rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Lehman Brothers<br><!-- End of picture text --> <!-- Start of picture text --> Lehman (t)<br>02jan2006 02jan2007 02jan2008 01jan2009 02jan2006 02jan2007 02jan2008 01jan2009 02jan2006 02jan2007 02jan2008 01jan2009<br>02jan2006 02jan2007 02jan2008 01jan2009 02jan2006 02jan2007 02jan2008 01jan2009 02jan2006 02jan2007 02jan2008 01jan2009<br>Note: eq=equity, cds=CDS, sen=senior debt, sub=subordinated debt, pfd=preferred stock, trp=trust preferred securities.<br>5 5 5<br>0 0 0<br>eq cds sen<br>-5 -5 -5<br>-10 -10 -10<br>5 5 5<br>0 0 0<br>sub pfd trp<br>-5 -5 -5<br>-10 -10 -10<br><!-- End of picture text --> <!-- Start of picture text --> Citigroup<br>Citigroup (t)<br>02jan2006 02jan2007 02jan2008 01jan2009 02jan2006 02jan2007 02jan2008 01jan2009 02jan2006 02jan2007 02jan2008 01jan2009<br>02jan2006 02jan2007 02jan2008 01jan2009 02jan2006 02jan2007 02jan2008 01jan2009<br>Note: eq=equity, cds=CDS, sen=senior debt, sub=subordinated debt, trp=trust preferred securities.<br>5 5 5<br>0 0 0<br>eq -5 cds -5 sen -5<br>-10 -10 -10<br>-15 -15 -15<br>5 5<br>0 0<br>sub -5 trp -5<br>-10 -10<br>-15 -15<br><!-- End of picture text --> 36
**Figure 1. Urban-rural divergence and then convergence as urbanization proceeds**

<!-- Start of picture text --> 4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>0 20 40 60 80 100<br>density (urban population share)<br>urban population share<br>ratio of urban consumption share to<br><!-- End of picture text -->

## _Source:_ World Bank 2009.

Asia and Sub-Saharan Africa are the two major world regions yet to fully urbanize. Urbanization in Asia is following the traditional pattern discussed above, but Sub-Saharan Africa differs. It has experienced substantial urbanization in the face of modest income growth,<sup>5</sup> with limited industrialization<sup>6</sup> and little technological progress in agriculture (figure 2).<sup>7</sup>

# **Figure 2. Urbanization, income, technology, and sector change**

a. Effective technology and urbanization level b. Income and urbanization level <!-- Start of picture text --> < Mean(Global GDP pc) - GDP pc < Mean(Global GDP pc) - GDP pc = PWT7.0<br>'URY' 'MLT' 'URY' 'MLT'<br>'CHL' 'CHL'<br>'BRA' 'JOR' 'KOR' 'JOR' 'BRA' 'KOR'<br>'SYR''GMB' 'SLV''MAR''TUR''COG''NIC''TUN' ' 'DOM''BGR'ZAF'PRY''ECU''BWA''COL''DZA''BOL' 'PER''PAN''MYS' 'MNG' 'GMB' 'NIC''COG' 'MNG''MAR''PRY''BOL''SYR''AGO''DZA''ECU''TUN''SLV''PER''COL''ZAF''BWA''TUR''DOM''PAN''BGR''MYS'<br>'GTM''CIV''LBR''IDN''CMR''HND''HTI' 'CHN''FJI''ALB''GHA''PHL''ROM' 'LBR' 'GHA''CIV''HTI''CMR''NGA' 'PHL''HND''IDN''FJI' 'GTM''ALB''CHN' 'ROM'<br>'MOZ''MLI''NER''TZA''BDI''RWA''PNG''MWI''SDN''UGA''MRT''SLE''KHM''LSO''SEN''ZAR''CAF''AFG''NPL''KEN''BEN''LAO''ZMB''NAM''VNM''IND''THA''TGO''BGD''SWZ''ZWE''PAK''MUS''EGY' 'GUY' 'LKA' 'ZWE' 'ZAR' 'BDI''SOM''NER''CAF''MWI''ETH''TGO''MDG''MOZ''GNB''GIN''SLE''BFA''MLI''RWA''BEN''UGA''AFG''TZA''KEN''NPL''TCD''LSO''BGD''SEN''MRT''ZMB''KHM''SDN''PAK''LAO''PNG''VNM''IND''SWZ''LKA''GUY''BTN''NAM''EGY' 'THA''MUS'<br>0 2 4 6 5 6 7 8 9 10<br>Aver yr school (high + college) ln(GDP per capita)<br>SSA NSSA SSA NSSA<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Urban share Urban share<br>.2 .2<br>0 0<br><!-- End of picture text --> 3
Policy Research Working Paper 9447

# **Abstract**

Flooding is among the most prevalent natural hazards affecting people around the world. This study provides a global estimate of the number of people who face the risk of intense fluvial, pluvial, or coastal flooding. The findings suggest that 1.47 billion people, or 19 percent of the world population, are directly exposed to substantial risks during 1-in-100 year flood events. The majority of flood exposed people, about 1.36 billion, are located in South and East Asia; China (329 million) and India (225 million) account for over a third of global exposure. Of the 1.47 billion people who are exposed to flood risk, 89 percent live in low- and middle-income countries. Of the 132 million people who are estimated to live in both extreme poverty (under $1.9 per day) and in high flood risk areas, 55 percent are in Sub-Saharan Africa. About 587 million people face high flood risk, while living on less than $5.5 per day. These findings are based on high-resolution flood hazard and population maps that enable global coverage, as well as poverty estimates from the World Bank’s Global Monitoring Database of harmonized household surveys.

This paper is a product of the Climate Change Group and the Global Facility for Disaster Reduction and Recovery, and a background paper for the 2020 Poverty and Shared Prosperity Report. 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://www.worldbank.org/prwp. The authors may be contacted at jrentschler@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
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 22. Benin’s pattern of capital investment in infrastructure and that of comparator countries** Investment in infrastructure sectors as percentage of GDP, by source

<!-- Start of picture text --> 2.5 Benin LIC-Non-fragile<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Public ODA Non-OECD Private<br>% of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ Private investment includes self-financing by households.

# How much more can be done within the existing resource envelope?

About $101 million of additional resources could be recovered each year by improving efficiency (table 19). The most vexing problem is underrecovery of the costs of generated power. Every year up to $50 million is lost due to underpricing of power. Underpricing of water services is also substantial, costing Benin $11 million per year. Reducing distributional losses could save another $11 million in power and $3 million in water. Collection of bills for these services needs to be improved. Benin is losing $5 million per year in the power sector and $3 million per year in the water sector because of undercollection of bills for services rendered. Overstaffing in the ICT sector is quite significant. Cutting staff levels to an economically viable benchmark could save an estimated $17 million in ICT.

Underexecution of the capital budget does not seem to be an issue for Benin, based on fiscal data from 2000–05 gathered by the AICD, but there is evidence that the public investment program has slowed down over the past couple years. The general budget execution rate, 97 percent in 2001, dropped to just 55 percent in 2010. Of particular concern is that execution rates for investment spending are persistently below 70 percent, averaging 63.3 percent between 2008 and 2010. The execution rate is lower for donor projects (61 percent) compared with internally financed projects (69.8 percent). The low execution rates on investment projects stem from a low capacity to plan and implement projects and a low capacity in procurement. Aside from addressing low execution rates, an improved approach for a systematic and rigorous analysis and prioritization of projects is needed (World Bank 2011). Detailed data on budget execution rates in the sectors covered in this report are not available after 2006.

Of all sectors, the power sector stands to benefit the most from reducing inefficiencies, followed by water and ICT. The country’s resource envelope could be expanded by $67 million if large inefficiencies in the power sector were tackled, in particular underpricing of services.

38
# **1. Introduction**

This paper develops a Ricardian farm model that allows farmers to choose the type of farm and irrigation based on the net productivity of each choice. Although the agriculture literature has carefully developed approaches to study the adoption of irrigation technology (Caswel and Zilberman 1986; Dinar and Yaron 1990; Negri and Brooks 1990; Dinar and Zilberman 1991; Dinar, Campbell, and Zilberman 1992), the literature has not explored how adoption may be related to climate. There have been several agronomic studies in Latin America of selected crops in a selected country (Downing 1992; De Siquerira et al. 1994; Magrin et al. 1997; Hofstadter et al. 1997; Conde et al. 1997) that suggest individual crops would be sensitive to warming. But this agronomic literature does not explore how farmers themselves would react to climate change. Mathematical programming (MP) has been used to explore how predicted yield losses from climate change would cause American farmers to change crops (Adams et al. 1994) and switch between crops and livestock (Adams et al. 1999). However, the MP approach has only been developed for the US and it places all the burden of including adaptation on the analyst. To the extent that the analyst is unaware of substitutions farmers can make or is unaware of reasons farmers cannot make substitutions, there is a possibility of error.

This paper presents an alternative methodology for measuring adaptation to climate by relying on cross sectional evidence. Cross sectional evidence has been widely used to measure the link between land value (or net revenue) and climate (Mendelsohn et al. 1994; 1996; 1999; 2001; Mendelsohn and Dinar 2003; Sanghi 1998; Seo et al. 2005; Kurukulasuriya et al 2006; Kurukulasuriya and Mendelsohn 2006a; Seo and Mendelsohn 2
Figure 3.3: SOUTH AFRICA, Kernel density estimates of PF variables (without M.V in PF variables and with M.V in any PF variable) <!-- Start of picture text --> A. Sales B. Materials<br>5 10 15 20 25 5 10 15 20 25<br>Log-sales Log-materials<br>Reported sales and the rest of PF vars Reported materials and the rest of PF vars<br>Reported sales with m.v in at least one PF var Reported materials with m.v in at least one PF var<br>C. Labor D. Capital<br>5 10 15 20 5 10 15 20 25<br>Log-employment (hours per year) Log-capital stock<br>Reported employment and the rest of PF vars Reported capital and the rest of PF vars<br>Reported empl. with m.v in at least one PF var Reported capital with m.v in at least one PF var<br>.25<br>.2<br>.2<br>.15<br>.15<br>Density .1Density<br>.1<br>.05<br>.05<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> Notes: Reported X and the rest of PF variable sis the distribution of those establishments reporting all PF variables Reported X with m.v in at least one of the rest of P.F is the distribution of those establishments reporting the corresponding PF variable and also reporting at least one missing value in the remaining PF variables Epanechnikov kernel. Each point estimated within a range of 300 values.

Source: Authors’ estimations with ICSs data.

43
and forestry enterprises (Koolwal 2007). Women in Nepal lag far behind men in literacy rates and educational attainments: the gap between male and female literacy rates is about 28 percentage points and males receive almost twice as many years of schooling as females (World Bank 2005).

Figure 1 shows the age-gender pyramid for the Nepali population with superimposed proportions of individuals participating in the labor market. The pyramid shows that for ages 15 to 35, the number of females substantially exceeds the number of males. While multiple forces could be at play, work-related migration of males is clearly one of the strongest factors that influence this distribution. On average, 55 percent of adult males and 19 percent of females were engaged in market wage-earning activities in 2004. The largest proportion of workers was among respondents 20 to 35 years of age, with 58 percent being male and 22 percent female. Participation in market work by both genders declined with age.

The levels of market work participation (MWP) by prime-age females were different depending on whether the households they lived in had migrant members and received remittances or not. Figure 2 shows the estimated distribution of MWP by percentiles of per capita expenditure, net of women’s market wages and by women’s levels of education. On average, only 13 percent of women from households with migrants participated in the labor market compared with 21 percent of women who lived in households with no migrants. The gap in the level of MWP between these two groups was widening for households from the top percentiles of expenditure distribution. The right panel of Figure 2 shows the female rates of MWP by years of education. Bettereducated women had a higher propensity to work. For all educational categories, except the highest, women from non-migrant households participated in the labor market at higher rates. MWP was least for women with only one to seven years of schooling.

# **4. Theoretical Framework and Empirical Strategy**

In Nepal, work-related migration by a household member has to be planned ahead. Before migration takes place, multiple arrangements need to be made. For example, if traveling abroad, a migrant has to obtain an international passport, apply for a visa and 7
tax office in the last six months, the amount of time spent on different activities during visits to the tax office,<sup>19</sup> and the amount of time spent calculating taxes and completing tax forms. Compliance cost measurement at endline is similar to at baseline, except that the endline survey also includes time spent on tax accounting.

In both baseline and endline surveys, we measure unofficial payments using a survey question asking how often it is true that firms “in their line of business” typically have to make unofficial payments to tax officials and create a dummy variable for firms saying it is “always true” or “often true.”<sup>20</sup> Our main measure of unofficial payments in the endline survey is a list experiment (Kuklinski et al., 1997). In response to the same question about _how many_ actions on a given list a company had taken in the last year to “solve or prevent problems with the Tax Administration,” half of the respondents receive a list of four options that are neutral and legal.<sup>21</sup> The other half receive a list of five options that includes “making unofficial payments” as an additional option. The difference between the average numbers selected by firms presented with the longer list and by those presented with the shorter list gives a measure of how common it is for firms to make unofficial payments.

To measure tax payments, we aggregate monthly payments from administrative records from the tax authority to obtain the total amount of tax paid over the course of the year. Given the long tail in the distribution of taxes paid, we also use the inverse hyperbolic sine function of total taxes paid as well as the median tax amount paid in that year. As an alternative measure, we use firms’ self-reports of their tax payments in two focal months

> 19 We ask firms separately about each of the following: Time to travel (both directions); Staying in line; Attendance at tax inspector office for checking reports and submission; Staying in line to sign tax reports by a head of tax office; Obtaining tax payments reconciliation act; Calculation of tax due and filling in tax reports at tax office; and Other.

> 20 The question is worded in this manner to avoid asking firms about their own behavior as corruption is a sensitive topic.

> 21 The four items on the short list are: 1. Received help from trade associations, 2. Had detailed discussions with tax officials, 3. Provided additional documents, 4. Pursued court action.

19
terms at the country-year level so that the standard errors are robust to within-group (serial) correlation.<sup>20</sup> The mix of macro- and microeconomic data allows to examine causality. Specifically, we can exclude the possibility of reverse causality since the innovation decision of a single firm has no significant feedback effect on the aggregate level of inflation. It may of course still be possible that inflation correlates with other country-level variables that influence an establishment’s investment decision. For this reason, we include the following control variables. First, we include country fixed effects to control for any time-invariant aggregate factors that may be correlated with inflation and firms’ incentives to innovate such as countries’ level of institutional and financial development (these move slowly over time and are hence considered quasi-fixed for the WBES time horizon). We thus measure the impact of the within-country variation in inflation. Second, we control for establishment-level variables and sector fixed effects to control for firm characteristics that may be correlated with their innovation decision and aggregate inflation. Third, we control for variation in countries’ economy activities over time.

When the dependent variable is an innovation dummy, we estimate (13) using a probit regression. The main coefficient of interest ( _β_ 1) measures the impact of the within-country variation in inflation on the average innovation activity of establishments operating in the same two-digit sector within this country, after controlling for changes in the establishment-specific control variables as well as for changes in the country’s aggregate economic activity. That is, _β_ 1 measures the effect of a change in a country’s inflation rate on the establishment’s innovation activity in that country. To estimate the impact of inflation on establishments’ financing sources for their working capital, we use a quasi maximum likelihood fractional multinomial logit estimator (fmlogit), which is efficient if the dependent variable ranges between zero and one and consists of multiple shares which add up to one for each observation (Papke and Wooldridge, 1996). The excluded category is given by ‘other’ financing sources.

To test the model-specific predictions (ii)-(iv) in Proposition 1, we additionally consider

> private sector in each country and year so that controlling for country fixed effects implies that we measure to what extent establishments within a given country adjust their innovation activity, on average, if the inflation rate in that country changes over time.

> 20Our standard errors are thus not subject to the Moulton (1990) critique in situations where the dependent variable is less aggregated (firm-level) than the independent one (country-year-level).

19
- [61] World Bank [2001b], _World Development Indicators_<sup>_2001CD-ROM._Washington,DC:</sup> World Bank.

- [62] World Trade Organization [1999], "Proposals<sup>forAddressingConcernsonMarginaliza-</sup> tion of Certain Small Economies," WT/GC//W/361, Geneva,<sup>October12.</sup>

- [63] World Trade Organization [2000], "Implementation<sup>of SpecialandDifferentialTreat-</sup> ment Provisions in WVTO Agreements and<sup>Decisions:NotebytheSecretariat,"</sup> WT/COMTD/W/77 (October 25), Geneva.

27
**Figure**<sup>**1: Mortality From Waterborne Disease**</sup>

<!-- Start of picture text --> _^~~~~~~-,<br>X<br>0 -84<br>843 - 2340--~4~ -<br>2340 -134<br>- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~~~~~-<br>18343 - 73257<br>_73257 - 336819<br>.n a. .<br>Source: Wang, et al. (2003)<br><!-- End of picture text --> 32
groups aged between 25 and 54, controlling for individual characteristics.<sup>6</sup> Another factor which warrants further research is on duration of youth unemployment. This is high in many countries, but is also often of short duration compared to older workers.

<!-- Start of picture text --> Figure 14: Preference of Enterprises for Age of Workers<br>120<br>100<br>80<br>60 79.26 82.35<br>Percent Enterprises<br>40<br>20<br>18.28 12.94<br>0 2.46 4.71<br>Formal Informal<br>Older (> 30 Years) Younger (< 30 years) No difference<br>Source: UES, 2004<br><!-- End of picture text --> 39. ( _ii) Recruiting Methods:_ Results of the survey indicate that the predominant source of recruiting employees is through informal means – family, friends, or word of mouth. Hardly any enterprises – formal or informal - use formal methods of hiring, again pointing to pervasive informality even in formal enterprises.

**<u>Table 7: Preferred hiring methods</u>**

|**Hiring Method**|**% of Formal**|**% of Informal**|
|---|---|---|
|Consulting workers/family/friends/word of mouth|75.9|94.6|
|Approaching educational institutions|0.7|3.6|
|Advertising|0.1|0.0|
|Acombinationof recruitingmethods above|0.1||
|Other|2.0|5.4|

Note – option of more than one method, and some enterprises failed to answer. Source: UES, 2004 40. (c) <u>Startup Capital and Insights into Profitability:</u> About one quarter of formal and almost 90 percent of informal firms have startup capital of less than USD 1,000 (Figure 15). This confirms the overall picture of very small enterprises, and greater similarities rather than differences between registered and unregistered (formal and informal) enterprises. Even the sources of startup capital indicate a remarkable degree of homogeneity among the two types of enterprises. Only about seven percent of either category use non-personal or family resources as startup capital, indicating that institutional sources are almost non-existent for the purposes for financing these ventures.

> 6 See Nguyen and Subbarao (forthcoming) on youth in TL.

19