small firms, it may be interesting to note that the entrepreneur is the brother-in-law of the Minister of Finance.) Clearly, recording and utilizing anecdotes becomes less manageable as sample size grows larger.

Finally, an interview is more than a train on a track -- surveyors should be sufficiently intelligent, enterprising and familiar with the survey to follow interesting lines of response to their conclusion, even if they must depart from the sequence of the questionnaire. If an entrepreneur complains about conflict resolution in response to a general regulatory question, that may be a good time for the interviewer to move to conflict resolution questions and to probe for an anecdote. This is part of the skill of maintaining flow and interest.

Entry and Analysis of Survey Responses Before responses can be analyzed, they must be put into a tractable form, generally by entry into a spreadsheet or statistical software program. In the process, most responses are "coded" so that a number represents a particular response (e.g. 0 for "no" and 1 for "yes"). Clearly, the accuracy of this process is vital to the usefulness of all the work that preceded it. Data should be scanned for obvious errors, and a sample of entries should be checked against the actual survey forms from which they were recorded. Data entry is tedious work, so it is advisable that, whenever possible, you _get someone else to do it._ Once entry is done, you will have a series of entries for each firm ready for analysis. It is natural to take averages, first for the entire sample, then for parts of the sample -- by size, sector or other characteristic (smal' firms, firms in manufacturing, etc.). Table IV shows a portion of the Brazil spreadsheet, which combines quantitative and short verbal entries. The spreadsheet program has been used to provide averages for each size classification of firm. However, popular spreadsheet programs are quite limited in their ability to perform statistical analyses beyond averages, and also quite slow compared to statistical software packages on large data sets. For analyses using standard deviations (or coefficients of variation), correlations, or nonlinear regressions, statistical packages are preferable.

Even simple correlations may indicate important relationships and distinguish them from statistically insignificant ones. For example, in the Kenya survey, following the path of statistically significant correlations revealed that the firms that faced overall "financing constraints" to growvth were those with collateral problems, due to lack of access to land ownership. By contrast, the large number of firms that complained about the "high interest rate" did not consistently find financing a constraint to growth. Clearly, caution is needed before concluding causality in the case of correlation, but there is a credible story to be told that merits further investigation.

Some responses can generally be expected from firms, reflecting near universal biases. Except in cases where there has been a recent decline in interest rates, firms generally find them too high. A high constraint score for interest rates does not alone argue for ir.tervention, unless there is other evidence of some policy or institutional failure in finance. In addition, businesses don't like a lot of competition, but that does not mean that reducing competition is a socially desirable goal. In general, firms would like the cost of their inputs to be lower, their profits higher. These inherent biases should be recognized in interpreting responses; just as every other source of data should be recognized as having its own inherent biases: government statistics and officials, trade associations, and even multilateral development institutions. Some overlap of questions across sections of a survey may provide a means of cross-checking responses. For example, respondents who report finance a problem but also report having several market-rate loans may be shedding crocodile tears.

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