rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6: Descriptive traits of workers in India hired by ethnicity of company contacts**

||Indicator for<br>hired worker|Self-reported||Sampl|e of experienced w|Worker's past<br>orkers|
|---|---|---|---|---|---|---|
||having prior<br>oDesk<br>experience|English<br>proficiency of<br>worker|Indicator for<br>missing English<br>proficiency|Worker's<br>average past<br>wages|Worker's total<br>oDesk hours<br>worked|average good<br>performance<br>rating|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Estim<br>Panel<br>Total|Colum<br>ates include fixe<br>A: Worker traits<br>sample of compa|n headers indicate<br>d effects for year x<br>for company conta<br>ny contacts locate|trait of worker a<br>job type x coun<br>cts not utilizing<br>d outside of Ind|nalyzed<br>try of company c<br>worker search fe<br>ia that are hiring a|ontact<br>atures<br>broad|
|Probability that hiring contact|0.001|-0.010|0.003|0.048|-0.523|0.010|
|is of ethnic Indian origin|(0.010)|(0.011)|(0.005)|(0.131)|(2.665)|(0.011)|
|Observations|70,364|67,245|70,364|54,168|54,168|26,622|
|Mean of dependent variable|0.770|4.789|0.044|7.569|62.73|0.550|
|Relative effect|0.001|-0.002|0.068|0.006|-0.008|0.018|
|||Panel B: Worker|traits for company|contacts utiliz|ing worker search||
|Probability that hiring contact|-0.007|0.032**|-0.005|-0.409**|1.438|-0.002|
|is of ethnic Indian origin|(0.010)|(0.014)|(0.005)|(0.162)|(3.650)|(0.011)|
|Observations|71,989|67,988|71,989|57,103|57,103|28,824|
|Mean of dependent variable|0.794|4.705|0.056|9.925|87.18|0.589|
|Relative effect|-0.009|0.007|0.000|-0.040|0.016|-0.003|

Notes: Contract-level regressions estimate differences in traits of initial workers hired by ethnicity of the hiring company contact outside of India. Panel A documents employers not using the search functionality, and Panel B considers cases where the functionality is employed. Traits of workers are indicated by column headers and further described in the text. Regressions are unweighted, include fixed effects for year x job type x country of company contact, and report standard errors that are clustered by originating company. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
the port to maximize cargo stay in their own facilities. A better understanding of the operational strategies of these bulking operators is needed.

|**Consignment type **|**# of containers**|**average dwell time**|
|---|---|---|
|FCL|29,698|19,8|
|LCL|26,524|22,6|

**Table 4 - Distribution of average dwell time with respect to bulking type** (Source: CAMEROONIAN CUSTOMS, 2009)

#  **Density of value**

Density of value is an important determinant of logistics strategies since it is a leading driver of holding and transportation costs and serves therefore as a control variable for the dimensioning of economic order quantities and inventory levels. An ABC analysis of cargo dwell times vs. density of value confirms the crucial importance of this variable. The correlation is positive: higher cargo value leads to higher dwell times.

The analysis of gaps between the three categories leads to the conclusions that low value goods are declared faster than high value goods (about 2 days less in average) which would be representative of a better performance of both shippers and brokers. A detailed analysis of performance by good type (HS code) will be done later on to better understand this fact. Another distinction is then to be made between categories B and C in the payment to gate dwell time. This specific feature of discretionary dwell time needs to be analyzed.

||**# of**|**average dwell**|**arrival to**|**lodging to**|**payment to**|
|---|---|---|---|---|---|
|**Density of value**|**containers**|**time**|**lodging**|**payment**|**gate**|
|less than 1000 FCFA/kg|38,432|20.3|**10.4**|4.3|<br>5.9|
|from 1000 to 6500 FCFA/kg|15,092|22.8|**12.1**|4.4|<br>**6.6**|
|More than 6500 FCFA/kg|2,318|24.7|12.4|4.9|<br>**7.5**|

**Table 5 - Distribution of average dwell time with respect to density of value** (Source: CAMEROONIAN CUSTOMS, 2009)

#  **Commodity type**

The variety of imports is significant at a country level and thorough commodity analysis of cargo dwell time is therefore difficult. By looking at cargo categories however - using 2-figure HS code - we can get a broad sense of clearance patterns with respect to cargo type. We use 15 categories which account for most of them for at least 5% of total volume of imports. The table below shows the important variance of average dwell time across different categories. While chemicals and allied industries inputs are cleared in 16 days in average, finished goods such as machinery, foodstuffs or transport vehicles and parts remain in the port terminal for more than 24 days in average. Further understanding of these differences is needed, and one needs to look at inventory management strategy in particular, but this brief analysis confirms that commodity type is a crucial determinant of cargo dwell time.

13
Table 4: Knowledge and Adoption False Reporting by SLM Technique and Gender of the Plot Manager

||Mul|ching|Strip|Tillage|Interc|ropping|
|---|---|---|---|---|---|---|
||Male|Female|Male|Female|Male|Female|
|Knowledge: Memory v.s. Exam|||||||
|% Correct (Memory=0 /1 & Exam=0/1)|64.63|64.46|58.82|59.74|57.93|51.76|
|% False Negative (Memory=0 & Exam=1)|33.10|33.93|38.39|37.43|40.29|46.85|
|% False Positive (Memory=1 & Exam=0)|2.26|1.61|2.79|2.83|1.78|1.39|
|# of Observation (ttl: 6076)|2477|3599|2477|3599|2477|3599|
|Knowledge: Name v.s. Exam|||||||
|% Correct (Name=0 /1 & Exam=0/1)|75.17|78.22|65.24|66.74|92.05|90.16|
|% False Negative (Name=0 & Exam=1)|21.52|19.53|29.83|28.15|3.75|6.11|
|% False Positive (Name=1 & Exam=0)|3.31|2.25|4.93|5.11|4.20|3.72|
|# of Observation (ttl: 6076)|2477|3599|2477|3599|2477|3599|
|Adoption:Subjective v.s. Objective|||||||
|% Correct (Sub=0/1 & Obj=0/1)|84.87|86.58|93.43|95.40|80.52|77.57|
|% False Positive (Sub=1 & Obj=0)|9.37|9.27|3.76|2.81|17.42|19.62|
|% False Negative (Sub=0 & Obj=1)|5.76|4.15|2.80|1.79|2.07|2.81|
|# of Observation (ttl: 2920)|1355|1565|1355|1565|1355|1565|

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|Table 10: Scorecard for<br>Objective|performance of count<br>Cost Recovery|ry’spower tariffs ag <br>Efficiency|ainst four key policyobjectiv<br>Affordability|es<br>Equity|
|---|---|---|---|---|
|Indicator|Ratio of<br>average effective<br>tariff to average<br>historic cost|Ratio of average<br>effective tariff to<br>LRMC|Share of population that<br>can afford subsistence<br>consumption priced at<br>average effective tariff|Percentage of subsidy<br>captured by poor as a<br>ration of percentage of<br>poor in the population|
|Benin|0.72|0.75|0.95||
|Botswana|0.54|1.00||0.06|
|Burkina Faso|1.00|0.87|0.51||
|Cameroon|0.63|1.00|1.00|0.36|
|Cape Verde|1.00||1.00|0.48|
|Chad|1.00|1.00||0.06|
|Congo,Dem. Rep. of|0.59|1.00|1.00|0.62|
|Congo|0.80|1.00|||
|Cote d'Ivoire|1.00|<br>0.91|1.00|0.51|
|Ethiopia|0.76|0.40|0.60||
|Ghana|0.81|1.00|0.97|0.31|
|Kenya|1.00|1.00|0.99||
|Lesotho|0.79|1.00|||
|Madagascar|0.93||0.92||
|Malawi|0.62|1.00|0.92||
|Mali|0.79|0.95|||
|Mozambique|0.87|1.00||0.31|
|Namibia|1.00|0.97|||
|Niger|0.44|0.47|0.55||
|Nigeria|0.44|0.32|0.97||
|Rwanda|0.88|1.00||0.01|
|Senegal|1.00|0.35|1.00||
|South Africa|0.84|0.72|1.00|0.41|
|Sudan|||||
|Tanzania|0.52|0.91|0.99||
|Uganda|1.00|1.00|0.20|0.02|
|Zambia|0.44|0.36|1.00||
|Zimbabwe||0.47|||
|**Average**|**0.78**|**0.82**|**0.87**|**0.29**|

_Source:_ Derived from Africa Infrastructure Country Diagnostic Power Tariff Database.

What becomes immediately clear is that some countries rank very high for cost recovery but do very poorly for equity and affordability and vice versa (table 11). Countries such as Chad, Mozambique, Rwanda, and Uganda tend to rank well for cost recovery but poorly for affordability and equity. On the other end, countries such as South Africa, the Democratic Republic of Congo, Tanzania, and Zambia fare relatively well in terms of equity and affordability but have not been able to achieve cost recovery. What is striking is that achieving all four objective simultaneously is almost impossible in the context of the high-cost low-income environment that characterizes much of SSA today. Hence most countries are caught between cost recovery and affordability.

37
the GDP (i.e. value-added) produced by a given country or industry (origin), the final demand in different countries and sectors (destination), or a combination of the two (when both origin and destination are specified).

We are aware that many questions can be addressed only partially with the current version of `icio` . Thus, our plan is to release new modules in the near future. First, we will add a much broader set of measures to assess the participation of countries and sectors in GVCs (Borin and Mancini, 2017; Wang et al., 2017) and their position (Antr`as and Chor, 2013; Fally 2012; Antr`as and Chor, 2018). Second, we will build a set of indicators to better evaluate the direct and indirect effects of trade policies, taking into account the GVC structure.

The rest of the paper is organized as follows. In Section 2 we show how to load ICIO tables in `icio` using the command `icio_load` . In Section 3 we show how supply, demand and supply-demand linkages can be computed in `icio` . This is useful when the empirical questions are related by supply, demand or require linking the origin of the value added to its absorption in final demand, without considering explicitly international trade flows. In Section 4 we provide the tools for gathering a comprehensive overview of the production process and cross-country relationships, i.e., how to obtain value-added decompositions of trade flows by country of origin and destination. In Section 5 we focus on the measurement of GVC participation. In several instances, and for illustration purposes, we show how to replicate some of the measures of GVC participation and figures presented in the World Bank’s Word Development Report 2020. Section 6 concludes.

Each section shares the same structure. At the beginning we provide a brief overview of the measures therein discussed. Then, we show how to compute those measures in `icio` , providing also some examples and a list of several relevant questions that could be addressed, with the related `icio` ’s syntax. In addition, in the Appendices we present the related conceptual frameworks.

# **2 Inter-Country Input-Output tables**

Input-Output (IO) models were developed by Leontief (1936) to represent and analyze production and consumption relationships within an economy. The related statistical tools, the IO tables, indicate the monetary amount of inputs of each sector necessary to produce the total output of a given industry and, in turn, how this output is used as final consumption (or investment) or as intermediate inputs for other productions. National IO tables distinguish only between domestic and foreign inputs; on the output side, exports represent one of the possible ‘final’ uses 3
we classify into Core, Language, Arts, Vocational and Other (math and science, civics, etc.). In addition, for public and charter schools we have reading and math proficiency rates (i.e., the fraction of students who pass D.C.’s reading and math tests); for charter schools we have per-student reimbursement by grade and year; and for private schools we have school type (Catholic, other religious and non-sectarian) and tuition.

Enrollment and proficiency for public and charter schools come from OSSE. Public school addresses and student demographics come from the Common Core of Data (CCD) from the National Center for Education Statistics (NCES) and OSSE. Curricular focus (henceforth, focus) for public schools come from Filardo et al (2008). Charters’ student body composition and proficiency rates come from OSSE and the School Performance Reports (SPRs). Charters’ focus comes from schools’ own statements, SPRs and Filardo et al (2008). Charter reimbursement rates come from D.C.’s Office of the Chief Financial Officer. Further information comes from past Internet archives and from Friends of Choice in Urban Schools. A complicating factor in charter data collection is the dispersion and inconsistencies of data sources, and their nonuniform treatment of multi-campus charters.

NCES’s Private School Survey (PSS) is our main source of private school data. Since PSS is biennial, we use the 2003, 2005 and 2007 waves. We impute 2004 data by linear interpolation of 2003 and 2005, and similarly for 2006. Average school tuition for school year 2002-2003 comes from Salisbury (2003). All dollar amounts are expressed in dollars of year 2000.

We follow DCPS’s criteria and classify schools into the following grade levels: elementary (covering grades in the K-6 range, which was the typical range for public elementary schools in 2003-2007), middle (covering grades 7th and/or 8th), high (covering grades in the 9th-12th grade range), and elementary/middle, middle/high, and elementary/middle/high. Mixed-level categories (such as middle/high) are quite common among charters. Note that a grade level is a _set_ of grades rather than a single grade.

Our neighborhood-level data comes from Filardo et al (2008)’s data appendices. Local urban planning agencies use the concept of “neighborhood cluster” to proxy for a neighborhood, and group D.C.’s Census tracts into 39 clusters. We observe each neighborhood’s fraction of children enrolled in charter schools relative to total public enrollment, and average distance traveled to public or charter schools. An alternative (but larger) measure of neighborhood is given by wards. The District has eight wards; ward 3, in the Northwest, is the most advantaged, and wards 7 and 8, in the Southeast, are the most disadvantaged. When convenient we split the city into three regions: West (ward 3 and some parts of ward 2), Southeast (wards 7 and 8) and Northeast.

For the sake of demand estimation, ideally we would observe the joint distribution of child grade, race, poverty status and parental income at the block group level (there are 433 block groups in D.C.) for every year between 2003 and 2007. Since 7
although this will be affected by country-specific enforcement factors; and positive for the agricultural opportunity value of the land (β7>0).

# **5.2  Estimation Areas**

We estimate the model for all cells in WWF’s moist tropical forest ecoregions of each country, which are discussed more fully in Section 7. Figures 4-6 identify the relevant ecoregions, which dominate northern and central Bolivia, southern Cameroon, and most of Myanmar.

# **5.3  Variable Measures**

Our data and sources are as follows.

**Hansen pixels cleared** : per 250 m cell, the percent of 30 m Hansen pixels cleared.  This incorporates two measures:  percent cleared prior to 2001, and annual clearing from 2001 to 2014 (Hansen et al. (2013)).

**Distance from road segment** :  distance from the centroid of each cell to the nearest road segment, calculated in ArcGIS 10.

**Distance traveled to the nearest urban center via secondary and primary road segments** : Calculated in ArcGIS 10 from Delorme digital road maps.

**Elevation** :  Average elevation for a cell, calculated from the CGIAR-SRTM dataset (3 seconds resolution), aggregated to 30 seconds resolution by DIVA-GIS (http://www.diva-gis.org/gdata).

**Terrain slope:** Standard deviation of pixel-level elevation measures within a cell.

**Legal protection status** :  1 if the cell is in a protected area identified by the World Database on Protected Areas (WDPA); 0 otherwise.  The WDPA shapefile has been downloaded from http://www.protectedplanet.net/.

**Agricultural opportunity value** :  mean value for a cell, calculated from the high-resolution global grid developed by Deveny et al. (2009).  Raster resolution: .0025 decimal degrees. The Deveny data have been drawn from two sources: (1)  Foregone agricultural rents from Naidoo and Iwamura (2007), who integrate spatial information on crop productivity, livestock density, and prices to produce a global map of the gross economic rents from agricultural lands. (2) Spatially-explicit calculation by Kindermann et al. (2006) of income foregone from a one-time timber harvest when timber rents exceed agricultural rents.

17
31

# **V. The Incentive Framework for Financial Institutions3 3**

Beyond the basic infrastructure, the behavior of the actors in the financial system-the providers ultimate users of funds and the intermediaries-depends crucially on the incentives they face. This is in turn influences financial institutions' efficiency, productive role in the allocation of resources, impact of growth and overall financial sector stability. Levine, Loayza, Beck (1999), for example, have recently linked the broader operating environment of financial institutions (the legal and accounting framework) to economic growth and show that reforms in the broader framework boost financial development and accelerate economic growth.

The following section reviews how the authorities should think about installing and operating a regulatory system that is incentive compatible, that is, encourages prudent behavior, and which financial sector entities should be subject to that framework. In particular it attempts to answer the following questions:

- What are the main pillars of the incentive framework in which financial institutions operate and how can the robustness of the financial system to adverse shocks be increased?

- X To what type of financial institutions should that regulatory framework be applied and why?

- Does the combination of commercial and investment banking activities make it harder for market participants to monitor the performance of financial institutions?

## **1. Introduction**

The regulatory and supervisory framework, along with accounting and auditing rules, disclosure requirements and the existence of a deposit insurance scheme, plays a crucial role in defining the incentive framework in which financial institutions operate. In particular, the extent to which excessive risk taking is curbed by regulation, penalized by the supervisory authority as well as by the market greatly influence the behavior of financial institutions. There are three potential groups that can monitor bank managers, namnely the owners, the market, and supervisors. What can the government do to ensure that each exerts pressure on managers to engage in prudent risk taking? In industrial economies, authorities erect some entry barriers; enforce modest capital requirements, usually above the BIS minimum of 8% (capital to risk-weighted assets); irntermediaries face market discipline in money and capital markets, which usually are uncovered by explicit government guarantees; and are supervised by one or more government agencies. Industrial country authorities have tended to permit bank exit, though banks still have engaged in excessive expansions that have caused systemic difficulties.

In developing and transitional economies, where risks are greater due to the small and often more concentrated economies, where shocks often are larger and volatility is greater, and where the market's ability to monitor banks is hampered by poor information, governments need to enhance the ability and incentives of these three potential groups.

33 This section draws heavily on World Bank, 1997.
# **4. Sovereign bond returns, 1815–2016**

# 4.1. Sovereign bond pricing database: Sample and sources

This section presents our newly assembled, comprehensive dataset on sovereign bond prices and explains how we compute bond returns. Compared with Section 3, we thus move beyond sovereign default and restructuring situations and instead track the performance of foreign-currency sovereign bonds using bond prices. We start in 1815, during the decade in which London emerged from the Napoleonic Wars as the world’s dominant financial center (Michie 2001). While our data span until September 2017, we mainly show results through end-2016 to include only complete years.

To assemble the long-run bond pricing database, we include all external sovereign bonds for which we could find pricing information on the London or New York Stock Exchange (LSE and NYSE). In line with the above, we focus on bonds issued by central governments in foreign (USD and GBP) currency. We include bonds with a maturity of at least one year and those with a fixed coupon rate, thus dropping a small number of floating rate instruments in the modern sample. Throughout, we coded end-of-month price quotations.

We rely on several main sources of bond price data. For the pre-1870 period, we use prices from the Money Market Review, The Economist, Circular to Bankers, Course of the Exchange, and Banker’s Magazine. For the 1870-1930 period, we greatly benefitted from the work by William Goetzmann and Geert Rouwenhoorst, by using their digitized bond-level pricing data from the British Investor Monthly Manual.<sup>31</sup> We contribute to this collection by adding bond-level information on the timing and scope of default, that is, on missed or partial coupon and principal payments as well as the restructuring terms. We also expand that dataset forward, by 50 years, adding monthly price quotations for external sovereign bonds trading on the LSE from 1930 to 1980, as provided by The Economist and the Financial Times.

Importantly, for the interwar period and post-WW2, we are the first to code and integrate into the analysis a large dataset of prices and returns for external sovereign bonds on the NYSE, which became the main trading platform for foreign sovereigns after 1914. Specifically, we coded NYSE sovereign bond price data from the Bank and Quotation Section of the Commercial Financial Chronicle (1905–27 and 1954– 78) and from the Bank and Quotation Record (from 1927–54). Taken together, the historical bond price sample spans the period from 1815 until 1980 and includes more than 900 external sovereign bonds.

For the modern (post-1990) period, we build on the extensive emerging market bond price collection by JP Morgan as part of their EMBI Global indices. EMBI data have been very widely used in the sovereign

> 31 Their dataset is hosted on the website of the International Center for Finance at Yale. A large literature has used their data and, more generally, data from the Investor’ Monthly Manual, for example, Ferguson and Schularick (2006), Mauro, Sussman, and Yafeh (2002), or Mitchener and Weidenmier (2010).

16