According to issue ownership theories, parties should focus on the issues they are considered strong. This is especially true for issues where public preferences tend to go into the same direction (valence issues). In most Western European countries, the public have a preference for maintaining immigration controls or making them more restrictive. The policy alternative of increasing immigration is not politically viable. Does this mean that the parties not owning the issue avoid it?
We examined nearly 200 party manifestos in 6 countries over a period of 20 years. Only 3.6% contained no reference to immigration — parties do not completely avoid the issue. At the same time, unsurprisingly, we find that anti-immigrant parties on average devote nearly three times as many word (in proportion) to the issue than other parties.
In this working paper (The Paradox of Manifestos) Ian Budge replies to some of the methodological critiques of the MARPOR/CMP/MRG manifesto projects. The paradox lies in the contrast between the positive research experience of most users of the manifesto data, and the at times rather harsh methodological critiques of manifesto data.
Unfortunately the paper is quite selective in which critiques it engages with (table 2 is rather short). The biggest issues I have come across with the data in question are a rigid coding scheme (this of course has advantages, but the data can struggle to reflect the situation on the ground adequately), and party rankings that defy common sense and expert judgements. In my view the many happy users Budge identifies are a sign of good enough data (not necessarily good data), and also of the extensive coverage of the data.
Having tried and compared different methods to measure party positions, I have serious doubts whether we’re even close to measuring party positions with precision — or do precise party positions exist at all? No, I don’t want to give up on measuring party positions, after all the different methods correlate enough to agree on the ranking of parties. We should, however, always express the precision and error in measuring party positions, not just the point estimates.
My paper on obtaining party positions from manifestos has just been published. It compares different methods to obtain party positions on immigration, mostly from party manifestos. Most approaches differentiate the same order of party positions, and there are high correlations between many methods. However, the different methods do not agree on the exact party positions.
Today I run into an unexpected error when using Wordscores in R. I used JFreq 0.5.4 to calculate the word frequencies from 35 parties with rather long party manifestos. This resulted in a 3.4M CSV file with 42462 columns. R would throw up an error regarding read.table when I called Austin‘s (0.2)
wfm function to import the word frequencies: “Error in read.table(file = file, header = header, sep = sep, quote = quote, : more columns than column names”. Well, the file seems too wide to open.
The solution I found was to use the old JFreq 0.2.5, which produces the output the other way around (rows/columns switched). Even if it is a bit slower than the newer JFreq, having a rather long (as opposed to wide) CSV with the word frequencies does not seem to pose problems.