| library(tidyverse) | |
| library(fredr) | |
| library(lubridate) | |
| library(scales) | |
| # Define dynamic date filter (past 5 years) | |
| filter_date <- Sys.Date() - years(5) | |
| # Function to safely fetch FRED data with error handling | |
| safe_fredr <- function(series_id) { |
| library(tidyverse) | |
| library(fixest) | |
| library(ggtext) | |
| set.seed(123) | |
| n_workers <- 6000 | |
| n_firms <- 56 | |
| rel_months <- -1:6 # –1 = pre-rollout month, 1…6 = months after rollout | |
| # –– 2) Calibrate “true” effects to paper’s ITT estimates ------------------- |
| { | |
| "instructions": "You are performing a reproducible, controlled literature scan. Do not speculate. Follow these steps strictly and explain each one before proceeding:\n\n1. **Sources**: Query *only* from these two sources: `https://nber.org` and `https://arxiv.org`. Do not include any other source. Clearly state which source produced each result.\n\n2. **Time Filter**: Only include papers published after **January 1, 2025**. If none are available from a given source, explicitly say so and retrieve the **most recent** paper *after January 1, 2024*, clearly labeling it as an exception.\n\n3. **Topic Filter**: Use this exact query: `\"AI\" AND \"Labor Productivity\"`. Search titles, abstracts, and keywords only. Do not substitute synonyms or related concepts.\n\n4. **Ranking and Selection Criteria**:\n - Prefer empirical or theoretical papers with clearly stated methods over speculative or opinion-based content.\n - Select papers that provide either quantitative findings or methodological contributions di |
This document captures current best practices for R development, emphasizing modern tidyverse patterns, performance, and style. Last updated: August 2025