rafmacalaba/data-use-mentions-extended huggingface dataset:
may capture aspects of well-being that are not captured by standard survey-based measures of poverty such as wealth and consumption.

To test for systematic misclassification of certain types of households, Table 4 displays the overlap in errors of exclusion and inclusion between methods. Our results suggest that the three classifiers misidentify the same households at a rate only slightly above random.<sup>8</sup>

# **3.3 Combining Targeting Methods**

Since the different targeting methods identify different populations as ultra-poor, there may be complementarities between asset, consumption, and CDR data. As shown in Panel A of Table 2, we find that a _combined method_ , which takes as input the wealth index, total consumption, and the output of the CDR-based method, performs better (AUC = 0.78) than methods using any one data source (AUC = 0.68 - 0.73). As shown in Table S5, the full method also outperforms methods based on any two data sources (AUC = 0.75 - 0.76). The method that combines CDR and asset data (AUC = 0.76) may, however, be more practical than the combined method, since consumption data is difficult to collect for large populations.

# **3.4 Targeting Households without Phones**

An important limitation of CDR-based targeting is that households without phones do not generate CDR. Here, we show how targeting performance is impacted when households without phones are included in the analysis. This analysis uses two additional samples of TUP households to evaluate targeting performance: (i) the _balanced sample_ , which adds all of the 472 households without phones to the sample of 535 for whom we have matched CDR; the balanced sample is intended to illustrate the performance of CDR-based targeting if — CDR were available from all operators in Afghanistan though it relies on the assumption that phone-owners observed on our mobile network are representative of all phone owners in Afghanistan (an assumption that is not fully satisfied, as shown in Table
**Figure 4 - Energy-Intensive Sectors Take Up to 40 Percent of Value Added in ECA**

Cumulative percentage of value added, by sector, 2008 <!-- Start of picture text --> 100%<br>energy intensive<br>50%<br>industries<br>20.30%<br>11.48%<br>2.96%<br>2.24%<br>0%<br>Mining Elec, Gas, Water Transport Manuf Hotel & Rest<br>Health Education Finance Public Admin Agriculture<br>Constr Retail Real Estate<br><!-- End of picture text --> Source: Authors’ calculation based on UN Data Note: The figure is a representation of unweighted averages across ECA countries. The names of the sectors had been shortened to fit the axis. See previous note for full names of sectors.

ECA countries are significantly more vulnerable than the more developed countries in Western Europe, with the exception of Norway being an oil producer (Figures 5 and 6). There is significant variation within the ECA region in the exposure or vulnerability of countries to an energy price shock.

Looking at the potential price increase data and assuming the long-run marginal cost of generation is determined by the average cost of building a gas-fired combined cycle power plant, the average cost recovery price of electricity supply in ECA will be around 12.5 cents per kWh.<sup>13</sup> Also assume an energy price at $15 per tones of CO2 equivalence. We estimate the potential price hikes facing individual countries. As shown in Figure 5, some CIS countries, notably Ukraine, Russia and Armenia, need to make the largest adjustments in order to move tariff close to cost recovery. Meanwhile, some EU and EU accession countries, such as Estonia, Bosnia and Herzegovina and Macedonia that rely more heavily on fossil fuel fired generation, are likely to be hit hard once an energy price is
specification, but the linear estimates from Table 2 are also instructive. The 3.3 year increase in years of schooling for adults and the over 50% increase in real per capita expenditure imply only a modest decrease in AL over this period (about 3%) for demographically similar households, with a small offset from the decrease in average household size from 6.3 to 5.3 individuals.

The fact that TEE is falling for adult men, adult women and children yet rising for the population reflects the important role played by populationlevel demographic change. The share of children aged 14 and under in the population fell from about 40% in 1983 to 30% by 2012. This implies considerably higher TEE, not just because children are physically smaller and have lower REE, but because children are also much less active than adults according to the TUS estimates, particularly in rural areas. In 1983 child TEE was about 60% of adult TEE, implying that the demographic shift over 11
exogenous to the firm’s export decisions.

Credit constraints may have an important impact on firm exports in ECA countries, especially given the large presence of foreign banks in the region. Based on data from Claessens and van Horen (2012), Figure 2 shows that the ECA region stands out in terms of presence of foreign banks. As European banks are the largest sources of FDI in the banking sector of ECA countries, this initial wave of transmission of credit shocks produced immediate effects on the credit markets of the countries we study.<sup>7</sup> There is indeed growing evidence that banks transmit credit shocks

> 6Alternative possible answers to question (1) are “Application procedures for loans or lines of credit are complex”, “Interest rates are not favorable”, “Collateral requirements are too high”, “Size of loan and maturity are insufficient”, “It is necessary to make informal payments to get bank loans”, “Did not think it would be approved”.

> 7Giannetti and Laeven (2012) show that the collapse of the global market for syndicated loans during financial crisis can in part be explained by a flight home effect whereby lenders rebalance their loan portfolios in favor of domestic

7
bias in road spending allocation decisions. Countries with Road Funds (represented by blue circles in Figure 18) are more likely to allocate adequate resources to the road sector, either on maintenance and/or investment, than those without. Nevertheless, the presence of a Road Fund is not of itself a guarantee that maintenance expenditure will be _enough_ . Countries like Lesotho and Sierra Leone, for instance, do not appear to capture adequate resources through their Road Funds to support full maintenance requirements.

> 13 The above estimates are based on the BOOST, Road Fund and OECD-DAC data and do not include any maintenance expenditure undertaken by toll roads as this data is not available. However, based on the earlier analysis of Road Funds, this is likely to be relatively small overall.

20
welfare indicators are not adjusted to spatial price differences in many countries. The World Bank however usually recommends spatial price adjustment when measuring poverty and inequality (Deaton and Zaidi, 2002; Ravallion, 1992, 1996). This is due to the fact that different price levels across geographical areas represent different levels of consumption and welfare for the same amount of expenditure.

In the past, PovcalNet, decided not to use spatially deflated household expenditures to facilitate inter‐ temporal and international comparability of extreme poverty indicators. Indeed, the ways spatial price adjustments are conducted differ largely across countries and are for some countries not properly documented. Recently however, the documentation of price adjustments has improved and the adopted methodologies have become more standardized. PovcalNet started to accept spatially adjusted welfare aggregates but for most countries, the extreme poverty and the shared prosperity indices are still estimated with spatially unadjusted welfare aggregates. It is important to examine implications of the use of spatial price adjustments for estimating the shared prosperity index.

Another potential issue is that when calculating shared prosperity for selected countries (and extreme poverty), mean expenditure or income of the poorest 40 percent of the population are estimated using grouped data rather than estimating directly from the distribution of the household expenditure or income. The grouped data approach first calculates means of household expenditure or income for deciles or ventiles and then estimates a Lorenz curve from the grouped data. Finally, the mean of the bottom 40 percent of the population of a country is estimated from the fitted Lorenz curve and the mean of household expenditure or income in the whole population (Datt, 1998; World Bank, 2008; Mitra et al., 2010; or Box 2 in the annex).

The mean of the poorest 40 percent of the population can be directly calculated from the microdata or grouped data. Creating grouped data and estimating a Lorenz curve using grouped data seems unnecessary and some might even argue that the additional steps add non‐negligible estimation errors (Azevedo and Mitra, forthcoming). The grouped data approach was adopted
bilaterally and disaggregated by gender.<sup>11</sup> Of the 3,500 sources detailed in the overarching Global Migration Database, 1,107 were suitable for analysis once repeated censuses were removed or combined. Of these, 951 record data disaggregated by gender, as reported in table 1.

# **{Table 1 about here}**

Despite the large number of primary sources, there are still inevitable gaps (table 2). This might be because a particular destination country did not conduct a census in a given decade or disseminate the relevant bilateral or gender-specific information. The majority of the migrants omitted from these censuses are in the Middle East and Africa. The countries of the Middle East are often reticent about releasing data, while many countries in Africa have a long history of conflict. Nonetheless, the 68 countries for which there are complete data account for 68 percent of the world migrant stock in 2000. The 17 countries for which there is only one census account for less than 2 percent of the total stock. The data for earlier decades reflect an identical pattern.

# **{Table 2 about here}**

# _II. HARMONIZING THE MATRICES_

Given the complexities of the underlying data, several major challenges arise in constructing global bilateral migration matrices. The most critical were explained above. In some cases, there is no choice but to recognize that the underlying processes that generated the data are less than ideal and to accept the data at face value. In others, every effort has been made to standardize the data.

# _Defining the Master Country List_

Over the period covered by the 1960–2000 censuses used to construct the global bilateral matrices of migrant stocks (1955–2004), the global political landscape underwent fundamental changes. Many countries, especially in Africa, Oceania, and the Caribbean, gained their independence. Following the end of the cold war, many countries redrew their political boundaries. Some fragmented into smaller nation states, such as the Soviet Union, Czechoslovakia
- Holz, C. A., & Sun, Y. (2018). Physical Capital Estimates for China’s Provinces, 1952–2015 and Beyond. China Economic Review, 51(October), 342–57.

- Hu, W., Ho, M. S., & Cao, J. (2019). Energy Consumption of Urban Households in China. China Economic Review, 58(December), 101343.

- IRENA. (2019a). Future of Solar Photovoltaic: Deployment, Investment, Technology, Grid Integration and Socio-Economic Aspects. A Global Energy Transformation: Paper. Abu Dhabi: International Renewable Energy Agency.

- IRENA. (2019b). Future of Wind: Deployment, Investment, Technology, Grid Integration and Socio-Economic Aspects. A Global Energy Transformation: Paper. Abu Dhabi: International Renewable Energy Agency.

- Jomini, P., Zeitsch, J. F., McDougall, R., Welsh, A., Brown, S., Hambley, J., & Kelly, J. (1991). SALTER: A General Equilibrium Model of the World Economy. Database and Parameters, Industry Commission, Canberra.

- State Information Center. (2020). Medium and Long-Term Goals, Strategies, and Paths of China’s Economic and Social Development. Beijing: State Information Center.

- Ross, M. T. (2014). Structure of the Dynamic Integrated Economy/Energy/Emissions Model: Computable General Equilibrium Component, DIEM-CGE. NI WP 14-12. Durham, NC: Duke University.

- RTI International. (2015). The Applied Dynamic Analysis of the Global Economy (RTIADAGE) Model. U.S. Regional Module Final Release.

- Shi, G., Zheng, X., & Song, F. (2012). Estimating Elasticity for Residential Electricity Demand in China. The Scientific World Journal.

- Zhang, L., Brooks, R., Ding, D., Ding, H.,
subsidizing education with taxpayer money, and then high-skilled individuals who emigrate not contributing back into the tax system. More recently, much of the concern has been with the supposed externalities health and education professionals have on the well-being of others in their communities. The potential importance of such externalities, complementarities, and fiscal costs in practice strike us as important economic questions.

Finally, it is also worth reiterating that in many developed countries, immigrants constitute important shares of the skilled labor force in many professions. Understanding the determinants of this migration should therefore be of interest even to those whose prime focus is the U.S. economy. To illustrate this, table 2 uses the ACS 2008 to show the share of all tertiary educated workers, and of all workers with Ph.D.s who are foreign-born, and who are born in developing countries for the professions identified in table 1 as being the main occupations for skilled migrants. We see developing country migrants constitute 47 percent of individuals with a Ph.D. working as computer software engineers in the U.S., 36 percent of medical scientists and 35 percent of engineers with Ph.Ds.

# **Question 3: Is Brain Drain Increasing?**

In absolute levels, skilled migration is increasing. However, skill levels in migrantsending countries are also rising, and for the world as a whole, skilled migration rose at about the same pace as overall education levels in the sending countries in recent decades, so that the brain drain rate remained quite stable for long periods of time and may have even fallen in the past decade.

Between 1960 and 2010, the global migrant stock increased from 74 to 188 million, only slightly faster than world population growth, so that the share of the world‘s population who are international migrants increased only from 2.7 to 2.8 percent (UNDP, 2009). However, while the global migration rate has been quite flat, the migrant flow has increasingly been from less developed to more developed countries
hold the incentive environment fixed and just consider how the selection of ( _τ, θ_ ) types produces test scores. Substituting for _e_<sup>_I_</sup> , we can write this second selection term as We see from this expression that there are also two selection channels. A further part of the reason that test scores are higher in schools with good people management practices is because: (i) the _τ_ -types selected in are intrinsically motivated to exert more _effort_ , and (ii) the _θ_ -types selected in are of greater _ability_ .

To see this, consider the numerical example illustrated in Figure 7. In the top panel, the grey shaded area depicts the set of ( _τ, θ_ ) types that are hired by a high management private school. The unshaded area depicts the set of ( _τ, θ_ ) types that are hired by an intermediate management public school. It is clear that the intermediate management public school experiences negative selection on both dimensions. More able teachers prefer the performance-contingent compensation schemes available either in private schools or the outside sector. And more intrinsically motivated teachers prefer private schools because they anticipate exerting higher effort (and hence higher pay).

# **3.3.2 Predictions for intermediate school outcomes and evidence from PISA**

Our theoretical framework suggests two mechanisms, teacher selection and teacher incentives, that could explain the positive correlation between people management scores and student learning outcomes apparent in the WMS, PISA and Prova Brasil data. If these mechanisms are correct, then we should see behavioural responses in intermediate school outcomes. In this section, we set out these predictions and then explore empirically whether they hold in our PISA data for Latin America.<sup>21</sup>

> 21These predictions are based on the numerical example illustrated in Figure 7 and are derived (via numerical integration) in Remarks 1 and 2 in Appendix A. We present the main results with our preferred specification, but include additional variations of the main explanatory variable in
# **1 Introduction**

There is a large consensus in the literature that female empowerment improves women’s and children’s outcomes and plays a positive role in economic development overall (e.g. Duflo, 2003 and Allendorf, 2007). Women living in the Middle East and North Africa (MENA) region score lowest worldwide on most measures of economic empowerment, including access to property ownership (World Economic Forum, 2023). In the region, inheritance has historically been the primary way through which women access property and it plays a decisive role in allowing women to participate in the economy, e.g. by enabling them to start their own businesses (IFC, 2017). Property ownership can insure women against unforeseen misfortunes and increase their bargaining power within the household. However, most MENA countries apply Islamic inheritance law, which systematically favors sons over daughters. The bequest foreseen for an individual’s children is split unevenly by gender: a son always receives twice the share of a daughter. This arrangement is commonly justified with a man’s obligation to take care of his wife, daughter or sister. Social reality in the MENA region, however, is changing. In Tunisia, women represent more than half of the college student population (World Bank, 2022b) and marry increasingly late or not at all (Assaad et al., 2017). The “gender gap” in education has reversed: in the most recent panel survey, 71 percent of Tunisian women were as educated as or even more highly educated than their husbands (Assaad et al., 2017). Even though female labor market participation remains relatively low, household surveys suggest that women contribute a significant share to household budgets (ERF, 2014). The persistence of gender discriminatory inheritance law has been associated with women’s precarization not only in Tunisia, but also in Morocco and Egypt, and particularly when it comes to land bequests: if not entirely left out, women tend to receive the least
<!-- Start of picture text -->
-8 -6 -4 -2 0 -10 -8 -6 -4 -2<br>Log of General Gov. employment Log of Central Gov. employment<br>4.5 4.5<br>4 4<br>3.5 3.5<br>3 3<br>log of General Gov. expenditure 2.5 log of General Gov. expenditure 2.5<br>2 2<br><!-- End of picture text -->

<!-- Start of picture text -->
-6 -4 -2 0<br>Log of Public Sector employment<br>Source: ILO Stats and IMF<br>0 20 40 60 80<br>General Government Expense (% GDP)<br>4.5<br>4<br>3.5<br>3<br>log of General Gov. expenditure 2.5<br>2<br>30<br>20<br>CE (% GDP)<br>10<br>0<br><!-- End of picture text -->

_Figure 2: The wage bill and total government expense_ Source: ILO Stats and IMF 5
# **_4.1.2. Empirical strategy_**

In order to make use of the retrospective information on school attendance provided in the dataset, we have constructed what we have called an _‘ad hoc panel’_ , whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset. In this way, we are able to obtain observations for each individual over three academic years. All key education variables are timevariant, while other individuals and households characteristics are time-invariant .

Within these three years, we focus our analysis on individuals that were of primary school age (between 7 and 12 years old) in each year. In practice, we keep all children aged at minimum 7 years old in 1998 and at maximum 12 years old in 2000. As a consequence, our panel data contains children aged 8-11 years in 1999, the year of the violence.<sup>11</sup> Since we are interested in looking at different effects across groups of individuals, we have split the sample between boys and girls and between younger children (aged 8-9 in 1999) and older children (aged 10-11 in 1999). We estimate the following equation: where are our outcome variables, i.e. attendance rate (which is a binary variable<sup>12</sup> ) or grade deficit. and are year dummies respectively for year 2, the year of the 1999 violence, and for year 3, the first year of the post-conflict period. The model includes individual fixed effects, . The variable is the random error. All standard errors are clustered at the village level.

As discussed above, we identify violence-affected individuals using two different measures, , with _j=1,2_ depending on which measure is included in the specification. The first one is whether the individual was displaced with the whole household. The second is whether the individual reports that her house was
Figure 11. Debt Composition of BRI-recipient EMs (In percent) <!-- Start of picture text --> a. By type of financing b. By debt vulnerability (2016)<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Bilateral concessional2007 2012Multilateral concessional2016 Bilateral concessionalLow Multilateral concessionalHigh scrutiny<br>IMF Bilateral non concessional IMF Bilateral non concessional<br>Multilateral non concessional Bond Multilateral non concessional Bond<br>Other private debt Other private debt<br><!-- End of picture text --> Source: International Debt Statistics.

Source: International Debt Statistics.

- c. Share of countries above investment grade*

<!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Source: Dealogic.<br>*Moody’s ratings of Baa3 or above.<br><!-- End of picture text -->

- d. Number of upgrades/downgrades

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Upgrades Downgrades Net change<br><!-- End of picture text --> <!-- Start of picture text --> Source: Moody’s ratings
|**Upazila Name**|**Number**<br>**of**<br>**Villages**|**HH on**<br>**the**<br>**validation**|**Prevalence**|**Average**<br>**prevalence**<br>**within**<br>**villages**|**Number of**<br>**villages**<br>**with 0%**<br>**prevalence**|
|---|---|---|---|---|---|
|Chakaria|6|1,630|1.10%|0.54%|4|
|Cox's Bazar Sadar|10|4,221|2.99%|2.03%|4|
|Pekua|2|501|0.00%|0.00%|2|
|Ramu|3|508|2.95%|3.16%|1|
|Teknaf|7|2,830|1.41%|1.36%|3|
|Ukhia|5|1,467|0.14%|0.08%|4|
|Total|33|11,157|1.80%|1.30%|18|

**<u>Source:</u>** <u>Validation household listing, 2018</u>

This two-step data collection on Rohingya refugee prevalence confirmed that prevalence in host communities was low, and that this was the case not only for newer Rohingya displaced, but for the older cohort of displaced, as well. These figures could still to some extent reflect the displaced population’s mobility, as well as Rohingya’s unwillingness to reveal