A new approach to the statistical estimation of age‐period‐cohort (APC) accounting models, called the intrinsic estimator (IE), recently has been developed. This article (1) further describes the IE algebraically, geometrically, and verbally, (2) reviews properties of the IE as a statistical estimator, (3) provides model validation evidence for the IE both from an empirical example and from a simulation exercise, (4) relates the coefficients of the IE to those of conventional constrained APC models using formal definitions of statistical estimability, hypothesis testing, and empirical applications that directly address a criticism that often has been lodged at general‐purpose methods of APC analysis, and (5) introduces computer software for application of the IE that interested users can readily access. The authors conclude that the IE holds the potential for applications not only to APC analysis but also to similar problems of structural underidentification in sociology.

https://www.jstor.org/stable/10.1086/587154

The intrinsic estimator for age-period-cohort analysis: What it is and how to use it

For more than 40 years, communities across the United States have grappled with Brown’s mandate to provide equality of educational opportunities to Black children by ending school segregation. Despite considerable unambiguous evidence that desegregation enhances students’ long-term outcomes such as educational and occupational attainment, the situation with respect to short-term outcomes is more ambiguous and more highly contested. Using survey data from the Charlotte-Mecklenburg Schools, a North Carolina district that used mandatory busing to desegregate its schools, the author demonstrates the direct and indirect negative effects of segregation on academic achievement in ways not employed previously. The distinctive research design includes a longitudinal measure of exposure to racially isolated Black elementary education, multiple indicators of educational outcomes, measures of track placement, and a large representative sample of grade 12 students from the entire school system. By demonstrating how bo...

Subverting Swann: First- and Second-Generation Segregation in the Charlotte-Mecklenburg Schools

SUMMARY

Some claim that when level of property rights protection is controlled, democracy lowers foreign direct investment (FDI) to developing countries (Li and Resnick 2003). We critically examine the theoretical claims of the pessimistic arguments and show that FDI responds to preferences of countries and that democracies have a clear preference for FDI given that the scarce factor – capital – will find it harder under democracy to seek rents by raising barriers to entry. On the other hand, labor (the abundant factor in developing countries) should profit from lower barriers to capital importation. We demonstrate conclusively that the most prominent pessimistic result on democracy in the literature is simply an artefact of sample size and testing procedure. We establish robust evidence suggesting that developing country democracies actually receive higher inflows of FDI, net of a number of control variables. Consistent with our view that host nations' attitudes are shaped by factor endowments, which in turn determine rent-seeking, we demonstrate that governments controlled by ‘leftist’ political parties also receive more FDI than ‘centrist’ or ‘rightist’ governments among democracies. Why this should be true is not obvious from a theory based on property rights risk alone. An extended sample of LDCs and a better operationalization show that property rights and democracy positively affect FDI. Our results suggest that globalization advances the fortunes of democracies in the developing world.

Do Foreign Investors Punish Democracy? Theory and Empirics, 1984–2001

http://dse.univr.it/espe/documents/Papers/E/5/E5_4.pdf

The impact of international outsourcing on individual employment security: A micro-level analysis

Male–male competition has historically been considered the major force driving sexual selection. However, female choice and inter-sexual conflict are increasingly recognized as important influences affecting differential mating and reproductive success. Many females exhibit preferences for particular males; however, male strategies may conflict with females’ ability to obtain their mate preferences. To influence paternity, females must affect both (1) whether or not sexual interactions occur, particularly during the periovulatory period (POP) and (2) the outcome of sexual interactions. This study focuses on the effectiveness of female choice in wild chimpanzees (Pan troglodytes verus). Over 2,600 h of data were collected on two habituated chimpanzee communities in the Tai National Park, Cote d’Ivoire. Female mate preferences were measured by quantifying proceptive and resistance behavior toward males in both the periovulatory period and non-POP phases of estrus. The efficacy of female preference was measured both (1) by measuring success rates of female proceptivity and resistance behaviors and (2) by determining how well measures of female mate preference (proceptivity and resistance rates) predict male mating success. Though male chimpanzees are clearly dominant to females, the results indicate that females could effectively resist male solicitations and, in most cases, unwanted copulations were averted. Both female proceptivity and resistance rates correlate (positively and inversely, respectively) with male mating success in POP. Outside POP, female proceptivity rates corresponded with male mating success, but resistance rates did not. Males (irrespective of rank) that were preferred by females obtained higher mating success compared to other males during the POP, suggesting that females were effective in their mate choice and that, despite clear male dominance, female choice influences paternity in wild chimpanzees.

/pdf/the-efficacy-of-female-choice-in-chimpanzees-of-the-tai-3nq4bynbuc.pdf

The efficacy of female choice in chimpanzees of the Taï Forest, Côte d’Ivoire

fail over time, not at a particular frequency. Two sensor audit strategies are employed in this chapter that allows the user to detect and diagnose sensor faults. One is based on the development of either PLS models (weak autocorrelation exists) or canonical variates state-space (CVSS) models (strong autocorrelation exists). The other strategy uses PCA models to assess the contribution of individual sensors to the T2 and SPE of the calibration model. The pasteurization model is used again to illustrate these strategies. (A note here is that Table 8.3, which has been scanned, is barely legible and must be replaced with a cleaner version.) Controller performance monitoring (CPM) is the subject of Chapter 9 and represents an important contribution to this text. CPM is the methodology for providing the operator (or other plant personnel) with the means of determining if performance targets and response attributes are within acceptable limits based on the performance of the controlled process variables. CPM of multivariable control systems is also considered. The final section discusses CPM for model predictive control (MPC) systems which is an alternative to CPM of multivariable control systems. An example to illustrate the application of CPM and diagnosis to MPC of a forced circulation evaporator is given at the end of the chapter. Chapter 10 focuses on the application of methods presented in the text to sheet forming processes. This is interesting since the independent variables are space and time. While process uniformity is the objective, the analysis of such a process involves the evaluation of data in both the cross-direction (CD) and machine (MD) of the sheet over time. The authors initially discuss how to look at an array of such data using traditional methods. Both the CD profile and MD trend can be extracted from the data, and the CD profile can be evaluated over time. Gram polynomials are introduced for discrete time data and compared to the CD profile itself and its residual measurement signal. Alternatively, principal components analysis is offered as another technique to define orthogonal basis functions which can be directly estimated from the process data. In the next section, the authors discuss the performance evaluation of the univariate MD controller and multivariate CD controllers. In summary, I highly recommend this text for chemical engineers and statisticians interested in learning how statistical methods can be integrated with process control methods. Others will also find this to be a very informative text. The only drawback is that the authors do not spend much time discussing how the reader can use commercially available software to do these analyses. Many readers will want to implement these methods right away, so helping to direct them (or better yet, show them) to what software is on the market would be a valuable asset to this already fine text.

A Handbook of Statistical Analysis Using Stata

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A Handbook of Statistical Analysis Using Stata