rafmacalaba/datause-encoder-data huggingface dataset:
- 35 -

of risk and inefficiency into the securities processing cycle and so a limited form of institutional participation is beneficial. Such systems are called "one-sided" to distinguish them from the "two-sided" comparison achieved between direct members of a clearing house.

One-sided comparison systems exist for institutional investors who may not wish or be permitted to join a two-sided comparison system associated with an exchange because they may be unwilling or unable to participate in a risk sharing arrangement, such as that typically found in two-sided comparison systems. They may, however, wish to have access to an automated comparison system which links them, their broker, and their operational agent (such as a custodian bank) in order to receive a timely and accurate list of trades to which they are the counterparty. This allows the institution to affirm or question the trades within an established time frame.

These systems require communication systems capability which tie the participants together. The most efficient system will require a high degree of automation, with all participants having on-li. a, real time access to information, such as clearing and settlement data (to manage positions) and accounts (to manage cash). The regulatory authority should ensure that the system is capable of reliably fulfilling this expanded role.

Proposal 3 Each country should have an effective and fully developed central securities depository, organized and managed to encourage the broadest possible industry particpation.

A Central Securities Depository's ("CSD") principal function is to immobilize or dematerialize securities, thereby assuring that the bulk of securities transactions are processed in 'book entry" form. The depository system provides the basis for achieving efficient and low-risk transaction settlement. The most important feature of the book entry method is that a transfer of a given quantity of an issue from one account to another can be effected by a simple debit or credit on the books of the CSD. This is inevitably done via computer data entry.

The CSD may also have the capability for trade clearance, safe custody, and settlement/post-settlement processing of securities and information, such as corporate actions and dividend/interest processing. All these functions are greatly facilitated by automation.
- **21** -

> **Tabe 10.** **_Deost_ of Norfm Acvty Ac States with DWfforkg AgrIcutural hobme, hida** 1981

|**States**|Rural Areas|Urban Areas**al**|
|---|---|---|
||(Full-tknu Workers**per**1,000|Population)|
|**High**Agricultural Income|||
|PunJab|67|263|
|Haryana|67|258|
|Medium Agricultural**Income**|||
|Karnataka|61|247|
|Gujarat|53|258|
|Low**Aoricultural hcome**|||
|Madhya Pradesh|44|241|
|**Bihar**|38|212|

a/ Urban **inud ail** localities over 5,000 in population. It encompasses both rural towns and large urban **areas.**

**Source: India (1QS1c).**
# **Figure 2: Evaluation Design**

<!-- Start of picture text --> First<br><!-- End of picture text --> <!-- Start of picture text --> Package A<br><!-- End of picture text --> <!-- Start of picture text --> ‐Regulatory<br>simplification<br>‐Provision of<br>tax filing and<br>payment<br>information<br><!-- End of picture text --> <!-- Start of picture text --> Samples of informal businesses<br><!-- End of picture text --> <!-- Start of picture text --> Third group<br><!-- End of picture text --> <!-- Start of picture text --> Second group<br><!-- End of picture text --> <!-- Start of picture text --> Control<br>group<br><!-- End of picture text --> <!-- Start of picture text --> Package B<br><!-- End of picture text --> <!-- Start of picture text --> Package B<br><!-- End of picture text --> <!-- Start of picture text --> Package A<br><!-- End of picture text --> <!-- Start of picture text --> Package A<br><!-- End of picture text --> <!-- Start of picture text --> Package C<br><!-- End of picture text --> <!-- Start of picture text --> ‐Regulatory<br>simplification<br>‐Provision of<br>tax filing and<br>payment<br>information<br><!-- End of picture text --> <!-- Start of picture text --> ‐Provision of<br>business<br>services and<br>trainings<br>‐Support with<br>banks<br>services<br><!-- End of picture text --> <!-- Start of picture text --> ‐Regulatory<br>simplification<br>‐Provision of<br>tax filing and<br>payment<br>information<br><!-- End of picture text --> <!-- Start of picture text --> ‐Provision of<br>business<br>services and<br>trainings<br>‐Support with<br>banks<br>services<br><!-- End of picture text --> <!-- Start of picture text --> ‐Provision of<br>tax<br>preparation<br>support<br>‐Tax<br>mediation<br>services<br><!-- End of picture text --> <!-- Start of picture text --> 300<br>informal<br>businesses<br><!-- End of picture text --> <!-- Start of picture text --> 900 informal businesses<br><!-- End of picture text --> <!-- Start of picture text --> 1200 informal businesses<br><!-- End of picture text --> <!-- Start of picture text --> 1200<br>informal<br>businesses<br><!-- End of picture text --> 27
A Duration Analysis of CONALEP two years less formal education than the parents of a student attending an upper secondary school (Colegio de Bachilleres), and three years less formal education than the parents of a student attending the high schools of the National Polytechnic Institute.

**Table 1  CONALEP Students Compared to Students from Selected Institutions**<sup>**2**</sup> **Centro Nacional de Evaluación**

|**All**<br>**Institutions**|**Option**<sup>1</sup><br>**Number**|**Global**<sup>2</sup><br>**Test Score**|**Family**<sup>3</sup><br>**Income**|**GPA in**<sup>4</sup><br>**Lower-**<br>**secondary**|**Mother’s**<br>**Schooling**<sup>5</sup><br>**(years)**|**Father’s**<br>**Schooling**<sup>5</sup><br>**(years)**|**Private Lower**<br>**Sec = 1**|
|---|---|---|---|---|---|---|---|
|**CONALEP**||||||||
|Mean|2.27|54.2|2271.2|7.627|7.1|8.2|8.70E-03|
|N|460|460|460|460|460|460|460|
|SD|2.13|15.3|2269|.6992|4.8|5.2|9.29E-02|
|**Colegio de**||||||||
|**Bachilleres**|3.05|66.4|3132|7.658|9.0|10.0|3.33E-02|
|Mean||||||||
|N|421|421|421|421|421|421|421|
|SD|2.28|13.2|2845|.75722|5.2|5.1|.18|
|**Estado de México**||||||||
|Mean|2.41|64.6|2721|7.931|8.452|9.9|1.76E-02|
|N|1192|1192|1192|1192|1192|1192|1192|
|SD|2.09|16.56|2436|.7600|4.9|5.2|.13|
|**DGETI**||||||||
|Mean|2.71|59.6|2610|7.7205|7.700|9.2|2.20E-02|
|N|682|682|682|682|682|682|682|
|SD|2.32|15.6|2488|.7271|5.0|5.4|.15|
|**IPN**||||||||
|Mean|1.97|80.7|3315|8.1865|9.8|11.3|5.81E-02|
|N|430|430|430|430|430|430|430|
|SD|1.61|13.9|2552|.7871|4.7|5.0|.23|
|**UNAM**||||||||
|Mean|1.46|88.1|3967|8.3935|9.8|11.4|9.41E-02|
|N|510|510|510|510|510|510|510|
|SD|.83|11.6|3385|.7864|5.212|5.0|.29|
|**Other**||||||||
|Mean|1.38|82.9|3896|8.5417|11.969|12.9|8.33E-02|
|N|48|48|48|48|48|48|48|
|SD|.96|15.8|3164|.8124|3.676|4.2|.28|
|**TOTAL**||||||||
|Mean|2.33|67.9|2945|7.9248|8.6|10.0|3.50E-02|
|N|3743|3743|3743|3743|3743|3743|3743|
|SD|2.03|18.5|2693|.7982|5.1|5.2|.18|

1. This is the average preference number toward each institution from students who applied and got in.

2. Out of 128 questions.

3. In net pesos per month.

4. Grades go from 5 (fail) to 10.

5. Years of schooling.

Students attending CONALEP do not necessarily do poorly on the entrance test, nor do they all come from low educated or low-income parents. About 20% of CONALEP students in this sample scored higher than the average student attending the Colegio de Bachilleres.

> 2 _Colegio de Bachilleres_ is the local answer to over-demand; Estado de México is a state-centralized high school system; DGETI is the Dirección General de Educación Técnica Profesional, a centralized institution; IPN is the Instituto Politécnico Nacional –centralized; and UNAM is the Universidad Nacional Autónoma de México – autonomous.

5
|**Table 9: Heterogeneity according to external pressu**<br>Dependent variable: Realprofits|**re**|||
|---|---|---|---|
|Interaction Category:|Household|Says there is|Number of|
||Size|pressure to<br>share with hh|Siblings<br>in Area|
|**Panel A: Pooling Treatment Effects across Gender**||||
|Cash Treatment|2.191|1.187|0.414|
||(8.350)|(9.886)|(8.624)|
|In‐kind Treatment|28.79***|31.54**|25.61**|
||(10.62)|(12.49)|(10.40)|
|Cash Treatment * Interaction|5.937|‐0.705|2.166|
||(4.238)|(17.82)|(3.284)|
|In‐kind Treatment *Interaction|0.451|‐23.61|6.469*|
||(5.022)|(20.36)|(3.835)|
|p‐value for testing interactions jointly zero|0.371|0.501|0.198|
|Observations|4,169|3,880|3,750|
|Number of firms|757|675|652|
|**Panel B: Females Sub‐sample only**||||
|Cash Treatment|‐3.461|‐3.369|0.0197|
||(8.711)|(10.76)|(9.261)|
|In‐kind Treatment|30.52**|35.56**|33.84**|
||(13.40)|(17.04)|(13.80)|
|Cash Treatment * Interaction|10.49**|16.55|5.174**|
||(4.602)|(16.63)|(2.092)|
|In‐kind Treatment *Interaction|‐1.392|‐10.42|2.144|
||(6.528)|(23.13)|(2.694)|
|p‐value for testing interactions jointly zero|0.064|0.479|0.045|
|Observations|2,592|2,398|2,375|
|Number of firms<br>Notes: Results from Fixed effects estimation on trim<br>*, ** and *** denote significant at the 10%, 5% and<br>All regressions include wave effects which vary with|471<br>med sample.<br>1% levels.<br>the interaction.|418|414|
# **6. Comparison with other asset classes**

In this section, we compare the returns on external sovereign bonds from our new database with those of other major asset classes traded on UK and US capital markets. As in the previous analysis, we use a global portfolio time series of returns on all active foreign-currency sovereign bonds in the sample, weighted by debt amounts.

# 6.1 Data preamble

For comparison, we start with “risk-free” assets in financial centers. Specifically, we use returns on US and UK three-month Treasury _bills_ as well as US and UK long-term government _bonds_ . The time series of US and UK sovereign bonds and UK bills are gathered for the full 200-year sample. For bills, the data for the 19th century are approximated using commercial bills and related rates (US Treasury bill rates data start in 1919, and for the UK in 1900; see Appendix A). For stocks, we use a spliced total return index for the UK (FTSE) and for the US (S&P index), both from 1815 onward. To measure total returns on US corporate bonds, we use the S&P AAA Corporate Bond Price Index, which is available over 1900– 84 and combine this with the Bank of America US Corporate AAA Bond Index from 1985 onward. More details on the data sources are shown in Appendix A.

In addition to the US and UK series, we draw on the cross-country return database by Jordà et al. (2017), which covers 16 advanced countries over 1870–2015. We use their data to compute advanced country returns on domestic sovereign bonds as well as equities. Specifically, we first convert their local currency return series on bonds and equities into US dollars, using their exchange rate data. In a second step, we compute yearly average returns using equal weights for the 16 countries since 1870.

To compute excess returns, we use two approaches. First, we benchmark against monthly UK or US Treasury _bills_ , depending on the currency denomination of the respective bond or asset. Using bills facilitates the comparison across asset classes, including to long-term US/UK government bonds. Moreover, most of the earlier work on long-run returns uses bills as the benchmark, for example, Dimson, Marsh, and Staunton (2001) or Jordá et al. (2017). However, as an alternative, we also report excess returns above long-term UK or US _bonds_ , which has been our approach so far but is less standard in the literature comparing asset classes. In what follows, the resulting excess returns series are also used to compute commonly used metrics in this literature, such as standard deviations and Sharpe ratios.

# 6.2. Results on asset comparisons

Table 7 shows the results for the full sample, as well as for the modern period subsample (1995–2016). Figure 14 visualizes these findings, by focusing on average returns and their respective Sharpe ratios 38
Policy Research Working Paper 9110

# **Abstract**

This paper provides the first systematic analysis of migration to, within, and from Libya. The data used in the analysis are from the Displacement Tracking Matrix data set of the International Organization for Migration. The analysis uses this unique source of data, combining several techniques to analyze various dimensions of migration in Libya. First, the paper provides a detailed description of the demographic characteristics and national composition of the migrant populations in Libya. Next, it discusses the determinants of migration flow within Libya. The findings show that migration in Libya can be characterized as forced migration, because conflict intensity is the main determinant of the decision to relocate across provinces. Finally, the paper describes the direction, composition, and evolution of international migration flows passing through Libya and identifies the mechanisms of location selection by migrants within Libya by identifying hotspots and cluster provinces.

This paper is a product of the Poverty and Equity 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 vmolini@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
# Comparison of Cross-Section and Time-Series Analyses

It is interesting to compare the equations resulting from the crosssection analysis of data from 98 countries (circa 1988) and from the time-series analysis of the U.S. data (1950 to 1988). The time-series equation PGNP = -3.4 + 1.24LPR was derived with constant 1982 dollars. To make it comparable with the cross-sectional equation, it should be expressed in 1988 constant dollars taking into account the change in the GNP implicit price deflator between 1982 and 1988. i.e., a factor of 1.213 (11). The resulting equation is: PGNP88 = -4.1 + 1.50 x LPR where PGNP88 is real per capita GNP (1988 $1,000/inhabitant) and LPR is the per capita length (or density) of paved roads (km/thousand population).

The inference spaces for both equations can be approximately defined by: (a) cross-sectional analysis: paved road density between 60 and 20,000 km/million population; and (b) time-series analysis: paved road density between 8,000 and 20,000 km/million population. Figure 5 depicts the two equations according to their inference space. As can be seen in the figure, there is relatively good consistence between both equations.

# Comparson of Road Supply in the World Economies

A comparison between the supply and condition of paved road networks in 98 developing and developed countries is shown in Figure 6. The country groups in the figure are defined as (6): (a) Low-incom **e** conomies are those with a GNP per capita of $545 or less in 1988; (b) Middle-incom **e** conomies are those with a GNP per capita of more than $545 but less than $6,000 in 1988; and (c) High-incom **e** conomies are those with a GNP per capita of $6,000 or more in 1988.

For the analyses described in this paper, data was available for 42 lowincome economies (average per capita GNP of $320); 43 middle-income economies (average PGNP=$1720/capita); and 13 high-incom **e** conomies (average PGNP =$ 17,420/capita).

As shown in Figure 6, the supply of road infrastructure in high-income economies is dramatically higher than in middle and low-incom **e** conomies. For instance, the average density of paved roads (km/million inhabitants) varies from 170 in low-incom **e** conomies to 1,660 (plus 876 percent) in middle and 10,110 in high7
|Cohort 2: 7-12 yrs in 1974|1.5618<br>(0.7125)*|1.6805<br>(0.7840)*|
|---|---|---|
|**_Pre-programme share of children in the population in region of b_**<br>|**_irth x birth coh_**<br>|**_ort_**<br>|
|Cohort 1: 1-6yrs in 1974|-3.5010|-4.3169|
||(2.6206)|(2.8959)|
|Cohort 2: 7-12 yrs in 1974|-5.5211<br>(2.7520)*|-7.5318<br>(2.9118)**|
|**_Pre-programme enrolment rate in region of birth x birth cohort_**|||
|Cohort 1: 1-6yrs in 1974|0.3892<br>(0.9844)|2.8362<br>(0.7983)**|
|Cohort 2: 7-12 yrs in 1974|-0.2422|2.6035|
||(1.1613)|(0.9028)**|
|Constant|-3.4222<br>(0.3712)**|-3.118<br>(0.3714)**|
|Observations<br>|3648|3648|
|Model Wald chi-squared<sup>(a)</sup>|2747.69|1111.92|
|Prob > chi-squared|0.0000|0.0000|
|**_Hausman specification test for endogeneity_**<sup>**_(b)_**</sup>|||
|chi-squared|261|.77|
|Prob > chi-squared|0.0|000|

Notes: (a) Wald statistics and p value for the test of the hypothesis that all of the slope coefficients are jointly zero (b) Haussman test of the null hypothesis that both the standard probit and IV probit estimators are consistent.

Robust standard errors in parentheses

* significant at 5%; ** significant at 1%

# _Hospital deliveries_

Regression estimates for the probability of a woman delivering her child in a hospital are presented in Table 8. The coefficient for schooling is positive, but significant only in the standard probit model. An additional year of schooling increases the probability of a woman with average education going to a hospital for child delivery by 4% in the IVprobit model. The marginal effect is only 1.85% in the standard probit model.

Hospital care is relatively expensive in Indonesia because of the absence of adequate insurance or other forms of health care safety nets. Household consumption per capita, a 48
Policy Research Working Paper 7256

# **Abstract**

With the recent release of the 2011 purchasing power parity (PPP) data from the International Comparison Program (ICP), analysts and institutions are confronted with the question of whether and how to use them for global poverty estimation. The previous round of PPP data from 2005 led to a large increase in the estimated number of poor in the world. The 2011 price data suggest that developing countries’ incomes in PPP-adjusted dollars are significantly higher than indicated by the 2005 PPP data. This has created the anticipation that the new PPP data will decrease significantly the count of poor people in the world. This paper presents evidence that if the global poverty line is updated with the 2011 PPP data based on the same set of national poverty lines that define the $1.25 line in 2005 PPPs, and if the 2011 PPP conversion factors are used without adjustments to selected countries, the 2011 poverty rate is within half a percentage point of the current global estimate based on 2005 PPPs. The analysis also indicates that the goal of ‘ending’ extreme poverty by 2030 continues to be an ambitious one.

This paper is a product of the Poverty and Inequality 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 authors may be contacted at djolliffe@worldbank.org and eprydz@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
payments, the government budget, sector structure, and structure of sector factor use, respectively. The data are all from Macro SAM III for the archetype low‐income economy (Table 3.6), i.e. a GDP‐weighted average of the low‐income countries for which sufficient data were available. Given this, they may be taken to show the situation for an average low‐ income country in 2015 (brushing aside the issue of whether the data for low‐income countries with sufficient data deviate systematically from those without data). Like Table 3.6, the unit in these tables is percent of GDP, a choice that makes the numbers more meaningful and easier to interpret.

In terms of GDP spending structure (Table 5.1), absorption (domestic final demands) exceeds total GDP by 5.6 percent thanks to a trade deficit of the same magnitude. Among domestic final demands, the private sector and the government account for 89.0 and 16.5 percent, respectively. Government final demands are relatively focused on fixed investment: its ratio between fixed investment and consumption ratio is 0.41 as opposed to 0.18 for the private sector.

The current account of the balance of payments (Table 5.2) shows that the trade deficit and absorption in excess of GDP were made possible by foreign savings (a current account deficit) at 4.9 percent and a surplus for non‐trade items in the current account at 0.7 percent (with non‐government transfers or “worker remittances” as the main inflow). In the capital account of the balance of payments, the current deficit is mainly covered by FDI (2.9 percent) and government financing (mainly borrowing; 1.5 percent).

‐20‐
29 boys) are much less likely to enroll in<sup>secondary school than urban girls, as</sup> shown by the steep drop in survival<sup>rates between grades five and six.</sup> <!-- Start of picture text --> Fguri: Progression Probabilities<br>1<br>0.8 ~I.a FejeJe<br>O(11 Mtalaa<br>OVA Fenales<br>0.6<br>0.4 - #eral Aefsle<br>0.2<br>0 1 2 3 4 5 8 7 8 9 10 11 12<br>Education In years<br><!-- End of picture text --> Table 10: Percentage of School Dropouts by Region

||L|ima|Other|Areas<br>urban|Rur|al|All|Peru|
|---|---|---|---|---|---|---|---|---|
|AgLe.|F|M|F|M|F|M|F|M|
|5-7|2.1|3.5|4.4|4.0|5.7|7.3|5.2<br>|<br>4.5<br> <br>|
|8-10|2.8|1.2|4.8|4.8|8.7|6.7|4.8|<br>6.3|
|11-13|2.5|2.6|6.9|5.8|15.8|8.4|6.6<br>|<br>9.4<br> <br>|
|14-16|8.5|4.1|15.5|12.9|51.5|30.4|18.6|<br>28.4<br>|
|17-19|34.4|35.0|44.6|38.2|75.3|66.8|49.1|<br>53.2|