rafmacalaba/data-use-mentions-extended huggingface dataset:
6 imports are computed as the difference between the consumption and production statistics available from the Energy Information Administration (EIA) of the U.S. Department of Energy. Although the EIA also has import and export statistics, the data for 2009 are not yet available. Vulnerability is computed for a total of 180 countries, including 131 developing countries.

2. For decomposition analysis of changes in vulnerability, net imports are always based on the difference between consumption and production because both appear in the accounting identity used. Changes in inventories are omitted because the data are available primarily for high-income countries only and the contributions are expected to be small in most cases. For countries for which data from the IEA are available, they are used unless the net imports calculated using the EIA data more closely match the net imports computed using the IEA data in 1 above. For all countries in which the IEA does not collect data, the EIA data are used. Decomposition analysis covers a total of 170 countries.

The results of decomposition analysis necessarily exclude countries missing data for any one of the parameters in the accounting identity. In addition, the analysis excludes those countries for which there are serious discrepancies in the oil statistics, as explained in appendix 1.

Although full results for 1999, 2008, and 2009 are presented in appendix 1, the main text of this paper presents results for ∆ V for only the period between 1999 and 2008. The differences between 1999–2008 and 1999–2009 are driven primarily by the difference in the price of oil. As expected, net oil importers were generally less vulnerable, and net oil exporters saw a smaller fraction of their GDP derived from oil export revenue, in 2009 than in 2008.

# **2.2 Passing through of world fuel price increases**

The pass-through coefficient for each fuel is calculated between January 2009 and January 2012 as where all fuel prices are expressed in current U.S. dollars and the reference world prices are the free-on-board (FOB) prices of the
<mark>remarks, we discuss several hypotheses that emerge from these facts regarding systematic differences between the labor markets of developing and advanced economies.</mark>

# Data

The International Income Distribution Database (I2D2) is a harmonized database of households and individuals drawn from nationally‐representative surveys, built to compare income equality across countries. Depending on the country, the I2D2 draws observations from different types of surveys, usually conducted by national statistical agencies, including Household Budget Surveys, Household Income and Consumption Surveys, Labor Force Surveys, and multi‐topic surveys (such as Living Standards Measurement Study Surveys). It includes major economies such as Bangladesh, China, Vietnam, Mexico and Chile. Crucially, along with hourly wages, the data include the size of the worker’s firm, along with indicators of worker demographics, occupation, industry and wage.

We restrict attention to the 69 surveys (25 countries) in which data on the size of the employer firm are provided, which cover 1988‐2015. Where data are available, I2D2 includes a categorical variable for the size of the workers’ firm (for instance, <100 employees, between 100‐500 employees, or more than 500). The breakpoint used varies across countries however, and in the lowest income countries very few or zero individuals report working at firms with more than 500 employees, or even 100. For this reason, to maximize the number of countries in the sample, we define the LFWP as the (log) difference in wages between those workers in firms with 100 or more employees, and those workers in firms with fewer employees. The share of workers in our sample in large firms ranges from 31 percent in China and 26 percent in Chile, to 0.5 percent in Mali and 0.3 percent in the Comoros.

# Results

The LFPW is estimated using the following regression specification, for worker i, in a survey in country k and year t: �,� where wage� is the hourly
# **5 Quantitative Analysis**

In this section, I present the quantitative analysis of the model. Subsection 5.1 presents the benchmark calibration. Subsection 5.2 presents key statistics in the data and model simulations. Subsection 5.3 inspects the mechanism of the model. Subsection 5.4 explores the social welfare implications of having an independent central bank that can accumulate international reserves.

## **5.1 Calibration**

**Functional Forms.** —The utility function with constant relative risk aversion is given by

with _σ_ = 1, and I follow Chatterjee and Eyingungor (2012) by adopting the functional form for default costs given by

**Parameter Values.** —The model is solved numerically to evaluate its quantitative predictions regarding the level of international reserves, public debt, and sovereign spreads. The appendix describes the computation of the model. I calibrate the model using data for Mexico from 1994 to 2017, a typical emerging economy with an independent central bank used commonly as a reference for studies on reserves accumulation.<sup>28</sup> A period in the model refers to a year. I choose a subset of parameter values (summarized in table 2) that can be directly pinned down from the data, and then I choose a second subset of parameter values (summarized in table 3) such that model simulations match key aspects of the data.

The risk aversion parameter value ( _σ_ = 2) is standard in quantitative business cycle and sovereign default studies. The international risk-free interest rate is set to match the average real interest rate for US Treasury Bills from 1980 to 2017, which is set to 1 _._ 1% ( _r_<sup>_∗_</sup> = 0 _._ 011). I use spreads from the EMBI+ to parameterize the lenders’ stochastic discount factor. I assume that a period with high risk aversion is one in which the global EMBI+ is one standard deviation above the
percent level across Tables 1 and 2, and only two differences are statistically significant at the 10 percent level, which is what would be expected by chance.

Second, further corroborating the conditions required for _γ_ to represent the arguably causal impacts of the earthquake, the identification strategy is able to uncover a pattern independently reported by humanitarian agencies using other data sources at the time of the earthquake- a growing food security crisis arising from the disaster that was worst felt in the Southern Highlands and Hela provinces. The DHS collected the Food Insecurity Experience Scale in the household survey, which is composed of both subjective and objective questions on food access.

Using an identical identification strategy as described in (1) but using the household survey information on food security as the dependent variable, Figure 3 reports the difference-in-differences estimate of the change in food security relative to the rest of the country. As expected by the sampling strategy that reaches more rural and remote households in later phases of the survey, there was a worsening of food access for all households following the earthquake in the fourth phase of the survey across the country. However, there was a substantially larger increase in the share of households reporting poor food access in the regions worst affected by the disaster. There was between a 6 and 15 larger percentage point increase in the share of poor food access in disasteraffected regions for the three objective food security measures (skip meals, run out of food, and going a whole day without food), which represents between 16 and 45 percent of the pre-disaster share of households that reported poor food access across the entire country.<sup>28</sup> Third, it is possible that the disaster led to changes in survey quality or in the willingness of individuals to respond to the IPV module in earthquake-affected regions, and this change might be driving some of the results and not an actual change the prevalence of IPV. Even though the low rates of non-response reported in the Data section suggest that this is likely not a significant problem
The data come from several sources. For financial variables, we use Lane and Ferretti (2007, updated in 2015). For the share of credit to the private sector, we use data from the World Development Indicators. For the exchange rate regime (Eichengreen et al., 2013), we use data from the International Monetary Fund’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER), which reports countries’ self-reported exchange rate regimes. The existence of an SWF was constructed using different websites of SWF (see Appendix A).

# **5. Empirical Findings**

We used the Lane and Ferretti (2007) data set, which was updated to include data covering the period 1970–2015. We ran a compendium of regressions, including various components of the NFA—including the capital account, FDI, portfolio assets and liabilities, external debt, international reserves, and total foreign assets—as dependent variables.

We first present the results on assets (with a focus on oil-exporting countries). We then report the results on liabilities (with a focus on oil-importing countries). We then report aggregates of the balance of payments, especially the capital account and reserves, for both oil-exporting and oil-importing countries.

Table 3 shows the basic specification for assets. The SWF variable is statistically significant and positively associated with FDI (Reddy, 2019); it does not seem to matter for other assets (portfolio and debt). This finding shows the extent to which an SWF can help improve the allocation of investment assets toward FDI rather than portfolio investments. Financial development (defined as credit to the private sector as a percent of GDP) is also statistically significant for all asset components, with the highest elasticity for portfolio investments. This result is highlighted by Vermeulen and de Haan (2014), who show that financially developed countries are likely to take more risks than developing countries, because development allows them to insure against risk. For this reason, financially developed countries tend to invest more
scenarios constructed to highlight the contribution of changes in inequality vs. mean incomes on poverty forecasts; thus, one should focus on the differences in projections generated under the different scenarios as opposed to the actual forecast levels. We set out to calculate: for each country _i_ , _t_ = _{T, T_ + 1 _, ...,_ 2030 _}_ , where _T_ denotes the latest survey year for each country. Then by region, we calculate average headcount poverty (weighted by country population): where _r_ denotes region, and _ωi_ denotes country _i_<sup>_′_</sup> _s_ population relative to the total population in the region.<sup>17</sup> Note, we could also project headcount poverty using an elasticity approach, e.g., _Hi,_ 2030 = _Hi,T_ +<sup>�2030</sup> _s_ =1<sup>_−T_</sup> ( _κy,i,s_ ̄ ∆ ̄ _ys_ + _κσ,i,s_ ∆ _σs_ ), but as discussed earlier, the elasticity approach only provides a first-order approximation for headcount poverty as changes in mean incomes and inequality are discrete.

To project headcount poverty, we need values for mean income and inequality out to 2030. As our baseline, we generate predictions for _y_ ̄ using data on real GDP per-capita from 1990-2022 from the Macro Poverty Outlook (MPO).<sup>18</sup> In particular, we estimate the following MA(2) regression for each country _i_ :<sup>19</sup> where log( _gdp_ _~~p~~ ci,t_ ) denotes the log of GDP per-capita in country _i_ in year _t_ . Using the coefficients estimated from these regressions, we then predict real GDP per-capita for 2023-2030 for each country. Then, using this GDP per-capita series (the MPO series up until 2022 plus our predicted series from 2023-2030), we calculate country specific
suggest that the EBA program was associated with more exports, holding other HighDimensional Fixed Effects (HDFEs) and RTAs constant. These estimates are slightly less than those for RTAs generally. In addition, the effects on Cambodian exports were especially acute. The interaction estimates in the second column suggest that the EBA program was associated with smaller increases in exports from Cambodia than from other countries.

**Table 3.6: Cambodia EBA Estimates**

|**VARIABLES**|**Zeros**|**Zeros**|
|---|---|---|
|RTA|0.581***<br>(0.099)|0.581***<br>(0.099)|
|EBA|1.103***<br>(0.090)|1.110***<br>(0.091)|
|Cambodia EBA||-0.323*<br>(0.181)|
|Constant|16.016***<br>(0.045)|16.016***<br>(0.045)|
|Observations|1,231,334|1,231,334|
|**Notes:**PPML HD<br>annual pair-wise C<br>spanning 1996-20<br>errors in parenthes<br>***p<0.01,**p<|FE estimation<br>OMTRADE/<br>21. Robust sta<br>es.<br>0.05,*p<0.1|using<br>Baci data<br>ndard<br>.|

One reason why the EBA program is associated with such large increases in exports from LDCs is that they explicitly reduce tariffs and quotas for LDC exports. Other agreements are more aspirational, such as the Trade and Investment Facilitation (TIF)
**MONGOLIA'S INFORMAL SECTOR -**

**Appendix B:** Methodologies and Descrptive Statistics

**46**

# **Generalized Informal Sector Survey Instrument**

<!-- Start of picture text --> Name of Researcher:<br>a. District surveyed ] b. Gender M F [2] c. Activity [3]<br>d. Day surveyed () © (D (a) i) © (7)[41 e. Age [5]<br>f. Hour surveyed [6] g. Married? y N [7]<br>h. Education: OD (Z (a ) [8]<br>primary secondary vocational higher<br>18. Do you have another job or source of income in addition to this activity? Y N 19]<br>If yes is the other job in [10)<br>O) state<br>(a) state enterprise, private company, cooperative, partnership, or public organization<br>(3) another form of self- or family-employment<br>If yes, is the job mentioned above from () active work, ( pension, ( leave? (I) ) (M [11]<br>If yes, what is your average monthly income from your other job? [121<br>If yes, how many hours per week do you work at your other job? [13]<br>19. Is your activity registered with the duureg? Y N [14]<br>20. When the duureg administration asks if you are employed, how do you answer? Y N [15]<br>If you respond that you are not employed, what do you give as the reason? (D M GD 0 ® [t6]<br>(I illness, or watching a | because of | not finding | fired, or
# 3 Empirical Strategy

Our analysis is based on comparing the national COVID-19 vaccine coverage rate<sup>5</sup> for a given population of interest estimated from survey data to the coverage rate reported in administrative data. Neither source can be regarded ex-ante as bias free or (close to) the “true” rate, so that we cannot observe data accuracy directly. Instead, we observe how different survey estimates vary in response to design choices and vis-à-vis the administrative data. This approach is common in the measurement literature in the absence of an objective truth against which estimates under different design choices can be benchmarked (Bardasi et al. 2011; Laajaj and Macours 2021; Das et al. 2012; Beaman and Dillon 2012; De Weerdt et al. 2020b). It is also in line with health policy practice in which the size of the gap between survey and administrative estimates of vaccine coverage is taken as the key metric by which data quality as a whole is judged (Gavi, The Vaccine Alliance 2022c) .

An important caveat is that the two data sources usually refer to different reference populations. The phone surveys generally cover the adult population (aged 15+)<sup>6</sup> whereas the administrative data is reported for the entire country population. As age-disaggregated administrative data is not consistently available across our sample, we assume that the administrative data contains no vaccinated children (younger than 15 years). This assumption is likely more accurate at the start of the pandemic but will possibly underestimate the true gap between survey data and administrative records as countries lowered the age threshold for COVID-19 vaccination.<sup>7</sup> We structure our analysis along six hypotheses and associated predictions that we empirically test by randomly varying one aspect of the survey design at a time (Table 1). We conduct each analysis on a per-country basis. In the following, we introduce each hypothesis, its predicted effect on the estimated COVID-19 vaccine coverage rate and our empirical strategy to test it.
1350|-8.3480|8.0050|
|log total hours|8.0160|1.4070|4.9130|14.6700|-0.0049|0.4130|-5.0790|5.8480|
|share with a degree|0.1470|0.1890|0.0000|1.0000|0.0164|0.0943|-1.0000|1.0000|
|N(obs.)||443|98|||22|199||

Notes: Table reports summary statistics on the firm-level data from the estimation sample for 2007-2018, both in levels and in changes. Levels refer to variables averaged over 3-year periods, changes refer to variation between 3-year periods.
discussion of their potential for research on internal migration. More generally, given their increasing availability and huge potential, there is a clear need to invest more in research aimed at developing methods for improving integration and interoperability of household surveys with these new data sources .

**Weather data.** To investigate climate-related migration outcomes, accurate and georeferenced weather information is needed. However, most household surveys include, at best, self-reported measures of weather shocks based on individuals’ recalls, which can hardly be reliable or comparable given their subjective nature. For this reason, household data from multi-topic surveys are almost always autonomously integrated with external weather information. The main public domain sources of weather data include NASA _Modern-Era Retrospective analysis for Research and Applications_ (MERRA-2), the _Terrestrial Air Temperature and Precipitation_ database from the Center for Climatic Research at the University of Delaware, and the _High-Resolution Gridded Datasets_ from the Climatic Research Unit of the University of East Anglia. Remote sensing weather data sets, which can take the form of gridded, satellite, or reanalysis data, have been used in many studies leveraging LSMS-ISA data to address weather- and climate-related research questions in Sub-Saharan African contexts (see, among many, d’Errico et al., 2019; Letta et al., 2018; Mueller et al., 2020; Di Falco et al., 2022).

While this integration is typically carried out directly by the research team, this practice can be suboptimal for several reasons. First, the intrinsic diversity of weather data products. For instance, satellite data provide less accurate data than ground stations in most locations and do not extend as far back in time. Gridded data, on the other hand, aggregate data from ground stations via interpolation and across a given space. This works well in developed countries, where there is wide and uniform coverage of weather stations across the entire territory, but not so much in developing contexts, where often gridded data aggregate weather information from a few old stations spread across the country. Sparse
(Figure 20). The cross-country differences are important and interesting because they help show the comparative value that these officials place on different kinds of education.

The gaps between envisioned and actual returns also differ from one country to another. In Indonesia, Vietnam and Mongolia, expected secondary premiums are not far from available published global data. In Lao PDR, secondary premiums are half as large as expected by officials, while they are underestimated significantly in the Philippines. At the tertiary level, education premiums are vastly overestimated by officials in all EAP countries. Actual returns at the tertiary level range from 20 percent in Lao PDR to just under 60 percent in the Philippines. Yet, expected returns are at least twice as large compared with the actual data for 2014 in all countries. This overvaluing of tertiary education may lead to disproportionate investments in tertiary education relative to primary education or other sectors.

_Figure 20: Estimated versus Actual Returns to Education by Sector._ <!-- Start of picture text --> 180%<br>160%<br>140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Secondary vs. primary Tertiary vs. secondary<br>Expected Actual<br>Indonesia Lao PDR Mongolia Philippines Vietnam Indonesia Lao PDR Mongolia Philippines Vietnam<br><!-- End of picture text --> Source: CGD–World Bank 2022 survey for envisioned premiums and Patrinos and Montenegro (2014) for a compilation of the actual premiums.

# 6.6 Ministry of finance officials overestimate the returns to tertiary education.

Officials of ministries of finance overestimate university premiums and underestimate returns to TVET and secondary by a larger factor than officials of ministries of education. On average, among EAP countries, expected university premiums are 52 percent larger for surveyed finance officials than for education officials (Figure 21). Conversely, finance officials’ estimates
- **Newbury, M. Catherine** , “Ebutumwa Bw’Emiogo: The Tyranny of Cassava A Women’s Tax Revolt in Eastern Za ̈ıre,” _Canadian Journal of African Studies / Revue Canadienne des Etudes_<sup>_ ́_</sup> _Africaines_ , 1984, _18_ (1), 35–54.

- **Northrup, David** , “The Ending of Slavery in the Eastern Belgian Congo,” _The End of Slavery in Africa_ , 1988, pp. 462–82.

- **Nunn, Nathan and Diego Puga** , “Ruggedness: the Blessing of Bad Geography in Africa,” _The Review of Economics and Statistics_ , 2012, _94_ , 20–36.

- **O’Mealia, T.** , “Violence, Protection, and the Political Economy of Security Provision in Ongoing Conflicts.” Phd dissertation, University of Michigan, Ann Arbor, MI 2022.

- **Pourtier, Roland** , “D ́esorganisation des transports et spirales du sous-d ́eveloppement au Za ̈ıre,” in Chantal Blanc-Pamard, ed., _Dynamique des syst`emes agraires: politiques agricoles et initiatives locales: adversaires ou partenaires_ , ORSTOM, 1993, pp. 49–69.

- **Raleigh, Clionadh and H ̊avard Hegre** , “Population size, concentration, and civil war: A geographically disaggregated analysis,” _Political Geography_ , 2009, _28_ (4), 224–238.

- **, Roudabeh Kishi, and Andrew Linke** , “Political Instability Patterns are Obscured by Conflict Dataset Scope Conditions, Sources, and Coding Choices,” _Humanities and Social Sciences Communications_ , 2023, _10_ (1), 1–17.

- **Rogall, Thorsten** , “Mobilizing the Masses for Genocide,” _American Economic Review_ , 2021, _111_ (1), 41–72.

- **Ross, Michael L.** , “Oil,