rafmacalaba/datause-encoder-data huggingface dataset:
and poor neighbour relations (see below) have rendered previously free-of-charge border crossings between Soviet provinces highly costly. The World Bank (2004a) provides a striking illustration. As the network configuration was initially implemented to satisfy the former Soviet Union’s industrial and strategic plans, little consideration was given to transport costs or to passengers’ needs. As a result, there are often no straightforward connections between locations within a country, while roads and railways cross new CIS borders several times. For instance, 800 kilometers of Russian railways are on Kazakh territory, while 300 kilometers of Kazakh railways are on Russian territory.

# **2.3 Additional costs of landlockedness in Central Asian countries**

Landlocked countries often lag behind their maritime neighbors in terms of external trade and economic development, even when they have similar distances to main trade partners or similar internal transport infrastructure. The reason is that landlocked countries also have to cope with long overland distances (notably to join ports) and are largely dependent on sovereign transit countries, which they have to cross in order to access international shipping markets (see Faye et al., 2004, Ojala, 2005, or Carcamo-Diaz, 2004 for surveys on the various forms of transit dependency).

# **_Overland distance_**

Landlockedness affects many countries, but some, such as Switzerland, nonetheless manage to prosper. However, the story is altogether different for Central Asia. Except for Batumi on the Black Sea (which can be reached only after trans-shipment over the Caspian Sea), all ports are more than 3,000 km away from their closest Central Asian border. Many are even farther away, the shortest route to the sea being the Southern one which happens to go through what is probably the most unstable region of the world. Such huge land distances imply problems of a different magnitude than those faced by European landlocked countries like Switzerland and the Czech Republic, which are 500-600 km away from major ports.

In terms of monetary transport costs (one dimension of the problem, although by no means the only one), Limao and Venables (2001) estimated that overland transport costs rise by as much as $1,380 per 1,000 km compared to only $190 for overseas transport.

9
Table 3: Parameter values used in simulation

|_δs_: Elasticity of substitution between the high and low skilled|1.75|
|---|---|
|_δa_: Elasticity of substitution between the old and young|5|
|_δm_: Elasticity of substitution between immigrants and natives|20|
|_γ_: Elasticity of labor supply|.10|

Parameters used in the intermediary scenario in DOP; _δa_ comes from Card and Lemieux (2001) 44
representative for the world as a whole even if recently there has been an apparent greater reluctance of the rich to participate in national household surveys. This in turn likely imparts a downward bias to national and perhaps to global inequality estimates.<sup>6</sup> Table 1 shows the coverage of world population by household surveys. The second row from the bottom shows the overall coverage which was in all years but one greater than 90%. This is quite good, but should not make us forget that the countries that are omitted because they do not conduct household surveys are not a random draw from among all countries in the world but are all poor countries such as Afghanistan, Sudan, Congo, Somalia, Eritrea, etc. This is reflected in substantially lower population and income coverage of Africa. While the population coverage of other continents never falls below 92 percent, African coverage at its peak is 78 percent (see Table 1). And, in a worrisome development, the number and availability of household surveys in Africa is currently less than five or ten years ago. If we could include all of the omitted countries, global inequality would increase. In other words, what we calculate here, the Gini of about 70, is a lower bound to global inequality, simply because we do not have data from many of the poorest countries. Thus both the decreasing participation of rich individuals in national surveys, and the fact that countries that do not have surveys are overwhelmingly poor, bias the global inequality numbers down.

> 6 The effects of non-participation in surveys on measured inequality is by definition difficult to estimate since income of people who refuse to participate is not known. It is only indirectly (e.g., by looking at the geographical distributions of refusals as in Korinek, Mistiaen and Ravallion, 2005) that we can conclude that it is the rich who comply less. The difficulty of figuring out the effects of rich’s non-participation exists despite the intuition that it must underestimate actual inequality. In a model proposed by Angus Deaton (2005), where compliance decreases with income following a Pareto-like function, standard deviation of income  distribution does not change and inequality, by most measures, is unaffected. However, with different non-compliance functions, inequality may indeed be underestimated.

10
the largest group in the sample, experience 0.1 more births. Regardless of whether women decrease or increase the number of births in the presence of HIV, most of the divergence in fertility rates happens in the second half of the women's fertile stage of the life-cycle. This is because older women assign higher likelihood to being infected, they are more likely to actually be infected, and the marginal utility from additional child reduces with the number of children.

In Panel C of Table 15 I present results from a simulation of an environment in which there is no mother-to-child transmission. This simulation can be thought of as an assessment of the potential eect of provision of antiviral treatment that prevents such transmission.<sup>23</sup> In the simulation, child mortality probabilities of children born to HIV-infected women are equated to those of children born to HIV-negative women. The average number of life-cycle births in this environment is 7.05, a slight dierence from the 7.07 births experienced in the baseline environment. Although the reduction in expected and experienced child mortality, less than one percent of women will change the number of births they give.

To study the impact of HIV testing on fertility outcomes, I simulate life-cycle fertility in counterfactual scenarios in which the respondents are oered a single test in dierent points in their life cycle: at the time of their marriage (age 17 on average), age 25 and age 35. The impact of testing on outcomes depends on how women update their beliefs after learning their test result. This updating depends on the discrepancy between the beliefs and actual HIV status and on the accuracy women assign to test results. The extent to which testing will aect outcomes is expected to be limited given the estimated low probabilities most women assign to the accuracy of test results (presented in Table10). In addition, the impact of testing also depends on the extent to which the updated beliefs alter the relative valuation of women's choices. This can vary with age and the number of children a woman has as life horizon shortens and marginal utility from additional children reduced. It is also important to keep in mind that while the test result gives information about one's status

> 23In high-income countries, the rate of mother-to-child transmission has been reduced to less than one percent (unaids.org).

31
Most of the UK RPI - X formulae allow for certain uncontrollable cost elements to be passed through to customers. The water companies were initially allowed to pass through a variety of uncontrollable cost elements, such as rates, construction expenditure and the costs of compliance with new legal requirements, although the number and extent of these 'relevant changes of circumstance' has since been curtailed.<sup>3</sup> In the electricity industry, the regional electricity companies (RECs) are permitted to pass on any changes in electricity generation costs (although these are subject to separate controls). In Argentina, on the other hand, electricity companies are not entitled to automatic passthrough of fuel costs, presumably to give them an incentive to generate electricity as cheaply as possible and to respond to changes in relative fuel prices by altering the fuel mix.

# **3.3 Revenue caps**

A revenue cap places a limit on the total income of the company rather than the per-unit price it charges. This system makes sense when the majority of a company's costs are fixed rather than increasing with the number of units sold. A company with high fixed costs that is under price-cap regulation faces excessive risk from demand fluctuations as these movements significantly affect the company's income but leave its cost level relatively unchanged. Thus, a revenue cap reduces the risk-exposure of the company without undermining cost incentives in any way (as long as the permitted revenue is not based on actual cost levels).

In the UK, Northern Ireland Electricity was privatised in June 1993 with a revenue-cap rather than a price-cap formula, while partial revenue caps have been introduced for the RECs and National Grid Company following periodic reviews. This change was due partly to arguments concerning the level of fixed costs in the industry, which the

> 3 The companies that agreed to the changes were allowed a higher rate of return at the 1994 Periodic Review in recognition of the increased cost of capital arising from the loss of cost pass-through.

10
Since SMEs tend to rely more on the use of collateral than larger firms, what constitutes acceptable collateral (“ _garantía idónea_ ”) is an important issue for them. In practice, most SMEs have few real estate assets and can only pledge moveable property – such as equipment, inventories, receivables or fixtures – as guarantee (“ _prenda_ ”). Besides the ineffectiveness of foreclosure and liquidation procedures that diminish the value of such guarantees, there also exist legal and regulatory impediments to their acceptability as collateral by credit institutions (Figures 4.7 and 4.8)<sup>38</sup> . An important impediment is the requirement for an excessively detailed description of the asset operating as collateral for the guarantee to be valid, which limits the possibility of pledging assets that cannot be fully identified or of settling pledges separately from the SME itself<sup>39</sup> . In addition, the absence of a comprehensive property registry limits the types of moveable collateral that can be acceptable by credit institutions. As a result, with the exception of motor vehicles and durable goods stored in warehouses, other forms of moveable collateral cannot easily be pledged and represent ‘dead capital’ for SMEs.

Figure 4.7: Regulatory Treatment of Collateral Figure 4.8: Registering Collateral as Obstacle as Obstacle to SME Lending to SME Lending <!-- Start of picture text --> Do you consider that the regulatory treatment of collateral Are there issues in registering collateral that inhibit SME<br>inhibits SME lending? secured lending?<br>100% 100%<br>80% 80%<br>60% 60%<br>50%<br>38% 38%<br>40% 38% 40%<br>20% 20%<br>0% 0%<br>Yes No Yes No<br><!-- End of picture text --> _Source: Authors’ analysis of bank questionnaires (simple averages across all banks). Note: The Figures may not add up to 100 percent because some credit institutions did not answer the question._ The recently-approved Credit Information Law could further increase the supply of credit to SMEs, although its ‘amnesty’ provision was cited by banks as being problematic. A well-functioning credit reporting industry allows lenders to more accurately measure credit risk based on a borrower’s payment behavior and other relevant information, thereby enabling them to expand credit and/or reduce its price. While credit reporting has been extensively used in Colombia, the absence of an effective legal and

> adoption of modern rules for the treatment of the international aspects of insolvencies with cross-border impact, and the suppression of “consensual priorities” and of “internal creditors”.

> 38 For example, as part of SARC, standard LGD rates are currently applied across all banks depending on the type of collateral backing a loan; the large difference in LGD rates between loans with no (or nonacceptable) collateral and those loans that have an FNG guarantee provides a strong incentive for banks to seek the latter type in order to reduce their loan loss provisions.

> 39 See the World Bank (November 2003) and Fleisig and de la Peña (September 2005) for more details.

26
||0<br>*|*<br>*<br>*<br>*|
|---|---|---|
||*<br>*|*<br>*<br>*<br>*|
||*<br>*<br>*|*<br>*<br>*<br>*|
|Network Effects(PageRank)|||
||0.||
||6||
|Trade Connectivity per capita‐t0|1<br>*||
||*||
||*||
||0.||
||5||
|FDI Connectivity per capita‐t0|9<br>*||
||*||
||*||
|||0.|
|Migration Connectivity per capita‐t0||3<br>4<br>*|
|||0.|
|ICT Connectivity per capita‐t0||1|
|||2|
|||0.|
|Portfolio Flows ‐ t0||1<br>7|
|||0.|
|Airline Connectivity||1<br>9|
|||*|
||0.<br>0.|0.<br>0.<br>0.<br>0.|
|Adj‐R2|5<br>5|5<br>5<br>5<br>5|
||9<br>8|6<br>4<br>5<br>6|

Note: All coefficients are estimated with ordinary least squares regression. ***,**,* represent significance levels at 99%, 95% and 90% respectively.

Compared to the base model and the standard measure of openness (trade/GDP), nearly every network connectivity measure manages to increase the explanatory power (Adj‐R<sup>2</sup> ) of the standard growth equation, although not every network layer is statistically significant at the minimum 10 percent level. Deeper integration along each individual dimension is associated with stronger per capita GDP growth over the subsequent 16‐year period. Unlike the traditional measure of openness and connectivity (trade/GDP), the PageRank‐based index, which was designed to capture the knowledge spillovers from connections, is associated with higher long‐term growth in the case of international trade, FDI, migration and airline connectivity. A one standard deviation increase in the trade connectivity of a country is associated with more than half a percentage point (0.6%) higher annual economic growth over the long 20
- 24 -

# **_Causality_**

Causality tests are one of the advantages of estimating a multivariate stochastic system; this feature cannot be used when analyzing only a univariate hydrological time series or imposing the meteorological or physical hydrologic relationship in advance. The conventional Granger causality test can reveal what causes what. In the Vishnugad Pipalkoti case, temperature Granger-causes water flow of the Alaknanda River (Table 10). The null hypothesis that temperature is irrelevant in the hydro equation _can_ be rejected at the 10 percent significance level, and the hypothesis of hydrology being unimportant to determine temperature _cannot_ be rejected. In the same manner, rainfall also Granger-causes the river flow.

By contrast, at the Kotmale River, temperature seems to cause the water level, and vice versa. The null hypothesis that temperature influences hydrology cannot be rejected, but at the same time, the hypothesis of hydrological series being critical in the temperature equation can also be accepted. Statistically, rainfall does not cause hydrology.

Finally, in the Thac Mo case there is no conclusive causal relationship between climate and hydrology, though temperature Granger-causes precipitation in the region. In sum, hydrological series are likely to be impacted on by climate changes, particularly temperature. However, it may vary on a case-by-case basis.

Table 10. Causality Test

||Vishnugad<br>Pipalkoti<br>(India)<br>Chi2|<br>Upper<br>Kotmale<br>(Sri Lanka)<br>Chi2|Thac Mo<br>(Vietnam)<br>Chi2|
|---|---|---|---|
|Null hypothesis|statistics|statistics|statistics|
|ln_TEMP_<br>=> ln_HYDRO_|4.789 <sup>*</sup>|11.508 <sup>***</sup>|1.990|
|ln_PREC_<br>=> ln_HYDRO_|6.935 <sup>**</sup>|5.788|0.450|
|ln_HYDRO_ => ln_TEMP_|4.046|6.485 <sup>*</sup>|0.003|
|ln_PREC_<br>=> ln_TEMP_|4.077|3.816|0.321|
|ln_HYDRO_ => ln_PREC_|0.427|1.364|1.928|
|ln_TEMP_<br>=> ln_PREC_|2.159|1.554|12.665 <sup>***</sup>|

*** 1 % significance level, ** 5 % significance level, and * 10% significance level.
Spanish, and 0.06 standard deviations for Mathematics. Aside from these quality improvements, the ETC pedagogic model aims to improve efficiency in the education system: reductions in repetition rates at the primary and secondary levels, and reductions in student dropout in secondary. As discussed previously, there are also expected social benefits that may be reaped. While these benefits are clearly an important part of the rationale for investing in ETC, this analysis considers only the benefits gained in terms of standard deviations in learning outcomes.

To compare the cost effectiveness of this intervention to other similar interventions for which data is available, we use the framework put forth by Dhaliwal et al. (2013) and place the Uruguay full-time schooling model alongside a range of other educational interventions for which cost effectiveness has been estimated (Figure 12). We use the Mathematics impact, which is the higher of the two. As such, this may be viewed as an upper bound of the cost effectiveness of the intervention. As is clear from Figure 13, full-time schooling is much less cost effective than other educational interventions. In fact, it is roughly one-tenth as cost effective as the next intervention (conditional cash transfers in Malawi). If the cost effectiveness numbers are adjusted for purchasing power parity (and there are reasons in favor and against doing this, as discussed in Dhaliwal et al. 2013), full-time schooling in Uruguay is one-fifth as cost effective as the next intervention, and still the least cost effective on the list. If one imagined finding a much larger learning impact of 0.4 standard deviations – the largest learning outcome we observe (Valenzuela 2005)<sup>2</sup> – and kept the costs the same, full-time schooling would be roughly as cost effective as cash transfers and far less cost effective than any of the other educational interventions listed. Of course, one is ignoring the non-educational impacts, but the same could be said of any of the other interventions. Purely from an education perspective, this is not very promising.

> 2 This draws from those studies that report outcomes in standard deviations. However, even among studies that do not, the magnitude is not substantively different.

23
<!-- Start of picture text --> 3500<br>8%<br>a 3000 4%<br>q<br>zgZ 0.06%<br>= 2500<br>o 4%<br>2ia -3%<br>zo@,&& 2000 -<br>=<br>3 1500<br>a<br>§<br>2<br>3<br>< 1000<br>s<br>)eRZz.I<br>500<br>0<br>2011 (nss) 2015 2016 2017 2018 2019<br>imputed nominal consumption per capita<br>@ predicted consumption per capita using 0.67 pass-thru on nomical PFCE growth per year<br><!-- End of picture text -->
markets. However, at 3-4 percent of GDP, FDI has not been a key source of financing in China. Indeed, as is the case in most countries, domestic savings has been the dominant source of financing. Thus, even as the investment to GDP ratio increased from 34 percent in 2000 to over 40 percent in 2005, gross domestic saving increased even more rapidly. As a result, the current account rose from 1.7 percent of GDP in 2000 to over 7 percent of GDP in 2005.<sup>6</sup>

# **II.3. Trying to explain China’s economy-wide saving**

A body of literature aims at explaining saving patterns across countries and over time. Focusing on the factors that affect household saving, the empirical literature aims at explaining saving by macro economic variables including government finance, interest rates, inflation, terms of trade, and the current account balance, supplemented by variables capturing demographic trends and per capita GDP. See for instance Masson, Bayoumi, and Samiei (1995) and IMF (2005).

Loayza, Schmidt-Hebbel, and Servén (2000) (hereafter called LSHS) provide an overview of empirical studies in this area in developed and developing countries. Their own cross country empirical work is among the richest in terms of the types of determinants considered. Based on their survey and their own work, they conclude that private sector and national savings are affected by:

- The level of development (per capita income), with the positive influence of per capita income larger in developing countries;

- Economic growth, with much of the causation running from growth to saving;

- Fiscal policy, with its impact on national savings typically only partly offset by responses in private saving;

- Pension reform, with direct short-term effects depending on the financing of the transition deficits and long term effects likely dominated by labor market effects;

- Financial liberalization, with the impact on saving of interest liberalization mixed, but a strong effect from expanding the supply of credit to people that had been credit-constrained;

- Demographics, with an increase in the share of young and  elderly dependents in the population tending to reduce private saving;

- Urbanization;

as well as by foreign borrowing and foreign aid, and uncertainty.

These factors explain only part of China’s saving. Kraay (2000) found, based on a crosscountry regression using the LSHS variables, that China’s high national saving rate—on average 37 percent between 1978 and 1995, compared to an international average of almost 21 percent—can be partly explained by high growth and, to a lesser extent, favorable demographics. However, even using this relatively rich set of explanatory variables, China’s national saving rate was 10 percentage points higher than what would

> 6 There is a discrepancy between on the one hand the difference between gross national saving and gross domestic capital formation from the national accounts and on the other hand the current account data. This discrepancy was about 3 percent of GDP in 2004.

5
# **In Someone Else’s Shoes: Reducing Prejudice Through Perspective Taking**<sup>*</sup>

Marisol Rodr ́ıguez Chatruc<sup>†</sup> Sandra V. Rozo<sup>‡</sup> Originally published in the Policy Research Working Paper Series on _November 2021._ This version is updated on _May 2024._ To obtain the originally published version, please email prwp@worldbank.org.

## **JEL Classification:** C91, D91, J15 **Keywords:** migration, prejudice, perspective taking, altruism, trust, prosocial behaviors

> *We thank the editor Andrew Foster and the two anonymous referees for insightful suggestions and comments. We are also extremely grateful to Mar ́ıa Jos ́e Urbina, Tatiana Hiller, Camila Cort ́es, and Lucina Rodr ́ıguez for providing excellent research assistance. We benefited from comments from Pablo Bachelet, Mariana Blanco, Guillermo Cruces, David McKenzie, Berk Ozler, Salma Mousa, Margaret Peters, Yannay Spitzer, Mart ́ın Rossi, Subha Mani, and audiences at the 2021 Symposium on Economic Experiments in Developing Countries, LACEA BRAIN 2021, the 14th International Conference on Migration and Development, Universitat de Barcelona, Universidad de San Andr ́es, IZA 2021 Migration Webinar, and LACEALAMES 2021. We also thank Mariana Blanco and Andr ́es Zambrano from the Experimental and Behavioral Economics Lab at Universidad del Rosario (REBEL) for implementing the experiment, Gast ́on Cleiman for developing the interactive applications, and Carlos Macher, who produced “Walking for Freedom,” for adapting the documentary for our purposes. Finally, we thank Adela Barrio, Marcela Colmenares, Alejandra Holguin, and Carolina Aclan for their administrative support. USC IRB approval UP-20-00522 and Universidad del Rosario IRB Approval CS239. This study is registered in the AEA RCT Registry with unique identifying number AEARCTR-0006190. The contents of this study are the responsibility of the authors and do not necessarily reflect the views of their employers, funding agencies, or governments. Marisol Rodr ́ıguez Chatruc acknowledges funding from the Inter-American Development Bank.

> †Inter-American Development Bank, email: marisolro@iadb.org.

> ‡Development Research Group, World Bank, email: sandrarozo@worldbank.org.