rafmacalaba/data-use-mentions-extended huggingface dataset:
3<br>B=BoBo 2BB8 |<br>3 z&<br>a3a<br>.8 |<br>3 \> \<br>. —S——. = : {| eZ NS<br>0 200 400 600 800 1000 -1000 0 1000<br>Previous month sales per worker worker Previous month profits per worker worker<br>PPP international USD international USD USD (2018) PPP international USD international USD USD (2018)<br>—— LE-SE _——— HE-SE ——— Employer —— LE-SE _——— HE-SE ——— Employer<br><!-- End of picture text -->

<!-- Start of picture text --> 8<br>+<br>3<br>B=BoBo<br>3<br>3 \><br>. —S——. = :<br>0 200 400 600 800 1000<br>Previous month sales per worker worker<br>PPP international USD international USD USD (2018)<br>—— LE-SE _——— HE-SE ——— Employer<br><!-- End of picture text --> <!-- Start of picture text --> 4ss<br>2BB8 |<br>z&<br>a<br>8 |<br>\<br>{| eZ NS<br>-1000 0 1000<br>Previous month profits per worker worker<br>PPP international USD international USD USD (2018)<br>—— LE-SE _——— HE-SE ——— Employer<br><!-- End of picture text -->
# **4.     Youth Education and Employment Decisions**

# **4.1.   Methods**

Youth face a decision whether to continue their education, join the labor force, or stay inactive. A rational decision depends on many factors, including the marginal utility to the household of youth continuing in school, working, or staying inactive, the income constraints, time constraints, and outside opportunities for youth to work. The theoretical model of education and employment decisions has been substantively discussed in the child labor literature (Bhalotra and Heady 2003; Edmonds 2007). In this paper I adapt the reduced form for the youth education and employment decisions.

where Y is the decision, EW is both in the labor force and in school, E is in school, W is in the labor force, and H is inactive; HH is household demographic information, and X is the vector of variables including individual and household characteristics.<sup>9</sup> The four choices can be sequential or simultaneous. For example, youth can choose whether to go to school, then choose whether to join the labor force or stay inactive. Or the choices can be made simultaneously. Because the order of the decisions is subjective in a sequential model, this paper uses a simultaneous model--i.e., a multinomial logit model--to understand which factors are significant in contributing to youth activity decisions (Ersado 2005). Four choices (to be both in labor force and in school; in school; in labor force; and inactive) are estimated, with school as the base outcome (Green 2003).

8
While the Global MPI has data for over 100 developing countries, not all countries have data for more than one year. As our analysis seeks to also investigate dynamics over time, we required at least two data points for each country—with the aim of an extended time lag in between years (as poverty dynamics can be slow to change). For this reason, we restrict the dataset to those economies that have surveys available for more than one year _and_ have a minimum gap of three years between the first and last survey. This strategy helps us to minimize noise in the changes in multidimensional poverty resulting from short-term measurements. As a result of these selection criteria, our dataset includes information for 67 countries for the period between 2003 and 2020 (Table A1.2 in Appendix 1 lists the covered countries and their survey years).

# **5. Results**

## **5.1. Mapping the Interconnected Structure of Poverty in 67 Countries**

We begin by showing how the Poverty Space and Poverty Centrality can help us understand the structural aspects of poverty within an economy. For illustration purposes, we focus on the countries of the Kyrgyz Republic and Ethiopia which present contrasting cases in terms of the structure and stability of their Poverty Spaces.

Figure 1 displays network visualizations of the Poverty Spaces for the Kyrgyz Republic and Ethiopia, comparing the initial and final years of the survey. In these graphics, nodes signify poverty indicators, and the edges between them indicate important connections (for the visualization, for each network we use a threshold value calculated as the minimum edge weight for which every poverty indicator has at least one connection). The node size corresponds to its poverty centrality (the larger the node, the more central the indicator), while its color matches the censored headcount ratio of the indicator (ranging from a low share of people deprived in the indicator in blue to a high share deprived in yellow).

Focusing on the Kyrgyz Republic (Figure 1 a), we see
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aerosol index. We find no significance for this variable.

Columns 5-6 re-introduce the S5P NO2 measure, since nitrates from NO2 emissions are a significant source of PM2.5 (Hodan and Barnard 2004). Here we find very high significance for both level and difference estimates.

Columns 7-8 test the relationship between CAMS-3 PM2.5 and MODIS Aerosol Optical Depth (AOD). We find very high significance for MODIS AOD, although we should note that relatively sparse observations yield many fewer degrees of freedom for the AOD case.

In summary, we find that CAMS-3 PM2.5 is strongly related to Sentinel-5P NO2 and MODIS AOD; CAMS-3 NO2 is strongly related to Sentinel-5P NO2.

> 6 We use one-day differences for this exercise.

10
We find that the discrepancies introduced by denominator issues are non-negligible and amount to a difference of up to 5 percentage points between coverage rates reported in the WHO dashboard and the OWID dataset. On average, reported coverage in the WHO dashboard is 1.6 percentage points higher than that reported in the OWID data. Similarly, coverage reported in the WHO dashboard is on average 1.9 percentage points higher in our sample of five Sub-Saharan African countries. This implies that, on average, the coverage rates reported in the WHO data assume a smaller population size. However, cases also exist in which assumed population size in the WHO data is higher, leading to up to 2.9 percentage points lower vaccination rates.

These discrepancies seem to arise because of differences in baseline years for which population figures are taken. The OWID dataset uses the latest, 2022 UN population projections (Mathieu et al. 2021), whereas population figures implied by the WHO’s reported coverage rates typically correspond to UN World Population Prospects for 2019 or 2020.

# 5 Discussion

Phone surveys have become commonplace during the COVID-19 pandemic and have informed a large body of (health) policy research. Our results from 57 survey waves on COVID-19 vaccination across 36 LMICs indicate that phone surveys may produce estimates of COVID-19 vaccine coverage that differ from administrative figures. In some cases, survey estimates exceed administrative figures by a margin that would suggest vastly different policy conclusions. Such misalignment is particularly concentrated in Sub-Saharan Africa while coverage rates estimated in other regions on average track administrative records more closely.

Upon investigating potential sources of bias, our findings largely maintain the reliability of the phone survey data. We find phone survey estimates from five Sub-Saharan African countries to be robust to a number of commonly feared representation and measurement errors, but they are affected by how respondents are selected (Figure 3 and Appendix Table A3).

We find little evidence that errors of measurement bias the phone survey estimates. Interviewing respondents in person rather than on the phone does not lead to survey estimates
- Oui-Yang, K. (2018). Lexical measures of social inequality from Pigou-Dalton to Hammond. _Review of Income and Wealth_ . DOI: 10.1111/roiw.12402.

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- Permanyer, I. and C. D’Ambrosio (2015). Measuring social polarization with ordinal and categorical data. _Journal of Public Economic Theory 17_ (3), 311–27.

- Ravallion, M. (1994). _Poverty Comparisons_ . Switzerland: Harwood Academic Press.

- Roberts, F. S. (1979). _Measurement Theory with Applications to Decisionmaking, Utility, and the Social Sciences_ , Volume 7 of _Encyclopedia of Mathematics and its Applications_ . Reading, Mass.: Addison-Wesley.

- Sen, A. K. (1976). Poverty: An ordinal approach to measurement. _Econometrica 44_ (2), 219–231.

- Seth, S. and M. McGillivray (2018). Composite indices, alternative weights, and comparison robustness. _Social Choice and Welfare 51_ (4), 657–679.

- Seth, S. and G. Yalonetzky (2018). Assessing deprivation with ordinal variables: Depth sensitivity and poverty aversion. OPHI Working Paper 123. University of Oxford.

- Shorrocks, A. F. (1995). Revisiting the sen poverty index. _Econometrica 63_ (5), 1225–1230.

- Stevens, S. S. (1946). On the theory of scales of measurement. _Science, New Series 103_ (2684), 677–680.

- Thon, D. (1979). On measuring poverty. _Review of Income and Wealth 25_ (4), 429–439.

39
and Dar es Salaam are within global best practice benchmarks, but the dwell

**Table 2.7. Comparative performance across East African ports**

||Djibouti|Mombasa|Port<br>Sudan|Dar es<br>Salaam|
|---|---|---|---|---|
|Container dwell time (days)|8|5|28|7|
|Truck processing time (hours)|12|5|24|5|
|Container crane productivity<br>(container per hour)|17|10|8|20|
|**Charges**|||||
|Container cargo handling charge<br>(US$ per TEU)|135|68|150|275|
|General cargo handling charge<br>(US$ per tonne)|8|7|10|14|
|Source: AICDports database.|||||

times in other ports are significantly longer, particularly in Port Sudan. Truck processing time at Port Sudan is almost five times that at Mombasa or Dar es Salaam. Crane productivity (in terms of containers handled per hour) in Dar es Salaam is twice that of Mombasa. Dar es Salaam, which performs slightly better than Mombasa in terms of productivity, levies higher port charge —four times more than Mombasa (the cheapest port in the region) for container handling. The prices for Mombasa are consistent with global best practice benchmarks.

Mombasa and Dar es Salaam are natural transshipment points on the East African coastline. While both ports have pursued transshipment, both now face severe challenges to increase their national cargo flows over the short term (box 2.2), and their limited capacity is likely to curtail their transshipment activities. Djibouti, on the other hand, may soon provide a new solution to transshipment along the East African coast. DP World is scheduled to bring a new container-terminal facility on stream at Djibouti, one
Table 6: Summary of systematic review and meta-analyses studies on burden of malnutrition/disease in children 5-19 y associated with their diet

|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key Findings|
|---|---|---|---|---|---|
|Undernutrition outcom|es|||||
|Zeinalabedini et al.<br>(2023)|SRM of 20 studies,<br>all LMICs|Children 5-18 y (6<br>studies were girls only)|Diet diversity(17 based<br>on 24HR, 1 7-d recall, 1<br>30-d FFQ, 1 unknown)|Stunting, thinness, wasting|Low diet diversity increased odds of<br>stunting (OR 1.43, 95% CI 1.08, 1.89)<br>and wasting (OR 2.18; 95% CI 1.41,<br>3.36) but not thinness (OR 1.10, 95%<br>CI 0.81,1.49)|
|Berhe et al. (2019)|SRM of 22 cross-<br>sectional studies,<br>Ethiopia|Adolescents<br>(n=17,854)|Dietary DiversityScore,<br>low (DDS<4)|Underweight (BMI-Z <-2<br>SD) (n=5 studies)|Low diet diversity associated with<br>underweight (OR 1.95; 95% CI 1.31,<br>2.92)|
|Berhe et al. (2022)|SRM of 15 cross-<br>sectional studies,<br>Ethiopia<br>(includes 2 national<br>surveys in 2016 –<br>EDHS & MNS)|Adolescent girls 10-19<br
Figure 4 below presents the same Lorenz curve for the IHS4, which shows that even asking only a “most knowledgeable” household member about others’ land ownership and valuation leads to a higher Gini coefficient than household per capita wealth. Table 10, however, shows that when comparing individuallevel wealth inequality estimates across the IHPS and IHS4, inequality across different measures is still significantly higher in the IHPS (and substantially greater in magnitude as well) compared to the IHS4. There were other differences as well; the Gini decomposition revealed that non-residential land contributed more to the Gini coefficient (factor share of 59 percent), compared to residential land (41 percent).

Apart from collecting individual-level data, therefore, the mode of reporting (self- versus proxy) also matters significantly in estimating inequality, and can vary by asset class. Lower inequality in individuallevel wealth in the IHS4 may be due to the key respondent reporting the same valuation among all joint owners of a given asset. Even among those not owning jointly, however, the intra-household variation in reporting (as measured by the standard deviation in land values) for the most knowledgeable household member was also significantly lower in the IHS4 than in the IHPS (results available upon request). Relying strictly on one individual per household to report on asset valuations therefore clearly leads to less variation in reporting in the context of Malawi. Going forward, conducting similar experiments across other country contexts — and perhaps for other key assets, such as financial accounts and durables — can shed further light on the role of respondent selection on how inequality is assessed within populations.

36
1.4 data

19 agencies. Collected during the period 2006-08, these indicators reflect regulations that prevailed before 2006 in each of the 87 countries.

In terms of the coverage of subjects, sectors, and countries, this data set on FDI regulations is the most comprehensive to date. It comprises both de jure indicators measuring laws and regulations on paper, and de facto indicators that measure the implementation of those laws. To the extent possible, I create sub-indices of primarily de facto indicators of FDI Arbitration and FDI Procedures to use in the empirical estimation. On FDI Openness, the indicators are only de jure, and these tend to do less justice to countries that are open to foreign investment in practice but have not enshrined it in written law. I expand on the main explanatory variables below.

# 1.4.1 _Investing Across Sectors_

Restriction on equity ownership across sectors is one of the most important indicators of a country’s policy attitude towards foreign investment. The index of FDI openness is created for 86 countries<sup>20</sup> by averaging the equity ownership permitted for foreign companies across 33 subsectors. These measure statutory (not de facto) restrictions to the ownership of equity by foreigners in new (greenfield) investment, and investment in existing firms through mergers and acquisitions. Sixty-six data points for each country are aggregated first into 33 subsectors, and then into 11 industries (eight of which are services, two are resources/primary, and one manufacturing). The final index shows on a scale of 0 to 100 the overall openness in a given country to ownership of firms by foreign investors (0 being least open).

20 Excluding Papua New Guinea for which equity data could not be confirmed.
# Figure A.5.3 Inflation targeting in Poland

## A. Inflation and inflation expectations

<!-- Start of picture text --> Percent, year-on-year<br>14 Target upper and lower limit<br>5-year-ahead expectations<br>1-year-ahead expectations<br>10 Actual inflation<br>6<br>2<br>-2<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

## C. General government structural balance and debt

<!-- Start of picture text --> Percent of GDP Percent of GDP<br>4 Primary balance Gross debt (RHS) 90<br>2 80<br>70<br>0<br>60<br>-2<br>50<br>-4 40<br>-6 30<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2017<br><!-- End of picture text -->

## B. Central bank transparency

<!-- Start of picture text --> Index (15=best)<br>12 Poland Median of EMDEs<br>9<br>6<br>3<br>0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> D. Sensitivity of inflation expectations to shocks <!-- Start of picture text --> Percentage points<br>1.0<br>0.5<br>0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Source: Bloomberg, Consensus Economics, Dincer and Eichengreen (2014), Haver Analytics, International Monetary Fund, World Bank.

A.-D. The start of the inflation targeting regime is shaded in gray
residing within 50 km of combustion power plants. Specifically, we use modeled population at 100-meter spatial resolution available at five-year intervals from the Global Human Settlement Layer (GHSL) database<sup>8</sup> (European Commission, 2023). This dataset represents a significant advancement compared to prior attempts at modeling population distribution in space. It achieves this by refining raw census data into grid cells through the integration of remote-sensed imagery, specifically by utilizing the volume of residential areas. To avoid double counting the population in cases where the 50 km radius for different power plants overlaps, we partition these areas using a Thiessen method. We then compute the sum of the population residing in these areas in 2000, 2005, 2010, 2015, and 2020. We linearly interpolate the data obtained in the previous step for each power plant to create an annual frequency times series of the exposed population.

Additionally, to better characterize the population residing in proximity of a power plant, we use the Thiessen polygons derived previously and 2019 Human Development Index (HDI) data down-scaled to 0.1<sup>_◦_</sup> x0.1<sup>_◦_</sup> resolution from Sherman et al. (2023) to compute mean plant level HDI. We then compare the plant-level HDI with country-level HDI data from Smits and Permanyer (2019) to determine whether the population residing near power plants is systematically different.

Last, to study how excess PM2 _._ 5 concentrations and exposure may change during the operational lifespan of power plants already in operation, we use runoff projections from 22 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). All these models provide runoff projections for the key Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, and SSP3-7.0. Using this information on 66 future paths of runoff (22 models _×_ 3 SSPs), we compute for each path and hydropower watershed the average runoff anomaly at monthly
of Education and Culture) and MoRA (Ministry of Religious Affairs), the two main ministries in the education sector, and RPJMN (Rencana Pembangunan Jangka Menengah Nasional/National Medium Term Development Plan).

Despite the massive expansion of school enrollment, Indonesia still faces challenges in its human capital development. The World Bank’s Human Capital Index (HCI) utilizes health and education indicators to measure the amount of human capital a child born today can expect to attain by the age of 18, ranking Indonesia 87 of 157 countries with a 2018 score of 0.53. This means that a child born in Indonesia today will be 53 percent as productive when she grows up as she could be if she enjoyed complete education and full health. While Indonesia’s overall score is higher than the average of lower-middle-income countries (0.48), its score still falls behind the global and East Asia Pacific (EAP) averages, at 0.57 and 0.61, respectively.<sup>5</sup> Across the five indicators that comprise the 2018 HCI, scores for girls are higher than for boys, with girls achieving an HCI of 0.55 compared with 0.52 for boys. With regard to education, girls’ learning-adjusted years of school is 8.1 compared with 7.8 for boys, demonstrating a pervasive female advantage when it comes to education in Indonesia; expected years of school and harmonized test scores are also higher for girls.<sup>6</sup>

> 1 Sri Mulyani Indrawati and Marsudi, R. (2017). Girls’ education and the future of a nation. World Bank: 3 November 2017. <u>http://blogs.worldbank.org/voices/girls-education-and-future-nation?CID=EDU_TT_Education_EN_EXT</u>

> 2 World Bank (2018). Population, total: <u>https://data.worldbank.org/indicator/SP.POP.TOTL</u>

> 3 The
with characteristic _k_ , which at endline is endogenous to the treatment itself.

We therefore also report the causal impact of the treatment on facilities with characteristic _k_ at baseline. In this case, the treatment effect is most precisely reported for the likelihood of exit and patient load; for the latter, we can correctly assign a value of zero when the facility is closed. For other characteristics, such as the JHIC score, we will have missing data for the 16% of all facilities in the census at randomization that exited by endline, and although we present these results in the appendix, they come with the caveat that they pertain only to surviving facilities. With this high rate of exit, any estimates based on bounds will be quite imprecise, underscoring the importance of the market-level randomization, which still allows us to back out the policy relevant impact of the treatment on regulated markets.

> 20The treatment estimators thus correspond to population intent-to-treat, but due to the high take-up and adherence to treatment status, as well as the high response rate at endline (97% of the census of facilities), they are unlikely to differ from treatment-on-the-treated effects.

17
. Dashed line indicates the baseline unconditional response. Shaded area denotes 90 percent confidence bands, based on standard errors clustered at the country level. High-efficiency and low-efficiency samples are based on the top and bottom quartiles of the IMF (2021b) public infrastructure efficiency index, which ranges from 0 to 100 (the values above 81 and below 47, respectively).

These results are consistent with empirical studies using other samples and methods and provide support for the argument that low public investment efficiency is problematic.<sup>27</sup> Poor design, evaluation, and implementation of investment projects, including issues with corruption and governance, can deplete valuable fiscal resources without necessarily increasing the quantity or quality of public infrastructure that supports growth (Dabla-Norris et al. 2012; IMF 2014; Pritchett

> 26 The analysis uses the IMF (2021b) public infrastructure efficiency index, which is a cross-sectional index available for 120 countries (including 93 EMDEs), produced using the data envelopment analysis. The index ranges from 0 to 100, with higher values indicating better efficiency. The model was also estimated using the Devadas and Pennings (2018) infrastructure efficiency index and the Dabla-Norris et al. (2012) public investment management index. Estimations using alternative measures also suggest statistically insignificant and lower output effects of public investment in low-efficiency economies (see the results in the robustness section).

> 27 See, for instance, Cavallo and Daude (2011); Furceri and Li (2017); IMF (2014); Izquierdo et al. (2019); Leduc and Wilson (2012); and Leeper et al. (2010).

20