rafmacalaba/data-use-mentions-extended huggingface dataset:
(Wacziarg and Welch 2008). Between 2000 and 2018, the employment share in agriculture declined from 55 percent to 33 percent (or by 22 percentage points), while the employment share in services increased from 28 percent to 47 percent (19 percentage points). After a long period of stagnation, the employment share in industry rose slightly after 2013, from 17 percent in that year to 20 percent in 2018 (3 percentage points).

Figure 2b plots trends in average labor productivity per worker by sector between 1990 and 2018. Agriculture and industry have seen steady gains in average labor productivity over this subperiod. Starting from the early 2000s, average labor productivity in services declined slightly (from about 18,300 cedis per worker in 2000 to about 17,600 cedis per worker in 2018). Between 1990 and 2000, when the country exhibited little structural transformation, a basic decomposition of annual average labor productivity growth shows that virtually all of the gain in average labor productivity is attributable to within-sector productivity gains.<sup>4</sup> Between 2000 and 2018, when structural transformation was much stronger in the country, roughly 46 percent of the gain in average labor productivity is attributable to between-sector gains (that is, the movement of workers between sectors, or structural transformation) while the remaining percentage is attributable to within-sector gains.

Turning to the picture across regions within Ghana, Figure 3 plots trends in employment shares in agriculture, industry, and services by region, based on data from the 2005/06, 2012/13, and 2016/17 GLSS rounds.<sup>5</sup> These rounds of data are also used for other results presented in the remainder of this section.

Based on the figure, we note three general patterns across the regions. First, the employment share in agriculture has declined. Second, employment has mainly shifted from agriculture to services. Third, in richer regions, services have replaced agriculture as the dominant sector in terms of employment.

The shift
There is also a key methodological difference between the SCI and other assessment tools. The latter methods collect data directly from national statistical offices’ staffs or local experts. While this procedure may provide a more in-depth analysis and uncover finer details in the organization of an NSS, it incurs high costs and is (far) more time-consuming. Furthermore, even the best evaluator can bring personal biases, non-uniform conceptions of capacity, or other subjective elements. Direct interviews of government officials might bias responses and complicate comparability across countries.<sup>5</sup> The SCI follows a different approach by using publicly available data and focusing on a set of easily observable and verifiable indicators. It provides internationally comparable, objective, country-level assessments across the globe. The World Bank has mainstreamed these indicators in its monitoring and assessment frameworks such as the Corporate Score Card (CSC) and the IDA Results Measurement System (RMS). The SCI is also used in various World Bank projects as a baseline indicator.

Yet, there are several areas in which the existing SCI can be improved. First, it comprises of a limited number of indicators and includes no indicators of some important surveys such as the labor force surveys and establishment surveys. Second, it ignores the data dissemination practices of an NSS, which is one of the key features of data usage. Third, the SCI has been criticized for placing too much weight on statistical output and activities, while neglecting the infrastructure and resource components of statistical systems. It was also criticized for its weighting scheme (i.e., equal weighting of each dimension and individual indicator) and aggregation method (i.e., simple arithmetic average).

> 5 Government officials may have an incentive to overestimate their country’s statistical capacity when there are concerns about their ability to deliver; the opposite can happen when they are requesting additional international aid or technical assistance.

5
education has almost tripled since FY09 from LE 39.8 billion to LE 92.3 billion in FY15.<sup>13</sup> However, as a percent of GDP the allocation has shown a slight decrease to 3.78 percent in FY15 from 3.83 percent in FY09.

In terms of total government expenditure, the share of education has increased to 12.6 percent in FY15 from 11.3 percent in FY09. In FY15, education was the sector with the third largest share in the budget, preceded by general public services and social protection. Compared to other countries in the region, Egypt ranks on the lower end of spending on education, both as share of total government expenditure and as a share of GDP (Figure 1 and Figure 2) over the period FY05-FY15. It is worth noting that, in this comparison, the Islamic Republic of Iran is the only country with a similar population to Egypt, and although the two rank close in education spending as a share of GDP, the Islamic Republic of Iran ranks much higher as a share of total government spending.

As is common in this sector, the majority of spending goes to the compensation of employees. However, since FY09, the share of compensation of employees in total education spending has grown from 78.7 percent to 83.6 percent in FY15 at the expense of investments in education (which mostly include the construction of new schools) and purchase of goods and services (which cover purchase of school supplies and equipment and maintenance of equipment and school buildings). Both items have each decreased by a little over 2 percentage points in the same period.

The available government expenditure data do not allow for the identification of the amounts spent on primary, preparatory, and secondary education separately, the data available group all expenditures that are in the levels below tertiary education into one figure. As such, the cost, in nominal terms, per student in pre-university education in FY09 was LE 1,408 and has
or, more importantly, due to bad governance, inadequate financing, and poor infrastructure.

To explore the answer to this question, it may be useful to compare the garment industries in Bangladesh (case 18 in Tables 1 and 2) and Ethiopia (case 8).<sup>8</sup> Both are least developed countries and regarded as relatively corrupt.<sup>9</sup> Both countries attempted to develop their export-oriented garment industries from scratch. However, they have had contrasting outcomes. In Bangladesh, export-oriented garment production dates from 1979, when a newly established firm, Desh, entered a technical cooperation agreement with the then leading garment exporter, Daewoo, in Korea (Rhee 1990; Easterly 2002). These firms teamed up because both were sure that the creation of a cadre of professionals would allow the latent comparative advantage of Bangladesh in garment production to materialize. The agreement sent 130 young employees of Desh, who were university graduates, to Korea for intensive training for eight months at Daewoo‟s factory in production skills, management skills, and international procurement and marketing. The garment industry in Bangladesh has since continued to grow rapidly for three decades with the continual learning of technology and management from abroad. It now has more than 4,000 manufacturing firms, employs more than four million workers directly, and ranks among the world‟s largest producers of garment items (Mottaleb and Sonobe 2011).

By contrast, the government of Ethiopia attached only secondary importance to learning from abroad when it began promoting export-oriented garment production in the mid-2000s. The promotion policy offered various preferential incentives including

> 8 See Sonobe and Otsuka (2011, chapters 8 and 9) for details.

> 9 In terms of corruption, probably Bangladesh was worse, as being ranked the most corrupt country in the world from 2001 to 2005, according to the Corruption Perceptions Index, which is available at <http://www.transparency.org/policy_research/surveys_indices/cpi>

9
selecting from nearby structures when the selected points do not fall on the roof of a dwelling, but these probabilities would be distorted if the household density were unevenly distributed or for households close to the boundary. Other public health studies, such as the World Health Organization Expanded Program on Immunization studies, use the “spin the pen” method to choose a starting household and then interview a tight cluster of households, though this method has been shown to be nonprobability (Bennett et al 1994, Grais et al 2007).

Outside the field of public health, Himelein et al (2014) used circles generated around random points to survey pastoralist populations in eastern Ethiopia, with the stratification developed from satellite imagery. A variation of this method was considered for the High Frequency Survey Pilot, but the methodology is likely unsuited to a dense metropolitan area, because it involves surveying all households within the selected circles. The uncertainty over the final cluster size was also an issue in Mogadishu as clusters with too few households increased costs, while clusters with too many households increased the time in the field and raised security concerns.

This paper brings together alternatives developed from this literature and applies them to a conflict environment. We take a rigorous approach using simulations and careful estimation of weights to compare the methods across a variety of potential field conditions. The results offer general guidelines for practitioners developing implementation plans for conflict settings.

# **2. Data**

To explore the challenges of the random walk and the four proposed alternatives, we simulated the use of each method in three example PSUs from Mogadishu, Somalia. We purposefully chose three census enumeration areas as the PSUs for this exercise to illustrate the variation in physical layout present in 4
.078|-|
|Agriculture value added share|-0.157**|-|-0.093|-|
|GDPc|-0.085|-|-0.195***|-|
|Log labor force|-|-30.421***|-|-27.100***|
|Log gross fixed capital formation|-|0.548|-|4.939*|
|Log exports of goods and services|-|9.189***|-|5.571**|
|Log REER|-|4.338*|-|3.613|
|World oil price index|-|0.084***|-|0.088***|
|World metal price index|-|0.027|-|0.011|
|Gini index|-|1.337**|-|1.096*|
|Observations|580|580|580|580|
|R2|0.162|0.229|0.903|0.835|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

The subsample analysis appears to confirm the robustness of the full-sample results. Restricting the analysis to resource-rich countries also supports the conclusion that the impact of resource dependence on income inequality and economic growth is determined by country-level characteristics that affect the management of natural resources. The overall conclusions of the analysis are presented in the following section.

15
processing algorithms to identify crash reports [20,21], developing an improved geoparsing algorithm to extract data on crash time and location [22–28], and ground truthing the results. The paper also contributes to a broader literature that uses machine learning methods for road safety analysis [29–31].

This study innovates on three fronts and demonstrates the value of using social media to expand data availability. (1) Geospatial Twitter data analysis usually uses the approximately 1% of tweets that have a geolocation tag <mark>[32–34]</mark> ; we improve this by using a machine learning geoparsing algorithm to leverage the 99% of tweets that do not contain a geotag. (2) To our knowledge there are no other studies that physically validate the locational accuracy of tweets in real time. Among verified tweets, 92% were found to be valid crashes, demonstrating the validity of crowdsourced crash data. (3) The work created an essential resource by generating one of the first real-time maps of RTCs in an African city (Nairobi). We identify 52,228 crash reports and geolocate those with enough information provided in the text (32,991 of them). In a context where there is no systematic georeferenced data on crashes to support policy planning, the process outlined here could be used to capture these data for cities all over the world that need this essential resource.

Overall, the method expands the coverage of road crashes that can be used to analyze road safety and to prioritize policy action around the locations where crashes occur more often. This is especially useful in country contexts where the only data available for analysis are aggregated statistics on total fatalities in the country, with no detailed breakdown of location or time. Crowdsourced data can help act as an additional input that can be used by policymakers in better understanding the situation. By using a clustering algorithm to identify and rank crash locations, we find that the top 15% of crash clusters (66 of 435
(SDG target 3.2), quality-adjusted years of schooling (SDG target 4.1), adult survival rate (SDG target 3.4), and stunting (SDG target 2.2).

- _The State of Social Safety Nets._ The WBG’s _State of Social Safety Nets_ tracks progress toward SDG target 1.3, on social protection coverage, and target 1.5, on the vulnerability of the poor to economic, social, and climate-related shocks and natural disasters (e.g., World Bank 2018c).

- _Health Equity and Financial Protection Indicators._ Through its Health Equity and Financial Protection Indicators database, the WBG tracks progress toward SDG 2 (on hunger and nutrition) and SDG target 3.8 (on universal health coverage; World Bank 2018d).

- _WASH Poverty Diagnostic._ The Water, Sanitation, and Hygiene Poverty Diagnostic tracks progress toward sustainable water and sanitation (SDG 6) in 18 countries (World Bank 2017b).

- _WBG flagship reports._ The _World Development Report_ (WDR) investigates a different development issue and outcome each year. Recent reports have examined the changing nature of work with key insights on SDG 3 (health) and SDG 4 (education) (World Bank 2019a); the issue of learning to realize education’s promise (World Bank 2018e); and the impact of governance and law on development (World Bank 2017c).<sup>15</sup> The _Global Economic Prospects_ (GEP) regularly assesses the economic growth of developing countries and the associated topical policy issues affecting the SDGs, such as fiscal space, fiscal policy, and structural policies  (World Bank 2015b and 2019b); potential growth and investment prospects (World Bank 2017d and 2018f); the prevalence of the informal economy (World Bank 2019c); and challenges of low-income countries (World Bank 2019b).

- _Joint monitoring reports._ In collaboration with other agencies, the WBG has produced reports to capture the global
sup>) is an option value of moving to a different job.This option value can be decomposed</sup> into internal and external option values. The external option value is defined as the option value from alternative job options in a different labor market. By netting out the effect from switching to a different job in the same labor market, the external option value is derived as The internal option value is the option value from alternative job options within the same labor market, which is given as the difference between the total and the external option values,

# **3.3 Relative Welfare and the Number of Job Options**

Although the model features various determinants of mobility and welfare, we can derive simple formulae describing the relationship between changes in welfare as well as the number of job options, and changes in an underlying policy, conditional on two assumptions. First, we assume that the change in an arbitrary policy variable _z_ does not change the moving costs.<sup>29</sup> Second, any change in _z_ of period _t_ is realized after workers receive their wage. Under these assumptions, the change in the relative welfare of workers in labor market _k_ compared to workers in labor market _l_ can be derived as

> 27This conditional probability equation will allow us to identify structural parameters using labor mobility data.

> 28By construction, _μk_ 0 _,t_<sup>+</sup><sup>_μk_</sup> 1 _,t_<sup>+</sup><sup>_μk_</sup> 2 _,t_<sup>+</sup><sup>_μk_</sup> _I,t_<sup>= 1holdsforany</sup><sup>_t_,</sup><sup>_k_.Usingequations(6)-(9),each</sup><sup>_μ_canbeexpressedas</sup> _μ_ 0<sup>_k_</sup> _,t_<
and road transportation infrastructure is exposed by urban flood risk. Second, the network-level effects of floods on urban connectivity and accessibility are assessed. Finally, it investigates whether flood risk affects infrastructure disruptions at the level of an individual firm. To address these questions, flood maps, employment density maps, infrastructure maps, and geocoded firm-level micro data is used for the 13 cities of Abidjan, Accra, Addis Ababa, Bamako, Cotonou, Douala, Dar es Salaam, Freetown, Kampala, Kigali, Monrovia, Nairobi, and Zanzibar.

The analysis finds that a significant fraction of economic activity is exposed to flood risk – though the level exposure varies significantly across cities. In Kampala, about 10% of all jobs are directly located in flood risk zones. Indeed, large fractions of the road network in these cities are located in areas that are potentially prone to flooding, often with depths that make traversing the roads impossible. In Kampala, hydrological flood models estimate flood depths at several road segments to exceed one meter (or three feet). Moreover, the impacts of flooding are felt not only in directly exposed flood zones, but across the entire city. Connectivity between firms is estimated to be affected significantly with travel times more than doubling for a quarter of all firms. Urban flooding is also estimated to make hospitals inaccessible to a third of all locations within the “golden hour”, i.e. the 60 minutes window following a critical medical incident beyond which survival rates are reduced drastically.

The remainder of this study is structured as follows. Section 2 outlines the types of data that are crucial to conduct city level hazard assessments, including high-resolution flood maps, and open source infrastructure network data. Section 3 presents evidence from a pooled dataset of up to 3,000 firms from 13 major cities in Sub-Saharan Africa, showing that infrastructure disruptions are transmitted across citywide networks and affect firms in- and outside of flood zones. Section 4 applies a geospatial network analysis tool to assess the direct exposure of jobs, road
**Figure 3.10: Total annual outage duration by cause in the US (top) and in Europe (bottom)**

<!-- Start of picture text --> 500,000 Combination of non-natural causes USA<br>Natural shock<br>450,000<br>Vandalism<br>400,000 unknown<br>350,000<br>300,000<br>250,000<br>200,000<br>150,000<br>100,000<br>50,000<br>0<br>30,000 Natural causes<br>Europe<br>Non-natural causes<br>25,000 Unknown<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>Total annual outage duration (minutes)<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Total annual outage duration (minutes)<br>2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> _Source: authors. Note: The peak in duration of outages caused by climatic events in 2014 in Europe is driven by one particular event: an ice storm in Slovenia that left the country out of power for several days._

# 4. Drivers of outages: natural shocks and external factors

Previous sections have shown that natural hazards, and storms in particular, are the leading cause of power outages in developed economies and an important factor elsewhere. Naturally, reducing corresponding economic costs presupposes understanding of exposure risk and identification of resilience factors to be harnessed for policy design. For example, this section will show that differential exposure to storms alone cannot fully explain the distribution of storm-related power outages. To assess underlying mechanisms, this section empirically tests a range of external factors that may modify the vulnerability of the electricity
Administration's Central Records and Subsidy Library in the Department of Commerce in Washington, DC. There were over 2000 petitions from firms that requested to have their products excluded from the safeguard.<sup>25</sup> Finally, we note again that a firm-specific product exclusion was typically granted to a product from a single exporting firm, which would be even more narrowly defined than a 10-digit HTS product category. Thus, not all products within a 10-digit product category would be excluded.

Table 4 provides the summary statistics for the yearly data that were used in the estimation of specifications that are reported in the next section.

# **4 Empirical Results**

This section considers the result of estimating variants of equation (2) on product-level imports and trade policy indicators for data that cover the period 1989-2003. Unfortunately our approach requires so many different policy variables that we are forced to split the estimation results of each regression into three _separate_ tables. Table 5 presents the estimated coefficients for the variables that are associated with the application of the steel SG in 2002, and Table 6 reports the impact of the application of AD/CVD. In unreported results (discussed in section 4.4 below) we have coefficient estimates for a smaller set of SGs that were imposed in 2000 as well as estimates for the trade impact of AD/CVD policy removals. In our tables, we ultimately present results from eight different model specifications.

# **4.1 Results for the 2002 steel safeguard**

Table 5 presents results for our estimates of equation (2) with regard to the trade effects that were associated with various aspects of the 2002 application of a US safeguard on steel. The dependent variable is the yearly growth rate of US imports of product _h_ from country _i_ . So as to facilitate a comparison of results across different policy instruments, we use simple indicators for application and removal of TTB policies in lieu of the applied tariff rates found in equation (2).<sup>26<