rafmacalaba/data-use-mentions-extended huggingface dataset:
statistics are increasingly available.” This is, in part, due to the growing number of countries that collect disability data in censuses. At least 120 of 214 countries or regions that conducted a census during the 2010 round included a set of questions on disability, a significant increase from approximately 19 countries or areas that did so during the 1970 census round. A major challenge though is how to collect the disability data that are needed to inform policy and advocacy efforts as part of the CRPD and the 2030 Agenda for Sustainable Development. As with any complex social science phenomenon, there are several options when it comes to the scale and methods for data collection efforts. One option is to have a qualitative and participatory effort involving multiple stakeholders including persons with the lived experience of a disability. It may go a long way in understanding the situation of persons with disabilities and informing policy and advocacy efforts at one place and one point in time. However, it would not provide nationally representative or internationally-comparable data that are needed to ensure that persons with disabilities worldwide are not left behind post-2015. Another option is to develop a national disability survey with a battery of questions to measure disability and identify service needs and barriers, as well as to understand the factors leading to disability related inequalities. This is suitable provided significant resources and capacity for data collection are available and have the potential to provide very rich information to inform national or even subnational policy and advocacy efforts. The World Bank and the World Health Organization have developed a Model Disability Survey to this end (Cieza et al 2018; Groc 2019; WHO 2020). It has a full stand-alone survey which takes 60 to 120 minutes to accomplish. It also has a brief version, taking an average of 10-15 minutes to complete and includes over 40 questions on environmental factors, functioning and health conditions. Few countries may, however, have the capacity or resources needed to develop and implement the stand-alone survey or include the brief version in an existing survey, let alone repeat this over time
excess of 10 percent a year.

Similarly, Figure 11 and 12 give a more detailed view of the evolving patterns of service exports from Malaysia. First, Figure 11 shows the export basket in 1995, 2000, 2007 and 2009 by the basic 10 categories available from the Balance of Payments. We note that travel and transport service dominate over 60 percent of Malaysia’s service exports, whereas as the relative share of modern business services progressed around 2000 but did not manage to keep up over the last decade. Hence, Figure 12 gives even a more detailed look at the 74 categories of service exports in 2000 and 2009, whereas the third column reports the change in share of the particular export in total service export basket. Modern services are coded orange and blue is used for traditional services. Several creative, visual services and digital services are not reported by Malaysia however there are signs of progress. The share of computer and information services is approximately 2 percent of service export basket, whereas business services have been declining. In particular, business travel services are continuing to grow very fast.

We now present a preliminary measure of the sophistication in service exports that seeks to mirror the EXPY measure of Hausmann, Hwang, and Rodrik for goods that we employed above.<sup>16</sup> Mishra, Lundstrom and Anand 2011 repeat the same exercise for service exports and shows that there is increasingly a stronger association between moving to more productive modern services and economic growth. We use the IMF Balance of Payments and plot the trend of the sophistication for selected emerging economies in Figure 13. Malaysia maintained a sophistication level in services above that of China until 2003, but since then the Chinese export service sophistication has increased while Malaysia has lagged behind the remaining sample of middle income countries. In contrast, India has emerged as a big player in the delivery of complex services as measured by EXPY in post 2000. India is followed by Singapore and Korea in their level of service export sophistication.

Figure 14 plots individual
Figure 10. Debt Composition of BRI-recipient LIDCs (In percent of total debt) <!-- Start of picture text --> a. By type of financing<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2007 2012 2016<br>Bilateral concessional Multilateral concessional<br>IMF Bilateral non concessional<br>Multilateral non concessional Bond<br>Other private debt<br><!-- End of picture text --> Source: International Debt Statistics.

<!-- Start of picture text --> b. By risk of debt distress (2016)<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Low Moderate High<br>Bilateral concessional Multilateral concessional<br>IMF Bilateral non concessional<br>Multilateral non concessional Bond<br>Other private debt<br><!-- End of picture text --> Source: International Debt Statistics.

<!-- Start of picture text --> c. By Type of creditor<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2009 2012 2014 2016<br>Paris Club bilateral Non-Paris Club bilateral<br>Multilateral Bond<br>Other private debt<br>Source: World Bank.<br><!-- End of picture text --> <!-- Start of picture text --> d. By type of creditor and risk of debt distress<br>
country specific characteristics, it initially focuses on a single, globally integrated, emerging economy (Mexico's) that has, during the period of observation, experienced both fixed and flexible foreign exchange regimnes, and that has seen adverse external financial shocks under both.

Mexico's flexible regime period is then compared with an array of emerging countries that maintained fixed exchange rates with various levels of rigidity. In all cases, persistence is estimated by using the impulse response functions obtained from estimating a vector autoregressive system for a set of variables traditionally used to describe a macroeconomic system.

# II. **Empirical Results**

This section is based on a four-variable VAR specification whereby current real output is ultimately determined by past output and the past values of the other three endogenous variables. That specification is shown in Equation (1) and consists of _Industrial Production (Y),_ our measure of real output, a domestic _Interest Rate (r), Money (m),_ and _Inflation (1),_ where the subscript i reflects the time period for the lagged variable and its respective coefficient estimate, and I represents the number of lags.<sup>2</sup> Table 1 shows the countries and their associated forms of foreign exchange regimes for which Equation 1 is estimated. Additionally, the number of lags for each country's VAR, and the order of exogeneity used in the impulse response functions are also shown in Table 1, as well as

> _2_

> _Money_ refers to _Broad Money,_ as defined in the IMF's International Financial Statistics, except for Estonia where M2 is employed. Additionally, we use an indexed measure of real total industrial sales for Estonia.

4
internationally, a change in international trade costs a¤ects a proportionately smaller subset of their total production. Accordingly, their price indices, and hence multilateral resistance, change by less. MR therefore provides a smaller dampening e¤ect on bilateral trade elasticities, such that the overall elasticity net of MR is larger. Conversely, smaller countries will have smaller elasticities net of MR. Given the skewness in the world’s distribution of country size, most countries are small. Therefore, standard estimates overstate the impact of changes in logistics on bilateral trade ‡ows for most countries. Conversely, for a handful of large developing countries, the impact is underestimated.

The second issue was addressed by Helpman, Melitz & Rubinstein (2008), who develop a method to account for the consequences of heterogeneous ...rm productivity in gravity models. Firm heterogeneity gives rise to two margins of adjustment to changes in trade barriers: the intensive margin, which captures exports per ...rm, and the extensive margin, which captures the number of exporting ...rms. If ...xed trade costs are su¢ ciently high, no ...rms in a given country may export to a particular destination, which explains the zeros observed in aggregate trade data. These issues a¤ect estimation and ignoring the e¤ects of trade costs on ...rm entry results in misleading country-level comparative statics even if the countries do trade.

Behar & Nelson (2009) develop a model which accommodates both MR and ...rm heterogeneity. They demonstrate the importance of these e¤ects for comparative statics when trade costs are captured by bilateral distance. In this paper, we adapt that approach to the case of logistics. Unlike distance, logistics might not be exogenous, so we take potential endogeneity seriously. Further, unlike bilateral distance, the logistics index is a country-speci...c variable which precludes the use of ...xed e¤ects to control for MR in estimation.
26.1% (22.5%) could not provide any response at all. Panel C shows that only 12.8% of respondents (24.7% of those aged 25–65) correctly chose shares in a single company stock as a riskier investment asset than shares in a unit fund. An important caveat is that the inflation and risk questions asked in the Russian survey differ from those in the U.S. Health and Retirement Study.

As shown in Panel D, a very small number of individuals correctly answered all three questions, i.e., 3.1% of the whole sample (3.4% of those aged 25–65). To the interest and inflation questions 21.8% responded correctly (23.9% aged 25–65). Furthermore, 31.8% gave all incorrect responses (28% aged 25–65) and 12.5% (9% aged 25–65) reply with ―I don‘t know‖ to every question. A remarkable 53.7% of respondents replied with ―I don‘t know‖ to at least one question (48.2% aged 25–65).

# **_3.2 The Demographics of Financial Literacy in Russia_**

Table 2a presents summary statistics of financial literacy in Russia, by demographic characteristics, disaggregated by correct and don‘t know responses. First, the data suggest that financial literacy is negatively related to age: to all three questions, younger groups are more likely to provide correct responses and less likely to indicate that they don‘t know an answer. Second, correct responses are not notably different by gender, although men are much less likely to state that they do not know what the answer is (47.2% of males versus 58.5% of females). Third, individuals with higher education offer a higher number of correct responses (and a lower percentage of don‘t know responses
# Appendix A: Cross national data and results

This appendix provides details on four aspects of the cross national regressions: data on health status, data on independent variables, data on health sector strategy.

# a) Data on health status

The data used here are as reported by UNICEF (1992) except for the mortality rates for Zaire which are unbelievable: under-5 mortality rate for 1990 is reported as 130 and infant mortality rate as 79. These are replaced with the, more reliable, estimates reported in United Nations (1992) for 1984. These are 200 for under-5 mortality and 126 for infant mortaLlity.

# b) Data on non-health sector variables

|_Income_|Real GDP per capita in 1995 international dollars (i.e. adjusted for Purchasing Power Parity)<br>are from the Penn World Tables 5.6.|
|---|---|
|_Education_|Average education levels for men and women over 15 are from Barro and Lee (1996).|
|_Income inequality_|Gini coefficient as calculated by Deininger and Squire (1996) multiplied by 100.|
|_PercRenturban_|Percent of the country's population that lives in urban areas. From the World Bank's Social<br>Indicators of Development database (World Bank, 1997).|
|_Predominantly Muslim_|Dummy equal to one if over 90 percent of the country's population is Muslim.|
|_Ethnolinguistic_<br>_fractionalization_|Index of ethnolinguistic fractionalization for 1960. Measures the probability that two randomly<br>selected people from a given country will not belong to the same ethnolinguistic group as<br>reported in Easterly and Levine (1996).|
|_Tropical country_|Dummy equal to one if part of the country's territory lies within 20 degrees of the equator.|
|_Access to safe water_|Percent of population with access to safe water. From the World Bank's Social Indicators of<br>Development database (World Bank, 1997).|
well, moreover, with other developing region trade blocs, such as Mercosur. For example, prior to the formal launch of Mercosur in 1991, its share of intra-regional trade stood at 14 percent.<sup>10</sup> Similarly, in ASEAN, a relatively high level of regional trade among member countries at the launching of their regional trading arrangements—16 percent—provided the necessary impetus for further regional integration.

If SAFTA is launched from low level of intra-regional trade, lower than any successful regional grouping, such a low base may be evidence that the South Asian countries are not, in fact, “natural trading partners.” The failure to meet this key conceptual criterion for beneficial integration may adversely affect the realization of a successful FTA in the region.

The above estimates of intra-regional trade are based on official trade data, which we know understates the extent of intra-regional trade. Could the above conclusion that the South Asian countries are not natural trading partners be altered if we take into account available “guesstimates” of informal trade in the region? For example, if the reported one billion dollars’ worth of informal trade between India and Pakistan cited by Taneja (1999) is incorporated, it would boost Pakistan’s share of intra-regional trade to around 14 percent of its total trade (referring to 1998 survey results), although it would still fall below the level of trade it undertakes with the United States (19 percent) and the European Union (22 percent) in the corresponding year. While informal exports to India from Bangladesh, Nepal, and Sri Lanka are relatively high, they comprise a sizable share of third-country goods that originate beyond regional sources. However, these national guesstimates of informal trade based on a sample survey of key locations may have high error margins and, hence, may not be fully reliable. Even with a less conservative

> 10 Mercosur’s initial members were Argentina, Brazil, Paraguay and Uruguay. Chile and Bolivia became members in 1996 in 1997, respectively.
consumption-based methods. Further, we find that methods combining survey data with CDR perform better than methods using a single data source. However, as we demonstrate empirically, low rates of phone ownership — or the inability to access data from all operators — can undermine the value of CDR-based targeting. In our setting, the CDR-based approach still works well if households without phones are targeted before the CDR-based algorithm selects the poorest households with phones. However, this approach may not be appropriate in other contexts where phone ownership is less predictive of wealth, or where potential beneficiaries have the ability to strategically underreport phone ownership (Bj ̈orkegren et al., 2020).

As mobile phone penetration rates continue to rise in LMICs (GSMA, 2020), and as

> 11Due to the design of the TUP survey, which interviewed women in the household, we cannot avoid this mismatch between the survey respondent and the phone owner.

15
<!-- Start of picture text --> 7 pSa‘-a<br>; as aa o..<br>~ : ”, : ‘I { vf ne ee<br><!-- End of picture text --> Table 3: Definitions of Variables for Measuring Relative Importance and Security of Existence of Local Government

|Name|Type|Definition|
|---|---|---|
|(Importance ofLG)|0-100|LG expenditures as% ofGG expenditures<br>1 - legislative safeguards against dismissal of LAG council by|
|LG<br>independence|Discrete:<br>0,|CG; 0.5- LG can be dismissed under certain circumstances|
|(Security<br>of<br>LG|0.35,<br>0.5,|(prescribed by law or constitution); 0- LG can be dismissed|
|existence)|0.75, 1|in an arbitrary situation. 0.25 or 0.75 - if LG are treated<br>asymmetrically|

Source: Authors’ calculations based upon data sources reported in Annex Table Al.
in the primary sector, while agricultural production the least. This trend is especially reflected in the developed economies and the Latin American and the Caribbean countries, where the share of FDI in mining and quarrying is 5.3 and 4.6 times, respectively, that of agriculture, fishery, and forestry.

13 The 9.1 percent FDI share estimated for coal and petroleum products is based on the figures of 5 countries, too small a sample size to be the basis of general conclusions.

14
production of pure information. And, the results of scientific inquiry should never be interpreted as immutable facts. For years the economics profession has used remotely sensed EO data, weather or otherwise, as if it perfectly reported on objective facts. We have done this ourselves, having chosen one EO product over another simply because it was easy to download or work with. By keeping Gould’s caution about science and scientific findings in the forefront of our minds, we reduce the probability of blindly accepting the truth or accuracy of the data we use. As researchers, we should cultivate skepticism, but we should also cultivate empathy for all of us engaged in this social phenomenon called science.

23
domestic consumption and services. Today resources are being reallocated from agriculture and, since 2013 and at a much slower pace, from industry into services.

Following Baumol (1967), the decline in productivity growth as the economy matures is almost inevitable but, in China’s case, there is scope to improve labor productivity in services. As incomes rise, households spend more of their income on services. Correspondingly, higher income countries tend to have a higher share of employment in services, where productivity is generally lower than in industry. However, China could implement policies that boost service sector productivity. One area, for example, is market competition, as services in China have been much less open to competition and FDI than industry. By one measure—the OECD services trade restrictiveness index (STRI)—China, along with the Russian Federation, Indonesia, and India, ranks considerably below the OECD countries and has shown limited improvement over time (Figure 6).

6
between 132 million and 587 million poor people are exposed to flood risks (depending on which poverty definition is used). About 1.2 billion flood-exposed people live in lower- and uppermiddle-income countries.

Taking into account the income levels of flood exposed populations is particularly important, as income is a relatively reliable proxy for people’s ability to mitigate, withstand, cope with, and recover from floods. For instance, while a large share of the Dutch population lives in flood risk areas, large-scale investments in flood protection infrastructure have enabled them to mitigate risks. Similarly, flood exposed populations in Canada or Japan are more likely to have access to rapid government support systems in post-disaster situations compared to people in Malawi or Bangladesh. Thus, action to strengthen disaster prevention and recovery capacity is most urgently needed in the hotspots where poverty and flood exposure coincide.

# References

- Braese, J., S. De Vries Robbe, J. Rentschler, Jun. (2020). Coastal Development between Opportunity and Disaster Risk: A Multisectoral Risk Assessment for Vietnam. Policy Research Working Paper; No. 9352. World Bank, Washington

- <mark>Bright, E. A., Rose, A. N. & Urban, M. L.</mark> _<mark>Landscan 2015 High-Resolution Global Population Data Set</mark>_ <mark>. (Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, USA, 2016).</mark>

- Chakraborty, J., T. Collins, M. Montgomery, S. Grineski. (2014). Social and Spatial Inequities in Exposure to Flood Risk in Miami, Florida. Natural Hazards Review. Vol.15(3)

- Erman, A., M. Tariverdi, M. Obolensky, X. Chen, R. Vincent, S. Malgioglio, J. Rentschler, S. Hallegatte, N. Yoshida (2019), Wading Out the Storm: The Role of Poverty in Exposure,
obtained by the International Growth Center and data for Bangladesh, Chile, Jordan, Kuwait, Lebanon, Mexico, Morocco, and the Republic of Yemen were obtained by the Trade and Integration Unit of the World Bank Research Department, as part of the Exporters Dynamics Database (EDD) project described in Cebeci _et al._ (2012). Pooling data from different developing countries allows us to provide results on regions which have received little attention so far. The structure of our dataset also allows us to observe countries with different development levels, facing different trade policy arrangements (in particular preferential tariffs), which implies a gain in statistical power.

For each country, all export transactions are covered over a certain time period (see Table 1). For each firm and year, the data includes a firm identifier, as well as the value (in local currency) and quantity (expressed in kilograms) sold by the firm for each destination country and HS product (at country-specific HS8-equivalent levels).<sup>15</sup> For each firm-destination-productyear, unit values are computed as the ratio of export value to quantity. Note that unit values are an imprecise proxy for prices because there may be more than one distinct product within a 8-digit code. Prices changes may be due to compositional changes within a product code or due

> 14The theories mentioned in this section are heterogeneous firms models where each firm produces a single good. It might be the case that trade policy has an impact on PTM through their effect on multi-product firms. We come back to this point when discussing our empirical results in Section 6.

> 15Product classifications are not harmonized between countries at sub-HS6 levels of disaggregation (HS8 or HS10). This is not a problem in our estimations as all regressions have fixed effects at the firm-destination-product level. However, for comparability of descriptive statistics, we aggregate products up to the harmonized HS6 level.

8
<!-- Start of picture text --> Figure 3: Adjusted Statistics: REER and CPI/PPI<br>REER (2005 = 100) CPI / PPI Ratio in Logs<br>Source: BIS, INDEC, PriceStats, and authors' calculations 5.1<br>250<br>4.9<br>200 4.7<br>4.5<br>150<br>4.3<br>Large Adj. CPI / PPI<br>100 4.1<br>Adjusted CPI / PPI<br>3.9 CPI / PPI<br>50<br>3.7<br>Inflation-adjusted REER REER<br>0 3.5<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> Figure 4: Output - Official vs. Private Sector Estimates <!-- Start of picture text --> Economic Activity Index ( 2007m1 = 100 ) 150 Industrial Production Index ( 2007m1 = 100 )<br>Source: INDEC, Elypsis, Orlando J. Ferreres & Asociados Source: INDEC, Orlando J. Ferreres & Asociados<br>210<br>130<br>190<br>170 110<br>150<br>EMAE 90 INDEC<br>130 ICAE OJF<br>IGA<br>70<br>110<br>90 50<br>Figure 5: Agriculture-Nontradables Labor Productivity Differentials<br>4.8 Agriculture/Nontradables Labor Productivity<br>4.7<br>4.6<br>4.5<br>4.4<br>Excl. utilities & transport<br>4.3 Excl. utilities<br>Baseline<br>