Gender and the knowledge of financing options by immigrant entrepreneurs in South Africa
Bibliographic record
Abstract
IntroductionTwo of the major economic challenges facing South Africa are weak economic growth and high rate of unemployment (Mahadea & Simson, 2010; Munyeka, 2014). South Africa's unemployment rate has increased from 24.2% in 2013 to 26.7% in 2016 (Statistics South Africa, 2016). The economic growth moved into the negative territory in the first quarter of 2016 as the South African economy contracted by 1.2% quarter-on-quarter (Statistics South Africa, 2016). The poverty level has gradually reduced since 1994. However, income inequality is still very high (Bhorat & van der Westhuizen, 2012; Statistics South Africa, 2014). To reduce unemployment, poverty and income inequality, South Africa needs a faster and more inclusive economic (National Panning Commission, 2012). However, there is a significant gap between the actual and the desired economic growth rate of South Africa (Fourie, 2013).Entrepreneurship and small business development is one of the solutions to the highlighted development challenges. Entrepreneurship leads to job creation and a reduction in the unemployment rate (Audretsch & Thurik, 2000; Musa & Semasinghe, 2013). Immigrant entrepreneurship is an important part of entrepreneurship and small business resurgence (Kloosterman & Rath, 2002). Immigrant-owned businesses contribute to employment and economic growth of host countries (Organisation for Economic Co-operation and Development (OECD), 2013). Immigrant entrepreneurship can help to drive the economic growth of host countries (Turkina & Thai 2013; Lofstrom, 2014; Anastasia, Dimitrios, Anastasios & Andreas, 2014). OECD (2013) points out that entrepreneurship is marginally higher among immigrants than natives. However, the survival rate of immigrant-owned businesses is often lower than that of their native counterparts (Desiderio, 2014).One of the primary factors that negatively impact on the performance of immigrant entrepreneurs is access to finance. Immigrant entrepreneurs often do not have sufficient capital to start and grow their business. (Rath, 2011; Anastasia, et al. 2014). Immigrant entrepreneurs face greater obstacles in accessing credit from financial institutions than their native born peers (Miller, Abreo, Farmer, Moon & McCullough, 2011; Desiderio, 2014). Capital acquisition is a one of the major issues facing small businesses. Without appropriate level of capital it is difficult for any business to survive and grow (Van Auken, 2003).According to Gregory (2013), access to finance by entrepreneurs can be affected by both demand and supply-side factors. It is of significance to recognise the demand-side factors that impact on access to finance by entrepreneurs ((Matshekga & Urban 2013; Rao 2015). Inadequate knowledge of the financing options available to entrepreneurs can lead to financial constraints (Seghers, Manigart & Vanacker 2009; Pangeran, 2015). In addition, owners' characteristics such as gender can affect the knowledge of financing options by entrepreneurs (Okafor & Amalu 2010; Kamukama & Natamba 2013). This study makes a significant contribution to the knowledge on access to finance by immigrant entrepreneurs. Understanding the factors that can affect the performance of immigrant entrepreneurs is of importance in improving their contribution to the host economy (Fairlie & Lofstrom 2013).The objectives of the studyAccess to finance is one of the major challenges facing immigrant entrepreneurs. The knowledge of the available financing options can help to improve access to finance by immigrant entrepreneurs. The objectives of this study are (1) to investigate the knowledge of financing options by immigrant entrepreneurs (2) to examine if there is a significant gender difference in knowledge of financing options by immigrant entrepreneurs.Immigrant entrepreneurshipAn immigrant can be described as an individual that comes from another country to a particular host country (Dalhammar, 2004). …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".