rafmacalaba/datause-encoder-data huggingface dataset:
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improving the organization, presentation, consistency, and general accessibility of project data. Availability of the relevant historical data on a consistent format cross different projects, and over reasonable periods of time, would enable statistical analysis at a much more sophisticated level than has been possible In this study. This Is highly desirable, since the Bank would then be In a position to better understand the underlying causal factors that Influence pro;3ct and utility performance. Such Information would be Invaluable In designing loan covenants, arresting adverse trends at a much earlier stage In the project cycle, and predicting power project and utility performance. While most of the useful Information on past and current lending may be ultimately retrievable, the efort Is likely to be prohibitively cnstly. Therefore, It Is recommended that while the comprehensive database compiled for this study be further developed, regularly updated, and made readily accessible to other Bank staff, a serious eftort be made to also collect and preserve all relevant data, for current and future power sector projects.

Beyond the data collection and organization level, there Is a need to build up and maintain a critical mass of staff who can provide Intellectual leadership and proactive guidance, by analyzing past Information, drawing useful lessons for the future, and developing new and viable policies for future operations. This problem Is compilcated by the tendency for the deep insights and knowledge that has been acquired by senior staff to vanish with their retirement. At the same time, daily pressures on operational staff act as a deterrent to those who wish to synthesize and preserve some of their valuable experience for the benefit of colleagues. The strengthening of the policy development capability Is particularly Important, given that Bank lending Is presently carrled out In an environment of rapid change and many relatively Inexperienced operational staff are being called upon to address more and more complex Issues, often under severe time and resource constraints.

Beyond the database development mentioned above, It would be useful to develop an agendr. ut more in-depth follow-up Bank studies In areas such as:
This paper proposes a roadmap for improving the design of in-service teacher training in Ecuador using available data from student and teacher assessments. Even though Ecuador has made substantial efforts to periodically evaluate student and teacher performance, the data resulting in these evaluations have not been used systematically to guide teacher development programs. The results of this study indicate that systematically using the data has the potential to help design more relevant programs that respond to observed skills gaps and allow prioritizing investments in teachers and students with the most substantial needs.

The study also intends to raise awareness about the potential gains of capitalizing on available information on student and teacher assessments. Many top education systems invest heavily in student and teacher evaluations. Finland, Japan, the Republic of Korea, China, the United States, and Singapore, among others, all have robust systems that rely on comprehensive student and teacher assessments that serve as inputs to develop more pertinent in-service teacher training programs. However, to date, the use of data resulting from teacher evaluations in Latin America for formative purposes is much more limited (Bruns and Luque 2014). Chile and Mexico are the education systems in Latin America with the most consolidated systems of teacher evaluation. Among other information, both systems collect information about teachers’ cognitive skills. Data resulting from these assessments are often used to make decisions about a teacher's career progression, but not necessarily to develop tailor-made in-service training programs. Thus, the framework presented by the study applies to other countries that, like Ecuador, dispose of detailed information about cognitive skills gaps of teachers, but largely underuse it.

The paper is structured as follows. Section II briefly describes the design and implementation of, and investments made in, in-service teacher training in Ecuador by the Ministry of Education (MINEDUC) under its main national teacher development program, the Comprehensive <mark>System for the Development of Education Professionals</mark> (SIPROFE) program. Section III provides a brief overview of the information collected by the national teacher’s assessment ( _Ser Maestro_ ) and discusses how the information included in this assessment could guide the design of in-service training courses. Section IV presents different policy scenarios that use data from student and teacher evaluations to better design in-service teacher training. A brief conclusion follows.

4
where **a** _,_ Φ _,_ Λ _,_ and Υ are the coefficient vectors. Since this specification includes the foreign variables (“star” variables) and the global variables, both of which are assumed to be weakly exogenous, the model is called VARX*.

When we estimate the country-specific VARX*, **x**<sup>_∗_</sup> _it_<sup>areconstructeddirectlyfromthe</sup> data. However, for dynamic analysis such as calculating the impulse response functions, the value of **x**<sup>_∗_</sup> _it_<sup>iscalculatedinternallyfromtheforecastedvaluesof</sup><sup>_{_</sup><sup>**x**</sup><sup>_∗_</sup> _jt_<sup>_}_for</sup><sup>_i_=</sup><sup>_j_,whichare</sup> obtained by solving the system of Equations (1) and (2). This is why the GVAR model can describe the interactions of variables not only within a country but also between countries.

# **3.2 Data and a related specification issue**

In this paper, following CPRX, we have estimated 26 country-specific VARX* models.<sup>8</sup> Based on the dataset obtained from Centre for Financial Analysis & Policy, Judge Business School, University of Cambridge,<sup>9</sup> which covers the periods from 1979Q1 to 2011Q2, we have extended the dataset up to 2014Q3 in order to investigate the up-to-date impact of the Chinese economy on the world. The GDP of these 26 countries adds up to approximately 90% of the world GDP; therefore we claim that the model covers the world economy.

The standard elements of three variables in Equation (2) in the related literature are as follows. The domestic variables, **x** _it_ , include the real GDP _yit_ , the inflation rate _πit_ , the real exchange rate _eit − pit_ , the real equity prices _qit_ , the short-term interest rate _ρ_<sup>_S_</sup> _it_<sup>,andthe</sup> long-term interest rate _ρ_<sup>_L_</sup> _it_<sup>.Since</sup><sup>_qit_,</sup><sup>_ρS_</sup> _it_<sup>,and</sup><sup>_ρL_</sup> _it_<sup>,aremissingforsomecountries,theyare</sup> included when available.<sup>10</sup> The foreign variables, **x**<sup>_∗_</sup> _it_<sup>, are constructed as defined by Equation</sup> (1). As for the global variables, **d** _it_ , the log of oil price index _p_<sup>_O_</sup> _t_<sup>isincludedinorderto</sup>

> 8 Since one of the economies is the Eurozone, which consists of eight countries, i.e. Germany, France, Italy, Spain, Netherlands, Belgium, Austria, and Finland, the total number of countries in our data is 33.

> 9We have downloaded GVAR Data 1979Q1-2011Q2 (2011 Vintage) from “The GVAR Toolbox” website at Centre for Financial Analysis & Policy, Judge Business School, University of Cambridge. URL is http://www.cfap.jbs.cam.ac.uk/research/gvartoolbox/download.html. The most updated dataset currently available is 2013 Vintage at “Global VAR Modelling” website created by Dr. L.Vanessa Smith. URL is https://sites.google.com/site/gvarmodelling/.

> 10Specifically, the definitions of the variables are as follows: _pit_ = ln(CPI _it_ ), _qit_ = ln(EQ _it/_ CPI _it_ ),

10
firms therefore tend to favor personal contacts in recruitment whereas larger firms are more likely to use formal channels in addition to informal ones (World Bank, 2013). Participants of the control group who followed the standard curriculum completed an internship prior to graduation and are therefore more likely to have gained personal contracts in the private sector. Graduates of the entrepreneurship track likely lacked these contacts and might have been more likely to seek recruitment in larger companies.

# **8. Conclusion**

This paper provides new experimental evidence on the medium-term impacts of entrepreneurship education on self-employment and labor market outcomes up to four years after university students graduate. The study sheds light on whether entrepreneurship education can successfully foster actual, nascent or latent entrepreneurship. It also analyzes whether a time lag between intentions and actions explains why the impacts of entrepreneurship education on employment outcomes is rarely documented. Based on short-term and medium-term follow-up surveys, as well as data on employment history and individuals’ attempts to set up projects going back to graduation, the study provides rare information on the time-paths of potential impacts of entrepreneurship education on self-employment and labor market outcomes.

The results show that small program impacts on self-employment were mostly concentrated in the year after graduation but were not sustained in the medium-term. Results show that participants were more likely to try to realize a project at some point. As a result of these additional attempts, they were more likely to set up a project and thus more likely to have had an active project at some point as well. However, they were not more likely to have an active project at the time of the endline survey, nor were they more likely to succeed in their attempts. Overall, patterns of heterogeneity are weak and the lack of medium-term impact on selfemployment holds across groups. Results on intermediary outcomes are also consistent. Nearly 4 years after graduation, program participants still have better knowledge on the content of a business plan, but less so than at midline. In contrast, changes in personality dimensions, aspirations and entrepreneurial traits that were observed one year after graduation did not persist. Ultimately, medium-term impacts on intermediary outcomes are narrowly concentrated on 22
38

# Table 1 - S_macy Statistics - Israel: 1980-1988 (Monthly Data)

# la. Means, Standard Deviations, and Coefficients of Variationa

|Variable|M|eanb|Stan<br>Devia|dard<br>tionb|Coeffi<br>of Var|cient<br>iationb|
|---|---|---|---|---|---|---|
|(2 rate of change)|J.L|(A)|(B)|(A)|(B)|(A)|
|CPI|9.36|<br>1.34|<br>5.03|0.89|2.70|<br>0.59|
|Price of Controlled Goods|9.48|<br>1.39|<br>6.44|1.04|4.38|<br>0.78|
|Price of Free Goods|9.32|<br>1.33|<br>4.95|0.98|2.63|<br>0.71|
|Nominal Wage|9.83|<br>2.46|<br>7.85|6.50|6.27|<br>17.18|
|Exchange Rate (Basket)|8.71|<br>0.70|<br>5.29|1.55|3.21|<br>3.43|
|Exchange Rate (Dollar)|9.37|<br>0.21|<br>5.05|1.35|2.72|<br>8.67|
|Ml Money|8.23|<br>3.81|<br>7.90|8.30|7.58|<br>18.08|
|M3 Money|10.03|<br>1.67|<br>6.27|2.74|3.92|<br>4.50|
|Credit|9.20|<br>3.22|<br>5.26|1.64|3.01|<br>0.84|
|Interest Ratec|11.44|<br>3.75|<br>5.12|0.62|2.29|<br>0.10|
|Industrial Production|0.73|<br>0.51|<br>8.78|9.62|lOS.60|181.46|
|Employment|0.20|<br>0.16|<br>1.52|1.31|11.55|<br>10.73|
|Rate of Unemploymentc|5.13|<br>6.58|<br>0.78|0.97|0.12|<br>0.14|
|Consumption|0.69|<br>0.91|<br>7.16|5.05|74.30|<br>28.03|
|Real Wage|0.47|<br>1.11|<br>7.80|6.48|129.45|<br>37.83|
|Real Exchange Rate (Basket)|-0.64|<br>-0.64|<br>3.25|1.78|-16.50|<br>-4.95|
|Real Exchanga Rate (Dollar)|1.43|<br>-1.13|<br>3.35|1.73|7.86|<br>-2.64|
|Real Relative Price of Controlled Goods|<br>0.13|<br>0.05|<br>3.24|0.85|80.75|<br>14.45|
|Real Interest Ratec|2.09|<br>2.41|<br>3.79|1.22|6.87|<br>0.62|
|Trade Deficitd|244.91|237.46|<br>62.65|66.09|16.03|<br>18.40|
|Trade Deficit/GDPe|0.12|<br>0.08|<br>0.031|<br>0.023|<br>0.008|<br>0.006|

Notes:

- a) Data definitions and sources are provided in Appendix 1.

- b) Columns (B) refer to the period 1980:2-1985:6; i.e., before the implementation of the 1985 stabilization program. Columns (A) apply to the 1986:1-1988:12 post

- stabilization period.

- c) Level (and not rate of change).

- d) Level, in millions of dollars.

- e) Trade deficit in domestic currency units divided by a monthly measure of GDP.
by 5–10 percentage points in an election year, particularly in districts with high level of electoral contestation. This pattern is not found in nonelection years or in lending by private banks. Cole also shows that this capture is costly as elections affect negatively loan repayment, and electionyear credit booms do not increase agricultural output either. An explanation for why government-run lending institutions in India are more generous during elections comes from Cole, Healy and Werker (2009) who using rainfall, public relief, and election data examine the reaction of voters to the response of governments to adverse shocks. They find that voters only respond to government relief efforts during the year immediately preceding an election.<sup>5</sup> Unfortunately, the culture of subsidies and frequent government reliefs undermines the culture of repayment as borrowers take advantage of the lax enforcement of credit contracts. The data clearly show that farmers anticipated that, with probability close to 50 percent, a relief package that benefits PACS (but not MFIs or informal lenders) would be announced in case of a drought. Since farmers do not benefit from a relief package if they repay the loan before the announcement is made, all farmers have an incentive to wait (even after the loan due date) until the announcement (if any) is made.

We also asked farmers about their perceived probability of different events related to loan repayment, and their answers showed similar patterns. For example, farmers perceived that their loan with a PACS would be rescheduled in the event of a drought with a 70 percent chance, while this probability dropped to 55 percent for Commercial Banks and only 25 percent for MFIs. The same pattern occurs with the perceived probability of loan forgiveness. Besides, PACSs appeared more likely to issue loans in amounts lower than requested (the difference between PACS and MFIs exceeds 30 percentage points) and less likely to issue new loans even if the previous ones were fully repaid. PACS were less trustworthy of savings and more likely to have a share of the loan deducted as a bribe. Not surprisingly, the loans most likely to suffer from this problem were those issued by PACS. MFI loans, on the contrary, were perceived as less likely to do so. There was a nearly 20 percentage point gap between the two loan sources.

> 5 The international evidence shows the same patterns. Dinç (2005), for example, shows that increased lending by government-owned banks right before elections is not specific to India but can be observed in data from 22 developing countries. Khwaja and Mian (2005) also find that in Pakistan, politically connected firms are able to secure larger and cheaper loans from state-owned banks and default on these loans much more than other nonconnected firms.

7
have information of the distance of the site to the center of the commune, as well as basic information on each partner NGO that is in charge of the site (such as size/personnel, whether it is local or national, their experience and sector of activity before joining Seecaline).

We complement the monitoring data with two additional sources of data. The first one is the Commune Census data, conducted in 2001 under a joint collaboration between Cornell University, the National Statistical Institute (INSTAT) and the agricultural research institute within the Ministry of Scientific Research (FOFIFA). The census contains detailed information on demographic and socio-economic characteristics of all communes in the country<sup>16</sup> . The most important aspect of this data is that it includes some key variables that capture the site selection process such as remoteness, main economic activities, and access to infrastructure. Second, we use commune level estimates<sup>17</sup> of poverty at the commune from the poverty map developed by Mistiaen et al (2002) by combining the 1993 household survey with the 1993 population census. The technique allows estimating consumption-based measures of poverty and inequality at very low level of geographic disaggregation. Mistiaen et al (2002) document a considerable degree of spatial heterogeneity in poverty across administrative units within provinces in particular across districts.

# **4.1. The Definition of Treatment**

As described earlier, the program is gradually phased-in each year to the intervention districts. In August and September each year, the provincial directors (of Seecaline) operate a bidding session in which partner NGOs are selected. These partner NGOs are responsible for opening new sites for each district. There exists a grace period between the time when the contract (between the Seecaline and the NGOs) is signed and the time when a site is actually opened and operated because it usually takes two to four months to select sites, elect a community nutrition worker, and train the NGO personnel and the community nutrition worker.

> 16 The Census covers 1385 (out of a total of 1394) communes in the country in 2001. The missing communes could not be reached because of the local security reasons. The questionnaire was administered to a focus group composed of residents of the commune.

> 17 The Communes were introduces in 1995, replacing Firaisanas as the smallest administrative unit. In order to create communes, some of the Firaisanas were subsequently split or changed some of the boundaries. Our unit of analysis is the community: we are able to assign all communities to old Firaisanas/new Communes with the help of a geographic mapping provided to us by the Statistical Institute.

11
# **E. Estimating Excess Costs**

1.16 Surveys of transport chains originating or ending in the Maghreb countries were conducted to generate data on the incidence of transport-related restrictive practices. These surveys identified representative routes for typical import and export commodities in intra-Maghreb and Europe-Maghreb trade. Product groups that represented different handling modes (bulk, containers, bags, general cargo) were analyzed, major product groups were identified (Table 1.3).

Table 1.3: Transport Chains

|_Tunisia_|_Morocco_|_Algeria_|
|---|---|---|
|Imports|Imports|Imports|
|Steel products<br>Marble/glass|Cereals<br>Steel products<br>Cement|Steel products<br>Cereals|
|ExQorts<br>Textiles and clothing<br>Citrus fruits<br>Dates<br>Industrial machinery<br>Leather products<br>Agrifoodstuffs (Olive oil)|Exports<br>Almonds<br>Textiles and clothing<br>Containers<br>Machinery<br>Canned foods<br>Sugar<br>Manufactured fertilizers|Exports<br>Wood<br>Agrifoodstuffs<br>Pharmaceuticals<br>Electrical machinery<br>Dairy products|

1.17 Studying these chains in the field made it possible to identify the restrictive practices that apply to each and to determine how each diverges from international standards on dwell times, double charges, inefficient tariff structures, billing systems, coordination between modes, logistics planning, loss and damage, and various forms of fraud. A detailed review of customs procedures considered the complexity of transactions and the cause of this complexity, namely document preparation, currency issues, and variations in procedure among countries. Transaction costs were estimated, taking into account the cost of transport by different modes, user charges, countervailing tariffs, and any differential tariffs for cabotage. The aim was to separate the costs attributable to transportation from other costs that act as barriers to trade. The excess costs were expressed in ad valorem terms for the identified products and aggregated to obtain an estimate of total excess costs.

1.18 Comparing the tonnages reported in Tables Al.1, Al.2, and Al.3 with total foreign trade tonnages reported in Table A1.4 clearly shows that the study has captured most of the foreign trade. The excess costs estimated from these figures amount to US$30 million for Morocco, US$94 million for Algeria, and US$71 million for Tunisia.
# GHANA: INFLATION PROJECTIONS

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# **_Table A4.6a Relationship between Task Team Leader (TTL) turnover and IEG outcome rating, ordered probit_**

||**Depen**<br>|**dent variable: I**<br>|**EG 1-6 outcom**<br>|**e rating**<br>|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Dummy for preparation TTL change|-0.056|||-0.020|
|in the 1st or 2nd ISR|(0.046)|||(0.047)|
|TTL turnover during first half (Number of||-0.412***||-0.305*|
|TTLs/Number of ISRs)||(0.158)||(0.170)|
|TTL turnover during second half (Number of|||-0.614***|-0.623***|
|TTLs/Number of ISRs)|||(0.182)|(0.176)|
|Observations|2,763|2,762|2,757|2,752|
|Pseudo R-squared|0.081|0.082|0.082|0.083|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01,

**p<0.05, *p<0.10). All regressions are estimated using Ordered Probit and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects.

# **_Table A4.6b. Relationship between Task Team Leader (TTL) turnover and IEG outcome rating,_** **_<u>probit</u>_**

||**Depend**<br>**(S**<br>|**ent variable: Bi**<br>**atisfactory=1, N**<br>|**nary IEG outco**<br>**on-satisfactory**<br>|**me rating**<br>**=0)**<br>|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Dummy for preparation TTL change|-0.116*|||-0.079|
|in the 1st or 2nd ISR|(0.062)|||(0.063)|
|TTL turnover during first half (Number of||-0.530***||-0.382*|
|TTLs/Number of ISRs)||(0.204)||(0.211)|
|TTL turnover during second half (Number of|||-0.614***|-0.682***|
|TTLs/Number of ISRs)|||(0.209)|(0.203)|
|Observations|2,568|2,567|2,563|2,558|
|Pseudo R-squared|0.101|0.101|0.102|0.105|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01,

**p<0.05, *p<0.10). All regressions are estimated using  Probit and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects.

54