rafmacalaba/data-use-mentions-extended huggingface dataset:
With the objective of measuring different aspects of adult farmers’ cognitive ability, we selected five different cognitive tests: i) The Raven Colored Progressive matrices, measuring visual processing and analytical reasoning; ii) The digit span forwards and backwards, measuring shortterm memory and executive functioning; iii) A written and timed test of basic math skills; iv) An oral 9-item test containing short math puzzles relevant for agriculture and increasing in level of difficulty; and v) A reading comprehension test. Table A1.A provides a detailed description of each of these tests.

# _<u>Noncognitive skills</u>_

The noncognitive part focuses on testing instruments derived from commonly used scales in noncognitive domains that the literature has emphasized as potentially predictive of success in life and that are potentially relevant for smallholder farmers. We use a subset of items from the 44-item BFI, a commonly used instrument for the Big Five personality traits. We also test commonly used instruments for lower-order constructs such as scales for locus of control, selfesteem, perceptions about the causes of poverty, attitudes towards change, organization, tenacity, meta-cognitive ability, optimism, learning orientation, and self-control. The majority of these subscales are derived from a set of questions asking the respondent the level at which he agrees or disagrees with general statements about himself, with answers on a Likert scale from 1 to 5.<sup>9</sup> In addition, we asked a set of locus-of control questions with visual aids in which people are asked to attribute success to effort & good decisions, luck or endowments. We also included the CESD, a commonly used depression scale, that has been used and validated in many developing countries, and which relates to some of the noncognitive domains also captured in other scales (such as neuroticism and optimism). A standard risk aversion game and time preference questions were also added, mostly for comparison and completeness.

Table A1.B in the appendix presents all items, and the first column indicates the sub-scale each
By providing examples of outlier countries, we do not imply the data are wrong or misreported in specific countries. While country circumstances will differ, we do consider that inequality in data quality has a significant role to play in the aggregate. The recording of vital statistics was already prior to the pandemic subject to considerable variation in data quality, where WHO (2018a) contains a detailed description of the various challenges. We believe that the pandemic accentuates these pre-existing challenges. As a result, the tally of the developing world is likely significantly underestimated by a margin that will far exceed that of the HICs.

Data quality concerns are not the preserve of the developing world. Indeed, we observe that the HICs themselves are grappling with considerable data quality challenges. Total deaths may be measured with more precision than infections—a reasonable assumption, especially in HICs that have better registration methods for vital statistics. Yet, there is a growing realization that COVID19 fatalities are being misattributed to other causes of death (The Economist, 2020b). By some estimates, reported death rates in selected countries could be underestimated by 60 percent, which would of course imply a significant increase in the already considerable severity of the mortality burden in those countries (Burn-Murdoch and others, 2020).

Perhaps for this reason it is unsurprising that Belgium—the country with the highest severity ratio in the world—has reported such a high death toll. Belgium has corrective measures to aggressively include deaths occurring in elderly-care homes based on symptoms shown and contacts made even if there was not a confirmed infection (Bloomberg, 2020). As a result, excess all-cause deaths closely match COVID-19 deaths, which inspires confidence in the quality of the data. While this may help explain why Belgium has the highest level of severity globally, it at the same time raises suspicion on the data of many other countries.

# **Perspective 2: Unrealized**

While data quality may play a significant role, the vast discrepancies cannot be reasonably explained by data quality alone. The
assignment of students across classes has been used in a number of papers. Ammermueller and Pischke (2009) follow a similar approach for estimating peer effects using PIRLS data of primary school children across six European countries. McEwan (2003) also bases his estimates of peer effects in Chilean secondary schools using school fixed effects, although the argument of randomization tends to be weaker at the secondary level, given that a fraction of all school aged children do attend this level. Vigdor and Nechyba (2004, 2006) take advantage of randomization in primary schools in North Carolina to estimate peer effects for fifth graders. In all of these cases, however, the peer effects were estimated using the mean of the peer characteristics and no composition or heterogenous peer effects were allowed.<sup>11</sup> My approach is also close to Hoxby (2000) who estimates non linear peer effects

> 11Ammermueller and Pischke also cast doubts on the validity of the random assignment

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who self-select into our sample are not all existing entrepreneurs and they fall well below median levels of consumption in rural Uganda, in contrast to most studies that focus on urban entrepreneurs. Our results highlight that the population where skills-based constraints bind may not be the population typically targeted by entrepreneurship programs.

Third, our paper makes two methodological contributions that provide a comprehensive understanding of the value of entrepreneurship skills in the lives of ultra-poor, rural women. First, we use high-frequency SMS data to better understand how the program we study affects income volatility and women’s ability to cope with large economic shocks. Understanding volatility is first-order to alleviating poverty in rural areas, but traditional surveys are ill-suited to measure such dynamics. Second, we demonstrate the effectiveness of a relatively fast, low-cost method for collecting social network data using randomly ordered photobooks of study participants. Our photobooks allow low-literacy respondents to fully participate, avoid concerns about different names being used for the same person, and reduce respondent fatigue by allowing respondents to quickly identify network links. This is helpful in any setting where measuring social networks is important but is not the central focus of an intervention. We additionally demonstrate that in settings where network-based spillovers may occur, researchers can use baseline network data to quantify them.

Positive spillovers through business networks, but not friendship networks, speak to peer effects.<sup>8</sup> Our results stand in contrast to Field et al. (2016), who Bjorvatn, and Tungodden (2015), Bruhn and Zia (2013), deMel, McKenzie, and Woodruff (2014), Brooks, Donovan, and Johnson (2018), Campos et al. (2017), Arráiz, Bhanot, and Calero (2019), Alibhai et al. (2019), Anderson et al. (2020)). By contrast, five out of seven studies on existing entrepreneurs in rural markets find positive effects from training (Calderon, Cunha, and Giorgi (2020), Bakhtiar, Bastian, and Goldstein (
complementarities between activities due to for example the need for specific job skills, shared infrastructure or need for specialized inputs and services (Hidalgo et al., 2007; Coniglio et al., 2021). In addition, it may also be related to less tangible spillovers in knowledge and soft technologies associated with the entrepreneurial discovery process (Hausmann and Rodrik, 2003). The remainder of this paper is organized as follows. Section 2 discusses data development. Section 3 explores basic patterns in activity exports as countries develop over time. Section 4 reports on the importance of proximity in determining the development of new specializations. Section 5 explores the degree of path dependence in specialization patterns of countries and relates it to economic growth. Section 6 concludes.

# **2. Data sources for exports of activities**

The analysis of trade in terms of activities rather than in terms of products or industries poses major data challenges. In this section, we discuss how these challenges have been met through combining two main data sets: data on exports of value added and data on the activities of workers. To this end we introduce variable _z_ j , the value added of activity type _j_ in country _c_ ’s exports. It is tracked by the labor income of the workers carrying out the activity _j_ in the production chains of country _c_ ’s exports. The first data set contains information on labor income of workers by occupational group and industry of work. The second data set contains information on the exports of value added by industry. These exports include value added in the exporting industry as well as other domestic industries that contribute more upstream in the production chain through delivery of inputs. The _z_ j ’s are derived for each country and year by joining the two data sets at the countryindustry dimension. A crucial part in data construction is to make sure that occupations and industries are consistently defined and measured across countries and over time. Data is constructed for a set of 52 economies, ranging from low-income to high-income, see Appendix
the data. Cross‐country comparisons were systematically undertaken when data comparability permitted it to do so.</mark> <mark>The twelve topics examined were chosen following a review of key policy documents and expert consultations, and retained because of their salient nature in ongoing academic and policy debates, in addition to the feasibility of the new LSMS‐ISA data to address and advance these debates. The papers speak to the prevailing, overarching notions that 1) Africa’s agricultural technology is backward; 2) that smallholder engagement with input, factor and product markets remains limited and 3) that Africa and its citizens are behind in the structural transformation of their economies, occupations and incomes.</mark>
investigations.<sup>16</sup> The failure to find evidence of our political economic determinants affecting the WTO-challenge decision would be consistent with the theory that the WTO membership is acting rationally and not challenging cases that would either have high market access gains and/or which would could be litigated at relatively low cost, because they anticipate that dispute settlement panels would indeed show “deference” and follow Article 17.6, thus placing an extraordinarily low probability on the expectation of a successful legal outcome to the case.

We classify a U.S.-imposed trade remedy as being challenged if we can find evidence of a formal dispute being initiated under the GATT or WTO. We consider all challenges to U.S.-imposed remedies, whether the challenge concerns the investigation of dumping, subsidies, or injury, or whether it is an investigation during an administrative or sunset review. Most of the data on _which_ trade remedies were formally challenged by foreign countries through GATT/WTO dispute settlement proceedings are also available in publicly available data bases. For example, the WTO’s website has all of the data on formal disputes covering the period since 1995, as well as some antidumping and countervailing disputes that resulted in formal panel reports under the GATT period. A handful of antidumping and countervailing duty disputes that took place under the dispute settlement provisions of the Tokyo Round Codes were pieced together by examining unpublished GATT documents. The summary of these GATT/WTO challenges to U.S. trade remedies imposed over the 1992-2003 period is listed in table 2.

In the next two sections we detail the variables and data we use to represent the expected benefits and costs of our empirical investigation.

# **3.2 Expected Benefits to Initiating a Dispute**

# **3.2.1 Exporter market access benefits from a successful dispute**

What are the expected benefits to initiating a dispute against a U.S.-imposed trade remedy, and when would they be large? For the
of the lack of telecommunication on households is rarely documented in the literature, some studies put in evidence consequences on emergency systems and public health structure. In the aftermath of hurricane Maria in Puerto Rico, Kishore et al. (2018) report that 8.8% of the surveyed population in the most remote areas of the island were not able to reach emergency services. Beatty et al. (2006) point out the need of improvement of communication during an electricity outage in a hospital setting. Kile et al. (2005) reach the same conclusion after conducting semi-structured interviews on the impact of a power outage in 2003 in the Midwest and Northeast of the US. Respondents underlined the fact that communication was limited, and that public health surveillance was affected along with emergency responses.

# 1.2. The economic cost of electricity outages to households is high

The cost of electricity outages depends on length and timing. Carlsson & Martinsson (2008) provide evidence that the willingness to pay (WTP) to avoid unplanned power outages varies depending on the time of the week and the season. The 2,400 households who responded to their choice experiment survey in Sweden reported being more affected by outages occurring during a weekend and during the winter. Households also place more value on the reduction of long electricity service interruptions. Equivalent studies based on survey methods in other developed countries reach the same conclusion about the nonlinear impact of outage durations on WTP and costs, and therefore on well-being.

Estimates of the WTP to avoid power outage yield very different results across countries and methodologies (Figure 4)<sup>3</sup> . According to the referenced studies, the average cost revealed by contingent valuation and choice experiments surveys are USD 11.9 and USD 0.74 per unserved hour respectively. This difference is coherent with theory: in contingent valuations, people tend to overstate their WTP as they think it will have an impact on the problem while not being directly charged to them. There is no clear

>
Furthermore, the work permit is taxable and the tax rate levied by the government would be a key policy instrument for the targeting. By taxing work permits, the government affects the size of the market, hence determining the set of beneficiaries. When the revenues from such taxation are transferred as lumpsum cash benefits, the government is further able to exploit market forces to determine both the breadth and depth of this market-based social protection scheme.

We provide a theoretical model of a market for work permits. We henceforth use the terminologies “rent in” (resp. “rent out”) and “buy” (resp. “sell”) interchangeably, since our model does not have a time dimension. Yet, the scheme of interest in this paper is a market of _temporary_ tradeable work permits. In our model, demand for work permits is driven by risk-neutral workers in the origin country who compare their current earnings with their expected earnings, were they to move (migrate) to the destination country. However, the migrants face two types of costs: (i) expenses associated with the actual costs of the journey and (ii) a non-monetary psychic cost. Workers in the origin country observe the equilibrium price of a work permit and decide whether to demand one accordingly. Symmetrically, workers in destination countries decide whether or not to sell their work permits given their opportunity cost of time, as we assume that all workers have some disutility from work. In equilibrium, the price of a work permit clears the market and the lowest earners in the destination countries are the sellers, while the buyers are those with the highest returns to migration. We next allow the government to levy a tax on work permits. The tax generates tax revenues but the market shrinks as permit prices drop.

To calibrate our model, we assume a fictitious market in the US for Mexican workers. We first estimate what earnings prospective migrants from Mexico would earn in the US by estimating Mincer-type regressions on US survey data and inferring migrant earnings using
Redistribution: Evidence from the Life in Transition Survey. European Journal of Political Economy, 34: 300-314.

- Cruces, G., R. Perez-Truglia, and M. Tetaz (2013). Biased Perceptions of Income Distribution and Preferences for Redistribution: Evidence from a Survey Experiment. Journal of Public Economics, 98: 100-112.

- Diermeier M, H. Goecke, J. Niehues J, and T. Thomas, 2017. Impact of inequality-related media coverage on the concerns of the citizens, DICE Discussion Paper 258.

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# **4. Implications for Poverty**

What would be the welfare implications of modifying the relevant factors determining the price differentials in South Sudan? To address this question, we match the expected price effect arising from changing a specific determinant with the only household survey data available, i.e. the 2009 National Baseline Household Survey (NBHS). These need to be interpreted as partial equilibrium effects (i.e. everything else equal). Among the possible factors, we consider road quality and diesel price as those are amenable to policy interventions and are significant determinants of price differentials in **Error! Reference source not found.** . .

Due to data limitations the estimation relies only on the welfare effects via consumption but not via production.<sup>23</sup> The basic principle consists of applying the changes in prices faced by the household from modifying a specific factor to the overall household’s tradable expenditures.

We start from the consideration that households minimize a household expenditure function C(u,p), which finds the minimum cost to attain utility level u given prices p (Deaton and Muellbauer, 1980; Friedman and Levinsohn, 2002). Any changes in the vector of prices that the household is exposed to would have a direct impact on its consumption, which can be measured as ∆����∆� where q is a vector of quantities consumed and Δp is a vector of price changes. This expression approximates the compensating variation, i.e. the change in income required to restore the household to the pre-price change utility level. The expression can be re-formulated in terms of initial expenditures of household j introducing the distinction between goods produced and consumed: Where P is the purchase of household j of good i, S is the sale and Exp is the total initial expenditure of household j. Expression (3) defines the effects of price changes on the basis of whether the household is a net seller or buyer of good i. In the
across a wide range of country categories (Foster and Briceño-Garmendia, 2010; Estache 2005; Prud’homme 2005; IMF 2008; and World Bank 2007). Values at the lower end may be expected for broader aggregates; for example, on the basis of a cross-country analysis for developing countries, Dessus and Herrera (2000) generate an average internal rate of return of 14.2 percent for aggregate government capital, a rate that is very similar to the return calculated for private capital, 14.9 percent. There is some indication that, for the period 2000-2003, returns from infrastructure investment in SubSaharan Africa and elsewhere were higher than in previous decades (Estache, 2005). Given the current low base of infrastructure provision in Congo, a higher than average IRR can be expected. In fact, estimates by Briceño-Garmendia and Foster (2009) yield an IRR ranging between 6 and 100 percent for isolated interventions and between 5 and 30 percent for country-level investments in the Republic of Congo.

Compared to the baseline, additional infrastructure investments lead to improvements in the macroeconomic situation for all scenarios (Table 5). This includes growth, private consumption and investment and trade indicators. Poverty rates and MDG indicators are also lower than in the baseline scenario (Table 6).

The results of the model show that delaying investments and improving their efficiency pays off. Higher growth rates and lower unemployment rates can be achieved when slightly delaying investment and simultaneously improving efficiency compared to frontloading investment with low efficiency. As described in section 2.3, Congo’s public investment management is far from being efficient. The brief delay in large investment projects could be used to improve government systems, especially in project 10
- Finally, the concept used in some countries to measure delays may differ from the definitions used here to ensure global comparability. For instance, in many places, containers may be moved for processing at terminal to satellite facilities in the same general location. Our definition consolidates the time spent at all facilities, not just at the port terminal.

## Potential for more indicators from the existing data

The current data can support more indicators than in Table 2. The following indicators could be considered in the future:

1. **How time at destination depends on origin** . Trade procedures and transfer of information or payment at destination may be influenced by the country of origin. An indicator could be a weighted variance of dwell time depending on origin.

2. **Efficiency of domestic logistics** (KPI based on the logistics of empty containers). The tracking data covers the responsibility of the international logistics operators, not logistics done by shippers upstream or consignees downstream the supply chain. Supply chain practices by the latter may vary. However, container data include information on the movement of empty containers that proxies the time taken to stuff export containers or deliver full import containers at destination. The lead time for an empty container to come back full is an indication of the efficiency of domestic logistics.

3. **Cold chain logistics** . Refrigerated containers (reefers) are important for food trade. Differences in lead time for reefers compared to ordinary containers could be informative, especially on the export side.

4. **Accessibility indicators** as weighted average over origin countries of the lead time to destination country.

# **4. Conclusions and future directions**

The experience of the 2023 analytics of the large tracking datasets is encouraging. KPI derived from Big Data provide new insights, more actionable than the traditional LPI (e.g., dwell time). The following directions could reinforce the value of the KPIs. The first is making the production sustainable by strengthening the data