Random Intercept Models including Level-2 Variables

To follow the thesis’s main hypotheses and their results in the multilevel analysis, I want to present one model for the hypotheses from chapter five.⁷⁷ The main independent variable refugee_camp is included in each of the models. The explanatory variable is then added to see if the results positively or negatively impact institutional trust and whether it improves the models in explaining institutional trust.

7.2.1 Refugee Camps and Institutional trust

The first level-2 variable, refugee_camp, is included in model three to test H1: People in regions with refugee camps express lower institutional trust than people in regions with no refugee camps present. It also includes an alternative measure for camp settlement, camp5yr. Based on the table below (Table 10), there is no significant change in institutional trust for either Kenya or Tanzania based either of these two variables. This might be because there are not enough camps in the two countries to find a significant change in institutional trust. H1 is rejected, and there is no relationship found for Kenya or Tanzania in the models below. Additional variables

77 Hypothesis 5: institutional trust is higher in Tanzania compared to Kenya, will be answered in regard to the final model, see table 13.

Unstandardised b-coefficients, Z-values and P-values: * p<0.1; ** p<0.05; *** p<0.01

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measuring camp presence were also tested out⁷⁸, but camp5yr and the refugee_camp variable was selected for this thesis.

A rule for regression states that there should be at least 10 observations for each independent variable. If there are fewer than 15-20 level-2 units (regions in this case) this leads to confidence intervals that are unreliable (Stegmueller 2013). It is important to mark that this thesis has a minimum of 29 level-2 units (Tanzania’s case) and a maximum of 47 regions (Kenya’s case).

This means that one can only include two level-2 variables in Tanzania’s case and four level-2 variables in Kenya’s case. I limit the use of level-2 variables to one in Tanzania’s models and a maximum of two in Kenya’s models.

The included variables for Kenya’s model three and four differ from Tanzania’s; this is due to the prevviol variable, which excludes regions where violence has decreased institutional trust drastically. Excluding these regions located at Kenya’s border did not provide more significant results in this case, and if one excludes these areas even fewer camps are present in Kenya. In these regions, close to the border of Somalia, one find many of Kenya’s refugee camps (Lochery 2012). Controlling for violence is important in studies of institutional trust, since this would decrease institutional trust, but as seen here, it does not provide enough camps to support for hypothesis one.

There is no significant result for the variables measuring refugee camps at the regional level in Tanzania's case. Since the variable refugee_camp shows camp data from 14 years in total, I want to keep this variable in the upcoming models that test the rest of the hypotheses. The reason for doing this is because the variable will rather be over-exclusive, rather than too narrow, to uncover the effects of camp settlement in Kenya and Tanzania.

78 The results from alternative measures measuring camp data from the three last years and the last year (2014) is found in appendix C6 and C7.

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Table 10: OLS Regression, Institutional Trust and Refugee Camps, Kenya and Tanzania

7.2.2 Employment and Institutional Trust

The upcoming intercept model see how the employment variable affect the results for institutional trust, testing hypothesis two: employed people have a stronger institutional trust than unemployed people in Kenya and Tanzania. In Kenya’s case, one cannot see any significant results for the variable employment. Therefore, one cannot verify that Kenyan’s work status affects their institutional trust levels. For Tanzania, employment shows a significant negative relationship at the 0.05-level in Table 11. Employed people are then less trusting towards their government than the unemployed, which tells how people with jobs might be more self-sufficient. They might not need to rely on their government as much as the unemployed do. The VPC shows that model five for Tanzania explains in total 7.7 per cent of the variance in the dependent variable. Employment did not show significant results in model two.

Kenya Tanzania

III IV III IV

Refugee_Camp 0.245 0.13

(0.96) (1.22)

Camp5yr 0.046 0.033

(0.58) (0.39)

Prevviol -0.075 -0.076 (0.35) (0.32)

N 1,954 1,954 2,154 2,154

Var(e) 0.408 0.408 0.477 0.477

Var(u) 0.091 0.092 0.04 0.042

VPC 18.24 18.4 7.74 8.09

Unstandardised b-coefficients, Z-values and P-values: * p<0.1; ** p<0.05; *** p<0.01

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Table 11: OLS Regression, Institutional Trust and Employment, Kenya and Tanzania

7.2.3 Insecurity and Institutional Trust

In the upcoming model six, the independent variable safety is included to the model, testing for hypothesis 3a: People that are not feeling safe in their neighbourhoods show a lower level of institutional trust, and 3b: The negative association between insecurity and institutional trust is stronger in Kenya than Tanzania. In model two the safety variable showed a significant negative relationship for both countries, and the effect is stronger for Kenya in relation to Tanzania, confirming H3b.

Kenya shows that the safety variable has a strong positive association for institutional trust in model six (see Table 12 below). The measured VPC for this model is higher than for the empty intercept model, explaining 15.57 per cent of the level-2 variance in institutional trust. An alternative explanation for this relationship is if one feel less safe if one is less trusting in the institutions. I exclude the safety variable from the model (see appendix C5a & C5b), and based on this revised model, reversed causality might be present here; people with lower institutional trust might feel less safe. If the multilevel analysis could take interaction effects into account, it would control for this.⁷⁹ For Tanzania, perceived safety is also significantly negatively associated with institutional trust, and the substantial effect is quite strong, but the effect is not as strong as in Kenya’s case supporting H3b. The effect of safety will also be investigated further in Tanzania’s case.

79 As stated, one could not take interaction effects into account, further discussion found in section 8.2.2.

Kenya Tanzania

V V

Refugee_Camp 0.219 0.124 (0.92) (1.18) Employment -0.026 -0.046

(1.28) (2.19)**

N 1,951 2,152

Var(e) 0.408 0.475

Var(u) 0.09 0.04

VPC 18.07 7.77

Unstandardised b-coefficients, Z-values and P-values: * p<0.1; ** p<0.05; *** p<0.01

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Table 12: OLS Regression, Institutional Trust and Safety, Kenya and Tanzania

7.2.4 Residency and Institutional Trust

Model seven will include the variable rural to test the two hypothesis 4a: People living in a rural area will have a higher institutional trust than people living in an urban area, and 4b: Tanzania will have higher levels of institutional trust for people living in rural areas in relation to urban areas than Kenya. Kenya’s institutional trust is lower for people living in rural areas compared to urban areas. This rejects H4a. Tanzania show a lower negative association for the relationship than Kenya, supporting H4b.

Table 13: OLS Regression, Institutional Trust and Rural, Kenya and Tanzania