rafmacalaba/datause-encoder-data huggingface dataset:
participate in the intervention after they have been treated and cured of the infection; learning is encouraged through positive reinforcement, and mistakes can be corrected and overcome.

In addition to these urine-based NAAT STI tests, we also measured syphilis and HSV2 using blood-based tests. Because of the low prevalence of syphilis, and because HSV2 detects whether the individual has ever contracted HSV2, we elected to only measure these blood-based STI‘s at baseline and 12 months, rather than at each 4-month interval.

Finally, we also measured HIV at baseline and one-year follow-up. We did not conditional reward payments on HIV status, in part due to ethical concerns about ―punishing‖ somebody at the very time when they learn that they have contracted a fatal disease. We were not powered to look at intervention arm effects on HIV, but for scientific purposes did want to measure baseline HIV to better characterize our population. Our HIV rates turned out to be lower (just under 4%) than anticipated based on regional Demographic and Health Survey data; given the reasonably high rates of HIV testing in the population it may be that HIV positive individuals selected themselves out of the sample. We decided to also test HIV at the 12-month endpoint due to the study population‘s strong interest in checking their results again. Although over half of the population had received a prior HIV test, study enrollees expressed concerns about confidentiality of testing at local clinics, and strong desire for retesting by RESPECT study personnel.

# **_Incentive Size and Frequency_**

A major design consideration is the size of the incentive. CCT programs focused on uptake have typically relied on formulas to compensate participants on the opportunity costs, or time requirements, of complying with programmatic requirements such as attending antenatal care visits or taking the child to the clinic for his or her regular checkups. However, in the case of CCT programs designed to encourage safer sexual practices and discourage risky ones, the purpose of the incentive is not primarily to compensate the individual for the opportunity costs of participating in the program, but rather to change the decision calculus of the individual regarding his or her sexual behavior. The goal 18
9

# **4. Data and background information**

Data for the four selected countries (Brazil, Colombia, Moldova, and Vietnam) have been taken from the International Benchmarking Network (IBNET). These four countries differ in many respects, in particular regarding their level of economic and social development.

Brazil has a diversified middle-income economy with wide variations in levels of economic development across the country. After decades of inflation, Brazil embarked on a successful economic stabilization program, the Real Plan in July 1994. Inflation, which had reached an annual level of nearly 5,000 percent at the end of 1993, fell sharply, reaching 8 percent in 2004. In 2004, Gross National Income (GNI) was US$3,000 per capita (see table 1).<sup>10</sup> After experiencing decades of steady growth (average GDP growth exceeded 4 percent in the 1970-1998 period), Colombia entered into a recession in 1999, and the recovery from that recession was long and painful. Colombia’s economy suffers from weak domestic and foreign demand, austere government budgets, and serious internal armed conflicts. Inflation was moderate in the last few years (about 7 percent in 2004). In 2004, GNI reached US$2,020 per capita (table 1).

Although the Moldovan economy experienced a constant economic growth after 2000 it still ranks low in terms of commonly-used living standards and human development indicators in comparison with other transition economies. Moldova remains the poorest country in Europe in terms of GDP per capita. In 2004, the registered GNI per capita was US$720 (table 1). An estimated 40 percent of population lives under the absolute poverty line.

In 1986, the Sixth Party Congress of the Communist Party of Vietnam formally began introducing market elements as part of a broad economic reform package. Vietnam achieved around 8 percent annual GDP growth from 1990 to 1997 and continued at around 7 percent from 2000 to 2002, making it the world’s second-fastest growing economy. Vietnam, however, is still a poor country with GNI of US$380 per capita in 2000, reaching US$540 per capita in 2004.

> 10 _Gross National Income or GNI (formerly GNP), current dollars_ is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Source: World Bank.
17 Here firms in Hong Kong (China) and Singapore score the best on average, although there is quite a large range in the performance of firms in Singapore (largely reflecting the performance of firms that are incorporated outside Singapore), followed by Malaysia, Korea, Thailand and Indonesia in that order. (The analysis did not cover China or the Philippines).

More broadly, the importance of corporate governance has increasingly been recognized and, over the past few years, countries in the region have made significant efforts to strengthen the various elements that shape it. Table 5 provides market perceptions of where countries in East Asia stand with respect to the key elements of corporate governance. Rules and regulations pertaining to the exercise of corporate governance— which include well defined rights that can protect outside shareholders from the actions of controlling shareholders, regulations that ensure the equitable treatment of shareholders and well defined responsibilities of boards—are seen to still require strengthening in several countries—more notably in China<sup>7</sup> , the Philippines and Indonesia<sup>8</sup> .

Table 5. Corporate governance scores—a market perspective

|**Economy**|**Rules and**<br>**regulations**|**Enforcement**|**Political**<br>**and**<br>**regulatory**|**IGAAP**|**CG culture**|**Economy**<br>**score**|
|---|---|---|---|---|---|---|
|China|5.3|4.2|5.0|7.5|2.3|4.8|
|Indonesia|5.3|2.7|3.8|6.0|2.7|4.0|
|Malaysia|7.1|5.0|5.0|9.0|4.6|6.0|
|Korea|6.1|5.0|5.0|8.0|5.0|5.8|
|Philippines|5.8|3.1|5.0|8.5|3.1|5.0|
|Thailand|6.1|3.8|5.0|8.5|3.5|5.3|
|Hong Kong|6.6|5.8|7.5|9.0|4.6|6.7|
|Singapore|7.9|6.5|8.1|9.5|5.8|7.4|
|Score ranges fr|om 1 (lowest) to|10 (highest). Source|: CLSA 2005||||

The enforcement of the rules is seen to be more problematic across a larger number of countries in the region. Indeed, while there are some aspects of the regulations pertaining to shareholder rights that could be strengthened in several countries, the key challenge for countries the region now lies in the implementation and enforcement of the regulations that are already in place.

In terms of accounting and auditing, East Asian countries are at different stages of convergence with international standards. From the market’s perspective, China and Indonesia are seen to be the furthest off from international standards and practices at present. Indeed, in China, while the government’s policy is to bring local accounting standards into line with international standards, there are currently significant

> 7 China has recently amended its Company Law, with several amendments relating to the strengthening of shareholder rights.

> 8 These assessments are based on a number of parameters under each category (see Credit Lyonnais Asia Pacific 2005 for the list of the parameters).
_18_ _Estimates and Projections of Mortality by Cause_ Table **8.** Percentage distribution by major cause of all deaths, deaths under 65, and potential years of life lost, world and developed and developi-. countries, 1970-2015

|_Region and_<br>_cause_|_19_<br>_All_<br>_deaths_|_70: Perc_<br>_Deaths_<br>_under_<br>_65_|_ent of_<br>_Life_<br>_years_<br>_lost_|_198_<br>_All_<br>_deaths_|_5: Pcent_<br>_Deaths_<br>_under_<br>_65_|_of_<br>_Life_<br>_yeats_<br>_lost_|_200_<br> <br>_All_<br>_deaths_|_0: Perce_<br>_Deaths_<br>_under_<br>_65_|_nt of_<br>_Life_<br>_years_<br>_lost_|_20_<br>_All_<br>_deaths_|_15: Perc_<br>_Deaths_<br>_under_<br>_65_|_ent of_<br>_Life_<br>_years_<br>_lost_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_World_|||||||||||||
|Infectious|35|<br>45|50|<br>30|39|44|21|30|37|<br>16|<br>23|32|
|Neoplasms|7|5|2|10|8|4|12|12|6|15|<br>15|9|
|<br>CIrculatory|<br>24|<br>12|6|26|13|7|35|20|10|<br>39|<br>25|14|
|Pregnancy|1|1|1|0|1|1|1|1|1|0|1|1|
|Perlnatal|6|9|12|<br>7|11|15|5|10|16|4|9|15|
|Injury|5|7|6|7|10|10|7|10|9|7|12|11|
|<br>Other|22|21|23|20|19|20|20|17|20|19|<br>16|18|
|_Developed Co_|_untries_||||||||||||
|Infectlous|12|16|20|9|13|18|7|4|5|7|3|3|
|Neoplasms|16|18|12|18|21|14|18|27|21|18|<br>29|24|
|Circulatory|<br>48|28|16|50|29|17|52|39|29|53|41|32|
|Pregnancy|0|0|0|0|0|0|0|0|0|0|0|0|
|Perinatal|2|6|11|1|4|8|1|5|14|1|5|14|
|Injury|7|17|21|6|15|17|6|16|20|5|15|19|
|Other|16|16|20|15|19|25|16|9|10|16|7|9|
|_Developing C_|_ountrks_||||||||||||
|Infectious|42|49|53|36|42|46|26|33|39|19|26|34|
|Neoplasmis|<br>5|4|_2_|7|6|3|11|10|5|14|14|8|
|Circulatory|<br>17|10|5|19|11|6|29|17|9|35|23|13|
|Pregnancy|1|1|1|1|1|1|1|1|1|1|1|1|
|Perlnatal|7|10|12|8|12|16|6|10|16|5|9|16|
|Injury|5|5|4|8|10|9|7|10|9|7|11|10|
|Other|24|22|23|21|19|20|21|18|20|19|17|19|

# Discussion

Since the expert assessments imply that at least some of the regression estimates are too low, we now consider the quality of these estimates and the biases that may exist in them.

The source of particular estimates should be borne in mind. For several regions, the current and past estimates are based substantially on data reported to WHO. Table 9 shows the proportions of regional populations covered by reported data. (The U.S.S.R. is not counted as covered, but China and rural India are.) The 1970 and 1985 estimates for industrial market economies are drawn almost entirely from reports. This is true to a lesser extent for Latin America and the Caibbean and industrial nonmarket economies. The situation for Asian data is more ambiguous because of reliance on special survey data for China and India. For Sub-Saharan Africa and North Africa and the Middle East, on the other hand, practically all the 1970 and 1985 figures are produced from the regression models.

As noted earlier, estimates based entirely on reports to WHO are not free from potential bias. The general issues of reliability and comparability have already been noted, but in addition some aspects of the coding appear not to conform with expert
This study combines a large set of near real-time indicators with the World Bank’s highfrequency phone surveys to assess the ability of the indicators to predict changes in a variety of proxies for household economic well-being. In addition to Google Mobility data, Google search trends, night-time lights, vegetation and pollution data are used to develop predictive models of trends in a variety of outcomes measured in household surveys. First, the study examines the ability of these real-time indicators to predict variation in the severity of the initial shock, as measured by the prevalence of self-reported decreases in income and job losses since the start of the crisis, recorded in the first survey wave that each country fielded between April and August 2020. Second, the study examines the ability of the real-time-indicators to predict variation within regions – typically states or provinces – over time as the impact of the crisis evolved.

While the use of real-time data is relatively new in economic research, several studies provide insight into best practices for predictive modeling and how this data can contribute to poverty research. Many real-time measures, such as Google mobility, night lights from satellites, nitrogen dioxide (NO2 ), and Google search of certain keywords, have the potential to predict traditional well-being measures. Google mobility has been recently used to predict industrial production growth (Sampi & Jooste, 2020) and economic growth (Putra & Arini, 2020) during the pandemic. The Google mobility index shows how visits to places are changing over time in each geographic region. The index is measured for six categories of places, including workplaces, residential, grocery and pharmacy, transit stations, retail and recreation, as well as parks. The data are not representative, however, as they are only collected from smartphone users that have location services turned on.

Economists increasingly use night lights detected by satellites as a proxy for economic indicators (Chen & Nordhaus, 2011; Gibson et al., 2021; Henderson et al., 2012; Ishizawa et al., 2017; Mellander et al., 2015). In developing countries with low-quality economic and social accounting systems, or when satellite images are more frequently available than such data, the satellite-based nightlight data may provide valuable information on social and economic development. In addition to the widely used night lights, other types of remotely sensed data have recently been explored in economics. For example, Castellanos & Boersma (2012) find that NO2 is correlated with economic activities and thus can be used to measure the magnitude of social and economic activities (Keola & Hayakawa, 2021). The Normalized Difference 3
understand how real price decline affects different households, according to their degree of participation in the rice market. We use for this illustrative analysis Bangladesh’s 1995 HIES.

In order to evaluate the overall welfare impact of price changes on households who are both rice producers and rice consumers, we use Deaton’s (1989) methodology. The overall welfare effect of a rice price decline, assuming that the consumption and production prices decline by the same percentage, is directly proportional to the net benefit ratio (NBR). This ratio is the difference between the net consumption ratio (CR) and the net production ration (PR), and the short-run distributional effect of rice price changes is directly proportional to the share of rice in income and consumption (Deaton 1989, 1997): where Δ _w_ is the first-order approximation of the net welfare effect of a rice price change on a household and the superscripts _p_ and _c_ are used to distinguish between the prices used to value rice production and consumption, respectively, PR and CR denote the production and consumption ratios, and<sup>_x_</sup> 0<sup>is total household budget.</sup> The longer-term direct welfare effect can be calculated using a Taylor series approximation, and it accounts for household production adjustment in response to the price change<sup>5</sup> : where Δ _w_ 2 is the second-order approximation of the net welfare effect of a rice price change on a household, and _ε_<sup>_s_</sup> and _ε_<sup>_H_</sup> are the elasticity of supply and demand, respectively. By adding the second and fourth terms on the right hand side of the equation above, we are partially capturing the direct second-round effects of a rice price decline that include the price-induced response on the supply and demand side.

Table 5 shows that the impact of a rice price decline varies across different household categories. Since most households even in the urban areas are net consumers

> 5 See Minot and Goletti (2000) for a derivation. Minot and Goletti used this method to study the impact of rice price increase on welfare in Vietnam.

15
|||**Ov**|**erall**|||**Firms**|||**Individua**|**ls**|
|---|---|---|---|---|---|---|---|---|---|---|
|Conditioningvariable|Tot obs|ρ (low)|ρ (high)|p-value|ρ (low)<br>|ρ (high)|p-value|ρ (low)|ρ (high)|p-value|
|**Fiscal freedom**|155|-0.508|-0.118|**0.007**|-0.025|0.024|0.767|-0.570|<br>-0.206|**0.008**|
|**Investment freedom**|154|-0.049|-0.436|**0.011**|-0.033|0.056|0.587|-0.052|<br>-0.513|**0.002**|
|Financial freedom|151|-0.141|-0.362|0.153|-0.092|0.176|0.104|-0.174|<br>-0.450|**0.065**|
|Strength of investor protection index (0 to 10)|156|-0.314|-0.345|0.831|-0.077|0.040|0.474|-0.338|<br>-0.471|0.337|
|Strength of insolvency framework index (0-16)|145|-0.173|-0.337|0.309|0.083|0.034|0.778|-0.265|<br>-0.382|0.454|
|Credit: Strength of legal rights index (0=weak to 10=strong)|156|-0.391|-0.307|0.558|-0.003|0.045|0.770|-0.443|<br>-0.419|0.855|
|Depth of credit information index (0=low to 6=high)|156|-0.368|-0.329|0.786|-0.055|0.066|0.463|-0.402|<br>-0.436|0.800|
|Strength ofgovernance structure index(0-10.5)|156|-0.456|-0.237|0.126|-0.278|0.213|**0.002**|-0.427|<br>-0.409|0.895|

27
62 <!-- Start of picture text --> . . .. * . 4 * *...... ...<br>1980 1981 1982 1983 1984 1985 1986 1987 1988<br>~~~~~~~0 .............. 0. ............ .... .. ..<br>6 PASTERN EUROPE (W) 96 96 95 95 95 95 95 95<br>(U) 97 97 99 95 96 95 95 98<br>.............. .............. *5*O ...................... 99. ** . ....<br>6 M4 Bu1garLe 97 99 99 99 99<br>6 M Czechoslovakia 95 99 99 99 99 99 99<br>6 M CDR 99 95 99 99 99 99 99 99<br>6 N Hungary 99 99 99 99 99 99<br>6 M Poland 93 95 95 95 95<br>6 M Romania 93 95<br>6 M Yugoslavia 99 85 84 85 84<br>... *0..t....S..*........ .* w0.....S....ff* ... .*S.e.S.... 0.. ....<br><!-- End of picture text --> Note: U - unweighted; W - weighted; L - low income; X - middle income; LW - low income weighted average; KW - middle Income weighted average. Source: Expanded Programme on ImmuniZation, WHO.
WPS3737

# **Global Antidumping Database Version 1.0**

## **Chad P. Bown**<sup>**†,‡**</sup>

## **Brandeis University**

## **Abstract**

This paper describes a newly collected, detailed database on national governments’ use of the antidumping trade policy instrument. The data collection project was funded by the Development Research Group of the World Bank and Brandeis University. While still preliminary, it goes beyond existing, publicly-used sets of antidumping data in a number of fundamental ways. It is a first attempt to use original source national government documentation to organize information on products, firms, the investigative procedure and outcomes of the historical use (since the 1980s) of the antidumping policy instrument across large importing country users. We also report more and recent data on a number of smaller users of antidumping, as well as some limited information on the use of countervailing measures from national governments that are users of countervailing duty laws.

### **Database Website:** **<u>http://www.brandeis.edu/~cbown/global_ad/</u>**

World Bank Policy Research Working Paper 3737, October 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ____________________

> **†** Department of Economics and International Business School, Brandeis University, MS 021, PO Box 549110 Waltham, MA 02454-9110, USA; tel: 781-736-4823; fax: 781-736-2269; email <u>cbown@brandeis.edu, web: http://www.brandeis.edu/~cbown/.</u>

> **‡** Funding for the data collection project is through the World Bank with contributions from a Mazer Award and Tomberg Funds from Brandeis University. I would also like to thank the hospitality of the World Bank (DECRG) which I was visiting while this project was initiated, as well as the Brookings Institution which I was visiting during some of the data collection. For helpful work collecting the data in this project I would especially like to thank the research assistance of David Cheong, Gloria Sheu, Nabeela Alam and Jaewoo Nakajima. Assistance in data acquisition at various stages of the process was also provided by Rebecca McKibbin, Gustav Brink, Honorio Kumé, Eric Haven, Jayanta Roy, Hilda Fridh, Raul Torres, Johann Human, and Kwang Hyok Yoo whose efforts are appreciated. I also thank Bernard Hoekman, Tom Prusa, Bruce Blonigen, James Durling, Nuno Limão, Robert Feinberg, Kara Reynolds, Michael Moore, Patrick Messerlin, J. Michael Finger, Maurizio Zanardi and Meredith Crowley for helpful discussions. Data errors and corrections should be emailed to <u>cbown@brandeis.edu.</u>
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18