rafmacalaba/data-use-mentions-extended huggingface dataset:
//doi.org/10.1093/wber/lhaa007</mark></u> Amin, Mohammad and Asif Islam (2015), “Does Mandating Nondiscrimination in Hiring Practices Influence Women's Employment? Evidence Using Firm-level Data,” _Feminist Economics_ , _forthcoming_ Amin, Mohammad and Asif Islam (2014), “Imports of Intermediate Inputs and Country Size,” _Applied Economics Letters_ , 21(11): 738-741.

Antecol, Heather (2000), “An Examination of Cross-Country Differences in the Gender Gap in Labor Force Participation Rates,” _Labour Economics_ , 7(4): 409-426.

<mark>Aterido, R., Hallward-Driemeier, M., & Pages, C. (2011). Big Constraints to Small Firms' Growth? Business Environment and Employment Growth Across Firms.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>, 59 (3): 609 – 647.</mark> Ballington, J. (2008) “Equality in Politics: A Survey of Men and Women in Parliaments,” InterParliamentary Union, Reports and Documents No. 54, p.31.

Barth, Erling, and Harald Dale-Olsen (2009) “Monopolistic Discrimination, Worker Turnover, and the Gender Wage Gap” _Labour Economics_ 16:589-597 31
10 Total Fertility Rate 11. Total fertility is specified to be a function of infant mortality, 7 literacy, income, urbanization, and family planning expenditures. Higher levels of fertility are caused by higher levels of mortality. Mortality derives its affects on fertility through the physiological effects of the cessation of breastfeeding in shortening post-partum ammenorea and through behaviocal or replacement effects resulting from the desire to achieve desired family size. Additionally, urbanization is generally associate%& with sociological changes that result in lower fertility, and income, over the short run or within cross section data, is expected to be positively 8 related to fertility. Literacy, in most studies, has been associated with a lowering of fertility although this effect, distinguished from education, has not always been found to be positive. Finally, higher levels of acceptance of family planning produced by government expenditures on family planning programs lead to lowered fertility. The relationship of the total fertility rate to its determinants can be written as TFR - F(IMR, ILLIT, YP, PJRB, REXFPP) (2), using the notation in Table 1.

7The determinants of fertility are surveyed in Bulatao (1984), Birdsall (1977), McGreevey and Birdsall (1974) and Cochrane (1979).

8Simon (1974) and Hull and Hull (1977).
countries to create two new global rankings of GDP accuracy.

_Objective Data Quality._ Existing rankings of GDP data quality are at least partially subjective. We assemble a new world ranking of GDP misstatement by country over 2005-2020, which ranges from 0.25 to 5 percentage points per annum on average, and is in line with Henderson et al. (2012)’s estimate of trend growth errors up to 3 percentage points. We find that existing subjective rankings are misleading, and our objective ranking implies a significant reshuffling.

_GDP Errors during the 2020 Pandemic._ We reconsider reported GDP during the 2020 COVID19 crisis and create a ranking of misstatement in this year specifically. We find that even for countries with very high-quality statistical systems including the United Kingdom, the unprecedented official GDP statistics reported during 2020 seem unlikely to be entirely accurate. We ultimately conclude that 2020 GDP systematically understated economic activity, globally.

Finally, we close by discussing other avenues for research we have no space for in this proofof-concept work, including subnational and sub-annual estimates, and nowcasting.

**Related Literature.** There is a wealth of economic research using satellite data, as summarized by Donaldson and Storeygard (2016). The primary signal which has been used towards measuring economic activity is the radiance of night lights. Levin et al. (2020) provides a comprehensive review of this literature.

Alternative satellite data such as high-resolution imagery has been used to measure world poverty (Jean et al., 2016) and detect urban markets (Baragwanath et al., 2019). Moreover, there have been many examples of using satellite air pollution data in the environmental and health literature (Jayachandran, 2009). However, to our knowledge the only previous study using air pollution data to measure cyclical economic activity is Morris and Zhang (2018), which uses NO2 to infer economic activity in China. The current study represents a more comprehensive and independent effort to carry out this seed of an idea to its full fruition, globally, with
# **5. Learning variance decomposition**

In this section, we examine how much of the total variability in QAT scores is attributable to the variability in QAT scores _between_ program schools (i.e., due to school heterogeneity) versus the variability in QAT scores between students _within_ program schools (i.e., due to student heterogeneity). Decomposing further, we examine how much of the total variability in QAT scores is attributable to the variability in QAT scores (1) between program districts, (2) between schools within district, (3) between grades within school, and (4) between students within grade. These are important questions as education policymakers and practitioners may have at their disposal the means to effectively reduce the extent of heterogeneity at the level of the school and grade (if they are found to be major contributing factors), thereby reducing total variability in student achievement.

To answer the above questions, we fit two-level and four-level variance-components models (nested random effects models) to the student-level QAT score data. This exercise is performed separately for each round of QAT score data.<sup>13</sup> It is also performed separately for phase-3 and phase-4 program schools.

The two-level variance-components model is specified as where _i_ indexes the student and _s_ the school, and<sup>_y_</sup> _is_<sup>denotes the mean QAT score in the core</sup> subjects of English, mathematics, and Urdu for student _i_ in school _s_ . Students are nested within schools—thus student QAT scores within schools are likely to be correlated. The QAT sometimes tested students in the same grades across schools. As a result, the grade _g_ is viewed as a cross factor with the school _s_ and is treated in the model as a fixed vector of grade dummies<sup>_x_</sup> _g_ with a vector of fixed parameters  to be estimated. The error components  _s_ and  _is_ are assumed to be independently
that not be possible, subregions<sup>17</sup> are created, and countries with insufficient data are assumed to have a similar propensity to accept migrants as other countries in the subregion. Failing this option, global propensity measures are constructed.<sup>18</sup> More than a single method of allocation is chosen so that the data already in the matrices can be used to maximum effect. All these allocations ignore the gender profile of migrants. This dimension is accounted for at a later stage, once all the aggregate categories have been assigned.

# _Varying Survey Dates_

During the 10-year window of each census round, there are no conventions on when a destination country should conduct its census. Although many destination countries conduct their censuses at the turn of the decade, the actual date is up to each country. Attempting to standardize census dates would require changing the numbers reported in the original census documents.

Most destination countries conduct their census within two years of the middle year of each census round—between 1998 and 2002 for the 2000 census round, for example (table 3). The census numbers thus are not changed, and the matrices report all censuses as comparable in each round. A full list of census dates is in appendix 1. An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer.

# **{Table 3 here}**

# _Calculating Missing Gender Splits_

Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits;
Patents offer an alternative measure of inventive activity. Patents provide a detailed record of each invention. From the bibliographic data on a patent, the researcher can learn the identity and home country of the inventor, read a description of the invention, and see references to earlier patents. Using patent data, it is possible for researchers to collect data in highly disaggregated forms. Whereas R&D data are typically available only for specific industries or general applications,<sup>5</sup> patent classifications can be used to distinguish between different types of R&D at great detail, such as air pollution control devices designed to reduce NOX emissions versus devices designed to control SO2 emissions.<sup>6</sup> In addition, economists have found that patents, sorted by their date of application, provide a good indicator of R&D activity, as patent applications are usually filed early in the research process (see, for example, Griliches 1990). As a result, patent counts not only serve as a measure of innovative output, but are indicative of the level of innovative activity itself. A recent OECD data collection effort makes data on 37 different types of environmental technologies readily available to researchers.<sup>7</sup> However, patent data also have drawbacks. While patent counts should be expected to increase as R&D activity increases, the correlation need not be exact. Variations in patent law, both across countries and across time, must be controlled for to properly interpret patent data. Furthermore, the existence of a patent does not mean that the technology has been adopted. Indeed, studies of the economic value of patents find that most patents have little commercial value, suggesting that adoption of most patented inventions is not widespread (see, for example, Lanjouw _et al_ . 1998). Moreover, firms are more likely to use patents to protect new products

> 5 For example, in the US, private R&D is available from 1972-1994 for air pollution
Cambodia, Ethiopia, Indonesia, Nepal and Zimbabwe, a comparison is carried out of the ideal variables summarizing the various dimensions or components of food security, environment and health, and child care against the measures available from current household surveys. These comparisons serve to highlight the limitations of most data sets and the potential gains associated with the collection and availability of additional information.

Second, bearing in mind the limitations imposed by the data available, the report also provides a practical diagnostic framework of the main correlates of child malnutrition that could be applied to identify potential “binding constraints” towards the effort to reduce child malnutrition. Specifically, the UNICEF conceptual framework is “operationalized” by serving as a guide for investigating the relationship between the prevalence of malnutrition in the country and inadequate levels and access to the three pillars summarizing the underlying causes of malnutrition. Next, for each indicator available from the survey used in country, a definition of “adequacy” is constructed using thresholds based on accepted international standards. The report aims to provide a “helicopter view” of the extent to which nutritional outcomes, as measured by a child’s height‐for‐age Z score (HAZ) at any given point in time, as well as over time, are associated with inadequate food security, inadequate environment and health, and inadequate child care practices. In consideration of the complexity of the linkages between the underlying causes of malnutrition and the economic situation of the family, the analysis is also carried out separately for resource‐rich (top 60%) and resource‐poor (bottom 40%) households based on an asset index constructed for that purpose.

Third, the report provides some of the first empirical evidence on the synergies at work in combating malnutrition in the set of countries used in this study when there is simultaneous access to adequate levels in two or more of the three pillars of the underlying causes of malnutrition. While intuitively appealing, the synergies among the three clusters of malnutrition have received little empirical validation. The recent
type of document they had.<sup>8</sup> Relying on this information, we are able to identify documented and undocumented migrants among the pool of return migrants. For most of the destination countries, Egyptian migrants are required to have a visa. For a few countries, like Sudan, Libya, Jordan, Lebanon, and the Republic of Yemen, Egyptians

> 7 In Table A.1 in the Online Appendix, we also examine the geographical transition matrices for non-migrants and return migrants in Panel A and Panel B, respectively. We examine individuals’ mobility between their region of birth and their region of residence, at the time of the survey, in order to investigate whether the geographical locations reported in Table 1 reflect settlement choices after return or whether returnees are simply returning to their regions of origin. The diagonal cells represent the percentage of individuals who stayed within the same region between birth and the time of the survey, while the cells below and above the diagonal represent the percentage of individuals who moved to a different region between the two time periods. For both non-migrants and return migrants, we compute the mobility rate as the sum of all cells below and above the diagonal. We find evidence of little mobility for both groups—the mobility rates are equal to 5.5% and 7.1% for non-migrants and return migrants, respectively. The descriptive statistics on geographical locations in Table 1 therefore do not reflect relocation choices, as the results suggest that return migrants eventually return to their origin regions.

> 8 This question is similar to what was used in the Mexican Migration Project questionnaire to elicit information on the documentation the migrant had.

9
which aims to assess resilience at country‐level based on globally available data complemented by country‐specific data. An example of a Climate Resilience

> 13 http://www.wcdrr.org/uploads/Sendai_Framework_for_Disaster_Risk_Reduction_2015‐2030.pdf

> 14 http://inform.jrc.ec.europa.eu/Results/Global

> 15 http://index.gain.org/

> 16 http://www.oecd.org/dac/risk‐resilience.htm

26
available. Using these data, and highly disaggregated information on import tariffs for the same period, we analyze how different trade policy interventions affect the ability of Indonesian exporters to adjust to sudden changes in foreign demand. Specifically, we focus on how exchange rate shocks in Chinese yuan affect Indonesian firms’ exports in Japan, the largest regional trading partner for Indonesia (after China) and the export destination for 8,353 Indonesian firms.<sup>1</sup> Indonesia provides a suitable context for the analysis for a number of reasons. First, it fits remarkably well the general pattern of trade policy in developing countries over the past three decades, namely import tariffs liberalization since the 1990s and a contemporaneous increase in NTMs. Second, many of these NTMs are applied on intermediate inputs and have been changed frequently over time, thus providing a rich source of variation for our analysis. Specifically, merging customs and policy information allows us to build a unique data set on the universe of exporters and their imported inputs with precise time-varying data on trade policy at a detailed product level. Finally, many Indonesian exporters compete with Chinese producers in third markets, particularly Japan. Importantly for our analysis, Indonesian exports are denominated in USD. These characteristics allow us to exploit the significant changes in the USD/yuan exchange rate in the 2010s as an exogenous demand shock to Indonesian exporters to Japan.

The demand shocks we use are monthly variations in exchange rates between the yuan and the USD. Such exchange rate movements—out of control of Indonesian firms—affect their exports by changing the relative prices of Indonesian and Chinese goods in Japan, where the largest share of imports are from China. Consider for instance a depreciation of the yuan versus the USD, which is the case for most of our sample period. One of the key strategies through which an Indonesian exporter could counter the increase in Chinese competition associated with this exchange rate shock is to lower its marginal costs. In the

> 1The largest regional trading partner is China.
with many recent industrial policies aiming to target key products or components in the value chains of these green technologies, it is helpful to have a sense of where latent comparative advantages are more likely to exist.

To address this gap, this paper makes several contributions. First, we construct a new dataset of key traded products, components and materials associated with solar PV, wind turbines and EVs and map this to country trade data. This dataset enables the exploration of historical and current trade patterns for over 200 countries between 1995-2021 in these global value chains. We focus these specific value chains for three reasons. First, worldwide consensus exists that these technologies are critical in the green transition, irrespective of countries’ economic conditions and political alignment. This contrasts with green and environmental goods whose classification can sometimes be controversial and subject to countries’ political sensitivities. Second, participating in the trade of these value chains offers important economic advantages for countries. As global demand is beginning to shift away from fossil-fuel based production and towards these technologies, developing the capabilities to competitively produce products and associated components can help countries achieve greater economic growth and export diversification prospects. This is especially true for technologically sophisticated products and components as they offer advantages for technological upgrading and knowledge spillovers into other industrial areas (Hidalgo & Hausmann, 2009). Third, given the potential supply-side risks facing these value chains, building greater resilience – through diversifying production across a greater number of countries with latent comparative advantage can help mitigate against potential disruptions or shortages in the global roll-out of these technologies. Further, such diversification can also help ensure that the rewards from participating in these high-growth global value chains are shared more broadly.

Second, we develop novel indices that summarize the breadth and depth of countries’ current export strengths in these value chains. While China, Germany and the US are the leaders in export competitiveness across all three technologies, middle-income countries such as Türkiye, Mexico, India, South Africa and Brazil have export strengths in a variety of key value chain products
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The establishment of comprehensive 'risk data' information bases, also permits the insurance industry to participate in loss mitigation incentives by providing tools to better understand and price local risks. Such information allows taking into account detailed characteristics such as building code ratings when determining potential loss costs to be used in rating insurance coverage for individual properties. This results not only in fairer prices for insurance but also provides an economic incentive for communities to strengthen their building codes and enforcement.

Insurers can take a number of steps to help promote investment in vulnerability mitigation measures and to reduce potential loss profiles on underwritten risks. For this purpose, industry service functions could be established under a specialized industry sponsored office (similar to the Insurance Services Office in the U.S.), in order to assign 'grades' to communities based on the quality of their building codes and their enforcement. For this, a grading schedule reflecting building code enforcement can be established for qualifying communities which may be eligible for premium discounts. Under such a program, grades would be assigned to local communities based on structural factors as well as public protection services (e.g.: disaster preparedness, fire fighting capability, etc.) available. Loss cost estimation and associated premium levels should, in addition to the above factors, be also based on the characteristics of individual properties including location and risk exposure in accordance with the hazard mapping informnation discussed above.

# **_Structural Measures_**

Structural measures have also made but limited headway in the Caribbean. The Caribbean Unified Building Code (CUBIC) has now progressed into useable codes in several nations but, as was recently observed, these improved codes lack any effective enforcement practices. Limited progress on retrofitting can be attributed primarily to the lack of incentives and concerted leadership in the promotion of benefit features and practices.

A fee based approach which might result in more effective enforcement actions would be to set up a Bureau de Controle type firm at the regional level, with the responsibility to inspect building during the
Very poor households, however, may not acquire many durable goods and spend most of their income on consumption, particularly food consumption. Therefore a more complete assessment of the household’s wealth would need to include a consumption measure as well. Even when available, household food related expenditure or consumption data is an imperfect measure of welfare, as it does not take into account seasonal changes, the distribution of food within the household, and the variability of all individual household members needs. An alternative is to use an outcome based indicator that would reflect how these factors come into play. The use of the body mass index (BMI), defined as the weight in kilograms divided by the square of height in meters, has been suggested as a possible indicator of household food availability (James and others, 1999). It seems reasonable to expect that changes in food availability, such as seasonal food shortages, would be reflected in a low BMI. As women tend to be the most vulnerable household members, their BMI has been chosen as a proxy for household food availability. The cut off point of a BMI of less than 18.5 is generally regarded as an indicator of malnutrition, with a BMI of less than 16 regarded as a sign of severe malnutrition. A dummy variable, which takes a value of one if the mother’s BMI is less than 18.5 is therefore used to proxy for household food insecurity.

Maternal education has been identified as a significant determinant of children’s health in the literature. The analysis incorporates information on the education of the mother and her husband or partner. Eighty two percent of the women in the sample report receiving no formal education. Thirteen percent report at least some primary school. Given the low level of education in the sample, an additional year of education may be a significant factor and therefore the mother’s education variable is defined as the number of years of education. The husband’s or partner’s level of education is also defined