rafmacalaba/datause-encoder-data huggingface dataset:
|US|Miami|8|33|Romania|Bucharest|29|83|
|---|---|---|---|---|---|---|---|
|US|Seattle|8|33|South Africa|Pretoria|30|85|
|Spain|Madrid|8|33|China|Dalian|30|85|
|Spain|Barcelona|8|33|South Africa|Johannesburg|30|85|
|US|Atlanta|9|41|Kuwait|Kuwait City|30|85|
|HongKong|HongKong|9|41|Brazil|Sao Paulo|31|89|
|US|Minneapolis|9|41|Thailand|Bangkok|31|89|
|UK|Manchester|9|41|Brazil|Rio de Janeiro|32|91|
|US|Washington,DC|9|41|Ukraine|Kiev|32|91|
|US|Detroit|9|41|Jordan|Amman|33|93|
|US|Houston|10|47|Serbia & Montenegro|Belgrade|33|93|
|Tunisia|Tunis|34|95|Kenya|Nairobi|45|111|
|Paraguay|Asuncion|34|95|Venezuela|Caracas|46|113|
|Brunei|Bandar Seri<br>Begawan|35|97|Vietnam|Hanoi|46|113|
|Mexico|Mexico City|36|98|Indonesia|Jakarta|48|115|
|Ecuador|Quito|38|99|India|Mumbai|48|115|
|Azerbaijan|Baku|38|99|Colombia|Bogotá|49|117|
|Saudi Arabia|Jeddah|38|99|Iran|Tehran|52|118|
|Turkey|Istanbul|39|102|Cameroon|Douala|53|119|
|Saudi Arabia|Al Khobar|39|102|Zimbabwe|Harare|53|119|
|Saudi Arabia|Riyadh|39|102|Cote d’Ivoire|Abidjan|54|121|
|Philippines|Manila|39|102|Cambodia|Phnom Penh|55|122|
|Egypt|Cairo|39|102|Nigeria|Lagos|59|123|
|Uzbekistan|Tashkent|42|107|Pakistan|Karachi|60|124|
|Guatemala|Guatemala City|43|108|Bangladesh|Dhaka|61|125|
|Sri Lanka|Colombo|43|108|Algeria|Algiers|66|126|
|India|New Delhi|43|108|PNG|Port Moresby|66|126|
|Vietnam|Ho Chi Minh|45|111|||||

# **13. 2006 SustainLane U.S. City Rankings**

©SustainLane, an online network that promotes practicing healthy living at home and in communities, conducted a nationwide study that measures the 50 largest cities in America on essential quality-of-life and economic factors that affect personal sustainability.

The Philosophy of this organization is that sustainability is a more appropriate approach for urban areas than environmental management because it combines environmental, economic, and social issues, and recognizes people and institutions as the primary actors that benefit from change, with indirect benefits also accruing for natural systems as a result.

The 2006 SustainLane U.S. City Rankings were developed using a combination of primary and secondary research directed by SustainLane. Fifty US Cities were included in the study. Data and information are drawn from surveys and interviews from 2005-2006, and publicly available sources published in the period 2002-2006. Overall rankings were determined by averaging 15 individual category rankings, each of which was given a weighting of 0.5, 1, or 1.5 (see below for details). Cumulative averages ranged from 85.08 out of 100 for the highest-scoring city (Portland, Oregon) to 32.50 out of 100 for the lowest-scoring city (Columbus, Ohio).

41
26

**Figure 17. A Measure of “lost time” in economic development: the Proust index**

<!-- Start of picture text --> a. Countries where economic clock was rewound by at least 5 years from 2012<br>MYS<br>SWE<br>FRA<br>FIN<br>NZL<br>CRI<br>LVA<br>CHE<br>EGY<br>LTU<br>LUX<br>DNK<br>TUR<br>NLD<br>GBR<br>CYP<br>SVN<br>PAK<br>HUN<br>MEX<br>USA<br>EST<br>JPN<br>ESP<br>PRT<br>IRL<br>ISL<br>GRC<br>16 14 12 10 8 6 4 2 0<br><!-- End of picture text --> b. The Proust index by region and income level <!-- Start of picture text --> Western Europe<br>Upper<br>middle<br>Africa income<br>South Asia<br>Lower<br>middle<br>North America income<br>Middle East and North Africa<br>High<br>income:<br>Latin America/Caribbean non OECD<br>East Asia and Pacific<br>High<br>income:<br>Central-Eastern Europe OECD<br>0 1 2 3 4 5 6 7 8 0 2 4 6 8<br>number of lost years number of lost years<br><!-- End of picture text --> _Source_ : Authors’ computations based on data from Bloomberg (equity prices); World Bank WDI for GDP and consumption per capita; and WDI and ILO for the unemployment rate. _Note_ : Lost time is computed as a simple average of lost time in terms of GDP per capita, household consumption per capita, equity prices, and unemployment for 52 countries for which data for all four indicators are available.
# _Strategic Reporting_

One concern is that couples might over-report IPV to increase chances of accessing future program benefits, given the attendance stipend. However, if couples were reporting strategically to appear as favorable program candidates, we would expect them to minimize reports of socially undesirable behavior. Instead, we find the opposite: both treated and spillover couples report increased alcohol consumption by husbands across multiple measures (table 3).

Strategic reporting would also suggest couples might claim greater poverty to justify need for program stipends. Instead, we find either no economic effects or slight improvements: both treated and spillover couples report more assets, and treated women report increased savings (table A8).

Moreover, strategic coordination would likely produce aligned responses between spouses on key program themes. Yet we observe significant divergence in gender attitudes between husbands and wives. If there were shared community understanding about reporting to gain program access, we would expect similar responses across all groups. Instead, we find different directions of impacts between spouses and between treated and spillover couples, with mechanisms and magnitudes differing across groups—inconsistent with coordinated strategic reporting. For example, we find diverging effects on gender attitudes across groups and genders, yet worsened acceptance of the justification for women refusing sex in all groups.

Finally, strategic reporting implies couples believed their responses affected program enrollment. However, the transparent public lottery process, confirmed by village leaders’ recollections and baseline balance in key variables, makes this unlikely.

# _Expanded Understanding of IPV Questions_

Second, the intervention may have expanded women’s understanding of IPV survey questions. If so, terms like “slapping,” “choking”, “force,” or “degrading” may have taken on different meanings for women in treated versus control groups. However, several patterns suggest expanded understanding does not drive our results. First, we find the largest effects among spillover women who had only indirect exposure to IPV discussions, rather than treated women who had direct program exposure.<sup>23</sup>

> 23Qualitative reports indicate that in community meetings, couples mainly shared personal testimonials about changes in their relationships rather than discussing IPV definitions and concepts.

24
better off under IMT. A larger percentage of the poor (32%) in IMT IAs are downstream farmers relative to the poor in non-IMT IAs (24%). Further, significantly more of the poor farmers in IMT IAs (relative to poor in non-IMT IAs) indicate that the IAs help resolve illegal use of water. Similarly, significantly more of the IMT IA poor indicate that the water distribution schedule is followed.<sup>6</sup> Thus, one explanation for the boost IMT appears to give the poor lies in the fact that IMT helps increase timeliness of water delivery in general, and, more specifically, downstream availability of water. A recent review of IMT worldwide suggests that one of the ways management transfer can help poor farmers is by increasing the flow of water from upstream to downstream areas (Araral 2005). Our results appear to back this conclusion.

# **6. Conclusions**

Irrigation management transfer is an important strategy among donors and governments to strengthen farmer control over water. IMT is also a means to reduce the financial burdens of fiscally strapped national irrigation associations. In this study, we seek to understand if IMT is meeting the promise of its commitments. Our objective is to understand whether IMT contributes to improvements in both irrigation system indicators and in some household indicators.

> 6T-tests show these differences between asset poor farmers in IMT IAs and non-IMT IAs are statistically significant.

33
and literacy and their too-often low levels, especially among disadvantaged children and youth. This public awareness can stimulate demand for change and provide governments with the necessary legitimacy to design and enact education reforms. However, critics of standardized tests argue that they promote a “reductionist” approach to education, emphasizing literacy and numeracy in detriment of other equally important subject areas. Moreover, critics point out that the reliability of standardized test scores is ruined by either the presence or absence of incentives. On the one hand, high-stakes test can create perverse incentives that lead to “gaming” or “teaching to the test,” without true improvements in learning.<sup>1</sup> On the other hand, in low-stakes tests students might not put in enough effort to pass the test if no consequences are attached to the results (Gneezy et al., 2017). The key questions, then, are: Despite their potentially limited scope and imperfect set of incentives, can standardized test scores in early grades be used to predict future education trajectories and labor market outcomes? Can standardized tests capture foundational skills—or a relevant subset of them-–in a meaningful way?

To address these questions, we construct two longitudinal datasets that track students along their education trajectories and through school to labor market transition. These datasets are among the first of their kind in a developing country. We use information from ENLACE, a census-based standardized test that primary and secondary school students in Mexico took from 2006 to 2013. Our dataset tracks students who took the test at the end of primary school (Grade 6) in 2007 through the end of lower secondary (Grade 9) and upper secondary (Grade 12) in 2010 and 2013, respectively. We take advantage of the large size of this student cohort (1.986 million individuals) and identify about 10,000 pairs of twins in our dataset (individuals with identical last names, birthdays and Grade 6 schools). A second dataset, merges Grade 12 test scores into a special module of the Mexican labor force survey (ENOE) applied to individuals aged 18 to 20 years old in 2010.

Our results show that, despite their limitations, large-scale standardized tests have a strong predictive power for future education and labor market outcomes and capture the

> 1For a discussion on incentives and strategic behavior in testing, see Koretz and Barron (1998), Deere and Strayer (2001), Koretz (2017), Figlio and Loeb (2011) and Neal (2011).

2
<!-- Start of picture text --> a. Absolute 6-convergence, 1992-2000<br>0.2<br>z<br>Es<br>#$ 01<br>a ‘ue<br>28 a oo<br>gege" eee5 - a—<br>mie a ee ayy a<br>BB ee? See ee,<br>3 8-01 ° y<br>a<br>-0.2<br>6 7 8 9<br>Municipalities’ mean per capita income in 1992<br>(log-scale)<br><!-- End of picture text --> <!-- Start of picture text --> b. Absolute f-convergence, 2000-2014<br>0.2<br>z<br>3g<br>BX 01<br>Pa<br>gS , 2 g<br>gege" owes<br>Emed<br>2B<br>6 8-01<br>a<br>-0.2<br>6 7 8 9<br>Municipalities’ mean per capita income in 2000<br>(log-scale)<br><!-- End of picture text -->
POLICY **RESEARCH WORKING PAPER** 12S6

# **Summary findings**

Larsen presents a simple empirical fiamework for estimating the level of world fossil fuel subsidies and analyzing their implications for carbon dioxide emissions. Larsen extends Larsen and Shah (1992) by applving a simple model with interfuel substitution, using a more detailed sectoral data set that includes energy prices and consumption for an expanded sample of countries.

Larsen concludes that substantial fossil fuel subsidies prevail in a hand&ul of large carboni-emitting countries.

The fiscal implications for some countries are significant - as much as 10 percent of C,DI) in sorne countries. World subsidies are estimated to he Ilmore than $2 1 **()** billion, or 20 to 25 percent of the value of world fossil fuel consumption at world prices.

Removing such subsidies, Larsen estimates, would reduce national carbon cmissions by more than 20 percent relative to baseline emissions in some countries. It would reduce global carbon emissions by 7 percent.

This paper-a product ofthe Public Econotnics Division, Policy Research Department- is an extension of Policy Research Working Paper 1002, "World Fossil Fuel Subsidies and Global Carbon Emissions," bv Bjorn Larsen and Anwar Shah October 1992. Copies ot this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433 Please contact Carlina Jones, room N10-063, extension 37699 (24 pages). February 1994.

_The Polic)' Researcb Working Paper Se, ies disserninates the findings of ttork in progress to encourage the exchange of ideas about development issues. An objective of the serie, is to get the findings out quickly, even if the presentations are less than fully polisbed. 7he papers carry the names ofthe authors and should be used and cited accordingly. 7he findings, interpretations, and conclusions are the auti rs' otvn and should not be attributed to the World Bank, its Executive Board of Directors, oir any of its member countries._ Produced by the Policy Research Dissemination Center
# **Table 1 – Survey of Islamic Micro-Finance Case Studies**

|**Paper/**<br>**Country**|**Institution**|**Purpose**|**Business Size**|**Client**<br>**size/volume**|**Islamic**<br>**instrument**<br>**of**<br>**financing**|
|---|---|---|---|---|---|
|**(Asaad 2007)**<br>**Syria**|UNDP supported<br>village funds|The village funds are self-managed and<br>autonomous in their decision-making,<br>which has included the adoption of<br>financial practices consistent with local<br>values.| Between Sep 2000 and Dec<br>2002, 22 village funds were<br>established.<br> UNDP contributed $370,000<br>in equity.<br> Repayment rate as of 31 Dec<br>2002 was 99.7%. ROE was<br>17%| Average loan<br>balance per<br>borrower/per<br>capita GDP is<br>61%.<br> Till the end of<br>2003, 5,674 loans<br>were disbursed.|Murabahah|
|**(Ahmed 2007)**<br>**Bangladesh**|Islamic Bank<br>Bangladesh<br>Limited (IBBL):<br>Rural<br>Development<br>Scheme (RDS)|RDS is a_Shariah_based microfinance<br>model of poverty alleviating program,<br>mainly for the poor woman of the rural<br>area of Bangladesh. Target group includes<br>destitute women, distressed people, and<br>households with less than 0.5 acres of land.| Up to Dec 2006, 1,368 Field<br>Officers engaged in supervision<br> An investment amount of<br>$135 mln<br> 118 branches in 8,057<br>villages<br> Recoveryrate was 99%.|294,908<br>borrowers, 92%<br>or which are<br>woman.|Bai-Muajjal, Bai-<br>Murabaha, Bai-Salam, and<br>Hire Purchase under<br>Sherkatul Melk (HPSM).|
|**(Mannan**<br>**2007)**<br>**Bangladesh**|Social Investment<br>Bank Ltd<br>“SME programs”<br>and “Family<br>Empowerment<br>Micro enterprise<br>Program”|Helping poor family to purchase materials<br>commodities for trading, manufacturing and<br>service concern.| As of 2005, the family<br>empowerment micro credit<br>program has a total outstanding<br>of $0.3mln, with a recovery rate<br>of 96%<br> SME program has a total<br>outstanding of $1.1mln, with a<br>recoveryrate of 94%|n.a.|_Waqf_and Mosque<br>properties, cash-_Waqf_<br>certificate, joint-venture<br>projects for management of<br>Hajj affairs|
|**(Kazem 2007)**<br>**Iran**|1,229_Qard-al-_<br>_Hassan_Funds<br>(GFs)|With the objective of helping low-income<br>group through short-run credit grant, GFs<br>provide_Shariah_compliance services to<br>individuals who are unable to fulfill banks<br>loan collateral requirements and thus were<br>deprived from obtaining credit.| Total value of loans is $169<br>mln<br> Total value of deposits is<br>$227 mln<br> 60% of the total loans had<br>been paid back.| 6,480,237<br>depositors<br> 1,777,583<br>borrowers|_Qard-al-_<br>_Hassan_Funds|

14
2723-2753

- Xu, Chenggang, 2011, The fundamental institutions of China's reforms and development. _Journal of Economic Literature_ 49, 1076-1151.

- Yi, Yuanyuan, Gunnar Kohlin, Jintao Xu. 2014. “Property Rights, Tenure Security and Forest Investment Incentives: Evidence from China’s Collective Forest Tenure Reform.” _Environment and Development Economics_ 19(1), 48-73.

- Young, Alwyn, 2000, The Razor's Edge: Distortions and Incremental Reform in the People's Republic of China _, Quarterly Journal of Economics_ 115, 1091-1135.

- Young, Alwyn, 2018, Consistency without Inference: Instrumental Variables in Practical Application, _mimeo_ , London School of Economics.

41
Table 6. Linear regression model estimates (education level, country fixed effects and interaction for managers and professionals)

|Dependent variable:|Log CO2 e<br>val|missions per i<br>ue-added (20|ndustry real<br>12)|Average<br>emissions<br>added(|annual growt<br>per industry<br>2010-2019),|h in CO2<br>real value-<br>%point|
|---|---|---|---|---|---|---|
|Skill measure domain|Literacy|Numeracy|Problem<br>solving|Literacy|Numeracy|Problem<br>solving|
|Skill measure|-0.01<br>(0.01)|0.02***<br>(0.01)|-0.01<br>(0.01)|0<br>(0.03)|-0.02<br>(0.03)|-0.02<br>(0.03)|
|female|-0.61***<br>(0.01)|-0.61***<br>(0.01)|-0.56***<br>(0.01)|-0.1***<br>(0.03)|-0.12***<br>(0.03)|-0.24***<br>(0.04)|
|Manager or professional occupation|-0.41***<br>(0.01)|-0.41***<br>(0.01)|-0.42***<br>(0.01)|-0.15***<br>(0.05)|-0.17***<br>(0.05)|-0.26***<br>(0.05)|
|Manager or professional occupation x skill|<br>-0.08***|<br>-0.1***|<br>-0.07***|<br>-0.45***|<br>-0.4***|<br>-0.25***|
|measure<br>Education level (relative to no education)|(0.01)|(0.01)|(0.01)|(0.04)|(0.04)|(0.06)|
|Primary|-0.13**|-0.14**|-0.11|-1.2***|-1.21***|-1.8***|
||(0.06)|(0.06)|(0.07)|(0.15)|(0.15)|(0.25)|
|Lower secondary|<br>-0.12**|<br>-0.14***|<br>-0.14***|<br>-0.09|<br>-0.09|<br>-0.53***|
||(0.05)|(0.05)|(0.04)|(0.14)|(0.14)|(0.14)|
|Vocational upper secondary|<br>-0.14***<br>(0.05)|<br>-0.16***<br>(0.05)|<br>-0.18***<br>(0.05)|<br>0.27**<br>(0.13)|<br>0.28**<br>(0.13)|<br>-0.13<br>(0.13)|
|General upper secondary|-0.31***<br>(0.05)|-0.35***<br>(0.05)|-0.37***<br>(0.05)|-0.03<br>(0.13)|-0.02<br>(0.13)|-0.49***<br>(0.12)|
|Technical post-secondary|<br>-0.46***<br>(0.05)|<br>-0.51***<br>(0.05)|<br>-0.54***<br>(0.05)|<br>0.2<br>(0.14)|<br>0.23<br>(0.14)|<br>-0.33***<br>(0.12)|
|General post-secondary|<br>-0.56***|<br>-0.61***|<br>-0.63***|<br>-0.1|<br>-0.08|<br>-0.62***|
||(0.05)|(0.05)|(0.05)|(0.15)|(0.14)|(0.12)|
|Other controls (age, age2, dummy for country)<br>Constant|yes<br>14.15***<br>(0.2)|yes<br>14.29***<br>(0.2)|yes<br>14.12***<br>(0.22)|yes<br>73.02***<br>(1.03)|yes<br>73.12***<br>(1.02)|yes<br>82.89***<br>(1.11)|
|Manager or professional occupation + Manager or<br>professional occupation x skill|-0.09***<br>(0.01)|-0.08***<br>(0.01)|-0.08***<br>(0.01)|-0.45***<br>(0.04)|-0.41***<br>(0.04)|-0.27***<br>(0.05)|
|obs|66665|66665|43701|66726|66726|43759|
|r2|0.26|0.26|0.28|0.21|0.21|0.22|
|Countries|19|19|15|19|19|15|

Estimated using ten plausible values for each skill measure following OECD (2013); jackknife standard errors reported in parentheses. Statistical significance at the 1, 5 and 10 percent levels denoted by ***, **, and * respectively.

19
14 minimum wages have the estimated effects on employment as described in the Table 8 regressions, then it would follow that a rise in the relative value of the large cities minimum wage should, _all else equal,_ decrease large cities employment relative to small cities employment over time. Because a majority of firms in our sample are in large cities and because these firms tend to be larger and more stable, I focus on employment trends in this group relative to the minimum wage. Smaller firms located in small cities are less stable and the employment numbers show less of a interpretable trend over time.

Figure 6 plots the ratio of the large cities minimum to average wage, and the relative employment share of large cities to small cities for both unskilled (top panel) and skilled (bottom panel) employment over time. As is clear from the figure, there is an inverse relationship between the two series over time, which is especially strong for unskilled employment-- the group at or near the minimum wage and most likely to be affected by value changes. Although suggestive at best, the minimum wage plot is supportive of the interpretation given to the Table 8 regression results.

Second, although the theoretical model directs that all input prices other than wages be used in the employment equations, limiting ourselves to this restriction forces estimation over the 1981-87 subperiod since input price data are not available prior to that date for Colombia, and therefore does not capture the period of most rapid rise in the real value of the minimum wage. Table 8.A tests the robustness of the employment demand equations by moving away from the reduced form equations of (6') and (6") in a variety of ways. In columns (l)-(4), I estimate the responsiveness of unskilled employment to mninimum wages (columns 1-2) and lagged minimum wages (columns 3-4) over the period 1977 to 1987, omitting by necessity both materials and energy input prices. In both column (1)-- the restrictive specification-- and column (2)-- the unrestricted specification-- the effect of the minimum wage on employment is insignificantly different from zero. Lagged minimum wage effects are generally stronger and in the case where input price levels are used, the estimated minimum wage elasticity is of similar magnitude to the earlier estimates. These results, especially for contemporaneous minimum wage effects, conflict with and therefore challenge the earlier estimates, although the 1981-87 calculations reproduced from Table 8 and estimated in columns (5) and (6) without input prices _also_ produce insignificant and muted coefficient estimates on the contemporaneous minimum wage variable. Presumably, input prices
<!-- Start of picture text --> indicator —*- SCI —* SPI Overall Score<br>100<br>75<br>joe ee]<br>f-——<br>1000a a<br>75 —O | |=".<br>eeeee ||<br>25 —_rt<br>0<br>100<br>50 ——<br>25 -—__ i<br>0<br>100<br>5)<br>50 +—— re<br>25<br>0<br>2016 2017 2018 20192016 2017 2018 2072016 2017 2018 2019<br><!-- End of picture text -->