Abstract
Developing indicators is considered an exercise associated with the measurement process.
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Notes
- 1.
The transition from the quantity to the quality paradigm implies a consistent choice of the indicators: this means, for example, adopting a “quantity” indicator like “life expectancy” as well as to a quality indicator like “healthy life expectancy.”
- 2.
In data analysis, indicators/items are technically defined “variables”; consequently, these are conceptually different from “latent variables”.
- 3.
Macro does not necessarily correspond to summing up micros and the micro level does not necessarily reflect what emerges at the macro level. Quality of life is typically observed at individual level, while other concepts, like economic and social cohesion, are observed at community level. Some concepts require both levels of observation, like sustainability, which can be defined through different dimensions (capitals) and two time perspectives observed at both micro and macro level.
- 4.
By using multiple measures, random errors tend to compensate each other. Consequently, the measurement turns out to be more accurate. The greater the error component in one single measure, the larger the number of required measures needs to be.
- 5.
In particular, the basic indicators defined in the multi-indicator approach are considered multiple measures, since they are assumed to cover the conceptual dimension’s variability.
- 6.
Generally, the formal representation of models of measurement uses symbols referring to the Greek alphabet:
Greek alphabet
Capital
Small
Capital
Small
Capital
Small
Capital
Small
A
α
alfa
H
η
eta
N
ν
nu
T
τ
tau
B
β
beta
Θ
θ
theta
Ξ
ξ
csi
Y
υ
upsilon
Γ
γ
gamma
I
ι
iota
O
o
omicron
Φ
ϕ
phi
Δ
δ
delta
K
κ
kappa
Π
π
pi
X
χ
chi
E
ε
epsilon
Λ
λ
lambda
P
ρ
rho
Ψ
ψ
psi
Z
ζ
zeta
M
μ
mu
Σ
σ
sigma
Ω
ω
omega
- 7.
In case of reflective indicators, the synthesis assessment (in terms of reliability and validity) can be accomplished through a statistical approach related to the factor models (scaling models), where an observed measure is presumed to be determined by a latent factor and a unique factor.
- 8.
An example is represented by decision makers who need to know and manage a composite mosaic of information in order to define and evaluate priorities to be translated into actions. With reference to this, we can distinguish between:
-
Conceptual aims (goals). In other words, goals refer to the direction to be adopted by the society. They are defined through a consensual process, by referring to cultural paradigms or normative demands, or through expert groups’ pressure or public opinion movement In the wellbeing perspective, they are not only time and space dependent but rely on political views. Consequently, it is important to set clear and shared goals, by giving philosophical and political debate (understandable for all) more democratic space. Goals require action/intervention proposals to be defined by taking into account that the taken decisions will influence all the domains, even when no resolution is made on each of them.
-
Operative aims (objectives) that represent the instruments identified in order to attain the conceptual aims. Objectives can have different temporal prospects (monthly, four-monthly, annual, bi-annual, etc.)
-
Planning aims (actions) that represent the specific activities identified to accomplish the objective. They can include developments and infrastructural changes in policies, institutions, in management instruments, and so on.
Each goal, objective and action has corresponding targets, representing those elements allowing each goal, objective and action to find measurable criteria and to define a timetable, and corresponding indicators defined in order to assess progress towards the target with goals and objectives and the accomplishment of actions.
-
- 9.
It should be taken into account that observing a wide territory does not entail that a lower level is necessarily covered. Beyond statistical representativeness, the conceptual model (in terms of dimensions and/or indicators) and the observation approach need to be reviewed and adapted in order to monitor the lower level (e.g., province, city, etc.). Seen in this perspective, the approach aimed at reaching small area estimations from representative data collected in wider areas appears questionable. Projects calibrated on smallest areas should be urged and encouraged.
- 10.
Generally, trend analysis is used to observe changes and is aimed at estimating future events. Data analysis approaches can be mainly distinguished according to the adopted design; particularly, each design allows analysis at different level (Maggino and Facioni 2015).
- 11.
Time of observation should not be necessarily equal for all selected indicators according with their different dynamics; in fact, some phenomena show “fast” dynamics while others show extended changing progression.
- 12.
Another way to look at the dichotomy objective-subjective is adopting the duality sensitive to individual observation, i.e. internal level and external level; in fact, at individual level the defined concepts should be observed at both “external” (e.g., objective living conditions, equity and sustainability of those conditions) and “internal” (e.g., subjective evaluations about the living conditions, subjective perceptions about equity and sustainability of living conditions) level.
- 13.
- 14.
Classifying indicators in terms of input and outcomes aspects is difficult; in fact, some aspects could be classified at the same time (or in subsequent times) as input or output; families’ lower expenses for foodstuffs could represent an output indicator related to a short-term situation but could also represent an input indicator towards a change (worsening?) in family members’ health status.
- 15.
The use of benchmarks plays an important role in the ambit of a program development. Used in combination with the program objectives they provide the basis for program accountability.
- 16.
A reference point can be actually represented by a reference group (e.g., percentage of people with a high level of satisfaction with life as a whole).
- 17.
With regression analysis cases that performed better than average can be rewarded while cases that performed worse than average can be penalized. Such benchmarking studies are used to create yardstick comparisons, allowing outsiders to evaluate the performance of operators in an industry. A variety of advanced statistical techniques, including stochastic frontier analysis, have been utilized to identify high performers and weak performers.
- 18.
Accountability is a concept with several meanings. It is often used synonymously with such concepts as responsibility, answerability, enforcement, liability. In governance perspective, it has been central to discussions related to problems in both the public and private worlds.
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Political accountability. Political accountability is the accountability of the government, civil servants, and politicians to the public and to legislative bodies (congress, parliament). Generally, voters do not have any direct way of holding elected representatives to account during the term for which they have been elected. Moreover, some officials and legislators may be appointed rather than elected. Constitution, or statute, can empower a legislative body to hold their own members, the government, and government bodies to account. This can be through holding an internal or independent inquiry. The powers, procedures, and sanctions vary from country to country. The legislature may have the power to impeach the individual, remove them, or suspend them from office for a period. The accused person might also decide to resign before trial. In parliamentary systems, the government relies on the support or parliament, which gives parliament power to hold the government to account. For example, some parliaments can motion for a vote of no confidence in the government.
-
Administrative accountability. It refers to internal rules and norms as well as some independent commission are mechanisms to hold civil servant within the administration of government accountable. Within department or ministry, firstly, behaviour is bounded by rules and regulations; secondly, civil servants are subordinates in a hierarchy and accountable to superiors. Apart from internal checks, some supervisory bodies accept complaints from citizens, bridging government and society to hold civil servants accountable to citizens, but not merely governmental departments.
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Market accountability. Nowadays, it is “customer-driven” and is aimed at providing convenience and various choices to citizens; ideally, this perspective should improve quality of service. The standard of assessment for accountability requires a neutral body. Government can choose among a shortlist of companies for outsourced service; within the contracting period, government can hold the company by rewriting contracts or by choosing another company.
-
Constituency relations. A particular agency or the government is accountable if voices from agencies, groups, or institutions, which is outside the public sector and representing citizens’ interests in a particular constituency or field, are heard. Moreover, the government is obliged to empower members of agencies with political rights to run for elections and be elected; or, appoint them into the public sector as a way to hold the government representative and ensure voices from all constituencies are included in policy-making process.
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- 19.
For example, “life expectancy” represents a quantitative aspect, while “healthy life expectancy” represents a more qualitative aspect.
- 20.
Some aspects could be classified at the same time (or in subsequent times) as input or output; families’ lower expenses for foodstuffs could represent an output indicator related to a short-term situation but could represent also an input indicator towards a change (worsening?) in family members’ health status.
References
Anand, S., & Sen A. (1997) Concepts of human development and poverty: A multidimensional perspective. Human Development Papers 1997. New York: UNDP.
Blalock, H. M. (1964). Causal inferences in nonexperimental research. Chapel Hill: University of North Carolina Press.
Berger-Schmitt, R. & Noll, H.-H. (2000). Conceptual framework and structure of a european system of social indicators, EuReporting Working Paper No. 9, Centre for Survey Research and Methodology (ZUMA) – Social Indicators Department, Mannheim.
Diamantopoulos, A., & Siguaw, J. A. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263–282
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277.
Eurostat. (2000a). Definition of quality in statistics, Eurostat Working Group on Assessment of Quality in Statistics, Eurostat/A4/Quality/00/General/Definition, Luxembourg, April 4–5.
Eurostat. (2000b). Standard quality report, Eurostat Working Group on Assessment of Quality in Statistics, Eurostat/A4/Quality/00/General/Standard Report, Luxembourg, April 4–5.
Felce, D., & Perry, J. (1995). Quality of life: Its definition and measurement. Research in Developmental Disabilities, 16(1), 51–74.
Horn, R. V. (1993). Statistical indicators. Cambridge: Cambridge University Press.
Johansson, S. (2002). Conceptualizing and measuring quality of life for national policy. Social Indicators Research, 58, 13–32.
Land, K. C. (1971). On the definition of social indicators. American Sociologist, 6, 322–325.
Land, K. C. (1975). Social indicator models: an overview. In K. C. Land, & S. Spilerman (Eds.), Social indicator models. New York: Russel Sage Foundation.
Maggino, F., & Facioni, C. (2015). Measuring stability and change. Methodological issues in quality of life studies. Social Indicators Research (first online issue) http://link.springer.com/article/10.1007/s11205-016-1241-5
Meadows, D. (2008). Thinking in systems: A primer. White River Junction: Chelsea Green Publishing.
Michalos A. (1992). Use and abuses of social indicators In Sinet, n. 32.
Noll, H.-H. (1996). Social indicators and social reporting: The international experience. http://www.ccsd.ca/noll1.html
Noll, H.-H. (2004). Social indicators and indicators systems: Tools for social monitoring and reporting. Paper presented at OECD, World Forum “Statistics, knowledge and policy”, Palermo, 10–13 November 2004.
Nuvolati, G. (1997). Uno specifico settore di applicazione degli indicatori sociali: La qualità della vita. In F. Zajczyk (Ed.), Il mondo degli indicatori sociali, una guida alla ricerca sulla qualità della vita (pp. 69–94). Roma: La Nuova Italia Scientifica.
Patel S., M. Hiraga, and L. Wang (World Bank) D. Drew and D. Lynd (Unesco) (2003). A framework for assessing the quality of education statistics, Development Data Group and Human Development Network, World Bank, Washington, DC
Sharpe, A., & Salzman, J. (2004). Methodological choices encountered in the construction of composite indices of economic and social well-being. Ottawa: Center for the Study of Living Standards.
Sirgy, M. J., Michalos, A. C., Ferriss, A. L., Easterlin, R. A., Patrick, D., & Pavot, W. (2006). The quality-of-life (QOL) research movement: Past, present, and future. Social Indicators Research, 76(3), 343–466.
Śleszyński, J. (2012). Prospects for synthetic sustainable development indicators, Paper presented at the conference “Quality of Life and Sustainable Development”, September 20–21, Wroclaw (Poland).
Stiglitz, J. E., Sen, A., & Fitoussi, J.-P. (2009). Report by the commission on the measurement of economic performance and social progress, Paris. http://www.stiglitz-sen-fitoussi.fr/en/index.htm
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Maggino, F. (2017). Developing Indicators and Managing the Complexity. In: Maggino, F. (eds) Complexity in Society: From Indicators Construction to their Synthesis. Social Indicators Research Series, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-319-60595-1_4
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