rafmacalaba/datause-encoder-data huggingface dataset:
activity in the week prior to the interview. Therefore we can identify whether children attend school and/or work. For those who report working, the survey asks the number of hours of work.

Of the original 208 experimental villages, eight are excluded from our analysis. Two villages could not be resurveyed due to concerns for enumerator safety; two villages were incorporated in PAL prior to the pretreatment survey; two villages were deemed ineligible for the experiment because they were receiving the conditional cash transfer program, _Oportunidades_ , contrary to PAL rules; and two villages are geographically contiguous and cannot be regarded as separate villages.<sup>10</sup> Observable characteristics of excluded villages are balanced across treatment arms (results available upon request). Of the remaining 200 villages, three received the wrong treatment (one in-kind village did not receive the program, one cash village received both in-kind and cash transfers, and one control village received in-kind transfers). We include these villages and interpret results as intent-to-treat estimates.

For all children born between 1998 and 2004 - those who were 6 years old or younger at the time of the first PAL survey - we merge individual and household level information both from the baseline and the follow-up evaluation surveys with grades 3 through 6 ENLACE test score data between 2007 and 2013.<sup>11</sup> The ENLACE identifies students through the government-issued identifier, the _Clave Única de Registro Poblacional_ (CURP), which is formed by an algorithm combining the first name, surname, date of birth, sex, state of birth, and two randomly generated digits. The PAL household surveys do not contain the CURP, but do contain all of its constituent demographics from which we generated a quasi-CURP (lacking the random digits). We thus form an unbalanced panel of seven cohorts spanning seven academic years.

Each year, 20 percent of exam takers are randomly selected to complete the _ENLACE de Contexto,_ a multiple choice survey which elicits information about child labor, parental and student sociodemographic characteristics, student and parent expectations, and student perceptions about their peers, teachers and parental involvement.

The Ministry of Education conducts two school censuses per year, known as Formato 911, which are completed by an authorized school representative. The census identifies whether the school belongs to the _general_ , _indigenous_ , or _community_ system and collects a large set of school characteristics, including the number of teachers, students, classrooms, and laboratories and whether a library is available. For general schools, it also provides information

> 10The contiguous villages are named “Section 3 of Adalberto Tejada” and “Section 4 of Adalberto Tejada,” so they appear to be part of the same administrative unit.

> 11We observe a small number of surveyed PAL children in grades 7 through 9, but exclude them from our analysis in order to focus on primary school outcomes.

8
Variable #180| Question #39 | **fbudgetextagency** | amount of budget allocated by external (government) agency | percent | float

Variable #181 | Question #39 | **fbudgetwhichagency** | name of external agency | 0 = n/a,1 = Government,2 = SRBC,3 = Provincial,4 = Central Gov’t,5 = Hydrologic funds,6 = Env. Ministry,7 = Fundo Hydricos,8 = Agric. Ministry,9 = BM,10 = BIRF | int

Variable #182 | Question #39 | **fbudgetstakeh** | amount of budget derived from basin stakeholders | percent | float

Variable #183 | Question #39 | **fbudgetothersources** | amount of budget from other sources | percent | float

Variable #184 | Question #40 | **fbudgetinvestmentbasin** | amount of budget used for investment in the basin | percent | float

Variable #185 | Question #41 | **fbudgetotheractivities** | amount of budget used for other development activities | percent | float

Variable #186 | Question #42 | **fbudgetom** | amount of budget used for O&M | percent | float

Variable #187 | Question #43 | **fbudgetwaterquality** | amount of budget used for water quality activities : percent | float

Variable #188 | Question #44 | **fbudgetother** | amount of budget used in other activities | percent | float

Variable #189 | Question #44 | **other44** | other activities | 1 = hydraulics,2 = hydrology,3 = communication,4 = national budget,5 = institutional cost,6 = personnel cost,7 = R & D,8 = education | int

# **Performance Indicators**

Variable #190 | Question #45 | **pigdpyesno** | Does the basin measure GDP | 0 = no,1 = yes | int

Variable #191-194 | Question #45 | **piyear1** | **piyear2** | **piyear3** | **piyear4** | year of GDP measurement | int

65
# **Mobility and Resilience: A Global Assessment of Flood Impacts on Road Transportation Networks**

## **Yiyi He**<sup>**a***</sup> **, Jun Rentschler**<sup>**b**</sup> **, Paolo Avner**<sup>**c**</sup> **, Jianxi Gao**<sup>**d**</sup> **, Xiangyu Yue**<sup>**e**</sup> **, John Radke**<sup>**a,f**</sup>

a College of Environmental Design, University of California, Berkeley, USA. b Office of the Chief Economist for Sustainable Development, The World Bank, Washington, D.C., USA. c The Global Facility for Disaster Reduction and Recovery, The World Bank, Washington, D.C., USA. d Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, USA.

e Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA. f Department of City and Regional Planning, University of California, Berkeley, USA.

> * _Corresponding author_

**Keywords:** Floods, Global road network, Infrastructure network resilience, Travel simulation **JEL codes:** Q54, R41, R12, C63
# **5b)  Days to Start a business (DB) versus Days to Obtain an Operating License (ES)**

<!-- Start of picture text --> 0 50 100 150 200 250<br>Days to start a business (DB)<br>excludes outside values<br>5c) “Time to Import Goods” (DB) versus “Days to Clear Customs” (ES)<br>0 25 50 75 100<br>Time to Import Goods (DB)<br>excludes outside values<br>250<br>200<br>150<br>100<br>50<br>Days to obtain operating license (ES)<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br>Days to clear import customs (Enterprise Surveys)<br><!-- End of picture text --> 48
Table D.4: Program impacts on anticriminal and antiviolent values, 12–13 month survey only

|||||ITT regre|ssion (N=9|47)||
|---|---|---|---|---|---|---|---|
||Control|Ther|apy only|Cas|h only||Both|
|Outcome|mean|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Summary index of anticriminal and antiviolent values|0.070|-0.087|[.101]|-0.005|[.102]|-0.203|[.099]**|
|Attitudes toward use of violence (11 questions)|0.051|0.074|[.120]|0.029|[.122]|0.001|[.119]|
|If a stranger in the community robs one of<br>your neighbors, is it okay for your neighbor<br>to send people to abuse the stranger?|0.127|0.032|[.039]|-0.037|[.038]|0.003|[.040]|
|If a man owes you money but refuses to pay,<br>would it be all right to take something from|0.036|-0.009|[.026]|0.002|[.028]|-0.016|[.026]|
|his home?||||||||
|If someone kills a known criminal in your<br>community, it is ok if the police don’t|0.122|0.022|[.035]|0.058|[.041]|-0.015|[.034]|
|investigate?||||||||
|If a rogue steals from a market, and the<br>storekeepers chase him and beat him,<br>should the storekeepers be punished? (-)|0.447|0.100|[.058]*|0.043|[.059]|0.052|[.058]|
|What if they mistakenly kill the rogue?<br>Should the storekeepers be punished? (-)|0.189|0.011|[.045]|0.063|[.050]|0.038|[.048]|
|Suppose your friend’s wife ran of with<br>another man and stole his money and<br>belongings. Would it be good for him to<br>chase and beat that woman?|0.138|0.093|[.041]**|0.048|[.042]|0.020|[.042]|
|If a ’highman’ is caught should his property<br>be destroyed?|0.434|-0.043|[.057]|-0.036|[.059]|-0.002|[.059]|
|Suppose someone tries to steal your friend’s<br>girlfriend. Your friend is talking about<br>getting the boys together to threaten the|0.107|-0.025|[.034]|0.005|[.035]|-0.035|[.034]|
|man. Would you join in?||||||||
|If a local leader is corrupt and taking<br>money for himself, is it all right if he is<br>beaten by the community?|0.163|-0.052|[.042]|-0.065|[.045]|-0.062|[.045]|
|If a wife challenges her husband in public, is<br>it ok for her husband to beat her?|0.179|0.002|[.043]|-0.036|[.044]|-0.027|[.045]|
|Suppose a man rapes a girl in your<br>community. He is arrested, but he bribes<br>the police and goes free. If some people<br>beat that police man, would you join in?|0.173|0.002|[.043]|-0.001|[.047]|0.037|[.046]|
|Attitudes toward criminality (12 questions)|0.044|-0.109|[.118]|-0.106|[.117]|-0.287|[.116]**|
|Imagine that a Chinese man cheats another|0.081|-0.049|[.026]*|-0.044|[.029]|-0.070|[.028]**|
|man of his wage for carrying a load. Would<br>it be ok for that man to take his friends to||||||||
|beat the Chinese man hard, and to take his<br>money?||||||||

Continued on following page.

xxix
Table 1. General technology deployment levels by scenario

||**Leapfrog**|**Lopsided**|**Lock-in**|
|---|---|---|---|
|**Power**<br>_Indicative stat:_|IOT-based digital grid<br>Acceleration of grid-scale<br>renewable energy and<br>battery storage<br>Construction and<br>incorporation of some EV<br>charging stations<br>Rapid diffusion of microgrids<br>_Electricity access improved_<br>_by ~95% versus 2018 base_|Investment in digital only in<br>privately-owned capacity<br>More renewables, but not<br>well integrated<br>Private microgrids for fleet<br>EV charging<br>Microgrids for gated<br>communities<br>_Electricity access improved_<br>_by ~65% versus 2018 base_|Lack of investment in digital<br>smarts and communications<br>infra.<br>More renewable capacity, but<br>poor grid integration<br>Grids too weak to support<br>much EV<br>Very few microgrids<br>_Electricity access improved_<br>_by ~75% versus 2018 base_|
|**Transport**<br>_Indicative stat:_|Shared, Autonomous EVs<br>(SAEVs) have become the<br>norm for millenials<br>Single passenger cars are a<br>rare luxury<br>Wide diffusion of mobility as<br>a service (MaaS)<br>Common package delivery<br>drones; occasional,<br>autonomous flying cars<br>_50% of cars on the road are_<br>_SAEVs_|Limited penetration of<br>AV/EVs<br>Limited implementation of<br>physical infrastructure<br>New techs most common for<br>the wealthy, new<br>neighborhoods<br>Resistance by incumbents to<br>TNCs & ride sharing<br>_Passenger VKT for the_<br>_wealthiest is up by 20-30%_|Requisite physical &<br>communication infrastructure<br>has not fully arrived<br>Resistance by incumbents to<br>TNCs & ride sharing<br>Grids not capable of handling<br>loads from electric vehicles<br>AVs create new problems for<br>“drivered” cars<br>_No more than 5% of cars are_<br>_SAEVs_|
|**Water**<br>_Indicative stat:_|Water mgt agencies<br>cooperate within & across<br>boundaries<br>Wide collection & use of data<br>use data for resource and<br>system mgt<br>Non-revenue water is<br>reduced substantially<br>Mix of new connections to<br>the main system & distributed<br>systems<br>Safe water access improved<br>by ~70% versus_2018 base_|Cooperation in closely<br>politically aligned areas<br>Advanced flookd control in<br>areas with industrial<br>operations or wealth<br>,<br>Private water utilities<br>incorporate digital strategies<br>to reduce non-revenue water<br>Wealthy nbhds<br>built with local water<br>reclamation and purification<br>Safe water access improved<br>by ~40% versus_2018 base_|“Stove-piping” & little<br>coordination of data<br>Legacy data systems and<br>untrained staff<br>Little approval for spending<br>on digital<br>Few distributed systems exist<br>Few “trenchers” ... piping<br>remains costly and disruptive<br>Safe water access improved<br>by ~50% versus_2018 base_|

12
- 16 -

33. In addition, the bus access variables can be replaced with the direct measurements of the distance to a bus route. The results are presented in the last two columns of the table. Both distance variables are found to have negative coefficients as expected. That is, as one moves away from a bus route, the land value would likely decline. But there is no statistical significance, except for one: In the last column model, the land value is found to decrease significantly with the distance to the nearest taxi-be route. The coefficient is estimated at - 0.038, meaning that a 10 percent increase in distance to taxi-be routes would reduce the land price by about 0.4 percent. All the indications are that the proximity to bus services is an important element of land value in Antananarivo.

**<u>Table 4. Spatial autoregressive models with alternative bus accessibility variables</u>**

||<br>Coef.|<br>Std.Err.|<br>Coef.|<br>Std.Err.|<br>Coef.|<br>Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|---|---|
|ln_MIN_|-0.062|(0.133)|||-0.350|(0.124) ***|||
|ln_JAI_|||0.106|(0.064) *|||0.094|(0.065)|
|ln_POPDEN_|0.029|(0.030)|0.033|(0.030)|0.021|(0.030)|0.029|(0.030)|
|ln_CSB_|-0.040|(0.028)|-0.031|(0.028)|-0.033|(0.028)|-0.032|(0.028)|
|_VUL_|-0.023|(0.008) ***|-0.023|(0.008) ***|-0.024|(0.008) ***|-0.023|(0.008) ***|
|_RD_|1.8513|0.5606 ***|1.8443|<br>0.555 ***|1.7186|0.5552 ***|1.7953|0.5565 ***|
|_ALLWEARD_|0.137|(0.069) **|0.163|(0.068) **|0.145|(0.071) **|0.143|(0.071) **|
|_RICE_|-0.076|(0.162)|<sup>-0.183</sup>|<sup>(0.160)</sup>|<sup>-0.230</sup>|<sup>(0.157)</sup>|<sup>-0.198</sup>|<sup>(0.160)</sup>|
|_AMENITY_|1.476|(0.784) *|1.810|(0.777) **|1.635|(0.784) **|1.859|(0.779) **|
|_TAXIBE500_|0.087|(0.101)|0.116|(0.097)|||||
|_SUBU500_|0.084|(0.078)|0.067|(0.079)|||||
|ln_KMTAXIBE_|||||-0.034|(0.022)|<sup>-0.038</sup>|<sup>(0.023) *</sup>|
|ln_KMSUBU_|||||-0.014|(0.016)|<sup>-0.012</sup>|<sup>(0.016)</sup>|
|Constant|-2.756|(1.431)*|-11.519|(3.115)***|-9.968|(2.926)***|-11.281|(3.148)***|
|Obs.|548||548||548||548||
|No. of district|||||||||
|dummies|3||3||3||3||
|Wald chi2|134.05||165.52||169.57||167.12||
|Spatial parameters:|||||||||
|_λ_|1.132|(0.124) ***|1.909|(0.306) ***|1.922|(0.276) ***|1.896|(0.309) ***|
|_ρ_|3.406|(0.178)***|2.673|(0.098)***|2.424|(0.047)***|2.678|(0.097)***|

34. Another matter of concern may be endogeneity. Especially, people’s locational choice is potentially endogenous with land prices. While the land prices increase where many people move in, people may refrain from living in the places where land prices are high. Thus, the
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8. REFERENCES Aizenman, J. & Marion, N. (1991): Policy Uncertainty, Persistence and Growth, NBER Working Paper, No 3848, Cambridge.

Al-Thaqeb, S.A. & Algharabali, B.G. (2019): Economic Policy Uncertainty: A Literature Review, The Journal of Economic Asymmetries, Vol 20, 1-11.

Aragón-Correa, J.A. % Sharma, Sanjay (2003): A Contingent Resource-Based View of Proactive Corporate Environmental Strategy, Academy of Management Review, Vol. 28(1), 71-88.

Baker, S.R.; Bloom, N, & Davis, S.J. (2016): Measuring Economic Policy Uncertainty, The Quarterly Journal of Economics, Vol. 131(4), 1593-1636 Baldwin, R. (2010). Better regulation. The search and the struggle. In R. Baldwin, M. Cave, & M. Lodge (Eds.), The Oxford handbook of regulation. Oxford University Press.

Barro, R.J. (1991): Economic Growth in a Cross Section of Countries. Quarterly Journal of Economics, Vol. 106(2), 407-443.

Bernanke, B.S. (1983): Irreversibility, Uncertainty, and Cyclical Investment, The Quarterly Journal of Economics, Vol. 98(1), 85-106.

Bloom, N. (2009): The Impacr of Uncertainty Shocks, Econometrica, Vol. 77(3), 623-685.

Bloom, N.; Bond, S. & van Reenen, J. (2007): Uncertainty and Investment Dynamics, The Review of Economic Studies, Vol.74(2),391-415.

Brenner, D. & Fazekas, M (2023) Can impact assessments tame legislative drift? Event history analysis of modifications of laws across Europe. Governance, 36(1) Carrera, A., Mesquita, L., Perkins, G. and Vassolo, R. (2003). ‘Business groups and their corporate strategieson the Argentine roller coaster competitive and anti-competitive shocks’.Academy of Management Execu-tive,17, 32–44 Chen, P-F; Lee, C-C, & Zeng, J-H. (2019): Economic Policy Uncertainty and Firm Investment: Evidence from the U.S. Market, Applied Economics, Vol. 51(31), 3423-3435.

Colak, G; Durnev A. & Qian, Y (2017): Political uncertainty and IPO activity: Evidence from the U.S gubernatorial elections, Journal of Financial and Quantitative Analysis, Vol. 52(6), 2523-2564.

23
|____________<br>Country|______<br>Year|___________________<br>Variable|_______|______|_____|_______|_______|______<br>In|______<br>dustry|_______|______|_______|______|_____|________|________|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|____________|______|___________________|DA<br>_______|DB<br>______|DC<br>_____|DD<br>_______|DE<br>_______|DF<br>______|DG<br>______|DH<br>_______|DI<br>______|DJ<br>_______|DK<br>______|DL<br>_____|DM<br>________|DN<br>________|
|East Germany|1995|Participation rate 21.5|58.8|54.7|15.6|39.0|63.6|74.4|42.4|23.1|32.8|54.1|49.6|47.4|54.1||
|||Export intensity 15.5|14.6|18.4|15.8|17.0|15.5|26.1|11.3|20.8|11.0|21.3|20.6|18.1|15.4||
|||<br>Number of firms 912|374|53|256|410|11|234|384|588|1,126|845|750|287|380||
||2004|Participation rate 20.8|67.7|61.5|46.0|45.2|62.5|79.8|66.5|41.9|47.6|63.6|55.1|68.9|61.8||
|||Export intensity 16.9|24.9|29.9|21.0|18.5|40.6|37.2|23.4|26.1|17.6|25.9|31.5|26.1|20.2||
|||<br>Number of firms 1,144|269|26|224|465|8|282|525|506|1,462|977|1,039|360|283||
|Italy|1989|Participation rate|52.0|56.6|77.1|45.4|51.1|29.1|78.6|74.3|54.8|57.8|83.8|66.3|68.4|71.9|
|||Export intensity 16.1|26.2|48.5|21.0|12.8|9.8|20.0|25.7|33.0|23.0|37.2|26.8|28.7|30.8||
|||<br>Number of firms 1,418|3,361|1,077|526|1,105|86|796|1,018|1,418|2,925|2,562|1,452|684|1,488||
||1997|Participation rate 65.3|59.3|72.3|59.6|61.7|44.8|83.6|79.3|60.9|63.1|82.5|70.3|71.3|79.3||
|||Export intensity 19.0|34.1|51.3|22.4|14.7|17.1|26.7|28.5|34.6|27.6|45.6|34.4|34.9|38.4||
|||Number of firms 1,066|2,158|611|413|931|67|639|922|963|2,561|2,120|1,331|526|1,208||
|Rep. of Ireland|1991|Participation rate 53.3|71.4|76.5|58.9|56.8|NA|86.8|81.9|54.1|67.8|72.6|84.5|62.5|71.3||
|||Export intensity 48.3|61.0|55.5|27.6|34.1|NA|72.0|54.3|39.8|50.8|69.1|81.8|70.2|56.0||
|||Number of firms 392|227|17|56|185|NA|129|105|98|143|124|226|48|94||
||2004|Participation rate 71.9|87.9||44.6|59.5|NA|90.4|72.4|43.3|52.6|84.3|88.9|76.6|65.4||
|||Export intensity|47.0|60.0||27.2|30.3|NA|72.4|50.3|28.5|36.2|60.4|79.1|77.1|43.1|
|||Number of firms 331|69||92|205|NA|136|127|120|209|115|217|47|107||
|Slovenia 1994|Partici|pation rate 48.8<br>59.5|68.4|75.1|34.0|100.0|85.9|71.7|64.2|60.8|78.0|59.3|82.1|63.2|||
|||<br>Export intensity 16.9|88.3|90.6|39.6|16.0|19.3|38.8|36.2|25.9|52.6|41.4|34.4|50.6|45.8||
|||<br>Number of firms 160|232|38|173|297|3|71|145|95|424|205|371|56|163||
||2002|Participation rate 65.0|77.6|87.0|79.5|72.2|100.0|90.1|95.9|80.6|83.4|90.5|88.7|90.2|94.4||
|||Export intensity 15.2|95.1|69.7|50.6|20.5|45.9|52.0|44.8|37.3|49.4|58.6|60.2|76.1|54.4||
|||Number of firms 100|147|23|78|101|3|55|74|62|223|126|150|41|89||

64
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# 1.1 Government Intervention and Agriculture in Sub-Saharan<sup>Africa: An Overview</sup>

Governments' interventiors in agriculture are wide spread<sup>and common to all countries</sup> developed as well as less developed alike. SSA has not been<sup>an exception as can be seen from</sup> the record of agricultural sector pricing policy in table<sup>(1.1) below. This form of intervention is</sup> explicit and operates along the supply curve and is executed<sup>through the instruments of forced</sup> procurement, administered pricing, export and local<sup>taxation, among others. Tne practical</sup> motivation for such practice on the part of governments<sup>fould be to raise revenue or to keep</sup> down the prices of food.<sup>7</sup> Such squeeze on agriculture<sup>is argued to be a main cause of rural</sup> poverty<sup>8</sup> , and a major obstacle p,-venting agriculture<sup>from accelerating thc process of growth and</sup> structural transformation.<sup>9</sup> The potential deleterious impact on agriculture of the above<sup>direct form of government</sup> policy, may be reversed, however, by another set of direct<sup>interventions which tend to shift tile</sup> agricultural supply curve. In this case government interventions<sup>throagh the 'shifters of supply'</sup> such as agricultural technology, credit, investment,<sup>and price stabilization schemes are helpful to</sup> agriculture. Indeed in the case of the countries of SSA such role<sup>for government is critical for</sup> the development of a viable agriculture. Micro and<sup>institutional considerations aside, the ability</sup> of governments in SSA to undertake such a role depends<sup>critically on the prevailing international</sup> economic environment and domestic macroeconomic<sup>conditions. For example after 1982 a</sup> 7There is now a vast body of literature on the underlining political economy of these policies. An interesting analysis for the case of African agriculture<sup>is p,ovided in Bates (1981,82), and</sup> Commins, Loftchies, and Payne (1986).

8Garcia (1981), for example, found agriculture prices to be a major determinants of rural wages.

'Me arguments regarding the implications of such sector-specific<sup>policies for the growth of</sup> agriculture are well understood and has occupied<sup>a central position in the literature. e.g. Eicher</sup> and Staatz (1986) and the literature cited therein.