|**Constant**|9.490***|9.518***|9.670***|9.584***|9.629***|9.690***|
|---|---|---|---|---|---|---|
||(0.209)|(0.216)|(0.204)|(0.236)|(0.209)|(0.211)|
|**Fixed effects**|Two‐digit in|dustrydummie|s,and countryan|d region(within|country)fixed e|ffects|
|**Observations**|3,774|3,774|3,774|3,774|3,774|3,774|
|**R‐squared**|0.348|0.346|0.344|0.345|0.350|0.344|

Notes: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Survey weights are used in all regressions.

Table 7 below presents the results of the regression of management practices on establishments’ total factor productivity and only for manufacturing firms.<sup>5</sup> Column (1) of Table 7 shows that, on average across all manufacturing establishments, there is no significant relationship between the average management score and total factor productivity. In terms of the other establishment‐level characteristics, Table 7 shows that only establishment size is significantly associated with total factor productivity, with larger establishments being more productive. Turning to the individual management practices, column (3) below presents some weak evidence of a negative relationship between the number of performance metrics monitored and total factor productivity, suggesting that more is not always better. On the other hand, column (5) confirms what was shown in Table 6 above, that communicating performance targets throughout the establishment is associated with higher productivity.

Table 7: Determinants of total factor productivity <u>(TFP) in manufacturing establishments</u>

||**Y = Total facto**<br>|**r productivity**<br>|||||
|---|---|---|---|---|---|---|
||(1) Average<br>score|(2)<br>Response to<br>problems|(3)<br>No. of metrics<br>monitored|(4)<br>Difficulty of<br>targets|(5)<br>Knowledge of<br>targets|(6)<br>Basis of<br>bonuses|
|**Management score**|0.210|‐0.121|‐0.205*|0.0842|0.260***|0.115|
||(0.211)|(0.141)|(0.108)|(0.130)|(0.0962)|(0.0941)|
|**Log(size)**|0.110***|0.124***|0.136***|0.119***|0.107***|0.114***|
||(0.0314)|(0.0308)|(0.0310)|(0.0304)|(0.0308)|(0.0309)|
|**Age **|‐0.000736|‐0.000854|‐0.00107|‐0.000926|‐0.000561|‐0.000750|
||(0.00221)|(0.00220)|(0.00221)|(0.00220)|(0.00220)|(0.00221)|
|**Foreign owned**|‐0.000205|‐0.000245|5.14e‐06|‐0.000185|‐0.000285|‐0.000166|
||(0.00140)|(0.00140)|(0.00140)|(0.00140)|(0.00140)|(0.00139)|
|**Multi‐establishment**|‐0.162|‐0.162|‐0.163|‐0.163|‐0.161|‐0.165|
||(0.108)|(0.109)|(0.110)|(0.108)|(0.107)|(0.109)|
|**Exporter**|0.000459|0.000545|0.000774|0.000549|0.000239|0.000545|
||(0.000979)|(0.00103)|(0.00101)|(0.00102)|(0.000981)|(0.00100)|
|**Top**<br>**manager's**<br>**experience**|‐0.00244|‐0.00207|‐0.00198|‐0.00222|‐0.00295|‐0.00220|
||(0.00273)|(0.00271)|(0.00268)|(0.00271)|(0.00270)|(0.00268)|
|**Female top manager**|0.00147|0.00151|0.00138|0.00148|0.00139|0.00147|
||(0.00110)|(0.00107)|(0.00103)|(0.00109)|(0.00112)|(0.00106)|

5 Note that Table A.3 in the appendix displays the results of the labor productivity estimation, restricting the sample to only manufacturing firms to ensure comparability with the results displayed in Table 7. The overall patterns do not change from the results for all firms displayed in Table 6.

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