Presentation at the HLEG thematic workshop on "Inequality of Opportunity", 14 January 2015, Paris, France, http://oe.cd/HLEG-workshop-inequality-opportunity-2015
HLEG thematic workshop on "Multidimensional Subjective Well-being", Carrie ExtonStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
HLEG thematic workshop on "Multidimensional Subjective Well-being", Arthur StoneStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
HLEG thematic workshop on "Inequality of Opportunity", Laura TachStatsCommunications
Presentation at the HLEG thematic workshop on "Inequality of Opportunity", 14 January 2015, Paris, France, http://oe.cd/HLEG-workshop-inequality-opportunity-2015
HLEG thematic workshop on "Inequality of Opportunity", Anders BjorklundStatsCommunications
This document compares four approaches to measuring the impact of family background on outcomes: intergenerational mobility, intergenerational effects, sibling correlations, and equality of opportunity. Intergenerational mobility measures descriptive associations between generations but does not capture causal effects. Intergenerational effects estimate causal impacts but explain little variance in outcomes. Sibling correlations provide a lower bound on family influences but do not address policy questions. Equality of opportunity aims to measure the impact of circumstances beyond an individual's control, but often omits important family factors not captured by data. Overall, no single approach provides a complete answer, as each leaves out key family influences captured by other methods.
HLEG thematic workshop on "Multidimensional Subjective Well-being", Angus DeatonStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
HLEG thematic workshop on "Intra-generational and Inter-generational Sustaina...StatsCommunications
Presentation at the HLEG thematic workshop on "Intra-generational and Inter-generational Sustainability", 22-23 September 2014, Rome, Italy, http://oe.cd/StrategicForum2014
HLEG thematic workshop on "Multidimensional Subjective Well-being", Dan BenjaminStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
HLEG thematic workshop on "Intra-generational and Inter-generational Sustaina...StatsCommunications
Presentation at the HLEG thematic workshop on "Intra-generational and Inter-generational Sustainability", 22-23 September 2014, Rome, Italy, http://oe.cd/StrategicForum2014
HLEG thematic workshop on "Multidimensional Subjective Well-being", Carrie ExtonStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
HLEG thematic workshop on "Multidimensional Subjective Well-being", Arthur StoneStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
HLEG thematic workshop on "Inequality of Opportunity", Laura TachStatsCommunications
Presentation at the HLEG thematic workshop on "Inequality of Opportunity", 14 January 2015, Paris, France, http://oe.cd/HLEG-workshop-inequality-opportunity-2015
HLEG thematic workshop on "Inequality of Opportunity", Anders BjorklundStatsCommunications
This document compares four approaches to measuring the impact of family background on outcomes: intergenerational mobility, intergenerational effects, sibling correlations, and equality of opportunity. Intergenerational mobility measures descriptive associations between generations but does not capture causal effects. Intergenerational effects estimate causal impacts but explain little variance in outcomes. Sibling correlations provide a lower bound on family influences but do not address policy questions. Equality of opportunity aims to measure the impact of circumstances beyond an individual's control, but often omits important family factors not captured by data. Overall, no single approach provides a complete answer, as each leaves out key family influences captured by other methods.
HLEG thematic workshop on "Multidimensional Subjective Well-being", Angus DeatonStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
HLEG thematic workshop on "Intra-generational and Inter-generational Sustaina...StatsCommunications
Presentation at the HLEG thematic workshop on "Intra-generational and Inter-generational Sustainability", 22-23 September 2014, Rome, Italy, http://oe.cd/StrategicForum2014
HLEG thematic workshop on "Multidimensional Subjective Well-being", Dan BenjaminStatsCommunications
Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014
HLEG thematic workshop on "Intra-generational and Inter-generational Sustaina...StatsCommunications
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Anne Annink, Public Administration
Fabian Dekker, Sociology
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This document summarizes research on how institutions impact social trust. It finds that institutions can slowly destroy social trust over time, but the effect is very small. Analyzing a survey of Swedish expats living abroad, it finds that those who lived in the most corrupt countries for over 30 years were still as trusting as Swedes in Sweden. However, among those who moved to these countries before age 30, trust was around 1/3 of a standard deviation lower for each 5 years lived in the country, suggesting that institutions mainly affect the development of trust among younger people.
This document provides background information on Dr. Nathan Smith and outlines his research interests in monitoring psychological function in isolated, confined, and extreme environments. Some key points:
- Dr. Smith's research focuses on extreme occupations like the military and collaborations with ESA on analog environments and the ISS.
- Isolated, confined, and extreme environments present physical, psychological, and interpersonal demands that require human adaptation for survival, performance, and health.
- In this presentation, Dr. Smith proposes using ecological momentary assessments, passive analysis of linguistics, and a digital monitoring tool to better understand dynamics related to behavior, performance, and psychological health in isolated environments.
Government Expenditures and Philanthropic Donations: Exploring Crowding-out w...Arjen de Wit
1) The study examines the relationship between government expenditures and individual philanthropic donations across 19 countries using cross-country data.
2) The results show evidence of crowding-in of donors rather than crowding-out, with higher government expenditures associated with higher probabilities of individuals donating.
3) However, there is less crowding-in observed in the health and social protection sectors. Social welfare expenditures seem to drive donors towards more "expressive" sectors like the arts and international aid.
This document reports on a study that examined the relationships between wealth, health, and happiness using data from 72 countries. It finds that while wealth is positively correlated with happiness and health, happiness fully mediates the relationship between wealth and health. Specifically, higher national wealth is associated with greater reported happiness, and greater happiness in turn is associated with better health outcomes. The study concludes that wealth alone cannot satisfy well-being, which depends more on experiencing happiness. It suggests countries focus on increasing opportunities for engagement and optimism once basic needs are met.
Here are the key steps to solve this problem:
1) The population mean lifetime for manufacturer A's bulbs is μ = 140 hours
2) We are sampling n = 20 bulbs
3) The sampling distribution of the sample mean (x-bar) is Normal with:
- Mean = Population mean (μ) = 140 hours
- Standard deviation = Population standard deviation (σ) / √n
= Let's assume σ = 10 hours
Then standard deviation of x-bar = 10/√20 = 2.22 hours
4) We want P(x-bar < 138)
5) Standardize: (138 - 140) / 2.22 = -0.89
6)
L. Becchetti, 30 Novembre - 1 Dicembre 2021 -
Webinar: Gli effetti della pandemia sulla soddisfazione per la vita e il benessere: analisi e prospettive
Titolo: La pandemia attraverso gli indicatori soggettivi a livello internazionale: un paradosso?
This study uses administrative data on Swedish lottery players to estimate the causal impact of wealth on health outcomes. The data allows for a large sample size with little attrition. Three lottery samples are pooled to examine the effect of wealth on players' health and their children's health and development. In the adult analyses, the researchers find no significant effects of wealth on mortality or health care utilization, with a possible small reduction in mental health drug use. In the intergenerational analyses, wealth increases children's health care utilization after the lottery and may reduce obesity risk, but other child outcomes are not significantly affected. Overall, the findings provide little evidence that wealth causally impacts health.
This document provides an overview of descriptive statistics. It discusses frequency distributions, histograms, measures of central tendency (mean, median), variability (range, standard deviation), and position (percentiles, quartiles). Bivariate descriptions include contingency tables and scatterplots. Key concepts are correlation, which measures the strength of association between two variables, and regression analysis, which predicts a response variable from an explanatory variable. Population parameters are distinguished from sample statistics.
Disability is associated with lower subjective well-being through two main pathways:
1) People with disabilities experience less positive and more negative emotions during daily activities like work, housework, leisure and self-care.
2) To compensate, people with disabilities allocate more time to activities with relatively higher positive emotions, like leisure, and less time to activities like work that induce more negative emotions.
However, the negative impact of reduced positive emotions during activities generally outweighs the positive impact of increased time in more enjoyable activities. So overall, disability is linked to lower levels of subjective well-being as measured by net affect.
This document summarizes key concepts in descriptive statistics, including:
1. Methods for describing data distribution through tables, graphs like histograms and stem-and-leaf plots, and numerical descriptions of center and variability.
2. Common measures of center like the mean and median, and variability like range and standard deviation.
3. Ways to describe the relationship between two variables through bivariate methods like contingency tables, scatterplots, correlation, and regression analysis.
This document summarizes key concepts in descriptive statistics including frequency distributions, measures of central tendency, variability, and relationships between two variables. It discusses graphical and numerical methods for describing one or two variables including histograms, box plots, measures of center, standard deviation, correlation, and regression. The summary provides an overview of techniques for exploring and summarizing data both within and between variables.
Statistics easy. Learn the descriptive statistics and understand concepts like measure of central tendency, variation, shape and correlation. central tendency,Statistics easy. Learn the descriptive statistics and understand concepts like measure of central tendency, variation, shape and correlation. central tendency,
This document provides an overview of descriptive statistics techniques for summarizing and describing data, including both categorical and quantitative variables. It discusses frequency distributions, histograms, stem-and-leaf plots, numerical descriptions of center and variability (mean, median, standard deviation), bivariate descriptions using tables, scatterplots and correlation, and simple linear regression. The goal of descriptive statistics is to organize and summarize sample data in order to make inferences about the underlying population.
This document provides an overview of descriptive statistics techniques for summarizing and describing data, including both categorical and quantitative variables. It discusses frequency distributions, histograms, stem-and-leaf plots, numerical descriptions of center and variability (mean, median, standard deviation), bivariate descriptions using tables, scatterplots and correlation, and simple linear regression. The goal of descriptive statistics is to organize and summarize sample data in order to make inferences about the corresponding population parameters.
This document summarizes key concepts in descriptive statistics, including:
1. Methods for describing data distribution through tables, graphs like histograms and stem-and-leaf plots, and numerical descriptions of center and variability.
2. Common measures of center like the mean and median, and variability like range and standard deviation.
3. Ways to describe the relationship between two variables through bivariate methods like contingency tables, scatterplots, correlation, and regression analysis.
Government Expenditures and Philanthropic Donations: Exploring Crowding-out w...Arjen de Wit
This document summarizes a study that explored the relationship between government expenditures and philanthropic donations across 19 countries using cross-country data. The study found evidence of crowding-in rather than crowding-out, with higher government expenditures associated with higher rates of individual donations. However, crowding-in was less for health and social protection sectors. Government support for social welfare also seemed to drive donors to donate more to expressive nonprofit subsectors like the arts and environment. Overall, there was no evidence that higher government expenditures crowded out either the number of donors or the amounts donated.
Polygynous family structure and child undernutrition in Africa: Empirical evi...CGIAR
This presentation was given by Mulubrhan Amare (IFPRI), as part of the Annual Gender Scientific Conference hosted by the CGIAR Collaborative Platform for Gender Research. The event took place on 25-27 September 2018 in Addis Ababa, Ethiopia, hosted by the International Livestock Research Institute (ILRI) and co-organized with KIT Royal Tropical Institute.
Read more: http://gender.cgiar.org/gender_events/annual-conference-2018/
OECD Knowledge Exchange Platform on Well-being Metrics and Policy Practice (KEP): Virtual Workshop 1, 13 June 2024
Summarising the complexity of well-being data and evidence: Reporting and communicating on well-being dashboards
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- Isolated, confined, and extreme environments present physical, psychological, and interpersonal demands that require human adaptation for survival, performance, and health.
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1) The study examines the relationship between government expenditures and individual philanthropic donations across 19 countries using cross-country data.
2) The results show evidence of crowding-in of donors rather than crowding-out, with higher government expenditures associated with higher probabilities of individuals donating.
3) However, there is less crowding-in observed in the health and social protection sectors. Social welfare expenditures seem to drive donors towards more "expressive" sectors like the arts and international aid.
This document reports on a study that examined the relationships between wealth, health, and happiness using data from 72 countries. It finds that while wealth is positively correlated with happiness and health, happiness fully mediates the relationship between wealth and health. Specifically, higher national wealth is associated with greater reported happiness, and greater happiness in turn is associated with better health outcomes. The study concludes that wealth alone cannot satisfy well-being, which depends more on experiencing happiness. It suggests countries focus on increasing opportunities for engagement and optimism once basic needs are met.
Here are the key steps to solve this problem:
1) The population mean lifetime for manufacturer A's bulbs is μ = 140 hours
2) We are sampling n = 20 bulbs
3) The sampling distribution of the sample mean (x-bar) is Normal with:
- Mean = Population mean (μ) = 140 hours
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= Let's assume σ = 10 hours
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L. Becchetti, 30 Novembre - 1 Dicembre 2021 -
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This study uses administrative data on Swedish lottery players to estimate the causal impact of wealth on health outcomes. The data allows for a large sample size with little attrition. Three lottery samples are pooled to examine the effect of wealth on players' health and their children's health and development. In the adult analyses, the researchers find no significant effects of wealth on mortality or health care utilization, with a possible small reduction in mental health drug use. In the intergenerational analyses, wealth increases children's health care utilization after the lottery and may reduce obesity risk, but other child outcomes are not significantly affected. Overall, the findings provide little evidence that wealth causally impacts health.
This document provides an overview of descriptive statistics. It discusses frequency distributions, histograms, measures of central tendency (mean, median), variability (range, standard deviation), and position (percentiles, quartiles). Bivariate descriptions include contingency tables and scatterplots. Key concepts are correlation, which measures the strength of association between two variables, and regression analysis, which predicts a response variable from an explanatory variable. Population parameters are distinguished from sample statistics.
Disability is associated with lower subjective well-being through two main pathways:
1) People with disabilities experience less positive and more negative emotions during daily activities like work, housework, leisure and self-care.
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2. Common measures of center like the mean and median, and variability like range and standard deviation.
3. Ways to describe the relationship between two variables through bivariate methods like contingency tables, scatterplots, correlation, and regression analysis.
This document summarizes key concepts in descriptive statistics including frequency distributions, measures of central tendency, variability, and relationships between two variables. It discusses graphical and numerical methods for describing one or two variables including histograms, box plots, measures of center, standard deviation, correlation, and regression. The summary provides an overview of techniques for exploring and summarizing data both within and between variables.
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Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
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Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
HLEG thematic workshop on "Inequality of Opportunity", Daniel Waldenstrom
1. Intergenerational wealth mobility
and the role of inheritance
Daniel Waldenström, Uppsala University
(joint with Adrian Adermon, UU, and Mikael Lindahl, UU)
1
Presentation at OECD, Jan 14, 2015
2. Our study
We have two main questions:
1. Does personal wealth status persist over more than two
generations?
2. Of all the potential mechanisms driving persitence in personal
wealth across generations (ability, contacts, norms, luck),
what is the specific importance of inheritance?
2
3. Previous literature
Wealth mobility
• Two-generational intergenerational elasticities (IGEs):
– US: Menchik (1979, wealthy, 0.7), Charles and Hurst (2003, PSID, 0.4),
Wahl (2003, top wealth, 0.6-0.8).
– Britain: Harbury and Hitchens (1979, wealthy families, 0.5)
• Three generational IGE
– US: Wahl (2gen, 0.6-0.8, 3gen 0.1).
– France: Arrondel and Grange (2006, 2gen 0.45, 3gen 0.3)
– Denmark: Halphern Boserup et al (2014, 2gen IGE 0.3, 3gen 0.1)
Role of inheritance
– No direct tests. Halphern Boserup et al find larger persistence when
parents have died. Charles and Hurst decompose wrt to transfers.
3
4. Data
• Population:
– Survey of all third-graders in Sweden’s third largest city, Malmö, in 1938
– Index generation (b. 1928) followed with added info about parents (b.
≈1898), spouses, children (b. ≈1955) and grandchildren (b. ≈1980)
– We observe wealth, income, education of these individuals
• Wealth data:
– Wealth: Non-financial assets + Financial assets ‒ Debt
– Mid-life wealth (gen1‒4):
• Taxable wealth: by household, measured at ages 45-60 (gen1-3)
• Third-party reported wealth: individual, observed in the 2000s (gen3-4)
– Wealth at death (gen1-2):
• Estate inventory reports
• Problem: parents die at different times. How to control for this?
• Inheritance data:
– Inheritance tax reports: final taxable inheritance lot/heir.
4
5. Method: How is wealth mobility measured?
• Standard 2-generational estimation of IG mobility:
𝑊𝑐ℎ𝑖𝑙𝑑 = 𝛼 + 𝛽𝑊𝑝𝑎𝑟𝑒𝑛𝑡 + 𝛿𝑋 + ε
– 𝛽 is called the intergenerational elasticity, a measure of transmission.
– 𝑊 can be in rank, in log, in IHS, in level
– X is vector of controls, mainly age (but also other things)
• We estimate 3- and 4-generational variants:
𝑊𝑐ℎ𝑖𝑙𝑑 = 𝛼 + 𝛽1 𝑊𝑝𝑎𝑟𝑒𝑛𝑡 + 𝛽2 𝑊𝑔𝑟𝑎𝑛𝑑𝑝. + 𝛿𝑋 + ε
𝑊𝑐ℎ𝑖𝑙𝑑 = 𝛼 + 𝛽1 𝑊𝑝𝑎𝑟𝑒𝑛𝑡 + 𝛽2 𝑊𝑔𝑟𝑎𝑛𝑑𝑝. + 𝛽3 𝑊𝑔𝑟𝑒𝑎𝑡𝑔𝑟𝑎𝑛𝑑𝑝. + 𝛿𝑋 + ε
5
6. Method: How is the role of inheritance measured?
• We estimate the role of receiving inheritance 𝐵 by estimating:
𝑊𝑐ℎ𝑖𝑙𝑑 = 𝛼 + 𝛽1 𝑊𝑝𝑎𝑟𝑒𝑛𝑡 + 𝛿𝑋 + ε
𝑊𝑐ℎ𝑖𝑙𝑑 = 𝛼 + 𝛽2 𝑊𝑝𝑎𝑟𝑒𝑛𝑡 + 𝛾𝐵 + 𝛿𝑋 + 𝑢
𝐵 = 𝛼 + 𝛽3 𝑊𝑝𝑎𝑟𝑒𝑛𝑡 + 𝛿𝑋 + 𝑒
• Assuming that 𝑢 and 𝑒 are uncorrelated (𝐵 is random), we can
retrieve two effects from 𝑊𝑝𝑎𝑟𝑒𝑛𝑡 onto 𝑊𝑐ℎ𝑖𝑙𝑑:
– The direct (non-mediated) effect: 𝛽1
– The mediated effect, i.e., the role of inheritance: 𝛽3 ∙ 𝛾 = 𝛽1 − 𝛽2
• But are 𝑢 and 𝑒 uncorrelated?
– We allow for different variants, using SUR and mediate in Stata.
6
7. Results: How persistence is personal wealth status?
7
Rank Log IHS Rank Log IHS
Dep: 2nd Gen Dep: 2nd Gen
1st Gen 0.350*** 0.232* 0.210*** 1st Gen 0.364*** 0.105
(0.059) (0.010) (0.039) (0.009) (0.080)
728 728 728 165 256
Dep: 3rd Gen Dep: 3rd Gen (mid-life W)
2nd Gen 0.321*** 0.237*** 0.369*** 2nd Gen 0.281*** 0.160*** 0.279***
(0.029) (0.037) (0.026) (0.052) (0.062) (0.007)
1485 1485 1485 556 556 556
Dep: 3rd Gen Dep: 3rd Gen (midlife)
1st Gen 0.284*** 0.201*** 0.326*** 1st Gen 0.147*** 0.164***
(0.005) (0.070) (0.069) (0.003) (0.064)
1485 1485 1485 1001 1472
Mid-life wealth Wealth at death (estates)
8. Results: Does wealth persistence differ across the
distribution?
8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
P0-50 P50-75 P75-90 P75-100
Intergenerationalelasticity
Parent's wealth fractile
Global (OLS)
Spline
9. Results: What is the role of inheritance?
• Very preliminary: Seems to be a large effect of inheritance
– Mediated effect much more than half of the total effect
– Prel findings on inheritances of Gen3 suggest about 50%
– Results sensitive to attempts to control for timing of inheritance
9
Only pos. Incl. zeros
Dep: 2nd Gen
1st Gen 0.325 0.357*
(0.177) (0.179)
Dep: 2nd Gen
1st Gen 0.051 0.067
(0.134) (0.134)
Inheritance (B) 0.889*** 0.894***
(0.155) (0.147)
Dep: Inheritance
1st Gen 0.308*** 0.324***
(0.090) (0.091)
Mediated effect 0.274 0.290
(0.009) (0.009)
10. Concluding remarks
Three main conclusions (so far):
1. Personal wealth status persists for more than two generations
– Not only parents’ wealth, but also grandparents’ wealth matter
2. Inheritance, i.e., direct transfer of capital to descendents,
seems to account for much of this wealth persistence
– Our preliminary estimates suggest that they are quite large
3. At what point in life when wealth is measured matters less
– Persistence effetcs are similar when using wealth during midlife and
wealth at death
10