Was the French 2022 honeymoon election one that defies the usual impact of such election timing? Not to offer a spoiler, but the answer is yes and no.

Back around the time of the presidential runoff, I restated what I often say about elections for assembly held shortly after a presidential election: they are not an opportunity for the voters to “check” the president they have just chosen; presidential and semi-presidential systems just do not work that way. Well, usually. It seems hard to escape the notion that voters did just that–by holding Emmanuel Macron’s allies in Ensemble to less than a majority of seats, and by delivering bigger than expected seat totals to the Mélenchon-led united left (Nupes) and even to Le Pen’s National Rally (RN).

There will not be cohabitation, which was what I really meant in the French context when saying that honeymoon elections were not an opportunity to check the president. The results have not offered up any conceivable assembly majority that would impose its own choice for premier on Macron. I was also generally careful to say that I thought Macron’s allies would win a majority of seats, *or close to it*. They are relatively close, but considerably farther away that I expected, on about 42%. So, how does this outcome compare to honeymoon elections generally?

I have prepared an updated version of a graph I have shared before. An earlier version appears in *Votes from Seats*, as Figure 12.2. The x-axis is elapsed time, *E*, defined as the share of the period between presidential elections at which the assembly election occurs. The y-axis is the presidential seat ratio, *R _{P}*, calculated by dividing the vote share of the party (or pre-electoral alliance) supporting the president by the president’s own vote share in the first or sole round. The diagonal line is a regression best fit on the nonconcurrent elections (those with E>0), and is

*R*=1.2–0.7

_{P}*E*.

I added the France 2022 data point and label a little larger than the others, to call attention to it. The most notable thing is that this is the only case of a really extreme honeymoon–defined loosely as those with *E*<.05 but *E*>0–to have a value of *R _{P}*<1.00. So in that sense, it is a poor performance. There are other honeymoons for which

*E*≤0.1 that are below

*R*=1.00, including Chile 1965 and Poland 2001. In the Chilean case, the result obtains simply because the right did not present its own presidential candidate, but ran separately in the congressional election. Although this post is focused on honeymoon and other nonconcurrent elections, I also added labels to the two cases of concurrent elections (

_{P}*E*=0) that have unusually low presidential vote ratios. Note that on average,

*R*in concurrent elections tends to be a bit below 1.00, as a combination of strategic voting and small-party abstention from the presidential contest leads assembly voting to be more fragmented than presidential voting, hence lowering

_{P}*R*. However, in very early term elections, the president’s party/alliance almost always gains. So France 2022 is unusual, but not a massive outlier. In fact, in terms of distance from the regression line, it is about equivalent to France 1997 or El Salvador 2006 (labelled).

_{P}We see that the 2022 election also features the lowest *R _{P}* of any of France’s six honeymoon elections to date. The 2002 election (Chirac) produced an especially huge boost, whereas the 2017 election, when Macron had just been elected the first time, is almost on the regression line. (The regression does not include elections after 2015 because the dataset was collected around then; I added these more recent ones to the graph directly.) I also want to call attention to Volodomyr Zelenskyy’s 2019 honeymoon result in Ukraine for Servants of the People, as it is also among the most extreme honeymoon vote surges recorded anywhere as expected, perhaps aided by how uninstitutionalized that country’s party system has been. (If I wanted to be provocative, I’d say that factor also has been present in France, given frequent realignments on the right, the emergence of Macron, etc.)

(As an aside, I was somewhat surprised that an outlier, the one case of *E*>0.6 to have *R _{P}*>1 is the French late-midterm election of 1986. This is remembered as the election that produced the first cohabitation of the French Fifth Republic. But the vote share of the Socialists was still considerably higher than Mitterrand’s own vote share in the presidential first round of 1981, when the Communists had presented their own candidate.

^{1})

So much for the votes. I was wondering what happens if we look at **seats**? Strangely I had never done this before (at least with this dataset). This graph has as its y-axis the *seat* share of the president’s party (or alliance) divided by the president’s own first or sole-round *votes*, which I will call *R _{Ps}*. The x-axis is the same. In addition to plotting a best fit line, the diagonal, I also added the 95% confidence intervals from the regression estimates to this graph. There is also a lowess (local regression) plotted as the very thin grey line. Note how flat it is for a long portion of the term, a fact related to a point I will come to at the end (and also suggesting a more complex than linear fit may be more accurate, but I want to keep it simple for now).

The regression line here is very close to ** R_{Ps}=1.5–E**, which is a wonderfully elegant formula! It says that at a midterm election, a president’s party’s seat share would be, all else equal, the same as his or her own vote share half a term earlier. At a truly extreme honeymoon election–imagine one held the day after the president was elected, but with the result known–the seat share would be about 1.5 times the president’s vote share. At an extreme counter-honeymoon it would drop to around 0.5. So where did Macron’s Ensemble come out in the election just concluded? His

*R*=

_{Ps}**1.52**! So the party actually did about what the average trend says to expect. It was his 2017 surge that was higher than we perhaps should have expected (although, again, not as high as Chirac’s in 2002).

The result in the second figure is obviously holding constant the electoral system, so it should be taken with a grain of salt, given the importance of variation in electoral systems in shaping the size of the largest party (which is usually the president’s party, at least until we get to midterms and beyond).

What I find particularly elegant about the equation is its suggestion that midterm elections are no-effect elections, in terms of seat share for the president’s party. This was presumably what major party leaders were going for in the Dominican Republic when they shifted to the world’s only ever case (to my knowledge) of an all-midterm cycle. Both president and congress were elected to four-year terms, each at the halfway point of the other. (Actual outcomes during were not always no-effect, though on average they were close^{2}; they have since changed back to their former concurrent elections.) This may seem a surprise to readers who know the American system and its infamous midterm decline, but actually the midterm-election median in the US is 0.969. In an almost pure two-party system, anything below 1.00 might look bad, and be both politically consequential and also somewhat over-interpreted. But 0.969 is not really that much below 1.00! Okay I am cheating just a little by reporting the median. The mean is 0.943; it is brought down by a few major “shellackings” like 2010 (0.891), although 1990 was worse (0.719, in this case because G.H.W. Bush had won such a big landslide of his own).^{3}

In concurrent elections, the regression suggests also that on average, *R _{Ps}* is around 1.00. For the US, the median is 0.979, and the mean is 1.009. Note how it is higher than the midterm average, but perhaps not as much as one might expect.

^{4}

At this point, both these equations are just empirical regression best fits, not logical models. There is logic behind the general effects of electoral cycles on a presidential party’s performance, but not a logical basis for the specific parameters observed. I would very much like to have such a logical basis, but I have not hit upon it. Yet.

(Considerably nerdier and some rather half-baked stuff the rest of the way.) Such a logical model may be closer now that there is a simple and elegant empirical connection between presidential votes and seats. Seat shares are more directly connected to parameters of the electoral system than votes shares are–even vote shares for assembly parties, but vote shares for presidential candidates are a good deal more remote from the assembly electoral system. Nonetheless, in *Votes from Seats* we do derive a predictive formula for the effective number of presidential candidates, based on the assembly’s seat product. A regression reported in the book confirms its plausibility, but with rather low R^{2}. From that formula one could get an expected relationship for the leading presidential candidate’s vote total, *v _{p}*. It would be

*v*= 2

_{p}^{–3/8}[(

*MS*)

^{1/4}+1]

^{–1/4}. We already have, for the seat share of the largest party,

*s*

_{1}=(

*MS*)

^{–1/8}. It so happens that these return the same value at around

*MS*=175. Expectations of

*v*<

_{p}*s*

_{1}or

*s*

_{1}<

*v*would then depend on whether

_{p}*MS*(mean district magnitude times assembly size) is higher or lower than 175; for most presidential systems it is a good deal higher (the median in this sample of elections, including semi-presidential, is 480). Tying this observation to the one about midterm elections (

*E*=0.5) yielding actual (not predicted)

*s*

_{p}=

*v*and accepting for simplification that the president’s party seat share (

_{p}*s*

_{p}) is also the largest party seat share, at least in elections that are not after the midterm, might be a path towards a model. But that may take a while yet. Below I will copy a table of what the formulas for

*v*and

_{p}*s*

_{1}yield at various values of seat product,

*MS*, for simple systems. These values of

*s*

_{1}are without regard to elapsed time when the assembly election takes place.

Table of expected values of presidential vote shares (pv) and largest assembly party seat share (s1)

MS | pv | s1 | ratio_s1_pv |

1 | 0.65 | 1.00 | 1.54 |

10 | 0.60 | 0.75 | 1.26 |

25 | 0.57 | 0.67 | 1.16 |

50 | 0.56 | 0.61 | 1.10 |

100 | 0.54 | 0.56 | 1.04 |

150 | 0.53 | 0.53 | 1.01 |

175 | 0.52 | 0.52 | 1.00 |

200 | 0.52 | 0.52 | 0.99 |

225 | 0.52 | 0.51 | 0.98 |

256 | 0.51 | 0.50 | 0.97 |

300 | 0.51 | 0.49 | 0.96 |

500 | 0.50 | 0.46 | 0.92 |

1000 | 0.48 | 0.42 | 0.88 |

10000 | 0.42 | 0.32 | 0.75 |

25000 | 0.40 | 0.28 | 0.70 |

50000 | 0.39 | 0.26 | 0.67 |

100000 | 0.37 | 0.24 | 0.64 |

200000 | 0.35 | 0.22 | 0.61 |

*MS*due to system disproportionality, but higher as

*MS*increases beyond 175, presumably because of strategic behavior being different around the majoritarian presidential election and the more permissive assembly electoral system. The smallest

*MS*observed in this dataset for a (semi-)presidential system is 124 (Sierra Leone, 2002, 2007). The largest is 202,500 (Ukraine, 2006, 2007). For nonconcurrent elections, the minimum

*MS*is 240 (Chile, 1997, 2001).

Footnotes

- Also, Mitterand himself had finished second in the first round, with 25.9% of the votes (the incumbent, Giscard, had 28.3%). The Communist candidate had 15.4%. In the 1986 election, Socialists won 31% of the votes, for
*R*=1.2. (I am not counting the Communists as part of Mitterrand’s alliance by then, as he had fired the Communist ministers that were in his initial cabinet.)_{P} - The values for
*R*in these Dominican elections were: 0.587 in 1998, 0.975 in 2002, 0.945 in 2006, and 1.067 in 2010. So other than that first run, if the no-effect was what they wanted, they basically got it._{Ps} - [Added, 21 June.] I somehow forgot that my first publication on this topic, in the APSR in 1995, also used seats as its outcome of interest–but it was change in seat percentage for the president’s party from the prior assembly election (with president’s vote share as a control). Looking back on that pub, I see that my regression there would agree with my updated analysis here in suggesting that midterm elections, all else constant, are
*no-effect elections*. The regression line clearly passes very near the*change*=0,*E*=0.5 point in the article’s Figure 1. And, yes, in that article I commented on this as a “particularly striking feature” (p. 332). - The way I set up the regression, its constant term would be the
*R*when_{Ps}*E*=0, a concurrent election. This constant is actually 0.95, but its 95% confidence interval includes 1.00 (it is 0.844–1.057). The coefficient on the nonconcurrent dummy is 0.552, from which I get the approximation, 1.5, in the equation in the second figure (summing this coefficient and the constant). The coefficient on*E*is –1.072. R^{2}=0.215.