Abstract
This paper empirically investigates whether financial development has played a role in the rise of market concentration over the past decades. Using an industry-level panel of 41 countries over the period 1989–2010, we document a strong threshold effect of financial development on market competition. Specifically, the deregulation of capital and financial markets promotes product market competition in countries with initially low level of financial development. However, such positive effects vanish with the deepening of credit and stock markets. We identify two mechanisms through which financial development affects market competition in a nonlinear form: external finance dependence and technology gap within an industry. Our results further suggest that financial development may play a role in explaining the modern productivity growth slowdown puzzle.
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Notes
Competition in banking industry reduces bank’s incentive to screen, creating inefficiencies as more incumbents with less innovation are more likely to obtain financing and remain in the market (Marquez 2002)
Note that OECD countries have a relatively higher level of credits.
For a definition of modern productivity growth slowdown, see Brynjolfsson et al. (2020)
For discussions of four mainstream explanations, see Brynjolfsson et al. (2020)
Source: https://comparativecompetitionlaw.org/data/ For more details, see Bradford et al. (2019).
For example, the start-up costs, number of Procedures necessary to incorporate a business, and days required to start a business. These variables are available on World dEVELOPMENT Indicator database (WDI), but there are many missing values.
Similar evidence can be found in the equity markets. The median capitalization was 48.98% in the period 1989–1999 and 71.17% in the period 2000–2010.
The estimated equation is \(y_t = \underset{(0.071)}{0.464}+ \underset{(0.093)}{0.412}y_{t-1} +\underset{(0.057)}{0.153}y_{t-2} + \underset{(0.071)}{0.088}y_{t-3} -\underset{(0.070)}{0.094}y_{t-4} + \underset{(0.054)}{0.058}y_{t-5}\), where \(y_t\) is the competition index. Robust standard errors are in parenthesis.
Specifically, we estimate the following model: \(y_{ci,t} = \sigma _0 + \alpha _L \textrm{FD}_{c,t} * I(\textrm{FD}_{c,t} \le \gamma _1 ) +\alpha _M \textrm{FD}_{c,t} * I(\gamma _1<\textrm{FD}_{c,t} \le \gamma _2 )+ \alpha _H \textrm{FD}_{c,t} * I(\textrm{FD}_{c,t} > \gamma _2 ) + {\textbf {X}}_{ci,t} \delta + \eta _{ci} +v_t + \varepsilon _{c,i,t}\), where \(\alpha _M\) is the coefficient on the middle regime. \({\textbf {X}}_{ci,t}\) includes the controls and lagged dependent variable.
To capture the picture of productivity growth slowdown, Fig. 3 and Table 11 show the annual total factor productivity growth rate for G7 from 1985 to 2017. The US TFP growth rate witnessed an increase from 0.732% to 1.623% between the 1980s to 2004, and then declined after 2004, even before the financial crisis. A similar pattern is observed in other G7 countries, with a mere exception of Italy, whose TFP growth rate has been declining since the mid-1980s. We can also capture a sharp decline in TFP growth rate in the G7 countries during 2007–2008, followed by an immediate recovery to a level below 1%. This has been a puzzle because we are in the midst of a technological revolution, driven by technological advances such as the emergence of artificial intelligence and industrial robots, which are perceived to contribute significantly to productivity growth.
Since the productivity growth rate had declined prior to the financial crisis, explanations related to the financial crisis do not explain the pre-crisis decline very well. Several theories have been proposed to explain the slowdown in productivity growth in the USA and other developed countries. For example, “The mismeasurement hypothesis” (Griliches 1994; Byrne et al. 2018),Footnote 12 weak business dynamism (Decker et al. 2016; De Loecker and Eeckhout 2017; Bijnens and Konings 2018), and low interest rate (Gopinath et al. 2017; Liu et al. 2019). The latter two explanations point to the same end that it is the rise in industry concentration and higher markups that lead to lower productivity growth, as lower financial constraints and real interest rates make it more difficult for high-productivity firms to crowd out the least efficient ones. Financial development is closely related to credit constraints, capital abundance, and interest rate, and thus, financial development is likely to be a potential explanation for the productivity growth decline in developed economies since the 1980s.
All the values are converted to US dollar base.
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Appendix: Construction of external financial dependence index
Appendix: Construction of external financial dependence index
Existing literature typically follows the approach of Rajan and Zingales (1998) to construct the index of external financial dependence, i.e., using firms listed on major US stock exchanges retrieved from Compustat North America to calculate the industry-level value in the manufacturing sector. An important assumption is that the dependence of US firms on external finance serves as a good proxy for the demand for external finance in other countries. According to Rajan and Zingales (1998), there are two important reasons underlying this assumption. First, industries’ demand for external finance is assumed to be homogeneous across countries when they respond to technological shocks. Second, due to data limitation, the authors do not have access to data on flow of funds in other countries. Several studies show that the technology gap between poor and rich countries has been widening and it is mainly driven by differences in the development of financial markets in different countries (Aghion et al. 2005b; Comin and Nanda 2019). This means that countries respond differently to technology shocks due to the heterogeneity of the financial environment. Second, the Worldscope database provides Funds from operations (Worldscope item 4201), which helps remove the barriers to building the external financial dependence for each country individually. The cross-country EFD is constructed to account for the heterogeneity of financial dependence across countries. Following Rajan and Zingales (1998) and Hsu et al. (2014), the cash flow from operations is defined as funds from operations (item 4201) plus decreases in inventories (item 4826), decreases in receivables (item 4825), and increases in account payable (item 4827). Capital expenditure and R &D expense are item 4601 and item 1201, respectively. Each firm’s dependence on external finance is calculated as capital expenditures plus R &D expenditures minus cash flows from operations, all divided by the sum of capital expenditures and R &D expense. Within a country, the time series of each industry’s dependence on external finance is calculated as the median of all firms’ dependence on external finance in a year.Footnote 13 We then compute the invariant external dependence of each industry in each country as the time series median of the industry’s dependence on external finance over the period 1989–2018. An industry with higher external finance dependence uses more external financing to fund its tangible and intangible investment.
Table 6 shows the correlation matrix between the US and other countries in terms of the External Finance Dependence index. We can see that several developed economies, such as Australia, Belgium, Denmark, Finland, France, Italy, Japan, South Korea, Sweden, and the UK, show a positive correlation with the USA in terms of External Financing Dependence. Therefore, it is legitimate to use the US EFD as a proxy for the external financing demand of industries in these countries. However, we can also observe that some developed economies, such as Germany, Greece, the Netherlands, and Singapore, have no or weak correlation with the USA. Regarding the emerging markets, countries such as Brazil, the Philippines, and Malaysia show a positive correlation with the USA, and many have no or negative correlation with the USA. For instance, Mexico and Thailand are negatively associated with the EFD of the USA. The facts presented in Table 6 clearly show that industries’ dependence on external financing varies across countries; therefore, using the US index may be valid if the study is limited to certain industrialized economies, but it may generate biased estimates if a larger number of emerging markets are included (Tables 7, 8, 9, 10, 11). This justifies our construction of a cross-country EFD dataset.
Multifactor productivity growth rate in G7 Group: 1985–2017. Note Annual growth rate trends are obtained using HP filter. The smooth parameter is set as 6.25. Data source: OECD (2019), Multifactor productivity (indicator)
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Zhu, X. Financial development and declining market dynamics: Another dark side of “too much finance”?. Empir Econ 65, 275–309 (2023). https://doi.org/10.1007/s00181-022-02327-0
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DOI: https://doi.org/10.1007/s00181-022-02327-0