rafmacalaba/datause-encoder-data huggingface dataset:
Magnier, A. and J. Toujias-Bernate, (1994) Technology and Trade: Empirical Evidences for the Major Five Industrialized Countries, _Weltwirstschaftliches Archiv_ band 130 heft 3, pp 494-520

> Mohnen P. (1996) "R&D Externalities and Productivity Growth" , _STI Review_ n. 18 OECD Paris

Nadiri M.I, S. Kim (1996) _International R&D Spillovers, Trade and Productivity in Major OECD Countries_ NBER w.p. 5801 Nelson, R.R. (ed.) (1993), _National Innovation Systems,_ New York, Oxford University Press OECD (1992) _Stan Databasefor Industrial Analysis,_ Paris, OECD OECD (1996) _Science, Technology and Industry Outlook,_ Paris OECD Padoan P.C. (1996) _Trade and the Accumulation and Diffusion of Knowledge,_ World Bank Policy Working Paper, 1679 Pavitt, K. (1984) Sectoral Patterns of Technical Change: Towards a Taxonomy and a Theory, _Research Policy_ vol. 13 p.p. 343-75 Reinicke W. 1996 _Deepening the Atlantic_ Bertelsmann Foundation Publishers, Gutersloh Sjoholm F. (1996) International Transfer of Knowledge: the Role of International Trade and Geographic Proximity, _Weltwirtschaftliches Archiv_ 132 (1) 97-115 Venables A. (1995), Economic Integration and the Allocation of Firms, _American Economic Association P&P,_ 85 296-300.

18
Using high resolution DEM maps, we downscale and prepare the flood inundation map (i.e. flood fraction 0 to 1.0) for each AOGCM (spatial resolutions: 2.5 min x 2.5 min) (Hu _et al._ , 2015). From these outcomes, we extract the flood extent for return periods greater than 1 in 100 years, 1 in 50 years and 1 in 30 years and produce the mean flood inundation map for 2016–2035 and 2036–2055 at RCP4.5 and RCP8.5 respectively. We selected these return periods because the flood protection standard for Chinese infrastructures typically ranges between 1 in 10 years to 1 in 100 years, as required by the law.

With the above outcomes, we evaluate the extent of changing flooding probability, calculating the difference between two selected subsequent periods. Here we subtract the inundation value for each grid (spatial resolutions: 2.5 min x 2.5 min) and estimate the total flooded area between period 1 as defined by the difference between 1986–2005 and 2016–2035, and period 2 as defined by the difference between 1986–2005 and 2036–2055, for both pathways RCP4.5 and RCP8.5. This difference indicates how specified grids in China would face different levels of changing flood probabilities – increasing, decreasing or a continuation of the status quo.

# **2.2.2. Estimating impact on WWTP**

To demonstrate how Chinese wastewater treatment plants may be exposed to the changing probability of flooding, we identify those wastewater plants that face increasing, decreasing or non‐ changing flooding hazard by intersecting them with inundation cells, as estimated above. We subsequently calculate the net change, as defined by the difference between the number of assets facing increasing hazard and those that face decreasing hazard. We also calculate the number of users each wastewater plant serves, as described in Section 2.1, as well as the those that face increasing/decreasing flood hazards. Below we explain the detailed process of estimating the flood exposure of China’s WWTPs.

To understand the exposure to flooding we look at the spatial intersection of particular assets with the flood grid cells. The flood grid cells are denoted by H��h�, ... , h�� is space. For each particular grid cell, h� , we first estimate the change in flood fraction ∆f� between the representative time period (e.g. 2016‐2035) and the baseline (1986‐2005) for the particular return period and RCP scenarios.

To estimate the numbers of WWTPs flooding we intersect the wastewater assets with the flood cells. We assemble the results based on whether the change is flood fractions are positive (for increasing flood exposures) or negative (for decreasing flood exposures). Hence, two types of results sets are created:(1) P� �a�: a� ∩h� and ∆f� �0� for all assets with positives (increases) in flood exposures; (2) N� �a�: a� ∩h� and ∆f� �0� . Subsequently the number of assets (and their users) at increasing exposure to flood are estimated as |P| , and those (and their users) at decreasing exposure to flooding are estimated as |N| , while the assets (and their users) with no‐changes in flood exposures are estimated as |I| �|P| �|N| where |I| is the total number of WWTPs.

7
- Schiffer, M. and Weder, B., 2001. Firm Size and the Business Environment: Worldwide Survey Results. International Finance Corporation Discussion Paper 43.

- Stephanou, C. and Rodriguez, C., 2008. Bank Financing to Small- and Medium-Sized Enterprises (SMEs) in Colombia. World Bank Policy Research Working Paper 4481.

- Strahan, P. E. and Weston, J., 1996. Small Business Lending and Bank Consolidation: Is There Cause for Concern? _Current Issues in Economics and Finance,_ Federal Reserve Bank of New York, 2:1-6.

- World Bank, 2007a. Bank Financing to Small and Medium Enterprises: Survey Results from Argentina and Chile. Mimeo.

- World Bank, 2007b. Bank Lending to Small and Medium Enterprises: The Republic of Serbia. Mimeo.

18
<!-- Start of picture text --> (a) Output Growth (b) Consumption Growth (c) Investment Growth<br>60 _ 140 _ 500 _<br>120 BRA BRA<br>50 BRA FJI 400 _ FJI<br>FJI 100 TUR TUR<br>TUR USA _ USA<br>40<br>USA ZWE 300 ZWE<br>_ ZWE 80 _<br>30 _ _ _<br>_ 60 _ 200 _ _<br>_<br>20100 _______ _ _______ _ _ _______ _ _ _______ _ __ 40200 ______ _ _ _ _____ _ _ _ _ _____ _ _ _____ _ _ _ __ 1000 ______ ________ _______ _ _ _______ _ __<br>1 2 3 4 1 2 3 4 1 2 3 4<br>Levels Levels Levels<br>Wavelet variance Wavelet variance Wavelet variance<br><!-- End of picture text --> Figure 8: Wavelet variance and non-Gaussian confidence intervals of the (a) Output, (b) Consumption, (c) Investment growth for BRA, FJI, TUR, USA, and ZWE Figure 8 also shows the wavelet variance estimates for consumption and investment growth series with associated confidence intervals. Changes in consumption growth rates show similar variability patterns (see panel (b) of Figure 8). Here, Zimbabwe, a country representing the low income group shows the most volatility for scales _j_ = 1 _,_ 2 _,_ 3. The accuracy of confidence interval estimates at level _j_ = 4 is rather low, due to small sample size and many coefficients missing because of wavelet circularity assumption. Panel (c) of Figure 8 shows that the changes in the Investment growth rates are much more volatile. It is worth mentioning that Brazil follows the same volatility pattern as the USA on highfrequency scales. Finally, the investment growth series for the other three countries (FJI, TUR, ZWE) show high short-term variability.

# **6 Co-movement Analysis**

When analyzing the growth and volatility in a group of economies, one question that arises is to what extent they are synchronized. In this section, we demonstrate how the wavelet based techniques can help to explore this synchronization. In particular, as these 19
|**Appendix Table 5: Ba**|**lance of Cont**|**rols among**<br>Regression-|**Boys**<br>|
|---|---|---|---|
||Mean among<br>Control|<br>Adjusted<br>Difference|Robust Standard<br>Errors|
|White Child|0.28|0.03|0.03|
|Black Child|0.13|-0.02|0.02|
|Mixed Race Child|0.57|-0.02|0.03|
|Other Race Child|0.03|4.9E-3|0.01|
|Birthweight (in kilos)|3.23|0.03|0.04|
|Birth Height (in cms)|49.48|0.41|0.25|
|Planned Birth|0.33|-0.01|0.03|
|First Born|0.45|-0.04|0.03|
|Age of the Mother at Birth|20.26|-0.16|0.33|
|Prenatal Care|0.96|-0.01|0.01|
|Natural Birth Delivery|0.69|-0.03|0.03|
|Premature Birth|0.12|0.04|0.02|
|Breastfed up to 6 Months|0.79|-0.06|0.03|
|Household per Capita Income|552.43|-61.19|43.88|
|Household Size|4.92|-0.08|0.32|
|Age of Caregiver|29.39|0.01|0.64|
|Caregiver can Read and Write|0.96|0.02|0.01|
|Caregiver Finished Middle School|0.69|0.04|0.03|
|Caregiver Graduated from High School|0.34|0.03|0.03|
|_Nt_Th tbl itit hth<br>Caregiver has a College Degree|h ttt<br>0.01|tl b<br>4.7E-3|bl i<br>0.01|

_Notes_ : The table investigates whether the treatment and control boys are comparable in terms of predetermined characteristics. The first column shows means among the control boys. The second column reports the coefficient on the treatment indicator from regressions of the dependent variable listed in the row on the treatment indicator and on dummies specific for childcare center × age group. The third column shows robust standard errors. _N_ = 1,108.

40
important complications. First, the two properties will be difficult for respondents to disentangle. A mechanism that prevents complications evolving into problems can also be viewed as a way to resolve issues. A mechanism that resolves problems, perhaps with the explicit threat of sanctions, also can prevent them from even arising because of the deterrence effect. Indeed, 'preventing' can be thought of as being produced by the shadow of 'resolving'. Second, respondents might view two very similar questions as repetitive, which reduces the overall quality of a survey.<sup>36</sup> Third, piggybacking on the regular cycle of the WBES meant a strict constraint on the total number of questions that we could ask. Given these conceptual and practical considerations, we chose to ask about both resolution and prevention in one question.

A further issue revolved around the phrasing of the term to connote success when resolving and preventing problems. We chose to use a single, simple term 'effective'.<sup>37</sup> We did not ask respondents to focus upon whether agreements work in an efficient way because this is too demanding a criterion and probably one that is less familiar for business managers than economists. In using this word, we avoided weighted terms such as importance, usefulness, or frequency of either use or occurrence, even as these elements, and perhaps more, enter in determining effectiveness.<sup>38</sup> The terms "important" or "useful" carry the meaning of value and can be prone to more subjective interpretation than the term "effective". Frequency of use or

> 36 Survey methodologists warn against such repetitive design since it contributes to respondents' survey fatigue and affects their responses through the rest of the questionnaire, and even their overall participation. 37 In the papers reviewed by Cao and Lumineau (2015), equivalent notions are satisfaction with the exchange relationship and performance of the partner in the exchange. These notions are entirely analogous to our 'effective', although measured in a variety of different ways in the papers reviewed. Poppo and Zhou (2016) refer to the notion of "harms" to exchange performance, which too is analogous to our 'effective'.

> 38 Hendley and Murrell (2003), whose survey question is adapted in this paper, mention all these terms but also provide detailed clarifications to respondents, which was not possible in the context of the WBES.

19
# A Few Good Masks:

Evidence from Mask Manufacturing in Rwanda during the COVID-19 Pandemic<sup>∗</sup> Kieran Byrne<sup>†</sup> Florence Kondylis<sup>†</sup> John Loeser<sup>†</sup> Denis Mukama<sup>‡,‡</sup> _JEL Classification:_ O10, O12, H32, I15, F14 _Keywords:_ masks; trade networks; COVID-19; non-pharmaceutical pandemic policy.

∗This paper benefited from comments from Pierre Bachas, Paul Christian, Ana Fernandes, Emma Frankham, Megan Lang, David McKenzie, Mushfiq Mobarak, Thiago Scot, Lucas Zavala, and seminar audiences at the World Bank. This research received research funding from the European Union, as well as the World Bank’s Rwanda Country Management Unit, the Research Support Budget, the Global Tax Program and the Knowledge for Change program. This work would not have been possible without the support of the leadership and technical staff at Rwanda Revenue Authority for this collaboration. In particular, we thank Dr. Innocente Murasi, John Karangwa, Naphtal Hakizimana, Theonille Mukamana, Felipe Aguirre Martinez, Raymond Kamana and Mellas Mpakaniye. The views expressed reflect neither the views of the World Bank, nor of the Rwanda Revenue Authority. All errors are our own.

> †Development Impact Evaluation, World Bank

> ‡Rwanda Revenue Authority
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|(0.07035)|(0.07011)|(0.12617)|(0.11184)|-0.00207|0.00068|-0.01460|0.03929|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||(0.08004)|(0.07903)|(0.13704)|(0.11993)|||||
|Log of refugees (50km) predicted by Log of|||||||||||||
|Distance IV (square-root of mean border<br>distance)|||||||||0.04785<br>(0.07721)|0.04642<br>(0.07728)|-0.04025<br>(0.13570)|0.00884<br>(0.12045)|
|Observations|843|842|533|533|843|842|533|533|843|842|533|533|
|Kleibergen-Paap rk Wald F|9.851|9.829|7.005|7.251|10.07|10.03|7.201|7.445|7.852|7.840|6.104|6.338|
|Root MSE|0.517|0.513|0.437|0.367|0.517|0.514|0.437|0.367|0.517|0.513|0.438|0.368|
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.03556|0.03295|0.04755|0.04190|Panel|D (Non-agricul|ture self-emplo|yment)|||||
||(0.08114)|(0.07635)|(0.14670)|(0.15579)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.01600<br>(0.07556)|0.01666<br>(0.07132)|0.00760<br>(0.13468)|-0.00204<br>(0.13978)|||||
|Log of refugees (50km) predicted by Log of|||||||||||||
|Distance IV (square-root of mean border<br>distance)|||||||||0.08898<br>(0.11750)|0.08291<br>(0.11115)|0.17809<br>(0.20970)|0.18533<br>(0.22979)|
|Observations|1,068|1,067|669|667|1,068|1,067|669|667|1,068|1,067|669|667|
|Kleibergen-Paap rk Wald F|8.789|8.719|4.676|4.530|8.380|8.323|5.320|5.117|6.527|6.468|3.440|3.363|
|Root MSE|0.515|0.511|0.414|0.327|0.515|0.511|0.413|0.325|0.517|0.513|0.426|0.345|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex “male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,|||||||||||||
|<br>education, occupation, other income sources,<br>land ownership)|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

38
# **2. The role of Special Economic Zones**

Accepting that agglomeration is both necessary and desired to achieve the benefits of regional scale, the question is how these processes can be best facilitated. The customs union and the wider processes of integration within the EAC should help overcome barriers to agglomeration caused by trade policies, border-related issues, investment, and the free movement of labor. But even assuming these processes are efficient, a number of barriers still remain. These include:

- Lack of reliable infrastructure, in particular transport, power, and telecommunications;

- Difficulty in accessing serviced industrial land and slow, cumbersome processes to secure it;

- Red tape and slow decision making relating to licenses, permits, and other aspects of starting and operating a business; and

- Information failures that prevent the private sector from coordinating investment activity.

One instrument used internationally to overcome such barriers to investment and agglomeration is special economic zones (SEZs). Special economic zones are spatially delimited areas within an economy that function with administrative, regulatory, and often fiscal regimes that are different (typically more liberal) from those of the domestic economy. Operating through a variety of different forms such as “export processing zones”, “economic processing zones”, “free zones”, and “foreign trade zones”, SEZs aim to overcome barriers that hinder investment in the wider economy, including restrictive policies, poor governance, inadequate infrastructure, and problematic access to land. SEZs have been an important policy instrument for many governments seeking to attract foreign investment, promote export-oriented growth, and generate employment. Their popularity as a policy tool has grown enormously in the past 20 years – in 1986, ILO’s database reported 176 zones in 47 countries. By 2006 it reported 3,500 zones in 130 countries (Boyenge, 2007).

While there is a long history of failure around the world in using instruments like SEZs to promote investment in remote regions, the evidence suggests they can be highly effective when targeting regions that already have natural or economic geography advantages (World Bank, 2008). While SEZs are unlikely to be a trigger for agglomeration in lagging regions with low population densities, in places like China, where SEZs targeted coastal trade gateways, they have proven to be powerful catalysts for growth. Thus, while the World Bank’s _World Development Report 2009_ (World Bank, 2008) suggests SEZs be approached cautiously, it supports the use of such hard and soft infrastructure to reinforce existing geographical advantages.

And so by leveraging agglomeration, SEZs have the potential to play a role in harnessing integration processes to support regional competitiveness. In an open regional integration context, SEZs may serve as regional public goods (see Box 1), as they may have significant spillover effects across the region – this gives them the potential to help entrench the integration process in the region.

5
3

# **1. Data Sources**

The analysis conducted in this paper is based on the 2004 National Household Survey, EPCV 2004. The EPCV 2004 is the last in a series of nationally representative surveys that the Mauritanian authorities have carried out since 1992. It was conducted to update the main household well-being indicators, with a particular focus on poverty, and support the preparation of Mauritania’s second PRSP, as well as the elaboration of regional poverty reduction strategies in the _wilayas_ (provinces) of Adrar, Brakna, Nouadhibou, Inchiri and Trarza. Four types of questionnaires were elaborated for the EPCV 2004: (i) a core welfare indicators questionnaire (CWIQ) covering basic social indicators<sup>6</sup> ; (ii) an expenditure questionnaire, focusing on current and occasional expenditures, transfers, revenues, credit and savings; (iii) a price questionnaire, focusing on the price of principal products in the different zones covered by the survey; and (iv) a community questionnaire, focusing on the local infrastructure in the areas covered by the survey (GIRM 2006a).

At the household level, the EPCV 2004 provides information on households’ assets, income, and expenditures. At the individual level, the data includes basic demographic characteristics (e.g. gender, age) as well as data on education, health, and employment. In order to better capture the important effects linked to seasonality, the EPCV 2004 was carried out during two separate phases: (i) a first 3- month phase – between end-August and end-November 2004 – during which information was collected on social indicators and household expenditure; and (ii) a second phase – between AprilJune 2005 – during which information was collected on education (which was not covered in the previous phase under the CWIQ) and household expenditure. The EPCV sampled a subset of households by using two-tier random sampling in each _wilaya_ of the country. The sampling base during the first phase (sampling of primary units) was established on the basis of a list of Census Districts (CD) extracted from the 2000 Census. Subsequently households were randomly selected within each CD. The surveyed population was in the order of 52,609 individuals, of which 29,038 are of working age (15 to 64 years old), belonging to 9,385 households. The sample comprised 95 percent of sedentary people. Poverty was determined at the household level based on per capita expenditures using a 1$ a day per capita poverty line. After taking into account inflation and exchange rates, this poverty line in the EPCV 2004 translates into 94,600 Mauritanian Ouguiyas (MRO) per year and per capita for 2004. The poverty line for extreme poverty was set at 71,550 MRO (see GIRM 2006a).

> 6 The CWIQ is a package which aims at collecting information to measure the access to, utilization of, and levels of satisfaction concerning the main social and economic services. The CWIQ sheets can be scanned, reducing the data entering, cleaning, and analysis phases.
from the guarantee beneficiary, and an entity’s ability to cover at least part of its debt service payments when they occur. At the moment, LGD is assumed at 100 percent.

**Table 6: Probabilities of initial distress by rating and time horizon in South Africa**

|**Moody's**<br>**rating**<br>**e**|**Internal rati**|**ng**<br>**Year 1**<br>**(%)**|**Year 2**<br>**(%)**|**Year 3**<br>**(%)**|**Year 4**<br>**(%)**|**Year 5**<br>**(%)**|**Year 6**<br>**(%)**|**Year 7**<br>**(%)**|**Year 8**<br>**(%)**|**Year 9**<br>**(%)**|**Year 10**<br>**(%)**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Aaa<br>**Grad**|1|0.003|0.006|0.012|0.023|0.039|0.057|0.076|0.093|0.103|0.107|
|Aa2<br>**ent**|2|0.022|0.047|0.073|0.108|0.136|0.150|0.150|0.141|0.129|0.120|
|A2<br>**estm**|3|0.062|0.151|0.183|0.255|0.314|0.351|0.365|0.360|0.347|0.334|
|Baa2<br> **Inv**|4|0.174|0.352|0.350|0.444|0.511|0.549|0.563|0.563|0.563|0.573|
|Ba2<br>**grade**|5|1.110|2.111|1.954|2.294|2.418|2.342|2.136|1.890|1.676|1.543|
|B2<br>**ent**|6|3.904|5.729|4.471|4.884|4.874|4.509|3.930|3.289|2.705|2.253|
|Caa2<br>**estm**|7|15.894|11.760|7.339|7.021|6.300|5.349|4.341|3.411|2.643|2.070|
|Ca<br>**b-inv**|8|54.147|10.977|5.315|4.427|3.569|2.793|2.133|1.603|1.202|0.918|
|C<br>**Su**|9|100.000|-|-|-|-|-|-|-|-|-|

_Source: National Treasury of South Africa, Risk Integrated, Moody’s_ Going forward, NTSA intends to calculate the present value of expected losses for the lifetime of a guarantee. To do so, annual expected losses need to be discounted to the present and summed up.

To use a simple example, imagine a guarantee on a one‐year loan, with a face of value 100 and no interest payments. Assume the likelihood of the entity not being able to repay, or PD, at 15 percent. Further assume that if the entity were unable to repay the loan, it would have cash of 30 (LGD) to service at least part of the loan. In this case expected loss = 100 x 15 percent x 70 percent = 1.05. At a discount rate of 10 percent, the present value of expected loss would be 1.05/1.1 = 0.95. Table 7 provides an illustrative example over a multi‐year period.

**Table 7: Illustrative example of expected loss from a guarantee**

|_Year_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Credit rating|6|6|6|6|6|6|6|6|6||
|Annual PD|1.11%|2.81%|3.63%|4.50%|5.19%|5.59%|5.67%|5.49%|5.15%|4.78%|
|LGD|50%|50%|50%|50%|50%|50%|50%|50%|50%|50%|
|Principal outstanding end of year|100|100|100|100|100|100|100|100|100||
|Principal payment|0|0|0|0|0|0|0|0|0|10|
|Interestpayment|5|5|5|5|5|5|5|5|5||
|Annual expected loss|0.03|0.07|0.09|0.11|0.13|0.14|0.14|0.14|0.13|2.51|
|Discount rate|5.0%|5.5%|6.0%|6.5%|7.0%|7.5%|8.0%|8.5%|9.0%|9.5%|
|NPV of annual EL|0.03|0.06|0.08|0.09|0.09|0.09|0.08|0.07|0.06|1.01|
|NPV of EL|1.66||||||||||

_Source: Risk Integrated, authors_ 19