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Recent reforms across Eastern European countries have given more flexibility and information to parties to engage in secured debt transactions. The menu of assets legally accepted as collateral was enlarged to include movable assets (e.g., machinery and equipment). Generalized difference-in-differences tests show that firms operating more movable assets borrowed more as a result. Those firms also invested more, hired more, and became more efficient and profitable following the changes in the contracting environment. The financial deepening we document triggered important reallocation effects: firms affected by the reforms increased their share of fixed assets and employment in the economy.

Private investment can be an important engine of economic growth in East African countries that are plagued with adverse economic conditions, despite recent growth rates. Against this backdrop, there has been substantial penetration of mobile money, moving beyond simple person-to-person exchanges towards adoption by private firms. This study explores whether there is a relationship between firm adoption of mobile money and firm investment. Using firm-level data that are nationally representative of the private sector in three East African countries—Kenya, Tanzania, and Uganda—a positive relationship is found between mobile money use and firm's purchase of fixed assets. This relationship is attributed to reduced transaction costs, increased liquidity, and increased credit worthiness associated with the use of mobile phone financial services. The finding is largely driven by small- and medium-sized enterprises (SMEs).

We study the valuation of collateral by comparing spreads on loans by the same bank, to the same borrower, at the same origination date, but backed by different types of collateral. Pledging collateral reduces borrowing costs by 23 BPS on average. The effect varies across different types of collateral, with marketable securities being most valuable, and real estate and accounts receivables and inventory being more valuable than fixed assets and a blanket lien. Further, the rate reduction from pledging collateral is sensitive to the value of the underlying collateral, and collateral tends to be more valuable for smaller and private firms and for loans with longer maturity.

This paper examines whether firms manipulating their reported financial results make suboptimal investment decisions. We examine fixed asset investments for a large sample of public companies during the 1978-2002 period and document that firms that manipulate their earnings-firms investigated by the SEC for accounting irregularities, firms sued by their shareholders for improper accounting, and firms that restated financial statements-over-invest substantially during the misreporting period. Furthermore, following the misreporting period, these firms no longer over-invest, consistent with corrected information leading to more efficient investment levels. We find similar patterns for firms with high discretionary revenues or accruals. Our findings suggest that earnings management, which is largely viewed as targeting parties external to the firm, can also influence internal decisions.

This paper examines the significance and robustness of four measures of growth of the firm with respect to firm-level Total Factor Productivity (hereinafter TFP). These four measures are (a) growth of total assets, (b) growth of sales, (c) growth of fixed assets, and (d) weighted growth of fixed assets. The four measures are examined in the business and economics literature in different contexts. The results of related studies do not include a consensus regarding the validity of a certain measure. The ultimate objective of this paper is to present a realistic view of growth of the firm.The data used in this paper represent the non-financial firms listed on DJIA30 and NASDAQ100. These data were obtained from Reuters Finance database© (https://www.reuters.com/markets/) and cover the quarterly periods from June 1992 till March 2018The results of the robustness test show that (a) firm-level TFP is positively associated with weighted growth of fixed assets, (b) the estimates of weighted growth of fixed assets are robust which is an indication to the intrinsic relationship with firm-level TFP, (c) the significance of weighted growth of fixed assets varies across industries which reflects an industry effect. This paper contributes to the related literature by examining the robustness of the common measures of growth of the firm. As far as growth of the firm and size are exchangeable, the lack of conformity in the literature raises a controversy regarding the search for a reliable measure of size and growth of the firm.

Many factors impact urban fixed-asset investment efficiency (FAIE) during urbanization. Significant academic attention has focused on the main factors that affect urban fixed-asset investment efficiency. However, it remains unclear what the main factors affecting urban fixed-asset investment efficiency are and how they influence it. First, we use the Group Method of Data Handling (GMDH) to automatically identify the main drivers influencing urban FAIE in western China. Then, we utilize the Nadaraya–Watson estimator of a non-parametric regression model to empirically test the nonlinear relationships between these drivers and urban FAIE. Finally, we construct a dynamic panel data model to explore the magnitude of their impact on urban FAIE. We find that the drivers influencing urban FAIE include the economic development level, urbanization investment, financial industry development, urbanization level, and education level. Urban FAIE is positively correlated with the economic development level and negatively correlated with urbanization investment. The relationship between urban FAIE and financial industry development shows a fluctuating pattern. Urban FAIE and the urbanization level have a U-shaped relationship. Urban FAIE and the education level do not appear to have a significant mutual relationship. The drivers have a significant positive impact on urban FAIE. Except for urban fixed-asset investment, economic development, financial industry development, education level, and urbanization level significantly promote urban FAIE. The education level and urbanization level interact with each other, and this interaction has a significant positive impact on urban investment efficiency.

This study used China’s accelerated depreciation policy (2014–2015) as an exogenous shock to examine the impact of tax incentives on firm financing structures. Based on data from China’s A-share listed companies from 2010 to 2017, we estimated a difference-in-differences model and found that the accelerated depreciation policy increased firms’ liability–asset ratio. Moreover, this rise was mainly seen in firms’ current liability–asset ratio (i.e., short-term leverage), while long-term leverage remained stable, which shortened firms’ debt maturity. The mechanism exploration showed that the accelerated depreciation policy stimulated fixed asset investment, and this investment increase was mainly financed by short-term debt, leading to greater maturity mismatch between firm assets and liabilities. Further heterogeneity analysis showed that the observed rise in short-term leverage was more serious among firms that were less likely to be allocated long-term credit from banks, including small-sized firms and those with a low share of tangible assets.

Abstract—The article considers methodological and informational problems related to calculating the dynamics of fixed assets (fixed capital) in the Russian economy over 1990–2000. Special aspects of measuring the fixed assets performance using various statistical classifiers, the effect produced by the base of comparable prices on assessing the performance and reproduction characteristics of fixed capital are analyzed. Calculated estimates of its rate of change in the context of certain types of economic activity for 2005–2019 are given; and a possible direction for their refinement is also proposed.

This research focused on the development of small farms, which in many countries form the basis of the agricultural sector. The specifics of this type of farm, as well as the way in which they operate, influence the possibilities for these farms to realise the model of sustainable agriculture. This study considers income and the rate of reproduction of fixed assets as the main measures of farm development, which are influenced by a number of endo- and exogenous factors. The research period covered 2017–2021, and the subjects of analysis were small individual farms located in Greece, Portugal, Lithuania, and Poland. The figures for the research were taken from the FADN system database. The purpose of this study was to assess the impact of endogenous agricultural factors on the development of small farms as measured by farm income and reproduction of fixed assets in four selected European Union (EU) countries, i.e., Greece, Portugal, Lithuania, and Poland. Spearman’s non-parametric rank correlation method was used to assess the impact of endogenous factors. Selected on the basis of correlation relationships, the farm development factors showed a significantly higher correlation with farm income than with the reproduction of the farm’s fixed assets. The analysis indicated that, irrespective of the location of the farm, factors significantly affecting income levels included the area of agricultural land and the number of full-time employees. Only in some countries was there a statistically significant correlation between farm income and the share of leased land, the number of full-time workers per 100 ha of UAA, the share of hired labour input, as well as the level of total farm subsidies received.

In recent years, with the complexity of market environment and diversification of asset structure, the identification and early warning of operational risk has become a key part of asset management. Traditional risk assessment methods are often difficult to give consideration to accuracy and interpretability, which easily leads to information loss and risk omission. Therefore, a risk early warning model based on rule integration learning is proposed. Through the dynamic weight distribution mechanism, the steps of rule extraction, conflict resolution, and priority ranking are integrated, which can flexibly deal with different business scenarios. In this paper, a multidimensional data system is constructed, and cross-validation and hierarchical evaluation are carried out to ensure the robustness of the model's performance. The results show that the model has good generalization ability and risk identification ability. This study provides reference and support for the risk early warning and decision-making of fixed assets operation.