rafmacalaba/datause-encoder-data huggingface dataset:
# **Table** 7-b **Doing Business Indicators Comparison (continued)**

||**Gettin**<br>|**g Credit**<br>||||**Enforc**<br>|**ing Contrac**<br>|**ts**<br>||
|---|---|---|---|---|---|---|---|---|---|
||Rank|Strength of<br>legal rights<br>index (0-10)|Depth of<br>credit<br>informatio<br>n index<br>(0-6)|Public<br>registry<br>coverage (%<br>of adults)|Private<br>bureau<br>coverage<br>(% of<br>adults)|Rank|Procedure<br>s<br>(number)|Time<br>(days)|Cost<br>(% of<br>claim)|
|**OIC**|117|4|2|5|6|116|42|667|39|
|**GCC**|94|4|4|5|17|117|47|590|20|
|**OPEC**|110|4|3|6|10|114|43|594|28|
|**OECD**|38|7|5|8|61|37|31|518|19|
|**OIC-GCC**|120|4|2|5|5|116|41|677|41|
|**Low income**|120|5|1|1|1|117|39|613|58|
|**MENA**|116|3|3|5|7|115|44|664|24|
|**developing**<br>**countries**|89|5|3|10|18|99|39|638|33|

Source: Doing Business Report 2011, The World Bank Getting Credit: the legal rights of borrowers and lenders with respect to secured transactions through one set of indicators and the sharing of credit information through another. The strength of legal rights index measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders and thus facilitate lending. The depth of credit information index measures rules and practices affecting the coverage, scope and accessibility of credit information available through either a public credit registry or a private credit bureau. The public credit registry coverage indicator reports the number of individuals and firms listed in a public credit registry with information on their borrowing history from the past 5 years. The private credit bureau coverage indicator reports the number of individuals and firms listed by a private credit bureau with information on their borrowing history from the past 5 years.

Enforcing contracts: the efficiency of the judicial system in resolving a commercial dispute. The list of procedural steps compiled for each economy traces the chronology of a commercial dispute before the relevant court. Time is recorded in calendar days, counted from the moment the plaintiff decides to file the lawsuit in court until payment. Cost is recorded as a percentage of the claim, assumed to be equivalent to 200% of income per capita.

53
<mark>comparative data, the definition of “employed” is the same as used in the online survey. That is, someone is defined as employed during the reference week if they performed some work for at least one hour or were temporarily not at work but had a job to go back to. Figure 5.2 presents a scatter plot of the employment to population ratio in online surveys and the comparative data.</mark><sup>24</sup> <mark>The 45-degree line is presented for reference. Evidently, the online survey estimates are mostly above the 45-degree line, indicating that the online survey perhaps overrepresents the employment to population ratio.</mark> We calculate the relative bias as the absolute value of the difference between the online estimates and the comparative data-based estimates divided by comparative data estimates. For the propensity score adjusted online estimates, the bias ranges from 9 percent to 55 percent, with an average of 30.0 percent. Without weight adjustments, the deviations ranged from 3 percent to 62 percent, with around the same average of 29.8 percent. Notably, the weight adjustment sometimes improved the accuracy of Internet sample surveys and sometimes reduced their accuracy. We also calculate bias reduction due to propensity score adjustment as (bias of unadjusted online survey estimates - bias of adjusted online survey estimates)/bias of unadjusted online survey estimates. We find the average of the bias reductions across countries/waves to be -18 percent, indicating that the weight adjustments overall worsened the accuracy of Internet sample surveys.<sup>25</sup>

**<mark>Figure 5.2: Employment to population ratio: Internet survey and comparative data</mark>**

<!-- Start of picture text --> Employment to Population Ratio comparisons<br>Avereage relative deviation of RIWI: 30.03%<br>90<br>45 deg line<br>80 Brazil<br>Indonesia<br>70<br>Kenya<br>Sri Lanka<br>60<br>Turkey<br>50<br>40<br>30<br>30 40 50 60 70 80 90<br>Comparative Data<br>RIWI<br><!-- End of picture text --> 24 The results for each country are presented as bar graphs in appendix figures A.4.6 to A.4.11. These figures also include comparative summary data from other nationally representative sources as line graphs whenever available. 25 We do not have enough information from the comparative data to test the statistical significance of employment measures between our online surveys and the comparative data.

19
less than 5 years in the 1960s to more than 10 years by 2013 (see Figure 3).

**Figure 3: Returns and years of schooling over time: Colombia**

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1974 1979 1984 1989 1994 1999 2004 2009 2014<br>Years of schooling Rate of return (%)<br><!-- End of picture text --> Focusing on Argentina, for which we have Mincerian returns (Kaboski 2003; Psacharopoulos 1994; Kugler and Psacharopoulos 1989; Lopez Boo 2010; Fiszbein, Giovagnoli and Patrinos 2007) over a 25-year period, we notice a remarkable stability of such returns even during the country’s economic crisis (Figure 4). Note that the wide variation of the returns over time is due to different samples and methodologies.

**Figure 4: Returns to Schooling and Economic Growth: Argentina**

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>1980 1985 1990 1995 2000 2005<br>Returns Growth<br><!-- End of picture text --> Note: Source of change in GDP per capita is World Bank World Development Indicators This finding can be interpreted using Tinbergen’s (1975) race between education and technology, in the sense that over time the education supply curve has been shifting more to the right relative 8
# **School Enrollment, Selection and Test Scores**<sup>♣</sup>

Deon Filmer Norbert Schady

## Development Research Group The World Bank

> ♣ We thank Felipe Barrera, Luis Benveniste, Pedro Carneiro, Stephanie Cellini, Richard Murnane, Jamele Rigolini, T. Paul Schultz, Miguel Urquiola, and various seminar participants for very helpful comments, as well as the World Bank Education Team for Cambodia and the members of Scholarship Team of the Royal Government of Cambodia’s Ministry of Education for valuable assistance in carrying out this work. This work benefited from funding from the World Bank’s Research Support Budget (P094396) as well as the Bank-Netherlands Partnership Program Trust Fund (TF055023). The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent.
# **6. Conclusions**

This paper constructs a database of growth and primary spending expenditure (i.e.net of interest payments) “events” over the period 1972-2005 for 118 developing and 22 high-income OECD countries. Fiscal expenditures were compiled by government function, and “events” were sought over 5-year rolling windows with a missing observation attributed if less than 4 out of 5 years of data were available. For the growth episodes, data was available for 87% of the potential of 6171 observations. For the fiscal episodes, data was available for 40% of the potential 4760 observations. In spite of more than half of the potential observations missing for the construction of fiscal events, in the end, the search for events was based on a sufficiently large data base allowing for statistical tests.

Significant “events” were approximately constructed as follows (see section 3 and annex A.3 for details). For GDP per capita, acceleration in the average annual growth rate of 2 percentage point per annum (ppa) between any rolling 5-year window would qualify for a growth “event”. For fiscal expenditures (expressed in GDP%), an increase in the average growth rate of approximately 1 ppa that would not be accompanied by an aggravation of the (consolidated central government) fiscal deficit beyond 2% of GDP would likewise qualify for a fiscal “event”. The resulting benchmark constructed data set (merging both fiscal and growth databases) had 58 growth events and 95 fiscal events over a sample included 107 countries (84 developing countries) over 1977-2000 (1452 observations).

For this sample, the (unconditional) probability of occurrence of a fiscal event is about 10%, and, for a large range of parameter values for the selection of a “significant” event, the probability of a growth event once a fiscal event had occurred is in the 22%- 28% range. The probability of occurrence of a fiscal event is higher for the bottom half of the income distribution of countries, but the probability that this fiscal event is followed by a growth event is higher for the third quartiles, corresponding to middle income countries (which are largely in Latin America). Finally, the probability of a fiscal event _not_ followed by a growth event is significantly higher for the Middle East and Africa region, prompting us to note that this result is coherent with the view (taken by the interim report presented to the Development Committee, 2006) that the success of a growth-oriented fiscal expenditure package hinges on the quality of the institutional environment.

For both high income and low and middle income countries, fiscal events involve a shift towards education, health and Housing & Community expenditures at the expense of defense, non-interest Public Services, and Transport & Communication expenditures, the shifts always being larger for developing countries, confirming more volatility (and probably a lesser 27
system reduces the risk associated with high external or local debt, since there is a foreign equity buffer to absorb losses in the event of a banking crisis. However, in the other direction, it is also possible that foreign-owned banks are more vulnerable to external credit crises that may induce parent banks to pull back from all locations. These issues are explored further section 4.3.

**Table 3.4c. International Debt Securities, Percent of GDP**

||Country|2002|2007|2009|2010|
|---|---|---|---|---|---|
|New Member States|Bulgaria<br>|26<br>|5<br>|5<br>|4<br>|
||Czech Republic|3|5|10|12|
||Estonia|9|8|3|3|
||Hungary|16|25|30|28|
||Latvia|5|5|6|6|
||Lithuania|11|15|22|29|
||Poland|4|10|12|13|
||Romania|6|3|3|3|
||Slovakia|12|7|9|10|
||Slovenia|10|8|29|36|
|Candidates and Potential Candidates|Albania|6|2|2|2|
||Bosnia and Herzegovina|0|0|||
||Croatia|20|13|12|13|
||Macedonia, FYR|||||
||Serbia|||||
||Turkey|10|6|7|6|
|Eastern Partnership Countries|Armenia|||||
||Georgia|0|2|6|8|
||Moldova|0|0|0|0|
||Ukraine|6|9|10|9|

_Source_ : World Bank, Joint External Debt Hub. Note: This table reports international debt securities, all maturities.

**Table 3.4d. International Debt Securities, Nonbanks, Percent of GDP**

||Country<br>|2002|2007|2009|2010|
|---|---|---|---|---|---|
|New Member States|Bulgaria<br>|26<br>|5<br>|5<br>|4<br>|
||Czech Republic|1|4|9|11|
||Estonia|5|2|3|3|
||Hungary|15|19|24|23|
||Latvia|5|3|4|5|
||Lithuania|11|15|22|29|
||Poland|4|10|12|13|
||Romania|6|2|2|3|
||Slovakia|12|7|9|10|
||Slovenia|9|7|22|29|
|Candidates and Potential Candidates|Albania|6|2|2|2|
||Bosnia and Herzegovina|||||
||Croatia|19|9|10|11|
||Macedonia, FYR|||||
||Serbia|||||
||Turkey|10|6|7|6|
|Eastern Partnership Countries|Armenia|||||
||Georgia|0|0|5|7|
||Moldova|0|0|0|0|
||Ukraine|6|6|7|7|

Source: World Bank, Joint External Debt Hub. Note: This table reports international debt securities, nonbanks.

14
14

# **_A. Cross-country regressions with instrumental variables_**

# **1.** Legal origin and financial development

To control for potential simultaneity bias, we first use instrumental variables developed **by** LLSV **(1998).** Legal systems with European origins can be classified into four major legal families [Reynolds and Flores **1996]:** the English common law, and the French, German, and Scandinavian civil law countries. **" All** four families descend from the Roman law as compiled **by** Byzantine Emperor Justinian in the sixth century and developed **by** the Glossators, Commentators, and in Canon Law through the **13"** century. The four legal families developed distinct characteristics during the last **_5_** centuries. In the **17'** and **18'** centuries the Scandinavian countries formed their own legal codes. The Scandinavian legal systems have remained relatively unaffected from the far reaching influences of the German and especially the French Civil Codes.

The French Civil Code was written in 1804, under the directions of Napoleon. Through occupation, it was adopted in other European countries, such as Italy and Poland. Through its influence on the Spanish and Portuguese legal systems, the legal French tradition spread to Latin America. Finally, through colonization, the Napoleonic code was adopted in many African countries, Indochina, French Guyana and the Caribbean.

The German Civil Code _(Birgerliches Gesetzbuch)_ was completed almost a century later in **1896.** The German Code exerted a big influence on Austria and Switzerland, as well as China (and hence Taiwan), Czechoslovakia, Greece, Hungary, Italy, and Yugoslavia. Also, the German Civil Code heavily influenced the Japanese Civil Code, which helped spread the German legal tradition to Korea.

> " This does not include countries with "conununist" or Islamic legal systems.
author the most likely explanation is an effect emanating from the negative press around involuntary demolitions occurring during this time.

Table 8 is structured to mirror the simple OLS associations reported in table (7). For instance, column (1) corresponds to column (1) in table (7) and focuses on whether there is any association between views on the fairness of policies and a belief that the country is going in the “right direction” on economic and social issues. As in the case of table (8), there is a negative and statistically significant correlation, signifying that people who changed their view to believing the policies are unfair were also more likely to change their view and feel that the country was not on the “right track.” Similar relationships are present for each of the variables described, including whether the government is engaging citizens in reform (negative), whether a household’s finances are worse than 2 years ago (positive), if the government is not doing enough on corruption (positive), and whether now is a good time to find a job or start a business (negative).

**Table 8: Panel Regression with Random Effects on Fairness of Policy for Current Homeowners**

||Country<br>going in<br>right<br>direction|Gov.<br>engages<br>citizens in<br>reform|Finances<br>worse<br>than 2<br>years ago|Gov. not<br>doing<br>enough<br>on<br>corruption|Good<br>time to<br>findjob|Good<br>time to<br>start<br>business|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Unfair Treatment|-0.01**|-0.06***|0.04***|0.05***|-0.06***|-0.06***|
||(0.003)|(0.012)|(0.013)|(0.011)|(0.016)|(0.015)|
|Constant|1.00***|0.94***|0.14***|0.06***|0.65***|0.78***|
||(0.001)|(0.004)|(0.006)|(0.004)|(0.010)|(0.008)|
|Observations|7,583|7,583|7,583|7,583|7,583|7,583|
|Number of hhid|1,584|1,584|1,584|1,584|1,584|1,584|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 These results are consistent with the view that involuntary demolitions and the publicity around them had a negative effect on support for the reform agenda, and aggregate measures of “citizen sentiment.” It is important to note that the shift in views was not large enough to leave the popularity of the reform agenda in question, nor did these changes have any meaningful impact on support for free movement and more urban housing construction. However, the results suggest that the treatment of citizens in these efforts matter a great deal to citizens, and that perceived unfairness could undermine support for the national reform program beyond the specific areas of housing and urban development.

43
have assets of at least $A250 million, high impact from $A2.5 billion and extreme impact from $A25 billion. About 50 superannuation funds are rated as high and the remainder as low. There are no extreme impact funds.

# **3.4 Supervisory stance (SOARS)**

The supervisory attention index is the geometric average of the riskiness/probability index and the impact index. Supervisory attention increases with both riskiness and impact, but the composite index recognizes that there are economies of scale in the level of extra supervisory effort required in response to marginal increases in both riskiness and size.

**Table 3:  Supervisory Attention Index**

||**Probab**<br>|**ility rating/**<br>|**index (PAIRS)**<br>|<br>||
|---|---|---|---|---|---|
||**Low**<br>**1**|**Me**|**dium**|**High**<br>**81**|**Extreme**<br>**256**|
|||**Low**<br>**5**|**High**<br>**16**|||
|**Extreme**<br>|22|50|89|201|358|
|**125 +**<br>**Impact**<br>|16|36|63|142|253|
|**Index**|11|25|45|101|179|
|**High**<br>**12.5 +**|4|8|14|32|57|
|**Medium**||||||
|**1.25 +**|1|3|4|10|18|
|**Low**||||||
|**0.25 +**|1|1|2|5|8|

_Source: APRA_ A score on the attention index maps to a ‘supervisory stance’ and action plan under the Supervisory Oversight and Response System (SOARS). The mapping, step H above, is illustrated in Table 4.

20
DATA AND METHODS **XCV**

# ADDENDUM

This section was produced after the projections were completed, just before the papers were Issued. Its purpose is to list the results of censuses and surveys that came to our attention too late to be incorporated. _Note_ that these are usually preliminary results that have not been evaluated for data quality, i.e., undercounts In censuses or sampling errors in surveys. Differences with the estimates In the main tables In this volume are therefore not necessarily Inconsistencies.

Bolivia: Preliminary result of June 1992 census gives total population of 6.3 million.

Djlboutl* Intercensal survey (EDI) of 1991 gives total population of 519,900.

Malaysia: Preliminary result of the August 1991 census is 17,566,982 persons. Underenumeration in the 1980 census was estimated at 4.4 percent.

Nigeria: Preliminary result of the November 1991 census shows a total of 88,514,501 million people.

Pakistan: Integrated Household Survey (PIHS) of 1991 estimates the IMR at 121 for 1986-90.

Peru: Preliminary estimates from the 1991-92 Encuesta DemogrMfica y de Salud Familiar (ENDES-DHS) shows the IMR at 55 for the five-year period preceding the survey and the TFR at 3.5 for the three years preceding the survey.

Zambia: Preliminary estimates from the Demographic and Health Survey (ZDHS) of 1992 show the IMR at 108 for the five-year period preceding the survey and the TFR at 6.3 for the three years preceding the survey.
<!-- Start of picture text --> Sweden<br><!-- End of picture text --> <!-- Start of picture text --> Change needed To reduce<br>i to reduce vulnerability<br>Risk to welfare 0.03%gee —5 tisk by 10% by 2.9%<br><!-- End of picture text --> <!-- Start of picture text --> 3.5%<br>Asset vulnerability 49Ss 60 =2.9 Access to early warning 9 80% 400. =<br>11%<br><!-- End of picture text --> <!-- Start of picture text --> Resilience 25eat 86 71 To improve<br>ite resilience<br><!-- End of picture text --> <!-- Start of picture text --> 54 yr Income share of bottom quintile 2porR10 6.5<br>10%<br><!-- End of picture text --> <!-- Start of picture text --> Driver wine ~— — Counies’ Time to reconstruct 9 . 10 -14<br>Sweden 3yr<br><!-- End of picture text --> <!-- Start of picture text --> Asset-vulnerability bias 9fat ae 321 140<br>1%<br>Social protection for non-poor people =fa 54 1800<br><!-- End of picture text --> <!-- Start of picture text --> This(expected scorecardwelfare showslosses the riskin percent to welfareof GDP),in Swedenand its Social protection for poor people 9NPR° 98 1900<br>drivers: hazard (captured with protection level), 46%<br>exposure,Orivers areasset rankedvulnerability,according toand theirresilience. efficacy to Ready to scale-up social protection 9 | Beer {itis 100 3700<br>reduce risk, from the most to the least promising. 73%<br>Limitedthe list canimprovementssubstantiallyin reducethe driversrisk, atwhilethe largetop of ul 1 nt ft Nee = _~7000<br>improvements would be required in drivers at the cilia 9 B% 28<br>bottominformationto obtainis quantifieda similarinreduction. the right-mostThis column, ee<br>which indicates how much each driver needs to change Basic education :25 oem” 7000<br>toscorecardreduce riskprovidesby 10%. the distributionFor each driver, of values acrossthe age N<br>all countries with the minimum and the maximum, to Access to health care 31 oe 7000<br>indicate where Sweden stands with respect to the 89<br>other countries. The number is green if the . aS Fe<br>corresponding change is achievable without exceeding Financial management 9 +100 7400<br>the best performance among all countries in the 100%<br>sample.achievableIt iswithout orange exceedingif one tenththeof thebest changeperformance.is Contingent. finance 9 \ \___»_h_4+| 100 7400<br>Otherwise, the number is red. On the right, asset 80%<br>vulnerabilityand socio-economicis brokenresilience down intointo 2 sub-indicators,14 sub- Access to finance for non-poor people 1WOR369 15000<br>indicators. Similarly to drivers, sub-indicators are 67%<br>rankedresilienceaccording by 7.1%to ortheir reduceefficacy assetto vulnerabilityimprove by Access to finance for poor people 9i 53000<br>2.9%. 59%<br><!-- End of picture text -->
_Table 4 - Sample replacement next year. All regression are Logit estimations, and margins show marginal effects at mean. Shock is measured as the year-on-year difference in log(USD to IRR rate) in each month._

# **Is the individual replaced next year in the sample?**

|**Sample**||**All indi**|**viduals**||**Mat**|**ched**|
|---|---|---|---|---|---|---|
||(1|)|(2|)|(3|)|
||Main|Margins|Main|Margins|Main|Margins|
|**Afghan dummy**|1.272***|0.045***|1.620***|0.049***|0.626***|0.052***|
||(0.048)|(0.002)|(0.051)|(0.002)|(0.076)|(0.006)|
|**Shock**|0.149***|0.005***|0.130***|0.004***|-0.061|-0.005|
||(0.044)|(0.002)|(0.044)|(0.001)|(0.186)|(0.015)|
|**Afghan * Shock**|0.505***|0.018***|0.495***|0.015***|0.570**|0.047**|
||(0.137)|(0.005)|(0.140)|(0.004)|(0.230)|(0.019)|
|**Head dummy**|||0.851***||||
||||(0.030)||||
|**Male dummy**|||-0.249***||||
||||(0.029)||||
|**Married dummy**|||0.439***||||
||||(0.026)||||
|**Age**|||-0.015***||||
||||(0.001)||||
|**Household size**|||-0.132***||||
||||(0.006)||||
|**Job experience**|||-0.002***||||
||||(0.000)||||
|**Constant**|-3.311***||-2.706***||-2.637***||
||(0.037)||(0.062)||(0.059)||
|**Observations**|542148||542148||12806||
|**Time FE**|Yes||Yes||No||
|**Education FE**|No||Yes||No||

25