rafmacalaba/datause-encoder-data huggingface dataset:
|**Name**|**Acronym**<sup>**10**</sup>|**Countries Included**|
|---|---|---|
|Central and Eastern Europe|CEE (2)|Hungary, Poland|
|South East Europe|SEE (8)|Albania, Bosnia, Bulgaria, Croatia, Macedonia, Romania, Serbia<br>and Montenegro, Turkey|
|Former Soviet Union – Low<br>Income Countries|FSU-LI (8)|Armenia, Azerbaijan, Georgia, Moldova, Kyrgyz Republic,<br>Tajikistan, Turkmenistan, Uzbekistan|
|Former Soviet Union –<br>Middle Income Countries|FSU-MI (4)|Belarus, Kazakhstan, Russia, Ukraine|

_Description of Variables_

|**Indicator **|**Unit**|**Definition **|
|---|---|---|
|Average Cost<br>Recovery Price<br>(ACRP)|US$/ kWh (power)<br>US$ per 1000 m3 or US$ per mcm(gas)|Average cost-recovery price at the end-user level defined as long<br>run operations and maintenance costs plus allowance for<br>reasonable investment and normative losses.|
|Collection Rate|rate (as a proportion of<br>billed consumption)|This is the proportion of the billed charges to consumers of the<br>respective energy that is actually collected in some form by the<br>utility/serviceprovider.|
|End-User<br>Consumption|kWh metered or otherwise<br>observed (power)<br>thousand m3 (mcm)<br>metered or otherwise<br>observed (gas)|End-user consumption measures the needs of the final consumers<br>of the country. They are broken down into several categories:<br>industry, transport, residential, tertiary, agriculture and non-<br>energetic uses. This measure is the actual final consumption that is<br>marketed by the utilities, which is the difference between domestic<br>consumption and the consumption that results from the<br>transformation process, and the losses involved, for each country.<br>In ENERDATA tables, the values correspond to Total Final<br>Consumption.|
|Normative Loss|Rate, as a proportion of<br>power injected into<br>transmission<br>Rate, as a proportion of<br>gasproduced for transport|Normative losses are that component of total losses that accrue<br>due to system design and technology constraints; they are<br>inevitable and predictable.|
|Total Loss|Rate, as a proportion of<br>power injected into<br>transmission<br>Rate, as a proportion of<br>gasproduced for transport|Rate of actual losses in transmission and distribution due to<br>technical issues and due to theft. ENERDATA is the primary<br>source for transmission and distribution losses, supplemented with<br>data from sector specialists where available.|
|WAET|USc/ kWh (power)<br>US$/ 1000 m3 or U$ per<br>mcm(gas)|Weighted average end-user tariff.|

> 10 Number of counties shown in parentheses.

22
distressed banks to estimate the probability of receiving government assistance (versus the probability of resolution of bank distress through a private/market solution) using a multinomial logit model that adjusts for a possible selection bias using the inverse Mills ratio constructed based on the first-stage probit regression estimates for bank distress. Third, we estimate the fiscal expense on bank bailouts using the Tobit model—again adjusting for possible selection bias using the inverse Mills ratio for bank distress. To project future contingent liabilities for 2022-23, the paper combines the estimation results from the three steps with the most up-to-date data from the financial statements of banks.

- _6.1 What factors can predict bank distress that could trigger resolution action?_

We begin by estimating the probability of bank distress using bank-level data. The results are summarized in Table 3. Column 1 presents the results when the CAMEL variables are lagged by one year while Column 2 reports the results when CAMEL variables are computed as averages over the previous two years. The CAMEL control estimates indicate that greater bank capitalization decreases the likelihood of distress. The total number of banks in distress is negatively related with individual bank trouble, mechanically suggesting that once the banking system problems are addressed, on average individual banks are less likely to experience distress. The results on the profitability ratio suggests that sustained profitability over the short (one year) and medium (average over two years) term decreases distress (Table 3). The estimation results highlight the role of timely and credible supervision to mitigate the likelihood of bank distress. Other macroeconomic variables appear to correlate with individual bank distress significantly (at the 5 percent level) only when we consider bank-level determinants lagged one year (Table 3, column 1) but there appears to be a weaker association when in the model specification we include the average value of CAMEL variables over the previous two-years (Table 3, column 2) —even though they may still be relevant for capturing the overall banking systemic risk.

**Table 3: Estimation of the probability of bank entering distress**

|VARIABLES|(1)<br>Distress|(2)<br>Distress|
|---|---|---|
|Regulatory Capital / Total Assets|-0.014**|-0.014**|
||(0.006)|(0.007)|
|Provisions / Gross Loans|0.004|0.005|
||(0.005)|(0.007)|
|Interest Margin / Total Assets|-0.101*|-0.195**|
||(0.052)|(0.082)|
|Liquidity Ratio|-0.000|0.000|
||(0.001)|(0.001)|
|Overhead Costs / Income|-0.004|-0.005|
||(0.003)|(0.004)|
|System distresst-1|-0.063***|-0.047*|
||(0.019)|(0.024)|
|Inflation rate|0.028***|0.009|
||(0.010)|(0.009)|
|Real GDP growth|0.057***|0.033*|
||(0.017)|(0.019)|

17
confirms the regression toward the mean effect and shows that high average growth rates and democratization are associated with improvements in social policy.

Building on Conley et al (2012), we also estimate a lower and upper bound for social policy prior actions, assuming that the instruments are only “plausibly exogenous”. Implementing the Union of Confidence Interval (UCI) approach – with gamma values ranging from - 0.004 to +0.004 for both instruments – generated a 90 percent lower bound of .0004 and an upper bound of .019. Even though this is a wide confidence interval, it further corroborates the finding of a positive influence of World Bank policy lending.

As mentioned above, prior actions related to social policy can be disaggregated and matched with the relevant components of CPIA Cluster C. Table 4 reports results for these tests, which in all other respects replicate those from tables 2 and 3. For brevity, the table reports only the coefficient estimates and standard errors for the conditions variables.

Table 4 shows considerable heterogeneity. While conditions related to building human resources (health and education) and equity of public resources use do not seem to affect the quality of policies in those areas,<sup>11</sup> prior actions targeting social protection and environmental sustainability do have a positive effect, with coefficient estimates for these variables several times larger than the estimates presented in tables 2 and 3. Note also that the coefficient estimates increase when estimating the model with 2SLS.<sup>12</sup> Caution is required however as the IV test statistics are generally less favorable. Nevertheless, the findings in Table 4 indicate that – even within a policy cluster – the right kind of conditionality is able to improve the quality of policies.<sup>13</sup> These results are in line with Smets and Knack (2015), who also find heterogeneity when disaggregating their analysis on the effect of Bank lending for public sector governance.

11 As mentioned in footnote 6, prior actions for equity of public resource use are imperfectly matched with the corresponding CPIA subcomponent. As Clemens et al (2012) have shown, this might attenuate the impact of policy lending in this sub-sector.

12 However, caution is required as IV test statistics are generally less favorable.

> 13 While overidentifying restrictions are met, the F-statistic of excluded instruments is on the low side for gender (F=2.35), public resource use (F=4.61), social protection (F=6.51) and environmental sustainability (F=7.13), mainly due to the weak predictive power of population. Weak instrument tests turn out favorable for social protection (e.g., confidence interval AR.95-test = [.005894, .]) and environmental sustainability (confidence interval AR.95-test = [.017852, .101367]), but not for gender equality and equity of public resource use.

13
To target enrollment, the NGO identifies out-of-school girls before each school year, using information from community members and government records. The program volunteers hold village meetings to prepare for a house-to-house enrollment drive, targeting girls who have never enrolled or who have dropped out of school. These efforts seek both to encourage parents to support their daughters’ education and to motivate girls themselves to come to school.<sup>8</sup> To target student learning, the NGO organizes in-school lessons led by the volunteer in grades three through five. The curriculum and instructional model was designed with Pratham Rajasthan and emphasizes activity-based and playful learning through games that teach English, Hindi, and Math. The methodology emphasizes group work and student involvement in the teaching and learning process. These lessons are held during school hours for approximately two hours per day, several days per week, over four to five months. This component of the program does not focus explicitly on girls, but rather aims to increase learning levels for both girls and boys.<sup>9</sup> In tandem with the peer group learning method, students are placed in three groups according to ability, measured by diagnostic pre-program tests similar to the Annual Survey of Education Report (ASER); the tests are designed to be quick to administer so that they can be conducted individually for each student.

The program was implemented and evaluated in the academic years of 2012 and 2013. Each year, in selected villages, village volunteers conducted the door-to-door enrollment drive,

> 8 The NGO identifies out-of-school girls (aged 6 to 14) using a two-step approach. Using data from the state government child tracking system (CTS) and school records, the program develops an initial list of girls to target. To prepare for the door-to-door enrollment drive, the program then engages community members to verify records, build awareness, and increase enrollment. Volunteers organize meetings of 20 to 40 individuals, to engage village leaders and community members as champions for increasing girls’ school enrollment, and jointly identify barriers and develop solutions. Volunteers also leverage local meetings with other programs.

> 9 The program involves a 12-week module focused on interactive teaching methods, “Catch Up” methodology for children who are behind their grade level, and peer group learning. After training, the program provides teachers with learning kits, which includes games and creative learning materials, and access to a telephone helpline and SMS updates for extra support. In schools with poorer performance, the NGO provides additional “handholding” visits to support teachers and the trained volunteer.

9
**Table 9.  Top Fifteen Intermediate Recommendations**

|||**HIC**|**MIC**|**LIC**|**Score**<br>**10**|
|---|---|---|---|---|---|
|1|_Strengthening - Debtor Registries_|0.3|1.4|0.7|23.2|
|2|_Consolidation - Global Oversight - Legal Framework_|0.3|0.4|0.2|22.0|
|3|_Independence & Accountability - Insurance Oversight_|0.7|0.8|0.2|19.8|
|4|_Supervisor based risk management - Pensions Supervision_|0.2|0.5|0.1|19.5|
|5|_Pensions – Supervision_|0.8|0.9|0.1|18.8|
|6|_Institutional framework - Debt Management_|0.7|0.9|0.7|18.7|
|7|_Independence & Accountability - Pensions Oversight_|0.2|0.3|0.1|17.3|
|8|_Banking - Market Competition_|0.7|0.9|0.3|17.2|
|9|_Security Registries_|0.5|0.5|0.0|16.9|
|10|_Resources & Capacity Building - Pensions Oversight_|0.2|0.2|0.1|16.7|
|11|_Standards – Accounting_|0.7|1.2|0.9|16.6|
|12|_Independence & Accountability - Capital Markets Oversight_|0.2|0.4|0.2|16.0|
|13|_Legal Framework - Payments System_|0.5|0.8|0.3|15.9|
|14|_Extra-judicial Resolution Arrangements_|0.0|0.5|0.3|13.9|
|15|_Standards – Auditing_|0.2|0.4|0.2|13.8|

**Table 10.  Top Fifteen Basic Recommendations**

|||**HIC**|**MIC**|**LIC**|**Score**<br>**11**|
|---|---|---|---|---|---|
|1|_Closure – Development Banks_|0.0|0.0|0.5|9.1|
|2|_Specialized Courts - Judiciary_|0.0|0.1|1.4|7.0|
|3|_Collection & Organizational Improvement - Pensions & Social Security_|0.0|0.1|0.8|6.6|
|4|_Microfinance – Supervision_|0.0|0.3|1.8|3.8|
|5|_Microfinance – Regulation_|0.0|0.3|1.9|3.4|
|6|_Microfinance - Legal and Org Framework_|0.0|0.6|2.7|3.1|
|7|_Procedural & Managerial Improvements - Insurance Oversight_|0.0|0.1|0.5|2.8|
|8|_Liquidity Forecasting - Monetary & FX Management_|0.0|0.5|1.4|2.7|
|9|_Strengthening - Company Registries_|0.2|0.3|0.8|2.3|
|10|_Other NBFIs – Regulation_|0.2|0.4|1.0|2.0|
|11|_Transparency - Judiciary_|0.0|0.1|0.5|1.7|
|12|_Pensions & Social Security - Direct Public Intervention_|0.5|1.7|3.8|1.7|
|13|_Introduction - Debtor Registries_|0.2|0.5|0.9|1.6|
|14|_Institutional Framework – Monetary & FX Management_|1.2|1.5|3.0|1.5|
|15|_Capital Markets – Tax Management_|0.0|0.3|0.9|1.4|

> 10 The score is obtained from the coefficient of the quadratic GDP per capita term in a regression in which both the level and the square of GDP per capita are introduced as explanatory variables.

> 11 The score is derived from average income elasticities obtained from a regression in which both the level and the square of GDP per capita are introduced as explanatory variables.

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Table 8: Effect of the Discontinuity on Teacher Characteristics

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|SE|RD (3 Year)|SE|RD (4 Year)|SE|
|Log(Salary)|-0.284***|0.062|-0.554*|0.273|-0.444**|0.205|
|Bachelor’s|0.318***|0.032|0.003|0.186|0.109|0.140|
|Some Training|0.003|0.031|0.013|0.120|0.010|0.096|
|Local|-0.017|0.037|-0.006|0.178|-0.066|0.134|
|Age Started|0.072***|0.024|1.193|1.550|0.943|1.116|
|Single|0.148***|0.032|-0.006|0.176|0.053|0.136|
|Female|-0.005|0.044|0.288|0.254|0.273|0.190|
|Mean Teacher Knowledge|0.129**|0.062|0.132|0.245|0.011|0.229|

This table presents OLS and fuzzy regression discontinuity results for the effect of temporary contracts on teacher characteristics. The RD includes either teachers hired 4 years before 1998 and 4 years after 2001 or teachers hired 3 years before 1998 and 3 years after 2001. Standard errors are clustered at the month hired level for the regression discontinuity results and the school level for the OLS results. Log(salaries), which were observed multiple times over several years, were normalized by calculating the teacher fixed effect, controlling for year fixed effects and de-meaned at the district-level. Each cell is a coefficient estimate (or standard error estimate) for the temporary contract teacher effect.

40
**Figure 19: Outflows Permitted under Liberalized Remittances Scheme for Residents and Overseas Direct Investment Scheme for Firms**

<!-- Start of picture text --> Outflows by residents under the Liberalized Restrictions on Outward Investment by Firms have<br>Remittances Scheme were liberalized during the been liberalized in sync with the capital flow cycle<br>“Surge”<br>300,000 500<br>250,000<br>400<br>200,000<br>300<br>150,000<br>200<br>100,000<br>50,000 100<br>* **<br>0<br>0<br>Feb 4, Dec 20, May 8, Sep 26,Aug 14, June Feb 3, 1992 2002 2004 2005 June 14, Sep 26, August<br>2004 2006 2007 2007 2013 14, 2015 2007 2007 14,<br>2014<br>2013<br>US $<br>% of net worth of the firm<br><!-- End of picture text --> Source: RBI Notifications on Liberalized Remittance Scheme for Resident Individuals, and on Overseas Direct Investment, various years.

* While overseas investment up to $4 mn can be made via the automatic route, those between 4 mn and 15 mn require the RBI's approval, and investmnets worth more than 15 mn can only be made with the approval of the Ministry of Finance.

** automatic approval limit raised to 100 mn.

<mark>In wake of the capital account reversal during the tapering talk, the RBI announced restrictions on capital outflows from Indian corporates and individuals. It lowered the limit on Overseas Direct Investment under the automatic route (i.e. the outflows which do not require prior approval of the RBI) from 400 percent to 100 percent of the net worth of Indian firms, reduced the limit on remittances by resident individuals (which were permitted under the so-called Liberalized Remittances Scheme) from $200,000 to $75,000, and discontinued remittances for acquisition of immovable property outside India.</mark> Basu et al (2015) note that the amounts remitted under the schemes were small, of <mark>the order of $100 million a month. There was no surge in remittances during the period of the tapering talk. Outflows were a paltry $92 million in June and $110 million in July 2013, hence there does not seem to be an apparent justification for this restriction. If anything, outflows once underway can be difficult to stem with such restrictions, with the incentive and scope for evasion remaining strong. Results in Basu et al (2015) indicate that in the five days from the time when this announcement was made, exchange rate depreciation and decline in stock market index were accentuated, while equity flows declined. Commentary in the international financial press reflected the fears that these controls evoked (</mark> _<mark>Economist</mark>_ <mark>, August 16, 2013, “.... India’s authorities have planted a seed of doubt: might India ‘do a</mark> 30
rise to classical externalities or spillover effects. The general level of education in the workforce also expands production possibilities, by facilitating the discovery, adaptation and use of more economically rewarding, albeit technologically more demanding and knowledge-intensive, production processes.

Besides its direct impact on economic production, education can yield other, communitylevel benefits. These non-market effects include the possible contribution of education to improving social equity, strengthening national cohesiveness, reducing environmental stress through its effect on fertility and population growth, reducing crime rates, and so on (see Table 2 for a partial list).

Some studies have succeeded in identifying positive externalities, and have quantified them (see Weisbrod 1964; Haveman and Wolfe 1984; Wolfe and Zuvekas 1997). If one could include externalities to typical estimates, then social rates of return might well be higher than private rates of return on education, perhaps more than double (Haveman and Wolfe 1984; Wolfe and Zuvekas 1997). Rauch (1993) finds higher social returns (at 8.1 percent), compared to private returns (4.8 percent), by comparing worker’s educational level and increases in wages with average educational level attained in United States metropolitan statistical areas. Acemoglu and Angrist (2000) also find higher social returns (9.1 percent versus 7.4 percent) by instrumenting quarter of birth dummies for individual education, and compulsory school attendance laws and child labor laws in states of birth for average education (see also Moretti 2002).

15
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39
job offers then had to go through the onboarding process at the firms which typically consisted of a training that lasted for several days or weeks depending on the hiring firm. This training typically focused on teaching technical skills related to fabric handling, stitching, pressing, packing and quality control among others.

# **3.2. Data**

The data collection for the baseline survey took place at screening centers directly after the eligibility checks of individual job applicants in order to minimize attrition and to ensure that the treatment and control groups are fully comparable. All eligible job applicants who agreed to take part in the study were interviewed at baseline and, in total, information from 935 respondents was collected between June-August 2016. Approximately eight months after the baseline data collection, an effort was made to re-interview all baseline respondents during the follow-up data collection. Among the baseline respondents, 827 were successfully tracked, which results in an attrition rate of 11.6 percent. The information from respondents interviewed both at baseline and at follow-up comprises the panel data set and only the sample of panel respondents will be at the core of the impact analysis.

Table 1 indicates that on average study participants randomized into the control group were roughly 24 years old at baseline. Around one-quarter of the respondents were married and slightly less than one-quarter were mothers at baseline. As expected, the number of migrants among applicants for factory work is high. Nearly 70 percent of the respondents had migrated to Addis Ababa. On average, respondents have more than nine years of completed formal education and nearly 10 percent of the sample is enrolled in some form of educational program at baseline. It should also be noted that the vast majority of study participants has had experience from either self or wage employment, with nearly 77% reporting to have at least one regular wage or salary paying experience (not shown in the table). We also find that around 30 percent of our sample has had some cash income within the 30 days leading up to the baseline survey. In the 7-day period preceding the baseline survey, however, 80% of our sample respondents were not engaged in any form of employment. Importantly, there do not seem to be any systematic differences across this wide range of observables between the treatment and the control group which suggests that the 11