rafmacalaba/datause-encoder-data huggingface dataset:
There are certainly many contributing factors to the resulting emphasis of research focused on the US antidumping process, for example. First, the United States is one of the most frequent “users” of the policy instrument. Until the mid-1990s, there were only four primary users of antidumping was– the US, EU, Canada and Australia. A second explanation for focus on the United States is the economic size of the market at stake and the value of imports affected by the policies. A third factor is that there is also readily available complementary data required for political-economic empirical analysis, such as data on product-level trade flows, disaggregated industrial level economic activity, and even political activity such as lobbying expenditures or political influence on Congressional committees that may affect the process.<sup>5</sup> In addition to these explanations, we believe that a fourth factor is an important determinant of substantial research on the US antidumping process relative to other countries: policy transparency and data availability. Researchers are more likely to scrutinize a policy when detailed information as to how it is being implemented is publicly available, which is the case for the United States’ use of antidumping.<sup>6</sup> Furthermore, since 2000, Bruce A. Blonigen at the University of Oregon has provided an additional boost to research in this field by posting on the Internet a collection of well-organized and comprehensive Finally, papers examining the ramifications of antidumping on international trade flows use include Bown and Crowley (2005a, 2005b, forthcoming) and Prusa and Durling (forthcoming).

5 For example, historical data at high frequencies (e.g., monthly) on product level (at the 10-digit Harmonized Tariff Schedule) trade flows is publicly available in the United States from the US International Trade Commission (http://dataweb.usitc.gov/ ). See also Feenstra, Romalis and Schott (2001). Furthermore, historical data on industry level indicators of economic activity at the 4-digit Standard Industrial Classification (SIC) or 6-digit North American Industrial Classification System (NAICS) is also available from surveys posted by the US Census (http://www.census.gov/mcd/index.html ). See also Bartelsman, Becker and Gray (2000).

> 6 Information on US antidumping investigations is published in the US government’s _Federal Register_ publication, which can be accessed on line at <u>http://www.gpoaccess.gov/fr/</u> . The US antidumping cases follow a numerically chronological categorizing process which makes tracking a single case over time through government records very straightforward. While information on European Union antidumping cases can similarly be found in its publicly - available government publication titled the _Official Journal_ (see <u>http://europa.eu.int/eur lex/en/oj/), its case</u> categorizing process is not as transparent so as to allow an individual only minimal effort to be able to track the process of a single case over time.

2
**Table 9: Latent Class Preference Estimates**

||**Class**<br>**(“Stabil**<br>**Seeking**|**1**<br>**ity**<br>**”) **|**Class**<br>**(“Incom**<br>**Seeking**|**2**<br>**e**<br>**”) **|**Class**<br>**(“Long-T**<br>**Planner**|**3**<br>**erm**<br>**s”) **|
|---|---|---|---|---|---|---|
||Coeff<br>||Coeff<br>||Coeff<br>||
||(se)<br>(1)|WTP<br>(2)|(se)<br>(3)|WTP<br>(4)|(se)<br>(5)|WTP<br>(6)|
|Contract -6months|2.62***<br>(0.49)|44.4|0.55***<br>(0.19)|5.7|0.86***<br>(0.25)|29.7|
|Contract - 1year|3.87***<br>(0.84)|65.6|1.24***<br>(0.19)|12.8|1.05***<br>(0.19)|36.2|
|Contract - long-term|6.27***<br>(0.84)|106.3|1.83***<br>(0.54)|18.9|2.2***<br>(0.25)|75.9|
|Notice|0.017***<br>(0.003)|0.29|0.019***<br>(0.002)|0.20|0.034***<br>(0.004)|1.17|
|Hours|-0.028***<br>(0.01)|<br>-0.47|-0.041***<br>(0.006)|-0.42|-0.028***<br>(0.005)|-0.97|
|Leave|0.004<br>(0.018)|0.07|0.056***<br>(0.008)|0.58|0.018<br>(0.011)|0.62|
|Provident Fund|0.73<br>(0.78)|12.4|0.75***<br>(0.23)|7.7|1.64***<br>(0.31)|56.6|
|Perc. ChangeIncome|0.059**<br>(0.029)|1.0|0.097***<br>(0.007)|1.0|0.029**<br>(0.013)|1.0|
|Class share|0.198||0.467||0.335||
|# (%) workers assigned<br>class|to<br>367<br>(18.7%)||949<br>(48.3%)||648<br>(33.0%)||

_Source_ : Authors’ analysis based on survey data.

_Note_ : WTP= willingness to pay. Results from a latent class analysis. Sampling weights are not applied. Omitted level for contract is none. Omitted level for Provident Fund is none. *, ** and *** represent statistical significance at the 10%, 5% and 1% levels, respectively.

49
NRM programs and projects, it is possible to support gender inclusion by making the communications and outreach strategy fit better the needs and motivations of these groups.

# **Notes and Acknowledgments**

This work was supported by the Climate Investment Funds (CIF) Evaluation and Learning Initiative and the Forest Carbon Partnership Facility (FCPF), which provided finance, time, and intellectual support.

The authors would like to thank Anne T. Kuriakose, Hanna Schweitzer, Joseph Dickman, Ines Angulo, Ezgi Canpolat, Eirini Pitta, Rahul Madhusudanan, Dora Cudjoe, and Emmanuel Kouadio of the CIF Administrative Unit for their support throughout the preparation of this study. The authors would like to thank Kilara C Suit from FCPF. The authors also thank the World Bank colleagues Lauren Manning, Abigail Dalton, Lucia Madrid Ramirez, Gabriela Guerrero Pacheco, Ana Maria Muñoz, Angela Armstrong, Hugo Cardenas, Valeria Hickey, Ximena Del Carpio, Loli Arribas-Banos, Jutta Kern, and Gregor Wolf, for their support and comments to the paper. From the National Laboratory of Public Policy at Centro de Investigación y Docencia Económicas (CIDE), the authors would like to thank Marina Benitez Kanter, Mayra Cabrera, Jorge Sosa, and Axel González for their support in the design, preparation and implementation of the RCT.

The team thanks Rainforest Alliance México-Alianza para Bosques A.C. (Rainforest Alliance) for providing data, comments on ideas and drafts, and their overall support.

# **References**

Agarwal, B. (2018). Gender equality, food security and the sustainable development goals. _Current opinion in environmental sustainability_ , 34, 26-32.

Armstrong, A.G., K. Siegmann, G. Alonso Mendieta, G. Reyes-Rentana, (2018). United Mexican States - Programmatic Forest Country Note (Spanish). Washington, D.C. : World Bank. http://documents.worldbank.org/curated/en/639041556249435926/United-Mexican-StatesProgrammatic-Forest-Country-Note Baland, J.M.; Bardhan, P.; Das, S.; Mookherjee, D.; Sarkar, R. (201= The environmental impact of poverty: Evidence from firewood collection in rural Nepal. _Econ. Dev. Cult. Chang._ 2010, _59_ , 23–61 Comisión Nacional Forestal (CONAFOR) (2017) _. Informe de Medio Término Proyecto Bosques y Cambio Climático_ . Document Técnico CONAFOR. Jalisco, Mexico.

Comisión Nacional Forestal (CONAFOR) (2020). Estimación de la tasa de deforestación en México para el periodo 2001-2018 mediante el método de muestreo. Documento Técnico CONAFOR. Jalisco, México.

Diario Oficial de la Federación (DOF) (2016). Acuerdo por el que se emiten las Reglas de Operación del Programa de Fomento a la Economía Social para el ejercicio fiscal 2017. DOF, México.

FAO (2018). _The State of the World’s Forests_ . Forest pathways to sustainable development. Rome.

19
**_Economic structure of South Africa._** The CGE model has 43 production activities.<sup>6</sup> For reporting purposes, the output results by activity are aggregated into three categories: agriculture, industry, and services (see table 1 for the composition of the aggregate categories).<sup>7</sup>

**Table 1:  CGE Model Sectors**

|**AGRICULTURE**|**INDUSTRY**|**SERVICES**|
|---|---|---|
|Agriculture|Coal Mining|Electricity & Gas & Steam|
||Gold & Uranium Ore Mining|Water Supply|
||Other Mining|Construction & Civil Engineering|
||Food|Catering & Accommodation|
||Beverages & Tobacco|Wholesale & Retail Trade|
||Textiles|Transportation & Storage|
||Wearing Apparel|Communication|
||Leather & Leather Products|Financial Services|
||Footwear|Business Services|
||Wood & Wood Products|Health & Community & Social & Personal Services|
||Paper & Paper Products|Other Producers|
||Printing & Publishing & Recorded Media|Government Services|
||Coke & Refined Petroleum Products||
||Basic Chemicals||
||Other Chemicals & Man-Made Fibers||
||Rubber Products||
||Plastic Products||
||Glass & Glass Products||
||Non-metallic Minerals||
||Basic Iron & Steel||
||Basic Non-ferrous Metals||
||Metal Products Excluding Machinery||
||Electrical Machinery||
||TV & Radio & Communication Equip||
||Professional & Scientific Equip||
||Motor Vehicles Parts & Accessories||
||Other Transport Equipment||
||Furniture||
||Other Industries||

Source: CGE model social accounting matrix (SAM) database.

Agriculture accounts for 4 percent of value added, industry accounts for 27 percent, and service accounts for 69 percent (Figure 1).

> 6 Full detail of the South Africa CGE model can be found in Essama _et. al._ (2007) and Kearney (2004); for a version of the model used to analyze Value Added Taxes (VAT) see Go et al. (2005). In this description we comment on new features of the model important for an analysis of a wage subsidy.

7 Note, we disaggregate crude oil from other mining, as described in Essama et al (2007).

- 8 -
(i.e., the proxy for the innovation frontier) are able to “escape” competition from new entrants when faced with the threat of entry. They innovate to become more competitive and reap productivity gains. Firms that are farther from the technological frontier, in turn, may be hurt by competition. In recent studies of firm behavior, some researchers have found evidence that supports this argument.

<!-- Start of picture text --> Figure 2: China and India in the catch up game<br>China: trying to close the gap - GDP pre capita (constant 2000 USD $)<br>1600<br>1400<br>Lower middle incom e<br>1200<br>1000<br>800<br>600 China India<br>400<br>200 Low income<br>0<br>India China Lower middle income Low income<br>Source: WDI<br>19651967196919711973197519771979198119831985198719891991199319951997199920012003<br>Constant 2000 USD<br><!-- End of picture text --> In this paper, we analyze how much the BICs are catching up with Korea and why. Our analysis is cast in the setting of a globalizing world market in which countries compete at the level of the industry to grow their per capita incomes and catch up. This makes sense. As tariffs, other trade barriers to imports and government policies for industrial development are usually applied at the level of an industry; hence, an industry-level analysis has policy relevance. In particular, we are interested in understanding the role of export orientation in a country’s catch up. We are also interested in evaluating whether domestic market-oriented production (i.e., import-substitution) has penalized productivity growth. We find three measures of competitiveness useful in analyzing the catch-up “game”: labor productivity, revealed comparative advantage (RCA) and the capability to export more high tech industrial products similar to those in the OECD export basket.

Aghion et al.’s new Schumpeterian paradigm provides the theoretical setting for our analysis. Its attention to the dynamic between competition and “distance” lends itself to our study of how trade liberalization and innovation affected the catch up of the BICs with Korea. Specifically, we apply industry level data (ISIC2 – 3 digit) to analyze why some industries in some BICs are catching up with Korea while others are not. Catch up is measured by growth in labor productivity as well as the level of labor productivity which serve as a proxy for total factor productivity.<sup>4</sup> Conventionally, the latter is considered a proxy for innovation. As Korea is the benchmark, its labor productivity is treated as the technological frontier. The distance from

> 4 In the absence of data on the stock of capital at the industry-level, we use labor productivity as a proxy for total factor productivity. This is a reasonable assumption and can be verified from the Investment Climate Assessment surveys of enterprises for Brazil (2002; 1485 firms), China (2002; 1153 firms) and India (2004; 1420 firms). An analysis of the survey data by industry as well as for the whole manufacturing sector using various definitions of TFP indicates that there is a strong correlation between value added per worker and TFP in the BICs.

4
European Community acting through financial interventions in dairy markets.<sup>25</sup> With the massive drop in global trade flows in late 2008 and early 2009, a number of governments (e.g., those of Brazil, India, China and Malaysia) intervened to shore up credit markets by providing export financing assistance.<sup>26</sup> There is also the concern about competitive devaluation of currencies, especially those that may be giving an advantage to exporters in one country at the expense of exporters (and competing local producers) in other economies. Finally, indirect evidence of increased subsidy use is also consistent with the trade remedy data observed and described in the last section (Bown, 2010), and in particular the increasing resort to countervailing measures being sought by industries that perceive they are being injured by unfair foreign subsidies that have affected their exports.

Subsidies are but one example of ways in which governments may have imposed WTO-inconsistent behind-the-border measures during the crisis, which have significant impacts on developing country exporters. The lowest set of rows in table 2 indicate that the GTA has found numerous other examples of new government policies such as export 25 See Global Trade Alert ‘United States of America: Support for General Motors and Chrysler,’ Measure #0274, Published 16 July 2009; Global Trade Alert ‘United States of America: Expanded bailout to mortgage guarantors,’ Measure #1048, Published 29 December 2009; and Global Trade Alert ‘EC: Measures to “stabilize” markets for certain dairy products,’ Measure #0653, Published 8 September 2009.

26 See Global Trade Alert ‘Brazil: Public financing for the production of goods for exports by small and medium companies (pre-shipment phase),’ Measure #0715, Published 16 September 2009; Global Trade Alert ‘Malaysia: Services Export Fund,’ Measure #0697, Published 10 September 2009; Global Trade Alert ‘India: Pre and post shipment export credit,’ Measure #0614, Published 7 September 2009; and Global Trade Alert ‘China: Restructuring of the ship-building industry,’ Measure #0348, Published 7 August 2009.

30
## **SEASONAL ADJUSTMENT OF THE SERIES**

Elimination, of the seasonal component of the series is done with the X- 11 census moving average procedure. This method consists of routines using a ratio-to-moving average decomposition, adjusted for trading day and holiday variations. One of the advantages of this approach (compared with other moving averages that remove seasonality) is that it allows seasonal factors to change from year to year.

# MCD **SMOOTHING OF** CLI COMPONENTS

Smoothing the variability of the components of the CLI requires application of a moving average to each component. The procedure used here is the months of cyclical dominance (MCD) moving average. This procedure has the advantage of deternining the length of the moving average necessary to control fluctuations in the irregular component of the series, with a minimal loss of values in the beginning/ending of the series (see Annex 1).

MCD is defined as the number of months needed for the average change in the irregular component to become smaller than the average change in the trend component. Changes in the irregular component dominate those in the trend-cycle during the first months. However, as the time span increases, changes in the trend cycle will become larger and dominate those in the irregular component. Once the MCD scalar has been obtained for each series, it will be used as the length of a moving average<sup>8</sup> that removes the irregular component of the series.

## **NORMALIZATION AND AGGREGATION OF THE SERIES**

With the MCD smoothed series we construct the CLI. It is necessary to normalize the series prior to aggregation, thus standardizing their amplitude. The normalized series are aggregated into a single leading indicator by a simple averaging process (see Annex 3). In case one of the series presents missing values for some period,<sup>9</sup> no linking factor is For practical reasons the length of this MA it is usually restrained in between 1 and 6 periods. 9 This would be the case of Real Effective Exchange Rate and Litin series for Lithuania.

6
# Credit rating agencies in emerging democracies: Guardians of fiscal disci line? <u>p</u>

Marek Hanusch<sup>a</sup> _The World Bank Washington, DC 20433, USA_ Paul M. Vaaler<sup>b</sup> _Carlson School of Management, University of Minnesota, Minneapolis, MN 55455, USA_ _JEL classifications_ : F30, F34, G24, H63, O10, O23 _Keywords_ : credit rating agencies, fiscal policy, political budget cycles _Sector Board:_ EPOL.

## **____________**

a: mhanusch@worldbank.org b: vaal0001@umn.edu 1
|||||CHS|
|---|---|---|---|---|
|||BEFORE MOV<br><br>|ING ON TO CH3<br><br><br>|TO EITHER QUESTION CH1 OR<br>|
|||.<br>Since 2013, have any ofthe community members ever<br>approached the village headman, other village leaders<br>or political leaders about expressing a need for [ITEM]<br>in the community?|:<br>=<br>=<br>|Since 2013, have the village headman or other village<br>|leader or political leaders organized the community<br>|members to discuss how to address a need for [ITEM]<br>in the community?|QUESTION CH2?|
|ITEM<br>|CODE<br>|¥ES..1<br>NO...2<br><br>|YES..1<br>NO...2<br>|¥YES..1<br>NO...2<br>>> NEXT ROW|
||||||
|<br><br>|||S<br>||
|ridge:Construction<br><br>|||R<br>|T<br>|
|<br>Ci<br>i<br><br>|<br><br>|i<br>ii|e|e|
||||||
|<br> <br>|<br>||||
|Maintenance/Improvement<br> <br>|||||
|<br><br>|||||
|<br><br>Secondary School:<br>|108<br>||||
||||||
|<br>Community Based Child Care Centers’ <br>Nursery: Construction<br>|<br> | 1081<br>||||
|fuerMantonancotmprovemont <br>Nursery: Maintenance/Improvement||[|f|f|
|<br>Health Center/Clinic/Dispensary:<br>Construction|<br>109<br>|<br>|<br>|<br>|
|a<br>Maintenance/Improvement<br>||ee|e|e|
|nc<br>Construction<br>|||<br>||
|erie<br>Maintenance/Improvement<br><br>||E<br>|||
|<br>Orphanage: Construction/<br>ftamennceimprovemont<br>|113<br><br>||||
|<br><br>Maintenance/Improvement<br>|<br><br>||||
|Public Transportation:<br>Intatonimprovement|115<br>||||
|<br>Agricultural/Fishery/ Livestock<br>Extension Services<br>|<br>116<br>||||
|ic <br>Improvement<br><br>|<br>||||
|<br>foien(Sper<br>a|<br>e||||
# TABLE 2: SUMMARY OF MATERNAL TETANUS IMMUNIZATION STUDIES

|STUDY<br>|LOCATION<br>|STUDY<br>PERIOD|STUDY POPULATION|MORTALITY IMPACT|DURATION OF<br>PROTECTION|
|---|---|---|---|---|---|
|SCHOFIELD,<br>ET AL.|NEWGUINEAU|1959-1960|0 TT:<br>86 BIRTHS<br>1 TT:<br>74 BIRTHS<br>2 TT: 234 BIRTHS<br>3 TT: 175 BIRTHS|REDUCTION IN NEONATAL<br>TETANUS DEATHS FROM<br>100 PER 1000 TO 34 PER<br>1000 WITH 2 TT DOSES<br>AND TO 6 PER 1000 WITH<br>3 TT DOSES|<12 MONTHS|
|NEWELL,<br>ET AL.|COLOMBIA|1961-1966|0 TT: 270 AND<br>347 BIRTHS<br>1 TT: 224 BIRTHS<br>2-3 TT: 341 BIRTHS|REDUCTION IN NEONATAL<br>TETANUS DEATHS FROM 78<br>TO 0 PER 1000 TO WOMEN<br>WITH 2-3 TT DOSES|UP TO 4 YEARS|
|BLACK,<br>ET AL.|MATLAB,<br>BANGLADESH|1974-1977|0TT:<br>4386 BIRTHS<br>1 TT: 1265 BIRTHS<br>2 TT: 2990 BIRTHS|33% REDUCTION IN<br>NEONATAL MORTALITY<br>WITH 2 TT DOSES DURING<br>1975-77<br>PERIOD|1TT:<br>UPTO 20 MOS.<br>2 TT: UP TO 32 MOS.|
|RAHMAN,<br>ETAL.|MATLAB,<br>BANGLADESH|1974-1979|0 TT: 7237 BIRTHS<br>1 TT:<br>486 BIRTHS<br>2 TT:<br>956 AND<br>934 BIRTHS|REDUCTIONS IN NEONATAL<br>MORTALITY OF 18% AND<br>49% WiTH 2 TT DOSES,<br>DEPENDING<br>ON DURATION|1TT:<br>NS<br>2 TT: UP TO 5 YEARS|
|KOENIG,<br>ET AL.|MATLAB,<br>BANGLADESH|1973-1989|0 TT: 10745 BIRTHS<br>1 TT:<br>2718 BIRTHS<br>2 TT<br>7891 BIRTHS|37% REDUCTION IN<br>NEONATAL MORTALITY<br>WITH 2 TT DOSES DURING<br>1975-79<br>PERIOD|1 TT: UP TO 4 YEARS<br>2 TT: UP TO 15 YRS.<br>i}|
trade--by 1993 per capita exports exceeded 1989 levels again. Whereas only some 29 percent<sup>of</sup> CEEC exports went to the EU in 1989, the proportion had risen to over 60 percent in 1994. The average annual growth rate of exports to the EU (20.4 percent) is two and a half times that of MENA countries (Table 1).

**Table 1: Non-Oil Exports to EU, 1989 and 1994 (ECU million)**

|Country||Value|||Market Share|in EU|
|---|---|---|---|---|---|
||**1989**|**1994**|Growth|**1989**|**1994**|
|MENA|9,940|14,485|7.8|2.68|2.68|
|Jordan|86|152|12.1|0.02|0.03|
|Lebanon|100|87|-2.8|0.03|0.02|
|Syria|90|234|21.0|0.02|0.04|
|Israel|3,014|4,043|6.1|0.81|0.75|
|Egypt|790|1,107|6.9|0.21|0.20|
|Morocco|2,612|3,652|7.0|0.70|0.67|
|Tunisia|1,596|2,784|14.8|0.43|0.51<br>|
|Algeria|219|328|8.4|0.06|0.06|
|Saudi Arabia|890|1,234|6.7|0.24|0.22|
|Kuwait|131|169|5.2|0.04|0.03|
|U.A.E.|414|695|10.9|0.11|0.12|
|CEEC|10,336|26,115|20.4|2.79|4.83|

Source: EUROSTAT, COMEXT database.

Intra-industry trade has been expanding very rapidly between the CEECs and the EU (Figure 2). Intra-industry trade is important because it is one mechanism through which transfers of technology can occur. The Europe Agreements with the CEECs have created incentives for EU suppliers/retailers to engage in so-called outward processing trade (OPT). This consists of shipping components or assemblies to a CEEC where further processing occurs. The processed good is then exported back to the EU supplier/retailer. Such processing trade benefits from liberal access to the EU, and has been used intensively for sectors such as garnents, electrical machinery and furniture. As part of the subcontracting that is involved, EU counterparts will often provide designs, monitor quality, take care of marketing, etc. This is a good way for firms in partner countries to reduce the 9