rafmacalaba/datause-encoder-data huggingface dataset:
Reported events are clearly accelerating over time but this does not necessarily prove that the number of extreme weather events that resulted in excess flows of water has increased. More flood events could arise from the same flows because of the increased presence of people in their path. The under-reporting of events in the past might also lead to a false perception that events are accelerating when they are not. Both these effects are probably operating to some extent in the growth in reported events. Certainly the profile sources of incident reports has changed over time as was illustrated in Guha-Sapir et al. (2004) such that the balance of sources is very different now from what it was 30 years ago. However, the distribution of scale of reported flood events is revealing because the amount of under-reporting is likely to be greatest in small scale and short duration floods which suggests that the increase in frequency observed in medium and large scale floods is more robust.

An increase in number and to some extent scale of flood events from static water flows may also be adding to the increase in reported events. Section 2 will discuss in detail the ways in which this may occur. This therefore represents a real acceleration in the number of flood events that destroy lives, property and livelihoods. The argument towards addressing the associated growth in impact is compelling.

# **1.4 Is loss of life increasing?**

**The most devastating floods in terms of loss of life have tended to occur in less developed nations.** The most tragic and irreversible impact of flooding on human settlements is the loss of human life. Mortality rates, some of which can be as a direct result of flood waters such as drowning, being swept away or collision with flood debris, are a key impact which varies widely across urban and rural floods, flood scale, type, speed of onset and development scale. Jonkman and Kelman (2005) have estimated that two-thirds of immediate deaths from flooding worldwide are from drowning, with the remaining one-third arising from physical trauma, heart attack, electrocution, carbon monoxide poisoning or fire. Other, more indirect, deaths are caused by disease, disruption of food supplies, ill-health or shock after the event and are also related to flood and development characteristics. Annex 2 details some of the most devastating events in terms of loss of life globally over the last century.

While it is unlikely that deaths from flooding can ever be entirely eliminated, the natural focus of flood mitigation programs naturally centers on the protection of human life. There is some evidence that such efforts have been successful. Identification of flood-related deaths is problematic as in many cases only immediate deaths from trauma are recorded. In addition, when examined over long time periods, loss of life statistics are dominated by a handful of disasters of epic proportions such as the large peak in 1999 relating to the Venezuelan (Caracas) disaster, which was a mud and debris flow event, and the 1931 Yangtze river flooding in China. This 8
importer of rice (which is becoming more important in the urban diets), rice markets are more closely linked with the international markets. Part of African rice production is also irrigated. The other cereals (teff, sorghum, millet) have seasonal gaps of around 20‐24 percent. They tend to store better—they have smaller grains and are cultivated in dryer areas. On average, seasonal gaps are 3.4 percent higher in wholesale than in retail markets. This is in line with experience in developed economies where a substantial proportion of the value of retail products is generated by transport costs and by labor costs in retailing.

Table 7 further shows the estimated country effects, with the caveat that they reflect both the country effects and potential short sample bias.<sup>21</sup> Niger, Burkina Faso, Malawi and Ghana are associated with the highest average seasonal gaps, all in excess of 30 percent at the wholesale level. Ethiopia has the lowest average gap at approximately 15 percent. Tanzania and Uganda, which also have the longest samples, are intermediate at around 25 percent. The findings for Niger and Burkina are intuitive and consistent with other studies.<sup>22</sup> Dryland agriculture is predominant in both countries and the raining season short (and erratic). The large gap observed in Ghana is less expected, however, and may be related to the short duration of the price series (only 6 years at most) implying higher potential bias. Ghana also displays the largest proportion of locations where its seasonality is not statistically significant (Appendix Tables A1 and A2).

Seasonal gaps measure the extent of seasonality. A second question posed in the introduction was that of the share of monthly price variation attributable to seasonality. This share is measured by the seasonal _R_<sup>2</sup> which is simply the standard regression _R_<sup>2</sup> in equation (9), (12) or (14), depending on the specification. Among crops, plantain/matoke and maize show the largest (0.32 and 0.25 respectively) and cassava and cowpeas the lowest seasonal _R_<sup>2</sup> s (0.08 and 0.09 respectively) – see Table 6. Across countries seasonality appears to explain around 17

> 21 The average statistics reported in the final row of Table 7 may differ from those in the final row of Table 6 because of differences in the number of reporting locations in each country and for each food crop.

> 22 Using simple monthly averages of the 1996‐2006 real prices, Aker (2012) also reports a high seasonal gap of for millet in Niger (44%).

21
of debt. These other debt instruments seem to be shorter term for both developing and developed countries, as reflected in the balance sheet evidence. Developing country firms appear to use these other markets more heavily. A third source can be smaller firms (outside of our sample) that might borrow shorter term than larger ones, to the extent that the patterns shown in this paper hold for them. Thus, the composition of instruments and firms matters when comparing the overall maturity structure across countries and when trying to understand its determinants.

Our findings are related to three other strands of the literature. First, this paper is related to recent research examining several debt instruments at once based on issuance data (as opposed to the vast number of papers focused on a single instrument). For example, a few studies analyze syndicated loan-bond substitutions during credit crises (Adrian et al., 2013; Becker and Ivashina, 2014; Cortina et al., 2016). By examining two widely used instruments across domestic and international markets, this paper provides new evidence on the firms’ aggregate debt maturity that contrasts with the evidence based on firm or bank balance sheets. We are able to characterize where the long-term debt financing comes from for firms from developing countries. Moreover, by distinguishing the use of domestic versus international bond markets, we provide evidence suggesting that firms from developing countries might consider these markets as complements, using international bond markets for longerterm financing and domestic bond markets for shorter-term financing.

Second, several policy reports and academic papers discuss the perils associated with the recent debt expansion in emerging markets (Shin, 2013; Acharya et al., 2015; IMF, 2015; McCauley et al., 2015; Sobrun and Turner, 2015; The Economist, 2015, 2016). These studies argue that the rise in foreign currency debt has increased corporate vulnerability to the extent that debt burdens would get exacerbated in the event of capital flights or sudden currency depreciations. Moreover, part of the literature argues that a large proportion of the foreign borrowing by developing countries has been held in cash, implying an increase in risk taking by firms that use that cash to lend to other corporations 5
Policy Research Working Paper 5957

# **Abstract**

This paper surveys qualitative crisis monitoring data from sites in 17 developing and transition countries to describe crisis impacts and analyze the responses and sources of support used by people to cope. These crises included shocks to export sectors as a result of the global financial crisis, as well as food and fuel price volatility, in the period from 2008 to early 2011. Respondents reported the crisis had resulted in significant hardships in the form of foregone meals, education, and health care, food insecurity, asset losses, stress, and worsening crime and community cohesion. Although the export-oriented formal sector was most exposed to the global economic downturn, the crises impacts were more damaging for informal sector workers, and some of the adverse impacts will be long-lasting and possibly irreversible. There were important gender and age differences in the distribution of impacts and coping responses, some of which diverged from what has been seen in previous crisis coping responses. The more common sources of assistance were family, friends, and community-based and religious organizations; formal social protection and finance were not widely cited as sources of support in most study countries. However, as the crisis deepened, the traditional informal safety nets of the poor became depleted because of the large and long-lasting shocks that ensued, pointing to the need for better formal social protection systems for coping with future shocks.

This paper is a product of the Social Development Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at rheltberg@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 5. Brain drain: most and least affected countries**

||Largest h|igh-skilled|net emigrat|ion rates||||Lowest hi|gh-skilled|net emigratio|n rates|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||2000|||1990||||2000|||1990||
||||non-|||non-||||non-|||non-|
|_Country_|Net<br>(%)|Gross<br>(%)|OECD<br>(%)|Net<br>(%)|Gross<br>(%)|OECD<br>(%)|_Country_|Net<br>(%)|Gross<br>(%)|OECD<br>(%)|Net<br>(%)|Gross<br>(%)|OECD<br>(%)|
|Jamaica|84.6|85.6|0.6|85.5|86.5|0.9|United Arab Emirates|-309.4|9.0|70.6|-103.5|3.0|77.1|
|Haiti|80.9|81.0|5.9|73.5|73.7|7.9|Saudi Arabia|-93.3|5.0|63.5|-132.4|5.6|61.8|
|Liberia|53.3|59.8|13.0|55.0|62.9|13.7|Israel|-77.1|18.4|18.2|-19.4|13.2|14.6|
|Sierra Leone|51.9|53.4|12.7|46.7|48.5|14.0|Oman|-58.7|33.1|98.2|-55.0|29.2|98.4|
|Eritrea|49.2|49.9|44.6|48.1|49.7|48.6|Kuwait|-54.6|42.2|71.8|-39.4|37.9|75.6|
|Laos|45.5|45.8|0.5|40.3|42.0|1.8|Australia|-51.9|4.7|8.9|-47.9|3.4|8.0|
|Somalia|45.1|46.2|26.0|32.2|34.0|30.8|Canada|-25.8|6.4|2.8|-22.9|6.5|3.5|
|Afghanistan|44.1|45.0|45.5|25.8|26.9|59.4|Switzerland|-17.7|11.2|8.0|-12.4|8.2|11.0|
|Bosnia and Herzegovina|41.7|44.6|51.5|29.7|33.8|29.0|Singapore|-16.0|12.0|19.6|1.0|11.1|13.4|
|Lebanon|39.8|57.5|17.6|52.3|65.4|16.9|Libya|-15.8|7.7|19.7|-22.0|9.3|14.7|
|Kenya|37.2|43.5|9.5|48.7|50.5|11.4|United States|-11.6|0.6|18.1|-10.0|0.7|18.9|
|Yemen|36.0|42.5|84.8|55.4|59.8|73.0|Sweden|-6.8|5.2|2.7|-3.9|4.3|3.9|
|Uganda|34.9|41.6|12.0|42.5|43.9|19.7|New Zealand|-6.4|30.0|1.3|-19.9|25.1|2.9|
|Macedonia|32.6|34.9|18.7|26.1|30.3|6.5|Netherlands|-4.9|12.0|6.7|-2.4|12.1|6.4|
|Sri Lanka|32.4|32.8|19.2|34.0|37.0|33.9|Paraguay|-4.2|6.2|33.8|-4.7|4.6|8.1|
|Congo, Rep. of the|32.3|33.6|21.0|14.2|22.0|25.6|Cote d'Ivoire|-4.1|12.9|49.8|-16.5|7.5|25.1|
|El Salvador|31.9|32.9|3.7|32.9|33.7|5.1|Kazakhstan|-3.8|43.2|95.1|-8.8|9.6|90.1|
|Nicaragua|31.8|33.9|13.1|28.6|30.0|8.1|Russia|-2.7|9.0|75.3|1.0|4.7|75.9|
|Cuba|29.4|29.5|3.4|31.6|31.8|2.9|Belgium|-2.6|6.7|9.2|-0.8|5.3|6.2|
|Azerbaijan|28.5|35.5|90.1|-4.0|5.3|75.6|France|-2.5|4.0|13.8|-1.1|3.1|12.1|
|Vietnam|27.4|27.7|3.1|23.9|24.0|1.1|Spain|-2.4|4.3|15.9|-0.8|3.4|11.6|
|Chad|26.8|30.4|75.8|25.3|27.3|77.6|Nepal|-1.7|5.9|27.5|21.1|23.6|79.3|
|Georgia|25.6|28.5|88.1|11.3|16.1|90.6|Latvia|-0.8|20.5|45.5|-34.8|27.1|63.9|
|Armenia|24.9|47.2|72.7|24.5|29.4|66.2|Germany|0.2|6.8|9.2|2.4|6.8|4.6|
|Cambodia|24.6|31.4|0.6|26.9|27.8|1.9|Costa Rica|0.2|8.4|8.8|-6.0|10.5|8.5|

Notes. Only countries with labor force above one million are included. Countries’ ranking is based on net emigration rates in 2000. The non-OECD share measures the share of non-OECD countries in gross emigration of college graduates.
universities, and companies to learn how they operate. Others used various forms of media to explore the topics in the textbook, such as computer simulations and videos.

Overall, teachers and educators felt the financial education program was valuable, that the textbooks were extremely relevant, and that they as well as their students enjoyed the new learning opportunities afforded to them.

# **5.2. Parent Workshops**

For the parent workshops, we asked schools to mail the filled-in parent exit questionnaires back to us after the workshop, and we determine which schools implemented the workshops based on our receipt of these questionnaires. We received 1,553 filled-in parent questionnaires from 109 treatment schools, implying that parent workshops did not take place in the other 153 treatment schools that had originally provided a current list of their students for the parent intervention (these schools either did not organize the workshop or parents did not attend the workshop). When planning the parent intervention, our counterparts had cautioned that attendance rates may be low since parents of public high school students in Brazil tend to not participate in school events.

Comparing the lists of current students we received from schools before the workshop to the filled-in exit questionnaires gives an average attendance rate of 46 percent across the 109 schools that returned questionnaires. The attendance rate does not differ across parents who were randomized into the treatment and control groups. Parents did not know whether they had been assigned to watch a financial education or health video until the video was screened at the workshop. That is, the decision to attend was independent from treatment status. Hence, in our impact analysis of the parents’ workshop, we only keep students and parents if the parent attended a workshop. The interpretation of the results is applicable to families who are more interested in school events and respond to school invitations.

# **6. Survey Data and Summary Statistics**

# **6.1. Survey Data**

For the data collection, we partnered with the _Centro de Políticas Públicas e Avaliação da Educação_ (CAEd/UFJF), a local education research and survey institute with extensive experience in designing and implementing knowledge tests in Brazilian schools. CAEd has a wide network of supervisors and surveyors across Brazil and was able to implement simultaneous surveys in all schools in our sample.

Survey implementation took three days per school during each survey round (baseline and both follow-ups). On the first day, CAEd staff administered a financial knowledge test and distributed parent questionnaires to students. The students were instructed to take the parent questionnaire home, ask one of their parents to fill out the questionnaire and return the questionnaire on one of 15
# **When Do In-service Teacher Training and Books Improve Student Achievement? Experimental Evidence from Mongolia**

Habtamu Fuje<sup>*</sup> Columbia University Prateek Tandon The World Bank

**Keywords** : In-service teacher training, RCT, matching, impact **JEL Classification** : I28, I21, O15

> * Corresponding author: <u>Habtamu.Fuje@columbia.edu or Habtamu_Fuje@post.harvard.edu</u> The study also benefited from discussion with faculty and graduate students at Columbia University. Yabibal Walle, University of Göttingen, Andinet Woldemichael, Georgia State University, and Kefyalew Endale, National Graduate Institute for Policy Studies, proofread the draft version. Charles Abelmann, Cristobal Ridao-Cano, and Katherine Nesmith of the World Bank originally designed the evaluation. D. Khishigbuyan, Project Coordinator of READ, provided assistance throughout project implementation and follow up. Deon P. Filmer and David Evans, from the World Bank, graciously provided invaluable comments and suggestions.  We thank you all.
49

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23
Table 3 - Influence of Large Doing Business Rankings Improvements (greater than 9 ranking places) on FDI Inflows into Developed and Developing Countries

|||<br>Dep|<br>endent Variabl<br>GMM based|<br>e: Value of FDI I<br>on the Arellano|<br>nflows (100s of<br>-Bond methodo|<br>million USD)<br>logy||||
|---|---|---|---|---|---|---|---|---|---|
||Column A|Column B|Column C|Column D|Column E|Column F|Column G|Column H|Column I|
|REFORMS|122.77|106.06|**130.10***|98.41|42.03|106.76|88.29|**117.30***|103.02|
||_76.26_|_71.36_|_78.34_|_88.13_|_104.49_|_72.02_|_71.64_|_69.54_|_65.2_|
|GDPGR|5.38|3.28|4.51|3.41|-1.37|**7.81****|-3.71|0.03|0.72|
||_3.54_|_3.46_|_3.48_|_4.6_|_5.6_|_3.33_|_4.54_|_4.04_|_3.53_|
|GDPCAP|**0.01****|**0.01****|**0.01****|**0.01***|**0.02****|||||
||_0_|_0.01_|_0.01_|_0.01_|_0.01_|||||
|REER|0.97|0.95|0.43|3.38|5.55|1.28|-0.56|-3.18|-2.95|
||_2.19_|_2.14_|_2.25_|_3.05_|_4.06_|_1.74_|_0.77_|_3.27_|_2.7_|
|INF|-1.87|-1.59|-2.76|-0.36|0.83|-7.27*||**-14.23****|**-15.66****|
||_5.2_|_5.03_|_5.04_|_6.19_|_6.83_|_3.94_||_5.87_|_5.04_|
|OPEN|-3.24|-2.81|-3.24|-2.58|-6.69|0.8|2.17|1.9||
||_3.06_|_3.17_|_3.07_|_3.16_|_4.24_|_3.27_|_2.64_|_2.58_||
|VA||-3.88||-5.47|**-12.28***|2.37||||
|||_4.03_||_7.01_|_6.97_|_3.61_||||
|PV||-1.16||0.08|5.59|**-4.23***||||
|||_2.42_||_3.87_|_3.67_|_2.36_||||
|CC||2.39||2.31|4.7|5.36||||
|||_3.02_||_4.59_|_4_|_3.77_||||
|GOV|||-2.06|||||||
||||_2.44_|||||||
|TAXPROFITS||||4.94<br>_3.75_||||||
|TAXINT|||||2.66|||||
||||||_4.9_|||||
|Hansen J test|0.24|0.33|0.31|0.48|0.49|0.34|0.52|0.45|0.14|
|No. of observations|248|248|248|163|136|248|254|248|264|
|No. of groups|88|88|88|63|54|88|89|88|90|
|No. of instruments|38|41|39|42|42|34|30|31|24|

Standard errors corrected for robustness in italics. *** Significance at the 1 percent level, ** Significance at the 5 percent level, * Significance at the 10 percent level. A negative coefficient for the DBR variable suggests on average, an increase in the Doing Business Ranking will increase FDI Inflows all else constant (i.e. higher rankings are a lower numerical value). For the remaining variables, the opposite is true; an increase in that variable suggests, on average, an increase in FDI Inflows all else constant.

REFORM represents a dummy for countries that have improved by greater than 9 places on the Doing Business Rankings from 2006 to 2007, 2007 to 2008 and 2008 to 2009 (D09) 11