rafmacalaba/data-use-mentions-extended huggingface dataset:
times which incorporates monsoon impacts and walking speeds in a rugged and sometimes high-altitude context.

# 2.3. Traditional rural transport valuation

Since available budgets for rural road building rarely match the observed needs, transportation planners and policy makers must choose the most productive subset of roads from a collection of potential investments. Valuation and prioritization exercises for rural roads investments traditionally define such productivity according to economic cost-benefit analysis (CBA). Van der Tak and Anandarup (1971) framed the classic method of evaluating rural roads construction benefits in terms of welfare gains within a CBA, as measured by society’s net savings from reductions in the cost of supplying goods, increased employment, and increases in commercial traffic linked to new roads. Improvements in travel times are principally used to measure changes in vehicle operating costs and opportunity costs of lost labor time. Lebo and Schelling (2001) updated this method to better emphasize year-round access to rural areas by incorporating seasonal access cost variations, the monetary value of improved access to services, and improved valuation of time savings into traditional cost-benefit analysis. As noted by Belli et al. (2001), the large budgets and timeframes required for these data-intensive methods make them difficult to scale to large sets of roads or road alternatives. Accordingly, Lebo and Schelling argued for analyzing roads’ cost-effectiveness – relative to stated development objectives, instead of economic outcomes – when evaluating lightly trafficked but locally critical rural transport infrastructure where comprehensive CBA may be prohibitively costly.

Accessibility indicators based on travel times may complement or outperform CBAs in rural areas as they present easily parsable decision criteria to lightly resourced rural governments, scale easily across large sets of roads, and readily translate to monetary opportunity costs of travel. Additionally, the travel time indicators generated by cost-time models have demonstrated correlations with changes in fields as varied as conservation (Nelson and Chomitz, 2011), poverty reduction (Porter, 2007; Ahlstr ̈om et al., 2011), trade (Schmitz et al., 2012), and disease prevention (Gilbert
to the “density” of exported products and their position in the “open forest”. Products that are “closer”<sup>50</sup> to other higher income value products (as defined by PRODY) are said to be in a “denser” part of the product space, implying that the ability of the country to “jump” (as one measure of self‐discovery) to these higher income value products is actually higher.

> 49 COMTRADE provides a good source of information, where goods can be disaggregated in thousands of subcategories, depending on the used nomenclature<sup>49</sup> . An additional advantage of using export data, as opposed to production data is that exporting can be seen as a sort of demonstration that a firm was able to pass a “quality” test in the international market. Being purchased in the world market, a locally produced and exported good can thus be regarded as one produced under satisfactory minimum standards, with entrepreneurs being able to break into a more competitive stage. On the flipside, using exports data omits considering the possibility that the goods were actually successfully produced within the country with good quality standards for already a long time and that the inability to bring them to the international market may be linked to some form of obstacle or intervention.  It also restricts the analysis to tradable goods and is silent in the –equally important‐ service area, including the touristic, and information technology sectors.

> 50 “Closeness” between product groups A and B refers to the probability that a country exporting product group “A” will also export product group “B”. In turn, this probability is derived from the export record, by product group, from the rest of the world. It is the minimum of the pairs of conditional probabilities going in both directions as an inverse measure of distance: min{P(A|B),P(B|A)}. See Hausmann and Klinger (2006)

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# **5 Summary and discussion**

## **5.1 Summary**

In this working paper, we presented a new quantitative risk assessment framework that evaluates (i) climate-induced asset risk to housing, infrastructure and agriculture, (ii) cascading service disruption risk caused by critical infrastructure failures, (iii) the exposure of households that suffer from multidimensional poverty (MDP) and (iv) the temporary incidence of MDP as a result of critical infrastructure service disruptions. We employ this risk assessment framework to explore the wider benefit of climate adaptation (through embankment improvement programs) in terms of their economic performance, through cost-benefit analysis, and in terms of their contribution towards equity-related objectives, more specifically their effectiveness at alleviating climate-induced MDP. We applied this framework to the highly vulnerable coastal zone of Bangladesh, utilizing a newly developed synthetic household dataset. The framework is designed to be generic and adaptable to other geographies where similar datasets are available.

Asset risk at present as a result of flooding is estimated to be 483 USD million, which is projected to increase up to 750 USD million in 2050 due to climate change alone, not accounting for socioeconomic projections. Extreme winds can add an additional 414 USD million to this risk, which can increase 53% in the future because of climate change. Note that we do not account for non-linear effects (e.g. compound flood-wind events).

As a result of flooding of critical infrastructure assets (which includes hospitals, health and education, electricity substations, WASH infrastructure and housing), households face, on average, a service disruption risk of 2 days per year, which can increase to 4.5 days per year by 2050. In four districts, the future service disruption risk is more than 6 days per year.

At present, 25.8% of the coastal population is exposed to flooding (100-year event), which can increase to 33.2% in 2050. Of the exposed population, 33.1% – 39.
The paper is organized as follows. The next section briefly summarizes similar studies. Section 3 describes the methodology of the data collection. Section 4 shows descriptive statistics of survey respondents. Section 5 presents major findings with analysis of the collected data. Finally, Section 6 summarizes and discusses implications for education provision and education policy in order to increase employability of engineering graduates.

# **2. Previous Employer Surveys and Related Literature on Skills**

This section reviews a selected set of previous employer surveys and related literature on skills that guided the design of this employer survey. A number of employer surveys have been conducted for graduates of different academic disciplines, e.g., business administration, education, economics, psychology, etc. Many of these surveys aim to identify which skills are demanded by employers and to examine how the supply of skills matches labor market demand.<sup>3</sup> Noel-Levitz (a higher education consulting firm) and Utah State University developed a comprehensive and well designed instrument for employer satisfaction surveys. The objective of the survey was to measure the employer satisfaction for benchmarking purposes (Kleinke, 2006). Seventeen universities participated in the survey in 2004. A survey instrument was mailed to 297 employers of the graduates, of which 112 employers responded (38% response rate). The questionnaire focused on graduates‘ knowledge and understanding within: the field of the graduates‘ major, general skills, and specialized skills. Employers rated graduates‘ knowledge and skills on a five-point scale in two aspects: satisfaction with the specific skills of the graduate and the importance of each of those skills. The survey found that employers were on average ― _very satisfied_ ‖ with the knowledge and skills of the graduates (average rating of 4.0 on a five point scale). The survey instrument was found useful especially in that it asked both importance and satisfaction levels of knowledge and skills. This structure was incorporated in our survey instrument.

Lattuca, Terenzini, and Volkwein, 2006, assessed the impact
**Table 2: Programs to Improve Breastfeeding and Complementary Feeding Rates in South Asia**

||Afghanistan|Bangladesh|Bhutan|India|Maldives|Nepal|Pakistan|Sri Lanka|
|---|---|---|---|---|---|---|---|---|
| Promotion of, and increased access to,<br>micronutrient powders for home fortification|No|Yes|No|No|No|Yes|Yes|Yes|
| Initiatives to increase access to, and promote<br>use of, diverse, local nutritious food at<br>household level|Yes|Yes|No|No|No|Yes|Yes|Yes|
| Promotion of, and increased access to, fortified<br>complementary foods|No|No|No|No|No|Yes|Yes|No|
| Social protection services for improved early<br>childhood nutrition (cash or in-kind transfers<br>combined with nutrition counseling)|No|No|No|Yes|No|Yes|No|No|
| Strengthened research and monitoring<br>activities on the quality of children’s diets|Yes|No|No|Yes|Yes|Yes|Yes|No|

_Sources:_ WHO 2017; UNICEF unpublished data; The Breastfeeding Collective Country Scorecards; Al-Jawaldeh and Abul-Fadl 2018.

_Note_ : BFHI = Baby-Friendly Hospital Initiative; IYCF = Infant and Young Child Feeding.

a. Formal designation has not been conducted in Sri Lanka. However, since 2017, the assessment is incorporated into quality assurance processes and tools.

b. Implementation at scale is defined as implementation at the national level or the subnational level (largest administrative subdivision of a country, e.g., state, province, or region), and excludes pilots.

21
, yet should only affect U.S. labor market outcomes through their impact on the choice of employment in affiliate locations.

B. Imports and Exports using Prices instead of Quantities In Table 2 in the paper, we examine the impact of import penetration and the quantity of exports on worker’s wages. It is worth examining the impact of prices of imported or exported goods on workers wages, since most traditional models of trade presume that changing relative prices are the catalyst for wage effects. In Table A4, we replace our quantity measures for imports and exports with price series data for each. Our data on the prices of imports were generously provided by Robert Lawrence, which catalogue prices within manufacturing by 4-digit NAICS industry codes. We used a concordance to translate this into prices for each CPS industry. Our data on export prices
years completed, number of high school repetitions, and percent entering college were 10.3, 0.127, and 42.4 percent, respectively. We observe the same pattern for the TEEP and younger cohorts. Although females outperformed males in schooling and migration, the monthly salary of females was only 84 percent of males’ salaries for the non-TEEP cohort and 96 percent of males’ salaries for the TEEP cohort.

Table 5.3 compares the means of key variables between the two modes of interview: face-to-face and phone. Except for years of schooling completed and number of repetitions in high school, all the variables show statistically significant differences. The migration rate was particularly higher among phone interviewees due to the above-mentioned method of our survey. Note that most of the face-to-face interviews were conducted within division. In our analyses, we include indicators of interview mode to control for potential differences in unobserved characteristics correlated with the mode.

# **_Cohort-Specific Variables_**

As discussed earlier, we control for factors (progress in social development and high school quality) that could have induced differentiated trends between TEEP and non-TEEP divisions. Figure 5.2 plots the percentage of households below the poverty threshold in each of the sample provinces from 1997 to 2009. The trends in poverty incidence during the 12 years are similar between the paired provinces for two pairs (Ifugao and Neuva Vizcaya, and Antique and Iloilo), slightly different for one pair (Leyte and West Samar), and very different for the last pair (Negros Oriental and Cebu).

In Table 5.4, we compare measures of access to public services and infrastructure between TEEP and non-TEEP areas. The household survey asked each mother or guardian whether the household had access to 14 types of public services or infrastructure in its barangay in 2000 and 2010. The table shows that except for public elementary school, health center, and paved road, we do not confirm significant differences in 2000 and/or 2010. Interestingly, trends in the period of 2000 to 2010
to the Energy Poverty Advisory Hub's report (2022), the driving forces behind energy poverty can be categorized into contextual and individual factors. Additionally, the report identifies three principal causes responsible for the occurrence of energy poverty: low income, energy inefficiency, and energy prices.

**To better understand what could be associated with energy poverty, we next describe the energy poor, using the multidimensional energy poverty index; characterizing the energy poor can also be helpful for the design of targeting strategies.** Table 4 shows that the energy poor are more likely to reside in households with at least one unemployed, with more than five members, and with at least one pensioner when compared to households that are not energy poor. They are more likely to live in rural areas, be elderly, and disabled. We confirm that these characteristics are correlated with energy poverty incidence rates by estimating a Probit regression, using the probability of energy poverty based on the energy poverty index as a binary outcome variable. Table 5 shows that these groups are still more likely to be energy poor, even when compared with households with similar household characteristics<sup>43</sup> . The fact that these characteristics relate to a higher probability of being energy poor can help with targeting mechanisms, such as proxy mean testing, and generate insights on what might be driving energy poverty in the first place (e.g., increased vulnerabilities, more generally speaking).

_Table 4: Descriptive Statistics of Energy Poor and Non-Energy Poor_

||(1)<br>Energy poor<br>Mean|sd|(2)<br>Non-energy<br>poor<br>mean|sd|
|---|---|---|---|---|
|Members|2.56|1.31|2.55|1.10|
|Adult equivalent|2.73|1.61|2.64|1.15
2007) “China's (Uneven) Progress Against Poverty,” Journal of Development Economics, 82(1) (January 2007): 1-42.

- Robalino DA, Zylberstajn E, Zylberstajn H, Afonso LE (2009) Ex ante methods to assess the impact of social insurance policies on labor supply with an application to Brazil, World Bank Social Protection Discussion Paper No 0929, Washington D.C.

- Sohn MJ (2007) The characteristics of the recent trend of self-employment. SERI Economic Focus 146: 39-55.

- Vere JP (2011) Social security and elderly labor supply: evidence from the Health and Retirement Study, Labor Economics (in press).

- Venti, S and Wise, D (2004) “Aging and housing equity: Another look” in _Perspectives on the Economics of Aging_ , David Wise (eds). Chicago: University of Chicago Press.

28
# Did the 2018 Trade War Improve Job Opportunities for US Workers?<sup>*</sup>

Beata Javorcik Benjamin Kett Oxford, EBRD & CEPR IMF Katherine Stapleton Layla O’Kane World Bank Burning Glass Technologies

**Keywords** : tariffs, trade barriers, online job adverts, trade war, local labor markets **JEL classification** : F14, F15, F16

> *Javorcik e-mail: beata.javorcik@economics.ox.ac.uk. Kett e-mail: bkett@imf.org. Stapleton email: kstapleton@worldbank.org. O’Kane email: lokane@burning-glass.com. We thank Burning Glass Technologies for providing us with the job postings data. The views presented in the paper are solely of the authors and not any institutions they may be affiliated with.
diversity distribution – often very poor households – experience large declines in dietary diversity as a result of the wheat flour price increases. Since households living at subsistence levels cannot make major cuts to caloric intake, they must adjust the compositions of their diet in order to maintain energy levels.

To the best of our knowledge, this paper is the first to utilize the UQR estimator to examine the impact of food price shocks on household food security. It represents one of a handful of empirical analyses that use nationally-representative household data collected prior to and during a significant price shock. Since such data are extremely rare, most empirical analyses rely on data collected during earlier periods and utilize simulation models to look at the potential impact of the price shock on household welfare. In this analysis, we seek to inform local and national policymakers and development agents working in Afghanistan by providing systematic quantitative analyses of household responses to food price shocks.

# **Data**

We use consumption and price data from the National Risk and Vulnerability Assessment (NRVA) 2007/08, conducted by the Government of Afghanistan Central Statistics Organization and the Ministry of Rural Rehabilitation and Development. The survey was administered between August 2007 and September 2008 and covered over 20,500 households (over 150,000 individuals) in 2,572 communities in all 34 provinces of Afghanistan. The long time frame made it possible to obtain seasonally-adjusted estimates of household food security and allowed for coverage of conflict-affected areas.

The sample was selected based on a stratified, multi-stage design. The survey was stratified explicitly geographically and implicitly over time.<sup>1</sup> The 11 provinces with the most populous provincial centers were each stratified into urban and rural areas. The remaining provinces were treated as separate rural strata, and the nomadic Kuchi population was treated as a separate stratum. The stratification resulted in 46 domains or strata. In the first stage of selection, 2,441 primary sampling units (PSU) from urban and rural settled

> 1 The population frame is based
exercise seeks to quantify the opportunity costs related to this lost productivity. We do not include the losses associated with other aspects of growth such as fewer sick days (Grossman and Kaestner 1997) of more educated workers or lower violence rates among children of adult (rather than teen)

> 5 The definition of youth depends on the sectoral and cultural context under study; for the purposes of this study youth female or girls, as per the United Nations definition, refer to females in the age bracket of 15 to 24. All cost data refer to girls in this age range with the exception of adolescent pregnancy, which refers to girls ages 15 to 19.

4
# 6.2. Firm-level capabilities and skills

Poor labor productivity trends among formal Zambian firms in the non-mining industry and service sectors also reflect firm-level capability and technological constraints. Firms often lack critical managerial, technological, and vocational skills. The Zambian training system provides inadequate training with respect to business and entrepreneurial skills, which are necessary for the establishment and growth of businesses (Government of the Republic of Zambia 2020). A relatively low percentage of firms in Zambia have licenses for the use of international technologies (Figure 37) or have internationally recognized quality certifications (Figure 38), proxies for the level of technology adoption, and managerial and operational sophistication. As a result, Zambia ranks 100th out of 132 countries with respect to business sophistication in the World Intellectual Property Organization’s Global Innovation Index (2022), below countries such as Peru (49th), Thailand (43rd), and even Zimbabwe (90th) (World Intellectual Property Organization 2022).

<!-- Start of picture text --> Figure 37. Firms using technology licensed from Figure 38. Firms with international recognized quality<br>foreign companies certification<br>(Percent of latest Enterprise Survey respondents) (Percent of latest Enterprise Survey respondents)<br>40% 25%<br>35%<br>20%<br>30%<br>25% 15%<br>20%<br>15% 10%<br>10%<br>5%<br>5%<br>0% 0%<br>Cross-country averages Structural peers Aspirational peers Zambia Cross-country averages Structural peers Aspirational peers Zambia<br>Note: Select aspirational peers such as Thailand also have lower scores, although in such cases the low score reflects the ready<br>availability of domestically developed technology rather than low business sophistication.<br>Source: World Bank staff calculations based on Enterprise Survey data and the Investment Climate Assessment 2.0 toolkit<br><!-
it is difficult to locate registered properties for tax administration because there is no proper address system and the tax database is not geocoded. The addresses provided at the time of registration are often vague (e.g., “18th Street” or “Catholic Hospital community”). As a result, officials from the LRA typically rely on information from community members to identify buildings from the tax database, especially in cases where there is no readily available photograph of the property.<sup>9</sup> Because of the lack of addresses, tax bills are not routinely delivered to property owners; instead, they are required to visit the LRA to request their bills.

Besides the challenges in identifying properties, the LRA faces legal, political, and logistical constraints to implementing the penalties for noncompliance. The most common consequence for noncompliance is that penalties (up to 25 percent of the tax liability per year) and interest (an average of 15 percent per year) are added to the tax liability. Although the revenue code allows for the closure and seizure of delinquent properties, these actions require a court ruling, which implies significant time and administrative costs. As a result, in practice, enforcement consists of repeated written notices and in-person visits to property owners and, at most, the posting of a delinquent notice on the property for 72 hours. Furthermore, given the limited staff and resources of the LRA, these enforcement actions are focused on select properties that have the highest revenue potential, leaving the majority of the tax base essentially free from enforcement.

An additional key feature of real estate tax in this context is the tax rate. It is 1.5 percent of the property value per annum for commercial properties and 0.25 percent for residential properties.<sup>10</sup> Given the relative illiquidity of the real estate market and the lack of transaction data, it is challenging to determine property values. To facilitate extensive margin compliance, property owners are allowed to self-declare their property