We study attitude toward redistribution by local policy-makers in the context of public childcare in Italy. Within a substantially homogeneous legislative framework, different municipalities autonomously define how participation fees vary with a compound indicator of income and wealth (ISEE), thus redistributing resources across households using the service. The nearly one hundred municipalities we take into account exhibit wide heterogeneity in redistributive attitudes. We find statistically significant correlations with a number of individual characteristics of policy-makers and municipalities, but not with those of the ex ante distribution of income, which should be central according to both normative and positive theory. Since the price of public childcare is subsidized, resources are also redistributed from taxpayers to users. The evidence we find is consistent with the hypothesis that this type of redistribution is a public good.
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Also on the taxation side, the part of the personal income tax due to local authorities, unlike the national component, is generally based on flat tax rates.
For a specific analysis of this aspect, see Antonelli and Grembi (2011).
The basis for the definition of ISEE is household income, which is then adjusted to account for household wealth and subsequently corrected through an equivalence scale. For a detailed description, see the “Appendix”.
Municipalities classified as “Capoluoghi di Provincia” were selected. These are relatively large municipalities (hosting on average 170,000 inhabitants each) in charge of the public administration at the district level. This is the third level of administration in Italy, which is made of four pillars (State, Regions, Districts, Municipalities).
However, there might be competition with bordering municipalities not in the sample. This might imply unobserved heterogeneity to the extent that different municipalities face different degrees of competition. Since our sample is made up of main towns, neighboring municipalities are, in general, substantially smaller. This leads us to believe that our results are not biased by strategic competition.
Note that for \(R_j\) to be a measure of redistribution from rich to poor it is required that fees are nondecreasing in ISEE. The assumption is satisfied for our sample.
We consider this wave because it refers to the last year in which all real estate properties are subject to taxation. We use this information to estimate the value of real estate wealth, which is part of the computation of ISEE. See the “Appendix” for details.
The size of this subsample is 1180 observations. Of course, a more appropriate subsample would include only households with children attending public childcare. However, this would have reduced the number of observations to 255.
Clearly, the selection problem due to option available to households not to deliver their ISEE statement and pay the maximum fee is particularly serious for the right-hand part of the distribution.
Although information on the distribution of pre-tax income is available at the municipality level, it is not possible to retrieve the specific distribution for households with children aged 0–2 (as we do for the distribution of ISEE). Therefore, we are implicitly assuming that the distribution for this population is not substantially different from that of the general population, or that the differences between these distributions, if any, are reasonably similar across municipalities.
This variable is available only at the district level.
However, municipalities can adjust the provision of public childcare by signing agreements with private providers of the service.
We are aware that the dependent variables may exhibit high serial correlation and the models would reach a better fit if we included lagged dependent and explanatory variables in the specification. However, our purpose is to explore possible correlation with distribution, demographic and political variables, and not to make forecasts of our dependent variables. Hence, we do not consider these dynamic regressions as they would entail a loss of observations in an already small sample. Nonetheless, in a robustness check (available upon request) we confirmed all our benchmark findings.
For example, in some of their empirical specifications Padovano and Turati (2012) find that left-wing governments tend to expand public expenditure but redistribute less.
In general, the reference core consists of the registrant, the spouse, and their children, as well as other people living with them, with some exceptions.
Since the reference year for our analysis is 2007, we considered the rules for the computation of ISEE for that year. Some changes have been introduced in 2015, which do not affect the main characteristics of the indicator.
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We are grateful to Lisa Grazzini and Claudio Zoli for helpful comments. Andrea Giaretta provided excellent support to the research.
The research was funded by the Italian Ministry of Education, FIRB programme, 2008, Grant RBFR0873ZM 001.
Conflict of interest
ISEE (”Indicatore della Situazione Economica Equivalente”) is a compound indicator of income and wealth introduced in Italy in 1997. It is used by different government levels in order to assess the economic situation of citizens who apply for some type of social benefits or subsidized care services. The indicator is computed starting from a declaration (Unique Substitute Declaration) provided by the applicant, who bears the full responsibility for its truthfulness. An ISEE certificate is issued on the basis of this declaration. The certificate is valid for one year.
The ISEE indicator takes into account income, plus a share of the asset value and the characteristics of the households.Footnote 16 Specifically, it incorporates income flows from different sources received by the household in the previous fiscal year, and an estimate of the value of property, assets, and capital owned by the family. The formula is defined as follows:Footnote 17
Y is the sum of gross employment income net of social security contributions, gross self-employment income net of social security contributions, gross income from unemployment benefits and gross income from real estate properties;
P is the gross pension income, which does not include social allowances for the elderly, and all the benefits for work-related disability or other disabilities;
\(i_W\) is the notional return on financial assets, computed applying to financial assets the average annual rate of return of 10-year Government Bonds;
\(D_R\) is the rent paid by tenants that can be deducted from taxable income up to a maximum of 5,164 euros;
\(W_f\) are the aggregate financial assets owned by the households, with a 15,500 euro allowance;
\(W_r\) is the value of the real property assets, such as primary and secondary residences; a maximum of 51,650 euros allowance is granted for the value of owner-occupied houses.
p(n, c) is an equivalence scale, and it depends on household size (n) and other characteristics of the household (c), such as (1) the number of cohabitating children, (2) the number of individuals with disability; (3) the status of single parent with children; (4) the presence of both parents working for at least 6 months.
The Survey on Income and Living Conditions EU-SILC allows us to estimate the ISEE indicator for households in the sample. Detailed information on Y, P, \(D_r\) and \(W_f\) is available in the dataset, and we use these variables to compute income and financial assets for each household. Regarding the real estate properties (\(W_r\)), the cadastral values of the properties of all the household members are needed. These values are not available in EU-SILC; therefore, we indirectly estimate them using data on the local council property tax paid by each single household member. Finally, EU-SILC includes all the demographic variables required to derive p(n, c). A detailed explanation of the assumptions and the variables used to construct ISEE indicator is available in Bucciol et al. (2014). Figure 4 shows the estimated distribution of ISEE for a households with at least one child aged 0–2 (1180 observations) that we use to calculate our progressivity and redistribution indicators.
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Bucciol, A., Cavalli, L., Pertile, P. et al. Redistribution at the local level: the case of public childcare in Italy. Int Rev Econ 63, 359–378 (2016). https://doi.org/10.1007/s12232-016-0258-z