The dynamic behaviour of income assistance recipients in British Columbia
Bibliographic record
Abstract
This thesis investigates the labour market behaviour and program participation of social assistance recipients. The research is based on the analysis of a unique data set derived from the adrriinistration of the income assistance programs in British Columbia (B.C.) for the period 1980- 1992 to analyse the length of time individuals and families spend on welfare. In chapter two the growth in social assistance caseloads and expenditures is documented and the institutional features of the program are laid out. The B.C. income assistance administrative data are then used to examine the changing demographic composition of the caseload. It is found that a substantial proportion of the B.C. caseload is comprised of employable single men and women without children. Further, an examination of program exit rates reveals that most welfare spells are relatively short; however, there is a very high incidence of recidivism. The deterrninants of the length of welfare spells are analysed in more detail in chapter three. More sophisticated econometric models are estimated which control for observed characteristics and "unobserved heterogeneity" (omitted variables). The estimated models are used to test for several forms of program dependence. It is found that there is strong evidence of negative duration dependence in program exit rates. This implies that the program, to some extent, acts as a "trap" whereby exit from the program becomes less likely the longer an individual remains on the program. Further, the evidence indicates the presence of negative occurrence dependence and negative lagged duration dependence, which imply that participating in welfare has a scarring effect on recipients' labour market careers. Policy implications of these finding are discussed. The research presented in chapter four uses data generated from the operation of the Unemployment Insurance (UI) and income assistance programs to test for broader forms of program dependence. Specifically, this research examines whether participating in welfare leads to greater reliance on Unemployment Insurance. After controlling for UI program parameters, demand conditions and individual characteristics, it is found that the UI exit rate for individuals with a recent welfare history was almost identical to that of other beneficiaries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".