sustainability Brief Report Demographic Transitions and Socioeconomic Development in Italy, 1862–2009: A Brief Overview Luca Salvati 1 , Margherita Carlucci 2 , Pere Serra 3 and Ilaria Zambon 4, * 1 Council for Agricultural Research and Economics (CREA), Viale Santa Margherita 80, I-52100 Arezzo, Italy; luca.salvati@crea.gov.it 2 Department of Social and Economic Sciences, Sapienza University of Rome, Piazzale A. Moro 5, I-00185 Rome, Italy; margherita.carlucci@uniroma1.it 3 Department of Geography, Autonomous University of Barcelona, Building B, Campus UAB, ES-08193 Bellaterra (Cerdanyola del Vallès), Barcelona, Spain; pere.serra@uab.cat 4 Department of Agricultural and Forestry Sciences (DAFNE), Tuscia University, Via San Camillo de Lellis, I-01100 Viterbo, Italy * Correspondence: ilaria.zambon@unitus.it  Received: 28 November 2018; Accepted: 26 December 2018; Published: 6 January 2019  Abstract: The present study investigates long-term population dynamics in Italy, 1862–2009, in light of Demographic Transitions (DTs). Under the assumption that DTs are multidimensional processes of change involving several aspects, including population structure and dynamics, an exploratory analysis was carried out in this study to verify temporal coherency of 15 indicators in Italy, identifying homogeneous time periods with distinct demographic characteristics. Indicators’ trends were identified using a multivariate statistical approach. The results of this study allow empirical testing of the assumption of temporal coherence between different aspects of a long-term DT, distinguishing distinctive population dynamics and the differential impact on population structure over two centuries. After a relatively long period of demographic stability, the time window encompassing the two World Wars—approximately between 1921 and 1951—was identified as a primary turning point of population dynamics in Italy; a second turning point was estimated at the beginning of the 1970s. These time intervals may represent conditions of dynamic equilibrium between demographic and socioeconomic contexts, highlighting latent system transitions. The study concludes by outlining the importance of a more effective integration of demographic transition theories into a broader sustainability framework, and implementing a diachronic analysis of political, economic, and social forces associated with population dynamics in both advanced economies and emerging countries. Keywords: population dynamics; socioeconomic structure; exploratory data analysis; Italy 1. Introduction While latent interplays between population trends and socioeconomic development have been frequently investigated at global, regional, and local spatial scales [1–7], feedback relationships between demographic dynamics and broader dimensions of sustainability were more rarely outlined in recent studies. In this regard, socioeconomic development and population trends were disentangled within the framework of ‘modernization’ theory, hypothesizing a process-based transformation from traditional (underdeveloped) societies to modern (wealthy or emerging) societies with an advanced economy [8,9]. Focusing on basic conditions for industrialization and tertiarization [9], the theory of modernization emerged in the 1950s with the aim of explaining how industrial societies developed thanks to pivotal socio-political and cultural forces in the process of economic expansion. Modernization implies a generalized development within societies, including (but not restricted to) Sustainability 2019, 11, 242; doi:10.3390/su11010242 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 242 2 of 12 agricultural mechanization, massive industrialization, and the rapid shift towards (traditional and more advanced) services. The theory assumes that economic development depends primarily on technological import and several additional socio-political, productive, cultural, and institutional forces [10]. This theory postulates that societies and economies develop in (more or less) predictable stages through which they become increasingly complex, proceeding through increasingly non-linear transitions and more articulated transformations of their components [11]. These include—but are not limited to—increased levels of education and development of transport, logistic, communication, mass media, and international trade [12–14]. Urbanization (together with a shift from compact to more dispersed forms of urban expansion) and demographic change (population aging, fertility decline, and a progressive rearrangement of traditional families) are at the same time the cause and the effect of modernization [12]. In this regard, modernization and the consequent socioeconomic development are intimately linked with demographic transitions. The concept of Demographic Transition (DT) refers to a continuous trend from high birth and death rates to low birth and death rates. This transition involves countries developing from pre-industrial to industrialized economic systems, with consequent socio-environmental changes identified and classified into four stages [15,16]. In the first stage, represented by a socioeconomic context typical of a pre-industrial society, birth and death rates are high and balanced [17]. In the second stage, death rates drop rapidly due to improvements in food supply and sanitation increasing life spans and reducing diseases. In the third stage, birth rates fall due to extended access to contraception, urbanization, reduced agricultural subsidies, wage increases, a rising status and education of women, containment of children’s work, and an intense expansion of parental investments in the education of children, together with other minor social changes [18]. During the fourth stage—corresponding to contemporary societies—low birth rates and low death rates were extensively observed in advanced economies [19]. In Europe, a declining birth rate was observed for the first time in the late 19th century, especially in northern countries. By the late 20th century, birth and death rates leveled off at lower rates [18]. Fertility decline was usually greater among younger women, which suggests postponement rather than forgoing of births [20]. Substantial increases occurred in the percentage of young single adults who lived with their parents, contributing to a long-term trend toward intergenerational co-residence [21]. A slight decline in divorce and separation was also recorded in socioeconomic contexts characterized by high unemployment [22]. At the same time, changes in mortality rate and income inequalities resulted from a complex combination of socioeconomic factors [23–26]. Regulatory feedbacks between modernization (i.e., socioeconomic development) and socio-environmental issues are intimately linked with DTs. These transformations are particularly evident when studying the long-term evolution of a given country (or region) based on social, cultural, and economic indicators [15]. Due to the supposed relationship between socioeconomic development and population dynamics, a more comprehensive understanding of DTs may shed light on long-term economic changes and socio-ecological transformations [27]. In this line of thinking, interpretative models are rather generalizations that apply to a group of countries and that may not accurately describe each individual case. A multidimensional, diachronic analysis of the most relevant attributes of demographic transitions, and their coherence with external factors supposed to influence population dynamics, is thus particularly useful when ascertaining latent interactions between sustainable development and demographic patterns/processes. Based on these premises, our analysis contributes to the assessment of latent aspects dealing with long-term socioeconomic development and population dynamics in a European country, under the assumption that DTs are complex processes characterized by multiple socioeconomic dimensions that lead to differentiated conditions advancing (or containing) sustainable development. Based on a multivariate analysis of demographic time series data representative of a relatively long time period (1862–2009), this study investigates multiple dimensions of the demographic transition in Sustainability 2019, 11, 242 3 of 12 Italy, discussing long-term population-economy interplays and their impact on the inherent transition toward a truly sustainable development. 2. Methodology The investigated area includes 301,330 km2 of Italian land. Demographic data considered in the present study refer to homogeneous geographical boundaries within the study period [28]. Population stock and vital statistics (births, deaths, and marriages), were used to detect recent demographic trends in Italy, which have been presented and discussed by [29] and [30]. A total of 15 demographic indicators derived from an extended set of country-scale time series annual data (1862–2009) released by the Italian National Institute of Statistics (ISTAT) were adopted with the aim to investigate DTs in Italy. Indicators were collected and harmonized from official data sources including population censuses and demographic registers. Indicators adopted in this study assess multivariate aspects of demographic change, investigating population structure and dynamics [31–33]. Indicators include (i) natural balance (%, per year, ‘GRO’), (ii) migratory balance (%, per year, ‘MIG’), (iii) crude sex ratio (males-to-females, ‘SEX’), (iv) crude birth rate (per 100 resident inhabitants, ‘BIR’), (v) crude death rate (per 100 resident inhabitants, ‘DEA’), (vi) stillbirth rate (per 100 births, ‘STI’), (vii) infant mortality rate (deaths at age 0–4 years per 100 resident inhabitants, ‘INF’), (viii) non-legitimate (natural) children (births outside marriage per 100 births, ‘NAT’), (ix) marriage rate (per 100 resident inhabitants, ‘MAR’), (x) absolute difference between groom and bride age (years, ‘BRI’), (xi) absolute difference between female and male median age (years, ‘MER’), (xii) median age at death, males (years, ‘AGE’), and, finally, (xiii) average age at marriage, groom (years, ‘SPO’). Two supplementary indicators were considered: (xiv) total population (logarithm, ‘POP’), and (xv) urbanization rate (%, ‘URB’). Indicators from (i) to (ix) assess demographic dynamics from different points of view (births, deaths, marriages); indicators from (x) to (xiii) quantify specific aspects of population structure. Indicators (xiv) and (xv) provide a context-based analysis of demographic trends, estimating total population density and urbanization processes over Italy. Non-parametric correlations and a multivariate statistical approach incorporating Principal Component Analysis (PCA) and Hierarchical Clustering (HC) were applied to classify indicators into homogeneous groups, relating demographic dynamics to specific time intervals during the study period. The PCA is an exploratory statistical technique extensively used to identify and summarize latent factors possibly influencing the multivariate relationship among variables within a data set [2,4,32]. The PCA allows reduction of redundancy resulting from high levels of serial autocorrelation in the observed data, identifying the most relevant (independent) dimensions of Italy’s DT. Selection of relevant components in a PCA is a subjective matter, since the technique is exploratory in its aims and not based on hypothesis testing. Using a correlation matrix as the input data matrix of a PCA, each input variable contributes with unitary variance to the overall matrix variance. Under the assumption that relevant components explain a higher variance than the variance of an individual variable, components with eigenvalue >1 were retained and analyzed in detail. Loading and score plot was extensively used to assess independent dimensions of DTs (the most characteristic indicators) in Italy. The PCA was supplemented with a hierarchical clustering (Euclidean distances, Ward’s agglomeration rule) assessing similarity patterns over time in the selected demographic indicators. A visual inspection of dendrograms was adopted to classify indicators in consistent groups with similar demographic and socioeconomic features. 3. Results 3.1. Descriptive Statistics Italy underwent a DT over the last two centuries reflected in specific trends of selected demographic indicators, as shown in Figure 1. A progressive decline of both fertility and mortality rates was observed (with a 30-year delay in long-term birth rate compared with death rate), respectively Sustainability 2019, 11, 242 4 of 12 stabilizing to the lowest values in the early 1950s and the mid-1980s. The time period between the early 1950s Sustainability and 2018, 10,the x FORmid-1980s was thus characterized by net population expansion; isolated 4spikes PEER REVIEW of 12 in time series of birth and death rates reflect demographic shocks caused by World Wars, as clearly evidenced the early 1980s,in theremaining time series of total population. relatively stable since Population growth decelerated the 1990s. Migration balance was in the early 1980s, systematically remaining relatively stable since the 1990s. Migration balance was systematically negative (emigration > immigration) up to the early 1970s, with a progressive increase afterward negative (emigration and a>structural immigration) peak up to the since recorded earlythe 1970s, earlywith a progressive 2000s. Urbanization increase afterward rate increased and a structural continuously duringpeakthe recorded study sinceaccelerating period, the early 2000s. sinceUrbanization World Warrate II. increased continuously Other indicators during displayed a the study period, temporal trend acceleratingsimilarly responding since World War II. Othershocks to demographic indicators (Worlddisplayed Wars) anda temporal long-termtrend responding population similarly trends. For to demographic instance, marriageshocks (World Wars) rate declined and long-term progressively population over time (with timetrends. For series instance, spikes marriage rate corresponding to declined World Wars progressively I and II) and over time (with stabilized time to the series lowest spikesincorresponding values the mid-1980s.toInfant Worldmortality Wars I and II) and followed astabilized similar time to the lowest pattern, values inrapidly decreasing the mid-1980s. and linearlyInfant mortality up to followed the mid-1950s anda then similar time pattern, declining more decreasing slowly up torapidly and linearly a quasi-zero valueup to the mid-1950s recorded and2000s. in the early then declining Crude birth more slowly and death uprates to a quasi-zero display a distinct (linear) relationship value recorded over two in the early 2000s. Crudetime intervals birth (before and death ratesand after aWorld display War distinct II). Moreover, (linear) relationship a separate over twoshort-term time intervalstrend(before was observed and afterfor the War World time II). interval encompassing Moreover, a separatethe two World short-term trendWars was (1915–1945), observed forconfirming previous the time interval results as shown encompassing in Figure the two World2.Wars (1915–1945), confirming previous results as shown in Figure 2. 4,80 36 36 4,75 32 32 28 28 4,70 Log (Pop) 24 24 4,65 DEA 20 20 BIR 4,60 16 16 4,55 12 12 4,50 8 8 4,45 4 4 4,40 0 0 16 32 48 64 80 96 112 128 144 16 32 48 64 80 96 112 128 144 16 32 48 64 80 96 112 128 144 Year(1=1862) Year(1=1862) Year (1=1862) 16 16 96 12 12 92 8 8 88 4 4 84 GRO URB MIG 0 0 80 -4 -4 76 -8 -8 72 -12 -12 68 -16 -16 64 -20 -20 60 16 32 48 64 80 96 112 128 144 16 32 48 64 80 96 112 128 144 16 32 48 64 80 96 112 128 144 Year(1=1862) Year(1=1862) Year(1=1862) Figure 1. Trends over time in selected demographic indicators in Italy (1862–2009); BIR: Crude birth Figure 1. Trends rate (per over time 100 resident in selected inhabitants), demographic DEA: Crude death indicators in 100 rate (per Italyresident (1862–2009); BIR: Crude inhabitants), birth Log(Pop): rate (per 100 absolute residentnumber population inhabitants), DEA: Crude (logarithm), death rate GRO: natural (per (% balance 100population resident inhabitants), Log(Pop): growth per year), MIG: absolute migratory balance (% population growth per year), URB: urbanization rate (%); some time series MIG: population number (logarithm), GRO: natural balance (% population growth per year), spikes migratory balance in the 1910s (%1940s and the population growth reflect World perI and War year), URB: World Warurbanization II. rate (%); some time series spikes in the 1910s and the 1940s reflect World War I and World War II. A descriptive analysis was carried out by computing average values and Coefficients of Variation (CV) for individual variables over two-time intervals as shown in Table 1, corresponding to a pre-war phase (1862–1945) and a more recent post-war phase (1946–2009). Some indicators increased substantially in the second time interval, displaying an increased variability over time. For instance, the migratory balance moved, on average, from negative values to slightly positive values, with a ten-fold increase of variability over time. A reverse pattern was observed for natural balance, crude birth rate, and stillbirth rate. Crude death rate clearly decreased over time in both average and variability over time. Taken together, population structure attributes showed moderate or more Sustainability 2019, 11, 242 5 of 12 relevant increases over time; conversely, demographic dynamics attributes showed moderate decreases over time. 2018, 10, x FOR PEER REVIEW Sustainability 5 of 12 36 1918 1867 32 1880 1875 1862 1872 1863 18741868 1870 1873 1871 1864 1865 1879 1878 1866 1876 28 18861877 1869 1887 1882 1883 1888 1881 1885 1884 1917 18901892 1891 1889 18951894 1893 24 1900 1896 1916 1898 1915 1903 1908 1901 19021899 1905 1897 1911 1909 1904 19071906 DEA 20 1910 1919 1913 1920 1922 1912 1914 1943 1921 1926 1925 1924 1923 16 1944 1929 1928 1927 1942 1941 1932 1931 1945 193719351938 1930 1936 1940 1933 1939 1934 12 1946 1947 1949 1948 1998 2003 1999 1976 1975 1951 1963 1956 1952 1953 1997 2000 1995 1994 1996 1993 2009 2008 1983 1980 1973 1970 1969 1968 1965 1962 1950 2002 2005 2001 2007 20061991 1986 1985 1992 1990 1987 20041989 1984 1981 1988 1982 1979 1978 1977 1974 19721971 1957 1967 1960 1964 1958 1961 1966 1955 1954 1959 8 4 0 12 15 18 21 24 27 30 33 36 BIR Figure 2. The relationship relationshipbetween betweencrude crudebirth birth rate rate (BIR) (BIR) and and crude crude death death raterate (DEA) (DEA) in Italy, in Italy, 1862– 1862–2009. 2009. Table 1. Descriptive statistics (average and coefficient of variation (CV) over time) of demographic A descriptive analysis was carried out by computing average values and Coefficients of indicators in Italy by time interval. Variation (CV) for individual variables over two-time intervals as shown in Table 1, corresponding to a pre-war phase (1862–1945) and 1862–1945 a more recent post-war phase (1946–2009). Some indicators 1946–2009 Variable increased substantially in the second Mean time interval, CV displaying an increased Mean variability CV over time. For instance, the migratory balance moved, on average, from negative values to slightly positive values, GRO 8.4 51.9 4.3 97.8 with a ten-fold increase SEX of variability1.0 over time. A2.7reverse pattern 1.0was observed 0.8for natural balance, crude birth rate, and BIRstillbirth rate. Crude death rate 31.1 19.3 clearly decreased 14.1 over time in both average and 30.9 DEA 22.7 26.5 9.8 4.8 variability over time. Taken together, population structure attributes showed moderate or more −1.8 relevant increasesMIGover time; conversely, 117 demographic 0.1 dynamics 2639 attributes showed moderate NAT 0.1 20.2 0.1 85.2 decreases over time. STI 3.5 20.5 1.4 74.6 BRI 3.7 8.8 3.3 10.5 Table 1. Descriptive MAR statistics (average 1.4 and coefficient 19.6 of variation 1.3(CV) over time) of demographic 22.1 SPOby time interval. indicators in Italy 29.4 2.5 29.5 6.1 AGE 60.1 9.7 72.4 4.1 MER 1.0 1862–1945 2.4 1.11946–2009 1.8 INF Variable 3.9 Mean 23.5 CV 0.6 Mean CV107 POP 4.5 1.5 4.7 0.7 GRO 8.4 51.9 4.3 97.8 URB 72.9 2.0 86.2 6.4 SEX 1.0 2.7 1.0 0.8 BIR 31.1 19.3 14.1 30.9 3.2. Multivariate Analysis DEA 22.7 26.5 9.8 4.8 MIG Analysis was A Principal Component −1.8 117 carried out on the data0.1 2639 matrix constituted of demographic NAT temporal 0.1 indicators, evidencing similar trends and20.2 0.1 linkages between 85.2 relevant indicators. the most As shown in Table 2, theSTI 3.5 first two components 20.5 for a cumulated accounted 1.4 74.6 variance larger than 80%, summarizing peculiar time BRIpatterns for each 3.7 analysis’8.8 dimension,3.3 10.5 and the indicators contributing the most to such trends. MAR 1.4 19.6 1.3 22.1 SPO 29.4 2.5 29.5 6.1 AGE 60.1 9.7 72.4 4.1 MER 1.0 2.4 1.1 1.8 INF 3.9 23.5 0.6 107 POP 4.5 1.5 4.7 0.7 URB 72.9 2.0 86.2 6.4 Sustainability 2019, 11, 242 6 of 12 Table 2. Indicators’ loadings to selected principal components (see Figure 3); bold indicates relevant loadings >|0.5|; * indicates a supplementary variable. Indicator Component 1 Component 2 GRO 0.65 −0.50 SEX 0.91 0.33 BIR 0.97 0.14 DEA 0.84 0.49 MIG −0.62 0.58 NAT −0.32 0.79 STI 0.83 −0.15 BRI 0.79 0.11 MAR 0.58 −0.56 SPO −0.26 0.86 AGE −0.90 −0.28 MER −0.90 −0.20 INF 0.95 0.24 *POP −0.94 −0.30 *URB −0.94 −0.04 Exp. Var. (%) 58.7 21.7 Sustainability 2018, 10, x FOR PEER REVIEW 7 of 12 2009 2008 2007 1918 2006 4,0 2004 2003 2005 3,2 1917 SPO 1867 2002 NAT 2,4 1916 1868 1866 18701871 MIG 1862 1880 1874 1872 1863 1,6 1864 1865 2001 1878 DEA 1873 18691875 Co mponen t 2 2000 1879 1877 1882 1876 1915 1886 1999 1883 1998 SEX 1881 1997 1885 1884 0,8 1887 INF 1890 1996 1943 1893 1892 1888 1919 1894 1889 1995 1944 BIR 1891 1994 BRI 1895 1896 1903 19011902 1991 1993 1941 1914 19041900 1898 1905 -6 -5 -4 1992 -3 -2 -1 1945 1942 1 2 1907 1897 3 1911 1909 190619081899 1990 STI 1910 1989 1988 1940 19871986 MER 1939 -0,8 1985 AGE 1984 1983 19351932 1936 19261925 1938 19291928 1927 1982 1980 1981 1979 1933 1931 1924 1978 1953 1934 1922 1912 1952 1951 1949 1930 1923 1976 1950 -1,6 1937 1946 1977 1956 19581954 GRO 1975 1973 1959 1957 1955 1974 1948 MAR 1921 1972 1960 1913 1962 1947 1968 1967 1965 1961 1969 1966 1963 1970 1964 1971 -2,4 1920 Component 1 Figure Figure 3. 3.Biplot Biplotillustrating illustratingloadings loadingsand andscores scorestotoprincipal principalcomponents components(see Table (see Table1).1). Component A hierarchical1 clustering was correlated with the was finally run natural balance on the same data(the interplay matrix with the between birth and aim to identify death similar trends with rates), overinfluence time in demographic indicators, on the age structure asItalian of the shownpopulation. in Figure 4.This Theaxis empirical analysis identifies may satisfactorily describe two clusters long-term that discriminate population patternsindicators assessing and relevant vital statistics consequences (births of the DT inand deaths) Italy. from indicators As shown in Figure 3, quantifying 2population Component structure was associated by migratory with the age, migration, balanceandandmarriage trendsdirectly vital statistics and more recentby influenced demographic social change, phenomena (e.g., children including marriage rate andout of marriage). children This last group out of marriage, and may displays providesimilarities with a more dynamic both urbanization rates and total population. These results complement the empirical description of social ‘modernization’ trends in Italy, specifically referring to short- and medium-term findings presented trends above,in observed distinguishing the aftermathlong-term of World trends War II. in the natural component of demographic balance and more volatile (and A hierarchical recent) trends clustering influenced was finally run by on socioeconomic the same datachangematrixatwith global theand aimlocal to scales. identify similar trends over time in demographic indicators, as shown in Figure 4. The empirical analysis identifies two clusters that discriminate indicators assessing vital statistics (births and deaths) from indicators quantifying population structure by age, migration, and marriage trends and more recent demographic phenomena (e.g., children out of marriage). This last group displays similarities with both urbanization rates and total population. These results complement the empirical findings presented A hierarchical clustering was finally run on the same data matrix with the aim to identify similar trends over time in demographic indicators, as shown in Figure 4. The empirical analysis identifies two clusters that discriminate indicators assessing vital statistics (births and deaths) from indicators quantifying2019, Sustainability population 11, 242 structure by age, migration, and marriage trends and more recent 7 of 12 demographic phenomena (e.g., children out of marriage). This last group displays similarities with both urbanization rates and total population. These results complement the empirical findings above, presenteddistinguishing long-term long-term above, distinguishing trends in the natural trends component in the of demographic natural component balance and of demographic more balance volatile (and recent) trends influenced by socioeconomic change at global and local scales. and more volatile (and recent) trends influenced by socioeconomic change at global and local scales. Figure 4.4.Hierarchical clustering Hierarchical of population clustering indicators of population during the indicators Italian during demographic the transition, Italian demographic 1862–2009.1862–2009. transition, 4. Discussion 4. Discussion Modernization Modernization waswasconsidered considereda aprocess processofof change change towards towards newnew types types of social, of social, economic, economic, and and political systems that have developed and consolidated in economically-advanced political systems that have developed and consolidated in economically-advanced countries, countries, especially in Western Europe and North America, over a relatively long time period (17th–19th century), spreading (more or less) rapidly to other countries [8,10,34]. In this framework, the present study explores and clarifies the empirical linkages between population indicators along a complete demographic transition in Italy, defining homogeneous time periods and breakdowns according to the dominant socioeconomic context and possibly reflecting different ‘modernization’ stages. The results of this work provide an overview of the long transition toward modernization in Italy, focusing on the interplay between demographic patterns and processes [35–39]. At the same time, our study contributes to the analysis of DTs by adopting a wide set of yearly-based indicators that reflect different demographic patterns and processes over a sufficiently long-time span. Being grounded on multivariate exploratory statistical techniques, our approach provides a conceptual and operational perspective to understand demographic trends at country and regional scales, with a special focus on past and present pre-modern societies and their transition to ‘Westernized’ patterns of economic growth and change in socio-political structures and cultural/institutional organizations. Advanced economies have shown a turning point from traditional to advanced economic systems in the years between the two World Wars, reflecting a transitional period that coincides with rapid population changes [19]. Although being characterized by a relatively large spatial variability across countries and regions [40–42], an improved characterization of this transitional period sheds light on the relationship between demographic and socioeconomic transitions. The strength of such a linkage is increasingly dependent on rapidly-changing socioeconomic contexts typical of advanced economies [2,43–45]. In these regards, the empirical findings of this study corroborate earlier evidence supporting the assumption that sequential demographic transformations are associated with a broad spectrum of socioeconomic processes driven by industrialization and urbanization [9,35–39,45]. Involving multiple dimensions that are difficult to identify and indeed acting as synergistic drivers of change, the interplay between demographic transitions and socioeconomic development is particularly Sustainability 2019, 11, 242 8 of 12 relevant in economically-advanced countries. While this study focused on a long period covering 160 years of Italian development, a comparable investigation can be extended to other countries in Southern Europe (such as Spain, Portugal or Greece), with the aim to identify common trends and specific demographic processes, under the assumption that changes in fertility and mortality rates are responsible for distinctive trends of population increase or loss [46]. Mechanisms through which socioeconomic development has influenced fertility rates in recent times include the effects of economic uncertainty [47], job instability [48], unemployment [49–51], volatility in housing markets and mortgage foreclosures [52]. These processes frequently resulted in marriage postponement [53] and declining marriage rates [30,54–58]. Our findings definitely highlight the importance of integrated assessment frameworks in evaluating economic and socio-demographic issues jointly. In this regard, demographic transitions in Italy were observed from a ‘co-evolutionary’ perspective, identifying distinct components of transition (long-term changes in vital statistics and short-term transformations in migratory patterns, in turn linked with important changes in marriage behavior). Implying an intense interplay between demographic and economic systems [59,60], co-evolution has been known as a main outline for understanding alteration in multifaceted systems [59,61–64], including biological [65,66], social [34,67–76], gene-culture [77], bio-social [78], and socio-ecological structures [79,80]. A comprehensive analysis of population trends in Italy contributes to identify homogeneous time intervals as progressive developmental waves featuring distinct demographic dynamics [18]. These time spans represent conditions of dynamic equilibrium between environmental and socioeconomic contexts [36,39]. Demographic dynamics have indirectly influenced these transitions, e.g., polarizing population distribution in high-density and low-density settlements and leaving large areas of the country under marginal economic conditions, low accessibility, and unbalanced demographic structures because of aging [40–44]. Based on these premises, our study confirms the interpretative framework grounded on the demographic transition theory, identifying distinct components of transition in Italy that outline the intimate relationship between historical development trends and long-term demographic dynamics. 5. Conclusions The approach proposed in this study allows identification of demographic patterns and processes while considering the evolving socioeconomic context over time [77–79]. Multivariate statistical techniques are used to examine long-term population dynamics in Italy during the study period, identifying the most relevant dimensions of Italy’s demographic transition. Empirical results of this study document a long period of demographic stability in Italy, with two primary turning points of population dynamics. While one is marked by the time window of the two World Wars, the other one is at the beginning of the 1970s. More specifically, the 1930s and the 1940s were identified as a time interval with a particularly evident turning point in demographic dynamics, as distinctive groups of indicators characterizing pre-transitional, transitional, and post-transitional developmental phases clearly outline. An explicit analysis of diachronic processes (demographic transition and socioeconomic development) allows a more comprehensive investigation of the rapidly-transforming socioeconomic context at the base of ‘modernization’ stages, providing a knowledge base that may advance strategies of sustainable development oriented toward more balanced socio-environmental complex systems. Author Contributions: Conceptualization, M.C. and L.S.; methodology, L.S.; validation, M.C., P.S., L.S. and I.Z.; formal analysis, I.Z.; investigation, M.C., P.S., L.S. and I.Z.; data curation, L.S.; writing—original draft preparation, L.S. and I.Z.; funding acquisition, M.C. and L.S. Funding: This research has been partly financed with funding from the project ’CRISIS’, Sapienza University of Rome. Conflicts of Interest: The authors declare no conflict of interest. Sustainability 2019, 11, 242 9 of 12 References 1. Karamesouti, M.; Detsis, V.; Kounalaki, A.; Vasiliou, P.; Salvati, L.; Kosmas, C. 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