rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7: Summary Statistics of Stock and Investor Characteristics in Finland**

Table 7 reports descriptive statistics for stock and investor characteristics in Finland. Summary statistics are calculated by pooling annual observations over the 1996-2003 time period. We report the mean, standard deviation, as well as median, the 25<sup>th</sup> and 75<sup>th</sup> percentile values for all variables used in the study. All liquidity measures are annual averages and are defined in the text. Price is the annual average of the daily closing prices. Mkt Cap is the average market capitalization in millions of Euros. AdjIlliq is the adjusted Amihud illiquidity ratio. Roll’s C is the Bayesian estimate of Roll’s (1984) transactions cost measure. Zerofreq is zero-return frequency which reports the percentage of zero-return days. Age in 1995 is the biological age of the investor in 1995. Gender (1=female) is a dummy variable that equals one for female traders. Portfolio concentration is defined as in Ivkovic, Sialm and Weisbenner (2008) and is calculated as the sum of squared value weights of each stock in a household’s portfolio. Average portfolio value is the average markto-market value of an investor’s portfolio in Euros using daily closing prices, calculated on an annual basis. Trades Option is a dummy variable that takes on a value of one for traders that have traded options at least once over the entire sample period.

|Summary Statistics for Finland||||||
|---|---|---|---|---|---|
||Mean|P25|Median|P75|Std.Dev|
|||StockCha<br>|racteristics<br>|||
|Price (€)|12.61|2.69|7.67|16.4|11.20|
|Mkt Cap (€mil)|1132|33|125|498|8414.34|
|AdjIlliq|10.61|1.07|6.21|20.12|10.25|
|Roll's C|2.96|0.66|2.30|3.88|3.51|
|Zerofreq|21.90%|13.50%|20.64%|27.75%|13.42%|
||I|nvestorCh|aracteristic|s||
|Age in 1995|39.5|27|40|52|18.48|
|Gender (1=female)|0.33|0|0|1|0.47|
|Portfolio Concentration|0.20|0.09|0.17|0.27|0.18|
|Average Portfolio Value, EUR|10,823|1,341|2,079|5,292|80,125|
|Trades Option|0.04|0|0|0|0.18|

49
# **Figure 1: GQ Network and Levels of Young Establishment Output, continued**

# Table 1a: Descriptive statistics for organized sector

||Le|vels of total acti|vity|Le|vels of young firm|activity|
|---|---|---|---|---|---|---|
||Plants|Employment|Output|Plants|Employment|Output|
||(1)|(2)|(3)|(4)|(5)|(6)|
||A. Averag|e levels of activ|ity in 1994 an|d 2000, com|bining districts wit|hin spatial range|
|Total|81,641|5,879,283|3.9E+11|12,166|563,479|4.5E+10|
|Nodal district for GQ|11,365|723,042|5.8E+10|1,403|71,037|5.2E+09|
|District 0-10 km from GQ|24,816|2,098,769|1.3E+11|4,054|196,958|1.5E+10|
|District 10-50 km from GQ|6,003|375,423|3.4E+10|1,069|44,087|5.8E+09|
|District over 50 km from GQ|39,456|2,682,049|1.7E+11|5,641|251,398|1.9E+10|
|Urban Total|55,503|3,879,188|2.4E+11|6,307|267,377|1.9E+10|
|Nodal district for GQ|10,430|666,887|5.3E+10|1,195|60,447|4.2E+09|
|District 0-10 km from GQ|16,600|1,456,570|7.7E+10|1,944|94,002|6.1E+09|
|District 10-50 km from GQ|3,960|214,911|1.8E+10|552|16,314|1.5E+09|
|District over 50 km from GQ|24,514|1,540,821|9.2E+10|2,616|96,614|7.4E+09|
|Urban Share|68%|66%|61%|52%|47%|43%|
|Nodal district for GQ|92%|92%|91%|85%|85%|81%|
|District 0-10 km from GQ|67%|69%|58%|48%|48%|40%|
|District 10-50 km from GQ|66%|57%|52%|52%|37%|25%|
|District over 50 km from GQ|62%|57%|54%|46%|38%|39%|
||B. Averag|e Levels of act|ivity in 2005 a|nd 2007 com|bining districts wit|hin spatial range|
|Total|100,951|3,571,552|1.1E+12|16,104|390,145|1.2E+11|
|Nodal district for GQ|13,751|489,068|1.7E+11|2,317|68,171|1.8E+10|
|District 0-10 km from GQ|33,774|1,208,338|4.0E+11|5,681|135,277|3.9E+10|
|District 10-50 km from GQ|7,410|222,213|8.4E+10|1,228|21,779|5.9E+09|
|District over 50 km from GQ|46,017|1,651,932|4.2E+11|6,878|164,918|5.7E+10|
|Urban Total|61,431|2,101,925|5.7E+11|7,621|186,840|4.3E+10|
|Nodal district for GQ|11,880|418,401|1.3E+11|1,909|56,496|1.4E+10|
|District 0-10 km from GQ|20,405|735,688|2.2E+11|2,629|67,447|1.6E+10|
|District 10-50 km from GQ|4,303|110,170|3.3E+10|550|8,615|2.2E+09|
|District over 50 km from GQ|24,843|837,667|1.9E+11|2,533|54,282|1.1E+10|
|Urban Share|61%|59%|53%|47%|48%|36%|
|Nodal district for GQ|86%|86%|75%|82%|83%|74%|
|District 0-10 km from GQ|60%|61%|55%|46%|50%|41%|
|District 10-50 km from GQ|58%|50%|40%|45%|40%|36%|
|District over 50 km from GQ|54%|51%|46%|37%|33%|20%|

Notes: Descriptive totals taken from the Annual Survey of Industries.

26
> Polky anning, and Resarch !

EnryStrategy, Mabnagement, and A"essen This paper presents guidelines for admin;stering houisehold energy surveys.

Typically, country energy balances, national budget surveys, and microstudies have been the source of information about residential energy consrmption. A dedicated nationwide household energy survey will generate more relevant data for planners, policymakers, and evaluators - but may overturn assumptions in the process.

The subsidized promotion of liquefied petroleum gas (LPG) as a charcoal substitute in Senegal, for example, was based on the assumption that subsidies alone would lead to widespread adoption of butane fuel and that as the use of butane increased, the use of charcoal would decline. But a 1987 survey indicated that although 65 percent of the households in Dakar have LPG stoves, only 2 percent use the fuel exclusively. And in the households that use both LPG and charcoal, consumption of charcoal has not changed.

In Niger, wealthy residents, because of their lifestyle and income, were considered the logical market for modem fuels. But a 1986 study indicated that they used a lot of wood because they could afford to buy it in bulk (which made it the cheapest fuel) and they suffered none of the health or other disadvantages of wood fires because their servants did all the cooking.

This paper, a product of .' e Household Energy Unit, Energy Strategy, Management and Assessment Division, Industry and Energy Department, has also appeared as an Industry and Energy Department Working Paper. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Janine Littlzford, room S2-274, extension 33627.

The PPR WoPking Paper Series disseminates the fr dings of work under way in the Bank's Policy Plamning, and Research Complex. An obiective of the series is to get these findings out quickly. even if presentations are less than fuilly polished. The findings, interpretations, and conclusions in these papeTs do not necessarily ,-epresent of ficial policy of the Bank.

Produced at the PPR Dissemination Center
Table 13: Climate-Neutralizing Female Education - Students and Costs Developing Regions

||CSIRO<br>Total<br>Cost<br>|NCAR<br>Total<br>Cost<br>|CSIRO<br>New<br>Primary<br>Students<br>|CSIRO<br>New<br>Secondary<br>Students<br>|NCAR<br>New<br>Primary<br>Students<br>|NCAR<br>New<br>Secondary<br>Students<br>|
|---|---|---|---|---|---|---|
||($’000)|($’000)|(‘000)|(‘000)|(‘000)|(‘000)|
|Year|||Sub-Saha|ran Africa|||
|2010|179,036|211,757|800|880|981|974|
|2020|562,746|672,806|1,847|2,258|2,422|2,508|
|2030|1,001,736|1,044,923|2,117|2,843|3,038|4,262|
|2040|1,680,756|1,623,282|2,038|3,311|3,383|6,481|
|2050|2,294,642|2,203,969|1,708|3,488|2,967|7,053|
||||Sout|h Asia|||
|2010|528,691|266,339|2,264|2,603|990|1,020|
|2020|1,567,993|784,403|4,354|5,277|1,961|2,024|
|2030|2,983,226|1,771,123|5,129|7,143|2,960|3,139|
|2040|4,101,156|3,502,443|4,470|6,040|3,752|4,056|
|2050|5,446,895|4,853,930|4,277|5,357|3,539|3,681|
|||E|ast Asia and|Pacific Islands|||
|2010|339,783|397,749|1,100|1,237|872|1,276|
|2020|883,470|1,019,321|1,423|2,580|1,561|1,984|
|2030|1,352,575|1,312,094|990|2,681|1,130|1,635|
|2040|2,104,735|1,723,739|768|3,367|820|1,554|
|2050|795,767|2,636,736|241|1,315|780|2,307|
|||Eas|tern Europe|and Central As|ia||
|2010|112,192|150,593|200|246|214|313|
|2020|339,834|479,968|419|436|459|561|
|2030|417,795|727,301|301|359|375|561|
|2040|476,751|868,335|216|265|311|429|
|2050|524959|1554609|148|156|345|447|
||,|,,<br>M|iddle East a|nd North Afric|a||
|2010|60,080|32,367|114|147|46|93|
|2020|171,062|95,127|269|318|101|205|
|2030|327,340|226,553|255|339|135|259|
|2040|573,419|443,764|232|322|181|312|
|2050|891,987|958,291|219|323|244|372|
|||Lat|in America a|nd the Caribbe|an||
|2010|396,497|513,496|465|597|619|959|
|2020|1,016,825|1,266,047|555|1,486|929|1,928|
|2030|617,302|1,187,125|412|833|700|1,687|
|2040|218,728|1,370,353|200|644|526|1,445|
|2050|962,013|1,396,755|365|703|407|1,032|

35
-7 -

In this section we focus on the distribution of hours of work among employed persons. The objective is to estimate inequality In distributlons of hours of work, I.e., elative dffferences in hours of work among employed persons. A similar study for children and households is reported in Aaberge and Dagsvik (1990).

Table 1. Employment Rates, Annual Mean Hours of Work and A-inequality In Distributions of Hours of Work for Males and Married and Unmarrieri .emales, by Region

||||||Femal|es|||||
|---|---|---|---|---|---|---|---|---|---|---|
|||Males||All||Married|||Unmar|ried|
||Em-<br>ploy-<br>ment<br>rates|An-<br>nual<br>mean<br>hours|Em-<br>ploy-<br>A-<br>ment<br>inequality rates|An-<br>nual<br>mean A-<br>hours Inequality|Em-<br>ploy-<br>ment<br>rates|An-<br>nual<br>mean<br>hours|A-<br>Inequality|Em-<br>ploy-<br>ment<br>rates|An-<br>nual<br>mean<br>hours|A-<br>inequality|
|Peru|.82|2,351|.396(.004) .64|1.746 .b21(.004|) .69|1,728|.521(.005)|.57|1,775|.521(.006)|
|Lima|.77|2,356|.398(.008) .51|1,594 .569(.008|) .55|1,580|.586(.011)|.47|1,611|.547(.012)|
|Other<br>urban|<br>.76|2,286|.434(.008) .56|1,656 .563(.007|) .62|1,613|.573(.009|) .49|1,717|.546(.011)|
|Rural|.91|2,388|.370(.006) .79|1,8S8 .467(.005|) .81|1,344|.455(.066|) .75|1,912|.483(.008)|

Note: Numbers In parenthesis are standard deviations.

1 able 1 examines regional employment and regional distributions of hoirs of work for employed rr ales and females aged 15-702'. The participation rates for males and females are considerably higher in rural than in urban areas. Rates for married females are higher than those for unmarried females, perhaps due to an income effect. Females In rural areas work consL.erably longer than females in urban areas. Males also work longer in rural areas, but the differencr is less significant.

The figures In table 1 may cover large Individual differences in hours of work. We now employ the A-coefficient as a measure of the relative differences in hours of work (see section 2.1); corresponding results based on the Gini coefficient are given In Appendix 1. The estimates of the A-coefficient are displayed in table 1.

The inequaliy estimates show large Individual variations in hours of work, particularly among females. Except for rural women, the inequality in the distribution of hours of work is significantly higher than i the Individual hours of work were generated randomly, I.e. trom a uniform (O,a) diLtribution for an arbftrary a. There are not, however, significant discrepancies In inequality between the distribution of hours of work for married and unmarried females. Inequality is lowest in the rural area for both males and females.

2/ Individuals are classified as employed i they worked one hour or more during the seven days or 12 months prior to the survey. The definition and measurement of annual hours of work are reported in Appendix 2.
**Future empirical work.** This study was a first step in understanding the dimensions of urban poverty by using existing survey data to provide an overview of the current situation.  This first step describes the current situation.  Subsequent work would be needed in order to provide understanding of the outcomes and to answer questions such as:  To what extent do institutional arrangements for service delivery explain the results?  How are fiscal and governance issues related to the outcomes? Has the recent improvement in the fiscal situation in some countries resulted in an improvement in service-delivery?  If not what are the implications?  How have changes in the social safety net contributed to poverty reduction in urban areas?  Why are mobility rates low and how does this affect urban poverty?  To what extent are slums emerging in ECA cities and how is this linked to country policies?

# **Annex 1. Measurement and Data Issues**

# **Annex 2. Country Data Tables**

# **Annex 3.  Payment Rates and Provisions of Different Infrastructure Services by Quintile and be Capital and Other Urban Areas**

62
Even for the basic R&D input proxies, some national specifications vary from the intemational norms set out in the Frascati Manual. Details of differences between national and in. rnational specifications are set out in footnotes of the UNESCO Statistical Yearbook and the supplementary tape. On the basis of individual footnotes', we exclude countries (or observations) if their specification is too different from the irternational norm. For example, we exclude the expenditure and manpower data for Greece and the manpower data for Norway since they do not include R&D activities in private productive sectors. We exclude Fiji, French Polynesia and Tonga since their data relate to one research institute only.

At the same time, to avoid excluding too many countries, we permit some variation in the R&D input definitions. The most severe example of this is the manpower variable for several countries, including Japan, which is based on the number of physical persons, rather than the international standard of full time equivalent persons. The R&D personnel for Japan are as a result estimated to be overstated by about 30 percent, according to other studies.<sup>8</sup> Our data set consists of manpower and expenditure data for 21 OECD countries and 19 developing countries, whose sample periods differ widely across countries. Table I shows the sample averages and the growth rates of our R&D input proxies for each :ountry. The first two columns list the average and the annual growth rates of R&D expenditures as a percentage of GDP between 1970 and 1985. The third column shows the average of the number of scientists and engineers (employed in R&D activities) per million population 'The proportion of footnoted observations is large, indicating the primitive state of intemnational standardization of R&D measurement.

'OECD, Proposed Standard Pi.ctice for Surveys of Research and Experimental Development: "Frascati Manual 1980", Paris, 1981.

4
4. _Regulatory Quality (RQ)_ – measuring the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development 5. _Rule of Law (RL)_ – measuring the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, the police, and the courts, as well as the likelihood of crime and violence 6. _Control of Corruption (CC)_ – measuring the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests In brief our methodology consists of identifying many individual sources of data on perceptions of governance that we can assign to these six broad categories. We then use a statistical methodology known as an unobserved components model to construct aggregate indicators from these individual measures. These aggregate indicators are weighted averages of the underlying data, with weights reflecting the precision of the individual data sources. Crucially our methodology also generates margins of error for the estimates of governance for each country, which need to be taken into account when making comparisons of governance across countries and over time.

# **2.1  Underlying Data Sources**

We rely on a large number of individual data sources that provide us with information on perceptions of governance of a wide range of stakeholders. These data sources consist of surveys of firms and individuals, as well as the assessments of commercial risk rating agencies, non-governmental organizations, and a number of multilateral aid agencies and other public sector organizations. A full list of these sources is presented in Table 1. For the 2006 round of the data, we rely on a total of 310 individual variables measuring different dimensions of governance. These are taken from 33 different sources, produced by 30 different organizations. Appendices A and B provide a detailed description of each data source, and document how we have assigned individual questions from these data sources to our six aggregate indicators. Almost all of our data sources are available annually, and we use the data only from the most recent year available from each source in our aggregate indicators. In a few 4
IPSL-CM4, SLR 32 cm, land subsidence 9 mm/year). We map the results for 110 _upazilas_ in Figures 2 and 3. The maps illustrate two critical dimensions for priority-setting: percent change in species counts, and poverty populations identified using lower and upper poverty lines.

Figure 2 overlays color-coded changes in fish species with black circles scaled by lower poverty line populations. Figure 2(a) displays the scenario with least change (B1), while 2(b) displays the scenario with most change (A2). Although the maps present a wealth of information, three patterns are immediately clear. First, the two scenarios exhibit a very similar pattern of species increase (colored blue) in the southwest, with growth somewhat more pronounced in 2(b). Second, the two scenarios exhibit widespread species decrease in both scenarios, and strikingly higher decrease rates in 2(b). Third, the distribution of the lower level poverty population is strikingly non-uniform across _upazila_ s, with the largest concentrations in the center of the eastern region.

Risk assessment should incorporate both species change and poverty population size, focusing particularly on _upazila_ s which have high species loss rates and large poverty populations. Visual inspection reveals two obvious priority candidates in Figure 2(b): Lakshmipur in Chittagong Division, and Bhola in Barisal. Both have large extreme poverty populations (defined by the lower poverty line) and species loss rates greater than 50%. Elsewhere, the diversity of change rates and poverty populations makes it more difficult to identify clear patterns. This is also true of Figure 3, because poverty populations are less skew-distributed when we employ the upper poverty line.

18
ratio than is income per capita. For the aging regression, the functional forms which produced the best fit were the quadratic regressions shown in these figures. The percentage over 60 was considered to be the best proxy available for the population with access to pension benefits since it corresponded to the effective retirement ages in most countries and produced better fits than regressions using the population over 65.

While both income and aging were positive and significant as individual regressors, including both in the same regression resulted in an insignificant income variable while the demographic variables retained its high t-statistic. Although these results are must be taken with caution given the proven collinearity of these two variables suggested by Figure 1.3, there are other reasons to believe that aging exerts the greatest influence for higher pension spending.

The higher proportion of GDP spent on public pensions is a function of the maturation of the systems over time, the aging of the population and increases in benefits that typically occurred in the second stage of the pay-as-you-go life cycle (see Issue Brief 6). Since aging and maturation tend to occur simultaneously, the demographic variable captures both of these effects. Income levels may remain stagnant or even fall, as was the case in Argentina or parts of Eastern Europe, while aging and maturation continue to drive up the percentage of national income devoted to pensions. Benefit increases, while arguably related to the prosperity of the 1950s and 60s in the industrial countries, have also been a function of the political power of pensioners who have managed to increase their relative income levels even in times of recession in many countries. Meanwhile, in young countries such as Venezuela, a relatively small number of old persons has been unable to maintain real pension levels even while the pension scheme was running a surplus. For these reasons, population aging seems to be tbe strongest causal factor behind the growth of public pension expenditures. A fuller discussion of other evidence in the literature, possible theoretical explanations for his result as well as alternative specifications of the relationship are discussed in Palacios (1996).

Figure 2 in the Overview chapter presents simulations based on World Bank demographic projections and the current, cross-sectional relationship which exists between the pension spending ratio and the percentage of the population over 60. A linear version of the regression presented in Figure 1.8 generated the coefficients which were used in the regional simulations. The demographic variable 13
|Recreation and culture|844|1,471|74%|46|60|62|4%|34%|
|---|---|---|---|---|---|---|---|---|
|Education|695|1,066|53%|2,134|2,887|3,763|30%|76%|
|Restaurants and hotels|705|334|‐53%|‐|‐|‐|||
|Miscellaneous|1,928|821|‐57%|1,648|1,850|2,546|38%|54%|
|Non‐food groups, monthly per<br>Alcohol tobacco and|adult equiva|lent consum|ption, inter|‐year deflato|rs (CPI)||||
|,<br>narcotics|1,359|309|‐77%|1,688|1,611|1,375|‐15%|‐19%|
|Clothing and footwear|5,108|4,180|‐18%|‐|‐|‐|||
|Housing, utilities and fuels|4,639|7,687|66%|2,119|2,603|2,858|10%|35%|
|Household maintenance|3,221|1,202|‐63%|1,246|1,137|1,149|1%|‐8%|
|Health|1,099|1,979|80%|2,492|2,179|2,572|18%|3%|
|Transport|2,719|4,596|69%|3,310|3,479|4,193|21%|27%|
|Communication|1,479|3,517|138%|2,223|2,213|2,648|20%|19%|
|Recreation and culture|1,407|1,471|5%|55.32138|59.92502|49.3421|‐18%|‐11%|
|Education|1,159|1,066|‐8%|2,554|2,887|2,992|4%|17%|
|Restaurants and hotels|1,175|334|‐72%|‐|‐|‐|||
|Miscellaneous|3,214|821|‐74%|1,973|1,850|2,025|9%|3%|

# **IV. Comparison of NPS and HBS Methods for Poverty Line and Poverty Indicators Measurement**

This section examines the differences in the measurement of the poverty line and estimation of the poverty indicators between HBS and NPS. It also investigates the potential sources of differences in the inequality indicators. More specifically, the analysis re‐evaluates the poverty line of NPS using the methodology of the HBS 2011/12, to separate differences in the poverty indicators resulting from the estimation approach (that can be addressed) and those stemming from the survey methods and that are more difficult to adjust. We also estimate the poverty numbers using the US$1.90 international poverty line to explore how the levels and trends of poverty compare between the two surveys. We finally examine the potential sources of discrepancy between the two surveys in the consumption distribution patterns for NPS data and decomposition methods.

# **A. Poverty line estimation**

The poverty line in the NPS is not directly comparable with the poverty line in the HBS due to the differences in the consumption measurement methods listed in the above sections as well as differences in the reference period, reference population, measurement of the cost per calorie and 34