rafmacalaba/datause-encoder-data huggingface dataset:
Table 1. Assumptions for the Peru Business-as-Usual Baseline (2023-50)

||Value|Unit of measurement|Time Series|Source/Target|
|---|---|---|---|---|
|Baseline<br>|||||
|A. Main Parameters<br>|||||
|Depreciation rate (δ)<br><br>|4.2%|Percent/year|2009-19 average|PWT 10.01, Average depreciation rate of the capital stock<br>PWT 10.01, Share of labor compensation in GDP at current|
|Labor share in the non-res sector (βN)<br> <br>|0.49|[0, 1]<br>|2019|prices|
|Capital share in mining sector (1-γ)<br>|0.84|[0, 1]|2004-14 average|GTAP, 2004, 2011, 2007, 2014|
|Govt. share in the resource sector (τ)<br>|0.62|Share of copper GDP<br>Share of extra res.|Last 20 years, average|WEO, IMF-WEC, UN-Comtrade|
|Marginal propensity to invest (θ)|1.0|Revenue|Hartwick Rule (invest windfalls)|WEO/Match historical public investment to expenditure ratio|
|Long-run public investment|4.7%|% of GDP|Last 10 years, average|WEO, general government investment|
|B. Initial Values|||||
|Per capita GDP|6,446.9|Real, 2015 US$|2021|WDI, Real GDP per capita (NY.GDP.MKTP.KD)|
|Exports of copper|8.2%|% of initial PC GDP|2021|UN-Comtrade|
|Capital-to-GDP ratio|3.6|[0, inf]|2021|PWT 10.01|
||||Equalize the VPMK across sectors in||
|Non-resource sector|3.15|[0, inf]|2019<br>Equalize the VPMK across sectors in||
|Copper sector|0.46|[0, inf]|2019||
|Reserves of natural resources|||||
|Copper sector|81|Million of Metrics tons|2017|U.S. Geological Survey|
|C. Assumed paths of standard drivers of grow|th, 2021-50||||
|Total Investment|22.4%|% of GDP|2004-2019 average|WEO total investment - WEO public investment|
|Private, Investment|17.7%|% of GDP|2004-2019 average|WEO total investment - WEO public investment|
|Public, Investment|4.7%|% of GDP|2004-2019 average|WEO total investment - WEO public investment|
|TFP, non-resource sector|0.6%|Annual growth rate|LTGM-TFP|Aggregate TFP growth from PWT 10.01|
|TFP, mining sector|-0.5%|Annual growth rate|TFP average growth in Chile in 2000-17|Productivity Commission Chile (2016)|
|Human Capital|0.2%|Annual growth rate|LTGM-HC|PWT 10.01, Human Capital index (hc)|
|Demographics:|||||
|Population|0.7%|Annual growth rate|2021-2050, average|ILO's estimates, total population|
|Working-age population|65.2%|% total population|2021-2050, average|ILO's estimates, population aged 15-64|
|Population, Male|49.2%|% total population<br>initial % of working age|2021-2050, average|ILO's estimates, population, male|
|Participation rate, Male|88.1%|pop.|2021|WDI, Labor force participation rate, male (SL.TLF.ACTI.MA.ZS)|
|Participation rate, Male|0.4%|Annual growth rate<br>initial % of working age|2021-2050, average|WDI, Labor force participation rate, male (SL.TLF.ACTI.MA.ZS)|
|Participation rate, Female|74.1%|pop.|2021|WDI, Labor force participation rate, female (SL.TLF.ACTI.FE.ZS)|
|Participation rate,Female|1.0%|Annualgrowth rate|2021-2050,average|WDI,Labor forceparticipation rate,female(SL.TLF.ACTI.FE.ZS)|

10
GDP; throughout the period, foreign and private savings are at around 2 and 14-16 percent of GDP, respectively. In order to make the jump in growth to 7 percent feasible, an initial jump in TFP growth to close to 4 percent is needed. Over time, factor accumulation growth increases to around 4 percent, permitting a decline in TFP growth to around 3 percent.

Figure 4.7. High Growth: Investment and savings data (% of GDP) <!-- Start of picture text --> 20<br>18 total investment &<br>16 savings<br>14<br>private investment<br>12<br>10<br>8 government<br>investment<br>6<br>4 private savings<br>2<br>0 government savings<br>-2<br>-4<br>foreign savings<br>-6<br>Figure 4.8. High Growth: Growth decomposition<br>8<br>7<br>6<br>5<br>GDP at factor cost<br>4<br>Factor employment<br>3<br>TFP<br>2<br>1<br>0<br>% of GDP<br>% growth<br><!-- End of picture text --> Tables 4.1-4.4 include additional results for _hi-grw_ . As shown in Table 4.1, compared to _base_ , growth in GDP and absorption increase by around 2.5 percentage points. For GDP, around 1.5 percentage points are due to TFP; the rest is the result of more rapid growth in factor accumulation. Among the final demands (which make up absorption), the growth acceleration is particularly strong for private and government investment but it also spills over into private consumption. Unemployment falls to 5 percent, the minimum level. The decline in government debt by 10 percentage points is roughly evenly split between foreign and domestic debt. Measured relative to GDP, the major changes in the government budget are shifts to infrastructure from other areas and to investment from consumption (Table 4.2), the latter 34
random terms, in order to compute a point estimate (average of _M_ simulations) and standard errors for each welfare measure. The ELL method has two main sources of errors in the welfare estimates: a) model error due to the fact that the parameters for the imputations are estimated; and b) idiosyncratic error associated with the fact that the actual welfare outcomes deviate from their expected value. The importance of the latter component decreases with the size of the target population. In this paper we employ the Delta Method originally presented in Elbers, Lanjouw and Lanjouw (2002) to calculate the error components.

# **III. Data Source**

The main source of data in this paper is the 2000 Brazilian Population Census for the state of Minas Gerais. By construction, the 12.5% sample of the Census is representative at the level of municipalities (5,564 in Brazil as a whole in 2005). It also contains considerably more detailed information about household characteristics than the full census and the household per capita income measure includes earnings and transfers from different sources for all members of the household: main occupation; other occupations, retirement pensions, rent, other pensions, government social transfers and others.

On the basis of these data, the census sample for Minas Gerais collects information from about 606 thousand households drawn from all 853 municipalities in the state. This constitutes the target population for our study. Accordingly, we will refer to this census sample as the “census” for simplicity and all sampling and predictions will be with respect to this sub-population of Minas Gerais.

Upon our request IBGE drew 41 samples from the census file, following a sampling design that mimics that of Brazil’s two main household surveys: the Household Expenditure Survey, known as POF, last collected in 2002/2003, and the Annual National Household Survey, known in 7
<!-- Start of picture text --> 40.0% mlnterquartile range Median<br>30.0%<br>20.0%10.0%“<br>Le |<br>0.0% Ae<br>Oil exporters Oil importers<br><!-- End of picture text -->

<!-- Start of picture text --> 15.0% mlnterquartile range Median<br>10.0%<br>()<br>-U'/o [6]<br>9.0%<br>0.0% <aee<br>Coal exporters Coal importers<br><!-- End of picture text --> <!-- Start of picture text --> 20.0% mlnterquartile range Median<br>15.0%<br>10.0%<br>5.0%<br>0.0% Am<br>Large Small<br><!-- End of picture text --> <!-- Start of picture text --> 25.0% mlnterquartile range Median<br>20.0%<br>15.0%<br>10.0% a<br>50%<br>0.0% Ae<br>Gas exporters Gas importers<br><!-- End of picture text --> <!-- Start of picture text --> 40.0% mlnterquartile range Median<br>30.0%<br>20.0%<br>10.0% [-]<br>ee<br>0.0% a<br>Surplus Deficit<br><!-- End of picture text --> <!-- Start of picture text --> 20.0% mlnterquartile range Median<br>15.0%<br>10.0%<br>5.0%<br>0.0% <a<br>More open Less open<br><!-- End of picture text -->
Figure 1.5 Average years of schooling among the working population ages 16–59 <!-- Start of picture text --> 10.90<br>10.80<br>10.80<br>10.68<br>10.70<br>10.60 10.56<br>10.50 10.43<br>10.40 10.33<br>10.30<br>10.20<br>10.20<br>10.10<br>10.00<br>9.90<br>9.80<br>2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> _Source._ Authors’ calculation.

Looking ahead, while starting from a low base, by 2030 China will be the leading contributor to the world’s talent pool. Between 2000 and 2015, the number of higher education institutions more than doubled from 1,041 to 2,560, and the enrollment of college students almost tripled from 2.3 million to 7.8 million. According to an Organization for Economic Co-operation and Development (OECD) analysis, as university enrollment in China continues to increase, the number of 25- to 34-year-old college graduates in the country will triple by 2030, or 10 times the 30 percent rise in Europe and the United States.<sup>1</sup> China will then contribute 27 percent of the talent pool of OECD and G-20 countries. At this scale, the volume of highly skilled labor will be a critical advantage to China’s knowledge economy. This can only be sustained in the medium term by a high-performing upper secondary sector.

In March 2017, the Ministry of Education (MOE) and three other state agencies released the Program for Promoting Senior High School Education (2017–2020), aimed at promoting educational equity and raising the gross enrollment rate (GER) in senior high schools to above 90 percent by 2020. This government initiative has a strong equity agenda—priority has been given to improving enrollment in poverty-stricken western and central regions, regions inhabited by ethnic minorities, remote inland areas, and old revolutionary base areas. The program also underlines the need to solve an array of outstanding issues, such as ensuring access to education for students with disabilities or from low-income households and reversing the declining enrollment ratio in vocational schools (MOE 2018).

Built on these insights, this report aims to summarize the progress and challenges of upper secondary education in China and provide policy recommendations on how to optimize the structure and improve the quality of upper secondary education to prepare children ages 15–17 who will be the leading force of China’s knowledge economy.

7
**<u>Table 8. Test Scores of</u>** **_<u>Telesecundaria</u>_** **<u>students</u>**

||2000|2001|2002|Ave|Trend|
|---|---|---|---|---|---|
||**Less-**|**Disadvant**<br>**Glob**|**aged Gro**<br>**al**|**up**||
|Non-tel|530.2|552.5|564.2|549.0|17.0|
|Tel.|523.9|539.2|562.6|541.9|19.3|
|Difference|-6.3|-13.3|-1.6|-7.1|2.4|
|||**Mat**|**h**|||
|Non-tel|527.8|534.2|546.4|536.1|9.3|
|Tel.|521.9|525.7|543.3|530.3|10.7|
|Difference|-5.9|-8.5|-3.1|-5.8|1.4|
|||**Spani**|**sh**|||
|Non-tel|535.9|570.4|583.6|563.3|23.9|
|Tel.|528.1|552.6|582.5|554.4|27.2|
|Difference|-7.8|-17.8|-1.1|-8.9|3.4|
|||**Mid-Range**<br>**Glob**<br>|**Group**<br>**al**<br>|||
|Non-tel|481.19|495.67|522.66|499.84|20.73|
|Tel.|473.98|488.58|517.05|493.2|21.53|
|Difference<br>|-7.2<br>|-7.09<br>**Mat**<br>|-5.61<br>**h**<br>|-6.64<br>|0.8<br>|
|Non-tel|497.67|507.98|523.08|509.58|12.71|
|Tel.|492.4|503.18|517.86|504.48|12.73|
|Difference<br>|-5.27|-4.8<br>**Spani**|-5.23<br>**sh**|-5.1|0.02|
|Non-tel|473.34|495.02|525.14|497.83|25.9|
|Tel.|465.27|486.94|518.65|490.28|26.69|
|Difference|-8.07|-8.08|-6.49|-7.54|0.79|

Source : _Estándares Nacionales_ 2000-2002.

# **Failure and Repetition Rates**

A main goal of CONAFE is to lower failure and repetition rates. CONAFE maintains databases on these indicators for all schools, which allowed us to measure CONAFE’s effect on failure and repetition rates.

We began with data on repetition and failure rates in every school that appeared in DGE’s sample. We compared repetition rates for a cohort of CONAFE and non-CONAFE schools by disadvantage group for second grade in 1998 through sixth grade in 2002. The 33
larger here); and imports decrease less rapidly by 16 percentage points (see table 6). Despite these changes, the main conclusions of the analysis remain robust.

The most important results are those obtained for poverty measures (tables 7-8). Under the first scenario, which corresponds to an increase in the price of rice of 21 percent in the country, the headcount index of poverty increases by 0.7 percentage point in the first year versus the baseline, and the overall increase after five years is 0.89 point. Given that Mali’s population is at around 12 million people, this means that 107,000 people would fall into poverty. If Mali had not been protected by the appreciation of its currency, so that the international price of rice would have increased by 110 percent and the average price in the country by 26 percent, the increase in poverty would have reached 0.99 percentage point under scenario (2) by the year 2012. These are substantial impacts, although they are lower than that estimates obtained using the 2006 ELIM survey by Joseph and Wodon (2008) who find an increase in the headcount index of about 1.5 percentage point with a 25 percent increase in the price of rice. The fact that under the CGE model the impact is lower than with the survey-based work was to be expected since the CGE model includes a supply response for rice production as well as other adjustments in the economy and in the consumption patterns of households, while the survey based work does not.

The beneficial impact of the import and other tax cuts on rice is fairly limited, with these policies generating a gain in poverty reduction of only a tenth of a percentage point for most scenarios and simulation years. By contrast, the impact of an increase in productivity is much larger, since as of the year 2009, this increase in productivity is such that poverty is actually reduced following the initial price shock. The largest reduction in poverty observed in 2012 comes from the combination of the tax cut and the productivity gains. The findings with the poverty gap and squared poverty gap are qualitatively similar to those obtained with the 18
TM Lowincome ® Lower middle income Upper middle income ® High income <!-- Start of picture text --> we “im wete<br>over 35 yg/m3 861 - 1im<br>0 1 2 3 4 5 6 7<br>Population (billions)<br><!-- End of picture text --> <!-- Start of picture text --> 3B<br>2B<br>1B =<br>0B —"lIC LMIC. UMIC.- HIC<br><!-- End of picture text --> <!-- Start of picture text --> 3B<br>2B<br>oB1B LIC LMIC UMIC —HIC<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%0, LIC LMIC UMIC HIC<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>LIC LMIC UMIC HIC<br><!-- End of picture text --> <!-- Start of picture text --> M > 10 ug/m3<br>< 10 ug/m3<br><!-- End of picture text --> <!-- Start of picture text --> ®@ > 5ug/m3<br>< 5 pg/m3<br><!-- End of picture text --> <!-- Start of picture text --> 3B<br>2B<br>0B1B aLIC =LMIC| UMIC—_— HIC<br><!-- End of picture text -->

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%0, LIC LMIC UMIC HIC<br><!-- End of picture text --> <!-- Start of picture text --> M > 35 ug/m3<br>< 35 pg/m3<br><!-- End of picture text -->
-3.

best of o%r knowledge, limited to one study in Zimbabwe (Riddell, 1989) and one in Thailand (Lockheed and Longford, 1989); even research employing crossectional designs occurs infrequently. The two studies that examined school effects in developing countries reached similar conclusions, however: that student background and prior achievement have significantly greater impact on present achievement than do school factors, and that different schools are relatively consistent in their transformation of prior achievement into present achievement.

3. This study contributes to the literature by providing evidence for a Latin American country and by exploring a larger variety of school types than previously examined in either developed or developing countries. The present study examines the relative effect of four types of secondary schools (technical, SENAI, teacher training, and general secondary) and two sectors (public and private) on student achievement in mathematics and Portuguese in four cities in Brazil.

# 2. Method

4. The Ministry of Education and the World Bank commissioned the Carlos Chagas Foundation to design and administer a standardized test of mathematics and Portuguese to a sample of secondary students in four cities (Fortaleza, Salvador, Sao Paulo and Curitiba). The achievement test was designed for students at the end of their third (and final) year of secondary school, and comprised test items that had appeared previously on entrance examinations for Brazilian universities (yestibular). The test was accompanied by a background questionnaire for each student and a questionnaire about the school that was completed by the school director. Both tests and questionnaires were
34

**Table 7 Structure of Natural Gas Deliveries to U.S. Consumers, 1995 (millions of cubic feet)**

|Type of delivery|**Resi**<br>Volume|**dential**<br>Percentage<br>of total|**Comm**<br>Volume|**ercial**<br>Percentage<br>of total|**Indu**<br>Volume|**strial**<br>Percentage<br>of total|**Electric**<br>Volume|**utilities**<br>Percentage<br>of total|All cat<br>Volume|egories<br>Percentage<br>ofttotal|
|---|---|---|---|---|---|---|---|---|---|---|
|_Firm_**_transportation_**|||||||||||
|Sales|4,846,360|<br>99.9|2,125,008|70|1,294,546|<br>_15_|431,014|15|8,696,928|<br>45|
|Transportation|0|0|525,404|17|3,845,501|<br>45|1,327,931|<br>46|5,698,836|<br>29|
|Total<br>,|4,846,360|99.9|2,650,412|87|5,140,047|60|1,758,945|61|14,395,764|74|
|**_Interruptible_**|||||||||||
|**_transportation_**|||||||||||
|Sales|3,958|0.1|199,930|7|767,687|9|365,507|13|1,337,082|7|
|Transportation|0|0|180j735|6|2,671,850|31|782,353|27|3,634,938|19|
|Total|3,958|0.1|380,665|13|3,439,537|40|1,147,860|39|4,972,020|26|
|**_All_**_transportation_|||||||||||
|Sales|4,850,318|100|2,324,938|77|2,032,233|24|796,521|27|10,034,010|52|
|Transportation|0|0|706,139|23|6.517,351|76|2,110,284|73|9,333,774|48|
|Total|4,850,318|100|3,031,077|100|8,579,584|1oo|2,906,805|100|19,367,784|0oo|

_Source: U.S._ Department of Energy, Energy Information Administration.

# **Primary transportation market**

The primary transportation market facilitates the initial distribution of transportation contracts. Pipeline companies sell transportation contracts to shippers for prices that are regulated by FERC. Transportation contracts differ primarily in the reliability, timing, and location of natural gas delivery. Shippers purchase transportation contracts in combinations that allow them to achieve the desired service reliability at the minimum cost and to take advantage of time and locational price differentials in the natural gas market.

FERC determines transportation charges using the straight fixed variable rate-making metnod. Although this method does not yield efficient prices for pipeline transportation, it ensures full cost recovery and provides fair and transparent pricing signals. But increasing competition among interstate pipeline companies has prompted a move toward market-based pricing of transportation services.

# _Transportation contracts_

Deregulation of the U.S. natural gas industry has led to the development of transportation contracts that differ in many dimensions. The most important and frequently used contract dimensions are reliability of transportation service, time and duration of shipment, location of points of injection and withdrawal, pipeline pressure, and charges for pipeline capacity and transportation services.

The contracts most commonly used in the U.S. natural gas industry are of four types (U.S. Department of Energy 1994):
conducted during our sample period (1984, 1989, 1993, 1997, 2000). Using the available surveys, we interpolate and extrapolate computer use rates for the entire window.<sup>8</sup> Summary statistics for the individual worker sample matched to our offshoring, trade, technology, and price data are available in Appendix Table 1.

We use Autor et al.’s (2003) distinction between routine and non-routine tasks to measure the “offshorability” of jobs. Autor et al. (2003) describe routine jobs as “tasks that can be expressed using procedural or ‘rules-based’ logic, that is, codified in a fully specified sequence of logical programming commands (“If-Then-Do” statements) that designate unambiguously what actions the machine will perform and in what sequence at each contingency to achieve the desired result.” While Autor et al. (2003) use routine-ness to designate which jobs can be easily performed by computers, we would argue that routine jobs are also more readily codified, communicated, and consequently transferred overseas. Examples of these jobs include: attaching hands to faces of watches, sewing fasteners and decorative trimming to articles and, though not mentioned explicitly in their paper, include services tasks that we think of as offshorable such as answering telephones.

Following Autor et al. (2003), we aggregate their five different measures of the routine-ness of tasks into a single index for each occupation _k_ . Two indicators measure how routine manual and cognitive tasks are (Routine Manual _i_ and Routine Cognitive _i_ , ranging from 1 for not routine to 10 for fully routine). The index of routine-ness by worker education level, industry, and year is given by: The index ranges from 0 to 1.<sup>9</sup> The input tasks include routine cognitive and routine manual, nonroutine analytic, non-routine interactive, and non-routine manual. The last three terms refer to

> 8 These data were also provided by David Autor and are used in Autor et al. (1998).

> 9 See Autor et al. (2003) for a thorough description of these variables. Our calculation of routine is the sum of routine manual tasks (Finger Dexterity) and routine non-manual (Set Limits, Tolerances or Standards), as a share of those tasks
higher-quality prenatal care. Better prenatal care identifying and preventing delivery risk factors will lower the need for a cesarean section.

**Table 5: Means of Outcome Indicators**

||Sample size|Overall|2004|2008|
|---|---|---|---|---|
|Prenatal care|||||
|Number of prenatal care visits|228,656|4.563|4.418|4.853|
|Tetanus toxoid vaccine|108,535|0.786|0.717|0.694|
|Cesarean section|282,042|0.235|0.223|0.260|
|Birth outcomes|||||
|Birth weight (in grams)|274,078|3,277|3,256|3,282|
|Low birth weight (<2,500 grams)|274,078|0.070|0.075|0.070|
|APGAR score at 5 minutes > 6|248,840|0.982|0.982|0.983|
|Mortality|||||
|Neonatal mortality per 1,000 live births|131,943|8.28|9.73|6.77|

_Source: Authors calculations based on medical records_ The sample sizes for tetanus toxoid vaccine are smaller than for the other indicators. This is primarily because the vaccine is prescribed only to a subset of pregnant women.<sup>11</sup> Based on these criteria 44 percent of women in the sample required a tetanus vaccine.

The sample size for neonatal mortality is also lower. We exclude one province and a number of hospitals that coded neonatal mortality aggregated with other outcomes such as abortion. In these cases we could not separate neonatal mortality from abortion. Overall, we have data from 40 hospitals representing around 45 percent of the births. While this should not affect the internal validity of our estimates of the causal impact, it could affect the external validity. However, the neonatal mortality rate from our sample (8.3 per thousand) is close to the neonatal mortality rate based on death certificate data (10.1 per thousand). We would expect the death rate from death certificates to be slightly higher than the in-hospital mortality rate as the former includes deaths that did not occur in hospitals.

> 11 Women requiring a tetanus vaccine include those who had not previously been vaccinated or had received only one dose, those whose previous immunization status is not known, those who have had two doses but whose last vaccination is more than three years ago, and those that have had three doses but the last dose or booster was more than five years ago (the maximum length recommended by clinical guidelines).

16