Effect of Population Dynamics on Economic Growth in Tanzania: 19902022
Sanga Dismas ^{1}, Dr. Wainyaragania Kennedy Arthur ^{2}, Dr. Warioba Robby Timoth ^{3}
^{1, 2, 3}^{ }St. Augustine University of Tanzania,
Department of Economics, Mwanza, Tanzania

ABSTRACT 

The study analysed the correlation between population dynamics and
economic expansion in Tanzania using time series information covering from
1990  2022. The study was influenced by persistent population growth in the
country. Comprehending this matter will offer a dependable basis for
formulating development policies. The author sought to determine the effect
of crude birth rate, crude death rate and net migration on her economic
growth. VECM was employed for estimation. Stationarity test results
demonstrated that variables became stationary after being differentiated
twice and cointegration test results detected that the variables were
cointegrated at rank 5 which compelled the use of the Vector Error Correction
Model (VECM). The VECM findings showed that the longterm expansion of the
economy is significantly affected by population dynamics. Moreover, crude
birth rate positively affects economic growth while crude death rate and net
migration negatively affect economic growth. The study recommends that the
government should control the outflow of Tanzanians to other countries to
avoid loss of human resources, the government is supposed to pump in more
resources on the health sector to minimize and delay mortalities thus
increasing output growth and the government must ensure that birth rate is
controlled although it is positively related with economic growth, precaution
must be observed. 

Received 02 November 2023 Accepted 03 December
2023 Published 19 December 2023 Corresponding Author Sanga
Dismas, sangadismas9@gmail.com DOI 10.29121/granthaalayah.v11.i11.2023.5396 Funding: This research
received no specific grant from any funding agency in the public, commercial,
or notforprofit sectors. Copyright: © 2023 The
Author(s). This work is licensed under a Creative Commons
Attribution 4.0 International License. With the
license CCBY, authors retain the copyright, allowing anyone to download,
reuse, reprint, modify, distribute, and/or copy their contribution. The work
must be properly attributed to its author. 

Keywords: Population Dynamics, Economic Growth,
VECM, Tanzania 
1. INTRODUCTION
Tanzania's
population stood at approximately 61.7 million people in 2022, up from 44.9
million in 2012. The country has witnessed significant
population growth over the past decade, boasting an average annual growth rate
of 3.2 percent between 2012 and 2022 NBS (2022). However,
future projections, such as those for the years 2044 (123.5 million people) and
2050 (151.3 million people) NBS (2022). The trend emphasise the urgency of
addressing population growth. This substantial population not only
complicates planning for national development but also poses serious risks to
the overall economic growth of the nation. Consequently, population dynamics
have raised concerns regarding their impact on the country's economic growth.
Similar
to many emerging economies globally, Tanzania has implemented various economic
policies to improve the quality of life for its citizens and ensure sustained
economic growth and development. With abundant natural resources such as coal,
natural gas, gold, timber, and more, along with a substantial population,
Tanzania ranks sixth in Africa by population size, according to the World Bank (2014). However, any
significant economic growth plan must carefully consider population factors,
particularly for a nation with such a sizable population.
The
famous work publication "An Essay on the Principle of Population" Malthus (1978) is credited
with demonstrating the connection between population growth and economic
expansion. Aidi and Ngwudiobu
(2016). The
population of Tanzania has been steadily increasing throughout the years.
However, evidence from the Bank of Tanzania. (2022) revealed that
economic growth has been fluctuating over time. For example, in 2019 and 2022
economic growth rate was 7 and 4.8 percent respectively. According to World
Bank, the birth rate steadily decreased from 49 births to 35 births per 1000
population between 1990 to 2022 and the death rate has relatively decreased
from 19 deaths to 6 deaths per 1000 population. Perhaps as a result of nations
improving their total medical services, natality and mortality rates have
started to drop. Additionally, there is an uneven migration trend, with both
entry and emigration of individuals from the nation. Drinkwater et al. (2003) argued that
because motivated and educated individuals are typically more likely to
relocate in quest of chances, migration may drain away prized abilities. It is
estimated that as of 2000 about 215.5 thousand Tanzania people dwelled and
worked in other countries and the number of emigrants progressively increased
to over 327.8 thousand in 2020 who could have contributed to the development of
Tanzania but their knowledge and intelligence are being used to contribute to
the growth of other nations.
The
Asian Development Bank (1997) asserts that
several factors contribute to economic growth, including trade policy,
investments, capital accumulation, health and education spending, savings
within the country, and technological breakthroughs. Nevertheless, demographics
have emerged as a major factor in determining economic growth, according to
economists Bloom and Sevilla (2001).
Klasen and Lawson (2007) argues for
negative impact. According to Savas. (2008), population
dynamics have a favourable impact on economic expansion. On the other hand,
some academics assert that there is no relationship between population dynamics
and economic growth Liddle (2003). Similarly
titled studies carried out in Tanzania have yielded varying conclusions. For
instance, Loiboo and Osoro (2021) who employed
VAR, discovered a strong and positive relationship between population growth
and economic growth throughout the analysis, while Ishumael and Akarro (2022), who applied
ARDL, discovered a Tanzania's population growth and economic growth are
negatively correlated.
Population dynamics is important to be
studied for promoting sustainable economic growth and development and improving
the quality of life for Tanzanians. Based on the trend of population dynamics in
Tanzania particularly for the period of 10 years back, there
is an increase of more than 16.8 million people from
44.9 to 61.7 million people NBS (2022). This raises an
important question: To what degree do population dynamics impact economic
growth?
The Tanzanian population has been increasing to an alarming threshold.
This is a very telling statistical reality if left unchecked over time it can
constrain socioeconomic growth sustainability depending on the government’s
policy in place. The implications can be persistent increase of unemployment low
life expectancy due to poverty rate caused by low per capita income and
decrease of the natural resources. Moreover, social service provisions like
health, education and clean water supply may be jeopardised by too huge
population. It is in this regard therefore; By
deploying Vector Error Correction Model, the current study aimed
to analyse the effect of population dynamics on economic growth in Tanzania
between 1990  2022.
1.1. Main Objective
The
study aimed to analyse the effect of population dynamics on economic growth in
Tanzania.
1.2. Specific Objectives
1)
To determine the effect of crude birth rate on economic growth in
Tanzania
2)
To find out the effect of crude death rate on economic growth in
Tanzania
3)
To estimate the effect of net migration on
economic growth in Tanzania
1.3. Research Hypothesis
In
light of the specific objectives and the statement of the problem, the study
intended to test the following null hypothesis;
1)
Ho: There is no significant link between the crude birth rate and
economic growth in Tanzania
2)
Ho: There is no significant link between the crude death rate and
economic growth in Tanzania
3)
Ho: There is no significant link between the net migration and
economic growth in Tanzania
2. LITERATURE REVIEW
This
study reviewed two theoretical learning which were Malthus's theory (classical
and Solow’s growth theory (neoclassical). According to Malthus's theory, the
economic growth of a nation may decline with an increase in population and
limited resources. Food would expand at an arithmetic pace (for example, 1, 2,
3, 4... th) due to significantly diminishing returns
to increasing scarce land, whereas population would grow at a geometric rate
(i.e., 2, 4, 8, 16... th) mostly due to lack of
conscious restrictions on fertility (Malthus, 1798). Starvation, fatalities,
and a shortage of food would be the result.
The
neoclassical growth model Solow (1956) concurred that rapid technological advancement promotes economic
expansion and advancement. Labour and capital are not
the only factors contributing to economic progress. Solow posits that economic
growth is contingent upon three factors: advancements in technology, capital
accumulation, and labour growth.
Hakeem and Ikenna (2016) Using timeseries data covering the years 1970–2014, examined the
connection between Nigeria's economic expansion and population demographics.
The data analysis method employed was the OLS estimation methodology. The
findings show that during the studied period, net migration, fertility, and
mortality had a negative relationship with economic growth. The analysis goes
on to show that domestic savings and total stationary capital creation are
significant contributors to Nigeria's economic expansion. The report finds that
immediate action should be taken by the Nigerian government to reduce the
worrying fertility rate. To raise productivity in Nigeria, more money should be
invested in education and skillbuilding initiatives to enhance the caliber of
the country's labor force.
Degu (2020) analysed the
correlation between economic growth and population increase in Ethiopia, second^{
}populated country in Africa devoted time to studying disagreement among
economists and academics regarding how population increase and economic
expansion interact in Ethiopia, second populated country in Africa Using annual
time series data from 1981 to 2018 with estimation techniques namely unit root tests (the Augmented Dickey Fuller,
PhillipsPerron) ARDL, Cointegration and TodaYamamoto Causality tests approaches
are used to study how population
increase and economic expansion interact. Results show that, the bound test
cointegration approach demonstrates a sustained partnership between population
increase and economic expansion meanwhile estimations from the ARDL model show
population dynamics have a short and longterm negative and considerable
impact on economic growth. It is therefore suggested to the relevant body that antinatal policies, which discourage the fertility
rate, be reconsidered to supplement with policies for economic growth.
Rizk (2018) “Does demographic
transition matter for economic growth”? was studied in the economy of Egypt
third^{ }populated country in Africa with population of working age,
Domestic saving, tertiary enrolment, trade openness and GDP per capita as
independent variables with economic growth being the dependent variable. An
expanded exogenous SolowSwan growth model was used along with a time series
framework from 1971 to 2015, estimation technique was multivariate
cointegration analysis. One of the main findings was that, both in the short
and long term, the rise of the workingage population was beneficial and
stimulated GDP per capita. For Egypt to benefit from the demographic dividend,
it is recommended therefore, the age structure of the population must be
prioritised.
Akintunde and Oladeji (2013) conducted a
study about how death and fertility have affected SubSaharan African economic
growth between 1970 and 2005. In contrast to other parts of the world,
SubSaharan Africa has a peculiar combination of rapid population increase and
slow economic growth. The study used dynamic panel data analysis and pooled
OLS. The findings indicate that while There was a benefit to the crude birth
death rate on economic growth, the total fertility rate had a negative impact.
If the region wants to have sustained economic progress, it must address the
high rate of population dynamics, the study concluded.
Loiboo and Osoro (2021) employed vector
autoregression (VAR) to analyse annual time series data from 1971 to 2017 to
test the following theories regarding the connection between economic expansion
and population growth: the first theory maintains that population dynamics promotes
economic growth, the second theory maintains that population dynamics hurts
economic growth, and the third school of thought maintains that population
growth is unrelated to economic growth and is determined in ways other than
those predicted by standard conventional growth models.
Results indicate that economic shocks due to
population dynamics are positive and negative. Moreover, there are both short
and extended correlations among population increase and economic expansion. The
study recommends a methodically planned population dynamics strategy as well as
the institutional and policy improvements that will ensure that the population
and economy are enhancing each other without worries that the country’s
population dynamics will cause famines and a lack of further socioeconomic resources.
The government must also make sure that economic growth outpaces population
growth. By doing this, it will be possible to meet the expanding demand for
services brought on by population dynamics. Economic benefits from having a
larger, healthier, and bettereducated workforce will only materialize if the
additional people can find employment. Countries may be able to benefit from
their demographic shift if they have open economies, adaptable labour markets,
and modern institutions that can win over the public's confidence.
Ishumael and Akarro (2022) investigated the causal association
among the population dynamics and expansion of the economy between 1980 – 2019
by applying time series information on an annual basis obtained from the World
Bank. The Granger causality test and the cointegration test were used to
determine whether the dynamics of population increase and economic growth are
correlated over the short or long term. The findings indicate that whereas
other parameters including population dynamics, total capital creation,
government spending, rate of fertility, crude death rate, dependence
percentage, and an increase of foreign direct investment hurt economic growth,
trade openness has a positive effect.
The only variable that is correlated with the dependent variable both
over the long and short terms is trade openness, the findings showed that there
is no longterm association between the variables. Thus, population dynamics
have a detrimental impact on Tanzania's economic expansion. As stated by
study's findings, the report suggests that to reduce the dynamics of population
increase, the government should be directed to give family planning policies
top priority. Increasing trade openness by creating opportunities both
domestically and internationally will facilitate the flow of resources more
efficiently and increase the availability of goods and services.
Research Gap
The variable
gap, to the best knowledge of the researcher of this study, none of the studies
on similar topics in the Tanzanian space has included net migration. Therefore, the study was planned to
bridge the identified gap in the literature about the impact of population dynamics on economic expansion in Tanzania.
3. METHODOLOGY OF THE STUDY
The study sought to determine how population dynamics (captured by crude birth rate, crude death rate and net migration)
relate to economic growth (using gross domestic product as a proxy) using data
spanning from 1990  2022. The control variables included in this study were trade openness, foreign direct investment (movement of
people cause population fluctuations) and savings is incorporated as an underlying cause of GDP. The study applied Vector Error Correction Model. Data were analysed by
using STATA a computer software for data analysis. The
researcher used a quantitative approach that made it possible to apply
econometric statistical techniques.
Since the study used time series data, a
quantitative correlational research design was appropriate for this study that
enabled to apply mathematical theories, models and hypotheses to describe the
situation under study. In addition, ethical considerations were observed such
as providing relevant citations Kothari (2004).
Neoclassical growth theory served as the
study's compass Solow (1956). This is because, according to Akintunde and Oladeji (2013) the theory provides a thorough examination of demographic variables in
the analysis of growth in any economy. Neoclassical economists hold the view
that positive economic outcomes and population increase are tied to
technological advancements. Moreover, the theory maintains that there are two
methods to enhance total output: either by increasing savings or by slowing
down the rate of population growth. Following Framework
was established to conceptualise the interrelationship between dependent and
independent variables.
Where:
Y =
(GDP)
L = the quantity of workers
K =
capital reserve and
P =
factor of productivity (i.e., exogenously determine
the degree of technological advancement).
The
study used the neoclassical growth model in which the vector of all other
variables that can affect economic growth, as well as the vector of variables
that are used to represent population dynamics, or the forces that influence
population change are included as a component of the demographic factor (P) as
well as the control variable (C) correspondingly. The total work force is
removed from the model because there is no trustworthy data for total labour in
Tanzania for the time frame being examined. Once more, take note that GDP and
domestic savings are employed as standins for economic growth and capital
reserve. As a result, equation 1 is reformulated as follows:
GDP=f (P,
DOMESTIC SAVINGS, C) ………………………….……………………………….……………2
Therefore,
equations 3 and 4 define the population component (P) and control variable (C)
correspondingly:
P=f (CBR, CDR, NM) …….………………………………………………………………. ….…………….…….3
C=f (OT, FDI) ………………………………………………………………………………….………...…………4
Domestic
savings was a control variable since it is an underlying cause of GDP also
openness to trade and foreign direct investment involves crossborder movement
of people that cause dynamics in population size.
Where;
CBR=crude
birth rate (live births per 1000 people as its proxy)
CDR
=crude death rate (mortality rate per 1000 people as its proxy)
NM=net
migration (defined as immigration minus emigration as its proxy)
DS=domestic
savings (a structural variable) total domestic saving in the economy percent of
GDP. It serves as a proxy of national capital reserve used for the productivity
of output
OT
= openness to trade
FDI=foreign
direct investment
GDP=f
(CBR, CDR, NM, DS, OT, FDI)
……………………………….………………………………...….5
Gujarati and Porter (2009) advise the use
of a loglinear model in determining every economic variable's growth rate. By
this advice, equation 5 was written as follows in log form:
lgGDP = f (lgCBR, lgCDR, lgNM, lgDS,
lgOT, lgFDI) ….……………………………………………...6
Equation
6's econometric form was written as the following equation 7 (noting that all
variable measurements are in rates hence, natural Logarithms are not applied).
GDP_{t}_{ }=
β_{o }+ β_{1}CBR_{t }– β_{2} CDR_{t}_{ }– β_{3}NM_{t}
+ β_{4}DS_{t }+ β_{5}OT_{t }+ β_{6}FDI_{t
}+ µ_{t }……………………………7
To
establish the link between population dynamics and economic growth, the above
(equation 7) was estimated using Vector Error Correction Model. 0.05 is the choice level of significance for all tests
because is relevant and most appropriate for social science studies.
4. FINDINGS AND DISCUSSION
4.1. Stationarity Test
A stationarity
test was performed for each variable used in the analysis as a necessary step
to prevent erroneous
regression. It
should be possible to ascertain from this test whether or not these variables'
variance and mean values
change over
time. In this investigation, the widely used Augmented DickeyFuller (ADF) test
*was utilized.
Findings
indicated that
the variables needed to be differentiated twice to become stationary because
they were not
stationary at
the level and the first difference. The hypotheses tested were;
Ho:
The variables are not stationary
H_{1}:
The variables are stationary
Decision
Rule:
The variables are statistically significant (i.e., stationary) at the 5
percent significance level since the tvalue (in absolute terms) of 2.986 is
more than 2 and the MacKinnon approximation pvalue for z(t) 0.0000 is lower
than 0.05. As a result, the null hypothesis was rejected.
Table 1
Table 1 Results of Stationarity Test (at Second
Difference) 

Variable 
Test statistic 
Critical value 
P value Z(t) 

GDP 
10.910 
2.986 
0.0000 
I (2) 
CBR 
5.286 
0.0000 
I (2) 

CDR 
5.302 
0.0000 
I (2) 

NM 
5.299 
2.986 
0.0000 

DS 
2.986 

OT 
7.161 
2.986 
0.0000 
I (2) 
FDI 
12.328 
2.986 
0.0000 
I (2) 
Critical Value 2.989 at 5% 




Source Author (2023) 



Table 1 shows that all
the variables became stationary after being differentiated twice since values
in the test statistic for each variable were greater than the critical value
(2.986) and the pvalue for all the variables was less than 0.05 level of
significance implying that all the variables were stationary.
4.2. Test of Cointegration
Two
order integration of the variables raises sufficient doubts about
cointegration. If two or more variables have an equilibrium or longterm
relationship, they are said to be cointegrated Engle and Granger (1987). When two
variables are cointegrated, the stochastic trend between the two series is
cancelled out by their liner combination so, the regression of the two
variables is meaningful and not erroneous Gujarati (2004). The hypotheses
tested were;
Ho:
There is no sustained correlation between the independent and dependent
variables.
H_{1}:
There is sustained longterm correlation between the independent and dependent
variables
Decision
rule:
If an asterisk sign (*) appears in the column of the trace statistic shows that
the regressant cointegrates with the regressors
Table 2
Table 2 Results of
Johansen Cointegration Test 




Trace 
5% critical 

Rank 
Parm 
LL 
Eigenvalue 
Statistic 
value 
0 
56 
165.86359 
. 
216.7558 
124.24 
1 
69 
135.38693 
0.86002 
155.8025 
94.15 
2 
80 
107.74482 
0.83193 
100.5183 
68.52 
3 
89 
85.762638 
0.75785 
56.5539 
47.21 
4 
96 
73.107325 
0.55801 
31.2433 
29.68 
5 
101 
62.19758 
0.50533 
9.4238* 
15.41 
6 
104 
57.677285 
0.25296 
0.3832 
3.76 
7 
105 
57.485686 
0.01229 


Note: At the 5% significance level, an asterisk * denotes the null
hypothesis' rejection. Source: Author (2023) 
Table 2 demonstrates
the rejection of the null hypothesis (Ho), which states that "there is no
cointegrating equation," and suggests the existence of five cointegration
equations, as shown by the asterisk (*) in the trace statistic (9.4238*), which
uses five lag lengths at the critical value (15.41) at the five percent
significance level. Because of their stationarity, the dependent and
independent variables can only have short and longterm associations
established by running a Vector Error Correction Model.
Table 3
Table 3 VECM Speed of Adjustment Results 

Sample: 1993  2022 



No. of obs 
= 30 

Log likelihood = 11.08188 



AIC 
=
7.127875 

Det (Sigma_ml) =1.13e09 



HQIC 
= 8.891011 





SBIC 
= 12.63925 


Coef. 
Std. Err. 
Z 
P>z 
[95%
Conf. Interval] 

D_gdp_ce1 L1. 
1.216244 
0.3101737 
3.92 
0.000 
1.824173 
.6083143 
Source: Author (2023) 
Table 3 shows that the
VECM (cel Ll) indicated a
negative coefficient of (1.216244), which is greater than 1 and is
statistically significant (0.000) at the 5 percent significance level implying
that dependent and independent variables oscillatoraly
adjusts to steady state equilibrium. The negative sign shows the presence of
the speed of adjustment of dependent and independent variables towards
equilibrium.
Table
4
VECM Longrun Johansen
normalization Estimates Results 

Coef. 
Std. Err. 
Z 
P>z 
[95% 
Conf. Interval] 

_ce1 GDP 
1 
. 
. 
. 
. 
. 
cbr 
1.32487 
.0299647 
44.21 
0.000 
1.383599 
1.26614 
cdr 
.5234815 
.0352219 
14.86 
0.000 
.4544478 
.5925151 
nm 
1.162132 
.0251716 
46.17 
0.000 
1.211468 
1.112797 
ds 
.1294 
.0069844 
18.53 
0.000 
.1430891 
.1157109 
ot 
.2157551 
.0026401 
81.72 
0.000 
.2105807 
.2209296 
fdi 
.6606158 
.0192197 
34.37 
0.000 
.6982858 
.6229458 
cons 
. 
. 
. 
. 
. 

Source: Author (2023) 
Table 4 shows that in
the longterm population dynamic significantly affect economic growth since
their corresponding pvalues (0.000) are less than 0.05 level of significance
but shortterm estimates were statistically insignificant.
4.3. Regression Result and Interpretation
Equation
7, which estimates our model will yield a dependable and constant result rather
than a spurious one since the cointegration test results proved that there was
of a longterm correlation among the regressant and
regressors of this research Gujarati and Porter (2009). The following
were the regression's results:
Table 5
Table 5 Synopsis of Findings for the Study's Estimated Model (Equation 7) Regressant GDP 

GDP 
Coef. 
Std. Err. 
t 
P> t  
[95% Conf. 
Interval] 
CONS 
22.34147 
10.30963 
2.17 
0.040 
43.53322 
1.149725 
CBR 
1.102733 
.33077 
3.33 
0.003 
.4228257 
1.782641 
CDR 
.3268072 
0.001 
1.852328 
.5088045 

NM 
.7027019 
.3152406 
2.23 
0.035 
1.350688 
.0547155 
DS 
.1012689 
.0827954 
1.22 
0.232 
.2714573 
.0689196 
OT 
.0308226 
1.33 
0.195 
.1043944 
.022319 

FDI 
.1*309448 
.260265 
0.50 
0.619 
.6659271 
.4040375 
Prob > F = 0.0000 RSquared = 0.6956 Adj Rsquared = 0.6254 Root MSE = 1.2146 Source: Author
(2023) 
Estimated
Empirical Model
GDP = 22.34147+1.102733CBR1.180566 CDR.7027019NM.1012689DS.0410377OT.1309448FDI
Std.
Err. (10.310) (0.331) (0.327) (0.315) (0.083) (0.031) (0.260)
pvalue (0.040)
(0.003) (0.001) (0.035) (0.232) (0.195) (0.619)
Constant: When the values of all explanatory variables are zero, the
intercept term clarifies or forecasts the value of the regressant
(GDP). There is no clear (meaningful) economic significance to be inferred from
the intercept term's coefficient (22.34147) which is negative and
statistically significant (tstatistics of 2.17 and Pvalue of 0.040).
Crude Birth
Rate: Considering
tvale (3.33) which is greater than 2 and pvalue (0.003) which is less than
0.05. The output showed that the intercept of this variable (1.102733)
is positive and statistically significant. It suggests that, if all other
variables remain constant, a one percent rise in the crude birth rate will on
average boost economic expansion by about 1.1 percent. This validates the results of Ogunbadejo and Zubair (2021) and conforms
to the prior sign of expectation.
Crude Death
Rate: It was
discovered that the mortality rate was statistically significant and correlated
adversely with GDP, the regressant. The Pvalue,
tstatistics, and coefficient are, in that order, 0.001, 3.61, and 1.1800566.
It can be deduced that a one percent increase in the death rate is anticipated
to result in an average reduction of 1.18 percent in GDP while keeping all
other factors fixed. Ishumael and Akarro (2022) found similar
results in Tanzania. Also, Turtiyus and Peter (2015) got the same
results in Nigeria.
Net migration: The intercept, tstatistics and Pvalue of 0.7027019, 2.23 and
0.035 correspondingly demonstrated that, when it comes to the study of economic
growth, net migration is a statistically relevant variable. Well, it is also
important to remember that the variable's inverse relationship with economic
growth is explained by the negative coefficient, as shown. Consequently,
assuming all other factors remain equal, GDP is predicted to decline by 0.7
percent for every 1 percent rise in net movement. This outcome is especially
consistent with the study of Hakeem and Ikenna (2016).
Domestic
savings: As can be seen
from the above table, the intercept of this variable is 0.1012689; the Pvalue
is 0.232, and the tstatistics are 1.22. This variable is statistically
unimportant (as seen by the Pvalue and tstatistics). This implies that
domestic savings is not a strong driver of economic growth in Tanzania.
Openness to
Trade: Openness to
trade and the dependent variable (GDP) have a negative connection, as
demonstrated by the minus sign of the coefficient (0.0410377) for this
variable. This suggests that the economy of Tanzania would have benefited
economically from the lifting of trade barriers, but the Pvalue (0.195) and
the tstatistics (1.33) demonstrate that the variable has no statistically
significant impact on economic growth in Tanzania.
Foreign Direct
Investment: The sign of the
intercept (0.1309448) demonstrates that it is negatively connected with regressant (GDP). The tstatistics (0.50) and the Pvalue
(0.619) show that the variable is statistically insignificant in the model.
Thus, foreign direct investment does not trigger economic growth in Tanzania.
RSquared: Based on the outcome that the goodness of fit metric is 0.6981
(i.e., 70 percent). The proportion of the regression of variability that is
impacted by the regressors is explained by the Rsquared. According to this
model, the regressors (CBR, CDR, NM, DS, OT, and FDI) account for about 70
percent of the variation in GDP with other factors which cause GDP growth
accounting for the remaining 30 percent.
FStatistics
(Ftest): The likelihood
value of the Ftest, also known as the Fstatistics, allows us to evaluate if
the model as a whole is statistically significant. The table's likelihood value
(i.e., Prob of Fstat) from the table is 0.0000. Given that the probability of
the Fstatistic is less than 0.05 percent, this suggests that the model is
statistically significant.
5. CONCLUSION AND RECOMMENDATIONS
The
primary goal of the research was to analyse the effect of population dynamics
on economic expansion in Tanzania. According to findings, population dynamics
had a significant longterm correlation with economic expansion. However, in
the short run crude birth rate, crude death rate and net migration were
statistically insignificant. The regression analysis revealed that the crude
birth rate positively affects economic growth which means an increase in the
number of births caused economic growth to increase while the crude death rate
and net migration were negatively affecting economic growth which implies that
an increase in crude death rate and net migration decreased economic growth in
Tanzania.
Generally,
the null hypothesis which stated that population dynamics do not affect
economic growth was rejected, meaning that in the long run, changes in the
headcount of individuals affect economic growth in Tanzania. These results are
locked up together with the neoclassical growth theories which believe that
population increase has a positive impact on economic expansion Solow (1956). Meanwhile,
the results contradict classical theories which believed increased population
is detrimental to economic growth Malthus (1798).
Aligning the results, the recommendations that follow are based on this study.
Since encouraging a high birth rate can lead to
negative outcomes like competition for resource extraction, usage and
unemployment challenges, a precaution must be observed, the government must
ensure that the birth rate is controlled although it is positively related to
economic growth. The
government is supposed to allocate more resources to the health sector to
minimize and delay mortalities to increase productivity as results have
confirmed that death rates significantly reduce economic growth. Appropriate
measures are to be enforced to control the outflow of Tanzanians to other
countries to avoid loss of human resources like engineers and doctors among
others which drags back output growth and provision of quality service within
the country.
Finally,
future studies should also focus on other econometric tests such as Machine
Learning Techniques and Bayesian Modelling among others.
CONFLICT OF INTERESTS
None.
ACKNOWLEDGMENTS
None.
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