knitr::opts_chunk$set(
  warning = FALSE, # show warnings during codebook generation
  message = FALSE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())

# set base directory
basedir = "/home/ecco_rais/data/clean/RAIS-homogenized/output/"
# adjust as necessary
startyear = 2003
endyear = 2004

# libraries
library(codebook)
library(rio)

# Start the codebook loop

#for ( year in startyear:endyear) {
year = "2017"
    # for CSV
    codebook_data <- fread(file.path(basedir, paste0("harmo_", year, ".csv")))

    # omit the following lines, if your missing values are already properly labelled
    codebook_data <- detect_missing(codebook_data,
        only_labelled = TRUE, # only labelled values are autodetected as
                              # missing
        negative_values_are_missing = FALSE, # negative values are missing values
        ninety_nine_problems = TRUE,   # 99/999 are missing values, if they
                                      # are more than 5 MAD from the median
    )

    # If you are not using formr, the codebook package needs to guess which items
    # form a scale. The following line finds item aggregates with names like this:
    # scale = scale_1 + scale_2R + scale_3R
    # identifying these aggregates allows the codebook function to
    # automatically compute reliabilities.
    # However, it will not reverse items automatically.
    codebook_data <- detect_scales(codebook_data)
## Warning in detect_scales(codebook_data): cbo items found, but no aggregate
## Warning in detect_scales(codebook_data): cnae items found, but no aggregate
## Warning in detect_scales(codebook_data): active items found, but no aggregate
    codebook_data <- as.data.table(codebook_data)
    codebook(codebook_data)
## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.
## Warning in grepl("^\\s+$", x): input string 1 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 2 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 3 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 4 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 5 is invalid in this locale
## Warning in max(f): no non-missing arguments to max; returning -Inf
## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.
## Warning in max(f): no non-missing arguments to max; returning -Inf
## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.
## Warning in grepl("^\\s+$", x): input string 1 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 2 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 3 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 4 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 5 is invalid in this locale
## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.
## Warning in grepl("^\\s+$", x): input string 1 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 2 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 3 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 4 is invalid in this locale
## Warning in grepl("^\\s+$", x): input string 5 is invalid in this locale

Metadata

Description

Dataset name: codebook_data

The dataset has N=65655882 rows and 67 columns. 0 rows have no missing values on any column.

Metadata for search engines
  • Date published: 2024-06-07
x
adm_date
cbo94
cbo02
cei
zip_establishment
cnae20
cnae20sub
cnae95
firmID
cnpj_root
cpf
dob
termination_day
schooling
ibge_subsetor
age
cei_avail
pat
disabled
simples
union
intermittent_contract
partial_job
alvara_ind
termination_month
separation_cause
muni_job
muni
nationality
legal_form
name
ctps
pis
hired_hours
race_color
estab_name
sex
establishment_size
tenure
adm_type
disability_type
firmID_type
wage_type
contract_type
active1231
earn_april
earn_august
earn_feb
earn_jan
earn_july
earn_june
earn_may
earn_march
earn_nov
earn_oct
earn_sept
earn_dec_mw
earn_dec
mean_earn_mw
mean_earn
hired_wage
last_wage
yr
termination_year
adm_day
adm_month
adm_year

#Variables

adm_date

Distribution

Distribution of values for adm_date

Distribution of values for adm_date

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
adm_date numeric 0 1 1e+06 5072017 3.1e+07 8776093 8510237 ▇▂▂▂▁ NA

cbo94

Distribution

## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed
## No non-missing values to show.

65655882 missing values.

Summary statistics

name data_type n_missing complete_rate count label
cbo94 logical 65655882 0 : NA

cbo02

Distribution

Distribution of values for cbo02

Distribution of values for cbo02

70101 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
cbo02 numeric 70101 0.9989323 10105 513435 992225 492739.3 205266.5 ▁▃▇▅▁ NA

cei

Distribution

## Error in `ggplot2::geom_histogram()`:
## ! Problem while computing position.
## ℹ Error occurred in the 1st layer.
## Caused by error in `if (...) NULL`:
## ! missing value where TRUE/FALSE needed

0 missing values.

Summary statistics

name data_type n_missing complete_rate n_unique empty min max whitespace label
cei character 0 1 46906 0 1 21 0 NA

zip_establishment

Distribution

Distribution of values for zip_establishment

Distribution of values for zip_establishment

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
zip_establishment numeric 0 1 1e+06 4.2e+07 1e+08 46633787 30946994 ▇▅▃▅▅ NA

cnae20

Distribution

Distribution of values for cnae20

Distribution of values for cnae20

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
cnae20 numeric 0 1 1113 52231 99008 56984.44 25982.18 ▂▂▇▂▇ NA

cnae20sub

Distribution

Distribution of values for cnae20sub

Distribution of values for cnae20sub

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
cnae20sub numeric 0 1 111301 5223100 9900800 5698451 2598216 ▂▂▇▂▇ NA

cnae95

Distribution

Distribution of values for cnae95

Distribution of values for cnae95

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
cnae95 numeric 0 1 1112 55247 99007 57084.57 22556.15 ▂▂▆▇▂ NA

firmID

Distribution

## Error in `ggplot2::geom_histogram()`:
## ! Problem while computing stat.
## ℹ Error occurred in the 1st layer.
## Caused by error in `seq_len()`:
## ! argument must be coercible to non-negative integer

0 missing values.

Summary statistics

name data_type n_missing complete_rate n_unique empty min max whitespace label
firmID character 0 1 3845034 0 15 21 0 NA

cnpj_root

Distribution

Distribution of values for cnpj_root

Distribution of values for cnpj_root

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
cnpj_root numeric 0 1 0 1.4e+07 1e+08 24372359 25816991 ▇▂▁▁▁ NA

cpf

Distribution

## Error in `ggplot2::geom_histogram()`:
## ! Problem while computing position.
## ℹ Error occurred in the 1st layer.
## Caused by error in `if (...) NULL`:
## ! missing value where TRUE/FALSE needed

0 missing values.

Summary statistics

name data_type n_missing complete_rate n_unique empty min max whitespace label
cpf character 0 1 55488531 0 1 21 0 NA

dob

Distribution

Distribution of values for dob

Distribution of values for dob

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
dob numeric 0 1 1e+06 1.6e+07 3.1e+07 15677804 8783089 ▇▇▇▇▇ NA

termination_day

Distribution

Distribution of values for termination_day

Distribution of values for termination_day

0 missing values.

Summary statistics

name data_type n_missing complete_rate n_unique empty min max whitespace label
termination_day character 0 1 32 0 2 2 0 NA

schooling

Distribution

Distribution of values for schooling

Distribution of values for schooling

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
schooling numeric 0 1 1 7 11 6.793991 1.701364 ▁▂▇▃▁ NA

ibge_subsetor

Distribution

Distribution of values for ibge_subsetor

Distribution of values for ibge_subsetor

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
ibge_subsetor numeric 0 1 1 19 25 18.12556 5.287318 ▁▁▂▇▇ NA

age

Distribution

Distribution of values for age

Distribution of values for age

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
age numeric 0 1 0 35 100 36.69655 11.72856 ▁▇▅▁▁ NA

cei_avail

Distribution

Distribution of values for cei_avail

Distribution of values for cei_avail

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
cei_avail numeric 0 1 0 0 1 0.0133853 0.1149178 ▇▁▁▁▁ NA

pat

Distribution

Distribution of values for pat

Distribution of values for pat

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
pat numeric 0 1 0 0 1 0.2888026 0.453206 ▇▁▁▁▃ NA

disabled

Distribution

Distribution of values for disabled

Distribution of values for disabled

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
disabled numeric 0 1 0 0 1 0.0086419 0.0925594 ▇▁▁▁▁ NA

simples

Distribution

Distribution of values for simples

Distribution of values for simples

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
simples numeric 0 1 0 0 1 0.2429833 0.4288851 ▇▁▁▁▂ NA

union

Distribution

Distribution of values for union

Distribution of values for union

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
union numeric 0 1 0 0 1 0.0738842 0.2615823 ▇▁▁▁▁ NA

intermittent_contract

Distribution

Distribution of values for intermittent_contract

Distribution of values for intermittent_contract

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
intermittent_contract numeric 0 1 0 0 1 0.0001243 0.0111503 ▇▁▁▁▁ NA

partial_job

Distribution

Distribution of values for partial_job

Distribution of values for partial_job

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
partial_job numeric 0 1 0 0 1 0.0030312 0.0549724 ▇▁▁▁▁ NA

alvara_ind

Distribution

Distribution of values for alvara_ind

Distribution of values for alvara_ind

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
alvara_ind numeric 0 1 0 0 1 4.32e-05 0.0065745 ▇▁▁▁▁ NA

termination_month

Distribution

Distribution of values for termination_month

Distribution of values for termination_month

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
termination_month numeric 0 1 0 0 12 1.974901 3.608127 ▇▁▁▁▁ NA

separation_cause

Distribution

Distribution of values for separation_cause

Distribution of values for separation_cause

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
separation_cause numeric 0 1 0 0 90 4.686708 8.904724 ▇▁▁▁▁ NA

muni_job

Distribution

Distribution of values for muni_job

Distribution of values for muni_job

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
muni_job numeric 0 1 0 0 530010 155566.7 180985.4 ▇▁▂▃▁ NA

muni

Distribution

Distribution of values for muni

Distribution of values for muni

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
muni numeric 0 1 110001 351880 530010 345536.5 88460.11 ▁▂▇▂▂ NA

nationality

Distribution

Distribution of values for nationality

Distribution of values for nationality

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
nationality numeric 0 1 10 10 80 10.08543 1.784163 ▇▁▁▁▁ NA

name

Distribution

Distribution of values for name

Distribution of values for name

0 missing values.

Summary statistics

name data_type n_missing complete_rate n_unique empty min max whitespace label
name character 0 1 40785749 0 2 52 0 NA

ctps

Distribution

Distribution of values for ctps

Distribution of values for ctps

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
ctps numeric 0 1 0 68175 1e+08 1448106 3795366 ▇▁▁▁▁ NA

pis

Distribution

## Error in `ggplot2::geom_histogram()`:
## ! Problem while computing stat.
## ℹ Error occurred in the 1st layer.
## Caused by error in `seq_len()`:
## ! argument must be coercible to non-negative integer

0 missing values.

Summary statistics

name data_type n_missing complete_rate n_unique empty min max whitespace label
pis character 0 1 55561692 0 13 21 0 NA

hired_hours

Distribution

Distribution of values for hired_hours

Distribution of values for hired_hours

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
hired_hours numeric 0 1 1 44 44 40.77122 6.827322 ▁▁▁▁▇ NA

race_color

Distribution

Distribution of values for race_color

Distribution of values for race_color

10319553 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
race_color numeric 10319553 0.8428236 1 4 9 4.900209 3.04816 ▇▁▁▁▇ NA

estab_name

Distribution

Distribution of values for estab_name

Distribution of values for estab_name

0 missing values.

Summary statistics

name data_type n_missing complete_rate n_unique empty min max whitespace label
estab_name character 0 1 3502272 0 3 52 0 NA

sex

Distribution

Distribution of values for sex

Distribution of values for sex

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
sex numeric 0 1 0 0 1 0.4326729 0.4954463 ▇▁▁▁▆ NA

establishment_size

Distribution

Distribution of values for establishment_size

Distribution of values for establishment_size

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
establishment_size numeric 0 1 1 6 10 6.278815 2.852224 ▃▅▆▅▇ NA

tenure

Distribution

Distribution of values for tenure

Distribution of values for tenure

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
tenure numeric 0 1 0 24 600 55.74033 80.1713 ▇▁▁▁▁ NA

adm_type

Distribution

Distribution of values for adm_type

Distribution of values for adm_type

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
adm_type numeric 0 1 0 0 14 0.6359371 1.036817 ▇▁▁▁▁ NA

disability_type

Distribution

Distribution of values for disability_type

Distribution of values for disability_type

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
disability_type numeric 0 1 0 0 6 0.0192074 0.2521726 ▇▁▁▁▁ NA

firmID_type

Distribution

Distribution of values for firmID_type

Distribution of values for firmID_type

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
firmID_type numeric 0 1 0 1 1 0.9716366 0.1660087 ▁▁▁▁▇ NA

wage_type

Distribution

Distribution of values for wage_type

Distribution of values for wage_type

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
wage_type numeric 0 1 1 1 7 1.309368 1.1056 ▇▁▁▁▁ NA

contract_type

Distribution

Distribution of values for contract_type

Distribution of values for contract_type

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
contract_type numeric 0 1 10 10 97 16.71512 14.84768 ▇▂▁▁▁ NA

active1231

Distribution

Distribution of values for active1231

Distribution of values for active1231

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
active1231 numeric 0 1 0 1 1 0.7049116 0.4560825 ▃▁▁▁▇ NA

earn_april

Distribution

Distribution of values for earn_april

Distribution of values for earn_april

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_april numeric 0 1 0 1124 140540 1726.511 3261.811 ▇▁▁▁▁ NA

earn_august

Distribution

Distribution of values for earn_august

Distribution of values for earn_august

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_august numeric 0 1 0 1155 140552 1758.658 3270.223 ▇▁▁▁▁ NA

earn_feb

Distribution

Distribution of values for earn_feb

Distribution of values for earn_feb

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_feb numeric 0 1 0 1085 140535 1691.723 3298.412 ▇▁▁▁▁ NA

earn_jan

Distribution

Distribution of values for earn_jan

Distribution of values for earn_jan

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_jan numeric 0 1 0 1119 140484 1776.853 3469.528 ▇▁▁▁▁ NA

earn_july

Distribution

Distribution of values for earn_july

Distribution of values for earn_july

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_july numeric 0 1 0 1157 140556 1776.223 3316.557 ▇▁▁▁▁ NA

earn_june

Distribution

Distribution of values for earn_june

Distribution of values for earn_june

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_june numeric 0 1 0 1142 140534 1754.728 3283.199 ▇▁▁▁▁ NA

earn_may

Distribution

Distribution of values for earn_may

Distribution of values for earn_may

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_may numeric 0 1 0 1149 140526 1750.196 3254.601 ▇▁▁▁▁ NA

earn_march

Distribution

Distribution of values for earn_march

Distribution of values for earn_march

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_march numeric 0 1 0 1123 140546 1726.374 3333.645 ▇▁▁▁▁ NA

earn_nov

Distribution

Distribution of values for earn_nov

Distribution of values for earn_nov

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_nov numeric 0 1 0 1171 140461 1779.624 3277.2 ▇▁▁▁▁ NA

earn_oct

Distribution

Distribution of values for earn_oct

Distribution of values for earn_oct

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_oct numeric 0 1 0 1178 140516 1778.049 3248.745 ▇▁▁▁▁ NA

earn_sept

Distribution

Distribution of values for earn_sept

Distribution of values for earn_sept

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_sept numeric 0 1 0 1153 140495 1752.634 3240.089 ▇▁▁▁▁ NA

earn_dec_mw

Distribution

Distribution of values for earn_dec_mw

Distribution of values for earn_dec_mw

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_dec_mw numeric 0 1 0 1.4 150 2.206376 3.824295 ▇▁▁▁▁ NA

earn_dec

Distribution

Distribution of values for earn_dec

Distribution of values for earn_dec

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
earn_dec numeric 0 1 0 1323 140556 2070.504 3584.209 ▇▁▁▁▁ NA

mean_earn_mw

Distribution

Distribution of values for mean_earn_mw

Distribution of values for mean_earn_mw

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
mean_earn_mw numeric 0 1 0 1.7 150 2.71563 3.803707 ▇▁▁▁▁ NA

mean_earn

Distribution

Distribution of values for mean_earn

Distribution of values for mean_earn

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
mean_earn numeric 0 1 0 1568 140550 2552.184 3564.445 ▇▁▁▁▁ NA

hired_wage

Distribution

Distribution of values for hired_wage

Distribution of values for hired_wage

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
hired_wage numeric 0 1 0 1027 1e+07 1411.773 5264.697 ▇▁▁▁▁ NA

last_wage

Distribution

Distribution of values for last_wage

Distribution of values for last_wage

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
last_wage numeric 0 1 0 1450 1e+07 2417.658 6576.111 ▇▁▁▁▁ NA

yr

Distribution

Distribution of values for yr

Distribution of values for yr

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
yr numeric 0 1 2017 2017 2017 2017 0 ▁▁▇▁▁ NA

termination_year

Distribution

Distribution of values for termination_year

Distribution of values for termination_year

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
termination_year numeric 0 1 2017 2017 2017 2017 0 ▁▁▇▁▁ NA

adm_day

Distribution

Distribution of values for adm_day

Distribution of values for adm_day

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
adm_day numeric 0 1 1 5 31 8.711481 8.509995 ▇▂▂▂▁ NA

adm_month

Distribution

Distribution of values for adm_month

Distribution of values for adm_month

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
adm_month numeric 0 1 1 6 12 6.259857 3.422801 ▇▅▅▅▆ NA

adm_year

Distribution

Distribution of values for adm_year

Distribution of values for adm_year

0 missing values.

Summary statistics

name data_type n_missing complete_rate min median max mean sd hist label
adm_year numeric 0 1 1938 2015 2017 2012.744 6.627319 ▁▁▁▁▇ NA

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "codebook_data",
  "datePublished": "2024-06-07",
  "description": "The dataset has N=65655882 rows and 67 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                  |label | n_missing|\n|:---------------------|:-----|---------:|\n|adm_date              |NA    |         0|\n|cbo94                 |NA    |  65655882|\n|cbo02                 |NA    |     70101|\n|cei                   |NA    |         0|\n|zip_establishment     |NA    |         0|\n|cnae20                |NA    |         0|\n|cnae20sub             |NA    |         0|\n|cnae95                |NA    |         0|\n|firmID                |NA    |         0|\n|cnpj_root             |NA    |         0|\n|cpf                   |NA    |         0|\n|dob                   |NA    |         0|\n|termination_day       |NA    |         0|\n|schooling             |NA    |         0|\n|ibge_subsetor         |NA    |         0|\n|age                   |NA    |         0|\n|cei_avail             |NA    |         0|\n|pat                   |NA    |         0|\n|disabled              |NA    |         0|\n|simples               |NA    |         0|\n|union                 |NA    |         0|\n|intermittent_contract |NA    |         0|\n|partial_job           |NA    |         0|\n|alvara_ind            |NA    |         0|\n|termination_month     |NA    |         0|\n|separation_cause      |NA    |         0|\n|muni_job              |NA    |         0|\n|muni                  |NA    |         0|\n|nationality           |NA    |         0|\n|legal_form            |NA    |         0|\n|name                  |NA    |         0|\n|ctps                  |NA    |         0|\n|pis                   |NA    |         0|\n|hired_hours           |NA    |         0|\n|race_color            |NA    |  10319553|\n|estab_name            |NA    |         0|\n|sex                   |NA    |         0|\n|establishment_size    |NA    |         0|\n|tenure                |NA    |         0|\n|adm_type              |NA    |         0|\n|disability_type       |NA    |         0|\n|firmID_type           |NA    |         0|\n|wage_type             |NA    |         0|\n|contract_type         |NA    |         0|\n|active1231            |NA    |         0|\n|earn_april            |NA    |         0|\n|earn_august           |NA    |         0|\n|earn_feb              |NA    |         0|\n|earn_jan              |NA    |         0|\n|earn_july             |NA    |         0|\n|earn_june             |NA    |         0|\n|earn_may              |NA    |         0|\n|earn_march            |NA    |         0|\n|earn_nov              |NA    |         0|\n|earn_oct              |NA    |         0|\n|earn_sept             |NA    |         0|\n|earn_dec_mw           |NA    |         0|\n|earn_dec              |NA    |         0|\n|mean_earn_mw          |NA    |         0|\n|mean_earn             |NA    |         0|\n|hired_wage            |NA    |         0|\n|last_wage             |NA    |         0|\n|yr                    |NA    |         0|\n|termination_year      |NA    |         0|\n|adm_day               |NA    |         0|\n|adm_month             |NA    |         0|\n|adm_year              |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["adm_date", "cbo94", "cbo02", "cei", "zip_establishment", "cnae20", "cnae20sub", "cnae95", "firmID", "cnpj_root", "cpf", "dob", "termination_day", "schooling", "ibge_subsetor", "age", "cei_avail", "pat", "disabled", "simples", "union", "intermittent_contract", "partial_job", "alvara_ind", "termination_month", "separation_cause", "muni_job", "muni", "nationality", "legal_form", "name", "ctps", "pis", "hired_hours", "race_color", "estab_name", "sex", "establishment_size", "tenure", "adm_type", "disability_type", "firmID_type", "wage_type", "contract_type", "active1231", "earn_april", "earn_august", "earn_feb", "earn_jan", "earn_july", "earn_june", "earn_may", "earn_march", "earn_nov", "earn_oct", "earn_sept", "earn_dec_mw", "earn_dec", "mean_earn_mw", "mean_earn", "hired_wage", "last_wage", "yr", "termination_year", "adm_day", "adm_month", "adm_year"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "adm_date",
      "@type": "propertyValue"
    },
    {
      "name": "cbo94",
      "@type": "propertyValue"
    },
    {
      "name": "cbo02",
      "@type": "propertyValue"
    },
    {
      "name": "cei",
      "@type": "propertyValue"
    },
    {
      "name": "zip_establishment",
      "@type": "propertyValue"
    },
    {
      "name": "cnae20",
      "@type": "propertyValue"
    },
    {
      "name": "cnae20sub",
      "@type": "propertyValue"
    },
    {
      "name": "cnae95",
      "@type": "propertyValue"
    },
    {
      "name": "firmID",
      "@type": "propertyValue"
    },
    {
      "name": "cnpj_root",
      "@type": "propertyValue"
    },
    {
      "name": "cpf",
      "@type": "propertyValue"
    },
    {
      "name": "dob",
      "@type": "propertyValue"
    },
    {
      "name": "termination_day",
      "@type": "propertyValue"
    },
    {
      "name": "schooling",
      "@type": "propertyValue"
    },
    {
      "name": "ibge_subsetor",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "@type": "propertyValue"
    },
    {
      "name": "cei_avail",
      "@type": "propertyValue"
    },
    {
      "name": "pat",
      "@type": "propertyValue"
    },
    {
      "name": "disabled",
      "@type": "propertyValue"
    },
    {
      "name": "simples",
      "@type": "propertyValue"
    },
    {
      "name": "union",
      "@type": "propertyValue"
    },
    {
      "name": "intermittent_contract",
      "@type": "propertyValue"
    },
    {
      "name": "partial_job",
      "@type": "propertyValue"
    },
    {
      "name": "alvara_ind",
      "@type": "propertyValue"
    },
    {
      "name": "termination_month",
      "@type": "propertyValue"
    },
    {
      "name": "separation_cause",
      "@type": "propertyValue"
    },
    {
      "name": "muni_job",
      "@type": "propertyValue"
    },
    {
      "name": "muni",
      "@type": "propertyValue"
    },
    {
      "name": "nationality",
      "@type": "propertyValue"
    },
    {
      "name": "legal_form",
      "@type": "propertyValue"
    },
    {
      "name": "name",
      "@type": "propertyValue"
    },
    {
      "name": "ctps",
      "@type": "propertyValue"
    },
    {
      "name": "pis",
      "@type": "propertyValue"
    },
    {
      "name": "hired_hours",
      "@type": "propertyValue"
    },
    {
      "name": "race_color",
      "@type": "propertyValue"
    },
    {
      "name": "estab_name",
      "@type": "propertyValue"
    },
    {
      "name": "sex",
      "@type": "propertyValue"
    },
    {
      "name": "establishment_size",
      "@type": "propertyValue"
    },
    {
      "name": "tenure",
      "@type": "propertyValue"
    },
    {
      "name": "adm_type",
      "@type": "propertyValue"
    },
    {
      "name": "disability_type",
      "@type": "propertyValue"
    },
    {
      "name": "firmID_type",
      "@type": "propertyValue"
    },
    {
      "name": "wage_type",
      "@type": "propertyValue"
    },
    {
      "name": "contract_type",
      "@type": "propertyValue"
    },
    {
      "name": "active1231",
      "@type": "propertyValue"
    },
    {
      "name": "earn_april",
      "@type": "propertyValue"
    },
    {
      "name": "earn_august",
      "@type": "propertyValue"
    },
    {
      "name": "earn_feb",
      "@type": "propertyValue"
    },
    {
      "name": "earn_jan",
      "@type": "propertyValue"
    },
    {
      "name": "earn_july",
      "@type": "propertyValue"
    },
    {
      "name": "earn_june",
      "@type": "propertyValue"
    },
    {
      "name": "earn_may",
      "@type": "propertyValue"
    },
    {
      "name": "earn_march",
      "@type": "propertyValue"
    },
    {
      "name": "earn_nov",
      "@type": "propertyValue"
    },
    {
      "name": "earn_oct",
      "@type": "propertyValue"
    },
    {
      "name": "earn_sept",
      "@type": "propertyValue"
    },
    {
      "name": "earn_dec_mw",
      "@type": "propertyValue"
    },
    {
      "name": "earn_dec",
      "@type": "propertyValue"
    },
    {
      "name": "mean_earn_mw",
      "@type": "propertyValue"
    },
    {
      "name": "mean_earn",
      "@type": "propertyValue"
    },
    {
      "name": "hired_wage",
      "@type": "propertyValue"
    },
    {
      "name": "last_wage",
      "@type": "propertyValue"
    },
    {
      "name": "yr",
      "@type": "propertyValue"
    },
    {
      "name": "termination_year",
      "@type": "propertyValue"
    },
    {
      "name": "adm_day",
      "@type": "propertyValue"
    },
    {
      "name": "adm_month",
      "@type": "propertyValue"
    },
    {
      "name": "adm_year",
      "@type": "propertyValue"
    }
  ]
}`
 # } # end year loop