**Table 6.**

*ARDL summary results.*


#### *Economic, Social, and Environmental Dimensions of Development in Sudan DOI: http://dx.doi.org/10.5772/intechopen.90752*

(ASW) + 1.15 L(ELC) + 0.04 L(CO2P) + 0.80\* L(TOP) 52.28)

*\*\*\*, \*\*, and \* indicate significance at 1%, 5%, and 10% level, respectively.*

#### **Table 7.**

coefficient of the error correction term estimated at 0.53 is highly significant confirming cointegration of the study variables and average speed of adjustment to equilibrium in the long run in response to the short-run shocks of the model variables. Results of the diagnostic tests show that the estimated ARDL suffers none of the conventional econometric problems associated with time series data. Thus, the estimated model is stable and robust and significantly captures the behavior of the association between economic growth and social and environmental progress indicators.

*Perspectives on Economic Development - Public Policy, Culture, and Economic Development*

**Variable Coefficient Std. error t-statistic Prob.\*** L(GDP)t-1 0.69 0.123 5.620 0.000\*\*\* L(GDP)t-2 0.22 0.126 1.737 0.096\* L(INV) 0.41 0.090 4.540 0.000\*\*\* L(INV)t-1 0.30 0.096 3.126 0.005\*\*\* L(INV)t-2 0.44 0.092 4.751 0.000\*\*\* L(YUN) 3.05 1.292 2.360 0.028\*\* L(LE) 0.61 6.138 0.099 0.922 L(LE)t-1 6.81 10.036 0.679 0.504 L(LE)t-2 16.45 9.763 1.685 0.106\* L(ASE) 0.28 0.430 0.650 0.523 L(ASF) 0.39 0.853 0.455 0.654 L(ASF)t-1 0.52 1.051 0.493 0.627 L(ASF)t-2 2.76 0.822 3.352 0.003\*\*\* L(ASW) 3.18 1.529 2.077 0.050\*\* L(ASW)t-1 5.55 1.879 2.955 0.007\*\*\* L(ASW)t-2 3.91 1.461 2.676 0.014\*\* L(ELC) 0.11 0.266 0.429 0.672 L(ELC)t-1 0.49 0.246 1.987 0.060\* L(CO2P) 0.02 0.125 0.164 0.871 L(TOP) 0.03 0.115 0.269 0.791 L(TOP)t-1 0.04 0.135 0.284 0.779 L(TOP)t-2 0.49 0.128 3.832 0.001\*\*\* DUM3 0.01 0.095 0.104 0.919 <sup>C</sup> 27.54 10.872 2.533 0.019\*\* R-squared = 0.994; adjusted R-squared = 0.988; SER = 0.102; SSR = 0.229; LL = 56.70; F-statistic = 167.22,

P(0.000); AIC = 1.422; SC = 0.468; HQ = 1.064; DW = 2.15.

*\*\*\*, \*\*, and \* indicate significance at 1%, 5%, and 10% level, respectively.*

**Table 6.**

**76**

*ARDL summary results.*

Diagnosis Stat. P. value D.W. Normality 0.04 (0.979) 2.15 Autocorrelation 0.59 (0.562) 2.05 Heteroskedasticity 0.32 (0.996) 2.36 Stability 0.12 (0.728) 2.23

*ARDL long-run form. Case 2: restricted constant and no trend.*


R-squared = 0.86; adjusted R-squared = 0.80; SER = 0.085; SSR = 0.229; LL = 56.70; AIC = 1.856; SC = 1.300; HQ = 1.648; DW = 2.15

*Note: Case 2—restricted constant and no trend;* \*\*\* *at 1% level.*

#### **Table 8.**

*ARDL short-run estimates.*

As evident from **Figures 12** and **13**, all plots of cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) statistics of the recursive residuals are well within the critical bounds, implying that the coefficients in the error correction model of the ARDL are stable.

**Figure 12.** *CUSUM of the residuals.*

direction of causality in which a series causes another series if the knowledge of the history of the first improves the prediction of the second. Therefore, consistent with the ARDL bound test and Johansen cointegration tests, the causal relationships among the economic, social, and environmental dimensions of development in Sudan is examined with lag length of 2. The Granger causality tests results are reported in **Table 10**. No causal relationship is found between investment and youth unemployment. Life expectancy at birth is found to cause GDP growth rather than the other way around. Also, education and access to sanitation facilities are found to cause GDP growth with no sign of feedback from these social and environmental indicators to GDP. A bidirectional relationship is found between access to drinking water and GDP growth. GDP is found to cause access to electricity rather than electricity causing GDP growth. GDP is also found to cause carbon dioxide emissions rather than the other way around. Trade openness is found to cause GDP growth with no sign of feedback from growth to trade openness as reported in **Table 10**. As for the causal relationships among the social and environmental indicators, we report the most notable and significant relationships. Education is found to cause investment. There exists a bidirectional relationship between access to sanitation facilities and investment. Both access to sanitation facilities and education are found to cause unemployment. Unemployment is found to cause access to drinking water. Both carbon dioxide emissions and trade openness are found to cause unemployment. Interestingly, a bidirectional causal relationship is found between life expectancy and education, which indicates the importance of investing simultaneously in both sectors. A unidirectional relationship is detected to run from life expectancy at birth to access to sanitation facilities, access to drinking water, access to electricity, and carbon dioxide emissions. Access to sanitation facilities is found to cause education, access to drinking water, and carbon dioxide emissions. Education is found to cause access to drinking water, access to electricity, and carbon dioxide emissions. A bidirectional relationship is detected between access to drinking water and access to sanitation facilities indicating the proximity

**H0 H1 Intercept only Intercept and trend**

**0.05 cri. value**

r = 0 r = 0 514.493\* 239.235 134.344\* 64.505 570.844\* 273.189 134.384\* 68.812 r ≤ 1 r = 1 380.149\* 197.371 101.701\* 58.434 436.460\* 228.298 106.157\* 62.752 r ≤ 2 r = 2 278.448\* 159.530 82.403\* 52.363 330.304\* 187.470 96.049\* 56.705 r ≤ 3 r = 3 196.046\* 125.615 50.966\* 46.231 234.254\* 150.559 53.479\* 50.600 r ≤ 4 r = 4 145.080\* 95.754 42.747\* 40.078 180.775\* 117.708 50.569\* 44.497 r ≤ 5 r = 5 102.333\* 69.819 36.978\* 33.877 130.206\* 88.804 37.727 38.331 r ≤ 6 r = 6 65.355\* 47.856 27.671 27.584 92.479\* 63.876 30.562 32.118 r ≤ 7 r = 7 37.684\* 29.797 20.444 21.132 61.917\* 42.915 27.665\* 25.823 r ≤ 8 r = 8 17.240\* 15.495 14.8780\* 14.265 34.252\* 25.872 19.378 19.387 r ≤ 9 r = 9 2.362 3.841 2.362 3.841 14.874\* 12.518 14.874\* 12.518 *Intercept only: Trace test indicates nine cointegrating equations; max. Eigenvalue test indicates seven cointegrating equations. Intercept and trend: Trace test indicates ten cointegrating equations; max. Eigenvalue test indicates five*

**Trace stat.**

**0.05 cri. value**

**Max. eigenvalue stat.**

**0.05 cri. value**

**Max. eigenvalue stat.**

*Economic, Social, and Environmental Dimensions of Development in Sudan*

**Trace stat.**

*cointegrating equations;*

*Johansen cointegration results.*

**Table 9.**

**79**

\* *denotes rejection of the hypothesis at a 0.05 level.*

**0.05 cri. value**

*DOI: http://dx.doi.org/10.5772/intechopen.90752*

#### *5.2.4 Johansen cointegration test*

In addition to the ARDL bound test approach, Johansen cointegration method is also employed at lag length of 2. The Johansen's cointegration test determines the number of cointegrating vectors of equations. It is based on the statistics of two different likelihood ratios (LR): the trace statistic and the maximum eigenvalue statistic. With the assumption of intercept only, the test shows the existence of nine cointegrating equations with the trace statistic and seven cointegrating equations with maximum eigenvalue, while with the assumption of intercept and trend, the test shows the existence of ten cointegrating equations using the trace statistic and five cointegrating equations when using the maximum eigenvalue as shown in **Table 9**. Thus, the test results show that a long-run equilibrium relationship exists among the variables of the study and also justifies the use of the ARDL bound test method to cointegration.

Thus both the ARDL and Johansen cointegration tests confirm the existence of a long-run equilibrium relationship which indicates that the social and environmental indicators, together with investment and trade openness, are simultaneously playing an important role in determining the GDP growth in Sudan.

#### *5.2.5 Granger causality analysis*

Cointegration implies that causality exists between the series, but it does not indicate the direction of causality. Granger causality test enables to detect the


*Economic, Social, and Environmental Dimensions of Development in Sudan DOI: http://dx.doi.org/10.5772/intechopen.90752*

*Intercept only: Trace test indicates nine cointegrating equations; max. Eigenvalue test indicates seven cointegrating equations. Intercept and trend: Trace test indicates ten cointegrating equations; max. Eigenvalue test indicates five cointegrating equations;*

\* *denotes rejection of the hypothesis at a 0.05 level.*

#### **Table 9.**

*5.2.4 Johansen cointegration test*

*CUSUM of squares of the residuals.*

**Figure 12.**

**Figure 13.**

*CUSUM of the residuals.*

method to cointegration.

*5.2.5 Granger causality analysis*

**78**

In addition to the ARDL bound test approach, Johansen cointegration method is also employed at lag length of 2. The Johansen's cointegration test determines the number of cointegrating vectors of equations. It is based on the statistics of two different likelihood ratios (LR): the trace statistic and the maximum eigenvalue statistic. With the assumption of intercept only, the test shows the existence of nine cointegrating equations with the trace statistic and seven cointegrating equations with maximum eigenvalue, while with the assumption of intercept and trend, the test shows the existence of ten cointegrating equations using the trace statistic and five cointegrating equations when using the maximum eigenvalue as shown in **Table 9**. Thus, the test results show that a long-run equilibrium relationship exists among the variables of the study and also justifies the use of the ARDL bound test

*Perspectives on Economic Development - Public Policy, Culture, and Economic Development*

Thus both the ARDL and Johansen cointegration tests confirm the existence of a long-run equilibrium relationship which indicates that the social and environmental indicators, together with investment and trade openness, are simultaneously

Cointegration implies that causality exists between the series, but it does not indicate the direction of causality. Granger causality test enables to detect the

playing an important role in determining the GDP growth in Sudan.

*Johansen cointegration results.*

direction of causality in which a series causes another series if the knowledge of the history of the first improves the prediction of the second. Therefore, consistent with the ARDL bound test and Johansen cointegration tests, the causal relationships among the economic, social, and environmental dimensions of development in Sudan is examined with lag length of 2. The Granger causality tests results are reported in **Table 10**. No causal relationship is found between investment and youth unemployment. Life expectancy at birth is found to cause GDP growth rather than the other way around. Also, education and access to sanitation facilities are found to cause GDP growth with no sign of feedback from these social and environmental indicators to GDP. A bidirectional relationship is found between access to drinking water and GDP growth. GDP is found to cause access to electricity rather than electricity causing GDP growth. GDP is also found to cause carbon dioxide emissions rather than the other way around. Trade openness is found to cause GDP growth with no sign of feedback from growth to trade openness as reported in **Table 10**. As for the causal relationships among the social and environmental indicators, we report the most notable and significant relationships. Education is found to cause investment. There exists a bidirectional relationship between access to sanitation facilities and investment. Both access to sanitation facilities and education are found to cause unemployment. Unemployment is found to cause access to drinking water. Both carbon dioxide emissions and trade openness are found to cause unemployment. Interestingly, a bidirectional causal relationship is found between life expectancy and education, which indicates the importance of investing simultaneously in both sectors. A unidirectional relationship is detected to run from life expectancy at birth to access to sanitation facilities, access to drinking water, access to electricity, and carbon dioxide emissions. Access to sanitation facilities is found to cause education, access to drinking water, and carbon dioxide emissions. Education is found to cause access to drinking water, access to electricity, and carbon dioxide emissions. A bidirectional relationship is detected between access to drinking water and access to sanitation facilities indicating the proximity


of these variables to one another. Trade openness is found to cause access to sanitation facilities which might indicate the importance of imported goods to the improvement of sanitation facilities. Access to drinking water is found to cause access to electricity. Only significant causal relations between social and environmental dimensions of development are extracted and reported

**H0 Obs. F-statistic Prob. Decision Direction of causality** H0: L(ASF) does not cause L(CO2P) 46 6.213 0.004 Reject ASF to CO2P H0: L(TOP) does not cause L(ASF) 46 5.610 0.007 Reject TOP to ASF H0: L(ASW) does not cause L(ELC) 46 4.568 0.016 Reject ASW to ELC

> **MMR SDG**

 17.29 49.47 308.06 45.78 58.93 26.90 29.11 17.27 48.09 231.05 46.81 59.09 26.60 29.61 17.28 45.85 215.64 48.41 59.27 26.40 30.08 17.15 43.62 200.24 52.40 60.33 28.95 32.86 17.03 41.38 184.84 56.40 61.39 31.50 35.64 16.91 39.14 169.43 60.39 62.45 34.05 38.42 16.79 36.91 154.03 64.39 63.50 36.60 41.20 16.67 34.67 138.63 68.38 64.56 39.15 43.98 16.54 32.43 123.22 72.38 65.62 41.70 46.76 16.42 30.20 107.82 76.37 66.67 44.25 49.54 16.30 27.96 92.42 80.37 67.73 46.80 52.32 16.18 25.72 77.02 84.36 68.79 49.35 55.10 16.06 23.49 61.61 88.36 69.85 51.90 57.88 15.93 21.25 46.21 92.35 70.90 54.45 60.66 15.81 19.01 30.81 96.35 71.96 57.00 63.44 15.69 16.78 15.40 100.34 73.02 59.55 66.22

**ASE SDG** **ASW SDG**

**ASF SDG**

**ELC SDG**

**SDG**

*Economic, Social, and Environmental Dimensions of Development in Sudan*

In the light of our findings on the performance of Sudan on socioeconomic components of development, we make some projections for the period of 2015– 2030. Projections are made according to the values of indicators in 2015 compared with that in 1990. We assume that the youth unemployment can be reduced to fluctuate around an average of 10%, and accordingly we projected the required job creation for youth in order to achieve this. Under five (UMR) and maternal mortality (MMR) ratios are projected according to the target of reduction by two third and 75%, respectively. Access to sanitation facilities, access to safe drinking water, and access to electricity are projected against full coverage of 100% of population.

in **Table 10**.

**81**

**Table 11.**

**Table 10.**

*Granger causality test results.*

**Year UNE required increase UMR**

*DOI: http://dx.doi.org/10.5772/intechopen.90752*

Projections are summarized in **Table 11**.

*Projection of key socioeconomic indicators toward SDGs.*

*Economic, Social, and Environmental Dimensions of Development in Sudan DOI: http://dx.doi.org/10.5772/intechopen.90752*


#### **Table 10.**

**H0 Obs. F-statistic Prob. Decision Direction of causality**

H0: L(INV) does not cause L(GDP) 46 0.050 0.951 Accept None H0: L(GDP) does not cause L(INV) 46 0.097 0.908 Accept None H0: L(YUN) does not cause L(GDP) 46 0.284 0.754 Accept None H0: L(GDP) does not cause L(YUN) 46 1.579 0.219 Accept None H0: L(LE) does not cause L(GDP) 46 3.557 0.038 Reject LE to GDP H0: L(GDP) does not cause L(LE) 46 1.122 0.335 Accept None H0: L(EDU) does not cause L(GDP) 46 4.927 0.012 Reject EDU to GDP H0: L(GDP) does not cause L(EDU) 46 0.268 0.766 Accept None H0: L(ASF) does not cause L(GDP) 46 2.633 0.084 Reject ASF to GDP H0: L(GDP) does not cause L(ASF) 46 0.111 0.895 Accept None H0: L(ASW) does not cause L(GDP) 46 3.575 0.037 Reject ASW to GDP H0: L(GDP) does not cause L(ASW) 46 5.142 0.010 Reject GDP to ASW H0: L(ELC) does not cause L(GDP) 46 1.094 0.344 Accept None H0: L(GDP) does not cause L(ELC) 46 9.590 0.000 Reject GDP to ELC H0: L(CO2P) does not cause L(GDP) 46 1.501 0.235 Accept None H0: L(GDP) does not cause L(CO2P) 46 4.860 0.013 Reject GDP to CO2P H0: L(TOP) does not cause L(GDP) 46 6.196 0.005 Reject TOP to GDP H0: L(GDP) does not cause L(TOP) 46 0.531 0.592 Accept None H0: Independent Variables Obs. F-Statistic Prob. Decision Direction of causality H0: L(EDU) does not cause L(INV) 46 3.243 0.050 Reject EDU to INV H0: L(ASF) does not cause L(INV) 46 3.967 0.027 Reject ASF to INV H0: L(INV) does not cause L(ASF) 46 4.141 0.023 Reject INV to ASF H0: L(EDU) does not cause L(YUN) 46 2.457 0.098 Accept EDU to YUN H0: L(ASF) does not cause L(YUN) 46 6.133 0.005 Reject ASF to YUN H0: L(YUN) does not cause L(ASW) 46 5.478 0.008 Reject YUN to ASW H0: L(CO2P) does not cause L(YUN) 46 4.850 0.013 Reject CO2P to YUN H0: L(TOP) does not cause L(YUN) 46 3.244 0.049 Reject TOP to YUN H0: L(EDU) does not cause L(LE) 46 2.628 0.084 Reject EDU to LE H0: L(LE) does not cause L(EDU) 46 4.134 0.023 Reject LE to EDU H0: L(LE) does not cause L(ASF) 46 4.282 0.021 Reject LE to ASF H0: L(LE) does not cause L(ASW) 46 3.619 0.036 Reject LE to ASW H0: L(LE) does not cause L(ELC) 46 11.616 0.000 Reject LE to ELC H0: L(LE) does not cause L(CO2P) 46 3.370 0.044 Reject LE to CO2P H0: L(ASF) does not cause L(EDU) 46 5.793 0.006 Reject ASF to EDU H0: L(EDU) does not cause L(ASW) 46 4.372 0.019 Reject EDU to ASW H0: L(EDU) does not cause L(ELC) 46 7.313 0.002 Reject EDU to ELC H0: L(EDU) does not cause L(CO2P) 46 2.952 0.063 Reject EDU to CO2P H0: L(ASW) does not cause L(ASF) 46 3.745 0.032 Reject ASW to ASF H0: L(ASF) does not cause L(ASW) 46 5.227 0.010 Reject ASF to ASW

*Perspectives on Economic Development - Public Policy, Culture, and Economic Development*

**80**

*Granger causality test results.*


#### **Table 11.**

*Projection of key socioeconomic indicators toward SDGs.*

of these variables to one another. Trade openness is found to cause access to sanitation facilities which might indicate the importance of imported goods to the improvement of sanitation facilities. Access to drinking water is found to cause access to electricity. Only significant causal relations between social and environmental dimensions of development are extracted and reported in **Table 10**.

In the light of our findings on the performance of Sudan on socioeconomic components of development, we make some projections for the period of 2015– 2030. Projections are made according to the values of indicators in 2015 compared with that in 1990. We assume that the youth unemployment can be reduced to fluctuate around an average of 10%, and accordingly we projected the required job creation for youth in order to achieve this. Under five (UMR) and maternal mortality (MMR) ratios are projected according to the target of reduction by two third and 75%, respectively. Access to sanitation facilities, access to safe drinking water, and access to electricity are projected against full coverage of 100% of population. Projections are summarized in **Table 11**.

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