From 61c7b6b16a044aa864aab5d6c57bfd6b71268a3b Mon Sep 17 00:00:00 2001 From: github-actions Date: Wed, 15 May 2024 01:57:11 +0000 Subject: [PATCH] Update PSL-catalog on 2024-05-15 --- Catalog/prs.json | 2452 +++++++++++++++++++++---------------------- Incubating/prs.json | 734 ++++++------- 2 files changed, 1582 insertions(+), 1604 deletions(-) diff --git a/Catalog/prs.json b/Catalog/prs.json index 6fbaf42..bea5df0 100644 --- a/Catalog/prs.json +++ b/Catalog/prs.json @@ -1,17 +1,17 @@ { "OG-USA": [ { - "url": "https://api.github.com/repos/PSLmodels/OG-USA/pulls/109", - "id": 1861349138, - "node_id": "PR_kwDODjb9iM5u8e8S", - "html_url": "https://github.com/PSLmodels/OG-USA/pull/109", - "diff_url": "https://github.com/PSLmodels/OG-USA/pull/109.diff", - "patch_url": "https://github.com/PSLmodels/OG-USA/pull/109.patch", - "issue_url": "https://api.github.com/repos/PSLmodels/OG-USA/issues/109", - "number": 109, - "state": "closed", + "url": "https://api.github.com/repos/PSLmodels/OG-USA/pulls/110", + "id": 1869818812, + "node_id": "PR_kwDODjb9iM5vcyu8", + "html_url": "https://github.com/PSLmodels/OG-USA/pull/110", + "diff_url": "https://github.com/PSLmodels/OG-USA/pull/110.diff", + "patch_url": "https://github.com/PSLmodels/OG-USA/pull/110.patch", + "issue_url": "https://api.github.com/repos/PSLmodels/OG-USA/issues/110", + "number": 110, + "state": "open", "locked": false, - "title": "[DO NOT MERGE] Add windows to build_and_test.py", + "title": "Updated ryp2 dependency", "user": { "login": "rickecon", "id": 4317690, @@ -32,12 +32,12 @@ "type": "User", "site_admin": false }, - "body": "This PR tests to see if adding `windows-latest` to the `build_and_test.yml` tests will work now that we are only testing Python 3.10 and 3.11.", - "created_at": "2024-05-09T03:32:10Z", - "updated_at": "2024-05-09T14:40:49Z", - "closed_at": "2024-05-09T14:40:49Z", + "body": "This PR updates the dependency `rpy2>=3.5.12` in `environment.yml` and `setup.py`. 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The functionality allows the user to enter a value of `eti` that is either a scalar (i.e., constant over income) or a dictionary with keys `knot_points` and `etc_values`, which each contain corresponding lists of income values and the ETI at those values. E.g., using the estimate of the ETI over the income distribution from [Gruber and Saez (2002)](http://piketty.pse.ens.fr/files/GruberSaez2002.pdf), this would look like:\r\n\r\n```python\r\neti_dict = {\r\n \"eti_values\": [0.18, 0.106, 0.567, 1.0],\r\n \"knot_points\": [30000, 75000, 250000, 10000000]\r\n}\r\n```\r\n\r\nWhere the last value is provided to help find just a gradual increase in the ETI after $250,000. Using a linear spline, the results look like:\r\n![linear_spline](https://github.com/PSLmodels/InverseOptimalTax/assets/10715924/918108e0-2bdd-47f9-b373-ac77e1626739)\r\n\r\nThe sharp changes in the ETI in this interpolated function are probably not realistic and introduce odd kinds in the implied social welfare weights. \r\n\r\nHowever, with so few datapoints, a cubic spline has its own problems (going off to values that are unrealistically high):\r\n![cubic_spline](https://github.com/PSLmodels/InverseOptimalTax/assets/10715924/40cd20ff-9a73-4537-b2d6-973d4ff19edd)\r\n\r\nI believe the solution is to find more data points on the ETI on different parts of the income distribution that can be used to inform a cubic spline. This should allow for some smoothness in the ETI spline, but without a steep slope in some values beyond those in the literature.\r\n\r\ncc @john-p-ryan \r\n\r\n\r\n\r\n", - "created_at": "2023-10-27T02:46:36Z", - "updated_at": "2023-11-27T15:15:46Z", - "closed_at": "2023-11-27T15:15:46Z", - "merged_at": "2023-11-27T15:15:46Z", - "merge_commit_sha": "7cb3657268877cec5cc7e092c6f58f0965e55089", + "body": "This PR adds a new notebook to produce the plots use in a presentation at the National Tax Association Annual Meetings in 2023.", + "created_at": "2024-01-31T19:39:43Z", + "updated_at": "2024-02-03T21:30:03Z", + "closed_at": "2024-02-03T21:30:00Z", + "merged_at": "2024-02-03T21:30:00Z", + "merge_commit_sha": "d0127007ee7b60a64c76dda3f425c0cfdcb93a6e", "assignee": null, "assignees": [], "requested_reviewers": [], @@ -1041,15 +1041,15 @@ "labels": [], "milestone": null, "draft": false, - "commits_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/23/commits", - "review_comments_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/23/comments", + "commits_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/25/commits", + "review_comments_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/25/comments", "review_comment_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/comments{/number}", - 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"title": "Add additional tests, approximate distributions from raw data", + "title": "Allow non-constant elasticity of taxable income", "user": { "login": "jdebacker", "id": 10715924, @@ -1360,12 +1360,12 @@ "type": "User", "site_admin": false }, - "body": "This PR adds some additional unit test, including one to compare the computational solution to an analytical one.\r\n\r\nIt also removes the binning of the raw data by income group and rather uses the raw data directly to approximate the income distribution (which should resolve Issue #16).", - "created_at": "2023-10-16T22:03:46Z", - "updated_at": "2023-10-26T20:44:15Z", - "closed_at": "2023-10-26T20:44:12Z", - "merged_at": "2023-10-26T20:44:11Z", - "merge_commit_sha": "614ba3f8e56832660ebda908d2633f4985d035d2", + "body": "This PR adds the ability for users to enter an elasticity of taxable income that varies over the earned income distribution, which would resolve Issue #21. The functionality allows the user to enter a value of `eti` that is either a scalar (i.e., constant over income) or a dictionary with keys `knot_points` and `etc_values`, which each contain corresponding lists of income values and the ETI at those values. E.g., using the estimate of the ETI over the income distribution from [Gruber and Saez (2002)](http://piketty.pse.ens.fr/files/GruberSaez2002.pdf), this would look like:\r\n\r\n```python\r\neti_dict = {\r\n \"eti_values\": [0.18, 0.106, 0.567, 1.0],\r\n \"knot_points\": [30000, 75000, 250000, 10000000]\r\n}\r\n```\r\n\r\nWhere the last value is provided to help find just a gradual increase in the ETI after $250,000. Using a linear spline, the results look like:\r\n![linear_spline](https://github.com/PSLmodels/InverseOptimalTax/assets/10715924/918108e0-2bdd-47f9-b373-ac77e1626739)\r\n\r\nThe sharp changes in the ETI in this interpolated function are probably not realistic and introduce odd kinds in the implied social welfare weights. \r\n\r\nHowever, with so few datapoints, a cubic spline has its own problems (going off to values that are unrealistically high):\r\n![cubic_spline](https://github.com/PSLmodels/InverseOptimalTax/assets/10715924/40cd20ff-9a73-4537-b2d6-973d4ff19edd)\r\n\r\nI believe the solution is to find more data points on the ETI on different parts of the income distribution that can be used to inform a cubic spline. This should allow for some smoothness in the ETI spline, but without a steep slope in some values beyond those in the literature.\r\n\r\ncc @john-p-ryan \r\n\r\n\r\n\r\n", + "created_at": "2023-10-27T02:46:36Z", + "updated_at": "2023-11-27T15:15:46Z", + "closed_at": "2023-11-27T15:15:46Z", + "merged_at": "2023-11-27T15:15:46Z", + "merge_commit_sha": "7cb3657268877cec5cc7e092c6f58f0965e55089", "assignee": null, "assignees": [], "requested_reviewers": [], @@ -1373,15 +1373,15 @@ "labels": [], "milestone": null, "draft": false, - "commits_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/22/commits", - "review_comments_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/22/comments", + "commits_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/23/commits", + "review_comments_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/23/comments", "review_comment_url": "https://api.github.com/repos/PSLmodels/InverseOptimalTax/pulls/comments{/number}", - 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