{
  "docId": "019dd923-5ca1-7489-b639-2e6db8a38ad7",
  "docSlug": "4dd6f8edb8b68a13",
  "documentTitle": "MTA Financial Impact COVID-19",
  "authorId": "McKinsey",
  "authorName": "McKinsey",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.779,
  "pageNumber": 16,
  "pageCount": 38,
  "prevPage": 15,
  "nextPage": 17,
  "slideType": "scenario_analysis",
  "function": "analyze_data",
  "density": "balanced",
  "nDataPoints": 45,
  "notes": "The slide compares two scenarios based on ridership percentages and revenue loss projections.",
  "elementsJson": [
    "headline_text",
    "subtitle_text",
    "data_table",
    "callout_box"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5ca1-7489-b639-2e6db8a38ad7/16",
  "deckHref": "/decks/019dd923-5ca1-7489-b639-2e6db8a38ad7",
  "deckJsonHref": "/decks/019dd923-5ca1-7489-b639-2e6db8a38ad7.json",
  "deckAnchorHref": "/decks/019dd923-5ca1-7489-b639-2e6db8a38ad7#slide-16",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "These analyses represent only potential scenarios based on discrete data from one point in time.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd951-cdee-73ea-b072-3bcdb6f12309",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Revenue loss: $1.02 B",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd951-cdee-73ea-b072-3c8cbbfc5732",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.02,
        "w": 0.35,
        "x": 0.04,
        "y": 0.13
      },
      "kind": "paragraph",
      "text": "% of typical ridership in a given month",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "9c66e155-3000-4b50-be96-8c9fa10f2d64",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.065,
        "w": 0.245,
        "x": 0.743,
        "y": 0.015
      },
      "kind": "source-note",
      "text": "Current as of 4/17. Please see disclaimer on page 3. These analyses represent only potential scenarios based on discrete data from one point in time.",
      "attrs": null,
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "46187b82-1bd3-4408-9333-9b6c79e66986",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.75,
        "w": 0.86,
        "x": 0.07,
        "y": 0.18
      },
      "kind": "table",
      "text": "Assumptions and scenario modeling table",
      "attrs": null,
      "subkind": "scenario",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f35039f9-8999-447c-9e40-4c975db683a7",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.04,
        "w": 0.65,
        "x": 0.04,
        "y": 0.08
      },
      "kind": "title",
      "text": "Resulting toll revenue assumptions and modeling",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "fd64eb38-8c84-477d-a356-35e211bc3212",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [
    {
      "metricRaw": "Revenue loss",
      "numberRaw": "$1.02 B",
      "numberKind": "money",
      "actionTitle": "Resulting toll revenue assumptions and modeling",
      "calloutText": "These analyses represent only potential scenarios based on discrete data from one point in time.",
      "numberScale": "b",
      "numberValue": 1.02,
      "metricFamily": "revenue",
      "numberCurrency": "$"
    }
  ],
  "tools": [
    {
      "name": "Scenario Analysis",
      "slug": "scenario-analysis",
      "agent": null,
      "layer": "slide",
      "matchId": "a3bc1b3f-a13d-4481-8fe0-545b5554a1d1",
      "evidence": "The slide presents resulting toll revenue assumptions and modeling, with a table showing assumptions and scenario modeling.",
      "confidence": 0.8
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 17,
      "from": 13,
      "beatId": "bea7e428-7853-44b1-b1e8-87a647d4ae0e",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Solution & Approach",
      "beatSlug": "consultants-gambit-solution-approach",
      "evidence": "The deck presents scenario analysis, fare revenue assumptions, and modeling.",
      "position": 2,
      "confidence": 0.8,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    }
  ],
  "loops": [],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}