rafmacalaba/datause-encoder-data huggingface dataset:
# **_Kingdom of Naples 1811_**

|Income<br>Class|Percentage of<br>population|Income per<br>family<br>(in ducats)|Income per capita<br>(in ducats pa)|Income in terms of<br>per capita mean|
|---|---|---|---|---|
|1|10|200|38|0.58|
|2|10|230|44|0.67|
|3|10|260|50|0.75|
|4|10|260|50|0.75|
|5|10|260|50|0.75|
|6|10|260|50|0.75|
|7|10|260|50|0.75|
|8|10|260|50|0.75|
|9|10|260|50|0.75|
|10|6|600|114|1.74|
|11|3.3|1500|286|4.34|
|12|0.7|5000|952|14.47|
|_Total_|_100_||_65.8_|_1_|

Note: Average household size (5.25) assumed to be the same across all income groups

> **Income distribution data.** The source is Malanima (2006: p. 31), who uses the tax census data from 1811. This tax census is, for the purposes of establishing an estimate of income distribution, better than others because it surveyed not only tax paying units but also the poor (the indigent). Each of the 14 provinces of the Kingdom was supposed to place people in predetermined nine categories, running from the poorest to the richest (by family income). The percentage of people placed in each category was “free” (that is, left to each village, city etc.) with the only stipulation that not more than one-sixth of the population may be placed in the bottom category (the “indigent”) and hence be exempt from taxation. The problem is that it imposes an equality of conditions across provinces and leads to an underestimation of incomes in the rich areas like Naples-city. For example, people with a same income may be placed in category III in Naples and in higher category IV in a poorer province. Similarly, the number of poor in Naples (which was probably high) might have been underestimated (because of the imposed threshold of one-sixth). Yet, with the exception of  the Naples-city (then the third largest European city  containing about 6 percent of the total Kingdom’s population), which also displayed relatively high inequality,<sup>27</sup> income differences between the provinces were too small to lead to significant and systematic misplacing of households. The ratio of mean rural incomes between the richest and poorest province was less than 1.5 to 1 (and rural population accounted for 85% of the total population).<sup>28</sup>

Another problem is that the authorities in each province might have been tempted to underestimate people’s incomes and to push more people into lower classes so that taxes

> 27 The Gini given by Malanima (2006) is 53.

> 28 Excluding Naples-city, the same ratio for the urban areas is even narrower: 1.4 to 1 (calculated from Malanima).

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Mental health plays a small role in explaining the variance of the seven behaviors in Mexico and Chile. Good mental health – proxied by feeling prepared for future employment, feeling optimistic about job prospects, or having a sense of well-being – emerge as key explanatory variables for not being inactive in Mexico and Chile, and for older age at first job for Mexican females and younger age at first parenthood for Mexican males. This is perhaps due to the small variance of the well-being variable in Mexico, and the focus of the outlook variables in Chile being primarily related to employment.

# **VI. Conclusion**

Of the risk and protective factors investigated in this study, higher socioeconomic status (proxied by parental education), good relationship with parents and peers (social inclusion), strong connection with local government and school institutions, urban residence, younger age, and feelings of spirituality repeatedly emerge as key explanatory factors in the stepwise regressions, being positively correlated with each of seven youth behaviors examined for Mexico and Chile – not inactive, school completion, later age of first job, safe sexual behavior, later age of sexual initiation/parenthood, participation in activities, and healthy attitudes toward alcohol. This finding is consistent with findings from other LAC countries - including the English-speaking Caribbean, Brazil, and Honduras - as well as studies using US data. Unlike other studies, though, self identified indigeneity and household abuse do not emerge as key explanatory factors for Chile, and the variables that proxy positive mental health do not emerge strongly in the analysis for either country.

Gender is also an important variable. The incidence of risk factors, protective factors, and behaviors differ by gender and the set of factors that is most important in explaining behaviors differed by gender. Most notably, the relationship with government institutions has differing effects on women’s and men’s behaviors (Chile) while local institutions (school) seem particularly relevant for women. Spirituality also emerges as a more influential factor for women than for men.

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**Figure 6. Performance and Poverty and Inequality Impacts of the Heating Allowance under Baseline Scenario (pre-reform)**

**a.Coverage (% of households in each decile) b. Benefit incidence  (% benefits allocated to each decile)**

<!-- Start of picture text --> 71.4<br>11.0<br>1<br>2<br>33.3<br>3<br>31.3<br>8.7 57.2<br>4<br>0.0 0.4<br>5<br> 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text -->

- **c. Generosity (benefits as % of MYPP)**

- **d.Inequality, poverty, and the poverty gap impact**

<!-- Start of picture text --> 10.2<br>Marginal Contributions to<br>0.00<br>Inequality<br>Change in Poverty Gap 0.40<br>Marginal Contributions to<br>2.0 0.17<br>Poverty<br>0.5<br>0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**When considering the combined effect of the Monthly Social Allowance (MSA) and the heating allowance, their aggregate impact on income inequality and poverty is still modest (Figure 7, panel b); this impact is slightly more pronounced than the effect of the heating allowance in isolation.** This observation underscores the limited magnitude of change brought about by these transfers in the broader context of income distribution and poverty reduction. Interestingly, one key takeaway is that the primary beneficiaries of this combined assistance are the households in the first decile. This is primarily because the MSA is exclusively directed toward this decile, augmenting the support provided by the heating allowance. In essence, these households in the lowest income bracket experience a more substantial increase in their total assistance due to the combined support of the MSA and the heating allowance (Figure 7 panel a).

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# **Table 4. Production Function Estimation**

||**Fixed Effectsl**<br>excluding effect of<br>education<br>distribution|**Fixed Effects**<sup>**1**</sup><br>log-linear allowing<br>for education<br>distribution effect<br>using coeff. of<br>variability of<br>education|**Non Linear**<br>**Fixed Effects**<sup>**2**</sup><br>allowing for education<br>distribution effects<br>using coeff. of<br>variability of<br>education|**Fixed Effectsl**<br>allowing for education<br>distribution effects<br>using standard<br>deviation of the log of<br>education|
|---|---|---|---|---|
||Coefficient<br>(Std. Error)|Coefficient<br>(Std. Error)|Coefficient<br>(Std. Error)|Coefficient<br>(Std. Error)|
|Human capital|0.496<br>(0.369)|-0.182*<br>(0.103)|0.088*<br>(0.046)|0.098**<br>(0.011)|
|Capital|0.299**|0.330**|0.327**|0.257**|
||(0.025)|(0.009)|(0.004)|(0.008)|
|Dummy 1982-85|-0.029**|-0.018*|-0.034*|-0.023**|
||(0.011)|(0.011)|(0.019)|(0.010)|
|Education distribution<br>effects|||||
|Brazil||I.130**|-0.404|-0.495**|
|||(0.569)|(0.930)|(0.083)|
|Chile||<br>-2.476**|<br>0.586|-0.986**|
|||(0.390)|(0.775)|(0.082)|
|China||<br>-5.163**|<br>-7.851*|0.923**|
|||(1.999)|(4.416)|(0.204)|
|Colombia||<br>1.044|<br>0.516|-0.891**|
|||(0.687)|(0.426)|(0.091)|
|India||<br>-1.224**|<br>1.480**|0.174|
|||(0.244)|(0.147)|(0.132)|
|Korea||-I.030**|-0.572|-0.297**|
|||(0.074)|(0.747)|(0.044)|
|Mexico||<br>0.402**|<br>-0.572|-0.923**|
|||(0.188)|(0.747)|(0.091)|
|Malaysia||<br>0.160|<br>0.543|-I.010**|
|||(0.657)|(0.846)|(0.092)|
|Peru||<br>1.040**|<br>0.745*|-1.556**|
|||(0.329)|(0.387)|(0.213)|
|Philippines||<br>0.451|<br>-0.475|-0.461 **|
|||(1.064)|(1.237)|(0.095)|
|Thailand||<br>-0.540|<br>-0.357|-0.245**|
|||(1.108)|(0.905)|(0.062)|
|Venezuela||<br>1.336**|<br>1.621*|-1.578**|
|||(0.496)|(0.905)|(0.097)|

Note: One star denotes significance at the 10% level. while two stars denote significance at the 5% level.

1. A first-order autoregressive coefficient was estimated by Maximum Likelihood for each country separately. This information wkas used to correct the data. Standard errors reported correspond to White's heteroscedastic consistent covariance estimator.

2. A first-order autoregressive coefficient was estimated by Maximum Likelihood for each country separately.

14
- 12 -

For induotry as a whole the fuel **sharea** for 1988 are shown for each country in table 2.5.

Table 2.5: Fuel Shares in Industry for O.B.C.D. Countries, 1988

||Coal and<br>Other<br>Solid Fuels|Oil|gas|Electricity|
|---|---|---|---|---|
|Australia|31.7|16.4|29.7|22.2|
|Austria|32.5|16.8|27.1|23.7|
|Belgium|29.5|28.0|22.9|18.2|
|Canada|19.6|21.5|33.1|25.0|
|Denmark|16.8|40.1|14.2|26.6|
|Finland|44.0|19.8|7.5|27.5|
|France|21.0|34.2|23.7|21.0|
|Germany|24.7|30.0|22.4<br>|22.8<br>|
|Greece|27.7|45.5|2.4|24.3|
|Iceland|16.7|25.0|0.0|61.1|
|Ireland|19.5|35.7|30.3|14.5|
|Italy|11.6|34.4|32.2|21.7|
|Japan|29.4|41.3|3.0|26.3|
|Luxembourg|58.8|15.8|11.5|3.9|
|Netherlands|10.8|34.9|41.0|13.2|
|Now Zealand|34.0|7.5|32.7|25.7|
|Norway|17.1|31.5|0.0|51.2|
|Portugal|24.2|58.2|0.0|16.9|
|Spain|19.6|47.8|10.5|22.1|
|Sweden|38.5|24.0|1.4|35.2|
|Switzerland|14.9|31.3|15.8|36.5|
|Turkey|41.0|40.7|1.4|16.9|
|United Kingdom|<br>21.0|32.6|27.6|18.7|
|U.S.A.|26.2|24.8|32.2|16.8|
|Yugoslavia|12.9|27.2|29.6|24.3|
|O.B.C.D.|25.2|31.4|24.7|20.6|

foureat As table 2.1.

This table shows that for industry as a whole there are very substantial variations in fuel shares between the industrialized countries at the same point in time. However these variations cannot be taken literally as defining a range of variations that is available for each and every country. Two major influences on fuel share may be largely country specific, thus placing 'imits on the potential for fuel switchings

- (i) the local availability of primary **sources** of energy. Although oil is freely traded and coal Lo also often traded, the trade in gas in much more limited by the enormous infrastructure costs involved. Countries with thelr own gas reserves (e.g. U.S.A., U.K., Netherlands) or very near to **gas** reserves tend to use much
<!-- Start of picture text --> FIGURE 1. REAL SALES, EXPORTING AND NON- FIGURE 2. PRODUCTION CONCENTRATION BY SECTOR,<br>EXPORTING FIRMS, 2008-2014 HERFINDAHL-HIRSCHMAN INDEX, 2008-2014<br>16.7 16.4 0.69 0.69<br>14.5<br>13.8<br>0.36<br>0.12 0.11<br>0.06<br>2008 2014 2008 2014 2008 2014 2008 2014 2008 2014<br>non-exporter exporter Manufacturing Services Construction<br>Source: Authors’ calculations based on panel data from the Source: Authors’ calculations based on panel data from the<br>World Bank Enterprise Surveys World Bank Enterprise Surveys<br>20 .8<br>15 .6<br>10 .4<br>Real sales (Log)<br>5 .2<br>Herfindahl-Hirschman (HH) index<br>0 0<br><!-- End of picture text -->

# **Access to finance**

The proportion of working capital financed by banks and the proportion of firms having a loan increased between 2008 and 2014. However, there is large heterogeneity across firms in the use of bank financing: while 7 percent of small firms reported having a line of credit or a loan facility, the proportion rose to 15 percent in medium sized firms, and further to 34 percent in large firms (Appendix 2). In total, only 12 percent of the firms had a loan or an overdraft facility in 2014, which is extremely low compared with regional peers where the proportion is more than 30 percent (Figure 3). Also, in 2014 less than 10 percent of Nigerian firms applied for a loan<sup>3</sup> (Appendix 2). Among firms that did not apply for a loan, 51 percent report that they do not need a loan, whereas the others quote respectively complexity in application procedure, unfavorable interest rate, high collateral requirements, insufficiency of size and maturity, and did not think it would be approved as the main reasons for not applying.

Figure 3. Proportion of firms having a loan – comparison <!-- Start of picture text --> 61.1<br>50.0<br>47.4<br>45.5 45.5<br>41.5<br>35.6<br>32.8 32.2 32.8<br>11.4<br>Source: Enterprise Surveys<br>Benin (2009) Botswana (2010)Burundi (2014)Cabo Verde (2009)Ethiopa (2015)Kenya (2013)Lesotho (2009)Mauritania (2014)Mauritus (2009)Nigeria (2014)Rwanda (2011)<br>60<br>40<br>20<br>0<br>% of firms having a credit line or overdraft facility<br><!-- End of picture text -->

> 3 The acceptance rate was 83 percent for all firms, but only 49 percent in the panel sample.

6
<!-- Start of picture text --> 50%<br>45%<br>40%<br>7% 35%<br>2 3 & 30%<br>=3 25%<br>= 20%<br>8<br>& 15%<br>10%<br>se<br>0%<br>a R 8~ g_ a Rg g 8~ Rg_ a<br>- 3 § & & # 3 $ & &<br>Men Women<br><!-- End of picture text --> <!-- Start of picture text --> 160%<br>10%<br>2 120%<br>©100%a 3<br>=3 80%<br>€<br>8 60%<br>a<br>40%<br>20%<br>0%<br>8 Rg 8 reN a 8 8 &> N5 a<br>§ 3 $ & & # 3 € & &<br>Men Women<br><!-- End of picture text --> <!-- Start of picture text --> 120,000<br>100,000<br>80,000<br>3B 60,000<br>40,000<br>20,000<br>0<br>38= R$# a2> N25 a;3 Rg== R§# g£2> a35 a;3<br>& = § & & = = &€ & 8&8<br>Men Women<br><!-- End of picture text --> <!-- Start of picture text --> 25%<br>20%<br>=3<br>S‘s2&Fa 15%<br>€ 10%<br>@fe]Fy<br>5%<br>0%<br>i5= agg$# a2> a35 N35 R3= a¢z a2> a3= N35<br>& 3s 8 & & = 3s 8 & &<br>Men Women<br><!-- End of picture text -->
|||Survey|noncognitive|gender as a|||||dummies;|
|---|---|---|---|---|---|---|---|---|---|
|||<br>(Encuesta<br>Nacional de<br>Habilidades,<br>ENHAB),<br>2010; 748<br>men and<br>|<br>skills as<br>latent<br>variables|<br>dummy<br>variable|||||whether<br>firstborn;<br>economic<br>sector|
|||women||||||||
|Diaz, Arias & Tudela<br>(2013)|Peru|Encuesta<br>Nacional de<br>Habilidades<br>(ENHAB)<br>(2010), final<br>sample of<br>1,140 men<br>and women<br>aged 14-50|Two-step IV<br>to address<br>endogeneity<br>of schooling|Log hourly<br>earnings|0.055***|0.088*** for<br>aggregate<br>cognitive<br>measure|-0.088*** for<br>agreeableness,<br>0.057* for emotional<br>stability|Not included|None|
|Glewwe, Huang & Park<br>(2013)|China<br>(Gansu<br>Province)|<br>Gansu<br>Survey of<br>Children and<br>Families|OLS|Log hourly<br>earnings,<br>with<br>selectivity|0.059** for<br>years of<br>schooling|0.028 for<br>literacy score|0.027 for self-<br>esteem; -0.086* for<br>depression scale;<br>0.097** for|-0.396***<br>for female<br>dummy|Years of<br>work<br>experience,<br>parents'|
|||(GSCF), 402<br>males and<br>females aged<br>17-21||bias<br>correction|||noncognitive factor||education,<br>Inverse<br>Mills ratio|
|Gunewardena (2015)|Sri Lanka|STEP (Skills|OLS|Log hourly|Not included|0.108*** for|Agreeableness***,|Not relevant|Experience,|
|||Measurement<br>Survey); 779||earnings||numeracy,<br>0.073** for|risk-taking***||experience-<br>squared;|
|||men aged 20-||||reading,|||location|
|||64||||0.056 for|||(region|
|||||||writing, 0.039<br>for core|||dummy,<br>urban/rural|
|||||||literacy|||dummy);<br>informal|
||||||||||employment,<br>full-time<br>|
||||||||||employment|
|Gunewardena (2015)|Sri Lanka|STEP (Skills|OLS|Log hourly|Not included|-0.000 for|Openness ***,|Not relevant|<br>Experience,|
|||Measurement||earnings||numeracy, -|emotional||experience-|
|||Survey); 510||||0.009 for|stability**,||squared;|
|||<br>women, aged||||reading,|<br>decisionmaking (-)*||<br>location|
|||<br>20-64||||<br>0.134*** for|||(region|
|||||||writing, 0.070|||dummy,|
|||||||for core|||urban/rural|
|||||||literacy|||dummy);|

10
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Another reason for the somewhat changed pattern of allocations in the past 2 years has been the strong thrust on completing the requirements of ‘universal physical access’. This has resulted in higher allocations for states and districts that had bigger deficits of school facilities. The other factor that has contributed to improving the EDI-PCA relationship has been the change in the approach of 2 major states, with poor educational indicators (viz. Bihar and West Bengal), especially. This helped to improve PCAs for about 60 districts that earlier had the lowest PCAs in the country while having some of the lowest EDIs in the country.

However, the process of criteria based resource allocations is far from perfect and could do with several improvements. Some suggestions are highlighted here as examples:

- a. Using indicators with gross numbers like 30,000 ‘out of school’ children or 3000 additional classrooms works in favor of bigger districts with larger population. These are not necessarily districts with the highest proportion (percentage) of ‘out of school children’ or ‘classroom deficits’. This needs to be modified to use indicators that are able to identify districts with the highest level of disadvantage or deprivation.

- b. It is useful to use one or more indices that combine some dimensions of educational status to identify the overall status of individual districts to slot them in deciles/quintiles or quartiles.   Overall per child allocations (PCA) of total funds could then be linked to such indices.  The EDI developed in this study is an example of such an index.  In addition, other untied funds could be preferentially allocated to districts that figure in the lowest bracket of EDIs.

- c. Use of size of population of social categories is often not the best indicator for identifying educational deprivation.  For example, districts with sizable SC population have very widely varying educational indicators. In some states like Kerala, Tamil Nadu, Karnataka and Maharashtra, the Muslim population has high levels of literacy and children’s educational status. Also, many districts with significant pockets of Muslim population, do not figure in the list of districts with ‘sizable’ Muslim population.  Any indicator that is based on presence of population of traditionally disadvantaged social groups should be used along with some indicator that measures educational deprivation.

At present, additional allocations are being made for these disadvantaged districts based mostly for improving physical access, especially construction of school buildings and additional classrooms, irrespective of the real nature of problems in particular districts. Thus, whether a district has high gender disparity in enrolment (owing to a traditional lower value for education of girls) or a high proportion of out of school children, a relatively higher allocation is made for construction of school buildings or additional classrooms.

24
stability, and - by way of lower deficits - decreased risk _premia_ and interest costs<sup>18</sup> , and is creating room for productive expenditure in the budget. Financial markets have reacted with optimism: stocks<sup>19</sup> , the peso and reserves have all risen significantly, as have foreign direct investment and capital inflows.<sup>20</sup>

**... but the fiscal adjustment might be too recent to convince investors.** The increase in the 2006 tax effort reflected implementation of the VAT reforms, while the collection of (corporate and personal) income taxes and excise taxes was insufficient.  The public-debt-toGDP ratio remains high at about 77 percent, the fiscal position is still fragile and significant interest payments (Table 4) still expose the economy to swings in financial market sentiment. Hence, containing the risk premium with further tax efforts and steady progress on reforms is crucial. Concerns about the long history of macroeconomic volatility, the unsettled political climate, and (the unpredictability in the incidence of) corruption add to risks for investors (World Bank, 2005c).

# **2b. The capital-intensive private sector** **_does not want_ to invest**

**The expectations on future profitability of investment are low.** The capital-intensive private sector _does not want_ to increase investment because it expects low returns (World Bank, 2005c). In the corporate sector, the MPK - calculated as market value/asset value, that is the Tobin’s Q - has been falling since the early 1990s, and, since the Asian crisis, it has been lower than in neighboring countries (Figure 11).<sup>21</sup> Despite the more favorable investment environment, the demand for domestic credit has been falling and the banks have been reluctant to lend (Figure 12). Indeed, a low MPK reduces the expectations about future profitability and thus the willingness to invest, and, over the years, has contributed to the decline of fixed investment.

> 18 Spreads for government borrowing have fallen along with inflation.

> 19 Equity prices are approaching their pre-Asian crisis highs.

> 20 The Philippine stock market was among the top East Asian performers, and in early 2007 continued to rise towards pre-Asian crisis highs.

> 21 Aquino (2003) calculates the Tobin’s Q of the Filipino corporate sector. While the firms with higher Tobin’s Q can more easily and less expensively finance their investment with external financing, firms with low Tobin’s Q are considered to have lower growth potential and the market might require higher external finance premiums in raising funds. This analysis, however, occurred when equity prices were considerably lower than current levels.

16
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