rafmacalaba/datause-encoder-data huggingface dataset:
encouraged common citizens to make their own investments in this prosperity.

For people who are unfamiliar with federalism or democracy, however, the benefits of federalism may be harder to appreciate than those of democracy. People everywhere can readily appreciate the democratic ideal that they should have a choice about who will lead them, and that authority in government should depend on broad popular approval.

But federal democracy asks more from voters. When power is divided among local and federal officials, there will inevitably be disagreements about the line dividing local from federal authority. In a democracy, the resolution of such constitutional disputes must ultimately depend on judgments by the voters. Voters must understand that their elected leaders at the national level need supreme power to serve and protect the broad interests of the entire country, but that their elected leaders at the local level also need some autonomous power to provide public services for their communities. An official at either level who acts to undermine the other level’s legitimate constitutional powers should be distrusted and rejected by voters in the future.

It may take some years of experience with federal democracy for voters to develop a broad understanding about what should be an appropriate balance between the different levels of government. America was indeed greatly blessed in that its local governments were elected long before its first national government. Even before the American republic was established, its citizens had decades of experience of power-sharing between their locally elected provincial assemblies and the greater government of the British Empire. The key lesson from American history is that those who would promote vigorous democratic development should appreciate the vital benefits of a balanced federal system—one in which the people can elect responsible local governments, as well as their sovereign national government.

> _AUTHOR'S ADDRESS:  Roger Myerson, Economics Dept., U. of Chicago, 1126 East 59th St, Chicago, IL 60637 USA. myerson@uchicago.edu,  http://home.uchicago.edu/~rmyerson/_

> _This paper is available at  http://home.uchicago.edu/~rmyerson/research/amerfed.pdf_

> _Version in HORIZONS (Autumn 2015), issue 5:  http://www.cirsd.org/publications/magazines_list_of_pages/english/5_

11
# **References**

- [1] Associated Press. Islamic state group’s war chest is growing daily, 15 September 2014.

- [2] Aymenn Jawad Al-Tamimi. Archive of islamic state administrative documents, 27 January 2015.

- [3] John Bellows and Edward Miguel. War and local collective action in sierra leone. _Journal of Public Economics_ , 93(11):1144–1157, 2009.

- [4] Bloomberg View. Cutting islamic state’s purse strings, 6 January 2015.

- [5] Jean-Charles Brisard and Damien Martinez. Islamic state: The economy-based terrorist funding. Technical report, Thomson Reuters Accelus, October 2014.

- [6] David Butter. Syria’s economy: Picking up the pieces. _Chatham House_ , 23:3241–3246, 2015.

- [7] Xi Chen and William Nordhaus. A test of the new viirs lights data set: Population and economic output in africa. _Remote Sensing_ , 7(4):4937, 2015.

- [8] David S. Cohen. Prepared testimony of under secretary cohen before the house financial services committee on “the islamic state and terrorist financing”, 2014.

- [9] Die Zeit. The business of the caliph, 4 December 2014.

- [10] Oeindrila Dube and Juan F Vargas. Commodity price shocks and civil conflict: Evidence from colombia. _The Review of Economic Studies_ , 80(4):1384–1421, 2013.

- [11] Christopher D Elvidge, Mikhail Zhizhin, Kimberly Baugh, and Feng-Chi Hsu. Automatic boat identification system for viirs low light imaging data. _Remote Sensing_ , 7(3):3020– 3036, 2015.

18
have a higher ridership-pollution elasticity compared to the TM? Note that the premetro (1995–96) _CO_ level in Taipei was 1,030  _g m_ / 3 , while the _CO_ level for Delhi 3 before the first extension of the Yellow Line was 2,212  _g m_ / .<sup>41</sup> Given that baseline pollution in Delhi is about twice that of Taipei, a one percentage point change in pollution would translate into a much larger absolute reduction in pollution for Delhi. Therefore, it is to be expected that Delhi would require a larger change in ridership to support a larger absolute change in pollution. Moreover, compared to Taipei, a much larger proportion of Delhi’s population uses public transport.<sup>42</sup> For traffic diversion from private vehicles to take effect, Delhi would therefore need a much larger increase in ridership.

# _Benefits from Reduced CO Pollution_

Drawing from literature that looks at health impacts of air pollution, we quantify the benefits of reduced _CO_ levels in Delhi in terms of infant lives saved. Currie, Neidell, and Schmieder (2009) study the effect of pollution on infant health in New Jersey in the 1990s. They account for avoidance behavior, and estimate that a 1 part per million (ppm) reduction in _CO_ , reduced infant mortality by 2.5 percent in proportion to the baseline risk. CW combine this with their estimate of a 15 percent reduction in _CO_ , and conclude that the opening of the TM resulted in 1.7 infant lives being saved in the year following the metro opening. We undertake a similar exercise and find that the expansion of the DM (first extension of the Yellow Line), resulted in about 61 infant lives being saved in the year following

> 41. CW present pollution levels in parts per million (ppm). We converted their figures into micro grams per cubic 3

> meter (  _g m_ / ) using an online convertor provided by Lenntech. Source url: http://www.lenntech.com/calculators/ppm/converter-parts-per-million.htm. Last accessed on March 31, 2015. 42. The travel mode shares for Taipei City in 2001 (five years after the opening of TM), were 8.8 percent TM, 16.1 percent bus, 34 percent car and 41.1 percent motorbike (Jou et al. as cited in CW). The travel mode shares for Delhi in 2007 (also 5 years since DM opening), were 21 percent walk, 12 percent cycle, 5 percent two-wheelers, 43 percent public transport, 14 percent car and 6 percent three wheelers (Ministry of Urban Development, 2008).

27
Figure 1: Timeline of the study

|1 week||1 week<br>1 to 2 weeks|4 months|
|---|---|---|---|
||||_time_|
|||Treatment 1:||
|||Drama||
|||25 min (3 episodes)||
|Facebook<br>recruiting|15-min<br>baseline<br>survey|Treatment 2:<br>Documentary<br>20 min (7 episodes)<br>15-min<br>short-term<br>survey|15-min<br>medium-term<br>survey|
|||Control:<br>Placebo<br>25 min (4 episodes)||

# **3.2 Interventions**

The studied edutainment content was produced by WEvolve, a multi-donor initiative supporting innovative campaigns against VAW, and Population Foundation of India, an NGO that advocates for the formulation and implementation of gender-sensitive development policies and programs. Both programs were of high-quality, with their content developed by professional teams and adjusted by extensive formative research with target audiences. To understand if explicit or implicit formats were more effective in influencing gender norms and VAW attitudes, we selected the following edutainment programs.

Treatment 1 (implicit) was a fake reality TV web series called Sex Ki Adalat (meaning Court of Sex). The program takes place in a fictitious court where myths and misconceptions around gender norms are discussed often in a humorous way. We showed three episodes of this series, for a total length of 25 minutes. These episodes focused on the determinants of the child’s gender (sex selection at birth), female and male virginity at marriage, and the “menstruation ritual”, which bans women from entering the kitchen or household shrine during their period.

Treatment 2 (explicit) was a series of WEvolve clips that aimed to raise awareness on VAW prevalence in India, including real-life stories and experiences from people similar to our target population (i.e., young, middle-class, city-dwelling Indians), with clear calls to action. The clips heavily used music and editing to be both contemporary and emotionally impactful and were often character-driven. Celebrities, including actor and screenwriter Farhan Akhtar, make appearances. We showed seven episodes for a total length of 20 minutes. The control group was exposed to a “placebo”. In particular, respondents in this condition were invited to 8
# TADLE 2: REAL CONSOUDATED PtBUC SECTOR<sup>DEFICIT (Con t)</sup> (Perceag of GNP)

|PAKISTAN|1601|1962-44<br>1|9540|PORTUGAL|1941|196244|196540|
|---|---|---|---|---|---|---|---|
|Total RealPSt..bcia<br>(Excl. K losses of For. Debt)|0.1|6.9|7.7|Total Real PS Deficit<br>(Excluding K lossesdlFor Debt)|7.6|5.2|3.4|
|A. Adjusted Money Finance|1.6|1.9|0.0|A. Adjusted Money Finance|5.9|3.5|4.2|
|Inlation Tax|1.6<br>|083<br>|0.9<br>|Inflation Tx<br>|385<br>|560<br>|2.0<br>|
|Selgnor|0.2|1.1|-0.2|Seignrage|2.1|-1.6|2.2|
|S.Not Domtnc DebtF fha|-2.0|3.3|5.9|8. Nt Domest Debt Finance|4.9|-1.2|4.3|
|||||Instruments<br>||||
|C. Not Foeign De Fbta|0.2|1.7|1.2|Bonds and Bills<br>|5.6<br>|-2.4<br>|6.2<br>|
|(Exci. K.kbexofdFor. De)||||Tresurybills<br>|0.0<br>|00<br>|2.4<br>|
|||||Government bolds and other<br>|5.6<br>|-2.4<br>|**5.8**<br>|
|**Incrsx**in Foeign Uabik<br>|2.2|2.7<br>|0.4<br>|Net Bank Cedit|-0.7|1.2|-3.9|
|**ncrse**in Foeign**Asseo**<br>**t**|2.0|1.1|-0.7|||||
|||||**Borrowers**<br>||||
|D. Nt Foreign Debt Finance|-2.4|26|46|Ge6ral Gonnment<br>|5.8<br>|-1.7<br>|7.5<br>|
|_(Incl._K. lobs of For. Debt)||||Nonfinancial PSEs|-0.9|0 5|-3.2|
|MEMO:||||C. Net Foreign Debt Finance<br>|-3.2|2.9|-5.2|
|Frigp<br>Exch. Loun (D-C)|-2.6|1.0|3.4|(Excluding K lome of For. Db)||||
|||||Increse in Foreign Labeitis<br>|6.6<br>|0.5<br>|1.9<br>|
|||||Incree in Foreign**Asst**|9.8|-2.5|7.1|
|||||D. Net Fortign Dbt Fince<br>(Including K**las**<br>of For. Debt)|-2.1|9.6|-7.4|
|||||MELO: Foreign Exchange Losses (D-C)|1.1|6.7|-2.2|
**Table B5. Number of Recipients and Average Value of Benefits from Public, Contractual and Individual Old Age Insurance, 2004.**

||All|Average<br>|Ofwhich||||||||
|---|---|---|---|---|---|---|---|---|---|---|
||persons|amount|Pblild|i|||C|lbfi|||
||<br>||uc o|ge penso|||ontractua|enet|||
||with|1000<br>|||With a|uarantee|||Private in|dividual|
||some|SEK||||<br>|||||
||<br>form of|||||benefit|||pensions||
||<br>pension|||Average<br>amount<br>|Number|Average<br>amount||Average<br>amount<br>||Average<br>amount|
|||||1000||1000||1000||1000|
|Age|||Number|SEK||SEK|Number|SEK|Number|SEK|
|**All**|1 863 855|135,5|1 571 252|113,8|1 563 149|112,1|1 461 832|40,4|<br>370 547|<br>40,1|
|55-60|143 788|58,6|0|0|0|0,0|110 588|59,4|<br>50 291|<br>36,8|
|61-64|178 470|105,0|36 207|63,1|28 569|55,0|137 264|100,6|<br>55 825|<br>47,6|
|65-69|404 411|167,8|397 646|118,0|397 442|111,0|358 079|42,8|<br>150 624|<br>37,3|
|70-74|345 153|156,3|345 128|125,1|345 128|125,1|288 900|28,7|<br>59 959|<br>41,5|
|75-79|312 167|140,9|312 154|117,5|312 154|117,5|242 082|25,8|<br>25 047|<br>41,6|
|80-84|264 352|130,0|264 349|108,9|264 349|108,9|189 950|25,9|<br>15 840|<br>42,5|
|85-89|143 187|122,3|143 183|101,3|143 183|101,3|94 249|27,9|<br>8 764|<br>42,9|
|90-|72 327|107,6|72 324|88,6|72 324|88,6|40 720|29,8|<br>4 197|<br>37,9|
|**Women**|1 045 573|105,2|889 155|93,4|884 154|92,4|783 549|27,1|<br>182 346|<br>31,7|
|55-60|80 106|39,2|0|0|0|0,0|63 061|38,4|<br>26 238|<br>27,4|
|61-64|89 049|68,7|15 551|43,9|10 809|44,1|69 396|64,4|<br>27 311|<br>35,2|
|65-69|207 541|134,8|204 601|100,6|204 490|95,1|180 422|27,9|<br>78 603|<br>30,0|
|70-74|185 573|124,6|185 563|103,9|185 563|103,9|150 882|19,6|<br>26 752|<br>33,0|
|75-79|176 604|110,8|176 598|95,3|176 598|95,3|130 503|18,3|<br>10 276|<br>35,2|
|80-84|159 370|101,2|159 369|87,0|159 369|87,0|106 121|18,9|<br>6 828|<br>38,9|
|85-89|94 063|96,7|94 061|82,0|94 061|82,0|56 227|21,9|<br>4 062|<br>39,3|
|90-|53 267|91,7|53 264|76,9|53 264|76,9|26 937|26,4|<br>2 276|<br>34,0|
|**Men**|818 282|174,2|682 097|140,3|678 995|137,8|678 283|55,7|<br>188 201|<br>48,2|
|55-60|63 682|82,9|0|0|0|0,0|47 527|87,2|<br>24 053|<br>47,1|
|61-64|89 421|141,2|20 656|77,6|17 760|61,7|67 868|137,5|<br>28 514|<br>59,5|
|65-69|196 870|202,6|193 045|136,5|192 952|127,8|177 657|57,9|<br>72 021|<br>45,3|
|70-74|159 580|193,3|159 565|149,7|159 565|149,7|138 018|38,7|<br>33 207|<br>48,3|
|75-79|135 563|180,0|135 556|146,5|135 556|146,5|111 579|34,6|<br>14 771|<br>46,1|
|80-84|104 982|173,7|104 980|142,1|104 980|142,1|83 829|34,7|<br>9 012|<br>45,2|
|85-89|49 124|171,2|49 122|138,4|49 122|138,4|38 022|36,8|<br>4 702|<br>45,9|
|90-|19 060|152,1|<br>19 060|121,4|19 060|121,4|<br>13 783|36,5|<br>1 921|<br>42,6|

Note.<sup>**1**</sup> In 2001 the _folkpension_ , a flat rate benefit pro-rated with years of residence up to 40 and with a reduction for spouses, was converted into a guarantee benefit in the new system Since the entire stock of pensioners at the time had a _folkpension_ as the first pillar in the public system, as a result of the conversion, they all received a guarantee benefit after the reform. Source. Statistics Sweden 68
been conducive for the benefits of structural improvements in Bangladesh to roll out and materialize over time.

**Table 2 Persistence and volatility**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLE|lnGDPpc|lnGDPpc|lnGDPpc|lnGDPpc|σ ofgrowth|
|Persistence|0.742***|0.743***|0.742***|0.866***||
||(0.0351)|(0.0351)|(0.0369)|(0.0597)||
|Persistence x BGD||0.125**<br>(0.0600)||||
|σ of growth|||-0.286<br>(0.282)|-0.681**<br>(0.305)||
|BGD|||||-0.0164***<br>(0.00208)|
|Constant|2.389***|2.373***|2.422***||<br>0.0463***|
||(0.305)|(0.303)|(0.312)||(0.00387)|
|Observations|635|635|635|635|967|
|R-squared|0.921|0.922|0.922|||
|<br>Countries|128|128|128|128|149|
|Other variables|Yes|Yes|Yes|Yes|period dum|
|Estimation|FE|FE|FE|GMM|<br>RE|

_Note:_ Robust standard errors are in parentheses. BGD = Bangladesh; FE = fixed effects; GDP = gross domestic product; GMM = generalized method of moments. *** p < 0.01, ** p < 0.05, * p < 0. 1.

-23-
Timor-Leste (2004) reports that survey managers checked the GPS points visited by the Census teams against detailed aerial photograph maps, and used this to detect areas missed in the enumeration, sending enumerators back to complete the surveys. Population counts from the Census are used in many countries for a variety of policy purposes, including the division of federal money and for defining political representation. Undercount can be particularly high in developing countries – Lalasz (2006) reports that the 1991 Census is thought to have undercounted Nigeria's population (officially put at 89 million) by perhaps 20 million people. The use of GPS can help show where such undercount has occurred, and help survey managers reduce it.

Another potential use of GPS is through reducing the cost and time taken to re-locate the same dwelling for follow-up surveys. Two reasons for follow-ups are to allow field managers to check errors made by enumerators, and for the collection of panel data. A recurrent problem in many developing countries is the lack of street addresses, making re-locating the same dwelling time consuming, especially in densely populated urban areas. A pilot study conducted by Dwolatsky et al. (2006) with the aim of tracing patients who left a tuberculosis control program in South Africa shows the potential for using GPS to re-locate dwellings. They compared the time taken to re-find a home given residential addresses with the time with a customized personal digital assistant (PDA) linked to GPS. The time taken to find the dwelling was found to be 20-50 percent less using the PDA/GPS device. The main limitation of this study was that it was a small pilot of only 20 houses, so further experiments are needed to confirm the promising results found here.

When panel surveys attempt the more difficult (but conceptually correct) task of tracking individuals rather than dwellings, GPS can be very useful for tracking people who had previously been co-residents in the same household. For example, the Kagera Health and Development Survey 2004 (KHDS 2004) in Tanzania used GPS to record the locations of 2700 households that contain members who had been in the baseline sample of 900 households first interviewed in 1991-94 (Beegle, de Weerdt and Dercon, 2006). Measures such as how far people have moved from either their baseline village center or from households with members who had been co-residents in the baseline surveys can be related to various socioeconomic characteristics.

Finally, collecting GPS data for households allows the possibility of linking the household data set to other surveys and other datasets. There is considerable option value in

- 14 -
# **F Back-of-Envelope Calculations: Cost of Harassment**

In this Appendix we interpret our empirical results to provide an estimate of the cost of harassment for those women who commute daily despite the potential for harassment.

Table F1 shows the calculations step by step. We first calculate the average willingness to pay for a ride on the reserved space, by multiplying the takeup at positive opportunity cost (row a) by the opportunity cost (20 cents). We cannot reject that responses to all opportunity cost levels are equal, so participants who show positive willingness to pay on a given ride are assumed to be willing to pay at least 20 cents for those rides. Average willingness to pay is shown on row (b).

We then rescale the willingness to pay for the reserved space, accounting for the fact that it only provides partial protection from harassment. We use the public space mean physical harassment (c) and treatment effect (d) from Table 3); this allows us to calculate the percentage change in row (e). We use this to rescale the willingness to pay: i.e. row (f) = (b) / (e).

Column 1 assumes that willingness to pay for the reserved space can be attributed to avoiding harassment. Since physical harassment drops by about half when a participant moves to the reserved space (row e), Column (1) row (f.1) implies that the per-ride cost of harassment is twice the value participants place on a ride on the reserved space. This adds up to approximately $1.46 per incident and $12 per year, or about 1.7% of the gender wage gap. Based on contemporaneous estimates of the supply elasticity of women’s labor in Brazil ranging from 1.2 to 1.5 (Vick, 2017), these estimates would imply a 0.4 - 0.5% decrease in the labor supply of a woman working at minimum wage. We replicate the estimates using the sub-sample in which the reservation rule is most closely followed, i.e. when there are very few men in the reserved space. Column 3 shows these estimates; the results are similar
Table 4. Balance sheet composition of debt in EMDEs

|||Median||Percent o<br>with det|f EMDEs<br>erioration|
|---|---|---|---|---|---|
||2010|2019|2020|2010-19|2010-20|
|Foreign currency government debt<br>(Percent of government debt)|48|49|49|45|50|
|Nonresident-held government debt<br>(Percent of government debt)|38|44|42|66|61|
|Short-term government debt<br>(Percent of GDP)|3|4|4|68|74|
|Average maturity of government debt<br>(Years)|8|9|9|54|49|
|Foreign currency external debt<br>(Percent of external debt)|91|94|95|45|55|
|Short-term external debt<br>(Percent of external debt)|11|10|13|43|41|
|Concessional external debt<br>(Percent of government debt)|20|14|...|84|...|

Note: Table shows medians of respective indicators across EMDEs, as well as the share of EMDEs by the sign of changes in those indicators. Deterioration refers to increases in all indicators except average maturity and concessional external debt.

Table 5. Countries with negative interest rate-growth differentials and sustainability gaps

|||P|ercent o|f countrie|s|Percent|<br>of country-year pairs<br>(1990-2020)|
|---|---|---|---|---|---|---|---|
|||1990s|2000|2010|2020|All|With negative gaps|
|Interest rate|World|28|53|68|11|55|14|
|below growth|Advanced economies|23|71|57|11|52|11|
||EMDEs|43|38|74|12|57|16|
|Negative|World|60|36|56|99|50|...|
|sustainability|Advanced economies|57|14|76|100|46|...|
|gap|EMDEs|46|53|45|99|53|...|

Note: Table shows the share of countries with negative interest rate-growth differentials and sustainability gaps in respective years. Sustainability gaps are measured with current growth and interest rates.

25
|P008059|1993|1993|1 Closed|Peru|
|---|---|---|---|---|
|P008059|1993|1994|2 Closed|Peru|
|P008059|1993|1995|5 Closed|Peru|
|P008059|1993|1996|7 Closed|Peru|
|P008059|1993|1997|9 Closed|Peru|
|P008059|1993|1998|10 Closed|Peru|
|P008059|1993|1999|11 Closed|Peru|
|P008062|1994|1994|4 Closed|Peru|
|P008062|1994|1995|6 Closed|Peru|
|P008062|1994|1996|8 Closed|Peru|
|P008062|1994|1997|10 Closed|Peru|
|P008064|1997|1997|1 Closed|Peru|
|P008065|1991|1991|1 Closed|St. Kitts and Nevis|
|P008065|1991|1992|3 Closed|St. Kitts and Nevis|
|P008065|1991|1993|6 Closed|St. Kitts and Nevis|
|P008065|1991|1994|9 Closed|St. Kitts and Nevis|
|P008065|1991|1995|10 Closed|St. Kitts and Nevis|
|P008065|1991|1996|12 Closed|St. Kitts and Nevis|
|P008065|1991|1997|15 Closed|St. Kitts and Nevis|
|P008065|1991|1998|16 Closed|St. Kitts and Nevis|
|P008065|1991|1999|18 Closed|St. Kitts and Nevis|
|P008071|1990|1990|2 Closed|St. Lucia|
|P008071|1990|1991|3 Closed|St. Lucia|
|P008071|1990|1992|5 Closed|St. Lucia|
|P008071|1990|1993|6 Closed|St. Lucia|
|P008071|1990|1994|7 Closed|St. Lucia|
|P008071|1990|1995|10 Closed|St. Lucia|
|P008071|1990|1996|12 Closed|St. Lucia|
|P008071|1990|1997|12 Closed|St. Lucia|
|P008072|1984|1985|0 Closed|St. Vincent and the Gr|
|P008072|1984|1986|0 Closed|St. Vincent and the Gr|
|P008072|1984|1987|0 Closed|St. Vincent and the Gr|
|P008072|1984|1988|0 Closed|St. Vincent and the Gr|
|P008072|1984|1989|5 Closed|St. Vincent and the Gr|
|P008072|1984|1990|7 Closed|St. Vincent and the Gr|
|P008072|1984|1991|9 Closed|St. Vincent and the Gr|
|P008072|1984|1992|10 Closed|St. Vincent and the Gr|
|P008072|1984|1993|11 Closed|St. Vincent and the Gr|
|P008076|1989|1989|3 Closed|St. Vincent and the Gr|
|P008076|1989|1990|6 Closed|St. Vincent and the Gr|
|P008076|1989|1991|8 Closed|St. Vincent and the Gr|
|P008076|1989|1992|11 Closed|St. Vincent and the Gr|
|P008076|1989|1993|15 Closed|St. Vincent and the Gr|
|P008076|1989|1994|18 Closed|St. Vincent and the Gr|
|P008076|1989|1995|19 Closed|St. Vincent and the Gr|
|P008076|1989|1996|21 Closed|St. Vincent and the Gr|
|P008076|1989|1997|23 Closed|St. Vincent and the Gr|