province {qcpm}

Province dataset example

Description

Province dataset example

Usage

province

Format

This data set allows to estimate the relationships among Health (HEALTH), Education and training (EDU) and Economic well-being (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 HEALTH, EDU and 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:

LV1

Education and training (EDU)

MV1 EDU1(O.2.2):

people with at least upper secondary education level (25-64 years old)

MV2 EDU2(O.2.3):

people having completed tertiary education (30-34 years old)

MV3 EDU3(O.2.4):

first-time entry rate to university by cohort of upper secondary graduates

MV4 EDU4(O.2.5aa):

people not in education, employment or training (Neet)

MV5 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

MV6 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

MV7 EDU7(O_2.7_2.8_A):

Differences between males and females students in the level of numeracy and literacy

LV2

Economic wellbeing (ECOW)

MV8 ECOW1(O.4.1):

per capita disposable income

MV9 ECOW2(O.4.4aa):

pensioners with low pension amount

MV10 ECOW3(O.4.5):

per capita net wealth

MV11 ECOW4(O.4.6aa):

rate of bad debts of the bank loans to families

MV12 ECOW5(O.4.2):

average annual salary of employees

MV13 ECOW6(O.4.3):

average annual amount of pension income per capita

#'

LV3

Health (HEALTH)

MV14 HEALTH1(O.1.1F):

life expectancy at birth of females

MV15 HEALTH2(O.1.1M):

life expectancy at birth of males

MV16 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. Social Indicators Research, doi:10.1007/s11205-020-02425-5.

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. Social Indicators Research, 136, pp. 999–1029, doi: 10.1007/s11205-016-1453-8

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.), Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (pp. 81–108). Cham: Springer.

ISTAT. (2019a). Misure del Benessere dei territori. Tavole di dati. Rome, Istat.

ISTAT. (2019b). Le differenze territoriali di benessere - Una lettura a livello provinciale. Rome, Istat.

ISTAT. (2018). Bes report 2018: Equitable and sustainable well-being in Italy. Rome, Istat.