We study dynamic unstructured bargaining with deadlines and one-sided private information, via theory and experiment. We predict the incidence of bargaining failures («strikes») and payoffs in each state by combining mechanism design and focal point approaches. Strikes are common in states with lower surpluses («pies») and strike incidence is decreasing in the pie size. Subjects reach equal splits when strikes are efficient, while payoffs are unbalanced in states where strikes are inefficient, with additional surplus accruing to the informed player. We employ a machine learning approach to explore the information content of bargaining process data.