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Two-dimensional methodological issues in Canadian municipal infrastructure time series. Marie-Claude Duval, Peter Elliott Statistics Canada ICES III Presentation / June 20, 2007. Overview. Background Data Sources Challenges Methodology Conclusion and lessons learned. Background.

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  1. Two-dimensional methodological issues in Canadian municipal infrastructure time series.Marie-Claude Duval, Peter ElliottStatistics CanadaICES III Presentation / June 20, 2007

  2. Overview • Background • Data Sources • Challenges • Methodology • Conclusion and lessons learned

  3. Background • Importance and current state of Canada’s public physical infrastructure • Infrastructure Canada required information in support of their research and public policy requirements • Project mandate: develop historical data series in currentand constant dollars of capital investment and stock, by: • asset and province, federal, provincial and municipal governments, 1961-2005 • function and province, municipal governments, 1988-2005

  4. Background (cont’d) – Assets - examples Code: Description: 1017 Parking Lots and Garages 1019 Indoor Recreational Buildings 1213 Waste Disposal Facilities 2202 Roads 2601 Sewage Treatment 8001 Computers

  5. Background (cont’d) – Functions Code/Description: 1 General Government Services 2 Protection of Persons & Property (police, firefighting) 3 Transportation and Communication (roads, snow removal, parking) 4 Environment (water supply, sewage & garbage collection & disposal) 5 Health 6 Social Services (welfare) 7 Resource conservation & Industrial development (Industrial parks, tourism) 8 Regional Planning & Development 9 Recreation & Culture (sport facilities, libraries)

  6. Data Sources Annual Capital Investment by Asset and Province, Current $: 1. ICSP - Investment and Capital Stock Program (Stocks) - 1871-2003, asset and industry detail (Canada), aggreg (prov) 2. CES - Capital Expenditures Survey (Flows) - 1988-2003 by province and asset detail; - pre-1988 less detail (building & engineering asset; no industry) 3. PISP -Public Institutions Statistical Program (Functions) - • 1993-2003 data series by province, function and asset; • Pre-1993: partial data available; used other sources

  7. Challenges – ICSP, CES, PISP • Data coherence • Slightly different universes (industry coding) • Asset disconnects between CES and PISP (concordances) • Acceptable asset-function combinations • ICSP adjustments (e.g. software, residential infrastructure) • Data Base Creation • Back-cast to 1871 to generate stocks (data gaps, imputation) • Level of details required by asset, function and province in order to derive data in constant dollars. • Benchmarking • Respect ICSP control totals – by asset and by province – • Respect local government data trends by asset and province and by function and province (PISP).

  8. Methodology Goal: • Develop coherent and consistent database on capital investment in current dollars from 1871 to 2003: • Part 1 - by asset and province; • Part 2 - by asset, function and province

  9. Methodology • Use of the following data sources: Controls = Investment and Capital Stock Program (ICSP) Source 1= Capital Expenditures Survey (CES) Source 2= Public Institutions Statistical Program (PISP)

  10. Methodology • Assumptions: • All information known at the requested level is better than no information. • The two sources are relevant even if different and were reconciled to be comparable.

  11. Methodology Part 1: Provide estimates on capital investment by asset and province

  12. Methodology Part 1 – Capital Investments by asset and province • Constraints: • Respect total by asset and total by province (controls). • Try to respect local government data trends by asset and province (source 2). • Capital Investment from two different sources (source 1 and source 2) might be different between them as well as with the controls. • Lack of data from sources 1 and 2 for some years.

  13. Methodology Part 1 – Capital Investments by asset and province • Process: • For each year, use raking ratio estimator to derive capital investment by asset and province using data from source 1 and source 2 and by respecting control total by asset and control total by province (controls).

  14. Methodology Part 1 – Capital investments by asset and province • Control data are the marginals: • Ca,.= Capital investment by asset • C.,p= Capital investment by province

  15. Methodology Part 1 - Capital investments by asset and province 2a. Use data from source 1 in the cells :Ca, p, S1

  16. Methodology Part 1 - Capital investments by asset and province 2b. Adjust the cell values to preserve the controls. • Raking ratio estimator (Ca,p,S1,rr)

  17. Methodology Part 1 - Capital investments by asset and province 2c. If no data available at the cell level, calculate the expected values by asset and province Ca,p,S1,e=Ca.*C.p /C

  18. Methodology Part 1 - Capital investments by asset and province 3.Redo the steps 2a to 2c using Source 2 data. 4. Take the mean of the two values obtained in steps 2 and 3.

  19. Methodology Part 1 - Capital investments by asset and province 5. Analysis • Analysis using the time series. • Biggest differences between the raking ratio values from Source 1 and Source 2. • Biggest differences between the raw data from Source 1 and Source 2. 6. Apply corrections if necessary. 7. Redo the raking ratio. 8. Repeat steps 5 to 7 until the results are satisfactory.

  20. Examples Ontario, asset 8001 (computers & related equipment) RAW DATA (source 1, source 2)RAKING RATIO (source 1, source 2, mean)

  21. Examples Ontario, asset 2602(Sanitary & Storm Sewers) RAW DATA (source 1, source 2) RAKING RATIO(source 1, source 2, mean)

  22. Methodology Part 2: Provide estimates on capital investment by asset, function and province

  23. Methodology Part 2 – Capital investment by asset, function and province • Constraints: • Respect totals by asset and province derived in Part 1. • Try to respect local government data trends by function and province (source 2). • Capital Investment from source 2 might be different then the one derived in part 1. • Lack of data from source 2 for some years.

  24. Methodology Part 2 – Capital Investments by asset, function and province • Process: • For each year, use ratio estimator to derive capital investment by asset, function and province using data from source 2 by respecting estimates by asset and province derived in part 1.

  25. Methodology Part 2 - Capital investment by asset, function and province 1. Ratio estimator:

  26. Methodology Part 2 - Capital investment by asset, function and province 2. If data from source 2 not available, use the mean of the ratio for years when source 2 is available: 3. Analysis and corrections • by function and province. Similar to part 1.

  27. Examples Ontario, function 72(regional planning and development) RAW DATA, RATIO ESTIMATOR

  28. Examples Alberta, function 22(policing) RAW DATA, RATIO ESTIMATOR

  29. Methodology • Step 1 of the project completed: • Capital investment by asset and province and capital investmentby asset, function and province from 1871 to 2003 in current dollars. • The subsequent steps performed to the data (but out of scope for this presentation) included: • Derive capital investment in constant dollars. • Derive stocks in current and constant dollars. • Estimates for years 2004 and 2005 • Estimates for other government levels.

  30. Conclusion and lessons learned Working with different sources, over a long period of time with many constraints, is feasible BUT: • Constraints to preserve totals: • Use of a raking ratio estimator • Use all sources available as long as they are relevant and comparable.

  31. Conclusion and lessons learned Working with different sources, over a long period of time with many constraints, is feasible BUT: • Data quality issues: • CONSISTENCY : Make sure to work with comparable sources. If not, apply adjustments to reconcile them (such as different coverage, different assets, differences over time....) • RELIABILITY : Data confrontation is important to validate the results and the data used (ex: time series, outliers,...). • LACK OF DATA : Strategy in place in case of lack of data.

  32. Conclusion and lessons learned Working with different sources, over a long period of time with many constraints, is feasible BUT: • Analysis of the results • Use of experts who know the topic. • Use of tools to validate the results (ex: time series, outliers,...). • Correct the data and repeat the process when necessary.

  33. For more information / Pour plus d’information: • Papers / Articles: • Daily(June 30, 2006) www.statcan.ca • STC Analytical Paper – The Age of Public Infrastructure in Canada (V. Gaudreault & P. Lemire, January 2006, cat no. 11-621-MIE – no. 035 ) • STC Contacts: • Methodology - Marie-Claude Duval, 613-951-7308 Gerrit Faber, 613-951-9438 • ICSP, CES – Irfan Hashmi, 613-951-3363 • PISP – Aldo Diaz, 613-951-8563 Peter Elliott, 613-951-4551

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