_Aggregate Productivity and Regional Trust._ Next, we test Prediction 4, that an increase in trust is associated with an increase in aggregate productivity. We begin by examining whether trust increases the correlation between size and productivity through the following specification We use TFPQ (as defined in Hsieh and Klenow 2009) as a measure of productivity<sup>24</sup> , but note that this has a high correlation of 0.9 with unadjusted TFPR.

Table V presents the results. Column (1) shows that family-managed firms are 24.8% less productive than their professionally-managed counterparts. Column (2) shows that firm size is more responsive to TFPQ in regions where trust is higher. In particular, a 10% increase in TFPQ is associated with a 3.3% increase in firm size (0.357 _×_ 0.466 + 0.163) for a region with the average level of regional trust, but this rises to a 3.9% increase (0.491 _×_ 0.466 + 0.163) in a region one standard deviation above the average. Increasing trust by one standard deviation within a country therefore increases the response of firm size to TFPQ by 18.2% (3.9/3.3 - 1). This suggests that trust increases aggregate productivity by improving the allocation of labor Finally, we use the statistic result of Proposition 1 to quantify the effect of trust on TFP via the mode choice channel highlighted in this paper. Recall that Proposition 1 relates changes in the share _<u>θ</u>_ <u>+</u> _<u>σ−</u>_ <u>1</u> of professionally-managed firms to changes in TFP with an elasticity _ν ≡ θ_ ( _σ−_ 1)<sup>.Thismotivatesthe</sup> following two-step method to map changes in trust to changes in TFP. First, we relate changes in regional trust to changes in the share of professionally-managed firms through the iso-elastic relationship 1 + _λP_ = _α_ (1 + Trust)<sup>_β_</sup> . We estimate the elasticity _β_ via OLS. Second, we combine this with the result in Proposition � _βν_ � 1 to compute changes in TFP resulting from a change in trust as _TFP_<sup>�</sup> = 1 + Trust , where 1 + Trust is a gross percentage change in 1 + Trust. We also perform the same decomposition using the rule of law index � at the country-level, where we posit a semi-elastic relationship of the form 1 + _λP_ = exp( _β_<sup> ̃</sup> Rule of Law) since our rule of law measure takes values less than zero. To obtain a value for _ν_ , we calibrate _σ_ and _θ_ to existing values from the literature and provide results for a range of parameters. In particular, based on the evidence on the firm size distribution cited in Chaney (2008) we use values of _θ, σ_ that match the sales-rank slope of 2. Since this only identifies the ratio, we provide TFP statistics for a range of _θ_ values and compute the _σ_ consistent with this sales-rank slope.<sup>25</sup> Table VI provides estimates of the elasticity _β_ . Consistent with our prior results, we provide results for the full sample as well as separate elasticities for low and high rule of law countries. Column (1) shows that a 10% increase in 1+Trust is associated with a 3.7% increase in 1+ _λP_ . However, columns (2) and (3) show significant heterogeneity across countries with a 4.9% (1.7%) effect in low (high) rule of law countries.

> 24We use a capital share of _α_ = 0 _._ 3, and calculate _TFPQ ∝_ ( _PY_ ) _σ−σ_ 1 _/K_<sup>_α_</sup> ( _wL_ )<sup>1</sup><sup>_−α_</sup> . All values are in 2012 dollars. See Hsieh and Klenow for a derivation of TFPQ, we use a conservative _σ_ = 3 as in their paper.

> 25In particular, this model predicts that the sales-rank slope is given by <u>1+</u> _σ_ _<u>θ</u>_<sup>,soforany</sup><sup>_θ_thereisaunique</sup><sup>_σ_thatmatchesthe</sup> sales-rank slope of 2.

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