Does the Wage Gap between Private and Public Sectors Encourage Political Corruption? - Fig 6

<p>(A) As a result of competing between two networks we show the phase space of network II. We used <i>τ</i> = 50, <i>T_h</i> = 0.5. (B) Close to a critical point, <i>p</i>1 = 0.004, <i>p</i>2 = 0.51, where <i>τ</i> = 50, <i>T_h</i> = 0.5 we show the phase flipping between two phases. We use <i>q</i> = 0.25.</p

Two regular network models where corrupt agents (red) are more intra-linked than uncorrupt agents (blue).

<p>Here <i>k</i>_<i>c</i> = 4, <i>k</i>_<i>u</i> = 2, and <i>k</i>_<i>cu</i> = 2.</p

EU countries: The higher the corruption (lower CPI) the higher the public sector wage premium.

<p>EU countries: The higher the corruption (lower CPI) the higher the public sector wage premium.</p

The smaller the wage gap between public and private sectors in favor of the public sector, the larger the average 5-year GDP growth rate.

<p>The smaller the wage gap between public and private sectors in favor of the public sector, the larger the average 5-year GDP growth rate.</p

Illustrations of effective potentials.

<p>For a given wage premium two phases are shown corresponding to low and high corruption levels.</p

Evolution of the wage gap with the level of development.

<p>In this and all other figures, the regression line t-values for the corresponding coefficients are shown in parentheses, and standard errors are robust to heteroskedasticity. The income separators are $15,000 and$35,000 of PPP adjusted GDP per capita.</p

Fraction of corrupt agents increases with the number of intra-links and the threshold.

<p>(A) With increasing the vulnerability of noncorrupt agents in a corrupt surrounding, here quantified by threshold <i>T</i>_<i>u</i>, the corrupt fraction increases. We use <i>p</i>₁ = 0.002, <i>p</i>₂ = 0.7, <i>k</i>_<i>u</i> = 2, <i>k</i>_<i>c</i> = 5, <i>k</i>_<i>uc</i> = 2. We use 2000 agents with 25% of corrupt agents. (B) With increasing the number of contacts established by corrupt agents, the corrupt fraction abruptly increases at one <i>k</i>_<i>c</i> value. We use <i>T</i>_<i>c</i> = 0.5, <i>k</i>_<i>u</i> = 2, and <i>k</i>_<i>uc</i> = 2.</p

HHI ROC analysis.

<p>(a) ROC curve of number tweets and number unique hashtags as classifiers for finding significant dates in the dataset. Number of tweets AUC = 0.42 and number of unique hashtags AUC = 0.36. (b) ROC curve of the HHI and Entropy classifiers. HHI AUC = 0.79, entropy AUC = 0.72. The focus-based classifiers provide the best classification when compared with the other methods, with the HHI being the best predictor. (c) ROC curve of the four classifiers - one minus number of tweets, one minus number of hashtags, and hashtag entropy - and their performance in identifying the ground truth. This is done as a below-random (<0.50) AUC means that the class labels should be inverted. (d) Distribution of the HHI AUC values for prediction of the ground truth for many random samples of the OWS dataset. The arrow in this figure represents the measure of the unshuffled data.</p

Empirical distribution of normalised returns for <i>American Express</i>.

<p>We build returns distributions for the 25 stocks of the DJIA for different time lags across the full period of analysis. We standardize each distribution by subtracting the mean return from each observation and dividing by the standard deviation. We depict in blue the cumulative distribution function of the positive component of the return distributions for <i>American Express</i> for a time lag of 300 seconds. We depict in red the positive tail of a Gaussian distribution with mean zero and standard deviation one. We observe a strong deviation of the empirical distribution from the Gaussian distribution. Instead, visual inspection of the distribution tail reveals consistency with a linear relationship on a log-log scale. This provides initial evidence for possible power law behavior at this time scale.</p

Time evolution of the number of tweets (top), number of hashtags (middle), and Herfindahl-Hirsch Index (HHI) parameter (bottom) for the OWS dataset, on a daily time horizon.

<p>The HHI calculates how diverse the discussion is on Twitter, by calculating how many messages are associated with a given hashtag, and ranges from a value of 0, for highly diverse discussion, to 1, when all messages are focused on only one hashtag.</p