rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4a. Poverty and Positive Behaviors across Youth Aged 18 to 24 – Mexico <!-- Start of picture text --> Mexico - 18 to 24 year olds<br>y = -0.819x + 1.158<br>1.00<br>R2 = 0.5993<br>t = -3.87<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.40 0.50 0.60 0.70 0.80 0.90<br>Poverty as a Risk Factor (parents with post graduate<br>education = 0, no education = 1)<br>Positive Behaviors<br><!-- End of picture text --> Figure 4b. Poverty and Positive Behaviors across Youth Aged 18 to 24 – Chile <!-- Start of picture text --> Chile - 18 to 24 year olds<br>y = -1.06x + 1.0725<br>1.00<br>R2 = 0.7987<br>0.80 t = -6.298<br>0.60<br>0.40<br>0.20<br>0.00<br>0.40 0.50 0.60 0.70 0.80<br>Poverty as a Risk Factor (parents with post<br>graduate education = 0, no education = 1)<br>Positive Behaviors<br><!-- End of picture text --> Figure 5a. Rural and Positive Behaviors across Youth Aged 18 to 24 – Chile <!-- Start of picture text --> Mexico - 18 to 24 year olds<br><!-- End of picture text --> <!-- Start of picture text --> y = -0.6895x + 0.8612<br>1.00<br>R2 = 0.7425<br>t = -6.11<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.00 0.20 0.40 0.60 0.80<br>Rural as a Risk Factor (rural=1, 0.5 = mid sized town,<br>urban=0)<br>Positive Behaviors<br><!-- End of picture text --> Figure 5b. Rural and Positive Behaviors across Youth Aged 18 to 24 – Chile <!-- Start of picture text --> Chile - 18 to 24 year olds<br>y = -1.3162x + 0.6401<br>1.00<br>R2 = 0.7465<br>t = -5.426<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.00 0.05 0.10 0.15 0.20 0.25 0.30<br>Rural as a Risk Factor (rural=1, urban=0)<br>Positive Behaviors<br><!-- End of picture text --> Figure 6. Chile Indigenous and Positive Behaviors across Youth aged 18 to 24

**Chile - 18 to 24 year olds**

<!-- Start of picture text --> y = 0.1454x + 0.4203<br>1.00<br>R2 = 0.1746<br>t = -0.186<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0.00 0.05 0.10 0.15 0.20 0.25<br>Indigenous as a Risk Factor (indigenous = 1, non-<br>indigenous=0)<br>Positive Behaviors<br><!-- End of picture text --> 40
such as regulating the supply of credit or strengthening consumer protection measures, may be more appropriate in helping individuals avoid costly financial options.

The rest of this paper proceeds as follows. Section II describes related literature on financial literacy. Section III describes the study design and setting. Section IV provides summary statistics and randomization checks, and Section V presents our results. Section VI concludes. Appendices I and II describe the content of our financial literacy and health videos, and present our financial knowledge survey questions, respectively.

# **II. Financial Literacy Mechanism and Measurement**

A growing literature in economics examines the impact of financial literacy programs and related interventions on financial outcomes, but the results so far have been inconclusive. In the developed world, for example, Duflo and Saez (2003) conduct a randomized experiment in which staff members of a US university were offered a small financial incentive to attend an employee benefits fair. The benefits fair was designed to promote awareness about retirement savings accounts, but the authors find only a small increase in retirement plan enrollment. Also in the US, Bertrand and Morse (2010) consider the effects of providing information on the costs of payday borrowing. Their results indicate that an information treatment reinforcing the addingup of interest over time reduces the likelihood that an individual renews a payday loan.

In developing countries, much less is known about the impact of financial literacy programs, although the findings have likewise been mixed. In Indonesia, Cole, Sampson and Zia (2011) study the impact of a financial literacy program tailored to teach unbanked households in Indonesia about savings accounts. While they find no effect on the general population, they find a modest increase in the demand for savings account among those with low initial levels of financial literacy. The impact of financial and business education on entrepreneurial outcomes is a little more promising: Karlan and Valdivia (2010) find that a business education program improves record-keeping, though not profits, among microfinance borrowers in Peru; Bruhn and Zia (2011) find significant impacts of a business training program on business investment and 6
# **Human Rights as Demands for Communicative Action**

**Varun Gauri and Daniel M. Brinks**<sup></sup>

**JEL** : O10, K30

**Keywords** : Human rights, social policy, courts

**Sector board** : Public Sector Management

>  For their comments and suggestions, the authors would like to thank Charles Beitz, Michael Goodhart, Aaron James, Thomas Pogge, Jorn Sonderholm, and the participants in panels and seminars at the Law and Society Association Meetings (Chicago, September, 2010), the University of Bergen (March 2011), and a Central European University conference on the right to health (Budapest, June 2011). Nevertheless, the views and opinions expressed in this paper are those of the authors alone. They do not necessarily represent the views of the World Bank or its Executive Directors. Varun Gauri is Senior Economist, The World Bank; and Daniel M. Brinks is Associate Professor in the Department of Government, University of Texas at Austin.
are of little use in understanding intergenerational economic mobility?<sup>6</sup> Are the different measures of intergenerational persistence affected by coresidency bias to the same degree, or are some measures more robust than others with relatively small bias? This paper makes progress on these questions, providing evidence, analysis, and guidance for working with coresident samples that have wide ranging implications for research on intergenerational economic mobility.

To understand the implications of the coresidency restriction, the challenge is to find surveys that (i) include all of the children and the parents irrespective of their residency status, and (ii) identify the subset of individuals coresident in a household at the time of the survey. We take advantage of two high quality household surveys in villages of India and Bangladesh, and estimate the two most widely used measures of intergenerational persistence in the literature: intergenerational regression coefficient (henceforth IGRC) and intergenerational correlation (henceforth IGC).<sup>7</sup> The evidence on intergenerational schooling persistence presented below in this paper shows that IGRC, the most widely used measure of intergenerational persistence, suffers from large downward bias because of truncation due to coresidency.<sup>8</sup> In contrast, the downward bias in the estimated IGC in coresident samples is much smaller; in many cases, less than one-third of the bias in the corresponding IGRC estimate. In the sample of 13-60 years age range, the average bias in IGRC estimates is 29.7 percent in the case of Bangladesh, while the corresponding bias in IGC estimates is only 8.7 percent. The extent of truncation bias in India is smaller because of higher coresidency rates observed in the data. However, the IGRC estimates in India are also substantially biased downward; the

> 6The prevailing view, in fact, holds that the estimates from coresident samples are not useful, and it is partly driven by the fact that the researchers do not know the direction of bias.

> 7IGRC shows how a one year of higher schooling of parents affects the schooling attainment of children. IGC shows what proportion of the variance in children’s schooling can be attributed to the variance in parents’ schooling. For a discussion, see Solon (1999), Hertz et al. (2007)).

> 8The bias is defined as [(Estimate from full sample - Estimate from coresident sample)/Estimate from coresident sample] _×_ 100. A downward bias implies that the estimate from coresident sample is smaller than that from the full sample, implying a positive bias estimate according to the formula above. This is done to avoid carrying a negative sign for the bias estimates.

2
# **_Post-Accession Payments_**

Total amount of payment appropriations for the period 2007-2009 is projected at close to € 3 bn in current prices, as shown in Table 4. EU transfers to Bulgaria’s national budget in relation to CAP present nearly half of total payments appropriations. Structural Funds and the Cohesion Fund combined are around one third of the total payments. The remainder is shared between Internal Policies (12%) and Budgetary compensations (9 %).

**Table 4: Payment Appropriations, € mn, current prices**<sup>**#**</sup>

||**2007**|**2008**|**2009**|**2007-2009**|
|---|---|---|---|---|
|**_Common Agricultural Policy_**|||||
|Marketmeasures|140|140|140|420|
|Direct payments|0|214|257|472|
|Ruraldevelopment|110|197|218|524|
|TotalCAP|250|551|615|1416|
|**_Structural Actions_**|||||
|Structural Funds|155|277|307|740|
|Cohesion Fund|6|49|113|168|
|TotalStructural Actions|161|327|421|908|
|**_Internal Policies_**|||||
|Existing policies|34|58|80|172|
|Nuclearsafety|0|76|77|153|
|Institutionbuilding<sup>1)</sup>|4|5|5|15|
|Total Internal Policies|37|140|163|340|
|**_Administration_**<sup>2)</sup>|||||
|**_Compensation_**|129|64|65|258|
|**Total Payments**|577|1081|1264|2922|

_Source_ : Own calculations, based on theFinancial Package for Bulgaria and Romania, indicative allocations 2007-2009 based on Commission Communication of 10 February 2004 and EU Accession Treaty for Bulgaria and Romania of April 2005.

_Notes:_<sup>#</sup> 2 percent per year deflator, routinely adopted by the EC for indexation of the EU funds, is used in these calculations.<sup>7</sup> See Annex II for Payment Appropriations, € mn, in 2004 prices.

> <sup>1)</sup> Institution building appropriations for commitments are calculated on the basis of a population key for Bulgaria and Romania.<sup>2)</sup> No administrative costs are specifically allocated for Bulgaria. Therefore, no commitment and payment appropriations under this heading are included.<sup>3)</sup> Payment appropriations in this table may differ from the calculated payments in the text below due to deflator-related base effects (see footnote 7 below).

# _Common Agricultural Policy_

The first sub-heading under this chapter is _Market Measures_ and includes export subsidies and expenditures to stabilize and regulate agricultural markets. Commitments covering market 7 To arrive at estimates of the appropriations for commitments and payments in current prices, one can alternatively deflate them by projections of the national GDP deflator. We have opted for the 2 percent deflator routinely used by the EU, because the funds are allocated out of the EU budget, and not the national budget.

6
# **Figure A6: Impact of U.S. interest rate shocks on EMDE fiscal variables**

A. Impact of 25 basis point shock on primary balance B. Impact of 25 basis point shock on government expenditure <!-- Start of picture text --> Percent of GDP Percent of GDP<br>3.0 Mean 90% confidence interval 1.0 Mean 90% confidence interval<br>2.5<br>0.5<br>2.0<br>1.5 0.0<br>1.0 -0.5<br>0.5<br>0.0 -1.0<br>-0.5 -1.5<br>-1.0<br>-2.0<br>-1.5<br>-2.0 -2.5<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction Real Inflation Reaction<br>C. Impact of 25 basis point shock on government D. Impact of 25 basis point shock on gross<br>revenue government debt<br>Percent of GDP Percent of GDP<br>0.8 Mean 90% confidence interval 2.0<br>Mean 90% confidence interval<br>0.6 1.5<br>0.4<br>1.0<br>0.2<br>0.0 0.5<br>-0.2 0.0<br>-0.4 -0.5<br>-0.6<br>-1.0<br>-0.8<br>-1.0 -1.5<br>-1.2 -2.0<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction Real Inflation Reaction<br><!-- End of picture text --> _Source:_ World Bank _Notes:_ Panel local projection models with fixed effects and robust standard errors. See table A3 for details. Models estimated over periods as long as 1997Q2-2019Q4; they exclude observations during global financial crisis (2008Q4-2009Q4) and the COVID-19 pandemic.

41
46 During the same period, the decreases of 3.67 per cent and 2.06 per cent, occurred in middle income countries in LAC (18.78 per cent to 15.11 per cent) and low income countries in Africa (15.93 per cent to 13.87 per cent), respectively. Examples of declining shares of education expenditure during 1975-85, include 10.03 per cent in Costa Rica (28.78 per cent to 18.75 per cent), 6.97 per cent in Mexico (18.52 per cent to 11.55 per cent), 5.72 per cent in Ethiopia (15.61 per cent to 9.82 per cent), 5.00 per cent in Uruguay (11.45 per cent to 6.42 per cent), 4.40 per cent in Peru (20.56 per cent to 16.16 per cent), 4.25 per cent in Kenya (24.00 per cent to 19.75 per cent), and 4.18 per cent in Tanzania (12.46 per cent to 8.28 per cent).

Health Expenditure as Percent of Total Government Expenditure. By and large, the share of government expenditure on health is lower than that on education expenditure in the group of 49 countries with complete datL 4r the periods 1975, 1980, and 1985. The share of health expenditure remained at around 6 per cent of total expenditure during the period 1975-1985 (6.03 per cent, 6.23 per cent, and 6.30 per cent, respectively). The share of health expenditure increased in East Asia (4.26 per cent to 5.28 per cent), South Asia (4.65 per cent to 4.10 per cent) and Eastern Europe (12.50 per cent to 0.38 per cent) decreased.

"Other Welfare" Expenditure as Percent of Total Government Expenditure. The share of "other welfare" expenditures in a group of 48 countries with complete data for the period 1975-1980 - 1985 declined from about 15 per cent to 13.33 per cent to 13.08 per cent (Expenditure Table 6). In 1985, the share of "Other welfare expenditures was lower than that of 1975 in all the regions
data on Gini coefficients and income shares (covering the period 1960-96 and some 80 countries) which represents a major step on the road to full data standardization. The data set is reviewed in detail in Deininger and Squire, 1996, who also provide a detailed guide for its use.

The data problems have particularly plagued the testing of the median voter hypotheses-a crucial hypothesis for our purposes-because the way the hypothesis is theoretically couched requires access to individual factor incomes. This is so because people vote on taxes and transfers based on their factor income levels, and the decisive voter is the one with the median factor (pre-fisc and pre-transfer) income. However, these data are typically very difficult to obtain for most countries and the researchers had therefore had to resort to using the post-tax and post-transfer income (net income) as the basis on which the median voter makes her decision (see Perotti, 1993 and 1996, Easterly and Rebelo 1993; Bassett, Burkett and Putterman 1999). Thus people's decisions about the optimal level of taxes and transfers were made to depend on the distribution which emerges as a consequence of that decision! Lack of data on the middle-class gain, furthermore, led to researchers approximating the gain by the share of transfers in GDP-a very imperfect proxy bearing in mind that many transfers may be captured by the rich. Only recently (Milanovic, 1999) have these two shortcomings, thanks to the rich data set provided by the Luxembourg Income Survey, been overcome, and the hypothesis tested in an empirically correct way.

# (b) _Political democracy_

The second major area of interest for the present survey has also suffered from definitional and measurement problems. Some "hard" measures of democracy, have been based, for example, on the degree of national electoral participation. On the **odier** 12
and 90% of the countries examined (65% and 69% if variables are de-trended). Similarly, ODA is stabilizing in a majority of countries, while REM is destabilizing or has no effect in more than three-quarters of the countries and FDI in close to 90% of the countries. In addition, the stability of ODA (FDI) decreases (increases) with countries’ income, REM and FDI are more pro (ODA is more counter)–cyclical in the lower and upper middle income groups than in the low income group, and the stabilizing impact of REM (ODA) increases (falls) with countries’ income. Finally, adding ODA to REM+FDI (REM to FDI+ODA, and FDI to REM+ODA) reduces (raises) the pro-cyclicality and raises (reduces) the stabilizing impact of these flows.

Though, as documented in a large part of the literature, remittances increase at times of major upheavals such as natural disasters, armed conflicts or economic crises in migrants’ source countries, we find them to be pro-cyclical as well as destabilizing for a majority of developing countries over large periods of times (1980 to 2007 in our analysis). Moreover, adding REM to FDI and ODA inflows raises the pro-cyclicality of these inflows as well as their destabilizing impact. Our results at this stage convey a cautiousness message, suggesting that the stabilizing virtues of remittances inflows be examined on a country-by-country basis. Explaining the patterns in the observed behavior of remittances and other inflows requires further empirical examination. The objective of this paper was to simply provide evidence on the behavior of remittances, as well as ODA and FDI. The examination of the motives underlying it, its determinants or its impact is on our research agenda.

It could be argued that there might be a problem with our conclusions being drawn solely based on the examination of formal remittance flows, while informal channels are estimated by the researchers to still attract about 50% of remittances (Ratha, 2006). However, all studies dealing with remittances only use official remittance data because of the lack of data on informal remittances. Consequently, the cyclical behavior of informal remittances cannot be ascertained, and neither is it possible to know the impact of including informal remittances on our findings. This lack of data obviously plagues the findings of all remittance studies.

As a final comment, whether REM flows are pro- or counter-cyclical and stabilizing or not, their impact might also depend on their importance relative to GDP and other sources of inflows. However, even though REM may be small as a share of GDP, it may amount to a large share of the income of recipient households and may therefore have a substantial impact on the stability of these households’ income. This issue will also be studied in future work.

17
<!-- Start of picture text --> Calinski.Harabasz Davies.Bouldin Hartigan<br>10-<br>1.70-<br>16- 1.65- 8-<br>1.60 -<br>14 - 1.55- 6 -<br>1.50- 4-<br>12-<br>1.45-<br>Krzanowski.Lai MSE Silhouette<br>0.23 -<br>2500 -<br>2.0-<br>0.21 -<br>2000 -<br>1.6- 1500- 0.18 4<br>1.2- 1000 - 0.17-<br>1 ' ' 1 ' 500 7 ' ' ' ' ' 0. 1 5 7 ' ' 1 ' '<br>234562345623456<br>K<br><!-- End of picture text -->
<!-- Start of picture text --> » 290 2013 2014 TM2015 TM2016 2017<br>g<br>S6 200<br>vo<br>Rad<br>ce<br>£~=c=6 150<br>ER<br>gem<br>><br>£<br>a 50<br>‘a<br>adoO 0 ——_<br>Railway Road Inland water Air<br>& maritime<br><!-- End of picture text --> <!-- Start of picture text --> 5 1,800<br>3B 1600 A<br>ao” Adi d<br>22 1,200 fidip 4<br>Seams) 800 |. 6. |EY mae: areneslA<br>re<br>5G 400<br>vo 2<br>@¢ 200<br>Es s -<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>sm Quarterly @ = Annual<br><!-- End of picture text -->

<!-- Start of picture text --> E 70<br>ve 60 .<br>= 50 ee =e i er ose oad .<br>fe} ae ed<br>Soecs Ld ee eoDeeeeeee<br>gvo<br>2. 10<br>»<br>ae<br>g 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>couse With delays (quarterly) ===» Without delays (quarterly)<br>@ With delays (annual) e Without delays (annual)<br><!-- End of picture text -->
is similar to that used in the MIT-EPPA integrated assessment model (Paltsev et al. 2005). We explicitly introduce in the production function an additional xed factor specic to the new technology, _φ,_ whose endowment in the economy is limited. As technology penetrates the market, the share of the technology xed factor in the production function declines with the rate of factor-specic technological progress, _θt_<sup>_φ_(Acemoglu1998,vanMeijlandvanTongeren1999).</sup> Under these assumptions the production of second generation biofuels, _x_<sup>_b,_2</sup> _,_ is determined by the following CES function where _θt_<sup>_b,_2</sup> and _α_<sup>_b_</sup> are, respectively, the technology parameter and the value share of xed factor in production of second generation biofuels at the benchmark time 0 . The parameter _ρb_ =<sup>_<u>σb</u>_</sup> _σ_<sup>_−_</sup> _b_<sup><u>1</u></sup> is a CES function parameter proportional to the elasticity of substitution of technology xed factor for cellulosic feed stocks, _σ_<sup>_b_</sup> _._ The agricultural products' conversion to renewable fuel incurs additional non-food processing costs, _c_<sup>_b,i_</sup> _._ We assume these costs are constant and scaleinvariant.<sup>5</sup> In the technical appendix, section C.1, we discuss parameterization of biofuels' production technologies and costs.

# 2.7 Energy Sector

The energy sector consumes petroleum products, _x_<sup>_e_</sup> _,_ and rst and second generation biofuels, _x_<sup>_b,i_</sup> . First generation biofuels (e.g., corn or sugarcane ethanol) blend with petroleum products in varying proportions,<sup>6</sup> and the resulting mix is further combusted to satisfy the demand for energy services.<sup>7</sup> Following the economic literature on biofuels modeling (Hertel et al. 2010), we assume that rst generation biofuels and petroleum products are imperfect substitutes. Second generation biofuels (e.g., cellulosic biomass-to-liquid diesel obtained through

> 5With introduction of second generation biofuels one would expect these costs to decline, and biofuels conversion rate to increase as the biofuels' production technology improves. We show the model sensitivity to changes in these parameters in section 5 below.

> 6Blends of E10 or less are used in more than twenty countries around the world, led by the United States, where ethanol represented 10% percent of the U.S. gasoline fuel supply in 2011. Blends from E20 to E25 have been used in Brazil since the late 1970s. E85 is commonly used in the U.S. and Europe for exible-fuel vehicles. Hydrous ethanol or E100 is used in Brazilian neat ethanol vehicles and ex-fuel light vehicles and in hydrous E15 called hE15 for modern petrol cars in Netherlands.

> 7The focus of this model is on services from liquid fuels, which account for about a third of global energy use EIA (2011).

11