rafmacalaba/data-use-mentions-extended huggingface dataset:
indicate that only one country (Finland) produces a conceptual statement with limited actual data and the rest of the responding countries indicate that the sovereign balance sheet is a financial statement with actual quantified data.

Regarding the accounting system used to produce the balance sheet, the survey indicates that the almost half (46 percent) of the respondents producing a sovereign balance sheet are using a full accrual accounting system (including Australia, Canada, New Zealand).<sup>11</sup> Another 18 percent of the respondents, producing a balance sheet, are using semi‐accrual accounting. Only 11 percent mentioned using cash‐based accounting systems, including Norway and Uruguay. Of the remaining 25 percent that indicated “other,” some mentioned that an accrual system is under implementation or is already used but specific transactions linked to the budget execution or tax revenues are still registered on a cash basis (this group includes Bulgaria and Poland which indicated they are not producing a balance sheet). These results are presented in graph 1 (based on the number of countries). Producing a balance sheet based on accrual accounting is important to ensure that policy makers can assess and monitor effectively the mismatches between stocks of assets and liabilities. This requires additional work compared to the traditional budget process that captures cash flows of revenues and expenditures, which explains why some of the respondents still produce a sovereign balance sheets based on semi‐accrual or cash accounting systems.

<!-- Start of picture text --> Graph 1 ‐ Accounting System<br>14 13<br>12<br>10<br>8 7<br>6 5<br>4 3<br>2<br>0<br>Cash Semi‐accrual Fully accrual Other<br><!-- End of picture text --> Source: Responses to SALM survey Concerning publication, in all cases except one, the sovereign balance sheet is published. The sole case of no publication concerns the country producing its
important for groundnut, sorghum and millet yield predictions. Finally, cowpea yield prediction benefits particularly from the inclusion of both SR yield and GPS extracted variables. The heterogeneity of the impact of the different combinations of covariates for each crop predictions is difficult to interpret especially since the coefficients are in general not statistically different to each other. As such, the main lesson that we retain here is that combining the subjective measure of yields and the objective covariates is always the most beneficial to improve the accuracy of the predictions. This conclusion is also robust to the use of alternative model performance metrics. This is confirmed in Table B.6 which shows how the inclusion of different covariates impacts the performance as measured by the root mean square of the predictions when we model level yields: we find that for all crops including both SR yields and geospatial variables improve the model performance (reduce the root mean square error) the most. The improvement is marginal although significant when SR is included without geospatial variables, highlighting the fact that geospatial variables are the most potent in improving the predictions. Looking at the base root mean square error across crops, it appears that in average the prediction from cowpea yield in average the best prediction, but that is because the average yield for cowpea is smaller than the other crops and as such small deviations from when squared are amplified.

> 16The GPS extracted variables used the publicly available coordinates which have been scrambled for privacy purposes. As discussed in Michler et al. (2022), the degree to which spatial anonymization introduces mismeasurement is a function of which remote sensing weather product is used in the analysis. Care must be taken in choosing a remote sensing weather product when looking to integrate it with publicly available survey data. As such, the results might vary with other types of weather products.

> 17See Table B.4 for the results for log yields, which do not differ significantly from the levels.

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# WPS3389

## Why Do Emerging Economies Borrow Short Term?

Fernando A. Broner Guido Lorenzoni Sergio L. Schmukler<sup>∗</sup>

#### Abstract

We argue that emerging economies borrow short term due to the high risk premium charged by international capital markets on long-term debt. First, we present a model where the debt maturity structure is the outcome of a risk-sharing problem between the government and bondholders. By issuing long-term debt, the government lowers the probability of a liquidity crisis, transferring risk to bondholders. In equilibrium, this risk is re fl ected in a higher risk premium and borrowing cost. Therefore, the government faces a trade-off between safer long-term borrowing and cheaper short-term debt. Second, we construct a new database of sovereign bond prices and issuance. We show that emerging economies pay a positive term premium (a higher risk premium on long-term bonds than on short-term bonds). During crises, the term premium increases, with issuance shifting toward shorter maturities. This suggests that changes in bondholders’ risk aversion are important to understand emerging market crises.

### JEL Classi fi cation Codes: E43; F30; F32; F34; F36; G15

Keywords: emerging market debt; maturity structure; sovereign spreads; risk premium; term premium; fi nancial crises

> ∗Affiliations: CREI - Universitat Pompeu Fabra and University of Maryland, Princeton University, World Bank. E-mail addresses: broner@econ.umd.edu, guido@princeton.edu, sschmukler@worldbank.org. We are grateful to Galina Barakova, Tatiana Didier, Lishan Shi, and, particularly, Marina Halac for superb research assistance. For valuable comments, we thank Mark Aguiar, Ricardo Caballero, Eduardo Levy Yeyati, Roberto Rigobon, Jaume Ventura, and participants at presentations held at the AEA Annual Meetings (Washington, DC), the Conference on Sovereign Debt at the Dallas Fed, CREI - Universitat Pompeu Fabra, George Washington University, the Inter-American Development Bank, the International Monetary Fund, the LACEA
risk from catastrophes may be more effective at instilling the urgency needed to address critical global challenges. Our findings suggest that a more nuanced understanding of the relationship between

> 5 These numbers correspond surprisingly well with the result of the 2012 All Russia Omnibus survey, which asked respondents to evaluate the number of deaths from the Chernobyl and Fukushima nuclear disasters using similar scales (Levada Center 2012). Thirty-three percent of Russian respondents thought that at least 100,000 people died in Chernobyl and 25 percent thought that as many people died in Fukushima; 10 percent of the respondents believed that millions for people died in Chernobyl, and 9 percent reported that millions died in Fukushima. A quarter of respondents responded “do not know” about Chernobyl and a third reporting not knowing about Fukushima.

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( _NA_ ), in which wages are also affected by climate but to a lesser degree; and (3) public sector ( _P_ ), as described in fn. 26, in which wages are assumed to be unaffected by climate. Based on a sample of households with at least one wage-earner in the 61st round employment-unemployment survey and using the same set of _X_ variables as in (i) and (ii), we predict the proportion of household wage earners whose main job is in each of these sectors and use the results to impute _e_ � _A,_ � _eNA_ , and _e_ � _P_ to households in NSS61.

- iv) _π_ and _w_ : Weights _λ_ , _φ_ , and _σ_ are functions of sector-specific land and labor prices. To obtain _πI_ and _πN_ in terms of an annualized flow, we take district median of per hectare land values for, respectively, irrigated and unirrigated land (from NSS59) multiplied by a discount rate, _δ_ = 0 _._ 05; we also try _δ_ = 0 _._ 10 as a robustness check.<sup>28</sup> For _wA_ , _wNA_ ,

> 28Deschˆenes and Greenstone (2007) arbitrarily choose the discount rate of 0.05 to compare estimates of climate impacts on land values and on agricultural profits. Jacoby (2000), using plot-level data from Nepal, finds a median rental income to land value ratio of 0.055 for a sample of rented plots and takes this as the discount rate for a calculation similar to ours. For India, we have plot-level data from a survey of rural households in the southern state of Andhra Pradesh collected by the World Bank and the Centre for Economic and Social Studies in 2004. The survey asks landowners for the market value of each plot along

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><sup>17</sup> During this period, 799 BH events in Nigeria were recorded in ACLED. Because of the intensity of the BH insurgency, interviewers during the field work of the 2013 NDHS could not reach some conflict-affected areas (NPC 2014). As we exploit variations in the timing and location of BH events, observations from the 2008 NDHS from LGAs not surveyed in the 2013 NDHS - Maidugiri in Borno State and Damaturu and Nangere in Yobe State - are excluded from our analysis.

# 4.2 Outcome variables

Both the 2008 and 2013 NDHS DV modules follow international global standards for the measurement of intimate partner violence. Ever partnered women were asked questions about experiences of violence using a modified version of the Conflict Tactics Scale (Straus 1990), a commonly used instrument in both the DHS and stand-alone violence against women and girls surveys.<sup>18</sup> To measure experience of physical or sexual IPV, women are asked if they ever

> 16 The NDHS is a nationally representative cross-sectional household survey that provides data on population, health, and nutrition for women <mark>aged 15-49.</mark>

> 17Because the ACLED often reviews specific periods of conflict to ensure accuracy, there may be changes to the data in selected countries or targeted conflicts throughout the year. The ACLED data used for the analysis in this study were downloaded from www.acleddata.com on August 30, 2018.

> 18 Sociologist Straus (1990) developed the original Conflict Tactics Scale (CTS), which consists of a series of individual acts of physical and sexual violence (Straus, 1979, 1990). The modified list used by the NDHS includes only fewer acts of physical and sexual violence (see appendix section A2). If a woman confirms that any one of the

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). No one seems to dispute that deprivations exist in multiple domains, and are often correlated. And that, therefore, considering information on those various dimensions, rather than only on incomes or consumption expenditures, is likely to be useful for designing policies to effectively combat poverty.

The disagreement is about how best to _measure_ this multidimensional poverty – i.e. how best to convey information about the extent of these various deprivations in a way that is useful for analysts and policy makers. Some, like Alkire and Foster (2011a) and Maasoumi and Lugo (2008), have proposed scalar indices that seek to combine, in a single number, information from those various dimensions. A key advantage of such scalar indices is that they generate a complete ordering of countries, regions or individuals, even when the rankings disagree across individual dimensions. The Multidimensional Poverty Index - or the Human Development Index, in the space of attainments - attracts a good deal international attention in large part because it
where census data and survey data with an anthropometric component are available.

Although estimated standard errors in our study are quite high for some communes, the magnitude of the standard errors for the estimated prevalence of stunting and underweight at the commune level is, on average, comparable to that for the existing stratum-level estimates derived only from the CDHS data. We argued that high levels of standard errors for each commune are not necessarily worrisome when a proposed policy intervention delivers assistance to a relatively large number of communes.

The commune-level estimates we derived in this study can be used for analyzing the current situation of malnutrition and for shaping targeting policies. We have presented the commune-level estimates as nutrition maps. These maps allow policy-makers to visually locate areas of severe malnutrition. We argued that overlaid maps help us identify possible causes of malnutrition in different locations. This in turn provides policy-makers with valuable information on the appropriate design of child nutrition programs. For example, direct food aid would be helpful if food shortage is the principal cause of malnutrition. However, local health clinics may prove more helpful where many children suffer from diseases such as malaria and diarrhea.

We demonstrated three applications of the commune-level estimates. First, we applied them to the analysis of correlation between consumption poverty, inequality and malnutrition. We found no simple relationship, but the regression results suggest that non-linear effects of consumption on the child nutrition status are crucial for understanding the relationship. Our small-area estimates of consumption and health are very helpful for exploring the relationship at a disaggregated level between malnutrition and other indicators, including consumption poverty and inequality.

Second, we conducted decomposition analyses of child health inequality. We first followed the approach by Pradhan et al. (2003), and then proposed two additional ways of decomposing inequality by groups. One of them is based on the analysis of variance, and the other uses 37
<u>2015). Despite this, international assessments show that the level of achievement of CABA students in</u> Science is still well below the OECD average (OECD 2016). Problems with teaching and learning Science in CABA were also highlighted by the last wave of the Trends in International Mathematics and Science Study (TIMSS), according to which CABA students (fourth and eighth graders) place at the bottom of the world ranking, just above Egypt and South Africa (Martin et al. 2016). These results are not surprising given the reality of Science education in Argentina, where lessons are mostly teacher centered and focused on the transmission of encyclopedic content, far from competency-based international learning standards, such as those assessed by TIMMS and PISA (Argentine Ministry of Education 2007). Countries with high levels of scientific literacy tend to implement inquiry-based approaches that position students as active knowledge producers in a classroom community of practice, placing importance on the development of specific Science skills and deep understandings (OECD 2016).

# 3. Experimental Design

A randomized controlled experiment was carried out to assess the effect of different teacher professional development approaches on student learning in Science. The intervention focused on a compulsory unit of the seventh-grade Science national curriculum: the Human Body.

The intervention consisted of a random allocation of 70 CABA state primary schools to one of three experimental groups (Appendix 1). Thus, the unit of randomization was the school.<sup>10</sup> All participating teachers involved received one in-service four-hour training session, and were then asked to teach the Human Body Science unit according to national curriculum guidelines, over the following 12 weeks. During the training session, teachers discussed and took part in inquiry-based activities related to the teaching of the

> 10 The randomization was at the school level and not at the classroom level because 46 percent of the schools in our sample shared the same Science teacher for at least one of their classrooms. Therefore, assigning classrooms to different treatments was operationally impossible.
)|(0.696)|(0.599)|(0.333)|(0.226)|
|Observations|116|238|228|224|239|
|Treatment vs Control,|2.938**|0.683|-0.866|-0.447|0.025|
|group 1, 2 and 3|(1.387)|(1.029)|(1.152)|(0.410)|(0.217)|
|Observations|1,205|2,580|2,482|2,500|2,581|

Notes: The estimations are contrasts between the matching differences of the indicator between the group of exposure and the comparison group. Variables for matching: in the provider’s height, eligibility score of PROSPERA in 1997 (linear and square), classification as poor household in 1997, gender, provider’s gender, provider’s age (linear and square), provider’s education, in 1997, two-months period when the transfers of PROSPERA were first received in the household of origin, number of persons in the household in 1997, and labor income of the household in 1997. Standard errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations by propensity score matching based on data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the panel of ENCEL 1997-2000.

_C. Returns to human capital: Intergenerational mobility and what is the effect of improvements in health and education on labor income?_ The translation of the estimated intergenerational mobility in height and schooling into social mobility is approximated by the returns in height and schooling on individuals’ labor income. Table 10 shows the returns model with estimations separated by gender based on the Heckman model. Columns 1 and 3
The magnitude of change in demand varies with government response to the pandemic. Countries with a higher number of cases and stricter social distancing and lockdown measures experience the largest drop in demand for services. Both supply disruptions due to business closures and demand-driven changes in economic activities are at play, with demand-side factors playing a greater role in most services. The varying impacts we find across sectors point to the need for targeted public policies that ease the economic burden of the pandemic on the sectors that are hit heavily. Finally, the paper shows the value of using near real-time "big data" to rapidly assess the impacts of the pandemic at a more granular level to inform immediate and medium-term policy responses.

Our paper is related to recent studies on the impacts of COVID-19 on consumer demand and spending. Andersen et al. (2020) and Baker et al. (2020) used high-frequency transaction-level data to study the impacts of the pandemic on household spending in Denmark and the United States (U.S.), respectively. Both studies report a sharp decline in spending in a range of consumption categories as COVID-19 spread. While these results are informative, it is difficult to replicate or extrapolate the findings to other countries or globally, mainly because such transaction-level data are difficult to find in several other countries.

Our paper also relates to a burgeoning body of macroeconomic studies that attempt to quantify the impacts of COVID-19 on national and global economies (Breisinger et al., 2020; Gourinchas, 2020; Eichenbaum et al., 2020; Guerrieri et al., 2020; Fornaro and Wolf, 2020). Many of these studies provide evidence on the macroeconomic implications of the pandemic, drawing largely on historical data, macroeconomic models, and simulations. A significant challenge in estimating the impact of the pandemic, as highlighted in these early studies, is a lack of reliable data on the spread of the pandemic and associated economic and health damages. These data are needed to design alternative containment policies at minimum economic cost (Stock, 2020
in Bangladesh).

The data set we use does not cover the 5.1 million Palestinian refugees who are under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA). The definition of Palestinian refugees in international law is distinct from other refugees. Palestinian refugees are people “whose normal place of residence was Mandatory Palestine between June 1946 and May 1948, who lost both their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict”. Importantly, their patrilineal descendants are also considered refugees regardless of citizenship (UNRWA 2009).

For the purpose of the analysis, we do not include Internally Displaced Persons (IDPs), who are defined as “persons who have been forced or obliged to flee or leave their home or place of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized border” (United Nations 2004). The categories “returnees”, “Stateless”, and “Others of concern” are also not included.

Overall, we account for more than 99 percent of the total number of persons of concern for UNHCR who are not either IDPs, Stateless, asylum-seekers, or returnees.

# **2 What do UNHCR data tell us?**

Figure 1 plots the total number of refugees worldwide since 1960. The total count stood at 15.5 million at the end of 2015. It has recorded wide variations since 1951, with a substantial increase over the last period. The current crisis is the second largest since UNHCR was established in 1951. The earlier peak, in the first half of the 1990s, was concommitant to the collapse of the Soviet Union, the dislocation of Former Yugoslavia, and the Rwanda genocide.

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phone owner sample by applying the abovementioned weights to the consumption data. The poverty rates among the biased HFPS subsample applying original weights, PSW (Lee) weights, PSW (Inverse) weights, and PSW & non-PSW weights are 16.6%, 22.3%, 23.0%, and 22.6%, respectively. These results show that different types of reweighting procedures reduce the bias due to sampling and nonresponses but do not fully eliminate the bias.

Figure 5 (subfigure 2) shows that the SWIFT model trained with the full sample of the reference data, when combined with reweighting techniques, accurately projects the national poverty rate, but does not show almost any clear improvement compared with the results with only reweighting. The SWIFT-based poverty rate using original weights is 18.7%, and the SWIFT-based poverty rate applying PSW and non-PSW weights is 21.4%. These rates are not as close to the poverty rate in the reference survey (23.5%) as the poverty rate estimates using reweighting procedures. This suggests that once reweighting alleviates sampling and nonresponse bias, the SWIFT projection method does not make additional improvements.

Lastly, Table 5 displays the summary statistics of variables included in the reweighting procedure, variables included in the SWIFT projection model, and other untargeted indicators not included in either procedure. All reweighting techniques produce close estimates for all the above-mentioned indicators, and the difference between reweighting procedures is minimal.

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