rafmacalaba/datause-encoder-data huggingface dataset:
- WTO (2006a) “Anti-dumping Initiations: by Reporting Member,” on-line excel spreadsheet available at http://www.wto.org/english/tratop_e/adp_e/adp_stattab2_e.xls , last accessed on 2 November 2006.

- WTO (2006b) “Anti-dumping Measures: by Reporting Member,” on-line excel spreadsheet available at http://www.wto.org/english/tratop_e/adp_e/adp_stattab7_e.xls , last accessed on 2 November 2006.

WTO (2006c) “Recommendations of the Taskforce on Aid for Trade,” WT/AFT/1 (July 27).

- WTO (2005) “Trade Policy Review Body - Trade Policy Review Mechanism - Report of the Trade Policy Review Body for 2005,” WT/TPR/173, November 8.

26
Noy, I., 2009. The Macroeconomic Consequences of Disasters, Journal of Development Economics 88, 221-231.

Okuyama, Y., 2004. Modeling spatial economic impacts of an earthquake: input-output approaches. Disaster Prevention and Management, 13(4):297-306 Okuyama, Y., Hewings, G., and Sonis,M., 2004. Measuring the economic impacts of disasters: Interregional input-output analysis using the sequential interindustry model. In Y. Okuyama and S. Chang (Eds.), Modeling Spatial and Economic Impacts of Disasters. Heidelberg: Springer.

Oosterhaven, J., 1988. On the plausibility of the supply-driven input-output model. Journal of Regional Science, 28(2), 203217 Pelling, M., A. Ozerdem and S. Barakat, 2002, The macro-economic impact of disasters, Progress in Development Studies 2, 283-305.

Raddatz C., 2009. The Wrath of God: Macroeconomic Costs of Natural Disasters, World Bank Policy Research Working Paper 5039, The World Bank, Washington D.C.

Ranger N., S. Hallegatte, S. Bhattacharya, M. Bachu, S. Priya, K. Dhore, F. Rafique, P. Mathur, N. Naville, F. Henriet, C. Herweijer, S. Pohit, J. Corfee-Morlot, 2011. A preliminary assessment of the potential impact of climate change on flood risk in Mumbai, Climatic Change, 104(1), 139-167 Rapp, G. C., 2006. Gouging: Terrorist Attacks, Hurricanes, and the Legal and Economic Aspects of Post-Disaster Price Regulation. Kentucky Law Journal, Vol. 94, p. 535. Available at SSRN: http://ssrn.com/abstract=800745 Romanoff, E. and Levine, S.H., 1977, Interregional sequential interindustry modeling: a prelim46
Table 2: Relationship between management and energy intensity (value).

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|
|General|-0.708<sup>_∗∗∗_</sup>|-0.424<sup>_∗_</sup>|-1.092<sup>_∗∗∗_</sup>|-0.015|0.017|-0.266|
||(0.185)|(0.222)|(0.356)|(0.184)|(0.256)|(0.341)|
|No. Employees||||1.107<sup>_∗∗∗_</sup>|0.920<sup>_∗∗∗_</sup>|0.967<sup>_∗∗∗_</sup>|
|||||(0.129)|(0.171)|(0.218)|
|Log(Sales)||||-1.576<sup>_∗∗∗_</sup>|-1.652<sup>_∗∗∗_</sup>|-1.670<sup>_∗∗∗_</sup>|
|||||(0.098)|(0.130)|(0.172)|
|Capital Stock|||||0.406<sup>_∗∗∗_</sup><br>(0.074)||
|3-digit sector|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|
|Observations|6143|3880|2063|6133|3000|2059|
|Adjusted _R_<sup>2</sup>|0.218|0.125|0.297|0.280|0.223|0.348|
|df<br>~~m~~|118|82|63|120|85|65|

Standard errors in parentheses Capital, labor, and output enter in logs. Management scores are the unweighted average of the z-scores for individual practices. We primarily observe capital input for firms in the manufacturing sector, and therefore have omitted capital from the pooled regression to avoid dropping services firms. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 services firms show the larger coefficients. See Appendix Tables A7 and A8.

Turning to energy intensity in physical quantity terms, we find a striking divergence from the energy expenditure results. Here the coefficient on management score is positive and significant, primarily driven by the manufacturing sector (columns (2) and (5)) (see Table 3. These results suggest that although better management practices enable manufacturing firms to limit the cost of energy use, they may not have the same effect on the consumption of physical quantity. One potential reason is that using greater quantities of energy may help a better managed firm substitute away from increasingly expensive labor, or enable automation, which may be a particular concern for the manufacturing sector. Management efforts in this case may be directed towards optimizing energy purchasing conditions, to 19
we supplement data gathered from Refinitiv with other sources including bond prospectuses, company press releases, available investor relations materials, and second-party opinion reports. Annex A lists the SLB features on which data is collected and their definitions.

**Figure 2: Sustainability-linked bond issuance (US$ Billions)**

<!-- Start of picture text --> 35.00<br> 30.00<br> 25.00<br> 20.00<br> 15.00<br> 10.00<br> 5.00<br> -<br>Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 Q4-2020 Q1-2021 Q2-2021 Q3-2021 Q4-2021<br>Year<br>East Asia and Pacific Europe and Central Asia Latin America and Caribbean<br>Middle East and North Africa North America South Asia<br>Sub-Saharan Africa<br>Amount issued (US$ billions)<br><!-- End of picture text --> In addition to the bond level data, we extract firm level financial data from Refinitiv for each issuer for the calendar year of the year of bond issuance, to be used as issuer-level control variables in the regression analysis. These include total issuer assets (in US$ billions) to proxy for issuer size, firm leverage (defined as total outstanding debt as a fraction of total reported assets) and an indicator for public listing status. These controls are intended to capture variation in monitoring or disclosure across issuers. Previous studies show that public shareholders and creditors may engage in greater monitoring of firms and demand more disclosure around its activities (Fung, 2014; Tagesson, Blank, Broberg, and Collin, 2009; Villiers & Staden, 2010). Likewise, larger firms may also be subject to greater scrutiny (Gamble et al., 1996; Neu et al., 1998; Hasseldine, Salama, and Toms, 2005; Villiers, Low, and Samkin, 2014).

# **4.2. Descriptive Statistics**

This section provides an overview of how KPIs and financial penalties are distributed in our sample. SLB proceeds, rather than being assigned to specific projects, are fungible so as to incentivize issuers to meet firm-level sustainability performance targets. Targets are set as measurable improvements in key performance indicators, which are quantifiable metrics that are pre-defined. A bond may have multiple KPIs, in which case each individual KPI has its own associated incentive/penalty. Our sample contains a total of 322 KPIs for 208 bonds. Figure 3 presents a breakdown of the KPIs in our sample. KPIs related to CO2 emissions are by far the most popular. This is in line with what is observed in green bonds, which also tend to focus on CO2 emissions reduction. Initial SLB offerings tended to focus more on ESG ratings, but this has become less favorable over time given concerns around transparency and 7
The distribution of global mortality due to ambient air pollution for year 2013, is presented in Figure 5 based on World Bank & IHME (2016). As can be seen from the figure, almost 90% of global mortality due to ambient air pollution occurs in developing countries with the greatest number in East Asia & Pacific region, followed by South Asia region. South Asian countries have the highest mortality rate due to ambient air pollution with 136.3, 135.5 and 133.2 deaths per hundred thousand population, respectively in Pakistan, India and Bangladesh in 2015 (Cohen et all. 2017).

**Figure 5. Geographic Distribution of Ambient Air Pollution Related Mortality in 2013**

<!-- Start of picture text --> Middle East and<br>North America, 2%<br>North Africa, 2%<br>Latin America and<br>Caribbean, 3%<br>Central Asia &<br>Easteran Europe,<br>9%<br>East Asia & Pacific,<br>40%<br>Sub-Saharan Africa,<br>11%<br>South Asia, 32%<br><!-- End of picture text --> Source: World Bank & IHME (2016)

# **5.2. Health Impacts of Household Air Pollution**

Household air pollution (HAP) also called indoor air pollution refers to the air pollution that occurs mostly inside a household and is mainly caused by burning of solid fuels, particularly, biomass used for cooking or home heating. WHO (2016) reports that burning biomass and fossil fuels in households is the primary source of indoor or household air pollution from elevated PM 18
performance of the simulated policy options and the financing requirements. The assumptions of the policy scenarios considered here are summarized in Table 6. They are not based on the actual reform proposals of the Government or the regulatory agency, as the Energy Law in Montenegro does not allow subsidizing electricity for particular groups of consumers using non-linear pricing.<sup>17</sup> We include these scenarios for illustrative purposes in order to show how well alternative pricing schemes would fare in Montenegro.

**_Table 6.  Scenarios of electricity_** **_<u>pricing policy reforms</u>_**<sup>**_1_**</sup>

|**Scenario**|**Description**|**Average**<br>**tariff**|**Lower**<br>**tariff**|**Upper**<br>**tariff**|**Threshold**|**Unfunded**<br>**subsidy**<sup>**2**</sup>|
|---|---|---|---|---|---|---|
|**1**|“Business as Usual Scenario” of<br>no price increase while the cost-<br>recovery pricerises|4.85||||32.6<br>million<br>Euro/year|
|**2**|Increasing Block Tariff (IBT)||4.85|7.0|300|14.1<br>million<br>Euro/year|
|**3**|Increasing Block Tariff (IBT)||4.85|8.6|300|None|
|**4**|Volume Differentiated Tariff<br>(VDT)||4.85|7.5|500|None|
|**5**|A linear tariff increase to<br>7c€/kWh combined with a<br>Targeted income transfer of 10<br>Euro/month to current FMS<br>recipienthouseholds|7.0||||2.8 million<br>Euro/year<br>(cost of<br>the FMS<br>transfer)|

Notes:<sup>1</sup> We assume that the current average tariff is 4.85 c€/kWh (including taxes), and it is equal to the cost-recovery tariff. The future cost-recovery tariff in these simulations is assumed to equal 7 c€/kWh.<sup>2</sup> In Scenarios 1 and 2 we calculated from the household survey data that this is the amount that would need to be financed either by the government or the energy company in scenarios where it is not covered by a cross-subsidy from high volume to low volume consumers.

**_What is the fiscal cost of each policy option?_** In the “Business as Usual Scenario” (Scenario 1) we assume that the cost-recovery price rises to 7.0 c€/kWh, but the average electricity tariff remains at the current level of 4.85 c€/kWh. If this subsidy were funded by the government, the fiscal cost of the electricity subsidy to the household sector in this case would be approximately 32.6 million Euros per year. The size of the subsidy would be so large that it would be comparable to the entire social protection budget of Montenegro or its 2004 budget deficit, and this is clearly not be a feasible policy option.<sup>18</sup>

In Scenario 2 all consumers pay 4.85 c€/kWh for the first 300 kWh they consume, and they pay the cost-recovery price (7.0 c€/kWh) for what they consume above this threshold. The fiscal cost of this subsidy would be 14.1 million Euros per year, since in this case only the first 300 kWh of electricity consumption is subsidized. If the

> 17 Personal comm. with the Ministry of Economy and EPCG (February 2006).

> 18The budget of the Ministry of Labor and Social Welfare is about 43 million Euros or about 3% of GDP, out of which about 90% or 38.7 million Euros is allocated to social benefits.  Budget deficit in MN in 2004 was about 32 million Euro or 2.1% of GDP.

13
The lack of employment growth is only one sign of labor market problems in Lithuania and in fact, there are more severe ones. The labor market slack has been substantial and labor supply has declined. Specifically, the unemployment rate peaked at 17 percent, and after some temporary decline in 1997-1999 is on the rise again. A more general measure of labor market conditions, the ratio of employment to working age population (the employment rate), has deteriorated substantially (a 4 percentage points decline) over the period 1995-2000 (Table 2). The labor force participation rate has declined even more (by over 6 percentage points). In addition, unemployment tends to be of long duration as over 50 percent of the unemployed have been jobless for more than a year.

**Table 2 Labor Force, 1995-2000**

||**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|
|---|---|---|---|---|---|---|
|Laborforce participationrate|66.9|65.4|61.5|61.7|61.9|60.4|
|Employmentrate|55.2|54.6|52.8|53.5|53.2|512|
|Unemploymentrate|17.1|16.4|14.1|13.3|14.1|15.4|
|Shareoflong-termunemployed|..|..|..|55|38.7|52.3|

Definitions: Labor force participation rate = labor force/population aged 14 or more ratio.

Employment rate = employment/population aged 14 or more ratio. Unemployment rate = unemployment/labor force ratio. Labor force = employment plus unemployment.

_Source: Laborforce, employment and unemployment 1997-2000 (Laborforce survey results). Statistcs Lithuania,_ **_Vilnius_** _2001._ These negative trends over recent years have led to a significant underemployment of labor resources. The Lithuanian employment rate-a summary measure of the degree of utilization of labor resources-is much below the OECD **2** average. While only 57 percent of persons of working age are employed in Lithuania, the OECD average employment rate is 66 percent (Table 3). This means that about 9 percent of working-age persons in Lithuania who could have been employed are not, and are either unemployed or out of the labor force. This is a considerable social loss, which translates into lower than potential GDP.

> 2 The employment rate is the ratio of employment to working age population, where in line with OECD practice, the working age **is** taken as 15 to 64.

7
# **Figure 14. Comparisons of Alternative Reform Scenarios: Baseline vs.  Scenarios 1 & 2 Revenue excluding grants (BIF bn)**

<!-- Start of picture text --> 1,200<br>1,100<br>1,000<br>900<br>800<br>700<br>600<br>500<br>2012 2013 2014 2015 2016 2017<br>Baseline Lower tax revenue Higher tax revenue<br>Fiscal balance to GDP (%GDP)<br>0.0<br>2012 2013 2014 2015 2016 2017<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>-6.0<br>Baseline Lower tax revenue Higher tax revenue<br>Debt to GDP (% GDP)<br>41<br>39<br>37<br>35<br>33<br>31<br>29<br>27<br>25<br>2012 2013 2014 2015 2016 2017<br>Baseline Lower tax revenue Higher tax revenue<br>Source : The author’s calculation<br><!-- End of picture text --> 21
**Figure 4. Grants in selective countries (percent of GDP)**

<!-- Start of picture text --> 40 2000-03 2004-07 2008-11<br>30<br>20<br>10<br>0<br>Burundi Chad Central DRC Niger Sierra Togo<br>African Leone<br>Republic<br>S ource : World Bank<br><!-- End of picture text -->

**Today, Burundi is at the crossroads.** As the country prepares for the next election in 2015, political and security pressures may make it difficult for the government to implement tight macroeconomic policies. The economic situation is still fragile and maintaining reform momentum is a challenge. Can the government strengthen the fiscal management, prevent the repeated cycles of policy slippage and tightening, and restore and strengthen confidence of the private sector to begin investing in the future prosperity of the country?

**This paper seeks to help the government and the international community think strategically about Burundi’s medium-term fiscal sustainability and priorities for reform.** It analyses key risks to the country’s fiscal sustainability and quantify their likely impact on the budget, in view of  helping the government prioritize policy actions to better manage future risks, build buffers, and protect priority spending on essential public goods to support sustained growth and poverty reduction.<sup>5</sup> To assess fiscal sustainability, the paper will initially draw on the standard inter-temporal sustainability framework, followed by sensitivity, stochastic, and scenario analyses. The standard framework is deterministic, and defines fiscal sustainability as a

> 5 The paper defines the fiscal sustainability as a situation where the government is expected to meet its current and future financial obligations without unrealistically large future corrections in the balance of revenue and expenditure. The current and future financial obligations will include the statutory payments such as debt service payments to the creditors as well as the implicit commitment to continue providing certain public goods, services, and transfers in the future (Hemming and Petrie 2000). Technically, the definition implies that the present value of government disbursements does not exceed the present value of revenues in the medium term (usually defined as 3 to 5 years), and the assessment of the fiscal sustainability relies on simulation of the debt to GDP ratio given specific macro forecast and fiscal policy assumptions for the period.

5
# 1. **Introduction**

Bangladesh is one of the largest consumers of tobacco in the world. Approximately 4 in 10 adults (46 million adults) use some form of tobacco, whether smoked (for example, cigarettes and bidis) or smokeless (for instance, betel, betel nut, tobacco leaf, and so on).<sup>2</sup> Tobacco consumption in Bangladesh varies by gender. Men are significantly more likely than women to smoke (40 percent of men versus 1 percent of women above the age of 15).<sup>3</sup> The prevalence of smokeless tobacco use is similar among men and women (about 30 percent).<sup>4</sup> Smoking is widespread in Bangladesh, but comparable with the prevalence rates in other countries in the region (figure 1).

**Figure 1. Prevalence of Smoking Any Tobacco Product, Individuals Ages > 15 Years**

<!-- Start of picture text --> 76.2<br>39.8 41.4 41.9<br>37.1<br>31.6<br>28.4<br>20.4<br>11.1<br>6.4<br>1.9 0.4 0.7 2.3 3 3.6<br>India Sri Lanka Myanmar Nepal Bangladesh Thailand Pakistan Indonesia<br>Female Male<br><!-- End of picture text --> _Source:_ WHO 2015a.

Information on households from the Household Income and Expenditure Survey (HIES) 2016/17 confirms the high rates of tobacco consumption (both smoked and smokeless). In 2016, approximately 65 percent of households had consumed some type of tobacco product during the month previous to the interview. Comparing across time, the share of households consuming tobacco has been decreasing during the last decade. Using previous rounds of the HIES and comparable measures, one may see that the share of households consuming tobacco was 82 percent in 2005, 71 percent in 2010, and 68 percent in 2016. Yet, this has been accompanied by an increase in the share of households consuming cigarettes from about 23 percent in 2005 to 30 percent in 2016.

> 2 Bidis are popular hand rolled tobacco products smoked in South Asia.

> 3 Note that the true smoking prevalence among women may be greater than estimated because evidence suggests that women (and those who proxy-respond for them in surveys) tend, for social reasons, to underreport risky behaviors.

> 4 Data on tobacco consumption vary in terms of coverage, period, and quality. These numbers come from the 2009 Global Adult Tobacco Survey Bangladesh.

2
young children (less than 9 years) have difficulty comprehending or responding to questions, someone else in the family, usually the mother or elder sister, may assist them (ILO, 2004). Although self-reporting (for respondents of some minimum age, typically 10 years or older) is the established standard for multi-topic household surveys (Schaffner, 2000), in practice proxy respondents are often used when individuals are away from the household working or otherwise unavailable to interview in the time allotted in an enumeration area to conduct interviews. In our survey experiment, the proxy respondent is randomly chosen from among the household members who are at least 15 years old. This age threshold reflects common practice in fieldwork as it is unlikely for an enumerator to choose another child (younger than 15) to be a proxy respondent for children (or adults) in the household. The proxy respondent is thus either the head of household, spouse of the head, or an older child or relative living in the household.<sup>3</sup> The proxy respondent then reports on two other household members who are at least 10 years old. In practice, proxy respondents are usually not randomly chosen but selected on the basis of availability and knowledge of the person for whom they will respond. Although, in this sense, the experiment does not exactly mimic the actual conditions of proxy respondents, the randomization of proxies allows us to investigate whether proxy characteristics may have an effect on the statistics generated.

Tables A1 and A2 in the Annex report the key employment questions in the short and detailed questionnaires and summarize the main features of the two experiments. The full English versions of the labor modules are presented in Bardasi et al. (2010).

Combining each of the above two dimensions in our experiment gives rise to a 2 x 2 randomized design that reflects commonly used approaches in practice: a detailed questionnaire with self-respondents, a detailed questionnaire with proxy respondents, a short questionnaire with self respondents, and a short questionnaire with proxy respondents. We use the results from the detailed self-report questionnaire as the benchmark reference for our analysis. This is generally considered to be the “best practice” approach of household surveys. It corresponds to ILO recommendations, which prescribe a detailed questionnaire with children self-reporting, as well as

> 3 The Tanzanian CWIQ 2006 data indicate that the average Tanzanian household has between two and three adults who could serve as a proxy.

7
of Rohingya outside camps. The IPA population count consisted of a census of unions in seven upazilas of Cox’s Bazar district, which accounted for 76 percent of all unions in Cox’s Bazar district.<sup>12</sup> According to the IPA count, 57 percent of unions in Cox’s Bazar reported a refugee prevalence of less than 10 percent, with 21 percent of unions reporting a prevalence of more than 10 percent. Unions reporting a positive refugee prevalence tended to be larger, covering about 11 villages on average. Figures 1 and 2 present figures on refugee prevalence outside camps at the union and upazila levels. Prevalence is reported starting from the union level (instead of the village), as this was the lowest unit for which complete refugee and host information was available.

**Figure 1:** Distribution of unions in Cox’s Bazar by Rohingya prevalence

**Figure 2:** Prevalence of Rohingya displaced by upazila

<!-- Start of picture text --> 45% 80.00%<br>40% 70.00%<br>35% 60.00%<br>50.00%<br>30%<br>40.00%<br>25%<br>30.00%<br>20%<br>20.00%<br>15%<br>10.00%<br>10%<br>0.00%<br>5% Chakaria Cox's Pekua Ramu Teknaf Ukhia Cox's<br>Bazar Bazar<br>0% Sadar District<br>0% prevalence >0%-10% >10% Unknown<br>Prevalence: Old Rohingya Prevalence: New Rohingya<br>prevalence prevalence prevalence<br><!-- End of picture text --> Source: 2011 census for host population count; IPA population count for Rohingya displaced.

Source: IPA Population Count, 2018. Old Rohingya refers to displaced population arrived before September 2017. New Rohingya refers to population arrived after September 2017.

At the upazila level, Rohingya prevalence was low outside Teknaf and Ukhia, the two upazilas that contain all the camps. Cox’s Bazar Sadar upazila, which is the district capital, was the only upazila where Rohingya prevalence exceeded 5 percent; almost all of these cases came from the older cohort of Rohingya displaced (Figure 2). In Ukhia and Teknaf, which reported a significant Rohingya presence, displaced households seemed to be located almost entirely in camps. In particular, villages with no camps reported prevalence rates of 0.8 and 4.2 percent respectively (Figure 3).

> 12 The IPA count excluded Kutubdia and Maheshkhali upazilas where refugee prevalence was expected to be low as there were islands.

5