rafmacalaba/datause-encoder-data huggingface dataset:
do, however, have results from previous studies that did have access to such data and were able perform the counterfactual exercise.

We begin by reviewing the result from what we believe is the only published study of zeroing that utilizes the same firm-level data as USDOC. We then examine evidence of the impact of zeroing as contained in submissions to the WTO AB where countries submit the results of the counterfactual calculations.

# **6.1 Firm-level evidence**

The only published firm-level analysis of the impact of zeroing is contained in series of papers by the CATO Institute (Lindsey and Ikenson, 2002a, 2002b; Ikenson, 2004). Lindsey and Ikenson were able to get 18 firms from five different countries to share the exact pricing data they had submitted to the USDOC as part of their dumping investigations. The determinations covered 14 original investigations and 4 administrative reviews. For each of these determinations, Lindsey and Ikenson used the USDOC’s own dumping calculation computer programs. They first recreated the dumping margins determined by the USDOC. They then altered those programs to gauge the effect of zeroing on margins. They state Using actual case data and the DOC’s dumping calculation computer programs, it was possible to calculate the actual effects of zeroing in these particular cases. In 17 of the 18 determinations, the dumping margin was inflated by zeroing. In 5 of the cases, the overall dumping margin would have been negative. On average, the dumping margins in the 17 cases – 33 –
# **Table 3: Variety Growth in China’s Exports, 1997-2005**

|**A: Extensive Margin using 8-digit China Data**|
|---|

||**Number**<br>**of Codes**|**Type **|**Partner**|**Feenstra**|**Shar**<br>**Intensive**|**e of Total**<br>**New**|**Export Growth f**<br>**Disappearing**|**rom:**<br>**Extensive**|**Total**<br>**Export**<br>**Growth %**|
|---|---|---|---|---|---|---|---|---|---|
|1|7951|All|World|0.10|0.74<br>[5501]|0.33<br>[1624]|0.07<br>[826]|0.26|187|
|2|6357|All|US|0.11|0.76<br>[3641]|0.29<br>[1980]|0.05<br>[736]|0.25|243|
|3|4826|Exist|US|0.01|0.98|0.02|0.00|0.02|212|
||76% of<br>codes||||[3641]|[935]|[250]|||

## **B: Extensive Margin using 10-digit U.S.  Data**

||**Number**<br>**of Codes**|**Type **|**Partner**|**Feenstra**|**Shar**<br>**Intensive**|**e of Total**<br>**New**|**Export Growth f**<br>**Disappearing**|**rom:**<br>**Extensive**|**Total**<br>**Export**<br>**Growth %**|
|---|---|---|---|---|---|---|---|---|---|
|1|14169|All|US|-0.03|0.83|0.29|0.12|0.17|168|
||||||[7576]|[5122]|[1471]|||
|2|11444|Exist|US|0.02|0.97|0.03|0.00|0.03|182|
||81% of<br>codes||||[7576]|[3506]|[362]|||

*Notes: the extensive and intensive margin may not sum exactly to one because of rounding error.

19
<!-- Start of picture text --> Figure 8: Total Revenues/GDP- Poland, Russia, Turkey (2000-2010)<br>50<br>» fA\<br>ee35 — —~—<br>30<br>25<br>20<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>—— Poland —— Russia ——Turkey<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 2: Rising Returns to Higher Education in Vietnam since 1992<br>20<br>15<br>10<br>5<br>0<br>Primary Secondary Tertiary<br>1992 2010<br><!-- End of picture text --> Source: Annex 1

# **Private and Social Returns to Higher Education**

The private returns to higher education are now higher than the returns to primary schooling (Montenegro and Patrinos 2014; see also Colclough, Kingdon and Patrinos 2010). The returns to primary schooling are just above 10 percent and returns to secondary schooling are 7 percent. The private rate of return to university is 15 percent. It is highest in Sub-Saharan Africa at 21 percent and ranges between 10 (Europe and Central Asia) and 17 percent (South Asia) for the rest.

High returns to higher education signal that university is a good investment – especially for the student and her family. If you add to that the social benefits and costs of higher education, then one can argue that higher education has social returns as well. Financing higher education, however, requires a sustainable financial model, which in most countries entails smart costrecovery (via, for example, income contingent student finance) and targeted support – which means guidance, information, supports, not just money – for those particularly disadvantaged.

How do we know that higher education might be a justified expenditure on the part of students (and their families) and society? Typically, we rely on cost-benefit analysis that gives us an estimate of the rate of return to investment in higher education. The rate of return to schooling equals the value of lifetime earnings of the graduate to the net present value of the costs of education. An economically justified investment has a positive rate of return which is higher than the alternative. For the prospective student, assessing costs and benefits means investing until the rate of return exceeds the private discount rate (the cost of borrowing and an allowance for risk). The costs are the student’s foregone earnings while studying, plus any fees or incidental expenses incurred while attending classes. The private benefits amount to how much extra money a graduate earns compared with someone with less education. In the case of higher education, the comparison is made with secondary school leavers.

The social rate of return is calculated by adding society’s spending on higher education on the cost side – for example, money spent on renting buildings and professorial salaries – and the benefits 6
reducing the relative importance of wage competitiveness, increased automation under Industry 4.0 can make it feasible for some leading firms to reshore labor-intensive activities back to highincome economies and closer to final consumers.

While the available evidence about the advent of reshoring, and resulting changes in globally fragmented production, is limited (De Backer et al. 2016), there are signs of a beginning. A report by Citigroup and the University of Oxford’s Oxford Martin School finds that 70 percent of Citi institutional clients surveyed believe automation will encourage companies to move their manufacturing closer to home, with North America seen as having the most to gain from this trend, while China, Association of Southeast Asian Nations (ASEAN) member countries, and Latin America are seen as having the most to lose (Citigroup 2016). Take the example of footwear manufacturing, where 3-D printing can dramatically shorten the design-to-production cycle from 18 months to less than a week ( _Economist_ 2017). Adidas, the German sporting goods company, has established “Speedfactories” in Ansbach, Germany, and Atlanta, which will use computerized knitting, robotic cutting, and 3-D printing almost exclusively to produce athletic footwear.

At the same time, China stands out as a middle-income country that is rapidly automating production through robotization to address declining wage competitiveness. Standard Chartered Global Research (2016) found that 48 percent of 290 manufacturers surveyed in the Pearl River Delta would consider automation as a response to labor shortages, while less than a third would consider moving capacity either inland or out of China. Some high-profile firms are already substituting a substantial number of workers with industrial robots. For example, Foxconn—the firm known for producing Apple and Samsung products in China’s Jiangsu province—recently replaced 60,000 factory workers with industrial robots ( _South China Morning Post_ 2016). Nationally, the country is projected to have more than 400,000 industrial robots in operational stock in the manufacturing sector by 2018, more than doubling the number in 2015 (figure 2). This would give China the distinction of having the highest number of installed industrial robots in the world, accounting for about one-fourth of total industrial robots projected to be installed globally.

**Figure 2: Operational Stock of Industrial Robots in the Manufacturing Sector, 1995-2018**

<!-- Start of picture text --> DE‐Germany<br>North America<br>KR‐Rep. of Korea<br>CN‐China<br>0 50 100 150 200 250 300 350 400 450<br>2018 2015 2010 2005 2000 1995<br><!-- End of picture text --> Source: Hallward-Driemeier and Nayyar (2017) 5
**Table 4. Odds ratios from logit “Model 2b” and “Model 3” of extended formality in ELMPS 2012 and 2018**

||**M**|**odel 2**|**M**|**odel 3**|
|---|---|---|---|---|
||**2012**|**2018**|**2012**|**2018**|
|**Ent. size (one worker omit.)**|||||
|2|1.224|1.510|1.215|1.572*|
||(0.271)|(0.324)|(0.270)|(0.332)|
|3-4|1.683|0.793|1.801|0.758|
||(0.500)|(0.236)|(0.584)|(0.241)|
|5-24|3.823*|4.572**|3.329*|4.460**|
||(2.066)|(2.623)|(1.744)|(2.566)|
|**Industry (wholesale/retail omit.)**|||||
|Manufacturing and related trades|0.501*|1.093|0.570|1.382|
||(0.136)|(0.338)|(0.169)|(0.462)|
|Construction|0.183**|0.874|0.174*|1.185|
||(0.114)|(0.777)|(0.126)|(1.064)|
|Transportation and storage|1.533|0.428|2.666|0.415|
||(1.489)|(0.362)|(2.600)|(0.372)|
|Accommodation and food service|1.041|1.416|1.337|1.236|
||(0.341)|(0.497)|(0.434)|(0.519)|
|Various professional acts.|1.002|1.991*|0.796|1.381|
||(0.324)|(0.626)|(0.265)|(0.493)|
|Other service|0.756|0.996|0.891|1.124|
||(0.213)|(0.279)|(0.282)|(0.336)|
|**Region (Greater Cairo omit.)**|||||
|Alexandria and Suez Canal|1.635|0.973|1.925*|1.072|
||(0.423)|(0.340)|(0.527)|(0.381)|
|Lower Egypt|1.257|1.049|1.327|1.078|
||(0.278)|(0.268)|(0.303)|(0.297)|

29
Table 5: Alternative instruments

|||IV|||IV||
|---|---|---|---|---|---|---|
|Dependent variable:|_log_(1 +_imp_)<br>(1)|_log_(1 +_exp_)<br>(2)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup><br>(3)|_log_(1 +_imp_)<br>(4)|_log_(1 +_exp_)<br>(5)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup><br>(6)|
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.5825**<br>(0.2333)|1.4069***<br>(0.3403)|-0.8244***<br>(0.2576)|0.5762**<br>(0.2745)|1.2756**<br>(0.4565)|-0.6995**<br>(0.2756)|
|First stage:|||||||
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>) in two countries</sup>|0.2743**|0.2743**|0.2743**||||
|with most similar GDP per capita|(0.1000)|(0.1000)|(0.1000)||||
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>) in four countries</sup>||||0.3247**|0.3247**|0.3247**|
|with most similar GDP per capita||||(0.1182)|(0.1182)|(0.1182)|
|Observations|786,797|786,797|786,797|854,589|854,589|854,589|
|_R_<sup>2</sup>|0.6282|0.7608|0.3855|0.6255|0.7580|0.3874|
|Kleibergen-Paap rk Wald-F-stat|7.523|7.523|7.523|7.546|7.546|7.546|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the baseline estimation sample and alternative instruments. Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

## **Figure 12. Evolution of seats and city pairs in Sudan**

<!-- Start of picture text --> a. Seats b. City pairs<br>4,000,000 60<br>3,500,000<br>50<br>3,000,000<br>40<br>2,500,000<br>2,000,000 30<br>1,500,000<br>20<br>1,000,000<br>10<br>500,000<br>- 0<br>2,001 2,004 2,007 2009 (Est)<br>2,001 2,004 2,007 2009 (Est)<br>Total Total International<br>International<br>Intercontinental excluding flights between NA and SSA<br>Between North and South Intercontinental Between North and South<br>Domestic<br>City pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems. NA = North Africa; SSA = Sub-Saharan Africa.

# Water supply and sanitation

## **Challenges**

Around 85 percent of Sudan’s populace lacks access to safe sources of water (figure 13). Over 25 percent of the population relies on surface water and almost 60 percent relies on wells and boreholes. Sudan’s reliance on wells and boreholes for water supply is higher than for any other country in its peer groups and is becoming a challenge for the country. Fewer than half the wells and boreholes in Africa provide safe water (table 10).

Sanitation access in Sudan is bimodal. On the one hand, 40 percent of the population has access to improved latrines; on the other, 40 percent continues to practice open defecation. Both ratios are substantially higher than for the relevant resource-rich and low-income African peer groups. In contrast to these peers, reliance on flush toilets and traditional latrines is much more limited.

A very small share of the population—15 percent—has access to utility water, well below all other African benchmarks. Available information suggests that there is almost no access to piped water, and stand posts are the only form of utility water that is accessed. Census data for all parts of Sudan indicate that in 1993 about 60 percent of the population had access to utility water but there has been a steep decline in access rates—down to 35 percent—in the 2000s. Since access to utility water is negligible in South Sudan, the access data for all parts of Sudan reported in the census relates primarily to Sudan itself.

> <sup>22</sup>
dwellers in Argentina have less access to safe water than do some of their peers in rural Africa such as Kenya (31 percent), Nigeria (39 percent), and Uganda (46 percent).<sup>16</sup> A fact having to do with the highly dispersed nature of Argentina’s rural poor population, especially compared to Africa’s village-based rural population. Moreover, the supply of services is seriously lacking in dispersed rural areas compared to national averages. In dispersed rural areas, only 21 percent of households have access to safe water, 7 percent to trash collection, 16 percent to paved roads, and 7 percent to a fixed telephone (Table 6.4).<sup>17</sup>

|Table 6.4:<br>Dispersed|Household<br>Rural Areas<br>|Access to Ba<br> of Argentin<br>|sic Infras<br>ain 2003 <br>|tructure in<br> (percent)<br>||
|---|---|---|---|---|---|
||Mendoza|Santiago<br>del<br>Estero|Chaco|Santa Fe|Total|
|Electricity|94.2|18.8|78.2|79.6|70.8|
|Water|43.4|10.4|18.2|0.8|21.0|
|Trash collection|15.0|0.0|6.6|0.8|6.6|
|Paved road|43.4|4.2|0.0|3.3|16.0|
|Public lighting|40.0|0.0|21.2|5.6|19.2|
|Fixed telephone|7.5|0.0|2.0|17.8|7.1|
|Mobile phone|18.3|3.1|6.7|31.6|15.6|
|Source: Own calculatio|n based on RH|S 2003.||||

Large differences exist in access to energy and electricity in rural Argentina. Energy and rural electrification contribute to the improvement of living conditions in rural areas. They facilitate social integration, contribute to increasing production value, and promote diversification. Some houses, most of them in dispersed areas and small localities far from main roads, use diesel generators. Large differences exist among provinces with regard to access to the electrical network and the type of energy used. Rural residents commonly use firewood or charcoal for cooking. In dispersed rural areas, public electricity connections range from 5.1 percent for the poorest quintile in Santiago del Estero to 85.1 percent for the wealthiest quintile in Santa Fe in 2003 (Figure 6.1 and Table 6.5). Therefore, there are extreme differences across income distribution and provinces in access to electricity. The growing trend toward successively higher income quintiles indicates the regressive nature of electrification in Chaco, Mendoza, Santiago del Estero, and Santa Fe. The clearest regressive pattern in incidence benefits is seen in Santiago del Estero. In Chaco, Santiago del Estero, and other provinces, it would not take a great deal of effort to increase access to electricity because in many places the power line runs directly over the lot, but the dwelling is not connected to the grid. During field visits rural dwellers mentioned that there is a one-time fee of AR$750-1,100 to connect the household to the electrical grid.

> 16 Source: UNICEF database (2000).

> 17 There may be representative problems at provincial level in the data set.

34
**Many of the current weaknesses in the performance management system were identified in 1989 already** . The modernization seminar’s conclusions in Table 2 demonstrate this. Among the identified weaknesses are direct recruitment, irrelevant performance criteria, inflated performance appraisals, the missing link between performance and promotion, a complex and opaque compensation system, and a constant and rapid increase in the wage bill.

**Table 2: Conclusions from the Seminar on Modernizing the Administration, 1989**

|**Recruitment**|**Weakn**|**esses**:|
|---|---|---|
||•<br>•<br>•|The Ministries of Education and Health bypass competitive processes and recruit<br>directly;<br>Competitive processes are often burdensome to manage and poorly adapted to the<br>desired profiles;<br>The probationary internship is not evaluated and tenure is practically automatic.|
||**Recom**<br>•<br>•<br>•<br>•|**mendations**:<br>Raise recruitment standards and relate these to diplomas earned;<br>Develop direct appointment paths for graduates from specific schools;<br>Preselect candidates through competitive examinations;<br>Appoint a trainer supervisor for each trainee and evaluate each training program.|
|**Evaluation**|**Weakn**<br>•<br>•<br>•<br>•|**esses**:<br>Employee performance and the quality of service are low;<br>Poor assiduity and discipline are common and go unpunished;<br>Professional grades are subjective and unrelated to the behavior or performance of<br>the employee, with the majority of employees scoring 100 percent;<br>Grades do not affect compensation orpromotion.|
||**Recom**<br>•<br>•<br>•<br>•|**mendations**:<br>Review criteria for evaluating professional performance and introduce bonuses to<br>link compensation to performance;<br>Introduce checks of absences and sick leave;<br>Reform the disciplinary system by expanding the range of sanctions and speeding up<br>the enforcement of first-degree sanctions;<br>Decentralize disciplinaryauthority.|
|**Compensation**|**Weakn**<br>•<br>•<br>•|**esses**:<br>Wage bill increases rapidly;<br>The remuneration system is complex due to the multitude of allowances and<br>differences between professional groups;<br>Allowances constitute a significantportion of overall salaries.|
||**Recom**<br>•<br>•|**mendations**:<br>Replace the index system with a base salary scale;<br>Put an end to the proliferation of allowances;|
||•|Create financial incentives forpostings in rural areas.|
|**Promotion**|**Weakn**<br>•<br>•<br>•<br>•<br>•|**esses**:<br>Internal candidates are given preference over external candidates;<br>Competitive testing is neither rigorous nor objective;<br>Continuing education and resulting internal promotions are underdeveloped;<br>Choice-based promotions are conducted without regard to the candidates’ merit;<br>Competition for civil servicejobs leads to a multiplication of structures.|
||**Recom**<br>•<br>•|**mendations**:<br>Ensure that 70 percent of recruitment is external, at least for category A positions;<br>Limit the number ofpermissible attempts in internal competitions;|

16
Policy Research Working Paper 5340

# **Abstract**

This paper examines the export behavior of Dominican Republic exporters following the implementation of the Dominican Republic-Central America Free Trade Agreement in 2007. Using a firm-level dataset for 2002–2009, the authors investigate the effects of a tariff reduction on the extensive margin. The analysis distinguishes the impact on the entry of new firms, exports of new products, and entry into the Agreement’s markets. The paper analyzes whether the agreement prevents incumbent exporters from exiting the market. The results suggest that tariff cuts had a positive although very small effect on the extensive margin. A decline in tariffs also seems to reduce the probability of exit, but the effect is also small. The evidence calls for complementary policies aiming at helping exporters maximize the benefits of the agreement.

This paper—a product of the Economic Policy Sector, Latin America and the Caribbean Region—is part of a larger effort in the department to identify challenges and opportunities of trade and integration in Central America and other subregions of the continent. Work for this paper was financed by the Multi-Donor Trust Fund. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at anacristina.molina@ graduateinstitute.ch, mbussolo@worldbank.org, and liacovone@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team