rafmacalaba/data-use-mentions-extended huggingface dataset:
While IRT might not be a reliable approach when there is limited item-by-item overlap, we conduct a few robustness tests where overlap is larger. We compare our results to the Linking International Comparative Student Assessment (LINCS) project which uses IRT methods and has significant overlap in items for a subset of international studies focused on reading at primary school from 1970 onwards (Strietholt, 2014; Strietholt and Rosén, 2016). If our expanded HLO database can produce similar results to original scores and IRT methods where there is overlap, we gain confidence in our results as well as an expanded dataset.

We note that while mean scores might vary by linking methods, and should be caveated appropriately, ranks and relative performance are relatively robust. While Sandefur (2016) finds large variation on mean scores depending on the equating method chosen, the Spearman rank correlations of the country averages are .97 or higher.

In building globally comparable education quality estimates, we rely on classical test theory (Holland and Hosken, 2003). Suppose that a population of pupils, sampled from the target population T, takes two different assessments X and Y. Here, we suppose that any differences in the score distributions on X and Y can be attributed entirely to the assessments themselves, since group ability is assumed to be constant.

The goal of linking is to summarize the difference in difficulty between two tests X and Y. We would like to link test X , the Anchored Test, to the scale of test Y, the Reference Test. For example, if we want to link PISA 2003 to TIMSS 2003, PISA 2003 is the Anchored Test X while TIMSS 2003 is the Reference Test Y.

Our main estimation method is ratio linking. To conduct ratio linking we index the difference in means in the Anchored Test X and Reference Test Y as follows: where 푙푖푛푘푖푛푔푌푝푙(푋) is the equation for converting observed scores on Anchored Test X to the scale of Reference Test Y. A limitation of this methodology is that it is sensitive to the scale
part is called the ‘foreign content of exports’ ( **FC** _sr_ , Koopman et al. 2014): In the same way gross exports are decomposed by country of origin in (2), they can be classified according to the country of ultimate absorption in final demand. **E** _sr_ consists of final goods ( **Y** _sr_ ) and intermediate inputs for the production of gross output in country _r_ ( **X** _r_ ): where **Y** _sr_ indicates the _N ×_ 1 vector of final goods completed in _s_ and consumed in _r_ . The intermediate inputs imported by country _r_ ( **X** _r_ ) can be linked with the country of final completion and the market of ultimate demand. According to basic I-O accounting relations (see equation A.2 in the Appendix), all the remaining (and potentially infinite) stages of production are accounted for by the Leontief inverse matrix **B** : where _k_ and _l_ might be the exporting country _s_ itself, and the generic **Y** _ij_ indicates the final goods completed in country _i_ and finally sold in country _j_ .

Relationships in (2) and in (4) add relevant information to traditional gross trade statistics (i.e. the very origin and the ultimate destination of exports). Nevertheless, it is still ‘gross accounting’, in the sense that these measures include the items that cross country _s_ ’s borders several times along the production process. Only by isolating these double counted items, we can measure the ‘net’ production (value-added) embedded in exports, akin to countries’ GDP.

Johnson and Noguera (2012) contribute to filling this gap by proposing a way to measure the share of a country’s GDP that is absorbed abroad. In particular, in a global input-output framework the ‘net’ value-added produced by _s_ and absorbed abroad (the so-called ‘value-added export’, *
60 60<br>40 40<br>20 20<br>0 0<br>45 55 65 75 45 55 65 75<br>age age<br>CHNS (1991) CHNS (2009) CHNS (1991) CHNS (2009)<br>Lowess Plot bw=0.3<br>Working Hours<br>Working Hours<br><!-- End of picture text --> Notes: Average hours worked per week by age cohort are calculated using non-parametric locally weighted regression (LOWESS) with a bandwidth of 0.3. Panel A presents results unconditional on working which use the 1991 and 2009 waves of the CHNS, and the 2008 CHARLS pilot. Because of the small sample of respondents who were working in urban areas in the CHARLS pilot, results conditional on working (Panel B) are only estimated using the CHNS.

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and the difference is statistically significant at the 0.01 level. Some of the differences are large. Public institutions have 3 more children per teacher, and 15% fewer teachers with post-secondary training, than private institutions. Indoor bathrooms are present in 80% of

> 7Anderson (2008) notes that some of the most influential randomized evaluations carried out in the U.S.— the Abecedarian Project, the Perry Preschool Program, and the Early Training Project—have serious statistical inference problems and confounds. DHHS (2010) finds that advantages gained during a year of the Head Start preschool program at age four dissipated by 2nd grade. Gormley _et al._ (2010) find that pre-K quality is key.

> 8There is also substantial within-country variation. For example, in the U.S., while Florida, Georgia, and Oklahoma provide free preschool to all four year-olds, 12 states do not fund any pre-primary (Pre-K Now 2010).

> 9In 2007, school became mandatory for six year olds, when primary education expanded from an eight to a nine year sequence. This explains the sharp spike in the ages 4-6 graph in 2007, as parents of six-year-olds no longer had the option to keep their children out of formal schooling.

> 10Averages are based on parental self reports in the National Household Sample Survey (PNAD). For a detailed discussion of heterogenous trends in access to pre-primary in Brazil during 1996-2008, see Evans and Kosec (2010).

> 11This includes any post-high school education, whether or not a degree was obtained.

> 12Some schools report using a teacher’s house, church, community center, local business, barn, or shed.

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**Figure 4. Distribution of wealth**

<!-- Start of picture text --> Asset Index Density, Non-Camp Households Asset Index Density, Camp Households<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Asset Index Asset Index<br>June (Gini=0.3325) June (Gini=0.2183)<br>February (Gini=0.3267) February (Gini=0.1970)<br>Before Earthquake (Gini=0.3346) Before Earthquake (Gini=0.2446)<br>kernel = epanechnikov, bandwidth = 4.6918 kernel = epanechnikov, bandwidth = 3.0994<br>.05<br>.02<br>.04<br>.015<br>.03<br>.01<br>.02<br>.005 .01<br>0 0<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. Weights are calculated using preearthquake assets.

Figure 5 presents the distribution of wealth losses in percentage of pre-earthquake wealth. The percentage of asset loss among households who lost assets is around 25%. This percentage does not seem to vary a lot according to wealth quintile.

**Figure 5. Wealth losses distribution**

<!-- Start of picture text --> Percentage of Asset Index Loss Percentage of Asset Index Loss, by Asset<br>Index Quintile (if Asset Loss>0)<br>35%<br>30%<br>25%<br>20% February<br>15% June<br>10%<br>-100 -50 Loss Percentage0 50 100 5%<br>February 0%<br>June 1
# **1 Introduction**

Since the 1940s, a five-day workweek followed by a two-day weekend has dictated the rhythm of economic activity in the US.<sup>1</sup> According to the American Time Use Survey, in 2013, 83% of employed persons worked on any given weekday, whereas only 34% of employed persons worked on a given weekend.<sup>2</sup> This suggests that the structure of the week has real economic consequences that are beyond the accounting of time. Yet, little is known about its normative properties: does it maximize welfare, or can we improve welfare by switching to a different labor-leisure cycle? For example, it has recently been proposed to transition to a four-day workweek, followed by a three-day weekend.<sup>3</sup> If adopted, how will this policy affect output and welfare?

The starting point of this paper is the observation that given the need for wide-scale coordination in both production and leisure, observing an equilibrium outcome in which the current workweek is practiced is uninformative of its optimality.<sup>4</sup> Given the multiple-equilibria nature of the calendar, assessing the optimality of the current workweek requires a more structural approach.

To this end, this paper develops a simple representative agent framework in which both labor productivity and the marginal utility of leisure evolve

> 1For an excellent survey on the history of the week, see Zerubavel [1989].

> 2See the American Time Use Survey Summary 2013, released on Wednesday, June 18, 2014.

> 3See, for example, “Should Thursday Be the New Friday? The Environmental and Economic Pluses of the 4-Day Workweek”, _Scientific American_ , July 24, 2009, by Lynne Peeples; or, “Where the Five-Day Workweek Came From: It’s a relatively new invention - is it time to shave another day off?”, _The Atlantic_ , August 21, 2014, by Philip Sopher.

> 4For example, a factory worker
|51|56|59|50|
|**Avg. Simulated**<br>**Output Loss**<br>**(%, Weighted**<br>**by Turnover)**|48|47|42|42|41|51|34|46|49|56|46|
|**Firms Proftable**<br>**at Baseline (%)**|75|69|72|99|75|69|87|80|59|100*|79|
|**Avg. Proft Margin (%)**|10|12|4|5|16|7|12|8|8|14|10|
|**Avg. Material Cost**<br>**(% Total Costs)**|52|41|65|26|54|38|53|29|38|19|41|
|**Avg. Labor Cost**<br>**(% Total Costs)**|11|29|6|11|15|29|24|28|32|26|21|
|**Avg. Fixed Cost**<br>**(% Total Costs)**|34|31|28|63|31|32|22|42|30|55|37|

Note: This table presents summary statistics on firms in ten countries. All statistics are from administrative corporate tax records, except the first row which is from the World Development Indicators. The allocation of firms to the high, low and medium impact group is based on the sector of economic activity and follows the classification in Vavra (2020), as displayed in Table A.1. We assume that the high impact sector experiences a 100% output loss during lockdown, and the medium and low impact sectors experience a 50% and 20% output
and bond markets.

The formulation and completion of the survey entailed a number of inter-related steps. Leveraging existing contacts at central banks and financial supervisory authorities, the initial outreach effort targeted the pertinent staff within local regulators and supervisors. However, given the sometimes-elusive nature of IRCs, with implementing authorities varying from central banks to different ministries, we also decided to contact the local banking associations, as these are in a prime position to provide specific references to the domestic or regional institutional arrangements. With the aim to cover as many countries as possible, the outreach effort included more than 150 jurisdictions.

Despite our best efforts, our survey faced several challenges. Not every country responded to the survey. Also, officials from the same country or even the same agency sometimes provided conflicting answers. Therefore, we had a second outreach effort to non-respondent local authorities relying on the contacts of World Bank Group financial sector experts, while we also followed up with the authorities that had provided inconsistent or incomplete responses. In a few countries where we still faced difficulties getting a response from the local authorities, we relied on the answers of World Bank Group financial sector experts.<sup>6</sup> In addition, in an attempt to reconcile any inconsistencies, we checked our responses with Ferrari et al. (2018), who recently took stock of lending interest rate caps collecting data from private and public sources; with the 2017 Global Financial Inclusion and Consumer Protection Survey, which included

> 6 These countries are Algeria, Argentina, Bangladesh, China, Ethiopia, Ghana, Kenya, South Africa and the United States.

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|4,258|0.4093|0.1375|-0.4691|2.0949|
|ln Population in 1991|4,267|9.5287|1.0888|6.6214|16.0821|
|Dependency Ratio in 1991|4267|0.4775|0.0466|0.3738|0.6205|
|ln Transport Cost to São Paulo in 1968|4266|7.6264|0.7874|3.2189|9.6385|
|ln Transport Cost to State Capital in<br>1968|4256|6.2548|0.7959|1.3863|8.6910|
|ln Proportion of Elderly in 1991|4267|-2.2502|0.2621|-3.9145|-1.5332|
|Illiteracy Rate in 1991|4267|35.5004|17.8266|2.4800|87.0500|
|Urbanization Rate in 1991|4267|53.6356|23.1683|2.1562|100.0000|
|Population Density in 1991|4267|93.6426|510.3321|0.0896|12199.7700|
|ln 5 to 15 over 15 to 55 years old Ratio|4267|-0.7491|0.2303|-1.6218|-0.0354|
|Repetition Rate in 1991|4267|4.2935|0.2071|2.3542|4.5756|
|ln Employment Rate in 1991|4267|-0.3760|0.1411|-1.2429|0.1601|
|Delta ln Education Market Potential<br>(2000/1991)|4251|0.9737|0.1736|0.0535|2.7704|
|Percentage of Good Soils|4266|0.7904|0.3071|0.0000|1.0002|
|Rainfall - First Principal Component|4266|0
ious sectors in Africa. In addition, we explore the role of complementary infrastructure, such as access to electricity and roads, in amplifying the impact of Internet connectivity. To this end, we assemble unique project-level data on sectoral FDI together with data on Internet infrastructure to estimate the spillover effects of fast Internet connectivity on FDI flows. The spatial and time granularity of our datasets allows us to undertake the analysis at the subnational level. Our identification strategy is a difference-in-difference (DiD) design that relies on the plausibly exogenous variation in access to high-speed Internet induced by the staggered arrival of submarine Internet cables across countries and the spatial variation in access to the terrestrial fiber backbone network within countries. Essentially, we compare districts connected to the terrestrial fiber network to those unconnected, and explore the changes in the FDI flows to these districts before and after the arrival of the submarine cables in the respective countries.

Three main findings emerge from the analysis. First, the arrival of high-speed Internet played a key role in stimulating FDI to Africa. However, the effects are largely concentrated in the service sector, with finance, technology, health and retail subsectors as the main beneficiaries. The effect on FDI in services is statistically and economically significant in both the intensive and extensive margins. On the extensive margin, the probability of a subnational district receiving FDI in services increases by 5.7 percentage points (pp) following the arrival of high-speed Internet connectivity. On the intensive margin, access to high-speed Internet connectivity is associated with a 9.6% and 23.6% increase the number and value of FDI in services. Within the service sector, we find positive and statistically significant effects of Internet connectivity on FDI into the various subsectors, viz, finance, technology, health and retail. The effects of the submarine Internet cables on FDI in other sectors such as manufacturing, energy and construction were not statistically significant.

Second, our findings show that the effects of high-speed Internet connectivity on FDI pertain largely to subnational districts with high access
To analyze the impacts of weather on child outcomes we estimate equation (3). We use the standardized height-for-age z-score for children 36-months or younger as our measure of health.<sup>29</sup> Unlike weight-for-age, height-for-age is not as sensitive to very short-term and immediate scarcities or illness, but would capture more chronic conditions.<sup>30</sup> Given that all the data were collected during the 1999 dry season, the year and season terms drop out. Given that the weather data are at the municipal level, we use state level fixed effects to control for the time invariant characteristics, such as the agro-climatic conditions at the state level.

Besides our measures of weather shocks, we also include information on household composition (the number of children, the number of adult males, and the number of adult females), on mother‘s characteristics (mother‘s education, height, and whether she speaks an indigenous language), information about the child (gender, if the child has an older sibling who was born alive within 2 years of the child‘s birth, multiple birth, the birth order of the child, whether the child was characterized as very small at birth, and the age of the child at the time anthropometric measurement was taken), an asset index,<sup>31</sup> housing characteristics (presence of indoor toilet, tap water, type of floor), and information about the child‘s locality (altitude) as regressors in the analyses.<sup>32</sup> Table 10 describes the variables used in the analyses. Given the differences in the average climate in the North and in the Centre/South regions, we also analyze the children in each region separately.

There are 1,995 rural children less than 36 months in the ENN dataset. Our sample consists of only 1,540 children.<sup>33</sup> We only include those children whose mother has not moved
>Ex-ante Vulnerable 0.01 0.00 0.04 1.00 0.13 0.17 0.04 0.03 0.17 0.21<br>Post-shock poor 0.23 0.79 1.00 1.00 0.39 0.45 0.23 0.14 0.61 0.65<br>Note: impacts are color-coded from dark red (highest impacts) to dark blue (lowest impact). Not all households declare<br>labor income.<br><!-- End of picture text --> Note, again, the importance of focusing attention on the right indicator. In particular, the use of the poverty headcount indicator delivers misleading results: in Albania, for instance, under the poverty headcount welfare metric using a PMT indicator would still indicate larger impacts than using a loss indicator. This is because the excessively simple metric used by the poverty headcount indicator, which focusses on an extremely narrow section of the income distribution around the poverty line, ignores any distributional impacts that happen below the line.

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of combining synthetic predictions with prior estimates based on survey data, which is important to generate more accurate and precise estimates for well-defined administrative levels (Masaki et al., 2022). Second, it is challenging when utilizing neural networks or other machine learning models to properly estimate uncertainty, which is crucial when evaluating the reliability of small area estimates. This paper aims to address both of these issues.

A few studies examine the extent to which combining survey data with geospatial data improves small area estimates of proxies related to household welfare, with generally encouraging results (Steele et al., 2017; Masaki et al., 2022). No studies to date, however, have examined the gain in both accuracy and efficiency from combining survey and remote sensing data for predicting monetary poverty for small areas. Furthermore, we know of no study that has compared predictions 3
trends model is that it extrapolates past trends to predict district mean welfare in 2014-15 rather than using actual data on district characteristics in 2014-15. While this is more likely to give a rate of poverty reduction in line with past trends, it will ignore the signal from district level indicators in characteristics.

The second alternative model tested includes dummy variables for the three types of expenditure instead of the actual expenditure amounts. The dummy variables are equal to one if the household incurred any expenditure for the particular item. We refer to this model as “Expenditures at the Extensive Margin”. This has the advantage of potentially being more robust to outliers in consumption, as well as changes in the way that the question was asked. However, it potentially ignores valuable information on the level of expenditures.

Lastly, we use only the 2011-12 data to predict into 2014-15. Because only one round of data is used to estimate the model, no variables are interacted with a time trend. We refer to this as the “Constant Coefficient” model, which has been utilized in past studies of survey to survey imputation.

We report predicted poverty rates of the four models in Table 5. We choose the primary model as our preferred specification because it utilizes all the available data, including levels of expenditure, imposes the least restrictive modeling assumptions on the data and has the most accurate predictions. We validate each model’s predictive quality by conducting two tests. First, for each specification we use data from 200405 and 2009-10 to predict poverty in 2011-12. We refer to this as forward projection. In the second analysis, we reverse project poverty in 2004-05 using the 2009-10 and 2011-12 data (see e.g., Deaton and Dreze (2002)). In the forward and reverse projections, we compare the actual versus predicted headcount of poverty, in 201112 and 2004-5 respectively.<sup>33</sup> Our final choice of model is based on which yields the most accurate estimates of poverty, on average, in both
secondary schools. Some countries had good internet connectivity and students had access to devices; these systems were well positioned to provide quality distance education online. Other countries, with limited internet connectivity and access to devices, broadcast lessons and used radio or other mass media to provide remote education.

As Table 2 shows, the longer the schools remained closed, the greater were the learning losses. For the 19 countries for which we have robust learning loss data, average school closures were 15 weeks, leading to average learning losses of 0.18 standard deviation. Put another way, for every week that schools were closed, learning declined by 1.2 point, or 0.01 standard deviation, on average.

Most of our robust data come from 12 European countries. Given similar starting conditions overall (such as GDP per capita, access to connectivity and devices), these countries provide compelling evidence that the pandemic-related school closures led to learning losses. In these European countries, average school closures were 11 weeks in duration, and average learning losses were 0.16 standard deviation. Therefore, for every week of school closure, learning losses were about 1.5 points, or 0.015 standard deviation, per month.

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Business_ team. The respondents to the community-level questionnaires in the LSMS and similar household conditions surveys are also expert respondents in this sense.

The sort of information that can be sought in this way includes such matters as how much it costs and how many days it takes to create a new limited liability company, or to recover on a collateral in the case of a loan default. Specific questions of law would also fall into the same category. Here there is no need to collect information about how many participants and the total value of assets, instead the facts being elicited relate to matters on which any well-informed expert should have a clear opinion based on their knowledge of the field and on which experts should generally agree. Sometimes the matters are rather subjective, but even here the hope is that experts will not differ by much and therefore that there will be no need to poll a large number of experts. As such, expert surveys are relatively rather inexpensive.<sup>31</sup> Expert surveys can be invaluable for

> 30 Thus the provider survey estimated 7.4% of households as having transactions accounts, whereas when ATM transaction-enabled savings accounts were included, 19 per cent of households reported their use.  46 per cent of households reported having savings accounts, compared with 28 per cent as estimated by the provide survey; the main reason was the wide use of informal savings clubs called Stokvels, in addition to the deposits at credit cooperatives which had been recorded in the provider surveys.

> 31 Indeed, the number of experts polled in some well-established expert surveys seems perilously small – leaving the surveys potentially open to manipulation as well as to small sample errors.  According to the _Doing Business_ website, even for a country the size of China, only 10 independent experts provided all of the responses; for five of the eight different themes explored, no more than one independent expert was

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