Credit scoring

4.5 Reti neurali

4.5.6 Algoritmo error back-propagation

5 10 15 20 25 30 35 40

1816 1818 1820 1822 1824 1826 1828 1830 1832 1834 1836 1838 1840 1842 1844 1846 1848 1850 1852 1854 1856 1858 1860

Wheat price

Florence Pistoia

Figure 1. Wheat price in the market of Florence and Pistoia, 1817-1859.

Table 3. Effects of price and cholera epidemic on mortality, Casalguidi 1819-59. Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2 taxed11111111111111111 -tax group1.0351.0211.1621.1571.1351.1221.0881.0621.0251.0241.0611.0500.4010.3700.7320.6890.5580.536 h-tax group0.9020.9080.8860.8910.9720.9800.5450.5570.4090.4090.4780.4820.3200.3470.2790.2840.3070.320 ged price at time t1.9521.8690.4960.4830.9510.9001.3161.2371.3501.3451.3451.2991.3341.0631.2901.0101.3081.030 ged price at time t-10.9830.7680.7840.7061.0270.8071.6680.9051.2271.1921.4381.0561.9510.5455.7311.8213.5901.082 lera1.6031.4551.7812.7901.0651.7744.5573.5943.996 son-months ths ALL Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2 taxed111111111111111111 -tax group0.5770.5610.4570.4500.5180.5040.3690.3540.6920.6640.5050.5090.4500.4510.6850.6610.5580.539 h-tax group0.4660.4730.2790.3000.3670.3810.3790.3860.6620.6700.5020.4850.4370.4360.4780.4960.4640.480 ged price at time t8.5228.0175.4844.8816.6726.0910.7830.6831.4111.2671.0300.9151.8901.6602.4162.0642.1711.883 ged price at time t-11.0900.6020.6470.2030.8260.3341.2950.7061.9451.0521.5770.8601.3090.6231.6980.6401.4790.626 lera2.1394.3463.1603.0942.4162.6922.9504.4633.203 son-months ths

Early childhood Mortality (1 years).Childhood Mortality (2-18 years). MFALLMFALLMFALL MFALLMFMFALL

Adult Mortality (19-54 years).Old-age Mortality (55+ years)Overall Mortality (2+ years)

Infant Mortality (0 years) 19,91419,81339,72712,01611,19023,20617,24416,84634,090 353336689181185366160206366 23,08623,19546,2816,0035,95411,95746,33345,99592,328 7047391443268587225265490319 Notes: The models control also for age, household head’s profession, and property of house. In bold, coefficients significant at p<0.05.

Table 4. Effects of price and cholera epidemic on marriage, fertility and household mobility, Casalguidi 1819-59.

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

Untaxed 1 1 1 1 1 1

Low-tax group 1.109 1.11 0.981 0.972 0.999 0.991

High-tax group 0.775 0.775 0.874 0.892 0.763 0.769

Logged price at time t 0.533 0.534 0.649 0.473 0.58 0.493

Logged price at time t-1 1.509 1.509 0.833 0.814 1.105 1.089

Cholera 0.995 1.54 1.257

Person-years Marriages

Model 1 Model 2 Model 1 Model 2

Untaxed 1 1 1 1

Low-tax group 0.999 0.991 0.667 0.661

High-tax group 1.288 1.259 0.399 0.408

Logged price at time t 0.927 1.098 1.778 1.115

Logged price at time t-1 0.938 0.848 0.922 0.884

Cholera 0.787 1.648

Person-years Births

High-tax group Logged price at time t

Cholera Household years Out-migrations 13,051


10,769 662

Logged price at time t-1 Untaxed

Low-tax group

6,148 2,091

17,618 590 Marriage (never-married people, 15-49 years)

Fertility (married women, 15-49 years) Household mobility (emigration) ALL F


23,820 1,192

Notes: The models control also for age, household head’s profession, and the property of house. In bold, coefficients are significant at p<0.05.

The effects of short-term economic stress prove to be less common but likewise important in this context. Firstly, regarding the time-lag aspect included in our analysis, these findings support the idea that the effects of short-term economic stress, namely at time t, were almost immediate on demographic events. Besides the obvious influence of grain prices, at t-1, on fertility, which naturally has an immediate effect on conceptions, the only significant impact of prices which lagged by 1 year is limited to child and adult mortality, although with signs of an opposite tendency limited to women. For children, the positive relationship between mortality at t-1 and price increase is linked to the cholera epidemic of 1855. Once a dichotomous variable aimed at capturing the effects of this epidemic event is included in the model, the size of the grain price coefficient at t-1 shows a noticeable drop leaving no real statistical significance. As for adults, the correlation between grain price at time t-1 and mortality is vice versa negative and unaffected by the introduction of the covariate concerning cholera. It is worth noting that this effect particularly concerns women once the effects of cholera have been

would also reduce the number of births in the following year, a decrease further accentuated by the drop in marriages due to grain price increase at time t. Hence, it is likely that female mortality associated with delivery and childbearing results as reduced by the limited number of births following periods of crisis.

We can note that at time t, grain price effects influence mortality, nuptiality and household emigration. While the absence of any significant effect of grain prices on infant mortality was expected, its extent on that of adults aged between 19-54 is somewhat surprising, even if this outcome is consistent with results of econometric analyses on time series. Our findings reveal a 12-18 percent rise in mortality in response to a 10 percent price increase. It also appears that those age brackets with a normally higher risk of death in regular periods―such as infants and the elderly―suffered less from price increases on the Florence market than the categories of active and working adults.

With regards to nuptiality and emigration, findings are consistent with previous expectations. The economic burden that first marriages put on families made them less likely to be celebrated in hard times, naturally both for males and females, and hence normally postponed to more propitious times. Out-migrations of entire households were usually associated to the circulation of day laborers, who moved around on a seasonal basis in search of work, as well as the possible expulsion of sharecroppers from farms.

However, the positive relationship between price increases and out-migration is simply the consequence of the exaggerated price of grains during the cholera epidemic of 1854-55. After having accounted for cholera, this association becomes insignificant and the exp-coefficient is largely reduced. Therefore, the reason behind the rise in household out-migration risk was the increased circulation of individuals and households that usually occurred during epidemics rather than the grain price itself (Manfredini 2003a).

Sharecropping contracts terminated and were up for renewal in November, and it is likely that landlords decided to expel those sharecropping families whose size had been dramatically reduced by cholera during the previous summer.

There is a close association, therefore, between the cholera epidemic and grain prices in mid-nineteenth century Tuscany. As shown in Figure 1, grain prices on both the Florence and Pistoia markets peaked in the biennium 1854-55, precisely when cholera hit Tuscany hard, reaching an increase of 40 and 50 percent respectively over the mean for the period. This epidemiological stress obviously affected mortality intensity, but it also had negative repercussions on fertility and household mobility, stimulating an increase in household out-migration. Indeed, all demographic events were concerned but marriage. All classes saw a significant rise in mortality risk, although less marked, as typical of cholera epidemics, in the early years of life. As expected, fertility also resulted as being affected, with a significant 22 percent drop of childbirth among married woman. As already mentioned, the circulation and emigration of entire households also results as being influenced by the 1854-55 epidemic, and, finally, no changes were made to marriage plans. In our case study, economic reasons appear to be much more important than epidemiological ones in determining marriage behavior and weddings. This can be said to be quite surprisingly since much previous data has confirmed the existence of a negative relationship between marriage and

mortality crisis. However, the reason for our findings could lie in the different seasonality patterns of cholera and marriages. In Casalguidi, most marriages were celebrated at the beginning of the year (January and February), in October and, especially, in November. Very few weddings were recorded in July and August, the very two months when the epidemic provoked the most deaths in the village. Thus, we can note both that the earlier seasonal peak of marriages would not have been affected by cholera, as well as that the later one would have been, to some extent, reinforced by remarriages and summer marriages that had been postponed.

Our final step was to investigate the extent of the relationship between short-term crises and SES. For each event and age-bracket of mortality, we used Model 2, with no differentiation by sex, as a base to form separate models for the untaxed and low-tax group on the one hand, and the high-tax group on the other. The results (Table 5) prove that wealthy families reacted very differently compared to poor and untaxed ones. Or better, they appear to have no reaction at all except for an increase in the risk of mortality associated to the cholera epidemic for young children and teenagers (2-18 years) and the elderly (55+). The demography of the poorest classes emerges as being greatly affected by epidemiological and, to a less extent, economic short-term crises.

Cholera, in particular, altered the entire demographic system of the poorest social groups by increasing mortality at all ages as well as household out-migration, and depressing fertility. Grain price resulted as being conversely effective in influencing adult mortality and nuptiality.

Table 5. Summary of price & epidemic effects by demographic event and SES (All individuals).

Untaxed + low-tax group High-tax group

Price t Price t-1 Cholera Price t Price t-1 Cholera Mortality

0 No No + No No No

1 No No + No No No

2-18 No No + No No +

19-54 + + No No No

55+ No No + No No +

Fertility No No No No No

Nuptiality No No No No No

Household emigration No No + No No No

Table 6. Summary of price & epidemic effects by demographic event and rural occupation (All individuals).

Day laborers Sharecroppers & tenants Price t Price t-1 Cholera Price t Price t-1 Cholera

Overall mortality + No + No +

Fertility No No No No No

Nuptiality No + No No No

Household emigration + No No No No +

The picture was even more complicated when tried to gain more insight into the large and predominant world of the very poorest classes (Table 6). We ran various models of adult mortality by household head’s occupation with the purpose of determining the effects of prices on both those who relied on the market for the purchase of food and those who lived off their farm’s produce. The question was not of being landless or not, but that of form of land tenure. In fact, the landless categories of sharecroppers and tenants were part of the latter group, whilst day laborers and rural wage earners formed the former one. The results demonstrate that the landless day laborers were undoubtedly the most fragile sector of the population. Day laborers suffered a 26 percent mortality increase in response to a 10 percent price increase, whereas this was true for only 7 percent of sharecroppers and tenants. Hence, it was poverty and the impossibility of direct access to land produce that determined a high risk of mortality in the presence of short-term economic stress. Clearly, the worsening of life conditions caused by rapid price increases had dramatic consequences on their survival. In most cases, they were in difficulty in even covering household expenses and therefore unable to cope with the minimum negative economic conjuncture. Conversely, sharecroppers, tenants and smallholders appear to be more capable of facing hard times induced by negative, economic, short-term cycles. Given the wide variety of Tuscan farm produce, these categories had little difficulty in finding what they needed, namely food and raw materials, such as textile fibers and wood, used to construct objects of everyday use. Indeed, one of the main characteristics of sharecropping was its high degree of auto-consumption.

On the other hand, it was virtually impossible for sharecropper or indeed wealthy families to avoid the effects of epidemics, so much so that cholera can be seen to have had an great impact on all Casalguidi’s social categories. Indeed, sharecropping households, due to their larger size and high housing density were extremely likely to be affected by infectious diseases such as cholera.

In conclusion, although the measuring of demographic effects from short-economic stress by SES proves to be extremely complex, the rich dataset we have been able to reconstruct for this Tuscan rural community has allowed us to assess the socioeconomic and professional status of individuals and families in great detail. Many social and economic aspects, such as occupation, wealth, and home ownership, were used to highlight demographic differentials by SES. This occasion to identify the day laborer category as the “poorest poor” and most fragile in absolute would seem to partly explain the paradox of the “Tuscany of the river” area, which while being the richest and most populated zone proves also to be the most sensitive to short-term economic stress and cycles. It is these day laborers and rural wage earners that became ever more present in rural Tuscany of 19th century11 and considered by contemporaries as the main source of moral and social disorder.

11 In the countryside around Pisa, the 1841 Grand Duchy census counted over one third of day laborers in the general category of rural workers (Doveri 1990).


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Demographic Responses to Short-Term Economic Stress

In document Sistema bancario e intelligenza artificiale: sviluppo di un processo di risk management, basato sui modelli di albero decisionale e rete neurale, per l'analisi del rischio di credito applicato al settore automotive italiano = Banking system and artificial (Page 122-127)