The main object of enquiry of this study is the variance in the types of women’s rights.
As the dependent variables used to analyse this phenomenon are of ordinal nature it is not possible to use the conventional ordinary least squares regression (OLS). I will thus estimate my statistical model with the help of an ordered logistic regression (henceforth:
OLOGIT) also known as theproportional odds model.
16If not stated otherwise, the control variables are derived from Landman (2005)
The ordered logit model
The OLOGIT assumes an underlying latent variable y* which in the case of this study will be the government’s compliance with women’s human rights (see Kohler and Kreuter, 2005: 289). Depending on the specific value of this variable at a given time and place, the value of the coded variables WOPOL will vary (see Long and Freese, 2006: 184-5). The statistical method of using OLOGIT predicts the probability that given an independent variable, such as the ratification of CEDAW, the specific values of y* will be in one of the categories of WOPOL.
The parallel regression assumption
As the nameproportional odds model indicates, OLOGIT builds on an important assump-tion, often referred to as theparallel regression assumption (PRA). It bases its predictions on the assumption that the explanatory variables have the same effect on every possibility of crossing from one category into the next (i.e. atJ-1 cut-points). To give an example, OLOGIT predicts that the probability of a state changing its level of respect for women’s political rights from ‘no rights’ to‘de jure rights’ is the same as the probability of chang-ing from‘de jure rights’ to‘de facto rights’ once a state has ratified CEDAW. In order to check if this assumption is true I will use the Brant test of parallel regression assumption to compare the probabilities and see if proportional odds are actually given in this case (see Long and Freese, 2006: 452-3)17.
Dealing with methodological challenges of cross-sectional time-series
The main challenges in using TSCS are problems of autocorrelation and heteroskedastic-ity (see Beck and Katz, 1995, Wooldridge, 2009: Ch.12). Heteroskedasticheteroskedastic-ity violates the underlying assumption of regression models that the unobserved errors will be indepen-dent and normally distributed (Fahrmeir, 2004: 479). The conventional way to deal with this issue is to employ panel-corrected standard errors (PCSE) when using TSCS data (see Beck and Katz, 1995). As the standard mode of employing PCSEs is designed for OLS regression, I cannot rely on this approach. Instead, I make use of robust standard errors that account for the non-independence of observations within the country-clusters.
The second problem I face is autocorrelation18. I follow previous studies dealing with similar issues (see Hafner-Burton, 2005; Kirschmann and Schneider, 2007) and compute lagged indicators of the countries’ previous womens rights values. However, the use of conventional lagged dependent variables would imply the assumption that state respect for women’s rights was linear. To avoid this generalization, Hafner-Burton includes J-1 binary variables that measure the previous year’s value. My model will thus include three
17Should the assumption be violated, a more elaborate model - the generalized ordered logit model (GOLOGIT2) can be used. For the variables that violated the assumption, the GOLOGIT2 regression relaxes the PRA. I will use this as a robustness check.
18Although the cluster() option in Stata regards the observations within one country as dependent, the temporal dependence of the human rights data is not yet reflected in the statistical model.
lagged dummy variables that account for the different categories of women’s rights, taking into consideration the temporal dependence without expecting a linear trend.
Multicollinearity
To prevent intercorrelation of the explanatory variables employed in the empirical mod-els, I use the variance inflator test (VIF) to distinguish whether the build of any of the models might be problematic The test reveals a very low correlation between the used vari-ables, which means that the separate effects of the variables are likely to be independent19.
5 Analysis
5.1 Descriptive Statistics
In order to get a first overview of the dataset, I commence my analysis with descriptive statistics. Tables 1 and 2 report summarising information on the dependent and inde-pendent variables. As we can see in Table 1, the political indicator seems to differ more strongly from the social and economic rights, with the most often reported category be-ing the de facto respect women’s rights in the political sphere. Spearman’s correlation20 reveals a particularly high and significant correlation between women’s economic and so-cial rights (.76) whereas the correlation of the socio-economic variables with the political indicator is much lower (both approx .4).
Table1: Descriptive Statistics: Dependent Variables
Dependent Variable Mode 1st Quartile. Median 3rd Quartile Min. Max.
Poiltical Rights 1 1 2 2 0 3
Social Rights 1 1 1 2 0 3
Economic Rights 1 1 1 2 0 3
Note: Number of observations for all variables = 2612.
If we take a closer look at the rights that are grouped in the two indicators (see Ap-pendix Table A1) we understand why this is the case: both include concepts that could be located in either categories. For example, the right to own, acquire, manage and retain property brought into marriage is classified as a social right, whereas classical property rights are usually located in the sphere of economic rights (see Freeman, 2002: Ch. 8).
On the other hand, the freedom to choose a profession or employment without the need to obtain a husband or male relative’s consent counts as an economic right, although this equally affects family and marriage law and could therefore just as well be classified as a social right. For this reason I will focus on the differences between women’s political and
19All values for the VIF test were<2.2.
20Spearman’s correlation is designed for ordinal ranked variables (Fahrmeir, 2007: 142).
social rights in this descriptive analysis. The WOSOC indicator covers rights that can be classified as both economic and social rights, including a much wider range of issues such as the right to education and the right to equal inheritance and the right to choose a residence; thus I will focus on this variable as approximate for the socio-economic sphere.
The empirical analysis will then take into account all three variables again.
Table 2: Descriptive Statistics: Independent Variables.
Variable N Mean Std. Dev. Min. Max.
CEDAW Ratification 2612 1 .4760 dummy dummy
Democracy 2611 11.124 7.575 0 20
ln p.c. GDP 2414 7.489 1.635 3.9865 10.936
ln Trade (% of GDP) 2427 4.097 .579 .426 5.672
IGO 2601 57.697 19.606 11 129
ln INGOs 2612 5.947 1.160 1.269 8.175
Muslim (%of Pop.) 2555 27.099 37.712 0 100
Catholic (%of Pop.) 2592 29.724 35.679 0 96.9
Time Trend 2612 10.889 5.775 1 20
Median reported for dummy variables, Std. Dev. = standard deviation.
Table 2 reports the summarised statistics for the independent variables that will be em-ployed in the OLOGIT model. As we can see, the median for the CEDAW ratification shows that most of the observations are in years where countries had already ratified the Convention. The number of IGOs a country belongs to accounts for the highest variation, followed by percentage of Muslim and Catholic Population.
In order to get a clearer picture of the general development of women’s political and social rights, Figure 1 provides a temporal overview of the different levels of women’s rights in all countries represented in this study, over time. The images reveal that between 1981 and 2000, approximately one third of these countries grantedno rights to the women who were citizens of their state, neither political nor social. Interestingly, there is no clear time trend visible that would indicate that the respect for women’s rights hasgenerally increased over time. On the contrary, the percentage of countries miss-respecting women’s social rights seems to increase at the beginning of the 1990s, though this effect is most likely due to the formation of new states of the former Soviet Union that weren’t included in the study before. Overall, not many differences can be found between the levels of respect between the two categories. If this general picture does not yield any differences, how does this image change if we only include the members of CEDAW in the presentation? If the next picture shows up differences to the overall situation, it would be a first acknowledgement of an impact by CEDAW on womens rights.
Figure 1: Level of State Respect of women’s political and social rights
Figure 2 gives the same temporal presentation as in the prior figure, however, this time only states parties to CEDAW are included. There does, indeed, seem to be a strong change in categories, compared with the visual including all countries. Keeping in mind that the rapid changes between the years might reflect new accessions, we are able to draw first deductions. Obviously, the states that joined CEDAW immediately after its legal in-stalment already displayed a basic respect for women’s rights. From the mid 1980s on, it seems that the states parties to CEDAW do not differ too much in their respect of social rights to those that haven’t ratified the Convention. The political rights however, seem to vary considerably in this section. On average it seems, that more countriesde jure orde facto respect political rights if they have ratified CEDAW than if not. Cross-tabulation of the occurrence of different categories of rights with the ratification status reveals that 74.4% of the observations of member states grantde facto political rights to their women, whereas only 46.5% of the non-members do so21, which amounts to a difference of 27.9%.
The difference only results in 9.3% in the case of social rights. As expected, the effect is similar for economic rights (11.1%) (see also, Appendix, FigureA1)
21See Appendix, Table A3a-c
Figure 2: CEDAW members level of Respect for women’s political and social rights First impression gained from the data thus gives credit to the hypothesised relationship between the ratification of CEDAW and state compliance. However, as the literature sug-gests, there are many factors influencing the status of women in society that could account for the actual variation of the figures. To control for these factors, I will now turn to the re-gression analysis of my actual statistical model, with the help of ordered logistic rere-gression.