**4.1. Democratic Republic of Congo (DRC)**

The lag selection process suggested lag 3 for DRC time series. VAR (3) was used to assess the interaction between climatic variables and people movement. In other words how rainfall variability and rising temperatures may have impacted people movement in Democratic Republic of Congo. VAR results have shown that temperature has a significant impact on rural and urban migrations in DRC (**Table 7**). The temperature (lag 2) is a significant variable––less than 10 and 5%—to explain DRC rural migration and urban migration, respectively. Further, the results indicate a strong relationship between rural migration and urban migration in DRC; where urban migration is statistically significant to explain changes in rural migration and vice versa.

Granger causality tests were conducted to test the VAR model and to determine whether each variable plays a significant role in each of the equations. The granger causality tests have shown that there is significant granger causality effect from Temp to both DRC MR and MU. Further, Temp may able to compensate for the other variables including rainfall and lead to a combined granger effect on DRC MR and MU. The combined effect of the rising temperature and rainfall variabilities seem to be significant at 5% level. Further, the results also suggest that there is a significant granger causes exist between rural migration and urban migration where each migration variable granger causes the other at less than 1% significance level. **Table 8** presents the granger causality Wald tests for MR and MU of DRC. The results suggest that future amounts of MR and MU in DRC can be predicted by using Eqs. (4) and (5).

temperature impacts urban migration in Kenya. Further, results also suggest that there is a significant relationship between rural migration and urban migration where each variable is

**Table 6.** Model fitting for VAR models for MR, MU, rain and temp of Congo DR, Kenya and Niger.

**Equation Parms RMSE R-sq chi2 P-value** DRC\_MR 13 10478.4 0.9439 857.9503 0.000 DRC\_MU 13 7176.59 0.9128 533.7465 0.000 DRC\_Rain 13 62.5479 0.3807 31.35537 0.002 DRC\_Temp 13 0.203218 0.7205 131.4498 0.000 KEN\_MR 13 9601.34 0.9529 1031.6 0.000 KEN\_MU 13 7453.29 0.9059 491.136 0.000 KEN\_Rain 13 107.421 0.1966 12.48087 0.408 KEN\_Temp 13 0.31654 0.5711 67.90203 0.000 NER\_MR 13 2916.02 0.7778 178.5192 0.000 NER\_MU 13 2882.49 0.5537 63.27865 0.000 NER\_Rain 13 29.2051 0.2938 21.21593 0.047 NER\_Temp 13 0.33013 0.6683 102.7623 0.000

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19

Granger causality tests were conducted to determine whether each variable plays a significant role in each of the equations. As shown in **Table 10**, both rain and temperature granger cause (<5%) rural migration in Kenya; rainfall and temperature together granger cause rural migration in Kenya at 1% significance level. Further, rising temperatures granger cause MU at less than 5%. The results also suggest that granger causality effects exist between rural and urban migrations, where each migration variable granger causes the other at less than 1% significance level. The VAR results suggest that future amounts of MR and MU in Kenya can be predicted by using Eqs. (6) and (7).

*KEN*\_*M Rt* = 143048.2 + 1.527*M Rt*−<sup>1</sup> − 1.121*M Rt*−<sup>2</sup> + 0.192*M Rt*−<sup>3</sup> + 1.105*M Ut*−<sup>1</sup> <sup>−</sup> 1.096*<sup>M</sup> Ut*−<sup>2</sup> <sup>+</sup> 0.475*<sup>M</sup> Ut*−<sup>3</sup> <sup>+</sup> 15.2*Rai nt*−<sup>1</sup> <sup>+</sup> 0.786*Rai nt*−2

*KEN*\_*M Ut* = − 207028 + 0.619*M Rt*−<sup>1</sup> − 0.47*M Rt*−<sup>2</sup> − 0.023*M Rt*−<sup>3</sup> + 0.56*M Ut*−<sup>1</sup> <sup>−</sup> 0.273*<sup>M</sup> Ut*−<sup>2</sup> <sup>+</sup> 0.268*<sup>M</sup> Ut*−<sup>3</sup> <sup>−</sup> 5.785*Rai nt*−<sup>1</sup> <sup>+</sup> 4.024*Rai nt*−2

VAR lag 3 was used to examine the interaction between climatic variables (namely rainfall and temperature) and migration variables (rural and urban). The VAR results have shown that rainfall

− 27.1*Rai nt*−<sup>3</sup> + 6401.1*Tem pt*−<sup>1</sup> − 14576.6*Tem pt*−<sup>2</sup> + 2526.64*Tem pt*−<sup>3</sup> + *ut*

+ 12.26*Rai nt*−<sup>3</sup> − 2708.18*Tem pt*−<sup>1</sup> + 8202.4*Tem pt*−<sup>2</sup> + 2833*Tem pt*−<sup>3</sup> + *ut*

(6)

(7)

statistically significant to explain changes in to the other.

**4.3. Niger (NER)**

$$\begin{aligned} \text{Maximum mass in LTC channel } v\_l &\text{ using } m\_{l-1} = 0 \text{ (}\mu\text{s)}\\ \text{ } & \text{ (}\text{RCR\\_MR}\_{l\_l} = 355348 + 1.476 \text{MR\\_l}\_{l\_{l-1}} - 1.237 \text{MR\\_l}\_{l\_{l-2}} + 0.23 \text{MR\\_l}\_{l\_{l-3}} + 1.274 \text{M\\_l}\_{l\_{l-1}}\\ & - 1.064 \text{M\\_l}\_{l\_{l-2}} + 0.531 \text{M\\_l}\_{l\_{l-3}} - 4.35 \text{R\\_l} \text{ in\\_}\_{l\_{l-1}} + 22.61 \text{R\\_l} \text{ in\\_}\_{l\_{l-2}}\\ & - 7.751 \text{R\\_in}\_{l\_{l-3}} + 1987.52 \text{Temp}\_{l\_{l-1}} - 11957.5 \text{Temp}\_{l\_{l-2}} - 5619.88 \text{Temp}\_{l\_{l-3}} + u\_l \end{aligned} \tag{4}$$

$$\begin{aligned} \text{RRC\\_M\,U}\_{\text{i}} &= -384210 + 0.586 \,\text{M\,R}\_{\text{i}-1} - 0.364 \,\text{M\,R}\_{\text{i}-2} - 0.067 \,\text{M\,R}\_{\text{i}-3} + 0.52 \,\text{M\,U}\_{\text{i}-1} \\ &- 0.227 \,\text{M\,U}\_{\text{i}-2} + 0.142 \,\text{M\,U}\_{\text{i}-3} + 0.857 \,\text{R\,i\,u}\_{\text{i}-1} - 24.366 \,\text{R\,i\,u}\_{\text{i}-2} \\ &+ 3.364 \,\text{R\,i\,u}\_{\text{i}-3} + 5775.5 \,\text{Temp}\_{\text{i}-1} + 11500 \,\text{Temp}\_{\text{i}-2} - 1731.45 \,\text{Temp}\_{\text{i}-3} + u\_{\text{i}} \end{aligned} (5)$$

#### **4.2. Kenya (KEN)**

The lag selection process suggested lag 3 for KEN time series. VAR (3) was conducted to assess the impact that rainfall fluctuations and rising temperatures have on rural and urban migrations in Kenya. This is to identify how rainfall variability and rising temperatures may impact rural and urban migrations. VAR results have shown that temperature has a significant impact on rural and urban migration in Kenya (**Table 9**). Both rainfall and temperature are statistically significant at less than 5% to explain rural migration in Kenya. Similarly, The Effects of Climate Change on Rural-Urban Migration in Sub-Saharan Africa (SSA)—The… http://dx.doi.org/10.5772/intechopen.72226 19


**Table 6.** Model fitting for VAR models for MR, MU, rain and temp of Congo DR, Kenya and Niger.

temperature impacts urban migration in Kenya. Further, results also suggest that there is a significant relationship between rural migration and urban migration where each variable is statistically significant to explain changes in to the other.

Granger causality tests were conducted to determine whether each variable plays a significant role in each of the equations. As shown in **Table 10**, both rain and temperature granger cause (<5%) rural migration in Kenya; rainfall and temperature together granger cause rural migration in Kenya at 1% significance level. Further, rising temperatures granger cause MU at less than 5%. The results also suggest that granger causality effects exist between rural and urban migrations, where each migration variable granger causes the other at less than 1% significance level. The VAR results suggest that future amounts of MR and MU in Kenya can be predicted by using Eqs. (6) and (7).

$$\begin{aligned} \text{Final numre amounts on kW and W.I.} \text{ } & \text{and } \text{ $m$ -} & \text{and } \text{ $m$ -} \\ \text{ } & \text{KEN\\_MR}\_{\text{.}} = 143048.2 + 1.527M \, R\_{\text{.}1} - 1.121M \, R\_{\text{.}2} + 0.192M \, R\_{\text{.}3} + 1.105M \, L\_{\text{.}1} \\ & - 1.096M \, U\_{\text{.}2} + 0.475M \, U\_{\text{.}3} + 15.2 \text{Rai } n\_{\text{.}1} + 0.786 \text{Rai } n\_{\text{.}2} \\ & - 27.1 \text{Rai } n\_{\text{.}3} + 6401.1 \text{Tem } p\_{\text{.}1} - 14576.6 \text{Tem } p\_{\text{.}2} + 2526.64 \text{Tem } p\_{\text{.}3} + u\_{\text{.}} \end{aligned} \tag{6}$$

*KEN*\_*M Ut* = − 207028 + 0.619*M Rt*−<sup>1</sup> − 0.47*M Rt*−<sup>2</sup> − 0.023*M Rt*−<sup>3</sup> + 0.56*M Ut*−<sup>1</sup> <sup>−</sup> 0.273*<sup>M</sup> Ut*−<sup>2</sup> <sup>+</sup> 0.268*<sup>M</sup> Ut*−<sup>3</sup> <sup>−</sup> 5.785*Rai nt*−<sup>1</sup> <sup>+</sup> 4.024*Rai nt*−2 + 12.26*Rai nt*−<sup>3</sup> − 2708.18*Tem pt*−<sup>1</sup> + 8202.4*Tem pt*−<sup>2</sup> + 2833*Tem pt*−<sup>3</sup> + *ut* (7)

#### **4.3. Niger (NER)**

(4)

(5)

the VAR model gave the R2

rural migration and vice versa.

**4.2. Kenya (KEN)**

results. As shown in **Table 6**, most of R2

18 Applications in Water Systems Management and Modeling

**4.1. Democratic Republic of Congo (DRC)**

values shown in **Table 6**. The test of the model fit gave favourable

values are above 0.70.

The lag selection process suggested lag 3 for DRC time series. VAR (3) was used to assess the interaction between climatic variables and people movement. In other words how rainfall variability and rising temperatures may have impacted people movement in Democratic Republic of Congo. VAR results have shown that temperature has a significant impact on rural and urban migrations in DRC (**Table 7**). The temperature (lag 2) is a significant variable––less than 10 and 5%—to explain DRC rural migration and urban migration, respectively. Further, the results indicate a strong relationship between rural migration and urban migration in DRC; where urban migration is statistically significant to explain changes in

Granger causality tests were conducted to test the VAR model and to determine whether each variable plays a significant role in each of the equations. The granger causality tests have shown that there is significant granger causality effect from Temp to both DRC MR and MU. Further, Temp may able to compensate for the other variables including rainfall and lead to a combined granger effect on DRC MR and MU. The combined effect of the rising temperature and rainfall variabilities seem to be significant at 5% level. Further, the results also suggest that there is a significant granger causes exist between rural migration and urban migration where each migration variable granger causes the other at less than 1% significance level. **Table 8** presents the granger causality Wald tests for MR and MU of DRC. The results suggest that future amounts

*DRC*\_*M Rt* = 355348 + 1.476*M Rt*−<sup>1</sup> − 1.237*M Rt*−<sup>2</sup> + 0.23*M Rt*−<sup>3</sup> + 1.274*M Ut*−<sup>1</sup> <sup>−</sup> 1.064*<sup>M</sup> Ut*−<sup>2</sup> <sup>+</sup> 0.531*<sup>M</sup> Ut*−<sup>3</sup> <sup>−</sup> 4.35*Rai nt*−<sup>1</sup> <sup>+</sup> 22.61*Rai nt*−2

− 7.751*Rai nt*−<sup>3</sup> + 1987.52*Tem pt*−<sup>1</sup> − 11957.5*Tem pt*−<sup>2</sup> − 5619.88*Tem pt*−<sup>3</sup> + *ut*

*DRC*\_*M Ut* = − 384210 + 0.586*M Rt*−<sup>1</sup> − 0.36*M Rt*−<sup>2</sup> − 0.067*M Rt*−<sup>3</sup> + 0.52*M Ut*−<sup>1</sup>

The lag selection process suggested lag 3 for KEN time series. VAR (3) was conducted to assess the impact that rainfall fluctuations and rising temperatures have on rural and urban migrations in Kenya. This is to identify how rainfall variability and rising temperatures may impact rural and urban migrations. VAR results have shown that temperature has a significant impact on rural and urban migration in Kenya (**Table 9**). Both rainfall and temperature are statistically significant at less than 5% to explain rural migration in Kenya. Similarly,

<sup>−</sup> 0.227*<sup>M</sup> Ut*−<sup>2</sup> <sup>+</sup> 0.142*<sup>M</sup> Ut*−<sup>3</sup> <sup>+</sup> 0.857*Rai nt*−<sup>1</sup> <sup>−</sup> 24.366*Rai nt*−2

+ 3.364*Rai nt*−<sup>3</sup> + 5775.5*Tem pt*−<sup>1</sup> + 11500*Tem pt*−<sup>2</sup> − 1731.45*Tem pt*−<sup>3</sup> + *ut*

of MR and MU in DRC can be predicted by using Eqs. (4) and (5).

VAR lag 3 was used to examine the interaction between climatic variables (namely rainfall and temperature) and migration variables (rural and urban). The VAR results have shown that rainfall


**Lags Coef. Std. Err. z P-value [95% Conf. Interval]**

L2. −1.12056 0.200759 −5.58 0.000 −1.51404 −0.72708 L3. 0.191674 0.130621 1.47 0.142 −0.06434 0.447688

L2. −1.09597 0.199596 −5.49 0.000 −1.48717 −0.70477 L3. 0.475334 0.153952 3.09 0.002 0.173592 0.777075

L2. 0.785979 12.16677 0.06 0.948 −23.0605 24.6324 L3. −27.1015 11.93806 −2.27 0.023 −50.4996 −3.70329

L2. −14576.6 4502.144 −3.24 0.001 −23400.6 −5752.53 L3. 2526.642 4568.531 0.55 0.580 −6427.51 11480.8 Cons 143048.2 114,720 1.25 0.212 −81798.8 367895.3

L2. −0.4728 0.155844 −3.03 0.002 −0.77825 −0.16736 L3. −0.02283 0.101398 −0.23 0.822 −0.22156 0.175912

L2. −0.27283 0.154942 −1.76 0.078 −0.57651 0.030848 L3. 0.267967 0.11951 2.24 0.025 0.033733 0.502201

L2. 4.023753 9.444765 0.43 0.670 −14.4877 22.53515 L3. 12.26245 9.267225 1.32 0.186 −5.90098 30.42588

KEN\_MR L1. 1.527326 0.124974 12.22 0.000 1.282381 1.772271

**Table 8.** Granger causality Wald tests for MR and MU of DRC.

**Equation Excluded chi2 df P-value** DRC\_MR DRC\_MU 43.642 3 0.000 DRC\_MR DRC\_Rain 0.90801 3 0.823 DRC\_MR DRC\_Temp 7.7732 3 0.051 DRC\_MR ALL 47.282 9 0.000 DRC\_MU DRC\_MR 82.511 3 0.000 DRC\_MU DRC\_Rain 5.2714 3 0.153 DRC\_MU DRC\_Temp 14.913 3 0.002 DRC\_MU ALL 126.24 9 0.000

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KEN\_MU L1. 1.105072 0.173335 6.38 0.000 0.765342 1.444802

KEN\_Rain L1. 15.18659 12.65142 1.2 0.230 −9.60974 39.98292

KEN\_Temp L1. 6401.065 3979.371 1.61 0.108 −1398.36 14200.49

KEN\_MR L1. 0.618856 0.097015 6.38 0.000 0.428711 0.809001

KEN\_MU L1. 0.563427 0.134556 4.19 0.000 0.299703 0.827151

KEN\_Rain L1. −5.7853 9.820989 −0.59 0.556 −25.0341 13.46348

**KEN\_MR**

**KEN\_MU**

**Table 7.** Democratic Republic of Congo MR and MU VAR results.

(lags 1 and 3) impacts rural migration in Niger and statistically significant. Moreover, temperature (lag 2) impacts rural migration at less than 10% significance level. On the other hand, the VAR results have shown that rainfall impacts urban migration and statistically significant (less than 5%); while temperature (lag 2) impacts urban migration at less than 5% significance level (**Table 11**). The Effects of Climate Change on Rural-Urban Migration in Sub-Saharan Africa (SSA)—The… http://dx.doi.org/10.5772/intechopen.72226 21


**Table 8.** Granger causality Wald tests for MR and MU of DRC.

(lags 1 and 3) impacts rural migration in Niger and statistically significant. Moreover, temperature (lag 2) impacts rural migration at less than 10% significance level. On the other hand, the VAR results have shown that rainfall impacts urban migration and statistically significant (less than 5%); while temperature (lag 2) impacts urban migration at less than 5% significance level (**Table 11**).

**Lags Coef. Std. Err. z P-value [95% conf. interval]**

L2. −1.23725 0.229631 −5.39 0.000 −1.68732 −0.78718 L3. 0.230204 0.144511 1.59 0.111 −0.05303 0.513439

L2. −1.06411 0.216905 −4.91 0.000 −1.48924 −0.63899 L3. 0.531081 0.185797 2.86 0.004 0.166926 0.895237

L2. 22.61041 23.87814 0.95 0.344 −24.1899 69.4107 L3. −7.75103 23.25511 −0.33 0.739 −53.3302 37.82815

L2. −11957.5 6233.197 −1.92 0.055 −24174.3 259.3549 L3. −5619.88 5994.979 −0.94 0.349 −17369.8 6130.059 Cons 355348.5 185244.5 1.92 0.055 −7723.95 718,421

L2. −0.36227 0.157273 −2.3 0.021 −0.67052 −0.05402 L3. −0.06749 0.098974 −0.68 0.495 −0.26148 0.126494

L2. −0.22655 0.148557 −1.53 0.127 −0.51772 0.064613 L3. 0.141994 0.127251 1.12 0.264 −0.10741 0.391401

L2. −24.3663 16.35395 −1.49 0.136 −56.4194 7.686882 L3. 33.36421 15.92725 2.09 0.036 2.147376 64.58104

L2. 11500.03 4269.069 2.69 0.007 3132.805 19867.25 L3. −1731.45 4105.915 −0.42 0.673 −9778.89 6315.998 Cons −384,210 126872.5 −3.03 0.002 −632,875 −135,544

DRC\_MR L1. 1.476003 0.133345 11.07 0.000 1.214651 1.737355

DRC\_MU L1. 1.273907 0.19951 6.39 0.000 0.882875 1.66494

DRC\_Rain L1. −4.34987 23.08613 −0.19 0.851 −49.5979 40.89811

DRC\_Temp L1. 1987.52 6380.53 0.31 0.755 −10518.1 14493.13

DRC\_MR L1. 0.585985 0.091327 6.42 0.000 0.406987 0.764983

DRC\_MU L1. 0.519761 0.136643 3.8 0.000 0.251946 0.787576

DRC\_Rain L1. 0.857312 15.81151 0.05 0.957 −30.1327 31.84731

DRC\_Temp L1. 5775.531 4369.976 1.32 0.186 −2789.47 14340.53

**Table 7.** Democratic Republic of Congo MR and MU VAR results.

**DRC\_MR**

20 Applications in Water Systems Management and Modeling

**DRC\_MU**



**Lags Coef. Std. Err. z P-value [95% Conf. Interval]**

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L2. −1.26126 1.001884 −1.26 0.208 −3.22492 0.702394 L3. 0.46737 0.568036 0.82 0.411 −0.64596 1.5807

L2. −1.1438 0.997921 −1.15 0.252 −3.09969 0.812092 L3. 0.378649 0.570321 0.66 0.507 −0.73916 1.496458

L2. 9.863682 13.98378 0.71 0.481 −17.544 37.27138 L3. −40.5639 15.41653 −2.63 0.009 −70.7798 −10.3481

L2. −1918.36 1100.37 −1.74 0.081 −4075.05 238.3239 L3. −356.347 1057.053 −0.34 0.736 −2428.13 1715.438 Cons 46384.24 39517.46 1.17 0.240 −31068.6 123,837

L2. −0.64425 0.990362 −0.65 0.515 −2.58532 1.296827 L3. 0.149531 0.561503 0.27 0.790 −0.951 1.250057

L2. −0.7102 0.986445 −0.72 0.472 −2.6436 1.223198 L3. 0.207188 0.563762 0.37 0.713 −0.89777 1.312142

L2. −8.85125 13.82296 −0.64 0.522 −35.9438 18.24125 L3. 42.98771 15.23924 2.82 0.005 13.11935 72.85607

L2. 2293.472 1087.715 2.11 0.035 161.5892 4425.355 L3. 247.8878 1044.897 0.24 0.812 −1800.07 2295.847 Cons −56333.9 39,063 −1.44 0.149 −132,896 20228.15

NER\_MR L1. 1.648669 0.548767 3 0.003 0.573106 2.724232

NER\_MU L1. 1.22845 0.547162 2.25 0.025 0.156032 2.300868

NER\_Rain L1. 26.30783 14.01253 1.88 0.060 −1.15623 53.77188

NER\_Temp L1. 622.3344 1061.896 0.59 0.558 −1458.94 2703.613

NER\_MR L1. 0.615852 0.542456 1.14 0.256 −0.44734 1.679046

NER\_MU L1. 1.013816 0.54087 1.87 0.061 −0.04627 2.073901

NER\_Rain L1. −32.8194 13.85138 −2.37 0.018 −59.9676 −5.67121

NER\_Temp L1. −498.241 1049.684 −0.47 0.635 −2555.58 1559.103

**NER\_MR**

**NER\_MU**

**Table 11.** Niger MR and MU VAR results.

**Table 9.** Kenya MR and MU VAR results.

Further, there is a significant relationship between urban migration and rural migration in Niger, where rural migration is statistically significant at less than 5% to explain changes that occur to urban migration in Niger.

Granger causality tests were conducted to examine whether each variable plays a significant role in each of the equations. The granger causality tests have indicated that there are granger causality effects from Rain to both MR and MU at less than 5% significance level (**Table 12**). Further, the results also suggest that there is a granger causality running from urban migration to rural migration at less than 1% significance level. The granger causality Wald tests have suggested that temperature does not granger cause people movement in Niger. The results suggest that future amounts of MR and MU in NER can be predicted by using Eqs. (8) and (9).

$$\begin{aligned} \text{heat future amounts of MR and MU in NER can be predicted by using Eqs. (8) and (9).}\\ \text{NER\\_MR}\_{\text{ }l} &= 46384.24 + 1.64 MR\_{\text{ }l-1} - 1.26 MR\_{\text{ }l-2} + 0.467 MR\_{\text{ }l-3} + 1.228 M \,\text{L}\_{\text{ }l-1} \\ &- 1.1444 \,\text{M} \,\text{L}\_{\text{ }l-2} + 0.378 M \,\text{L}\_{\text{ }l-3} + 26.3 \text{Rai }n\_{\text{ }l-1} + 9.86 \text{Rai }n\_{\text{ }l-2} \\ &- 40.56 \,\text{Rai }n\_{\text{ }l-3} + 62.7 \text{L} \,\text{m }p\_{\text{ }l-1} - 1918 \,\text{Tem }p\_{\text{ }l-2} - 356 \,\text{Tem }p\_{\text{ }l-3} + u\_{\text{ }l} \end{aligned} \tag{8}$$

*NER*\_*M Ut* = −56334 + 0.616*M Rt*−<sup>1</sup> − 0.644*M Rt*−<sup>2</sup> + 0.15*M Rt*−<sup>3</sup> + 1.014*M Ut*−<sup>1</sup> <sup>−</sup> 0.7*<sup>M</sup> Ut*−<sup>2</sup> <sup>+</sup> 0.21*<sup>M</sup> Ut*−<sup>3</sup> <sup>−</sup> 32.82*Rai nt*−<sup>1</sup> <sup>−</sup> 8.85*Rai nt*−2 + 42.987*Rai nt*−<sup>3</sup> − 498.24*Tem pt*−<sup>1</sup> + 2293.47*Tem pt*−<sup>2</sup> + 247.89*Tem pt*−<sup>3</sup> + *ut* (9)


**Table 10.** Granger causality Wald tests for MR and MU of Kenya.

The Effects of Climate Change on Rural-Urban Migration in Sub-Saharan Africa (SSA)—The… http://dx.doi.org/10.5772/intechopen.72226 23


**Table 11.** Niger MR and MU VAR results.

Further, there is a significant relationship between urban migration and rural migration in Niger, where rural migration is statistically significant at less than 5% to explain changes that

**Lags Coef. Std. Err. z P-value [95% Conf. Interval]**

L2. 8202.401 3494.905 2.35 0.019 1352.513 15052.29 L3. 2832.964 3546.439 0.8 0.424 −4117.93 9783.857 Cons −207,028 89054.35 −2.32 0.020 −381,572 −32485.1

KEN\_Temp L1. −2708.18 3089.089 −0.88 0.381 −8762.68 3346.322

Granger causality tests were conducted to examine whether each variable plays a significant role in each of the equations. The granger causality tests have indicated that there are granger causality effects from Rain to both MR and MU at less than 5% significance level (**Table 12**). Further, the results also suggest that there is a granger causality running from urban migration to rural migration at less than 1% significance level. The granger causality Wald tests have suggested that temperature does not granger cause people movement in Niger. The results suggest that future amounts of MR and MU in NER can be predicted by using Eqs. (8) and (9).

*NER*\_*M Rt* = 46384.24 + 1.64*M Rt*−<sup>1</sup> − 1.26*M Rt*−<sup>2</sup> + 0.467*M Rt*−<sup>3</sup> + 1.228*M Ut*−<sup>1</sup> <sup>−</sup> 1.144*<sup>M</sup> Ut*−<sup>2</sup> <sup>+</sup> 0.378*<sup>M</sup> Ut*−<sup>3</sup> <sup>+</sup> 26.3*Rai nt*−<sup>1</sup> <sup>+</sup> 9.86*Rai nt*−2

*NER*\_*M Ut* = −56334 + 0.616*M Rt*−<sup>1</sup> − 0.644*M Rt*−<sup>2</sup> + 0.15*M Rt*−<sup>3</sup> + 1.014*M Ut*−<sup>1</sup> <sup>−</sup> 0.7*<sup>M</sup> Ut*−<sup>2</sup> <sup>+</sup> 0.21*<sup>M</sup> Ut*−<sup>3</sup> <sup>−</sup> 32.82*Rai nt*−<sup>1</sup> <sup>−</sup> 8.85*Rai nt*−2

**Equation Excluded chi2 df P-value** KEN\_MR KEN\_MU 47.081 3 0.000 KEN\_MR KEN\_Rain 7.961 3 0.047 KEN\_MR KEN\_Temp 11.625 3 0.009 KEN\_MR ALL 66.058 9 0.000 KEN\_MU KEN\_MR 76.582 3 0.000 KEN\_MU KEN\_Rain 2.7587 3 0.430 KEN\_MU KEN\_Temp 10.582 3 0.014 KEN\_MU ALL 113.33 9 0.000

**Table 10.** Granger causality Wald tests for MR and MU of Kenya.

− 40.56*Rai nt*−<sup>3</sup> + 622*Tem pt*−<sup>1</sup> − 1918*Tem pt*−<sup>2</sup> − 356*Tem pt*−<sup>3</sup> + *ut*

+ 42.987*Rai nt*−<sup>3</sup> − 498.24*Tem pt*−<sup>1</sup> + 2293.47*Tem pt*−<sup>2</sup> + 247.89*Tem pt*−<sup>3</sup> + *ut*

(8)

(9)

occur to urban migration in Niger.

**Table 9.** Kenya MR and MU VAR results.

22 Applications in Water Systems Management and Modeling


predicted that these climatic conditions will continue to exist. Populations in these countries have been growing rapidly. These unfavourable climatic conditions and increasing population have together compounded the water scarcity and rural-urban migration conditions in Kenya and Niger. In contacts to that, Democratic Republic of Congo receives high annual rainfalls and medium low average annual temperature. Democratic Republic of Congo has abundant

The Effects of Climate Change on Rural-Urban Migration in Sub-Saharan Africa (SSA)—The…

high. During the vector auto-regression analyses, the lag selection process suggested lag 3 in all cases. VAR was used to investigate the interaction between climatic variables—rainfall and temperature—and people movement variables—rural and urban migrations. By using VAR (3), it was tested how rainfall variabilities and rising temperatures impact rural-urban migrations in Democratic Republic of Congo, Kenya and Niger. The VAR results have suggested that both rainfall and temperature impact rural migration and statistically significant. Further, the results also indicated that there is a significant relationship between rural and urban migrations.

Moreover, granger causality Wald tests were conducted to test the VAR model and to determine whether each variable plays a significant role in each of the equations. The granger causality tests have shown that there is significant granger causality effect from Rain and/or Temp to both rural and urban migrations in Democratic Republic of Congo and Kenya. Moreover, Rain and Temp together have a combined granger effect on MR and MU; this is statistically significant. In contrast, the granger causality test has shown that there is significant granger causality effect from Rain to both rural and urban migrations in Niger. However, rising temperatures does not appear to granger cause people movement in Niger. The results also suggest that there is a significant granger causes exist between rural migration and urban migration where each migration variable granger causes the other at less than 1% significance level. VAR auto forecasting in Stata and Eqs. (4), (5), (6), (7), (8) and (9) (inclusive) were used to predict changes of rural migration, urban migration, rainfall and temperature of Democratic Republic of Congo, Kenya and Niger in the next 10 years. The study predicts increasing rural-urban migrations in the next decade due to high rainfall variabilities and increasing temperatures. This study suggests that there will be a large number of rural communities leaving from their villages to urban areas

) is significantly

25

http://dx.doi.org/10.5772/intechopen.72226

water resources and the renewable internal freshwater resources per capita (m3

due to water availability conditions and poor agricultural production levels.

This study investigated Sub-Saharan Africa water availability and water security conditions in relation to rural-urban migration numbers. The SSA countries considered in this study are Democratic Republic of Congo (DRC), Kenya (KEN) and Niger (NER). The countries were selected based on specific factors such as water resources availability, population growth, migration processes and urbanization situation in recent times. The study finds that all three countries have fast growing populations with Niger having the highest fertility rate in the world. The countries are experiencing rapid urbanization where rural communities have been moving to urban areas at increased rates. The study finds that the renewable internal freshwater resources per capita (cubic meters) in Niger and Kenya have reached well below the water scar-

tively. On the other hand, Democratic Republic of Congo is known to be the SSA's water rich

/capita/annum. In 2014, these countries had renewable internal freshwater

/capita/annum and 450 m3

/capita/annum, respec-

**6. Conclusion**

city level of 1000 m<sup>3</sup>

resources per capita (cubic meters) of 183 m3

**Table 12.** Granger causality Wald tests for MR and MU of Niger.
