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Methodological Choices and Data Quality Issues for Official Poverty Measures: Evidences from Italy


The plurality of the official poverty estimates in Italy covers both absolute and relative approaches, ranges from consumption to income-based measures, follows different methodologies and uses several data sources. We can therefore expect that each measure gives a somewhat different picture of poverty, in its level as well as in its change across subgroups of the population. This paper investigates the effect of methodological choices together with the effect of different data quality aspects on the official poverty estimates. Usually, methodological issues attract much attention both in literature and empirical studies. However, the results of the sensitivity analysis suggest that more specific attention should be paid to data quality issues and to the definition of the variables. Our main conclusion is that an improvement in the quality as well as the inclusion of some items in the definition of the variable may result in large changes in poverty indicators. This finding signals that the data quality aspects have a higher impact on poverty estimates than some methodological issues.

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Fig. 1

Source: own calculations based on HBS, EU-Silc and SHIW data

Fig. 2

Source: own calculations based on EU-Silc data

Fig. 3

Source: own calculations based on HBS and NHBS data

Fig. 4
Fig. 5

Source: own calculations based on HBS and NHBS data

Fig. 6

Source: own calculations based on HBS and NHBS data

Fig. 7

Source: own calculations based on HBS and NHBS data


  1. 1.

    The willingness to overpass the limits of this type of measures has led Eurostat to produce indicators of material deprivation (Eurostat 2012a) and the multidimensional indicator of poverty or social exclusion (Eurostat 2016). However they are still considered as marginal or complementary estimates.

  2. 2.

    Some of these items have been discussed within the Interinstitutional Working Group on Poverty Estimates established by Italian National Institute of Statistics (

  3. 3.

    Elasticity accounts for the extent to which economic needs change with household size (Förster and Mira D’Ercole 2012).

  4. 4.

    The absolute term refers to the fact that the measure is independent on the distribution of the proxy variable; however, its definition is obviously dependent on (relative to) the reference context.

  5. 5.

    For a survey of the most used poverty indices and the axioms they satisfy, see Chakravarty (2009).

  6. 6.

    It should be emphasised that the SHIW by Bank of Italy, conducted by a professional interviewer network, shows a lower response rate (53.3% in 2014) (Bank of Italy 2015) and that several European HBSs (using again professional interviewer networks) show even lower levels of coverage (in 2010, the values varied between 5% in Belgium, 42% in Denmark, 51% in UK and Sweden to over 80% in Turkey and Romania) (Eurostat 2012b).

  7. 7.

    When the sample selection steps and weighting system arrangement had to be done, the information from the tax returns was not yet available and, therefore, the fiscal income was not used as a variable for either sample stratification or post-stratification. The fiscal information became available after 1 year and, at that moment, it was linked to HBS and NHBS data by individual fiscal codes (tax identification numbers).

  8. 8.

    It follows that the use of tax information in the sampling phase (for stratification) would not be of great utility, while it could be very useful in defining substitutions; likewise, a number of constraints could be added to the final calibration to comply with the tax income classes.

  9. 9.

    It must be underlined that some people are not properly represented in tax statistics, as the population not liable for taxation due to their low levels of income and tax evaders.

  10. 10.

    This analysis has been conducted as one of the activities of the already mentioned Interinstitutional Working Group on Poverty Estimates.

  11. 11.

    The consumption expenditure is the net of extraordinary maintenance expenses, premiums paid for life insurance and annuities, mortgage rate and repay loans; the household disposable income is derived as the sum of the income from employment, property income and transfers, net of taxes and social contributions.

  12. 12.

    HBS does not allow us to properly single out the component of own consumption from the total expenditure, whereas own consumption is not collected at all by the SHIW. Therefore, the own consumption is always included in the definition from the former survey, while it is always excluded from the latter survey.

  13. 13.

    It must be noted that the monthly imputed rent for the main accommodation ranges from 531 euros in EU-Silc to 576 euros in NHBS. Nevertheless, the NHBS data also include the imputed rent from the main accommodation appurtenances (garages, basements, etc.) and also from any second house.

  14. 14.

    The coefficients of this scale are as follows: 1 for a 1 member household, 1.9 for a 2 member household, 2.7 for a 3 member household, 3.5 for a 4 member households, 4.2 for a 5 member households, 5.0 for a 6 member households and 5.7 if the members are 7 or more.

  15. 15.

    The elasticities resulted equal to 0.53 for S1, 0.67 for S2, 0.73 for S3 and 0.87 for S4 (Betti et al. 2016).

  16. 16.

    The reference method corresponds to the calculation of poverty incidence on HBS data including imputed rent and goods produced for own consumption, using the traditional approach, S2 scale and poverty line at 60% of the mean of per-capita distribution.


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We would like to thank three anonymous reviewers for their helpful comments and suggestions. The opinions expressed in this paper solely represent those of the authors and do not necessarily reflect the official viewpoint of Istat.

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Lemmi, A., Grassi, D., Masi, A. et al. Methodological Choices and Data Quality Issues for Official Poverty Measures: Evidences from Italy. Soc Indic Res 141, 299–330 (2019).

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  • Poverty measures
  • Consumption expenditure
  • Income
  • Sensitivity analysis
  • Italy