rafmacalaba/datause-encoder-data huggingface dataset:
shock during the mother’s pregnancy (1st, 2nd, or 3rd trimester) counting backwards from the time of the birth.

Between 1999 and 2008, 7.215 million births occurred in Colombia. The analysis sample is limited to rural areas, 1.498 million births (which represent approximately 20% of all births) We also limit the sample to first births, as there are well-known differences in average outcomes by birth order (Currie and Moretti, 2003). This decision yields a potential sample of 425,403 births. We drop information on births with missing maternal and child characteristics, which leaves us with 410,837 observations. We also drop the 4,774 observations with birth at weight below the 1 percentile (less than 1,500 grams) and above the 99 percentile (more than 4,300 grams).

# **2.2 Data on weather shocks**

The climatic data used to measure the presence of severe weather shocks in Colombia are publicly available and come from the University of East Anglia Climate Research Unit (UEA CRU-TS2p1). The data are monthly time-series of temperature and rainfall from 1901 to 2008. The monthly series are available as interpolated gridded data with a spatial resolution of 0.5 x 0.5 degrees (Mitchell, 2005). This data set was spatially merged with a map of Colombia for 2009 with the geographic limits to construct a longitudinal data set with information on precipitation and temperature at the municipality level over the period 1901 to 2008.

In the estimations, binary variables for exposure to a rainy (possibly flood) or dry (possibly drought) event and a hot or cold wave are used to analyze the impact of weather shocks during pregnancy on health outcomes at birth. These variables are based on the standardized precipitation and temperature z-scores, which measure the number of standard deviations from the long-term mean (1901-1997) for each municipality-month pair. The use of standardized precipitation and temperature z-scores to identify weather shocks is supported by extensive applications in the climatology literature (Heim 2002; Keyantash and Dracup 2002). The threshold to identify weather shocks in the main analysis is 0.7/-0.7 standard deviations. A flood (heat wave) in the municipalitymonth of birth is identified whenever the standardized precipitation (temperature) z-score is above 0.7 standard deviations from the long-term mean. A drought (cold wave) month in the municipalitymonth of birth is identified whenever the standardized precipitation (temperature) z-score is below 8
**Table 3. The impact of remittances on real exchange rate misalignment**

||(I)<br>FE|(II)<br>FE|(III)<br>FE/IV1|(IV)<br>FE/IV1|(V)<br>FE/IV2|(VI)<br>FE|(VII)<br>FE|(VIII)<br>FE/IV1|(IX)<br>FE/IV1|(X)<br>FE/IV2|
|---|---|---|---|---|---|---|---|---|---|---|
|Change Remittances|1.29|1.31|10.28|11.14|14.19|1.33|1.33|14.87|14.62|19.83|
|(% of GDP)|_2.30_|_2.31_|_2.66_|_2.65_|_2.27_|_1.69_|_1.68_|_2.36_|_2.42_|_2.05_|
|Change Remittances x LAC||||||-0.08|-0.04|-9.89|-8.23|-11.31|
|(% of GDP)||||||_-0.07_|_-0.03_|_-1.00_|_-0.85_|_-0.86_|
|Interest Rates|0.02|0.02|0.02|0.02|0.02|0.02|0.02|0.01|0.01|0.01|
|(US 6-month rate)|_2.65_|_2.65_|_1.97_|_1.92_|_1.69_|_2.62_|_2.62_|_0.77_|_0.89_|_0.67_|
|Terms of Trade|0.26|0.26|0.24|0.24|0.26|0.26|0.26|0.28|0.27|0.31|
|(Change, %)|_4.04_|_4.07_|_2.40_|_2.31_|_1.98_|_4.03_|_4.05_|_2.36_|_2.34_|_1.93_|
|Government consumption|0.29|0.26|0.67|0.64|0.50|0.29|0.26|0.60|0.59|0.41|
|(% of GDP)|_0.87_|_0.77_|_1.22_|_1.10_|_0.69_|_0.87_|_0.77_|_0.97_|_0.96_|_0.48_|
|Growth||-0.05||-0.13|-0.61||-0.05||-0.07|-0.67|
|(%)||_-0.58_||_-0.89_|_-1.05_||_-0.58_||_-0.44_|_-1.01_|
|Number of observations|250|250|221|221|221|250|250|221|221|221|
|Anderson canon. Correlation (p-val)|||0.05|0.06|0.15|||0.06|0.05|0.09|
|Sargan test of overidentification(p-val)|||0.31|0.44|0.6|||0.39|0.44|0.85|

The table reports the results of regressing the changes in the estimated real effective exchange rate misalignment on the variables in the first column, allowing for fixed effects. IV1 Indicates that remittances are treated as endogenous; IV2 indicates that remittances and growth are treated as endogenous. Instruments are in both cases the level and first differences of (logged) output per capita of the host countries of migrant workers weighted by (i) distance between sending and receiving country and (ii) share of migrants of the receiving country in the sending country. t-statistics in italics. Source: Author’s calculations.

18
hand middle-income countries’ exports tend to concentrate more in less labor intensive extractive and manufacturing sectors. As shows below, however, middle-income countries have a relatively low direct labor intensity of exports even for the same sectors.

In terms of the total LVAX share, the high- and low-income groups are again similar (38% and 36%, respectively) and the middle-income group is at a lower level (32%). Other literature has found that emerging economies tend to export relatively more capital and import labor in value terms, with the opposite pattern for advanced economies (Stehrer et al., 2012). This also suggests that while the three income groups differ in the direct LVAX share, the indirect share via the backward linkages is similar between all groups (between 18% and 19%). Comparing the ratios of direct-to-total LVAX shares shows that the indirect linkages are relatively more important for exporting labor value added in middle-income countries than in low- or high-income countries.

**Figure 3. Direct LVAX share by income group**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>1995 1997 2001 2004 2007 2011<br>High, whole sample High<br>Middle, whole sample Middle<br><!-- End of picture text --> Note: Income group is held constant for the time series and is defined according to the World Bank country group 1995 classification. In the subsample of countries present in the database since 1995, low-income countries are represented by China, India, Sri Lanka and Vietnam; middle-income countries by Argentina, Brazil, Chile, Colombia, Indonesia, the Republic of Korea, Morocco, Mexico, Malaysia, Philippines, Thailand, Turkey, Uruguay and República Bolivariana de Venezuela; high-income countries by Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, New Zealand, Singapore, Sweden, Taiwan, China, UK and USA. Income group for the point comparison is defined according to the World Bank country group 2011 classification.

11
Table 1: Characteristics of the victims of civil war in Nepal

|Total casualties|14982|Political aliation (%)||
|---|---|---|---|
|Killed|13210|Nepali Congress|3.19|
|Disappeared|998|CPN-UML or ML|1.50|
|Injuried|774|CPN Maoist (rebel)|48.32|
|||Other parties|0.91|
|Perpetrator||No aliation|46.07|
|State|9208|||
|Maoist|5302|Occupation (%)||
|Other|472|Agriculture|21.01|
|||Wage laborer|2.27|
|Age (mean)|28.34|Employed|1.47|
|Female (%)|11.10|Teacher|1.68|
|||Police|11.92|
|Social caste (%)||Army|6.53|
|Bramin or Chettrey|44.76|Lawyer|0.05|
|Janajati, Aadibashi or Dalit|46.82|Doctor|0.04|
|Madeshi or Muslim|6.25|Politician|43.89|
|Other|2.17|Social worker|0.16|
|||Rights activists|0.03|
|Education (%)||Sports personality|0.05|
|Bachelor's degree or more|2.61|Driver|0.23|
|Intermediate|7.30|Student|5.50|
|Secondary school|26.26|Journalist|0.03|
|Lower secondary school|21.99|Businessman|1.57|
|Primary school|14.32|Ex-security personnel|0.01|
|Literate|15.05|Other, not clear|3.56|
|Illiterate|12.47|||

Source: Author's calculation based on the INSEC's archive on the conict victims (http: //www.insec.org.np/victim/). Note: While classes 8 to 10 are dened as secondary school, 6 and 7 are lower secondary school. Nepali Congress (Democratic) and Nepali Congress are combined as one as the former was formed due to a vertical split of Nepali Congress into two in 2002. However, the parties merged into one in 2007. Similarly, Communist Party of Nepal - Marxist Leninists (CPN-ML) was reunited with the Communist Party of Nepal - Unied Marxist Leninists (CPN-UML) in 2002 but few members refused to go along the merger forming a new party with the same name. Party's sister organizations and student wings are also accounted for while assigning party aliation.

39
|**Countries with a**|**Table 2:**<br>**Further Extension of the Transition Period for Export Subsidies (S**|**CM Art. 27.4)**|
|---|---|---|
|<br>**WTO Member**|<br>**Notified Programs (bolded if concerning SEZs)**|<br>**WTO Action**|
|Antigua &<br>Barbuda|**_Free Trade/Processing Zones_**_._Fiscal Incentives Act|Extensions<br>granted|
|Barbados|Fiscal Incentive Program. Export Allowance. Research & Development<br>Allowance. International Business Incentives. Societies with Restricted<br>Liability. Export Re-discount Facility. Export Credit Insurance Scheme.<br>ExportFinance Guarantee Scheme.Export Grant &Incentive Scheme.|Extensions<br>granted|
|Belize|**_Export Processing Zone Act_**_. Commercial Free Zone Act._Fiscal Incentives<br>Program. Conditional DutyExemption Facility.|Extensions<br>granted|
|Bolivia<br>(AnnexVII(b))|**_Free Zone_**_._Temporary Admission Regime for Inward Processing.|Reservation<br>of rights|
|Costa Rica|**_Duty Free Zone Regime_**_._Inward Processing Regime. Fiscal Incentives<br>Program.|Extension<br>granted|
|Dominica|Fiscal Incentives Program.|Extension<br>granted|
|Dominican<br>Republic|**_Law to Promote the Establishment of Free Trade Zones_**_._|Extension<br>granted|
|El Salvador|**_Export Processing Zones & Marketing Act_**_._|Extension<br>granted|
|Fiji|**_Export Processing Factories/Zones Scheme_**_._Short-Term Export Profit<br>Deduction.|Extension<br>granted|
|Grenada|Fiscal Incentives Act. Qualified Enterprise Act. Statutory Rules and Orders.|Extension<br>granted|
|Guatemala|**_Free Zones. Industrial and Free Trade Zones (ZOLIC)._** Special Customs<br>Regimes.|Extension<br>granted|
|Honduras<br>(AnnexVII(b))|**_Free Trade Zone of Puerto Cortes. Export Processing Zones_**_._Temporary<br>ImportRegime.|Reservation<br>of rights|
|Jamaica|**_Export Free Zone Act_**_._Export Industry Encouragement Act. Foreign Sales<br>Corporation Act.Industrial IncentivesAct.|Extension<br>granted|
|Jordan|Income Tax Law of 1985, amended.|Extension<br>granted|
|Kenya<br>(AnnexVII(b))|**_Export Processing Zones_**_._Export Promotion Program. Customs & Excise<br>Regulation.|Reservation<br>of rights|
|Mauritius|**_Freeport Scheme_**_._Export Enterprise Scheme. Pioneer Status Enterprise<br>Scheme.ExportPromotion.|Extension<br>granted|
|Panama|**_Export Processing Zones_**_._Official Industry Register.|Extension<br>granted|
|Papua New<br>Guinea|Income Tax Act|Extension<br>granted|
|Sri Lanka<br>(Annex VII(b))|Income Tax Concessions. Tax Holidays & Profits Generated.<br>Concessionary Tax on Dividends. Indirect Tax Concessions-Internal Tax<br>Exemptions. Export Development Investment Support Scheme. Import<br>DutyExemption.Exemption from Exchange Control.|Reservation<br>of rights|
|St. Kitts & Nevis|Fiscal Incentives Act.|Extension<br>granted|
|St. Lucia|**_Free Zone Act_**_._Fiscal Incentives Act. Micro & Small Scale Business<br>EnterpriseAct.|Extension<br>granted|
|St. Vincent and<br>the Grenadines|Fiscal Incentives Act|Extension<br>granted|
|Uruguay|Automotive Industry Export Promotion Regime.|Extension<br>granted|

_Sources_ : Subsidies Enforcement Annual Report to the US Congress (February 2008); WTO notifications.

23
population within each subbasin-country area, using the spatial data from the Gridded Population of the World version 3 (GPWv3), which provides estimates of population density in 2000 (CIESIN 2005).<sup>15</sup>

# **3. Data Analysis and Results**

Our basic econometric model estimates the determinants of the number of dams in an area: in which _Damsij_ is the count of dams in the portion of country _i_ that lies within river subbasin _j_ , _Cij_ is either an indicator for any downstream foreign countries or the count of these countries, _Xij_ is a vector of other characteristics of a country-subbasin, _αi_ is a country fixed effect, _uk_ is a river basin fixed effect, and ε _ij_ is the standard econometric error term. A log-log relationship was chosen for the relationship to allow proportionality between the number of dams and the major variables, especially area.<sup>16</sup>

## **_3.1 Main results_**

Table 4 reports estimates of the coefficients for equation (1). All of the estimated equations include dummies for the country and the major river basin, although these coefficients are not reported.

Standard errors are clustered at the river basin level to address concerns about spatial heterogeneity.

precipitation were small and statistically insignificant in all equations and are not currently shown in the tables. Dams do not appear to be either a substitute or complement for rainfall, once we control for basin and country effects and other covariates.

15 We use the GPWv3 grid that adjusts local population estimates to make them consistent with UN population estimates. We use the population grid for 2000, the most recent one available based on observed data. GPWv3 shows density in 2.5 arc-minute grid cells, which are about 5 km wide at the equator. Superimposing our subbasincountry areas on this grid allows us to calculate population density for each observation.

16 To allow areas with no dams to remain in the analysis, .1 was added to all dam counts before taking logs. In Column 5 of Table 4, the conditional-on-positive estimates, the values revert to the true value of the dam counts in logs. The statements in the text about statistical significance of the downstream-country variables largely remain true if the functional form is linear and the dam count variable is an average geographic density of dams.

10
32

**Table SI-4:** Change in exposure for migrants

||Destination exposure|Diference in exposure|
|---|---|---|
|1(Storm year)=1|-0.002|-0.005|
||(0.003)|(0.007)|
|N|62,877|44,886|
|Fixed Efects:|||
|County|Yes|Yes|
|Year|Yes|Yes|

Notes: All columns report the results of a fixed effects specification. The outcome in column 1 is the IHS transformation of the average total number of storms experienced over our full sample in each of the counties that received migrants from a given sending county in a given year. The outcome in column 2 is the IHS transformation of the average difference in exposure between a sending county and all the counties that received migrants from that county in a given year. We calculate this difference as exposure in the sending county minus exposure in the receiving county for each sending-receiving pair in a given year, then averaged within sending counties and years. A more positive difference indicates that migrants moved to places with lower storm risk. This measure is the outcome in our regressions so a positive coefficient indicates that the average exposure difference is larger and more positive in storm years relative to non-storm years. That, in turn, indicates that in storm years migrants move to places with a greater reduction in risk between the county they leave and where they go compared to migrants who move in non-storm years. Regressions are weighted by the total migrants from a county in each year. Counties are defined as exposed to a storm if at least one hurricane resulted in a flood warning during the year or the county experienced wind speeds of at least 21 m/s - the speed at which structural damage begins to occur - during a hurricane in the year.<sup>_∗_</sup> p=0.1,<sup>_∗∗_</sup> p=0.05,<sup>_∗∗∗_</sup> p=0.01.

**Table SI-5:** Difference in storm exposure in storm-year migration, all storms

||Mean|SE|_t-stat_|
|---|---|---|---|
|Receiving county storms|7.39|4.38||
|Sending county storms|7.35|5.04||
|Diference|0.04||0.46|

Notes: For counties that experience a storm, we calculate the total number of storms that county experiences over our full sample (Sending county storms). In each year a sending county experiences a storm, we calculate the weighted average total number of storms that the counties receiving migrants from that county in that year experience over our sample (Receiving county storms). Receiving counties are weighted based on the number of migrants they receive from a given county in a given year. We report the difference (a positive difference indicating that receiving counties experience more storms on average) and a t-test of the significance of that difference.
**Table 4: Illustration of aggregated cash flows in scenario analysis model in South Africa**<sup>**_17_**</sup>

|**R'billions**|**FY18**|**FY19**|**FY20**|**FY21**|**FY22**|
|---|---|---|---|---|---|
|**Opening balance**|**100,00**|**33,00**|**3,00**|**11,00**|**50,00**|
|Cash from operations|90,00|120,00|170,00|200,00|210,00|
|Cash from financing activities|-127,00|-100,00|-127,00|-101,00|-198,50|
|_Total debt payments needed_|_-130,00_|_-105,00_|_-130,00_|_-100,00_|_-210,00_|
|_Required Repayment loans_|-30,00|-25,00|-40,00|-60,00|-120,00|
|_Required Interest to be paid_|-100,00|-80,00|-90,00|-70,00|-110,00|
|Interest received|3,00|3,00|2,00|2,00|1,50|
|Other financing activities|-|2,00|1,00|-3,00|10,00|
|**Cash available for investment**|**-37,00**|**20,00**|**43,00**|**99,00**|**11,50**|
|Investment activities|-80,00|-100,00|-90,00|-110,00|-60,00|
|Net borrowings|50,00|50,00|55,00|50,00|60,00|
|Cash available to pay debt|**163,00**|**108,00**|**141,00**|**150,00**|**271,50**|
|Required debt payments|-130,00|-105,00|-130,00|-100,00|-210,00|
|Shortfall/Cash remaining|**33,00**|**3,00**|**11,00**|**50,00**|**61,50**|

_Source: NTSA, authors_

# **iii) Modified Merton model**

Robert Merton pioneered structural models for credit risk assessment (Merton, 1977). Structural models assume default of an entity occurs when an entity’s asset value falls below the value of its debt.

A guarantee may be viewed as a put option.<sup>18</sup> A guarantee is a put option on the assets of the firm with an exercise price equal to the face value of the debt. If an entity’s asset values are larger than debt, full repayment of debt can be expected and the put option is not exercised. If, however, an entity’s asset values fall below the value of debt, the creditors exercise the put option. The guarantor has to buy the entities’ assets at the exercise price.

The Black‐Scholes’ theory to price options (Black, Scholes, 1973) can be applied to price the guarantee as a put option. To price the put option, the volatility of assets and growth rate of assets are important inputs. Given that Eskom is fully government owned, no market prices for its equity<sup>19</sup> can be observed, and there is no forward‐looking measure of its volatility. Hence NTSA considered a modification. Historical book values were used to estimate the firm’s volatility and the exercise price for a put option.<sup>20</sup> NTSA considered employing such a modified Merton model to complement credit rating and scenario analysis. NTSA, however, decided against using this model. NTSA assessed the marginal insights gained

> 17 Numbers have been changed and do not reflect the actual situation.

> 18 A put option gives the owner the right, but not the obligation, to sell an asset for a pre-specified price (the exercise price) on or before a certain maturity date.

> 19 Equity prices may be used as proxies for asset values in the Merton model.

> 20 For example, an assumption was made for the value of the debt service coverage ratio at which Eskom would be unable to service debt and hence experience credit distress.

16
is Mexico MFA The difference between the Mexican export F.O.B. price and the U.S. unit value of production at the group level can then be expressed: Data only exist for pjm, pjus, pfm, axid wim. To get around this problem we assume that the<sup>following</sup> relationship holds for each sub-group (MFA category) i within each group j: _(5)_ pj<sup>m</sup> **=** cj + p,jPiUs + P2jXjj + edj . where Xij is a general term for the independent variables included in equation (1): the level of the quota, the utilzation rate, and the Herfindahl index of concentration in export supply. Note that we assume a, Pl, and P2 are constant over all the members of each group j. Solving for pjus and substituting into (4) yields: where yj = cj/plj , 8j = I/Plj , and<sup>**8**</sup> **2j** = P2j/Pjj . With one key assumption we can estimate equation (6) and test the coefficient on pijm to see if the composition effect is statistically significant. The assumption is that<sup>**8**</sup> **2j** = 0 for all j, that is, that the change in the Mexican export price is entirely determined by the change in the U.S. price, and variables such as quota levels, quota utilization, and concentration have no systematic effect. This seems to be a strong assumption, but it is borne out in the aggregate by the results of estimating equation (1) for all groups. Introducing this assumption, the regression equation then becomes: where 00 = (wijm - **8iwiju),** and uj = 8iIZwijuseiJ In order for E(uj) = 0 and the regression to be well specified, we have to make the additional assumption that the E(wije1j) = 0, that is , that the U.S. quantity weights and the error term are independent random variables.
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18