> of the propensity score is in principle zero, since _P_  _X_ is a continuous variable. Various methods have been proposed in the literature to overcome this problem, and three of the most widely used are nearest neighbor matching, radius matching, and kernel matching.

Based on the literatures on the selection and implementation of matching estimators, we decided to analyze the effect of credit guarantee with kernel matching which showed good precision when the unsupported-to-supported ratio is high (Frolich 2004) and can avoid the criticism expressed by Abadie and Imbens (2006), which pointed out the problem of nonsmoothness in calculating standard errors with bootstrapping for the case of nearest neighbor matching or radius matching.

# **4 Data and Variables**

# 4.1 The Data

The statistical source used for this analysis is the World Bank‘s Enterprise Surveys (ES). The ES collect data from key manufacturing and service sectors in every region of the world. The Surveys use standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across the world‘s economies.

To generate internationally comparable data, the questions in the Core questionnaire are asked in all countries and for all industries where the survey is implemented. In addition to this Core instrument, the Manufacturing Module and Services Module questions are asked to establishments in the manufacturing and services sectors, respectively.

The Core instrument is comprised of eleven sections: the first eight sections contain qualitative questions, asking for the manager‘s opinion on the business environment and for his motivation for business decisions<sup>16</sup> . The last three sections of the questionnaire deal with facts and figures specific to the transactions businesses make in order to operate<sup>17</sup> . The Manufacturing Module contains questions about capacity (use of production capacity and hours of operation).

The sampling methodology of the World Bank‘s Enterprise Survey generates sample sizes appropriate for achieving two main objectives: (1) a sample representative of the whole economy that substantiates assertions about the whole economy, not only about the manufacturing sector. In addition to selected manufacturing industries, the overall sample should include services industries and other relevant sectors of the economy, and (2) a sample that is large enough in size for selected industries to conduct statistically robust analyses with levels of precision at a minimum 7.5% for 90% confidence intervals about<sup>18</sup> : (a) estimates of population proportions (percentages), at the industry level, and (b) estimates of the average mean of log of sales at the industry level.

> 16 These sections deals with the characteristics of the business and the investment climate in which it operates including: Control Information, General information (ownership, start-up), Infrastructure and Services (power, water, transport, and communication technologies), Sales and Supplies (imports, exports, supply and demand conditions), Degree of Competition (price and supply changes, competitors), Land (land ownership, land access issues), Crime (extent and losses due to crime), Business-Government Relations (quality of public services, consistency of policy, regulatory compliance costs), and Investment Climate Constraints (evaluation of general obstacles).

> 17 More specifically, these sections contain questions on production costs, investment flows, balance sheet information and workforce statistics. These sections include: Finance (sources of finance, terms of finance, financial services), Labor (worker skills training, skill availability, employment, education levels of workers) and Productivity (Numbers and figures needed to estimate productivity).

> 18 A 7.5% precision of an estimate in a 90% confidence interval means that we can guarantee that the population parameter is within the 7.5% range of the observed sample parameter, except in 10% of the cases.

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