Nghiên cứu các yếu tố ảnh hưởng đến khả năng tiếp cận cho vay của các doanh nghiệp vừa và nhỏ tại ngân hàng nông nghiệp và phát triển nông thôn việt nam chi nhánh tỉnh long an

BỘ GIÁO DỤC VÀ ĐÀO TẠO NGÂN HÀNG NHÀ NƯỚC VIỆT NAM TRƯỜNG ĐẠI HỌC NGÂN HÀNG TP.HỒ CHÍ MINH LÊ HỒNG DIỄM NGHIÊN CỨU CÁC YẾU TỐ ẢNH HƯỞNG ĐẾN KHẢ NĂNG TIẾP CẬN CHO VAY CỦA CÁC DOANH NGHIỆP VỪA VÀ NHỎ TẠI NGÂN HÀNG NÔNG NGHIỆP VÀ PHÁT TRIỂN NÔNG THÔN VIỆT NAM – CHI NHÁNH LONG AN LUẬN VĂN THẠC SĨ Chuyên ngành: Tài - Ngân hàng Mã số: 34 02 01 Người hướng dẫn khoa học: PGS., TS ĐOÀN THANH HÀ TP HỒ CHÍ MINH - NĂM 2018 TĨM TẮT LUẬN VĂN Hoạt động tín dụng mảng kinh doanh lớn ngân hàng thương mại, nhiên xét hoạt động tín dụng doanh nghiệp vừa nhỏ (DNVVN) thị trường nhiều bỏ ngõ Trong thời gian qua, hoạt động tín dụng DNVVN địa bàn Tỉnh Long An dường tăng trưởng chậm, chương trình tín dụng dành riêng cho nhóm khách hàng Agribank Chi Nhánh tỉnh Long An chiếm tỷ trọng thấp dư nợ tín dụng đơn vị Đến việc tiếp cận vốn vay DNVVN nhiều khó khăn, chưa kết nối tạo dòng chảy vốn đến doanh nghiệp, để phục hồi sản xuất, kinh doanh, góp phần phát triển kinh tế - xã hội Vì vậy, đề tài: “Nghiên cứu yếu tố ảnh hưởng đến khả tiếp cận cho vay doanh nghiệp vừa nhỏ Ngân hàng Nông Nghiệp Phát Triển Nông Thôn Việt Nam - Chi Nhánh Long An ” nghiên cứu sở lý luận để xác định đo lường mức độ ảnh hưởng yếu tố ảnh hưởng đến khả tiếp cận cho vay vốn DNVVN Agribank Chi Nhánh tỉnh Long An, nhằm đưa hàm ý giúp DNVVN tiếp cận cho vay vốn hiệu Bằng mơ hình nghiên cứu kiểm chứng báo khoa học với đề tài “Testing SMEs Determinants of Access to Debt Financing by Using Logistic Regression Model” Alex Reuben (năm 2015), tác giả đưa giả thiết thử nghiệm thông qua việc áp dụng kỹ thuật mơ hình hóa nghiên cứu Các số liệu định lượng định tính thu thập, phân tích hồi quy thơng qua mơ hình kinh tế lượng với mơ hình Binary Logistic để kiểm tra giả thiết dựa mối quan hệ biến phụ thuộc biến độc lập Các giả thiết nghiên cứu đưa gồm yếu tố (quy mô doanh nghiệp, khu vực, lĩnh vực kinh doanh, thông tin kinh doanh, tuổi doanh nghiệp, loại hình doanh nghiệp, tài sản đảm bảo, trình độ học vấn, kinh nghiệm lĩnh vực quản lý) khảo sát mẫu ngẫu nhiên 200 doanh nghiệp Kết nghiên cứu cho thấy có yếu tố ảnh hưởng đến khả tiếp cận vốn vay DNVVN Agribank Chi Nhánh tỉnh Long An gồm quy mô doanh nghiệp, khu vực, thông tin kinh doanh, tuổi doanh nghiệp, tài sản đảm bảo kinh nghiệm quản lý Từ tác giả đưa hàm ý sách phù hợp i LỜI CAM ĐOAN Tôi xin cam đoan luận văn “Nghiên cứu yếu tố ảnh hưởng đến khả tiếp cận cho vay doanh nghiệp vừa nhỏ Ngân hàng Nông Nghiệp Phát Triển Nông Thôn Việt Nam - Chi Nhánh Long An” cơng trình nghiên cứu tơi thực hướng dẫn PGS.TS Đoàn Thanh Hà Các số liệu đề tài thu thập sử dụng cách hoàn toàn trung thực Kết nghiên cứu trình bày luận văn khơng chép luận văn chưa trình bày hay cơng bố cơng trình nghiên cứu khác trước Tơi hồn tồn chịu trách nhiệm pháp lí q trình nghiên cứu khoa học luận văn Tác giả Lê Hồng Diễm ii LỜI CẢM ƠN Đầu tiên, xin chân thành cảm ơn Quý Thầy Cô trường Đại học Ngân Hàng TPHCM trang bị cho kiến thức truyền đạt cho kinh nghiệm quý báu làm tảng cho việc thực luận văn Tơi xin chân thành cảm ơn PGS.TS Đồn Thanh Hà tận tình hướng dẫn bảo để tơi hồn thành luận văn cao học Cuối gửi lời biết ơn sâu sắc đến gia đình, người thân ln tin tưởng, động viên tạo điều kiện tốt cho học tập Tác giả Lê Hồng Diễm iii MỤC LỤC CHƯƠNG TỔNG QUAN VỀ ĐỀ TÀI NGHIÊN CỨU 1.1 Lý chọn đề tài 1.2 Một số cơng trình nghiên cứu nước nước .2 1.3 Mục tiêu nghiên cứu 1.4 Câu hỏi nghiên cứu 1.5 Đối tượng phạm vi nghiên cứu 1.5.1 Đối tượng 1.5.2 Phạm vi nghiên cứu 1.6 Phương pháp nghiên cứu 1.7 Ý nghĩa thực tiễn đề tài 1.7.1 Đóng góp mặt lý thuyết 1.7.2 Đóng góp mặt thực tiễn 1.8 Kết cấu đề tài CHƯƠNG CƠ SỞ LÝ THUYẾT 2.1 Lý thuyết tiếp cận tín dụng doanh nghiệp nhỏ vừa 2.1.1 Những vấn đề doanh nghiệp nhỏ vừa 2.1.2 Những vấn đề tín dụng ngân hàng 12 2.1.2.1 Khái niệm cho vay 12 2.1.2.2 Vai trò hoạt động cho vay doanh nghiệp 123 2.1.3 Lý thuyết tiếp cận vốn 14 2.1.3.1 Lý thuyết tiếp cận truyền thống 14 2.1.3.2 Lý thuyết tiếp cận hạn chế tài 14 2.1.3.3.Lý thuyết tiếp cận kinh tế học định chế 16 2.2 Các yếu tố ảnh hưởng đến khả tiếp cận vốn vay ngân hàng doanh nghiệp nhỏ vừa 17 2.2.1 Quy mô doanh nghiệp 17 2.2.2 Khu vực (Địa điểm kinh doanh) 18 2.2.3 Lĩnh vực kinh doanh 18 2.2.4 Thông tin kinh doanh (Thơng tin báo cáo tài chính) 19 2.2.5 Tuổi doanh nghiệp 19 iv 2.2.6 Loại hình doanh nghiệp 20 2.2.7 Tài sản đảm bảo 20 2.2.8 Trình độ học vấn lãnh đạo 20 2.2.9 Kinh nghiệm lĩnh vực 21 KẾT LUẬN CHƯƠNG 21 CHƯƠNG MƠ HÌNH PHƯƠNG PHÁP NGHIÊN CỨU 23 3.1 Quy trình nghiên cứu 23 3.2 Mơ hình nghiên cứu giả thuyết nghiên cứu 23 3.2.1 Mô hình nghiên cứu đề xuất 23 3.2.2 Giả thuyết nghiên cứu 25 3.3 Phương pháp nghiên cứu 29 3.3.1 Phương pháp chọn mẫu 30 3.3.2 Mơ hình hồi quy Logistic 30 3.3.3.Các phương pháp đưa biến độc lập vào mô hình hồi quy Binary Logistic 31 3.3.4 Các kiểm định mơ hình hồi quy Binary Logistic 32 3.4 Phương pháp xử lý liệu 34 KẾT LUẬN CHƯƠNG 34 CHƯƠNG KẾT QUẢ NGHIÊN CỨU VÀ THẢO LUẬN 35 4.1 Thực trạng tiếp cận tín dụng ngân hàng doanh nghiệp Ngân hàng Nông Nghiệp Phát Triển Nông Thôn- Chi Nhánh Tỉnh Long An 35 4.1.1 Tổng Dư nợ cho vay DNVVN Ngân hàng Nông Nghiệp Phát Triển Nông Thôn Việt Nam - Chi Nhánh Long An 35 4.1.2 Dư nợ cho vay doanh nghiệp phân theo tài sản đảm bảo ngân hàng Agribank – CN Long An 35 4.2 Kết nghiên cứu 36 4.2.1 Thống kê mô tả mẫu điều tra 36 4.2.2 Phân tích yếu tố đến khả tiếp cận vốn vay DNVVN Ngân hàng Nông Nghiệp Phát Triển Nông Thôn Việt Nam - CN Long An 39 4.2.2.1 Mơ hình xác định yếu tố đến khả tiếp cho vay vốn DNVVN Ngân hàng Nông Nghiệp Phát Triển Nông Thôn Việt Nam - CN Long An 39 v 4.2.2.2 Kết mơ hình hồi quy logistic yếu tố đến khả tiếp cận vốn vay DNVVN Ngân hàng Nông Nghiệp Phát Triển Nông Thôn Việt Nam - CN Long An 40 4.2.2.2.1 Kiểm định hệ số tương quan 40 4.2.2.2.2 Phân tích kết mơ hình hồi quy Binary logistic tối ưu 42 KẾT LUẬN CHƯƠNG 48 CHƯƠNG KẾT LUẬN VÀ HÀM Ý CHÍNH SÁCH 49 5.1 Kết luận 49 5.2 Hàm ý giúp DNVVN tiếp cận vốn 50 5.2.1 Đối với kinh nghiệm lĩnh vực kinh doanh 50 5.2.2 Chính sách tài sản đảm bảo 50 5.2.3 Chính sách khu vực kinh doanh 51 5.2.4 Chính sách tuổi doanh nghiệp 51 5.2.5 Chính sách Thông tin kinh doanh 52 5.2.6 Chính sách tổng tài sản 52 5.3 Hạn chế đề tài hướng nghiên cứu 53 KẾT LUẬN CHƯƠNG 54 TÀI LIỆU THAM KHẢO 55 PHỤ LỤC - Danh sách DNVVN chọn khảo sát Ngân hàng Nông Nghiệp Phát Triển Nông Thôn Việt Nam - Chi Nhánh tỉnh Long An - Thống kê mơ tả thang đo - Phân tích tương quan, hồi quy - Mơ hình nghiên cứu áp dụng vi DANH MỤC TỪ VIẾT TẮT Từ viết tắt Ý nghĩa Chi nhánh Chính Phủ Đồng sơng Cửu Long Doanh nghiệp Doanh nghiệp quốc doanh Doanh nghiệp vừa nhỏ Đơn vị tính Kinh nghiệm Khảo sát mức sống Khu vực Loại hình doanh nghiệp Lĩnh vực kinh doanh Nghị định Ngân hàng thương mại Sản phẩm dịch vụ Trình độ học vấn Tài sản đảm bảo Thông tin kinh doanh Tổng tài sản Vốn chủ sở hữu CN CP ĐBSCL DN DNNQD DNVVN ĐVT KN KSMS KV LHDN LVKD NĐ NHTM SPDV TDHV TSDB TTKD TTS VCSH vii DANH MỤC BẢNG BIỂU Tên Bảng 2.1 Bảng 2.2 Bảng 2.3 Bảng 3.1 Bảng 4.1 Bảng 4.2 Bảng 4.3 Bảng 4.4 Bảng 4.5 Bảng 4.6 Bảng 4.7 Bảng 4.8 Bảng 4.9 Bảng 4.10 Bảng 4.11 Bảng 4.12 Bảng 4.13 Bảng 4.14 Bảng 4.15 Bảng 4.16 Bảng 4.17 Nội dung Trang Phân loại DNVVN theo WB Phân loại DNVVN theo khối liên minh Châu Âu 10 Phân loại DNVVN theo ngành hoạt động Việt Nam 11 Các giả thiết nghiên cứu dấu kỳ vọng 28 Tổng Dư nợ cho vay Ngân hàng Nông Nghiệp Phát Triển 34 Nông Thôn Việt Nam - Chi Nhánh Long An Dư nợ cho vay phân theo TSBĐ Ngân hàng Nông Nghiệp 35 Phát Triển Nông Thôn Việt Nam - Chi Nhánh Long An Khả tiếp cận vốn vay 35 Khu vực kinh doanh 36 Lĩnh vực kinh doanh DNVVN 36 Thông tin kinh doanh DNVVN 36 Loại hình doanh nghiệp 37 Tài sản đảm bảo 37 Trình độ học vấn 37 Kinh nghiệm vay vốn 38 Ma trận tương quan biến 40 Kiểm dịnh Chi-bình phương độ phù hợp tổng quát 41 Hệ số -2LL 41 Khả dự báo xác mơ hình 42 Kiểm định mức ý nghĩa hệ số hồi quy lần 43 Kiểm định mức ý nghĩa hệ số hồi quy lần 43 Tổng hợp yếu tố có ý nghĩa thống kê 46 viii DANH MỤC SƠ ĐỒ, HÌNH ẢNH Tên Nội dung Trang Hình 3.1 Quy trình nghiên cứu 22 Hình 3.2 Mơ hình nghiên cứu đề xuất 24 ix A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 Fatoki and Assah (2011) found out that owner-manager’s management competency (education and experience) has an influence on access to debt financing from commercial banks Herrington and Wood (2003) points out that lack of education and training has reduced management capability in SMEs and account for one of the reasons for their high failure rates Timmons (1994) found out that the new business failure rates over the last 50-year period prior to 1994 occurrences were: 23.7% of the failures occurred in the first two years, 51.7% occurred within four years and 62.7% occurred within six years The failure can also be attributed to a general downturn in economic activity and owner-manager characteristics which include lack of knowledge or experience in key business areas can lead to failure Storey (1994) confirmed that lack of technical and managerial skills could have serious consequences for future venture performances Failure of business means even servicing the debt financing might be difficult, that’s why this study evaluates owner-manager characteristics impact the access to debt financing Therefore, the SMEs’ management education and experience impact the firm’s performance hence ability to access external debt financing by SMEs Similar research studies on SME sector covering the SMEs’ access to debt financing by SMEs are much less common in the economics literature, particularly those with a focus on the developing countries’ SME sector, especially Tanzanian SMEs Shortage of financing to SMEs in developing countries led the sector not to provide enough support to economic development and growth (Sacerdoti, 2005, Kira, 2013) The SMEs business activities in Tanzania have not been thoroughly unveiled entirely in reflecting the determinants on their access to external financing The initial capital and expansion capital fund for Tanzanian SMEs has been a perpetual problem even though the government struggles to empower the sector through various programmes These strategies and programs are still unable to support the SME sector since the sector is still vulnerable and few manage to survive due to shortage of finance Financial institutions have the capacity to pull their financial resources together to meet the credit demands for SMEs but are reluctant to act The financing gap still exists between the supply capabilities of financing sources and the demanding needs of the SMEs This study intended to create a bridge to solve this problem and improve the existing policies towards creating a permanent path to make debt financing available to SME sector The main objective of this study was to investigate the firms’ internal factors that impact access on the debt financing and the growth of SMEs sector The study investigated empirically the determinants on the gap of SMEs to gain permanent access on debt financing in Tanzania The appraisal of firm’s and the entrepreneur’s (owner-manager) attributes on the access to finance were conducted Le, et al., (2006) pointed out that; the main serious achievement characteristic for any SMEs is to gain adequate access on the external sources of financing To attain the study’s main objective, firms’ and owner-managers’ characteristics were employed as variables to be assessed in determining the access of debt financing by SMEs The research hypotheses were constructed from other studies which have been conducted in different aspects on SMEs to attain the objective of this study The study is organized into parts: introduction; research methodology and model; results and discussion; and finally recommendations and conclusion The programmes are: “MKUKUTA” National Strategy for Growth and Reduction of Poverty I & II (NSGRP I&II): MKUKUTA I (2005/06 – 2009/10) focused on accelerating economic growth, reducing poverty, improving standard of living and social welfare, good governance and accountability.” MKUKUTA II”, like predecessor, is a vehicle for realizing Tanzania’s Development Vision 2025, The Millennium Development Goals (MDGs) and “MKURABITA” Poverty and Business Formalization Programme A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 2.0 RESEARCH METHODOLOGY AND MODEL The methodology of this study focused on hypotheses testing, through application of research modeling techniques The quantitative and qualitative data were collected to determine the relationships among the independent variables that affect the dependent variable, access to debt financing by SMEs Correlation analysis was conducted to test the existence of the multicollinearity problem Multivariate regression analysis was used to test the significance of the impact of each independent variable’s influence on firm’s access to debt financing I64 participants were recruited from business operators from Tanzania: Coastal zone (Dar es Salaam); Northern zone (Arusha); Southern and Highlanders zone (Mbeya); Lake Zone (Mwanza) and Zanzibar (Unguja) The selected SMEs operators are those recognized by the Ministry of Industry, Trade and Marketing; Tanzania Chamber of Commerce, Industry and Agriculture (TCCIA); and Small Industries Development Organization (SIDO) The population of the study was 250 respondents and to establish the sample the study used Raosoft sample size calculator which provided the minimum of 152 SMEs’ respondents for the study Data collection was conducted through survey questionnaires encompassing open and closed questions administered to the owner-managers of SMEs 200 questionnaires were administered because of data collection limitation, that is, non-response whereby 164 respondents were successful This study had a number of limitations, which had to be taken into consideration when interpreting the findings First, the study was limited to urban representative areas in Tanzania; these areas are relatively more urbanized and more affluent compared to the rest of the country However these areas share many similarities with the rest of the country which led the data collected to still be unbiased representative to affect the findings Secondly, the data did not permit the researcher to involve other the noticeable features such as political and socioeconomic variables which possibly might impact SMEs’ access to debt financing because these variables were difficult to be captured quantitatively in the study During data collection, since Tanzania is multi-religions, sensitive questions involving religious issues were not involved to avoid confusion and to jeopardize the research’s objective since some of the financial institutions are owned by religious groups 2.1 The Logistic Regression Model The study opted to use logistic regression model because the nature of the dependent variable was binary; for SME operator to access debt financing only two options are available: either the loan/credit application will be approved (1) or will be rejected (0); that’s what is referred to as binary The independent variables are a categorical or a mix of continuous and categorical that is why a logistic regression was more preferred The logistic regression model forms a best fitting equation or function by using the maximum likelihood method which maximizes the probability of classifying the observed data into the appropriate category given the regression coefficients Also, logistic regression provides a coefficient ‘β’, which measures each independent variable’s partial contribution to variations in the dependent variable The fundamental model underlying multiple regression equation adopted by this study is as follow: q Y = α + β X + β X + + β q X q + ε = α + ∑ β j X j + ε ( ) j −1 Where: Y-intercept i.e., the expected value of Y when all X's are set to 0; βj is a multiple (partial) regression coefficient i.e., the expected change in Y per unit change in Xj assuming all other X's Raosoft sample size calculator is statistical software that enables researchers to determine the sample size given the following variables: The margin of error, the confidence level, the population and the expected response distribution (Raosoft, 2010) A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 are held constant; and ε is the error of prediction The multiple regression equation with all the variables to be associated in establishing a relationship with access to debt financing by SMEs was formed as shown from equation (1) to (6) In this study, logistic regression is used to evaluate the determinants of external finance where the result of the analysis is the probability of developing proper criteria after controlling for other associated risks The logistic regression provides knowledge of the relationships and strengths among the variables (e.g., lack of collateral puts a firm at a higher risk for failure to raise external capital than its location in the metropolitan) Logistic regression is used to predict the odds of being a case based on the predictor(s) The nature of data involved in this study was categorical which led the researcher to opt for logistic regression model analysis to establish the extent to which each of the variables plays part in firm’s access to debt financing The variables obtained from the survey were tested for their influence on firms’ and owner-managers’ characteristics over the access to debt financing A number of variables were correlated individually with access to debt finance; then were regressed with access to debt financing Of the greatest interest are the multiple correlations of those variables which were correlated collectively and simultaneously with access to debt finance in a statistically significant manner AtDF j = f ( FC , OC ) + ε j = βFC j + γOC j + ε ( ) Where: AtDF j is the probability that firm j will access debt or credit from a financial institution; f ( FC , OC ) = access to debt financing is the function of firm’s characteristics (FC) and ownermanager’s characteristics (OC); ε is the error component that varies over both individual firms AtDF = α + β (Size + Location + Industrial sector + BusinessInformation + Age + Incorporation + Collateral) + γ (Education + Experience) + ε .( ) AtDF j = α + β Size j + β Loc j + β Ind j + β Binfo j + β Age j + β Inco j + β Coll j + γ Medu j + γ MExp j + ε j ( ) Where: Size j is the size of firm j (measured by the number of employees); Loc j is the location of a firm j; Ind j is the industrial sector in which firm j operates; Age j is the age of a firm j; Binfo j is the business information (maintenance/preparation of financial statements annually) maintained by firm j; Inco j is the form of business organization (incorporation) or legal status of firm j during the access of debt/loan; Coll j is the status of collateral owned or controlled by a firm while applying for a loan/debt financing; Medu j is the owner-manager education level of firm j; MExp j is the owner-manager business experiences of firm j; (in terms of years working in the business/industry) β > ; β > ; β > ; β > ; β > ; β > ; β > ( ) γ > ; γ > ( ) Where: (β) is representing the firm characteristics parameters or coefficients to be estimated: β , β , β , β , β , β and β while (γ) is representing the entrepreneur (owner-manager) characteristics parameters or coefficients to be estimated: γ and γ 3.0 RESULTS AND DISCUSSION The presentation of this study results consist of the correlation, regression of the variables, presentation of the results and final discussion of the results 3.1 Correlations Correlation analysis was conducted to establish the degree of correlation on the variables intended for this study To establish the relationship between variables the correlation analysis was conducted to find out the association that exists between the firm’s size, location, industry, A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 business information, age, incorporation and management education and experience as independent variable against access to debt finance which is the dependent variable to establish whether the multicollinearity problem exists among the independent variables The correlation analysis was used to describe the strength and direction of the linear relationship between two variables (Pallant, 2001) In this study, the procedure for obtaining and interpreting a Pearson’s correlation coefficient was used Given that correlations between independent variables can cause problems with multicollinearity in regression analysis (Mendenhall & Sincich, 1993; Mason & Perreault, 1991), in examining the significance of the correlation coefficients takes on added importance Table demonstrates correlation matrix in this study, All results indicate that variables are correlated with access to debt finance as per Field (2005) that malticollinearity is likely to be a problem in data if the correlation coefficient between predictors is greater than 0.90 (r > 0.90) The study’s results indicate that accessibility of debt finance from external sources in Tanzania is positively impacted by the firm’s size, location, industry, age, incorporation, the availability of collateral and business information Some owner-managers’ traits are correlated with access to debt financing Furthermore, the correlation coefficients show that there is no high correlation among independent variables used (i.e r > 0.90) in this analysis and thus making the studied variable demonstrate that no multicollinearity problem exists among the variables A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 Table 1: Correlation Matrix for the Selected Tanzanian SMEs Attributes Variables AtDF Size Loc Indu Binfo Age Inco Coll MEdu Access to Debt Finance Size 0.455** * Location 0.450 -0.147* ** * Industry 0.235 -0.391 0.186* Business 0.328** -0.396** 0.078* 0.030* information Age 0.453* 0.229** 0.010* 0.048* -0.242** * ** ** * ** Incorporation 0.512 -0.340 0.203 0.160 0.260 -0.087* ** ** * * ** ** Collateral 0.831 0.296 -0.013 -0.143 -0.220 0.371 -0.014* Management 0.359** 0.504** -0.390** 0.13 0.251** Education 0.204** 0.347** 0.292** Management 0.812* 0.105* -0.003* -0.086* -0.047* 0.282** -0.110* -0.040* 0.051* Experience ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) Source: Source: Author’s calculations based on Survey data from selected Tanzanian SMEs Table heading abbreviations: AtDF – Access to Debt Financing; Loc – Location; Indu – Industry; Binfo – Business Information; Inco – Incorporation; Coll – Collateral; MEdu – Management Education Mexp A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 3.2 The Regression Results Study results presented in Table as per equation (1) to (6) focus on establishing relationships between the binary firm outcome on access to debt financing (measured by the variable with “1” indicating firm succeeded to access debt financing from lenders (financial institutions) and “0” failure) and nine firm and owner-manager characteristics that might have affected the chances of firm’s access to external debt financing, namely: Firm size (small, medium and large); Location (urban and rural); Firm industry (primary, secondary and tertiary); Business information (firm keep information and firm which not keep); Firm age (age – or less than years) and age and above years); Incorporation (limited companies, proprietorships and partnerships); Collateral (firms with collaterals and firms without collaterals); Top management education (less than secondary, secondary some colleges/university trainings and graduate); and Top management experience (low, medium, high and very high) Table shows each of the variables individually in the model which yields some useful insights into the nature of the interrelationships between the variables in the model The results, in aggregate, indicate various significant factors that influence access to debt financing by SMEs In summary, the results in Table demonstrate clearly that access to debt financing is significantly related to Firm Location: It is clear that access to debt financing to SMEs is significantly related to firm location (Score test: X2(1) = 53.432, p < 0.001) The inclusion of the firm location factor increases the percentage of correct classification by about 25.6% (76.2%50.6%) from the null model that compares access to debt financing predictions made on the basis of the fitted model The likelihood ratio (LR) test results detect a significant effect of firm location (LR test: X2(1) = 61.2, p < 0.001) The odd ratio for firm located in urban on access to debt financing compared to firm located in rural was 7.12 times with (C I from 3.51 to 11.52) Clearly, the chances of success on access to debt financing by a firm are significantly impacted by the location The firm industry in which a firm operates demonstrate impact on the access to debt financing by SMEs, firm industry was statistically significant (Score test: X2(2) = 14.9, p < 0.05) The inclusion of the firm industry factor increases the percentage of correct classification by about 14% from the null model that compares access to debt financing predictions made on the basis of the fitted model The likelihood ratio (LR) test results detect a significant effect of firm industry (LR test: X2(2) = 15.2, p < 0.001) Firm industry odd ratios on access to debt financing compared to tertiary industry were 2.78 and 4.38 with C.I (1.33 to 5.79 and 1.88 to 10.20) for primary and secondary sectors respectively Noticeably, the chances of success on access to debt financing by a firm are significantly impacted by the firm industry Firm maintain business information: The study observed that access to debt financing to SMEs is significantly related to maintenance of business operations records (Score test: X2(1) = 34.67, p < 0.001) The inclusion of the firm which maintain financial records factor increases the percentage of correct classification by about 22% (72.6%-50.6%) from the null model that compares access to debt financing predictions made on the basis of the fitted model The likelihood ratio (LR) test results detect a significant effect of firm which maintain records (LR test: X2(1) = 37.3, p < 0.001) The odd ratio for firm which maintain on success is higher 7.95 times (C I from 3.84 to 16.47) than the firm which not maintain its business information on access to debt financing Clearly, the chances of success on access to debt financing by a firm are significantly impacted by the proper maintenance of firm records A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 Table 2: Unadjusted Effects of Categorical Predictor on Access to Debt from Logistic Regression Odd Confidence Interval Variable LR Test Ratio (95%) Firm Size Large Firms (Reference) 1.00 X2(2) = 37.2, Small Enterprises 2.68 2.75 - 3.32 p < 0.001 Medium 5.35 3.11 - 6.58 Location Rural (Reference) 1.00 X2(1) = 61.2, Urban 7.12 3.51 - 11.52 p < 0.001 Firm Industry Tertiary (Reference) 1.00 X2(2) = 15.2, Primary 2.78 1.33 - 5.79 p < 0.001 Secondary 4.38 1.88 - 10.20 Business Information (Financial Records) X2(1) = 37.3, Do not keep Business Information p < 0.001 1.00 (reference) Firms maintain Business Information 7.95 3.84 – 16.47 Firm Age X2(3) = 75.3, Age -4 years (Reference) 1.00 p < 0.001 Age 5-19 years 2.20 1.64 - 2.94 Age 20 years and above 4.74 3.58 - 22 Incorporation X2(2) = 10.1, Private Ltd company (Reference) 1.00 p < 0.05 Sole proprietorship 3.59 1.78 - 7.25 Partnership 3.69 1.55 - 8.84 X2(1) = Collateral 164.0, Firm with no collateral (Reference) 1.00 p < 0.001 Firms with collateral 7.99 5.84 - 14.47 Education Graduate (Reference) 1.00 X2(3) = 28.1, Less than secondary education 2.04 1.42 - 2.49 p < 0.001 Secondary education 1.34 3.06 - 4.33 Vocational/Some University trainings 2.08 1.09 - 5.46 Experience Low 0.73 0.26 – 2.03 X2(3) = 11.0, Medium 0.23 0.08 - 0.63 p < 0.05 High 0.47 0.18-1.24 Source: Author’s calculations based on Survey data from selected Tanzanian SMEs A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 The firm age demonstrate impact on the access to debt financing by SMEs, age was statistically significant (Score test: X2(3) = 67.5, p < 0.001) The inclusion of the firm age factor increases the percentage of correct classification by about 28.1% from the null model that compares access to debt financing predictions made on the basis of the fitted model The likelihood ratio (LR) test results detect a significant effect of firm age (LR test: X2(3) = 75.3, p < 0.001) Firm age odd ratios on access to debt financing compared to young firms (age to years) were higher 2.20 and 4.74 times with C.I (1.64 to 2.94 and 3.58 to 8.22) for firm aged to 19 years and firm above 20 years respectively Evidently, the chances of success on access to debt financing by a firm are significantly impacted by the firm age The firm legal status (incorporation) in which a firm originated demonstrate impact on the access to debt financing by SMEs, firm incorporation was statistically significant (Score test: X2(2) = 10.0, p < 0.05) The inclusion of the firm legal status factor increases the percentage of correct classification by about 11.6% (62.2%-50.6%) from the null model that compares access to debt financing predictions made on the basis of the fitted model The likelihood ratio (LR) test results detect a significant effect of firm incorporation (LR test: X2(2) = 10.1, p < 0.05) Firm incorporation odd ratios on access to debt financing compared to private limited companies were lower 3.59 and 3.69 times with C.I (1.78 to 7.25 and 1.55 to 8.84) for sole proprietorships and partnerships respectively Noticeably, the chances of success on access to debt financing by a firm are significantly impacted by the firm’s legal status (incorporation) Firm with collaterals: The study observed that access to debt financing to SMEs is significantly related to firm’s own assets to be pledged as collateral (Score test: X2(1) = 164, p < 0.001) The inclusion of the firm which owns assets to be pledged as collateral assured higher possibility for a firm to access debt financing The likelihood ratio (LR) test results detect a significant effect of firm which maintain records (LR test: X2(1) = 227, p < 0.001) The odd ratio for firm which maintain on success is higher 7.99 times (C I from 5.84 to 14.47) than the firm which not own collaterals to be pledged as collateral during access to debt financing Evidently, the chances of success on access to debt financing by a firm are significantly impacted by the ownership of collateral The firm owner-managers education demonstrate impact on the access to debt financing by SMEs, owner-manager education was statistically significant (Score test: X2(3) = 27.0, p < 0.001) The inclusion of the owner-manager education factor increases the percentage of correct classification by about 18.9% from the null model that compares access to debt financing predictions made on the basis of the fitted model The likelihood ratio (LR) test results detect a significant effect of owner-manager’s education (LR test: X2(3) = 28.1, p < 0.001) Ownermanager education odd ratios on access to debt financing compared to graduate education were 2.04, 1.34 and 2.08 with C.I (1.42 to 2.49; 3.06 to 4.33 and 1.09 to 5.46) for less than secondary education; secondary and some vocation/university training respectively Noticeably, the chances of success on access to debt financing by the owner-manager’s education significantly impact the firm access to external debt financing The owner-managers’ experience demonstrate impact on the access to debt financing by SMEs, owner-manager education was statistically significant (Score test: X2(3) = 10.8, p < 0.05) The inclusion of the owner-manager experience factor increases the percentage of correct classification by about 9.8% from the null model that compares access to debt financing predictions made on the basis of the fitted model The likelihood ratio (LR) test results detect a significant effect of owner-manager’s experience (LR test: X2(3) = 11.01, p < 0.05) Owner- A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 manager experience odd ratios on access to debt financing compared to highly experience were 0.73, 0.23, and 0.47 with C.I (0.26 to 2.03; 0.08 to 0.63 and 0.18 to 1.24) for low, medium and high experiences respectively Markedly, the chances of success on access to debt financing by the owner-manager’s experience significantly impact the firm access to external debt financing Having found that all potential predictors are associated with access to debt financing individually; the next step was to model their effects simultaneously In this way, the estimation of the effect of each adjusted for the variables were taken into consideration A logistic regression model that accounts for the effects of all nine explanatory variables can be fitted to the model by the help of SPSS The final results from the logistic regression model showed that that access to debt financing to SMEs is significantly related to several factors (Score test: X2(16) = 106.7, p < 0.001) The inclusion of the firm size, firm location, firm’s business information, firm age, collateral and some attributes of owner-manager’s experience (which are statistically significantly) factors increases the percentage of correct classification by about 39.6% from the null model that compares access to debt financing predictions made on the basis of the fitted model The likelihood ratio (LR) test results detect a significant effect of firm which maintain records (LR test: X2(16) = 154.3, p < 0.001) The final results show that the variables in Table contributes significantly to explaining determinants of access to debt financing in developing countries whereby Tanzanian firms’ data were used Variables which were not significant were dropped from the model The final main effects model contains firm size, location, firms’ business information, firm age and ownermanagers’ experience as the significant variables influences access to debt financing in Tanzania Table 3: Parameter estimates for the final logistic regression for debt financing probabilities for selected SMEs in Tanzania 95% C.I for Exp (B) B S.E Wald df Sig Exp(B) Lower Upper Step 1a Size 4.303 1.259 11.683 001 13.887 6.268 87.999 Loc 1.406 876 2.578 018 8.573 1.845 31.210 Binfo 2.395 911 6.911 009 4.081 3.733 22.710 Age 6.059 1.246 23.652 000 10.965 1.839 65.378 Coll 5.406 1.836 32.578 001 4.325 3.256 72.710 MExp 4.664 1.550 17.236 002 4.821 3.733 21.265 a Variable(s) entered on step 1: Size, Location, Business info, Age, Collateral, and Management’s Experience Source: Author’s calculations based on Survey data from selected Tanzanian SMEs Location of the firm: The coefficient of firm’s location was positive and statistically significant and confirms the study’s expectations on the hypothesis The odd ratio for a firm situated in the urban location is 4.081 with a p-value 0.018 while for a firm located in rural areas has odd ratio of 1.01 with p-value 0.453 indicating that an access to debt financing is positively A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 related to firm’s location The study results supported Berger & Udell (2002) and Gilbert (2008) that firms located in urban areas have a higher possibility on access to loan or debt financing than firms located in the rural area Size of the firm: The coefficient of size of business confirms the study’s hypothesis, that is statistically significant with the odd ratio for small firms is 13.887 with a p-value 0.001 The study results conform to Bukart and Ellingsen (2004) and Cassar (2004) that larger firms have higher access to debt financing than smaller and medium sized firms because of economies of scale The study observed that large size firms access debt financing easily from financial institutions therefore enjoy the economies of scale in comparison with SMEs Business Information: The result indicates that firms which maintain business information have higher possibility to access debt financing than those which not maintain financial information The odd ratio for firms’ that maintain financial information is 4.081 with a p-value 0.009 Therefore, this study confirms the existence of a positive relationship between the availability of business information and access to debt financing by SMEs Age of the firm: The coefficient of the firm’s age indicates a positive association between access to credit loan and age of the firm as per our hypothesis The odd ratio for firm age is 10.965 The result supported Klapper (2010) and Ngoc et al., (2009) that younger firms find it difficult to access debt financing from lenders while older firms find it easier Regarding the study results, the Tanzanian business environment demonstrates a positive relationship between access of loan and age of business which is a burden to young firms whereby SMEs are dominant Collateral: The availability of collateral coefficient indicates a positive relationship with access to finance Collateral is a decisive aspect for borrower to succeed in accessibility of debt financing from lenders The odd ratio for firms’ with collateral is 4.325 with a p-value 0.001 The results indicate that lack of collateral by SMEs hinder their access to debt financing Most of the firms rejected were those which lack assets to be pledged as collateral Therefore our results support Bougheas et al., (2005) and Fatoki & Assah (2011) that collateral requirement is a key characteristic for SMEs to succeed to access debt finance from financial institutions Management experience: The regression results of owner – managers’ working experience was another factor which this study found out to be used by financial institutions to decide on access to debt financing by firms Owner-managers with more than five years and above of experience (medium level to very high) were more favored compared to less experienced owner-managers The owner-manager odd ratio was 4.821 and p-value = 0.002 This study’s results comply with Fatoki and Assah (2011) that owner-managers competence influences firms access to debt finance by SMEs 4.0 CONCLUSION AND RECOMMENDATIONS SMEs are the core solution to the unemployment problem which is growing in the developing countries However, SMEs still reported acute problems of access to external finance This study explored the factors that impact the access to external debt financing which would enable policy makers and planners to formulate proper measures to encounter firms’ financing obstacles The study found out that most of the firms experiencing financing obstacles tended to possess SMEs’ features i.e firms located in rural, small and medium, young, sole proprietorship and partnership and firms without collateral Firms owned or managed by managers with low level of experience A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 also encounter difficulties to access debt financing from lenders The study findings from Tanzanian SMEs proved that the sector face major barriers to access debt financing from lenders in developing countries In this aspect, actions to help SME sector to access debt financing are required Developing country’s policies, strategies and programs need to be modified to adjust with SMEs finance needs Harmonization measures to improve the structural, economic and most important regulatory conditions for financial institutions to finance SME sector are required SMEs operators should maintain proper records management; this includes a proper financial accounting system for SMEs to ensure adequate disclosure of financial information at least in three basic components of the budgetary period: the income statement, the balance sheet and the cash flow statement The information disclosure might enable financial institutions to be better informed on the governance of SMEs, in order to evaluate more adequately their credit risk that might be involved SMEs should start to save to be able to invest in tangible assets to be pledged as security in the future to access loan at low interest rates Collateral is an important determinant factor for any firm to gain access to debt from lenders This indicates that SMEs without collateral will find it difficult to obtain debt finance from lenders i.e commercial banks, finance companies Therefore, it is necessary for SMEs owners to improve their investment on tangible assets Consequently, SMEs owners should be ready to learn the requirement of credit providers since lenders look out for specific criteria for funding to be approved SMEs’ owner-managers must consult professionals’ advice and training on the requirements of financial institutions i.e banks, finance companies, trade creditors etc to improve their access to debt financing The study discovered that location traceability is limited by most of the SMEs Most businesses operate from isolated, temporary, informal and even illegal sites or premises Some businesses operate on location not surveyed, no street names, no accessibility which limits their traceability and their properties (i.e buildings) to be used as collateral by financial institutions Lack of business expertise and managerial competencies (education and experiences) are also important reasons why finances are not available to SMEs For SMEs to improve their access to debt financing, there is a need for SMEs’ owner-managers to develop themselves in the area of business and management skills through training and where necessary they have to hire consultants Most of Tanzanian universities, colleges and vocational training colleges have entrepreneurs’ centers for SME short courses, consultancy and incubators for practical training to educate SMEs stakeholders The literature suggests that economic growth can be accelerated through improvement of the financial institutions services and the overall business environment Through resolving financial constraints the SME sector’s facing could be the most effective way of allowing the sector’s contributions to the economic growth Conversely, permanent SMEs access to debt financing is a long term process and in the interim innovative access to debt financing technologies implementation could provide more appropriate ways of relaxing the financing constraints that SMEs face An approach that is particularly promising in the absence of SMEs financing gap is factoring as it relies on their account receivable and become lesser degree risk to financial institutions Others methods include hire purchase, leasing, invoice discounting, forfeiting, and syndicated lending (guaranteed by the government); the effective uses of these methods with development of financial institutions over time could help SMEs to solve financing constraints A Kira Journal of Social Sciencies (JSS), Volume (1) 2015, 76-92 This study recommends the Tanzanian government to assist SME sector’s financing gap through speeding up provision of the national identification system (NIDA) and to develop a national database of all firms operating in Tanzania to improve creditors information i.e traceability, addresses, which impact on trust for financial institutions to improve their credit to SMEs operators; government to accelerate the development of local stock exchange (DSE) and introduction of credit rating agency to improve access to finance from the stock market by SMEs and to rate credibility of participants (including SMEs operators) in the money market (financial institutions) and capital markets (DSE) Therefore, the possibility of faster economic growth will not be possible without extending the financial system particularly by ensuring more financial support to the SME sector This study shows that access to debt finance is an 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