Different linearizations have been proposed to cast dependency parsing as\nsequence labeling and solve the task as: (i) a head selection problem, (ii)\nfinding a representation of the token arcs as bracket strings, or (iii)\nassociating partial transition sequences of a transition-based parser to words.\nYet, there is little understanding about how these linearizations behave in\nlow-resource setups. Here, we first study their data efficiency, simulating\ndata-restricted setups from a diverse set of rich-resource treebanks. Second,\nwe test whether such differences manifest in truly low-resource setups. The\nresults show that head selection encodings are more data-efficient and perform\nbetter in an ideal (gold) framework, but that such advantage greatly vanishes\nin favour of bracketing formats when the running setup resembles a real-world\nlow-resource configuration.\n