rafmacalaba/datause-encoder-data huggingface dataset:
|YUNNAN ENVIRONMENT PROJECT|Shenhua Wang|
|---|---|
|YUNNAN ENVIRONMENT PROJECT|Shenhua Wang|
|YUNNAN ENVIRONMENT PROJECT|Shenhua Wang|
|TechnologyDevelopment Project|LilyUyHale|
|TechnologyDevelopment Project|LilyUyHale|
|TechnologyDevelopment Project|LilyUyHale|
|TechnologyDevelopment Project|LilyUyHale|
|TechnologyDevelopment Project|LilyUyHale|
|TechnologyDevelopment Project|LilyUyHale|
|TechnologyDevelopment Project|LilyUyHale|
|TechnologyDevelopment Project|LilyUyHale|
|TechnologyDevelopment Project|LilyUyHale|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|HUBEI URBAN ENVIRONMENT PROJECT|Mats Andersson|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|ENTERPRISE HOUSING AND SOCIAL SECURITY REFORM PROJECT|Songsu Choi|
|EnergyConservation Project|Robert P. Taylor|
|EnergyConservation Project|Robert P. Taylor|
|EnergyConservation Project|Robert P. Taylor|
|EnergyConservation Project|Robert P. Taylor|
|EnergyConservation Project|Robert P. Taylor|
|EnergyConservation Project|Robert P. Taylor|
|EnergyConservation Project|Robert P. Taylor|
|EnergyConservation Project|Robert P. Taylor|
|EnergyConservation Project|Robert P. Taylor|
|EnergyConservation Project|Robert P. Taylor|
|Sichuan Gas Development and Conservation Project|Noureddine Berrah|
|Sichuan Gas Development and Conservation Project|Noureddine Berrah|
|Sichuan Gas Development and Conservation Project|Noureddine Berrah|
across all issuances on a developing country dummy. We also estimate these regressions separately for bond and syndicated loan issuers.

The estimates in Table 6 confirm the robustness of our findings across the FSD. They show that firms from developing countries access debt markets at maturities similar to (and sometimes even longer than) those from developed ones across the entire FSD for debt issuers. The regressions also show that bond financing is shorter term in developing countries for firms of all sizes, except for those at the bottom quintile. The differences in bond maturity are relatively similar across the top three quintiles at around 2.6 years. In syndicated loan markets, the estimates show that firms from developing countries borrow longer term than those from developed countries across all quintiles of the FSD (except for bottom one). Moreover, the differences in maturity at issuance for syndicated loans increase with firm size. Whereas firms from developing countries at the 2<sup>nd</sup> quintile borrow with maturities 1 year longer than those from developed countries, firms at the 5<sup>th</sup> quintile obtain syndicated loans that are almost 3 years longer in maturity.<sup>20</sup>

# **5. Long-term Debt in Balance Sheet Data**

The results documented thus far based on transaction-level data stand in contrast with the shorttermism of debt in developing countries documented in the literature, which uses firm or bank balance sheet data. Our results imply at least two possible reasons for the relatively shorter-term liability structure of firms from developing countries. One is that other debt markets, such as non-syndicated bank financing and commercial paper, are shorter term in developing countries. A second (not mutually exclusive) possibility is that bond and syndicated loan markets (that are probably longer term) represent a smaller share of the total external finance to firms from the developing world.

> 20 The results also hold when including industry fixed effects in the estimations.

20
||||Wi|fe|||Husba|nd|
|---|---|---|---|---|---|---|---|---|
||Tran<br>To anyo|sfers sent<br>ne|To kin|Transfe<br>From any|rs receive<br>one|d<br>From kin|Transfers <br>To anyo|sent<br>ne|
||Amount (log)<br>(1)|Dummy<br>(2)|Dummy<br>(3)|Amount (log)<br>(4)|Dummy<br>(5)|Dummy<br>(6)|Amount (log)<br>(7)|Dummy<br>(8)|
|Panel A: equation (3), incl.|time,<br>t and t?, unio|n FEt|||||||
|Estimatedriskofpolygamy,|Bi<br>12.101*|1.165*|0.682|4.672|0.901|0.803|18.066**|2.058***|
||<br><br>(6.337)|(0.691)|(0.698)|(7.402)|(0.634)|(0.596)|(7.358)|(0.777)|
|Number of observations|1548|1548|1546|1542|1548|1546|1414|1414|
|Number of unions|774|774|773|771|774|773|707|707|
|Within R2|0.076|0.088|0.049|0.071|0.063|0.059|0.089|0.10|
|Panel B: equation (4), incl.|time-varying control|s X,,<br>t an|d t?, union|FE*|||||
|Estimatedriskofpolygamy,|Bi<br>13.430*|1.267+|0.844|4.904|0.877|0.801|19.849°**|2.239***|
||<br><br>(7.228)|(0.777)|(0.744)|(7.507)|(0.711)|(0.652)|(7.209)|<br>(0.751)|
|Number ofobservations|1482|1482|1480|1476|1482|1480|1396|1396|
|Number of unions|741|741|740|738|741|740|698|698|
|Within R2|0.081|0.092|0.058|0.071|0.062|0.058|0.11|0.12|
|Panel C: equation (5), incl.|1st-step controls x|time X, x|t, X;, t, a|nd t?, union FE*|||||
|Estimated risk ofpolygamy,|Fy<br>0.440|-0.699|-0.363|0.527|0.088|0.139|2.651|0.144|
||(9.109)|(0.979)|(0.896)|(9.072)|(0.915)|(0.835)|(9.034)|(0.970)|
|Numberofobservations|1482|1482|1480|1476|1482|1480|1396|1396|
|<br>Number of unions|741|741|740|738|741|740|698|698|
|Within R2|0.14|0.17|0.10|0.12|0.11|0.11|0.18|0.20|
|Unconditionalmean(wave1|)<br>3.72|0.38|0.30|2.35|0.23|0.21|4.19|0.41|

Panel union fixed effect model estimates. Standard errors in parentheses are bootstrapped (300 replications);+ p<0.12 * p<0.10, ** p<0.05, *** p<0.01.

Dependent variables: transfers sent and received from individuals out of the household over the past 12 months.

Sample: monogamous unions in the two waves. F;: predicted failure probability estimated by a Cox survival model stratified by the polygamy status of the husband’s father (see Table 1). All regressions include union fixed-effects + Controls not shown: time (in months) between the two survey waves and its square + Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents. * Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents, all step-1 controls (see Table 1) interacted with time Data source: PSF Survey, waves 1 and 2 (2006-2007 and 2010-2012)
flows are limited as there are no expanding regions which can absorb labor released from the contracting regions. Instead, the only promising destination for labor migration is the capital region (setting aside migration abroad), which can aggravate rather than ameliorate regional imbalances (see below).

# **Figure 4  Job Creation by Region, 2001**

<!-- Start of picture text --> 15 2<br>0<br>10<br>-2<br>5<br>-4<br>0<br>Capital Taraclia Chisinau Cahul Balti Soroca Tighina Gaugauzia Edinet Orhei Ungheni Lapusna<br>-6<br>-5<br>-8<br>-10<br>-10<br>-15<br>-12<br>-20 -14<br>Job creation Job destruction Employment growth<br><!-- End of picture text --> Note: the rate of employment growth is shown on the right-hand axis. Source: Annual Survey of Enterprises, 2000 and 2001; author’s calculations.

Cross-regional correlations of the components of job turnover further support the negative assessment of regional developments in Moldova. Three results are worth noting:<sup>23</sup>

- Across regions, job creation is delinked from job destruction, that is the destruction of old jobs is not followed by the creation of new jobs;

- Low regional job creation rates, falling uniformly short of job destruction rates, leave little room for regional restructuring;

> 23 The results of cross-regional correlations are strikingly similar to that of cross-industry correlations.

28
# **1. Introduction**

Does one need connections to get ahead in life? Three-quarters of adults, according to the data from the latest round of the Life in Transition Survey that queries adults across all transition economies and several Western European countries on their attitudes toward many aspects of life, report that connections are at least moderately important (more than half think them very important or essential) to get a good job in the government sector. More than two-thirds think connections are at least moderately important to get a good job in the private sector (EBRD, 2017). If socio-economic mobility is a universal human aspiration, of parents hoping for a better life for their children, as argued in the recent World Bank report _Fair Progress? Economic Mobility Across Generations Around the World_ (Narayan, Van der Weide et al., 2018), then these subjective assessments, based on nationally representative data covering a region with a population of about 500 million adults, present a very bleak picture of the perceived equality of opportunity for success in life.

A related, but different question is whether the perceived need for (and availability of) connections to access opportunities affects one’s expectations of future mobility? In other words, if chances for success are not equal, then the extent of one’s future mobility may be a function of access to connections or other factors that facilitate mobility. However, perceptions of inequality may influence not only where one ends up in the future, but also one’s own belief of where he or she will end up in the future, that is, one’s expectations of (or aspirations for) future mobility. The importance of the link between inequality (and inequality of opportunity) on one hand and mobility on the other, is illustrated prominently by the _The Great Gatsby Curve_ (Corak, 2017) which shows high levels of inequality to be associated with low levels of experienced mobility. The link between inequality and expectations of mobility is important in its own right because (i) expectations of mobility may affect investments in human capital for self and children, which will then affect actual mobility (Hoff, 2012; Genicot and Ray, 2017); and because (ii) expectations of future mobility are important determinants of current policy preferences, in particular with respect to redistribution (Benabou and Ok, 2001; Ravallion and Lokshin, 2000; Checchi and Filippin, 2004; 2
Hungary: Financial Sector Reform in a Socialist Economy by Mario I. Blejer and Silvia B. Sagari Table of Contents

|1.<br>The Framework|1|
|---|---|
|II.<br>Overview of Macroeconomic<br>Developments<br>Since 1968|4|
|Ill.<br>Financial<br>Sector Reform in Hungary|6|
|A.<br>The Pre-Refomi<br>Financial<br>Sector<br>B.<br>Key Developnmernts in the Process of Financial<br>Sector Reform|6<br>8|
|IV.<br>**An**Assessmernt<br>of the Financial<br>Reforms|13|
|V.<br>Concluding<br>Reiimarks|18|
|Tabhles|21|
|Charts|24|

Thanks are due to Bela Balassa, Tirm Condoin, and Christirne Ker, ides for their helpful conmients. and to 13o W'ang for excellent research assistance.
# Does Child Sponsorship Pay Off In Adulthood? An International Study of Impacts

# on Income and Wealth

_Bruce Wydick, Paul Glewwe, and Laine Rutledge_ Keywords: Childbearing, child sponsorship, developing countries, income, wealth.

Bruce Wydick is a professor in the Department of Economics at the University of San Francisco, 2130 Fulton Street, San Francisco, CA 94117‐1080; his email address is wydick@usfca.edu; Paul Glewwe (corresponding author) is a professor in the Department of Applied Economics at the University of Minnesota, 1994 Buford Ave, St. Paul, MN 55108; his email address is pglewwe@umn.edu; Laine Rutledge is a doctoral student in the Department of Economics at the University of Washington, 319 Savery Hall, Seattle, WA 98195‐3330; his email address is lainemr@u.washington.edu. We would like to thank Wess Stafford, Joel Vanderhart, Scott Todd, Alistair Sim, Herbert Turyatunga, Jose‐Ernesto Mazariegos, Ester Battz, Noel Pabiona, Rowena Campos, Sofia Florance, Catherine Mbotela, Sam Wambugu, Boris Zegarra, Marcela Bakir, and other local Compassion staff and enumerators in Bolivia, Guatemala, India, Kenya, the Philippines, and Uganda for logistical help and support in carrying out our field research. Thanks to graduate students Joanna Chu, Ben Bottorff, Jennifer Meredith, Phillip Ross, and Herman Ramirez for outstanding work in the field. We also appreciate support and helpful comments from Christian Ahlin, Michael Anderson, Jesse Anttila‐Hughes, Chris Barrett, Jere Behrman, Michael Carter, Alessandra Cassar, Pascaline Dupas, Giacomo De Giorgi, Alain de Janvry, Fred Finan, Pauline Grosjean, Phil Keefer, David Levine, Jeremy Magruder, Craig McIntosh, David McKenzie, Ted Miguel, Douglas Miller, Jeff Nugent, Jon Robinson, Elizabeth Sadoulet, John Strauss, and seminar participants at the University of California at Berkeley, Stanford University, the World Bank, the University of Southern California, the University of California at Davis, the Georgia Institute of Technology, the 2010 and 2011 Pacific Conferences for Development Economics, and the Institutions, Behavior, and the Escape from Persistent Poverty (IBEPP) conference at Cornell University. Finally, we thank the editor, Andrew Foster, and three anonymous referees for very helpful comments. We are grateful to BASIS/USAID for substantial funding for this project, to two generous South Korean donors, and to the University of San Francisco's graduate program in International and Development Economics.
<!-- Start of picture text --> & Groundwater Risks<br>my ey MH Extremely high risk<br>Pafichagaéry aA, ; ee . .<br>z ee) xX L & ; WE High risk<br>a° vena rere?. i oe eeik 4 ta; LE [7] Medium. risk.<br>Dinajpur Ran i \ i Low risk<br>. Sy ar - Arsenic only scenario<br>Zz j deypurhd s, ‘st ates A re LI =<br>qQ “~ 's Naogaon | foal af 4 ; o_o 7 a amg : +<br><!-- End of picture text -->

<!-- Start of picture text --> “SEL A Natore > i: Cow i : bigany . A<br>2 Tle ; = / woe | a<br>a ,{i a bigrebna5 cmA : & eaGazipur‘s a ——_ rd ooq fia<br>r ‘| fushtia 9 arayar me 4<br>Hee ( ar<br>Se ieae ee.<br>oe<br>S~ ao 4 eC<br><!-- End of picture text -->

<!-- Start of picture text --> ¢Zz]. 73 OURNG% fee K A ‘ ae3 (is: of & arguwi Ano}af ey. rs ot z 3‘ \\ ‘<br>SN ceea , be A Yugs — see m= %<br>QL pourhak ce: Ce Ae LX “ O} ae )<br>/} pid on see ae Bandarban<br>Cor Sparr |<br>Zz wafre Bay of Bengal N<br>0 50 100 km .<br>Lp<br>88°E 89°E 90°E 91°E 92°E<br><!-- End of picture text -->
prices). Second, we study the maturity of debt issues, defined as the number of years between the date of issuance and the final maturity date. Third, we analyze the currency denomination of bonds. For our regressions, we use a dummy variable that equals one if the bond is denominated in a foreign currency and zero otherwise. We define a foreign currency as one that is different from the currency of the issuing firm‘s home country. Finally, we analyze whether issues have a floating or fixed rate, by using a dummy variable that equals one if the bond has a floating rate and zero otherwise.

In addition, we compare yields of bonds issued at home and abroad. Following the literature, we measure the cost of debt using the yield spread at issue, defined as the difference between the yield to maturity of a bond at the time of issuance and the yield to maturity of a riskfree bond with the same maturity on the same date. As risk-free bonds, we use the constant maturity U.S. Treasury security series obtained from the Federal Reserve Board. If there are no Treasury securities with the same maturity as the corporate bond, we follow the literature and compute the risk-free rate as a linear interpolation between the rates of the two Treasury bonds with the closest maturity.<sup>15</sup> After eliminating issues with missing data on bond characteristics and outliers (i.e., observations in the top and bottom one percent), we are left with a sample of 116,338 corporate bond issues by 13,920 firms from 99 economies covering the period 1991-2008. Appendix Table 1 lists the countries included in our dataset and their regional and income level classification.

To illustrate the development and internationalization of corporate bond markets, Figure 1 displays the evolution of the aggregate amount raised by firms through debt issues in capital

> 15 As mentioned above, for the comparison of yields across markets, we restrict the sample to U.S. dollardenominated bond issues. This explains why we only consider U.S. Treasury securities as risk free bonds to calculate yield spreads.

12
**Table 4.3 Private Companies by Region, 2000**

|**Provinces**|**Firms**|**Share**|
|---|---|---|
|Red River Delta|5,732|18.1%|
|_- Hanoi_|_3,666_|_11.6%_|
|North East|1,114|3.5%|
|North West|146|0.5%|
|**_The North_**|**_6,992_**|**_22.1%_**|
|North Central Coast|1,336|4.2%|
|South Central Coast|2,383|7.5%|
|**_The Center_**|**_3,719_**|**_11.8%_**|
|Central Highlands|1,248|4.0%|
|Northeast South|12,296|38.9%|
|_- Ho Chi Minh City_|_9,089_|_28.8%_|
|Mekong River Delta|7,338|23.2%|
|**_The South_**|**_20,882_**|**_66.1%_**|
|**Total**|**31,593 **|**100.0%**|

Source: GSO, 2001 25. The survey showed that private companies in the South are more successful than their counterparts in the North. Southern firms claimed mean revenues of nearly three quarters more than firms in the North—a finding consistent with the 1999 MPDF study.<sup>20</sup> Revenues from exports showed an even more dramatic regional difference, with firms in the South reporting a mean four times higher than that for firms in the North. This difference occurs despite the fact that northern companies tend to actually be larger with regard to workers per firm. It is important to note, however, that there is significant variation between provinces within regions.<sup>21</sup>

**Table 4.4 Firm Revenues by Region, 2000 (VND Million)**

|**Region**|**Mean**|**Number of Firms**|**Standard Deviation**|
|---|---|---|---|
|North|7,626|122|12,408|
|South|13,079|162|32,301|
|**_Vietnam_**|**_10,736_**|**_284_**|**_25,821_**|

Source: World Bank Survey, 2001 26. Private firms have also concentrated on a limited number of economic industries. Fifty-seven percent of all domestic private companies are trade and retail services companies. Manufacturing firms account for a fifth of all private companies, but are responsible for 57 percent of formal private sector employment.

> 20 Webster and Taussig (1999). _Vietnam’s Undersized Engine_ , p. 28.

> 21 Furthermore, the makeup of official regions used by the GSO has changed in recent years.  The authors have made efforts to maintain consistency among regional categories used in this report.  The GSO has explained that some movement of provinces from one region to another has been done specifically based on those provinces economic performance—for example, when one province exhibited high growth over a few years, it was moved into a neighboring higher growth region, as officials decided it now had more in common with the higher growth region. This, of course, makes comparing regional performance over a given number of years quite problematic.

13
in the microdata. We are then left with corporate profits and property income of the households.

Here, we note two features of WID National Account aggregates related to corporate profits and property income of households, summarized in Figure C3. First, at close to 18% in more recent periods, the share of corporate profits over NNI in Honduras is much higher than most countries in LAC, with the exception of Nicaragua, and also much higher than high-income countries such as Spain, France and the United Kingdom. In Latin America we see a very large variation, with shares below 5% for Mexico for example, and in the range of 5 - 15% for Peru and Costa Rica. At the same time, the share of property income of households, which is mostly slightly negative in Honduras, is much lower than in other countries in the region and in high-income countries, where it ranges from 5 - 20% in recent years. While we are unable to assert the precise method to arrive at these figures for Honduras, these differences are substantial.

Given the negative amounts of property income of households we observe, adjusting directly our measure of capital income in our microdata to these aggregates would mean income to match these amounts – we lack more subtracting negative granular measures on property income on national accounts, so we could not adjust income from interests and dividends separately, for example. Instead, and informed by these large gaps in NA measures between Honduras and other countries, we decide to sum distributed income with undistributed and the up property corporate profits, adjust total amount to national accounts. Therefore, when we adjust our microdata to national accounts we multiply the total amount of distributed capital income and undistributed corporate profits in the microdata by a yearly factor that matches the total to national account aggregates.<sup>4</sup>

> 4In panel (e) of Figure C3, we show that the sum of corporate profits and household property income for Honduras is less of an outlier when compared to their LAC countries.

52
# **Table Sup 5: Trimming data**

This table presents robustness checks for our main result presented in Table 1, column 1. We exclude extreme precipitation values. In column 2, we exclude the observations with the lowest and highest five percent annual level of rainfall. In column 3, we exclude the observations with the lowest and highest 1 percent annual level of rainfall. Results remain robust.

||(1)|(2)<br>Trim top / bottom|(3)<br>Trim top / bottom|
|---|---|---|---|
||All data|5%|1%|
|Rainfall|1.261**|1.857*|2.275**|
||(0.399)|(0.715)|(0.776)|
|Rainfall<sup>�</sup>|-0.211***|-0.424*|-0.614*|
||(0.052)|(0.183)|(0.276)|
|Temperature|0.574**|0.572**|0.555**|
||(0.205)|(0.203)|(0.189)|
|Temperature<sup>�</sup>|-0.015*|-0.015*|-0.014*|
||(0.006)|(0.006)|(0.006)|
|Observations|869,447|854,141|787,508|
|R-squared|0.359|0.360|0.369|

Clustered standard errors at the administrative 1 level in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1.

19
# **Unlocking SME Finance in Fragile and Conflict Affected Situations**<sup>**¶**</sup>

Pietro Calice<sup>†</sup> JEL Classification Numbers: D22; D25; O17; Q34.

Keywords: Small and Medium Sized Enterprises; Financial Inclusion; Fragility; Conflict Author’s E-Mail Address: pcalice@worldbank.org

> ¶ The author is indebted to Nabila Assaf, Ana Carvajal, Tatiana Didier, Jade Ndiaye, Vincent Palmade, and Nadia Piffaretti for their comments and suggestions on an earlier version of this paper. The findings, interpretations, and conclusions of this paper do not necessarily represent the views of the World Bank, their Executive Directors, or the countries they represent.

> † World Bank.