Analyzing Socio-Economic Indicators in Regional Economic Modeling

Using Socio-Economic Indicators in
Regional Economic Modeling
Regional Economic Models, Inc.
Introduction
Employment by Race and Gender
Inequality Coefficient
Model Simulation
Employment by Educational Attainment
Q&A
Agenda
Prices by Income Level
Compensation Distribution
Introduction
Analysts use regional macroeconomic models to answer “what if?...” questions about the effects of policies on
national, state, local, and other regional economies.
Typically, decision-makers and the public have been most interested in aggregate indicators (total
employment, GDP, personal income) in assessing policies.
Increasingly, policy makers would like to understand the effects of policies on the distribution of
wages/income, on racial/ethnic groups, on male/female workers, and on employment by educational
attainment.
This paper presents REMI SEI, showing the distributional effects of regional economic policies.
In particular, REMI SEI addresses the need for public policy to provide broad-based benefits to the residents
of a region.
Compensation
Distribution
Employment Changes
by Earnings Quintile
Inequality
Coefficient
Consumption Price
by Earnings Quintile
and Range
Measurements of Disparities in REMI-SEI
Per Capita
Income
Unemployment
Labor Force
Participation 
by Race & Gender
Employment by
Educational
Attainment
Employment by
 Race & Gender
Compensation Distribution
Employment by Race and Gender
Inequality Coefficient
Model Simulation
Employment by Educational Attainment
Q&A
Agenda
Prices by Income Level
Introduction
Used regionally calculated industry quintiles​​
Sorted industries using the ‘All Regions’ compensation rate, and divided into
5 groups with approximately the same number of sectors
Calculated a weighted average for each industry’s average annual
compensation rate​​
Used each industry’s share of total employment in the baseline as the weight​
Compensation Distribution
REMI Model Linkages
 
 
Compensation Distribution
Inequality Coefficient
Employment by Race and Gender
Compensation Distribution
Model Simulation
Employment by Educational Attainment
Q&A
Agenda
Prices by Income Level
Introduction
The SEI module’s compensation 
Inequality Coefficient 
result is a measure of
inequality in the distribution of industry-average compensation rates across
jobs.
The coefficient is calculated by applying the 
Gini methodology 
to REMI’s
employment by industry and average annual compensation rate by industry
data.
Inequality Coefficient
Inequality Coefficient
Employment by Race and Gender
Inequality Coefficient
Compensation Distribution
Model Simulation
Employment by Educational Attainment
Q&A
Agenda
Prices by Income Level
Introduction
Calculated national shares by race and gender for each occupation using ACS
data​
​​Calculated national occupation weights for each race, gender, and occupation
Applied the national rates to the regions
Took the national employment rates by race and gender
Multiplied them by the labor force shares by race and gender
Scaled up to 100%
Employment by Race and Gender
Estimated labor force demographic shares by place of work​​​
Converted commuter data from number of jobs to number of laborers​
Calculated commuter inflows and outflows by race and gender​
​Calculated 
labor force by place of work​​​
Employment by Race and Gender
Employment by Race and Gender
Employment by Educational Attainment
Inequality Coefficient
Compensation Distribution
Model Simulation
Employment by Race and Gender
Q&A
Agenda
Prices by Income Level
Introduction
Calculated number of new jobs created that do not require college education by
multiplying 
employment by occupation 
by 
percentage of educational
attainment by occupation
, from BLS data​​
Aggregated educational levels to create summary of occupations by “Bachelor’s
degree and above” and “no Bachelor’s degree”​
Employment by Educational Attainment
&
&
Employment by Educational Attainment
Prices by Income Level
Inequality Coefficient
Compensation Distribution
Model Simulation
Employment by Race and Gender
Q&A
Agenda
Employment by Educational Attainment
Introduction
Calculated as the weighted average of Consumption Commodity Prices for each
income group (either quintiles or eight income ranges).
The weights for each commodity price are based on each group's national
average share of spending devoted to that commodity category.
Spending data by income group come from the BLS Consumer Expenditure
Survey.
Prices by Income Level
&
&
Model Simulation
Inequality Coefficient
Compensation Distribution
Prices by Income Level
Employment by Race and Gender
Q&A
Agenda
Employment by Educational Attainment
Introduction
Model Simulation: Set Up
Economic Summary –
Center Cities
Model Simulation: REMI
 
SEI
Components of Population
Change – Center Cities
Model Simulation: REMI
 
SEI
Compensation Rate by
Industry Quintile – Center
Cities
Workers in center cities need to be
paid higher wages to keep them in
cities with declining amenity
factors. The labor supply is also
reduced over time. Workers in the
rest of the U.S. are paid less and
experience a more competitive
labor market.
Model Simulation: REMI
 
SEI
Employment by Industry Quintile
– Center Cities
Model Simulation: REMI
 
SEI
Employment by Education – Center
Cities (Summary)
Year 2040
Approximately a third of the U.S. population
has a bachelor’s degree and above. These
job losses in center cities are proportional
to the national metric.
However, jobs requiring bachelor’s degrees
are more “footloose,” so the loss of this kind
of job is more conducive to finding another
similar job elsewhere. Workers without
bachelor’s degrees face more challenges in
center cities finding comparable jobs. 
Model Simulation: REMI
 
SEI
Employment by Education –
Center Cities (Detail)
Year 2040
Model Simulation: REMI
 
SEI
Employment by Race –
All Regions
COVID migration trends
affect SEI across the entire
nation, not just within center
cities, through the widening
of racial/ethnic disparities.
Model Simulation: REMI
 
SEI
Prices by Income Level –
All Regions
Model Simulation: REMI
 
SEI
REMI-SEI shows the distributional effects of regional economic policies
Public policies are meant to benefit the public; analysis of the effect of policies for
compensation, educational and demographic groups helps policy-makers evaluate policy
implications for all residents
Racial, gender, regional, and educational disparities are driving the political and policy agenda at
the national, state and local level
REMI-SEI allows these questions to be addressed in a constructive, data-based framework.
Conclusion
Q&A
Inequality Coefficient
Compensation Distribution
Prices by Income Level
Employment by Race and Gender
Model Simulation
Agenda
Employment by Educational Attainment
Introduction
Thank you for attending!
For more information, please contact
info@remi.com
Q&A
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Analysts utilize regional macroeconomic models to assess the impact of policies on various economic factors including employment, income distribution, and workforce demographics. REMI-SEI framework focuses on understanding the distributional effects of regional economic policies to offer broad-based benefits. Measurements of Disparities in REMI-SEI encompass factors like compensation, employment changes, inequality coefficients, and labor force participation by race and gender. The analysis helps policymakers make informed decisions for the overall economic well-being of a region.

  • Economic Indicators
  • Regional Economic Modeling
  • Socio-Economic
  • REMI-SEI
  • Distributional Effects

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  1. Using Socio Using Socio- -Economic Indicators in Economic Indicators in Regional Economic Modeling Regional Economic Modeling Regional Economic Models, Inc.

  2. Agenda Introduction Introduction Compensation Distribution Inequality Coefficient Employment by Race and Gender Employment by Educational Attainment Prices by Income Level Model Simulation Q&A

  3. Introduction Analysts use regional macroeconomic models to answer what if?... questions about the effects of policies on Analysts use regional macroeconomic models to answer what if?... questions about the effects of policies on national, state, local, and other regional economies. national, state, local, and other regional economies. Typically, decision Typically, decision- -makers and the public have been most interested in aggregate indicators (total makers and the public have been most interested in aggregate indicators (total employment, GDP, personal income) in assessing policies. employment, GDP, personal income) in assessing policies. Increasingly, policy makers would like to understand the effects of policies on the distribution of Increasingly, policy makers would like to understand the effects of policies on the distribution of wages/income, on racial/ethnic groups, on male/female workers, and on employment by educational wages/income, on racial/ethnic groups, on male/female workers, and on employment by educational attainment. attainment. This paper presents REMI SEI, showing the distributional effects of regional economic policies. This paper presents REMI SEI, showing the distributional effects of regional economic policies. In particular, REMI SEI addresses the need for public policy to provide broad In particular, REMI SEI addresses the need for public policy to provide broad- -based benefits to the residents of a region. of a region. based benefits to the residents

  4. Measurements of Disparities in REMI-SEI Consumption Price by Earnings Quintile and Range Compensation Distribution Employment Changes by Earnings Quintile Inequality Coefficient Labor Force Participation by Race & Gender Employment by Race & Gender Employment by Educational Attainment Unemployment Per Capita Income

  5. Agenda Introduction Compensation Compensation Distribution Distribution Inequality Coefficient Employment by Race and Gender Employment by Educational Attainment Prices by Income Level Model Simulation Q&A

  6. Compensation Distribution Used regionally calculated industry quintiles Sorted industries using the All Regions compensation rate, and divided into 5 groups with approximately the same number of sectors Calculated a weighted average for each industry s average annual compensation rate Used each industry s share of total employment in the baseline as the weight Industrial employment & Industrial compensation Industrial compensation rate Weighted Weighted Compensation Compensation Rate Rate Policy change REMI models

  7. REMI Model Linkages

  8. Compensation Distribution Industrial employment: Industrial employment: ? ? ??? ? 1 100, j = 1, ,J, I= 1, ,5 ??= ? ??? where ?? is percentage change of employment for industry group I, and ?? j (in group I) from a (alternative) and c (control) forecasts. ? and ?? ? are employment for industry Industrial compensation: Industrial compensation: ? ? ????? ? 1 100, j = 1, ,J, I= 1, ,5 ????= ? ????? where ???? is percentage change of compensation for industry group I. Industrial compensation rate: Industrial compensation rate: ? ? ????? ? ? ??? 1 100, j = 1, ,J, I= 1, ,5 ??= ? ? ????? ? ? ??? where ?? is percentage change of compensation rate for industry group I.

  9. Agenda Introduction Compensation Distribution Inequality Coefficient Inequality Coefficient Employment by Race and Gender Employment by Educational Attainment Prices by Income Level Model Simulation Q&A

  10. Inequality Coefficient The SEI module s compensation Inequality Coefficient inequality in the distribution of industry-average compensation rates across jobs. The coefficient is calculated by applying the Gini methodology employment by industry and average annual compensation rate by industry data. Inequality Coefficient result is a measure of Gini methodology to REMI s Industrial employment & Average annual compensation rate by industry Policy change REMI models Inequality Inequality Coefficient Coefficient Gini methodology Gini methodology

  11. Inequality Coefficient The Compensation Inequality Coefficient (????) for region k in time t is determined by the following equations, which assume that industries are first put in order of increasing average annual compensation rate (???,? ?). ? ??,? ????,? ?) = ?(???,? ? ?=1 ? ?= ????,? ? ??,? ? ???,? ?=1 ?0,? ?= 0 ???(???,? ?) ?? 1,? ???,? ? ? ????= 1 ?=1 + ??,? ? where ???,? ??,? ? ???,? each industry pay the industry average rate, this fraction is also equal to the share of total jobs being paid ???,? ??,? industry i, for region k in time t. ???,? = Average annual compensation rate across all industries, weighted by industry employment, for region k in time t. ????= Compensation Inequality Gini Coefficient for region k in time t. ?= Average annual compensation rate for industry i and region k in time t. ?= Employment for industry i and region k in time t. ? = Fraction of total employment belonging to industry i for region k in time t. Due to the assumption that all jobs within ?. ?= Contribution to the employment-weighted average of compensation rates for the lowest-paying industry through ?

  12. Agenda Introduction Compensation Distribution Inequality Coefficient Employment Employment by Race and by Race and Gender Gender Employment by Educational Attainment Prices by Income Level Model Simulation Q&A

  13. Employment by Race and Gender Calculated national shares by race and gender for each occupation using ACS data Calculated national occupation weights for each race, gender, and occupation Employment by race and gender shares for each occupation ACS National Occupation Weights Occupation Weights (Apply to all regions) National Labor Force by Race and Gender REMI Model Applied the national rates to the regions Took the national employment rates by race and gender Multiplied them by the labor force shares by race and gender Scaled up to 100%

  14. Employment by Race and Gender Estimated labor force demographic shares by place of work Converted commuter data from number of jobs to number of laborers Calculated commuter inflows and outflows by race and gender Calculated labor force by place of work labor force by place of work County-level Commuter Income Flows & Earnings by Place of Residence & county Labor Force Labor commuters inflows and outflows Adjusted Labor Force Adjusted Labor Force by Place of Work by Place of Work Labor Force by Place of Residence REMI Model

  15. Employment by Race and Gender Employment by occupation, race, and gender shares are calculated using new national Occupation Weights and Labor Force by Place of Work Shares, which give the distribution of employment by race and gender within individual occupations and counties. Calculate the employment by occupation, race and gender shares Calculate the employment by occupation, race and gender shares For region k, demographic group i, occupation j: ?????????? ? ?????,?,?= ????? ????? ?? ????? ?? ???? ? ?????,? ?????????? ???? ???,? where: Demographic group i = 1,2, ......, 8 Occupation j = 1,2, ......, 95

  16. Agenda Introduction Compensation Distribution Inequality Coefficient Employment by Race and Gender Employment Employment by Educational Attainment by Educational Attainment Prices by Income Level Model Simulation Q&A

  17. Employment by Educational Attainment Calculated number of new jobs created that do not require college education by multiplying employment employment by by occupation occupation by percentage of attainment attainment by by occupation occupation, from BLS data Aggregated educational levels to create summary of occupations by Bachelor s degree and above and no Bachelor s degree percentage of educational educational Policy change REMI Model Employment by Occupation & & Employment by Employment by educational attainment educational attainment Percentage of educational attainment by Occupation

  18. Employment by Educational Attainment Step 1: Calculate the employment by educational attainment and occupation. Step 1: Calculate the employment by educational attainment and occupation. For educational attainment i, occupation j of the total J occupations: ???????????,?= ??????????? ??????????? ??????????? ?????????? ???? ???,? where i = 1,2, ......, 7, j = 1,2, ......, J, Step 2: Calculate the employment by educational attainment. Step 2: Calculate the employment by educational attainment. For educational attainment i, occupation j of the total J occupations: ? ???????????= ???????????,? ?=1 where i = 1,2, ......, 7, j = 1,2, ......, J

  19. Agenda Introduction Compensation Distribution Inequality Coefficient Employment by Race and Gender Employment by Educational Attainment Prices Prices by Income by Income Level Level Model Simulation Q&A

  20. Prices by Income Level Calculated as the weighted average of Consumption Commodity Prices for each income group (either quintiles or eight income ranges). The weights for each commodity price are based on each group's national average share of spending devoted to that commodity category. Spending data by income group come from the BLS Consumer Expenditure Survey. BLS Consumer Expenditure Survey Weights for each commodity price & & Prices by Income Prices by Income Level Level Consumption Commodity Prices for each income group REMI Model

  21. Agenda Introduction Compensation Distribution Inequality Coefficient Employment by Race and Gender Employment by Educational Attainment Prices by Income Level Model Simulation Model Simulation Q&A

  22. Model Simulation: Set Up Two-region, 70-sector PI+ model Regions are center cites and the rest of U.S. Model Decrease amenity by 5% in the center cities Make no change to amenities in the rest of U.S. Scenario 2020 through 2040 Amenity decrease implemented each year Years

  23. Model Simulation: REMISEI Economic Summary Economic Summary Center Cities Center Cities

  24. Model Simulation: REMISEI Components of Population Components of Population Change Change Center Cities Center Cities Economic Migrants Out of Center Cities Economic Migrants Out of Center Cities 0 -50 Number of Migrants (Thousands) -100 -150 -200 -250 -300 -350 -400

  25. Model Simulation: REMISEI Compensation Rate by Compensation Rate by Industry Quintile Industry Quintile Center Cities Cities Center Workers in center cities need to be paid higher wages to keep them in cities with declining amenity factors. The labor supply is also reduced over time. Workers in the rest of the U.S. are paid less and experience a more competitive labor market.

  26. Model Simulation: REMISEI Employment by Education Employment by Education Center Cities (Summary) Cities (Summary) Center Year 2040 - -370 370 Approximately a third of the U.S. population has a bachelor s degree and above. These job losses in center cities are proportional to the national metric. - -180 180 However, jobs requiring bachelor s degrees are more footloose, so the loss of this kind of job is more conducive to finding another similar job elsewhere. Workers without bachelor s degrees face more challenges in center cities finding comparable jobs.

  27. Model Simulation: REMISEI High school diploma or equivalent Doctoral or professional degree Less than high school diploma Some college, no degree Associate's degree Bachelor's degree Master's degree Employment by Education Employment by Education Center Cities (Detail) Center Cities (Detail) -50.602 -147.448 -119.366 -53.420 -114.081 -48.150 -17.780 Year 2040

  28. Model Simulation: REMISEI Employment by Race Employment by Race All Regions All Regions COVID migration trends affect SEI across the entire nation, not just within center cities, through the widening of racial/ethnic disparities.

  29. Model Simulation: REMISEI Prices by Income Level Prices by Income Level All Regions All Regions

  30. Conclusion REMI-SEI shows the distributional effects of regional economic policies Public policies are meant to benefit the public; analysis of the effect of policies for compensation, educational and demographic groups helps policy-makers evaluate policy implications for all residents Racial, gender, regional, and educational disparities are driving the political and policy agenda at the national, state and local level REMI-SEI allows these questions to be addressed in a constructive, data-based framework.

  31. Agenda Introduction Compensation Distribution Inequality Coefficient Employment by Race and Gender Employment by Educational Attainment Prices by Income Level Model Simulation Q&A Q&A

  32. Q&A Thank you for attending! For more information, please contact info@remi.com

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