rafmacalaba/datause-encoder-data huggingface dataset:
and syndicated loans around the world from 1991 through 2014. Our data on firm debt issuances come from the Thomson Reuters Security Data Corporation (SDC) Platinum database (June 2015 version), which provides information on new issuances of publicly and privately placed bonds and syndicated loans with an original maturity of more than one year.<sup>4</sup> Given that the SDC Platinum database does not collect data on debt issuances with maturities shorter than one year, the data set does not cover commercial paper. We exclude all financial sector issuances and focus our analysis on financing to the non-financial sector. We also exclude all public sector issuances, comprising securities issued by national, local, and regional governments, government agencies, regional agencies, and multilateral organizations. The data set includes 57,513 (listed and unlisted) firms and 266,539 capital raising issuances: 105,734 bond issuances and 160,805 syndicated loan issuances.<sup>5</sup> One source of variation in debt maturity studied in the paper is the market location, namely, domestic and international markets. For corporate bonds, we compare the market location in which bonds are issued to the issuing firm’s nationality to classify the capital raising issuances as domestic or international. For offerings that take place simultaneously in more than one market, we consider tranches in each market as separate issuances. The data set includes 80,162 bond issuances in domestic markets and 25,572 bond issuances in international markets. For syndicated loans, we compare the nationality of the banks that participate in the deal with the issuing firm’s nationality to distinguish between domestic or cross-border bank lending. Domestic loans are those in which only domestic banks participate in the syndication, whereas international loans entail the participation of at least one foreign bank.<sup>6</sup> The data set includes 77,712 domestic syndicated loans and 83,093 international syndicated loans.

> 4 The data set does not include mortgage-backed securities or any other asset-backed securities.

> 5 Unlisted firms issued about 45 and 36 percent of the bonds and syndicated loans, respectively, in the sample.

> 6 For robustness, we consider two alternative definitions of international syndicated lending: when only foreign banks participate in the deal; or when more than half of the banks that participate in the deal are foreign. The results are qualitatively similar to the ones reported here.

7
Lama-Tamang, M. S., Gurung, S. M., Swarnakar, D., & Magar, S. R. (2003). Social Change in Conflict Areas: Assessment Report. _Prepared for UK Department for International Development (DFID) Nepal._ Jayaraman, A, Gebreselassie, T & Chandrasekhar, S. (2009) Effect of Conflict on Age at Marriage and Age at First Birth in Rwanda, _Population Research and Policy Review_ , 28 (5), 551-567.

Macours, K. (2011). Increasing inequality and civil conflict in Nepal, Oxford Economic Papers, 63(1), 1-26 Manchanda, R. (2004). Maoist insurgency in Nepal. Radicalizing gendered narratives. _Cultural Dynamics,_ 16(2/3), 237-258.

Menon, N. & Rodgers, Y. (2011). War and Women’s Work: Evidence from the conflict in Nepal. Rutgers University. Mimeo.

Miguel, E., & Roland, G. (2006). The Long Run Impact of Bombing Vietnam: NBER Working Paper No. W11954.

Ministry of Health [Nepal], New ERA, and ORC Macro. 2002. _Nepal Demographic and Health Survey 2001_ . Calverton, Maryland, USA: Family Health Division, Ministry of Health; New ERA; and ORC Macro.

Murshed, S., & Gates, S. (2005). Spatial-horizontal inequality and the Maoist insurgency in Nepal. _Review of Development Economics, 9_ (1), 121-134.

Niraula, B., & Morgan, S. (1996). Marriage formation, post-marital contact with natal kin and autonomy of women: evidence from two Nepali settings. _Population Studies, 50_ (1), 3550.

Nobles, Jenna and Alison Buttenheim (2006). Marriage in Periods of Crisis: Evidence from Indonesia, California Center for Population Research On-Line Working Paper Series. University of California, Los Angeles.

Ojha, D. P. (2004). Inventory of Poverty and Impact Monitoring (PIM) Approaches in Nepal _Poverty Monitoring Unit, GTZ, Nepal_ Palloni, A., Hill, K., and Aguirre, G. P. (1996). Economic Swings and Demographic Changes in the History of Latin America, _Population Studies_ , 50, 105-32.

Rodriguez, C., & Sanchez, F. (2009). Armed Conflict Exposure, Human Capital Investments and Child Labor: Evidence from Colombia _HiCN Working Paper No. 68_ .

Shemyakina, O. (forthcoming). The Effect of Armed Conflict on Accumulation of Education: Results from Tajikistan. _Journal of Development Economics_ .

31
Brazil own-sector linkages (within the unorganized sector) are more important than linkages with the other sector.

[Boehme and Thiele (2012)] use a survey of 5755 enterprises in urban Africa (the survey asks detailed questions about the sale/purchase of inputs and sources of inputs thus this analysis has much more direct measures of IO linkages). Their descriptive analysis reveals that about 80% of unorganized output is sold to final consumers. Further intermediate product sales are more important within the unorganized sector than from unorganized to organized sector. Thus they identify an asymmetry in the bi-directional flow of intermediates between the two sectors: the unorganized sector is more likely to buy from the unorganized sector than from the organized sector. This is an interesting contrast to our results for Indian manufacturing – we find that the _organized_ sector is much more likely to buy inputs from the unorganized sector. Further they model the plant-level determinants of greater vertical linkages with the organized sector and find a positive correlation with size of the plants, education levels, access to credit and capital-stock.

For India recent studies have focused on the unorganized sector. [Ghani et al. (2011a)] identify four stylized facts about the unorganized sector (including both services and manufacturing) in India.

1. The unorganized sector is large, accounting for 99% of plants and 81% of employment.

2. The unorganized sector is persistent: the share of unorganized activity is very stable over long periods of time.

3. The persistence is not due to specific states or specific industries becoming more unorganized. It is a pervasive phenomenon that is true for all states and industries.

4. They find evidence that increasing concentration of unorganized economic activity weakens the production function for all manufacturing firms.

Further they find that unorganized sector shares fall when the size of the organized sector grows (there is a negative correlation between unorganized sector share and the rate of growth of organized sector). However an expansion of the organized sector does not shrink the _absolute_ size of the unorganized sector. They also find that the share of unorganized sector falls as employment rises in large organized plants (those with employment greater than 500). This result identifies as important source of heterogeneity in the relationship between organized and 7
# Table C.2 : Potential top-down solutions: fiscal councils (correlation 2009-2016)

|Fiscal council (FC)|without|N (without)|with|N (with)|p-value ≠ 0|
|---|---|---|---|---|---|
|FC|0.13<br>(0.02)|811|-0.02<br>(0.06)|96|0.00|
|FC with large coverage|0.13<br>(0.02)|811|0.07<br>(0.08)|64|0.25|
|FC with forecast prep.|0.13<br>(0.02)|811|0.03<br>(0.07)|66|0.07|
|FC with forecast asses.|0.13<br>(0.02)|811|0.04<br>(0.06)|79|0.10|
|FC with recommendation|0.13<br>(0.02)|811|-0.06<br>(0.07)|74|0.00|
|FC with sustainability|0.13<br>(0.02)|811|-0.38<br>(0.07)|40|0.00|
|FC with consistency|0.13<br>(0.02)|811|-0.10<br>(0.07)|73|0.00|
|FC evaluating costs of measures|0.13<br>(0.02)|811|-0.29<br>(0.06)|54|0.00|
|FC with ex-post analysis|0.13<br>(0.02)|811|-0.08<br>(0.07)|67|0.00|
|FC with reports|0.13<br>(0.02)|811|0.01<br>(0.06)|93|0.01|
|FC with media impact|0.13<br>(0.02)|811|0.02<br>(0.07)|67|0.06|

Notes: For consistency, the control group systematically exclude FCers countries, so not any country in the control group has a FC, irrespective to the specification.

41
**Table 1: Basic Data, Guangdong and Project Counties, 2014**

|**Province / Counties**|**GDP per**<br>**capita (Y)**|**GDP from**<br>**agriculture (%)**|**Rural**<br>**population (%)**|**Population**|
|---|---|---|---|---|
|Guangdong|63,469|1.7|32.0|107,240,000|
|Wuhua|11,452|21.9|86.7|1,363,000|
|Dianbai|28,969|19.5|61.4|1,460,000|
|Lianjiang|24,006|25.9|79.2|1,485,000|
|Average of 16 project<br>counties|18,957|20.1|74.3|1,231,024|

Source: Project counties, Guangdong Department of Statistics 2015.

For this study, administrative data from Guangdong province were collected on county performance on the Zhongkao, a county-level test taken by all 9<sup>th</sup> grade students to determine where they will attend uppersecondary school. Data were collected from the 16 project counties as well as 20 comparator counties within Guangdong. Ten of the comparator counties were designated as impoverished by the province; 10 were not. The 16 project counties generally underperform the comparator counties. Of the 16 project counties, 13 scored in the bottom half of the 36-county sample. The average score for the 16 project counties was 6 percentage points lower than the average of the 20 comparator counties, and 10 percentage points lower than the non-impoverished counties.

Taken together, these factors indicate the need for a comprehensive teacher assessment and training system that can both address the difficulties in measuring effective teaching and help improve those areas in which teachers are falling behind.

## **1.2 Roadmap**

Section 2 presents a short literature review, the CLASS instrument, the data, and collection methods. It also presents evidence of reliability of the data collected. Section 3 analyzes the results, looking at differences across CLASS domains and dimensions, and by pilot county and by each of the teachers observed. It then attempts to tease out strands in the relationship between teacher beliefs and teaching quality. Section 4 presents some conclusions and recommendations. There are four appendixes.<sup>2</sup>

# **2 LITERATURE REVIEW AND METHODOLOGY**

## **2.1 Literature review**

Evans and Popova (2017) conducted a literature review of meta-analyses and found pedagogical interventions (e.g., Conn 2014; Kremer, Brannen, and Glennerster 2013) and teacher training (e.g., McEwan 2014) to be among the most effective interventions in improving student learning outcomes. But 2 Appendix A examines the CLASS results in further detail. Appendix B goes into greater detail regarding the statistical validity of the CLASS instrument in the pilot counties. Appendix C proposes a framework for a resultsbased financing mechanism. Appendix D details the complete protocols for CLASS observations.

1
WPS3382

# Skilled migration: the perspective of developing countries<sup>¤</sup>

Frédéric Docquier<sup>a;b</sup> and Hillel Rapoport<sup>a;c;d</sup>

> a CADRE, University of Lille 2, France

b IZA, Institute for the Study of Labor, Germany

> c Department of Economics, Bar Ilan University, Israel

d Stanford Center for International Development, Stanford University, USA

## Abstract

In this paper we focus on the consequences of skilled migration for source (developing) countries. We ...rst present new evidence on the magnitude of the "brain drain” at the international level and then discuss its direct and indirect e¤ects on human capital formation in developing countries in a uni...ed stylized model. Finally we turn to policy implications, with emphasis on migration and education policy in a context of international migration.

> ¤The ...rst author thanks the World Bank for ...nancial support (Contract PO. 7620076 - UPI 269656). We thank Arye Hillman and Maurice Schi¤ for helpful comments. The usual disclaimer applies. Corresponding author: Frederic Docquier, CADRE, University of Lille 2, 1 Place Deliot, 59024 Lille, France. Email: f.docquier@skynet.be.

1
<!-- Start of picture text --> Total Weights Assigned by Stakeholders to Groups of Criteria<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Short-term Short-term Long-term Long-term Economic<br>Environmental Economic Environmental<br><!-- End of picture text --> _Figure 4. Weights assigned by participating stakeholders to the four groups of sustainability criteria_ Next, the PROMETHEE II method was applied in which each DM evaluated the performance of all green interventions with a score in the scale 1-5, indicating an evaluation ranging from ‘very low’ to ‘very high’. Scores for each recovery measure by criterion, in line with this method, were provided by each stakeholder in Tables D.8 and D.9 in Appendix D. It should be noted that the scores for four criteria (energy savings, carbon emission savings, economic multiplier, and new jobs created) have been calculated through simulations with the relevant models described in Section 3, therefore stakeholders could not change these scores. Tables E.3 and E.4 in Appendix E show an example of the evaluation scores provided by one stakeholder.

# 4.3. Results and discussion

Before arriving at the final results of this analysis, it is necessary to calculate the net flow from the input of each stakeholder using equations (6) and (7). This intermediate result is shown in Table D.6 in Appendix D. Then, the final step is the application of the PROMETHEE GDSS in order to calculate the global ranking of all measures. For this part we assumed that each of the 10 stakeholders has the same weight (i.e. each DM has a weight of 0.1), and – in line with the methodology described in Appendix C – a type 5 preference function was implemented, with the indifference threshold set to 0.05 and the preference threshold to 0.25. Obviously, the ranking of alternatives is affected by both the weights assigned by each DM and their respective scoring by measure and criterion.

Figure 5 displays the results of the evaluation, averaged over all 10 DMs, before weighting the four groups of criteria. The carbon tax reform (measure M13) received a high score for its environmental performance in both the short and the long term, and actions related to sustainable mobility (M8 and M9) also had a good score on long-term environmental performance. Conversely, measures M5, M6 and M7, which mainly target enterprises, were assigned by DMs the highest scores regarding longterm economic effectiveness.

22
||(370.76)|(2616.16)|(4101.33)|(370.76)|(23624.46)|-26970.97|
|---|---|---|---|---|---|---|
|R<sup>2</sup>|0.14|0.12|0.05|0.17|0.13|0.04|
|#obs.|6916|6916|6916|3471|3471|3471|

Significance level, *: p<0.10, **: p<0.05, ***: p<0.01. The threshold for the first 3 columns is 1963 and the sample consists of all girls born between 1955 and 1968 with at least KCPE. The threshold for the last 3 columns is 1975 and the sample consists of all girls born between 1971 and 1980 with at least KCPE.

39
East and North Africa (MNA) lag other EMDEs. However, there is also significant heterogeneity within country groups. Therefore, the regional averages should be interpreted with caution.

**Table 2.1. Drivers of investment project performance**

||(1)<br>POLS<br>2000-21|(2)<br>FE<br>2000-21|(3)<br>POLS<br>2000-09|(4)<br>POLS<br>2012-21|(5)<br>OProbit<br>2000-21|(6)<br>OProbit FE<br>2000-21|(7)<br>OLogit<br>2000-21|
|---|---|---|---|---|---|---|---|
|Real GDP growth|0.008**|0.004|0.011*|0.003|0.013**|0.005|0.023**|
||(0.003)|(0.004)|(0.006)|(0.003)|(0.006)|(0.006)|(0.010)|
|Real GDP per capita, log|0.025|0.108|0.037|0.024|0.049*|0.207*|0.075|
||(0.019)|(0.073)|(0.028)|(0.024)|(0.030)|(0.118)|(0.052)|
|Project length|0.011*|0.013**|0.006|0.011|0.010|0.013|0.012|
||(0.007)|(0.006)|(0.011)|(0.008)|(0.010)|(0.010)|(0.018)|
|Project vol. 10-25 mn|0.116***|0.130***|0.133**|0.092*|0.175***|0.199***|0.238***|
||(0.034)|(0.038)|(0.053)|(0.051)|(0.053)|(0.058)|(0.092)|
|Project vol. 25-50 mn|0.150***|0.178***|0.194***|0.097|0.234***|0.279***|0.327***|
||(0.042)|(0.046)|(0.062)|(0.062)|(0.065)|(0.071)|(0.113)|
|Project vol. 50-100 mn|0.187***|0.229***|0.257***|0.150**|0.290***|0.358***|0.462***|
||(0.039)|(0.045)|(0.060)|(0.061)|(0.060)|(0.069)|(0.106)|
|Project vol. > 100 mn|0.183***|0.224***|0.208***|0.155**|0.303***|0.365***|0.431***|
||(0.050)|(0.058)|(0.060)|(0.065)|(0.079)|(0.093)|(0.140)|
|Quality at entry|0.374***|0.372***|0.344***|0.450***|0.579***|0.596***|1.076***|
||(0.015)|(0.016)|(0.020)|(0.021)|(0.027)|(0.028)|(0.047)|
|Quality of supervision|0.437***|0.422***|0.394***|0.503***|0.634***|0.637***|1.181***|
||(0.014)|(0.014)|(0.020)|(0.022)|(0.027)|(0.027)|(0.052)|
|Sector FE|yes|yes|yes|yes|yes|yes|yes|
|Agreement type FE|yes|yes|yes|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|yes|yes|yes|
|Country FE|no|yes|no|no|no|yes|no|
|Observations|4,440|4,440|2,119|1,901|4,440|4,440|4,440|
|Adj. R-squared|0.560|0.573|0.522|0.620|0.280|0.302|0.290|

_Source:_ Authors’ estimates. _Note:_ POLS indicates estimation results using pooled OLS specification (baseline model). FE indicates a specification with country fixed effects (column 2). Specifications for 2000-09 and 2012-21 are reported in columns 3 and 4. OProbit and OLogit indicate ordered probit and ordered logit models. OProbit FE indicates ordered probit with country fixed effects. Standard errors clustered at the country level are in parentheses. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively.

**Table 2.2. Summary statistics for the PIQ indexes**

|**Index**|**Observations**|**Mean**|**Std.**<br>**dev.**|**Min**|**Max**|**Num. of**<br>**countries**|**Time span**|**Projects per country-**<br>**period, mean**|
|---|---|---|---|---|---|---|---|---|
|PIQ-F (static)|120|0.003|0.195|-0.600|0.549|120|2000-2021|71|
|PIQ-D (dynamic)|898|0.003|0.179|-0.651|0.634|107|2009-2021|32-35|
|PIQ-Q (dynamic)|894|0.004|0.213|-0.847|0.729|91|2004-2021|16-20|

_Source:_ Authors’ estimates.

10
|Electricity Sector Quasi Fiscal Deficit|12.5|7.3<br>7.8**|
|---|---|---|

*  End 2006

** Calculated on the basis of a revised estimated of the long run average incremental cost of supply

Sources: MOF and IMF for budget aggregates, IMF and World Bank 2009 for debt stock, World Bank for QFD Fiscal deficits in Tajikistan were low in the 2000s, although they rose in 2007 and 2008 to accommodate higher capital expenditure financed with concessional external loans (table 5). Nevertheless, the fiscal space for increased public borrowing is highly constrained. As discussed above, the domestic financial system does not have the capacity to fund deficits in a non inflationary manner; while Tajikistan’s fragile external debt sustainability constrains its access to external finance. Although Tajikistan’s external public and publicly guaranteed (PPG) debt indicators currently fall within all the thresholds for sustainability stipulated in the Low Income Countries DSA framework, debt sustainability is highly vulnerable to macroeconomic shocks, such as lower real GDP growth and real exchange rate depreciation. The most recent joint Fund-Bank DSA, completed in March 2009, concluded that “Tajikistan is at high risk of debt distress” (IMF and World Bank, 2009, paragraph 8).

Fiscal space is also constrained by the potential long term consequences for the budget of quasi fiscal deficits (QFDs). In addition to those of the NBT already mentioned, the domestic energy sector incurs QFDs, estimated at about 8 percent of GDP in 2008 (table 5) and state owned enterprises also undertake quasi fiscal activities. The pension system will also start to incur deficits (benefits will exceed contributions) from 2010 onwards. The potential for QFDs to undermine fiscal sustainability, by imposing large future claims on the budget, reinforce the importance of the government adopting a very prudent attitude to the accumulation of public debt.

17
reduction in 4-14 day mortality of 69 percent; the effect of having received two tetanus toxoid injections through the vaccine trial an average of 4 1/2 years earlier also resulted in a substantially lower 4-14 day mortality rate (by 41 percent), although this difference was statistically significant in only one of the two areas.

# Reductions in Neonatal Mortality

The considerable geographical variability in the importance of neonatal tetanus as a cause of death has long been recognized (Bytchenko, 1966). Data from retrospective surveys in a number of Asian and African countries indicated neonatal tetanus death rates ranging from 3 to 31 per 1000 live births, with tetanus accounting for from 8 to 60 percent of all neonatal deaths (Foster, 1984). In addition to true variability across settings in the importance of neonatal tetanus as a cause of death, these figures are also likely to reflect differences in both under- as well as over-reporting of tetanus as a cause of neonatal death.

The Matlab study area in rural Bangladesh has provided much of the reliable evidence on the actual or potential reductions in mortality resulting from tetanus immunization (Table 3). The study by Black, et al. showed that two injections of tetanus toxoid reduced neonatal mortality in subsequent births by 33 percent. Similar results are obtained from the data presented by Koenig, et al. in the -xtension of this study. Over the five year period following the vaccine trial (1975-79), two doses of tetanus toxoid were associated with a 37 percent reduction in neornatal mortality. The findings of Rahman, et al. were more variable, with reductions in neonatal mortality ranging as low as 18 percent among births to mothers who received two injections of tetanus toxoid an average of almost five years early as part of the cholera vaccine trial, to as high as 49 percent among births whose mothers had recently been immunized while pregnant with two tetanus toxoid injections, as part of the MCH-family planning intervention program.

Given the very high efficacy of two doses of tetanus toxoid in eliminating tetanus in at least the short-term, accurate data on diagnosed cause of neonatal death among unimmunized populations should also provide a reasonably precise picture of the potential reductions in neonatal mortality resulting from tetanus immunization programs. Table 3 also shows data from other studies in Bangladesh on the prevalence of neonatal tetanus death. Data presented by Koenig, et al. (1991a) on causes of death in the Matlab comparison area, when adjusted for the confounding effects of tetanus immunization delivered through the 1974 cholera vaccine trial described earlier, indicated that tetanus accounted for 27 percent of neonatal deaths in this area 21