The power of positivity? The influence of positive psychological capital language on crowdfunding performance

We extend the entrepreneurship literature to include positive psychological capital — an individual or organization's level of psychological resources consisting of hope, optimism, resilience, and confidence — as a salient signal in crowdfunding. We draw from the costless signaling literature to argue that positive psychological capital language usage enhances crowdfunding performance. We examine 1726 crowdfunding campaigns from Kickstarter, finding that entrepreneurs conveying positive psychological capital experience superior fundraising performance. Human capital moderates this relationship while social capital does not, suggesting that costly signals may, at times, enhance the influence of costless signals. Post hoc analyses suggest findings generalize across crowdfunding types, but not to IPOs.     

Inequality and entrepreneurial thresholds

We explore the relationship between inequality and entrepreneurial activity. Drawing on cross-sectional data from a largescale survey of the economic conditions of individuals across India, we develop a number of dimensions of inequality to explore empirically how inequality interacts with entrepreneurship, operationalized as self-employment or as employing other people. We find compelling evidence that there are thresholds to becoming self-employed, and even more so to assembling the combinations of resources and personal attributes required to become an employer. Greater inequality leaves more people unable to make the transition to self-employment, leaving casual laboring as the occupation of necessity. At the same time, inequality increases the number of employers in a society, by concentrating resources - particularly land and finance - enough for significant numbers of people to be able to cross this higher threshold. Lastly, greater differentiation into social or religious groups curtails the ability to cross either entrepreneurial threshold, presumably by limiting the extent and benefits of social networks of value for entrepreneurship.     

Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison

We use several U.S. and euro‐area surveys of professional forecasters to estimate a dynamic factor model of inflation featuring time‐varying uncertainty. We obtain survey‐consistent distributions of future inflation at any horizon, both in the U.S. and the euro area. Equipped with this model, we propose a novel measure of the anchoring of inflation expectations that accounts for inflation uncertainty. Our results suggest that following the Great Recession, inflation anchoring improved in the United States, while mild de‐anchoring occurred in the euro area. As of our sample end, both areas appear to be almost equally anchored.     

Social capital and well-being in the Italian provinces

Recent years witnessed a growing interest in the concept of well-being and quality of life, as alternative to the Gross Domestic Product (GDP). The determinants of well-being, especially at the regional and provincial levels, is understudied in a macro perspective, as opposed to a micro perspective (individuals, survey data) that has been the dominant approach until recently. In this paper, we estimate an empirical model for the Italian NUTS-3 provinces to evaluate the role of social capital on well-being by using aggregated (provincial) data. Our findings suggest that social capital, social security programs, income, and grant-making activities by Bank Foundations, even though not uniformly distributed across Italy, positively affect well-being, thus contributing to explain the persistent dualism that characterizes the Italian economy.     

Cultural Baggage: Do Immigrants Import Corruption?

Do immigrants undermine culture in a way that destroys productivity in destination countries? Some scholars have argued that because immigrants come from countries with dysfunctional social capital—norms and institutions—they will import it and pollute the social capital in destination countries. One potential channel through which this could occur is corruption. We examine stocks and flows of immigrants over a 20‐year time period to see if corruption increased in destination countries. We generally find that immigration is not associated with increases in corruption. Additionally, we find that immigration tends to decrease corruption in destination countries with low levels of corruption or high levels of economic freedom.     

Personal Income Tax Reform in China in 2018 and Its Impact on Income Distribution

In the context of personal income tax (PIT) reform in China in 2018, this paper examines some of the major issues of concern regarding the reform and income distribution. Using the China Personal Income Tax Micro‐simulation model, the paper compares the differences between the 2011 and 2018 PIT systems, and finds that residents relying on different income sources may face a large degree of real tax rate change. Once the tax system is altered to PIT 2018, the coverage of PIT for wage earners will decrease from 46.9 to 23.4 percent, the income redistributive effect will drop from 1.95 to 1.22 percent and the PIT's role in fiscal revenue will also be negatively affected. Nevertheless, if individual income continues to grow, the share of PIT in fiscal revenue is expected to return to the 2018 level in 2022, but its income redistribution function is difficult to recover in the short term. The paper finds that the effect of PIT on income distribution depends on the tax structure. Gradual transition to an “entirely comprehensive” tax system when conditions are appropriate will achieve better income redistribution results at a lower average tax rate.     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

Does corruption slow down innovation? Evidence from a cointegrated panel of U.S. states

I investigate the long-run relationship between corruption and innovative activity using annual data from 48 contiguous U.S. states between 1977 and 2006. Using U.S. data allows me to work with a panel long enough to exploit time series properties of the data. I use two different measures of innovative activity: one measuring the quantity and the other measuring the quality of the patents granted. I also use two different measures of corruption: one based on the number of corruption convictions, the other based on number of corruption stories covered in Associated Press news wires. Following Pedroni (1999, 2000), I estimate the cointegrating relationship between corruption and innovative activity with Fully Modified Ordinary Least Squares (FMOLS). The results indicate that corruption indeed slows down innovation in the long-run.     

Increasing linkages among European regions. The role of sectoral composition

This paper analyses changes in economic regional interlinkages in Europe over time and investigates the factors that could explain the dynamics of these changes. Our four main findings are the following: (i) we detect a significant surge in regional synchronisation after the Great Recession; (ii) we identify the regions most interrelated with the rest of Europe, namely, Ile de France, Inner London and Lombardia; (iii) we find that sectoral composition explains regional synchronisation in Europe, mainly after the Great Recession and (iv) we document that sectoral composition has important implications for aggregate economic fluctuations, in particular, that similarities in services-related sectors across regions explain a nonlinear relationship between sectoral composition and regional business cycle synchronisation. We also propose a new method to measure time-varying synchronisation in small samples that combines regime-switching models and dynamic model averaging.     

The Great Recession,Austerity and Inequality: Lessons from Ireland

The advent of the Great Recession and the widespread adoption of fiscal austerity policies have heightened concern about inequality and its effects. We examine how the distribution of income in Ireland—a country which experienced one of the most severe economic contractions—has evolved over the years 2008 to 2013. Standard cross‐sectional analysis of the income distribution shows broad stability in the Gini coefficient and in decile shares, with one main exception: the share of the bottom decile fell sharply, with the largest fall in average incomes being for that group. Longitudinal analysis shows that the falls in the average income for the bottom decile were not due to decreasing income for those remaining in the bottom decile, but to falls in income from those initially located in higher deciles. The extent of redistribution through taxes and transfers increased strongly, as measured by the Reynolds‐Smolensky index, which rose from 0.20 before the onset of the crisis to 0.27 in 2013. Analysis indicates that about three‐quarters of this increased redistribution is due to automatic stabilisers and one‐quarter to discretionary policy changes.