rafmacalaba/datause-encoder-data huggingface dataset:
# 1: GwmiU Aitiattiv g:le: 1967-0614g

|Scale|<br>post|am.67|mne71|July 75|Jan 79|Jan.80|Jan. 82|Jan. 83|_Nov._83|Oct. 85|July 86|
|---|---|---|---|---|---|---|---|---|---|---|---|
|33|iamr-Sscretsry|6.250|7.600|7,812|7.914|10.176|10,176|10.176|10,176|11,536|12,216|
|87<br>|Assistant Director|5,120|5,800|6.324|6,468|7.440|7,40|7,440|7,440|8,680|9.300|
||U12<br>Oniv. Grad.(gntzfl(b)|<sup>2.184</sup>|2,340|2,976|3,132|3.852|3,870|3,876|3.992|4.931<br>|5,400<br>|
|S13|Diploa<br>Intry|1.72S|1,860|2,388|2,544|3.264|3,300|33,336|3,465|4,283<br>|4.692|
|Sal|Lmst<br>FPid_Salaried_<br>bvior|312|396|540|696|984|1.164|1,324|1,470|2.:46|2,464|
||Unskilled<br>non-established|**n.G.**|342|480|636|840|1,056|1,236|1.382|2.060|2,400|

:

**(a)** lb dates abn represent dates of lplentation for all salaries coiasboas fra 1967 arwards.

(b) k entry **polnt** mm moved upwardr<sup>In 1979. Salaries aomnbefore that datewere not entry points,butwro theequivalent</sup> polint co the scale to tUe _shwam_ for later years.

": Ibr scace prior to Octobr 1965, **see**<sup>Neeook</sup><sup>**at**</sup> .1, 1986, Table A.l.3: 76. For October 1905 and<sup>Jam 1986, see GRZ1906s.</sup>
Small companies also report a lower reinsurance ratio<sup>thanlargecompanies(37</sup> against 54 percent). However, the aggregate data conceal<sup>thefactthat, becauseof their</sup> smaller capital and risk retention capacity, small companies<sup>tendtorelymoreon</sup> reinsurance in each class of business. This is hidden<sup>inthereportedoverallreinsurance</sup> ratios because motor insurance, in which small companies<sup>specialize more,isa linethat</sup> relies less on reinsurance.

Large companies receive much more on commissions from their<sup>reinsurersthan</sup> what they pay out to their agents. Their administration<sup>costsarealso muchlower than for</sup> medium and small companies. As a result, their net costs<sup>absorb19 percentof net earned</sup> premiums against 40 in the case of medium and small<sup>companies.</sup> Large companies report a significant positive underwriting<sup>resultin2001against</sup> losses suffered by medium and small companies. This<sup>was partlyor fullyoffset by higher</sup> investment returns earned by medium and small<sup>companiesrespectively. Theoverall</sup> profit margin is quite healthy, although no firm conclusions<sup>can be derivedwithout more</sup> precise knowledge of the adequacy and appropriateness<sup>of reservingpolicies.</sup> VI. Motor Insurance Motor insurance is the largest class of general insurance,<sup>accountingfor45</sup> percent of total nonlife premiums in 2001. It<sup>representsthemainlongtailclassof</sup> business, but generally reports low levels of claims ratios,<sup>particularlyby the standardsof</sup> most developing countries.

Motor claims, especially those that involve only material<sup>damage,tendtobe</sup> settled quickly. However, claims arising from accidents<sup>involvingbodilyinjuriesare</sup> sometimes protracted by disputes between some small<sup>companiesand claimants.</sup> Motor insurance, even the compulsory part, is not subject<sup>topremiumcontrol.</sup> However, bodily injury liability is unlimited and<sup>thiscreatesproblemswithreinsurers</sup> who press for a cap on liability.

The market for motor insurance is much less concentrated<sup>thanthatforother</sup> classes of business. The largest 4 companies accounted<sup>for43percentoftotalmotor</sup> premiums in 2001, while the smallest 7 had 15 percent of<sup>premiums.Thelatter compared</sup> with less than 6 percent in each of the other classes.<sup>TheHerfindahlindexamountedto</sup> 866 in 2001. Not surprisingly, small companies had 22<sup>percentof thenumberof motor</sup> policies in force.

As in most other countries, and despite the absence of tariff<sup>controlsand</sup> prevalence of low loss ratios, motor insurance faces<sup>some pressingpolicyissues.Atone</sup> level, there are the problems created by the companies<sup>that havebeen putinliquidation</sup> and the need for the creation of a viable compensation<sup>fund.Then,thereisaneedto</sup> establish an insurance information bureau to collect<sup>market-widestatisticson</sup> underwriting practices, loss experience, and incidents of<sup>fraudulentclaims.</sup> 13
# **Table 6 Long-Run Estimates from Panel Autoregressive Distributed Lag Model, Nominal Prices, MPG Estimator**

||**‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐‐ Lag 1 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐ Lag 2 ‐‐‐**|**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**|
|---|---|---|---|---|---|
||**‐‐‐‐**��**‐‐‐**|**‐‐‐‐**��**‐‐‐‐**<br>**‐‐‐‐ λ ‐‐‐‐**|**‐‐‐‐**��**‐‐‐**|**‐‐‐‐**��**‐‐‐‐**|**‐‐‐‐ λ ‐‐‐‐**|
|**GDP measured at Mar**|**ket Prices, cons**|**tant US$ 2000, total**||||
|World|‐0.07|0.55***<br>‐0.52***|‐0.03|0.46***|‐0.57***|
|High income|‐0.08|0.55***<br>‐0.53***|‐0.06|0.49***|‐0.58***|
|Middle income|0.04|0.42***<br>‐0.52***|0.15|0.21|‐0.58***|
|Low income|‐0.02|0.46***<br>‐0.53***|0.19|0.22**|‐0.59***|
|**GDP measured at Purc**<br>|**hasing Power**|**Parity, total**<br> <br>||||
|World|‐0.09|0.57***<br>‐0.51***|‐0.03|0.46***|‐0.56***|
|High income|‐0.10|0.58***<br>‐0.53***|‐0.07|0.50***|‐0.58***|
|Middle income|0.03|0.44***<br>‐0.52***|0.13|0.24*|‐0.57***|
|Low income|0.00|0.46***<br>‐0.53***|0.21|0.20**|‐0.59***|
|<br>**GDP measured at Mar**<br>|<br>**ket Prices, cons**|<br> <br>**tant US$ 2000, per capita**||||
|World|‐0.12|0.52***<br>‐0.52***|‐0.021|0.41***|‐0.57***|
|High income|‐0.14|0.57***<br>‐0.54***|‐0.14|0.52***|‐0.59***|
|Middle income|0.12|0.40***<br>‐0.53***|0.32**|0.19**|‐0.59***|
|Low income|0.09|0.44***<br>‐0.54***|0.56***|0.33***|‐0.62***|
|**GDP measured at Purc**|**hasing Power**|**Parity, per capita**||||
|World|‐0.10|0.52***<br>‐0.52***|0.051|0.36***|‐0.57***|
|High income|‐0.17|0.59***<br>‐0.53***|‐0.15|0.53***|‐0.58***|
|Middle income|0.09|0.42***<br>‐0.52***|0.27*|0.22**|‐0.58***|
|Low income|0.11|0.44***<br>‐0.54***|0.56***|0.32***|‐0.63***|

**Notes** : �� is the long‐run impact of income on nominal prices, �� is the long‐run impact of manufacturing prices on nominal prices, λ is the speed of adjustment to long‐run equilibrium. The model is estimated based on equa‐ tion (7) in text (the nominal price version). One (*), two (**), and three (***) asterisks indicate significance at 10%, 5%, and 1% levels, respectively.

_21_
- Fay, M. and T. Yepes. 2003. “Investing in Infrastructure: What is needed from 2000‐2010”. **_World Bank Policy Research Working Paper_** , 3102.

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16
17 second is particularly dangerous: in this study the two coders knew the purpose of the empirical exercise and may have had some strong prior beliefs as to the expected outcome.

There is nothing we can do about the potential whalo effect" of the original evaluations. We do know that the project reports were regular parts of institutional evaluation cycle and that it is doubtful that the financing agencies had a particular stake in promoting participation. It could also be expected those many different individuals writing the project documents would have had widely different beliefs about the importance of participation so that a uniform bias in the first hand assessments would be unlikely.

As for the potentially serious 'halo effect" in the coding process, we explore three avenues to address the problem. Note that the results in table 2 are based on the average of the two coders assessments. Alternatively, we estimated the same models using only data from coder A and from coder B. Differences in these two assessments may reflect differences in the "halo effect" between the coders. Second, in the same models, we used coder A's assessment of the explanatory variables (mcluding OBP) with coder B's assessment of the dependent variable (OPE). Since coder A's assessments of participation and the other potential determinants are not affected by coder B's performance assessment, this should reduce the halo effect bias (although the confounding effect of pure measurement error in the coder's assessments will be important). Third, the coders created some project performance indicators that, by their nature, are more objective than others. If they were present halo effects would be more likely for the more subjective indicators.
studies commonly do not distinguish between domestic<sup>4</sup> and international investors when investigating the relative importance of risk factors. Yet, their risk preferences might differ due to the exposure and sensitivity to different risk classes. In terms of power sector segments, there is limited research investigating the differences between the different segments (generation, transmission, distribution, retail, off-grid, and mini-grid), even though there are likely differences between segments. With the increasing importance of index data to track power sector liberalization efforts, there is a growing need to ground-proof data and provide weighting of different factors (i.e., what is most important to actors in the power sector) (RISE, 2019). Lastly, there is a wide array of risk categories used across studies, increasing the need for a standardized risk assessment approach that can be easily replicated across different countries and regions.

This paper contributes to filling the existing gaps in the literature by first condensing the wide array of existing risk categories into a comprehensive and coherent framework. We then used this framework to survey 51 equity investors and lenders in the power sector in SubSaharan Africa between January and May 2019. We used a risk factor-based framework to structure the survey, which is described in detail in Section 3 (Waissbein _et al._ , 2013). This framework distinguished 10 risk factors, grouped into three categories, to evaluate the attractiveness of investment environments in the power sector. The survey focused on three sets of questions: (1) How important are different risks when evaluating a new power sector investment? (2) How ready are different segments of the power sector for private solutions? (3) How are different countries in Sub-Saharan Africa perceived in terms of past investment experience and prospects for new investments?

This paper is organized as follows. After reviewing the existing literature in Section 2, in Section 3 we introduce the aforementioned framework, sample and survey methodology, and Section 4 presents the results. Section 6 then ‘reality-checks’ the existing, data based assessments of countries’ readiness for investment by comparing it to the results from the investor survey. Section 6 provides a summary and lessons for policy.

> 4 A further challenge is the definition of what constitutes a ‘domestic’ investor. For our purposes, we define domestic investors as those investors that are incorporated on the African continent.

3
country where firms are headquartered or within the jurisdictions of third countries. This is the version of the index computed in this article.

# **Conditional probability of observing zero cases**

It is also important to demonstrate the varying precision with which the PACI measures corruption, which is influenced by the total number of transactions between countries as represented by bilateral trade flows. To fix ideas, consider the case of a foreign country that has no reported incident of cross-border corruption. Such a situation could be interpreted as a signal that corruption in the said foreign country is relatively low, but not necessarily equal to zero. The strength of such a signal depends on the number of cross-border transactions, proxied by imports. To express this concept, the “probability of observing zero cases, conditional on the probability of corruption being equal in all foreign countries” is introduced: where Pr corr_ FOz_ is the underlying probability that a public official accepts a bribe (see Appendix A) and _c_ is a constant. The measure expresses the probability that no case involving country _z_ public officials (first enforced in the headquarters country) is observed when the true country _z_ probability that a cross-border transaction involving its public officials is corrupt is the _PACI_ same everywhere in the world. Pr zero_ casesz_ may be computed as the product of individual probabilities: Each one of these probabilities, referring to a rare event, is described by a Poisson distribution and may be easily computed by making the condition Pr corr_ FOz = c_ correspond to an ideal situation where occurrences of corruption are distributed according to trade shares.

9
# **4. Evidence on World War 2 and the COVID-19 pandemic in Europe**

In the following analysis we test for an effect of exposure to WW2 on outcomes under the COVID-19 pandemic. In addition to the potential impact of WW2 experience, we take it that mortality from COVID-19 depends on a country’s demographic composition, total population, population density, the average level of education in the country, and the measures of government effectiveness or democracy (Bosancianu et al., 2020). In terms of our model in Section 2, these variables can be thought of as determinants of the probability of a big shock occurring. The variable of interest in our analysis is the country’s death rate during WW2, as the indicator of prior experience with this big shock.

We use multiple data sources. Data on COVID-19 come from the European Center for Disease Prevention and Control (2020). We use COVID-19 related-deaths-per-million of the population as our main dependent variable. Data on war military casualties is derived from UCDP/PRIO Battle Death Data (Bethany et al. 2006) and Wikipedia; civilian casualties are based on multiple sources of mostly country-specific data (Wikipedia 2020). Civilian casualties in the post-Soviet countries come from Erlikman (2004). We use death as a proportion of the population as our control variable. The total deaths as a proportion of the population come directly from the Wikipedia dataset. Military deaths as a proportion of the population are calculated as a ratio of military (battle) deaths and the population of that country in 1939. Both total and military deaths for several countries that did not exist during WW2 are imputed based on the corresponding losses of the “parent” countries.<sup>4</sup> The list of countries with WW2 casualties and COVID-19 statistics are shown in the Appendix (Table A1).

Recall from Section 2 that our hypothesis about the response to past big shocks is conditional on the probability of such shocks occurring ( ), which one would expect to differ systematically across countries. There may also be differences in the welfare function ) at pp given values of and . For example, richer countries will presumably be in a better position to tt uu(ττ, ss protect their citizens through the health care system. We will test the predictions of our model tt ττ ss controlling for GDP per capita (in constant 2011 PPP $) drawing on the World Development Indicators (World Bank 2020). We will also allow for differences in voice and accountability, using data from the World Governance Indicators (WGI) database produced by the World Bank

> 4 For example, losses for Balkan countries are imputed based on the losses of Yugoslavia.

9
R&D in 2003 (Asian Productivity Association, 2003), whereas in most OECD countries and China around 2% of GDP is devoted to this purpose. Moreover, most publicly funded R&D is conducted in government research institutes. Only a limited number of university faculties have adequate resources for significant R&D. The research infrastructure is below international standards, and what research is being carried out tends to be theoretical, supply-driven, and not connected to the needs of the productive sector. The national R&D system is "organized, financed and managed in such a way that technology transfer is difficult and expensive" (Bezanson et al., 2000).

# **_Table 1: R&D Personnel Comparisons in 1990-2001_**

|||||R&D|
|---|---|---|---|---|
|Country|R&D personnel<br>per 1 million<br>people|GDP<br>(billion USD)|Population<br>(millions)|Personnel<br>per billion<br>USD of<br>GDP|
|Vietnam|274|35.1|80.3|629|
|China|584|1,266.1|1,294.9|597.3|
|Korea|2,880|476.7|47.4|286.4|

Source: Calculated from UNDP Human Development Report Statistics 2004, <u>http://hdr.undp.org/statistics/data</u> According to MOET data, in 2000 Vietnam had 77 universities and 114 colleges, housing 160 institutes and research centers. Total teaching staff was 55,887, of which 35,938 were lecturers with university degrees and above. Until the beginning of 2003, the 6
subsidized, at rates exceeding 85 percent of the products’ market value (Table 3), with the highest subsidies provided for LPG and kerosene.

**Table 3: Energy Prices and Subsidies (2013)**

||Subsidized price (Libyan<br>Dinars per unit)|Market price (Libyan<br>Dinars unit)|Subsidy (percent of the<br>market price)|
|---|---|---|---|
|Gasoline (Liters)|0.150|1.072|86|
|Diesel (Liters)|0.150|1.110|86|
|Electricity (kWh)|0.020|0.156|87|
|LPG (Liters)|2.000|20.939|90|
|Kerosene (Liters)|0.090|1.089|92|

Source: Libyan Authorities and World Bank staff calculations. Market prices refer to first quarter 2013.

It is important to stress that estimates of subsidies in Libya vary significantly across sources. For example, government figures for 2012 indicated that the total amount for food and energy subsidies in 2012 was 9.5 billion dinars, equivalent to about 9.2 percent of GDP,<sup>5</sup> while the IMF, by including estimates on electricity and other subsidies, reaches an amount of 14.8 billion dinars or 13.8% of GDP (IMF, 2013). These estimates vary in absolute terms and relatively to GDP. Absolute estimates vary partly due to what is considered a subsidy and partly on whether subsidies include or exclude administrative costs. Estimates of subsidies as percentage of GDP can also vary because GDP figures are themselves volatile estimates in Libya due to weak national accounts and the prominence of oil as a source of revenues. Despite these caveats, it is clear that consumers’ subsidies in Libya are among the highest in the North Africa and Middle East (MENA) region (Zaptia 2013).

# **3. Baseline data, assumptions, and limitations**

The analysis provided in this paper is based on the 2007-08 Libyan Household Expenditure Survey (LHES), with all figures presented in the distributional and simulation analyses estimated at 2013 prices. This survey is the most recent household expenditure survey administered by the national statistical agency, and the only survey available in Libya today for this type of analysis. Data are projected from 2008 to 2013 using official population estimates and IMF estimates for inflation and real GDP growth for the period 2008-2013. Table 4 shows the parameters used for the 2008-2013 extrapolations.

**Table 4: Parameters used for the 2007-2013 extrapolations**

||**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|
|---|---|---|---|---|---|---|---|
|Gross domestic product (in billions of LYD/constant prices)|44.5|45.7|45.3|47.6|18.1|36.9|44.4|
|Inflation (average percent change in CPI; base year 2003)|112.0|123.7|126.7|129.8|150.5|159.6|162.8|
|Population (in millions)|6.0|6.2|||||6.4|

**Source:** International Monetary Fund, World Economic Outlook Database, April 2013, and Libyan authorities.

5 Preliminary data on government spending in 2012 indicated that food, electricity, and other energy subsidies cost respectively 2.1 billion, 1.1 billion and 6.3 billion dinars to the budget.

6
37 question in some countries and unemployed new entrants to the labor force in many countries. The wage and salary fraction of employment is based on only those members in the labor force who report a specified industry and one of the three primary job types (employer-employee, own account-self employed, and family worker). The other or not specified elsewhere residual groups are thus omitted from all calculations and weighted comparisons across countries as noted in the text. See also footnc;e 10.