rafmacalaba/datause-encoder-data huggingface dataset:
Shapiro, Jeremy. 2009. Weather Insurance and Investment Choice. Mimeo. MIT Department of Economics.

Shleifer, Andrei, and Robert Vishny. 1992. Liquidation Values and Debt Capacity: A Market Equilibrium Approach. _Journal of Finance_ 47: 1343–1366.

Sinha, S. 2004. Agriculture Insurance in India: Scope for Participation of Private Insurers.

_Economic and Political Weekly_ , June 19, 2605–2612.

Skees, J. R., J. R. Black, and B. J. Barnett. 1997. Designing and Rating an Area Yield Crop Insurance Contract. _American Journal of Agricultural Economics_ 79: 430–438.

Skees, Jerry, Peter Hazell, and Mario Miranda. 1999. New Approaches to Crop Yield Insurance in Developing Countries. EPTD Discussion Paper 55. International Food Policy Research Institute.

Suri, Tavneet. 2011. Selection and Comparative Advantage in Technology Adoption _Econometrica_ , 79 (1): 159-209.

Townsend, Robert. 1994. Risk and Insurance in Village India. _Econometrica_ 62 (3): 539–592. World Bank. 2005. _Managing Agricultural Production Risk: Innovations in Developing_ _Countries_ . World Bank Agriculture and Rural Development Department. Washington, DC.

- ———. 2006. _Overcoming Drought: Adaptation Strategies for Andhra Pradesh, India_ . Washington, DC.

———. 2010. _Innovative Finance for Development Solutions: Initiatives of the World Bank Group_ . Washington, DC.

Yang, Dean, and HwaJung Choi. 2007. Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines. _World Bank Economic Review_ 21: 219–248.
random variable distributions often required to simplify model solutions, but whose realism is often difficult to test<sup>2</sup> .

There is no golden rule on the appropriate combination of non-parametric and parametric inferences. In this case, the analytical intractability of sufficiently general models of credit risk and the statistical complexity of testing the restrictions of analytically simpler models suggest the usefulness of a Montecarlo simulation approach. Sampling from the universe of past realizations will provide a faithful replica of the unobservable loan loss distribution from which historical realizations have been generated. In other words, a Montecarlo simulation gives us nothing less than an “X-ray” of the hidden distribution of credit risk and an important support in the formulation of accurate diagnostics and appropriate financial policies.

Let’s now turn to the “main assumption and definitions” adopted in the construction of the universe(s) of reference of the bootstrapping exercises.

• <u>Universe of reference. The universe of reference is that of commercial loans to non-</u> financial institutions granted by Chilean supervised financial institutions and censored by the Chilean Credit register. Loans are defined as the consolidated exposures of individual borrowers in place at the initial date of the analysis (December 31 of each year). The positions may include a small amount of nonperforming loans (less than 5 percent of total exposures) to account for delays due to inefficiencies, rather than to inability to repay. We have then partitioned all the loans censored in the Chilean Credit Register into three dimensional categories (Table 1). The first includes loans of unit value smaller than 1 million Chilean pesos (approximately US$2,000), the second includes loans between 1 and 10 million Chilean pesos (approximately US$2,000 and US$20,000), and the third includes all the loans larger than 10 million Chilean pesos. We selected the second and third groups of loans (excluding the group with the smallest loans) as our two universes of reference. We were not able to link individual loans to the size of the borrower (as measured by sales, assets or number of employees) but, given current firm

> 2 For an interesting discussion of the relevance of model risk in taking normative decisions in the domain of banking regulation see Borio and Tsatsaronis (2005).

6
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That CFs and NCFs are not completely independent is perhaps suggested by our analysis of proposed CFs. We find that proposed CFs that are clearly affected by, but not yet part of, the Nepal Community Forestry Programme sequester carbon on a level similar to that of our broadest collective action measure (CanIDFUGyear) and have a variety of positive effects on forest quality. It is very important to emphasize that government forests that we here denote NCFs are often weakly controlled by governments. That so many NCFs are no more degraded than CFs as we would expect to occur under open access suggests collective action exists. We are not able to pinpoint why this collective action occurs, but it appears to be important. We conjecture that over time NCF communities have adopted norms from the CF system.

# **Bibliography**

- <mark>Adhikari, B. and Lovett, J. 2006.Transaction costs and community-based natural resource management in Nepal.</mark> _<mark>Journal of Environmental Management</mark>_ <mark>78 (2006) 5–15.</mark>

- Agarwal, B. 2010. _Gender and Green Governance: The Political Economy of Women’s Presence Within and Beyond Community Forestry_ , Oxford University Press: Oxford

- Baland, Jean-Marie and Jean-Philippe Platteau. 1996. Halting degradation of natural resources: Is there a role for rural communities? Oxford, U.K.: Clarendon Press.

- <mark>Beyene AD, Bluffstone R and Mekonnen A. 2013. Community Controlled Forests, Carbon Sequestration and REDD+: Some Evidence from Ethiopia. Discussion paper Series.</mark> Environment for Development.

- <mark>Bohra, P. and Massey, D. 2009. Processes of Internal and International Migration from Chitwan,</mark> Nepal. _International Migration Review_ 43 (3): 621-651.

- <mark>Bottazoi P, Crespo D, Soria H, Dao H, Serrudo M, Benavides JP, Schwarzer S and Rist S. 2014. Carbon Sequestration in Community Forests: Trade-offs, Multiple Outcomes and Institutional Diversity in the Bolivian Amazon.</mark> _<mark>Development and Change</mark>_ <mark>. Vol.</mark> **<mark>45</mark>** <mark>(1): 105-131.</mark>

- Branney P. and Yadav KP. 1998. Changes in Community Forest Condition and Management 1994-1998. Analysis of Information from the Forest resource assessment Study and Socio-economic Study in the Koshi Hills, Nepal. Project Report No. G/NUCFP/32. Kathmandu: UK Community Forestry Project.

- <mark>Campbell, B.M. 2009. ‘Beyond Copenhagen: REDD+, Agriculture, Adaptation Strategies and Poverty’,</mark> _<mark>Global Environmental Change</mark>_ **<mark>19</mark>** <mark>: 397–99.</mark>

**41 |** P a g e
# **3.1 Estimation of consistent poverty lines**

To identify poverty trends, it is common practice to select a poverty line for a base year and project this line into subsequent years using an appropriate temporal consumer price index (CPI). Alternatively, the reference poverty line can be held constant by expressing all monetary variables, previously adjusted for spatial cost-of-living differences, at the base year's constant values. Thus, the temporal price index plays a crucial role in dynamic poverty analysis. In the context of Bhutan, there is evidence that the official CPI for Bhutan underestimates the inflation faced by the poor over the period at hand: inflation recorded by the true cost of living index, based on the ratio of the nominal food poverty lines, is 87 percent, while the official food CPI only records 39 percent inflation (Amendola et al. 2023). For this reason, in this section we develop an analytical framework for identifying the poverty trend with no reliance on the official CPI. The proposed method anchors the trend to the official food poverty line for 2022: this means that the endpoint of the poverty trend will be close to the official point estimates for 2022 (NSB 2022).<sup>2</sup> The method consists in calculating the change in the monetary value of a _fixed_ reference food basket, which can be interpreted as a basic-needs basket consistent with the consumption pattern of poor households in Bhutan. The cost of such a basket can be used as the basis for calculating a food cost-of-living index. As detailed below, the construction of such an index is equivalent to the calculation of a Laspeyres-type price index based on the consumption basket of poor households. The index is then used to “move” the 2022 food poverty line back in time, which provides an anchor for estimating a new upper-bound poverty line.<sup>3</sup> _h_ : h h h h 0 1 2 � JJ qq = �qq , qq ,∙∙∙, qq Let us define a reference food basket for household _h_ : where _J_ denotes the number of food items included in the basket. The reference basic needs food basket is identified by averaging h0 over the first decile of the expenditure qq qq

> 2 There is, in fact, a small discrepancy between the 2022 poverty estimates presented in this paper and the ones published in NSB (2022): the reason is that the consumption aggregate and upper bound poverty line used for trend estimation have been computed to maximize comparability with 2017.

> 3 For a complete and detailed description of the method, see Amendola et al. (2023).

5
19 Table 6: Panel Data Estimates: Bond Flows, 1988.01-1992.09 Latin America (9 countries) - Asia (9 countries).

||La|tin America||Asia|
|---|---|---|---|---|
||Nominal interest<br>rates|Real interest rates|Nominal interest<br>rates|<br>real interest rates|
|Method|GLS|GLS|GLS|GLS|
|Credit rating|||1.318<br>(0.281)|1.304-<br>(0.281)|
|PCS|3.8930<br>(2.431)|8.329-<br>(2.114)|||
|Pll|-13.094|-8.737-|-8.370-|-9.563**|
||(2.413)|(2.104)|(2.628)|(2.620)|
|Adjusted R'|0.982|0.982|0.041|0.047|
|_X'1_|-0.0381|-0.0002|0.0003<br>(0.9871)|0.0181<br>(0.8930)|
|NOBS|513|513|513|513|

Note: See Table 5. Three dummies are used for Latin America to capture<sup>the Brady Plan effect. One dummy is</sup> eliminated because with 4 dummies the algorithm for GLS breaks down (square root of negative element).<sup>"#n, 'o',</sup> "*", and "**': significant at the _15%,_ 10%, _5%_ and **_I_** % levels, respectively.
# **(b) By Province**

|Province|Free A|llowances|(Grandfath|ering)||Aucti|oning||
|---|---|---|---|---|---|---|---|---|
||Without|linkage|With li|nkage|Without|linkage|With l|inkage|
||Emissio<br>n based|Output<br>-based|Emissio<br>n based|Output<br>-based|Revenue<br>GOV|Revenue<br>HH|Revenue<br>GOV|Revenue<br>HH|
|Anhui|-26.8|-29.2|-11.7|-13.2|-29.8|-29.7|-13.5|<br>-13.5|
|Beijing|-25.5|-27.3|-11.4|-12.9|-32.3|-32.4|-15.4|<br>-15.4|
|<br>Chongqing|-21.1|-14.7|-8.6|-6.0|-14.9|-14.4|-6.0|<br>-5.8|
|Fujian|-29.3|-23.5|-14.2|-10.9|-23.3|-23.1|-10.8|<br>-10.7|
|<br>Gansu|-27.1|-24.7|-12.2|-11.1|-24.5|-24.3|-11.0|<br>-10.9|
|Guangdong|-31.7|-32.2|-15.7|-15.9|-32.0|-32.1|-15.7|<br>-15.8|
|Guangxi|-24.7|-18.1|-10.8|-7.7|-18.2|-17.9|-7.8|<br>-7.6|
|Guizhou|-32.0|-38.6|-15.4|-18.9|-37.5|-37.5|-18.2|<br>-18.2|
|Hainan|-20.4|-12.5|-8.6|-5.1|-13.6|-13.3|-5.7|<br>-5.6|
|Hebei|-29.8|-29.6|-13.8|-13.8|-29.7|-29.8|-13.9|<br>-13.9|
|Heilongjiang|-28.6|-29.0|-13.3|-13.6|-28.2|-28.3|-13.1|<br>-13.2|
|Henan|-29.6|-34.8|-14.3|-17.2|-33.9|-33.8|-16.6|<br>-16.5|
|Hubei|-32.3|-29.7|-16.0|-14.0|-28.5|-28.8|-13.3|<br>-13.4|
|Hunan|-28.0|-30.5|-12.6|-14.1|-29.6|-29.5|-13.6|<br>-13.5|
|Jiangsu|-25.3|-21.6|-10.9|-9.3|-21.6|-21.6|-9.3|<br>-9.3|
|Jiangxi|-23.4|-16.4|-9.5|-6.6|-18.5|-18.1|-7.6|<br>-7.4|
|<br>Jilin|-26.1|-23.9|-11.4|-10.8|-23.7|-23.5|-10.6|<br>-10.5|
|Liaoning|-24.6|-19.1|-11.1|-8.6|-20.0|-19.7|-9.1|<br>-8.9|
|<br>Inner Mongolia|-37.8|-42.0|-19.5|-21.8|-41.0|-40.9|-20.9|<br>-21.0|
|<br>Ningxia|-28.4|-30.6|-13.1|-14.4|-30.7|-30.6|-14.4|<br>-14.3|
|<br>Qinghai|-24.2|12.7|-10.0|11.0|-16.7|-17.9|-4.1|<br>-4.6|
|<br>Shaanxi|-29.7|-24.5|-13.3|-10.6|-24.1|-24.0|-10.4|<br>-10.3|
|Shandong|-27.0|-23.8|-12.0|-10.5|-25.7|-25.7|-11.6|<br>-11.5|
|Shanghai|-24.2|-24.4|-10.7|-10.9|-25.6|-25.9|-11.6|<br>-11.7|
|Shanxi|-34.4|-43.4|-17.2|-21.8|-41.8|-42.1|-20.7|<br>-20.9|
|Sichuan|-30.3|-27.3|-14.1|-12.4|-26.5|-26.4|-12.0|<br>-12.0|
|Tianjin|-31.8|-32.9|-15.2|-16.1|-34.6|-34.7|-17.0|<br>-17.1|
|Xinjiang|-22.4|-18.2|-9.3|-7.5|-17.9|-17.9|-7.5|<br>-7.4|
|<br>Xizang|-15.8|-9.7|-6.9|-4.2|-11.0|-10.4|-4.8|<br>-4.5|
|<br>Yunnan|-25.4|-22.5|-11.0|-9.8|-22.4|-22.2|-9.8|<br>-9.7|
|Zhejiang|-27.2|-24.6|-12.3|-11.1|-24.6|-24.6|-11.1|<br>-11.1|
|China|-28.7|-28.2|-13.3|-13.2|-28.5|-28.5|-13.3|-13.3|

The national level emission reductions are not much sensitive across the allocations rules by design because the emission caps and corresponding emission reductions are fixed or exogenous. Provincial level emission reductions vary across the allowance allocation rules. The percentage emission reductions from a province are not different significantly between the revenue recycling schemes under the auctioning.

46
# **Appendix: Sources of Data Used**

Note: Classifications where cell sizes are 30 or less were not used, and are marked s.s. (small sample) in Tables

# **_Data Sources_**

_Albania:_ Data on return migration is from the 2005 Albania Living Standards Measurement Survey, a nationally representative survey which includes data on 1419 return migrants.

_Argentina:_ We combine seven waves of the EPH labor force survey, taken between October 1999 and October 2002, in order to achieve a sufficiently large sample of migrants. As this is a rotating panel, we drop subsequent observations on the same individuals in order to sample each migrant only once. This yields a sample of 16,890 born outside of Argentina, of whom 1,247 are aged 12 to 24 and are from developing countries. Of these, 183 came to Argentina within two years of the survey year. The native youth sample are taken from the October 2002 wave of the same survey.

_Canada_ : Data used are from the 2.7% public use file on individuals from the 2001 Census. This data contains individual records for 154,280 individuals born outside of Canada, of whom 12,386 are aged 12 to 24 and are from developing countries. Of these, 1,944 came to Canada in 1999 or later. The public use data only contains 25 occupation categories, and so Herfindahl indices of occupational concentration are not calculated.

_Côte d’Ivoire:_ Data used are from the May 2002 Enquete Niveau de Vie des Menages (Household Living Standards Survey). This yields a sample of 4,548 individuals born outside of Côte d’Ivoire, of whom 973 are aged 12 to 24. Of these, 662 came to Côte d’Ivoire within five years of the survey date. Data on country of birth is not available, but

- 18 -
**Table 4. Consumption Poverty Matrix under No Shocks**

|||**National Expected Poverty in 2013: Expected Shoc**|**ks=0**|
|---|---|---|---|
|||_Non‐Vulnerable_<br>_Vulnerable_|_Totals_|
|**Observed 2013**|_Non‐poor_|57.1<br>9.4|66.4|
||_Poor_|20.6<br>12.9|33.6|
||_Totals_|77.7<br>22.3|100|
|**Table 4a. Consumpti**|**on Poverty Matrix u**|**nder Expected Shocks**<br>**National Expected Poverty in 2013: Regional Mean S**|**hocks**|
|||_Non‐Vulnerable_<br>_Vulnerable_|_Totals_|
|**Observed 2013**|_Non‐poor_|45.8<br>20.6|66.4|
||_Poor_|10.5<br>23.0|33.6|
|**Table 4b. Northern R**|_Totals_<br>**egion Consumption**|56.4<br>43.6<br>**Poverty Matrix under Expected Shocks**|100|
|||**Northern Region Expected Poverty in 2013: Regional Me**|**an Shocks**|
|||_Non‐Vulnerable_<br>_Vulnerable_|_Totals_|
|**Observed 2013**|_Non‐poor_|35.1<br>28.3|63.4|
||_Poor_|6.4<br>30.2|36.6|
||_Totals_|41.5<br>58.5|100|

**Table 4c. Central Region Consumption Poverty Matrix under Expected Shocks**

|||**Central Region Expected P**|**overty in 2013: Regional Mea**|**n Shocks**|
|---|---|---|---|---|
|||_Non‐Vulnerable_|_Vulnerable_|_Totals_|
|**Observed 2013**|_Non‐poor_|59.1|9.9|68.9|
||_Poor_|19.3|11.7|31.1|
||_Totals_|78.4|21.6|100|

**Table 4d. Southern Region Consumption Poverty Matrix under Expected Shocks**

|||**Southern Region Expected**|**Poverty in 2013: Regional Me**|**an Shocks**|
|---|---|---|---|---|
|||_Non‐Vulnerable_|_Vulnerable_|_Totals_|
|**Observed 2013**|_Non‐poor_|44.6|21.1|65.6|
||_Poor_|9.9|24.5|34.4|
||_Totals_|54.5|45.6|100|

_Note_ : Tables compare observed poverty in 2013 with predicted vulnerability. Estimates are expressed in percentage terms.

Tables 4a‐4d present the national and regional transition matrices where we set the average annual rainfall and price shocks observed in the region historically, and the malaria and formal employment shocks to the average observed within the region in 2013,<sup>10</sup> which of course results in much higher predicted vulnerable households than estimates based on expected shocks. At the national level, the observed poverty rate in 2013 was ~34 percent, whereas the proportion of vulnerable people within the population was estimated to be ~44 percent, almost evenly split between poor households expected to

> 10 This produces conservative estimates of vulnerability since it assumes that all households, irrespective of wealth, landholdings, etc., would be equally likely to be affected by a shock next year.  In most cases, however, shocks are more likely to occur for poorer households due to more precarious job opportunities, access to preventative health care, etc.

19
indicated as above). There are no significant differences for any characteristics between the sample and the frame, suggesting that the balanced sampling approach performed well, and the Nigeria HFPS sample retains the representativeness present in the frame.

**Table 4.2: Sample representativeness in Nigeria HFPS**

||**Nigeria (G**|**HS-Panel)**<br>**With**|**HFPS**|**Samle**|
|---|---|---|---|---|
||**All**|<br>**Contact**<br>**info**||**p**<br>_Di †_|
||_Mean_|_Mean_|_Mean_|_fference_<br> _(3) -(2)_|
|**Characteristic**|_(1)_|_(2)_|_(3)_|_(4)_|
|**Sample size(number of households)**|_4,976_|_4,934_|_3,000_||
|Rural|0.686|0.683|0.687|-0.001|
|Average household size|5.528|5.515|5.530|-0.002|
|Dependencyratio|1.016|1.014|1.028|-0.012|
|Female head(%)|0.186|0.187|0.186|0.001|
|Age of head|48.797|48.775|48.646|0.151|
|**_Religion of head_**|||||
|Orthodox|||||
|Muslim|0.425|0.422|0.425|0.000|
|Christian|0.557|0.560|0.556|0.001|
|**_Marital status of head_**|||||
|Married|0.750|0.749|0.749|0.000|
|Formerlymarried|0.193|0.194|0.192|0.001|
|Never married|0.057|0.057|0.058|-0.001|
|**_Education level of head_**|||||
|Head Literate|0.744|0.745|0.748|-0.005|
|None|0.375|0.371|0.364|0.011|
|Primary|0.241|0.242|0.253|-0.012|
|Secondary|0.225|0.226|0.231|-0.006|
|Tertiary|0.160|0.161|0.152|0.007|
|**_Working status of head_**|||||
|Any|0.827|0.828|0.830|-0.003|
|Wage|0.177|0.178|0.186|-0.009|
|Agriculture|0.442|0.442|0.430|0.012|
|Nonfarm enterprise|0.367|0.367|0.375|-0.007|
|**_Consumption_**|||||
|Mean expenditure(LCUper capita)|219408|220234|220372|-964|
|**_Quintiles_**|||||
|Quintile 1 -poorest|0.201|0.197|0.201|0.000|
|Quintile 2 -poorer|0.199|0.200|0.196|0.004|
|Quintile 3 - middle|0.200|0.201|0.201|-0.001|
|Quintile 4 - richer|0.200|0.201|0.197|0.003|
|Quintile 5 - richest|0.200|0.201|0.206|-0.006|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

18
# **Voluntary Solidarity Contribution (VSC) on Airline Tickets**

A proposal to raise funds by introducing a VSC providing individuals and corporations who purchase airline tickets with the opportunity to voluntarily donate a small sum for every ticket purchased; the levy would not be mandatory for consumers.

Further information: <u>www.who.int/phi/UNITAID_Jan09.ppt</u> See also: Voluntary Solidarity Contribution (VSC)

# **Vouchers**

A voucher is a certificate, usually issued by a government by which users can pay for a specific service at a service provider of their choice, rather than assigning the user to a service provider. Vouchers are typically used in the education or health sector. Voucher programs are explicitly aimed at improving service quality by increasing users’ choices therefore increasing competition.

Proponents of vouchers argue that vouchers promote free market competition among service providers. Opponents criticize vouchers for the potentially ambiguous effect on the poor: Voucher schemes tend to increase sorting—for example with richer students concentrating in the private schools.<sup>122</sup> Reading: Elson-Rogers and European Centre for the Development of Vocational Training 2000, World Bank 2005

# **Weather derivatives**

Weather derivatives are linked to an index that measures weather related risks such as low rainfall or drought. Unlike insurance, weather derivatives do not cover the loss from weather related events but the value of the derivative changes depending on movements of the underlying index. Weather derivatives may avoid moral hazard problems that come with insurance products because the value of the derivative solely depends on exogenous variables, for example rainfall Reading: Cao and Wei 2004; Hess, et al. 2002; Turvey 2001

> 122 World Bank 2004.

89
birth. Learning from the experience of its European neighbor countries might help Turkey to avoid fertility rates declining below replacement level in the near future. Dissolving the negative association between employment and fertility emerges as a major challenge for Turkey. To stimulate female employment and fertility at the same time, encouraging work-life balance for families has been proven to be a fruitful option.

# **6. Appendix**

**figure A. Poportion of women (cohorts)  by education in Turkey**

<!-- Start of picture text --> 0,9<br>0,8<br>0,7<br>0,6<br>0,5<br>0,4<br>0,3<br>0,2<br>0,1<br>0<br>1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980<br>Less than primary completed Primary completed<br>Secondary completed University completed<br><!-- End of picture text --> Source: Census (i-pums) 2000 SILC data is not used here as census data covers cohorts 1969 to 1974 which serve to calculate approximate completed fertility. Education proportions for these cohorts are approximately the same in the census and the SILC data.

25