IrpetDin. A Dynamic Microsimulation Model for Italy and the Region of Tuscany
- Istituto Regionale Programmazione Economica della Toscana, Italy
Cite this article
as: M. Luisa Maitino, L. Ravagli, N. Sciclone; 2020; IrpetDin. A Dynamic Microsimulation Model for Italy and the Region of Tuscany; International Journal of Microsimulation; 13(3); 27-53.
doi: 10.34196/ijm.00224
Figures
Figure 1
Italian population by age. Italy.
Figure 2
IrpetDin’s structure.
Figure 3
IrpetDin’s alignements to Dante.
Figure 4
Validation of demography. Year 2017. Italy.
Figure 5
Validation of labour market indicators. Italy.
Figure 6
Demographic dependence for Italy and Tuscany (%).
Figure 7
Education. Italy.
Figure 8
Labour supply and unemployment. Italy.
Figure 9
Quantitative and qualitative mismatch. Italy.
Figure 10
Pensions flows and stocks. Italy.
Figure 11
Pensions: intra-generational equity. Italy.
Figure 12
Pensions: financial sustainability and inter-generational equity. Italy.
Figure 13
Social assistance for old people and in-kind services.
Tables
Table 1
Validation of pensions. Italy
| IrpetDin | ISTAT | Ratio IreptDin ISTAT | |
|---|---|---|---|
| Stock of retirees in 2017 | 10,800,000 | 11,039,137 | 0.98 |
| Pension flows 2016-2017-2018 | 284,138 | 291,115 | 0.98 |
| Pension expenditure (billion euro) in 2017 | 236 | 232 | 1.01 |
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Source: IrpetDin, ISTAT.
Table 2
Exogenous indicators/variables
| Indicator/variabile | External source | |
|---|---|---|
| Demographic trends | Life expectancy | ISTAT forecast, central scenario |
| Fertility | ISTAT forecast, central scenario | |
| Migration flows | ISTAT forecast, central scenario | |
| Economic trends | GDP real growth rate | Dante until 2035, then our assumption |
| GDP nominal growth rate | Dante until 2035, then our assumption | |
| Standard Labour Units | Dante until 2035, then our assumption | |
| Ratio SLU/employed | Our assumption | |
| Labour income growth rate | Our assumption | |
| Part time work | Our assumption | |
| Social security programmes | Pensions and social assistance for old-people | As current legislation |
| Health and long term care | As current legislation | |
| Health and LTC costs growth rate | Our assumption |
Table 3
Unemployment rate, percentiles of replacement rates for employees and employed, Gini among retirees
| 2018-2032 | 2033-2042 | 2043-2050 | ||
|---|---|---|---|---|
| Basic scenario | Unemployment | 11,3% | 4,6% | 0,1% |
| p25 employees | 62,4% | 57,8% | 54,0% | |
| p75 employees | 78,5% | 74,2% | 72,4% | |
| p25 self-employed | 42,5% | 34,0% | 30,1% | |
| p75 self-employed | 53,4% | 43,7% | 42,2% | |
| Gini | 0,322 | 0,305 | 0,305 | |
| Official scenario | Unemployment | 9,7% | 6,8% | 5,4% |
| p25 employees | 62,4% | 58,0% | 54,2% | |
| p75 employees | 78,5% | 74,3% | 72,5% | |
| p25 self-employed | 42,5% | 34,1% | 30,2% | |
| p75 self-employed | 53,4% | 43,8% | 42,2% | |
| Gini | 0,322 | 0,305 | 0,305 |
Table A1
Main features of IrpetDin’s modules
| EVENT | POTENTIAL CANDIDATES | PROBABILITIES ESTIMATION METHOD | VARIABLES USED TO DETERMINE EVENTS | DATA SOURCE FOR PROBABILITIES ESTIMATION | DATA SOUCE FOREVENTUAL ALIGNEMNTS | |
|---|---|---|---|---|---|---|
| Ageing | All individuals | |||||
| Mortality | All individuals | Rates taken from external sources | Territory, age, gender | Mortality tables ISTAT (2008-16) | ISTAT forecast, central scenario (2008–2050) | |
| Marriage | Single, divorced, widowed aged 18–59 | Our calculated rates | Territory, age, gender, education | Official data on marriage ISTAT (2008–2013) | ||
| Fertility | Married/cohabitant women aged 18–45 | Our calculated rates | Territory, age, n° children, education, nationality | Birth attendance certificates RT (2007–2013) + ISTAT survey on births (2012) | ISTAT forecast, central scenario (2008–2050) | |
| Dissolution | Married/cohabitant aged 20–64, at least 3 years of marriage | Our calculated rates | Territory, age, gender, nationality | Official data on civil status ISTAT (2008–2013) | ||
| Leaving home | Individuals aged 18–59, unmarried, employed, not the head of the family | Our calculated rates | Territory, age, gender | Survey “Famiglia e soggetti sociali” ISTAT (2013) | ||
| DEMOGRAPHY | Migration flows | All individuals | Our calculated rates | Territory, gender, education, occupational status, type and size of the family | Individual data on registrations and cancellations ISTAT + Demographic balances of foreign citizens ISTAT (2009–2017) | ISTAT forecast, central scenario (2008–2050) |
| Choice of secondary school | Individuals aged below 16 | Our estimation with multinomial logit | Territory, gender, parents’ education | Survey on secondary school graduates ISTAT (2011; 2015) | ||
| Educational attainments at secondary school (drop-out, repeating, high school certificate) | Enrolled to 1° year of secondary school | Our calculated rates and estimation with multinomial logit | Territory, gender, parents’ education, type of secondary school | School register RT (2008-13) + Survey on secondary school graduates ISTAT (2011; 2015) | ||
| Entry to tertiary school | Individuals with secondary school diploma | Our calculated rates | Territory, gender, type of secondary school, mark, year of study | University register (2008–2013) + Survey on secondary school graduates ISTAT (2011; 2015) | ||
| EDUCATION | University career (drop outs, three- and five-year degree) | Enrolled to university | Our calculated rates | Territory, age, gender, type of course | Survey on university graduates ISTAT (2011; 2015) | |
| Entry in the labour force | Individuals leaving the school (aged 15–39) and inactive people | Calculated | Territory, gender, age, education, role within HH | Labour Force Survey ISTAT (2009–2013; 2014–2016) | ||
| Employment status | Individuals belong to the labour force | Matching between labour demand (Dante) and labour supply (IrpetDin) | Territory, education e sector | INPS, data on hours of redundancy funds and Unioncamere – Minister of Labour, Excelsior survey. (2008–2014) | Labour demand aligned to Dante 2008–2035, then our assumption | |
| Career employment | All individuals employed | Our calculated rates | Sector | Labour Force Survey ISTAT (2009–2013) | ||
| LABOUR AND INCOME | Wages and earnings | All individuals employed | Our estimation with OLS | Territory, age, gender, contributory seniority, educational level, work status, number of hours worked, citizenship | EU-SILC ISTAT (2003–2013) | |
| SOCIAL SECURITY | Retirement | All non-pensioners accruing retirement requirements | Pensions rules | |||
| Pension amount | All pensioners | Pensions rules | ||||
| Social pension | Individual aged above 65 with economic condition requirements | Pensions rules | ||||
| Integration at minimum pensions and pension supplements | Pensioners fulfilling age and economic condition requirements | Pensions rules | ||||
| Health | All individuals (insurance value approach) | Age, gender, education, nationality | Regional administrative data on specialist, pharmaceutical and hospital services (only Region of Tuscany) (2011) | |||
| Long Term Care | All individuals | Our estimation with logit | Age, gender and education | Survey “Multiscopo”, ISTAT (2014) |
Table A2
Multinomial logit of high school choice
| Italy | Tuscany | ||||
|---|---|---|---|---|---|
| lyceum (base) | Coef. | P>z | Coef. | P>z | |
| technical | |||||
| female | –1,277 | 0,00 | –1,408 | 0,00 | |
| father with secondary education | –0,503 | 0,00 | –0,264 | 0,00 | |
| father with tertiray education | –1,398 | 0,00 | –1,704 | 0,00 | |
| mather with secondary education | –0,675 | 0,00 | –0,742 | 0,00 | |
| mather with tertiray education | –1,613 | 0,00 | –1,531 | 0,00 | |
| intercept | 1,051 | 0,00 | 0,998 | 0,00 | |
| professionalising | |||||
| female | –0,795 | 0,00 | –1,018 | 0,00 | |
| father with secondary education | –0,850 | 0,00 | –0,817 | 0,00 | |
| father with tertiray education | –1,849 | 0,00 | –1,412 | 0,00 | |
| mather with secondary education | –0,946 | 0,00 | –1,130 | 0,00 | |
| mather with tertiray education | –1,916 | 0,00 | –2,163 | 0,00 | |
| intercept | 0,308 | 0,00 | 0,479 | 0,00 | |
| others | |||||
| female | 0,133 | 0,00 | 0,238 | 0,00 | |
| father with secondary education | –0,519 | 0,00 | –0,512 | 0,00 | |
| father with tertiray education | –0,896 | 0,00 | –1,566 | 0,00 | |
| mather with secondary education | –0,288 | 0,00 | –0,333 | 0,00 | |
| mather with tertiray education | –0,544 | 0,00 | –0,379 | 0,00 | |
| intercept | –2,189 | 0,00 | –2,049 | 0,00 | |
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Source: our estimation on survey on secondary school graduates ISTAT (2011; 2015).
Table A3
Multinomial logit of high school mark
| Italy | Tuscany | ||||
|---|---|---|---|---|---|
| under 70 (base) | Coef, | P>z | Coef, | P>z | |
| 70–80 | |||||
| female | 0,383 | 0,00 | 0,371 | 0,00 | |
| father with secondary education | 0,159 | 0,00 | 0,178 | 0,00 | |
| father with tertiray education | 0,316 | 0,00 | –0,106 | 0,04 | |
| mather with secondary education | 0,098 | 0,00 | 0,174 | 0,00 | |
| mather with tertiray education | 0,266 | 0,00 | 0,283 | 0,00 | |
| intercept | –0,739 | 0,00 | –0,536 | 0,00 | |
| 80–90 | |||||
| female | 0,639 | 0,00 | 0,792 | 0,00 | |
| father with secondary education | 0,191 | 0,00 | 0,501 | 0,00 | |
| father with tertiray education | 0,499 | 0,00 | 0,469 | 0,00 | |
| mather with secondary education | 0,230 | 0,00 | 0,006 | 0,88 | |
| mather with tertiray education | 0,483 | 0,00 | –0,158 | 0,02 | |
| intercept | –1,518 | 0,00 | –1,404 | 0,00 | |
| 90–100 | |||||
| female | 0,789 | 0,00 | 0,584 | 0,00 | |
| father with secondary education | 0,501 | 0,00 | 0,258 | 0,00 | |
| father with tertiray education | 0,974 | 0,00 | –0,030 | 0,65 | |
| mather with secondary education | 0,482 | 0,00 | 0,809 | 0,00 | |
| mather with tertiray education | 0,827 | 0,00 | 1,229 | 0,00 | |
| intercept | –2,168 | 0,00 | –2,113 | 0,00 |
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Source: our estimation on survey on secondary school graduates ISTAT (2011; 2015).
Table A4
Logit of the being a part-timer
| 2009-2013 | 2018-2019 | |||||||
|---|---|---|---|---|---|---|---|---|
| Coef. | Std. Err. | Wald Chi-Square | Pr > ChiQuadr | Coef. | Std. Err. | Wald Chi-Square | Pr > ChiQuadr | |
| Woman | 2,0279 | 0,0076 | 71247,14 | <.0001 | 1,781 | 0,0106 | 28357,22 | <.0001 |
| Age | –0,017 | 0,000311 | 2995,31 | <.0001 | –0,0165 | 0,000423 | 1517,71 | <.0001 |
| Number of children | 0,216 | 0,00484 | 1991,45 | <.0001 | 0,1288 | 0,00648 | 394,59 | <.0001 |
| Intercept | –2,2154 | 0,0148 | 22545,18 | <.0001 | –1,7406 | 0,0209 | 6913,29 | <.0001 |
| Number of obs | 791,126 | 307,954 | ||||||
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Source: our estimation on Italian Labour Force survey 2009-2013 2018-2019, ISTAT.
Table A5
OLS of the logarithm of income from employee work
| Coef. | Std. Err. | t | Pr > |t| | |
|---|---|---|---|---|
| Intercept | 7.70048 | 0.01596 | 482.35 | <.0001 |
| Age | 0.04376 | 0.000727 | 60.15 | <.0001 |
| Age(squared) | –0.0004 | 8.74E-06 | –46.05 | <.0001 |
| Man | 0.18824 | 0.00259 | 72.54 | <.0001 |
| Primary education | –0.12254 | 0.00469 | –26.14 | <.0001 |
| Tertiary education | –0.16541 | 0.00875 | –18.91 | <.0001 |
| Tertiary Education (squared) | 0.00664 | 0.000213 | 31.15 | <.0001 |
| Exceutive | 0.52652 | 0.00418 | 125.91 | <.0001 |
| Office worker | 0.28028 | 0.00268 | 104.45 | <.0001 |
| Head of the household | 0.07564 | 0.00248 | 30.46 | <.0001 |
| Women with children<18 | –0.00165 | 0.00482 | –0.34 | 0.7327 |
| Ptime | 0.14081 | 0.0055 | 25.62 | <.0001 |
| Working hours | 0.0194 | 0.000148 | 130.86 | <.0001 |
| R2 0.4226 |
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Source: our estimation on EU-SILC2008. ISTAT.
Table A6
OLS of the logarithm of income from self-employed work
| Coef. | Std. Err. | t | Pr > |t| | |
|---|---|---|---|---|
| Intercept | 8.52019 | 0.03519 | 242.15 | <.0001 |
| Age | 0.01755 | 0.00116 | 15.07 | <.0001 |
| Age(squared) | –5.5E-05 | 1.18E-05 | –4.67 | <.0001 |
| Secondary Education | 0.1194 | 0.01231 | 9.7 | <.0001 |
| Tertiary education | –0.0409 | 0.03009 | –1.36 | 0.1741 |
| Tertiary education (squared) | 0.00904 | 0.000563 | 16.06 | <.0001 |
| Professionals | 0.01318 | 0.01036 | 1.27 | 0.2036 |
| Head of the household | 0.14279 | 0.0074 | 19.29 | <.0001 |
| Services | –0.02719 | 0.00781 | –3.48 | 0.0005 |
| Man | 0.20699 | 0.00779 | 26.59 | <.0001 |
| Working hours | 0.00615 | 0.00029 | 21.21 | <.0001 |
| R2 0.1549 |
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Source: our estimation on EU-SILC2008. ISTAT.
Table A7
Logit of the probability of being not self-sufficient
| Coef. | Std. Err. | z | P>z | [95% Conf.Interval] | ||
|---|---|---|---|---|---|---|
| Tertiary education | –1.110 | 0.635 | –1.750 | 0.080 | –2.354 | 0.134 |
| Woman | 0.652 | 0.149 | 4.360 | 0.000 | 0.359 | 0.945 |
| Age | 0.091 | 0.008 | 11.650 | 0.000 | 0.076 | 0.106 |
| Constant | –8.845 | 0.579 | –15.290 | 0.000 | –9.979 | –7.711 |
| Number of obs 7.049Pseudo R2 0.288 | ||||||
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Source: our estimation on Survey “Multiscopo”, ISTAT (2014).
Data and code availability
IrpetDin has been developed using SAS, a general-purpose statistics package. The code and the executable are proprietary and not publicly available. The code is made up of approximately 2,500 rows.
EU-SILC data used to build IrpetDin are proprietary. The authors had access to the EU-SILC data thanks to an agreement with the Region of Tuscany, which belongs to the Italian Statistical System (SISTAN).
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