rafmacalaba/datause-encoder-data huggingface dataset:
Overall, sovereign ratings are reliable predictors of sovereign default risk.<sup>18</sup> However, various authors conclude that the credit rating agencies were not able to predict and to warn the market participants of imminent default risks with the onset of financial turbulence and crisis.<sup>19</sup> During the Asian financial crises, Korea’s rating, for example, fell on average four letter grades and ten rating notches within a three-month period; sovereign rating changes of that magnitude had never been seen before. Reinhart (2002) concludes that sovereign credit ratings systematically fail to predict currency crises but do considerably better in predicting defaults. Downgrades in credit ratings usually follow currency crises, possibly suggesting that currency instability increases the risk of default. He also finds that in emerging market economies there is a strong link between currency crises and default.

# **Sovereign Foreign Currency Selective Defaults**

|Country|Selective<br>Default<br>Date|Rating one<br>year before<br>SD|Downgrade<br>in Notches|Time in SD|Date of<br>Emergence|Rating at<br>Emergence|
|---|---|---|---|---|---|---|
|Russia|01/27/99|BB-|(-8)|10 months|12/08/00|B-|
|Pakistan|01/29/99|B+|(-6)|10 months|12/21/99|B-|
|Indonesia|03/30/99|B-|(-5)|1 day|03/31/99|CCC+|
|Indonesia|04/17/00|CCC+|(-4)|4.5 months|10/02/00|B-|
|Indonesia|04/23/02|B-|(-5)|3.5 months|09/05/02|CCC+|
|Argentina|11/06/01|BB|(-9)|42 months|06/01/05|B-|
|Paraguay|02/13/03|B|(-6)|15.5 months|07/26/04|B-|
|Uruguay|05/16/03|BB-|(-8)|0.5 months|06/02/03|B-|
|Grenada|12/30/04|BB-|(-8)|11.5 months|11/18/05|B-|
|Venezuela|01/18/05|B-|(-5)|1.5 months|03/03/05|B|
|Dominican|02/01/05|CCC||5 months|06/29/05|B|
|Rep.|||||||
|Belize|12/07/06|CCC-|||SD||
|Data Source: S|&P (2007b)||||||

**Sovereign Local Currency Selective Defaults**

|Country|Selective<br>Default<br>Date|Rating one<br>year before<br>SD|Downgrade<br>in Notches|Time in SD|Date of<br>Emergence|Rating at<br>Emergence|
|---|---|---|---|---|---|---|
|Argentina|11/06/01|BBB-|(-11)|42 months|06/01/05|B-|
|Dominican<br>Rep.|04/09/99|BB|(-9)|26 months|06/12/01|B-|
|Grenada|01/01/05|BB-|(-8)|10.5 months|11/18/05|B-|
|Cameroon|09/01/04|B|(-6)|3 months|12/03/04|B-|
|Data Source: S|&P (2007b)||||||

18 According to S&P (2007a), over the last thirty years (1975-2006), on average 16.3% of ‘BBB’ rated sovereigns lost their position by two or more categories. And in the category ‘B’, approximately 1/3 of the rated sovereigns received downgrading to selected default. All of the S&P’s default studies have found a clear correlation between credit quality and default remoteness: the higher the rating the lower the historic average of default, and vice versa. 19 For example, see Reisen and Maltzan (1999) and Reihard (2002).

18
For sampling in urban areas, each town (or city) was divided into enumeration blocks consisting of 200–250 households. Each enumeration block was classified as belonging to one of three income groups (low, middle, and high), based on the living standard of the majority of the people in the block. In rural areas, the list of villages from the Population Census 1998 was used as the sample frame. Urban blocks and villages were the primary sampling units for the survey. Sixteen households were selected from each sample village and 12 from each enumeration block, using a systematic sampling scheme with a random start. Because the PSLM survey reports an electrification rate close to 100 percent in urban areas, the analysis was restricted to the rural sample, which included about 4,300 households.

Data from a second survey, conducted in 2014 under the IFC’s Pakistan Off‐Grid Lighting Consumer Perceptions Study (IFC 2015), were used to gauge the impact of power outages on households. This survey explored the market potential for off‐grid solar lighting in Pakistan. It included 6,000 households, both on and off grid, across all four of the country’s provinces. This cross‐sectional survey lacks the multifaceted measurement of welfare used in the PSLM, but it provides important information on the quality of electricity supply, because it asked on‐grid households to report the average duration of outages.

Table 2 shows the geographic distribution and electricity access rate of the population surveyed by the PSLM. Punjab accounts for the largest share of the sample, followed by Sindh and Khyber Pakhtunkhwa. Balochistan has the lowest electrification rate and the smallest number of households surveyed. Sindh and Balochistan have the lowest access rates, at less than 75 percent in 2010.

The average electrification rate rose by just 2 percentage points between 2008 and 2010. It rose by about 17 percentage points in Balochistan and 4 percentage points in Sindh.

Table 3 compares mean outcomes of households and individuals by electrification status. It shows that on average, households with access to grid electricity consume less kerosene than off‐grid households in both survey years, with the differences statistically significant. Grid‐ connected households also have higher income and expenditure than off‐grid households.

6
21 In addition, compared to their current volume of business, micro enterprises have the highest financial liabilities. The total liabilities (long and short term, all sources together except own funds) of micro firms amount to 14x their sales on average, compare to less than 7x for all other sizes of enterprises. In addition, the average debt coverage ratio of micro firms is lower than for any other firm (despite their lower leverage), because of their low profitability. Therefore the data suggest that those micro firms which have access to bank loans get sufficient financing compared to their business size and profitability.

**Figure 19:  Liabilities by Size of Firms Compared to Measures of their Size**

<!-- Start of picture text --> Access to finance Vs financial needs Interest cover EBITDA / Interest charges<br>EBITDA / (interest charges + LT maturing within a year)<br>Total loans / sales<br>16 14 300 282<br>14<br>12 250 183<br>108 7 7 200150 169 148 153<br>6 4 100 85 66<br>4 2 29<br>2 50 18 11<br>- -<br>Mega Large Medium Small Micro Mega Large Medium Small Micro<br>Source: FECUs<br><!-- End of picture text --> However, many micro firms have access to consumer loans rather than commercial loans; this shortens maturities and increases the cost of funding for them. Micro firms are the only ones to rely indeed on consumer loans for a large part of their financing needs (38 percent), compared to 5 percent for small firms and zero for all other sizes of firms. This reflects the well-functioning of credit bureaus in Chile (which makes scoring-based lending feasible) and the low quality of the financial statements of small firms (which makes cashflow based lending difficult). It means that the average maturity of the loans micro firms get from banks is shorter and their cost higher. The author’s estimates of the average external funding cost of firms of different sizes show wide differences across them, from 11 percent for mega large firms to 39 percent for micro firms.
introduction of the climate change policy—such as an alleged loss in terms of output, market share, profit or any other indicator of competitiveness.<sup>19</sup> These “carbon conditional measures” (CCMs) differ from carbon tariffs in several respects. They are subjected to legal procedures (complaint, inquiry, causation determination, decision to impose a CCM or not, review, etc.) quite different from those required for adopting carbon tariffs. They also tend to focus on products, not on countries (as carbon tariffs do).

Calculations by Mattoo et al. [2009] give a sense of the pressures for getting CCMs. Contrary to Table 2 which deals with all the industrial products, Table 4 focuses exclusively on carbon-intensive products. It provides the changes in imports, exports and outputs in the U.S. and in the EC (once again, the results reflect the fact that the U.S. and EC commitments of cutting carbon emissions by 17 percent are fully implemented).

Table 4 shows serious effects on imports _and_ exports, particularly in the case of tradebased and specific-based border taxes on imports. For instance, the trade contraction created by a trade-based border tax has a magnitude close to the trade collapse observed during the 2008-2009 Crisis.

**Table 4.  Impact of border tax regimes on carbon-intensive products, percent changes**

|Border tax regimes [a]|U.S.|EC|U.S.|EC|U.S.|EC|
|---|---|---|---|---|---|---|
||imports|imports|exports|exports|output|output|
|1. BT only on imports (trade‐based)|‐10.1|‐38.7|‐15.9|‐21.5|‐2.5|1.8|
|2. BT only on imports (specific‐based)|‐4.6|‐11.3|‐14.1|‐7.8|‐3.6|‐0.5|
|3. Two‐way BT (specific‐based)|‐1.1|‐7.8|0.7|4.1|‐0.8|1.0|
|4. No BT|3.5|3.1|‐11.6|‐5.2|‐4.4|‐1.9|

Source: Mattoo et al. [2009]. Developed countries are assumed to reduce unilaterally their emissions by 17 percent. Notes: [a] BT: border taxes. Two way BT: elimination of the carbon tax of the exporting countries and imposition of the carbon tax of the importing countries. [b] Low and middle income developing countries.

More importantly, all the regimes show the emergence of negative outputs in the U.S., and, to less systematically, in the EC. This result reveals the magnitude of the pressures for getting CCMs. The U.S. output decline may be substantial enough to induce U.S. carbonintensive sectors to request trade barriers on the top of two-way carbon border taxes (despite a slight increase in exports and decline in imports). The situation may look better in the EC, but probably not enough to induce EC carbon-intensive sectors not to follow U.S. industries’ requests, and not to argue that they also face “unfair” competition from foreign industries

> 19 This is not new.  It already occurred during the 1970s when the U.S. steel industry tried to impose antisubsidy measures on the top of border taxes after the EC move to a VAT regime [Hufbauer and Gabyzon 1996, Ruffin 1979].

22
Complementary feedings for six‐ to eight‐month olds was high in Bangladesh (in the IFPRI sample), Nepal, Zimbabwe and Bolivia, where more than 80% of the children of the age range received complementary foods. In Nepal, only 44% of the six‐ to eight‐month olds received complementary feedings from one of the dietary diversity food groups.

<!-- Start of picture text --> Figure 6a: Components of Adequate Care<br>Components of Adequate Care<br>100 100<br>98<br>96<br>91<br>86<br>83 82 81 80 84 82<br>78 80 80 78<br>74<br>68 69 67 69 69 67<br>63<br>59<br>55<br>51<br>49 51 50 48<br>45 44<br>30<br>BGD (HK, '11) BOL ('08) IDN (RKD, '10) NPL ('11) ZWE ('10)<br>BGD (IFPRI, '11) ETH ('11) KHM ('10) PER ('12)<br>Exclusive breastfeeding Early breastfeeding initiation<br>Complementary feedings Breast-fed up to 24 months<br>Source: Author estimates.<br>Note: (1) Exclusive breastfeeding for first 6 months; (2) Complementary feedings for 6- to 8-month olds;<br>(3) Breastfeeding within 1 hour of birth (100 minutes for Bolivia and Peru);<br>(4) Breast-fed for 24 months or currently breastfeeding if less than 24 months.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text --> Applying stricter standards by requiring simultaneous access to adequate levels of each component of child care, access to adequate care has increased significantly over time in most countries. Adequate care increased by 24 percentage points in Cambodia, 14 percentage points in Nepal, 9 percentage points in Ethiopia, 6 percentage points in Peru, 5 percentage points in Bangladesh, and 3 percentage points in Zimbabwe (Figure 6b).<sup>15</sup> For the most recent year of data, most countries had about one‐third to two‐thirds of the children living in situations with adequate care. However, adequate care was also the measure for which many of the ideal components were missing. Nonetheless, the results suggest an increased awareness of the importance of early initiation of breastfeeding and continued breastfeeding until two years of age, as well as following the recommendations on age‐appropriate complementary feedings.

> 15 In Bolivia the prevalence of adequate care was the same for the two surveys.  However, the 2003 definition does not include vitamin A supplementation (as the information was not collected) and the 2008 definition does.

26
prices series for white maize were obtained for the period March 1996‐December 2012; monthly Bangkok rice prices for 2000‐2012.<sup>14</sup> Tanzania is a large country exhibiting significant differences in climate and terrain across regions. Reflecting these differences, the estimated seasonal patterns for each market differ both in amplitude (and hence gap size) and phase (seasonal peak and trough dates) (Figure 1). Comparison of a seasonal model (trigonometric or dummy), with a model without seasonality, using the Bayesian (Schwartz) Information Criterion (BIC) which penalizes excess parameterization, confirms seasonality in 19 of the 20 markets for maize and 16 of the 20 markets for rice (Table 2, column 1). Comparison of the trigonometric with the dummy variable approach further shows that trigonometric model is preferred in most markets (17 out of 19 for maize and in all markets for rice). This provides confidence in the use of the more parsimonious trigonometric model pursued here. The dummy specification is preferred for both the SAFEX and Bangkok exchange prices.

Maize prices are on average 26.6 percent higher during the peak month than during the trough (Table 2, trigonometric specification (column 6)). The seasonal price gap for maize in Tanzania is sizeable. It is also almost twice as large as the gap for rice (15.2 percent on average). Rice is a tradable product, although Tanzanian rice imports remain small, and as a product consumed predominantly in urban areas, it is subject to lower intermediation costs. The estimated gaps based on the dummy variable approach are slightly higher (30 and 17.8 percent on average for maize and rice respectively) (Table 2, column 5). This is consistent with the Monte Carlo results, which suggest a slight upward bias for 20 year samples, if there are no or unclear seasonal patterns in the underlying data generating process.<sup>15</sup> There is also substantial variation in the

> 14 ‐ Sources: Johannesburg Stock Exchange, <u>https://www.jse.co.za/downloadable files?RequestNode=/Safex/PriceHistory/Spot percent20Months percent20on percent20all percent20Grain percent20Products</u> and IMF, _International Financial Statistics_ .

> 15 As Gilbert, Christiaensen and Kaminski (2015) show, when the samples are shorter and seasonal patterns are less clear as in many other countries and crops, the dummy variable approach tends to generate sizably larger seasonal gap estimates than those obtained through the trigonometric specification.  A referee suggested that we should set the seasonal factors to zero in those cases in which the _F_ tests fail to reject the hypothesis of no seasonal variation. Such non‐rejections are consistent either with an absence of seasonality or with insufficient evidence in the data to support the claim that the series is seasonal. Setting the seasonal factors to zero in cases where these are positive but statistically insignificant would bias the estimate of the average seasonal factors towards zero.

15
# **Assessing Fiscal Space in Sub-Saharan Africa**<sup>*</sup>

César Calderón, Punam Chuhan-Pole and Yirbehogre Modeste Some _The World Bank, 1818 H Street NW, Washington DC 20433, USA_

**JEL Codes:** E62, E65

**Keywords:** Fiscal space, primary surplus, general government gross debt, Sub-Saharan Africa

> * We are grateful to Ayhan Kose for providing the cross-country data on fiscal space indicators. We also would like to thank Albert G. Zeufack for his comments and suggestions. The usual disclaimer applies. The views expressed in this paper are those of the authors, and do not necessarily reflect those of the World Bank or its Boards of Directors. Calderón: The World Bank, Office of the Chief Economist of the Africa Region (AFRCE). E-mail: <u>ccalderon@worldbank.org. Chuhan-Pole: The World Bank, Office of the Chief Economist of the Africa Region</u> (AFRCE). E-mail: <u>pchuhan@worldbank.org. Some: Development Economics Prospects Group (DECPG). E-mail: ysome@worldbank.org.</u>
27

> _QiZ<QW2_ BGT must buy the missing gas in the flexibility market to restore system balance. BGT accepts flexibility bids 1-3.

SMPbuy, The system marginal price of gas bought by BGT from winning bidders. It is equal to the price of the last accepted flexibility bid.

Data for July 1996 support the relation between flexibility and spot market prices predicted above, although the period is short (table 4). During 10 days of that month BGT sold gas at a system marginal price below the prevailing spot market price and bought gas at a system marginal price above the spot market price. The system average price was close to the system marginal price on all but one day of trade, reflecting the small spread of prices in flexibility bids. The exception was July 28, when the system average price exceeded the spot market price even though the system was selling. This exception was caused by the high price of £3.11 per therm for 170,847 therms sold to National Power, compared with the prevailing system marginal price for system sell of 10.07 pence per therm. According to British Spot Gas Markets (July 29, 1996), the trader for National Power who placed this expensive bid had confused therms with kilowatt-hours.

**Table 4 Trading and Prices in the Flexibility Market, July 19-28, 1996** (1 pence per them, unless otherwise indicated)

||Volumea||||SAP|Spot market|
|---|---|---|---|---|---|---|
|Dav|(millions of therms)|SAP|SMP buy|SMP sell|30-day|price|
|19|-2.56|**15.04**|**15.05**|_8.90_|12.45|12.1275|
|20|2.81|**11.72**|_34.87_|**11.71**|12.56|12.1275|
|21|0|_12.49_|_35.58_|_8.99_|12.49|12.1275|
|22|2.00|**10.88**|_35.31_|**10.24**|12.51|12.1275|
|23|0|_12.47_|_35.03_|_9.04_|12.47|12.1275|
|24|0|_12.49_|_34.74_|_9.04_|12.49|12.1275|
|25|0|_12.58_|_34.44_|_9.01_|12.58|12.1275|
|26|0|_12.59_|_34.12_|_9.02_|12.59|12.1275|
|27|3.79|**10.72**|_33.78_|**10.54**|12.61|12.1275|
|28|2.67|**32.28**|_33.44_|**10.07**|12.56|12.1275|

Note: SAP is system average price; SMP is system marginal price. Numbers in bold are the system average or marginal price of accepted flexibility bids, and numbers in italics the system average or marginal price of nonaccepted bids.

Note: The spot market price is approximated by the Heren Index, as calculated by PH Energy Analysis for July 1996 for the Bacton terminal.

a. A positive number indicates a system sell, a negative number a system buy.

Source: The Heren Index, July 1996
**Table 5: Malmquist Index summary by country means**

|||||**Of w**<br>|**hich:**<br>|
|---|---|---|---|---|---|
|**Country**|**Total Factor**<br>**Productivity**<br>**Change**<br>**(TFPCH)**|**Technological**<br>**Change**<br>**(TECCH)**|**Technical**<br>**Efficiency**<br>**Change (EC)**|**Scale**<br>**Efficiency**<br>**Change (SECH)**|**Pure Efficiency**<br>**Change (PECH)**|
|**Afghanistan**|0.02|1.22|0.01|0.02|0.90|
|**Bulgaria**|0.76|0.94|0.81|0.71|1.13|
|**Burkina Faso**|0.20|0.41|0.48|0.49|0.97|
|**Costa Rica**|0.84|1.18|0.71|0.74|0.96|
|**Ethiopia**|1.44|1.51|0.96|0.90|1.06|
|**Guatemala**|0.81|1.20|0.68|0.63|1.07|
|**Kenya**|1.24|0.97|1.27|1.01|1.26|
|**Kosovo**|1.03|1.08|0.95|0.70|1.36|
|**Macedonia**|1.50|0.55|2.75|2.08|1.32|
|**Mauritius**|1.70|0.78|2.18|2.01|1.09|
|**Mexico**|1.17|1.17|1.00|1.00|1.00|
|**Namibia**|0.30|1.13|0.26|0.26|1.00|
|**Niger**|0.93|0.74|1.26|1.26|1.00|
|**Paraguay**|1.37|1.41|0.97|0.95|1.02|
|**Peru**|1.03|1.13|0.91|1.24|0.74|
|**Senegal**|1.37|0.94|1.46|1.20|1.23|
|**Tanzania**|0.92|0.97|0.95|0.88|1.07|
|**Tunisia**|0.97|0.98|0.99|1.34|0.74|
|**Mean**|1.03|1.04|1.00|0.95|1.06|

Source: Authors’ elaboration based on BOOST data. This table presents the results for the input–oriented Malmquist index and its components for the period 2006 – 2018.

**Figure 20: Mean Deviations from the Malmquist Index**

<!-- Start of picture text -->
1.00<br>0.50<br>0.00<br>-0.50<br>-1.00<br><!-- End of picture text -->

Source: Authors’ elaboration based on BOOST database. Mean deviation is calculated by subtracting 1 from the Malmquist Index presented in Table 5.

24
�1 < a1 for individuals with more self-commitment problems proxied by zi.

# 3 The Experiment

## 3.1 The Sample

We purposively chose urban Ghana as the setting for this study. The choice of Ghana was motivated by the desire to provide evidence in an African context, in a country known for a history of involvement of women in business which provides a setting that is conducive to female business success. Women in Ghana have similar labor force participation rates to men, and are more likely to be self-employed. Evidence of this is seen in data from the 2000 Ghanaian Census: the labor-force participation rates for 15-60 year olds are 69.6 percent for females and 73.9 percent for males, and in urban areas 45 percent of females are non-agricultural own-account workers, compared to 33 percent of males. This contrasts sharply with Sri Lanka, the setting for the experiment in de Mel et al. (2009a), where only 7.8 percent of prime age females are self-employed, compared to 29.7 percent of prime age males.

Within Ghana we chose Accra, the capital and largest city, and the nearby industrial city of Tema. A sample of microenterprises (which we term the Ghana Microenterprise Survey) was then constructed as follows. First, enumeration areas (EAs) were selected with probability proportional to the number of households in these EAs according to the 2000 census. We randomly selected 70 EAs in Accra and 30 in Tema. Then, to reduce the costs of listing, we subdivided EAs into equal areas, such that each area would contain approximately 70 to 80 households. This typically required dividing an EA into half or thirds. One of these areas was then randomly selected from each EA. Enumerators went door to door in this area to carry out a screening survey of each household. Households were screened to identify those with an individual age 20 to 55 who was self-employed and working 30 or more hours per week in a business with no paid employees and no motorized vehicle. These criteria were used to select full-time microenterprise owners who were not so large that the grants in our experiment would have little e¤ect.

The gender and business sector of all individuals passing this screen were then recorded. This resulted in screening 7,567 households to identify 3,907 individuals who passed the screen. Only 19.4 percent of these individuals were male, con...rming the predominance of women among small enterprise owners in urban Ghana. Based on the gender mix of self-employed in these 14