rafmacalaba/datause-encoder-data huggingface dataset:
disaster). We include non-linear combinations of these variables to enhance model fit. We then impute the value corresponding to the category with the highest probability.<sup>8</sup>

# _3.3 Other variables_

We use data from the World Development Indicators (WDI) for country-level indicators such as GDP per capita and population size (World Bank 2024). We derive the distance to catastrophe variable based on the centroid of the event (locations of the plants or the epicenter of the earthquake). We construct the distance to nuclear plants from the respondent’s primary sampling unit (PSU) indicator was constructed based on the coordinates of each nuclear plant in operation compiled by the <u>International Atomic Energy Agency (2023).</u><sup>9</sup> <u>Table 1 provides descriptive</u> statistics of all the variables in the analysis.

# **4. Empirical strategy**

We examine the effects of factors determining the respondent’s evaluation of the number of deaths by estimating the following equation for each disaster _d_ : i i i i i , (2) dd dd dd dd dd ii where, i is a set of individual characteristics such as the respondent’s age, gender, highest level OO = �RRFF −AAFF �= αα XX + γγ GG + θθ + εε , jj= 1, ... ,4 of education, marital status, religion, mental health, and main source of information; i is a vector XX of regional characteristics, such as distance to the disaster site, the distance to a nuclear station GG index, and the type of location (urban or rural); is the time-invariant country _c_ fixed effect, and i is an innovation term. ii θθ εε

> 8 In each estimation, we use 80 percent of the observations with no missing responses for a particular catastrophe question to train the model and evaluate the model’s performance on the remaining 20 percent of observations. We then impute missing responses based on the model out-of-sample prediction. We opted for bootstrapping as our sampling method, constructing 1,000 trees in the forest (bootstrap replications) to optimize prediction performance, as in Lechner and Okasa (2019). Our model consistently outperformed a model in which the most frequent category of each outcome was used as a predictor. The accuracy rate of our model exceeds that of the “most frequent category” model by 8.6 percent for Bhopal, 11.5 percent for Chernobyl, 21.4 percent for Haiti, and 9.9 percent for Fukushima. McNemar’s test (McNemar 1947) rejects the null hypothesis that the “most frequent category” model outperforms our model with 99 percent significance in all four scenarios.

> 9 To assess the closeness of a PSU to nuclear plants, we calculated an index of distances from that PSU to all nuclear plants in the world as a sum of inverted distances: = ∑NN p 1 �� ~~p~~ , where is a distance indicator for PSU _i_ , p pp nnppiinn are coordinates of PSU _i_ and DDnuclear plantii jj=1 ��CC _j_ ii , pp _p_ −CC is distance penalizing parameter, and N is the number of nuclear stations in the world.    jjnnppn DDii CCii and CCjj

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gets and for the common case of multi-programme assessment highlighted by Tohari et al. [2019].

In view of the huge logistical scale of many targeting surveys, sample population surveys are perhaps the most closely related information collection exercise. Looking at their economic aspects, Groves and Heeringa [2006] proposed a responsive survey design to reduce survey cost while maintaining accuracy. Focusing on settings with cost and operational constraints on data collection, Pape and Mistiaen [2018] proposed an effective imputation approach to estimate the population distribution of consumption rather than a full LSMS-style survey. A similar focus on a reduced length survey is Christiaensen et al. [2021], who investigated the use of components of consumption to estimate household values but find both theoretical and empirical problems with this approach.

# **2.2 PMT design**

Three essential design criteria, echoed in Grosh et al. [2022], shape PMT:

- _Accuracy._ Identifying the intended beneficiaries accurately is the essential paradigm of a targeting mechanism.

- _Cost._ The multiannual nation-wide PMT survey sweeps common in low- and lower-middle income countries require major fiscal outlays. The resource needs of on-demand registration, which countries with sufficient administrative capacity tend to invest in, are also high and imply the need for concise surveys that economize enumerator time.

- _Verifiability._ Survey responses determine PMT outcomes, creating a monetary incentive for misreporting personal and household characteristics towards responses that are associated with poverty<sup>2</sup> . As a result, targeting surveys are often restricted to variables that are observable by enumerators, such as physical assets, or that are verifiable via documentary evidence.

In comparison to other commonly used targeting methods such as geographic targeting, community-based targeting, means testing or hybrid means testing, PMT uses easy-to-verify characteristics. It is most appropriate when informality is high and when some form of household specific ranking is desired. At lower levels of informality in an economy, means or hybrid means tests would be more appropriate and yield higher accuracy than a PMT [Grosh et al., 2022].

> 2Banerjee et al. [2020] provide evidence of such strategic behaviour, which creates an unfair advantage for those willing to be dishonest.

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# Contents

Section 1: The Origins of Antidumping Regulation I The political rhetoric of protection 2 The first antidumninq 1Aw: Canada, 1904 **q** Other early laws 6 New Zealand, 1905 6 Australia, 1906 7 Great Britain, 1921 8 Why the outburst of antidumping laws in 1921? 9 The evolution of the U.S. antidumping law of 1921 11 The Sherman Antitrust Act of 1890 12 Section 73, Wilson Teriff Act of 1894 12 Antidumping law of 1916 12 The U.S. Tariff Commission study of 1919 13 Antidumping law of 1921 15 The rhetoric versus the regulation: from antitrust to anti-imports 16 Criteria: what the offense is 17 Standards of proof 18 Early use of antidumping 22 A summing up 23 Section 2: Contempor' ry Antidumping 24 Antidumping and t:e GATT 24 Extension to pricing below full cost 28 Substantive scope 28 Power politics: national 29 Power politics: international 30 Administrative discretion 31 The importance of adjustments 31 Detail brings complexity, not precision 33 Compledity provides cover for abuse 35 Examples of skewed procedures 35 Treatment of selling costs 35 Averaging of home-market price 36 The end result 37 The "new" conception of antidumping 39 Section 3: Summing up the Paper 41 Notes 45
**Table lb. Regional Breakdown of Government Expenditure by Type**

|Type of<br>Government<br>Expenditure by<br>Function<br>in % of Total<br>Expenditure||Regional A|verages||Type of<br>Government<br>Expenditure by<br>Economic<br>Classification<br>in % of Total<br>Expenditure|
|---|---|---|---|---|---|
||OECD|Centra|l Europe|Asia &<br>Latin America||
|||1988/89(*)|1992|||
|Subsidies|2.03|**26**|7|17.96|Subsidies|
|Economic<br>Services|10.59||18.90|16.74||
|Health|12.62||13.20<br>X|<br>7.07l||
|Education|12.35||8.58|15.09||
|Defence|2.54||1.73|11.72||
||13.61|8.08|13.33( )|21.94|Wages|
||6.64|5.62|4.15|17.52|Investment|
|General Public<br>Services &<br>Other|25.33||6.14|7.64||
||43.18|40.52|31.07|18.8|Goods and<br>Services & Other|

Source: Fakin & de Crombrugghe (1996), p.<sup>7</sup> .

Note: (*) Data for Slovenia are non available.

_6_
(formal health insurance holders) and household heads not covered by any health insurance.<sup>10</sup> Green Card holding household heads record, on average, less educational attainment and a higher likelihood of employment in the informal sector than other groups (note that the latter is one of the beneficiary selection criteria as discussed above). In Table 5B, the same variables are presented for the sample of households in the poorest asset quintile only. Here, the difference across insurance types by educational attainment is not as pronounced. The proportion of household heads employed in agriculture and industry are also roughly similar between Green Card holding households heads and those without any insurance. The only difference remains in the proportion of household heads formally employed with 4.3 percent of Green Card beneficiaries holding formal jobs and 21.1 percent of household heads without any insurance. This difference is a function of the eligibility criteria for the card and derives from the targeting process described above.

# **6.1 Non-parametric Estimation**

We use three different estimation techniques with the TWMS to assess the protective impact of the Green Card program during the economic crisis in Turkey. We first employ the below non-parametric comparison between the group of households with access to the Green Card and those that have no health insurance coverage: where Gi is the treatment variable for having access to the Green Card in period t1; Hi is the variable for having access to other health insurance at the household level (through formal sector health insurance _SGK_ or private insurance); and x is log income per capita in the household. The outcome variable is a binary variable that represents the reduction in health utilization between October 2008 and May 2009 and takes on two different variable definitions: (i) reduction in utilization of curative care and (ii) reduction in utilization of preventive care. We consider the responses of households to the following two questions in the baseline data: _(i) “Have you had to reduce visits to the doctor between October 2008May 2009?” and (ii) “Have you had to reduce the utilization of preventive health services_

> 10 Note that private insurance holders make up less than 1 percent of the urban sample and have hence been excluded from the summary statistics in this section.

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# **Table 3. Average MFN tariff rates in Russia in 2001-2005 at industry level**<sup>a</sup>

||||||**_Ye_**|**_ar_**|||||
|---|---|---|---|---|---|---|---|---|---|---|
|**_Industry_**||**_2001_**|**_2_**|**_002_**|**_2_**|**_003_**||**_2004_**||**_2005_**|
||_mean_|_weighted_|_mean_|_weighted_|_mean_|_weighted_|_mean_|_weighted_|_mean_|_weighted_|
|||_mean_||_mean_||_mean_||_mean_||_mean_|
|_Electric industry_|5|5|5|5|5|5|5|5|5|5|
||_.._|_(0.0)_|_.._|_(0.0)_|_.._|_(0.0)_|_.._|_(0.0)_|_.._|<br>_(0.0)_|
|_Oil extraction_|5|5|5||5|5|5.0|5.0|5.0|<br>5.0|
||_(0.0)_|_(0.0)_|_(0.0)_||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|<br>_(0.0)_|
|_Oil processing_|5.2|5.0|5.2|5.0|5.2|5.0|5.2|5.0|5.2|<br>5.0|
||_(1.8)_|_(0.4)_|_(1.8)_|_(0.3)_|_(1.8)_|_(0.3)_|_(1.8)_|_(0.4)_|_(1.8)_|<br>_(0.4)_|
|_Gas_|5||5|5|5||5|5|5|5|
||_(0.0)_||_(0.0)_|_(0.0)_|_(0.0)_||_(0.0)_|_(0.0)_|_(0.0)_|<br>_(0.0)_|
|_Coalmining_|5|5|5|5|5|5|5|5|5|<br>5|
||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|<br>_(0.0)_|
|_Other fuel industries_|5|5|5|5|5|5|5|5|5|<br>5|
||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|<br>_(0.0)_|
|_Ferrous metallurgy_|8.0|8.7|8.0|9.0|8.0|9.9|8.0|11.0|8.0|<br>10.4|
||_(4.8)_|_(5.2)_|_(4.8)_|_(5.3)_|_(4.8)_|_(5.6)_|_(4.8)_|_(6.)_|_(4.8)_|<br>_(5.9)_|
|_Non-ferrous metallurgy_|10.6|12.1|10.6|13.8|10.8|14.5|10.7|14.4|10.6|<br>13.7|
||_(6.)_|_(6.7)_|_(6.)_|_(6.8)_|_(6.1)_|_(6.5)_|_(6.)_|_(6.5)_|_(6.)_|<br>_(6.7)_|
|_Chemicals and_|7.8|8.7|8.0|9.2|8.0|9.2|8.0|9.2|7.9|<br>9.1|
|_petrochemicals_|_(5.1)_|_(5.1)_|_(7.1)_|_(8.4)_|_(7.4)_|_(7.2)_|_(7.5)_|_(6.)_|_(6.9)_|<br>_(5.1)_|
|_Mechanical_|10.5|9.7|11.0|10.3|11.6|10.4|11.0|12.3|10.6|<br>12.6|
|_engineering and metal-_<br>_rki_|_(7.7)_|_(8.5)_|_(15.9)_|_(15.7)_|_(28.3)_|_(11.2)_|_(15.)_|_(11.4)_|_(13.8)_|<br>_(12.3)_|
|_wong_<br>_Timber, wood, pulp and_|14.9<br>|14.0|15.1|14.7|15.5|14.3|15.4|14.2|15.1|<br>13.9|
|_paper_|_(5.5)_|_(7.4)_|_(6.6)_|_(8.5)_|_(7.7)_|_(9.6)_|_(7.5)_|_(9.2)_|_(6.6)_|<br>_(8.5)_|
|_Construction materials_|13.5|14.3|13.6|14.7|13.6|15.5|13.4|15.3|13.4|<br>15.4|
||_(5.2)_|_(5.)_|_(6.5)_|_(4.9)_|_(5.8)_|_(5.7)_|_(5.)_|_(5.)_|_(5.2)_|<br>_(5.2)_|
|_Light industry_|16.6|17.8|17.3|20.9|17.9|19.8|16.2|19.3|15.5|<br>19.5|
||_(19.9)_|_(11.1)_|_(16.5)_|_(13.6)_|_(17.9)_|_(12.)_|_(11.2)_|_(9.8)_|_(8.1)_|<br>_(8.4)_|
|_Food industry_|14.5|16.0|14.9|20.3|16.4|26.1|16.7|23.3|16.3|<br>23.1|
||_(10.9)_|_(11.2)_|_(16.1)_|_(16.6)_|_(17.)_|_(30.5)_|_(18.)_|_(31.8)_|_(19.)_|<br>_(26.6)_|
|_Other industries_|11.7|8.9|12.0|9.5|12.0|9.5|12.1|9.6|12.1|<br>9.0|
||_(7.6)_|_(7.9)_|_(8.7)_|_(8.2)_|_(8.4)_|_(8.3)_|_(8.5)_|_(7.9)_|_(8.8)_|<br>_(7.6)_|
|_Agriculture and_|7.8|8.8|9.2|13.7|9.8|15.7|9.5|14.5|9.1|<br>13.3|
|_forestry_|_(5.8)_|_(6.)_|_(8.)_|_(12.)_|_(9.7)_|_(14.2)_|_(8.6)_|_(12.6)_|_(8.)_|<br>_(11.1)_|
|_Other goods-producing_|18.6|10.0|18.2|6.6|18.3|7.1|17.8|6.4|17.3|<br>9.7|
|_sectors_<br>_Notes: a Mapping from_<br>_b Standard devia_|_(16.)_<br>_10 digit_<br>_tion in p_|_(33.3)_<br>_codes to sect_<br>_arentheses_|_(13.)_<br>_ors is based_|_(24.)_<br>_on Goskoms_|_(14.2)_<br>_tat classi_|_(25.4)_<br>_fication_|_(10.5)_|_(19.6)_|_(8.4)_|<br>_(14.)_|

18
recently. This reduction in the number of participants was reflected in a sharp increase in concentration ratios. As shown in Figure 4, the 3 largest AFPs increased their combined market share significantly and now account for more than 70 percent of total assets. The increase in the Herfindahl index was even more pronounced.<sup>13</sup> By contrast, the fast increase in the number of annuity contracts in the 1990s attracted new entrants to the life insurance market, increasing the total number of life insurance companies to 33 by 2000, 21 of which were providing annuities. At that time foreign participation was substantial, accounting for two-thirds of total capital. The annuity market was very concentrated in its early stages – by the end of the 1980s, the share of the 3 largest firms in the annuity business amounted to 87 percent. The increase in the number of participants in the 1990s led to a continuous decrease in concentration ratios, and one decade later the share of the 3 largest firms had declined to less than 30 percent (about 10 percent each).

**Figure 3: Number of Life Insurance Companies, Annuity Providers and AFPs, 1990-2008**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1990 1995 2000 2005 2008<br>Life Insurance Companies Annuity Providers AFPs<br> Source: SVS<br><!-- End of picture text --> The past decade has been characterized by the active entry and exit of companies from the annuity segment. The annuity market is highly competitive, offers thin intermediation spreads and relatively low returns on equity. The electronic quotation system for annuities introduced in 2004 has contributed to greater transparency and price competition, opening room for some firms to gain market share and encouraging others to leave the market. These factors have resulted in an increase in concentration ratios, whether measured by the 3 firm concentration ratio or the Herfindahl index. However, the annuity sector in Chile today still remains more competitive than the AFP sector, whether measured by the number of participants or concentration ratios. It is also important to note that life insurance companies that decide to exit the annuity market can enter the market again if the conditions prove attractive, that is, the annuity market not only remains less concentrated than the AFP market, but also looks more contestable. In 2008, there were 29 life insurance companies, 18 of which were offering annuities. Some life insurance companies with small market share in the segment of mandatory savings for retirement have found more profitable business in the less competitive market of voluntary savings.

> 13 It should, however, be noted that, attracted by high profit margins, the number of AFPs had increased during the second half of the 1980s from 12 to 20, before the recent trend of consolidation. The latter has been stimulated by the importance of scale economies and the failure of most new entrants to attain critical mass.

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8

**Figure 6: The Thai Cassava Value Chains**

<!-- Start of picture text --> Farmers<br>roots roots<br>starch waste<br>Drying yards Native starch factories<br>normal<br>chips Export (ethanol) China dried native starch<br>or starch slurry<br>clean chips<br>Modified starch factories<br>starch waste<br>Domestic (feed)<br>normal chips<br>starch waste Food industry<br>Pellet plants domestic / export<br>via exporters<br>Non food industry<br>domestic / export<br>Export (feed) EU, etc<br>Starch derivatives<br>domestic / export<br><!-- End of picture text --> _Source:_ Tijaja’s fieldwork interviews

# **_2.2. Market Requirements for Cassava-Based Products_**

There are two established and one emerging export markets for Thailand’s dried cassava (chips and pellets) – the EU and China, and in recent years Korea. Each of these markets has particular trajectories and requirements. The smaller starch export market is more diversified.

# <u>The EU</u>

The origin of Thailand’s dried cassava industry can be traced back to the EU’s Common Agricultural Policy (CAP). Demand for Thai cassava pellets expanded rapidly after the introduction of the CAP in 1962, where the resulting high domestic cereal price triggered the search by EU feed manufacturers for cheaper alternative feed ingredients. Exports of cassava to the EU expanded rapidly, reaching a peak of almost nine million tonnes in 1989. Initially these involved exports of cassava chips, but for a series of reasons, pellets became the dominant and then the exclusive cassava product exported to the EU. Since dried cassava was used in compound-feed production, cleanliness and uniformity of shape and size were important to ease the large-scale mechanized mixing of dried cassava with other feed ingredients. In addition, the distance between EU and Thailand required less bulky products, favoring pellets over chips. Moreover, the transportation of chips is dusty, and in 1978 EU environmental regulation mandated the introduction of a less dusty form of dried cassava, again favoring pellets over chips. Finally, as cassava pellets are used in feed production, imports are governed by the EU farm-to-fork policy, which requires
counterfactuals, we can isolate the endogenous responses of governments to shocks and identify the structural elasticity. Our findings show that over one-third of our reduced-form elasticity is explained by endogenous government responses that decrease default risk. Last, we use our calibrated model to analyze the implications of facing an inelastic bond demand. We find that downward-sloping demand curves act as a commitment device that limits governments’ debt issuances and reduces default risk.

We start our analysis with a simple framework to guide our identification strategy. This setup features heterogeneous investors who differ in how they allocate their funds across risky assets. Specifically, they exhibit differences in their levels of activism and passivism. We define passive demand as the portion of investors’ holdings aimed at replicating the composition of the index they follow. This demand is perfectly inelastic and shifts with changes in the index. For any asset in fixed supply, an increase in the passive demand implies a leftward shift in the “effective supply,” namely the quantity available to active investors. If this shift is exogenous, and under the assumption that asset payoffs are fixed, one can use that variation to examine whether demand curves for active investors slope downward (Pandolfi and Williams, 2019; Pavlova and Sikorskaya, 2022). Nevertheless, if expected payoffs endogenously respond to changes in the effective supply, any observed price variation resulting from the shift could over or underestimate the demand elasticity.

On the empirical front, we identify exogenous shifts in a country’s effective supply of sovereign bonds by using monthly rebalancings in the J.P. Morgan Emerging Markets Bond Index Global Diversified (EMBIGD), the most widely tracked index by institutional investors for U.S. dollar-denominated sovereign bonds issued by emerging economies. Changes in the composition of this index affect the effective bond supply because they lead to similar rebalancings in the portfolios of passive investors. Due to potential tracking error costs, passive investors tend not to deviate from the index. Given the EMBIGD’s popularity, these rebalancings can have market-wide effects.

We derive a measure of flows implied by rebalancings (FIR) by combining the assets passively tracking the EMBIGD with the index’s monthly rebalancings. Qualifying new bond issuances are incorporated into the EMBIGD each month, while maturing bonds are removed. These frequent adjustments lead to changes in country weights within the index, generating passive funds flows. To avoid endogeneity issues, we construct an instrument that exploits changes in the FIR generated by the issuance or retirement of bonds from other countries in the index. As such, these changes are orthogonal to a country’s own fundamentals. In addition, we focus on changes in the face amount of the FIR (as opposed to market value) to 2
<!-- Start of picture text --> Minimum wage compliance ee,<br>Low incidence of child labor ee,<br>Low incidence of relative underpayment fF<br>Low incidence of compensatory moonlighting fF e@<br>Social security benefits | | e<br>Tenure }<br>Low incidence of overqualification |<br>-10 5 0 5 10 15 20 25<br>[| 2014 @ 2019<br><!-- End of picture text -->
that the empirical application presented in Section 7 uses data for a group of Eastern European banks. Most of the banks in Eastern European countries are either national or regional banks, or subsidiaries of foreign banks, rather than international banks. Entries for G10 countries are also provided in the QIS5 study and can be retrieved as required.

**Table 1:** Selected Through-the-cycle Probabilities of Default (PDs) and Losses Given Default (LGDs).

|Asset Class|CEBS-|Group1|CEBS-|Group2|non-G10|-Group2|
|---|---|---|---|---|---|---|
||PDs|LGDs|PDs|LGDs|PDs|LGDs|
|Corporates|2.20|38.1|0.83|35.2|1.47|na|
|SMEs<sup>(1)</sup>|3.26|38.8|3.66|31.7|4.31|49.6|
|Consumer Mortgage Loans|1.52|21.4|1.39|21.4|17.72|40.4|
|Consumer Loans<sup>(2)</sup>|3.69|55.0|2.33|51.9|11.34|55.7|
|Other Consumer Loans|4.33|47.9|2.32|42.2|6.22|45.1|
|Sovereigns<sup>(3)</sup>|0.13|27.7|0.04|38.2|0.24|na|
|Banks<sup>(4)</sup>|0.22|39.4|0.11|39.4|0.74|na|

**Notes:**<sup>(1)</sup> Retail,<sup>(2)</sup> QRE retail,<sup>(3)</sup> loans to public institutions and state-owned enterprizes, and<sup>(4)</sup> loans to credit institutions. Data are taken from BIS (2006).

The main point of reference in our proposed stress testing approach is the steady-state of the macroeconomy which is associated with the equilibrium through-the-cycle PDs and LGDs. We then model departures from this steady-state under the PIT and Stress scenarios using a mapping that relates the changes in overall macroeconomic conditions to the changes in credit risk factors as captured by the PDs and LGDs. This is implemented by estimating the elasticities of NPLs with respect to the four main macroeconomic variables of interest. This systemic component of credit risk factors is discussed in the next section. The role of individual bank characteristics, i.e., the idiosyncratic component of risk factors, is described in Section 4.6.

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