43:["$","title",null,{"children":"Simulating Relief Payments: An Interactive Model | James Hawkins | Observable"}] 4b:Td6e,md`## Introduction The damage caused by the COVID-19 pandemic is unprecedented in the experience of modern Americans. Beyond the public health crisis that continues to wreak havoc on American communities, the country also faces an ongoing and significant economic crisis. Starting in March, the economic fallout was severe and swift. By April, the number of open small businesses [was down by 44%](https://tracktherecovery.org/?pni2w) and revenues had [dropped by 50%](https://tracktherecovery.org/?ofklu) relative to January of 2020. This led to a dramatic increase in the U.S. unemployment rate from an over 50-year low of 3.5% in February of 2020 to [14.8% in the span of only two months](https://fred.stlouisfed.org/series/UNRATE), leaving 22.8 million Americans searching but unable to find a job. And the economic damage was felt disproportionately across the population: the worst of the immediate downturn fell most acutely on jobs in [leisure and hospitality](https://www.stlouisfed.org/publications/regional-economist/third-quarter-2020/jobs-hit-hardest-covid-19), where [unemployment increased](https://fred.stlouisfed.org/series/LNU04032241) from 5.7% to 39.3% between February and April and [young](https://www.epi.org/publication/young-workers-covid-recession/) [adults](https://budgetmodel.wharton.upenn.edu/issues/2020/3/19/the-demographics-of-the-coronavirus-crisis-impact-on-hospitality), [people of color](https://www.npr.org/2020/04/22/840276956/minorities-often-work-these-jobs-they-were-among-first-to-go-in-coronavirus-layo), and [women](https://www.kansascityfed.org/en/publications/research/eb/articles/2020/women-take-bigger-hit-job-losses-covid19) are overrepresented. Since the start of the initial COVID-fueled downturn, the U.S. government has implemented expansive stimulus and relief programs, including the $1.9 trillion American Rescue Plan Act and the $2.2 trillion Coronavirus Aid, Relief, and Economic Security (CARES) Act. The relief packages included expanded Unemployment Insurance and distributed direct relief payments (i.e., stimulus checks), among other provisions. The weight of policy/academic research from the past year points in the same direction: that the scale of public relief had a [substantial](https://www.nytimes.com/2020/06/21/us/politics/coronavirus-poverty.html) and [positive](https://www.jpmorganchase.com/institute/research/household-income-spending/jpmorgan-chase-institute-take) [impact](https://equitablegrowth.org/get-more-money-immediately-to-u-s-families-and-help-them-out-of-the-coronavirus-recession/) on [people’s finances](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3689187) and [ability to spend](https://opportunityinsights.org/paper/tracker/) into the economy. Delivering relief in the form of direct cash to individuals and families worked — but it only worked for a time. As the virus continues to devastate American communities, [data shows](https://fred.stlouisfed.org/graph/?g=B8lo) that a speedy recovery is far from [assured](https://www.nytimes.com/2021/01/14/business/economy/unemployment-claims.html). As policymakers consider the best path forward, The Berkeley Institute for Young Americans (BIFYA) is publishing an interactive calculator (see below) to facilitate real-time modeling of potential policy designs for national relief payments. We hope that this will help inform ongoing debates as new income supports are considered. `4c:Tb89,chart1 = { const svg = d3.create("svg") .attr("viewBox", [0, 0, width + 20, height1]); svg.append("g") .call(xAxis1); svg.append("g") .call(yAxis1); svg.append("path") .datum(dataline1) .attr("fill", "none") .attr("stroke", "red") .attr("stroke-width", 2.5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .attr("d", line) svg.append("path") .datum(dataline2) .attr("fill", "none") .attr("stroke", "steelblue") .attr("stroke-width", 2.5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .attr("d", line) svg.selectAll("text") .datum(dataline_text1) .enter() .append("text") .text("Max. Credit (Single Adult)") .attr("x", 250) .attr("y", 250) .attr("font-family", "sans-serif") .attr("font-size", "11px") .attr("fill", "black"); svg.append("text") .datum(dataline_text1) .attr("x", margin.left + 3) .attr("y", (d) => y1(d.yvalues) - height / 50) .attr("font-family", "sans-serif") .attr("font-size", "12px") .attr("fill", "red") .text(d => "Maximum Payment for a Single Adult: " + "$" + d3.format(".0f")(d.yvalues)); svg.append("text") .datum(dataline_text2) .attr("x", margin.left + 3) .attr("y", (d) => y1(d.yvalues) - height / 50) .attr("font-family", "sans-serif") .attr("font-size", "12px") .attr("fill", "steelblue") .text(d => "Maximum Payment for a Two Adult, Two Child Family: " + "$" + d3.format(".0f")(d.yvalues)); /* svg.append("text") .attr("x", 0) .attr("y", height - height/7.5) .attr("font-family", "sans-serif") .attr("font-size", "10px") .attr("fill", "black") .text("Source:"); svg.append("text") .attr("x", 0) .attr("y", height - height/9.5) .attr("font-family", "sans-serif") .attr("font-size", "10px") .attr("fill", "black") .text("Notes:"); */ svg.append("text") .datum(phaseout_text) .attr("x", width) .attr("y", 10) .attr("font-family", "sans-serif") .attr("font-size", "12px") .attr("fill", "red") .attr('text-anchor', 'end') .text(d => "Single Adult Phaseout Rate: " + d3.format(".1f")(d.single * 100) + "%"); svg.append("text") .datum(phaseout_family_text) .attr("x", width) .attr("y", 25) .attr("font-family", "sans-serif") .attr("font-size", "12px") .attr("fill", "steelblue") .attr('text-anchor', 'end') .text(d => "Family Phaseout Rate: " + d3.format(".1f")(d.family * 100) + "%"); /* {if (equivadj === 1) { svg.append("text") .datum(phaseout_text) .attr("x", width - 125) .attr("y", 15) .attr("font-family", "sans-serif") .attr("font-size", "12px") .attr("fill", "steelblue") .text(d => "Single Adult Phaseout Rate: " + d3.format(".1f")(d.single * 100) + "%"); }} */ return svg.node(); }4d:T533,barchart = { const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]); const barpadding = 1 svg.append("g") .attr("fill", color) .selectAll("rect") .enter() .data(databar) .join("rect") .attr("x", d => x0(0)) .attr("y", (d, i) => y0(i)) .attr("width", d => x0(d.credit_avg) - x(0) - 5) // not sure why the -5 constant is necessary .attr("height", y0.bandwidth()) svg.selectAll("text") .data(databar) .enter() .append("text") .text(d => "$" + d3.format(".0f")(d.credit_avg)) .attr("x", (d) => x0(d.credit_avg) - width/30 - 30) .attr("y", (d, i) => y0(i) + y0.bandwidth()/1.5) .attr("font-family", "sans-serif") .attr("font-size", "12") .attr("fill", "white"); // .call(text => text.filter(d => x0(d.credit_avg) - x(0) < 50)) // short bars /* svg.append("text") .attr("x", 0) .attr("y", height - height/25) .attr("font-family", "sans-serif") .attr("font-size", "10px") .attr("fill", "black") .text("Source:"); svg.append("text") .attr("x", 0) .attr("y", height - height/100) .attr("font-family", "sans-serif") .attr("font-size", "10px") .attr("fill", "black") .text("Notes:"); */ svg.append("g") .call(yAxis); return svg.node(); }4e:T37e1,md`## Methodology ### Data We use the 2017 extract (the 2018 CPS ASEC) from the [Urban Institute’s Transfer Income Model, version 3](https://boreas.urban.org/T3Welcome.php) (TRIM3) as our primary data source for the interactive model. TRIM3 is copyrighted by the Office of the Assistant Secretary for Planning and Evaluation at the Department of Health and Human Services and developed and maintained at the Urban Institute. To compile and modify TRIM3 data for use with other CPS-based analysis files, we rely primarily on [publicly available](http://zachparolin.com/data/) code and instructions from Zach Parolin, Columbia University/Bocconi University to merge the TRIM3 extracts, remove high-income clones and alien replicates from the sample, and merge with our sample from IPUMS-CPS. We use the following variables from TRIM3 directly in the calculator: the adjusted gross income of the tax unit (AGIOFTAXUNIT); the number of adults in the tax unit for determining filing status (SIZEOFUNIT); the number of dependent children qualifying for the Child Tax Credit in the tax unit (CHILDTAXCREDITQUALIFYINGKIDS); the number of adult dependent children in the tax unit (calculated using NUMADULTDEPKIDS, NUMBEROFDEPENDENTS, NUMBERDEPKIDS, and CHILDTAXCREDITQUALIFYINGKIDS); and the 2018 CPS person weight of the head of the tax unit (calculated using PersonWeight and PersonWeightRatio), which we use as the basis of our primary weight. We use the flag for tax unit heads in TRIM3 (EXPANDEDUNITHEAD and PERSONID) to restrict the sample to a single observation per unique tax unit. The findings presented here required additional imputations and assumptions to the TRIM3 data and are attributable only to the author of this analysis. TRIM3 is copyrighted by the Office of the Assistant Secretary for Planning and Evaluation at the Department of Health and Human Services and developed and maintained at the Urban Institute. We also supplement the TRIM3 data with cleaning/analysis using the University of Minnesota’s [IPUMS-CPS](https://cps.ipums.org/cps/); however, this data is not used directly in the calculations for our interactive model. ### Unit of Analysis The relief payments in the CARES Act and the December Consolidated Appropriations Act are administered through the U.S. tax system; therefore, our primary unit of analysis is the “tax unit,” which encompasses the individuals counted on a single tax return. Households can have multiple tax units depending on the number of families and the tax situation of particular family members. We restrict the sample so that it contains a single observation for each unique tax unit. ### Analysis Our interactive calculator relies on a “target budget” methodology to determine the generosity of relief checks, given several parameters set by the user. Whereas most policy analyses of income support programs use the size of individual payments as an input and calculate the totalprogram budget as one of the primary outputs, we reverse this process by using the budget as one of the primary inputs, along with other user-inputted parameters like the eligibility threshold and dependent multiplier, and back into the size of relief payments. This allows the user to determine the amount of relief payments from a target budget, while simultaneously changing assumptions about other elements of the simulated program’s design (see the accompanying Appendix below for documentation of our approach). We reweight the data using a raking algorithm targeted to administrative targets using data published by the IRS (see the Appendix). The 2018 CPS ASEC includes data on income for 2017; therefore, we inflate all Adjusted Gross Income observations from 2017 dollars to 2019 dollars using the [R-CPI-U-RS series](https://www.bls.gov/cpi/research-series/r-cpi-u-rs-home.htm). ### Interactive Parameters Our interactive allows users to modify various “policy parameters,” which are detailed below, to control the simulated relief payment programs. The “slider parameters” have a fixed minimum and maximum range and defined steps (for instance, the Program Budget ranges from $0 to $650 billion in steps of $5 billion). * *Default Programs*: Allows the user to choose between different “default” simulation programs. The “Custom” option leaves all policy parameters available to the user. The “Consolidated Appropriations Act ($600)” program simulates the second round of relief payments, which included $600 payments for adults and $600 payments for child dependents. The “CARES Act ($1,200)” simulates the original relief payments distributed in 2020. * *Program Budget*: Determines the total simulated program budget (aggregate expenditures) for relief payments in billions of dollars. The program budget is adjusted to reflect expenditures on payments to foreign addresses (e.g., U.S. residents living abroad). See “Adjustment to the Budget” in the Appendix. * *Universal Eligibility*: Governs whether the simulation will use a defined eligibility threshold (“Not Universal”) for calculating relief payments or if it will eliminate phase outs of payments at higher income levels (“Universal”). It is important to note that the Universal setting does not affect Categorical Eligibility. The Universal setting disables the Equivalence Adjustment, Eligibility Threshold, Kink Point, and the Joint Filer Kink Point Multiplier parameters. * *Phaseout Adjustment*: Determines Eligibility Thresholds for tax units with one or more dependents. If “Equivalent Phaseouts” is selected, then the Eligibility Threshold will be set proportionate to the additional payment the tax unit receives, which results in equal phaseouts for all tax units within a particular filing group (i.e., head of household or joint filers) regardless of the number of dependents. Alternatively, “Force Phaseouts” will force Eligibility Thresholds to the same point for all tax units within a particular filing category. As an example, if the Eligibility Threshold for a joint filing tax unit with no dependents were set at $150k (this could be accomplished by setting the Eligibility Threshold parameter at $75k and the Joint filer Kink Point Multiplier at 1.0), the Force Phaseouts setting would cause the simulated payments to any joint filing tax unit with one or more dependents to also phaseout at $150k. This would lead to a steeper phaseout for tax units with one or more dependents. Alternatively, the Equivalent Phaseouts setting would further extend the Eligibility Threshold for joint filing tax units with one or more dependents such that the phaseouts were equivalent for all tax units within the joint filer group. * *Eligibility Threshold*: This parameter sets (in dollars) the end of the phaseout range for single adult tax units (no dependents). Single adult units with Adjusted Gross Incomes at or above this threshold are ineligible for simulated payments. The Eligibility Threshold for other filing groups (head of household and joint filers) is most directly controlled by the setting on the “Joint Filer Kink Point Multiplier” parameter; however, other parameters will indirectly determine this value as well. For instance, if the Phaseout Adjustments parameter is set to “Equivalent Phaseouts” then a higher Dependent Multiplier will push the Eligibility Threshold up for head of household and joint filing tax units. * *Kink Point*: This parameter sets (in dollars) the start of the phaseout range for single adult tax units (no dependents). Tax units with Adjusted Gross Incomes at or below this point are eligible for the maximum payment under the simulated relief program. Relief payments are reduced by the phaseout rate for tax units with Adjusted Gross Incomes above the Kink Point and below the Eligibility Threshold. * *Joint Filer Multiplier*: Determines the generosity of payments for families that file their taxes jointly relative to tax units with a single adult. A Joint Filer Multiplier of 1.0 will result in maximum payments to joint filer tax units that are 100% higher than the maximum payment for single adults. A setting of .5 would correspond with maximum payments that are 50% higher. * *Joint Filer Kink Point Multiplier*: Sets the Kink Point -- the start of the phaseout range -- for joint filer tax units relative to the Kink Point for single adult tax units. A Joint Filer Kink Point Multiplier of 1.0 will result in the start of the phaseout for joint filer tax units being set 100% higher than the start of the phaseout for single adult tax units. A value of 0 will result in equivalent Kink Points for single adult tax units and joint filer tax units. For example, if the Kink Point is set at $75k for single adult tax units (with no dependents), and the Joint Filer Kink Point Multiplier is set to 1.0, then joint filer tax units (with no dependents) will have a Kink Point of $150k, representing the start of their phaseout range. The start of the phaseout for tax units with a head of household status is set to a fixed 50% of the difference between the start of the phaseout for single adult tax units and joint filer tax units. * *Categorical Eligibility*: We include two categorical eligibility options: inclusion of tax units without a Social Security Number (SSN) and inclusion of adult dependents. As we discuss in the Methodology, we do not have direct data for SSN status within our sample of the U.S. householder population. Therefore, we conduct our own imputations to infer this status. We also do not directly impute “mixed household status” as one of our assumptions is that if one spouse is imputed to have an SSN, both spouses do. We also do not directly observe or impute whether respondents have a Individual Tax Identification Number (ITIN), which many undocumented immigrants use to pay taxes. Adult dependents include any dependents who do not qualify for the Child Tax Credit. * *Weight Specification*: We compute custom weights that are adjusted to target aggregated administrative data released by the Internal Revenue Service for the CARES Act. These “raked weights” match the number of payments distributed in the CARES Act along a number of dimensions (see “Raked Weights” in the Appendix). We recommend using these weights for simulations with the interactive; however, we also include the original CPS weights for any interested users. ### Limitations Our sample includes the “usual residents” of U.S. households and non-institutional group quarters, which excludes correctional facilities, nursing homes, and mental hospitals, and individuals who are experiencing homelessness. We assume 100% take-up among our eligible sample of tax units. The best available evidence suggests that upwards of [9 million individuals](https://www.irs.gov/newsroom/irs-releases-state-by-state-breakdown-of-nearly-9-million-non-filers-who-will-be-mailed-letters-about-economic-impact-payments) who were potentially eligible for CARES Act relief payments did not receive them -- with missed payments concentrated among individuals who are traditionally [non-filers](https://www.taxpolicycenter.org/sites/default/files/publication/159746/who-did-not-get-the-economic-impact-payments-by-mid-to-late-may-and-why_1.pdf). Using the original 2018 CPS weights, we generally underestimate the number of relief payments. Using the raked weights, the original weights are adjusted to match targets published by the IRS for the number of relief payments. The model also generally overestimates the typical size of relief payments; therefore, our simulations with the raked weights will tend to overestimate the cost of a given program (see the Appendix for benchmarking of our model). Relief payments are technically treated as “[advanced refunds](https://www.jct.gov/CMSPages/GetFile.aspx?guid=10f1800c-c656-4fa8-bcf3-48e0825feca3)" by the Internal Revenue Service; therefore, some tax units will qualify for supplemental “recovery rebates” if their next year’s tax return’s reported income or number of dependents would qualify them for a larger relief payment. We do not attempt to model whether tax units will receive a supplemental recovery rebate due to updated information on their tax return. In lieu of a direct question in TRIM3 or the CPS related to whether the respondent holds a valid Social Security Number (SSN), which is required to qualify for relief payments, we use various survey questions in the CPS, including respondent’s birthplace, year of immigration, and father and mother’s birthplace, to impute legal immigration status. We assume that if one spouse holds an SSN, the other spouse also does; therefore, we do not model ineligibility for “mixed status” tax units where one spouse does not hold an SSN. We allow for broad flexibility of user inputs into the model, which means that some combinations of parameters will yield programs that are not feasible on certain political or policy criteria. We restrict the kink point to 95% of the accompanying eligibility threshold to avoid benefit cliffs. Still, some combinations of parameters could have sizable phaseout rates. Relatedly, we do not make an attempt to account for any potential labor supply effects; however, the calculator does report phaseout rates so that the user can evaluate potential feasibility for themselves. ### Acknowledgments The author is grateful for comments and feedback on versions of the simulation model from Hilary Hoynes, Steven Raphael, Linda Giannarelli, Henry Brady, Vasco Yasenov, and Nicholas Adams-Cohen. This work is the result of guidance provided by the [Open Policy Analysis](https://academic.oup.com/spp/advance-article/doi/10.1093/scipol/scaa067/6018524?guestAccessKey=ac30d681-5391-4f91-b4d8-44c6aed11161) framework from the Berkeley Initiative for Transparency in the Social Sciences. The author also owes a debt of gratitude to the Observable community, whose tutorials, forum posts, and many examples helped greatly with this project. Any errors are the author’s alone and all interpretation and analysis is made solely by the author. Support for this project was provided by the Robert Wood Johnson Foundation. The views expressed here do not necessarily reflect the views of the Foundation. `4f:T246f,md`## Appendix ### Loading a Custom Input File Researchers who are interested in forking the Observablehq code behind our interactive and loading their own input file can do so by constructing their data set to include the following variables. * *agi*: Includes the Adjusted Gross Income of the tax unit. * *adults*: Includes the number of adults in the tax unit. Single adult filers should be coded as 1, head of household filers as 1.5, and joint filers as 2. * *adults_eq*: Includes the number of adults for the purposes of calculating the unit’s equivalence scale (the generosity of the payment). Single adult and head of household filers should be coded as 1 and joint filers as 2. * *children*: Includes the number of dependents in the tax unit who qualify as child dependents. * *adult_children*: Includes the number of dependents who do not qualify as child dependents. * *ssn*: Includes a dummy variable for whether the head of the tax unit, and their spouse if joint filing, holds a Social Security Number (SSN). Coded as 1 if they do hold an SSN and 0 if not. * *decile*: Denotes the decile of the tax unit within the income distribution. Tax units in the first decile should be coded as 1, second decile as 2, etc. * *weight*: Includes the probability weight for the tax unit. If no “raked_weight” variable is included in the input file, click the “Original Weights (2018 CPS) setting for the Weight Specification parameter –- this can be renamed by the user. ### Relief Payment Calculations Our basic analytical approach involves solving for the budget of a hypothetical income support program based on a set of policy parameters and eligibility thresholds. It is useful to first consider the straightforward example of a universal payment, wherein an income support is provided equally to the entire U.S. population (this is popularly referred to as a “universal basic income”). The universal payment for this program can be solved by dividing the budget of the program by the total size of the population of tax units (under the assumption that the payments are administered by the IRS). ${tex.block` \tag{1} \begin{aligned} P = B/U \end{aligned} `} Where P is the program payment, B is the program budget, and U is the population of tax units. Next, we introduce a complication to this payment structure by examining a non-universal program, which includes a maximum payment for tax units with no income (the “income guarantee”) and “phases out” at a constant rate for each additional dollar of a tax unit’s income. For instance, a program with a $1,000 income guarantee and a 5% phaseout rate would phase out completely at $20k. A tax unit with $5,000 of income would receive a payment of $750 -- $250 less than the maximum payment. A payment for a tax unit for such a program is given by the following piecewise function. ${tex.block` \tag{2} \begin{aligned} P &= \begin{cases} G-I\tau & \text{if $I < T$} \\ 0 & \text{otherwise} \end{cases} \\ &\text{where $\tau > 0$} \end{aligned} `} Where G is the income guarantee (the maximum payment); I is the income of the tax unit; T is an exogenously defined eligibility threshold, at or above which tax units are no longer eligible and receive zero payment; and tau is the positively-defined phaseout rate. Tax units with incomes below the eligibility threshold are in what we refer to as the “phaseout region.” For the purpose of reducing the number of unknown variables in the equation, it is useful to redefine G as the product of T and tau. ${tex.block` \tag{3} \begin{aligned} G&=T\tau \\ P &= \begin{cases} (T-I)\tau & \text{if $I < T$} \\ 0 & \text{otherwise} \end{cases} \\ &\text{where $\tau > 0$} \end{aligned} `} The intuition behind this is given by the fact that the phaseout rate is the rise (G) over the run (T) of the income support program. Thus, G is equal to the product of T and tau. We can aggregate this expression to the population level to facilitate policy analysis. ${tex.block` \tag{4} \begin{aligned} \sum_{i=1}^{n_1}P_i&=\sum_{i=1}^{n_1}(T-I_i)\tau \\ \sum_{i=1}^{n_2}P_i&={n_2}0\\ \end{aligned} `} Where each tax unit is indexed with subscript i, the number of tax units with incomes below the eligibility threshold -- the phaseout region -- is represented by n1, and the number of tax units with incomes at or above the eligibility threshold is represented by n2. ${tex.block` \tag{5} \begin{aligned} B &\equiv \sum_{i=1}^{n_1} P_i + \sum_{i=1}^{n_2} P_i \\ \end{aligned} `} By definition, the budget of the program, denoted by B, is equivalent to the sum of all payments in the program. As we have defined it, tax units above the eligibility threshold receive zero dollars in payment. Thus, payments for these units sum to zero (i.e., they receive no payments) and this term can be dropped from equation 5, leaving the budget equal to the sum of the payments in the phaseout region. ${tex.block` \tag{6} \begin{aligned} B &=\sum_{i=1}^{n_1}(T-I_i)\tau \\ \end{aligned} `} We can rearrange equation 6 after factoring out tau -- since it is a constant -- so that the phaseout rate is a function of the budget and the eligibility threshold. This provides a solution for this income support program given exogenous values for the budget and the eligibility threshold. ${tex.block` \tag{7} \begin{aligned} \tau&= \frac{B}{\sum_{i=1}^{n_1}(T-I_i)} \\ \end{aligned} `} We can extend this framework to model relief payments of a similar structure as those enacted via the CARES Act. These payments introduce a “kink point,” with tax units with incomes at or below that point receiving an equivalent maximum payment. This is depicted in Figure A1 as the “payment guarantee” region. Any tax unit with income above the kink point experiences a constant phaseout of their payment for the portion of their income greater than the kink point. This is similar to the phaseout in Equation 3; however, the phaseout rate is not applied to a tax unit’s entire income -- it only applies to that portion above the kink point. ${tex.block` \tag{8} \begin{aligned} P &= \begin{cases} G & \text{if $I \leq K$}\\ G-(I-K)\tau & \text{if $I > K$ and $I < T$}\\ 0 & \text{otherwise} \end{cases} \\&\text{where $\tau > 0$} \end{aligned} `} Where K is defined as the kink point. As with the previous iteration of the relief program model, it is useful to convert G into a function of tau to reduce the number of unknowns. As with Equation 3, G is the rise over the run; however, since we now have an entire region where the payment is flat, we need to subtract the kink point from the eligibility threshold to calculate the proper length of the run, which excludes the distance between zero and the kink point (see Figure A1). ${tex.block` \tag{9} \begin{aligned} G&=(T-K)\tau \\ P&= \begin{cases} (T-K)\tau & \text{if $I \leq K$}\\ (T-K)\tau-(I-K)\tau & \text{if $I > K$ and $I < T$}\\ 0 & \text{otherwise} \end{cases} \\&\text{where $\tau > 0$} \end{aligned} `} As with the previous program model, we can aggregate this expression to the population level to facilitate policy analysis. ${tex.block` \tag{10} \begin{aligned} \sum_{i=1}^{n_1}P_i&=n_1(T-K)\tau \\ \sum_{i=1}^{n_2}P_i&=n_2(T-K)\tau - \tau\sum_{i=1}^{n_2}(I_i-K) \\ \sum_{i=1}^{n_3}P_i&={n_3}0\\ \end{aligned} `} Where each tax unit is again indexed with subscript i, the number of tax units with incomes at or below the kink point -- the payment guarantee region -- is represented by n1; the number of units with incomes above the kink point and below the eligibility threshold, the new phaseout region, is represented by n2; and the number of tax units with incomes at or above the eligibility threshold is represented by n3. Since G is a constant (represented by T minus K, multiplied by tau in Equation 10), we can multiply it by the number of tax units in each of the eligible segments of the program, n1 and n2, in Equation 10. ${tex.block` \tag{11} \begin{aligned} B &\equiv \sum_{i=1}^{n_1} P_i + \sum_{i=1}^{n_2} P_i + \sum_{i=1}^{n_3} P_i \\ \end{aligned} `} By definition, the budget is equal to the sum of all payments. Since the payments for tax units above the eligibility threshold are equal to zero, we can drop this from Equation 11 to yield our complete budget constraint. ${tex.block` \tag{12} \begin{aligned} B=n_1(T-K)\tau + n_2(T-K)\tau - \tau\sum_{i=1}^{n_2}(I_i-K) \\ \end{aligned} `} Finally, we can factor out the tau’s from Equation 12 and rearrange to yield a new formula for the phaseout rate as a function of the budget, the eligibility threshold, and the kink point. ${tex.block` \tag{13} \begin{aligned} \tau=\frac{B}{n_1(T-K) + n_2(T-K) - \sum_{i=1}^{n_2}(I_i-K)} \\ \end{aligned} `} We can then use the phaseout rate calculated in equation 13, given values for the budget, eligibility threshold, and kink point, and plug it back into equation 9, to yield the relief payment for each tax unit. This formula can be extended to account for tax units of different sizes (equivalence scales), differential phaseout rates for different filing categories, and categorical eligibility conditions, as we do in the code of our simulation. `50:T8d5,md`### Raked Weights We reweight our sample of tax units that we identify as eligible for CARES Act relief payments based on a set of administrative targets from [data published by the IRS](https://www.irs.gov/statistics/soi-tax-stats-coronavirus-aid-relief-and-economic-security-act-cares-act-statistics). Specifically, we use the raking algorithm implemented by the [Stata ipfraking package](https://www.stata-journal.com/sjpdf.html?articlenum=st0323) to make adjustments to the original CPS weights to match administrative aggregates published by the IRS for the total number of relief payments in eight Adjusted Gross Income brackets, total number of relief payments in each state, total number of relief payments in each state for tax units with no qualifying dependents, and total number of relief payments in each state for tax units with qualifying dependents. We remove payments to eligible residents not residing at a U.S. state address from the state-level targets (according to the IRS, this includes residents of U.S. territories, U.S. citizens living abroad, and U.S. armed forces overseas. The IRS does not report the number of payments going to eligible residents at non-state addresses at the AGI level; therefore, we calculate the share of payments going to non-state addresses based on the state-level data and adjust the AGI bracket-based targets downward uniformly based on this state-calculated figure. The raking algorithm generally upweights underreporting of relief payments in lower income brackets and down-weights overreporting of relief payments in the highest income brackets (see “Benchmarks” in the Appendix). The raked weight and the original weight are equivalent for tax units who we simulated as ineligible for the CARES Act relief payments since we do not have administrative targets for these units. In Figure A2 and A3, we show how the distribution of the raked weights differ from the original weights and the distribution of adjustment factors (the relative difference between the raked weights and the original weights). The peak in the distribution of adjustment factors to the original weight occurs between 1.14 and 1.18, with 90% of the observations occurring between an adjustment factor of .84 and 1.298. `51:Tf7f,md`### Adjustments to Budget We make adjustments to the total program budget to bring our simulated estimates in closer alignment with real-world outcomes for our sample of residents of U.S. states. Specifically, our sample does not include data on residents eligible for the CARES Act residing at non-U.S. state addresses. Based on [data published by the IRS](https://www.irs.gov/statistics/soi-tax-stats-coronavirus-aid-relief-and-economic-security-act-cares-act-statistics), relief payments were sent to 1.18 million tax units for an aggregate of $1.8 billion relief payments paid to eligible tax units residing at a non-state address. This $1.8 billion accounts for approximately .6635% of the $271.4 billion aggregate payments for the CARES Act. We adjust the available share of the total user-inputted budgets downward to reflect the share of the budget for payments made to units at non-state addresses. For example, a user-inputted budget of $300 billion would be adjusted downward by $1.9 billion so that $298.1 billion was available for relief payments to state-based tax units. ### Benchmarks We conduct benchmarking of the accuracy of our simulation model using published data of CARES Act relief payments [from the IRS](https://www.irs.gov/statistics/soi-tax-stats-coronavirus-aid-relief-and-economic-security-act-cares-act-statistics). Generally, we find that our simulation of the CARES Act with the original CPS weights underreports the number of relief payments for tax units with lower incomes and overreports the number of relief payments for tax units with the highest incomes. After adjusting for the share of payments going to eligible tax units residing at foreign addresses, we find that our simulation of aggregate number of payments with the original CPS weights is about 91.8% of the IRS figure. Our simulation of aggregate expenditures with the original weights is approximately 97.7% of the IRS figures. Estimates of the aggregate number of relief payments to eligible residents of U.S. states using the raked weights are 100% of the same measure from the IRS. Our simulation of aggregate expenditures using the raked weights is 106.2% of IRS figures, after excluding payments to non-state tax units. The typical payment in our simulation is greater than those reported by the IRS for the CARES Act. Therefore, once we implement our raked weights to match the IRS targets for the aggregate number of payments, our simulation will tend to overestimate total expenditures relative to the IRS. Based on this benchmarking, we generally expect that any simulation using our calculator will overestimate the program budget by about 6%. Put in different terms, our simulation will underreport the dollar amount of relief payments possible with a given budget, relative to what IRS reports for the CARES Act. This bias may differ depending on the eligibility settings chosen in the interactive calculator. For instance, if the components of Adjusted Gross Income, which is a key input for our calculator, are misreported for low- or upper-incomes in the CPS, then simulations that are highly targeted based on income or more universal may not approximate real-world outcomes. In Figures A3 through A6 we show benchmarks of our simulation of the CARES Act for different Adjusted Gross Income brackets. We group all tax units with AGI below $30k into a single bracket and include non-filers and units with negative AGI in this category to avoid the need for imputations of who are filers/non-filers in the CPS data. Unlike the aggregate benchmarks we discuss above, we do not adjust the IRS data used in Figures A4 through A7 for the number of payments going to eligible residents at foreign addresses. Thus, the data shown in these figures uses the raw data provided by the IRS with no adjustments. The discrepancies shown in Figure A4 between the number of payments using the raked weights and the IRS data is due to this lack of adjustment. `52:T437,// Import input file data_import = { if (weight_radio === "original") { return d3.csvParse(await FileAttachment("input_file_@1.csv").text(), d3.autoType) // replace with input file .map(function(d) { return {ssn: d.ssn, adults: d.adults, children: d.children, adultchildren: d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, decile: d.decile, id: d.id, weight: d.weight}}) } else if (weight_radio === "raked") { return d3.csvParse(await FileAttachment("input_file_@1.csv").text(), d3.autoType) // replace with input file .map(function(d) { return {ssn: d.ssn, adults: d.adults, children: d.children, adultchildren: d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, decile: d.decile_raked, id: d.id, weight: d.weight_raked}}) } } 53:T2339,/* The following code loads in data titled "input_file" under various conditions to create the main analysis data array for the interactive. The first chunk of code is dedicated to formatting the input file for various custom program scenarios. Data is formatted separately for ssn categorical eligibility in order to simplify code for credit calculations. If ssn categorical eligibility is off (unchecked), units without an imputed ssn are included in later code within the program so that any analysis is conducted on the entire population of tax units. Code is filtered separately for single adult units, head of household units, and joint filer units to format potential separate eligibility thresholds and phaseouts for these groups of units. */ data = // CUSTOM PROGRAMS // no additional categorical eligibility { if (programs === "0" && cat_elig[0] === undefined && cat_elig[1] === undefined) { return data_import // single adult units .filter(f => f.adults === 1 && f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1, children: d.children, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}}) // head of household units .concat(data_import .filter(f => f.adults === 1.5 && f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1 + (.5 * jointfiler_phaseout), children: d.children, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}})) // joint filer units .concat(data_import .filter(f => f.adults === 2 && f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1 + (jointfiler_phaseout), children: d.children, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: 1 + jointfiler, weight: d.weight, decile: d.decile, id: d.id}})); // categorical eligibility for tax units without an SSN } else if (programs === "0" && cat_elig[0] === "ssn" && cat_elig[1] === undefined) { return data_import // single adult units .filter(f => f.adults === 1) // note: no filtering for SSN .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1, children: d.children, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}}) // head of household units .concat(data_import .filter(f => f.adults === 1.5) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1 + (.5 * jointfiler_phaseout), children: d.children, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}})) // joint filer units .concat(data_import .filter(f => f.adults === 2) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1 + (jointfiler_phaseout), children: d.children, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: 1 + jointfiler, weight: d.weight, decile: d.decile, id: d.id}})); // categorical eligibility for adult dependents } else if (programs === "0" && cat_elig[0] === "adult") { return data_import // single adult units .filter(f => f.adults === 1 && f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, children: d.children + d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, adults_phaseout: 1, weight: d.weight, decile: d.decile, id: d.id}}) // head of household units .concat(data_import .filter(f => f.adults === 1.5 && f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1 + (.5 * jointfiler_phaseout), children: d.children + d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}})) // joint filer units .concat(data_import .filter(f => f.adults === 2 && f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1 + (jointfiler_phaseout), children: d.children + d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: 1 + jointfiler, weight: d.weight, decile: d.decile, id: d.id}})); // categorical eligibility for tax units without an SSN AND adult dependents } else if (programs === "0" && cat_elig[0] === "ssn" && cat_elig[1] === "adult") { return data_import // single adult units .filter(f => f.adults === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, children: d.children + d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, adults_phaseout: 1, weight: d.weight, decile: d.decile, id: d.id}}) // head of household units .concat(data_import .filter(f => f.adults === 1.5) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1 + (.5 * jointfiler_phaseout), children: d.children + d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}})) // joint filer units .concat(data_import .filter(f => f.adults === 2) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: 1 + (jointfiler_phaseout), children: d.children + d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: 1 + jointfiler, weight: d.weight, decile: d.decile, id: d.id}})); // DEFAULT PROGRAMS // $600 relief payments } else if (programs === "1") { return data_import .filter(f => f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: d.adults, children: d.children, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}}); // $1,200 relief payments (CARES Act) } else if (programs === "2") { return data_import .filter(f => f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: d.adults, children: d.children, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}}); // $1,400 relief payments } else if (programs === "3") { return data_import .filter(f => f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: d.adults, children: d.children + d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}}); // $2,000 relief payments } else if (programs === "4") { return data_import .filter(f => f.ssn === 1) .map(function(d) { return {ssn: d.ssn, adults: d.adults, adults_phaseout: d.adults, children: d.children + d.adultchildren, agi: d.agi, agi_bracket: d.agi_bracket, adults_eq: d.adults_eq, weight: d.weight, decile: d.decile, id: d.id}}); } }54:T1e59,// Concatenating all credit calculations credit = { // Non-universal credit // SSN required for credit (the final concatenate includes units without an ssn that do not qualify for a credit) // No equivalence adjustment for phaseouts of units with multiple dependents if (universality === "0" && equivadj === "0" && cat_elig[0] !== "ssn") { // units below the kink point return data.filter(f => f.agi <= kink * f.adults_phaseout) .map(function(d) { return {credit: (elig - kink) * (d.adults_eq + (d.children * depmult)) * phaseout, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id}}) // units above the kink point but below the eligibility threshold .concat(data.filter(f => f.agi > kink * f.adults_phaseout && f.agi < (kink * f.adults_phaseout) + ((elig - kink) * ((f.adults_eq) + (f.children * depmult)))) .map(function(d) { return {credit: (((elig - kink) * ((d.adults_eq) + (d.children * depmult))) - (d.agi - (kink * d.adults_phaseout))) * phaseout, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id}})) // units above the eligibility threshold (with ssn) .concat(data.filter(f => f.agi >= (kink * f.adults_phaseout) + ((elig - kink) * (f.adults_eq + (f.children * depmult)))) .map(function(d) { return {credit: 0, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id}})) // units without an ssn and do not qualify for a credit in this iteration .concat(data_import.filter(f => f.ssn === 0 ) .map(function(d) { return {credit: 0, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} })); // Equivalence adjustment for phaseouts of units with multiple dependents } else if (universality === "0" && equivadj === "1" && cat_elig[0] !== "ssn") { // units below the kink point return data.filter(f => f.agi <= kink * f.adults_phaseout) .map(function(d) { return {credit: (elig - kink) * (d.adults_eq + (d.children * depmult)) * phaseout, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id}}) // units above the kink point but below the eligibility threshold .concat(data.filter(f => f.agi > kink * f.adults_phaseout && f.agi < (kink * f.adults_phaseout) + ((elig - kink) * f.adults_eq)) .map(function(d) { return {credit: (((elig - kink) * (d.adults_eq + (d.children * depmult))) - ((d.agi - (kink * d.adults_phaseout)) * ((d.adults_eq + (d.children * depmult))/(d.adults_eq)))) * phaseout, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id}})) // units above the eligibility threshold (with ssn) .concat(data.filter(f => f.agi >= (kink * f.adults_phaseout) + ((elig - kink) * f.adults_eq)) .map(function(d) { return {credit: 0, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id}})) // units without an ssn and do not qualify for a credit in this iteration .concat(data_import.filter(f => f.ssn === 0 ) .map(function(d) { return {credit: 0, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} })); // Non-universal credit // SSN not required // No equivalence adjustment for phaseouts of units with multiple dependents } else if (universality === "0" && equivadj === "0" && cat_elig[0] === "ssn") { // units below the kink point return data.filter(f => f.agi <= kink * f.adults_phaseout) .map(function(d) { return {credit: (elig - kink) * (d.adults_eq + (d.children * depmult)) * phaseout, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} // units above the kink point but below the eligibility threshold }).concat(data.filter(f => f.agi > kink * f.adults_phaseout && f.agi < (kink * f.adults_phaseout) + ((elig - kink) * ((f.adults_eq) + (f.children * depmult)))) .map(function(d) { return {credit: (((elig - kink) * ((d.adults_eq) + (d.children * depmult))) - (d.agi - (kink * d.adults_phaseout))) * phaseout, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} // units above the eligibility threshold })).concat(data.filter(f => f.agi >= (kink * f.adults_phaseout) + ((elig - kink) * (f.adults_eq + (f.children * depmult)))) .map(function(d) { return {credit: 0, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} })); // Equivalence adjustment for phaseouts of units with multiple dependents } else if (universality === "0" && equivadj === "1" && cat_elig[0] === "ssn") { return data.filter(f => f.agi <= kink * f.adults_phaseout) .map(function(d) { return {credit: (elig - kink) * (d.adults_eq + (d.children * depmult)) * phaseout, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} }).concat(data.filter(f => f.agi > kink * f.adults_phaseout && f.agi < (kink * f.adults_phaseout) + ((elig - kink) * f.adults_eq)) .map(function(d) { return {credit: (((elig - kink) * (d.adults_eq + (d.children * depmult))) - ((d.agi - (kink * d.adults_phaseout)) * ((d.adults_eq + (d.children * depmult))/(d.adults_eq)))) * phaseout, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} })).concat(data.filter(f => f.agi >= (kink * f.adults_phaseout) + ((elig - kink) * f.adults_eq)) .map(function(d) { return {credit: 0, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} })); // universal credit if ssn required } else if (universality === "1" && cat_elig[0] !== "ssn") { return data.map(function(d) { return {credit: per_capita * (d.adults_eq + (d.children * depmult)), agi: d.agi, weight: d.weight, decile: d.decile, id: d.id}}) .concat(data_import.filter(f => f.ssn === 0) // filtering to non-ssn group .map(function(d) { return {credit: 0, agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} })); // universal credit if ssn not required } else if (universality === "1" && cat_elig[0] === "ssn") { return data .map(function(d) { return {credit: per_capita * (d.adults_eq + (d.children * depmult)), agi: d.agi, weight: d.weight, decile: d.decile, id: d.id} }); } 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Payments: An Interactive Model","thumbnail":"3f03338e9313427a53cf678bd73ff59490257a5fe578af2beec575edbfeb0637","access_level":"public","create_time":"$D2021-02-14T21:25:08.174Z","update_time":"$D2021-06-10T18:43:44.361Z","view_time":null,"first_public_time":"$D2021-02-18T19:21:15.685Z","has_unpublished_edits":null,"fork":true,"stars":2,"starred":false,"owner":{"type":"team","id":"36fd468829736a6b","login":"jamesohawkins-obs","name":"James Hawkins","avatar_url":"https://avatars-next.observableusercontent.com/3f78aeea97d2072b148743deb134f72c55b003b4b85e12cb3d1cf4c036465f02","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":6365,"publish_version":6365,"role":null,"authored":false,"authors":[{"type":"individual","id":"2921417af20b049f","login":"jamesohawkins-obs","name":"James Hawkins","avatar_url":"https://avatars-next.observableusercontent.com/3f78aeea97d2072b148743deb134f72c55b003b4b85e12cb3d1cf4c036465f02","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"2921417af20b049f","login":"jamesohawkins-obs","name":"James Hawkins","avatar_url":"https://avatars-next.observableusercontent.com/3f78aeea97d2072b148743deb134f72c55b003b4b85e12cb3d1cf4c036465f02","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Simulating Relief Payments: An Interactive Model","cells":[{"id":0,"value":"md`# Simulating Relief Payments: An Interactive Model `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6210,"value":"html`

\n${await FileAttachment(\"stimulus@1.png\").image()}\n

Mega Pixel/Shutterstock.com

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2825,"value":"md`### **[Berkeley Institute for Young Americans](https://youngamericans.berkeley.edu/)** \n#### James Hawkins`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6350,"value":"md` *Update 3/23/21: Fixed an error in the display of family phaseouts and updated how the calculator implements the option to force phaseouts for families with one or more dependents.*`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6005,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3239,"value":"md`## Interactive Simulation`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3581,"value":"md`### Simulated Economic Relief Payments for a Single Adult and Family of Four`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1590,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5406,"value":"viewof text1 = html`

Source: Author’s analysis of March 2018 CPS ASEC data augmented with tax unit identification produced by the TRIM3 microsimulation model (trim3.urban.org) and SSN status imputed by the author.

Notes: Each line shows the full range of credits for eligible single adults and families of four with two adults and two children.

Program Budget${budget_slider_value_text}

\n${viewof budget_slider}\n

Total program budget for simulated relief payments.

${universality_switch}

\n`,\n html`\n

${equivadj_switch}

\n`, \n ],\n [\n\n ],\n [ \n\n ]\n ],\n\n [\n\n ]\n\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4474,"value":"viewof values2 = inputsGroup(\n \n[\n [\n html`\n

Eligibility Threshold${elig_slider_value_text}

${viewof elig_slider}

End of phaseout range for single adults.

`,\n html`\n

Dependent Multiplier${depmult_slider_value_text}

${viewof depmult_slider}

Size of payments for dependents relative to payments for adults.

`, ],\n [\n\n ],\n [ \n\n ]\n],\n \n[\n\n]\n \n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4626,"value":"viewof values3 = inputsGroup(\n \n[\n [\n html`\n

Kink Point${kink_slider_value_text}

${viewof kink_slider}

Start of phaseout range for single adults.

`,\n html`\n

Joint Filer Multiplier

${viewof jointfiler_slider}

Start of phaseout range for single adults.

`, ],\n [\n\n ],\n [ \n\n ]\n],\n \n[\n\n]\n \n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4903,"value":"viewof values4 = inputsGroup(\n \n[\n [\n html`\n

Joint Filer Kink Point Multiplier

${viewof jointfiler_phaseout_slider}

Location of joint filing units' kink point relative to single adult units.

`\n ],\n [\n\n ],\n [ \n\n ]\n],\n \n[\n\n]\n \n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3790,"value":"viewof values5 = inputsGroup(\n \n[\n [\n html`\n${cat_elig_switch}`\n ],\n [\n\n ],\n [ \n\n ]\n],\n \n[\n\n]\n \n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2175,"value":"md`### Distribution of Simulated Economic Relief Payments, by Decile of Tax Units' Adjusted Gross Income`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":395,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5430,"value":"viewof text2 = html`

Source: Author’s analysis of March 2018 CPS ASEC data augmented with tax unit identification produced by the TRIM3 microsimulation model (trim3.urban.org) and SSN status imputed by the author.

Notes: Each bar shows tax units' mean credit for that particular decile. Means are calculated on the entire population of tax units, including those that receive no credit due to eligibility restrictions.

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4121,"value":"viewof Table_values = Table(table)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5445,"value":"viewof weight_radio = radio({\n title: 'Weight Specification',\n description: 'Probability weight options. Recommendation: raked weights. See Appendix for details.',\n \n options: [\n { label: 'Raked Weights', value: `raked` },\n { label: 'Original Weights (2018 CPS)', value: `original` },\n ],\n value: 'raked'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6026,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6051,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6105,"value":"A1 = FileAttachment(\"Figure A1@1.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6116,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6112,"value":"A2 = FileAttachment(\"Figure A2.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6107,"value":"A3 = FileAttachment(\"Figure A3.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6127,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6136,"value":"A4 = FileAttachment(\"Figure A4.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6140,"value":"A5 = FileAttachment(\"Figure A5.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6142,"value":"A6 = FileAttachment(\"Figure A6.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6144,"value":"A7 = FileAttachment(\"Figure A7.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3269,"value":"md`## Code Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"md`### Input File`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5909,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3811,"value":"md`### HTML Switches`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3987,"value":"viewof budget_slider = Range([0, 650], {step: 5, value: 288.127123456, disabled: programs !== \"0\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3761,"value":"universality_switch = {\n let result;\n if (programs === \"0\") {\n result = viewof universality;\n }\n else {\n result = html`

Universal Eligibility: Off

`;\n }\n return result;\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2232,"value":"viewof universality = radio({\n title: 'Universal Eligibility',\n description: 'Universality of program. \"Universal\" sets flat payment schedule.',\n \n options: [\n { label: 'Not Universal', value: '0' },\n { label: 'Universal', value: '1' },\n ],\n value: '0'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4436,"value":"equivadj_switch = {\n let result;\n if (programs === \"0\" && universality != \"1\") {\n result = viewof equivadj;\n }\n else {\n result = html`

Equivalence Adjustment: Off

Categorical Eligibility: SSN required; No Adult Dependents

`;\n }\n else if (programs === \"2\") {\n result = html`

Categorical Eligibility: SSN required; No Adult Dependents

`;\n }\n else if (programs === \"3\") {\n result = html`

Categorical Eligibility: SSN required; Adult Dependents

`;\n }\n else if (programs === \"4\") {\n result = html`

Categorical Eligibility: SSN required; Adult Dependents

`;\n }\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2403,"value":"viewof cat_elig = checkbox({\n title: \"Categorical Eligibility\",\n description: \"\",\n options: [\n { value: \"ssn\", label: \"Tax units without a Social Security Number\" },\n { value: \"adult\", label: \"Adult Dependents\" },\n ],\n value: cat_elig_value,\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":328,"value":"md`### Model Parameters`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2506,"value":"budget_slider_value_text = {\n if (programs === \"0\") {\n return \" (Billions of Dollars)\";\n } else if (programs === \"1\" && weight_radio == \"raked\") {\n return \": $163.5 Billion\";\n } else if (programs === \"2\" && weight_radio == \"raked\") { \n return \": $288.1 Billion\";\n } else if (programs === \"3\" && weight_radio == \"raked\") {\n return \": $392.35 Billion\";\n } else if (programs === \"4\" && weight_radio == \"raked\") {\n return \": $570.3 Billion\";\n } else if (programs === \"1\" && weight_radio == \"original\") {\n return \": $148.9 Billion\";\n } else if (programs === \"2\" && weight_radio == \"original\") { \n return \": $265.1 Billion\";\n } else if (programs === \"3\" && weight_radio == \"original\") {\n return \": $392.35 Billion\";\n } else if (programs === \"4\" && weight_radio == \"original\") {\n return \": $570.3 Billion\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4049,"value":"budget_slider_value = {\n if (programs === \"0\") {\n return \" (Billions of Dollars)\";\n } else if (programs === \"1\" && weight_radio == \"raked\") {\n return 163.546611712;\n } else if (programs === \"2\" && weight_radio == \"raked\") { \n return 288.127123456;\n } else if (programs === \"3\" && weight_radio == \"raked\") {\n return 392.35;\n } else if (programs === \"4\" && weight_radio == \"raked\") {\n return 570.3;\n } else if (programs === \"1\" && weight_radio == \"original\") {\n return 148.947910656;\n } else if (programs === \"2\" && weight_radio == \"original\") { \n return 265.146204160;\n } else if (programs === \"3\" && weight_radio == \"original\") {\n return 392.35;\n } else if (programs === \"4\" && weight_radio == \"original\") {\n return 570.3;\n}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":351,"value":"// constant adjustment for portion of budget attributable to foreign addresses and portion for U.S. territories\nbudget = {\n if (programs === \"0\") {\n return budget_slider - (budget_slider * .00663543)\n } else {\n return budget_slider_value - (budget_slider_value * .00663543);\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2305,"value":"elig_slider_value = { \n if (universality === \"1\" && programs == \"0\") {\n return 99999999; \n } else if (programs === \"0\") {\n return 99000;\n } else if (programs === \"1\") {\n return 87000;\n } else if (programs === \"2\") { \n return 99000;\n } else if (programs === \"3\") {\n return 103000;\n } else if (programs === \"4\") {\n return 115000;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4060,"value":"elig_slider_value_text = { \n if (universality === \"1\" && programs == \"0\") {\n return \": Universal Program\"; \n } else if (programs === \"0\") {\n return \" (Dollars)\";\n } else if (programs === \"1\") {\n return \": $87k\";\n } else if (programs === \"2\") { \n return \": $99k\";\n } else if (programs === \"3\") {\n return \": $103k\";\n } else if (programs === \"4\") {\n return \": $115k\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2435,"value":"elig = {\n if (programs === \"0\") {\n return elig_slider\n } else {\n return elig_slider_value;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3658,"value":"kink_slider_value = { \n if (universality === \"1\" && programs == \"0\") {\n return 0; \n } else {\n return 75000;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4073,"value":"kink_slider_value_text = { \n if (universality === \"1\" && programs == \"0\") {\n return \": None\";\n } else if (programs === \"0\") {\n return \" (Dollars)\";\n } else {\n return \": $75k\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2070,"value":"kink = {\n if (programs === \"0\") {\n return kink_slider\n } else {\n return kink_slider_value;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2514,"value":"depmult_slider_value = { \n if (programs === \"0\") {\n return 500/1200;\n } else if (programs === \"1\") {\n return 1;\n } else if (programs === \"2\") { \n return 500/1200;\n } else if (programs === \"3\") {\n return 1;\n } else if (programs === \"4\") {\n return 1;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4103,"value":"depmult_slider_value_text = { \n if (programs === \"0\") {\n return \"\";\n } else if (programs === \"1\") {\n return \": \" + (1).toFixed(1);\n } else if (programs === \"2\") { \n return \": \" + (500/1200).toFixed(3);\n } else if (programs === \"3\") {\n return \": \" + (1).toFixed(1);\n } else if (programs === \"4\") {\n return \": \" + (1).toFixed(1);\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":570,"value":"depmult = {\n if (programs === \"0\") {\n return depmult_slider\n } else {\n return depmult_slider_value;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4798,"value":"jointfiler_slider_value = { \n if (programs === \"0\") {\n return 1;\n } else if (programs === \"1\") {\n return 1;\n } else if (programs === \"2\") { \n return 1;\n } else if (programs === \"3\") {\n return 1;\n } else if (programs === \"4\") {\n return 1;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4914,"value":"jointfiler_phaseout_slider_value = { \n if (programs === \"0\") {\n return 1;\n } else if (programs === \"1\") {\n return 1;\n } else if (programs === \"2\") { \n return 1;\n } else if (programs === \"3\") {\n return 1;\n } else if (programs === \"4\") {\n return 1;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4817,"value":"jointfiler = {\n if (programs === \"0\") {\n return jointfiler_slider\n } else {\n return jointfiler_slider_value;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4910,"value":"jointfiler_phaseout = {\n if (programs === \"0\") {\n return jointfiler_phaseout_slider\n } else {\n return jointfiler_phaseout_slider_value;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3591,"value":"cat_elig_value = {\n if (programs === \"0\") {\n return [undefined, undefined];\n } else if (programs === \"1\") {\n return [undefined, undefined];\n } else if (programs === \"2\") { \n return [undefined, undefined];\n } else if (programs === \"3\") {\n return [\"adult\"];\n } else if (programs === \"4\") {\n return [\"adult\"];\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5207,"value":"obs_count = d3.count(data_import, d => d.id)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2685,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":560,"value":"md`### Model Calculations`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1926,"value":"// Weighted sum of adjusted gross income\nagi_sum = \n\n // No equivalence adjustment\n { if (equivadj === \"0\") { \n return d3.sum(data\n .filter(f => \n f.agi > kink * f.adults_phaseout &&\n f.agi < (kink * f.adults_phaseout) + ((elig - kink) * (f.adults_eq + (f.children * depmult)))),\n d => (d.agi - (kink * d.adults_phaseout)) * d.weight)\n\n // Equivalence adjustment\n } else if (equivadj === \"1\") {\n return d3.sum(data\n .filter(f => f.agi > kink * f.adults_phaseout &&\n f.agi < (kink * f.adults_phaseout) + ((elig - kink) * f.adults_eq)),\n d => (d.agi - (kink * d.adults_phaseout)) * ((d.adults_eq + (d.children * depmult))/(d.adults_eq)) * d.weight)\n}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":679,"value":"// Weighted sum of threshold values in guarantee region\nthresh_flat_sum = d3.sum(data.filter(f => f.agi <= kink * f.adults_phaseout),\n d => (elig - kink) * (d.adults_eq + (d.children * depmult)) * d.weight)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":585,"value":"// Weighted sum of threshold values in phaseout region\nthresh_sum =\n \n // No equivalence adjustment\n { if (equivadj === \"0\") {\n return d3.sum(data\n .filter(f => \n f.agi > kink * f.adults_phaseout && \n f.agi < (kink * f.adults_phaseout) + ((elig - kink) * (f.adults_eq + (f.children * depmult))) ), \n d => (elig - kink) * (d.adults_eq + (d.children * depmult)) * d.weight)\n \n // Equivalence adjustment\n } else if (equivadj == \"1\") {\n return d3.sum(data.filter(f =>\n f.agi > kink * f.adults_phaseout && \n f.agi < (kink * f.adults_phaseout) + ((elig - kink) * (f.adults_eq))),\n d => (elig - kink) * (d.adults_eq + (d.children * depmult)) * d.weight)\n}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":707,"value":"// Phaseout rate\nphaseout = [(budget * 1000000000) / (thresh_flat_sum + thresh_sum - agi_sum)]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3474,"value":"phaseout_array = phaseout.map(function(item, i) {\n return {single: item}\n}) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3481,"value":"phaseout_text = {\n if (universality === \"0\" || programs !== \"0\") {\nreturn phaseout_array[0]\n } else {\nreturn {single: [\"0\"]}\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6269,"value":"phaseout_family = {\n if ((universality === \"0\" || programs != 0) && equivadj === \"0\") {\n const data = []\n data.push(((elig - kink) * ((1 + jointfiler) + (2 * depmult)) * phaseout) / ((elig - kink) * ((1 + jointfiler) + (2 * depmult))))\n return data\n } else if ((universality === \"0\" || programs != 0) && equivadj === \"1\") {\n const data = []\n data.push(((elig - kink) * ((1 + jointfiler) + (2 * depmult)) * phaseout) / ((elig - kink) * (1 + jointfiler)))\n return data\n } else if (universality === \"1\") {\n const data = []\n return data\n }\n }\n/*[((elig - kink) * ((1 + jointfiler) + (2 * depmult)) * phaseout) / ((elig - kink) * ((2) + (2 * depmult)))]*/","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6278,"value":"phaseout_family_array = phaseout_family.map(function(item, i) {\n return {family: item}\n}) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6283,"value":"phaseout_family_text = {\n if (universality === \"0\" || programs !== \"0\") {\nreturn phaseout_family_array[0]\n } else {\nreturn {family: [\"0\"]}\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2561,"value":"population = d3.sum(data, \n d => ((d.adults_eq) + (d.children * depmult)) * d.weight)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2568,"value":"per_capita = (budget * 1000000000) / population","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":767,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"md`### Analysis`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6000,"value":"// Verifying sum of budget\nd3.sum(credit, d => d.credit * d.weight) / (1 - .00663543)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"// Weighted sum of credits by decile\ncreditSumWeight = d3.rollups(credit, v => d3.sum(v, d => d.credit * d.weight), d => d.decile)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":271,"value":"// Sum of weights by decile\nweightSum = d3.rollups(data_import, v => d3.sum(v, d => d.weight), d => d.decile)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":229,"value":"creditSumWeightMap = creditSumWeight.map(x => x[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":254,"value":"weightSumMap = weightSum.map(x => x[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":314,"value":"dataset = creditSumWeightMap.map(function(n, i) { return n / weightSumMap[i]; })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":527,"value":"deciles = [\"1st Decile\", \"2nd\", \"3rd\", \"4th\", \"5th\", \"6th\", \"7th\", \"8th\", \"9th\", \"10th\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":521,"value":"databar = dataset.map(function(item, i) {\n return {credit_avg: item, decile: deciles[i]}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4140,"value":"deciles_text = [\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth\", \"Sixth\", \"Seventh\", \"Eighth\", \"Ninth\", \"Tenth\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5346,"value":"// Weighted sum of number of payments\ntotalPayments = d3.rollups(credit.filter(f => f.credit > 0), v => d3.sum(v, d => d.weight), d => d.decile).map(x => x[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4134,"value":"table = deciles_text.map(function(item, i) {\n return {Deciles: item, Total_Payments: totalPayments[i], Average_Payment: dataset[i]}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5922,"value":"({\n Deciles: table.map(d => d.Deciles),\n Total_Payments: table.map(d => d.Total_Payments),\n Average_Payment: table.map(d => d.Average_Payment)\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1622,"value":"// x-coordinates for budget schedule\ndatax = {\n if (universality === \"0\" || programs != 0) {\n const data = []\n data.push(0)\n data.push(kink)\n data.push(elig)\n return data\n } else if (universality === \"1\" & programs === \"0\") {\n const data = []\n data.push(0)\n data.push(kink)\n data.push(99999999)\n return data\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1595,"value":"// y-coordinates for budget schedule\ndatay = {\n if (universality === \"0\" || programs != 0) {\n const data = []\n data.push((elig - kink) * (1 + (0 * depmult)) * phaseout)\n data.push((elig - kink) * (1 + (0 * depmult)) * phaseout)\n data.push(0)\n return data\n } else if (universality === \"1\" && programs === \"0\") {\n const data = []\n data.push(per_capita * (1 + (0 * depmult)))\n data.push(per_capita * (1 + (0 * depmult)))\n data.push(per_capita * (1 + (0 * depmult)))\n return data\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1629,"value":"// Combining y- and x-coordinates for budget schedule\ndataline1 = datay.map(function(item, i) {\n return {yvalues: item, xvalues: datax[i]}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2462,"value":"datax2 = {\n if ((universality === \"0\" || programs != 0) && equivadj === \"0\") {\n const data = []\n data.push(0)\n data.push(kink * (1 + jointfiler_phaseout))\n data.push((kink * (1 + jointfiler_phaseout)) + ((elig - kink) * ((1 + jointfiler) + (2 * depmult))))\n return data\n } else if ((universality === \"0\" || programs != 0) && equivadj === \"1\") {\n const data = []\n data.push(0)\n data.push(kink * (1 + jointfiler_phaseout))\n data.push((kink * (1 + jointfiler_phaseout)) + ((elig - kink) * (1 + jointfiler)))\n return data\n } else if (universality === \"1\" && programs === \"0\") {\n const data = []\n data.push(0)\n data.push((kink * 2))\n data.push(99999999)\n return data\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2468,"value":"// x-coordinates for budget schedule\ndatay2 = {\n if (universality === \"0\" || programs != 0) {\n const data = []\n data.push((elig - kink) * ((1 + jointfiler) + (2 * depmult)) * phaseout)\n data.push((elig - kink) * ((1 + jointfiler) + (2 * depmult)) * phaseout)\n data.push(0)\n return data\n } else if (universality === \"1\" && programs === \"0\") {\n const data = []\n data.push(per_capita * ((1 + jointfiler) + (2 * depmult)))\n data.push(per_capita * ((1 + jointfiler) + (2 * depmult)))\n data.push(per_capita * ((1 + jointfiler) + (2 * depmult)))\n return data\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6265,"value":"phaseout","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2475,"value":"// Combining y- and x-coordinates for budget schedule\ndataline2 = datay2.map(function(item, i) {\n return {yvalues: item, xvalues: datax2[i]}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3145,"value":"dataline_text1 = dataline1[0]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3148,"value":"dataline_text2 = dataline2[0]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5997,"value":"/*chart = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, 500]);\n\n svg.append(\"g\")\n .call(xAxis_scatter);\n\n svg.append(\"g\")\n .call(yAxis_scatter);\n\n svg.append(\"g\")\n .attr(\"stroke\", \"steelblue\")\n .attr(\"stroke-width\", 1.5)\n .attr(\"fill\", \"none\")\n .selectAll(\"circle\")\n .data(credit)\n .join(\"circle\")\n .attr(\"cx\", d => x_scatter(d.agi))\n .attr(\"cy\", d => y_scatter(d.credit))\n .attr(\"r\", 3);\n\n return svg.node();\n}*/","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2016,"value":"md`### Charts`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1409,"value":"function updateBar(){\n svg.selectAll(\"rect\")\n .data(databar)\n .transition() // <---- Here is the transition\n .duration(5000) // 2 seconds\n .attr(\"y\", d => y(d.credit_avg))\n .attr(\"height\", d => y(0) - y(d.credit_avg))\n .attr(\"fill\", \"white\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":397,"value":"x = d3.scaleBand()\n .domain(d3.range(databar.length))\n .range([margin.left, width - margin.right])\n .padding(0.1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":399,"value":"y = d3.scaleLinear()\n .domain([0, d3.max(databar, d => d.credit_avg)]).nice()\n .range([height - margin.bottom, margin.top])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2901,"value":"x0 = d3.scaleLinear()\n .domain([0, d3.max(databar, d => d.credit_avg)]).nice()\n .range([margin_bar.left, width - margin_bar.right])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2899,"value":"y0 = d3.scaleBand()\n .domain(d3.range(databar.length))\n .rangeRound([margin_bar.top, height - margin_bar.bottom])\n .padding(.1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":401,"value":"xAxis = g => g\n .attr(\"transform\", `translate(0,${margin_bar.top})`)\n .call(d3.axisTop(x0).ticks(null, databar.format).tickFormat(v => `$${v}`))\n .call(g => g.select(\".domain\").remove())\n .call(g => g.append(\"text\")\n .attr(\"x\", width - margin.left - margin.right - 45)\n .attr(\"y\", -22)\n .attr(\"fill\", \"currentColor\")\n .attr(\"text-anchor\", \"start\")\n .text(\"→ Size of Payment\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":403,"value":"yAxis = g => g\n .attr(\"transform\", `translate(${margin_bar.left},0)`)\n .call(d3.axisLeft(y0).tickFormat(i => databar[i].decile).tickSizeOuter(0))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1642,"value":"line = d3.line()\n .defined(d => !isNaN(d.yvalues))\n .x(d => x1(d.xvalues))\n .y(d => y1(d.yvalues))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1664,"value":"x1 = d3.scaleLinear()\n .domain([0, 240000])\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1680,"value":"y1 = d3.scaleLinear()\n .domain([0, d3.max(dataline2, d => d.yvalues)]).nice()\n .range([height1 - margin.bottom, margin.top])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1651,"value":"xAxis1 = g => g\n .attr(\"transform\", `translate(0,${height1 - margin.bottom})`)\n .call(d3.axisBottom(x1).ticks(width / 80).tickSizeOuter(0).tickFormat(v => `$${v/1000}k`))\n .call(g => g.append(\"text\")\n .attr(\"x\", width)\n .attr(\"y\", 28)\n .attr(\"fill\", \"currentColor\")\n .attr(\"text-anchor\", \"end\")\n .text(\"→ Adjusted Gross Income\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1658,"value":"yAxis1 = g => g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(y1).ticks(null, dataline1.format).tickFormat(v => `$${v}`))\n .call(g => g.select(\".domain\").remove())\n .call(g => g.append(\"text\")\n .attr(\"x\", -margin.left)\n .attr(\"y\", 10)\n .attr(\"fill\", \"currentColor\")\n .attr(\"text-anchor\", \"start\")\n .text(\"↑ Size of Payment\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5015,"value":"xAxis_scatter = g => g\n .attr(\"transform\", `translate(0,${500 - margin.bottom})`)\n .call(d3.axisBottom(x_scatter).ticks(width / 80).tickSizeOuter(0).tickFormat(v => `$${v/1000}k`))\n .call(g => g.append(\"text\")\n .attr(\"x\", width - margin.left - margin.right - 55)\n .attr(\"y\", 28)\n .attr(\"fill\", \"currentColor\")\n .attr(\"text-anchor\", \"start\")\n .text(\"→ Adjusted Gross Income\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5018,"value":"yAxis_scatter = g => g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(y_scatter).ticks(null, credit.format).tickFormat(v => `$${v}`))\n .call(g => g.select(\".domain\").remove())\n .call(g => g.append(\"text\")\n .attr(\"x\", -margin.left)\n .attr(\"y\", 10)\n .attr(\"fill\", \"currentColor\")\n .attr(\"text-anchor\", \"start\")\n .text(\"↑ Size of Payment\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5026,"value":"x_scatter = d3.scaleLinear()\n .domain([0, 200000]).nice()\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5020,"value":"y_scatter = d3.scaleLinear()\n .domain([0, d3.max(credit, d => d.credit)]).nice()\n .range([500 - margin.bottom, margin.top])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":405,"value":"color = \"steelblue\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":407,"value":"height = 335","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1711,"value":"height1 = 335","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":365,"value":"margin = ({top: 30, right: 0, bottom: 30, left: 50})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2924,"value":"margin_bar = ({top: 0, right: 0, bottom: 0, left: 55})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4316,"value":"import {inputsGroup} from \"@bumbeishvili/input-groups\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1555,"value":"import {slider,button,select,text,radio,checkbox,number} from \"@jashkenas/inputs\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3927,"value":"import {Range, Radio, Table} from \"@observablehq/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W55","stdlib":"1"}} 42:null