rafmacalaba/data-use-mentions-extended huggingface dataset:
measured by the Theil index) and (the logarithm of) inflation over the period. Table 7 presents simple correlation coefficients for each of our main sub-periods (1981-1993 and 1993-2004), and both simple and partial correlation coefficients for the period as a whole.<sup>36</sup> The simple correlation coefficient (of 0.5) between inflation and inequality for the whole period is statistically significant

> 35 While the effects of channels (iv) and (v) are not captured by PNAD income data, the first three channels affect capital or labor incomes, and their effects should therefore be registered. 36 The partial correlation coefficient between two variables X and Y, holding a third variable Z constant, is given by

> _rXY_ − _rXZ rYZ rXY_ | _Z_ = ( 1 − _rXZ_ 2 )( 1 − _rYZ_ 2 ) , where rAB denote the simple correlation coefficient between A and B.
3 describes the South Sudan survey, Section 4 assesses patterns of non-response and survey results, and Section 5 concludes.

# **2. Mobiles for Data Collection**

For mobile phones to be useful as a survey device, they need to satisfy a number of criteria. A mobilebased survey should 1) yield a sample size large enough to draw statistically reliable inferences about a population of interest, 2) be economically viable compared to alternative data collection, and 3) generate data of adequate quality. The third criterion relates to sampling, attrition, and non-response as well as respondent behavior when interacting with a mobile phone interface.

2
# Teacher practices - (Preprimary & Post-preprimary - LMICs)

Study countrycode Timeline Type of intervention ES (95% Cl) Outcome_variable Low & Middle Income - Pre-primary (Wolf et al. 20794) GHA 1year after TT & Coaching ~~ 0.56 (0.35, 0.77) 141, Fidelity checklist (no. of activities) (Wolf et al. 20794) GHA 1year after TT & Coaching -0.11 (0.63, 0.41) 71, Facilitating deeper learning (Wolf etal. 20719a) GHA lyear after TT & Coaching —_— 0.65 (0.16, 1.13) 71, Emotional support and behavior management (Wolf et al. 20794) GHA year after TT & Coaching —p—_ 0.52 (0.04, 1.00) 11, Supporting student expression (Wolf et al. 20794) GHA year after TT & Coaching & Parental Engagement ~~ 0.61 (0.39, 0.34) T2, Fidelity checklist (no. of activities) (Wolf et al. 20794) GHA year after TT & Coaching & Parental Engagement -0.16 (-0.73, 0.41) T2, Facilitating deeper learning (Wolf etal. 20719a) GHA year after TT & Coaching & Parental Engagement —_e— 0.65 (0.16, 1.15) 12, Emotional support and behavior management (Wolf et al. 20794) GHA year after TT & Coaching & Parental Engagement 0.09 (-0.41, 0.60) T2, Supporting student expression (Ozleretal. 2013) MW! 1.5 years after TT & Coaching 0.56 (0.01, 1.10) 11, Classroom observation index (5.5 y.o.) (Ozleret al. 2013) MW 1.5 years after TT & Coaching & Teacher Stipend —_—~eo— 4.21 (0.64
|<br>1677.799|<br>1677.799|
|STD of dep. var.|1543.028|1543.028|1543.028|1543.028|1543.028|1543.028|
|No. of obs. (village councils)|52,780|52,780|52,780|52,780|52,780|52,780|
|R<sup>2</sup>|0.959|0.958|0.962|0.954|0.955|0.951|

_Source:_ Original table for this paper. _Note:_ Conflict indicators, damaged crop area and fatalities cover the period mid-March to end-August in 2022 and 2023 and zero for other years. Standard errors in parentheses: * p < 0.10, ** p < 0.05, and *** p < 0.010. ACLED = Armed Conflict Location and Events Data; DA = area damaged due to conflict; F = fatalities; GDD = growing degree days; mm = millimeters.

36
round), 2013 (12th round), and the first round of the Quarterly Labor Force Survey introduced in 2015–16.

Our analysis includes the following variables: gender, age, wages, educational status, marital status, individual’s primary occupational status, and employment. The survey questionnaire and reported variables changed within the period 2006–15. To ensure compatibility over time, we harmonize key variables: the “education” variable is reaggregated from 19 to 6 categories and the “informality” variable is created from multiple categories of the “principle activity status” which were harmonized overtime. Workers are then classified as informal who are either self-employed, contributing family members, and day laborers across years. We also harmonized geographic and industrial codes overtime for effective comparison.

We found some irregularities in wage earnings variables across years. Wages increase substantially between 2005 and 2013. Specifically, we noticed that workers’ wages were way higher – about 10 times – for those who report a monthly payment frequency in 2005. It was found that workers who reported a monthly frequency of payment appeared to have reported their monthly wage instead of the weekly wage, as it was reported indicated in the questionnaire. We corrected for this anomaly and present how the variables look in the final dataset through the distributional plots of logarithm of wages across years, by division across years, and by different skills across years in figure B2, B3 and B4 in annex.

According to 2015-16 labor force survey, agriculture continues to be the presiding source of employment, generating nearly 42.7 percent of total employment in Bangladesh. The importance of industry and services in Bangladesh have also grown overtime with over 20.5 percent of employment being generated by industry and 36.9 percent by services in 2015-16. Analysis of these labor force surveys highlights three acute challenges faced by the Bangladeshi labor market _—informal employment, gender differences, and spatial disparities in key labor market characteristics._ While there has been a modest 3 percentage point decline in informal employment
shares of individuals in each employment status/sector in 2005 Q1, with the shares in 2006 Q1 reported in brackets. Panel weights are used. The lower panel relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to individuals who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices by sex to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets. Panel weights are used.

17

**Official Use**
employment rate are further decomposed into changes in the employment rate by sector. _Third_ , changes in the aggregate output per worker are decomposed into changes linked to variations in output per worker within sectors (within-sector productivity growth) and changes linked

> 3 For reasons of data availability, this study uses output (or value-added) instead of GDP. In contrast to GDP, output does not include taxes and subsidies; differences between GDP and output are usually very small. The sum of sectoral outputs from DOSM is used to compute aggregate output.

5
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experiment**

The Tanzania survey experiment, conducted to shed light on the implications of survey design variations in food consumption measurement, systematically contrasts various design features. We strategically selected eight survey designs to reflect the most common methods utilized in low-income countries and that are typical of the scope of variation one is likely to find in consumption surveys. We then randomly assigned these eight designs to over 4,000 total households. Given the sample size and the random assignment of survey designs, differences in mean measurement performance may be attributed with a high degree of confidence to the survey design rather than potential confounders.

The designs differ by method of data capture (diary or recall survey), designated respondent (household head or other household member), length of reference period, number of items in the recall list, and nature of the cognitive task required of the respondents. Table 1 summarizes each of these designs. The modules we number 1–5 are recall designs, and modules 6–8 are diaries. For the food recall modules, households report the value of items consumed from three sources: purchases, home production, and gifts or payments. Modules 1 and 2 contain a list of 58 food items. Module 3 is associated with a subset list that consists of the 17 most important food items, which constitute, on average, 77 percent of food consumption expenditure in Tanzania based on the national Household Budget Survey 2000–01. To make module 3 comparable, we scale up reported expenditures for that module (by 1/0.77). Module 4 is associated with a list of 11 food items. It is an aggregated version of the list of 58 food items whereby, for example, several

> 3 Beegle et al. (2012) find a drop from 49 to 41 minutes in interview times if the food list is cut from 58 to 17 items in a one-week recall. Times for a 58-item list rise to 76 minutes if the typical, more cognitively demanding “usual” month recall is
percentage points (23.7% and 7.5%) more likely to work in low-skilled occupation and the agricultural sector (Columns (3)–(4)). Finally, worse mental health reduces the number of work hours by 10.1 (30.9%) and life satisfaction by 0.6 points (7.9%) on a 0-to-10 scale (Columns (5)–(6)). This is perhaps unsurprising given that mental health and life satisfaction are closely related (Danzer and Danzer, 2016). Our estimation results are thus consistent with the negative impacts of worse mental health on the probability of being employed and labor income discussed earlier (Table 1).

Once we establish strong evidence of the impacts of mental illness on refugees’ labor market outcomes, we shift our attention to its spill-over effects on their family members. Table 3 shows that higher mental health scores lead to a lower probability of refugee partners being employed, although the coefficient is statistically insignificant (Column (1)). Mental illness also reduces life satisfaction of the partners by 1.0 point (13.4%), although the impact is statistically significant at only 10% level (Column (2)).

Next, we employ the unique data on refugee children in wave 3 and examine two child outcomes, mental health status and school performance. The medical literature suggests that the mental health of refugee parents has an important role in child refugee mental health (Fazel et al., 2012). For example, lack of parental support is shown to be a predictor of PTSD in children. Our study reaffirms this finding by showing that a one standard deviation increase in refugee mental health scores causes a 3.2 unit increase (35.4%) in the mental health score of their children (Column (3)). A one standard deviation increase in parental mental health scores also leads to a 1.6 percentage point (26.2%) increase
<!-- Start of picture text --> World − Regions World − Income<br>−11 −9 −8 −7 −6 −5 −10 −9 −8 −7<br>Total trade intensity Total trade intensity<br>0.5<br>0.6<br>0.4<br>0.4<br>0.3<br>0.2<br>0.2<br>Corr GDP HP Corr GDP HP<br>0.0<br>0.1<br>−0.2 0.0<br><!-- End of picture text -->

**Figure 3.** Logged and HP-filtered GDP correlation and total trade intensity between region groups (left panel) income groups (right panel).

<!-- Start of picture text --> −0.5 0.0 0.5 1.0<br>Change in Production Connectivity<br>4<br>3<br>2<br>1<br>0<br>Change in GDP synchronization<br><!-- End of picture text -->

**Figure 4.** WDR 2020 team, based on World Bank’s World Development Indicators (database) and World Integrated Trade Solution (database). Note: Each dot represents a pair of regions—for example, East Asia and Pacific and Sub-Saharan Africa, and Latin America and Caribbean and South Asia are two different observations). The horizontal axis measures the change over time in production connectivity defined as the total trade in intermediates as a share of GDP of both regions. The vertical axis measures the proportional change in GDP correlation over time.

12
there is an additional source of error due to generation of welfare in each simulation from a different set of parameter values in step 3, instead of using the initial estimates from step 1.

Moreover, if our target indicator is the mean welfare in location _c_ , _τc_ = _Nc_<sup>_−_1</sup> � _Nh_ =1 _d_<sup>_ych_,andthewelfare</sup> is not transformed with log, then _E_ ( _τc_ ; _θ_ ) = _E_ ( ̄ _xcβ_ + _ηc_ + ̄ _ec_ ) = _x_ ̄ _cβ_ , where _x_ ̄ _c_ = _Nc_<sup>_−_1</sup> � _Nh_ =1 _d_<sup>_xch_isthe</sup> area mean of the correlates and _e_ ̄ _c_ = _Nc_<sup>_−_1</sup> � _Nh_ =1 _d_<sup>_ech_istheareameanofthehousehold-levelerrors.This</sup> means that the area effect _ηc_ vanishes in the final ELL estimator, which becomes basically the regression ̄ estimator _xcβ_ . This estimator is called “synthetic” in the SAE literature because it does not account for between-area heterogeneity (or idiosyncrasy of the areas) beyond that explained by the correlates. Note that, if the regression parameter _β_ was actually known (best case), _x_ ̄ _cβ_ would not be using at all the actual survey data, only the census auxiliary information. Synthetic estimators rely very strongly on the assumed regression model and might be misleading if the model is incorrectly specified. In fact, standard errors are obtained assuming that the model actually holds and do not account for model misspecification. Hence, under model failure, they might still be small, giving a lot of credibility to the (misleading) estimates. As will be shown, even if the model is correctly specified, the MSE of the ELL estimators can be substantial, especially if the prediction power of the model is weak.

# **3 Molina and Rao’s EB and Census EB estimators**

Molina and Rao (2010
# **3. How to attract more foreign investment? A cross country approach to the determinants of FDI**

## **3.1. An eclectic empirical approach to the determinants of FDI**

This section revisits the determinants of foreign direct investment in Eastern Europe and Turkey by adopting the cross country analytical framework proposed by Sánchez-Martín et al. (2014). This empirical approach combines different theories concerning foreign direct investment to identify relevant variables to be included in panel data regressions.

According to Dunning (1998), developer of the so-called eclectic Ownership-LocationInternalization (OLI) paradigm, beyond firm-specific determinants, labor endowments and locationbound factors play an important role in multinational decisions to invest. Thus, the empirical model incorporates _enrollment in tertiary education_ and _telephone and mobile phone lines per capita_ available from the World Development Indicator database of the World Bank (2015), as proxies for human capital and telecommunications infrastructure, respectively. The first is expected to influence foreign direct investment attraction, especially for companies requiring a skilled labor force, whereas the penetration of telecommunications would benefit all multinationals by lowering costs and improving business competitiveness.

Drawing from the OLI paradigm, the Knowledge-Capital Model (Markusen and Maskus, 1999) allows for multiple production facilities, separating cross-support centralized services and disperse production in some multinationals. This approach distinguishes three types of FDI depending on the motivation: export-oriented (vertical), market seeking (horizontal), and resource seeking. The empirical framework presented in this paper incorporates _GDP per capita growth_ as proxy for market expansion. It has been found to be one the most robust determinants of horizontal FDI inflow attraction in econometric studies (Artige and Nicolini, 2005). It also includes the level of _trade openness_ , defined as the sum of exports and imports as a percentage of GDP, as this would signal economies that are “international trade friendly”, which would positively contribute to vertical FDI attraction. Nonetheless, it is worth noticing that this macro-level cross country analysis does not explicitly distinguish between horizontal and vertical FDI
in terms of the debt structure metrics. For example, the share of local debt will be higher for a ―currency risk reduction‖ strategy compared to a ―maintain current profile‖ strategy. Debt structure metrics for alternative strategies is then reported to senior management together with the expected cost and risk levels of these strategies for each year in the decision horizon. An illustrative result for the distribution of cash-based interest costs under 5,000 simulation paths for a strategy tested is presented in Graph 2.

31
example, Clemens et al. (2019) compare the wages of immigrants from 42 countries in the United States with those of individuals of the same gender, age, and education level in the origin countries to get an estimate of the wage gain from migrating. Methods such as propensity score matching attempt to match migrants to similar non-migrants on the basis of much larger sets of covariates, whilst ensuring that these comparisons lie in the common support and do not involve the linear extrapolation that regression can entail. For example, Gibson and McKenzie (2014) match Tongan and ni-Vanuatu households that participate in a seasonal worker program to similar households matched on household demographics, characteristics of the males most likely to migrate such as their health status and work experience, previous migration histories and networks, household assets and infrastructure, geography, and past wage and salary history. Another example is Dustmann and Fabbri (2003), who use a matching approach to estimate the impact of English language proficiency among UK immigrants.

The challenge for these methods is that migration theory is full of examples of how individuals may self-select into migrating on the basis of variables that may be difficult to observe or measure, such as unobserved ability (Borjas, 1987), risk preferences, language skills, exposure to shocks, entrepreneurial aptitude, etc. This raises two issues for practical use. The first is that since many surveys will not measure all of these features, it will typically be difficult to convince a skeptical reader that one has controlled for all of the important characteristics of individuals that both influence their migration decisions and that may also be correlated with the outcomes of interest. The second is that because migration is typically a rare event, most individuals in the population will not be good matches for those who have migrated, dramatically reducing the effective sample size. For example, McKenzie et al. (2010) match migrants from Tonga who applied for a migration lottery program in New Zealand to Tongans in a nationally representative labor force survey. They find that their sample
landscape in Vietnam (Rentschler et. al 2020, chapter 3). In this section, the data employed to model each hazard are described.

## 2.1. Flooding

This analysis uses one dataset modelling fluvial and pluvial flooding and a second one modelling coastal flooding. For sake of simplicity the combination of fluvial and pluvial flooding is addressed as riverine flooding in the main report. Fluvial flooding occurs when excessive rainfall over an extended period of time causes a river to exceed its capacity. A pluvial, or surface water flood, is caused when heavy rainfall creates a flood event independent of an overflowing water body. A coastal flood occurs in areas that lie on the coast of a sea, ocean, or other large body of open water, and is typically the result of extreme tidal conditions caused by severe weather (World Meteorological Organization, 2011).

The first dataset combines information on flood extent and flood depth from pluvial and fluvial flooding into one flood map. It was developed by the company Fathom, formerly known as SSBN. The map covers all of Vietnam at a resolution of about 90 by 90 meters. Flood events with return periods of 5, 20, 50, 100, 250, and 500 years are modelled. A 100-year event, for example, is expected to occur once every 100 years, on average (figure 2.1.a). As with most large-scale flood maps, the effects of man-made flood protection structures like dikes are not incorporated. The dataset is in raster format (Bangalore et al. 2017).

Existing coastal flood maps covering Vietnam, most prominently the Global Tide and Surge Reanalysis (GTSR) data set (Muis et al. 2016), are expected to underestimate coastal flood risk in Vietnam. Resulting from coarse resolution in modelling elevation, bathymetry and meteorological forcing, the dataset underestimates extreme sea levels. In addition, the largest tropical cyclone-induced surges are not all 4