rafmacalaba/datause-encoder-data huggingface dataset:
of origin can be computed by specifying `output(gtrade)` .

As far as import flows are concerned (i.e. `[` _output_ _~~i~~ mports_ `]` in Section 4.1), the distinction between domestic and foreign items is less relevant, as the former would refer only to the items produced, exported and then re-imported by the importing country itself. For this reason, the _default_ in this case is to compute the gross trade value (i.e. `gtrade` ). The imported value-added (gross-content) can be traced back to the country of origin specifying the option `origin(` _country_ _~~c~~ ode_ `[,` _sector_ _~~-~~ code_ `])` together with `output(va)` ( `output(gtrade)` ). Of special note is that it is possible to pin down also a specific market/sector of final demand by specifying the `destination(` _country_ _~~c~~ ode_ `[,` _sector_ _~~c~~ ode_ `])` option.

As for the standard options, (i.e. `[` _standard_ _~~o~~ ptions_ `]` in Section 4.1), the `save()` and `group()` options are available for both export and import flows (see Section 3.1 for a description of these options).

# **4.1.3 Examples: Value-added in trade flows**

In this section we provide some examples of the insights that `icio` , dealing with the break down of value-added in trade flows, can bring for the economic analysis of ICIO tables.

As running example, we select a specific trade flow, the Chinese total aggregate exports. After having loaded the year 2014 of the last release of the WIOD tables using ``` icio_load,iciot(wiodn)year(2014) ``` the user can easily obtain a detailed breakdown of the selected trade flow, both in millions of US dollars and as a share, using ``` .icio,exporter(chn) Decompositionofgrossexports: Perspective:exporter Exporter:CHN Importer:totalCHNexports ```

||`Millions of $`|`% of export`|
|---|---|---|
|`Gross exports (GEXP)`|`2425406.15`|_`100.00`_|
|`Domestic content (DC)`|`2039474.07`|_`84.09`_|
|`Domestic Value-Added (DVA)`|`2016712.86`|_`83.15`_|
|`VAX -> DVA absorbed abroad`|`1957739.47`|_`80.72`_|
|`Reflection`|`58973.39`|_`2.43`_|
|`Domestic double counting`|`22761.21`|_`0.94`_|
|`Foreign content (FC)`|`385932.09`|_`15.91`_|
|`Foreign Value-Added (FVA)`|`380473.47`|_`15.69`_|

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# **: Rate-Based Emissions Trading with Overlapping Policies Insights from Theory and an Application to China**<sup>*****</sup>

Carolyn Fischer,<sup>†</sup> Chenfei Qu,<sup>‡</sup> and Lawrence H. Goulder<sup>§</sup>

**Keywords** : emissions trading; market-based regulations; subsidies; renewable energy **JEL codes** : Q21, Q28, Q48, O38

* The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

- World Bank Group, CESifo, and Resources for the Future (cfischer2@worldbank.org)

- Institute of Energy, Environment and Economy, Tsinghua University

- § Stanford University, NBER, and Resources for the Future
O.A.M.D.G.

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in poverty reduction by first computing the joint impact of a subset of variables, and then subtracting the marginal impact of each variable at a time. For instance, in step 2 in Table 1, they first compute the joint impact of inserting both the share of adults and the income per adult from the first period into the distribution of the second period. They then compute the impact of only changing the share of adults, and take the difference of these two simulations to approximate the marginal impact that changing the share of adults had on the distribution. However, in step 4, instead of computing the impact of income per adult on its own, they compute the impact of changing both the labor and non-labor income per adult. This is done because in principle, the sum of labor and non-labor income should be equivalent to changing total income per adult. However, the results of these two simulations are different. Moreover, the simulation of labor income is not done explicitly, but rather ends up being a ―residual‖ in step 8, to ensure that the cumulative effect adds up to the total distributional change.

|**Ta**<br>|**ble A1. Barros et al(2006) Metho**<br> <br> <br>|**dology**<br>|
|---|---|---|
|**1.**|<br> <br> <br> <br> <br> <br>|Initial poverty rate|
|**2.**|<br> <br> <br> <br>|Contribution of the interaction between share of adults<br>and income per adult is <br>|
|**3.**|<br> <br> <br> <br> <br>|Contribution of share of household adults is <br> <br>|
|**4.**|<br> <br> <br> <br> <br> <br> <br> <br> <br> <br>|Contribution of the interaction between labor and<br>nonlabor income is <br> <br> .|
|**5.**|<br> <br> <br> <br> <br> <br> <br> <br> <br> <br>|Contribution of non-labor income is <br> <br> .|
|**6.**|<br> <br> <br> <br> <br> <br> <br>|Contribution of the interaction between labor income<br>and the share of occupied adults is <br> <br> .|
|**7.**|<br> <br> <br> <br> <br> <br> <br> <br> <br>|Contribution of the share of occupied adults is<br> <br> <br> .|
|**8.**|<br> <br> <br> <br> <br> <br> <br> <br>|Final poverty rate, . The contribution of labor<br>income, <br> ,is calculated as a residual: <br> .|

In contrast, we compute a cumulative counterfactual distribution by adding one variable at a time. The impact of changes in each variable and its interactions with all other variables is calculated as the difference between the cumulative counterfactuals (Table A2). In contrast to the Barros et al (2006) approach, this method does not separately identify the contribution of the interaction between variables on poverty reduction, since doing so is partial at best given that changing any variable can potentially affect all other variables. Moreover, the approach adopted in this paper has the advantage that it avoids attributing the residual to the last variable being considered and allows for a more straight-forward interpretation of the results. The order in which the cumulative effects are built up matter, as described below, so we adopt an ordering 39
**Table 3: Percent of workers in each employment category; by country, region and income group**

|**Region and Income Level **||**NON-AGR**|**ICULTURE**||**AGRICULTURE**|
|---|---|---|---|---|---|
|**(number of countries in**<br>**sample)**|**wage and**<br>**salary**<br>**employee**|**non-paid**<br>**employee**|**employer**|**own account**||
|**All Countries (90)**|**45.2**|**2.6**|**2.1**|**14.4**|**35.8**|
|**Low and Middle Income**<br>**Countries (68)**|**37.9**|**3.0**|**1.8**|**15.7**|**41.7**|
|**Region (Low and Middle Income Co**|**untries)**|||||
|East Asia and Pacific (6)|35.7|4.1|1.8|17.2|41.2|
|Europe and Central Asia (13)|74.3|0.6|2.6|5.0|17.5|
|Latin America and the Caribbean<br>(18)|59.2|2.2|3.8|18.5|16.3|
|Middle East and North Africa (4)|48.0|2.3|4.0|8.7|37.1|
|South Asia (4)|28.7|3.8|0.7|15.6|51.2|
|Sub-Saharan Africa (21)|13.4|2.4|1.4|19.0|63.7|
|**Per Capita GNI**||||||
|Low Income (17)|18.6|2.1|1.0|17.9|60.4|
|Lower Middle Income (27)|32.2|3.8|1.3|15.6|47.1|
|Upper Middle Income (22)|65.2|1.7|3.6|14.3|15.1|
|High Income(24)|84.0|0.4|3.5|7.5|4.6|

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**Figure 7. Impact of 2014-16 oil price plunge on the largest energy importers**

**A. Consumption of fuels, 2018**

**B. Contribution of mining investment to U.S. GDP growth and U.S. industrial production growth**

<!-- Start of picture text --> Percent of total energy Oil Natural gas Percentage points Percentage points<br>consumption CoalNuclear energy HydroelectricRenewables Mining contribution<br>100 0.4 Industrial production (RHS) 20<br>75<br>0.2 10<br>50<br>0.0 0<br>25<br>-0.2 -10<br>0<br>Brazil Mexico Indonesia Advanced economies Turkey India South Africa China -0.4 2000 2002 2004 2006 2008 2010 2012 2014 2016 2019 -20<br><!-- End of picture text --> Source: BP Statistical Review; Federal Reserve Bank of St. Louis; U.S. Bureau of Economic Analysis; World Bank.

A. Oil consumption is measured in million tonnes; other fuels in million tonnes of oil equivalent. Renewables are based on gross generation from renewable sources including wind, geothermal, solar, biomass, and waste, but not accounting for cross-border electricity supply.

B. Mining investment is real private fixed investment of nonresidential structures for mining exploration, shafts, and wells.

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<!-- Start of picture text --> Figure U4: Percentage of Firms Using E-filing by Industries<br>efilers [ME non-efilers<br>Agriculture<br>Manufacturing a<br>consucion|<br>Trade<br>Hotels & Catering<br>Services a<br>0 20 40 60 80 100<br>Percent (%)<br>Soure: IFC 2009 [Ukraine]<br><!-- End of picture text -->

<!-- Start of picture text --> TABLE U1: Taxesby E-filing Usage<br>581 1063) 866 779 |422 1222/1039 606 | 170 1474) 526 1119<br>Non-Efilers |85% 76% | 85% 73% |82% 78% | 75% 86% |77% 79% |83% 77%<br>103 335] 147 290} 92 345} 337 100} 52 385)107 331<br>Efilers 15% 24% | 15% 27% |18% 22% |25% 14% |23% 21% {17% 23%<br>*Source: IFC 2009 [Ukraine]<br><!-- End of picture text -->
# Table 3: ITT Treatment Effect Estimates, Short-Run Business Outcomes OLS

|Outcome variable|ITT tre<br>effect e|atment<br>stimates|Control<br>group<br>mean<br>(std. dev.)|
|---|---|---|---|
||(1)|(2)|(3)|
|Full-time paid employees|1.475|0.516|12.428|
||(1.421)|(1.260)|(22.281)|
||378|378|243|
|Avg. sales Dec 2008, Jan and Feb 2009 (1000s USD)|-5.108|-11.886|63.384|
||(15.452)|(10.876)|(163.643)|
||307|307|200|
|Log (Avg. sales Dec 2008, Jan and Feb 2009 in 1000s USD)|0.006|0.050|2.391|
||(0.175)|(0.144)|(2.023)|
||307|307|200|
|Feb 2009 costs (1000s USD)|5.525|5.657|43.157|
||(14.694)|(14.551)|(113.758)|
||304|304|204|
|Profits (Feb 2009 sales minus costs, 1000s USD)|5.802|5.330|11.460|
||(5.831)|(5.705)|(97.044)|
||265|265|176|
|Log (business assets)|-0.055|-0.106|4.307|
||(0.176)|(0.157)|(1.699)|
||319|319|203|
|Productivity residual|0.270*|0.250*|-0.095|
|Residual from regression of log Feb 2009 sales|(0.141)|(0.130)|(1.272)|
|on log employees and log business assets|250|250|174|
|Return on assets (ROA)|0.105*|0.098|0.012|
|Feb 2009 sales minus costs divided by assets|(0.060)|(0.064)|(0.471)|
||236|236|154|
|Controls for baseline value of outcome|No|Yes|-|

Note: Each row in Columns 1 and 2 contains the treatment effect point estimate, robust standard error, and number of observations, for a separate OLS estimation. For the regressions that control for the outcome variable measured at baseline (Column 2), when the baseline outcome variable is missing, the missing value is filled-in with zero and a dummy variable indicating that the baseline observation is missing is added to the model. All regressions include controls for strata dummies and re-randomization variables, as well as a dummy for having been surveyed in March 2009 (vs. April, May or June) at follow-up. Column 3 contains means and standard deviations for the control group at follow-up. Significance levels: *10 percent, **5 percent, ***1 percent.

25
ˆ variable in equation (2) the log of actual trip speed and estimate city fixed effects _sc_<sup>_f e_that we</sup> can interpret as an index of mobility. To measure mobility _in the absence of traffic_ , we repeat the same estimation as with actual speed but use as dependent variable the log of speed in the absence of traffic returned by Google Maps for each query. The resulting city fixed effects ˆ _ntc_<sup>_f e_are our index of uncongested mobility.21</sup> To measure congestion, we repeat the same estimation using the difference between log trip duration with traffic and log trip duration without traffic, log _Ti −_ log _Ti_<sup>_nt_</sup> = log( _Ti_ / _Ti_<sup>_nt_),</sup> as the dependent variable. While strictly speaking, the city fixed effects, _f_<sup>ˆ</sup> _c_<sup>_f e_, that we estimate</sup> are a measure of delay, we can interpret them as a broad index of congestion, which we refer to as the congestion factor.

The dependent variable when estimating mobility is log _Si_ = log _Di −_ log _Ti_ . The dependent variable when estimating mobility in the absence of traffic is log _Si_<sup>_nt_</sup> = log _Di −_ log _Ti_<sup>_nt_.</sup> It then follows that when estimating the congestion factor we have log _Ti −_ log _Ti_<sup>_nt_</sup> = _−_ (log _Si_<sup>_nt−_log</sup><sup>_Si_).</sup> Our third regression thus uses as dependent variable the difference between the dependent variables of the first two regressions. Because we estimate these three regressions for the same trip instances using the same set of covariates, it follows directly from simple econometrics that a city’s congestion factor is the difference between its uncongested mobility factor and its overall mobility factor: This result is useful on two counts. First, it provides us with an exact decomposition which we exploit below. Second, when we regress these three city fixed effects on the same set of city determinants below, the estimated coefficients will also conveniently add up. For instance, the estimated effect of city population on mobility will be equal to the estimated effect of city population on mobility in the absence of traffic minus the estimated effect of city population on the congestion factor.

> 21Alternatively, recall that we observe each trip an average of ten times and oversample times in the middle of the night when we expect very little traffic. We can treat the speed of the fastest trip instance as an estimate of uncongested speed. In practice, these two methods yield city congestion indices with a Spearman correlation coefficient of 0.96.

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Note: The figure assumes producers are subsidized only for production in the domestic market. It also assumes domestic consumer subsidy occurs relative to the world price. For alternative assumption on both, see Figure 3. Produced by the authors based on stylized facts regarding the structure of the oil markets among oil producers.

We will examine whether a higher world price might require higher domestic subsidies if the domestic demand is to stay constant (Figure 3a). This proposition is empirically examined in Section 5 and is found to hold for MENA oil exporters.

# **3. Method and Data**

Our data is from International Energy Agency (IEA, 2021) and covers the years 2011-2018 for 41 countries for which complete data for our purposes exists. The data covers subsidies for four distinct products _, oil, gas, electricity, and coal_ . It also provides separate coverage of “Transport subsidy.” Although the data nominally covers most countries, there are numerous missing observations with the result that 328 observations corresponding to 41 countries are covered (Appendix 2, Table A2.1). One question is whether this produces a biased sample. To answer this question, we consider both the probability density for CO2 emissions and descriptive statistics for subsidies as well CO2 emissions (Appendix 2, Figure A2.1 and Table A2.2) . Despite the fact the sample includes the largest emitters, both the density function and the descriptive statistics confirm that it also includes nearly zero polluters as well zero subsidies. Given this large variance we believe that there is sufficient heterogeneity in the sample to pre-empt a biased outcome. These are nominally consumption subsidies. But as was shown in Figure 3 and 3a, in practice, any subsidy that lowers the retail price of fossil fuels to the users beyond an equilibrium price can only achieve this goal by reducing production and transportation cost and this amounts to an outward shift of the supply curve. Thus, in principle, there can be no conceptual distinction between production and consumption subsidies. That said, differential gains of both users and producers from a subsidy can certainly be assessed, as was illustrated in Figures 3 and 3a.

Our empirical methodology, detailed below, is supplemented with a scenario-based forecast of countries using the World Bank-IMF joint analytical tool, Climate Policy Assessment Tool (CPAT), which is an Excel based large macro accounting tool calibrated to detailed country-level data. Although CPAT is a partial equilibrium tool, this limitation is overcome by the consistency that we find between our empirical results and the CPAT scenario-based forecasts, at least in terms of focus on CO2 emissions. This is confirmed in nearly all cases. Beyond that, however, the use of this methodology allows us to compare the effectiveness of a possible 12
A teacher with a positive attitude toward students’ challenges can have a positive influence on student motivation and achievement. For instance, Zentall and Morris (2010) examined student responses to various scenarios illustrating failed behavior. Ultimately, they found that when most of the praise students received was non-generic (e.g., “you did a good job drawing”), students reported feeling happy about the scenarios, suggesting the emergence of a mastery orientation toward learning.

Finally, academic learning is an intensely social experience. Positive interactions with peers of the same age contribute to students’ academic, psychosocial, behavioral, and emotional well-being. These peer interactions take on increasing importance as children proceed through development (Parker & Asher, 1993; Hartup, 2009). Through peer relationships and experiences, children establish their concepts of trust, practice critical social skills, develop a sense of their own identity, and develop perceptions of other people and the world with lasting effects into later life.

The promotion of student collaboration in the classroom has benefits for children’s socioemotional development as well as their academic performance. For instance, in a study of Bruneian students, Kani and Shahrill (2015) found that where teachers assigned students to work in pairs to think-aloud and solve a set of math problems, improvements were observed in students’ problem-solving strategies and their understanding of the problem. Further, when paired with peers who are working at a slightly higher level of knowledge, scaffolding can occur; that is, the less-skilled peers’ memory recall and use of learning strategies improve while also increasing the more-skilled peers’ self-esteem (Manion & Alexander, 1997). This is also consistent with Wharton-MacDonald, Pressley, and Hampston’s (1998) study, which found that the most effective teachers with the highest performing students tended to encourage instructional groupings, where students would be encouraged to read or write with a partner during some part of the lesson or work in small-group activities cooperatively. Together this suggests that collaborations between students and peers, when structured well, can be conducive to positive learning outcomes for both parties.

3.1.3. Designed for Low- and Middle-Income Countries This subsection provides evidence associated with issues that arise when applying existing instruments in low- and middle-income countries and how these issues have been addressed by _Teach_ .

9
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