Survey data on entrepreneurs׳ subjective plan and perceptions of the likelihood of success

Entrepreneurship is an important economic process in both developed and developing worlds. Nonetheless, many of its concepts appear to be difficult to ‘operationalize’ due to lack of empirical data; and this is particularly true with emerging economy. The data set described in this paper is available in Mendeley Data׳s “Vietnamese entrepreneurs’ decisiveness and perceptions of the likelihood of success/continuity, Vuong (2015) [1]” http://dx.doi.org/10.17632/kbrtrf6hh4.2; and can enable the modeling after useful discrete data models such as BCL.


Experimental features
The experiment focuses on perceptions and subjective understanding of prospective and extant entrepreneurs in Vietnam.

Data source location
Hanoi, Ho Chi Minh City, Buon Ma Thuot, Da Nang of Vietnam

Value of the data
The data offer an opportunity to measure the decisiveness and preparedness of an entrepreneur given various conditions that characterize an emerging market.
Information and deeper insights that might be obtained through discrete data analysis can help predict behaviors of entrepreneurs in typical situations, and formulate policy responses if the government wishes to improve the business/economic environment.
Important aspects of entrepreneurship such as creativity/innovation, previous professional experience, personal perceptions of socio-cultural values, and the like can be researched and later 'operationalized'.
The data reflect the transition of the emerging market economy of Vietnam.

Data
The data set contains 3071 records obtained from a nationwide survey of perceptions, intentions and assessments from entrepreneurs, existing and prospective, about the socio-economic conditions, values of their previous employment, need of government-and society-supported entrepreneurshipenabling programs. The data also provide subjective evaluation of the likelihood of success or continuity of entrepreneurs' project given certain environmental conditions. The following discrete (categorical) variables are measured in the survey:

Experimental design, materials and methods
The survey was designed to obtain discrete data that can be employed by the multi-category logit models to enable analysis based on baseline-category logits (BCL), which helps provide estimated coefficients for computing probabilities upon events of hypothetical influence. The logic for designing the experiment and thus data set is described as follows. For designing both the survey and prepare the data set and suitable subset, an entrepreneur (among n) is treated as independent and identical. Each data point has outcome in any of J categories for each factor to be investigated. Let y ij ¼ 1 if entrepreneur i has outcome in category j and  y ij ¼ 0 therwise. Then, y ij ¼ y i1 ; y i2 ; …; y ic À Á represents a multinomial trial, with P j y ij ¼ 1. As n j ¼ P j y ij the number of trials having outcome in category j, the design is based on the assumption that n 1 ; n 2 ; …; n c ð Þ show a multinomial distribution. Let π j ¼P(Y ij ¼ 1) denote the probability of outcome in category j for each entrepreneur, the multinomial PMF is: p n 1 ; n 2 ; …; n c ð Þ ¼ n! n 1 !n 2 !⋯n c ! π n 1 1 π n 2 2 ⋯π nc c ; with: multinomial with corresponding sets of probabilities π 1 x ð Þ; …; π j x ð Þ È É . Thus, each response is aligned with a baseline category: BCL analysis simultaneously models the effects of x on (J À 1) logits, which in general vary according to the response paired with the baseline category. The estimating of (J À 1) equations employing a given empirical data set would provide for parameters for these logits, as: The empirical data set enables the computing of Pearson-type likelihood ratio test statistics (X 2 , G 2 ) for goodness-of-fit, following a multivariate generalized linear model (GLM) estimations: Technical details for practical modeling of polytomous logistic models is provided in [2]. Applied analysis can be performed in R (see [3]). Practical uses of survey data can be referred to [4].
Explanation of data subsets filtered for different analysis purposes (from [1]) (Fig. 1). The impact of entrepreneurs' past employment together with self-assessmet of economic conditions, product innovations on the startup decision and likely continuity.
One example of the analysis is to compute response probabilities from multinomial logits, i.e.
Picking two different trends, the contrast shown by the empirical data becomes apparent in Fig. 2, suggesting that, if a government aims to promote entrepreneurship, it is better to improve general socio-economic conditions.