What is the total local economic impact of sustained workforce investment in Maui County when earnings gains recirculate through the local economy, compared with no intervention?
Workforce Understory Episode: Episode Four — Mobility Through Lifelong Learning
Geography: Maui County
Topic: STARs, living-wage attainment, upskilling, workforce investment, and net economic mobility
The takeaway
This modeled scenario builds on the additional earnings generated by sustained workforce investment supporting 651 Maui County workers into higher-wage roles each year.
Applying a 1.4 local economic multiplier, the model estimates that those earnings gains could produce approximately $360 million in additional cumulative economic activity by 2036.
The projected impact accelerates as workers supported in earlier years continue earning and spending more while successive cohorts begin generating gains of their own.
The model captures the economic value associated with workers moving upward. It does not show what happens to the lower-wage positions they leave behind or whether the county’s overall number of workers below a living wage declines.
Workforce investment can generate substantial economic activity, but Maui County’s broader living-wage gap will shrink only if upward mobility changes the quality and distribution of jobs—not merely the people occupying them.
What this visualization shows
This visualization compares cumulative economic activity in Maui County under two scenarios from 2027 through 2036:
Economic activity without the modeled workforce investment
Economic activity with sustained annual investment in worker upskilling and movement into higher-wage employment
The model begins with the approximately $255 million in additional cumulative worker earnings projected in the previous visualization. Applying a 1.4 multiplier produces an estimated economic impact of approximately $357 million, presented in rounded form as roughly $360 million.
A multiplier of 1.4 assumes that each dollar of additional worker earnings generates approximately 40 cents in further economic activity beyond the initial income gain.
That additional activity may occur when workers spend more on housing, food, transportation, childcare, healthcare, recreation, and other goods and services. Businesses receiving that spending may then purchase supplies, expand employee hours, hire workers, or make additional expenditures.
The cumulative effect grows more quickly in later years because the model adds a new worker cohort each year while retaining the earnings gains associated with earlier cohorts.
This is a prospective model rather than a record of observed economic activity. Its results depend on assumptions about worker participation, placement, wage gains, employment continuity, household spending, and the share of additional earnings that remains within Maui County.
The projection measures gross upward movement among participating workers. It does not model the vacancy chains created when those workers leave lower-wage positions, the characteristics of the people who replace them, or whether employers improve the jobs that become vacant.
Why this matters
Helping workers earn more can strengthen households and generate demand throughout Maui County’s economy.
Additional spending may support local businesses, stabilize families, and contribute to broader economic activity. In a regional economy recovering from major disruption, those gains could have meaningful effects on workers, employers, and communities.
But individual advancement does not automatically translate into a smaller countywide living-wage gap.
When a worker moves from a low-wage position into a higher-paying role, several outcomes are possible. The former position may be eliminated, redesigned, automated, left vacant, filled by another resident, or filled by a worker moving into Maui County.
Some vacancy chains may create useful entry points. A new worker may gain experience, enter an industry, and eventually advance through the same pathway.
But if the vacated job continues to offer the same low wages, unstable schedule, and limited advancement, the underlying economic problem remains. One worker has moved upward, while another person now occupies the position below the living-wage threshold.
This distinction matters especially in Maui County’s tourism-dependent economy.
Hospitality, food service, retail, cleaning, transportation, and other service industries require large numbers of workers. Upskilling people out of those occupations without improving the jobs themselves could increase vacancies, turnover, and reliance on new entrants or in-migration without reducing the number of low-wage positions the economy produces.
Employers may respond to labor shortages by increasing wages, improving schedules, redesigning jobs, adopting technology, or creating clearer advancement pathways. Those responses could turn worker mobility into broader improvements in job quality.
They could also recruit replacement workers into unchanged positions, preserving the same wage structure.
A complete assessment of workforce investment therefore needs to distinguish between:
Gross mobility: How many participating workers move into higher-paying roles?
Net mobility: Does the total number or share of county workers earning below a living wage decline?
Job-quality change: Do the positions workers leave become better jobs?
Economic restructuring: Does Maui County reduce its dependence on industries and business models built around persistently low wages?
This evidence invites Maui County to ask:
Does workforce investment reduce the number of low-wage jobs and workers—or primarily change which individuals occupy those jobs?
Evidence:
Questions this visualization helps answer
How does Maui County’s modeled economic activity change with sustained workforce investment?
What local economic multiplier is applied to the projected earnings gains?
What does a 1.4 multiplier mean in practical terms?
How much additional cumulative economic activity could be generated by 2036?
How does this projection build on the previous cumulative earnings model?
Why does the economic-impact curve accelerate in later years?
How do earlier and later worker cohorts contribute simultaneously to the result?
How can increased worker earnings affect businesses and workers beyond the participants?
What is the difference between direct earnings gains and broader economic activity?
How closely does the rounded $360 million estimate align with the underlying earnings projection?
What aspect of workforce mobility does the model capture?
What labor-market effects remain outside the model?
Curiosity:
Questions this visualization raises
What happens to the lower-wage jobs that participating workers leave?
How many vacated positions are eliminated, redesigned, or left unfilled?
How many are filled by other Maui County residents?
How many are filled by workers moving into the county?
Do replacement workers receive the same wages and working conditions as the people who moved up?
Does the total number of below-living-wage jobs decline?
Does the countywide share of workers earning a living wage increase?
How does gross participant mobility compare with net workforce mobility?
Do vacancy chains create meaningful entry and advancement opportunities for new workers?
How long do replacement workers remain in the lower-wage roles?
Are they eventually able to advance through the same pathway?
Could upward mobility increase turnover in Hospitality and other service industries?
Would persistent vacancies cause employers to raise wages?
Could employers respond by improving schedules, benefits, or working conditions?
Would some businesses automate, consolidate, or reduce services instead?
Which low-wage occupations are most likely to be vacated by upskilled workers?
Which occupations are most likely to receive those workers?
Are the destination jobs genuinely additional opportunities or positions that would have been filled without the intervention?
Does the model account for displacement among workers competing for the same higher-paying jobs?
How much of the economic impact represents new earnings rather than redistribution among workers?
Could in-migration expand Maui County’s labor force while preserving its low-wage job structure?
Could workforce investment unintentionally increase housing pressure if it attracts additional workers?
How do migration, commuting, and multiple-job holding affect the countywide result?
What role do youth and other new labor-market entrants play in filling vacated positions?
Could entry-level jobs become the first step in a functioning career ladder rather than a permanent low-wage destination?
What employer commitments would ensure that replacement workers also have opportunities to advance?
Could public funding be conditioned on wage growth, job-quality improvements, or internal promotion?
Which Hospitality employers already demonstrate strong advancement and retention practices?
Can those practices be expanded across the industry?
Does Maui County need fewer low-wage tourism jobs, better tourism jobs, or both?
How much sector diversification is necessary to reduce dependence on persistently low-wage occupations?
Would Healthcare, Construction, Utilities, Transportation, or professional services provide enough destination jobs?
How much additional worker spending remains within Maui County?
Does the multiplier account for spending that leaks off-island through imports and nonlocal ownership?
Could local ownership and procurement increase the realized economic impact?
How would the projection change under a lower county-specific multiplier?
What measures should accompany the cumulative economic-impact estimate?
Should future analysis track the number of jobs below a living wage as well as the number of workers moving upward?
What data would allow Maui County to follow vacancy chains and replacement workers over time?
How can workforce leaders determine whether investment is shrinking the living-wage gap or redistributing it?
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