rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> TM in percent of US GDP thereafter Official international<br>ra 10% In percent of British GDP until 1914,<br>os lending<br>Ss ao<br>3 Private capital<br>5) flows<br>& 50%<br>a oe \<br>—<br>fe)<br><<br>‘5<br>0<br>1790 1815 1840 1865 1890 1915 1940 1965 1990 2015<br>HE Official commitments<br>——— Private capital flows (RRT)<br><!-- End of picture text -->
<!-- Start of picture text --> Employment creation ees ee Re Seas<br>Community Health Jocccee sce c eae e cece cece eee eeeeeeeenerseteeeeeesanteeretceeeneeriberedecnahsnireeeeeesnaeeneriag<br>& social spaces Tirrttsoreeeseeececsees@ecusceseasseerssesecssseriscscersssasecipecepeasapenecreseecessss sees<br>Poverty reduction Ce ce ee cr ee ee ee ee ee ee a<br>Price control nr SOP Or On Pa<br>National welfare rerensecartereerersnssatierereree essa terre eae srristererecesssrtstheredeesshsastrerrercesasaariee<br>SafetyHousing |-os-:s1rstrrerr errs titre eres e sneer err ers adbrriierersersni ri iberedeessbertirerenscssasnies<br>Transport & infrastructure mets [oocece 0-0 cece eee ee cece eee eeeeenssese eet eaterBCtips ccc ceeessetbeeetire dlc eeee e eeeeewaaaeeeener renee s<br>Water & sanitation i aes ae ae cen<br>Goverance [------- sees ee ee ee eee eee<br>Food& agriculture |--------++seee-+--+-sccs eee e eseeeet e sceneeeceece scesssaheced ecb e ceeee e e cscseeer eeee ee e es<br>Improve powerRenewablemanag e nergyment SnDDODRDREEESROIBEDDEDISOAOESSDIIODOSSSSSAOEEEOEDIOOOSSSSRORODOEDDIee ee eek LdNee (000)ee DUDSDORAOOOEOEDRIOESS:eee re<br>NaturalIndustryresourees Jecesssecsereeisececcccceceeeeeeeccuuueuevsueeeeeasaccrereecercaccccci beced@ecsbercereceuececuarnrias<br>Humanitarian PESPIOISE& trade [oresWirrteeorr eee e esseescesseeseesee e sesadeossressereesssessssssecssssccouipecsmesapeserssecerssssse sees<br>Technology erryeee eee Sree NB FD leerareeee rareeee<br>Electricity<br>and fuel |-------+++++++0-----sserttesees sesttet teen ecentat teens eee ntet epee ge cee bee ttidpeceeree e ennttee<br>-60 -40 -20 0 20<br>Chi-squared (7) statistic<br>Node: This figure shows differences in the relative frequency of attermative policies measures between respondents who unconditionally and only .<br>(aloethat tndatioa uch St bacpianty aereang racpencionda wis onlycoveiiiestalyshes! tafe. Wise Chi-arpannecl stideeac ts hac ar ee oulepoch<br>a@izo theremarked arewith mo oia arancascharmond between a opposed theseto groupsa orc. of respondents. iis significant at a p-value of 0.06 if the value falls outside the dotied lines (this<br><!-- End of picture text -->
by more than 1 percentage point during most of the Plan Years. The growth rate remains above the 7 percent mark till 2026 and does not fall below 6.5 percent even after 15 years.

<!-- Start of picture text --> 10 Real GDP Growth (%) 10 Real GDP Growth (%)<br>Baseline Participation<br>9 9<br>8 Plan Target 8 Plan Target<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>10 Real GDP Growth (%) 10 Real GDP Growth (%)<br>High Growth<br>9 9<br>8 Plan Target 8 Plan Target<br>7 7<br>High Growth II<br>6 6<br>5 5<br>4 4<br>3 High Growth I 3<br>2 2<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> The implied investment share of GDP obtained using equation (12) is higher than the investment shares used in section 3.1. While the national savings as a share of GDP is lower than used the investment share even under the assumption that <u>public capital is perfectly</u>

||**Average**|**Annual Real GDP Gro**|**wth(%)**|
|---|---|---|---|
|**Scenario**|**2016 – 2020**|**2021 – 2025**|**2026 – 2030**|
|Baseline|5.70|5.26|4.31|
|Participation|6.76|5.46|4.44|
|High Growth I|7.98|6.90|5.98|
|High Growth II|8.59|7.63|6.77|
|**7**<sup>**th**</sup>**Five Year Plan Target**|**7.44**|‐|‐|

22
# **Table 4:  Regression Results, by Country, Sub-Saharan Africa, 1998-2013**

||**Burkina Faso**|**Cameroon**|**Congo, D. R.**|**Cote d'Ivoire**|**Ethiopia**|**Ghana**|**Madagascar**|**Madagascar**|**Mauritius**|**Mozambique**|
|---|---|---|---|---|---|---|---|---|---|---|
|ln(relative food inflation)|||||‐0.147***|‐0.032||||0.244***|
||||||(0.007)|(0.023)||||(0.083)|
|ln(real total consumption)|‐0.117***|‐0.056***|‐0.078***|‐0.071***|‐0.095***|‐0.077***|‐0.093***|‐0.079***|‐0.117***|‐0.091***|
||(0.003)|(0.002)|(0.002)|(0.002)|(0.001)|(0.003)|(0.003)|(0.002)|(0.001)|(0.002)|
|Time dummy|0.004|‐0.004*|‐0.032***|‐0.043***|‐0.063***|0.026***|‐0.090***|‐0.042***|‐0.015***|‐0.043**|
||(0.004)|(0.002)|(0.003)|(0.003)|(0.001)|(0.009)|(0.003)|(0.003)|(0.001)|(0.020)|
|Sample size|4,992|12,830|14,566|11,151|44,600|8,852|8,834|11,480|12,736|8,852|
|Adjusted R2|0.306|0.237|0.294|0.184|0.228|0.353|0.316|0.268|0.412|0.353|
|_Availability of regional CPI_|_No_|_No_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_No_|_Yes_|
|_Sample coverage_|_Urban_|_Urban_|_Urban_|_Urban_|_National_|_Urban_|_Urban_|_Urban_|_National_|_Urban_|
|_Year of survey rounds_<br>_Significance level: * = 10 percent_<br>_Note: Regressions are controlle_<br>_head's age, head's gender, hea_|_1998, 2003_<br>_, ** = 5 percent,_<br>_d for household_<br>_d's education, h_|_2001, 2007_<br>_*** = 1 percent_<br>_size, share of ho_<br>_ead's marital sta_|_2004, 2012_<br>_usehold younge_<br>_tus, and region_|_2002, 2008_<br>_r than 5 year old,_<br>_al dummies._|_2004, 2010_<br>_share of househ_|_2006, 2012_<br>_old between 5_|_2001, 2010_<br>_and 10 year old, s_|_2005, 2010_<br>_hare of househo_|_2006, 2012_<br>_ld between 10_|_2002, 2008_<br>_and 17 year old,_|

# **(continued)**

||**Nigeria**|**Rwanda**|**Senegal**|**South Africa**|**Tanzania**|**Tanzania**|**Tanzania**|**Togo**|**Uganda**|
|---|---|---|---|---|---|---|---|---|---|
|ln(relative food inflation)|||||||||‐0.714***|
||||||||||(0.196)|
|ln(real total consumption)|‐0.013***|‐0.089***|‐0.090***|‐0.074***|‐0.040***|‐0.044***|‐0.049***|‐0.069***|‐0.082***|
||(0.003)|(0.002)|(0.002)|(0.001)|(0.001)|(0.003)|(0.003)|(0.003)|(0.004)|
|Time dummy|‐0.033***|‐0.064***|‐0.050***|‐0.220***|‐0.072***|‐0.056***|‐0.021***|‐0.101***|0.026***|
||(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|(0.004)|(0.007)|
|Sample size|8,824|19,715|11,002|25,969|20,990|3,612|4,499|4,832|2,986|
|Adjusted R2|0.297|0.341|0.337|0.492|0.197|0.161|0.121|0.331|0.262|
|_Availability of regional CPI_|_No_|_No_|_No_|_No_|_No_|_No_|_No_|_No_|_Yes_|
|_Sample coverage_|_National_|_National_|_Urban_|_Urban_|_Urban_|_Urban_|_Urban_|_Urban_|_Urban_|
|_Year of survey rounds_|_2011, 2013_|_2005, 2010_|_2005, 2011_|_2005, 2010_|_2001, 2007_|_2008, 2012_|_2010, 2012_|_2006, 2011_|_2009, 2012_|

_Note: Regressions are controlled for household size, share of household younger than 5 year old, share of household between 5 and 10 year old, share of household between 10 and 17 year old, head's age, head's gender, head's education, head's marital status, and regional dummies._ _Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent_ 23
# **5.2.4 Further robustness checks**

Table 7 provides a final set of additional robustness checks to further investigate the potential sensitivity of our results to alternative specifications. In particular, specification (5) uses an alternative variable in lieu of the measure of diversification of the foreign exporters targeted by the U.S. remedy. Instead, the variable is defined as the growth in (6-digit HS) exports of the targeted country to the rest of the world _after_ the U.S. remedy has been imposed, i.e., its growth between _t_ and _t+1_ . We expect the parameter estimate on this variable to be negative – the more the targeted exporters increase exports of the disputed product to third markets (proxying for whether they actually do “deflect” trade), the less likely would be the foreign country to litigate the issue of the U.S. remedy at the WTO. Nevertheless, the estimate is once again _positive_ (though it is marginally insignificant), which provides some confirmation of our earlier result of concern that less diversified exporters are less likely to challenge measures at the WTO.

Next, in specification (6), we further investigate whether there is a link between political-economic relationships between countries and GATT/WTO dispute settlement filing behavior in this sample of data. In theory, a second potentially important expected _cost_ to developing country exporters that may affect their decision of whether to challenge the U.S. at the WTO does not relate to the cost of litigation, but to the political economic costs of publicizing a grievance through a formal international confrontation with the U.S. One reason why the U.S. might be “important” is that the country affected by the trade remedy could be particularly reliant on the United States for bilateral assistance. Therefore, we expect the larger is the share of total aid received by the foreign country that derives from the U.S., the less likely is that country to initiate a dispute against the U.S. The bilateral aid data is derived from OECD (2001). In order to implement this, in specification (6) we also _drop_ the foreign exporter’s diversification variable, in case this is highly collinear with either the aid relationship or legal capacity variables so as to confound their estimated impact. While the results do indicate a negative relationship between the foreign country’s reliance on the U.S. for bilateral aid and its dispute settlement decision, the size of the estimate is not statistically significant at conventional levels. Furthermore, the parameter estimates for the variables on legal capacity in specification (6) are also virtually unchanged, and the log likelihood is substantially higher than in the other specifications when the diversity variable is included. Thus, the lack of empirical importance of the proxies for the litigation costs and the political economy relationship stands in contrast to the evidence presented in the related research of Bown (2004b), which investigates the participation decisions of exporting countries adversely affected 21
Policy Research Working Paper 6825

# **Abstract**

For young adults living in countries with AIDS epidemics, getting an HIV test may influence near-term decisions, such as when to leave school, when to marry, and when to have a first child. These behaviors, which define the transition from adolescence to adulthood, have long-term implications on well-being and directly affect a person’s risk of contracting HIV. Using an experimental design embedded in a panel survey from Malawi, this study assesses the impact of voluntary counseling and testing of young adults for HIV on these decisions. The results show negligible intent-to-treat effect of HIV testing on behaviors. There is some suggestive evidence on differential response by wealth and by prior beliefs about one’s status.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kbeegle@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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# **Do Resource-Rich Countries Suffer from a Lack of Fiscal Discipline?**

Michael Bleaney* and Håvard Halland** University of Nottingham* World Bank**<sup>1</sup>

**JEL classification codes:** E62, Q3 **(** Keywords: oil, fuel, ores, metals, natural resources, government expenditure, government revenue, fiscal balance).

> 1 The authors are grateful to Kevin Carey and Peter Mullins for their insightful comments on the final draft for this paper; to Marijn Verhoeven, Nicola Smithers and Jay-Hyung Kim for feedback on earlier drafts; and to Robert Beschel and William Dorotinsky for their support for this project. The authors are also grateful to the Governance Partnership Facility, its manager Piet van Heesewijk, and its donor partners—the U.K. Department for International Development (DFID), the Australian Department of Foreign Affairs and Trade (DFAT), the Netherlands’ Ministry of Foreign Affairs, and Norway’s Ministry of Foreign Affairs—for providing full funding for this work.
<!-- Start of picture text -->
1.5<br>Dpvo x“<br>=<br>S<br>q<br>5<br>2 1<br>So<br>n<br>a<br>o<br>&a)= --"- ec =— a<br>B 5 _ -<br>a)& -_— - x<br>$ ee x<br>oa cae<br>2 iS _-- -- x<br>5 * ae - -_— “_— aoex < *<br>f [b=<br>=b<br>g<br>a2]<br>jan}<br>-.5<br>1 2 3 4 5 6 7 8 9 10<br>VenRePS Share, 2018 (Deciles) - Origins of Venezuelan Migrants in Colombia<br>95% CI —-—-—-—- Fitted linear trend<br>x Decile means<br><!-- End of picture text -->
together, sum to one statistical life.<sup>13</sup> The challenge is in estimating what a rational individual would pay for a small reduction in his risk of dying.

In benefit-cost analyses of health and safety regulations conducted in the United States the European Union, and in many other countries around the world, mortality risks are valued using the VSL. When estimates of the VSL are unavailable, the human capital approach (foregone earnings) is often used to place a lower bound on the VSL.<sup>14</sup> This section describes the theory behind the VSL and methods that have been used to estimate it. The paper briefly reviews the empirical literature on the VSL in both high income and developing countries. Because of the paucity of estimates of the VSL in developing countries, analysts often use benefits-transfer techniques to extrapolate estimates of the VSL from high income to developing countries. We evaluate various approaches to benefits transfer, concluding with recommendations for estimating the VSL in benefitcost analyses of disaster risk reduction projects.

- A. The Concept of Willingness to Pay (WTP) for Reductions in Risk of Death<sup>15</sup>

The amount that a rational person would pay to reduce his risk of dying should reflect the enjoyment he will receive over the remainder of his lifetime. This, in turn, should depend on his current age and remaining life expectancy, his expected consumption over the remainder of his lifetime and factors that affect his utility of consumption. The lifecycle consumption model with uncertain lifetime (Yaari, 1965) provides the theoretical framework for examining willingness to pay (WTP) for changes in mortality risk. The implications of the model for valuation are summarized below and the details presented in Appendix A.

The lifecycle model assumes that at age _j_ the individual chooses his future consumption stream to maximize expected lifetime utility, where _V j_ is the present value of expected utility of lifetime consumption, _U t_ (<sup>_C_</sup> _t_ ) is utility of consumption at age _t_ , _q j_ , _t_ is the probability that the individual survives to age _t_ , given that he is alive at age _j_ , and δ his subjective rate of time preference.16 Yaari (1965) assumed that utility is maximized subject to a budget constraint that allows the individual to invest in annuities and to borrow via life-insured loans. This is equivalent to assuming

> 13 If each person would pay $10 for a 1 in 50,000 risk reduction, the value of a statistical life would be $500,000 (50,000*$10).

> 14 The human capital approach values a statistical life by the present discounted value of expected future earnings.

> 15 This section follows Cropper and Sussman (1990).  Other analyses of the value of mortality risks in the context of the life cycle model include Shepard and Zeckhauser (1984) and Rosen (1988).

> 16 Equation (1) implicitly assumes that the individual has no bequest motive, hence utility of consumption = 0 when the individual is dead.

12
Table 12 Testing for Sensitivity of Resuits to Inclusion of Changes in Government Spendingi'

||ANNUA|L DATA|SIX-YEAR A|VERAGES|PERIOD A|VERAGES|
|---|---|---|---|---|---|---|
||OPENNESS|GOV'T.<br>SPENDIN|OPENNESS<br>G|GOV'T.<br>SPENDING|OPENNESS<br>|GOV'T.<br>SPENDING|
|(1) TR I|0.08<br>(0.09)|0.86<br>(.39)|0.25**<br>(.12)|1.35<br>(1.06)|-0.30<br>(.74)|1.14<br>(2.46)|
|(2) TR II|0.02**<br>(0.0)|0.22<br>(.21)|0.02*<br>(.01)|-1.26<br>(1.24)|0.0<br>(0.0)|0.96<br>(.77)|
|(3) BLACK|-0.01<br>(.01)|0.44<br>(.14)|-0.03**<br>(.01)|40.30<br>(.43)|-0.02**<br>(.01)|1.41<br>(.55)|
|(4) DOLLAR|-0.06**<br>(.02)|0.80<br>(.20)|-0.06*<br>(.03)|-0.03<br>_(.-'5)_|-0.01<br>(.02)|0.27<br>(.93)|
|_(5)_TRADE<br>SHARES|-0.06<br>(.06)|0.42<br>(.14)|-0.10<br>(.27)|-. 44<br>(.48j|0.41<br>_(.51)_|1.59<br>(.60)|
|(6)INDIR<br>.___________|1.50<br>_______<br>(3.09)|0.33<br>(.20)|6.00**<br>(2.91)|-0.82<br>(.42)|27.11<br>(28.85)|_-0.15_<br>(1.08)|
|(7) MOVEMENT|0.05**<br>(.02)|0.63<br>(.21)|0.07<br>(.05)|-0.05<br>(.62)|0.08<br>(.08)|0.68<br>_(.55)_|

1/ Levels of policy variables used for all variables except trade shares, in dir, and movement towards - international prices which use changes in policies. Standard errors in (). Significance levels for openness variables (only) indicated by ** (5 percent), and * (10 percent).

33
We examine the correlates of attendance of the migrant and of the family member and find few predictors of who takes up the training. Migrant attendance is unrelated to baseline household income, or to the education, baseline knowledge of financial terms, or baseline numeracy of the survey respondent. Attendance by a family member is also unrelated to baseline financial knowledge, education, or numeracy, but is higher among households with higher baseline income – perhaps reflecting the impact of travel costs on poorer households attending.

# **2.6 Follow-up Surveys and Attrition**

Three rounds of follow-up surveys were conducted via in-person interviews. The first follow-up survey took place in March 2011 and interviewed 392 of the 400 households (98%). At this time, 83 percent of households reported the migrant to be abroad, and 9 percent to have had the migrant return, with the mean and median time abroad being 9 months. A second followup survey took place in September 2011, and successfully re-interviewed 376 of the 400 households (94%), at which time 79 percent of households still reported having a migrant abroad, with median time abroad of 15 months. The third and final follow-up survey took place in January 2012 and interviewed 365 households (91%), at which time 77 percent of households still had a migrant abroad, with median time abroad of 19 months.

The follow-up surveys were aimed at the family member in charge of receiving remittances and making financial decisions in the household, and effort was made to re-interview the same member each follow-up round. In the few cases where this was not possible, we control for a change in the identity of the respondent in the regressions. The surveys collected information on financial knowledge, behaviors, and outcomes of the household. An important caveat is that we do not re-interview the migrant workers, since the feasibility and cost of interviewing them while abroad did not make this possible. Our focus is thus on outcomes for the remaining household members.

Table 2 tests whether attrition rates at baseline and at each follow-up survey vary by treatment status. We regress a dummy variable for being present in the survey round on treatment assignment, and report both the coefficients, as well as p-values for test of equality across the different treatments. The last column looks at being present in any of the follow-up rounds. The regression coefficients are small, and we cannot reject the null hypothesis of equality of attrition rates across treatments for any of the survey rounds. Given the low level of attrition and that it is unrelated to treatment status, we therefore ignore attrition in our analysis.

10
unemployment exceeds 33 percent. Sugar mills in Mpumalanga provide input and technical support to about 100 commercial farmers and 1,400 smallholder farmers. The study is forward looking and examines the effects of potential changes in the European Union’s sugar regime that may well create opportunities for smallholder production to expand and the consequences for conservation and sustained development in the face of competing demands for the area’s limited supply of fresh water.

# <u>Chile</u>

The Chilean study has two areas of focus. One is the country’s diverse forest ecology; the other is its rich coastal aquatic ecology. Chile is home to nearly one-third of the world’s remaining large tracts of temperate forests (Global Forest Watch, 2002). Rates of endemism in Chile’s forests are high and its temperate forests are included in the World Wildlife Fund’s top conservation priorities. The forests contribute to global climate regulation and provide local benefits by reducing erosion and flooding, by providing catchments for fresh water and by preserving soil nutrients. Part of the study focuses on how the forestry industry has affected one of the richest zones of native forest and biodiversity in regions VIII to X. The other part focuses on the freshwater and estuarine ecosystems of southern Chile and the development of Chile’s salmon industry.

In contrast to many of the studies that focus on household interactions with a specific natural resource, the Chile study looks at the longer-term structural changes that have given rise to regional differences in rates of economic growth and poverty reduction and in shifting sources of livelihood. Noting that, on average, rural poverty fell from 41 percent to 24 percent during the last decade of the 20<sup>th</sup> century, regional disparities emerged and have persisted, with incomes lagging especially in the study areas. The study focuses on the reasons for this and on the consequences for how the region’s natural resources are used.

# <u>Madagascar</u>

The Madagascar study has two components. The first focuses on traditional practices of maize cultivation and its affect on the Mikea Forest of southwest Madagascar. The second looks at the role of contract farming on the preservation of soil fertility, a long-standing resource management problem in Africa. The Mikea is a dry and spiny forest, rich in endemism and home to the radiated tortoise, the subdersert mesite and the white-footed 10