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Economic geography and misallocation in Pakistan’s manufacturing hub

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Abstract

In this article, we explore whether localization of industries can reduce economic distortions and dispersion in total factor productivity (TFP) among firms in Punjab, Pakistan’s largest province economically. We consider two types of misallocation: (i) dispersion in the distribution of output-based TFP (TFPQ), in particular the survival of low productivity firms in the left tail; and (ii) dispersion in revenue-based TFP (TFPR), indicative of allocative inefficiency. The results are mixed: On the one hand, we find that the distribution of TFPQ is less dispersed in more agglomerated areas, measured by the localization quotient, local productive concentration, and average firm size. At the same time, we find that average TFPQ is also positively related to localization, especially the presence of small firms in the same sector, even though own-firm TFP is lowest for small firms. On the other hand, we do not find evidence that agglomeration improves allocative efficiency measured as deviations in TFPR from the sector average, concluding rather that greater localization of small firms is associated with firms being more output and capital constrained.

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Fig. 1

Source: Authors’ calculations based on CMI Punjab, 2005–2006, trimmed data

Fig. 2

Source: Authors’ calculations based on CMI Punjab, 2005–2006, trimmed data

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Notes

  1. Migration restrictions, such as those facing both labor and firms in China, can lead to firms losing out from scale and agglomeration economies (Au and Henderson 2006).

  2. TFPR is proportional to a ratio of output and capital distortions (see Hsieh and Klenow 2009, p. 1410).

  3. The findings of the existing empirical literature on agglomeration have largely documented that agglomeration, in the form of localization and/or urbanization, enhances the productivity of firms (Andersson and Lööf 2011; Ehrl 2013; Henderson 1986, 2003; Hu et al. 2015; Henderson et al. 1995; Hansen 1990; Hanson 1996, 1997). On the other hand, other aspects of agglomeration might lower productivity, for example, if congestion sets in, or if firms in agglomerated sectors remain small and, due to their large number, are unable to achieve economies of scale. According to Shaver and Flyer (2000), localization economies disproportionately enhance the competitiveness of weak firms possibly at the expense of stronger firms, leading the latter to avoid concentrated areas. Folta et al. (2006) find diminishing returns to agglomeration as a cluster grows. Spatial proximity, by facilitating communication among firms, may even enable non-competitive behavior such as collusion. Evidence from Chinese manufacturing firms reveals non-competitive pricing in industrial clusters (Brooks, Kaboski and Li).

  4. Duranton (2015) survey of the current literature does not identify any strong evidence to date that productivity growth in urban areas is responsive to higher rates of entry and exit.

  5. We lack the panel data for firms in Pakistan required to apply semiparametric methods such as Olley and Pakes (1996) and Levinsohn and Petrin (2003), or generalized methods of moments (GMM) dynamic panel models.

  6. Discussions with the agency responsible for collecting the data indicate that the sample is skewed toward smaller units in the sampling frame. After dropping cotton-ginning activities (as it is no longer considered a manufacturing activity according to most international studies), the raw data set is left with just over 3000 firms.

  7. According to Hsieh and Klenow (2009), an output distortion is observed where the labor share is different from the elasticity of output with respect to labor and a capital distortion is observed where the ratio of a plant’s wage bill to its capital stock differs from the ratio of the respective output elasticities.

  8. The data requirements include labor compensation, nominal output (revenue), expenditures on input materials and energy, book value of capital, and the industry level cost shares for labor and capital. Details of calculations of the firm-level TFPQ and TFPR in Pakistan can be found in Haseeb and Chaudhry (2014).

  9. Punjab’s districts were ranked by their location quotient and divided into quartiles. The firms in those districts in each quartile of agglomeration were included in the distribution. For example, the firms in districts considered most agglomerated were included in the distribution for “very highly agglomerated” areas.

  10. That dispersion in TFPR is in general less than that for TFPQ is an expected result since firms that are more productive (with higher \(A_{si}\) or TFPQ), should be producing more output at lower prices (Hsieh and Klenow 2009).

  11. Like our Eq. (12), Kalemli-Ozcan and Sørensen (2014) have used the log value of the Hsieh and Klenow capital distortion in a regression analysis, but they were not examining the role of agglomeration but rather institutional failures in Africa.

  12. For example, the small firm location quotient is defined as (employees of small firms in region x and industry y)/(total employees in industry y). Small firms are defined as firms with less than 10 workers, medium size as firms with 10–49 workers, and large firms having employment level greater than 49 workers.

  13. Reed (2015) has suggested that instrumenting current values with lagged values may be a better solution to simultaneity than using lagged values alone.

  14. We include district dummies in most specifications to control for levels of infrastructure and human capital and proximity to markets.

  15. Examples of where large vendor segments support clusters include electric fans in Gujrat/Gujranwala and surgical goods in Sialkot.

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Authors and Affiliations

  1. Department of Economics, Lahore School of Economics, Intersection Main Boulevard, Phase VI, DHA and Burki Road, Burki, Lahore, 53200, Pakistan

    Theresa Chaudhry

  2. Department of Economics, University of Warwick, Coventry, CV4 7AL, UK

    Muhammad Haseeb

  3. Centre for Research in Economics and Business, Lahore School of Economics, Intersection Main Boulevard, Phase VI, DHA and Burki Road, Burki, Lahore, 53200, Pakistan

    Maryiam Haroon

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  1. Theresa Chaudhry
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  2. Muhammad Haseeb
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  3. Maryiam Haroon
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Correspondence to Theresa Chaudhry.

Appendix

Appendix

See Table 8.

Table 8 Kolmogorov–Smirnov tests for the equality of distributions by level of agglomeration
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Chaudhry, T., Haseeb, M. & Haroon, M. Economic geography and misallocation in Pakistan’s manufacturing hub. Ann Reg Sci 59, 189–208 (2017). https://doi.org/10.1007/s00168-017-0824-7

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