rafmacalaba/datause-encoder-data huggingface dataset:
titling has too often been under a single name, that of the male head of household. As a consequence, many women have been excluded from ownership. Some countries, such as Ethiopia, have tried to address this unbalance by mandating joint titling of land, thus increasing women’s control over land. (Deininger et al. 2008). The benefit of such policies is apparent from Field’s (2007) study of a titling program in the slums of Peru, where she finds a strong impact on labor supply, especially for women. Without the need to physically protect plots and dwellings, women have more time to work away from their home.

Thus the literature shows that economic rights can affect women’s employment, health and education outcomes in particular countries and for particular reforms. This paper contributes to the literature by expanding both the country coverage and time dimension of legal indicators available for analysis. It also broadens the set of outcome variables analyzed. It provides further evidence of the scope for legal reforms to expand women’s wellbeing and empowerment. But it also shows that benefits are not automatic. This is true both as rising country incomes is not necessarily associated with closing gender gaps in these economic rights, and because the impact of giving women and girls equal rights is shown to vary by level of development and rule of law. Ensuring women and girls obtain and benefit from stronger protections of the property rights and legal capacity is an active agenda.

# **Data and Methodology**

The paper uses the newly compiled “50 Years of Women’s Legal Rights” database. The methodology is explained in more detail in the appendix. It draws on the constitutions as well as family, succession and land laws to look at three sets of legal rights for women. The indicators are based on an assessment of de jure rights based on the reading of the constitutions and 7
# **Table 7. Stock market performance and bank leverage: Separate pre-crisis and crisis regressions**

The dependent variable is quarterly bank stock returns. See Table 4 for a detailed description of the estimated model. In this table report estimates for three separate sample period: (1) the pre-crisis period Q1/2006 to Q2/2007; (2) the crisis period Q3/2007 to Q1/2009; (3) and the period following Lehman bankruptcy Q3/2008 to Q1/2009. We also allow all coefficients to vary by bank size. Large is a dummy variable that takes value one if the bank has total assets above 50 billion US $ and small a dummy that takes value one for all other banks. Tier1 (Tier2) is Tier1 (Tier2) capital scaled either by risk-weighted assets (RWR) or total un-weighted assets (LR). Tier1 capital includes shareholder funds plus perpetual, non-cumulative preference shares, plus retained earnings. Tier2 capital includes subordinated debt, hybrid capital, loan loss reserves, and valuation reserves. Common equity is shareholders fund and other capital total regulatory capital minus common equity. Standard errors clustered by bank are reported in brackets and the p-value for the test of significant difference between the pre-crisis and crisis coefficients in parentheses.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Pre-|Crisis|C|risis|Post-|Lehman|
||RWR|LR|RWR|LR|RWR|LR|
|Tier1*Large|-0.034|0.088|0.165|0.536***|0.123|0.720**|
||[0.090]|[0.093]|[0.154]|[0.181]|[0.257]|[0.323]|
|Tier1*Small|0.036|0.036|0.08|0.151|0.033|0.141|
||[0.045]|[0.066]|[0.114]|[0.148]|[0.158]|[0.214]|
|Tier2*Large|0.039|-0.053|0.103|0.357|0.008|0.269|
||[0.101]|[0.158]|[0.189]|[0.283]|[0.295]|[0.388]|
|Tier2*Small|0.08|-0.052|0.049|0.161|-0.126|0.28|
||[0.060]|[0.076]|[0.130]|[0.214]|[0.248]|[0.369]|
|Constant|-2.575|-5.352**|-2.104|-2.248|-0.826|-6.642|
||[2.417]|[2.313]|[4.755]|[4.258]|[6.481]|[7.101]|
|Controls*Large|x|x|x|x|x|x|
|Controls*Small|x|x|x|x|x|x|
|Country*Year FE|x|x|x|x|x|x|
|Nber. Obser.|1820|1650|2229|2076|949|897|
|R squared|0.23|0.23|0.15|0.16|0.22|0.23|

Note: (*), (**) and (***) stand for statistically significant at the 10%, 5 % and 1 % level, respectively. Robust standard errors clustered by bank reported in brackets.

# **Table 8. Robustness check: Weighted least squares and alternative clustering**

24
**Table 7: Which individuals go to more intercept locations?**

Dependent variable: Number of locations visited in past 2 weeks

|(1)<br>OLS|(2)<br>OLS|(3)<br>Negative<br>Binomial|(4)<br>Negative<br>Binomial|
|---|---|---|---|
|Male<br>-0.288*|-0.294*|-0.0913*|-0.0902*|
|(0.17)|(0.17)|(0.053)|(0.052)|
|Age<br>0.0151***|0.00719|0.00467***|0.00229|
|(0.0052)|(0.0061)|(0.0016)|(0.0019)|
|Married<br>0.101|0.0965|0.0349|0.0324|
|(0.18)|(0.18)|(0.056)|(0.055)|
|Has University Education<br>0.00686|0.0842|0.00622|0.0338|
|(0.18)|(0.18)|(0.056)|(0.056)|
|Has worked/studied in Japan<br>0.614***|0.313|0.193***|0.0983*|
|(0.18)|(0.19)|(0.055)|(0.060)|
|Works for Pay<br>0.0787|0.125|0.0310|0.0439|
|(0.19)|(0.19)|(0.060)|(0.059)|
|Receives Remittances<br>0.436|0.433|0.119|0.119|
|(0.29)|(0.28)|(0.083)|(0.081)|
|Issei|0.485||0.148|
||(0.34)||(0.099)|
|Nissei|0.433**||0.145**|
||(0.21)||(0.067)|
|Reads Japanese newspapers|0.688***<br>(0.20)||0.210***<br>(0.059)|
|Refuses to give income range|-1.005***<br>(0.34)||-0.377***<br>(0.12)|
|Constant<br>2.209***|2.173***|0.837***|0.811***|
|(0.32)|(0.32)|(0.10)|(0.10)|
|Observations<br>492|491|492|491|
|R-squared<br>0.07|0.13|||
|Standard errors in parentheses, *** p<0.01, ** p<0.05,|* p<0.1|||

These results show that individuals who are more strongly linked to the Nikkei community have higher likelihoods of being sampled in the intercept survey. Therefore, to obtain a sample which is representative of anyone who visits any of the different intercept locations, we need to place less weight on individuals who are more likely to be found. In particular, the probability that individual _i_ is sampled is proportional to: 25

-  -
Table 2: Cognitive score and average years of schooling by country/region: Males, 22-65

|Country/Region|IALS/ALL<br>Score (rank)|Relative Mean<br>Deviation<br>(rank)|Years of<br>Schooling<br>(rank)|**(1- δ1:α1)/(δ1:α1) **<br>**(SLR/NLR)**|
|---|---|---|---|---|
|**Norway***|302.5 (1)|0.056 (10)|13.5 (4)|64%/36%|
|**Finland**|298.3 (2)|0.059 (9)|13.1 (9)|31%/69%|
|**Denmark**|298.1 (3)|0.050 (13)|13.5 (4)|44%/56%|
|**Switzerland-German**|297.5 (4)|0.056 (11)|14.8 (1)|54%/46%|
|**Czech Republic**|291.6 (5)|0.063 (7)|13.4 (6)|34%/66%|
|**Switzerland-French**|285.4 (6)|0.054 (12)|14.2 (2)|41%/59%|
|**Bermuda**|280.9 (7)|0.078 (4)|14.1 (3)|37%/63%|
|**_Mean_**|**_293.5_**|**_0.059_**|**_13.8_**|**_44%/56%_**|
|Switzerland-Italian*|280.6 (8)|0.069 (6)|13.4 (6)|21%/79%|
|Korea|261.9 (9)|0.062 (8)|12.7 (8)|5%/%95|
|Hungary|259.7 (10)|0.071 (5)|12.2 (10)|35%/65%|
|Italy|250.6 (11)|0.083 (3)|11.8 (11)|15%/85%|
|Slovenia|240.6 (12)|0.104 (2)|11.5 (12)|10%/90%|
|Nuevo Leon**|-|-|10.6 (13)|0%/100%|
|Chile|204.2 (13)|0.126 (1)|8.9 (14)|0%/100%|
|**_Mean_**|**_249.6_**|**_0.086_**|**_11.6_**|**_12%/88%_**|

* Average of IALS and ALL.

**The literacy score derived from the Nuevo Leon data is not comparable to the scores in the rest of the surveys, as the scale is different.

Another observation from looking at column 4 in the Appendix tables 1-14, is that for the first group of countries, generally, the coefficient of SL (δ2) is clearly larger than the coefficient of NSL (δ3). In the second group of countries, however, the opposite is true. This may have to do with signaling and whether better educated workers, after entering the labor market can obtain jobs which are a better match to their skill quality. With incomplete information, employers cannot observe workers’ real 16
Our study also finds that the annual rate of deforestation does not increase proportionally along with annual rate of biofuel expansion; instead, marginal deforestation and corresponding emissions decrease even if the production of biofuels increases. This is because, a higher target of biofuels causes more GHG emissions from land-use change, but at the same time, it also reduces more GHG emissions through fossil fuel substitution.

This study has some limitations, such as the restriction of GHG emissions to CO2. Other GHG emissions, particularly methane emissions from rice plantation and emission of nitrous oxide from fertilizer use, could be significant. However, land use for rice plantation decreases due to the expansion of biofuels, implying a reduction in total methane emissions from rice fields. The estimates of GHG reduction are thus likely to be conservative.

# **References**

- Al-Riffai, P., B. Dimaranan, and D. Laborde, 2010. "Global Trade and Environmental Impact Study of the EU Biofuels Mandate", Report for the European Commission, DG TRADE, ATLASS Consortium.

- Danielsen, F., Beukema, H., Burgess, N.D., Parish, F., Bruhl, C.A., Donald, P.F., Murdiyarso, D., Phelan, B., Reijnders, L., Struebig, M., Fitzherbert, E.B. Biofuel Plantations on Forested Lands: Double Jeopardy for Biodiversity and Climate. Conservation Biology, April 2009. Vol.23, Iss.2; p.348-358.

- Fargione, F., Hill, J.; Tilman, D., Polasky, S. and Hawthorne, P. 2008. Land clearing and the biofuel carbon debt. Science 2008: 319: 1235 – 1238.

- Fischer, G., E. Hizsnyik, S. Prieler, M. Shah and H. van Velthuizen. 2009. Biofuels and Food Security, OPEC Fund for International Development, Vienna, Austria.

- Goldemberg, J., et al. The Sustainability of Ethanol Production from Sugarcane. Energy Policy 2008: 36(4).

- Horridge, M. 2005. Splitcom – Programs to Disaggregate a GTAP Sector. Centre of Policy Studies, Monash University, Melbourne, Australia.

http://www.monash.edu.au/policy/SplitCom.htm .

25
|P130888|2014|2019|6/29/2019|8 Closed|Western Africa|
|---|---|---|---|---|---|
|P130890|2012|2013|5/18/2013|1 Closed|Kenya|
|P130890|2012|2015|8/1/2014|2 Closed|Kenya|
|P130890|2012|2016|6/30/2016|3 Closed|Kenya|
|P130891|2014|2015|12/9/2014|1 Active|Mongolia|
|P130891|2014|2015|6/8/2015|2 Active|Mongolia|
|P130891|2014|2016|12/3/2015|3 Active|Mongolia|
|P130891|2014|2016|6/20/2016|4 Active|Mongolia|
|P130891|2014|2017|12/29/2016|5 Active|Mongolia|
|P130891|2014|2017|6/30/2017|6 Active|Mongolia|
|P130891|2014|2018|4/8/2018|7 Active|Mongolia|
|P130891|2014|2020|11/29/2019|8 Active|Mongolia|
|P130891|2014|2020|3/18/2020|9 Active|Mongolia|
|P130891|2014|2020|6/30/2020|10 Active|Mongolia|
|P130897|2014|2013|5/17/2013|1 Dropped|Ecuador|
|P130903|2014|2015|7/13/2014|1 Closed|Morocco|
|P130909|2012|2012||0 Closed|Indonesia|
|P130909|2012|2013||0 Closed|Indonesia|
|P130925|2013|2013||0 Closed|Sao Tome and Principe|
|P130925|2013|2014|6/27/2014|1 Closed|Sao Tome and Principe|
|P130972|2013|2013||0 Closed|Colombia|
|P130972|2013|2019|5/7/2019|1 Closed|Colombia|
|P130992|2012|2013|6/5/2013|1 Closed|Uganda|
|P130992|2012|2015|12/5/2014|2 Closed|Uganda|
|P130992|2012|2016|6/30/2016|3 Closed|Uganda|
|P130994|2014|2015|6/8/2015|1 Closed|Sao Tome and Principe|
|P131003|2013|2014|11/3/2013|1 Closed|Armenia|
|P131003|2013|2015|4/13/2015|2 Closed|Armenia|
|P131007|2012|2013|5/16/2013|1 Closed|Yemen,Republic of|
|P131007|2012|2014|5/13/2014|2 Closed|Yemen,Republic of|
|P131007|2012|2015|5/26/2015|3 Closed|Yemen,Republic of|
|P131013|2014|2014|4/11/2014|1 Active|Peru|
|P131013|2014|2015|11/7/2014|2 Active|Peru|
|P131013|2014|2015|5/26/2015|3 Active|Peru|
|P131013|2014|2016|12/31/2015|4 Active|Peru|
|P131013|2014|2016|6/26/2016|5 Active|Peru|
|P131013|2014|2017|12/13/2016|6 Active|Peru|
|P131013|2014|2017|6/13/2017|7 Active|Peru|
|P131013|2014|2018|12/24/2017|8 Active|Peru|
|P131013|2014|2018|6/6/2018|9 Active|Peru|
|P131013|2014|2019|11/14/2018|10 Active|Peru|
|P131013|2014|2019|4/24/2019|11 Active|Peru|
|P131013|2014|2020|10/23/2019|12 Active|Peru|
|P131013|2014|2020|3/14/2020|13 Active|Peru|
|P131019|2012|2012||0 Closed|Chad|
|P131019|2012|2013|10/31/2012|1 Closed|Chad|
|P131019|2012|2013|12/15/2012|2 Closed|Chad|
Policy Research Working Paper 6140

# **Abstract**

The initial occupational placements of male immigrants in the United States labor market vary significantly by country of origin even when education and other individual factors are taken into account. Does the heterogeneity persist over time? Using data from the 1980, 1990, and 2000 Censuses, this paper finds that the performance of migrants from countries with lower initial occupational placement levels improves at a higher rate compared with that of migrants originating from countries with higher initial performance levels. Nevertheless, the magnitude of convergence suggests that full catch-up is unlikely. The impact of country specific attributes on the immigrants’ occupational placement occurs mainly through their effect on initial performance and they lose significance when initial occupational levels are controlled for in the estimation.

This paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at cozden@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 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, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 18 -

3. Productivity of RD It is likely that in developing countries the productivity of RD is lower than in industrial countries. Several reasons could be listed<sup>including</sup> skill deficiencies, poor research equipment, poor quality of inputs, etc. **Also,** industrial countries having a richer experience with RD have accumulated experience which likely enhancen their productivity of RD.

Table 2: !NTERNATIONAL DATA: PATENTINC AND RELATED INDICATORS (Percent)

||Indu<br>Econ<br>USA|S<br>strial<br>omies<br>Other|emi-industr<br>Econ<br>Rapid<br>Growth|ialized<br>omies<br>Slow<br>Growth|Developing<br>Economies|Planned<br> <sup>Economies</sup>|
|---|---|---|---|---|---|---|
|Share of inventi<br>|ons<br>|||||277|
|1969-71<br>|31.6<br>|36.1<br>|2.6<br>|1.7<br>|0.4<br>|.<br>|
|1976-79|23.3|38.5|3.3|1.0|0.4|33.6|
|Proportion natio<br>|nal<br>|||||6|
|1967-71<br>|75<br>|33<br>|25<br>|17<br>|11<br>|7<br>|
|1976-79|62|45|27|20|12|84|
|Ratio:<br>R&D/CDP<br>|||||||
|1967-71<br>|2.7<br>|2.0<br>|0.8<br>|0.4<br>|0.6<br>|3.1<br>|
|1976-79|2.0|1.7|0.5|0.4|0.4|3.3|

Source: Evenson and Putnam (1987).

If the productivity of RD in developing countries is low, then increasing patent protection will not have a significant impact on innovations, but will imply higher prices for<sup>the technology</sup> they acquire.

# 4. Uncertainty

So far, the discussion has been carried<sup>out under the assumption of</sup>
investigation. A study of anthropological sources to determine family structure and living patterns may be necessary in this connection.

The following three methodological questions require further empirical investigation: (i) the effect of the recall period on the reliability of diagnosis; (ii) the effect of differences in proximity of residence between the respondent and the deceased prior to the death; and (iii) the use of medical or lay interviewers. The choice of interviewer may influence the type of questionnaire required. Lay interviewers will require a highly structured questionnaire, and considerable effort will need to be devoted to the development of a suitable instrument. If diagnostic algorithms are to be used, this too will require a considerable amount of development work. It may be desirable to use structured quastionnaires or algorithms even if physicians are used as interviewers, but this Point requires further research.

Preliminary field tests will be reqvired to pilot the questionnaire and to collect empirical data with respect to the methodological questions listed above. Several approaches are possible: a) A study of adults admitted to hospital with life-threatening diseases. The diagnosis based on an interview with the relative who had been caring for the patient prior to admission could be compared with the final medical diagnosis. If it were possible to interview relatives immediately at the time of admission, preferably before they were questioned by medical staff, their responses should not be influenced by medical contact, although selection biases would still operate.

b) A similar study could be conducted by interviewing relatives of patients who had recently died in the hospital while the relative was still at or near the hospital. This may not prove feasible because it may be considered inappropriate to intrude immediately after a relative has been b-.reaved.

c) A retrospective study of hospital deaths over a period of time could be conducted if the hospital records were adequate to allow tracing of a close relative and to allow an accurate medical diagnosis to be extracted from the notes.

Whichever approach is adopted, both lay and medical interviewers could be employed, and a comparison made of their findings. Approach (c) would be especially useful for investigation of the effects of differing recall periods and proximity of residence of the respondent and deceased.

Following this preliminary testing, and further development and modification of the survey instrument, a full scale field trial of the methodology would need to be carried out in a developing .country, preferably in an area where alternative sources of inforaation on adult mortality would allow validation of the results. Further investigation of methodology could be incorporated in the field trial itself, for example by allowing the time and proximity criteria for questioning of relatives to be more relaxed than would finally be envisaged, so that the effects of differing criteria could be examined.

27
<mark>corporate debt. For the use-of-funds analysis, we merge the SDC data with Worldscope data, which provide information on the financial statements of firms. Those data include important information on firms’ assets, cash holdings, and sales (reported in balance sheets, income statements, and cash flow statements). Worldscope data are available for 44% of the firms in the SDC database, resulting in a merged data set of 2,190 firms.</mark>

# **<mark>4. Corporate Bond Issuances</mark>**

# _<mark>4.1. New Findings on Yields and Issuance Behavior</mark>_

<mark>As discussed in Section 2, we conjecture that part of the surge in investor interest in emerging market corporate debt after the GFC reflected a change in the investor base. We hypothesize that this compositional shift, together with the existence of the CEMBI Narrow index, with a $500 million minimum cutoff, produced an increase in the interest of international investors for large ($500 million and greater) emerging market corporate bonds.</mark> <mark>To study how the shifts in size-dependent investor interest affected market yields, we begin with simple comparisons. In Figure 2, we plot the evolution of the yield to maturity during 2000-2016 for bonds issued by emerging market corporates with face value below $300 million, between $300 million and below $500 million, and equal to or above $500 million. We observe that yields for all issuance sizes declined after the GFC, but the effect is particularly pronounced for $500 million bonds.</mark> <mark>In Figure 3, Panel A, we aggregate within the pre- and post-crisis periods and compare the average yield to maturity of bonds of different issuance sizes for the two time periods. We observe that, on average, yield to maturity decreases with issuance size. More importantly, consistent with Hypothesis 1, after 2008, we observe a sharp decline in the yield when moving to issuance sizes of $500 million (a fall of 115 basis points). This decline at the $500 million threshold is much more pronounced than that observed in the pre-</mark> 16
|Consulting<br>Services|8%|8%|
|---|---|---|

Expenditures on third parties hired to process an organization’s personal data have become commonplace, with 90% of the IAPP’s respondents reporting that their processing was outsourced.<sup>107</sup> The GDPR mandates that personal data should be outsourced to third parties for processing only when those processors provide sufficient guarantees that processing will occur in accordance with the GDPR through a written contract.<sup>108</sup> Data controllers remain responsible for non-compliance by the processors with which they share data. The IAPP reports that only 26% of respondents conducted onsite audits to ensure GDPR compliance, with several respondents observing that doing so was laborintensive and potentially cost-prohibitive. An overwhelming majority of respondents (94%) rely on the assurances in the contract instead, with 57% of respondents supplementing the contact with questionnaires provided to processors to verify GDPR compliance.<sup>109</sup> The GDPR does not outline specific technologies that organizations should use, though the use of encryption and pseudonymization are encouraged and required whenever feasible.<sup>110</sup> The IAPP found an average of $172,000 spent on technology expenditures.<sup>111</sup> Of the 301 privacy professionals involved in the decision-making process of their respective organizations, 58% of those surveyed by DataGrail purchased commercial technology solutions in pursuit of GDPR compliance and 57% invested in developing internal technology solutions.<sup>112</sup> Showing similar results from a surveyed pool of 370 privacy professionals, 46% of the IAPPs respondents invested in new technologies or services in preparation for GDPR compliance.<sup>113</sup> Company Spending on Consulting Services and/or Technology in Preparation for GDPR Compliance<sup>114</sup> <!-- Start of picture text --> 6% 5%<br><$50K $5M+ 14%<br>$1M-$4.9M<br>20%<br>$50K-$99K<br>21%<br>$500K-$999K<br>34%<br>$100K-$499K<br><!-- End of picture text -->

> 107 _Id._

> 108 Council Directive 2016/679, art. 28, 2016 O.J. (L 119) 1, 2.

> 109 Hughes & Saverice-Rohan, _supra_ note 74, at xv.

> 110 _Id._ art. 32, 2016 O.J. (L 119) 1, 2.

> 111 Hughes & Saverice-Rohan, _supra_ note 74. .

> 112 DataGrail, _supra_ note 73.

> 113 Hughes & Saverice-Rohan, _supra_ note 74.

> 114 DataGrail, _supra_ note 73.

19