rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Measures of corruption

|Name|Type<br>Source|Defnition|Years|obs.|countr.|
|---|---|---|---|---|---|
|paid bribe|binary: 0,<br>1<br>TI<br>GCB|Question 5 in survey; 1 if Yes, 0<br>if No|2005|50704<br>(0’s-88%)|68|
|bribe burden|cont.,<br>corner at<br>0<br>TI<br>GCB|Question5.1 in survey; lower es-<br>timates of bribe paid divided by<br>current GDP per capita|2005|48470<br>(0’s-86%)|66|
|informal gift|binary: 0,<br>1<br>WBES|Surveys 2002-05:<br>maximum by<br>questions 40(v), 40(vi), 42(i);<br>Surveys 2006-09:<br>maximum by<br>questionsj5, j12, j15; 1 if at least<br>one Yes, 0 if all No|2004-<br>2006|40721<br>(0’s-77%)|80|
|bribe %sales|cont.,<br>corner at<br>0<br>WBES|Surveys 2002-05:<br>question 39;<br>Surveys 2002-05:<br>question j7;<br>percent of annual sales spent on<br>bribes|2004-<br>2006|40309<br>(0’s-71%)|79|
|corruption<br>perception|approx.<br>cont.<br>TI<br>CPI|aggregate index of diferent cor-<br>ruption perception indexes|2005,<br>2007|175|175|
|Question 5: I<br>form? Answer<br>Question 5.1: <br>in the past 12 <br>Question 42(i<br>expected or re<br>Question 40(v<br>payment expe<br>Question 40(v<br>or payment ex<br>Question 39-j7<br>payments to p<br>services etc. O<br>informal paym|n the past 12 months, <br>s: 1) Yes; 2) No<br> What was the approxi<br> months? Answers: 1)<br>)-j5:<br>In any of the t<br>quested? Answers: 1) <br>)-j12: In reference to <br>cted or requested? An<br>i)-j15: In reference to <br>pected or requested? <br>: We’ve heard that es<br>ublic ofcials to ”get th<br>n average, what perce<br>ents or gifts to public|have you or anyone living in this ho<br>mate amount of money paid overall i<br> Under 30 USD;... 11) 1000 USD a<br>ax inspections or meetings was a <br> Yes; 2) No<br> the application for an import licens<br>swers: 1) Yes; 2) No<br> the application for an operating lic<br>Answers: 1) Yes; 2) No<br>tablishments are sometimes require<br>ings done” with regard to customs, t<br>nt of total annual sales do establish<br> ofcials for this purpose? Answer:|usehol<br>n bribes<br>nd mor<br> gift or <br>e, was <br>ense, w<br>d to ma<br>axes, li<br>ments l<br> % of sa|d paid bribe <br>by your hou<br>e<br> informal pa<br> an informal <br>as an inform<br>ke gifts or in<br>censes, regul<br>ike this one <br>les (0-100)|in any<br>sehold<br>yment<br> gift or<br>al gift<br>formal<br>ations,<br> pay in|

Note Measures of corruption used in the paper are described. Abbreviations: TI GCB - Transparency International Global Corruption Barometer, TI CPI - Transparency International Corruption Perception Index, WBES - World Bank Enterprises Surveys.

15
**Table 8:** Pairwise Spearman rank correlations with our benchmark mobility index

|Index|Corr.|Std. Dev.|Min|Max|
|---|---|---|---|---|
|Panel A: Columns from table 4|||||
|(1)|0.916|0.100|-0.232|0.332|
|(2)|>0.999|0.105|-0.321|0.347|
|(3)|0.992|0.108|-0.337|0.355|
|(4)<br>|0.918|0.101|-0.240|0.332|
|(6)|0.991|0.109|-0.347|0.356|
|(7)|0.983|0.115|-0.329|0.374|
|Panel B: Trip subsamples|||||
|Radial|0.926|0.117|-0.318|0.373|
|Circumferential|0.900|0.113|-0.286|0.330|
|Gravity|0.966|0.112|-0.375|0.308|
|Amenities|0.966|0.107|-0.345|0.373|
|Interact time/daywith triptype|>0.999|0.105|-0.322|0.347|
|Panel C: Mean speeds|||||
|Simple mean|0.476|3.790|16.212|34.903|
|Mean unweighted by length|0.619|2.899|15.7|31.4|
|<br>Mean of “typical” traffc speed|0.452|3.814|16.2|35.1|
|<br>Mean of uncongested speed|0.340|4.494|16.3|38.1|
|Mean effective speed|0.410|2.768|11.6|24.0|
|Panel D: Table 7 variants|||||
|Effective speed|0.864|0.118|-0.430|0.392|
|“Typical” traffc|0.997|0.102|-0.301|0.345|
|No traffc|0.850|0.100|-0.242|0.339|
|Fastest trip instance|0.851|0.101|-0.261|0.298|
|Off peak|0.881|0.099|-0.255|0.316|
|<br>Peak|0.991|0.113|-0.388|0.361|
|High peak|0.948|0.130|-0.430|0.367|
|<br>Peak radial|0.915|0.133|-0.450|0.405|
|Panel E: Full indices|||||
|Laspeyres|0.794|0.151|0.105|1.478|
|Paasche|0.941|0.107|0.767|1.478|
|Fisher|0.910|0.126|0.322|1.478|
|Logit/CES(_σ_=0)|0.923|0.098|0.675|1.255|
|Logit/CES(_σ_=2)|0.836|0.099|0.694|1.221|
|Logit/CES (_σ_=4)|0.687|0.108|0.648|1.182|
|Panel F: Distance to center|||||
|Trips within 5 km of center|0.970|0.108|-0.278|0.350|
|<br>Trips within 3 km of center|0.918|0.111|-0.268|0.356|
|Trips within 2 km of center|0.827|0.116|-0.261|0.336|
|Weight byinverse dist. to center|0.959|0.106|-0.293|0.341|
|Panel G: Weight by powered con|gestion|factor|||
|_λ_=0.2|0.910|0.137|-0.533|0.378|
|_λ_=0.3|0.927|0.123|-0.396|0.373|

_Notes:_ 154 cities in all rows except in the last row of panel A which uses 107. The first column reports the Spearman rank correlation between the index at hand and our preferred index from column 5 of table 4. The second column reports the standard deviation. The third and fourth column report the maximum and minimum respectively.

24
Table 1: Total schools enrolled in _Mais Educação_ between 2008 and 2011

||2008|2009|2010|2011|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|North|163|879|1,301|1791|
||0.6%|3.1%|4.5%|6.1%|
|Northeast|791|1,893|3,511|4,770|
||0.9%|2.1%|3.9%|5.2%|
|Southeast|207|1,105|3,066|3,544|
||0.4%|2.1%|5.7%|6.6%|
|South|99|326|1,034|1,792|
||0.4%|1.3%|4.1%|7.1%|
|Midwest|120|437|745|1,027|
||1.4%|4.9%|8.2%|11.2%|
|Ttl|1,380|4,640|9,657|12,924|
|oa|0.7%|2.3%|4.7%|6.2%|

_Source:_ MEC/FNDE and School Census (INEP), 2008–2011.

_Note:_ Table reports the total number of schools enrolled in _Mais Educação_ between 2008 and 2011 in different regions and nationwide. Percentages show the share _Mais Educação_ 's schools represent on the region's public schools and nationwide. We calculate the percentages by dividing the number of schools enrolled in _Mais Educação_ by total public schools by regions and nationwide, using school census. Column (1) presents data for the year 2008, (2) for 2009, (3) for 2010, and (4) for 2011.
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 23

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Grassmueck, G., & Shields, M. (2010). Does government fragmentation enhance or hinder metropolitan economic growth?. Papers in Regional Science, 89(3), 641-657.

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Hendrick, R., & Shi, Y. (2015). Macro-level determinants of local government interaction: How metropolitan regions in the United States compare. Urban Affairs Review, 51(3), 414-438.

- Hill, R. C. (1974). Separate and unequal: governmental inequality in the metropolis. American Political Science Review, 68(4), 1557-1568.

- Hsu, F. C., Baugh, K. E., Ghosh, T., Zhizhin, M., & Elvidge, C. D. (2015). DMSP-OLS radiance calibrated nighttime lights time series with intercalibration. Remote Sensing, 7(2), 1855-1876.

- Kim, S. J., Schumann, A., & Ahrend, R. (2014). What Governance for Metropolitan Areas? OECD Regional Development Working Papers.

- Kim, Yoonhee, and Bontje Zangerling, eds (2016). Mexico Urbanization Review: Managing Spatial Growth for Productive and Livable Cities in Mexico. Directions in Development. Washington, DC: World Bank. doi:10.1596/978-1-4648-0916-3. License: Creative Commons Attribution CC BY 3.0 IGO
# TABLE 1 ( _continued_ )

||Obs.|**Ethi**<br>Mean|**opia**<br>Std. dev.|Loadings|Obs.|**Sud**<br>Mean|**an**<br>Std. dev.|Loadings|
|---|---|---|---|---|---|---|---|---|
|**5. Reputation mechanism**|||||||||
|If you have a dispute with a customer, will other<br>customers find out?<sup>(d)</sup>|304|1.049|0.948|0.47|400|0.808|0.934|0.48|
|If another firm has a dispute with a customer, will you<br>refuse to deal with that customer?<sup>(d)</sup>|304|0.457|0.815|0.67|401|0.783|0.954|0.65|
|If you have a dispute with a customer, will other firms<br>refuse to deal with that customer?<sup>(d)</sup>|304|0.474|0.717|0.43|401|0.788|0.899|0.63|
|If you have a dispute with a supplier, will other<br>suppliers find out?<sup>(d)</sup>|304|0.914|0.926|0.46|401|0.783|0.925|0.69|
|If you have a dispute with a supplier, will other firms<br>refuse to deal with that supplier?<sup>(d)</sup>|304|0.398|0.682|0.47|401|0.656|0.861|0.64|

_Source:_ Authors’ computations based on data described in the text.

(a) 1 = less than secondary, 2 = secondary, 3 = vocational, 4 = university.

(b) 0 = no, 1 = yes.

(c) Nonproduction workers include professionals, managers, administrators, and sales personnel.

(d) 0 = no, 1 = maybe, 2 = yes.

36
# **6. Conclusion**

This paper investigated the effect that the institutional setup of financial sector supervision can have on the ability of countries to prevent systemic banking crises. Specifically, the paper examined whether the possible synergy effects from having bank supervision in the central bank can help countries avoid banking crises. We used a standard early-warning model of banking crises with robust crisis predictors and data from around the time of global financial crisis for this investigation. We also controlled for the possibility that greater financial stability could be explained by the quality of bank microprudential supervision or by the quality of financial stability oversight rather than by the placement of bank supervision together with macroprudential supervision in the central bank. The literature has been divided about whether to place bank supervision in the central bank.

Our study provides evidence that countries with deeper financial markets and countries undergoing rapid financial deepening can benefit from having bank supervision in the central bank to better foster financial stability. This result holds regardless of whether rapid financial deepening occurs because of domestic credit policies influenced by the risk appetite of domestic policy makers for taking systemic risk or because of exogenous factors such as capital inflows after liberalization of external financial accounts. Especially in these circumstances, our results suggest that policy makers can benefit from having a good knowledge of the financial system’s microstructure when safeguarding the stability of the financial system as a whole. Indeed, countries like the Netherlands and the United Kingdom, and most recently Hungary, have moved bank supervision into the central bank to reap these benefits. Other countries, such as Poland, Turkey, and several in Latin America, could find encouragement in our results to implement similar reform in the near future.

21
The data contain nutrition and health information for children under six, health and service use information for adults, and health spending for households. Children’s nutrition measures, such as underweight and stunting, are constructed using objective measures of weight and height. Health measures include self-reported disability and chronic diseases, illness, and accidents. Health care service information includes each child’s immunization, assisted child delivery, and regular health checkups with health professionals.<sup>14</sup>

# <u>Health and Health Care in Yemen</u>

The most serious health problem faced by Yemenis is women and children’s health, where the achievement of Millennium Development Goals (MDGs) proves challenging. Child malnutrition is strikingly prevalent in Yemen where more than half of children under five suffer from malnutrition.<sup>15</sup> This is among the world’s highest child malnutrition rate along with Iraq and Sudan. Progress towards reducing the child malnutrition rate by half has been slow. The maternal mortality rate, 365 deaths per 100,000 lives, is the highest among the Middle East and North Africa (MENA) region countries.<sup>16</sup> While health indicators in Yemen pose a serious problem, access to health services are low, and services for major treatments are prohibitively expensive. The government, Ministry of Public Health and Population (MOPHP), is the main health service provider in Yemen. They operate a threetier network of primary, secondary, and tertiary level facilities through governorate and district health offices. One of the major challenges of the Yemeni health system is low coverage among the population. Furthermore, large discrepancies exist in levels of coverage between urban and rural areas (Fairbank, 2009). Due to the limited delivery of health services, particularly in rural areas where

> 14 World Bank (2007) noted that the questionnaire of HBS 2005 did not clearly distinguish if the question meant to ask about delivery by a doctor, midwife or a medically trained professional. 15 See UNICEF (2006).

> 16 See World Bank (2009).

8
The technology was used following the 2012 Ebola outbreak to help implement quarantines and other protective measures (SDSN TReNDS 2018b).

By construction, machine-generated data also have the potential to bring about quick and relevant data. One example relates to water pumps, of which one-third in rural Africa has been found to be inoperable. In Kenya, sensors on water handpumps provide real-time data on their functionality, which helped reduce the average time to repair a broken pump from 27 to 3 days and the median time from 6 days to 1 day (Figure 2) (SDSN TReNDS 2018a and Oxford/RFL 2014).

**Figure 2: Real-time sensor data on broken handpumps greatly reduced repair times in Kenya**

<!-- Start of picture text --> Median<br>1<br>repair<br>6<br>time<br>Mean<br>2.6<br>repair<br>27<br>time<br>0 5 10 15 20 25 30<br>Days<br>With mobile-enabled data Baseline<br><!-- End of picture text --> _Source_ : Oxford/RFL 2014 Satellite data can likewise offer a timely response to emerging threats. In Sri Lanka, where more than two million hectares of natural forest are home to over 30 endangered mammals, satellite data have been used to safely and quickly respond to deforestation of protected areas (Jamilla and Ruiz, 2020). The Department of Forest Conservation used to rely mostly on routine patrolling, which can be a strain on resources and be a dangerous task due to the terrain and animals that may be encountered by patrollers. Propelled by COVID-19, which restricted the use of patrolling, the department now relies on a mobile app provided by the Global Forest Watch. The app relays real-time satellite imagery to alert users of possible deforestation and provide evidence when deforestation has occurred. In one case, a popular local TV channel reported that encroachments had taken place without being addressed by the department upon which the department used the satellite data and alerts to identify two locations of encroachment. As a result of this, legal actions were taken to stop the encroachments from expanding.

# **Granularity**

Data can be granular along a number of dimensions, for example granular by space, granular by time, or granular by demographic attribute, such as gender. A long-running and rich example of the value of granular data by gender is the Demographic and Health Surveys, which cover topics 13
4 the country's external and internal outstanding debt had reached 125 percent and 37 percent of GDP respectively.

A series of structural adjustment programs in response to these developments were implemented by the government, beginning in the early 1980s. Although there was an improvement in performance between 1984-1986, due to a rise in world prices of coffee and cocoa and an appreciation of the dollar, _snce_ 1986 earlier decijnes in real output have continued, with real GDP falling by more than a quarter. Along with this deterioration in economic performance, government revenues have also s.A4"Uily fallen as a percentage of GDP from 31.1 percent in 1984 to 23.6 percent in 1990, with this fall mainly reflecting declining Commodity Stabilization Fund revenues. Coffee and cocoa are no longer the majority share in exports, although in 1990 they still accounted for 35 percent of total exports of goods and services.

# Applied General Equilibrium Models

The general equilibrium-based social policy model we present here is primarily aimed at aiding assessment of the social consequences of the adjustment programs pursued in C6te d'lvoire since 1981. It captures relative product- and factor-price movements, and shows how resources reallocate as policies change. Because the model utilizes a base-year Social Accounting Matrices (SAM), which details how incomes generated by production activities are mapped across to households and how expenditure decisions by households are allocated
# **Appendix A: CCT (Controlled Cooking Test) Protocol for Analyzing Cooking of Injera in Traditional and** **_Mirt_ (BEST) Stoves**

_Adapted from Protocol Available at_ http://www.aprovecho.org/lab/pubs/testing (Accessed 10 April 2013) The controlled cooking test (CCT) is designed to assess the performance of the improved stove relative to traditional stoves. Stoves are compared as they perform a standard cooking task that is close to the actual cooking that people do every day. However, the tests are designed in a way that minimizes the influence of other factors and allows for the test conditions to be reproduced.

# _Equipment Required_

- **Fuel:** For all tests within a given household, if possible the same type of air-dried fuelwood should be used. Use local input to determine the quantity of fuel required to cook a “standard meal” on a traditional stove.

- **Food and water:** Testers should be sure they have sufficient food and water for the tests. Like fuel, the food should be homogenous so that variability in food does not bias the results of the test.

- **Injera pans** : if possible, use standard pans.  Record the specifications in the Data and Calculation form. If possible, the same type (size, shape, and material) of pots should be used to test each stove.

- **Scale:** At least 6kg capacity and 1 gram accuracy: (see note in WBT section).

- **Heat resistant pad** to protect scale when weighing hot charcoal.

- **Tape measure**

- **Wood moisture meter**

- **Timer**

- **Thermometer** to record ambient temperature.

- **Small shovel/spatula** to remove charcoal from stove for weighing.

- **Dust pan** for transferring charcoal.

- **Metal tray** to hold charcoal for weighing.

- **Heat resistant gloves** .

## CCT testing procedure

1. Choose an appropriate cooking task. In our case, this will be cooking Injera.

- Decide on the exact amount of food on which to base the test.

- Ensure that sufficient food is available to conduct the tests.

2. Precisely describe the cooking task in writing in as much detail as possible in a way that both stove users and testers can understand and follow.

33 | P a g e
|Wait for water connection (days)|Number of days between application for a<br>water connection and the receipt of the<br>service|World Bank Enterprise Surveys|
|---|---|---|
|Establishment has checking or savings<br>account Y/N|Dummy variable that takes the value of 1<br>if the firm has a checking or savings<br>account at this time|World Bank Enterprise Surveys|
|Female manager Y/N|Dummy variable that takes the value of 1<br>if the top manager of the firm is female|World Bank Enterprise Surveys|
|Use technology licensed from foreign<br>companies Y/N|Dummy variable that takes the value of 1 if<br>the firm uses technology licensed from<br>foreign companies|World Bank Enterprise Surveys|
|Average historical temperature 1900-2014|Gridded dataset that contains monthly<br>observations of precipitation and average<br>temperature at the 0.5 degree grid cell<br>level. Data transformed into average<br>monthly temperature, and total<br>precipitation (mm), per year, for each grid<br>cell.|Matsuura and Willmott, (2012a)|
|Average historical precipitation 1900-2014|Gridded dataset that contains monthly<br>observations of precipitation and average<br>temperature at the 0.5 degree grid cell<br>level. Data transformed into average<br>monthly temperature, and total<br>precipitation (mm), per year, for each grid<br>cell. Precipitation shocks defined based on<br>the long run mean and standard deviation<br>of annual precipitation for each grid cell<br>from 1900-2014.|Matsuura and Willmott, (2012b)|
|Standardized Precipitation Index (SPI)|Gamma distribution fitted to precipitation<br>data to correct for the fact that<br>precipitation data is not normally<br>distributed.|Computed from Matsuura and Willmott,<br>(2012b) precipitation data.|

# **<u>Table A2: Country Composition</u>**

|**Country**|**Year**|**Country**|**Year**|
|---|---|---|---|
|Afghanistan|2014|Madagascar|2013|
|Albania|2013|Malawi|2014|

31
40

|Collection|Title in full|Type of collection|Institution|Geographic<br>focus|Type of survey(s)|Further information on data and access|
|---|---|---|---|---|---|---|
|SHES|World Bank<br>standardized<br>household<br>expenditure surveys|Ex post<br>harmonized|World Bank|Global|HBS, HIES,<br>multipurpose surveys|Surveys produced by World Bank’s Data<br>Group as part of the International<br>Comparison program. Accessible only to<br>World Bank staff|
|SHIP|World Bank Sub-<br>Saharan Africa<br>harmonized<br>household survey<br>collection|Ex post<br>harmonized|World Bank|World Bank’s<br>Sub-Saharan<br>Africa region|HBS, HIES,<br>multipurpose surveys|Some publicly accessible via the World Bank<br>Microdata Catalog. Rest accessible only to<br>World Bank staff via World Bank Microdata<br>Library.|
|WHS|WHO World Health<br>Survey|Multi-country<br>survey initiative<br>– highly<br>standardized|World Health<br>Organization|Global|Health survey,<br>including out-of-<br>pocket expenses and<br>some data on<br>household<br>consumption|Details of the WHS survey can be found at the<br>WHS webpage, and access is via the WHO<br>Central Data Catalog.<sup>45</sup>|

45 The WHS webpage is http://www.who.int/healthinfo/survey/en/. The WHO Central Data Catalog is at http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog.
# **Table 15: Receiving SMS vs. Reporting Self-Isolation Quarantine**

||Self-Is|olation/Quar|antine Measu|res=1; Do|not=0||Self-Is|olation/Quar|antine Meas|ures=1; Do|not=0|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)||(6)|(7)|(8)|(9)|(10)|
|SMS primary||||||Gov. SMS past||||||
|source|0.128***|0.107***|0.105***|0.000|0.000|week|-0.296***|-0.271***|-0.275***|-0.041***|-0.040***|
||(0.013)|(0.012)|(0.012)|(0.004)|(0.004)||(0.018)|(0.021)|(0.022)|(0.015)|(0.016)|
|Urban||0.107***|0.106***|||Urban||0.109***|0.109***|||
|||(0.012)|(0.012)|||||(0.012)|(0.012)|||
|Ln(income pc)||0.053***|0.053***||0.002**|Ln(income pc)||0.054***|0.054***||0.002**|
|||(0.002)|(0.002)||(0.001)|||(0.002)|(0.002)||(0.001)|
|Constant|0.312***|0.125***|-0.051**|0.332***|0.327***|Constant|0.343***|0.148***|-0.026|0.333***|0.328***|
||(0.006)|(0.008)|(0.025)|(0.001)|(0.002)||(0.005)|(0.008)|(0.025)|(0.000)|(0.002)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.012|0.082|0.086|0.000|0.001|R-squared|0.005|0.078|0.082|0.002|0.003|
|Unique individuals|2,153|2,153|2,153|2,153|2,153|Unique individuals|2,153|2,153|2,153|2,153|2,153|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 21