rafmacalaba/datause-encoder-data huggingface dataset:
- **Policy levers (L)** are actions that decision makers want to consider, in this case the components of SEDAPAL’s Master Plan and related investments;

- **Exogenous uncertainties (X)** are factors like climate change and demand that are outside the control of decision makers but that may affect the ability of actions to achieve decision makers’ goals;

- **Metrics (M)** are the performance standards used to evaluate whether or not a choice of policy levers achieves decision makers’ goals, e.g. water reliability; and

- **Relationships (R)** , generally represented by simulation models, define how the policy levers perform, as measured by the metrics, under the various uncertainties.

The XLRM matrix developed for this study is summarized in Table 4-1. This section continues to describe the policy levers (L) under consideration, the metrics (M) used to judge the effectiveness of those policies, the uncertainties (X), and lastly the models (R) that quantify the relationships among the factors.

**Table 4-1: XLRM framework of key factors in this analysis**

|**Uncertainties (X)**|**Policy Levers (L)**|
|---|---|
|●<br>Future water demand<br>●<br>Future stream flow<br>●<br>Project feasibility|●<br>12 projects in SEDAPAL’s Master Plan<br>●<br>2 additional projects<br>●<br>Budget for infrastructure<br>●<br>(Efficiency and demand management)|
|**Models (R)**|**Metrics (M)**|
|●<br>WEAP Model<br>●<br>Interactive, analytic decision<br>support tool|●<br>90th percentile of monthly met demand, as a<br>percent of total demand<br>●<br>Cost of plan|

# 4.1 Policy Levers (L)

SEDAPAL’s Master Plan consists of twelve investment projects; a mix of dams, tunnels, water treatment plants, desalination plants, and tunnel transfers. SEDAPAL is also considering two additional investment projects—Cañete transfer/water treatment plant (WTP) and Chancay Reservoir—that were considered important to evaluate but not part of this plan. Table 4-2summarizes the key characteristics of these projects. The first column identifies the shorthand name we use for each project throughout this paper, the second summarizes the key elements of each project, the third notes the amount of water the project is intended to supply, and the fourth is the estimated cost of each project.

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Table 5: Robustness, alternative definition of agricultural goods and restricted sample

|Dep. variable|ln simp<br>(1)|le tarifs<br>(2)|ln weig<br>(3)|hted tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.039|-0.015|0.106|0.040|
||[0.092]|[0.089]|[0.113]|[0.110]|
|ln rain_jt ∗_agric_i_||0.261***<br>[0.056]||0.317***<br>[0.063]|
|Observations|32,436|32,436|32,117|32,117|
|Number of countries|51|51|51|51|
|Number of products|65|65|65|65|
|R-squared|0.579|0.583|0.415|0.418|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 3
number of observations in that period, we cannot reject the null hypothesis of equivalence of the coefficients prior to 1985 and following 1996. Strikingly, despite the fact that food aid was administered very differently in the 2000s than in the 1970s, the coefficients for these two periods are nearly identical. Thus, one is left with two possible conclusions. Either, the delivery mechanisms behind food aid is irrelevant to the degree to which food aid translates into elevated conflict – which seems unlikely – or the NQ IV strategy is picking up something other than a causal effect of aid on conflict.

# _C. Incompatibility of OLS Results with Stated Goals of Aid Agencies_

An additional indication of a problem with interpreting NQ’s results is their implausible claim that the modest, statistically insignificant negative association between food aid and conflict in their OLS estimates suffers significant negative bias. The NQ explanation for that claim is the possibility that donors condition food aid flows on characteristics correlated with low levels of conflict, i.e., that the US actively seeks to avoid sending foreign aid to conflict-affected countries. This explanation directly contradicts USAID’s Food for Peace program’s stated objective <mark>“FFP provides emergency food assistance to those affected by</mark> _<mark>conflict</mark>_ <mark>and natural disasters and provides development food assistance to address the underlying causes of hunger.” (USAID, 2015, emphasis added)</mark> In the NQ dataset, less than 22% of countries experience conflict in the average year, and yet between 1975 and 2006, there were only 2 years when there were more countries receiving food aid and not experiencing conflict than countries who were both receiving food aid and experiencing conflict.

As US food aid shifted from Title I lending to governments to Title II emergency assistance through PVS/NGOs and WFP, food aid has grown increasingly concentrated on populations dealing with conflict, contrary to the NQ hypothesis.

37
HZJZ, Croatian Health Service Yearbook 2005, Zagreb: Croatian National Institute for Public Health, November 2006.

HZJZ, Croatian Health Service Yearbook 2006, Zagreb: Croatian National Institute for Public Health, November 2007.

HZJZ, Croatian Health Service Yearbook 2007, Zagreb: Croatian National Institute for Public Health, November 2008.

HZJZ, Croatian Health Service Yearbook 2008, Zagreb: Croatian National Institute for Public Health, November 2009.

HZJZ, Croatian Health Service Yearbook 2009, Zagreb: Croatian National Institute for Public Health, November 2010.

HZJZ, Report on Hospital Activities in 2004, Zagreb: Croatian National Institute for Public Health, July 2005.

HZJZ, Report on Hospital Activities in 2006, Zagreb: Croatian National Institute for Public Health, September 2007.

HZJZ, Report on Hospital Activities in 2007, Zagreb: Croatian National Institute for Public Health, August 2008.

HZJZ, Report on Hospital Activities in 2008, Zagreb: Croatian National Institute for Public Health, November 2009.

HZJZ, Report on Hospital Activities in 2009, Zagreb: Croatian National Institute for Public Health, November 2010.

HZZO. _Izvješće o poslovanju Hrvatskog zavoda za zdravstveno osiguranje za 2000_ .; Report of the Croatian Institute for Health Insurance 2000., Zagreb: HZZO 2001.

HZZO. _Izvješće o poslovanju Hrvatskog zavoda za zdravstveno osiguranje za 2001.;_ Report of the Croatian Institute for Health Insurance 2001., Zagreb: HZZO 2002.

HZZO. _Izvješće o poslovanju Hrvatskog zavoda za zdravstveno osiguranje za 2002.;_ Report of the Croatian Institute for Health Insurance 2002., Zagreb: HZZO 2003.

HZZO. _Izvješće o poslovanju Hrvatskog zavoda za zdravstveno osiguranje za 2003.;_ Report of the Croatian Institute for Health Insurance 2003., Zagreb: HZZO 2004.

HZZO. _Izvješće o poslovanju Hrvatskog zavoda za zdravstveno osiguranje za 2004.;_ Report of the Croatian Institute for Health Insurance 2004., Zagreb: HZZO 2005.

HZZO. _Izvješće o poslovanju Hrvatskog zavoda za zdravstveno osiguranje za 2005.;_ Report of the Croatian Institute for Health Insurance 2005., Zagreb: HZZO 2006.

HZZO. _Izvješće o poslovanju Hrvatskog zavoda za zdravstveno osiguranje za 2006.;_ Report of the Croatian Institute for Health Insurance 2006., Zagreb: HZZO 2007.

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prior to benchmarking, and therefore is unaffected by benchmarking. Benchmarking has minor effects for in-sample municipalities because of the presence of many state-level dummy variables in the model. Conversely, state dummy variables were not included for many out-of-sample municipalities, and as seen in the mean poverty column in Table 2, the non-benchmarked predictions significantly underestimate poverty in these cases. As in Masaki et al. (2022), the mean squared error was multiplied by the same scaling factor that was used for benchmarking in each municipality, which increases mean squared error and improves coverage rates, particularly in outof-sample municipalities. For in-sample municipalities, benchmarking improves the correlation with the CONEVAL estimates by about a percentage point, from 0.853 to 0.862, and slightly reduces root mean squared deviation. For out-of-sample prediction municipalities, meanwhile, benchmarking has a more substantial beneficial effect on accuracy, as the correlation with the CONEVAL estimates improves by about 3 percentage points from 0.669 to 0.701, and root mean squared deviation falls from 0.196 to 0.181. Overall, benchmarking improves the accuracy of the estimates, particularly for out-of-sample municipalities, and the benchmarked estimates are therefore preferred.

# _6.2 Weights_

Table 5 considers the impact of using sample and population weights when estimating the household-level model. The population weights in this case are estimated average household size at the state level, obtained by the MCS-ENIGH survey. As noted in section 3, this is assigned to each household in the synthetic census. Sample population weights, defined as the provided household weight times household size, are provided with the MCS-ENIGH data, and are used in the preferred specification when estimating the mixed effect model. Table 8 shows that failing to use sample weights decreases the average poverty rates prior to benchmarking from about 28.1%, which nearly matches the direct estimates, to about 26.4%. This suggests that failing to incorporate weights can moderately increase bias. Interestingly, the use of sample weights makes the estimates slightly less accurate for in-sample municipalities, but slightly improves accuracy for out-ofsample municipalities. This suggests that the method that was used to apply the weights to the sample may not be optimal for generating in-sample predictions in empirical best models. We nonetheless prefer the weighted estimates because they are more accurate for out-of-sample 26
The effort to train the FVA agency representatives on the principles of risk management and produce the sampling plan began in the Spring of 2013. The first sampling plans were produced for use on January 2014, but implementation was delayed until July 2014. An updated sampling plan was produced for 2015. Our investigation of the effects of the FVA reforms includes data through the end of 2015.

A subsequent crucial event that is relevant for our evaluation is a funding dispute between the FVA and the laboratories that conduct the testing. Due to postponed public procurement for a certain sub-period within 2015, the FVA did not have a contract with the Veterinary Institute where the analysis for food of animal origin and animal feeds are conducted. This dispute limited FVA’s use of laboratory testing facilities in 2015. We observe in our data a large drop in the number of samples sent to the laboratory for testing in 2015. Reduced sampling activity was a key objective of the reform, but a large share of the dramatic drop in sampling activity in 2015 is due to the funding dispute, not the reform. European Commission (2015) indicates that payment arrears jeopardized the FVA’s ability to implement animal health programs. We lack further specific information on this issue, but we note that reduced sampling and testing generated by this dispute helps achieve some goals of the reform (lower sampling rates), while undermining others (targeting risky shipments). In our evaluation, we will sometimes include 2015 data in the estimation process, and other times exclude it. The funding dispute with the public laboratories makes it very difficult to identify separate effects of the reform (except the immediate impacts visible in the second half of 2014). Our estimates that include 2015 data should be interpreted as due to the joint effects of reform and the funding dispute with the public laboratories. We will discuss the implications of the funding dispute as we implement each portion of the evaluation.

The slow implementation of the reform, the funding dispute with the public laboratories, and the budget cycle funding for our evaluation mean that we are only able to investigate the short-run effects of the reform. As will be described in Section III, we have daily data on the timing of shipments and monthly data on import values so we are able to construct 6-month semesters and estimate impacts over the second semester of 2014 that follow the implementation of the sampling plans. We are also able to investigate sampling rates, measured compliance and import value for the entirety of 2015, but these estimates are conflated with the effects of the funding dispute with the public laboratories. The reforms were difficult to implement and so one might consider the latter half of 2014 to be a transition period. Our inability to cover a longer period in the evaluation of the reform is unfortunate. Trade facilitation reforms in the developing world frequently occur in environments associated with economic shocks and implementation difficulties, which is one reason why so few studies exist.

# **III. Data**

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# **4 Empirical Strategy**

The main challenge in estimating the impact of child labor on learning is overcoming the potential endogeneity of child labor. The decision about the child’s time allocation could be made based on unobservable characteristics of the individual that also determine her proficiency. It is very likely that _ability_ is correlated with proficiency and the parents’ perception of the value of education which determines the child’s time allocation. In this case, a simple OLS estimator for child labor and proficiency will be biased. Depending on the correlation between the unobservables and time allocation decisions and between the unobservables and proficiency, the OLS estimator could be upward or downward biased.

In the above example, if there is a positive relationship between _ability_ and the perception of the value of education, meaning parents with high ability children are more likely to prioritize schooling, we would expect that a naive approach would overestimate the actual impact of child labor on proficiency. On the other hand, one can imagine that more able children have better opportunities in the labor market. In this case, the OLS estimator would underestimate the e↵ect of child labor on proficiency.

Therefore controlling for such unobservable characteristics is essential to consistently estimate the impact of child labor on proficiency. The longitudinal dataset of _Prova S ̃ao Paulo_ allows us to control for unobservable characteristics that are over time.

Our ‘benchmark’ strategy is, therefore, a fixed e↵ect estimator, we run the following regression separately for boys and girls and for math and Portuguese: where _Tigt_ is the math or Portuguese language proficiency test score of student _i_ in grade _g_ , 13
Japan, for example, shows that one possible outcome of career systems is that younger judges who too aggressively challenge accepted ideas are likely to find themselves with less desirable appointments. For this bias to be avoided, the composition of the authority in charge of appointments and promotion is relevant (Ramseyer 2001).

Recognition models run the risk of biased views due to previous political or business experience. Previous political activity is also the focus of recent research on France, which looks at the economic consequences of judges’ decisions by collecting information on more than 145,000 appeal court rulings, combined with administrative firm-level records covering the whole universe of French firms. The quasi-random assignment of judges to cases reveals that judge bias due to previous political activity has statistically significant effects on the survival of small lowperforming firms (Cahuc and Carcillo 2021).

Another study on the topic of political activity analyzes judicial decisions to estimate the bias due to prior political activism among judges arbitrating dismissal disputes in Australian labor courts. The political color of the appointing political party and judges’ previous political background affect probability of employee success by about 10% points (Booth and Freyens 2014).

The appointing process can be a determining factor in independence. Research on Ukraine finds that the practice of conducting competitive selection of judges is a critical part of independence of the judiciary (Shcherbanyuk 2018). A study on Pakistan finds that when the judge selection procedure in Pakistan changed, from the President appointing judges to appointments by judge peers, rulings in favor of the government decreased significantly and the quality of judicial decisions improved (Mehmood 2021). A study conducted on Albania investigates the role of assets assessments as one of 3 criteria for the vetting of judges. The study finds it to be an effective criterion when paired with background assessments (previous links with political parties or organized crime) and professional competence (Hasmuça 2017).

The length of tenure is also a determining factor of independence. Studies confer iconic status on the clause of England’s Act of Settlement (1701) mandating secure tenure for judges. A 13
Following the restriction on smallholder households, the sample includes 5,714 children with height measurements.<sup>15</sup> Table 1 presents the sample means and the results from the tests of mean differences by household tobacco adoption status in the year of or the year after child birth.<sup>16</sup> The rate of stunting among children born into tobacco-producing households stands at 51 percent, significantly higher than the 43 percent for children in non-tobacco-producing households. The aggregate household income statistics, however, show tobacco households to have more overall income than non-tobacco-producing farm households. On average, tobacco producing households also control more land, have larger families, have younger households heads and are more likely to live in communities without a health clinic.

# **4**

If we assume that systematic differences between children from tobacco-farming households and their comparators from non-tobacco farming households are captured conditional on observables, the coefficient _θ_ from the following linear regression would yield an unbiased estimate of the impact of household tobacco adoption on child stunting: where _i_ denotes individual; _h_ denotes household; _y_ is the height-for-age z-score for children 6 to 59 months old, with lower z-scores implying worse nutrition outcomes; _α_ is the constant; _D_ captures household tobacco adoption as defined above; _C_ , _H_ and _V_ are vectors of child-, household-, and community-specific variables, respectively; and _ε_ is the error term.

Previous work on health outcomes provides a wealth of information on significant contributing factors to reducing nutritional deficiencies.<sup>17</sup> The range of controls introduced in our specification account for variables that enter the health production function described by Strauss and Thomas

> 15Mei and Grummer-Strawn (2007) suggest excluding from analysis observations with height for age measurements that are either below six deviations or above five deviations with respect to the mean. Applying this criterion, we omitted 97 observations, accounting for less than 2 percent of the whole sample.

> 16All of the variables originate from the IHS2 data. The income variables are obtained from the Food and Agricultural Organization’s Rural Income Generating Activities (RIGA) database. We elected to use the version of the total income aggregate that calculates the value of own consumption from the food consumption module of the IHS2 household questionnaire.

> 17Recent literature questions past results in clarifying the links between health and development (Deaton, 2003).

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<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C3.3. SOSD: Social Sustainability Index C3.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.92*** 0.93***<br>H<br>Kendall tau-b 0.89*** 0.78*** 0.83***<br>Spearman 1*** 0.95*** 0.98***<br>A<br>Kendall tau-b 1*** 0.89*** 0.94***<br>Spearman 0.95*** 0.95*** 0.93***<br>M0<br>Kendall tau-b 0.89*** 0.89*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1 .1<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference<br>-.1<br>.2<br>0 -.2<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C4. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Medical attention indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._

# Peru

# C4.1. FOSD: Counting Vector

# C4.2. Dominance Analysis

<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.1<br>1<br>.8 .05<br>.6<br>0<br>CCDF<br>.4 Difference<br>.2 -.05<br>0<br>-.1<br><!-- End of picture text --> C4.3. SOSD: Social Sustainability Index C4.4. Rank correlations 50
uneven. This is especially problematic as most refugee-producing crises are protracted, implying that the composition of the “main host countries” remains somewhat stable over large periods of time. Sharing the responsibility for hosting refugees also implies, therefore, that increased support is warranted to maintain the current system and the international protection that it provides for those who are subject to persecution and violence.

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- **Crisp, Jeff** , “No Solution in Sight : the Problem of Protracted Refugee Situations in Africa,” 2003.

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- **Eaton, Jonathan, Samuel S. Kortum, and Sebastian Sotelo** , “International Trade: Linking Micro and Macro,” February 2012. NBER Working Paper 17864.

- **Hathaway, James C. and Alexander Neve** , “Making International Refugee Law Relevant Again: A Proposal for Collectivized and Solution-Oriented Protection,” _Harvard Human Rights Journal_ , 1997, _10_ , 115–211.

- **Schuck, Peter H.** , “Refugee Burden-Sharing: A Modest Proposal,” _Faculty Scholarship Series_ , 1997.

- **Smith, Merrill** , “Warehousing Refugees,” _World Refugee Survey_ , 2004, _38_ , 38–56.

- **Suhrke, Astri** , “Burden-Sharing during Refugee Emergencies: The Logic of Collective versus National Action,” _Journal of Refugee Studies_ , 1998, _11_ (4), 396–415.

- **Temprano Arroyo, Heliodoro** , _Using EU aid to address the root causes of migration and refugee flows_ , Florence: European University Institute, 2019.

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