rafmacalaba/datause-encoder-data huggingface dataset:
|(a)|Dermidons .........................<br>..<br>43|
|---|---|
|Ob|Records<br>and Reports<br>..................<br>.<br>.43|
|(c)|Accounts ...........................<br>43|
|(d)|Producdon<br>Rates .......................<br>. 43|
|(e)|MeasurementofPetroleum<br>....................."4|
|Mt|Confidenthialt<br>.......................<br>..<br>44|
|(g)|indem ity<br>.................<br>a<br>4|
|<br>(h)|<br> Penaltie<br>...........................<br>44|
|(10) Mode|l Forms ............................<br>44|

|Tv.|Model Con|tract(s)<br>..............................<br>45|
|---|---|---|
||(1)|Scope,<br>Duration,<br>and Grant<br>of<br>Rghts...................<br>45|
|||(a)<br>Scope<br>*...................<br>46|
|||(b) Feriods,<br>Phaes and Terms ...............................<br>47|
|||(c) Grant<br>of Rights<br>......................................<br>47|
||(2)|MnWO, Work Program<br>and Guarantee ...........................<br>47|
||(3)|R0nuh*ls.........................4|
||<br>(4)|DedaratiDn<br>ofCommerdalDiscovery<br>...........................<br>49|
||(5)|Joint<br>[ngn<br>Advisory]Committe<br>........................<br>49|
||(6)|Cost<br>Reovery,Expenses<br>and Pirduction<br>Shars ....................<br>_SO_|
||(7)|Taxes<br>and Dudes ......................................<br>_SI_|
||(8|Feesand<br>Bonuses ........................................<br>_53_|
||9)|Proectonof<br>the Environment<br>.................................<br>53|
||*(10|) Supply<br>of Domestic<br>Market;<br>Emgncy<br>Ruisition<br>..................<br>54|
||iii)|1YoHTrainiPy<br>ofaHost State Personnel<br>................<br>55<br>90....|
||<br>(12)|<br>Other<br>Standard<br>Pvisions ...................................<br>55|
|V.|Condusion|..............................................<br>56|

|=-k I|<br>.ForeigInvestment<br>..................<br>2<br> <br> <br>|
|---|---|
|;ffi;Sti<{<Xb<br>{-<iSf>|5<br>-2.HostCounbtr's<br>Objwdves .................<br>... 5|
||3. IOC's<br>Objectives .....................<br>.8|
||4. ExplorationPeriod<br>...........<br>35|
||5. License<br>orConeson........<br>0<br>39|
||6. PA Variants...........<br>.<br>.<br>.<br>46|
||7. PSC<br>..<br>0<br>52|

ANNEX ... TABLE OF BANK GROULS ................................ 59 RELEVANT PEITROLEUM SECTOR LOANS, CREDITS, PAETSDMENTS AND LEGAL TA (EFE^CTIVE AND PROPOSED) 1980 - 1994
24 Table 7 Projected Secondary Market Valuation of the New Instruments

|||Witho|ut enhancements;|With enhancements;||
|---|---|---|---|---|---|
||Percentage<br>chosen|Perc.<br>face|of new<br>value|Perc. of new<br>Perc.<br>face value<br>face|of old<br>value|
|IR|0.467||0.34|0.44|0.44|
|PR|0.382||0.49|0.61|0.39|
|NM|0.151||0.26|NA|0.26|

IR : Interest Rate Reduction NA : Not Applicable PR : Principal Reduction NM : New Money The table suggests that as a percentage of the new **face value and without** any enhancement, the principal **reduction exit bond would be quoted** at the highest price, because it receives **market interest rates as opposed to** 6.25% fixed. With interest coverage as stipulated in the tentative agreement, the value would increase further. The low-interest instrument would trade for less, simply because it carries a lower interest rate (and, something that is not incorporated here, because the interest rate is fixed).

However, the unit value of the new claims is not the **only factor** entering the decision on which option to choose. After all, with principal reduction, old claims are exchanged at a 35% discount for new claims, while the low interest bond would be exchanged at par. To incorporate that discount, the secondary market valuation needs to be compared to the old face value; in this way any discount at the time of the exchange of the old debt for the new instrument is taken into account. This reverses the outcome of the comparison: **the** low interest option would remain the same with enhancements, since old and new face value are the same; but the value of the discount bond, inclusive of enhancsments, would fall by 35%.

The second striking result in the table is the low valuation of the new money option. This option has clearly been presented as junior debt by the Mexican authorities, junior to the exit instruments. This has a major impact on valuation. Without subordination, the new money option would have traded at close to the unenhanced, new face value quotation of the principal reduction deal, since it carries market interest rates too but no guarantees. Its junior status reduces the valuation to 26 cts, however. This is the marginal price of Mexico's debt.

It is not possible to perform a similarly general analysis of the consequences of tax and regulatory aspects, since they are different per country; they could even depend on the particular balance sheet and profitability situation of individual creditors. In addition they have not fully been clarified in all countries.

In the US both instruments will be treated similarly f,r financial disclosure and accounting purposes. But for regulatory capital purposes it is likely that banks will have to recognize the 35% loss with the discounted bond, while apparently banks opting for the par bond may be able to avoid the capital loss recognition, thus reinforcing the attractiveness of the interest
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# How much more can be done within the existing resource envelope?

Around $34 million of additional resources could be recovered each year by improving efficiency (table 21). Capital budgets’ low ratios of execution point the way to an easy and budget-neutral increase in public investment—if only execution ratios can be raised. The CAR could expand its resource envelope by $14 million per year if it raises its budget execution ratios.

Reducing inefficiencies in infrastructure operations is also a practical and realistic way of making more resources available for infrastructure in the CAR. While the CAR needs to devote considerable effort to improving infrastructure, it is also severely constrained in what it can spend. The CAR has trouble raising domestic revenue and reallocating revenue from other uses that might require structural reforms. By contrast, efficiency improvements (that is, the reduction of distributional losses and the undercollection of bills) could enlarge the CAR’s availability of funds by $12 million per year, or around 11 percent of the current spending flows to infrastructure (table 21). Distributional losses in power cost $6 million per year, and another $2 million in the water sector. Undercollection of bills for electricity services could save another $4 million.

Increasing tariffs to cost-recovery levels could save further $6 million per year: $4 million in power and $2 million in the water sector.

Looking across sectors, power offers the greatest savings—up to $22 million per year—by tackling the inefficiencies of ENERCA and reallocating resources within the sector.

**Table 21. Potential gains from greater operational efficiency**

|$ million per year|||||||
|---|---|---|---|---|---|---|
||**ICT**|**Irrigation**|**Power**|**Transport**|**WSS**|**Total**|
|Low budget execution|0|—|3|10|—|14|
|Distribution losses|n.a.|n.a.|6|n.a.|2|8|
|Undercollection|n.a.|—|4|—|0|4|
|Underrecovery of costs|n.a.|—|4|—|2|6|
|Reallocation potential within sector|0|0|5|0|0|5|
|Total|0|0|22|10|5|37|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ ICT = information and communications technology; WSS = water supply and sanitation — = Not available; n.a. = Not applicable.

## **Raising capital budget execution**

Budget underexecution is a significant issue in the transport sector, leading to the underspending of about $14 million in available funds annually. In particular, in the transport sector underexecution of the budget leads to an average loss of $10 million per year.

## **Reducing distributional losses and utilities’ undercollection of bills**

ENERCA suffers from high power losses and low bill-collection efficiency. In 2009 ENERCA’s distributional losses, at 48.3 percent of production, were almost five times the best-practice 10 percent benchmark. If transmission and distributional losses were tackled, it could result in $6 million savings 44
# **4. The Distributional Impact of Cambodia’s Fiscal System**

## **Size effect of fiscal interventions**

Comparisons of average income per capita at each income concept reveals the size effect of fiscal interventions. The difference between _market income_ and _disposable income_ reflects the net effect of direct taxes and direct transfers. The difference between _disposable income_ and _consumable income_ reflects the net effect of indirect taxes and subsidies. The difference between _consumable income_ and _final income_ reflects the net effect of in-kind transfers. The difference between _market income_ and _final income_ reveals the overall change in average per capita income from the operation of the full fiscal system.

Among three main categories of fiscal interventions, indirect and in-kind fiscal interventions have the largest size effect on incomes in Cambodia. There is a sizable decline in average per capita income due to indirect fiscal interventions as revealed in the transition from _disposable income_ to _consumable income_ . The reduction in income indicates that on average indirect taxes paid exceed subsidies received. There is a sizable increase in average per capita income due to the receipt of public education and health services as revealed in the transition from _consumable income_ to _final income_ . Direct taxes and transfers have a small size effect on incomes, reflecting the small size of the formal sector in Cambodia and limited social protection spending as is shown later. We ought to bear in mind though that a high proportion of salary taxes, and other direct taxes, on the highest earners cannot be simulated on the CSES data due to underrepresentation of high-income households, as is common in household survey data. This implies that both the total and the average values of incomes, and the differences between them would have been different if we could account for the top income households correctly. Similarly, average implications of different forms of fiscal interventions would look differently if richest households were fully represented in the data.

Figure 6: Average monthly per capita income (KHR) by income concept Figure 7: Net effect of the fiscal system and its elements on average monthly per capita income (%) <!-- Start of picture text --> Market income Full system -0.6<br>Direct<br>Disposable income -0.6<br>interventions<br>Indirect<br>Consumable income<br>-3.5<br>interventions<br>Final income In-kind<br>3.7<br>interventions<br>400,000 500,000 600,000 700,000<br>-4.0 -2.0 0.0 2.0 4.0 6.0<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

16
Table 6. Gains in Infant Deaths Averted **through** Female Education **versus** Health Servces and **GDP** per capita change: Simulation Results<sup>1</sup> ' Infant Deatss per year, 1985 (millions)

|CountrueP<br>(1)<br>_ _{(2)|Actual|Remnarks<br>(3)|Simulated<br>(4)|Averted<br>(5)|Col. S**asS **<br>ofCol.2<br>(6)|Remarks<br>(7)|Simulbted<br>(8)|Averted<br>t9)|Col. 9 u%<br>of Col. 2<br>(10)|Remarks<br>(II)|Simulated<br>(12)|Averted<br>(13)|Col. 3as%<br>of Col. 2<br>(14)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Low&middle income<br>(n=65)F|8.0|Thescettio<br>if<br>felmale<br>gros<br> <br>|2.9|5.1|64|Scenrio<br>halvingthe<br> <br>|7.8|0.2|2.5|Scenario<br>raising<br>GDP|8.0|0|0|
|Lowincome (n=31)Y|6.8|enollment<br>in<br>1975 were<br>dbld f|2.3|4.5|66|ratio<br>of<br>ppop<sup>t</sup>jti-o<br>pe<br>|6.6|0.2|2.9|per capita<br>level<br>from<br>|6.8|0|0|
|Sub,Ssamn Africa<br>(n=260|2.1<br>|oue tron<br>19% (actual) to<br>38%.1'<br>|0.4|1.7|81|physician in<br>1984 from<br>11,851(mean)<br> <br>'|1.9<br>|0.2<br>|9.5|S650 in 1985<br>to the median<br>ofS13C0.'|2.0|0.1|4.8|
|Soutb Asfit (n=5)P|South Asia (n =<br>3.2|)11|0.7|2.5|78|~~~~~~~~<br>5926.|~~~~~~<br>3.2|~~to<br>0|0||3.2|0|0|

It The2e simulations ar based on equation 2 in Table 4.

21 According to World Bank classifgeation.

2_ These 65 countries eresent 76% of the worlds population and 93 or the LDC population, induding Chins.

fI Keeping all other variables constant at their mean values.

_I These 31 countrics represent 61% of the worad'soppulation and 97% of the low-income LDC population, including China. 61 These 26 countries **are:** Benin, Botswana, Burkina Faso, Burundi. Central African Republic, Cote d'lvoire, Ethiopia. Ghana, Guinea, Kenya. Lesoth, Liberia, Madagascar, Malawi, Mali. Maurnius. Mozambique, Niger, Nigeria, Rwsnda, Simfma Leone, Somalia. Sudan, Tanzania, Togo. Uganda. Zaire, Zambia. and Zimbabwe. Thcy represent 8.7% o' .ae world's popumation and 94% of SSA's population. 7/ Thse S countries are: Bangladesh, Nepal. India, Pakistan, and Sri Lanka. They represent 23.1 % of the world's population and 96.3% of South Asia's population.

0
# **9 Tables**

Table 1: What predicts average monthly fires over 2003-2005?

|Outcome: Average cropland fres by district|(1)|(2)|
|---|---|---|
|Avg. Per Capita GDP|.099<br>( .115)|.124<br>( .132)|
|Avg. Per Capita GDP, Ag|.323***<br>( .078)|.13<br>( .093)|
|Avg. Area Planted in Rice (000s HA)|.44***<br>( .099)|.237*<br>( .121)|
|Avg. Area Planted in Wheat (000s HA)|.389***<br>( .034)|.189***<br>( .07)|
|Avg. Area Planted in Sugarcane (000s HA)|.238***<br>( .046)|.23***<br>( .014)|
|Avg. Area Planted in Other Crops (000s HA)|-.025<br>( .062)|-.122<br>( .105)|
|Combines in 2006 (000s)|.119***<br>( 9.0e-03)|.018<br>( .015)|
|Share of holdings _>_4 HA|.5***<br>( .064)|.471***<br>( .091)|
|Covariates regressed separately|X||
|State FE|X|X|

Notes: In all columns the outcome is the average number of monthly fires in a district averaged over the years 2003-2005. In column 1 each row is a seperate regression. In column 2 all covariates listed down the left are included in the same regression. In all cases the specification is a fixed effects poisson regression. All independent variables are measured as Z scores so that units are comparable. Per Capita GDP (Ag) reports the average per capita value of (agricultural) GDP in the district in from 2003 to 2005 measured in Lakh |. Average Area Planted in Wheat, Rice, and Sugarcane measures the district average area in each crop from 2003-2005 in 1,000s of hectares as reported in the ICRISAT Meso data (Rao et al., 2012). Total area reports the total area in all other crops also in 000s of HA from the same data. Combines reports the number (in 000s) of self-propelled combines in the district as recorded in the 2006 Agricultural Input Survey. The Share of Holdings in each size class reports the share of acreage in the district in holdings within each size class in 2005 as reported in the ICRISAT Meso data. All columns include state fixed effects. Standard errors clustered at the state level are reported in parentheses.

37
observable in poor countries as in rich countries (decreased protection associated with increased inter-industry wage differences) would also tend to support our contention that in poor countries too low-wage or lower-skill sectors tend to benefit from protection.

Table 6. Relationship between inter-industry wage inequality and level of protection (average tariff rate) in poor and rich countries

|||Poorcountri<br>|es<br>||Richcountr<br>|ies<br>|
|---|---|---|---|---|---|---|
|Change in<br>average<br>tariff rate|Mean<br>change in<br>Theil<br>(Theil<br>points)|Standard<br>deviation<br>(Theil<br>points)|Number of<br>observations|Mean<br>change<br>in Theil<br>(Theil<br>points)|Standard<br>deviation<br>(Theil<br>points)|Number of<br>observations|
|Decrease|+0.02|2.56|219|+0.15|0.85|137|
|No change|+0.13|2.49|44|+0.05|0.60|72|
|Increase|-0.08|2.00|113|-0.32|3.41|23|
|_Total_|_-0.01_|_2.39_|_376_|_+0.07_|_1.29_|_232_|

Note: Poor countries are defined as those with GDP per capita less than $9000 at international 1995 prices. Rich, those above that threshold.

Figure 10 shows the change in inter-industry Theil when protection is reduced. In rich countries, the effect does not vary that much between the countries and is bunched around zero with a longer right-end tails (which explains the positive sign of the average). For the poor countries, both right- and left-end tail are approximately equally long and the distribution is flatter.

47
# **The Determinants of Regional Foreign Direct Investment and Its Spatial Dependence:**

# **Evidence from Tunisia**

**Bechir N. Bouzid Sofiene Toumi** World Bank University of Manouba Washington D.C. Tunisia

**JEL Classification :** _C33 ; F21; R12._

**Keywords** **_:_** _FDI determinants; Regional Disparities; GMM; Spatial Dependence._
# **I. Introduction**

Land area measurement has been of concern to humanity since the dawn of time. Some of the buildings constructed in ancient Egypt as early as 2700 BC testify to the knowledge of land surveying techniques. Evidence of boundary surveying in Mesopotamia and the Nile Valley from around 1400 BC have also been found (Lyman and Wright, 2015). The word ‘geometry’ originated in ancient Greece from the Greek words for ‘measure’ and ‘Earth’. Similarly, ancient traces of the command of surveying techniques have been found in China (Swetz, 1992) and India (Joseph, 1997). The need to measure land originated from concerns as diverse as the construction of buildings, tax collection, and the need to demarcate properties and boundaries. Those concerns are still valid today, and many of the basic principles and techniques have remained remarkably similar over the millennia, even though technology has certainly advanced and allows for measurements that are both easier and more precise than they might have once been.

With over 70 percent of the developing world’s poor residing in rural areas where agriculture is the primary means of livelihood, high quality agricultural data and analysis are paramount to informing policy aimed at poverty reduction (IFAD, 2010). Land is a key measure of absolute and relative farmer wealth, a critical input in production, and a key variable for normalizing agricultural input use and output measures. Although easily overlooked by analysts, the quality of land area measurement can have nontrivial implications for agricultural statistics, economics, and policy analysis (Carletto et al., 2013 and 2015; Dillon et al., 2016).

Area measurement holds significant value in the developed country context as well. In Europe, national authorities conduct a regular Farm Structure Survey employing a common methodology devised by Eurostat (Istat, 2008). The EU FIELDFACT Project is one of many schemes that aim to raise farmer awareness on the use of GPS in land measurement for the purposes of precision agriculture and more accurate and transparent subsidy claims through the EU’s Common Agricultural Policy.<sup>1</sup> Frequent land measurement is encouraged by several developed country governments (e.g. USA, UK, Germany, Australia) at the individual farmer level in promotion of precision agriculture, whereby the production process is tailored to farmland size estimates obtained via GPS/GNSS and remote sensing. By knowing exact area measurements, farmers are able to adjust input use accordingly and, thus, optimize yields while cutting down on costs. Effective land area measurement, therefore, also contributes to a more focused application of fertilizers and pesticides that could, in turn, alleviate environmental degradation and pollution.

> 1 <u>http://www.gsa.europa.eu/introduction-and-promotion-gnss-agriculture</u>

3
# 5 Regional infrastructure funding

Completing and maintaining the Southern African Development Community’s (SADC’s) regional infrastructure backbone would entail sustained spending of $2.1 billion a year over the course of a decade. The preceding sections identified a number of key gaps. The basic regional infrastructure package recommended would enable full regional power trade, a complete regional road network, and fiber-optic links connecting all countries to submarine cables. Meanwhile, the total spending needed in the SADC region to fulfill both regional and national infrastructure demands amounts to $30 billion a year. Hence, the regional portion—$2.1 billion—accounts for only 7 percent of the overall requirement.

The amount of spending needed varies widely across countries and sectors. The largest spending requirements in terms of investments and operations and maintenance (O&M) are in power ($1.4 billion per year), followed by transport ($728 million), and information and communication technology (ICT) (around $15 million). The Democratic Republic of Congo, followed by Mozambique, has the highest spending needs in the region in absolute terms. The Democratic Republic of Congo has to spend $961 annually to meet regional spending needs for infrastructure, mainly in the power sector. Mozambique needs to spend $265 million on regional integration, much of it also devoted to power-related investments.

**Table 5.1 Regional spending needs by sector**

||Trans|port|ICT||Pow|er|Tota|l||
|---|---|---|---|---|---|---|---|---|---|
||||||||||Total|
||Inv|O&M|Inv|O&M|Inv|O&M|Inv|O&M|<br>needs|
|Angola|34|36|2|0|88||125|36|160|
|Botswana|12|23|||9||21|23|44|
|Congo,<br>Dem. Rep.<br>of|139|69|5|0|748||892|69|961|
|Lesotho|0|0|0|0|||0|0|0|
|Madagascar|15|23|2|0|||17|23|40|
|Malawi|2|11|1|0|1||5|11|16|
|Mozambique|14|36|0|0|216||229|36|265|
|Mauritius|0|0|0|0|0||0|0|0|
|Namibia|37|34|||30||67|34|101|
|Seychelles|0|0|?|?|0|||||
|South Africa|11|79|0|0|2||14|79|93|
|Swaziland|11|26|0|0|0||11|26|37|
|Tanzania|28|32|3|0|44||76|32|108|
|Zambia|10|34|||141||151|34|185|
|Zimbabwe|4|7|1|0|73||77|7|84|
|**SADC**|**319**|**409**|**14**|**1**|**1,352**|**0**|**1,685**|**410**|**2,095**|

Source: Derived from Carruthers and others 2009, Rosnes and Vennamo 2009 and Mayer and others 2009 _Note:_ O&M = operations and maintenance; ICT = information and communication technology.