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The importance of choosing the data set for tax-benefit analysis

  1. Lidia Ceriani  Is a corresponding author
  2. Carlo V. Fiorio  Is a corresponding author
  3. Chiara Gigliarano  Is a corresponding author
  1. Bocconi University and Econpubblica, Italy
  2. University of Milan and Econpubblica, Italy
  3. Università Politecnica delle Marche, Italy
Research article
Volume 6(1) Spring 2013
Cite this article as: L. Ceriani, C. V. Fiorio, C. Gigliarano; 2013; The importance of choosing the data set for tax-benefit analysis; International Journal of Microsimulation; 6(1); 86-121. doi: 10.34196/ijm.00078
14 tables

Tables

Table 1
Comparison between 2008 SHIW and 2009 IT-SILC.
SHIW IT-SILC
Provider Bank of Italy Istat
Frequency of data collection Biannual Annual
Period of data collection January-September 2009 September-October 2009
Income reference period 2008 2008
Coverage Private households Private households
Statistical units Households and individuals Households and individuals
Sample size (households) 7,977 20,492
Sample size (individuals) 19,907 51,196
Response rate 56.1% 85.5%
Geographical Representativeness Three Macro regions Regional
International comparability No1 Yes
Sample design Two-stage (first stratified) Two-stage (first stratified)
Data collection methodology CAPI/PAPI PAPI
Gross income variables No yes (starting from 2007)

  1. Source: Our calculation and analysis of SHIW and IT-SILC.

  2. Note: 1International comparability is not among the main aims of the SHIW. It can however be obtained upon adequate harmonization of main variables, such as the “lissification” procedure (see LIS project).

Table 2
SHIW additional Information: Real-Estate Properties different from principal residence -Average yearly effective and imputed rent.
Frequencies % Rented properties Effective Rent (c) Euro Unrented properties Imputed Rent (d) Euro (d/c)-1*100 Diff. %
Total units of building and real estate 100 5,217 2,610 −49.97
Residential buildings 51.53 4,985 3,528 −29.22
Offices 1.46 7,914 5,686 −28.15
Storehouses 4.21 7,665 4,734 −38.23
Shops 4.11 7,195 5,743 −20.18
Workshops 0.52 4,860 11,892 144.68
Garages and basements 7.45 3,179 1,280 −59.74
Arable land 25.56 2,570 1,146 −55.39
Non-arable land 5.15 4,350 856 −80.33

  1. Source: our elaboration on SHIW data.

Table 3
Population Statistics: frequencies and their standard errors.
External source Frequencies (a) Frequencies (b) SHIW Standard error Diff. % (b/a-1)*100 Frequencies (c) IT-SILC Standard error Diff. % (c/a-1)*100
Panel a: total population
Total 60,045,068 59,721,994 425,204 −0.54 59,726,481 187,436 −0.53
Males 29,152,423 29,039,856 298,942 −0.39 29,034,258 133,299 −0.41
Females 30,892,645 30,682,138 302,382 −0.68 30,692,223 131,737 −0.65
Panel b: Population by Geographical Partition
North West 15,917,376 15,832,738 243,504 −0.53 15,821,674 99,431 −0.60
North East 11,473,120 11,351,612 162,575 −1.06 11,387,593 63,830 −0.75
Center 11,798,328 11,708,006 192,699 −0.77 11,720,956 76,598 −0.66
South 20,856,244 20,829,638 237,817 −0.13 20,796,258 116,865 −0.29
Panel c: Population by Regions
Piemonte 4,432,571 4,040,054 69,762 −8.86 4,404,240 43,130 −0.64
Valle D’Aosta 127,065 213,437 9,954 67.97 128,551 1,773 1.17
Lombardia 9,742,676 9,877,494 209,934 1.38 9,684,939 64,349 −0.59
Trentino Alto Adige 1,018,657 926,708 42,773 −9.03 1,018,870 13,472 0.02
Veneto 4,885,548 4,985,755 122,846 2.05 4,849,150 34,935 −0.75
Friuli Venezia Giulia 1,230,936 1,268,878 30,745 3.08 1,221,728 15,022 −0.75
Liguria 1,615,064 1,701,753 44,885 5.37 1,603,944 26,705 −0.69
Emilia Romagna 4,337,979 4,170,271 87,184 −3.87 4,297,845 33,274 −0.93
Toscana 3,707,818 4,064,424 81,482 9.62 3,678,686 34,202 −0.79
Umbria 894,222 899,923 26,000 0.64 893,152 13,906 −0.12
Marche 1,569,578 1,632,901 35,183 4.03 1,559,290 18,397 −0.66
Lazio 5,626,710 5,110,758 146,875 −9.17 5,589,828 46,453 −0.66
Abruzzo 1,334,675 989,980 37,424 −25.83 1,322,333 22,113 −0.92
Molise 320,795 563,074 27,802 75.52 319,701 5,599 −0.34
Campania 5,812,962 5,121,838 125,890 −11.89 5,796,817 52,115 −0.28
Puglia 4,079,702 3,859,146 106,992 −5.41 4,074,872 41,321 −0.12
Basilicata 590,601 1,621,012 59,568 174.47 590,344 10,574 −0.04
Calabria 2,008,709 1,990,086 51,394 −0.93 2,008,038 30,027 −0.03
Sicilia 5,037,799 4,811,870 126,081 −4.48 5,024,861 62,789 −0.26
Sardegna 1,671,001 1,872,632 36,650 12.07 1,659,292 35,834 −0.70

  1. Source: our elaboration on DemoIstat, values as 1st January 2009 (External Source) and SHIW and IT-SILC data.

  2. Note: Following EUROMOD conventions, in IT-SILC individuals who were born after the income reference period have been dropped (see Ceriani et al., 2012).

Table 4
Population Statistics: frequencies and their standard errors.
Variable External source Frequencies (a) Frequencies (b) SHIW Standard error Diff. % (b/a-1)*100 Frequencies (c) IT-SILC Standard error Diff. % (c/a-1)*100
Age 0–19
Italy 11,408,746 10,669,166 179,935 −6.48 10,760,931 83,853 −5.68
North West 2,811,590 2,530,113 96,815 −10.01 2,673,483 43,519 −4.91
North East 2,069,019 2,164,051 72,273 4.59 1,958,606 28,342 −5.34
Center 2,106,040 1,737,163 75,409 −17.52 1,955,060 32,628 −7.17
South 4,422,097 4,237,839 109,069 −4.17 4,173,782 53,824 −5.62
Age 20–65
Italy 37,179,179 36,436,238 337,064 −2.00 36,948,154 151,911 −0.62
North West 9,872,235 9,598,375 194,968 −2.77 9,760,948 80,082 −1.13
North East 7,124,107 7,241,443 134,291 1.65 7,039,530 51,288 −1.19
Center 7,293,877 6,809,238 147,241 −6.64 7,254,419 62,513 −0.54
South 12,888,960 12,787,182 186,642 −0.79 12,893,257 95,458 0.03
Age > 65
Italy 11,457,143 12,616,590 185,011 10.12 12,017,396 70,712 4.89
North West 3,233,551 3,704,250 107,748 14.56 3,387,243 39,170 4.75
North East 2,279,994 1,946,118 53,937 −14.64 2,389,457 25,007 4.80
Center 2,398,411 3,161,605 98,630 31.82 2,511,477 29,813 4.71
South 3,545,187 3,804,617 98,284 7.32 3,729,219 40,583 5.19

  1. Source: our elaboration on DemoIstat, values as 1st January 2009 (External Source) and SHIW and IT-SILC data.

  2. Note: Following EUROMOD conventions, in IT-SILC individuals who were born after the income reference period have been dropped (see Ceriani et al., 2012).

Table 5
Population by highest level of education attained: frequencies and their standard errors.
Variable External source Frequencies (a) Frequencies (b) SHIW Standard error Diff. % (b/a-1)*100 Frequencies (c) IT-SILC Standard error Diff. % (c/a-1)*100
Elementary Education
Italy 12,379,000 11,980,676 174,686 −3.22 11,576,464 73,665 −6.48
North West 2,980,000 2,740,537 95,718 −8.04 2,854,287 37,062 −4.22
North East 2,323,000 1,748,543 51,924 −24.73 2,234,921 24,530 −3.79
Center 2,271,000 2,533,318 79,516 11.55 2,226,255 28,632 −1.97
South 4,805,000 4,958,278 110,357 3.19 4,261,001 48,345 −11.32
Lower Secondary
Italy 16,285,000 15,950,263 215,876 −2.06 15,690,681 99,741 −3.65
North West 4,393,000 4,307,785 130,857 −1.94 4,276,672 54,038 −2.65
North East 2,976,000 2,990,028 76,817 0.47 2,831,499 32,224 −4.86
Center 2,925,000 2,843,163 86,899 −2.80 2,786,859 38,068 −4.72
South 5,991,000 5,809,287 125,616 −3.03 5,795,651 63,650 −3.26
Upper Secondary
Italy 17,077,000 17,915,782 238,861 4.91 17,830,531 101,128 4.41
North West 4,751,000 5,009,707 135,033 5.45 4,886,090 53,287 2.84
North East 3,471,000 3,882,779 99,289 11.86 3,628,807 36,691 4.55
Center 3,666,000 3,529,855 107,904 −3.71 3,798,268 44,818 3.61
South 5,190,000 5,493,441 129,806 5.85 5,517,366 60,352 6.31
Tertiary
Italy 5,574,000 5,566,073 140,786 −0.14 5,780,476 60,781 3.70
North West 1,574,000 1,814,768 81,955 15.30 1,654,332 34,060 5.10
North East 1,053,000 983,817 57,247 −6.57 1,087,134 19,946 3.24
Center 1,297,000 1,421,667 80,259 9.61 1,305,711 25,910 0.67
South 1,650,000 1,345,821 52,594 −18.44 1,733,299 35,096 5.05

  1. Source: our elaboration on Istat (2010d), SHIW and IT-SILC.

  2. Note: Elementary education include the set of population who did not complete any education level or completed just the elementary school (grade 1 to 5). Lower secondary education is the group of people whose highest level of education is grade eight. Upper secondary education refers to people who received a diploma and Tertiary is the set of individuals who attain an undergraduate or graduate degree. Population is age 15 and more in the External Source and in SHIW, while it is age 16 and more in IT-SILC due to data availability.

Table 6
Population Statistics: frequencies and their standard errors.
Variable External source Frequencies (a) Frequencies (b) SHIW Standard error Diff. % (b/a-1)*100 Frequencies (c) IT-SILC Standard error (c Diff. % /a-1)*100
Employees
Italy 21,611,778 18,734,187 228,015 −13.31 22,485,222 118,991 4.04
North West 5,985,582 5,266,597 133,671 −12.01 6,325,447 64,905 5.68
North East 4,637,931 4,296,697 95,571 −7.36 4,273,878 43,170 −7.85
Center 3,944,874 3,752,849 104,983 −4.87 4,627,595 48,960 17.31
South 5,734,778 5,418,044 116,004 −5.52 7,258,302 70,304 26.57
Self-employed
Italy 6,117,343 5,438,250 155,809 −11.10 6,717,910 71,173 9.82
North West 2,067,939 1,712,138 98,638 −17.21 1,840,272 40,080 −11.01
North East 1,280,320 1,217,732 65,355 −4.89 1,308,766 24,974 2.22
Center 1,488,679 1,045,498 68,217 −29.77 1,393,713 26,769 −6.38
South 2,128,093 1,462,882 68,435 −31.26 2,175,159 43,040 2.21
Pensioners
Italy 15,323,148 14,844,649 198,842 −3.12 15,877,444 86,908 3.62
North West 4,439,474 4,671,715 122,667 5.23 4,531,924 47,265 2.08
North East 2,864,306 2,504,802 64,410 −12.55 2,957,492 29,571 3.25
Center 3,055,415 3,412,963 97,978 11.70 3,232,561 36,895 5.80
South 4,476,872 4,255,169 101,464 −4.95 5,155,467 51,590 15.16

  1. Source: our elaboration on MEF (2013) (external source) and SHIW and IT-SILC data.

  2. Note: We count among employees all those who receive employment income and similarly for self-employment and pensioners, consistently with the external sources. Notice that the sum of frequencies in the external sources by geographic partition does not sum up to the total. This is because there is income which cannot be assigned to any Italian region.

Table 7
Population by sector of activity: frequencies and their standard errors.
Variable External source Frequencies (a) Frequencies (b) SHIW Standard error Diff. % (b/a-1)*100 Frequencies (c) IT-SILC Standard error ( Diff. % c/a-1)*100
Agriculture 1,287,100 878,337 44,619 −31.76 1,107,662 30,118 −13.94
Industry 6,988,500 7,486,766 150,641 7.13 7,145,046 70,310 2.24
Services 16,662,900 14,908,539 231,390 −10.53 15,034,130 102,976 −9.77

  1. Source: our elaboration on Istat, Employment breakdown by industry (NACE Rev.2) – annual national data, 2009, wired at: http://dati.istat.it/?lang=en.

Table 8
Yearly Net Incomes: averages and their standard errors.
SHIW IT-SILC Diff %
euro (b) Std. Err. euro (c) Std. Err. (c/b-1)*100
Employment Income
Italy 15,950 2.17 15,887 2.13 −0.40
North West 17,633 4.63 17,277 4.24 −2.02
North East 15,724 3.82 16,212 4.32 3.11
Center 16,998 5.02 16,177 4.90 −4.83
South 13,759 3.66 14,115 3.60 2.59
Self-Employment Income
Italy 21,109 8.16 18,685 7.84 −11.48
North West 23,018 17.53 20,848 16.22 −9.43
North East 24,151 20.28 21,052 20.66 −12.83
Center 20,043 13.94 19,447 17.58 −2.98
South 17,105 9.78 14,527 9.00 −15.07
Pension Income
Italy 12,354 2.04 12,164 2.53 −1.54
North West 12,807 3.24 12,301 4.88 −3.95
North East 12,450 8.39 11,877 6.75 −4.60
Center 13,210 2.37 12,831 5.14 −2.87
South 10,881 2.11 11,708 2.95 7.61

  1. Source: our elaboration on SHIW and IT-SILC data.

Table 9
Yearly Net Incomes: poverty index, inequality index and their standard errors.
SHIW IT-SILC Diff %
index (b) Std. Err. index (c) Std. Err. (c/b-1)*100
Poverty Headcount index 20.53 0.005 19.34 0.004 −5.82
Inequality Gini Index 35.00 0.006 31.60 0.003 −9.81

  1. Source: our elaboration on SHIW and IT-SILC data.

  2. Note: Poverty and Gini inequality computed on total household disposable income divided by the square root of household size

Table 10
Comparison between External Sources and TABEITA (SHIWT and IT-SILCT) gross incomes.
Variable External Source No evasion Evasion
SHIWT IT-SILCT SHIWT IT-SILCT
Frequencies Amounts euro Freq Diff% Amount Diff% Freq Diff% Amount Diff% Freq Diff% Amount Diff% Freq Diff% Amount Diff%
Total taxable income 41,466,397 782,593,452 3.54 −4.47 −0.72 9.64 3.54 −8.69 −0.72 3.23
Employment income 21,611,778 418,740,720 −13.31 −14.61 4.04 5.94 −13.31 −14.67 4.04 5.72
Self-employment income 6,117,343 109,565,087 −11.10 30.30 9.82 44.63 −11.10 0.96 9.82 0.34
Pension income 15,323,148 213,594,560 −3.12 −1.75 3.62 6.53 −3.12 −1.96 3.62 6.28
Total deductions 12,687,840 21,721,425 −44.31 30.25 −22.60 43.59 −44.55 5.74 −22.05 7.10
Net taxable income 40,249,514 753,556,569 5.19 −5.81 2.16 8.43 5.17 −9.48 2.16 2.82
Total tax credit 39,423,594 62,917,813 −6.00 1.04 2.10 0.67 −5.67 2.55 2.49 2.40
Net personal income tax Regional 31,087,681 146,157,039 6.32 −11.54 7.57 9.20 5.39 −18.17 5.59 −0.85
additional income tax 30,652,846 8,633,217 7.83 −9.20 9.10 7.87 6.88 −13.46 7.09 1.13
Cadastral income main residence 29,776,305 10,551,000 −18.70 0.00 −21.19 0.00 −18.70 0.00 −21.19 0.00
Cadastral income other buildings 17,513,880 7,723,079 −14.19 2.69 −86.97 −55.97 −14.19 2.69 −86.97 −52.71

  1. Source: our elaboration on MEF (2013) (External Source) and SHIW and IT-SILC data.

  2. Note: Freq Diff % = frequencies SHIWT (IT-SILCT) over external source frequencies; Amount Diff % = amount in euro SHIWT (IT-SILCT) over external source amounts. The net personal income tax is the due tax, net of all tax credits. The net taxable income is the tax base, i.e. gross taxable income net of tax allowances.

Table 11
Comparisons between External Sources and TABEITA (IT-SILCT) and IT-SILC gross incomes.
Variable External source IT-SILCT IT-SILC
Frequencies Amounts million euro Frequencies Amounts million euro Frequencies Amounts million euro
Total taxable income 41,466,397 782,593 40,993,736 795,174 41,048,992 848,645
Employment income na na 21,485,120 428,534 21,485,120 431,602
Self-employment income na na 7,498,613 129,802 7,498,613 171,175
Pension income na na 16,541,269 240,099 16,677,270 245,868

  1. Source: our elaboration on MEF (2013) (External Source) and IT-SILC data.

  2. Note: “na” stands for not available. In fact, IT-SILC definition of gross employment, self-employment and pension income is not consistent with what provided in MEF aggregate tables used for validation of TABEITA (Table 10). As there exist no external sources to validate IT-SILC aggregated variables except for total taxable income we aggregated gross variables simulated using TABEITA (IT-SILCT) consistently with IT-SILC definition of gross variables and indirectly validate IT-SILC data. IT-SILC gross incomes are released as aggregate variables subject to administrative exact matching that we are not able to totally replicate in TABEITA. This explains the small differences between IT-SILCT and IT-SILC frequencies for pension income.

Table 12
Comparisons between External Sources and TABEITA with calibration (IT – SILCTC) of cadastral incomes for building different from principal residence.
Variable External Source IT – SILCTC
Frequencies Amounts euro Evasion Freq Diff % Amount Diff % No evasion Freq Diff % Amount Diff %
Total taxable income 41,466,397 782,593,452 0.59 10.39 0.59 4.04
Employment income 21,611,778 418,740,720 4.04 6.10 4.04 5.87
Self-employment income 6,117,343 109,565,087 9.82 44.76 9.82 0.53
Pension income 15,323,148 213,594,560 3.62 6.62 3.62 6.39
Total deductions 12,687,840 21,721,425 −22.72 43.75 −22.07 7.20
Net taxable income 40,249,514 753,556,569 3.35 9.19 3.36 3.65
Total tax credit 39,423,594 62,917,813 2.12 0.38 2.36 2.05
Net personal income tax 31,087,681 146,157,039 9.27 10.55 7.39 0.59
Regional additional income tax 30,652,846 8,633,217 10.82 8.82 8.91 2.16
Cadastral income main residence 29,776,305 10,551,000 −21.19 0.00 −21.19 0.00
Cadastral income other buildings 17,513,880 7,723,079 −56.83 −6.65 −56.83 0.26

  1. Source: our elaboration on MEF (2013) (External Source) and IT-SILC data.

  2. Note: Freq Diff % = frequencies IT – SILCf over external source frequencies; Amount Diff % = amount in euro IT – SILCf over external source amounts.

Table 13
TABEITA (SHIWT and IT-SILCT) gross incomes. Averages and their standard errors.
Variable No evasion Evasion
SHIWT IT-SILCT SHIWT IT-SILCT
Average euro Std. Err. Average euro Std. Err. Average euro Std. Err. Average euro Std. Err.
Total taxable income 17,413.61 176.07 20,842.63 113.96 16,644.55 162.32 19,624.06 99.61
Employment income 19,086.86 184.06 19,728.63 119.78 19,072.34 183.68 19,687.35 119.43
Self-employment income 26,251.73 831.85 23,588.21 400.25 20,341.09 648.51 16,365.29 280.38
Pension income 14,136.62 205.80 14,331.29 93.27 14,106.22 206.52 14,297.66 92.81
Total deductions 4,003.99 82.94 3,176.11 40.75 3,264.82 69.93 2,352.12 30.27
Net taxable income 16,764.81 170.18 19,870.83 106.74 16,113.46 158.51 18,843.63 95.18
Total tax credit 1,715.53 7.12 1,573.64 3.97 1,735.05 7.16 1,594.42 3.95
Net personal income tax 3,911.55 79.73 4,772.70 47.73 3,650.65 73.93 4,414.69 41.75
Regional additional income tax 237.15 2.80 278.49 1.69 228.04 2.61 265.96 1.51
Cadastral income main residence 435.84 3.68 449.60 2.82 435.84 3.68 449.60 2.82
Cadastral income other buildings 527.71 20.24 1,490.07 39.08 527.71 20.24 1,600.44 41.98

  1. Source: our elaboration on SHIW and IT-SILC data.

    Note: Std. Err. refers to the standard error of the average.

Table 14
TABEITA with calibration (IT – SILCτ) of cadastral incomes for building different from principal residence. Averages and their standard errors.
Variable IT-SILCTC
No evasion Evasion
Average Euro Std. Err. Average Euro Std. Err.
Total taxable income 20,712.56 113.76 19,520.17 99.66
Employment income 19,758.79 119.96 19,716.68 119.53
Self-employment income 23,609.04 400.31 16,395.09 280.91
Pension income 14,343.90 93.15 14,312.55 92.81
Total deductions 3,184.75 40.78 2,355.07 30.36
Net taxable income 19,780.29 106.7 18,774.24 95.33
Total tax credit 1,568.60 3.95 1,591.00 3.96
Net personal income tax 4,756.20 47.40 4,404.03 41.51
Regional additional income tax 276.55 1.68 264.19 1.51
Cadastral income main residence 449.60 2.82 449.60 2.82
Cadastral income other buildings 953.51 13.88 1,024.14 14.91

  1. Source: our elaboration on SHIW and IT-SILC data.

  2. Note: Std. Err. refers to the standard error of the average.

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