnostic tests
.
4.1 Panel Unit Root Test
The power of Panel unit root test is by far more than the same test in time series, but considering the point that there is a possibility of conflict in various unit root tests, all tests have been considered. In general, the usual unit roots tests, such as Dickey- Fuller (DF), Augmented Dickey -Fuller (ADF) and Phillips - Perron (PP), which are used for a time series, are of low test power and have a bias towards accepting null hypothesis. When the sample size is small (n<50), it becomes more serious (worse). One of the methods that have been proposed to solve this problem is to use panel data to increase the sample size and the unit root test in panel data.
Table 1: Panel Unit Root Test (Levin-Lin-Chu)
Variables Model with intercept Model with intercept and trend Lags Oder of Integration t-statistic t-critical t-statistic t-critical
Log GDP -36891* -4.6466 -4.3239*** -4.8883 2 I(1)
BR -3.6891*** -5.2916 -4.3239** -5.6673 2 I(1)
NMig -3.6891*** -5.6659 -4.3239*** -5.5528 2 I(1) Sources: Stata Version 11
.
Table 2: Panel Unit Root Test (Im-Pesaran-Shin)
Variables
Model with intercept Model with intercept and trend
Lags Oder of Integration t-statistic t-critical t-statistic t-critical
Log GDP -3.6891*** -4.6859 -4.3239*** -4.8974 2 I(1) BR -3.6891*** -5.2311 -4.3239*** -5.6621 2 I(1)
DR -3.6891** -5.9754 -4.3239* -5.8652 2 I(1)
NMig -3.6891* -5.7416 -4.3239*** -5.6164 2 I(1) Sources: Stata Version 11.
Note: (*), (**), (***) indicates 1%, 5% and 10% level respectively.
According to the results of Table1 and 2, the tests emphasize the existence of the unit root i.e. they are non-stationary at level, but after taking the first difference they become stationary.
4.2 Panel Co-integration Test
In co-integration analyses, long-term economic relations are tested and estimated. If an economic theory is correct, a special set of variables specified by the theory are linked together in the long run. In addition, the economic theory just specifies the relations as static (long-term) and the does not provide information on the short-term dynamics among the variables.
Table 3: Results of Pedroni Co-integration Test
Test Result without intercept
Within Group
𝛾 𝑃𝑎𝑛𝑒𝑙 –𝑆𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐
Test Result 0.765
P-Value 0.046
Panel Phillips - Perron type P –Statistic
Test Result -0.699
P-Value 0.064
Panel Phillips - Perron type t –Statistic Test Result -1.428
P-Value 0.000
Augmented Dickey - Fuller (ADF) Type t-Statistic Test Result -1.123
P-Value 0.230
Between Groups
Group Phillips - Perron Type p- Statistic Test Result -0.315
P-Value 0.004
Group Phillips - Perron t- Statistic Test Result -2.175
P-Value 0.000
Group ADF Type t- Statistic Test Result -2.041
P-Value 0.002
co-integration test
P-Value 0.000
Sources: Stata Version 8.
H0: There is no co-integration in heterogeneous panels
H1: there is one co-integration in heterogeneous panel
According to the results of Pedroni co-integration tests above, most test statistics (in each case, at least six statistics) strongly reject the null hypothesis of no co-integration vector for all variables. Besides, the result of Kao integration test also rejects the lack of co-integration relationship among the model variables at 5 per-cent level.
4.3. Granger Causality Test Results
Therefore, if the variables are found to be co-integrated as in the case above, we can specify an error correction model and estimate using standard methods and diagnostic test.
The co-integration tested above indicates that causality existed between the four variables i.e. GDP, Birth rate, Death rate and Net migration but it fails to show us the direction of the causal relationship.
Engel and Granger suggested that if co-integration existed between two or more variables in the long-run, then, there must be either unidirectional or bi directional Granger-Causality between these variable.
Engle and Granger illustrated that the co-integrating variables can be represented by ECM (Error Correction Model) representation. In other words, according to Granger, if there is evidence of co-integration between two or more variables, then a valid error correction model should also exist between them.
As GDP, Birth rate, Death rate, and Migration are co-integrated, a ECM (error correction model) representation could have the table below.
Table 4: Granger Causality Test Result for the Long-run
Directions ECT Coefficient Standard Error t-statistic P-Value Decisions BR→LnGDP LnGDP→BR ECT1t-1 ECT2t-1 -0.0383 -0.2142 0.2424 0.0892 0.8536 -2.4004 0.4100 0.0335 Do not reject H0 Reject H0 DR→LnGDP LnGDP→DR ECT3t-1 ECT4t-1 0.1069 -0.4995 0.1716 0.1391 0.6231 -3.6414 0.5449 0.0034 Do not reject Ho Reject H0 Mig→LnGDP LnGDP→Mig ECT5t-1 ECT6t-1 -0.3920 -0.3547 0.1406 0.3637 -2.7885 -0.9754 0.0164 0.3486 Reject H0 Do not reject H0 Source: Author’s Computation Using stata version 9.
Table 5: Granger Causality Test Result for the Short-run Directions of Causality F-statistic (F) Chi-square (χ2) P-Value Decisions
BR→LnGDP LnGDP→BR 0.7919 3.1094 3.9595 15.5474 0.5553 0.0083 Do not reject H0 Reject H0 DR→LnGDP LnGDP→DR 0.7097 2.4961 3.5489 12.4807 0.6160 0.0288 Do not reject H0 Reject H0 Mig→LnGDP Mig→LnGDP 1.3961 0.2021 6.9808 1.0105 0.2221 0.9617 Do not reject H0 Do not reject H0 Source: Author’s Computation Using stata version 9.
Table 6: Granger Causality Test Result for the Short and Long-run Direction Short-run Causality Long-run Causality BR→GDP GDP→BR No causality There is causality No causality There is causality DR→GDP GDP→DR No causality There is causality No causality There is causality NMig→GDP GDP→NMig No causality No causality There is causality No causality
Check appendix table 1&2 for the computation
From the above table, we accept the null hypothesis of long-run causality running from the independent variable to dependent variable if and only if the coefficient of the lagged Error Correction Term ECTt-j is negative and statistically significant, and we do not reject the
null hypothesis otherwise.
The result has shown that, the birthrate has no cause unto the GDP both in the short-run and long-run, but there is both short-run and long-run causality running from the GDP to birthrate. This shows a unidirectional causality, and it hold true that higher economic growth is among the significant factors leading to higher birthrate in the developing countries particularly Sub-Saharan Africans.
The result also shown that, the death-rate has no cause unto the GDP both in the short and long-run and this is hold true in the economic theory, and confirming the previous researches in the same field. On the other hand, there is short and long-run causality running from GDP to death-rate indicating a unidirectional causality. Meaning that higher economic growth and increase in income per head among the citizens does not automatically reflect the falls in the death-rate rather aggravate it. This prevalence or phenomenon in most developing economies could be attributed to uneven distribution and allocation of increasing income realized from
the growth in the provision of heath and healthcare facilities, and some elements of corruption perpetuated.
Moreover, the result has shown a long-run unidirectional causality running from the migration to the GDP, and the economic theory (migration) also holds true. The influxes of skilled labor, capital and Foreign Direct Investment (FDI) have positive long-run effect onto the GDP not in the short-run.
.
4.4. Estimation from the Old Theoretical Perspective
As it’s clearly stated, the research would employ both the new (Co-integration) method, as well as the old method of panel estimation (fixed/random effect) model.
In order to estimate model from the old theoretical perspective, if Chow test result indicates that the model is panel, to determine the fixed (constant) and random effects, Hauseman test must be used. According to Chow and Hausman test results, the model is of panel type with random effects.
Table 7: Results of Diagnostic Panel Tests Test P-value Statistics Results
Chow 0.0000 13.16 panel
Hausman 0.5330 2.032 random effect
4.5. Random Effect Model Estimation Result
Table 8: Random Effect Model Estimation Result
Log GDP Coefficient Standard Error t-statistics P >|t| Birth rate -0.0861 0.0037 -22.84 0.0000
Death rate -1.0004 0.0002 -1.47 0.3329
Net Migration -1.47e-08 3.47e-08 -0.41 0.0421
Constant 6.50057 0.2199 29.55 0.0000
Probability >F R2 Within R2 Between R2 Overall Wald Chi
0.0000 0.674 0.681 0.795 490.02
Source: Stata Version 9
The results of the above table shows that all model estimate coefficients are significant with the highest level of confidence, and are in line with the research expectations with the exception of the death rate which is not statistically significance in explaining the GDP in most developing countries. So, that a one per-cent increase in Birth-rate and one per-cent increase in the Net migration leads respectively to a -0.0861 per cent and a -1.47e-08 per cent decrease in the log of GDP.
5. Summary Conclusion and Recommendation
5.1 summary and conclusion
Pure time series econometrics tools were used by previous researches in explaining the time dimensions of the implications of the rate of population growth with respect to birth rate, death rate and net migration on GDP growth of most developing nations. However, the present research as stated earlier has resort to panel data econometric analysis so as to take in to account both the time as well as the size or space dimensions of these implications.
The main objective of this study is to estimate by using an econometric model of panel data from a sample of thirty developing countries for the period of fourteen years to empirically analyse the impact of several dimensions of the demographic transition on per capita GDP growth. We observe that the results are more robust when interactive variables of random effect are used to estimate the model. We are able to draw the following conclusions: 1. Based on the research findings, causality test has been used, fixed and random effect of GDP of developing countries relative to birth rate, death rate, and migration are respectively 0.1073, 0.0006, 1.81e0762 and 0.0854, 1.0004, -1.29e-08
2. We find that the birth rate has a negative impact on economic growth, and this finding is robust even after we control for a number of demographic and institutional variables. Our finding provides some new evidence in the developing countries that shows the negative causal effect of population on economic growth in both the short and long-run, as asserted by Malthus.
3. The effect of population growth on per capita GDP growth is linear and everywhere negative. It is stronger when interaction terms are included in the statistical model.
4. The unilateral causality running from GDP to death rate both in the short and long-run shows that despite the tremendous effort made by various government of developing countries for the last three decade to reduce the death rate, shows no evidences of the decline in the death rate in many developing countries. 5. The level of emigration growth has no statistically significant impact or causality
on per capita GDP growth.
6. The panel data econometrics estimation is more powerful in separating the individual effects within, between and overall effect of the group or panel than the pure time series econometrics which provides the time effect only.
5.2 Policy Recommendations
Based on the major and minor findings of this research, however, I recommend the following 1. Governments in developing countries can influence population growth in order to
stimulate growth. China provides a clear example by suddenly introducing a collection of highly coercive methods to reduce the total birth rate reduction policy from about 5.8 to 2.2 births per woman between 1970 and 1980, whereas India policy on the fertility rate rather than the birth rate from about 6.7 to 4.1 per woman, and these policies were put in to social awareness campaign in many other developing countries.
2. Fiscal and monetary policy should not be seen as the utmost instruments of achieving target growth in developing countries, but should be combined with population policy instrument especially in the high birth rate economies such as Niger, Angola, Iraq, etc.
3. There is the need for the full participation in global market by removing the import impediments, and enactment of sound trade policies and security so as to attract influx of both physical and human capital, because of the exist a long-run causality running from the net migration and GDP not on the other way round.
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