rafmacalaba/datause-encoder-data huggingface dataset:
12 representative sample of 257 government, mosque and private schools that were first surveyed by Ali and Reed (1994) as part of a textbook study. In each school, we selected one teacher in each of the first three grades; this selection was random if there was more than one teacher in a grade. We collected data on teachers and children in _kachi_ or kindergarten, _pakki_ or grade one, and grade two, but our analysis focuses only on the last two grades as the _kachi_ were too young to take the tests. The survey elicited information on teachers’ socioeconomic backgrounds. Similarly, in each class we randomly selected two students for inclusion in the household survey. Enumerators visited those households to obtain information on the sample child and on the socioeconomic attributes of the child's family. Variable definitions and sample statistics are reported in the Appendix.<sup>11</sup> During the course of the school year, the enumerators conducted two unannounced spot checks on teacher and student absenteeism. The first check occurred in the first two months of the term, and the second occurred in the final two months. Data on monthly student and teacher attendance over the school year were also obtained from the school’s attendance register. The average teacher spot-check absence rate was 19% which is comparable to the absence rates found by Chaudhury et al. (2006) for six developing countries using similar methods to those employed in our study. Students had a lower average spot-check absenteeism rate of 13%.<sup>12</sup> Grade-one level exams in mathematics and languages were administered to the secondgraders at the start of the academic year, and to first-graders at the end of the term. The tests were designed by the late Sar Khan. The exam was based on the official curriculum which all schools, public or private, were expected to follow. The curriculum sets minimum objectives for each grade level, and the exam assessed student attainment of these minimum competencies for grade one or _pakki_ . The language test was in the language of instruction to avoid giving undue advantage to any one language group.<sup>13</sup> Out of a possible score of 20, average scores for the math and language tests were 14.3 and 11.3, respectively.
for each treated or untreated municipality. From equation (1) we specify a new linear equation that considers the number of nearby treated municipalities for each municipality: where _N_ is the number of treated municipalities within a radius of 50 kilometers. The coefficient �� captures the spillover effects. If significant, then the spillover effects cannot be rejected.

# **5. Results**

Table 1 summarizes the main descriptive statistics in our study. We present data on different types of crime provided by the National Police Department, as well as the number of municipalities receiving PATI for the 263 country municipalities. The average is calculated over the eight-year period between 2007 and 2014 on a monthly basis, comprising a total of 24,960 observations. The average crime rate per municipality per year is 56.9 per 100,000 people. Theft, injury, and homicide are the most commonly reported crimes.

**Table 1: Descriptive Statics**

|**Variables:**|**Mean**|**Std**<br>**Deviation**|**25th**<br>**percentile**|**Median**|**75th**<br>**percentile**|**# Obs**|
|---|---|---|---|---|---|---|
|**PATI**|0.04|0.21|0.00|0.00|0.00|24,960|
|**Total Crimes**|56.85|45.08|27.14|48.69|76.34|24,960|
|**Extortion**|2.73|6.76|0.00|0.00|2.68|24,960|
|**Homicide**|4.93|9.17|0.00|0.00|7.95|24,960|
|**Vehicle theft**|1.28|4.22|0.00|0.00|0.00|24,960|
|**Theft**|11.12|16.05|0.00|6.70|16.52|24,960|
|**Injury**|5.88|11.83|0.00|0.00|8.15|24,960|
|**Other (other + kidnapped)**|24.68|28.10|4.07|17.74|35.10|24,960|
|**Robbery**|5.41|10.30|0.00|0.00|8.20|24,960|
|**Rape**|0.82|3.75|0.00|0.00|0.00|24,960|

_Source:_ Program administrative data and National Police Reports.

Table 2 presents the estimations of ��in (1). Each column shows the results for each type of crime and the total. As noted, all econometric models include municipality and time fixed effects 10
|||||mitigation<br>measure)|mitigation<br>measure)|
|---|---|---|---|---|---|
|Extremepoverty|2.9|7.4|6.9|4.5|4|
|Poverty|13.7|20.9|20.2|7.3|6.5|
|Poverty gapLower|0.5|2.0|0.7|1.5|1.3|
|Poverty gapupper|3.2|6.4|4.2|3.2|1|
|Inequality|37.0|39.5|39.2|2.5|2.2|

Source: Author’s calculations based on EBCNV 2015

# **5. Policy Implications and Conclusions**

Our findings show that the COVID-19 pandemic is likely to affect household welfare depending on the socio-economic characteristics of the population, sector of employment and location. The poor (more likely to be living in overcrowded conditions) and those with chronic diseases are at greater risk to contract the infection, while those without health insurance (largely the poor and those in informal sectors) are faced with greater inability to avail health care. Also given that the dependence on schools for meals and books is the highest among poor, they are most likely to be negatively affected.

The labor market would be impacted by the COVID-19 crisis in various ways. There are six sectors that have been identified as high risk and are expected to be worst hit in Tunisia- Tourism/Hotels and Cafes/Restaurants, Textiles, Mechanical and Electric Industry, Transport, Commerce and, Construction or civil engineering. These sectors engage a larger share of those employed in the Centre and north east regions and 47 percent from the poorest decile. Secondly, the pandemic will negatively affect those employed informally which is widespread and the highest in construction (68 percent), commerce (51 percent), tourism (45 percent) and textiles (43 percent) sectors. Third, jobs which cannot be done from home, including agriculture and industries, face more uncertainty. Last, those who spend more on food will be affected by price shocks, especially the poor for whom food is a major share of their consumption expenditure.

Our analysis combines the labor shock and price shock induced by COVID-19 simultaneously and simulates post-pandemic consumption. Our estimates indicate that poverty is expected to increase by 50 percent under Scenario 1 (optimistic), and to almost double from 13.7 to 25.9 percent under Scenario 2 (pessimistic). At the same time, inequality is expected to increase slightly due to the COVID-19 outbreak in Tunisia. In fact, households with per capita consumption in the poorest 20 percent of the distribution are simulated to be hit the hardest.

Using the post-crisis welfare distribution, this analysis also helps identify the individuals who are expected to fall into poverty due to COVID-19. Individuals who are expected to fall into poverty due to COVID-19 are likely to be disproportionately resident in the Center west and South east regions and are also more likely to be women, stay in larger sized households, be employed with no contract, and have no access to health coverage.

These findings show that, going forward, it would be extremely important to ensure that economic growth benefits the poor and thus be inclusive. Fostering jobs as well as alternative livelihood mechanisms in the 18
# **TABLE A22:  Political Economic Risk Consultancy (PRC)**

## **A22. Political Economic Risk Consultancy (PRC)**

http://www.asiarisk.com/ Founded in 1976 and headquartered in Hong Kong, the Political and Economic Risk Consultancy specializes in strategic information and analysis for companies doing business in the countries in East and Southeast Asia.

PERC has conducted various surveys of expatriate business managers in the East Asia region. The original results of these surveys were published under the titles “Corruption in Asia in 1999” . Based on the average responses in these surveys, PERC has produced country ratings. In this paper, we use data from the 1998, 2000, 2002-2005 surveys.

## **Corruption in Asia**

In this survey, foreign managers working within the East Asia region were questioned about their perception of corruption, the quality of the legal system, and the professionalism and reliability of the police and judiciary. We have obtained their data on corruption for 12 countries, based on a total of 427 responses. With respect to corruption, respondents were asked “To what extent does corruption exist in a way that detracts from the business environment for foreign companies?”

|**Table A22: Political Economic Risk Consultanc**|2005<br>**y (10 As**|2004<br>**ian c**|2003<br>**ountr**|2002<br>**ies)**|2000|1998|1996|
|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||
|NA|..|..|..|..|..|..|..|
|**Political Stability**||||||||
|NA|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|NA|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||
|NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|NA|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||
|To what extent does corruption exist in a way that detracts from the<br>business environment for foreign companies?|X|X|X|X|X|X|..|

72
percent of all studies were conducted in rural environments, while 29 percent analyzed urban and peri‐ urban populations, and 5 percent analyzed interventions in both rural and urban settings.

**Figure 2. Regional Distribution of Studies**

<!-- Start of picture text --> 1%<br>4% 2%<br>SA<br>5%<br>SSA<br>LAC<br>9% 34%<br>EA<br>MENA<br>18%<br>NA<br>ECA<br>27%<br>LAC, SA<br><!-- End of picture text --> _Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database.

_Note:_ SA = South Asia, SSA = Sub‐Saharan Africa, LAC = Latin America and the Caribbean, EA = East Asia and Pacific, MENA = Middle East and North Africa, NA = North America, and ECA = Europe and Central Asia. The LAC, SA category (1 percent) represents studies done jointly in both regions.

# **_Interventions_**

Studies were classified into one of five conventional subgroups by type of WASH intervention: _hygiene, sanitation, water quality, water supply,_ and _multiple_ (Table 1).

**Table 1. Description of WASH Intervention Subgroups**

|**Intervention**|**Description**|
|---|---|
|Hygiene|Sought to prevent the oral transmission of pathogens through proper handwashing after<br>defecation, before and after handling food, and cleaning infants. Often combined with<br>handwashing infrastructure, water storage and management, training in household waste<br>management,and strongeducational components designed to drive behavioral change.|
|Sanitation|Aimed to prevent the transmission of disease by correctly separating feces, especially in rural<br>areas, by: (i) improving waste disposal facilities; (ii) building simple sanitation facilities (e.g.<br>latrines, toilets, etc.); and (iii) building complex infrastructure (e.g., sewage systems).<br>Although some of the interventions were innovative, such as subsidized training in the<br>adoption of new technologies, most relied on information campaigns to reduce open<br>defecation, promote utilization of facilities,and adopt healthybehaviors.|
|Water quality|Interventions that tested affordable point‐of‐use treatments, such as flocculation,<br>chlorination, solar treatment, and ceramic filters, to remove pathogens or prevent them<br>from entering water collected for drinking or food preparation. Most such efforts took<br>place in remote and vulnerable environments, were implemented by nongovernmental<br>organizations, and were small‐scale and thus involved small sample sizes.|
|Water supply|Designed to ensure access to sufficient water for basic hygiene through: (i) new sources<br>(wells, springs, etc.); (ii) reconstructing and maintaining current infrastructure (network<br>rehabilitation, piped water, etc.); and (iii) introducing management and financial support<br>innovations to spur access (such as credit, private‐public partnerships, and community<br>participation).|
|Multiple|Efforts that included two or more interventions in combination.|
||_Source:_World Bank GWSP‐SIEF WASH Impact Evaluation Database.|

5
We have time-invariant measures of such attributes and we can control for them in the cross-sectional regressions. Gold deposits lie underneath chains of gentle hills in an otherwise undifferentiated landscape of South-Western Ghana. This could co-determine suitability for agriculture. Furthermore, access to the road network influences both the placement of mines, and the potential for trade and specialization. We control for slope and elevation of the terrain, and distance to the nearest primary and secondary roads of a location. This does not affect the results.<sup>12</sup> Although we exploit substantial variation in mine openings and closures, we do not observe all mines from the start. Locations with more gold production in 2010 could be different from others because of a history of mining in their vicinity. In an attempt to separate the legacy effects from contemporaneous effects of mining, we reestimate Figures 5 and 6 with 2010 data and control for 2000 mining output. The _γk_ coefficients that are attributable to variations in gold production over and above historical production give if anything a stronger indication of agglomeration effects.

The dynamic specifications control for time-invariant unobservable characteristics, but they rely on the assumptions of common trends in labor markets, industry composition, and other measures of agglomeration we use. We can control for differential trends given a number of observable characteristics that may mediate the impact of Ghana’s economic growth and structural transformation across locations: initial city population, terrain, and distance to roads. We find little evidence for differential trends in the first place, and our main results are unchanged.

We perform several other robustness checks. First we show that our results do not change if we let mines affect outcomes more than 100 km away. Secondly, we repeat all the analysis replacing _mijt_ with a dummy equal to 1 if gold mine _j_ is in operation at time _t_ . We get similar results. Third, we demonstrate robustiness to using the average of the last 5 years instead of 10 years as an alternative measure of gold production. Finally, we reestimate all the regressions weighting the data by population instead of using EA means. Results are similar.

> 12This and all of the following robustness checks are relegated to the online appendix.

22
Credit flow between t − 1 and t − 2 is obtained by adding and subtracting △d<sup>s</sup> t−2<sup>=△dt−2+</sup> �τ<sup>d</sup> t−2<sup>+ τ s</sup> t−2� on the LHS and RHS of (27): where △τ<sup>d</sup> t−1<sup>and△τ s</sup> t−1<sup>denotesupply/demandfinancialshocksduringtherecessionaryphase.</sup> We define the probability of credit-less recoveries as the joint probability that ∆yt > 0 and △dt ⩽ 0. The determinants of this probability can be identified from the reduced form model implied by the three equations (26), (27) and (28): Hence our testable implications on the drivers of credit-less recoveries imply deleveraging, credit impulse, underutilization capacity or dominance of frictions in real/financial demand and supply during recessions. As we document in Figure 1 during credit-less episodes, real GDP growth was on average lower than during "credit-with" recoveries. Indeed, investment and consumption are the two components of demand mostly affected by the bounce-back effect channelled by changes in liquidity, as illustrated in the model above.

# 3 Liquidity Shocks and the Shape of The Recovery: Empirical Analysis

## 3.1 Data, Methodology and Stylized Facts

Our sample includes 42 advanced and emerging economies, covering the period between 1980 and 2014.<sup>4</sup> Crises episodes and credit-less recoveries are identified following a methodology similar to Braun and Larrain (2005) and Abiad et al. (2011). A contraction in output is considered as a

> 4The sample of advanced economies includes: Australia, Austria, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, New-Zealand, Norway, Spain, Sweden, Switzerland, United Kingdom and United States. The sample of emerging economies includes: Algeria, Argentina, Brazil, Chile, China, Colombia, Hungary, India, Indonesia, The Republic of Korea, Malaysia, Mexico, Peru, Philippines, Poland, The Russian Federation, South Africa, Thailand, Turkey, Ukraine, Uruguay and Venezuela.

11
<!-- Start of picture text --> Gross Output Specification (YKLM)<br>15<br>2<br>cS 1 aa , ] , |<br>D<br>0 eLAL SMLP PAPP HPA PPPSSX of HS ©WK GLP PAPP HA PPGSX of SF<br>Low Income High Income<br>G Capital (Labor (1 Materials<br>Value Added Specification (VAKL)<br>15<br>2<br>cS 1<br>a7)<br>&<br>w 5<br>0<br>NSK LOPS DMMP PED PPEKP CN POH DDH PED PPL YS &<br>%v SF of N v & of<br>Low Income High Income<br>G Capital (Labor<br><!-- End of picture text -->
higher proportion of intangible assets may find it optimal to adopt stricter governance mechanisms to prevent misuse of these assets. We find support for this hypothesis using a capital intensity measure, which is significantly negatively correlated with governance. We also include capital intensity as an additional control variable in our performance regressions and the governance results remain significant.

The paper proceeds as follows: section 2 describes the CLSA corporate governance survey and summarizes our firm- and country-level data. Section 3 discusses potential endogeneity of governance and reports results on the determinants of corporate governance behavior. Section 4 reports our results, which include correlation tests between measures of corporate governance and legality, Tobin's-Q and return on equity (ROE.) Section 5 concludes.

# 2. Data

The CLSA report contains CG ranking on 495 companies in 25 countries. The CLSA sample is selected based on two criteria - firms' size and investor interest. A recent paper by Khanna et al (2001) uses this data to study convergence of corporate governance practices across countries and confirms the sample selection criteria based on their detailed study of India.

The corporate governance (CG) ranking compiled by CLSA is a composite of 57 qualitative, binary (yes/no) questions, designed to avoid subjectivity. Appendix 1 reports an abbreviated version of the questionnaire. Each question is constructed such that answer 'Yes' adds one point to the governance score. The analysts are given strict instructions to answer negatively if they have any doubts or if there was any unresolved controversy over 6
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not distinguish between municipal and state public schools. Table 7 below presents the distribution of 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grade students across public (municipal and state) and private schools. These figures are presented for students who work and who do not work separately.

According to the census, there are around 540,000 individuals living in the City of S ̃ao Paulo in 2010 that reported that they attend 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grades. Of all of them, 82% attend public schools. Of all middle school students, around 11% work in the labor market. However, these figures are sharply di↵erent between public and private school students. Among public school students, 12.8% work in the labor market, whereas among private school students, 2.4% work in the labor market. As expected, the incidence of child and adolescent work increase with grade. Among 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> graders in public schools, the incidences of working the labor market are 7%, 12.6%, and 20.4%, respectively.<sup>11</sup> If the proportion of child workers among 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grade students is similar between municipal and state school students, then there are roughly 37,000 municipal middle school students working in the labor market of the City of S ̃ao Paulo in 2010.

What do these working students do? Table 8 below presents the occupational distribution of the working public school students in 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grade in the City of S ̃ao Paulo according to the _2010 Demographic Census_ .

Most students who work, work in the service sector. Indeed, 26.1% of them work as domestic servants, street vendors, car washers, and others; 25.4% work as service and retail vendors; 14.8% are in office work; and 10.2% are in military service occupations.

> 11In our pooled sample the averages (across all three grades) for the market labor variable are 12% for boys and 6% for girls.

12
Table 5: Deep trade agreements and GVC integration in goods, PPML

||(1)<br>DVA|(2)<br>DVA<br>final|(3)<br>DVA<br>intermediate|(4)<br>DVA int. re‐<br>exported|(5)<br>FVA|(6)<br>FVA final|(7)<br>FVA<br>intermediate|
|---|---|---|---|---|---|---|---|
|No. of provisions|0.00235*|0.00195|0.00230*|0.00111|0.00161|5.89e‐05|0.00259**|
||(0.0013)|(0.0017)|(0.0013)|(0.0015)|(0.0014)|(0.0017)|(0.0013)|
|BIT|0.139***|0.131**|0.142***|0.148***|0.0920**|0.110**|0.0608|
||(0.0436)|(0.0596)|(0.0443)|(0.0468)|(0.0389)|(0.0502)|(0.0398)|
|PTA (in force or||||||||
|inactive)|0.0254|0.174***|‐0.0667|‐0.0527|0.00409|0.159***|‐0.0939**|
||(0.0425)|(0.0455)|(0.0464)|(0.0646)|(0.0428)|(0.0521)|(0.0413)|
|Observations|26,520|26,520|26,520|26,520|26,520|26,520|26,520|
|R‐squared|0.997|0.997|0.996|0.996|0.997|0.996|0.997|
|Country‐pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Clustered standard errors at the country pair level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Table 6: Deep trade agreements and GVC integration in services, PPML

||(1)<br>DVA|(2)<br>DVA<br>final|(3)<br>DVA<br>intermediate|(4)<br>DVA int.<br>re‐exported|(5)<br> <br>FVA|(6)<br>FVA<br>final|(7)<br>FVA<br>intermediate|
|---|---|---|---|---|---|---|---|
|No. of provisions|0.00474**|0.00310|0.00468**|0.00493**|0.00450**|0.00353|0.00453*|
||(0.0022)|(0.0024)|(0.0023)|(0.0024)|(0.0023)|(0.0023)|(0.0025)|
|BIT|0.0632|‐0.0670|0.0868|0.0980|0.133|0.0394|0.173**|
||(0.0759)|(0.0948)|(0.0750)|(0.0765)|(0.0830)|(0.0938)|(0.0841)|
|PTA (in force or||||||||
|inactive)|‐0.0856|‐0.108|‐0.0476|0.0323|0.0188|0.0334|0.0298|
||(0.0833)|(0.0964)|(0.0797)|(0.0744)|(0.0904)|(0.110)|(0.0828)|
|Observations|26,401|26,350|26,401|26,401|26,401|26,350|26,401|
|R‐squared|0.959|0.959|0.957|0.966|0.951|0.946|0.951|
|Country‐pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Clustered standard errors at the country pair level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 25