Data-Driven Decision Making in Fragile Contexts by Alexander Hamilton & Craig Hammer

Author:Alexander Hamilton & Craig Hammer
Language: eng
Format: epub

Source: Based on regressions from columns (3.2), (3.4), and (3.6) in table 5.3.

Finally, in most cases, the dummy variable that distinguishes between the two samples is significant, although less so in most of the trust equations. We cannot say whether this reflects genuine changes caused by an overall change in culture or whether it reflects sampling variations. However, because the socioeconomic variables were included in this regression, it cannot be due to differences in the socioeconomic characteristics of the two samples. Further insights into this will need to wait until more surveys are available.

We have one more set of regressions to discuss. These relate to locational variables alone, with no individual or state variables. They were estimated using the Tobit methodology. The Tobit model was proposed by James Tobin and is a limited dependent variable technique. The term Tobit was derived by combining Tobin with probit. The model can be used when the variable is bounded from above, from below, or both. The results are shown in table 5.4, and there are, of course, substantially fewer observations than before. The first three regressions, in columns (4.1), (4.2), and (4.3), include the majority of possible locational variables, and the final three, in columns (4.4), (4.5), and (4.6), focus on the significant variables. We note the significance of education across the board, bribery for the two fairness regressions, and the differential impact of services. For example, better sanitation increases both dimensions of perceived fairness. Better access to the courts increases both perceived overall fairness and trust and better access to electricity reduces trust. Thus these results largely confirm the individual-based regressions.

Table 5.4 Regressions Based on Location Variables Only

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