Staff Report
PREPARED BY: MONICA RENN
Economic Vitality Manager
_______________________________________________________________________________________
Reviewed by: Assistant Town Manager Town Attorney Finance
______________________________________________________________________________
S:\COUNCIL REPORTS\2016\11-01-16\05 Minimum Wage\06 Staff Report FINAL.docx
MEETING DATE: 11/01/16
ITEM NO. 5
TOWN COUNCIL REPORT
DATE: OCTOBER 17, 2016
TO: MAYOR AND TOWN COUNCIL
FROM: LAUREL PREVETTI, TOWN MANAGER
SUBJECT: RECEIVE A REPORT ON EFFORTS IN SANTA CLARA COUNTY
REGARDING A MINIMUM WAGE INCREASE AND PROVIDE DIRECTION
ON NEXT STEPS.
RECOMMENDATION:
Receive a report on efforts in Santa Clara County regarding a minimum wage increase and
provide direction on next steps.
BACKGROUND:
The Cities Association of Santa Clara County has asked each city to consider adopting a
minimum wage increase of $15 per hour by 2019 (see Attachment 3). Beginning in September
2015, an advisory team made up of representatives from the Cities Association of Santa Clara
County, San Jose/Silicon Valley Chamber of Commerce, Working Partnerships, and the City of
San Jose convened to explore a regional approach to increasing the minimum wage. Two studies
have been conducted by third party vendors as a part of the effort to study the effect of minimum
wage on our region (see Attachments 1 and 2).
In the spring of 2016, a County-wide minimum wage employer survey was conducted on
including an analysis of regional economic impact of increasing the minimum wage to $15 by
2019 in all cities in Santa Clara County. A third party consultant, BW Research Partners, Inc.,
conducted this survey. Highlights of the survey’s findings are below, with the full survey results
attached, Attachment 1.
The industries most likely to be impacted are those such as retail and food service
industries with typically more than half of their staff earning minimum wage.
PAGE 2
MAYOR AND TOWN COUNCIL
SUBJECT: RECEIVE A REPORT ON EFFORTS IN SANTA CLARA COUNTY
REGARDING A MINIMUM WAGE INCREASE AND PROVIDE DIRECTION
ON NEXT STEPS.
OCTOBER 17, 2016
BACKGROUND (cont’d):
The majority of surveyed employers report that they will likely pass along wage increases
to the consumer by raising prices while agreeing that their employees may be more
satisfied and productive if they earn a higher wage.
Three out of four employers surveyed agreed with the statement, “An increase in the
minimum wage makes sense for our community, given our high cost of living.” Although
the results of the survey also indicate that an increase would make it more difficult for
smaller independent businesses to thrive.
In June 2016, the Center on Wage and Employment Dynamics conducted a study to look at the
effects of a $15 minimum wage by 2019 in San Jose and Santa Clara County. This report,
Attachment 2, specifically looked at the impacts of the increase over time, and incorporated
historical information regarding inflation since 2007. While much of this study was based on the
specific population of employers and employees in the City of San Jose, the information provides
a benchmark for the region, and offers a regional perspective on the issue. For Santa Clara
County as a whole, this study indicates:
After accounting for inflation, the earning of typical workers in the County has
declined by 8.3 percent between pre-recession level in 2007 and 2014. Median annual
earning in Santa Clara County are 49.6 percent higher than in the state as a whole.
Increasing the minimum would increase earnings for about 25.3 percent of the County
population.
95.5 percent of those earing minimum wage are over the age of 20, with a median age
of 32 years. 37 percent are married and nearly 34 percent have children.
The two most effected industries are food service and retail trade.
Total wages would increase by about one percent for all employers, while restaurants
would experience the highest increase in wages at about 9.5 percent.
The Board of Directors of the Cities Association voted to recommend the adoption of a minimum
wage ordinance on June 9, 2016 shortly after issuing the final report of the economic analysis.
Subsequently, a letter addressed to all Mayors and City Managers in Santa Clara County was sent
on July 27, with an updated version on August 23, 2016, Attachment 3, urging support of the
regional minimum wage proposal along with a model ordinance for cities to bring to their Council
for consideration.
PAGE 3
MAYOR AND TOWN COUNCIL
SUBJECT: RECEIVE A REPORT ON EFFORTS IN SANTA CLARA COUNTY
REGARDING A MINIMUM WAGE INCREASE AND PROVIDE DIRECTION
ON NEXT STEPS.
OCTOBER 17, 2016
BACKGROUND (cont’d):
The chart below shows an update on actions taken by other Santa Clara County cities to date:
Regional Status on Minimum Wage
Jurisdiction
Current
Min.
Wage
Response to Cities Association Recommendation and Next
Steps
Campbell $10.00 Scheduled for consideration on November 1, 2016
Cupertino $10.00 Adopted increase to $15 by 2019
Gilroy $10.00 Council declined to consider
Los Altos $10.00 Adopted increase to $15 by 2019
Los Altos Hills $10.00 Council determined it was not applicable to Los Altos Hills due to
the absence of commercial zones/industry in July 2016
Los Gatos $10.00 Council discussion scheduled for November 1, 2016
Milpitas $10.00 Outreach efforts continuing through October
Monte Sereno $10.00
Considered in September 2016, determined it was not applicable to
the City given the lack of commercial zones, however moved to
support the Cities Associations recommendation on a County level.
Morgan Hill $10.00 Council considered in August 2016, future consideration TBD
Mountain View $11.00 Adopted increase to $15 by 2018.
Palo Alto $11.00
In October 2016, Council conducted a second reading of an
ordinance which will go into effect 31 days thereafter. The new
ordinance will supersede the current ordinance and local minimum
wage will be increased to $12.00 on January 1, 2017.
San Jose $10.30 Voter approved initiative increased minimum wage in 2012;
consideration of Cities Association recommendation TBD.
Santa Clara $11.00 Adopted minimum wage increase January 2016; consideration of
Cities Association recommendation TBD.
Saratoga $10.00 Council to consider in November 2016
Sunnyvale $11.00 Adopted increase to $15 by 2018.
Aside from the regional efforts by the Cities Association to increase minimum wage, on April 4,
2016, Governor Jerry Brown signed legislation (SB 3, Leno) raising California’s minimum wage
to $15 per hour by 2022, with incremental increases starting in 2017. After January 1, 2023,
future wage increases are tied to inflation. Under the new state law, the wage increase schedule
may be temporarily suspended by the Governor during economic downturns. The law also
maintains existing exemptions in the state’s minimum wage law. California's minimum wage does
not apply to outside salespeople, or employees who are in the immediate family of their employer.
Student workers may be paid as little as 85% of the minimum wage for their first 160 hours of w ork.
PAGE 4
MAYOR AND TOWN COUNCIL
SUBJECT: RECEIVE A REPORT ON EFFORTS IN SANTA CLARA COUNTY
REGARDING A MINIMUM WAGE INCREASE AND PROVIDE DIRECTION
ON NEXT STEPS.
OCTOBER 17, 2016
BACKGROUND (cont’d):
Additional exemptions exist for disabled employees and workers at nonprofits where the employer has
obtained a certificate from the California Division of Labor Standards Enforcement.
It’s important to note that the economic analysis did not factor in the state’s new minimum wage
law, which likely reduces the negative impacts of a regional increase.
DISCUSSION:
For the purpose of this discussion, staff is providing a background on the topic of minimum wage
in Santa Clara County, including ordinance language provided by the Cities Association of Santa
Clara County for the Town Council to discuss and consider. There is no specific action before the
Council at this time; however, the Council may wish to provide direction if this is an ordinance
that it would like to consider formally (see Conclusion and Alternatives below).
The studies attached to this report indicate that food service and retail are the two highest
impacted industries by a change in minimum wage. The Town currently has approximately 200
food service businesses and 700 general retail businesses in operating in Town limits. It is likely
that industries who pay minimum wage will begin to raise their wage rates as a way to maintain
the market demand of workers, unelated to an action taken by the Town. As the ordinance
language is currently written, government agencies, including the Town and School Districts
would be exempt from the mandatory wage increase.
Assuring compliance with any Town mandated minimum wage increase would be subject to the
Town’s Code Compliance process, and a specific process and/or fines associated with non-
compliance could also be adopted.
CONCLUSION AND ALTERNATIVES:
The Council may choose to direct staff to bring this item back for further action including:
Receive this report and take no additional action. As a result, businesses in Town would
follow the State of California’s requirements for a minimum wage increase; or
Direct staff to bring back an ordinance as suggested by the Cities Association to increase
the minimum wage to $15 per hour by 2019; or
Direct staff to bring back an ordinance as suggested by the Cities Association to increase
the minimum wage to $15 per hour by 2019 with language modifications, such as not
exempting the Town from these provisions.
PAGE 5
MAYOR AND TOWN COUNCIL
SUBJECT: RECEIVE A REPORT ON EFFORTS IN SANTA CLARA COUNTY
REGARDING A MINIMUM WAGE INCREASE AND PROVIDE DIRECTION
ON NEXT STEPS.
OCTOBER 17, 2016
ENVIRONMENTAL ASSESSMENT:
This item is not a project under the California Environmental Quality Act.
FISCAL IMPACT:
In the current language recommended by the Cities Association of Santa Clara County,
government agencies, including School Districts are excluded from the mandatory minimum
wage increase. Therefore, there would be no fiscal impact to the Town.
If the Council is interested in adopting a minimum wage increase without exempting the Town,
the total estimated fiscal impact would be approximately $16,300 per year. The Town of Los
Gatos has three temporary/hourly classifications (Clerical Aide, Facility Attendant, and Library
Page) that have minimum salary ranges below $15 per hour. Five (5) employees are currently
assigned to the temporary/hourly classification of Library Page and earn hourly rates below the
proposed $15 per hour minimum wage.
Attachments:
1. Santa Clara County Minimum Wage Employer Survey
2. The Effects of a $15 minimum wage by 2019 in San Jose and Santa Clara County
3. Letter from the Cities Association of Santa Clara County dated August 23, 2016
2725 JEFFERSON STREET, SUITE 13, CARLSBAD CA 92008
50 MILL POND DRIVE, WRENTHAM, MA 02093
T (760) 730-9325 F (888) 457-9598
bwresearch.com
twitter.com/BW-Research
facebook.com/bwresearch
Santa Clara County
Minimum Wage Employer
Survey
April 2016
TABLE OF CONTENTS
Table of Contents .............................................................................................................................. i
List of Figures .................................................................................................................................... ii
List of Tables ..................................................................................................................................... ii
Executive Summary ......................................................................................................................... 1
Minimum Wage – Impacted Employers and Industries .................................................................. 2
Profile of Survey Participants ...................................................................................................... 2
Current Minimum Wage Employment ........................................................................................ 4
Minimum Wage – Stated Impacts and Attitudes ............................................................................ 7
Appendix A: Survey Methodology ................................................................................................. 12
Appendix B: Survey Toplines ......................................................................................................... 14
LIST OF FIGURES
Figure 1. Participating Business Industries ...................................................................................... 2
Figure 3. Firm Size ........................................................................................................................... 3
Figure 6. Employees that Earn $10 to $11 per Hour ....................................................................... 4
Figure 7. Employees that Earn $10 to $11 per Hour, by Employment Status and Age ................... 5
Figure 8. Employees that Earn between $11.01 and $15 per Hour ................................................ 6
Figure 9. Minimum Wage Increase – Anticipated Impacts ............................................................. 7
Figure 10. Minimum Wage Increase – Employer Attitudes ............................................................ 9
LIST OF TABLES
Table 1. Anticipated Impacts by Impacted and Non-Impacted Businesses .................................... 8
Table 2. Employer Attitudes by Impacted and Non-Impacted Businesses ................................... 10
EXECUTIVE SUMMARY
BW Research Partnership, Inc. (BW Research) in collaboration with the City of San Jose and the
Institute for Research on Labor and Employment (IRLE) developed and implemented a survey of
over 500 (n=518) businesses In Santa Clara County. The purpose of the survey was to assess the
attitudes, priorities and anticipated responses of Santa Clara County and City of San Jose
businesses as they relate to a potential minimum wage increase.
The telephone and online survey was completed from February 17 to March 4, 2016 and the
telephone survey was offered in English, Spanish and Vietnamese. The sampling plan for the
survey was segmented by industry, firm size, and geography within Santa Clara County to ensure
that a broad range of Santa Clara County businesses were included in the quantitative survey
findings. Drawing on IRLE’s research in comparable regions, the sampling plan was designed to
reflect industries that are most likely be impacted by an increase in the minimum wage, and
does not necessarily reflect the industry profile of the entire business community.
The majority of surveyed employers report that they will likely have to increase prices for
customers, but that their employees will be more satisfied and productive under a minimum
wage increase. Though the majority of surveyed employers agree that an increase in the
minimum wage will positively impact the community, most also feel increasing the minimum
wage will make it harder for new and emerging businesses. Impacted businesses more often
selected “very likely” across all anticipated impacts compared to non-impacted industries,
indicating that they agreed with both the positive and negative impacts of a minimum wage
increase. Almost half of impacted businesses report that it is very likely their employees will be
more satisfied and productive (46%) under a minimum wage increase, compared to 14% of non-
impacted businesses. Forty-five percent of impacted businesses also report they will very likely
increase prices given a minimum wage increase, compared 21% of non-impacted firms.
The survey found that both retail and food service firms are more likely to staff at least half of
their workforce with employees at the current minimum wage, and are therefore more likely to
be impacted by a minimum wage increase. Though these firms agree that their employees will
be more satisfied and productive under a minimum wage increase, they also report that this will
likely result in increased consumer pricing and a shift towards automation – firms will invest in
technologies that reduce the need for labor. Food service firms were also particularly more
likely to agree that an increase in the minimum wage will make it more difficult for companies to
locate and grow new businesses in the region.
While participating Santa Clara County businesses indicated some concerns about the increased
minimum wage, three out of four respondents stated they agreed (58%) or somewhat agreed
(18%) with the statement “An increase in the minimum wage makes sense for our community,
given our high cost of living.” Only 14 percent of respondents disagreed (9%) or somewhat
disagreed (4%) with the statement.
MINIMUM WAGE – IMPACTED EMPLOYERS AND INDUSTRIES
PROFILE OF SURVEY PARTICIPANTS
The impacted business community, those employers that are more likely to be affected by an
increase in the minimum wage, is largely comprised of four sectors – retail, residential care
and social assistance, administrative services and waste management, and food service.1
Together, these industries represent over half (59%) of those employers that participated in the
survey.
What industry or industries best describes the work that your firm is involved in and connected
to?
Figure 1. Participating Business Industries
Businesses that participated in the survey were fairly evenly distributed in terms of how long
they have had a business location in Santa Clara County. About three in ten firms (32%) have
been in business in the area for under five years, another quarter report about five to ten years,
1 Includes both full-service and fast food or fast casual restaurants
2.1%
4.1%
4.1%
4.2%
4.4%
4.6%
5.4%
5.4%
6.9%
7.1%
8.9%
9.7%
10.4%
22.6%
All other
Education or Healthcare
Repair and Maintenance or Other Services
Manufacturing
Lodging Accommodations or Other Food Services
Non-Profit
Construction
Wholesale Trade and/or Transportation
Information, Legal, Finance, Insurance, Real Estate, or Professional
Services
Limited Service Restaurant –Fast Food or Fast Casual
Full Service Restaurant –Table Service Dining
Administrative and/or Waste Management Services
Residential Care and/or Social Assistance
Retail
and roughly four in ten firms (43%) have been located in the County for ten years or more. See
Appendix B: Survey Toplines for a detailed breakdown of respondents by length of business
tenure in Santa Clara County.
By design of the sampling plan, participating businesses were evenly distributed between
small, medium, and large firms. The largest chunk of firms have between 10 and 35 employees
(37%), about three in ten employers also report either less than nine employees (28%) and
almost two in ten reported 100 or more (18%).
Including all full-time and part-time employees, how many permanent, seasonal, and temporary
employees work at or from your location?
Figure 2. Firm Size
Just under half of surveyed businesses (46%) expect to grow total employment at their current
business location, while the other half expect employment to remain the same. Only three
percent of surveyed firms project a decline in employment in the next 12 months. See Appendix
B: Survey Toplines for a detailed breakdown of responses regarding projected growth for the
next 12 months.
However, just over half of surveyed employers report that hiring had remained flat over the
last 3 years, or 36 months. Fifty-three percent of firms note that employment across
permanent, seasonal, and temporary workers has stayed the same in the last 3 years; three in
ten firms did report employment growth during this time, with one in ten noting decline. See
Appendix B: Survey Toplines for a detailed breakdown of responses regarding historical growth
over the past 36 months.
17.7%
18.1%
36.5%
27.8%
100 or more
Between 36 and 99
Between 10 and 35
Between 2 and 9
CURRENT MINIMUM WAGE EMPLOYMENT
Participating businesses were next asked, what percentage of their current workforce makes at
or within a dollar of the minimum wage. Nearly half (47%) of respondents report that about
half to all of their employees are paid a wage at or around the minimum wage ($10 to $11 an
hour). Surveyed firms could be split into three groups, those that do not hire anyone at the
minimum wage (30%), those that hire less than half of their employees at the minimum wage
(22%), and those with about half or more at minimum wage (47%).
Thinking about the current employees at your location, approximately how many are paid a
wage of $10 to $11 an hour?
Figure 3. Employees that Earn $10 to $11 per Hour
1.7%
29.5%
6.2%
15.6%
10.2%
14.5%
22.2%
Don't know/ Refused
None, all of our employees make more than the
minimum wage
Very few, 1% to 10%
Some but less than half, 11% to 40%
About half, 41% to 59%
Most but not all, 60% to 89%
All or close to it, 90% to 100%
Minimum wage employees across surveyed firms are more likely to be in permanent full- or
part-time positions. Fifty-seven percent of firms report that about half to all of their permanent
full-time employees are paid the minimum wage, and four in ten (37%) surveyed employers also
reported that their permanent part-time employees earn the minimum wage. Very few
minimum wage workers are seasonal employees or teenagers; six in ten employers (61%) report
that they have no minimum wage employees that are 19 years old or younger, and just over half
of surveyed firms (52%) also reported they have no minimum wage employees that are seasonal
or temporary workers.
How many of your current employees making between $10 and $11 an hour are permanent, full-
time workers; permanent-part time workers; seasonal or temporary workers; 19 years old or
younger?
Figure 4. Employees that Earn $10 to $11 per Hour, by Employment Status and Age
6.6%
61.3%
6.1%
3.9%
2.8%
11.9%
7.5%
14.4%
52.2%
3.0%
3.9%
4.7%
15.7%
6.1%
21.0%
17.1%
13.5%
11.6%
11.9%
21.0%
3.9%
11.3%
16.6%
34.8%
13.0%
8.8%
12.7%
2.8%
Don't know/ Refused
None are making the minimum
wage
All or close to it, 90% to 100%
Most but not all, 60% to 89%
About half, 41% to 59%
Some but less than half, 11% to
40%
Very few, 1% to 10%Permanent full-time
employees that are
paid a wage of $10 to
$11 an hour
Permanent part-time
employees that are
paid a wage of $10 to
$11 an hour
Seasonal or
temporary employees
that are paid a wage
of $10 to $11 an hour
Current employees
making between $10
and $11 an hour that
are 19 years old or
younger
Of those firms that provided an estimate of wages for this question, just over 50 percent
indicated that some to about half (11 to 59 percent) of their employees earn between $11.01
and $15 an hour2.
How many of your current employees make between $11.01 and $15 an hour?
Figure 5. Employees that Earn between $11.01 and $15 per Hour
2 The proportion is derived based on the 79 percent of respondents that provided an estimate of their
workforce that makes between $11.01 and $15 an hour.
21.0%
13.3%
12.7%
31.1%
9.7%
7.9%
4.2%
Don't know/ Refused
None of our employees make more than the minimum wage
Very few, 1% to 10%
Some but less than half, 11% to 40%
About half, 41% to 59%
Most but not all, 60% to 89%
All or close to it, 90% to 100%
MINIMUM WAGE – STATED IMPACTS AND ATTITUDES
Participating businesses were asked how they would respond if the minimum wage in Santa
Clara County was gradually increased to $15 an hour by 2019. Survey respondents were given
different options, and asked how likely that scenario was for their business.
The majority of surveyed employers report that they will likely (very + somewhat likely) have
to increase prices for customers, but that their employees will be more satisfied and
productive given a minimum wage increase. Approximately two out of three firms (66%)
reported that they will likely (very + somewhat) raise prices in order to pay for increased wages
and just over three in five firms (63%) noted that employees will likely be more satisfied and
productive with an increase in the minimum wage. About four in ten surveyed employers also
believe a minimum wage increase will likely reduce employee turnover (45%), but increase
likelihood of automation (42%) and generally reduce the number of employed workers (40%).
The majority of survey participants indicated that it was not at all likely that they would move or
close their business because of a minimum wage increase.
How likely are the following statements to occur at your business location, if the minimum wage
in Santa Clara County is gradually increased to $15 an hour by 2019: very likely, somewhat likely,
not at all likely, or not sure?
Figure 6. Minimum Wage Increase – Anticipated Impacts
8.3%
12.5%
18.0%
17.8%
21.2%
22.0%
42.1%
40.9%
12.7%
14.1%
21.2%
22.2%
20.7%
23.2%
20.7%
24.7%
58.5%
57.5%
45.0%
46.7%
43.4%
34.9%
20.7%
22.0%
10.4%
8.5%
9.5%
7.9%
7.9%
11.4%
9.7%
6.8%
You will have to close the business
You will move the business to a community that has a lower
minimum wage
You will reduce the hours for your minimum wage employees
You will reduce the total number of workers that you employ
You will invest in technologies that reduces the need for workers
and lowers labor costs
Your costs of employee turnover will decrease because employees
will be less likely to quit
Your employees at the minimum wage will be more satisfied and
more productive
You will need to increase prices to your customers to pay for the
increased wages
Very likely Somewhat likely Not at all likely It depends/Don't know or Refused (Not read)Not sure
The questions for anticipated impacts and employer attitudes were further analyzed by key
segments within the surveyed business community. Of particular interest was the difference in
opinion among “impacted and “non-impacted” business communities. Survey respondents were
delineated by the degree of impact given a minimum wage increase based on their current
minimum wage staffing. Impacted businesses are defined as those that support any portion of
their workforce at the current minimum wage or up to $15 an hour and they represent 84
percent of the survey respondents. Non-impacted businesses are those that currently employ
all of their workers above $15 an hour, and they represent eight percent of the survey
respondents. The remaining eight percent of survey respondents did not respond to the inquiry
regarding the portion of their workforce that makes between $11.01 and $15 an hour.
Impacted businesses more often selected “very likely” across all anticipated impacts
compared to non-impacted industries. Almost half of impacted businesses report that it is very
likely their employees will be more satisfied and productive (46%) under a minimum wage
increase, compared to 14% of non-impacted businesses. Forty-five percent of impacted
businesses also report they will very likely increase prices given a minimum wage increase,
compared 21% of non-impacted firms.
Table 1. Anticipated Impacts by Impacted and Non-Impacted Businesses
Very likely Somewhat
likely
Not at all
likely
It depends/Don't
know or Refused
(DON'T READ)
Not sure
Your employees at the
minimum wage will be
more satisfied and more
productive
Impacted 46.0% 21.6% 19.8% 7.4% 5.3%
Not impacted 14.0% 7.0% 34.9% 34.9% 9.3%
You will reduce the total
number of workers that
you employ
Impacted 19.3% 25.3% 42.8% 7.6% 5.1%
Not impacted 4.7% 4.7% 79.1% 9.3% 2.3%
You will reduce the hours
for your minimum wage
employees
Impacted 20.2% 23.7% 42.3% 8.0% 5.7%
Not impacted 7.0% 7.0% 69.8% 14.0% 2.3%
Your costs of employee
turnover will decrease
because employees will
be less likely to quit
Impacted 24.4% 24.6% 32.9% 10.1% 8.0%
Not impacted 4.7% 11.6% 58.1% 20.9% 4.7%
You will have to close the
business
Impacted 9.4% 14.0% 55.6% 10.8% 10.1%
Not impacted 0.0% 4.7% 81.4% 11.6% 2.3%
You will need to increase
prices to your customers
to pay for the increased
wages
Impacted 45.1% 26.2% 18.2% 5.7% 4.8%
Not impacted 20.9% 9.3% 58.1% 9.3% 2.3%
You will move the
business to a community
that has a lower
minimum wage
Impacted 14.0% 15.2% 55.2% 9.2% 6.4%
Not impacted 7.0% 2.3% 81.4% 7.0% 2.3%
You will invest in
technologies that reduces
the need for workers and
lowers labor costs
Impacted 23.7% 21.1% 42.3% 6.2% 6.7%
Not impacted 4.7% 11.6% 62.8% 18.6% 2.3%
Participating businesses were next asked their level of agreement with different statements
regarding a minimum wage increase.
Though the majority of surveyed employers agree that an increase in the minimum wage will
positively impact the community, most also feel increasing the minimum wage will make it
harder for new and emerging businesses. Three-quarters of surveyed firms (76%) agree that a
minimum wage increase makes sense for the community, especially given the region’s high cost-
of-living; in fact, 65% of employers also agree that increasing the minimum wage will alleviate
income inequality. Despite this, six in ten firms (61%) also agree that if the minimum wage is
increased, new businesses located in Santa Clara County will face more barriers to growth.
Please tell me whether you agree or disagree with each of the following statements.
Here’s the (first/next) one: ____________. (READ ITEM AND ASK:) Do you agree, somewhat
agree, neither agree nor disagree, somewhat disagree, or disagree with the statement?
Figure 7. Minimum Wage Increase – Employer Attitudes
37.8%
41.9%
55.2%
58.3%
22.8%
23.4%
19.5%
17.8%
11.2%
10.0%
11.0%
9.5%
7.3%
5.4%
4.4%
3.5%
18.1%
15.8%
7.3%
9.1%
If the minimum wage increases, it will make it harder
to start and grow businesses in our community
An increase in the minimum wage will help reduce
income inequality in our community
It would be better to increase the minimum wage the
same for all cities in the County, rather than having
different rates for different cities
An increase in the minimum wage makes sense for
our community, given our high cost of living
Agree Somewhat agree Neither agree nor disagree
Somewhat disagree Disagree Don't know/ Refused
The majority of firms across both impacted and non-impacted business communities agree that
an increase in the minimum wage makes sense given the cost of living and that a minimum
wage increase would be best implemented at the county-level.
Table 2. Employer Attitudes by Impacted and Non-Impacted Businesses
Agree Somewhat
agree
Neither agree
nor disagree
Somewhat
disagree Disagree
Don't
know/
Refused
(DON'T
READ)
An increase in the
minimum wage will
help reduce income
inequality in our
community
Impacted 41.1% 25.1% 10.3% 6.0% 15.2% 2.3%
Not impacted 46.5% 14.0% 7.0% 2.3% 23.3% 7.0%
If the minimum wage
increases, it will
make it harder to
start and grow
businesses in our
community
Impacted 39.8% 24.1% 11.5% 6.9% 15.9% 1.8%
Not impacted 34.9% 14.0% 7.0% 7.0% 34.9% 2.3%
An increase in the
minimum wage
makes sense for our
community, given
our high cost of living
Impacted 57.7% 17.9% 10.3% 3.9% 9.0% 1.1%
Not impacted 62.8% 14.0% 4.7% 0.0% 16.3% 2.3%
It would be better to
have the same
increase in the
minimum wage
throughout the
County than to have
different rates in
different cities
Impacted 55.9% 20.2% 10.1% 5.1% 7.4% 1.4%
Not impacted 58.1% 11.6% 14.0% 0.0% 11.6% 4.7%
How Manufacturing and Logistics Firms Responded
Though the sample size for Manufacturing and Logistics is relatively small (n=50), the
following key findings illustrate relevant cross-tabulation data for these firms:
Manufacturing and Logistics have fewer current workers at the minimum wage. Just over a
third (36%) of surveyed firms in these industries employ at least four in ten workers at $10 to
$11 an hour, compared to 51% of all respondents.
These firms are less likely to agree with the benefits of increasing the minimum wage.
Twenty-eight percent are very likely to agree that increasing the minimum wage would result
in worker satisfaction and productivity, compared to 42% of all respondents.
Manufacturing and Logistics firms are also less likely to agree with the negative impacts of
an increased minimum wage. Two in ten surveyed firms (20%) report that it is very likely a
minimum wage increase would cause an increase in prices, compared to 41% of all
respondents.
How Small Businesses Responded (35 employees or less at a location)
Small businesses account for 64% or 331 out of 515 survey participants. The following cross-
tabs illustrate key findings from small businesses with 35 or less employees at their
establishment location:
Small businesses have slightly fewer employees at the current minimum wage. Less than half
(48%) of respondent small business firms employ 40% or more of their employees at $10 to $11
an hour, compared to 57% of firms with 36 or more employees.
Small businesses are less likely to agree with benefits resulting from an increased minimum
wage. Thirty-eight percent report that it is very likely that a minimum wage increase would
result in greater worker productivity and satisfaction, compared to 50% of surveyed firms with
36 or more employees.
Small businesses are also less likely to agree with the negative impacts of an increased
minimum wage. One in ten note that it is very likely an increased minimum wage would result
in them moving their business to a community with a lower minimum wage, compared to 18%
of firms who have 36 or more employees.
APPENDIX A: SURVEY METHODOLOGY
A telephone and web survey of 518 Santa Clara County businesses was conducted for this study.
The sampling plan was designed to represent those employers that were more likely to be
impacted by an increase in the minimum wage.
Survey Design
Through an iterative process, BW Research worked closely with City of San Jose staff, IRLE
(Institute for Research on Labor and Employment) staff and an advisory committee to develop a
survey instrument that met the research objectives of the study. In developing the survey
instrument, BW Research utilized techniques to overcome known biases in survey research and
minimize potential sources of measurement error within the survey.
After the survey was finalized it was translated into Spanish and Vietnamese and offered in
those languages, for the phone portion of the survey, for those business respondents that were
not comfortable speaking English.
Sampling Method
BW Research developed a sampling plan by industry, employer size, and geography within Santa
Clara County to reflect those businesses that were more likely to currently hire at a minimum
wage and be impacted by a raise to the minimum wage. The sampling plan was based upon
previous analyses3 done by IRLE of industries that were more likely to currently hire or be
impacted by an increase in the minimum wage.
To implement the sampling plan, a database of 8,604 Santa Clara County firms was acquired
from InfoUSA for interviews over the phone and additional online web panels were secured for
web completes. The sampling followed a detailed plan targeting industries at the two and three
digit NAICS level and with developed quotas for firm size: 2 to 9 employees at a location, 10 to
35 employees at a location, 36 to 99 employees at a location, 100 or more employees at a
location, and geography within Santa Clara County (within the City of San Jose and outside the
City, but within Santa Clara County). Quotas were closed as they were filled by industry, size and
geography within Santa Clara County.
Data Collection
Prior to beginning data collection, BW Research conducted interviewer training and also pre-
tested the survey instrument to ensure that all words and questions were easily understood by
the respondents. Telephone interviews were generally conducted from 9:00am to 4:30pm
Monday through Friday. The data collection period was February 17, 2016 – March 4, 2016.
3 Based upon previous research completed by IRLE on Contra Costa County, Oakland, Sacramento and San
Francisco.
A web version of the survey was also developed and businesses in Santa Clara County were
contacted through web panels.
A Note about Margin of Error and Analysis of Sub-Groups
The overall maximum margin of error for the survey, at the 95 percent level of confidence, is +/-
4.29 percent for questions answered by all 518 respondents, for the approximately 72,0004
business locations with 2 or more employees in Santa Clara County. It is important to note that
questions asked of smaller groups of respondents (such as questions that were only asked to
firms based off their previous responses) as well as results presented separately for industry
clusters will have a margin of error greater than +/- 4.29 percent, with the exact margin of error
dependent on the number of respondents in each sub-group and the distribution of responses
to a given question.
4 Source: InfoUSA, March 2016
APPENDIX B: SURVEY TOPLINES
Screener Questions
A. Do you have a business location with at least one employee other than yourself in Santa
Clara County, California?
56.6% Yes, we have a location in San Jose
43.4% Yes, we have a location in Santa Clara County, but not San Jose
SECTION 1 - Organization-Related Questions – Business PROFILE
For this survey, we will just be asking about the employees that work from or directly report to
your current location.
1. How many years have you had at least one business location in Santa Clara County?
11.4% 0 to 2 years
20.5% more than 2 up to 5 years
24.3% more than 5 up to 10 years
18.3% more than 10 years up to 20 years
25.1% more than 20 years
0.4% (DON’T READ) Don't know/ Refused
Next I would like to ask about the industry that is most important to your firm.
2. What industry or industries best describes the work that your firm is involved in and
connected to?
22.6% Retail
10.4% Residential Care and/or Social Assistance
9.7% Administrative and/or Waste Management Services
8.9% Full Service Restaurant – Table Service Dining
7.1% Limited Service Restaurant – Fast Food or Fast Casual Dining
6.9% Information, Legal, Finance, Insurance, Real Estate, or Professional Services
5.4% Construction
5.4% Wholesale Trade and/or Transportation
4.6% Non-Profit
4.4% Lodging Accommodations or Other Food Services (catering, banquet, etc.)
4.2% Manufacturing
4.1% Education or Healthcare
4.1% Repair and Maintenance or Other Services
2.1% All other
3. Including all full-time and part-time employees, how many permanent, seasonal and
temporary employees work at or from your location? [IF NEEDED: As of today, how many
people work at or from your current location]
12.9% Less than 5
14.9% Between 5 and 9
26.8% Between 10 and 24
15.6% Between 25 and 49
12.2% Between 50 and 99
17.6% 100 or more
4. If you currently have [TAKE Q3 #] full-time and part-time permanent, seasonal and
temporary employees at your location, how many more or how many fewer employees do
you expect to have at your location 12 months from now?
45.6% More
3.3% Fewer
49.8% Same number of employees
1.4% (DON’T READ) Don't know/ Refused
Expected Employment in 12 months – Outliers Removed (companies expecting to add 50 or
more employees and an expected growth rate of 50% or higher)
(Calculated by only examining businesses with both current and projected data)
Current
12 months
n 454 454
Mean 126.09 129.49
Median 15.00 16.00
Total Employees 57,247 58,790
Change 1,543
% Growth 2.7%
[IF Q1>1 THEN ASK Q5, OTHERWISE SKIP]
5. Over the last three years, has your company grown, declined or stayed about the same in terms of
permanent, seasonal and temporary employees at your current location? [If it has grown or declined,
ask] By about how many people?
31.4% Grown
10.3% Declined
53.0% Stayed the same
5.3% (DON’T READ) Don't know/ Refused
Reported employment growth over the last 36 months – Outliers Removed (companies that
added 50 or more employees with a growth rate of 50% or higher)
(Calculated by only examining businesses with both current and past data)
36 months ago
Current
n 346 346
Mean 119.63 129.45
Median 12.50 13.00
Total Employees 41,391 44,789
Change 3,398
% Growth 8.2%
SECTION 2 – MW BUSINESS PROFILE
Now I would like to ask about your organization’s payment structure.
6. Thinking about the current employees at your location, approximately how many are paid a wage of
$10 to $11 an hour?
6.2% Very few, 1% to 10%
15.6% Some but less than half, 11% to 40%
10.2% About half, 41% to 59%
14.5% Most but not all, 60% to 89%
22.2% All or close to it, 90% to 100%
29.5% None, all of our employees make more than $10 an hour the minimum wage
1.7% (DON’T READ) Don't know/ Refused
[IF Q6>0, ask Q7 – Q10 OTHERWISE SKIP TO Q11]
7. How many of your current employees making between $10 and $11 an hour are permanent, full-time
workers? (n=362)
2.8% Very few, 1% to 10%
12.7% Some but less than half, 11% to 40%
8.8% About half, 41% to 59%
13.0% Most but not all, 60% to 89%
34.8% All or close to it, 90% to 100%
16.6% None of our permanent full-time employees make the minimum wage
11.3% (DON’T READ) Don't know/ Refused
8. How many of your current employees making between $10 and $11 an hour are permanent, part-
time workers? (n=362)
3.9% Very few, 1% to 10%
21.0% Some but less than half, 11% to 40%
11.9% About half, 41% to 59%
11.6% Most but not all, 60% to 89%
13.5% All or close to it, 90% to 100%
17.1% None of our permanent part-time employees make the minimum wage
21.0% (DON’T READ) Don't know/ Refused
9. How many of your current employees making between $10 and $11 an hour are seasonal or
temporary workers? (n=362)
6.1% Very few, 1% to 10%
15.7% Some but less than half, 11% to 40%
4.7% About half, 41% to 59%
3.9% Most but not all, 60% to 89%
3.0% All or close to it, 90% to 100%
52.2% None of our seasonal or temporary employees make the minimum wage
14.4% (DON’T READ) Don't know/ Refused
10. How many of your current workers making between $10 and $11 an hour are 19 years old or
younger? (n=362)
7.5% Very few, 1% to 10%
11.9% Some but less than half, 11% to 40%
2.8% About half, 41% to 59%
3.9% Most but not all, 60% to 89%
6.1% All or close to it, 90% to 100%
61.3% None of our minimum wage employees are 19 years old or younger
6.6% (DON’T READ) Don't know/ Refused
11. How many of your current employees make between $11.01 and $15 an hour?
12.7% Very few, 1% to 10%
31.1% Some but less than half, 11% to 40%
9.7% About half, 41% to 59%
7.9% Most but not all, 60% to 89%
4.2% All or close to it, 90% to 100%
13.3% None of our employees make between $11.01 and $15 an hour
21.0% (DON’T READ) Don't know/ Refused
SECTION 3 – Minimum Wage – Impact on Business
Now I would like to ask how an increase in the minimum wage would impact your business at
the current location.
12. How likely are the following statements to occur at your business location, if the minimum
wage in Santa Clara County is gradually increased to $15 an hour by 2019: very likely,
somewhat likely, not at all likely or not sure?
RANDOMIZE
Very likely
Somewhat
likely
Not at all
likely
(DON’T
READ)
DKNA/It
depends
Not
sure
A. Your employees at the minimum
wage will be more satisfied and
more productive
42.1% 20.7% 20.7% 9.7% 6.9%
B. You will reduce the total number
of workers that you employ 17.8% 22.2% 46.7% 7.9% 5.4%
C. You will reduce the hours for
your minimum wage employees 18.0% 21.2% 45.0% 9.5% 6.4%
D. Your costs of employee turnover
will decrease because
employees will be less likely to
quit
22.0% 23.2% 34.9% 11.4% 8.5%
E. You will have to close the
business 8.3% 12.7% 58.5% 10.4% 10.0%
F. You will need to increase prices
to your customers to pay for
the increased wages
40.9% 24.7% 22.0% 6.8% 5.6%
G. You will move the business to a
community that has a lower
minimum wage
12.5% 14.1% 57.5% 8.5% 7.3%
H. You will invest in technologies
that reduce the need for
workers and lowers labor costs
21.2% 20.7% 43.4% 7.9% 6.8%
13. Please tell me whether you agree or disagree with each of the following statements.
Here’s the (first/next) one: ____________. (READ ITEM AND ASK:) Do you agree, somewhat agree, neither
agree nor disagree, somewhat disagree, or disagree with the statement?
RANDOMIZE
Agree
Somewhat
agree
Neither
agree nor
disagree
Somewhat
disagree Disagree
(DON’T
READ)
Don't
know/
Refused
A. An increase in the minimum
wage will help reduce
income inequality in our
community
41.9% 23.4% 10.0% 5.4% 15.8% 3.5%
B. If the minimum wage
increases, it will make it
harder to start and grow
businesses in our community
37.8% 22.8% 11.2% 7.3% 18.1% 2.7%
C. An increase in the minimum
wage makes sense for our
community, given our high
cost of living
58.3% 17.8% 9.5% 3.5% 9.1% 1.9%
D. It would be better to have the
same increase in the
minimum wage throughout
the County than to have
different rates in different
cities
55.2% 19.5% 11.0% 4.4% 7.3% 2.5%
Since it sometimes becomes necessary for the project manager to call back and confirm
responses to certain questions, I would like to verify your contact information.
A. First and Last Name___________________
B. Position__________________________
C. Phone_____________
D. Email ______________
E. Company Name___________________
F. Company Address (including City, State, Zip) ___________________
Those are all the questions I have.
Thank you very much for your time.
The Effects of a $15 Minimum Wage by
2019 in San Jose and Santa Clara County
By Michael Reich, Claire Montialoux, Sylvia Allegretto, Ken Jacobs,
Annette Bernhardt, and Sarah Thomason
With the assistance of Saika Belal and Ian Perry
Michael Reich is a Professor at UC Berkeley and Chair of the Center on Wage and Employment
Dynamics at UC Berkeley’s Institute for Research on Labor and Employment (IRLE). Claire
Montialoux is an Economics Researcher at IRLE. Sylvia Allegretto is Co-Chair of the Center on
Wage and Employment Dynamics at IRLE. Ken Jacobs is the Chair of the UC Berkeley Center for
Labor Research and Education at IRLE. Annette Bernhardt is a senior researcher at IRLE.
Sarah Thomason is a data analyst at the Center for Labor Research and Education at IRLE.
Saika Belal and Ian Perry are members of the UC Berkeley IRLE Minimum Wage Research
Group.
POLICY BRIEF
June 2016
CONTENTS
KEY FINDINGS ........................................................................................................................................ 1
Scenario A: Key findings for a $15 minimum wage increase in San Jose ..................................... 4
Scenario B: Key findings for a $15 minimum wage increase in all of Santa Clara County........... 7
PART 1. THE POLICY CONTEXT ............................................................................................................ 10
1. The economic context .................................................................................................................. 11
2. The minimum wage increase schedules .................................................................................... 14
PART 2. EMPLOYMENT IMPACT ANALYSIS IN SAN JOSE AND SANTA CLARA COUNTY .................... 15
1. Previous minimum wage research .............................................................................................. 16
2. The UC Berkeley IRLE minimum wage model ............................................................................ 18
3. Effects on workers ....................................................................................................................... 22
4. Effects on businesses .................................................................................................................. 29
5. Effects on employment ................................................................................................................ 34
PART 3. POLICY ISSUES ....................................................................................................................... 47
Impacts on Specific Subpopulations ............................................................................................... 48
Wage Level ........................................................................................................................................ 53
CONCLUSION ........................................................................................................................................ 56
APPENDIX: DATA AND METHODS ........................................................................................................ 59
A1. The wage simulation model ...................................................................................................... 60
A2. Calibrating the UC Berkeley IRLE minimum wage model ........................................................ 63
Endnotes ............................................................................................................................................... 72
References ............................................................................................................................................ 76
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 1
KEY FINDINGS
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 2
We present here, at the request of the City of San Jose, an analysis of the impact of minimum
wage increases for both San Jose and all of Santa Clara County. Both scenarios begin on January
1, 2017 and increase to $15 by January 1, 2019.1
Critics of minimum wage increases often cite factors that will reduce employment, such as
automation or reduced sales, as firms raise prices to recoup their increased costs. Advocates
often argue that better-paid workers are less likely to quit and will be more productive, and that a
minimum wage increase positively affects jobs and economic output as workers can increase
their consumer spending. Here we take into account all of these often competing factors to
assess the net effects of the policy.
Our analysis applies a new structural labor market model that we created specifically to analyze
the effects of a $15 minimum wage. We take into account how workers, businesses, and
consumers are affected and respond to such a policy and we integrate these responses in a
unified manner. In doing so, we draw upon modern economic analyses of labor and product
markets. As we explain in the report, the main effects of minimum wages are made up of
substitution, scale, and income effects. The figure below provides a guide to the structure of our
model.
Figure 1. UC Berkeley IRLE minimum wage model
Source: UC Berkeley IRLE Minimum Wage Research Group
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 3
Our data are drawn from the Census Bureau’s American Community Survey and from other
Census and U.S. Bureau of Labor Statistics datasets. We also make use of the extensive research
conducted by economists—including ourselves—in recent years on minimum wages, and upon
research on related economic topics.
Our estimates of the effects of a $15 minimum wage are also based upon existing research on
labor markets, business operations, and consumer markets. Our estimates compare employment
numbers if the policy were to be adopted to employment numbers if the policy is not adopted.
Other factors that may affect employment by 2019 are therefore outside the scope of our
analysis. We have successfully tested our model with a set of robustness exercises.
Our analysis does not incorporate the recent state minimum wage law passed in April 2016.
Since the San Jose and Santa Clara County scenarios are on a faster timeline, the number and
demographics of workers affected would be similar if we had included the scheduled statewide
increases. However, the size of the average wage increase and the effect on firms compared to
the new baseline established by the state would be somewhat smaller.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 4
SCENARIO A: KEY FINDINGS FOR A $15 MINIMUM WAGE INCREASE IN
SAN JOSE – BY 2019
Economic context
• When accounting for inflation, median earnings in San Jose were 10.5 percent lower in 2014
compared to their 2007 pre-recession level. Median annual earnings in San Jose are 20.9
percent higher than the state as a whole, but 17.3 percent less than median earnings in
Santa Clara County.
• Unemployment rates have declined significantly for the state and San Jose. The April 2016
unemployment rate for California was 5.3 percent, down to its 2007 pre-recession rate.
Annual unemployment in San Jose had was 4.5 percent in 2015, lower than its pre-recession
rate (5.2 percent in 2007).2
Effects on workers – by the end of 2019
• Increasing the minimum wage to $15 would increase earnings for 115,000 workers, or 31.1
percent of the city’s workforce.
• Among those getting raises in San Jose, annual pay would increase 17.8 percent, or about
$3,000 (in 2014 dollars) on average. These estimates include a ripple effect: some workers
who already earn $15 will also receive an increase.
• 96 percent of workers who would get increases are over 20 and 56 percent are over 30—with
a median age of 32.
• The proposed minimum wage increase would disproportionately benefit Latinos, who
represent 53 percent of affected workers.
• Workers who would get pay increases are less-educated than the overall workforce, but
almost half (48 percent) have some college experience or higher.
• The median annual earnings of workers who would get raises ($18,100 in 2014 dollars) are
36 percent of median earnings for all workers in San Jose ($50,507). Workers getting
increases are disproportionately employed in part-time jobs, and are also less likely to have
health insurance through their employer.
• Workers who would get pay increases disproportionately live in low-income families; on
average, they earn close to half of their family’s income.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 5
• The research literature suggests downstream benefits from the proposed wage increase, such
as improved health outcomes for both workers and their children, and increases in children’s
school achievement and cognitive and behavioral outcomes.
Effects on businesses and consumers – by the end of 2019
• Three industries account for over half of the private sector workers getting increases in San
Jose: restaurants (21.0 percent), retail trade (19.1 percent), and administrative and waste
management services (14.7 percent).
• 77.8 percent of workers in the restaurant industry in the private sector would receive a wage
increase, compared to 11.5 percent in manufacturing.
• Total wages would increase by 10.1 percent for restaurants and 1.3 percent across all
employers. This increase is much smaller than the minimum wage increase because many
businesses already pay over $15 and many workers who would get pay increases are already
paid more than the current minimum wage. In addition, the workers who would receive pay
increases are the lowest paid workers in San Jose and their wages represent only 8.3 percent
of total wages.
• Employee turnover reductions, automation, and increases in worker productivity would offset
some of these payroll cost increases.
• Businesses could absorb the remaining payroll cost increases by increasing prices slightly—by
0.3 percent through 2019. This price increase is well below annual inflation of 2.5 percent
over the past five years. Price increases in restaurants would be higher, 3.1 percent.
• Price increases would be much smaller than labor cost increases because labor costs
average about 22 percent of operating costs; compared to 31 percent for restaurants and 11
percent for retail.
• The consumers who would pay these increased prices range across the entire income
distribution.
Net effect on employment in San Jose, Santa Clara County and nine nearby counties
– by the end of 2019
• Our estimate projects slightly slower employment growth during the phase-in period than
without the minimum wage increase: cumulatively, 960 fewer jobs by the end of 2019 in San
Jose, which corresponds to 0.3 percent of projected 2019 employment. In comparison,
employment in the state is projected to grow 1.32 percent annually in the same time period.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 6
• Most of the reduction in job growth in San Jose reflects leakage of the increased spending by
workers getting increases into the rest of the region. A substantial share of San Jose workers
who would get pay increases live and spend their increased income in neighboring areas.
Taking into account the increased spending in surrounding areas, we estimate there would be
80 fewer jobs over the larger regional area than without the wage increase. This area includes
the following counties: Santa Clara, Alameda, San Mateo, San Francisco, Santa Cruz,
Monterey, and San Benito.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 7
SCENARIO B: KEY FINDINGS FOR A $15 MINIMUM WAGE INCREASE IN
ALL OF SANTA CLARA COUNTY – BY 2019
Economic context
• After accounting for inflation, the earnings of typical workers in the county declined by 8.3
percent between their pre-recession level in 2007 and 2014. Median annual earnings in
Santa Clara County are 49.6 percent higher than in the state as a whole.
• Santa Clara County has experienced rapid employment growth in the recovery from the
recession. Over 62 percent of Santa Clara County’s working age residents are employed,
compared to 57 percent in the state as a whole.
• The unemployment rate in Santa Clara County was 4.2 percent in 2015, significantly
below the pre-recession rate and falling.
Effects on workers – by the end of 2019
• Increasing the minimum wage to $15 would increase earnings for about 250,000 workers
in Santa Clara County, or 25.3 percent of the county’s workforce.
• Among those getting raises in Santa Clara County, annual pay would increase 19.4
percent, or $3,200 (in 2014 dollars) on average. These estimates include a ripple effect in
which some workers who already earn $15 will also receive an increase.
• The demographics of the affected workers in Santa Clara County mirror those in San Jose:
95.5 percent are over the age of 20, with a median age of 32; 37.0 percent are married;
33.9 percent have children; nearly half are Latino.
• The median annual earnings of affected workers ($17,821 in 2014 dollars) are about
one-third of the median for all workers in Santa Clara County ($57,956).
Effects on businesses and consumers – by the end of 2019
• Three industries account for nearly half of the private sector workers getting increases in
Santa Clara County: food services (20.2 percent), retail trade (16.1 percent), and
administrative and waste management services (11.9 percent).
• 71 percent of workers in the restaurant industry in the private sector would receive a
wage increase, compared to 11.2 percent in manufacturing.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 8
• Total wages would increase by 9.5 percent for restaurants and one percent across all
employers. This increase is much smaller than the minimum wage increase because many
businesses already pay over $15 and many workers who will get pay increases are already
paid over the current minimum wage. In addition, the workers who would receive pay
increases are the lowest paid workers in Santa Clara County and their wages represent
only 6.1 percent of total wages.
• Employee turnover reductions, automation, and increases in worker productivity would
offset some of these payroll cost increases.
• Businesses would absorb the remaining payroll cost increases by increasing prices
slightly—by 0.2 percent through 2019. This price increase is well below annual inflation of
nearly 2.5 percent over the past five years. Price increases in restaurants would be higher
at 2.9 percent.
• Price increases would be much smaller than labor cost increases because labor costs
average about 22 percent of operating costs; compared to 31 percent for restaurants and
11 percent for retail.
• The consumers who would pay these increased prices range across the entire income
distribution.
Net effect on employment in Santa Clara County and nine nearby counties – by 2019
• Our estimate projects slower employment growth over the phase-in period than without
the minimum wage increase: cumulatively, 1,350 fewer jobs by the end of 2019 in Santa
Clara County, which corresponds to 0.1 percent of projected 2019 employment. In
comparison, employment in the state is projected to grow 1.32 percent annually in the
same time period.
• Based upon regional commuting and spending patterns, we estimate a net gain of less
than one hundred jobs over the larger region that includes the counties of Santa Clara,
Alameda, San Mateo, San Francisco, Santa Cruz, Monterey, and San Benito. The
employment gains generated by a $15 minimum wage within Santa Clara County are
spread over nearby counties.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 9
LIMITS TO OUR STUDY
• Any prospective impact study involves an inherent level of uncertainty. Actual effects may
differ from our estimates if future economic conditions vary from current forecasts.
• We estimate the net effects on jobs in the city, county and region. The effects will vary for
particular industries.
• We do not take into account the effects of higher wages on worker health and on worker
training, which are likely to be positive. Also, although higher parental earnings have well-
documented effects on children’s health, educational outcomes, and future earnings, these
long-run effects are beyond the time scope of our study.
• These results cannot be generalized to minimum wages higher than $15. Our model predicts
additional negative effects would occur at some higher minimum wage.
CONCLUSION
• Like all forecasts, our results may differ if other economic conditions change.
• A $15 countywide minimum wage by 2019 would generate a significant increase in earnings
for about 115,000 workers in San Jose and 250,000 workers in Santa Clara County. The
improvement in living standards would outweigh the small effect on employment.
• How can such a major improvement in living standards occur without adverse employment
effects? While a higher minimum wage induces some automation, as well as increased
worker productivity and slightly higher prices, it simultaneously increases worker purchasing
power. These positive and negative effects on employment largely offset each other. In the
end, the impacts of the minimum wage will be employee turnover reductions, productivity
increases and modest price increases.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 10
PART 1. THE POLICY CONTEXT
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 11
1. THE ECONOMIC CONTEXT
We review here the current economic conditions in Santa Clara County, the City of San Jose and,
for context, California. We focus on four economic indicators over the Great Recession and
recovery: unemployment rates, job growth, employment rates, and earnings. Each provides a
somewhat different perspective on the nature of the current recovery.
The Great Recession started near the end of 2007 and officially lasted until June 2009.
California was hit hard by the recession as state unemployment rates soared into double digits as
did the rates for San Jose and Santa Clara County (Figure 2). Unemployment rates started to
decline as the economy improved. The April 2016 unemployment rate for California was 5.3
percent, down to its 2007 pre-recession rate. The 2015 annual unemployment in San Jose was
4.5 percent, lower than its pre-recession rate (5.2 percent in 2007).
Figure 2. Annual unemployment rates, 2007-2015
Source: Annual unemployment rates are from the California Employment Development Department.
Unemployment rates improved as job growth strengthened over the last several years. Figure 3
shows the sizable job losses in Santa Clara County and California during the recession. Job
growth returned in 2011—at a faster pace in Santa Clara County than in California—and that
higher pace of job growth in Santa Clara County has increased even as job growth in the state
steadily improved.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 12
Figure 3. Job growth, California and Santa Clara County, 2007-2015
Source: Authors’ calculation of growth in total nonfarm payrolls (annual averages) from Current Employment
Statistics.
Note: *Data for Santa Clara County refers to the San Jose–Sunnyvale–Santa Clara MSA
Figure 4. The employment rate (EPOPS), 2007-2014
Sources: California state employment-to-population ratios are calculated using annual employment data
from the CPS and annual population data from the U.S. Census. Santa Clara County ratios are calculated
using annual employment data from EDD and annual population data from the U.S. Census.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 13
Figure 4 depicts trends in the employment rate - the share of the working age population that is
employed. This indicator is a companion to the unemployment rate as it counts workers who
stopped looking for work and those who want more hours of work. Santa Clara County has
experienced rapid employment growth over the recovery. Over 62 percent of Santa Clara County
residents are employed compared to 57 percent for the state as a whole. Figure 4 shows that the
earnings of typical workers in Santa Clara County far outpace earnings for workers in San Jose
and the state overall. Median annual earnings in Santa Clara County are $52,377 (in 2014
dollars) which is 49.6 percent higher than the state as a whole. Annual earnings in San Jose are
$43,313 (in 2014 dollars), which is 20.9 percent higher than the state as a whole, but 17.3
percent less than median earnings in Santa Clara County.
However, pay in both the county and the state is lower than it was in 2007. In Santa Clara
County, after accounting for inflation, earnings of typical workers have declined by 8.3 percent,
compared to pre-recession levels. The pay of typical workers in the City of San Jose is 10.5
percent lower compared to the 2007 per-recession level. These patterns suggest that inequality
has continued to increase even during economic expansions.3
Figure 5. Real median earnings, 2007-2014
Source: American Community Surveys 2007-2014.
Note: Median annual earnings for workplace geography in real 2014 inflation-adjusted dollars for
workers 16 years and over with earnings.
In summary, unemployment and employment trends show that California’s economic recovery
has strengthened substantially in recent years—and even more so in Santa Clara County and San
Jose. Median annual earnings are considerably higher in Santa Clara County and San Jose than
in the state as a whole. However, the earnings of typical workers have declined despite the
economy recovery.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 14
2. THE MINIMUM WAGE INCREASE SCHEDULES
Both of the scenarios considered in this report would phase in minimum wage increases over
three years, starting with $12 an hour in 2017 and reaching $15 an hour in 2019. In Scenario A,
this minimum wage schedule is adopted in San Jose. In Scenario B, this minimum wage schedule
is adopte throughout Santa Clara County, including San Jose. Tables 1 and 2 compare these two
minimum wage scenarios to the “baseline” schedules currently in effect (as of March 1, 2016).
In the impact analyses that follow, our logic will be to estimate the effects of Scenario A and B,
relative to their respective baseline schedules. (Our analysis does not take into account the
recent state minimum wage increase signed into law in April 2016).
Table 1. San Jose Minimum Wage Schedule: Scenario A
2017 2018 2019
Baseline schedule* $10.53 $10.76 $11.00
Scenario schedule $12.00 $13.50 $15.00
* Based on San Jose’s minimum wage schedule as of March 1, 2016. It does not take into account the state minimum wage
increase enacted on April 4, 2016. San Jose’s minimum wage was indexed to the U.S. All Cities CPI-W. We estimate each year’s
minimum wage using the average annual increase in the CPI-W over the past 10 years.
Table 2. Santa Clara County Minimum Wage Schedule: Scenario B
2015 workforce 2017 2018 2019
Baseline schedules
San Jose & Sunnyvale 431,000 $10.53* $10.76* $11.00*
Palo Alto & Santa Clara
City 211,000 $11.25* $11.50* $11.75*
Mountain View 84,000 $13.00 $15.00 $15.37*
Rest of Santa Clara
County (state schedule) 180,000 $10.00 $10.00 $10.00
Scenario schedule
Santa Clara County
(except Mountain View) 906,000 $12.00 $13.50 $15.00
Note: The baselines for these schedules were in effect as of March 1, 2016. Proposals being considered by individual cities were
not used. We do not take into account the state minimum wage increase enacted on April 4, 2016.
* Where minimum wages are scheduled to increase according to CPI, we estimate the increase using the average annual CPI
increase over the past 10 years. Mountain View’s minimum wage is indexed to the San Francisco CMSA CPI-W. All other cities
are indexed to the U.S. All Cities CPI-W.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 15
PART 2. EMPLOYMENT IMPACT
ANALYSIS IN SAN JOSE AND
SANTA CLARA COUNTY
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 16
1. PREVIOUS MINIMUM WAGE RESEARCH
In the past two decades, economists have conducted numerous econometric studies of the
effects of minimum wages. The overwhelming majority have focused on the employment effects
(Belman and Wolfson 2014; Belman and Wolfson 2015; Schmitt 2015). Typically these studies
make use of panel data on workers or firms from standard government sources such as the
Current Population Survey or the Quarterly Census on Employment and Wages.
Most extant research on minimum wages does not detect significant effects on workers age 20
and over. Some observers attribute the lack of visible effects to the relatively small proportion of
adults who were affected by past minimum wage increases in the U.S.4 These observers argue
that minimum wage effects should be detectible by examining groups that are more affected,
notably teens and restaurant workers (Brown 1999).
Economists have therefore focused on these two groups. After two decades of methodological
controversy among researchers, the literature has produced some areas of agreement. In
particular, recent studies of the effects on restaurant workers by researchers with opposing
methodological views have arrived at a consensus: the employment effects are either extremely
small or non-existent.5 The effects of minimum wages on teen employment remain somewhat
controversial. Some researchers find significant but not large negative effects (Neumark, Salas,
and Wascher 2014) while others find effects that are much smaller, close to zero (Allegretto et al.
2015).
The remaining controversy over effects on teens has become less relevant than it once was.
While teens once represented one-fourth of all workers affected by minimum wages nationwide,
their importance has fallen to less than half that level today. We find that teens represent only
4.5 percent of the workers who would be affected by the proposed $15 Santa Clara County
minimum wage. Moreover, compared to teens, the rest of the low-wage workforce is older and
has more work experience and schooling than was the case in previous decades. Results that are
specific to teens are therefore not as informative for the effects on the workforce as a whole.
This minimum wage research uses quasi-experimental methods, exploiting time and state
variation between 1979 and 2012 in federal and state minimum wages to identify causal effects.
The most credible of the studies use state of the art statistical methods to ensure that the causal
comparisons are apples to apples. However, the minimum wage changes in these past
experiences, which peak at about $10, generated increases for at most 8-10 percent of the
workforce. In contrast, approximately 31 percent of all workers would receive a wage increase in
the $15 San Jose scenario and 25 percent in the $15 Santa Clara County scenario, far higher
than is the case in the minimum wage research literature to date. As a result, this previous
research is at best only suggestive of the effects we consider here.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 17
Moreover, this quasi-experimental econometric approach does not tell us whether employment
effects are the result of automation, or price increases, or other possible mechanisms. Instead, it
incorporates the results of all these mechanisms without identifying which are at work.
Since the quasi-experimental econometric approach is not appropriate for our study, we draw
here upon the other major empirical method used by economists—building and calibrating a
structural model. Thus, in order to better understand the impacts of a larger minimum wage
increase, we model how the minimum wage policy works its way through the San Jose and Santa
Clara County economy, examining workers, businesses, and consumers. We incorporate
outcomes from economists’ best research on labor markets, business practices, and consumer
spending to construct a structural, multi-iterative model to estimate the effects of the scenarios
for San Jose and Santa Clara County.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 18
2. THE UC BERKELEY IRLE MINIMUM WAGE MODEL
In 2015, the UC Berkeley Institute for Research on Labor and Employment (IRLE) minimum wage
group developed a structural model to study the prospective impacts of a $15 minimum wage in
Los Angeles.6 This model was further enhanced to study the effects of a $15 minimum wage in
New York State (Reich et al. 2016). The current report, which uses that model, contains two
components:
• A wage simulation model that predicts the number of workers that will be affected by (i.e.,
receive) minimum wage increases. The results of this model are described in the first part
of this report, and the model itself is described in detail in the appendix.
• An economic impact model that predicts the effect of minimum wage increases, given the
structure of the workforce affected, on consumer demand. We focus on the latter in this
section.
We also adapt the model to apply to San Jose and Santa Clara County in particular. Our estimates
draw on standard government data sources, the large body of economic research on the
minimum wage, other research studies, and a standard regional economic model (IMPLAN).
These data sources and models are fully documented in the text, accompanying endnotes, and in
the appendix.
Our economic impact model recognizes that higher minimum wages will affect labor supply and
labor demand. Adjustments to labor supply include lower employee turnover and lower job
vacancy rates. Adjustments to labor demand include possible substitutions of capital for labor
and skilled labor for unskilled labor, greater worker productivity when wages rise, reductions in
employment because higher prices reduce sales, and increases in employment because workers’
spending out of their higher income will increase sales and employment. The net effect depends
upon the magnitudes of the individual adjustments, again taking into account interactions among
them.
The labor demand model draws from standard labor economic textbook analyses. For industry
labor demand, these analyses incorporate “substitution” and “scale” effects in labor, capital, and
goods markets. For a formal version of this labor demand model, see Cahuc, Carcillo and Zylberg
(2014). Since our concern here is on the effects of an economy-wide minimum wage, we add an
“income effect.” The income effect accounts for changes in the level of economic output when
wage increases lead to increased consumer demand.
Model Structure
Figure 6 summarizes our model qualitatively in a flow diagram. The green boxes refer to the
effects on workers and the red boxes refer to the effects on businesses. The automation and
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 19
productivity box is placed first to highlight how businesses will respond to a minimum wage.
Automation here refers only to capital-labor substitution that is induced by the minimum wage,
not to the much larger degree of automation that has taken place for decades. Productivity
growth can come from automation, from workers working harder or smarter when pay is high,
and from workers having more experience, as when minimum wages reduce employee turnover.
Figure 6. UC Berkeley IRLE minimum wage model
Source: UC Berkeley IRLE Minimum Wage Research Group
Examine next the effects on workers, shown in the green boxes and move from left to right. The
first green box refers to the higher wages received by lower-paid workers. The next green box
accounts for the net effect of taxes and reduced receipt of public benefit programs on workers’
income. Workers will pay more in taxes as their wages increase and eligibility for public benefits
will decline. The third box refers to how workers’ increased spending power out of their higher net
income translates into higher consumer demand and more jobs. We will refer to this mechanism
as the income effect of minimum wages.
Examine now the effects on businesses and again move from left to right. The higher minimum
wage will increase businesses’ payroll costs, but some of these higher costs will be offset
because employee turnover will fall, generating savings in recruitment and retention costs. Firms
may also find that higher-paid and more experienced workers will be more productive, which
could also offset payroll cost increases. In other words, one effect of a higher minimum wage is to
induce more efficient management practices.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 20
Higher payroll costs (net of turnover and productivity savings) will lead firms to increase prices,
leading to reduced consumer demand. We will refer to this adjustment mechanism as the scale
effect, as it identifies reductions in the scale of output that will reduce the demand for workers.
As we have already mentioned, businesses may also respond to higher minimum wages by
increasing their investment in equipment. This substitution effect (think automation) also reduces
their demand for workers.
The income effect has a positive effect on employment, while the scale and substitution effects
each have negative effects on employment. The sum of the income, scale, and substitution
effects determines the net employment effect of the minimum wage, as shown in the blue box on
the right side of Figure 6.
Figure 6 is useful for understanding the basic structure of our model. But it leaves out some
important details. First, the effects on businesses and workers in the red and green boxes of the
model occur simultaneously, not sequentially. The effects in reality are therefore captured only by
examining the net effects on the economy and employment. These net effects are symbolized by
the blue box at the right of the diagram. Second, Figure 6 omits some feedback loops that would
make the figure unwieldy, but which are included in our calculations.
Model calibration and dynamics
The net effect of minimum wages on employment equals the sum of the income, scale, and
substitution effects. The income effect will always be positive, while the scale and substitution
effects will always be negative. Whether the net effect is positive, zero, or negative therefore
depends upon the relative magnitudes of its three components.
These relative magnitudes in turn depend upon the quantitative responses of workers and
businesses to a minimum wage increase. We refer to the model’s parameters as the inputs that
determine these multiple quantitative responses. Some of these parameters, such as the
propensity to substitute capital for labor, may not vary with the magnitude of the minimum wage
increase. Other parameters, such as turnover cost savings, are likely to vary with the size of the
increase. As with any economic model, we calibrate our model using the best data and research
findings available. The details are presented in Section 5 below and in Appendix A2.
The model’s parameters and dynamics must be consistent with two conditions. First, the model
must be consistent with the very small effects that researchers find for the smaller pre-2015
increases in federal and state minimum wages. Second, although labor demand in low-wage
labor markets may be much less responsive to wages than is commonly thought, labor demand is
not completely unresponsive. The model must therefore be consistent with growing negative
effects if minimum wages were to reach extremely high levels, such as at $25 or $40 per hour.
The big unknown, of course, is: At what level do the effects become visibly negative and how
quickly do they become more negative?
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 21
In a forthcoming paper, Reich et al. (2016) show that our calibrated model predicts extremely
small effects for minimum wage increases of up to 25 percent, to a minimum wage of $10. At
this minimum wage, the income, scale, and substitution effects are each very small. As the
minimum wage reaches higher levels, the (positive) income effect weakens since the increase in
the proportion of workers getting pay increases slows down, and because the propensity to
consume of higher-paid workers is lower than that of lower paid workers. At the same time, the
(negative) scale effect strengthens because turnover cost savings diminish and the price
elasticity of consumer demand becomes higher for higher-priced goods.7 Our model is thus
consistent with growing negative employment effects at higher minimum wage levels.
We have tested our model’s calibration by undertaking a series of robustness tests. The tests
show that this net effect changes by small amounts when we vary the model’s parameters (Reich
et al. 2016). In the next sections, we discuss how we quantify the effects in each of the boxes in
Figure 6.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 22
3. EFFECTS ON WORKERS
We begin by analyzing the effects of the Scenario A (San Jose) and Scenario B (Santa Clara
County) minimum wage increases on workers. To estimate these effects, we use publicly-
available government datasets to model (a) the number of workers who would receive pay
increases under the two minimum wage scenarios and (b) the size of those wage increases. We
exclude federal and state government employees, local school district employees, In Home
Supportive Services (IHSS) workers, and self-employed workers from our analysis, since those
groups of workers would not be eligible for local minimum wage laws.
Specifically, for each scenario, our model produces two different simulations of the future wage
distribution. First, we conduct a baseline simulation, in which we assume that the minimum wage
will increase each year according to minimum wage laws that are already in effect (see Tables 1
and 2 above). For Scenario B (Santa Clara County), we assume that cities that do not have their
own minimum wage law will follow the state minimum wage schedule in effect as of January 1,
2016 (again, this analysis does not take into account the new state minimum wage law signed in
April 2016). Second, we conduct a simulation that models the future wage distribution under
each of the two minimum wage increase scenarios.
We then compare the baseline and scenario simulations and estimate, for each yearly phase-in
step, the number of workers that would be affected by the scenario and the additional wages
they would receive as a result, above and beyond any currently scheduled minimum wage
increases. In constructing these estimates, our model adjusts for expected growth in
employment, wages and inflation over time. Our estimates also take into account what is often
referred to as a “ripple” or “compression” effect: workers who make slightly more that the
scenario minimum wage are also likely to receive wage increases. More information on our
methodology is available in Appendix A1.
3.1 Workforce Impacts
Table 3 shows the estimated number and percentage of eligible workers affected under Scenario
A (San Jose) and Scenario B (Santa Clara County). Under Scenario A, we estimate that 115,000
workers in San Jose will receive a pay raise by 2019, or about 31.1 percent of the eligible
workforce. Of these, 92,000 are directly affected workers (earning less than $15 per hour when
the scenario would be fully implemented in 2019) and 23,000 are indirectly affected (earning
slightly more than $15 per hour when the scenario would be fully implemented in 2019).
Under Scenario B, 250,000 workers, or about 25.3 percent of the eligible workforce in Santa
Clara County, would receive a pay raise by 2019. Of these, 198,000 are directly affected workers
and 52,000 are indirectly affected workers. Estimates for Santa Clara County include San Jose.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 23
Table 3. Estimated cumulative impacts on workers by 2019
Cumulative workforce impacts Scenario A:
San Jose Scenario B:
Santa Clara County
Percent of eligible workforce receiving pay increases 31.1% 25.3%
Total number of workers receiving increases 115,000 250,000
Number of workers affected directly 92,000 198,000
Number of workers affected indirectly 23,000 52,000
Average hourly wage increase (2014 dollars) $1.81 $1.92
Average annual earnings increase for workers receiving increases (2014
dollars) $3,000 $3,200
Average percent annual earnings increase for workers receiving increases 17.8% 19.4%
Total aggregate increase in wages (2014 dollars) $345 million $796 million
Source: Authors’ analysis of ACS, OES, and QCEW data. See Appendix A1 for details.
Note: Santa Clara County impacts include those for the entire county, including San Jose. Eligible workers are those that work in
the city/county where the new minimum wage policy is implemented. Directly affected workers earned between 50% of the old
minimum wage and 100% of the new minimum wage. Indirectly affected workers earned between 100% and 115% of the new
minimum wage. Average annual earnings is per worker, not per job.
We also estimate the additional earnings that affected workers would receive under each
scenario, relative to their earnings under current minimum wage schedules. Table 3 shows the
estimated cumulative increase in affected workers’ hourly wages, annual earnings, and
percentage increase in annual earnings, as well as the cumulative total earnings increase for all
affected workers. By full implementation in 2019, we estimate that the wages of affected
workers will have risen by about $1.92 per hour in Santa Clara County and $1.81 per hour in San
Jose. That amounts to an estimated additional $3,000 in earnings per year for impacted workers
in San Jose and $3,200 for impacted workers in Santa Clara County. In total, we estimate that
affected workers will earn an additional $796 million by 2019 in Santa Clara County. In San Jose,
we estimate that affected workers will earn an additional $345 million by 2019. All estimates are
listed in 2014 dollars.8
3.2 Impact on Benefits Eligibility and Poverty
Some policymakers have expressed concern that affected workers and their families could
ultimately be worse off after minimum wage increases if they are no longer eligible for means-
tested social assistance programs. However, research suggests that most workers will come out
well ahead financially, because the benefits from most social assistance programs phase out as
recipients’ income rises. This means that as the earnings of affected workers rise, the benefits
they receive will gradually decline instead of being eliminated all at once.9 In fact, the
Congressional Budget Office (Congressional Budget Office 2012) has estimated that the average
marginal tax rate for low-and moderate-income workers is 34.8 percent, meaning that affected
workers will keep 65.2 cents of each additional dollar they earn. So while taxes and reductions in
social assistance benefits will erode some of the additional earnings for affected workers, most
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 24
families will still see significant gains in income under the scenario minimum wage increases.
Finally, Arin Dube has estimated that for each percentage increase in the minimum wage,
household poverty is reduced by -0.24 percent (2013). Applying this measure of the elasticity of
poverty with respect to the minimum wage, we estimate that an increase to $15 would reduce
the number of households in poverty by 8.5 percent in San Jose and 8.2 percent in Santa Clara
County.
3.3 Demographics of Affected Workers
Next, we analyze the demographic and job characteristics of the workers who would be affected
by the two minimum wage scenarios (including both directly and indirectly affected workers).
Table 4 profiles workers affected by Scenario A in San Jose. In the first column, we display the
characteristics of all eligible workers. For example, 58.3 percent of San Jose workers are men
and 41.7 percent are women. In the second column, we show the distribution of affected workers
by 2019. For example, we estimate that 51.4 percent of affected workers are men and 48.6
percent are women. In the third column, we present the share of each demographic group that
will receive a wage increase. For example, we estimate that 27.4 percent of male workers and
36.2 percent of female workers eligible for the proposed increase will receive a raise.
Contrary to the common perception that minimum wage workers are mainly teens, we estimate
that 95.6 percent of affected workers in San Jose are in their twenties or older and 56.3 percent
are in their thirties or older. The scenario will be particularly beneficial to Latino/a workers in San
Jose, as half of these workers (50.8 percent) will receive a raise. Workers of all education levels
would benefit from the scenario, with less educated workers benefitting the most. About half of
affected workers have no college education (51.2 percent)
We estimate that over a third of affected workers in San Jose have children (33.9 percent) and
37.1 percent are married. Affected workers in San Jose disproportionately live in low-income
families, with 40.3 percent at or below 200 percent of the federal poverty level. Fully 91.8
percent of workers in poor families will receive a pay increase. On average, affected workers in
San Jose bring home 48.5 percent of their family’s income, suggesting that they are primary
breadwinners in their families and are not providing supplementary income.
We estimate that the median annual earnings of affected workers ($18,100 in 2014 dollars) is
less than half (35.8 percent) of the median earnings for all workers in San Jose. Affected workers
are disproportionately employed in part-time or part-year jobs, and are much less likely to have
health insurance provided by their employer than the overall San Jose workforce.10
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 25
Table 4. Demographic and job characteristics of affected workers in Scenario A - San Jose
Percent of eligible
workers
Percent of eligible
workers getting a raise
Percent of group
getting a raise
Gender
Male 58.3 51.4 27.4
Female 41.7 48.6 36.2
Median Age 39 32
Age
16-19 1.6 4.4 86.6
20-29 22.4 39.3 54.4
30-39 27.2 22.8 26.1
40-54 35.6 23.7 20.7
55-64 13.3 9.8 22.9
Race/Ethnicity
White (Non-Latino) 33.8 20.9 19.2
Black (Non-Latino) 2.6 3.1 37.5
Latino/a 29.9 50.8 52.8
Asian (Non-Latino) 31.0 22.7 22.7
Other 2.6 2.4 28.7
Education
Less than High School 11.1 23.9 66.7
High School or G.E.D. 16.5 27.3 51.4
Some College 20.2 26.7 41.0
Associate’s Degree 7.1 7.7 33.4
Bachelor’s Degree or Higher 45.0 14.4 9.9
Country of Birth
U.S. Born 51.8 48.0 28.8
Foreign Born 48.2 52.0 33.5
Family Structure
Married 55.0 37.1 20.9
Has Children 44.2 33.9 23.8
Family Income Relative to Poverty Level (FPL)
Less than 100% of FPL 3.8 11.4 91.8
100% to 150% of FPL 5.1 14.3 86.6
150% to 200% of FPL 6.0 14.7 75.8
200% to 300% of FPL 13.0 24.2 57.7
Greater than 300% of FPL 72.1 35.5 15.3
Average Worker Share of Family Income 62.9 48.5
Median Individual Annual Earnings (2014 Dollars) $50,507 $18,100
Full-Time / Part-Time Worker
Full-Time (35 or More Hours per Week) 82.8 64.6 24.2
Part-Time (Fewer than 35 Hours per Week) 17.2 35.4 64.0
Full-Year / Part-Year Worker
Full-Year (50-52 Weeks per Year) 87.1 80.2 28.6
Part-Year (Fewer than 50 Weeks per Year) 12.9 19.8 47.7
Health Insurance Provided by Employer
Yes 77.1 52.0 20.9
No 22.9 48.0 65.1
Source: Authors’ analysis of ACS, OES, and QCEW data. See Appendix A1 for details.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 26
In Table 5, we show the demographic and job characteristics of the affected workers under
Scenario B in Santa Clara County. Affected workers in Santa Clara County as a whole share many
of the same characteristics as affected workers in San Jose. Nearly half of Latino/a workers
would receive a raise as a result of the proposed law. Over half are in their thirties or older (56.6
percent) and most are in their twenties or older (95.5 percent). About a third have children (33.9
percent).
As in San Jose, a disproportionate number of affected workers in Santa Clara County live in
families at or below 200 percent of the federal poverty level (39.9 percent), and most workers
living in families below the poverty line will receive a pay increase (91.1 percent). On average,
affected workers bring home almost half of their family’s income (48.0 percent).
The earnings gap between affected workers and the overall workforce is higher for Santa Clara
County than for San Jose. We estimate that the median annual earnings of affected workers
($17,821 in 2014 dollars) is less than one third (30.7 percent) of the median earnings for all
workers in Santa Clara County. As in San Jose, affected workers in Santa Clara County are
disproportionately employed in part-time or part-year jobs, and are much less likely to have health
insurance provided by their employer than the overall Santa Clara County workforce.11
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 27
Table 5. Demographic and job characteristics of affected workers in Scenario B - Santa Clara County
Percent of eligible
workers
Percent of eligible
workers getting a raise
Percent of group
getting a raise
Gender
Male 57.3 49.2 24.4
Female 42.7 50.8 33.3
Median Age 39 32
Age
16-19 1.4 4.5 86.7
20-29 21.6 38.9 50.6
30-39 28.0 22.7 23.3
40-54 35.9 24.1 18.9
55-64 13.2 9.8 20.6
Race/Ethnicity
White (Non-Latino) 34.9 21.1 17.3
Black (Non-Latino) 2.5 3.2 35.6
Latino/a 26.2 49.3 50.8
Asian (Non-Latino) 33.6 23.9 20.6
Other 2.8 2.5 25.8
Education
Less than High School 9.3 22.9 66.0
High School or G.E.D. 14.2 26.5 50.0
Some College 18.8 26.8 39.0
Associate’s Degree 7.0 8.0 31.3
Bachelor’s Degree or Higher 50.7 15.9 9.2
Country of Birth
U.S. Born 51.5 48.2 26.3
Foreign Born 48.5 51.8 30.2
Family Structure
Married 56.2 37.0 18.7
Has Children 44.8 33.9 21.4
Family Income Relative to Poverty Level (FPL)
Less than 100% of FPL 3.3 11.2 91.1
100% to 150% of FPL 4.4 14.2 86.4
150% to 200% of FPL 5.2 14.5 75.2
200% to 300% of FPL 11.7 24.0 55.3
Greater than 300% of FPL 75.4 36.1 13.7
Average Worker Share of Family Income 63.9 48.0
Median Individual Annual Earnings (2014 Dollars) $57,956 $17,821
Full-Time / Part-Time Worker
Full-Time (35 or More Hours per Week) 84.1 64.7 21.9
Part-Time (Fewer than 35 Hours per Week) 15.9 35.3 60.3
Full-Year / Part-Year Worker
Full-Year (50-52 Weeks per Year) 87.4 79.7 25.8
Part-Year (Fewer than 50 Weeks per Year) 12.6 20.3 44.8
Health Insurance Provided by Employer
Yes 79.8 53.2 19.0
No 20.2 46.8 62.7
Source: Authors’ analysis of ACS, OES, and QCEW data. See Appendix Section A1 for details.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 28
3.5 Downstream effects
The increases in earnings shown in Tables 4 and 5 would be substantial and would have an
immediate impact on the lives of low-wage workers and their families in San Jose and Santa
Clara County. But it is important to recognize that there are longer-term effects of minimum wage
increases as well.
Low wages have been shown to affect workers negatively in a variety of ways, but the health
impacts are most pronounced. All else being equal, low wages (and in turn poverty) result in
increased rates of high blood pressure and high levels of stress, as well as shorter life expectancy
(Leigh and Du 2012). A recent study from the United Kingdom found that by reducing the
financial strain on low-wage workers, an increase in the minimum wage improves mental health
at a level comparable to the effect of antidepressants on depression (Reeves et al. 2016). In
another study, additional income led to fewer arrests for parents and increases in parental
supervision of their children (Akee et al. 2010). Similarly, increases in Earned Income Tax Credit
(EITC) program payments led to improvements in the mental health of mothers (Evans and
Garthwaite 2010; Congressional Budget Office 2012).
Multiple rigorous studies also establish a causal negative effect of low incomes on outcomes for
children. A recent review of peer-reviewed articles found that 29 of 34 studies established a
negative effect of poverty on children’s outcomes (K. Cooper and Stewart 2013). Using data from
a randomized control trial of the Minnesota Family Investment Program, researchers found
positive, significant effects on children’s social behavior and school engagement due to increases
in income (Morris and Gennetian 2003). Other researchers analyzed data from ten such studies
and found significant effects of increased income on school achievement (Rodgers 2004).
Generally, these studies show that additional income has a positive effect on the outcomes of
children in households of all income levels. However, multiple studies also suggest that
additional income has a larger effect in very-low-income households compared to middle-income
households (Dahl and Lochner 2012); (Akee et al. 2010); (Costello et al. 2003). Some evidence
indicates that additional income early in life is important to cognitive outcomes, whereas
additional income in later childhood may be more important in terms of behavioral outcomes (K.
Cooper and Stewart 2013).
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 29
4. EFFECTS ON BUSINESSES
How a higher minimum wage affects a firm depends on how much the firm’s operating costs
change and on how the firm responds to those changes. In this section, we first identify the
industries that will be highly affected by the two minimum wage increase scenarios. We then
estimate the impact of the minimum wage increases on firms’ operating costs across the entire
economy and for highly affected industries, taking into account savings from reduced turnover.
We describe the effects on businesses separately for Scenario A (San Jose) and Scenario B
(Santa Clara County).
4.1 Scenario A: San Jose
Minimum wage increases do not affect all industries equally. We therefore begin with an analysis
of the impact of Scenario A at the industry level. Table 6 shows the estimated distribution of
affected workers across San Jose’s industries by 2019. In the first column, we show the
percentage of the overall eligible San Jose workforce in each industry. The second column
displays our estimate of the distribution across industries of workers getting a raise under the
scenario. The third column presents our estimate of the percentage of workers getting a raise
within each industry.
Over half of affected workers are employed in just three service sector industries: food services
(21.0 percent), retail (19.1 percent), and administrative and waste management services (14.7
percent), which is comprised mainly of building services contractors and employment agencies.
The service sector also dominates the list of industries that have high rates of low-wage work—
that is, industries where we estimate a high share of workers will get a raise (for example, 77.8
percent in food services and 50.7 percent in administrative and waste management services).
We also examine the sectoral distribution of affected workers in Table 6. Our estimates show that
affected workers are largely employed in the private, for-profit sector. Nonprofit and public sector
workers are less likely to be affected than the overall San Jose workforce.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 30
Table 6. Cumulative impact estimates for major industries in San Jose by 2019
Percent of eligible
workers
Percent of eligible
workers getting a
raise
Percent of industry
getting a raise
All Sectors
Agriculture, Forestry, Fishing, Hunting, and Mining 0.2 0.3
Construction 6.0 6.5 33.3
Manufacturing 16.5 6.1 11.5
Wholesale Trade 4.6 3.2 21.2
Retail Trade 12.7 19.1 46.8
Transportation, Warehousing, and Utilities 2.8 2.8 31.0
Information 3.1 0.9 9.5
Finance, Insurance, Real Estate, and Rental and Leasing 4.8 3.1 20.1
Professional, Scientific, and Management 11.9 2.7 7.2
Administrative and Waste Management Services 9.0 14.7 50.7
Educational Services 1.9 1.6 25.9
Health Services 8.5 5.5 20.4
Social Assistance 1.7 2.4 45.4
Arts, Entertainment, Recreation, and Accommodation 2.8 4.5 49.2
Food Services 8.4 21.0 77.8
Other Services 3.1 4.7 47.9
Public Administration 2.0 0.7 10.7
Total 100.0 100.0
By Sector
Private, For-Profit 90.0 93.8 32.4
Private, Non-Profit 6.0 4.6 23.6
Public 4.0 1.6 12.6
Total 100.0 100.0
Source: Authors’ analysis of ACS, OES, and QCEW data. See Appendix A2 part B for details.
Note: Blank value for “Percent of Industry That is Getting a Raise” indicates insufficient sample size for that category.
Changes in a firm’s operating costs due to a minimum wage increase are determined by the
following factors: the share of workers receiving wage increases, the average size of the wage
increases, and the labor share of operating costs within the firm. As we saw in Table 6, in most
industries only a minority of workers in San Jose will receive a wage increase under Scenario A.
Furthermore, among workers that do receive an increase, not everyone will receive the full
increase (because many of the affected workers already earn more than the current minimum).
Specifically, we estimate that the total wages of all affected workers will increase by 15.3 percent
in San Jose. However, affected workers’ wages represent only 8.3 percent of all workers’ wages
in San Jose. As a result, total wages in San Jose will increase by 1.3 percent.
Economic research suggests that some of the increased labor costs that businesses face as a
result of a higher minimum wage can be offset through lower turnover. In our calculations below,
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 31
we take the midpoint of those estimates and assume that 17.5 percent of increased labor costs
are absorbed via turnover savings in the first year.12 These savings are likely to accrue at smaller
rates as wage levels go higher; we therefore assume that by 2019 the marginal increase in
earnings relative to 2017 no longer yields any additional turnover savings. As a result, we
estimate that the total savings from turnover at a $15 minimum wage in 2019 would be 11.3
percent of increased labor costs.13
Table 7 shows our estimates of the increase in business operating costs (net of savings from
reduced turnover) in retail and restaurants, the two industries with the largest number of workers
receiving a raise under Scenario A. By 2019, we estimate that businesses in the restaurant
industry would see their payroll costs rise by 10.2 percent and businesses in the retail industry
would see their payroll costs rise by 2.2 percent; these cost estimates include payroll taxes and
workers’ compensation insurance expenses.14 Across the entire San Jose economy, we estimate
that payroll costs would rise by 1.2 percent by 2019.
However, operating costs will rise by a much smaller amount, because labor costs only make up a
portion of the total costs that businesses face. We estimate that labor costs excluding health
benefits currently account for 30.7 percent of restaurant operating costs, 10.8 percent of retail
operating costs, and 22.1 percent for the overall economy (these percentages will increase over
time as labor costs rise faster than other costs due to the proposed minimum wage increase). We
therefore estimate that by 2019, total operating costs would rise by 3.1 percent for restaurants,
0.2 percent for retail, and 0.3 percent for the overall economy. (See Appendix A2.2 for more
detail on how we estimate the labor share of operating costs by industry.)
Table 7. Cost impacts for businesses in San Jose by 2019
Percent change in payroll costs Labor costs as percent of
operating costs
Percent change in
operating costs and
prices
All 1.2 22.1 0.3
Restaurants 10.2 30.7 3.1
Retail 2.2 10.8 0.2
Source: US Census Annual Wholesale Trade Report and authors’ analysis of ACS, OES, and QCEW data. See Appendix A2 Part B
for details.
4.2 Scenario B: Santa Clara County
Table 8 shows the estimated distribution of affected workers across industries in Santa Clara
County under Scenario B. As in Scenario A, over half of affected workers are employed in three
service sector industries: food services (20.2 percent), retail (16.1 percent), and administrative
and waste management services (11.9 percent). These same industries have a high proportion of
low-wage workers who would get a raise in the scenario (for example, 71.0 percent in food
services and 47.6 percent in administrative and waste management services).
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 32
Affected workers in Santa Clara County are also mostly employed in the private, for-profit sector.
Nonprofit and public sector workers are less likely to be affected than the overall Santa Clara
County workforce.
Table 8. Cumulative impact estimates for major industries in Santa Clara County by 2019
Percent of eligible
workers
Percent of eligible
workers getting a
raise
Percent of industry
getting a raise
All Sectors
Agriculture, Forestry, Fishing, Hunting, and Mining 0.3 0.9 67.8
Construction 4.4 5.5 31.9
Manufacturing 16.6 7.4 11.2
Wholesale Trade 3.8 3.0 20.1
Retail Trade 9.2 16.1 44.4
Transportation, Warehousing, and Utilities 1.9 2.2 28.7
Information 7.5 1.4 4.6
Finance, Insurance, Real Estate, and Rental and Leasing 3.7 2.7 18.9
Professional, Scientific, and Management 16.0 4.1 6.5
Administrative and Waste Management Services 6.4 11.9 47.6
Educational Services 3.8 3.8 25.2
Health Services 10.2 7.7 19.1
Social Assistance 2.0 3.3 43.0
Arts, Entertainment, Recreation, and Accommodation 2.3 4.2 46.2
Food Services 7.2 20.2 71.0
Other Services 2.7 4.8 45.4
Public Administration 2.0 0.7 9.4
Total 100.0 100.0
By Sector
Private, For-Profit 88.7 92.3 26.4
Private, Non-Profit 7.3 5.9 20.4
Public 4.0 1.8 11.3
Total 100.0 100.0
Source: Authors’ analysis of ACS, OES, and QCEW data. See Appendix A2 Part B for details.
Note: Blank value for “Percent of Industry That is Getting a Raise” indicates insufficient sample size for that category.
We estimate that the total wages of all affected workers in Santa Clara County will increase by
16.4 percent. But again, because affected workers’ wages represent only 6.1 percent of all
workers’ wages in Santa Clara County, total wages in the county will increase by 1.0 percent.
Table 9 shows our estimates of the increase in business operating costs for Santa Clara County
for retail and restaurants, the two industries with the largest number of workers receiving a raise
under the proposed minimum wage law, as well as for businesses across all industries. After
accounting for reductions in turnover we estimate that businesses in the restaurant industry will
see their payroll costs rise by 9.5 percent and businesses in the retail industry will see their
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 33
payroll costs rise by 2.1 percent.15 Across the entire Santa Clara County economy, we estimate
that payroll costs will rise by 1.0 percent by 2019.
We therefore estimate that by 2019, total operating costs will rise by 2.9 percent for restaurants,
0.2 percent for retail, and 0.2 percent for the overall economy.
Table 9. Cost impacts for businesses in Santa Clara County by 2019
Percent change in payroll costs Labor costs as percent of
operating costs
Percent change in
operating costs and
prices
All 1.0 22.1 0.2
Restaurants 9.5 30.7 2.9
Retail 2.1 10.8 0.2
Source: US Census Annual Wholesale Trade Report and authors’ analysis of ACS, OES, and QCEW data. See Appendix A2 Part B
for details.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 34
5. EFFECTS ON EMPLOYMENT
A principal goal of the proposed minimum wage policy for San Jose (Santa Clara County) is to
raise the earnings of low-wage workers, while minimizing the tradeoffs in economic costs. In
previous sections, we have assessed the benefits to low-wage workers as well as the impact on
businesses’ operating costs in particular industries. In this section we consider whether the
proposed policy would generate net gains or losses to the city’s (county’s) economy.
In Section 5.1, the key issues concern how much employers will substitute equipment or skilled
labor for unskilled labor and how much of their cost increases employers will pass on in the form
of higher prices. In Section 5.2, we discuss who might pay the costs of the higher minimum wage.
Higher prices reduce consumption demand, which translates into reductions in employment and
economic activity.
Section 5.3 examines the increased spending that derives from the higher income of low-wage
workers. We take into account the effects of taxes and reduction in public benefits on the
affected workers’ take-home pay and the rate at which their households spend income compared
to others. Greater spending from consumers increases economic demand, which translates into
increases in employment and economic activity.
The net effects on the economy will then depend upon the sum of the effects estimated in each
of these three sections. Section 5.4 estimates these net impacts on economic activity and
employment.
5.1 Automation, productivity and substitution away from unskilled labor
It is often argued that a higher minimum wage will lead firms to reduce their use of workers. This
reduction in labor demand can occur through two different channels: one involves substituting
capital for labor, i.e., automation or mechanization of jobs while keeping sales at the same level;
the other involves lower demand for workers when prices increase and sales fall. We discuss
here the automation channel and consider the effect on sales in the following section.
Automation: economic theory and measurement
Mechanization does not necessarily lead to a net loss of jobs. As David Autor (2014a; 2014b)
points out, machines (including smart robots) do not just substitute for labor; they are also
complements to existing jobs and they can lead to the creation of new jobs and industries.
Indeed, previous rounds of automation and computerization have created more jobs than they
destroyed. Moreover, automation does not involve only the replacement of labor by machines. It
also involves the replacement of old machines (think manual cash registers) with newer ones
(think electronic cash registers and electronic screens like iPads).
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 35
In general, the effect of automation on employment depends upon the elasticity of substitution of
capital for labor (sigma)—the change in the relative prices of capital and labor—and the share of
profits in revenue. The lower is sigma, the more difficult it is to substitute capital for labor. Robert
Chirinko, the leading economist specializing in estimates of sigma, finds an economy-wide sigma
of about 0.4 (Chirinko and Mallick 2016). While the estimates in this study are identified across
all economic sectors, most of the variation occurs among manufacturing industries. Lawrence
(Lawrence 2015) also finds that the economy-wide sigma is less than 1 and that it is lower still in
low-wage manufacturing industries than in high-wage manufacturing industries.
Alvarez-Cuadrado, Van Long and Poschke (2015) estimate substitution elasticities separately for
manufacturing and services using data on 16 countries. They find that service sector elasticities
are considerably lower than in manufacturing. However, their study does not examine low-wage
services separately. The results in these papers nonetheless suggest, as Autor et al. conjectured,
that automation possibilities are lower in low-service jobs.
Aaronson and Phelan (Aaronson and Phelan 2015) have carefully studied the short-run impact of
minimum wages on the automation of different kinds of low-wage jobs. Their study is the first to
examine automation within low-wage industry contexts. Aaronson and Phelan find that minimum
wage increases do reduce routinized low-wage jobs (such as cashiers) and increase the number
of less-routinized low-wage jobs (such as food preparation). As it turns out, the changes offset
each other almost equally, resulting in no net change in employment. Thus, Aaronson and Phelan
(2015) find that sigma is essentially zero in low-wage occupations.
We use a sigma of 0.2 in our calculations, half-way between Chirinko and Mallick and Aaronson
and Phelan. This conservative assumption may therefore result in an over-estimate of the
magnitude of the automation effect.
Aaronson and Phelan’s findings also suggest very little substitution of highly skilled workers for
lower skilled workers. Dube, Lester and Reich (2016) obtained a similar result. Consequently, we
do not include any effect of skilled labor being substituted for unskilled labor in our model.
Automation in practice
Machines that process automated transactions—at airports and in airplanes, banks, self-
checkout stations in retail stores, parking garages, and gasoline stations—have become
particularly widespread over the past 30 years. During this period, the price of computer-related
machines has rapidly declined. Labor-saving automation will occur even when wages do not rise,
insofar as the technological change continues to push down the price of equipment, making
investments in new equipment and software profitable.
The effects of a rising minimum wage on actual automation depend in part upon whether new
labor-saving technology that has not yet been adopted continues to become available. We
suggest that much of existing labor-saving technological change has already been embodied in
low-wage industries, in equipment and software such as smart electronic cash registers, remote
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 36
reservations, and ordering systems. An increase in the minimum wage is likely only to generate
small increases in the adoption of more automated systems.
Equally important, the rate of adoption of technical change depends on changes in the relative
prices of capital and labor, not just on the price of low-wage labor. Although the prices of
computer-related equipment and software have fallen dramatically, by approximately a factor of
ten in the past several decades, the decline in the past five years is much smaller. Meanwhile,
median wages have stagnated and real minimum wages remain lower than they were in the early
1970s.
The declining cost of capital is also reflected in declines in long-term interest rates in recent
decades. Five-year and ten-year inflation-protected interest rates have also fallen dramatically.
These changes in relative prices have been the main impetus to increased automation. Even a
doubling of the minimum wage policy, which would imply (according to (Allegretto et al. 2015) an
average wage increase of about 22 percent, would have very little impact in comparison.
However, interest rates are unlikely to fall further. It is therefore likely that actual automation in
low-wage industries is slowing.
To summarize, empirical estimates of the elasticity of substitution of capital for labor that include
low-wage industries in their sample range between 0 and 0.4. We use 0.2, the midpoint of this
range. Since Aaronson and Phelan find a much smaller elasticity, our use of 0.2 is conservative.
Reductions in paid hours relative to working hours
Some commentators assert that a higher minimum wage will lead employers to cheat workers of
a portion of their wages. However, such practices already exist; the question at hand is how much
the minimum wage increase will increase their prevalence and intensity. Although it is difficult to
measure changes in wage theft, we know that employee-reported increases in pay (to a census
surveyor) after a minimum wage increase match up well to employer-reported increases in pay on
administrative reports that determine payroll taxes (Dube, Lester, and Reich 2010). These results
suggest that most employers comply about as much after the increase as before.
Employee turnover and employer recruitment and retention costs
The correlation between low wages and high employee turnover is well known (Cotton and Tuttle
1986).16 Over the last decade, annual employee turnover in accommodation and food service
averaged 70 percent a year, compared to 41.4 percent in other services, 30.5 percent in health
care and social assistance, and 32 percent in non-durable manufacturing (Statistics 2014).17
Quits are higher in low-wage occupations because workers leave to find higher-wage jobs or
because they are unable to stay in their jobs due to problems such as difficulties with
transportation, child care, or health.
Recent labor market research has gone beyond establishing a correlation between pay and
turnover. We now know minimum wage increases have well-identified causal impacts that reduce
worker turnover. Dube, Naidu and Reich (2007) found that worker tenure increased substantially
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 37
in San Francisco restaurants after the 2003 minimum wage law, especially in limited service
restaurants. Dube, Lester and Reich (2016) found that a 10 percent increase in the minimum
wage results in a 2.1 percent reduction in turnover for restaurant workers and for teens. Jacobs
and Graham-Squire (2010) reviewed studies of the impact of living wage laws on employment
separations and found that a 1 percent increase in wages is associated with a decline in
separations of 1.45 percent.
Turnover creates financial costs for employers (Blake 2000; Dube, Freeman, and Reich 2010;
Hinkin and Tracey 2000). These costs include both direct costs for administrative activities
associated with departure, recruitment, selection, orientation, and training of workers, and the
indirect costs associated with lost sales and lower productivity as new workers learn on the job.
Hinkin and Tracey (2000) estimate the average turnover cost for hotel front desk employees at
$5,864. A study of the cost of supermarket turnover by the Coca Cola Research Council
estimates the replacement cost for an $8 an hour non-union worker at $4,199 (Blake 2000).
Boushey and Glynn (2012) estimate that the median replacement cost for jobs paying $30,000
or less equals 16 percent of an employee’s annual salary.
Pollin and Wicks-Lim (2015) estimate that 20 percent of the increased costs from a minimum
wage increase are offset by reductions in turnover. Similar estimates can be found in Fairris
(2005) and Jacobs and Graham-Squire (2010). In a small case study of quick service restaurants
in Georgia and Alabama (Hirsch, Kaufman, and Zelenska 2011), managers reported they offset
23 percent of the labor cost increases through operational efficiencies.
For our calculations below, we assume that 17.5 percent of the increase in payroll costs is
absorbed through lower turnover in the early years of the proposed minimum wage increase.18
However, these turnover savings do not continue to grow at higher wage levels. Dube, Lester and
Reich (2016) find that most of the reduction in turnover occurs among workers with less than
three months of job tenure.
This result suggests that the effect of higher wages on increasing tenure dissipates as wage
levels increase. We therefore assume that the increases in wages after 2018 no longer result in
turnover reductions, yielding an overall lower rate of savings from turnover of 13.4 percent in
2019.
Impact of higher wages on worker performance
Paying workers more can also affect worker performance, morale, absenteeism, the number of
grievances, customer service, and work effort, among other metrics (Hirsch, Kaufman, and
Zelenska 2011; Reich, Jacobs, and Dietz 2014; Ton 2012; Wolfers and Zilinsky 2015).
Efficiency wage models of the labor market argue that wage increases elicit higher worker
productivity, either because when employers pay workers more, workers are more willing to be
more productive, or because they remain with the firm longer and thereby gain valuable
experience, or because higher pay tends to reduce idleness on the job. This theoretical result
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 38
holds whether one company raises its wage above the market-clearing level, or whether all do
(Akerlof and Yellen 1986).
Reduced employee turnover means that workers will have more tenure with the same employer,
which creates incentives for both employers and workers to increase training and therefore
worker productivity. A large scholarly literature makes this point, and it has been emphasized
recently by firms such as Walmart, TJ Maxx, and The Gap as principal reasons underlying their
announced policies to increase their minimum wages nationally to $10. However, because of the
lack individual- or firm-level productivity data, the earlier efficiency wage literature does not
provide a reliable quantitative assessment of the importance of the effect on worker productivity
among low-wage workers.
A new paper by Burda, Gedanek and Hamermesh (2016) does just that. Using microdata for
2003- 2012 from the American Time Use Study, Burda et al. find that working time while on the
job increases when wages are higher. Their results imply that an increase in hourly pay from $10
to $15 increases the level of productivity by 0.05 percent.
Burda et al.’s estimate may be too high, given the difficulty of disentangling cause from effect in
their loafing data. On the other hand, they do not have measures of worker engagement while
working, which could make the actual worker productivity improvement potentially twice as large.
To capture this range of productivity effects in our model, we use the Burda et al. estimate of
0.05 percent.19
Another relevant new paper (Card et al. 2016) appeared after the analysis for this report was
completed. This paper uses firm-based data on value added per worker and pay to examine how
much the rise of wage inequality derives from increases in firm-based productivity differences.
The results in this paper (Card, personal communication) imply that a one percent wage increase
leads to a 0.04 percent increase in log of productivity, which translates into a productivity
increase of 0.1 percent. Consequently, our productivity estimate may be too low, which offsets
our automation estimates, which may be too high.
A recent study by John Abowd et al. (Abowd et al. 2012) demonstrates the substantial room for
productivity and wage growth in low-wage industries in the U.S. Using longitudinally linked
employer-employee data, Abowd et al. disentangle wage differentials among industries that are
attributable to individual heterogeneity (such as the demographic, educational, and work
experience characteristics of workers in the industry), which they label person effects, from the
characteristics of the product market and bargaining power of firms in the industry, which they
label industry effects.
Abowd et al. can observe wage changes when individual workers move from one employer to
another. They find very strong industry average firm effects, particularly for industries that have
high average pay and low average pay. Among restaurants, for example, they find that 70 percent
of the relatively low wages in the industry are attributable to firm effects, and only 30 percent to
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 39
person effects. These findings suggest that a change in an industry’s environment can have large
effects on worker pay.
Effects on prices
As we have seen, previous prospective studies have made different assumptions on how much
costs will affect prices—and therefore also profits. Card and Krueger (1995) provide an extensive
discussion of this issue. As they point out, from the point of view of an individual employer in a
perfectly competitive industry, profits would be unaffected only in the extreme case in which firms
can costlessly replace low-wage labor with high-skill labor and/or capital, and without cutting
output. Since such substitutions are costly, from this perspective a minimum wage increase
would have to reduce profits. Firms do not envision a price increase as a solution, as it fears
losing sales to its competitors.
A different result emerges when Card and Krueger consider the point of view of an industry as a
whole. This perspective is necessary since the minimum wage increase applies to all the firms in
an industry. Now, when individual firms respond to the prospect of reduced profits by raising their
prices, they find that other firms are doing the same. Some of the price increases will stick and
the industry will recapture some of the reduced profits. However, since demand for the industry’s
product is not fixed, this increase in price entails some reduction in product demand, implying
that industry output (and therefore employment) will fall. In other words, the price increase will
permit employers to recover only a portion of their reduced profits. Card and Krueger do not,
however, take into account the income effect that will increase sales when a minimum wage
applies to an entire economy, not just a single industry.
The evidence on whether profits do fall is extremely scant. The most important study remains the
one in Card and Krueger (1995). These authors obtained mixed results when examining the
effects of minimum wage changes on shareholder returns for fast-food restaurant chains. Using
British data, Draca et al. (2011) find a small negative effect on profits. However, one segment of
this study uses data for firms in the British residential care industry. Firms in this industry were
not permitted to increase prices, making the results not very useful for other sectors. Harasztosi
and Lindner (2015) examine a large (60 percent) and persistent increase in the Hungarian
minimum wage, which affected much of manufacturing. These authors find that cost increases
were entirely passed through, but employment did not change and profits did not fall. Of course,
the relevance of the British and Hungarian studies for the U.S. is highly uncertain.
In our model, employers pass all of the increase in operating costs stemming from a minimum
wage increase onto prices, after accounting for the above-mentioned turnover savings,
automation, and productivity growth. Studies of price effects of minimum wages are consistent
with this model. These studies generally examine data on restaurants. Aaronson (2001) and
Aaronson, French and MacDonald (2008) both find complete pass through of costs. However,
their data come from a period of much higher inflation, are based on a handful of observations
per metro area, and they do not correct their standard errors for clustering. In contrast, Allegretto
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 40
and Reich (2015) collected a large sample of restaurant price data in and near San Jose, before
and after a 25 percent minimum wage increase in 2013 (from $8.00 to $10.00). Their results
indicate that most of the costs are passed through to consumers in higher prices. Using scanner
data from supermarkets, Montialoux et al. (forthcoming) find a similar effect for retail prices.
Effects on profits and rent
Some economists have argued that many firms have captured above-normal profits in recent
decades. An increase in the minimum wage could therefore reduce such economic rents. We
attempted to include such an effect in our model, but were stymied by limited data on the
proportion of reduced profits that would be borne within the study area.
Our simulations did confirm that insofar as payroll cost increases are partly absorbed by profits,
then the scale effect is smaller. The reduced profits have much less effect on the income effect
because propensities to spend are low among shareholders and managers, and because much
of the profit decline affects capital owners outside of the study area. As a consequence, including
a fall in profits in our model would have led to more positive effects on employment.
Minimum wage increases will likely affect the composition of businesses within and among
industries. Aaronson, French and Sorkin (2015) find that minimum wage increases raise both exit
and entry rates among restaurants, suggesting that entering firms arrive with a business model
that is more oriented to the higher wage minimums. These higher-wage firms could be instituting
business methods that improve productivity or improve product quality, or both. It is not possible
for us to quantify these secondary effects, as they require more data on such adjustment
mechanisms than are available.
Franchisee-franchiser relationships and commercial rental leases could also be altered by
minimum wage increases. Franchises are particularly important among restaurants. In principle,
franchisees could pass their increased costs to franchisers, either through a relaxation of fees or
land rent. However, data on such changes are not available, to our knowledge. Effects on
commercial rents are also difficult to detect, in part because of the lack of data and in part
because such leases are typically of longer duration.
5.2 Scale effects of increased prices on reduced sales of consumer goods
Economists use the term price elasticity of consumer demand to refer to the effect of an increase
in prices on reducing consumer demand. Taylor and Houthakker (2010) report price elasticities
for six categories of goods and services that together cover all of consumption. We adjust their
health care elasticity to -0.20, to take into account changes in the structure of health care
provision since the 1990s, and then compute a weighted average elasticity across the six
categories using personal consumption expenditure shares from the U.S. Consumer Expenditure
Survey (McCully 2011). The result is a price elasticity of consumer demand of -0.72.20
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 41
This estimate is compatible with, but somewhat larger than, price elasticities estimated from
aggregate panel data. Hall (2009), for example, obtains a price elasticity of -0.50. On the other
hand, our estimate is very close to that of Blundell et al. (1993).
5.3 Income effects
We consider here the increased spending that derives from the higher income of low-wage
workers. Our model takes into account the effects of taxes and reduction in public benefits on the
affected workers’ take-home pay and the rate at which their households spend (as opposed to
save) income compared to others. Greater spending by consumers increases economic demand,
which translates into increases in employment and economic activity.
We do not expect all of the increases in household incomes to translate into increased
consumption demand. A substantial portion of minimum wage earners come from households in
the middle of the household wage distribution. These households will save some of their
increased income. The amount of such savings will depend on their current savings rates and on
the extent to which they view the increase in income as permanent, rather than a short-term
windfall.
Economic research has found that changes in permanent income generate much higher
consumption effects than changes that are, or are perceived as, transitory. Low wage-earners
who are young and have more education may regard their low-wage status as transitory. These
earners may regard a minimum wage increase as transitory.
However, recent research has found that an increasing proportion of minimum wage workers are
stuck in minimum wage careers (Boushey 2005; Casselman 2015). These results suggest that
the proportion of workers who regard a minimum wage increase as constituting a one-time
increase will be small. Moreover, economic theory and evidence suggests strongly that the
distinction between permanent and transitory income does not apply to workers who are credit-
constrained and whose households have accumulated very little in assets (Achdou et al. 2014).
The majority of minimum wage workers fit this description.
The IMPLAN model does not account for savings that come from transitory income. The
considerations above indicate that any such effects are likely to be small. This is nonetheless a
topic for future research.
5.4 Model calculations and net effects on employment for scenario A: a $15
minimum wage increase in San Jose
Table 10 displays the results of our model for 2019. Note that the estimates in this table are
cumulative. They are estimated relative to the city’s minimum wage in each year, and therefore
capture the full effect of increases in the suggested city minimum wage in previous years.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 42
Table 10. Scenario A: Cumulative net changes in employment in San Jose
Impacts in San Jose
Additional impact in
the rest of Santa Clara
County & nine nearby
counties
Total impact of a $15
MW increase in San
Jose, the rest of Santa
Clara County and nine
nearby counties
A. Cumulative reduction in wage bill due to capital-labor substitution and productivity gains
Reduction in number of jobs from substitution
effects and productivity gains -1,190 n.a. -1,190
B. Scale effect: Cumulative reduction in consumer spending
Reduction in consumer spending from price
increase (millions) -$107 n.a. n.a.
Reduction in number of jobs due to the scale
effect -580 n.a. n.a.
Reduction in GDP due to the scale effect
(millions) -$64 n.a. n.a.
C. Income effect: Cumulative increase in consumer demand
Aggregate increase in consumer spending
(millions) $204 +$101 $305
Increase in number of jobs due to the income
effect 800 +890 1,690
Increase in GDP due to the income effect $92 +$105 $197
D. Cumulative net change in employment
Net change in employment -960 +880 -80
Net change in employment, as a percent of
total employment -0.3% +0.3% 0.0%
Net change in GDP (millions) $25 +$105 $130
Net change in GDP, as a percent of total GDP 0.0% +0.1% 0.1%
Source: Authors’ calculations using the regional economic impact model IMPLAN.
Note: The nine nearby counties taken into account are: Alameda, San Mateo, San Francisco, Santa Cruz, Monterey, San Benito,
Contra Costa, San Joaquin, and Merced. All estimates are in 2019 dollars.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 43
Panel A: Reduction in employment due to capital-labor substitution and productivity gains
Panel A in Table 10 shows our estimates for the reduction in the number of jobs due to both
capital-labor substitution effects and productivity gains. With an assumed capital-labor
substitution elasticity of 0.2 and a productivity effect of 0.005, we find a negative employment
effect of about 1,190 jobs.
Panel B: Scale effects due to reduced consumer spending
Panel B in Table 10 presents our estimates of the reductions in consumer spending from the
higher payroll costs that are generated by the suggested minimum wage increase in 2019, in
both (1) San Jose and (2) in San Jose, the rest of Santa Clara County and 9 nearby counties
(Alameda, San Mateo, San Francisco, Santa Cruz, Monterey, San Benito, Contra Costa, San
Joaquin, and Merced). Row 3 restates the total net percentage increase in payroll costs from the
proposed policy, accounting for savings from reduced turnover costs. This number comes from
the top line of Table 6, using the same assumption that expected savings from reduced turnover
will be 17.5 percent in 2017, 17.5 percent in 2018 and 11.30 in 2019. Similarly, Row 4 in Table
8 restates the percentage change in prices from Table 6. Percentage changes in prices are equal
to the percentage change in operating costs (after accounting for savings from turnover).
Row 5 presents our estimate of the reduction in consumer spending in San Jose from the price
increase. As previously discussed, we estimate that each 1 percent increase in consumer prices
results in a -0.72 percent decline in consumer spending. We apply this price elasticity of demand
to the percentage increase in prices and then multiply by annual consumer spending in San
Jose.21
The result is an estimate of $64 million cumulative reduction in consumer spending by 2019. We
then use IMPLAN to estimate the total reduction in consumer demand, including multiplier
effects.22 Row 6 then translates these results into numbers of jobs.
Panel C: Income effect-- cumulative increases in wages from proposed minimum wage increase
Panel C of Table 10 presents the estimated income effect: increases in consumer demand
deriving from increased incomes of low-paid workers. The income effects are presented first for
San Jose (column 1), and then detailed for a broader region (column 3). The additional increase
in income effects coming from the broader region is detail in column 2. We estimate that only 65
percent of workers are affected by scenario A work and live in San Jose. As a consequence, the
income effect presented in column 1 captures only the positive effects of a boosted consumption
for 65 percent of affected workers. Column 3 presents a more complete picture of these income
effects: 99 percent of affected San Jose workers live in Santa Clara County and nine nearby
counties.
Row 7 shows the total wage increase from the proposed law for all affected workers. These
estimates are taken from Table 4, converted to nominal dollars in 2019. Row 8 adjusts the total
wage increase for an estimated loss of 14.75 percent due to reduced eligibility for public
assistance programs, as well as lost worker income due to reductions in consumer spending from
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 44
Panel A.23 The result is an estimated net income increase of $204 million by 2019 in San Jose,
and $305 million in Santa Clara County and nine nearby counties. We then use IMPLAN to
estimate the increase in employment for San Jose resulting from the increased household
spending triggered by the income increase, accounting for multiplier effects and spending
leakage outside the city (respectively outside Santa Clara County and nine nearby counties).24
Row 9 shows the employment change associated with this increase in income in San Jose
(column 1), and in Santa Clara County and nine nearby counties (column 3).
Panel D: Net effect
As we have previously mentioned, the substitution productivity, scale, and income effects in Parts
A to C occur simultaneously, not sequentially. It is thus not correct to infer that the employment
changes in Parts A to C actually occur. Net employment changes occur only to the extent that is
registered after we add Parts A to C together to obtain the net effects.
Panels A to C do tell us that the net effects will likely differ by job wage rates. In particular, the
automation and productivity effects in Part A will occur entirely among low-wage jobs. The scale
and income effects of Parts B and C, however, will affect jobs throughout the state’s consumer
demand industries and among a much broader wage distribution. We have not been able to
quantify these differences, as they depend on the relative concentration of scale and income
effects in low-wage industries.
In Panel D of Table 10, we present our estimate of the net change in employment from scenario
A.
• For San Jose only (column 1), we estimate a cumulative net loss in employment, due to
the policy, of 960 jobs by 2019, or -0.3 percent of total employment. To put this estimate
in context, we project, based on past QCEW data on employment that San Jose will grow
annually by 1.32 percent from 2014 to 2019. (For more details see Appendix A2.)
• For Santa Clara County as a whole and nine nearby counties (column 3), we estimate a
cumulative net loss in employment, due to the policy, of 80 jobs by 2019, or -0.0 percent
of total employment. We’ve also assumed that this broader region will grow annually by
1.32 percent from 2014 to 2019, at the same pace as San Jose. (For more details see
Appendix A2.)
We emphasize again that our cumulative estimate will be spread over the preceding years of the
minimum wage increase—the 2019 estimate includes effects in 2016, 2017, 2018 and 2019.
The key point in Table 10 is that a $15 minimum wage has negligible effect on net on
employment when examining the region as a whole.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 45
5.5 Model calculations and net effects on employment for scenario B: a $15
minimum wage increase in Santa Clara County
We conduct a similar analysis as in section 5.3 for a $15 minimum wage increase in Santa Clara
County. In Table 11 we present our results for Santa Clara County in column 1 and for Santa
Clara County and nine nearby counties. We estimate that 84 percent of Santa Clara County
affected workers are also living in Santa Clara County and therefore spend their additional
income in this county. We also estimate that 99 percent of Santa Clara County affected workers
live in Santa Clara County and nine surrounding counties.
Panel A: Reduction in employment due to capital-labor substitution and productivity gains
Panel A in Table 11 shows our estimates for the reduction in the number of jobs due to both
capital-labor substitution effects and productivity gains. With an assumed capital-labor
substitution elasticity of 0.2 and a productivity effect of 0.005, we find a negative employment
effect of about 2,700 jobs.
Panel B: Scale effects due to reduced consumer spending
Panel B in Table 11 presents our estimates of the reductions in consumer spending from the
higher payroll costs that are generated by the proposed minimum wage law in 2019.
We estimate that scenario B would lead to a $214 million cumulative reduction in consumer
spending by 2019. We then use IMPLAN to estimate the total reduction in consumer demand,
including multiplier effects. Row 6 then translates these results into numbers of jobs.
Panel C: Income effect--cumulative increases in wages from proposed minimum wage increase
Panel C of Table 11 presents the estimated income effect: increases in consumer demand
deriving from increased incomes of low-paid workers.
We estimate that scenario B could trigger a net income increase of $602 million by 2019 in
Santa Clara County, and $706 million in Santa Clara County and nine nearby counties (column
3), i.e. an additional $104 million (column 2). We then use IMPLAN to estimate the increase in
employment for Santa Clara County resulting from the increased household spending triggered by
the income increase, accounting for multiplier effects and spending leakage outside the city
(respectively outside Santa Clara County and nine nearby counties).25 Row 9 shows the
employment change associated with this increase in income in Santa Clara County (column 1),
and in Santa Clara County and nine nearby counties (column 3).
Panel D: Net effect
Panel D of Table 11 presents our estimate of the net change in employment in scenario B.
• For Santa Clara County only (column 1), we estimate a cumulative net loss in employment,
due to the policy, of 1,350 jobs by 2019, or -0.1 percent of total employment.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 46
• For Santa Clara County and nine nearby counties (column 3), we estimate a cumulative
net gain in employment, due to the policy, of 60 jobs by 2019, or 0.0 percent of total
employment.
Scenario B, as scenario A would lead to negligible effect on net employment by 2019 if the
benefits of the income effect are fully taken into account. The job losses are greater if the area of
study is smaller.
Table 11. Scenario A: Cumulative net changes in employment in Santa Clara County
Impacts in Santa
Clara County only
Additional impact in
nine nearby
counties
Total impact of a $15
MW increase in Santa
Clara County and nine
nearby counties
A. Cumulative reduction in wage bill due to capital-labor substitution and productivity gains
Reduction in number of jobs from substitution effects
and productivity gains -2,700 n.a. -2,700
B. Scale effect: Cumulative reduction in consumer spending
Reduction in consumer spending from price increase
(billions) -$214 n.a. n.a.
Reduction in number of jobs due to the scale effect -1,120 n.a. n.a.
Reduction in GDP due to the scale effect (millions) -$130 n.a. n.a.
C. Income effect: Cumulative increase in consumer demand
Aggregate increase in consumer spending (millions) $602 +$104 $706
Increase in number of jobs due to the income effect 2,480 +1,410 3,890
Increase in GDP due to the income effect (millions) $285 +$170 $455
D. Cumulative net change in employment
Net change in employment -1,350 +1,410 60
Net change in employment, as a percent of total
employment -0.1% +0.1% 0.0%
Net change in GDP (in millions) $160 +$170 $330
Net change in GDP, as a percent of total GDP 0.1% +0.0% 0.1%
Source: Authors’ calculations using the regional economic impact model IMPLAN.
Note: The nine nearby counties taken into account are: Alameda, San Mateo, San Francisco, Santa Cruz, Monterey, San Benito,
Contra Costa, San Joaquin, and Merced. All estimates are in 2019 dollars.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 47
PART 3. POLICY ISSUES
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 48
IMPACTS ON SPECIFIC SUBPOPULATIONS
Young Adults and Learners
California regulation allows for “learner” employees to be paid 85 percent of the minimum wage
during their first 160 hours of employment in occupations in which the employee has no previous
similar or related experience (California Department of Industrial Relations 2013).
Local minimum wage laws typically incorporate state definitions of which employees are covered
by state labor law. Of the 18 local minimum wage laws in California:
• 11 have no other special provisions for teens or learners
• 4 exempt youth training programs operated by a non-profit corporation or government
agency (Sacramento, Richmond, Berkeley, San Diego).
• 1 exempts publicly subsidized job-training and apprenticeship programs for teens (San
Francisco)
• 2 extend the state learner provision to 480 hours or 6 months (Santa Monica, Long
Beach)
• 2 restrict the learner provision to youth under the age of 18 (Los Angles, Pasadena)
The goal behind exempting young workers from minimum wage requirements is to avoid creating
disincentives for hiring such workers. In theory, higher minimum wages could reduce the
incentive for employers to hire less skilled workers, thus disadvantaging teens. On the other
hand, higher minimum wages might draw more teen workers into the labor market, leading to an
increase in teen employment.
Teens make up a shrinking share of the workforce. We estimate that teens will constitute 4
percent of workers affected by the proposed increase (see Table 4). A large body of research
suggests that the effect of minimum wage laws on teen employment is either negligible or very
small, and may run in either direction (Manning 2016). Giuliano (2013) finds a small increase in
relative employment of teens after a minimum wage increase using personnel data from a large
U.S. retail firm. Neumark and Wascher (1992) find a modest negative impact on teen
employment through cross-state comparisons. Allegretto, Dube and Reich (2011) follow Neumark
and Wascher’s methods, but control for regional differences and find no measurable impact on
teen employment.26
On the downside, subminimum or training wages for teens may create an incentive to hire
middle-class teenagers over low-wage adult workers in high-turnover industries such as food-fast
restaurants. When state or federal law has included a subminimum wage for teens, very few
employers made use of it (Card and Krueger 1995).27
To summarize, it appears that differential treatment for teens beyond what is already permissible
in California law is not necessary.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 49
Transitional Jobs Programs
Transitional jobs programs provide short-term, subsidized employment and supportive services
through a non-profit organization to help participants overcome barriers to employment. This may
include programs for the formerly incarcerated, youth from disadvantaged backgrounds, adults
with mental health challenges and the homeless. The programs typically provide a mix of services
to their client employees including vocational training, legal services, counseling, etc.
Most minimum wage laws treat transitional jobs programs the same as other non-profit
organizations. To the degree the programs are funded by public contracts and philanthropy, the
considerations for these programs may not be significantly different from other non-profit health
and human service agencies. In Los Angeles and Santa Monica, participants in transitional jobs
programs that meet specified criteria are exempted from the higher minimum wage for a
maximum of 18 months.
Other Exemptions
General exemptions under state law
As discussed above, local minimum wage laws generally incorporate the definition of who is an
eligible employee from state law. Under California law the following employees are exempt from
the state minimum wage:
• A parent, child or spouse of the employer.
• A person under the age of 18 employed as a babysitter for a minor child of the employer in
the employer’s home.
• Persons employed by the federal government.
• “Outside salespersons” who spend more than half of their time away from their
employer’s place of business.
People employed in “executive, administrative or professional capacities” are exempt from most
state wage orders (overtime, meal breaks, etc.). In order to be an exempt employee in California,
the employee must earn a salary equal to twice the state minimum wage.
Subminimum wages for workers with severe mental or physical disabilities
Workers with severe mental or physical disabilities may be paid a sub-minimum wage if an
employer has received a special license from the state labor commissioner. Wages are set based
on the individuals’ productivity and the prevailing wage for similar work. There is no legal wage
floor for these programs.
This practice, which dates back to the passage of the Fair Labor Standards Act in 1938 has
become more controversial in recent years. Opponents include the National Disability Rights
Network and the National Federation for the Blind (“Groups Supporting the Repeal of Section
14(c) of the Fair Labor Standards Act” 2016). They argue that this allows for exploitation of
disabled individuals. Proponents, such as Goodwill Industries, argue that it provides opportunities
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 50
to work for people who otherwise would not be employable due to their lower productivity.
Maryland abolished the subminimum wage for people with disabilities earlier this year (Marans
2016).
Nonprofit organizations
Nonprofits comprise a wide range of organizations. Some are large institutions (universities,
hospitals, large services providers) that have sizeable annual budgets with varied funding
streams and that are therefore able to absorb minimum wage increases. Such institutions
account for a significant portion of the nonprofit sector. At the same time, other nonprofits may
face real constraints on their ability to adjust to minimum wage. These are typically smaller
nonprofits dependent on a few public funding streams that are fixed over the short or even
medium term, and over which they have little leverage.
A local minimum wage policy offers an opportunity to address the problem of low-wage work in
certain nonprofit service-providing sectors—a problem that impacts the well-being of both workers
and program clients through the quality of care provided. Raising wages in human services and
early care and education has benefits for clients as well as workers.
There is a well-documented link between quality jobs, worker turnover and quality care in human
services and early care and education.
Larson et al. (2004) found that, in the field of developmental disability services, high vacancies
are associated with lower consumer and family satisfaction. Furthermore, families reported
increased stress, greater financial challenges, and more job losses due to reduction in services
that were at least in part connected to high turnover and vacancies. Wage increases have been
shown to reduce turnover and vacancies. For example, after Wyoming legislation increased
wages for developmental disability industry workers, turnover rates fell from 52 percent to 32
percent in just two years (Harmuth and Dyson 2005). Similarly, turnover decreased 17 percent
among home care workers in San Francisco after an increase in wages (Howes 2002).
Other studies have directly linked wages and quality care. The National Childcare Staffing Study
(Whitebook, Howes, and Phillips 1989) found that staff wages provided the strongest predictor of
child care quality. Observations in child care centers in Wisconsin revealed an increase in the
quality of care after a wage increase (Center on Wisconsin Strategy (COWS) 2002). Child care
quality in turn has long-term impacts on children’s learning, health and development (Whitebook,
Howes, and Phillips 2014). Larson et al.’s 2004 study similarly found a link between lower wages
in developmental disabilities services and lower quality of life assessments for consumers
(Larson et al. 2004).
A higher minimum wage would help to reduce turnover in lower paid occupations within the
nonprofit sector and improve quality outcomes for consumers. Exempting groups of nonprofit
organizations from a minimum wage increase, conversely, could have negative effects on the
quality of care by increasing employee turnover. If certain nonprofits pay lower wages than the
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 51
rest of the market, it will make it harder for them to attract and retain workers. But requiring
higher wages without addressing the need to increase funding streams, or without providing
sufficient phase-in time, is likely to result in cuts to services.
Ultimately, the solution is to increase public funding for the services provided by these nonprofits.
San Jose and Santa Clara County could choose to fund the higher wages in certain sectors. San
Francisco’s C-Wages program, for example, provides County wage subsidies to child care centers
and family child care providers that meet certain quality measures and enroll at least 25 percent
of their children from low-income families. Funding for this program was increased to assist
providers in meeting the higher minimum wage in 2015. San Jose could also engage with private
philanthropy to help support nonprofit agencies through the transition. This should include both
financial aid and technical assistance and management support in adjusting to the higher wage
rate.
A number of city minimum wage laws have provided for slower phase-ins for nonprofit
organizations to provide more time to adjust to the higher minimum wage. In San Francisco’s
2003 law, implementation was delayed by one year for nonprofits; however, its recent 2014 law
had no such phase-in. Berkeley’s 2014 law exempts nonprofits for one year, at which point they
are required to pay the same minimum wage as for-profit firms. Los Angeles allows nonprofit
organizations to seek a one year deferral provided that either the chief executive officer earns a
salary that is less than five times the lowest paid employee; it is a transitional employer as
discussed above; it serves as a child care provider; or it is primarily funded by public grants or
reimbursements. The new California minimum wage law treats nonprofits the same as all
employers.
Small Businesses
The California State minimum wage law and a number of the city laws that reach $15 an hour
have provided slower phase-ins for small businesses. The State of California, Los Angeles, Los
Angeles County, Long Beach and Santa Monica all delay the raises by one year for businesses
with 25 or fewer employees. Emeryville has a slower phase-in for businesses with 55 or fewer
employees (combined with a one year 60 percent increase in the minimum wage for larger firms).
San Francisco, Sunnyvale, Mountain View and El Cerrito treat all firms equally, regardless of size.
In all of these cases the wages ultimately converge between large and small firms. This is
important to reduce any perverse incentives created by permanently different wage structures for
different business sizes. The State of California and Los Angeles area policies all begin indexing
the year after the small firms reach the final mandated wage level, leaving the wage for larger
firms at $15 for two consecutive years. Emeryville increased the wage for large firms to $14.44 in
one step in 2015 and began indexing the following year. Wages for small firms reach $15 in
2018 and are increased to match the rate for larger businesses the following year (estimated at
$16 an hour).
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 52
If San Jose or other cities in Santa Clara County choose to go this route, another important
consideration is the definition of what counts as a business for the purpose of counting
employees. Large firms often operate via multiple small establishments (i.e., retail clothing stores
or bank branches); therefore, a small business definition based on establishment size will
erroneously include large national or multinational firms. We would therefore recommend a
definition based on firm, rather than establishment size. The same principle holds in the case of
franchises—i.e., all franchises or other businesses owned by a given owner or group of owners
should be counted toward firm size.29
Whether or not the City institutes a longer phase-in period for certain small businesses, the Cities
may want to seek ways to assist small businesses through the transition, including providing
access to small business loans and technical assistance and training.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 53
WAGE LEVEL
Economists often look at two summary statistics when assessing a proposed minimum wage
increase schedule. The first measures the ratio of the minimum wage to the median full-time
wage, a common metric used both in the U.S. and in other countries (Organization for Economic
Co-operation and Development (OECD) 2013). The second estimates the percentage of the
workforce directly or indirectly affected by the minimum wage increase. Both metrics provide a
measure of scale of impact and therefore give us insight into the ability of an economy to absorb
higher minimum wage levels (the two metrics are related but do not necessarily move in strict
tandem). Table 11 shows our estimates of these metrics for the San Jose and Santa Clara County
minimum wage scenarios at $15 in 2019.
We begin with the ratio of the minimum wage to the median full-time wage (minimum-to-median
ratio for short). Historically, this ratio reached a high of 55 percent in 1968 at the federal level
(Dube 2014). The average for OECD countries is 49 percent; five, including France and New
Zealand, have minimum-to-median ratios of 60 percent or more (2013). The United Kingdom
recently pegged the minimum wage to a ratio of 60 percent (O’Connor 2016).
Table 11 shows that $15 an hour in 2019 would result in a minimum to median ratio of 41
percent in San Jose and Santa Clara County, well within the historical range in the United States.
Even at $20, the minimum to median ratio in San Jose or Santa Clara County would be below 55
percent. This compares to 62 percent for $15 in California when full phased in in 2023. New York
City is projected to reach 57 percent, Los Angeles 62 percent, Seattle 53 percent and San
Francisco 46 percent at the point of full implementation in each of those cities.
It is important not to place too much weight on the minimum to median wage measure. While the
minimum to median ratio provides a simple tool of thumb for comparisons across geographical
areas, it can be misleading on its own, especially for small geographic areas, and is best used in
combination with other measures.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 54
Table 11. Minimum wage to median ratio, bite and average percent increase per year
Minimum Wage to Median
Full-Time Ratio
Share of workers getting pay
increases
(Percent)
Average Percent Earnings
Increase
(Percent)
San Jose ($15) 0.41 31 18
Santa Clara County ($15) 0.41 25 19
San Jose ($20) 0.55 NA NA
Santa Clara County ($20) 0.54 NA NA
California 0.62 39 24
New York City 0.57 35 28
Los Angeles City 0.62 39 29
Seattle 0.53 29 NA
San Francisco 0.46 23 16
Sources: UC Berkeley-IRLE calculations using ACS data and Cooper (2016) for New York State; Reich et al. (2015) for a $15.25
minimum wage in Los Angeles and in Seattle; Reich et al. (2014) for a $15 minimum wage by 2018 in San Francisco.
Notes: The figures are provided for the end point of the minimum wage increase. The end point for California is 2023. It is 2019
for New York City and Los Angeles and 2018 for Seattle and San Francisco. The Share of workers getting pay increases for
Seattle is the percent of employees who earn $15 or under and live and work in Seattle.
Our second metric shows that that the percentage of workers directly and indirectly affected
under the proposed law. The share of affected workers in San Jose (31 percent) and Santa Clara
County (25 percent), are below each of the other $15 minimum wage laws, with the exception of
San Francisco (23 percent). Similarly, the average projected increase per worker in San Jose (18
percent) and Santa Clara County (19 percent) are well below the other policies, again with the
exception of San Francisco (16 percent). In contrast, state and federal minimum wage increases
between 1979 and 2012 have generally affected 10 percent or less of the workforce (D. H. Autor,
Manning, and Smith 2016).
Effects of a $20 Minimum Wage
Setting a higher minimum wage (such as $20) can be expected to amplify each of the effects
discussed in the minimum wage model, but not to the same degree. The higher wage level is
likely to increase the negative consumption effects caused by higher prices, and negative
employment effects from automation and increased productivity. Since more of the individuals
receiving wage increases would have higher income levels, either as a result of the wage increase
or because the increases are reaching farther up into the wage distribution, a greater portion of
the increased wages is likely to be saved rather than spent. This means that the positive
consumption effects from higher wages will decline as the size of the increase goes up. As a
result, a $20 minimum wage in 2019 is likely to generate larger negative net employment
effects. To understand the size of those effects would require further research. Any projections at
wage levels much higher than previously studied necessarily entail greater uncertainty.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 55
Raising the minimum wage steeply over a short period of time is also likely to generate greater
disruption of existing firms (Aaronson and Phelan 2015). While by some of the indicators
discussed above San Jose and Santa Clara County may well be able to absorb a higher minimum
wage than $15 an hour, if the City and County were to pursue such an option, a longer phase in
time should be considered and assistance provided to non-profit human service agencies and
small businesses as they make the transition to higher wages.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 56
CONCLUSION
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 57
The proposal to increase the minimum wage to $15 by 2019 will generate benefits and costs for
workers and businesses in Santa Clara County and San Jose. Like all forecasts, our estimates of
the benefits and costs are subject to some uncertainty. First, economic conditions, such as
employment and wage growth in the absence of the policy, may differ in future years from the
standard forecasts that we rely upon in this report. For example, in a recession employment
would fall and wages would not grow as quickly. Our cost estimates might then be somewhat
larger, but then so would our benefit estimates. Our estimates of the net effects are therefore
likely to change, but not by a large amount. Second, our estimates rely on parameters that are
themselves estimated with some uncertainty. We have tested the sensitivity or our calculations to
these parameters. The results were encouraging, but require further research.
The proposed policy would result in substantial benefits to low-wage workers and their families.
The policy will raise wages for 115,000 workers in San Jose and 250,000 in Santa Clara County
by 2019. On average, for workers getting increases, their annual earnings will increase by 17.8
percent or $3,000, in San Jose and $3,200 or 19.4 percent in Santa Clara County by 2019.
These large increases in pay will raise overall wages in for-profit businesses by only 1.3 percent in
San Jose and one percent in Santa Clara County. This amount is surprisingly small because many
businesses already pay more than $15, because many of the workers who are now paid below
$15 are already paid above the current minimum wage, and because the pay of low-wage
workers makes up a smaller share of total payroll costs.
Businesses will absorb the additional payroll costs partly through savings on employee turnover
costs, higher worker productivity gains, and some automation (the substitution effect). Most of
the increase in costs will likely be passed on to consumers via increased prices. Since labor costs
make up only about one-fourth of operating costs, consumer prices will increase only slightly—
about 0.3 percent in San Jose and 0.2 percent in Santa Clara County over the entire phase-in
period. Prices will be most affected in the restaurant industry, where they will increase by 3.1
percent in San Jose and 2.9 percent in Santa Clara County.
These higher prices by themselves would reduce consumer sales and reduce the demand for
labor (the scale effect). But simultaneous positive effects on increased consumer spending from
workers receiving wage increases will offset the scale and substitution effects.
After taking into account all of these factors, we estimate that the proposed minimum wage
policy would result in slower employment growth, reducing overall net employment (as a percent
of total employment) in San Jose by 0.3 percent and in Santa Clara County by 0.1 percent by
2019, over the baseline. This estimate is cumulative (and so will be spread over several the
phase-in period). In comparison, employment in the state is projected to grow 1.32 percent
annually in the same time period. Most of the job declines reflect leakage of the increased
spending into the rest of the region. When taking into account the surrounding counties, the net
effect on jobs is close to zero.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 58
In sum, it is possible to effect a substantial improvement in living standards for a quarter of the
workforce in San Jose and nearly a third of the workforce in Santa Clara County without
generating a significant net adverse employment effect. It can do so through induced efficiencies
(more automation, productivity gains, and turnover savings) and slight price increases borne by
all consumers. Based on our analysis, we conclude that the proposed minimum wage will have its
intended effects in improving incomes for low-wage workers. Any effects on employment and
overall economic growth are likely to be small. The net impact of the policy will therefore be
positive.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 59
APPENDIX: DATA AND METHODS
In this appendix, we document the data and methods we use in this study. Section A1 details how
the Census’ American Community Survey was used both to estimate pay increases for affected
workers and the median full-time wages in San Jose and Santa Clara County. Section A2
describes the data and methods we use to calibrate the UC Berkeley IRLE minimum wage model.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 60
A1. THE WAGE SIMULATION MODEL
In this section, we describe our simulation model for estimating the number of workers that
would be affected by the Scenario A and Scenario B minimum wage increases. We provide a
general overview of our methodology here. For full documentation of the model and data we use,
see Perry, Thomason and Bernhardt (Forthcoming).
The logic of our method is to simulate the future San Jose and Santa Clara County wage
distributions with and without the scenario minimum wage increases. First, we use our model to
run a “baseline” simulation of the wage distribution through 2019 assuming existing minimum
wage schedules (see Table 2 and Table 3). We then use our model to run a “scenario” simulation
of the wage distribution through 2019 assuming the minimum wage increases specified in the
two scenarios.
We then compare the baseline and scenario simulated wage distributions to identify the impact
of the minimum wage increase scenarios above and beyond currently scheduled minimum wage
increases. With this comparison, we are able to estimate (a) the number of workers affected by
each scenario, and (b) the additional wages earned as a result of the increase. In our estimate of
affected workers, we include those workers who earn just above the new minimum wage but who
also receive an increase via the ripple effect (see below). Our estimates are adjusted for
projected wage and employment growth.
Dataset
We combine the 2013 and 2014 IPUMS American Community Survey (ACS)
(https://usa.ipums.org/usa/) in order to attain sufficient sample size for our analysis (Ruggles et
al. 2015). The American Community Survey is the largest annual survey conducted by the U.S.
Census Bureau, and interviews more than 2.3 million households throughout the United States.
The ACS is better suited than the Current Population Survey (CPS) for conducting labor market
analyses at the state or sub-state level for two main reasons: first, the ACS sample size is much
larger than the CPS; and second, the ACS contains place of work data, while the CPS data are
limited to place of residence. This allows us to disaggregate wage and employment data for sub-
state geographical units.
Sample definition
We make the following adjustments to our ACS sample:
1. We restrict the sample to individuals age 16 to 64 who had positive wage and salary
income in the previous 12 months, who worked in the previous 12 months, and who were
not self-employed or unpaid family workers.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 61
2. We exclude the following workers from our sample who would not be eligible for a
municipal or county minimum wage law:
a. Federal and state government workers would not be eligible for the minimum wage
increases in Scenario A and Scenario B because local governments do not have
jurisdiction over federal or state employees.
b. Public education employees are excluded from our sample because local school
districts are state entities and are exempt from local minimum wage laws.
c. In-Home Supportive Service (IHSS) workers are also excluded because IHSS
programs are administered at the county level and are exempt from local minimum
wage laws.
Wage measure
Because the ACS only records workers’ annual earnings, it is necessary to estimate an hourly
wage variable in order to perform simulations of the effects of minimum wage increases. The
hourly wage is estimated for all workers in the sample using their reported annual earnings, usual
hours of work per week, and weeks worked in the previous year. The annual earnings measure
includes wages, salaries, commissions, cash bonuses, and tips from all jobs, before deductions
for taxes. The “number of weeks worked in the previous year” variable is a categorical variable of
intervals of weeks worked (such as 14–26 weeks or 50–52 weeks). This variable is converted to
a discrete variable using the mid-point of each interval. The hourly wage variable is then
estimated as annual earnings divided by the product of the number of weeks worked in the
previous year and usual hours worked per week. Workers in occupations that receive tips as the
majority of their earnings are coded with hourly wage values equal to state minimum wage, since
we only want to measure wages paid by their employer in this study.
Geography
The smallest geographic unit for the ACS place-of-work variable is the county. In order to
estimate the impact of the minimum wage scenarios for cities within Santa Clara County, we
conduct our simulation as described above using county-level data, and then estimate the
number of affected workers in the city by applying the percentage of affected workers to city-level
employment estimates from the Quarterly Census of Employment and Wages (QCEW). This step
introduces additional assumptions; namely, that the wage distribution of those who work in the
city (not all of whom live in the city) is the same as the wage distribution of those who work in the
county, and that future wage and employment growth trends in the city will mirror those at the
county level. We therefore make two adjustments to our county-level ACS data to better
approximate the city-level wage distribution:
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 62
1. We use data from the California Employment Development Department to adjust the
industry and sector distribution of the county-level ACS data to match the city’s
distribution.
2. We adjust wages for two high-impact industries where QCEW data show a significant
difference in wages in San Jose and Santa Clara County.
Our model for Santa Clara County takes into account the different local minimum wage laws in
effect within the county (see Table 3).
Identifying affected workers
Our model estimates the impact of minimum wage increases on three groups of affected
workers: minimum wage workers, subminimum wage workers, and those who are indirectly
affected (via spill-over effects). The spill-over effect means that workers who make slightly more
than the new proposed minimum wage level are also likely to receive wage increases.
The main group of affected workers – minimum wage workers – consists of those who earn
between the old minimum wage and the new minimum wage. Given measurement error, we
include in this group workers who earn somewhat below the old minimum wage (down to 90
percent of the old minimum wage). Subminimum wage workers include those earning between
50 percent to 89 percent of the old minimum wage. Indirectly affected workers are those earning
between 100 and 115 percent of the new minimum wage.30
We then estimate the additional wages earned by affected workers as a result of the minimum
wage increase scenario, as summarized in Table A1. Minimum wage workers simply receive the
new minimum wage. Subminimum wage workers receive a percentage wage increase of the
same size as the percentage change in the statutory minimum wage. Indirectly affected workers
receive a quarter of the difference between their current wage and the upper bound of the spill-
over band (115 percent of the new minimum wage).
This model is used to simulate the scenario minimum increases for each of the phase-in years
from 2017 to 2019, but also to simulate baseline minimum wage increases between 2013 and
2019 (i.e. minimum wage increases that have already occurred or are planned under existing
law). We model overall regional wage growth over time using the average annual growth rate of
the San Francisco CMSA CPI-W Urban Wage Earners & Clerical Workers between 2005 and 2014
(2.45 percent).
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 63
A2. CALIBRATING THE UC BERKELEY IRLE MINIMUM WAGE MODEL
A2.1 Structure of the model, and calculations step by step
Table A1 summarizes the structure of our model. The table has four components. The top part
describes the number of workers in the state who will receive pay increases by 2021. Part A
describes the effects of automation and worker productivity gains. Part B describes how much
consumer prices will increase and how much those increases will reduce consumer demand and
employment. Part C describes how we calculate the income effect: how pay increases will
increase consumer spending and employment. Part D describes how we calculate the net effect
on employment. In this section we document in detail the data and methods that we use in each
part of Table A1. In section A2.2, we document the source of the key parameters used to
calibrate our model.
Top part: Workers affected and wage increase
Lines [1] to [3] in Table A1 use our estimates (described in detail in the first section of the
appendix) on how the labor force will grow and how the proposed minimum wage increase would
affect the wage distribution of workers in San Jose (respectively Santa Clara County). The wage
estimates include the number of workers directly and indirectly affected by the two scenarios,
and their nominal wages with and without the policy. We also use our estimate of the total wage
bill by 2019: it will be $31.1 billion in San Jose with minimum wage increase (as described in
scenario A) and $30.7 billion without the minimum wage increase. In Santa Clara County, we
estimate that the total wage bill will be 90.0 billion with the minimum wage increase (as
described in scenario B) and 89.1 billion without the minimum wage increase.
Part A: Impact of capital-labor substitution and productivity gains
Part A calculates the impact of capital-labor substitution and productivity gains on employment
and the total wage bill. Our estimates are calculated as follows:
The reduction in number of jobs from substitution effects (line [5] in Table A2) is calculated by
multiplying four components: (i) the capital-labor substitution elasticity (see section A2.2) (ii) the
average wage increase of workers getting increases, that we estimate to be 18 percent based in
San Jose (respectively 19 percent in Santa Clara County), (iii) the profit share of revenues (see
section A2.2), and (iv) the total number of affected workers.
The reduction in number of jobs from productivity gains ([6]) is calculated by multiplying two
components: (i) the productivity gains (see section A2.2 for a description of the values we use to
calibrate the model) and (ii) the total number of affected workers (that we estimate to be
115,000 in San Jose and 250,000 million in Santa Clara County according to our wage
simulation model).
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 64
The reduction in wage bill due to substitution effects and productivity gains ([7]) is calculated by
multiplying the reduction in number of jobs due to capital-labor substitution and productivity
gains ([8]) by the nominal average annual earnings of workers who would otherwise remained
employed ([9]).
Table A1. Structure of the UC Berkeley IRLE minimum wage model for the case of San Jose
A. Workers affected and wage increases
Total employment [1]
Total number of affected (directly and indirectly) workers in San Jose in 2019 [2]
Working age population growth from 2014 to 2019 [3]
B. Impact of K-L substitution and productivity gains on number of jobs and wage bill
Reduction in # of jobs from substitution effects and productivity gains [4]=[5]+[6]
Reduction in # of jobs from substitution effects in 2019 [5]
Reduction in # of jobs from productivity gains in 2019 [6]
Reduction in wage bill due to substitution effects and productivity gains job loss (in millions) [7]=[8]*[9]/1e6
Reduction in # of jobs from substitution effects and productivity gains [8]=[4]
Nominal average annual earnings of directly and indirectly affected workers without the
policy [9]
C. Scale effects: increase in consumer prices and reduction in consumer demand
Percentage increase in consumer prices [10]=[11]
Percentage increase in operating costs [11]=[12]*[13]
Payroll share of operating costs [12]
Net percentage payroll increase, accounting for savings from reduced turnover and
productivity gains [13]
Annual reduction in consumer demand from price increase (in millions) [14]=[15]*[16]
Percentage reduction in demand from price increase [15]
Annual aggregate consumer spending in San Jose (in millions) [16]
Reduction in # of jobs from consumer spending reduction in San Jose [17]
Reduction in # of jobs, as a percentage of total employment [18]
D. Income effects: effects of pay increases on consumer spending and employment
Net change in compensation for workers in San Jose (in millions) [19]=[20]-[21]
Total wage increase for state workers in San Jose from proposed minimum wage increase
(in millions) [20]
SNAP and ACA benefit reduction [21]
Increase in # of jobs from wage increase in San Jose (respectively in SC county and nine
counties) [22]
Increase in # of jobs, as a percentage of total employment [23]
E. Net effects
Cumulative net change in # of jobs in San Jose [24]
Cumulative net change in # of jobs, as a percent of total employment [25]=[24]/[1]
Annual net change in # of jobs in San Jose [26]=[24]/5
Annual net change in # of jobs, as a percent of total employment [27]=[25]/5
Source: UC Berkeley minimum wage model.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 65
Part B: Scale effects: increase in consumer prices and reduction in consumer demand
Part B of Table A1 estimates the percentage increase in consumer prices due to an increase in
operating costs for firms and the annual reduction in consumer demand from price increase. We
use the 2014 IMPLAN model to calculate the impact of this reduction in consumer spending on
employment. Our estimates are calculated as follows:
• The percentage increase in consumer prices ([10]) is assumed to be equal to the
percentage increase in operating costs ([11]), following the widely-used Dixit-Stiglitz model
of monopolistic competition (Dixit and Stiglitz 1977).
• The percentage increase in operating costs ([11]) is obtained by multiplying the net
percentage payroll increase ([13]) by the labor share of operating costs ([12]).
• The net percentage payroll increase ([13]) includes savings from reduced turnover and the
reduction in wage bill due to substitution effects and productivity gains. We estimate the
total wage bill increase to be $389 million in San Jose by 2019 (respectively $899 million
in Santa Clara County). We subtract the reduction in total wage bill due to substitution
effects and productivity gains ([1]). We also account for the increase in payroll costs that
corresponds to Medicare, Social Security, and Workers’ Compensation costs. This share
equals 10.36 percent in 2019 (see section A2.2 for the source). To compute the net
percentage increase in payroll costs, we apply a partial offset for turnover cost savings
(see section A2.2 for the source).
• The labor share of operating costs ([12]): we estimate the economy-wide labor share of
operating costs to be 22.1 percent in 2016 (see section A2.2 for the source).
• The reduction in consumer demand from price increase ([14]) is obtained by multiplying
the percentage reduction in demand from price increase ([15]) by the annual aggregate
consumer spending in San Jose (respectively Santa Clara County) ([16]). The estimated
reduction in consumer demand due to higher prices equals $107 million in San Jose
(respectively $214 million in Santa Clara County). The key components of this calculation
are:
o The percentage reduction in consumer demand from price increase ([14]). It
depends on two parameters: (i) the percentage increase in consumer prices as
calculated in line [10], and (ii) the price elasticity of demand (see section A2.2 for
the source). The bigger the price elasticity of demand is, the more sensitive the
consumers are to a price change and the greater the percentage reduction in
demand from price increase is.
o Annual aggregate consumer spending ([16]) is obtained by multiplying the
projected annual GDP for San Jose and Santa Clara County in 2019 by an overall
estimated share of consumer spending in GDP. We estimate San Jose GDP and
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 66
Santa Clara County GDP so that it is consistent with the underlying value of the
GDP in IMPLAN in 2019 (see section A2.2), and we estimate that the share of
consumer spending in GDP is 58.8 percent (see section A2.2). We estimate that
the annual aggregate consumer spending is $57.9 billion in 2019 in San Jose and
146.5 billion in Santa Clara County.
• The annual reduction in jobs resulting from price increases is estimated using the 2014
IMPLAN model (see (Day 2013) for documentation on this software). We adjust those
estimates by working age population growth from 2014 to 2019, estimated to be 6.79
percent for the overall period in both San Jose and Santa Clara County (see section A2.2).
Part C: Income effects
Part C of Table A1 estimates the income effects resulting from pay increases for low-wage
workers, the resultant increase in consumer demand, and its impact on employment. Our
estimates are calculated as follows:
• The net change in compensation for affected workers ([19]) is calculated as the total wage
bill increase for affected workers ([20]) minus the wage bill reduction from a reduction in
the Supplemental Nutrition Assistance Program (SNAP) and in premium tax credits under
the Affordable Care Act benefit reduction ([21]).
• The offset from SNAP and premium tax credits ([21]) under the ACA is estimated to be
14.75 percent of the total wage increase (see Appendix A2) and is applied to the total
wage bill increase for all households, as there is no easy way to separate this out by
income brackets.
• The annual increase in jobs resulting from higher consumer demand is estimated using
the 2014 IMPLAN model. We adjust those estimates by the working age population growth
from 2014 to 2019, estimated to be 6.79 percent for the overall period in both San Jose
and Santa Clara County (see section A2.2 for the source).
Part D: Net effects
Part D of Table A1 estimates the cumulative net effect on employment ([24]), simply by
subtracting the reduction in employment due to substitution effects, productivity gains ([4]), and
scale effects ([17]) from ([ the employment gains due to income effects 22]). We compute the
annual estimates by dividing the cumulative effects on employment by five, to account for the
number of years needed for the policy to be fully phased in. These numbers are therefore
approximate annual averages.
A2.2 Key parameters and assumptions used in the model
Our key parameters are drawn from the best available evidence. We vary some of them in our
robustness tests. We explain and document below the range of those parameters and the
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 67
sources we used. The values of the key parameters used in the model are summarized in table
A2.
Table A2. Key parameters of the model
In San
Jose
In Santa
Clara County
A. Workers affected and wage increases
Working age population growth from Dec 31 2012 to July 1 2021 6.79% 6.79%
B. Impact of K-L substitution and productivity gains on number of jobs and wage bill
Capital-Labor substitution 0.2 0.2
Profit share (taking into account the share going to intermediate inputs and materials) of revenues 0.15 0.15
Productivity gains - in levels 0.005 0.005
C. Scale effects: increase in consumer prices and reduction in consumer demand
Labor percent of operating costs 22.1% 22.1%
Percent of wage costs for Medicare, Social Security, and worker compensation 10.36% 10.36%
Turnover reduction (as share of payroll increase) 0.11 0.11
Price elasticity of demand -0.72 -0.72
Annual GDP in 2019 (in millions) $98,420 $249,225
Share of consumer spending in GDP 0.588 0.588
D. Income effects: effects of pay increases on consumer spending and employment and employment
Percentage offset from reduced SNAP benefits and lower premium tax credits 14.75% 14.75%
Offset from reduced EITC 0.60% 0.60%
Offset from reduced SNAP benefits 4.20% 4.20%
Offset from lower premium tax credits under the ACA 2.30% 2.30%
Offset from reduced payroll taxes 7.65% 7.65%
E. Net effects
No key parameters used in this section
Source: UC Berkeley minimum wage model.
Future Employment Growth
Our estimate of future employment growth in San Jose and Santa Clara County comes from data
supplied by the California Employment Development Department (EDD) (2015).
Capital-labor substitution
For a discussion about capital-labor substitution and the sources we used, see section 4.2 in the
main report.
Profit share of revenues
We use Table 1.14. “Gross Value Added of Domestic Corporate Business in Current Dollars and
Gross Value Added of Nonfinancial Domestic Corporate Business in Current and Chained Dollars”
of the National Income and Product Accounts Tables (NIPA) published by the Bureau of Economic
Analysis to estimate the labor and capital share of national income. Using the 2014 data, we
estimate that the labor share of national income is 62 percent and the capital share of national
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 68
income (including capital depreciation) is 38 percent. Knowing that the labor share of operating
costs is 22.1 percent in 2016, we apply the growth rate of payroll costs to estimate the labor
share of operating costs in 2019 and estimate that the profit share of revenues is therefore
estimated to be 0.15 in 2021. The remainder of businesses revenues is composed of materials,
intermediate inputs and rent.
Productivity gains
For a discussion of productivity gains and the sources we used, see section 5.1 in the main
report.
Labor share of operating costs
Net payroll cost increases for businesses are a function of three factors: (1) the total wage bill
increase, after reduction due to substitution effects and productivity gains; (2) Medicare, Social
Security, and Workers’ Compensation increases, and (3) turnover costs savings. The payroll costs
increase as total compensation increases and decrease with turnover costs savings.
• The total wage bill increase from 2016 to 2019 is estimated with our wage simulation model
based on micro data. For each year, we calculate the reduction in wage bill due to job losses
from substitution effects and productivity gains, assuming that capital-labor substitution and
productivity gains are constant over the years. We assume in our calculations that capital-
labor substitution is equal to 20 percent every year, and that productivity gains are equal to 5
percent every year.
• Employers’ costs for Medicare, Social Security, and Workers’ Compensation will equal 10.36
percent of wages from 2016 to 2019. We estimate the three components—Medicare (1.45
percent), Social Security (6.2 percent), and Workers’ Compensation costs—separately. Since
we are estimating only the effects of a minimum wage increase, we assume the Medicare and
Social Security rates will not change between 2016 and 2019. For Workers’ Compensation
costs, we draw from a report of the National Academy of Social Insurance {Citation}(2013).
Table 14 (p. 37) of this report indicates that Workers’ Compensation employer costs in 2013
amounted to $1.50 per $100 of eligible wages. These costs increased $0.11 cent increase a
year over 2011–2013, slightly more than the 2009–2011 change. To account for these cost
increases, we adjust the 2013 cost by $0.34. Consequently, we estimate that Workers’
Compensation costs will equal 1.84 percent of wages in San Jose and Santa Clara County
from 2016 to 2019.
• Turnover costs savings are based on the estimates of Pollin and Wicks-Lim (2015), Fairris
(2005), Dube, Freeman and Reich (2010), Dube, Lester and Reich (2016), Boushey and
Glynn (2012), and Jacobs and Graham-Squire (2010). See section 5.1 in the main report.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 69
The labor share of operating costs by industry
For each industry, we estimate labor costs as the sum of the annual wage costs, payroll taxes
and employer paid insurance premiums (except health insurance), and other benefits (other than
contributions to pension plans). The labor share is estimated using 2012 Census Bureau
surveys—the most recent year available. We use these surveys only for select individual
industries: retail trade; food services; wholesale trade; manufacturing; administrative and waste
management services; health care and social assistance (including ambulatory care, hospitals,
and long-term care); and other services. We document here our sources and methods for these
individual industries as well as for our estimates of the labor share of operating costs in the
overall economy.
• Retail trade (including grocery stores): The 2012 U.S. Census Annual Retail Trade Reports
provides data on retail sales, payroll costs, merchandise purchased for resale, and detailed
operating expenses. We add operating expenses and purchases together to determine total
operating costs. We add the costs of payroll taxes, employer paid insurance premiums, and
employer benefits (excluding health insurance and retirement benefits) to annual payroll to
estimate total labor costs. Health and retirement benefits are excluded since, unlike payroll
taxes and Workers’ Compensation insurance, the costs of the benefits will not change if
wages are increased. Dividing labor costs by operating costs gives us the labor share in retail
trade.
• Food services industry: Industry data on gross operating surplus are available from the
Bureau of Economic Analysis Input-Output Account Data, before Redefinitions, Producer
Value. We subtract gross operating surplus from sales to obtain total restaurant operating
costs, and then proceed as we did for retail to obtain labor cost data.
• Wholesale trade: Data are from the U.S. Census Annual Wholesale Trade Report. We follow
the same methods as with retail trade.
• Manufacturing: Data are from the 2012 Economic Census (Table EC1231I1). To determine
operating expenses we add together payroll costs and benefits, total cost of materials, total
capital expenditures, depreciation, rental or lease payments, and all other operating
expenses. To determine labor costs we add together payroll costs and payroll taxes, employer
paid insurance premiums, and employer benefits (excluding health insurance and retirement
benefits).
• Administrative and waste management services, health care and social assistance (including
ambulatory care, hospitals, and long-term care), and other services: Data are from the U.S.
Census Annual Services Report, which provides data on payroll and operating expenses. Total
operating expenses are reported directly in the data. To determine labor costs we add
together payroll costs and payroll taxes, employer paid insurance premiums, and employer
benefits (excluding health insurance and retirement benefits).
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 70
• Overall economy: We sum the total labor and operating costs across all industries with
available data and then divide the aggregate labor costs by the aggregate operating costs. In
addition to the industries listed above, we are able to use the Annual Services Report to
gather data on the following industries: utilities; transportation and warehousing; information;
finance and insurance; real estate and rental and leasing; professional, scientific, and
technical services; educational services; and arts, entertainment, and recreation. We are
missing data for the following industries, and as a result they are not included in our
calculation: agriculture, forestry, fishing, and hunting; mining, quarrying, and oil and gas
extraction; construction; accommodation; and public administration. Overall, we estimate that
the labor share of operating costs is 22.1 percent in 2012, and assume it is constant
between 2012 and 2016.
Share of payroll costs for Medicare, Social Security and Workers’ compensation
The share of Medicare, Social Security, and Workers’ Compensation is assumed to continue to be
10.36 percent from 2016 to 2019. We estimate the Medicare, Social Security, and Workers’
Compensation costs separately. Employers are liable for 6.2 percent Social Security taxes and
1.45 percent Medicare taxes. We estimate that the Workers’ Compensation employer cost is
2.71 percent of wages in California. The estimate of 2.71 comes from Workers’ Compensation
Insurance Rating Bureau of California (2014), Chart 6 for “all industries”:
http://www.wcirb.com/sites/default/files/documents/state_of_the_wc_system_report_140815.
pdf.
Turnover reduction
For a discussion on savings generated by turnover reduction and the sources we used, see
section 5.1 in the main report.
Price elasticity of demand
The price elasticity of demand measures the effect of a price increase on reducing consumer
demand. We use a price elasticity of 0.72. This estimate is based on Taylor and Houthakker
(2010), who report price elasticities for six categories of goods and services. We adjust their
estimates to account for changes in the elasticity of health care spending attributable to the
Affordable Care Act and other changes in the health care system.
GDP for San Jose and Santa Clara County in 2019
The 2019 GDP used in our model has been forecasted using the following methodology:
• We start with the 2014 GDP reported in IMPLAN, i.e. $84.4 billion in San Jose, and
$213.7 billion in Santa Clara County;
• We then forecast the GDP for San Jose (respectively for Santa Clara County) by applying
the employment growth of 6.79 percent from 2014 to 2019 (respectively 6.79 percent for
Santa Clara County), the projected wage growth using the last 10 years of CPI-W growth of
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 71
12.9 percent (respectively 12.9 percent for Santa Clara County), and the GDP deflator in
IMPLAN for 2019 (1.039 for both San Jose and Santa Clara County).
Share of consumer spending in GDP
Our estimate of the share of consumer spending in GDP includes only consumer spending that
flows through households. We therefore reduce the BEA’s estimate of the consumption share by
14.1 percent.
Offsets from benefit reductions and payroll tax increases
We estimate that the total offset from reduced EITC to be 0.6 percent, the offset from reduced
SNAP benefits to be 4.20 percent, the offset from lower premium tax credits under the ACA to be
2.3 percent, and the offset from reduced payroll taxes to be 7.65 percent (the remaining
personal income taxes are removed by IMPLAN). These estimates have been calculated using
Congressional Budget Office (2012). These results are for the year 2012, and we assume they
will remain constant until 2021.
Share of in-commuters
We use 2014 ACS data to estimate the proportion of affected workers in Santa Clara County who
live outside of the county (16.2 percent). However, we are not able to estimate the share of in-
commuters for San Jose with ACS data alone because the ACS does not provide place of work
data at the city level. LEHD Origin Destination Employment Statistics (LODES) data accessed
through the Census Bureau’s On the Map website provides employer location and worker
residence data at the city level, but is not as reliable as ACS data because employers’ addresses
do not always correspond to a worker’s physical workplace. To estimate the share of in-
commuters for San Jose, we therefore first calculate the ratio of the ACS estimate of the share of
in-commuters in Santa Clara County to the LODES estimate of the share of in-commuters in
Santa Clara County. We then apply that ratio to the LODES estimate to the share of in-commuters
in San Jose.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 72
ENDNOTES
1 Portions of this report draw from Reich et al. 2016.
2 The April 2016 non-seasonally adjusted unemployment rate for San Jose reported by California
EDD was 4.1 percent. We do not include this statistic here because it is not seasonally adjusted.
3 See, for example, the report on inequality from the California Budget and Policy Center:
http://calbudgetcenter.org/wp-content/uploads/Inequality-and-Economic-Security-in-Silicon-Valley-05.25.2016.pdf
4 However, Aaronson, Agarwal and French (2012), Table A-3, report a positive earnings effect for
adults and nonetheless find no detectable effect on employment.
5 Neumark, Salas and Wascher (2014), the best-known researchers who find negative effects,
report a 0.06 minimum wage employment elasticity for restaurants, very close to the findings in
Allegretto et al. (2015).
6 The study was prepared for the Los Angeles City Council; see Reich, Jacobs, Bernhardt and Perry
(2015).
7 The capital-labor substitution elasticity is not likely to be higher or lower at higher minimum
wage rates.
8 Constant dollar values are calculated using the average annual change for the past ten years of
the San Francisco-Oakland-San Jose Consumer Price Index for Urban Wage Earners and Clerical
Workers (CPI-W).
9 One exception is child care assistance, which does have a maximum income threshold that,
once exceeded, results in the immediate loss of benefits. However, since there is a substantial
waiting list for child care assistance benefits, any affected workers who lose eligibility will be
replaced by lower-wage workers not currently receiving benefits. Workers who are no longer
eligible for Medi-Cal will be eligible for subsidized health care through Covered CA. While most
families will come out well ahead financially, the change in costs for specific families will depend
on income and health care utilization.
10 This analysis is based on data gathered before the full implementation of the Affordable Care
Act.
11 This analysis is based on data gathered before the full implementation of the Affordable Care
Act.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 73
12 Hirsch, Kaufman, and Zelenska (2011) and Reich, Hall, and Jacobs (2003) found
improvements in worker productivity following higher wage mandates.
13 The turnover savings are considered constant in 2017 and 2018, at 17.5 percent of increased
labor costs, a midpoint estimate in the literature (Hirsch, Kaufman, and Zelenska 2011; Reich,
Hall, and Jacobs 2003). These savings are likely to accrue at smaller rates as wage levels go
higher; we therefore assume that by 2019 the marginal increase in earnings relative to 2017 no
longer yields any additional turnover savings. As a result, we estimate that the total savings from
turnover at a $15 minimum wage in 2019 would be 11.3 percent of increased labor costs for
San Jose and 11.9 percent of increased labor costs for Santa Clara County.
14 We use a payroll tax rate of 7.65 percent (6.2 percent for Social Security and 1.45 percent for
Medicare). Workers’ compensation insurance rates vary by industry (see Table 6:
http://www.wcirb.com/sites/default/files/documents/state_of_the_wc_system_report_140815.
pdf.
15 The turnover savings are considered constant in 2017 and 2018, at 17.5 percent of increased
labor costs, a midpoint estimate in the literature (Hirsch, Kaufman, and Zelenska 2011; Reich,
Hall, and Jacobs 2003). These savings are likely to accrue at smaller rates as wage levels go
higher; we therefore assume that by 2019 the marginal increase in earnings relative to 2017 no
longer yields any additional turnover savings. As a result, we estimate that the total savings from
turnover at a $15 minimum wage in 2019 would be 11.3 percent of increased labor costs for
San Jose and 11.9 percent of increased labor costs for Santa Clara County.
16 Since workers often increase their wages by moving from one employer to another, we cannot
assume that the correlation between wages and turnover indicates that low wages are causing
higher turnover. As we discuss below, however, policy experiments with living wages and
minimum wages have provided the evidence needed to determine that wages do, in fact, affect
turnover.
17 These averages include the low-turnover period of the Great Recession, and can be expected to
increase towards higher pre-recession levels as the labor market tightens.
18 The estimate of 17.5 percent represents the midpoint between the 20 percent estimate of
Pollin and Wicks-Lim (2015) and a 15 percent (unpublished) estimate that draws upon Dube,
Freeman and Reich (2010) and Dube, Lester and Reich (2016).
19 Burda et al. 2016, Table 6 (cols. 3 and 5) reports that a $1 increase in weekly pay reduces the
incidence of shirking by -.027 (.0054), on a base of .032 (from Table 1). For a full-time worker,
going from $10 to $15 per hour raises weekly pay by $200, so the effect on productivity would be
about .2x.027 = .005, or 0.5 percent. This estimate measures just the effect of reducing
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 74
idleness. Positive effects on absenteeism and worker engagement would add to the productivity
improvement.
20 Taylor and Houthakker’s industry elasticities are based on regressions of U.S. panel data
across over 300 cities and pooled over 1996-99. As we discuss below in Section 5.5, we do not
expect that a substantial component of consumer sales will move outside the state’s borders. Liu
and Chollet (2006)’s review essay suggests that the price elasticity of demand for out-of-pocket
individual healthcare expenses is -0.2. Our health care elasticity recognizes that employers shift
their cost of health care on to employees. We also recognize that for those with subsidized
coverage, increases in premium costs for lower-income families—who are more price-sensitive—
are borne by the federal government.
21 Annual consumer spending for San Jose (respectively Santa Clara County) is estimated at 58.8
percent of IMPLAN’s estimated GDP for San Jose (respectively Santa Clara County). This
percentage excludes the government share of health care costs.
22 IMPLAN household spending model (proportional to city consumer spending patterns by
household income level), using reduced consumer spending in Row 3 and forcing IMPLAN to
apply 100 percent of the reduction in the city; see the appendix for details on IMPLAN modeling.
23 This includes an offset of 4.20 percent for reduction in SNAP, and 2.3 percent in lower
premium tax credits and cost sharing subsidies under the ACA (Congressional Budget Office
2012). We also reduce the aggregate increase in wages by lost earnings due to estimated job
loss in Panel A. This offset may be too high. According to Chodorow-Reich and Karabarbounis
(2015), the consumption expenditures of the unemployed equal 75 percent of the consumption
expenditures of the employed, even after taking into account the limited duration of
unemployment insurance benefits. Their result echoes a similar result by Aguiar and Hurst (2005)
for food expenditures only.
24 IMPLAN household income model for New York State, using net wage increase from Row 5 and
subtracting net wage increase going to affected workers who live outside New York State; see
Appendix A2 and Day (2013) for more details on IMPLAN. The net wage increase is distributed
across household income categories by the household distribution of increased wages from the
minimum wage increase. Our wage simulation model estimates that 6.6 percent of increased
wages will go to workers living outside the state.
25 IMPLAN household income model for New York State, using net wage increase from Row 5 and
subtracting net wage increase going to affected workers who live outside New York State; see
Appendix A2 and Day (2013) for more details on IMPLAN. The net wage increase is distributed
across household income categories by the household distribution of increased wages from the
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 75
minimum wage increase. Our wage simulation model estimates that 6.6 percent of increased
wages will go to workers living outside the state.
26 Neumark, Salas and Wascher (2014) have criticized these findings. A response paper
(Allegretto et al. 2015) refutes the criticisms.
27 Federal law permits a 90-day subminimum wage for workers under the age of 20.
29 For example, the State of California uses the following definition in SB-3 Sec. 3(b)(4):
“Employees who are treated as employed by a single qualified taxpayer under subdivision (h) of
Section 23626 of the [California] Revenue and Taxation Code, as it read on the effective date of
this section, shall be considered employees of that taxpayer for the purposes of this ordinance.”
30 There is no single consensus estimate of the size of the ripple-effect from minimum wage
increases. We draw on Wicks-Lim (2006), who finds a modal ripple effect of 115 percent across
state and federal minimum wage increases from 1983-2002. Cooper (2013) uses a common
convention of defining the ripple-effect band as equal to the new minimum wage plus the
absolute value of the minimum wage increase being studied.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 76
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http://www.peri.umass.edu/fileadmin/pdf/WP116.pdf.
Wolfers, Justin, and Jan Zilinsky. 2015. “Higher Wages for Low-Income Workers Lead to Higher
Productivity.” PIEE Briefing 2–15. Peterson Institute for International Economics.
http://www.iie.com/publications/briefings/piieb15-2.pdf.
The Effects of a $15 Minimum Wage by 2019 in Santa Clara County and San Jose 84
Institute for Research on Labor and Employment
irle.berkeley.edu
IRLE promotes multidisciplinary research on all aspects of the world of work and conducts
numerous outreach programs. Its funding sources include the University of California, research
grants and contracts, revenue from programs and publications, and contributions from
individuals, business, unions, and community-based organizations.
Center on Wage and Employment Dynamics
irle.berkeley.edu/cwed
CWED was established within IRLE in 2007 to provide a focus for research and policy analysis on
wage and employment dynamics in contemporary labor markets.
Center on Wage and Employment Dynamics
University of California, Berkeley
2521 Channing Way #5555
Berkeley, CA 94720-5555
(510) 643-8140
http://www.irle.berkeley.edu/cwed
MEMO_Minimum
Wage
Recommendation_August
23,
2016
To:
Mayors
and
City
Managers
From:
Raania
Mohsen,
Executive
Director
Cc:
Board
of
Directors
and
City
Council
Administration
Subject:
Update
on
Minimum
Wage
Recommendation
Date:
August
23,
2016
The
collaboration
amongst
Mayors
across
the
County
and
the
Cities
Association
Board
of
Directors
has
led
to
groundbreaking
efforts
on
a
regional
approach
to
a
minimum
wage,
providing
a
common
path
for
cities
throughout
Santa
Clara
County
to
help
ensure
that
more
residents
benefit
from
the
region’s
growing
economic
prosperity.
At
the
June
9th
Board
of
Directors
Meeting,
the
Cities
Association
of
Santa
Clara
County
recommended
a
regional
minimum
wage
increase
according
to
the
following:
o Increase
minimum
wage
to
$15
by
2019
in
three
steps:
1. $12.00
on
1/1/17
2. $13.50
on
1/1/18
3. $15.00
on
1/1/19;
o “Off-‐ramp”
triggers
during
ramp-‐up
phase
that
would
allow
for
scheduled
increases
to
be
delayed
under
certain
economic
conditions;
o Index
to
Bay
Area
CPI-‐W
after
2019,
capped
at
0-‐5%;
o Round
to
nearest
5
cents;
o No
exemptions.
The
recommendation
was
endorsed
based
on
the
results
of
the
countywide
study
led
by
the
City
of
San
Jose,
Cities
Association
Minimum
Wage
Subcommittee
leadership,
and
Board
Member
and
community
input.
View
the
full
economic
analysis
report
and
accompanying
employer
survey.
A
letter
requesting
consideration
of
the
recommendation
was
sent
to
all
Mayors
and
City
Managers
on
July
27,
2016.
Though
several
cities
have
already
increased
minimum
wage,
the
proposed
increase
and
schedule
would
lead
cities
to
land
at
$15
one
year
after
Mountain
View
and
Sunnyvale,
and
three
years
before
the
State
of
California.
Ultimately,
the
goal
is
to
have
all
or
most
of
our
cities
at
nearly
the
same
wage
by
January
1,
2019
and
ahead
of
the
State
due
to
the
region’s
high
cost
of
living.
To
ease
implementation,
a
model
ordinance
was
provided.
It
is
expected
that
each
city
and
council
will
need
to
do
the
necessary
outreach
to
its
businesses
and
constituents
and
will
ultimately
decide
whether
or
not
to
increase
the
minimum
wage
within
its
own
jurisdiction
according
to
the
needs
of
its
community.
MEMO_Minimum
Wage
Recommendation_August
23,
2016
Since
the
distribution
of
the
recommendation,
various
cities
have
scheduled
the
recommendation
for
consideration
by
council
and
have
begun
community
outreach.
To
provide
an
overview
of
when
and
which
cities
are
moving
forward
with
consideration
of
the
recommendation,
a
table
of
each
city’s
current
minimum
wage,
recent
actions,
and
probable
next
steps
are
outlined
below.
Jurisdiction
Current
Minimum
Wage
Response
to
Cities
Association
Recommendation
and
Next
Steps
Campbell
$10.00
Conducted
community
outreach
in
2015;
Council
agreed
to
wait
for
regional
direction;
to
consider
Cities
Association
Recommendation
at
October
4th
Council
Meeting.
Cupertino
$10.00
Began
community
outreach
in
June;
considering
Cities
Association
Recommendation
at
September
20th
Council
Meeting.
Gilroy
$10.00
Is
not
currently
planning
to
consider
the
recommendation
and
will
continue
to
follow
State’s
minimum
wage
ordinance.
Los
Altos
$10.00
Council
to
consider
Cities
Association
Recommendation
at
September
Council
Meeting.
Los
Altos
Hills
$10.00
Council
considered
recommendation
at
July
21st
Council
Meeting
and
determined
it
was
not
applicable
to
Los
Altos
Hills
due
to
the
absence
of
commercial
zones/industry.
Los
Gatos
$10.00
The
Mayor
is
scheduling
a
study
session
to
discuss
minimum
wage
options
and
determine
Council
direction
for
future
consideration.
Milpitas
$10.00
Council
provided
direction
in
June
to
continue
with
outreach
to
businesses,
especially
with
translation
services;
outreach
to
continue
through
October.
Monte
Sereno
$10.00
Council
to
consider
recommendation
in
September
or
October.
Morgan
Hill
$10.00
Cities
Association
recommendation
to
be
considered
at
August
24th
Council
Meeting;
community
outreach
underway.
Mountain
View
$11.00
Adopted
ordinance
to
increase
minimum
wage
to
reach
$15
by
January
1,
2018.
Palo
Alto
$11.00
Adopted
minimum
wage
increase
January
1,
2016.
The
Council’s
Policy
and
Services
Committee
discussed
current
ordinance
and
Cities
Association
Recommendation
at
August
16
th
Meeting;
Committee
supported
recommendation
and
forwarded
to
Council
for
consideration
before
October.
San
Jose
$10.30
Voter
approved
initiative
increased
minimum
wage
in
2012;
consideration
of
Cities
Association
Recommendation
TBD.
Santa
Clara
$11.00
Adopted
minimum
wage
increase
January
1,
2016;
consideration
of
Cities
Association
Recommendation
TBD.
Saratoga
$10.00
Cities
Association
Recommendation
to
be
considered
in
November.
Sunnyvale
$11.00
Adopted
ordinance
to
increase
minimum
wage
to
reach
$15
by
January
1,
2018.
MEMO_Minimum
Wage
Recommendation_August
23,
2016
Based
on
preliminary
feedback,
items
of
interest
for
consideration
and
in
support
of
efforts
to
move
forward
with
a
minimum
wage
ordinance
include:
o Cities
Association
Subcommittee
and
staff
to
provide
direction
and
administrative
support
for
cities
as
needed,
specifically
during
the
ramp-‐up
period.
o Enforcement
-‐
several
cities
with
existing
minimum
wage
ordinances
in
Santa
Clara
County
have
contracted
with
the
City
of
San
Jose
to
enforce
the
ordinance
through
a
complaint
driven
model.
The
contract
between
Mountain
View
and
San
Jose
is
attached
for
review
as
an
example.
City
of
San
Jose
is
willing
to
consider
similar
contracts
with
other
cities
as
needed.
o Administration
-‐
the
City
of
San
Jose
has
provided
a
number
of
documents
as
a
“starter
kit”
for
cities
who
are
interested
in
modifying
them
for
their
jurisdictions,
e.g.
Public
Service
Announcement,
Minimum
Wage
Notification
Templates
in
various
languages,
Press
Release,
Minimum
Wage
Ordinance
Regulation,
etc.,
The
“starter
kit”
is
available
for
download
at:
https://drive.google.com/drive/folders/0B2Yz4EClXj5Cby1tckFIaS13ZmM?ths=true
o For
reference,
the
Cities
of
Mountain
View,
Palo
Alto,
San
Jose,
Santa
Clara,
and
Sunnyvale
have
web
pages
with
information
about
their
current
minimum
wage
ordinances,
notifications,
and
FAQ’s:
o http://www.mountainview.gov/depts/comdev/economicdev/city_minimum_w
age.asp
o http://www.cityofpaloalto.org/gov/topics/minimum_wage.asp
o https://www.sanjoseca.gov/minimumwage
o http://santaclaraca.gov/government/departments/city-‐manager/minimum-‐
wage-‐ordinance
o http://sunnyvale.ca.gov/DoingBusiness/EconomicDevelopment/MinimumWage
.aspx
The
Cities
Association
is
interested
in
hearing
feedback
from
cities
on
their
efforts.
Please
contact
Raania
Mohsen
at
408-‐766-‐9534
or
raania.mohsen@citiesassociation.org
for
inquiries
or
to
provide
feedback.