rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5 SIDS public debt ratios (% of GDP) and composition of debt (% of total public debt), 2022**

|Country|PPG debt/|PV PPG debt/|PV PPG external||Compositio|n of PPG d|ebt %||
|---|---|---|---|---|---|---|---|---|
||GDP %|GDP %|debt/GDP %||Extern|al||Domestic|
|||||Multilateral|Bilateral|Private|other||
|Suriname|132.2|||20|18|19|8|34|
|Antigua & Barbuda|91.2|||||||46|
|São Tomé and Príncipe|94|74(35)|25.3(30)|19|29|||51|
|Grenada|69|63.7(55)|50.7(40)|||||22|
|Guinea Bissau|81|65.6(35)|26.2(30)|41|10|||49|
|Cabo Verde|128.8|90.7(70)|51.3(55)|38|14|18||29|
|Comoros|33.7|23.4(55)|18.3(40)|31|52|||17|
|Tonga|47.6|35.2(70)|26.7(55)|35|48|||17|
|Dominica|100.3|78(55)|44.2(40)|37.7|18||8|36|
|Haiti|29.4|23.3(55)|6.5(40)|5|40|||55|
|Maldives|122.6||||||||
|Marshall Islands|26.2|21(35)|21(30)|||||0|
|Micronesia|14.6|12.5(35)|12.5(30)|71.3|18.1|9.4||1|
|Mauritius|92.4|||||||68|
|Papua New Guinea|49.8|48.4(35)|21.2(30)|||||54|
|Samoa|51.3|38.9(70)|30(55)|44|42|||15|
|Tuvalu|5|4(35)|2.7(30)|74||||26|
|St Vincent and Grenadines|87.8|80(55)|62(40)|49|20|10||22|
|St Lucia|90.1|||33|||24|43|
|Bahamas|109.2|||||||55|
|Fiji|88.7|||24||2|4|72|
|Palau|90.3|||||||1|
|Dominican Republic|59.2|||||||42|
|Seychelles|67.9|||||||53|
|Nauru|27.3|||||||49|
|Belize|63.7|35.2(70)|26.7(55)|30|15||20|35|
|Guyana|22.8|19.2(35)|6.5(30)|29|14|1||56|
|Jamaica|100.3|||22|5|38||35|
|Barbados|122.5|||24|2|10||64|
|St Kitts and Nevis|58.4|||||||84|
|Trinidad and Tobago|53.8|||||||68|
|Solomon Islands|16.9|16.3(35)|10.6(30)|49|12|||39|
|Timor Leste|14.7|10.3(35)|10.3(30)|91|9|||0|
|_Notes: Figures in pa_<br>Vanuatu|_rentheses_<br>46.6|_in the col_<br>31.1(55)|_umns for the_<br>20.8(40)|_preset val_<br>32|_ue (PV)_<br>44|_of Publi_|_c and_<br>2|_Publicly_<br>22|

_<u>Notes: Figures in parentheses in the columns for the preset value (PV) of Public and Publicly</u> Guaranteed (PPG) debt and external PPG debt are the applicable thresholds or benchmarks for each country, which are determined by its assessed debt carrying capacity._ _<u>Source: Debt Sustainability Analyses</u>_ 18
In this section, we present and discuss the estimation results of transaction cost equations. Subsequently, we assess these estimation results on the basis of survey data of traders. We continue with presenting selected regime probabilities computed on the basis of predicted transactions costs. The section is completed with estimations that attempt to explain fluctuations in regime probabilities.

# _<<B>>Estimation of transaction cost equations_

To apply the proposed approach, we use Malawi district-level monthly maize price data complemented with other variables over the period from June 1999 to October 2009 (see appendix for details). The district is the unit of observation: buyer (seller) or deficit (surplus) locations refer to deficit districts (surplus districts), where the difference between district production and district requirements determines whether a district is a seller district (production – requirements > 0) or a buyer district (production – requirements < 0). For the estimation of transaction cost equations, we use the specification proposed in equation (1) conditioned on a selection of observations specified in equation (6). For comparisons over time, unit transaction costs and fuel prices are deflated with the Malawi consumer price index. Additionally, the unit transaction cost, distance, and fuel price are transformed into natural logarithms. Table 1 reports estimated transaction cost equations. Absolute t-statistics are given in parentheses below the coefficient. We have omitted the coefficients and statistics of constant terms and dummy variables. Columns in the table represent estimation results with different values for three parameters: minimum per person per day caloric intake (2200 or 2300 kcal), excess production over requirements above which district exports are triggered (two or four months of maize requirements, respectively; φ in equation (3a)), and the distance above which domestic trade is unlikely to occur (200 or 300 km).

|**Table 1**|**Estimating Transaction **|**Cost Equ**|**ations: Sample Restricted by Distance**<br>|
|---|---|---|---|
|Dependent va|riable: natural logarithm ofper kil|ogram trans|action cost orprice difference(ln(pt<br>j−pt<br>k))|
||[1]<br>[2]|[3]|[4]<br>[5]<br>[6]<br>[7]<br>[8]|

18
Table 6: Basic Statistics on Urban Infrastructure Coverage

|Item|1990|1995|2000|2002|2003|
|---|---|---|---|---|---|
|Developed areas (sq. km)|12856|19264|22439|25973|28308|
|Population density of urban districts (person/sqkm)|279|322|442|754|847|
|Total floor space of buildings (100 million sqm)|39.8|57.3|76.6|131.8|140.9|
|Per capita water consumption for residential use (ton)|67.9|71.3|95.5|77.8|77.1|
|Percentage of population with access to tap water (%)|48|58.7|63.9|77.9|86.2|
|Percentage of population with access to gas (%)|19.1|34.3|45.4|67.2|76.7|
|Area of paved roads per 1000 population ( sqm)|3.1|4.4|6.1|7.9|9.3|
|Length of sewer pipelines per capita (m)|3.9|6.0|6.8|--|--|
|Density of sewer pipelines (km/sqkm)|4.5|5.7|6.3|6.7|7.0|
|% of Treated Sewerage|14.9|20.0|34.2|36.5|--|
|Number of public transportation vehicles per 100|2.2|3.6|5.3|6.7|7.7|
|population (unit)||||||
|Per capita public green areas (sqm)|1.8|2.5|3.7|5.4|6.5|
|Volume of garbage disposal (10000 tons)|6727|10671|11818.|13650|14857|

Sources: China statistical yearbook 2004 Despite this growth, many Chinese experts and policy officials believe that urban infrastructure investment lags behind the rate appropriate to the country’s growth in urbanization, industrialization, and income level, and that urban infrastructure investment will have to accelerate in the future.

32
26

-

, Table 2

# Saving measures: availability and basic properties

||{a|} Sampie com|position<br>||
|---|---|---|---|
||All|LDC|OECD|
|1970-79|267|132|135|
|1980-89|436|251|185|
|1990-95|169|92|77|
|Total|872|475|397|

|(b)<br>(Ful|Correlation mat<br>l sample: upper <br>|rix ofalternat<br>triangle. Cro<br>|ive saving rate<br>ss section: lo<br>|definitions<br>wer triangle)<br>||
|---|---|---|---|---|---|
||GNS/GNDI|GPS/GPDI<br>(PA)|GPS/GPDI<br>(PU)|GPS/GPDI<br>(CA)|GPS/GPDI<br>(CU)|
|GNS/GNDI|1.00|0.82|0.82|0.85|0.85|
|GPS/GPDI (PA)|0.89|1.00|0.95|0.94|0.91|
|GPS/GPDI (PU)|0.92|0.96|1.00|0.92|0.96|
|GPS/GPDI (CA)|0.94|0.94|0.95|4.00|0.97|
|GPS/GPDI(CU)|0.94|0.97|0.97|0.98|1.00|

## (c) Saving Ratios: Descriptive statistics

|||(Full sampl|e)||
|---|---|---|---|---|
||Mean|Std Dev|Min|Max|
|GNS/GNDI|0.206|0.075|-0.147|0.486|
|GPS/GPDI (PA)|0.193|0.083|-0.253|0.461|
|GPS/GPDI (PU)|0.215|0.081|-0.077|0.462|
|GPS/GPDI (CA)|0.210|0.085|-0.166|0.457|
|GPS/GPDI(CU)|0.226|0.084|-0.166|0.466|

Note: Approximate standard errors are 0.031 for the full-sample correlations and 0.178 for the cross-section correlations.

GNS = Gross National Saving, GNDI = Gross National Disposable Income. GPS = Gross Private Saving, GPDI = Gross Private Disposable Income (4 alternative definitions, as in the text).
- 41 -

**Table 18**

||**Comimercia**<br>|**l Bank Net F**<br>|**oreign Asset**<br>|**s as Percen**<br>|**tages of Bro**<br>|**ad Money**<br>||
|---|---|---|---|---|---|---|---|
||**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|1997|
|Turkey|5.3%|6.0%|3.9%|**17.0%**|**11.1%**|**2.6%**|**-1.7%**|
|Thailand|-2.8%|-4.2%|-7.8%|-21.6%|-28.0%|-28.7%|-33.0%|
|**Indonesia**|-0.9%|-2.7%|-6.4%|-7.0%|-4.5%|-6.4%||
|**Mexico**|**-4.3%**|-3.3%|-4.3%|-5.4%|**-2.1%**|**-1.5%**|-0.7%|
|**Chile**|**-7.8%**|**-16.5%**|**-15.1%**|**-14.2%**|**-9.5%**|**-6.3%**|1.6%|

Source: **own** calculation based on IFS.

The fastest growing foreign liabilities have been interbank deposits and foreign currency deposits by non-residents. These deposits remained stable even during the 1994 currency crisis. Loans from non-residents declined sharply during 1994, and although this item has also been growing very fast in recent years, the growth has not been strong enough for the loans to recover to the level as recorded in 1993. It is noteworthy that there has been a structural shift in the composition of foreign loans. In pre-1994 crisis years, trade credits (imports and pre-export finance) were not as important as other purpose foreign loans. Since the crisis, however, trade credits have been more important than other purpose foreign loans. The fastest growing foreign assets have been portfolio investments abroad. Overall though, the level of foreign assets has remained much less volatile than the level of foreign liabilities.

Foreign liabilities reached 24% of domrestic liabilities by the end of 1997, as compared to 32% aLt the end of 1993 and 14% at the end of 1990. Data of the maturity structure of foreign liabilities are hard to come by, but according to BIS, of the US$29.2 billion total claims on Turkey at the end of 1997 by BIS-reporting international banks, 56.3% were claims of maturity up to and including one year. This figure was an average of all the claims to banks, the public sector and the non-bank private sector. For the claims to banks, it is likely that a higher percentage was of short-tern. According to the Turkish Treasury, the short-term external debt of the deposit money banks reached US$8.4 billion at the end of 1996 and US$8.5 billion at the end of 1997, out of total external debt of the deposit money banks of US$1 1.1 billion and US$13.6 billion at the end of 1996 and 1997 respectively<sup>1 7</sup> . Thus in 1996 75% of deposit money banks' foreign borrowings were short-term, and in 1997 the ratio declined to 62%.

It is noteworthy that short-term loans and credits from abroad have been subject to a levy of 4 percent by the Resource Utilization Support Fund.'<sup>18</sup> Thus despite the existence of 17 There is sorne discrepancy between Treasury data and Central Bank data of the foreign liabilities of the banking system. The Treasury data show larger foreign liabilities of the banks than Central Bank data. The discrepancy may have to do with the definition of "banks" in their prospective coverage.

**18** This levy was lifted in early 1999 in response to the Russian crisis.
<!-- Start of picture text --> 20<br>18<br>16<br>i4<br>12<br>10<br>3<br>6<br>4<br>2<br>o<br>1982 1983 1984 1985 1956 1987 1958 1989 19950 1991 1992 1995 1994 1995 1996 1997 1998 1999 2000 2001 2002 2008 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>—— Average returns to schooling ——Average years of education<br><!-- End of picture text -->
problem with this study is rather that a comprehensive view of adaptation costs is missing, as only for particular sectors have costs been explicitly calculated, and thus included.

Currently no pure ”bottom-up” study exists for overall adaptation cost assessment. Such studies would be necessary to complement and refine the top-down studies, not least in giving more precise assessments for individual sectors.

**Table 2.1: Overview of adaptation financing needs from top-down modelling routines. Figures in US$billion.**

|Assessment<br>conducted by|Annual<br>adaptation<br>financing<br>need|Time<br>frame|Countries<br>included|Sectors included|Comments|
|---|---|---|---|---|---|
|World Bank|9-41|Present|LICs|Unspecified|Based on WB and<br>OECD analyses of<br>official flows|
|Stern|4-37|Present|LICs|Unspecified|Update of WB study|
|Oxfam|50 (min.)|Present|LICs|Unspecified|WB study including<br>estimates from<br>NAPAs and NGO<br>projects|
|UNDP|86-109|2015|LICs|Unspecified|WB study, plus<br>poverty reduction<br>programmes and<br>disaster responses|
|UNFCCC|28-67|2030|LICs|Agriculture,<br>forestry, health,<br>coastal zones,<br>infrastructure|Detailed costing for<br>water, infrastructure,<br>coastal, less for<br>ecosystems,<br>infrastructure|
|UNFCCC|44-166|2030|Global|Agriculture,<br>forestry, health,<br>coastal zones,<br>infrastructure|Infrastructure<br>adaptation costs<br>overlap with coastal<br>zones, water costing|
|Project<br>Catalyst|15-37|2030|Global|Capacity building,<br>research, disaster<br>management|Covers most<br>vulnerable countries<br>only; only public<br>sector|

Source: OECD (2008), World Bank (2009).

It is also a question how good these assessments are for indicating the magnitude of _international financial_ flows to be generated by adaptation to climate change. In most cases little is said about the distribution of costs between outside assistance, and domestic costs and how these are to be borne by the public or private sector (the implicit assumption is in most cases that most of adaptation costs will be public and not private).

A further crucial issue is whether the funds in question will be additional to already disbursed flows such as existing ODA. When not, the (additional) effects of adaptation finance will be less or could vanish altogether. From that perspective, the numbers derived here should perhaps be considered as upper limits to the related international financial flows, given that the basic premises behind the stated adaptation cost needs are valid.

6
30 same shift in the mean of an otherwise stable relationship, as in the Poland case. On the other hand, BULDIS remains above MEXDIS (even if the distance between the two decreases) and follows a behavior similar to ECUDIS. 32 <!-- Start of picture text --> 2000<br>1000<br>1500 800<br>600<br>1000- ~~~~~~~~~~~~400<br>712 /4 1 2I16174 ~I0~I9'5 9/2 /95 /I0~I~6 /I2~I96 6/96 - 200 72 7/2 /94 i)4K 1~/16/94 ~/0~/ m i2 i96<br>- MEXPAR ---- PHIPAR - DIFF=MEXPAR-PHIPAR<br>Figure 16 Figure 17<br>3000<br>2500<br>2000,<br>i50 ' ' 'A'z --- .,.-Rw<br>7/2nj)§ W i I0'I9fl 9/23;95 iO159 ki2A96<br>-MEXDIS ----- BULDIS<br><!-- End of picture text --> _Figure 18_ From this brief analysis we reach two conclusions: i) there is evidence of comovements during the Mexican crisis (and for the Venezuelan crisis, even if in this case the evidence is less strong); 32 We could do a similar exercise for the political crisis that hit Venezuela in 92/93. A military coup failed in February 1992 and a second military coup was attempted in November 1992. The political crisis continued for the entire year 93 until the December 93 elections, but it was less acute after the summer when an interim government was appointed after the suspension from office of president Perez on May 20. By January 92 the Brady bonds already issued were those of Nigeria and Mexico. The date of issue for Argentina was 4/93 and for Philippines was 12/92. Note that before January 92 the Venezuelan bonds were the less volatile and with the lowest mean among the Brady bonds. The data suggest that: NIGPAR reacted to the crisis in such a way that it is not easy to trace out comovements of the two variables; MEXDIS, which was a relatively stable title at the time, is only slightly affected by the crisis; on the other hand, the newly issued bonds of Argentina and Philippines show signs of comovements with VENDIS and VENPAR respectively.
21 Notwithstanding these normative considerations, the possibility remains that limited offers on the part of developing countries can be explained by limited offers on the part of industrialized countries. As of early 1993 all OECD members had presented an initial offer. Those of the two major participants in the negotiations, the EC and the U.S.,3 cover about two-thirds and one-half of the GNS list of sectors, respectively. The average unweighted coverage ratio of initial offers of industrialized countries exceeds 75 percent.<sup>40</sup> Given the wide coverage of the offers, there is less interest in calculating weighted coverage ratios than for the offers of developing countries. The main issue for developing countries is what measures are retained for scheduled services in which they have a comparative advantage.

Existing balance-of-payments data show that many developing countries have a revealed comparative advantage in services (Hoekman and Karsenty, 1992).41 Unfortunately, the high level of aggregation of the data makes it impossible to determine which sectors are of greatest export interest - actual or potential - for developing countries. Abstracting from tourism, there is a general belief that developing counitries have a comparative advantage in labor intensive products, and that market access for labor intensive services therefore should be of greatest interest. These include professional services such as legal, accounting, engineering, consulting, medical and quasi-medical services as well as activities such as data processing, software development or cleaning services. Many of these business services are included in the offers of major industrialized countries. Thus, in this respect it appears that developing countries may have gained a significant increase in their export potential. Even though few countries have included personal services such as domestic help in initial offers - and are unlikely to do so - the list of business services includes many in which developing countries should be competitive. Even if it should turn out that final offers do not go much beyond the status quo regarding visa and licensing requirements, implementation of the contact/enquiry point mechanisms should
_Panel B. Gap in level between survey mean and GDP mean_

||||**All surve**|**ys**|||**Co**|**nsumptio**|**n survey**|**s**|||**Income s**|**urveys**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Group**|**O**|**.**<br>**bs**|**Gap**||**S.E.**|**O**|**bs.**|**Ga**|**p**|**S.E.**|**Ob**|**s.**|**Ga**|**p**|**S.E.**|
|All|2082|(165)|-0.465|***|0.015|990|(138)|-0.462|***|0.018|1092|(75)|-0.516|***|0.017|
|1980s|122|(61)|-0.459|***|0.027|53|(37)|-0.439|***|0.041|69|(33)|-0.506|***|0.022|
|1990s|385|(122)|-0.422|***|0.027|208|(97)|-0.414|***|0.033|177|(58)|-0.484|***|0.025|
|2000s|764|(150)|-0.484|***|0.016|385|(126)|-0.487|***|0.019|379|(58)|-0.545|***|0.015|
|2010s|806|(150)|-0.508|***|0.015|344|(123)|-0.518|***|0.017|462|(62)|-0.546|***|0.014|
|Low income|219|(55)|-0.304|***|0.033|209|(52)|-0.299|***|0.035|10|(8)|-0.224|*|0.116|
|Lower middle income|534|(95)|-0.485|***|0.018|356|(81)|-0.505|***|0.020|178|(25)|-0.412|***|0.036|
|Upper middle income|532|(60)|-0.578|***|0.019|242|(45)|-0.590|***|0.025|290|(29)|-0.593|***|0.017|
|High income|768|(42)|-0.576|***|0.009|168|(30)|-0.606|***|0.013|600|(41)|-0.571|***|0.009|
|East Asia & Pacific|175|(23)|-0.431|***|0.040|132|(19)|-0.415|***|0.048|43|(6)|-0.519|***|0.006|
|Europe & Central Asia|1076|(49)|-0.542|***|0.022|486|(47)|-0.549|***|0.024|590|(38)|-0.548|***|0.025|
|Latin America & Caribbean|410|(24)|-0.463|***|0.034|30|(6)|-0.432|***|0.083|380|(23)|-0.464|***|0.036|
|Middle East & North Africa|96|(15)|-0.460|***|0.035|70|(13)|-0.446|***|0.038|26|(3)|-0.546|***|0.026|
|North America|81|(2)|-0.511|**|0.034|32|(1)|-0.564||0.000|49|(2)|-0.501|**|0.024|
|South Asia|48|(7)|-0.535|***|0.053|47|(7)|-0.534|***|0.054||||||
|Sub-Saharan Africa|196|(45)|-0.386|***|0.037|193|(45)|-0.385|***|0.037|3|(2)|-0.586|**|0.041|

**Notes:** The gap is estimated as the proportional difference (per cent) of survey means with respect to national accounts means. It is estimated as the ratio of per capita survey mean over the per capita national accounts mean, minus one. A negative (positive) gap estimate indicates that surveys means within the sample are smaller (larger) than national account means. Number of countries in each group given in parenthesis. Robust standard errors clustered at the country level (based on sandwich estimator). Statistical significance from zero, denoted by *p < = .1, **p < = .05, ***p < = .01. Each country is given equal weight within each sub-sample/group estimate, regardless of the number of observations for the country. Additional estimates with alternatives weights are available in Appendix 2. The column labelled “All surveys” pools consumption and income surveys. Due to weights and the fact that not all countries having both consumption and income surveys, estimates in “All surveys” does in some cases not fall within range of the estimates for the samples separated by survey type.
|Bridges Improvement and Maintenance Program|A.K. Farhad Ahmed|
|---|---|
|Bridges Improvement and Maintenance Program|A.K. Farhad Ahmed|
|Bridges Improvement and Maintenance Program|A.K. Farhad Ahmed|
|Bridges Improvement and Maintenance Program|A.K. Farhad Ahmed|
|Bridges Improvement and Maintenance Program|A.K. Farhad Ahmed|
|Bridges Improvement and Maintenance Program|A.K. Farhad Ahmed|
|Bridges Improvement and Maintenance Program|A.K. Farhad Ahmed|
|Bridges Improvement and Maintenance Program|A.K. Farhad Ahmed|
|Bridges Improvement and Maintenance Program|A.K. Farhad Ahmed|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|Integrated Modern Agriculture Development Project|Jianwen Liu|
|CapacityBuildingfor the Philippines' Public-Private Partners|hipCen Victor Dato|
|CapacityBuildingfor the Philippines' Public-Private Partners|hipCen Victor Dato|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|SN:Casamance Development Pole Project|Nicolas Perrin|
|DRC Catalytic Project to Strengthen the INS|Yele Maweki Batana|
|DRC Catalytic Project to Strengthen the INS|Yele Maweki Batana|
|DRC Catalytic Project to Strengthen the INS|Yele Maweki Batana|
|DRC Catalytic Project to Strengthen the INS|Yele Maweki Batana|
|DRC Catalytic Project to Strengthen the INS|Yele Maweki Batana|
|DRC Catalytic Project to Strengthen the INS|Yele Maweki Batana|
|DRC Catalytic Project to Strengthen the INS|Yele Maweki Batana|