rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Elderly**

Next, we turn attention to social pensions given to beneficiaries of age 65 and older (Figures 4 to 6). Observe that some countries in the sample have already in place large and widespread contributory and non-contributory pensions systems (Argentina, Brazil, and Chile) that have already dramatically lowered poverty among the elderly to reach less than 5 percent of this demographic group. In these countries, it makes little sense to compare the effectiveness of categorical vs. targeted programs, and we have therefore excluded them from the analysis.

**Figure 4: Changes in Extreme Poverty Rates - Elderly**

<!-- Start of picture text --> 80%<br>Categorical<br>60% Targeted<br>40%<br>20%<br>0%<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases

**Figure 5: Changes in Poverty Gap - Elderly**

<!-- Start of picture text --> 80%<br>Categorical<br>60% Targeted<br>40%<br>20%<br>0%<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases 12
10 slowly than income, paved road networks expand at the same rate as income. At the urban level, per capita road length is positively associated with per capita income across world cities but changes little over time.

The empirical results from the current analyses are summarized in Table 3. At the national level, income elasticities of road length are 0.5 for total roads and 1.0 for paved roads. The variable income elasticity estimates are statistically insignificant even at the 0.10 Ilevel for both total roads and paved roads, suggesting that the income elasticity of road length is constant. The piecewise regression analysis produces a statistically significant non-linear relation for total road length, but not for paved road length (Appendix Table 6, Eq. 2 and 3). Both total road length and paved road length are unit elastic with population, and the variable population elasticities are not significant, indicating that the constant elasticity specification for population is consistent with the data. Again, population demonstrates a constant scale relation with road length because its elasticities are 1. These estimates are based on the cross-sectional variation in panel data, but fixed effect estimates based on first differences over time in the same panel data produced generally similar results for income. The first difference results differ for population: The population elasticity is insignificant for total roads (value of 0.4) and significant and larger for paved roads (value of 1.3) compared to the cross section results..

**Table 3. Elasticities of Road Length with Respect to Per Capita Income, Population, and Population Density**

||El<br>|asticity Estimat|es<br>||
|---|---|---|---|---|
|Type of Elasticity Estimates|Per capita<br>income|Population|Population<br>density|R-squared|
|**INational: iengtn o0 total roac**|||||
|Constant|0.5
# **Risk Shifting and Long-Term Contracts: Evidence from the Ras Gas Project**

## _1._ **_Introduction_**

The presence of risky debt in a firm's capital structure is<sup>known to lead to</sup><sup>_ex post_</sup> conflicts of interests between the firm's equity holders and<sup>bondholders.' Given eguity's convex</sup> pay-off profile, firms that are managed in the interest of equity holders<sup>have an incentive to shift</sup> risk to their bondholder. Excessive risk taking, if not controlled<sup>through contractual and pre-</sup> commitment devices, could result in suboptimal investment<sup>decisions (over- or under-</sup> investment). The associated investment distortions cause deadweight<sup>losses to the economy. The</sup> risk shifting incentives of risky debt and the associated suboptimal<sup>investment problem arise</sup> from the interplay of pay-off profiles and informational asymmetries between<sup>equity holders and</sup> bondholders, e.g., in circumstances where monitoring and contracting<sup>costs prevent bondholders</sup> from sufficiently observing investment choices made by inside<sup>managers.</sup> To address such agency problems, recent research has advanced a<sup>variety of incentive</sup> compatible solutions, including financial contracting (restrictive<sup>bond covenants, and</sup> convertibility clauses, Kahan and Yermack, 1998), project finance<sup>(Berkovitch and Kim, 1990),</sup> tax policy (John, John and Senbet 1991), and reform of bankruptcy<sup>procedures (Schwartz, 1997).</sup> Through or because of such devices, it is claimed, borrowers can<sup>credibly signal to lenders that</sup> they choose efficient levels
**Figure 9: Health insurance (as beneficiary) by income quintile**

<!-- Start of picture text --> 34.9%<br>47.9%<br>58.6%<br>72.2% 72.4%<br>63.4%<br>50.0%<br>40.0%<br>25.9% 24.4%<br>Q1 Q2 Q3 Q4 Q 5<br>Source: ENIGH 2007 Public (SESPAS + IDSS) Private<br><!-- End of picture text --> <!-- Start of picture text --> Q3<br><!-- End of picture text -->

**Figure 10: Average expenditure in medical consultation on the month before the survey, by type of centre**

<!-- Start of picture text --> 3,472.60<br>SESPAS (Public) Private<br>2,273.30 2,270.60<br>2,075.40<br>1,809.40 1,764.30<br>1,425.60<br>896.2 952.4 988.5<br>Q1 Q2 Q3 Q4 Q5<br>Source: ENDESA survey, 2007<br>DR pesos<br><!-- End of picture text --> As expected, the strata of the population with a higher income are more inclined to attend to private hospitals and clinics when they feel the need. It is somehow surprising to see

[16]
|---|---|---|---|
|**Country**|**Simple Av.(%) **|**Weighted Av.(%) **|**Simple Av.(%) **|**Weighted Av.(%)**|
|China|7.649|3.469|9.289|4.089|
|Indonesia|4.742|2.358|6.876|5.193|
|Japon|2.534|1.511|3.204|1.833|
|Korea|10.084|7.832|10.284|7.949|

Source: Authors’ calculations using data from UNCTAD accessed through WITS (2012).

The TTRI and OTRI show intermediate levels of protection in relative terms for China, Indonesia, and Malaysia. The equivalent uniform tariffs excluding NTMs are 5.06%, 4.47%, and 3.01%, respectively, ranking the countries in positions 68<sup>th</sup> , 62<sup>nd</sup> , and 45<sup>th</sup> (out of 125 countries), respectively. If we compare the TTRI with the OTRI rates, we observe that the latter are larger than the former, highlighting the fact that NTMs are important. For example, China’s equivalent uniform tariff increases from 5.06% to 9.83% 6
- [38] Smith, T. B. (2004). Electricity Theft: A Comparative Analysis. _Energy Policy_ , 32(18), 2067-2076.

- [39] Steinbuks, J., de Wit, J., Kochnakyan A. and Foster, V. (2017). Forecasting Electricity Demand: An Aid for Practitioners. _Live Wire_ 2017/1. World Bank, Washington, DC.

- [40] Suganthi, L., and Samuel, A. A. (2012). Energy Models for Demand Forecasting—A Review. _Renewable and Sustainable Energy Reviews_ , 16(2), 1223-1240

- [41] Tserkezos, E. D. (1992). Forecasting Residential Electricity Consumption in Greece using Monthly and Quarterly Data. _Energy Economics_ , 14(3), 226-232.

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# **D APPENDIX D: ROBUSTNESS TESTS**

# **D. A Raw Data Trends**

<!-- Start of picture text --> Election Turnout Share of Votes for Left<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>Election Year Election Year<br>PEP No-PEP PEP No-PEP<br>Share of Votes for Center Share of Votes for Right<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>Election Year Election Year<br>PEP No-PEP PEP No-PEP<br>Electoral Competition<br>2000 2003 2007 2011 2015 2019<br>Election Year<br>PEP No-PEP<br>.75<br>.3<br>.25<br>.7<br>.2<br>.65<br>.15<br>.1<br>.6<br>.8 .4<br>.75 .3<br>.7 .2<br>.65 .1<br>.6 0<br>.7<br>.65<br>.6<br>.55<br>.5<br><!-- End of picture text --> Figure D.1. Evolution of Outcomes by PEP and Non-PEP Status – Mayoral Elections _Notes:_ The figure illustrates the yearly evolution of the electoral outcomes in municipalities with and without PEP take-up. We changed the variables with zero for missing values.

68
as is the usual custom in developing countries, then intergenerational regression coefficients for older cohorts will be biased upward. Tracking the same younger age cohort [16-27] between years has the added advantage that our estimates are comparable and are not un-duly influenced by changes in coresidency pattern over the life cycle.

While we are not aware of any paper that provides direct estimates of sibling and intergenerational correlations in education for India, some indirect evidence on intergenerational persistence in education can be found in two recent studies. Our empirical approach, however, differs in some important ways from that of the existing studies. Jalan and Murgai (2008) use the NFHS 1998/99 data to estimate intergenerational regression coefficients for different age cohorts.<sup>24</sup> Hnatkovska, Lahiri and Paul (2011) examine the probability of children having a different level of education compared with their parents among the socially dis-advantaged Scheduled Caste/Tribes relative to rest of the population using different rounds of NSS data.<sup>25</sup> In contrast to Jalan and Murgai (2008), we track influences of family background and parental education directly for the same age cohort between 1992/93 (year immediately following economic liberalization) and 2006 (15 years after liberalization). For the reasons mentioned above, we restrict our sample to younger age cohort (16-27 years), whereas the sample used in Hnatkovska et al. (2011) consists of 13 to 65 year olds.

As noted before, our main sample consists of all children in the age group 16-27 who are co-resident with the mother.<sup>26</sup> Estimation was carried out for all children and separately for brothers and sisters. Since an important objective of our study is to uncover spatial differences in intergenerational mobility, we also estimate the sibling and intergenerational correlations for subsamples defined on the basis of geographical location such as rural and urban areas, and developed and less developed regions/states. The number of observations for different sub-samples is reported

> 24
susceptible to natural disasters, and is regularly affected by floods, landslides, earthquakes, and droughts. Up to 40 percent of the country’s national workforce is employed abroad (mostly in Russia) and sends home remittances equal to more than one-third of its gross domestic product.

3. The above factors combine to make Tajikistan one of the poorest and most vulnerable economies in the world.<sup>1</sup> Given its unique combination of geography, topography, climate, and low income, it is also especially prone to the adverse impacts of global climate change. How well informed are households in Tajikistan about the different causes and consequences of climate change? How concerned are they about its various potential impacts, and how do these concerns compare with other development challenges the country currently faces? What are some of the main transmission channels through which households could be impacted? Which potential impacts, if realized, would be of greatest public concern? Is there evidence of some of these changes already taking place? Using data from a nationally representative household survey carried out in Tajikistan recently, this paper examines some salient aspects of public perceptions of climate change in the country to help address these and other such important questions of policy interest.

4. The Pilot Program on Climate Resilience has recently been launched in Tajikistan with donor support, which will provide the Government of Tajikistan with USD50 million in grant financing to strengthen institutional capacities for climate resilience and to fund investment projects. There is also interest among other donors and national counterparts in integrating a climate change perspective in their operations. Do the survey findings have any bearings on how Tajikistan should adapt to ongoing and future climate change, in particular given the many pressing development challenges it

> 1 Tajikistan's GDP per capita is $751 (current US$, 2008 estimate) / $PPP 1,906 (current international $, 2008 estimate). In 2004, more than one-half (51 percent) the country’s population was estimated to subsist below the $
were sent out in May 2019. Before randomization, some of the identified property owners had become compliant and were thus excluded. The final sample to which notices were sent included 529 individuals who own 580 properties. Of these, 3 percent did not receive notices due to not meeting anyone at the property during all delivery attempts. The delivery rates for both the treatment and control groups were equal at 97 percent of property owners (table A5), so we use this subsample for the subsequent analysis.

# **5.2 Data and Empirical Specification**

The data for this study come from two main sources: the property location verification data and administrative records on tax payments. The property location verification data provide the name of the property owner, as well as the address (directions), a photograph, and the GPS coordinates of the property. They also include descriptions of external property characteristics provided by the verification team on the basis of their observations, such as the number of floors, the presence of a fence, the quality of landscaping, and the kind of road the property is on.

The administrative tax records provide the payment history of each property owner from 2014 to 2020. In addition, clerks at the tax office made note of each property owner that visited the tax office in response to the notice: even if no payment was recorded, taxpayers might visit the office to set up a payment plan or dispute the tax notice.

Table 3 provides summary statistics and shows that the randomization achieved balance across different characteristics. The average property value is about US$38,000. About 8 percent of property owners own more than one property, and over 90 percent own solely residential properties. About 84 percent of properties have only one floor, and 56 percent do not have a fence. For property owners with multiple properties, we use the characteristics of the highest-value property.

We use the following equation to examine the impact of the enforcement notice in promoting the payment of property tax arrears: 19
HIV/AIDS program?

These questions raise a broader concern about the conditions under which the provision of services by donors and non-state actors is likely to undermine or strengthen the fiscal contract between citizens and a government and citizens’ legitimating beliefs. Before addressing this concern, I will first examine whether the logic of the fiscal contract, which is largely based on the statebuilding experiences of Europe, is relevant to developing countries with very low and idiosyncratic taxation levels. I will then assess whether there is empirical support for the fiscal contract argument using cross-sectional survey data, the Afrobarometer, which was collected across 19 Sub-Saharan African countries in 2008.

Second, extending the logic of the fiscal contract arguement, there is 2
**The evidence for Togo shows that products who did not have “Relative Comparative Advantages” (RCA) in the early 1980s but were located in the “denser” part of the product space (were close to many other products with higher income value) did “jump” to become products with RCA in the early 2000s.** This in contrast to products that were more isolated in the product space (see Figure 56).

**Figure 56: Histogram of Density for Jumps to New Products (i.e. products with RCA in 2000/04 but not in 1980/84) and No Jumps Products (i.e. no RCA in either period), Togo.**

<!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-6 -5.5 -5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5<br>ln(density)<br>No RCA in 80-84 and RCA in 00-04 No RCA in 80-84 nor in 00-04<br>Relative Frequency<br><!-- End of picture text --> _Source_ : Authors calculations, based on WITS, COMTRADE data.

**Moreover, between 1980‐1984 and 2000‐2004, Togo’s export basket significantly approached or became surrounded by groups of products of implicit high income value, positioning the country in a much better position to jump to a basket of products with even higher level of sophistication.** In other words, using Hausmann et al (2005) terminology, the “option value” of the countries unexploited opportunities has increased as the so‐called “open forest” for new products has become denser, more attainable
# Table A.2: Election and District Characteristics

||N|Mean|S.D.|
|---|---|---|---|
|**Panel A: Election data**||||
|Share of constituencies won by women in a district (F)|628|0.09|0.13|
|Share of male-female close elections won by women in a district (FC)|628|0.01|0.05|
|Share of close male-female elections in a district|628|0.03|0.08|
|Whether the district had at least one seat won by a woman|628|0.40|0.49|
|Whether the district had at least one male-female election|628|0.57|0.49|
|Whether the district had at least one male-female close election|628|0.13|0.34|
|Whether the district had at least one male-female close election won by a woman|628|0.07|0.25|
|**Panel B: Census data**||||
|Fraction of female population|628|0.49|0.02|
|Fraction of urban population|628|0.26|0.20|
|Fraction of SC/ST population|628|0.33|0.22|
|Female literacy rate|628|0.55|0.12|
|Male literacy rate|628|0.69|0.09|

_Notes_ : The table presents the number of observations, the means, and standard deviations (S.D.) for district-level characteristics of the election data and the Census data. Panel A presents these summary statistics for the electoral data from the Election Commission of India, which was obtained at the constituency level and aggregated to the district level. Panel B presents them for the district characteristics obtained from
We explore the effect of this constraint in two ways. The first is to vary the size of transfers based on the PMT scores. The second is to reformulate the problem as one of optimizing the transfers as a function of the variables going into the PMT. Quite generally one can think of the informationallyfeasible transfers as a function of _m_ observed _x_ ’s. The policy maker only observes the covariates _x_ for each person; it is not known who is poor and who is not. However, the policy maker has a survey with much more information available for a sample. The problem is to choose the parameters of a score for assigning the real-world transfers based on the _x_ ’s, as given by: 0 0 _k_ (Here _xijt_  1 so that  _jt_ is the intercept—the transfer received by someone with _xijt_  0 for _k_ =1,..., _m_ .) In one version transfers are linear in the _x_ ’s, i.e.,  1. We call this the linear optimization. We also estimate a nonlinear version with  2, which introduces squared terms and interaction effects among the _x’_ s.<sup>36</sup> The choice of the score parameters  _kjt_ is made to minimize the Watts index in the sample survey data: The choice is constrained by the budget: We solve this problem numerically.<sup>37</sup> One start value we use for the optimization is the uniform 0 _k_ case obtained by setting  _jt_  _B jt_ and  _ijt_  0 for _k_ =1,..., _m._ Other start values are tested. When there are multiple local optima the solution for the parameters  _kjt_ that gives the lowest value of the poverty measure is chosen.

**_Data:_** The data for implementing these measures come from the World Bank’s well-known LSMS.<sup>38</sup> Table 2 lists the countries,
of gender-based violence, force women to live with ongoing abuse, and block women’s attempts to access the police and the courts, sometimes threatening to testify against women in court (Human Rights Watch, 2003a). Human Rights Watch argues that reforming these traditional councils in Central and South Asia is urgently needed to improve women’s access to justice and human rights.

Such an approach appears to have achieved some success in settings such as rural Gujarat, Uttar Pradesh and West Bengal, India (International Center for Research on Women, 2002). In West Bengal, rural women's groups known as the Samity adapted the traditional village dispute resolution system-- _shalishi_ -- to address violence against women (Sadasivam, 2000; International Center for Research on Women, 2002; Shramajibee Mahila Samity, 2003). Cases include physical violence by husbands and in-laws, forced sex by husbands, dowry disputes, and other forms of violence against women. Facilitators of the _shalishis_ conduct an inquiry, hold a public hearing (depending on the sensitivity of the case), try to negotiate a resolution, and then call on the community to enforce the decision. Police are asked to intervene only if the _shalishi_ fails. By 2002, caseloads had grown to over 400 a year. Key informant interviews, focus groups and a survey of 151 women suggested that the _shalishi_ was vastly more accessible than the formal legal system. While the formal legal system focuses almost exclusively on punishing the perpetrator, the _shalishi_ aimed to restore women’s safety while keeping the family intact. Some criticize the emphasis on preserving the family, but researchers note that rural women often have no means of survival outside the family in this setting. The _shalishi_ appeared to be effective in stopping most cases of physical violence and most women were satisfied with the process.

# **_Community mobilization to influence and monitor policy reforms_**

An important role of community mobilization is to pressure policy-makers to change laws, implement reform, or monitor the performance of law enforcement
was a village leaders survey administered between June and October 2015. It provides information on village characteristics, public facilities, impacts of Ebola (in terms of the number of households quarantined, Ebola related cases and deaths), and policy responses (such as the functioning of schools and health facilities, and other relief efforts).<sup>23</sup> After fieldwork restrictions were lifted, the endline survey was fielded between February and May 2016 (so like the baseline, taking place during the school year). As Figure 1 shows, this is around six months after cases of Ebola declined to near zero, and well after schools and markets

> 22The exact wording of the question used for the broader categories is, _”Now I would like you to do a simple exercise. Here on these cards are some ways you can spend your time in a typical week. Here are 25 beans. Please divide these beans between the cards according to how much time you spend in each activity.”_ For time use related to leisure, the exact wording of the key question is, _”Here are the 25 beans again. Here on these cards are some ways you can spend your free (leisure) time. Please divide these beans between the cards according to how much time you spend in each activity. If there are any other activities not listed on these cards, you can write them on these blank cards.”_

> 23The village leader survey collects data coded from focus group discussions held in every village. Prominent members of the socioeconomic and administrative life of the community attended these meetings, with the average focus group involving 11 participants (the minimum (maximum) was 5 (18)). 85% of these meetings were attended by a Chief (either a Paramount, Section, Regent or Village Chief). Village elders, women’s and youth leaders, imams, pastors, head teachers, nurses and ELA club mentors were also invited.

13
per household was 3.3.

# _2.4.2 Data on nutrients_

In addition to macronutrients, we focused on six micronutrients, namely calcium, iron, zinc, Vit. A, Vit. B12, and folate, in terms of nutritional analysis. We based our decision of micronutrients on level of deficiency and availability of a micronutrient’s unit amount as a component of Bangladeshi local foods. Previous studies including Nutrients Background Paper (Talukder et al. 2015) and National Micronutrients Status Survey (ICDDR’B et al. 2013) mostly focus on Vitamin

> 7 Bangladesh Bureau of Statistics (BBS). Household Income and Expenditure Survey 2000/2016. Ministry of Planning, Government of Bangladesh. Data requests can be made at dg@bbs.gov.bd.

7