Employee Nomination Quotes

Intellectuals are now typically public employees, even if they work for nominally private institutions or foundations. Almost completely protected from the vagaries of consumer demand ("tenured"), their number has dramatically increased and their compensation is on average far above their genuine market value. At the same time the quality of their intellectual output has constantly fallen.

SCANDALS AND MISMANAGEMENT If Secretary Clinton’s political career had ended with her defeat for the Democratic presidential nomination in 2008, her skills as a manager would have been judged by her disorganized and drama-filled campaign for the presidency and her disastrous Health Care Task Force as First Lady. President Obama, who defeated her calamitously run campaign, should have been wary of nominating Clinton to a post that was responsible for tens of thousands of federal employees throughout the world. While her tenure in Foggy Bottom didn’t have the highly publicized backstabbing element that tarnished her presidential campaign, Secretary Clinton’s deficiencies as a manager were no less evident. There was one department within State that Secretary Clinton oversaw with great care: the Global Partnerships Initiative (GPI), which was run by long-time Clinton family aide Kris Balderston. Balderston was known in political circles for creating a “hit list” that ranked members of Congress based on loyalty to the Clintons during the 2008 presidential primaries.[434] Balderston was brought to Foggy Bottom to “keep the Clinton political network humming at State.”[435] He focused his efforts on connecting CEOs and business interests—all potential Clinton 2016 donors—to State Department public/private partnerships. Balderston worked alongside Clinton’s long-time aide Huma Abedin, who was given a “special government employee” waiver, allowing her to work both as Secretary Clinton’s deputy chief of staff, and for other private sector clients. With the arrangement, Abedin would serve as a consultant to the top Clinton allied firm, Teneo, in a role in which, as the New York Times reported, “the lines were blurred between Ms. Abedin’s work in the high echelons of one of the government’s most sensitive executive departments and her role as a Clinton family insider.”[436] Secretary Clinton and her allies have placed great emphasis on the secretary of state’s historic role in promoting American business interests overseas, dubbing the effort “economic statecraft.”[437] The efforts of the GPI, Abedin, and Balderston ensured that Secretary Clinton’s “economic statecraft” agenda would be rife with the potential for conflicts of interest reminiscent of the favor-trading scandals that emanated from her husband’s White House. While the political office and donor maintenance program was managed with extreme meticulousness, Secretary Clinton ignored her role as manager of the rest of the sprawling government agency.[438] When it came to these more mundane tasks, Secretary Clinton was not on top of what was really going on in the department she ran. While Secretary Clinton was preoccupied with being filmed and photographed all around the world, the State Department was plagued by chronic management problems and scandals, from visa programs to security contractors. And when Secretary Clinton did weigh in on management issues, it was almost always after a raft of bad press forced her to, and not from any proactive steps she took. In fact, she and her department’s first reaction in certain instances was to silence critics or intimidate whistleblowers, rather than get to the bottom of what was actually going on. The events that unfolded in Benghazi were the worst example of Secretary Clinton neglecting her managerial responsibilities. This pattern of behavior, which led to the tragedy, was characteristic of her management style throughout her four years at Foggy Bottom. “Economic Statecraft” A big part of Secretary Clinton’s record-breaking travel—112 countries visited—was her work as a salesperson for select U.S. business interests.[439] Today, her supporters would have us believe her “economic statecraft” agenda was a major accomplishment.[440] Yet, as always seems to be the case with the Clintons, there was one family that benefited more than any other from all this economic statecraft—the Clinton family.

The Scheffe test is the most conservative, the Tukey test is best when many comparisons are made (when there are many groups), and the Bonferroni test is preferred when few comparisons are made. However, these post-hoc tests often support the same conclusions.3 To illustrate, let’s say the independent variable has three categories. Then, a post-hoc test will examine hypotheses for whether . In addition, these tests will also examine which categories have means that are not significantly different from each other, hence, providing homogeneous subsets. An example of this approach is given later in this chapter. Knowing such subsets can be useful when the independent variable has many categories (for example, classes of employees). Figure 13.1 ANOVA: Significant and Insignificant Differences Eta-squared (η2) is a measure of association for mixed nominal-interval variables and is appropriate for ANOVA. Its values range from zero to one, and it is interpreted as the percentage of variation explained. It is a directional measure, and computer programs produce two statistics, alternating specification of the dependent variable. Finally, ANOVA can be used for testing interval-ordinal relationships. We can ask whether the change in means follows a linear pattern that is either increasing or decreasing. For example, assume we want to know whether incomes increase according to the political orientation of respondents, when measured on a seven-point Likert scale that ranges from very liberal to very conservative. If a linear pattern of increase exists, then a linear relationship is said to exist between these variables. Most statistical software packages can test for a variety of progressive relationships. ANOVA Assumptions ANOVA assumptions are essentially the same as those of the t-test: (1) the dependent variable is continuous, and the independent variable is ordinal or nominal, (2) the groups have equal variances, (3) observations are independent, and (4) the variable is normally distributed in each of the groups. The assumptions are tested in a similar manner. Relative to the t-test, ANOVA requires a little more concern regarding the assumptions of normality and homogeneity. First, like the t-test, ANOVA is not robust for the presence of outliers, and analysts examine the presence of outliers for each group. Also, ANOVA appears to be less robust than the t-test for deviations from normality. Second, regarding groups having equal variances, our main concern with homogeneity is that there are no substantial differences in the amount of variance across the groups; the test of homogeneity is a strict test, testing for any departure from equal variances, and in practice, groups may have neither equal variances nor substantial differences in the amount of variances. In these instances, a visual finding of no substantial differences suffices. Other strategies for dealing with heterogeneity are variable transformations and the removal of outliers, which increase variance, especially in small groups. Such outliers are detected by examining boxplots for each group separately. Also, some statistical software packages (such as SPSS), now offer post-hoc tests when equal variances are not assumed.4 A Working Example The U.S. Environmental Protection Agency (EPA) measured the percentage of wetland loss in watersheds between 1982 and 1992, the most recent period for which data are available (government statistics are sometimes a little old).5 An analyst wants to know whether watersheds with large surrounding populations have

A NONPARAMETRIC ALTERNATIVE A nonparametric alternative to one-way ANOVA is Kruskal-Wallis’ H test of one-way ANOVA. Instead of using the actual values of the variables, Kruskal-Wallis’ H test assigns ranks to the variables, as shown in Chapter 11. As a nonparametric method, Kruskal-Wallis’ H test does not assume normal populations, but the test does assume similarly shaped distributions for each group. This test is applied readily to our one-way ANOVA example, and the results are shown in Table 13.5. Table 13.5 Kruskal-Wallis’ H-Test of One-Way ANOVA Kruskal-Wallis’ H one-way ANOVA test shows that population is significantly associated with watershed loss (p = .013). This is one instance in which the general rule that nonparametric tests have higher levels of significance is not seen. Although Kruskal-Wallis’ H test does not report mean values of the dependent variable, the pattern of mean ranks is consistent with Figure 13.2. A limitation of this nonparametric test is that it does not provide post-hoc tests or analysis of homogeneous groups, nor are there nonparametric n-way ANOVA tests such as for the two-way ANOVA test described earlier. SUMMARY One-way ANOVA extends the t-test by allowing analysts to test whether two or more groups have different means of a continuous variable. The t-test is limited to only two groups. One-way ANOVA can be used, for example, when analysts want to know if the mean of a variable varies across regions, racial or ethnic groups, population or employee categories, or another grouping with multiple categories. ANOVA is family of statistical techniques, and one-way ANOVA is the most basic of these methods. ANOVA is a parametric test that makes the following assumptions: The dependent variable is continuous. The independent variable is ordinal or nominal. The groups have equal variances. The variable is normally distributed in each of the groups. Relative to the t-test, ANOVA requires more attention to the assumptions of normality and homogeneity. ANOVA is not robust for the presence of outliers, and it appears to be less robust than the t-test for deviations from normality. Variable transformations and the removal of outliers are to be expected when using ANOVA. ANOVA also includes three other types of tests of interest: post-hoc tests of mean differences among categories, tests of homogeneous subsets, and tests for the linearity of mean differences across categories. Two-way ANOVA addresses the effect of two independent variables on a continuous dependent variable. When using two-way ANOVA, the analyst is able to distinguish main effects from interaction effects. Kruskal-Wallis’ H test is a nonparametric alternative to one-way ANOVA. KEY TERMS   Analysis of variance (ANOVA) ANOVA assumptions Covariates Factors Global F-test Homogeneous subsets Interaction effect Kruskal-Wallis’ H test of one-way ANOVA Main effect One-way ANOVA Post-hoc test Two-way ANOVA Notes   1. The between-group variance is

The journalist Dan Lyons joined a tech start-up after being downsized from Newsweek in 2012, and the experience inspired him to write a book about how Bay Area norms have infected the American workplace, Lab Rats: How Silicon Valley Made Work Miserable for the Rest of Us. Nominally egalitarian but oppressive in practice, the start-up spirit insists that everyone be super psyched about their jobs all the time. No one is actually loyal to the organziation in the sense of intending to work there for longer than five years, but what employees lack in commitment, they must make up for in enthusiasm. This mandatory passion is made worse by the smartphone. No one is every off duty anymore. The BlackBerry’s original tagline was “Always On. Always Connected.” Bizarrely, this made people want to buy it.

During this decade, one of the most important features of the new Koch Industries was the impervious strength of its corporate veil—the legal barrier that separated Koch’s various divisions. Under the new structure, Koch Industries became little more than a holding company, a big investment firm that owned a lot of smaller, nominally independent firms. And those companies would be strictly segregated from one another, and from Koch central, by a thick wall designed to be legally impenetrable. The corporate veil became reflected in the vocabulary of Koch employees. They didn’t refer to the company’s subsidiaries as units or divisions, but as “companies,” reinforcing the notion that each unit was fully independent. Many of these “companies” developed their own internal systems for human resources, information technology, and other services, creating just the kind of big, redundant systems that most US corporations were striving to eliminate. These redundancies might have cost Koch money, but their value far outstripped the cost. Koch could now argue persuasively that each company division was a stand-alone company, one that could assume its own liabilities. Never again would angry creditors be able to threaten the cash reserves of Koch Industries’ central treasury, as the lawyers from Purina Mills had done. Now liability would only travel to the top of each company that Koch held.