rafmacalaba/datause-encoder-data huggingface dataset:
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 49

# **A.2.3      Pragmatic approaches for improving the global unit cost estimates of sea dikes**

This section discusses a pragmatic approach to improve global unit cost estimates of sea dikes, and the current data limitations to achieve this goal. Dronkers et al. (1990) and Hoozemans et al. (1993) multiplied unit cost estimates of the Netherlands by country unit cost factors to derive unit costs for all countries. More recent unit cost estimates of sea dikes are now available for the Netherlands. Jonkman et al. (2013) report that unit cost estimates of sea dikes range from 4.9 M to 13.5 M euros (2014) for rural areas in the Netherlands, and from 16.9 M to 24.4 M euros for urban areas.<sup>1</sup> One pragmatic approach to derive new rural and urban unit cost estimates at the global level would be to multiply the recent rural and urban unit cost ranges of the Netherlands with the original country unit cost factors. However, we hypothesize that applying the Dutch urban unit cost ranges would lead to an upward bias of unit cost estimates at the global level due to a set of specific circumstances in the Netherlands that have not been reflected in the country cost factors. These include the large national budget available for defense works, the large share of land below sea level, high protection standards, high protected value in rural areas, a strong solidarity which favors protection of rural areas more than in other countries, and the polycentric urban structure. Unfortunately, we cannot statistically test this hypothesis due to the low number of case studies reporting urban unit costs at the country level.

Alternatively, we apply the rural ranges of the Netherlands to the rest of the world by multiplying them with the DIVA country factors and compare the results with unit costs found in available case studies. Table A3 shows this comparison for the United States, Canada, the United Kingdom and Vietnam. Appendix 2 contains a complete overview of DIVA country cost factors, previous DIVA estimates of unit costs and the newly estimated unit cost ranges. The case studies suggest that country unit cost factors are accurate for some countries, and inaccurate for others. The unit cost estimates reported for New Orleans, USA from the US Army Corps of Engineers in Jonkman et al. (2013), range from 4.0 M to 12.8 M euros (reference year 2014). These estimates resemble the unit cost estimate range in Table A3. Similarly, Canada’s dike unit cost may be in the range of 2 M to 12 M euros based on Lenk et al. (2015). However, the rural estimates of dike unit cost for Hai Phong and Nam Dinh, Vietnam reported in Jonkman et al. (2013) are 10 times lower than those reported in Table A3 for rural areas.

> 1 Converted to 2014 price levels.
# Can Business Input Improve the Effectiveness of Worker Training? Evidence from Brazil’s _Pronatec-MDIC_

Stephen D. O’Connell, Lucas Ferreira Mation, Jo ̃ao Bevilaqua T. Basto, Mark A. Dutz July 31, 2017

**Keywords:** Vocational education and technical training; Training programs; Business development services; Labor demand; Unemployment; Brazil. (JEL J24, J23, J31, J68, J62, M53)

**Author affiliations and contact:** O’Connell: Corresponding author; Department of Economics, Massachusetts Institute of Technology (MIT) and the IZA Institute of Labor Economics, soconnel@mit.edu; Ferreira Mation: Instituto de Pesquisa Econˆomica Aplicada (IPEA), lucas.mation@ipea.gov.br; Bevilaqua: World Bank, jbevilaqua@worldbank.org; Dutz: World Bank, mdutz@worldbank.org.

**Acknowledgments:** The authors are grateful for invaluable research assistance by Nicolas Soares Pinto and Pui Shen Yoong. The authors thank several colleagues for helpful input and discussions, including Onur Altında ̆g, John Giles, Aguinaldo Nogueira Maciente, David McKenzie, Pedro Olinto, Truman Packard, and Romero Rocha, as well as participants in seminars at The World Bank, Pontificia Universidad Javeriana, the 2016 Jobs for Development Conference, Secretaria Especial do Assuntos Estrat ́egicos - Presidˆencia da Rep ́ublica, and the Minist ́erio da Ind ́ustria, Com ́ercio Exterior e Servi ̧cos. Staff at MDIC and MEC were instrumental in providing data and details of program administration. The study was conducted in partnership with the Instituto de Pesquisa Econˆomica Aplicada (IPEA) and funded as part of Brazil’s Productivity Programmatic Approach (P152871) at the World Bank. Views expressed are those of the authors and not of any institution with which they are associated.
PPP (both growth and level), ICT users (as a share of total population), population by age, exports and imports (both as a share of GDP).

# **4. Methodology**

To estimate our skill indexes, we first construct a conceptual link between tasks and skill categories following the same approach used by Autor, Levy, and Murnane (2003), Acemoglu and Autor (2011), Handel (2012), Spitz‐Oener (2006) and several other studies.

Within this approach, two main methods can be distinguished in the literature. The first one relies on occupational level task indexes estimated by experts, who rank occupations based on worker interviews. The O*NET data set is the outcome of such analysis for the US economy, with 44 different scores being assigned to each detailed level occupation. The second approach, instead, relies on direct worker-level information on the specific tasks performed on the job. It was pioneered by Handel (2008), who developed and used the STAMP survey (that later became the PDII), for the US. This approach allows observation of the tasks at a more disaggregated level, making within-occupation analyses possible. Our methodology falls into this second category, as we employ task information at the worker-level, exploiting the STEP surveys for several developing countries. As our objective is to compare our findings with the counterfactual results that one would obtain using the US classifications, we employ two different specifications, each of which as close as possible to the O*NET and to the PDII specifications, respectively. Since the results using the PDII specification lead to similar conclusions to the ones we obtain using the O*NET one, we only discuss the latter because it provides a greater disaggregation of skill groups (5 vs. 3 categories).

The O*NET specification refers back to the study of Autor, Levy, and Murnane (2003). This specification uses 5 different skill categories: Non-Routine Analytical, Non-Routine Interpersonal, Routine Cognitive, Routine Manual, Non-Routine Manual. In the original work of Autor et al. (2003), they make a map between DOT (the predecessor of O*NET) variables and these five skill brackets, with a single variable eliciting the information for each of them. In general, the five indexes measure the following:

- Non-routine cognitive analytical tasks (analytic reasoning skills).

6
Figure 12. Global growth forecasts

# A. Current-year growth forecast revisions

<!-- Start of picture text --> A. Current-year growth forecast revisions B. Long-term growth forecasts<br>Percentage points Percent Percent<br>2 4 7<br>World<br>Advanced economies<br>1 EMDEs 3 6<br>0 2 5<br>World<br>Advanced economies<br>EMDEs (RHS)<br>-1 1 4<br>2010 2013 2016 2019 1998 2001 2004 2007 2010 2013 2016 2019<br><!-- End of picture text --> Sources: Consensus Economics, World Bank.

Note: Panel A shows differences in growth forecasts for current years as of December of the year and those made a year ago, in percentage points. The sample includes 85 countries, consisting of 33 advanced economies and 52 EMDEs, weighted by GDP in U.S. dollars. In Panel B, the horizontal axis refers to the year of consensus forecast surveys. Annual averages of results from multiple surveys conducted in each year are presented. For example, forecasts for 2019 are based on surveys that were conducted in January, April, July, and October. The sample includes 38 countries, consisting of 20 advanced economies and 18 EMDEs, weighted by GDP in U.S. dollars.

48
no significant impact on the number of people hired by the firms in the last 12 months, nor on the types of people hired by the firms, whether it be hiring of candidates with degrees, or hiring more candidates on permanent contracts. This suggests that the job fairs did not significantly change how, and whom, firms hired, over the 12 month period.

## _<_ **Table 4 here.** _>_

Unsurprisingly, therefore, we find no impact of the firms’ overall work-force composition (Table 5). We asked firms about their entire current workforce (not just workers hired in the last few months). We find no impacts on the types of contracts held by different workers, their starting salaries, or the firms’ assessment of how well qualified their workers are, on average.

# **4.2 Impact on job seekers**

We use the same specification as equation 1 to analyze the ITT for job-seekers; we report results in Tables 6, A.2 and 7. These tables show regressions of key employment and search outcomes at an endline survey conducted four months after the second job fair. We cluster errors at the level of the enumeration area in which respondents live, to correct for the fact that the treatments were randomized at the level of the enumeration area. As in our earlier results, we report both conventional _p_ -values and False Discovery Rate _q_ -values.

We find no average effect on either endline employment outcomes or search methods. This is hardly surprising, in light of our results on firms. Table 6 reports effects on employment outcomes. The effect on key job quality outcomes such as ‘formal’ work or ‘permanent’ work are positive, but not significant. (These estimates are very much in line with the results suggesting that about 14 job seekers found jobs at the large formal firms at the job fairs, which would no doubt have been formal and permanent contracts; this effect would register as a 1.5 percentage point increase in the probability of having such a job.) We find that the fairs have no impacts on the types of jobs held by workers either.<sup>20</sup>

## _<_ **Table 6 here.** _>_

In Table 7, we consider average effects on search methods. We find that treated individuals were more likely to have found jobs by interview. Similarly, we find that treated respondents

> 20 These results can be found in Table A.2 in the online appendix, which reports effects on employment amenities.

13
Tol, R. S. (2009). The economic effects of climate change. _Journal of economic perspectives_ , _23_ (2), 29–51.

World Bank. (2016). _Global consumption database._ World Bank.

World Bank. (2018). _Poverty and shared prosperity 2018 : Piecing together the poverty puzzle_ . Washington, D.C.: World Bank Group. Retrieved from `http://documents.worldbank.org/curated/ en/104451542202552048/Poverty-and-Shared-Prosperity-2018-Piecing-Together-the-Poverty -Puzzle` World Bank. (2020). _Pandemic, recession: The global economy in crisis. global economic prospect._

# **Appendix 1**

The table below shows the household survey year for each country included the analysis.

|**No**|**Country**|**Survey year**|
|---|---|---|
|1|AGO|2018|
|2|AUT|2018|
|3|BDI|2013|
|4<br>|BEL<br>|2018<br>|
|5|BEN|2015|
|6|BFA|2014|
|7|BGD|2016|
|8|BGR|2018|
|9|BIH|2011|
|10|BOL<br>|2018|
|11|BRA|2018|
|12|BTN|2017|
|13|BWA|2015|
|14|CHE|2018|
|15|CIV|2015|
|16|CMR|2014|
|17|COD|2012|
|18|COL|2018|
|19|COM|2013|
|20|CRI|2018|
|21|CYP|2018|
|22|CZE|2018|
|23|DNK|2018|
|24|DOM|2018|
|25|ECU|2018|
|26|ESP|2018|
|27|EST|2018|
|28|ETH|2015|
|29|FIN|2018|
|30|FRA|2018|
|31|GBR|2017|
|32|GHA|2016|
|33|GRC|2018|
|34|GTM|2014|
|35|HND|2018|
|36|HRV|2018|

13
We begin by documenting the status quo of the marketplace for professional business services in Lagos and Abuja. We assess brand recognition of service providers amongst applicants to a government firm support program to show that the majority of service providers are unknown by most firms. We then conduct a survey of these providers to determine how easy it is to find providers, and to examine how they gain clients and the service guarantees they provide. We find there is no centralized database of providers, and that providers typically do not offer any sort of financial guarantee on their services, and rely largely on word-of-mouth for gaining new clients. As such, existing use of these services relies more on informal than formal reputation mechanisms, and potential clients have limited information about competing providers in the market. We then send mystery shoppers to visit and rate the quality of a set of accounting, marketing, and human resource specialists, to give us an independent measure of provider quality.

With these pieces, we develop a business services marketplace, consisting of a website that contains information about the different providers operating in a given geographic location, along with their brand logos, taglines describing their services, and contact information. We conduct a randomized experiment with 1,054 firms with 2 to 15 workers in Lagos and Abuja. The control group just receives a baseline interview and follow-up interview six months later. The treatment group is shown the business services marketplace, and randomized into one of four sub-treatments that vary whether they are just shown information, or also quality ratings (taken from the mystery shoppers). Firms are asked to make incentivized rankings over providers on the platform. We find that firms do respond to the quality ratings provided, with the average star rating (out of 5) of their top three-ranked providers increasing by 0.2 to 0.4 stars. Firms that receive the quality ratings treatment are less likely to choose low-quality (3 star or lower) providers and more likely to choose the top-ranked (5 star) providers. Providing quality ratings services therefore does change firm preferences over providers.

Finally, we conducted a six-month follow-up survey, which tests whether providing information about business service providers, and quality ratings, is enough to get more firms to use this market for business services. We find use of the market for business services is low in the control group, with only 10 percent of firms using a service provider, and that the information and ratings treatments have no significant impact on use of these services. Our estimated treatment effects are 3
Quality and Accountability in Healthcare Delivery: Audit Evidence from Primary Care Providers in India Jishnu Das, Alaka Holla, Aakash Mohpal, and Karthik Muralidharan<sup>_∗_</sup>

**Keywords** : Standardized patients, quality of healthcare, healthcare markets, audit studies, India

**JEL Codes** : D40, H10, H41, I11

> _∗_ Das: The World Bank Group, Washington, DC, jdas1@worldbank.org. Holla: The World Bank Group, Washington, DC, aholla@worldbank.org. Mohpal: University of Michigan, Ann Arbor, MI, amohpal@umich.edu. Muralidharan: University of California, San Diego, CA, kamurali@ucsd.edu. Acknowledgements: We are especially grateful to Michael Kremer for his involvement as a collaborator in the early stages of this project and for subsequent discussions. We thank Brian Chan, Veena Das, Ranendra Kumar Das, Diana Tabak and Manoj Mohanan for their contribution to designing and implementing the SP methodology, and thank Eun-Young Shim for her contribution to the theoretical appendix. We are grateful to Innovations for Poverty Action, CT, and Pratap Bhanu Mehta and the Center for Policy Research, New Delhi for hosting the project and providing logistical support and infrastructure. We also thank Sreela Dasgupta, L. Ravi and Anvesha Khandelwal for project management support, and Monisha Ashok, Carl Liebersohn, Prerna Mukharya, Suzanne Plant and Anand Shukla for excellent research assistance. The project would not have been possible without the dedication of our SPs and Purshottam, Rajan Singh, Devender, Charu Nanda, Simi Bajaj, Geeta and other staff at Institute of Socioeconomic Research on Democracy and Development (ISERDD) in Delhi. The Social and Rural Research Institute (SRI), New Delhi oversaw data collection of all other aspects other than SPs. This study was funded by the Global Health Program of the Bill and Melinda Gates Foundation through Grant No. 50728 and the Knowledge for Change Program at the World Bank. The findings, interpretations and conclusions expressed in this article are those of the authors and do not necessarily represent the views of the World Bank, its executive directors, or the governments they represent.
# **FIGURES**

**<u>Figure 1:Macroeconomic Indicators in Bulgaria, 1991-2006e</u>**

<!-- Start of picture text --> Panel (A): Annual CPI (Dec.-to-Dec.) Panel (B): Annual growth in real GDP<br>06' 7.4 06' 5.6<br>05' 6.7 05' 5.5<br>04' 4.0 04' 5.7<br>03' 5.6 03' 4.5<br>02' 3.8 02' 4.9<br>01' 4.8 01' 4.1<br>00' 11.3 00' 5.4<br>99' 7.0 99' 2.3<br>98' 1.6 98' 4.0<br>97' 547.7 5.6- 97'<br>96' 364.1 9.4- 96'<br>95' 32.9 95' 2.9<br>94' 121.9 94' 1.8<br>93' 63.9 1.5- 93'<br>92' 79.5 7.3- 92'<br>91' 473.7 11.7- 91'<br><!-- End of picture text --> Source: Statistical Agency of Bulgaria.

- 19 -
reflected a reassessment of long-term growth prospects of energy exporters in supply-driven oil price drops. Meanwhile, growth gains in energy importers were gradual and delayed (de Michalis, Ferreira, and Iacovelli forthcoming).

**_Policies mattered._** Energy-exporters tend to be particularly hard-hit by supply-driven oil price plunges, but even in those plunges, energy-exporting EMDEs with flexible exchange rates, lower debt, and more diversified export bases suffered smaller output losses than those with fixed exchange rates, higher debt, and less diversified export bases.<sup>13</sup>

# **5. Lessons from the 2014-16 oil price plunge and comparison with the 2020 episode**

The 2014-16 oil price plunge was followed by a sharp global growth decline. The oil price plunge of 2020 is similarly unlikely, by itself, to trigger a global growth rebound because of its demanddriven nature and the disruptions that it may cause in energy-exporting EMDEs.

# **5.1 The 2014-16 oil price plunge**

In late 2014, the 50 percent decline in oil prices between June and November 2014 was expected to lift global GDP by around 0.3-0.7 percent (Arezki and Blanchard 2014). The cheaper cost of a critical input into global production was expected to raise global activity, and the transfer of income and wealth from energy-exporting economies with higher savings rates to energyimporting economies, with higher propensities to spend, was also expected to boost global demand (Baffes et al. 2015; World Bank 2015a). While lower oil prices were expected to depress investment in the oil industry, this was expected to be more than offset by the boost to consumption and energy-intensive sectors (transportation, manufacturing, and agriculture).

However, the expected “shot in the arm” to global growth was slow to materialize. Instead, in 2016, global growth slowed to a near-post-crisis low of 2.6 percent. Global growth only picked up in 2017-18 once considerable policy stimulus was put in place in major economies. The disappointing short-term growth trajectory reflected several factors as outlined below.

> 13 In demand-driven plunges, similar patterns emerged but differences were less pronounced and there was wide heterogeneity between countries.

14
Esty, Daniel C. 1996 Greening World Trade, in the World Trading System: Challenges head (ed. Jeffrey Schott), Washington: Institute for International Economics Esty, Daniel C. 1994. Greening the GATT: Trade, Environment and the Future. Washington: Institute for International Economics.

European Commission, 2009. <u>Decision of determining, pursuant to Directive 2003/87/EC of the European Parliament and of the Council, a list of sectors and subsectors which are deemed to be exposed to a significant risk of carbon leakage. Website europa.eu.</u> de Gorter, H. and E. Kliauga, 2006. Reducing tariffs versus expanding tariff rate quotas. In K. Anderson and W. Martin, Editors. <u>Agricultural trade reforms and the Doha Development Agenda.</u> World Bank. Washington, DC.

Graichen, V., K. Schumacher, F.C. Matthes, L. Mohr, V. Duscha, J. Schleich and J. Diekman, 2008. <u>Impacts of the EU Emissions Trading Scheme on the industrial competitiveness in Germany.</u> Unweltbundesamt Research report 3707-41-501.

Hoekman, B., W. Martin and A. Mattoo, 2010. Conclude Doha: It matters!” Policy research working paper 5135, World Bank. Washington, DC.

Horn, H. and P. Mavroidis, 2008. The permissible reach of national environmental policies. Mimeo 24 May 2008. ENWINED project.

Horn, H. and P. Mavroidis, 2010. <u>Border Carbon Adjustments and the WTO. Mimeo. CEPR,</u> London.

Hourcade, J.C., D. Demailly, K. Neuhoff and M. Sato, 2008. Differentiation and dynamics of EU ETS <u>industrial competitiveness impact. Climate Strategies, available at http://www.climate-strategies.org</u> Houser, T., R. Bradley, B. Childs, J. Werksman and R. Heilmayr, 2008. Leveling the carbon playing <u>field. Peterson Institute for International Economics and World Resources Institute. Washington, DC.</u> Hufbauer, G.C. and C. Gabyzon, 1996. Fundamental tax reform and border tax adjustments. Peterson Institute for International Economics. Washington, DC.

Hufbauer, G.C., Charnovitz, S. and J. Kim, 2009. <u>Global warming and the world trading system.</u> Peterson Institute for International Economics. Washington, DC.

Hufbauer, G.C. and J. Kim, 2010. <u>Climate change and trade: Searching for ways to avoid a train wreck. Second Conference on climate change, trade and competitiveness, The Graduate Institute</u> Geneva, Center for Trade and Economic Integration.

- Jensen, M.F., 2009. <u>Leveling or mining the playing field?  Implementation problems of carbon motivated adjustment taxes.  Mimeo, November.  Global Trade and Finance Architecture (GTFA),</u> World Bank-DFID.

Kanter, J. 2009. Council in France blocks a carbon tax as weak on polluters. Green Inc. Available on <u>http://greeninc.blogs.nytimes.com</u> Kanter, J. 2010. Moments of truth for CO2 permits. International Herald Tribune. April 1, 2010.

Kovallyova, S. 2010. Subsidy cuts in Europe to help China. International Herald Tribune. February 16, 2010.

30
Policy Research Working Paper 5225

# **Abstract**

Despite recent efforts toward collecting evidence of vertical specialization for developed economies, scarce work has been dedicated to developing economies and to Argentina, in particular. The key contribution of this paper is to provide comparable estimates of vertical specialization as reflected in the imported content of exports in Argentina. Drawing on the 1997 input-output matrix for Argentina, the authors estimate that the imported content of the country’s exports is about 14.5 percent of total exports, lower than for other emerging economies. They also provide estimates of imported content for more disaggregated product categories.

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 gain a deeper understanding of the main trade patterns of LAC countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at panoscasero@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