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Proponents of a basic income (BI) claim that it could bring significant reductions in financial poverty, on top of many other benefits, including greatly reduced administrative complexity and cost. Using microsimulation analysis in a comparative two-country setting, we show that the potential poverty-reducing impact of BI strongly depends on exactly how and where it is implemented. Implementing a BI requires far more choices than advocates seem to realize. The level at which the BI is set matters, but its exact specification matters even more. Which parts of the existing tax-benefit system are maintained, and which parts are abolished, modified or replaced? The impact of a BI, be it a low or a high one, thus strongly depends on the characteristics of the system that it is (partially) replacing or complementing, as well as the socio-economic context in which it is introduced. Some versions of BI could potentially help to reduce poverty but always at a significant cost and with substantial sections of the population incurring significant losses, which matters for political feasibility. A partial basic income complementing existing provisions appears to make more potential sense than a full basic income replacing them. The simplicity of BI, however, tends to be vastly overstated.

Recent data from EU-SILC and Destatis seemed to indicate a dramatic increase in German inequality and poverty in the pandemic year 2020. But Destatis had changed its data collection method. Based on several studies, it is likely that the actual rise has been much weaker while the previous data collection by Destatis might have underestimated the true disparities within German society in the past.

We describe methods of combining administrative and survey data to improve the measurement of income. We begin by decomposing the total survey error in the mean of survey reports of dollars received from a government transfer program. We decompose this error into three parts, generalized coverage error (which combines coverage and unit non-response error and any error from weighting), item non-response or imputation error, and measurement error. We then discuss these three sources of error in turn and how linked administrative and survey data can assess and reduce each of these sources. We then illustrate the potential of linked data by showing how using linked administrative variables improves the measurement of income and poverty in the Current Population Survey, focusing on the substitution of administrative for survey data for three government transfer programs. Finally, we discuss how one can examine the accuracy of the underlying links used in the combined data.

We produce estimates of the full distribution of all national income in Australia for the period 1991 to 2018, by combining household survey with administrative tax microdata and adjusting to match National Accounts aggregates. From these estimates, we are able to rigorously document the shifts in income shares over the period, contrasting changes in the distribution of pre-tax and post-tax national income. Comparing Australia to the US and to France, we also compare our new results to traditional household survey-based estimates of inequality. Moreover, we exploit the richness of our unique microdata to shed light on the distribution of national income across and within various population groups not usually identifiable in the tax datasets that underpin reliable top-income estimates. Among our most surprising findings, inequality of post-tax national income is less than inequality of survey-based (post-transfer, disposable) income for Australia. The gender gap in income has stubbornly remained over the past three decades. Finally, we find that Australian inequality of national income is much lower than that of the United States, while it is similar to that of France, although those at the bottom of the income distribution fare better in France than in Australia.

We collect, digitize, and supplement the Swiss rich list for the years 1989–2020 published in the “BILANZ” business magazine to gain new insights on the structure and dynamics of top wealth in Switzerland. Using this data allows us study the super-rich in Switzerland in ways that were not possible in previous research based largely on tax data. In addition to presenting this valuable data source, and also discussing its limitations, we make three distinctive contributions to the literature. First, we present a number of new facts on the wealth elite in Switzerland. We show that about 60% of the super-rich are heirs—a much larger fraction than in the United States where many of the super-rich are self-made—and that five in ten super-rich residing in Switzerland are foreign-born. Second, we estimate the sensitivity of the location-decision of super-rich foreigners to a preferential tax scheme that offers wealthy foreigners to be taxed on their expenses rather than on their true income and wealth. We are the first to evaluate this policy—similar to “non- dom” taxation that exists in other countries like the UK or Italy—and show that when some of the Swiss cantons abolished this practice, they lost about 30% of their stock of super-rich taxpayers. Third, we use the wealth series compiled in our BILANZ dataset to estimate the wealth shares of the top 0.01% in Switzerland and show how they compare to earlier estimates by Föllmi and Martínez (2017) based on wealth tax data. We find that top wealth concentration is higher than previously assumed, an conclude that top wealth shares based on tax data constitute a lower bound, while the estimates based on our BILANZ data are upper bounds., CESifo Working Papers, 9778, ISSN:2364-1428, ISSN:1617-9595

I focus on one of the most-commonly-cited 'facts'; about UK income inequality - that it has changed little over the last 30 years - and reflect on how robust that description is. I look at a number of fundamental issues in inequality measurement related to inequality concepts (e.g., inequality aversion, relative versus absolute inequality, and inequality of opportunity versus outcome), definitions of 'income', the income-receiving unit, and the reference period, and related data issues. There are grounds for arguing that income inequality levels are higher, and the inequality increase over time greater, than conventional approaches indicate.

We analyse the top tail of the wealth distribution in France, Germany, and Spain using the first and second waves of the Household Finance and Consumption Survey (HFCS). Since top wealth is likely to be under-represented in household surveys, we integrate big fortunes from rich lists, estimate a Pareto distribution, and impute the missing rich. In addition to theForbeslist, we rely on national rich lists since they represent a broader base of the big fortunes in those countries. As a result, the top 1% wealth share increases notably for the three selected countries after imputing the top wealth. We find that national rich lists can improve the estimation of the Pareto coefficient in particular when the list of national USD billionaires is short.

We link survey data containing Danish people’s perceptions of where they rank in various reference groups and fairness views with administrative records on their income history, life events, and reference groups. People know their income positions well, but believe others are closer to themselves than they really are. The perceived fairness of inequalities is strongly related to current social position, moves with shocks to social position (e.g., unemployment or promotions), and changes when people are experimentally shown their actual positions. People view inequalities within education group and co-workers as most unfair, but underestimate inequality the most exactly within these reference groups.

We link survey data containing Danish people's perceptions of where they rank in various reference groups and fairness views with administrative records on their income history, life events, and reference groups. People know their income positions well, but believe others are closer to themselves than they really are. The perceived fairness of inequalities is strongly related to current social position, moves with shocks to social position (e.g., unemployment or promotions), and changes when people are experimentally shown their actual positions. People view inequalities within education group and co-workers as most unfair, but underestimate inequality the most exactly within these reference groups.

We contribute new UK evidence about measurement errors and employment earnings to a field dominated by findings about the USA. We develop and apply new econometric models for linked survey and administrative data that generalize those of Kapteyn and Ypma (Journal of Labor Economics, 2007). Our models incorporate mean-reverting measurement error in administrative data in addition to linkage mismatch and mean-reverting survey measurement error and 'reference period' error, while also allowing error distributions to vary across individuals. Annualised survey earnings underestimate true annual earnings on average. Mean-reversion in survey measurement errors is absent. Both earnings sources underestimate true earnings inequality. The survey earning measure is more reliable than the administrative data earnings measure, but hybrid earnings predictors based on both sources are distinctly more reliable than either source-specific measure. The models with heterogeneous measurement error distributions indicate how data quality may be improved. For example, for survey quality, our results highlight how respondents showing payslips to interviewers have smaller survey error variances. For administrative data, our results suggest that greater error variances are associated with non-standard jobs, private sector jobs, and employers without good payroll systems.

Researchers use finite mixture models to analyze linked survey and administrative data on labour earnings (or similar variables), taking account of various types of measurement error in each data source. Different combinations of error-ridden and/or error-free observations characterize latent classes. Latent class probabilities depend on the probabilities of the different types of error. We introduce a set of Stata commands to fit a general class of finite mixture models to fit to linked survey-administrative data We also provide post-estimation commands for assessment of reliability, marginal effects, data simulation, and prediction of hybrid earnings variables that combine information from both data sources.

UK official statistics on income distribution have incorporated top-income adjustments to household survey data since 1992. This article reviews the work undertaken by the Department for Work and Pensions and the Office for National Statistics, and the academic research that influenced them, and reflects on the lessons to learn from the UK experience.

Although the effect of fiscal drag is well studied in the industrialized world, empirical evidence from developing economies remains limited. Against this backdrop, this study aims to explore the effect of fiscal drag on income distribution and work incentives. To this end, the study employs SOUTHMOD, the tax-benefit microsimulation model, for six African countries: Ethiopia, South Africa, Tanzania, Uganda, Mozambique, and Zambia. Three important conclusions are drawn from our empirical investigation. First, in the absence of proper tax parameter adjustment, the distribution of fiscal drag is determined by the liability progression of personal income tax in the pre-inflation period. Second, the impact of fiscal drag on the redistributive effects and progressivity of personal income taxes is differentiated among countries. On the one hand, it reduces the progressivity of personal income tax in Ethiopia, South Africa, Tanzania, Uganda, and Zambia; on the other hand, it improves progressivity in Mozambique. However, it decreases the redistributive effect of personal income tax only in Ethiopia, Tanzania, and Uganda. Third, fiscal drag reduces financial work incentives to increase earnings in all countries. Therefore, a comprehensive and frequent inflationary adjustment of tax parameters to circumvent fiscal drag would be welcome.

This paper explores the impact of the first wave of the COVID-19 pandemic in South Africa on income poverty and inequality in South Africa. Using a static tax-benefit microsimulation model with input datasets that were adjusted to reflect people's earned incomes just before the pandemic (March 2020) and during the first national lockdown (April 2020), we investigate how well the social protection system in South Africa was able to mitigate the economic losses to the public. We take into account both the existing benefit system that was in place before the crisis and the role of the new policy measures that were introduced in April, May, and June 2020.

Meijer, Rohwedder, and Wansbeek (MRW, Journal of Business & Economic Statistics, 2012) develop methods for prediction of a single earnings figure per worker from mixture factor models fitted using earnings data from multiple linked data sources. MRW apply their method using parameter estimates of Kapteyn and Ypma's mixture factor model (KY, Journal of Labour Economics 2007) fitted to earnings data for Swedish workers aged 50+. First, we replicate MRW's empirical analysis using the Swedish model estimates. Second, we check the generality of their empirical finding with a new application. Using estimates of a KY model fit to a linked dataset on earnings for UK employees of all ages, we confirm that MRW's principal findings about the performance of their various predictors of true earnings also hold in this different setting.

We document the extent, nature, and consequences of survey errors for receipt of cash welfare and SNAP in three major U.S. household surveys linked to administrative program records. Our results confirm high rates of misreporting of program receipt, particularly failure to report receipt. The surveys inaccurately capture patterns of participation in multiple programs, even though there is little evidence of program confusion. Error rates are higher among imputed observations, which also account for a large share of false positive errors. Many household characteristics have significant effects on errors in reporting receipt, both false positives and false negatives. We find large differences in survey errors by race, ethnicity, income and other household characteristics. We provide evidence on the consequences of these errors for models of program receipt. Estimated effects of income and race are noticeably biased. We then examine error due to item non-response and imputation, as well as whether imputation improves estimates. Item non-respondents have higher receipt rates than the population, even conditional on many covariates. The assumptions for consistent estimates in multivariate models fail both when excluding item non-respondents and when using the imputed values. In binary choice models of program receipt, estimates from the linked data favor excluding item non-respondents rather than using their imputed values. The biases in each case are well predicted by the error patterns we document, so such analyses can help researchers make more informed decisions on the use of imputed values.

The quality of data on employment income is explored using Tanzanian and Zambian household survey datasets. The extent of missing and implausible income data is assessed and four different methods are applied to impute missing or implausible values. The four imputation methods are also applied to artificial missing data for Tanzania and Zambia, and-using one approach-for a South Africa dataset. Post-imputation results are assessed. It is argued that the treatment of missing data cannot be generalized, and that tax compliance should also be taken into account when assessing the validity of a microsimulation model's simulation of direct taxes. Update (September 2021): The technical note mentioned in the text is available as WIDER Technical Note 2021/15 here: https://doi.org/10.35188/UNU-WIDER/WTN/2021-15

In this paper we explore South Africa's personal income tax system using two microsimulation models. The first, SAMOD, simulates personal income tax and social benefits using a dataset derived from the nationally representative National Income Dynamics Study survey. The second, PITMOD, simulates the personal income tax system and is underpinned by a dataset comprising a full extract of anonymized individual-level administrative tax data especially constructed for this purpose. The two models have a common framework in the form of the EUROMOD microsimulation software and interface, and have a common policy timepoint of 1 March 2017. Discrepancies between the simulated personal income tax generated by each model are explored in order to better understand the strengths and weaknesses of the two models.

We use a new Australian longitudinal income tax dataset, Alife, covering 1991–2017, to examine levels and trends in the persistence in top-income group membership, focussing on the top 1%. We summarize persistence in multiple ways, documenting levels and trends in rates of remaining in top-income groups; re-entry to the top; the income changes associated with top-income transitions; and we also compare top-income persistence rates for annual and 'permanent' incomes. Regardless of the perspective taken, top-income persistence increased markedly over the period, with most of the increase occurring in the mid-2000s and early 2010s. In the mid- to late-2010s, Australian top-income persistence rates appear to have been near the top of the range of tax-data estimates for other countries. Using univariate breakdowns and multivariate regression, we show that the rise in top-income persistence in Australia was experienced by many population subgroups.

A major difficulty faced by researchers who want to study the consumption and savings behavior of households is the lack of reliable panel data on household expenditures. One possibility is to use surveys that follow the same households over time, but such data are rare and they typically have small sample sizes and face significant measurement issues. An alternative approach is to use the accounting identity that total household spending is equal to income plus capital gains minus the change in wealth over the period. The goal of this paper is to examine the advantages and difficulties of using this accounting identity to construct a population panel data with information on household expenditure. To derive such measures of consumption expenditure, we combine several data sources from Norway over the period 1994–2014. This allows us to link tax records on income and wealth to other administrative data with information on financial and real estate transactions. Using this data, we derive household expenditure from the accounting identity, before assessing the sensitivity of this measure of consumption expenditure to the assumptions made and the data used. We then compare our measures of household expenditure to those reported in expenditure surveys and to the aggregates from national accounts. We also illustrate the research opportunities arising from the derived measures of consumption expenditure through two applications: the first is an examination of how relative wage movements among birth cohorts and education groups affected the distribution of household expenditure, while the second is a study of the transmission of income shocks to household consumption.

Although household well-being is anchored in long-term average rates of consumption, welfare comparisons typically rely on shorter-duration survey measurements. This paper develops a new strategy to identify the distribution of these long-term rates by leveraging a large-scale randomization that elicited repeated short-duration measurements from diaries and recall questions. Identification stems from diary-recall differences in reports from the same household, does not require these reports to be error-free, and hinges on a research design with broad replicability. This strategy delivers cost-effective suggestions for designing survey modules to yield the most accurate measurements of consumption well-being, and offers new insights for interpreting and reconciling diary-recall differences in household expenditure surveys.

This paper analyses the extent to which the Italian welfare system provides monetary compensation for those who lost their earnings due to the lockdown imposed by the government in order to contain the COVID-19 pandemic in March 2020. In assessing first-order effects of the businesses temporarily shut down and the government's policy measures on household income, counterfactual scenarios are simulated with EUROMOD, the EU-wide microsimulation model, integrated with information on the workers who the lockdown is more likely to affect. This paper provides timely evidence on the differing degrees of relative and absolute resilience of the household incomes of the individuals affected by the lockdown. These arise from the variations in the protection offered by the tax-benefit system, coupled with personal and household circumstances of the individuals at risk of income loss.

The share of women in the top 1% of the UK's income distribution has been growing over the last two decades (as in several other countries). Our first contribution is to account for this secular change using regressions of the probability of being in the top 1%, fitted separately for men and women, in order to contrast between the sexes the role of changes in characteristics and changes in returns to characteristics. We show that the rise of women in the top 1% is primarily accounted for by their greater increases (relative to men) in the number of years spent in full-time education. Although most top income analysis uses tax return data, we derive our findings taking advantage of the much more extensive information about personal characteristics that is available in survey data. Our use of survey data requires justification given survey under-coverage of top incomes. Providing this justification is our second contribution.

The "Minimum Vital Income" (IMV) constitutes a novelty in the panorama for fighting poverty by guaranteeing minimum incomes after the COVID-19 crisis. This work simulates the distributional and poverty effects of the IMV introduction across Spanish regions using EUROMOD. Our results show that the IMV reduces inequality and poverty - general and extreme - for all regions. The regional minimum income schemes (RMI) have been a fundamental measure to fight poverty in Spain from the regional level, although this power has not been as effective as it was expected in reducing inequality. This work also simulates the effects on inequality and poverty that the elimination of current RMI and the introduction of the new IMV would generate. Considering the simultaneous introduction of IMV and RMI elimination, the negative effects of RMI would be offset by positive effects of IMV, leading also to a big additional saving for the Spanish Public Accounts.

Underreporting and undersampling biases in top tail wealth, although widely acknowledged, have not been statistically quantified so far, essentially because they are not readily observable. Here we exploit the functional form of power law-like regimes in top tail wealth to derive analytical expressions for these biases, and employ German microdata from a popular survey and rich list to illustrate that tiny differences in non-response rates lead to tail wealth estimates that differ by an order of magnitude, in our case ranging from one to nine trillion euros. Underreporting seriously compounds the problem, and we find that the estimation of totals in scale-free systems oftentimes tends to be spurious. Our findings also suggest that recent debates on the existence of scale- or type-dependence in returns to wealth are ill-posed because the available data cannot discriminate between scale- or typedependence on the one hand, and statistical biases on the other. Yet both economic theory and mathematical formalism indicate that sampling and reporting biases are more plausible explanations for the observed data than scale- or type-dependence.

Kapteyn and Ypma (Journal of Labour Economics 2007) is an influential study of errors in survey and administrative data on employment earnings. To fit their mixture models, Kapteyn and Ypma assume a specific fraction of their sample have error-free earnings. Using a new UK dataset, we assess the sensitivity of model estimates and post-estimation statistics to variations in this fraction and find some lack of robustness.