Investigating the Incidence of Value Added Tax on Households Income: New Evidence from Italy

Since the introduction of the Value Added Tax in Italy, in 1973, VAT rates were subject to a number of changes and the recent debate on tax-shift from direct to indirect taxation revamped the attention on this topic and the need to rigorously assess the effects produced by fiscal policies changes. To this end, along the years, researchers and institutions developed different instruments to assess the economic effects produced by variations in VAT rates. In particular, microsimulation models were proven to be powerful tools to predict the economic effects of fiscal changes for governments, firms and households. In Italy, there exist several microsimulation models on VAT and the objective of this paper is to contribute to the existing literature by proposing a new non-behavioural distributional model, the Value Added Tax Simulation Model (VATSIM-DF II).

The goals of VATSIM-DF (II) are: (i) to estimate actual and expected VAT amounts; (ii) to estimate the VAT incidence on households’ disposable income; (iii) and to simulate the distributional effects of changes in fiscal policies. The idea to develop VATSIM-DF (II) origins from the need to produce an original model to check the distributional effects of VAT and to support Italian policy makers in designing fiscal policies. Compared to existing models, the main achievements of this study are the creation of a unique original dataset, which includes Tax Register data, and the implementation of a matching procedure, which outperforms other data fusion strategies used in the existing literature. Our model has the great advantage to benefit from several data sources that are not accessible by all research institutions.

Our model contributes to a rich literature that, along the years, proposed powerful microsimulation models on the Italian VAT, which contributed to shed a light on this topic. One of the first works in Italy was due to Marenzi (1989) who, relying on the SHIW (Survey on Household Income and Wealth) data, simulated the distributional effect of taxation in Italy. Then, in 1999, the Microsimulation Unit in the Department of Applied Economics at the University of Cambridge, ¹ launched Euromod, a tax-benefit tool which covers 15 European countries, including Italy, that was then updated in 2017 with the creation of Euromod-ITT (Bourguignon et al., 1997; Immervoll et al., 1999; De Agostini et al., 2017; Ceriani et al., 2017). In 2001, Baldini published a study about MAPP98, a static tax-benefit microsimulation model, based on SHIW and consumption data, which was then updated in 2009, by Baldini and Pacifico, to assess the dynamic effects of changes in personal income taxes in the labour market (Baldini, 2000, Baldini and Pacifico, 2009). In 2007, the Department of the Treasury, at the Italian Ministry of Economy and Finance, developed the ITaxSIM (Italian TAXation SImulation Model) which relies on SILC data (Statistics on Income and Living Conditions) and focuses on the analysis of personal income tax, VAT, property tax, in-kind benefits and family allowances. ² More recently, Arachi et al. (2012) and Gastaldi et al. (2014) built their microsimulation models to assess the effect of a shift from personal and corporate income taxes to indirect taxation. In particular, Gastaldi et al. (2014) rely on an integrated dataset, produced by Pisano and Tedeschi (2014) for their model (EGaLiTe). While Arachi et al. (2012) updated a microsimulation model originally developed by Pellegrino et al. (2010) to study the distributive impact of housing taxation in Italy. Another Italian micro-simulation tool is the BIMic model, developed by Curci et al. (2017) at the Bank of Italy. BIMic originally focused on the budgetary impact and on the distributional effects of social security contributions, family allowances, social benefits, personal income tax and property taxes. Recently, Curci and Savegnano (2019) extended the scope of BIMic by including the simulation of VAT to assess the effects of tax shifts from labour to consumption. Finally, at regional level, Maitino et al. (2013) developed the MicroReg model, which is a static model aimed at assessing the effects of PIT and VAT.

The common challenge and limitation faced by researchers and institutions to develop these microsimulation models is the lack of a dataset, which includes detailed information on both income and consumption. Indeed, in Italy a dataset on income and consumption does not exist. To match data on income and consumption from different datasets, researchers rely on a third data source used as “bridge” dataset (to calculate estimated income or consumption) or they match similar households basing on socio-demographic variables only. These procedures show limitations related to the reliability of a matching based on socio-demographic variables only, and to the small sample size and accuracy of aggregate data on consumption of the “bridge” dataset. ³ To address these issues, we take advantage of our unique access to the Tax Register data of all the households surveyed in the expenditure and income surveys. Indeed, we create a unique dataset on VAT, income and consumption (VATIC dataset) that is a strong innovation with respect to existing dataset produced before. Indeed, instead of relying on estimated consumption or socio-demographic variables only, we use Tax Register income data to match, with a high level of precision, households in the income survey with similar households from the expenditure dataset.

The value added of VATSIM-DF (II), with respect to the existing models, are: (i) the creation of a unique dataset with data on income and consumption with a very high level of reliability, since the matching procedure relies on tax codes and Tax Register data; (ii) and the adjustment of survey data on income and consumption to National Accounts data at the year of the simulation, which makes our estimations on VAT coherent with the most updated macroeconomic data. To the best of our knowledge, there are not existing models in Italy with these features and this makes VATSIM-DF (II) a promising tool for reliable microsimulations about the distributional effects of VAT.

In our paper, we show how our matching procedure, to create the VATIC dataset, outperforms those used in other microsimulation models to match different data sources. Also, we analyse the current VAT burden on Italian households showing the regressivity of VAT. Finally, we show the distributional effects that a revenue neutral reform of the VAT system could produce on the Italian households. The reform sets two VAT rates and applies the reduced rate also to female sanitary towel and babies nappies.

The paper is structured as follows: in Section 2 we describe the data used in our model; in Section 3 we briefly illustrate the strategies used in the literature to integrate different Italian data sources, we describe our method, and we test the reliability of our procedure with respect to those used in the literature; in Section 4 we report the steps to estimate theoretical and actual VAT amounts and VAT incidence on individuals’ disposable income, and we show the simulation outputs for 2019 at current legislation; Section 5 describes a revenue neutral reform and the expected VAT burden for households; Section 6 concludes.

In Italy, a database including detailed information on both income and consumption expenditures is not available. This is the main challenge faced by researchers and institutions when it comes to create a reliable and rigorous dataset for microsimulation models. We rely on three different data sources: Statistics on Income and Living Conditions (SILC), Household Budget Survey (HBS), and Tax Register data. Unfortunately, SILC includes data on income but not on expenditure, vice versa for HBS. ⁴ The access to Tax Register data constitutes one of the main advantages of our model because it allows us to match households from the other two surveys by relying on declared income variables. ⁵ In this paper, we describe the model for 2019, that is based on surveys data from 2017, which are then updated to 2019 statistics by relying on National Accounts data (details are provided in Section 4). Below we provide a description of each dataset, while in Section 3 we provide details about the integration of the different data sources. ⁶

In order to develop our model, we need a dataset which includes, for each family, detailed information on income sources and expenditure items. In Section 3.1, we describe the methods used by existing studies to integrate Italian data sources, while in Section 3.2 we show the procedure used in our model and its value added.

3.2. Method

To create the dataset for our microsimulation, the first step is to match SILC and HBS datasets with Tax Register data. This process is feasible because we can access the tax codes (anonymized through the creation of a unique identifier) of households included in SILC, HBS, and the Tax Register. This allows an exact matching between SILC households and Tax Register households, and between HBS households and Tax Register households. After this step, for each household we have data on the declared income, which constitutes one of the key variables used to match SILC and HBS households.

Indeed, during the second step the objective is to match SILC and HBS families. As explained in the previous section, we cannot rely on an exact matching because the households interviewed by these surveys are not the same. For this purpose, we need to integrate them through a statistical matching based on observable common variables. There are several techniques to implement a statistical matching. One of the most used is the Propensity Score Matching, a method originally developed by Rosenbaum and Rubin (1983) to identify a reliable control group in the setting of quasi-experimental impact evaluations. However, this technique can be used also to identify similar observations in the context of data fusion and can be based on the Euclidean distance (calculated through the propensity score) or on the Mahalanobis metric distance. In our model, we use a Nearest Neighbor matching (with replacement) based on the Mahalanobis distance. ⁹

To implement the statistical matching, we need to select variables that are comparable across the databases. The common variables to HBS and SILC are those included in the Tax Register (namely, household total income, household taxable income, household land and buildings income, first-earner total income, first-earner taxable income, other income) and a set of socio-demographic variables (such as, household size, type of family, first-earner age, sex, type of occupation, and education level) that were recoded and harmonized to be comparable across the two surveys. In Table 2, we show descriptive statistics about the common variables in HBS and SILC and we compare the two survey samples through a t-test. The p-value indicates that the two groups, on average, are not significantly different in income but they are significantly different in all the socio-demographic variables.

Table 2

	Mean in SILC	Mean in HBS	p-value for difference	95% C.I.
Tax Register household total income	31,474.26	31,375.21	0.795	-648.206	846.317
Tax Register household taxable income	30,584.36	30,553.10	0.930	-668.825	731.349
Tax Register first-earner total income	23,972.73	24,195.20	0.479	-837.955	393.008
Tax Register first-earner taxable income	23,158.80	23,285.98	0.673	-717.342	462.993
Tax Register household land and buildings income	1,641.48	1,592.43	0.463	-81.94	180.034
Tax Register other household income	3,260.84	3,365.34	0.649	-554.238	345.246
First-earner age	59.49	59.02	0.006	0.136	0.798
First-earner sex	0.59	0.60	0.002	-0.026	-0.006
Household size	2.16	2.32	0.000	-0.186	-0.137
Number of children	0.26	0.32	0.000	-0.077	-0.05
First-earner occupation
Employed	0.52	0.49	0.000	0.032	0.032
Unemployed	0.03	0.05	0.000	-0.03	-0.022
Homemaker	0.04	0.08	0.000	-0.041	-0.032
Retired	0.38	0.36	0.000	0.017	0.036
Student, unable to work or other	0.03	0.02	0.000	0.012	0.019
First-earner education level
None	0.04	0.04	0.002	-0.01	-0.002
Primary education	0.17	0.18	0.072	-0.015	0.001
Lower secondary education	0.27	0.28	0.423	-0.013	0.005
Vocational education (2-3 years)	0.07	0.08	0.326	-0.008	0.003
Upper secondary education	0.29	0.27	0.000	0.013	0.031
University bachelor’s degree	0.13	0.14	0.000	-0.024	-0.01
Post graduate education	0.03	0.01	0.000	0.012	0.017
Type of Family
Single	0.37	0.31	0.000	0.055	0.074
Couple with no children	0.21	0.24	0.000	-0.038	-0.021
Couple with 1 child	0.16	0.17	0.016	-0.017	-0.002
Couple with 2 or more children	0.12	0.17	0.000	-0.063	-0.049
Other	0.14	0.11	0.000	0.023	0.036

1. Source: Authors elaboration.

2. Notes: sample weights are not used for estimations.

After the identification of common variables, we stratify the HBS and SILC samples by the 20 Italian regions and then within each of them by 18 income quantiles, based on the household total declared income from the Tax Register data. We obtain 380 strata of observations plus an additional stratum for all households with a negative declared income. Now the objective is to find, for each SILC household, the most similar household in the HBS dataset, given a set of characteristics. To this purpose, within each stratum, we implement a Nearest Neighbor matching (with replacement) based on the Mahalanobis distance relying on the following covariates (listed in Table 2): household total income, household taxable income, household land and buildings income, first-earner total income, first-earner taxable income, other income, household size, type of family, first-earner age, sex, type of job, education level.

Thanks to the matching, for each household of the SILC dataset we can identify the most similar household in the HBS survey. Therefore, we can assign to the SILC household the expenditure variables of the paired household. It is worth mentioning that, the matching procedure allows for replacement, hence one HBS household can be matched with more than one SILC household. ¹⁰

Generally, when statistical matching is used for quasi-experiment impact evaluations, verifying the matching performance is very important to produce reliable results. Indeed, after the statistical matching, the two groups (the treated and the control group) should not show significant differences in the covariates. However, in this case, we use matching to integrate two different data sources therefore also other aspects must be considered. As described by Rässler (2004), when the statistical matching is used for data fusion, the most important objective after matching is to preserve original individual values, joint distributions, correlation structures, and marginal distributions.

Hence, in order to check the matching: (i) we compare the SILC and HSB observations on average and in each income decile to check whether they are significantly different; (ii) and we provide a graphical representation of the variables distribution before and after the matching to verify whether they are preserved.

To the best of our knowledge, the existing studies on Italian microsimulation model do not test whether the two survey groups are, on average, significantly different after the matching. We provide this information to have a complete overview of the sample, even though the preservation of variables distributions is the most important aspect to check in case of data fusion. In Table 3, we compare SILC and HBS matched observations in the variables used for the matching. We want to check whether the two groups, after the matching, are not significantly different, on average, in terms of income.

Table 3

	Mean in SILC	Mean in HBS	p-value for difference	95% C.I.
Tax Register household total income	31,474.26	30,638.12	0.075	-83.54	1,755.83
Tax Register household taxable income	30,584.36	30,178.54	0.360	-463.892	1,275.54
Tax Register first-earner total income	23,972.73	23,783.18	0.595	-508.693	887.787
Tax Register first-earner taxable income	23,158.80	23,201.90	0.900	-717.76	631.569
Tax Register household land and buildings income	1,641.48	1,033.65	0.000	483.753	731.91
Tax Register other household income	3,260.84	1,501.07	0.000	1,363.42	2,156.12
First-earner age	59.49	60.67	0.000	-1.672	-0.676
First-earner sex	0.59	0.62	0.000	-0.05	-0.02
Household size	2.16	2.01	0.000	0.123	0.187
Number of children	0.26	0.22	0.000	0.029	0.059
First-earner occupation
Employed	0.52	0.48	0.000	0.048	0.048
Unemployed	0.03	0.02	0.020	0.001	0.008
Homemaker	0.04	0.03	0.017	0.001	0.01
Retired	0.38	0.45	0.000	-0.076	-0.046
Student, unable to work or other	0.03	0.02	0.000	0.015	0.023
First-earner education level
None	0.04	0.03	0.002	0.003	0.012
Primary education	0.17	0.20	0.000	-0.043	-0.018
Lower secondary education	0.27	0.29	0.009	-0.032	-0.004
Vocational education (2-3 years)	0.07	0.05	0.000	0.014	0.026
Upper secondary education	0.29	0.31	0.085	-0.027	0.002
University bachelor’s degree	0.13	0.11	0.000	0.007	0.024
Post graduate education	0.03	0.01	0.000	0.015	0.021
Type of Family
Single	0.37	0.41	0.000	-0.058	-0.027
Couple with no children	0.21	0.26	0.000	-0.058	-0.032
Couple with 1 child	0.16	0.15	0.012	0.003	0.023
Couple with 2 or more children	0.12	0.11	0.270	-0.004	0.013
Other	0.14	0.07	0.000	0.063	0.077

1. Source: Authors elaboration.

2. Notes: sample weights are not used for estimations.

Indeed, the p-value for the difference between SILC and HBS for the most important income categories shows that the two samples are not significantly different on average in these variables. However, the two groups are still significantly different in several socio-demographic variables (first-earner age and sex, household size, type of job, education level and type of family) and this is in line with what we observed before the matching.

An explanation for this unchanged difference in socio-demographic variables can be found in the sample stratification, implemented in the first step of the matching procedure. On the one hand, the stratification of households by income quantiles, allows us to obtain a good matching in terms of income, on the other, the creation of very small sub-sample of observations with similar income reduces the possibility for each SILC observation to find a similar HBS household in terms of socio-demographic characteristics. While in the setting of impact evaluation this could impair the validity of the study, for the purpose of our model this difference does not represent a limitation.

To further inspect the matching results, we compare the samples by the households total income deciles, that we are going to use to compute VAT incidence. Table 4 shows that households are not significantly different in each income decile except in the last one. This is due to the fact that in the SILC sample there are outliers with very high-income values, while in HBS sample there are not such extremely high values. While we can discard HBS observations who are not similar to SILC households, we cannot discard any SILC household because we need to keep the survey representative at the country level. Therefore, if there are SILC households that are very different from all the HBS observations, instead of discarding them, we compare them with the nearest HBS household even though it is not strictly similar. Finally, the most important aspect to be preserved after the matching are the variables distributions. In order to check whether distributions are preserved we show the Kernel density distribution of the most important income categories for SILC and HBS samples, before and after the matching (Figure 1).

Table 4

	Average Household Total Income(Tax Register Data)	p-value for difference	95% C.I.
SILC	HBS
Decile 1	109.84	97.09	0.362	-14.690	40.191
Decile 2	6,123.82	6,100.1	0.853	-227.601	275.042
Decile 3	12,119.33	12,100.75	0.808	-227.601	275.042
Decile 4	17,048.14	17,003.47	0.479	-79.197	168.543
Decile 5	21,418.1	21,422.48	0.944	-127.396	118.644
Decile 6	26,253.72	26,184.46	0.337	-72.261	210.775
Decile 7	32,128.57	32,141.58	0.886	-191.308	165.297
Decile 8	40,270.44	40,385.72	0.382	-373.659	143.105
Decile 9	52,844.87	52,972.46	0.561	-557.837	302.661
Decile 10	100,286.9	92,542.72	0.000	3642.137	11,846.200

1. Source: Authors elaboration.

Figure 1

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Before the matching the distribution represents all HBS household, while after the matching the distribution represents only HBS observations not discarded by the matching and it takes into account the fact that some observation is repeated more than once to be paired with SILC households. Figure 1 shows that the income distributions of SILC and HBS households is really similar with original samples and that this similarity is preserved even after the matching.

Additionally, we provide a graphic representation of the Kernel density distribution of expenditures, before and after the matching. We want to check whether the exclusion of some HBS household during the matching procedure could have altered the original distribution of the most important expenditure aggregates (namely: total expenditure, food expenditure, non-durable expenditures, durable expenditures). In Figure 2, we show that the two distributions are extremely similar before and after the matching.

Figure 2

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In general, we can consider the matching performance good for the purpose of our microsimulation model because the distributions are preserved, and the two groups, on average, are not significantly different in income.

Finally, we compared the performance of our matching with other two matching procedures commonly used by scholars. First, we tested a nearest neighbour matching with replacement (using the Mahalanobis distance) based on socio-demographic variables only and we stratify observations by type of job (as a proxy for income). After the matching, we compared the Tax Register declared income of the paired households and they are significantly different. Second, we replicate the most common procedure used in the literature, by matching SILC and HBS relying on the estimated consumption in the SHIW dataset. After the matching, we compare the Tax Register data of the two groups and we found that this procedure matches households that are significantly different in income. Also, the consumption distribution after matching is different from the consumption distribution observed before the matching (see Table A1). Basing on our estimations, the matching procedure used in this paper outperforms (thanks to the access to Tax Register data) those implemented in the existing literature.

Once we produced a reliable dataset, which includes both data on income and expenditures, we start to model the VAT microsimulation. In this section we describe the steps to estimate the expected and actual VAT amounts and the VAT incidence on households disposable income for the year 2019 with current legislation. We show how we compute the core microsimulation variables (Section 4.1) and then we describe the outputs of a microsimulation for 2019 with current VAT legislation (Section 4.2). Currently, the Italian VAT legislation establishes: (i) the application of a super reduced rate of 4% for basic food items, some medical services, books and newspapers; (ii) the application of two reduced rates of 5% and 10% for other food items and some house maintenance services; (iii) the application of the standard rate of 22% for other assets; (iv) the VAT exemption for some categories of goods or services, such as education. The same asset may be subject to different VAT rates (i.e. for heating gas a reduced rate of 10% is applied for domestic uses and up to a certain consumption threshold; in all other cases, the standard rate is 22%). Indeed, for each consumption item, we need to consider five weights, which represent the share of that item consumption subject to each VAT rate (4%, 5%, 10%, 22%, exempt).

4.2. Microsimulation outputs with current VAT legislation

In this section, we describe the VATSIM-DF (II) outputs. Figure 3 shows net consumption (that is the taxable VAT base) by household income deciles and VAT rates. We find that households in the first and tenth decile devote, respectively, around 8.6% and 5.8% of their total expenditure to goods with super reduced VAT rate (4%). While they spend, respectively, 33% and 35.1% of their total expenditure for goods with standard VAT rate (22%). For each VAT rate, we compute the expected and actual VAT revenue and the VAT gap (see Table 5). As expected, most of the VAT revenue is driven by goods with the standard VAT rate (22%) that are also those with the highest VAT gap in absolute terms.

Figure 3

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Table 5

VAT rate	Expected VAT	VAT Gap	Actual VAT
4%	2,909,905,600	307,970,616	2,601,934,984
5%	114,201,667	17,058,621	97,143,046
10%	23,543,919,955	3,817,302,226	19,726,617,733
22%	76,787,961,011	8,186,778,456	68,601,182,579
Total	103,355,988,233	12,329,109,919	91,026,878,342

1. Source: Authors elaboration.

Figure 4 shows the actual and expected VAT incidence on households disposable income by equivalent disposable income deciles. ¹⁹ As expected, VAT is a regressive tax because low-income households spend a higher share of their income for consumption (and consequently for VAT) with respect to high-income households. Indeed, we find that in 2019, at aggregate level, households in the lowest income decile devote 13.4% of their income to VAT expenditure. Conversely, the highest income households spend only 4.7% of their income to pay VAT on goods (for more details see Appendix 3, Table A3).

Figure 4

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Our results on VAT incidence are not very different from recent findings by Centro Studi Confindustria (2019) but we found a slightly higher level of regressivity. ²⁰ While we found a similar VAT incidence of 13% for the for the lowest income decile, for all the other deciles, Centro Studi Confindustria show a higher VAT incidence (between 10% and 9%) with respect to our findings (between 9% and 6%). For the highest income decile, this figure is even more pronounced because we found a VAT incidence of 4.7% while they find a VAT incidence of around 9%. The regressivity of VAT was confirmed also using different methodologies. For instance, Benassi and Randon (2020) rely on the stochastic dominance relationship between the income share distribution and the VAT burden distribution and provide evidence of VAT regressivity. Finally, our results show that the VAT incidence trend in Italy is similar to the one calculated on the average of other 26 OECD countries (see Thomas, 2020). ²¹

In order to have a complete picture of the households that bear the highest burden of VAT, we disaggregate data on VAT incidence by VAT rates (Figure 5). For goods with super reduced and reduced VAT rates (namely, 4%, 5%, and 10%), the incidence curve is flatter with respect to the one related to goods with a standard VAT rate (22%). However, the absolute difference in incidence between the highest and the lowest income deciles are 0.3, 1.9, and 6.3 percentage points, for super-reduced, reduced and standard VAT rates, respectively. This result shows that VAT is regressive, although by different degrees, for all VAT rates.

Figure 5

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More interestingly, the VAT incidence and the VAT gap vary by bundles of goods. Indeed, the VAT incidence curve is steeper for goods related to food and non-alcoholic beverages, transport, clothing and footwear, showing a stronger regressivity and a high burden for low-income households (see Appendix 4, Figure A4)²². Transport, food, and non-alcoholic beverages are (jointly with housing, water, gas, electricity, and other fuels) also the goods that absorb the highest share of households income. Moreover, not surprisingly, the share of income devoted to these goods is much higher for low-income households in comparison with the highest income ones (see Appendix 5,Figure A5).

The results showed so far are based on the choice to measure the VAT incidence on current income. However, the high incidence of VAT on the lowest income deciles, can be due to two factors. The first one is usually related to under-reporting or misreporting errors in income data for the lowest deciles. To address this problem, we adjusted income by imputing unplausible low-income values to positive values (Section 4.1). The second one, is related to the use of current income instead of permanent income. Indeed, according to Gastaldi et al. (2016), in single-period analysis, findings about the regressivity of VAT may be upward biased by the presence, in the sample, of consumption smoothers, whose expenditure choices may not be affected by negative fluctuations in current income. The best option to check for this hypothesis would be to measure VAT incidence on lifetime income (Levell et al., 2020), but this data availability is extremely limited. To overcome these issues, several scholars suggest relying on expenditure as reference value for VAT incidence (for instance, Adam et al., 2011). Indeed, in some cases, and under the hypothesis of consumption smoothing, expenditure is used as a proxy for lifetime income. To account for this, we also estimate the VAT incidence on expenditure, and we found that, overall, the incidence curve is flat (Appendix 6,Figure A6). However, using expenditure as a reference to calculate VAT incidence has its drawbacks. First, as argued by Mirrlees et al. (2011), since low-income households may face borrowing constraints to smooth consumption, the most important indicator for them is still current income. Second, while measuring VAT incidence on current income may lead to an overestimation of VAT regressivity, the use of expenditure, as reference value, may lead to an underestimation of VAT regressivity. Underestimating regressivity is much risky, as it could favour policies which can produce long-term detrimental effects on low-income households at risk of poverty.

In this section, we show an application of VATSIM-DF (II). It is worth noting that the proposed application is an exercise to show how a reform of the VAT system may affect the VAT burden on households. The proposed reform is an exercise to test the simulation model. Indeed, to design a reform of the VAT system one should consider the entire fiscal systems and account for the individuals behavioral responses to changes in VAT rates. ²³

We analyse a scenario with a revenue neutral reform which sets two VAT rates of 7% and 20%. ²⁴ In particular, the reduced VAT rate of 7% is applied to food and non-alcoholic beverage and to other goods and services currently subject to a VAT rate of 4%, 5%, and 10%. Also, we applied the VAT rate of 7% to some articles and products for personal care (such as, female sanitary towels and babies’ nappies). In our simulation, we assign a VAT rate of 20% to goods and services which are currently subject to the standard VAT rate of 22%. Appendix 7 (Table A7) provides details about the changes we applied to existing VAT rates. The primary role of VAT is not to stimulate or discourage the consumption of certain types of goods, which is the goal of other types of fiscal measures. ²⁵ However, in our simulation we take into account social, cultural and environmental considerations made in the past by policy makers. Figure 6 shows the results of our simulation in terms of VAT incidence in aggregate terms (see Appendix 8,Figure A8, for further details). The simulation results show that the changes applied to VAT rates do not strongly affect the VAT burden on households, which is very similar to the one observed at current legislation. Indeed, in Figure 7, we report the difference in incidence between simulation and current legislation. Although the VAT burden on households is not subject to strong changes, we can observe that there is a slight reduction in incidence for low-income households and an even smaller increase in incidence for high income households. Most importantly, in Appendix 8 (Figure B8), we show that the VAT incidence, for food and non-alcoholic beverages, decreases for all households but it benefits most low-income households. The same applies to other sectors, such as clothing and footwear and housing, water, gas, electricity and other fuels. Conversely, the VAT burden on households visibly increases for restaurants and hotels, especially for low-income households.

Figure 6

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Figure 7

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The objective of this paper was to describe the VATSIM-DF (II), a new microsimulation model whose goals are to: (i) to estimate actual and expected VAT amounts; (ii) to assess the VAT incidence on household disposable income; (iii) and to simulate the effects of changes in fiscal policies. The main innovation of our model, with respect to the existing literature, is the creation and the use of a new dataset developed by implementing an original matching procedure based on tax register data, which outperforms other matching procedures commonly used in previous studies.

The model relies on two surveys about income and consumption which are merged one at a time with Tax Register data, through an exact matching based on tax codes. Then the two surveys are merged, between them, by a statistical matching relying on Tax Register data on income and on socio-demographic households characteristics. The resulting dataset (VATIC 2019) is a unique source of detailed data on income and expenditure, at the household level. Then survey data are adjusted to National Accounts data to obtain the most updated variables coherent with macroeconomic data. Finally, we provide the main microsimulation outputs. Indeed, thanks to this model, we can assess the actual and expected VAT revenue and the VAT gap. Most importantly, we estimate the level of regressivity of VAT in Italy, which is a crucial information to design policies able to account for the trade-off between equity and efficiency.

The motivation for this model was twofold. From the one side, we wanted to produce a reliable microsimulation tool which can support Italian policy makers to design VAT related policies. From the other, we aimed at contributing to the existing literature by providing a microsimulation model with original features. Indeed, the value added of VATSIM-DF (II), with respect to the existing models, are: (i) the creation of a unique dataset with data on income and consumption with a very high level of reliability, since the matching procedure relies on tax codes and Tax Register data; (ii) the adjustment of survey data on income and consumption to National Accounts data at the year of the simulation, which makes our estimations on VAT extremely coherent with the most updated macroeconomic data. To the best of our knowledge, there are not existing models in Italy with these features and this makes VATSIM-DF (II) a promising tool for reliable microsimulations about the distributional effects of VAT. We show that the procedure we used to create the dataset VATIC 2019, outperforms other methods used in the literature about microsimulation models in Italy. Also, we show an application of our model by testing a revenue neutral reform with two VAT rates. The reform consists only of two rates: a reduced VAT rate of 7% and an ordinary VAT rate of 20%. In addition to other basic goods, the reduced VAT rate is also applied to female sanitary towels and babies nappies. We show that this reform does not strongly affect the VAT burden on households. Conversely, it benefits low-income households reducing the VAT burden for basic goods and services (e.g. food and non-alcoholic beverages; clothing and footwear; and housing, water, gas, electricity and other fuels). However, the VAT burden on households visibly increases for restaurants and hotels, in particular for low-income households. A further extension of the VATSIM-DF (II) model, which will be described in a forthcoming paper, focuses on the behavioural effects produced by tax-shifts and changes in fiscal policies.

Below, we show the results after a matching procedure based on estimated consumption relying on the SHIW data (see Table A1). Since we have access to the Tax register data, we show that a matching based on estimated consumption may pair households that are significantly different, on average, in Tax Register income (see Table B1). Finally, we show the consumption distribution before and after the matching based on SHIW as bridge dataset (see Figure A1). Comparing Figure 2 (Section 3.2) and Figure A1, we find that our matching procedure, based on Tax Register data, is more able to preserve the original consumption distribution with respect to a matching procedure based on estimated consumption.

Figure A1

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1. 1
   TAXUD/2010/DE/328, FWC No.TAXUD/2010/CC/104, Institute for Fiscal Studies (Project Leader)

   1. S Adam
   2. D Phillips
   3. S Smith
   4. L Bettendorf
   5. S Boeters
   6. H Kox
   7. S Giannini

   (2011)

   A retrospective evaluation of elements of the EU VAT system: Final report, TAXUD/2010/DE/328, FWC No.TAXUD/2010/CC/104, Institute for Fiscal Studies (Project Leader).

   • Google Scholar
2. 2
   Fiscal Reforms during Fiscal Consolidation: The Case of Italy

   1. G Arachi
   2. V Bucci
   3. E Longobardi
   4. PM Panteghini
   5. ML Parisi
   6. S Pellegrino
   7. A Zanardi

   (2012)

   Finanz-Archiv: Zeitschrift Fur Das Gesamte Finanzwesen 68 :445–465.

   https://doi.org/10.1628/001522112X659574

   • Google Scholar
3. 3
   Dipartimento Di Economia Politica, Università Di Modena

   1. M Baldini

   (2000)

   MAPP98: un modello di analisi delle politiche pubbliche, in «Materiali di discussione», n. 331, Dipartimento Di Economia Politica, Università Di Modena.

   • Google Scholar
4. 4
   The recent reforms of the Italian personal income tax: distributive and efficiency effects

   1. M Baldini
   2. D Pacifico

   (2009)

   Rivista Italiana Degli Economisti 14 :191–218.

   • Google Scholar
5. 5
   DEMB Working Paper Series, Dipartimento Di Economia Marco Biagi - Università Di Modena e Reggio Emilia

   1. M Baldini
   2. D Pacifico
   3. F Termini

   (2015)

   Imputation of missing expenditure information in standard household income surveys, DEMB Working Paper Series, Dipartimento Di Economia Marco Biagi - Università Di Modena e Reggio Emilia.

   • Google Scholar
6. 6
   The distribution of the tax burden and the income distribution: theory and empirical evidence

   1. C Benassi
   2. E Randon

   (2020)

   Economia Politica 38 :1087–1108.

   https://doi.org/10.1007/s40888-020-00207-3

   • PubMed
   • Google Scholar
7. 7
   Faculty of Economics, University of Cambridge

   1. F Bourguignon
   2. C O’Donoghue
   3. J Sastre-Descals
   4. A Spadara
   5. F Utili

   (1997)

   Eur3: A prototype European tax-benefit model (no.9723), Faculty of Economics, University of Cambridge.

   • Google Scholar
8. 8
   Ministry of Economy and Finance, Department of Finance

   1. VF Calà
   2. S Carta
   3. M Manzo
   4. L Signorelli

   (2020)

   A Methodological Note on the Value Added Tax Simulation Model - DF (I), Ministry of Economy and Finance, Department of Finance.

   • Google Scholar
9. 9
   Toward a Comprehensive Tax Reform for Italy

   1. E Cammeraat
   2. E Crivelli

   (2020)

   IMF Working Papers 20 :1.

   https://doi.org/10.5089/9781513529226.001

   • PubMed
   • Google Scholar
10. 10
    Dove va l’economia italiana e gli scenari di politica economica. Rapporti di previsione. Confindustria Servizi

    1. Centro Studi Confindustria

    (2019)
11. 11
    Euromod Country Reports

    1. L Ceriani
    2. F Figari
    3. C Fiorio

    (2017)

    Euromod Country Report: Italy 2014-2017, Euromod Country Reports.

    • Google Scholar
12. 12
    Bank of Italy Temi Di Discussione (Working Paper) No, 908

    1. G Cifaldi
    2. A Neri

    (2013)

    Asking income and consumption questions in the same survey: what are the risks?, Bank of Italy Temi Di Discussione (Working Paper) No, 908.

    • Google Scholar
13. 13
    Bank of Italy Occasional Paper, (394)

    1. N Curci
    2. M Savegnago
    3. M Cioffi

    (2017)

    BIMic: The Bank of Italy microsimulation model for the Italian tax and benefit system, Bank of Italy Occasional Paper, (394).

    • Google Scholar
14. 14
    (No. 1244) Bank of Italy, Economic Research and International Relations Area.

    1. N Curci
    2. M Savegnano

    (2019)

    Shifting taxes from labor to consumption: the efficiency equity trade-off, (No. 1244) Bank of Italy, Economic Research and International Relations Area..

    • Google Scholar
15. 15
    EUROMOD Technical Note EMTN 3.0

    1. P De Agostini
    2. B Capéau
    3. A Decoster
    4. F Figari
    5. J Kneeshaw
    6. C Leventi
    7. K Manios
    8. A Paulus A
    9. H Sutherland
    10. T Vanheukelom

    (2017)

    EUROMOD Extension to Indirect Taxation: Final Report, EUROMOD Technical Note EMTN 3.0, Colchester, Institute for Social and Economic Research, University of Essex.

    • Google Scholar
16. 16
    Ediesse

    1. C De Vincenti
    2. C Pollastri

    (2004)

    La partita doppia del welfare: una base informativa originale per dibattere di tassazione e riforma dell’intervento pubblico: XIV rapporto CER-SPI, Ediesse.

    • Google Scholar
17. 17
    Statistical matching of income and consumption expenditures

    1. G Donatiello
    2. M D’Orazio
    3. D Frattarola
    4. A Rizzi
    5. M Scanu
    6. M Spaziani

    (2014)

    International Journal of Economic Sciences 3 :50–65.

    • Google Scholar
18. 18
    Working Paper SIEP 09

    1. F Gastaldi
    2. P Liberati
    3. E Pisano
    4. S Tedeschi

    (2014)

    Progressivity-Improving VAT Reforms in Italy, Working Paper SIEP 09.

    • Google Scholar
19. 19
    Regressivity-Reducing VAT Reforms

    1. F Gastaldi
    2. P Liberati
    3. E Pisano
    4. S Tedeschi

    (2016)

    International Journal of Microsimulation 10 :39–72.

    https://doi.org/10.34196/ijm.00149

    • Google Scholar
20. 20
    A Tax-Benefit Microsimulation Model for Italy: A Partial Evaluation of Fiscal Consolidation in the Period 2011-2014

    1. E Giarda
    2. M Baldini
    3. A Olivieri

    (2015)

    SSRN Electronic Journal 1.

    https://doi.org/10.2139/ssrn.2665547

    • Google Scholar
21. 21
    Microsimulation Unit, Department of Applied Economics, University of Cambridge

    1. H Immervoll
    2. C O’donoghue
    3. H Sutherland

    (1999)

    An introduction to EUROMOD, Microsimulation Unit, Department of Applied Economics, University of Cambridge.

    • Google Scholar
22. 22
    Istat

    1. Istat

    (2018)

    Indagine sul reddito e le condizioni di vita delle famiglie (EU-SILC). Dati trasversali 2018 Dati longitudinali 2015-2018. Aspetti metodologici dell’indagine, Istat.

    • Google Scholar
23. 23
    Istat

    1. Istat

    (2018)

    Indagine sulle spese delle famiglie, Periodo di riferimento: anno 2017. Aspetti metodologici dell’indagine, Istat.

    • Google Scholar
24. 24
    Tavole conti economici nazionali, Settembre 2020 (Tavola 20). Retrieved 23/11/2020 from

    1. Istat

    (2020a)
25. 25
    Dataset: Nic - pesi dal 2016: Classificazione Ecoicop (5 cifre). Retrieved 23/11/2020 from

    1. Istat

    (2020b)
26. 26
    Tavole economia non osservata nei conti nazionali, Anni: 2011-2018. Retrieved from

    1. Istat

    (2020c)
27. 27
    Dataset: Stock di attività non finanziarie e sequenza dei conti per settore istituzionale: Sequenza dei conti – Ed. ott. 2020. Retrieved 23/11/2020 from

    1. Istat

    (2020d)
28. 28
    Statistical Software Components S432001, Boston College Department of Economics, Revised 01 Feb 2018

    1. E Leuven
    2. B Sianesi

    (2003)

    PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing, Statistical Software Components S432001, Boston College Department of Economics, Revised 01 Feb 2018.

    • Google Scholar
29. 29
    Mobility and the lifetime distributional impact of tax and transfer reforms

    1. P Levell
    2. B Roantree
    3. J Shaw

    (2020)

    International Tax and Public Finance 28 :751–793.

    https://doi.org/10.1007/s10797-020-09627-0

    • Google Scholar
30. 30
    Studi e Approfondimenti IRPET

    1. ML Maitino
    2. L Ravagli
    3. N Sciclone

    (2013)

    Il modello di microsimulazione dell’Irpet MicroReg: aggiornamenti ed estensioni, Studi e Approfondimenti IRPET.

    • Google Scholar
31. 31
    Pavia, Dipartimento Di Economia Pubblica e Territoriale

    1. A Marenzi

    (1989)

    La distribuzione del carico fiscale in Italia: un modello di microsimulazione, Pavia, Dipartimento Di Economia Pubblica e Territoriale.

    • Google Scholar
32. 32
    Institute for Fiscal Studies

    1. SJ Mirrlees
    2. S Adam
    3. T Besley
    4. R Blundell
    5. S Bond
    6. R Chote
    7. M Gammie
    8. P Johnson
    9. G Myles
    10. J Poterba

    (2011)

    Tax by Design: The Mirrlees Review, Institute for Fiscal Studies.

    • Google Scholar
33. 33
    OECD Tax Policy Studies, No.22

    1. OECD and KIPF

    (2014)

    The Distributional Effects of Consumption Taxes in OECD Countries, OECD Tax Policy Studies, No.22, Paris, OECD Publishing, 10.1787/9789264224520-en.

    • Google Scholar
34. 34
    Developing a static microsimulation model for the analysis of housing taxation in Italy

    1. S Pellegrino
    2. M Piacenza
    3. G Turati

    (2010)

    International Journal of Microsimulation 4 :73–85.

    https://doi.org/10.34196/ijm.00054

    • Google Scholar
35. 35
    Working Papers 164, University of Rome La Sapienza, Department of Public Economics

    1. E Pisano
    2. S Tedeschi

    (2014)

    Micro data fusion of italian expenditures and incomes surveys, Working Papers 164, University of Rome La Sapienza, Department of Public Economics.

    • Google Scholar
36. 36
    Data fusion: identification problems, validity, and multiple imputation

    1. S Rässler

    (2004)

    Austrian Journal of Statistics 33 :153–171.

    • Google Scholar
37. 37
    The central role of the propensity score in observational studies for causal effects

    1. PR Rosenbaum
    2. DB Rubin

    (1983)

    Biometrika 70 :41–55.

    https://doi.org/10.1093/biomet/70.1.41

    • Google Scholar
38. 38
    Department of Public Policy and Public Choice-POLIS, Working Paper 63

    1. A Sisto

    (2006)

    Propensity score matching: un’applicazione per la creazione di un database integrato ISTAT-Banca d’Italia, Department of Public Policy and Public Choice-POLIS, Working Paper 63.

    • Google Scholar
39. 39
    University of Genova DEP Working Paper, 7

    1. A Taddei

    (2012)

    L’ipotesi di tax-shift dal lavoro ai consumi in Italia: un’analisi di microsimulazione fiscale utilizzando il modello EUROMOD, University of Genova DEP Working Paper, 7.

    • Google Scholar
40. 40
    OECD Taxation Working Papers, (49), 0_1-51

    1. A Thomas

    (2020)

    Reassessing the regressivity of the VAT, OECD Taxation Working Papers, (49), 0_1-51.

    • Google Scholar

Author details

1. Cristina Cirillo

   Ministry of Economy and Finance, Department of Finance, Rome, Italy

   For correspondence

   cristina.cirillo@mef.gov.it

   Competing interests

   No competing interests reported.

   "This ORCID iD identifies the author of this article:" 0000-0003-2131-6023

2. Lucia Imperioli

   Ministry of Economy and Finance, Department of Finance, Rome, Italy

   Competing interests

   No competing interests reported.

3. Marco Manzo

   Ministry of Economy and Finance, Department of Finance, Rome, Italy

   Competing interests

   No competing interests reported.