The share of self-employment in total employment has been growing in Canada throughout the 1990s. Recent research for Canada and elsewhere suggests that some workers may be ‘pushed’ into self-employment as a response to inadequate opportunities in the paid sector. Examining transitions from paid work to selfemployment using the Labour Market Activity Survey, this push hypothesis is tested using a number of indicators of the economic opportunities facing the newly selfemployed. It is found: (i) longer spells of joblessness favour self-employment, (ii) workers who collect unemployment benefits between jobs are less likely to become self-employed than are workers who did not, (iii) workers who left their previous, paid jobs involuntarily - i.e., due to layoff - were more likely to become selfemployed than those who left voluntarily, but less likely than workers who specified personal reasons for leaving, and (iv) self-employment decisions are independent of the health of the labour market as measured by the unemployment rate. These results are generally consistent with the push hypothesis but provide more ambiguous evidence than found in some other studies.
Separation bonus eligibility 6 The Selective Reenlistment Bonus. 8 The civilian unemployment rate. 9 Career variables 9 Personal characteristics.. Fiscal year of decision. Estimation method Results Descriptive statistics • • VSI/SSB eligibility and retention Other variables Estimation results VSI/SSB eligibility Retention Policy questions and marginal effects Appendix A: Selecting the Zone B sample Appendix B: Determining VSI/SSB eligibility Appendix C: Unemployment rates 35 References 37 List of tables 39 Distribution list 41 1. For a model of Zone A retention, see [2].
We were originally asked to calculate TTT for each officer community, but we had only enough time and resources to calculate TTT for the large unrestricted line (URL) communities. We also present some basic early training information for the supply community. To construct a complete database, CNA must start with FY 1993 accessions, which constrains our analysis to the mid-and post-drawdown Navy. Making TTT comparisons between the pre-and post-drawdown Navy would be problematic even if we had complete data for earlier accession cohorts because the officer corps differed greatly in size and composition across those two periods. At the time of this report, CNA had on hand officer accession information through FY 2001. Because the TTT to first assignment can be as long as 3 to 4 years (for jet pilots, for example), we can observe all the completed training outcomes for only the FY 1993-96 accessions. For officer communities with shorter training pipelines, we can include data for the accession cohorts of more recent fiscal years. We will update the database annually or as resources allow. CNA receives Navy officer personnel file extracts (Officer Master Tapes (OMTs)) in March and September of each year. Using the September extracts, CNA has created a longitudinal officer file (LOF) that follows the career of every officer who has accessed since FY 1976. The personnel data contain information about each officer at accession, the history of billets held, and Navy career milestones. They contain far less information on specific training details, such as when and where an officer took a particular class and what the outcome was. Instead, the detailed training information for each officer is found in the Navy Integrated Training Resources Administration System (NITRAS) II database. However, data from NITRAS II are available only from FY 1993 forward. We merged officer accessions from 1993 forward from the LOF with the NITRAS data to create the OSTF database.
Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE: Mar 20022. REPORT TYPE: Final TITLE:Inside the Black Box: Assessing the Navy's Manpower Requirements Process AUTHOR(S):Moore Carol S., Hattiangadi, Anita U. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES):Center for Naval Analyses 4825 Mark Center Drive Alexandria, Virginia 22311-1850 SPONSORING AGENCY NAME(S) AND ADDRESS(ES):Total SPONSOR ACRONYM(S): N/A SPONSOR REPORT NO.: N/A SUPPLEMENTARY NOTES: N/A ABSTRACT:The Navy determines the number of sailors it needs on board ships through a complex and demanding process. To those not directly involved, it is a black box. This study describes the Navy's methods and compares them to practices used by private-sector firms. It identifies assumptions that drive the number of people needed, or "manpower requirements," and quantifies the impact of those assumptions on billets and costs. The study concludes that the Navy's manpower requirements process is thorough, accountable, and meets the Navy's stated goals. However, it does not adequately consider manning alternatives. In setting requirements, the Navy takes technology as given and uses decades-old assumptions about work hours, labor productivity, and the paygrade mix of the crew. Such assumptions, which are "hardwired" into the Navy's requirements computation model, are costly and merit revalidation. Other problems include limited cost incentives and a lack of performance metrics with which to assess different manning configurations. The study recommends that the Navy (a) make the costs (and benefits) of requirements more visible; (b) shift the focus from workload validation toward innovation and improvement; and (c) charge an agent or organization with identifying avenues for manpower savings, through methodological, technological, or organizational changes.
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