rafmacalaba/datause-encoder-data huggingface dataset:
in regression [2] of Table 2 when controlling for IMF data transparency (SDDS). Our overall results support that countries with higher income per capita tend to exhibit lower spreads. Data transparency interacted with institutional quality (proxied by the ICRG index) reduces sovereign bond spreads significantly while data transparency interacted with public debt increases spreads.

We calculate the benefits of investing in data transparency across Sub-Saharan African countries using the coefficients from regression [4] in Table 2. If the level of data transparency in SubSaharan Africa (measured by the average of the statistical capacity index of the World Bank) improves to that of the top quartile of the middle-income country group, sovereign bond spreads could decrease by 47.99 percentage points. Data transparency, therefore, reduces spreads by 119 basis points and will improve an additional 36 basis points when combined with institutional improvement (as measured by increases in the ICRG index). The total benefits are 155 basis points from improving both data transparency and ICRG together in 2018. The stock of Eurobonds issued in the Africa region by the end of 2018 is about US$82.3 billion and the average spread for the region is 405 basis points in the same year. Therefore, Sub-Saharan African countries could have saved US$9.8 billion in 2018 if they had the level of data transparency of Thailand. The total dollar amount saved from improving both data transparency and institutional quality could have reached US$12.8 billion in 2018.

CPI inflation seems to be another significant pull factor. Higher inflation increases the sovereign spreads (Table 2). Therefore, inflation stabilization and adequate management of inflation expectations by the central bank are important to reduce spreads. Primary balance to GDP and current account to GDP unfortunately did not show any significance in our regressions. Some push factors such as global market volatility, as proxied by the VIX, and the US Treasury bond yield matter: the coefficients of global market volatility are positive and significant while the coefficients of US Treasury bond yield are negative and significant (Table 2). Higher global market volatility and lower US bond yields increase the sovereign spread. Therefore, both pull and push factors matter for sovereign bond spreads.

# _Channels of Transmission_

Table 3 explores whether the different components of data transparency have an impact on sovereign spreads. Similar to the results from Table 2, greater GDP per capita and higher economic growth reduce sovereign bond spreads in developing countries. An inspection of the regression estimates using the components of data transparency shows that, for example, the interaction coefficient between methodology assessment and ICRG as well as periodicity and timeliness assessment and ICRG would contribute to reducing sovereign spreads together. Improving the methodology and periodicity components of data transparency as an integral part of an overall institutional reform is associated with a greater reduction in sovereign spreads.

10
||||Tabl<br>Number of|e 2. Informatio<br>Number of|n on SME Exchanges|Total Caital|Averae|Total<br>Market|Average|
|---|---|---|---|---|---|---|---|---|---|
|Exchange|Launch<br>Date|Target Companies and<br>Startups|Companies<br>Listed since<br>Launch|Companies<br>Listed as of<br>March 2014|Current Sector Breakdown|p<br>Raised since<br>Launch|g<br>Issue Size of<br>an IPO|<br>Cap as of<br>March<br>2014|Market<br>Cap of<br>Issuers|
|NSE India<br>Emerge|2012|No specific sector or type<br>of company targeted. Post<br>issue paid-up capital ><br>$4.16 million. Startups<br>allowed.|5|5<br>(no foreign<br>listings)|80% manufacturing, 20% service<br>industries|$14.4<br>million|$2.9 million|$49.8<br>million|$10<br>million|
|JSE AltX|2003|No specific sector or type<br>of company targeted.<br>Share capital > $200,000.<br>No startups allowed.|106|60<br>(has foreign<br>listings)|71% financials, 15% industrials,<br>7% technology, 5% basic<br>materials, 1% consumer goods, 1%<br>remaining sectors|$2 billion|$18 million|$2.3<br>billion|$38<br>million|
|GreTai|1994|No specific sector, type,<br>or size of company<br>targeted. No startups<br>allowed.|1,133|663<sup>a</sup><br>(22 foreign<br>listings)|70% electronics, 14.5% biotech<br>and medical care, 8.4% traditional<br>business, 7.1% other|$14.1<br>billion<sup>b</sup>|$12.4<br>million<sup>c</sup>|$119<br>billion|$180<br>million|
|BOVESPA<br>MAIS|2005|No specific sector, type,<br>or size of company<br>targeted. Startups<br>allowed.|9|9<br>(no foreign<br>listings)|22% noncyclical consumer, 22%<br>IT, 22% public utilities, 11% basic<br>materials, 11% capital goods, 11%<br>telecommunications|$37.1<br>million<sup>d</sup>|$18.5<br>million|—|—|
|WSE<br>NewConnect|2007|No specific sector or type<br>of company targeted.<br>Issuer capital > $ 0.16<br>million. No startups<br>allowed.|508|442<br>(11 foreign<br>listings)|15% retail, 13% support services,<br>10% financial services, 9% IT, 8%<br>media, 8% technology, 7%<br>construction, 5% health care, 5%<br>investment services, 4% real<br>estate,16%other|$551<br>million<sup>e</sup>|$1.1 million|$3.54<br>billion|$8 million|
|BIST ECM|2009|No specific sector, type,<br>or size of company<br>targeted. Startups are<br>allowed.|23|22<br>(no foreign<br>listings)|9% agriculture, 4.5% apparel,<br>4.5% financial services, 13.6%<br>food and beverages, 54.5%<br>industrial goods and services, 9%<br>investment companies, 4.5%<br>wholesale trade|$102<br>million|$4.4 million|$358<br>million|$16<br>million|

_Source:_ WBG compilation of exchange information. _Note:_ IT = information technology; _— =_ not available.

a. In addition to 663 listed companies on GreTai listed board, 253 companies (seven foreign) are registered with the GreTai Emerging Stock Board.

b. Total capital raised on GreTai from 2002 to March 2014.

c. The average size of an IPO on GreTai listed board between 2011 and 2013 is $10.5 million.

- d. Only two companies conducted an IPO on Bovespa Mais.

- e. Total capital raised through secondary offerings on WSE NewConnect is $595 million.

11
# **3. The Behavior of Capital Flows**

# 3.1 Net vs. Gross Flows

As cross-border assets and liabilities increased, signaling greater financial globalization, a new debate has emerged among academics on whether this growth has been driven by gross or net capital flows.<sup>4</sup> A central question is whether financial globalization is propelled by countries simultaneously buying and selling foreign assets or, instead, by some countries accumulating net foreign asset positions that finance foreign liabilities in other countries.

Economic theory has conjectured that financial globalization would mainly proceed through two different channels: efficient allocation of capital around the world and risk diversification (Fischer, 1997; Obstfeld and Taylor, 2004). On the one hand, standard theoretical models predict that financial globalization leads to resources flowing from rich (North) countries (where capital is abundant and expected returns are lower) to poorer (South) countries (which are capital scarce and have higher returns to capital). These capital inflows would temporarily boost investment and economic growth, permanently raising the living standards in developing economies (Barro et al., 1995; Eichengreen et al., 1998; Henry, 2007; Gourinchas and Rey, 2014). On the other hand, financial globalization allows investors to diversify their portfolio worldwide. Investors could purchase assets from other countries where risk is imperfectly correlated and, therefore, attain higher risk-adjusted rates and higher consumption smoothing, increasing welfare (Obstfeld, 1994).<sup>5</sup> The trends in gross and net capital flows can shed light on the relative importance of these two mechanisms (Figure 2). Gross capital flows in the world have experienced a significant increase. Whereas the median value of average gross capital flows across all countries amounted

> 4 Throughout the paper, we use the definition of capital flows of the International Monetary Fund (IMF). Capital inflows are defined as net acquisition of domestic assets by nonresidents. Capital outflows are defined as net acquisition of foreign assets by residents, excluding reserve assets. Net capital inflows are defined as the difference between capital inflows and outflows (IMF, 2016).

> 5 To be clear, net financing from rich to poor countries could be partly driven by risk diversification motives as well, but for simplicity we omit that aspect from the discussion in this paper.

13
# **Annex I. Operation of Catastrophe Insurance Pools**

**Project management unit.** An important key to success will be establishing implementation arrangements within the government to address each of the steps on the critical path needed to put the pool into operation.  Before legislation is passed and the catastrophe pool is established as a legal entity, the government will have to establish an internal unit or working group with responsibility for managing the project.  This group should have the participation and support of the insurance supervisor and appropriate government ministries, and include  in a consultative process key domestic insurance and reinsurance companies, mortgage lenders and other relevant market participants.  Such a group is likely to be hosted by the Ministry of Finance or by the office of insurance supervision.

**Selection of pool manager and board of directors.** One of the key initial functions of the project management unit will be the selection through a formal procurement process of the pool manager and other advisers that will be needed during the pool design and implementation stage.  The unit could also be involved in recommending to the government the membership of the board of directors.  Once the pool becomes a legal entity, the board of directors has been formed and the pool manager is selected and contracted, then the government unit can revert to providing occasional support and monitoring the performance of the pool manager in project implementation.

The responsibilities of the pool manager should include implementing the policies set by the board; supervising the distribution of policies through insurance companies; managing claims payments; arranging for risk transfer to the global reinsurance and capital markets; and managing the pool’s operations so as to ensure its financial viability. Management of operational matters such as policy processing, information technology, marketing, claims settlements and investments can often be handled most effectively through outsourcing.

**Preparation of business plan and budget.** Subsequent to the selection of the board of directors and managers of the pool, the manager should focus initially on the preparation of a business plan and budget for the implementation phase of the pool, to be approved by the board of directors.

The design and implementation phase will require the completion of a number of demanding tasks, some of which will necessitate the procurement of expert advice. The establishment of a twinning arrangement between the pool manager and an existing catastrophe pool can be a useful source of expertise and assistance. Engaging specialized consultants will be necessary for some activities.

The key tasks that have to be accomplished include:

**Risk modeling and pricing.** It may be necessary to conduct additional risk measurement and analysis studies in order to develop loss estimates to provide the basis for premium and coverage calculations.  Engineering consultants should analyze and quantify the

30
**<u>Table 1: Daily wage rate in Rupees for the week before interview</u>**

|||Mean|St.dev.|Median|N|
|---|---|---|---|---|---|
|Round 1||||||
|All|Public works|82.7|27.4|89.0|54|
||Other casual labor|72.2|31.2|70.0|1031|
|Men|Public works|85.8|25.2|89.0|41|
||Other casual labor|79.1|29.2|80.0|815|
|Women|Public works|73.0|32.6|80.0|13|
||Other casual labor|46.0|23.6|45.0|216|
|Round 2||||||
|All|Public works|94.8|22.3|100.0|118|
||Other casual labor|87.2|42.0|100.0|796|
|Men|Public works|99.4|18.2|100.0|75|
||Other casual labor|97.9|39.3|100.0|574|
|Women|Public works|86.8|26.6|100.0|43|
||Other casual labor|59.4|35.2|50.00|222|

_Note_ : Based on Block 23. We treat wages over 200 or less than 10 Rupees per day as missing values. Wage data are in nominal terms, and reported as unweighted means and medians from the sample. _Source:_ Authors’ estimates based on BREGS Survey.

**Table 2: Average wage rates before and after increases in the BREGS stipulated wage**

|||Public<br>works<br>wage|Round 1<br>Non-PW<br>wage|Stipulated<br>BREGS<br>wage|Public<br>works<br>wage|Round 2<br>Non-PW<br>wage|Stipulated<br>BREGS<br>wage|
|---|---|---|---|---|---|---|---|
|Before increase of||||||||
|stipulated wage|mean|80.7|71.4|89.0|93.8|91.5|104.0|
||s.d.|20.4|30.6|.|24.8|39.8|.|
||N|46|946|.|54|226|.|
|t-test: Ho: mean<br>||-2.8|-17.8|.|-3.0|-4.7|.|
|actual wage = stip.<br>||(0.008)|(0.000)|.|(0.004)|(0.000)|.|
|wage (prob.)||||||||
|After increase of||||||||
|stipulated wage|mean|92.2|74.7|102.0|95.5|83.9|114.0|
||s.d.|32.8|30.1|.|24.3|39.2|.|
||N|20|377|.|81.0|649.0|.|
|t-test: Ho: mean||-1.3|-17.6|.|-6.9|-19.6|.|
|actual wage = stip.<br>wage(prob.)||(0.199)|(0.000)|.|(0.000)|(0.000)|.|

_Note_ : The wage was increased to Rs 102 on June 16, 2009 in R1, then to Rs 104, and to Rs 114 on May 18, 2010 in R2. T-tests use only daily wages greater than 10 and lower than 200 Rupees/day. Wage data are un-weighted. _Source:_ Authors’ estimates based on BREGS Survey.

34
Figure 3. Crises during rapid debt accumulation episodes in EMDEs <!-- Start of picture text --> A. Government debt accumulation episodes B. Private debt accumulation episodes<br>associated with crises associated with crises<br>Percent of episodes Percent of episodes<br>60 60<br>40 40<br>20 20<br>0 0<br>All Banking Currency Debt All Banking Currency Debt<br>Source: International Monetary Fund; World Bank.<br>Note: Figures show the share of government and private debt accumulation episodes that ended in crises, both for all<br>types of crises, and for individual types of crises.<br><!-- End of picture text --> 20
study, the YOLL approach is used both in cases where the hazard has a significant latency period before impact (mortality associated with chronic exposure), and cases where the impact takes place within a short period of time (mortality associated with acute exposure). In estimating the values of mortality arising from chronic exposure to particulate matter we assume that latency and mortality risks are spread out evenly over a period of 15 years and the life time reduction is 5 years on average (ExternE 1999). For mortality associated with acute exposure in the general population we assume no latency and the average life time reduction to be 0.75 years (ExternE 1999). The resulting "value of life-year" lost (VOLY) based on benefit transfer using the PPP approach is reproduced in table 6.5.

**Table 6.5 Value of Life Year (VOLY)**

(in US$, (2010 values in 1999 prices, 3 percent discount rate)<sup>**a**</sup>

||_VOLY mortality_|_-acuteexposure_|_VOLYmortality-_|_chronic exposure_|
|---|---|---|---|---|
||_Male_|_Female_|_Male_|_Female_|
|Incomeelasticity**=**0|184,750|179,776|140,611|138,308|
|Incomeelasticity**=**0.4|131,961|128,409|100,434|98,789|
|Incomeelasticity=1.0|79,660|77,515|60,628|59,635|

a. Using a VSL of 4.28, 3.06, and 1.85 million US$ (2010 values in 1999 prices) after benefit transfer of the European estimate of VSL of 3.36 million US$ (1999 values in 1999 prices) with income elasticity 0, 0.4 and 1, respectively.

b. Differences in values for males and females arise from unequal distributions<sup>of survivalprob-</sup> abilities and life expectancy.

## **Results**

This section presents the main results of the economic valuation of the benefits of improving air quality in Mexico City. A distinction is made between economic health benefits for the air pollution reduction scenarios presented in section 4 and the benefits arising from the reduction in environmental contingencies. Both categories of effects are then aggregated and summarized.

# **_Economic Health Benefits_**

As explained in earlier sections health-related benefits consist of effects resulting from reducing acute morbidity, mortality associated with acute exposure, chronic morbidity and mortality associated with chronic exposure. For the first three categories a straightforward procedure is followed by multiplying the physical health impacts (see section 5) by the monetary values for each unit of health impact (see previous section). The procedure to assess<sup>the</sup> damages from mortality associated with chronic exposure-combining information on life expectancy, age dependent mortality rates, and VOLYs-is more complicated.

_36_
ELL approach, disturbance terms are decomposed into a location-specific effect (usually at the cluster-level) and a household-specific effect. However, in our study, it is important to allow as well for an unobserved individual-specific effect in addition to location- and household-specific effects.

The third difference is related to the second point. The number of children under five in a household is very limited, with no more than two for most of the households. Hence, we cannot rely on large-sample properties when estimating the parameters or distribution of the individual-specific effect. Thus, unlike the ELL approach, we make finite-sample corrections. The fourth point is the number of left-hand-side variables used in this study. In the ELL approach, only a single consumption or income measure is considered, but we consider multiple indicators. Because the unobserved parts of the different indicators may be correlated, this must be taken into account when computing the parameter estimates. We call correlation across indicators the intra-personal effect, and allow for it in the model. Given that weight-for-age is a medium-term indicator of nutritional status, which is partly affected by height-for-age, it would be natural to assume that intra-personal effect may exist. Consequently, our approach involves simultaneous estimation of several models, unlike the single-model procedure applied by ELL (2002; 2003a).

**Parameter Estimation** We denote the set of all clusters by _C_ , the set of all households in cluster _c_ ( _∈C_ ) by _Hc_ and the set of all the children under five in household _h_ ( _∈Hc_ ) by _Ich_ . Let _ychi_<sup>(</sup><sup>_k_)bethe</sup><sup>_k_-th(1</sup><sup>_≤k≤K_)anthropometricindicatorofinterestfortheindividual</sup> _i_ in cluster _c_ and household _h_ .<sup>4</sup> In our particular application, _K_ = 2, with _k_ = 1 and _k_ = 2 being the standardized height and weight respectively, but in general _K_ could be larger. Our goal is to find an estimate of an aggregate index **W**<sup>(</sup> _V_<sup>_k_)</sup> _≡_ **W** ( _{yi_<sup>(</sup><sup>_k_)</sup><sup>_}i∈V_)suchas</sup> the prevalence of malnutrition for a set of individuals _V_ . _ychi_<sup>(</sup><sup>_k_)isrelatedtoa</sup><sup>_d_(</sup><sup>_k_)-vectorof</sup>

> 4A cluster is the primary sampling unit for the survey, which is a village for CDHS 2000.

8
# **<u>Table</u> 6: Safeguard Investigations, 1999-2003.**

|<br>**D.O.**<br>**Initiation**<br>**Date**|<br>**Name of**<br>**Product**|<br>**Measure**<br>**level**|<br>**Country**<br>**of Origin**|<br>**D.O.**<br>**Definitive**<br>**Measure**<br>**Date**|**Measure**<br>**Effective**<br>**Date**|<br>**Measure**<br>**Termination**<br>**Date**|<br>**Measure**<br>**Duration**|
|---|---|---|---|---|---|---|---|
|9 -09- 1999<br>(*)|Wheat, flour,<br>sugar and<br>vegetable oils|Variable|All|22-01-00|22-01-00|26 –11-00|10 months|
|09-02-00|Synthetic fiber<br>socks|13%|All|08-11-00|08-11-00|27-04-01|4 months|
|04-11-00|Wheat, flour,<br>sugar and<br>vegetable oils<br>(**)|Variable|All|25-11-00|25-11-00|27-11-01|12 months|
|21-06-00|Powdered and<br>liquid UHT milk|<br>12%|All|10-01-01|10-01-01|13-07-01|6 months|
|19-04-01|Synthetic fiber<br>socks (***)|6%|All ****|30-04-01|30-04-01|31-10-01|6 months|
|05-04-02|Steel products:<br>hot-rolled steel<br>and wire rod rolls<br>and sheets|10%|All ****|16-07-02|16-07-02|16-07-03|12 months|
|08-06-02|The remaining<br>fructose and<br>fructose syrups,<br>with a fructose<br>content over the<br>dry product<br>higher than 50%<br>in weight|14%|All ****|19-11-02|19-11-02|14-02-03|3 months|

(*) As from this investigation they correspond to safeguard measures.

(**) Safeguard extension dated 22/01/00. For wheat and wheat flour, the measure was terminated by DH 244 exempted D.O. 27/07/01 and for vegetable oils, by DH 559 exempted, D.O. 20/11/01 (***) Safeguard extension dated 08/11/00 (****) Except imports from Canada, Mexico and Peru <u>Source: Central Bank of Chile, www.cndp.cl</u> In the 1999 – 2002 period, seven safeguard measures were adopted (Table 6). The traditional agricultural sector was the main user of the measures, which have been successfully questioned by Argentina and other trading partners in the context of the WTO. These questions led to the creation of an Expert Group to analyze the safeguard measures that Chile applied to wheat imports in the light of the provisions of the WTO Agreement<sup>14</sup> .

> 14 The Expert Group analyzed two different matters: the price bands system that is applied to wheat, wheat flour and vegetable oil imports, and the safeguard measures that benefited these products. SEE WT/DS207/R, CHILE-PRICE BAND SYSTEM AND SAFEGUARD MEASURES APPLIED TO CERTAIN AGRICULTURAL, WTO.

24
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