Capitulo I V: Presentación e Interpretación de Resultados
Categoría 3: “Existencia de prácticas de autocuidado espontaneas”
3.1 Sub Categoría: “Practicas espontaneas de autocuidado en el ámbito personal”: Dentro de esta sub categoría se exponen las diversas prácticas de
In this section, H1a and H2a are tested. The results of the OLS regression analysis on the expenditures on traffic, transport and water management (TTWM) per inhabitant per year are presented in Table 7. The first regression model explains 14.6 per cent of the variance in the expenditures on TTWM per inhabitant per year (R2=0.146). The share of women in the MC (b=0.353) and the share of women in the ME (b=0.109) are statistically significant. This means that as the share of women in the MC increases, the expenditures on TTWM per inhabitant per year increase as well; the same applies to the share of women in the ME. Even though the share of women in the MC and the ME have an effect on the expenditures on TTWM per inhabitant per year, this effect is in the opposite direction of H1a (positive instead of negative).
As the control variables on political party preference and year are added to the second regression model, 29.2 per cent of the variance in expenditures on TTWM per inhabitant per year is explained by the variables in this model (R2=0.292). Regarding the independent variables, the results show that the share of women in the MC has a statistically significant effect on the expenditures on TTWM per year (b=0.306). However, as in the first regression model, this effect is in the opposite direction of H1a. Moreover, the share of women in the ME no longer has a statistically significant
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effect on the expenditures on TTWM per inhabitant per year, now that control variables have been added to the model. The share of left political party in the MC (b=0.297) and the political party preference of the Mayor (b=0.258) have a statistically significant effect on TTWM expenditures per inhabitant per year as well. This means that a left political party preference of the MC and the Mayor influences TTWM expenditures per inhabitant per year. Also, the year has a statistically significant effect on TTWM expenditures per inhabitant per year (b=0.292), indicating that an increase in expenditures on TTWM is explained in part because it is a year ahead.In the third model, when TTWM income per inhabitant per year is added to the model, 80.3 per cent of the variance in TTWM expenditures per inhabitant per year is explained by the variables in this model (R2=0.803). Regarding the dependent variables, both the share of women in MC (b=0.141) and the gender of the Mayor (b=0.070) have a statistically significant effect on the expenditures on TTWM, even when controlled for political party preference, year, and TTWM income per inhabitant per year. This indicates that gender has an effect on the expenditures on TTWM per inhabitant per year that have not been budgeted. Besides gender and income, the left political party preference of the MC (b=-0.123) and the Mayor (b=0.082) have an effect on TTWM expenditures per inhabitant per year as well. Interesting to see is that the expenditures per inhabitant per year on TTWM decrease as the political orientation of the MC is more left-winged, which is in accordance with the party ideology of left-winged political parties. Moreover, year no longer has a statistically significant effect in the third regression model, indicating that expenditures on TTWM do not increase because it is a year ahead, but because of other effects, such as the share of women in politics, political party preference, and income on TTWM.
The fact that the share of women in the MC has a statistically significant effect on TTWM expenditures throughout all three regression models is important, because this indicates that the share of women in the MC has an effect on TTWM expenditures, even when controlled for political party preference, year, and TTWM income. Especially on TTWM income, as this control variable explains most of the variance in TTWM expenditures (b=0.850), and even then, the share of women has a statistically significant effect on TTWM expenditures (b=0.141). Interesting to see is that the gender of the Mayor did not have an effect on TTWM expenditures in the first and second regression model, and in the third regression model the gender of the Mayor does have an effect. Despite the fact that the income on TTWM and the effect of the political party preference of the Mayor have a (large) statistically significant effect, the gender of the Mayor has a statistically significant effect as well. Even though the share of women in the MC and the gender of the Mayor have a statistically significant effect on TTWM expenditures per inhabitant per year, this effect is positive instead of negative, as presumed by H1a. Thus, gender does have an effect on TTWM expenditures per inhabitant per year, however not in the right direction. Therefore, H1a must be rejected. H2a must be
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rejected as well, as the gender of the Clerk to Council has no statistically significant effect on TTWM expenditures per inhabitant per year in either of the three regression models.Table 7: OLS regression analysis on traffic, transport and water management expenditures per inhabitant per year
First model Second model Third model Independent variables
Share of women in MC 0.353*** 0.306*** 0.141***
Share of women in ME 0.109* 0.036 0.017
Mayor gender 0.031 0.073 0.070*
Clerk to Council gender 0.039 0.023 -0.019
Control variables
Share of left political party in MC - 0.297*** -0.123**
Share of left political party in ME - -0.079 0.051 Dummy Mayor political party - 0.258*** 0.082*
Dummy Clerk to Council political party - -0.035 0.035 Traffic, transport and water management income per inhabitant per
year
- - 0.850***
Year - 0.159* -0.015
R2 0.146*** 0.292*** 0.803***
***. Correlation is significant at p<0.001 (2-tailed). **. Correlation is significant at p<0.01 (2-tailed). *. Correlation is significant at p<0.05 + p<0.10 (2-tailed). Standardized regression coefficients (b) are presented. N=349.