Reference
Teaching guide: social policy
A second course built on the Social conditions modules, for economics, public policy, social work and urban planning students. The same data skills as the environment course apply: tidy CSVs, maps, regression and careful interpretation.
A twelve-week sequence
| Week | Topic | Module | Concepts |
|---|---|---|---|
| 1 | Measuring well-being | Inequality | Gini, Lorenz curves, household-size adjustment |
| 2 | Poverty lines | Poverty | Market Basket Measure vs Low Income Measure; regional baskets |
| 3 | Income support | Poverty, Inequality | Transfers by family type; the 2020 natural experiment |
| 4 | Food insecurity | Food insecurity | Survey measurement, severity, who is affected |
| 5 | Charity and the state | Food banks | HungerCount as a signal; social assistance adequacy |
| 6 | Housing markets | Housing affordability | Core housing need, vacancy and rents, the 30% rule |
| 7 | Housing policy | Housing affordability | Rent control, allowances and non-market supply |
| 8 | Homelessness: counting | Homelessness | Stocks and flows; shelter data vs point-in-time counts |
| 9 | Homelessness: policy | Homelessness | Housing First and the At Home/Chez Soi trial |
| 10 | Indigenous overrepresentation | Homelessness, Food insecurity | Data, causes and self-determined responses |
| 11 | Empirical lab | Stats lab: provinces | Cross-province regression, small samples, ecological fallacy |
| 12 | Synthesis | All modules | Policy brief presentations |
The modules
- Food insecurity: measurement, trends by family type, provinces.
- Poverty: the official poverty line, age and family patterns, provinces.
- Inequality: the Gini coefficient and what transfers do.
- Housing affordability: core housing need, rents, and an affordability calculator.
- Homelessness: shelter use since 2005, point-in-time counts, shelter capacity.
- Food banks: HungerCount trends and client profile.
- Stats lab: provinces: regressions across provinces.
Teaching sensitive topics
Some students will have lived experience of poverty, food insecurity or homelessness. Frame discussions around systems and policies rather than individual choices, use person-first language (“people experiencing homelessness”), and avoid asking students to disclose their circumstances.
When discussing Indigenous overrepresentation, pair the numbers with the causes identified by Indigenous organizations and by the Truth and Reconciliation Commission, and with Indigenous-led responses.
Assessment ideas
- Data brief: recreate one chart in Python or R from its CSV and write 300 words for a city councillor.
- Policy costing: design a benefit to cut child food insecurity by a third; estimate cost from the number of children affected.
- Measurement memo: explain to a journalist why the shelter study and the point-in-time count give different numbers.
- Regression report: extend the provinces lab with one more indicator, test it, and discuss influential observations.
- Local project: compare your community’s point-in-time count with national results.
Keeping the data current
Most social figures are entered from official reports into data-pipeline/seed/social.json, then rebuilt with python data-pipeline/build.py. Release calendar:
- Canadian Income Survey (poverty, food insecurity, income): each spring.
- HungerCount: each October.
- National Shelter Study and Shelter Capacity Report: usually once a year.
- CMHC Rental Market Report: each December.
- Canadian Housing Survey (core housing need): every two to three years.