rafmacalaba/datause-encoder-data huggingface dataset:
# **_A.2. CO2 emissions accounting_**

- 1.20. CO2 emissions are generated by fossil fuel combustion. There are three types of fossil fuels ( _ff_ ) in the model: fluid (natural gas), solid (coal), and liquid (oil). Based on the input-output tables, the model contains data at the sector and region level of how intensively each sector in each region uses each fossil fuel; that is, we have the cost share of each fossil fuel for each sector and region. We lack, however, detailed data on differences in emission intensity across sectors and regions for a given value of fossil fuel use. Due to lack of detailed data on CO2 emissions intensity per ruble of expenditure on fossil fuel, by industry and regional level, aggregated data for CO2 emissions for each type of fuel in 2001 was used for emission intensity per ruble of expenditure. We assume that all sectors and regions that spend 1 billion rubles on a given fossil fuel, will emit the same amount of CO2. Differences in CO2 emissions per ruble of output in the WTO model are therefore only due to the differences in the intensity of fossil fuel use across sectors and regions, not due to the efficiency with which a sector in a given region controls its emission of CO2.

- 1.21. Aggregated CO2 emissions for 2001 for each type of fuel (SRCO22001ff ) is presented in Table A32. The source of CO2 emission data in year 2001 is Rosgidromet (2013, page 36, Table A3.3).

- 1.22. CO2 emission intensity index (CO2intff)  for a fuel ( _ff_ ) is defined as total CO2 emissions of this type of fuel (measured in billions of tons, also called gigatons) divided by total demand for this type of fuel (in billions of rubles of 2001). More specifically, let  ffd0ff,reg,g equal demand for a particular fossil fuel by a given sector in one of the model’s regions in billions of 2001 rubles. The CO2 emission intensity index (CO2intff)  is presented in Equation 7: it equals ratio of aggregated CO2 emissions by a specific type of fuel ( SRCO22001ff ) and total value of fossil fuels demand in 2001 aggregated over regions and sectors, in billions of 2001 rubles.

## **Equation 7**

- 1.23. Values of CO2 emission intensity index (CO2intff)  are presented in Table A33.

- 1.24. See figures 7 and 8 for emissions intensity at the sector level in our model. These figures combine the emission intensities of Table A33, with the different cost shares of fossil fuels across sectors based on the input-output tables. Since our regional input-output tables exhibit identical cost shares for a given sector across our ten regio **n** s, the national average for CO2 emission intensity of a sector applies to all ten regions.

# **_Appendix References_**

1. SUST-RUS. 2012(a). Deliverable 2.2: The spatial-economic-environmental database for the model, SUST-RUS project web site, accessed at May 28, 2014, <u>http://sust-rus.org/wp-content/uploads/2010/08/regionalsam_sust-rus.xls</u>

89
# 102(7), 3531-60.

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29
# **<u>Supply and Demand per Commodity on a Geographical basis</u>**

Supply and demand on a geographical basis per commodity is determined as per equations 1 and 2 below.

����� ������������������� �������� ������������ �������� �������������� (1) ����� ������������������������� (2) Total supply equals total demand which corresponds to the market equilibrium condition (Ivanova, 2014). The modeling of total supply and demand addresses a concern raised by Lyk‐Jensen (2011) that freight traffic forecasting typically does not adequately incorporate international trade flows.

An official spatially disaggregated I‐O table was not available for the Indian economy, thus final supply and demand tables had to be constructed (the tables are therefore referred to as supply and demand tables, not I‐O tables, as the upstream and downstream interdependencies are not automated). The model is a hybrid, dependent on actual and modeled data, but differs from many other models in that the main inputs are actual data, while the overall supply and demand balance is maintained, implying modeling of unknown data points. In the case of India, the latter refers mainly to final regional consumption data, as well as the regionalization of aggregate data. Actual data is hardcoded in the supply and demand tables, which is much more reliable and useful than an assumption‐based disaggregation. For example, if new data on totals or regional supply and/or demand elements become known, the supply and demand tables are updated and rebalanced (the same holds true for district‐ level data, import and export data). Discussions with key players were also conducted for gathering spatially disaggregated actual data for larger commodities. The key was to rebalance both the aggregate and regional supply and demand tables when actual data replaced modeled data, resulting in improved disaggregated supply and demand tables. The same process is followed for flows where known flows (such as rail data) are hardcoded and the remainder modeled.

The sectoral disaggregation is informed by existing classification protocols. Following a number of iterations in the 1970s and 1980s to standardize data collection and reporting, the Annual Survey of Industries Commodity Classification scheme (ASICC) was developed in India in the late 1990s while maintaining linkages with the Standard International Trade Classification (SITC) which is maintained by the United Nations Statistics Division, and the Harmonized System (HS) which is maintained by the World Customs Organization (South Asian Association for Regional Cooperation, 2006). The final commodity group list for the India FDM maintains the link with the HS classification, informed by the practicality of data availability. As shown in Table 2, the model contains data for 31 commodity groups (to identify specific supply chains and to understand cost drivers) grouped into 8 cargo types (to identify logistic solution criteria and to identify required infrastructure, policy and operations).

20
played by public procurement in the empirical ndings they document. They suggest that an eective anti-corruption policy would be to improve governance and best practices in public procurement.<sup>26</sup>

# 5 Reforms and productivity growth

This section will discuss the literature that analyzes the eect on productivity of several reforms implemented since the 1990s. Several of them tried to reduce distortions that were discussed in the previous section and thus improve the eciency of factor allocation.

## 5.1 Trade liberalization

In the late 1980s Brazil started a process of trade liberalization that lasted until the mid1990s. This episode consisted of large, unilateral, import tari reductions. Average taris fell from 31 to 13 percent, and there was ample variation in the magnitude of tari cuts across sectors. After 1995, taris remained relatively constant. Using a panel of manufacturing rms, Ferreira and Rossi (2003) measure the impact of trade reform on productivity growth. The results show that the tari reduction had a substantial positive impact on the growth rates of TFP and output per worker. Moreover, this eect was widespread in the manufacturing sector, with most activities experiencing large productivity improvements. Lisboa et al. (2010) show that one important mechanism through which the tari reduction increased productivity was that it allowed national producers to have access to more technologically advanced equipment and components from abroad. Despite the evidence that the trade liberalization reform increased productivity at the rm level, there were not sizable aggregate eects in the 1990s. Menezes Filho and Muendler (2011) provide evidence that is informative about this issue. They nd that even though output shifted to highproductivity export-oriented rms, there was a labor ow away from these rms because their labor productivity increased faster than their production. The labor that was shed from these productive rms moved to low-productivity service activities, unemployment and out of the labor force. As a result, despite sizable gains at the rm level, the labor reallocation induced by trade liberalization seems to have reduced its aggregate eect. Using

> 26Zaourak (2019) argues that rent-seeking activities, in the form of bribes, have aggregate eects through two channels. First, they generate misallocation of resources across rms, since they prevent resources from owing to the most productive rms. Second, rent-seeking activities aect the allocation of resources within rms, since they drive resources away from innovation activities. He then quantitatively shows that these two channels can help explain why Brazil has both more misallocation across rms and less investment in research and development than developed economies.

23
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|**COMPARA**|**TIVE ANALY**|**TABLE 3: SUM**<br>**SIS of SOUTH A**<br>**P**<br>Dependent variab|**MARY of COEFF**<br>**FRICA vs. other**<br>**RODUCERS**<br>le: Automotive Ex|**ICIENTS**<br>**MIDDLE INCOM**<br>ports(log)|**E AUTOMOTIVE**|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|||||Multile ear|Multile ear countr|
||Base case|Base case with<br>main controls|Base case with<br>additional<br>controls|p y<br>and interaction<br>dummies|p y, y<br>and interaction<br>dummies<br>(fixed effects)|
|**SA * >95**|0.179|0.339|**0.429**|-||
|**(effect MIDP)**|[0.379]|[0.266]|[0.267]*****|||
|SA|**1.931**<br>[0.319]*******|**2.339**<br>[0.236]*******|**2.312**<br>[0.239]*******|**2.548**<br>[0.305]*******||
|>95|**0.978**<br>[0.352]|**0.415**<br>[0.242]|**0.407**<br>[0.237]|||
|Economic||**0.009**|**0.008**|**0.009**|**0.001**|
|density||[0.008]|[0.0009]|[0.0009]|[0.0008]|
|Terms of trade||**-0.032**|**-0.029**|**-0.034**|**-0.025**|
|||[0.005]|[0.0047]|[0.006]|[0.002]|
|Infrastructure|||**0.030**<br>[0.0139]|||
|Inflation|||0.012<br>[0.003]|||
|GDP per capita<br>|||-0.005<br>|||
|growth|||[0.021]|||
|SA 93||||0.424<br>[0.449]|0.389<br>[0.319]|
|SA 94||||0.244<br>[0.518]|0.229<br>[0.306]|
|SA 95||||0.551<br>[0.483]|0.392<br>[0.289]|
|||||0471|0243|
|SA 96||||.<br>[0.430]|.<br>[0.358]|
|||||0.717|**0.539**|
|SA 97||||[0.499]|[0.273]**|
|SA 98||||0.835<br>[0.540]|**0.636**<br>[0.286]**|
|SA 99||||**0.929**<br>[0.527]*****|**0.750**<br>[0.253]***|
|SA 00||||**0.871**|**0.441**|
|||||[0.421]*****|[0.270]*|
|SA 01||||**1.334**|**0.656**|
|||||[0.037]*******|[0.272]***|
|SA 02||||**1.122**<br>[0.498]******|**0.698**<br>[0.304]**|
|||||0.602|0.363|
|SA 03||||[0.468]|[0.346]|
|SA 04||||0.146<br>[0.445]|0.017<br>[0.282]|
|SA 05||||0.255<br>[0.517]|0.099<br>[0.272]|
|SA 06||||0.638<br>[0.555]|0.133<br>[0.282]|
|SA 07||||0.589<br>[0.483]|0.196<br>[0.280]|
|Year Dummies||||Yes|Yes|
|Country<br>Dummies||||_-_|Yes|
||**11.274**|**13.399**|**12.767**|**14.240**|**15.612**|
|Constant|[0.319]|[0.529]|[0.488]|[0.679]|[0.525]|
|Observations|191|191|191|191|191|
|<br>R-squared|<br>**0.14**|<br>**0.58**|<br>**0.63**|<br>**0.62 **|<br>**-**|

Robust std errors in brackets * significant at 10%, ** significant at 5%, *** significant at 1% 24
Policy Research Working Paper 9847

# **Abstract**

This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection operator are applied to matched student-teacher data for math and Kiswahili from grades 2 and 3 in 392 schools across Tanzania. These two machine learning methods produce consistent results and outperform standard ordinary least squares in out-of-sample prediction by 14–24 percent. As in previous research, commonly used teacher covariates like teacher gender, education, experience, and so forth are not good predictors of teacher effectiveness. Instead, teacher practice (what teachers do, measured through classroom observations and student surveys) and _teacher beliefs_ (measured through teacher surveys) emerge as much more important. Overall, teacher covariates are stronger predictors of teacher effectiveness in math than in Kiswahili. Teacher beliefs that they can help disadvantaged and struggling students learn (for math) and they have good relationships within schools (for Kiswahili), _teacher practice_ of providing written feedback and reviewing key concepts at the end of class (for math), and spending extra time with struggling students (for Kiswahili) are highly predictive of teacher effectiveness. As is _teacher preparation_ on how to teach foundational topics (for both Math and Kiswahili). These results demonstrate the need to pay more systematic attention to teacher preparation, practice, and beliefs in teacher research and policy.

This paper is a product of the Development Research Group, Development Economics and the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at ssabarwal@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Lower Urban|0.00858|0.0435***|
|---|---|---|
||(0.00298)|(0.00211)|
|Lower Rural|0.0205|0.0639***|
||(0.00238)|(0.00168)|
|Upper Urban|-0.0193|0.0217***|
||(0.00307)|(0.00218)|
|Upper Rural|0.00783|0.0375***|
||(0.00271)|(0.00182)|
|Borders Urban|0.0168|0.0825***|
||(0.00802)|(0.00603)|
|Borders Rural|0.0363|0.100***|
||(0.0111)|(0.00782)|
|Constant|0.586|0.592***|
||(0.00652)|(0.00430)|
|Observations|11,091|23,466|
|R-squared|0.324|0.383|

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 **Source** : Authors’ calculations based on HIECS 2015 and 2017/18. **Note** : Regression run using total expenditures.

Table A2 contains the value of coefficients for the regression for the hedonic regressions in 2015 and 2017/18.

_Table A2: Values of coefficients for the hedonic regression (dependent variable log of rents)_

||(1)|(1)|
|---|---|---|
|VARIABLES|2015|2017/18|
|HH size|0.00121|-0.00637***|
||(0.00288)|(0.00211)|
|Apartment|0.170***|0.150***|
||(0.0327)|(0.0167)|
|Country House|0.0653**|0.341***|
||(0.0328)|(0.0841)|
|#rooms|0.135***|0.108***|
||(0.0253)|(0.0221)|
|(#rooms)^2|-0.00874***|-0.00871***|
||(0.00276)|(0.00256)|
|area in m2|0.00598***|0.00669***|
||(0.000610)|(0.000537)|
|(area in m2)^2|-6.26e-06***|-1.09e-05***|
||(2.23e-06)|(2.12e-06)|
|tap water|0.0298|0.0340**|
||(0.0201)|(0.0136)|
|Dwelling connected to public network|0.187***|0.110***|
||(0.0168)|(0.0107)|
|Dwelling connected to local network|0.0282|0.0561***|

22
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15
21 short-run total cost function:a it ishnort-run additiveln £ fction: d i is not additive in fixed costs and variable costs, and variable costs are not a decreasing function of the stock of capital.

# 5.2 Inefficiency **i-n Ethiocian hosoitals**

Bitran and Dunlop do not analyze efficiency explicitly. Nor do they include any 'non-traditional' variables in their cost function which might be said to capture inefficiency implicitly.

**5.3** Economies of scope in **EthioDian hospitals**

Bitran and Dunlop have, in effect, five different outputs in their cost function: inpatient days, outpatient visits, deliveries, surgical procedures Table 3: Parameter Estimates of Bitran and Dunlop's Equation

|Variable<br>|Parameter<br>Value<br>|
|---|---|
|Constant|5.45|
|B|01<br>4.71E-3|
|I|Rl<br>**~~~~~2.18E-5**|
|12|a<br>2<br>-1.65E-12|
|OUTP|3<br>1.91E-6|
|OUTP<sup>2</sup>|a<br>4<br>1.42E-10|
|I*OUTP|5<br>-7.50E-10|
|DELIV|_6_<br>1.68E-4|
|SURG<br>|_67_<br>3.21 E-6<br><br>|
|LAB|a<br>8<br>7.63E-6|
|Adjusted R<sup>2</sup>|0.96|

Note: Dependent variable is InC.

and lab tests. In calculating the economies of scope associated with eq (22) it is important to be clear about which fixed costs would be incurred and which would avoided in each scenario. An extreme and clearly implausible assumption would be that all outputs would, if produced alone, require the same level of beds as at present. In this case projected stand-alone production costs are those indicated in column 1 of table 4. The projected costs incurred by the average hospital at present are 1006231 Birr (Bitran and Dunlop (op cit, table A.3)). The overall degree of economies of scope are therefore Sc = (3155873-10062313/1006231 = 2.136, which implies economies of scope. If, instead, one assumes that producing outpatient visits and lab tests alone would not necessitate any beds, the stand-alone production costs are those indicated in column 2 of table 4 and the implied degree of economies of scope is equal to Sc = (2527777-10062313/1006231 = 1.512, which, unsurprisingly, is smaller than in the previous case. But even this probably overstates the true degree of economies of scope, the reason being that the production of each of the inpatient outputs alone is unlikely to require the full amount of beds currently being used. This highlights a weakness of the specification used by Bitran and Dunlop, namely that unlike the flexible fixed cost functional form proposed by Baumol et al. (op cit), it does not allow for the
> <sup>- 15 -</sup>

**<u>Table 7  Asset Composition</u>**

|||**ATP**||
|---|---|---|---|
||1995|1998|2004|
|Danish Bonds||55.0%|26.1%|
|Foreign Bonds||2.4%|32.2%|
|Danish Equities||25.2%|9.6%|
|Foreign Equities||10.7%|7.9%|
|InvestmentFunds||1.2%|3.1%|
|Real Estate||2.0%|1.8%|
|Derivatives|||10.2%|
|Other||3.5%|9.1%|
||**Life Insu**|**rance Com**|**panies**|
||1995|1998|2004|
|Danish Bonds|65.6%|55.2%|49.6%|
|Foreign Bonds|0.5%|1.6%|15.5%|
|Danish Equities|7.7%|12.2%|4.1%|
|Foreign Equities|4.7%|12.0%|7.7%|
|InvestmentFunds|0.4%|0.8%|6.4%|
|Real Estate|1.4%|1.2%|1.2%|
|Derivatives|..|..|..|
|Other|19.7%|17.0%|15.5%|
||**Multi-E**|**mployer P**<br>**Funds**|**ension**|
||1995|1998|2004|
|Danish Bonds|59.2%|52.6%|27.9%|
|Foreign Bonds|0.6%|0.7%|12.3%|
|Danish Equities|14.3%|16.2%|5.8%|
|Foreign Equities|6.3%|13.0%|9.7%|
|InvestmentFunds|2.8%|4.4%|26.3%|
|Real Estate|6.3%|4.6%|5.4%|
|Derivatives|..|..|..|
|Other|11.5%|8.5%|12.6%|

Note: Data on the use of derivatives is incomplete. Many life insurance companies and pension funds have hedged the interest rate risk on their liabilities through the use of derivatives. Source: The Danish Financial Supervisory Authority and ATP