rafmacalaba/data-use-mentions-extended huggingface dataset:
to the affected trading partners. This compensation could take the form either of a mutually agreeable tariff reduction in some other sector or of the affected trading partner being permitted to raise its tariff in some other sector in response. A second common feature is that, unlike other GATT provisions introduced below, these changes to tariffs were meant to apply only to limited and well-defined _products_ , and not entire sectors or bundles of imports.<sup>71</sup> Over the entire GATT period of 1947-1994, countries invoked Article XIX on 150 different occasions to increase their tariffs temporarily and Article XXVIII on 275 occasions to increase their tariff bindings permanently (WTO, 1995). While there is little empirical research examining use of these provisions, access to some newly available archival data from the 1950s allows us to investigate patterns to some of the new import restrictions triggered right after the GATT’s inception.<sup>72</sup> For example, the early GATT negotiations for tariff reductions could have significantly over-estimated the amount of tariff-cutting liberalization that government might be able to sustain, thus triggering the need to raise We begin with the permanent tariff increases triggered under Article XXVIII. Between 1950 and 1959, the GATT Contracting Parties invoked Article XXVIII only 70 times (WTO, 1995). Thus it appears that formal actions to raise tariffs permanently under Article XXVIII were relatively rare, and when they arose, the requests were scattered across countries and sectors. The lack of major permanent tariff increases, at least for the major GATT members, is consistent with the downward trend in average applied tariffs for these countries over the GATT period (see again Figure 12).

Next, consider the frequency with which countries resorted to _temporary_ escape from their tariff commitments (Article XIX). A distinctive element of this channel is the mandate that, in order to justify use of Article XIX, governments were required to provide some evidence of unforeseen events having taking place, and that increased imports of the product were causing injury to the domestic,
projectspecific datasets. Furthermore, some survey datasets are stored as separate entries in the WB and UNHCR MDL even though they are indeed part of the same survey (e.g., entries by camp or by wave). Those independent entries are collapsed as one when they are part of the same survey.

> 28 Throughout this paper, we adopt the regional classification of the World Bank:

<u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.</u>
or collect relief items; and (d) community outreach programs (Brookings 2013). However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013).

> 92 The absence of data on new displacement may simply mean that no displacement has taken place (IDMC 2016).

> 93 IDMC reports that data, disaggregated by age and sex, were available for 15 of the 60 countries it monitored in 2014, however these data were not comprehensive and are not published. Additionally, in some countries there are data provided by IOM on IDP populations by location from which the urban or rural character of the population may be inferred (e.g. if the camp is located in the capital), but data are not comprehensive and not published. While the majority of humanitarian profile data does not typically cover IDPs living outside of camp or camp-like settings (the large majority of IDPs), IOM’s DTM in countries such as Nigeria, Iraq, Yemen and Libya do include information about those residing in host communities.
manner that is consistent with the standard semi-logarithmic formulation for estimating returns to schooling at the micro level.

Nevertheless, Temple (2001) argues that the parameter _ψ_ may not be interpreted as the social returns to schooling because it does not incorporate the opportunity costs of the resources used in educational provision. Still, it remains of considerable interest since an empirical estimate provides a way of either confirming or rejecting the importance of education suggested by micro studies.

For the Guatemalan case, the econometric specification is similar to the previous equations. The production function is first converted into a logarithmic expression: (23) log _yt_ = log _At_ + _α_ ⋅ log _kt_ + 1( − _α_ ) ⋅ _ψ_ ⋅ _ht_ Then, the production function is transformed into an error-correction formulation, which allows the long-run schooling parameter to be identified: Finally, the error-correction model is re-parameterized and includes a series of dummy variables: Notice that the implicit return to schooling can be calculated with knowledge of _α_ and _γ_ 3 . In principle, this approach would also allow the productivity effect of schooling to be differentiated by education level, as mentioned by Wößmann (2003). Unfortunately, the results here were found unstable for disaggregated education data. This is presumably due to the missing logarithmic transformation of the schooling variables.

Insofar, the specification provides an attractive way for comparing macro and micro evidence on the returns to schooling, but in a time series context tends to produce fragile parameter estimates. Nevertheless, when using aggregated data on human capital the regressions perform quite satisfactorily. Table 7 presents the results. Controlling for endogeneity does not distort the empirics. In the IV specification 1

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precarious zone, (e) medium-density precarious zone, (f) high-density precarious zone, and (g) rural area.

While the Kinshasa Survey is prepared with an innovative approach, the use of the survey requires caution against its data quality and comparability. It is extremely challenging to design a sampling survey to collect information about households and individuals that represents Kinshasa, due to the lack of a recent population census. The sampling of the Kinshasa Survey, which is the primary data for this study, was prepared by drawing on satellite imagery. In addition, unlike the 1-2-3 household survey of 2012, the stratification of the 2018 survey explicitly captures precarious areas. Despite such an innovative sampling approach, the Kinshasa Survey has concerns about the data quality because of a problem in the data collection process (see Batana et al. 2021 for detailed descriptions). Considering all these factors, this study does not report any absolute number of population based on the survey and does not make a direct comparison between the Kinshasa Survey and the 1-2-3 household survey.

This note also draws on other existing survey data, such as the 1-2-3 household survey of 2012, Water, Sanitation, and Hygiene (WASH) survey, and Demographic and Health Survey (DHS) 2013–2014, as well as satellite imageries and other spatial datasets, to capture trends in the levels and qualities of services and also identify spatial determinants of service access.

Throughout the paper, we follow the definitions of water and sanitation services as provided by World Health Organization (WHO)/United Nations Children’s Fund (UNICEF) Joint Monitoring Programme (JMP) for Water Supply, Sanitation, and Hygiene. To distinguish quality of water and sanitation access, they are classified into the following categories.

In the case of water access, it is classified into (a) improved water; drinking water that includes piped water and protected wells/springs, (b) piped water; water sources coming from public/private taps at home or in neighborhoods and (c
transformation has to be used to assign first period characteristics to the second-period observations (Inchauste et al., 2012; Azevedo et al., 2013a; Azevedo et al., 2013b).

Rank preservation implies anonymity, where the identity of the individual experiencing a particular outcome (such as income) is irrelevant—a permutation of outcomes between any two individuals in any of the two distributions being compared has no effect on the comparison. With this assumption, one can compare across the same quantiles of two distributions (including distributions at two periods of time) _as if_ they are the same observations. But such a comparison may not account for the true nature of mobility trajectories (Bourguignon, 2010).<sup>5</sup> By using panel data, where the assumption of rank preservation is not needed since households can be observed over both periods, we account for the true trajectories of mobility, which results in more precise estimates of the contribution of each component to poverty reduction. As we will see later, the ability to account for mobility is particularly important in this case, given the vast differences in the distribution of income growth across households that are observed, depending on whether one uses an anonymous or nonanonymous measure of growth incidence.

# **3. Data and Descriptive Statistics**

We use the India Human Development Survey (IHDS) for 2005 and 2012, a nationally representative survey that was conducted in all states and union territories of India (with the exception of Andaman and Nicobar and Lakshadweep). Both rounds of the IHDS provide household level aggregates for job status, job sector, earnings from labor activities and other sources of income (i.e., remittances, government transfers, etc.), and consumption.

The data presents some limitations, which require making the reasonable assumption that only individuals 15 years of age or older can be considered as workers, which also implies that households with positive labor income must be deemed to have at least one working adult.<sup
endogenous, the coefficient remains positive, but increases

> 26 This is consistent with results for five African countries (including Tanzania and Kenya) presented in Söderbom and Teal (2003).

> 27 Lal (2004) shows that Indian firms that use e-business technologies more intensively were more likely to export than other firms were.  Using data from manufacturing enterprises in 27 low and middle income countries in Eastern Europe and Central Asia, Clarke (2001) shows that enterprises that were connected to the Internet exported more than other enterprises, even after controlling for self-selectivity bias.

> 28 Wood and Jordan (2000) argue that human capital in management—and in particular connections with potential international partners—might partially explains the difference between Uganda and Zimbabwe with respect to exporting.

18
most likely through existing beneficiary-control institutions called Health Users Management Committees (HUMCs). This is a case in which information, and possibly complementary mobilization, activated a client power mechanism that the state has already set up.

However, similar meetings that were organized by a CSO (Pratham, mentioned earlier) in India around citizen report cards on the quality of education services in 195 intervention villages had no impact on teacher effort, or on learning outcomes in public schools (Banerjee et al, 2010). The intervention also had no impact on a beneficiary-control institution existing in the state (Uttar Pradesh) where the study was conducted—the Village Education Committee (VEC), which is authorized by the state and in principle operates much like the HUMCs for health services in Uganda. In both UP and Uganda these existing institutions were found to be inactive at the baseline.

Yet, perhaps the most striking finding of the India study is that in one set of intervention villages, where the citizen report card was combined with an offer by Pratham to train village volunteers to hold remedial classes outside school, a large number of local youths volunteered to hold such classes, completely bypassing the public school system. In these villages, the children who attended the volunteer-led
behavioral changes observed in the information only group were not significantly different even though their knowledge gains were half as large. This is surprising since one might have expected that cash would be more important in affecting behavior, such as increased regular feeding for young children, than in building new knowledge.

Given that women did implement behavioral changes, we then study child outcomes to see if the improvements in behaviors are passed on to children. In the information plus cash group we find that child cognitive development, as measured by the Ages and Stages Questionnaire, improves by 0.1 SD. This is statistically significant and different from the essentially zero improvement in the information only group. While we find no increases in child anthropometrics in the treatment groups, we find significant anthropometric improvements among the _older siblings_ of the treated children (these siblings were between 25-36 months at baseline) in the information plus cash intervention group. Hence, the intervention, and especially the provision of cash as a short term safety net, seems to have resulted in a marked improvement in maternal knowledge about best practices, maternal behaviors, child development, and nutrition.

This paper is related to existing research in epidemiology and economics focusing on the role of information campaigns and cash transfers to improve health outcomes (see Bhutta et al. (2013) for a review on interventions related to maternal undernutrition and Lagarde, Haines, and Palmer (2007) and Fiszbein, Schady, and Ferreira (2009) for a review on conditional cash transfers and take up of health interventions). Our results are a robust contribution to the literature specifically focusing on the connection between social safety nets, nutrition, and early childhood development, which has shown inconclusive links (Ruel, Alderman, and Maternal and Child Nutrition Study Group, 2013). We also link our findings with the vast literature in medicine and economics which has identified the in utero phase and the first 2 years of life as the most critical in terms of determining future outcomes related to human capital (see Almond and Currie
# **Tables and Figures**

**Figure 1:** Number of RTAs by year, period 1958-2018.

<!-- Start of picture text --> 1960 1980 2000 2020<br>RTA with Visa RTA without Visa<br>Cumulative number of RTA with Visa<br>40 100<br>80<br>30<br>60<br>20<br>40<br>10<br>20<br>0 0<br><!-- End of picture text --> Note: cumulated number of RTA with visa provision on the right axis. Source: World Bank Deep Trade Agreement database.

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of the top ten countries are the same in both surveys. Moreover, for most countries the EWCS-2005 total informality rate is very close either to both ESS-2004 and ESS-2006 rates or at least to one of them. Situation with the dependent informality rates is broadly similar. The exceptions in both cases include Slovenia, Norway and the Netherlands.

The coherent findings from ESS and EWCS raise concerns about the quality of field work performed in the countries of Southern Europe, as well as UK, Ireland and Poland for the Special Eurobarometer Survey on Undeclared Work in the European Union (Riedmann and Fischer (2008), - according to this survey, even after adding together positive responses and non-response, the level of informal employment in these countries is significantly lower that it follows from the ESS data (detailed comparisons are available on request).

When recent informal employment is accounted for, the largest increases in the informality level are found in countries where it was already high. As the result, the total level of informal employment is [at least] around 50% in Ireland, Israel, Greece, and Cyprus, around 25% in Austria, the UK, Poland, Portugal, and Italy, and close to 20% in Spain, Ukraine, Slovenia, and Bulgaria. Most of the other European countries feature informality level from 14 to 16%, whilst it is 11% to 13% in France, Hungary, Finland, Belgium, and Denmark, and just 9% in Sweden and Lithuania.

In terms of relative size of dependent and self-employed informal workforce, three groups of countries emerge:

- (i) In Cyprus, Israel, and Ireland both groups are large, but employees without contracts dominate the informal sector (even despite seasonal migrant workers are likely to be not covered, see footnote 13);

- (ii) In Russia, Ukraine, Bulgaria, Romania, Slovenia, Latvia, the United Kingdom, and Austria  the two groups are of comparable size;

- (iii) In remaining countries (i
**

Both business training programs were implemented in April 2014 in the form of 36 hours of inperson classroom training spread over four weeks, followed by a trainer visiting the business for three hours, once per month, for each of the next four months to assist with implementing the concepts learned. Both programs had a similar delivery cost of approximately $750 per invited business, and charged firms a highly subsidized fee of $10. Take-up rates were equal in the two training programs, at 84 percent.

The traditional business training used the Business Edge program developed by the International Finance Corporation and adapted to the Togo context. It focused on teaching standard business

> 1 We convert all financial outcomes into real September 2021 U.S. dollars using an exchange rate of 550.5 CFA per USD and the Togo Consumer Price Index collected by INSEED (https://inseed.tg/).

4
|**1970-**<br>|**1980**<br>|**1960-1**<br>|**970**<br>|
|---|---|---|---|
|United States|27.5%|United States|27.3%|
|Japan|18.3%|Japan|22.9%|
|Germany|6.0%|Germany|7.2%|
|France|5.1%|France|5.5%|
|**_Brazil_**|4.7%|Italy|4.8%|
|Italy|4.5%|United||
|**_Mexico_**|3.2%|Kingdom|3.9%|
|United||Spain|2.6%|
|Kingdom|3.0%|Canada|2.4%|
|Canada|2.6%|**_Mexico_**|1.9%|
|Saudi Arabia|2.1%|**_Brazil_**|1.9%|
|Spain|1.9%|Netherlands|1.5%|
|**_China_**|1.6%|Australia|1.4%|
|Korea, Rep.|1.3%|Switzerland|1.3%|
|Netherlands|1.2%|**_Argentina_**|1.1%|
|Australia|1.1%|Sweden|1.1%|

*Not included: Russian Federation nor FSU nor ECA former Communist states, for 1980- 1990 period. Source:  World Development Indicators, IMF database (for selected countries for 1960 data). Calculations based on change in country‘s GDP in constant US dollars as a share of the change in world GDP in constant US dollars, by decade.
answerable research questions (Sala et al., 2018; Englander, 2019), but they do not measure the most important outcomes caused by fishing: the quantities and types of fish that vessels catch. I obtained from the regulator two administrative datasets that contain these variables. Both datasets contain the tons of anchoveta and the percentage juvenile caught by all vessels in the North-Central zone during the six fishing seasons of 2017, 2018, and 2019. However, they differ in important ways which allow me to accurately calculate the number of juvenile anchoveta each vessel catches each time it sets its net (an individual fishing operation).

The first dataset is _electronic logbook data_ (Englander, 2022). Vessels report to the regulator when a fishing trip begins, when a fishing trip ends, and the location, time, tons caught, and percentage juvenile caught from each set during a fishing trip. Vessels record this information on a smartphone or tablet application, which transmits data in real-time to the regulator through the vessel’s onboard GPS transponder. Vessels perform sets once they have located anchoveta in the water. They encircle the anchoveta with a large net (a “purse seine”), close the net, and transfer the anchoveta from the net into the vessel’s hold. While transferring anchoveta from the net into the hold, a trained fisher estimates the percentage of juveniles by taking three samples using a standardized bucket: once during the first 30% of transference and two more times during the remaining 70% (PRODUCE, 2016 _b_ ). The fisher measures each fish in the sample in half-cm intervals, producing data on the length distribution of anchoveta caught by that set (e.g., 10 individuals between 11 and 11.5 cm, 17 individuals between 11.5 and 12 cm, etc.). Out of the several million individual anchoveta caught in a typical set, fishers measure approximately 200. The percentage juvenile for the set is the percentage of measured individuals that are shorter than 12 cm
per Incumbent exporter, etc. While not shown in Table 1, note that we used suffixes to identify means (i), medians (ii), and standard deviations (iii) of certain measures in the stata files. For example, the variable ‘A7i’ represents the mean of the Export Value per Incumbent exporter in a given country and year; ‘A7ii’ represents the median of the same statistic and ‘A7iii’ represents its standard deviation.

> 29 Although the files CY.dta, CYH2.dta, CYH4.dta, CYH6.dta, and CYD.dta are all based on the cross-country input dataset excluding oil exports in HS Chapter 27, interested users can request from the authors an alternative version of the files where the measures are calculated including exports in HS Chapter 27 (for the countries which provided such information).

15
approaches based on historical data and use risk assessment models instead. This was actually made mandatory in Florida after Hurricane Andrew in 1992. Second, the increase in the probability of occurrence of extreme events and the potentially higher correlation between risks make default risks greater for insurers and _in fine_ for reinsurers, eventually justifying higher level of reserves. The insurance sector is very highly regulated in all countries, and these changes will lead to modifications of these regulations. In some countries, the central (or local) government is also a stakeholder in the insurance or reinsurance sector. This is the case in France with the CAT-NAT system in which the government is responsible for losses linked to most natural disasters (with the notable exception of storms), as well as in Florida where the public insurer, Citizens, has a large share of the market, following the withdrawal of a large number of private insurers unable to face ever-increasing risks.

**More generally, insurance can also be considered as a tool for adaptation.** In particular, a high penetration of insurance allows economic stakeholders affected by a disaster to rebuild more rapidly and to avoid bankruptcies (particularly small businesses), and to therefore get the economy back on its feet more rapidly, limiting indirect economic losses (see an example on Mumbai in Ranger et al., _forthcoming_ ). Moreover, the presence of insurance (particularly for operating costs) enables stakeholders to proceed with reconstruction in a more peaceful atmosphere and to therefore seize improvement opportunities, i.e., to reconstruct "better" rather than reconstructing "identically".  Finally, access to insurance against natural risks also allows some stakeholders to take higher risks as a result of the fact that it is shared and transferred to stakeholders who can handle it.  The risk can appear as a production factor, making it possible to strengthen the producers' position.  Thus, better risk distribution, thanks to insurers, increases the economy's ability to make the changes necessary to adapt to climate change.

**
effect is relatively modest, however. The point estimate of the coefficient suggests that a 1-point average increase in the

> 16 Wood and Jordan (2000) note that it is 870 miles from Kampala to the nearest port, Mombasa (in Kenya).  Overall, they argue that poor infrastructure partially explains why Uganda exports less manufacturing goods than Zimbabwe.  However, as noted above, it is important to note that most exports for the small and medium-sized enterprises in the investment climate surveys are to neighboring countries.

14