Probability Of Attrition Research Articles

Modeling the Combined Effects of Credit Limit Management and Pricing Actions on Profitability of Credit Card Operations

The profitability of any credit card portfolio is influenced by complex interactions between several conflictingfactors like credit risk, probability of attrition, propensity to revolve, credit limit utilization and revenuegenerated. In this context, the allocation and maintenance of appropriate credit limits, and optimum pricing arethe most critical parameters, as they affect a number of these factors. Going beyond previously reported workdealing with pricing and revenue optimization, and credit limit management in isolation, this paper proposes amethod of studying the combined effects of credit limit management, and pricing actions on profitability using asystem of empirical behavioral models for the individual factors; and discusses how simulation can be used toarrive at ‘optimum’ pricing and credit limit combinations for each credit card account in a portfolio.

O atrito nas pesquisas longitudinais: o caso da pesquisa mensal de emprego (PME/IBGE)

O objetivo deste artigo é estimar os determinantes da permanência de pessoas no painel da Pesquisa Mensal de Emprego (PME) e, além disso, testar se ignorar a não-aleatoriedade do desgaste no painel causa algum viés em análises de regressão. Os resultados apontam que há uma forte correlação da taxa de atrito com as características geralmente associadas a uma mobilidade geográfica maior. Além disso, tanto características do processo de entrevista quanto fatores socioeconômicos estão significativamente relacionados à probabilidade de atrito no painel. Nos modelos adotados para testar o viés do desgaste no painel, assim como de outros critérios de seleção amostral, constatamos que todos estes são endógenos, apesar de alguns não estarem diretamente correlacionados com a equação de interesse. Por fim, mostramos que a ausência do controle para seleção amostral, o que inclui a retenção no painel, pode incorrer em algumas análises enviesadas.

Hedge Fund Attrition, Survivorship Bias, and Performance: Perspectives from the Global Financial Crisis

The impact of the global financial crisis since 2007 has been deep and broad, blanketing the financial and economic landscape and hammering the hedge fund industry. Using both active and inactive hedge fund return data from the CISDM database from January of 1994 to March of 2009, we measure survivorship bias, account for attrition rates, estimate a multi-factor model to explain hedge fund performance, and conduct a cross-sectional probit analysis to predict hedge fund attrition during the crisis. Following prior research, we surprisingly do not find survivorship bias to be prevalent during the global financial crisis. Moving a step beyond the literature, however, we identify a hidden survivorship bias attributed to the lack of reporting during the final months of the eventual demise of a fund. We also find unprecedented attrition rates, along with record declines in assets under management and fund closures during the crisis. Using a multi-factor model with both linear and nonlinear risk factors, we find four structural breaks in the data: October of 1998, April of 2000, March of 2003, and February of 2007. Estimating the model for each of the five sub-periods and for the entire period, we find that hedge fund returns can be explained by the linear risk factors (such as the three factors of Fama and French model, the change in credit spread, and the term structure spread) and the non-linear trend-following risk factors of Fung and Hsieh (2001). The evidence also indicates that the average hedge fund manager did not deliver a significantly positive alpha during the crisis. In addition, the cross-sectional probit results reveal that the more seasoned the fund, the higher its pre-crisis return performance, and the more regularly audited its books, the lower was its probability of attrition during the crisis.

Student Teaching and Teacher Attrition in Special Education

Research suggests that substantial pre-service student teaching is essential for the preparation and retention of special educators. Using logistic regression analysis, survey responses of beginning special educators were analyzed to answer the following questions: Are beginning special educators more likely to leave the field if they have had minimal or no pre-service student teaching? Are other characteristics of preparation related to the probability of attrition? It was found that substantial pre-service student teaching experience has a strong effect on the probability that a beginning special educator will remain in the field 1 year later (estimated odds ratio = 2.18). In addition, none of the effects of aspects of teacher pre-service preparation or teacher or school demographics analyzed had an impact on attrition, when controlling for the number of weeks of student teaching.

Early participant attrition from clinical trials: role of trial design and logistics

Participant attrition from randomized controlled trials reduces the statistical power of the study and can potentially introduce bias. Early identification of potential causes of attrition can help reduce patient attrition. We performed secondary analyses of two trials involving cancer patients. To identify predictors of attrition during two early phases, i.e., from consent to screening (Phase-1), and from screening to intake interview (Phase-2) in two clinical trials. Cancer patients undergoing chemotherapy were asked to enroll in one of two clinical trials. In each trial the benefits of a cognitive behavioral intervention were compared with a psycho-educational intervention to assist patients to manage cancer and treatment-related symptoms. Following consent patients were screened for their symptoms' severity to determine their eligibility. Of the 885 consenters 785 completed screening and of the 782 eligible for participation, 713 completed intake interview. In the first phase, longer delays between consent and first contact attempt, lower levels of patient education, minority race, and prolonged duration of screening increased the likelihood of dropping out with a significantly stronger effect on minorities than white patients. In the second phase, low education, being a minority, longer screening delays, and impact of symptom severity on enjoyment of life significantly increased probability of attrition. Participant reported causes of attrition were not modeled; however, exclusion of patients who died during the time period of this research meant that most patients leaving the study made a conscious decision to do so. To assure preservation of external validity, the time between consent and randomization into the arms of a trial must be held to a minimum. Delays between contacts and run in time, that may include screening patients to assure they will benefit from a trial, must be balanced against rates of attrition. Compressing intervals between contacts is particularly important to retain minorities.

EXPLORING ATTRITION BIAS: THE CASE OF THE KHAYELITSHA PANEL STUDY (2000‐2004)

AbstractAttrition bias is a problem for users of panel data. Researchers need to know what socio‐economic factors are associated with attrition, and whether this is of relevance for the kind of analysis they want to conduct. This paper discusses attrition bias in the 2000/2004 Khayelitsha panel study. It shows that women, shack‐dwellers and people living in smaller households are more likely to attrit, but that the impact of these variables on the probability of attrition is relatively small. The implications for labour‐market analysis are then explored using Mincerian earnings functions and a probit regression on whether respondents are wage‐employed or not. The coefficients generated using a restricted sample of non‐attritors do not differ significantly from those generated by the entire sample. This suggests that attrition bias in this particular data set is not a problem for this kind of labour market analysis.

The Impact of Attrition on the Children of the NLSY79

Abstract This paper examines the impact of attrition among the women of the National Longitudinal Survey of Youth 1979 (NLSY79) and children in the NLSY79 Mother-Child Supplement (NLSY79-C). Attrition among the children is nonrandom with respect to mother’s marital status, grandfather’s completed schooling, and family income. These differences that are related to the probability of attrition do not appear to impact estimates of the effects of family income or maternal employment early in the child’s life on either PPVT or BPI standard scores. However, the women who are not interviewed in any child-supplement year and the children for whom supplemental information is never collected appear to be the most disadvantaged. The omission of these children from the NLSY79-C may impact estimates of family characteristics on child outcomes, but because there are relatively few such children, the effects of their omission are likely to be small.

Do Unions Help or Hinder Women in Training? Apprenticeship Programs in the United States

Trade unions are frequently criticized for excluding women from skilled crafts by denying them training. This article examines this argument by eestimatin the retention and attrition probabilities of men and women in the joint union‐management and the unilateral employer‐sponsored apprenticeship programs. While men, on average, have higher retention and lower attrition rates than women, joint sponsorship raises women's graduation probability above (and lowers their quit probability below) those of men or women apprentices in unilateral programs.

Combining Panel Data Sets with Attrition and Refreshment Samples

tistical models that allow for more complex relationships than can be inferred using only cross-sectional data. Panel, i.e., longitudinal, data, in which the same units are observed repeatedly at different points in time, can often provide the richer data needed for such models (e.g., Chamberlain (1984), Hsiao (1986), Baltagi (1995), Arellano and Honore (forthcoming)). Missing data problems, however, can be more severe in panels, because even those units that respond in initial waves of the panel may drop out of the sample in subsequent waves (e.g., Hausman and Wise (1979), Robins and West (1986), Ridder (1990), Verbeek and Nijman (1992), Abowd, Crepon, Kramarz, and Trognon (1995), Fitzgerald, Gottschalk, and Moffitt (1998), and Vella (1998)). Sometimes, in the hope of mitigating the effects of such attrition, panel data sets are augmented by replacing the units that have dropped out with new units randomly sampled from the original population. Following Ridder (1992), who used such replacement units to test alternative models for attrition, we call such additional samples refreshment samples. Here we explore the benefits of refreshment samples for inference in the presence of attrition. Two general approaches are often used to deal with attrition in panel data sets when refreshment samples are not available. One model, based on the missing at random assumption (MAR, Rubin (1976), Little and Rubin (1987)), allows the probability of attrition to depend on lagged but not on contemporaneous variables that have missing values. The other model (denoted by HW in the remainder of the paper, given the similarity to a model developed by Hausman and Wise (1979)), allows the probability of attrition to depend on such contemporaneous, but not on lagged, variables. Both sets of models have some theoretical plausibility, but they rely on fundamentally different restrictions on the

A Community-Based Vocational Preparation/Training Programme for Delinquent Youths: General Aptitudes as Predictors of Attrition?

Client attrition in community-based programmes for delinquent youths is a serious and widespread problem. The current study examined whether delinquents' general aptitude test scores would predict attrition in a vocational preparation focused programme. Results showed that completers (n = 26) and non-completers (n = 15) were significantly differentiated on general learning ability, verbal, numerical and spatial aptitudes, with completers displaying higher levels of functioning. Numerical and, to a lesser degree, spatial aptitudes emerged as the best discriminators between the two groups. The derived discriminant function produced a correct prediction ("hit") rate of 71%, accurately predicting almost all of the non-completers (13 out of IS). However, it mis-classified 10 of the 26 completers. In view of this tendency to overpredict the number of non-completers, low aptitude scores alone appear to be limited in their usefulness to predict attrition in individual youths. It is suggested that future research should examine whether aptitude deficits inter-acting with other factors can differentially influence the probability of attrition.

Factors affecting attrition in the Baltimore longitudinal study of aging

In a longitudinal study of aging, the influence on attrition of age, marital status, education, occupation, distance between a subject's residence and study center, self-health and financial assessment, and method of recruitment was examined. Prospective follow-up information on the subsequent active/dropout and alive/dead status on the 1088 subjects, who joined the Baltimore Longitudinal Study of Aging during the first twenty years (1958–1977), were studied using a proportional hazards model. Three analyses, each controlling for death in a different manner, produced similar results. The analysis indicates that age, education and distance have the strongest association with attrition ( p < 0.001), while self-health assessment has a lesser association ( p < 0.05). The lowest risk in age, for example, occurs in 40–49.9 year olds with subjects less than 30 having an increased risk of 91% and subjects 70 or older having an increased risk of 189%. Examination of attrition probabilities show that subjects living 500 or more miles from study center, ages 70 and older, with less than bachelors degree, and perceiving their health as average or below have the greatest probability of dropping out — approximately 3-fold greater than the average subject's probability.