rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: Marginal effects of(x100) gender, wealth, and the presence of primary and secondary schools in the community on the probability of enrollment of 6-14 year olds, rural areas (Probit results for selected variables)

||Male|Middle|Richest|Male*<br>Middle|Male<br>Richest|Primary<br>school<br>only|Primary<br>and<br>seconda<br>ry<br>schools|Male<br> <br>Primary<br>school<br>only|Male<br>Primary<br>and<br>seconda<br>ry|
|---|---|---|---|---|---|---|---|---|---|
|Benin 1993|35.0 s|12.0|s<br>22.5|s<br>1.9|-4.4|25.2|s<br>49.5|s<br>-17.3|s<br>-14.1 s|
|Burkina Faso 1992-93|15.5 s|5.2|s<br>10.1|s<br>-0.9|9.0|12.5|s<br>12.2|s<br>2.4|-0.2|
|Cameroon 1991|33.6 s|9.1|s<br>11.5|-4.3|14.8|0.1|.|-0.1||
|Chad 1998|17.7 s|2.7|6.4|-0.9|-2.0|21.0|s<br>14.3|<br>2.6|2.4|
|Cote d'lvoire 1994|27.2 s|15.4|s<br>19.8|s<br>-2.7|7.4|17.9|s<br>-3.8|<br>-3.4|31.0|
|Mali 1995-96|11.6 s|5.3|s<br>13.4|s<br>-0.6|0.9|20.9|s<br>.|-0.6||
|Niger 1992|10.3 s|1.6|<br>8.6|-2.0|-4.3|s<br>9.6|s<br>3.7|<br>0.5|2.4|
|<br>Niger 1997|10.3 s|0.6|6.0|1.4|1.7|<br>14.4|s<br>56.3|s<br>1.1|-0.3|
|Senegal 1992-93|12.6 s|6.8|s<br>-7.2|-2.2|25.4|-2.9|-8.2|<br>-1.8|-0.7|
|Morocco 1992|37.6 s|36.9|s<br>54.4|s<br>-4.9|-10.7|8.6|-10.1|<br>1.4|1.1|
|Bangladesh 1993-94|-9.6|6.8|s<br>10.6|s<br>1.6|-6.6|2.4|3.4|<br>8.4|s<br>9.1 s|
|Bangladesh 1996-97|-4.2|4.8|s<br>4.4|0.6|8.2|s<br>4.8|4.7|<br>1.3|2.3|
|India 1992-93|18.9 s|14.6|s<br>21.0|s<br>0.3|-0.6|4.4|9.0|s<br>-1.2|-6.2 s|
|Madagascar 1992|9.4|10.8|s<br>27.6|s<br>-5.0|-12.5|s<br>14.9|s<br>21.1|s<br>-7.6|-1.1|
|<br>Tanzania 1991-92|-6.7|2.0|19.7|s<br>1.3|-4.3|6.7|-0.5|<br>0.0|4.2|
|Uganda 1995|12.6 s|12.0|s<br>17.1|s<br>-6.6 s|-13.4|s<br>-0.5|4.9|<br>-0.5|-7.9|
|<br>Zimbabwe 1994|8.8 s|3.9|s<br>10.1|s<br>-2.5|-38.4|<br>5.4|s<br>9.1|s<br>-0.3|-10.8 s|
|Dominican Rep. 1991|-15.2|22.0|s<br>42.9|s<br>-16.7|-27.7|2.3|11.5|<br>7.9|-3.6|
|<br>Haiti 1994-95|2.2|21.9|s<br>23.4|s<br>1.9|-28.3|7.5|8.9|<br>-2.5|1.4|
|Philippines 1993|-8.1|6.9|s<br>5.7|s<br>0.5|1.6|<br>4.0|4.8|<br>-1.5|-2.3|
|Bolivia 1993-94|4.6|8.8|s<br>13.5|s<br>0.9|-88.8|s<br>4.7|9.4|s<br>0.1|-1.8|

Notes: Each marginal effect (change in the dummy from zero to one) is evaluated at the means of all other regressors. Significance at the 5 percent level is indicated by an "s".

Other variables in the regression are age and age squared, average education of adult males in the household (ages 20-64) and adult females in the household, a dummy variable for whether or not the head of the household is male, the age of the head of the household, and a set of community infrastructure variables (e.g. presence of a post office, a cinema, health facilities, distance to the nearest urban center)
_15_ We use a two-stage stratified randomization design where we first assign each village to one of three experimental groups and then schools within these villages to treatment. Stratification is based on village size and village average revenues, as both these variables are highly auto-correlated in our panel dataset (Bruhn and McKenzie, 2009). Based on power calculations,<sup><u>3</u></sup> 7<sup>ofthevillagesareassigned</sup> to the _L_ arm, and<sup><u>2</u></sup> 7<sup>tothe</sup><sup>_H_armandthecontrolgroup;atotalof342schools</sup> across 189 villages receive grant o↵ers (see Appendix Figure C1). In the second stage, for the _L_ arm, we randomly select one school in the village to receive the grant o↵er; in the _H_ arm, all schools receive o↵ers; and, in the control group, no schools receive o↵ers.

The randomization was conducted through a public computerized ballot in Lahore on September 5, 2012, with third-party observers (funders, private school owners and local NGOs) in attendance. The public nature of the ballot and the presence of third-party observers ensured that there were no concerns about fairness; consequently, we did not receive any complaints from untreated schools regarding the assignment process. Once the ballot was completed, schools received a text message informing them of their own ballot outcome. Given village structures, information on which schools received the grant in the _L_ arm was not likely to have remained private, so we assume that the receipt of the grant was public information.

_Intervention.—_ We o↵er unconditional cash grants of Rs.50,000 (approximately $500 in 2012) to every treated school in both _L_ and _H_ arms. The size of the grant represents 5 months of operating profits for the median school and reflects both our overall budget constraint and our estimate of an amount that would allow for meaningful fixed and variable cost investments. For instance, the median wage for a private school teacher in our sample is Rs. 24,000 per year; the grant thus allows the school to hire 2 additional teachers a year. Similarly, the costs of desks and chairs in the local markets range from Rs. 500 to Rs. 2,000, allowing the school to purchase 25-100 additional desks and chairs.

We deliberately do not impose any conditions on the use of the grant apart from submission of a (non-binding) business plan (see below). School owners retain complete flexibility over how and when they spend the grant and the amount they spend on schooling investments with no requirements of returning unused funds. As we show below, most schools choose not to spend the full amount in the first year and the total spending varies by the treatment arm. Our decision not to impose any conditions follows our desire to provide policy-relevant estimates for the simplest possible design; the returns we observe therefore provide a ‘baseline’ for what can be achieved through a relatively ‘hands-o↵’ approach to private school financing.

_Grant Disbursement.—_ All schools selected to receive grant o↵ers are visited three times. In the first visit, schools choose to accept or reject the grant o↵er: 95
Figure 3: Probability Distribution of Net Present Value

## _Riskfactor: Total Revenue_

<!-- Start of picture text --> Histogram of Monte Carlo Simulation, 1000 iterations<br>180 ---<br>160 1 Mean: 25.71<br>1- Median: 27.89<br>j ') 120 +Prob. <0: 19.7%<br>(D 100 -<br>80-l[ .:l|ll[<br>u 60~<br>4 0<br>-+<br>20 -n0<br>Net present value of Indiantown Power Project<br><!-- End of picture text -->

Figure 4: Probability Distribution of Dividend in Year 2005 _Riskfactor: Total Revenue_

# **Histogram of Monte Carlo Simulation, 1000 iterations**

<!-- Start of picture text --> 180-<br>180 - I Mean: 35.71<br>160 I<br>140 2 Median: 36.32<br>120 Probability < 0: 9.5%<br>a100 il|l<br>- 80<br>60<br>40-'-<br>20<br>Dividend, 2005<br><!-- End of picture text -->

11
describes the range of temperature outcomes reported for this emissions scenario across many climate models (IPCC 2001). CNRM predicts a global warming of 2.9<sup>◦</sup> C, ECHAM predicts 3.4<sup>◦</sup> C, GFDL predicts 2.7<sup>◦</sup> C, and MIROC predicts 4.5<sup>◦</sup> C. These changes in climate warm sea surface temperatures which in turn fuel the hurricanes. However, there are other changes in wind shear and wind direction that can affect hurricane intensity as well, and changing wind patterns can affect where storms strike and how many storms actually intercept the U.S. coastline.

Using a hurricane generator in the Atlantic basin, thousands of storms are seeded into the Atlantic Ocean. Given the climate conditions projected by each climate model, the storms are allowed to develop. Only some of the seeded storms develop into hurricanes and only some strike the United States. A total of 5,000 hurricane tracks are followed that eventually strike the US for each climate case (Emanuel et al. 2008). This is equivalent to observing over 2000 years of hurricane experience with each climate scenario. For each climate model, there are 5,000 hurricanes for the current climate and 5000 hurricanes for the future climate. Given that there are four climate models, there are a total of 40,000 hurricanes followed in the study. Note that this approach explicitly models the location and intensity of each storm. Most earlier hurricane studies simply assumed that all storms would become more intense by a common percentage (Nordhaus 2006; 2010; Pielke 2007).

We assume that hurricanes that remain at sea cause minimal damage. Most of the damage caused by tropical cyclones occurs when they strike land. The minimum barometric pressure and the maximum wind speed at landfall of each storm are recorded. Storms striking Florida and Long Island, New York can continue and strike another location. We count the damages from these storms in both locations.

A sample of hurricane tracks is shown in Figure 1. The tracks are both more frequent and more powerful (red) near Florida and the Gulf Coast states. As hurricanes move north along the US coast they tend to lose power. This is true for both the current 7
coal, which emits higher amounts of harmful pollutants. Therefore, coal washing and dressing is needed. Coal gasification is a source of natural gas in China. Gasification technologies used include pure oxygenation, hydro-gasification and steam gasification.

Coal and natural gas are the main sources of thermal generation in China. The setting of coal power technology in the model includes conventional power generation technologies such as pulverized combustion and circulating fluidized bed combustion and advanced supercritical and ultra-supercritical technologies. It also includes integrated gasification combined cycle (IGCC) technology and cogeneration technology. The natural gas power generation technology includes gas turbine and combined cycle technologies. The heat production is mainly for industrial application and district heating. Coal-fired boilers account for almost 80% of the total industrial boilers. Data on thermal efficiency and capital costs of thermal power plants are provided in Table B2.

**Table B2.** Thermal power supply efficiency,technology and unit investment

|**Unit generating**<br>**mode**|**Power**<br>**generation**<br>**technology**|**Power**<br>**generation**<br>**efficiency (%)**|**Capital cost**<br>**(Yuan/kW)**|
|---|---|---|---|
|**Coal-fired power**<br>**generation**|Ultra-<br>supercritical|45%~47%|3600~3800|
||Supercritical|41%~42%|3700~3850|
||Subcritical|38%~39%|4400~4600|
||Circulating<br>fluidized bed|35%~40%|4500~6000|
||IGCC|40%~43%|8000~10000|
|**Gas power**<br>**generation**|Natural gas<br>boiler|Less than 35%|3100~3400|
||NGCC|55%~67%|3282~3350|
|**Fuel power**<br>**generation**|Direct<br>combustion<br>boiler|Less than 25%|3300~3500|
|**Coal gangue**<br>**power**<br>**generation**|Circulating<br>fluidized bed|37%|4500~6000|
|**Biomass power**<br>**generation**|Biomass direct<br>combustion|23%|6500~8500|
|**Unit generating**<br>**mode**|Biomass<br>gasification|36%|6500~12000|

Note: The data needed for the power sector sub-module are obtained from sources including _China Electric Power Yearbook (2010-2013)_<sup>[5]-[8]</sup> , _China Energy Statistical Yearbook (20062014_ )<sup>[9]-[17]</sup> , _China Electric Power Statistics Information Collection (2010-2013)_<sup>[18]-</sup> 42
<!-- Start of picture text --> Figure S2: Percentage of Firms Using E-filing by Turnover<br>EE ecfiiers HEEEMM non-efilers<br>aSSa a<br>0 20 40 60 80 100<br>Percent (%)<br>Source: USAID 2008 [South Africa]<br>* In thousands of Rands ('000)<br>*DN/Us/Re: Don't know, Unsure, Refused to answer<br>Figure S3: Percentage of Firms Using E-filing by Legal Form<br>a i i iS Se<br>(@) 20 40 60 80 100<br>Percent (%)<br>Source: USAID 2008 [South Africa]<br><!-- End of picture text -->
_4_ _Ways Out of Povert& - Diffusina Best Practices and Creating_<sup>_Capabilities_</sup> the 20th century remains hotly disputed.<sup>8</sup> In any case,<sup>roughly 1.2billion people still live</sup> on less that $1 a day, and 2.8 billion on less then<sup>$2 a day.9</sup>

**Figure 1: Shares of world population living on less**<sup>**than$1 a day**</sup>

<!-- Start of picture text --> 0.450 -<br>> 0.400 -<br>0.350-<br>6 0.300 -<br>; 0.250 -<br>_ 0200<br>0.150<br>0<br>X, 0100<br>0050<br>0.000<br>1970 1980 1990 1998<br>- World - Asia<br>&- China -x-- Asia rinus China<br>- - Latin Anerica + Europe & Central Asia<br>i Africa<br><!-- End of picture text --> Source: Sala-i-Martn (2002). Data for Europe & Central Asia are<sup>from</sup> World Bank (2000a).

Worldwide, the average income per person is about $6,000<sup>a year,no more than that for</sup> the United States in 1900, then the richest country in<sup>the world.The world has thus</sup> shown that it can produce enough for everyone to lead<sup>a better life.But many people</sup> have not benefited. And so, the century that has<sup>given rise to unprecedentedhope hasalso</sup> given rise to anger that many people are left out. Precisely<sup>becausewe can now imaginea</sup> world without poverty, we can be angry about the slow<sup>progress.In the wordsof Nobel</sup> laureate Joseph Stiglitz: "The experience of the past<sup>fifty years has demonstratedthat</sup> development is possible, but not inevitable."'<sup>0</sup>

> **"** Milanovic, 1999; Ravallion, 2000; Sala-i-Martin,<sup>2002.</sup>

> **9** World Bank, 2002a. 'O **Stiglitz, 1998a.**
<!-- Start of picture text --> InWw;,,) = BrFemales wu; + &;<br>Females U;<br>eit<br>Br<br>Br<br>In(w;, ) = BpmPrimM,, + ByyJunMi + Bum HighM(,<br>NoPrimF,;<br>PrimF,, PrimM;,)<br>JunF;, JunMj,)<br>HighF;, (HighMi,)<br>NoPrimM;,<br>Bi<br><!-- End of picture text -->
# **Appendix A Variable Definitions**

This appendix consists of four tables (tables A.1–A.5) that define the variables used in the paper.

**Table A.1 Definition of variables used from 2010, 2012 and 2013 rounds of the Ghana Urban Panel Survey**

|Variable|Definition|
|---|---|
|Male|Dummy variable = 1 if male, 0 if female|
|Age|Age in years|
|Education|Years of formal schooling|
|Married|Married = 1, 0 otherwise|
|Self-employed|Dummy variable = 1 if primary work activity is self-employment, 0 if<br>primary work activity is wage employment, out of labor force, or<br>unemployed|
|Wage-employed|Dummy variable = 1 if primary work activity is wage-employment, 0 if<br>primary work activity is self-employment, out of labor force, or<br>unemployed|
|Dependency ratio|Number of household members younger than 15 or older than 64 divided<br>by household size divided by household size|
|Log hh assets|Log of household assets|
|Accra|Dummy variable taking value 1 if the individual lives in Accra, 0 otherwise|
||Dummy variable taking value 1 if the individual lives in Cape|
|Cape Coast|Coast/Takoradi,0 otherwise|

|**Table A.2 Definition of baseline variables**<br>Variable<br>Definition|
|---|
|Self-employed<br>Dummy variable = 1 if primary work activity is self-employment, 0 if<br>primary work activity is wage employment, out of labor force, or<br>unemployed|
|Wage-employed<br>Dummy variable = 1 if primary work activity is wage employment, 0<br>if primary work activity is self-employment, out of labor force, or<br>unemployed|
|Manufacturing<br>Dummy variable=1 if respondent works in the manufacturing<br>sector, 0 otherwise|
|Trade<br>Dummy variable=1 if respondent works in the trade sector, 0<br>otherwise|
|Services<br>Dummy variable=1 if respondent works in the services sector, 0<br>otherwise|
|Public sector<br>Dummy variable=1 if respondent works in the public sector, 0<br>otherwise|
|Pay per week<br>Usual weekly earnings in Ghanaian Cedis|
|Days worked per week<br>Total number of days worked per week|
|Hours worked per week<br>Total number of hours worked in a typical week (calculated by<br>multiplying the number of days worked per week with the number<br>of hours workedper day)|

28
ASSESSING THE MACROECONOMIC IMPACTS OF NATURAL

# DISASTERS: ARE THERE ANY?

## **Stefan Hochrainer**<sup>1</sup>

International Institute for Applied Systems Analysis (IIASA)

**JEL:** C22, C53, E01

**Keywords:** Natural disasters, macroeconomic consequences, time-series analysis, ARIMA process, vulnerability.

> 1 We gratefully acknowledge support by the UN/World Bank project “Economics of Disaster Risk Reduction.” We would like to thank the team leader of this project, Apurva Sanghi, as well as Sebnem Sahin of the World Bank for ongoing support and stimulating discussions, and Jesus Crespo Cuaresma, as well as a number of anonymous referees for very helpful and stimulating comments.
Table A.8: Productivity Decompositions and Policy Changes: Without State-Industry Weights

||Aggregate|Average|Reallocation|
|---|---|---|---|
||(1)|(2)|(3)|
|Final Goods Tariff|-.059|-.050|-.009|
||(.018)<sup>_∗∗∗_</sup>|(.020)<sup>_∗∗_</sup>|(.015)|
|Input Tariff|-.713|-.674|-.039|
||(.089)<sup>_∗∗∗_</sup>|(.098)<sup>_∗∗∗_</sup>|(.064)|
|FDI Reform|.049|.069|-.020|
||(.011)<sup>_∗∗∗_</sup>|(.012)<sup>_∗∗∗_</sup>|(.009)<sup>_∗∗_</sup>|
|Delicensed|-.026|-.023|-.003|
||(.012)<sup>_∗∗_</sup>|(.013)<sup>_∗_</sup>|(.010)|
|Obs.|17074|17074|17074|
|_R_<sup>2</sup>|.056|.048|.003|

Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is given equal weight, and standard errors are clustered at the stateindustry level.

60
# Table 4. Contributions of global shocks to alternative measures of global inflation

||||Struc<br>|tural Shocks<br>||
|---|---|---|---|---|---|
|Sensitivity|Proxy variables|Oil<br>Price|Global<br>Supply|Global<br>Demand|Global<br>Interest rate|
|Baseline (Headline CPI)||38.4<br>[12.9 66.9]|17.6<br>[2.4 33.3]|27.8<br>[4.5 59.5]|16.2<br>[1.2 34.4]|
|Ali Ifli M|Core CPI|6.9<br>[0.5 14.5]|41.1<br>[8.3 77.3]|25.7<br>[1.9 55.6]|26.4<br>[2.5 54.3]|
|ternatve naton easures|Producer price index|38.0<br>[12.4 68.9]|22.5<br>[3.0 42.2]|22.9<br>[3.4 48.5]|16.5<br>[1.5 35.7]|

Note: This table reports variance decompositions of global inflation (in percent) based on median from 1000 successful Bayesian draws. The numbers in the brackets are 16-84 percentiles draws. “Baseline” indicates the variance decompositions of global inflation based on the global FAVAR model that consists of oil price growth, and the global factors extracted of inflation, output growth, and interest rates from the dataset of G7 countries. “Alternative inflation measures” indicates that global inflation factor is replaced by either global core CPI inflation factor or global producer price index inflation factor.

33
We calculate a Market Accessibility Index (MAI) as another agglomeration measure. Treating the population size within the town administrative boundaries could mismeasure agglomerations. For instance, the presence of economically vibrant, small satellite towns surrounding major towns could result in underestimating our estimates. To take account of such connectivity between towns, we calculate the MAI, following Donaldson and Hornbeck (2016) and Berg, Blankespoor, and Selod (2018). It is calculated as the sum of its own population and surrounding towns’ populations that are discounted based on its travel time from the town (see Appendix A for the details).

# **3.2 Labor Outcomes**

Table 1: Employment Characteristics of Whole Sample and Among Employed

||Total|Size 1|Size 2|Size 3|Size 4|Addis<br>Ababa|Min|Max|
|---|---|---|---|---|---|---|---|---|
|**Employment Status**|||||||||
|Employed|0.62|0.72|0.66|0.61|0.60|0.55|0|1|
|Unemployed|0.13|0.08|0.11|0.13|0.14|0.17|0|1|
|Non-Labor Force|0.25|0.20|0.23|0.26|0.25|0.28|0|1|
|Observations|124,354|9,240|16,727|43,732|26,774|27,486|||
|**Among Employed**|||||||||
|Sector of employment|||||||||
|Government|0.23|0.24|0.22|0.24|0.23|0.23|0|1|
|Private|0.21|0.07|0.14|0.19|0.28|0.37|0|1|
|Self-employed|0.48|0.65|0.59|0.51|0.39|0.27|0|1|
|Other|0.08|0.04|0.05|0.06|0.10|0.13|0|1|
|Formality|||||||||
|Book account of enterprise|||||||||
|Have book account|0.41|0.30|0.30|0.38|0.45|0.62|0|1|
|Do not have book account|0.33|0.64|0.38|0.29|0.29|0.17|0|1|
|Do not know|0.24|0.07|0.31|0.31|0.22|0.17|0|1|
|License of enterprise|||||||||
|Have License|0.66|0.55|0.58|0.62|0.72|0.84|0|1|
|Do not have License|0.33|0.44|0.41|0.36|0.26|0.13|0|1|
|Do not know|0.02|0.01|0.01|0.02|0.02|0.03|0|1|
|Labor outcome|||||||||
|Working hours|43|35|39|43|47|50|0|97|
|Underemployment|0.13|0.21|0.19|0.14|0.09|0.05|0|1|
|Monthly nominal earnings|1047|762|798|920|952|1293|50|81000|
|Observations|74,578|6,566|10,946|25,900|16,057|14,828|||

Notes: The city size is grouped in quantile excluding Addis Ababa; Size 1 is up to 15,000, Size 2 is up to 40,000, Size 3 is up to 150,000, and size 4 is up to 500,000. Underemployment is defined as workers who work less than 30 hours per week and are willing to work more. Unit of nominal earnings is Ethiopian Birr (ETB).

The labor status of urban individuals is associated with the town population size. Table 1 10
Fraser Institute Index of Regulatory Freedom. Appendix 1 provides additional information on definitions and sources of the three explanatory variables.

<!-- Start of picture text --> Figure 2. Technological Innovation and Regulatory Freedom<br>Personal Computers by Level of Regulatory Freedom<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>>75pc 25-75pc <25pc<br>Internet Users by Level of Regulatory Freedom<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>>75pc 25-75pc <25pc<br>Notes: 1. Lines show average per group (top quarter, inter-quartile range, bottom quarter)<br>according to Regulatory Freedom as of mid 90s. 2. Data on Personal Computers and Internet<br>Users are from World Development Indicators. Data on Regulatory Freedom are from the Fraser<br>Institute.<br>Personal Computers (per 1,000 people)<br>Internet Users<br>(per 1,000 people)<br><!-- End of picture text --> 9
the cost-of-basic needs method using the 1993 Vietnam Living Standards (VNLSS) household survey data.<sup>4</sup> P'overty in Vietnam has a distinctive regional dimension. The headcount index of rural poverty ranges from a low of 45 percent in the rural South East to a high of 74 percent in the North Central region. The South is in general less poor than the North and the coastal and mountainous areas of the Center. Poor regions, especially mountainous ones, tend to be the most disadvantaged in terms of infrastructure, remoteness from market and information centers, lack of water and water management, and deficits of food staples. Here markets are least developed. The majority of Vietnam's ethnic minorities, who experience some of the most severe poverty, inhabit remote mountainous regions.

The center's coastal regions tend to have high population densities but poor quality soils threatened by encroaching salinity and acid sulphate contamination. They suffer especially from frequent natural disasters such as typhoons and devastating floods, followed by serious bouts of drought. But other regions also experience water related hazards. It is estimated that close to three-quarters of the country's population is susceptible to flooding.<sup>5</sup> Mountainous areas are prone to flash floods and landslides, while the low-lying river deltas often experience severe river flooding and typhoons. Moreover, analysis of trends over time suggests that the frequency and severity of natural catastrophes are on the rise as a result of environmental degradation.<sup>6</sup> The rural household economy, and therefore poverty, is intrinsically related to agriculture. Agriculture is the primary occupation of 84 percent of all rural workers aged 6 years and older.' The highest incidence of poverty is found among farmers. Poor households 6