The 2030 Agenda for Sustainable Development (2015) can be considered the synthesis of a debate, which sets the sustainable development as a priority for the International Community. The achievement of the sustainable development goals has made necessary to develop a system of indicators. Indicators and data should be collected and reported sub-nationally, giving attention to the territory. This is a necessity even more for Italy, a country historically characterized by strong regional specificities and differences, which find their radicalization in the so-called North–South gap. In this paper, we want to examine and monitor the Italian situation as to the achievement of the SDGs, based on the analysis of the Regions, to highlight potential differences or territorial homogeneity. In particular, we want to emphasize not only how there is actually a gap between the North and the South of the country, but also how the synthesis tends often to be representative of situations profoundly different from each other, as a result of different values in the basic indicators, or similar situations between them. Due to the difficulty of reporting on a paper a detailed analysis of all 17 sustainable development goals, we focus only on the first three goals one. In particular, for each goal we select indicators all useful for the analysis of regional realities and appropriate some for monitoring the present condition, others for providing information on the future one (risk). The research methodology is to use the Adjusted Mazziotta–Pareto Index for creating a composite index for each goal considered. This analysis is preceded by an exploratory analysis of the basic indicators over time through the use of within and between correlations and the average PCA.
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For a complete analysis of the ecological paradigm on sustainable development, please see Elliott (2013).
The Millennium Summit of the United Nations in 2000 established the MDGs, these eight international development goals for the year 2015:
To eradicate extreme poverty and hunger.
To achieve universal primary education.
To promote gender equality and empower women.
To reduce child mortality.
To improve maternal health.
To combat HIV/AIDS, malaria, and other diseases.
To ensure environmental sustainability.
To develop a global partnership for development.
Some authors point out that even at the time of national unification Italy was characterized by evident inequalities and backwardness of the South compared to the rest of the country and how this situation has influenced the future development. Other authors, on the contrary, reduce the importance and role of the initial gap between the different areas in future development. For a review, please see Lepore (2012).
Regions have legislative power, except for the determination of fundamental principles, reserved to the State.
Within the composite indicator framework, methods for weighting indicators can be broadly categorized into three main groups: equal weighting, statistic-based weighting, public/expert opinion-based weighting (Gan et al. 2017, 492).
For example, in the case of goal 3, if we intended to construct a composite whose increase coincides with an improvement in health, the life expectancy would have positive polarity, while the smoking rate would be negative. If, on the contrary, we wanted to construct a composite whose increase indicates a worsening of health (for instance, a risk indicator), the life expectancy would have negative polarity, while the smoking rate would be positive.
It is possible to observe (also in this work) how in reality the composite often has values outside this range. What might seem a limit of AMPI, on the contrary, is one of its qualities, as it allows highlighting the presence of a strong variability in the time series of the basic indicators.
For more information, please see: Rutherford (2011).
Rmcorr is an atypical application of ANCOVA, with an opposite purpose. The analysis of covariance is a statistical method used to test the effects of categorical variables on a continuous dependent variable, controlling for the effects of selected other continuous nuisance variables, which co-vary with the dependent. On the contrary, we can use “rmcorr to determine the relationship between two continuous variables, while controlling for the effect of the categorical variable, which in this case is the between-participants variance” (Bakdash and Marusich 2017, 3).
This method is equivalent to fitting parallel lines through the data of each observation and the residual sum of squares represents the variation about these lines.
The rmcorr generally has much greater statistical power than a standard Pearson correlation using averaged data, and its power increases exponentially when either the value of the number of repeated observations, or that of the total number of unique observations, increases.
These approaches may lead to misleading conclusions, because they do not offer an explicit description of the three-way interaction in the data. For this reason, some specific techniques have been developed to obtain direct solutions for three-way dataset (for a review, please see: Kroonenberg 1983; Kiers and Mechelen 2001; Kroonenberg 2008). The strength of three-way methods is that they summarize the entities of each mode through a few components and describe the relations between these components (Kiers and Mechelen 2001, 84). For the objectives of exploratory analysis in the construction of composites, these techniques would probably be too sophisticated and would still require more specific and broader treatment.
We would have been possible to include the indicator of adequate nutrition among the risk factors. As is evident, it can be considered both in the current condition and in the risk. However, we decided not to include it as a risk factor for Goal 2, especially because we did not have other risk indicators available. Considering it as the only risk factor would probably be limiting for the concept.
We must point out that healthy life expectancy is constructed using the prevalence of individuals who respond positively ("well" or "very well") to the perceived health question. The indicator includes in itself part of the good health index. Therefore, the high values of the CB and CW are also due to the way in which the indicator in question was constructed.
The trends are fluctuating for both indicators considered.
The decrease concerns all three indicators: the life expectancy at birth passes from 82.8 to 82.7; the healthy life expectancy at birth from 58.8 to 58.7 and the life expectancy without activity limitations at 65 years goes from 9.8 to 9.7. These decreases are very slight. However, we must consider that life expectancies vary very slowly over time and, therefore, could have a very strong weight, especially when compared to a positive trend in previous years.
Other behaviours may affect health; however, we cannot include them in our analysis because of lack of data.
All individuals who practice at least one of the behaviours at risk are identified as "consumers at risk", exceeding the daily consumption of alcohol (according to specific thresholds for sex and age) or concentrating in a single occasion of consumption the assumption of more than 6 alcoholic units of any drink (binge drinking).
The indicator has a decreasing trend until 2015. In 2016, it pick-ups, followed in 2017 by a slight decrease.
Looking at the average values for simplicity, 25.5% of regional population present at least one behaviour at risk in the consumption of alcohol compared to 17.7% of the national population.
The trend of the adequate nutrition index in 2017 is the highest amongst all the regional ones (23.6%).
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Alaimo, L.S., Maggino, F. Sustainable Development Goals Indicators at Territorial Level: Conceptual and Methodological Issues—The Italian Perspective. Soc Indic Res 147, 383–419 (2020). https://doi.org/10.1007/s11205-019-02162-4
- Sustainable development
- Italian regions
- Composite indicators
- Repeated measures correlation analysis
- Aggregation-through-compensation fallacy