Social Spending Generosity and Income Inequality: A Dynamic Panel Approach

This paper explores whether more generous social spending polices in fact lead to less income inequality, or if redistributive outcomes are offset by behavioral disincentive effects. To account for the inherent endogeneity of social policies with regard to inequality levels, I apply the System GMM estimator and use the presumably random incidence of certain diseases as instruments for social spending levels. The regression results suggest that more social spending effectively reduces inequality levels. The result is robust with respect to the instrument count and different data restrictions. Looking at the structure of benefits, particularly unemployment benefits and public pensions are responsible for the inequality reducing impact. More targeted benefits, however, do not significantly reduce income inequality. Rather, their positive effect on pre-government income inequality hints at substantial disinctive effects.Social Benefits, Redistribution, Income Inequality, System GMM

Social Spending Generosity and Income Inequality: A Dynamic Panel Approach

This paper explores if more generous social spending polices in fact lead to less income inequality, or if redistributive outcomes are offset by behavioral disincentive effects. To account for the inherent endogeneity of social policies with regard to inequality levels, I apply the System GMM estimator and use the presumably random incidence of certain diseases as instruments for social spending levels. The regression results suggest that more social spending effectively reduces inequality levels. The result is robust with respect to the instrument count and different data restrictions. Looking at the structure of benefits, particularly unemployment benefits and public pensions are responsible for the inequality reducing impact. More targeted benefits, however, do not significantly reduce income inequality. Rather, their positive effect on pre-government income inequality hints at substantial disincentive effects.social benefits, redistribution, income inequality, System GMM

Lower and Upper Bounds of Unfair Inequality: Theory and Evidence for Germany and the US

Previous estimates of unfair inequality of opportunity (IOp) are only lower bounds because of the unobservability of the full set of endowed circumstances beyond the sphere of individual responsibility. In this paper, we suggest a new estimator based on a fixed effects panel model which additionally allows identifying an upper bound. We illustrate our approach by comparing Germany and the US based on harmonized micro data. We find significant and robust differences between lower and upper bound estimates - both for gross and net earnings based either on periodical or permanent income - for both countries. We discuss the cross-country differences and similarities in IOp in the light of differences in social mobility and persistence.Equality of opportunity, fairness, redistribution, wage inequality

Adapting End-to-End Speech Recognition for Readable Subtitles

Automatic speech recognition (ASR) systems are primarily evaluated on transcription accuracy. However, in some use cases such as subtitling, verbatim transcription would reduce output readability given limited screen size and reading time. Therefore, this work focuses on ASR with output compression, a task challenging for supervised approaches due to the scarcity of training data. We first investigate a cascaded system, where an unsupervised compression model is used to post-edit the transcribed speech. We then compare several methods of end-to-end speech recognition under output length constraints. The experiments show that with limited data far less than needed for training a model from scratch, we can adapt a Transformer-based ASR model to incorporate both transcription and compression capabilities. Furthermore, the best performance in terms of WER and ROUGE scores is achieved by explicitly modeling the length constraints within the end-to-end ASR system.Comment: IWSLT 202

Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection

Encoder-decoder models provide a generic architecture for sequence-to-sequence tasks such as speech recognition and translation. While offline systems are often evaluated on quality metrics like word error rates (WER) and BLEU, latency is also a crucial factor in many practical use-cases. We propose three latency reduction techniques for chunk-based incremental inference and evaluate their efficiency in terms of accuracy-latency trade-off. On the 300-hour How2 dataset, we reduce latency by 83% to 0.8 second by sacrificing 1% WER (6% rel.) compared to offline transcription. Although our experiments use the Transformer, the hypothesis selection strategies are applicable to other encoder-decoder models. To avoid expensive re-computation, we use a unidirectionally-attending encoder. After an adaptation procedure to partial sequences, the unidirectional model performs on-par with the original model. We further show that our approach is also applicable to low-latency speech translation. On How2 English-Portuguese speech translation, we reduce latency to 0.7 second (-84% rel.) while incurring a loss of 2.4 BLEU points (5% rel.) compared to the offline system