Credit Constraints and the Productivity of Small and Medium-sized Enterprises: Evidence from Canada

Abstract

Small and medium-sized enterprises (SMEs) are regulators of the business environment. In Canada, SMEs represent about 50 percent of businesses and are responsible for over 60 percent of the country’s employment. The role of SMEs in the development of a country can’t be ignored, as they are vital indicators of economic development. The size and cash flow of a company's assets are reliable indicators of credit constraints (CC), which results in a CC agent for models that use an asset-to-liability ratio. We focus on the actual impact of a previously estimated score in cases where corporate credit is limited. Investment and employment decisions are based on productivity shocks (PS) and the possibility of CC. Using variables, our model indicates the importance of measured credit restrictions being distinguished, such as cash flows that indicate productivity levels and the probability of CC. The data samples are from 2009 to 2014, although the measurement of CC is only available from 2011. Therefore, we use the model of credit constraint estimation to anticipate the likelihood of CC in the months before and after 2011. The findings reflect that the firm’s size, debt to assets ratio, and cash flow are significant factors in the evaluation of the CC, whereas long-term debt (LTD) to asset ratio wasn’t found to be significant. The study also evaluates and estimates firm-level productivity. This research paper makes two important and constructive contributions towards effectively understanding and forecasting financial constraints (FC), such as sales growth and LTDs, as well as their direct impacts on the performance level of the firm. First, the present study adds to existing research on CC, specifically in relation to small businesses. To our knowledge, the present research is the first to analyze FC in SMEs independently from examined outcomes and results of EF. Second, the following study significantly contributes to effectively forecasting the relationship between FC and productivity at an organizational level, which is mainly based on productivity measurements and a consideration of FC.