rafmacalaba/datause-encoder-data huggingface dataset:
health worker training, to support a particular health intervention or a myriad of other potential development-oriented government actions that the World Bank finances. In some cases, projects simply take the form of budget support to receiving countries. A document describing the project is prepared by World Bank staff and includes a proposed amount of World Bank funding. An important ingredient in this initial document is a statement of the project’s “development objective,” which summarizes what the project intends to achieve. This development objective is important because the evaluation data we use rates the performance of the project relative to this objective. Once the project is approved by the Board of Executive Directors of the World Bank, it is implemented over several years, with the project spending financed by disbursements on loans provided by the World Bank.

On the World Bank side, each project is staffed by a project team and led by a task manager (formally known as a "task team leader"). At least twice a year, World Bank task managers are required to report on the status of the projects for which they are responsible, by completing an Implementation and Status Results (ISR) report (previously known as a Project Status Report (PSR)). As discussed below, these ISRs provide us with a rich set of project-level variables measured over the life of the project. Naturally, the World Bank’s information systems also capture the annual disbursements on loans associated with each project over its duration. We use this information to construct disbursement rates on projects. Once a project is concluded, the responsible task manager produces an Implementation Completion Report (ICR) (previously known as a Project Completion Report (PCR)), which provides a comprehensive review of various dimensions of project outcomes. As we discuss further below, the reports form the basis of several of our outcome measures.<sup>2</sup> All of the results that follow are representative only of the set of World Bank projects actually implemented over the past 30 years and for which we have detailed data. There should be no presumption that this set of World Bank projects is necessarily representative of all potential public investment projects in developing countries, and our results should be interpreted with this in mind. Indeed, the process by which projects are selected and implemented reflects a complex balancing of World Bank and recipient country interests and priorities, and therefore has implications for the broader relevance of our results. For instance, it is plausible that the World Bank has much more influence over the set of projects it finances in the poorest, aid dependent countries than those implemented in richer

> 2 It is worth noting that projects are typically “complete” in the data once disbursements are completed and the implementation stage of the project is finished.  Of course many projects are intended to continue delivering benefits for many years after this completion date.  And by the same token, these benefits may not be apparent for many years after “completion” as well.

6
smallest quintile of firms (who we know have limited access to bank finance) who report productivity increases when their primary financing sources include internal, informal, family or other financing.

# **6. Discussion**

In this section, we discuss our findings in the context of recent evidence on the link between finance and growth in China. Allen et al. (2005) find that while the Private Sector dominates the State and Listed sectors in both the size of the output and the growth trend, there is a huge reliance on non-bank financing sources amongst firms in the Private sector. Given the weak external markets and poor legal protection of minority and outside investors in China, Allen et al. (2005) attribute the growth of the Private sector to the reliance on alternative financing channels and corporate governance mechanisms such as reputation and relationships. They interpret this as evidence against the finance and growth literature that the development of stock markets and a banking system is important for growth of firms and economies. However, due to data limitations, the results in Allen et al. (2006) are based on an analysis of the 17 largest, and perhaps unrepresentative, firms in the two most developed regions in the country. Further, their definition of informal financing channels includes retained earnings, informal financing, issuance of equity and all other sources of fund raising except bank financing, state funding and foreign investment.

While our data on 2400 firms (including 1720 non-publicly traded and non-state companies) confirm the wide use of financing channels other than Bank Finance, our regression results suggest that it is the formal financing channel, specifically Bank 41
antitrust cases againct chemical firms, the EC Cormission found that industry agreements existed that had "as their object or effect the prevention, restriction, or distortion of competition within the common market" (Article 851 of the EC Treaty). Many cther EC industries that were accused of antitrust activities were involved in the antidumping compl.ints as well. Examples include EC producers of low-density polyethylene (antidumping cases were brought against Czechoslovakia, East Germany, Poland, and the Soviet Union, among others), polyvinyl chloride (Czechoslovakia, East Germany, Hungary, and Romania were among the accused), and soda ash (Bulgaria, East Germany, Poland, Romania, and the Soviet Union). Collusion could have been involved among EC producer of other products as well, for which antitrust investigations had not yet been launched.

Messerlin (1989, 12) and other studies (UNCTAD 1982) also show that EC producers might have tried to use antidumping complaints to temporarily affe(prices. The announcement of an investigation has a "freezing" effect on falling prices (as in the case of low-density polyethylene and polyvinyl chloride). The prices remain stable for the period of investigation and then increase sharply if the case is "positively" resolved and protective measures are imposed. Several empirical studies of antidumping measures have documented their more frequent use in combating low-price imports than lower-export-thanhome-price imports.7 These two motives well explain the large number of complaints lodged overall by the EC chemical industry, as well as a number of cases against its largest competitor, the U.S. industry. In their struggle against low-price imnorta. Rutropean companies used all the weapons at their disposal, including antidumping complaints. But what explains the large number of complaints against producers from Eastern Europe, which accounted for a very small percentage of the E- iarket and did not exercise any significant competitive pressures?8 The answer is straightforward: because it was easyl It is easy -- both politically and technically -- to make a case against a socialist country.

-8-
|spouse was<br>informal wage|-0.13<br>(0.13)|-0.4*<br>(0.16)|0.024<br>(0.2)|||---|0.09<br>_(0.55)_|0.23<br>(0.77)|-1.26<br>(0.71)|
|---|---|---|---|---|---|---|---|---|---|
|spouse was<br>formal wage|0.13<br>(0.093)<br>|0.22<br>(0.13)<br>|0.36*<br>(0.15)|||---|-0.0038<br>(0.31)|0.63<br>_(0.55)_|0.41<br>(0.57)|
|spouse wage<br>contract|-0.29*<br>(0.13)|-0.32*<br>(0.17)|-0.42<br>(0.19)|_|---|---|-1.0<br>(0.8)|-0.71<br>(1.06)|0.12<br>(1.18)|
|sample size<br>||12223<br>|||801|||1054||
|log likelihood<br>||-11804|||-766.9|||-1105.36||
|Chi<br>||4151**<br>|||283**|||175**||
|(d.o.f.)<br>||(114)<br>|||(99)<br>|||(108)<br>||

the estimates are from Model 3, the Model that had the best fit. The coefficient values for Models I and 1I differ slightly, but the sign and magnitude of the coefficient estimates is very similar among the three models. ** significant at the 1% level

> * significant at the 5% level

46
**Table 6c:  Mexico - Log hourly wages for the labor force as a whole and with Heckman correction***

||**Hour**<br>|**ly wage est**<br>|**imation**<br>|**Hec**<br>|**kman corre**<br>|**cted**<br>|
|---|---|---|---|---|---|---|
||All|Men|Women|All|Men|Women|
|**_Wage Ear_**|**_ners_**<br>||||||
|_Female_|-0.13<br>(-32.0)|---|---|-0.16<br>(-42.99)|---|---|
|_Age1525_|-0.35<br>(-56.74)|-0.33<br>(-45.06)|-0.37<br>(-34.49)|-0.47<br>(-81.98)|-0.48<br>(-66.41)|-0.47<br>(-48.37)|
|_Age2645_|-0.048<br>(-8.53)|-0.031<br>(-4.52)|-0.08<br>(-8.0)|-0.098<br>(-18.67)|-0.096<br>(-14.85)|-0.1<br>(-11.39)|
|_Nosch_|-0.76<br>(-77.03)|-0.67<br>(-52.57)|-0.87<br>(-54.91)|-1.07<br>(-111.3)|-1.03<br>(-82.57)|-1.13<br>(-74.27)|
|_Pri_|-0.35<br>(-71.52)|-0.29<br>(-44.59)|-0.42<br>(-55.93)|-0.63<br>(-162.8)|-0.61<br>(-123.85)|-0.65<br>(-106.2)|
|_Home_|-0.19<br>(-18.69)|-0.038<br>(-2.34)|-0.27<br>(-20.54)|-0.21<br>(-21.67)|-0.074<br>(-4.83)|-0.29<br>(-23.4)|
|_Constant_<br>|1.69<br>(252.46)|1.62<br>(196.3)|1.63<br>(154.9)|2.05<br>(342.3)|2.029<br>(266.37)|1.92<br>(193.5)|
|R<sup>~~2~~</sup>|0.12|0.09|0.16|---|---|---|
|Sample<br>size|104936|67653|37283|156642|97662|58980|

* t- and z-values in parentheses

38
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE states (figure 18). Nevertheless, as before, this is partly explained by the fact that the level of coverage is very low.

**Figure 18. Liberia’s power and water utilities are more efficient than those in other low-income countries**

- a. Uncollected bills and unaccounted losses in the power sector, as a percentage of GDP

<!-- Start of picture text --> undercollection distribution losses<br>b. Uncollected bills and unaccounted losses in the water sector, as a percentage of GDP<br>undercollection distribution losses<br>% of GDP<br>% of GDP<br><!-- End of picture text -->

- b. Uncollected bills and unaccounted losses in the water sector, as a percentage of GDP

_Source_ : Derived from Briceño-Garmendia and others 2009.

# Annual funding gap

Liberia’s annual infrastructure funding gap ranges between $250 million in the pragmatic scenario to $500 million in the base case (table 16). The largest funding gaps are found in the power and transport sectors. The funding gap looks implausibly large when measured relative to existing GDP—between 40 and 60 percent. The wide variation between the two scenarios illustrates the importance of making shrewd choices with respect to infrastructure standards. At the same time, in this case even the pragmatic scenario 32
through 2015. During the period from 1985 to 2016, the Dartmouth Flood Observatory (DFO) registered 3,808 floods of magnitude 4 or more and 1,175 floods of magnitude 6 and up.<sup>3</sup> _Myanmar_ . The second country used in our analysis is Myanmar. It has the smallest GDP and population of our five countries, while at the same time being the second largest. During the period 2006‐2016, the UN Office for the Coordination of Humanitarian Affairs (OCHA) mentions several major natural hazards affecting Myanmar (OCHA 2016).

There were three large earthquakes in 2011, 2012 and 2016, affecting more than 30,000 people and killing 100. Furthermore, a major flood occurred in 2015, displacing 1.7 million people in Western Myanmar and killing 172 people. Finally, more than 2.5 million people were affected by strong cyclones in 2008, 2010 and 2013.

_Philippines_ . The Philippines is – like Indonesia – a country consisting of thousands of islands ranging in size from tiny and deserted to large and densely populated. It is the second smallest country in our sample, with the second highest population just in excess of 100 million. It has the third largest GDP.

According to the Global Facility for Disaster Reduction and Recovery (GFDRR), the Philippines is at high risk from several types of natural hazards (GFDRR 2019). Prime among them are cyclones, where an average of 20 make landfall every year. In 2013, typhoon Yolanda led to 6,000 casualties and damaged more than 1.1 million houses. The Philippines are also exposed to earthquake and flood risks.

_Thailand_ . As for Thailand, it is the third largest country, fourth most populous and boasts the second largest GDP of our countries. In terms of disasters, Thailand is mostly at risk to floods and typhoons, but earthquakes happen occasionally, with the largest recorded being a 6.1 magnitude earthquake that occurred in May 2014. The lower risk compared to our other countries is also mentioned by INFORM in their country profile of Thailand (INFORM 2019).

_Vietnam_ . The final country is Vietnam, which is the smallest, but has the third highest population just below 100 million people. In terms of GDP, Vietnam rank fourth among our five countries. When it comes to disaster risk, they are on par with Thailand in that INFORM ranks them low and exposed primarily to floods and typhoons (INFORM 2019).

# _B. VIIRS_

To find and identify areas with economic activity and their exposure to natural hazards, satellite images of nighttime lights will be used as a proxy. This is due to the highly localized nature of natural hazards and how they affect economic activity within populated areas. In an optimal scenario, one would prefer highly spatially disaggregated economic data, but lacking

> 3 Magnitude is defined as: M =  log(D * S * AA), with D being the duration of the flood in days; S is the severity on a scale consisting of 1 (large event), 1.5 (very large event) and 2 (extreme event); and AA is the size of the affected area in square kilometers. Flood events registered by DFO have mainly been derived from news and governmental sources.

6
garment-making) which move on quickly or disappear the moment a visit by the labor inspectors seems likely." De Grazia (1982) also reports that in Milan only 5,000 homeworkers, from a total of 100,000, and 1,000 small enterprises, from a total of 50,000, are listed on the city's commercial register. Indeed, the practice of subcontracting as a means to avoid labor-protection legislation (including prohibitions to employ illegal immigrants) has become particularly popular in developed countries, especially the United States, Canada, Italy, Spain, and France (Portes, Castells, and Benton, 1989.) 6. The model presented here is a substantially modified version of the model in Braun and Loayza (1994).

7. From equation (9), an interior solution for I requires the following parameter restrictions: 8. We assume that the technology, policy, and preference parameters are such that The second inequality ensures positive growth, and the first one ensures that attainable utility is bounded and the transversality condition holds (see Barro and Sala-i-Martin, 1995).

9. If 1-6 < _a1/(a+X+aX),_ the growth-maximizing tax rate implies some informality.

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|Transport|Lesotho|saneeinail<br>ow<br>mene|SSA|Middle<br> adi <br>“<br>Africa|ae<br>"Pa|East<br>Asia <br>cific|Latin<br>America <br> «=&<br> Carithea<br>hn|Europe& <br>(Central <br>Asia|.<br>|.<br> Ihe<br>ome|Lower<br>Middle<br>Income|=‘Upper<br>Middle<br>Income|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|RoadDensityinTerms ofPopulationfroad-k/1000 people)|34|3.0|33|48|24|42|a7|$4|Td|49|92|67|
|RoadDensityinTerms ofLand(road-dew/1000 sq keert)|195.7|181|155.7|6089|54460-|27640|F127|580.3|702|327.7|10764|=840.8|
|TravelTime toWorkinMainCities (minutes/one-wayworktrip)|15|33|34|23|a?|36|23|23|23|40|23|31|
|Paved Roads (% oftatalroads)|18|30|25|56|38|32|36|76|5a|47|it|50|
|Note:Datausedisfromthelatestobservationavailablebetween1|997and2663,||||||||||||

Source: A "Research" Database of Infrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrasiructure Network, Office of the Vice President, June 2005.
- L74 -

# Ftpre l.C.25 LABOR USES UNDER FOOD SELF-SUFFICIENCY

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the credit program because many mega firms, who did not participate in the program, either chose not to, or were unable to, roll over their existing debt maturing in 2020, resulting in a decline in outstanding non-program debt.<sup>22</sup>

# **3 Data**

We use three administrative data sets from various sources in Chile. These data sets cover the entire formal private sector in Chile in rich detail, including credit flows and balances, default history, and terms of individual transactions. The firm-level data contain financial statements, input use, and sales collected monthly, along with the industry and location of firms. Below we describe the data sources, sample selection, and key variables.

First, we use granular confidential bank-to-firm information compiled by the Financial Market Commission (the financial supervisory agency) for all firms using the entire banking system. We have information on stocks and flows of credit. For stocks (i.e., loans outstanding), we have data on the amount of debt each firm has with each bank in the system every month. We also know the number of days each loan in the system is past due. For flows, we have transaction-level data on each loan received by each firm, including information on the loan amount, interest rate, and loan maturity. We complement the bank data with unique data on the credit program we analyze, including detailed information on loan applications by firms (such as the amount requested) as well as banks’ decisions (such as whether a loan request is approved or rejected and the approved amount). These data allow us to measure selection and disentangle supply and demand factors in the allocation of credit across the whole economy.

Second, we use confidential administrative tax records from Chile’s tax authority (Servicio de Impuestos Internos). These data contain monthly, firm-level information, including sales, materials expenditure, value added, number of workers, wage bill, net worth, age, industry, and municipality. They allow us to construct measures of pre-pandemic firm attributes (such as productivity, measured as value added per worker) as well as firm performance during the pandemic, using monthly sales during 2020.

Third, we work with publicly available firm-level data on firms that use the employment program. The employment authority (Direcci ́on del Trabajo) publishes these data monthly, containing the dates that each firm uses the program and the number of workers in each firm

> 22In Appendix Figure 1, we plot the evolution of the number of firms using each program, distinguishing between SMEs and non-SMEs. In both programs, SMEs make up the vast majority of participants, implying that the discrepancy in the dollar value of both programs reflects a strategic policy decision to scale up the credit program, not differences in firm size across programs.

10
With respect to the gap between the MDGs and current mortality rates, we find that providing all children with access to basic improved water and sanitation could close about 15% of the current difference in Sub-Saharan Africa, and 10% in the average country of our sample (Table 6, column 8). Upgrading to private water connections and flush toilets (scenario 2) would close approximately 43% of the remaining gap in Sub-Saharan Africa, and 38% for the rest of the world in our sample (Table 6, column 8).

Given the pronounced differences in the estimated absolute mortality impact, the large variations in the expected contribution towards MDG 4 are not very surprising. The two countries with the highest potential contributions are Liberia and Niger, where we estimate that over 80% of the remaining gap could be closed by investment in sewers and piped water infrastructure. It is worth pointing out that the gap between status quo and the MDG target remains large for most countries, so that even countries with substantial improvements through water and sanitation would remain unlikely to actually achieve the (ambitious) MDG targets.

Nevertheless, the potential impact of water and sanitation on global health in general, and child mortality in particular, is remarkably large. In our sample of 38 developing countries - which covers about 62 million of the 102.5 million children born per year in developing countries - we estimate that private water connections and flush toilets could potentially save up to 1.3 million child lives per year. This corresponds to approximately 20% of the total estimated number of 6 million children under the age of five who die each year in the 38 countries in our sample (Table 7). Assuming that the health improvements are roughly comparable in the rest of the developing world, this means that investment in high-end water and sanitation could save up to 2.2 million child lives in developing countries (World Population Prospects, United Nations Population Division). Plugging the previous estimates into equation (5), and assuming a discount rate of 3% and a global life expectancy of 80 years (i.e. a life expectancy of 75 years at the age of 5), this implies a total of 39.5 million life-years per cohort in the countries covered in this paper, and a total of 66 million life-years in all developing countries combined (Table 7, last column).

21
_National Water Planning:_ The National Water Plan should follow the Water and Flood Management Strategy for future public sector investment and management programs in the water sector. This plan should draw heavily from the lessons of the FAP studies, other country experiences, and past experience of planning in Bangladesh.

_Public Investment Program:_ The National Water Plan should lay down the basis of public water investment program in Bangladesh to be implemented by BWDB.

_Implementation and O&M Program:_ Specific projects will have to be designed by the line ministries of the government in conformity with the public investment program. The O&M program, likewise prepared by the line ministries, should reflect joint public and private participation as specified in the National Water Plan.

_Data and Information Management:_ The collection of hydrological data, its processing and dissemnination to project designers, beneficiaries and the general public will have to be streamlined. Considerable improvement of the data and information management process has been accomplished under FAP. These have to be properly institutionalized and developed further.

# **C. Institutions (Organizations)**

The institutional aspects of water management in the public sector, besides laws and regulation, are organizations, enforcement, monitoring and data collection, planning, investment, and O&M. It is important to incorporate public participation in as many aspect of these activities as possible.

The major organizations in the water sector are shown in Figure 2. The principal public organizations, under the Ministry of Water Resources, are the Water Resource Planning Organization (WARPO), Bangladesh Water Development Board (BWDB), River Research Institute (RRI) and Surface Water Monitoring Center (SWMC) under it. Organizations in other sectors that have interlinkages with this sector are the Public Health Engineering Department (PHE), Local Government Engineering Department (LGED), Water and Sewerage authority (WASA), Department of Fisheries (DOF), Inland Water Transport Authority (BIWTA), Roads and Highways Department (R&H), Department of Environment (DOE), and respective municipal corporations. The National Water Council presides over all these institutions in matters of water policy and legislation.

The National Water Council, with representations from all water-related ministries, approves policy for presentation to the Cabinet. WARPO is supposed to evolve national policies and plans for water resources, ensuring optimum utilization among various users such as agriculture, fisheries, navigation, public health, industry etc.21 WARPO is also supposed to consolidate data on water collected by various agencies. The organization, however, has not operated satisfactorily for various constrains and very little has been

> 23 See FAP 26: Institutional Development Program, Report No.2, Dhaka, February 1995.

21