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on Entrepreneurship |
| By: | Norbäck, Pehr-Johan (Research Institute of Industrial Economics (IFN)); Persson, Lars (Research Institute of Industrial Economics (IFN)) |
| Abstract: | The application of machine learning (ML) to big data has become increasingly important. We propose a model where firms have access to the same ML, but incumbents have access to historical data. We show that big data raises entrepreneurial barriers making the creative destruction process less destructive (less business-stealing) if the entrepreneur has weak access to the incumbent’s data. It is also shown that this induces entrepreneurs to take on more risk and be more creative. Policies making data generally available may therefore be suboptimal. Supporting entrepreneurs’ access to ML might be preferable since it stimulates creative entrepreneurship. |
| Keywords: | Machine Learning; Big Data; Creative Destruction; Entrepreneurship; Operational Data |
| JEL: | L10 L20 M13 O30 |
| Date: | 2023–02–22 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:iuiwop:1454&r=ent |
| By: | Stefania Basiglio; Andrea Ricci; Mariacristina Rossi |
| Abstract: | This paper analyses the impact of entrepreneurs’ preferences (time impatience and risk attitudes) on firms’ propensity to make general investments and also specific investments in digital technologies. To fulfil this aim, we use the responses to the questions intended to measure risk attitude and patience included in the Rilevazione su Imprese e Lavoro (RIL) survey conducted by INAPP on a representative sample of Italian firms. The regression estimates show that time impatience has at most a weak effect on firms’ ‘general’ investments, while it reduces the propensity to undertake investments in digital technologies. Risk attitude is positively correlated with digital investment, even though the estimates are weaker in magnitude and statistical significance than those found for impatience. These results are robust to simultaneity and endogeneity issues. |
| Keywords: | Time preferences, Impatience, Investments, Digital technologies |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:cca:wpaper:688&r=ent |
| By: | David Audretsch; Paul P. Momtaz; Hanna Motuzenko; Silvio Vismara |
| Abstract: | This synthetic control study quantifies the economic costs of the Russo-Ukrainian war in terms of foregone entrepreneurial activity in both countries since the invasion of Crimea in 2014. Relative to its synthetic counterfactual, Ukraine's number of self-employed dropped by 675, 000, corresponding to a relative loss of 20%. The number of Ukrainian SMEs temporarily dropped by 71, 000 (14%) and recovered within five years of the conflict. In contrast, Russia had lost more than 1.4 million SMEs (42%) five years into the conflict. The disappearance of Russian SMEs is driven by both fewer new businesses created and more existing business closures. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.02773&r=ent |
| By: | Yuan Gao; Biao Jiang; Jietong Zhou |
| Abstract: | Financial Distress Prediction plays a crucial role in the economy by accurately forecasting the number and probability of failing structures, providing insight into the growth and stability of a country's economy. However, predicting financial distress for Small and Medium Enterprises is challenging due to their inherent ambiguity, leading to increased funding costs and decreased chances of receiving funds. While several strategies have been developed for effective FCP, their implementation, accuracy, and data security fall short of practical applications. Additionally, many of these strategies perform well for a portion of the dataset but are not adaptable to various datasets. As a result, there is a need to develop a productive prediction model for better order execution and adaptability to different datasets. In this review, we propose a feature selection algorithm for FCP based on element credits and data source collection. Current financial distress prediction models rely mainly on financial statements and disregard the timeliness of organization tests. Therefore, we propose a corporate FCP model that better aligns with industry practice and incorporates the gathering of thin-head component analysis of financial data, corporate governance qualities, and market exchange data with a Relevant Vector Machine. Experimental results demonstrate that this strategy can improve the forecast efficiency of financial distress with fewer characteristic factors. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.12118&r=ent |
| By: | HAMAGUCHI Nobuaki; João Carlos FERRAZ |
| Abstract: | What accounts for the expansion of high-tech startups? This paper provides a selective but reasonably comprehensive review of studies attempting to answer this question, drawing from the economic and business literature, especially from the resource-based view of the firm. We seek a scholarly foundation to propose a set of hypotheses to guide the design of a questionnaire-based survey of Japanese startup companies. Our basic proposition is that the growth of a startup is a function of predominantly positive interaction among all or a subset of the following elements: the Schumpeterian spirit of the entrepreneur, the existence of a dynamic set of capabilities within the firm, including the capacity to market and explore potential demand, its location within reach of an innovatively dense ecosystem, the availability of adequate financing, and the effectiveness of appropriate public support instruments. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:eti:dpaper:23012&r=ent |
| By: | Iman Cheratian (Tarbiat Modares University); Saleh Goltabar (Tarbiat Modares University); Hassan Gholipour Fereidouni (Western Sydney University); Mohammad Reza Farzanegan (Marburg University) |
| Abstract: | This study examines the relationship between access to finance and growth in sales for Micro, Small, and Medium Enterprises (MSMEs) in Iran. Using data from 486 firms in five provinces, our findings indicate that external financing positively impacts sales growth for MSMEs. The results suggest that financing for research and development expenditures, production diversification, new employment and advertising can significantly contribute to increased sales growth. We also find that spending on intellectual property, labor training and land and building acquisition have a negative moderating effect on the relationship between finance and sales growth. |
| Keywords: | Finance-sale growth nexus; Micro, Small, and Medium Enterprises (MSMEs); Iranian economy; External financing |
| JEL: | G21 G32 G38 O16 O53 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:mar:magkse:202308&r=ent |
| By: | Schilling, Linda |
| Abstract: | The article 'Entrepreneurial Incentives and the Role of Initial Coin Offerings' by Garratt and van Oordt revisits a classical principal-agent problem in corporate finance: How do financing choices of firms and the according firm ownership structure impact effort choices of the management and therefore firm value? Garratt and Oordt extend this classic discussion by analyzing initial coin offerings (ICOs) as a startup financing instrument in contrast to classic financing instruments such as debt, (entrepreneurial) equity and venture capital. Their paper shows, depending on the firm's characteristics, ICO financing can align the entrepreneur's incentives with those of his investors better or worse, in comparison to venture capital and debt financing. Therefore, the novel ICO financing instrument is a valuable alternative to credit and venture capital also from a social perspective. I find the efficiency comparison of financing choices for entrepreneurial incentives to be a very interesting and important contribution. In this discussion, I shed light on this topic from a different angle. Garratt and van Oordt base their analysis on the simplifying assumption that demand for the platform product realizes once and for all in t=1. This allows Garratt and van Oordt to abstract from token-exchange rate risk. Here, instead, I will allow platform demand to evolve stochastically across time. Moreover, unlike in Garratt and van Oordt, I analyze the general case where not necessarily all tokens are sold in the market at every point in time. Instead, there can exist token speculation where investors hold on to tokens for a certain period of time, effectively reducing the token supply, or additional tokens can be mined by the entrepreneur, which increases the token supply, even though the latter activity might constitute a breach of contract. By allowing the token supply and product demand to fluctuate across time, the token exchange rate becomes stochastic, causing exchange rate risk to the ICO investors, the entrepreneur, and platform retailers. |
| Keywords: | Initial coin offerings, financing instruments |
| JEL: | E52 G3 G32 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:116492&r=ent |
| By: | Aubhik Khan; Soyoung Lee |
| Abstract: | We study an economy with a time-varying distribution of production to examine the role of debt in amplifying and propagating recessions. In our model, entrepreneurs use risky, long-term debt to finance capital. Liquid assets serve as collateral and transaction costs make debt illiquid. Debt payments increase the volatility of earnings relative to output, deterring entrepreneurs with insufficient collateral from financing efficient levels of capital. This results in a misallocation of resources. In a large recession, productive entrepreneurs with high levels of debt deleverage, amplifying the downturn. The model economy exhibits asymmetries over the business cycle. Recessions involve a rapid deterioration of economic activity, while expansions are more gradual. When a recession coincides with a rise in leverage resulting from a fall in assets, fewer producers operate at efficient levels. When the aggregate business leverage is ten percentage points above average, the half-life of the recovery doubles. |
| Keywords: | Business fluctuations and cycles; Firm dynamics, Productivity |
| JEL: | E23 E32 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:23-17&r=ent |