Juan Carlos Córdoba, Anni T. Isojärvi, and Haoran LiThis paper revisits the conditions under which search models generate balanced growth paths (BGPs)—equilibria where unemployment, vacancies, and job flows remain steady as search frictions decline. Martellini and Menzio (2020) claim that such paths exist only when matches are “inspection goods” and match quality follows a Pareto distribution. We show that these conditions are sufficient but not necessary. Their implementation assumes a strong form of stationarity—requiring the endogenous distribution of match qualities to remain invariant under proportional scaling. This restriction forces the reservation quality to grow at a constant, strictly positive rate, mechanically tying declining frictions to long-term growth and yielding counterfactual implications of eliminating search frictions—persistent unemployment and infinite welfare gains. Relaxing this restriction, balanced growth can arise under alternative forms of scaling, such as additive transformations that restore stationarity without Pareto tails or inspection. We further show that biased technological progress, when vacancies and unemployed workers are complementary inputs, also generates well-behaved BGPs with finite welfare gains and vanishing unemployment as search frictions disappear.
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