The Social Policy Indicators (SPIN) database provides the foundations for new comparative and longitudinal research on the causes behind, and the consequences of, welfare states and social citizenship rights. The SPIN database is oriented towards analyses of institutions as manifested in social policy legislation. To date, SPIN covers 40 countries, of which several have data on core social policy programmes from 1930. There are currently six data modules in SPIN, covering different social policy areas. The following research note describes the theoretical and conceptual basis of the SPIN project, as well as the data it contains.

Active labour market policies (ALMP) are important tools that welfare states utilize to influence the labour market. This study analyses the macroeconomic effects of different types of ALMP spending on aggregate unemployment rates, and especially if there is evidence of interdependencies between policies. The types of policies scrutinized are public employment services (PES), training programs, public job creation and subsidized employment, where the PES is singled out as a crucial factor that moderates the effects of other types of labour market programs. The study examines 19 welfare states between 1985 and 2013, using error correction modelling to separate between short-and long-term effects. The results indicate that PES, training and subsidized employment reduce unemployment in the short-run, whereas PES and wage subsidies are associated with reduced unemployment when considering long-term effects. However, PES is found to have indirect effects on other policy types and increased spending on PES is shown to reinforce long-term effects of training programs.

What, if any, similarities and differences between song and speech are consistent across cultures? Both song and speech are found in all known human societies and are argued to share evolutionary roots and cognitive resources, yet no studies have compared similarities and differences between song and speech across languages on a global scale. We will compare sets of matched song/speech recordings produced by our 81 coauthors whose 1st/heritage languages span 23 language families. Each recording set consists of singing, recited lyrics, and spoken description, plus an optional instrumental version of the sung melody to allow us to capture a “musi-linguistic continuum” from instrumental music to naturalistic speech. Our literature review and pilot analysis using five audio recording sets (by speakers of Japanese, English, Farsi, Yoruba, and Marathi) led us to make six predictions for confirmatory analysis comparing song vs. spoken descriptions: three consistent differences and three consistent similarities. For differences, we predict that: 1) songs will have higher pitch than speech, 2) songs will be slower than speech, and 3) songs will have more stable pitch than speech. For similarities, we predict that 4) pitch interval size, 5) timbral brightness, and 6) pitch declination will be similar for song and speech. Because our opportunistic language sample (approximately half are Indo-European languages) and unusual design involving coauthors as participants (approximately 1/5 of coauthors had some awareness of our hypotheses when we recorded our singing/speaking) could affect our results, we will include robustness analyses to ensure our conclusions are robust to these biases, should they exist. Other features (e.g., rhythmic isochronicity, loudness) and comparisons involving instrumental melodies and recited lyrics will be investigated through post-hoc exploratory analyses. Our sample size of n=80 people providing sung/spoken recordings already exceeds the required number of recordings (i.e. 60) to achieve 95% power with the alpha level of 0.05 for the hypothesis testing of the selected six features. Our study will provide diverse cross-linguistic empirical evidence regarding the existence of cross-cultural regularities in song and speech, shed light on factors shaping humanity’s two universal vocal communication forms, and provide rich cross-cultural data to generate new hypotheses and inform future analyses of other factors (e.g., functional context, sex, age, musical/linguistic experience) that may shape global musical and linguistic diversity.