rafmacalaba/datause-encoder-data huggingface dataset:
cyclone shelters is expected to decline if polders are raised adequately and maintained properly, cyclone shelters will still be needed in the following cases:

- a) a number of smaller yet inhabited islands may turn out to be cost ineffective to be protected by polders.  For those islanders, cyclone shelters will remain a vital necessity;

- b) in localities where projected inundation depth exceeds 3 m and inhabitants of single storied houses would need evacuation.

The total number of additional cyclone shelters required will depend on, and have been calculated from the population likely to be exposed to inundation depth of more than 3m from accentuated storm surges in a changing climate and the capacity of existing cyclone shelters<sup>49</sup> . See figure 4 for location of existing cyclone shelters<sup>50</sup> (see Figure 4).

# d) _<u>Early Warning and evacuation System</u>_ <u>:</u>

In the past, the early warning and evacuation system of Bangladesh has played an important role in saving lives during cyclones. In general, Bangladesh Meteorological Department issues forewarning for any impending cyclone and storm surge; newspapers, television channels and radio stations broadcast the warning; and the local government administration and the local Cyclone Preparedness Program (CPP) volunteers run by the Red Crescent Society lead the evacuation of the people<sup>51</sup> .

Although the overall quality of forecasting cyclones and storm surges has improved over the years, the general consensus is that there is scope for further improvement. At present, the forecast of an area likely to experience a storm surge is usually over‐estimated and delineated over a large section of the coastal zone, which can lead to unnecessary repetitive evacuations and loss of popular faith in the early warning system. More precision in forecasting, especially in the landfall location and location‐specific inundation depth, is 49 Data and information on existing cyclone shelters are from the IWM and CEGIS, compiled from the Public Works Department (PWD), LGED, Education Engineering Department, Red Crescent Society, donor agencies, NGOs and local experts.

> 50 Many of the existing cyclone shelters are in dilapidated condition. In a 2004 survey, CEGIS found that more than 65% of the shelters had no provision for the special needs of women; almost no facility was user friendly for people with disabilities; and 80% had no provision for livestock.

> 51 Red Cross in Bangladesh
Table Al.8 - Ranking of Item. United States-Brazil, Grain Mill Products. (US 1977) (Brazil 1975)

|-------<br>_---_------------_----------------------------_---------__----------<br>UNIT|--__-------------_--------------_---<br>EDSTATES|BRAZIL||
|---|---|---|---|
|rank<br>value**as**%<br>of ite"<br>of total value<br> <br>|cumulative<br>maizhed<br>S of total<br>with BRAZIL<br>|rank<br>value**as**t<br>cumulativt<br>of item<br>of total value<br>*of total<br> <br>|matched<br>with USA|
|of output<br>v|alue of output|of output<br>**value**of output||
|<br> <br>-------------------------------------------|<br>_-----------------------__--------__-|<br> <br>-------------------------------||
|a) Items Specified by Quantit<br>|y and Value,<br>|a) Items Specified by Quantitv and Value.<br>||
|contributing more then1p<br> <br>|er cent to Output:<br>|contributing more than1per cent to Out<br> <br>|put:|
|1<br>19.50|19.50<br>Yes|1<br>59.51<br>59.51|Yes|
|2<br>7.61|27.11<br>No|2<br>7.26<br>66.77|Yeo|
|3<br>7.16|34.27<br>**Yes**|3<br>7.07<br>73.85|Yes|
|4<br>5.16|39.43<br>**Yes**|4<br>0.55<br>74.40|No|
|5<br>4.61|44.05<br>Yes|5<br>5.52<br>79.92|Yes|
|6<br>3.98|48.02<br>No|6<br>3.82<br>83.74|No|
|7<br>3.40|51.43<br>Yen|7<br>2.86<br>06.60|No|
|8<br>2.82|54.24<br>**Yeo**|8<br>2.58<br>89.18|Yes|
|9<br>2.55|56.79<br>**Yea**|9<br>1.79<br>90.97|No|
|10<br>2.50|59.29<br>Yes|10<br>1.22<br>92.20|Yes|
|11<br>2.43|61.72<br>Yes|||
|12<br>2.14|63.87<br>No|||
|13<br>2.07|65.93<br>No|||
|14<br>1.94|67.88<br>No|||
|15<br>1.30|69.18<br>**Yea**|||
|16<br>1.27|70.45<br>**Yes**|||
|17<br>1.11|71.56<br>**Yeo**|||
|18<br>1.05|72.61<br>Yes|||
|19<br>1.03|73.64<br>Yes|||
|Percentage matched:|55.90|Percentage matched:|83.17|
|<br>b) Items Specified by Quantit<br>|y and Value.<br>|<br>b) Items Specified by Quantity and Value.<br>||
|contributing**less than**1 p|er cent to Output:|contributing**less**than 1 per cent to Out|put:|
|20-37<br>8.16|81.80|<br>11-21<br>2.07<br>94.26||
|Percentage matched:|2.16|Percentage matched:|0.00|
|c) Output not Specified by Qu|antity:|<br>C)Output not Specified by Quantity:||
|<br>18.20|<br>100.00|<br>5.74<br>100.00||
|Percentage matched:|0.00|Percentage matched:|0.00|
|||...----------------------------------.---------------------------__-----|-------__-----------____---_---------|
|Total percentage matched:|58.06<br>|| Total percentage matched:|83.17|

Source: US figures derived from tables A1.2 and A1.5; Brazilian figures derived from tables A1.3 and A1.5;
# **Appendix Figure 1A - France**

# **The main variables during 1997-2010 for firms from France**

<!-- Start of picture text --> 8.00% Gross Savings 14% Gross Capital Formation<br>7.00% 12%<br>10%<br>6.00%<br>8%<br>5.00%<br>6%<br>4.00%<br>4%<br>3.00%<br>2%<br>2.00%<br>0%<br>6%<br>Excess Savings Non-Operating Assets<br>45%<br>4%<br>40%<br>2% 35%<br>0% 30%<br>25%<br>-2%<br>20%<br>-4%<br>15%<br>-6% 10%<br>-8% 5%<br>0%<br>-10%<br>80% Leverage 13% Liquidity Holdings<br>75%<br>12%<br>70%<br>11%<br>65%<br>10%<br>60%<br>9%<br>55%<br>8%<br>50%<br>45% 7%<br>40% 6%<br>Median<br>Asset-Weighted Average<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 19... 19... 19... 20... 20... 20... 20... 20... 20... 20... 20... 20... 20... 20... 20...<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Excess Savings, Gross Savings, Gross Capital Formation, Non-Operating Assets, Leverage, and Liquidity Holdings, during the period 1997-2010, according to median and asset-weighted average, for the sample of manufacturing firms from France.
<!-- Start of picture text --> ALGERIA<br>MAURITANIA Tombouctou<br>Kidal<br>ahsP wr ae<br>- ie Gao<br>, q - a . . - ia 7 7 ‘ ;<br>ss Pig Mopt =~ NIGER<br>SENEGAL s& ‘es pe .<br>acl aw BURKINA FASO<br>a - og ¢ne5 5 fismh, - NIGERIA<br>GUINEA ye Cae. GHANA BENIN<br>—_—o. COTE D'IVOIRE a.<br><!-- End of picture text --> <!-- Start of picture text --> ALGERIA<br>MAURITANIA<br>NIGER<br>SENEGAL<br>BURKINA FASO<br>NIGERIA<br>GUINEAjl BENIN<br>GHANA<br>—_—o COTE D'IVOIRE ———<br><!-- End of picture text --> <!-- Start of picture text --> Density per Kma<br><4<br>Tet<br>io- 40<br>OO s- im<br>@ too.se0<br>@ +500<br><!-- End of picture text --> <!-- Start of picture text --> lmcidones in %<br>edi<br>) 4-5<br>® m-s0<br>® wen<br>® =<br><!-- End of picture text -->
probability of not having had a birth yet by spell duration. The survival curves are presented for two reasons. First, they show the predicted progression to the next birth by quarter and how quickly it occurs, if at all. Secondly, they provide “weighting” for the associated predicted percentage boys born. The steeper the survival curve, the more weight should be assigned to a given spell period because it is based on more births, whereas if the survival curve is flat the period should be given little weight because the predicted percentage boys is based on few births. The survival curve for quarter _t_ is or equivalently

# **6 Data**

The data come from the three rounds of the National Family Health Survey (NFHS-1, NFHS-2 and NFHS-3), collected in 1992-93, 1998-99 and 2005-2006, respectively.<sup>16</sup> They were collected by the International Institute for Population Sciences in Mumbai and have nationwide coverage. There are three advantages to using the NFHS. First, the data are considered high quality. There still is a potential issue of recall error, but, as shown below, it is more easily addressed than in the data used by Jha et al. (2006). Secondly, it has complete birth histories for a large number of women. Finally, by combining the three NFHS rounds it is possible to show the development from before sex-selective abortions were available until 2006. Specifically, it is possible to validate the method using data before sex selection and to show whether the law banning providing information about the sex of a fetus has changed the use of sex-selective abortions.

The surveys are large: NFHS-1 covered 89,777 ever-married women aged 13-49 from 88,562 households, NFHS-2 covered 90,303 ever-married women aged 15-49 from 92,486 households and NFHS-3 covered 124,385 never-married and ever-married women aged 15-49 from 109,041 16 NFHS-2 also has a small number of observation collected in 2000, due to a delay in the survey for Tripura.

15
_56_ _Table 4.2: Ghana: Energy Cost Shares and Index ofinduslralProducrion_

|||**C!nd.harspA**||**_TrnA_**|**_as nf_imhim**|**tri.lXocutef**|**inn(1010**|**=1(In1**|
|---|---|---|---|---|---|---|---|---|
||Fuels|EMctricitY|Ener*y|1981|1983|198S|1987|1989|
|Gold|1.39%|4.99%|6.38%|96|78|85|93|116|
|Diamond|12.0S%|7.99%<br>|20.04%|<br>73|30|_SS_<br>|_38_|33|
|Baix|0.78%|0.61%|1.38%|81|31|75|87|103|
|Mainganoso|4.97%|1.78%|6.75%|89|69|126|101|141|
|**_Ma_**<br>**_duwing_**|||||||||
|Milk|**_2.15%_**|0.86%|3.02%|119|_75_|109|175|174|
|Whdat flour|0.47%|1.30%|1.78%|82|20|Si|80|92|
|Cocoa buter|1.60%|_0.9%_|2.19%|90|80|70|74|_65_|
|cocoa Lqur|1.60%|0.59%|2.19%|123|69|133|218|238|
|Coco cale|1.60%|_0.59%_|2.19%|85|90|73|82|67|
|_Boe_|0.73%|2.06%|2.78%|_118_|70|96|132|_140_|
|SoftdrinDa||n.a.|n.a.|129|_28_|**_53_**|_99_|_La._|
|**_Cigae_**|0.27%|0.14%|0.41%|79|53|96|86|80|
|_Ckodh_|4-51%|2.36%|6.87%|84|_19_|35|_53_|_61_|
|Jutebap|_1.02%_|2.42%|3.44%|96|53|_75_|48|46|
|Margarine|2.88%|0.96%|3.84%|136|29|101|256|295|
|caue|0.80%|2.79%|_3.59%_<br>|135|_94_|121|100|**_152_**|
|harozods|4.17%|1.21%|_5.38%_|_95_|29|62|_58_|27|
|_Sop_|3.13%|0.48%|3.61%|121|_41_|_145_|374|419|

**_Sowee.:_** _'Ghana_ Prspaoa on adjusunent,t Rcpost No. 947S-Gh, AfUica Region, World BuiL

The energ and individual fuel cost shares in Ghana, except for the diamond miing sector, lies betwee 0.4 and nearly 7 percent Compared to Malaysi **a** nd Indonesia, these cost shares are high, which coud be iprtet as lower eiciny i energy use in the industrial sector, but whi _may_ also have oer causes. Howv, most industries had their lowest output around 1983, before the energ price increases, with an expansion in output from 1983 to 1989, a result that must reflect other simultaneous changes. Among de ener tsive mdusties, diamonds and iron rods are the exceptions (not recoverng). Others xanded from 1983 onwards through 1989, under and after the energy price increases. One explanation is the presence of the stuctural constraints in the economy at the time of energy price changes. Admnsrative cortrols and constaint on import capacity during the 1970s and 1980s resulted in rationing of importnt us for most finns in the industrial sector, resulting in underutilizati **o** n findusria capacty
coverage is determined by data availability. The 16 manufacturing industries are either at the two-digit or three-digit level according to the International Standard Industry Classification (ISIC), revision 2.<sup>7</sup> The TFP index is calculated as the difference between the logs of output and factor income, with inputs (labor and capital) weighted by their income shares, i.e., log _TFP_ = log _Y_ −α log _L_ − 1( −α ) log _K_ , with α equal to labor’s share, measured as the ratio of wages over value added. The capital stocks are derived from the investment series using the perpetual inventory model with a 5% depreciation rate. Raw data on value added, labor, wages, and fixed capital formation are taken from the World Bank (Nicita and Olarreaga 2001), and were converted to 1990 US dollars, before computing the TFP index.

The R&D flow data are taken from the ANBERD 2000 (OECD) database (DSTI/EAS Division). It covers 15 OECD countries from 1973 to 1998 at either the two-, three- or four-digit level. From this, we construct R&D flow data for the 16 manufacturing industries at the two- or three-digit level (according to the United Nations International Standard Industrial Classification (ISIC), Revision 2). R&D flows cover all intramural business enterprise expenditures. Cumulative R&D stocks are derived using the perpetual inventory method with a 10% depreciation rate.

The national input-output matrix is used as a proxy, which is derived from GTAP (1998). Bilateral openness shares are derived from the World Bank database “Trade and

> 7 They are: (1) 31 Food, Beverage & Tobacco; (2) 32 Textiles, Apparel & Leather; (3) 33 Wood Products & Furniture; (4) 34 Paper, Paper Products & Printing; (5) 351/2 Chemicals, Drugs & Medicines; (6) 353/4 Petroleum Refineries & Products; (7) 355/6 Rubber & Plastic Products; (8) 36 Non-Metallic Mineral Products; (9) 371 Iron & Steel; (10) 372 Non-Ferrous Metals; (11) 381 Metal Products; (12) 382 NonElectrical Machinery, Office & Computing Machinery; (13) 383 Electrical Machinery and Communication

5
of individuals experiencing ill-health or other medically relevant conditions at the time of Lhe main survey. This could yield a much better understanding of the longer-term implications of such experiences. Some specific possibilities are: to follow up pregnant women to examine determinants of their health status and pregnancy outcomes in the context of the use of MCH services and of their work activities. This study could be extended to examine the implications of a new b)irth for the health of the infant's older sibling; to follow up sick and malnourished children to clarify the interaction between morbidity and growth faltering and the process by which disease may develop into long-term disability;

-

to examine the economic and health consequences of serious illness among adults, in particular mothers and principal earners, both for the household as a whole and for the children in it.

# 5.6 Ouestionnaire development and ore-testina

The complexity of the social processes affecting health suggest that small-scale observational studies could represent a useful counterpart to the examination of existing statistical information (see section 5.2) during the planning of country health studies. These could be both clinic and community based. As well as suggesting relevant issues ano hypotheses, such studies have an important role to play in questionnaire development. For example, a good understanding of traditional concepts and terminology describing diseases and svmptoms is essential for the design of algorithms intended to identify particular diseases and causes of death. Moreover the sensitivity and appropriateness of questions about many issues vary between cultures. Such studies would also be useful for identifying and clarifying the nature of social aggregates (ie. communities) that are likely to be of relevance to the health status and health-related actions of individual households (see section 4.7).

40
trending techniques to estimate potential output growth, or vector autoregressions.<sup>9</sup> The IMF has chosen to construct long term growth scenarios for the transition countries based on the long term growth experiences in other parts of the world.<sup>10</sup> A major shortcoming of this approach is the need to estimate the rate of total factor productivity growth, which typically varies substantially over time and across countries.<sup>11</sup> Although we could choose from the myriad of cross-country growth regression models in the literature, we adopted the growth framework and forecasting methodology in Loayza _et al._ (2005). The main reason for our choice is the fact that we had access to their panel dataset. It meant that we could avoid the cost of building a large panel data. Another reason is the fact that Loayza _et al._ (2005) employed state-of-the-art panel estimation techniques. Their published forecasts for 20 countries in Latin America and the Caribbean for the period 20012010 are already widely used in the World Bank’s economic and sector work.<sup>12</sup> Loayza _et al._ (2005) obtain these forecasts by estimating a cross-country growth regression model based on a panel dataset that included 78 countries from various regions in the world (Table 1)<sup>13</sup> spanning the period 1961-99 and then forecasting real per capita GDP growth by generating simple univariate forecasting models for the growth determinants in the model.

Loayza _et al._ (2005) depart from the standard practice and emphasize the importance of differentiating between trend output growth and cyclical output movements (Figure 4). The cyclical output movements are bound to be important in their panel dataset representing

> 9 Kraay and Monokroussos (1999) suggest that vector autoregressions in a small set of key macroeconomic variables, estimated country by country, may be a way to improve over the forecast performance of both univariate time series and cross-country growth regression models. The advantage of this approach over the univariate time series models is that it draws on a larger information set. Another advantage is that it relaxes the restrictive assumption of cross-country regression models that the parameters of the model are equal across countries.

> 10 See IMF (1996), Chapter 5.

> 11 The IMF uses an endogenous growth model to assess the rate of technical change (De Masi 1997). 12 See, for instance, El Salvador’s Country Economic Memorandum 2003.

> 13 Table 1 displays the developing countries in the panel data. The developed countries in the panel, not shown in Table 1, are Austria, Australia, Belgium, Canada, Denmark, Finland, France, Greece, Iceland, Ireland, Israel, Italy, Japan, Netherlands, Norway, Sweden, Switzerland, United Kingdom, and United States.

12
number of zeros has been declining over the years. This suggests that product diversification is on the rise everywhere. A more careful inspection of the graph suggests that the decline is more substantial for East Asian economies than for the remaining regions. This point is confirmed by the actual data country by country.<sup>9</sup> The variation is the lowest for developed countries and the group of poorest countries in our sample. This is due to very different reasons. Developed countries display a lower reduction in the number of zeros because there are few 6-digit categories that are not being exported yet. Although by definition all of these categories are tradable, empirical evidence shows that there is some sort of lower bound to the number of zeros. This can be evidenced by intra-European trade (the most integrated trading area in the world) where some categories remain not traded. Further research is needed to understand what the nature of these “rigidities” may be. The poorest countries in our sample, on the other hand, are experiencing a totally different type of constraint. It may be the case that their economic structure is very basic and they concentrate their exports in just a few products (mostly commodities). Another explanation is that they lack the capacity (e.g. knowledge, infrastructure) to innovate (e.g. diversify their production) or simply that the national production does not meet the standards to export.

**Figure 2: Evolution of the number of zeros by region (1990 – 2005)**

<!-- Start of picture text --> Number of Zeros by area<br>1990 1995 2000 2005<br>Year<br>LAC DEV<br>ASIA SSA<br>ECA & MENA<br>5000<br>4500<br>4000<br>3500<br>Number of zeros<br>3000<br>2500<br><!-- End of picture text --> Export diversification seems to be influenced by the proximity and the size to the destination markets.<sup>10</sup> The figures in Appendix 2 show the scatter plots of the count of

- SSA: Ghana, Kenya, Mauritius, South Africa and Uganda.

-

- ASIA: Bangladesh, China, Indonesia, India, Malaysia and Thailand.

- ECA-MENA: Morocco, Tunisia and Turkey.

- Details for individual countries are available from the authors upon request.

> 9 See Table 6 in Appendix 5.

> 10 Note that in the previous graph we saw diversification in terms of the evolution of number of zeros, now we switch to observe the evolution in the count of goods exported.

6
As shown at the bottom of Table 12, the exclusion restriction for the instruments is convincing: the existence of a CB in other countries should not impact access to finance by firms in a given country directly or through other channels besides its impact on the endogenous reform variable. We test for overidentifying restrictions, and report the Hansen’s J statistics and p-values. As shown in Table 12, for all 5 regressions, we fail to reject the null hypothesis that the instruments are jointly valid. We also establish that instrument relevance is strong: more countries in the region with established CB’s and recently introduced CB’s is more likely to lead a country in that region to introduce its own CB. We report the first stage of the 2SLS regressions in Table 12. As expected, both the exogenous excluded instruments have a positive impact on the introduction of a CB. We formally test weak identification in the first stage by calculating Kleibergen-Paap rk statistic, and confirming they are above Stock-Yogo critical values for all specifications except interest rate.<sup>14</sup> In Table 12, we also present 2<sup>nd</sup> stage estimates for the impact of CB introduction on all five dependent variables.<sup>15</sup> We find that the estimates are qualitatively consistent with the baseline results, albeit with higher point estimates.<sup>16</sup> The result in our baseline specification and subsequent analysis using the same quasi difference-in-difference approach does not appear to have been driven by endogeneity.

> 14 The more commonly used Cragg-Donald Wald statistics and corresponding Stock-Yogo critical values are only valid with i.i.d errors. Our comparison of more generalized KP rk statistics with Stock-Yogo critical values is not strictly valid and only meant as suggestive. The KP rk statistics are also above the general critical value of 10.

> 15 For the case of clustered standard errors like ours, in the tables we present the weak-instrument robust inference Anderson-Rubin statistics for testing the significance of the endogenous regressor in the structural equation (Finlay et al 2009).

> 16 We also estimated the instrumental variable regressions using 2 step GMM estimators. Results (not reported) do not change.

22