5.14.17 finalworkonthisversion report_caleitc

Evaluating Cal EITC Education
and Outreach Grants
Marina Balleria, Paulina Maqueda, Jonathan Palisoc, Sonya Zhu
Goldman School of Public Policy
2607 Hearst Ave.
Berkeley, CA 94720
May 2017

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EXECUTIVE SUMMARY
Created in 2015, the California Earned Income Tax Credit (Cal EITC) is a
new credit for working families and individuals who earn less than $14,161
annually. Estimates suggest that between 600,000 and 2 million Californians
are eligible for the Cal EITC; however, during the inaugural 2015
filing season, only 369,956 families filed for and received the credit.
The exact reason for the low utilization rate of Cal EITC is unknown;
however, preliminary evidence suggests that eligible individuals are
not aware of the credit. In response to the low take-up rate and to
increase the number of low-income tax filers in 2016-17, the
Franchise Tax Board (FTB) allocated $2 million to a coordinated education and outreach
campaign, and a state workgroup was formed to oversee delivery of education and
outreach, as led by the California Department of Community Services and Development
(CSD).
Using a mixed methods approach, this evaluation assesses the outreach and education
activities conducted by recipients of the grant. The objective is to identify the most
effective strategies for promoting take-up and to provide recommendations for future Cal
EITC outreach. Through a qualitative analytic method, combined with an original scoring
system for outreach and multivariate and difference-in-difference regression analyses, we
aim to discern the effect of certain types of outreach methods on Cal EITC filings.
Although results indicate a significant positive association between canvassing and filing,
and a negative association between social media outreach and filing, we conclude that
due to the limitations of the data and a short time period of outreach implementation,
limited significance can be found between most outreach methods and filing at this time.
However, this report builds upon the research as to what kinds of outreach methods are
effective at increasing take-up of public benefit programs.
Programmatic recommendations include augmenting the grant process to better fit
grantee capacities, leveraging strategic outreach activities to reach targeted populations,
developing clearly defined metrics for tracking outreach activities, and increasing
coordination amongst grantees, sub-grantees, and other community partners such as
VITA sites. Evaluative recommendations include improving data collection, surveying
reached individuals, and refining models for data analysis. Future directions for Cal EITC
outreach could explore alternative grant models, such as pay-for-performance.

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TABLE OF CONTENTS
I. INTRODUCTION 3
A. Background 3
B. Stakeholder Overview 5
C. Project Motivation 6
D. Objectives 8
E. Literature Review 9
F. Education & Outreach Activities 12
II. METHODS 13
A. Data Sources 13
B. Qualitative Methods 14
C. Quantitative Methods 17
III. RESULTS 23
A. Descriptive Statistics 23
B. Outreach Activity Scoring 28
C. Statistical Tests and Regression Output 29
IV. STRENGTHS & LIMITATIONS 38
A. Strengths 38
B. Limitations 38
V. RECOMMENDATIONS & FUTURE DIRECTIONS 41
A. Programmatic Recommendations 41
B. Evaluative Recommendations 45
C. Future Directions 46
VI. CONCLUSION 49
VII. ACKNOWLEDGEMENTS 51
VIII. REFERENCES 52
IX. APPENDICES 57
A. Variable Coding 57
B. Grantee Interview Questions 62
C. Coding Scheme 63
D. Results: Additional Tables and Figures 65

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I. INTRODUCTION
A. Background
In 2015, Governor Jerry Brown signed the California Earned Income Tax Credit (Cal EITC)
into law, creating a new credit for working families and individuals who earn less than
$14,161 annually (State of California Franchise Tax Board, 2016). Combined with the
federal Earned Income Tax Credit (EITC), families may receive up to $8,975 in tax credits.
As seen in Table 1, very low-income households with children have the most to gain, as
the federal and Cal EITC income taxes may raise their incomes by nearly 75% (California
Budget & Policy Center, 2015). Individuals who are eligible for Cal EITC are predominantly
single women working part time. As a reference, individuals earning under $15,000 make
up approximately the bottom 10% of Californian households (Watt, 2016).
Comparatively, the federal EITC and Cal EITC are both designed to support low-income
workers. Yet the eligibility criteria differ. For the 2016 tax year, the federal EITC refunded
individuals and families making under $53,505 annually, while Cal EITC is only applicable
to those earning up to $14,161 annually. Both the federal and state programs’ payouts
increase with household size, with a significant boost for families (Table 1). Finally, both
require prospective claimants to file income taxes as a first step, and subsequently deliver
the credit as a portion of claimants’ overall returns. This credit may then contribute to the
refund amount provided to the claimant, if no taxes are owed.

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Table 1. Family size and Cal EITC and federal EITC tax credits (State of California FTB,
2016).
Estimates suggest that between 600,000 and 2 million Californians are eligible for the Cal
EITC (CalEITC4Me, 2017; Montialoux and Rothstein, 2015). However, during the1
inaugural 2015 filing season, only 362,000 families filed for and received the credit. On
average, the Cal EITC credit refund amount was $524 per household (Miller, 2016).
The exact reason for the low utilization rate of Cal EITC is unknown; however, preliminary
evidence suggests that eligible individuals are not aware of the credit. An April 2017
survey of people potentially eligible for Cal EITC found that only 18% of respondents
were aware of the credit (California Budget and Policy Center, 2017). Moreover, only 50%
of the potentially eligible respondents reported filing their taxes in the past year,
precluding half from receiving the credit.
In response to the low take-up rate of the credit, the State Interagency Team (SIT)
Workgroup to Reduce Poverty was formed to coordinate the delivery of education and
outreach for Cal EITC, and thereby increase the number of low-income tax filers. For the
fiscal year 2016-17, FTB allocated $2 million to a coordinated education and outreach
Maximum Cal
EITC value
Maximum Federal
EITC value
Highest earnings
to receive
No child $217 $506 $6,717
One child $1,452 $3,373 $10,087
Two children $2,406 $5,572 $14,161
Three children or more $2,706 $6,269 $14,161
It is difficult to estimate exactly how many Californians are eligible because the credit requires filers to be both low-1
income and actively working. Most estimates use the low-income population as a proxy, however, this includes retired,
disabled, unemployed and other individuals that are not working. Moreover, this measure encompassed households
earning between $15,000 and $13,870 (the cut-off for 2015 filers), some of which are not eligible for the credit. CSD was
constricted by data availability and used this measure as the best proxy to identify potential filers.

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campaign, administered by the California Department of Community Services and
Development (CSD).
B. Stakeholder Overview
CSD acts as the main convener of the SIT Workgroup, which aims to:
Figure 1 outlines the various agencies and partners involved in the project.
Figure 1. Stakeholder overview.
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“...reduce poverty in California by increasing the
number of EITCs claimed by eligible low-income
populations, increasing awareness and outreach
for the state EITC, and increasing Volunteer
Income Tax Assistance participation among low-
income eligible populations. The members of the
SIT Reducing Poverty Workgroup represent public
and private organizations” (California Department
of Community Services & Development, 2016).

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The stakeholder overview illustrates how the Cal EITC education and outreach campaign
is a large-scale effort bringing together state government and nonprofit agencies, as well
as community partners and individuals who may serve as “trusted community members”
for spreading the word about the tax credit.
C. Project Motivation
CSD prioritized counties with the highest number of potential Cal EITC filers–defined as
those who were eligible but did not file–and chose partner organizations through a
competitive application process to lead education and outreach efforts in the target
counties and across the state. Grantees were selected based on their demonstrated
experience in conducting outreach with relevant populations, their level of detail and
quality of their outreach plan, and their trusted presence in their respective communities
(CSD, 2016).
CSD identified the counties with the most potential filers for the 2016 tax year. The
number of potential filers was measured by calculating the number of households earning
below $15,000 in each county compared to the Cal EITC credit filing rates for the 2015
tax year in each of those counties.
As specified in the Notice of Funding Availability (NOFA), grantees are required to use
web and social media outreach methods, and recommended to utilize traditional media
outlets, distribute educational materials, conduct community canvassing, and participate
in outreach events. The specifics of each type of activity are explained further in Section F.
The scope of work for grantees contained thorough explanations of the outreach methods
chosen to reach the targeted areas, the type of data that was to be collected, detailed
descriptions of the tools used to track progress, and performance measures (CSD, 2016).
Agencies adapted their outreach strategies to best reach targeted communities, such as
through providing materials in multiple languages and harnessing their network of
partners. Table 2 provides a list of the final grantees, counties, and each respective award
amount.
Grantees in Target Areas 1-10 house the highest proportion of potentially eligible credit
claimants, as determined by income, who did not file for the credit in 2015 (CSD, 2016).
These areas are a mix of urban, suburban, and rural regions, and most are characterized

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by high population density with low-income communities. Amador Tuolumne Community
Action Agency (ATCAA), the agency in Target Area 11, targets the counties that are
predominantly rural: Amador, El Dorado, Mariposa, Calaveras and Tuolumne. Finally,
Target Area 12 grantees were responsible for coordinating statewide initiatives and
several counties not specified in the Target Areas.
Table 2. Grantees and amounts awarded (NOFA).
County Organization
Amount
Awarded
Los Angeles County-Target Area 1
Koreatown Youth and Community
Center $300,000
Youth Policy Institute $300,000
San Diego County-Target Area 2 United Way of San Diego County $92,461
INFO LINE of San Diego $77,539
Orange County-Target Area 3 Orange County United Way $110,000
Riverside County-Target Area 4 Golden State Opportunity $100,000
San Bernardino County-Target Area 5 Golden State Opportunity $100,000
Sacramento County-Target Area 6 United Way California Capital Region $90,000
Alameda County-Target Area 7 United Way of the Bay Area $90,000
San Francisco County-Target Area 8 United Way of the Bay Area $80,000
Santa Clara County-Target Area 9 United Way of the Bay $70,000
Fresno County-Target Area 10
United Way of Fresno and Madera
County $70,000

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D. Objectives
As Cal EITC presents a valuable opportunity to help reduce poverty across California,
CSD’s aim is to raise awareness of the program among low-income populations and
uptake of Cal EITC. The present evaluation aims to answer the following key question:
Table 3 outlines the research questions used to answer the key question. Findings will
inform subsequent planning, implementation, and evaluation of Cal EITC education and
outreach.
Table 3. Project research questions.
Rural Counties (Amador, Mariposa,
Calaveras, Tuolumne and El Dorado)-
Target Area 11
Amador Tuolumne Community
Action Agency $110,000
Statewide-Target Area 12 Golden State Opportunity $221,400
United Ways of California $188,600
Research Questions
Did Cal EITC filing changes from last year to this year differ between grantee and non-
grantee covered counties?
Do certain outreach activities improve the amount of Cal EITC filings in grantee-covered
counties, compared to filings in non-grantee covered counties?
Does outreach at a certain level of intensity improve the amount of Cal EITC filings in
grantee-covered counties, compared to filings in non-grantee covered counties?
What effect did education and outreach activities have
on the number of Cal EITC ﬁlings in counties that were
provided grant funding?

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E. Participation in Public Beneﬁt Programs and Outreach Strategies for
Program Take-Up
Evaluating education and outreach for public benefit programs can provide valuable
insight to enhance program take-up. Research has shown that several major means-
tested programs, such as Temporary Assistance for Needy Families (TANF),
Supplemental Nutrition and Food Stamp Program (SNAP), and Medicaid, face low or
incomplete utilization from eligible populations (Center on Budget & Policy Priorities,
2016; Currie, 2004). On the other hand, take-up rates for the federal EITC are relatively
higher, ranging from 70 to 85 percent of eligible taxpayers over the past couple of
decades (Scholz, 1994; IRS & ACS, 2017). Still, approximately 20 percent of the eligible
population does not claim the federal EITC (IRS & ACS, 2017).
Factors influencing take-up of public assistance programs range from a variety of
individual to state-level factors, including state policy designs (Bansak & Raphael, 2016;
Floyd et al., 2017); transaction costs such as paperwork, program hours of operation,
transportation to social services sites, and administrative barriers (Bertrand et al., 2004;
Currie, 2004); language access resources and English proficiency (Holcomb et al., 2003);
immigration status related to eligibility barriers and fear of deportation (Wasem, 2014;
Ku & Bruen, 2013); psychological factors such as stigma associated with receiving aid,
misperceptions about the service, and lack of trust in service providers (Stuber &
Kronebusch, 2004; Bertrand et al., 2004); and information resources (Hirasuna & Stinson,
2006; Currie, 2004). There is also broad consensus that individuals will weigh the costs
and benefits before participating in a program. If the costs (including both financial and
non-financial costs) exceed the benefits of receiving the public assistance, participation
is less likely (Craig, 1991; Bertrand et al., 2004; Currie, 2004).
To lower barriers to take-up for means-tested programs, evidence suggests that
automatic or default enrollment processes, fewer administrative barriers, and support
from institutions (including private organizations such as hospitals) that are incentivized
to enroll eligible individuals can generally improve participation (Currie, 2004). Tapping
into channel factors--situational details such as close physical proximity to service site,
knowledge of site location, and an a priori commitment--may reduce the perceived costs
and encourage take-up (Bertrand et al., 2004). For immigrants and limited English
proficient speakers, strategies that take into account the complexity of the application
process, provide support in a non-welfare agency setting (e.g., schools, health clinics),
and integrate multiple language access strategies (e.g., bilingual staff, language phone
lines, translated materials) can be especially helpful (Holcomb et al., 2003).

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Specific to the federal EITC, studies have found that awareness of the program may play
a significant role in take-up. In areas with high-knowledge of the EITC, self-employed
filers will manipulate their earnings to maximize their credit (Chetty et al., 2012).
Furthermore, there is evidence that information about the EITC spreads geographically
over time as increased numbers of self-employed filers shift their earnings to exactly the
point that maximizes their credit (Chetty et al., 2012). Additionally, when EITC eligible
households are required to file a tax return, these households are more likely to claim
the credit than those not required to file (Blumenthal et al., 2005). Furthermore, there is
significant income mobility among the EITC eligible population, such that many
individuals claim the credit for only short periods of time (e.g., 1-2 years), and it is
possible that each year there are many new individuals claiming the credit for the first
time (Dowd & Horowitz, 2011).
Previous research has documented a variety of findings on federal EITC take-up rates by
demographic group. Some studies have found that among low-income tax filers, food
stamp recipients, women, persons with more children, and separated/divorced/widowed
persons are more likely to claim the federal EITC (Blank, 2000; Blank, Card, & Robbins,
1999; Center on Budget & Policy Priorities, 1998; Ellwood, 2000b; Eissa & Hoynes, 1998;
Greenstein & Shapiro, 1998; Caputo, 2006). Other studies point to lower take-up rates
as being more likely amongst single parents, persons with lower educational attainment,
persons without children, being age 65 or older, low-income Hispanic parents, and
welfare recipients (Caputo, 2006; Phillips, 2001; Berube, 2003). In California, take-up of
the federal EITC varies by region, where the Bay Area counties have slightly lower
participation than rural counties (Hotz, Mullin, & Scholz, 2003).

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In spite of EITC outreach conducted by the federal government, state governments, and
nonprofit organizations, there is a substantial lack of research on the effectiveness of
EITC outreach efforts on tax credit claims, and whether the benefits exceed the costs.
Some studies have begun to identify specific strategies to increase take-up rates. A
study on Virginia’s EITC program found that an outreach strategy combining mailers and
telephone calls to public assistance recipients had a small positive effect on tax filings
and EITC claims, and that the outreach effort was a cost-effective method to increase
EITC benefits (Beecroft, 2012). Another study on Minnesota’s EITC program found that
among Aid to Families with Dependent Children (AFDC) and TANF recipients, take-up of
the state EITC may depend upon TANF requirements and incentives: for people with
less incentive to work under TANF, they may likely perceive the state EITC as little to no
benefit (Hirasuna & Stinson, 2006). Furthermore, an IRS field experiment found that
simplifying the application process and displaying the benefits of filing significantly
increased the take-up rate of federal EITC (Bhargava & Manoli, 2012).
Considering the multitude of reasons for why eligible individuals and households do not
participate in social safety net programs, strategic and increased outreach efforts may be
key to increase points of entry to programs and promote uptake among eligible
populations (Scholz, 1994; Caputo, 2006; Anderson, 2017). Yet in spite of the
burgeoning literature on why low-income people do not take-up various public benefit
programs, there is still limited evidence-based knowledge with regard to strategies that
can effectively lower both financial and non-financial costs to encourage program take-
up (Currie, 2004).
In California, Cal EITC may face similar challenges with uptake as the federal EITC (and
other public benefit programs), if not to a greater extent. People who qualify for Cal
EITC earn substantially less than those who qualify for the federal EITC. Their low-
incomes imply that they may be likely be isolated from resources and are hard-to-reach.
As Cal EITC has completed its second year of implementation, and the outreach grant is
in its inaugural year, the present project’s preliminary evaluation of Cal EITC outreach can
pinpoint areas of success, improvement, and possibility for the program.

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F. Education & Outreach Activities
During the 2016-17 grant process, grantees employed a variety of education and
outreach activities (Table 4). Per the NOFA, CSD provided grantees with a description of
the recommended outreach activities and strategies.
Table 4. Grantee education & outreach activities.
Outreach Activity NOFA Description
Web Presence
Host web banner on website, add educational information and
updates about the credit and VITA to website, provide links to Cal
EITC educational materials, tools, calculators, maps.
Social Media
Utilize social media platforms to disseminate educational messages,
share success stories, and inform the public about outreach events in
targeted counties and local communities. Platforms may include but
are not limited to: Facebook, Instagram, Twitter, YouTube.
Media
Education through media outlets including: magazines, newspapers,
radio, TV (e.g., solicit local news coverage and participate in
interviews to educate viewers in targeted areas).
Distribution of
Educational Materials
Distribute the following types of materials in multiple languages as
appropriate for the target audience and demographic populations:
flyers, brochures, posters, mailers, emails, letters, memos,
newsletters, text messaging, informational call centers.
Community
Canvassing
Make person-to-person contact in targeted residential neighborhoods
and community gathering places to engage individuals and families.
Outreach Events
Host or participate in coordinated outreach events to engage groups
of eligible people at places such as: community gatherings, resource
fairs, mega events, local businesses, bus tours, educational forums,
local free tax preparation and filing services (e.g., VITA sites).

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II. METHODS
We undertook a mixed methods approach using quantitative and qualitative methods to
assess the grantees’ outreach experiences and activities (Burch & Heinrich, 2016). Given
the substantial differences between two statewide grantees (United Way and Golden
State Opportunity) and 211-Infoline, these three grantees were not included in the
evaluation. However, this report’s recommendations may also apply to these grantees’2
outreach efforts.
A. Data Sources
Table 5 provides an overview of the data used for the evaluation.
Table 5. Data sources.
Data set Description
County-level Cal
EITC filing data
FTB provided Cal EITC filing data from 2015-16 and 2016-17 across
counties in California.
Monthly progress
reports
Grantees reported on outreach activities conducted, subsequent levels of
engagement, and any challenges faced during the month. CSD specified
metrics for reporting. Reports were available from November 2016 to March
2017.
County
demographics
The U.S. Census Bureau’s American Community Survey (ACS) provided
demographic information on county population, race/ethnicity, median
household income, unemployment rate, and participation in public benefit
programs (e.g., SNAP, TANF). Additionally, the Department of Housing and
Urban Development produces yearly estimates of median incomes for
Metropolitan Statistical Areas. Finally, California State Association of
counties categorized counties as being rural, suburban or urban. Details on
exact coding for each variable is included in the Appendix A.
These three grantees conducted different outreach strategies than what was required and/or provided support directly2
to the other grantees.

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B. Qualitative Methods
In an effort to add further depth to the evaluation, we assessed outreach activities from a
qualitative standpoint. The grantees’ experiences were systematically analyzed using
thematic analysis. Specifically, we drew from grantee interview notes and the
“Challenges” section of the progress reports to form the qualitative data set.
Thematic analysis is a popular and flexible tool used in the social sciences to identify,
analyze, and report patterns in qualitative data, and can provide a rich illustration of the
topic of inquiry (Braun & Clarke, 2006). A theme represents a meaningful aspect about the
data in connection to the research question, and occurs in a patterned response within
the data set. The current project adopted an inductive approach to thematic analysis–
exploring and analyzing the data without a pre-existing theoretical framework. This
allowed for the analytic process to aim directly at capturing grantees’ experiences.
Based on Braun & Clarke’s (2006) conception of thematic analysis as a distinct analytical
method, the steps undertaken to analyze grantee interviews and monthly reports are as
follows:
1. Familiarize yourself with the data: Actively read through the all notes from grantee
interviews and monthly progress reports, with repeated readings. Note ideas and
potential coding schemes.
2. Generate initial codes: Each code refers to the most basic element of the data that
can be assessed in a meaningful way (Boyatzis, 1998). Read through monthly
reports and grantee interviews to identify interesting segments of the data, which
may eventually form repeated patterns, or themes. Code all data extracts and
collate them together within codes. It is possible to have multiple codes for the
same data extract. The relative importance of a code does not necessarily depend
on its frequency, but more so on the code’s ability to capture a meaningful aspect
of the data.
3. Search for themes: Organize the codes into potential themes. Consider the
relationship between codes, between themes, and between levels of themes (i.e.,
Grantee
leadership
interviews
We conducted interviews with the grantees’ primary contacts for Cal EITC
outreach, using a predetermined set of questions along with an open dialogue
format. Notes taken from the interviews were used in thematic analysis. See
Appendix B for interview questions.

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main and sub-themes). Eliminate, add, or revise codes as needed.
4. Review the themes: Review the collated extracts for each theme and determine
whether they collectively produce a meaningful pattern. Review the validity of each
theme in relation to the data set as a whole; if the theme no longer fits, incorporate
it into another theme, revise it, or eliminate it altogether.
5. Define and name themes: Clearly describe each theme in terms of what it
represents and how it fits into the data set overall. It is possible for themes to be in
tension with one another, but when taken together, the themes tell a coherent
story. Create a thematic map of the themes and sub-themes, as displayed in the
results section.
6. Produce the report: Connect the thematic analysis back to the main research
question of how outreach and education impact Cal EITC utilization rates and the
literature on public benefits and tax credits. The final analysis should go further
than serving as a description of the data, but instead is an argument addressing the
research question.

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Table 9 depicts an example of the coding process that analyzes a grantee interview. The
far left-hand column includes collated notes from the interview, with the next column
containing the relevant code. The codes were then sorted into themes and sub-themes.
Once the themes were defined, a map of themes and sub-themes was generated to
illustrate the analytic narrative (see Results section).
Table 9. Example of collating data extracts into codes and themes.
Notes Code Sub-theme Theme
The funding helped to hire a professional to make
marketing more uniform amongst their partners,
so the same message could be relayed to the
public.
Marketing
Uniform
messaging
Communicatio
n
Canvassing and leveraging existing relationships
was possible on a greater scale. It was also
necessary as the agency went through staffing
changes that left them with limited capacity.
Capacity
building
New hires
New
opportunities
The agency could have reached out to more
people if the timing of when they can use the
funding happened earlier. Planning for this agency
begins in the summer and they want to be sure
that they are going to be able to take advantage of
this funding when they are in their planning
phase.
Planning
Earlier grant
process
Timing

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C. Quantitative Methods
a. Scoring system
A scoring system was created to standardize evaluation criteria for the education and
outreach activities of each individual grantee. As grantees provided data on their levels of
outreach for each month leading up to the tax deadline, monthly reports from the start of
outreach in November 2016 to the most recently received in March 2017 were analyzed
(April 2017 reports were not available at the time of this analysis). Scores were then
weighted according to the month that the activities were conducted; as many grantees
did not start reporting and conducting their outreach until later months, their level of
outreach and subsequent scores were weighted heavier in those months and less in
earlier planning months. The hypothesized relevance of the activity was also scored and
weighted. Weights for subscores within each outreach score were determined by two
general principles: subcategories where actual touches to possible eligible filers could be
measured were weighted heavier, while subcategories where the outreach was improved,
but not in measureable terms of touches (number of platforms, number of ads, etc.), were
weighted at a lesser amount. Finally, scores were incorporated into the statistical models
described below to examine the effect of certain outreach methods on Cal EITC claims
across counties.
Each activity category is specified by a set of codes, which were developed based upon
the NOFA definitions of activities, research on federal EITC education and outreach, and
our team’s collective background in relevant community engagement activities, including
financial counseling, canvassing, social media, and programmatic analysis (see Appendix
C for complete description of coding scheme). If a grantee did not report using that
outreach method, they were dropped from the analysis for that particular method. They
were not scored as a 0 because grantees interviews implied that an omission in the
monthly report did not mean that no outreach occurred during that month. This can be
further clarified by CSD when all of the monthly reports are completed.

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Table 6. Scoring of outreach intensity.
Social Media
Web Presence
Media
Distribution of Educational Materials & Messaging
Category Frequency Platforms Engagement Paid Promotion
Metrics
(per month)
Count of posts/
tweets
Count of social
media platforms
used (i.e.
Facebook,
Twitter,
Instagram)
Count of likes,
reactions,
favorites, shares,
and retweets
across platforms
Count of social
media ads
Weight 0.50 0.10 0.25 0.15
Category Overall Engagement Unique Visitor Engagement
Metrics
(per month)
County population-adjusted
count of clicks to website
County population-adjusted
unique visitors to website
Weight 0.50 0.50
Category Outdoor Broadcast Print Reach
Metrics (per
month)
Dummy variable
indicating any
use of transit or
billboard
advertising
Dummy variable
indicating any
use of television
or radio
promotion
Dummy variable
indicating any
engagement with
newspapers or
printed shoppers
Population-adjusted
reach per month
based on projected
readership
Category Reach Partners
Metrics (per month) Population-adjusted count of
materials distributed
Count of partners used to
distribute materials

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Community Canvassing
Outreach Events
Weighting per month
b. Descriptive analysis
Univariate and bivariate analyses were used to examine differences in counties that
received funding and those that did not. The literature suggests that demographic trends,
such as race and ethnicity, may play a role in public benefit take-up rates. Furthermore,
labor market trends in each county were also examined to account for any variation in
wages and employment opportunities, as changing economic conditions may enable
individuals to begin working or may push the incomes of formerly eligible individuals
above the Cal EITC threshold.
c. Statistical tests and models
The study undertook three statistical methods to identify the relationship between grant
funding and Cal EITC filings.
Category Reach Sites
individuals contacted
Count of locations where
materials were distributed
Category Reach Events
individuals attending event
Count of events that grantee
or partners participated in
November December January February March Total
.05 .10 .15 .30 .40 1

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Figure 2. Evaluation framework.
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Table 7. Research questions.
i. Two proportion z-test
First, a two proportion z-test was used to discover if there was a statistically significant
difference between grantee and non-grantee counties. This method compared the
percent change of filing numbers between the 2015 tax year and the 2016 tax year to
discern if there was a significant difference between grantee and non-grantee counties.
ii. Multivariate regressions
Second, a standard Ordinary Least Squares (OLS) regression model was used to estimate
the effectiveness of grantee outreach and education on Cal EITC filings. In each of these
cases, the outcome variable, Y, is the percent change in Cal EITC filings from 2015 to
2016.
Education & Outreach
Activities
Covariates: race,
economic conditions,
median income, other
public benefit programs
Cal EITC Claims by
County
?
Method Research Question
z-test &
multivariate
regression
Did Cal EITC filing changes from last year to this year differ between grantee
and non-grantee covered counties?
Multivariate
regression
Do certain outreach activities improve the amount of Cal EITC filings in
grantee-covered counties, compared to filings in non-grantee covered
counties?
Difference-in-
difference
Does outreach at a certain level of intensity improve the amount of Cal EITC
filings in grantee-covered counties, compared to filings in non-grantee
covered counties?

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Y = β0 + β1-8(outreach indicators) + 𝛆
This method aimed to discover if a certain type of outreach had a statistically significant
effect on the percentage change in Cal EITC filings. Using data from grantee-provided
monthly reports and grantee interviews, any reported outreach during any month was
coded as a 1. This method used the percent change in Cal EITC filings, as provided by
the FTB (note: April 15-18th, 2017 filings were not available when this report was
completed).
A more robust multivariate model was also created that included covariates that
controlled for county-level economic conditions, including the percent change in
unemployment, county median income, percentage receiving SNAP, and percentage
receiving TANF.
Y = β0 + β1-8(outreach indicators) + β9-n(local economic controls) + 𝛆
iii. Difference-in-difference
Third, to control for changes by year, the difference-in-difference (DD) approach was
undertaken in comparing grantee and non-grantee areas:
Y = β0 + β1(time) + β2-7 (outreach) + β8-13(time*outreach) + ɛ
Table 8. DD framework.
YType/Time Cal EITC Filings
2015
Cal EITC Filings
2016
Change
Grantee Counties
(with outreach
intensity scoring)
YG15 YG16 YG16-YG15
Non-Grantee
Counties
(with all outreach
intensity scores = 0)
YN15 YN16 YN16-YN15
Difference YG15-YN15 YG16-YN16 (YG16-YG15)-(YN16-YN15)

!22
In addition to the main explanatory variables, a set of covariates was included in the
regression to avoid omitting relevant variables and to minimize potential confounding,
which would otherwise bias the estimate of the program’s effect and limit the ability to
draw appropriate conclusions about outreach effectiveness. Covariates included county-
level demographic variables, including population size, median household income, race-
ethnicity, local economic conditions, and participation in other public benefit programs
(i.e., cash public assistance or TANF, SNAP or food stamps).

!23
III. RESULTS
A. Descriptive statistics
The following section reports the results from univariate and bivariate analyses.
a. Overall comparison of grantee- and non-grantee-covered counties
Figure 3: Comparing grantee- and non-grantee counties.
In 2016, the vast majority of Cal EITC filings occurred in grantee counties. As mentioned
previously, the estimates of the eligible population overstate the true eligible numbers
because they include those who do not work and are therefore not eligible for the credit.

!24
b. Race, country of origin and Cal EITC filings
Figure 4. Race, foreign-born, and Cal EITC filings.
!
Legend: Orange represents grantee counties and gray represents non-grantee counties.
On the whole, grantee counties are more diverse than non-grantee counties with higher
proportions of every racial and ethnic group besides whites (Appendix D). Additionally,
grantee counties are home to a higher proportion of Californians born outside of the
United States. Appendix Figure D-2 highlights the comparative diversity of grantee
counties. However, there is not a meaningful correlation between the percentage of
nonwhite individuals in a county and the percent change in filings nor the raw change in
the amount of Cal EITC filings in 2016.

!25
c. Changes in Cal EITC filings across counties
Figure 5. Changes in Cal EITC filings across grantee-covered counties.
!
In California as a whole, Cal EITC filings decreased between 2015 and 2016. The vast
majority of California counties experienced a negative change in filings, while a small
number of sparsely populated rural counties experienced a positive change in filings. For
example, Sierra County experienced the highest percent increase in filings with a filing
increase from eleven filers to fifteen filers (Appendix D). Non-grantee counties
experienced a 5.1% decrease in filings and grantees ranged from a -15.9% to -3.1%
decrease (Appendix D). Reasons for the filing decrease are complex and include changes
in local economic conditions and the demographic differences between grantee and non-
grantee counties. These will be explored further through our analysis and
recommendations.

!26
!
Legend: Orange represents counties with less filings than the previous year while blue shows
counties with more filings than the previous year.
d. Labor market conditions across counties
Both CSD and grantees hypothesized that regional labor market conditions could have
had a profound effect on filings. Improved employment opportunities or wages may push
individuals’ incomes out of the Cal EITC eligibility ranges. Between 2015 and 2016,
unemployment decreased or remained steady in most of California. The grantee counties
have overwhelmingly experienced no change in unemployment compared to non-grantee
counties (Appendix D). We also find unemployment is not strongly correlated with the
change in Cal EITC filings.
However, the vast majority of grantee counties saw significant increases in wages. On
average, grantee counties saw a median income increase of 2.86%, or $804, compared to
non-grantee counties, who saw a median increase of .75%, or $194 (Appendix D). As
demonstrated in Table 10 and Figures 6, grantee counties saw significant increases in
wages, ranging from $50 to $1,750, compared to California as a whole. The income range
to qualify for Cal EITC is tight, meaning that a few hundred dollar increase in income can
push many low-income people outside of the eligibility criteria.

!27
Table 10. Changes in economic conditions across grantee counties.
!
Figure 6. Percent change in income by county.
!

!28
B. Outreach Activity Scoring
Figure 7. Examples of scored activities.
!
As described in the Methods section, a set of criteria were used to create scores for each
type of outreach. The above bar charts demonstrate the variation in scores for each
grantee across various outreach methods. Some methods, such as the distribution of
educational materials, were fairly uniform across counties, while the intensity of
community canvassing across counties was extremely variable. It must be emphasized that
grantee reporting was inconsistent and the findings generated from the reported activities
are incomplete. For example, one grantee did not report their sub-grantee’s canvassing
activity. Complete reporting by every grantee and their sub-grantees/partners would be
required to create a more robust analysis. A full set of scores for all methods and grantees
can be found in Appendix D.

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C. Statistical Tests and Regression Output
a. Two-proportion z-test output
Table 11. Cal EITC filing changes from 2015-16 to 2016-17.
Using a comparison of proportions z-test, the percentage decrease in filings for non-
grantee and grantee counties was compared to see if they were significantly different. The
test shows statistically significant results. As such, we are very confident that there is a
significant difference in filings between grantee and non-grantee counties.
Surprisingly, though, grantee counties have a significantly larger decrease in percent
filing, which runs counter to the hypothesis that outreach has significantly helped grantee
county filings. This result describes the raw change in filings; however, it does not include
any control variables that may help explain the change. Reasons as to why grantee county
filings might have seen a larger decrease will be explored later.
Grantee Non-Grantee
Cal EITC File Change
-7.13% -6.86%
Cal EITC Filings
201,891 78,579
Z-statistic
2.60*
p < .001

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Cal EITC funding indicator, eligible population, and controls.
!
As shown in the figure above, funding and eligible population have a significant
relationship at the .01 level. This means that there is a strong positive relationship
between whether or not a county received funding and how much of their population was
eligible for the credit. This is in line with CSD’s targeting for funding towards counties
where the highest about of Cal EITC individuals were located.

!31
b. Multivariate regression output
Table 12. Model 2.1: Multivariate regression output of Cal EITC filing change and
outreach.
!
Model 2.1 uses dummy variables to indicate whether or not each county provided each
type of outreach. The model shows no significant relationship between Cal EITC filing
change and whether or not a type of outreach was provided. The indicators for web,
broadcast, educational materials, and outreach were dropped due to multicollinearity; this
is because all counties provided these types of outreach and therefore made it impossible
to distinguish individual effects. All outreach dummies were interacted and tested to see if
certain outreach activities were only effective when provided in tandem with others, but
none were significant.

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Table 13. Model 2.2: Multivariate regression output with covariates.
!
!
Model 2.2 integrates county median income, unemployment change, the proportion of
individuals on SNAP, and the proportion of individuals on TANF as controls for local
economic conditions. As shown, the indicators for canvassing and social media are
statistically significant, although only at the 10% level. This suggests that if a county
provides social media outreach, we would expect their filings to be 11.30% lower than a
county that did not provide social media outreach, on average. If a county provides
canvassing outreach, we would expect their filings to be 7.91% higher than a county that
did not provide canvassing outreach, on average.
While we do find significant results towards canvassing and social media, it must be
stated that filing change and outreach is a complex issue. It would be unlikely that these
outreach methods caused this significant of a filing change, and it is dubious that social
media’s effect is negative; it is possible there are omitted variables or other biases in the
model. As our sample size is small (n=53 counties), and our results show that outreach
combined with local economic conditions only explain a small portion of the total change

!33
in filings, these results should be interpreted with caution. In addition, correlation and
significance between variables does not imply causation between the two. The
Recommendations section describes more robust methods to discern the relationship
between outreach and Cal EITC claims.
c. Difference-in-difference regression output
Table 14. Model 3.1: Difference-in-difference regression output.
!
Model 3.1 examines filing over the two time periods of 2015 and 2016 while testing the
treatment effect of providing grant-funded outreach at a certain level of intensity. In this
model, outreach methods are represented using the scores described above. As such, the
variables of interest are the interaction terms of treatment*time (smtime to outtime). No
variables of interest are found to be significant. All outreach intensity scores were
interacted and tested to see if certain outreach activities were only effective when
provided at a certain level of intensity together, but none were significant.

!34
Table 15. Model 3.2: Difference-in-difference regression output.
!
As with Model 2.2, controls were integrated to help explain the variation in filings. The
same controls were used, plus an additional variable representing the proportion of
foreign born in a county. In Model 3.1, no significance was found for the variables of
interest. Potential reasons for the lack of significant results is explained in the Limitations
section.
d. Analysis summary
No significance related to outreach intensity was found at this time. However, our second
model found a significant positive effect of canvassing and a significant negative effect of

!35
social media on filing change. As described above, there are issues that limit our
interpretation; however, significant results are suggestive of a pattern.
e. Thematic analysis results
Based on the thematic analysis of grantee interviews and monthly reports, a map was
generated to illustrate the themes and sub-themes.
Figure 8. Thematic analysis map.
!
i. Description of themes and sub-themes.
Theme: Communication
Communicating the right messages that resonate with target populations and knowing
when to use the most appropriate channel is an ongoing challenge.
a. Subtheme: Uniform messaging
Grantees and their partners aimed to coordinate messaging to reflect local needs,
while staying consistent amongst each other and the statewide campaign. This was
a challenge as each of the Target Areas serve different communities with diverse
needs.
b. Subtheme: Low social media engagement
Social media is difficult to reach targeted communities and has had limited success

!36
in engagement from the broader public.
Theme: Measuring Progress
Monitoring and assessing progress was
structured via Monthly Progress Reports,
but knowing how to fully capture the
extent and impact of outreach activities is
an ongoing question.
a.Sub-theme: Accurate partner reporting
The supporting activities of sub-grantees
and other partners were difficult to
ascertain and not completely accounted
for in the Monthly Progress Reports.
Technical assistance had to be provided
often to ensure more accurate reporting of
grantees and their sub-grantees/partners.
b. Sub-theme: Lack of narrative and VITA site data
Having a record of the demographics of Cal EITC eligible individuals reached may
improve outreach. Currently, there is no tracking of how potential Cal EITC filers
heard about the credit, which could be through the VITA sites.
Theme: Timing
Timing is critical to planning outreach, negotiating with partners, and implementing
activities and has to include flexibility to address changing conditions.
a. Sub-theme: Earlier grant process
The late start of the grant timeline presented a significant challenge in having
adequate time to prepare for outreach.
b. Sub-theme: Exploring VITA collaboration
Although legislative constraints and the scope of the grant prohibited funding from
being used for any VITA activities, grantees and VITA sites have a significant
amount of shared goals. If allowed in future years, grantees have expressed
significant interest in tying Cal EITC outreach funding, or using alternative
resources, with VITA site establishment, engagement, and promotion.
Theme: New opportunities
Funding provided the opportunity to pursue new avenues of engagement and bolster
“[Before] we had to look at
what we could afford to do.
People need to hear things
multiple times to have it sink
in. They have to see it in
multiple ways, [even] in
tactile ways [such as through
flyers and brochures].”

!37
existing outreach.
a. Sub-theme: New hires
Grantees were able to hire new staff for activities such as marketing, reporting,
material development, and canvassing.
b. Sub-theme: Unconventional outreach methods
Grantees that had previously limited or no resources for certain outreach methods
were able to explore activities such as transit and TV ads, text messaging, and
social media platforms.

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IV. STRENGTHS AND LIMITATIONS
A. Strengths
a. Multiple data sources and mixed methods
Using several sources of data and a combination of quantitative and qualitative methods,
the evaluation presents a multi-faceted picture of Cal EITC outreach. In particular, the
scoring of activities derived from progress reports and county-level claims data allowed us
to investigate a possible treatment effect of the grant outreach.
b. Quantitative analysis
The standardized scoring system of outreach activities allowed us to provide analytical
output for the effectiveness of outreach and education. The multivariate regression
allowed us to examine whether or not certain outreach methods were effective, while
controlling for other variables. The difference in difference method has the advantage of
controlling for unmeasurable characteristics that remain the same over time. This allows us
to compare groups that are not exactly the same but both experience the same changes
in filings. For example, a difference in difference approach would control for the overall
economic health of counties, if these characteristics remained stable. If an area
experiences an outsized change in the economy, then that variation would violate the
assumptions of the model.
c. Qualitative analysis
Through the interviews, we were well-positioned to gather firsthand insight from grantees
that may not have been captured in the monthly reports. Additionally, using the method
of thematic analysis, we were able to draw common themes from stakeholders and
highlight meaningful pieces of information not otherwise reflected in the quantitative data
sets. Finally, our qualitative findings provided further avenues of inquiry, which guided our
quantitative process.
B. Limitations
a. Lack of uniform and robust data
The main source of data used in this evaluation are the monthly reports submitted to CSD
by each grantee. While current reporting processes requested data in a uniform manner
and worked with grantees to improve their process, there was significant variation in how
grantees reported. Some grantees failed to report sub-grantee activities, forcing the sub-

!39
grantee activity to be omitted from our analysis. The varied nature of the data may create
issues and so the results of our analyses should be viewed with this in mind.
b. Lack of specificity in data
While data was provided at a county level, outreach efforts from each organization were
often targeted to specific areas within the county. If zip code or census tract level data
was provided where outreach methods were used, our statistical analysis would be much
more targeted. In addition, this would increase the sample size allowing further
evaluations to have greater potential to find significance.
c. Sources of Bias
i. Selection Bias
Grantee outreach methods, which served as the treatment in our model, were not
randomly assigned. This made it difficult to compare groups, as there was a systematic
bias in which counties received treatment. As demonstrated in our descriptive statistics,
there were systematic differences between grantee and non-grantee counties that made
them ill-suited for comparison. We used statistical techniques to mitigate this issue;
however, the counties are so dissimilar that the assumptions of this model do not hold.
ii. Omitted variable bias
The models used in this analysis attempted to control for demographics and economic
conditions using variables such as change in income and population proportions on
public benefits. However, there may be other variables that affected filing that were not
included in the model, which would then bias the estimated effect of outreach.
iii. Attenuation Bias
The lack of consistency in grantee reporting creates considerable irregularity in the
outreach scores. This means that our results are more likely to show no relationship
between outreach efforts and Cal EITC filings, even if a significant relationship exists.
d. Subjectivity in outreach scoring
Due to the qualitative nature of the outreach, there is a certain amount of subjectivity
when analyzing the data. Grantees faced challenges with gauging the exact number of
touches each outreach method created. Moreover, for certain outreach strategies such as
social media, web presence, and traditional media, it is impossible to know whether or
not the touch was to a person who qualifies for Cal EITC. While the regression and

!40
variables created for this analysis can weigh and adjust for the outreach methods believed
to be most effective, there is still considerable variation within the data. Lastly, while
scoring outreach methods conforms the activities to a standardized set of metrics, it is
necessary to acknowledge that the scoring system adds another level of subjectivity to
the results.

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V. RECOMMENDATIONS
Both the quantitative and qualitative analyses of Cal EITC outreach
highlight areas of success and room for growth. Findings are integrated
into the following set of recommendations across programmatic and
evaluative components. Programmatic suggestions propose
improvements for the grant process and outreach implementation;
evaluative recommendations offer directions for more refined research
across data collection, analysis, and evaluation activities.
A. Programmatic: Grant Process and Outreach Activities
a. Augment the grant process timeline
Grantee organizations would have benefited from the opportunity to
apply for the grant and plan outreach activities earlier in the year, as
preparation for the tax filing season begins in the summer. The short
turnaround time between being awarded the grant and conducting
outreach presented a significant challenge in ensuring adequate time to
plan activities, meet and negotiate with partner organizations, and
implement activities with room for iteration.
b. Leverage strategic outreach activities
It is not possible for the present evaluation to draw causal inferences on
which outreach activities effectively contribute to take-up of Cal EITC.
However, given that we employed both quantitative and qualitative
methods to assess outreach, it is possible to identify potentially fruitful
outreach strategies by combining insights from our interviews with the
grantees, thematic analysis, and our quantitative results. Moreover, given
that the broader empirical research is sparse on whether or not outreach
activities are effective for increasing utilization of public assistance
programs, the activities undertaken by Cal EITC grantees may provide a
window into further understanding of strategic outreach activities.
The most effective outreach strategies in terms of converting eligible filers
into Cal EITC claimants potentially include the following:
Programmatic
Execution of outreach
conducting by grantees
could be more strategic,
like targeting speciﬁc zip
codes versus entire
counties.
Evaluative
More structured/
outlined reporting
system for grantees’
education & outreach
activities.
Analysis
Other types of analysis
that could be conducted
are Cost-Effectiveness or
Cost-Beneﬁt analysis on
outreach activities.
Recommendations

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● Word-of-Mouth: Grantees strongly hypothesized that potential filers who hear
about the benefits of Cal EITC from other trusted members of their community (not
just service providers), from the media, or through educational materials, are more
likely to claim the credit. While word-of-mouth is perhaps the most difficult
outreach “strategy” to track due to the fluid and informal means through which
information is communicated, outreach activities can aim to integrate word-of-
mouth through referral programs, focused outreach to community leaders, and by
coaching VITA site volunteers to encourage Cal EITC filers to spread the word to
their friends and family.
● One-on-One: Direct contact between agencies’ internal staff, partner staff,
volunteers, and hired people can provide information about the credit directly to
potential filers by talking about the credit and providing educational materials.
● Flyers: Educational materials in plain language, a language relevant to the target
audience, and formatted as a one-page document have been described as
impactful on potential filers.
● Direct mailing and texting: Grantees who purchased address information and cell
phone numbers of potential eligible filers were able to directly mail educational
materials and send targeted text messaging to raise awareness of the credit, similar
to the approach taken on by political campaigns.
● Social media: There are ample social media toolkits that future grantees may
reference to improve social media engagement. These include social media
checklists, best practices for different platforms and types of content that can
engage audiences, and editorial calendar guides to organize postings and outline
broader communication strategies. While there is limited research on whether
social media itself can improve take-up of government programs, studies of
nonprofit organizations find that strategically engaging stakeholders on social
media through providing information, fostering an online community, and making
calls to action are key elements to social media posting (Lovejoy & Saxton, 2012).
Our preliminary findings suggest that social media does not increase Cal EITC
filings; however, outside research shows that it may inform regional service
providers about the credit.
● Collaborating with outreach for other public benefit programs: While some
grantees conducted outreach at sites servicing other public benefit programs (e.g.,
WIC offices), future grantees can more strategically partner with local community
services that promote and provide public assistance for programs such as CalFresh,
Covered California, and WIC, as they serve similar clientele.

!43
c. Developing clearly defined metrics for outreach activities
The monthly progress reports provided an overarching structure for grantees to track their
inputs and outputs, as found in the Description of Activities Conducted And Progress
Made This Report Period section. However, because the level of detail in which metrics
were reported on varied across grantees, as well as the types of metrics reported within
each activity, this presented a challenge from an evaluative standpoint. As such, it is
recommended that the reporting be updated with the following considerations for
metrics. This should be accompanied by existing upfront training for grantees alongside
consistent technical assistance to address inconsistencies as they arise. This list aims to be
provide a menu of options; CSD should choose the metrics most relevant to their
purposes:
● Web presence
○ Metrics: number of clicks, number of visitors, number of page views to
grantee websites, number of website ads.
● Social media
○ Metrics: number of posts, likes on posts (not on page) and shares; post
reach, engagement, and impressions up to the last 28 days (accessible on
Facebook Insights and Twitter Analytics, which are free to the user and
provide a wealth of information).
● Media
○ Metrics: type of medium used (e.g., TV, radio, transit, newspaper), estimated
number of impressions or readership.
● Distribution of educational material and messaging
○ Metrics: types of materials used, raw number of materials distributed, and
sites or avenues of distribution.
● Community canvassing
○ Distinguish between a canvassing engagement (e.g., one-on-one through
active canvassing) vs. an event engagement (e.g., hosting an event and
presenting to attendees).
○ Metrics: # of doors knocked, # of geographic sites visited, # of one-on-one
interactions, type of canvassing (e.g., door-to-door, high traffic).
● Outreach events
○ Metrics: # of events hosted or attended, # of people who attended and
were reached.

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● General reporting
○ If applicable, sub-grantee activities should always be denoted separately
within grantee reports.
○ For activities that received support from in-kind funding, include this detail
in the report in order to differentiate from the grant’s funding.
○ Grantees should separately report their time spent planning and the actual
implementation of activities. Some grantees conflated the two, making their
actual outreach activities difficult to distinguish from the planning process.
○ Consistency in reporting expectations: Reporting guidelines were changed
throughout the tax filing season. Some agencies reported that it was difficult
to change their reporting as they were understaffed during the tax filing
season.
d. Increase collaboration with VITA sites
Although the state legislature budget and grant scope did not allow grant funding to be
used towards VITA sites, grantees commonly expressed the desire for funding to support
collaboration with VITA site activities, in order to match the increase in resources
dedicated to the Cal EITC campaign. Specifically, funding support for VITA sites could
entail hiring new staff
members, purchasing
additional computers,
providing larger spaces to
accommodate the
anticipated increase in
filers, covering overhead
costs related to fulfilling
reporting requirements,
converting volunteer
outreach to paid outreach
(e.g., canvassing), and/or
managing sub-grantees.
While this may not be
covered under Cal EITC grant funding, an alternative resource should be explored to
pursue collaboration with VITA sites. Indeed, the literature suggests eligible beneficiaries
are less likely to use a public benefit if the process is onerous; VITA site support can help
ameliorate this issue by ensuring all filers have an efficient experience.

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f. Micro-outreach grants
Evidence from research on the federal EITC suggests that knowledge about tax credits
spreads quickly through zip code areas when word spreads about an additional source of
income (Chetty et al., 2012). CSD can exploit this phenomenon through micro-outreach
grants that focus on zip codes with a high concentration of potential filers. Grantees
reported issues engaging with a diverse populations across large counties. Additionally,
our preliminary findings suggest that high-contact outreach methods may be the most
effective. Micro-outreach grants encourage grantees to use intensive, one-on-one
outreach in concentrated areas.
B. Evaluative
a. Obtaining more uniform and robust quantitative data
As detailed above, clearly defined metrics for each outreach category can build a more
comprehensive and reflective picture of grantees’ community engagement. It is strongly
recommended that grantees report their progress using a well-defined, consistent, and
reliable set of metrics for outreach, in order to eventually procure a more robust data set
for evaluation. In particular, a review of best practices for how to measure each type of
outreach category–with special attention to canvassing, social media engagement, and
traditional media–would support the validity of metrics selected for reporting.
b. Surveying reached individuals
To build the qualitative evidence base for the effectiveness of Cal EITC outreach, the
grant funding might stipulate and cover costs of grantee data collection activities. For
example, grantees may conduct short interviews with individuals they have engaged with
and who have decided to file for the tax credit. Alternatively, a brief survey form
distributed to VITA site attendees can ask individuals whether or not they had heard of
Cal EITC, and if so, from what source(s). Both narrative and quantitative data can be
obtained through surveying reached communities, and inform subsequent evaluations.
c. Models for data analysis
Other directions that were not explored in this evaluation but may be of interest are:
● Time-series analysis and measuring the effect of implementation timelines.
● Effectiveness of outreach by zipcode.
● Evaluating filings based on the proximity to a VITA site.

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● Inclusion of more demographic and economic-related variables.
● Genetic matching algorithm: An algorithm could be used to match grantee areas
and non-grantee areas with extremely similar demographic, economic, and
geographic characteristics. This would allow for a more direct comparison, making
it simpler to estimate the true effect of outreach. However, this requires variation
between grantee and non-grantee counties. For example, major metropolitan
areas would need to be divided between those receiving the grant and those that
do not, regardless of the need within each respective county. For example, the
GAM method would require one major metropolitan area–like Los Angeles–to
receive grant funding while another–the San Francisco Bay Area–would not. This
allows for comparison between the two, but there are practical limitations to
providing funding to one area and not the other.
C. Future Directions
With consideration of the above mentioned Recommendations, looking ahead, one
possibility to pursue in Cal EITC outreach is a pay-for-performance (P4P) model.
a. Pay-for-Performance Model
If desired, CSD should explore using a P4P model when distributing grant funds for Cal
EITC outreach. P4P models have gained traction in recent years due to their ability to
have the applicant be more invested in their funding through shared risk. In a P4P model,
participants are awarded a proportion of their predetermined funding award based on
their performance in the program on an ongoing basis. In contrast, non-P4P programs like
the current Cal EITC grant give all funding up front, regardless of whether the grantee
reaches pre-specified goals.
i. Pro et contra
The benefits of a P4P model are numerous, and the model has enjoyed significant success
within social programs such as healthcare (Gates et al., 2014). For the Cal EITC grant
process, the P4P model could allow more flexibility for grant development, hold grantees
accountable to goals, normalize data and improve grantee reporting and evaluation
processes.
As long as incentives are enticing to grantees, P4P models would allow for CSD to set
ambitious targets in numerous areas within grant outreach. The Department could create

!47
a menu of metrics it wishes to see achieved and grantees could then pick a mutually
agreeable number to attempt. By basing payment on performance, grantees would be
increasingly incentivized to hit grant targets and devote increased time and resources
towards those predetermined targets.
Most P4P programs require that a basket of measures is defined and incorporated into a
scorecard (Heider et al., 2015). Grantees can then earn their funding based on their
performance on the scorecard, which is evaluated using predetermined metrics by the
operating agency. Generally, P4P programs require strict reporting from each entity
involved in the program. Alongside this, evaluations must be conducted on each entity to
determine if they have met thresholds to receive funding.
While generally successful, P4P programs are not fit for every grant program. Some
challenges that the model face are added pressure on grantees, limitations with outcome
measures, and significant incentives. While P4P can incentivize grantees, it also creates a
significant amount of risk for them as well. If an agency put in a significant amount of
investment but still failed to meet their metrics, there is the possibility of considerable
financial harm to their organization.
Similarly, a common mistake in P4P programs is when the operating entity requires
extremely difficult performance goals, but provides low amounts of funding. This may
cause entities to be disinterested in participating in the program and be harmful to
outreach efforts on the whole. In healthcare fields, clinical outcomes, such as longer
survival, are difficult to measure. Because of this, pay for performance systems usually
evaluate process quality and efficiency, such as measuring blood pressure, lowering blood
pressure, or counseling patients to stop smoking. In the case of Cal EITC grants, metrics
for success would likely be goals such as hiring additional staff for filing outreach and
reaching a certain amount of low-income potential filers rather than increasing Cal EITC
filings.

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iii. Possible Cal EITC P4P Framework
If CSD were to implement a P4P model, metrics should be set towards process rather than
outcomes such as increasing filing.
For example, metrics could include:
● Hiring new employees to work exclusively for Cal EITC outreach efforts.
● Reaching a predetermined amount of confirmed potential Cal EITC filers through a
certain outreach method.
● Holding a set number of outreach events.
● Increasing percentage of residents aware of Cal EITC: Use survey data from VITA
sites for last year’s filings and calculate the proportion of people filing who knew
about Cal EITC. Propose a higher percentage for the current year, then measure
again at VITA sites this year.
As the grantee organizations for outreach are modest in size, it is likely that a significant
amount of funding would still have to be provided for start-up costs. A funding scheme
split could be 50% of funding up front, 25% at a midpoint time for meeting infrastructure
goals, and 25% after filing is done on more strenuous goals.
ii. Case Study: PRIME
In California’s most recently approved Section 1115 Waiver, Medi-Cal 2020, public
hospitals participate in the Public Hospital Redesign and Incentives in Medi-Cal
program, or PRIME. PRIME’s goal is to significantly improve care delivery to maximize
health care value, and to move toward other risk-sharing arrangements (Pagel &
Schwartz, 2017). The program is intentionally designed to be ambitious in scope and
time-limited.
Using evidence-based, quality improvement methods, the program established
performance baselines followed by target setting and the implementation and
ongoing evaluation of quality improvement interventions. DHCS approved plans
submitted by public hospitals; these entities then receive funding over five years for
achieving metrics in certain health care areas.

!49
VI. CONCLUSION
As the state of California’s
2017-2018 budget proposal
renews funding for the Cal EITC
benefit for the upcoming year,
there is the opportunity to improve
education and outreach endeavors
and to refine evaluation of those
activities. Building the evidence
base for how outreach can
effectively improve take-up is
critical for both programmatic and
evaluative reasons–not only for Cal
EITC success, but for the success
of other public benefit programs that CSD supports. Indeed, identifying effective means
of reaching target populations and encouraging take-up supports the most
disadvantaged individuals and families who can benefit substantially from the tax credit.
The present evaluation aimed to answer the question of whether or not education and
outreach activities promoting Cal EITC could have an effect on the number of claims filed
across counties. While there is a substantial amount of research on the levels of uptake for
public assistance and barriers to participation, broader research on outreach strategy
effectiveness for these programs is limited. This project has helped lay the groundwork for
future Cal EITC outreach and evaluation.
Through our descriptive statistics, we were able to observe differences in demographic
info among the counties. This information allowed us to discern that improvement in the
economy may have reduced filings from 2015 to 2016. There was a high correlation
between grantee counties and an increase in the minimum wage. Additionally, counties
with racially diverse populations and high rates of public benefit use saw some of the
largest drops in filings, a trend that is consistent with the literature.
Based on our statistical analyses, we were able to find some, albeit limited, findings that
indicated a significant relationship between certain outreach activities and Cal EITC filing.

!50
In particular, one model indicated that canvassing had a significantly positive effect on
filing, while social media had a negative effect. This paralleled our interviews with
grantees, in which many praised the effectiveness of “boots on the ground” and one-on-
one interactions with their target populations.
However, it is not clear in general whether the outreach grant had an effect on filings.
Even though our results were significant in some cases, it must be noted that significance
in a model does not imply causation. This is understandable, as the limitations in our
model–including lack of randomization, small sample size, and difficulty of measuring
outreach–all created significant barriers in trying to tease out the true effect of outreach
for Cal EITC. While grantee counties varied by demographics, geography, and economy,
most shared one characteristic: a large population size. As non-grantee counties served as
a control comparison group, this became problematic; controls work best when they share
the same characteristics as the treatment group. Because grantees were the majority of
the urban areas and population centers in California, this was not possible.
Overall, it must be emphasized that Cal EITC outreach is a new campaign, and that the
evaluation itself is new. The best analyses are run with a wealth of data over long periods
of time. Even though a small amount of significance was found in our model, it does not
mean that most outreach is not effective. If this grant continues with varied outreach in
varied counties, the true effect of outreach activities will be revealed.
To help better serve the lowest-income Californians through Cal EITC and the outreach
grant, we recommend changes across multiple areas. At the programmatic level, grantees
have stressed more support for VITA sites and an earlier timeline for outreach funding.
Secondly, in terms of evaluation, requiring more consistent reporting will assist greatly in
finding what works. Lastly, at the outcomes level, the effects of canvassing and social
media should be closely examined, as well as other outreach activities. In the continuing
years, more data and more types of outreach will allow analysis to discern the most
effective methods; and through this, Cal EITC can be accessed by the neediest
Californians and provide them with essential support.

!51
VII. ACKNOWLEDGMENTS
The consultants at the Goldman School of Public Policy, Marina Balleria, Paulina
Maqueda, Jonathan Palisoc, and Sonya Zhu, would like to thank:
The State of California Department of Community & Services Development and their staff,
Sylmia Britt, Moneshia Campus, Adam Gosney, Sukie Montes, Benjamin Yeager, and
Shkiba Amri, for their passion to reduce poverty in California through efforts that help the
most economically disadvantaged, as well as their patience and guidance in interpreting
the mountains of data on Cal EITC outreach.
The grantees, Amador Tuolumne Community Action Agency, United Way Bay Area,
United Way of Fresno & Madera Counties, Youth Policy Institute, Koreatown Youth &
Community Center, Orange County United Way, Golden State Opportunity, United Way
California Capital Region, INFO LINE of San Diego County (San Diego 2-1-1), United Way
of San Diego County, and Golden State Opportunity, for gifting us their time to conduct
interviews where we learned about their unique communities and the incredibly hard work
they conduct--not only for Cal EITC outreach but for the wellbeing of their community
members. We respect and appreciate what you do for all Californians.
Dr. Amy Lerman, our coach and professor at the Goldman School of Public Policy, for her
accessibility and the empowering and constructive conversations that made this
evaluation as comprehensive as possible. Her lessons inside and outside of the classroom
were crucial to our edification.
Additional gratitude is extended to Dr. Avi Feller and Dr. Jesse Rothstein, for their
advice on the quantitative methods that directed us in evaluating the effectiveness of
outreach activities.
Last but not least, we would like to thank the Goldman School of Public Policy as the
institution that allows us to enhance our experiences through projects such as this one, as
we become Masters of Public Policy.

!52
VIII. REFERENCES
Anderson, A. (2017). California should do more to raise awareness of the California
Earned Income Tax Credit (Cal EITC). California Budget & Policy Center. Retrieved from
http://calbudgetcenter.org/resources/california-raise-awareness-california-earned-income-
tax-credit-caleitc/.
Bansak, C., & Raphael, S. (2006). The effects of state policy design features on take-up
and crowd-out rates for the state children's health insurance program. Journal of Policy
Analysis and Management, 26(1)149-75.
Beecroft, E. (2012). EITC take-up by recipients of public assistance in Virginia, and results
of a low-cost experiment to increase EITC claims. Virginia Department of Social Services.
Retrieved from http://www.dss.state.va.us/files/about/reports/financial_assistance/eitc/
2012/The_Effectiveness_of_EITC_Outreach_2012-05-29.pdf.
Bertrand, M., Sendhil M., & Eldar, S. (2004). A behavioral economics view of poverty.
American Economic Review, 94(2): 419-23.
Berube, A. (2003). Rewarding work through the tax code: The power and potential of the
Earned Income Tax Credit in 27 cities and rural areas. The Brookings Institution. Retrieved
from https://www.brookings.edu/wp-content/uploads/2016/06/berubetaxcode.pdf.
Berube, A., Kim, A., Forman, B., & Burns, M. (2002). The price of paying taxes: How tax
preparation and refund loan fees erode the benefits of the EITC. The Brookings
Institution. Retrieved from https://www.brookings.edu/wp-content/uploads/2016/06/
berubekimeitc.pdf
Bhargava, S., & Manoli, D. (2012). Why are benefits left on the table? Assessing the role of
information, complexity, and stigma on take-up with an IRS field experiment. NA-
Advances in Consumer Research, 40: 298-302.
Blank, R. M. (2000). Fighting poverty: Lessons from recent history. Journal of Economic
Perspectives, 14(2): 3-19.
Blank, R. M., Card, D., & Robbins, P. K. (1999). Financial incentives for increasing work and
income among low-income families (No. w6998). NBER Working Paper Series. Retrieved
from http://www.nber.org/papers/w6998.pdf.
Blumenthal, M., Erard, B., & Ho, C. C. (2005). Participation and compliance with the

!53
earned income tax credit. National Tax Journal, 58(2): 189-213.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research
in Psychology, 3(2), 77-101.
Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code
development. Thousand Oaks, CA: SAGE Publications.
Burch, P., & Heinrich, C. (2016). Mixed methods for policy research and program
evaluation. United States: SAGE Publications.
CalEITC 4 Me. (2017). Retrieved from http://caleitc4me.org/.
California Budget & Policy Center (2015, June 25). First look: 2015-16 budget creates a
state EITC while investing in education, health coverage, and child care and preschool,
but leaves some key supports diminished. California Budget & Policy Center. Retrieved
from http://calbudgetcenter.org/blog/first-look-the-2015-16-state-budget-2/.
California Department of Community Services & Development. (2016). California Earned
Income Tax Credit Education and Outreach Grant: 2016 Cal EITC Notice of Funding
Availability (NOFA).
California Department Community Services and Development. (2016, 12 Sept.). Cal EITC
Education and Outreach Grant Q&A. Retrieved from www.csd.ca.gov/Portals/0/
Documents/ContractingOpportunities/2016%20NOFA%20EITC/
Cal%20EITC%20NOFA%20QA%209.12.16.pdf.
Caputo, R. K. (2006). The Earned Income Tax Credit: A study of eligible participants vs.
non-participants. Journal of Sociology & Social Welfare, 33(1):9-29.
Center on Budget and Policy Priorities. (2016, June 17). Policy Basics: State Earned
Income Tax Credits. (2016). Center on Budget and Policy Priorities. Retrieved from http://
www.cbpp.org/
research/state-budget-and-tax/policy-basics-state-earned-income-tax-credits.
Center on Budget and Policy Priorities (1998, March 9). Strengths of the safety net: How
the 
EITC, Social Security, and other government programs affect poverty. Retrieved from
http://www.cbpp.org/archives/snd98-rep.htm.
Chetty, R., Friedman, J. N., & Saez, E. (2013). Using differences in knowledge across

!54
neighborhoods to uncover the impacts of the EITC on earnings. The American Economic
Review, 103(7), 2683-2721.
Craig, P. (1991). Costs and benefits: a review of research on take-up of income-related
benefits. Journal of Social Policy, 20(04), 537-565.
Currie, J. (2004). The take up of social benefits (No. w6998). NBER Working Paper Series.
Retrieved from http://www.nber.org/papers/w10488.
Dollins, D. R., & Maynard, M. (2002). Participation in the Earned Income Tax Credit
program for tax year 1996. Internal Revenue Service, SB/SE Research.
Dowd, T., & Horowitz, J. B. (2011). Income mobility and the Earned Income Tax Credit:
Short-term safety net or long-term income support. Public Finance Review, 39(5): 619-52.
Eissa, N., & Hoynes, H. W. (1998). The Earned Income Tax Credit and the labor supply of
married couples (No. w6856). NBER Working Paper Series. Retrieved from http://
www.nber.org/papers/w6856.
Ellwood, D. T. (2000b). The impact of the earned income tax credit and social policy
reforms on work, marriage, and living arrangements. National Tax Journal, 53(4),
1063-1105.
Floyd, I., Pavetti, L., & Schott, L. (30, March 2017). Policy Brief: TANF reaching few poor
families. Center on Budget and Policy Priorities. Retrieved from http://www.cbpp.org/
research/family-income-support/tanf-reaching-few-poor-families.
Gates, A., Guyer, J., & Rudowitz, R. (2014, Sept. 29). An Overview of Delivery System
Reform Incentive Payment (DSRIP) waivers. California Department of Health Care Services.
Retrieved from http://kff.org/medicaid/issue-brief/an-overview-of-delivery-system-reform-
incentive-
payment-waivers/.
Greenstein, R., & Shapiro, I. (1998, March 11). New research findings on the effects of the
earned income tax credit. Center on Budget and Policy Priorities. Retrieved from http://
www.cbpp.org/archives/311eitc.htm.
Heider, F., Kaye, N., Rosenthal, J., Schoenberg, M., & Schwartz, C. (2015). State
Experiences Designing and Implementing Medicaid Delivery System Reform Incentive
Payment (DSRIP) Pools. Medicaid and CHIP Payment and Access Commission. Retrieved

!55
from https://www.macpac.gov/wp-content/uploads/2015/06/State-Experiences-
Designing-DSRIP-Pools.pdf.
Hirasuna, D. P., & Stinson, T. F. (2006). Earned income credit utilization by welfare
recipients: A case study of Minnesota's earned income credit program. Journal of Policy
Analysis and Management, 26(1):125-48.
Holcomb, P. A., Tumlin, K., Koralek, R., Capps, R., & Zuberi, A. (2003). The application
process for TANF, Food Stamps, Medicaid, and SCHIP. Office of the Assistant Secretary
for Planning and Evaluation. U. S. Department of Health and Human Services.
Retrieved from http://webarchive.urban.org/publications/410640.html.
Hotz, V. J., Mullin, C., & Scholz, J. K. (2003). Trends in EITC take-up and receipt for
California’s welfare population, 1992–1999. University of California, Los Angeles.
Internal Revenue Service & American Census Bureau. (2017, April 12). “EITC participation
rates by state (TY2013, TY2012, and TY2011 Reports).” IRS-ACS Match-Center for
Administrative Records Research and Applications. Retrieved from https://
www.eitc.irs.gov/
EITC-Central/Participation-Rate.
Johnson, N., & Lazere, E. (1998, Sept. 14). Rising number of states offer earned income
tax credits. Center on Budget and Policy Priorities. Retrieved from http://www.cbpp.org/
archives/9-14-98sfp.htm.
Ku, L., & Bruen, B. (2013, March 4). Poor immigrants use public benefits at a lower rate
than poor native-born citizens. Economic Development Bulletin No. 17. CATO Institute.
Retrieved from https://www.cato.org/publications/economic-development-bulletin/poor-
immigrants
-use-public-benefits-lower-rate-poor.
Lovejoy, K., & Saxton, G. D. (2012). Information, community, and action: How nonprofit
organizations use social media. Journal of Computer-Mediated Communication, 17(3),
337-353.
McCubbin, J. (2000). EITC noncompliance: The determinants of the misreporting of
children. National Tax Journal, 53(4): 1135-1164.
Miller, J. (2016, June 14). California budget scales back estimated use of anti-poverty
credit. The Sacramento Bee. Retrieved from http://www.sacbee.com/news/politics-
government/

!56
capitol-alert/article83722012.html.
Pagel, L. & Schwartz, T. (2017). The Public Hospital Redesign and Incentives in Medi-Cal
(PRIME) Program: Continuing California’s Delivery System Transformation. California
Department of Health Care Services. Retrieved from http://www.dhcs.ca.gov/
provgovpart/Documents/PRIME/PRIME_Fact-Sheet_Final_1_18_17.pdf. 
Phillips, K. R. (2001). The earned income tax credit: Knowledge is money. Political Science
Quarterly, 116(3), 413-424.
Scholz, J. K. (1994). The earned income tax credit: Participation, compliance, and
antipoverty effectiveness. National Tax Journal, 47(1), 63-87.
State of California Franchise Tax Board. (2016). “Report to the Legislature: Expansion of
Earned Income Tax Credit to include self-employment income.” State of California
Franchise Tax Board. Retrived from https://www.ftb.ca.gov/aboutftb/
EITCReportToBudgetCommittee.pdf.
State of California Franchise Tax Board. (2016). “California Earned Income Tax Credit.”
State of California Franchise Tax Board. Retrieved from https://www.ftb.ca.gov/
individuals/faq/net/900.shtml.
Stuber, J. & Kronebusch, K. (2004). Stigma and other determinants of participation in
TANF and Medicaid. Journal of Policy Analysis and Management, 23(3): 509-30.
Wasem, R. E. (2014). Noncitizen eligibility for federal public assistance: Policy overview
and trends. Congressional Research Service. Retrieved from http://
digitalcommons.ilr.cornell.edu/key_workplace/1327/?
utm_source=digitalcommons.ilr.cornell.edu%2Fkey_workplace%2F1327&utm_medium=P
DF&utm_campaign=PDFCoverPages.
Watt, B. (2016, Feb. 5). "Who qualifies for the state's new tax credit?" Southern California
Public Radio. Retrieved from http://www.scpr.org/news/2016/02/05/57261/california-s
-new-tax-credit-for-the-working-poor-h/.

!57
IX. APPENDICES
Appendix A: Variable coding.
Source Variable
name
Description* Year of data** Scale (e.g., percentage,
proportion, raw)
CSD county County in California - -
CSD 2015file Amount of filers in 2015 2015 Raw
CSD 2016file Amount of filers in 2016 2016 Raw
CSD filediffpct % change in filings
2015-2016
2015-2016 Percent
Authors smed Score created by authors 2017 Intensity Weighted Score
Authors webclick Score created by authors 2017 Proportional Weighted
Score
Authors webvisit Score created by authors 2017 Proportional Weighted
Score
Authors outdoor Score created by authors 2017 Proportional Weighted
Score
Authors broadcast Score created by authors 2017 Proportional Weighted
Score
Authors print Score created by authors 2017 Proportional Weighted
Score
Authors medprop Score created by authors 2017 Proportional Weighted
Score
Authors edumatprop Score created by authors 2017 Proportional Weighted
Score
Authors edusite Score created by authors 2017 Intensity Score
Authors canvprop Score created by authors 2017 Proportional Weighted
Score
Authors outrprop Score created by authors 2017 Proportional Weighted
Score

!58
Authors outrscore Score created by authors 2017 Intensity Score
Authors smtreat Score created by authors 2017 Proportional Weighted
Score
Authors webtreat Score created by authors 2017 Intensity Score
Authors mtreat Score created by authors 2017 Intensity Score
Authors edutreat Score created by authors 2017 Intensity Score
Authors canvtreat Score created by authors 2017 Intensity Score
Authors outrtreat Score created by authors 2017 Intensity Score
Authors SMInd Indicator based on monthly
reports
2017 Dummy (1=Yes, 0=No)
Authors WebInd Indicator based on monthly
reports
Authors OutdoorInd Indicator based on monthly
reports
Authors BroadcastInd Indicator based on monthly
reports
Authors PrintInd Indicator based on monthly
reports
Authors EduInd Indicator based on monthly
reports
Authors CanvInd Indicator based on monthly
reports
Authors OutrInd Indicator based on monthly
reports
Authors smtime DD Interaction 2015-2016 Interaction Term
Authors webtime DD Interaction 2015-2017 Interaction Term
Authors mtime DD Interaction 2015-2018 Interaction Term

!59
Authors edutime DD Interaction 2015-2019 Interaction Term
Authors canvtime DD Interaction 2015-2020 Interaction Term
Authors outtime DD Interaction 2015-2021 Interaction Term
CSD population County-level population 2015 Raw
CSD eligible pop County-level eligible
population, estimated based
on households with incomes
under $15,000
2015 Raw
Authors popprop Proportion of total county
population estimated to be
eligible
2015 Percent
ACS whiteonly Estimated % of county
residents reporting as white
and non-Hispanic
2015 Percent
ACS nonwhite Estimated % of county
residents reporting as
anything other than white
and non-Hispanic
2015 Percent
ACS black Estimated % of county
residents reporting as black
2015 Percent
ACS native Estimated % of county
residents reporting as Native
American
2015 Percent
ACS asian Estimated % of county
residents reporting as Asian
2015 Percent
ACS pacific Estimated % of county
residents reporting as Pacific
Islander
2015 Percent
ACS latino Estimated % of county
residents reporting as Latino
or Hispanic
2015 Percent
ACS foreign Estimated % of county
residents reporting as being
foreign-born
2011-2015 Percent

!60
ACS working Estimated % of county
residents that are employed
2011-2015 Percent
ACS medincome Estimated median income by
county
(in 2015
dollars),
2011-2015
Raw
ACS percpov Estimated % of county
residents in poverty
No year in file;
assuming
2011-2015
Percent
BLS unemprtchang
e
Percent change in
unemployment by county,
not seasonably adjusted
2015-2016 Percent
CSD caleitcamount
15
Amount of dollars refunded
through Cal EITC in 2015
FY 2016 Raw
CSD caleitcamount
16
Amount of dollars refunded
through Cal EITC in 2015
FY 2017 Raw
Authors amtperchange % change in the amount of
dollars refunded through Cal
EITC from 2015-2016
caleitcamount1
5/16
Percent
ACS cashpubasst Estimated % of county
residents receiving cash
public assistance, such as
TANF
2015 Percent
ACS SNAPfoodstm
p
Estimated % of county
residents receiving SNAP
benefits
2015 Percent
Authors funding Indicator whether a county
received grant
2017 Dummy (1=Grantee,
0=Non-Grantee)
CSAC ruralind Indicator whether a county is
rural (0) or urban/suburban
(1)
2016 Dummy (1=Rural,
0=Urban Or Suburban)
HUD medinc2015 Estimated for a very low-
income family of four in
2015
2015 Raw
HUD medinc2016 Estimated*** for a very
low-income family of four in
2016
2016 Raw

!61
HUD medinc2017 Estimated for a very low-
income family of four in
2017
2017 Raw
Authors medincchg201
6
Raw change in median
income between 2015-2016
2016 Raw
Authors medincchg201
7
Raw change in median
2017 Raw
Authors medincper201
6
Percent change in median
2016 Percent
Authors medincper201
7
Percent change in median
2017 Percent
*Years refer to the tax year. For example, 2015 refers to income earned in 2015. That income is claimed
on taxes that were filed in 2016. For Cal EITC claimants, they would have received that refund in 2016.
**Year of data refers to the year that this data was created. For example, smed refers to the social media
outreach grantees conducted in the 2016-17 grant process. For demographic information, whiteonly
refers to the white population in that county in 2015.
***Median family incomes are adjusted by HUD to describe a "very low income for a family of four,"
or 50% of overall MFI. Counties are generally aggregated into metropolitan areas and data reported
reflect metropolitan area median income, not that for a member county.

!62
Appendix B: Grantee interview questions.
All grantees were interviewed between March and April 2017 to gain more insight into
the grant and outreach process. Below are the questions that were asked of each grantee.
Questions aimed to understand the programmatic changes made because of the grants,
ongoing issues or concerns, and clarify inconsistencies in the Monthly Reports provided.
Unfortunately, only the first monthly reports were available, and therefore there was little
clarification that applied to our overall analysis. Interviews were used in the thematic
analysis and informed our subsequent recommendations.
Interview Questions
1. What outreach activities are you currently engaging in? What is new, what’s been
ongoing before grant (e.g. were you doing outreach on federal EITC before,
combining outreach on other public assistance programs w/ Cal EITC)?
2. How much were you spending previously on Cal EITC outreach?
3. Can you provide more detail about how you conduct outreach activities?
a. Web presence
b. Social media
c. Media
d. Distribution of educational material and messaging
e. Canvassing–e.g., high traffic canvassing, door-to-door
f. Outreach events
g. Other
4. How did you decide which outreach strategies to use?
5. Do you have any sense of how you would rank your different outreach methods in
terms of effectiveness (has social media, school events, etc… what has worked
best)? $/impressions (cost-effectiveness)
6. How do you define a “trusted community member”?
7. If you believe that “word-of-mouth” is the most successful outreach method, do
you believe that there a “trusted community member” in your community?
8. Why do you think x strategy is effective (or not)?
9. How do you track/record each outreach activity (e.g., # of staff members/
volunteers, amount spent, # of hours, # of people reached)? Would we be able to
receive a document of these activities?
10.Recommendations for improving the grants?

!63
Appendix C. Coding Scheme
Outreach
Method
CSD Definition (per
NOFA)
IPA Team Definition Not Part of the
Definition
Web Presence
-Host web banner on
website
-Add educational
information and updates
about the credits and VITA
to website
-Provide links to Cal EITC
educational materials,
tools, calculators, maps,
etc.
Anything on a website
(Can be an
advertisement, link or
post)
Planning or
Negotiations
Social Media
Utilize social media
outlets to disseminate
educational messages,
share success stories, and
inform the public about
outreach events in targeted
counties and local
communities. Social media
outlets may include but are
not limited to:
-Facebook/Instagram
-Twitter
-YouTube
Posts, likes, shares,
number of platforms,
social media ads
Planning or
Negotiations
Media
Educate through media
outlets including:
-Magazines
-Newspapers·
-Radio
-TV (e.g., solicit local
news coverage and
participate in interviews to
educate viewers in
targeted areas)
Billboards, TV, radio,
newspaper, shoppers,
published press
releases, transit ads (on
buses/bus stops)
Planning,
Negotiations,
Sending a Press
Release

!64
Distribution of
Educational
Materials
Distribute the following
types of materials in
multiple languages as
appropriate for the target
audience and
demographics:
-Flyers/Brochures Posters
-Mailers/Emails
-Letters/Memos
-Newsletters
-Text Messaging
-Informational Call
Centers, Etc.
Mailers, handing out
flyers, partner agencies
distributing collateral.
Limited interaction w/
recipients of materials.
Planning or
Negotiations,
Attending Meetings
with Staff or Sub-
grantees, Canvassing
and Outreach event-
type activities
Community
Canvassing &
Outreach
Events
Make person-to-person
contact in targeted
residential neighborhoods
and community gathering
places to engage
individuals and families.
Host or participate in
coordinated outreach
events to engage groups of
eligible people at places
such as:
-Community gatherings/
Resource fairs
-Mega events
-Local businesses
-Bus tours
-Educational forums
-Local, Free Tax
preparation and Filing
Services, such as VITA
sites
-Grocery stores/Libraries
-Food banks
-Churches
-Health care clinics
-Schools
-Etc.
One-on-one contact
with community
members, including
door-to-door, or
providing information
in high traffic areas
such as grocery stores
or department stores.
Planning and
Negotiations, staff or
sub-grantee
education/trainings

!65
Appendix D. Results: Additional tables and ﬁgures
Figure D-1. Demographics and percent change in filings between 2015-2016.
!
!
!
!

!66
Table D-1. Changes in Cal EITC filings among grantee counties.
!
!!

!67
Figure D-2. County unemployment levels and percentage change in Cal EITC filings.
!
Figure D-3. Relationship between change in median income and funding.
!

!68
Outreach Scoring Across Grantees
The following table presents the scores generated from the scoring system of outreach
activities. These scores were then incorporated into the statistical models.
Table D-2. Grantee outreach scores.
*Proportional scoring
Grante
e
Social
Medi
a
Web
Click
*
Web
Visit
*
Out
-
doo
r
Broad
-
cast
Prin
t
Medi
a
Educ.
Mat.
*
Educ
.
Mat.
Site
Canvas
s
-ing*
Outreac
h
Events*
Outreach
Events
Sites
KYCC 2.02 0.07 0 0.85 0.3 0.3 1.6 0.01 2.55 0 0 2.85
YPI 2.13 0 0 0 0.15 0.3 0 0.02 2.5 0.01 0 2.4
UW SD 1.91 0 0.05 0.55 0.4 0.4 24.72 0.5 2.65 0 0.05 2.55
UW OC 2.86 0 0.78 0 0.7 0.7
243.9
7 0.31 2.75 0 0.02 2.85
GSO-
Riversid
e 0.62 0 0 0 0.4 0.7 0.63 0.27 1.25 0 0.01 2.1
GSO-
SB 1.04 0 0 0 0.4 0.7 1.05 0.26 2.1 0 0 0.7
UW
CCR 2.69 0.01 1.71 0 0.85 0.45 3.16 0.31 1.1 0.01 0.01 1.7
UW
BA-
Alamed
a 2.92 0.32 0.04 0.85 0.85 0.85 54.42 0.11 2.7 0.01 0.01 1.2
UW
BA-SF 2.92 0.41 0.05 0.85 0.85 0.85
206.1
9 0.75 2.55 0 0 1.2
UW
BA-SC 2.92 0.43 0.05 0.85 0.85 0.85
134.0
7 0.26 2.7 0.01 0.01 0.6
UW
F&M 2.49 0.64 0.38 0 0.85 0 1.5 1.1 2.85 0.08 0.08 2.15
ATCAA 2.58 0 0.85 0.95 6.96 0.14 2.85 0.01 0.44 2.6

!69
Figure D-4. Social Media Scoring
!
Figure D-5. Web Presence Scoring
!
Figure D-6: Media Scoring
!

!70
Figure D-7: Distribution of Educational Materials Scoring
!
Figure D-8: Canvassing Scoring
!
Figure D-9: Outreach Events Scoring
!

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