rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 17. LAC: Investment needs**

_A number of LAC countries rank poorly on access to quality infrastructure. Important among current investment needs are infrastructure and education, in terms of both quantity and quality._

# **A. Quality of infrastructure**

<!-- Start of picture text --> Rank<br>LAC average<br>140<br>EMDE average<br>120 AE average<br>100<br>80<br>60<br>40<br>20<br>0<br>Barbados Panama Chile Ecuador Mexico Jamaica Honduras Uruguay Guatemala El Salvador Dominican Republic Nicaragua Costa Rica Argentina Colombia Bolivia Peru Brazil Venezuela, RB Paraguay<br><!-- End of picture text -->

# **C. Selected education indicators**

<!-- Start of picture text --> Percent of per capita<br>income, ratio<br>45<br>Range AE<br>40<br>EMDEs LAC<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Government Pupil-teacher ratio<br>expenditure<br><!-- End of picture text -->

# **B. Ease of accessing electricity**

<!-- Start of picture text --> Rank LAC average<br>140 EMDE average<br>120 AE average<br>100<br>80<br>60<br>40<br>20<br>0<br>D. Selected health care indicators<br>Per capita Percent of population<br>4500 120<br>100<br>3000 80<br>60<br>1500 40<br>Range AE 20<br>EMDE LAC<br>0 0<br>Health Improved Improved<br>expenditure sanitation water<br>(LHS) (RHS) source<br>(RHS)<br>Guatemala Panama Costa Rica Uruguay Brazil Peru Chile Colombia Argentina Barbados Ecuador Mexico Nicaragua Bolivia Jamaica Paraguay El Salvador Haiti Honduras Dominican Republic Venezuela, RB<br><!-- End of picture text --> Sources: World Bank (2017), World Economic Forum (2016).

A. Rankings out of 138 countries.

B. Rankings out of 190 countries.

C. Blue bars denote range of unweighted regional averages across EMDE regions. Government expenditure per primary student (in percent of per capita income), unweighted averages of 87 EMDEs, 32 AEs, and 20 LAC economies. Pupil-teacher ratio in primary education (headcount basis), unweighted averages for 165 EMDEs, 31 AEs, and 23 LAC economies. Latest data available during 2011-15.

D. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 31 LAC economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 28 LAC economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 30 LAC economies. Latest data available during 2011-15.

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**Table S4.5.** ITT effects on number of adopted ISFM technologies and integrated adoption of the full ISFM package, FREG- and non-FREG samples separately; using a different set of matching variables.

|||O<br>|Numbe<br>LS<br>|r of ISFM te<br>|chnologies a<br>|dopted<br>Pois<br>|son<br>||Inte<br>|grated adopt<br>|ion of full pa<br>|ckage<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Non-FRE|G sample|FREG|sample|Non-FRE|G sample|FREG|sample|Non-FRE|G sample|FREG|sample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|T1 (extension)|0.296|0.286***|1.057***|1.091***|0.302|0.301***|1.056***|1.091***|0.046|0.047*|0.228***|0.235***|
||(0.189)|(0.076)|(0.194)|(0.126)|(0.190)|(0.083)|(0.186)|(0.127)|(0.047)|(0.024)|(0.065)|(0.050)|
|p-value|0.120|0.000|0.000|0.000|0.112|0.000|0.000|0.000|0.326|0.055|0.000|0.000|
|T2 (extension + video)|0.527***|0.494***|0.940***|0.987***|0.517***|0.492***|0.954***|1.005***|0.093**|0.096***|0.184**|0.200***|
||(0.177)|(0.081)|(0.178)|(0.126)|(0.169)|(0.083)|(0.175)|(0.130)|(0.045)|(0.025)|(0.073)|(0.043)|
|p-value|0.003|0.000|0.000|0.000|0.002|0.000|0.000|0.000|0.040|0.000|0.011|0.000|
|Test T1 >= T2 (p-value)|0.141|0.010|0.711|0.777|0.141|0.013|0.712|0.757|0.184|0.038|0.719|0.742|
|Endline control mean|2.4|65|2.9|75|2.4|65|2.9|75|0.|153|0.|275|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.029|0.489|0.185|0.503|0.005|0.089|0.020|0.056|0.010|0.300|0.037|0.262|
|Observations|1,604|1,604|400|400|1,604|1,604|400|400|1,604|1,604|400|400|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (columns (5) to (12)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and their matched controls. Matching variables are those listed in notes of Table 2 (ITT regressions). Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F- tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

13
Figure 15 below benchmarks poverty estimates for Syria obtained using the distribution neutral approach based on GDP current priced adjusted by CPI and baseline 2009. Overall, estimates based on both unit value pass through and conflict adjusted pass through lead to estimates that could be consistent with countries at similar levels of development, as proxied by per capita GDP at current prices.

_Figure 15: Benchmarking of_ _<u>poverty estimates for Syria, 2020</u>_ <!-- Start of picture text --> LIC($2.15,2017 PPP) LMIC($3.65, 2017 PPP)<br>100% 100%<br>UZB<br>ZMB<br>SYR-Unit<br>80% MDG 80% Passthrough TLS<br>COD MWI TZA PNGSLB<br>BDI MOZ ZWE COG<br>60% CAF 60% SYR- 0.7 HTIBENLSOKEN NGA SWZ<br>NER RWA Passthrough BGDGHA AGO<br>40%20% SYR-Unit PassthroughSYR-Conflict-adjusted GNBTCDGMBLBRMLIETHSLE GINBFA SYR- 0.7 Passthrough UGATGO SDN 40%20% SYR-Conflict-Passthroughadjusted NPL CMRTJKCOMKGZSENKIRNICSTPPAKHNDCIVMRTMMRINDBOL BTNFSMDJILAOCPVPSEMARVUTPHL IDNSLVDZAMNG WSMEGYLKAIRN<br>Passthrough 0% UKR VNM TUN<br>0% $- $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 $4,500 $5,000 $5,500<br> $- $200 $400 $600 $800 $1,000 $1,200 $1,400 $1,600 $1,800 $2,000<br>GDP per capita, 2020 (constant 2015 US$) GDP per capita, 2020 (constant 2015 US$)<br>Poverty Rate Poverty Rate<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on WDI and CBS data

# 5 Triangulating poverty projections with available evidence

As discussed in Section 3, the HNAP 2022 survey could provide a reasonably good assessment of the current extent of monetary poverty in Syria. While the consumption aggregate based on the HNAP 2022 survey is not comparable with the comprehensive recording of consumption from pre-conflict official household budget surveys, it is most likely providing a more accurate characterization of monetary poverty in Syria compared to projections based on a distribution neutral nowcasting approach, including on the subnational profile of monetary poverty. Using the nominal consumption aggregate obtained from the HNAP 2022, monetary poverty in Syria is the highest in northeastern governorates (Figure 8). Can this evidence be corroborated?

Similarly, as discussed in Section 4, distribution neutral approaches could lead to very different dynamics of the temporal evolution of poverty, depending on the underlying NA aggregate used to project growth in average household consumption. Based on the assessment of the likely quality of each aggregate, estimates of growth based on per capita GDP in current prices deflated using the CPI should be preferred over the default nowcasting estimates obtained using constant prices per capita GDP. As shown in Figure 11 and Figure 12, poverty trends based on per capita GDP in constant prices would lead to a monotonic poverty increase over the conflict period, whereas estimates using per capita GDP in current prices deflated with the CPI would lead to an increase in poverty over the period 2011-2016, followed by a poverty decline between 2016 and 2019, and again an increase thereafter. Can these trend dynamics be corroborated?
Table C6: Robustness on GDP Growth: Drivers of Total Debt Inflows, by Sector - Emerging Markets (Quarterly AHKS data, missing filled from Public Sources)

||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|
|---|---|---|---|---|
|log(VIX_t−_1)|-2.505<sup>_∗∗∗_</sup>|1.188|-2.562<sup>_∗∗∗_</sup>|-1.132<sup>_∗∗∗_</sup>|
||(0.862)|(0.677)|(0.747)|(0.253)|
|GDP Growth_it−_1|0.133<sup>_∗∗∗_</sup>|-0.0390<sup>_∗∗∗_</sup>|0.124<sup>_∗∗∗_</sup>|0.0478<sup>_∗∗∗_</sup>|
||(0.0440)|(0.0136)|(0.0430)|(0.0112)|
|Observations<br>|1372|1372|1372|1372|
|_R_<sup>2</sup>|0.066|0.017|0.099|0.078|
|CountryFE|Yes|Yes|Yes|Yes|

Sample is from 2002q4-2014q4, countries as listed in Appendix A.4. Total Debt is the sum of Portfolio Debt and Other Investment Debt inflow data, constructed by AHKS as described in Section 2. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as country year-on-year percentage GDP growth minus aggregate advanced economy year-on-year GDP growth. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01 100
The disassociations between the formal and informal aspects of agencies’ mechanisms and procedures to achieve transparency might be explained by two factors: 1) not all agencies’ procedures translate into practice (for instance, the mere existence of the agency’s annual report vs. its publication on the agency’s website or bulletin); and 2) the agency has developed good practices of transparency that are not established in the agency’s statute or the electricity law. This is the case of Bolivia and Guatemala, whose positions drastically change from formal to informal transparency. Bolivia moves from a score of 0.7 in formal transparency to the first place in informal transparency (0.9). Guatemala leaves the last position in the formal transparency index (0.5) to reach the sixth place (0.75) in the Informal Transparency Index.

Consistent with the Transparency Index, Trinidad and Tobago heads the Formal Transparency Index, reaching one of the leading positions in informal transparency.

# **4.3.3 Accountability:**

Accountability was not disaggregated into different aspects. Its indicators represent different institutional elements ( _e.g._ reporting obligations to the Executive and the Parliament and ability to appeal its decisions before the Executive and the Judiciary) of the agency’s relationships with the Executive, the Legislative, and the Judiciary. Hence, we only considered the institutional aspect of agencies’ accountability design.

Our benchmark of analysis gives higher scores to the dual accountability of the agency to the Executive and the Legislative, with stronger influence of the latter. Moreover, it emphasizes the appeal of the agency’s decisions before the judiciary, giving a lower score to agencies whose decisions need a previous denial from the Executive before the judicial review.

**Figure 18: Accountability Index**

<!-- Start of picture text --> Accountability Index<br>Source: LAC Electricity Regulatory Governance Database, The World Bank, 2007.<br>1<br>.8<br>.6<br>Index (0-1)<br>.4<br>.2<br>0<br>T&T T1 GUA BRA BOL BAR ESV COL R D PER MEX NIC C R ARG T2 URU ECU JAM HON PAN CHI<br><!-- End of picture text --> Trinidad and Tobago is the only country in Tier 1, showing a desirable governance design and practices to keep the agency accountable to institutional actors. Different from other indexes, there are a vast number of countries that are under Tier 3. Chile, Panama, and Honduras are the countries with the lowest results. The situation of Chile among Tier 3 countries might be due to the oversight of the agency by the Executive and not the Parliament. Nevertheless, we do not make a judgment regarding the effectiveness of Chile’s accountability mechanism. We limit our assessment to establish whether agencies are subject to stronger controls by the Executive or the Legislative,

- 30 -
Panel A: Burkina Faso Panel B: Mali Source: Nathan Associates Inc. (2013), Port Autonome d’Abidjian, Ghana Ports and Harbours Authority, Port of Lome Notes: Panel A of Figure 8 presents historical trends in the physical trade imbalances between Burkina Faso and its four main ports. The physical trade imbalance is defined as the difference between exports (kg) and imports (kg), divided by the maximum of exports and imports. Panel B presents historical trends in export-import imbalances between Mali and its five main ports.

Figure 8: Demand Imbalances between Major West African Ports and Burkina Faso/Mali 20
Table 2: Impact on FGT2 of Targeting at Different Levels of Geographic Disaggregation Optimal Targeting Scheme

||1999|2009|1999|2009|
|---|---|---|---|---|
|Original FGT2|0.0159|0.0234|1.00|1.00|
|FGT2 after:|||||
|Uniform transfer|0.0110|0.0166|0.69 (1.00)|0.71 (1.00)|
|Province Level Targeting|0.0080|0.0096|0.50 (0.72)|0.41 (0.58)|
|District-level targeting|0.0066|0.0070|0.42 (0.61)|0.30 (0.42)|
|Commune-level targeting|0.0058|0.0057|0.36 (0.57)|0.24 (0.34)|

Note: Budget=5% of (Total Population * 25<sup>th</sup> Percentile Per Capita Expenditure). Poverty Line= Per capita expenditure defining bottom quintile of population (pre-transfer). The two last columns are the poverty gap indexes normalized to one. The figures in parentheses are the ratio of normalized FGT2 in the case of targeting transfer to the normalized FGT2 in the case of uniform transfer. The number of provinces, districts and communes in 1999 is 61, 614, and 10474, respectively. The number of provinces, districts and communes in 2009 is 63, 685, and 10896, respectively.

Table 3: Impact on FGT0 of Targeting at Different Levels of Geographic Disaggregation Optimal Targeting Scheme

||1999|2009|1999|2009|
|---|---|---|---|---|
|Original FGT2|0.2000|0.2000|1.00|1.00|
|FGT2 after:|||||
|Uniform transfer|0.1673|0.1724|0.84 (1.00)|0.86 (1.00)|
|Province Level Targeting|0.1522|0.1555|0.76 (0.90)|0.78 (0.91)|
|District-level targeting|0.1443|0.1465|0.72 (0.86)|0.73 (0.85)|
|Commune-level targeting|0.1390|0.1372|0.70 (0.83)|0.69 (0.80)|

Note: Budget=5% of (Total Population * 25<sup>th</sup> Percentile Per Capita Expenditure). Poverty Line= Per capita expenditure defining bottom quintile of population (pre-transfer). The two last columns are the poverty gap indexes normalized to one. The figures in parentheses are the ratio of normalized P0 in the case of targeting transfer to the normalized P0 in the case of uniform transfer. The number of provinces, districts and communes in 1999 is 61, 614, and 10474, respectively. The number of provinces, districts and communes in 2009 is 63, 685, and 10896, respectively.

We only have access to a 15% sample of the Population Census, which is not representative on a commune-level. Therefore, it must be noted that the commune-level estimates of poverty may not be representative of the actual welfare levels, making our district-level results more on the point. However, as the government does have the full Census in its possession, a potential geographic targeting policy could successfully target aggregation levels as low as commune or even village, reaping the benefits of more disaggregated spatial 28
level rise scenarios and confusion between the greenhouse effect and ozone depletion. Bell concludes that, “this misunderstanding is socially and politically disabling because it misleads people away from dealing with an issue at the heart of western consumer society—the consumption of fossil fuels.” Jacques, Peter 2006. “ The Rearguard of Modernity: Environmental Skepticism as a Struggle of Citizenship” Global Environmental Politics. 6(1): 76-101.

See above.

Ungar, Sheldon 2003. “Misplaced Metaphor: A Critical Analysis of the ‘Knowledge Society’” <u>Canadian Review of Sociology and Anthropology</u> 40(3): 331-347.

Ungar rejects the conception of a “knowledge society” metaphor, arguing instead that we live in a “knowledge averse” culture. The author examines the idea of the well-informed citizen, the notions that our present society contains institutional arrangements and social expectations for being knowledgeable, and the role of the Internet in providing critical underpinnings of a “knowledge society.”Ungar argues that while social processes create a high degree of knowledge specialization in the workplace, this very phenomenon may increase ignorance across society in general.

Ungar, Sheldon 2007 “Public scares: changing the issue culture” in Moser, Susanne C. and Lisa Dilling. eds Creating a Climate for Change: Communicating Climate Change and Facilitating <u>Social Change.</u> New York: Cambridge University Press.

Ungar, Sheldon 2008. “Ignorance as an under-identified social problem. British Journal of <u>Sociology, 59(2): 301-326.</u> Similar to earlier paper on the misplaced metaphor of a knowledge society, Ungar examines “the persistence of ignorance in the ostensible knowledge society.” Ungar identifies contradictory dynamics of the knowledge society, specifically information explosions in the knowledge economy and the resultant “knowledgeignorance paradox.” Ungar counters that “pockets of observed public knowledge - rather than ignorance - are exceptional and require specific explanation.” While ignorance among individuals, as well as experts and organizations, is a serious social problem with potentially deadly consequences, ignorance remains relatively unrecognized since it has major liabilities as a marketable issue. Ungar argues that we are in need of more research on “the cultural and institutional production of ignorance.”

# **Media Analysis**

Boykoff, Maxwell T. and Jules M. Boykoff. 2004. "Balance as Bias: Global Warming and the 63
Policy Research Working Paper 10750

# **Abstract**

Men die at higher rates in nearly all places and at all ages beyond age 45. Using World Health Organization excess mortality estimates by sex and age groups for 75 countries in 2020 and 62 countries in 2021, this paper analyzes how patterns of excess mortality varied by sex and age groups across countries during the COVID-19 pandemic and their association with country income level. In 2020, the pandemic amplified the gender mortality gap for the world, but with variation across countries and by country income level. In high-income countries, rates of excess mortality were much higher for men than women. In contrast, in middle-income countries, the sex ratio of excess mortality was similar to the sex ratio of expected all-cause mortality. The exacerbation of the sex ratio of excess mortality observed in 2020 in high-income countries declined in 2021, likely as a result of the faster rollout of vaccination against COVID-19.

This paper is a product of the the Human Development Global Practice and the Development Research Group, Development Economics. 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:// www.worldbank.org/prwp. The authors may be contacted at kbeegle@worldbank.org, gdemombynes@worldbank.org, ddewalque@worldbank.org, pgubbins@worldbank.org and jveillard@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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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