rafmacalaba/datause-encoder-data huggingface dataset:
# **Non-OECD Countries**

||**Column 1**|**Column 2**|**Column 3**|**Column 4**|
|---|---|---|---|---|
||||**Printing**|**Household**|
||**Aggregate**<br>**Paper**|**Newsprint**|<br>**and**<br>**Writing**|**and**<br>**Sanitary**|
|Internet|||||
|Pentration (per<br>100 people)|-0.00994**|-0.0294***|-0.00863|-0.0187|
||(0.0047)|(0.0047)|(0.0062)|(0.0142)|
|GDP per capita (in<br>logarithm)|2.071***|1.121**|2.292***|4.114***|
||(0.4530)|(0.4850)|(0.6040)|(1.3180)|
|Percentage Post|||||
|High School<br>Students (over<br>whole population)|-5.102|18.38*|5.538|30.13|
||(8.9410)|(9.3320)|(11.5900)|(28.1900)|
|Workers in|||||
|Primary Sector<br>(percentage)|-1.277|-2.031|4.207**|8.100**|
||(1.2420)|(1.3120)|(1.9550)|(3.8060)|
|Workers in|||||
|Manufacturing<br>Sector|-1.504|-8.944***|1.325|6.191|
|(percentage)|||||
||(2.1570)|(2.1550)|(2.8630)|(5.6440)|
|Workers in the|||||
|Services<br>(percentage)|1.921***|0.759|-0.723|0.523|
||(0.6710)|(0.6750)|(0.8870)|(1.6760)|
|Constant|-15.10***|-7.344*|-19.55***|-39.22***|
||(3.8980)|(4.1920)|(5.3230)|(11.6600)|
|Observations|203|198|183|132|
|R-squared|0.941|0.944|0.931|0.862|

29
50

# Table 6 'ogistic Regression Results for Infant Mortality Components for Birth Cohorts 1970-86 in Cote DO Ivoire (LSMS 1985/86)

||NeonatAl Mortality<br>|Postneonat|al Mortality|
|---|---|---|---|
|Variable|Coefficient<br>Chi-square|Coefficient|<br>Chi-square|
|Intercept|-3.39226<br>152.27*|-2.80905|109.66*|
|Time2<br>|0.15771<br>0.19|-0.28819|0.59|
|Time3|-0.10228<br>0.07|0.00759|0.00|
|Ordl<br>|0.38778<br>5.68**|0.14689|0.62|
|Ord2O|0.71340<br>22.04*|6.54**||
|Male|0.14130<br>1.34|-0.06799|0.25|
|Magebl<br>|0.03004<br>0.04<br> <br>|0.17842<br>|1.28|
|Mageb2|-0.09816<br>0.16|-0.16455|0.32|
|Urbanl|-0.49365<br>2.31|-0.31418|1.12|
|Urban2<br>|-0.01402<br>0.00<br> <br>|-1.13590|4.37**|
|Rurall|0.39930<br>2.76***|-0.19990|0.60|
|Savingsl|0.20246<br>0.70|-0.12521|0.24|
|Savings2|0.19392<br>0.66|-0.32094|1.42|
|Meduc2<br>|0.22020<br>0.73<br> <br>|-0,45176|1.61|
|Meduc3|-0.18936<br>0.21|-1.07151|3.11***|
|Time2*Urbanl<br>|0.07393<br>0.03|-0.45794|0.89|
|Time2*Urban2<br>|-0.28729<br>0.30<br> <br>|0.65811|0.83|
|Time2*Rurell|-0.44130<br>1.53|0.35481.|0.84|
|Time3*Urbanl<br>|0.32111<br>0.44|-0.46590|0.96|
|Time3*Urban2<br>|0.17022<br>0.10|1.26450|3.69***|
|Time3*Rurall|-0.19119<br>0.24|-0.47631|1.29|
|Time2*Savingsl|0.23976<br>0.46|0.35849|0.88|
|Time2*Savings2|-0.08854<br>0.06|0.21924|0.30|
|Time3*Savingsl<br>|0.06511<br>0.03<br>|0.91607|5.43**|
|Time3*Savings2|0.18999<br>0.27|0.23648|0.29|
|Model Chi-square<br>|49.73*<br>53.02*<br> <br> <br>|||
|Degrees of freedo|m<br>24<br>24|||
|Number of observat|ions<br>5,354<br>4,801|||
# **3 Methodological framework and data**

In our simulations we deploy the simplest implementation of the shared prosperity premium, where everyone in the bottom 40% grows at the same rate, corresponding to the step-function in panel A of Figure 3. This is less pro-poor than the other two GICs discussed in the previous section, and therefore our assumption on shared prosperity is perhaps not overly optimistic. However, it is unrealistic to induce such a growth incidence in real life and sustain such a pattern of growth over almost 20 years, as is done in our simulations. The interest of this exercise, nonetheless, does not lie in producing a ‘plausible’ transformation but rather simulating one stylized version of ‘shared prosperity’. This allows us to provide, _ceteris paribus_ , estimates of the changes in poverty headcounts due exclusively to a specific pattern of economic growth imposed per country.

The three scenarios for the growth rate of mean income or consumption are as follows: (1) Each country’s annualized growth rate from national accounts for the last 10 years for which we have poverty data (2001-2011), taken from World Bank (2015)<sup>13</sup> ; (2) each country’s annualized growth rate for the latest 20 years using the same database and methods; and (3) a scenario which assumes zero growth of per capita income or consumption to isolate the pure redistributive effects of our shared prosperity premiums. The objective of this paper is not to assess the impact of different average growth scenarios on poverty, but rather focus on the effect of differential growth across the distribution. We therefore stick to the average growth scenarios used by the projections in World Bank (2015). The projections relying on the 10 year historic growth rates (2001-2011) may be optimistic as the rapid growth experienced in the early 2000s is showing signs of slowing down. Furthermore, Pritchett and Summers (2014) make the point that “the single most robust empirical finding about economic growth is low persistence of growth rates” and that “extrapolation of current growth rates into the future is at odds with all empirical evidence about the strength of regression to the mean in growth rates”. Rodrik (2014) also suggests that the rapid growth experienced by emerging economies in recent decades is unlikely to persist indefinitely and that convergence will slow down in coming decades. Following World Bank (2015), we focus on the growth scenario based on average growth rates for the period 2001 to 2011. We consider five scenarios for – the growth rate differential of the bottom 40% relative to the mean. Our baseline scenario is a distribution-neutral growth where each percentile group grows at the same mm

> 13 The growth rates from World Bank (2015) have been calculated in the following way. They are based on annualized growth rates of real GDP per capita (countries in Sub-Saharan Africa) or national accounts real household final consumption per capita (all other countries). They are adjusted by a factor based on observed differences between national accounts growth and survey growth, as explained on p. 68 (endnote 14) in World Bank (2015).

9
<!-- Start of picture text --> 45 Vietnam<br>4.0<br>3.5<br>3.0 Population SSP5<br>25<br><!-- End of picture text --> <!-- Start of picture text -->
1.5 .<br>—_ Population+structural change<br>0.51.0 / “a<br>0.0<br>-0.5 0.0 0.5 1.0 1.5 2.0 2.5<br>Income distribution (daily income, log10)<br><!-- End of picture text -->

aj Aj(ty) = aj (to) * (1 + gr)” Qiskitlea (tn) = Qi unskilled (tn) * skill_ premium; oN; skill_ premium; Yealc (tn) —_ Yiz1ys Qi (tn) * cat; Yeatc(to) Yeatc (tn) e(h, ~~Yoatc (tn)~~ ty)" = e(h,ty) * ~~>—~~~ ° Yeaic (to)
(before the Doha round), they are imposed largely unilaterally. Agricultural tariff data are therefore appropriate for testing the Grossman-Helpman model.

The subsidy measures are taken from Hoekman, Ng and Olarreaga.<sup>29</sup> Unlike the protection measures, the closest we can get to an ad valorem equivalent is the ratio of the dollar value of the subsidy to the dollar value of exports. Two subsidy measures are available, export subsidies as a proportion of exports, XS, and total subsidies (including production and export subsidies) as a proportion of exports, TS.<sup>30</sup> All protection and subsidy data are for the year 1999.

# _Trade and Output_

The USDA’s Production, Supply and Distribution database PSD Online (at <u>http://www.fas.usda.gov/psd/complete_files/default.asp) is the source for trade and production</u> data. The data are kept at a commodity level that map neatly into the harmonized system (HS) of trade data-keeping at the 4-digit or 5-digit level. Since imports and production are both measured in physical units in the database, they yield precise measures of the inverse import-to-output ( _z_<sup>M</sup> ) and inverse export-to-output ratios ( _z_<sup>X</sup> ).

We use the following criteria to categorize commodities as pure import or pure export goods: if for any commodity _z_<sup>M</sup> >30 (imports small relative to output) and _z_<sup>X</sup> <30 then it is categorized as a pure export good; if for a commodity _z_<sup>X</sup> >30 (exports small relative to output) and _z_<sup>M</sup> <30 then it is categorized as a pure import good. In the data, in sectors where one ratio is greater than the cutoff of 30, the other is considerably less than 30, so that the cutoff clearly separates the exported commodities from import-competing ones.<sup>31</sup>

> 29 Hoekman, Ng and Olarreaga (2004).

30 The range of included production subsidy measures can influence imports as well as exports -- production subsidies can curtail imports (by increasing domestic production) or promote exports (by lowering production costs). For goods with one-way trade (goods that are purely exported or imported), it is clear what the subsidies achieve. For goods with two-way trade, we solve the problem by replicating them in the sample -- once for imports, and once for exports. Protection data are used only with the imports sub-sample, while subsidy data are used only with the exports sub-sample.

> 31 M X This applies to commodities with zero imports (so that _z_ is infinite), and zero exports ( _z_ =∞).

14
21

**Table 5. Robustness I: varying the time window for calculating quality growth**

||C|ross-section 1|/|5-year non|-overlapping|windows 2/|
|---|---|---|---|---|---|---|
||Manufacturing|Agriculture|Natural Res.|Manufacturing|Agriculture|Natural Res.|
|Ln(Initial Quality)|-5.04***|-5.02***|-4.67***|-20.3***|-22.6***|-22.2***|
||(0.12)|(0.16)|(0.47)|(0.11)|(0.19)|(0.35)|
|Ln(Initial GDP p.c.)|0.0933|-0.0103|0.1640|0.4940***|0.6880***|0.0778|
||(0.0598)|(0.1610)|(0.3440)|(0.0317)|(0.1070)|(0.1780)|
|Initial Institutional Quality|0.0012|0.0027|-0.0047|0.0017**|0.0140***|-0.0002|
||(0.0022)|(0.0055)|(0.0105)|(0.0007)|(0.0025)|(0.0041)|
|Initial Human Capital|0.0011|0.0215|0.0327|0.0106***|0.0104|-0.0136|
||(0.0064)|(0.0164)|(0.0375)|(0.0020)|(0.0064)|(0.0113)|
|Initial FDI inflows|0.0033|-0.0078|-0.0127|-0.0059**|0.0021|-0.0011|
||(0.0052)|(0.0097)|(0.0249)|(0.0026)|(0.0081)|(0.0140)|
|Initial Trade Lib.|0.0458|0.3500|-0.6120|0.2020***|0.7890***|0.6090*|
||(0.162)|(0.4040)|(0.9080)|(0.0628)|(0.2180)|(0.3640)|
|Initial Agric. Lib.||0.7340**<br>(0.2850)|||0.1830<br>(0.1610)||
|Observations|17,632|4,138|1,479|152,022|46,126|12,798|
|R-squared<br>1/ Includes country and p<br>2/ Includes country-produ<br>Notes: The dependent va<br>significance at the 10 per<br>multiplied by 100 for pres|0.147<br>roduct fixed effe<br>ct and product-<br>riable is the an<br>cent, 5 percent<br>entation purpos|0.282<br>cts.<br>time fixed effe<br>nualized growt<br>and 1 percent<br>es.|0.292<br>cts.<br>h rate of produc<br>level, respectiv|0.739<br>t quality. *, **, an<br>ely. All coefficient|0.717<br>d *** denote<br>s and standa|0.724<br>statistical<br>rd errors are|
still twice as large as the coefficient on own family background. This means that raising the average background of the peers has twice the effect on a student’s test score as increasing his own socioeconomic background.

School fixed effects, however, cannot address all sources of bias. Apparent peer effects may still be driven by differences in the quality of instruction or school management across classes, whenever these differences are correlated with the peer variables. This concern would arise if, for instance, richer parents are able to exert pressure on the school for a better teacher or if the school tracks children according to their performance. In other words, there is the concern of _selection of students into classes_ . In terms of the previous equation, the problem stems from the correlation between _bcs_ and peer characteristics _Ycs_ ( _−i_ ).

To address these concerns, I exploit random variation in peer-groups between sixth-grade classrooms within schools. Two-thirds of the pupils in Argentina attend schools that have more than one class in each grade. The assignation of pupils to a class is determined at the beginning of primary school (at six to seven years of age) and remains unchanged throughout the whole seven years of education. Also, unlike the United States or the United Kingdom, in Argentina children are not streamed on the basis of their performance year by year. Instead, the initial allocation of classes is done randomly and it remains fixed for the entirety of primary education. In other words, _bcs_ and peer characteristics are not systematically related. Combined with school fixed effects, the effect of the peers on a student’s performance is identified by the existence of variations in class composition across classes within a given school.

The strategy of combining school fixed effects when there is a random assignment of students across classes has been used in a number of papers. Ammermueller and Pischke (2009) follow a similar approach for estimating peer effects using PIRLS data of primary school children across six European countries. McEwan (2003) also bases his estimates of peer effects in Chilean secondary schools using school fixed effects, although the argument of randomization tends to be weaker at the secondary level, given that a fraction of all school aged children do attend this level. Vigdor and Nechyba (2004, 2006) take advantage of randomization in primary schools in North Carolina to estimate peer effects for fifth graders. In all of these cases, however, the peer effects were estimated using the mean of the peer characteristics and no composition or heterogenous peer effects were allowed.<sup>11</sup> My approach is also close to Hoxby (2000) who estimates non linear peer effects

> 11Ammermueller and Pischke also cast doubts on the validity of the random assignment

17
4 more than 40% in the decrease of the primary surplus. States and local governments and public enterprises were responsible for 30% each for the worsening of the results.

Due mostly to the 2002 debt crisis (analyzed in the next section) and to tight monetary policy, interest payments rose from 4.8% of GDP in the 1995-98 period to 5.5 percent of GDP in 1999-2005. The operational balance improved by 3.5% of GDP, from –5% of GDP to -1.5% of GDP corresponding to an improvement of 4.2% of GDP in the primary balance. The Federal government contributed half the adjustment, while state and local governments and public enterprises with 25% of the adjustment in primary accounts<sup>2</sup> .

**Table 1: Fiscal Balances*, 1990-2005**

||**An**<br>|**nual Averages (% of G**<br>|**DP)**<br>|
|---|---|---|---|
||**1990-1994**|**1995-1998**|**1999-2005**|
||**_(A)_**|**_(B)_**|**_(C)_**|
|**I Operational Balance (III -**<br>**II)**|-0.05|-5.01|-1.46|
|**Federal Government**|0.52|-2.48|-1.72|
|**States and Municipalities**|-0.25|-1.98|-0.30|
|**Public Enterprises**|-0.31|-0.55|0.54|
|**II Real Interest Payments**|3.33|4.84|5.48|
|**Federal Government**|1.26|2.78|4.13|
|**States and Municipalities**|0.86|1.64|1.08|
|**Public Enterprises**|1.20|0.42|0.24|
|**III Primary Balance**|3.27|-0.17|4.01|
|**Federal Government**|1.78|0.30|2.41|
|**States and Municipalities**|0.61|-0.34|0.78|
|**Public Enterprises**|0.89|-0.13|0.82|

*** ( + ) Surplus ( - ) Deficit**

# _B. The Flexible Primary Surplus as a Device to Signal Fiscal Sustainability_

How do governments that are not fully credible signal regime sustainability? Based on the Drudi-Prati (2000)<sup>3</sup> model that rationalizes debt accumulation and delayed stabilization, we analyze the Brazilian case. The main testable implication of the DrudiPrati (DP) model is the existence of a positive relationship between the spreads and the

> 2 Regarding the operational balance, the federal contribution was low (only 16%) due to the impact of the greater effect of interest rates on federal accounts. On the other hand, the interest payments for state and municipalities have been reduced because of the bail-out operation of 1997-98. This operation has substituted state bonds for federal bonds and re-scheduled state debt, producing a subsidy from the federal government to the states.  This means higher interest payments for the federal government and lower ones for state governments.

> 3 Drudi, F. and A. Prati. (2000). “Signaling fiscal regime sustainability,” _European Economic Review_ , Vol. 44 pp. 1897-1930.
|Diesel<br>Generators|* Temporary relief and backup for other<br>power plants (NDRRMC 2014; Steve<br>Mufson 2012; Ardagh et al. 2012)<br>* Reliant on immediate on-demand fuel<br>supply (Powell, Hanfling, and Gostin<br>2012)|* Serve as backup<br>during times of grid<br>related power outages<br>(NDRRMC 2014;<br>Ardagh et al. 2012)<br>* Severe shaking can<br>cause sump sludge<br>which leads to failures<br>(Ardagh et al. 2012)|
|---|---|---|

_Vulnerabilities of wind, hydro, and geothermal._ Wind farms were found to be particularly vulnerable to hurricane force winds and debris, resulting in damages in Puerto Rico and Texas. Damages to geothermal facilities in The Philippines were seen due to hurricane force winds and flooding resulting from Typhoon Haiyan.

_Supply Chain and Interdependencies of wind, hydro, and geothermal._ Hydroelectric facilities and diesel generators were seen to be heavily reliant on their supply chain for fuel – in this case, water and diesel gas, respectively. In Malawi, capacity was reduced by half because of low water levels at the largest hydroelectric plant during a 2017 drought and heatwave. Nearly all of Malawi’s electricity is generated through hydroelectric plants, highlighting a potential vulnerability if precipitation patterns change in the future. Similar impacts from drought and heatwaves were seen in Tanzania in 2015 and Mozambique in 2016 (Alvaro 2018). Hydroelectric plants account for 12% of all Fuel Supply Emergency outages in the United States in the period 2000-2015 (Table 2) (Alvaro 2018).

_Costs, Impacts and Resilient Design Considerations of wind, hydro, and geothermal._ The resilience of wind farms was found to depend in part on the wind velocity that the turbines were designed to withstand. Hydroelectric facilities were seen to be vulnerable to blockages, but also flooding (through potential erosion of dam structures). Repair costs were found to be dependent on damage type and extent. Design considerations from the studies considered in this analysis are summarized in Table 16.

**Table 16 – Other Generation Facilities, Costs and Resilient Design** **<u>Considerations</u>**

||Hurricane|Drought/Heat Wave|Earthquake|
|---|---|---|---|
|Wind|* Poles and blades<br>reinforced to withstand<br>Category 4-5 wind speeds<br>210 kph (U.S. Department<br>of Energy 2018b)|* Can help prevent outages water supply is<br>low (Scanlon, Duncan, and Reedy 2013;<br>Faeth 2013)||
|Hydro||* Install power mix that doesn't all depend<br>on water for generation (Alvaro 2018)||

# **SECTION 4.0 – PUERTO RICO CASE STUDY**

The Commonwealth of Puerto Rico is a set of islands in the Caribbean Sea southeast of the continental United States. It consists of several islands, with the majority of occupants on the largest and main island of Puerto Rico. Energy use in the Puerto Rico is about 1/3 the consumption per capita of the United States. The generation portfolio consists of many fuel types including coal, oil, natural gas and renewables including solar, wind and hydroelectric. The vast majority of electricity is generated using 21
whole set of scenarios published around each basic storyline, but results from the six marker model runs carried out by the core teams involved.

# **1.3) “Stick to the data as much as possible”**

We report the original values whenever they are available if they correspond to the year 2010 or the year t+30 in the original scenario. When this value is not available<sup>5</sup> we interpolated it. In both cases, shares for 2010 are then computed for all the recorded scenarios and a median energy portfolio share is computed across all the scenarios and for each of the three energy sources (nuclear, RE and fossil fuels).The same procedure is applied to t+30 figures.

# **1.4) Extrapolate the real trajectories of primary energy consumption**

Where extrapolations or interpolations are needed, growth rates were used for each separate primary energy component. Thus, each component is then allowed to grow to the target year at its own rate based on explicit or implicit growth trends.<sup>6</sup> Then all components are totaled and shares are computed for the given year.

# **1.5) Retain the broadest primary energy base possible**

Energy studies do not always report the same total primary energy consumption. The most important difference lies in whether the study incorporates biomass and traditional energies: commercial energy only without biomass, commercial energy + biomass, commercial energy and total traditional energy. In all cases we use the broadest definition of primary energy originally presented in the scenarios. This means that when traditional energy and biomass are present, we use the total primary energy consumption including them to compute the shares to 2010 and t+30. This leads to the last step of our methodology.

# **1.6) Special attention to renewable energy**

The renewable energy shares displayed are defined in a narrow sense: excluding biomass, waste and traditional energy. Obviously, under this narrow definition, the share of RE in primary energy consumption will be lower. Note that this narrow definition essentially covers the market-oriented RE systems like solar, wind or hydraulic RE. On some occasions it was not possible to compute the narrow RE share from available data, so the figures originally reported in the studies are given. Finally, most scenarios (especially the older ones) do report very broad categories of RE technologies like hydraulic, other RE and biomass + traditional energies. We always stick to the original level of detail, according to the policy of section (1.3).

> 5 In the Energy for a Sustainable World IIASA scenarios, there are two scenarios and two variants. Unfortunately, the two variants are not as detailed as the reference scenarios, we decided that for the two variants, the trajectory was the same up to 2000 and diverged after.

> 6 That is, for in-between years, we used the average growth rate between these two years; for out-of-sample years, we extrapolated from the past growth path. This latter approach was frequently applied to older scenarios whose forecasts are often just to the year 2000.

4
Appendix A

# **Coding of the Variables from the PNAD dataset (1981-98) Table A1: Variable Names**

|**Category**|**Specification**||||**Serie**|**s and Coding**||||**0-1 dummies**<br>**(1:yes)**|
|---|---|---|---|---|---|---|---|---|---|---|
|||**1981**|**1985**|**1988**|**1990**|**1993**|**1995**|**1997**|**1998**||
|**Gender**||**v0303**|**v0303**|**v0303**|**v0303**|**v0302**|**v0302**|**v0302**|**v0302**||
||male|1|1|1|1|2|2|2|2|male|
||female|3|3|3|3|4|4|4|4|female|
|**Age**||**v0805**|**v0805**|**v0805**|**v0805**|**v03034**|**v03034**|**v8005**|**v8005**||
|**Race**||||**v0304**|**v0304**|**v0404**|**v0404**|**v0404**|**v0404**||
||white|||2|2|2|2|2|2|white|
||black|||4|4|4|4|4|4|black|
||Asian|||6|6|6|6|6|6|asian|
||mulatto|||8|8|8|8|8|8|mulatto|
||indigenous|||||0|0|0|0|indigenous|
||ignored|||9|9|9|9|9|9||
|**Urban/Rural**||**v03**|**v03**|**v03**|**v03**|**v4728**|**v4728**|**v4728**|**v4728**||
||urban|1,3|1,3|1,3|1,3|1,2,3|1,2,3|1,2,3|1,2,3|urban|
||rural|5,7|5,7|5,7|5,7|4,5,6,7,8|4,5,6,7,8|4,5,6,7,8|4,5,6,7,8|rural|
|**Typ of Region**<br>**(Metropolitan)**||**v05**|**v05**|**v05**|**v05**|**v4727**|**v4727**|**v4727**|**v4727**||
||metropolitan|1|1|1|1|1|1|1|1|metropol|
||self-representative|2|2|2|2|2|2|2|2|selfrep|
||not self-representative|3|3|3|3|3|3|3|3|notselfrep|
|**Read and Write?**||**v0311**|**v0311**|**v0311**|**v0311**|**v0601**|**v0601**|**v0601**|**v0601**||
||yes|1|1|1|1|1|1|1|1|read|
||no|3|3|3|3|3|3|3|3||
||NA|9|9|9|9|0,9|0,9|0,9|0,9||

43