2d:null 2e:["$","title",null,{"children":"A Decade of NYC City Employee Pay | Matt Khinda | Observable"}] 33:Ta23,Plot.plot({ width: width, height: 600, grid: true, marginLeft: 30, inset: 10, x: { type: "log", label: "Payroll size (log scale) →" }, y: { label: "↑ Pay ratio (highest pay/lowest pay)", domain: [3, 19] }, color: { scheme: "RdYlBu", reverse: true, label: "Change in pay ratio from 2015 to 2025", legend: true, tickFormat: "+f", domain: [-10,10], pivot: 0 }, marks: [ // Invisible big dot for hover Plot.dot(data.filter(d => d.num_employees_2015 >= 10), { x: "num_employees_2025", y: (d) => getExtremes(d, 2025)[1] / getExtremes(d, 2025)[0], r: 15, fill: "transparent", stroke: "none", tip: width < breakpoint ? false : { fontSize: 12, lineHeight: 1.3, }, title: (d) => { const [lo15, hi15] = getExtremes(d, 2015); const [lo25, hi25] = getExtremes(d, 2025); const fmt = n => `$${Math.round(n).toLocaleString()}`; return `${titleCase(d.agency_name)} (2015) high: ${fmt(hi15)} ↔ low: ${fmt(lo15)} (2025) high: ${fmt(hi25)} ↔ low: ${fmt(lo25)}`; } }), Plot.arrow(data.filter(d => d.num_employees_2015 >= 10), { x1: "num_employees_2015", y1: (d) => getExtremes(d, 2015)[1]/getExtremes(d, 2015)[0], x2: "num_employees_2025", y2: (d) => getExtremes(d, 2025)[1]/getExtremes(d, 2025)[0], bend: true, stroke: (d) => (getExtremes(d, 2025)[1]/getExtremes(d, 2025)[0]) - (getExtremes(d, 2015)[1]/getExtremes(d, 2015)[0]), strokeWidth: 1.75 }), Plot.circle(data.filter(d => d.num_employees_2015 >= 10), { x: "num_employees_2015", y: (d) => getExtremes(d, 2015)[1]/getExtremes(d, 2015)[0], r: 3, stroke: "white", strokeWidth: 1, fill: "black" }), Plot.text(data.filter(d => d.num_employees_2015 >= 10), { x: "num_employees_2025", y: (d) => getExtremes(d, 2025)[1]/getExtremes(d, 2025)[0], filter: (d) => highlight.includes(getAgencyAbbrev(d.agency_name)), text: (d) => getAgencyAbbrev(d.agency_name), fill: "currentColor", stroke: "white", fontWeight: "bold", fontSize: 11, dy: -12, }), Plot.text( [payType === "Yes" ? "All pay (overtime and bonuses)" : "Regular pay only (no overtime)"], { frameAnchor: "top-left", dx: 18, dy: 22, fill: "currentColor", fontSize: 13, fontWeight: "bold", stroke: "white", strokeWidth: 12, textAnchor: "start" }) ] })34:T17d9,getAgencyAbbrev = function(name) { const map = { "ADMIN FOR CHILDREN'S SVCS": "ACS", "ADMIN TRIALS AND HEARINGS": "OATH", "BOARD OF CORRECTION": "BOC", "BOARD OF ELECTION": "BOE", "BOROUGH PRESIDENT-BRONX": "BX BP", "BOROUGH PRESIDENT-BROOKLYN": "BK BP", "BOROUGH PRESIDENT-QUEENS": "QN BP", "BOROUGH PRESIDENT-STATEN IS": "SI BP", "PRESIDENT BOROUGH OF MANHATTAN": "MN BP", "BRONX COMMUNITY BOARD #1": "BX CB1", "BRONX COMMUNITY BOARD #2": "BX CB2", "BRONX COMMUNITY BOARD #3": "BX CB3", "BRONX COMMUNITY BOARD #4": "BX CB4", "BRONX COMMUNITY BOARD #5": "BX CB5", "BRONX COMMUNITY BOARD #6": "BX CB6", "BRONX COMMUNITY BOARD #7": "BX CB7", "BRONX COMMUNITY BOARD #8": "BX CB8", "BRONX COMMUNITY BOARD #9": "BX CB9", "BRONX COMMUNITY BOARD #10": "BX CB10", "BRONX COMMUNITY BOARD #11": "BX CB11", "BRONX COMMUNITY BOARD #12": "BX CB12", "BROOKLYN COMMUNITY BOARD #1": "BK CB1", "BROOKLYN COMMUNITY BOARD #2": "BK CB2", "BROOKLYN COMMUNITY BOARD #3": "BK CB3", "BROOKLYN COMMUNITY BOARD #4": "BK CB4", "BROOKLYN COMMUNITY BOARD #5": "BK CB5", "BROOKLYN COMMUNITY BOARD #6": "BK CB6", "BROOKLYN COMMUNITY BOARD #7": "BK CB7", "BROOKLYN COMMUNITY BOARD #8": "BK CB8", "BROOKLYN COMMUNITY BOARD #9": "BK CB9", "BROOKLYN COMMUNITY BOARD #10": "BK CB10", "BROOKLYN COMMUNITY BOARD #11": "BK CB11", "BROOKLYN COMMUNITY BOARD #12": "BK CB12", "BROOKLYN COMMUNITY BOARD #13": "BK CB13", "BROOKLYN COMMUNITY BOARD #14": "BK CB14", "BROOKLYN COMMUNITY BOARD #15": "BK CB15", "BROOKLYN COMMUNITY BOARD #16": "BK CB16", "BROOKLYN COMMUNITY BOARD #17": "BK CB17", "BROOKLYN COMMUNITY BOARD #18": "BK CB18", "MANHATTAN COMMUNITY BOARD #1": "MN CB1", "MANHATTAN COMMUNITY BOARD #2": "MN CB2", "MANHATTAN COMMUNITY BOARD #3": "MN CB3", "MANHATTAN COMMUNITY BOARD #4": "MN CB4", "MANHATTAN COMMUNITY BOARD #5": "MN CB5", "MANHATTAN COMMUNITY BOARD #6": "MN CB6", "MANHATTAN COMMUNITY BOARD #7": "MN CB7", "MANHATTAN COMMUNITY BOARD #8": "MN CB8", "MANHATTAN COMMUNITY BOARD #9": "MN CB9", "MANHATTAN COMMUNITY BOARD #10": "MN CB10", "MANHATTAN COMMUNITY BOARD #11": "MN CB11", "MANHATTAN COMMUNITY BOARD #12": "MN CB12", "QUEENS COMMUNITY BOARD #1": "QN CB1", "QUEENS COMMUNITY BOARD #2": "QN CB2", "QUEENS COMMUNITY BOARD #3": "QN CB3", "QUEENS COMMUNITY BOARD #4": "QN CB4", "QUEENS COMMUNITY BOARD #5": "QN CB5", "QUEENS COMMUNITY BOARD #6": "QN CB6", "QUEENS COMMUNITY BOARD #7": "QN CB7", "QUEENS COMMUNITY BOARD #8": "QN CB8", "QUEENS COMMUNITY BOARD #9": "QN CB9", "QUEENS COMMUNITY BOARD #10": "QN CB10", "QUEENS COMMUNITY BOARD #11": "QN CB11", "QUEENS COMMUNITY BOARD #12": "QN CB12", "QUEENS COMMUNITY BOARD #13": "QN CB13", "QUEENS COMMUNITY BOARD #14": "QN CB14", "STATEN ISLAND COMMUNITY BD #1": "SI CB1", "STATEN ISLAND COMMUNITY BD #2": "SI CB2", "STATEN ISLAND COMMUNITY BD #3": "SI CB3", "BRONX DISTRICT ATTORNEY": "BX DA", "DISTRICT ATTORNEY KINGS COUNTY": "BK DA", "DISTRICT ATTORNEY QNS COUNTY": "QN DA", "DISTRICT ATTORNEY RICHMOND COU": "SI DA", "DISTRICT ATTORNEY-MANHATTAN": "MN DA", "DISTRICT ATTORNEY-SPECIAL NARC": "SNP DA", "BUSINESS INTEGRITY COMMISSION": "BIC", "CAMPAIGN FINANCE BOARD": "CFB", "CITY CLERK": "City Clerk", "CITY COUNCIL": "Council", "CIVIL SERVICE COMMISSION": "CSC", "CIVILIAN COMPLAINT REVIEW BD": "CCRB", "COMMUNITY COLLEGE (BRONX)": "BCC", "COMMUNITY COLLEGE (HOSTOS)": "Hostos CC", "COMMUNITY COLLEGE (KINGSBORO)": "KCC", "COMMUNITY COLLEGE (LAGUARDIA)": "LaGuardia CC", "COMMUNITY COLLEGE (MANHATTAN)": "BMCC", "COMMUNITY COLLEGE (QUEENSBORO)": "QCC", "GUTTMAN COMMUNITY COLLEGE": "Guttman CC", "HUNTER COLLEGE HIGH SCHOOL": "Hunter HS", "CONFLICTS OF INTEREST BOARD": "COIB", "CULTURAL AFFAIRS": "DCLA", "DEPARTMENT FOR THE AGING": "DFTA", "DEPARTMENT OF BUILDINGS": "DOB", "DEPARTMENT OF BUSINESS SERV.": "SBS", "DEPARTMENT OF CITY PLANNING": "DCP", "DEPARTMENT OF CORRECTION": "DOC", "DEPARTMENT OF EDUCATION ADMIN": "DOE", "DEPARTMENT OF FINANCE": "DOF", "DEPARTMENT OF INVESTIGATION": "DOI", "DEPARTMENT OF PROBATION": "DOP", "DEPARTMENT OF SANITATION": "DSNY", "DEPARTMENT OF TRANSPORTATION": "DOT", "DEPT OF CITYWIDE ADMIN SVCS": "DCAS", "DEPT OF ENVIRONMENT PROTECTION": "DEP", "DEPT OF HEALTH/MENTAL HYGIENE": "DOHMH", "DEPT OF PARKS & RECREATION": "Parks", "DEPT OF RECORDS & INFO SERVICE": "DORIS", "DEPT OF YOUTH & COMM DEV SRVS": "DYCD", "DEPT. OF DESIGN & CONSTRUCTION": "DDC", "DEPT. OF HOMELESS SERVICES": "DHS", "EQUAL EMPLOY PRACTICES COMM": "EEPC", "FINANCIAL INFO SVCS AGENCY": "FISA", "FIRE DEPARTMENT": "FDNY", "HOUSING PRESERVATION & DVLPMNT": "HPD", "HRA/DEPT OF SOCIAL SERVICES": "HRA/DSS", "HUMAN RIGHTS COMMISSION": "CCHR", "INDEPENDENT BUDGET OFFICE": "IBO", "LANDMARKS PRESERVATION COMM": "LPC", "LAW DEPARTMENT": "Law", "MUNICIPAL WATER FIN AUTHORITY": "NYW", "NYC EMPLOYEES RETIREMENT SYS": "NYCERS", "NYC HOUSING AUTHORITY": "NYCHA", "NYC POLICE PENSION FUND": "PPF", "OFF OF PAYROLL ADMINISTRATION": "OPA", "OFFICE OF COLLECTIVE BARGAININ": "OCB", "OFFICE OF EMERGENCY MANAGEMENT": "NYCEM", "OFFICE OF LABOR RELATIONS": "OLR", "OFFICE OF MANAGEMENT & BUDGET": "OMB", "OFFICE OF THE ACTUARY": "Actuary", "OFFICE OF THE COMPTROLLER": "Comptroller", "OFFICE OF THE MAYOR": "Mayor", "POLICE DEPARTMENT": "NYPD", "PUBLIC ADMINISTRATOR-BRONX": "BX PA", "PUBLIC ADMINISTRATOR-KINGS": "BK PA", "PUBLIC ADMINISTRATOR-NEW YORK": "MN PA", "PUBLIC ADMINISTRATOR-QUEENS": "QN PA", "PUBLIC ADMINISTRATOR-RICHMOND": "SI PA", "PUBLIC ADVOCATE": "PA", "TAX COMMISSION": "Tax Comm", "TAXI & LIMOUSINE COMMISSION": "TLC", "TEACHERS RETIREMENT SYSTEM": "TRS", }; return map[name] || name; }35:[{"id":"093e1e0dcd23696e2adb89de279270ef3f6e5760bc41e73298b85035f4b33de9119240946d59bcbeaf303046b49d9e86981cb238822537dfd239cc386d81ea59","href":"https://static.observableusercontent.com/files/093e1e0dcd23696e2adb89de279270ef3f6e5760bc41e73298b85035f4b33de9119240946d59bcbeaf303046b49d9e86981cb238822537dfd239cc386d81ea59","name":"nyc_payroll_summary_2015-2025.csv","type":"text/csv","lastModified":1779648141238.659,"size":45113}] 31:{"id":"ad86b1ad1b6c6a1a","slug":"nyc-city-pay","title":"A Decade of NYC City Employee Pay","thumbnail":"3f269dd9d5de282e3873733fa7caaa56c4176d0dd7f1d7eb752255effe276494","access_level":"public","create_time":"$D2026-05-24T15:20:23.218Z","update_time":"$D2026-05-26T02:10:31.548Z","view_time":null,"first_public_time":"$D2026-05-24T20:44:33.772Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"bd1a04fba6aa0b96","login":"mpkhinda","name":"Matt Khinda","avatar_url":"https://avatars-next.observableusercontent.com/d3ce820bb0a0da07120f1d18fc5462f4d2dda95075bbb7bd7ac6113a256128dd","tier":"starter_2024"},"version":583,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"7ed01765dbd656ee","login":"mpkhinda","name":"Matt Khinda","avatar_url":"https://avatars-next.observableusercontent.com/381c8bb4edddbcd7e2319cbd390f7477b1b4934289bf039a4e881d2d3711d20e","tier":null}],"members":[{"type":"individual","id":"7ed01765dbd656ee","login":"mpkhinda","name":"Matt Khinda","avatar_url":"https://avatars-next.observableusercontent.com/381c8bb4edddbcd7e2319cbd390f7477b1b4934289bf039a4e881d2d3711d20e","tier":null,"role":"editor"},{"type":"team","id":"bd1a04fba6aa0b96","login":"mpkhinda","name":"Matt Khinda","avatar_url":"https://avatars-next.observableusercontent.com/d3ce820bb0a0da07120f1d18fc5462f4d2dda95075bbb7bd7ac6113a256128dd","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":{"id":"247c83cdea677d59","slug":"nyc-data-visualizations","title":"🗽 NYC Open Data Visualizations","owner":{"id":"bd1a04fba6aa0b96","login":"mpkhinda","name":"Matt Khinda"}},"type":"notebook","body":{"title":"A Decade of NYC City Employee Pay","cells":[{"id":0,"value":"# A Decade of NYC City Employee Pay","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"This arrow chart (modeled after [this notebook by Mike Bostock](https://observablehq.com/@observablehq/plot-arrow-variation-chart)) shows the change, from 2015 to 2025, in the ratio between the highest paid full-time employee and the lowest at ${data.length} NYC city agencies. This measure provides a useful snapshot of pay disparity, but is heavily influenced by outliers like the ${titleCase(highestPaidEmployee.max_total_title_2025)} at the ${titleCase(highestPaidEmployee.agency_name)} who made $${numFormat(highestPaidEmployee.max_total_pay_2025)} (including overtime) in fiscal year 2025.\n\n*Details about the filtering and aggregation process at the bottom of the page*\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":26,"value":"viewof payType = Inputs.radio([\"Yes\", \"No\"], {value: \"Yes\", label: html`Include overtime and other pay?`})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"legend = {\n const arrowId = DOM.uid(\"arrow\");\n return svg``;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":495,"value":"Source: Office of Payroll Administration (OPA) via NYC Open Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":9,"value":"#### key variables","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":98,"value":"highlight = [\n \"NYPD\", \"NYCHA\", \"FDNY\", \"DOT\", \"DCAS\", \"BOC\", \"DOHMH\", \"DOC\", \"HRA/DSS\", \"Comptroller\",\"DOP\", \"Mayor\"\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":411,"value":"breakpoint = 650","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"#### helper functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":131,"value":"function arc(x1, y1, x2, y2) {\n const r = Math.hypot(x1 - x2, y1 - y2) * 2;\n return `M${x1},${y1} A${r},${r} 0,0,1 ${x2},${y2}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"titleCase = function titleCase(str) {\n const lower = new Set([\n \"and\", \"for\", \"in\", \"of\", \"the\"\n ]);\n const upper = new Set([\"nyc\"]);\n const words = str.toLowerCase().split(\" \");\n return words.map((w, i) => {\n if (upper.has(w)) {\n return w.toUpperCase();\n }\n if (i === 0 || i === words.length - 1 || !lower.has(w)) {\n return w.charAt(0).toUpperCase() + w.slice(1);\n }\n return w;\n }).join(\" \");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":331,"value":"numFormat = function(num) {\n return num.toLocaleString(\"en-US\", {\n minimumFractionDigits: 2,\n maximumFractionDigits: 2\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"getMed = function(d, year) {\n if (payType === \"Yes\") {\n return d[`med_total_pay_${year}`];\n } else {\n return d[`med_reg_pay_${year}`];\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"getExtremes = function(d, year) {\n if (payType === \"Yes\") {\n return [d[`min_total_pay_${year}`], d[`max_total_pay_${year}`]];\n } else {\n return [d[`min_reg_pay_${year}`], d[`max_reg_pay_${year}`]];\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"#### data structures","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":306,"value":"highestPaidEmployee = d3.greatest(data, d => d.max_total_pay_2025)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"data = FileAttachment(\"nyc_payroll_summary_2015-2025.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"### Dependencies","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":17,"value":"d3 = require(\"d3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"### Sources:\n[Citywide Payroll Data](https://data.cityofnewyork.us/City-Government/Citywide-Payroll-Data-Fiscal-Year-/k397-673e/about_data) from the NYC Office of Payroll Administration\n\n*All data retrieved via the [NYC Open Data Portal](https://opendata.cityofnewyork.us/) (Accessed 05/24/2026)*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":32,"value":"### Process Notes:\nNYC Citywide Payroll Data includes a wide variety of employee types (seasonal workers, interns, part-time employees, etc...) resulting in a massive range of salaries and actual pay amounts (some even negative!). This noise had to be appropriately filtered before any useful comparison in pay between full time employees could be made. To do so, I cleaned and aggregated the data in Python using the following criteria: \n\n- Must have had total pay above $0\n- Must have worked at least 1800 hours that year (_to estimate full-time employment_)\n- Must have an average hourly wage of at least $17 (_NYC minimum wage_) which is confirmed by dividing both total pay and non-overtime pay by hours worked\n\nPayroll size was used as a stand-in for total number of employees, but the same individual may be double counted in the case they changed titles or if they held multiple titles within the fiscal year (though some of that would still have been filtered by the criteria above).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 32:"$31:body"