rafmacalaba/datause-encoder-data huggingface dataset:
spreading rapidly. Treating Corrigan's assertion about "banking problems" as a scientific hypothesis about bank risk that is applicable to developing countries implies that initial efforts to transfer risk-based capital standards to developing countries ought to concentrate on using existing U.S. credit-analysis and database software as a paradigm for improving the information and credit-analysis systems used in developing economies. Credit-Apglication EIles As **a** Datahase TO qualify for a bank loan, every would-be borrower must provide enough information on its affairs and prospects to convince the bank that it is prudent to advance the funds requested. Bank credit analysis turns on an assessmcnt of the traditional "five Cs" of creditworthiness: character, capacity (or cash flow), capital, collateral, and conditional economic invulnerability (Sinkey, 1992, p. 515). Any bank's loan application formn can be regarded as a questionnaire that seeks to extract infornation on these attributes. Analysis of this information by bank credit experts looks to assign a riskrating to the various deals that customers offer.

"Character" is a subjective variable that summarizes a borrower's reputation for probity and fair play. Lenders prefer to deal with honorable parties who are detemined to live up to their obligations if they possibly can. Bank files on character begin with the information that each applicant supplies on its credit history. Borrower honesty is assessed by testing this infornation against credit-bureau and other data-service files and by cross-checking other falsifiable facts with suppliers, customers, and other lenders. Additional evidence is sometimes collected by asking around about the fuim's reputation in the communities and markets in which it operates.

Double-checking applicant-supplied information is an important test of character. This is why it would be useful for the World Bank or BIS to help developing countries establish or strengthen the presence or competence of private credit bureaus and credit rating agencies. Because government as well as private debt needs to be rated in these countries, it would also be beneficial to seek ways to insulate the work of creditinfornation agencies from political influence.

The other four "Cs" focus on the borrower's ability to pay. They seek to clarify how funds to repay the loan can be generated. Data for analyzing a business firn's capacity and potental vulnerability to changes in economic conditions ame collected in the **form** of business plans and from questions that focus on management skills and
we apply a minimum-distance procedure as in Nevo (2000, 2001) to estimate _β_ and _ξ j_ separately (see Appendix E.1 for details). Finally, we use our estimates of ∆ _ξ jgt_ s for all schools and of _ξ j_ s for entrants to obtain the empirical counterparts of ϝ _ξ_ and ϝ∆ _ξ_ .

# **5.3 Supply estimation**

̆ Supply-side parameters are _θ_<sup>_s_</sup> = _b_ , _ζ_ , _V_ , _F_ , _σν_ , _α_ , _β_<sup> ̆</sup> , _a_<sup>_π_</sup> , _b_<sup>_π_</sup> , _a_<sup>_q_</sup> , _b_<sup>_q_</sup> , _c_<sup>_q_�</sup> . In our � application, the economy includes _L_ = 39 locations (neighborhood clusters), _Y_ = 5 focuses (Core curriculum, Arts, Language, Vocational, Other) and _K_ = 5 grade levels (elementary, middle, high, elementary/middle, middle/high), for a total of _E_ = 975 potential entrants per year. For each school year _t_ we observe the schools operating in the market. We also observe the following: a) new charter entries, authorized in _t_ − 2 based on **M** _t_ −1 as described in the game’s Step 4; b) charter closings; c) charter relocations.<sup>25</sup> Let _Ct_ be the total number of charters operating in _t_ , including incumbents from _t_ − 1 that remain open in _t_ as well as new entrants. Let _C_<sup> ̃</sup> _t_ be the number of incumbent charters that remain in the same location as in _t_ − 1. Let l _jt_ be _j_ ’s location in _t_ , and let l _jt_ be its observed counterpart. Variable _d_<sup>_e_</sup> _jt_<sup>∈{0,1}indicateswhetherthereisanew</sup> entrant in entry point _j_ in year _t_ ; variable _d_<sup>_x_</sup> _jt_<sup>∈{0,1}indicateswhetherincumbent</sup><sup>_j_</sup> closes at the end of _t_ − 1; and variables _d_<sup>_~~e~~_</sup> _jt_<sup>and</sup> _d_<sup>_~~x~~_</sup> _jt_<sup>are the observed counterparts of</sup><sup>_de_</sup> _jt_ and _d_<sup>_x_</sup> _jt_<sup>respectively.The likelihood function is:26</sup> Infrequent entry and exit in our sample complicate the estimation of _θ_<sup>_s_</sup> . Nonetheless, we can calibrate some parameters. According to Buckley and Schneider (2007), there were 71 charter entry applications between Fall 2004 and Fall 2007, which results in _b_<sup>ˆ</sup> = 71 / (975 entry points * 4 years) = 0.018.<sup>27</sup> In order to calibrate _V_ and _F_ , we use budget data for school year 2009-2010, which is the closest to our sample period with publicly available financial data. We use data for the charters in our sample that were still open in 2009, including the new

> 25Closing year is the first year that the school is not in the data. Relocation year is the first year that the school operates in its new location.

> 26Note that the likelihood for 2003 cannot be calculated as we lack data on market structure, charter locations and charter proficiency in 2002.

> 27In practice we assume that the distribution of entries across grade levels is the same as the distribution of entry applications, and use a different value of _b_ for each grade level. This feature improves the performance of our MLE estimator.

24
behavior of health care providers. An alternative interpretation is that the public sector may have serious, systematic, problems ensuring quality in publicly supplied medical care and that major improvements in true public goods and non-medical control of infectious disease are needed before turning to policies that are hard to implement.

# **Improved Access to Health Care in Low-Income Countries**

The expansion of physical health care infrastructure has improved the extent to which people in low-income countries can meet with a health care provider. In 2004 data from the Demographic and Health Surveys show that 68 percent of urban and 58 percent of rural Tanzanian reported that they took their child to a health facility when he or she showed signs of acute respiratory infection (viral or bacterial colds and coughs). In India, for the same period, 78 percent of urban and 60 percent of rural residents report having done so (National Family Health Survey-3 data for 2005-06). In Indonesia, the overall rate is 62 percent, and in Paraguay in 1990 the rate was 53 percent. Although, there is no precise comparison in the U.S., data from the National Medical Expenditure Survey (1988) show that 52 percent of children seek care at a health facility when they are sick with pharyngitis (throat infection); among uninsured the rate is 32 percent. The comparability of these numbers to US data, in conjunction with the fact that the rural health facility usage rates are within 80 percent of the urban rates, suggests that most residents of low-income countries can access health care when they need to.

Detailed surveys on health care utilization in low-income countries also often show many contacts with health care providers. People in rural Rajasthan—a low-income and low-density state in India--visit a doctor about six times a year (Banerjee, Deaton and Duflo, 2004). In urban India, individuals visited doctors about five times a year (Das and Sanchez-Paramo, 2003). This 6
30 most integrated seem to be producers from the Czech Republic, Estonia, Hungary, Slovakia and Slovenia. Bulgaria, Latvia and Lithuania appear yet to take advantage of opportunities offered by fragmentation of production.

# 5. INTEGRATION INTO EU PRODUCTION SHARING ARRANGEMENTS

Trade in parts tends to be highly concentrated. This is a universal phenomenon that has been observed in trade of OECD countries as well as in that of East Asian economies (Yeats 1998 and Ng and Yeats 1999). Top 10 categories account for between 69 percent (Bulgaria) and 95 percent (Estonia) on the export side and 80 (Slovenia) and 92 percent (Estonia) on the import side. There are two other interesting features. First, automotive industry drives production sharing for most CEEC followed by telecommunication and furniture. Motor vehicle parts (SITC 784) rank first or second on both export and import lists of the Czech Republic, Hungary, Poland, Slovenia and Slovakia. Turkey's trade in parts and components with the EU shares the same characteristics. While telecommunication equipment appears high on both export and import lists, it is ranked first only in Estonian trade. The composition of Estonia's trade in parts with its focus on telecommunication and office equipment resembles that of Malaysia. While furniture accounts for a substantial portion of trade of each CEEC, it ranks first in exports of parts from Latvia, Lithuania and Slovenia.

Second, even a cursory examination of categories among top imports and those among top exports shows strong similarity. Products appearing in the top ten are roughly the same and appear in the same order in each CEEC. Clearly, imports are indispensable for exports, and each country follows a very similar pattern.

But does it necessarily imply a division of labor based on fragmentation of production? The participation in a single global process of production involves two-directional transborder flows. One way of capturing their importance is by first assessing the extent of these flows within each important part category and then focusing on selected networks of production and distribution. The latter include not only parts but also components and 'final' products, as identified earlier in Table 1.

# 5.1. **Horizontal Trade Specialization**

One way of assessing their scope is to use the index of horizontal trade specialization (HTS), which is a slightly modified version of a G-L index earlier applied to determine the scope of intra-industry trade. The index is defined as follows: HTSi = 1 - I **Xij** - **Mij I** / (Xij + M1j), where Mq and Xd are imports and exports of goods respectively, i is a product, and j is a reporting
4 Table 2. COPIRUNCING IN PEN PROJECTS APPRDVED IN FY90 (La US$ millions)

||**_L_**|**_OJIa.OMIT_**|TOTAL<br> **PRDJRC**||||
|---|---|---|---|---|---|---|
|UNTuT|PRojTr<br>A|NGUT|cms|AMOUT|XOF<br>TOTAL<br>**Pi**<br>**COST**|Z OF<br>**_LhNICRED_**ANY<br>**(ADDITIONALIT)**|
|APR!CA|||||||
|Kenya|Population IV|35.0|S.3|<br>2.2|5.3|6.3|
|Lesotho|Population, Health &||||||
||<br>Nutrition II|12.1|22.1|7.1|32.1|58.7|
|Nigeria|National Essential Drugs|68.1|85.1|0.0|0.0|0.0|
|Tanzania|Health and Nutrition|47.6|70.0|15.0|21.4|31.5|
|||162.8|21865|24.3|11.1|14.9|
|ASIA|||||||
|Indla|Population VII|96.7|141.5|0.0|0.0|0.0|
|India|Tamil Nadu Nutrition It|95.8|139.1|0.0|0.0|0.0|
|||192.5|280.6|0.0|0.0|0.0|
|EKO U|||||||
|Morocco|BealthSector Development|104.0|171.3|0.0|0.0|0.0|
|Yemen A.**)U.**|Health II|13.0|19.1|0.0|0.0|0.0|
|||119.0|190.4|0.0|0.0|0.0|
|LAC|||||||
|Bolivia|Integrated Health Developmen|t 20.0|38.6|6.2|16.1|31.0|
|Braail|Northeast Basic Health II|267.0|610.6|0.0|0.0|0.0|
|Colombia|Child Care and Nutrition|24.0|40.2|0.0|0.0|0.0|
|Halti|Health and Populstion|28.2|33S|2.4|7.1|8.5|
|||339.2|723.1|6.6|1.2|2.5|
||GAHDTOTAL|813.5|1,412.6|32.9|2.3|4.0|

# Social Development Projects

& In concert with the increased emphasis on human resource development as an essential part of the develooment process, subscribed to by national governments as well as by the Bank, and facilitated by the merger in 1987 of the PHN and education sectors into human resources divisions, six social development projects, all containing substantial PHN components, were approved during the year. Of these, four were from the Africa Region and two from LAC 9. As ibble 3 indicates, Bank funds approved for these PHN components increased the amount of Bank-approved funds for PHN in FY90 by US$31.6 million, or about 4 p)ercent over the US$813.5 million in approved PHN projects, for a total of US$845.1 mill-on or 4.1
**Figure 18. Expansion in access to water and sanitation has not keep pace with population growth and increases reliance on untreated surface water and open defecation, 2001-2006**

Population gaining access per year between 2001–06 <!-- Start of picture text --> a. Water b. Sanitation<br>6<br>6<br>5<br>5<br>4<br>4<br>3.5<br>3.5<br>3 2.9 2.8 3 2.9 2.8<br>2<br>2<br>1<br>1<br>0<br>0<br>Nationa l Urban Rural<br>National Urban Rural<br>‐1<br>‐1<br>‐2 ‐2<br>Flush Toilets<br>Piped Water Standposts<br>Wells/Boreholes Surface Water Improved Latrines<br>Traditional Latrines<br>Annualized Growth<br>Population gaining access per year (%)<br>Population gaining access per year (%)<br><!-- End of picture text --> _Source:_ WHO (2010) from the Demographic and Health Surveys 2001 and 2006.

# Power

## **Achievements**

_In the late 1990s, the Government of Uganda undertook a comprehensive power reform program that placed the power sector under private management operating on commercial principles_ . The sector has subsequently been unbundled, legal and regulatory reforms have been introduced, and the operation of the main generation and distribution assets has been turned over to the private sector under long-term concession agreements.

_There has been a doubling of generation infrastructure over the last few years_ . Following a serious drought and resulting power supply crisis in the mid-2000s, Uganda has made serious efforts both to expand its power generation assets and diversify its mix to become less reliant on hydropower. Generation capacity increased from 280 megawatts (MW) to 568 MW between 2005 and 2009. Despite this increase, generation capacity in 2009—at 18 MW per million of population—remained somewhat lower than the average for the African LIC peer group. But the Bujagali hydropower project will begin to come on stream during 2011 but will not be fully operational until 2012 (EIU 2011). With the additional 250 MW that will be available, capacity is expected to increase to 25 MW per million people, higher than in LIC peers. Although Uganda’s generation portfolio has moved from a predominantly hydro-based 32
**Table 4. Net Present Value (NPV) and Economic Rates of Return (ERR) of GDEC. Project Horizon: 1987-1998.**

**Percent of Work-Year Lost Per Case of Dracunculiasis: 12.5% (5 weeks out of 40).**

|Discount Rate|Net Present Value (NPV)|
|---|---|
|3%|$7,235,744|
|10%|$6,257,928|
|_Economnic Rate of Return (ERR):_|29%|

As can be seen from Table 4, even under relatively conservative assumptions of infection-related disability and limiting the calculation of benefits to the productive age group, GDEC appears to represent an efficacious use of resources. The ERR is very respectable at 29% and the NPV at a 10% discount rate is almost US$6.3 million in constant dollars. These economic returns compare very favorably with those from other health-sector projects. Typically, institutions such as the World Bank consider ERRs in excess of 10% to represent sound economic investments in "productive" sectors such as transport, energy, and agriculture.<sup>2 6</sup>

## **_Switching Value_**

We calculate the switching value _(V)_ which is the value of a case prevented that would make the ERR equal 10%. In other terms, V times the present value (at 10% discount) of prevented cases equals the present value of costs (also at a 10% discount). Using this formulation, we find _V_ to equal about US$10 in constant dollars. Therefore, if the value of a case prevented is at least US$ 10 in the productive age group, then the intervention can be viewed as being sound in economic terms with an ERR of 10%.

# **Sensitivity Analysis**

The above analysis is based upon several crucial assumptions regarding: (a) the average degree of incapacitation caused by Guinea worm infection _(5 weeks),_ (b) the projected annual incidence of Guinea worm in the absence of GDEC _(2.2 million every year), and_ (c) the project horizon _(1987-1998)._ In this section, we determine the sensitivity of our results to variances in these estimates.

- 25 The Economic Rate of Return (ERR) is that discount rate which makes the present value of the stream of net returns (benefits minus costs) equal to zero. If the ERR is greater than the cost of borrowing funds, the project is usually considered to be worth undertaking from an economic point of view.

- 26 Social sectors such as health and education are evaluated against lower "social" discount rates, reflecting the difficulties in quantifying the broader economy-wide benefits (such as benefits to future generations from disease eradication) accruing from investments in such sectors.

10
Table A5: Early Life Exposure to Coffee Mills and Test Scores: Effects by Gender

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||||Male|||||
|CatchmentArea X Post|0.0597***|0.0622***|0.0607***|0.0460**|0.0492**|0.0466**|0.0636***|0.0656***|0.0648***|
||(0.0210)|(0.0217)|(0.0215)|(0.0195)|(0.0210)|(0.0208)|(0.0216)|(0.0222)|(0.0220)|
||[0.015]|[0.015]|[0.015]|[0.034]|[0.035]|[0.043]|[0.015]|[0.015]|[0.015]|
|R-squared|0.3792|0.4221|0.4222|0.3698|0.4099|0.4099|0.3574|0.4026|0.4026|
|Observations|668639|668285|668285|668639|668285|668285|668638|668284|668284|
||||||Female|||||
|CatchmentArea X Post|0.0379**|0.0409**|0.0345**|0.0243|0.0254*|0.0201|0.0434**|0.0464***|0.0397**|
||(0.0171)|(0.0160)|(0.0158)|(0.0151)|(0.0148)|(0.0148)|(0.0181)|(0.0170)|(0.0167)|
||[ 0.042 ]|[ 0.023 ]|[ 0.043 ]|[ 0.124 ]|[ 0.103 ]|[ 0.196 ]|[ 0.031 ]|[ 0.015 ]|[ 0.032 ]|
|R-squared|0.3983|0.4432|0.4432|0.3888|0.4299|0.4300|0.3712|0.4187|0.4187|
|Observations|818714|818396|818396|818717|818399|818399|818714|818396|818396|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|

Notes: Dependent variable is the standardized aggregate test scores in the respective subject groups in the Rwanda Primary School National exam. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. Mills X Post is an indicator variable equal to 1 if a student was born within the vicinity ( _≤_ 5 _km_ ) of an operational coffee mill and 0 if otherwise. Controls include gender of the student, average annual temperature and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at school level in parenthesis. False discovery rate (FDR) adjusted p-values for _MillsX Post_ are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda.

> _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level

44
by the models. Resource-poor countries, such as Moldova, Ukraine, or Kyrgyz Republic, appear below the 45-degree line; their export structures are more diversified than predicted by the model.

Figure 4: (Root) Herfindahl Index Against Prediction <!-- Start of picture text --> AZE<br>TKM<br>KAZ TJK<br>RUSBLRUZBGEOARM<br>KGZ<br>MDA<br>UKR<br>-.2 0 .2 .4 .6 .8<br>Predicted Concentration Index<br>All Countries<br>Fitted values<br>Countries in ECA Region<br>Figure 6: (Log) Count of Export Products Against<br>Prediction<br>RUS<br>UKR<br>BLR<br>MDAKGZ ARMGEOAZEUZB KAZ<br>TJK TKM<br>4 6 8 10<br>Predicted No. of Products Exported (in Logs)<br>All Countries<br>Fitted values<br>Countries in ECA Region<br>1<br>.5<br>Actual Concentration Index<br>0<br>10<br>8<br>6<br>4<br>2<br>Actual No. of Products Exported (in Logs)<br><!-- End of picture text --> Figure 5: Share of Top 5 Products Against Prediction <!-- Start of picture text --> AZETKM<br>TJK<br>KAZ<br>RUS<br>BLR UZBGEOARM<br>KGZ<br>MDA<br>UKR<br>0 .2 .4 .6 .8 1<br>Predicted Share in Top 5 Products<br>All Countries<br>Fitted values<br>Countries in ECA Region<br>1<br>.8<br>.6<br>.4<br>Share in Top 5 Products .2<br>0<br><!-- End of picture text --> The same pattern emerges from Figure 6 that compared the predicted and actual number of exported products. In this case, the variables considered in the estimated model explain more than 80 percent of the total variation of the diversification indicators, which suggests that resource abundance, development and size explain the largest portion of diversification patterns. This shows by how much closer each country is to the 45-degree line in which the predicted count matches the actual count of export products.

Source: Authors’ calculations based on WDI and Comtrade data from WITS.

Still, most of the resource rich countries of the region (Turkmenistan, Azerbaijan, and Kazakhstan) are below the 45-degree line, which means that they export fewer products than is predicted by the model. Russia and Uzbekistan’s numbers of export products match those predicted by the model, while Tajikistan exports more products than predicted.

# **Diversification and the Commodity Boom**

We assess whether the diversification performance of ECA countries during the commodity boom period differed between resource-rich and resource-poor countries, also taking into consideration diversification of exports into services.

6