rafmacalaba/datause-encoder-data huggingface dataset:
# Table 3

How about the much feared conflict between the anchoring role of the exchange rate and the need to keep the economy internationally competitive? Real appreciation has been massive throughout the zone, ranging from over 40% in Hungary to nearly 600% in Lithuania (Table 4). This is partly a catch-up following massive undervaluation at the time when exchange rates were unified and allowed to respond to market forces. The crucial question is whether the real appreciation goes on too far and eventually results in overvaluation. Trend real appreciation is to be expected in countries which are undergoing deep restructuring and fast productivity growth.'<sup>9</sup> Estimates by Halpern and Wyplosz 1997, 1998a, Krajnyak and Zettelmeyer 1998, and the EBRD 1998 indicate that, by 1998, there was no case of overvaluation yet, even though some countries are now nearing that situation. Yet, the issue has been very lively in domestic discussions and weighs on policy choices. It illustrates that one key difficulty of operating a fixed exchange rate regime or a crawling band is that the exchange rate becomes a political issue.

# Table 4

# _Currency Boards_

Currency boards have three main merits: 1) they are robust; 2) as a tight rule, they establish credibility; 3) inflation becomes endogenous. They also have three main disadvantages.

First, a currency board eliminates the ability to conduct lender of last resort operations. Since banking systems are fragile in most transition economies this may be a serious cost but solutions exist. For example, foreign ownership of banks, as in Estonia, transfers the responsibility to the bank shareholders. If the country is small, the cost of bank rescue is well within the means of parent banks. Next, the Treasury 18
scholarship. Out of the 13 baseline characteristics, only one is statically different for treatment and control institutes, namely the admission score obtained in the application process. This variable is only available for one-third of the students, as many institutes did not provide the score. Nevertheless, it deserves careful consideration since the variable correlates with student outcomes and displays a large and significant imbalance (p < 0.01). For this reason, we include it in our baseline specification.

The average number of professors per institute is relatively high (40) compared to the number of students (168). Furthermore, the SENESCYT disposes of information on professors’ cognitive and non-cognitive skills (collected using the DESCAES standardized test), a proxy for their human capital. These variables' information does not provide evidence of a significant imbalance between treatment and control schools.<sup>6</sup>

# _4.3. Intervention’s Take-up_

After randomization, 6,069 students in the 39 TTCs were assigned to receive ALEKS. However, only 84 percent (or 5,077 students) used the license. One possible explanation for the initial drop in the sample is that some students only confirmed their enrollment in the course after their license was issued or failed to follow through with their intention to enroll. Of the 5,077 participants, 97 percent used the platform at least once, and 74 percent used it for 360 minutes or more per month during at least one of the five intervention months. McGraw Hill recommends using the platform for at least 90 minutes per week or 360 minutes per month. Although teachers encouraged using the platform, doing so was not compulsory. Using ALEKS did not affect student grades, which may have decreased students' incentives to use it during the mandated times.

**Figure 2** shows that the take-up of ALEKS fluctuated from month to month, starting a little above 50 percent in January and achieving its peak in February and March, when 86 percent of the students with licenses used the platform for at least one minute. In April, the percentage of students that used the platform dropped to 70 percent, and in May, which coincides with the end of the semester, the take-up of the platform dropped sharply to 7 percent.

> 6 The DESCAES assessment is a standardized, online test that can diagnose skills and measure competencies using task-based exercises that confront individuals with real situations.

11
Deflating all nominal expenditures to real terms and using the same poverty line produce the same result as inflating the poverty line and comparing it with nominal expenditures. This procedure can therefore be thought of as equivalent to using the deflator which uses the food share of those at the poverty line ((O(e=PL)) in constructing a price index for the poverty line.

Using this inflation rate for the poverty line, table 4 shows that the poverty rate increased by 5.53 percentage points using CPI for food and non-food inflation rates (in the range of the distributionally neutral "expected" values from the rules of thumb elaborated above of between 5.32 and 5.83 percentage points) and around 7.12 percentage points when using Susenas unit prices for the food inflation and CPI for non-food inflation.'<sup>0</sup> Obviously this arises because of the differences in food price inflation in the CPI versus Susenas unit prices. Table _5_ shows that in aggregate the Susenas food price inflation is 8 percentage points higher than CPI food inflation, resulting in 5 percentage points higher in total inflation in method L"

> '° The food and non-food price inflation of CPI used are the provincial level. Meanwhile, the food price inflation of Susenas unit prices are not only varied across provinces, but also across urban-rural areas within province.

> " The aggregate inflation rates are obtained as weighted averages of inflation rates faced by households in the sample.

15
> <sup>8</sup>

The cost of capital is expressed as a rate, to be added to the pure premium rate, and is therefore equal to this aggregate cost to the insurer of acquiring risk capital for a given portfolio divided by the total sum insured for that portfolio.

Where the insurer must quote premiums for products before the entire portfolio is finalized, judgment must be applied in assessing the level of diversification in the forthcoming season’s mNAIS portfolio. The more diversified the portfolio, the less risk capital will be required, and the lower the cost of capital will be. If there is some uncertainty surrounding the level of diversification in the forthcoming portfolio the insurer may wish to estimate the cost of capital on a prudent, or cautious, basis.

The risk management team can support management decisions about the risk financing strategy by estimating the portfolio Probable Maximum Loss (PML) using historical data. See Chapters 4 and 5 and Annex G of World Bank (2011) for examples of such calculations for the WBCIS and NAIS portfolios.

# <u>Actuarial justification</u>

The cost of acquiring capital to allow for the risk of random variation from expected claims should be included in the commercial premium rate. The insurer must acquire capital in the form of reserves, reinsurance and contingent credit to ensure that it is able to pay all claims as they fall due. Most regulatory regimes require private insurance companies to demonstrate that they hold enough capital to be able to survive specified extreme events; for example the Europe Union’s forthcoming Solvency II will require each (re)insurer to demonstrate that it is be able to meet its obligations over the next 12 months with a probability of at least 99.5%.

# _Administrative Load_

# <u>Description of step</u>

The administrative load should be sufficient to cover all costs to the insurer of administering the mNAIS and is a decision for the insurer’s management team.

# <u>Actuarial justification</u>

All costs to the insurer of providing benefits, including administrative costs, should be included in commercial premium rates.

> 8 A formula for the contingent credit multiple is given in Clarke and Mahul (2011).  The contingent credit multiple is less than unity, and therefore the contribution of contingent credit to the cost of capital is zero, when the opportunity cost of capital is greater than the rate of interest that would be paid on drawn down credit.  If this does not hold then the contingent credit multiple will be greater than unity.  For example, if the opportunity cost of capital is 2%, the rate of interest payable on drawn down credit is 5%, and the term of any drawn down credit is 30 years with a five year grace period the contingent credit multiple would be approximately 1.4 (Clarke and Mahul 2011).

26
of annuitization. There is an inherent contradiction in mandating workers to save for retirement during their working lives but allowing unrestricted access to lump sums in retirement. However, the historical application of clawback provisions for the universal pension has encouraged the emergence of a so-called lump-sum mentality, while the public has shown a strong preference for flexibility and financial control over retirement wealth.

# **3.6 The Value of Annuities and Money Worth's Ratios**

Money worth's ratios (MWRs) measure the value of an annuity to its purchaser at the time the annuity is bought. The MWR is defined as the ratio of the expected value of benefits payable under the contract to the paid premium. A cohort life table and an interest rate yield curve are required to calculate the present value of promised benefits.

MWRs are calculated by using two life tables, one covering the general population and the other the population of annuitants, and two interest rate yield curves, one based on risk-free interest rates on government bonds and the other on interest rates on corporate bonds. In most countries, calculations are based on annuity quotes since data on sold annuities are not readily available. Chile is a major exception because it has a very rich database on sold annuities.

The calculation based on the population of annuitants and corporate bond rates reflects the expected longevity experience of annuitants and the risk of insurance insolvency faced by them, especially when insurance companies invest primarily in corporate bonds. However, country comparisons are often focused on calculations based on the general population and government bond rates because the underlying data are more reliable. Moreover, in countries where the corporate bond market is underdeveloped and insurers invest primarily in government bonds, use of the risk-free rate would be more appropriate. Use of the risk-free rate would also be appropriate when governments guarantee annuity payments, at least up to the level of the government guaranteed benefits.

A MWR that is close to unity, perhaps around 0.97, would indicate an efficient and competitive annuity market that offers fair prices to annuitants. This would allow for a 3 percent load factor to cover commissions paid to brokers and other expenses as well as risk premiums and profit margins. In most countries where such calculations have been made, MWRs based on the annuitant population and government bond (risk-free) rates have been higher or close to this level. But MWRs based on corporate bond rates have been significantly below this level. This has reflected higher load factors because of higher expenses and higher risk premiums resulting from greater uncertainty about future longevity and asset/liability mismatching.

In the country studies commissioned for this project and presented in the papers listed in footnote 1, MWRs were calculated for only two countries, Chile and Switzerland, and in the latter case, only for annuities from the mandatory component of the second pillar. In Australia, the very small size of the annuity market did not warrant a detailed calculation 29
2.10 Thrughout the projecon peiod net crret fer, nct direct rbr?ign investment, offici gram and short-term captal are deemined by their respective ratios<sup>**to**</sup> GDP<sup>estiated</sup> for 1987-89. Long-term public and publicly guamteed inflows, Don-guaranteed private medium and long-tem inflows together with their amorzation and interest obligadons are<sup>calculated in</sup> the debt module. Short-term public and private debt are assumed to be a fixed ratio of total<sup>long-</sup> term inflvuw. Interest on short-term extenal debt and on reserves is determined<sup>by applying</sup> the exogenous foreign interest rate to their stocks. Reserve accumlation is derived<sup>from the</sup> overall balance of payments or stated as an explicit target.

# National accounts

2.11 Several items for the national accounts are determined in the sectoral accounts described in paras. 2.6 to 2.10. They include indirect taxes, subsidies, the net current<sup>transfers in the</sup> balance of payments, Government consumption and investment. Domestic<sup>savings is simply the</sup> difference between total investment and the external resource gap. LikbwiLe, national<sup>savings</sup> is the difference between total investment and the current account balance. Private savings<sup>is the</sup> residual after public savings-defined as the current balances in the fiscal accounts-is subtracted from national savings.

2.12 GDP at ftctor cost is broken down into value added in agriculture,<sup>mining,</sup> imanufacturing, other industry and services. Growth in each<sup>of these sectors is projected</sup> exogenously. GDP at market prices is then determined in conjunction<sup>with net direct taxes which</sup> are calculated in the fiscal account.

2.13 Public investment and consumption are projected in the public sector<sup>accounts. Private</sup> Iinestment is a negative function of the change in the real domestic loan<sup>interest rate and a</sup>
**Table 6: Market penetration of biofuel**

<!-- Start of picture text --> Market penetration of<br>biofuel in % of share<br>Reference 0,31<br>sim1a 3,74<br>sim1b 3,74<br>sim1c 3,73<br>sim2a 0,38<br>sim2b 2,65<br>sim2c 2,65<br>sim2c 2,65<br><!-- End of picture text --> We will follow this section with the distributional analysis. It will be important to keep in mind the variations presented in the previous tables.

# **_4.2 Distributional impacts_**

In this section, we present and analyze poverty and income distribution changes following the different scenarios. We perform this analysis at the national level and also present a ruralurban decomposition analysis. It is important to restate that the CGE model does not take into account this household decomposition. The decomposition is performed at the distributional analysis level. The CGE-microsimulation model allows us to compute changes in real income and equivalent variations for each 4,494 household in the model. Table 7 presents the share of households as well as average per capita expenditure for each of the two groups. We observe that 31.70% of Malian households live in urban area and 68.3% in the rural zone. The average per capita expenditure of urban households is more than double (631 064 Fcfa) compared to what is observed for rural households (225 416 Fcfa).

**Table 7: Descriptive statistics for groups**

||**% of households**<br>**Mean of**per<br>capita<br>**expenditure (Fcfa)**|
|---|---|
|**Urban**|31.7%<br>631064.8|
|**Rural**|68.3%<br>225416.8|
|**Mali**|100%<br>377035.72|

Sources: Elim, 2006 20
Table 5: Indicators of gender disparity at the level of Indian States from various sources.

|State|Child mo|rtality'|Female /<br>Populatio|Male<br>n Ratiob|Female<br> <br>ratio o<br>treatment<br>or fe|/ male<br>f no<br>for ARI<br>vera|Female /<br>of school e<br> <br>(ages 1|Male ratio<br>nrollment<br>1-14)'|Male f<br>difference<br>litera|emale<br>in rural<br>cyb|Rural fe<br>labor f<br>particip|male<br>orce<br>ationb|Differe<br>Gende<br>unadjust|nce of<br>r and<br>ed HDId|Status of|women'|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Level|Rank|<br>Level|Rank|<br>Level|Rank|<br>Level|Rank|Level|<br>Rank|Level|Rank|<br>Level|Rank|<br>Level|Rank|
|Andrha Pradesh|1.28|7|973|3|1.45|15|0.67|22|19.3|7|46.60|8|7.3%|6|34.6|10|
|Arunachal Pradesh|||861|23|||0.92|8|18.3|16|67.10|2|||27.0|19|
|Assam|1.12|4|925|13|1.27|7|0.90|10|15.7|19|55.40|6|8.4%|8|24.2|22|
|Bihar|1.55|13|912|18|1.59|17|0.55|24|24.1|21|15.30|18|13.6%|<br>13|20.2|24|
|Delhi|_1.55_|12|||1.16|4|1.00|3||3||25|||36.9|6|
|Goa|1.10|3|||1.38|13|0.98|_5_||||24|||49.0|1|
|Gujarat|1.42|9|936|10|1.39|14|0.79|16|23.4|9|20.20|14|6.4%|4|34.2|12|
|Haryana|2.34|19|874|22|1.28|8|0.83|14|24.7|12|7.60|23|24.3%|16|30.6|15|
|Himachal Pradesh|1.43|10|996|2|1.28|9|0.92|7|19.0|6|29.20|12|4.8%|1|34.7|9|
|Janmuu and Kashmir|1.69|16|923|15|1.34|10|0.86|13||13|9.20|22|||30.1|16|
|Karnataka|1.30|8|961|6|0.62|1|0.77|19|20.9|8|33.40|11|6.9%|5|34.6|11|
|Kerala|0.94|2|1040|1|0.87|3|1.00|2|5.8||20.20|13|6.3%|3|47.6|2|
|Madhya Pradesh|1.21|5|932|12|1.19|5|0.74|21|24.9|17|39.70|10|10.6%|<br>9|25.5|20|
|Maharashtra|1.24|6|935|11|1.58|16|0.81|15|23.9|4|47.30|7|5.9%|2|36.9|7|
|Manipur|||961|7|||0.88|11|18.3||61.20|3|||41.6|3|
|Meghalaya|||947|8|||0.99|4|4.8|11|60.80|4|||33.7|14|
|Mizoram|||924|14|||0.96|6|8.4|1|60.60|_5_|||40.9|4|
|Nagaland|||890|19|||1.01|1|11.0|2|72.60|1|||40.8|5|
|Orissa|1.45|11|972|5|1.37|12|0.75|20|23.8|20|16.70|15|11.8%|<br>10|24.0|23|
|Punjab|1.81|18|888|20|1.67|18|0.91|9|13.7|10|16.10|16|19.8%|<br>15|33.7|13|
|Rajastan|1.59|14|913|17|1.79|19|0.49|25|28.8|18|16.10|17|13.2%|<br>12|25.2|21|
|<br>Tamil Nadu|0.80|1|972|4|0.85|2|0.79|18|21.5|5|39.80|9|8.2%|7|36.1|8|
|Tripura|||946|9|||0.86|12|18.8|14|14.30|19|||29.3|17|
|Uttar Pradesh|1.70|17|881|21|1.34|11|0.60|23|25.8|22|9.40|21|15.8%|<br>14|19.9|25|
|West Bengal|1.63|15|917|16|1.22|6|0.79|17|19.0|15|10.00|20|13.1%|<br>11|28.3|18|

Sources: (a) This study (b) Agarwal (1997) (c) Srivastava (1997) (d) Kumar (1996) 18
**Table 2: Household Ownership of Consumer Durables in South Sudan** _Percentage of Households Owning Item_

||20|06 SHH|S|200|8/2009 N|BHS|20|10 SHH|S|
|---|---|---|---|---|---|---|---|---|---|
|Consumer Durable|Overall|Rural|Urban|Overall|Rural|Urban|Overall|Rural|Urban|
|Radio|21.9|16.8|32.6|24.6|19.6|54.4|30|27.3|60.2|
|Bicycle|26.2|19.9|39.2|25.0|23.5|34.0|26.3|24.5|46.9|
|Mobile Phone|2|0.6|5|15.1|7.8|58.5|18.3|14.7|58.5|
|Motorcycle/ Scooter|1.9|1.1|3.6|3.3|2.0|11.1|4.3|3.5|13.6|
|Animal Drawn Cart|1|0.8|1.6|2.9|1.8|3.1|2.4|2.4|3.2|
|Television|1.1|0.4|2.4|3.3|0.6|19.4|2.2|0.8|17.3|
|Car or Truck|0.6|0.3|1.3|1.7|1.0|6.3|0.9|0.5|5.5|
|Refrigerator|0.9|0.6|1.7|0.9|0.2|5.0|0.6|0.2|5|
|Computer|0.5|0.2|1.3|0.7|0.3|3.3|0.5|0.3|3.4|
|Boat with motor|0.9|0.6|1.5|1.4|1.5|0.8|0.4|0.4|0.7|

Source: Authorss calculations using 2006 Sudan Household Health Survey, 2008 National Baseline Household Survey, and 2010 Sudan Household Health Survey.

Notes: The table is sorted by highest to lowest overall ownership in 2010.

# _Design, Implementation and Timeline_

Implementation of SSEPS involved two major phases: (1) Distributing mobile phones to individuals in households in South Sudan’s ten state capitals and training them on their use and (2) using a Nairobibased call center (Horizon Contact Center) to call respondents on a monthly basis and conduct a 15-20 minute survey. To provide an incentive for participation, individuals who successfully completed a survey were given pre-paid airtime credit for the mobile phone. Planning for the survey began in January 2010, a pilot survey was conducted in June and July of 2010, and the main implementation of the project was conducted between November 2010 and March of 2011 (Figure 2).<sup>3</sup> Specific design choices were influenced by context. South Sudan’s population is widely dispersed over rural areas without good connections to urban centers, less than 25 percent of the population has any formal education, half of households are poor, and very little infrastructure exists outside of state capitals. Given poor network coverage in rural areas, the sample was limited to households in urban settings. To avoid systematically omitting households without a phone, a phone was given to individuals in sampled households, regardless of whether they previously owned a phone or not. It was also hoped that distributing phones would help vest in participants a sense of ownership in the project.

> 3 After March, the project was handed off to Cordaid (an international NGO) which continued making calls on a monthly basis to participants through early 2012.

7
2. EPA: Health Effects of Air Pollution, accessed at May 28, 2014, http://www.epa.gov/region7/air/quality/health.htm .

3. Ministry of Natural Resources and Environment of the Russian Federation. 2007. State Report On the State of Environmental Protection in the Russian Federation in 2006 (in Russian), accessed at May 28, 2014, http://www.mnr.gov.ru/regulatory/list.php?part=1269 4. SUST-RUS. 2012 (b). SUST-RUS Deliverable 5: Description of the environmental, international and social part of the model, SUST-RUS project - web site, accessed at May 28, 2014, http://sust <u>rus.org/wp content/uploads/2012/03/d5-description-of-the-environmental-international-andsocial-part-of-the-model.pdf</u> 5. Rosgidromet. 2013. State inventory report of anthropogenic emissions by sources and removals by sinks of greenhouse gases not controlled by the Montreal Protocol for 1990 - 2011 years, Part 1, Moscow 2013.

6. SUST-RUS. 2012 (c). SUST-RUS Deliverable 2.1: Description of the constructed database, data quality and data collection methods, SUST-RUS project web site, accessed at May 28, 2014,http://sust-rus.org/wpcontent/uploads/2010/08/deliverable-2.pdf 90
**<u>Table 2: Parcel Level Characteristics</u>**

||**Total**|**Irrigation**|**Scheme**||**Crop Esta**<br>**Mode (K**|**blishment**<br>**pong only)**||
|---|---|---|---|---|---|---|---|
|||**Kpong**|**Weta**||**Broadcast**|**Transplant**||
|Harvest per hectare (Kg)|4572|5033|3143|***|4756|5315|***|
|Value of yield per hectare (GHC)|4486|5033|2794|***|4756|5315|***|
|**Plot characteristics & yield**||||||||
|Planted area (Ha)|0.83|0.86|0.73|***|0.80|0.91|***|
|Parcel owned|74|71|84|***|71|70||
|Parcel rented-in|26|29|16|***|29|30||
|**Non-Labor inputs**||||||||
|<br>Value of seed applied/Ha (GHC)|194|207|152|*|262|151|***|
|Any herbicide used|100|100|99|***|100|100||
|<br>Any insecticide used|91|98|72|***|98|98||
|<br>Any fungicide used|68|80|29|***|79|82||
|Nitrogen/ha (kg)|151|150|154||150|150||
|Phosphate/ha (kg)|58|62|43|***|61|63||
|<br>Transplanting used|38|49|3|***|0|100||
|**Labor inputs**||||||||
|Male family labor\ha (days)|24.8|24.2|26.4||25.2|23.3||
|Female family labor\ha (days)|17.8|14.5|27.9|***|15.1|14.0||
|Male child family labor\ha (days)|6.4|5.1|10.3|***|6.2|4.0|*|
|Fem. Child family labor\ha (days)|3.9|3.3|5.6|**|4.3|2.3|**|
|Male hired labor\ha (days)|30.5|34.0|19.6|***|28.9|39.3|***|
|Female hired labor\ha (days)|25.3|19.7|42.6|***|16.7|22.8|***|
|Land preparation labor (days)|20.4|21.9|15.8|***|18.6|25.3|***|
|<br>Field management labor (days)|66.5|58.2|92.0|***|56.8|59.6||
|Harvest labor (days)|24.8|23.9|27.4|**|22.9|25.0||
|**Mechanization**||||||||
|Ploughed using power tiller|71|90|12|***|91|89||
|Ploughed using tractor|28|10|85|***|8|11||
|<br>Cutting & threshing by combine|48|51|38|***|52|51||
|Crop cut manually|51|48|59|***|47|49||
|**Expenses per ha**||||||||
|<br>Ploughing (GHC)|326|364|210|***|354|373|**|
|<br>Threshing (GHC)|252|265|210|***|265|265||
|<br>Cutting (GHC)|14|11|24|***|13|10||
|<br>Transport (GHC)|190|213|117|***|195|232|**|
|<br>Drying (GHC)|86|84|94||75|92|***|
|Milling (GHC)|61|71|31|***|51|91|***|
|No. of observations|1778|1344|434||679|665||

_Source:_ Own computation from World Bank Kpong and Weta irrigation scheme survey. Asterisks denote significance of t-tests for equality of means between the preceding columns: *** p<0.01, ** p<0.05, * p<0.1.

18