rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Impact of Heat Exposure on Performance in Common Subjects

|||Standardiz|ed Score||
|---|---|---|---|---|
||English|Math|Civics|Aptitude|
||(1)|(2)|(3)|(4)|
|T_>_33C|_−_0_._003<sup>_∗_</sup>|_−_0_._001|_−_0_._011<sup>_∗∗∗_</sup>|_−_0_._006<sup>_∗∗∗_</sup>|
||(0_._001)|(0_._001)|(0_._002)|(0_._002)|
|T 30-33C|0_._00001|0_._001|_−_0_._005<sup>_∗∗∗_</sup>|_−_0_._002|
||(0_._001)|(0_._001)|(0_._001)|(0_._001)|
|T 27-30C|0_._001<sup>_∗∗_</sup>|0_._002<sup>_∗∗∗_</sup>|_−_0_._003<sup>_∗∗_</sup>|0_._001|
||(0_._001)|(0_._001)|(0_._001)|(0_._001)|
|T 24-27C|_−_0_._00002|0_._001<sup>_∗∗_</sup>|_−_0_._003<sup>_∗∗∗_</sup>|_−_0_._0002|
||(0_._0005)|(0_._001)|(0_._001)|(0_._001)|
|T 21-24C|_−_0_._0004|0_._0005|_−_0_._003<sup>_∗∗∗_</sup>|_−_0_._001|
||(0_._0004)|(0_._0005)|(0_._001)|(0_._0005)|
|T_<_18C|_−_0_._002<sup>_∗∗∗_</sup>|_−_0_._003<sup>_∗_</sup>|_−_0_._002|_−_0_._004<sup>_∗∗∗_</sup>|
||(0_._001)|(0_._001)|(0_._001)|(0_._001)|
|Female Control|X|X|X|X|
|**Fixed Effects**|||||
|Year|X|X|X|X|
|School|X|X|X|X|
|Stream|X|X|X|X|
|Observations|2,132,635|2,132,635|2,104,972|2,132,635|
|R<sup>2</sup>|0.271|0.140|0.144|0.196|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variables are the subject-wise standardized test scores of students taking the 12th grade exams (for the common subjects across both streams) and is constructed to have a mean of (or close to) zero. All regressors include the number of days in the temperature bins during the school-year cycle: June-May. All regressions control for the number of days in the precipitation bins. Errors are clustered at the school level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

21
# **IV. Conclusion**

In this paper we provide an analysis of welfare dynamics in the Sub-Saharan Africa region. In the absence of actual panel data, we construct synthetic panel data from cross sectional surveys using recently developed statistical methods that can offer insights into welfare dynamics for the region. Our findings generally point to strong performance for the region in terms of pro-poor growth and upward mobility. We find that one-third of the poor population escaped poverty, and the size of the middle class increased by 28 percent, albeit from a low base. Chronic poverty, however, still remains high for a number of countries. Furthermore, while many escaped poverty, they remain vulnerable, as is evident from the 12 percent increase in the share of the vulnerable population.

We also find some limited evidence suggesting that resource-rich and middle-income countries mostly have more upward mobility than downward mobility, and that (some) tertiary education is strongly associated with higher upward mobility and less downward mobility, which holds true to some extent for households with a female household head and urban residence.

These mostly positive outcomes were possible because of favorable global economic conditions from which many African countries benefited in the 2000s. However, the circumstances are changing and domestic economic conditions have deteriorated for many African countries. Although the long-term goal is to increase upward mobility, or exit from poverty, the immediate and medium term goals may be to protect the incomes of the poor and to minimize downward mobility, especially for the vulnerable. Some of the policies that have been shown to achieve these goals include safety net programs and building the assets (especially human capital – education and health) of the poor and the vulnerable, such as investments in early years of the children of the poor, and providing a basic package of health services.

21
clients of the development banks-see below), while stronger states would see the price of loans fall.

5. Registration of sub-national loans with the federal government was made conditional upon the borrowing state or municipality being current on its publication of debt and associated fiscal statistics from the preceding year's final accounts, and on all of its debt service obligations toward the Government's development banks. At the same time, and to make that registration appealing, unregistered loans are automatically risk weighted by the regulators at 150 percent. This additional incentive to achieve transparency has the purpose of ensuring that private contracting between the subnational borrowing and the credit rating agencies does not lead to the withholding of a minimum of quantitative information on borrower finances. Ultimately, the discipline on these governments will arise only if voters and opposition parties have access to full information about the fiscal behavior of the administration in office.

6. Finally, and as a matter of corporate policy, federally-owned development banks are to make new loans to states and municipalities only when the loan in question qualifies for registration and its corresponding capital risk weighting is less than 100 percent. This policy, coupled with the previously mentioned conditions for registration, makes the development banks part of the rigor of the new regulatory scheme, rather than allowing them to function as a potential loophole.

Lending to weaker sub-nationals is not forbidden, after all, these are the clients whom the development banks are mandated to assist. Instead, sub-national loans with risk weightings of more than 100 percent are allowed if the loan package 8
**Table 3c. Heterogeneous Impacts of EPAG on Participation in Income Generating Activities—by Wealth, Education and Age**

|||**Panel 1: By**|**WealthQuintil**<br>Quintiles o|**e**<br>OLS<br>f asset-based w|elfare index||
|---|---|---|---|---|---|---|
||All|Q1|Q2|Q3|Q4|Q5|
|Any IGA|0.181***|0.006|0.178***|0.337***|0.256***|0.074|
||(0.026)|(0.049)|(0.055)|(0.051)|(0.048)|(0.046)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3200|606|646|659|634|655|
|||**Panel 2**<br>|**: By Education**<br>Edu|OLS<br>cation level of t|he Girl||
|||None/||||High school|
|||Informal||Junior high||graduate|
||All|only|Primary|school|High school|and above|
|Any IGA|0.181***|0.219|0.192***|0.171***|0.140***|0.222***|
||(0.026)|(0.149)|(0.047)|(0.050)|(0.044)|(0.036)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3200|88|612|758|835|907|
|||**Panel 3:**|**By Age Group**<br>A|OLS<br>gegroupof the|girl||
||All||16-19|20-24|25-27||
|Any IGA|0.181***||0.227***|0.187***|0.151***||
||(0.026)||(0.049)|(0.032)|(0.038)||
|Controls|Yes||Yes|Yes|Yes||
|Observations|3200||484|1753|963||

Controls used are household size, sex of household head, asset based quintiles (excluded for “by wealth” regressions), community, age of adolescent (excluded for “by age group” regressions), educational status of adolescent (excluded for “by education” regressions), marital status of respondent, parental status of respondent (indicator variables for adolescent having a child and being pregnant), orphan status of respondent (one parent or both parents being deceased), and month of interview at midline; all controls are from baseline data except month of midline interview.

36
20,000-people dead or missing<sup>1</sup> in Japan, underscores the importance of considering resilience to disaster in infrastructure investment decisions. The disaster heavily affected Sendai, the capital city of Miyagi Prefecture and a regional economic hub. The tsunami completely submerged the city's primary wastewater treatment, while some 500,000 residents lost access to water. Also, the tsunami damaged 325 kilometers of coastal railway assets and flooded about 100 kilometers of the national highway in the Tohoku region, leaving devastated towns in need of assistance without inland transport access.

This paper presents a practical approach to bring the concept of infrastructure resilience to the project level, with direct application to transport infrastructure. The proposed approach complements the IPF and offers additional indicators as potential inputs to the framework that can help decision makers discern between projects from a resilience perspective. The paper begins with a discussion of the concept of infrastructure resilience and follows with proposed indicators to measure aspects of resilience in transport at the project level.

# Applying the Concept of Resilience to Infrastructure Projects

The concept of _resilience_ has been discussed in various academic fields and conceptualized in numerous ways (Cutter _et al._ , 2010). For social sciences, the main interest is the resilience of communities, whereas for engineering fields, the focus is on the resilience and robustness of structures. Organizational resilience, conversely, focuses on the ability of an organization to absorb shocks and to adapt in a changing environment (ISO 22316). In most fields, the concept is recognized to be multifaceted. Bruneau (2003) conceptualizes resilience as the ability of a system to reduce the chances of a shock, to absorb a shock capable of causing abrupt reduction of performance, and to recover quickly after the occurrence of a shock.

In line with these definitions, we present an application of the resilience concept that describes (a) an asset's ability to withstand shocks in such a way that minimizes functionality losses (i.e., robustness or resistance), and (b) the asset's capacity to recover functionality across multiple dimensions following a disaster event (i.e., recoverability). Dealing with the expected

> 1 World Bank. 2014. Learning from Megadisasters: Lessons from the Great East Japan Earthquake. Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/18864

3
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vehicle use. A second car usually brings additional use; this not only substitutes the restricted trips but also expands the possibility of using the entire car capacity for other purposes. On the other hand, used cars tend to be less fuel-efficient and more polluting than new ones.<sup>32</sup> In Bogotá, the lifetime of the TWC has already expired for some used cars, and more than 50 percent of the regular cars do not have a TWC. Thus, the use of old cars may have contributed to the lack of decline in CO for a given total stock of cars.

# VII. TRANSPORT COSTS

Here I provide some calculations of the transport costs imposed by PYP. As Davis (2008) indicates, one might consider a model that takes into account people’s transportation choice set to analyze the costs induced by suboptimal choices due to driving restrictions. In that model, individuals derive utility from air quality, a nonpolluting composite consumption good, and transportation goods. Under the assumption that the marginal utility of the composite good is constant, the social costs imposed by the program would be equal to the willingness to pay (WTP) to avoid the restriction (see Davis 2008). WTP values are not available for Bogotá, but recently, Blackman et al. (2015) used contingent valuation to obtain the WTP for being exempt from HNC in Mexico. Analogous to the benefit transfer approach, I used Blackman et al.’s WTP values to estimate the driving restriction costs of the drastic PYP phase. Costs are not computed for the moderate phase as Blackman et al.’s estimates were obtained in 2013 when HNC had been in force for many years—that is, WTP is conditional on households’ past adaptation investments. This setting is much closer to the drastic phase as households already had some experience facing restrictions.

32. Basic statistics reported by Secretaría de Movilidad (2009) show that used vehicle sales increased four times more in the first month of the drastic phase than in the same month in the preceding year.

24
- 11 -

technologies, i.e., low-input, high-input and irrigated production systems. The last two figures are used for our calculation of areas with fertilizer application.

The average fertilizer consumption is calculated based on IFA/IFDC/IPI/PPI/FAO (2002) and Liu et al. (2010). Our fertilizer variable is the sum of three major elements of fertilizer: nitrogenous (N), phosphate (P2O5) and potash (K2O), because the actual mix and content of N, P and K varies across fertilizers. They are simply the sum of all the three elements for each crop.

Transport connectivity is also a crucial input for agricultural production. Farmers need to go to the markets to purchase necessary inputs and sell their products. Transport connectivity can be measured at different levels. First, the distance to the nearest road—denoted by _T_ 0—is fundamental regardless of the type of crop, because farmers need to get to the road network no matter which crop is produced and where the products are transported to. In East Africa, rural accessibility is still a challenge and the accessibility indices are estimated at about 2535 percent (Figure 5). Using the spatial data of road networks, the distance to the nearest road is calculated from the centroid of each location _i_ . This varies from less than 1km to over 100 km, depending on location (Figure 6).

Beyond the local accessibility, required transport connectivity may differ depending on the destination of the products. For domestically consumed food crops, such as maize and bananas, the destination is assumed to be a large town/ city, which is defined by the nearest populated area with more than 50,000 inhabitants.<sup>9</sup> Under the cost minimization assumption, the transport network cost is calculated by summing up the transport unit costs of road, railway and port along the optimal path from the production area to the city (Figure 7). This is denoted by _T_ 1. Note that the unit road transport costs vary, depending on type and class of road, and road conditions. In addition, not only road user costs and transport tariffs, but also

> 9 For small countries, such as Burundi, some other cities below this threshold are also included, because there would otherwise be only one domestic market in the country, i.e., the capital city, Bujumbura.
6. Cities that were less exposed to SOE reform experienced higher increase in services employment share and higher growth in college enrollment. The effect of FDI inflow shock on structural transformation and college enrollment remains significant in all columns but column (5). The effect of FDI shock on city level structural transformation and school enrollment remains robust.

Table <u>6. Robustness check: control SOE reform</u>

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Panel A:<br>|structural transf<br>|ormation<br>|Panel B: school<br>|enrollment<br>|
|Dependent var|∆agri_emps|∆indu_emps|∆serv_emps|∆Highschool|∆College|
|FDI_shock|-2.751***|1.498**|1.151**|0.735**|0.507|
||(0.985)|(0.686)|(0.481)|(0.333)|(0.338)|
|L.FDI_empshare|0.00250|0.0508|-0.0546|-0.0485|0.0188|
||(0.0439)|(0.0369)|(0.0409)|(0.0307)|(0.0281)|
|Observations|607|607|607|607|607|
|R-squared|0.542|0.554|0.452|0.813|0.482|
|Prov-Year FE|Y|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|Y|

Note: This table reports the effect of FDI shock on city level structural transformation and school enrollment when SOE reform is controlled for. Lagged city characteristics include population, average age, share of Han ethnicity, log GDP per capita in both panels and average years of schooling in panel A. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 4.4 Migration

Migration creates another major threat to the validity of our findings. FDI constitutes a local labor market shock and workers can respond to such shocks by moving across cities. When a city receives huge manufacturing FDI inflows, labor demand in industry and related services sectors will rise, lifting wage rates in the city and attracting workers from other places. More and better work opportunities also attract students to this city for college education. Migration thus magnifies the effect of FDI on local structural transformation and school enrollment. On the other hand, migration could reduce school enrollment in poorer areas as youths drop out of school to work in richer cities (De Brauw and Giles 2017). Fortunately, migration should not affect our results too much because China adopts a strict Hukou system that limits the free movement of people across locations, and the share of migrants indeed remained relatively low during our study period. The population census records individual’s Hukou address, current residence address and where the individual lived 5 years before the census date. We can therefore identify individuals that migrated to the current location within five years. While the number of cross-county migrants increased dramatically from 18 million to 76 million during 1990-2005, these migrants only accounted for a fraction of total working-age population (Colas and Ge 2019). The number of cross-township migrants are larger, estimated at nearly 50 million in 1995 and 153 million in 2005, primarily involving rural labor moving to 21
subsidiary usage (regression 9). In the IV regressions 7-9, estimated coefficients for creditor rights in borrower countries, and the time to enforce contracts in borrower and lender countries are very similar to regressions 4-6. The IV regressions appear to be well specified according to reported overidentification and under-identification tests.

Overall, the results of Table 4 indicate that international banks are more likely to originate loan through foreign subsidiaries located in especially third countries if subject to stricter capital regulation and greater supervisory power in their home countries, consistent with regulatory arbitrage. In contrast, better institutions in lender countries relative to borrower countries lead to a lower subsidiary usage in the provision of cross-border syndicated loans. In particular, the foreign subsidiary share is negatively related to the quality of monitoring institutions and the speed of contract enforcement in lender countries, while it is positively related to creditor rights and the speed of contract enforcement in borrower countries.

As discussed, Figure 3 suggests that arbitrage with respect to capital regulations was relatively prevalent in the first half of our sample period. To test this formally, we re-estimate the subsidiary ratio regressions 4-9 from Table 4 separately with data for the years 1995-2005 and 2007-2016. The results using data for the years 1995-2005 are displayed as columns 1-6 in Table 5. These results provide consistent evidence of higher subsidiary usage in case of more stringency in lender countries as related to capital regulation (columns 1, 3, 4 and 6), overall activity restrictions (columns 4 and 6), and overall supervisory power (columns 1 and 3). There is some evidence that stronger monitoring incentives in lender countries reduce subsidiary usage (column 3).

Estimation results for the years 2006-2016 are shown in columns 7-12. Overall, these results suggest that regulatory arbitrage was more limited in the later period. In particular, the capital regulatory index for the lender country is insignificant in all of these regressions. The 23
Mexican population. Each year, SEP adjusts the schools and students in the sample to ensure that it represents Mexico’s national profile.

For choosing students within the selected schools, SEP uses two algorithms. For primary schools with fewer than 80 students, SEP included every student in the sample who took the exam. Since SEP gave exams in few grades each year, not every student in the school appeared in the exam. For primary schools with more than 80 students, SEP randomly selected 25 students to include in the sample. For secondary schools with fewer than 200 students, SEP included every student in the sample who took the exam. For secondary schools with more than 200 students, SEP randomly selected 36 students to include in the sample. So the likelihood that a student in a small school appears in the sample is higher than the likelihood that a student in a large school appears in the sample. This bias suggests that our results over-represent students in small schools.

The EN database included data from a variety of grades and years. To allow comparison of a cohort, we used primary school test data from second grade in 1998, third grade in 1999, fourth grade in 2000, fifth grade in 2001 and sixth grade in 2002. Those grades represent the grade of test content; a fourth grade exam includes material that a student should have learned in fourth grade. Similarly, we used secondary school test data from third grade in the year 2000 through third grade in the year 2002. The exact students in each grade-year varied, so we did not exactly use a cohort. Rather, we followed a group of schools through time while allowing the students within those schools to vary randomly.

For each student in the EN sample, the Evaluation Division of Mexico’s Secretariat of Public Education (DGE) reports three scores. The first is a Spanish score based on a multiplechoice Spanish language exam. The second is a mathematics score also based on a multiple17
managed by the Carolina Population Center, University of North Carolina, and Russia’s National Research University Higher School of Economics. The ongoing panel survey started in 1994, and has been implemented every year since then, except for a break in 1997 and 1999. The RLMS collects nationally representative data on various topics including household demographics, income and consumption, occupation characteristics, and others. The sample size is between 4,000 and 6,000 households, capturing between 8,000 and 17,000 individuals for each year, which have been replenished several times due to panel attrition over time. Hardly any middle-income countries can offer such long-running and nationally representative panel data as the RLMS.

However, one data challenge with the RLMS is the considerable attrition rate over time. For example, out of the original 11,290 individuals in the 1994 round, the proportion that remains in the survey drops to 44 percent (4,917) in the 2005 round and 24 percent (2,702) in the 2015 round. We use a three-pronged approach to address attrition issues. First, we offer estimates for time periods of varying lengths. The attrition rate is far lower for shorter panels. For example, out of the original 11,290 individuals in the 1994 round, 63 percent remain in the 1998 round; the corresponding figure between the 2000 and 2004 rounds is 76 percent. Since these shorter panels and longer panels have different sample sizes due to different attrition rates, if estimation results are consistent, it will provide robustness checks on our findings. Second, we offer robustness checks that utilize econometric techniques that adjust for attrition bias (Fitzgerald _et al._ , 1998; Wooldridge, 2002). Finally, to keep reasonable sample sizes, we restrict our analysis of mobility patterns to three income categories only. To avoid any potential bias with the panel data attrition, we define these income categories using the cross-sectional data, which are nationally representative in each year. We mostly use the RLMS’s panel data for analysis, but we also supplement it with analysis based on the repeated cross sections.

14