rafmacalaba/datause-encoder-data huggingface dataset:
**Annex 5.7. Median distance to city center (in km) by tenure status at time of transaction**

|**Tenure status**|**Median**<br>**All plots**|**distance to city**<br>**Residential**<br>**plots**|**center**<br>**Agricultural**<br>**plots**|
|---|---|---|---|
|**No administrative**<br>**document**|33|23|38|
|**Administrative**<br>**document**|22|21|29|
|**Precarious title**|18|17|21|
|**Property title**|16|13|23|

_Source: authors’ survey, 2012_

**Annex 5.8. Tenure transition matrix – residential plots only (in %)**

|**Tenure at time of survey**<br>**Tenure at time of transaction **|**No administrative**<br>**document**|**Administrative**<br>**document**|**Precarious**<br>**title**|**Property**<br>**right title**|**Unknow**<br>**n**|**Total**|
|---|---|---|---|---|---|---|
|**No administrative document**|7.6|7.0|1.0|0.2|0.4|16.1|
|**Administrative document**|0.0|54.0|8.1|0.5|0.0|62.5|
|**Precarious title**|0.0|0.0|14.9|0.5|0.1|15.6|
|**Property title**|0.0|0.0|0.0|5.7|0.0|5.7|
|**Unknown **|0.0|0.0|0.0|0.0|0.1|0.1|
|**Total **|7.6|61.0|24.0|6.9|0.5|100.0|

_Source: authors’ survey, 2012_ 56
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**Table 7. Effect of income on log-normal location and scale parameters (mu and sigma, respectively), with regional differences controlled by dummies.** Dependent variables: mu and sigma. Observations: individual respondents in the models in the first two columns; countries in the models in the last two columns.

||Individual<br>|predictions<br>|Countr<br>|y means<br>|
|---|---|---|---|---|
|Variables|Mu|Sigma|Mu|Sigma|
|ln(GDP per capita, PPP)|0.735***|-0.0422|0.932***|-0.0268|
||(0.154)|(0.0800)|(0.157)|(0.0775)|
|Dummy: plan A|0.293***|-0.0114|0.317***|-0.000672|
||(0.0367)|(0.0185)|(0.0315)|(0.0256)|
|Dummy: Asia|0.369|-0.343**|0.894**|-0.284**|
||(0.321)|(0.140)|(0.368)|(0.131)|
|Dummy: North America|0.757**|-0.140**|0.661***|-0.104|
||(0.314)|(0.0696)|(0.253)|(0.0764)|
|Dummy: Oceania|-0.298|-0.0876|-0.141|0.0145|
||(0.475)|(0.165)|(0.251)|(0.112)|
|Country FEs?|No|No|No|No|
|Weighted regression?|Yes|Yes|No|No|
|_R_<sup>2</sup>|0.283|0.084|0.611|0.211|
|_F_statistic|33.3|7.61|21.1|3.52|
|Observations|428|428|73|73|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors  are in parentheses and were estimated jointly for the pair of equations (mu, sigma), with clustering by country. Weights: inverse number of respondents from a given country.

Returning to the analysis of WTP, the models in Table 6 did not control for the characteristics of the individual experts. The two leftmost columns in Table 8 show results for models that include such controls. In addition to PPP-adjusted GDP per capita and dummies for Plan A and the three non-European regions (as in Table 6), the models in Table 8 include controls based on all survey questions that provided objective information on expert characteristics; questions that asked experts to evaluate their own expertise were excluded. The controls included the expert’s perceived error in the Delphi estimate; the expert’s perceived difficulty with doing such valuation; the expert’s reported number of surveys, publications, reviewed papers, and benefit transfer exercises; the journals the expert reads regularly; the expert’s highest academic degree; and the expert’s gender.

With one exception, we set the controls equal to the experts’ responses. The exception was for the journals they read regularly. Instead of including dummy variables for the various 19
Reversely calculating the insurance capital required for financial robustness in 95 (99) percent of the cases over the 10-year time horizon leads to the following capital requirements per person with associated confidence intervals of the outcomes in brackets assuming a normal distribution (Table 6). Each simulation was performed 100 times and the mean and standard deviation were used to calculate the confidence levels.

Table 6: Insurance capital required for financial robustness in 95 (99) percent of the cases over the 10 year time horizon

||Back-up capital to<br>avoid insolvency<br>with 95%<br>probability<br>(kwacha/person)|Back-up capital<br>in % of premium<br>(and additional<br>back-up capital<br>necessary<br>compared to the<br>baseline case in %<br>of premium)|Back-up capital to<br>avoid insolvency<br>with 99%<br>probability<br>(kwacha/person)|Back-up capital<br>in % of premium<br>(and additional<br>back-up capital<br>necessary<br>compared to the<br>baseline case in %<br>of premium)|
|---|---|---|---|---|
|Empirical|1013.3<br>[1006.4 1020.2]|220|2179.1<br>[2166.3 2191.9]|473|
|MM5<br>near future|1078.0<br>[1070.4 1085.6]|234 (14)|2283.2<br>[2267.7 2298.7]|495 (23)|
|Precis<br>near future|1027.8<br>[1021.9 1033.7]|223 (3)|2196.2<br>[2181.2 2211.2]|476 (4)|
|MM5<br>future|3874.2<br>[3861.6 3886.8]|840 (621)|5943.5<br>[5918.9 5968.1]|1289 (871)|
|PRECIS<br>future|1473.5<br>[1466.2 1480.8]|320 (100)|2730.6<br>[2715.9 2745.3]|592 (120)|

In all cases, additional backup capital would be needed to remain solvent at the 95 (99) percent levels. Backup capital expressed in terms of premium ranges from 220% and 473% for the empirical, non-climate change case up to 840% and 1289% for the MM5 case for 2070. Generally, Precis estimates were lower than those for MM5. The additional back-up capital required as a percentage of the premium for the next 10 years compared to the empirical case is significant, if small: 14% and 23% for the 5% resp. 1% ruin probabilities for MM5, and about 3% and 4% for the Precis model. For more significant climate change (here based on the A2 scenario to occur in the distant future around 2070), these additional requirements would rise very substantially for the MM5 to 621% and 817% and substantially for the Precis to 100% and 120% of the premium.

One additional important consideration here is the confidence of these estimates. As Monte Carlo simulations are used, there is uncertainty in these results. Confidence intervals of the outcomes of the simulations are shown. No overlap (with one exception for the Precis data at the 1% level) between the empirical and modeled results occurred indicating that the differences are (mostly) significant. The analyses of mean changes, therefore, show negative effects on the insurance pool to be expected in the near future and dramatic negative effects in the distant future. In the next subsection, an analysis of mean <u>and</u> variability changes is presented, whereby variability is added in the form of sensitivity analysis.

16
# - 84 -

91. In a rigorous welfare analysis, Wilson (1988) uses a joint mode choice and work start time choice model to analyze the same set of individual **s** urveyed in a pre- and post-ALS set of data by the World Bank. His results indicate that: a) some travelers are worse off as a result of being tolled off when entering the Restricted Zone during its operating hours and are diverted onto a different time period or even onto public tsanport, and having to incur the resultant rescheduling costs which exceed the payment of the ALS fee itself; b) some motorists (who used to travel, say, just outside the restricted hours) had to suffer increased congestion as a result of those motorists who were tolled off the restricted times, and those public transport commuters who had to face a more congested public tranwsport environment following AMS would also be worse off; and c) those commuters who chose to remain _as is_ and be tolled would be worse off unless their values of time savings are sufficiently high." He concludes that a constant, flatrate toll may in fact result in a decrease in social welfare as in Nash-Bentham-Sen, contrary to the -exuberantu feeling surrounding the positive results of the AIS scheme in 1975-78. Wlon simulates the effects of making various revenue-neutal assumptions by explicitly retuning the toll revenues to the individuals in the sampled popUiation and comparing the pre- and post-AMS results. However, he acknowledges that he has not taken into account incentive effects. The setting of the AMS fee in 1975-76 may have been widely observed to be on the high side and it resulted in the underutilization of the road network in the Restrcted Zone. Subsequently, the ALS fee was lowered, on June 1, 1989. However, it is unclear how the simulation results would have panned out given a real decre in daily licenses exceeding 40%.

# E. Lessons for ImDlementng Area Licensing

92. A few lesons emerge from analyzing the Singapore ALS for the purpose of chargng for congeson: a) ALS, being fairly low cost and requiring one gantry per entry point, would be suitable for cities with limited access;6 b) ALS defers hefty investment in roads and requires " Botb intuind twy sggest tht these _bt_ are to be ctd (u a 1992).

- For a tpicl United Sats inner-city si of two sque mles and _ming_ 12 major shteets to a mile, dio toa numberof prcigpointsorgies ndod woudw be96 (or48 if half ao e-way dreeb). fdeed econoica;lly efficient, a few mmo **r** oads locdad a tb ecdon bonday could be dod off (as was don
<!-- Start of picture text --> b. Concentration shares: taxes & contributions<br>30<br>20<br>10<br>0<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Decile of Market income<br>Direct taxes Contributions Indirect taxes Market income<br>Source: Authors' estimates based on PECS 2016/2017; LFS 2017.<br>Notes:<br>[1] Concentration shares show the proportion of each tax/transfer paid by / benefitting each decile. They sum to 100%.<br>[2] Households are ranked over Market income<br>Concentration shares (%)<br><!-- End of picture text -->

# **7.2. Transfers**

**The incidence of the social protection direct transfers is higher in poorer deciles; and the largest direct transfer is the CTP.** The incidence of the CTP is largest in the first decile and decreases steeply with income (Figure 7-2a) from 56.3 percent of Market Income in decile 1 to 0.04 in decile 10; this is consistent with the fact that the CTP is targeted to the poor through a PMT. The incidence of the social protection nearcash transfers is much smaller than the CTP in all but the top decile, ranging from 4.4 in decile 1 to 0.06 in decile 10.

**Poor households receive a larger share of direct transfers.** Almost 60 percent of the direct transfers are received by decile 1 and only 1.2 percent in decile 10.

**The incidence of the indirect subsidies is small.** The electricity subsidies are visible in deciles 1 to 5 only, they start at 1.8 percent of Market Income in decile 1, decrease to 0.6 percent in decile 5, and further to 0.2 percent in decile 10. The fuel subsidies are not visible at all – they start at 0.1 percent of Market income in decile 1 and increase to 0.2 percent in decile 10.<sup>75</sup>

**The share of indirect subsidies received by decile is increasing monotonically with income.** While decile 1 receives 6.8 percent of the total indirect subsidies, decile 10 receives 16.1 percent.

**In-kind benefits from public hospital healthcare constitute a substantial benefit for households.** They range from 16.8 percent of Market Income in decile 1 to 0.7 percent in decile 10, followed by in-kind

> 75 We do not separate out the direct and indirect effects in the graph as they would be too small to be visible.

50
The Pulse of Public Opinion: Using Twitter Data to Analyze Public Perception of Reform in El Salvador Skipper Seabold Alex Rutherford `American University United Nations Global Pulse` Olivia De Backer Andrea Coppola `United Nations Global Pulse The World Bank`<sup>_∗_</sup> JEL Classification: C55,C8,H2. Keywords: Political Economy of Reform, Fuel Subsidy, Big Data

> _∗_ Corresponding author e-mail: jsseabold@gmail.com. This is a preliminary draft. The authors would like to thank Diana Lachy Castillo for translating as well as Marcelo Echague Pastore and Juliana Torres for tagging tweets.
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lags both regional nonoil-exporting economies (Jordan, 0.04 and Lebanon, 0.08) as well as oil exporters (Saudi Arabia, 0.07 and UAE, 0.20).<sup>14</sup> The dynamic changes in Syria’s export structure can also be captured by indices of revealed comparative advantage (RCA) (Balassa 1965), which capture the degree to which a country is specialized in exporting a given product relative to other countries exporting the same product. For a given country _c_ in line _k_ the index is computed as where _l ∈ L_ are the countries that export _k_ . Since (5) is the export share of the country relative to the rest of the world, a value of _RCAc,k >_ 1 ( _RCAc,k <_ 1) indicates a _revealed_ comparative advantage (no revealed comparative advantage) in line _k_ .

At the most aggregated level, Syrian RCA patterns display a rising comparative advantage in agricultural products, and a concomitant decline in mineral fuels.<sup>15</sup> These shifts coincide with the overall rise of agriculture as a productive segment of the Syrian economy—as evidenced by increases in the amount of irrigated cultivable land—and the decline of the energy sector, manifested by Syria’s move away from being a net exporter of oil in 2007.

The broad RCA patterns are better understood at the section level, where sufficient variability emerges so that it is useful to present those lines that lie close to the bounds of calculated revealed comparative advantage, as well as those registering the greatest changes. The former are the product lines for which Syria has the strongest (and weakest) relative global presence in 2007, while the latter is suggestive of rising (and falling) stars. These 2-digit lines are listed in Tables 5 and A.3, respectively, along with their key underlying 4-digit drivers.<sup>16</sup> The calculations presented in Table 5 suggest that the strength of Syrian exports in the agricultural sector derive from live animals, especially sheep and poultry, as well as edible vegetables. Although not reported, RCA values for many other processed agricultural products are also high. These include products traditionally associated with Syrian agricultural exports, such as animal and vegetable oils (chapter 15), especially olive oil

> 14As for our Herfindahl calculations, since export penetration indices are relatively stable over the period, these values are averages for the period 2001–2007, inclusive.

> 15A more detailed discussion of the aggregated data, in terms of both HS and SITC sections, is provided in the annex.

> 16We identify these drivers by taking bivariate regressions of the 2-digit line on the 4-digit line, and reporting the variables that yielded the top two _R_<sup>2</sup> values. Since this methodology allows for both positive and negative coefficients, it is important to keep in mind that a significant amount of the variation could be due to the negative contribution of a given 4-digit driver.

14
_COTE D’IVOIRE: Volatility, Shocks and Growth_

# **Cote d’Ivoire and World Cocoa Markets**

To better articulate our characterization of the role cocoa in the economy of Cote d’Ivoire, we turn to a review of production and market share statistics. Figure 19 provides statistics of cocoa production in metric tons, while Figure 20 shows Cote d’Ivoire’s percentage share of world output.

In 1970 Cote d’Ivoire produced 179,000 metric tons of cocoa, about 11.6% of world production. In 1980 Cote d’Ivoire produced 417,000 metric tons, about 25% of world output. So, sometime in the 1970s, the country began to dominate world cocoa output, and went on to produce over 41% of world output by 2000.

At the about the same time, physical cocoa production displaced coffee production as the leading commodity of Cote d’Ivoire in quantity. In 1970, Cote d’Ivoire produced 276,000 metric tons of coffee beans versus 179,000 MT of cocoa beans. In 1974 for the first time, cocoa production tonnage exceeded that of coffee, 241,000 versus 196,000. In 1976, Cote d’Ivoire’s cocoa output tonnage moved permanently ahead of coffee. World coffee production greatly exceeds world cocoa production in volume.

**Figure 19: Physical Production of Raw Cocoa in the World and Cote d’Ivoire**

<!-- Start of picture text --> Physical Cocoa Bean Production<br> In the World and Cote d'Ivoire<br>3.5<br>Millions of Metric Tons<br>1961-2003<br>3<br>COTE D'IVOIRE<br>WORLD<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Annual Output<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>Millions<br>Metric Tons<br><!-- End of picture text -->
At first blush, this rather mixed set of analyses suggests a lack of uniformity in compliance with the BCA requirements. This result, impressionistic as it is, however, based on a sample of three, is generally consistent with the results of an often-cited, quite rigorous, review of U.S. RIAs. Work by Robert Hahn and colleagues (2000, 2002) examined 48 RIAs in federal agencies in the United States prepared between 1996 and 1999, paying particular attention to two aspects: whether the RIAs met the requirements of executive order 12866, and whether they satisfied the guidelines produced by the OMB. Their conclusion: RIAs often do not contain all the required items. Whereas 90 percent of the RIAs examined monetized costs, only 50 percent monetized benefits and 29 percent calculated net benefits. Just two-thirds of the RIAs discussed alternatives to the regulation, and only 25 percent of the cases calculated benefits and costs of alternatives.

A follow-up study by Hahn and Dudley (2007) focused exclusively on the EPA, based on a sample of 74 RIAs issued by the agency between 1982 and 1999, roughly balanced among the Reagan, Bush, and Clinton administrations. Using a scorecard similar to the earlier studies, the authors found that while all the RIAs monetized at least some costs, not all provided estimates of total costs. Beyond that, a significant percentage of the analyses done by the EPA did not report the benefits, consider alternatives to the selected policy option, or adequately discount costs and/or benefits. Further, Hahn and Dudley reported no statistically significant changes in the observed quality of RIAs over the 17year sample period or across the three presidential administrations studied. Overall, they concluded that the EPA RIAs fall far short of the formal requirements, although they also observed a great deal of variation among individual RIAs.

25
- 31 -

**Table 8. Sectoral Breakdown of Lending Activities**

(percentage of total lending)

||**198**|**8**|**199**|**6**|
|---|---|---|---|---|
||**Comm. Banks**|**Fincos**|**Comm. Banks**|**Fincos**|
|**Agriculture, Forestry, and**|7.1%|1.3%|3.9%|0.9%|
|**Mining**|||||
|**Manufacturing**|25.8%|21.8%|26.8%|15.30%)|
|**Construction**|4.3%|3.5%|4.6%|3.7%|
|**Real estate business**|6.3%|14.8%|9.1%|24.3%|
|**Imports**|5.3%|2.8%|3.2%|1.70/o|
|**Exports**|8.3%|1.3%|4.2%|0.8%/o|
|**Wholesale and retail trade**|18.9%|12.7%|17.8%|7.8%|
|**Public utilities and services**|7.3%|7.3%|10.6%|7.60/o|
|**Banking and otherfinancial**|_6.3%_|9.1%|7.5%|11.0%0|
|**business**|||||
|**Personal consumption**|10.3%|25.5%|12.3%|27.09/o|
|Total|100.0%|100.0%|100.0%|100.090%|

_Source:_ Bank of Thailand.

_Lending_ **_to Risky Sectors._** Commercial banks and finance companies did not only increase their share of risky assets on their balance sheets but also expanded their exposure to "higher risk" and the non-tradable sectors of the economy (compare Table 8 and Figure 9). This was even more the case for finance companies which expanded rapidly their lending activities in the real estate sector (from an average 15 percent in 1988 to 24 percent in 1996 with some finance companies real estate exposure exceeding 40 percent) and to loans to consumers.42 These "official" figures, however, are likely to be understated because of existing loopholes in the categorization of loans.<sup>43</sup> Market estimates have put the overall exposure of Thai financials in the real estate sector at 30 to 40 percent.<sup>44</sup>

> 42 47 percent of Financial Restructuring Agency's assets consisted of loans to real estate and construction business, which absorbed the loan portfolio of defunct finance companies.

> 43 Loans are categorized according to business of the borrower not the purpose.

> 44 J. P. Morgan (1998).