Mazziotta Pareto Index Research Articles

Analyzing Economic Resilience of Rural Tourism in Indonesia Using Synthetic Composite Index

Research Originality: This study used a new method, namely the Adjusted Mazziotta-Pareto Index (AMPI) to measure and categorize economic resilience using clustering analysis. In addition, the innovative method had not been previously applied to tourism resilience in Indonesia, making this study the first to measure tourism village resilience.Research Objectives: This study aimed to analyze economic resilience of tourism village destinations in Indonesia from 2019 to 2022 during the COVID-19 pandemic. Research Methods: The procedures were carried out using a quantitative method to determine resilience index of tourism village in Indonesia during the COVID-19 pandemic. Data were obtained from form management documents as well as 24 tourism villages across Java, Bali, and West Nusa Tenggara Provinces. Empirical Results: The results showed that the majority of villages were significantly impacted in the initial year of COVID-19, but were able to recover, demonstrating strong capacity and performance in recovering from the pandemic shock. In addition, economic aspects of capacity and performance showed high adaptability after the pandemic, indicating relative resilience to the shock. Implications: The results of this study could inform policies to enhance tourism village resilience in Indonesia. JEL Classification: O18, Z32, Z38How to Cite:Ariyani, N., & Fauzi, A. (2024). Analyzing Economic Resilience of Rural Tourism in Indonesia Using Synthetic Composite Index. Etikonomi, 23(2), 415 – 432. https://doi.org/10.15408/etk.v23i2.33355.

A new class of composite indicators: The penalized power mean

In this paper we propose a new aggregation method for constructing composite indicators based on a penalization of the power mean. The idea underlying this approach consists in multiplying the power mean by a factor that accounts for the horizontal heterogeneity among indicators while penalizing units with a larger heterogeneity. In line with the minimum loss of information principle, the penalization factor proposed is proven to be linked to the loss of information generated when the indicators are substituted with their power means. As a consequence, the aggregation approach gives rise to the class of penalized power means and the penalized Benefit of the Doubt aggregative approach. Including heterogeneity makes the aggregation approach more suitable for refined rankings. Interestingly, the penalized power mean of order one coincides with the Mazziotta Pareto Index. Some theoretical properties of the penalized power means are proven, thus supporting the Mazziotta Pareto index. An empirical analysis of the Human Development Index in 2019 is presented. Comparisons of the rankings induced by the penalized and non-penalized Benefit of the Doubt and power mean aggregation approaches are shown. There are three main findings: the penalized power means satisfy the properties characterizing weakly monotone aggregation functions; the penalization reduces ranking variations while differentiating units with close means; and the geometric mean provides composite indicators whose ranking is closest to those obtained with power means of different order.

Measuring the resilience of rural tourism in Indonesia using the Adjusted Mazziotta-Pareto index

Rural tourism plays a crucial role in rural development in Indonesia by providing employment opportunities, livelihood, infrastructure, cultural preservation, and environmental preservation. However, it is prone to external shocks such as natural disasters, public health events, and volatility in the national and global economy. This study measures the resilience of rural tourism to external shocks caused by the COVID-19 pandemic in 24 rural tourism destinations in Indonesia covering four years from 2019 to 2022. A synthetic composite index of the Adjusted Mazziotta-Pareto index (AMPI) is used to measure rural tourism resilience followed by clustering analysis to determine the typology of the resilience. The AMPI measure is also compared with the conventional Mazziotta-Pareto index (MPI) method. The resilience index is composed of capacity and performance components related to resilience. The results show that in the first year of COVID-19, most tourism villages in Indonesia were severely affected by the pandemic, yet they were able to recover afterward, as indicated by positive differences in the AMPI index before and after COVID-19. Thus, rural tourism villages in Indonesia have a strong capacity and performance to recover from pandemic shock. Lessons learned from this analysis can be applied to policies related to rural tourism resilience in developing countries.

Measuring Economic Resilience of Tourism Villages: A Spatiotemporal Analysis of Pre and Post-Covid-19 Pandemic

Tourist village plays an important role in rural development in Indonesia. Nevertheless, tourist village is also prone to external shocks such as national and global economic volatilities and recent public health events of the Covid-19 pandemic. This study attempts to analyze a temporal variations of tourist village economic resilience from pandemic shock in 24 tourist village destinations covering the period of 2019-2022 in Indonesia. A synthetic composite index of the Adjusted Mazziotta-Pareto Index (AMPI) was used to measure resilience, followed by clustering analysis to determine the typology of the resilience. The resilience index was composed of capacity and performance dimension related to resilience. The results show that most villages were severely affected in the first year of Covid-19, yet they recovered afterward, as indicated by positive differences in the AMPI index before and after Covid-19. This result shows that tourist villages in Indonesia have a tendency of strong capacity and performance to recover from the pandemic shock. The economic components of the capacity and performance were able to readjust after the pandemic indicating that these components are relatively adaptable to the shocks. The indicator that has the most significant influence on the typology of resilience in the performance dimension is the number of visitors. Meanwhile, the Development Village Index (DVI) indicator is the most significant influence on the capacity dimension.

Indexing blood banking performance in India: A retrospective cross-sectional analysis of states and districts

BackgroundMultiple international assessments have highlighted gaps in blood banking globally. However, there is a dearth of subnational assessments. We applied a thematic framework with multiple composite indices to assess blood bank performance across Indian states and districts. MethodsIn this retrospective cross-sectional analysis, data for 2493 blood banks in 35 states/union territories (UTs) and 616 districts was extracted from the National Blood Transfusion Council for 2016. The framework involved seven themes (accreditation, ownership, safety, volume, infrastructure, regulation, and workforce) with several indicators nested under them. Composite thematic indices and an overall index (0-100, with 100 being the best performance) were constructed using the adjusted Mazziotta-Pareto index method that can provide composite indices that are partially non-compensatory and easily interpretable. ResultsThe state-level median [interquartile range] value of the overall index was 59.61 [46.35, 71.67]. Chandigarh had the highest values for safety, regulation, workforce, and ownership indices, Maharashtra for volume and infrastructure indices, and Manipur for accreditation index. Districts in southern and western states performed well on the overall index with inter- and intra-state variations for themes. District-level correlations depicted positive associations among indices with the strongest correlation between ownership and accreditation indices (n=616, R=0.92, p<0.001). ConclusionBlood banking in India is fragmented, with variations in themes across geographies. The northern and northeastern states require more attention for volume, accreditation, infrastructure, and ownership. While the southern and western-central regions need to prioritize safety. The framework with thematic indices, when applied to routine data, can be useful for monitoring and evaluation to decide local policies and resource allocations.

Periodical measurement of multidimensional phenomena

AbstractSynthesizing multidimensional phenomena such as well-being in the form of composite indicators has been gaining popularity in recent years. The Mazziotta-Pareto Index is one of the methods of constructing non-compensatory composite indices. The paper proposes a modification of this method, which (unlike the original) enables periodical measurements and can be used to compare countries in research on East European transition. The essence of this modification is the use of anti-pattern normalization, during which only current data is used, and yet after normalization, the indicators are in a certain way comparable over time. The Anti-Pattern Normalized Mazziotta-Pareto Index (APMPI), unlike the original, does not change the previously determined values after the inclusion of new data. Both the imperfection of the original approach and the new proposal are illustrated by an empirical example. Indices of well-being for the OECD countries are constructed. The example shows that although AMPI and APMPI values are not comparable, the rankings based on them are not very different.

A Novel Approach to Measuring Spatiotemporal Changes in Social Vulnerability at the Local Level in Portugal

Social vulnerability, as one of the risk components, partially explains the magnitude of the impacts observed after a disaster. In this study, a spatiotemporally comparable assessment of social vulnerability and its drivers was conducted in Portugal, at the civil parish level, for three census frames. The first challenging step consisted of the selection of meaningful and consistent variables over time. Data were normalized using the Adjusted Mazziotta-Pareto Index (AMPI) to obtain comparable adimensional-normalized values. A joint principal component analysis (PCA) was applied, resulting in a robust set of variables, interpretable from the point of view of their self-grouping around vulnerability drivers. A separate PCA for each census was also conducted, which proved to be useful in analyzing changes in the composition and type of drivers, although only the joint PCA allows the monitoring of spatiotemporal changes in social vulnerability scores and drivers from 1991 to 2011. A general improvement in social vulnerability was observed for Portugal. The two main drivers are the economic condition (PC1), and aging and depopulation (PC2). The remaining drivers highlighted are uprooting and internal mobility, and daily commuting. Census data proved their value in the territorial, social, and demographic characterization of the country, to support medium- and long-term disaster risk reduction measures.

Measuring Competitiveness: A Composite Indicator for Italian Municipalities

This study measures territorial competitiveness at the municipal level in Italy, by proposing a robust composite indicator based on variables not yet used in the literature. The underlying theoretical framework is identified based on the literature on regional competitiveness. The proposed indicator consists of the following seven dimensions of competitiveness: Education, Job, Economic Wellbeing, Territory and Environment, Entrepreneurship, Innovation, and Infrastructures and Mobility. Data are retrieved mainly from administrative sources, for 2014 and 2015. In the building process, three aggregation methods are compared: a compensatory method, the arithmetic mean, and two partially compensatory methods, the geometric mean and the Adjusted Mazziotta-Pareto Index (AMPI). The arithmetic mean turns out to be the most robust method among the three considered, but the AMPI is the most robust method among the two partially compensatory methods. All the methods considered agree in identifying Innovation and Entrepreneurship as the most influential pillars in 2014 and 2015, respectively. The detailed geographical focus provides specific insights into territorial competitiveness in Italy. It emerges a rather heterogeneous picture of municipal competitiveness within the Italian regions. Highly competitive municipalities are present in every region, though with different concentration levels.

Aggregating Composite Indicators through the Geometric Mean: A Penalization Approach

In this paper, we introduce a penalized version of the geometric mean. In analogy with the Mazziotta Pareto Index, this composite indicator is derived as a product between the geometric mean and a penalization term to account for the unbalance among indicators. The unbalance is measured in terms of the (horizontal) variability of the normalized indicators opportunely scaled and transformed via the Box–Cox function of order zero. The penalized geometric mean is used to compute the penalized Human Development Index (HDI), and a comparison with the geometric mean approach is presented. Data come from the Human Development Data Center for 2019 and refer to the classical three dimensions of HDI. The results show that the new method does not upset the original ranking produced by the HDI but it impacts more on countries with poor performances. The paper has the merit of proposing a new reading of the Mazziotta Pareto Index in terms of the reliability of the arithmetic mean as well as of generalizing this reading to the geometric mean approach.

Covid-19 y desarrollo sostenible en Europa: una comparación temporal.

Covid-19 has posed major challenges globally. One of these is undoubtedly the achievement of the Sustainable Development Goals (SDGs) of the 2030 Agenda.This study aims to analyze, in a time perspective, the effects of the pandemic on the levels of sustainable development achieved in Europe. To measure the impact of Covid-19, 3 different sustainability indices (SIs) will be constructed referring to 2015, 2018 and 2020. To construct these indices, 17 indicators (one per SDG) from Eurostat’s free database will be used. The Adjusted Mazziotta-Pareto Index (AMPI) method will be used to aggregate these indicators. This method relying on a Min-Max transformation allows for an absolute time comparison. In addition, this method allows for high replicability of results. Furthermore, in order to capture the differences between different European specificities, a hierarchical clustering will be performed according to the Ward method. The results confirm the negative effects of Covid-19. In fact, there is a general worsening of sustainable development levels between 2015 and 2020.

Measuring Regional Performance in the Italian NHS: Are Disparities Decreasing?

Given the regional disparities that historically characterize the Italian context, in this paper we propose a framework to evaluate the regional health care systems’ performance in order to contribute to the debate on the relationship between decentralisation of health care and equity. To investigate the regional health systems performance, we refer to the OECD Health Care Quality Indicators project to construct of a set of five composite indexes. The composite indexes are built on the basis of the non-compensatory Adjusted Mazziotta-Pareto Index, that allows comparability of the data across units and over time. We propose three indexes of health system performance, namely Quality Index, Accessibility Index and Cost-Expenditure Index, along with a Health Status Index and a Lifestyles Index. Our framework highlights that regional disparities still persist. Consistently with the evidence at the institutional level, there are regions, particularly in Southern Italy, which record lower levels of performance with high levels of expenditure. Continuous research is needed to provide policy makers with appropriate data and tools to build a cohesive health care system for the benefit of the whole population. Even if future research is needed to integrate our framework with new indicators for the calculation of the indexes and with the identification of new indexes, the study shows that a scientific reflection on decentralisation of health systems is necessary in order to reduce inequalities.

Well-Being and Sustainability in Crisis Areas: The Case of Taranto

Unresilient and fragile regions need methods and data able to make policy-makers acknowledge the specific criticalities by which they are affected, so as to build effective development strategies and policies. This research explores whether and to what extent well-being and sustainability measurement frameworks are able to recognize crisis areas. We identified Taranto (Italy), declared as both a National Priority Contaminated Site and a Complex Industrial Crisis area, as a paradigmatic and extreme case of crisis areas and adopted the single case approach to address our research question. After reviewing several frameworks able to measure well-being at local level, we focused on Benessere Equo e Sostenibile dei Territori (Equitable and Sustainable Territorial Well-being, BESdT). We used two aggregate indexes to analyze data, namely the Adjusted Mazziotta-Pareto Index and the Adjusted Differences Mean Index. The study shows that, although BESdT does detect some criticalities of the examined area, it seems not able to adequately frame the multifaceted crisis that affects the area of Taranto. Even in presence of a full-blown crisis, the problematic situation does not always reflect into lower territorial performance, neither at the level of single indicators nor at the level of entire domains. Such discrepancy appears to be particularly evident within the economic domain. The paper ends with a discussion on the research and policy implications and some proposals for further research.

Environment, logistics and infrastructure: the three dimensions of influence of Italian coastal tourism

This paper examines the various dimensions that affect the tourist sector in order to provide an assessment of the environmental quality and logistic and infrastructural dimensions of Italian coastal municipalities. The analysis starts from the assumption that the environmental resources and logistic and infrastructural dimensions of each area represent an asset for the whole territory, since they are able not only to increase the quality of life of residents but, above all, to attract travellers, thus contributing to the development of the tourism sector. Therefore, it can be said that the dimensions assessed in this work increase the capacity of the territory to generate income. In the analysis a quantitative aggregative partially non-compensatory method, known as Mazziotta Pareto Index (MPI) was used; by means of the MPI it was possible to express the magnitude of the tourist phenomenon reducing the compensation effect that occurs every time indicators with non-homogeneous values are aggregated. The contribution provides further insights into coastal tourism, a particularly important tourist sector on a national scale.

Synthesis of Multi-indicator System Over Time: A Poset-based Approach

In recent years, sustainable development has become one of the main issues of scientific and institutional debate. The literature on this concept is wide and often presents conflicting positions. This leads to considering sustainable development as a contested concept. The growing interest and importance of this topic has also led to an increasing focus on the aspect of its measurement. Dealing with the measurement of complex phenomena, like sustainable development, means dealing with synthesis. The traditional and dominant approach to the synthesis of multi-indicator systems of cardinal variables is the use of the aggregative-compensative approach. Despite its success, this approach presents a series of critical issues. In this paper, we propose a method of synthesizing multi-indicator systems over time based on the Partial Order Theory. Applying and comparing the method we proposed and an aggregative method, the Adjusted Mazziotta–Pareto Index, to one of the 15 sustainable development goals, we highlight the strengths of the new methodological proposal.

Health and Deprivation: A New Approach Applied to 32 Argentinian Urban Areas

Deprivation indexes have been used to analyze various urban contexts in different areas of the world. Argentina offers many examples of fragmented urban realities, as segregation and inequality in the inner city. Our first objective is to examine deprivation in relation to health in the urban Argentinian areas and to propose the Argentina Deprivation Index, extracted from the 2017 Argentina Permanent Household Survey (EPH). Five variables—four related to material and the other to social deprivation—were identified. Subsequently the Adjusted Mazziotta–Pareto Index and a hierarchical cluster analysis were applied to calculate the classes of the index. The results are encouraging, and they allow us to state that the deprivation index proposed explain in a significant way the variability of life expectancy at birth. Even though this study is not free of limitations, these results are expected to be helpful in the formulation of a new approach to the study of the deprivation in Argentina.

REVIEW OF SOME STATISTICAL METHODS FOR CONSTRUCTING COMPOSITE INDICATORS

The methodology for the construction process of composite indicators is reviewed in a step-by-step approach ranging from the ex-ante definition of the latent variable that is intended to be measure, through the construction process of the composite indicators. We focus particularly on four aggregations methods in order analayze weighting and aggregation approach, Distance P_2, Principal Component Analysis, Data Envelopment Analysis and Mazziotta-Pareto Index. An empirical comparison among them is provided and the composite indices divergences are shown.

An interpretable approach to modeling the impact of an environmental risk factor on correlated neurobehavioral subscales

PDS 69: Methods and statistics, Johan Friso Foyer, Floor 1, August 26, 2019, 4:30 PM - 5:30 PM Background/Aim: Neurobehavioral tests are often correlated and tap into overlapping domains, but are commonly analyzed separately. Statistical approaches that aggregate test data could increase power. We introduce easily implementable approaches to assess how an environmental risk factor predicts correlated neurobehavioral subscales, and identify which subscales are most sensitive to the risk factor. We illustrate these approaches by identifying a neurobehavioral signature for arsenic, a known neurotoxicant. Methods: We modeled the prospective association between third trimester arsenic and 19 clinical, adaptive and content t-scored subscales of the Behavioral Assessment System for Children (BASC-2) Parent Rating Scales, assessed at 4 years of age in a birth cohort study (PROGRESS) in Mexico City (n = 425). We used three different approaches to quantify the global neurobehavioral burden using all subscale data: (1) clinical burden (sum-score based on clinical significance cutoffs, [t-score >= 70 or <= 30], adjusting for the subscale’s directionality), (2) sample-based burden (number of subscales in the highest risk quartile); and (3) the Mazziotta-Pareto Index (MPI), a nonlinear composite of continuous subscale data. We used generalized linear models adjusted for child sex, socio-economic status, maternal IQ and maternal age. To identify subscales most sensitive to arsenic, we explore a leave-one-subscale-out step and compare model performance. We also compared results from separately modeled subscales. Results: We detected a positive association between arsenic and clinical burden index (p = 0.0081), as well as with the MPI (p = 0.043) and the sample-based burden score (p = 0.049). However, arsenic was not associated with any BASC-2 individual subscale or composite scale after adjusting for multiple comparisons using the Benjamini-Hochberg method. Conclusions: This easy-to-implement framework could be used to increase power and uncover relationships not seen when subscales are separately modeled, providing a succinct way to account for correlated subscales.

Sustainable Development Goals Indicators at Territorial Level: Conceptual and Methodological Issues—The Italian Perspective

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.

Development and Poverty Assessment Using an Alternate Non-compensatory Composite Index of Rajasthan State in India

The present study is an attempt to measure diverse perspectives of development and poverty using an alternate non-compensatory composite index denoted as Mazziotta–Pareto Index (MPI). The study identifies the critical districts of the Rajasthan state, which is the largest state of India with lots of diversity. The index facilitates linear aggregation and introduces penalties for unbalance values in the index in both positive as well as negative phenomena. Region specific dimension and indicators selected for the study highlights the socio-economic status, infrastructure, health care facilities, literacy and employment conditions in different districts. MPI results in the form of rankings, cluster maps and radial charts, classify all the districts in five ranks according to the combined performance in both the phenomena. The results highlights that districts Pratapgarh, Dungarpur, Banswara, Dhaulpur and Jaisalmer situated in south, northeast and west border of the state are falling in the category of low development and high poverty conditions, whereas districts Jaipur (capital of state) and Kota from south eastern region are the best performing districts of the state with high development and low poverty conditions. Further, important factors for each class are highlighted from indicator and sub-indices radial charts. Overall, the present research generates a case study results for preliminary application of an alternate index MPI in district level vulnerability mapping to strengthen the decentralized approach in policy reforms. The further detail study is suggested to bring out area specific policy interventions to overcome spatial disparities in the state.

Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices

Most of the socio-economic phenomena such as development, well-being, and quality of life have a multidimensional nature and require the definition of a set of individual indicators to be properly assessed. Often, individual indicators are summarized and a composite index is created. One of the main problems in constructing composite indices is the choice of a method which allows time comparisons. In this paper, we consider the Adjusted Mazziotta–Pareto Index, a non-compensatory composite index used by the Italian National Institute of Statistics for measuring “Equitable and Sustainable Well-being” in Italy. An empirical comparison with some traditional non-compensatory indices is presented and an Influence Analysis is, for the first time, performed in order to assess the robustness of the index.