rafmacalaba/datause-encoder-data huggingface dataset:
# **2 Causes of increased impacts**

**The situation described above has emphasized the need to analyze the causes of flooding and to understand what can be done to reduce impacts.** This calls for an investigation in the field of physical and anthropogenic issues related to urban flooding. Impact from flooding in the urban environment is caused by the action of hazard on exposed and vulnerable receptors. Increases in impacts from flooding can result from increases in hazard, in the exposure of populations and their assets, or in the vulnerability of these exposed populations and assets to flooding. Recent increases in impacts observed by sources such as the CRED database are seen to be fuelled by changes in all three underlying factors. Hazard, exposure and vulnerability all appear to be increasing and are likely to continue to do so in the future unless active steps are taken to prevent this. Table 2 shows the major factors underlying increases in hazard, exposure and vulnerability of urban populations to flooding. Some of these factors affect more than one underlying driver, but in subtly different ways. For example, urban expansion affects the flood hazard by widening the area of impermeable surface and increasing runoff while simultaneously increasing the exposure by increasing the development outside existing defenses. Increases in urban density, however, often expose more receptors to overtopping of existing defenses while also making areas more vulnerable to damage by higher volumes of flood debris.

**Table 2: Causes of changes in hazard, exposure and vulnerability**

|Hazard|Exposure|Vulnerability|
|---|---|---|
|Climate change and variability<br>Increased rainfall|Larger population in urban areas<br>Higher urban density|Changing wealth/ poverty of population|
|Sea level rise|Development outside|Changing demographics of populations|
|Increased storminess|defenses||
|Changing rainfall patterns|Uncontrolled development|Building design without regard to flood<br>risk|
|Land use change|Lack of operations and<br>maintenance|Lack of preparedness|
|Urban expansion|||
|Decreased permeability<br>Poor drainage|Reliance on insurance and aid|Over reliance on defenses|
|Urban microclimate|Land use change|Poor operations and maintenance|
|Ageing infrastructure||Urban density|
|Land subsidence||Increased efficiency and just in time<br>management|

16
Table 6.7. Poverty rate transition matrix estimates by the unweighted, current UNPS, and calibrated GWSM base estimators

||Unweighted|2009/15<br>UNPS|<br>Calibrated<br>GWSM base|2<br>Unweighte<br>d|013/15<br>UNPS|Calibrate<br>d GWSM<br>base|
|---|---|---|---|---|---|---|
|In non-poor HH in 2009|||||||
|(or 2013) and 2015|63.1|64.8|63.6|65.0|66.5|65.4|
|In non-poor HH in 2009|||||||
|(or 2013) and poor HH<br>2015|12.3|12.1|13.0|8.3|8.1|8.3|
|In poor HH in 2009 (or<br>2013) and poor HH<br>2015|9.6|9.0|8.4|12.2|11.2|11.7|
|In poor HH in 2009 (or|||||||
|2013) and non-poor HH<br>2015|15.0|14.2|15.1|14.4|14.1|14.5|

Table 6.8 shows the transition matrix for the area of residence, for which the 2009/2015 panel estimates are greater than 2013/2015 estimates. The overestimations of poor people suggest that non-poor individuals, especially in rural areas, are more dynamic and tend to move out of the UNPS sample more frequently than poor individuals. In the 20013/2015 panels, figures stabilize. All statistics are consistent with the national-level estimates from the UNPS 2015 cross-sectional sample. The refresh of the sample in 2013/14 seems to have mitigated the attrition of non-poor individuals. Rural and urban areas of residence show the same trends. Comparing the percent relative variance among the three estimates of the poverty headcount ratio, two from the longitudinal estimates and one from the cross-sectional estimator, we note that for the rural sub-population, the UNPS estimators has 5.3% of percent relative variance whilethe calibrated GWSM estimator has 3.5%. The estimates for the urban sub-population show the UNPS estimators has 0.6% percent relative variance, the calibrated GWSM estimator has 3.0%, while the unweighted has 5.4%. So in this case we are not able to indicate which one is the more stable estimator.

31
Lambsdorff, J.G. 2006. Consequences and causes of corruption: What do we know from a cross-section of countries? In Susan Rose-Ackerman (Ed), _The International Handbook on the Economics of Corruption_ , Edward Elgar Publishing.

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begin, and more likely to experience peaceful settlement attempts if claims do begin, between two democracies as opposed to dyads with less democratic forms of government. Tir and Ackerman (2009) make a similar conjecture and find that dyads with joint democracies are more likely to negotiate water treaties.

Despite this democratic peace rationale, which suggests that democratic countries are better stewards of the environment and will be more likely to cooperate with other democracies, the literature has also found insignificant and even negative and significant results (Brochmann 2012).

We test the democracy argument and calculate a combined democracy score for the relevant period of each dyad ( **_Democracy_** ) based on the Polity IV database (Marshall, Gurr, and Jaggers, 2013), which ranges from -10 for institutionalized autocracies to 10 for institutionalized democracies. The data for the democracy variable are derived for the 1945-2008 period.

# _<u>Overall Relations: Alliances</u>_

Overall political relations between countries should also affect their hydropolitical relations (Yoffe, Wolf, and Giordano, 2003: 1117; Brochmann and Hensel, 2009: 415). We use measures of a history of alliances to proxy for overall relations between states sharing a river basin.

Following Tir and Ackermann (2009), we expect states with common security interests to form military alliances, which afford them the opportunity to enhance security, stability and order. In turn, these shared interests can lead to an increase in mutual trust and result in the attainment of additional objectives in other realms (e.g. environmental) (Keohane 1984). We therefore hypothesize that countries with an overall robust history of alliances will be more likely to experience cooperative events and less likely to experience conflict. The intensity of these events should also be affected. To measure the existence and robustness of alliances, we use the Correlates of War (COW) data set (Militarized Interstate Disputes, v3.1). We include a dummy variable ( **_Alliance_** ) depicting whether an alliance exists between the riparians over the respective period of the dyad. The data for the alliance variable are derived for the 1945-2008 period.

# _<u>Power and Wealth</u>_

The literature provides a variety of views relating to the role of power in international hydropolitics. Some authors have claimed that an asymmetric inter-riparian power relationship impedes international cooperation over shared rivers (Hijri and Gray, 1998; Just and Netanyahu, 1998). Others (particularly those echoing neorealist assumptions) have argued that hegemony, or 10
**Table 4: Girls' Enrollment - Grade 5**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||OLS|FE|FE + Controls|Excluding mosque and<br>primaryschools|Excluding mosque and<br>primary schools+Multan<br>+Khanewal|
|Stipend District|-3.185***|||||
||(0.105)|||||
|2004|0.156***|0.242***|0.377***|0.739**|0.756**|
||(0.0397)|(0.0394)|(0.0417)|(0.273)|(0.289)|
|2005|0.338***|0.411***|0.545***|1.460***|1.494***|
||(0.0433)|(0.0427)|(0.0439)|(0.281)|(0.297)|
|2006|0.364***|0.206***|0.210***|0.0838|0.0873|
||(0.0460)|(0.0447)|(0.0452)|(0.172)|(0.171)|
|2007|-0.148***|-0.0567|-0.0459|-0.757***|-0.753***|
||(0.0440)|(0.0436)|(0.0448)|(0.171)|(0.173)|
|2008|0.231***|0.320***|0.332***|-0.0865|-0.0729|
||(0.0440)|(0.0436)|(0.0451)|(0.169)|(0.174)|
|Stipend District * 2004|0.175***|0.163**|0.165***|0.252|0.356|
||(0.0501)|(0.0498)|(0.0496)|(0.199)|(0.209)|
|Stipend District * 2005|0.0425|0.0596|0.0618|0.0304|0.131|
||(0.0543)|(0.0531)|(0.0527)|(0.216)|(0.226)|
|Stipend District * 2006|0.319***|0.467***|0.464***|1.011***|1.055***|
||(0.0592)|(0.0581)|(0.0581)|(0.244)|(0.252)|
|Stipend District * 2007|0.835***|0.820***|0.820***|1.781***|1.831***|
||(0.0561)|(0.0557)|(0.0558)|(0.224)|(0.233)|
|Stipend District * 2008|0.839***|0.826***|0.824***|1.616***|1.640***|
||(0.0580)|(0.0577)|(0.0577)|(0.233)|(0.246)|
# **S.** Investuent in Vocational and **Technical** Education

To see the implications of the above findings for educational policy in Peru requires that the data on marginal costs and marginal returns be placed in juxtaposition. That the returns to a year of VTE and a year of general education are found to be essentially the same implies one thing if VTE is more expensive to provide than general education and quite another if the unit costs of the two are much the same. Table 12 summarizes the findings from earlier sections on the costs and returns to VTE and general education in Peru.

The comparison between costs and returns is clear-cut in the case of post-secondary education. Here, in the four locational/sectoral samples the return to a year of post-secondary nonuniversity education varies between 901 and 1101 of the return to a year of university education. Averaging across the four samples, one may conclude that the return to VTE in the country as a whole is just slightly below the return to university education. This is quite consistent with the information on costs, taken from table 11 in the previous section. On a year by year basis, unit costs in post-secondary nonuniversity education have varied as a percentage of unit costs in university education from a low of 601 to a high of 1201 and averaged about 80x. In comparing costs and benefits, one can say that the competition' between VTE and general education at the post-secondary level amounts to a virtual draw.

The situation with regard to secondary education is more complicated. In terms of averages, once again the picture suggests a high degree of equilibrium between the additional costs and additional returns of VTE relative to general education. Both costs and returns are approximately 10 higher on average. For secondary education, however, the data points are very dispersed. For the self-employed in Lima, though the difference is not statistically significant, each year of VTE is associated with a proportionate increase in earnings three and a half times greater than a corresponding year of general education. On the other hand, for the self-employed in OUAs, VTE is negatively related to earnings (though not significantly so) whereas general education yields a moderately high return. In between, for both groups of wage workers, the marginal return to VTE is just slightly below the return to general education.

The pzttern of unit costs in secondary education, as the reader will recall, is even less stable. Although the figures show considerable uniformity for the years between 1970 and 1977 (the period for which th¶e 1.1:1 ratio -- expenditure on secondary technical education to expenditure on secondary general -- has been derived), the data for the years before 1970 and years since 1977 show VTE to be much more expensive than this, at least twice as much relative to general education. Disregarding the earliest period -- which, even if the figures are accurate, is now "ancient history' -- one must consider the implications of the very latest figures.

28
firms and not-for-profit organizations engaged in production. The public sector relates to organizations not engaged in production but belonging to the executive branch of governments.

An additional category of the sources of R&D funding is foreign financing or “funding from abroad.” The final number of observations on the structure of R&D is broken down as follows:

- R&D funded by the productive sector: 1143 observations

- R&D funded by the public sector: 924

- R&D funded from abroad: 924

- R&D performed by the productive sector: 878

- R&D performed by the public sector: 872

- R&D performed by higher education (as percent of total expenditure): 1020

# _C. R&D personnel_

The number of scientists and engineers working in R&D activities is another proxy for innovation inputs. Some of UNESCO’s yearbooks had them classified under “researchers.” As with R&D investment, we had difficulties when we had two different sources for a country with different numbers for a given year. This was common for some Latin American countries when we used RICYT in addition to UNESCO since they use slightly different definitions (see Annex). Again, we examined the time variation of the resulting series to ensure consistency. The total number of observations obtained was 1,393.

6
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not available, which is qualitatively consistent with the empirical drop in borrowing associated with the use of the employment program documented in the empirical section.

In sum, the model yields four lessons. First, the aggregate expected credit loss and the government’s burden remain limited even after accounting for firms’ and banks’ endogenous credit response and the general equilibrium effects of the credit policy. We obtain comparable theoretical and empirical counterfactual results. Second, although the credit guarantee raises credit and default, the interest rate cap constrains them. Eliminating the cap increases credit and also expected credit losses. An analogous empirical counterfactual that allows firms in default to receive credit produces a similar outcome. An additional insight concerns endogenous recovery rates under default, which further reduce the government’s burden. Given that default and interest rates are comparable in the data and banks face a funding cost, the model implies that recovery rates must be substantial for lenders to break even. Third, when the lower interest rate cap is absent, credit and credit loss are higher in the theoretical than in the empirical counterfactuals because banks are endogenously willing to lend more to high-risk firms by charging a higher interest rate. The resulting increase in default probability is similar to that observed under a systemic shock. Lastly, under a systemic shock such as the Global Financial Crisis, risks rise sharply, with expected credit losses more than doubling relative to normal times.

# **8 Conclusions**

This paper uses a large-scale episode of a credit program and an employment program together with unique financial and real data for the universe of firms and banks in Chile to shed new light on how these policies influence the distribution of credit and the implied potential financial risks. The programs give different incentives to firms: firms internalize the cost of using the employment program, but do not do so for the credit program. As a result, higher-risk firms disproportionately borrow through the credit program, which leads to a rapid and substantial increase in indebtedness across a broad class of firms. Still, most credit volume is granted to large firms, which have a significant weight on the macroeconomic allocation.

Whereas our findings are based on the COVID-19 pandemic, we can draw more general lessons about circumstances and policy actions that can limit risk while broadly expanding credit. Although loose credit conditions inevitably generate incentives for risky firms to obtain credit at low cost, selection can also be mitigated by design or in practice. Firms with 32
_For labor or for divorce?_

# **Table 8 - Results decomposition and sensitivity checks for labor market outcomes of unilateral divorce reform by marital status**

|||**Iftida’ effe**|**ct in Jordan**|||**Khul' effect in**|**Egypt Arab.**|**Rep.**||**Chikak' eff**|**ect in Morocc**|**o**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
||Window 1|: 1997 to 2007|Window|2: 1992 to 2012|Window 1|: 1992 to 2005|Window 2|: 1992 to 2014|Window 1|: 1992 to 2007|Window|2: 1992 to 2014|
||Married|Divorced|Married|Divorced|Married|Divorced|Married|Divorced|Married|Divorced|Married|Divorced|
|Treatment x child age cutoff|<br>0.166|0.0477|0.0195*|0.0460|0.0177|-0.0325|0.0271**|-0.00516|0.0379*|0.0584|-0.0245**|0.0460|
||(0.0125)|(1.12)|(1.75)|(1.50)|(1.43)|(-0.61)|(3.04)|(-0.18)|(2.33)|(0.87)|(-3.18)|(1.50)|
|Household Size|-0.0033|-0.00583|-0.00167|0.0131|-0.00566|-0.0199|-0.000743|-0.0431**|-0.00398|0.00253|<br>0.00724|0.0131|
||(0.0087)|(-0.20)|(-0.27)|(0.59)|(-0.78)|(-0.79)|(-0.13)|(-2.92)|(-0.53)|(0.12)|(1.00)|(0.59)|
|World Bank Law Index|0.0534**|0.0262|0.0189**|0.00523|0.0701***|0.0228|0.00470|0.0362|-0.00560|-0.0162|0.00658***|<br>0.00523|
||(0.0174)|(0.46)|(6.40)|(0.65)|(6.12)|(0.17)|(0.61)|(1.17)|(-1.85)|(-1.27)|(17.51)|(0.65)|
|Observations|10124|290|19295|670|18838|743|29589|1333|8184|548|17012|670|
|Mean of outcome variable|0.135|0.127|0.137|0.155|0.23|0.32|0.22|0.30|0.214|0.47|0.259|0.53|
|WB p-value|0.314|0.608|0.0940|0.262|0.396|0.460|0.006|0.260|0.738|0.158|<br>0.002|0.262|
|N clusters|42|28|59|45|45|40|58|43|44|34|54|43|
|Time trends|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Authors calculations. Notes: The dependent variable is a binary indicator for whether the interviewed woman is currently working. The estimation samples are restricted mothers whose youngest child's age is around 5 years window around the custody cutoff and whom did not have children after the reform. Wild Bootstrap p-value for 1000 reps. *p<0.10, **p<0.05, ***p<0.01.

28
shocks lead to a 9 ppt increase in STI rates for unmarried women, while shocks have a smaller and insignificant effect on STIs for married women (Shock + Shock X Married: p-value = .223). While we cannot reject the null that the effects of shocks are the same for both unmarried and married women, these results suggest that shocks have a stronger effect on unmarried women.

The second group we examine are women in the lowest SES groups. Using the SES groupings discussed in the previous section, we find that women in the lowest SES group are 12 ppt more likely to contract an STI after experiencing a shock (Column 7). For women in the higher SES group, we are unable to reject the null that shocks lead to any change in STI rates (Shock + Shock X High SES: p-value=.512). The effect of shocks on women in the higher SES group is also significantly different (at the 10% level) compared to those in the lowest SES group. We note that unmarried women are more likely to be in the lowest SES group, but given smaller cell sizes, we are unable to differentiate the effects of shocks by both marital status and SES groupings.

# _Self-Reported Sexual Behavior_

We now examine whether the effects of shocks on self-reported sexual behavior is consistent with what we find with STIs. We note that the questionnaires used in the study collected a wide range of sexual behaviors. Since, unlike STIs, sexual behaviors are self-reported and therefore more prone to bias, we use these results to provide corroborating evidence to our main STI results.

We select three outcomes from the self-reported sexual data based on the following criteria. The first two outcomes, unprotected sex and the number of partners, are standard measures commonly used in the literature on sexual behavior. The third outcome, whether a woman received cash or gifts in exchange for sex, is one which will provide evidence of the behavioral response we believe links shocks to increased STI risk (transactional sex).

In Table 4, we examine the effects of shocks on unprotected sex (Panel A), number of partners (Panel B), and paid sex (Panel C). We find that shocks lead to significant 13