In an earlier post, I analyzed the pool of NIH R-mechanism funded investigators from FY2007-FY2013 on a longitudinal basis (here and here). An additional set of features that I did not examine in the previous analyses are gaps in funding. I tracked situations were a particular investigator had funding in 1 fiscal year, no funding listed in the next year, but then funding listed again in a subsequent year. By this definition, there were 10451 gaps among the 53528 investigators in this data set.
If a given investigator has a year with no reported funding, what is the likelihood that they will show funding again in a subsequent year? For investigators who were funded in FY2006, but not FY2007, there are 6 possible years (FY2008-FY2013) for them to be re-funded. For gaps in years after FY2007, there are fewer years of “follow-up” available. The results are shown below:
The “re-funding" curves are relatively consistent from year to year with a probability of being re-funded after a 1-year gap of approximately 18% and an overall re-funding probability approaching 45% after 6 or more years.
In order to examine the distribution of gaps over time, I extended the longitudinal analysis back to FY2000. Note that data about grant costs available through NIH RePORTER are limited prior to FY2000. In a subsequent post, I will examine the dynamics of the investigator pool over the period from FY2000 to FY2005. For my present purpose, I examined the distribution of gaps and over-ended breaks (i.e. breaks in funding where subsequent funding has not (yet) be obtained). The results are shown below:
This plot shows that the number of gaps per year increased by 22 percent (from 1909 to 2327) from 2001 to 2006 while the number of open-ended breaks increased by 44% over the same period.
The ends of these curves are distorted by two effects. First, the ARRA funding in FY2009 and FY2010 decreased the number of gaps. Second, the number of gaps falls (and the number of open-ended breaks increases) at the end of the period since there are no data for subsequent years (end-effects). It may be possible to correct approximately for these end-effects with the re-funding curves shown above although I have not yet sorted this out to my satisfaction.
Another parameter of interest is the number of investigators with a given number of years of uninterrupted funding as a function of time. For 4 consecutive years of funding, little change over time was observed. However, for 6 consecutive years of funding (frequently requiring renewal of an R01 grant), a downward trend is observed as shown below:
The number of investigators with 6 years of uninterrupted funding fell from 10310 in the period from FY2000-FY2005 to 9127 in FY2008-FY2013, a drop of 11%.
These parameters provide some quantitative measures that capture the sense of uncertainty and insecurity that many investigators feel despite increased efforts to obtain sustained funding.