rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 shows that the observed correlation between directly reported profits and revenue-expenses is low, with Pearson correlations of 0.2-0.3.<sup>5</sup> These correlations are similar to those found in microenterprise surveys in other countries. For example, the ENAMIN survey in Mexico gives a Pearson correlation of 0.32 between profits and revenues-expenses for firms with less than US$1000 in capital stock. Vijverberg (1991) finds a correlation between revenue-expenses and reported earnings between 0.27 and 0.43 in Côte d’Ivoire and negative and close to zero in Ghana, and Daniels (2001) reports a correlation of 0.24 between these measures in Zimbabwean microenterprises.

Secondly, we see that mean and median reported profits are higher than mean and median reported revenue-expenses, while the standard deviation of revenue-expenses is three times larger than that of profits. Finally, no entrepreneur reports that his or her profits are negative, whereas over a quarter of firms have negative reported revenue-expenses. Again these findings mirror results obtained in other countries. Vijverberg (1991) finds average revenue-expenses to be negative in three out of four industries studied in Côte d’Ivoire and Ghana, with large standard deviations; Vijverberg and Mead (2000) report 20 percent of enterprises have negative revenue-expenses in household surveys in Ecuador and Pakistan, and Daniels (2001) reports 37 percent of firms have negative revenue-expenses in Zimbabwe.

Does profits or revenue-expenses provide the more realistic level of income? One indication can be found in the wages paid to entry-level workers in similar industries to those our firms operate in. Interviews with owners of firms with 5 to 45 workers in the garment, coir, lottery ticket sales, fishing, and boat-building industries revealed typical daily wages of 300-350 Rupees, leading to monthly wages of 6000-7000 Rupees. As a second indication, we asked SLMS firms what is the lowest monthly wage they would be prepared to accept to close their business and work in a wage job. The median response was 8000 Rupees. Even allowing for a premium for the flexibility of self-employment, this leads us to believe that a reasonable measure of firm profits should have average

> 5 Note the correlations are lower for July in the KMS than for August. Both July and August data were asked at the start of September. We interpret the lower correlations in July as therefore arising from greater recall errors with two month compared to one month recall.

7
**<u>Table 5: Reasons for Not Having a Loan</u>**

||Avg.for Femal|e-OwnedFirms|Difference (|Female- Male)|
|---|---|---|---|---|
||Mexico<sup>#</sup>|Enterprise<br>Surveys|Mexico<sup>#</sup>|Enterprise<br>Surveys|
|Loan application was rejected|0.5|5.5|-0.5***|-0.3|
|Did not apply for loan|91.4|48.8|1.4**|-4.7|
|_Reasons for not applying for a loan_|||||
|No need|69.7|38.5|2.4**|-6.9|
|Cumbersome application procedures|8.8|18.9|-1.4*|10.7***|
|Stringent collateral requirements||12.2||2.2|
|Interest rates are too high|14.4|25.7|-0.6|-6.6*|
|Corruption in the allocation of bank credit||1.4||0.5|
|Did not expect to be approved|2.6|2.7|-0.7*|-0.3|
|Amount and/or maturity not convenient|1.6||-0.3||
|Other|2.8|0.0|0.6|0.0|

_Note_ :<sup>#</sup> In the Mexican data, all measure refers to ever having applied for a loan since starting the business. Significance levels: *** 1 percent, ** 5 percent, * 10 percent. _Source_ : Encuesta Nacional de Micronegocios (ENAMIN), 2002, Bolivian World Bank Micro Enterprises Survey, 2007, Peruvian World Bank Micro Enterprises Survey, 2008, World Bank Enterprise Surveys for Brazil, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua, 2003.

# **4.b. Other Obstacles to Firm Operation and Growth**

This section relies on a series of perception-based questions to investigate whether female and male-owned firms face different types of obstacles to firm operation and growth. The Bolivian, Peruvian, and Enterprise Survey data ask the respondents to gauge whether a series of different factors represent an obstacle to the firm’s operation and growth. Table 6 displays the percentage of female-owned firms that perceive each factor as an obstacle, along with the corresponding difference to male-owned firms<sup>10</sup> . For ease of comparison with the average percentages, the differences do not control for region or industry effects. However, the results do not change significantly when including region and industry fixed effects.

Many of the variables listed in Table 6 are equally perceived as obstacles by female-owned and male-owned firms. Moreover, for most variables where there is a statistically significant difference, female-owned firms are actually less likely to perceive the variable as an obstacle than male-owned firms. For example, in the Bolivian data, female-owned firms are 10.9 percent less likely to perceive access to finance as an obstacle than male-owned firms.

There are only three variables that a greater percentage of female-owned firms perceive to be an obstacle than male-owned firms. The first is that, in the Enterprise Surveys, female-owned firms are 3.9 percent more likely to see macroeconomic instability as an obstacle than male-owned firms. However, a total of about 90 percent of firms report macroeconomic instability to be an obstacle, implying that the economic relevance of the 3.9 percent difference is quite small. The

> 10 Appendix Table A3 displays the results separately for the different countries covered in the Enterprise Surveys.

16
Figure 7: Price and welfare changes <!-- Start of picture text --> S<br>PV<br>CV<br>CS<br>EV<br>LV<br>0 10 20 30 40 50<br>The increase in price (in %)<br>Figure 8: Price and welfare changes<br>The increase in price of good 2 (in %)<br>0 5 10 15 20 25<br>S<br>PV<br>CV<br>CS<br>EV<br>LV<br>0 10 20 30 40 50<br>The increase in price of good 1 (in %)<br>10<br>0<br>−10<br>The impact on welfare<br>−20<br>−30<br>0<br>−10<br>−20<br>The impact on welfare<br>−30<br><!-- End of picture text --> 32
# **_D. Price Relationships and Identities_**

18. Components of Domestic Sales Di = DiC + DiIF + IiI + DiG 19. Components of Import Sales Mi = MiN + MiC + MiIF + MiG N m 20. Domestic Price of Intermediate Imports pri = (1 + ui + tri)pri (holds also for imports for G) C m 21. Domestic Price of Imports for C pri = (1 + ui + tri)pri (holds also for imports for I<sup>F</sup> ) 22. Consumer Price of Domestic Goods piC = (1 + vi)pi 22. Consumer Price of Domestic Goods (holds also for purchases for I<sup>F</sup> )

- τK1 + wK1 = ... = τKn + wKn  (mobile capital sectors)

23. Capital-Market Equilibrium

# **_LIST OF VARIABLES_**

Li Domestic labor inputs, sector i (i=1,..,34) Ki Capital (other value added) inputs, both mobile and immobile Vi Value added Mi Total imports Mri Imports from region r (r = EU, MENA, ROW) MiN Imports of commodity i for intermediate use mriN Imports for intermediate use from region r (r = EU, MENA, ROW) zji Composite intermediate input of j into i (j=1,...,34) dji, mji Intermediate usages of domestic and imported goods Yi Output of good i Di, Xi Output for domestic sales and exports DiC, DiG, DiIF Domestic sales: private and public consumption, capital formation Xri Exports of good i to region r ci Index of marginal cost of production pi Domestic producer price index Z C IF G ~pi , ~pi , ~pi , ~pi Domestic price indexes (home and imported prices) wK, wL Factor price indexes U Utility<sup>~</sup> pi Composite price index for total domestic supply Ci, Gi Private and public consumption IiF, IiI Fixed capital formation and inventory investment MiC, MiG Imports for private and public consumption 22
# **The Energy Transition of the Transition Economies: An empirical analysis**<sup>**1**</sup>

Fan Zhang<sup>2</sup>

**Key Words** : Manufacturing Energy Intensity, Energy Productivity Convergence, Index Decomposition, Trade, Eastern Europe and Central Asia

**JEL Classification** : O13, O47, P28, Q40

**Sector Board** : Energy and Mining

> 1 I thank Uwe Deichmann and Indermit Gill for useful comments and suggestions. I thank Sofia Chiarucci and Po Ying Wong for excellent research assistance. The usual disclaimer applies.

> 2 World Bank, 1818 H ST NW, Washington DC. 20433.  fzhang1@worldbank.org
**Table 2: Use of Remote Learning Opportunities by Student Characteristics**

|||||Last Seve|n Days|||Since Scho|ol Closed T|eachers:|
|---|---|---|---|---|---|---|---|---|---|---|
||Sample<br>Size|Any Online<br>or Telelearning|Educa|DOV|YouTube|Google<br>Class/Zoom|EdModo|Held Online<br>Class|Sent<br>Materials|Set<br> Homework|
|**Full Sample**|**1552**|**0.74**|**0.08**|**0.11**|**0.48**|**0.41**|**0.19**|**0.37**|**0.83**|**0.94**|
|Males|710|0.72|0.05|0.10|0.47|0.40|0.17|0.35|0.81|0.93|
|Females|842|0.77|0.10|0.12|0.48|0.42|0.20|0.39|0.85|0.94|
|_test of gender equality_||0.034|0.001|0.197|0.648|0.570|0.088|0.086|0.018|0.273|
|Grade 10|545|0.72|0.07|0.11|0.44|0.42|0.17|0.36|0.79|0.90|
|Grade 11|595|0.72|0.08|0.10|0.46|0.35|0.15|0.31|0.84|0.95|
|Grade 12|412|0.82|0.09|0.12|0.55|0.50|0.26|0.47|0.88|0.96|
|_test of grade equality_||0.000|0.473|0.613|0.002|0.000|0.000|0.000|0.001|0.000|
|Lowest Wealth Quartile|375|0.64|0.07|0.09|0.33|0.29|0.15|0.26|0.77|0.91|
|Highest Wealth Quartile|405|0.81|0.07|0.12|0.58|0.52|0.21|0.50|0.88|0.97|
|_test of wealth equality_||0.000|0.694|0.210|0.000|0.000|0.042|0.000|0.000|0.001|
|Mother's Education:high school or less|693|0.71|0.07|0.10|0.43|0.34|0.17|0.27|0.80|0.93|
|Mother's Education:beyond high school|859|0.77|0.08|0.11|0.52|0.47|0.20|0.45|0.86|0.95|
|_test of educational background equality_||0.009|0.811|0.727|0.001|0.000|0.247|0.000|0.001|0.128|
|Mestizo or White|1306|0.75|0.08|0.11|0.50|0.44|0.20|0.39|0.84|0.94|
|Indigneous or Other|246|0.68|0.05|0.09|0.34|0.28|0.14|0.25|0.76|0.92|
|_test of ethnicity equality_||0.022|0.153|0.480|0.000|0.000|0.035|0.000|0.002|0.306|
|Pichincha|1066|0.76|0.08|0.10|0.52|0.48|0.23|0.42|0.85|0.92|
|Napo or Orellana|486|0.71|0.07|0.13|0.38|0.27|0.09|0.26|0.79|0.97|
|_test of provincial equality_||0.049|0.464|0.048|0.000|0.000|0.000|0.000|0.007|0.000|
|Have Computer & Internet|914|0.82|0.09|0.13|0.59|0.52|0.23|0.48|0.89|0.96|
|No Internet|404|0.61|0.05|0.07|0.28|0.22|0.11|0.17|0.73|0.91|
|_test of access equality_||0.000|0.014|0.001|0.000|0.000|0.000|0.000|0.000|0.000|

Notes:

**Educa** online and by radio is the platform of the Ministry of Education; **DOV** is Showing Life Opportunities, a platform provided by the authors. **YouTube** refers only to educational use of this platform; **Google Class/Zoom** is use of Google Classroom or Zoom; **EdModo** is an online educational platform.

20
# **I The Puzzle. Motivation**

This paper presents an analysis of some of the root causes for persisting poor public service delivery in the Dominican Republic, by looking at the behavior of the middle class in the Dominican Republic, characterized by opting for private services and presenting low-intensity engagement in collective action demands. This background paper is a contribution to the LCR Flagship Report 2012: _Socio-Economic Mobility and the Rise of the Middle Class in Latin America and the Caribbean._ The middle class is alleged to support economic development and good governance by organizing demand for public goods and government accountability, which could lead to an enhanced welfare state, and thus reduced inequality and poverty. In the Dominican Republic, however, the middle class has not frequently participated in collective actions and demands for public accountability, and the quality of public services remains poor.

The **aim** of this paper is to provide a plausible (but perhaps not definite) explanation for this puzzle, as well as to understand what interdependences within the Dominican middle class could motivate collective arrangements and citizen demand for public accountability. Departing from the theoretical framework for service delivery presented in the World Development Report (2004), we **focus** in analyzing the relation between the citizens and the state, and, concretely, in the behavior of the Dominican middle class regarding public goods, their institutional perception and their collective engagement.

We **find** that the Dominican middle class is different from that of developed nations, as it is more vulnerable to poverty and it has resorted to individualized solutions to substitute faulty public goods. Examples of this “adaptive behavior” include the heavy reliance on domestic generation of electricity, the digging of wells to get running water at home, the use of private companies to report car accidents (instead of doing it at the police station), and the use of private services for education and health – these last two also common in other countries of the region. Additionally, the middle class seems to be skeptical and presents low levels of trust in public institutions.

[2]
### **5. Quick Ratio**

<!-- Start of picture text --> Quick Ratio<br>1.3<br>1.2<br>1.1<br>1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>EAP ECA LAC MENA SA SSA<br><!-- End of picture text --> Note: Quick Ratio: (Current Assets-Inventories)/Current liabilities.

Source: Bloomberg, own calculations

# 4 Results

## 4.1 Trends in the Corporate Vulnerability Index

Debt of non-financial listed firms in EMDEs has increased in both level and riskiness (Figure 6): EMDEs’ corporate debt increased by 46 percent, from $2.6 trillion to $3.8 trillion between 2010 and 2016. Over the same period, debt in the hands of firms considered vulnerable based on at least one indicator grew by 120 percent, from US $1 trillion to US $2.2 trillion. In 2016, about 58 percent of debt was considered ‘at risk’ based on at least one indicator; 35 percent according to two or more indicators; 12 percent based on three or more indicators; and 4 percent according to four or more indicators.

13
Figure 3: Country level correlates of gender differences in the enrollment of 6 to 14 year olds.

<!-- Start of picture text --> Mae-leealegap R-io- -.229N 41 pval= .7 and GNP per capita (PPP) Nible-6rrIle R-*-- -38 gap and Gin W 23 pval= i ndex.07<br>30 - 30<br>C V<br>E ro bm pa E Pa roi<br>o 20 - nd 20 - ln<br>cr lcd rsca r a r.i,<br>rotC LncY rz m c a N tur 0X ew n crsi<br>10 -rg _ ta 1w cd 10-<br>w car ~~~~~~gtm (<br>E rr ml ns b pHt nam E m b pHni<br>mcci 0-~~~~~~p ti r<br>-10- -10<br>403 1097 2981 8103 25 35 45 55 65<br>GNP per cpila tnl$ PPP (log scao Gne index<br>?Nle-female gap and GN per capita growth IWe-ferraleap and public spending on paryeducation<br>Rio- .1 N= 41 pval= .51 Rio- .03 N2 pVal= gmin89 n<br>30 - 30-<br>E bnpVE b<br>o 20- mr 20- mni<br>i, . ctrd id c tlcd<br>o ~~~~~~~~~~~~igo C g,<br>-10- ' ~ ~ ~ ~ ~ ~ ~ ~ ~ m ta m2e L ccc r 1 -ma 0- ir ne5>in 10 u )m1<br>Oc rrs sh~~~~~~~~~~~~ba gtm tus cLi g tr<br>E bdE<br>cc mcci~~~~w va ee e ci Nti kenc<br>0 -cc kNi bg Id c 0- mwac llbbdio Cd<br>ai, u taCD,<br>-10- -10-<br>-6 -4 -2 0 2 4 6 7.4 20 55 148 403<br>GNP per capita groWh Public speniding per stent L$ - Prin-ary le~ (log ae)<br><!-- End of picture text --> 22
# **TABLE 10**

||Residential<br>Conn.per<br>Employee|Distribut.<br>Losses|Coverage|Energy Sold<br>per Conn.|Duration<br>of<br>interrupt's|Frequency<br>of<br>interrupt's|OPEX per<br>Connection<br>(in dollars)|OPEX per<br>MWH sold<br>(in dollars)|Avg Resid.<br>Tariff<br>(in dollars)|Avg Indust.<br>Tariff<br>(in dollars)|Cost<br>Recovery<br>Ratio|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Dummy Transition of|0.122***|0.027**|-0.014***|0.059***|-0.043|0.046|-0.730*|-0.808**|0.147***|0.087***|0.068|
|PSP|(0.012)|(0.013)|(0.003)|(0.004)|(0.037)|(0.044)|(0.397)|(0.400)|(0.019)|(0.021)|(0.062)|
|Dummy Post Transition|0.084***|-0.124***|0.002|-0.008|-0.358***|-0.366***|-0.193***|-0.137***|0.049***|-0.016|0.176***|
|of PSP|(0.008)|(0.013)|(0.002)|(0.005)|(0.025)|(0.024)|(0.039)|(0.049)|(0.013)|(0.021)|(0.053)|
|PCA 1 - Informal|0.001|-0.027***|-0.001|-0.048***|0.014|0.010|0.046|0.053|0.087***|0.010|-0.003|
||(0.007)|(0.007)|(0.001)|(0.004)|(0.018)|(0.018)|(0.042)|(0.050)|(0.010)|(0.021)|(0.018)|
|PCA 2 - Formal|0.107***|-0.006|0.004*|0.037***|-0.024|-0.103***|0.050|0.092|-0.145***|-0.051***|-0.071*|
||(0.008)|(0.006)|(0.002)|(0.004)|(0.026)|(0.028)|(0.084)|(0.085)|(0.014)|(0.016)|(0.037)|
|PCA 3 - Formal Autonomy|0.085***|-0.069***|0.012***|-0.009|-0.144***|-0.080|-0.405***|-0.339**|-0.036*|-0.030|0.266***|
|and Tariffs|(0.015)|(0.012)|(0.004)|(0.009)|(0.053)|(0.049)|(0.111)|(0.132)|(0.020)|(0.029)|(0.068)|
|Utility FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Utility Specific<br>Time trend|Yes|No|Yes|No|No|No|No|No|No|No|No|
|Observations|1782|1917|1190|2253|974|882|800|809|1596|820|619|
|Number of utilities|175|169|131|189|134|123|121|121|153|84|93|

Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%

- 24 -
- 12 -

The skewed distribution of traffic coincides with an uneven geographical distribution (Table 6): whereas the ADT on roads in the Greater Accra region is close to 2,000 ve1icles, in the Eastern, Central and Ashanti regions it is 600-750; the average in the rest of the country is only 200 vehicles (in the Upper West and Northern regions it is less than 100 vehicles).

Table 6. Average Daily Traffic by Region, 1980-1988

|Region|1980|<br>1981|<br>1982|1983|<br>1984|1985|<br>1986|<br>1987|1988|
|---|---|---|---|---|---|---|---|---|---|
|Greater||||||||||
|Accra|2,318|1,103|1,772|n.a.|1,310|1,828|1,823|1,787|1,976|
|Volta|281|296|269|218|224|257|<br>213|232|<br>297|
|Eastern|750|831|744|636|492|510|623|656|<br>764|
|Central|603|668|653|571|594|654|624|628|<br>615|
|Western|259|229|246|182|190|296|295|327|<br>334|
|Ashanti|597|594|674|550|647|727|751|739|<br>756|
|Brong Ahafo|259|230|243|230|250|253|239|221|<br>236|
|Northern|137|107|94|62|n.a.|75|83|91|82|
|Upper East.|202|230|171|125|n.a.|129|<br>203|214|<br>182|
|Upper West.|57|75|101|63|64|n.a.|57|47|82|
|**Totala**|**417**|**381**|**395**|**288**|**397**|**383**|**382**|**390**|**421**|
|Greater||||||||||
|Accra|2,318|1,103|1,772|n.a.|1,310|1,828|1,823|1,787|1,976|
|Ashanti||||||||||
|Central|664|714|699|592|567|616|665|676|<br>725|
|Eastern||||||||||
|Other|212|196|191|152|209|197|184|193|<br>208|

* Mean of available data.

Data on urban traffic is much more limited. A recent study by DeLeuw-Cather International indicates that the average traffic volume on Accra inner streets is about 840 vehicles per hour (slightly higher in the afternoon peak hours and lower at noon). The distribution of traffic in different parts of Accra is far from even. Thus, whereas traffic on major arteries in peak hours reaches 1,600-2,300 vehicles per hour, traffic on noncircumferent city streets is much lower. Accra has by far the most dense concentration of vehicles. Traffic counts in Tema and Sekondi-Takoradi indicate an average hourly traffic volume in peak hours of 450 and 360, respectively.
# **_Table 13: Saving Behavior_**

|Savings Behavior<br>Followed up with an MFB<br>after the event|Currently has any<br>form of formal<br>savings account|I saved some<br>money last<br>month|<br>Do you currently have<br>savings of less than or<br>equal to 1 month of<br>income?|
|---|---|---|---|
|(1)|(2)|(3)|(4)|
|_Treatments_||||
|Movie<br>0.02***|‐0.01|0.02|0.01|
|(0.006)|(0.029)|(0.037)|(0.039)|
|MFB<br>0.00|‐0.04|0.01|0.07*|
|(0.006)|(0.030)|(0.038)|(0.040)|
|Movie + MFB<br>0.00|‐0.04|‐0.04|0.02|
|(0.006)|(0.030)|(0.038)|(0.040)|
|_Gender disaggregated interaction effects (female in_|_teraction)_|||
|Movie<br>0.02***|0.02|0.05|0.03|
|(0.007)|(0.034)|(0.043)|(0.045)|
|MFB<br>0.00|‐0.05|0.02|0.05|
|(0.008)|(0.035)|(0.045)|‐0.047|
|Movie + MFB<br>0.00|‐0.04|0.01|0.03|
|(0.008)|(0.035)|(0.044)|(0.047)|
|_Gender disaggregated interaction effects (male int_|_eraction)_|||
|Male<br>0.00|0.07|0.03|0.05|
|(0.011)|(0.050)|(0.064)|(0.067)|
|Male*Movie<br>‐0.02*|‐0.09|‐0.10|‐0.05|
|(0.014)|(0.066)|(0.084)|(0.089)|
|Male*MFB<br>‐0.01|0.02|‐0.05|0.05|
|(0.015)|(0.068)|(0.086)|(0.091)|
|Male*(Movie + MFB)<br>‐0.01|‐0.01|‐0.17**|‐0.02|
|(0.014)|(0.067)|(0.085)|(0.089)|
|p‐values δ1+ γ1≠0<br>0.92|0.18|0.43|0.73|
|for F‐tests δ2+ γ2≠0<br>0.82|0.64|0.76|0.17|
|δ3+γ3≠0<br>0.78|0.38|0.03|0.93|
|_Observations_<br>_1,261_|_1,261_|1,256|_1,261_|
|_R‐squared_<br>0.03|_0.34_|0.08|_0.05_|
|_Controls_<br>YES|_YES_|YES|_YES_|
|_Restricted Model_<br>YES|_YES_|YES|_YES_|
|_Control Mean:_<br>0|_0.738_|0.650|_0.415_|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 37
# Sources of Wealth Data

Wealth data is collected less frequently than incomes, which are themselves not as common as consumption, particularly in low-income countries. But wealth surveys do exist- China and India have household surveys that collect detailed data on assets and liabilities of households- the Household Income Project (CHIP) for China and the All-India Debt and Investment Survey (AIDIS). There are also wealth focused surveys in Latin America- the Chilean Household Financial Survey, Colombian Household Financial Burden and Financial Education Survey, Mexican National Survey on Household Finances and the Financial Survey of Uruguayan Households (EFHU) (Gandelman and Lluberas, 2023). But such surveys are not common in LMICs. One recent example to improve data on assets is the World Bank Living Standards Measurement Study-Plus (LSMS+) program to measure the ownership of, and rights to, selected physical and financial assets in various African countries ( <mark>Hasanbasri et al., 2021</mark> ). These surveys collected the value of assets but do not measure liabilities, so net wealth cannot be computed.

Administrative data may provide information on wealth, although this is sometimes harder to utilize than administrative data on incomes. This is because most tax administration systems do not collect data on all or most forms of wealth directly. The most common wealth data collected through tax administration systems is estate records (Piketty and Saez, 2006). Wealth data is also collected when countries have wealth taxes, but these are not common. Some tax authorities ask questions about assets, even though they are not taxed, but, given that they are not taxed, the accuracy of the data can be questioned. To estimate wealth from income tax data, researchers use the capitalization method, in which capital income and an assumed or observed rate of return are used to estimate the value of the capital generating the observed capital income (Roine and Waldenstrom, 2015, Saez and Zucman, 2016).

In recent times other less traditional forms of data have been used to measure the top of the wealth distribution. One source is rich lists compiled by Forbes or other organizations (Piketty et al. 2022, Bach et al. (2019), Xie and Jin (2015)). These sources provide wealth estimates for the extreme top of the wealth distribution and can be used as a check on the household survey or administrative data wealth estimates.

A second set of non-traditional data sources was used by Alstadsæter et al. (2019). These sources were an HSBC Switzerland leak of customer data, voluntary declarations of hidden assets from tax amnesties in Norway, Sweden and Denmark and leaked information of shell company owners in Panama, the Panama Papers.

23