rafmacalaba/datause-encoder-data huggingface dataset:
designed fiscal rules, supported by strong governance and institutions, are essential for ensuring effective fiscal discipline.

While the literature on fiscal rules is vast, certain aspects key to their effectiveness have not received sufficient attention. In particular, we have limited understanding of how the effects of fiscal rules develop over time.<sup>2</sup> Most studies estimate the average effects of fiscal rules, sometimes accounting for specific conditions. However, this approach implicitly assumes that these effects remain constant in both the short and medium-to-long term— an assumption that is unlikely to hold. Credibility, a cornerstone for the success of a fiscal rule, takes time to develop. Furthermore, over time, the conditions that led to the adoption of the rule may have changed, potentially weakening the motivation of governments to stick to the rule’s constraint(s). The evolution of fiscal rule effectiveness likely depends on country-specific characteristics, offering valuable insights into how these factors shape medium-to-long term outcomes. For example, better governance structures and higher-quality institutions may enhance the effectiveness of fiscal rules by helping to build the credibility necessary to ensure their long-term survival. In contrast, the absence of such institutional support may yield only short-to-medium term effects.

Studying the dynamic effects of fiscal rule adoption also helps us understand whether initial conditions—the environment in which these rules are introduced—matter. The notion that initial conditions might influence the long-term success of fiscal rules can be inferred from the literature on economic reform. Several studies show that the origin of economic reforms, along with the political and economic conditions at the time of adoption, play a crucial role for their success (Rodrik 1996; Duval, Furceri, and Miethe 2020; Alesina et al. 2024; IMF 2024). The same logic can be applied to the environment in which fiscal rules are implemented. For instance, the effect of rules introduced during economic downturns may evolve differently compared to those implemented in more stable times. Likewise, fiscal rules adopted in a political climate of strong consensus may yield different effects than those established with limited political support. In their early survey of fiscal rules, Kopits and Symansky (1998) emphasized the importance of

> 2 There are some studies that have looked at the dynamic effects of fiscal rules on fiscal policy, but they typically have a narrow focus.  Afonso and Jalles (2019) study how the effects of fiscal rules on sovereign yield spreads evolve over the years. Apeti et al. (2024) examine the effect of fiscal rule adoption on the share of borrowing in foreign currency. Chrysanthakopoulos and Tagkalakis 2024 also examine the dynamic effects of fiscal rules. However, due to their model specification, their focus is primarily on the level-shift effects of fiscal rules (i.e., the difference between having a rule and not having one) over the medium term, rather than on the adoption process itself.

2
c) <!-- Start of picture text --> Total Managed Forest Land (billion hectares) Difference between Stochastic and Deterministic Solutions<br>1.75<br>0<br>−2<br>−4<br>1.7<br>−6<br>−8<br>1.65 −10<br>−12 Range of all sample paths<br>10% quantile<br>−14 25% quantile50% quantile<br>1.6<br>−16<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090<br>Year Year<br>d)<br>Protected Land (billion hectares) Difference between Stochastic and Deterministic Solutions<br>0.36 0.6 Range of all sample paths10% quantile25% quantile<br>50% quantile<br>0.34<br>0.4<br>0.32<br>0.2<br>0.3<br>0<br>0.28<br>−0.2<br>0.26<br>−0.4<br>0.24<br>0.22 −0.6<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090<br>Year Year<br>million hectares<br>million hectares<br><!-- End of picture text --> Figure 3: Optimal Global Land Use Path under Perfect Foresight of Crop Productivity The expansion in cropland is associated with significant losses in unmanaged natural land (panel b), which declines by 220 million hectares (or by 9 percent compared to 2004) around 2040. Managed forest area declines by a small amount in near decades and then increases, returning to its 2004 levels by mid-century (panel c). In the second half of the coming century growing demand for timber products results in a stronger growth in managed forest land. Managed forest area increases to 1.75 billion hectares by the end of the century, which is 8 percent larger than in 2004. Unmanaged forest area continues to decline, although at slower pace in the second half of the century, falling to 2.11 billion hectares, which is 18 percent small than in 2004. The decline in unmanaged forest land is less environmentally damaging in the second half of the coming century, as deforestation does not occur, and most of that land is converted to managed 23
While increased honey collection can be beneficial, we should stress that this activity is fraught with hazards from twice-daily tidal inundations and attacks by man-eating tigers,

**Table 5: Median changes in key mangrove species by** **_upazila_ , 27 scenarios**

|Upazila|<br>_Avicennia_<br>_alba,_<br>_marina,_<br>_officinalis_<br>(Baen)|Med<br>_Excoecaria_<br>_agallocha_<br>(Gewa)|ian% Chan<br> _Ceriops_<br>_decandra_<br>(Goran)|ge<br>_Sonneratia_<br>_apetala_<br>(keora)|_Heritiera_<br>_fomes_<br>(Sundri)|
|---|---|---|---|---|---|
|Dacope|0|0|0|0|0|
|Koyra|45.6|20.0|20.0|-0.3|-58.0|
|Mongla|0|47.4|(a)|0|-2.3|
|Sarankhola|0|0|0|0|0|
|Shyamnagar|-71.0|-67.3|4.2|-68.1|0|

(a) Ceriops decandra (Goran) has an extremely small median increase in Mongla, where the species has a tiny presence in 2013. This would translate to a huge but misleading percent increase.

crocodiles, venomous snakes and sharks.<sup>19</sup> Unfortunately, augmented production of honey is likely to increase conflicts between humans and wildlife.

# **6.  Summary and Conclusions**

In this paper, we have used a detailed scenario analysis to assess possible impacts of climate change, sea level rise and land subsidence on aquatic salinity, mangrove species migration and poor communities in the Bangladesh Sundarbans. Our analysis draws on spatially-formatted information from Dasgupta et al. (2015a), including aquatic salinity in 2012, and 27 projections for 2050, developed from combinations of three IPCC climate change scenarios (B1, A1B, A2),

> 19  The available statistics indicates that thousands of honey collectors have been killed by tigers, crocodiles and snakes since human habitation of the Sundarbans began.  It is believed that approximately 80 people are killed every year by tigers alone.

24
|Multilateral|48<br>7%|3<br>0%|493<br>72%|46<br>7%|77<br>11%|11<br>2%|2<br>0%|
|---|---|---|---|---|---|---|---|
|Bilateral|224|1|172|22|415|35|135|
||22%|0%|17%|2%|41%|3%|13%|

_Source: REFD (2024)_ Core contributions by UN and other international organizations (IO) are excluded from the above analysis, yet the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA)<sup>20</sup> is a major financer of refugee education for Palestinians in Jordan, Lebanon, Syria, and the West Bank and Gaza Strip. In 2021, the organization allocated US$ 944 million of its core resources for refugee education to the MENA region (REFD, 2024). When UN and other IO's core contributions are included in financing allocations for refugee education, the MENA region receives the highest volume of donor financing, with 55 percent of all ODA for refugee education in LICs and MICs going to the region, followed by 25 percent to ECA, 10 percent to SSA, 3 percent to LAC, 2 percent to SCA and over 5 percent unspecified.

# Key Fact 8: ODA allocations for refugee education are distributed unevenly across income groups and regions

In 2021, LICs hosted 29 percent of school-age refugees in LICs and MICs, yet received just 11 percent of ODA allocated for refugee education in LICs and MICs. By comparison, UMICs received 54 percent of ODA for refugee education, while hosting 33 percent of school-age refugees in LICs and MICs (see Table 2). While ODA allocations do not correspond to the number of school-age refugees hosted by each income group, this financing is somewhat proportional to the costs of educating refugees in these income categories.

education shows that ODA allocations broadly align with average costs in each income group, with UMICs receiving a greater share of ODA in line with their greater costs (see Table 2, columns 4 and 5). This does not indicate these allocations are progressive. Rather, this analysis implies that a country’s income level does not have a significant impact on the ODA it receives for refugee education, when arguably LICs should receive more ODA given their limited fiscal capacity and typically over-stretched education systems.

_Table 2: Distribution of school-age refugee children and ODA for refugee education received by host country income-category_

|Income Group|ODA for<br>education (US$ Millions)|Share of school-<br>age refugees in<br>LICs & MICs|Share of costs of<br>refugee<br>education|Share of ODA for<br>refugee<br>education|
|---|---|---|---|---|
|Low income|296.2|29%|3%|11%|
|Lower middle income|714|37%|25%|27%|
|Upper middle income|1,433|33%|72%|54%|
|Unspecified|187|-|-|7%|

20 UNRWA is funded almost entirely by voluntary contributions, with state and regional governments providing 94 percent of all contributions in 2021. Financing for core contributions cannot be disaggregated by sector. However, the largest financers of the UNRWA’s programs budget in 2021 were the United States, Germany, European Union, Sweden and Japan, which combined made up 70 percent of the total program budget (UNRWA, 2022).

17
supply of other food products such as meat, cheese, milk, and fish. Other affected countries such as Brazil, Ecuador, Malaysia and Indonesia also represent a significant share of world supply of respectively citrus fruit (48.7), bananas (30.4), palm oil (31.8), and coconut oil (46.6) (Appendix Table 1).

**Figure 4. World imports from top-50 most affected by Covid-19**

<!-- Start of picture text --> Meat 82.8% 17.2%<br>Fresh Fruits - Vegetables 81.8% 18.2%<br>Vegetable and animal oils 78.2% 21.8%<br>Sugar 73.3% 26.7%<br>Cereals 69.6% 30.4%<br>Fish 65.9% 34.1%<br>Legumes and pulses 64.8% 35.2%<br>Dairy and eggs 63.4% 36.6%<br>Nuts and derived products 58.3% 41.7%<br>Oil-bearing 51.8% 48.2%<br>Stimulant crops 38.3% 61.7%<br>World imports from 50 worst affected COVID Rest of the world<br><!-- End of picture text --> _Source_ **_:_** <u>Espitia, Rocha, Ruta (2020). “Database on COVID-19 trade flows and policies”.</u> The negative impact of disruptions in food supply as a result of Covid-19 will be potentially higher for import food dependent countries,<sup>11</sup> with high levels of concentration of imports coming from the top-50 most affected countries. Import food dependent developing countries would be particularly vulnerable to supply shocks in key staples such as cereals, meat and fresh fruits, where more than 74 percent of imports on average come from the top-3 exporters that are most affected by Covid-19 (Table 2).<sup>12</sup> Import food dependent developing countries such as Botswana, Mexico and Jamaica import more than 95 percent of rice, wheat and corn from the top-3 exporters most affected by Covid-19. Least developed countries such as Lesotho and Angola have concentration of imports of rice and wheat from the top-3 most affected countries by Covid-19 that are above 95 percent. Almost the totality of imports of poultry for Botswana, Mexico and Lesotho and imports of bovine meat for Jamaica, Bosnia and Herzegovina and The Gambia come from the top-3 most affected Covid-19 countries (Appendix Table 4 and Appendix Table 5).<sup>13</sup>

**Table 2: Developing countries’ concentration of imports from top-3 Covid-19 most affected countries, by level of food dependence**

||**Low food dependence**|**Medium food dependence**|**High food dependence**|
|---|---|---|---|
|Cereals|69.4|71.0|77.5|
|Dairy and eggs|59.7|63.2|72.0|

> 11 Import food dependence is calculated as the share of net food imports over domestic food supply: F F d d i = 100 × (I i ⁄ i + (I i ) , where _i_ is importer and _k_ is product. This share is positive for net importers and negative for net exporters. For net importers, the share ranges between 0 (no import dependence) FFd FFd and 100 (maximum import dependence). For this analysis, country-products with food dependence higher than 20 percent are ddFFI II−E ddFFI II) (ddIIF ppddIIppFFdd ddFFI II −E ddFFI II) considered as import food dependent. See FAO (2018).

> 12 See Appendix Table 2 for results at the product level.

> 13 For information on the top-10 import food dependent  advanced countries in terms of concentration of imports from the most affected Covid-19 countries, see Appendix Table 3.

10
Check that the value lies within the permissible range. Negative precipitation is clearly nonsensical, so the minimum permissible precipitation is 0 mm.

The ‘scenario selector’ file was designed to inform our own Fortran software, and contains the names of the ‘response pattern’ and ‘global warming’ files to use for each scenario, and the value by which to divide the pattern files to obtain responses expressed in units per degree of global warming. These values are duplicated in Table 6 for clarity. It may be noted that the pattern files for ECHam4 are already stored in units per degree of global warming.

The ‘minimum’ and ‘maximum’ files were included in order to simplify the additions that had to be made to an existing code base. They are not likely to be convenient if writing one’s own software. The same effect can be achieved by applying the values in Table 7 to every grid-box.

# **3.2 Climate change results for case studies**

The results were as follows:

- i. WatBal, the water model, was run for base climate at a monthly time step for 1961– 1990, producing 360 values for every district. We developed a ‘monthly average’ base for January to December by averaging over the period 1961–1990 (the standard IPCC base) for each of the 12 months. The monthly ‘average’ base values are the first set of data giving for all results for climate change.

- ii. For climate change, we ran ten climate change scenarios, described in the previous section: five models for A2 and B2 monthly for the 100 years 2001–2100, producing 1200 monthly values. As requested by the economic modelers we developed decadal average values (monthly) for precipitation , temperature and streamflow.

We provided this data in two formats:

- i. Absolute value for the base case and ten sets of decadal average _absolute_ values of precipitation, temperature and streamflow.

20
Phase I when spot prices were low, investment studies will need to reconcile the conflicting incentives given by the pricing of Phase I and Phase II allowances.

Prices for natural gas, petroleum, and coal paid by electricity generators in the United Kingdom are directly observable on a monthly basis from the United Kingdom (2009). For the other countries, this study must estimate monthly prices based upon regional monthly wholesale prices published by Platts (2009) for various market hubs in the EU and quarterly prices published by the International Energy Agency (2009) that measure prices paid by end users including taxes. Quarterly averages are computed from the monthly data from Platts. Next, we compute the ratio of these quarterly averages to the quarterly data reported by the IEA. These ratios represent the spreads between prices in each country and the market hub. Monthly estimates for prices in the remaining eleven countries result from multiplying these ratios by the monthly data from Platts.

# **6. Model Estimation Results**

The above econometric model of the restricted variable cost function (4) and the three variables input demand functions given by (5) are estimated as a system of equations. Given that fuel and carbon permit prices and output could be endogenous, an instrumental variables estimator is needed. The Generalized Method of Moments (GMM) estimator provides for consistent parameter estimates and allows correction of the standard errors for heteroscedasticity and autocorrelation in the error terms. The instruments include lagged values of input prices, generation levels for nuclear, hydroelectric, and renewable generation, total power generation, and country and monthly dummy variables. The lagged instruments vary for each equation and correspond with the specific specification of the right-hand side variables in equations (4) and (5). So, for example, the instruments for the input demand functions include square roots of lagged price ratios. This approach is intended to ensure that the instruments are correlated with the explanatory variables but remain independent of the error terms. Country and monthly dummy variables are included as instruments in all four equations.

13
Policy **R_searohe** JSorking Paper Series

||Title|Authoe|Date|**Contact**<br>**for**paper|
|---|---|---|---|---|
|WPS1979|Bankingon Crises: Expensive<br>Lessons fromnRecent Finarr ,`al<br>Crises|GerardCapric, Jr.|September 1998|P.Sintim-Aboagye<br>38526|
|WPS1980|The Effect of Househoid Wealth<br>on Educational Attainment<br>Demographic and Health SL.rvey<br>Evidence|DDeonFilmer<br>Lant Pritchett|September 1998|S. Fallon<br>38009|
|WPS1981|Evaluating Public Expenditures<br>When Governments Must Refly<br>on Distortionary Taxation|James E. Anderson<br>Will Martin|September 1998|L. Tabada<br>36896|
|WPS1982|Analyzing Financial Sectors in<br>Transition: With Special Reference<br>to the Former Soviet Union|Alan Roe<br>Paul Siegelbaum<br>Tim King|September 1998|D. Cortijo<br>84005|
|WPS1983|Pension Reform in Small Developing<br>Countries|Thomas Charles Glaessne<br>Salvador Vald6s-Prieto|r September 19C8|M. Navarro<br>84722|
|WPS1984|NAFTA, Capital Mobility, ancl<br>Mexico's Financial System|Thomas Charles Glaessne<br>Daniel Oks|r September 1998|M. Navarro<br>84722|
|WPS1985|The Optimality of Being Efficient:<br>Designing Auctions|Lawrence M. Ausubel<br>Peter Cramton|September 1998|S. Vivas<br>82809|
|WPS1986|Putting Auction Theory to Work:<br>The Simultaneous Ascending Auction|Paui Milgrom|September 1998|S. Vivas<br>82809|
|WPS1987|Political Economy and Political<br>Risks of Institutional Reform in<br>the Water Sector|Ariel Dinar<br>Trichur K. Balakrishnan<br>Joseph 'Wambia|September 1993|F. Toppin<br>30450|
# **Appendix A:** _Sample selection issues_

We set out to obtain a sample of enterprises with less than 100,000 LKR in capital stock, excluding capital invested in land and buildings. The initial sample for the experiment was from a random selection of households in 25 Grama Niladharis in the Sri Lankan districts of Kalutara, Galle and Matara. Grama Niladharis, or GNs, are administrative units with an average of about 400 households. The 25 GNs selected had high rates of self employment and moderate or low levels of education. A screening survey was used to select households into the final sample. The screen asked whether there was anyone in the household between the ages of 22 and 65 who was self employed. Those working in professional services, fishing, transportation and agriculture were eliminated. Those owning a motorized vehicle or hiring paid employees were also eliminated. These restrictions were made because enterprises in these industries, or those with employees or motor vehicles, are likely to have more than 100,000 LKR in capital. Finally, near the beginning of the survey given to enterprise owners meeting these criteria, we ask the owner whether the market value of invested capital excluding land and buildings was less than 5,000 LKR, between 5,000 and 50,000 LKR, between 50,000 and 100,000 LKR, or greater than 100,000 LKR. Those reporting capital stocks in excess of 100,000 LKR were not interviewed.

Because returns to capital are expected to be correlated with entrepreneurial ability, we are particularly interested in whether and how the sample’s selection on ability differs between females and males. This will be affected first by decisions to participate in the labor force. Not surprisingly, men are much more likely to be active in the labor market than women. According to the 2006 Sri Lankan labor force survey, the labor force participation rate of 30-39 year old urban males is 96.4 percent, compared to only 34.4 percent for the same age urban females (DCS, 2007). Furthermore, only 22.7 percent of female workers are self-employed, compared to 30.8 percent of male workers. Thus selfemployed individuals are only 7.8 percent of prime age females, compared to 29.7 percent of prime age males. While we expect that selection of females into the labor market is determined at least in part by ability, attitudes toward risk, and related factors. But if capital flows mainly to those with entrepreneurial experience, then those active in 32

-  -
- 12 -

Table 2. Mean Earnings, Years of Schooling and Mincerian Returns to Education: by Gender, circa 1989

|Country|Year|Ear<br>(ocal|nings<br>curr.)|Ye<br>Sch|ars of<br>ooling|Rate o<br>(pe|f Return<br>rcent)|
|---|---|---|---|---|---|---|---|
|||Males|Females|Males|Females|Males|Females|
|Argentina|1989|<br>8,563|5,629|8.7|9.8|10.7|11.2|
|Bolivia|1989|<br>402|270|9.5|_11.5_|7.3|7.7|
|Brazil|1989|7,151|6,640|_5.0_|5.9|15.4|.14.2|
|Chile|1989|43,878|31,373|8.2|9.4|12.1|13.2|
|Colombia|1989|60,592|43,124|8.1|8.3|14.5|12.9|
|Costa Rica|1989|17,283|13,928|6.4|8.1|_10.5_|13.5|
|Dominican Republic|1989|722|522|8.4|9.5|7.8|12.0|
|Ecuador|1987|32,049|19,360|9.7|9.5|9.8|11.5|
|El Salvador|1990|988|766|7.0|6.7|9.6|9.8|
|Guatemala|1989|256|207|4.0|4.8|14.2|16.3|
|Honduras|1989|488|384|6.1|7.1|17.2|19.8|
|Jamaica|1989|9,829|1,047|7.0|7.4|28.0|31.7|
|Mexico|1984|84,520|71,533|6.3|_7.5_|14.1|15.0|
|Panama|1989|312|258|8.6|10.1|12.6|17.1|
|Paraguay|1990|290,496|165,787|9.1|8.9|10.3|12.1|
|Peru|1990|11,482|6,937|10.2|9.8|8.5|_6.5_|
|Uruguay|1989|178,086|97,865|8.7|9.3|9.0|10.6|
|Venezuela|1989|7,858|6,067|7.9|10.2|8.4|8.0|

Source: Annex 3.