Figures and data in The Italian Treasury Dynamic Microsimulation Model: Data, Structure and Validation

Table 1

Aligned processes in T-DYMM.

Module	Process	Source
Demographic module	Fertility, mortality, immigration and emigration flows	ISTAT (historical data) and Eurostat - Europop 2019 projections
	Education, age of exit from original household, informal and formal marriages, divorces	ISTAT and OECD (for education achievements of immigrants)
Labour Market module	Employment rate, inflation growth, GDP growth, productivity growth	ISTAT (historical data) and European Commission 2022 Spring Forecasts and Working Group on Ageing Populations and Sustainability (AWG) assumptions
	Take-up rate of unemployment benefits	INPS
	Quota of permanent public employees	ISTAT
Pension module	Number of disability allowances and incapacity pensions	INPS
	Enrolment in private pension plans	Italian Supervisory Authority on Pension funds (COVIP)
Wealth module	Households that pay rent	Department of Finance
	Returns on financial and housing assets	European Commission 2022 Spring Forecasts and AWG assumptions, Italian Housing Observatory (Osservatorio del Mercato Immobiliare, OMI) and Standard & Poor’s (S&P) 500
	Houses sold and bought, average propensity to consume	ISTAT
Tax-Benefit module	Beneficiaries of selected tax expenditures and substitute tax regimes	Department of Finance
	Take-up rates of specific social assistance measures	INPS

Table 2

Eligibility requirements for retirement as simulated in T-DYMM.

Criteria	Regime	Requirements	2022
Old age 1	NDC	age	64 years
		seniority	20 years
		amount	2.8*assegno sociale
Old age 2	NDC, mixed	age	67 years
	NDC, mixed	seniority	20 years
	NDC	amount	1.5*assegno sociale
Old age 3	NDC	age	71 years
		seniority	5 years
Seniority	NDC, mixed	seniority, males	42 years, 6 months
		seniority, females	41 years, 6 months
Seniority - young workers	mixed	seniority	41 years, 12 months accrued before turning 19
Seniority - Quota 100/102	mixed	age	62/64 years
		seniority	38 years

Note: The assegno sociale is the social allowance for the elderly. Since 2018, age requirements are aligned with ‘Old age 2’. Concerning ‘Old age 2’ seniority requirement, 15 years suffice for workers with at least 15 years of seniority as of Dec 31, 1992. For 2022, ‘Seniority - Quota 102’ replaces ‘Quota 100’ and the age requirement rises to 64.

Table 3

Projected rate of nominal returns adopted in the Wealth module.

Wealth component	Gain	2016–2021	2022–2070
House wealth	Income gain	OMI	Projections based on OMI
Government bonds	Income gain	Implicit rate of return on public debt, Italian Treasury Department	Implicit rate of return on public debt, EU Commission
Corporate bonds	Income gain	S&P 500	Implicit rate of return on public debt, EU Commission
	Capital gain	S&P 500	Projections based on S&P 500*
Stocks	Income gain	S&P 500	Implicit rate of return on public debt, EU Commission
	Capital gain	S&P 500	Mark-up stocks–bonds*
Mortgages		Long-term interest rate, Italian Treasury Department	Long-term interest rate, EU Commission

*

For the 2022–2023 period a linear convergence is applied to ensure a smoother transition from historical data to long-term assumptions.

Table 4

SICs and taxes simulated in T-DYMM.

SICs
Employer social insurance contributions
Employee social insurance contributions
Contributions paid by self-employed workers
Proportional taxes and tax regimes that substitute the personal income tax for:
i) Capital income: government/corporate bonds and shares^a
ii) Private pensions: Pillars II and III^b
iii) Self-employment income subject to substitute tax regimes (regime fiscale di vantaggio^c or regime forfetario^b)
iv) Rental income subject to cedolare secca (assigned to the head of the household)^b
v) Productivity bonuses^b
Personal income tax (Imposta sul reddito delle persone fisiche – IRPEF)^d

Note: The order of appearance follows the module sequence: a) The ‘Financial investment decision’ process is modelled through dynamic regressions based on SHIW data (see Section 3.4). b) Recipients are aligned with aggregate administrative data in the 2016–2020 interval, while from 2021 onwards we align recipients by taking as reference the external totals as of 2020 and updating them with: the population growth at the individual level for ii; the population growth of self-employed workers for iii; the population growth at the household level for iv; and the population growth of employees for v. c) Recipients are bound to gradually diminish to zero under current legislation. We assume that there are no recipients by 2030. d) Recipients of residual tax expenditures are aligned with external totals derived from tax return micro data for the 2015 tax period, annually updated to the population growth of recipients of gross income subject to PIT.

Table 5

In-cash benefits simulated in T-DYMM.

Unemployment benefits (NASpI and DIS-COLL)^a,b

In-work bonus for employees and atypical workers (Bonus IRPEF, which has replaced Bonus 80 euro)

Means-tested disability allowances (Pensione di inabilità agli invalidi civili up to the standard pensionable age and Assegno sociale sostitutivo afterward)^c

Non-means-tested disability allowances (Indennità di accompagnamento for those aged 18 or above and Indennità di frequenza for those aged under 18)^c

War pensions and indemnity annuities (Rendite indennitarie)^d

14th month pension^d (Quattordicesima)

Social allowance for the elderly and related increases (Assegno sociale and Maggiorazioni sociali)

Increases to old-age/seniority, survivor and incapacity integrated pensions (Maggiorazioni sociali del minimo)^e

Family allowances for employees’ and pensioners’ households (Assegni al nucleo familiare – ANF, up to 2021 for households with dependent children)^f

Newborn bonus (Bonus bebè, up to 2021)

Mother bonus (Bonus mamma domani, from 2017 to 2021)

Universal unique allowance (Assegno unico e universale – AUU, from 2022 onwards)^g,h

Minimum income schemes:^b,h

- SIA (Sostegno all’inclusione attiva, 2017)

- REI (Reddito di inclusione, 2018)

- RdC (Reddito di cittadinanza, from 2019 onwards)

Note: The order of appearance follows the module sequence. a) Unemployment benefits are actually simulated prior to the Tax-Benefit module because they are subject to the personal income tax. b) For the first two years of the simulation, administrative totals are employed for the alignments. From the latest available figures onwards, the ratio between actual (administrative data) and potential (obtained from T-DYMM’s simulations) recipients is kept constant. c) The average probabilities of receiving disability allowances are aligned by gender and five-year age group with the INPS statistics available for the period 2016–2020; beyond 2020, probabilities are projected following the same logic adopted for disability probabilities (which in turn follow the Reference Scenario of the 2021 Ageing Report). d) Recipients in T-DYMM’s base-year sample will hold these benefits until death. New occurrences are not simulated. e) Integrations to old-age/seniority, survivor and inability pensions (Integrazione al trattamento minimo) are included among pension benefits subject to PIT and thus not listed in the above table. f) ANF continue to be granted to households without children – but conditional to specific requirements in terms of household members and their disability status – following the introduction of AUU. g) The new allowance has replaced family allowances for employees’ and pensioners’ households, the newborn bonus, the mother bonus, family allowances granted at the municipal level (not simulated) and PIT tax credits for dependent children under the age of 21. h) In the simulation of AUU and minimium income schemes, we grant benefits for twelve months in a year following first introduction.

Table A1.1

Comparison between sample totals and external totals for selected distributions.

Variable	(1) IT-SILC totals	(2) T-DYMM totals	(3) External totals	(1)/(3)	(2)/(3)
Individuals	60,323,126	60,631,408	60,665,551	0.994	0.999
Households (in thousands)	25,821	25,429	25,386	1.017	1.002
Ratio individuals/households	2.336	2.384	2.390	0.977	0.998
Foreign population by gender, area of birth and educational attainment:
Female – EU – Upper secondary	566,866	540,119	532,951	1.064	1.013
Female – EU – Tertiary	140,621	186,397	194,004	0.725	0.961
Female – non-EU – Upper secondary	689,547	706,173	695,296	0.992	1.016
Female – non-EU – Tertiary	311,078	307,186	302,485	1.028	1.016
Male – EU – Upper secondary	287,465	334,650	345,220	0.833	0.969
Male – EU – Tertiary	93,373	61,285	63,090	1.480	0.971
Male – non-EU – Upper secondary	609,550	572,669	573,095	1.064	0.999
Male – non-EU – Tertiary	151,188	205,723	206,235	0.733	0.998
Population by number of household members (in thousands):
1	8,369	8,032	8,016	1.044	1.002
2	14,332	13,866	13,838	1.036	1.002
3	15,141	15,043	15,111	1.002	0.995
4	16,112	16,075	16,200	0.995	0.992
5	4,890	5,400	5,290	0.924	1.021
6 or more	1,478	2,214	2,211	0.668	1.001
Individuals with retirement income at the macro-regional level by gender:
North West – Female	2,218,088	2,367,979	2,378,635	0.933	0.996
North East – Female	1,546,322	1,675,793	1,675,812	0.923	1
Middle – Female	1,599,179	1,667,401	1,671,085	0.957	0.998
South – Female	1,701,624	1,753,352	1,766,032	0.964	0.993
Islands – Female	834,713	835,608	832,585	1.003	1.004
North West – Male	1,993,336	2,033,838	2,059,497	0.968	0.988
North East – Male	1,473,823	1,498,042	1,479,615	0.996	1.012
Middle – Male	1,443,607	1,485,073	1,486,315	0.971	0.999
South – Male	1,608,304	1,593,656	1,616,762	0.995	0.986
Islands – Male	796,957	787,546	795,288	1.002	0.990
Individuals with retirement income by gender and six age groups:
Female – 0-54	564,506	600,895	604,675	0.934	0.994
Female – 55-64	1,023,141	1,067,015	1,072,665	0.954	0.995
Female – 65-69	1,443,927	1,487,870	1,488,105	0.970	1
Female – 70-74	1,208,479	1,276,298	1,269,707	0.952	1.005
Female – 75-79	1,264,050	1,348,517	1,364,252	0.927	0.988
Female – 80 and over	2,395,823	2,519,538	2,524,745	0.949	0.998
Male – 0-54	741,462	694,984	711,781	1.042	0.976
Male – 0-54	741,462	694,984	711,781	1.042	0.976
Male – 65-69	1,606,143	1,605,099	1,607,490	0.999	0.999
Male – 70-74	1,262,353	1,271,212	1,301,312	0.970	0.977
Male – 75-79	1,175,818	1,227,220	1,218,628	0.965	1.007
Male – 80 and over	1,392,296	1,469,307	1,472,586	0.945	0.998
Individuals with retirement income by gender and type of pension:
Female – Old-age and seniority pensions	4,709,966	5,204,113	5,196,325	0.906	1.001
Female – Inability pensions	565,080	617,424	624,406	0.905	0.989
Female – Survivors’ pensions	3,605,291	4,034,618	4,032,187	0.894	1.001
Female – Disability pensions	1,416,051	1,813,111	1,824,118	0.776	0.994
Female – Social pension	518,856	536,305	541,679	0.958	0.990
Male – Old-age and seniority pensions	5,716,676	6,319,952	6,377,756	0.896	0.991
Male – Inability pensions	606,074	642,345	652,098	0.929	0.985
Male – Survivors’ pensions	583,017	603,947	617,234	0.945	0.978
Male – Disability pensions	992,888	1,201,027	1,219,673	0.814	0.985
Male – Social pension	266,998	297,015	304,413	0.877	0.976
Retired workers by gender (in thousands):
Female	196	115	111	1.766	1.036
Male	536	321	331	1.619	0.970
In-work individuals by area of birth (in thousands):
Italy	20,107	20,132	20,106	1	1.001
EU or non-EU	3,033	2,326	2,359	1.286	0.986
In-work individuals by employment status (in thousands):
Employees	18,042	17,008	16,988	1.062	1.001
Freelancers	1,161	1,314	1,327	0.875	0.990
Craftsmen, traders and farmers	3,387	3,793	3,801	0.891	0.998
Atypical workers (co.co.co.)	550	344	349	1.576	0.986
Employees by type of contract (in thousands):
Open-ended	15,290	14,615	14,605	1.047	1.001
Fixed-term	2,752	2,393	2,383	1.155	1.004
Full-time	14,324	13,634	13,642	1.050	0.999
Part-time	3,718	3,373	3,346	1.111	1.008
Population by gender and civil status:
Female – Single	11,444,708	11,878,040	11,900,653	0.962	0.998
Female – Married or separated	14,719,913	14,669,927	14,683,481	1.002	0.999
Female – Divorced	824,754	862,563	871,345	0.947	0.990
Female – Widow	4,022,839	3,753,115	3,753,751	1.072	1
Male – Single	13,305,714	13,646,650	13,641,747	0.975	1
Male – Married or separated	14,586,291	14,448,378	14,485,092	1.007	1
Male – Divorced	477,936	579,280	584,343	0.818	0.991
Households at the regional level (in thousands):
Male – Widower	940,970	752,454	745,139	1.263	1.010
Piemonte	2,008	1,963	1,950	1.030	1.007
Valle d’Aosta	61	125	62	0.984	2.016
Liguria	771	788	756	1.020	1.042
Lombardia	4,418	4,299	4,222	1.046	1.018
Bolzano	217	246	215	1.009	1.144
Trento	233	254	228	1.022	1.114
Veneto	2,059	2,022	1,983	1.038	1.020
Friuli-Venezia Giulia	559	622	544	1.028	1.143
Emilia-Romagna	1,993	1,928	1,961	1.016	0.983
Toscana	1,643	1,614	1,636	1.004	0.987
Umbria	383	420	379	1.011	1.108
Marche	644	683	641	1.005	1.066
Lazio	2,631	2,528	2,601	1.012	0.972
Abruzzo	555	532	549	1.011	0.969
Molise	131	167	131	1	1.275
Campania	2,157	2,068	2,173	0.993	0.952
Puglia	1,587	1,549	1,593	0.996	0.972
Basilicata	231	248	238	0.971	1.042
Calabria	800	740	806	0.993	0.918
Sicilia	2,022	1,930	2,022	1	0.955
Sardegna	719	699	696	1.033	1.004
Households by type (in thousands):
Single	8,369	8,175	8,312	1.007	0.984
Single with children	2,216	2,083	2,360	0.939	0.883
Couple	5,142	4,973	5,110	1.006	0.973
Couple with children	8,692	8,701	8,754	0.993	0.994
Other	1,403	1,494	850	1.651	1.758

Source: Authors’ elaborations of AD-SILC 2015 and statistics from different institutes (Eurostat, ISTAT, INPS and DF).

Table A3.1

Probability of being employed.

	Male		Female
	b	se	b	se
extra-EU born	0.318***	(0.042)	0.250***	(0.038)
studying	-1.022***	(0.033)	-1.029***	(0.033)
retired	-1.327***	(0.049)	-1.209***	(0.069)
age	0.383***	(0.012)	0.064***	(0.005)
age²	-0.010***	(0.000)	-0.001***	(0.000)
age³	0.000***	(0.000)
upper sec. degree	0.250***	(0.020)	0.385***	(0.021)
tertiary degree	0.578***	(0.032)	0.850***	(0.031)
disabled	-0.335***	(0.050)	-0.306***	(0.058)
disability pension	-1.048***	(0.100)	-1.030***	(0.096)
disability allowance	-1.021***	(0.125)	-0.864***	(0.131)
incapacity pension	-1.018***	(0.086)	-1.285***	(0.138)
in couple	0.107***	(0.027)	-0.318***	(0.033)
partner working (lag)	0.204***	(0.027)	0.156***	(0.030)
experience	0.085***	(0.003)	0.098***	(0.004)
experience²	-0.001***	(0.000)	-0.002***	(0.000)
last spell duration (out-of-work)	-0.199***	(0.007)	-0.201***	(0.007)
last spell duration (working)	0.027***	(0.001)	0.031***	(0.002)
pen-ended private (lag)	3.439***	(0.034)	3.666***	(0.036)
fixed-term private (lag)	2.783***	(0.038)	3.028***	(0.038)
pen-ended public (lag)	3.760***	(0.058)	4.506***	(0.063)
fixed-term public (lag)	2.969***	(0.125)	3.549***	(0.084)
professional (lag)	4.470***	(0.101)	4.133***	(0.125)
self-employed (lag)	4.082***	(0.053)	4.415***	(0.068)
atypical (lag)	3.600***	(0.071)	3.144***	(0.070)
children aged 0-6			-0.376***	(0.030)
constant	-5.725***	(0.163)	-2.242***	(0.101)
pseudo-R²	0.719		0.739
no. of observations	253,274		250,143

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category for the educational level is compulsory education. Experience refers to years of work. Children aged 0-6 is equal to one when children in that age group are present. All other variables except for age and duration in last spell are dummies. Source: Authors’ elaborations on AD-SILC data.

Table A3.2

Probability of being employed in each employment category for males.

	Fixed-term employee		Professionals		Self-employed		Atypical
	b	se	b	se	b	se	b	se
EU born	0.095	(0.082)	-0.785**	(0.362)	-0.016	(0.148)	-0.218	(0.242)
extra-EU born	0.123**	(0.050)	-0.817***	(0.227)	-0.049	(0.102)	-0.403***	(0.145)
age	-0.024***	(0.009)	0.227***	(0.028)	0.078***	(0.015)	0.097***	(0.019)
age²	0.000***	(0.000)	-0.002***	(0.000)	-0.001***	(0.000)	-0.001***	(0.000)
upper sec. degree	-0.336***	(0.029)	1.407***	(0.148)	0.068	(0.050)	0.392***	(0.072)
tertiary degree	-0.494***	(0.043)	1.902***	(0.162)	-0.275***	(0.076)	0.745***	(0.094)
studying	0.403***	(0.054)	0.156	(0.180)	-0.356***	(0.106)	0.844***	(0.110)
in couple	-0.214***	(0.032)	-0.329***	(0.103)	0.019	(0.056)	-0.063	(0.076)
exp. as open-ended	-0.060***	(0.006)	-0.191***	(0.017)	-0.061***	(0.010)	-0.097***	(0.012)
exp. as fixed-term	0.143***	(0.016)	-0.321***	(0.089)	-0.186***	(0.032)	-0.266***	(0.058)
exp. as atypical	0.012	(0.021)	0.081*	(0.046)	-0.053*	(0.028)	0.392***	(0.024)
exp. as self-employed	0.026***	(0.009)	-0.025	(0.026)	0.174***	(0.009)	0.072***	(0.013)
exp. as professional	-0.068***	(0.020)	0.267***	(0.019)	-0.010	(0.029)	0.020	(0.024)
exp.² as open-ended	0.000	(0.000)	0.003***	(0.001)	0.000	(0.000)	0.001***	(0.000)
exp.² as fixed-term	0.005***	(0.002)	0.019	(0.013)	0.018***	(0.003)	0.017***	(0.006)
exp.² as atypical	-0.003	(0.002)	-0.004	(0.003)	0.005**	(0.002)	-0.013***	(0.002)
exp.² as self-employed	-0.001***	(0.000)	-0.002	(0.001)	-0.004***	(0.000)	-0.002***	(0.000)
exp.² as professional	0.001	(0.001)	-0.006***	(0.001)	-0.001	(0.001)	-0.000	(0.001)
pen-ended (lag)	-2.813***	(0.041)	-3.589***	(0.153)	-3.038***	(0.072)	-3.735***	(0.107)
fixed-term (lag)	0.989***	(0.040)	-1.620***	(0.216)	-1.401***	(0.103)	-1.219***	(0.138)
professional (lag)	-0.373**	(0.190)	4.160***	(0.139)	0.083	(0.231)	-0.006	(0.222)
self-employed (lag)	0.226**	(0.097)	0.047	(0.267)	4.861***	(0.082)	1.043***	(0.120)
atypical (lag)	-0.023	(0.107)	0.727***	(0.184)	0.874***	(0.116)	2.752***	(0.097)
constant	0.317*	(0.166)	-8.894***	(0.546)	-3.057***	(0.284)	-5.095***	(0.371)
pseudo-R²	0.732
no. of observations	140,331

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category in the dependent variable is open-ended employee. The omitted category for the nationality is Italian, while for the educational level, it is compulsory education. Exp. refers to years of work. All other variables except for age are dummies. Coefficients in units of log odds. Source: Authors’ elaborations of AD-SILC data.

Table A3.3

Probability of being employed in each employment category for females.

	Fixed-term employee		Professionals		Self-employed		Atypical
	b	se	b	se	b	se	b	se
EU born	-0.045	(0.071)	-0.644***	(0.248)	-0.418**	(0.165)	-0.357**	(0.164)
extra-EU born	-0.147***	(0.054)	-0.747***	(0.231)	-0.339***	(0.125)	-0.475***	(0.131)
age	0.039***	(0.010)	0.249***	(0.036)	0.078***	(0.021)	0.089***	(0.019)
age²	-0.001***	(0.000)	-0.003***	(0.000)	-0.001***	(0.000)	-0.001***	(0.000)
upper sec. degree	-0.310***	(0.032)	0.995***	(0.184)	-0.005	(0.070)	0.172**	(0.073)
tertiary degree	-0.439***	(0.041)	2.117***	(0.185)	-0.604***	(0.098)	0.634***	(0.085)
studying	0.362***	(0.056)	-0.158	(0.179)	-0.252*	(0.137)	0.720***	(0.095)
in couple	0.064*	(0.034)	-0.242**	(0.117)	0.312***	(0.074)	-0.116	(0.071)
children aged 0-6	-0.085**	(0.040)	-0.161	(0.139)	0.160*	(0.085)	-0.187**	(0.086)
exp. as open-ended	-0.044***	(0.006)	-0.122***	(0.022)	-0.028**	(0.013)	-0.048***	(0.014)
exp. as fixed-term	0.149***	(0.015)	-0.351***	(0.094)	-0.310***	(0.042)	-0.249***	(0.049)
exp. as atypical	0.012	(0.020)	0.041	(0.054)	-0.127***	(0.041)	0.353***	(0.027)
exp. as self-employed	0.043***	(0.011)	-0.001	(0.035)	0.160***	(0.013)	0.052***	(0.020)
exp. as professional	-0.020	(0.021)	0.309***	(0.025)	-0.008	(0.052)	-0.031	(0.035)
exp.² as open-ended	-0.000	(0.000)	0.002*	(0.001)	-0.001***	(0.001)	-0.001	(0.001)
exp.² as fixed-term	0.001	(0.001)	0.015	(0.013)	0.025***	(0.004)	0.015***	(0.005)
exp.² as atypical	-0.002	(0.002)	-0.004	(0.004)	0.006*	(0.003)	-0.015***	(0.002)
exp.² as self-employed	-0.002***	(0.000)	-0.000	(0.001)	-0.004***	(0.000)	-0.001*	(0.001)
exp.² as professional	0.001	(0.001)	-0.008***	(0.001)	-0.002	(0.003)	-0.000	(0.001)
pen-ended (lag)	-3.319***	(0.043)	-4.417***	(0.201)	-3.419***	(0.096)	-4.158***	(0.109)
fixed-term (lag)	0.990***	(0.041)	-1.562***	(0.206)	-1.179***	(0.130)	-1.056***	(0.110)
professional (lag)	-0.122	(0.192)	4.112***	(0.163)	0.214	(0.337)	0.726***	(0.219)
self-employed (lag)	0.120	(0.130)	0.213	(0.325)	5.234***	(0.113)	0.258	(0.181)
atypical (lag)	-0.233***	(0.090)	0.288*	(0.172)	0.172	(0.160)	2.215***	(0.088)
constant	-0.480***	(0.186)	-7.982***	(0.702)	-3.335***	(0.395)	-4.213***	(0.359)
pseudo-R²	0.699
no. of observations	110,553

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category in the dependent variable is open-ended employee. The omitted category for the nationality is Italian, for the educational level it is compulsory education. Exp. refers to the experience in the labour market. All other variables except for age are dummies. Coefficients in units of log odds. Source: Authors’ elaborations of AD-SILC data.

Table A3.4

Probability of being employed in each employment category for working pensioners.

	Open-ended private		Fixed-term private		Professional		Atypical
	b	se	b	se	b	se	b	se
partner working	-0.800***	(0.181)	-0.769***	(0.230)	-1.620***	(0.378)	-0.705***	(0.175)
exp. as open-ended private	0.086***	(0.019)	0.061***	(0.023)	-0.031	(0.031)	0.002	(0.021)
exp. as fixed-term private	2.251***	(0.521)	3.048***	(0.536)	2.009***	(0.611)	1.829***	(0.537)
exp. as atypical	-0.017	(0.077)	0.094	(0.079)	0.245***	(0.092)	0.808***	(0.051)
exp. as self-employed	-0.123***	(0.018)	-0.179***	(0.021)	-0.324***	(0.036)	-0.207***	(0.018)
exp. as professional	-0.231	(0.249)	-0.176	(0.254)	0.782***	(0.133)	0.178	(0.128)
exp.² as open-ended private	-0.001**	(0.000)	-0.001**	(0.001)	0.000	(0.001)	-0.000	(0.001)
exp.² as fixed-term private	-0.147***	(0.029)	-0.177***	(0.030)	-0.129***	(0.036)	-0.118***	(0.033)
exp.² as atypical	-0.002	(0.004)	-0.005	(0.005)	-0.013**	(0.005)	-0.032***	(0.003)
exp.² as self-employed	0.001	(0.000)	0.002***	(0.000)	0.004***	(0.001)	0.002***	(0.000)
exp.² as professional	0.006	(0.005)	0.003	(0.005)	-0.014***	(0.003)	-0.002	(0.003)
constant	-0.279	(0.309)	-0.927**	(0.415)	-0.048	(0.445)	-0.182	(0.323)
pseudo-R²	0.581
no. of observations	9,627

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category in the dependent variable is self-employed. Exp. refers to years of work. Partner working is a dummy. Coefficients in units of log odds. Source: Authors’ elaborations of AD-SILC data.

Table A3.5

Monthly wages of private employees.

	Male		Female
	b	se	b	se
EU born	-0.033**	(0.013)	-0.235***	(0.014)
extra-EU born	-0.102***	(0.008)	-0.308***	(0.011)
upper sec. degree	0.126***	(0.004)	0.126***	(0.005)
tertiary degree	0.334***	(0.008)	0.277***	(0.008)
children aged 0-3	0.035***	(0.004)	-0.051***	(0.005)
children aged 4 and over	0.024***	(0.004)	-0.047***	(0.005)
exp. as private employee	0.037***	(0.001)	0.025***	(0.001)
exp.² as private employee	-0.001***	(0.000)	-0.000***	(0.000)
pen-ended contract	0.034***	(0.005)
part-time	-0.563***	(0.010)	-0.403***	(0.005)
partner working	0.024***	(0.003)	0.016***	(0.004)
constant	7.169***	(0.008)	7.145***	(0.008)
$σ$ _u	0.375		0.369
$σ$ _e	0.152		0.144
$ρ$	0.860		0.868
R²-within	0.142		0.192
R²-between	0.460		0.396
R²-overall	0.438		0.391
no. of observations	88,966		63,934

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category for the nationality is Italian, while for the educational level it is compulsory education. Exp. refers to years of work. Children aged 0-3 or 4 and over is equal to one when children in that age group are present. All other variables are dummies. Source: Authors’ elaborations of AD-SILC data.

Table A3.6

Monthly wages of public employees.

	Male		Female
	b	se	b	se
upper sec. degree	0.091***	(0.015)	0.073***	(0.012)
tertiary degree	0.431***	(0.018)	0.242***	(0.013)
exp. as public employee	0.026***	(0.002)	0.024***	(0.002)
exp.² as public employee	-0.000***	(0.000)	-0.000***	(0.000)
children aged 0-3	0.048*	(0.025)	-0.067***	(0.014)
children aged 4 and over	0.060***	(0.014)	-0.033***	(0.009)
pen-ended contract	0.254***	(0.029)	0.222***	(0.017)
part-time	-0.437***	(0.042)	-0.348***	(0.018)
constant	7.198***	(0.030)	7.159***	(0.022)
$σ$ _u	0.473		0.366
$σ$ _e	0.341		0.266
$ρ$	0.658		0.654
R²-within	0.034		0.019
R²-between	0.229		0.288
R²-overall	0.209		0.260
no. of observations	16,637		24,175

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category for the educational level is compulsory education. Exp. refers to years of work. Children aged 0-3 or 4 and over is equal to one when children in that age group are present. All other variables are dummies. Source: Authors’ elaborations of AD-SILC data.

Table A3.7

Monthly wages of professionals.

	b	se
female	-0.225***	(0.043)
exp. as professional	0.049***	(0.005)
exp.² as professional	-0.001***	(0.000)
constant	7.221***	(0.045)
$σ$ _u	0.856
$σ$ _e	0.457
$ρ$	0.778
R²-within	0.014
R²-between	0.085
R²-overall	0.090
no. of observations	8,311

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. Exp. refers to years of work. All other variables are dummies. Source: Authors’ elaborations of AD-SILC data.

Table A3.8

Monthly wages of self-employed workers.

	b	se
female	-0.178***	(0.024)
upper sec. degree	0.092***	(0.015)
tertiary degree	0.161***	(0.027)
exp. as self-employed	0.022***	(0.003)
exp.² as self-employed	-0.001***	(0.000)
constant	6.884***	(0.028)
$σ$ _u	0.777
$σ$ _e	0.583
$ρ$	0.640
R²-within	0.003
R²-between	0.044
R²-overall	0.044
no. of observations	31,470

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category for the educational level is compulsory education. Exp. refers to years of work. All other variables are dummies. Source: Authors’ elaborations of AD-SILC data.

Table A3.9

Monthly wages of atypical workers.

	Male		Female
	b	se	b	se
pen-ended (lag)	0.396***	(0.046)	0.393***	(0.048)
fixed-term (lag)	0.316***	(0.066)	0.230***	(0.047)
experience	0.052***	(0.005)	0.022***	(0.004)
experience²	-0.001***	(0.000)	-0.000	(0.000)
partner working	0.088***	(0.031)	-	-
children aged 0-3	0.076*	(0.041)	-0.039	(0.041)
constant	7.516***	(0.053)	7.593***	(0.055)
$σ$ _u	0.592		0.430
$σ$ _e	0.406		0.462
$ρ$	0.680		0.464
R²-within	0.053		0.034
R²-between	0.160		0.079
R²-overall	0.157		0.072
no. of observations	3,747		3,440

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. Exp. refers to years of work. Children aged 0-3 is equal to one when children in that age group are present. All other variables are dummies. Source: Authors’ elaborations of AD-SILC data.

Table A3.10

Monthly wages of working pensioners.

	b	se
female	-0.130***	(0.040)
upper sec. degree	0.171***	(0.031)
tertiary degree	0.253***	(0.050)
experience	0.006***	(0.002)
fixed-term private	0.104*	(0.055)
professional	0.599***	(0.070)
self-employed	-0.141***	(0.045)
atypical	0.608***	(0.052)
constant	6.616***	(0.083)
$σ$ _u	0.893
$σ$ _e	0.485
$ρ$	0.772
R²-within	0.008
R²-between	0.139
R²-overall	0.126
no. of observations	7,632

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category for the educational level is compulsory education. Experience refers to years of work. All other variables are dummies. Source: Authors’ elaborations of AD-SILC data.

Table A3.11

Probability of working all year.

	Male		Female
	b	se	b	se
working all year (lag)	1.257***	(0.065)	1.197***	(0.063)
partner working	0.133***	(0.047)
no. of months worked last year	0.191***	(0.008)	0.171***	(0.007)
part-time	0.851***	(0.068)	1.118***	(0.053)
experience	0.004**	(0.002)	0.009***	(0.003)
upper sec. degree	0.414***	(0.046)	0.333***	(0.054)
tertiary degree	0.694***	(0.070)	0.669***	(0.067)
fixed-term public	0.629***	(0.095)	0.619***	(0.064)
atypical	0.964***	(0.052)	0.520***	(0.060)
children aged 0-3			-0.323***	(0.074)
children aged 4 and over			-0.163***	(0.048)
constant	-3.638***	(0.108)	-3.971***	(0.119)
pseudo-R²	0.327		0.267
no. of observations	19,829		19,702

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category for the educational level is compulsory education. Experience refers to years of work. Children aged 0-3 or 4 and over is equal to one when children in that age group are present. All other variables are dummies except for no. of months worked. Source: Authors’ elaborations of AD-SILC data.

Table A3.12

Number of months in work if working less than 12 months.

	Male		Female
	b	se	b	se
experience	0.071***	(0.009)	0.063***	(0.010)
experience²	-0.001***	(0.000)	-0.002***	(0.000)
studying	-1.088***	(0.093)	-0.806***	(0.084)
foreign	0.142	(0.095)
upper sec. degree	0.573***	(0.062)	0.593***	(0.065)
tertiary degree	1.007***	(0.101)	1.299***	(0.088)
children aged 0-6	0.326***	(0.090)
fixed-term private employee	-1.276***	(0.150)	-0.968***	(0.091)
atypical	-2.436***	(0.162)	-2.461***	(0.103)
retired	-0.542***	(0.160)	-0.520**	(0.215)
working (lag)	1.293***	(0.058)	1.400***	(0.056)
constant	4.819***	(0.188)	4.416***	(0.152)
$σ$ _u	2.246		2.087
$σ$ _e	1.900		2.006
$ρ$	0.583		0.520
R²-within	0.064		0.051
R²-between	0.136		0.172
R²-overall	0.113		0.143
no. of observations	13,932		14,793

Note: Significance levels are indicated by * < .1, ** < .05, *** < .01. The omitted category for the educational level is compulsory education. Experience refers to years of work. Children aged 0-6 is equal to one when children in that age group are present. All other variables are dummies. Source: Authors’ elaborations of AD-SILC data.

Table A4.1

List of regressions adopted in the Wealth module and in the private pensions sub-module.

Process	Regression dependent variable	Data source
Financial investment decision	Ownership of government bonds	SHIW 2010-16
Financial investment decision	Ownership of corporate bonds	SHIW 2010-16
Financial investment decision	Ownership of stocks	SHIW 2010-16
Financial investment decision	Ratio of liquidity over total fin. wealth	SHIW 2010-16
Financial investment decision	Ratio of gov. bonds over total fin. wealth	SHIW 2010-16
Financial investment decision	Ratio of corp. bonds over total fin. wealth	SHIW 2010-16
Financial investment decision	Ratio of stocks over total fin. wealth	SHIW 2010-16
Inter vivos transfers	Probability of making transfers	SHIW 2014
Inter vivos transfers	Amount transferred (absolute value)	SHIW 2014
Inter vivos transfers	Probability of receiving transfers	SHIW 2014
Inter vivos transfers	Amount received (absolute value)	SHIW 2014
Inheritance	Probability of receiving inheritance	SHIW 2014
Inheritance	Amount received (absolute value)	SHIW 2014
House investment decision	Probability of buying house	SHIW 2010-16
House investment decision	Log-value of purchased house	SHIW 2010-16
Rent	Probability of paying rent	SHIW 2010-16
Rent	Ratio of rent paid over household income	SHIW 2010-16
Rent	Probability of received rent	AD-SILC 2015
Rent	Ratio of rent received over household income	AD-SILC 2015
Consumption	Log-level of household consumption	SHIW 2002-16
Private pensions	Probability of investing in II pillar	SHIW 2010-16
Private pensions	Probability of investing in III pillar	SHIW 2010-16
Private pensions	Amount of income invested in III pillar	SHIW 2010-16
Private pensions	Probability of investing zero in III pillar	SHIW 2010-16

Table A4.2

Probability of investing in financial activities.

	F A2_t		F A3_t		F A4_t
	b	se	b	se	b	se
FA2_t−₁	1.662***	(0.150)
FA2₀	0.886***	(0.150)
FA3_t−₁			1.296***	(0.185)
FA3₀			0.899***	(0.187)
FA4_t−₁					1.350***	(0.143)
FA4₀					0.741***	(0.145)
age	-0.026	(0.019)	-0.099***	(0.021)	-0.038**	(0.017)
log fin. wealth	1.603***	(0.062)	2.230***	(0.088)	1.328***	(0.057)
avg age	0.056***	(0.019)	0.079***	(0.022)	-0.005	(0.017)
avg fin. wealth	-0.134**	(0.061)	-0.197**	(0.085)	0.014	(0.068)
female	0.337***	(0.117)
tertiary degree	-0.776***	(0.134)			0.299**	(0.117)
constant	-19.730***	(0.727)	-22.684***	(0.855)	-13.969***	(0.522)
pseudo-R²	0.654		0.774		0.639
no. of obs.	6,019		6,019		6,019

Note: Significance levels are indicated by ^∗ < .1, ** < .05, *** < .01. FA2 corresponds to government bonds, FA3 corresponds to corporate bonds and FA4 corresponds to stocks. The subscript t − 1 denotes the lagged variables. The subscript 0 denotes the initial conditions. The dependent variables are the probabilities of investing in one of the three forms of financial activities, named FA (households who own financial wealth always have a positive probability of detaining liquidity). To correctly estimate the dynamic relationship between ownership at time t and at time t-1 we check for the initial conditions in the status (whether the household owned the financial activity in 2010) and we average time-varying variables, following the approach by Wooldridge (2005). Source: Authors’ elaborations of SHIW data (2010–2016).

Table A4.3

Panel estimates of log-consumption.

	b	se
age	0.011***	(0.001)
2nd income decile	0.213***	(0.012)
3rd income decile	0.304***	(0.013)
4th income decile	0.375***	(0.014)
5th income decile	0.447***	(0.015)
6th income decile	0.516***	(0.016)
7h income decile	0.575***	(0.016)
8th income decile	0.653***	(0.018)
9th income decile	0.690***	(0.019)
10th income decile	0.777***	(0.022)
2nd fin. wealth quintile	0.037***	(0.008)
3rd fin. wealth quintile	0.056***	(0.008)
4th fin. wealth quintile	0.075***	(0.009)
5th fin. wealth quintile	0.118***	(0.011)
no. hh members	0.038***	(0.006)
retired	-0.067***	(0.011)
no. of income earners	0.035***	(0.007)
constant	8.176***	(0.045)
$σ$ _u	0.382
$σ$ _e	0.354
$ρ$	0.538
R²-within	0.146
R²-between	0.459
R²-overall	0.381
no. of observations	39,559

Note: Significance levels are indicated by ^∗ < .1, ** < .05, *** < .01. The dependent variable is the logarithm of consumption. We adopt a fixed effects estimator, where the correlation between the error component and unobserved time-invariant household effect is introduced in the simula- tion. The main explanatory variables are the level of household income (in deciles) and financial wealth (in quintiles); the regression coefficients illus- trate a positive correlation between these two variables and consumption, as expected. The number of household members and of income earners increase the total consumption level, whereas the retired status of the head of the household reduces consumption and, as a result, increases savings. This result, typical of Italy, is known in the literature as the ‘retirement consumption puzzle’ (Battistin et al., 2009). Source: Authors’ elaborations of SHIW data (2010–2016).

Table A4.4

Probability of investing in private pension plans.

	II pillar		III pillar
	b	se	b	se
age	0.247***	(0.036)	0.246***	(0.025)
age²	-0.003***	(0.000)	-0.003***	(0.000)
log income	1.415***	(0.127)	0.811***	(0.071)
tertiary degree	0.473***	(0.087)	0.326***	(0.067)
high wealth	0.403***	(0.115)	0.414***	(0.091)
employee	-		0.425***	(0.080)
constant	-	(1.302)	-	(0.824)
	21.679***		16.029***
pseudo-R²	0.108		0.059
no. of observations	14,288		25,198

Note: Significance levels are indicated by ^∗ < .1, ** < .05, *** < .01. The regressions for private pensions are performed at the individual level. The estimate for the II pillar is made for private employees only. Among the explanatory variables are age, the level of individual income, tertiary education and the highest quintile of household net wealth. The estimate for the III pillar is made for all employed individuals. The high wealth variable is a dummy that denotes the highest quintile of wealth distribution. The employee dummy denotes the employment status. The coefficients of the two types of investment are similar, this means that the profile of the individuals who invest in collective schemes are similar to the ones who invest in individual plans. Source: Authors’ elaborations of SHIW data (2010–2016).

Figures

Modular structure of T-DYMM

Structure of the Labour Market module

Structure of the Pension module, work-related public pensions (first pillar)

Structure of the Pension module, private pensions (second and third pillars)

Structure of the Wealth module. Note: If individuals do not sell or buy a house, they move to financial investment decisions and subsequent processes.

Frequency density function for gross income (values in thousands of euros on the horizontal axis). Note: Gross income is the sum of gross income subject to PIT and rental income subject to the cedolare secca. Source: Authors’ elaborations of simulation results and DF statistics.

Distribution of PIT taxpayers by gross income group (values in thousands of euros on the horizontal axis). Note: Gross income is the sum of gross income subject to PIT and rental income subject to the cedolare secca. Source: Authors’ elaborations of simulation results and DF statistics.

Gini index for equivalised income definitions by age group. Source: Authors’ elaborations of simulation results and Eurostat statistics.

At-risk-of-poverty rate by age group. Source: Authors’ elaborations of simulation results and Eurostat statistics.

Distribution of the population by gender and one-year age group (individuals in millions on the horizontal axis). Source: Authors’ elaborations of AD-SILC 2015 and ISTAT statistics.

Frequency density function for gross income (values in thousands of euros on the horizontal axis). Note: Gross income is the sum of gross income subject to PIT and rental income subject to the cedolare secca. Source: Authors’ elaborations of AD-SILC 2015 and DF statistics.

Tables