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A Reassessment of the Fertility RatePoverty Rate Nexus: Evidence from Nigeria
 Oru Patricia Oghenekevwe
 11821198
 May 8, 2024
 Economics
A Reassessment of the Fertility RatePoverty Rate Nexus: Evidence from Nigeria
Oru Patricia Oghenekevwe
Institution: Department of Economics, School of Secondary Education (Business), Federal College of Education (Technical), Asaba, Delta State, Nigeria
DOI: https://dx.doi.org/10.47772/IJRISS.2024.804090
Received: 11 March 2024; Accepted: 04 April 2024; Published: 08 May 2024
ABSTRACT
This research aimed to uncover the connection between two variables specifically within Nigeria. Using data from 1980 to 2016 and a modified Solow model, researchers identified a clear positive relationship between high fertility rates and poverty levels in the country. The study indicates that the causality primarily stems from fertility rates leading to poverty, emphasizing the necessity for interventions to address these challenges. The absence of social safety nets and support programs in Nigeria contributes to larger family sizes, resulting in inadequate healthcare, limited educational opportunities, and restricted economic prospects. These results highlight the critical need to revisit and strengthen the National Population Policy to effectively tackle these issues, emphasizing the essential role of government leadership in driving these muchneeded reforms.
The correlation between the main variables was established using the SVAR and innovation accounting method. The findings reveal a significant and strong positive correlation between poverty and fertility rate, with this correlation surpassing that of other variables examined. The study suggests that this may be attributed to the lack of social safety nets and family support programs in Nigeria, leading to the consequences of larger family sizes, including inadequate healthcare, limited access to education, lack of economic opportunities, and restricted social mobility. Based on these empirical findings, an immediate reassessment and revitalization of the National Population Policy (NPP) are warranted, with strong leadership from the Federal Government required from both the Executive and the National Assembly.
Keywords: Fertility Rate, Poverty Rate, Population Growth, SVAR, Solow Growth Model
INTRODUCTION
Scholastics, arrangement creators, devout educators, as well as third division associations have been debating on the causal relationship between ripeness rate and destitution, without any frame of agreement or closure in seeing. The wrangle about has centred around the questions of how the number of children in a family lessens the family’s show wellbeing and future prospects, whether destitution contributes to tall richness, and in case tall ripeness rate may be a indication, instead of a cause of destitution. This talk about, which happens at both the large scale and the microlevels, are approximately the linkage and heading of causality.
Despite these talks, a watched slant in a few created nations of Europe, and Central Asia, appears that over time, as incomes rise, richness tends to drop. In other words, there’s not much wrangling about the presence of a relationship between progressed living conditions and lower ripeness. Where talk about remains dynamic and at times very disagreeable must do with the course of causality —i.e. “Does decrease ripeness make strides the financial prospects of families and societies?” or “Does the advancement of the financial status of families and social orders lead to diminished fertility?”
In order to determine whether lower ripeness rates—and particularly, open approaches intended to lower richness rates—can result in improved living standards and higher livelihoods, or whether concentrating on improving living standards will undoubtedly result in lower ripeness rates—this paper contextualizes these issues to Nigeria and attempts to address the heading of causality.
The goal of this paper is to determine the kind of interaction (direction of causality) that exists between the ripeness rate and the destitution rate and to experimentally illustrate a specific state of such in Nigeria. Of course, a great deal of research has been done on this topic. The goal is to first ascertain what conclusions policymakers might draw from Nigeria’s engagement, and then to ascertain. The reason is to undertake to distinguish what policymakers can conclude from Nigeria’s involvement, and after that to distinguish the tall affect approaches that might ensure Nigeria reaps maximum advantage from the rising statistic reward.
Statement of Problem
The enduringly high fertility rate poses a greater risk to reducing poverty in many African nations compared to the impact of HIV/AIDS (UNFPA, 2016). Failure to address this issue could undermine poverty alleviation efforts undertaken by governments, civil society, and aid organizations, potentially endangering the longterm economic growth prospects of these countries. Based on the World Bank Global Development Report 2015/2016, extreme poverty persists at alarming levels, with approximately 700 million individuals – onetenth of the global population – living on less than $1.90.SubSaharan Africa harbors the majority of profoundly impoverished populations, with Nigeria ranking among the countries with the highest number of impoverished individuals.
According to the 2016 poverty report from the National Bureau of Statistics (NBS), around 112 million Nigerians, constitute 67%. A noticeable trend associated with the high poverty rates in SubSaharan Africa is the rapid population growth surpassing advancements in economic development (UNFPA, 2014; Pew Research Center, 2015). of the 30 countries projected to have the most rapid population growth between 2015 and 2050, 29 are situated in SubSaharan Africa, with 13 of these countries currently exhibiting total fertility rates of 5.
Collectively, these nations are expected to contribute to 20% of the global population growth during the same period, with Nigeria alone accounting for 2% of the total increase. Nigeria boasts one of the highest fertility rates globally, ranking 13th out of 223 countries (CIA World Factbook, 2016), with little indication of decline.
These statistics underscore two critical issues: the country’s population, bolstered by the high fertility rate, is expanding faster than the resources available to support them, and a significant number of women and children continue to succumb due to complications during childbirth, with inadequate family planning identified as a major contributor (Akanni et al.2015)
With the observed dearth in studies that deal with the macroeconomic effects of high fertility rates in Nigeria, this study develops from previous studies in other jurisdictions, but with evidence from Nigeria. Specifically, this study improves on those work in two key dimensions. First, it will determine the direction of causality between fertility and poverty as a basis for addressing the poverty dilemma facing the country, and then ground estimate of the magnitudes of the impact of poverty on fertility rate (and vice versa) on advanced econometric methodology with an improved theoretical framework (see Weil, (2008) and Coleman and Rowthorn (2011) for base framework developed upon).
LITERATURE REVIEW
This section presents a review of pieces of the literature to put the study in context. The review covers empirical findings.
Empirical Literature
There is a noticeable lack of literature discussing the connection between the fertility rate and poverty in Nigeria. Historically, the absence of access to modern contraceptives led to the belief that managing fertility was costly and perpetuated vicious cycles of high fertility and poverty (Ehrlich and Holdren 1971, Ehrlich and Ehrlich 1990). However, the conviction regarding the role of fertility rate in reducing poverty often surpasses the supporting evidence. Challenges arise due to the complexity of where family planning programs are implemented, primarily influenced by the demand for children, making it difficult to conduct thorough evaluations (Pritchett 1994, Schultz 1994 and 2005). Despite these difficulties, the few existing studies warrant examination.
Cleland, Bernstein, Ezeh, Faundes, Glasier, and Innis (2006) in their sexual and reproductive world survey, discovered that encouraging family planning in nations with high birth rates can potentially prevent 32% of all maternal deaths and almost 10% of child deaths, as well as alleviate poverty and hunger. They continued by saying that it would also make a significant contribution to longterm environmental sustainability, the accomplishment of universal primary education, and the empowerment of women.
Kamphuis (2012) investigated China`s family planning policies and their economic consequences since the 1970s. His research indicates that declining fertility rates initially coincide with increased income per capita but later result in rapid population aging, declining workingage population ratios, and ultimately lead to increased poverty rates in China.
Asogwa and Ugwunta (2013) identified uncontrolled fertility as a significant factor driving population growth in Nigeria and established its correlation with poverty. They concluded that high fertility rates contribute to population growth and negatively impact per capita income, leading to high poverty rates, inadequate housing, poor sanitation, low quality of life, high unemployment, inflation, and excessive strain on resources.
Ashraf, Weil, and Wilde (2013) quantitatively studied the impact of fertility reduction on output per capita, affirming that lowering fertility rates by transitioning to a lower projection can significantly increase output per capita.
Aidi, Emecheta, Chisom, and Ikenna (2016) explored the intertwined relationship between population dynamics and economic growth in Nigeria from 1970 to 2014, revealing an inverse correlation between core demographic variables and economic growth, stressing the need for the Nigerian government to address high fertility rates.
Karra, Canning, and Wilde (2017) used a demographic–economic macrosimulation model to estimate the impact of a decline in fertility on economic growth in Nigeria. They improved on previous modeling approaches by including four previously disregarded channels: the impact of fertility on savings; the relationship between education and fertility; the impact of fertility on health; and the impact of a more realistic threesector model that takes into account market imperfections, which are common in developing nations. According to their model, a decrease in fertility will have more beneficial consequences and accelerate economic growth. Using the simulation exercise, they deduce that these hitherto disregarded routes could potentially have greater significance than the conventional channels that have been contemplated up to this point. They contended that reduced fertility over time will lower fertility and increase female education, which in turn lowers fertility in the next generation and produces a multiplier effect from any initial change in fertility.
One critical question persists – the direction of causality. While previous studies(see Ademoju, Alemide, Ibekwe, Nweke, Ogunwole, and Waziri, 2000; Caldwell, and Wane, 2002; Adeleye, and Adeleye, 2003; Okediji, 2003; Anyanwu, Ezegbe, Eskey, 2013)focused mainly on microeconomic analyses and family planning methods, few macroeconomic studies have empirically examined the interrelationship between fertility rates and poverty levels in Nigeria. This research seeks to bridge this gap by scrutinizing the dynamics between these variables within a Structural Vector Autoregressive (SVAR) framework, allowing for a comprehensive analysis of their mutual influence. This type of exercise requires a Structural Vector Autoregressive (SVAR) framework, which addresses the identification problem associated with the regression of simultaneous equation models and models all endogenous variables simultaneously rather than one equation at a time (Watson, 1994; Lutkepohl, 2005, 2012).
THEORETICAL FRAMEWORK AND RESEARCH METHODOLOGY
Theoretical Framework
The fertility rate has a direct impact on the rate of growth, which in turn influences both the capacity and consumption needs of an economy. This relationship is elucidated in a modified version of the Solow Model, chosen for its ability to analyze the economic repercussions of a decline in population growth resulting from a decrease in fertility rates. The Solow model, a straightforward framework based on a CobbDouglas production function, primarily focuses on the level of income per capita determined by the amount of capital available per worker.
The Solow Model with Population Growth
In addition to capital depreciation and investment in capital stocks, population growth also interacts with the amount of capital per worker in the economy. Positive population growth, all else equal, will lead to a decline in the level of capital available per worker.
Furthermore, this will then lead to reduced production output per worker. The negative relationship between positive population growth and the decline in capital per worker is called capital dilution.
A country with high population growth must invest most of its output in new capital to keep the level of capital per worker constant due to capital dilution. The effects of depreciation, population growth, and investment on capital accumulation are then discussed.
Theoretical Analysis of the Solow Model
This section will make use of a CobbDouglas production function with input factors capital (K) and labour (L), and including a parameter for the level of productivity (A):
Y = AKαL(1α) (1)
The parameter α in the production function measures the share of capital in National Income. The analysis starts with rewriting the CobbDouglas production function in perworker terms, dividing the equation (1) by L.
y = A (K/L) α (L/L) (1α) = Akα = f(k) (2)
Due to the fact that the labour term in the perworker production function does not change, output per worker can be written as a function of capital perworker.
First, the fundamental process of capital accumulation in terms of workers will be the main emphasis, with population increase being ignored. Along with the premise that a constant portion, γ, of output per worker is invested in capital each period, a constant fraction, δ, of the current capital stock per worker is assumed to decay. This results in a function that represents how the capital per worker changes over time under the combined effects of investment and depreciation:
∆k= γ f(k) – δk (3)
Secondly, this basic function of the accumulation of capital per worker can be extended by adding the negative effect of population growth on capital per worker to equation (3). This capital dilution works in the exact same way as depreciation. Labour force growth is assumed to be equal to the level of population growth and is measured by n:
∆k= γ f(k) – δk – nk = γ f(k) – (δ + n) k (4)
“Parameter n is influenced by both fertility and mortality rates. In this paper’s theoretical analysis, it is assumed that mortality rates remain constant, meaning that parameter n is solely impacted by fertility rates. This assumption is considered valid when evaluating the economic consequences of a decline in population growth during the final two stages of the demographic transition, where mortality rates are deemed stable, as illustrated in Figure 3. The capital stock perworker rises when investment exceeds the combined negative impact of depreciation and capital dilution. Conversely, the capital stock perworker decreases when investment falls short of the combined effects of depreciation and population growth.”.
A steadystate stock of capital perworker will be reached if the capital stock does not change, in other words; if equation (4) equals zero. This would imply that the investment in the capital stock has to be equal to the combined negative effect of depreciation and population growth on the capital stock perworker:
γ f(k) = (δ + n) k (5)
Rewriting equation (5) leads to the steadystate stock of capital perworker, ksteadystate:
ksteadystate = [γA/ (δ + n)](1/1 α) (6)
Substituting this steadystate stock of capital per worker into equation (2) will yield the steadystate level of output per worker, ysteadystate:
ysteadystate = A(ksteadystate) α = A (1/1 α) (δ + n)](1/1 α) (7)
Thirdly, the impact of population growth on income per worker in a country within the Solow model will be discussed. Population growth affects the steadystate level of income per capita negatively (see equation (7)). A lower population growth rate, measured by n, causes the (δ + n) term to decline and therefore causes the income perworker in the steadystate, steadystate, to increase. The drop in the population growth rates, resulting from the drop in fertility rates, in Nigeria should have had a positive effect on income per capita because of less capital dilution. The impact of population growth on the steadystate income per capita level in the Solow model can also be seen in figure 1.
Determining the steadystate levels of capital and output perworker can also be done in a figure. In Figure 1, the steadystate capital stock perworker is found at the point where the investment curve and the line representing depreciation and population growth intersect:
Figure 3: The Solow Model Incorporating a Decline in Population Growth
Source: Author’s based on the Solow model
Figure 1 shows that the line representing the depreciation and capital dilution effect rotates outward if the level of n drops from, where. This causes the steadystate level of output perworker to increase as a result of a higher steadystate capital stock perworker.
With the use of Solow model’s theoretical framework, it can be argued therefore that reductions in fertility rates, while assuming mortality rates to be constant, will cause the capital stock per worker to increase and hence lead to higher levels of income per capita
Research Methodology
Applying the Solovian framework to analyze the relationship between fertility rates and poverty in Nigeria, this research employs the structural vector autoregression (SVAR) method to investigate the impact of birth rates on poverty within the model. It also examines impulse response functions (IRF) and forecast error variance (FEVD) decomposition as discussed by Adebiyi (2009) and Adrangi& Allender (1998). SVAR, a more refined version of VAR, has gained popularity as a useful tool for assessing economic models, particularly in macroeconomic literature according to Sarte (1997). This method extends the conventional VAR analysis by identifying independent disturbances through constraints based on economic theory rather than the traditional VAR’s theoretical constraints as proposed by McCoy (1997). The key advantage of this technique lies in its capacity to capture responses, transmit shocks, and understand the economic impact among interconnected variables within a given economy or research context as indicated by Udoh (2009).
We consider the interaction of fertility rate through population change and poverty (or vice versa)in Nigeria. So, we are looking at three (3) endogenous economic time series and p lags. The endogenous linear equations can be explicitly specified as follows:
A0 yt = A1 yt1 + ……+ Ak ytk + CDt+ Bet
where yt= (y1t, y2t,….ynt) is an nx1 vector of nonpolicy and policy variables and Ai and C are parameter matrices of order n x n. Dt contains all deterministic variables which may consist of a constant, a linear trend, seasonal dummy variables as well as other specified dummy variables. Moreover, et, which is an nx1 vector of structural shock or innovations in policy and nonpolicy variables, is assumed to be a white noise process with (0,1n).
Drawing on the theoretical and empirical literature of Solow and Ashraf, Weil, and Wilde, (2013) respectively, the model for this study is represented by a twocomponent vector (yt) of endogenous variables defined as
yt= (fr, pov,) (2)
Where fr is the variable that measure fertility rate and pov is the variable that measures poverty,
In Equation (2) above, all variables are in logarithmic form. Moreover, to achieve identification of the SVAR, this study draws from the theoretical and empirical literature as well as the ‘trickle down model’ adopted from Jalilian and Weiss (2004).
Given that matrix B is diagonal and of order 3 x 3, matrix A now has the following nonrecursive structure:
Matrix B
frtt pgrtpovt
1 0*
*1 *
** 1
The nonrecursive identification scheme described above is defined simply a constraint indicated by zero (0) an asterisk (\*), which represents parameters estimated freely. Implementing this scheme is logical because the impulse response function it produces is not influenced by the variable order in the SVAR system.
In the first instance, we have the birth rate equation, which is solely determined by the poverty level, aligning with the principles of the “demographic dividend theory.” This theory suggests that the birth rate adapts in response to fluctuations in the economic growth rate, influenced by changes in poverty.
Furthermore, the subsequent line highlights that the rate of population growth in an economy is contingent on both fertility rates and poverty levels, while the third line underscores that poverty is influenced by both the birth rate and population growth.
To analyze the model represented by Equation (2) above, a Structural Vector Autoregressive (SVAR) framework which models all endogenous variables jointly rather than one equation at a time and also deals with the issue of identification common with regression of simultaneous equation models will be utilized (see Watson, 1994; Lutkepohl, 2005, 2011).
Given a VAR(p) model of Yt is;
Yt= A1Yt1 + ApYtp + Ƞt or A(L)Yt = Ƞt (1)
Where A(L) = I – A1L – … – APLP and L is the lag operator, and where the disturbance Ƞt is a martingale difference sequence with covariance matrix ΣȠ, so that Ƞt is serially uncorrelated.
In practice, Yt will generally have a non zero mean and the VAR in (1) would include an intercept. the assumption of zero mean and no intercept in the var is made without loss of generality to simplify notation.
The VAR (1) is the reducedform VAR. The ithequation in (1) is the population regression of Yit onto lagged values of Yt..Because (1) is the population regression of Yt. onto its lags, its parameters A(L) and ΣȠ are identified.
The innovation in Yit is the onestep ahead forecast error, Ƞit, in the ithequation in (1). The vector moving average representation of Yt, which in general will be infinite order, expresses Ytin terms of current and past values of the innovations:
Yt= C(L)Ƞt, where C(L) = I + C1L + C2L2 + …. = A(L)1 (2)
The SVAR model represents Ytnot in terms of its innovations Ƞt, but rather in terms of underlying structural shocks et, where these structural shocks represent unexpected exogenous disturbances to structural economic relationships.
The SVAR assumes that the innovations are a linear combination of the unobserved structural shocks:
Ƞt = Het (3)
The structural shocks are assumed to be uncorrelated.
Substituting (3) into (2) and (1) delivers the structural VAR and the Structural Moving Average
A(L) = Het or B(L)Yt = et, where B(L) = H1A(L) (Structural VAR) (4)
Yt= D(L)et, where D(L) = C(L)H, (Structural MA) (5)
The second expression in (4) holds if H1 exist.
The structural forecast error variance decomposition (SFEVD) and structural impulse response functions (SIRFs) are two of the SVAR analytical methods that will be used in this investigation. Assuming that the errors are equal to zero, the SIRFs plot the reaction of each variable’s present and future values to a oneunit rise in the current value of one of the SVAR errors (Gottschalk, 2001). Conversely, the SFEVD represents the amount of variance in the forecasting error caused by a particular shock at a certain time horizon (Kilian, 2011).
The structural MA in (5) summarizes the dynamic causal effect of the shocks on current and future Yt, and it directly delivers two key objects in the SVAR analysis: the SIRF and the decomposition of Ytinto structural shocks. With the assumption of uncorrelation in the structural shocks, the structural MA representation also delivers the structural forecast error variance decomposition.
The fertility rate passed through to poverty will also be computed. It will be calculated from the impulse response function results. The pass through can be defined as the accumulated effect of a structural one standard deviation to the nominal effective fertility rate in period t on the poverty rate in period t. Note that the accumulated response measures the effects of fertility rate changes on the poverty rate. The dynamic passthrough elasticity (φ) of poverty at time t is given by:
FRTPTφ = %∆POV
%∆FRT
The numerator is the percentage change in the level of the poverty rate between period zero, when the initial fertility rate shock strikes, and at time t. The denominator is the percentage change in the nominal effective fertility rate at time 0.
Data Description and Source
The variables to be utilize in this study are shown in the table below
Table 1: Abbreviations and Corresponding Economic Variables
S/N 
Variable Name 
Abbreviation 
1 
Population Growth rate 
PGR 
2 
Poverty Rate 
POV 
4 
Fertility Rate 
FRT 
Analysis of Estimation Results and findings
Unit Root
Empirical findings have shown that most economic time series are strongly trended and hence nonstationary (Iyoha M.A. 2004). The Unit Root Test is conducted to verify the stationarity or otherwise of the selected macroeconomic variables. The result of the Augmented Dickey Fuller (ADF) and PhillipsPerron (PP) used in the study are shown in table 4.1 below:
Table 2: Unit Root test

LEVELS 
FIRST DIFFERENCE 

Variables 
ADF 
PP 
ADF 
PP 
FRT 
0.131 
1.090 
2.879** 
2.735** 
POV 
1.522 
1.517 
5.834* 
5.834* 
PGR 
3.386** 
4.114* 
1.641 
2.986** 














Lag 
LogL 
LR 
FPE 
AIC 
SC 
HQ 














0 
152.2726 
NA 
1.32e08 
9.630491 
9.491718 
9.585254 
1 
316.9185 
286.8026 
5.77e13 
19.67216 
19.11707 
19.49122 
2 
356.8960 
61.90065 
7.97e14 
21.67071 
20.69930 
21.35406 
3 
407.6703 
68.79093 
5.65e15 
24.36583 
22.97810* 
23.91346 
4 
416.0149 
9.690477 
6.47e15 
24.32354 
22.51949 
23.73546 
5 
437.2444 
20.54469* 
3.46e15 
25.11254 
22.89217 
24.38876 
6 
450.7240 
10.43586 
3.41e15* 
25.40155* 
22.76486 
24.54206* 







Source: Authors Computation from Eviews 7
Going further with the cointegration, since all the variables are either integrated of order 0 or 1 and none of the variable is I(2) in the model, therefore, ARDL approach to co integration is the most appropriate technique of estimation (Pesaran, Shin, and Smith, 1996).
Estimating the ARDL model, the computed F statistic with lag of order 6 is given in Table 4. The value of F statistics lies below the upper bound value of F statistics. Therefore, null hypothesis of no long run relationship cannot be rejected and we conclude that there is no long run relationship among variables. Thus, for the rest of the analysis the VAR model is carried out in first differences and no errorcorrection terms are included (Sims, 1990).
Table 4: ARDL Bounds Test
Test Statistic 
Critical Value 

Fstatistics 
3.52560 

Critical Value Bounds 

Significance 
I0 Bounds 
I1 Bounds 

10% 
3.17 
4.14 

5% 
3.79 
4.85 

2.5% 
4.41 
5.52 

1% 
5.15 
6.35 
Source: Authors Computation from Eviews 7
SVAR Estimation Result
The model consists of three variables. Prior to the estimation of the Structural VAR, the time series data was transformed to a stationary series via differencing. As stated above in the absence of cointegration among the variables, SVAR was estimated in first differences. That is; ΔFRT, ΔPOV and ΔPGR, denote the first differences of the Fertility Rate, Population Growth Rate, and Poverty Rate. The lag length was selected using same criteria as used above. Since these criteria suggested 6 as the order of the unrestricted VAR model in first difference, a lag
To find out more about the type of residual errors, diagnostic tests are run. At delays between 1 and 3, the hypothesis of no serial autocorrelation could not be rejected by the Lagrange multiplier (LM) or the BreuschGodfrey test with a high pvalue larger than 5 percent (see Table 7). In a similar vein, the calculated JarqueBera normalcy test is shown in Table 8. Because there is extra kurtosis in the residuals, this test contradicts the null hypothesis of normalcy. Upon eye inspection, several outliers may be seen in the residuals. Relevantly, it should be noted that Monte Carlo tests for serial autocorrelation should remain very accurate even if the normality assumption is disregarded (Lutkepohl, 1991, and Mackinnon, 2005).
For the SVAR model, the likelihood ratio test (LRtest) is calculated.The likelihood ratio test (LRtest) is computed for the SVAR model. That is whether the covariance matrix of the residual for SVAR model is diagonal. They were found to be nonzero. The relevance of this test is, if the covariance of the matrix residuals is zero there is no point using contemporaneous restrictions to identify the SVAR system (Sanusi, 2010).
The LR statistic is found to be greater than the critical value, so we reject the null hypothesis that the restriction is not valid. Therefore, we can accept the imposed identification restrictions within matrix B. Similarly, this suggests that shocks in the entire equations have contemporaneous correlation in the system – hence this gives justification for structural VAR to take into consideration the contemporaneous correlation among the variables. Without the SVAR, this contemporaneous correlation among the variables would have been neglected by the unrestricted VAR model.
Table 7: VAR Residual Serial Correlation LM Tests






Lags 
LMStat 
Prob 






1 
32.47028 
0.0002 
2 
34.09806 
0.0001 
3 
27.98274 
0.0010 
4 
5.708102 
0.7687 
5 
13.23662 
0.1522 
6 
7.821806 
0.5522 






Source: Authors Computation from Eviews 7
Table 8: VAR Residual Normality Tests of the individual equations
Variables 
FRT 
PGR 
POV 
Normality JB 
2.260607 
957.9898 
105.8900 
Prob 
0.3229 
0.0000 
0.0000 
Skewness 
0.090306 
1.930701 
0.928774 
Prob 
0.6649 
0.0000 
0.0000 
Kurtosis 
3.600440 
15.31651 
6.868485 
Prob 
0.1499 
0.0000 
0.0000 
(0.8344)
ƐPGR = 3.4295ƐFRT+ 0.5452ƐPOV …………………………….(7)
(0.0000) (0.5860)
ƐPOV= 5.338ƐFRT+ 2.789ƐPGR
(0.0000) (0.0000)
From the equation above, it is seen that fertility rate and poverty rate have a positive impact on each other. What is also clear, is that while the impact of poverty rate on fertility rate is not significant, the impact of fertility rate on poverty rate was highly significant. This will be examined further using innovation accounting.
Impulse Response (IR) Analysis
The results show that in case of a onetime shock of a positive one standard deviation innovation in FRT, the effect on FRT will positive throughout the observed 10 periods, while the effect will be negative for population growth rateup to period 10. On poverty rate, the effect will be positive up till period 10, but then becomes positive thereafter.
A onetime shock of a positive one standard deviation innovation in PGR will cause poverty to decline and remain negative all through the 10 periods.
A onetime shock of a positive one standard deviation innovation in poverty will leave set fertility rate in decline, hitting negative in period 4 and remaining negative even at 10 periods.
Figure 3: Impulse response function
Source: Authors Computation from Eviews 7
Variance decomposition (VD) Analysis
From the figure, variance decomposition of fertility rate indicates that short run dynamics in fertility rate are explained mostly by its own fluctuations, followed by Population Growth Rate, and then rate and Poverty rate. The weight of its own shock ranges from 100% in period one to 76% in period 10. And this percentage decreases as the forecast horizon increases. The percentage explained by both Population Growth Rate and poverty rate are quiet low, ranging from zero contribution from both in period one, to 17% in period 10 for Population Growth Rate and 6% forpoverty rate after 10 periods.
The variance decomposition of poverty rate shows that short run dynamics in poverty rate are explained mostly by its own fluctuations up to the sixth period ( between 51% and 96%), then fertility rate explains a significantly higher proportionof between 50 and 57%.
Figure 4: Variance Decomposition Analysis
Source: Authors Computation from Eviews 7
Fertility Rate Pass Through
Table 5 Shows the passthrough effect of changes in fertility rate to Poverty Rate. The result shows that elasticity of poverty in response to a change in fertility was greater than 1 (one) within the 10 period, showing higher degree of elasticity. It is clear, that a change in fertility rate leads to a more than proportionate change in poverty rate.
Table 5: Fertility Rate Pass Through effect
Time 
elasticity (φ) of Pov 
3 
2.51 
4 
14.22 
5 
1.46 
6 
6.08 
7 
1.19 
8 
3.51 
9 
3.52 
10 
16.03 
Source: Authors Computation from Eviews 7
The Granger Causality Result
The Granger causality test shows the interrelationship among variables. According to the Granger causality test result presented in table 4.7 below, it is seen fertility rate granger causes both GDP Growth rate and Poverty rate, and fertility rate is granger caused by both poverty and GDPGR. The summary of the Granger Causality Result is shown in the table below. The estimation result, as well as the decision(at the 10* level) is shown in table 6 below.
Table 6: Granger Causality result
Null Hypothesis: 
Obs 
FStatistic 
Prob. 
Decision 
PGR does not Granger Cause FRT 
31 
1.72920 
0.1715 
Accept 
FRT does not Granger Cause PGR 
2.61445 
0.0531 
Reject 

POV does not Granger Cause FRT 
31 
1.61425 
0.2004 
Accept 
FRT does not Granger Cause POV 
3.60460 
0.0159 
Reject 

POV does not Granger Cause PGR 
31 
2.61588 
0.0530 
Reject 
PGR does not Granger Cause POV 
0.82943 
0.5624 
Accept 
Source: Authors Computation from Eviews 7
The result above shows that while fertility rate granger causes poverty rate, the opposite does not hold. Same relationship also holds true for fertility rate and population growth. While fertility rate granger causes population growth rate, the opposite does not hold.
SUMMARY OF FINDINGS AND CONCLUSIONS
The findings of this study indicate a strong correlation between birth rates and poverty rates, with birth rates having a more significant impact on poverty rates than vice versa
The SVAR result shows that there is significant and positive impact of fertility rate on poverty, the result however shows an insignificant but positive impact of poverty on fertility rate. Also, the variance decomposition of poverty rate shows that short run dynamics in poverty rate are significantly explained in most part by fertility rate, accounting between 50% and 57%.Again, the passthrough effect showed that poverty rate was highly elastic to changes in the fertility rate.
The Granger Causality result shows bidirection causality from fertility rate to poverty.
Particularly in Nigeria, where there is a lack of social safety nets or family support systems, larger families often experience lower standards of living due to the financial burden of education and skills development.(Knodel, Havanon, and Sittitrai, 1990). Conversely, smaller families are more inclined to save and are less susceptible to income fluctuations. Furthermore, the presence of multiple children in large families can lead to competition for resources, resulting in each child receiving a smaller share of the family’s income, time, nutrition, and attention.
RECOMMENDATIONS
Based on the empirical findings, this study extends the following recommendations aimed at repositioning Nigeria for a new growth and propoor development trajectory that adequately caters for the fertility rate of the country.
i. Donors and governments (Federal and State) should explicitly state the urgency of voluntary family planning as a poverty reduction intervention, and persuasively explain its longterm benefits.
ii. There is the need toimmediately review and revive the National PopulationPolicy (NPP) (with one of its main objective of reducing fertility rate), with strongleadership from the Executive and the National Assembly.
iii. There is also the need to develop an action plan for the NPP. Key elements of the action plan should include (a) the targeted groups; (b) specific services, goods, or other benefits provided; (c) providers of assistance; (d) geographic coverage; (e) collaboration with public and private agencies, community organizations, and local leaders; and (f) the budget
iv. Various stakeholders should accelerate ongoing efforts to improve maternal andchild health, and ensure all needs for family planningare met.
v. There is the need for governments at all level to accelerate access to and the quality of basic,secondary and postsecondary education for girls andboys.
vi. There is also the need to enact culturallyappropriate affirmative action policiesand laws to encourage women’s participation in theworkforce and in business opportunities, particularly inthe northern parts of the country.
vii. Each state should review its own population policy andimplementation approaches, deriving guidance fromthe reviewed national policy.
viii. Finally, it is observed that the poor are an underutilized resource in policy formulation and implementation.National decisionmakers and government officials should make concerted effort to understand the situation of the poor and other marginalized groups in order to meet their needs more effectively; Involve the poor and organizations that represent their interests in setting program priorities and directions; and insist that program planners and implementers involve beneficiaries in all stages of program planning and implementation.
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