Anti-poverty policy and health: Attributes and diffusion of state earned income tax credits across U.S. states from 1980 to 2020.

PURPOSE: The U.S. federal Earned Income Tax Credit (EITC) is often considered the most effective antipoverty program for families in the U.S., leading to a variety of improved outcomes such as educational attainment, work incentives, economic activity, income, and health benefits for mothers, infants and children. State EITC supplements to the federal credit can significantly enhance the magnitude of this intervention. In this paper we advance EITC and health research by: 1) describing the diffusion of state EITC policies over 40 years, 2) presenting patterns in important EITC policy dimensions across space and time, and 3) disseminating a robust data set to advance future research by policy analysts and scientists.
METHODS: We used current public health law research methods to systematically collect, conduct textual legal analysis, and numerically code all EITC legislative changes from 1980 through 2020 in the 50 states and Washington, D.C.
RESULTS: First, the pattern of diffusion across states and time shows initial introductions during the 1990s in the Midwest, then spreading to the Northeast, with more recent expansions in the West and South. Second, differences by state and time of important policy dimensions are evident, including size of credit and refundability. Third, state EITC benefits vary considerably by household structure.
CONCLUSION: Continued research on health outcomes is warranted to capture the full range of potential beneficial effects of EITCs on family and child wellbeing. Lawyers and policy analysts can collaborate with epidemiologists and economists on other high-quality empirical studies to assess the many dimensions of policy and law that potentially affect the social determinants of health.

Introduction

Across the world, health organizations acknowledge family economic conditions as an important social determinant of health [1, 2]. In the United States, the relationship between income and health operates on a continuum, with the most economically disadvantaged experiencing the poorest health outcomes [3, 4]. Living in poverty has negative implications across the lifespan and across many health outcomes [3, 5, 6]. Despite substantial evidence of poverty’s deleterious health effects, the economic gap between the wealthiest and poorest families continues to widen and health disparities persist [4]. Public policies aimed at reducing poverty have been shown to raise low incomes [7-9]. State governments are uniquely positioned to address state-level income inequality that has been linked with poor health outcomes [8, 10–15]. The state-level earned income tax credit (EITC) is a promising policy strategy that has been used to reduce poverty and improve related outcomes, including health [16-19].

The U.S. federal EITC was first introduced in 1975 as a tax credit to help relieve tax burdens and supplement incomes of low-earning, working families [20]. Single mothers with dependent children were the initial intended recipient population, and the EITC was expanded during welfare reform in the early 1990s to facilitate the transition from the existing welfare system into the workforce [20]. Since its introduction, modifications to the federal EITC policy include provisions for accommodating the number of children in a household, extending eligibility to childless workers, considering presence of a spouse, and adjusting for inflation [20]. The value of the federal EITC is determined by a combination of amount of earned income and family structure, and is fully refundable. Refundability means that if tax liability falls to zero and there is still some portion of the credit remaining, the government will send a refund check equal to that remaining amount to the worker [17, 19]. By design, the EITC primarily benefits working families; workers with children receive a much larger credit than workers without qualifying children. In the 2020 tax year, the maximum federal credit for families with one child is $3,584, while the maximum federal credit for families with three or more children is $6,660 [21]. In contrast, a childless worker between the ages of 25 and 65 (filing singly or, if married, jointly) can receive a maximum federal earned income tax credit of $538 [21]. Many economists consider the federal EITC as the largest and most effective antipoverty program for families in the United States [22]. Additionally, state EITC supplements to the federal credit can significantly enhance the magnitude of this intervention [23], and a few states began to introduce EITCs in the late 1980s. As of 2020, 29 states and the District of Columbia offer their own EITCs, which vary widely in eligibility, credit amount, and refundability [24].

While research on economic outcomes of the federal EITC dates back to the initial implementation of the program, much of the evidence on health outcomes has emerged within the last decade. Research has associated the federal EITC expansions with improved overall self-reported health and positive mental health outcomes among adults [25, 26], but finds conflicting results regarding effects on adult health behaviors [27-30]. The research is more consistent regarding beneficial effects of federal EITC expansions on infant and child health. An increase in the federal EITC is associated with decreased infant mortality, higher birth weights and improved overall perinatal health [27, 30]. In addition, one study found that the EITC was associated with increased supportive home environments for child development [28] and two studies estimated that parental receipt of federal tax credits was associated with a child’s increased likelihood of college enrollment and higher lifetime earnings [31, 32]. The EITC may also be relevant to reducing risk factors for child abuse and maltreatment (e.g., poverty, maternal stress and depression) [33].

The body of literature on effects of state EITCs on health outcomes has grown as more states have adopted their own policies. These studies find similar results to those assessing effects of changes to the federal EITC. A study by Muennig and colleagues concludes that a state EITC is a cost-effective policy for increasing health-related quality of life and reducing mortality [33]. Similar to studies that examine federal EITC expansions, those measuring effects of state-level credits report inconsistent findings regarding effects on adult health, including smoking and obesity [34-36]. Findings from studies examining state EITC effects on infant and child health have been more consistent [37]. State EITC introductions are associated with fewer low-birthweight births and increased gestation weeks [23, 34, 38–40], as well as improvement in overall child health [41]. Studies also find state EITCs are associated with improved educational outcomes at primary, secondary, and post-secondary education levels [42, 43].

Given numerous expansions and changes over time in specific EITC policy features at the state level, continual policy surveillance to track those changes can facilitate further research on the health effects across the lifespan [44]. The extant literature suggests that widespread state EITC adoption appears likely to have important health benefits, especially among infants and children [19, 30–32, 35–38]. Similarly, given the current evidence regarding the beneficial effects of EITC on educational outcomes [39, 40], researchers can further contribute to the available EITC literature by assessing potential effects of varied EITC policies on reduction of intergenerational poverty. Studies of state-level policy changes are able to include important design elements, such as multiple comparison groups across space and time, that can be used to generate unbiased estimates [45]. Careful legal research and coding combined with science-based measurement methods are required to establish reliable and valid indicators of policy prevalence, timing, and dose. Research has shown that reliance on policy indicators from websites maintained by administrative agencies or advocacy organizations often include measurement error [46]. In order to scientifically evaluate health effects of state-level EITC policies, our multidisciplinary team of epidemiologists, economists, lawyers, and statisticians conducted rigorous surveillance of these policies over time and across states. In this paper we seek to advance EITC and health research by: 1) describing the diffusion of EITC policies across the U.S. states over 40 years, 2) presenting patterns in important EITC policy dimensions (such as credit amount and refundability) across space and time, and 3) disseminating a robust data set that can be used to advance future research by other policy analysts and scientists and to inform decision making by stakeholders seeking to improve a variety of outcomes.

Methods

We used the public health law research method known as legal epidemiology [44, 47] to systematically collect, conduct textual legal analysis, and numerically code all EITC legislative changes from 1980 through 2020 in the 50 states and Washington, D.C. (collectively, the states). The Emory authors funded and worked with a team of legal researchers at Temple University’s Public Health Law Research Program. This legal team began the legal analysis with a sample of five states, producing policy memos that detailed trends, variations, and key features of laws from the initial sample. Key search terms drawn from the literature review included “tax credits” with “earned income,” “low income,” “working families,” “family,” and “refundable.” Additional terms were included based on the table of contents of the tax section in each state’s codified law. Based on this preliminary legal research and the published literature, the authors worked closely with the legal team to develop a policy surveillance codebook and a detailed coding protocol to capture important EITC policy dimensions, including eligibility criteria, as well as the amount and refundability of the tax credit (see S1 and S2 Appendices, respectively, available online).

The legal team then reviewed the text of all U.S. federal and state laws (statutes and regulations) on EITCs from all 50 states and Washington, D.C., using the Westlaw and LexisNexis legal databases and session laws from each state’s legislative website. The search terms included: earned income tax credit, earned income credit, low-income tax credit, working families tax credit, family tax credit, refundable tax credit, section 32 Internal Revenue Code. The study includes all laws outlining eligibility and benefit information on state earned income tax credits aimed at providing tax relief or refunds to low- or moderate-income working taxpayers. For the purposes of this study, laws providing credits or other income supports to low- to moderate-income workers for assistance with food, housing, or childcare were excluded.

The initial legal coding was conducted in 2013, with updates in 2015 and 2016. The legal data was collected and coded with extensive quality control procedures, including blinded independent coding of a 20% random sample of items by two trained legal researchers. As part of Temple’s work, all legal coders were closely supervised by a senior attorney, who reviewed protocols with coders for any variable showing 5% or higher cross-coder disagreement rate. Divergence rates were below 10% for the original coding and 2015 and 2016 updates. All divergences between two coders were resolved by the supervising attorney after meeting with the two coders and examining the original legal text. Following completion of the legal coding, quality control checks included inspecting descriptive data and comparing results with other EITC sources. If discrepancies were noted, the legal team returned to the original legal text to verify or update the data set. The legal team compared their results with a similar study conducted by NCSL available at: http://www.ncsl.org/research/labor-and-employment/state-earned-income-tax-creadit-enactments.aspx; https://users.nber.org/~taxsim/state-eitc.html. Any discrepancies were resolved. In 2020, a team of legal researchers at CDC’s Policy Research, Analysis, and Development Office conducted an update to a subset of questions relevant to this research. They followed the methods in the protocol and outlined above with minor variations (see S5 Appendix, available online). The CDC legal researchers updated the coding for a subset of questions as developed. The researchers expanded the search terms to reflect the current state of the laws based upon an independent review of literature and sample searches. All laws were collected using the database WestlawNext. Every law was independently coded by two legal researchers and de minimis discrepancies were resolved through consensus. During this process, some anomalies were identified in data related to these variables that had been included in the original dataset. The 2020 update expanded to include a retrospective quality control review and correction, as needed, of previously coded data related to these variables. This research made use of publicly available data sets and was determined to be exempt by the Emory University Institutional Review Board.

Results

Dimensions of state EITC laws

Table 1 includes the core legal data set, stratified by state and time from 1980 through 2020 tax year, including effective tax year(s) of introduction and amendments, credit amount (i.e., percent of federal EITC), variation by number of children in the household, refundability, and, finally, specific legal citations (see S3 and S4 Appendices for data codebook and cvs data file, respectively). As of 2020, 29 states—as well as Washington D.C.—had a state EITC. Nine states do not have an earned income tax (Alaska, Florida, Nevada, New Hampshire, South Dakota, Tennessee, Texas, Washington and Wyoming). In 2020, the majority of states with an EITC calculate the state credit as a percent of the federal EITC (27 of 29 states) and offer the state EITC as a refundable tax credit (23 of 29 states). Being refundable means that if the EITC reduces the taxpayer’s tax liability to zero, any remaining credit amount will be refunded to the taxpayer, usually in the form of a refund check sent from the state to the taxpayer [48]. Six state EITCs are nonrefundable credits, which provide no further income benefit beyond a zero tax liability [48]. Although the state of Washington does not have an income tax, it does have an EITC law. Enacted in 2008 as a refundable credit, Washington state’s EITC has not yet been implemented as policymakers have not financed the credit.

Table 1

State EITC as a percent of the federal EITC and refundability, 1980–2020.

State	Tax Year	Percent of federal EITC	If different by number of dependents				Refundable	Legal citation
			None(a)	One(a)	Two(a)	Three(a)
AL	1980–2020	None
AK(b)	1980–2020	None
AZ	1980–2020	None
AR	1980–2020	None
CA(c)	1980–2014	None
	2015	44					Yes	Cal Rev & Tax Code § 17052
	2016	43					Yes
	2017	44					Yes
	2018–2020	45					Yes	2018 Cal. Legis. Serv.
								Ch 52 (SB 855)
CO(d)	1980–1998	None
	1999	8.5					Yes	C.R.S. § 39-22-123
	2000–2001	10					Yes	C.R.S. § 39-22-123.5
	2002–2004	10*
	2005–2010	0
	2011–2014	10*
	2015–2020	10					Yes
CT	1980–2010	None
	2011–2012	30					Yes	Conn. Gen. Stat. § 12-704e
	2013	25					Yes
								2017 Conn LS June Sp Sess PA 17–2 (SB 1502) §645
	2014–2016	27.5					Yes
	2017–2020	23					Yes
DE	1980–2005	None
	2006–2020	20					No	30 Del. C. § 1117
DC(e)	1980–1999	None
	2000	10					Yes	D.C. Code § 47-1806-04
	2001–2004	25					Yes
	2005–2007	35					Yes
	2008–2014	40
	2015–2020		100	40	40	40	Yes	2014 DC Laws 20–155 (Act 20–424) Chpt 1C §6(B)
FL(b)	1980–2020	None
GA	1980–2020	None
HI	1980–2017	None
	2018–2020	20					No	Hawaii Rev Stat. § 235–55.75
ID	1980–2020	None
IL	1980–1999	None
	2000–2002	5					No	30 ILCS 5–212
	2003–2011	5					Yes
	2012	7.5					Yes
	2013–2016	10					Yes
	2017	14					Yes	2017 Ill. Legis. Serv. P.A. 100–22 (S.B. 9)
	2018–2020	18					Yes	35 ILCS 5–212
IN(f)	1980–1998	None
	1999–2002		0	3.4	3.4	3.4	Yes	Burns Ind. Code Ann. § 6–3.1-21-6
	2003–2008	6					Yes
	2009–2020	9					Yes
IA(g)	1980–1989	None
	1990	5					No	Iowa Code § 422-12B
	1991–2006	6.5					No
	2007–2012	7					Yes
	2013	14					Yes
	2014–2020	15					Yes
KS	1980–1997	None
	1998–2001	10					Yes	K.S.A. § 79–32,205
	2002–2006	15					Yes
	2007–2009	17					Yes
	2010–2012	18					Yes
	2013–2020	17					Yes
KY	1980–2020	None
LA	1980–2007	None
	2008–2018	3.5					Yes	LA R.S. § 47:297.8
	2019–2020	5					Yes	Acts 2018, 2nd Ex.Sess., No. 6, § 1
ME	1980–1999	None
	2000–2002	5					No	36 M.R.S. § 5219-S
	2003–2007	4.92					No
	2008	5					No
	2009–2010	4					No
	2011–2015	5					No
	2016–2019	5					Yes
	2020		25	12	12	12	Yes	2019 Me. Legis. Serv. Ch. 527 (H.P. 1198) (L.D. 1671)
MD(g,h)	1980–1986	None
	1987–1997	50 (NR)					See Note	Md. TAX-GENERAL Code Ann. § 10–704
	1998–1999	50 (NR)	0 (R)	10 (R)	10 (R)	10 (R)	See Note
	2000	50 (NR)	0 (R)	15 (R)	15 (R)	15 (R)	See Note
	2001–2002	50 (NR)	0 (R)	16 (R)	16 (R)	16 (R)	See Note
	2003	50 (NR)	0 (R)	18 (R)	18 (R)	18 (R)	See Note
	2004–2006	50 (NR)	0 (R)	20 (R)	20 (R)	20 (R)	See Note
	2007–2014	50 (NR)					See Note
		25 (R)
	2015	50 (NR)					See Note
		25.5 (R)
	2016	50 (NR)					See Note
		26 (R)
	2017	50(NR)					See Note
		27(R)
	2018–2020	50 (NR)					See Note	2018 MD Laws Ch. 611 (SB 647)
		28(R)
MA	1980–1996	None
	1997–2000	10					Yes	ALM GL ch. 62, § 6
	2001–2015	15					Yes
	2016–2018	23					Yes
	2019–2020	30					Yes	2018, 154, Sec. 30, 111
MI	1980–2007	None
	2008	10					Yes	MCL § 206.272
	2009–2011	20					Yes
	2012–2020	6					Yes
MN(g,i)	1980–1990	None
	1991–1992	10					Yes	Minn. Stat. § 290.0671
	1993–1997	15					Yes
	1998		15	25	30	30	Yes
	1999		15	27	32	32	Yes
	2000–2013		25	30	35	35	Yes
	2014		26	31	37	37	Yes
	2015–2016		26	31	36	36	Yes
	2017		25	31	36	36	Yes
	2018		25	30	35	35	Yes
	2019		53	32	37	38	Yes
	2020		52	31	36	38	Yes
MS	1980–2020	None
MO	1980–2020	None
MT	1980–2018	None
	2019–2020	3					Yes	MT ST 15-30-2318; 2017 Montana Laws Ch. 381 (H.B. 391)
NE(j)	1980–2005	None
	2006	8					Yes	R.R.S. Neb. § 77-2715-07
	2007–2020	10					Yes
NV(b)	1980–2020	None
NH(b)	1980–2020	None
NJ	1980–1999	None
	2000		0	10	10	10	Yes	N.J. Stat. § 54A-4-7
	2001		0	15	15	15	Yes
	2002		0	17.5	17.5	17.5	Yes
	2003–2006		0	20	20	20	Yes
	2007	20					Yes
	2008	22.5					Yes
	2009	25					Yes
	2010–2014	20					Yes
	2015	30					Yes
	2016–2017	35					Yes
	2018	37					Yes	L.2018, c. 45, § 4
	2019	39					Yes
	2020	40					Yes
NM	1980–2006	None
	2007	8					Yes	N.M. Stat. Ann. § 7-2-18.15
	2008–2018	10					Yes
	2019–2020	17					Yes	L. 2019, Ch. 270, §§ 13, 59
NY	1980–1993	None
	1994	7.5					Yes	NY CLS Tax § 606
	1995	10					Yes
	1996–1999	20					Yes
	2000	22.5					Yes
	2001	25					Yes
	2002	27.5					Yes
	2003–2020	30					Yes
NC	1980–2007	None
	2008	3.5					Yes	N.C. Gen. Stat. § 105-151-31
	2009–2012	5					Yes
	2013	4.5					Yes
	2014–2020	None
ND	1980–2020	None
OH	1980–2012	None
	2013	5					No	ORC Ann. § 5747.71
	2014–2018	10					No
	2019–2020	30					No	2019 H 62, section 757.100
OK	1980–2001	None
	2002–2015	5					Yes	68 Okl. St. § 2357–43
	2016–2020	5					No	Laws 2016, c. 341, § 1;
								OK ADC 710:50-15-90
OR(k)	1980–1996	None
	1997–2005	5					No	ORS § 315.266
	2006–2007	5					Yes
	2008–2013	6					Yes
	2014–2019	8					Yes	ORS §§ 315.266(a), (b);
								Laws 2016, c. 98, § 1
	2020	9					Yes	Laws 2019, c. 579, §§ 31, 32a
PA	1980–2020	None
RI(l)	1980–2000	None
	2001	25.5					No	R.I. Gen. Laws § 44-30-2.6
	2002	25					No
	2003–2014	25					Partial Refund
	2015	10					Yes	P.L. 2014, ch. 145, art. 12, §§ 7, 22
	2016	12.5					Yes
	2017–2020	15					Yes	P.L. 2016, ch. 142, art. 13, § 15, 20
SC	1980–2017	None
	2018	20.83					No	SC ST § 12-6-3632; 2017 Act No. 40 (H.3516), § 16.A.
	2019	41.66					No
	2020	62.49					No
SD(b)	1980–2020	None
TN(b)	1980–2020	None
TX(b)	1980–2020	None
UT	1980–2020	None
VT	1980–1987	None
	1988	23					Yes	32 V.S.A. § 5828b
	1989–1990	25					Yes
	1991–1993	28					Yes
	1994–1999	25					Yes
	2000–2017	32					Yes
	2018–2020	36					Yes	2017, Adj. Sess., Sp. Sess., No. 11, §§ H.4, H.31
VA(m)	1980–1999	None
	2000–2005	Other
		See Notes
	2006–2020	20					No	Va. Code Ann. § 58.1–339.8
WA(b,n)	1980–2007	None
	2008–2009	5					Yes	Rev. Code Wash. (ARCW) § 82.08.0206
	2010–2020	10					Yes
WV	1980–2020	None
WI(o)	1980–1983	None
	1984–1985		0	30	30	30	No	Wis. Stat. § 71.09
	1986–1988	None
	1989–1993		0	5	25	75	Yes	Wis. Stat. § 71.07
	1994		0	1.15	6.25	18.75	Yes
	1995		0	4	16	50	Yes
	1996–2010		0	4	14	43	Yes
	2011–2020		0	4	11	34	Yes
WY(b)	1980–2020	None

Notes:

NR—Non-Refundable.

R—Refundable.

(a) Variation from federal EITC percentage if different by number of dependents.

(b) No state earned income tax.

(c) California uses a different income eligibility requirement than the federal EITC, imposing a lower maximum income threshold to qualify for the credit; the rates listed in Table 1 are effective percentages of the federal EITC, calculated to account for the differences between the California and federal EITC. The California EITC had a minimum age requirement of 25 years old for taxpayers without dependents from 2015–2017; the minimum age was lowered to 18 years old effective in tax year 2018. Beginning in 2019, taxpayers eligible for EITC under Section 17052 who had one or more children under the age of 6 were also allowed a young child tax credit with a maximum benefit of $1,000 and a phase-out threshold of $25,000. Cal Rev & Tax Code § 17052.1; added by CA Stats. 2019, c. 39 (AB 91).

(d) The Colorado EITC was established as a refund mechanism under CO’s Taxpayer Bill of Rights (TABOR) and, prior to 2016, required a budget surplus for the EITC credit to be financed. Thus, in some years tax payers did not receive the credit (*). From 2005 to 2010, TABOR was temporarily suspended by referendum, effectively suspending the EITC. In 2013, a law was passed establishing a separate permanent EITC law that would be available the year after the next year in which an EITC refund would be triggered under TABOR. This occurred in 2015 with the permanent EITC becoming available starting in 2016. *No surplus was available from 2002 to 2004 or from 2011 to 2014, and the EITC was unfunded during these periods. As a TABOR refund mechanism, the EITC was funded only from 1999–2001.

(e) DC taxpayers 25 years and older with no dependents were allowed a credit that is 100% of the federal EITC beginning in 2015. The DC phase-out threshold and completed phase-out amounts are greater than the respective federal amounts, which means that the effective percentage for these taxpayers can exceed 100% of the federal.

(f) Indiana specifies a fixed dollar amount with a maximum credit of $408 from 1999 to 2002. The percentage shown is calculated based on the federal amount for those years.

(g) The majority of states use the federal EITC eligibility requirements to determine whether an individual is eligible for state EITC credits. As such, it is important to note that childless adults were not eligible for the federal EITC prior to the passage of the Omnibus Budget Reconciliation Act of 1993 (OBRA93). Thus, while IA, MD, MN, and VT did not differentiate the state credit percentage based on the number of dependents, it would not be applicable to childless adults. Once OBRA93 became fully effective in 1994, childless adults between the ages of 25 and 65 years old are eligible for state credits unless they have state-specific eligibility requirements.

(h) Maryland residents calculate a nonrefundable credit, which is equal to the lesser of 50% of the federal credit or the state income tax liability in the taxable year. In 1998, a refundable tax credit was also implemented. Individuals can select either the refundable or the non-refundable credit, but not both. However, from 1998 to 2006, an individual had to have one or more qualifying dependents to have the refundable credit as an option. In 2018, qualified adult taxpayers under the age of 25 without dependents became eligible for the Maryland EITC.

(i) Minnesota’s Working Family Credit was specified as a percentage of the federal EITC from 1991–1997. Starting in 1998, the credit was restructured as a percentage of earnings, with maximum credits and income eligibility requirements specified. We use these maximum credits and the respective income eligibility requirements to calculate the effective maximum percent of federal EITC. Values in table reflect this maximum. Implemented in 2019, Minnesota created a separate category for taxpayers with three or more children and lower the age of eligibility for taxpayers with no qualifying children to 21 years of age.

(j) Effective in 2015, Nebraska modified its EITC so that net operating loss carryforward must be added back in as income when qualifying for the EITC.

(k) Beginning in 2017, Oregon implemented an additional child tax credit worth 3% of the federal EITC. To qualify for this credit, you must qualify for the federal EITC as well as have a qualifying dependent under 3 years of age.

(l) Rhode Island had a partially refundable EITC from 2003 to 2014. The policy allows a percentage of the amount exceeding the tax liability to be refundable. For years 2003 and 2004, the percentage is 5. For 2005, the percentage was increased to 10. Then from 2006 to 2014, the percentage was 15.

(m) Virginia also has a direct amount available that begin on January 1, 2000. The eligibility for the direct amount differs from the federal EITC eligibility by imposing a lower maximum income threshold. Additionally, the number of direct amount credits that can be claimed is based on the number of qualified dependents and whether a spouse is on the tax return. The credit based on the federal amount and eligibility requirements began on January 1, 2006. When both of these options are in effect (January 1, 2006 and onward), an individual decides which of the amounts to utilize.

(n) Washington passed legislation to create a state-based EITC in 2009 with an automatic rate increase in 2010. However, this policy was never implemented because lawmakers have not financed the credit. Additionally, Washington has a direct amount of $25 from 2008 to 2010 and $50 from 2011 to 2016, with the individual receiving the greater of the direct amount or refund percentage.

(o) Wisconsin’s credit in 1994 is not based on the federal credit. Maximum credits are specified. We use these maximum credits to calculate the maximum percent of federal EITC. Values in table reflect this effective maximum for 1994 only. All other years are percent of federal EITC.

Trends over time

Fig 1 shows cumulative EITC policy adoption over time. The pattern roughly follows the well-known S-curve of diffusion [19, 49] -a slow start with 5 states adopting from the mid-1980s to mid-1990s, followed by a more rapid increase in state adoptions from the mid-1990s to 2010 (19 additional jurisdictions), followed by a decrease in rate of additional adoptions over the 2010–2020 decade (ending with 30 jurisdictions having an EITC). Given that remaining states continue to introduce EITC legislation, it appears that the curve is not yet complete, and it is not yet known the degree to which the next decade will follow the traditional S-curve expectation of further slowing of the adoption rate (i.e., among “laggard” states). Geographic diffusion of state EITC legislation from 1993 (following the major expansion of the federal EITC in the Omnibus Budget Reconciliation Act of 1993) through 2020 is illustrated in Fig 2, with a series of maps of state EITC features in 1993, 2000, 2010, and 2020. Only five states had an EITC prior to 1993: IA, MD, MN, VT, and WI (see Table 1). In 1993, three had a refundable EITC. Iowa and Maryland had a nonrefundable EITC. By 2000, eleven additional states introduced an EITC, seven of which were refundable. These state credits ranged widely in magnitude, from 3.4% (IN for one or more dependents) to 43% (WI for three dependents) of the federal amount for refundable credits, and 5% (IL, ME, OR) to 50% (MD nonrefundable option) for nonrefundable credits. By 2010, two additional states introduced nonrefundable credits (DE, RI) (At that time, Rhode Island’s EITC was partially refundable. See Table 1, note l for discussion.) and seven introduced refundable credits. In addition, the three states that had had nonrefundable tax credits changed them to refundable (IA, IL, OR). By 2020, two (ME, RI) of the four nonrefundable state EITCs became refundable and two additional states (OH, HI) introduced a nonrefundable EITC. An additional three states introduced a refundable credit (CA, CT, MT), Oklahoma changed its refundable EITC to a nonrefundable credit, and North Carolina eliminated its EITC. In 2020, state credits ranged in size from 3% (MT) to 45% (CA) and 52% (MN with no dependents) of the federal EITC for refundable credits, and 20% (HI) to 62% (SC) for nonrefundable credits.

Fig 1

U.S. states adoption of EITC by year.

Fig 2

Maps of state EITC credits and refundability based on having one dependent: 1993, 2000, 2010, 2020.

Fig 3 presents EITC levels, refundability, and legislative changes from 1993 through 2020 by state and year. From 1993 through 2020, 114 state EITC legislative changes occurred. Notably active years were in 2000 (n = 9 changes), 2008 (n = 8 changes), and 2019 (n = 8 changes).

Fig 3

Trend of state EITC credits and refundability based on one dependent: 1993–2020.

In 1993 five states had an EITC (IA, MN, WI, VT, MD). Between 1993 and 2020, an additional 24 states and the District of Columbia introduced an EITC and 28 states amended EITC legislation. Most amendments increased the credit (i.e., increased percent of the federal EITC) (n = 18: DC, CA, CO, IA, IL, IN, KS, MA, MN, NE, NJ, NM, NY, OH, OR, SC, VT, WA) and/or changed from a nonrefundable to a refundable credit (n = 6: IA, IL, ME, OR, RI, WI).

There are also a handful of states that have reduced their EITC credit, removed refundability, or eliminated their EITC altogether. For example, New Jersey incrementally increased their credit to 25% in 2009, then decreased it to 20% from 2010–2014, and then incrementally increased it to 40% in 2020. Connecticut’s credit was introduced in 2011 at 30%, then was reduced to 25% in 2013, followed by an increase to 27.5% for 2014 to 2016, followed by a reduction to 23% from 2017–2020. Rhode Island reduced the credit from 25.5% in 2001 to 15% in 2020 along with a transition from nonrefundable to refundable credit during the period. Michigan and Wisconsin decreased the amount of their refundable credit.

Regional variations

Three of the five early adopter states were in the upper Midwest. States in the upper Midwest and Northeast have implemented the most legislative changes, and have had the highest credit amounts, especially the states in the Northeast. Over 75% of states in the Midwest and Northeast have state EITCs, compared with around 40% of states in the West and South. In 2020, the highest state EITCs, as a percent of the federal EITC amount, were in Washington, D.C. (40%), California (43%), and in Minnesota for those with no dependents (52%).

Discussion

Our legal research documents the diffusion of EITC legislation across states and time from 1980 to 2020. Since the late 1980s, there has been a gradual increase in policy adoption with 29 of 50 states and Washington D.C. having adopted a state EITC by 2020. Three key findings inform future research and policy development in this area. First, the pattern of diffusion across states and time shows initial introductions during the 1990s in the Midwest, then spreading to the Northeast, with more recent expansions in the West and South. A few states initially began to pass EITC legislation during the 1980s. By 1993, when the major expansion of the federal EITC occurred as part of welfare reform, only five states had an EITC, three of which were in the upper Midwest. After the relatively slow start, the late 1990s through 2008 was a particularly active period for adoption of a state-level EITC.

By 2000 an additional eleven states introduced an EITC bringing the total to 16 states. Most of this wave of state EITC implementation occurred in the Northeast and Midwest. From 2001 to 2010 another eight states introduced an EITC, bringing the total to 24. Again, uptake occurred largely in the Northeast and Midwest, with credit enactment also beginning to spread to the West and South. Following the major waves of expansion prior to 2010, the pace of state EITC introductions slowed.

From 2010 to 2020, seven states adopted an EITC, and one state eliminated its EITC, bringing the total, as of 2020, to 29 states and D.C., including Washington state, which has not yet implemented its EITC. The majority of states in the Midwest and Northeast and over a third of states in the West and South now have an EITC. As noted, the overall diffusion pattern of this public policy roughly approximates the conventional S-curve of cumulative adoptions over time—a few innovator states slowly start the process, followed by a period of more rapid spread, ending with a gradually slowing spread to the remaining laggard states. Given that a number of states have enacted EITC laws in the past few years (Table 1), combined with the fact that 40% of states do not yet have a state-level EITC, suggests that we likely have not yet reached the top of the S-curve—that is, a complete leveling off of the adoption rate [49, 50].

Differences across state and time of fundamental policy dimensions are evident, including size of credit and refundability. As of 2020, the 29 states and D.C. with an EITC varied widely in credit amount (5% to 52% of the federal EITC) and varied slightly in refundability (23 of 29 were refundable). Following the introduction of an EITC, about half of the states continued to increase the credit amount and/or convert a nonrefundable credit to a refundable credit. About a third of the states have made adjustments to increase or decrease credits throughout the years. One state recently eliminated its EITC.

State EITC benefits vary considerably by household structure. Most state EITCs are based on the federal EITC. The federal EITC was designed to incentivize work, especially for households with children [20]. EITCs for individuals or couples with no children are limited. For example, based on 2019 U.S. federal poverty thresholds, the 2019 federal EITC credit is nearly phased out for a household with no children at the poverty threshold (e.g., household with one adult with an income of $12,490 would receive a $237 refund; household with two adults and no dependents with an income of $16,910 would receive a $340 refund). In comparison, households with children with income at the poverty level would receive near the maximum EITC (e.g., household with one adult and two dependents with an income of $21,330 would receive a $5,345 credit; household with two adults and two dependents with an income of $25,750 would receive a $5,627 refund) [51]. These numbers translate to a 2% boost in income among households with no children compared with a 22% to 25% boost for households with two children. Nearly all state EITCs are based on the federal eligibility and income requirements; Minnesota and Wisconsin, however, use different income thresholds, which results in higher state credits to households with children. Consistent with how EITC benefits are structured, population segments that receive larger credits are those found to benefit the most in regard to health and well-being, especially among infants and children [23, 34, 37–43].

A broader question that arises whenever observing the spread of a particular state policy is: What drives the adoption of this distinct policy in a given state at a given time, and what drives the pattern of diffusion to other states? Understanding the complex mix of causal factors affecting state-level policy adoption across time is of theoretical and practical importance—and is an important focus of current and future research in political science [50]. There is a wide range of conceptually distinct plausible drivers of policy adoption [50]. In broad categories these drivers include, at a minimum, geospatial effects, role of multiple dimensions of political and social ideologies, effects of coercion and incentives (from higher level federal government, as one key example), and resource availability and distribution [50]. Moreover, recent research suggests drivers of policy adoption likely vary in their relevance across the adoption curve (i.e., across time within each adoption curve) [50]. There remains considerable poorly understood heterogeneity in policy adoption speed, and continuing research on causes of adoption patterns, both for EITC and more generally for all state-level policies, is warranted.

Separate from such research on policy adoption processes, research continues to accumulate regarding the societal benefits of EITCs, including reductions in family poverty and improved health outcomes [23, 33–41]. The analysis of policy trends reported here extends to 2020. Our research highlights that legislative debate and action at the state level has recently been and continues to be active, underscoring the utility of continued policy surveillance and public health law research evaluating health and well-being effects of new credits and other legislative changes to tax credit law. Additionally, cost-benefit analysis of state EITCs would also be helpful, given consistent findings of beneficial effects on birth weight and gestation weeks, and the annual social and health costs of preterm or low-birth-weight births in the U.S [52].

Conclusion

State-level EITC legislation continues to diffuse across states, with the majority of states in the Midwest and Northeast and over one-third of states in the West and South having adopted state EITCs as of 2020. States continue to modify their laws, with most increasing the EITC benefits over time. Appendices to this paper provide the policy surveillance codebook, the coding protocol, the data codebook, and a cvs data file ready for further analyses. We hope these resources support future researchers conducting multistate and multi-policy studies of predictors of policy adoption and evaluations of the effects of tax credit laws on the wide range of potential health and education outcomes. Finally, we hope this collaboration—between lawyers, policy analysts, epidemiologists, and economists—serves as an example for high-quality empirical studies of other dimensions of policy and law with the potential to affect the social determinants of health.

LawAtlas EITC codebook.

(PDF)

Click here for additional data file.

LawAtlas EITC research protocol.

(DOCX)

Click here for additional data file.

EITC data codebook 2020.

(DOCX)

Click here for additional data file.

CSV data file.

(CSV)

Click here for additional data file.

CDC EITC research protocol.

(DOCX)

Click here for additional data file.

25 Sep 2020

PONE-D-20-25783

Anti-poverty policy and health: Policy diffusion of state Earned Income Tax Credits across the U.S. states from 1980 to 2020

PLOS ONE

Dear Dr. Komro,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Please especially note reviewer two's comments.  Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Nov 09 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Sze Yan Liu, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating the following in the Financial Disclosure section:

'The National Institute on Minority Health and Health Disparities, National Institutes of Health (https://www.nimhd.nih.gov) through award R01MD010241 to KAK and ACW and the Policy Research, Analysis, and Development Office; Office of the Associate Director for Policy and Strategy; Centers for Disease Control and Prevention (https://www.cdc.gov/policy/about/index.html) supported this research. The content is solely the responsibility of the authors and does not necessarily represent the views of the National Institutes of Health or the Centers for Disease Control and Prevention. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. '

We note that one or more of the authors are employed by a commercial company: RTI International

a. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

b. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

c. Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information

4. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Congratulations to the authors for the great work! It is very important to study policy diffusion while such study is limited, especially in public health. I found that this initial study on Policy diffusion of state Earned Income Tax Credits (EITC) will be beneficial for other works to evaluate and assess the impacts in population.. Authors has also created a clear structure, so the manuscript is well-written and precise.

I do not have major comments for this manuscript, but there are few comments for the authors.

1. The authors use maps to describe the diffusion. When authors describe how the EITC policies diffused over 40 years and explain the diffusion within the cardinal direction, I wonder whether it diffused through adjacent states or not. I need to ask this as studying the policy diffusion is very important to incorporate how adjacent area stimulates the policy diffusion while the authors do not consider the vertical diffusion pattern in the study. For me, this is very important for international readers and experts to understand the geographical factors in the EITC policy diffusion aside from the cardinal direction. Perhaps the authors may consider to add one or two sentences explaining the adjacent states’ influence in the diffusion.

2. “The overall diffusion pattern of this public policy roughly approximates the conventional S-curve of diffusion—a few innovator states slowly start the diffusion process, followed by a period of rapid spread, ending with a gradually slowing spread to the remaining laggard states.” Just to make this sentence stronger, perhaps the authors can explain more about this in the results and add the period/year for each phase to explain when the innovators, early adopters, etc began. This claim seems very weak without a support explaining each phase.

Reviewer #2: This article looks at the diffusion of the earned income tax credit from 1980-2020, with an eye toward out the geographic locations of the diffusion. While this article is fundamentally sound in its methodological approach, I see no theory (or even postulation) about why these policies are diffusing from one point to the next. Are states learning from one another, emulating other’s policies? Is this competition? A statement needs to be made regarding why states are adopting this specific policy, and how this diffusion (the spreading of the policy from one jurisdiction to the next) is occurring across states. Since you are looking at just adoption years with no modeling to connect sates to one another (many studies rely on survival analyses), I would at least postulate a driving mechanism for states adopting this policy. I would also consider doing a cumulative adoption graph and looking at how many adoptions occurred per year. Also, I would look at the newer literature relevant to diffusion research.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Joshua L Mitchell

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

16 Oct 2020

PONE-D-20-25783

Anti-poverty policy and health: Policy diffusion of state Earned Income Tax Credits across the U.S. states from 1980 to 2020

PLOS ONE

Sze Yan Liu, PhD

Academic Editor

PLOS ONE

Dear Dr. Sze Yan Liu:

Thank you for the opportunity to revise and resubmit our manuscript for consideration of publication in PLOS ONE. We have carefully reviewed the comments and revised the paper accordingly. Below we have copied each point raised by the editor and reviewers and summarize our response with italicized text following each point. We have also included a marked-up and unmarked versions of the manuscript.

Journal Requirements:

1. We have followed the PLOS ONE’s style requirements.

2. We have revised our Financial Disclosure statement.

We originally listed RTI International (https://www.rti.org), a nonprofit research institute, as an affiliation for Nolan Sroczynski. However, this research was conducted while Mr. Sroczynski was a student at Emory University Rollins School of Public Health. While Mr. Sroczynski is currently employed at RTI International, his involvement in this research occurred while he was a student at Emory and recently during his own time. RTI International did not provide any support for this research, even in the way of overhead salary. Therefore, we feel it is more appropriate to remove the RTI affiliation for him.

Phenesse Dunlap receives funding from a training grant and we would like to include this source of funding in our financial disclosure section.

The revised Financial Disclosure section:

'The National Institute on Minority Health and Health Disparities, National Institutes of Health (https://www.nimhd.nih.gov) through award R01MD010241 to KAK and ACW, The National Heart, Lung, and Blood Institute, National Institutes of Health (https://www.nhlbi.nih.gov) through training award T32 HL130025 to PD, and the Policy Research, Analysis, and Development Office; Office of the Associate Director for Policy and Strategy; Centers for Disease Control and Prevention (https://www.cdc.gov/policy/about/index.html) supported this research. The findings and conclusions of this paper are solely the responsibility of the authors and do not necessarily represent the official position of the National Institutes of Health or the Centers for Disease Control and Prevention. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. '

3. We have included captions for your Supporting Information files at the end of our manuscript, and update any in-text citations to match accordingly.

4. We inserted our ethics statement in the Methods section of our manuscript.

Reviewers' comments:

Reviewer #1: Congratulations to the authors for the great work! It is very important to study policy diffusion while such study is limited, especially in public health. I found that this initial study on Policy diffusion of state Earned Income Tax Credits (EITC) will be beneficial for other works to evaluate and assess the impacts in population. Authors has also created a clear structure, so the manuscript is well-written and precise.

Thank you for your positive feedback.

I do not have major comments for this manuscript, but there are few comments for the authors.

1. The authors use maps to describe the diffusion. When authors describe how the EITC policies diffused over 40 years and explain the diffusion within the cardinal direction, I wonder whether it diffused through adjacent states or not. I need to ask this as studying the policy diffusion is very important to incorporate how adjacent area stimulates the policy diffusion while the authors do not consider the vertical diffusion pattern in the study. For me, this is very important for international readers and experts to understand the geographical factors in the EITC policy diffusion aside from the cardinal direction. Perhaps the authors may consider to add one or two sentences explaining the adjacent states’ influence in the diffusion.

Thank you for your thoughtful comment. Understanding the complex mix of causal factors driving state-level policy adoption across time is of theoretical and practical importance—and is an important focus of current and future research in political science. There is a wide range of conceptually distinct plausible drivers of policy adoption. In broad categories they include, at a minimum, geospatial effects, role of multiple dimensions of political and social ideology, effects of coercion and incentives (from higher level federal government, as one key example), and resource availability and distribution issues. Moreover, recent research suggests drivers of policy adoption likely vary in their importance across the adoption curve (i.e., across time within each adoption curve). Additionally, there remains considerable poorly understood heterogeneity in adoption speed. A study with the goal of advancing understanding of the relative role of factors affecting policy adoption across states would require study of dozens or hundreds of policy adoptions; a study of one policy is essentially an n=1 study in such a context. Our study is a descriptive study of EITC adoption and EITC policy content across states; the objective is describing the diffusion of this one policy and, most importantly, providing the requisite datasets for further empirical evaluations of the health effects of this particular economic security policy. Table 1 provides the complete clean policy dataset, and Figure 1 provides maps illustrating the spatial spread of adoption over time. To better address these broader conceptual issues raised by reviewer 1 we have: (1) added to the Discussion section text on the causes of policy diffusion and how that is one direction for future research, and (2) added a new Figure 1 showing the cumulative policy diffusion curve for this policy, and added description of that curve in more detail. The figure provides data from this single additional policy case of interest to the general political science literature on factors affecting diffusion, while also showing the data behind our statement that EITC policy diffusion roughly appears to be following the standard S-curve of innovation diffusion.

2. “The overall diffusion pattern of this public policy roughly approximates the conventional S-curve of diffusion—a few innovator states slowly start the diffusion process, followed by a period of rapid spread, ending with a gradually slowing spread to the remaining laggard states.” Just to make this sentence stronger, perhaps the authors can explain more about this in the results and add the period/year for each phase to explain when the innovators, early adopters, etc began. This claim seems very weak without a support explaining each phase.

This is a good idea. We have added a new Figure 1, illustrating cumulative adoptions over time, and describe period/years for each phase in the text. We introduce this figure in the beginning of the Time Trends in the Results section. Then Figure 2 and 3 follow, providing additional details of adoptions over time, with the details summarized in the Results and Discussion section—these are helpful to forward the primary objective of the paper—fostering additional studies of the health effects of EITC and similar policies

Reviewer #2: This article looks at the diffusion of the earned income tax credit from 1980-2020, with an eye toward out the geographic locations of the diffusion. While this article is fundamentally sound in its methodological approach, I see no theory (or even postulation) about why these policies are diffusing from one point to the next. Are states learning from one another, emulating other’s policies? Is this competition? A statement needs to be made regarding why states are adopting this specific policy, and how this diffusion (the spreading of the policy from one jurisdiction to the next) is occurring across states. Since you are looking at just adoption years with no modeling to connect sates to one another (many studies rely on survival analyses), I would at least postulate a driving mechanism for states adopting this policy. I would also consider doing a cumulative adoption graph and looking at how many adoptions occurred per year. Also, I would look at the newer literature relevant to diffusion research.

Thank you for your thoughtful suggestions.

1. As noted in response to reviewer 1 above, we have added a new Figure 1 presenting cumulative adoptions over time, and describe the pattern in the text. We present this figure in the beginning of the Time Trends in the Results section on page 7.

2. We added a citation to a great 2020 publication specifically focused on this issue of the causal drivers of state-level policy adoption (Mallinson, D.J. (2020). Policy innovation and adoption across the diffusion like course. Policy Studies Journal, 1-24. doi: 10.1111/psj.12406). We added text to the Discussion section on the many possible factors influencing policy adoption.

3. We also suggest future research to study predictors of policy adoption. In response to reviewer 1 we point out that our study was not designed to answer that question, and a fundamentally different study design would be required to substantively contribute to the political science literature on drivers of state policy diffusion. Our goal was to describe state-level EITC policy spread and provide a complete policy dataset, for the purpose of facilitating additional empirical research evaluating the health effects of EITC and similar policies that directly address family economic security.

4. We changed the title to better encompass the study purpose and lesson the sole focus on the term “diffusion.”

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

4 Nov 2020

Anti-poverty policy and health: Attributes and diffusion of state Earned Income Tax Credits across U.S. states from 1980 to 2020

PONE-D-20-25783R1

Dear Dr. Komro,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Sze Yan Liu, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The additional revisions and clarification greatly strengthens your paper.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The author/s addressed all of my comments pretty well. I look forward to seeing this article in print. I would recommends the author/s give the paper a final edit before submission.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: Joshua L Mitchell

9 Nov 2020

PONE-D-20-25783R1

Anti-poverty policy and health: Attributes and diffusion of state Earned Income Tax Credits across U.S. states from 1980 to 2020

Dear Dr. Komro:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Sze Yan Liu

Academic Editor

PLOS ONE