rafmacalaba/data-use-mentions-extended huggingface dataset:
difference, and there are two broad ways in which individuals’ answers can be read. On the one hand, we can assume that, when asked about their position in Cantril’s ladder, individuals’ answers have to be understood as such - where do they place themselves with respect to other members of society. Being in the fifth step of the ladder would imply having roughly half of the population "above" in terms of welfare and half of the population "below". In essence, the answer to the question is _distributional_ . On the other hand, that answer can be understood as their position with respect to some ideal benchmark - for instance, being "absolutely rich" or "absolutely poor". Being in the fifth step of the ladder would mean that individuals find themselves to be as far in terms of welfare from the richest and from the poorest of their country. In this sense, the position in Cantril’s ladder refers more to individuals’ perception of their _well-being_ , even though the question is not phrased in those terms.

The reasons behind the difference between perceived and objective welfare ranking will be very distinct depending on whether it is assumed that individuals’ self-reported positions in Cantril’s ladder are _distributional_ or that they refer to their _well-being_ . These represent two different analytical frameworks and in the next subsections we will address them separately.

# **3.1** **_Distributional_ explanations of the difference between objective and subjective ranking**

If we assume that individuals’ answers refer to their perceived position in the welfare distribution, then any explanation will ultimately have to address why responses are "normally" distributed, rather than uniformly distributed.

There are several possible explanations: on the one hand there are data issues: (i) the survey data miss rich and poor people, thus more people are actually in the middle because the sample does not include the full population. On the other hand, there are misperceptions or misinterpretations of the questions: (ii) people
The composition and practices of supervisory colleges are confidential. However, the Basel Committee’s good practice paper states that the home supervisor should periodically notify host supervisors and the Basel Committee of the structure of the college and relevant participants. It remains unclear what is actually the objective of the notification to the Basel Committee. Over the summer of 2009, the Basel Committee performed a survey on college arrangements and practices in the banking sector. The International Association of Insurance Supervisors (IAIS) undertook a similar exercise in the insurance sector. Unlike the IAIS, the survey results for the banking sector were not published but formed the basis for the Basel Committee s good practices paper.

# **Box 2: Supervisory Colleges in the European Union**

EU banks operate in a more closely integrated political and legal framework, and the extent of financial market integration and cross border banking is more developed than in the rest of the world. The state of affairs of supervisory colleges, and cross border supervision in general, in the EU is thus fundamentally different.

The EU also has a legally binding framework for home host cooperation among supervisors. This legal framework is implemented by a set of principles, guidelines and written agreements allowing common sharing and delegation of tasks, common risk assessments and conflict resolution processes. It includes exchange of information, cooperation in emerging situations and even some joint decision making. Indeed, Article 129 (3) of the revised Capital Requirements Directive requires that the consolidating supervisor (generally the home supervisor) and supervisors of subsidiaries involved in the supervision of a cross-border banking group do everything within their power to reach a joint decision on the application of the Pillar 2 provisions related to the Internal Capital Adequacy Assessment Process and to the Supervisory Review and Evaluation Process. The joint decision should cover the adequacy of the consolidated level of own funds held by the banking group with respect to its financial situation and risk profile, as well as the required level of own funds above the regulatory minimum, applied to each entity within the group
to exert either a negative or an insignificant effect on the productivity of Chinese domestic firms.

One of the potential explanations for the limited horizontal spillover is that domestic producers also face crowding out effects in product and factor markets when foreign competitors enter the market. Such crowding out effects tend to be nonexistent or relatively small for firms that are vertically linked to foreign firms (i.e. suppliers or customers of the foreign firms). Other explanations suggested in the literature include, in particular, mediating factors that are discussed below.

While the evidence is ambiguous about the existence of positive and significant horizontal spillover, most studies find positive and significant productivity spillover through backward linkages. A leading study in this literature, Javorcik (2004), finds that a one-standarddeviation increase of foreign presence in the sourcing sectors is associated with 15 percent rise in domestic firms’ output in supplying industries in Lithuania. Blalock and Gertler (2008) use firm level panel data from 1988-1996 and similarly find evidence of positive spillover through backward linkage in Indonesia. In Hungary, Halpern and Murakzy (2007) find evidence of positive horizontal spillover but only for firms that are geographically close to foreign-owned firms. Javorcik and Spatareanu (2009) find evidence of backward linkage spillover in the Czech Republic. Kee (2015) explores a new channel of productivity spillover and finds firms sharing the same suppliers with foreign firms in Bangladesh to experience productivity improvement.

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html` and `https://www.prsgroup.com/about-us/our-two-methodologies/icrg.`

> 12The database of SWF transparency measured by the Linaburg-Maduell index and capitalization are publicly available at `http://www.swfinstitute.org/`

> 13The list of members are publicly available at `http://www.oic-oci.org/oicv2/states/`

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suggests consumption magnitudes (for individual items) are themselves often imprecisely estimated. In Mexico in 2016, for example, the coverage rate – defined as the cumulative consumption expenditure magnitude for an item covered in a household survey divided by cumulative consumption expenditure (for the same item) from national accounts – of alcohol and tobacco expenditures was less than 10 percent while the coverage rate of education expenditures was more than 160 percent (Zwijnenburg and others 2021).

The third disadvantage helps distinguish our approach from empirical approaches to intrahousehold decision-making that make use of exclusive or assignable expenditures. In order to determine how individuals would allocate income gains to maximize their own individual utility, it is usually assumed that preferences for single individuals without dependents or spouses also describe the preference of similar individuals in households with children or elderly dependents (or both); with one, two, three, or more caretakers who may also be income earners; and with members that have both a fiduciary responsibility and intrinsic motivation to care for and

> 10. Individual-level data helpful for more precise estimates of the incidence of fiscal policies on individuals is time-use data, or records summarizing the differential time-use patterns of males and females, children, and the elderly, and (potentially) household members who are either temporarily or permanently absent. Beyond that, detailed individual-level information on assets, employment, income, and consumption expenditure could be used to provide individual-level impact estimates of fiscal policies.

> 11. Past expenditures on single items may also be unreliable for predicting future intrahousehold allocation or decision-making processes.

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are highly beneficial for all children involved. Conversely, enforcement-oriented policies tend to hinder children’s development, particularly among immigrant children, by reducing service uptake and damaging the mental health of children and their parents.

The review also draws attention to the vulnerability of left-behind children, who despite observing positive income shocks might still be negatively affected by parental absence. Notably, the evidence highlighted the large negative effects of parental absence on cognitive outcomes, mental health, and socioemotional behaviors. Future research and programming should cover effective ways to support these children. Specifically, the Chinese case raises a crucial question about the trade-off between relocation policies to spur growth and the human capital costs of such policies.

Forcibly displaced children are one of the most vulnerable groups since they not only experience migration but do so unexpectedly, often after traumatic events. Yet, without representative and longitudinal data to comprehensively measure children’s outcomes, it is difficult to diagnose the precise challenges they face. This includes an effective evaluation of their cognitive and noncognitive development relative to native children and its impact on future outcomes, such as adult income. These issues are particularly important in developing countries and present a significant opportunity to expand our knowledge in the future.

Finally, more evaluation of programs that effectively target migrant and forced migrant children’s outcomes will be extremely valuable in guiding policymakers about the most effective programs to support these populations and their hosts. This is particularly crucial for long-term outcomes because the majority of existing work concerns the immediate impact of such programs due to various constraints. Even though the forcibly displaced population requires urgent attention, there is wide scope for action in understanding the long-term impacts of these experiences during their teenage years and into adulthood.

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multiple visits (and fees) would improve access to essential financial services in remote areas. Given the low capital base of many remote municipalities placing a bank in each municipality will be financially difficult and alternative delivery channels are a more sustainable mechanism for financial inclusion (Shakya _et al._ 2014). Sakcyham’s Branchless Banking Initative and UNCDF’s Mobile Money for the Poor (MM4P) project are the most prominent existing examples of this approach.

For medical services, the Nepalese government will need to continue expanding its network of health and sub‐ health posts if it is to reach its goal of 90% coverage within 30 minutes travel. More urgently, expanding the public hospital network or incentivizing private hospital construction in less developed areas is justified given the widespread inaccessibility of advanced medical services. Tax breaks or other inducements to private hospital construction in less developed areas could encourage this.

Given the high costs of building and maintaining infrastructure in mountainous areas, raising human development indicators and planning new services will present a stiff governance challenge for new municipal governments in remote municipalities. Provincial and municipal governments can enhance administrative access by enhancing infrastructure connections to municipal headquarters, localizing essential administrative functions and enforcing staff attendance in municipal headquarters. These moves would reduce dependence on far away district headquarters and thus raise overall access. This may require significant investment in staff training and the necessary offices and equipment, in addition to improved bridges, trails and roads.

At a more advanced level, policy mechanisms that streamline collaboration among neighboring municipalities and municipalities and provinces would enable more economical solutions to shared accessibility challenges that are beyond the resources of any one municipality. Hospital siting, for example, may best be addressed at the provincial level. Such expensive services will require strong planning coordination between local and provincial governments, as well as between neighboring local governments facing similar service shortfalls. In this light the new Provincial Planning Commissions are a positive governance step.

Finally, we must note the strongly gendered implications of the above accessibility challenges. Many women will avoid overnight
/sup> On adoption of expenditure prioritization mechanisms (MTBFs), MENA ranks lowest globally, followed by LAC. This raises significant questions as to the medium term mechanisms MENAs governments can adopt to sustainably reprioritize distribution programs in the context of political transitions, especially as the traditional 26 These are based on an extensive global dataset recently constructed by the World Bank‘s PREM Public Sector & Governance Group ( _forthcoming)_ . The three different MTEF—MTFF, MTBF, and MTPF—were coded as one if a country met the following criteria: (i) MTFF: the government has rolling aggregate, expenditure, revenue, and other fiscal forecasts. Features include the availability of a macro-fiscal strategy, macroeconomic and fiscal forecasts, and debt sustainability analysis, (ii) MTBF: the budget, spending agency or other reports explain aggregate and sectoral expenditure objectives and strategies, budget circulars detail medium-term expenditure ceilings and revenue forecasts, and budget documents contain some detail for medium-term estimates. MTPF: the budget, spending agency or other reports explain program objectives and strategies, listing specific agency and/or program output or outcome targets, as well as results.

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costs (Euro/km) by dividing it by average speed. Their imputation suggests a per-km cost of 1.23 Euro/km in 1980, which fell to 1.02 Euro/km in 2007. Their methodology helps to decompose the drop to its productivity and infrastructure capital components. Persyn et al. (2020) apply the same methodology to all of Europe starting from a one-square km resolution. They sample centroids for each NUTS-2 European region based on the spatial population distribution.

Imputation-based transport cost calculations lend themselves to practical applications. For example, in an effort to give shippers a benchmark for freight costs, the Colombian Ministry of Transportation developed a simulation tool called _SICETAC_ . Using this web-based application, shippers can obtain trucking cost estimates for all routes within the country.<sup>5</sup> The main data used in this application comes from a survey of per-unit freight prices paid to truck owners at the origin-destinationcommodity level. The developers used an imputation-based approach to calculate the determinants of unit costs based on the characteristics (such as distance and slope) of sampled routes. Through a GIS interface that contains information about these characteristics in the entire road network of the country, the simulation tool then yields predictions on monetary costs on any hypothetical route. As in this example, data collection efforts can provide useful inputs to imputation-based calculations that generalize cost calculations to a wider geography. While imputation-based methods can predict monetary shipping costs across hypothetical routes, they cannot inform how these costs affect shipping demand and trade between locations. Doing so requires additional conceptual tools discussed in the subsequent section.

# **5 Estimation-based Methods**

Before delving into the estimation of trade costs, it is in order to note that the methodology discussed in the previous section provides some of the key variables to be used. In what follows, it is assumed that the travel distance and travel time variables are the output of a network-based optimal itinerary/route calculation along the lines described above.

## **5.1 Gravity
Estrades, Maliszewska, Osorio-Rodarte, and Seara E Pereira. (2021) these policies is costless, resulting in upper bound estimates of potential gains. Fourth, as found in previous studies, trade liberalization leads to an increase in productivity, and thus we simulated an increase in productivity associated with the falling trade costs (the “productivity kick”).<sup>12</sup> Table 1. Simulation scenarios

|**Scenario**|**Policy instrument**|**Shock**|
|---|---|---|
|_RCEP_tar_<br>Tariffs alone|Tariffs|RCEP Tariff reduction schedule|
|_RCEP_<br>Full scenario|Tariffs and NTM|RCEP Tariff reduction schedule<br>Preferential NTM reduction:<br>-35% agricultural goods<br>-25% manufacturing goods<br>-25% on services<br>10% non-preferential NTM reduction|
|_RCEP_roo_<br>ROO liberalization|Tariffs, NTM, and trade costs|RCEP Tariff reduction schedule<br>NTM reduction as in RCEP<br>1% reduction in trade costs among RCEP<br>members|
|_RCEP_prod_<br>Productivity kick|Tariffs, NTM, and trade costs|RCEP_roo with productivity increase|

Source: own elaboration As sensitivity scenarios, we simulated the same shocks, calibrating the model with the ad valorem equivalents of non-tariff measures on goods from Kee and Nicita (2018), and we run an alternative scenario in which we assume that the trade cost reduction due to the common rule of origin regime only affects trade in manufacturing goods (see Appendix II). Manufacturing relies heavily on intermediate goods and other inputs, making it a sector susceptible to changes due to the implementation of rules of origin.

We complement the general equilibrium analysis with a simple global microeconomic model to obtain impacts on poverty and income distribution. The initial global distribution of per capita consumption/income was constructed with household-based data. Country-specific growth
the country-month fixed effects, bond characteristics-month fixed effects, and bond controls with bond-month fixed effects.

We also estimate a leads and lags regression in which the instrumented FIR is interacted with trading-day dummies. This analysis allows us to both explore the dynamic effect of the FIR and test for parallel trends before the rebalancing.

# **3.2 Data and Summary Statistics**

We collect data from different sources to compute the FIR and our instrument. Most of the variables used in the analysis come directly from J.P. Morgan. However, one variable is not straightforward to measure: the AUM of funds that passively track the EMBIGD, _At_ . While J.P. Morgan provides data on the amount of assets benchmarked against their indexes, it does not distinguish between passive and active funds. Additionally, even if these data were available, many active funds might passively manage a significant share of their portfolios, as highlighted by Pavlova and Sikorskaya (2022).

To compute _At_ , we start with J.P. Morgan data on assets tracking the EMBIGD, which we then adjust based on an estimate of the share of passive funds. The estimation of this share involves the following steps. We retrieve data from Morningstar on the asset holdings 14
event - Transition economies</u>**

|||Bef|ore|Just afte|ronset|
|---|---|---|---|---|---|
|BGR|Bulgaria, 1996|31.1|_1995_|26.4|_1997_|
|CZE|Czech Republic, 1989|19.4|_1988_|26.5|_1993_|
|EST|Estonia, 1992|23.0|_1988_|39.5|_1993_|
|HUN|Hungary, 1991|25.0|_1989_|27.9|_1993_|
|LVA|Latvia, 1995|27.0|_1993_|31.6|_1996_|
|LTU|Lithuania, 1995|37.2|_1994_|32.4|_1996_|
|POL|Poland, 1992|26.9|_1989_|32.7|_1996_|
|ROM|Romania, 1990|23.3|_1989_|25.5|_1992_|
|RUS|Russia, 1995|46.2|_1996_|45.6|_2000_|
|RUS|Russia, 1998|48.3|_1993_|46.2|_1996_|
|SVK|Slovakia, 1991|19.5|_1988_|19.5|_1992_|
|UKR|Ukraine,1997|33.2|_1996_|29.0|_1999 _|
||Mean|30.0||31.9||
||Standard deviation|9.6||8.1||

Note: Crisis dates are drawn from Caprio et al. 2004. For panel (a), in respect of each country in the Povcal database, other than Transition economies, Gini coefficients were extracted where available for dates (i) _before any_ crisis hit (within 10 years); (ii) _soon after the end_ of all crises (within 5 years); (iii) _long after the end_
equivalence between national and foreign degrees both in terms of quality and subject coverage (such as the case in law degrees). In particular, highly educated immigrants trained in developing countries could be less productive in high-skill jobs than natives with similar educational degrees.

Evidence of such heterogeneity in the quality of education is provided by Coulombe and Tremblay (2009), who compare the skill intensity and schooling levels of Canadian immigrants and natives who were both submitted to standardized tests in literacy, math, and problem solving. These tests provide measures of pro...ciency that are comparable across countries and over time. On this basis, Coulombe and Tremblay estimate a ’skill-schooling gap’for each origin country, de...ned as the di¤erence between the mean years of schooling of the immigrant subgroup and the typical native with the same pro...ciency level. A positive skill-schooling gap of n years means that Canadian nationals with y years of schooling are as productive as immigrants with y+n years of schooling. The larger the skill-schooling gap, the lower is the quality of education in the country of origin. Simple bivariate OLS regressions show that the skill-schooling gap is a decreasing function of per capita income of the origin country. Their -0.10 point estimate of the slope coe¢ cient indicates that the skill-schooling gap is one year smaller when per capita income increases by US$10 000 in the origin country. Using this estimate and cross-country data on per capita income, we construct an indicator of skill-schooling gap for each origin country. Then, assuming that one year of schooling generates a productivity gain of 8 percent, we estimate the relative productivity of educated immigrants and natives in each country, with a benchmark value of one for workers trained in Canada (as well as workers trained in richer origin countries, i.e. the upper bound of this index is one). For example, college graduate immigrants from Angola and Portugal are 17
, partly due to discomfort with assuming a prior distribution. Finally, multiple well-documented and user-friendly software packages employ EBP methods, such as the EMDI and SAE packages in R, and the SAE package in Stata (Kreutzmann et al, 2019, Molina and Marhuenda, 2015, Nguyen et al, 2018).

Here, I focus on EBP models because they incorporate a conditional random effect that conditions on the sample, which is effectively used as a prior estimate. This distinguishes EBP models from two other popular alternatives: M-quantile (Chambers and Tzavidis, 2006) and ELL (Elbers, Lanjouw, and Lanjouw, 2003). Including a conditional random effect is particularly important when the auxiliary data are linked to the survey at the sub-area or area-level (Masaki et al, 2022). In this case, the sample contains more information relative to the auxiliary data than when using a typical household census, because the auxiliary data is the same for all households within a sub-area. This mechanically introduces correlation across households in a village, increasing the variance of the area effect. This in turn increases the weight given to the sample relative to the prediction in the empirical best predictor model when using aggregate predictors, as opposed to household-level predictors.

Within the set of EBP models, three main classes of model can be used to generate area-level poverty estimates using existing publicly available software packages.

The first type of EBP model for poverty estimation is a household-level model, as follows: (2) G(Yrasi) = β1Xras + β2Xra + β3Ir + ηra + εrasi , where G(Yrasi) is a transformed measure of household economic welfare of household i, typically measured as capita income or consumption. Household i lives in subarea s, target area a, and region r.

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