rafmacalaba/datause-encoder-data huggingface dataset:
44

**Table 9**

# **Robustness: geography**

_(dummy remittances: 75_<sup>_th_</sup> _percentile)_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||ED4|ED4|BP5|BP5|
||Africa|DevelopingAsia|Africa|DevelopingAsia|
|CA|-2.800|-2.790|-1.771|-1.765|
||[0.227]***|[0.226]***|[0.213]***|[0.212]***|
|REM|0.098<br>|0.097<br>|0.037<br>|0.081<br>|
||[0.522]|[0.523]|[0.436]|[0.435]|
|RES|-0.993|-0.942|-1.176|-1.227|
||[0.433]**|[0.406]**|[0.481]**|[0.463]***|
|EXTD|0.215|0.203|0.147|0.192|
||[0.102]**|[0.085]**|[0.090]|[0.073]***|
|SHORTD|<br>-0.010|<br>-0.019|<br>-0.017|<br>-0.004|
||[0.146]|[0.146]|[0.110]|[0.110]|
|CREGRO|-0.001|-0.001|-0.001|-0.001|
||[0.002]|[0.002]|[0.001]|[0.001]|
|PPPGDP|-0.018|-0.017|-0.007|-0.007|
||[0.025]|[0.025]|[0.025]|[0.025]|
|OPEN|<br>0.201|<br>0.208|<br>0.241|<br>0.231|
||[0.108]*|[0.106]*|[0.098]**|[0.097]**|
|OT|-0.735|-0.735|-0.529|-0.501|
||[0.344]**|[0.341]**|[0.297]*|[0.294]*|
|CONCD|<br>-0.005|<br>-0.014|<br>0.012|<br>0.020|
||[0.115]|[0.113]|[0.105]|[0.102]|
|RES^2|1.510|1.445|1.943|2.088|
||[0.807]*|[0.803]*|[1.008]*|[1.042]**|
|EXTD^2|<br>-0.039|<br>-0.038|<br>-0.043|<br>-0.048|
||[0.021]*|[0.021]*|[0.017]**|[0.016]***|
|RES*DREM|0.638|0.615|0.742|0.772|
||[0.317]**|[0.319]*|[0.304]**|[0.307]**|
|EXTD *DREM|-0.100|-0.098|-0.083|-0.089|
||[0.052]*|[0.051]*|[0.042]*|[0.041]**|
|RES*AFRICA|<br>0.025||<br>0.033||
||[0.384]||[0.377]||
|EXTD *AFRICA|-0.023||0.047||
||[0.078]||[0.071]||
|AFRICA|0.006||-0.417||
||[0.176]||[0.190]**||
|RES*ASIA||-0.173||0.423|
|||[0.583]||[0.414]|
|EXTD *ASIA||-0.073||-0.227|
|||[0.145]||[0.118]*|
|ASIA||<br>-0.190||<br>-0.100|
|||[0.174]||[0.191]|
|Observations|1214|1214|1191|1191|
|R-squared|0.31|0.31|0.22|0.22|

_Notes_ : see note to Table 5. DREM is a dummy variable that takes the value 1 if in a given year the level of workers’ remittances over GDP of a country is above the 75th percentile value of that year’s (cross-country) distribution. * significant at 10%; ** significant at 5%; *** significant at 1%
# **Figure 3: Machinery Exports and Processing Trade**

<!-- Start of picture text --> Telecom Electric Office Machinery Road Vehicles Power Gen. Specialized Transport Metalworking<br>(SITC 76) (SITC 77) (SITC 75) (SITC 74) (SITC 78) (SITC 71) (SITC 72) (SITC 79) (SITC 73)<br>2005 - All Exports 2005 - Processing Exports<br>.15<br>.1<br>Share of Total Exports<br>.05<br>0<br><!-- End of picture text -->

# Note: Column headings include the following industries:

- SITC 71: Boilers, turbines, internal combustion engines, and power generating machinery. SITC 72: Agricultural machinery, civil engineering and contractors’ equipment, printing and bookbinding machinery, and textile and leather machinery.

- SITC 73: Lathes, machines for finishing and polishing metal, soldering equipment, metal forging equipment, and metal foundry equipment.

- SITC 74: Heating and cooling equipment, pumps, ball bearings, valves for pipes, and nonelectrical machines.

- SITC 75: Typewriters, photocopiers, and data processing machines.

- SITC 76: Television receivers, radio receivers, and sound recorders.

- SITC 77: Equipment for distributing electricity, electro-diagnostic apparatus, and semiconductors.

SITC 78: Automobiles, trucks, trailers, and motorcycles.

SITC 79: Railroad equipment, aircraft, ships, boats, and floating structures.

21
-4-

reduction in the lower level positions, the functions of which have been absorbed by the private sector through contracting arrangements. The large degree of dependence on force account work has been significantly reduced. Road maintenance under the Regional Roads Engineer has been almost entirely contracted out through the Integrated Roads I Project (IDA Credit No. 2149-TA). Urban Water and Sewerage Departments and Urban Water and Sewerage Boards have been newly established on a pilot basis in three urban centers in 1994 and proven to be successful in bringing about improved efficiency in the sector. Efforts in Dar es Salaam and in eight project towns to reassess the property valuation policies and procedures through the Urban Sector Engineering Project (IDA Credit No. 2291-TA) have led to significant improvements in revenue generation for the municipalities through renewed efforts in property tax billing and collection.

# **Urban Service Coverage**

1.13 Relatively high rates of urban population growth, low levels of urban management capacity, and low revenue collection have resulted in low levels of service coverage, affecting the poor more than the middle and upper income groups. According to recent findings of a Social Sector Survey, 60% of national expenditures for water supply benefited the 20% in the highest income group. Table 1.1 lists some indicative figures for urban services.

**Table 1.1: Urban Service Coverage**

|**SERVICE PROVISION**|**COVERAGE**|
|---|---|
|Roads|70% - 85% in poor condition|
|Stormnwater Drainage|0% - 50% coverage|
|Solid Waste Collection|10% - 42% by councils|
|Water Supply|6% - 28% of population has no coverage<br>14% - 52% of population has basic coverage|
|Sanitation|2% - 5% of population has no facilities<sup>2</sup>|
||3% - 6% use public facilities|

_Source: COWIConsult, 1992_

1.14 The poor quality and quantity of infrastructure services increases the unit costs of production and accelerates the depreciation of assets for the urban-based manufacturing sector. Over the past two decades the routine diversion of operations and maintenance (O&M) funds to finance small capital works has led to severe deficiencies in infrastructure services. The limited amount of historical budget data available indicates

- 2 While these figures indicate that only 2% -5% of urban residents have no sanitation facilities, avery high percentage of those counted as having service is served with on-site latrines that are in very poor condition and would be classified as unhygienic service. These estimates are confirmed by the April 1995 World Bank _Social Sector Review._
# **3.5 Role of Hydropower for Peak Load Management**

One salient feature of connecting a hydropower dominant power system with a thermal power dominant power system is that it significantly helps manage peak loads in both systems, especially in the smaller system. The interconnection would significantly reduce the ratio of installed capacity to peak load in hydropower dominant power systems, such as Afghanistan, Bhutan and Nepal. In the absence of cross-border interconnection, total installed capacity to meet peak load in Nepal, for example, would be much higher not only because of low capacity factors of hydropower plants but also ‘peaky’ characteristics of some end-uses, such as lighting and cooking. Figure 9 illustrates the hourly load curve of Nepal for a typical day. The shape of the load curve is unlikely to vary much across days although the magnitude or height of the curve in each hour could be different. The curve shows that electricity demand around an hour in the evening (6 PM) is more than twice as high as off-peak hours (1-3 PM). This high peak demand is caused by lighting load. If electric cooking is promoted in Nepal, evening peak is likely to increase significantly as evening cooking hours also fall during the existing peak hours (6-7 PM). The load forecasts made by WECS (2017) under the policy intervention scenarios assume complete electrification of urban cooking. If that is the case, one can wonder how much extra capacity would be needed to meet the peak load. WECS (2017) shows that in the reference scenario (7.2% per year GDP growth), required installed capacity would double if water heating and urban cooking are electrified as compared to the situation not doing so. A very high and short peak load may not be a good indicator for a power system, especially when such a system is an isolated system (not connected with other grids). This type of peak load implies the requirement of large capacity which will not be utilized other times.

23
without any specific leadership or management preparation. Second, school actors have limited information on _who_ is at risk of dropping out. Data on mandatory “dropout risk flags” reported by school principals and teachers show that they identify on average fewer than 5% of the students who end up dropping out in the transition from primary to lower secondary.<sup>1</sup> Third, there are several parallel demands and multiple tasks that compete for the attention of principals and teachers, which may prevent dropout prevention from being top-of-mind, particularly since dropout in the transition from primary to lower secondary occurs after students have left their primary schools and are no longer under their official responsibility. For instance, surveys of school principals show that reducing dropout is not a top priority for them, as they rank it third on average out of five options (below teaching/curriculum and internal administrative tasks).

Considering these gaps, the government designed ENTRE to combine three key components: a brief training on actionable, evidence-based strategies to support students to stay in school; information on sixth-grade students at high risk of dropping out; and small behavioral nudges to try and keep dropout prevention top-of-mind for principals and teachers.

# <u>Component 1: Short training on actionable strategies to prevent dropout</u>

The first component consists of a half-day training for school principals and sixth-grade teachers on specific, evidence-based methods to help students make the transition from primary to lower secondary – in essence, providing them more information on _how_ to help students stay in school. The training was complemented by a short and user-friendly guidance manual (13 pages of text and informative illustrations in the main manual, plus 13 pages of additional resources in annex) that summarizes the training content, a copy of which was given to each participant at the beginning of the half-day training. Along with the guide, the school principals received a personalized letter from the Minister of Education that emphasized the importance of every Guatemalan child completing at least nine years of basic education and urged the school principal to lead her or his staff in implementing what they learned through the ENTRE program.

> 1Part of this could be explained by perverse incentives to under-report students at risks. The data mentioned is collected by a program that asked school actors to identify students at risk. After identifying students at risk, several administrative tasks were triggered for each student, creating significant additional work for principals and teachers.

8
**N**

**Table IL** Country-wise distribution of Indian Banks Overseu

||_3_|_nrchcs_|Banking|Subsidlaies|||
|---|---|---|---|---|---|---|
|<br>Countrv|Convention|al Ofthor Banking<br>Units|WhoNy<br>Owned|Majrity<br>Owred|Affiliates|Representative<br>Offices|
|Australia|-|-|-|-|-|1|
|Bangladeh|I|-|||||
|Bahamas|-|2|-|-|-|_|
|Bahraln|-|2|||||
|Belgium|2|-|-|-|-|-|
|Bhutan|-|_|_|_|||
|Canada|-||||||
|Cayman hlands|-|2|||||
|Channel Islands|-|1<br>I|-|-|-||
|Egypt|-|-|-|-|-|1|
|<br>FijlIslands|11|-|||||
|France|2|-|||||
|Guyana|1|-|||||
|Federal Republic of Germany|<br>I|-|||||
|HongKong|I|-|3||||
|Indonesia|-|-|-|-|-|3|
|Iran|-|-|-|-|-|1<br>|
|Italy|-|-<br>-||-|-|1|
|Japan|4|-|-|-|-|-|
|Kenya|9||-||-|-|
|Malaysia|-|-|-|-|1||
|Maldive Islands|I|-|-|-|-|-|
|Mauritius|6|-|-|-|-|-|
|Nigeria|-|-|-|-|2|-|
|Oman|3||-||-||
|Panawna|I|-|||||
|Seychelles|I|-|||||
|<br>SouthKora|1|-|||||
|Sri Lanka|6|-|||||
|Singaporo|6|1|||||
|Thailand|I|-|||||
|UnitedArabEmirates|6|-|||||
|UntdK1npdom|33|-|||||
|Unlted St<br>of America|7|-|1|-|-|1|
|Uo|~|~~-<br>-|_|1|_-||
|U99|~-|-|-|-|-|1|
|**Zimbaw**|-|-|-|-|-|1|
|**Total**|**114**|8|**6**|**2**|**4**|**10**|

Note. as at end December 1987. _Source:_ **Reserv** Bank af Intia.
natural resources that are likely to be critical inputs to achieve transition, such as lithium and copper.

# 7.2. Evolution and drivers of potential growth in LAC

During 2011-21, potential output growth in LAC is estimated to have averaged around 2.2 percent a year, below the 2000-10 annual average of 2.7 percent (figure 11).<sup>21</sup> The slowing of potential growth is accounted for by shrinking contributions from the growth of TFP and labor. The finding that potential growth declined is robust to the method of estimation.

Potential TFP growth in LAC, which has long been below that in other EMDE regions, slowed to virtually zero after peaking in 2007; potential TFP was essentially flat between 2015 and 2019. Weak investment growth, starting in the mid-2010s, held back the absorption of productivity-enhancing new technologies, with commodity-exporting economies struggling to adapt to falling commodity prices (OECD 2016). Worsening terms of trade, a consequence of the downturn in commodity prices, may also have dampened TFP growth in the region’s commodity exporters by reducing spending on research and development (R&D) and slowing innovation (Aslam et al. 2016). This hypothesis is supported by evidence that improving terms-of-trade during 2001-07 explained more than one-quarter of average TFP growth in this period in Mexico, Chile, and Peru (Castillo Bardález and Rojas Zia 2014). In keeping with anemic TFP growth and a severe cyclical downturn, per capita growth fell far below its estimated potential level of 1.2 percent per year during 2011-21, registering actual per capita income growth of only 0.4 percent per year.

Shortcomings in education and training have long dampened productivity growth in LAC. Although access to education has steadily risen in recent decades, the low quality of primary and secondary education, relative to international standards and countries with similar per capita incomes, has hindered productivity gains (OECD 2015; OECD/CAF/ECLAC 2016; World Bank 2021f). Further, at the tertiary level, graduation rates are low, and quality appears to have suffered as demand has expanded rapidly (World Bank 2021g). Regional productivity growth is further impeded by still-stringent labor and product market regulations and high levels of informality, as well as institutional weaknesses, reflected in such problems as elevated levels of wasteful government expenditure and corruption (de Paulo, de Andrade Lima, and Tigre 2022; IDB 2018).

Numerous studies have documented that weak TFP growth has been the principal contributor to the region’s low potential output growth (Aravena, Friedman, and Hofman 2017; IMF 2017b; Loayza, Fajnzylber, and Calderón 2005 ).<sup>22</sup> One study found that in the nearly half-century leading up to the financial crisis of 2008-09, relatively low TFP growth, rather than relatively weak capital accumulation or labor force growth, was the main

> 21 For the period 2000-22, 20 LAC economies are included in the estimation, representing 99 percent of 2020 LAC GDP.

> 22 See also, for instance, Faal (2005) on Mexico and Ollivaud, Guillemette, and Turner (2016) on Chile.
concern about the impacts of global warming; (e) public support for policy action in light of potential economic costs; and (f) public support for the Kyoto climate treaty. This work provides a useful summary of a series of sometimes contradictory studies.

Overall they report that, “Twenty years after scientists and journalists first alerted the public to the potential problem of global warming, few Americans are confident that they fully grasp the complexities of the issue, and on questions measuring actual knowledge about either the science or the policy involved, the public scores very low. As of 1992, only 11 percent of the public answered that they understood the issue of global warming “very well,” and across Gallup surveys taken each year between 2001 and 2005, this figure ranged between only 15 percent and 18 percent of respondents, increasing to 22 percent of respondents in 2007” (ibid).

# **_Concern_**

A second important category of public opinion polls on climate change are questions about the level of concern held by citizens. Here again we review a combination of studies focused solely on the US and those which provide international comparison. The earliest high profile cross national work was conducted by Riley Dunlap in 1998 as part of the Gallup Health of the Planet survey (mentioned above for measures of knowledge). Dunlap’s survey contains results also on concern (again, these data are from six nations: Canada, the US, Mexico, Brazil, Portugal, and Russia). Most respondents interviewed saw global warming as a problem, although it was not generally ranked as seriously as ozone depletion or rain forest destruction. Dunlap’s work points to two important issues. First, as predicted by the concept of a risk society, his results show that perceptions of global warming do not vary consistently across differing social strata within nations. Dunlap notes that: “Perhaps more directly supportive of the risk society hypothesis is that neither ratings of global warming nor of all seven problems combined correlate significantly and consistently with age, sex, education or residence across the six nations, suggesting that Beck (1992) is justified in arguing that concern over global mega-hazards is broadly diffused throughout society” (479). Secondly, despite limitations in knowledge, citizens in all nations except Russia believed that climate change would have consequences in their lifetimes. Thus he notes that detailed public understanding is not actually necessary for policy response.

8
Table 6: Spillovers from Contract Enforcement

||Made at <br>payment|least one<br>within. . .|Total a<br>paid wi|mount<br>thin. . .|Propor<br>bala<br>paid wi|tion of<br>nce<br>thin. . .|
|---|---|---|---|---|---|---|
||1 month<br>(1)|9 months<br>(2)|1 month<br>(3)|9 months<br>(4)|1 month<br>(5)|9 months<br>(6)|
|**_Panel A: Spillovers o_**|**_n compound_**|**_s eligible for _**|**_disconnecti_**|**_on_**|||
|Coefcient|0.010|-0.020|-0.424|-1.721|0.019|0.008|
||(0.023)|(0.039)|(1.353)|(3.924)|(0.025)|(0.027)|
||r0.681s|r0.610s|r0.754s|r0.662s|r0.449s|r0.760s|
|Control Mean|0.110|0.334|5.026|24.722|0.268|0.300|
|Observations|996|996|996|996|996|996|
|Number of Clusters|144|144|144|144|144|144|
|**_Panel B: Spillovers o_**|**_n compound_**|**_s not eligible _**|**_for discon_**|**_nection_**|||
|Coefcient|-0.015|-0.009|-0.340|0.303|-0.002|0.006|
||(0.023)|(0.029)|(0.723)|(3.617)|(0.025)|(0.026)|
||r0.507s|r0.752s|r0.639s|r0.933s|r0.924s|r0.815s|
|Control Mean|0.409|0.734|8.544|51.156|0.722|0.655|
|Observations|1930|1930|1930|1930|1930|1930|
|Number of Clusters|143|143|143|143|143|143|

_Notes_ . Clustered standard errors based on the level of randomization (street) in parentheses and associated p-value in brackets. *** p<0.01, ** p<0.05, * p<0.1. Panel A includes all compounds that were eligible for the enforcement intervention. The comparison is between compounds assigned to the control group in treatment clusters with all disconnection-eligible compounds residing in control clusters. Panel B includes all compounds that were not eligible for the disconnection intervention. The comparison is between disconnection-ineligible compounds in treatment clusters with all disconnection-ineligible compounds residing in control clusters. Time periods are benchmarked on the end date of the intervention (November 2018) and use data downloaded from the Nairobi Water billing data for December 2018 and September 2019 to estimate impacts 1 and 9 months after the intervention respectively. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated.

34
**Table 1. Geolocation Algorithm Results**

||Any Location<br>Captured by<br>Algorithm Close to<br>True Crash Location|Crash<br>Deter<br>Algorith<br>True Cra|Location<br>mined by<br>m Close to<br>sh Location|
|---|---|---|---|
||Recall<br>Precision|Recall|Precision|
|LNEx|0.674<br>0.686|0.129|0.132|
|Alg., Raw Gaz|0.695<br>0.757|0.579|0.756|
|Alg., Aug Gaz|0.798<br>0.857|0.651|0.811|
|Alg., Aug Gaz [Cluster]||0.656|0.774|

‘N Crashes’ refers to the number of correctly identified crashes. ‘Raw Gaz’ refers to the raw gazetteer (ie, dictionary of landmarks with original names) and ‘Aug Gaz’ refers to the augmented gazetteer. We use our raw gazetteer as an input into LNEX, which implements its own augmentation process. For LNEx, the crash location is determined by taking the centroid of all locations captured by the algorithm. Locations are considered close if they are within 500 meters of each other.

landmarks within 500 m and within 4 hours of each other (see additional details in SI for how parameters were chosen).

(d) **Ground truth.** To ensure that the crowdsourced data are reliable and provide correct information, we conduct a ground-truthing exercise to validate the quality of the data and the performance of the underlying algorithm. We processed tweets in real-time and dispatched a motorcycle delivery service (Sendy) to the site of the crash within minutes. The Sendy driver was tasked with verifying and reporting whether a crash actually happened in that location. If a driver could not see the crash, they were instructed to ask a bystander whether a crash had occurred but was cleared or whether a crash occurred nearby. Drivers were able to arrive at the crash location quickly; the median time between being alerted of a crash and arriving at the scene was 26 minutes.

# **Results**

The methods laid out here created a georeferenced RTC dataset that was previously unattainable and produced one of the first real-time maps of RTCs in Nairobi. We classify 52,228 tweets as crash-related out of a universe of 874,588 tweets during 2012 - 2020 (Panel A of Figure 2). This is based on the SVM algorithm, which we find performs better than the Naive Bayes algorithm according to the F1 statistic (see Table S4 in the SI). We geolocate 32,991 time-stamped crash tweets from August 2012 to July 2020 and cluster them into 22,872 unique geolocated crashes (panels B and C of Figure 2 show the unique crashes generated by Twitter daily using the algorithm and clustering). In our truth dataset, where we manually 7