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In this paper we estimate the effect of tuition fees on the intentions of high school graduates in Germany to enroll in higher education. Based on representative survey data collected by the HIS institute between 2002 and 2008, we are able to analyze the effect of tuition fees using a quasi-experimental design. We take advantage of the variation between the German federal states in the introduction of tuition fees to examine the impact of tuition fees and employ a difference-in-differences estimation strategy. We do not find empirical evidence that tuition fees lower the intentions to enroll in higher education among high school graduates. This holds true for both the whole sample and for different subgroups, such as women or high school graduates with no family background of higher education.
This paper compares the usability of data stemming from probability sampling with data stemming from nonprobability sampling. It develops six research scenarios that differ in their research goals and assumptions about the data generating process. It is shown that inferences from data stemming from nonprobability sampling implies demanding assumptions on the homogeneity of the units being studied. Researchers who are not willing to pose these assumptions are generally better off using data from probability sampling, regardless of the amount of nonresponse. However, even in cases when data from probability sampling is clearly advertised, data stemming from nonprobability sampling may contribute to the cumulative scientific endeavour of pinpointing a plausible interval for the parameter of interest.