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The results indicate that land prices exert pressure on retail performance (RP) and that the enhancement of digital means has a positive effect on RP. Additionally, digital instruments (DI) play a significant moderating role in the relationship between land prices and RP.

This paper empirically examines the impact of land prices on RP using panel data from 239 Chinese cities between 2011 and 2022.

The use of lagged land prices as instrumental variables effectively alleviates endogeneity issues. Both two-stage least squares (2SLS) and generalized method of moments (GMM) regression results suggest that higher land prices are associated with improved RP. Further analysis reveals that the increase in land prices leads to scale effects, structural effects and technological effects, contributing to the enhancement of RP. The impact of land prices on RP becomes more pronounced in larger cities and economically developed regions experience the pressure from land prices earlier.

The findings of this study have practical implications for discussions on retail industry development, site selection for retail businesses and the establishment of sustainable mechanisms for expanding domestic demand.

The study examined the impact of financial development, foreign direct investment, market size and trade openness on domestic investment for 119 countries divided into four panels that are low-income countries (LIC), lower middle-income countries (LMIC), upper middle-income countries (UMIC) and high-income countries (HIC) between 1995 and 2019.

The present study bases its empirical procedure on the bases of the data mix. To this end, based on the presence of cross-sectional dependence, covariate-augmented Dickey–Fuller unit root and Westerlund cointegration second-generation tests were employed to validate the stationarity and cointegration of the variables, respectively. The novel Dynamic Common Correlation Effects estimator was employed to estimate the heterogeneous parameters while the Dumitrescu and Hurlin test was used to test for causality direction of the highlighted variables.

The empirical results show that market size and trade openness had a positive and statistically significant effect on domestic investment for all the income groups. Results also show that financial development had a positive and statically significant effect on domestic investment only for LMIC and HIC economies, while a positive and statistically insignificant effect was obtained for LIC, UMIC and the global panel. The causality results revealed a bidirectional relationship between domestic investment and the exogenous variables – financial development, foreign direct investment, market size and trade openness.

It is therefore, recommended that LIC and LMIC need to consider harmonising the financial system to lower credit limitations and adopt business-friendly policies. HIC and UMIC should seek more outward FDI policies and harmonise their trade policy, to reap more benefits from FDI and international trade.

On novelty, previous studies have been criticised for the effect on technical innovation of bank financing and institutional quality. This research tackles the deficiency using systematic institutional quality indicators and by taking other variables into account.

Nonfamily farms are responsible for a disproportionate amount of US agriculture production. The importance of these operations to the volume of agriculture production in the United States has led researchers and policymakers to understand nonfamily farms as large commercial operations. This paper examines whether the distinction between family and nonfamily helps explain the financial outcomes of farm operations and households.

We test for differences in financial outcomes of the household and operations of family and nonfamily farms using an Oaxaca-Blinder decomposition. We compare these results to a decomposition of other possible typologies.

We present evidence that nonfamily farms are a heterogeneous group with a majority of small operations that are dominated by a small number of large operations. We discover that differences associated with the family-nonfamily distinction are largely explained by observable farm and operator characteristics that arise mechanically from the definition. However, we find suggestive evidence that family-nonfamily classification captures differences in economic behavior that lead to higher profitability measures to nonfamily farms. We find little evidence of any inherent structural differences between family and nonfamily farms that helps explain financial outcomes related to leverage or household finances.

We conclude that including nonfamily farms in official statistics of farm households may provide a more comprehensive overview of the farm sector, as our results suggest that family and nonfamily farms do not have innate differences that help explain many of their financial outcomes.

We incorporate previously unused data on nonfamily farm households and test the difference in mean financial outcomes between family and nonfamily farms.