The SNAP state cost-shift policy leaves the program’s existence to chance
Until the One Big Beautiful Bill Act (OBBBA), the federal government alone was responsible for paying for Supplemental Nutrition Assistance Program (SNAP) benefits.
在《美丽大法案》(OBBBA) 出台之前，联邦政府独自负责支付补充营养援助计划 (SNAP) 福利。
Under OBBBA, starting in October 2027, states potentially will be required to pay a share of SNAP benefits, with the required share determined solely by the state’s “payment error rate” for the relevant fiscal year.
根据 OBBBA，从 2027 年 10 月开始，各州可能需要支付一部分 SNAP 福利，所需份额仅由各州相关财政年度的“支付错误率”决定。
After the first year, a state’s payment error rate from three years prior will determine the state’s cost shift, meaning that a state’s FY2026 payment error rate will be the basis for the FY2029 cost-shift amount.
第一年之后，一个州三年前的支付错误率将决定该州的成本转移，这意味着该州 2026 财年的支付错误率将成为 2029 财年成本转移金额的基础。
We have examined the harms that will result from OBBBA ending the guarantee of full federal funding for SNAP benefits.
我们已经研究了 OBBBA 终止对 SNAP 福利提供全额联邦资助的保证所带来的危害。
As we , the Congressional Budget Office , governors , officials in states like Alabama and Arizona , and other analysts have concluded, the state cost shift will likely lead some states to exit SNAP altogether while other states will restrict eligibility, cut benefits, or make it harder for eligible residents to enroll in SNAP.
正如我们、国会预算办公室、州长、阿拉巴马州和亚利桑那州等州的官员以及其他分析师得出的结论，州成本转移可能会导致一些州完全退出 SNAP，而其他州将限制资格、削减福利，或让符合资格的居民更难加入 SNAP。
Because of balanced budget laws, declining revenue, and other factors, this policy also makes it more likely that states will restrict access or drop out of the program when an economic downturn occurs and need increases, leaving Americans without adequate food assistance, sharply increasing poverty and hardship, and deepening the downturn.
由于平衡预算法、收入下降等因素，这一政策也使得各州在经济衰退、需求增加时更有可能限制准入或退出该计划，导致美国人得不到足够的粮食援助，贫困和困难急剧增加，经济衰退加深。
In this analysis, we focus on characterizing the statistical shortcomings of SNAP payment error rates, which each year will solely determine whether and how much a state must pay for SNAP benefits.
在本次分析中，我们重点关注 SNAP 支付错误率的统计缺陷，每年的错误率将仅决定一个州是否必须为 SNAP 福利支付费用以及支付多少费用。
The SNAP payment error rate is a measure of how accurately states make eligibility and benefit determinations for participating households.
SNAP 支付错误率是衡量各州为参与家庭做出资格和福利决定的准确程度的指标。
If there is a payment error among eligible households, defined as a benefit overpayment or underpayment in excess of $58 in FY2026, the difference between the erroneous payment and the correct payment counts toward the error rate.
如果符合资格的家庭存在支付错误（定义为 2026 财年福利多付或少付超过 58 美元），则错误支付与正确支付之间的差额将计入错误率。
In some cases, a procedural error that awarded benefits to an otherwise eligible family can result in their entire benefit allotment being counted as error.
在某些情况下，向原本符合条件的家庭发放福利的程序错误可能会导致其全部福利分配被计为错误。
The opposite does not hold: Wrongly rejecting benefits for an eligible applicant is not considered an error.
相反的情况则不成立：错误地拒绝符合资格的申请人的福利不被视为错误。
The payment error rate is calculated as the sum of all over- and under-payments to participating households divided by all payments.
支付错误率的计算方式为参与家庭的所有多付和少付的总和除以所有付款。
Our focal analysis characterizes FY2023 payment error rates and shows that the penalty tier assigned to a state is substantially a product of statistical chance.
我们的重点分析描述了 2023 财年的支付错误率，并表明分配给某个州的处罚等级基本上是统计机会的产物。
An analogy to public opinion polling may be helpful, as every poll reports its findings along with an assessment of its statistical uncertainty.
与民意调查进行类比可能会有所帮助，因为每次民意调查都会报告其调查结果以及对其统计不确定性的评估。
When a pollster reports likely voter support as “48 percent with a margin of error of ±3 points at a 95 percent confidence level,” the interval—45 to 51 percent—is the pollster’s way of saying: “We only talked to a sample.
当民意调查机构报告可能的选民支持率为“48%，在 95% 的置信水平下，误差幅度为 ±3 个百分点”时，这个区间（45% 到 51%）就是民意调查机构的说法：“我们只与样本进行了交谈。
If we drew 20 fresh samples the same way, we would expect about 19 of them to land somewhere in this range.” The statistical uncertainty is not a mistake; it is the unavoidable price of not interviewing every voter.
如果我们以同样的方式抽取 20 个新样本，我们预计其中大约 19 个样本会落在这个范围内。”统计上的不确定性并不是一个错误；而是一个错误。这是不采访每一位选民的不可避免的代价。
And if candidate A is at 48 percent ±3 while candidate B is at 46 percent ±3, one doesn’t declare a likely winner because the race is a statistical tie.
如果候选人 A 的支持率为 48% ±3，而候选人 B 的支持率为 46% ±3，则不会宣布可能的获胜者，因为这场比赛在统计上是平局。
The SNAP payment error rate is estimated the same way and comes with the same kind of statistical uncertainty.
SNAP 支付错误率的估算方式相同，并且具有相同的统计不确定性。
States review a statistical sample of cases, and the U.S. Department of Agriculture ( USD A ) then calculates the official rate based on the state review findings and its own re-review of a subsample of those cases.
各州审查案件的统计样本，然后美国农业部 (USD A) 根据州审查结果以及对这些案件子样本的重新审查来计算官方比率。
USD A ’s Quality Control Annual Report publishes each state’s payment error rate together with its standard error and computes a 95 perfect confidence interval as the payment error rate ±1.96 × standard error—the same formula a pollster uses.
USD A 的质量控制年度报告公布了每个州的支付错误率及其标准误差，并计算了 95 完美置信区间作为支付错误率 ±1.96 × 标准误差——与民意调查机构使用的公式相同。
The problem we identify in this analysis is that the uncertainty in the payment error rate is large relative to the size of the cost-shift bins: The confidence intervals are wider than most of the bins themselves.
我们在此分析中发现的问题是，相对于成本转移箱的大小，支付错误率的不确定性很大：置信区间比大多数箱本身更宽。
The average standard error on a state’s annual payment error rate is about 1.1 percentage points, meaning a typical 95 percent confidence interval spans roughly 4.4 percentage points.
一个州年度支付错误率的平均标准误差约为 1.1 个百分点，这意味着典型的 95% 置信区间约为 4.4 个百分点。
In Alaska, the standard error is 3.9 percentage points, so its 95 percent confidence interval spans roughly 15 points.
在阿拉斯加，标准误差为 3.9 个百分点，因此其 95% 置信区间大约为 15 个百分点。
Compare that to the 2-percentage-point bins now used to set state cost-shares: Zero percent of benefits if the error rate is below 6 percent, 5 percent if it is between 6 and 7.99 percent, 10 percent if it is between 8 and 9.99 percent, and 15 percent if it 10 percent or higher (after an implementation delay for states with errors of 13.33 percent or higher in FY2025 or FY2026).
与现在用于设置州成本分摊的 2 个百分点的区间相比：如果错误率低于 6%，则收益为 0%；如果错误率在 6% 到 7.99% 之间，则收益为 5%；如果错误率在 8% 到 9.99% 之间，则收益为 10%；如果错误率在 10% 或更高，则收益为 15%（在 2025 财年或 13.33% 或更高错误率的州实施延迟之后）。 2026 财年）。
A state whose point estimate (the “point estimate” is the statistical term for the payment error rate number) lands at 8.5 percent cannot, in a statistical sense, be reliably distinguished from a state at 7.1 percent or 10.9 percent—yet the state landing at 8.5 percent would owe twice as much as the state landing at 7.1 percent.
从统计意义上讲，一个点估计值（“点估计值”是支付错误率数字的统计术语）达到 8.5% 的州，无法与 7.1% 或 10.9% 的州可靠地区分开来——然而，点估计值达到 8.5% 的州所欠债务是达到 7.1% 的州的两倍。
Sorting states into high-stakes penalty tiers on the basis of a single estimate treats a noisy measurement as if it were not, with hundreds of millions of state dollars on the line.
根据单一估计将各州划分为高风险惩罚等级，将噪声测量视为不存在，这将导致数亿州的损失。
Many federal funding allocations rely on statistics computed from survey samples such as the American Community Survey.
许多联邦资金分配依赖于根据美国社区调查等调查样本计算得出的统计数据。
What makes SNAP different is that the sample used to estimate each state’s payment error rate is quite small, the resulting uncertainty is large relative to the width of the cost-shift bins, and the fiscal impacts for states of small changes in payment error rates are also quite large.
SNAP 的不同之处在于，用于估计每个州支付错误率的样本非常小，相对于成本转移箱的宽度而言，由此产生的不确定性很大，而且支付错误率的微小变化对各州的财政影响也相当大。
As a result, random chance will substantially contribute to determining what a state will be required to pay in SNAP benefits starting in October 2027.
因此，随机机会将极大地有助于确定从 2027 年 10 月开始各州需要支付的 SNAP 福利金额。
USDA’s existing quality control regime already recognizes this uncertainty.
美国农业部现有的质量控制制度已经认识到这种不确定性。
Since 2002, the payment error rate has been used to subject states to corrective action and to levy financial penalties, but those penalties have been based on the statistical lower bound of the payment error rate confidence interval, not the point estimate: A state could be sanctioned only if, for two consecutive years, the lower bound of its confidence interval exceeded 105 percent of the national payment error rate.
自2002年以来，支付错误率一直被用来让国家采取纠正行动并征收经济处罚，但这些处罚是基于支付错误率置信区间的统计下限，而不是点估计：只有当一个国家的置信区间下限连续两年超过国家支付错误率的105%时，才会受到制裁。
The previously existing policy regime, which remains in place, accounts for uncertainty in payment error rate estimates.
先前存在的政策制度仍然存在，导致了支付错误率估计的不确定性。
The new high-stakes state cost-shift policy does not.
新的高风险国家成本转移政策则不然。
Because random chance is a substantial factor in producing a state’s payment error rate in any single year, it is also true across time: The cost shift to states will fluctuate unpredictably and considerably from year to year based on random chance alone.
由于随机机会是在任何一年中产生一个州支付错误率的重要因素，因此随着时间的推移也是如此：仅根据随机机会，向各州的成本转移将每年出现不可预测且相当大的波动。
We find that changes in payment error rates are not at all persistent across years and are more likely to reflect differences in a state’s luck of the draw in the quality control sample than changes in program administration.
我们发现，多年来支付错误率的变化根本不会持续存在，并且更可能反映一个州在质量控制样本中抽签的运气差异，而不是项目管理的变化。
Consequently—and consequentially—we find that there is no guarantee that good-faith efforts to improve program administration will be reflected in the form of a lower payment error rate.
因此，我们发现，无法保证改善项目管理的真诚努力会以较低的支付错误率的形式得到体现。
A policy that could cause states to drop out of the SNAP program entirely should not be left to chance; instead, the federal government should continue to fully fund SNAP benefits.
一项可能导致各州完全退出 SNAP 计划的政策不应被视为偶然；相反，联邦政府应继续为 SNAP 福利提供全额资金。
For the first time in program history, OBBBA requires states to pay part of SNAP benefit costs.
OBBBA 在计划历史上首次要求各州支付部分 SNAP 福利费用。
The share charged to a state each year is tied to the state’s estimated SNAP payment error rate alone.
每年向各州收取的份额仅与该州估计的 SNAP 付款错误率挂钩。
Table 1 summarizes the relationship between a state’s estimated payment error rate point estimate and the share of SNAP benefits that a state will have to pay.
表 1 总结了各州的估计支付错误率点估计值与各州必须支付的 SNAP 福利份额之间的关系。
(Appendix figure 1 visualizes the payment schedule.) A state will be required to pay a portion of SNAP benefit costs if the state’s estimated SNAP payment error rate exceeds 6 percent, with the state’s portion rising as the payment error rate increases from there until it hits a maximum when the state’s payment error rate exceeds 10 percent.
（附录图 1 显示了付款时间表。）如果该州估计的 SNAP 付款错误率超过 6%，则该州将被要求支付一部分 SNAP 福利费用，该州支付的部分会随着付款错误率的增加而增加，直到该州的付款错误率超过 10% 时达到最大值。
There is a temporary exception in the first two years of implementation, however, during which time states with SNAP payment error rate point estimates of 13.33 percent or higher will not be required to contribute payment.
然而，在实施的前两年有一个暂时的例外，在此期间，SNAP 付款错误率点估计为 13.33% 或更高的州将不需要缴纳付款。
This delay for states with especially high error rates is sometimes referred to as the “Alaska carveout.” Beginning in FY2030, all states with payment error rates above 10 percent will have to pay 15 percent of benefit costs.
错误率特别高的州的这种延迟有时被称为“阿拉斯加剥离”。从 2030 财年开始，所有支付错误率超过 10% 的州都必须支付 15% 的福利成本。
Figure 1 shows FY2022–25 payment error rates (PER) by state and year, with that year’s payment bin color coded as:
图 1 按州和年份显示了 2022-25 财年支付错误率 (PER)，该年的支付箱颜色编码为：
The variation in colors across rows highlights the fact that SNAP payment error rates fluctuate frequently across these 2-percentage-point bins.
各行颜色的变化凸显了这样一个事实：SNAP 支付错误率在这些 2 个百分点的区间内频繁波动。
This fluctuation will make planning for the expected cost shift even more difficult for states.
这种波动将使各州规划预期的成本转移变得更加困难。
Forty-three states had payment error rates over this four-year period that fell into at least two bins: Twenty-eight states had payment error rates in two different bins, 14 states in three different bins, and one state (North Carolina) had an error rate in each of the four different bins.
在这四年期间，43 个州的支付错误率至少分为两个类别：28 个州的支付错误率属于两个不同的类别，14 个州属于三个不同的类别，一个州（北卡罗来纳州）的错误率属于四个不同的类别。
Thirty-three states and D.C. had an error rate above 10 percent at least once.
三十三个州和华盛顿特区的错误率至少有一次超过 10%。
Only five states—Idaho, South Dakota, Vermont, Wisconsin, and Wyoming—have had payment error rates consistently below 6 percent since 2022.
自 2022 年以来，只有爱达荷州、南达科他州、佛蒙特州、威斯康星州和怀俄明州这五个州的支付错误率一直低于 6%。
In FY2028 (starting in October 2027), 15 states and Washington, D.C., will have SNAP benefits fully paid for by the federal government because they have a payment error rate below 6 percent (nine states) or above 13.33 percent (six states and DC).
2028 财年（从 2027 年 10 月开始），15 个州和华盛顿特区将获得由联邦政府全额支付的 SNAP 福利，因为这些州的支付错误率低于 6%（9 个州）或高于 13.33%（6 个州和华盛顿特区）。
If FY2025 payment error rates are used, the remaining 35 states may have to pay portions ranging from 5 percent (six states), 10 percent (16 states), to 15 percent (13 states) of benefits.
如果使用 2025 财年支付错误率，其余 35 个州可能必须支付 5%（6 个州）、10%（16 个州）到 15%（13 个州）的福利。
States instead may opt to use their FY2026 payment error rate to determine the share of benefits to be paid (information that will likely be released in June 2027).
相反，各州可能会选择使用 2026 财年支付错误率来确定要支付的福利份额（信息可能会在 2027 年 6 月发布）。
What is not addressed or accounted for under OBBBA is that there is substantial and consequential statistical noise in the measurement of the SNAP payment error rate.
OBBBA 没有解决或解释的是，在 SNAP 支付错误率的测量中存在大量且相应的统计噪音。
The SNAP payment error rate is a statistic: It is not an inherently precise number, but rather the best estimate given the underlying random sample of cases.
SNAP 支付错误率是一项统计数据：它本身并不是一个精确的数字，而是给定潜在随机案例样本的最佳估计值。
As with all statistics, the estimated payment error rate is likely to be different from the “true” rate that would be measured if one were to audit every single case instead of a sample drawn from the full caseload.
与所有统计数据一样，如果审计每一个案例而不是从整个案例中抽取样本，则估计的支付错误率可能与测量的“真实”率不同。
If, due to luck of the draw, the sample happens to consist disproportionately of simple, well-documented cases, the estimated payment error rate will be lower than the rate that would be calculated if we could audit every single case.
如果由于抽签的运气，样本恰好由不成比例的简单、有据可查的案例组成，则估计的支付错误率将低于如果我们能够审核每个案例所计算出的错误率。
If, on the other hand, the sample happens to overrepresent complex cases, the estimated payment error rate will be higher than the true error rate that would be computed if the entire caseload were used to produce the calculation.
另一方面，如果样本恰好过度代表了复杂的案例，则估计的支付错误率将高于使用整个案例进行计算时计算出的真实错误率。
A state does not—and should not—review every single case on file to produce the payment error rate; in FY2023, the SNAP caseload was 22.2 million households, and 45,467 cases were reviewed.
国家不会也不应该审查每个案例以得出支付错误率； 2023 财年，SNAP 案件数量为 2220 万户家庭，审查了 45,467 起案件。
Identifying errors requires a deep dive into the data, reinterviewing participants, and careful consideration of the SNAP program rules by dedicated quality control staff; more time and information are given per case during quality control than is given for eligibility and benefit determinations.
识别错误需要深入研究数据，重新采访参与者，并由专门的质量控制人员仔细考虑 SNAP 计划规则；质量控制期间为每个案例提供的时间和信息比资格和福利确定时提供的时间和信息要多。
Due to costs (our estimate is that these reviews cost about $100 million a year 1 ) and time constraints, quality control is only conducted on a small subsample of SNAP cases.
由于成本（我们估计这些审查每年花费约 1 亿美元 1）和时间限制，质量控制仅对 SNAP 案例的一小部分子样本进行。
Each state draws a “statistical sample” of SNAP-participating households from active cases.
每个州都会从活跃病例中抽取参与 SNAP 家庭的“统计样本”。
The state agency reviews this statistical sample of cases to identify and quantify payment errors, meaning that the agency checks each of the sampled cases to determine whether the state either overpaid or underpaid a household by more than the maximum allowable amount for that fiscal year ($58 in FY2026).
州政府机构审查此统计案例样本，以识别和量化支付错误，这意味着该机构会检查每个样本案例，以确定州政府是否多付或少付给家庭，其金额是否超过该财政年度允许的最大金额（2026 财年为 58 美元）。
The absolute values of overpayments (which include payments to ineligible households) and underpayments to participating households are summed.
超额支付（包括向不符合资格的家庭支付的款项）和向参与家庭支付的不足的绝对值相加。
The value of the total dollar amount of over- or underpayments is divided by the total dollar amount in benefits to calculate the error rate estimate.
将多付或少付的总金额除以福利总金额即可计算错误率估计值。
Next, the federal government re-reviews a sample of the state’s review of the statistical sample of cases.
接下来，联邦政府重新审查各州统计案件样本的审查样本。
On average, the federal government reviews about one-third of the cases that were in the state review sample.
平均而言，联邦政府审查州审查样本中约三分之一的案件。
This results in further revision to the payment error rate statistic to produce the final point estimate and the confidence interval around that estimate (though with an extended lag).
这导致对支付错误率统计数据的进一步修改，以产生最终的点估计和该估计周围的置信区间（尽管有较长的滞后）。
But the sampling variability in the statistic is only part of its uncertainty.
但统计数据中的抽样变异性只是其不确定性的一部分。
Not all case reviews can be completed satisfactorily.
并非所有案件审查都能圆满完成。
Behind the scenes, federal auditors must make imputations to assign payment error values to incomplete cases.
在幕后，联邦审计员必须进行估算，将支付错误值分配给不完整的案例。
A state’s official payment error rate point estimate, i.e., the single number that is the state’s SNAP payment error rate for the fiscal year, is what the federal government determines from its review of the state’s initial review.
州的官方支付错误率点估计值，即该州本财年 SNAP 支付错误率的单一数字，是联邦政府根据该州的初步审查确定的。
The federal government review also produces a 95 percent confidence interval around the estimated error rate in a separate report, which is released with a multi-year delay from the release of the point estimate.
联邦政府的审查还在另一份报告中得出了围绕估计错误率的 95% 置信区间，该报告的发布比点估计的发布延迟了多年。
(Standard errors for FY2024 and 2025 error rates are not available yet.)
（2024 财年和 2025 财年错误率的标准错误尚不可用。）
The precision of a statistic is related to its sample size; thus, the variability of a state’s payment error rate statistic is related to the sample size of the number of cases in the federal review of each state.
统计的精度与其样本量有关；因此，一个州支付错误率统计数据的变异性与每个州联邦审查案件数量的样本量有关。
In FY2023 the number of cases reviewed ranged from 155 in Delaware to 428 in Wisconsin.
2023 财年，审查的案件数量从特拉华州的 155 起到威斯康星州的 428 起不等。
Appendix figure 3 shows that there is little relationship between the size of a state’s caseload and the sample size.
附录图 3 显示，州的案件数量与样本量之间几乎没有关系。
The core of this analysis is an evaluation of the SNAP payment error rate for a single year.
本分析的核心是对单年 SNAP 支付错误率的评估。
To illustrate the concepts at issue, we estimate statistical uncertainty using FY2023 SNAP payment error rates, the most recent year with complete USDA-published payment error rate data—including the relevant statistical uncertainty—available (see table 27 ).
为了说明有争议的概念，我们使用 2023 财年 SNAP 支付错误率来估计统计不确定性，这是美国农业部发布的完整支付错误率数据（包括相关统计不确定性）的最新一年（见表 27）。
USDA does not release the QC Annual Report for a given fiscal year until a couple of years later, which is why in this analysis, instead of using the most recent (2025) error rates we use the 2023 error rates—that is, the most recent year for which USDA has published standard errors.
美国农业部直到几年后才会发布特定财政年度的质量控制年度报告，这就是为什么在本次分析中，我们使用 2023 年的错误率（即美国农业部发布标准误差的最近一年），而不是使用最新的（2025 年）错误率。
Because the payment error rate statistic varies so widely and unpredictably from year to year, which states will have to make payments toward SNAP benefits, and what their contribution rate will be, varies by year.
由于支付错误率统计数据每年变化很大且不可预测，因此哪些州必须支付 SNAP 福利以及其缴款率每年都会有所不同。
The point we make here is true no matter the year we use to illustrate the issues: Statistical uncertainty in the payment error rate will drive substantial random variation in cost-shift rates because the bin sizes are small relative to the size of the statistical uncertainty.
无论我们用哪一年来说明问题，我们在这里提出的观点都是正确的：支付错误率的统计不确定性将导致成本转移率的显着随机变化，因为相对于统计不确定性的大小，分箱大小很小。
We show the payment error rate point estimate (figure 2) and the confidence intervals around the FY2023 payment error rates (figure 3) as well as for each state from FY2002–23 (a data interactive; figure 4).
我们显示了支付错误率点估计（图 2）和 2023 财年支付错误率周围的置信区间（图 3）以及 2002-23 财年每个州的置信区间（数据交互；图 4）。
In table 2, we then calculate expected state cost shifts using FY2023 SNAP benefit expenditures to size the uncertainty in terms of both SNAP benefit expenditures in millions of dollars and as a share of a state’s FY2023 expenditures (table 2).
然后，在表 2 中，我们使用 2023 财年 SNAP 福利支出计算预期的州成本变化，以衡量 SNAP 福利支出（以百万美元为单位）和占州 2023 财年支出的份额的不确定性（表 2）。
We emphasize that if we were to replicate this analysis with SNAP payment error rates from other years, states would land in completely different places; one should not expect that the list of states doing relatively well or worse will be persistent across years.
我们强调，如果我们用其他年份的 SNAP 支付错误率来重复这一分析，各州将处于完全不同的位置；人们不应期望表现相对较好或较差的国家名单会持续多年。
Furthermore, the “Alaska carveout,” with zero cost-shift payments for states with error rates 13.33 percent or higher, is only in effect for two years (until FY2030); to illustrate expected payments after this provision sunsets, we also remove the exception criteria for high error rates and show the results in table 2.
此外，“阿拉斯加剥离”政策仅对错误率 13.33% 或更高的州实行零成本转移支付，有效期仅两年（直到 2030 财年）；为了说明该规定取消后的预期付款，我们还删除了高错误率的例外标准，并在表 2 中显示了结果。
Figure 2 shows the point estimate for the FY2023 payment error rate by state, ordered from lowest to highest.
图 2 显示了按州划分的 2023 财年支付错误率的点估计值，按从最低到最高的顺序排列。
Had OBBBA’s policy for FY2029 been in place during this period, seven states would have paid nothing due to a low estimated error rate, and 11 states and D.C. would have paid nothing due to having a high estimated error rate.
如果 OBBBA 2029 财年的政策在此期间实施，则 7 个州将因估计错误率较低而无需支付任何费用，而 11 个州和华盛顿特区将因估计错误率较高而无需支付任何费用。
The remaining 32 states would have been required to pay between 5 and 15 percent of benefit costs.
其余 32 个州将被要求支付 5% 至 15% 的福利成本。
The reported SNAP payment error rate is the best estimate of the “true” error rate that would be produced if every case were completely audited.
报告的 SNAP 付款错误率是对每个案例都经过彻底审核后产生的“真实”错误率的最佳估计。
While there is only one “true,” unobserved error rate, standard statistical methods produce a best estimate of that rate—plus a range around the estimate reflecting that the “true” error rate may be higher or lower.
虽然只有一个“真实的”、未观察到的错误率，但标准统计方法会产生该错误率的最佳估计值，加上该估计值周围的一个范围，反映出“真实的”错误率可能更高或更低。
This range is the “95 percent confidence interval.” It means, in essence, that if we drew 100 samples and calculated an estimated rate and range from each, about 95 of those ranges would contain the true but unobserved error rate.
该范围是“95% 置信区间”。从本质上讲，这意味着如果我们抽取 100 个样本并计算每个样本的估计率和范围，那么其中大约 95 个范围将包含真实但未观察到的错误率。
The wider this range, the greater the uncertainty in the estimate—meaning the reported rate could sit well above or below the true error rate.
该范围越宽，估计的不确定性就越大，这意味着报告的错误率可能远高于或低于真实错误率。
Under the cost-shift policy, a wider range also means a greater chance that a state’s required payment rate reflects sampling luck rather than its actual performance.
在成本转移政策下，更大的范围也意味着一个州要求的支付率更有可能反映抽样运气而不是其实际表现。
Figure 3 shows the 95 percent confidence intervals around each state’s estimated payment error rate for FY2023.
图 3 显示了每个州 2023 财年估计付款错误率的 95% 置信区间。
The point estimate (dot) and the lower and upper bounds (triangles) are color coded to show which OBBBA cost-shift bin each of these three values falls into.
点估计（点）以及下限和上限（三角形）采用颜色编码，以显示这三个值各自属于哪个 OBBBA 成本转移箱。
The “true” payment error rate is not equally likely to land equally throughout the confidence interval (see figure 7 for further discussion).
“真实”支付错误率在整个置信区间内出现的可能性并不相同（进一步讨论参见图 7）。
Only among those states with SNAP payment error rates at the very low end (three states) and very high end (eight states and D.C.) does the 95 percent confidence interval fall entirely within a single bin—meaning that we can be confident that the state’s true payment error rate is below 6 percent or equal to or above 13.33 percent.
只有在 SNAP 支付错误率处于极低端（三个州）和非常高端（八个州和华盛顿特区）的州中，95% 的置信区间才完全落在一个区间内，这意味着我们可以确信该州的真实支付错误率低于 6% 或等于或高于 13.33%。
This is partly a function of the width of the relevant cost-shift bin, which is 6 percentage points at the low end (0 to 5.99 percent) and about 87 percentage points at the high end (13.33 to 100 percent)—far wider than the narrow 2-percentage-point bins in between.
这在一定程度上是相关成本转移区间宽度的函数，在低端为 6 个百分点（0 至 5.99%），在高端为约 87 个百分点（13.33 至 100%）——远比其间狭窄的 2 个百分点区间宽。
But we reiterate that SNAP payment error rates differ widely from year to year (figure 4), and “precision” at below 6 percent or above 13.33 percent in one year has little predictive power for the next year.
但我们重申，SNAP 支付错误率每年差异很大（图 4），一年内低于 6% 或高于 13.33% 的“精确度”对下一年的预测能力不大。
Due to random chance from statistical sampling variability, each year it is unpredictable whether a state will pay nothing or 5, 10, or 15 percent of benefit costs.
由于统计抽样变异性的随机性，每年都无法预测某个州是否会不支付任何费用，或者支付 5%、10% 或 15% 的福利成本。
When the confidence interval extends across multiple bins—as is the case for most states—we cannot be confident that the state’s “true” error rate falls in the same bin as the noisy point estimate produced from the available sample.
当置信区间延伸到多个容器时（就像大多数状态的情况一样），我们无法确信该状态的“真实”错误率与从可用样本生成的噪声点估计落在同一容器中。
For these states, the obligation could be too high or too low purely due to random chance.
对于这些州来说，义务可能纯粹由于随机机会而太高或太低。
In this section, we quantify this uncertainty in terms of billions of dollars and as a share of a state’s budget in a single year.
在本节中，我们将这种不确定性量化为数十亿美元并占州一年预算的份额。
Again, we emphasize that the states and error rates vary widely from year to year, so FY2023 is an illustrative example.
我们再次强调，每年的状态和错误率差异很大，因此 2023 财年就是一个说明性的例子。
Table 2 first quantifies the uncertainty that a state would face in billions of dollars, showing the range of SNAP benefit payments in FY2023 that would have been statistically likely based on the 95 percent confidence interval around the FY2023 payment error rate.
表 2 首先量化了一个州将面临的数十亿美元的不确定性，显示了 2023 财年 SNAP 福利支付的范围，该范围在统计上可能是基于 2023 财年支付错误率周围的 95% 置信区间。
For this illustration, we treat each state’s estimated payment error rate as if it were the true, unobservable rate, then use the confidence interval to show how much the resulting obligation could vary from sampling alone.
在这个例子中，我们将每个州的估计支付错误率视为真实的、不可观察的比率，然后使用置信区间来显示由此产生的义务可能与单独抽样有多大差异。
Eleven states and D.C. would have been likely to pay nothing, because their 95 percent confidence interval fell either entirely below 6 percent or entirely at or above 13.33 percent (the two-year “Alaska carveout” group).
十一个州和华盛顿特区可能不会支付任何费用，因为它们的 95% 置信区间要么完全低于 6%，要么完全等于或高于 13.33%（两年“阿拉斯加剥离”组）。
For all other states, though, there is substantial chance—due to luck of the draw—that they will be required to pay hundreds of millions of dollars more, or hundreds of millions of dollars less, toward their SNAP benefits.
然而，对于所有其他州来说，由于运气好，他们很有可能被要求为 SNAP 福利多支付数亿美元或少支付数亿美元。
These include states such as California, Florida, Georgia, New York, and Texas.
这些州包括加利福尼亚州、佛罗里达州、佐治亚州、纽约州和德克萨斯州。
At the extreme, due to random chance, California could pay nothing or more than $2 billion.
在极端情况下，由于随机性，加州可能不支付任何费用或超过 20 亿美元。
In other words, due to the variability in the payment error rate estimation, large changes in cost share liabilities basically come down to a coin flip.
换句话说，由于支付错误率估计的可变性，成本分摊负债的大幅变化基本上可以归结为抛硬币。
In Montana, the FY2023 payment error rate calculations indicate that it was almost equally likely that the state would fall into the 5 percent payment bin (51.8 percent chance) as it was that it would fall into the zero benefit costs payment bin (47.8 percent chance).
在蒙大拿州，2023 财年付款错误率计算表明，该州落入 5% 付款区间（51.8% 的机会）的可能性几乎与落入零福利成本付款区间（47.8% 的机会）的可能性相同。
Meanwhile in California, it is a higher-stakes coin toss between an estimated rate falling into the 15 percent payment bin (47.5 percent chance) or the no payment bin (52.1 percent chance) due to have an imprecisely measured higher error rate.
与此同时，在加利福尼亚州，由于不精确测量的较高错误率，估计的利率落入 15% 的付款区间（47.5% 的机会）或不付款区间（52.1% 的机会）之间，这是一场更高风险的硬币掷硬币。
The imprecision in Illinois’ estimated rate gave them nearly equal odds at falling into the 10 percent cost-shift bin (50.8 percent chance) or the 15 percent bin (46.4 percent chance).
伊利诺伊州估计率的不精确性使他们落入 10% 成本转移区间（50.8% 的机会）和 15% 成本转移区间（46.4% 的机会）的几率几乎相同。