The package aims to implement in a simple way the methodologies of INE Chile, ECLAC 2020 and ECLAC 2023 for the quality assessment of estimates from household surveys and INE Chile Economics Surveys 2024 for the quality assessment of estimates from economics surveys.
This tutorial shows the basic use of the package and includes the main functions to create the necessary inputs to implement the quality standards.
We will use three datasets:
Those datasets are loaded into the package and they can be used when the package is loaded in the session 1. The data edition in the case of ENE has the purpuse of creating some subpopulations (work force, unemployed and unemployed).
library(survey)
library(calidad)
library(dplyr)
ene <- ene %>%
mutate(fdt = if_else(cae_especifico >= 1 & cae_especifico <= 9, 1, 0), # labour force
ocupado = if_else(cae_especifico >= 1 & cae_especifico <= 7, 1, 0), # employed
desocupado = if_else(cae_especifico >= 8 & cae_especifico <= 9, 1, 0),
hombre = if_else(sexo == 1, 1, 0),
mujer = if_else(sexo == 2, 1, 0)) # unemployed
# One row per household
epf <- epf_personas %>%
group_by(folio) %>%
slice(1) %>%
ungroup()C:4qfZS5cc861292365.R
Before starting to use the package, it is necessary to declare the
sample design of the survey, for which we use the survey
package. The primary sample unit, the stratum and weights must be
declared. It is also possible to use a design with only weights,
nevertheless in that case the variance will be estimated under simple
random sampling assumption. In this case we will declare a complex
design for the two surveys (EPF and ENE). Additionally, it may be useful
to declare an option for strata that only have one PSU.
C:4qfZS5cc861292365.R
# Complex sample design for ENE
dc_ene <- svydesign(ids = ~conglomerado , strata = ~estrato_unico, data = ene, weights = ~fact_cal)
# Complex sample design for EPF
dc_epf <- svydesign(ids = ~varunit, strata = ~varstrat, data = epf, weights = ~fe)
options(survey.lonely.psu = "certainty")C:4qfZS5cc861292365.R
For ELE survey, we also must to declare the fpc (finite population correction)
# Complex sample design for ELE
dc_ele <- svydesign(ids = ~rol_ficticio, weights = ~fe_transversal, strata = ~estrato, fpc = ~pob, data = ELE7)
options(survey.lonely.psu = 'remove')C:4qfZS5cc861292365.R
To assess the quality of an estimate, the INE methodology establishes differentiated criteria for estimates of proportion or ratio between 0 and 1, on the one hand, and estimates of mean, size and total, on the other. In the case of proportion estimation, it is necessary to have the sample size, the degrees of freedom and the standard error. The other estimates require the sample size, the degrees of freedom, and the coefficient of variation.
The package includes separate functions to create the inputs for estimates of mean, proportion, totals and size. The following example shows how the proportion and size functions are used.
insumos_prop <- create_prop(var = "desocupado", domains = "sexo", subpop = "fdt", design = dc_ene) # proportion of unemployed people
insumos_total <- create_size(var = "desocupado", domains = "sexo", subpop = "fdt", design = dc_ene) # number of unemployed peopleC:4qfZS5cc861292365.R
var: variable to be estimated. Must be a dummy
variabledomains: required domains.subpop: reference subpopulation. It is optional and
works as a filter (must be a dummy variable)design: sample designThe function returns all the neccesary inputs to implement the standard
insumos_total
#> sexo stat se df n cv
#> 1 1 399299.4 14530.92 880 1564 0.03639102
#> 2 2 402505.4 14688.19 913 1629 0.03649190C:4qfZS5cc861292365.R
To get more domains, we can use the “+” symbol as follows:
desagregar <- create_prop(var = "desocupado", domains = "sexo+region", subpop = "fdt", design = dc_ene)C:4qfZS5cc861292365.R
A useful parameter is eclac_input. It allows to return
the ECLAC inputs. By default this parameter is FALSE and
with the option TRUE we can activate it.
eclac_inputs <- create_prop(var = "desocupado", domains = "sexo+region", subpop = "fdt", design = dc_ene, eclac_input = TRUE)C:4qfZS5cc861292365.R
In some cases, it may be of interest to assess the estimate of a
ratio. The create_prop function allows ratio estimation by
explicitly specifying the numerator and the denominator. To do this, the
function includes the denominator parameter, where the variable to be
used in the denominator of the ratio must be provided.
For ECLAC standards, it is necessary to specify the version of the
methodology using the scheme parameter, since ratio estimation is only
supported under the ECLAC 2023 standard (eclac_2023). The
default option is eclac_2020, which does not include
support for ratio estimation. An example using eclac_2023
is shown below:
create_prop(var = "mujer", denominator = "hombre", domains = "ocupado", design = dc_ene,
eclac_input = TRUE, scheme = 'eclac_2023')C:4qfZS5cc861292365.R
We may also be interested in assessing the estimate of a sum. For
example, the sum of all the income of the EPF at the geographical area
level (Gran Santiago and other regional capitals). For this, there is
the create_total function. This function receives a
continuous variable such as hours, expense, or income and generates
totals at the requested level. The ending “with” of the function alludes
to the fact that a continuous variable is being used.
C:4qfZS5cc861292365.R
If we want to assess the estimate of a mean, we have the function
create_mean. In this case, we will calculate the average
expenditure of households, according to geographical area.
C:4qfZS5cc861292365.R
The default usage is not to disaggregate, in which case the functions should be used as follows:
# ENE dataset
insumos_prop_nacional <- create_prop("desocupado", subpop = "fdt", design = dc_ene)
insumos_total_nacional <- create_total("desocupado", subpop = "fdt", design = dc_ene)
# EPF dataset
insumos_suma_nacional <- create_total("gastot_hd", design = dc_epf)
insumos_media_nacional <- create_mean("gastot_hd", design = dc_epf)C:4qfZS5cc861292365.R
The create functions also include a ci parameter, which
allows returning the confidence interval. By default, this parameter is
set to FALSE. For proportions, the logistic confidence
interval is available. To use it, you need to set
ci_logit = TRUE, as shown below:
# ENE dataset
prop_nacional_ci <- create_prop("desocupado", subpop = "fdt", design = dc_ene, ci = TRUE)
prop_nacional_ci_logit <- create_prop("desocupado", subpop = "fdt", design = dc_ene, ci_logit = TRUE) C:4qfZS5cc861292365.R
In the case of economic surveys, the estimates are carried out in a similar way. For example, in the Longitudinal Enterprise Survey (ELE), wage productivity is estimated as the ratio between value added and the total gross remuneration of personnel hired by the company.
prod_salarial <- create_prop('VA_2022f', denominator = 'REMP_TOTAL', domains = 'cod_actividad+cod_tamano', design = dc_ele)C:4qfZS5cc861292365.R
Once the inputs have been generated, we can proceed with the
assessment using the assess function. By default, the INE
Chile criteria are applied (scheme = 'chile'). However, if
we want to evaluate using the ECLAC or Chile Economics criteria, we need
to specify scheme = 'eclac_2020',
scheme = 'eclac_2023', or
scheme = 'chile_economics'.
For the ECLAC 2023 standard (eclac_2023), we can use the
parameters domain_info and low_df_justified to
indicate whether the estimation corresponds to a planned study domain
and whether low degrees of freedom are justified. Both parameters are
set to FALSE by default.
# INE Chile
evaluacion_prop <- assess(insumos_prop)
evaluacion_tot <- assess(insumos_total)
evaluacion_suma <- assess(insumos_suma)
evaluacion_media <- assess(insumos_media)
# ECLAC
evaluacion_cepal_2020 <- assess(eclac_inputs, scheme = 'eclac_2020')
evaluacion_cepal_2023 <- assess(eclac_inputs, scheme = 'eclac_2023', domain_info = TRUE, low_df_justified = TRUE)C:4qfZS5cc861292365.R
In the INE Chile Economics standard (chile_economics),
the domain_info parameter is also available, and the
table_n_obj parameter can be used to specify the target
sample size for the domains to be evaluated, using a data frame. This
data frame must include the target sample size in a column named
n_obj. By default, table_n_obj is
NULL, and the sample recovery verification will be
skipped.
For the assessment of ratios, we can indicate whether the estimated
value falls between 0 and 1 using the parameter
ratio_between_0_1. By default, it is set to
TRUE. If a value greater than 1 is detected, the
coefficient of variation (CV) will be used instead.
# INE Economics
## target sample size
table_n_obj <- ELE7_n_obj %>%
dplyr::mutate(cod_actividad = cod_actividad_letra,
cod_tamano = as.character(cod_tamano)) %>%
dplyr::select(-cod_actividad_letra)
eval_ratio <- assess(prod_salarial, scheme = 'chile_economics',
domain_info = TRUE, table_n_obj = table_n_obj, ratio_between_0_1 = FALSE)C:4qfZS5cc861292365.R
The output is a dataframe that, in addition to
containing the information already generated, includes a column that
indicates whether the estimate is unreliable, less reliable or
reliable.
The function assess has a parameter that allows us to
know if the table should be published or not. Following the criteria of
the standard, if more than 50% of the estimates of a table are not
reliable, it should not be published.
# Unemployment by region
desagregar <- create_size(var = "desocupado", domains = "region", subpop = "fdt", design = dc_ene)
# assess output
evaluacion_tot_desagreg <- assess(desagregar, publish = T)
evaluacion_tot_desagregC:4qfZS5cc861292365.R
C:4qfZS5cc861292365.R
The data contained within the package has some editions. It is important to note that haven::labelled objects may have some collision with quality functions. If you want to import a dta file, all variables that are of type haven::labelled must be converted to numeric or character.↩︎