Economic Policy Visualization

Wealth · Scales

Dr. Matthias Schnetzer

November 4, 2024

What is wealth and how do we measure it?

Definition of private wealth

Non-financial assets

• Dwellings (owner-occupied residence, other real estate)
• Consumer durables (vehicles, etc.)
• Valuables
• Intellectual property

Financial assets

• Currency and deposits
• Net equity in own unincorporated business
• Mututal funds and investment funds
• (Private) Pensions funds
• Bonds and other debt securities
• Shares and other equity
• Life insurance funds -Other financial assets

Liabilities

• Owner-occupied residence loans
• Consumer durable loans (e.g. for vehicles)
• Other investment loans (collateralized)
• Other loans (e.g. education loans)

No human, social and cultural capital; No public social security pensions (marketable vs. augmented wealth)

Source: OECD (2013)

HFCS sampling and underreporting

• Target population in Austria 2021: 4 million households
• Gross sample in HFCS 2021: 6,300 households
• Realized interviews: 2,293 households
• Response rate in HFCS 2021: 39%
• Residual: refused interviews, invalid addresses, households not available, etc.

• Response refusal correlates with wealth and is highest at the top (Vermeulen, 2016)
• Wealthy households own a greater number of assets and miss some components more easily (Kennickell/Woodburn, 1999)

Upstream strategy against underreporting: Oversampling

• Undercoverage and Underreporting
• Oversampling is crucial for wealth surveys
• Oversampling in HFCS 2021: 🇧🇪 🇨🇾 🇩🇪 🇪🇪 🇪🇸 🇮🇹 🇫🇮 🇫🇷 🇬🇷 🇭🇷 🇭🇺 🇮🇪 🇱🇻 🇱🇹 🇱🇺 🇵🇹 🇸🇰
• No oversampling: 🇦🇹 🇨🇿 🇲🇹 🇳🇱 🇸🇮
• How does oversampling work?
  • External personal wealth data (🇫🇷 🇪🇸 🇱🇹)
  • List of streets with high-income people (🇩🇪)
  • Income tax data (🇱🇺 20% of the sample from top 10% earners)
  • Regions with higher average income or house prices (🇧🇪 🇬🇷)
  • Electricity consumption (🇨🇾)

Downstream strategy against underreporting: Pareto estimation

• Pareto-Distribution is a sensible approximation to the distribution of large wealth
• Two parameters:
  • Threshold for “large” wealth \(m\)
  • Pareto-Index \(\alpha\)

\[P_i(x_i) = Pr(X_i \leqslant x_i) = 1 - \left(\frac{m_i}{x_i}\right)^{\alpha_i}\] \[\forall ~\text{implicates} ~i = 1...5 \wedge x_i \geqslant m_i\]

A smaller \(\alpha\) means greater inequality. Empirically, \(\alpha\) often is around 1.5 for wealth.

Reading recommendation: Gabaix (2016)

European Rich List Database (ERLDB) and HFCS

Source: Disslbacher et al. (2020)

Cumulative density function of wealth in Germany

Source: Disslbacher et al. (2020)

New data source: Distributional wealth accounts (DWA)

Source: DWA, ECB

Wealth distribution in Austria

Net wealth distribution in Austria

Self-positioning in the wealth distribution

• Correct positioning of households in the richest decile: 0
• Average estimated decile in the richest decile: 6
• Average estimated decile in the poorest decile: 3

Note: for animated version, see here

Gender wealth gap in Austria

	Mean wealth	Mean GWG (in €)	Mean GWG (in %)	Median Wealth	Median GWG (in €)	Median GWG (in %)
All respondents
Couples	356,553			173,683
Male	207,485	58,417	28	82,285	13,862	17
Female	149,068			68,422
Male respondent
Male	270,307	110,995	41.1	99,347	27,085	27.3
Female	159,312			72,262
Female respondent
Male	138,830	957	0.7	63,825	−1,395	-2.2
Female	137,873			65,220

Note: The respondent in HFCS is the financially most knowledgeable person who is best informed about the household’s finances.
Source: Rehm et al. (2022)

Migrant wealth gap in Austria

	Mean wealth	Mean MWG (in €)	Mean MWG (in %)	Median Wealth	Median MWG (in €)	Median MWG (in %)
Natives	165,730			59,001
Migrants
Total	98,007	67,723	41	15,931	43,070	73
1st generation	63,001	102,729	62	9,917	49,084	83
2nd generation	139,775	25,955	16	32,763	26,238	44
1st generation migrants only
Short stay	39,598	126,132	76	4,935	54,066	92
Long stay	86,317	79,413	48	20,196	38,805	66

Note: First-generation migrants are distinguished into short (⩽20 years) and long (>20 years) time since arrival in Austria.
Source: Muckenhuber et al. (2022)

Perceptions of wealth inequality

Income and wealth inequality across Europe

Perceptions of fairness in wealth disparities

Schnetzer et al. (2024)

Private-public wealth gap

Source: Chancel et al. (2022)

Scales

Axes

Continuous

ggplot(aes(x = life_expectancy, y = poverty_rate)) +
  geom_point() +
  scale_x_continuous(limits = c(40, 100),
                     breaks = seq(40, 100, 20),
                     labels = scales::number_format(suffix = "years")) +
  scale_y_continuous(labels = scales::percent)

Dates

ggplot(aes(x = year, y = poverty_rate)) +
  geom_line() +
  scale_x_date(limits = c(as.Date("2020-01-01"), as.Date("2024-01-01")),
                breaks = date_breaks = "2 years",
                labels = date_labels = "%Y")

Others

scale_x_discrete(), scale_x_log10(), scale_x_reverse(), scale_x_sqrt(), …

Colors

Manual

ggplot(aes(x = year, y = poverty_rate, color = country)) +
  geom_line() +
  scale_color_manual(values = c("red", "blue", "green"))

Gradient

ggplot(aes(x = gdp, y = life_expectancy, color = poverty_rate)) +
  geom_point() +
  scale_color_gradient(low = "green", high = "red", na.value = "gray80")

Brewer

ggplot(aes(x = year, y = poverty_rate, fill = country)) +
  geom_area() +
  scale_fill_brewer(palette = "Set1")

Others

scale_color_binned(), scale_color_distiller(), scale_color_grey(), …

Shape, size and alpha

tribble(~x, ~y, ~significance, ~gdp, ~continent,
        0.5, 0.5, "yes", 140, "Asia",
        1.0, 0.5, "yes", 100, "Africa",
        1.5, 0.5, "no",  250, "Europe") |> 
  ggplot(aes(x = x, y = y)) +
  geom_point(aes(alpha = significance, size = gdp, shape = continent)) +
  scale_alpha_discrete(range = c(0.5, 1), guide = guide_none()) +
  scale_size_continuous(range = c(5, 12), guide = guide_none()) +
  scale_shape_manual(values = c(17, 19, 15),
                     guide = guide_legend(title = "Continent", 
                                          override.aes = list(size = 5))) +
  theme_minimal()

Guides

ggplot(aes(x = life_expectancy, y = poverty_rate, color = continent)) +
  geom_point() +
  scale_color_viridis_d(guide = guide_legend(title.position = "top",
                                             title.theme = element_text(size = 2),  
                                             title.hjust = 0, title.vjust = 0.5,
                                             label.position = "bottom",
                                             label.hjust = 0, label.vjust = 0.5,
                                             keywidth = 2, keyheight = 2,
                                             direction = "horizontal",
                                             override.aes = list(size = 4),
                                             nrow = 1, ncol = 4,
                                             byrow = FALSE, reverse = FALSE, ...))

Scale type	Default guide type
continuous scales for colour/fill aesthetics	colourbar
binned scales for colour/fill aesthetics	coloursteps
position scales (continuous, binned and discrete)	axis
discrete scales (except position scales)	legend
binned scales (except position/colour/fill scales)	bins

Bibliography

Chancel, Lucas/Piketty, Thomas/Saez, Emmanuel/Zucman, Gabriel (2022). World inequality report 2022. World Inequality Lab.

Disslbacher, Franziska/Ertl, Michael/List, Emanuel/Mokre, Patrick/Schnetzer, Matthias (2020). On top of the top - adjusting wealth distributions using national rich lists (Working Paper Series No. 20). INEQ.

Gabaix, Xavier (2016). Power Laws in Economics: An Introduction. Journal of Economic Perspectives, 30(1), 185–206. DOI: 10.1257/jep.30.1.185

Kennickell, Arthur B./Woodburn, R. Louise (1999). Consistent Weight Design for the 1989, 1992 and 1995 SCFs, and the Distribution of Wealth. Review of Income and Wealth, 45(2), 193–215. DOI: 10.1111/j.1475-4991.1999.tb00328.x

Muckenhuber, Mattias/Rehm, Miriam/Schnetzer, Matthias (2022). A Tale of Integration? The Migrant Wealth Gap in Austria. European Journal of Population, 38(2), 163–190. DOI: 10.1007/s10680-021-09604-1

OECD (2013). OECD guidelines for micro statistics on household wealth. OECD. DOI: 10.1787/9789264194878-en

Rehm, Miriam/Schneebaum, Alyssa/Schuster, Barbara (2022). Intra-Couple Wealth Inequality: What’s Socio-Demographics Got to Do with It? European Journal of Population, 38(4), 681–720. DOI: 10.1007/s10680-022-09633-4

Schnetzer, Matthias/Hofmann, Julia/Marterbauer, Markus (2024). Fairness Perceptions of Wealth Inequality in Europe. Review of Income and Wealth (R&R).

Vermeulen, Philip (2016). Estimating the Top Tail of the Wealth Distribution. The American Economic Review, 106(5), 646–650. DOI: 10.1257/aer.p20161021