rafmacalaba/datause-encoder-data huggingface dataset:
Research Ennponents of World Bank Education Projects, since 1982.

|**Area**|| Country<br>and Loan/Creditl<br>Number|Approved<br>|Research<br>Components<br>[I|Current<br>Status<br>of Studies,<br>including<br>CompletedDocunients/Data|
|---|---|---|---|---|
||||(v) study MEN organization;<br>(vi) establish study fund ($300,000)||
|AFRIC|A Comoros CR 1751-CON|1987|Second Education. Planning Unit of Education<br>Planning Directorate to urdertake<br>studies to assess factors detrimental<br>to student achievement and collect<br>edication statistics<br>(para. 33.03 p.15)|Excellent statistics<br>being produced by planning<br>unit with praject support. No other studies.|
|AFRIC|A Comoros CR 1195-COa|1982|First Education. No research coaponent.||
|AFRIC<br>AFRIC|A Djibouti CR 1543-DJI<br>A Equatorial Guinea<br>CR 1797-EG|1985<br>1987|First Education. Evaluation studies to focus<br>on; (i) teacher recruitment, training and<br>upgrading,mndcommitment;<br>(ii)<br>curricula adjustment;<br>**(iii)**<br>relationship<br>betweenexmscoresand<br>curricula/teacher<br>qualifications;<br>in relation to test scores (para.4.06 p.21)<br>and, (iv) repetition<br>and double shift teaching<br>Primary Education. As part of strengthening<br>the education sector planning capabilities;<br>(i)<br>school map updating (March 1968),<br>(ii)<br>coltect and publish school statist,cs<br>(iii)<br>prepareastudy on the future needs of<br>secondary and university education|No informtion<br>supplied.<br>(i) and (ii):<br>work uderway<br>(iii):<br>not initiated.|

28
commodity prices (e.g. oil). This situation led to large fiscal surpluses, eliminated the need for debt issues and hindered the development of a benchmark yield curve. Moreover, private issuers have not issued instruments because of abundant access to cheap capital from domestic banks and abroad. Pension funds have been forced to invest primarily in foreign assets due to the lack of domestic instruments, and have earned earn negative returns in domestic currency due to the currency appreciation.<sup>17</sup> The analysis of actual reform cases supports the view that the optimal financing strategy for most emerging countries is a mixed one **.** Most countries have initiated reforms with fiscal deficits of around 3 percent of GDP or higher, savings ratios around 20 percent of GDP or lower, and debt ratios above 30 percent of GDP. In these cases, a mixed strategy with a strong tax component would have produced more positive outcomes on savings, portfolio diversification, and capital market development, possibly contributing to a better growth performance. At the same time, the analysis of actual reform cases also shows that commodity exporters would need to reevaluate the appropriateness of introducing a second pillar, as in these cases the implicit returns on a balanced PAYG system may be higher than the returns on a funded scheme.<sup>18</sup>

# **FINANCIAL INFRASTRUCTURE**

This section addresses financial sector pre-conditions for a pension reform, and is divided into two parts. The first part identifies the preconditions that need to be in place at the time of the reform, while the second part provides a roadmap for further institutional and regulatory improvements that should be introduced within the first five years of reform implementation. Moreover, the preconditions at the initial stage are divided in those that are essential and those that are highly desirable. The preconditions classified as highly desirable at the start of the reform become essential within a period of five years. The analysis assumes that an important part of the pension assets are invested in the domestic market.<sup>19</sup>

# **_Preconditions at the Start of the Pension Reform_**

# **Legal Framework**

**It is essential to ensure the existence of rule of law and the provision of a basic legal framework to the protection of citizens against the arbitrary use of state**

> 17 See World Bank (2006).

> 18  Under these conditions the economy has excessive savings and is dynamically inefficient (see, e.g. Feldstein (1995)).  The authors are unaware of theoretical research examining how countries can meet debt solvency conditions and the dynamic efficiency condition simultaneously.

> 19 Most of the preconditions are not necessary in the case that pension assets are fully invested in foreign securities through foreign platforms.  The main challenge in this case are in the legal framework, payment system and the provision of currency hedges (foreign exchange derivatives may not be available at reasonable prices and maturities).

8
urban and rural areas.<sup>12</sup> Referring to Nicita (2004) for a detailed explanation of the model, the estimating equation is given by: where _X gt_ is the primary control variable (the international price of good _g_ expressed in domestic currency), while _Z gtr_ is a vector of control variables that includes local supply and regional income, _R_ and _U_ denote rural and urban dummy, and ε _gtr_ is an i.i.d. error term. The coefficients of interest are: γ that represents the tariff pass-through elasticity and<sup>γ</sup> 1<sup>and</sup> γ 2 that are its adjustment for distance from the US border. The pass-through is “full” or “complete” if γ = 1 and the pass-through is “incomplete” if γ < 1. Similarly, the effect of the pass-through will be identical in all urban areas if γ 1 = 0 . On the other hand, if local prices vary as a consequence of movement in the tariff, then γ 1 ≠ 0 . Similar reasoning is applied in the case of rural areas, where the coefficient of interest is γ 2 .

The econometric estimation of equation (1) combines a time series of crosssectional data set into a pseudo panel.<sup>13</sup> The data consist of domestic prices for 63 regions and six time periods. Average prices for each region are arranged into a panel data set and the estimation is performed separately for agriculture and for manufacturing. Table 3

> 12 Higher transport costs and local supply suggests a lower price pass-through in rural areas, especially on agricultural products.

> 13 From a time series of six cross section surveys (from 1989 to 2000), synthetic cohorts are defined as groups with fixed membership, whose individuals (or households) can be identified as they show up in the surveys. For this reason, groups are defined according to some time invariant variables. Means within each cohort are calculated and followed for each temporal unit under examination: this cohort aggregation is defined as a _pseudo panel_ .

11
# _(a) The shock: Destruction, relief, and our data_

The tsunami struck Aceh in late December 2004. After late winter and spring fieldwork, in the summer and fall of 2005, we surveyed village heads and local heads of the fishermen’s association ( _Panglima Laot_ ) in 111 fishing villages. In the fall of 2007 and of 2009, we resurveyed the 111 villages and added another 88 villages, which were further away from the capital Banda Aceh and were inaccessible in 2005. The intent was to cover the universe of fishing villages as we moved some distance south and north-east of the capital Banda Aceh, as explained in the appendix. Our villages are in the five districts most affected by the tsunami and account for about 30% of all house aid in Aceh, with urban Banda Aceh accounting for much of the rest. The timing of the tsunami, fieldwork, and surveys are in appendix table A1. One set of surveys focuses on village level characteristics and outcomes. As noted in the appendix, they ask in detail about the public and private capital destruction and the aid process and then ask background questions on village demographic and socio-economic characteristics. Table A2 in the appendix gives statistics on village characteristics we use in estimation. For these questionnaires, in the majority of cases, the official interviewee is the village head, who is also the respondent to the PODES, a tri-annual census of Indonesian village populations and facilities. But often multiple village officials were present and village heads were not shy about asking other prominent villagers to supply or confirm information.<sup>5</sup>

> 5. In 2007, for example, 62% of the time the official respondent was the village head, 25% the secretary, and the rest at least both. But often the mullah or head of the local fisherman’s association would drop by and participate in the interview. Surveyors generally did not record this, as these are not village officials per se.

7
<!-- Start of picture text --> ModelB_1<br>[)-n rar 382<br>[> -01:091 381 - o.000000<br>[) a.cocco1 - 0 250000<br>(OU 0.250001 - 0. 250000<br>(EE 02500m - 0 so0000<br>HB 0.500001 - 0.750000<br>HEB 0.750001 - 1.con000<br>HE 000001 - 1.250000<br>HE 260001 - 100000<br>HB | 500001 - 1.750000<br><!-- End of picture text -->

Figure 6: Estimated Relative Risk of Conflict Events for Locations in Figure 3, based on Model B1
21 regimes. Accordingly, Table 3 presents predicted values of the employment size distributions based on regression results from Table 2.

The top panel illustrates how the employment size distribution shifts as the import shars rises, the middle panel illustrates how it shifts as the export share rises, and the bottom panel illustrates .hifts with changes in effective protection. The left size vf the table describes a low turnover industry while the right side corresponds to a high turnover industry. Within each panel, columns present "low", "medium' and "high" export or import shares.<sup>22</sup> Finally, rows of the table give predicted employment levels for the 5th through 95th percentiles, as well as the mean and standard deviation of the employment distribution.

First, focussing on the size distribution for low turnover industries, the leftward shift in the size distribution as import shares increase is marked. For example, the mean plant size falls from 73.4 to 31.1 employees as the import share rises. This leftward shift is particularly large for the 7 5<sup>th,</sup> 90<sup>th, and9</sup> 5<sup>th</sup> percentiles. Similarly, both the mean and the standard deviation drop substantially with increases in import share. Recall, however, that high turnover moderates the extent to which import shares reduce plant size. This appears in Table 3 when one moves from the low turnovter to the high turnover figures, especially among large plants.

Relative to import shares, export shares appear to covary less with the employment size distribution. For example, among low turnover industries, the mean plant size declines only from 54.2 to 51.1 employees as the export share increases. Also, although plants in high turnover industries are generally more concentrated in the lower employment ranges, changes in export shares appear to have little effect on location or shape of the distribution.
This argument is consistent with an increase in private capital inflows before the crisis; a stylized fact associated with the currency crisis episodes of the eighties and nineties.

Capital flight in this context becomes both, a measure of private expectations regarding the exchange rate and the future relative price of domestic and foreign assets, and a measure of domestic agents' ability to prevent expropriation of their assets.

Thus, to test the insurance model one would like to have access to data on capital flight, measured consistently across a large sample of countries, and gross foreign liabilities, stocks as well as flows.

> Ideally, the insurance logic could also be tested using some criteria of bank inefficiency such

> as non-performing loans, or by using banking crises as a proxy for a weak financial sector, since a test of Granger causality can be used to check that currency crises are not the source of banking crises. At the same time, there are arguments that a banking crisis can occur if a devaluation spirals up the liabilities side of the financial sector's balance sheet. This is also however an indication of an imperfection in the domestic banking sector, since in a perfect world there should be risk pooling, so banks would insure themselves against exchange-risk. While the database developed by Caprio _et al_ (World Bank, 2001), fills a useful gap, it is

> noteworthy that the countries surveyed mostly refused to disclose any information in their response to the question on non-performing loans.

> A.3: The Illiquidity Model: The stylized fact that currency crises are associated with a cessation or reduction in capital inflows implies, by definition, that there must be a decrease

> in the current account deficit or in foreign reserves or both. If the exchange rate is pegged, the authorities are intervening in the exchange market to adjust quantities, as prices cannot change. This makes the central bank vulnerable to runs since any reallocation of portfolios away from' domestic assets involves a fall in their international reserves. The central bank, as lender of last resort, is committed to stand ready to swap its foreign assets for domestic currency. The entire stock of domestic currency is the liability of the monetary authorities. Thus, the absence of capital restrictions generates a situation that is similar to that of an

6
expenditure patterns both between products and over<sup>time.Whymightweexpectagreater</sup> availability of economic data to be associated with better<sup>quality government?For a number of</sup> reasons more widely available data can help governments<sup>governbetter.For one,the publiccan</sup> judge their governments' ability to make sound<sup>policyby lookingatsuchdata.The abilityto</sup> judge leaders according to how they perform<sup>in theeconomic spherecanaffectthelevelof</sup> support the government has and determines how long<sup>theystayin power.Incountries where</sup> different constituents are able to gauge economic<sup>performance,and wherecitizensarewell</sup> informed, people are more likely to demand<sup>governmentsthatgovernbetterandgovernments</sup> have more of an incentive to do well. That is governments<sup>become moreaccountabletotheir</sup> people. Even in non-democratic. countries<sup>policymakersmayfeelboundtoproducebetter</sup> economic policy because they are monitored more<sup>effectively andtheycareabouttheir</sup> reputations. They will be more wary of making large<sup>mistakes.</sup> Second, data can help better coordination between members<sup>of government.For example,</sup> the budgetary process can benefit from data<sup>onoutcomesrelatedto fiscalexpenditures.Third,</sup> the use of data to design policy can improve policymaking,<sup>helpidentifygoalsandevaluate</sup> alternative policies; and it can help policymakers to<sup>understandthe relativemagnitudesof the</sup> issues for which they may have had only a qualitative<sup>feel.A better understandingof the effects</sup> of policies can lead to a change in the nature of the<sup>policiesadopted.Forthesereasons, the</sup> provision of timely and good quality economic<sup>datacan improve governance.</sup> Countries which produce economic data on a<sup>timelybasisandpromotetheir</sup> dissemination are also likely to be countries which<sup>supportbetter informationflowsallaround.</sup> In other words, economic data can be thought of as<sup>a proxy for other kindsof data.It isof course</sup> an imperfect proxy since experience clearly shows that<sup>governmentsmayonoccasiondivulge</sup> economic data but not political data.

5
# >>> TABLE 9 ABOUT HERE

Table 9 presents the results of the count data and interval regression models with household controls. We see once again that experimental cooperation can make a difference for reported forest-related behaviors. CONTRIBUTION1 is positively correlated with all three dependent variables, but only the estimated effect on numbers of trees planted on respondents’ own land during the previous five years is statistically significant at least at the 10% level. We note, though, that the estimated effects of CONTRIBUTION1 on MONITORING and NUMTREES_CF are very close to 10% significance levels.

CFP members are estimated to monitor common forests less than others, which is not wholly unexpected because in our study area the Nepal Community Forestry Programme is not a unique path to forest collective action and non-Community Forestry Programme forests are known to engage in significant collective action (Bluffstone et al., 2014). We do find, though, that CFP officers spent more hours in monitoring forests during the previous month than those who are not officers. Low caste Dalit respondents report planting fewer trees, whether on their own land or in common forests. In our communities there is little evidence that low caste user group members do more work (e.g. monitoring or tree planting) than higher caste groups, which has been an issue noted by others (e.g. Adhikari 2005). Respondents with more education plant more trees on their own land and those in larger families do more monitoring.

# **5. Conclusions**

This paper examines whether cooperative behavior of respondents as measured by contributions in a one-shot public goods game correlates with reported pro-forest behaviors which are highlighted in the literature as contributing to forest quality, including reduced forest 23
# **TABLE A17:  IJET's Country Security Risk Ratings (IJT)**

## **A17: IJET's Country Security Risk Ratings**

https://worldcue.ijet.com/tic/login.jsp iJET is a privately held company founded in October 1999 and is based in Annapolis, MD. iJET monitors the world around-the-clock and alerts travelers, expatriates and decision-makers to events and situations in real-time to help them avoid or minimize risk and travel disruptions abroad. iJET's professional services offer in-depth analysis of changing risks around the world, and allows organizations to monitor, locate and communicate with traveling employees and expatriates.

In our paper, we use the iJET's security risk ratings for 167 countries worldwide in 2004 and 2005.

|**Table A17: IJET Secutiry Ratings **|2005<br> **(167 developed and develo**|2004<br>**ping c**|2003<br>**ountr**|2002<br>**ies)**|2000|1998|1996|
|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||
|NA|..|..|..|..|..|..|..|
|**Political Stability**||||||||
|Security Risk Rating|X|X|..|..|..|..|..|
|**Government Effectiveness**||||||||
|NA|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||
|NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|NA|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||
|NA|..|..|..|..|..|..|..|

67
43

**Figure 10: Net income of the refining, transportation, and marketing segment of Petrobras**

<!-- Start of picture text --> 10<br>5<br>0<br>‐5<br>‐10<br>‐15<br>‐20<br>2007 2008 2009 2010 2011 2012 2013 2014<br>US$ billion<br><!-- End of picture text --> _Source_ : <u>http://www.investidorpetrobras.com.br/en/financial‐results/.</u> The producer price of LPG sold in 13‐kg cylinders, used by households, was frozen between December 2002 and September 2015 and remained R$0.87 (varying between US$0.25 and US$0.55 depending on the exchange rate) per kg before tax. In September 2015, the producer price was raised to R$1.00 (US$0.26) per kg (ANP 2015b). Brazil is a net importer of LPG, but the producer price has been significantly below the import‐parity price of LPG. The producer price of LPG sold to other consumers has been adjusted but infrequently: Since 2003, there have been seven adjustments, of which the one in February 2009 was a downward adjustment and all others (January, April, and June 2008, January 2010, December 2014, and September 2015) were upward. In 2015, the producer price of other LPG was two‐ thirds higher than that of LPG sold in 13‐kg cylinders until September. While the producer price and taxes for the latter was frozen between December 2002 and September 2015, distribution and retail margins more than doubled in local currency, increasing the end‐user price over the years.

There are subsidies for natural gas used in power generation in Brazil. For example, in April 2015, the price of domestic gas charged to distributors was US$8.80 per mmBtu but there was a discount of US$2.37 per mmBtu offered to the power sector. Even a bigger discount is offered to legacy plants benefiting from the Thermoelectric Priority Program. In the same month, the price natural gas under the program, before tax, was only US$4.03 per mmBtu, against US$11–14 per mmBtu after tax paid by large industrial consumers of natural gas, depending on location. The price before tax in fact averaged US$4.03 during the first nine months of 2015, falling from US$4.20 in January to US$3.78 by September (MME 2015a, 2015b).

# **Cameroon**

The Caisse de Stabilisation des Prix des Hydrocarbures (CSPH, Hydrocarbon Price Stabilization Board) is a regulatory agency and a public company in Cameroon with a legal status and financial autonomy established in 1974. The CSPH sets and equalizes the prices of petroleum products at all depots throughout the country for the purpose of protecting consumers from price fluctuations, in principle within the limits of its funds. The CSPH is supposed to review prices every month and make recommendations to the government. It is financed in part by equalization fees it levies on petroleum products for pan‐territorial pricing.
Table 6. Labor Force Participation by Cohort Matching group: Age 17-65. Three nearest neighbors based on propensity scores

|||**_Working people_**||**_Se_**|**_arching for aj_**|**_ob_**|
|---|---|---|---|---|---|---|
|_Cohort_|_CtrL Group_|_CONALEP_|_Difference_|_Ctr/ Group_|_CONALEP_|_Difference_|
|90- 93|94.1|93.0|-1.0|5.9|7.0|1.0|
|91 - 94|96.4|93.6|-2.8|3.6|6.4|2.8|
|92 - 95|95.2|89.9|-5.3|4.8|10.1|5.3|
|93-96|94.7|88.9|-5.8|5.3|11.1|5.8|
|94 - 97|93.1|90.5|-2.6|6.9|9.5|2.6|
|Total'|94.8|91.2|-3.6|5.2|8.8|3.6|
|_ENE 982_|97.5|||2.5|||
|_ENE 98, LS'_|94.5|||5.5|||
|_ENE 98, US_|95.7|||4.3|||

1. Sample: Workers in **the** matching group.

2. Sample: All workers.

3. Sample: Workers with lower-secondary complete and 3 years of experience (18 and 19 years old).

4. Sample: Workers with upper-secondary complete and 1-5 years of experience (22-26 years old).

Table 7. Labor Force by Cohort

**Matching group: Age 17-65. Three nearest neighbo based on propensity scores**

|||**_Working peopl_**|**_e_**|**_Se_**|**_arching for_**_aj_|_ob_<br>|
|---|---|---|---|---|---|---|
|_Cohort_|_Ctrl. Group_|_CONALEP_|_Difference_|_Ctrl. Group_|_CONALEP_|_Difference_|
|90 - 93|97.0|94.5|-2.5|3.0|5.5|2.5|
|91 - 94|95.7|93.1|-2.7|4.3|6.9|2.7|
|92 - 95|96.3|88.3|-8.0|3.7|11.7|8.0|
|93 - 96|94.7|88.8|-5.9|5.3|11.2|5.9|
|94 - 97|95.7|87.9|-7.8|4.3|12.1|7.8|
|Total'|95.9|90.8|-5.1|4.1|9.2|_5.1_|
|_ENECE992_|98.1|||1.9|||
|_ENVECE 99, LS_<sup>_3_</sup>|95.7|||4.3|||
|_ENECE 99, US'_|98.4|||1.6|||

1. Sample: Workers in the matching group.

2. Sample: All workers.

3. Sample: Workers with lower-secondary complete and 3 years of experience (18 and 19 years old).

4. Sample: Workers with upper-secondary complete and 1-5 years of experience (22-26 years old).

28