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Crucial public sector financial activities gravitate around, but are not limited to, revenue generation, borrowing, and government spending. These activities are usually targeted to define certain critical economic causation along the lines of aggregate output and income growth, full employment, reduced inflation, and favorable balance of payments, which every mixed economy seeks to attain. In other words, public spending has been given a pride of place when targeted to achieve a wide range of specific economic objectives, such as to improve the infrastructure, reduce unemployment, achieve more equity, poverty reduction, generate positive externalities, and generally stimulate growth of the economy. Public expenditure is the rallying point of these activities, since it is only when the generated revenue or borrowed fund is put to use by way of spending that the economy would be significantly affected.

More specifically, government spends money for a plethora of reasons, including provision of public and merit goods and services that the private sector would find it an upheaval task to handle. Such public goods would include defense, roads and bridges, and merit goods as exemplified by hospitals and schools. Government makes welfare payments and benefits like social security, unemployment and disability benefits. Governments direct their expenditure in a bid to achieve supply-side innovations and improvements in the economy. Spending on human capital development such as in education and training to improve technological and labor productivity is also a crucial part of the overall activity. It is a major thesis in public finance theory that government intervention in the economy by spending seeks to reduce the negative effects of externalities such as pollution controls. Government expenditure is also directed to subsidize certain preferred sectors (such as agriculture) and infant or pioneer industries that may need assistance in critical times. Income redistribution and equity are also important reasons for public spending. Government expenditure is equally occasioned by the need to increase aggregate demand and general economic activity, which is an integral part of discretionary fiscal policy (Ezirim et al., 2015).

Economic theory appears to have linked economic output growth, inflation and unemployment in certain defined terms. It is no wonder that the potential stimulus effects of fiscal policy tool of government spending have attracted much commentaries and empirical concerns over time. In particular, the important point of inquiry relates to whether or not an increase in government spending stimulates output growth, causes inflation or raises employment. Thus, it makes sense to devote as much attention to the employment effects of government spending as to the output and inflation effects, since it is common economic sense that job creation is at least as important a goal as stimulating output or curbing hyper-inflation. It is even more interesting to note some obvious connecting nexus (Ezirim et al., 2015).

For instance, government spending activities aimed at promoting growth are somewhat consistent with reducing unemployment. These activities, on the other hand, are likely to be inflationary, especially when the concerned government spending originates from debts (such as government bonds and treasury securities) borrowed from the financial markets. Another way the inflationary stance is accentuated is if and when the spending is rising too quickly (in peaks and jerks) as described in Wiseman and Peacock theory. This is exemplified by a situation where public sector overheads rises (example, pay raises) without corresponding efficiency gains. Just as the New-classical economists would posit; if laissez fairs is violated and government intervenes by increasing spending, people will expect an inflationary effect on the economy, and will bargain for higher wages. The pay raises shifts the AS curve to the left, with no gain in aggregate output (Ezirim et al., 2014).

The Phillips curve hypothesis would however clarify that where the intention of public spending is to create jobs, there is the strong possibility that prices will jerk up, and any growth in jobs will only be temporary as the economy quickly readjusts to the previous level of unemployment. This is premised on the positions that there exist a potential trade-off between unemployment and inflation, and that markets tend to clear if left alone. This argument is, however, against the stance of the Keynesians who see government spending as economic growth stimulant and antidote of unemployment, since the government is the best guardian of the state, where the market constantly fails, and the economy as a result requires concerted actions. Thus, Government spending or expenditure has been particularly nominated as a major stimulant of economic growth as well as an indispensable tool of inflation targeting (Ezirim et al., 2015).

The experiences of Sub-Saharan African economies in matters of inflation, employment, and growth have been documented in various words as suboptimal growth, hyper-inflation, perennial unemployment. Unemployment particularly is said to be the major cause of many socio-economic crimes in these countries. These are tendencies which concerted and strategic spending policy of governments would have addressed. The question becomes: Is government spending the anti-thesis of goal attainment especially in the area of fuller employment of the countries' abundant resources? Against the background of perennial double-digit unemployment and ever increasing public spending in the Sub-Saharan African Region, the study set out to investigate the empirical nexus between unemployment and government expenditure. Using the Generalized Method of Moments, the fully modified Phillips-Hassen estimation, Johanse Co-integration, and Granger Causality techniques applied to Nigeria data, the study investigates the long-run and short-run unemployment- expenditure relation.


Many studies have however clarified the real relationship between government spending and output growth, and also between expenditure and inflation. Some of these include the works of Ram (1986), Ranjan and Sharma (2008), and Cooray (2009) who found positive and significant effects of government expenditure on economic growth. Some mixed results were however found by Nurudeen and Usman (2010) in their disaggregated or sectoral analysis of Nigerian data. Their results indicated that government total capital expenditure (TCAP), total recurrent expenditures (TREC), and government expenditure on education (EDU) have negative effect on economic growth; while government expenditure on transport and communication (TRACO), and health (HEA) results in an increase in economic growth, on the aggregate.

In a more recent study, Ezirim et al. (2015) found the existence of sustainable long run equilibrium relationship between the GDP growth and the growth in the government expenditure variables. Equally, in each of the two cases, there exists uni-directional causality flowing from recurrent and capital spending of government to the GDP with no feedback effects for Nigeria. The general evidence largely supports the fact that government spending boosts output and income growth for developing countries such as Nigeria; and thus lends support to the Keynesian theory. In the case of inflation, Ezirim et al. (2014) analyzed the inflation-expenditure nexus. The result of their follow-up study indicated that capital expenditure is not a significant inflation driver in Nigeria, but recurrent expenditure is. This is in line with the theory of the neo-classicals who posited that increased overheads can be inflationary.

The above submissions notwithstanding, what can be said about the employment effects of government expenditure? A notable approach employed to explain the relationship between government spending and unemployment relates to the 'crowding out' phenomenon. The Crowding-out theory of Bacon and Eltis (1976) has been seen in the light of the process of 'squeezing' out the privately owned manufacturing sector by the expansion of the public sector. The basic contention is that crowding out occurs because of the inherent scarcity of financial and real resources; a condition that makes the (inefficient) public sector utilize more scarce resources, and thus leaving very limited resources in the hands of the more efficient and productive private sector. In a situation, for instance, where the government sector expands and is required to borrow using financial market's bonds and treasury securities, interest rates may escalate and cause a private sector investment squeeze.

Similarly, where public sector expansion dictates a rise in demand for other resources that stimulate price increase, such as increase in wages and rents, the tendency is for the private sector to suffer a 'squeezing' out in the economy. Ramey (2012) appears to have provided a somewhat empirically support to the above perspective when he studied the effect of government spending on private spending, and employment. Defining private spending to be GDP less government spending. the results indicate that an increase in government spending never leads to a significant rise in private spending. In fact, in most cases it leads to a significant fall. These results imply that the government spending multiplier is more likely below one rather than above one.

Some other studies have investigated the actual relationship between government employment and private employment with evidence that the former crowds out the latter. For instance, in a study of OECD countries for 40 years, Algan, Cahuc, and Zylberberg (2002) reported that the "creation of 100 public jobs may have eliminated about 150 private sector jobs". More direct studies buttressing the unemployment effects of public expenditure were not equally recognized in their dearth. For instance, Bruckner and Pappa (2010) studied the effects of fiscal expansions on unemployment in a sample of OECD countries using quarterly data and found that a fiscal expansion often increases the unemployment rate very significantly. Feldmann (2006) analyzed the relationship between a large government sector and unemployment using evidence from 19 countries over 17 years, and found that large government sector is likely to increase unemployment, with women and the low skilled suffering more than other groups.

Some other studies suggest that an increase in government expenditure impairs labor market performance. Karras (1993), for instance, observed negative employment effects of government spending in eight countries in his sample of 18 countries. Yuan and Li (2000) found similar results for the US. Abrams (1999) found that the government expenditure ratio was positively related to the unemployment rate In a cross-country study of 15 major industrial countries, Christopoulos and Tsionas (2002) examined the relationship between the government expenditure ratio and the unemployment rate for 10 European countries over the period 1961 to 1999 and found that there was unidirectional causality from government size represented by its spending to unemployment rate. The magnitude of the government (un)employment effect was quite statistically significant.

It is worthy of note that not all studies found the labor market effects of government spending to be negative. Monacelli, Tommaso, and Trigari (2010) analyzed the effects of government spending shocks on a number of labor market variables including unemployment, vacancies, job finding rates and separation rates in the post-1954 period. Their point estimates suggested that positive shocks to government spending lowered the unemployment rate and the separation rate, and increased vacancies and the job finding rate. It is noteworthy that their point estimates are not statistically significant at the conventional levels.

In Ramey (2012), the bulk of evidence just presented suggests that a rise in government spending tends to lower the unemployment rate. Specifically, his study also investigated the effects of government spending on unemployment and employment and found that an increase in government spending lowers unemployment. It was also underscored that in all cases, "the increase in employment after a positive shock to government spending is due to an increase in government employment, not private employment" with only one exception. These lead to the conclusion that the employment effects of government spending work through direct hiring of workers and not through stimulating the private sector to hire more workers (Ramey, 2012).

It is quite apparent that notable points of separation are noticeable from the earlier studies. These disagreements accentuates the need for further enquiry on the unemployment effects of public spending. Furthermore, empirical evidence from the South Saharan countries is not robust in literature to the best of the authors' knowledge. Thus, the critical question in the present inquiry is how does government expenditure affect unemployment in South Saharan countries using Nigeria as a test case? Has the increased public spending reduced unemployment as anticipated by policy makers and economic managers? To what extent have the capital and recurrent expenditures of government affected unemployment, and by default employment situations of Nigeria. These are the main stay of our ensuing investigation.


The critical objective of this study is to investigate the causal link between unemployment and government expenditure, decomposed into capital and recurrent expenditure, in Sub-Saharan African countries using Nigeria as a test case. For the purpose of this study, it is hypothesized, therefore, that unemployment is a negative function of capital and recurrent expenditure of government.


Macrofinametric Techniques and Procedure Employed

MacroFinametric modeling which involves the application of statistical and quantitative techniques to analyze relationships among macro financial phenomena, or to investigate problems in the macro economy that are of financial origin, is employed in this study. The implicated financial econometric modeling gave rise to estimable equations that were used to examine the relationship between the variables identified in the hypotheses. Descriptive statistical analysis of data was also done before the resulting model was estimated. Also, a critical diagnostic check, related to Jaque-Bera normality test of the variables, and empirical distribution test were done.

The first step employed in estimation was to check the stationarity status of the variables using the Augmented Dickey-Fuller test procedure. These are followed by the Unrestricted Cointegration Rank Tests (Trace and Maximum Eigenvalue varieties) in order to determine the long run effects. The next step in the estimation procedure of the study used the Generalized Method of Moments, the fully modified Phillips-Hassen estimation, and Granger Causality techniques to determine the short-run effects. The results of these techniques aided the test of relevant hypotheses. Computations were done using the Eviews and Microfit software.

The Models and Variables

The rate of unemployment in the country is hypothesized to be a negative function of capital and recurrent expenditures of the government. This presupposes that the total spending of government is supposed to curb the rate of unemployment and by default promote employment in the country. Thus, the expenditure - unemployment relations can be explicitly expressed as

[UNER.sub.t] = [[beta].sub.0] + [[beta].sub.1][CEXR.sub.t] + [[beta].sub.2] [REXR.sub.t] + [V.sub.t] ; [[beta].sub.1] < O, [[beta].sub.2] <O (1)

Where [UNER.sub.t] denotes unemployment rate over time, [CEXR.sub.t] denotes capital expenditure rate over time, and [REXR.sub.t] denotes the recurrent expenditure rate over time, the [beta]'s are the parameters, and Vt is the stochastic error term. Expression 1 is simply the classical linear equation for the hypothesized expenditure--unemployment relation, which does not reflect the causal implications. In order to ascertain whether or not government spending actually causes unemployment, it becomes necessary to invoke the vector autoregressive model to have

[UNER.sub.t] = Constant + [1.summation over (i=0)] [[psi].sub.i][CEXR.sub.t-i] [1.summation over (i=0)] [[lambda].sub.i][REXR.sub.t-i] + [E.sub.t] ......... (2)

Where, [[PSI]] < 0; [[??].sub.i] < 0and [] are stochastic error terms.

Expression 2 represents the dynamic distributed-lag rendering of expression 1 that would permit the examination of the causality imperatives of the relationship between unemployment and government spending.


Data Sources and Descriptive Statistical Tools

The research data are obtained from the Central Bank of Nigeria (CBN) publications, namely CBN Annual Reports and Statement of Accounts, and the CBN Statistical Bulletin for various years. Descriptive Statistical analyses are used to present and undertake preliminary analysis of the data. The study utilizes such measures as the mean, median, standard deviations, skewness and kurtosis, and the Jarque-Bera statistic.

As can be seen from the Figure 1, the variables unemployment rate (UNER), recurrent expenditure (REXR), and capital expenditure (CEXR) pass the Jarque-Bera normality test where all observed probabilities are not significant at the conventional levels of 5% and 1%. We cannot therefore accept the non-normality hypothesis. Thus the variables are normally distributed, which would warrant their employment in further analysis of the study. In terms of kurtosis, the variables posted the values that are less than 3; thus the distributions are flat (platykurtic) relative to the normal. Apart from REXR that posted negative skewness, and thus a distribution having a long left tail, the other variables CEXR and UNER have positive skewness implying that their distributions have long right tails.


Relationship between Unemployment and Public Expenditure

Table 1 depicts the results of the unit root tests of the variables: UNER, REXR, and CEXR. As seen,

Stationarity Analysis of the Variables

Table 1 shows that the three variables attain stationarity at first differencing and thus, they are series and permit the employment of the Johansen cointegration procedure..

Cointegration between UNER, REXR, AND CEXR

Being integrated as a group, the analysis was pushed further to ascertain whether the variables are co-integrated or not. Thus, the study employed the Unrestricted Cointegration Rank Tests (Trace and Maximum Eigenvalue) after the order of linear deterministic trend; the results of which are depicted on Table 2 and 3, respectively. From Table 2, it can be seen that the Trace Statistic is computed to be 39.5 while the critical value at alpha 0.05 is 29.8, which indicates a rejection of the null of no co-integrating equation. Thus the alternate hypothesis of at least one cointegrating equation is accepted. Equally, the Max-eigenvalue test as in Table 3 indicates 1 cointegrating equation(s) at the 0.05 level (statistic = 32.9; critical value = 21.13). These results indicate that there exist a sustainable long run equilibrium relationship between the UNER and the duo of REXR and CEXR. Invariably, the results of the short-run relationships are sustainable in the long-run.

Relative Long Run Relationships Between UNER, REXR, AND CEXR

Table 4 depicts the long run cointegration equation showing the nature and magnitude of the observed long run relationships. The equation is normalized for INFR - the dependent variable.

The normalized beta coefficient representing the long run relative statistical relationship between the UNER and CEXR is shown to be -53.29 and Standard error of 6.33, suggesting a t-statistic of -8.42. This is significant at the 5% level. By implication, there exist a statistically significant long run relationship between the UNER and the CEXR variables. The sign suggests a negative relationship which agrees with the priori expectation. On the other hand, the normalized beta coefficient representing the long run relative statistical relationship between the UNER and REXR is calculated to be -8.71 with a standard error of 6.75 (t-statistic = - 1.29). The computed t-statistic is not significant at the 5% significance level. Thus, the relationship between UNER and REXR is negative in line with a priori expectation. However, it is not statistically significant at the conventional 5% level (see Table 4). It appears that the two expenditure variables do not have the same effect on unemployment. By implication, government spending in Nigeria reserves the potential to generate employment instead of unemployment.

Relative Short Run Relationship between INFR AND REXR AND CEXR

Equally, the study employed the Generalized Method of Moments method to estimate the short-run relationship between INFR and UNER and CEXR. The results are summarized on Table 5.

As can be seen, the beta coefficient representing the relationship between UNER and REXR is -6.57, while observed t-statistic is -2.114 which is significant at the 5% level (prob. = 0.0479). Given these results, we cannot accept a null hypothesis of no significant relationship between UNER and CEXR, in the short run. More so, the observed relationship is negative which is in line with a priori expectation.

On the other hand, though the relationship between UNER and REXR is negative, it is not statistically significant at 5% level (Beta = 0.81; t-stat = 0.117; prob = 0.91). Thus we cannot reject the null hypothesis of no significant relationship between UNER and REXR in the short run.

Causality Between UNER AND REXR AND CEXR

To test the existence of causality or otherwise, the study employed the Granger Causality procedure to test the direction of causality among the nominated variables of UNER, REXR, and CEXR. The results of the pairwise Granger Causality test are summarized in Table 7. It can be seen from the Table that REXR did not granger-cause UNER (F= 3.01; prob = 0.072) at the 5% alpha level. Also, CEXR does not granger-cause UNER (F= 0.61; prob = 0.55). Thus, we cannot accept a hypothesis that government spending causes unemployment in Nigeria. This is theoretically plausible and may suggest that public expenditure does indeed cause employment.

Furthermore, it is easy to see from Table 7 that UNER does granger-cause REXR since the observed F-stat is 6.52 and probability of 0.0066. This implies that causality flows from UNER to REXR but not vice versa. Thus, we cannot reject a null hypothesis of no causal relationship flowing from UNER to REXR. This may be interpreted to mean that unemployment conditions in the country drives the government to increase its recurrent spending, may be in the area of social security and associated spending.

However, UNER does not granger-cause CEXR (F= 0.215; prob = 0.80), implying that causality did not flow from UNER to CEXR. It is noteworthy that none of the expenditure variables did granger cause each other, indicating that there is no feedback effects and possibly a situation that would lend credence to a claim of absence of multicollinearity among the independent variables.


The policy implications of these results are obvious. Since capital expenditure happens to be a significant causation factor of employment in the country, evidently, increasing capital expenditure relative to recurrent expenditure has the potential to cause significant reversal to the problem of unemployment in Nigeria, and by extension, the entire Sub-Saharan region of Africa. Thus governments of the countries should sustainably increase capital expenditure for the good of their economies.


This study is limited to the broad categorization of government expenditure into recurrent and capital expenditure. It did not cover the dichotomization into various sectors such as services, agriculture, manufacturing and production, defense, and others.


It is the suggestion of the researcher that further work be carried out on the sectoral imperatives of government expenditure and their implication for employment, inflation and economic growth. This will help to ascertain which sectors of the economy are most rewarding when government pumps funds into them.


It was the main objective of the study to investigate the empirical relation between unemployment and government expenditure in Sub-Saharan African countries using Nigeria as a test case. The study utilized the Generalized Method of Moments, fully modified Phillips-Hassen estimation, Johansen Co-integration, and Granger Causality techniques as applied to Nigeria data. The study investigates the long-run and short-run unemployment-expenditure relation. It was revealed that there exists a long-run equilibrium relationship between unemployment and government expenditure.

The results indicated that a negative and significant relationship existed between capital expenditure of government and unemployment both in the long-run and short-run. Invariably, capital expenditure actually boosts employment in the country. Recurrent expenditure, being just overheads, was found to relate negatively with unemployment both in the long-run and short-run, but not significantly. Thus, recurrent expenditure does not cause unemployment.


Ezirim, C. B., Eniekezimene, D., Amuzie, A. E., & Charles-Anyaogu, N. (2014a). Output - Expenditure Relation: A Macroeconometric Evidence From Nigeria. International Academy of Business Review, 1(2), 19-31.

Ezirim, C. B., Eniekezimene, D., Amuzie, A. E., & Charles-Anyaogu, N. (2014). The Inflation - Expenditure Relation: Macrofinametric Evidence from Nigeria. International Journal of Economics and Business; 4(1, November), 47-60.

Ram, R. (1986). Government Size and Economic Growth: A New Framework and Some Evidence from Cross-Section and Time-Series Data. American Economic Review, 76, 191-203.

Ranjan, K. D., & Sharma, C. (2008). Government Expenditure and Economic Growth: Evidence from India. The ICFAI University Journal of Public Finance, 6(3), 60-69. []

Cooray, A. (2009). Government Expenditure, Governance and Economic Growth. Comparative Economic Studies, 51(3), 401-418. From: [;jsessionid=q1g8lgkzfvms.alice]

Nurudeen, A., & Usman, A. (2010). Government Expenditure and Economic Growth in Nigeria, 1970-2008: A Disaggregated Analysis; Business and Economics Journal, Volume 2010: BEJ-4 ; Accessed 10/04/2015

Bacon, R., & Eltis, W. (1976). Britain's Economic Problem - too few producers, London: McMillan Press

Economics Online Ltd. (2015) The public sector; News Analysis Theory Comment, From:

Mitchel, M. (2010). The Government (Un)employment Effect. From:

Ramey, V.A. (2012). Government Spending and Private Activity. From:,273,275

Monacelli, R., Perotti, T., & Trigari, A. (2010). Unemployment Fiscal Multipliers. Journal of Monetary Economics, Carnegie-Rochester Conference 57, 531-553

Bruckner, M., & Evi, P. (2010). "Fiscal Expansions May Affect Unemployment, but They May Increase It." Working paper DP7766, Centre for Economic Policy Research

About the Authors:

Chinedu B. Ezirim is a Professor of Finance, Banking and Finametrics, University of Port Harcourt, Nigeria. He has published several papers in reputable International Journals. He has served as Conference and Program Chair, Workshop Facilitator, Discussant, and Session Chair, and presented over 150 papers in several International and National Conferences. He holds the fellowship and distinguished fellowship of a number International and National Academies and Institutes.

Daniel Eniekezimene is a doctoral candidate of the Department of Finance and Banking, Faculty of Management Sciences, University of Port Harcourt.

Dr. Amuzie Edith Azuka is a director of Finance at the Office of the Accountant General, Ministry of Finance, Imo State Nigeria. She has attended and participated in many national and international conferences and has published in many reputed Journals.

Dr. Nneka Charles-Anyaogu is a lecturer with the Imo Stae Polytechnic Umuagwu Owerri Imo State of Nigeria. She has published a number of papers in reputable journals and has attended several national and international conferences.

Chinedu B. Ezirim Daniel Eniekezimene

University of Port Harcourt, Port Harcourt, Nigeria

Azuka E. Amuzie

Ministry of Finance, Imo State, Nigeria

Nneka Charles-Anyaogu

Imo State Polytechnics, Umuagwo, Owerri, Nigeria
Table 1 Unit Root Test Summary for the Series: UNER, REXR, CEXR

Panel 2a: Null Hypothesis: D(UNER) has a unit root

                                               t-Statistic  Prob. (*)
Augmented Dickey-Fuller test                   -4.912175    0.0006
Test critical values:                1% level  -3.724070
                                     5% level  -2.986225
                                    10% level  -2.632604
Panel 2b: Null Hypothesis: D(CEXR) has a unit root
                                               t-Statistic  Prob. (*)
Augmented Dickey-Fuller test                   -4.186267    0.0034
                                     1% level  -3.724070
                                     5% level  -2.986225
                                    10% level  -2.632604
(*) MacKinnon (1996) one-sided
Panel 2c: Null Hypothesis: D(REXR) has a unit root
                                               t-Statistic  Prob. (*)
Augmented Dickey-Fuller test                   -8.655965    0.0000
Test critical values:                1% level  -3.737853
                                     5% level  -2.991878
                                    10% level  -2.635542

(*) MacKinnon (1996) one-sided p-values.

Table 2 Unrestricted Cointegration Rank Test (Trace
Series: UNER REXR CEXR ; Trend assumption: Linear deterministic trend

Hypothesized                Trace        0.05
No. of CE(s)   Eigenvalue   Statistic   Critical Value   Prob. (**)

None (*)       0.731994     39.50006    29.79707         0.0028
At most 1      0.226714      6.581385   15.49471         0.6268
At most 2      0.006130      0.153719    3.841466        0.6950

Trace test indicates 1 cointegrating eqn(s) at the 0.05 level

Table 3 Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized                Trace        0.05
No. of CE(s)   Eigenvalue   Statistic   Critical Value   Prob. (**)

None (*)       0.731994     32.91867    21.13162         0.0007
At most 1      0.226714      6.427666   14.26460         0.5590
At most 2      0.006130      0.153719    3.841466        0.6950

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
(*) denotes rejection of the hypothesis at the 0.05 level
(**) MacKinnon-Haug-Michelis (1999) p-values

Table 4 Normalized Cointegrating Coefficients (Standard Error in

UNER   CEXR         REXR

       - 53.29220   -8.706346
         (6.33489)  (6.75096)

Table 5 Results of Estimation using the Generalized Method of Moments:
Dependent Variable: UNER
Instrument specification: LNUNER LNREXR LNCEXR; Constant added to
instrument list

Variable   Coefficient   Std. Error   t-Statistic

CEXR       -6.571442       3.108280   -2.114173
REXR       -0.814803       6.943657   -0.117345
C          11.56031        2.765282    4.180518

Table 6 Fully Modified Phillips-Hansen Estimates (using Bartlett
weights), truncation lag= 1, Trended Case
Dependent Variable: UNER:
26 observations used for estimation from 1986 to 2011

Regressor   Coefficient   Std. Error   t-Statistic (Prob)

Intercept   13.1723       2.1469        6.1356 (.000)
REXR        -2.1495       2.5052        -.85803 (.400)
CEXR        -9.0129       3.5823       -2.5160 (.019)

Table 7 Pairwise Granger Causality Tests

Null Hypothesis:                   Obs   F-Statistic   Prob.

REXR does not Granger Cause UNER   25    3.01341       0.0718
UNER does not Granger Cause REXR         6.51585       0.0066
CEXR does not Granger Cause UNER   25    0.60875       0.5538
UNER does not Granger Cause CEXR         0.21502       0.8084
CEXR does not Granger Cause REXR         0.95675       0.4010
REXR does not Granger Cause CEXR   25    2.24130       0.4010
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Article Details
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Author:Ezirim, Chinedu B.; Eniekezimene, Daniel; Amuzie, Azuka E.; Charles-Anyaogu, Nneka
Publication:International Journal of Business and Economics Perspectives (IJBEP)
Article Type:Report
Geographic Code:6NIGR
Date:Sep 22, 2017

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