rafmacalaba/datause-encoder-data huggingface dataset:
|**Children aged 36+ months ((n=705)**|**Percentage**<br>**(weighted)**|**n (raw)**|
|---|---|---|
|ECE enrollment|||
|Yes|62.28|435|
|No|37.72|270|
|ECE class***|||
|Playgroup|32.48|156|
|Nursery|24.28|103|
|Katchi|31.69|125|
|Other ECE/Missing|11.55|51|
|School Type|||
|Government|72.11|288|
|Private|25.37|136|
|Refugee School|.79|2|
|Other [_deeni madaris_, non-formal basic education, distance|||
|education/privately]|1.74|9|
|Frequency of ECE attendance (days/week)*****|||
|1-3 days|10.57|38|
|4-5 days|24.20|116|
|6 days|65.23|276|
|Frequency of ECE attendance (hours/day)|||
|1-3 hours|12.00|43|
|4-5 hours|52.29|232|
|6 hours+|35.71|155|

*Urban includes urban, semi-urban, and inner city

**No education= never attended school, some primary= completed grades 1-4, completed primary= completed grades 5-7, includes those who completed some middle school, completed middle= completed grades 8-10, includes those who completed some secondary school, completed secondary= completed F.A/ F. Sc/ I. Com/ ICS/A-Level or Polytechnic Diploma, completed Bachelor’s degree= completed B.A/B.Sc./ B. Com/ B. Ed/ Degree in Engineering/Accounting/Law/Agriculture, completed graduate degree= completed M.A/M. Sc or M.Phil or PhD, religious education=completed religious education or classified as “Islamic scholar”

***Other ECE/Missing includes Class 1+, Prep, Madrasa, and Kindergarten;

****Data was missing for 3 household heads indicating that these household heads did not live in the household

*****Data was missing for 5 children on days per week and hours per day attendance in ECE

CREDI Z-Scores are norm-referenced and standardized against a global population<sup>7</sup> of children from low-and middle-income countries who are from an “advantageous environment.” An advantageous environment is defined as children with maternal educational attainment of secondary or higher and adequate levels of early stimulation (indicated as participated in 4 or more

> 7 <mark>A total of 4,652 children from Bangladesh, Brazil, Cambodia, Chile, Colombia, Ghana, Guatemala, India, Jordan, the Lao People’s Democratic Republic, Lebanon, Nepal and Pakistan were included in the CREDI reference population (24% of the full cross-country sample).</mark>

12
**Panel 1C: Percentage of children with more than primary education conditional on parents' education**

|**The probability**<br>**of children to**<br>**attain more than**<br>**primary**<br>**education**|**Neither parents'**<br>**education is more**<br>**thanprimary school**|**At least one parent's**<br>**education is more than**<br>**primary school**|**Both parents'**<br>**education are more**<br>**thanprimary school**|
|---|---|---|---|
|_Daughters_||||
|1988|34.3|56.6|74.8|
|2002|77.7|91.7|97.8|
|_Sons_||||
|1988|55.1|73.1|84.9|
|2002|81.4|94.2|97.6|
|NOTES||||

1. Children were born in 1967 or later;

2. Adult children's education dummy: 1=more than primary school; 3. The samples are: 2057 (1988, daughters), 1884 (1988, sons), 2264 (2002, daughters), 3616 (2002, daughters)

**Panel 1D: Percentage of Children with more than junior secondary schooling conditional on parent’s education**

|**The probab**<br>**attain m**<br>**second**|**ility of children to**<br>**ore than junior**<br>**ary education**|**Neither parents'**<br>**education is more than**<br>**primary school**|**At least one**<br>**parent's education**<br>**is more than**<br>**primary school**|**Both parents'**<br>**education are more**<br>**thanprimary school**|
|---|---|---|---|---|
|_Daughters_|||||
||1988|3.4|11.1|25.1|
||2002|18.4|34.7|43.5|
|_Sons_|||||
||1988|5.3|14.7|29.4|
||2002|16.9|35.2|43.2|
|NOTES|||||

1. Children were born in 1967 or later;

2. Adult children's education dummy: 1=more than junior secondary school; 3. The samples are: 2057 (1988, daughters), 1884 (1988, sons), 2264 (2002, daughters), 3616 (2002, daughters) 50
they add regular grades.

Below is the list of variables for charter schools:

- Address: geographic location of the campus. For PCSB-authorized charters, the main source is the School Performance Reports (SPRs). For BOE-authorized charters, the main source is the CCD. We supplement these sources with web and Internet archive searches. We geocode all addresses. Several schools moved in the middle of the school year, temporarily relocated, or closed during the sample period. We consult the SPRs and various web sites to handle these cases. If the school moved in the middle of a school year, the address variable contains the more recent address. Some schools relocated some students for a few months during renovations. Since this was a temporary, anticipated arrangement, we do not consider these cases as address changes.

- Statement: the school’s mission statement. Source: schools’ web sites, FOCUS , SPRs.

- Focus: the school’s curricular focus. Source: school statements, and Filardo et al (2008).

- School enrollment: total school enrollment, excluding ungraded and adult students. Source: own calculations based on OSSE.

- Grade-level enrollment (grades kindergarten through 12). Source: OSSE.

- Percent of white students: for PCSB charters, the source is the SPRs when available; if not, we use data reported for NCLB purposes. For BOE charters in 2007, the source is the SPRs (in 2007, the PCSB began including the BOE-authorized charters in its reports). For BOE charters before 2007, the source is the NCLB web site. If necessary, we supplement these sources with CCD data. When the school has multiple campuses but we only have one set of ethnic composition data, we impute it to all campuses.

- Percent of black students, percent of Hispanic students: constructed similarly to percent of white students.

- Percent of low income students: for PCSB charters, the source is the SPRs. For BOE charters, the source is NCLB information in http://www.nclb.osse.dc.gov/. These sources are supplemented by CCD when necessary and possible. When the school has multiple campuses but we only have one set of low-income variables, we impute it to all campuses.

- Reading proficiency: sources are the same as for public schools. Some schools span elementary as well as secondary grades and, for some years, have separate proficiency rates per grade level. In these cases we combine the rates into a single proficiency indicator for comparability with years for which we have a single indicator. For multi-campus charters we usually have proficiency data per campus. When we do not, we impute the available data to all the campuses. As with public schools, we do not have proficiency data for some campuses and years for the reasons described above. In the case of PCSB schools for which the NCLB web site does not report test scores due to low enrollment, we obtain proficiency rates from the SPRs. This is not possible for BOE schools with low enrollment since

43
- 16 -

dummy is included or not. 1/ This suggests that what may be relevant for the smaller countries in Latin America are developments in a core set of countries in the region, rather than developments in a single country.

# V. Concluding Remarks

Several results emerge from the preceding analysis. Eirs, there is evidence that the degree of comovement across weekly equity and Brady bond returns for emerging markets in Latin America increased in the wake of the Mexican crisis. Given the<sup>heterogeneity in macroeconomic</sup> fundamentals across countries, such comovement could be interpreted as indications<sup>of herding</sup> behavior on the part of investors. Alternatively, an open-end emerging market<sup>mutual fund</sup> expecting an increasing amount of redemptions will sell off its holdings of equity<sup>in several</sup> emerging markets in an effort to raise cash. Given the illiquidity<sup>that characterizes most emerging</sup> markets the sell-off by a few large investors will drive stock prices in other<sup>markets. Second, while</sup> the degree of comovement following the crisis increased in both Asia and Latin<sup>America, regional</sup> patterns differed, suggesting contagion may be more regional<sup>than global.</sup> Tblrd, as in several earlier studies, our results suggest that international<sup>capital movements,</sup> in our case, the balance on the capital account, are all significantly affected<sup>by swings in interest</sup> rates in the United States. Increases in U.S. interest rates, other<sup>things equal, are associated with</sup> capital outflows from Latin America. Small and large countries<sup>appear to be equally vulnerable in</sup> this respect. Fourth, in addition to external factors, the capital<sup>account balance of small countries</sup> appears to be affected by developments and trends in the larger countries<sup>in the region--these are</sup> more persistent forms of contagion than those that are typically associated<sup>with a crisis.</sup> Specifically, capital inflows/outflows into the larger countries in<sup>the region, other things equal, tend</sup> to encourage increased inflows/outflows to the small countries. The opposite<sup>is not true. Capital</sup> 1/ These results are not reported but are available from the<sup>authors.</sup>
# Appendices

## A Data

Brazil: Quantity of industrial production indexes are the “Produção industrial da indústria geral” from IGBE (the series can be found at http://www.ipeadata.gov.br) while GDP for the whole economy is obtained from Datastream. Nominal hourly wages and employment in the private sector, both for the manufacturing and whole economy, are obtained from the micro data of “Pesquisa Mensal de Emprego”, a survey representative of the eight largest urban areas in Brazil. There is an important methodological change in 2000, with a new survey launched in 2001q1.

Chile: all series for Chile are taken from the Instituto Nacional de Estadística (INE). The industrial physical production index and the industrial employment index are representative for sectors CIIU Rev 2 311-390 between 1993-2002 and CIIU Rev 3 D15-D36 between 2002-2010. For the nominal remuneration per hour index, the series correspond to CIIU Rev 3. D15-D36. The employment series are calculated using the “Encuesta Nacional de Empleo” (ENE) and the “Nueva Encuesta Nacional de Empleo” (NENE) that changed in 2010. For wages the INE uses the “Encuesta Estructural de Remuneraciones y Costos Laborales”; and for industrial production they use the baseline of the sample of firms from the “Encuesta Industrial Anual”.

Colombia: the Industrial employment index and the nominal industrial production index are taken from the Departamento Nacional de estadística (DANE), and the nominal wage index is taken from the Central Bank. The indexes are defined for CIIU Rev 3. A.C. 1510-3690. The survey used to calculate the series is the “Encuesta Mensual Manufacturera”(MMM).

Mexico: the employment index, volume of industrial production index and the nominal remuneration index are taken from INEGI and are representative of 125 classes (scian) from 1980-1995 and 205 classes from 1995-2008. All the variables are calculated using the “Encuesta Industrial Mensual”(http://dgcnesyp.inegi.org.mx).

All employment indexes refer to number of people employed, not to employment rates, and all wage series refer to average hourly wages. For each country we have price 25
with synthetic data that increases population density (given that Eswatini is a generally sparsely populated country) and shows that ACS’s benefits largely hold throughout.

# **5. Implementing ACS in practice**

Practically, implementers must first clearly define delineated geographical areas—that is . While ACS can be applied in rural areas, this paper focuses on urban settings. When defining this geographical area, three broad sources of information are considered, often in combination: NN (1) administrative and natural boundaries; (2) supplementary information likely to be correlated with informal business activity; and (3) local knowledge. Implementers should balance the tradeoffs of relying on any single one or particular combination of these information sources. Administrative boundaries (or delineated statistical areas like a census tract) can correspond to official statistics, important for policy-relevant comparisons. However, administrative boundaries may be insufficient. This will be the case when political boundaries are outdated, or when informality occurs in the outskirts of administrative areas. Implementers will likely need to consult alternative sources of information—such as land-use or, even, night light intensity—as well as local knowledge.

The grid of block areas, BAs, can be easily generated using GIS<sup>27</sup> software, such as ArcGIS or QGIS. BAs should be small enough that they can be enumerated in a reasonable timeframe, but large enough to capture concentrations of informal businesses. Since 2016, the World Bank’s Enterprise Analysis Unit has been piloting an ACS approach in a survey of informal businesses, known as the Informal Sector Business Survey (ISBS). From several initial rounds of the implementation of the ISBS, a practical suggestion is to begin with BAs sized at 150 by 150 meters. However, implementers will want to consider their own restrictions, such as budget and staffing, the relative density of informal businesses, and the appropriateness of other design decisions, such as the choice of neighborhood definitions. All of these choices will be interlinked. Stratification will require mutually exclusive categories and in the case of the ISBS, six stratification categories have proven quite useful: residential, commercial/industrial, mixed residential and commercial/industrial, market areas, open areas, and inaccessible.

> 27 Geographic Information Systems.

21
<u>Human capital: We find that the availability of workers who have primary or</u> more schooling has a considerable impact on location decisions in manufacturing industries. The only exception is the chemical and petroleum industry where the effects are not statistically significant. In general terms, a pool of semi skilled workers makes it easier for firms to scale up production by hiring more workers. In fact, investment climate surveys in many developing countries identify the lack of skilled workers as being a major impediment to increasing firm size and productivity.

# **4. Geographically prioritizing infrastructure investments**

# _Identifying high return areas_

We now move from describing the empirical analysis of location choices to identifying _where_ public infrastructure investments will produce the highest economic returns in terms of national industrial promotion. Addressing this policy concern requires that we recall how firms in various sectors value district specific endowments. In particular, agglomeration economies – from economic diversity are important location determinants. As firm performance depends on being located in a diverse and large urban environment, it is extremely difficult for policies to successfully move and sustain these activities in secondary locations. This is because successful relocation policies will need to coordinate decisions of firms across sectors. In addition, the stock of human capital is important for location decisions of manufacturing establishments.

At least in the short-to-medium term, these ‘preconditions’ are likely to be fixed, and the effects of infrastructure improvements will depend on the relative ‘stock’ of these attributes across districts. For example, consider education attainment. 421,000 people of working age in Kampala have completed primary or higher education. In comparison the ‘human - capital’ stock in Lira is 92,000 and 56,700 in Gulu. Other things being equal, for industries that value skilled labor, Kampala becomes more attractive than upcountry centers.

Firms consider a package of amenities that a region offers in making location decisions. From our model, we can predict the relative profitability to manufacturing 18 -
Table 2.1: Major government spending items, 1991-2009 (Percent of spending)

||1991|1994|1997|2000|2003|2006|2009|
|---|---|---|---|---|---|---|---|
|Defence|23.3|39.0|18.2|16.8|18.2|15.0|16.4|
|Health|3.7|4.1|3.3|4.1|4.0|3.9|3.5|
|Education|15.0|21.6|15.2|18.0|17.0|13.8|16.3|

Source: Authors' calculations based on Bulletin of Government Finance Statistics.

# _Evolution of MDG indicators 1990-2009_

Yemen is off track with respect to meeting the MDGs (MOPIC, 2003; MOPIC, 2010). As shown in Table 2.2, progress has been made for primary net enrolment (MDG 2), underfive mortality (MDG 4), and water and sanitation access (MDGs 7w and 7s, respectively). However, no target has been reached and Yemen is off track for all MDGs. On the malnutrition front, due to a combination of stagnating per capita income levels and worsening nutritional patterns (including rising consumption of junk food and qat), the situation has deteriorated over time, with the proportion of children under five who are underweight rising from 30 percent in 1992 to 46 percent by 1997/98 and remaining at that level through 2003.

Table 2.2: Key MDG indicators and target by 2015 for Yemen

|MDG and associated indicator|1990(1)|2004|Most recent(2)|2015 target|
|---|---|---|---|---|
|MDG 1: Povertyrate(per cent ofpopulation)3/|40.1|34.8|42.4|20.1|
|MDG 2: Net enrolment rate in basic education(per cent)|52.7|62.5|69.8|100.0|
|MDG 4: Under-five mortalityrate(per 1,000 births)|122.0|93.4|78.2|40.6|
|MDG 5: Maternal mortalityrate(per 100,000 live births)|351.0|365.0|365.0|87.8|
|MDG 7w: Access to safe water(per cent ofpopulation)|34.9|43.9|48.0|67.4|
|MDG 7s: Access to improved sanitation(per cent ofpopulation)|10.6|15.9|23.0|55.3|

(1) Nearest available year if data are not available for 1990; 2004 values for MDGs 4 and 7w interpolated using data for years close to 2004.

(2) Most recent: 2010 for MDG 1, 2008 for MDGs 2 and 7, 2006 for MDG 4, 2003 for MDG 5.

(3) The national poverty line is used to estimate the poverty rate; the rate in the 1990 column is for 1998.

Source: Authors’ calculations based on the Central Statistical Organization (of the Government of Yemen), MOPIC (2010) and MOPIC (2011).

12
# **Table A13:  Transparency International Global Corruption Barometer (GCB)**

**Data Provider** Transparency International **Description** Nongovernmental organization devoted to fighting corruption **Website** www.transparency.org **Data Source** Global Corruption Barometer **Type** Survey **Respondents** Households **Frequency** Annual since 2004 **Coverage** Global sample of countries **Public Access** Country-level aggregate responses and some breakdowns are reported on TI's website **Description** This survey commissioned by TI collects data on households' experiences with petty corruption and their perceptions of the overall incidence of corruption.    Note that we do NOT use data from the TI Corruption Perceptions Index (TI-CPI).  The TI-CPI, in contrast with the GCB, is a composite indicator of corruption based on an aggregation of a subset of the same data sources that we also use for aggregation into our Control of Corruption indicator. Note that in each year we have carried forward GCB scores for those countries that were covered in earlier years (up to two) but not in current year.

||2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||||
|Frequency of corruption among public institutions|X|X|X|X|..|..|..|..|..|
|Frequency of household bribery|X|X|X|X|..|..|..|..|..|
|**Country coverage**|79|79|74|62|..|..|..|..|..|
|**Year of Publication**|**2007**|**2006**|**2005**|**2004**|**..**|**..**|**..**|**..**|**..**|

51
# **– 4.1.2 PNAD Brazil**

The Brazilian data come from the Pesquisa Nacional por Amostra de Domic ́ılios (PNAD), an annual Brazilian household survey that samples households across (almost) the entire country.<sup>10</sup> It collects information on various household characteristics, as well as individual incomes and education levels. We use PNAD data for 1995 and 2007. For comparability, we use the same four variables as for the CPS: real hourly labor earnings, age, gender, and a categorical educational attainment variable. IBGE, the Brazilian Statistics Bureau that is responsible for running PNAD, started including the rural Northern region in the PNAD sample after 2004 but, for comparability across years, we do not use information on the rural North for 2007.

As for the US, we restrict the sample to individuals aged 16 to 65 that reported a positive labor income in the previous year. Individuals with missing values for income or any of our three covariates were excluded from the sample. The top and bottom 0.5% of the distribution of hourly wages in each year was trimmed, as in the CPS. Hourly wages are in Brazilian reais (BRL) of September 2007, which means that nominal values in 1995 were multiplied by 2.89 to be expressed in 2007 prices. The final sample contains a total of 275,749 observations. Summary statistics are presented in Table 2.

– Table 2: Summary Statistics Brazil

||Mean|S.D.|Min.|Max.|Observations|
|---|---|---|---|---|---|
|PNAD 1995||||||
|Hourly Work Earnings|5.565|7.312|0.300|61.317|119,770|
|Age|34.924|11.936|16|65|119,770|
|Male|0.631||||119,770|
|High School|0.131||||119,770|
|College Incomplete|0.046||||119,770|
|College|0.064||||119,770|
|PNAD 2007||||||
|Hourly Work Earnings|5.659|6.978|0.312|62.500|155,979|
|Age|36.367|12.025|16|65|155,979|
|Male|0.589||||155,979|
|High School|0.259||||155,979|
|College Incomplete|0.085||||155,979|
|College|0.094||||155,979|

A comparison of Tables 1 and 2 reveals considerable differences between the two labor 10In 1995, PNAD did not survey households in the rural areas of Acre, Amap ́a, Amazonas, Par ́a, Rondˆonia and Roraima — six states in the Amazon region.

25
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