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Abstract
This paper examines the role of the external institutional environment captured by effective state–business relations (SBRs) on firm performance. By effective SBRs, we mean a set of highly institutionalised, responsive and public interactions between the state and the business sector. We find that effective SBRs have had a discernible positive impact on firm productivity in Indian formal manufacturing over the period 1989–1990 to 2004–2005. We also find internal and external institutional factors are complementary to firm performance – smaller firms, firms in urban areas and firms in simpler organisational forms benefit more.
Acknowledgements
This paper forms a part of a larger study examining the effect of SBRs on the productivity of Indian firms funded by IPPG-University of Manchester, UK (www.ippg.org.uk). The authors would like to thank two anonymous referees for their thoughtful comments on the paper. The financial support from IPPG-University of Manchester and DFID-UK is gratefully acknowledged. The authors are also thankful to the Central Statistical Organisation, for providing them access to the data, and to Nilachal Ray for his many suggestions and comments. The usual disclaimers apply.
Notes
1. An alternate set of arguments on the external institutional determinants of firm performance, propounded by the World Bank, among others, is that the investment climate – understood to be the institutional, policy and regulatory environment in which firms operate – has a strong impact on firm performance. However, as Moore and Schmitz (Citation2008, 10) have argued, ‘the core conceptual problem with (this view) is that government and political power are viewed primarily as persistent threats to capital, investment and economic growth. From that perspective, the policy mission is to curtail the influence of political power through formal rules, laws and institutions. If that mission fails, politicians are expected at least to maltreat the private economy, and possibly to loot it, and thus, undercut economic growth’. Thus, there is a strong assumption in this literature that the state, by its very nature, is always predatory, and cannot be developmental in most instances of its manifestations. In this paper, we take an opposite view: that ‘good growth-enhancing relations between business and government elites are possible’ (Maxfield and Schneider Citation1997) and that effective SBRs are the core external institutional determinants of firm performance.
2. See Qureshi and te Velde (Citation2007) and Sen and te Velde (Citation2009) for evidence that improvements in SBRs improve economic performance both at the micro- and macro-levels, for Sub Saharan Africa.
3. The primary data are obtained from structured and semi-structured interviews with business associations in each state and state government officials from the industry department of almost every state. CMP also collected data from secondary sources whenever they were available. Given the constraints of measuring the effectiveness of SBRs in a sub-national context and for a long time period, CMP needed to strike a difficult balance between the representativeness of the variables in capturing the essence of SBRs and their availability. However, they go a long way in gathering data for variables that are as close as possible to the ideal notion of effective SBRs that is present in the previous literature (e.g. Evans Citation1995; Maxfield and Schneider Citation1997; Harriss Citation2006).
4. In our empirical analysis, we use the SBR measure for 1985–2006 due to the non-availability of the state domestic product data prior to 1985.
5. This variable takes a value of zero in any year in which the organisation does not have a website and 1 otherwise.
6. This variable is coded as the number of times the website is updated in a month, thus a monthly update has the value of 1, a weekly update has the value of 4.5, a daily update is equal to 30, an annual update is equal to 1/12, etc.
7. We investigated further whether and to what extent the frequency of website updates used in the CMP measure is a good proxy for the level of activity of business associations. An important indicator of the level of activity of a business association is the number of Google occurrences (total or unique) of the association's name (normalised by the state's population, as business associations in a larger state would tend to have more of an internet presence, regardless of level of activity) – the more active the association is, the more likely it will be present in websites (of the media, business sites, etc.). This measure is obviously available for a point in time and not over time. We obtain Google occurrences of the peak business associations in our sample of states, and compute the correlation of both total and unique Google occurrences of the association's name with the frequency of updating of the association's website. We find a strong correlation between total and unique Google hits and the frequency of website updates (correlation coefficients of 0.64 and 0.60 respectively). This suggests that the frequency of website updates is a good indicator of the overall level of activities of the business association.
8. CMP code this variable as 1 when the association owns its office premises (and the year when it occurred), 0 otherwise.
9. This is corroborated by CMP fieldwork results which indicate that only industry departments engage with the business sector in a ‘significant’ manner, identifying its needs and facilitating its operations.
10. There are a number of other potential labour market indicators that are likely to represent the quality/intensity of interaction between state governments and businesses. These include the number of inspected factories as a proportion of total registered factories, convictions as a percentage of number of factories inspected, the proportion of industrial disputes resulting in adjudication awards in favour of workers and the officially announced minimum wage. However, as CMP note, the coverage of such variables is patchy and their inclusion in the computation of SBR indices significantly reduces the number of observations. For this reason, they focus only on labour regulation as a proxy for state–business interactions.
11. CMP update this index to 2006.
12. This variable is coded analogously to the frequency of website update.
13. To obtain the SBR private component, the apex business association is given a weight of 0.5 and the two sectoral associations are given weights of 0.25 each. CMP experiment with different weights for apex and sectoral business associations and find that there is a strong correlation between SBR measures obtained under different weighting schemes. We adopt these weights for the main regressions but our results are also robust to the use of SBR indices calculated through different weights (i.e. assigning the same weight of one third to each, or assigning all the weight to the apex body).
14. We follow similar periodisation in the regression equation estimation, where we use SBR index averaged for four different periods to test the effectiveness of SBR on firm performance.
15. We use the term ‘firm’ instead of ‘plant’ as majority of firms are single-plant firms.
16. The enterprises which employ less than 20 workers without the use of electricity or 10 workers with the use of electricity or are not producing hazardous substances (such as chemicals) fall under the unorganised/informal sector, as these are firms that are not required to register with the authorities under the Indian Factories Act of 1948.
17. As in 2000, the states of Bihar, Madhya Pradesh and Uttar Pradesh were bifurcated to form new states Uttrakhand, Chattisgarh and Jharkhand. For the present analysis, these three states were merged with their parent states so as to have consistency with the SBR variable.
18. The ASI do not provide data on hours worked by workers, so we use total employees (managerial and factory workers) instead. However, given the strong government regulations on total hours that are permitted in a given working day, we do not expect that total hours will vary differently from total employees.
19. Omitted variable bias need not be a major concern here as we control for all important variables that could exert an impact on TFP performance.
20. All estimations were carried out in STATA 11.
21. First-stage results of different 3SLS models are available from authors on request.
22. Ayyagari and Maksimovic (Citation2008) demonstrate that a good business environment improves the growth of industries that are naturally composed of small firms more than large-firm industries. In their view, small firm dominated industries gain from less stringent and more business friendly regulations associated with starting and closing a business, licensing requirements, exporting and importing, employment hiring and firing decisions, paying taxes, protecting investors and obtaining credit.
23. Our finding at the micro-level is supported by the macro-level study of Cali and Sen (Citation2011) which find that increases in the effectiveness of SBRs in Indian states had a strong positive effect on state-level per capita growth.