rafmacalaba/data-use-mentions-extended huggingface dataset:
While mirroring reduces the extent of missing data in the bilateral datacube, for CGE modeling this is not enough. This is because, to balance trade data, we need to somehow allocate missing destinations if we are to reconcile bilateral data with total values of trade with world (for example based on data from the IMF). On top of this, official statistics can be inconsistent, in that services data can vary widely depending on the reporting countries. In the case of international shipping and transport, reported data may be inconsistent with flows implied by the _cif − fob_ margins for goods. The most comprehensive attempt to confront these challenges in the context of balancing global and bilateral service flows has been through the GTAP project. The efforts to reconcile services data within the project are well documented, and reflect rather creative approaches, based on entropy methods, to flesh out the pattern of trade enough to allow construction of a global dataset for modeling cross-border trade in services (see MacDougall and Hagemejer, 2005; as well as Van Leeuwen and Lejour, 2006). Indeed, for the transport and shipping logistics sector, reconciliation of trade data in services is explicitly linked to the parallel reconciliation of data on trade in goods, as services play a critical role in bridging the gap between reported export flows for goods in one country’s national accounts data, and reported import flows in another country’s data (Gelhar, 1996).

While data on cross-border services trade have a reputation for being crude, they are easy to work with and much more complete in comparison with data on FDI stocks and flows in services. FDI data are plagued by limited coverage, as well as by confidentiality constraints that imply that even if available they cannot legally be reported. As discussed below, FDI data compilation efforts have sometimes been linked to specific modeling projects, like FTAP (Hanslow et al., 2000), the Michigan model (Brown and Stern, 2001) and the Worldscan model (Lejour, Rojas-Romagosa and Verweij, 2008)
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of job protection in developing countries, particularly among females (Bonnet, Vanek and Chen, 2019; de Paz Nieves, Gaddis and Muller, 2021). Jobs that are amenable to working from home, which have been found to moderate the impact of the pandemic on employment (Dingel and Neiman, 2020), while also reducing its gender effects (Alon et al., 2021), are relatively scarce across LMIC countries. As such, the impact of the COVID-19 crisis within these countries may differ substantially from the impacts that have been observed across developed countries.

Indeed, there are indications that the declines in employment seen in developing countries are steeper than those observed in developed countries; descriptive evidence from the HFPS studies undertaken in developing countries suggests that about 30 to 40 percent of workers stopped working during the second quarter of 2020<sup>10</sup> (Khamis et al., 2021; Bundervoet, Davalos and Garcia, 2021; Schotte et al., 2021). Such a magnitude is similar to that reported in other non-HFPS studies in developing countries as well (Jain et al., 2020, in South Africa; Biscaye, Egger and Pape, 2021, in Kenya and Nigeria). Consistent with the findings in developed countries, some of these studies report that females are significantly more likely to lose their jobs and to report larger declines in earnings (Hoehn-Velasco et al., 2021; Kugler et al., 2021; Deshpande, 2020; Jain et al., 2020). In South Africa, the findings of Jain et al. (2020) suggest that women experienced a 49 percent reduction in active employment over the February–April 2020 period, which is 15 percentage points larger than the reduction experienced by men in the same period.

The negative employment effects among females are also confirmed by similar results from simulation exercises using pre-pandemic data. For example, Lavado et al. (2022) find that, during the onset of the pandemic, female workers in the Philippines
where _T_<sup>_t_</sup> _is_<sup>is the test-score performance of student</sup><sup>_i_in school</sup><sup>_s_at time</sup><sup>_t_,</sup><sup>_B_are student</sup> background characteristics and _S_ are school characteristics. This value-added specification is considered preferable to simple contemporaneous specifications because it accounts for the cumulative character of the education process by controlling for prior performance at time _t_ –1 (cf. Hanushek 2002). Given the proxy of pre-school reading performance we derived from the PIRLS questionnaire, we can implement a version of this value-added specification using the PIRLS data. This is not a perfect solution because ideally, we would either have the students tested at the start of fourth grade or cumulative measures of school inputs _S_ from first to fourth grade. However, given relative constancy of school factors over the course of the first four years of schooling, the approximations introduced by this specification should be limited.

Treating the input factors as additively separable and considering the imputed character of some observations, we implement the following microeconometric estimation equation of the education production function: where _T_<sup>_t_</sup> _is_<sup>is the PIRLS test score of student</sup><sup>_i_in school</sup><sup>_s_in fourth grade,</sup><sup>_Tt_–1</sup> _is_<sup>is the measure</sup> of pre-school reading performance, _B_ is a vector of student background data, _S_ is a vector of data on school characteristics and _ε_ is a student-specific error term. The parameter vectors β 1 to β 4 will be estimated in the regressions.

The vector _D_ contains one dummy for each variable in _B_ and _S_ that has missing data. The dummies equal 1 if a value was missing and thus imputed and 0 for original data (cf. Section 2.1 above on the
assessment, could be achieved by the same means?

Secondly, given their differing perspectives, can<sup>the needs of</sup> developing country institutions, donor organisations and the research community all be identified and accommodated within the same study design? It will be argued by the research community<sup>that the</sup> muitifactorial nature Gf many health problems in the developing world, particularly those associated with poverty, means<sup>that better</sup> knowledge of determinants can only come irom research aimed at elucidating the processes whereby environmental, behavioural and economic factors interact to cause disease and mortality: to the extent that this view is correct, it would seem that a<sup>survey approach</sup> is either inappropriate, or that at least it should be regarded as only one part of a more extensive strategy aimed at understanding mecharisms and processes. Consideration of the requirements of the other users, points to the need to decide on the emphasis to<sup>be placed</sup> on achieving internationally comparable data on health, particularly for those indicators which are considered to be useful for advocacy, or for allocation of aid resources between countries. This might conflict with the need of countries themselves for information which could improve their own policy-making capacity, or<sup>assist in the</sup> management of their own health infrastructure.

Finally, however these issues are resolved, there would need to be consideration of the relationship between any proposed strategy for a WHS, and existing international and national initiatives providing information on health and on health interventions, in particular:- surveys such as the Demographic and Health Survey (DHS), UN Household Survey Capabi'.ity Programme (UNHSCP) and World Bank's Living Standards Measurement Study (LSMS) - internatLonal and national initiatives to develop and strengthen routine and continuous collection and analysis of informatior. on health (eg development of primary health cara (PHC) reporting, use of sentinel monitoring, nutrition surveillance
these countries, standards and quality assurance systems are often nonexistent or under-resourced and learning spaces often do not meet minimum safety and

> 1 In contrast to the practice of “red-shirting” in higher-income countries, in many low-income countries, parents try to enroll their children in primary school before the children are age eligible.

> 2 UIS use a variety of data sources to measure enrolment: administrative data from schools or household survey data on enrolment; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment); administrative data from ministries of education on the structure (entrance age and duration) of early childhood education. Data on provision are limited but suggest that around 38 percent of countries offer some free preprimary education, with most of these countries offering between one and three years of service. There is also wide variation of private provision across regions, ranging from 7 percent in Europe and Central Asia to 72 percent in the Middle East and North Africa (UIS, 2018).

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of measurement|||
||||GP (all members of|
|H3: Survey mode|P3: In-person surveying will display reported vaccine<br>uptake similar to administrative records.|ESS 5 in-person<br>vaccination survey in<br>Ethiopia (Apr – Jun ‘22)|<br>phone survey HHs)<br>and AP (main<br>respondents): June 5,<br>2022|
|H4: Panel<br>conditioning|P4: Respondents interviewed for the first time, as opposed<br>to repeat respondents, will display lower rates of vaccine<br>uptake.|Phone survey round in<br>Nigeria (Jan ’22)|AP: Jan 16, 2022|
|H5: Proxy<br>reporting|P5: Self-reported vaccine uptake will be significantly lower<br>than proxy-reported vaccine uptake.|Phone survey rounds in<br>Burkina Faso (Oct ’22),<br>Malawi (Sep ’22) and<br>Uganda(Aug’22)|AP: Oct 2, 2022<br>(BFA), Sep 4, 2022<br>(MWI, UGA)|
|H6:<br>Experimenter<br>demand|P6.1: Deliberately inducing positive experimenter demand<br>will lead to significantly higher reported vaccine uptake.<br>P6.2: Deliberately inducing negative experimenter demand<br>will lead to significantlylower reported vaccine uptake.|Phone survey rounds in<br>Burkina Faso (Jan ’23)<br>and Ethiopia (Jan ’23)|AP: Feb 19, 2023<br>(BFA), Jan 22, 2023<br>(ETH)|

Note: The table summarizes the empirical hypotheses we test in the vaccine survey data, the associated prediction(s) and survey data source. It also indicates the reference administrative coverage rate to which estimates can be compared.
pattern of consumption and investment responses to changes in public spending looks much more like the patterns the theory would predict for an _unanticipated_ change in spending rather than an anticipated change. And this in turn casts some doubt on the importance of anticipation effects in accounting for the small estimated multipliers in my benchmark specifications.<sup>12</sup>

> 11 Data on private and government consumption, total investment, and net exports are taken from the national accounts as reported in the World Development Indicators.  I use data on total public investment from the IMF’s WEO database to separate total investment into public and private investment.  Data on the expenditure components of GDP and/or public investment are missing for a handful of observations in my sample.  However, the fit of the first‐stage regressions of changes in government spending on changes in disbursements on World Bank loans are nearly the same as those reported in Table 3 for the full sample, and so are not repeated for this subsample.

> 12 A possible explanation for this pattern can be found in the work of Leeper, Walker and Yang (2010), who use a calibrated model of the US economy to investigate the short and long‐run effects of productive government

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2015) that improves the previous two approaches for their collinearity problem. A final TFP measure that has lately been developed is from Hsieh and Klenow (2009, 2014). In their methodology the authors follow a structural approach using a Cobb‐Douglas estimate with commonly used labor shares so as to explicitly take into account policy distortions that would otherwise affect traditional TFP measures in their revenue as opposed to physical TFP. In our paper, we use all four TFP proxies so as to compare results.

Moreover, since we are dealing with a cross‐sectional panel dimension we can also employ country‐ consistent indexes of services reform. In this way we could endorse whether reform indexes found in previous research papers are stable with our findings across a like‐minded group of countries with similar level of development as opposed to a single developing or emerging economy. Ideally, the results would be consistent across our four performance measures. The fact that we chose the EU is furthermore interesting since this part of the world has embarked on a great effort to synchronize its differing services sectors across member states into a common market. Over the years, the experience of the EU has proven to be a difficult one, but could nonetheless amount to considerable economic benefits as some studies suggest. Earlier works suggesting significant trade and welfare gains stemming from services reform are mostly done for an individual sector such as Maijoor, Buijink, Meuwissen and van Witteloostuijn (1998) and Cummins and Rubio‐Misas (2006). One likely reason why these studies have taken a sectoral approach lies is the inherent difficulty of finding international comparable performance estimates. Taken from this perspective, a third major contribution that this paper develops is to use sector and country‐specific TFP and regulatory policy measures that use firm‐level data from one common source.<sup>7</sup> The results of this paper suggest that regulatory barriers in services have diverse effects on downstream manufacturing performance depending on the _type_ of regulatory measure in question. Our policy variables are split into
weight to match the total population reported in the World Development Indicators. Section 3 below reports estimates based on this method.

In section 4, we test the sensitivity of key results to the choice of line-up method. In particular, we compare key results to an alternative approach that simply takes the population weighted average of country-level profiles, without any attempt to line-up data from different survey years to a common year, and find minor changes. Even though the results are generally similar under this approach, they cannot be referenced to a specific year and thus must be interpreted with caution. Appendix 1 describes the line-up procedures in greater detail.

Once the household surveys have been lined up, it is straightforward to use the pooled household survey data to construct poverty profiles. The figures displayed below are based on the international poverty lines of $1.90 and $3.10 per person per day in 2011 PPP as currently reported by the World Bank. The $1.90 line is the average national poverty lines from the 15 poorest countries originally used by Chen and Ravallion (2010) to establish the $1.25 a day poverty line in 2005 PPP terms.<sup>20</sup> As mentioned earlier, statistics are reported separately for the extreme poor, the moderate poor, and the non-poor. The moderate poor are defined as individuals whose household per capita consumption (or income) lies between $1.90 and $3.10

> 19 This reweighting procedure multiplies household weights by a scale factor in order to maintain a constant weight within each household. However, in cases where that is not sufficient to match the age group and genderdisaggregated UNDESA population estimates, weights are rescaled according to individuals’ age and gender groupings to exactly match these totals, meaning that weights for different members of the household may vary.

> 20 See Ferreira et al (2015) for more details on how these lines are derived.

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discussed in Section 2.4, this means to either ignore the monetary dimension or ignore the health dimension. The global MPI made the former choice while the World Bank’s MPM made the latter choice.<sup>19</sup> Currently, a key challenge reasoning may provide a rationale for attributing a quite high weight to education in the global MPI, even though the intrinsic value of education to well-being is disputed (Ravallion, 2011), at least if education is strongly correlated with consumption.

> 15Subjective well-being data suggests that good health and longevity are key determinant of wellbeing (Adler et al., 2017; Diener et al., 2018). The causal relationships behind the statistical association between subjective well-being, health and mortality are still an active area of research (Steptoe et al., 2015).

> 16A string of applied papers suggest that adding longevity significantly affects cross-country well-being comparisons (Becker et al., 2005; Jones and Klenow, 2016; Boarini et al., 2016).

> 17See Chapter 3 of the Poverty and Shared Prosperity Report (Bank, 2022) where the multidimensional impact of Covid-19 is analyzed through lenses different from the World Bank’s MPM.

> 18For instance, prices are imperfectly captured through unit values, rents implicitly perceived by homeowners are imperfectly captured through hedonic regressions, recall periods used in household consumption surveys affects welfare aggregates, etc.

> 19At this stage, it is an open question which of these two choices should yield the larger “signal-tonoise ratio” given the data available. On the one hand, the global MPI include a few variables capturing “standard of living”, which may serve as a noisy proxy for consumption. On the other hand, the variables capturing “health” in the global MPI provide a limited coverage of that dimension, as discussed in Section 2.4. It would be interesting to compare the correlation at household level between subjective well-being

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theoretical analysis but makes the predictions nearly impossible to test because multilateral rounds are so infrequent—eight since GATT was signed in 1947. Moreover, countries can choose to conclude a multilateral round with either considerable or little liberalization. Thus, the focus should be on whether PTAs affect the change in multilateral tariffs and not simply the probability of concluding any given multilateral round.<sup>4</sup> The model that we develop, which builds on Limão (2002), captures key features of the multilateral system and of recent PTAs and generates several speci fi c predictions that we test. The main prediction is that multilateral tariffs are higher on products that a country imports duty-free from preferential partners than on an otherwise similar good. The basic intuition for this result is the following. Suppose the European Union (EU) offers duty-free access in a set of products to some North African countries. The latter bene fi t from facing a lower tariff than their competitors; the fact that the EU signs the PTA indicates that its member governments value it at given multilateral tariffs. If the EU eliminated its multilateral tariff on that same set of products, it would effectively eliminate the PTA that it valued. We show that this additional cost of MTL is only present for the subset of PTA goods and affects multilateral tariff levels only when the preferential tariff is already zero since otherwise, the preferential tariff can be reduced to maintain the preferential margin. The model also predicts that if there is a common external tariff and the ability for direct cash transfers, that is generally present when there is a common tariff, then no stumbling block effect is present. This occurs because the EU can now offset any reduction in preferential margins due to MTL through a direct transfer to the preferential partner.

We estimate the model’s structural equation for the equilibrium trade policy using detailed product level data for the EU. There are several compelling reasons for focusing on the EU to analyze whether there is a clash of liberalizations. First, a key concern
suggestions for improving the teaching of development economics in developing countries, and to help students with the proficiencies of being critical readers and users of data, and creators of new knowledge:

- i) Take advantage of the availability of macro and micro data to have students do basic data analysis for their home countries. This could involve calculating poverty rates, plotting Lorenz curves, or estimating simple regressions that do not require complicated statistical knowledge. Free statistical packages like R, and simple calculations built into the sheets of Google Docs mean that such calculations can be carried out without requiring students to have licenses for expensive software.

- ii) Given the growing emphasis on experiential learning and teamwork, have students work in teams to collect and analyze basic data about their home country. This could include, for example, visiting local marketplaces to attempt to understand how small shops compete with one another, collecting wage data from spot labor markets during different weeks of the class to see how stable wage labor is, or visiting firms to learn how they hedge against exchange rate risk. More ambitiously, one could develop small panel surveys by having each new intake of classes return to the same settings year after year in order to build up rich knowledge of how the local economy works.  This appears to be very rarely done, although one class in Guatemala had a weekend visit to a rural community to apply the concepts learned in class.

- iii) Assign students readings from current newspapers, blogs, or magazines in order that they can apply what they are learning to current economic debate in their country.

- iv) Consider using textbooks that emphasize more the use and interpretation of data and/or supplement these textbooks with content that does this.

- **v)** Consider ways to better share what is taught across instructors. Our analysis found very few syllabi were on the web. One noteworthy exception was a repository of Brazilian development economics syllabi collected by the Brazilian Network of

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