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In this paper, I use administrative data to estimate the number of deaths, the number of infections, and mortality rates from COVID-19 in Lombardia, the hot spot of the disease in Italy and Europe. The information will assist policy makers in reaching correct decisions and the public in adopting appropriate behaviors. As the available data suffer from sample selection bias, I use partial identification to derive the above quantities. Partial identification combines assumptions with the data to deliver a set of admissible values or bounds. Stronger assumptions yield stronger conclusions but decrease the credibility of the inference. Therefore, I start with assumptions that are always satisfied, then I impose increasingly more restrictive assumptions. Using my preferred bounds, during March 2020 in Lombardia, there were between 10,000 and 18,500 more deaths than in previous years. The narrowest bounds of mortality rates from COVID-19 are between 0.1 and 7.5%, much smaller than the 17.5% discussed in earlier reports. This finding suggests that the case of Lombardia may not be as special as some argue.

ZusammenfassungDie großen Einkommensunterschiede in der Europäischen Union sind maßgebliche Treiber der innereuropäischen Migration und Standortkonkurrenz. Beides hat ärmere Menschen in den reicheren Mitgliedstaaten beunruhigt. In Großbritannien war die Einwanderung ein Hauptmotiv für den Brexit. Der Stand und die Entwicklung der europaweiten Einkommensverteilung zeigt, dass die Ungleichheit in der EU insgesamt höher ist als innerhalb der Mitgliedstaaten. Sie erreicht oder überschreitet sogar das Niveau der USA.

Philosophy of current knowledge distinguishes facts from values. It maintains that facts are objective, indisputable, universally verifiable and do not require to persuade. Since rhetoric is persuasion it is assumed to be deceptive and overlook reality. Therefore, statistics in its current form disregards rhetoric and emphasizes only numbers. It ignores meanings and interpretation of numbers that involve subjectivity and value judgements. In real world, numbers and values are entangled in a way that it becomes impossible to avoid subjectivity. So, it is used with an appearance of objectivity. We illustrate how apparently objective statistics conceal subjective choices. Most of real-world experiences cannot be reduced to numbers, but scientific approach compels us to measure everything. In the attempt to measure the unmeasurables like trust, intelligence and wealth etc.it is inevitable make subjective choices. There is no objective way to reduce multiple measures into one. In the field of economics values are involved even in seemingly indisputable numbers like GDP. It is value laden for the choice of factors, weights and their signs. Making comparisons on such measures without awareness have harmful implications for policy development. Moreover, it is also desirable to understand hidden values to avoid deception.

Policy makers put great emphasis on the role of information about carbon emissions in achieving sustainable decisions by consumers. We conduct two studies to understand the optimal targeting of such information and its effects. First, we conduct an incentivized and representative survey among US consumers (N = 1, 022) to investigate awareness of climate impact and willingness to mitigate it. We find a large variation in the perceptions of the carbon emissions of different consumption behaviors, with an overall tendency to underestimate these emissions. We also find a positive but highly concave willingness to mitigate climate impact. We combine elicited misperceptions and willingness to mitigate in a structural model that delivers sharp predictions about where to best target information campaigns. In an experiment with actual consumption decisions (N = 2, 081), we then test for the effect of CO2 information on the demand for beef, a product predicted to be a productive target for information. Correcting misperceptions has no effect on the demand for beef, both in absolute terms and compared to a predictably less productive target of information, i.e. the demand for poultry. Our dataset allows us to hone in on the underlying reason for this null effect.

Empirical studies in the economics of education, the measurement of skill gaps across demographic groups, and the impacts of interventions on skill formation rely on psychometrically validated test scores that record the proportion of items correctly answered. Test scores are sometimes taken as measures of an invariant scale of human capital that can be compared over time and people. We show that for a prototypical test, invariance is violated. We use an unusually rich data set from an early childhood intervention program that measures knowledge of narrowly defined skills on essentially equivalent subsets of tasks. We examine if conventional, broadly-defined measures of skill are the same across people who are comparable on detailed knowledge measures. We reject the hypothesis of aggregate scale invariance and call into question the uncritical use of test scores in research on education and on skill formation. We compare different measures of skill and ability and reject the hypothesis of valid aggregate measures of skill.

Die Produktionsperspektiven der Unternehmen in Deutschland für das Jahr 2022 haben sich gemäß der aktuellen IW-Konjunkturumfrage vom Juni 2022 weiter eingetrübt. Eine ausgeprägte Erwartungsrevision fand bereits zwischen den Umfragen vom Spätherbst 2021 und Frühjahr 2022 statt. Der Saldo aus positiven und negativen Geschäftserwartungen für 2022 hat sich von 34 Prozentpunkten im Herbst 2021 auf 15 Prozentpunkte im Frühjahr und auf zuletzt 12 Prozentpunkte zurückgebildet. Dabei erwartet aktuell ein Viertel der befragten Betriebe eine niedrigere Produktion als im Jahr 2021 und 37 Prozent der Firmen gehen von einem Anstieg aus. Fast 40 Prozent sehen einer stabilen Geschäftstätigkeit entgegen. Vor allem die Produktionsperspektiven der deutschen Bauunternehmen haben sich nochmals massiv verschlechtert. Hier bremsen Materialengpässe, hohe Kostensteigerungen und fehlende Mitarbeiter die wirtschaftlichen Aktivitäten. Diese Faktoren belasten auch die Industrie - hinzu kommen die globalen Verunsicherungen infolge der Geopolitik. Am aktuellen Rand gehen 37 Prozent der Industriefirmen von einer besseren Produktion als im Jahr 2021 aus, aber 28 Prozent sehen einen Rückgang. Der Saldo aus positiven und negativen Werten hat sich seit Spätherbst 2021 von 41 auf 16 Prozentpunkte stark vermindert. Die Dienstleister weisen zwar immer noch einen deutlichen positiven Saldo auf. Dieser ist mit 18 Prozentpunkten jedoch ebenfalls ernüchternd niedriger als im Herbst 2021 mit 44 Prozentpunkten. Die regionalen Wirtschaftserwartungen fallen zwar unterschiedlich aus, der Großteil der Regionen liegt aber ziemlich nahe am landesweiten Durchschnitt. Die Beschäftigungsperspektiven in den deutschen Unternehmen liegen weiterhin im positiven Bereich. Ein Drittel der Unternehmen erwartet im Jahr 2022 eine höhere Anzahl an Mitarbeitern. Dagegen sieht ein Fünftel der Befragten einen Personalrückgang. Die Investitionserwartungen haben sich empfindlich zurückgebildet. Derzeit erwarten 36 Prozent der befragten Unternehmen höhere Investitionen als im Jahr 2021. Zugleich geht aber ein Viertel der Firmen von niedrigeren Investitionen aus. Damit wird die im Gefolge der Corona-Pandemie entstandene Investitionslücke im Jahr 2022 wohl nicht geschlossen werden.

The paucity of reliable, timely household consumption data in many low- and middleincome countries has made it difficult to assess how global poverty has evolved during the COVID-19 pandemic. Standard poverty measurement requires collecting household consumption data, which is rarely done by phone. To test the feasibility of collecting consumption data over the phone, we conducted a survey experiment in urban Ethiopia, randomly assigning households to either phone or in-person interviews. In the phone survey, average per capita consumption was 23 per cent lower than in the in-person survey, and the estimated poverty headcount was twice as high. There is evidence of survey fatigue occurring early in phone interviews but not in in-person interviews; the bias is correlated with household characteristics. While the phone survey mode provides comparable estimates when measuring diet-based food security, it is not amenable to measuring consumption using the 'best practice' approach originally devised for in-person surveys.

The Mahabub Hossain Panel Data (MHPD) was initiated in 1988 and maintained by and named after the late Dr. Mahabub Hossain, a well-known agricultural and development economist who led a number of reputed organizations in Bangladesh (Bangladesh Institute of Development Studies and BRAC) and in the region (International Rice Research Institute). This background report portrays the historical context, sampling evolution, survey structure and methodology, and academic and policy contributions of the MHPD with some lessons learned for the next step forward. The MHPD has tracked rural households for a period of over three decades (1988-2014) with five waves of household surveys covering over 2,800 households and has collected a wide range of information on household composition, schooling of household members, assets, cropping intensity and patterns including cost and return, employment and income, consumption, participation in different government and nongovernment programs. To produce this document, we reviewed several books and journal articles authored by Mahabub Hossain and related academic papers and documents and collated information on MHPD, including (i) mapping out information on past and ongoing panel or cross-sectional household survey data series in Bangladesh; (ii) undertaking the review of all past rounds of MHPD survey documents, such as survey implementation plans, questionnaires, codebooks, databases, and processed data; (iii) consulting relevant stakeholders, including the past implementers of the surveys and the users of the data as needed to validate documented information; (iv) taking stock of the contribution of MHPD to academic literature and policy development; and (v) drawing a number of lessons learned for future data collection and policy making. The report aims to i) serve as a comprehensive reference document for scholars and policymakers who wish to understand MHPD for possible use in their research; ii) provide a comprehensive baseline from which we can consider ways to enhance MHPD further to continue contributing to understanding the economic and social issues of today and near future. By compiling all associated research work based on MHPD, the report also offers a historical landscape of Bangladesh's social and economic development and a credible explanation for the Bangladesh development model for global comparison.

I propose a generalized method of moments type procedure to estimate parametric binary choice models when the researcher only observes degenerate pure choices-based or presence-only data and has some information about the distribution of the covariates. This auxiliary information comes in the form of moments. I present an application based on the data on all COVID-19-positive tests from British Columbia. Publicly available demographic information on the population in British Columbia allows me to estimate the conditional probability of a person being COVID-19-positively tested conditional on demographics.

Die russische Invasion in der Ukraine hat die wirtschaftlichen Perspektiven in Deutschland erheblich eingetrübt und das Investitionsklima verschlechtert. Die im Gefolge der Corona-Pandemie entstandene Investitionslücke wird vorerst nicht geschlossen werden. Dies belastet nicht nur das konjunkturelle Tempo, sondern schafft zugleich langwierige Defizite beim gesamtwirtschaftlichen Kapitalstock und den damit einhergehenden Produktions- und Produktivitätspotenzialen. Die Konjunkturumfrage des Instituts der deutschen Wirtschaft vom Sommer 2022 liefert eine empirische Grundlage, um die aktuelle Investitionsschwäche in Deutschland zu erklären. Diese Unternehmensdaten zeigen zum einen, welchen Einfluss bestimmte Faktoren auf die allgemeine, vom Ukraine-Krieg unbeeinflusste Investitionstätigkeit haben. Die Digitalisierung und Dekarbonisierung wirken sich positiv auf die Höhe der Investitionen aus. Hohe Arbeitskosten, Produktionsprobleme durch fehlende Zulieferungen und insbesondere die herrschende weltwirtschaftliche Unsicherheit belasten dagegen das Investitionsklima. Unsicherheiten hinsichtlich der Energieversorgung, fehlende Mitarbeiter und die Höhe der Energiekosten sind ebenfalls dominierende Investitionshemmnisse. Zum anderen wurden die Unternehmen befragt, ob und wie aus ihrer Sicht diese Investitionsdeterminanten durch den Krieg in der Ukraine beeinflusst werden. Während sich die wenigen positiven Treiber von Investitionen aus Sicht der Unternehmen durch den Krieg nur wenig veränderten, hat es massive Verschlechterungen vor allem bei den bereits bestehenden Investitionshemmnissen gegeben. Die stark angestiegenen Energiekosten, Unsicherheiten hinsichtlich der Energieversorgung, globale Verunsicherungen und Störungen in den internationalen Lieferketten stellen eine nochmals höhere Investitionsbarriere dar. Die unabhängig vom Krieg in der Ukraine negativ auf die Investitionstätigkeit wirkende Höhe der Arbeitskosten in Deutschland hat durch die geopolitischen Probleme zusätzlich an Relevanz gewonnen.

We document the occurrence of process claims in granted U.S. patents over the last century. Using novel data on the type of independent patent claims, we show an increase in the annual share of process claims of about 25 percentage points (from below 10% in 1920). This rise in process intensity is not limited to a few patent classes but can be observed across a broad spectrum of technologies. Process intensity varies by applicant type: companies file more process-intense patents than individuals, and U.S. applicants file more process-intense patents than foreign applicants. We further show that patents with higher process intensity are more valuable but are not necessarily cited more often. Last, process claims are on average shorter than product claims; but this gap has narrowed since the 1970s. These patterns suggest that the patent breadth and scope of process-intense patents are overestimated when claim types are not accounted for. We conclude by describing in detail the code used to construct the claim-type data, showing results from a data-validation exercise (using close to 10,000 manually classified patent claims), and providing guidance for researchers on how to alter the classification outcome to adapt to researchers' needs.

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 document the development and operation of the SWITCH model, Ireland's taxbenefit microsimulation model. Using the EUROMOD platform and process of creating an input model, we discuss adjustments to the underlying data, such as reweighting and uprating, aiming at improving representativeness in terms of the income tax base and beneficiaries of social welfare schemes. The validation of the model with respect to a range of external sources as well as the capabilities of the model in terms of results produced is also described.

In the monthly ifo Business Survey around 9,000 German companies answer questions about their current business situation, expectations and plans for the near future as well as on other business variables. This paper provides an overview of all regular questions (monthly, quarterly, bi-annually, annually). Furthermore, a special focus lies on detailed information about all supplementary questions since 2009. This overview can be used as a guide for future research projects. The ifo Institute provides the data via two channels: On one side, researchers can work with the firm-level microdata of all survey questions at the LMU-ifo Economics & Business Data Center (EBDC). On the other side, data sets of aggregated time-series for all regularly surveyed variables can be obtained from the ifo Center for Macroeconomics and Surveys.

Immigration is one of the most divisive political issues in many countries today. Competing narratives, circulated via the media, are crucial in shaping how immigrants' role in society is perceived. We propose a new method combining advanced natural language processing tools with dictionaries to identify sentences containing one or more of seven immigrant narrative themes and assign a sentiment to each of these. Our narrative dataset covers 107,428 newspaper articles from 70 German newspapers over the 2000 to 2019 period. Using 16 human coders to evaluate our method, we find that it clearly outperforms simple word-matching methods and sentiment dictionaries. Empirically, culture narratives are more common than economy-related narratives. Narratives related to work and entrepreneurship are particularly positive, while foreign religion and welfare narratives tend to be negative. We use three distinct events to show how different types of shocks influence narratives, decomposing sentiment shifts into theme-composition and within-theme changes.

We measure individual-level loss aversion using three incentivized, representative surveys of the U.S. population (combined N = 3,000). We find that around 50% of the U.S. population is loss tolerant, with many participants accepting negative-expected-value gambles. This is counter to earlier findings−which mostly come from lab/student samples−and expert predictions that 70-90% of participants are loss averse. Consistent with the difference between our study and the prior literature, loss aversion is more prevalent in people with high cognitive ability. Loss-tolerant individuals are more likely to report recent gambling and to have experienced financial shocks. These results support the general hypothesis that individuals value gains and losses differently, although the tendency in a large proportion of the population to emphasize gains over losses is an overlooked behavioral phenomenon.

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, and expert classification of any documents with any scheme. To demonstrate this process for building data from text with Machine Learning, we publish open-source resources: the software, a new public document corpus, and a replicable analysis to build an interpretable classifier of suspected "no poach" clauses in franchise documents.

This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across establishments is highly skewed. To address these difficulties, this paper develops a probabilistic record linkage methodology that combines machine learning (ML) with multiple imputation (MI). This ML-MI methodology is applied to link survey respondents in the Health and Retirement Study to their workplaces in the Census Business Register. The linked data reveal new evidence that non-sampling errors in household survey data are correlated with respondents' workplace characteristics.

Online technologies enable lower-cost, rapid data collection, but concerns about access and data quality impede their use in global research. I conduct a randomized experiment in the Philippines to test the effectiveness of web-form and chatbot surveys of K–12 teachers recruited through social media and compare their effectiveness with phone surveys of teachers recruited from a pre-existing frame. Chatbot surveys yield higher response rates and higher-quality data than web-form surveys in terms of missed question and item differentiation. The results suggest that chatbot responses match CATI responses on multiple dimensions of quality. Relative to CATI, online methods also yield higher rates or information disclosure on potentially sensitive topics, revealing substantially higher levels of distress among teachers. I show that social-media-based recruitment can be an attractive alternative for targeted sampling and that online surveys can be implemented effectively at a fraction of the cost of phone surveys.

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

We provide new evidence on the extent that survey items in the Preference Survey Module and the resulting Global Preference Survey measuring social preferences − trust, altruism, positive and negative reciprocity − predict behavior in corresponding experimental games outside the original student sample from Bonn, Germany. Our results, which are based on a replication study in Tehran, Iran, are somewhat mixed. While quantitative items considering hypothetical versions of the experimental games correlate significantly and economically meaningfully with individual behavior, none of the qualitative items show significant correlations. The only exception is altruism where results correspond more closely to the original findings.

In 2020, Belgian ports generated € 31.8 billion in direct and indirect value added (7% of Belgian GDP) and employed 254 611 full-time equivalents (FTEs) either directly or indirectly (5.9% of Belgian domestic employment including the self-employed). Despite the COVID-19 outbreak, direct employment at Belgian ports remained quite stable in 2020. The temporary lay-off system - more flexible during the pandemic - played a vital role in avoiding redundancies. Job losses in the port population were especially visible in the non-maritime cluster because the maritime activities were considered as essential and allowed to operate continuously. Direct value added at Belgian ports fell by 1.2% in 2020 against a drop of 4% for the whole of the Belgian economy. The drop was particularly visible in the non-maritime cluster more precisely in those branches hit by the temporary imposed closure of businesses or impacted by demand contractions and supplychain disruptions. In 2020, direct investment by all Belgian ports taken together rose by 5.1% to € 5.1 billion, thanks to higher sums invested in the chemicals industry and the cargo-handling sector. Analysing the investment degree levels by branch of activity, results indicate that shipping companies and port authorities invested relatively more given the competitive businesses in which they operate. A relatively high degree of investment is also notified for the energy sector and the industrial branches, whose operational activity is largely based on high technological knowledge and whose business is substantially subject to future developments. During the pandemic, total turnover figures fell. Downscaling costs to the same extend as declining sales was difficult given the presence of fixed costs, accordingly the profitability level of a median port company declined slowly. However, while strong performing1 port companies in terms of operating profit suffered a drop in profitability, weak performing businesses enhanced theirs owing to the generous government support measures. (In)direct support to wage and (para)fiscal payments endorsed port companies to maintain or even slightly strengthen their liquidity position, while their solvency was sustained as well.

Studies on health insurance coverage often rely on measures self-reported by respondents, but the accuracy of such measures has not been thoroughly validated. This paper is the first to use linked Australian National Health Survey and administrative population tax data to explore the accuracy of self-reported private health insurance (PHI) coverage in survey data. We find that 9% of individuals misreport their PHI coverage status, with 5% of true PHI holders reporting that they are uninsured and 16% of true non-insured persons self-identifying as insured. Our results show reporting errors are systematically correlated with individual and household characteristics. Our evidence on the determinants of errors is supportive of common reasons for misreporting. We directly investigate biases in the determinants of PHI enrolment using survey data. We find that, as compared to administrative data, survey data depict a quantitatively different picture of PHI enrolment determinants, especially those capturing age, language proficiency, labour force status or the number of children. We also show that PHI coverage misreporting is subsequently associated with misreporting of reasons for purchasing PHI, type of cover and length of cover.

This paper examines the quality of quarterly records on work hours collected from employers in the State of Washington to administer the unemployment insurance (UI) system, specifically to determine eligibility for UI. We subject the administrative records to four "trials," all of which suggest the records reliably measure paid hours of work. First, distributions of hours in the administrative records and Current Population Survey outgoing rotation groups (CPS) both suggest that 52-54% of workers work approximately 40 hours per week. Second, in the administrative records, quarter-to-quarter changes in the log of earnings are highly correlated with quarter-to-quarter changes in the log of paid hours. Third, annual changes in Washington's minimum wage rate (which is indexed) are clearly reflected in year-to-year changes in the distribution of paid hours in the administrative data. Fourth, Mincer-style wage rate and earnings regressions using the administrative data produce estimates similar to those found elsewhere in the literature.

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.

This paper presents a system of greenhouse gas indicators for markets and policymakers. The system is lean and informative. It condenses the relevant product and enterprise-specific information into a single number: the greenhouse gas (GHG) value. Like prices, GHG values are easy to understand, manage and communicate. The envisaged scenario is one in which, at all levels of production, goods and services have two tags - the financial price to pay and the GHG value. GHG values are interdependent. The value for any product will depend on the carbon costs of all inputs. This paper shows that the massive information processing this simultaneity involves can be handed over to the market. Analytically, the system of product-level indicators is solved for the reduced form, the vector of true GHG values. This vector is shown to be the fixed point to which measurement will converge if producers comput e GHG values using the information they have: data from their input providers if available, and estimates elsewhere. This amounts to a process of collective, decentralised learning. Technological changes will automatically be accounted for. A micro-simulation exercise is carried out based on sectoral information on production structure from Germany. The results indicate that convergence is fast. W ith appropriate institutional underpinning, the disclosure of GHG values by producers may become self-sustaining.

We assess whether online data on vacancies and applications to a job board are a suitable source for studying skills dynamics outside of Europe and the United States, where a rich literature has examined skills dynamics using online vacancy data. Yet, the knowledge on skills dynamics is scarce for other countries, irrespective of their level of development. We first propose a taxonomy that systematically aggregates three broad categories of skills - cognitive, socioemotional and manual - and fourteen commonly observed and recognizable skills sub-categories, which we define based on unique skills identified through keywords and expressions. Our aim is to develop a taxonomy that is comprehensive but succinct, suitable for the labour market realities of developing and emerging economies and adapted to online vacancies and applicants' data. Using machine-learning techniques, we then develop a methodology that allows implementing the skills taxonomy in online vacancy and applicants' data, thus capturing both the supply and the demand side. Implementing the methodology with Uruguayan data from the job board BuscoJobs, we assign skills to 64 per cent of applicants' employment spells and 94 per cent of vacancies. We consider this a successful implementation since the exploited text information often does not follow a standardized format. The advantage of our approach is its reliance on data that is currently available in many countries across the world, thereby allowing for country-specific analysis that does not need to assume that occupational skills bundles are the same across countries. To the best of our knowledge, we are the first to explore this approach in the context of emerging economies. 4950

Due to the omnipresence of digital technologies in the economy, measuring firm digitalisation is of high importance. However, current indicators show several shortcomings, e.g., they lack timeliness and regional granularity. In this study, we show that advances in text mining and comprehensive firm website content can be leveraged to generate real-time and large-scale estimates of firm digitalisation. We use a transfer learning approach to capture the latent definition of digitalisation. For this purpose, we train a random forest regression model on labeled German newspaper articles and apply it on firm's website content. The predictions are used as a continuous indicator for firm digitalisation. Plausibility checks confirm the link to established digitalisation indicators at the firm and sectoral level as well as for firm size classes and regions. Lastly, we illustrate the indicator's potential for giving timely answers to pressing economic issues by analysing the link between digitalisation and firm resilience during the Covid-19 shock.

We present a new method to measure business opening and closure rates using real-time information from Google Places, the dataset behind the Google Maps service. Our Canadian application confirms the importance of temporary closures and reopenings during the COVID-19 pandemic. Over 50% of the temporarily closed food and retail businesses during the April 2021 lockdown reopened by the end of September. Our estimates align well with the timing of COVID-19 restrictions and are validated by a survey of recently opened businesses. Our framework provides policy-makers with a tool for the timely monitoring of business dynamics.

The monthly ifo Business Survey is the basis for numerous economic indicators, such as the ifo Business Climate Index Germany. Moreover, the anonymized micro data can be used for a wide range of research projects. In order to obtain robust results, the panel of survey participants must be large enough and as representative as possible for the German economy. This article describes the composition of the panel participants based on various criteria, such as representativeness at the sectoral and industry level, regional distribution, and distribution by firm size. The investigation shows that the business panel is representative for the German economy.