profile in equation 5, we compute each firm’s _TFPR_ and _TFPQ_ and calibrate _γ_ as the regression coefficient resulting from regressing _log_ � _TFPRTFPRsi_ � on _log_ � _TFPQTFP_ _<u>Qsi</u>_ �, where industry averages are computed at the 4-digit level. Equipped with an estimate of the elasticity parameter of the idiosyncratic distortion profile, we infer the value of the entry tax that equates Ghana’s average firm size in equilibrium with Ghana’s observed average firm size in the manufacturing formal-sector data.

The parameter values resulting from the first and second calibration steps are reported in table 4.

Table 4: Parameter Values

|Parameter|Values|Target|
|---|---|---|
|_δ_1_, δ_2|0.025,0.0125|Exit rate large formal frms, exit rate informal frms|
|_μ_<br>_δ_1|0.3|Employment share of the top 10 largest frms in the informal sector = 20%|
|_λ_|3|Bachas et al. (2021)|
|_θ_|4|Hsieh and Klenow (2009)|
|_ζ_|4.5|Average size of entrant relative to incumbent U.S. manufacturing|
|_η, φ_<br><br>|15,0.00056|Life-cycle growth and employment share of the top 10% largest frms in the U.S.|
|_fe_1<br>_fe_2 <sup>_,_</sup> <sup>_A_1</sup><br>_A_2|2.56, 7.15<br>|Average frm size formal relative to informal and informal employment share -8%<br>|
|_γ_1|0.285|Regression coefcient_log_(_TFPR_)vs_log_(_TFPQ_)<br>|
|_τ _<sup>_e_</sup>|1.6|Average frm size in Ghana’s manufacturingsector relative to the U.S.|

_Source:_ The average size, the employment concentration at the top 10 largest firms, and the average size ratio between 21-25 year old and 1 year old firms in U.S.’ manufacturing are drawn from the BDS database for the year 2007. To make the U.S. data comparable with Ghana’s formal Manufacturing Census, from which we infer idiosyncratic distortions and entry barriers, we compute statistics conditional on firms with ten workers or more, which is the smallest firm size covered in the Ghanaian data. The average firm size in Ghana’s informal sector is computed from the full establishment census. The _log_ ( _TFPR_ ) and _log_ ( _TFPQ_ ) are computed exactly as in (Hsieh & Klenow, 2009), estimated from Ghana’s National Industrial Census of 2003. The entry barrier _τ_<sup>_e_</sup> is drawn directly from (Fattal-Jaef, 2022)’s estimate for Ghana.

# **6.2 Quantifying the Role of Entry Barriers and Idiosyncratic Distortions**

Our main quantitative experiment involves starting from a stationary equilibrium calibrated to the U.S. economy and introducing the measures of distortions targeted to Ghana. Can the distorted stationary equilibrium resemble the micro-level features of the Ghanaian economy? In particular, can the extent of informal production and its implied consequences on the shape of the size distribution generate a missing middle as in the data? If so, what are the aggregate implications?

Consider first the effect of the various combinations of distortions on the shape of the size distribution, reported in figure 15. The top-left panel replicates the model’s representation of the U.S. economy’s size distribution, an outcome of the calibration. The bottom right panel illustrates the size distribution assuming only idiosyncratic distortions are in place, whereas the bottom left panel reproduces the outcome under entry barriers only. The top right plot corresponds to both distortions interacting.

The size distribution under idiosyncratic distortions (bottom right) reallocates employment away from the top and towards the bottom but also expands some of the middle-size categories. Two forces are at play. Firstly, idiosyncratic distortions in the formal sector reduce the aggregate productivity among formal firms and increase the relative price of formal varieties, thereby reallocating 35