rafmacalaba/datause-encoder-data huggingface dataset:
- WSE NewConnect has brought very small companies to market: 68 percent of companies on NewConnect have a market capitalization of less than $5 million, and 30 percent are even smaller than $1 million. As of March 2014, the average market capitalization of a company is $8 million, and the average size of an initial offering is around $1.1 million.

- GreTai has 663 companies listed, as of March 2014; the average market capitalization of companies is $133 million. The average IPO size is $14.60 million for 2011, 2012, and 2013. The average market size of a GreTai ESB company is smaller at $82 million.

- Since 2011, Bovespa Mais has targeted a new generation of entrepreneurs with mediumsized companies (not only the traditional family owned) that wanted to go public but were too small to list and raise capital on Novo Mercado. These companies even considered listing outside of Brazil

|Table|6. Company Siz<br>|es (Market Capit<br>% between $5|alization) Serv<br>% between|ed by SME Exc<br>% between|hanges<br>|
|---|---|---|---|---|---|
|Exchange|% below $5<br>million|<br>and $20<br>million|<br>$20 and $50<br>million|<br>$50 and $100<br>million|% above $100<br>million|
|NSE India<br>Emerge|0%|0%|20%|80%|0%|
|JSE AltX|35%|40%|12%|8%|5%|
|GreTai|0.7%|9%|32%|27%|31%|
|Bovespa Mais|0%|50%|50%<sup>7</sup>|0%|0%|
|WSE<br>NewConnect|68%|23%|7%|1%|1%|
|BIST ECM|9%|55%|36%|0%|0%|

_Source:_ WBG compilation of exchange information.

Two questions frequently raised are whether to allow startups and micro-companies. It is generally accepted that micro-companies are not suitable for capital markets, because costs such as prospectuses, annual general meetings, and advisors are too expensive and the personnel requirements to meet ongoing listing requirements are not available. Often, however, policy makers push for them to be included, because they make up the bulk of SMEs.

Allowing startups requires a higher tolerance for failures and challenges to quality that may be damaging for the exchange’s reputation, particularly in the early years, when a loss of investor trust in the market’s integrity is extremely hard to mend.

- NewConnect allowed startups to list but then stopped after a number of startups failed or changed their business model after listing. It now has minimum size requirements of approximately $160,000 in own equity and requires financial statements for one year prior to listing.

> 7 Only two companies have conducted an IPO and therefore have a market capitalization based on public prices. The other companies’ shares are not traded on the market.

19
liberalization in industries in which the country has an incentive to use its trade policy to shift the terms of trade in its favor.

Second, consider a comparison of the adoption of two different types of trade agreements – free trade areas versus customs unions – and the potential impact of the _change_ in the level of import market power that country _k_ experiences under each type of agreement. First, to clarify, there should be no change in the level of import market power arising under an FTA. There will, however, be an increase in the level of market power that country _k_ experiences arising under a customs union due to the adoption of the common external MFN tariff toward non-partners _-k_ . Thus, if we are measuring the change in the level of import market correctly, we would expect that larger increases (changes) in market power when −k country k adopts the customs union be positively related to ∆τi .

However, suppose we introduce a naïve (and imperfect) measure of the change in country _j_ ’s import market power defined as the difference between its individual level of market power, and the highest level of market power of any of the other PTA partners _–k_ . As we have indicated, during the period that MERCOSUR is a customs union, the theory predicts that an increase in market power will generate terms-of-trade incentives to increase external barriers against PTA outsiders, ∆τi−k . Note, however, that we also anticipate this effect to be more likely to arise for Argentina relative to Brazil based on the way in which we have been forced to construct this variable. In most instances, the MERCOSUR partner with the most individual market power is Brazil, given that it is so much larger (in population) than the other members.<sup>42</sup> Thus, a limitation of our measure is that the change in import market power variable for Brazil is likely to exhibit less variation around zero, which may make it difficult to identify any effect.

Finally, in addition to investigating whether the level or change in import market power affects ∆τi−k directly, our approach also allows us to examine whether the failure to include the influence of market power in our regressions in Tables 3 and 4 resulted in omitted variables bias for our estimates of β̂ . I.e., we will also take care to examine whether our estimates of the impact of changes in levels of import protection offered to PTA insiders are affected by inclusion of these market power concerns.

In terms of implementation, we consider and ultimately introduce a number of measures of import market power as a new explanatory variable into **x** i in our estimation equation (2). Again, we are interested in capturing the potential effect that market power may have on changes in external tariffs

> 42 The 1994 populations for the MERCOSUR countries were Argentina (34.4 million), Brazil (159.4 million), Paraguay (4.7 million) and Uruguay (3.2 million). Thus the adoption of the common external tariff in 1994 increases the size of Argentina’s market by nearly 500 percent (in population terms) compared to only 27 percent for Brazil.

25
# _General Youth Well-Being Index_

The addition of the socioeconomic variables to the C-YWI gives us the General Youth Well-Being Index (GYWI), which finds that most states retain their ranking with a few exceptions (Figure 4, Table 4). The top five are joined by Rio de Janeiro, due to above average levels of wealth, formal sector employment opportunities, and health care accessibility, relative to the rest of Brazil. The bigger issue in the state of Rio de Janeiro, which is not captured by these indicators, is access to these factors, since their youth are faring particularly poorly given the relative wealth of the state, relative to most of the other states in Brazil. The bottom eight ranking states are still the bottom ranking states. This is not surprising since they are among the poorest states, so they perform poorly in the socioeconomic category (Table 3, last column).

Besides Rio de Janeiro, the only other significant change in ranking was observed for Espíritu Santo, where the YWI ranking was 18<sup>th</sup> , the C-YWI ranking was 14<sup>th</sup> , and the GYWI ranking was 11<sup>th</sup> . The last improvement is due to better than average rankings in all the socioeconomic indicators, with the exception of a formal sector employment rate, equivalent to the national average (Annex 5). This has the result of raising the state to a ranking higher than the median position.

There were not significant declines in rank, either. However, it is notable that Rio Grande do Sul regained some of the position it lost when the child variables were added. It moved from 10<sup>th</sup> position in the C-YWI to 8<sup>th</sup> in the GYWI. This is still not as good as its 6<sup>th</sup> place ranking in the YWI, but it does suggest that the future of Rio Grande do Sul youth is perhaps better than the C-YWI would indicate. The improvement in ranking is due to its very high rankings in dual parent households, number of physicians per 1000 inhabitants, and low poverty (household income per capita) (Annex 5).

# IV. **Conclusions**

The Brazilian Youth Well-Being Index shows that the situation of youth across Brazil varies greatly. While youth in some states, such as the Federal District and Santa Catarina, are doing well across nearly all indicators, others, particularly those in the Northeast and the North, consistently perform poorly. To track the progress of these 13
App. Table 1a: Pre-post estimations of the impact of GQ improvements on unorganized activity

||Lo|g levels of total|activity|L|og levels of yo|ung firm activity|
|---|---|---|---|---|---|---|
||Plants|Employmen|t<br>Output|Plants|Employmen|t<br>Output|
||(1)|(2)|(3)|(4)|(5)|(6)|
||A. Base|spatial horizon|measuring eff|ects relative to|districts 50+ km|from the GQ network|
|Post GQ upgrades *|0.184|0.259|0.126|0.376|0.370|0.031|
|Nodal district|(0.177)|(0.146)|(0.180)|(0.279)|(0.223)|(0.363)|
|Post GQ upgrades *|-0.086|-0.096|-0.077|0.059|0.093|0.038|
|District 0-10 km from GQ|(0.105)|(0.109)|(0.131)|(0.208)|(0.212)|(0.234)|
|Post GQ upgrades *|-0.031|0.006|0.112|-0.123|-0.102|-0.019|
|District 10-50 km from GQ|(0.116)|(0.122)|(0.186)|(0.247)|(0.246)|(0.295)|
|District and year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1089|1089|1089|1089|1089|1089|
||B. Extend|ed spatial horizo|n measuring ef|fects relative to|districts 200+|km from the GQ network|
|Post GQ upgrades *|0.168|0.227|0.148|0.661|0.644|0.348|
|Nodal district|(0.185)|(0.157)|(0.188)|(0.297)|(0.247)|(0.386)|
|Post GQ upgrades *|-0.102|-0.127|-0.054|0.344|0.367|0.356|
|District 0-10 km from GQ|(0.117)|(0.124)|(0.141)|(0.232)|(0.237)|(0.266)|
|Post GQ upgrades *|-0.047|-0.026|0.134|0.160|0.170|0.295|
|District 10-50 km from GQ|(0.127)|(0.136)|(0.193)|(0.267)|(0.268)|(0.321)|
|Post GQ upgrades *|-0.074|-0.130|-0.112|0.392|0.293|0.127|
|District 50-125 km from GQ|(0.118)|(0.130)|(0.144)|(0.240)|(0.247)|(0.296)|
|Post GQ upgrades *|0.014|0.012|0.205|0.706|0.765|1.106|
|District 125-200 km from GQ|(0.126)|(0.132)|(0.170)|(0.256)|(0.270)|(0.333)|
|District and year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1089|1089|1089|1089|1089|1089|

Notes: Estimations consider the location of unorganized sector manufacturing activity in 312 Indian districts for 1994, 2000, and 2005 from the National Sample Statistics. Young plants are those less than four years old. Panel A estimates effects of GQ upgrades for nearby districts relative to districts more than 50 km from the GQ network; Panel B includes extended spatial rings to measure effects relative to districts 200 km away from the GQ network. The Post GQ upgrades variable takes unit value for the year 2005 after the GQ upgrades commenced in 2001. Outcome variables are winsorized at their 1% and 99% levels, and entry variables are coded at the 1% level where no entry is observed to maintain a consistent sample. Estimations report standard errors clustered by district, include district and year fixed effects, and weight observations by log total district population in 2001.

34
||||Table A1: Main variables|
|---|---|---|---|
|Database|Source|Variable|Description|
|||Dropout rates|Percentage of students who drop out of school in a given grade or school cycle|
|||Urban|Dummy variable, equals one if schools are in urban areas|
|||Basic sanitation|Dummy variable, equals one if schools have appropriate conditions of basic sanitation|
|||Library|Dummy variable, equals one if school has a library|
|||Sports court|Dummy variable, equals one if school has a sports court|
|||Number of classrooms|Number of classrooms available in the school|
|||Number of classrooms used|Number of classrooms used in the school|
|||Computers|Computers available in the school|
|||Science lab|Dummy variable, equals one if school has a science lab|
|||Computer lab|Dummy variable, equals one if school has a computer lab|
|||Teachers' room|Dummy variable, equals one if school has teachers rooms|
|School Census|INEP|DVD|Dummy variable, equals one if school has a DVD|
|||Overhead projector|Dummy variable, equals one if school has an overhead projector|
|||School meals|Dummy variable, equals one if school provides meals to its students|
|||Number of employees|Number of employees working at the school|
|||Morning lessons|Dummy variable, equals one if schools have morning lessons|
|||Child care|Dummy variable, equals one if schools have child care (for babies)|
|||Kindergarten|Dummy variable, equals one if schools have kindergartens|
|||High school|Dummy variable, equals one if schools have high school|
|||State schools|Dummy variable, equals one for state schools|
|||Duration of school day|Average duration of school day in hours|
|||% of classes with full-time education|Percentage of classes with at least 7 hours duration of school day|
|||Students' enrollments|Number of students enrolled in the school in agivenyear|
||INEP|Portuguese grades|Schools' average grades in Portuguese|
||INEP|∆Portuguese grades|Difference between schools' average grades in Portuguese in t and t-1|
|_Prova Brasil_|INEP|Mathematics grades|Schools' average grades in mathematics|
||INEP|∆ Mathematics grades|Difference between schools' average grades in mathematics in t and t-1|
|IDEB|INEP|IDEB Index|Educationalqualityindex,aggregatingstudents' learningand school flow measures|
|||_Mais Educação_|Dummy variable, equals one if schools adhered to_Mais Educação_|
|||Fields of knowledge|Dummy variable, indicating which fields of knowledge schools had chosen|
|_Mais Educação_|MEC/FNDE|Activities|Dummy variable, indicating which_Mais Educação_activities are being offered|
|||Program's costs|Total program's costs by school, divided into fixed and variable costs|
|||Students enrolled in_Mais Educação_|Number of students from the school that enrolled in_Mais Educação_|
|IPEA||GDPper capita|Gross Domestic Production divided by population|
|National Treasury|Secretariat|Spendingon education and culture|Municipal spendingon education and culture|
|Demographic Census|IBGE|Population|Population estimates for 2007, 2009, and 2011|
||UNDP/UN|IDHM|Municipal human development index(2010)|
# **Figure 21. Investment needs in South Asia**

_Despite improvements since 2010, sizable investment needs remain in public infrastructure (energy, transport) and human capital development._

# **A. Quality of infrastructure**

# **B. Infrastructure investment needs**

<!-- Start of picture text --> Index, 1=low to 5=high 2010 Percent of GDP<br>2014 12<br>3<br>2010 EMDE average<br>2014 EMDE average 10<br>8<br>2<br>6<br>4<br>1<br>2<br>0<br>0<br>India Pakistan Bangladesh Sri Lanka Energy Telecom Overall<br>C. Selected health indicators D. Selected education indicators<br>Per capita Percent of population Percent of per capita<br>5000 120 income, ratio<br>45<br>4000 100<br>80 40 Range AE<br>3000 35 EMDEs SAR<br>60 30<br>2000<br>40 25<br>1000 Range AE 20 20<br>EMDE SAR<br>0 0 15<br>Health Improved Improved 10<br>expenditure sanitation water 5<br>(LHS) (RHS) source 0<br>(RHS)<br>Government expenditure Pupil-teacher ratio<br><!-- End of picture text --> Sources: Bhattacharya (2012), Haver Analytics, Inderst (2016), World Bank.

B. This represents investment as a share of GDP required every year during 2010-12 to meet investment needs. The authors use “bottom-up” approach based on identified pipeline regional infrastructure projects across SAR. C. Latest data available during 2011-15. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 6 SAR economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 8 SAR economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 8 SAR economies.

D. Latest data available during 2011-15. Blue bars denote range of unweighted regional averages across EMDE regions. Government expenditure per primary student (in percent of per capita income), unweighted averages of 87 EMDEs, 32 AEs, and 5 SAR economies. Pupil-teacher ratio in primary education (headcount basis), unweighted averages for 165 EMDEs, 31 AEs, and 8 SAR economies.

# **VI.3. Which policies can help address infrastructure needs in the SAR region?**

The alleviation of some longstanding obstacles to growth would help increase the level and productivity of investment of all forms. A more targeted, multi-pronged, policy strategy could also encourage investment by increasing returns to investment, and by expanding the financing envelope (Henckel and Mckibbin, 2010; Nataraj 2007).

42
low education group wages initially drop by 9.0 percent, the gap narrows to 5 percent in the second year and vanishes completely after the sixth year. For the high education group, the effect is considerably larger and permanent, with an initial drop of 18.4 percent, which narrows to 11.6 percent between the third and the sixth year and persists up to the ninth year with a wage gap of 6 percent (figure 11).

The effect on hours of work is stronger for the low education group, with an estimate of 3 additional hours per week, an impact that persist over the entire horizon of analysis. For the high education group, the estimates point to 2 additional hours of work during first three years, with a gradual reduction over time that stabilizes at 1 additional hour at the end of the nine-year horizon (figure 12). Finally, there is a marked difference in the impact on the probability of working in the formal sector (figure 13). For the high education group, there is a transitory negative impact, with an initial drop of -10.0 pp., which narrows rapidly to -1.0 pp. between the third and sixth year, vanishing completely in the seventh year. The opposite occurs for the low education group, with a permanent increase in the probability that range between 7 and 10 pp. over the nine-year period.

**<u>Table 5. Estimates of job displacement on labor market outcomes by education categories</u>**

||<br>Log of hour<br>|<br>ly wage|<br>Hours o<br>|<br>f work|<br>Form<br>|<br>ality|
|---|---|---|---|---|---|---|
||Primary and<br>Secondary|Tertiary|Primary and<br>Secondary|Tertiary|Primary and<br>Secondary|Tertiary|
|Plant Closing|-0.0536***|-0.1218***|1.4798***|0.8475***|0.0461***|-0.0480***|
||(0.006)|(0.006)|(0.177)|(0.125)|(0.004)|(0.005)|
|Fired|-0.0624***|-0.1229***|-0.1674|-0.0864|-0.0377***|-0.1099***|
||(0.010)|(0.007)|(0.203)|(0.137)|(0.005)|(0.007)|
|Quit|-0.0120**|-0.0412***|1.9146***|0.9811***|0.0545***|-0.0081|
||(0.006)|(0.006)|(0.159)|(0.127)|(0.004)|(0.005)|
|Closed Own Business|-0.0285***|-0.1266***|0.0998|0.6714***|-0.0561***|-0.1265***|
||(0.005)|(0.012)|(0.220)|(0.238)|(0.007)|(0.009)|
|Observations|932,874|296,406|1,157,290|422,294|1,221,797|449,234|
|R-squared|0.2072|0.2782|0.1300|0.1470|0.2277|0.1218|

Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.10. All regressions control for year of education, gender, marital status, age, age square, survey period, state and industry fixed effects. Standars errors are clustered at the state level. The omitted category is non-displaced workers.

21
# **Table A18:  Cingranelli Richards Human Rights Database & Political Terror Scale (HUM)**

**Data Provider** University of Binghamton Cingranelli-Richards Human rights database (CIRI) and  University of North Carolina Political Terror Scale (PTS) **Description** United States based universities **Website** www.humanrightsdata.com **Data Source** Cingranelli-Richards Human Rights Dataset (CIRI) and the Political Terror Scale (PTS) **Type** Expert assessment **Respondents** Coding by Cingranelli-Richards and Gibney teams **Frequency** Annually since 1980 **Coverage** Global sample of countries **Public Access** Yes **Description** The Cingranelli-Richards dataset is a numerical coding on a 2 or 3 point scale of data on 13 human rights, as reported in Amnesty International Human Rights Reports and the U.S. Department of State Country Reports on Human Rights Practices.  It is produced by Professor David Cingranelli at the University of Binghamton, U.S.A. and Professor David Richards at the University of Memphis, U.S.A. and is available at www.humanrightsdata.com.  The Political Terror Scale is a numerical coding on a 5-point scale of state-sponsored domestic political terror through imprisonments, torture, disappearances and violations of the rule of law.  It is produced by Prof. Marc Gibney at the University of North Carolina and is available electronically at: http://www.unca.edu/politicalscience/images/Colloquium/faculty-staff/gibney.html.  Note that the two sources are averaged and treated as a single source in Political Stability and Absence of Violence since they are based on the same underlying publications.

|**Voice and Accountability**|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|
|Restrictions on domestic and foreign travel (CIRI)|X|X|X|X|X|X|X|X|
|Freedom of political participation (CIRI)|X|X|X|X|X|X|X|X|
|Imprisonments because of ethnicity, race, or<br>political, religious beliefs? (CIRI)|X|X|X|X|X|X|X|X|
|<br>Government censorship (CIRI)|X|X|X|X|X|X|X|X|
|**Political Stability**|||||||||
|Frequency of political killings (CIRI)|X|X|X|X|X|X|X|X|
|Frequency of disappearances (CIRI)|X|X|X|X|X|X|X|X|
|Frequency of tortures (CIRI)|X|X|X|X|X|X|X|X|
|Political terror scale (PTS)|X|X|X|X|X|X|X|X|
|**Government Effectiveness**<br>NA|..|..|..|..|..|..|..|..|
|**Regulatory Quality**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||
|Independence of judiciary (CIRI)|X|X|X|X|X|X|X|X|
|**Control of Corruption**<br>NA|..|..|..|..|..|..|..|..|
|**Country coverage (CIRI)**|**192**|**192**|**192**|**159**|**159**|**159**|**159**|**159**|
|**Country coverage (PTS)**|**176**|**176**|**178**|**178**|**178**|**178**|**177**|**174**|
|**Year of Publication**|**2006 **|**2006 **|**2005 **|**2004 **|**2003 **|**2001 **|**1999 **|**1997**|

56
- 12 -

The other model is the crop sales equation. The road improvements are expected to enhance farmers’ participation in output markets. To this end, the following specification is considered with the crop production value replaced with the total amount of crops sold at market, _SALE_ : Our data cover 28 crops, such as teff, maize, coffee and beans. To aggregate them, producer price data are used from FAOSTAT. Note that as in the case of advanced inputs used, not many farmers have yet participated in market transactions in rural Ethiopia. In our survey data, about one-fourth of households went to market and sold some crops that they produced. Therefore, again, the truncated regression model is used: The total value of crop sales at market in logarithm is distributed closely to a normal distribution, which validates the truncated regression model ( **Figure 5** ).

**Figure 5. Distributions of total crop sales**

<!-- Start of picture text -->
-5 0 5 10 15<br>ln(Total crop sales, US$)<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text -->