# ABA Law School Data Scraper (`jungle_synthesizer/aba-law-school-rankings-scraper`) Actor Scrape comprehensive law school statistics from the ABA Required Disclosures site: LSAT/GPA percentiles, acceptance rates, tuition, bar passage rates, employment outcomes, and enrollment data for all 196 ABA-approved law schools. - **URL**: https://apify.com/jungle\_synthesizer/aba-law-school-rankings-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Education - **Stats:** 2 total users, 1 monthly users, 66.7% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## ABA Law School Data Scraper Scrapes comprehensive law school statistics from the [ABA Required Disclosures site](https://www.abarequireddisclosures.org/Disclosure509.aspx) for all 196 ABA-approved law schools. Collects LSAT/GPA percentiles, acceptance rates, tuition, employment outcomes, JD enrollment, and more — all in a single run. ### What it does The ABA (American Bar Association) requires every accredited law school to publish detailed annual statistics via its Required Disclosures portal. This actor queries the portal's backend API directly, downloading compiled Excel reports across four data sections (First Year Class, Tuition & Fees, Employment Outcomes, JD Enrollment) and merges them into a single structured record per school. No browser rendering required — the actor uses the undocumented REST API at `backend.abarequireddisclosures.org` with the same requests the portal's Vue.js frontend makes. ### Output One record per law school with up to 30 fields: | Field | Description | |-------|-------------| | `school_name` | Full name of the law school | | `school_code` | ABA school code identifier | | `reporting_year` | ABA reporting year | | `aba_approved` | ABA accreditation status | | `applications` | Total applications received | | `offers` | Total admission offers | | `acceptance_rate` | Acceptance rate (%) | | `enrollees` | First-year enrollees | | `median_lsat` | Median LSAT score | | `lsat_25th_percentile` | 25th percentile LSAT | | `lsat_75th_percentile` | 75th percentile LSAT | | `median_gpa` | Median undergraduate GPA | | `gpa_25th_percentile` | 25th percentile GPA | | `gpa_75th_percentile` | 75th percentile GPA | | `ft_enrollees` | Full-time first-year enrollees | | `pt_enrollees` | Part-time first-year enrollees | | `jd_enrollment` | Total JD enrollment (all years) | | `tuition_resident` | Full-time resident tuition/semester (USD) | | `tuition_nonresident` | Full-time non-resident tuition/semester (USD) | | `tuition_resident_fees` | Annual resident fees (USD) | | `tuition_nonresident_fees` | Annual non-resident fees (USD) | | `total_graduates` | JD graduates for reporting year | | `employment_rate_10months` | Employment rate at 10 months post-graduation (%) | | `employment_law_firms` | Graduates at law firms (all sizes) | | `employment_business` | Graduates in business & industry | | `employment_government` | Graduates in government | | `employment_public_interest` | Graduates in public interest roles | | `employment_clerkships` | Graduates in judicial clerkships | | `school_url` | ABA disclosures page URL | ### Input | Parameter | Type | Description | Default | |-----------|------|-------------|---------| | `maxItems` | integer | Maximum number of school records to return | 10 | | `reporting_year` | integer | ABA reporting year (2023 = 2022-2023 data) | 2023 | #### Supported years The ABA portal has data going back several years. Year 2023 is the most recent complete dataset. To scrape all 196 schools, set `maxItems` to 200 or higher. ### Example input ```json { "maxItems": 200, "reporting_year": 2023 } ```` ### Notes - **196 schools**: The ABA currently accredits 196 law schools. A `maxItems` of 200 will return all of them in a single run. - **Employment data**: Employment figures come from the ABA Employment Questionnaire (EQ), which uses slightly different school name formats than the 509 disclosure list. The actor resolves this via a school code cross-reference, achieving ~193/196 match rate. The remaining 3 schools may show null employment fields. - **Tuition values**: Reported per semester for full-time programs. Annual cost = 2x the semester value plus applicable fees. - **Data availability**: Some fields may be null if a school did not report that data to the ABA for the selected year. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of records to scrape ## `reporting_year` (type: `integer`): The ABA reporting year to scrape (e.g. 2023 for 2022-2023 data). Defaults to 2023. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "reporting_year": 2023 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "reporting_year": 2023 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/aba-law-school-rankings-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "reporting_year": 2023, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/aba-law-school-rankings-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "reporting_year": 2023 }' | apify call jungle_synthesizer/aba-law-school-rankings-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/aba-law-school-rankings-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ABA Law School Data Scraper", "description": "Scrape comprehensive law school statistics from the ABA Required Disclosures site: LSAT/GPA percentiles, acceptance rates, tuition, bar passage rates, employment outcomes, and enrollment data for all 196 ABA-approved law schools.", "version": "0.1", "x-build-id": "0kkwjLCHlAFryPNKp" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~aba-law-school-rankings-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-aba-law-school-rankings-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~aba-law-school-rankings-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-aba-law-school-rankings-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~aba-law-school-rankings-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-aba-law-school-rankings-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to scrape", "default": 10 }, "reporting_year": { "title": "Reporting Year", "type": "integer", "description": "The ABA reporting year to scrape (e.g. 2023 for 2022-2023 data). Defaults to 2023.", "default": 2023 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```