rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 6: Determinants of land values per acre</u>**

||**No tenure or knowledge**<br>**variables**|**All variables**|
|---|---|---|
|Total number of trees per acre (Log)|0.144***|0.125***|
||(7.74)|(6.75)|
|Soil conservation dummy|0.022<br>|0.018<br>|
||(0.33)|(0.28)|
|Knowledge of land law (index)||0.236**|
|||(2.38)|
|Unconditional transfer rights||0.253*|
|||(1.71)|
|Conditional transfer rights||0.138|
|||(0.93)|
|Freehold tenure||0.196*|
|||(1.82)|
|_Mailo_tenure|1.361***|1.377***|
||(6.27)|(6.43)|
|Parcel area in acres|-0.212***|-0.213***|
||(14.60)|(14.77)|
|Parcel area squared|0.004***|0.004***|
||(10.15)|(10.26)|
|Household head's age|-0.011|-0.014|
||(0.96)|(1.27)|
|Head's age squared|0.000*|0.000*|
||(1.74)|(1.89)|
|Head's education in years|0.041***|0.039***|
||(5.19)|(4.91)|
|Female headed household|-0.147|-0.078|
||(1.53)|(0.81)|
|Value of all assets three years ago (Log)|0.138***|0.123***|
||(7.07)|(6.34)|
|No. of years possessed|-0.004|-0.002|
||(1.37)|(0.70)|
|Distance from house|-0.002***|-0.002***|
||(3.37)|(3.95)|
|Good land quality|0.184***<br>|0.168***<br>|
||(2.96)|(2.73)|
|Poor land quality|0.168*|0.129|
||(1.85)|(1.43)|
|Flat land|-0.225***|-0.198***|
||(3.49)|(3.08)|
|Steeply sloped land|-0.202*<br>|-0.173<br>|
||(1.74)|(1.50)|
|Irrigated land|0.632|0.419|
||(1.29)|(0.87)|
|Swamp/Wetland|0.532**|0.488**|
||(2.16)|(2.01)|
|Distance to the nearest district town in miles|-0.026***|-0.029***|
||(2.61)|(2.85)|
|Public electricity accessible to households|<br>0.675***|0.663***|
||(7.70)|(7.58)|
|Constant|<br>4.999***|4.209***|
||(17.73)|(6.45)|
|Observations<br>|1237<br>|1237<br>|
|R-squared|0.43|0.45|
|Absolute value of z statistics in parentheses. * significan|t at 10%; ** significant at 5%; *** significa|nt at 1%|

27
direct as well as indirect health benefits of water and sanitation infrastructure, it does not come as a surprise that improvements in water and sanitation have been nominated as one of the official targets of the Millennium Development Goals (MDGs). MDG 7 demands that _„...by 2015, the proportion of the population without sustainable access to safe drinking water and basic sanitation”_ shall be reduced by half (MDG7, UNDP, 2007/2008).

From an economic perspective it is easy to see why countries cannot solely rely on market-based solutions for an optimal level of investments in water and sanitation infrastructure. First, with high initial cost and health benefits in the future, individuals with limited access to credit and/or hyperbolic discounting rates will generally under-invest in precautionary health care measures. Second, with disposal of human feces in public areas as a natural alternative to sanitation facilities, social benefits of proper sanitation infrastructure are likely to substantially exceed private benefits. From a welfare perspective, these positive externalities imply that in the absence of government intervention private investment will be sub-optimal. This problem is further aggravated by the fact that health benefits of improved water and sanitation – involving invisible bacteria and parasites – are hard to understand and internalize, especially for populations with little or no formal education.

However, with limited public resources available to most developing countries, the optimal level of public investment in water and sanitation is not obvious, and critically hinges on a comprehensive analysis of the associated health benefits as well as of more broad welfare measures. The same is true for the international financial contributions towards the water and sanitation sectors. Of the US$ 90 billion development aid spent in 2006, only about US $3.9 billion (4.3%) percent were invested in the improvement of water supply and sanitation (OECD). A normative judgment on whether the current international support for Millennium Development Goal 7 is adequate or not certainly also depends on the magnitude of the identifiable health benefits.

Despite the large number of observational and intervention studies on improved water and sanitation supply, a comprehensive empirical evidence base on their private and public health impact is still lacking. A major part of the existing (mainly epidemiological) literature on 3/36
<!-- Start of picture text --> Income growth at 10th pctile Income growth at 50th pctile<br>Ss =:<br>ae og<br>3 an<br>So a s<br>cc<br>oo<br>3 8<br>1960-70 1970-80 1980-90 1990-00 2000-10 1960-70 1970-80 1980-90 1990-00 2000-10<br>excludes outside values excludes outside values<br>Income growth at 99th pctile Income inequality<br>3 <<br>£<br>53 = 8<br>es | = g°<br>N<br>50<br>on<br>ro : = °‘ : =<br>es =<br>1960-70 1970-80 1980-90 1990-00 2000-10 1960 1970 1980 1990 2000 2010<br>excludes outside values excludes outside values<br><!-- End of picture text -->
with better outcomes, it may take years before those outcomes truly flourish, and by then, it may be impossible to distinguish the contribution of any one action to the better outcome. If local actors do decide to innovate in ways designed to increase vitality, readiness, and connectedness, however, they can do so by comparing areas or people who do and do not benefit from the innovation.

Metrics are important, that is, during this phase of building more inclusive economies because it is still an experimental one. The role of metrics in this experimental phase may be to encourage stakeholders to identify their most important objectives (i.e., desired outcomes of the intervention), develop more robust logic models connecting interventions with outputs and outcomes, and roll out interventions that they agree are most likely to help reach their expectations. Building consensus about the outcomes is an important exercise in itself, because stakeholder commitment to actions that will improve those outcomes can foster better preparation in the face of future economic challenges.

Measurement of local outcomes is also challenging because people and businesses move into and out of cities all the time. The beneficiaries of local actions might relocate. An investment in children’s health and education in one city may pay off in another city (or state, or country) entirely. Encouragement of entrepreneurs, tax breaks for relocating businesses, and other initiatives can all offer short‐term gains that evaporate with relocation to other places. Additionally, some actions involve redistribution of resources from a mobile group to a less mobile one—in particular, taxation of high‐income or high‐ wealth individuals or businesses to fund services for low‐income households. If few enough members of the more mobile group support the overall balance between what they pay and the advantages the location affords, they may be inclined to move away (and others not to move in). As the most sensitive people and businesses trickle away from the city or decide not to move in, the burden may shift to those whose tolerance is tested and even overstressed, causing new people and businesses to move away.

Tracking who moves, where they move, and why can be done to an extent using existing national data sources, but only with some delay. For example, data on residents of small areas by age, sex, and race can be used to infer movement between censuses. The residents of a neighborhood consisting entirely of exactly the same number of 20‐ to 24‐ year‐olds in both 2000 and 2010, for example, must be completely different people (i.e., 36
returns (-30% to -60%) is below 1% initially and decreases to zero percent which includes the long and severe Argentine default, as well as the years following the financial crisis of 2007–08. Thus, over the decades since the crisis of the 1980s, only a small subgroup of bonds saw protracted losses.

Figure 6: The bottom bin: Share of bonds with negative returns Panel A: Full sample, 1815-2016 <!-- Start of picture text --> 30<br>20<br>10<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Holding period in years<br>Share of bonds with returns < 0%<br>Share of bonds with returns < -30%<br>Share of bonds with returns < -60%<br>Panel B: Modern sample, 1995-2016<br>30<br>20<br>10<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Holding period in years<br>Share of bonds with returns < 0%<br>Share of bonds with returns < -30%<br>Share of bonds with returns < -60%<br>Share of bonds in %<br>Share of bonds in %<br><!-- End of picture text --> _Note:_ This figure shows the share of country bond portfolios with a negative, annual real return over different holding periods (years 1 to 10). Panel A includes all years, while Panel B focuses on the modern (post-1995) sample.

# 4.4.  Returns by country: The role of credit history and risk

This section shows bond returns for individual countries and by country groups, in particular for the group of serial defaulters. The returns by country or group are averaged across all active bonds in the subsample and volume-weighted, analogous to the construction of the global portfolio. We also show excess returns above “risk-free” bonds as described above, using UK and US government bonds as the benchmarks for bonds issued in GBP and USD, respectively.

27
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37
**Table A5: Impact of PBF on content of care**

||**Overall**|**PBF**|**p-**||
|---|---|---|---|---|
|**Variable**|**mean**|**impact**|**value**|**N**|
|**Panel A: Adult consultations history taki**|**ng**<sup>**a**</sup>||||
|Physical activity|0.36|0.22*|0.066|2,614|
|Alcohol|0.12|0.08*|0.085|2,614|
|Blood Pressure measurement|0.81|0.07|0.620|2,614|
|Chest pain|0.41|0.00|0.978|2,614|
|Diabetes status|0.44|0.18|0.120|2,614|
|Diet|0.60|0.19**|0.047|2,614|
|Family history of heart disease or stroke|0.31|0.21|0.124|2,614|
|<br>Hypertension|0.55|0.09|0.558|<br>2,614|
|Kidney disease history|0.26|0.11|0.209|2,614|
|Medication|0.31|0.04|0.624|2,614|
|Nausea|0.50|0.14|0.391|2,614|
|Oliguria|0.13|0.02|0.671|2,614|
|<br>Smoking status|0.13|0.08**|0.048|2,614|
|Duration of symptoms|0.58|0.02|0.876|2,614|
|Symptoms|0.81|-0.02|0.767|2,614|
|<br>Vomiting|0.35|0.10|0.450|2,614|
|<br>Patient measures weight|0.33|0.16|0.213|2,614|
|Weight gain/loss|0.26|0.14|0.147|2,614|
|**Panel A: child consultations: physical ex**|**aminations**<sup>**b**</sup>||||
|Assess ability to drink or breastfeed|0.32|0.10|0.265|2,582|
|<br>Breathing|0.42|0.08|0.475|2,582|
|Examine ear|0.33|0.01|0.886|2,582|
|Check for ear infection|0.28|0.21***|0.010|2,582|
|Check for edema|0.10|0.06|0.241|2,582|
|Examine eye infection|0.21|0.04|0.474|2,582|
|<br>Assess lethargy|0.13|0.12*|0.058|2,582|
|Check for mouth ulcers|0.27|-0.00|0.963|2,582|
|Measure radial pulse|0.39|0.31*|0.052|2,582|
|<br>Pinch skin|0.63|0.17*|0.057|<br>2,582|
|Stridor|0.25|0.13|0.146|<br>2,582|
|Temperature|0.91|0.05|0.391|2,582|
|Check for visible wasting|0.19|0.11|0.131|2,582|

Note: data from direct clinical observations conducted at the rural health centers. The PBF impact is estimated using a propensity score weighting specification as only follow-up observations are being included in the analysis. Standard errors are clustered at the district level.

a Data from consultations of adults aged 40 and above. Regressions include controls for facility’s exposure to the collaborative quality improvement and citizen scorecards interventions, Region, age and gender of patient, gender age and position of provider, and a binary indicator for experience of over 20 years. b Data from consultations of children under-5. Regressions include controls for facility’s exposure to the collaborative quality improvement and citizen scorecards interventions, Region, gender age and position of provider, and a binary indicator for experience of over 20 years.

Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 35
TabDl 6: EFFECTS OF EXTRA W1iSlCrNS ON UNilED KINGl BANKS _(Al iIn_ billions 9 USSI * 0.61)

||||||||||--<br>Asssing|tax|relief<br>of 35-|----|_--_<br>_smlng_|no tax relief---|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Books|Oaeloplag<br>Country<br>Exposure<br>lacl SA|Exlsting<br>sasrve|Extra<br>Prov:<br>Increas<br>to 30S|1986<br>e Pro-tax<br>Profit|PrM-tax<br>Profit<br>after<br>Prov|1966<br>Equity|1966<br>Assets|1986<br>Equity/<br>Assets|Extra Prov<br>Batof<br>Tix Ralof|If6<br>Equity<br>tat of<br>IBt Prow|1966 Assets<br>Not of<br>Full<br> Extra Prow|Equity<br>Bat of<br>oBt Prow<br>Assets|I96<br>EquityNot<br>ofFull<br>Extr Prov<br>w|EquityNot<br>of Full<br>Full Extra<br> <br>Prow/Assets|
|Berlass<br>|3.2!|7.0|0.730|0.096|0.165|3.826|76.169|5.02|0.474|3.352|75.439|4.44|3.096|4.10|
|Lloyds|3.60|5.0|0.900|0.700|-0.200|2.755|47.829|5.76|0.585|2.170|46.929|4.62|1.655|3.9<br>5I|
|MIdl ad|4.3|7.0*|_0.962_|0.434|-0.54f|2.111|53.169|3.97|0.636|1.473|52.161|2.12|1.129|2.16|
|Net. vast<br>|2.0|I 3.2|0.466|1.053|0.567|4.702|63.325|<br>5.6!|0.303|4.399|82.059|5.31|4.236|5.11|
|DyalBankof Scotland|0.2|9.0|0.036|0.185|0.147|0.964|16.507|5.61|9.025|0.99|16.559|5.67|0.926|5.59|

Bates: a Estlstad 1. Icludes South Africa _(cross-border),_ which at nd-1906 exchangp rates _was_ 726 million.

Rate of prowlsians: 30 pWrcnt Tea relfIf rate: 35 perenat
|Yunnan Technical and Vocational Education and TrainingProject|LipingXiao|
|---|---|
|Yunnan Technical and Vocational Education and TrainingProject|LipingXiao|
|Yunnan Technical and Vocational Education and TrainingProject|LipingXiao|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|MA-Ouarzazate Concentrated Solar Power|Manaf Touati|
|IraqPublic Distribution System|Kevin Carey|
|IraqPublic Distribution System|Kevin Carey|
|ID-TF ADDITIONAL FINANCING OF PNPM RURAL III FOR GENERASI|(CRobert Wrobel|
|ID-TF ADDITIONAL FINANCING OF PNPM RURAL III FOR GENERASI|(CRobert Wrobel|
|ID-TF ADDITIONAL FINANCING OF PNPM RURAL III FOR GENERASI|(CRobert Wrobel|
|ID-TF ADDITIONAL FINANCING OF PNPM RURAL III FOR GENERASI|(CRobert Wrobel|
|ID-TF ADDITIONAL FINANCING OF PNPM RURAL III FOR GENERASI|(CRobert Wrobel|
|ID-TF ADDITIONAL FINANCING OF PNPM RURAL III FOR GENERASI|(CRobert Wrobel|
|ID-TF ADDITIONAL FINANCING OF PNPM RURAL III FOR GENERASI|(CRobert Wrobel|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Social SafetyNet StrengtheningProject|OleksiyA. Sluchynskyy|
|Guinea-Bissau BiodiversityConservation Project|Maman-Sani Issa|
|Guinea-Bissau BiodiversityConservation Project|Maman-Sani Issa|
|Guinea-Bissau BiodiversityConservation Project|Maman-Sani Issa|
|Guinea-Bissau BiodiversityConservation Project|Maman-Sani Issa|
|Guinea-Bissau BiodiversityConservation Project|Maman-Sani Issa|
|Guinea-Bissau BiodiversityConservation Project|Maman-Sani Issa|
|Guinea-Bissau BiodiversityConservation Project|Maman-Sani Issa|
|Guinea-Bissau BiodiversityConservation Project|Maman-Sani Issa|
|Guinea-Bissau BiodiversityConservation Project|Maman-Sani Issa|
# **Table 1a OTRI and TTRI (percent), by Region and for the Four Largest Traders, 2006**

|**Region **|All trade|Agriculture|Manufacturing|
|---|---|---|---|
|Middle East and North|**21.6**|**32.3**|**19.4**|
|Africa|_11.9_|_12.1_|_11.8_|
|South Asia|**19.5**<br>_14.0_|**46.4**<br>_31.4_|**18.2**<br>_13.2_|
|Latin America and the|**15.0**|**28.1**|**13.8**|
|Caribbean|_5.4_|_6.6_|_5.3_|
|Sub-Sahara Africa|**14.4**<br>_8.4_|**24.9**<br>_13.8_|**12.9**<br>_7.6_|
|East Asia and Pacific|**11.3**<br>_5.0_|**26.6**<br>_8.7_|**10.4**<br>_4.8_|
|Europe and Central<br>Asia|**10.1**<br>_4.5_|**25.9**<br>_10.3_|**9.0**<br>_4.0_|
|**QUAD**|All trade|Agriculture|Manufacturing|
|United States|**6.4**<br>_1.6_|**18.4**<br>_3.8_|**5.7**<br>_1.5_|
|European Union|**6.6**<br>_1.4_|**48.7**<br>_5.9_|**2.9**<br>_1.1_|
|J|**11.4**|**55.8**|**5.7**|
|apan|_4.5_|_31.1_|_1.1_|
|Chi|**9.9**|**17.1**|**9.5**|
|na|_5.1_|_8.8_|_4.9_|

_Note_ : TTRI is in italics; OTRI is in boldface font. _Source_ : Global Monitoring Report (2008), World Bank.

9