Abstract
In this paper, we study whether or not transportation infrastructure disrupts local monopsony power in labor markets using an expansion of the national highway system in India. Using panel data on manufacturing firms, we find that monopsony power in labor markets is reduced among firms near newly constructed highways relative to firms that remain far from highways. We estimate that the highways reduce labor markdowns significantly. We use changes in the composition of inputs to identify these effects separately from the reduction in output markups that occurs simultaneously. The impacts of highway construction are therefore pro-competitive in both output and input markets and act to increase the share of income that labor receives by 1.8–2.3 percentage points.
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Notes
See also Alder (2016) for a study of the aggregate and regional effects of the GQ and counterfactual network designs using a Ricardian spatial trade model.
Brooks et al. (2021) develop this model with an arbitrary number of inputs. Our results extend to this case so long as the firm is a price-taker in at least one of them.
We believe this assumption is reasonable among the Indian manufacturing firms being studied in this paper, but we admit it may not be appropriate in every context. See Morlacco (2019) for a discussion of market power among large French importers.
Alternatively, the model could be extended to include a competitive wholesale sector in each location that buys the local output from the firms, then sells it to many locations based on transportation costs and variation in local demand. The firms internalize that their choice of output affects wholesaler demand so that there are still markups for their output. This case is not presented here for ease of exposition.
In principle, the ratio may also reflect other frictions that can distort the firm’s labor input choice away from the price-taking allocation, but we emphasize that the component associated with market share reflects the exercise of monopsony power.
The expressway was not fully completed until 2013.
The district classification follows the 2011 Census of India. The number of districts has increased from 640 in the year 2011 to 720 as of 2018.
Moreover, in the appendix we show that our results are largely robust to the inclusion of state*time dummies, which indicates that the identification comes off of within-state, across-district variation.
An additional limitation is that it assumes a production function that is constant across firms (within an industry) and only differs by a factor-neutral productivity parameter.
It is, however, less restrictive along other dimensions. It allows for firm-specific production functions that are time-varying, for example. In this sense, it also allows for more general forms of technological change, including factor augmenting technical change.
The reported averages are not equal because of the winsorizing process.
To be more precise, we start with unnormalized values \({\tilde{\mu }}^L_{nkit} = p_{nkit} y_{nkit} / w^L_{kit} x^L_{nkit}\). We then regress \({\tilde{\mu }}^L_{nkit}/\mu ^M_{nkit}\) on labor share and a constant:
$$\begin{aligned} \frac{{\tilde{\mu }}_{nkit}^L}{\mu _{nkit}^M}=\chi +\gamma s^L_{nkit}+u_{nkit} \end{aligned}$$We then set \({\mu }^L_{nkit}\) equal to \({\tilde{\mu }}^L_{nkit}/{\hat{\chi }}\), effectively assigning \(\theta ^L=1/{\hat{\chi }}\). This guarantees that the predicted value of that ratio is equal to one when the labor share is zero.
If \(s_{i,t}\) is the sampling weight for firm i at time t and \(c_{i,t}\) is the average labor compensation of firm i, then the new sampling weight is:
$$\begin{aligned} \text {weight}_{i,t} = \frac{s_{i,t} c_{i,t}}{\sum _j s_{j,t} c_{j,t}} \end{aligned}$$.
The coefficients on the triple interaction of the firm share with the highway implementation in Table 5 are not inconsistent with the results being weaker for the largest firms. However, if one is concerned that measurement for the smallest firms may be poor, our results are robust to dropping the smallest 20% of firms as shown in Table 12 of the appendix.
To see that this has no impact on aggregate labor’s share, note that \(\theta ^L_{nki}\) in the denominator of the right-hand side in Eq. (22) cancels with the implicit \(\theta ^L\) in the numerator of \(\mu ^L_{nki}/\mu ^M_{nki}.\)
For example, since the sampling-weighted median markdown in Table 1 is 0.5 for the CD benchmark, an alternative normalization in which we rescale so that the median markdown ratio is one would merely imply multiplying markdowns, regression coefficients, and standard errors that follow by 2. This alternative scaling would be consistent with an assumption that measurement error for the median firm is zero and the median firm has a negligible market share. The median market share is just 0.003, and this is nearly identical for firms with markdown ratios near the median.
Moreover, the Brooks et al. (2021) results, and those of Hershbein et al. (2020) for the USA, show that the markdowns are not capturing labor adjustment costs. Hershbein et al. (2020) use a number of other specifications that go beyond Cobb–Douglas and find very similar results across their specifications.
The magnitude of the change in labor’s share is lower than the magnitude of the drop in the markdown, however. Presumably this reflects a negative correlation between increases in labor compensation and increases in material expenditures across firms in connected districts.
The coefficients drop precipitously once we control for log output and the implied elasticities become quite large, 12 and 44, respectively. We view these elasticities as implausibly large, and we therefore interpret the log output control as a second proxy for labor market share.
It is surprising that weighted markdowns are rising over time in Fig. 1, but their impact on labor’s share is falling over time. We conjecture that this is driven by a changing correlation between markdowns and labor’s share.
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Appendix
Appendix
See Tables 6, 7, 8, 9, 10, 11, and 12.
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Brooks, W.J., Kaboski, J.P., Kondo, I.O. et al. Infrastructure Investment and Labor Monopsony Power. IMF Econ Rev 69, 470–504 (2021). https://doi.org/10.1057/s41308-021-00144-6
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DOI: https://doi.org/10.1057/s41308-021-00144-6