rafmacalaba/datause-encoder-data huggingface dataset:
|P116170|2010|2018|6/1/2018|16 Active|Brazil|
|---|---|---|---|---|---|
|P116170|2010|2019|10/24/2018|17 Active|Brazil|
|P116170|2010|2019|4/13/2019|18 Active|Brazil|
|P116170|2010|2020|11/1/2019|19 Active|Brazil|
|P116170|2010|2020|4/9/2020|20 Active|Brazil|
|P116176|2009|2009||0 Closed|Algeria|
|P116176|2009|2010||0 Closed|Algeria|
|P116176|2009|2011||0 Closed|Algeria|
|P116176|2009|2012||0 Closed|Algeria|
|P116176|2009|2013||0 Closed|Algeria|
|P116176|2009|2020|3/3/2020|1 Closed|Algeria|
|P116194|2010|2010|6/24/2010|1 Closed|Egypt,Arab Republic o|
|P116194|2010|2011|12/22/2010|2 Closed|Egypt,Arab Republic o|
|P116194|2010|2011|5/29/2011|3 Closed|Egypt,Arab Republic o|
|P116194|2010|2012|12/17/2011|4 Closed|Egypt,Arab Republic o|
|P116194|2010|2012|6/29/2012|5 Closed|Egypt,Arab Republic o|
|P116194|2010|2013|5/18/2013|6 Closed|Egypt,Arab Republic o|
|P116194|2010|2014|12/24/2013|7 Closed|Egypt,Arab Republic o|
|P116194|2010|2014|6/16/2014|8 Closed|Egypt,Arab Republic o|
|P116194|2010|2015|12/14/2014|9 Closed|Egypt,Arab Republic o|
|P116194|2010|2015|6/10/2015|10 Closed|Egypt,Arab Republic o|
|P116194|2010|2016|12/21/2015|11 Closed|Egypt,Arab Republic o|
|P116194|2010|2016|6/27/2016|12 Closed|Egypt,Arab Republic o|
|P116194|2010|2017|12/27/2016|13 Closed|Egypt,Arab Republic o|
|P116194|2010|2017|5/30/2017|14 Closed|Egypt,Arab Republic o|
|P116194|2010|2021|7/22/2020|15 Closed|Egypt,Arab Republic o|
|P116199|2012|2012|5/7/2012|1 Closed|West Bank and Gaza|
|P116199|2012|2013|9/24/2012|2 Closed|West Bank and Gaza|
|P116199|2012|2013|5/17/2013<br>|3 Closed|West Bank and Gaza|
|P116199|2012|2014|12/21/2013|4 Closed|West Bank and Gaza|
|P116199|2012|2014|6/16/2014|5 Closed|West Bank and Gaza|
|P116199|2012|2015|12/1/2014|6 Closed|West Bank and Gaza|
|P116199|2012|2015|6/17/2015|7 Closed|West Bank and Gaza|
|P116199|2012|2016|12/30/2015|8 Closed|West Bank and Gaza|
|P116199|2012|2017|7/7/2016|9 Closed|West Bank and Gaza|
|P116199|2012|2017|1/13/2017|10 Closed|West Bank and Gaza|
|P116199|2012|2018|9/29/2017|11 Closed|West Bank and Gaza|
|P116199|2012|2018|5/1/2018|12 Closed|West Bank and Gaza|
|P116199|2012|2019|12/31/2018|13 Closed|West Bank and Gaza|
|P116199|2012|2020|10/25/2019|14 Closed|West Bank and Gaza|
|P116199|2012|2020|6/29/2020|15 Closed|West Bank and Gaza|
|P116201|2012|2012||0 Closed|Morocco|
|P116201|2012|2013|12/9/2012|1 Closed|Morocco|
|P116201|2012|2014|8/3/2013|2 Closed|Morocco|
|P116201|2012|2014|3/10/2014|3 Closed|Morocco|
|P116201|2012|2015|10/11/2014|4 Closed|Morocco|
|P116201|2012|2015|6/8/2015|5 Closed|Morocco|
Figure 11: Fraction of businesses in arrears or expected to fall in arrears.

<!-- Start of picture text --> Unconditional mean<br>100<br>90<br>83<br>79<br>80<br>69 70 71<br>64 68 65<br>60 55 51 51 58 54 57 54 51 52 56 54 50<br>40 38 40 39 41 41 42 40 44<br>24 26 30 27 26 23<br>20<br>12 13<br>6<br>0<br>TCD RUS IDN CYP GRC GEO BGR POL SVN ALB TUR ITA ZMB ROU ZWE GTM SLV XKX TGO NIC SDN GIN TZA PAK JOR MDA HND IND VNM NER GAB LKA SEN KEN MNG NPL BGD ZAF<br>Predicted mean<br>100<br>88<br>80<br>80 74 76 79<br>63 65 67<br>60 49 50 50 51 51 52 52 54 55 58 58 58 59<br>40 36 38 39 39 40 41 41 45<br>23 26 26 27 27 28<br>20<br>13 13<br>5<br>0<br>TCD RUS IDN CYP GRC GEO BGR POL SVN ALB TUR ITA ZMB ROU ZWE GTM SLV XKX TGO NIC SDN GIN TZA PAK JOR MDA HND IND VNM NER GAB LKA SEN KEN MNG NPL BGD ZAF<br>Fraction of businesses in arrears<br>(now or who believe will fall in 6 months)<br>Fraction of businesses in arrears<br>(now or who believe will fall in 6 months)<br><!-- End of picture text --> Note: Predicted probability from a Probit model that controls for country, size, sector, and weeks before and after the peak of the mobility shock.

23
presence of NTMs: We then use the semi-elasticity of trade with respect to a one percentage point increase in the tari¤, �<sup>^</sup> tnij<sup>;toconverttheproportionatechangeinquantityimportedduetoNTMsto</sup> the ad valorem equivalent tari¤: De...nition 1 The ad valorem equivalent of NTM (AV Enij) of product n; in importing country i; from exporting country j; measures the ad valorem tari¤ that induces the same proportionate change in the quantity imported as the presence of NTMnij, or<sup>23</sup> Equation (19) above makes it clear that AVE by construction will have product-importerexporter variation. Variation across products is obvious, because all the regressions are estimated at the HS-6 product level, so all the coe¢ cients will have product variations. The reasons we have variation across importers and exporters are twofold. First, even if NTMs

> 23There are other ways to de...ne the AVE, such as the equivalent tari¤ that induces the same change in the quantity imported, or the equivalent tari¤ that induces the same rate ratio change in the quantity imported. In those cases, the formula for the AVE could be slightly di¤erent. Kee, Nicita and Olarreaga (2009) use the import demand elasticity, "; to obtain the following:

23
Taking Nigeria as a representative case study, we see a strong negative relationship between heat stress and household wealth in the following scatterplot (with data points binned by percentile of the temperature distribution, Figure 4).

<!-- Start of picture text --> Temperature and Household Wealth - Nigeria<br>28 30 32 34<br>Average Daily Temperature of Hottest Month (C)<br>70<br>60<br>50<br>40<br>Percentile of Household Wealth Distribution<br>30<br><!-- End of picture text --> _Figure 4: Average hottest month temperature and Nigerian household wealth by temperature bin. Controls for precipitation, elevation, household size, and urban/rural included._ Among the 38,144 households surveyed, households located in places that are exposed to greater degrees of heat stress within the country have systematically lower levels of aggregate wealth, once again, controlling for size of household, whether or not the household is located in an urban or rural setting, and other climatic factors such as average precipitation.<sup>28</sup> Households located in a grid cell with 1 degree Celsius warmer summer temperature tend to be 6.2 percentiles poorer on average. Nigeria is a hot country with an average temperature of 27.3 degrees C (81.2 degrees F), and as such, one would expect that future climate change would increase heat stress for most if not all households, since most households already reside in areas that are subject to more heat stress than cold stress.

# _Country Case Study: Lesotho (Cold)_

Lesotho illustrates the opposite effect: a positive relationship between temperature and wealth. Among the 9,226 households surveyed, a one degree Celsius warmer hottest month temperature is associated with a 4.3 percentile higher household wealth score. Lesotho is a relatively cold country, with average annual temperatures in the low teens (14.4 degrees Celsius,

> 28 Similar scatterplots for various other countries are presented in the Appendix.

29
-35 -

expense, as a proportion of premiums. The tota! cost to the annuitant is the sum of profit and expense.

To check our estimate of the charges on annuitants we can use information on average spreads provided by Friedman and Warshawsky (1990). They report that in the period 1968-1983, on average the yield on individual annuities was 2.43% below the yield on comparable duration government bonds. Both annuities and bonds pay in dollars (nominal). Considering an average level for long-term yields of 10%, and a duration of 8 years (at age 65, life expectancy was 17.2 years in 1990 for the whole population, including those that do not purchase annuities), this spread implies a discount of 16.37 % over the premium'<sup>7</sup> . This is not far from our accounting number for individual annuities, which was 17.4%.

# Total lifetime costs of private pensions

In this section we produce some hypothetical figures to add together the charges for pension services that a U.S. worker must pay over the life cycle. We attempt to add the costs of defined contribution plans with the charges by life insurance companies, in order to compare them with the costs of defined benefit plans.

A dollar figure for average annuity costs should be based on the active part of life, so that it could be directly added to other administrative costs such as those incurred in defined contribution plans. Comparability also depends of the amount of savings devoted to purchase the annuity.

We will consider a case of a defined contribution plan that saves 10% of the average covered wage to be fully invested in an annuity at retirementL<sup>8</sup> . The 10% savings rate is less than standard, so the resulting figure is biased downward. In this case we apply the contribution rate to the average accounting data on charges for 1989-91, to find the amount that must be put aside while active to pay the cost of an annuity. The resulting figure is 1.74% of the covered wage for individually purchased annuities. For a group annuity, that figure falls to 0.71% of the covered wage. The actual size of the pension will depend of the investment returns, so we do not meet any target

> 17 [(1.10 - 0.0243)/1.1018 = 0.8363 = I - 0.1637

> **18** This is not impossible. In 1984 houscholds whose head was aged _55-64_ held $ 47,900 in **all** types of life insurance both individual and employer-based (ACLI 1992 Factbook. page 38). When considering an inflation of 4% per **year** in 1984-1992 and a real rate of return of 3% real available to the worker for 35 years. we find **that** this sum could have been accumulated by putting aside S3,051 a year. This is .16.5% of the average 1992 annual salary but only 8.2% of the nnuial salary of persons with double average income, which are typical purchasers of insurance.
Hsiang and Jina (2014) The Causal Effect of Environmental Catastrophe on Long‐Run Economic Growth: Evidence From 6,700 Cyclones, NBER Working Paper.

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James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An introduction to statistical learning. New York: Springer, 2013.

<mark>Leong, Airriess, Li, Chen, and Keith (2007) Resilient History and the Rebuilding of a Community: The</mark> Vietnamese American Community in New Orleans East, Journal of American History, 2007.

Muir‐Wood (2014) The use of probabilistic catastrophe risk models to assess the impact of disasters on poverty, World Bank publications, 2014.

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Rossetto T., Ioannou I., Grant D.N., Maqsood T. (2014) Guidelines for empirical vulnerability assessment, GEM Technical Report 2014‐X, GEM Foundation, Pavia Smith M, and Steduto, P, Yield response to water: the original FAO water production function Food and Agricultural Organisation Irrigation and Drainage Paper No. 66. Rome, FAO Souch, C, (RMS) (2010). Post Catastrophe Loss Amplification. International Underwriting Association of London (IUA), 2010.

<mark>Todd, P E., and K. I. Wolpin. "The production of cognitive achievement in children: Home, school, and racial test score gaps."</mark> _<mark>Journal of Human capital</mark>_ <mark>1.1 (2007): 91‐136.</mark> Verma, V, Marchant, T, and Scott, C, (1988). Evaluation of Crop‐Cut Methods and Farmer Reports for Estimating Crop Production: Results of a Methodological Study in Five African Countries. London, Longacre Agricultural Development Centre Ltd World Bank (2013). Pacific Catastrophe Risk Assessment and Financing Initiative – Catastrophe Risk Assessment Methodology, World Bank publications, 2013.

World Bank (2014) Ethiopia Poverty Assessment, World Bank Working Paper.

World Food Programme (2008). FSCB/WFP Workshop Livelihood Early Assessment and Protection (LEAP): its potential application, benefits and limits, NOTE FOR THE RECORD. 21<sup>st</sup> January 2008.

31
<!-- Start of picture text --> Change in<br>Index Value<br>Change in Diversity 3 <025<br>1989 - 2010 HS -0.25 - 0.00<br>HB 0.01- 0.25<br>f<br>Pf Sep MM 0.26-0.50<br>a. |] Ne Data<br>La]= MH > 0.50<br>bY ee Meoy<br>g NCA |<br>Mumba! Po gp yw" F is<br>* * |<br>Bangalorero Chennai sp<br>oa " o<br>, Sf, ‘i<br>Z 4<br>4+ oO 125 250 500: 750 1,000<br><!-- End of picture text -->
A third difference is that while PCRs may compel regulated institutions to supply data, they rarely seem to include data supplied voluntarily from other sources. Of the 57 PCRs surveyed worldwide, only three (France, Slovenia and Taiwan) indicated they received any data on a voluntary basis and in the case of Slovenia, the voluntary data were information received from another government agency. It appears that firms and businesses do not wish to report sometimes sensitive financial data to the government on a voluntary basis in most countries. Important sources of credit data, typically beyond the scope of PCRs, include retail credit, trade credit, microfinance and information from other non-regulated financial institutions such as leasing firms and finance companies or independent credit card issuers.<sup>14</sup> Private credit registries not only seek to expand their access to information from a variety of lenders, but they also dedicate significant resources to obtaining data from other sources, such as court records and diverse public databases, which may not otherwise be readily available.

Another important difference between public credit registries and private credit reporting firms is the distribution (or degree of transparency) of the data. PCRs frequently provide access to data only to those regulated financial institutions that are also compelled to report their data. This practice, known as reciprocity, is followed by 49 of the 57 PCRs in the survey. As a result, not only are non-reporting financial institutions denied any access to the data but also small businesses, non-financial businesses that provide credit and insurance firms, among others, do not typically have access to the data.<sup>15</sup> Limiting access to the data to these groups, which have may have justifiable business reasons for requesting the data including even the data subject’s consent reduces the overall impact of the data and limits the role they can play in contributing to transparency in an economy. In only a handful of countries do PCRs share their data with private credit bureaus; this also restricts the development of the credit information industry. Finally, only 15 of the 57 PCRs in the survey are required by law to allow consumers access to their own credit reports and 29 PCRs explicitly deny borrowers any access – a violation of internationally recognized data protection rules. Not surprisingly therefore, countries with PCRs tend also to

> 14 Argentina’s PCR does include data from credit card companies given on a voluntary basis under an agreement of reciprocity.

> 15 According to survey results, non-financial firms may access PCR data in only 2 countries, insurance firms may only access PCR data in 3 and in general small businesses cannot request reports from the PCR, even with the authorization of the borrower - these remarks exclude the case of Argentina where information was freely available to all on the internet for “punctual inquiries” - inquiries regarding a single individual.

15
Table 11: Placebo Test: Gorontalo (Restaurants)

|**Control vs. POS reci**|**pient resta**|**urants**||
|---|---|---|---|
|**Dependent Variable:**|**(1)**|**(2)**|**(3)**|
|**Log (restaurant tax receipt)**|**RPOS**|**RPOS-**<br>**Grab**|**TMD**|
|ATET (given RPOS vs. not)|0.149**<br>(0.059)|||
|ATET (given RPOS-Grab vs. not)||0.343***<br>(0.105)||
|ATET (given TMD vs. not)|||-0.055<br>(0.098)|
|Constant|0.913***|0.832***|0.939***|
||(0.025)|(0.026)|(0.026)|
|Observations|5,969|5,086|5,201|
|Adj. R<sup>2</sup>|0.001|0.005|0.001|

Robust standard errors in parentheses *** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1 Table 12: Placebo Test: Gorontalo (Hotels)

|**Control vs. POS recipient **|**hotels**||
|---|---|---|
|**Dependent Variable:**|**(1)**|**(2)**|
|**Log (hotel tax receipt)**|**PAC**<br>**Hotel**|**TMD**|
|ATET (given access to PAC Hotel vs. not)|-<br>0.134***<br>(0.017)||
|ATET (given TMD vs. not)||-0.501***<br>(0.025)|
|Constant|0.321***<br>(0.008)|0.371***<br>(0.007)|
|Observations|8,330|8,394|
|Adj. R<sup>2</sup>|0.000|0.019|
|Robust standard errors in parent<br>*** _p <_0_._01, ** _p <_0_._05, * _p <_|heses<br> 0_._1||

26
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emerge with risk and return, the real commodity prices component seems to be positively correlated with US economic growth and the real price of oil.

**Figure 3. Real Commodity Prices and Macroeconomic Determinants**

<!-- Start of picture text --> 6.8 6.8<br>6.4 6.4<br>6.0 6.0<br>5.6 5.6<br>5.2 5.2<br>4.8 4.8<br>4.4 4.4<br>4.0 4.0<br>3.6 3.6<br>-20 -10 0 10 20 .00 .04 .08 .12 .16 .20<br>R RISK<br>6.8 6.8<br>6.4 6.4<br>6.0 6.0<br>5.6 5.6<br>5.2 5.2<br>4.8 4.8<br>4.4 4.4<br>4.0 4.0<br>3.6 3.6<br>-.2 -.1 .0 .1 .2 -0.8 -0.4 0.0 0.4 0.8 1.2<br>DY OIL<br>PC<br>PC<br>PC<br>PC<br><!-- End of picture text --> _Impulse Responses_ Figure 4 presents Impulse Responses from our FAVAR. Impulse responses of commodity prices to real interest rate shock are displayed in the left hand side charts, while the responses to a one standard deviation innovation in risk are presented on the right. We find that a real interest rate shock has a significant negative impact in the first five years, while an increase in risk leads to an immediate and significant decrease in the level of commodity prices during the first four years. Our findings are 19
value approach of delaying irreversible investment decisions when one knows that new information will arrive over time Dixit et al. (1994). The climate change adaptation challenge appears to be a prime example. Future research should explore how the value of holding a real option affects the timing of firm irreversible investments in location specific capital.

# **3.2 Adapting to the Uncertainty of Extreme Rare Events**

There are two components of extreme weather uncertainty: (a) the incidence of uncertainty: regarding where, when, and whether an extreme weather event will occur, and (b) the impact of uncertainty: how severe is the impact of the weather event’s effect conditional on an event occurring.

Business-related uncertainty is known to affect firms (Bernanke, 1983), however, the salience of such uncertainties in the context of climate change are only now being recognized (Fabrizio, 2012; Dorsey, 2019). Uncertainty about future climate shocks affect investments in innovation and technologies as well as decisions regarding other aspects of production such as input choice, location of firm and the sourcing of materials (Jia et al., 2022).

How firms and investors respond to uncertainty related to climate change also has implications for adaptation. Firms’ expectations about future weather events or shocks may influence their decisions concerning sector level innovation as well as the choice of inputs, spatial sourcing of material inputs, location of production and so on which in turn affects their productivity. Yet, these dimensions have been examined mostly in response to actual climate changes rather than uncertainty per say. One exception to this is a study by (Huang et al., 2018) that uses Global Climate Risk Index to capture likelihood of losses from natural hazards at the country level, which was found to be associated with firms’ lower and more volatile earnings and cash flows. These firms also tend to hold more cash and pay less cash dividends, suggesting that more exposed firms tend to hedge more against cash flow volatility and illiquidity due to higher climate risk. Increased climate uncertainty can also affect firms by increasing their insurance premiums. This can manifest through a decline in coverage agreements and, consequently, an increase in insurer exits because of limited revenue streams (Born and Viscusi, 2006), both will inevitably lead to higher insurance premiums.

Trading networks raises the possibility that a spatial shock to one area has a ripple effect that impacts non-treated areas. If a supply chain is not diversified and features a type of O-Ring such that a break in the chain shuts down production, then the whole network is sensitive to extreme disasters and weather events Carvalho et al. (2021). Access to geocoded micro data is creating new opportunities to study how firms adapt to extreme weather events.

14
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