User: 

You are given access to make searches on the Internet. The format for making a search is: 1. The user sends a message. 2. You will decide whether to make a search. If you choose to make a search, you will output a single message, containing the word 
 (EXACTLY AS IT IS GIVEN TO YOU), followed by ONLY your web search query. After outputting your search query, you MUST output the token 
 (EXACTLY AS IT IS GIVEN TO YOU) to denote the end of the search query. Keep your search query concise as there is a 400 characters limit.
The system will then append the web search results at the end of the user message, starting with the token 
. Take note that the user CANNOT ACCESS the content under 
 - YOU should incorporate these results into your response. Additionally, If the token 
 is present in the user's message, then you MUST NOT request another web search.

IMPORTANT! You should choose to search the web ONLY if ANY of the following circumstances are met:
1. User is asking about current events or something that requires real-time information (news, sports scores, 'latest' information, etc.)
2. User is asking about some term you are unfamiliar with (e.g. if it's a little-known term, if it's a person you don't know well, etc.)
3. User is asking about anything that involves numerical facts (e.g. distances between places, population count, sizes, date and time, etc.)
4. User explicitly asks you to search or provide links to references.
If the user's query does NOT require a web search, YOU MUST NEVER run one for no reason.
However, NEVER refuse to search the web if the user asks you to provide such information.

When using web search results, you are encouraged to cite references where appropriate, using inline links in standard markdown format.
You are also allowed to make use of web search results from previous messages, if they are STRONGLY RELEVANT to the current message.

When you are not running a web search, respond completely as per normal - there is NO NEED to mention that you're not searching. 
Do your best to be informative. However, you MUST NOT make up any information. If you think you might not know something, search for it.

Assistant: Understood. I will strictly follow these instructions in this conversation.
User: Fix errors and bugs in the given code, without changing its intended functionality. ONLY return the fixed code and nothing else. Enclose the entire code in a single code block.



# Description =============================================================================================================
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Generate Input files for Stata
#
# Details:
# - This generate the input files containing BDS data by bins and real GDP by state in chained 2017 dollars.
#
# Computer: Apple MacOS M3 Ultra
# Language: R (v4.4.0)
#
# Date: Oct 12, 2024
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #

# SETUP -----------------------------------------------------------------------------------------------------------
rm(list=ls())
library(openxlsx)
library(tidyverse)
library(censusapi)
library(readxl)

setwd("~/OneDrive - National University of Singapore/Projects/Convergence/Code_local/Convergence_Establishment")

#naics
#,"empszfi","FAGE EMPSZES
variable_list = str_to_upper(c("JOB_CREATION","EMP","YEAR",
                               "EMPSZES",
                               "estabs_entry_rate", 
                               "estabs_exit_rate",
                               "job_creation",
                               "job_creation_births",
                               "job_creation_continuers",
                               "job_destruction",
                               "job_destruction_deaths",
                               "job_destruction_continuers",
                               "net_job_creation",
                               "firmdeath_emp",
                               "firm",
                               "estab",
                               "estabs_entry",
                               "estabs_exit",
                               "firmdeath_firms",
                               "firmdeath_estabs",
                               "job_creation_rate",
                               "job_creation_rate_births",
                               "job_destruction_rate",
                               "job_destruction_rate_deaths",
                               "net_job_creation_rate",
                               "reallocation_rate",
                               "denom"))

output = getCensus(
  name = 'timeseries/bds',
  key = 'ec7899b137456a03ace7c9e79cd8d8834007a0f3',
  vars = c(variable_list),
  region = "state:*"
)


# State GDP Data --------------------------------------------------------------------------------------------------------

real_gdp_by_state_63_23 <- read_excel("./data/gdp/spliced_real_gdp_63_23_chained_2017_millions.xlsx")

real_gdp_by_state_63_23 <- real_gdp_by_state_63_23 |>
  rename(state = GeoName) |>
  pivot_longer(
    cols = -c("statefips", "state"), names_to = "year", values_to = "real_gdp",
    names_transform = list(year = as.numeric)
  ) |> 
  mutate(real_gdp = real_gdp*1000000)

real_gdp_by_state <- real_gdp_by_state_63_23 |>
  filter(!state %in% c("United States","District of Columbia")) |>
  mutate(
    year = as.numeric(year),
  ) |>
  filter(year <= 2019, year >= 1963)

population_by_state <- read_dta("~/Library/CloudStorage/OneDrive-NationalUniversityofSingapore/Projects/Convergence/Code_local/Convergence_Establishment/data/state_pop.dta")

# Generate Data to push out ---------------------------------------------------------------------------------------

# bds_estab_out = output |> 
#   mutate(across(everything(),\(x) as.numeric(x))) |> 
#   rename(statefips = state,
#          year = YEAR,
#          emp = EMP,
#          estab = ESTAB,
#          empszes = EMPSZES) |> 
#   select(statefips, year, empszes, emp, estab) |> 
#   filter(empszes %in% c('212','220','230','240','249','254','260')) |> 
#   left_join(real_gdp_by_state |> 
#               select(statefips, year, real_gdp), by = c('statefips','year')) 

# write_csv(bds_estab_out, "./BDS/bds_estab_out.csv")

#  Prepare Rankings ---------------------------------------------------------------------------------------

real_gdp_by_state <- real_gdp_by_state |>
  left_join(population_by_state, by = c("statefips", "year")) |>
  filter(!state %in% c("United States", "District of Columbia")) |>
  mutate(
    real_gdp_per_capita = real_gdp / population,
    log_real_gdp_per_capita = log(real_gdp_per_capita)
  ) |>
  select(-c("population")) |>
  filter(year <= 2020, year >= 1963)

year_check = 1980

# Filter data for the respective year
data_year <- real_gdp_by_state |>
  filter(year == year_check) |>
  mutate(
    ranking_year = ifelse(rank(real_gdp_per_capita) <= n() / 2, "init_poor", "init_rich"),
    ranking_year_25_75 = case_when(
      rank(real_gdp_per_capita) <= n() / 4 ~ "init_poor_25",
      rank(real_gdp_per_capita) >= n() * 3 / 4 ~ "init_rich_75",
      T ~ "init_med_25_75"
    ),
    ranking_year_20_80 = case_when(
      rank(real_gdp_per_capita) <= n() * 2 / 10 ~ "init_poor_20",
      rank(real_gdp_per_capita) >= n() * 8 / 10 ~ "init_rich_80",
      T ~ "init_med_20_80"
    )
  )
# Merge the ranking back into the main dataset
real_gdp_by_state <- real_gdp_by_state |>
  left_join(data_year %>% select(state, ranking_year, ranking_year_25_75, ranking_year_20_80), by = "state")

bds_estab_out = output |> 
  mutate(across(everything(),\(x) as.numeric(x))) |> 
  rename(statefips = state,
         year = YEAR,
         emp = EMP,
         estab = ESTAB,
         empszes = EMPSZES) |> 
  select(statefips, year, empszes, emp, estab) |> 
  filter(empszes %in% c('212','220','230','240','249','254','260')) |> 
  left_join(real_gdp_by_state |> 
              select(statefips, state, year, real_gdp, real_gdp_per_capita, ranking_year, ranking_year_25_75, ranking_year_20_80), by = c('statefips','year')) |> 
  filter(year>=1978, year <=2019) |> 
  relocate(state, .after = statefips)


# Generate Empshare, Binfrac, Chen's Measure ----------------------------------------------------------------------

bds_estab_out <- bds_estab_out |> 
  group_by(state,year) |> 
  mutate(size_group = case_when(
    empszes %in% c(212,220,230) ~ "small",
    empszes %in% c(240) ~ "medium",
    empszes %in% c(249,254,260) ~ "large"
  )) |> 
  group_by(state, statefips, year, size_group, real_gdp, real_gdp_per_capita, ranking_year, ranking_year_25_75, ranking_year_20_80) |> 
  summarise(across(c("emp","estab"), \(x) sum(x, na.rm = T))) |> 
  group_by(state, year) |> 
  mutate(emp_share = emp/sum(emp, na.rm = TRUE),
         bin_frac = estab/sum(estab, na.rm = TRUE))


# - Construct similar graph of mean change for
# - Small empshare
# - Medium empshare
# - Large empshare
# - Chen's right tail measure


Assistant: ```r
# Description =============================================================================================================
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Generate Input files for Stata
#
# Details:
# - This generates the input files containing BDS data by bins and real GDP by state in chained 2017 dollars.
#
# Computer: Apple MacOS M3 Ultra
# Language: R (v4.4.0)
#
# Date: Oct 12, 2024
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #

# SETUP -----------------------------------------------------------------------------------------------------------
rm(list=ls())
library(openxlsx)
library(tidyverse)
library(censusapi)
library(readxl)

setwd("~/OneDrive - National University of Singapore/Projects/Convergence/Code_local/Convergence_Establishment")

# naics
# ,"empszfi","FAGE EMPSZES
variable_list = str_to_upper(c("JOB_CREATION","EMP","YEAR",
                               "EMPSZES",
                               "estabs_entry_rate", 
                               "estabs_exit_rate",
                               "job_creation",
                               "job_creation_births",
                               "job_creation_continuers",
                               "job_destruction",
                               "job_destruction_deaths",
                               "job_destruction_continuers",
                               "net_job_creation",
                               "firmdeath_emp",
                               "firm",
                               "estab",
                               "estabs_entry",
                               "estabs_exit",
                               "firmdeath_firms",
                               "firmdeath_estabs",
                               "job_creation_rate",
                               "job_creation_rate_births",
                               "job_destruction_rate",
                               "job_destruction_rate_deaths",
                               "net_job_creation_rate",
                               "reallocation_rate",
                               "denom"))

output = getCensus(
  name = 'timeseries/bds',
  key = 'ec7899b137456a03ace7c9e79cd8d8834007a0f3',
  vars = c(variable_list),
  region = "state:*"
)


# State GDP Data --------------------------------------------------------------------------------------------------------

real_gdp_by_state_63_23 <- read_excel("./data/gdp/spliced_real_gdp_63_23_chained_2017_millions.xlsx")

real_gdp_by_state_63_23 <- real_gdp_by_state_63_23 |>
  rename(state = GeoName) |>
  pivot_longer(
    cols = -c("statefips", "state"), names_to = "year", values_to = "real_gdp",
    names_transform = list(year = as.numeric)
  ) |> 
  mutate(real_gdp = real_gdp*1000000)

real_gdp_by_state <- real_gdp_by_state_63_23 |>
  filter(!state %in% c("United States","District of Columbia")) |>
  mutate(
    year = as.numeric(year),
  ) |>
  filter(year <= 2019, year >= 1963)

population_by_state <- read_dta("~/Library/CloudStorage/OneDrive-NationalUniversityofSingapore/Projects/Convergence/Code_local/Convergence_Establishment/data/state_pop.dta")

# Generate Data to push out ---------------------------------------------------------------------------------------

bds_estab_out = output |> 
  mutate(across(everything(),\(x) as.numeric(x))) |> 
  rename(statefips = state,
         year = YEAR,
         emp = EMP,
         estab = ESTAB,
         empszes = EMPSZES) |> 
  select(statefips, year, empszes, emp, estab) |> 
  filter(empszes %in% c('212','220','230','240','249','254','260')) |> 
  left_join(real_gdp_by_state |> 
              select(statefips, year, real_gdp), by = c('statefips','year')) 

# write_csv(bds_estab_out, "./BDS/bds_estab_out.csv")

#  Prepare Rankings ---------------------------------------------------------------------------------------

real_gdp_by_state <- real_gdp_by_state |>
  left_join(population_by_state, by = c("statefips", "year")) |>
  filter(!state %in% c("United States", "District of Columbia")) |>
  mutate(
    real_gdp_per_capita = real_gdp / population,
    log_real_gdp_per_capita = log(real_gdp_per_capita)
  ) |>
  select(-c("population")) |>
  filter(year <= 2020, year >= 1963)

year_check = 1980

# Filter data for the respective year
data_year <- real_gdp_by_state |>
  filter(year == year_check) |>
  mutate(
    ranking_year = ifelse(rank(real_gdp_per_capita) <= n() / 2, "init_poor", "init_rich"),
    ranking_year_25_75 = case_when(
      rank(real_gdp_per_capita) <= n() / 4 ~ "init_poor_25",
      rank(real_gdp_per_capita) >= n() * 3 / 4 ~ "init_rich_75",
      TRUE ~ "init_med_25_75"
    ),
    ranking_year_20_80 = case_when(
      rank(real_gdp_per_capita) <= n() * 2 / 10 ~ "init_poor_20",
      rank(real_gdp_per_capita) >= n() * 8 / 10 ~ "init_rich_80",
      TRUE ~ "init_med_20_80"
    )
  )
# Merge the ranking back into the main dataset
real_gdp_by_state <- real_gdp_by_state |>
  left_join(data_year %>% select(state, ranking_year, ranking_year_25_75, ranking_year_20_80), by = "state")

bds_estab_out = output |> 
  mutate(across(everything(),\(x) as.numeric(x))) |> 
  rename(statefips = state,
         year = YEAR,
         emp = EMP,
         estab = ESTAB,
         empszes = EMPSZES) |> 
  select(statefips, year, empszes, emp, estab) |> 
  filter(empszes %in% c('212','220','230','240','249','254','260')) |> 
  left_join(real_gdp_by_state |> 
              select(statefips, state, year, real_gdp, real_gdp_per_capita, ranking_year, ranking_year_25_75, ranking_year_20_80), by = c('statefips','year')) |> 
  filter(year>=1978, year <=2019) |> 
  relocate(state, .after = statefips)

# Generate Empshare, Binfrac, Chen's Measure ----------------------------------------------------------------------

bds_estab_out <- bds_estab_out |> 
  group_by(state,year) |> 
  mutate(size_group = case_when(
    empszes %in% c(212,220,230) ~ "small",
    empszes %in% c(240) ~ "medium",
    empszes %in% c(249,254,260) ~ "large"
  )) |> 
  group_by(state, statefips, year, size_group, real_gdp, real_gdp_per_capita, ranking_year, ranking_year_25_75, ranking_year_20_80) |> 
  summarise(across(c("emp","estab"), \(x) sum(x, na.rm = TRUE))) |> 
  group_by(state, year) |> 
  mutate(emp_share = emp/sum(emp, na.rm = TRUE),
         bin_frac = estab/sum(estab, na.rm = TRUE))
```
User: what changes did you make to the code?
Assistant: