rafmacalaba/data-use-mentions-extended huggingface dataset:
> emerging markets, with particular attention to their size and holdings. Section IV analyzes

> the behavior of mutual funds during crises. Section V concludes.

# **H.** Brief istory of Capital Flows

> Private capital flows are important for emerging economies, and that importance continues to increase over time. Private flows now constitute most of the capital going to emerging markets. **By 1996,** private flows accounted for **80** percent of total flows to all developing countries.<sup>6</sup> Figure **1** and 2 summarize the major trends **by** plotting, respectively, net private capital flows to the three largest emerging regions and the composition of flows over the past **30** years.

Figure 1 shows that the 1970s witnessed a remarkable surge of capital flows to emerging economies. The surge in flows was triggered **by** the oil shock in **1973-74,** helped **by** the growth of the Eurodollar market, and amplified **by** a spurt of bank lending during **1979-81.** Latin America was the main recipient of this heavy capital inflow, with net flows to the region peaking at $41 billion in **1981.** Relative to regional **GDP,** inflows reached about **6** percent. Flows in this episode mostly took the form of syndicated bank loans, as is shown in Figure 2. The pace of international lending came to an abrupt end in **1982** with the hike in world real interest rates to levels not seen since the 1930s. Suddenly, emerging countries became the pariahs of international capital markets. Not only were they excluded

> Emerging markets are typically defined as middle-income developing countries.

> The data on capital flows comes from the World Bank databases, and from the World Bank publications Private Capital Flows to Developing Countries and Global Development Finance.

4
16 Initial inspection of Tables 1 and 2 as well as Fgj (allows easily to detect a positive link between aggregate inflation and relative price variability. Fig. depicts three different measures of standard deviation'<sup>8</sup> used in the analysis along with the aggregate inflation. Because of disproportionately big values of these statistics for 4 initial observations (see table 1 , they have been dropped from the sample.'<sup>9</sup> This enables to observe the relationship in a much more detailed scale.

Figure 2: INFLATION AND VARIOUS MEASURES OF VARIANCE <!-- Start of picture text --> 30A' T -p5<br>I ~~~ 9<br>Il, 1 g= inflation - left scale 4<br>-2-- sd - right scale 3.5<br>20% / o- wsd - right scale<br>15 n<br>S10<br>- CO 0') -r CO .- ~~ 02 - CO 02 ~~~~- - 02 CO<br>multiplied by 500 and sd I!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ by 15 to fit the tsdgureghtsscale .<br>COD - CO~ CO' - CO C CO -- CO 02 C02 cO'CO 02 2 C CO CO C\O CO CO, C\2O' r - C3 2 ~,c " rc<br>CO 0O OC OCOC OC CO CO CO C CO CO CO<br>source: author's calculations using GUS data<br><!-- End of picture text -->

> **18** Standard deviation was used instead of variance because of its bigger correlation with inflation (see Table 3). _Wsd_ has been multiplied by **500** and _sd by_
Bank of Tanzania (2007), Tanzania Tourism Sector Survey: The 2005 International Visitors’ Exit Survey Report, October.

Bank of Tanzania, Directorate of Banking Supervision Annual Report 2006, 2007.

Bank of Tanzania, Balance of Payment Data for Services for the Years 2000-2006, unpublished data.

- Blalock, Garrick and Paul Gertler ( 2008), “Welfare Gains from Foreign Direct Investment through Technology Transfer to Local Suppliers,” _Journal of International Economics_ , 74:401-21

- Broda, Christian and David Weinstein (2004), “Variety, Growth and World Welfare,” _American Economic Review_ , 94 (2), May, 139-144.

- Broda, Christian, Josh Greenfield and David Weinstein (2006), “From Groundnuts to Globalization: A Structural Estimate of Trade and Growth,” National Bureau of Economic Research Working Paper No. 12512. Available at: http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/BrodaGroundnuts.pdf .

- Brown, Drusilla, Alan Deardorf, Alan Fox and Robert Stern (1996), “Liberalization of Services Trade,” in  W. Martin and L. A. Winters, eds., _The Uruguay Round and the Developing Countries_ , Cambridge: Cambridge University Press.

- Brown, Drusilla and Robert Stern (2001), “Measurement and Modeling of the Economic Effects of Trade and Investment Barriers in Services,” _Review of International Economics_ , 9(2): 262-286.

- Chinitz, B. (1961), “Contrast in agglomeration: New York and Pittsburgh,” _American Economic Review, Papers and Proceedings_ , 51:279-89.

- Coe, David T., and .Elhanen Helpman (1995), "International R&D Spillovers,” _European Economic Review_ 39 (5): 859-887.

- Coe, David T., .Elhanen Helpman and Alexander W. Hoffinaister (1997), "North-South R&D Spillovers", _Economic Journal_ , Vol 107, 134-149.

- Coe,
.7 billion items shipped.<sup>81</sup> Customers’ expectations about efficient logistics are also growing. According to a 2018 study, 91 percent of U.S. online shoppers said they will leave a retail website if critical services such as “fast, free shipping” are not available.<sup>82</sup> Poor logistics remains a barrier for the growth of both domestic and cross-border e-commerce in many developing countries. A lack of quality physical infrastructure such as ports, railroads, and roads is the first bottleneck. According to the Logistics Performance Index, low-income countries score 2.2 out of 5 on the physical infrastructure indicator, compared with high-income countries’ score of 3.5 on average. An infrastructure investment gap also exists between rural and urban areas. In the Lao People’s Democratic Republic, 65 percent of the rural population does not have access to an all-weather road.<sup>83</sup> Beyond the need for investment in physical infrastructure, governments should also support the provision of logistics services through better administrative governance. In many countries, home delivery of purchases is still challenging because of lack of a national addressing system. A large percentage of the population has to pick up or send mail from a postal establishment, which gives them no incentive to engage in e-commerce activities (figure 9).

# **Figure 9. Some countries lack a national addressing system to support e-commerce**

21
the preceding year as nearcasts. For _nearcasting_ , one can rely on all data that are produced with less than a one-year time lag. We will use more than 1,000 variables from the World Development Indicators, the World Economic Outlook, and the Google Earth Engine. For _nowcasting_ , only variables that themselves have been nowcasted by others are available, as well as variables that are produced with little or no time lag, such as certain remote sensing indicators.

We combine all of these possible predictors with the PovcalNet database, which contains more than 2,000 international poverty estimates covering 168 countries. We train our model on these past surveys – essentially pretending a subset of them do not exist – and evaluate the models how well by measuring they approximated the true poverty rates of these held-out surveys. The best performing 2
In this paper we explain some of the above findings by examining the role of foreign currency (FC) denominated debt as a source of external capital. We argue, and find confirming evidence, that FC debt is an important _missing_ piece of the puzzle. To the best of our knowledge, our analysis is the first to thoroughly examine the relation between foreign currency debt and legal effectiveness. We hypothesize that large publicly traded firms usually have access to FC debt, irrespective of the legal environment of the home country. This is because foreign loans often are structured to reduce the reliance on the local legal system (e.g., foreign jurisdictions apply, collateral is off-shore, guarantors’ assets are off-shore, etc.). Access to this market loosens borrowing constraints associated with poor legal systems so that the total debt of firms in these countries is comparable to that of firms in the best legal environments.<sup>2</sup> This also has implications for the maturity structure of debt since, consistent with the findings of DemirgucKunt and Maksimovic (1999) and Fan, Titman, and Twite (2003), local lenders in poor legal environments (the primary source of capital for smaller firms) prefer short-term loans. Consequently, legal factors are important for determining several aspects of debt, including the amount, the maturity, and the currency denomination of debt, but the importance of legal factors is obscured unless access to foreign currency debt markets is explicitly considered.<sup>3</sup> To test our hypotheses we examine the capital structure of over 1,600 firms in nine East Asian countries. We combine publicly available data from the WorldScope database with a proprietary database describing the currency denomination of debt. Although our sample of

> 2 A similar argument in spirit is made in Faulkender and Petersen (2003) regarding US firms, in which access to public bond market allows US firms to increase their debt level above that of firms without access to public bond markets. In our case,
# **7.1 Guaranteed return**

For this Subsection, we will use the target functions outlined in 5.2, that is: We defined N by applying a 5.88% compound rate for 25 periods on the total investment denominated in each local currency.<sup>35</sup> It is important to note, however, that this value can be determined in different ways for distinct countries, including a least present value auction that captures the market’s expected return for a given project in a country with certain characteristics. Therefore, the franchise will be terminated when the value in mixed currency is ~~α~~ at least as high as the target function in mixed currency (that is, V<sup>α</sup> ⩾ V as defined in (15). Results for the simulations are shown in Table 6.

35 This is the internal rate of return of the project when α = 0, obtained using data from the project and Brazil’s CPI inflation as the discount factor. This is further clarified in Appendix B.

36
Farole, Thomas. 2011. _Special Economic Zones in Africa: Comparing Performance and Learning from Global Experience._ World Bank Publications, The World Bank, number 2268, June.

Fernandes, Ana, Caroline Freund, and Martha Denisse Pierola. 2016. “Exporter Behavior, Country Size and Stage of Development: Evidence from the Exporter Dynamics Database,” _Journal of Development Economics_ 119: 121-137.

Fernandes, Ana, Aaditya Mattoo, Huy Nguyen and Marc Schiffbauer. 2019. “The Internet and Chinese Exports in the Pre-Ali Baba Era,” _Journal of Development Economics_ 138: 57-76.

<mark>Frazer, Garth and Van Biesebroeck, Johannes. 2010. “Trade Growth under the African Growth and Opportunity Act,”</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>92(1): 128-144.</mark> <mark>Gil-Pareja, Salvador, Llorca-Vivero, Rafael and Martínez-Serrano, José Antonio. 2014. “Do Non-reciprocal Preferential Trade Agreements Increase Beneficiaries' Exports?,”</mark> _<mark>Journal of Development Economics</mark>_ <mark>107(C): 291-304.</mark> <mark>Grossman, Gene and Esteban Rossi-Hansberg. 2010. “External Economies and International Trade Redux,”</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>125(2): 829-858.</mark> <mark>Guimaraes, Paulo and Portugal, Pedro. 2010. “A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects,”</mark> _<mark>Stata Journal</mark>_ <mark>10(4): 628-649</mark> .

Hakobyan, Shushanik. 2015. “Accounting for Underutilization of Trade Preference Programs: the US Generalized System of Preferences,” _Canadian Journal of Economics_ 48(2): 408-436.

Hakobyan, Shushanik. 2020. “GSP Expiration
7%|
|Wall Insulation|USD/building||1395.8|cu.m/ building<br>/year||-36.0%|

Sources: analysis of technologies sold on Georgian market for electric appliances, for building insulation measures – energy audits (Sustainable Development Centre Remissia 2014) Table A.2 presents penetration rates of different technologies in households and Table A.3 presents penetration rates of efficient appliances.

**Table A.2. Penetration rates of technology in households (%)**

|Appliance|2014|2035|
|---|---|---|
|Refrigerator|84%|93%|
|TV|100%|100%|
|Washingmachine|68%|83%|

Table A.3 shows the assumption on share of efficient technologies in new stock of technologies for baseline and mitigation scenarios, and value of early switching parameter. For example for refrigerators, in baseline case it is assumed that every ear 20% of newly bought refrigerators are efficient, in mitigation scenario share starts from 25% in 2015 and goes up to 82% by 2034. The baseline values are based on 2014 data from residential survey (Winrock international, 2014), and shares for mitigation scenarios are based on expert assessment for moderate mitigation measures.

31
targeting approaches at identifying the actual beneficiaries of the TUP program: (i) our _CDR-based method_ , which applies machine learning to data from the mobile phone company; (ii) an _asset-based wealth index_ , which uses asset ownership to approximate poverty; and (iii) _consumption_ , a common benchmark for measuring poverty in LMICs.

Our analysis produces three main results. First, by comparing errors of inclusion and exclusion using the program’s hybrid method as a benchmark, we find that the CDR-based method is nearly as accurate as the commonly employed asset and consumption-based methods for identifying the phone-owning ultra-poor households. Second, we find that methods combining CDR data with measures of assets and consumption are more accurate than methods using any single data source. Third, we find that when non-phone-owning households are included in the analysis, the CDR-based method remains accurate if non-phone-owning households are classified as ultra-poor; however, targeting performance is quite poor if house2
bottom (RTB)". However, empirical evidence of the PHH and subsequent RTB predictions is not at all conclusive. Early cross sectional analyses did not find environmental regulation to have a statistically significant effect on firm location, while recent panel data studies<sup>6</sup> do find evidence of PHH. In their comprehensive review of the empirical literature, Brunnermeier and Levinson (2004) conclude that although such studies can shed light on the sensitivity of capital and goods flows to regional differences in regulation, they cannot demonstrate that governments purposely set suboptimal environmental regulations to attract business. Antweiler et al. (2001) analyze the above outlined trade environment composition effect and find evidence that international trade creates only small changes in pollution concentrations and that the opposing technique and scale effects result in a net reduction of pollution. Cole et al. (2003) similarly take per capita income as a proxy for stringency of environmental regulation and try to reproduce the results of Antweiler et al. (2001). They similarly find evidence for both the environmental regulation and factor endowment effects for SO2, however, these results do not hold for other pollutants and the pollution reducing net effect of trade is not found for per capita emissions while it seems to exist for pollution intensities. Furthermore, Birdsall and Wheeler (1993) found that in Latin America pollution intensive industries tend to be located in those countries most protected from trade, while openness brings higher international standards.

Besides the trade-environment relationship, a development-regulation nexus has been approached under the label of the Environmental Kuznets Curve, which posits an inverted U relationship between income per capita and pollution. Carson (2010) concisely states that there is robust evidence that pollution levels fall at high income levels, however, different political jurisdictions can follow very different paths, income may influence pollution in slow and subtle ways<sup>7</sup> , and it may be hard to disentangle growth from diffusion of clean technology. Along these lines and according to empirical work by Dasgupta et al
men, and the gap stays at those levels at higher income quantiles. Therefore, while for the majority of those at the top 1% women and men have similar income composition in terms of labor vs. capital, women’s income is more dependent on capital income at the very top (Atkinson _et al._ , 2018, Bobilev _et al._ , 2019).

# **8 Conclusion**

In this paper we have used rich administrative tax records, including detailed shareholder list of all corporations, to offer new measures of top income shares in Honduras.

Our main results can be summarized as follows. First, we document very large top income shares for Honduras – the top 1% receive approximately 30% of total pretax net national income. We provide evidence not only that the inequality levels in Honduras are among the highest in the world (for which detailed measurements are available), but also that they persist with little change over two decades. Second, we also show the predominance of capital income at the top of the distribution, and particularly of undistributed corporate profits. The ability to assign corporate profits to individual shareholders is only possible due to the construction of a shareholder dataset for the majority of corporations in the country. We also show that this information matters: using standard methods in the literature, where undistributed profits are distributed according to other distributed income, substantially underestimates income concentration within the top 0.1%. Finally, the panel dataset nature of our tax records also allows us to document a high persistence of the same individuals at the top of the distribution over time.

Our estimates are obtained following the most recent literature on measuring top incomes, rendering them broadly comparable to several similar efforts. Throughout the paper, we carefully document the methodological decisions we take and caveat the limitations of our approach - more generally, all estimates of top incomes combining survey data, tax records and macroeconomic national accounts carry a significant degree of uncertainty. Nonetheless, all our estimates are consistent in
Table C: Reviewed surveys

|Country|LSMS‐ISA|LSMS|LFS|DHS|LSMS‐type<br>survey|
|---|---|---|---|---|---|
|Burkina Faso|2014||n.a.|2014||
|Ethiopia|2015/16||2013|2016||
|Malawi|2016/17||2013|2015/16||
|Mali|2014||2013|2015||
|Niger|2014||2014|2012||
|Nigeria|2015/16||n.a.|2013||
|Tanzania|2014/15||2014|2009/10||
|Uganda|2013/14||2009|2016||
|Guatemala||2000|2014|2014/15||
|Nepal||2010/11|2007/08|2016||
|Nicaragua||2014|2005|2001||
|Tajikistan||2009|n.a.|2012||
|Indonesia|||2012|2012|2010|
|South Africa|||2017|1998|2016|

Source: Based on LSMS(‐ISA), LFS and DHS surveys. Note: n.a.: questionnaire is not publicly available.

36
||**Overall**|0.978|
|---|---|---|
|**Teach ECE**|Classroom Culture|0.651|
||Guided Learning|0.820|
||Socio-Emotional Skills|0.771|
||**Overall**|0.889|

# **Analysis**

Given the sampling design, results do not require weighting to be representative of the PCHIP project area. In addition, on reporting the results of composite items calculated as described above, we also present results based on the proportion of **segments** demonstrating a certain degree of quality and the proportion of **children** with different skills. Below, we outline the process used to set benchmarks needed to dichotomize these results on AIM-ECD and Teach ECE. In addition to the simple descriptive statistics and disaggregation, we also build a statistical model for inferential purposes. The details of this model-building process are outlined below.

# **_Quality Benchmarks, Exemplar Items, and Standardized Scores_**

As described above, both the AIM-ECD DA and Teach ECE generate continuous scores to describe child developmental outcomes and quality of teaching practices, respectively. AIMECD scores range from 0-100% while Teach ECE element and area scores range from 1-5.

These continuous scores are limited in their comprehensibility. To improve the communicability of our results, we also utilize benchmarks on Teach ECE to report on how much of the time (i.e., what proportion of segments) classrooms are demonstrating minimal quality during lessons. When reporting on the proportion of segments demonstrating minimum quality, we use the proportion of segments that received at least a “Medium” rating, i.e., an average rating of at least 3. Three represents the score achieved by a class with no extremely serious quality issues and thus is a reasonably interpretable benchmark. Teach ECE quality benchmarks are useful for creating concrete descriptives about the status of ECD in South Punjab but
present given the distribution of hazards is changing over time with climate change and historical data forms the basis of all simulations presented in this paper.

The models built in this paper are an important first step toward constructing vulnerability or damage functions as used in the catastrophe risk modeling literature, and can inform disaster response planning and financing. However, these limitations mean that care is needed in interpreting the results of the analysis. We envisage that the set of tools developed herein will provide the basis for refining the analysis as more data become available, and expanding coverage to other regions. Accompanying this paper is code designed to extract a large set of potential shock measures and determine which of these are consistent with changes in the welfare measures (outof-sample). The modular code used is intended to be transparent, to aid in replicability, and to be available for others to improve upon. The goal is to encourage both learning and common standards, which in turn contribute to the implementation of drought-responsive policies that are objective and transparent.

The paper is structured as follows. Section 2 presents the empirical framework for assessing the relationship between welfare and hazards in rural areas where rainfall-dependent agricultural production is dominant, and where rainfall deficits (droughts) thus may have very serious welfare consequences. It sets out the approach used to estimate the vulnerability function and an exceedance probability curve that describes the welfare losses likely to occur because of rainfall deficits of increasing severity. Section 3 discusses in detail the historical hazard database and the household survey data used, the merging of the hazard data to the household survey data, and the specifications used for the hazard measures. Section 4 presents estimates of vulnerability functions. Section 5 shows how these vulnerability functions can be used to project welfare losses and presents some exceedance probability curves. Section 6 offers some conclusions.

# 2. An Empirical Framework for an Ex Ante Assessment of the Welfare Costs of Drought

A catastrophe risk modeling framework uses three components to generate an ex ante assessment of the costs of a catastrophe: hazard, exposure