{ "docId": "019dd923-5de0-76bd-a165-994c2a270db1", "docSlug": "09cd0f1face7682d", "documentTitle": "The economic and social impact of investment in the nbn network Methodology Report", "authorId": "misc", "authorName": "Accenture", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 45, "pageCount": 54, "prevPage": 44, "nextPage": 46, "slideType": "appendix_methodology", "function": "diagnose", "density": "dense", "nDataPoints": 10, "notes": "The slide presents a linear regression model specification and a summary table of coefficients and p-values.", "elementsJson": [ "headline_text", "paragraph", "data_table" ], "metadataConfidence": 1, "imagePath": null, "slideHref": "/slides/019dd923-5de0-76bd-a165-994c2a270db1/45", "deckHref": "/decks/019dd923-5de0-76bd-a165-994c2a270db1", "deckJsonHref": "/decks/019dd923-5de0-76bd-a165-994c2a270db1.json", "deckAnchorHref": "/decks/019dd923-5de0-76bd-a165-994c2a270db1#slide-45", "components": [ { "bbox": null, "kind": "metric", "text": "Coefficient: 0.0209", "attrs": null, "subkind": "primary", "toolName": "Quantification", "toolSlug": "quantification", "confidence": null, "componentId": "019dd951-edac-778d-bcc1-13c9da15a2c5", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.7, "w": 0.55, "x": 0.03, "y": 0.2 }, "kind": "paragraph", "text": "Overview of model specification\nTo estimate the impact of fast broadband on higher education attainment at an SA2 level the following model specification was developed:\n%EDUC_bachelor degrees or higher = β1Broadband.Speedit + β2Population.Densityit + β3Average.Incomeit + β4Unemployment.Rateit + β5Remotenessit + εit\nWhere i = SA2 and t = time period (year, calendar year 2012 to 2019 inclusive)\nWith the following variables:\n• %EDUC_bachelor degrees or higher = Proportion with a bachelor degree or higher within an SA2\n• Broadband. Speedit = Average broadband speeds, estimated based on nbn and non-nbn connections, including unconnected households, who are assumed to have a speed of zero.\n• Population. Densityit = The quotient of the residential population in an SA2 and its area in square kilometres\n• Average. Incomeit = Personal income on an SA2 level\n• Unemployment. Rateit = Unemployment rate, by SA2 from Small Area Labour Markets data, by SA2\n• Remotenessit = Classification of an SA2 into one of five remoteness categories (major cities, inner regional, outer regional, remote and very remote) based on the Australian Statistical Geography Standard\n• εit = error term\nWe applied this model to estimate the implied percentage of people with a bachelors degree or higher in the absence of fast broadband (%EDUC_bachelor degrees or higher – β1 * Increase in broadband speed since 2011 / 100). Our model describes a relationship between total average broadband speeds and number of bachelor and higher degree completions. To fairly represent the impact of the nbn network (only one part of the total broadband story in Australia), we then disaggregate this total number of bachelor and higher degree completions into nbn and non-nbn effects. In each year we calculate the weighted impact of the nbn network on the all-Australian average broadband speed, accounting for take-up and average speeds, using the following formula:\n%Completion of a bachelor degree or higher upliftnbn = (Householdsnbn * Speednbn) / SpeedAll\nThis fraction increases over time with the nbn roll out, from 0.6% in 2012 to close to 90% in 2023. This fraction accounts for the fact the other non-nbn technologies exist and these too are providing some of the GDP benefit.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "6969909b-3e63-4685-a163-46793d829113", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.03, "w": 0.1, "x": 0.03, "y": 0.95 }, "kind": "source-note", "text": "Source: Accenture", "attrs": null, "subkind": null, "toolName": null, "toolSlug": null, "confidence": null, "componentId": "ae57954c-12a1-44df-a58d-c80d2677057c", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.45, "w": 0.36, "x": 0.61, "y": 0.2 }, "kind": "table", "text": "Summary results\nVariable | Coefficient | P-value\nBroadband. Speed | 0.0209 | 0.000\nPopulation. Density | 0.0025 | 0.000\nAverage. Income | 0.0000451 | 0.000\nUnemployment. Rate | -0.0371 | 0.000\nRemoteness | 0.7894 | 0.0108", "attrs": null, "subkind": "data", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "7099da54-43f0-4841-8845-b220757e8137", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.08, "w": 0.94, "x": 0.03, "y": 0.05 }, "kind": "title", "text": "An additional model to estimate the impact of the nbn network on education (bachelor and higher degree completions) was developed", "attrs": null, "subkind": "headline", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "adcd8ce3-2e8f-4fa0-b8cb-d5eaa47a91f7", "frameworkName": null, "frameworkSlug": null } ], "metrics": [], "tools": [ { "name": "Table data", "slug": "table-data", "agent": null, "layer": "slide", "matchId": "4c812cbc-3eab-4144-8b4e-56c3f91c8319", "evidence": "The slide contains a table/data of model specification results, including coefficients and p-values.", "confidence": 0.7 } ], "frameworks": [], "arcBeats": [ { "to": 54, "from": 31, "beatId": "de2b4a7e-820d-4666-a484-aed0b25c4e96", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "The document provides detailed results and implications of the analysis", "position": 3, "confidence": 0.8, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "loops": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }