rafmacalaba/datause-encoder-data huggingface dataset:
The census unit records are publicly available for 10 percent of the population in 1990 and 2000, and for 5 percent in 2010.<sup>14</sup> The unit record data contain employment records of all household members.<sup>15</sup> We define total employment level in each activity from these unit records using appropriate weights. The total employment defined from census data includes employment in both large and small firms, with the large firms in manufacturing and services producing the _T_ -good and the small firms producing the _S−_ good in our model. The population census contains a aggregated industrial classification of employment (top level). Specifically, the 2010 census distinguishes only between agriculture, manufacturing and services activities, and large and small firms cannot be distinguished. We thus use these data to understand the effects of agricultural productivity on all firms (farms) without any distinction between large and small firms.

Data on employment in large-scale formal non-farm activities (corresponding to _T_ -good in the model) are drawn from three rounds of economic censuses (2000, 2006 and 2009). The economic census provides a detailed list of all firms engaged in industrial and services activities (at 4-digit level) with number of employees more than 10. These relatively larger firms are mostly registered, and tend to produce better quality manufacturing and services to meet demand in larger markets. The larger is the firm size, the greater is the possibility that it produces goods and services for a wider geographical area. These data are useful in two ways. First, we use them to estimate the effects of agricultural productivity on large-scale manufacturing and services. A comparison of the estimates on all firms (farms) from the population census with the estimates for large firms from the economic census helps us to analyze possible differential effects across firm size. Given that these data are available at the 4-digit level, we rely on them to perform disaggregated analysis of employment (for example, in construction, education, health etc).

The productivity growth in agriculture is measured by growth in crop yields. Rice (paddy) is the predominant crop in Bangladesh, of which three different types (Boro, Aman and Aus) are grown.<sup>16</sup> The official source of agricultural statistics provides yield data at the district level, but the data are at aggregate

> 14The sample sizes are 10.6, 12.4 and 7.2 million individuals in 1990, 2000 and 2010 censuses respectively.

> 15Total numbers of employment records in census data are 3.1, 3.5 and 2.1 million for 1990, 2000 and 2010 censuses respectively.

> 16High yielding variety of Boro rice now accounts for more than half of rice production (56%). Aman is the next important crop accounting for 44% of rice production. Yields of both of these varieties are much higher than Aus.

18
By merging these sources, we create an individual-level dataset for Brazil for students who entered our surveyed programs in 2014. It includes student gender, age, and mother’s education level; graduation status as of 2016; and pre- and post-program labor market outcomes for graduates who were formally employed after graduation. We merge the individual-level dataset with information at the program- and HEI- levels from the WBSCPS and other administrative data. The resulting dataset includes 29,453 students and 401 programs (relative to 601 surveyed programs).<sup>17</sup>

# **Ecuador**

**_2019 Higher Education Census (HEC)._** This is the universe of higher education graduates who obtained their degrees between January and December of 2019. It comes from the Science, Technology, and Innovation Secretariat ( _Secretaria Nacional de Ciencia, Tecnologia e Innovacion_ , SENESCYT), and contains information on approximately 29,000 SCP graduates, including field of study, institution, program name, and graduation date. Based on these data, we define a student’s peers as those who also graduated from her program in 2019.

**_National Educational Entrance Examination._** This dataset comes from the National Institute

for Educational Assessment ( _Instituto Nacional de Evaluacion,_ INEVAL) at the Ministry of Education. It records test scores on the mandatory high school exit exam ( _Ser Bachiller_ ) and selfreported student socioeconomic background at the time of the exam. We obtained access to a subset of this dataset through the Higher Education Access Unit ( _Subsecretaria de Acceso a la Educacion Superior_ ) at SENESCYT, with information on students who took the test in 2017 and

> 17 Some programs do not match because they did not yet exist in 2014, which is our cohort’s entry year. Others did exist in 2014 but did not have graduates who were formally employed during our sample period. Table A8 presents descriptive statistics for the subsample of 401 programs matched to individual-level data. It also shows t-tests for mean differences between those programs and the remaining 200. A few means are statistically different. For example, matched programs are more likely to have sufficient infrastructure for practical training or coordinate job interviews with firms than unmatched programs. They also tend to be older, are taught by smaller institutions, and have higher shares of part-time students.

18
5 A third line of inquiry focuses on project/program level outcomes and seeks to uncover factors that increase the likelihood of success in achieving the intended objectives and outcomes. Independent variables are typically country characteristics, task team leader skills, and project design. An early example is the work of Kilby (1995, 2000) which examines the effects of supervision on the ratings of World Bank-funded investment projects from 1981 to 1991, finding a positive impact of early supervision on performance. Chauvet _et al._ (2010) use data from World Bank-funded loans (mixing together investment loans and old structural adjustment and new policy-based loans) from 1977 to 2002. They similarly find that supervision is one of the factors that raise the chance of success, and that projects in private sector development are more likely to fail than those in urban development or transport. They also find that the probability of success of projects in post-conflict situations increases the longer the peace holds. Using data between 1990 and 2002 on World Bank investment projects and structural adjustment loans together, Wane (2004) finds that capable and accountable governments take on projects with better design, but low capacity and less democratic governments tend to accept lower-quality projects because they lack the ability to screen them well and because they are driven by the urgency to secure the financing regardless of the result.

Dollar and Svensson (1998, 2000) focus on 179 structural adjustment loans between 1980 and 1995. They posit a binary probit as their main model, with country-level political economy variables, variables relating to the Bank’s project preparation and supervision, and some variables about the operation. They hypothesize that the preparation and supervision effort would influence the probability of success, but note that these are endogenous: if the task team leader suspects impending failure, s/he may increase the effort level. One of their key findings is that when the Bank’s preparation and supervision are treated as endogenous, neither has any statistically significant effect on the probability of success. Their explanation is that _country_ variables such as democratic elections are a powerful determinant of outcomes, and so if the Bank makes a bad choice of country, its adjustment programs are fated to fail, irrespective of its efforts in preparation and supervision. They imply that the best way to improve programs would be to select more promising countries in the first place. One drawback to their approach, however, is that there were no variables about the innards of the operations: the strength or relevance of the conditions, the kinds of results expected, and other aspects of the project or program design.
The division fixed effect estimation shows that returns to school and teachers’ resources significantly differ between city and non-city divisions. Impacts of PCR and PTR on NAT scores are significant in non-city divisions, while impacts of per-pupil teacher salary are rather significant in city divisions, implying that it is optimal to assign young (inexperienced) teachers to rural schools, and then reallocate them, once accumulating more experience, to urban schools, which together maximizes the average test score in the country. The results in the Theil decomposition analysis further imply that gains in the test scores from reallocating teachers and classrooms within division are potentially large due to the concavity built into the education production function.

# **2. Data**

Two data sets are used in this study: the Basic Education Information System (BEIS) and the Grade-6 National Achievement Test (NAT) score data. BEIS, a school census collected every school year, has a variety of information on school characteristics and student performance. NAT data cover total and subject-wise test scores (mathematics, English, Filipino, science and hekasi: social sciences). Panel data (2005 and 2010) are constructed with the above two data sources. All elementary schools, located in both city and non-city divisions, are used in the analysis.

From BEIS, we construct school resource and human capital measures: pupil-classroom ratio, pupilteacher ratio (both quantity) and per-pupil teachers’ salary (quality). BEIS has information on the numbers of principals and teachers, differentiated by their categories and ranks. For example, principals are ranked into four levels. Teachers are categorized into master teachers (two levels) and normal teachers (three levels). For each level/category, we have the salary scale, so the total salary payment can be computed. Per-pupil teacher salary is calculated from the total salary payment for principals and teachers, divided by total enrollment. PCR and PTR represent quantities of resources available at the school, while per pupil teachers’ salary represents the quality (and experience) of teachers.

4
|N|33,454|17,270|16,184|19,460|11,423|1,161|1,410|16,563|16,891|11,181|11,127|11,146|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|N Control|12,426|6,415|6,011|6,880|4,605|476|465|6,228|6,198|4,078|4,187|4,161|
|N Treatment 1|10,995|5,711|5,284|6,657|3,527|262|549|5,442|5,553|3,683|3,688|3,624|
|N Treatment 2|10,033|5,144|4,889|5,923|3,291|423|396|4,893|5,140|3,420|3,252|3,361|
|_R2_|_0.935_|_0.934_|_0.930_|_0.930_<br>* p < 0.0|_0.925_<br>5, ** p < 0.|_0.913_<br>01, *** p <|_0.953_<br>0.001|_0.930_|_0.942_|_0.671_|_0.592_|_0.518_|

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Own survey data, administrative data, school year 2015/2016, own calculations.

18
In addition to describing who is credit constrained and how firms finance themselves, we also analyze the link between access to credit and firm performance and the association between access to credit -at the firm level- and equivalent macro variables. First, we find that firms with higher performance, as measured by labor productivity, are less likely to be credit constrained, which we take as an indication of well-functioning financial markets. A closer examination of this result shows that this relationship is weaker for small firm than for medium and large firms. Second, we find that countries with a higher level of private credit-to-GDP ratios have on average lower percentages of firms that are credit constrained. These results are based on correlations and should not be interpreted as causal.

The structure of the paper is as follows. The next section describes the data set being used in detail, highlighting its richness and uniqueness. Section 3 explains the grouping of firms according to their level of being credit constrained. Section 4 presents both the descriptive results and the regression analysis on the determinants of being credit constrained. Finally, section 5 concludes the paper.

# **2. Data**

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving business environments as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms’ experiences and enterprises’ perception of the environment in which they operate. The studies are implemented using firm-level surveys and over the last 10 years have evolved into a mature product that since 2005 uses a standardized methodology of 4
<!-- Start of picture text --> WdDevelopmnlon R pI<br><!-- End of picture text --> Taxpayers are most concemed about the private burden (costs) of taxation as measured by what they pay to the government. However, taxes can generate a net gain to society if governments use the revenues wisely. Economists, on the other hand, are more concerned about the social burden (costs) of taxation independently of the way in which the revenue is used. Taxes change prices and factor returns (wage rates, the rate of return on capital, and so on). When taxes are imposed, consumers and producers try to avothe tax by consuming or producing less of the taxed item (good, activity, or income). The extent of avoidance accelerates with increases in the tax rate. This distortion of taxpayer behavior reduces their welfare and results in a net ("deadweight") loss to society when markets are functioning well.

To illustrate this broader welfare notion of the economic inefficiency of taxation, some heu*istic examples are chosen, first from historical and then from more recent accounts. It is shown that in some cases the behavioral effect of taxation is dramatic (for example, in production, trade, and consumption) while in other cases the evidence is mixed (for example, in savings and investment). In general, very low tax rates have little impact on economic behavior, while very high tax rates generate efficiency costs that nobody can ignore. However, when taxes are moderately distorting, they have the greatest potential to damage as their welfare effects are likely to be substantial but difficult to measure quantitatively.

The latier half of the paper reviews the literature on a more direct but narrower measure of the incentive effects of taxation: how do taxes affect output growth rates? Some new evidence on this issue is provided using a comprehensive sample of 111 countries. Empirical estimates from this cross-country data show the growth rate of output to be negatively correlated with the level of government spending but positively correlated with the growth rate of government spending. Estimates of the effect of tax rates on output growth rate are also mixed: they are sometimes negative, sometimes zero.

This is a background paper for the 1988 World Development Report. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact the World Development Report office, room S13-060, extension 31393 (38 pages).

The PPR Working Paper Series disseminates the f:ndings of work under way in the Bank's Policy, Plamning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
utilities before they defaulted, DGFRM checked if their score card was useful in picking up deteriorating credit quality. Based on insights from this back testing, DGFRM was able to further refine its score card.

To institutionalize the process of developing credit rating methodologies and to conduct the assessment of individual beneficiaries, the Ministry of Finance is planning to form a credit committee staffed by professionals from various related backgrounds and headed by the head of the middle office of DGFRM with a leading role for the head of the CL unit at DGFRM. This committee can have an important role in further refining score cards, developing new scoring methodologies, and discussing the assessment of entities thereby ensuring that the major risks have been identified.

**Table 3.2: DGFRM's score card for electric utilities**

|**Internal Credit Rating SPU f**|**or Company XYZ**|**Weight S **|**ub Score **|**Final Score**|
|---|---|---|---|---|
|**Regulatory Framework**||**20.0%**||**0.70**|
|a. Legislative and Judicial Under|pinnings of the|10.0%|4||
|b. Consistencyand Predictability|of Regulation|10.0%|3||
|**Ability to Recover Cost and E**|**arn Return**|**20.0%**||**2.60**|
|a. Timeliness of Recovery of Op|erating and Capital|10.0%|12||
|b. Sufficiencyof Rates and Retu|rn|10.0%|14||
|**Diversification**||**10.0%**||**1.30**|
|a. Market Position||5.0%|12||
|b. Generation and Fuel Diversity||5.0%|14||
|**Management**||**10.0%**||**1.40**|
|a. Strategic Positioning||3.3%|15||
|b. Operational Effectiveness||3.3%|12||
|c. Governance(BoD)||3.3%|15||
|**Key Credit Metrics**||**40.0%**||**5.1**|
|a. (CFO pre + Interest)/(Interest|+ Principal)|15.0%|12||
|b. CFO pre/WC Debt||15.0%|12||
|c. Debt/Total Asset||5.0%|18||
|d. Foreign CurrencyDebt/Dome|stic CurrencyDebt|5.0%|12||
|||||**Ba1**|
|**Base Rating**||||**11.0**|
|**Modifiers**<br>**Factors**|**Base Notch Ad**|**justed Sc**|**ore**||
|Base Rating|11.10||||
|**Liquidity**|**3**<br>**0**|**11.10**|||
|**Financial Policy**|**2**<br>**0**|**11.11**|||

_Source_ : Indonesian Directorate General of Financing and Risk Management Next steps in risk analysis for DGFRM will be to further refine the existing score cards based on insights from their application and discussions in the credit committee. DGFRM may consider applying the score cards not only for guaranteed beneficiaries but also entities in the same industry as beneficiaries to broaden the scored universe. Also, DGFRM is in the process of developing additional score cards for municipalities (given the recourse agreement to municipalities in investment guarantees) and more complex structures such as project finance deals in sectors currently in the pipeline (e.g. toll roads, 34 | P a g e
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Restructuring and privatizing CAMTEL will yield significant fiscal benefits. CAMTEL has been the recipient of large indirect subsidies in infrastructure, highlighting the significant costs of the telecom sector to government. In most other African countries, the telecom sector is the first to be privatized and to operate without government support, as its sector fundamentals allow for cost recovery through commercial tariffs and a relatively short path to breaking even because of relatively low capital expenditures. The privatization of CAMTEL was launched in 1997, but the process stalled in 2002 after negotiations with the first two bidders failed.

The country has yet to benefit fully from its connection to the SAT3 undersea fiber-optic cables. Connection to SAT3 led to an increase in Internet connectivity from 0.2 to 13 bits per person between 2000 and 2009 (figure 26a), but that rate of connectivity is low in comparison to Sub-Saharan African peers (table 12, figure 26b).

The full benefit of the submarine cable connection has been mitigated by CAMTEL’s monopoly over the gateway: Although prices are cheaper where there is access to submarine cable, they are even lower when there is a competitive international gateway. As a result, Internet service providers continue to rely on costly VSAT infrastructure. Although Cameroon’s Internet and international call prices are level with the Sub-Saharan Africa average (table 12), ample room remains for lower prices (table 13). As an example, the launch of competitive wireless offerings by mobile operators forced down fixed broadband prices from $104 in 2009 to $61 in 2010.

**Figure 26. The Internet market in Cameroon and elsewhere in Central Africa**

<!-- Start of picture text --> a. Cameroon<br>6 12<br>5 10<br>4 8<br>3 6<br>2 4<br>1 2<br>0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br><!-- End of picture text --> <!-- Start of picture text --> b. Selected Central African countries, 2008<br>14 250<br>12<br>200<br>10<br>150<br>8<br>6<br>100<br>4<br>50<br>2<br>0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br><!-- End of picture text --> Source: AICD database.

38
exercise and then randomly selected households from the lists using a tablet-based random number generator. Only households engaged in crop agriculture and/or raising livestock were eligible for listing.

The survey was conducted by 20 interviewers and 5 supervisors, organized into five teams, over a month-long period. Interviewers were not randomly assigned to respondents. Interviewers and supervisors took part in a five-day training and a one-day pilot prior to data collection.

# _Selection of Variables_

For calculating interviewer ICCs, we use the ten numerical variables from the household module with over 800 responses.<sup>5</sup> All variables selected are analyzed as recorded by interviewers and are not based on post0interview processing. This avoids potential attenuation or amplification of enumerator effects. For example, rather than calculating hours worked per year, we analyze separately months worked in past 12 months, days worked in those months, and hours worked in those days – as was asked and recorded during the interview. Similarly, weekly expenditures on food items and monthly expenditures on non-food items were directly recorded for each item and our analysis is based on these separate variables and not on post interview multiplication of price and quantity. We use the two food and two non-food item expenditure results with more than 800 responses.

We classify our ten dependent variables into three categories based on their difficulty as described in Table 1. We expect that more difficult to record variables will elicit more interviewer-based measurement error and have higher associated ICCs. However, the evidence that would allow for a clear classification of questions on the basis of their propensity for inducing interviewer effects is ambiguous (Schnell and Kreuter, 2005).

# **Table 1: Dependent variables**

|**Classification**|**Variable**|
|---|---|
|Easier questions that involve<br>NO recall or computation<br>beyond counting|Number of people in HH<br>Age of HH head|
|More difficult question<br>requiring recall OR<br>computationbeyond counting|Months out of past 12 worked in primary occupation by HH head|
|Most difficult questions<br>requiring recall AND<br>computation beyond counting|Days per month worked in primary occupation by HH head<br>Hours per day worked in primary occupation by HH head<br>Meals per day for HH members over 5<br>HH expenditure on sorghum (including sorghum flour) in last 7 days<br>(SDG)<br>HH expenditure on sugar or high-sugar products in last 7 days (SDG)<br>HH expenditure on matches in last 30 days (SDG)<br>HH expenditure on laundry detergent in last 30 days (SDG)|

> 5 The exception is volume of food items consumed due to food weight units which was deemed unreliable.

5