Encuesta Longitudinal de Empresas

Description

ELE data for the year 2022. Contains only a few variables.

Usage

ELE7

Format

dataframe with 6.592 rows y 13 columns

rol_ficticio

Company ID

cod_actividad

Economic activity

cod_tamano

Company size by sales

tramo

Inclusion range

fe_transversal

Cross-sectional weights

fe_longitudinal

Longitudinal weights

panel

Panel sample

estrato

Strata

pob

Finite population correction

VA_2022

Value added 2022, difference between production value and intermediate consumption

VA_2022f

VA_2022f is an adjusted version of VA_2022, where negative values are replaced with 0, while non-negative values remain unchanged.

EMP

Total personnel employed and hired by the company on a monthly basis

REMP_TOTAL

Total gross remuneration of personnel hired by the company

Source

https://www.ine.gob.cl/docs/default-source/encuesta-longitudinal-de-empresas/bbdd/ele-2022/base-de-datos-ele7.csv?sfvrsn=1504c58d_4&download=true

Examples

data(ELE7)

Tamano muestra objetivo Encuesta Longitudinal de Empresas

Description

Target cross-sectional sample size ELE data for the year 2022.

Usage

ELE7_n_obj

Format

dataframe with 59 rows y 4 columns

cod_tamano

Company size by sales

cod_actividad_letra

Economic activity

cod_actividad

Economic activity ID

n_obj

Target sample size

Source

https://www.ine.gob.cl/docs/default-source/encuesta-longitudinal-de-empresas/metodologias/ele-2022/informe-de-calidad-ele7.pdf?sfvrsn=6ca73eb5_4

Examples

data(ELE7_n_obj)

Assess the quality of mean estimations

Description

assess evaluates the quality of mean estimations using the methodology created by INE Chile, which considers sample size, degrees of freedom, and coefficient of variation.

Usage

assess(
  table,
  publish = FALSE,
  scheme = c("chile", "eclac_2020", "eclac_2023", "chile_economics"),
  domain_info = FALSE,
  low_df_justified = FALSE,
  table_n_obj = NULL,
  ratio_between_0_1 = TRUE,
  ...
)

Arguments

Value

dataframe with all the columns included in the input table, plus a new column containing a label indicating the evaluation of each estimation: reliable, bit reliable, or unreliable.

Examples

dc <- survey::svydesign(ids = ~varunit, strata = ~varstrat, data = epf_personas, weights = ~fe)
assess(create_mean("gastot_hd", domains = "zona+sexo", design = dc))

Encuesta de Caracterización Socioeconómica Nacional 2020 - CASEN en Pandemia 2020

Description

CASEN data for the year 2020. Contains only a few variables.

Usage

casen

Format

dataframe with 185.437 rows y 6 columns

folio

household id

sexo

1 = man; 2 = woman

edad

age

activ

Economic activity status

ing_aut_hog

Household Income

pobreza

poverty status: 1 = extreme poverty, 2 = non-extreme poverty, 3 = non-poverty

expr

regional sample weights

estrato

strata

cod_upm

PSU

Source

http://observatorio.ministeriodesarrollosocial.gob.cl/encuesta-casen-en-pandemia-2020

Examples

data(casen)

Create html table with the results of the evaluation

Description

Create html table with the results of the evaluation

Usage

create_html(table)

Arguments

Value

html table

Examples

library(survey)
library(dplyr)

hogar <- epf_personas %>%
  group_by(folio) %>%
  slice(1)
dc <- survey::svydesign(ids = ~varunit, strata = ~varstrat, data = hogar, weights = ~fe)
table <- assess(create_prop("ocupado", domains = "zona+sexo", design = dc))

Create the inputs to evaluate the quality of mean estimations

Description

create_mean generates a dataframe with the following elements: mean, degrees of freedom, sample size, and coefficient of variation. The function allows grouping in several domains.

Usage

create_mean(
  var,
  domains = NULL,
  subpop = NULL,
  design,
  ci = FALSE,
  ess = FALSE,
  ajuste_ene = FALSE,
  standard_eval = FALSE,
  rm.na = FALSE,
  deff = FALSE,
  rel_error = FALSE,
  unweighted = FALSE,
  eclac_input = FALSE
)

Arguments

Value

dataframe that contains the inputs and all domains to be evaluated.

Examples

dc <- survey::svydesign(ids = ~varunit, strata = ~varstrat, data = epf_personas, weights = ~fe)
create_mean("gastot_hd", "zona+sexo", design = dc)

Create the inputs to evaluate the quality of proportion estimations

Description

create_prop generates a dataframe with the following elements: sum, degrees of freedom, sample size, standard error, and coefficient of variation. The function allows grouping in several domains.

Usage

create_prop(
  var,
  denominator = NULL,
  domains = NULL,
  subpop = NULL,
  design,
  ci = FALSE,
  deff = FALSE,
  ess = FALSE,
  ajuste_ene = FALSE,
  rel_error = FALSE,
  log_cv = FALSE,
  unweighted = FALSE,
  standard_eval = FALSE,
  eclac_input = FALSE,
  ci_logit = FALSE,
  scheme = c("eclac_2020", "eclac_2023")
)

Arguments

Value

dataframe that contains the inputs and all domains to be evaluated.

Examples

library(survey)
library(dplyr)

epf <- mutate(epf_personas, gasto_zona1 = if_else(zona == 1, gastot_hd, 0))
dc <- svydesign(ids = ~varunit, strata = ~varstrat, data = epf, weights = ~fe)
old_options <- options()
options(survey.lonely.psu = "certainty")

create_prop(var = "gasto_zona1", denominator = "gastot_hd", design = dc)

enusc <- filter(enusc, Kish == 1)

dc <- svydesign(ids = ~Conglomerado, strata = ~VarStrat, data = enusc, weights = ~Fact_Pers)
options(survey.lonely.psu = "certainty")
create_prop(var = "VP_DC", denominator = "hom_insg_taxi", design = dc)
options(old_options)

internal function to calculate proportion estimations

Description

internal function to calculate proportion estimations

Usage

create_prop_internal(
  var,
  domains = NULL,
  subpop = NULL,
  disenio,
  ci = FALSE,
  deff = FALSE,
  ess = FALSE,
  ajuste_ene = FALSE,
  rel_error = FALSE,
  log_cv = FALSE,
  unweighted = FALSE,
  standard_eval = TRUE,
  rm.na = FALSE,
  env = parent.frame(),
  ci_logit = FALSE
)

Arguments

Value

dataframe that contains the inputs and all domains to be evaluated

internal function to calculate ratios estimations

Description

internal function to calculate ratios estimations

Usage

create_ratio_internal(
  var,
  denominator,
  domains = NULL,
  subpop = NULL,
  disenio,
  ci = FALSE,
  deff = FALSE,
  ess = FALSE,
  ajuste_ene = FALSE,
  unweighted = FALSE,
  rel_error = FALSE,
  log_cv = FALSE,
  rm.na = FALSE
)

Arguments

Value

dataframe that contains the inputs and all domains to be evaluated

Create the inputs to evaluate the quality of total estimations

Description

create_size generates a dataframe with the following elements: sum, degrees of freedom, sample size, and coefficient of variation. The function allows grouping in several domains.

Usage

create_size(
  var,
  domains = NULL,
  subpop = NULL,
  design,
  ci = FALSE,
  ess = FALSE,
  ajuste_ene = FALSE,
  standard_eval = FALSE,
  rm.na = FALSE,
  deff = FALSE,
  rel_error = FALSE,
  unweighted = FALSE,
  df_type = c("chile", "eclac"),
  eclac_input = FALSE
)

Arguments

Value

dataframe that contains the inputs and all domains to be evaluated.

Examples

dc <- survey::svydesign(ids = ~varunit, strata = ~varstrat, data = epf_personas, weights = ~fe)
create_size("ocupado", "zona+sexo", design = dc)

Create the inputs to evaluate the quality of the sum of continuous variables

Description

create_total generates a dataframe with the following elements: sum, degrees of freedom, sample size, and coefficient of variation. The function allows grouping in several domains.

Usage

create_total(
  var,
  domains = NULL,
  subpop = NULL,
  design,
  ci = FALSE,
  ess = FALSE,
  ajuste_ene = FALSE,
  standard_eval = FALSE,
  rm.na = FALSE,
  deff = FALSE,
  rel_error = FALSE,
  unweighted = FALSE,
  eclac_input = FALSE
)

Arguments

Value

dataframe that contains the inputs and all domains to be evaluated.

Examples

dc <- survey::svydesign(ids = ~varunit, strata = ~varstrat, data = epf_personas, weights = ~fe)
create_total("gastot_hd", "zona+sexo", subpop = "ocupado", design = dc)

Turn on all the indicators needed for the eclac standard

Description

This function activates the appropriate indicators based on the selected eclac standard and whether proportion indicators are needed.

Usage

eclac_standard(eclac, env = parent.frame(), proportion = FALSE)

Arguments

Value

A list of logical values indicating which indicators are turned on.

Encuesta Nacional de Empleo - ENE. 2020-efm

Description

Reduced version of the ENE database. Contains some sociodemographic variables and the necessary information to work with complex design

Usage

ene

Format

dataframe with 87.842 rows y 7 columns

sexo

1 = man; 2 = woman

region

region

cae_especifico

Economic activity status

fe

sample weights

varunit

PSU

varstrat

strata

fdt

It shows if the person belongs to labour force: 1 = yes; 0 = no

ocupado

1 = employed; 0 = non-employed

desocupado

1 = non-employed; 0 = employed

Source

https://www.ine.cl/estadisticas/sociales/mercado-laboral/ocupacion-y-desocupacion

Examples

data(ene)

Encuesta Nacional Urbana de Seguridad Ciudadana 2019 - ENUSC 2019

Description

ENUSC data for the year 2019. Contains only a few variables.

Usage

enusc

Format

dataframe with 24.465 rows y 22 columns

rph_sexo

1 = man; 2 = woman

region

16 regions

Fact_Pers

person sample weights

Fact_Hog

household sample weights

Conglomerado

PSU

VarStrat

strata

VP_DC

Individual victimization. It works combined with Fact_Pers

VA_DC

Household victimization. It works combined with Fact_Hog

rph_edad

age

P3_1_1

Perception of increased crime in the country. It works combined with Fact_Pers

P8_1_1

Cause of increased crime in the neighborhood. It works combined with Fact_Pers

muj_insg_taxi

Female perception of insecurity inside taxis. Variable elaborated with variables P9 and rph_sexo . It works combined with Fact_Pers

hom_insg_taxi

Male perception of insecurity inside taxis. Variable elaborated with variables P9 and rph_sexo. It works combined with Fact_Pers

muj_insg_micro

Female perception of insecurity inside buses. Variable elaborated with variables P9 and rph_sexo. It works combined with Fact_Pers

hom_insg_micro

Male perception of insecurity inside buses. Variable elaborated with variables P9 and rph_sexo. It works combined with Fact_Pers

muj_insg_centr.com

Female perception of insecurity inside malls. Variable elaborated with variables P9 and rph_sexo. It works combined with Fact_Pers

hom_insg_centr.com

Male perception of insecurity inside malls. Variable elaborated with variables P9 and rph_sexo. It works combined with Fact_Pers

muj_insg_loc.col

Female perception of insecurity public transport. Variable elaborated with variables P9 and rph_sexo. It works combined with Fact_Pers

hom_insg_loc.col

Male perception of insecurity public transport. Variable elaborated with variables P9 and rph_sexo. It works combined with Fact_Pers

muj_insg_barrio

Female perception of insecurity neighborhood. Variable elaborated with variables P9 and rph_sexo. It works combined with Fact_Pers

hom_insg_barrio

Male perception of insecurity neighborhood. Variable elaborated with variables P9 and rph_sexo. It works combined with Fact_Pers

Source

https://www.ine.cl/docs/default-source/seguridad-ciudadana/bbdd/2019/base-de-datos—xvi-enusc-2019-(csv).csv?sfvrsn=d3465758_2&download=true

Examples

data(enusc)

Encuesta Nacional Urbana de Seguridad Ciudadana 2023 - ENUSC 2023

Description

ENUSC data for the year 2023. Contains only a few variables.

Usage

enusc_2023

Format

dataframe with 49.813 rows y 15 columns

enc_region

16 regions

enc_rpc

Code of region, province and commune

Fact_Pers_Reg

Person sample weights at region level

Fact_Pers_Com

Person sample weights at commune level

Fact_Hog_Reg

Household sample weights at region level

Fact_Hog_Com

Household sample weights at commune level

VarStrat

Strata

Conglomerado

PSU

VH_DV

Households victimized by violent crimes. It works combined with Fact_Hog_*

VH_DC

Household victimization. It works combined with Fact_Hog_*

VP_DV

People victimized by violent crimes. It works combined with Fact_Pers_*

VP_DC

Individual victimization. It works combined with Fact_Pers_*

PAD

Perception of increased crime in the country. It works combined with Fact_Pers_*

rph_sexo

1 = man; 2 = woman

rph_edad

Age

Source

https://www.ine.gob.cl/docs/default-source/seguridad-ciudadana/bbdd/2023/base-usuario-20-enusc-2023.csv?sfvrsn=34653b72_2&download=true

Examples

data(enusc_2023)

VIII Encuesta de Presupuestos Familiares

Description

Reduced version of the VIII EPF database. Contains some sociodemographic variables and the necessary information to work with complex design.

Usage

epf_personas

Format

dataframe compuesto por 48.308 observaciones y 8 variables

sexo

1 = male; 2 = female

zona

1 = metropolitan area; 2 = rest of the regional capitals

ecivil

marital status

fe

sample weights

varunit

PSU

varstrat

strata

gastot_hd

household expenditure

ocupado

1 = employed; 0 = non-employed

Source

https://www.ine.cl/estadisticas/sociales/ingresos-y-gastos/encuesta-de-presupuestos-familiares

Examples

data(epf_personas)

Get the coefficient of variation

Description

Receive a table created with survey and return the coefficient of variation for each cell

Usage

get_cv(table, design, domains, type_est = "all", env = parent.frame())

Arguments

Value

dataframe with results including including CV

Get degrees of freedom

Description

Receive data and domains. Returns a data frame with the psu (if available), strata and degrees of freedom (df) for each cell

Usage

get_df(data, domains, df_type = "eclac")

Arguments

Value

dataframe with results including degrees of freedom

Calculates multiple estimations. Internal wrapper for survey package

Description

Generates a table with estimates for a given aggregation

Usage

get_survey_table(
  var,
  domains,
  complex_design,
  estimation = "mean",
  env = parent.frame(),
  fun,
  denom = NULL,
  type_est = "all"
)

Arguments

Value

dataframe containing main results from survey

Calcula el valor de una función cuadrática

Description

quadratic returns the output of a particular function created by INE Chile, which is assessed at the value of the estimated proportion from a sample. If the output of the function is higher than the standard error, it is interpreted as a signal that the estimation is not reliable.

Usage

quadratic(p)

Arguments

Value

numeric vector

standardize and sort column names

Description

Receive the survey table in raw state and sort it

Usage

standardize_columns(data, var, denom)

Arguments

Value

dataframe with standardized data

Standardize the name of design variables

Description

Rename design variables, so we can use the later

Usage

standardize_design_variables(design)

Arguments

Value

design survey