rafmacalaba/datause-encoder-data huggingface dataset:
(2019), IEA (2015), EIA (2020), IRENA (2020), and NREL (2019).<sup>9</sup> All sources, with the exception of EIA, provide ranges of capital costs for a given technology. For example, Lazard (2019) provides maximum and minimum values for capital costs for each technology it has included. Others (i.e., IRENA, IEA, NREL) provide a range of values. We also chose maximum and minimum values from IRENA, IEA, and NREL as well. EIA provides one capital cost for a given technology; we used the same value for the minimum and maximum categories. Whenever the capital costs are used in different years’ prices, for example, IEA (2015), we expressed them in the same year (2019 here) using the US GDP deflator.

When selecting maximum and minimum values, there is a danger of choosing an extreme value from an outlier. We dropped such values based on expert judgment. For example, IEA used US$9,400/kW as the maximum value for hydropower capital cost; this is too high so we did not use it. The maximum capital cost used by NREL for geothermal technology is US$35,701/kW; we excluded it as well. IRENA excludes the lowest 5% of samples and the highest 5% of samples. There could be several reasons for these extreme values, such as a reporting error during the survey. The value could be a true value but from a single respondent. For hydropower plants in some developing countries, the construction time is often lengthened by delays. The actual costs of the project would be much higher than the design estimate. It also depends on the cost items included in a project. The capital costs of a project would be high if the project costs include the construction of access roads (especially for hydro or wind projects located away from existing roads). If the project costs include the cost of transmission lines to access the existing grids and the project site is away from the existing grid, it will cause the capital costs of the project to increase.

While we tried to standardize other data except for the capital costs so that the LCOEs are comparable, some data could not be standardized. Heat rate and capacity factors could not be standardized in some cases. For example, a thermal power plant with higher capital costs tends to have higher thermal efficiency or low heat rate. Therefore, we assign a low heat rate for a power plant with higher capital costs. A CSP plant with storage facilities has a higher capacity factor. For

> 9 IEA (2015) collected data from 181 electric power plants in 22 countries; IRENA (2020) data are collected through surveys of 17,000 renewable power generation projects around the world that represent more than 1,770 GW of total installed or pipeline capacity. Data used by EIA (2020), NREL (2019) and Lazard (2019) are for the United States.

9
autocorrelation data available for the study populations being considered. We see from Table 2 that autocorrelations are often in the 0.2-0.3 range for household income and consumption. In rural Ghana, the autocorrelation in Boozer et al (2010)’s data is 0.32 when wives report on both their own and their husband’s consumption, but increase to 0.58 to 0.66 when husbands and wives report separately on consumption and the results are combined. The autocorrelations are lower for the poor – in both Mexico and Argentina the autocorrelation of labor income is around 0.2-0.3 for individuals with first period labor income below the median, compared to autocorrelations of 0.5-0.8 for those with this income above the median. This is consistent with the evidence in Collins et al. (2009) that incomes for the poor are very volatile.

The autocorrelation will typically be lower in the more homogeneous samples typical of many randomized experiments than it is in general samples of the population. It will also be lower if one stratifies the randomization or uses fixed effects and thereby controls for time-invariant reasons why one household or firm may have higher income, expenditure or profit levels than another. To see this in a simple example, write the error term , where _X_ is a characteristic that is used to stratify the randomization on, or to restrict an experiment only to individuals with certain values of _X._ Then: And Controlling for _X_ via stratified randomization, or restricting the sample only to those with certain values of _X_ will therefore reduce the variance of , which increases the power of an experiment. However, it also reduces the autocorrelation (by removing a factor which would otherwise cause the error term to be correlated from one period to the next), thereby increasing the value of more measurements.

# **2.4. What should one do if the autocorrelations are not constant?**

The empirical evidence therefore suggests that the assumption that the autocorrelation is constant over different time horizons and is the same for the treatment and control is likely to be a 12
regressions for MNC a¢ liates’location choices and investments drawing on economic geography models provide extensive evidence that high corporate income tax rates are negatively associated with FDI in‡ows.<sup>31</sup> Using statutory corporate tax rates as an instrument for FDI in‡ows could satisfy the exclusion restriction, as a priori there is no clear relationship between corporate tax rates and GVC participation other than through the FDI channel, conditioning on institutional quality, domestic industrial capacity and endowments.

# 3.1.4 Transitional Economy Status

The late 1980s ushered the fall of communism and gave rise to a group of countries that embraced market capitalism and abandoned central planning, collectively referred to as transitional economies (IMF, 2000). Most of these economies are the former Soviet Union and its satellite states, including Poland, Hungary, and Bulgaria, while other countries are in East Asia, such as China and Vietnam. These transitional economies opened up engaging in tari¤ and FDI liberalization, and several were subsequently successful in participating in GVCs. We use the transitional economy status as an instrument for tari¤s and FDI in‡ows, and since a priori GVC participation is not correlated with the transitional economy status of the countries, that status satis...es the exclusion restriction, conditioning on institutional quality, factor endowments and domestic industrial capacity.

# 3.1.5 Instrumental Variables Summary and Tests

In summary, we have two endogenous variables in our reduced form speci...cation Equation (1): tari¤s and FDI in‡ows. We use ...ve excluded exogenous variables as instruments: import elasticity, total population, the interaction between import elasticity and total population, statutory corporate tax rates, and transitional economy status. We test for weak IVs in the ...rst-stage regressions, based on the Kleibergen-Paap Wald F-statistic, which indicates that these IVs jointly have signi...cant explanatory power for tari¤s and for FDI in‡ows if the F-statistic is higher than the critical value given by Stock and Yogo (2005). We also test that (the instrumented) tari¤s and FDI in‡ows can jointly explain GVC participation, based on

> 31See e.g. Desai et al. (2004), Head and Mayer (2004), and Mutti and Grubert (2004).

15
Table 3a: Industry-level urbanization rates for India's manufacturing sector

||Tot|al establishm<br>counts|ent|Urb<br>es|anization<br>tablishme|rate<br>nts|Urb<br>e|anization<br>mployme|rate<br>nt|
|---|---|---|---|---|---|---|---|---|---|
||1994|2000|2005|1994|2000|2005|1994|2000|2005|
|15 Food products and beverages|2,391,234|2,962,970|2,572,043|0.19|0.22|0.20|0.24|0.27|0.25|
|16 Tobacco products|1,004,510|2,062,543|2,753,644|0.20|0.22|0.17|0.32|0.28|0.24|
|17 Textiles|1,971,821|2,239,348|2,312,117|0.24|0.31|0.32|0.37|0.42|0.45|
|18 Wearing apparel; dressing and dyeing of fur|90,952|2,785,199|3,158,538|0.67|0.40|0.40|0.85|0.52|0.52|
|19 Leather tanning; luggage, handbags, footwear|190,786|171,759|144,328|0.44|0.49|0.74|0.63|0.68|0.80|
|20 Wood and wood products; straw and plating articles|1,957,120|2,720,752|1,895,690|0.15|0.13|0.11|0.20|0.16|0.15|
|21 Paper and paper products|63,172|90,214|165,652|0.59|0.70|0.38|0.60|0.66|0.47|
|22 Publishing, printing and media reproduction|105,479|144,293|116,764|0.76|0.84|0.86|0.84|0.88|0.87|
|23 Coke, refined petroleum and nuclear fuel|5,075|7,429|6,435|0.48|0.27|0.20|0.48|0.43|0.34|
|24 Chemicals and chemical products|98,048|216,410|401,055|0.44|0.51|0.29|0.60|0.53|0.42|
|25 Rubber and plastic products|74,771|95,352|74,108|0.76|0.65|0.67|0.81|0.69|0.61|
|26 Other non-metallic mineral products|686,560|784,551|606,049|0.15|0.16|0.17|0.22|0.22|0.22|
|27 Basic metals|38,086|43,127|39,461|0.80|0.65|0.62|0.74|0.70|0.57|
|28 Fabricated metal products, except machinery|422,420|640,256|616,937|0.44|0.43|0.41|0.64|0.61|0.60|
|29 Machinery and equipment, n.e.c.|319,227|171,138|178,220|0.33|0.53|0.61|0.63|0.73|0.76|
|30 Office, accounting and computing machinery|498|303|931|0.95|0.94|0.91|0.77|0.88|0.86|
|31 Electrical machinery and apparatus, n.e.c.|29,495|67,896|112,788|0.67|0.66|0.54|0.83|0.79|0.66|
|32 Radio, television, and comm. equipment|7,355|7,589|5,863|0.75|0.84|0.71|0.76|0.79|0.70|
|33 Medical, precision and optical instruments, watches|11,715|9,190|10,283|0.96|0.82|0.85|0.82|0.80|0.77|
|34 Motor vehicles, trailers and semi-trailers|6,924|24,186|16,664|0.65|0.84|0.63|0.84|0.77|0.60|
|35 Other transport equipment|22,955|17,495|26,002|0.76|0.70|0.78|0.82|0.77|0.76|
|36 Furniture, manufacturing n.e.c.|1,429,878|1,255,784|1,094,058|0.38|0.54|0.54|0.47|0.60|0.62|
|Totals and wtd averages|10,928,081|16,517,785|16,307,628|0.26|0.30|0.30|0.40|0.41|0.41|
|Unweighted averages|496,731|750,808|741,256|0.52|0.53|0.50|0.60|0.59|0.55|

Notes: See Table 2.
“log” surveys of neighbors on international labor migration by the respondent, and inclusion of these log surveys raises our total endline response rate (for the “migrate abroad” outcome) to 98.5%. We show in Appendix A.5 that our estimated impacts on migration are robust to use of the full (98.5%) endline sample, which includes the log surveys.

# **3.1 Interventions**

_Information and website assistance_ During the baseline survey, we randomly assigned respondents to a control group or to one of four treatment groups designed to improve their information about and access to overseas work opportunities (Figure 1). These groups were application information [T1], financial information [T2], application and financial information [T1] + [T2], and website assistance [T4]. The application information consisted of information on typical overseas costs; the steps needed to apply for work abroad; an advertisement to enroll in Pilijobs.org, an overseas job-finding website designed as part of this project;<sup>9</sup> and a list of ways to avoid illegal recruitment from the Philippine Overseas Employment Agency. Financial information consisted of typical placement fees for work abroad and a list of Manila-based financial companies that provide loans for placement fees.

To facilitate job-matching, we worked with several Manila-based overseas recruitment agencies and a Sorsogon microfinance NGO to develop a website, Pilijobs.org, to help respondents easily contact and apply with reputable recruitment agencies and to allow those agencies to directly post job opportunities that could be accessed by respondents. While several widely used job-finding websites for overseas work already exist in the Philippines, we developed a separate one to ensure that applicants would be put in 9
- ?9

Tab 1 e 3 (Con t rue t ion **and I oan** rtepayinint

|Part<br>icipatory<br>systemlls|Nonparticipatory<br>svst ellsl|
|---|---|
|Payment<br>(n<br>11)<br>a/|(n<br>19)<br>b/|
|Mean amortization||
|payment due a/<br>15,088|41,667|
|Mean amortization payment<br>remitted to the NIA<br>12,429|21,065|
|Mean % of amortization||
|due actually paid<br>82%|50%|
|(z<br>2.17;|p<0.05)|

a. Of the twenty-four sample participatory systems, four were still under construction during the research fieldwork, one had no loan because the association raised a 30 percent equity, and two were not yet due to begin paying their amortization.

b. Of the twenty-two sample nonparticipatory systems, two associations had not accepted the final turnover of the system, while one association had no loan because it raised a 30 percent equity.

c. Nonparticipatory systems had larger amortization payments due because most had been completed in 1981. A larger proportion of participatory systems were completed in 1982 and 1983.

Source: de los Reyes and Jopillo, 1988:111 in Korten and Siy, Jr.

wet season, expansion has been similar ,or the two types of systems--18 percent for participatory and 17 percent for nonparticipatory. In the dry season, however, there has been a substantial difference between the two systems: under the participatory systems the area irrigated expanded by 35 percent in contrast to only 18 percent under the nonparticipatory schemes (see Table 4). Jopillo and de los Reyes (1988:98) speculate that the difference in dry season expansion may reflect better water management practices (for example, more efficient water rotations) in the participatory systems as
# **5. Impact on Interactions with Partners and Household Efficiency**

# **5.1 Defining Partners**

At the time of migration the principal applicants are typically adults in their late twenties or early thirties, who move with their spouse and children. When applying some would still have lived with their parents or siblings, or otherwise would typically have recently set up their own dwellings in the same small villages as their extended family members. A decade later, by the time of our follow‐up survey at about age 40, most would have formed their own households in Tonga if they had not been selected to migrate. We therefore defined for each principal applicant in the long survey a partner household in Tonga containing either a parent or elder sibling (with priority on parents over siblings, and females over males). A strict rule was used for matching so that the choice of partner household is orthogonal to winning the lottery. In about 15 percent of cases the partner was living in the same dwelling in Tonga as the ballot loser.

Each partner was then interviewed separately from the principal applicant. Table 4 provides some descriptive statistics on partners by treatment group. The partner is the parent of the principal applicant in 44 percent of the cases, and their sibling in the remainder. This is reasonably balanced by treatment status, although the ballot losers are slightly more likely to have a parent rather than sibling interviewed as the partner, but not significantly so. The partner age averages 66 for parents of the principal applicant and 52 for their siblings, for an average age of 54. This is slightly higher for the ballot loser sample reflecting the higher incidence of parents. We see no significant difference by treatment status on other partner characteristics such as gender, education, or earnings. The latter result is consistent with Gibson et al. (2015) who find that the massive gains in income to the migrants are not accompanied by changes in the income of these partner households. We control for partner type (parent or not), partner gender, and partner age in our trust game regressions.

# **5.2 Interactions with Partners**

Table 5 then examines how international migration has affected the different types of interactions principal applicants have with these partners. Column 1 shows that there is a large and significant reduction in regular communication. Over three‐quarters (77%) of ballot losers communicate at least weekly with their partner, while migrating lowers this by 35 percentage points. Column 2 shows there is a small positive, but insignificant, effect of migration on the likelihood the principal applicant sends money to the partner. This might seem surprising as we might expect migrants to be remitting, and non‐migrants not to be. But this question does not capture the size of the transfer – migrants are likely to be making 12
price effects of such announcements (ADB, 2004).<sup>20</sup> More importantly, the costs of going public seem to surmount their benefits, as public firms are subject to more scrutiny by tax authorities.

# **5. The role of government in financial service provision**

While most economists agree by now on the role of government in providing a stable monetary environment and government involvement in the contractual and informational framework, there has been an ongoing debate over the past decades on the role of government as financial service provider and on the regulation and supervision of financial service providers by government. This section presents three different approaches to the role of government: the laissez-faire view, the market-failure view and the market-enabling view. Finally, we will contrast these views with the current situation in Bangladesh.

## **5.1. The laissez-faire approach**

Under the laissez-faire or invisible-hand approach, there is no role for government involvement in the financial system. Market discipline rules the relationship between banks and depositors and between banks and borrowers. Depositors and creditors of financial institutions monitor them and exercise market discipline by either withdrawing funds or demanding higher interest rates. Failing banks are taken over by more successful banks or are liquidated. Similarly, banks monitor and discipline borrowers; borrowers unable or unwilling to repay are liquidated or rehabilitated through a restructuring process. Under the pure laissez-faire approach, there is thus no role for government involvement. A modified laissez-faire approach stresses the importance

> 20 Sobhan and Werner (2003) even quote the example where a bank’s stock price increased after irregularities in its lending practices became public, most likely due to manipulating transactions in the market.

20
# **Method**

The goals of this analysis are to create data on road crashes with times and locations and understand how these incidents cluster in the city, which allows for the spatial prioritization of urban investments in road safety. The technical challenges this study addresses are: i) improve the protocols for geolocation, ii) apply applications of AI to classify tweets reporting crashes and identify their location from multiple geographical references, iii) cluster the crashes geographically and identify areas with many crashes. See the Supplemental Information (SI) for the detailed methodology. The components are as follows:

1. **Scrape data.** We scrape 874,588 tweets posted by Ma3Route, an existing urban mobility platform with 1.1 million followers, since its inception in May 2012 through July 2020 (see SI for examples of tweets and for a figure of the daily number of tweets across time).

2. **Develop and augment a gazetteer.** We build a gazetteer of landmarks for the five counties that constitute the Nairobi metro area using: OpenStreetMap, Geonames and Google Places. The gazetteer includes the landmark name, geocoordinates and type of landmark (e.g., school, bus stop). We use consecutive combinations of 2 and 3 words (known as n-grams) and skip-grams of landmarks in the gazetteer, alternate spellings and abbreviations, and splitting of landmarks with select punctuation (e.g., slashes, parentheses, commas). We innovate by developing alternate names that exclude the landmark type from the name (e.g., excluding “Hotel” from the name).

3. **Develop a truth dataset.** We develop a truth dataset to train the algorithm. Taking all tweets for July 2017 - July 2018, we restrict tweets to the ones most likely related to a crash based on a broad list of words and their variations. Each tweet is manually coded, indicating (1) if the tweet reported a crash and (2) the approximate latitude and longitude of any reported crash whenever enough information is provided. A total of 9,480 tweets were coded, of which 69% (6,602) reported a crash and of these, 63% (4,192) identified an approximate location of the crash. On average, users posted 10 crash reports that could be geolocated to Twitter daily.

4. **Identify RTCs and their location.** We use a three-step process to convert unstructured crowdsourced text into a dataset. The first is to identify relevant reports from hundreds of thousands of reports. The second is to extract necessary information from the relevant reports. The third is to consolidate unique record information from multiple reports of the same event. In Figure 1, we illustrate how the algorithm works to classify and geolocate RTCs. We use the tweet “Bad accident on Waiyaki Way next to Kianda heading towards ABC Place.” 4
Table <u>9 Dynamic effects of drought on schooling progression in Ethiopia and Malawi: Pre-flood assets and ex-post public assistance</u>

|Dependent: Change in grades<br>|Ethiopia|Malawi|
|---|---|---|
|Drought 2001 -0.2168<br>|-0.4370<br>|-0.2776 -0.1077 -0.2083 -1.1084<br>|
|(1.100)|(2.520)|(0.890) (0.600) (1.050) (3.840)|
|Public work -0.7253<br>|-0.7673<br>|-0.7563<br>|
|(1.970)|(2.170)|(2.230)|
|Food aid 0.2390<br>|0.2638<br>|0.3538 -0.1912 -0.1921 -0.2057<br>|
|(0.850)|(0.980)|(0.980) (1.430) (1.340) (1.380)|
|Drought * public work 0.5498<br>|0.6392<br>|0.6143<br>|
|(1.910)|(2.480)|(1.770)|
|Drought * food aid -0.5224<br>|-0.4860<br>|-0.6428 0.3554 0.3093 0.3824<br>|
|(1.090)|(1.050)|(0.990) (1.420) (1.230) (1.630)|
|Drought * asset<br>|0.0006<br>|0.00002<br>|
||(3.560)|(3.790)|
|Drought * max educ<br>|<br>|0.0150 0.1112<br>|
|||(0.360) (6.150)|
|Drought * land<br>|<br>|0.0539 0.0112<br>|
|||(0.290) (0.280)|
|Drought * household size<br>|<br>|-0.0056 0.0354<br>|
|||(0.130) (1.440)|
|Drought * livestock||-0.00004 -3.70E06|
|||(1.430) (0.860)|
|Drought * male 0.2390<br>|0.2522<br>|0.3020 -0.0627 -0.0786 -0.0744<br>|
|(0.670)|(0.710)|(0.930) (0.300) (0.390) (0.360)|
|Region FE|||
|Number of observations 842|838|813 447 433 431|
|Number of regions 6|6|6 4 4 4|
|R squared (within) 0.0670|0.0724|0.0685 0.0553 0.0875 0.1214|

Numbers in parentheses are absolute t values using robust standard errors with region clusters. Age and gender dummies are included in all specifications.

31
8

- States (n 8), noticeable effects appeared in the British sample, which included 47 single-sex schools. Girls in English coeducational schools showed a decline in science and vocabulary relative to male peers, while girls in single-sex schools excelled in reading and science.12/

American research. Two recent studies which have focused on American Catholic secondary schools are somewhat stronger than the studies cited above, since the samples of students and schools are large and randomly selected. Lee and Bryk examined a random sample of 75 Catholic schools from the High Sclhool and Beyord (HS&B) study, which included longitudinal information on student background, attitudes, behaviors, and achievement.1L8 Whether concerning academic achievement, achievement gains, educational aspirations, locus of control, sex role stereotyping, or attitudes and behaviors related to academics, their results indicated that single-sex schools deliver specific advantages, especially to their female students. Analyses included statistical adjustment for student background and contextual differences between schools. Another study compared white students in public schools with Catholic single-sex and coeuucational school students. Using the 1972 National Longitudinal Study (NLS) data, positive achievement effects were found for single-sex schools, particularly for girls, with limited statistical controls employed.<sup>12</sup> / The relation between sex-stereotyped attitudes and behaviors and American sex-segregated education has received some research attention. In all these studies, which examined students from first to twelfth grades, girls' school students were found to hold less stereotypic attitudes. These ranged from views on women's roles in societ , including the appropriateness of women ent3ring typically male professions, to more favorable attitudes toward