In a bipartite max-min LP, we are given a bipartite graph $\myG = (V \cup I \cup K, E)$, where each agent $v \in V$ is adjacent to exactly one constraint $i \in I$ and exactly one objective $k \in K$. Each agent $v$ controls a variable $x_v$. For each $i \in I$ we have a nonnegative linear constraint on the variables of adjacent agents. For each $k \in K$ we have a nonnegative linear objective function of the variables of adjacent agents. The task is to maximise the minimum of the objective functions. We study local algorithms where each agent $v$ must choose $x_v$ based on input within its constant-radius neighbourhood in $\myG$. We show that for every $ε>0$ there exists a local algorithm achieving the approximation ratio ${Δ_I (1 - 1/Δ_K)} + ε$. We also show that this result is the best possible -- no local algorithm can achieve the approximation ratio ${Δ_I (1 - 1/Δ_K)}$. Here $Δ_I$ is the maximum degree of a vertex $i \in I$, and $Δ_K$ is the maximum degree of a vertex $k \in K$. As a methodological contribution, we introduce the technique of graph unfolding for the design of local approximation algorithms.