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This paper seeks to examine the use of the cohort component method as a method of deriving replacement demand for manpower forecasting.

The paper explains the principal concepts of replacement demand and how replacement demand has been estimated in two alternative ways: the cohort component method and using longitudinal data on individuals. The paper focuses on one of these ways, the cohort component method, and illustrates how this method can fail to capture all the relevant flows driving replacement demand. It also compares the method to the alternative approach based on individual data and discusses US and Irish results using both methods.

The cohort component method is found to underestimate replacement demand significantly in many occupations.

Research estimates of replacement demand should be based on individual longitudinal data rather than the cohort component method.

Many countries undertake some form of occupational employment forecasting including, in many cases, making estimates of replacement demand. This paper should help to clarify the appropriate choice of methodology for estimating replacement demand.

This paper's objective is to provide improvement plans that take into account problems in the housing sector, as well as low fertility and demographic problems caused by aging, by suggesting long‐term policy directions considering all future possible changes in fertility and housing policies.

This research analyzes age specific fertility rates by socioeconomic index, composition of three scenarios about the total fertility rate, the forecast of the future population by the cohort component method, and the forecast of housing demand according to the Mankiw‐Weil (M‐W) model.

If the fertility rate increases through improvements in socioeconomic conditions, then we see that housing demand also increases. If the fertility rate level is higher than that of scenario 3, then a housing demand decrease comes later. However, even if the low fertility rate issue is addressed, the problem with the housing market due to the aging is expected to continue for the time being.

If the population decrease cohort is accumulated due to the continuously low fertility, then it will cause an increase in the reduction effect of housing demand. Considering housing spaces, the elderly require a relatively large amount of available space in Korea where the aged population is rapidly increasing. This increase in seniors could counterbalance the reduced demand for housing.

This is a long‐term‐oriented paper about housing demand and the changing trends in Korea, which is undergoing demographic changes due to low fertility and ongoing aging. We need to monitor fertility rates and the structure of population changes to achieve a stable housing market, and we should also recognize that these structural changes by age will have diverse ripple effects on housing demand.

Information on job openings by occupation is important for those looking for jobs, for those contemplating education and training options and for policy makers in education and training. Job openings in an occupation are a result of employment growth and the replacement of workers who leave the occupation. In this paper, three concepts of replacement demand are outlined and net replacement demand is estimated and projected for 81 occupational groups, covering the whole of employment in Australia. The results show the net replacement demand rate to vary substantially across occupations, with an average annual rate of 2 per cent.

As an important carrier of sustainable economy and social development, population is the foundation of the whole society. Scientific predictions of future population growth will bring great reference to macro-economic and social planning. For China, as a country of the biggest population, the research on its population policy is worthwhile.

Previous literatures on population growth prediction are generally based on time-series analysis. However, the new two-child policy in China provides us an opportunity to predict the population growth from the perspective of the welfare efficiency, since each family is able to determine whether to have the second child on account of the family’s utility. The welfare efficiency is calculated through the database of newborn babies, disposable income per capita, living resource per capita, and health expenditure pre capital. These are the main factors by which each family decides whether to bear additional babies. In this chapter, we perform the micro-economic analysis on a new policy and propose a Data Envelopment Analysis (DEA) method to predict the population growth. Under the condition of policy adjustment, we successfully predict the population growth with this method. We also propose some suggestions concerning the implementation of the new policy.

This paper seeks to provide researchers and librarians with an overview of the US Census Bureau's American Community Survey (ACS), with a specific focus on practical issues that users must face when choosing and using ACS datasets.

Each of the following issues are explored subsequent to a general overview of the ACS: choosing among census datasets from different census programs, interpreting and choosing between the different ACS period estimates, selecting census geography, understanding and recalculating margins of error, and accessing the data. Samples of ACS tables and formulas for creating derived estimates are used to illustrate how to interpret and work with the data.

The ACS datasets are fundamentally different from the decennial census as they are period estimates created from rolling sample surveys. The ACS has a steeper learning curve; this complexity is due in part to the number of choices users must make between datasets, but the primary challenge is learning how to understand and work with estimates as opposed to population counts.

While other papers have discussed the benefits and challenges of the ACS, this paper is structured around the practical issues that researchers must face when using it. Special consideration is given to calculating derived estimates using spreadsheet formulas, as this is a key task that many users will need to perform and spreadsheets are the most likely tool users will employ to manipulate the data.

This chapter proposes an optimized innovative information technology as a means for achieving operational functionalities of real-time portable electronic health records, system interoperability, longitudinal health-risks research cohort and surveillance of adverse events infrastructure, and clinical, genome regions – disease and interventional prevention infrastructure. In application to the Dod-VA (Department of Defense and Veteran's Administration) health information systems, the proposed modernization can be carried out as an “add-on” expansion (estimated at $288 million in constant dollars) or as a “stand-alone” innovative information technology system (estimated at $489.7 million), and either solution will prototype an infrastructure for nation-wide health information systems interoperability, portable real-time electronic health records (EHRs), adverse events surveillance, and interventional prevention based on targeted single nucleotide polymorphisms (SNPs) discovery.

Synthesis of the customer lifetime value and the shareholder value (SHV) approach in order to develop an integrated, marketing‐based method for corporate valuation.

Discusses the limitations and assumptions of existing methods to estimate customer value components and examines the limitations of the SHV concept. By linking the customer equity (CE) and the SHV approach, a formal model to calculate corporate value is developed. The discounted cash flow method is used for modelling the profit streams.

Provides formulas for the estimation of both the individual lifetime value of a customer and CE. Provides a comprehensive model to estimate corporate value based on customer‐related cash flows and traditional financial metrics. Introduces typical cases, in which the use of a customer‐based valuation seems beneficial. Illustrates how our approach can be applied by using a simple case study on M&A in the telecommunication industry. Gives suggestions on how to obtain the necessary data, partially even from publicly available sources.

Advancement of the quantitative techniques for modelling the customer value components would allow for relaxing some restrictive assumptions. The explicit modelling of the future growth of the customer base (the acquisition rate) would increase the applicability of the model. Additionally, taking into account heterogeneity within the customer cohorts is a task for future research. Finally, our model needs to be applied more extensively using real data for the input variables.

A CE‐based valuation approach can guide marketing investments and helps to avoid misallocation of resources. Based on an example in the field of M&A, we demonstrate the usefulness of the approach for obtaining a realistic indicator of firm value. It helps to assess whether an acquisition is economically sensible. We provide evidence for the superiority of a customer‐based approach over traditional financial methods.

While the traditional SHV method considers cash flows at a highly aggregated level, our approach employs disaggregated cash flows on the level of individual customers. Thereby we do incorporate the lifetime values of future customers by considering different cohorts. We do capture customer defection by incorporating retention rates. Our model enables a more detailed and valid estimation of corporate value by accounting for the single customer activities that drive marketing actions. This enables a better forecasting of the free cash flow. Incorporating customer‐related drivers into financial valuation models makes easier to assess the return on marketing investments.

This article estimates a control function estimator with training effect modelled as a random coefficient, partitioned into an observed and unobserved component. The article analyzes the earnings effect of participating in labor market training programs for three cohorts during the 1980s and the beginning of the 1990s. It separates the analysis between Swedish and foreign‐born to identify differences in their responses to training. The results indicate that there is positive sorting in training: slightly positive effects for both groups but somewhat larger for the foreign‐born. Further, consistent with results from several previous studies, the article finds that being young often means no positive pay‐off from training, and the same is found for persons with only a primary education. In conflict with earlier studies, the article finds that males have a better pay‐off from training than females. Rewards from training are higher for foreign‐ than for native‐born and rewards among the former vary by place of birth and how long they have been in the country.

This study aims to present a thematic analysis on cohort-based teaching and learning from four education doctorate degree programs. Recommendations are then presented to other scholars engaging in research on cohort-based, graduate degree programs.

Yin’s (2018) embedded, multiple case study approach guided the design of this study. Data collection consisted of three- to four-day site visits to each program and included the following data sources: program documents (e.g. handbooks, syllabi and third-party evaluations), class observations and semi-structured interviews with students, faculty and program directors.

This study describes how collaboration and collective learning were key components in each program’s coursework and milestone expectations, arguing that such an emphasis contributed to opportunities for collaboration and collective learning experiences.

Research has documented a number of outcomes associated with cohort-based programs in terms of group dynamics. The authors examine this quality further by showing how specific structures and practices within each program’s cohort model supported not only peer collaboration but also overall student learning.

On the basis of an exploratory research, the purpose of this paper is to identify the criteria used by new entrants to the workforce (specifically premium millennial talent) while making job choices.

Using signal theory, the study attempts to explore what drives prospective millennial candidates to apply to a particular job role by identifying the components that form the initial anticipatory psychological contract (iAPC) of millennial talent from a tier-1 technological school in India and the factors that drive their job application intent. It is an exploratory study which uses survey data from 335 respondents.

The study identifies the critical components of the iAPC of this set of talent. It highlights the transactional approach of this cohort to evaluate prospective job roles.

This research is based on an exploratory study which was carried out in a single school in India and may be used as a concept for designing and implementing more generalized studies using standardized tools of measurement in the future. The study highlights the fact that this cohort attends to the transactional components of the signals sent out by recruiters to a larger extent than to other kinds of signals which may be communicated by employers. HR communication strategies for connecting with this set may emphasize the unique employment proposition to potential recruits using the findings from this study to better engage with this cohort.

The current research gives indications on the components of the iAPC for this cohort and would help HR form appropriate strategies to attract this set of talent.

This research takes a fresh line of reasoning to explain what parameters would drive fresher millennial talent to apply for a job at an organization. It views the group through a generational theory paradigm and signaling theory forms the backdrop for this study. Further, since the study is based on an Indian sample, it contributes to literature from a non-western context.