rafmacalaba/datause-encoder-data huggingface dataset:
consistent across a range of different indicators measured independently<sup>25</sup> . Overall, these results suggest that program participants perceived a heightened sense of opportunities for the future.

# <u>6.5 Access to credit</u>

Access to credit is one of the most frequently cited barriers to entrepreneurship, particularly in Tunisia.<sup>26</sup> The entrepreneurship track did not directly aim to alleviate credit constraints, but some aspects of the training involved providing information to students about credit applications, as well as connecting them to bankers. Table 7 (Panel A) shows that the treatment group was more likely to be confident to be able to obtain credit and to have actually applied for credit (conditional on having a business idea). However, they were not more likely to know how to apply for credit and were also no more likely to have obtained credit. These findings suggest that the intervention improved beneficiaries’ confidence in obtaining outside credit. Still, they do not provide direct evidence that the intervention alleviated credit constraints.

As a robustness check, Panels B, C, and D in Table 7 present impact estimates for the main employment outcomes after removing prize winners from the sample. This is done by removing the 50 students who chose to enter the business plan competition and won a prize, even though in practice fewer than 15 students cashed their prize. While there is clearly selection into winning a prize, the winners were arguably students with the best prospects to become self-employed to start with. As such, removing winners from the sample most likely provide conservative estimates of program impact. Table 7 shows that, although program impacts on self-employment in permanent activities are not significant anymore, the results are overall very similar. As such, this robustness test suggests that the results in section 5 are mainly driven by the effects of business training and coaching, not so much by the seed capital attached to the competition, even though we cannot formally disentangle the two.

Finally, in the follow-up survey, graduates in the treatment group report that lack of access to credit remains the most binding constraint for entry into self-employment. In fact more than 70

> 25 The optimism indicator itself is aggregated from 6 questions

> 26 Tunisia ranks 87th on the “ease of getting credit” indicator (see World Bank, 2012).  Start-up financing is provided by the BFPME (Bank for financing Small and Medium Enterprises of 100,000 – 10 million TND) and the BTS (Bank for financing micro enterprises of <100,000 TND). Own funds required for a credit is between 35-40 percent at the MFPME and 10 percent at the BTS. There are also a number of support funds available such as FOPRODI (Industrial Decentralization and Investment Fund), or SICAR (a venture capital firm).

24
-57-

Table 4.2 represents the first stage in the calculation procedure. It shows figures for that part of the 1975 gross value of output of Brazil and Mexico which is covered by our matching of individual product items. Figures are presented in terms of national currency and in prices of the other country. The correspondinF gross output ratios are also presented in this table, as indicated by formulae (<sup>4</sup> .2a) and (4.2b): and <!-- Start of picture text --> I (QM pm<br>YL15 ---- * pM ) (4.2a)<br>1 (QB PM-<br>y 75 y,75 c<br>I (Qm PB<br>---- YL75 --- lYL75-s ~~~~~~~~(4. 2b)<br>I (QB * pB )(<br>y,75 y,75 c<br><!-- End of picture text --> We did not, as we did in chapter III, make a quality adjustment for passenger cars because quality differences between Mexico and Brazil are much smaller than those between these countries and the USA.

Table 4.3 shows the coverage ratios for the products matched in this direct comparison between Mexico and Brazil, and compares them with the coverage ratios for the Brazil/USA and Mexico/USA comparisons of chapter III. The greater homogeneity of Mexican and Brazilian output might be expected to make it possible to match more product items than in chapter III, but table 4.3 shows that in practice there was not much difference in the matching in the two chapters.

Chapter III discussed two methods of adjusting the covered part of output in terms of prices of the other country, X(QB * pM ) and M y y c (Qy * py)c to total gross value of output. In practice we prefer the method which assumes the price relationships for the covered part of output to be representative for uncovered output as well (see chapter III).

Table 4.4 presents the gross output quantity comparison calculated both in terms of pesos and cruzeiros. The Brazilian figures at national prices are directly derived from the Censo Industrial, and for Mexico from the Resumen General (see also table 4.1). The figures in country B at prices of country N are derived by adjusting the matched figure by the inverse coverage ratio for country B; for country M at prices of country B by using the coverage ratio of country M (compare with formula 3.5a and 3.5b in chapter III). Table 4.4 also shows the corresponding ratios of Mexican to Brazilian output.
**Table A.1 Cancer incidence rate**

<u>(per 10,000 a year, average of 1993-1997)</u>

|**Age**<br>**ranges**|**National**|**average**|**Jian**|**gsu**|**Tia**|**njin**|
|---|---|---|---|---|---|---|
||**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|
|TOTAL|24.78|19.38|25.69|13.54|24.52|20.49|
|0_4|1.28|0.90|0.62|0.20|1.41|1.18|
|5_9|0.97|0.78|0.88|0.56|1.01|1.14|
|10_14|1.06|0.79|0.76|0.53|1.29|0.63|
|15_19|1.11|0.84|0.77|0.55|1.34|0.76|
|20_24|1.33|1.59|1.15|0.83|1.29|1.88|
|25_29|2.03|2.47|2.92|1.55|1.62|2.80|
|30_34|3.67|4.68|8.23|3.98|2.84|4.81|
|35_39|7.11|8.65|18.64|7.96|5.03|7.19|
|40_44|12.93|13.14|31.65|16.42|11.09|11.03|
|45_49|19.57|19.10|45.55|22.13|19.92|17.71|
|50_54|28.94|24.62|43.69|23.39|31.68|26.58|
|55_59|40.84|34.88|59.83|27.73|40.44|44.30|
|60_64|75.68|53.77|93.69|39.65|76.38|64.52|
|65_69|112.53|69.40|114.45|48.66|123.23|80.11|
|70_74|151.01|89.10|134.68|55.79|178.94|98.49|
|75_79|164.10|97.63|149.87|72.04|183.12|107.65|
|80_84|153.46|92.79|102.43|40.19|182.95|99.55|
|85|106.89|79.35|--|--|130.89|78.41|

1. Data Source : Data for Tianjin and Jiangsu come from Parkin, D.M., S.L. Whelan, J. Ferlay and H. Storm (2005). Cancer Incidence in Five Continents, Volumes I to VIII. International Agency for Research on Cancer and the -

World Health Organisation. IARC CancerBase No 7, Lyon, 2005. Website : <u>http://www dep.iarc.fr/</u> 2. Data for national average are calculated by authors according to the IRAC database.

**<u>Table A.2 National average cancer mortality rate</u>**<sup>**1**</sup> <u>(per 10,000 persons)</u>

||**1**<br>|**991**<br>|**19**<br>|**95**<sup>**2**</sup><br>|<br>|**2000**<br>|
|---|---|---|---|---|---|---|
|**Age rage**|**male**|**female**|**male**|**female**|**male**|**female**|
|15-44|3.12|2.00|3.26|2.00|3.29|2.13|
|45-54|22.32|12.62|23.49|13.19|24.3|14.14|
|55-64|56.23|28.33|48.97|25.41|47.7|26.30|
|65-74|104.27|53.10|93.18|38.10|104.89|51.58|
|75+|128.24|67.84|126.99|67.29|135.39|67.39|

1. the data for 1991 and 2000 come from Yang, L., DM Parkin, LD, Li, YD, Chen and F. Bray (2004): Estimation and projection of the national profile of cancer mortality in China: 1991-2005. British Journal of Cancer (2004) 90, 2157-2166.

2. The data for year 1995 is projected by authors. We suppose a linear growth for both the cancer incidence and population between 1991 and 2000.

3. This data also used in mortality rate estimation reported in table 7.

**Table A.3 Province-specific cancer incidence rate used in estimation**

<u>(per 10000 persons)</u>

|**Age ranges**|**National **|**average**|**Jian**|**gsu**|**Tia**|**njin**|
|---|---|---|---|---|---|---|
||**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|
|15_44|5.20|6.09|10.484|4.995|4.205|5.210|
|45_55|23.55|21.39|44.730|22.668|24.800|21.446|
|55_65|58.13|44.45|75.832|33.466|58.331|54.173|
|65_75|128.46|77.78|122.593|51.193|145.114|85.854|
|75+|151.92|93.35|116.020|51.852|174.414|97.546|

1. calculated directly by authors from the IRAC database f with a less precise age-classification.

34
LAC Region 28.7 7.2 3.602 1.618 66.4 56 23.1 6.2 2.762 1.285 69.6 42 16.4 7.2 2.102 1.005 73.5 25 1 Brazil 28.0 7.5 3.415 1.531 65.3 63 22.6 6.5 2.730 1.270 68.8 48 16.3 7.6 2.090 1.000 73.1 28 2 Central America 30.4 6.0 3.909 1.786 67.6 49 25.0 5.1 2.863 1.343 70.7 35 16.7 6.1 2.093 1.004 74.0 22 3 Caribboan and Other 30.2 7.5 3.725 1.623 65.3 60 23.4 6.5 2.795 1.275 68.5 42 16.7 7.3 2.149 1.021 72.6 26 4 Temperate and Tropical 26.2 8.2 3.416 1.559 67.7 51 21.1 7.2 2.633 1.231 70.5 35 15.9 7.9 2.090 1.000 73.9 18 Note: Crude birth rates (CBR)<sup>**and**crude</sup> death rates (CDR) are per<sup>1,000 population; total fertility</sup> rates (TFR) and net reprodcktion<sup>rates</sup> {"RR) are per woan 15-49 years of<sup>age; life expectancy at birth, E(0),isgiven in years; and infant mortality rates (INR) are per 1.000 live</sup> births. (See definitions in World Bank<sup>1988.) World Bank regions and country departments are defined in Table 14.</sup>
The selection of households as PROGRESA beneficiaries was accomplished by first identifying the communities to be covered by the program (geographic targeting) and then selecting the beneficiary households within the chosen communities.<sup>6</sup> The household selection within communities covered by the program involves conducting a census of all the households in the communities to be covered by the program and collecting detailed information on household demographic composition, assets and income. Adult per capita income is then compared with the cost of the _Standard Food Basket_ (which is roughly equivalent to two times the minimum wage) of 320 pesos per capita per month in order to generate a new binary variable taking the value of 1 for poor households (if income is less than the cost of the standard food basket) and 0 for nonpoor. Finally, discriminant analysis is applied, separately for each <u>geographical region, in order to identify the variables that discriminate best between poor and</u> nonpoor households and compute an index (discriminant score) that represents parsimoniously the differences between the poor and nonpoor households.<sup>7</sup> In the early stages of the program (i.e. during 1998) the PROGRESA beneficiary selection method led to approximately 52% of the households in the evaluation sample to be classified as eligible for the program benefits. By July 1999, PROGRESA had added new households to the list of beneficiaries since it was felt that the original selection method was biased against the elderly poor who no longer lived with their children.<sup>8</sup> As a result of the revised selection process the fraction of households classified as eligible for program benefits increased from 52%

> 5 See Skoufias (2001) for a synthesis of all the available results of PROGRESA’s evaluation.

> 6 For a more detailed discussion see Skoufias et al. (2001).

> 7 Discriminant analysis may be considered as analogous to a logit probability model for a binary dependent variable. Its main difference from the logit model is that it allows a variety of ways for classifying observations into groups (for more details see Sharma, 1996).

> 8 The Spanish term used to describe this revised selection process is _densificacion_ . The revised selection process did not simply increase the region-specific thresholds but instead it revised the

6
sector of banks in state _s_ in August 2014, the month prior to the oil price shock. Although the average exposure of banks across states is 12.1 percent, for states in the bottom decile it is 9.6 percent, and for states in the top decile it is 14.3 percent.

# **3. Methodology**

We use the 2014 collapse of global energy prices as an exogenous shock to the Mexican banking sector to assess the implications for banks of large exposures to a troubled sector. Banks with large exposures to ailing sectors may suffer due to weaker capital ratios as loans become delinquent, or losses on those exposures as loans default. Therefore, the banks might have incentives to expand lending to these borrowers. However, these actions can come at the expense of increased risk and lower returns, by taking lending away from borrowers in unaffected sectors.

To investigate the impact of this external shock on banks’ balance sheets and credit allocation, we adopt a difference-in-differences approach in which treatment is continuous and corresponds to the banks’ exposure to borrowers in energy-related sectors in the month prior to the unanticipated shock. This measure of bank exposure consists of the August 2014 ratio of loans to firms in energy-related sectors issued by a bank over its tier 1 capital.<sup>12</sup> In Figure 2, we classify banks into two groups according to their August 2014 exposure to the energy sector. The three panels in the figure provide descriptive evidence that the effect of the global energy price collapse was more pronounced among banks with greater exposure to the energy sector prior to the price drop. The group labeled “high exposure” includes banks with exposures above the median (5 percent) for the selected date, while “low exposure” banks had exposures below the median. For each group, we plot their exposures to the energy sector (panel A), CDS spreads (panel B), and stock prices (panel C) from January 2013 to June 2016.

Panel A shows that although there were substantial differences in the level of exposure to the energy sector across banks prior to the shock, the variation across these groups was constant from January 2013 through August 2014 and followed a parallel trend. The shares of lending to energy firms of banks above and below the median exposure were on average around 10 and 3.8

> 12 However, our results remain unchanged using alternative measures of exposure or different periods (Table A6 in the appendix). These measures correspond to the December 2012—the month prior to the start of our sample— exposure to energy firms of banks and the December 2012 number of branches in energy-intensive municipalities over the total number of branches of a bank.

10
# References

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Baker, Michael, Jonathan Gruber, and Kevin Milligan. “Universal Child Care, Maternal Labor Supply, and Family Well‐Being.” Journal of Political Economy 116, no. 4 (August 2008): 709–45. <u>https://doi.org/10.1086/591908.</u> Currie, J. (2009). ‘Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development’. Journal of Economic Literature, 47(1): 87–122. <u>https://doi.org/10.1257/jel.47.1.87</u> Cheryl Doss, Intrahousehold Bargaining and Resource Allocation in Developing Countries, The World Bank Research Observer, Volume 28, Issue 1, February 2013, Pages 52–78, https://doi.org/10.1093/wbro/lkt001 Duflo, Esther. 2003. “Grandmothers and Granddaughters: Old Age Pension and Intra-Household Allocation in South Africa.” World Bank Economic Review 17 (1): 1–25.

Dunbar, Geoffrey R, Arthur Lewbel, and Krishna Pendakur. “Children’s Resources in Collective Households: Identification, Estimation, and an Application to Child Poverty in Malawi.” American Economic Review 103, no. 1 (February 1, 2013): 438–71. https://doi.org/10.1257/aer.103.1.438.

Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen (eds.). 2022. World Values Survey: Round Seven - Country-Pooled Datafile Version 5.0. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. doi:10.14281/18241.20 Kling, J., Liebman, J., and Katz, L. (2007). Experimental analysis of neighborhood effects. Econometrica, 75(1):83–119.

Lépine, A., and E. Strobl (2013). ‘The Effect of Women’s Bargaining Power on Child Nutrition in Rural Senegal’. World Development, 45: 17–30. https://doi.org/10.1016/j.worlddev.2012.12.018 John A. List & Azeem M. Shaikh & Yang Xu, 2019. "Multiple hypothesis testing in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 773-793, December.

Lundberg, S. J, R. A Pollak, and T. J Wales. 1997. “Do Husbands and Wives Pool Their Resources? Evidence from the United Kingdom Child Benefit.” _Journal of Human Resources_ 32 (3): 463–80.

7
<!-- Start of picture text --> 0.9<br>0.8<br>Commercial Banks<br>0.7<br>Investment Bank/Securities<br>0.6 House<br>0.5 Non-banking Credit Institution<br>0.4 Other<br>0.3<br>0.2<br>1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br><!-- End of picture text -->

**Figure 3. Trend of the fee income share by categories of bank type.**

The fee income share is the share of non-interest income in total operating income. This plot shows the trend of the fee income share by bank categories from 1999 to 2007. Bank categories are commercial banks, investment bank or securities house, non-banking credit institution and other. Subcategories of _commercial banks_ are bank holdings and holding companies, and commercial banks. Subcategories of _other_ are cooperative banks, Islamic banks, medium and long term credit banks, real estate mortgage banks and savings banks. The data are from Bankscope.

59
**Figure 2.  Persons utilizing a tax deduction for pension saving. Percent of all population 20-64.**

<!-- Start of picture text --> Figure 2. Persons utliizing a tax deduction for<br>pension saving. Percent of all population 20-64.<br>50<br>40<br>30 Women<br>20 Men<br>10<br>0<br>Year<br>Source: Data from Statistics Sweden.<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> A third major structural change was a substantial reform of the tax system, which went into effect in 1991. With this reform all forms of saving were given the same income tax status. This improved the relative price of saving in financial instruments compared with borrowing for current consumption or for housing investments. It is noteworthy that individual saving had reached a post-war low at the end of the 1980s, and a major focus of policy in the initial years of the 1990s was on reversing this downward trend.

In 1993, a fourth major change occurred when legislation enabling unit-linked insurance went into effect. Whereas traditional life insurance still followed conservative insurance principles, unit-linked insurance allowed the individual to make his/her own investment choices during the accumulation phase, including choices from among equity funds. Since 1993, an increasing share of growth in the assets of life insurance companies has come through unitlinked contracts.

A fifth legislative change influencing the development of private insurance was the introduction of individual retirement saving (IRS) accounts in 1994. New saving in an IRS account is tax deductible under the same (gross) rule as premiums paid for private insurance. As with private insurance, accounts must be held at least to age 55, and withdrawals must be made over a period of at least five years. Banks and investment funds provide the account services, and the saver can usually choose among a number of alternative funds. Individual retirement accounts are not a perfect substitute for insurance, since they do not cover the risk of longevity, but are more liquid compared to traditional insurance with life annuities. For this reason, they have become an attractive saving form that competes with insurance policies.

A sixth major structural change was the reform of the public pension system, already discussed. The reform of the public pension system, first with the presentation of the proposed framework in 1992 and the ensuing discussion, and then with the presentation of the actual proposal and its passage by Parliament in June 1994, had three important effects on the development of the private insurance market.

20
**Figure 15c. Unconditional quantile regressions coefficients: occupations**

<!-- Start of picture text --> Legislators and Sr. Managers Professionals Technicians & Assoc. Prof<br>0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100<br>Quantile Quantile Quantile<br>2002 2007 2012 2002 2007 2012 2002 2007 2012<br>Service & market workers Skilled agriculture & fishery Craft and related trades<br>0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100<br>Quantile Quantile Quantile<br>2002 2007 2012 2002 2007 2012 2002 2007 2012<br>Operators & Assemblers Unskilled workers<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Quantile Quantile<br>2002 2007 2012 2002 2007 2012<br>.4<br>.6 .4<br>.4 .2<br>.2<br>.2<br>0<br>0 0<br>-.2<br>-.2<br>-.4 -.4 -.2<br>.2 .5 .2<br>0 0<br>0<br>-.2 -.2<br>-.5<br>-.4 -.4<br>-.6 -1 -.6<br>0<br>0<br>-.1 -.2<br>-.2<br>-.4<br>-.3<br>-.4 -.6<br><!-- End of picture text --> 31
countries over 23 _,_ 000 like Kazakhstan, already attained 0% poverty rates in the published data. After, 49 _._ 54% of poverty remains missing because most countries are not complete in the time dimension. We first interpolate these variables by taking a weighted average over time. This works well for most variables, but may yield poor results for poverty, as we have seen a tremendous improvement in most countries in past years. We improve the time dynamics in the interpolated poverty data by using information about time dynamics contained in our other variables. We vectorize the interpolated values, and fit the kernel model using the full set of undernourishment, logarithmic GDP per capita, the share of manufacturing, services, urban population shares, and bottom 40 income shares. The model reaches an _R_<sup>2</sup> ’s of _._ 91. We use this model to smoothen the interpolated poverty values by taking an average of the interpolated values and the values predicted by this nonlinear model.

Table 1: Summary of the data used in our empirical application. Statistics are not weighted and not necessarily representative of the world averages.

|Statistic|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|
|Annual % Tree loss|0.437|0.406|0.009|2.924|
|Urban PM2_._5 mcg/m3|18.924|12.609|0.311|63.498|
|CO2 kg / $|0.239|0.199|0.014|1.990|
|CO2 ton p.c.|3.402|4.162|0.015|20.208|
|GDP ppp p.c. 2011 international $|13,468.880|15,056.710|555.560|64,979.840|
|Population density, people / sqkm|101.592|138.270|1.524|1,148.514|
|Undernourishment rate|15.514|13.546|0.000|64.500|
|Poverty 1.90$ at 2011 international $|21.177|22.040|0.000|84.740|
|Manufacturing GDP share|14.371|6.721|0.237|38.733|
|Services GDP share|70.065|12.734|29.279|93.881|
|Urban population share|53.685|22.754|12.082|97.818|
|Bottom 40% income share|15.970|4.130|7.510|28.024|
|NDVI annual mean|0.502|0.163|0.111|0.762|
|NDVI annual min|0.327|0.164|_−_0.027|0.657|
|NDVI annual max|0.655|0.161|0.170|0.862|
|Forest cover 2000 extent million ha|3.628|2.815|0.0005|9.883|
|Country area sqkm|978,152|1,999,320|15,007|9,904,700|

# **3.6 Transformation to degradation intensities**

To address homogeneity concerns related to the scales of sovereign economies, we model standardized units of deforestation, pollution, and emissions, subsequently standardized per unit of GDP per capita in 2011 international dollars adjusted for purchasing-power parity. These environmental intensities follow a nonlinear trend with variance declining 16