farach

{
  "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

farach

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 -------------------

farach

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) {

farach

library(tidyverse)
library(gganimate)

# Parameters
h <- 0.02 # Time cost per worker for the solver
r <- 2 # Rental rate for one unit of compute
worker_grid <- seq(0.2, 0.8, by = 0.1) # Range of human worker knowledge
solver_grid <- seq(0.3, 1.0, by = 0.1) # Range of human solver knowledge
zAI_values <- seq(0.25, 0.95, by = 0.05) # Range of AI knowledge levels

farach

# This R script generates synthetic data representing job functions in various languages, translates them to English using a local language model, and detects the original language. The output includes the original job function, the translated job function, and the detected language, and is saved to a CSV file for further use.

# Load necessary libraries
library(httr)
library(jsonlite)
library(textcat)
library(tidyverse)
library(glue)
library(here)

farach

# Load necessary libraries
library(janitor)
library(httr)
library(jsonlite)
library(tidyverse)
library(furrr)
library(stringr)
library(glue)

# Setup parallel processing

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# Instructions:
# - Download LM Studio
# - Download Phi-3 Model (within LM Studio)
# - Load the model into LM Studio
# - Start the Local Server (instructions here: https://lmstudio.ai/docs/local-server)

# Load necessary libraries
library(httr)
library(jsonlite)
library(tidyverse)

farach

import pandas as pd

def pep_talk ():
  pep_csv = pd.read_csv(
    "https://raw.githubusercontent.com/farach/pep/main/pep_talk.csv",
    encoding = 'unicode_escape'
    )
  
  pepText = list(
    map(

farach

pep_talk <- function() {
  read.csv("https://raw.githubusercontent.com/farach/pep/main/pep_talk.csv") |> 
    purrr::map_chr(~ sample(.x, 1)) |> 
    glue::glue_collapse(sep = " ")
}

farach

library(fredr)
library(tidyverse)
library(geofacet)
library(ggrepel)
library(ggtext)

set.seed(42) # For reproducibility

# Prepare state series IDs
state_ids <- c(