JLARC staff used Regional Economic Models, Inc.'s (REMI) Tax Policy Insight Multi Region model (Tax-PI MR) to model three scenarios that illustrate potential employment impacts of the LIFT program.

This technical appendix provides background detail and supporting information for the JLARC staff analysis that led to the results summarized in section 3.

This appendix is divided into three sections:

1. REMI overview explains what the REMI Tax-PI MR model is, and how and why it is used.
2. Modeling LIFT using REMI details how JLARC staff set up the Tax-PI MR program and modeled scenarios to reflect the range of possible results.
3. Detailed assumptions and results shows the employment changes resulting from each scenario at the county level.

 

REMI Overview

JLARC staff used REMI's Tax-PI MR software (version 2.3) to model the economic impacts of LIFT funding. REMI software is used by approximately 30 state governments and dozens of private sector consulting firms, research universities, and international clients.

Model is tailored to Washington and includes government sector

Tax-PI MR is an economic impact tool used to evaluate the fiscal, economic, and demographic impacts of policy changes at the state and county levels. The software includes various features that make it particularly useful for analyzing the economic and fiscal impacts of tax policies such as LIFT.

• Tax-PI MR uses economic and demographic data from federal government agencies such as the U.S. Census Bureau, U.S. Energy Information Administration, the Bureau of Labor Statistics, and the Bureau of Economic Analysis. REMI staff consulted with staff from the Office of Financial Management (OFM) and customized a model to reflect Washington's economy.
• The model contains 70 industry sectors, based on the North American Industry Classification System (NAICS) codes.
• Tax-PI MR includes state and local government as a sector. This permits users to see the trade-offs associated with tax policy changes. For example, users can model the effects on Washington's economy from both increased expenditures by businesses due to a tax preference, along with decreased spending by government due to the associated revenue loss.
• For current revenue and expenditure data, users can input information to reflect their state's economic and fiscal situation.

Model simulates the direct, indirect, and induced impact of a policy change

The Tax-PI MR model accounts for the direct, indirect, and induced effects as they spread through the state's economy, which allows users to simulate the full impact of a policy change over time.

• Direct effects are industry specific and capture how a target industry responds to a particular policy change (e.g., changes in industry employment following a change in tax policy).
• Indirect effects capture employment and spending decisions by businesses in the targeted industry's supply chain that provide goods and services.
• Induced effects capture the in-state spending and consumption habits of employees in targeted and related industries.

The Tax-PI MR model produces year-by-year estimates of the total statewide and county effects of a tax policy change. Impacts are measured as the difference between a baseline economic and revenue forecast and the estimated economic and revenue effects after the policy change.

Model includes economic, demographic, and fiscal variables

The Tax-PI MR model is a macroeconomic impact model that incorporates aspects of four major economic modeling approaches: input-output, general equilibrium, econometric, and new economic geography. The foundation of the model — the inter-industry matrices found in the input-output models — captures Washington's industry structure and the transactions between industries. Layered on top of this structure is a complex set of mathematical equations used to estimate how private industry, consumers, and state and local governments respond to a policy change over time.

• The supply side of the model includes many economic variables representing labor supply, consumer prices, and capital and energy costs with elasticities for both the consumer and business sectors.
• Regional competitiveness is modeled via imports, exports, and output.
• Demographics are modeled using population dynamics (births, deaths, and economic and retirement migration) and includes cohorts for age, sex, race, and retirement.
• Demographic information informs the model's estimates for economic consumption and labor supply.
• The dynamic aspect comes from the ability to adjust variables over time as forecasted economic conditions change.

While the model is complex and forecasting involves some degree of uncertainty, Tax-PI MR provides a tool for practitioners to simulate how policy and the resulting industry changes affect Washington's economy, population, and fiscal situation.

 

Modeling LIFT using REMI

Before running modeling scenarios, users must customize the model by inputting information about the state's budget. JLARC staff created budget and revenue assumptions in the model using revenue estimates from the Economic and Revenue Forecast Council (ERFC) and budgeted expenditures from the Legislative Evaluation and Accountability Program (LEAP) Committee. This results in a baseline economy, which allows comparison between different modeled scenarios.

Because Tax-PI MR is a forecasting tool, the ability to model policy changes from past years is not built in. To account for this, REMI staff advised JLARC staff on a method to adjust baseline assumptions for employment and population, setting 2018 levels to reflect the economy and population in 2007.

 

Detailed Assumptions and Results

LIFT provides state government funds for infrastructure construction. The corresponding Tax-PI MR policy variables are state government spending and construction industry sales. Within the construction industry, there are three sub-industries: transportation, other non-residential construction, and residential construction. JLARC staff, with the help of the cities, categorized each construction project as either transportation or other non-residential construction (LIFT does not fund residential construction).

State LIFT contributions from FY 2011 (the first year of contributions) through FY 2018 totaled $35.1 million. We used that figure as the state government spending policy variable. Although the FY 2019 LIFT contribution amounts were available, we excluded them in order to align with the reporting cycle of the cities' infrastructure investments. The most recent reported data was CY 2018. We allocated the reduction in state government spending across all counties using the amount of the state general fund each county receives, as reported by the Office of Financial Management.

JLARC staff selected the change in number of jobs as the result to display.

Reading and using the table

• Six counties are listed in the table: King (which had two RDAs), Pierce, Snohomish, Spokane, Whatcom and Yakima. They had active RDAs with public construction spending from CY 2007 to 2018.
• Two counties – Clark and Skagit - had RDAs that did not have any public construction spending. Results for those counties are reported along with the 31 other counties that do not have RDAs.

• Use the buttons on the left side of the graphic to select a scenario to display.

Exhibit A1: Assumptions and results for modeled scenarios

Use the buttons to select a scenario. Descriptions are in the text below.

Scenario 1: LIFT caused cities to spend the state contribution on infrastructure construction. State spending was reduced by the amount of the state contribution provided to cities. Result: Net loss of 60 jobs.

Assumptions: the state contributed $35.1 million to cities and as a result:

• Construction sales increased by $35.1 million compared to the baseline. This increase took place in the counties with LIFT projects, and was split between transportation and other non-residential construction based on actual spending data from the cities.
• LIFT did not cause any other infrastructure investment. This scenario assumes that the local government spending would have occurred in the absence of LIFT.
• State spending decreased by $35.1 million compared to the baseline. This reduction was spread across all counties based on Office of Financial Management (OFM) estimates of state government spending in each county.

Results: In this scenario, the state economy loses an average of 60 jobs per year from 2011-2018, the time period in which cities received LIFT distributions. Increases in construction industry jobs do not offset job losses in government or other industries.

Scenario 2: LIFT caused all reported infrastructure construction. State spending was reduced by the amount of the state contribution provided to cities. Result: Net gain of 216 jobs.

Assumptions: the state contributed $35.1 million to cities and as a result:

• Construction sales increased by $165.9 million compared to the baseline. That is the amount that local governments reported spending on infrastructure construction in the RDAs. This increase took place in the counties with LIFT projects, and was split between transportation and other non-residential construction based on actual spending dataThe city of Bothell reported an additional $100 million in spending on other projects within its RDA on its annual reports. For this analysis, JLARC staff considered only the spending related to the Crossroads project, for which the city dedicated all LIFT funding to servicing bonds. from the cities.
• State spending decreased by $35.1 million compared to the baseline. This reduction was spread across all counties based on Office of Financial Management (OFM) estimates of state government spending in each county.

Results: Overall employment is an average of 216 jobs higher compared to the baseline from 2007-2018, the time period in which cities have made infrastructure investments.

Scenario 3: LIFT caused all reported infrastructure construction, and state spending was not reduced. Result: Net gain of 300 jobs.

Assumptions: the state contributed $35.1 million to cities and as a result:

• Construction sales increased by $165.9 million compared to the baseline. This increase took place in the counties with LIFT projects, and is split between transportation and other non-residential construction based on actual spending data from the cities.
• New economic activity in the RDAs increased enough to generate $35.1 million in new state tax revenues. That new tax revenue offset the state contribution so there was no net decrease in state government spending.

Results: Overall employment is an average of 300 jobs higher compared to the baseline from 2007-2018, the time period in which cities have made infrastructure investments.

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