Land conservation efforts throughout the U.S. enhance ecological amenities while generating wealth in the housing market through capitalization of amenities. This paper estimates the benefits of conservation that are capitalized into proximate home values and quantifies how those benefits are distributed across demographic groups. Using detailed property and household-level data from Massachusetts, we estimate that new land conservation led to $62 million in new housing wealth equity. However, houses owned by low-income or Black or Hispanic households are less likely to be located near protected areas, and hence, these populations are less likely to benefit financially. Direct study of the distribution of this new wealth from capitalized conservation is highly unequal, with the richest quartile of households receiving 43%, White households receiving 91%, and the richest White households receiving 40%, which is nearly 140% more than would be expected under equal distribution. We extend our analysis using census data for the entire United States and observe parallel patterns. We estimate that recent land conservation generated $9.8 billion in wealth through the housing market and that wealthier and White households benefited disproportionately. These findings suggest regressive and racially disparate incidence of the wealth benefits of land conservation policy.

The data contains approximately 36,000 personal names derived from medieval Russian documentation. More preciously, names are collected from an edited version of the census book of Vodskaja pjatina, which was one of the five administrative areas in the late 15th century Novgorod.

Editions were compiled in parts and the first two, which cover the northernmost region, are called Переписная окладная книга по новугороду вотской пятины (1851, 1852)(POKV I‒II). The third part of the book series Новгородские пистсовые книги (1868)(NPK III) covers the southern and western parts of the study area.

The process of obtaining the personal from the inscription has been following: First, editions of the census book were obtained as scanned PDF files. These were transformed as editable copies by using OCR (=Optical Character Recognition) software Abbyy. The program read the original mid-19th century Russian text adequately with its old Russian alphabet package.

After the initial corrections, a Python script was written to harvest the personal names. This was based on exploiting the systematic formalities in how most of the names were presented in the census book. The script looked for abbreviations “дв.” and “д.” and extracted all following capitalized words until section end markers “.”, “;” or “:”. As an output, a name to pogost matrix was produced, which held the raw frequencies of each word in each pogost.

The process of cleaning the name data, in turn, has been done mostly by data wrangling program OpenRefine in following manner: For starters, all name forms shorter than four characters were removed as there were no personal names consisting of three or less letters. Furthermore, nouns that were not names were removed. This meant discarding expressions that described person’s special feature or profession, like such as being a widow (“вдова”) or working as a deacon (“діакъ”). For some reason, editors followed inconsistent conventions in capitalizing these non-name nouns.

In addition, some orthographical and morphological harmonization was done on the data. The letter ы was cut from the end of bynames, where it denotes plurality. Similarity of so called soft and hard signs, ь and ъ caused some problems. As the latter one is not used in contemporary Russian and was not used in the original documents either (Неволин 1853 : 4 (in Appendix 1)) it was removed. The soft sign ь was also removed because it was absent in the original documents and it had been used inconsistently by the editors. The letter ѣ (yat) is rarely used in personal names but nevertheless, it was changed to е (like as it is in contemporary Russian) as since it was often confused with soft and hard signs (ь and ъ). Furthermore, the letter ѳ (fita) was often erroneously recognized as о or е. As it is only found in NPK III and only in the beginning of certain names, which all are also written with “Ф” (e.g. “Ѳедко” vs. “Федко”), it was replaced with Ф.

In the second phase most of the erroneous orthographies were corrected. We do not detail herescribe all the OCR-errors here that were found, but in the following a short description is given of the most significant corrections. There were, for example, many letters whose similarity caused problems for the OCR-program (e.g. и / й and б / в). In these cases, the correct orthography was sought in the census book editions and accordingly, Openrefine was used to change erroneous forms to right correct ones.

After the corrections were made, the number of name types (= name variants) was reduced from 4942 to 2748. The Overall overall number of name tokens was dropped as well: from 36,405 to 35,726. Of the name types, more than half (1484) have only one occurrence.

The refined and harmonized data is published as pogost-by-name frequency tabulations (pogost, equivalent of English parish). The file is in tab-delimited file (.tsv) format.

References:

Неволин, К. А. 1853, О пятинах и погостах новгородских в XVI веке, с приложением карты, Санкт-Петербург (Из Записок Императорского русского географического общества, Кн. VIII).

NPK III = Новгородские писцовые книги, Т. 3 : Переписная оброчная книга Вотской пятины, 1500 года, 1868, 1868, Санкт Петербург.

POKV I, II = Переписная окладная книга по Новугороду Вотьской пятины, 1851, 1852, Имп. Моск. о-во истории и древностей рос., Москва.

Key information about Hong Kong SAR (China) Market Capitalization: % of GDP

• Hong Kong SAR (China) Market Capitalization accounted for 1,110.0 % of its Nominal GDP in Dec 2024, compared with a percentage of 1,038.5 % in the previous year
• Hong Kong SAR (China) Market Capitalization: % Nominal GDP is updated yearly, available from Dec 1985 to Dec 2024
• The data reached an all-time high of 1,771.1 % in Dec 2020 and a record low of 96.9 % in Dec 1985

CEIC calculates Market Capitalization as % of Nominal GDP from monthly Market Capitalization and annual Nominal GDP. Hong Kong Exchange provides Market Capitalization in local currency. Census and Statistic Department provides Nominal GDP in local currency.


Further information about Hong Kong SAR (China) Market Capitalization: % of GDP

• In the latest reports, Hang Seng recorded a daily P/E ratio of 13.3 in Feb 2025
• Hang Seng closed at 20,225.1 points in Jan 2025

Key information about Sri Lanka Market Capitalization: % of GDP

• Sri Lanka Market Capitalization accounted for 15.4 % of its Nominal GDP in Dec 2023, compared with a percentage of 16.0 % in the previous year
• Sri Lanka Market Capitalization: % Nominal GDP is updated yearly, available from Dec 1991 to Dec 2023
• The data reached an all-time high of 33.3 % in Dec 2010 and a record low of 7.5 % in Dec 2000

CEIC calculates Market Capitalization as % of Nominal GDP from annual Market Capitalization and annual Nominal GDP. Colombo Stock Exchange provides Market Capitalization in local currency. The Department of Census and Statistics provides Nominal GDP in local currency. Nominal GDP prior to 2010 is sourced from the World Bank.


Further information about Sri Lanka Market Capitalization: % of GDP

• In the latest reports, All Shares recorded a daily P/E ratio of 9.0 in Feb 2025
• All Share closed at 17,122.7 points in Jan 2025

Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point. Project Tycho level 1 data include data counts that have been standardized for a specific, published, analysis. Standardization of level 1 data included representing various types of data counts into a common format and excluding data counts that are not required for the intended analysis. In addition, external data such as population data may have been integrated with disease data to derive rates or for other applications.

Version 1.0.0 of level 1 data includes counts at the state level for smallpox, polio, measles, mumps, rubella, hepatitis A, and whooping cough and at the city level for diphtheria. The time period of data varies per disease somewhere between 1916 and 2011. This version includes cases as well as incidence rates per 100,000 population based on historical population estimates. These data have been used by investigators at the University of Pittsburgh to estimate the impact of vaccination programs in the United States, published in the New England Journal of Medicine: http://www.nejm.org/doi/full/10.1056/NEJMms1215400. See this paper for additional methods and detail about the origin of level 1 version 1.0.0 data.

Level 1 version 1.0.0 data is represented in a CSV file with 7 columns:

• epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)
• state: the two digit postal code state abbreviation that represents the state for which a count has been reported
• loc: the name of a state or city for which a count has been reported, capitalized
• loc_type: the type of location (STATE or CITY) for which a count has been reported
• disease: the disease for which a count has been reported: HEPATITIS A, MEASLES, MUMPS, PERTUSSIS, POLIO, RUBELLA, SMALLPOX, or DIPHTHERIA
• cases: the number of cases reported for the specified disease, epidemiological week, and location
• incidence_per_100000: the number of cases per 100,000 people, computed using historical population counts for cities and states as reported by the US Census Bureau