R/01_province_data.R

In qcpm: Quantile Composite Path Modeling

#' Province dataset example
#' 
#' @docType data
#' @name province
#' @usage province
#' @format This data set allows to estimate  the relationships among Health  (\code{HEALTH}), 
#' Education and training (\code{EDU}) and Economic well-being (\code{ECOW}) 
#' in the Italian provinces using a subset of the indicators collected by the Italian Statistical 
#' Institute (ISTAT) to measure equitable and sustainable well-being (BES, from the Italian Benessere 
#' Equo e Sostenibile) in territories. Data refers to the 2019 edition of the BES report (ISTAT, 2018, 
#' 2019a, 2019b). A subset of 16 indicators (manifest variables) are observed on the 110 Italian provinces 
#' and metropolitan cities (i.e. at NUTS3 level) to measure the latent variables \code{HEALTH}, \code{EDU} 
#' and \code{ECOW}. The interest in such an application concerns both advances in knowledge 
#' about the dynamics producing the well-being outcomes at local level (multiplier effects or trade-offs) 
#' and a more complete evaluation of regional inequalities of well-being.
#' 
#' Data Strucuture
#' 
#' A data frame with 110 Italian provinces and metropolitan cities and 16 variables (i.e., indicators) related to 
#' three latent variables: Health (3 indicators), Education and training (7 indicators), and Economic well-being 
#' (6 indicators).
#' 
#'
#' Manifest variables description for each latent variable:
#'
#'\describe{
#'\item{LV1}{Education and training (\code{EDU})} 
#'\describe{
#'\item{MV1 \code{EDU1}(O.2.2):}{people with at least upper secondary education level (25-64 years old)}
#'\item{MV2 \code{EDU2}(O.2.3):}{people having completed tertiary education (30-34 years old)}
#'\item{MV3 \code{EDU3}(O.2.4):}{first-time entry rate to university by cohort of upper secondary graduates}
#'\item{MV4 \code{EDU4}(O.2.5aa):}{people not in education, employment or training (Neet)}
#'\item{MV5 \code{EDU5}(O.2.6):}{ratio of people aged 25-64 years participating in formal 
#'or non-formal education to the total people aged 25-64 years}
#'\item{MV6 \code{EDU6}(O_2.7_2.8):}{scores obtained in the tests of functional skills of the 
#'students in the II classes of upper secondary education}
#'\item{MV7 \code{EDU7}(O_2.7_2.8_A):}{Differences between males and females students in the level of 
#'numeracy and literacy}
#'}
#'\item{LV2}{Economic wellbeing (\code{ECOW})} 
#'\describe{
#'\item{MV8 \code{ECOW1}(O.4.1):}{per capita disposable income}
#'\item{MV9 \code{ECOW2}(O.4.4aa):}{pensioners with low pension amount}
#'\item{MV10 \code{ECOW3}(O.4.5):}{per capita net wealth}
#'\item{MV11 \code{ECOW4}(O.4.6aa):}{rate of bad debts of the bank loans to families}
#'\item{MV12 \code{ECOW5}(O.4.2):}{average annual salary of employees}
#'\item{MV13 \code{ECOW6}(O.4.3):}{average annual amount of pension income per capita}
#'}
#'#'\item{LV3}{Health (\code{HEALTH})} 
#'\describe{
#'\item{MV14 \code{HEALTH1}(O.1.1F):}{life expectancy at birth of females}
#'\item{MV15 \code{HEALTH2}(O.1.1M):}{life expectancy at birth of males}
#'\item{MV16 \code{HEALTH3}(O.1.2.MEAN_aa):}{infant mortality rate}
#'}
#'}
#'
#' For a full description of the variables, see table 3 of Davino et al. (2020).
#'
#'
#' @references Davino, C., Dolce, P., Taralli, S. and Vistocco, D. (2020). Composite-based 
#' path modeling for conditional quantiles prediction. An application to assess 
#' health differences at local level in a well-being perspective.
#' \emph{Social Indicators Research}, doi:10.1007/s11205-020-02425-5.
#'
#' @references Davino, C., Dolce, P., Taralli, S., Esposito Vinzi, V. (2018). A quantile 
#' composite-indicator approach for the measurement of equitable and sustainable well-being: 
#' A case study of the italian provinces. \emph{Social Indicators Research}, \bold{136}, pp. 999--1029, 
#' doi: 10.1007/s11205-016-1453-8
#'
#' @references Davino, C., Dolce, P., Taralli, S. (2017). Quantile composite-based model: 
#' A recent advance in pls-pm. A preliminary approach to handle heterogeneity in the measurement 
#' of equitable and sustainable well-being. In Latan, H. and Noonan, R. (eds.), \emph{Partial Least 
#' Squares Path Modeling: Basic Concepts, Methodological Issues and Applications} (pp. 81--108). 
#' Cham: Springer.  
#'
#'@references ISTAT. (2019a). Misure del Benessere dei territori. Tavole di dati. Rome, 
#'Istat. 
#'
#'@references ISTAT. (2019b). Le differenze territoriali di benessere - Una lettura a livello 
#'provinciale. Rome, Istat.
#'
#'@references ISTAT. (2018). Bes report 2018: Equitable and sustainable well-being in Italy. 
#'Rome, Istat. 
#'
#' @keywords datasets
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