rafmacalaba/datause-encoder-data huggingface dataset:
Figure 9.C. Interest rates, confidence, and uncertainty during global recoveries

# A. Nominal interest rate

# B. Real interest rate

<!-- Start of picture text --> Percent Percent 1975 1982<br>15 1975 1982 6 1991 2009<br>1991 2009 Average<br>Average<br>3<br>10<br>0<br>5<br>-3<br>0 -6<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br>C. Business confidence D. Policy uncertainty<br>Index, t = 100 Index, t = 100<br>104 140<br>1975 1982<br>1991 2009 1991 2009 Average<br>103 120<br>Average<br>102 100<br>101 80<br>100 60<br>99 40<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br><!-- End of picture text --> Sources: Bank for International Settlements; Davis (2016); Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-operation and Development; World Bank. Note: Year “t” denotes the year of the respective global recessions (shaded in gray). Average refers to an average of four global recessions with available data. Variables are weighted by GDP in U.S. dollars. Business confidence and policy uncertainty are index numbers equal to 100 in recession years. For details, see Appendix 2.

43
26

**Table 5:** The effect specific types of visa provisions on bilateral migration stocks.

|Dep var:|B|ilateral Stoc|k of Migrant|s|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Dispute Settlement|0.112***<br>(0.034)||||
|Freedom of Movement||0.070**<br>(0.027)|||
|Residency or Citizenship|||-0.032<br>(0.025)||
|Migration formalities||||0.112***<br>(0.034)|
|GDP diference (ln)|-0.001|-0.001|-0.001|-0.002|
||(0.008)|(0.008)|(0.008)|(0.008)|
|RTA|0.027|0.037*|0.052**|0.029|
||(0.019)|(0.020)|(0.020)|(0.020)|
|Schenghen|0.189***|0.182***|0.188***|0.187***|
||(0.039)|(0.039)|(0.039)|(0.039)|
|Estimator|PPML|PPML|PPML|PPML|
|Country-Year FE|Yes|Yes|Yes|Yes|
|Country-Pair FE|Yes|Yes|Yes|Yes|
|Sample period|1990-2020|1990-2020|1990-2020|1990-2020|
|Observations|63,803|63,803|63,803|63,803|
|Pseudo R-squared|0.99|0.99|0.99|0.99|

Note: Dependent variable is the stock of immigrants in country _i_ originating from _j_ . Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.
The estimated regression coefficient on storm intensity from the US analysis and the estimated coefficients o income and population density from the international regression are then used to predict the damages that would be caused by each storm in the generated data set. The global data set of 17,000 storms for both current and future climate is a rich data set that describes the expected value and distribution of tropical cyclone damages. The comparison of the results across the four climate models also provides an additional sense of the uncertainty of the estimates with respect to climate. We use both a minimum pressure damage model and a maximum wind speed model to reflect some of the uncertainty in the damage function. In each case, the damages to each country are estimated and then aggregated.

The analysis first calculates the damages to each country from tropical cyclones with the current economic baseline and current climate. The damages are then predicted for the future using projected populations and economies in 2100. This analysis reveals how much baseline damages will increase because of higher incomes and populations given the current climate.

The second analysis estimates the impact of climate change on tropical cyclone damages. Climate change impacts are calculated as the tropical cyclone damages with a 2100 climate minus the tropical cyclone damages given the current climate, both estimated using future income and population.

The next section of the paper describes the methodology in more detail. The empirical findings of the paper are then reviewed in Section III. The paper concludes with a review of the major findings and a few policy observations.

# II. Theoretical Methodology

The economic damage ( _D_ ) from each tropical cyclone is the sum of all the losses caused by it. In this analysis, we focus primarily on lost buildings and infrastructure. The economic damage of capital losses is the present value of lost future rents. This should be equal to the market value of the building. Note that the market value of capital is often less than the replacement cost.

In order to model tropical cyclones, it is critical to recognize that they are rare events. An important component of expected damages is the frequency or probability ( _π_ ) the 4
less reliant is the U.S. on the targeted foreign country for its own exports (-0.720). In specification (2), the U.S. authorities are also more likely to impose a trade remedy, the larger is the recent surge in product-level imports under investigation (0.107). The only estimates that run counter to the theory are the variables designed to capture the “injury” to the domestic industry (change in employment, change in capacity utilization), though these estimates are not statistically different from zero.<sup>27</sup> Since the estimates presented in table 6a are not of particular concern to the ultimate question of interest regarding the question of whether to formally challenge a U.S.-imposed remedy at the GATT/WTO, we will not further discuss them here.

# **5.2 Regression Equation: Determinants of Foreign Country Decisions to Formally Challenge a U.S. Remedy with a WTO Dispute**

Table 6b provides results from our estimation of the decision of a foreign country to challenge a U.S.imposed trade remedy at the WTO.

## **5.2.1 Expected benefits and costs to dispute initiation**

Consider initially the first three rows of table 6b which describe whether the size of the market access benefits from the U.S. removing the imposed trade remedy are associated with the WTO member’s decision to formally challenge the measure with a dispute. First, the size of lost imports matters. The larger the dollar value of the investigated products in the U.S. import market in _t-1_ (the year before the trade remedy investigation), the more likely is the affected foreign country to challenge the measure (0.056). Since the import data is converted to logs – the size of the effect is not particularly easy to interpret. Nevertheless, the impact of an increase from the mean value of the explanatory variable of 16.857 (the log of roughly $21 million in targeted imports) to 17.857 (the log of roughly $57 million in targeted imports, less than a one standard deviation increase) increased the likelihood of dispute initiation by 5.6 percentage points. Keeping in mind that the model’s predicted probability at the means of the data is roughly a 10% likelihood of WTO dispute initiation, this is a sizable effect.

> 27 A potential contributing explanation for the result that the “injury” determinants are not particularly successful in predicting antidumping decision-making is the _sample_ of U.S. trade remedy investigations used in the estimation here – i.e., we only examine investigations against GATT/WTO members. For example, this implies omitting antidumping and countervailing duty investigations against China occurring before its WTO accession in 2001.

17
trade finance with market shares typically higher than 80 percent (Exhibit II.8).<sup>13</sup> For example, in 2011, European banks were part of deals worth $229 billion—only $57.9 billion did not involve European banks. However, the volume of deals which involved European banks dropped significantly in 2007, 2008, and 2011 when European bank stress conditions soared.<sup>14</sup>

**Other banks took advantage of receding European banks. As a result, volumes of deals** **_without_ European banks have increased although they do not fully fill the gap**<sup>15</sup> . During peak stress conditions when dollar funding was severely curtailed, the market share of deals with European involvement fell steeply for dollar-denominated lending, compared to deals in other currencies (Exhibit II.9). Since 2011, trade finance<sup>16</sup> deal volumes to EMDEs with European bank involvement have weakened significantly, with little substitution from other lenders (Exhibit II.10). While other financial institutions will step in to fill the gap for general syndicated loans, it will be more difficult to do so for specialized finance areas<sup>17</sup> which require more knowhow and patience and typically have longer maturities.

**Despite a fall in volumes, the average maturity of all syndicated deals with European bank involvement to EMDEs held up relatively well during the early stages of the financial crisis, but dropped in 2009 and 2010 and stabilized in 2011** (Exhibit II.11). However, the average maturity of deals with European involvement has been decreasing again. Average maturities of deals without European banks have typically been higher.

> 13 In this paper, a syndicated loan is categorized as “European” if at least one European bank is involved in the deal and “non-European” otherwise. Purely domestic deals and deals with only one lender are excluded. Deals with supra-national agency involvement are included.

> 14 This is consistent with a flight home effect in syndicated markets during financial crises, as documented in Gianetti and Laeven (2012). This effect is distinct from a flight to quality effect which is also active during crises. 15 In 2012, lenders from the Asia-Pacific area were the most active project financiers. Bank of Tokyo led the market with a share of 8 per cent, followed by Sumitomo Mitsui Banking Corporation.

> 16 Following the definition by data provider Dealogic, the trade finance data include structured commodity finance, export-credit agency financing, trade flows, trade financing, and supply chain finance.

> 17 These include project, export, structured commodity, and ship and aviation finance.

11
|ELETROBRAS Distribution Rehabilitation|Christophe de Gouvello|
|---|---|
|ELETROBRAS Distribution Rehabilitation|Christophe de Gouvello|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|Agricultural Productivityand Food SecurityProject|Elisee Ouedraogo|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|The Gambia - Growth & Competitiveness|Kofi-BoatengAgyen|
|Education for All Fast-Track Initiative Catalytic Fund Phase III|Tomomi Miyajima|
|Education for All Fast-Track Initiative Catalytic Fund Phase III|Tomomi Miyajima|
|Education for All Fast-Track Initiative Catalytic Fund Phase III|Tomomi Miyajima|
|Education for All Fast-Track Initiative Catalytic Fund Phase III|Tomomi Miyajima|
|Education for All Fast-Track Initiative Catalytic Fund Phase III|Tomomi Miyajima|
|Education for All Fast-Track Initiative Catalytic Fund Phase III|Tomomi Miyajima|
|Education for All Fast-Track Initiative Catalytic Fund Phase III|Tomomi Miyajima|
|Education for All Fast-Track Initiative Catalytic Fund Phase III|Tomomi Miyajima|
|Education for All Fast-Track Initiative Catalytic Fund Phase III|Tomomi Miyajima|
|Ghana Commercial Agriculture|Sheu Salau|
|Ghana Commercial Agriculture|Sheu Salau|
|Ghana Commercial Agriculture|Sheu Salau|
|Ghana Commercial Agriculture|Sheu Salau|
|Ghana Commercial Agriculture|Sheu Salau|
|Ghana Commercial Agriculture|Sheu Salau|
|Ghana Commercial Agriculture|Sheu Salau|
|Ghana Commercial Agriculture|Sheu Salau|
# **5.  Aggregate WTP for Europe, and Concluding Remarks**

This study reports on average assessments among European environmental valuation experts, of mean population WTP for protecting the Amazon rainforest in their own countries and in Europe as a whole. The answers were provided through two rounds of a Delphi expert survey. In the first round, experts gave independent WTP estimates, which could be changed by experts in the second round after having observed the overall Round 1 results from the entire survey. Mean WTP estimates across all the European experts was approximately 34 €/hh/y, and 29 €/hh/y, respectively, in Round 1 for preserving by 2050, respectively 85 % (Plan A), and 75 % (Plan B), of the original Amazon by 2050; and where the “no-policy” (or business-as-usual) alternative was presumed to imply that only 60% of the original forest will be preserved by 2050. Plan A corresponds to _no further forest loss_ , whereas Plan B implies an additional _10% loss_ of the original forest. In Round 2, in which experts could adjust their initial value answers after having seen the summary statistics from Round 1, mean answers were approximately 32 €/hh/y, and 24 €/hh/y, for Plans A and B respectively.

Experts were also asked to provide similar valuation assessments for an equivalent Europe-wide CV survey. Average answers were then slightly lower than the averages given for their individual countries.

We have also analyzed the relationships between WTP and average national income, in the form of both regular GDP per capita, and PPP-adjusted GDP per capita. Income elasticities were found to lie in the range 0.5–0.8 with respect to regular GDP per capita (from Table 7). This implies that mean WTP increase with regular per-capita GDP of the respective experts’ countries, but at a rate somewhat less than proportional to income. When considering PPPadjusted per-capita GDP, by contrast, income elasticities are overall slightly above unity (mostly in the range 1-1.5, as seen from Table 8). While these results are interesting and plausible, they can however only be considered as suggestive due to the limited number of participants in our survey (with 48 participating experts).

Assuming that our study permits us to add up such values across all European households, and assuming that experts represent their respective populations correctly, our results indicate that the annual WTP among European households for execution of Plan A (after both rounds) could be close to 28 €/hh/y x 300 mill hh, or approximately 8.4 billion €/y, given that experts are correct in their assessments of this population value.<sup>16</sup> This indicates that benefits to distant

> 16 This result is provided under the reservation that experts’ answers need to be weighted with their respective population sizes, which they are generally not. Weighting might bring overall population WTP numbers down somewhat as experts in high income, and high WTP, regions (such as Scandinavia) seem to be over-represented in our sample of experts.

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where _ND_ = number of neonatal deaths averted by Plan Nacer _LBW_ = number of LBW babies averted by Plan Nacer _r_ = discount rate _LE_ = Life expectancy at birth _Dlbw_ = Disability weight for low birth weight The disability weight for low birth weight is an estimate of the loss in value of life from living with the disability associated with low birth weight and provides a common metric for summing the loss due to disability and mortality across disease and injury types.

The DALYs associated with a neonatal death are the present value of life expectancy at birth. Life expectancy at birth in Argentina is 76 years and assuming a 3 percent discount rate, the present value of years of life lost from a neonatal mortality is 30. A child who experiences low birth weight will live with the associated disability for her whole life. The discounted present value of the years of life living with disability associated with is 30 as well. The disability weight associated with low birth weight is estimated as 0.106 (WHO 2004). Then the DALYs associated with a low birth weight birth in Argentina are 3.18.

The number of neonatal deaths averted is estimated as the product of the total number of births from beneficiary mothers and the absolute value of the estimated coefficient on the effect of Plan Nacer on the probability of an in-hospital neonatal death. Between 2005 and 2008 there were 104,748 beneficiary mothers of Plan Nacer giving birth in the seven provinces under analysis.<sup>13</sup> Hence we estimate that Plan Nacer averted 773 neonate deaths with a 95% confidence region of 117 to 1,349. Then the DALYs associated with neonate deaths averters are 21,997.

The number of LBW babies is estimated as the product of the total number of births from beneficiary mothers and the absolute value of the estimated coefficient on the effect of Plan Nacer on the probability of a low weight birth from Table 8. Recall that half of the averted neonate mortality was through a reduction in low birth weight babies. Hence we need to reduce the estimated number of LBW babies averted by the number of neonate deaths averted due to preventing LBW to avoid double counting. We estimate that the number of LBW babies averted adjusted for neonatal mortality to be 1,071 with a 95% confidence region 171 - 1,970. Then the total number DALYs averted through preventing LBW is 3,404.

> 13 See Informes de Gestion, 2005-2008, total beneficiaries by December each year.  Total averted deaths are calculated as 733 = 104,748*0.007 for the point estimate.

30
America and the Caribbean, 21 countries from Eastern Europe and Central Asia and 6 countries from North Africa (Turkey is grouped here). Note that for ECA the data may overstate export growth since Russia does not appear as an importer. Redirection of exports away from Russia to new destinations will appear as export growth. Bearing this in mind, Africa was the region that experienced the lowest growth of exports. Of the 9 countries in the sample for which exports contracted 8 are in Africa. The average growth of exports across African countries is lower than in any other region.

# **Decomposing export growth on the intensive and extensive margins**

We decompose total export growth (the sum of exports to the 102 importers) between the two years as _Change in total exports =_ _(Increase in exports of existing products to current markets_

- _decrease in exports of existing products to current markets_

Intensive Margin }

- _extinction of exports of existing products to current markets)_

+ _(new exports of existing products to new markets_ _+ new exports of new products to existing markets_ Extensive Margin }

- _+ new exports of new products to new markets)_

Hence, we identify the extent to which existing bilateral flows have changed; intensifying, declining or becoming extinct (appearing zeros). This we define as the intensive margin.

We also identify the cause of disappearing zeros in the matrix; whether due to existing products being exported to new markets, a new product being exported to an existing market, or a new product being exported to a new market. This is defined as the extensive margin.

Figure 1 presents this decomposition for the aggregate of our 99 developing countries. This is the sum across exporters of the change in exports of each element divided by the change 8
POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA

# **7.** **_Annex_**

||SNA|TA|BLE A1 — KE<br>Poverty-weig|RNEL STABI<br>hted PPPs:_Ke_|LITY,28 CO<br>_rnel based_vs.|UNTRIES<br> _Below IPL_|||
|---|---|---|---|---|---|---|---|---|
||-<br>based vs.<br>Below<br>IPL PPPs|Uniform;<br>Bandwidth<br>=1.0|Uniform;<br>Bandwidth<br>=0.5|Uniform;<br>Bandwidth<br>=0.25|Bi-weight;<br>Bandwidth<br>=1.0|Bi-weight;<br>Bandwidth<br>=0.5|Bi-weight;<br>Bandwidth<br>=0.25|SD for kernels|
|Burkina Faso|1.0398|1.0046|1.0041|1.0048|1.0043|1.0041|1.0040|0.031%|
|Burundi|1.0204|1.0146|1.0160|1.0165|1.0154|1.0159|1.0172|0.081%|
|Cameroon|0.9671|0.9923|0.9931|0.9948|0.9927|0.9944|0.9951|0.109%|
|Cabo Verde|1.0332|1.0028|1.0014|1.0017|1.0018|1.0007|0.9968|0.192%|
|Congo, Rep.|0.9848|0.9960|0.9978|0.9983|0.9973|0.9985|0.9979|0.081%|
|Congo, D.R.|0.9835|0.9865|0.9870|0.9878|0.9868|0.9870|0.9823|0.180%|
|Côte d'Ivoire|0.9980|0.9992|0.9996|0.9998|0.9994|0.9994|0.9992|0.021%|
|Ethiopia|0.9632|0.9968|0.9968|0.9951|0.9966|0.9958|0.9951|0.073%|
|Gabon|0.9540|0.9867|0.9868|0.9947|0.9889|0.9920|0.9977|0.409%|
|Ghana|0.9418|0.9872|0.9870|0.9895|0.9872|0.9887|0.9910|0.148%|
|Guinea|0.9570|0.9938|0.9950|0.9962|0.9944|0.9955|0.9965|0.094%|
|Kenya|1.0144|1.0019|1.0014|1.0021|1.0018|1.0018|1.0030|0.051%|
|Liberia|0.9791|0.9829|0.9818|0.9794|0.9821|0.9800|0.9776|0.183%|
|Madagascar|0.9767|0.9956|0.9949|0.9954|0.9952|0.9952|0.9968|0.062%|
|Malawi|1.0382|0.9914|0.9920|0.9901|0.9916|0.9905|0.9898|0.083%|
|Mali|1.0296|1.0077|1.0059|1.0042|1.0055|1.0044|1.0044|0.122%|
|Mozambique|1.0300|0.9988|0.9975|0.9967|0.9980|0.9969|0.9964|0.083%|
|Namibia|0.9820|0.9878|0.9888|0.9882|0.9887|0.9889|0.9896|0.055%|
|Niger|0.9978|1.0019|1.0025|1.0026|1.0027|1.0030|1.0025|0.033%|
|Nigeria|0.9589|1.0031|1.0043|1.0007|1.0027|1.0026|0.9997|0.153%|
|Rwanda|1.0270|1.0317|1.0347|1.0360|1.0340|1.0359|1.0380|0.194%|
|Senegal|1.0102|1.0027|1.0028|1.0026|1.0028|1.0027|1.0023|0.016%|
|South Africa|1.0759|1.0079|1.0063|1.0061|1.0068|1.0061|1.0053|0.079%|
|Swaziland|0.9946|1.0010|0.9988|0.9972|0.9994|0.9984|1.0006|0.129%|
|Tanzania|1.0084|0.9995|0.9995|0.9965|0.9993|0.9979|0.9970|0.121%|
|Togo|0.9998|1.0069|1.0068|1.0057|1.0069|1.0062|1.0058|0.050%|
|Uganda|1.0330|1.0023|1.0014|1.0009|1.0017|1.0012|1.0008|0.051%|
|Zambia|1.0155|1.0180|1.0177|1.0176|1.0176|1.0177|1.0193|0.061%|

_Notes_ : All ratios are based on country PPPs normalized with respect to the regional geometric mean. _Source_ : Authors’ calculations.

16