rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 4. Distribution of emission allowances under auctioning to meet China’s NDC in 2030**

|~~Shandong~~|~~816~~|ELECT|2011|
|---|---|---|---|
|~~Jiangsu~~|~~770~~||,|
|~~Hebei~~|~~652~~|NMETA|1307|
|~~Henan~~|~~540~~||,|
|~~Guangdong~~|~~465~~|METAL|1,172|
|~~Inner Mongolia~~|~~418~~|||
|~~Zhejiang~~|~~401~~|~~PETRO~~|~~658~~|
|~~Hunan~~|~~354~~|||
|~~Anhui~~|~~330~~|~~COAL~~|~~655~~|
|~~Sichuan~~|~~320~~|||
|Liaoning|~~309~~|~~CHEMI~~|~~651~~|
|Shanxi|284|||
|Shaanxi|267|CONST|~~513~~|
|Shanghai|267|||
|Beijing|228|TRANS|455|
|Heilongjiang|211|||
|Tianjin|202|SERVI|293|
|Hubei|197|||
|Jiangxi|192|OTHMF|211|
|Jilin|190|||
|Guizhou|168|FTPMF|172|
|Yunnan|125|||
|Guangxi|119|~~MINE~~|~~144~~|
|Fujian|117|||
|Xinjiang<br>|108|~~GAS~~|~~107~~|
|Chongqing|102|||
|~~Gansu~~|~~101~~|~~AGRI~~|~~55~~|
|~~Ningxia~~|~~83~~|||
|Qinghai|49|~~OILNG~~|~~7~~|
|Hainan|26|||
|~~Xizang~~|~~1~~|~~WATER~~|2|

When emission allowances are distributed through auctioning, the central government first decides the total or national cap corresponding to the NDC target. A sector or emitter buys emission allowances from the government in a similar manner as if it is ready to pay a carbon tax to meet the national CO2 mitigation target. The underlying assumption here is that the auction price and permit price are equal, meaning that if an emitter purchases a higher amount of permits than it needs it can sell in the market at the same price it purchased. While this approach provides incentives to an emitter which has a lower marginal cost of its reduction, it may not be easy to implement in practice because the auction price and permit price may be different thereby creating a possibility of arbitrage.

40
In fact (Table 2) using any of the variants of the new data series presented in Appendix 2, we do find that access is correlated with poverty rates. This is true even of the microfinance-only data (referred to in the Appendix as the WSBI series – see regression 2.D). It is also true of the synthetic access percentages (2.E) or the composite series (2.F). But access percentages are, of course, strongly correlated with per capita income (Figure 8),<sup>21</sup> and since the latter is highly correlated with poverty rates, the more interesting question is whether access remains significant in a regression which also includes per capita income. In fact, when per capita income and other controls found to be significant in earlier work (see 2.A-C) are added to the specification, none of the access series retains significance (2.G).

Given the numerous deficiencies of the aggregate access data series, these negative macro cross-country results should not be taken as proving that access has little effect, but neither do they provide any strong evidence in favor of that hypothesis. Besides, the equation is clearly underspecified, the omission of relevant variables could mask or bias the estimated role of access. For instance, access might be more effective in the presence of other elements of the financial infrastructure such as credit registries or effective protection of property rights (Johnson et al., 2002; Djankov et al., 2006), or if financial liberalization is more advanced (Vlachos & Waldenström, 2005). The strength and presence of informal credit channels could also be a factor. Several other control variables reflecting for example education and labor market conditions deserve to be included also. Future work will report the results of further analysis along these lines.

> 21 In a regression of data on the summary access indicator for 147 countries, per capita income explains 60 percent of cross country variation.  After exclusion of 5 outliers, Hong Kong, Lebanon, Luxembourg, Macao, China and United Arab Emirates, this jumps to 73 percent.  It is likely that these five outliers should be omitted from most analyses with this data.

17
distortions. However, voluntary savings programs are also likely to incur social costs, particularly when they involve fiscal incentives.<sup>8</sup> To develop measures to improve the financial well-being of the elderly and incorporate an element of self-control in retirement planning, policy makers must first understand worldwide retirement savings patterns. In the next sections, we evaluate these patterns using a binary measure of saving for old age, which capture whether an individual has saved for old age during the last year. Data limitations prevent us from measuring the volume of savings for old age. However, examining savings behavior can reveal a more complete picture of consumer characteristics and inform policy design.

# **3. Who saves for old age?**

# **3.1      The data**

We use data from the Global Findex database, which provides in-depth data showing how people save, borrow, make payments, and manage risk.<sup>9</sup> It is the world’s most comprehensive set of data providing consistent measures of people’s use of financial services across economies and over time. The data were collected in partnership with Gallup, Inc. and with funding from the Bill & Melinda Gates Foundation. The survey was carried out over the 2014 calendar year as part of the Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160

> 8 Such incentives involve tax exemptions and subsidies. These might require changes in fiscal policy that can be socially costly because they increase distortions. Moreover, such schemes might suffer from limited or lower coverage as well as volume of savings, particularly among the poor, compared to the remaining pillars. Valdés-Prieto (2002a) suggests that most third-pillar fiscal incentives are regressive because the incentives are taken up mostly by higher-income workers while the increase in general tax rates fall to some degree upon middle- and lower-income workers. There is already a rich literature on the effects of social security incentives and provisions on labor market distortions (e.g. Gruber and Wise, 1998; 2002; Coile, 2015), on the strong connections between subjective retirement expectations and future work behavior (e.g. Chan and Stevens, 2004), on the role of financial incentives on retirement choices (e.g. Kingston, 2000; Belloni and Alessie, 2009), and the role of fluctuations on the business cycle, financial markets and the housing market on retirement choices (e.g. Crawford and Lilien, 1981; Coile and Levine, 2010).

> 9 The complete country-level database is available at: http://datatopics.worldbank.org/financialinclusion/

7
differ much across the population (with the exception of the 1<sup>st</sup> and 10<sup>th</sup> deciles), and most of the goods and services are taxed at the same VAT rate.

**Table 3. Share of VAT in consumer prices by expenditure line**

|**Expenditure line(COICOP)**|**Industry (ISIC)**|**VAT rate**|
|---|---|---|
|Food*|Food products|9.1|
|Alcohol and tobacco*||16.7|
|Clothing|Textiles, and textile products|16.7|
|Footwear|Leather and footwear|16.7|
|Fabrics|Textiles, and textile products|16.7|
|Housing, fuel for heating dwellings**|Electricity, gas and water supply and forestry|7.6|
|Housing, utilities***|Electricity, gas and water supply and other community, social and<br>personal services|7.3|
|Housing, other|Real estate activities and renting|0.0|
|Household appliances|Computer, electronic and optical equipment,|16.7|
|Furniture|Wood and products of wood and cork|16.7|
|Health care|Health and social work|3.9|
|Public transportation|Transport|16.7|
|Maintenance of private vehicles|Coke, refined petroleum products and nuclear fuel|16.7|
|Purchase of cars and other vehicles|Motor vehicles, trailers and semi-trailers|16.7|
|Communication services|Post and telecommunications|16.7|
|Culture, recreation and sports|Hotels and restaurants|16.7|
|Secondary and higher education|Education|3.0|
|Preschool education|Education|3.0|
|Eating out and restorans|Hotels and restaurants|16.7|
|Personal care|Chemicals and chemical products|16.7|
|Other goods and services|Manufacturing nec|16.7|
|Food purchased for animals and for cultivation of|Agriculture|9.1|
|land plot|||
|Construction andpurchase of real estate|Construction|8.1|

_Notes_ .

* VAT rate for alcohol and tobacco is 20%, while for majority of food products it is set at 10%.

** Structure of fuel used for heating in houses with autonomic heating is following: 40% wood fuel, 60% gas<sup>16</sup> . Based on these weights we estimated VAT rate for expenditures on fuel for heating as weighted average VAT rate for “electricity, gas and water supply” and “forestry”. *** VAT rate is estimated as average for “electricity, gas and water supply” and “other community, social and personal services” weighted on sectors’ total output.

_Source_ : own estimates based on the Tax Code of Belarus and Input-Output Tables for 2014.

**Figure 8. Incidence of indirect taxes by deciles**

<!-- Start of picture text --> 6 USD PPP % 10 1.0 USD PPP % 6<br>9 0.9<br>5 5<br>8 0.8<br>7 0.7<br>4 4<br>6 0.6<br>3 5 0.5 3<br>4 0.4<br>2 2<br>3 0.3<br>2 0.2<br>1 1<br>1 0.1<br>0 0 0.0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>% of disposable income (right axis) % of disposable income (right axis)<br>USD PPP per day USD PPP per day<br>(a) VAT (b) excises<br><!-- End of picture text --> _Note_ . Deciles are ranked by disposable income. _Source_ : own estimates based on HBS data.

> 16 <u>http://www.belstat.gov.by/upload-belstat/upload-belstat-pdf/oficial_statistika/Potreblenie_energii_v_dom_hoz.pdf.</u>

15
# Report Cards: The Impact of Providing School and Child Test Scores on Educational Markets

_By_ Tahir Andrabi, Jishnu Das and Asim Ijaz Khwaja<sup>*</sup> <mark>June 2014</mark> _JEL Codes: O12, I25, L15, L22, D22, D82 Keywords: Educational Markets, Information Provision, Private Schools, Market-level Experiments_

> * Andrabi: Pomona College, 425 N. College Ave, Claremont, CA, 91711 (tandrabi@pomona.edu); Das: Development Research Group, World Bank, 1818 H St NW, Washington, DC, 20433 (jdas1@worldbank.org); and Khwaja: Harvard Kennedy School, 79 JFK St, Cambridge, MA, 02138 (akhwaja@hks.harvard.edu). The data used in this paper is part of the multi-year LEAPS project. We are grateful to Khurram Ali, Sarah Bishop, Alexandra Cirone, Ina Ganguli, Sean LewisFaupel, Emily Myers, Paul Novosad, Niharika Singh and Tristan Zajonc for research assistance and to the LEAPS team for all their hard work. We are also grateful to Abhijit Banerjee, Prashant Bharadwaj, Pinar Dogan, Quy-Toan Do, Matthew Gentzkow, Justine Hastings, Robert Jensen, Maciej Kotowski, Nolan Miller, Rohini Pande, Doug Staiger, Tara Viswanath and seminar participants at Boston University, BREAD (Duke), Brown, Chicago, Cornell, Dartmouth, NBER, Stanford, Syracuse University, UC Berkeley, UCLA, University of Illinois at UC, University of Wisconsin at Madison, and Yale for comments. All errors are our own.
# **List of tables**

|Table 1: Sources of rural energy for various end-uses at different household incomes<br>(Barnes 1996).......................................................................................................... 100|
|---|
|Table 2: Comparison of characteristics of traditional and modern biofuels................... 101|
|Table 3: Biofuel technology matrix ............................................................................... 101|
|Table 4: Land and water intensity of potential sources for ethanol (Rajagopal 2007)... 102|
|Table 5: Land and water intensity of major oilseed crops (Rajagopal 2007)................. 102|
|Table 6: Potential for ethanol production from major crops........................................... 102|
|Table 7: Potential for ethanol from perennial grasses in future based on predictions.... 103|
|Table 8: Estimate of land needed to electrify rural homes in India using biodiesel<br>(Rajagopal 2007)..................................................................................................... 103|
|Table 9: List of LCA studies reviewed........................................................................... 104|
|Table 10: Average cost of production of ethanol in various countries (OECD 2006) ... 105|
|Table 11: List of policy tools and some examples.......................................................... 105|
|Table 12: Summary of current production, future targets and policies in various countries<br>................................................................................................................................. 106|
|Table 13: Possible impact of policy on economic and environmental indicators........... 107|

4
||Description|Resp|onse wa|s justifed|N|
|---|---|---|---|---|---|
|||_L_|_H_|_H_-_L_||
|V0|Dinesh robs Mahesh’s house, after which Mahesh beats<br>Dinesh, reports him to the police, and Dinesh receives a<br>jail sentence.|0.90|0.93|0.03|61|
|V1|Dinesh digs a canal through Mahesh’s feld without<br>permission. Mahesh later argues with Dinesh in the<br>village and beats him up.|0.03|0.38|0.35*|59|
|V2|Dinesh lets his cattle graze on Mahesh’s felds without<br>permission. Mahesh later argues with Dinesh in the<br>village and beats him up.|0.20|0.43|0.23*|60|
|V3|Dinesh calls Mahesh names and roughs him up. They<br>later meet in public, argue, and Mahesh beats up<br>Dinesh.|0.10|0.33|0.23***|59|
|V4|Dinesh’s daughter marries Mahesh, even though Mahesh<br>is lower caste than the daughter. Dinesh later beats up<br>Mahesh.|0.22|0.70|0.48***|203|

- ** ***

- _p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001

Table 4: Proportion of respondents who agree that the response was justified in the punishment survey. Standard errors have been clustered by village. Of the 241 respondents, 38 were not able to answer for vignette V4, one was not able to answer for V1, and one was unable to answer for V3. Among the men who did respond for V4, 99 were low-caste and 104 were high-caste.

The caste difference in the proportion who would respond with aggression is in every case statistically significant ( _p <_ 0 _._ 01). A few examples of aggressive responses are:

- “I would do the same as Mahesh because I lost my honor.”

- “I would do tit for tat, otherwise people will think I am weak.”

- “I would do the same, it is wrong to cause a loss.”

- “I would do the same as Mahesh because a mistake is a mistake.”

For the low-caste respondents, non-aggression was much more common. 78 percent of responses for V1-V3 and 63 percent of responses for V4 indicated a non-aggressive approach, and suggested intent to solve the problem, forgive, reconcile, or let things be. Examples of non-aggressive responses were:

- “I would talk and find an agreement.”

- “I would deal with it peacefully.”

- “I would do nothing, tolerate it.”

- “I would forgive.”

18
**<u>Table 5: Predicted direct and indirect effects of the conflict on winter and summer crop area, 2022 and 2023</u>**

||<br>**Total**|<br>**Conflict aff**|<br>**ected**|<br>**...if yes, c**|<br>**ontrolled by**<br>**Ri**|
|---|---|---|---|---|---|
|||**No**|**Yes**|**Ukraine**|**ussan**<br>**Federation**|
|**Panel A: Winter crop area**||||||
|_2022 cropping season_||||||
|(1) Full model (B)|8.38|5.68|2.70|0.70|2.00|
|(2) No conflict (S1)|8.49|5.68|2.81|0.75|2.06|
|(3) No macro and conflict (S2)|9.23|6.28|2.95|0.80|2.15|
|<br>(4) Net conflict effect (S1-B)|0.11|0.00|0.11|0.05|0.06|
|(5) Net macro effect (S2-S1)|0.74|0.61|0.14|0.05|0.09|
|(6) Conflict and macro effect (S2-B)|0.85|0.61|0.25|0.10|0.15|
|No. of VCs|10,002|8,147|1,855|624|1,231|
|_2023 cropping season_||||||
|<br>(1) Full model (B)|6.76|4.79|1.97|0.46|1.50|
|(2) No conflict (S1)|7.09|4.79|2.31|0.54|1.76|
|(3) No macro and conflict (S2)|8.20|5.76|2.45|0.58|1.87|
|(4) Net conflict effect (S1-B)|0.34|0.00|0.34|0.08|0.26|
|(5) Net macro effect (S2-S1)|1.11|0.97|0.14|0.03|0.11|
|<br>(6) Conflict and macro effect (S2-B)|1.45|0.97|0.48|0.11|0.37|
|No. of VCs|10,002|8,718|1,284|313|971|
|**Panel B: Summer crop area**||||||
|_2022 cropping season_||||||
|<br>(1) Full model (B)|17.06|13.10|3.95|1.93|2.02|
|<br>(2) No conflict (S1)|17.87|13.10|4.76|2.16|2.61|
|(3) No macro and conflict (S2)|19.22|14.21|5.02|2.29|2.73|
|(4) Adj. for w. crops in (3) (S3)|19.00|14.06|4.94|2.27|2.67|
|(5) Net conflict effect (S1-B)|0.81|0.00|0.81|0.23|0.59|
|(6) Net macro effect (S2-S1)|1.36|1.10|0.25|0.14|0.12|
|<br>(7) Conflict and macro effect (S2-B)|2.17|1.10|1.07|0.36|0.70|
|(8) Adjusting for w. crop (S3-B)|1.94|0.95|0.99|0.34|0.65|
|No. of VCs|10,556|8,576|1,980|1,075|905|
|_2023 cropping season_||||||
|(1) Full model (B)|18.12|15.48|2.64|1.21|1.43|
|<br>(2) No conflict (S1)|18.68|15.48|3.20|1.33|1.87|
|<br>(3) No macro and conflict (S2)|19.20|15.95|3.25|1.35|1.90|
|(4) Adj. for w. crops in (3) (S3)|18.06|15.12|2.95|1.26|1.68|
|<br>(5) Net conflict effect (S1-B)|0.56|0.00|0.56|0.12|0.44|
|(6) Net macro effect (S2-S1)|0.52|0.47|0.06|0.02|0.03|
|<br>(7) Conflict and macro effect (S2-B)|1.08|0.47|0.61|0.14|0.47|
|(8) Adjusting for w. crop (S3-B)|-0.06|-0.37|0.31|0.05|0.25|
|No. of VCs|10,556|9,425|1,131|442|689|

_Source:_ Original table for this paper.

_Note:_ Figures are in millions of hectares and constructed based on regression results from table 4. VC = village council.

22
# WPS4104

## **Road Infrastructure in Europe and Central Asia: Does Network Quality Affect Trade?**

Ben Shepherd and John S. Wilson<sup>*,</sup> Development Economics Research Group—Trade The World Bank Mail Stop MC3-303 1818 H St. NW Washington D.C. 20433

**Abstract:** We present a new database of minimum distance road routes connecting 138 cities in 27 countries across Europe and Central Asia. We use it to show that improved road network quality is robustly associated with higher intraregional trade flows. Gravity model simulations suggest that an ambitious but feasible road upgrade could increase trade by 50% over baseline, exceeding the expected gains from tariff reductions or trade facilitation programs of comparable scope. Cross-country spillovers due to overland transit are important: total intraregional trade could be increased by 30% by upgrading roads in just three countries—Albania, Hungary and Romania.

**Keywords:** International Trade; Europe and Central Asia; Road Transport; Trade Facilitation; Gravity Model.

**JEL Codes:** F13; F15; H54.

World Bank Policy Research Working Paper 4104, December 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* The authors are respectively Consultant and Lead Economist (DECRG). This work is part of a broader project on trade facilitation and development supported through a Trust Fund of the U.K. Department for International Development. Sincere thanks to Piet Buys, Uwe Deichmann and David Wheeler (World Bank) for their advice and assistance with the data and analytical framework, and to Yu Cheng Kuo for helping with compiling the road distance database. We are also grateful to Andreas Kopp (OECD), Henry Kerali, Bernard Hoekman, Beata Smarzynska Javorcik, David Cieslikowski, Tsukasa Hattori, Leonardo Iacovone and Souleymane Coulibaly (World Bank) for their comments and suggestions, as well as to seminar participants in Washington, D.C. and an OECDECMT conference in Berlin. Ayako Suzuki and Witold Czubala provided very helpful and expert research assistance. Comments to: <u>bshepherd@worldbank.org</u> or <u>jswilson@worldbank.org.</u>

- 1 -
surprising, considering the high correlation coefficient (0.70) between privately funded and government funded R&D expenditures to GDP ratios.' The last two colurrns in Table 6 show typical regression results when the dependent variable is the growth rates of the number of scientists and engineers per million population (GSCP7085). As expected from our previous regressions in Table 3 and 5, the per capita GDP growth rate is a significant determninant of GSCP7085. In fact, we find other variables we used in this paper are not robustly related to GSCP7085 using Levine and Renelt's [1992] approach to assessing robustness. Note that GSCP7085 is negatively related to the initial level of income, GDP70, which is a major determinant of R&D expenditures. Ex post, this finding illustrates the reason for the negative correlation between our manpower and expenditure proxies for R&D activities (see footnote 14). We believe that R&D activities are incomeelastic and led by high income countries, consistent with the results in columns 1-4. Therefore, at least for the OECD countries, expenditure data appears to provide a better proxy for growth in R&D activities than manpower data. At the same time, the finding in columns 5 and 6 confirms our earlier supposition of reverse causality, i.e., growth of income induces growth of R&D manpower indices in all countries.

# VI. Conclusions

Using the UNESCO data for R&D expenditures and personnel, this paper documents international differences in R&D activities and assesses empirically the link between R&D and economic growth, and the deterninants of R&D differentials. Within our group of OECD 'The correlation coefficient between fundamental R&D and applied R&D expenditures to GDP rations is 0.89.

15
sources including the World Development Indicators (WDI, World Bank) and La Porta et al. (1999).

The ES consist of a stratified random sample of firms that is representative of the country’s formal (registered) private sector. Sampling weights are provided in the survey to allow for inferences to the targeted universe of firms. The ES do not cover firms in the informal sector, firms with fewer than five employees, the agriculture and mining sectors, as well as some services sectors such as banking and insurance, health care, and education. A common survey methodology and questionnaire is used across surveys. We use the latest round of the ES available in each country. The sample we use consists of over 48,647 private firms located in 66 different countries that are mostly developing countries.<sup>2</sup> The survey year for the ES varies across countries ranging from 2007 to 2016. Summary statistics of the variables used are provided in table 1; correlations between our main explanatory variables are provided in table 2.<sup>3</sup>

# _Dependent variable_

Our dependent variable is the percentage of all permanent full-time workers at the end of the last fiscal year at the firm that are females ( _Female workers_ ). The variable ranges between 0 and 100, with a mean value of 32 (percent), median value of 26 and the standard deviation equals 27.3. The percentage of female workers in a typical firm is highest in Belarus (51%) and lowest in Pakistan (5%). Note that our dependent variable is women’s employment _relative_ to total employment at the firm. Thus, factors such as overall economic development, job availability and labor market conditions that affect men’s and women’s employment equally do not pose any omitted variable

> 2 Of the total 66 countries in our sample, there are 5 high-income countries per the definition of the World Bank. The high-income countries include Chile, Estonia, Poland, Latvia and Uruguay.

> 3 For brevity, in table 2, only the correlations between our main explanatory variable ( _Maternity leave_ ) and GDP per capita with all the other explanatory variables are shown.

7