Model Of Statistical Discrimination Research Articles

Racial Discrimination in the Labor Market: Theory and Empirics

We review theories of race discrimination in the labor market. Taste-based models can generate wage and unemployment duration differentials when combined with either random or directed search even when strong prejudice is not widespread, but no existing model explains the unemployment rate differential. Models of statistical discrimination based on differential observability of productivity across races can explain the pattern and magnitudes of wage differentials but do not address employment and unemployment. At their current state of development, models of statistical discrimination based on rational stereotypes have little empirical content. It is plausible that models combining elements of the search models with statistical discrimination could fit the data. We suggest possible avenues to be pursued and comment briefly on the implication of existing theory for public policy. (JEL J15, J31, J64, J71)

Application of Progressively Statistical Discriminant Models

Discriminant analysis is an important multivariate statistical analysis, and plays an important part in pattern classification, data mining, machine learning et al. In this paper, based on principle of progressively statistical discriminant analysis under Fisher rule, a progressively statistical discriminant model is set up. The authors analyzed the data about the occurrence of the second generation of the corn borer in 21 years from 1985 to 2006 (except 1990) at Linyi, Shandong Province, and then set up three graded recognition pattern. The results tested the pest data showed that the fitting rate is 95.24%, 92.31% and 100% respectively, and that accuracy of forecast is satisfactory.

Projected Reorganization of Florida Bay Seagrass Communities in Response to the Increased Freshwater Inflow of Everglades Restoration

Historic changes in water-use management in the Florida Everglades have caused the quantity of freshwater inflow to Florida Bay to decline by approximately 60% while altering its timing and spatial distribution. Two consequences have been (1) increased salinity throughout the bay, including occurrences of hypersalinity, coupled with a decrease in salinity variability, and (2) change in benthic habitat structure. Restoration goals have been proposed to return the salinity climates (salinity and its variability) of Florida Bay to more estuarine conditions through changes in upstream water management, thereby returning seagrass species cover to a more historic state. To assess the potential for meeting those goals, we used two modeling approaches and long-term monitoring data. First, we applied the hydrological mass balance model FATHOM to predict salinity climate changes in sub-basins throughout the bay in response to a broad range of freshwater inflow from the Everglades. Second, because seagrass species exhibit different sensitivities to salinity climates, we used the FATHOM-modeled salinity climates as input to a statistical discriminant function model that associates eight seagrass community types with water quality variables including salinity, salinity variability, total organic carbon, total phosphorus, nitrate, and ammonium, as well as sediment depth and light reaching the benthos. Salinity climates in the western sub-basins bordering the Gulf of Mexico were insensitive to even the largest (5-fold) modeled increases in freshwater inflow. However, the north, northeastern, and eastern sub-basins were highly sensitive to freshwater inflow and responded to comparatively small increases with decreased salinity and increased salinity variability. The discriminant function model predicted increased occurrences of Halodule wrightii communities and decreased occurrences of Thalassia testudinum communities in response to the more estuarine salinity climates. The shift in community composition represents a return to the historically observed state and suggests that restoration goals for Florida Bay can be achieved through restoration of freshwater inflow from the Everglades.

Testing for Statistical Discrimination: The Case of Immigrant Physicians in Israel

Two models have been proposed to explain why two groups of workers who have the same level of productivity can receive different wages: (i) the statistical discrimination model, according to which the discrimination is the result of a rational response to uncertainty about productivity that consists in adopting certain characteristics as proxies for productivity and (ii) the taste-based discrimination model, according to which the discrimination is based on prejudice against a particular group. It is usually difficult to determine empirically whether the gap in earnings is due to statistical or taste-based discrimination. However, the conditions for a natural experiment were created in 1989 when Israel introduced a licensing examination for immigrant physicians arriving from that point onward, thus making it possible to determine which type of discrimination is responsible for the wage gap between immigrant and native physicians. Controlling for other factors, the paper finds that: (1) the earnings of physicians who immigrated to Israel after 1989 are significantly higher than those of physicians who immigrated previously and (2) the earnings of immigrant physicians who have taken the licensing examination converge to those of native physicians after an average of 5.5 years. These results constitute evidence that the difference in earnings between native and immigrant physicians is due to statistical, rather than taste-based, discrimination.

Do expert patients get better treatment than others? Agency discrimination and statistical discrimination in obstetrics

We address models that can explain why expert patients (obstetricians, midwives and doctors) are treated better than non-experts (mainly non-medical training). Models of statistical discrimination show that benevolent doctors treat expert patients better, since experts are better at communicating with the doctor. Agency theory suggests that doctors have an incentive to limit hospital costs by distorting information to non-expert patients, but not to expert patients. The hypotheses were tested on a large set of data, which contained information about the highest education of the parents, and detailed medical information about all births in Norway during the period 1967–2005 (Medical Birth Registry). The empirical analyses show that expert parents have a higher rate of Caesarean section than non-expert parents. The educational disparities were considerable 40 years ago, but have become markedly less over time. The analyses provide support for statistical discrimination theory, though agency theory cannot be totally excluded.

The role of the sediment budget in understanding debris flow susceptibility

AbstractThis study proposes a sediment‐budget model to predict the temporal variation of debris volume stored in a debris‐flow prone watershed. The sediment‐budget is dominated by shallow landslides and debris outflow. The basin topography and the debris volume stored in the source area of the debris‐flow prone watershed help evaluating its debris‐flow susceptibility. The susceptibility model is applied to the Tungshih area of central western Taiwan. The importance of the debris volume in predicting debris‐flow susceptibility is reflected in the standardized coefficients of the proposed statistical discriminant model. The high prediction rate (0·874) for the occurrence of debris flows justifies the capability of the proposed susceptibility models to predict the occurrence of debris flows. This model is then used to evaluate the temporal evolution of the debris‐flow susceptibility index. The analysis results show that the numbers of watershed which are classified as a debris‐flow group correspond well to storage of sediment at different time periods. These numbers are 10 before the occurrence of Chi‐Chi earthquake, 13 after the occurrence of Chi‐Chi earthquake, 16 after the occurrence of landslides induced by Typhoon Mindulle (Typhoon M), and 14 after the occurrence of debris flows induced by Typhoon M. It indicates that the occurrence of 7·6 Chi‐Chi earthquake had significant impact on the debris flow occurrence during subsequent typhoons. Copyright © 2009 John Wiley & Sons, Ltd.

Assessment of Overall Rate Expressions and Multiscale, Microkinetic Model Uniqueness via Experimental Data Injection: Ammonia Decomposition on Ru/γ-Al2O3 for Hydrogen Production

We introduce a framework for parameter estimation of microkinetic models via injecting data, collected in optimal regions of the “entire” experimental operating space, in models. We demonstrate this framework by combining differential conversion experimental data without transport limitations and models for ammonia decomposition on Ru/γ-Al2O3 for hydrogen production. Experiments indicate that there is significant H2 inhibition even at low ammonia conversions. Statistical model discrimination techniques indicate that multiple microkinetic parameter sets are able to describe quantitatively the experimental data, and some global rate expressions are also adequate. Among the best microkinetic models, nitrogen adsorption/desorption and the NHx dehydrogenation reactions are the kinetically significant reactions. It is found that macroscopic data (conversion) are insufficient for complete model discrimination; microscopic scale data are proposed for further model discrimination.

Model Discrimination and Mechanistic Interpretation of Kinetic Data in Protein Aggregation Studies

Given the importance of protein aggregation in amyloid diseases and in the manufacture of protein pharmaceuticals, there has been increased interest in measuring and modeling the kinetics of protein aggregation. Several groups have analyzed aggregation data quantitatively, typically measuring aggregation kinetics by following the loss of protein monomer over time and invoking a nucleated growth mechanism. Such analysis has led to mechanistic conclusions about the size and nature of the nucleus, the aggregation pathway, and/or the physicochemical properties of aggregation-prone proteins. We have examined some of the difficulties that arise when extracting mechanistic meaning from monomer-loss kinetic data. Using literature data on the aggregation of polyglutamine, a mutant β-clam protein, and protein L, we determined parameter values for 18 different kinetic models. We developed a statistical model discrimination method to analyze protein aggregation data in light of competing mechanisms; a key feature of the method is that it penalizes overparameterization. We show that, for typical monomer-loss kinetic data, multiple models provide equivalent fits, making mechanistic determination impossible. We also define the type and quality of experimental data needed to make more definitive conclusions about the mechanism of aggregation. Specifically, we demonstrate how direct measurement of fibril size provides robust discrimination.

Bayesian Bigot? Statistical Discrimination, Stereotypes, and Employer Decision Making

Much of the debate over the underlying causes of discrimination centers on the rationality of employer decision making. Economic models of statistical discrimination emphasize the cognitive utility of group estimates as a means of dealing with the problems of uncertainty. Sociological and social-psychological models, by contrast, question the accuracy of group-level attributions. Although mean differences may exist between groups on productivity-related characteristics, these differences are often inflated in their application, leading to much larger differences in individual evaluations than would be warranted by actual group-level trait distributions. In this study, the authors examine the nature of employer attitudes about black and white workers and the extent to which these views are calibrated against their direct experiences with workers from each group. They use data from fifty-five in-depth interviews with hiring managers to explore employers' group-level attributions and their direct observations to develop a model of attitude formation and employer learning.

Effect of gallium, aluminium, and chromium on silica supported V-Mg-O catalysts during oxidative dehydrogenation of propane: Kinetic study

The oxidative dehydrogenation (ODH) of propane was conducted on gallium, aluminum, and chromium doped Si30VMgO catalysts. On doping, the concentrations of the phases responsible for the activity and selectivity increased in their concentrations. The reaction studies were conducted in a tubular steel reactor at temperatures of 753, 783, 813, and 843 K and atmospheric pressure. The total flow rates of the feed were chosen as 30, 40, 50, and 60 ml/min. The propane to oxygen ratios were chosen at 1 : 1, 2 : 1, and 3 : 1, respectively. The effect of various dopants on the activity and selectivity of the catalysts was studied. Deactivation studies were conducted over all the catalysts. The kinetic data were analyzed in terms of power law models and Langmuir-Hinshelwood (LH) models. The kinetic data results were analyzed by comparing the effect of dopants. Statistical model discrimination was done for the proposed models. AIC and BIC criteria were used for discrimination of the models.

Why Do Women's Wages Increase So Slowly Throughout Their Career? A Dynamic Model of Statistical Discrimination

Abstract. The aim of this paper is to explain the growing wage differentials between men and women during their working careers. We provide a dynamic model of statistical discrimination, which integrates specific human capital decisions: on‐the‐job training investment and wages are endogenously determined. We reveal a small wage differential at the beginning of women's career, but women's wages increase more slowly; this is partly due to a lower level of human capital investment by women and partly because firms smooth training costs between different periods.

Statistical Discrimination with Peer Effects: Can Integration Eliminate Negative Stereotypes?

We introduce peer effects in the costs of human capital acquisition into a model of statistical dis crimination in labour markets. This creates a link between the level of segregation in social networks and racial disparities in job assignment and wages. We show that this relationship is characterized by disconti nuities: there is a threshold level of segregation below which negative stereotypes become unsustainable, and steady-state skill levels can change dramatically. This change can work in either direction: skill levels may either rise or fall in both groups. VvTiich of these outcomes arises depends on the population share of the disadvantaged group and on the distribution of the costs of human capital investments. We also examine the effects of affirmative action policies in the presence of peer effects and provide conditions under which such policies eliminate negative stereotypes.

Why Do Women's Wages Increase So Slowly Throughout Their Career? A Dynamic Model of Statistical Discrimination

The aim of this paper is to explain the growing wage differentials between men and women during their working careers. We provide a dynamic model of statistical discrimination, which integrates specific human capital decisions: on-the-job training investment and wages are endogenously determined. We reveal a small wage differential at the beginning of women's career, but women's wages increase more slowly; this is partly due to a lower level of human capital investment by women and partly because firms smooth training costs between different periods.

Sexual Orientation and Labor Market Discrimination

We develop empirical estimates of the return to sexual orientation in the labor market and utilize the 2004 Current Population Survey to determine if lesbians and gay men are treated differently from their heterosexual counterparts. We find strong evidence consistent with the hypothesis of discriminatory treatment against gay men, and this evidence differs substantially by occupation. On the other hand, we find no evidence of discrimination against lesbians. These findings are consistent with priors based on economic theory of the standard taste for discrimination and statistical discrimination models. The results for both men and women are consistent across wage and total compensation regressions.

Statistical Discrimination in the Criminal Justice System: The Case for Fines Instead of Jail

We develop a model of statistical discrimination in criminal trials. Agents carry publicly observable labels of no economic significance (race, etc.) and choose to commit crimes if their privately observed utility from doing so is high enough. A crime generates noisy evidence, and defendants are convicted when the realized amount of evidence is suciently strong. Agents may also be convicted even when no crime has occurred. Convicted oenders are penalized either by incarceration or by monetary fines. In the case of prison sentences, discriminatory equilibria can exist in which members of one group face a prior prejudice in trials and are convicted with less evidence than members of the other group: If income is inversely related to prejudice, prison sentences have a lesser deterrence eect on the disadvantaged group whose members will thus commit more crimes, justifying the initial prejudice. Applying this argument to lifetime income, it is even possible that all individuals earn the same wage, but racial dierences persist in the crime rate because the expected duration of employment is less for persons who face a higher prejudice (because they will be jailed more often). Such discriminatory equilibria cannot exist with monetary fines instead of prison sentences, as they represent a stronger penalty for persons with lower earnings. Our findings have implications for potential reforms of the American criminal justice system.

Belief flipping in a dynamic model of statistical discrimination

The literature on statistical discrimination shows that ex-ante identical groups may be differentially treated in discriminatory equilibria. This paper constructs a dynamic model of statistical discrimination and explores what happens to the individuals who nonetheless overcome the initial discrimination. If an employer discriminates against a group of workers in her initial hiring, she may actually favor the successful members of that group when she promotes from within the firm. The worker's welfare implications (i.e. who benefits from an employer's discriminatory hiring practices) are unclear. Even though agents face discrimination initially, some may be better off because of it.

The Dynamics of Statistical Discrimination

This article demonstrates how learning dynamics select among equilibria in a statistical discrimination model of employment. The static market model exhibits multiple equilibria. The belief revision dynamics generate a Markov market process which, in the long run, is mostly near one and only one of the static equilibria, regardless of initial conditions. Usually policy effects appear in comparative statics, where the equilibrium set moves with different policies. But here policy is also seen to effect which equilibrium is selected even when it has no comparative statics effect at all. Social scientists have identified self-fulfilling expectations as an important source of discrimination in labour markets and other social exchanges. Various theories highlight the role of negative racial attitudes in creating social outcomes that in turn help to perpetuate the initial beliefs. In this account, discriminatory outcomes appear as an equilibrium of beliefs and behaviour. Among sociologists, Merton (1948) is one source for this view.1 A similar view of discrimination was put forward by Myrdal (1944), who referred to an equilibrium of beliefs and behaviour as 'the principle of cumulation' and, less abstractly, as 'the vicious circle'. Myrdal observes that social systems may have multiple equilibria, and that different equilibria may be more or less robust to perturbations. Remedial policies, he notes, might work by destabilising one equilibrium in favour of another.

Diskriminace žen v ekonomické teorii vybrané problémy

Paper outlines economic connections of labor market discrimination. It is intended on discrimination based upon gender, because this type of discrimination is on the point of our social background. In the introductory part are defined discrimination and delineated the various types of discrimination. Further there are presented data to suggest the possibility of discrimination. The major part of paper presents four important labor market models of discrimination: taste for discrimination model, monopsony model of discrimination (market power model), statistical discrimination model and the crowding model of discrimination (occupational segregation). In the following part are discussed the other nondiscriminatory factors determinating male - female earnings differentials. Discrimination in law is the last part of this paper.

Trade unions vs. statistical discrimination: Theory and application to post-apartheid South Africa

A simple model of statistical discrimination is analyzed, which captures some stylized facts of the South African labor market. It shows that this type of discrimination disappears when the wage rates are determined by efficient bargaining between a representative firm and a union, with endogenous membership. This may explain why the wage gap between Black and White workers in post-apartheid South Africa is smaller among unionized workers than among non-unionized ones.

Biological Gender Differences, Absenteeism, and the Earnings Gap

In most countries, women are absent from work more frequently than men. Using personnel data, we find that the absences of women below the age of 45 follow a 28-day cycle, while the absences of men and of women over the age of 45 do not. We interpret this as evidence that the menstrual cycle increases female absenteeism. To investigate the effect on women's earnings, we use a simple model of statistical discrimination. Consistent with the model, we find absenteeism has a more negative effect on men's earnings and this difference declines with seniority. The increased absenteeism induced by the 28-day cycle explains at least 14 percent of the earnings gender differential. (JEL J16, J22, J31)