GOVERNMENT CAPITAL EXPENDITURE ON ADMINISTRATION, SOCIAL & COMMUNITY, ECONOMIC SERVICES, TRANSFERS

GOVERNMENT CAPITAL EXPENDITURE ON ADMINISTRATION, SOCIAL & COMMUNITY, ECONOMIC SERVICES, TRANSFERS
CHAPTER FOUR
DATA ANALYSIS
This study was carried out based the annual time series data on; Gross Domestic Product (GDP) as the dependent variable and as a proxy for Economic Growth. This is because Economic Growth is considered a function of the GDP. Government Capital Expenditure on Administration (GCEA), Government Capital Expenditure on Social & Community Services (GCESCS), Government Capital Expenditure on Economic Services (GCEES) and Government Capital Expenditure on Transfers (GCET) were used as the explanatory variables and as proxies for Federal Government Capital Expenditure. In this chapter the researcher presented and analyzed the results from the estimation of the working model as specified.

4.1 Descriptive Statistics
With the data for the variables covering the sample period employed for this study, we begin our analysis by observing the descriptive statistics of each of the variables, all of which are summarized below:

LOG(GDP)
LOG(GCEA)
LOG(GCEES)
LOG(GCESCS)
LOG(GCET)
 Mean
 16.62315
 11.91214
 12.49111
 11.19788
 9.338442
 Median
 16.73677
 12.12932
 12.48762
 11.27316
 10.17629
 Maximum
 17.51790
 12.66312
 13.13431
 11.93278
 12.49088
 Minimum
 15.71964
 10.80476
 11.49254
 10.23872
 0.000000
 Std. Dev.
 0.640480
 0.638507
 0.523594
 0.611912
 3.783153
 Skewness
-0.088938
-0.620441
-0.641946
-0.330042
-1.680142
 Kurtosis
 1.645476
 1.975837
 2.422351
 1.803890
 4.476644






 Jarque-Bera
 1.010953
 1.402211
 1.073615
 1.010962
 7.297323
 Probability
 0.603218
 0.496037
 0.584612
 0.603215
 0.026026






 Sum
 216.1009
 154.8578
 162.3844
 145.5724
 121.3997
 Sum Sq. Dev.
 4.922583
 4.892297
 3.289812
 4.493229
 171.7469






 Observations
 13
 13
 13
 13
 13

Source: Researcher’s Calculations using Eviews 7

Average (mean) GDP was more than the mean of all other variables within the period. Same goes with the median. The measure of dispersion/spread (i.e. standard deviation) is also highest for GDP, with GCET having the lowest. The data for all the variables are negatively skewed. However, the distribution of GCET has the longest tail, indicating that it has more extreme large values than others. The kurtosis of GCET is highest among all. The probability of the Jarque-Bera statistic for each of the series is low and leads weak rejection of the null hypothesis of a normal distribution, further confirming that the skewness and kurtosis of each of the sample data do not match a normal distribution, and suggesting that the data series for the variables are not normally distributed.
4.2 Augumented Dickeyfuller Unit Root Test
The results from stationary test for all the variables of the study are presented below (see appendix for full results):
Null Hypothesis: D(LOG(GCEA)) has a unit root

Exogenous: Constant, Linear Trend

Lag Length: 0 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-4.648111
 0.0189
Test critical values:
1% level

-5.124875


5% level

-3.933364


10% level

-3.420030











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

Since the ADF t-statistic (absolute value) is greater than the test critical values of 5% as well as 10% and the p-value is less than 0.05, the researcher rejects the null hypothesis and concludes that LOG(GCEA) is stationary at first difference level and at both 5% and 10% level of significance.

Null Hypothesis: D(LOG(GCEES),2) has a unit root
Exogenous: Constant, Linear Trend

Lag Length: 1 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-17.13786
 0.0001
Test critical values:
1% level

-5.521860


5% level

-4.107833


10% level

-3.515047











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


Since the ADF t-statistic (absolute value) is greater than the test critical values for all percentage levels and the p-value is less than 0.05, the researcher rejects the null hypothesis and concludes that LOG(GCEES) is stationary at 2nd difference level and at both 1%, 5% and 10% level of significance.

Null Hypothesis: D(LOG(GCESCS)) has a unit root
Exogenous: Constant, Linear Trend

Lag Length: 0 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-6.180769
 0.0027
Test critical values:
1% level

-5.124875


5% level

-3.933364


10% level

-3.420030











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


Since the ADF t-statistic (absolute value) is greater than the test critical values for all percentage levels and the p-value is less than 0.05, the researcher rejects the null hypothesis and concludes that LOG(GCESCS) is stationary at 1st difference level and at both 1%, 5% and 10% level of significance.

Null Hypothesis: LOG(GCET) has a unit root

Exogenous: Constant, Linear Trend

Lag Length: 1 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-7.657553
 0.0005
Test critical values:
1% level

-5.124875


5% level

-3.933364


10% level

-3.420030











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


Since the ADF t-statistic (absolute value) is greater than the test critical values for all percentage levels and the p-value is less than 0.05, the researcher rejects the null hypothesis and concludes that LOG(GCET) is stationary at level and at both 1%, 5% and 10% level of significance.
Null Hypothesis: D(LOG(GDP)) has a unit root

Exogenous: Constant, Linear Trend

Lag Length: 0 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-4.088843
 0.0405
Test critical values:
1% level

-5.124875


5% level

-3.933364


10% level

-3.420030











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


Since the ADF t-statistic (absolute value) is greater than the test critical values of 5% as well as 10% and the p-value is less than 0.05, the researcher rejects the null hypothesis and concludes that LOG(GCEA) is stationary at first difference level and at both 5% and 10% level of significance.




4.3 Estimation of the Model
The result from the model estimation using Eviews 7 is presented below:
Dependent Variable: LOG(GDP)


Method: Least Squares


Date: 12/10/14   Time: 13:55


Sample: 1 13



Included observations: 13












Variable
Coefficient
Std. Error
t-Statistic
Prob.  










C
5.817319
2.386775
2.437313
0.0407
LOG(GCEA)
0.666456
0.278461
2.393353
0.0436
LOG(GCEES)
0.053479
0.332434
0.160872
0.8762
LOG(GCESCS)
0.188220
0.323939
0.581037
0.5772
LOG(GCET)
0.010792
0.016381
0.658845
0.5285










R-squared
0.825071
    Mean dependent var
16.62315
Adjusted R-squared
0.737606
    S.D. dependent var
0.640480
S.E. of regression
0.328082
    Akaike info criterion
0.892619
Sum squared resid
0.861105
    Schwarz criterion
1.109908
Log likelihood
-0.802026
    Hannan-Quinn criter.
0.847957
F-statistic
9.433180
    Durbin-Watson stat
0.500317
Prob(F-statistic)
0.004027













Source: Eviews 7.0

4.4 Interpretation of Result
From the regression result presented 4.3, other factors (affecting GDP) remaining constant, the researcher deduced as follows:
1.      As Government Capital Expenditure on Administration (GCEA) increased by, say, N1, Gross Domestic Product (GDP) on the average increased by about N0.7 (70 kobo).
2.      As Government Capital Expenditure on Economic Services (GCEES) increased by, say, N1, Gross Domestic Product (GDP) on the average increased by about N0.05 (5 kobo).
3.      As Government Capital Expenditure on Social & Community Services (GCESCS) increases by, say, N1, Gross Domestic Product (GDP) on the average increased by about N0.19 (19 kobo).
4.      As Government Capital Expenditure on Transfers (GCET) increased by, say, N1, Gross Domestic Product (GDP) on the average increased by about N0.01 (1 kobo).
4.3 EVALUATION OF RESULT
Evaluation and test of hypothesis as regards estimated parameters consists of ascertaining whether the estimated parameters are theoretically meaningful and statistically satisfactory. We shall evaluate the parameters using economic a priori criterion and some key statistics from the result the model estimation.
1.       Economic A Priori Criterion
Economic theory imposes a restriction on the signs and magnitudes of economic relationships. In view of this, the coefficients of the explanatory variables in the estimated model presented in 4.3 all conform to the a priori expectations as analyzed below:
Table 4.1: Evaluation Based on a priori Criterion
Variable
Parameter estimate
Expected Sign
Remark
C
b0 = 5.817319
+
Conforms
GCEA
b1 = 0.666456
+
Conforms
GCEES
b2 = 0.053479
+
Conforms
GCESCS
b3 = 0.188220
+
Conforms
GCET
b4 = 0.010792
+
Conforms

From the analysis in table 4.1, it can be observed that all the variables of the model in this study, met the a priori expectations with respect to size and magnitude of their coefficients/parameter estimates.
2. Coefficient of Determination R2
Being a measure of goodness of fit, an R2 of 0.825071 (82 percent) as in the estimated model presented in 4.1, shows that the regression line fits the data well. Also GDP is highly responsive to the changes in the explanatory variables in the model.
3. The t-statistic/Test of Hypotheses
Hypothesis One: H0: Government Capital Expenditure on Administration (GCEA) has not impacted significantly on Nigeria’s Economic Growth.
Decision: From the regression result presented in section 4.3, the low probability of the critical value of 0.0436 (which is lower than the corresponding t-statistic 2.393353) indicates the significance of the null hypothesis. Thus, the researcher rejects the null hypothesis at 5% level of significance, and concludes that Government Capital Expenditure on Administration (GCEA) has impacted significantly on Nigeria’s Economic Growth. Hence the observed impact as indicated by the parameter estimate of GCEA is statistically significant. Hypothesis Two: H0: As Government Capital Expenditure on Economic Services (GCEES) has not impacted significantly on Nigeria’s Economic Growth.
Decision: The high probability of the critical value of 0.8762 which is higher than the absolute value of the t-statistics (0.160872) indicates the acceptance of the null hypothesis. The researcher as such accepts the Ho at 5% significance level and concludes that Government Capital Expenditure on Economic Services (GCEES) have not impacted significantly on Nigeria’s Economic Growth. The observed positive impact as indicated by the parameter estimate of GCEES in the estimated model is therefore proved to be statistically significant. There is therefore need to beef up the GCEES and as well as ensure an implementation system that would translate to a significant impact on the economic growth of Nigeria. This is because economic services/activities are the major driver of economic growth in any economy.
Hypothesis Three: H0: Government Capital Expenditure on Social & Community Services (GCESCS) has not impacted significantly on Nigeria’s Economic Growth.
Decision: The regression result presented in 4.3 above indicates the probability of the critical value as 0.5772 which is lower than the absolute value of t-statistics (0.581037). This is indicative of the rejection of the null hypothesis. Thus the researcher as such rejects the Ho at 5% significance level and concludes that GCESCS has impacted significantly on Nigeria’s Economic Growth. This also indicates that the observed impact as indicated by the parameter estimate of GCESCS as seen in the result of the estimated model (in 4.1) is statistically significant.
Hypothesis Four: H0: Government Capital Expenditure on Transfers (GCET) has not impacted significantly on Nigeria’s Economic Growth.
Decision: The regression result presented in 4.3 above indicates the probability of the critical value as 0.5285 which is lower than the absolute value of t-statistics (0.581037). This is indicative of the rejection of the null hypothesis. Thus the researcher as such rejects the Ho at 5% significance level and concludes that Government Capital Expenditure on Transfers (GCET) has impacted significantly on Nigeria’s Economic Growth. This also indicates that the observed impact as indicated by the parameter estimate of GCET as seen in the result of the estimated model (in 4.1) is statistically significant.
4.      Durbin Watson Test Statistic
Durbin Watson statistic is used to test for the presence of autocorrelation with the following decision rule: if D.W < dl (lower limit), it implies there is an evidence of positive first order serial correlation (autocorrelation). But if D.W > du (upper limit), there is no evidence of positive first-order serial correlation. However, if dl<DW<du, there is inconclusiveness regarding the presence or absence of autocorrelation.
At 5 percent level of significance; where n = 13, k’ = 4, dl = 0.4991 and du = 1.126. Also, observed D.W for the estimated model is 0.500317. Since dl > DW < du (i.e 0.4991>0.500317< 1.126), the researcher therefore concludes that there is inconclusiveness regarding the presence or absence of autocorrelation.


APPENDIX
DATA FOR ANALYSIS
Year
GDP
GCEA
GCEES
GCESCS
GCET
2000
6,713,574.84
53,279.5
111,508.6
27,965.2
46,697.6
2001
6,895,198.33
49,254.9
259,757.8
53,336.0
76,347.8
2002
7,795,758.35
73,577.4
215,333.4
32,467.3
0.0
2003
9,913,518.19
87,958.9
97,982.1
55,736.0
11.3
2004
11,411,066.91
137,765.9
167,721.8
30,032.5
15,729.8
2005
14,610,881.45
171,574.1
265,034.7
71,361.2
11,500.0
2006
18,564,594.73
185,224.3
262,207.3
78,681.3
26,272.9
2007
20,657,317.67
226,974.4
358,375.6
150,895.2
23,036.0
2008
24,296,329.29
287,103.6
504,286.9
152,174.6
17,325.0
2009
24,794,238.66
315,880.0
506,010.0
120,710.0
210,200.0
2010
33,984,754.13
264,554.2
412,245.2
147,409.5
59,661.1
2011
37,409,860.61
232,600.0
386,500.0
91,900.0
207,500.0
2012 1
40,544,099.94
190,500.0
321,000.0
97,400.0
265,900.0
 Source: CBN Statistical Bulletin, CBN Annual Report and Financial Statements
RESULT OF THE ESTIMATED REGRESSION MODEL
Dependent Variable: LOG(GDP)


Method: Least Squares


Date: 12/10/14   Time: 13:55


Sample: 1 13



Included observations: 13












Variable
Coefficient
Std. Error
t-Statistic
Prob.  










C
5.817319
2.386775
2.437313
0.0407
LOG(GCEA)
0.666456
0.278461
2.393353
0.0436
LOG(GCEES)
0.053479
0.332434
0.160872
0.8762
LOG(GCESCS)
0.188220
0.323939
0.581037
0.5772
LOG(GCET)
0.010792
0.016381
0.658845
0.5285










R-squared
0.825071
    Mean dependent var
16.62315
Adjusted R-squared
0.737606
    S.D. dependent var
0.640480
S.E. of regression
0.328082
    Akaike info criterion
0.892619
Sum squared resid
0.861105
    Schwarz criterion
1.109908
Log likelihood
-0.802026
    Hannan-Quinn criter.
0.847957
F-statistic
9.433180
    Durbin-Watson stat
0.500317
Prob(F-statistic)
0.004027















UNIT ROOT TEST

LOG(GCEA) @ 1st dif
Null Hypothesis: D(LOG(GCEA)) has a unit root

Exogenous: Constant, Linear Trend

Lag Length: 0 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-4.648111
 0.0189
Test critical values:
1% level

-5.124875


5% level

-3.933364


10% level

-3.420030











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













Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LOG(GCEA),2)

Method: Least Squares


Date: 12/16/14   Time: 05:17


Sample (adjusted): 3 13


Included observations: 11 after adjustments











Variable
Coefficient
Std. Error
t-Statistic
Prob.  










D(LOG(GCEA(-1)))
-1.038310
0.223383
-4.648111
0.0016
C
0.530681
0.119255
4.449956
0.0021
@TREND(1)
-0.057512
0.013649
-4.213572
0.0029










R-squared
0.768977
    Mean dependent var
-0.011011
Adjusted R-squared
0.711221
    S.D. dependent var
0.232999
S.E. of regression
0.125209
    Akaike info criterion
-1.090657
Sum squared resid
0.125419
    Schwarz criterion
-0.982140
Log likelihood
8.998615
    Hannan-Quinn criter.
-1.159062
F-statistic
13.31431
    Durbin-Watson stat
2.102071
Prob(F-statistic)
0.002849






























LOG(GCEES) @ 2nd dif
Null Hypothesis: D(LOG(GCEES),2) has a unit root
Exogenous: Constant, Linear Trend

Lag Length: 1 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-17.13786
 0.0001
Test critical values:
1% level

-5.521860


5% level

-4.107833


10% level

-3.515047











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













Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LOG(GCEES),3)

Method: Least Squares


Date: 12/16/14   Time: 05:27


Sample (adjusted): 5 13


Included observations: 9 after adjustments











Variable
Coefficient
Std. Error
t-Statistic
Prob.  










D(LOG(GCEES(-1)),2)
-2.090403
0.121976
-17.13786
0.0000
D(LOG(GCEES(-1)),3)
0.581303
0.075978
7.650951
0.0006
C
0.673489
0.156876
4.293115
0.0078
@TREND(1)
-0.083441
0.018526
-4.504087
0.0064










R-squared
0.985452
    Mean dependent var
0.053182
Adjusted R-squared
0.976723
    S.D. dependent var
0.925912
S.E. of regression
0.141263
    Akaike info criterion
-0.775278
Sum squared resid
0.099777
    Schwarz criterion
-0.687622
Log likelihood
7.488750
    Hannan-Quinn criter.
-0.964438
F-statistic
112.8973
    Durbin-Watson stat
1.470436
Prob(F-statistic)
0.000052






















LOG(GCESCS) @ 1st dif
Null Hypothesis: D(LOG(GCESCS)) has a unit root
Exogenous: Constant, Linear Trend

Lag Length: 0 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-6.180769
 0.0027
Test critical values:
1% level

-5.124875


5% level

-3.933364


10% level

-3.420030











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













Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LOG(GCESCS),2)

Method: Least Squares


Date: 12/16/14   Time: 05:29


Sample (adjusted): 3 13


Included observations: 11 after adjustments











Variable
Coefficient
Std. Error
t-Statistic
Prob.  










D(LOG(GCESCS(-1)))
-1.618245
0.261819
-6.180769
0.0003
C
0.352756
0.321394
1.097580
0.3043
@TREND(1)
-0.033020
0.041084
-0.803730
0.4448










R-squared
0.828550
    Mean dependent var
-0.053411
Adjusted R-squared
0.785688
    S.D. dependent var
0.904356
S.E. of regression
0.418661
    Akaike info criterion
1.323493
Sum squared resid
1.402220
    Schwarz criterion
1.432010
Log likelihood
-4.279210
    Hannan-Quinn criter.
1.255088
F-statistic
19.33044
    Durbin-Watson stat
1.248972
Prob(F-statistic)
0.000864





















LOG(GCET) @level
Null Hypothesis: LOG(GCET) has a unit root

Exogenous: Constant, Linear Trend

Lag Length: 1 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-7.657553
 0.0005
Test critical values:
1% level

-5.124875


5% level

-3.933364


10% level

-3.420030











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













Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LOG(GCET))

Method: Least Squares


Date: 12/16/14   Time: 05:33


Sample (adjusted): 3 13


Included observations: 11 after adjustments











Variable
Coefficient
Std. Error
t-Statistic
Prob.  










LOG(GCET(-1))
-1.561499
0.203916
-7.657553
0.0001
D(LOG(GCET(-1)))
0.502582
0.149488
3.362020
0.0121
C
5.001276
1.549317
3.228051
0.0145
@TREND(1)
1.282558
0.203420
6.304973
0.0004










R-squared
0.901273
    Mean dependent var
0.113438
Adjusted R-squared
0.858961
    S.D. dependent var
4.414185
S.E. of regression
1.657752
    Akaike info criterion
4.124090
Sum squared resid
19.23699
    Schwarz criterion
4.268779
Log likelihood
-18.68249
    Hannan-Quinn criter.
4.032883
F-statistic
21.30086
    Durbin-Watson stat
1.350074
Prob(F-statistic)
0.000676





















LOG(GDP) @ 1st dif
Null Hypothesis: D(LOG(GDP)) has a unit root

Exogenous: Constant, Linear Trend

Lag Length: 0 (Automatic - based on SIC, maxlag=2)













t-Statistic
  Prob.*










Augmented Dickey-Fuller test statistic
-4.088843
 0.0405
Test critical values:
1% level

-5.124875


5% level

-3.933364


10% level

-3.420030











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











Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LOG(GDP),2)

Method: Least Squares


Date: 12/16/14   Time: 05:34


Sample (adjusted): 3 13


Included observations: 11 after adjustments











Variable
Coefficient
Std. Error
t-Statistic
Prob.  










D(LOG(GDP(-1)))
-1.256857
0.307387
-4.088843
0.0035
C
0.245933
0.078712
3.124447
0.0141
@TREND(1)
-0.006396
0.008807
-0.726171
0.4884










R-squared
0.694185
    Mean dependent var
0.004887
Adjusted R-squared
0.617731
    S.D. dependent var
0.148427
S.E. of regression
0.091769
    Akaike info criterion
-1.712076
Sum squared resid
0.067373
    Schwarz criterion
-1.603559
Log likelihood
12.41642
    Hannan-Quinn criter.
-1.780480
F-statistic
9.079789
    Durbin-Watson stat
2.368791
Prob(F-statistic)
0.008747














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