2. Reasonable; deceleration of growth rates as the region matures

3. Very hard to explain this one. Must be a drastic increase in income

4. Yes, very positive income outlook

5. It would be hard to imagine income growth without a correspondent increase in wages. And in fact, it happens. However, what’s puzzling is that, in real terms the wages didn’t grow since early 2000s. However, the future is radically different. There’s the answer for why TRS are growing

6. DEFM is using BEA income; and BEA income is not the same as we think as income. Since early 1990s, the gap between the BEA income and money income began to get wider.

7. And the same happens with per capita income; BEA’s income continues to grow, while the money income doesn’t

8. BEA Personal Income Includes Money and “Not Money” Income
Some of the items in the BEA’s “Not Money” Income
Interest on private pension plans
IRA and Keogh dividends
Small business corporation income
Rental value of owner-occupied housing
Capital consumption adjustment
Employer contributions to private pension and profit-sharing funds

12. Issues with DEFM
Everything is connected in DEFM, which makes it overly deterministic and inflexible
Inable to handle multiple equilibria (different states); e.g., different outputs from the same inputs
The salient feature of DEFM is its dependence on an external forecast for the US economy
Demographic module aside, DEFM is just an elaborately repackaged Moody’s (Global Insight’s) forecast
Our proposal
Switch to a different TYPE of model

13. From Forecasts to Scenarios
Not just rebuild DEFM, but pivot to a different modeling approach
Scenarios instead of predictions
In DEFM, key indicators of the future are supplied by an external forecast for the US (we’ve used Moody’s and Global Insight)
We want to treat these indicators as clearly stated “IF” assumptions
Migration rates
Labor force participation rates
Unemployment rates (overall and cohort-specific)
Average wage
Average non-wage income (cohort-specific)
The forecast then becomes a plausible “what if” scenario
The goal is to generate an internaly consistent and reasonable future for a regional system

26. Issues with DEFM TRS growing faster than income
Income growth rates are high
Uses BEA Total Personal Income
Includes non-money income
While total income is misleading, it doesn’t by itself lead to biased TRS estimates
As long as the share of non-money income stays the same
Higher base levels of wage & salary income
Extremely cheerful average wage growth rates

27. Long-Range Forecasting
Future is not like a content of an unopened envelop, which can be somehow revelead; the future hasn’t been written yet
Forecasting (predicting the inevitable) is all about extending trajectories
However, trajectories have relatively short lives; that’s why forecasting is suited for short-term
Unemployment rate forecast for next quarter is meaningful
But long-term is different
Trajectories (and forces behind them) will change many times
Forecast for unemployment rate in 2050 is meaningless because it is unknowable
So we have to make an assertion and use it as an assumption

28. Changes to the Macro Model
Migration
Cohort-specific gross migration (in/out), domestic and international
Employment-Population Coupling
Cohort-specific labor-force participation and unemployment rates
Income
Industry-specific average wages
Cohort-specific self-employment income and unearned income (persions, social security, rental income, supplemental income)
Housing Supply
Belongs in the LU model

34. Integrated Models SANDAG’s suite of models and tools
Evolved “organically”
DEFM,Concep,Pacef,ABM,PopSyn,PECAS,UrbanSim
Overlaps, inconsistencies, conflicts
From a jerry-rigged solution to
First, an integrated modeling platform
Streamlined, logical data flow
Then, a fully disaggregated behavioral system
Real Estate Markets (Parcels in UrbanSim)
Daily Activity Schedules (Individuals in ABM)
Socioeconomic Evolution (Households in the “Demographic” model)

35. Idea #1: Annual Release
Update (release) frequency should match the temporal resolution of a forecast
Daily forecast is updated daily
Annual forecast is updated annualy
Incorporation of most recent data
Demographic and Employment
Land use (actual and planned development, zoning changes)
Linking to the annual estimates
Production schedule with firm dates

36. Idea #2: Interval instead of Point
Components of demographic change
Births, deaths, migration
Cumulative impact
The illusion of determinism
SD County (2013): 43,627 births and 20,602 deaths
Were the exact numbers somehow preordained???
Inherent uncertainty in demographic forecasts
Even if we get all the trends right
Solution
High/Low Bounds
Update (correct) often

37. Idea #3: Model Integrity
We need to maintain the integrity of the model by enforcing a clear distinction between model inputs and outputs
Inputs
current conditions (facts)
operating rules (model’s logic)
policy instruments (transporation, zoning)
Outputs
what comes out of the model (forecast)
if the output is “wrong”, we need to know why (so that we can make appropriate corrections)
Ok to change inputs (correct factual errors, deficient rules, test different policies), but not outputs

38. Idea #4: LUT Scenarios Components of the “land use” system
A. Regional transportation policy
Transportation investment scenarios
Directly affects accessability land rent
B. Local LU regulatory policy (zoning, fees)
Zoning scenarios
Prescribes (but does not esure!) development form
C. Private development enterprize
What developers actually build
PECAS/UrbanSim: predict what/when/where developes will build in the future
C responds to A, is constrained by B
A and B are given, C is to be determined (i.e., forecasted)

39. Idea #4: LUT Scenarios (cont.)
A land use forecast is our best guess about the consequence of certain policy decisions, it should be informed by, and be consistent with, policy decisions
At minimum, each transportation investment scenario should beget its own land use scenario (and perhaps a demographic one too)
Ideally, zoning scenarios should be used as well (to leverage transporation investments)
Implications for the RTP
Create transportation scenarios first, then generate a forecast

41. Outline Rethinking the “Macro” model (DEFM)
Developing an integrated suite of models
New ideas for forecasting and modeling

42. 150,000 people move to San Diego county a year

50. Changes to the Macro Model
Migration
Cohort-specific gross migration (in/out), domestic and international
Employment-Population Coupling
Cohort-specific labor-force participation and unemployment rates
Income (in addition to wage&salary income)
Proprietors (self-employment) income
Unearned income (persions, social security, rental income, supplemental income)
Housing Supply
Belongs in the LU model