LOS LÍMITES DE LA OPINIÓN PÚBLICA COMO “DATO AGREGADO”

Previous research has emphasized that a quantitative analysis of cross-country data is quite sensitive to outliers, i.e. that effects of country characteristics are often driven by single countries (Van der Meer et al. 2010). To exclude this, an additional analysis of the central analyses is performed which aims to show how strong the coefficients vary if single countries are excluded from the analysis, a strategy also known from estimating dfbeta to identify outliers in traditional regression analyses. These analyses are performed only for the models testing the central hypotheses, and the focus is on the effects of the variables that are of interests only.

In a first step it is shown how strong the effect of social norms and preferences measured by gender-role attitudes varies. For this purpose, model 3 shown in table 6.1 is estimated 83 times excluding each country once. This model estimates the effect of social norms and gender roles on the probability of being active in the labor market when controlling for natural rents, GDP, and living in Sub-Saharan Africa. In this model the difference between societies in which everyone agrees with the traditional gender role statement and societies in which no one agrees with the statement is 62 percentage points if all countries of the sample are included. The analyses for potential outliers reveal that the effect of social norms ranges between -0.68 and -0.56 depending on the countries included in the analyses. Figure 9.1

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shows the distribution of the effect. It can be seen that the range of the great majority of effects is extremely small with only a few outliers within the above described range.

Figure 9.1 Distribution of the social-norm effect

Note: Boxplot for the distribution of the social norm effect in 83 different samples. Source: EVS/WVS data; own calculations.

Nevertheless, the central conclusion does not depend on outliers. Even if we would rely on the lowest estimation of -0.56 we would conclude that the social norm of the society has a strong effect on the labor force participation of women.

For the effect of individual gender-role beliefs similar results can be found. In the model relying on the full sample, the effect of gender-role beliefs on individual level was estimated as an 8 percentage point lower probability of being active in the labor market for women with traditional gender-role beliefs compared to women with modern gender-role beliefs. The outlier analysis shows that this effect varies between a value of -0.075 and a value of -0.083 (see figure 9.2), a range which leaves no room for different interpretations depending on the sample. -.7 -.6 5 -.6 -.5 5

132 Figure 9.2 Distribution of the preference effect

Note: Boxplot for the distribution of the preference effect in 83 different samples. Source: EVS/WVS data; own calculations.

For the results of chapter 7 – the influence of religion on female labor force participation – outlier analyses were performed as well. Again, the focus is on the central result, i.e. the effect of religion without controlling for potential mechanisms. The model tested is model 2 from table 7.2 in which religious denomination is estimated on country and individual level while controlling for the share of people without denomination. Table 9.1. shows the results. For each coefficient both the estimated value of the model of the full sample as well as the range of the coefficient is shown. It can be seen that, for most coefficients, the maximal deviance is 2 percentage points, and this would not result in a substantially different interpretation of the results. -.0 8 2 -.0 8 -.0 7 8 -.0 7 6 -.0 7 4

133 Table 9.1 Distribution of the effects of religion

Full sample Min Max

Denomination country (Ref. Protestant)

Mixed -0.13 -0.15 -0.11 Buddhist -0.05 -0.18 0.08 Catholic -0.14 -0.16 -0.12 Hindu -0.38 -0.40 -0.36 Muslim -0.31 -0.33 -0.29 Orthodox -0.14 -0.16 -0.12 Share of Atheists 0.13 0.09 0.19

Denomination individual (Ref. Protestant)

no rel 0.01 0.00 0.01 Buddhist -0.03 -0.05 0.00 Catholic 0.00 -0.07 0.00 Hindu -0.18 -0.20 -0.15 Jewish 0.07 0.05 0.10 Muslim -0.12 -0.14 -0.10 Orthodox 0.01 0.00 0.02 Other -0.01 -0.02 0.00

Source: EVS/WVS data; own calculations.

One exception is the effect of living in a Buddhist country for which two extreme values can be found. If Thailand is excluded from the sample the model shows an 18 percentage point lower probability of being active in the labor market for women living in predominantly Buddhist countries compared to women living in predominantly Protestant countries. If China is dropped from the sample, the probability for women living in Buddhist countries is 8 percentage points higher than for women living in Protestant countries, but it is important to note that both coefficients are not significant. If these two extreme values are ignored, the range of the coefficient lies between -0.07 and -0.04. The reason for these two extreme values is that China and Thailand are the only two available predominantly Buddhist countries in the sample. Furthermore, China has an extremely high number of respondents without a religious belonging. The estimated Buddhist effect is hence an average effect for China and Thailand, and dropping one of the countries from the sample leaves a country-fixed effect. Accordingly, the effect of living in a Buddhist country is hardly transferable to other Buddhist countries. For the Hindu effect only 82 different estimates are available since excluding the only Hindu country in the sample (India) makes the effect inestimable. The effect of the share of people without denomination varies between 9 percentage points and 19 percentage points. Given the

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scale of this variable and the variation in the data (range from 0-0.85; sd=0.19), this range is not as large as it first seems.

The deviations of the individual-level effects are small as well. This is also true for the two relevant effects, the probability of being active in the labor market for Hindu and Muslim women, for which the estimated coefficient under exclusion of specific countries varies only slightly. Again we would not come to a substantially different conclusion with a different sample.

Finally, the results of the last empirical chapter about the low female labor force participation in the MENA region are investigated with respect to outliers. For this purpose the cultural model, which fully explains the MENA effect in the full sample, i.e. while controlling for religion, attitudes, and institutions, is estimated 83 times excluding each country once. As can be seen in figure 9.3 the majority of the results lie within a very small range of values, the 10th percentile is -0.037, the 90th percentile 0.029, but the exclusion of a few countries produces some more deviating results.

Figure 9.3 Distribution of the MENA effect

Note: Boxplot for the distribution of the MENA effect in 83 different samples. Source: EVS/WVS data; own calculations.

-.1 5 -.1 -.0 5 0 .0 5

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After excluding Morocco from the sample, the MENA effect in this model is significant on a 10 percent level and shows a 16 percentage point lower probability of women living in the MENA region of being active in the labor market than women living outside the MENA region. This is related to the fact that female labor force participation in Morocco as estimated by the WVS data is very high and, accordingly, the gap in labor force participation between MENA and other regions is higher when Morocco is excluded. Since the female labor force participation in Morocco estimated by the WVS data strongly deviates from the official numbers, the impact of excluding Morocco from the sample is discussed in more detail in part 2 of this chapter. All other estimations of the MENA effect are not significant and hence would result in the same conclusions as those made in chapter 8.