rafmacalaba/data-use-mentions-extended huggingface dataset:
to use alternative measures to target issues in a specific sector or supplement such a broad measure using other sectoral measures (as discussed in the next section).

In summary, a countercyclical capital buffer can be a useful macroprudential tool, particularly in increasing the resilience of the financial system. However, the reference guide for systemic risk identification using the credit-to-GDP gap—which is developed using historical data for the BCBS member countries—may not be appropriate for all countries at various stages of development. The BCBS rule already emphasizes the importance of judgment. Many developing countries may find it especially important to focus on the element of national discretion due to different patterns of growth and cycles, stages of financial development, and policy priorities. In

> 51 For example, Russia has a de facto prohibition of foreign branches. China has a differentiating capital requirement for foreign branches, while locally incorporated subsidiaries are subject to the same requirement as domestic banks after the geographic and customer restrictions were removed in 2006 following WTO accession.  New Zealand requires that systemically important foreign entities be organized as subsidiaries and conduct a substantial part of their business locally. After the onset of the crisis, the UK FSA has proposed to increase the power of the host supervisor, possibly by requiring subsidiarization (see FSA 2009a).  The Reserve Bank of India also issued a discussion paper (RBI 2011) expressing views in favor of a subsidiary structure over a branch structure and proposing incentivizing subsidiarization, although the existing laws and regulations already provide a ring-fenced structure with requirements on locally assigned capital.

> 52 For a recent discussion on subsidiarization, see Fiechter and others (2011).

26
income) by the inverse of the proportion of Ulaanbaatar households sampled.

Using a Living Standard Measurement Survey (LSMS), the World Bank found an incidence of poverty in Ulaanbaatar in June 1995 of 35 percent. See World Bank 1996 for a detailed discussion of poverty in Mongolia.

52.From SSO statistics, compiled from official police registrations.

53.The lack of a market for urban land does not mean that urban land is completely unavailable for development. But the procedures for obtaining permission to build on land are relatively more nebulous and subject to the whims of local authorities than would be the case with private ownership.

The reference to utilities reflects the difficulties of adding new demands to an already stretched energy sector. The system of centralized heating in Ulaanbaatar means that any construction of a new building requires new heating connections; any connection of a new building, therefore, requires permission to supply heat to the new
important to understand its consequences.

Table 1: FDI

||Share of regions<br>with FDI_>_0|Avg nb of projects<br>per region|Avg nb of sectors<br>per region|
|---|---|---|---|
|||2004||
|Brazil|0.74|14.8|2.5|
|Colombia|0.38|1.9|0.8|
|Ethiopia|0.10|0.1|0.1|
|Mexico|0.81|6.5|1.8|
|Philippines|1.00|12.0|3.0|
|South Africa|0.80|7.1|2.2|
|Vietnam|0.38|3.3|0.9|
||20|08, 2011, 2012 or 20|14*|
|Brazil|0.96|73.5|4.0|
|Colombia|0.79|21.7|2.8|
|Ethiopia|0.55|5.1|1.3|
|Mexico|0.97|29.3|3.7|
|Philippines|0.81|11.8|1.9|
|South Africa|1.00|28.9|4.2|
|Vietnam|0.52|5.3|1.1|

Source: fDiIntelligence. *Note: The end-of-period year varies by country due to the availability of the household survey data. The last years for Brazil, Colombia, and Ethiopia is 2014. For Mexico it is 2012. For the Philippines 2011. For Vietnam South Africa and it is 2008. The end-of-period year information is based on cumulative number of greenfield FDI projects over the last period interval for which the data is available: 2009-2014 for Brazil and Colombia, 2010-2014 for Ethiopia,
VOCATIONAL SECONDARY SCHOOLING IN ISRAEL:

# **A Study of Labor Market** Outcomes

# Summary and Implications

This paper examines the efficacy (in terms of labor market outcomes) of vocational school education in relation to that of the academic secondary school; the focus is on non-postsecondary school attenders. Given the relatively small fraction of youth that attend, and complete, tertiary education in developing countries, the secondary school completer population that does not continue into tertiary education, plays an important role in economic development. Since vocational schooling in Israel is sizeable - with over half of Israeli secondary school pupils attending vocational schools or vocational streams in comprehensive schools, it is quantitatively far more important than in most other countries - the Israeli setting seems to be a fitting one for a case study comparing the outcomes of academic and vocational schooling.

Using data from the 1983 population census, the study shows vocational schooling to be more cost-effective than general academic education. In particular, those vocational school completers that work in occupations related to the course of study pursued at school earn more (by up to about 10 percent annually) than their counterparts that attended general secondary schools or those from vocational schools employed in non course related occupacions.
_Figure 4.1 Yearly Rates of Return to Education Level, Mexico Urban Areas, 1988-2002_ <!-- Start of picture text --> 1 2 %<br>1 0 %<br>8 %<br>6 %<br>4 %<br>2 %<br>0 %<br>1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2<br>P r im a r y C o m p le te L o w e r S e c o n d a r y C o m p le te<br>U p p e r S e c o n d a r y C o m p le te T e r tia r y C o m p le te<br>Yearly Rates of Return<br><!-- End of picture text --> _Note:_ The yearly rate of return represents the additional contribution to wages from an additional year of a certain level of education. All the coefficients are statistically significant at the 5 per cent level and conditioned on age, squared age, gender, region (North, Center, South, and Mexico City).

_Source:_ Calculations using third quarter of the National Urban Employment Survey (ENEU) from 1988 to 2001 and third quarter and urban section of ENET 2002.

27
are very close to the average reported in 2017 given the very small share of the population that lives in the regions classified as IPC 2.

And lastly, prior to the 2017 IPC announcement, there were significant differences across IPC regions in the share of respondents that received food assistance in the past month. Essentially nobody received food assistance in IPC 2 regions; approximately 10 percent of respondents received assistance in IPC 3 regions; and slightly more than 20 percent received food assistance in both IPC 3+ and IPC 4 regions.<sup>25</sup> However, it is important to stress that the lack of difference in food access between all regions with an IPC classification that was more severe than IPC 2 is difficult to precisely interpret. For example, the lack of a difference could be driven by differences in the underlying data, where the mVAM is a mobile phone survey and the 2017 IPC used a variety of face-toface surveys, key informant information, and other types of sources. Or the results could be consistent with the IPC classification being driven by some of the other indicators aside from food access. But regardless of the exact reason, food access was similarly poor across much of the mobile phone-using population in the country at the time of the 2017 IPC.

# **5. The Evolution of Food Assistance and Food Access over the Course of the Food Emergency**

# **5a. Empirical Strategy**

This article primarily focuses on the change in food assistance and food access following the 2017 IPC announcement. The overall goal is to better understand if the humanitarian community can quickly and effectively respond to new threats, and the degree to which assistance helps improve food access. Focusing on the change in food assistance is important to better understand the speed with which the humanitarian community responds, and to better understand if the population with the most needs receive the most increase in assistance. Furthermore, the change further allows us to estimate the impact that assistance has on food access, which can be easier
# **Figure 3.4: TFP growth by manufacturing sectors between exporters and non‐exporters (2014)**

<!-- Start of picture text --> Servicification productivitiy premium<br>Non-exporters<br>Servicification productivitiy premium<br>Exporters<br>Basic metalsICT & electronicsBeveragesRubber and plasticsChemicalsElectircal machineryWoodNon-metallic mineralsPaperAgribusinessFabricated metalsTextilesMotor vehiclesPharmaceuticalsLeatherManufacturing necmachineryCoke, petroleumFurniturePrinting, publishWearing apparelOther transport eqTobacco<br>Basic metalsChemicalsElectircal machineryTextilesWearing apparelFabricated metalsManufacturing necRubber and plasticsmachineryNon-metallic mineralsBeverages FurnitureWoodMotor vehiclesLeatherAgribusinessPrinting, publishICT & electronicsPharmaceuticalsPaperOther transport eq<br>.1<br>0<br>-.1<br>TFP premium<br>-.2<br>.1<br>0<br>-.1<br>TFP premium<br>-.2<br><!-- End of picture text --> Source: EIS data from Turkey; author’s calculations. Note: TFP based on Ackerberg et al. (2015) Types of services that Turkish manufacturing firms perform and productivity

**To better understand why servicified firms have lower productivity in Turkey, this section decomposes services into stages of the production process.** It then highlights productivity differences between manufacturing firms that provide services in each stage. This is intended to shed light on where services sector regulatory reform is likely to have more impact **.**

18
**Table A2.1. Disaggregation of MAMS LIC archetype database**

|_Private sectors_|Agriculture: food, other<br>Industry: food, refined petroleum (only imports), other<br>Services: transportation,other|
|---|---|
|_Government services_|education (divided into primary, secondary, and tertiary)<br>health<br>agricultural infrastructure<br>other infrastructure<br>othergovernment|
|_Factors_|labor - unskilled (< completed secondary)<br>labor - skilled (completed secondary)<br>labor - high-skilled (completed tertiary)<br>agricultural land<br>private capital<br>government capital stocks(onepergovernment sector)|
|_Institutions -- current_<br>_accounts_|households: rural and urban, both split into top and bottom half<br>government<br>rest of world|
|_Auxiliary institutional_<br>_accounts_|taxes: direct, import, other indirect<br>domestic interest -- on domestic government debt<br>foreign interest -- on foreigngovernment debt|
|_Institutions -- capital_<br>_accounts_|One account for every institution with a current account|
|_Investment_|private capital<br>one investment account for each government service/capital stock<br>stock change|

29
explorers, an additional US$31 billion in exploration spending would have been undertaken between 1997 and 2022. Zambia would have received an additional US$7 billion.

_Table 3: Missing Copper Grassroots Exploration Spending in DRC and Zambia, 1997-2022_

||Land Area (sq km)|Spending per<br>10,000sqKm($M)|Potential Project<br>Spending ($M)|Actual Project<br>Spending ($M)|Missing Project<br>Spending ($M)|
|---|---|---|---|---|---|
|Chile|756,950|150.6|11,399|11,399|0|
|DRC|2,345,000|16.7|35,513|3,907|31,406|
|Zambia|572,614|28.5|8,623|1,630|6,993|

_Source: authors’ calculations based on S&P Capital IQ Database._ Note that we only report missing exploration project spending. A fraction of these projects end-up finding mineral resources and mineral reserves, and another fraction end up in production. The missing total investment spending over this 25-year period is many multiples of the missing exploration spending. To estimate this lost spending, we have compiled fairly complete economic data on a subset of 48 of the 53 projects in Figure 16. The average initial capital spending on a copper project that reaches the go-ahead stage post exploration is US$1 billion. The average operating cost per ton of ore then extracted and processed is US$47. The average amount of copper ore planned to be mined and processed at a project over its life is 387 million tonnes, for an average planned project operating cost spent of US$18 billion over the life of mine, much of which is spent domestically. It is impossible to say how many additional operating projects DRC and
Crozet, M and E Milet (2015), “Should Everybody Be in Services? The Effect of Servitization on Manufacturing Firm Performance”, GSEM working paper series 15-10-2.

Cusumano, M. A., S. J. Kahl, and F. F. Suarez (2015). Services, Industry Evolution, and the Competitive Strategies of Product Firms. Strategic Management Journal 36 (4), 559-575.

Dachs, B., Biege, S., Borowiecki, M., Lay, G., Jäger, A., & Schartinger, D. (2012). The Servitization of European Manufacturing Industries (MPRA Paper No. 38995). Vienna, Austria: AIT Austrian Institute of Technology.

Fang, E.; Palmatier, R. and Steenkamp, J. (2008) “Effect of service transition strategies on firm value”, Journal of Marketing, 72, pp. 1-14.

Federico, S., & Tosti, E. (2012). Exporters and importers of services: firm-level evidence on Italy: Bank of Italy, Economic Research and International Relations Area.

Gebauer, H. and E. Fleisch (2007). An Investigation of the Relationship Between Behavioral Processes, Motivation, Investments in the Service Business and Service Revenue. Industrial Marketing Management 36 (3), 337-348.

Gebauer, H., Fleisch, E. and Friedli, T. (2005) “Overcoming the service paradox in manufacturing companies”, European Management Journal 23(1): 14-26.

Khandelwal, A. (2010). The Long and Short (of) Quality Ladders. The Review of Economic Studies, 77(4), 14501476.

Lodefalk, M. (2013). Servicification of Manufacturing - Evidence from Sweden. International Journal of Economics and Business Research 6 (1), 87-113.

Lodefalk, M (2015), "Servicification of Manufacturing Firms Makes Divides in Trade Policy-Making Antiquated", Örebro University, Working Paper 2015:1.

Malleret, V.
, and RW’s net debt-GDP ratios are moderate on average: around 36%, 57%, and -35% of GDP, respectively. Yet, these debt levels have increased sharply in recent years (OECD 2022). Despite a low net debt vis-a-vis the RW, T ̈urkiye imports around 87% of its total primary energy supply.

## **2.1 High- and low-skilled labor households**

Capturing to some extent how the low-carbon transition affects households differently, the model features _high-skilled labor_ H-households (50% of the labor force) and liquidity-constrained _low-skilled labor_ L-households (50% of the labor force). We assume that L-households hold no financial wealth. In T ̈urkiye, the 50% top-income households account for two-thirds of all consumption expenditures. Hence, we assume H-households consume twice as much as L-households while providing the same labor hours. The former receive capital income as well as a higher real wage. The overall wage share in total income (household income and retained earnings) is around 50%. L-households also receive transfer payments from the government (around 7.3% of GDP).

H-households own the firms and domestic carbon mines (oil, coal, and natural gas), provide high-skilled labor services, and save. Because a specific household’s labor variety is unique, each H-household has market power. Wage adjustment costs allow the model to capture the persistence in high-skilled wage contracts as observed in empirical data (Taylor

> 4Appendix A reports the model in detail.

> 5Appendix B reports the model calibration and lists the respective data sources and references.

6
Over the past decade, BRI-recipient LIDCs, particularly those with high risk of debt distress, have increased their exposure to non-concessional debt and debt from non-traditional creditors, including China (Figure 10). Debt from multilateral and traditional bilateral creditors represented more than 70 percent of all external public debt of BRI-recipient LIDCs in 2016 (panel a). Consistent with the general trend of increased debt vulnerabilities in LIDCs (IMF, 2018 and World Bank, 2018a and 2018b),<sup>22</sup> the group of BRIrecipient LIDCs has over time increased reliance on private financing, including bonds and bank financing, which has doubled in the past 10 years (panel b). Non-concessional financing has increased especially in those BRI-LIDCs with moderate risk of debt distress. Exposure to non-Paris Club official creditors, including China, has also increased for the group of BRI LIDCs, reaching 22 percent of total external public debt. The largest increase of the exposure to non-traditional creditors took place in LIDCs with moderate and high risk of debt distress, with most of the increase in the latter group due to large borrowing from Laos (panel c and d).

<!-- Start of picture text --> Figure 6. General Government Gross Debt<br>(% of GDP)<br>55%<br>50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>LIDCs LIDCs - BRI<br>EMEs EMEs - BRI<br><!-- End of picture text --> Source: International Debt Statistics.

<!-- Start of picture text --> Figure 7. Public and Publicly Guaranteed<br>External Debt<br>(Median levels, % of GDP)<br>30%<br>25%<br>20%<br>15%<br>10%
# **4.2 Estimating program impacts on refugee well-being**

The primary approach to estimate program treatment effects on the treated (TOT) is instrumental variables (IV) estimation, as represented by the following two equations: Equation (1) estimates the first-stage effect of assignment to treatment on the household’s takeup status (with results presented above), and equation (2) estimates the TOT effects. To enhance statistical power, data from the midline, endline, and follow-up surveys are combined in the main analysis when possible, as pre-specified. The data includes multiple rounds _t_ of survey data for each household, and the data are stacked. _Tic_ denotes the treatment take-up for household _i_ in community _c_ . _Zc_ is an indicator variable signifying whether community _c_ was randomly assigned to HSP treatment, where all eligible individuals were then assigned to the treatment. The outcome variable of interest is _yict_ , and the predicted treatment take-up, _T_<sup>�</sup> _ic_ , is derived from equation (1).

The vector _Xc_ encompasses community stratification variables used in the randomization, including indicators for whether a community is in Mafraq or Irbid and the community’s district’s population quartile<sup>12</sup> The vector _Wic_ includes individual baseline covariates, accounting for household-level demographic variables from the integrated assessment; in particular the vulnerability quartile, month of assessment, household size, number of children, and respondent gender and age. _μict_ is a vector of enumerator-round fixed effects. _ηic_ and _εict_ are error terms and are clustered at the community level, which is the level of randomization.

To assess dynamics, the analysis also estimates TOT effects separately with the midline, endline, and follow-up data, while the regression specification remains effectively the same as previously described, with enumerator fixed effects (rather than enumerator-round fixed effects). The

> 12As noted before, communities are defined by the Jordanian administrative unit “locality”. “District” is a more aggregated, administrative geographic unit
ones such as 2016/17 (-0.14) that are unfavorable to development of fungi and the associated spread of stem rot so that soybean’s ability to fix nitrogen outweighs its role as potential disease vector. While other crops have a slightly larger negative effect, monocropping of sunflower is estimated to have the most negative effects with coefficients of -0.94 in 2016/17 when, in addition to disease permanence, water access is likely to have acted as a constraint, and -0.61 in 2017/18. With coefficients of -0.45 and -0.90 a three-year sunflower succession has a more negative impact than a two-year one.

As limited training data made it impossible to distinguish between wheat and barley as the main cereal crops in satellite imagery, cereals’ high levels of self-incompatibility, with coefficients of -0.30 and -0.79 for two-year cereal successions in 2016/17 and 2017/18, respectively are only slightly above those for maize (-0.22 and -0.19 in the two periods) and other crops (-0.29 and -0.51), reflecting the positive contribution of soybeans (the excluded category) as a predecessor crop due to N fixation. With -0.83, the estimated coefficient on a three-year cereal monocrop is not significantly different from that on a two-year sequence (-0.76).

# **4.2 Using alternative outcome variables**

Vegetation indices based on remote sensing data can complement statistical data two ways. First, rather than having one outcome variable (yield), they offer additional variables such as soil water content or leaf area at much higher temporal resolution. This allows to take corrective action in near real time<sup>13</sup> and to assess channels for observed effects _ex post_ . Second, by providing an independent source of information, they can allay concerns about bias in statistical data that may result from misreporting to statistical agencies
itself the key problem or the ‘binding constraint’; as such, the priority issue is discerning what _is_ in fact the key policy problem and its constituent elements. From this starting point, more and better data can be part of, but not a substitute for, strategies for enhancing the effectiveness of public sector agencies.

Even if both data management and broad institutional capability are functioning at high and complementary levels, there remains the structural necessity of interpreting what the data _means_ . Policy inference from even the best data and most rigorous methodology is never self-evident; it must always be undertaken in the light of theory. This might sound like an abstract academic concern, but it is especially important when seeking to draw lessons from, and/or make big decisions regarding the fate of, complex interventions. This is so because a defining characteristic of a complex problem is that it generates highly variable outcomes across time, space, and groups.

> 6 If such agencies/departments do in fact happen to perform especially strongly – in the spirit of the ‘positive deviance’ cases of government performance in Ghana provided in McDonnell (2020) – then it would be useful to understand how and why this has been attained. For present purposes, our point is that, perhaps paradoxically, we should not expect, ex ante, that agencies/departments in the business of collecting and curating data for guiding policy and performance to themselves be exemplary exponents of the deployment of that data to guide their _own_ performance – because doing so is a separate ontological task, requiring distinctive professional capabilities. Like the proverbial doctors, if data analysts cannot “heal thyselves” we should not expect other public agencies to be able to do so merely by infusing them with more and better data.

> 7 A special issue of _The Journal of Development Studies_ (Volume 51, Issue 2) was dedicated to this issue. For example, on the enduring challenges associated with agricultural data – another sector with a long history of data collection experience – see <