Finding out why parallel queries not avoided

I’m trying to find out why parallel queries are sometimes not used.

For example, I have 2 tables, calendar (1 row per day, ~3K rows) and measure (~300M rows) which includes a FK to
calendar.

I.e knowing two day numbers, I can find out how many measures there are between these two dates with a
select count(*) from measure m where m.fromdateid >=1462 and m.fromdateid < 1826;
(1462 and 1826 are the calendar ids corresponding to 2015-01-01 and 2015-12-31)

This uses parallel query:
explain select count(*) from measure m where m.fromdateid >=1462 and m.fromdateid < 1826;
                                                 QUERY PLAN
--------------------------------------------------------------------------------------------------------------
Finalize Aggregate  (cost=3894860.64..3894860.65 rows=1 width=8)
  ->  Gather  (cost=3894860.61..3894860.62 rows=8 width=8)
        Workers Planned: 8
        ->  Partial Aggregate  (cost=3894860.61..3894860.62 rows=1 width=8)
              ->  Parallel Bitmap Heap Scan on measure m  (cost=11265.96..3881068.52 rows=5516835 width=0)
                    Recheck Cond: ((fromdateid >= 1462) AND (fromdateid < 1826))
                    ->  Bitmap Index Scan on idx_measure_fromdate  (cost=0.00..232.29 rows=44134699 width=0)
                          Index Cond: ((fromdateid >= 1462) AND (fromdateid < 1826))


The “equivalent" query without hard coding the day numbers gives this query plan:

explain select count(*) from calendar c1, calendar c2, measure m where
 c1.stddate='2015-01-01' and c2.stddate='2015-12-31' and m.fromdateid >=c1.calendarid and m.fromdateid < c2.calendarid;
                                                  QUERY PLAN
--------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=5073362.73..5073362.74 rows=1 width=8)
   ->  Nested Loop  (cost=8718.47..4988195.81 rows=34066770 width=0)
         ->  Index Scan using calendar_stddate_unique on calendar c2  (cost=0.28..2.30 rows=1 width=4)
               Index Cond: (stddate = '2015-12-31 00:00:00+01'::timestamp with time zone)
         ->  Nested Loop  (cost=8718.19..4647525.81 rows=34066770 width=4)
               ->  Index Scan using calendar_stddate_unique on calendar c1  (cost=0.28..2.30 rows=1 width=4)
                     Index Cond: (stddate = '2015-01-01 00:00:00+01'::timestamp with time zone)
               ->  Bitmap Heap Scan on measure m  (cost=8717.91..4306855.81 rows=34066770 width=4)
                     Recheck Cond: ((fromdateid >= c1.calendarid) AND (fromdateid < c2.calendarid))
                     ->  Bitmap Index Scan on idx_measure_fromdate  (cost=0.00..201.22 rows=34072527 width=0)
                           Index Cond: ((fromdateid >= c1.calendarid) AND (fromdateid < c2.calendarid))

Both queries return the same answers but I don't see why the second one doesn't use parallel query.
I've tried a few different ways to express the same thing, e.g subselect, CTE etc in order to try to ease the query
plannerwork but it always avoids the parallel query. 
I also set the parallel_tuple_cost and parallel_setup_cost to 0 without success.

Any idea ? Or is there a way to ask the query planner more details about the decisions it makes ?

Kind regards,
Didier