rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 4. Poverty Estimates 2009-2013 Rest of the Country <!-- Start of picture text --> Host communities<br>80<br>70<br>60<br>50<br>40<br>30<br>20 21.1 20.8 19.3 18.4 17.9<br>10<br>0<br>2009 2010 2011 2012 2013<br>Settled Migrant Households in the Rest of the Country (>4 years in Turkey)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10 9.1 10.7 9.3<br>7.0 6.6<br>0<br>2009 2010 2011 2012 2013<br>Recent Migrant Households (Rest of the Country, <=4 years in Turkey)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>16.1<br>13.3 13.2<br>10<br>7.3<br>5.2<br>0<br>-10<br>2009 2010 2011 2012 2013<br>Poverty Rate (%)<br>Poverty Rate (%)<br>Poverty Rate (%)<br><!-- End of picture text --> _Notes_ : Regionally deflated, OLS imputation model, with time-invariant characteristics. Vertical line represents standard error. Poverty line is $5/day PPP per capita. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. Results displayed at the 95% Confidence Interval.

_Source_ : LFS 2009-2014. World Bank author’s calculations
that financial consumer protection regulation in low-access environments should make sure that plain language is used and that the use of complex formulas and calculations is avoided. Other studies (Collins _et al._ , 2009, and FSD-Kenya, 2009) support this claim. For example, consumers prefer and better understand when they are quoted the dollar amount of payments and the number of months it will take them to pay off the loan, instead of the details of compounding. However, certain products are necessarily more complex and will require more information to be disclosed, though in the absence of financial literacy, it is unlikely that this complex information will be understood by the consumer. For example, a U.S. Government Accountability Office (GAO) study finds that people with limited language proficiency are less likely to have bank accounts and more likely to be susceptible to fraudulent practices (US GAO, 2010). In an earlier study on review of regulations on the disclosure of rates and fees, the U.S. GAO (2006) comes to a conclusion that disclosure forms are complicated, and may contain conflicting information, confusing disclaimers.

Unfortunately systematic data on the levels of financial literacy and awareness of financial concepts remains limited. Lusardi and Tufano (2009) present the results of a household survey in the U.S. and find that awareness of financial concepts among borrowers is low, especially among women, elderly, and minorities. The authors find that borrowers with lower levels of financial literacy tend to transact in high-cost manners such as incurring fees. Based on household interviews in Australia, Wilson _et al._ (2009) report similar findings and suggest that low income consumers are not only more likely victims of abuse by lenders, but they are also less aware of consumer protection provisions and their rights to seek recourse.

Policy papers focusing on the review of consumer protection and financial literacy indicate that there is no one-size-fits-all approach when it comes to designing consumer protection and financial literacy policy. Financial literacy is low among the poor
- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997a), Economic Implications for Turkey of a Customs Union with the European Union, _European Economic Review_ , 41(3-5), 861-870.

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (2002), ―Trade Policy Options for Chile: The Importance of Market Access,‖ _World Bank Economic Review_ , Vol. 16, Number 1.

- Harrison, Glenn H., Thomas F. Rutherford, David G. Tarr and Angelo Gurgel (2004) ―Trade Policy and Poverty Reduction in Brazil,‖ _The World Bank Economic Review_ , Vol. 18, 289-317, 2004.

- Heston, Alan, Robert Summers and Bettina Aten (2006), Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September.

- Holmes, T. (1995), ―Localization of Industry and Vertical Disintegration,‖ Federal Reserve Bank of Minneapolis.

- Hummels, D (1995), ―Global Income Clustering and Trade in Intermediate Goods,‖ Graduate School of Business, University of Chicago.

- Jabbour, Liza and Jean Louis Mucchielli (2007), ―Technology Transfer through Vertical Linkages: The Case of the Spanish Manufacturing Industry,‖ _Journal of Applied Economics_ , 10(1): 115-36

- Javorcik, Beata (2004), ―Does Foreign Investment Increase Productivity of Domestic Firms? In Search of Spillovers through Backward Linkages,‖ _American Economic Review_ , Vol. 94 (3), June, 605627.

- Javorcik Beata S. and Mariana Spatareanu (2008), ―To Share or not to Share: Does Local Participation Matter for Spillovers from Foreign Direct Investment?‘‖ _Journal of Development Economics_ , 85(1-2): 194-217

- Jensen, Jesper, Thomas Rutherford and David Tarr (2010), ―Modeling Services Liberalization: the Case of Tanzania,‖ _Journal of Economic Integration_ .  Also
%|
|General Cargo|1|9.1%|1|0.4%|0.00|0.0%|
|Bulk|1|9.1%|13|5.4%|0.48|7.3%|
|Total|11|100.0%|239|100.0%|6.66|100.0%|
|||Panel D. Jones Act v|essels - NBIC|database|||
|Barges|8<sup>_†_</sup>|57.1%|124|37.9%|1.52*|21.0%|
|Container ships|5<sup>_†_</sup>|35.7%|192|58.7%|5.31|73.3%|
|Bulk|1|7.1%|11|3.4%|0.41|5.7%|
|Total|14|100.0%|327|100.0%|7.24|100.0%|

Note: Number of vessels, port calls, and dead-weight tonnage (DWT) of freight vessels arriving in Caribbean and Puerto Rican ports in 2019, by type of vessel and inferred JA status. Panels A.-C. are constructed from ship arrival data in the Caribbean purchased from LLI (2021). Panel D. summarizes cargo ship arrival data in PR sourced from NBIC (2024). Vessels’ JA status are identified as those that are U.S.-flagged and U.S.-owned. (Some vessels serving PR have been exempted from the JA’s U.S.-build requirement.) The NBIC data better capture barge traffic on U.S.-PR routes, while the LLI data allow comparisons with the broader Caribbean. _∗_ indicates an understatement of DWT in a
The results in Table 4.5a have provided evidence that the bias adjustment methods employed in the HFPS in the four countries have been effective at reducing the bias present in the interviewed sample. The benefits can be further illustrated by examining not just the point estimates for the considered characteristics but also the confidence intervals. Figures 4.2 to 4.8 plot the point estimates and 95 percent confidence intervals for selected characteristics for the full LSMS-ISA (F2F) sample, HFPS sample with adjusted weights, and HFPS sample with unadjusted weights. In the figures, all estimates are standardized by subtracting the F2F survey mean (so the F2F mean will always be zero) in order to allow comparison across indicators. For household size and head age, the estimates were further standardized by dividing by the F2F mean. Thus, the confidence intervals represent the percent deviation from the mean for these two variables.

Looking through the figures, it is clear that the adjustments for bias result in substantially improved alignment with the LSMS-ISA survey point estimates and confidence intervals. Although the level of reduction in the bias achieved varies across indicators, the adjustment for nearly all indicators does result in a shift of HFPS estimates towards the LSMS-ISA. For many indicators, the reduction is the bias from the adjustments is substantial, for example literacy in Figure 4.2. As expected, the bias adjustment was especially effective for the indicators that were included in the bias adjustments in each country (listed in Table 3.2). However, for other indicators, the reduction in bias is more modest. For example, the housing characteristics in Figure 4.5 show substantial bias without the adjustment to the weights and the bias is significantly reduced after applying the adjustments, but there still remains a gap between the point estimates for the F2F and HFPS samples (though not statistically significant).

Comparing the results for the four countries, there is remarkable consistency in the direction of the bias across the indicators, though the magnitude of the bias varies. The bias is
# **1 Introduction**

The _U.S. Merchant Marine Act of 1920_ (the Jones Act) requires that maritime vessels moving goods from one U.S. port to another must be U.S.-built, U.S.-flagged, U.S.-owned and U.S.-crewed.<sup>1</sup> This protectionist policy raises the cost of intranational maritime shipping in the U.S., and imposes a disproportionate burden on residents of U.S. islands. These effects of the policy are generally understood as a qualitative matter, but there are few quantitative estimates of the economic burden the policy puts on U.S. outlying areas. In this paper we estimate the economic effects of the Jones Act (JA) on Puerto Rico (PR).<sup>2</sup> We begin by exploring data documenting ship movements in the Caribbean. Supplementary data from the same source reveal each ship’s type, country of build, country of ownership and the flag under which it operates. We use these data to identify ships that call in Puerto Rican ports and satisfy JA regulations. We compare the characteristics of vessels that satisfy JA requirements with the broader fleet calling in PR, and with vessels calling elsewhere in the Caribbean. This exercise reveals a striking pattern; the JA fleet serving PR is dominated by container ships and barges. Other cargo vessels that appear regularly in the rest of the Caribbean are nearly absent from the U.S.-PR market. We hypothesize that the JA raises costs on all-seaborne shipping, but more so on physically heavy and/or bulky products that are difficult to containerize.

> 1Specifically, the Jones Act requires that every vessel serving any U.S. domestic route must be (1) at least 75% owned by U.S. citizens; (2) built in the U.S.; (3) crewed by U.S. citizens
. . . . . . . .|. . . .<br>68|
|**C Additional results on cross-frm diferences in technology**|**70**|
|**D Robustness to using indices based on logarithmic transformations**|**of tech-**|
|**nology sophistication rankings**|**77**|
|**E Additional results on within-frm technology**|**84**|
|**F Additional results on technology and productivity**|**92**|
|**G Detailed acknowledgments**|**106**|

47
International, 2011). It ranks countries by the perceived levels of public sector corruption, as determined by expert assessments and opinion surveys.<sup>6</sup> In particular, the CPI is a composite index based on a combination of surveys and assessments from 13 different sources, with each individual indicator of corruption standardized to have the same weight in the aggregate score.<sup>7</sup> As the CPI measures only public sector corruption, two additional subjective

> 5Besides ICRG, other objective measures of corruption exploit variations in conviction rates (Fiorino et al. 2012, Hill 2003) or press reports (Rehren 1996). However, these methods remain mostly unsystematic, thus leading to validity and reliability problems (Morris 2008, p. 390).

> 6As mentioned by Eurostat: “As there is no meaningful way to assess absolute levels of corruption in countries or territories on the basis of hard empirical data, capturing perceptions of corruption of those in a position to offer assessments of public sector corruption is so far the most reliable method of comparing relative corruption levels across countries.”

> 7The main weakness of this measure is that the number of sources used to construct it varies over time, especially pre-2012. However, because the CPI uses sources from the last three years preceding each publication of the index, it is still possible to use the CPI year average to make comparisons over short periods of time (Persson and Tabellini 2003). There is a very strong significant correlation between the CPI and other proxies for corruption: black market activity and an overabundance of regulation (Wilhelm 2002),

4
and Niehaus et al. [2013] in South Asia find evidence of manipulation on the survey administration side. Tohari et al. [2019] made a case for considering the full set of programmes with socio-economic criteria when conducting targeting simulations via their extensive linking of Indonesian survey data.

A growing literature considers PMT from a predictive modelling perspective. McBride and Nichols (2018) highlighted the importance of using out-of-sample validation data in model selection, which has been adopted in Indonesia. A simplified scorecard approach supported by machine learning prediction was suggested by Kshirsagar et al. [2017], who showed impressive results with very limited data collection needs for Zambian data. A systematic evaluation of machine learning methods for construction of the predictive model in PMT was carried out by Areias and Wai-Poi [2022] with data from 12 African countries, but it found that accuracy gains tend to be limited and context specific; no clear machine learning works best across the board. A similar conclusion emerged from a study of Indonesia’s PMT model [Ohlenburg, 2020].

The model presented here has a household-specific stopping criterion as its adaptive component. Adaptive methods link to extensive literatures on the design of experiments, adaptive algorithms and active learning. As an example of the latter, Saar-Tsechansky et al. [2009] studied active feature acquisition when data collection is costly and variables differ in their information value. They proposed a framework that aims to maximize the expected utility of each data item and tailors the sequence and length of data acquisition to each instance, achieving a cost reduction for a given level of accuracy.

A line of research that focuses on the aspect of fairness in targeting emerged in the work of Noriega-Campero et al. [2020]. It puts an emphasis on achieving equitable accuracy rates across subgroups of the population, thereby avoiding systematic disadvantages in programme eligibility for specific groups. In contrast to the evidence of limited accuracy improvements mentioned in the machine learning for targeting literature mentioned above, this work also suggests scope for meaningful improvements in targeting accuracy
Estimating the Economic and Distributional Impacts of the RCEP of the world. Therefore, the highest reductions of imposed and faced NTMs by Vietnam are in sectors with initial high levels, that is, public administration and services, trade, and tourist services.

Figure 20. NTMs imposed by Vietnam vis-à-vis RCEP members in 2020 and 2035 <!-- Start of picture text --> Public administration and services<br>Trade<br>Tourist services<br>Communications<br>Wood and paper products<br>Construction<br>Wearing apparel and leather<br>Road transport<br>Air transport<br>Water transport<br>Motor vehicles and parts<br>Food and beverages<br>Meat products<br>Financial services and insurance<br>Textiles<br>Other manufactures<br>Business services<br>Metals<br>Basic pharmaceuticals<br>Chemical, rubber, plastics<br>Non metallic minerals<br>Electrical equipment and machinery<br>Crops<br>Petroleum, coal products<br>Other transport equipment<br>Animal products<br>Fossil fuels<br>Other extraction<br>Total services<br>Total natural resources<br>Total manufacturing<br>Total agriculture<br>Total<br>0 10 20 30 40 50<br>2020 2035<br><!-- End of picture text --> Source: NTMs in goods by Cadot et al. (2018) for NTMs in goods. For services, Hoekman and Shepherd (2019).
The authors tested the proposed model using FX reserve and external debt data from seven countries—Albania, Ghana, the former Yugoslav Republic of Macedonia, South Africa, the Republic of Korea, Tunisia and Uruguay. Among the ones disclosing currency composition of international reserves and external debt portfolio, the sample countries were selected based on three main criteria, including broad regional representation; issuance of foreign currency debt; and minimum reserves benchmark of three months of imports and short-term foreign currency debt service.<sup>6</sup> The objective of the paper was to explore the impact of an overarching ALM strategy and integrated approach on the efficient management of FX risk. The outcome of this process is several policy recommendations on ways to minimize the risk of FX mismatches and increase the return on public FX reserves.

This paper is comprised of six sections. Section II provides a brief overview of the sovereign asset and liability management approach. Section III discusses the challenges of managing FX risk and opportunities under a joint ALM approach. Section IV assesses the status of FX risk in the selected country sample. Section V measures the impact of a joint ALM approach on portfolio optimization. Finally, section VI offers policy recommendations that account for governance and institutional constraints.

# **II. Sovereign Asset and Liability Management**

The Revised Guidelines for Public Debt Management recommends establishing and executing a debt management strategy that enables a government to raise required amount of funds at the lowest possible cost over the medium to long term with a prudent level of risk. Key elements of this approach include the identification of the preferred composition of a debt portfolio, including the share of debt denominated in foreign currency. The structure of preferred debt portfolio and the degree of risk exposure may differ based on the size and types of debt portfolios and the level of risk aversion of the decision makers.

Identifying the appropriate share of debt denominated in foreign currency is a multi-step process. First, a debt manager must define the share of foreign currency debt given the inability of
28|-0.23|
|region||||
|2|-0.33|-0.01<br>-0.15|-0.04|
|3-4|-0.64|-0.44<br>-0.40|-0.36|
|5-6|-0.60|-0.27<br>-0.33|-0.32|
|Panel|E: Engl|ish and Math s|core, male|
|total|-0.21|-0.14<br>-0.18|-0.09|
|region||||
|2|-0.01|0.11<br>-0.04|0.01|
|3-4|-0.51|-0.37<br>-0.30|-0.23|
|5-6|-0.18|-0.05<br>-0.26|-0.11|

Sample is Grade 12 student exam records, public schools Regions 2-6, 1998-2012. Test scores are mean 2-scores, standardized based on universe of students taking exam in that year, including Region 1.
Policy Research Working Paper 5536

# **Abstract**

Consumer protection and financial literacy can contribute to improved efficiency, transparency, competition, and access in retail financial markets by reducing information asymmetries and power imbalances between providers and users of financial services. Financial consumer protection has gained significance in policy debates, especially since the onset of the financial crisis in 2008. This paper presents the results of a survey on consumer protection regulations in 142 countries. The findings indicate that although consumer protection legislation is in place in the majority of countries, these do not necessarily address the issues specific to financial services. There is some evidence that enforcement powers and monitoring capacity are limited in many countries, obstructing the effective implementation of the existing regulations. Furthermore, independent third party dispute resolution mechanisms are not widespread. The paper also compiles comprehensive information on laws and regulations relevant for consumer protection and discusses a number of challenges related to empirical analyses of financial consumer protection to enable crosscountry comparison.

This paper is a product of the Financial Access Team in Consultative Group to Assist the Poor, Financial and Private Sector Development. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at nmylenko@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments
equation (18) and replacing ∆UNILIBt with a common term for the 1990-1992 period (REFt) during which the liberalization took place step by step (column 2). In this version, β1 becomes significant at the 10% level and β2 at the 5%. REFt has a negative and significant (at the 1% level) coefficient capturing the common downward trend. In column 3, I further allow for a common constant term on top of REFt recognizing the small changes in the other years, and both β1 and β2 become significant at the 5% level. These results indicate that due to political economy, the extent of liberalization is smaller for the sectors with a smaller reduction or a higher increase in their productivity as compared to similar sectors. Note that in all differenced results, β1 and β2 are statistically identical, which is predicted by the model. In levels, this may not occur because of Although the theoretical section involves protection through tariffs, I repeat the specifications in Table 5 and Table 6 with the effective rates of protection (ERP) in order to see whether the results hold with a different measure of protection. Effective rates are based on value added and essentially take into account the effect of tariffs on the inputs as well. ERP data are provided by the National Planning Department of Colombia (DNP) and I am limited by their computations since I do not have the detailed data to calculate them myself. As can be observed from Figure 1 and Table 4, the effective rates are higher than the regular tariff rates but otherwise display a similar trend. I exclude the three sectors<sup>44</sup> that exhibit negative ERP (in levels not logs), because it is hard to argue that these sectors are indeed protected. In Table 7, I repeat the specifications from Table 5 and the results appear to be totally consistent. The only difference is that the significance levels for the main variables are lower, and the constant term becomes insignificant in columns 2 and