The Markov Model of Labor Force Activity 2012-17: Extended Tables of Central Tendency, Shape, Percentile Points, and Bootstrap Standard Errors

2019 ◽  
Vol 28 (1-2) ◽  
pp. 15-108 ◽  
Author(s):  
Gary R. Skoog ◽  
James E. Ciecka ◽  
Kurt V. Krueger
Keyword(s):  
Markov Model ◽  
Labor Force ◽  
Transition Probabilities ◽  
Standard Errors ◽  
Central Tendency ◽  
Statistical Measures ◽  
Gender And Education ◽  
Labor Force Activity ◽  
Force Data

Abstract This paper updates the Skoog-Ciecka-Krueger (2011) study which used 2005-09 U.S. population labor force data to estimate worklife expectancies. This update presents estimates using 2012-17 labor force data for persons ages 18 and over by sex and education. These updated estimates are presented as before as a set of worklife tables, including extended probability calculations and other statistical measures useful to forensic economists. Transition probabilities, by age, gender, and education, are contained in the electronic supplementary materials.

The Markov Model of Years to Final Separation from the Labor Force 2012-17: Extended Tables of Central Tendency, Shape, Percentile Points, and Bootstrap Standard Errors

2019 ◽  
Vol 28 (1-2) ◽  
pp. 109-195
Author(s):  
Gary R. Skoog ◽  
James E. Ciecka ◽  
Kurt V. Krueger
Keyword(s):  
Labor Market ◽  
Markov Model ◽  
Labor Force ◽  
Standard Errors ◽  
Central Tendency ◽  
Data Set ◽  
Time Period ◽  
Labor Force Activity ◽  
Education Groups ◽  
Labor Market Activity

Abstract In this paper, we estimate characteristics of years to final separation from the labor force. We use data for the same time period, the same data set, the same education groups for both genders, and similar recursive methods as in our paper on years of labor force activity (Skoog, Ciecka, and Krueger, 2019). Years of labor market activity are a subset of years to final separation from the labor force, with the latter including any years of inactivity that occur prior to final labor force separation. The last set of extended tables for years to final separation from the labor force appeared in 2003 and was based on 1997-98 data (Skoog and Ciecka, 2003).

The Markov Process Model of Labor Force Activity: Extended Tables of Central Tendency, Shape, Percentile Points, and Bootstrap Standard Errors

2011 ◽  
Vol 22 (2) ◽  
pp. 165-229 ◽  
Author(s):  
Gary R. Skoog ◽  
James E. Ciecka ◽  
Kurt V. Krueger
Keyword(s):  
Markov Process ◽  
Business Cycle ◽  
Labor Force ◽  
Process Model ◽  
Standard Errors ◽  
Central Tendency ◽  
Statistical Measures ◽  
Markov Process Model ◽  
Labor Force Activity

Abstract This paper updates the Skoog-Ciecka (2001) worklife tables, which used 1997–1998 data, and the Krueger (2005) worklife tables, which used 1998–2004 data. The present paper uses data generated by the methodology Krueger devised in his 2003 PhD dissertation. We have pooled the data beginning January 2005 and continuing through December 2009, a period of five years, using observations matched a year apart. Thus, we have roughly four times the data in the first of the previous studies, and about that of the second. We chose this period for a variety of reasons, including recency, business cycle and trend considerations. The result is the most current and disaggregated set of worklife tables, along with extended probability calculations and statistical measures available to forensic economists.

scholarly journals Labor Market Dynamics

2019 ◽  
Vol 2019 (282) ◽  
Author(s):  
Ippei Shibata
Keyword(s):  
Labor Market ◽  
Markov Model ◽  
Labor Force ◽  
Transition Probabilities ◽  
Market Dynamics ◽  
Demographic Groups ◽  
First Order ◽  
Labor Force Status ◽  
Long Run ◽  
Labor Market Dynamics

This paper proposes a hidden state Markov model (HMM) that incorporates workers’ unobserved labor market attachment into the analysis of labor market dynamics. Unlike previous literature, which typically assumes that a worker’s observed labor force status follows a first-order Markov process, the proposed HMM allows workers with the same labor force status to have different history-dependent transition probabilities. I show that the estimated HMM generates labor market transition probabilities that match those observed in the data, while the first-order Markov model (FOM) and its many-state extensions cannot. Even compared with the extended FOM, the HMM improves the fit of the empirical transition probabilities by a factor of 30. I apply the HMM to (1) calculate the long-run consequences of separation from stable employment, (2) study evolutions of employment stability across different demographic groups over the past several decades, (3) compare the dynamics of labor market flows during the Great Recession to those during the 1981 recession, and (4) highlight the importance of looking beyond distributions of current labor force status.

scholarly journals States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model

2021 ◽  
Vol 18 (14) ◽  
pp. 7449
Author(s):  
Juan Xiong ◽  
Qiyu Fang ◽  
Jialing Chen ◽  
Yingxin Li ◽  
Huiyi Li ◽  
...  
Keyword(s):  
Postpartum Depression ◽  
Markov Model ◽  
Normal State ◽  
Sojourn Time ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Public Health Problem ◽  
Mental States ◽  
Hazard Ratio ◽  
Multiple Time

Background: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. Methods: In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. Results: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42–0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46–2.27). Conclusions: The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.

scholarly journals The marriage unemployment gap

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Sekyu Choi ◽  
Arnau Valladares-Esteban
Keyword(s):  
Labor Market ◽  
Labor Force ◽  
Unemployment Rate ◽  
Crucial Role ◽  
Transition Probabilities ◽  
Demographic Changes ◽  
Men And Women ◽  
Main Driver ◽  
Married Individuals ◽  
Flow Decomposition

Abstract In this paper we document that married individuals face a lower unemployment rate than their single counterparts. We refer to this phenomenon as the marriage unemployment gap. Despite dramatic demographic changes in the labor market over the last decades, this gap has been remarkably stable both for men and women. Using a flow-decomposition exercise, we assess which transition probabilities (across labor force states) are behind this phenomenon: For men, the main driver is the higher job losing probabilities faced by single workers. For females, the participation margin also plays a crucial role.

scholarly journals Forecasting the Long-Run Behavior of the Stock Price of Some Selected Companies in the Malaysian Construction Sector: A Markov Chain Approach

2020 ◽  
Vol 5 (2) ◽  
pp. 296-308
Author(s):  
Wajeeh Mustafa Sarsour ◽  
Shamsul Rijal Muhammad Sabri
Keyword(s):  
Markov Model ◽  
Stock Prices ◽  
Stock Price ◽  
Transition Probabilities ◽  
Return Time ◽  
Price Increase ◽  
State Model ◽  
Construction Sector ◽  
Long Run ◽  
Three State Model

The fluctuations in stock prices produce a high risk that makes investors uncertain about their investment decisions. The present paper provides a methodology to forecast the long-term behavior of five randomly selected equities operating in the Malaysian construction sector. The method used in this study involves Markov chains as a stochastic analysis, assuming that the price changes have the proparty of Markov dependency with their transition probabilities. We identified a three-state Markov model (i.e., increase, stable, fall) and a two-state Markov model (i.e., increase and fall). The findings suggested that the chains had limiting distributions. The mean return time was computed for respective equities as well as to determine the average duration to return to a stock price increase. The analysis might aid investors in improving their investment knowledge, and they will be able to make better decisions when an equity portfolio possesses higher transition probabilities, higher limiting distribution, and lowest mean return time in response to a price increase. Finally, our investigations suggest that investors are more likely to invest in the GKent based on the three-state model, while VIZIONE seems to be a better investment choice based on a two-state model.

Does Urban Public Housing Diminish the Social Capital and Labor Force Activity of Its Tenants?

2001 ◽  
Vol 20 (3) ◽  
pp. 485-504 ◽  
Author(s):  
David A. Reingold ◽  
Gregg G. Van Ryzin ◽  
Michelle Ronda
Keyword(s):  
Social Capital ◽  
Labor Force ◽  
Public Housing ◽  
The Social ◽  
Labor Force Activity

A semi-markov model for clinical trials

1965 ◽  
Vol 2 (02) ◽  
pp. 269-285 ◽  
Author(s):  
George H. Weiss ◽  
Marvin Zelen
Keyword(s):  
Clinical Trials ◽  
Probability Distribution ◽  
Acute Leukemia ◽  
Markov Model ◽  
Markov Models ◽  
Transition Probabilities ◽  
State Transitions ◽  
Semi Markov Processes ◽  
Absorbing States ◽  
Epidemic Diseases

This paper applies the theory of semi-Markov processes to the construction of a stochastic model for interpreting data obtained from clinical trials. The model characterizes the patient as being in one of a finite number of states at any given time with an arbitrary probability distribution to describe the length of stay in a state. Transitions between states are assumed to be chosen according to a stationary finite Markov chain.Other attempts have been made to develop stochastic models of clinical trials. However, these have all been essentially Markovian with constant transition probabilities which implies that the distribution of time spent during a visit to a state is exponential (or geometric for discrete Markov chains). Markov models need also to assume that the transitions in the state of a patient depend only on absolute time whereas the semi-Markov model assumes that transitions depend on time relative to a patient. Thus the models are applicable to degenerative diseases (cancer, acute leukemia), while Markov models with time dependent transition probabilities are applicable to colds and epidemic diseases. In this paper the Laplace transforms are obtained for (i) probability of being in a state at timet, (ii) probability distribution to reach absorption state and (iii) the probability distribution of the first passage times to go from initial states to transient or absorbing states, transient to transient, and transient to absorbing. The model is applied to a clinical study of acute leukemia in which patients have been treated with methotrexate and 6-mercaptopurine. The agreement between the data and the model is very good.

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