rafmacalaba/datause-encoder-data huggingface dataset:
incomes, and life expectancies experienced in different Peruvian districts, so that the magnitude of climate impacts can be seen in the appropriate perspective. 95% confidence intervals on the estimated relationships have also been included in the figure.

Panel (a) shows a hump-shaped relationship between average annual temperature and per capita income, with inhabitants in the regions located in the optimal temperature range earning at least 50% more than inhabitants living in either the coldest or the hottest regions.

Panel (b) also shows a hump-shaped relationship between temperatures and life expectancy, with a difference of more than 10 years between the optimal temperature and the coldest temperature.

<!-- Start of picture text --> Figure 2: Estimated contemporary relations between<br>temperature/rainfall and income/life expectancy in Peru<br>(a) Temperature and Income (b) Temperature and life expectancy<br>76<br>1087<br>71<br>887<br>66<br>687<br>61<br>487<br>56<br>287<br>51<br>87<br>8 10 12 14 16 18 20 22 24 26<br>8 10 12 14 16 18 20 22 24 26<br>Average annual temperature (oC)<br>Average annual temperature (oC)<br>(c) Rainfall and Income (d) Rainfall and life expectancy<br>76<br>1087<br>71<br>887<br>66<br>687<br>61<br>487<br>56<br>287<br>51<br>87<br>0 1 2 3 4 5<br>0 1 2 3 4 5<br>Average annual precipitation (m)<br>Average annual precipitation (m)<br>Income per capita (Soles/Year) Life expectancy at birth (years)<br>Income per capita (Soles/Year) Life expectancy at birth (years)<br><!-- End of picture text --> _Source:_ Graphical representation of the estimation results from Table 3. _Notes:_ The thick red line represents the point estimate from the regressions in Table 3 while the thin black lines 8
<!-- Start of picture text --> Generation Pool Transmission Distribution/ | Consumers<br>Supply<br>Privatized —_——._ooOnNuJ—X——_<br>Gencos WESM Contestable<br>a —<$<—* Retail > 750kw<br>Privatized Supply consumer<br>NPC-IPPs bum (and ability<br>to aggregate<br>, System contiguous<br>oeIPPs Control loads)<br>Distribution<br>Own<br>Generation Wires -> Captive<br>-----------> et 2 Business)<br>Legend: anytae Regulated<br>=== === D Power Flow<br>Genco = generation company, IPP = independent power producer, NPC = National Power Corporation,<br>WESM = wholesale electricity spot market.<br>Note: TransCo is the public sector asset owner and the National Grid Corporation of the Philippines the<br>private operator and and concessionaire for the transmission system.<br>Source: KPMG. 2013. The Energy Report Philippines, 2013-2014. Manila<br><!-- End of picture text -->

Genco = generation company, IPP = independent power producer, NPC = National Power Corporation, WESM = wholesale electricity spot market. Note: TransCo is the public sector asset owner and the National Grid Corporation of the Philippines the private operator and and concessionaire for the transmission system. Source: KPMG. 2013. The Energy Report Philippines, 2013-2014. Manila
the impact of human rights treaties in three significant ways. First, it utilizes immunization coverage rates as the dependent variable. Immunization coverage is one of the most widely and reliably measured indicators of international well-being now available. As a result, this paper uses a far more precise outcome indicator than other papers, which have typically studied reported killings or incidents of torture, repression of civil and political liberties, and restrictions on speech. Because of the relatively good quality of the immunization data, the paper arguably offers a stronger test of the direct impact of human rights treaties. Second, the paper studies a key indicator related to the rights to survival and health, whereas the majority of existing analyses focus primarily on civil and political rights. Third, the paper investigates heterogeneity of ratification effects, according the level of development of the ratifying country, not just the average effect of treaty ratification.

The next section below forwards three hypotheses regarding ratification of the CRC, and in the process, very briefly reviews the literature on the impact of complying with international human rights treaties. After that the succeeding sections address the reasons why countries ratified the CRC; the data used in the estimations of this paper; the analytical approach; and the key findings.

# **Theoretical expectations**

# _Nonsense upon stilts_

Bentham called natural rights “simple nonsense” and natural and imprescriptible rights “nonsense upon stilts.”(Bentham 2002) Since then, many commentators have similarly argued that natural rights, human rights, and basic rights are unenforceable, meaningless, and ineffectual. In more contemporary guise, these arguments have appeared in the work of international relations realists, who have contended that considerations of state power, rather than international law, will always dominate action in the international realm. Hathaway summarizes the views of realists this way: “if compliance with international law occurs, it is not because the law is effective, but merely because compliance is coincident with the path dictated by self-interest in a world governed by anarchy and relative state power” (Hathaway 2002: 1945-6). Analysts adopting an institutionalist perspective, on the other hand, believe 3
We also performed a mobile phone survey in April 2019. Households were first reached through random digit dialing and were given an initial automated interactive voice response (IVR) survey, where they were asked about the region in which they live and whether they would be willing to respond to an in-person mobile phone survey. Then, 750 households were given the final survey that included identical questions fielded in the internet survey. The share of households in the final survey that lived in DFA and IRGcontrolled territory was equal to their share in the population, and we further limited the share that lived in the largest cities in each region (Sana’a and Aden) to the share of the total population that lived in each city.

In both surveys, we include 11 identical questions in three broad groups, which are listed in Tables 1 and 2. First, we collect demographic information related to the likelihood of responding to an internet-based survey, including age, location, highest school grade completed, subjective assessments of income, and employment in the past month; second, we collect information on non-sensitive questions that are not obviously related to the likelihood of responding to an internet-based survey; and third, we collect information on sensitive questions to which respondents in traditional surveys might not answer truthfully.

For the sensitive questions, we vary the source of sensitivity- broken up into those potentially sensitive due to social desirability bias and those potentially sensitive due to both social desirability bias and safety concerns. For example, the question asking who the respondent held accountable for the violence they had witnessed is likely sensitive due to safety concerns or the potential judgement of others for favoring another party in the conflict; and the question on whether it could ever be morally justified to engage in violence is likely sensitive because it is not socially acceptable to all to support violence. The source of sensitivity for each sensitive question is listed in Table 2.<sup>13</sup>

> associated with the capital city in some countries. In order to get accurate information on the region, it is important to validate this region with self-reported regions in the survey.

> 13There are also potential variations in the level of sensitivity in sensitive questions. For example, one question asks whether the respondent disagrees with a humanitarian targeting plan, where the sensitivity could come from a belief that humanitarian agencies are wrong in their assessments, which is not necessarily sensitive to all individuals, or from the opinion that assistance should not be targeted at those most in need if the respondent does not receive assistance in those scenarios, which is a response that is potentially socially unacceptable to some. These potential ambiguities, or the lack of any ambiguity,

11
# GHANA: DEFICIT PROJECTIONS

## 1988 -1991

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Duranton 2016 for Colombia). Glaeser and Maré (2001) and Combes, Duranton, and Gobillon (2008) include worker fixed effects such that the wage elasticity is identified from differences in earnings across changing work location. Since plant relocations are much less frequent than worker relocations, firm sorting is more difficult to control for (see Faberman and Freedman, 2016; Mare and Graham, 2013).

# **IV. Results**

# Base OLS elasticity estimates

Table 2 panel a presents the estimates from equation (1) for all plant-level productivity indicators. The patterns identified in the recent literature are confirmed for all four countries: a 1 percent increase in density leads to wage gains of .017 percent for Chile, .038 percent for Colombia, .057 percent for Indonesia; and .052 percent for Ethiopia. The wage elasticities are high and decreasing in national GDP.

However, consistent with the meta-analysis in Grover, Lall and Timmis (2021), the elasticity associated with TFPR is much smaller than that for wages, falling to .01 for Chile, 0.03 percent for Colombia, .022 in Indonesia and becoming negative .023 percent but insignificant in Ethiopia. This does not negate the benefits of agglomerations arising from easier capital accumulation that has been stripped out, although our explorations suggest that this effect is positive and significant only in Indonesia. However, it does suggest that other potential gains from matching or externalities are probably overstated using wages.

In fact, such efficiency gains disappear altogether or become negative when we control for prices. TFPQ using Foster et al. (2008) and TFPQ à la De Loecker et al. suggest that the elasticity for Colombia, Indonesia and Ethiopia is either close to zero or _negative:_ -0.04, -.011 and -.061 (significant) for the former, and 0.04, -0.02 percent and -0.07 (none of them being significant) for the latter. In fact, the De Loecker estimates will be generally small and insignificant throughout the exercise, supporting the point that productivity gains are unlikely to be large. Only for Chile, our highest income country, does the elasticity enter positively and significantly at .03 suggesting some productivity gains from agglomeration. As detailed in Annex table C.1, the pattern of results remains largely robust to alternative estimation techniques and function forms.

Consistent with these findings, prices and marginal costs also rise with agglomeration and increasingly so in poorer countries.<sup>13</sup> In Indonesia, the output price elasticity is .033, the mark-up elasticity -.016 and the marginal cost elasticity is .047. In Ethiopia, the output price elasticity is slightly higher at .038, but the marginal costs elasticity is much higher at .11, driven by a negative mark-up elasticity of -.07. Colombia follows roughly the

> 13 This result is in line with that of Handbury and Weinstein (2015), who use detailed barcode data and find a positive elasticity of price with respect to population.

9
**Figure 1: Electricity Tariffs in SEE**

<!-- Start of picture text --> 8.0<br>7.0<br>Residential tariffs (c€/kWh) ECA benchmark<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>EU-8 SEE<br> Euro C ents / kW h<br>Estonia H ungary Latvia Lithuania Poland Slovak Republic Albania Bulgaria C roatia FYR M acedonia M ontenegro R om ania Serbia<br><!-- End of picture text --> Source: World Bank staff estimates, as reported in Alam et al (2005)

# **2. Overview of energy consumption patterns in Montenegro**

**_How much do the poor spend on energy?_** In this overview of energy consumption patterns in Montenegro we focus on winter energy consumption, because space heating accounts for a substantial share of household energy consumption. Also, we want to be able to examine the trade offs between the two main sources of heating energy used during the winter months in Montenegro, which are electricity and fuelwood. Since energy consumption during the winter months is a necessity, it is not surprising that poor households spend a higher share of total expenditures on energy. Households in the poorest quintile spend more than twice as much (12.9% versus 5.2%) of their budget on energy expenditures than households in the highest quintile of the income distribution. Although many households do not heat their entire living space, energy consumption in Montenegro is very inefficient. Average energy consumption per square meter of living space in Montenegro is about 2.5 times greater than in Northern Europe, where the climate is more severe.<sup>7</sup> Higher electricity tariffs would encourage energy efficiency investments, however, the burden of higher electricity tariffs on the poor and their choice of heating fuel must also be considered.

**_Poverty and the choice of space heating fuel._** Households in Montenegro use primarily electricity and fuelwood for heating during the winter months. We find that more than half of the population (56.6%) uses fuelwood for heating.<sup>8</sup> Among the poor the dependence on fuelwood is even higher, with 86 percent of poor households relying to some extent on fuelwood for heating. The percentage of households using fuelwood is higher in rural areas (79.3%) and in the northern part of the country (71.1%), where the availability of fuelwood is greater and the climate is colder. A more detailed analysis follows, examining the pattern of

> 7 United Nations Development Program (2004), “Stuck in the Past: Energy, Environment and Poverty in Serbia and Montenegro.”

> 8 Forty five percent of the population relies solely on fuelwood to meet their heating needs during the winter months.

3
**Table S3.1:  Financial education treatment effect on financial behavior under various models**

|Outcome|(1)<br>OLS<br>Full pooling|(2)<br>OLS<br>Synthetic ES|(3)<br>RE-Metareg|<br>(4)<br>RE<br>GLS|(5)<br>WLS<br>1/ SEg|(6)<br>Robumeta|
|---|---|---|---|---|---|---|
|_Panel A : All_|||||||
|Fin. Behavior|0.086***|0.102***|0.079***|0.093***|0.026**|0.064***|
||(0.012)|(0.013)|(0.009)|(0.012)|(0.011)|(0.008)|
|n(Studies)|90|90|90|90|90|90|
|n(Effect sizes)|349|90|90|349|349|349|
|_Panel B: RCTs_|||||||
|Fin. behavior|0.082***|0.102***|0.075***|0.089***|0.067***|0.078***|
||(0.014)|(0.023)|(0.013)|(0.021)|(0.013)|(0.012)|
|n(Studies)|40|40|40|40|40|40|
|n(Effect sizes|227|40|40|227|227|227|
|_Panel C: Quasi exp._|||||||
|Fin. behavior|0.093***|0.102***|0.083***|0.100***|0.015*|0.059***|
||(0.022)|(0.015)|(0.012)|(0.015)|(0.008)|(0.010)|
|n(Studies)|50|50|50|50|50|50|
|n(Effect sizes)|122|50|50|122|122|122|

Notes: Column (1) shows the average effect size on fin. behavior estimated via OLS with standard errors clustered by Study ID. Column (2) shows the average effect using only one synthetic (weighted average) effect size per study. Synthetic effect sizes are estimated via within-study random effects meta-regression (DerSimonian and Laird 1986). Column (3) shows the average weighted treatment effect estimated via random effects meta-regression (DerSimonian and Laird 1986) and Knapp Hartung (2003) adjusted standard errors. The Stata command is “metareg”. Column (4) shows the average treatment effect of fin. edu on fin. behavior utilizing a study random-effects GLS model. Column (5) presents results using unrestricted weighted least squares where a weight of the respective inverse standard error is assigned to each observation. Column (6) presents results from robust variance meta-regression with dependent effect size estimates (Tanner-Smith and Tipton 2014). The Stata command is “robumeta”. Standard errors (clustered at the study-level for Columns (1), (4), (5), and (6)) in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level.

52
Table 3. Correlations between Key Variables

||Fixed Yield-<br>to-Maturity|Maturity of<br>Bondissued|Log of Size of<br>BondIssued|
|---|---|---|---|
|Global push factors||||
|VIX|0.1891|0.0251|0.0945|
|RISK|-0.0677|-0.087|-0.0006|
|FED|-0.5751|-0.2587|-0.2324|
|LIBOR|-0.1391|-0.0951|-0.0471|
|MOVE|0.3311|0.1099|0.1641|
|USREER|-0.1492|-0.1159|-0.0979|
|XRATE|-0.0962|-0.0397|-0.0575|
|UST10Y|0.5469|0.2319|0.2039|
|Domestic Pull Factors||||
|GDPPC|-0.2249|-0.0394|-0.0429|
|GROWTH|-0.358|-0.2328|-0.3234|
|EXT|0.3692|0.2112|0.2807|
|CA|-0.2592|-0.1892|-0.2212|
|PCRED|-0.6565|-0.3912|-0.4685|

# **<u>Global push factors</u>**

||VIX|RISK|FED|LIBOR|MOVE|USREER|XRATE|
|---|---|---|---|---|---|---|---|
|RISK|0.7864|||||||
|FED|-0.2948|0.1282||||||
|LIBOR|0.6738|0.8203|0.1469|||||
|MOVE|0.7908|0.5221|-0.5604|0.3982||||
|USREER|-0.0118|0.0171|0.2012|0.0792|-0.2156|||
|XRATE|-0.0885|-0.0361|0.1398|0.0065|-0.146|0.3598||
|UST10Y|0.0983|-0.3783|-0.836|-0.4811|0.408|0.8279|0.0249|

# **<u>Domestic Pull Factors</u>**

||EXT|CA|PCRED|
|---|---|---|---|
|GDPPC|-0.0936|-0.0028|0.2108|
|GROWTH|-0.3165|0.3445|0.5031|
|EXT||-0.5258|-0.3839|
|CA|-0.5258||0.3815|
|PCRED|-0.3839|0.3815||

31
methodology updated to 2012 (with an EOCK of about 10 percent), give us an indication of the recommended EOCK value for Mexico: a rounded 10 percent discount rate.

## **Table 11** **<u>Comparing the two approaches to estimate the EOCK, 2003–2011</u>**

|Bottom-upand top-down estimation|s|||||
|---|---|---|---|---|---|
|Gross return excluding share of indir|ect taxes (Table|4)|||10.9%|
|Taxes paid out of return on capital (T|able 9, less indir|ect taxes)|||1.7%|
|Net of tax return, private sector|||||**9.2%**[top-down]|
||||Weights in|||
|Market representation:||Real rates<br>of return|private<br>investment|||
|Corporate bonds (Table 5)||5.1%|30%|1.5%||
|Real return on equity (Table 5)||13.0%|40%|5.2%||
|Housing return (from Annex III)||8.3%|30%|2.5%|**9.2%**[same]|
|||Domestic|Foreign|||
||Investment|savings|savings|||
||f1|f2|f3|||
|Average weights|65.8%|24.7%|9.6%|||
|Net of tax return on investment or<br>gross of tax return on savings|**9.2%**|8.3%|8.2%|||
|Weighted supply cost of capital (priv|ate sector)||**8.9%**|**WACC [A]**||
|Adding back the tax externality:||||||
|Weighted average cost of capital (W|ACC for private s|ector)|**8.9%**|[A]||
|EOCK externality/unit of capital|||**1.4%**|[B, from Tab|le 10]|
|**_EOCK, bottom-up approach_**|||**10.3%**|[A+B]||
|||Domestic.|Foreign|||
||Investment|savings|savings|||
|**Top-down**|f1|f2|f3|||
|Average weights|65.8%|24.7%|9.6%|||
|Rates|11.6%|7.5%|9.7%|||
|**_EOCK, top-down approach_**|||**10.4%**|[same magn|itude]|

# **4.3 Estimating the tax externality in the case of disaggregation of PEMEX from the rest of the economy**

**In the final step of our analysis we assess the impact of PEMEX on EOCK externalities.** The impact of the tax externalities per unit of investment in PEMEX and the rest of the economy is investigated. From the tax revenue for general government information and from national

25
Table 9c: Difference in Difference Estimates of the Impact of PROSPERA on ENLACE Achievement Tests: All 6<sup>th</sup> Graders in 2008 in Medium, High or Very Highly Marginated Communities 2008.

|Variables|7th grade- 6th grade<br>score<br>9th grade -6th grade<br>score<br>Propensityscore matchingwith exact matchingon age<br>_Panel A: Spanish_|Increase in category<br>of score from 6<sup>th</sup>grade<br>to 12<sup>th</sup>grade<br>and state|
|---|---|---|
|Girls|2.41<br>6.389|-0.0608|
||(4.022)<br>(5.29)|(0.0335)|
|Boys|7.29<br>14.67**|0.0604|
||(5.358)<br>(6.501)|(1.79)|
|Observations|4,565<br>4,415|2,253|
||_Panel B: Mathematics_||
|Girls|7.478*<br>7.145|-0.0157|
||(4.23)<br>(6.623)|(0.0499)|
|Boys|9.99*<br>14.097*|0.01|
||(5.19)<br>(7.558)|(0.004)|
|Observations|3,072<br>2,908|1,494|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

We now turn to results concentrating on children enrolled in indigenous primary schools. Tables 10a to 10c presents impacts on enrollment in seventh grade, ninth grade and twelfth grade for the set of children in indigenous schools in sixth grade. The results show important impacts on enrollment in all grades, and significantly larger than the impacts reported for all sixth graders in medium to very high marginalized areas. We observe increases in enrollment of 9 to 11 percentage points in seventh grade, 11 to 12 percentages points in ninth grade and about 8 to 9 percentage points in twelfth grade. There are no differences in these enrollment effects by gender.

27
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