rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2.  Fraction of Coverage in Catastrophe Reinsurance and ILS Markets, by Geographic Zone**

<!-- Start of picture text --> US<br>Europe<br>Japan<br>Other<br>0% 10% 20% 30% 40% 50% 60%<br>ILS Limits by zone, all CAT ILS issued in calendar 2006<br>Catastrophe Reinsurance Protection Purchased 2006<br><!-- End of picture text --> Source: Lane Financial LLC (2008).

Among the pitfalls of the proposed approach is the extrapolation from the ILS market to the catastrophe reinsurance market as a whole. And while it is true that the risks covered by the ILS market are similar to those covered in the market as a whole, the ILS market is less rich and deep compared to the traditional reinsurance market. Finally, of course, the capital standards of the ILS market are quite different from the traditional market. All ILS are fully collateralized; traditional reinsurance is collateralized to some acceptable level of certainty (usually 1 in 250 years, i.e., 0.4%). It is acceptable, that is, to cedants, shareholders and rating agencies. ILS risk assumption is unleveraged; traditional market risk assumption is leveraged to some degree. Inferences can nevertheless be made concerning the risk profiles of the ILS market which are comparable to the market as a whole.

The paper proceeds in four parts as follows. Section 2 examines the issue prices of 247 ILS tranches and their respective risk statistics since 1997. Importantly, the risk statistics are broken down into component perils since peril diversity can be a determinant of capital use. Prices are then deflated by an index of general market prices to see if the cyclic effect of prices can be isolated.<sup>3</sup> In Section 3, price relationships are examined without cyclic effects. Ideally this would be done by simultaneously examining all the ILS issued on a particular date. Given the small number of ILS this simultaneity of issue is a rarity<sup>4</sup> . However, an insight can be made with simultaneous evaluation, if not simultaneous issue. This is essentially the information that comes from the secondary market. Two cross section analyses are conducted at different points in the cycle: the most recent hard part in mid-2006 and the most recent part at the end of the first quarter of 2008. The ILS market is not the only part of the catastrophe market that is visible. Since the mid-1990s another vehicle that trades non-traditionally is the Industry Loss Warranty (ILW) market<sup>5</sup> . This market resides somewhere between the traditional market and the securities market in a series of private transactions. ILWs have become sufficiently commonplace for brokers to exist and provide potential participants with price “indication” sheets for a range of

> 3 See Annex 1 for a description of the data used in this analysis.

> 4 The closest the market ever came to a simultaneous issue of many securities at once was the Successor series of Swiss Re in June 2006.

> 5  The ILW market is also known interchangeably as the OLW market, meaning Original Market Loss Warranty.

4
In our model, between about 700 (Ghana) and 1000 (Kenya) settlements with known or estimated population represent spatially distributed electricity demand points.<sup>6</sup> These settlements are modeled as nodes in a transmission and distribution grid. Residual rural populations are identified from high resolution population maps (ORNL 2008). The residual populations are allocated to the closest settlements using a simple Thiessen polygon approach. Assuming that the entire population lives in settlements will yield a lower bound estimate for grid distribution costs at the margin, but does not significantly influence stand-alone cost estimates. Dividing population assigned to each settlement by average household size yields the number of households. Multiplication by the targeted energy supply provides the estimate of total demand at each location.

Our model estimates the costs of providing electricity to all households in a country. This must be the ultimate goal in any country, but is clearly unrealistic in the short or even medium term in Sub-Saharan Africa. The average access rate across subnational areas in a sample of African countries is 23 percent, with half of all areas below 11 percent (DHS 2009). Current operational or policy goals are relatively modest. In one scenario (UN-ENERGY/Africa, 2007), USD 4 billion invested annually in the energy sector will supply approximately half of African households with electricity by 2030.

# **3.2. The Economics of Network Expansion**

Previous modeling of electricity networks by Bergey, et al. (2003 a,b) has considered the optimal partition of a national monopoly grid into competitive power districts. We extend this approach to include non-grid service options. Also related are the approaches to optimal planning in Hongwei, et al. (1996), who focus on the locations and sizes of power grid substations; and Klose and Drexl (2005), who review more general algorithms for locating facilities and allocating customers in product distribution systems. Most closely related to our work are a study by Parshall et al (2009) and a companion paper by Zvoleff et al. (2009). They propose a comprehensive engineering-

> 6 Settlement locations are from the Global Insights Plus v.6.1 database (Europa Technologies; www.europa.uk.com).

8
**Figure III-1: Medium-term Credit Cycle and Credit Cycle Peaks**

<!-- Start of picture text --> SAU: Medium Term Credit Cycle and Credit Cycle Peaks EGY: Medium Term Credit Cycle and Credit Cycle Peaks<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br>Medium-term Credit Cycle Peak Medium-term Credit Cycle Peak<br>USA: Medium Term Credit Cycle and Credit Cycle Peaks<br>1967 1977 1987 1997 2007 2017<br>year<br>Medium-term Credit Cycle Peak<br>15 1 20 1<br>10 .8 .8<br>10<br>5 .6 .6<br>0<br>percent0 .4 percent .4<br>local_peak_SAU local_peak_EGY<br>-5 -10<br>.2 .2<br>-10<br>0 -20 0<br>1<br>5<br>.8<br>.6<br>0<br>percent .4<br>local_peak_USA<br>.2<br>-5<br>0<br><!-- End of picture text --> As the second step, we investigate the relation between the peaks in credit cycles and recessions, measured by the GDP growth of the countries in our sample. According to the literature for Western countries and Japan, every peak in a credit cycle is followed by a period of recession with a little lag.

To check the relation between credit cycles and the GDP growth of the countries, Figure III-2 demonstrates the credit cycle peaks with the growth of GDP. In the United States, after every peak in credit cycle a sharp drop in GDP growth follows even if the GDP growth is quite high before and during the peak. Nonetheless, in Saudi Arabia and Egypt the drop in GDP growth after the credit cycle peaks is not as evident and it is not clear whether after the peak GDP growth falls significantly.

10
between employment of men and the work status of wives in urban areas, but a strong and roughly equal positive association between the employment of husbands and wives in rural Korea, which likely reflects joint decisions to retire from farming among older rural Koreans.

**_Employment and Spouse Health Status_** . Inclusion of Spouse Difficulty-ADL and Spouse Unable-ADL z-scores among covariates in model (3), and presented in Table 3, allow us to shed light on the relationship between spouse health status and respondent work decisions. Similar to the relationship with own health status, we find no correlation between spouse health status and employment in urban China. In rural China, by contrast, increases in the number of functions that a spouse has difficulty performing (Spouse Difficulty-ADL z-score) are positively associated with labor supply, suggesting an added worker effect dominates. A one standard deviation increase in the Spouse Difficulty-ADL z-score is associated with 3.1 and 5.2 percent increases that men and women, respectively, will be employed. The gender difference in the added worker effect, evident in rural areas of China and Indonesia, stands in contrast to the results using data from the US Health and Retirement Study, which suggest that shocks to health of a spouse have a small positive effect on labor force participation of men and no effect on women (Coile, 2004). While results for the US can be interpreted as suggesting that there is little opportunity to smooth income loss associated with health shocks through the labor market (as Coile concludes), the employment response for both genders in in rural China and men in rural Indonesia likely reflects both a stronger need to smooth income and fewer constraints to returning to work on the family farm than when looking for formal wage work.

# **Conclusions**

As in other regions of the developing and developed world, population aging in China raises the prospect that both formal and informal mechanisms for supporting the elderly will come under strain over the next twenty years. In common with Indonesia and other developing countries, however, China is experiencing population aging at lower income levels and prior to the extension of pensions to rural residents and to urban residents in the informal sector. In a sense, China has two retirement systems: a _formal_ system, under which urban employees receive generous pensions and face mandatory retirement by 60, and an _informal_ system, under which rural residents and individuals in the urban informal sector rely on family support in old age and have much longer working lives. The retirement patterns presented in this paper illuminate the 22
# **B. ECONOMETRIC METHODOLOGY**

The model discussed above is estimated using the following general framework in which the real effective exchange rate in the CEMAC and UEMOA zones is viewed as a function of a vector of fundamentals- terms of trade, openness of the economy, net aid inflows, and government consumption. The dependent variable of the analysis is the log of the real effective exchange rate, essentially a trade-weighted index derived from 1970 to 2005. All variables were put in logarithmic form to ensure that parameters represented elasticities, and annual time series data were used to estimate the following model:

# (3) RER = f (TOT, OPEN, AID, DUMMY)

The two principal techniques for the time series estimation of the long-run relationship and cointegration of two variables are the Engel-Granger (1987) and the Johansen methodology (1988), the key diagnostic tools to test for systemic patterns in time series. The essence of cointegration is that even if certain time series themselves are nonstationary, a linear combination of them will be stationary, and that there is a long-run co-movement between the variables. The Engel-Granger technique is a two-stage method that examines cointegration and is based on the assumption that a single cointegration relationship exists, which can be estimated through ordinary least squares.

First, standard unit root tests were performed on all the macroeconomic variables prior to the estimation to ensure that the two variables are integrated of the same order because traditional estimation and inference procedures based on traditional t and F tests do not apply in the presence of nonstationary variables. The optimal lag lengths were determined using the Akaike Criterion. The Augmented Dickey-Fuller test was used to examine whether one can reject the null of a unit root at traditional significance levels. Econometric tests were conducted in order to assess whether the variables were first-difference stationary.

Secondly, the following the Engle-Granger methodology, the long-run equilibrium equation was tested using ordinary least squares (OLS) relating the levels of the real effective exchange rate to the economic fundamentals that affect it (Seddighi, Lawler, and Katos, 2000).

The general “cointegrating regression” is:

- (4)   Yt  =  β0 +  β1 Xt    +  ε t

Two models were specified, one excluding the dummy variable and one including the dummy:

- (5) _log_ RERt = β0 +  β1 log TOTt    + β2 log OPEN t + β3 log AID t   + β4 DUMMY t<sup>+ RESIDUAL</sup> t

Thirdly, the residuals, ε t , of the estimated OLS long-run relationships were saved from the previous equation as an estimate of the equilibrium error , ε t and subsequently, tested for stationarity using the Augmented Dickey-Fuller test. It is important to establish the stationarity condition for residuals, which would be an indication of the existence of long-run relationships between these variables. In the absence of cointegrating relationships, OLS estimations produce spurious regressions, but if cointegration does exist, then a simple OLS regression can provide consistent estimates of long-run parameters.

16
dropping by 1.6 percentage points to 4.9%. The second and third columns of the table do show, however, that the impact of the agreement is likely to be attenuated significantly by determining a list of products to be excluded from liberalization. Depending on the size of the exclusion list, our model projects a reduction in total revenues from import taxes of between 3.2–5.1%. Given that an appropriate sequencing of the reform may allow the spread of these losses over the course of a 20-year period, we do not regard these losses as excessive.

Irrespective of the revenue implications of the agreement, the fall in prices of imported goods in response to tariff liberalization is advantageous for consumers to the extent that these tariffs are not protecting significant amounts of production and employment.<sup>21</sup> It is therefore helpful to examine the sectors and subsectors that will experience the largest price changes as a result of the EUSAA.

Recall, in our model, the implementation of the EUSAA impacts import prices through two channels: first, directly via the reduction in tariffs (affecting demand); and second, indirectly via the change in the share of EU-sourced imports in overall imports of the product (affecting exporter substitution). The impact of the EUSAA, as measured by these changes in protection and import prices, is highly asymmetric. Table 4 lists the 20 (ISIC) subsectors that will experience the largest product price declines as a result of the EUSAA, and also aggregates this information for each ISIC 1-digit sector. The calculations for this exercise are performed for the “no exclusion list” (upper panel) and “20 percent exclusion list” (lower panel) scenarios. The results indicate that the extent to which subsectors are impacted by the agreement in terms of changes in protection and import prices is extremely heterogeneous across subsectors. For example, whereas subsector 742 (architectural, engineering and other technical activities) experiences a reduction in protection from 30% to zero and an average import price decline of about 34.6% when no exclusion list is used, most other subsectors are affected only marginally.

Table 4 also shows that a given choice of exclusion list can greatly impact the relative vulnerability of different subsectors. To continue with our example, subsector 742 is not even among the 20 most affected sectors when the 20% exclusion list is used. Similarly, whereas sector D (manufacturing) experiences the largest import price change in the scenario without an exclusion list, it is much better shielded from the impact of reform when the 20% exclusion list is used; sector C (mining and quarrying) becomes the sector experiencing the largest reduction in import prices in the latter case.

We now consider the more realistic scenario of a sequential phased-in agreement (Ta-

> 21If sufficiently detailed sectoral production data was available, it would be straightforward to determine likely changes in employment and production in response to the reform, but that exercise would take us beyond the scope of this paper.

15
between primary and secondary education. In the case of higher education and training, however, the norm is specified in terms of number of students. Health expenditures too are determined on a per capita basis. Administrative expenditures are determined on the basis of number of employees, although all provincial recruitment and the salary scales have to be approved by the Central government.

Another problem is that resource constraints mean that the norms are set too low to provide reasonable levels of service. In the case of education, for example, on average the expenditure per student in 1991-92 was only D 106,800 and expenditure per person of schoolgoing aoe was just only D 57,600 (about U.S. $6). Since teachers' salaries itself accounted for about 80 per cent of this amount, little was left for books, furniture and educational aids. Moreover, alEhough the norm system in effect allocates expenditure resources among the provinces, it does not seem to do so in a very satisfactory way. In Soc Trang, one of the poor provinces. for instance, per student expenditure of D 19,600 was actually the lowest. Indeed, all spending on education was less than 2 per cent of GDP, or less than half the average ratio in the group of developing countries with less than $ 300 per capita income.'<sup>6</sup> The situation is no different in respect of economic services. In Thanh Hoa province. where cyclones are a recurring phenomenon, the sanctioned budget for strengthening and maintaining 937 kms of embankments is generally only about 10 per cent of what is requested by the province - which itself is only a fraction of what is actually needed for its proper maintenance. Even after mobilizing conmnunity help by way of voluntary labor (through embankment protection committee), the province is thus able to select only the very weakest segments of embanlknent every year for strengthening. The story appears to be the same in other sectors and in other provinces as well.

> 16 The estimate for the developing countries is for 1987, and is taken from the Statistical Year Book, 1989, UNESCO.

28
|= 0_, X_)<br>(5)<br>IV<br>0.077**<br>(0.039)<br>-0.044<br>(0.033)<br>0.015<br>(0.034)<br>0.022<br>(0.042)<br>-0.006<br>(0.028)<br>0.015<br>(0.010)<br>0.007<br>(0.016)<br>-0.028<br>(0.027)<br>-0.033<br>(0.023)<br>Yes<br>-|ted to control<br>l groups using<br>ing campaign<br>l3). Standard<br>e respondent,<br>characteristics<br>dicator on the<br>variables. The<br>h smaller than|
|---|---|
|1_, X_)_−E_(_Y |T_<br>(4)<br>OLS<br>0.062*<br>(0.032)<br>-0.027<br>(0.024)<br>0.014<br>(0.026)<br>0.019<br>(0.031)<br>-0.001<br>(0.023)<br>0.010<br>(0.008)<br>0.005<br>(0.013)<br>-0.021<br>(0.020)<br>-0.022<br>(0.018)<br>Yes<br>0.5405|ple means restric<br>tment and contro<br>eceived the spray<br>indicator (mode<br>rriage status of th<br>mies and village<br>f the treatment in<br>on the selected<br>variables is muc|
|_E_(_Y |T_ =<br>(3)<br>OLS<br>0.031<br>(0.032)<br>-0.011<br>(0.020)<br>0.030<br>(0.031)<br>0.029<br>(0.039)<br>0.015<br>(0.035)<br>0.004<br>(0.009)<br>0.012<br>(0.014)<br>-0.006<br>(0.018)<br>-0.014<br>(0.021)<br>No<br>0.8851|(2) report sam<br>e between trea<br>port to have r<br>eatment group<br>cation and ma<br>sub-zone dum<br>it regression o<br>he coeffcients<br>wers for these|
|(2)<br>Treatment<br>0.807<br>[0.395]<br>0.942<br>[0.234]<br>0.834<br>[0.372]<br>0.678<br>[0.467]<br>0.225<br>[0.418]<br>0.025<br>[0.156]<br>0.058<br>[0.234]<br>0.048<br>[0.215]<br>0.106<br>[0.309]<br>p-values =|olumns (1) and <br>ort the differenc<br>useholds who re<br>ation with the tr<br>gender, age, edu<br>characteristics,<br>ce, we run a prob<br>signifcance of t<br>non-missing ans|
|(1)<br>Control<br>0.776<br>[0.417]<br>0.953<br>[0.212]<br>0.804<br>[0.397]<br>0.649<br>[0.478]<br>0.210<br>[0.408]<br>0.021<br>[0.143]<br>0.046<br>[0.209]<br>0.054<br>[0.226]<br>0.120<br>[0.325]<br>3-9|ousehold. C<br>3) and (4) rep<br> between ho<br>gram particip<br>trols include<br>ater, dwelling<br>nt signifcan<br>for the joint<br>e sample of|
|1. Household keeps livestock_>_100m from home<br>2. Household covers stored water<br>3. Respondent does anything to prevent mosquito bites<br>4. Respondent mentions using net<br>5. Respondent mentions burning coils<br>6. Respondent mentions using spray<br>7. Respondent mentions burning animal dung<br>8. Respondent mentions burning herbs<br>9. Respondent mentions draining stagnant water<br>Controls<br>Joint tests on variables (with comparable sample size):|Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observation per h<br>and treatment group, standard deviations in brackets. Columns (<br>OLS regression (model 2). Column (5) estimates the difference<br>in the last 5 months and those who didn’t by instrumenting pro<br>errors clustered at village level are reported in parentheses. Con<br>household size, tribe and religion, information about access to w<br>(size and share of women living in the village). To control for joi<br>selected groups of variables and we report p-values of an F-test<br>frst two variables are omitted from the joint test only because th<br>for the remaining variables in the Table.|

32
high natural resources may establish sound institutions and increase their compliance with FATF recommendations, or—on the opposite—be confronted with various forms of the “Dutch disease” (e.g. Sachs & Warner, 1995); in our estimations, the former explanation seems to drive a positive impact of natural resources on the probability of Non-Cooperation. Finally, good institutions are expected to signal well-governed countries that generally meet their commitments regarding the international cooperation against IFFs. As shown by columns [1][6], whenever significant, a better institutional quality—measured by various indexes—is associated with a lower probability of Non-Cooperation. Based on propensity scores computed from Table 1, we estimate in the following the effect of being on the FATF list on tax revenues.

**Table 1. Probit estimates of the propensity score of being a Non-Cooperative country**

||**[1]**|**[2]**|**[3]**|**[4]**|**[5]**|**[6]**|
|---|---|---|---|---|---|---|
|Lag (IFFs)|0.421***<br>(0.0682)|0.415***<br>(0.0703)|0.434***<br>(0.0729)|0.359***<br>(0.0662)|0.413***<br>(0.0655)|0.375***<br>(0.0681)|
|L.inflation|0.0229*|0.0211*|0.0204|0.0309**|0.0270**|0.0224*|
||(0.0128)|(0.0125)|(0.0133)|(0.0126)|(0.0134)|(0.0118)|
|Trade|-0.00508*|-0.00634**|-0.00793**|-0.00411|-0.00446|-0.00650**|
||(0.00290)|(0.00306)|(0.00369)|(0.00270)|(0.00279)|(0.00304)|
|L.public debt|0.00422*|0.00292|0.00417*|0.00253|0.00420*|0.00432**|
||(0.00221)|(0.00216)|(0.00229)|(0.00238)|(0.00217)|(0.00216)|
|L.debt service|<br>0.0644***<br>(0.0176)|<br>0.0652***<br>(0.0173)|<br>0.0741***<br>(0.0193)|<br>0.0638***<br>(0.0174)|<br>0.0648***<br>(0.0172)|<br>0.0639***<br>(0.0170)|
|L.GDP growth|<br>0.0171<br>(0.0196)|<br>0.0187<br>(0.0200)|<br>0.0185<br>(0.0197)|<br>0.0183<br>(0.0206)|<br>0.0149<br>(0.0201)|<br>0.0215<br>(0.0200)|
|L.FDI|<br>-0.121***|<br>-0.105***|<br>-0.103***|<br>-0.0875**|<br>-0.129***|<br>-0.118***|
|L.agriculture|(0.0405)<br>0.0267**<br>(0.0127)|(0.0397)<br>0.0239**<br>(0.0113)|(0.0384)<br>0.0291***<br>(0.0102)|(0.0354)<br>0.0160<br>(0.0132)|(0.0455)<br>0.0328***<br>(0.0109)|(0.0400)<br>0.0131<br>(0.0132)|
|L.natural resources<br>Effectiveness|<br>0.0457***<br>(0.0118)<br>-0.214<br>(0.258)|<br>0.0399***<br>(0.0119)|<br>0.0351**<br>(0.0142)|<br>0.0399***<br>(0.0112)|<br>0.0509***<br>(0.0123)|<br>0.0380***<br>(0.0117)|
|Law||-0.557**<br>(0.273)|||||
|Voice|||-0.401*<br>(0.229)||||
|Stability||||-0.441***<br>(0.153)|||
|Regulatory|||||0.0686<br>(0.203)||
|Corruption||||||-0.801**<br>(0.327)|
|Constant|-5.102***<br>(0.717)|-5.094***<br>(0.743)|-5.112***<br>(0.722)|-4.788***<br>(0.706)|-5.094***<br>(0.703)|<br>-4.771***<br>(0.725)|
|N|419|419|419|419|419|419|
|Pseudo R2|0.300|0.312|0.309|0.315|0.298|0.318|

Note: Standard errors in parentheses. * _p_ <0.10, ** _p_ <0.05, *** _p_ <0.01.

9
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