rafmacalaba/datause-encoder-data huggingface dataset:
|January x [Northern Hemisphere]|3.408***|2.853***|3.124***|2.628***|3.039***|2.599***|3.040***|2.647***|
|---|---|---|---|---|---|---|---|---|
||(24.37)|(20.76)|(43.28)|(37.06)|(61.01)|(53.14)|(38.12)|(33.57)|
|February x [Northern Hemisphere]|4.142***|3.643***|3.979***|3.514***|3.780***|3.419***|4.076***|3.717***|
||(31.51)|(27.13)|(55.37)|(48.71)|(77.40)|(69.91)|(54.78)|(49.31)|
|March x [Northern Hemisphere]|4.896***|4.549***|4.658***|4.312***|4.483***|4.180***|4.533***|4.266***|
||(38.10)|(35.92)|(67.55)|(63.49)|(93.78)|(88.77)|(59.31)|(56.25)|
|April x [Northern Hemisphere]|5.108***|5.027***|5.043***|4.938***|4.996***|4.878***|5.045***|4.962***|
||(45.33)|(44.50)|(81.92)|(80.37)|(120.60)|(117.67)|(73.04)|(71.50)|
|May x [Northern Hemisphere]|4.407***|4.368***|4.241***|4.250***|4.088***|4.149***|4.367***|4.376***|
||(38.67)|(38.26)|(75.88)|(75.36)|(109.52)|(109.59)|(65.27)|(65.09)|
|June x [Northern Hemisphere]|1.709***|1.680***|1.547***|1.610***|1.475***|1.571***|1.956***|1.982***|
||(16.23)|(15.66)|(30.44)|(30.64)|(43.85)|(44.88)|(29.64)|(29.71)|
|July x [Northern Hemisphere]|-0.591***|-0.616***|-0.866***|-0.842***|-1.016***|-0.956***|-0.548***|-0.506***|
||(-5.84)|(-5.93)|(-18.41)|(-17.18)|(-32.81)|(-29.32)|(-9.35)|(-8.38)|
|August x [Northern Hemisphere]|-2.337***|-2.370***|-2.434***|-2.398***|-2.424***|-2.357***|-2.313***|-2.251***|
||(-21.76)|(-21.84)|(-47.44)|(-45.88)|(-70.77)|(-67.31)|(-37.12)|(-35.71)|
|September x [Northern Hemisphere]|-1.811***|-1.844***|-1.830***|-1.873***|-1.860***|-1.874***|-1.806***|-1.798***|
||(-21.12)|(-20.96)|(-39.44)|(-39.57)|(-57.51)|(-56.81)|(-33.35)|(-32.65)|
|October x [Northern Hemisphere]|-0.567***|-0.695***|-0.525***|-0.646***|-0.622***|-0.733***|-0.415***|-0.531***|
||(-6.16)|(-7.39)|(-10.31)|(-12.57)|(-17.50)|(-20.46)|(-6.91)|(-8.70)|
|November x [Northern Hemisphere]|0.917***|0.662***|0.919***|0.666***|0.822***|0.556***|1.164***|0.925***|
||(9.73)|(6.76)|(17.00)|(11.95)|(21.14)|(13.98)|(18.08)|(13.97)|
|December x [Northern Hemisphere]|2.442***|1.918***|2.432***|1.946***|2.319***|1.858***|2.529***|2.092***|
||(24.75)|(19.10)|(42.34)|(33.63)|(56.87)|(45.38)|(39.49)|(32.35)|
|Constant|254.3***|254.7***|255.0***|255.3***|255.4***|255.7***|255.5***|255.7***|
||(736.01)|(775.48)|(1395.77)|(1472.50)|(1971.11)|(2080.00)|(1181.10)|(1235.86)|
|Observations|37,788|37,788|129,851|129,851|247,592|247,592|89,157|89,157|

Absolute value of t statistics in parentheses

- significant at 5%; ** significant at 1% *** significant at 0.1%

22
**_Energy demand models for policy_** **_<u>formulation</u>_**

|Criteria|Kuwait model|DTI|ERASME|NEMS|MAED/<br>MEDEE|LEAP|POLES|
|---|---|---|---|---|---|---|---|
|requirement|econometric<br>estimation|econometric<br>analysis|econometric<br>analysis|running<br>a<br>complex model|||running<br>a<br>global model|
|Versatility|Low – country<br>specific|Low – country<br>specific|Low – region<br>specific|Low – country<br>specific|High – general<br>model|High-general<br>model|Low – specific<br>global model|
|Portability<br>to<br>anothercountry|Difficult|Difficult|Difficult|Difficult|Easy|Easy|Difficult|
|Documentation|Limited|Limited|Limited|Excellent|Excellent|Excellent|Poor|
|Capability<br>to<br>analyse<br>price-<br>induced policies|High|High|High|High|Does not exist|Does not exist|High|
|Capability<br>to<br>analyse<br>non-<br>price policies|Low|Good|Low|Good|High|High|High|
|Rural energy|Not<br>covered<br>separately|Not<br>covered<br>separately|Not<br>covered<br>separately|Included<br>through<br>geographical<br>coverage|Can<br>be<br>included|Can<br>be<br>included|Not<br>covered<br>specifically|

92
**Table 1:  Poverty in Mexico (headcount)**

||Ex<br>|treme Pover<br>|ty<br>|M<br>|oderate Pove<br>|rty<br>|
|---|---|---|---|---|---|---|
||Total|Urban|Rural|Total|Urban|Rural|
|Federal District|11.2|11.2||43.3|43.3||
|US Border|15.9|7.5|32.9|51.2|35.4|67.6|
|North|23.1|16.1|43.2|52.8|41.4|73.3|
|Center|27.6|16.5|55.7|59.2|49.4|90.0|
|South|45.4|25.3|78.6|71.3|56.8|93.8|
|Total|24.2|13.7|58.5|53.7|43.7|83.8|

**Table 2:  Scenarios – Doha implementations and Full Trade Liberalization**

|Sector||Change in f|actor returns||
|---|---|---|---|---|
||Do|ha|Full liber|alization|
|Return to Land|1.0%||-16.4%||
|Unskilled Labor|-0.1%||-0.1%||
|Skilled Labor|-0.2%||0.1%||
|Capital|-0.2%||0.0%||
|Natural Resources|1.6%||1.1%||
||Do|ha|Full liber|alization|
|Product Group|Price<br>change (%)|Change in<br>Production<br>(million<br>USD)|Price change<br>(%)|Change in<br>Production<br>(million<br>USD)|
|Cereals|0.4%|18.9|14.6%|-351.6|
|Dairy|0.2%|-35.6|-0.6%|-418.0|
|Meat products|0.2%|135.6|0.1%|-495.8|
|Oils and Fats|3.0%|12.3|15.2%|-57.9|
|Sugar|0.2%|-2.2|0.0%|-27.2|
|Vegetables|0.6%|11.9|-1.8%|80.7|
|Other Agricultural|0.5%|61.4|-1.9%|222.3|
|Food Products|0.0%|44.4|0.3%|-62.6|
|Household Items|0.0%|746.9|-0.6%|147.4|
|Textiles and Apparel|0.0%|-565.1|0.1%|-2,506.0|
|Other Manufacturing|0.0%|122.6|-0.3%|-1,760.9|
|Other products|0.3%|31.0|0.8%|81.4|
|Services|-0.1%|241.4|0.6%|361.3|

24
.5inactive accounts is very large. If these records are not kept, evasion becomes rampant. Therefore, a higher share of informal labor implies higher administrative charges.

Another way of expressing these differences is that the mix of services provided by a pension system must change as the structure of the labor market changes. For example, the substantial flows of workers between the formal and informal sectors, and in and out of the labor force, must be squared with the need to define some minimum residence penod before insurance coverage is granted. Many workers do not meet it and receive a reduced -- frequently zero --level of services. The result is that the mix of services - for example, -old age pensions versus disability - varies substantially across countries. These differences help to explain large variations in administrative charges.

# The appropriate cost ratio

Once a figure for total charges is found, it is usually presented as a ratio. Typical candidates for the denominator are the volume of pension assets, the volume of contributions or pension benefits, the volume of covered wages and the number of covered workers. Each ratio emphasizes a different aspect of charges, but has important defects.

Pension assets have the defect that published government statistics exclude the value of outstanding pension entitlements (liabilities), so PAYGfinanced systems appear as lacking assets. On the other h&d, most pension systems in the world have not achieved maturity, in the sense that citizens of al ages are participating to the same degree in the pension system. This reduces pension assets by more than administrative costs, so biases emerge. This measure also tends to present countries where the working population is older, closer to retirement, or where the informal sector is larger, as less costly in administrative charges. Finally, pension assets tend to be higher in countries with higher contribution rates, diluting administrative charges as discussed below. In the end, these biases make appear developing country pension systems as mare costly.

The ratio of administrative charges to the flow of contributions or pensions is also misleading because contribution rates vary enormously across countries, from 2% in Iceland to 40% in Singapore. They also vary substantially over time in a given country. Usually these changes overwhelm changes in administrative charges. As developing countries tend to have lower contribution rates because of' their youth or because of a higher rate of return on investments, and they have lower coverage rates, their administrative charges appear as excessive when this ratio is used.
# **Annex Table 6. NAMA tariffs levied, %**

||Base|Formula|Formula plus<br>flexibility|
|---|---|---|---|
|Australia NZ<br>|3.6<br>|2.4<br>|2.4<br>|
|Argentina<br>|6.1<br>|5.2<br>|5.4<br>|
|Bangladesh|18.3|12.3|18.3|
|Brazil|8.5|7.4|7.8|
|Canada|0.9<br>|0.5|0.5|
|China|5.6|3.9|4.4|
|EU-27|1.8|1.0|1.0|
|Korea and Taiwan Pr.|4.0|2.9|3.1|
|HK & Singapore|0.0|0.0|0.0|
|India|12.9|11.7|12.0|
|Japan|1.3|0.7|0.7|
|Cambodia|14.8|9.4|14.8|
|Mexico|3.0|2.5|2.5|
|M.E. & N. Africa|5.8|4.5|5.0|
|Nigeria|21.4|12.8|21.4|
|Pakistan<br>|15.3<br>|10.8<br>|15.3<br>|
|Selected LDCs|7.3|6.3|7.3|
|South Africa|4.9|4.9|4.9|
|Sri Lanka|5.3|4.8|5.3|
|Thailand|8.1|5.4|6.7|
|Turkey<br>|1.1<br>|1.0<br>|1.1<br>|
|United States of America<br>|1.5|0.8|0.8|
|**World Bank Classification**||||
|All countries|2.9|2.1|2.3|
|High income countries|1.7|1.1|1.1|
|Developing countries, non LDC|6.4|4.8|5.6|
|LDCs|10.9|8.0|10.9|
|**WTO Classification**||||
|Developed WTO|1.7|1.0|1.0|
|Developing WTO non LDCs|4.8|3.7|4.3|
|_Normal Developing WTO_|3.9|3.1|3.4|
|_RAM WTO_|4.9|3.5|3.9|
|_SVE WTO_|8.9|6.7|8.9|

# **_3:  Trade in Services_**

An evaluation of what is on the table in services is much harder than in goods. First, there is no database of actual trade policies in specific services sectors –the counterpart of “applied” tariffs. Second, the Doha negotiations in services are not based on an agreed formula for cuts in protection but on offers by each member of market access (and national treatment) in specific sectors. Third, it is hard to quantify services trade policies, which are akin to non-tariff barriers and include prohibitions, quotas, and discriminatory regulation. We describe here efforts to overcome these difficulties and construct a picture of what Doha offers in services.

24
can be sorted into two groups displaying clearly different relationships between GDP levels and nightlight intensity. Estimating the relationship allowing for this difference results in rather different elasticities. For Afghanistan, Bangladesh, India, and Maldives it is 0.17 (South Asia I in Table 3) and for Bhutan, Nepal, Pakistan and Sri Lanka it is 0.35 (South Asia II in Table 3). Using these two regressions yields predicted levels of GDP that track National Accounts GDP very closely.<sup>6</sup> _Figure 2: Sum of mean squared prediction errors for South Asia._ <!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>World regression South Asia regression South Asia I and II<br><!-- End of picture text -->

# _b._ DISTRICT-LEVEL ECONOMIC ACTIVITY FROM OUTER SPACE

Nightlight intensity predicts GDP at subnational levels relatively well. The performance of the spatial approach can be assessed for India, where data on economic activity at subnational levels is available. The number of observations that can be used for this assessment is maximized when using GDP data at the state level for FY2014, and at the district level for FY2005. Figure 3 shows an application of the spatial approach for Indian states and districts. The correlation

> 6 See figures with measured and predicted GDP in Appendix B. Figures based on the other regressions can be requested from the authors.

13
# **Income Inequality and Violent Crime: Evidence from Mexico’s Drug War**

Ted Enamorado<sup>*</sup> Carlos Rodríguez-Castelán<sup>‡</sup> Luis-Felipe López-Calva<sup>†</sup> Hernán Winkler<sup>§</sup>

**Keywords:** Income Inequality, Crime, Instrumental Variables, Mexico **JEL codes:** C26, D74, H70, I3, O54 **Sector Board:** Poverty (POV)

The authors would like to thank Eduardo Ortiz-Juarez and Daniel Valderrama for invaluable research assistance. The findings, interpretations and conclusions in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank Group, its Executive Directors, or the countries they represent.

> * Princeton University. E-mail: tede@princeton.edu

> † World Bank. E-mail: lflopezcalva@worldbank.org

> ‡ World Bank. E-mail: crodriguezc@worldbank.org (corresponding author).

> § World Bank. E-mail: hwinkler@worldbank.org
**Annex 1 Measurement and Data Issues**

# AVAILABILITY OF RELEVANT INFORMATION

The questionnaires from the most recent surveys were reviewed to determine their relevance for analysis of infrastructure and urban poverty issues.

|Table 2<br>ECA INFRASTRUCTURE AND URBAN<br>SCREENING FOR INFRASTRUCTURE R|POVERTY STUDY<br>ELEVANCE|
|---|---|
|Type of Infrastructure Service|Criteria|
|water, sewage, district heating, electricity,<br>naturalgas|connection, service, use, expenditures,|
|solid waste|type of service available,expenditures|
|transport|type of service available,expenditures|
|housing|type, ownership, condition, maintenance, additional<br>plot|

Level of service data came in various forms: data not available (question not asked) number of hours of operation per day less than 24 hours service available (yes/no) <u>Overall in the region, the coverage of variables to measure infrastructure and urban poverty was poor.</u> This is discussed in Chapter 2 of the main report and illustrated there in Table 2.2. Out of the 27 desired indicators constructed for this study, only about 70 percent could be measured with recent available data. There were also large disparities across countries in terms of survey coverage of infrastructure and energy poverty and the percentage of desired indicators that could be measured. A detailed listing of what questions, covering what aspects of infrastructure, were available by country is provided in Attachment 2 of this Annex.

Attachment 2 also shows that while most surveys provided information on the availability of infrastructure and energy services, few contained indications of whether these services were reliable and <u>paid for and even fewer provided information on the consumption of infrastructure and energy services. Not all types of infrastructure and energy services were covered equally. In terms of availability, public</u> transportation and electricity connections were the least well documented in the region, although for different reasons. The failure to ask about public transportation is especially noteworthy as the region remains heavily dependent on public transport. In the case of electricity, this reflects an assumption that all households are connected to electricity, hence there is no need for the question. As regards reliability, information on the quality of district heating was extremely limited and even information on water and electricity reliability was available from less than half the surveys. Finally, in terms of payment rates, the information provided for natural gas was extremely poor.

65
instructors, to leverage computer-aided learning programs to supplement teachers, or to support teachers with scripted lesson plans (Banerjee, et al. 2017).<sup>54</sup> The data reveal large variation both within and across countries, with implications for policy. For example, in countries like Kenya where teacher content knowledge is relatively high, but teacher absence is also high, a focus on increasing teacher effort (reducing absence) should be the priority. On the other hand, a similar approach in countries like Togo and Mozambique would be unlikely to yield the same results given the low teacher content knowledge.

Overall, the results presented here—along with our other work based on these same data (Bold et al. 2017)—suggest that there are important complementarities between various dimensions of teacher quality: Increasing the effort of teachers who are well-trained and educated will likely go a long way toward increasing human capital accumulation.

> 54 See, also, for example, the reviews in Murnane and Ganimian (2014), Glewwe and Muralidharan (2015), and Evans and Popova (2016), as well as the discussion in World Bank (2018).

31
Kaldor-Hicks efficiency criterion can be defended even if it produces undesirable or negative outcomes for some individuals or groups, so long as the overall benefits are greater than the overall costs to society (Manning, 2008) . However, we propose that the conceptual foundation of CBA (Kaldor-Hicks Criterion) does not adequately address the issue of heterogeneity in program impacts. We argue that an important consequence of accounting for heterogeneity is that international development assistance-driven justice sector gains, net of costs, disproportionately target those in extreme poverty and those in the bottom 40% of the population.

# **_Average and quartile treatment effects_**

The availability of data now makes obtaining average treatment effect (ATE) easier, particularly when random control trials (RCT) are employed. In short, the average treatment of the treated group (ATT) equals the ATE (for the population). The RCT lends itself to a simple nonparametric analysis of the treatment effect, given by 1 0 where, 1 is, for example, the average number of episodes of political violence (using the ATT = ATE = YY<sup>�</sup> −YY<sup>�</sup> Armed Conflict Location and Event Database (ACLED)) for the experimental group, while YY<sup>�</sup> 0 is the average number of episodes of political violence for the control group. The average effect of the intervention of interest is simply the difference between the two averages. As YY<sup>�</sup> stated above, however, the shortcoming of ATE is our lack of understanding of the distribution of effects. In addition, the mechanisms that make the prevention of conflict (economically) successful.

It is inherently difficult to measure the policy impact for each individual, since, by definition, the counterfactual for each individual under the policy may be unobserved depending on the possibility for appropriate matching of experimental and control groups. Often, even when these groups are matched, the heterogeneity of policy impact is buried in the average effect. We can, however, disaggregate the population based on certain characteristics or key factors and compute the distribution of outcomes within the population (Heckman et al., 1997). A sign of heterogeneity is revealed by an uneven distance across individuals. Although we cannot fully appreciate the distribution of effects, we can nevertheless gain useful knowledge about whether there exist heterogeneous effects across groups in the population.

14
Very high capex costs in Sierra Leone ($0.21 per kWh billed) are driven by the reliance on capitalintensive hydropower (Bambuna) which is operating at a relatively low capacity-factor (26 percent in the reference year) and very high T&D losses of 39 percent. Very high capex costs in Ethiopia ($0.15 per kWh billed) are driven by more than 1,400 MW of hydropower installed since 2000, and associated 9,000 km of HV transmission lines to evacuate the power. The majority of these investments were financed through the state and with debt servicing not being the responsibility of the utility (see section 5.1 for further discussion of the case of Ethiopia). The high capex costs in Ethiopia are consistent with earlier findings in the AICD. Similarly, relatively high capex costs in Kenya (US$0.09/kWh) are driven by 1,400 MW of hydropower and geothermal combined (out of a total 2,300 MW of installed capacity), with both technologies being heavily capital-intensive.

As explained above, the distinction between capex and opex is an artifact of the methodology selected in this study in countries where a sizable fraction of electricity is purchased from imports, private producers, or both, or where upstream state-owned utility has not issued financial statements. In all these cases, the entire purchase is classified as opex. These purchases represent more than 40 percent of total costs in two countries: Guinea with power purchases from an EPP, and Uganda with a concession and an IPP (Eskom has a concession on one of the generation companies, and Bujagali is an IPP).

There are several important divers for electricity costs (Figure 5). As expected, the 14 countries that depend on oil-based generation (HFO and diesel) have significantly higher opex while those with non-oil based generation (hydropower, coal and gas) have lower opex and similar capex. In terms of geography, SSA’s island nations bear a significant power cost disadvantage vis-à-vis the coastal and landlocked countries on account of high dependence on oil-based power generation. High power costs are also driven by the size of markets, with small markets relying more on thermal generation resulting in higher opex.

**Figure 5** : **Cost drivers in 2014 U.S. dollars per kWh billed**

<!-- Start of picture text --> Source of energy Geography System size<br>$0.30 $0.30 $0.30<br>$0.25 $0.25 $0.25<br>$0.20<br>$0.20 $0.20<br>$0.15<br>$0.15 $0.15<br>$0.10<br>$0.10 $0.10<br>$0.05<br>$0.05 $0.05<br>$0.00<br>Total cost Opex Capex $0.00 $0.00<br>Predominantly oil based Total cost Opex Capex Total cost Opex Capex<br>Predominantly non-oil based Coastal Landlocked Island <150 MW 150-1000 MW >1000 MW<br><!-- End of picture text --> _Source_ : World Bank staff calculations.

_Note_ : The figure excludes three countries with a large portion of domestic power purchases classified as opex (Republic of Congo, Ghana, and Mozambique).

# 4.2 Comparison of costs and cash collected

Figure 6 compares current costs (aggregated into opex and capex for simplicity, with unrebated taxes captured in opex) with cash collected. Under the assumptions applied, only two countries cover the 29
**Figure 1: CERS from New Projects and Total CERs from all Projects in the Pipeline from December 2003 to August 2009.**

<!-- Start of picture text --> 25 600<br>500<br>20<br>400<br>15<br>300<br>10<br>200<br>5<br>100<br>0 0<br>Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07 Dec-08<br>Start of CDM Public Comment Period<br>New Projects All Projects<br>CERs, MtCO2e<br>Cumulative CERs, MtCO2e<br><!-- End of picture text --> 20
Table **1. Rview** of Bank-Assisted liigher Education Projects

|||1972 -|1981|||||1982 -|1992?||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Africa|S.Asia|E.Asia|EMENA|LAC|Total|Africa|S.Asia|E.Aia|EMENA|LAC|Total|
|1.All<br>Education Projects|62|II|36|35|30|174|71|23|58|40|37|229|
|2. EducationP°toics withhighereducation<br>components (inclutling teatcher training)<br>(a) Nuwmberof projcts<br>(b)<br>Total<br>invesument<br>(Millions ofUSsJ|46<br>363.4|6<br>194.9|23<br>2282.7|28<br>1300.4|19<br>65.8|122<br>4207.2|35<br>417.2|13<br>623.6|41<br>5102.2|29<br>1027.1|13<br>622.0|131<br>7792.1|
|3. Thost acknowledgin**g**ender issues<br>(a) Numberof pmojcts<br>(b) Total investment (Millions of USSr|9<br>162.9|3<br>83.6|4<br>T7.5|13<br>669.4|3<br>29.8|32<br>1023.4|9<br>198.6|S<br>530.9|20<br>2310.1|10<br>419.7|6<br>84.3|53<br>3543.6|
|4. Projects with signifrcant attion to addres<br>gender problem.<br>|||||||||||||
|(a) Numbcrof pro.cts<br>|4<br>|2<br>|3<br>|4|2|13|5|6|14|a|3|36|
|(b) Total investment (Millions of USS)_|113.2|71.8|7.8|205.2|21.8|419.9|312.3|520.3|1832.5|321.0|15.8|2800.9|
|5. (a) Row 3as**a**% of mw 2<br>0)Number of projects<br>|19.6%<br>|50.0%<br>|17.4%<br>|46.4%<br>|15.8%|26.2%|25.7%|61.5%|48.7%|34.5%|46.2%|40.5%|
|(ii)Total investmen (Millions of USS)<br>(b) Row4 as a% ofsow 2<br>|44.9%|42.9%|3.4%|51.5%|45.3%|24.3%|47.6%|85.1%|45.3%|40.9%|13.6%|4S.5%|
|(i) Number of projects<br>|8.7%<br>|33.3%<br>|4.3%<br>|14.3%<br>|10.5%<br>|10.6%|14.3%|46.1%|34.1%|27.6%|23.1%|27.5%|
|(6i4Total invcetmcnt (Millions of Uss)|31.2%|36.8%|0.3%|15.8%|33.1%|9.9%|26.9%|83.4%|35.9%|31.3%|2.5%|35.9%|

# Source: World Bank documents.

Notes: -eu-off-<sup>t</sup> date Mcy 31. 1992; **Expressed in 1985 dollars. **0**** lnclwive of monitoring, needs assssment and otber govememtt invterventions.