rafmacalaba/data-use-mentions-extended huggingface dataset:
variables are best represented as trend or first-difference stationary (see, for example, Nelson and Plosser, 1982). Although in theory the hypothesis that the series is difference stationary can be tested, it is widely recognized that those tests have very low power, particularly in small samples (Enders, 2003). Also, although the long run properties of the alternative representations are very different, from a practical standpoint the short run forecasts obtained from them tend to be similar. In our particular case, using the Levin et al. (2002) test for unit roots in panel data, we reject the null hypothesis of a unit root for all the panel variables, _GDP , DMTT ,_ and _AID,_ but standard Dickey-Fuller tests cannot reject the hypothesis for the variables capturing the state of the world business cycle ( _GDPH_ and _R_ ), which are common to all countries (the results of the unit root test are reported in Table A1 in the appendix). However, for these common variables we cannot take advantage of the cross-section dimension to increase the power of the tests, so we have only 31 observations. Given the low power of these test, and the small number of observations we prefer to follow the results obtained for the panel variables and estimate the system assuming that the series are trend stationary. Nevertheless, we will also estimate the model in first differences and the results, reported in the robustness section of the paper, will prove qualitatively similar.<sup>7</sup> The model corresponds to a _panel VAR_ in which it is assumed that the dynamics, represented by the _A_ matrices, are common across cross-sectional units. This is a standard assumption in this literature (see Broda, 2001, 2004; Ahmed, 2003; Uribe and Yue, 2003) because, given the length of the time series dimension of the data (around 30 annual observations), it is not possible to estimate country specific dynamics unless we reduce importantly the number of exogenous shocks under consideration, the
# _Calibrating the Model Using Korea Data_

We begin by discussing the debt and threshold rule parameters _bo, fo, do, 0,_ and 'Q. The domestic and foreign debt levels of the public sector in our model were calibrated using the consolidated debt levels of the Korean government and central bank. The Korean Ministry of Finance (MOFE) reports that the level of total government and public sector bonds outstanding at the end of December 1996 was equal to approximately 37.4 trillion won.<sup>15</sup> According to the International Financial Statistics (IFS) of the International Monetary Fund (IMF), the Bank of Korea held claims on the government equal to 2 trillion won as of the end of December 1996. To avoid double counting, we subtracted this number from the reported level of total government and public sector bonds. The IFS also reports that the Bank of Korea had net domestic liabilities (excluding those vis-a-vis the government, official entities and base money) of 4.7 trillion won. Based on this evidence, we conclude that consolidated government domestic liabilities equaled roughly 37.4-2+4.7 = 40.1 trillion won, or 10.3 percent of 1996 GDP. Accordingly we set bo = 0.103.

The Korea Institute for International Economic Policy (KIEP) estimated that the foreign debt of the public sector in June 1997 was equal to 2.0 trillion won.<sup>16</sup> According to the IFS, the value of the central bank's net foreign assets was approximately 28.0 trillion won. This suggests that the overall net foreign assets of the consolidated public sector was equal to roughly 26.0 trillion won or 6.7 percent of 1996 GDP. We set _fo_ = 0.067 and bo _-_ fo = 0.036. According to the Bank of Korea, the value of private sector net foreign debt at the end of 1996 was roughly 13.9 percent of GDP. Accordingly we set
To construct the dataset for DPCMREL, we started with the UNIDO database and cleaned the data by excluding observations where value added exceeded the value of output (7,785 observations) and wages and salaries exceeded value added (4,776 observations). Because of missing variables, we are able to construct DPCMREL for 915 observations. ICRG coverage of institutional variables starts in 1984, therefore the set decreases to 340 observations. Because we have no external dependence measure for Industrial Chemicals and Tobacco, we lose another 24 observations. Hence, we end up with a dataset containing 316 observations, which includes Algeria, Brazil, Bulgaria, Finland, Hungary, Indonesia, Japan, Korea, Madagascar, Panama, Poland, Senegal, Sweden, Venezuela, Zambia, and Zimbabwe. Coverage is good (over 20 industries per country on average), however Algeria has 1 observation.

To construct the dataset for DESTREL, we started with the UNIDO database and cleaned the data as already described. Because of missing variables, we are able to construct DESTREL for 583 observations. ICRG coverage of institutional variables starts in 1984, therefore the dataset shrinks further to 324 observations. Because we have no external dependence measure for Industrial Chemicals and Tobacco, we lose another 23 observations. Like Laeven, Klingebiel, and Krozner (2002), we drop observations for industries which have increased over 100% during the crisis-period, since this is most probably due to increased coverage or reclassification of firms. Hence, we end up with a dataset containing 260 observations, which includes Brazil, Bulgaria, Finland, Hungary, Indonesia, Japan, Korea, Panama, Poland, Senegal, Sweden, Venezuela, Zambia, and Zimbabwe. Coverage of industries is good (on average more than 20 industries), except for Bulgaria, Hungary, and Senegal which have fewer than 3 observations.

17
# Figure 8. Risks to EMDE growth prospects: Other adverse shocks

# A. Share of countries with growth slowdowns

# B. Spillovers from the United States, euro area and China

<!-- Start of picture text --> Percent Percentage points<br>90 EMDE Global<br>Advanced economies EMDEs<br>0.0<br>80<br>70<br>-0.5<br>60<br>50 -1.0<br>40<br>30 -1.5<br>20<br>-2.0<br>United States Euro Area China<br>C. Spillovers from EM7 D. Number of armed conflicts<br>Percentage points EMDEs Number<br>55<br>0.2 1990-2017 average<br>50<br>-0.1<br>45<br>-0.4<br>-0.7 40<br>-1.0 35<br>30<br>E. Oil production F. Weather-related events<br>Mb/d 1000 2017<br>100<br>Rest of the World MENA 1960-2009<br>800<br>2010-2018<br>80 2005<br>600<br>60<br>400<br>40<br>200<br>20<br>0<br>0 0 50 100 150 200 250 300 350 400<br>2007 2018 Occurance<br>China Russia India Brazil Mexico Indonesia Turkey<br>2015 2016 2017 2018 2019<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2017<br>US$ million<br>Damage per event, Real<br><!-- End of picture text --> Source: Centre for the Study of Civil War at the Peace Research Institute Oslo (PRIO), CRED/OFDA International Disaster Database, Huidrom et al (2019),
||||
|21|<br>Health economics worldwide|6%|16|278|52|195|254|235|8|177|0.116|235|20%|343|
|21|Healthy markets? The new competition in<br>medical care|36%|14|324|60|170|495|132|8|177|0.225|129|44%|117|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. An _h_ -index of 10 means that the author has 10 publications to his or her name each of which has been cited at least 10 times. The quadratic influence function is defined in eqn (1). The measure _I_<sup>3</sup> is defined in eqn (2). A normalized h-index of 20% means that 20% of articles in the journal have been cited at least 20 times.
11 of support to local suppliers, as evidenced by survey findings for China by Liao and Wei (2013) and Poland by Gentile-Ludecke and Giroud (2012).

The _nationality or country/economy of origin_ of MNC affiliates. This factor can affect the use of local suppliers and productivity spillovers in various ways. Wei et al. (2012) report that affiliates from Hong Kong SAR, China; Macao SAR, China; Taiwan, China; and Singapore source more local inputs in China compared to MNC affiliates from other countries, suggesting that cultural proximity facilitates the use of local suppliers. Ni et al. (2017) estimate productivity spillovers among Vietnamese firms and find that only MNC affiliates of Asian origin generate significant effects.

However, it may also be the case that MNC affiliates from home economies located far away from their host economies use more local suppliers, as high transportation costs can prevent the use of suppliers located in the home economies (Rodriguez-Clare 1996). This is in line with findings by Amendolagine et al. (2019) for Sub-Saharan Africa that show that the distance between the MNC affiliates’ home and host economies has a positive effect on local sourcing. It also provides an explanation for findings presented by Javorcik and Spatareanu (2011) for Romania that only MNC affiliates from the United States create significant productivity spillovers. Related to this, Javorcik, Lo Turco, and Maggioni (2018) find in their study on Turkey that only MNC affiliates originating from highincome home economies generate positive productivity spillovers. This finding suggests that affiliates from high-income countries have larger technological capabilities that increase the scope for positive externalities.<sup>8</sup>

# **3.2. Characteristics of Domestic Firms**

_Unavailability of inputs_ . A key factor that many studies find limits the use of local suppliers by MNC affiliates is the _unavailability of particular inputs_ . As for inputs that can (potentially) be sourced from local suppliers, MNC affiliates often report that various characteristics of these suppliers limit their degree of local
underestimation (overestimation) of urban (rural) poverty. Furthermore, housing costs are often excluded from spatial price deflators in global poverty measurement. However, housing costs tend to vary the most across different locations, particularly in urban areas. By excluding housing costs from the price deflators, the cost of living in urban areas is underestimated.<sup>9</sup> This can lead to an overestimation of the living standards of urban households and an underestimation of urban poverty, as well as an underestimation of rural living standards and an overestimation of rural poverty.

Ravallion et al. (2007) and Ferré et al. (2012) are important studies that have contributed to our understanding of the spatial dimension of poverty from a global perspective. Ravallion et al. (2007) conducted a groundbreaking study that examined the urbanization of global poverty using data from over 200 household surveys in approximately 90 countries between 1993 and 2002. The study introduced several innovative approaches. Firstly, it utilized global poverty lines to measure poverty, providing a standardized measure across countries. Secondly, it addressed the differences in the cost of living between urban and rural areas by calculating the ratio of urban and rural poverty lines for each country.<sup>10</sup> Thirdly, the authors analyzed the dynamic aspects of urbanization and poverty reduction, as well as the and heterogeneity across different world regions, using multiple data points over time. However, one limitation of Ravallion et al. (2007) is the possible bias from the inconsistent urban definitions across countries.

> 9A counterargument here is that higher housing costs in urban than in rural areas for properties that share the same structural characteristics (i.e., size, number of rooms, etc.) reflect, at least in part, the existence of superior urban amenities, including access to basic services, which are welfare-enhancing. To the extent that this is the case, it is unclear how to incorporate the value of these amenities into the welfare measure.

>
countries. In order to disentangle the e¤ects of the di¤erent MTEF phases (MTFF, MTBF, and MTPF) from other factors and to correct for reverse causality we apply a dynamic panel data approach to a newly-collected panel dataset of 181 countries over the period 1990-2008, the most comprehensive dataset to date on worldwide MTEF adoption.

The econometric ...ndings suggest that, unlike in previous small-sample and case-study analyses, MTEF adoption is associated with strong improvement in ...scal discipline, the effects increasing with each successive MTEF phase. The adoption of an MTBF and an MTPF decrease the volatility of health spending per capita, which we interpret as an improvement in allocative e¢ ciency. Finally, the MTPF seems to be the only MTEF phase that exerts a signi...cant e¤ect on technical e¢ ciency in the health sector, although due to insu¢ cient within-country variation in technical e¢ ciency this e¤ect is less precisely estimated. Overall these results are more favorable than the conclusions of prior work, and suggest that budget institutions that restrain short-term incentives to manipulate the budget can have measurable bene...ts for ...scal performance.

Our analysis may be limited by the fact that an MTEF could be in place only in law (de jure) and not in practice (de facto). However, if this phenomenon were widespread it would induce an attenuation bias and our estimates could still be regarded as a lower bound on the actual e¤ect. Being in e¤ect commitment mechanisms, transparency and enforcement are critical components of MTEFs. Studying which features of the broader civic, juridical, and political environment enhance MTEF e¤ectiveness may lead to a better understanding of these institutions. Also, our analysis of MTEF impacts on allocative e¢ ciency and technical e¢ ciency is limited to the health sector. Whether the results carry over to other categories of productive spending is an interesting question for future research. To create more precise measures
Policy **Research Working Paper Series**

||Title|Author|Date|**Contact**<br>**for paper**|
|---|---|---|---|---|
|WPS1760|Credibiiity of Rules and Economic<br>Growth: Evidence from a Worldwide<br>Survey of the Private Sector|Aymo Brunetti<br>Gregory Kisunko<br>Beatrice Weder|April 1997|M. Geller<br>31393|
|WPS1761|Bending the Rules: Discretionary<br>Pollution Control in China|Susmita Dasgupta<br>Mainul Huq<br>David Wheeler|May 1997|E. de Castro<br>89121|
|WPS1762|Strategies for Pricing Publicly<br>Provided Health Services|Paul J. Gertler<br>Jeffrey S. Hammer|May 1997|C. Bernardo<br>31148|
|WPS1763|China and the Multilateral Investmen<br>Guarantee Agency|t Yushu Feng|May 1997|E. Beers<br>36175|
|WPS1764|A Reversal of Fortune for Korean<br>Women: Explaining 1983's Upward<br>Turn in Relative Eamings|Yaba vab der Neykeb<br>Rodgers|May 1997|D. Ballantyne<br>87198|
|WPS1765|Explaining Agricultural and Agrarian<br>Policies in Developing Countries|Hans P. Binswanger<br>Klaus Deininger|May 1997|D. Housden<br>36637|
**A1.10 Political Risk Services (PRS)**

The PRS group is an affiliate of Investment Business with Knowledge (IBC), a United States-based corporation providing up-to-date country information for international business. PRS was founded in 1980 and is headquartered in Syracuse, New York. As of May 1999 can be reached at http:l/www.prsgroup. com.

Since 1982, PRS produces the International Country Risk Guide (ICRG) which provides assessments of a political, economic and financial risks is a large number of developed and developing countries. These assessments are based on the analysis of a worldwide network of experts, and is subject to a peer review process at subject and regional levels to ensure the coherence and comparability across countries. In this paper we use data from the 1998 edition of the Intemational Country Risk Guide.

The ICRG assesses three mnajor categories of risk: political (with 12 components), financial (5 components) and economic (6 components). We use components of the Political Risk Index, which report subjective assessments of the-factors influencing the business environment in a particular country. Table Al.10 presents the different components of the political risk ratings.

49
Because of the importance of this age group, the NEET phenomenon has increasingly gained attention in both developed and developing countries. In Morocco, by contrast, labor market analysis (see, for example, Verme et al., 2016a, 2016b) has tended to focus on understanding what determines unemployment or inactivity within the entire population, with no specific focus on those between 15 and 24 years old. The present paper aims at filling this informational gap by providing a first-of-itstype comprehensive analysis of the characteristics and dynamics of NEETs in the last decade.

Examining NEET profiles is a relatively straightforward exercise using repeated cross-sections on the labor force, but without access to longitudinal data, it is rather more complicated to construct transition matrices that trace their movement in and out of the NEET condition. The Moroccan Labor Force Surveys do have a panel component (50 percent of the sample) but it rotates every two years, making it all but impossible to construct a transition matrix over a longer period than that.

Yet because of the extreme importance of gaining an understanding of the duration and persistence of the NEET condition, our team worked diligently to overcome the data limitations of existing methods by constructing a Synthetic Panel (SP) of individuals between 15 and 24 over a nine-year period (2010-2018). To our knowledge, this represents a primer in the synthetic panel literature, which has so far focused on welfare dynamics and only occasionally on labor market outcomes.<sup>1</sup> The adaptation of synthetic panel methodology to the labor market also opens up the possibility of conducting panel-type analyses in this field without relying on panel data sets, which are often scarce in developing countries. An important advantage we intend to exploit in constructing the SP is the possibility of comparing year-on-year estimates (for example, from 2010 to 2011) to the corresponding panel component within the data.

The paper is organized in six sections. The first is the current Introduction. The second section profiles NEETs, drawing comparisons in different countries around the world. Section
Policy Research Working Paper 9225

# **Abstract**

This study investigates the World Bank’s use of lending and non-lending instruments to affect the policy priorities of developing countries. In a typical year, the World Bank lends more than $30 billion to its client countries. It also spends approximately $200 million on the provision of analytical and advisory products each year. However, insufficiently granular data on the nature, timing, and distribution of these analytical and advisory products and the policy priorities of client countries has made it difficult for policymakers and scholars to understand which World Bank instruments are most useful for effectuating change in the direction of government policy. With new data on the delivery of analytical and advisory products and microlevel survey data from 1,244 public sector officials in 121 developing countries, this study demonstrates that the organization’s non-lending instruments are more effective than its lending instruments at influencing the policy priorities of client countries. The World Bank’s analytical and advisory products not only affect the direction of government policy, but also its design and implementation.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bparks@aiddata.wm.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations,
parallel downward shift and reach 6.92 percent in 2050.

**Figure 4a. Poverty has Fallen from 1995 to 2007;** **<u>Two Scenarios for Future Poverty Headcount Ratio</u>**

**Figure 4b. Household Saving Rate May Increase** **<u>by almost 3 Percentage Points by 2050</u>**

<!-- Start of picture text --> 30% 8%<br>7%<br>25%<br>6%<br>20% 5%<br>15% 4%<br>3%<br>10%<br>2%<br>5% 1%<br>0% 0%<br>2010 2015 2020 2025 2030 2035 2040 2045 2050<br>1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br>Saving rate (8.93% (trend) annual decline in inequality; incl. publ. pensions)<br>Share below poverty line (1995-2008)<br>Saving rate (unchanged inequality; incl. publ. pensions)<br>Projected share below poverty line, unchanged inequality<br>Saving rate (8.93% (trend) annual decline in inequality; excl. publ. pensions)<br>Projected share below poverty line, 1% annual decline in inequality<br>Saving rate (unchanged inequality; excl. publ. pensions)<br>Projected share below poverty line, 8.93% (trend) annual decline in inequality<br><!-- End of picture text --> Source: Author’s estimations based on PNAD survey 1995-2008 for the poverty line of less than $2 PPP in income per day; scenarios for unchanged poverty levels, and 1 percent reduction in poverty.

Source: Author’s projections for the saving rate depend on scenarios for poverty and the exclusion or inclusion of public
Policy Research Working Paper 10821

# **Abstract**

This paper builds on recent advances in machine-based pattern recognition to estimate species occurrence maps, using georeferenced open-source data from the Global Biodiversity Information Facility. With currently available data, the estimation algorithm has produced maps for more than 600,000 vertebrates, invertebrates, other animals, vascular plants, and fungi. The algorithm is designed for rapid map updates and estimation of new maps with continued increases in Global Biodiversity Information Facility occurrence reports. The paper compares the algorithm-produced maps with species-matched sets of expert maps for mammals, ants, and vascular plants. Using comparative species density counts in a high-resolution grid, it finds close similarity in global distribution patterns. It also traces regional differences to technical differences in estimation methods or cases where the boundaries of existing expert maps could be revised to reflect species-level patterns in Global Biodiversity Information Facility reports. The paper uses the estimated Global Biodiversity Information Facility maps to explore the global distributions of endemic species and species whose small occurrence regions increase their extinction risks. It finds a high overall incidence of endemism, with significant variations across major species groups. It also identifies about 118,000 species whose small occurrence regions create significant extinction risks. For both endemic and small-occurrence region species, the paper finds patterns of spatial clustering that identify candidate areas for cost-effective protection.

This paper is a product of the Development Research Group, the Development Data Group, Development Economics and the Environment, Natural Resources and Blue Economy Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bblankespoor@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even