This paper examines the selected issues related to the economy of Denmark: divergence in house prices, house prices in Denmark's cities, macroprudential policies, and product market reform and firm productivity. Recent house price developments in Denmark have been characterized by a growing divergence between different parts of the country, with big cities experiencing much more rapid price increases than other parts. House price booms and busts in Denmark, like in many other countries, are a big-city phenomenon. Macroprudential policies can help contain risks for households, the financial system, and the broader economy, but they should be carefully calibrated to avoid an undue drag on growth.

Abstract

This paper examines the selected issues related to the economy of Denmark: divergence in house prices, house prices in Denmark's cities, macroprudential policies, and product market reform and firm productivity. Recent house price developments in Denmark have been characterized by a growing divergence between different parts of the country, with big cities experiencing much more rapid price increases than other parts. House price booms and busts in Denmark, like in many other countries, are a big-city phenomenon. Macroprudential policies can help contain risks for households, the financial system, and the broader economy, but they should be carefully calibrated to avoid an undue drag on growth.

Product Market Reform and Firm Productivity in Denmark1

Productivity growth in Denmark has lagged that in European peers for an extended period, weighing on GDP growth. Whereas labor and capital markets are relatively flexible already, there is scope for further relaxing product market regulation in some network sectors and retail trade. The paper uses firm-level data to assess the impact of product market reforms in these sectors on firm productivity in Denmark both in the deregulated markets and in downstream sectors. The results point to potentially large productivity gains for the entire economy.

A. Background

1. Denmark’s productivity growth has lagged behind most of its peers. During 2000–14, growth in labor productivity has been weaker in Denmark than in other OECD countries (Figure 1). Much of this is driven by sluggish growth in total factor productivity (TFP). For other countries, the TFP contribution to GDP growth relative to other factors of production has been much higher. Previous analyses have also pointed to weak productivity growth in Denmark as an important factor dragging down economic performance (e.g., Copenhagen Economics, 2013a; OECD, 2014a).

Figure 1.
Figure 1.

Denmark: Labor and Total Factor Productivity in Selected Countries

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A004

2. Over the past years, product market regulations have been relaxed to enhance productivity growth. Product markets have been liberalized substantially in a number of sectors, as reflected in an improvement in the OECD’s Product Market Regulation (PMR) indicator over 2003–13. In the network sectors, public ownership was reduced in varying degrees and at different speeds, contributing to significant economic gains for the entire economy (Copenhagen Economics, 2013b).2 In the retail sector, the gradual liberalization of laws regulating opening hours since 2012 has also had an important impact on productivity growth (Copenhagen Economics, 2013c).3 These product market reforms and other structural reforms—including the strengthening of education and labor market institutions and higher R&D investment—are positively reflected across many business environment indicators on which Denmark scores relatively high (IMF, 2014).

A04ufig1

Product Market Regulation: Change 2003-13. 1/

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A004

Sources: OECD and Fund staff calculations.1/ A higher level of PMR indicates more regulation. A negative change implies less regulations.

3. However, a regulatory gap remains between Denmark and the OECD frontier. To assess the regulatory burden in Denmark relative to peer countries, we use OECD indicators (scaled between 0 and 6) of PMR in individual sectors. While these OECD indicators are not perfect measures of the state of regulation in individual countries, they provide a useful cross-country perspective and are arguably the best measure available for international comparisons of regulation in network sectors and retail trade.4 A cross-country comparison on the basis of PMR indicators points to a sizable deregulation gap between Denmark and the OECD or European OECD frontiers, notably for the electricity, gas, retail, and rail sectors.5 A breakdown of the PMR indicators provided by the OECD suggests that for the network industries the sizeable gap with the frontier is explained in particular by regulation pertaining to public ownership, vertical integration, and market structure.6 For the retail sector, regulation in Denmark is most restrictive in areas of licensing, large outlets, protection of existing firms, shop opening hours, and price controls.7

A04ufig2

Product Market Regulation in 2013, Denmark vs. OECD 1/

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A004

Sources: OECD and Fund staff calculations.1/ European OECD frontiers are calculated as the averages for the three best European performers in OECD, which are very close to the OECD frontiers, except for road where it is much worse.2/ A negative value implies less regulations.

4. Product market regulations in Denmark can impede competition and may help explain low productivity growth. Several studies have highlighted that anti-competitive regulations in Denmark effectively raise barriers to entry, thereby limiting competition and productivity growth (McKinsey, 2010; Copenhagen Economics, 2013a and 2013b). Insufficient competition between a small number of incumbentshinders business dynamics—e.g., inefficient firms are not forced out of business and existing stores are not able to achieve economies of scale. By removing barriers to competition (such as regulated prices, licensing requirements, or zoning restrictions), new entrants could intensify competition thereby putting pressure on firms to cut product prices and/or improve their quality and thus improve productivity. In this context, the report of the Productivity Commission—appointed in 2012 by the Government to investigate the slowdown in productivity growth—already highlighted that regulation in network sectors and retail trade needs that is not justified by other objectives to be reconsidered (OECD, 2014a). 8

A04ufig3

Intensity of Local Competition, 2015

(Score range: 0-100; 141 economies)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A004

Sources: Global Innovation Index 2015 and Fund staff calculations.

5. This paper uses firm-level data to estimate potential productivity gains from product market reforms in Denmark’s regulated sectors. Unlike previous productivity analyses that rely on aggregate indicators, this study uses firm-level data in a panel setting. It also goes beyond assessing the direct impact that reforms may bring to the regulated industries, as relaxing regulatory burden in those product markets may also reverberate on “downstream” firm productivity across the entire economy. This aspect of deregulation is receiving increasing attention in other advanced economies (IMF World Economic Outlook, April 2016b; Barone and Cingano, 2011; Bourlès and others, 2013; Lanau and Topalova, 2016), but it has been less explored in the context of Denmark. We find that relaxing product market regulation could significantly improve firm productivity in Denmark, and that the effect of reforms would be largest for smaller downstream firms. In a stylized exercise, we estimate that closing half of the PMR gap between Denmark and the European OECD frontier would increase average firm TFP by 12 percent for the network industries and by 20 percent for retail trade.

6. The rest of the paper is organized as follows: Section B describes the state of product market regulation in the network and retail sectors in Denmark. Section C describes the data and empirical specification. Section D discusses the findings and conducts policy experiments. Section E concludes.

B. Product Market Regulation in Network and Retail Industries

Regulation in Network Sectors

7. Network industries provide important services to the entire economy. They use a delivery network as their main asset (e.g., transmission of electricity from generators to users, network of postmen, etc.) and they tend to be important suppliers to many other industries and consumers. Therefore, by ensuring high-quality products and services at competitive prices, well-functioning network industries could have significant beneficial impacts on the competitiveness of other Danish businesses.

8. Substantial progress has been made in deregulating network industries in Denmark. Deregulation of network industries in Denmark started early in the 1990s with the reduction of public ownership and has progressed well relative to peers. Sectors such as telecommunications, rail freight, and to some degree electricity were opened to more competition. Deregulation generated economic gains in terms of welfare, consumption, and employment while leading to significant spillover effects to the rest of the economy (Copenhagen Economics, 2005). Historically, the market in telecommunications was more open in Denmark than in other EU member states, the urban transport market started to open much earlier than in other countries, and significant progress was made towards opening the gas market. For electricity, the speed of market opening was slower than for other countries. Also, some steps were taken to open postal services, but much less was done in the area of passenger rail.

9. Nevertheless, network industries in Denmark present scope for further deregulation. Recent studies have identified the postal sector and transport services—notably passenger rail9—as having large potential for further deregulation. The energy sector also presents more potential (Copenhagen Economics, 2013a and 2013b; OECD, 2014a). To that end, the government has recently commissioned McKinsey & Company to analyze the utilities sector—including electricity, gas, water, waste water, and district heating—and estimate potential efficiency gains from improving governance, consolidation, and regulation in these sectors. As for rail freight and telecommunications, there is little scope for more market opening.

10. Productivity gains from network sector deregulation can be significant. Network sectors hold potential for productivity growth resulting from regulatory changes (Copenhagen Economics, 2013a). The Productivity Commission had estimated the potential gain from increasing competition in the utilities sector at DKr 3.3 billion by 2020, mostly in postal services and rail transport of passengers (Produktivitets Kommissionen, 2014). Similarly, estimates of productivity gains by Copenhagen Economics (2013b) from market opening in the network sectors are sizable, ranging from 2 percent for the electricity sector to 24 percent in telecom, 28 percent in postal services, and can be as high as 47 percent for rail freight. As for rail passenger liberalization, productivity and welfare gains can also be expected with the 2013 European Commission package that opens the sector to new entrants by 2019 (OECD, 2014a).

Regulation in Retail Sectors

11. The retail sector continues to face strong barriers to competition. Despite the removal of restrictions on opening hours other than on public holidays and the possibility to open on Sundays since 2012, retail trade in Denmark remains subject to substantial restrictions (Copenhagen Economics, 2013c). For example, the Danish planning law constitutes a market entry barrier for foreign retail business models, including bans on outlets above a certain surface threshold that depend on location and strict rules concerning outlet locations in city and local centers (European Commission, 2015). These regulations are among the most restrictive in Europe and may inhibit investment in the retail sector, which is characterized by high concentration, high prices, and a low proportion of foreign companies.10,11 The Productivity Commission recommended that ownership restrictions of businesses be removed as far as possible, with permission to establish larger stores, easing the rules regarding the location of shops, and freedom to establish pharmacies.

12. The government’s Growth Plan proposed reforms in several areas pointed out by the report of the Productivity Commission. The Growth Plan 2014 comprised various measures to accelerate productivity growth in Denmark, including by strengthening the institutional framework for competition, streamlining business regulations, and lowering corporate income taxes (Danish Ministry of Business and Growth, 2014). However, it did not include action to address the barriers to competition in the retail sector such as permits to engage in commercial activity, specific regulations for large outlets, and zoning regulations that limit the location of stores and their size (OECD, 2014a; European Commission, 2016). To bring competition in line with best practice among EU countries, planning laws need to be liberalized and competition considerations need to be incorporated, while making information about the relevant laws and regulations also accessible to foreigner retailers.

13. More recently, the government has proposed liberalizing the Planning Act to reduce barriers to entry and alleviate the regulatory burden in retail services. The new Growth and Development Strategy proposed in November 2015 envisages removing the floor cap for stores selling books, electronic goods, clothes, and furniture in all cities, allowing them to better respond to competition from e-commerce foreign companies. Municipalities will be able to decide to what extent bigger and more productive grocery shops can be opened in different areas in Denmark. But the strategy does not provide the possibility to establish significantly larger grocery stores (hypermarkets or stores above 10,000 square meters) because of concerns that allowing such larger stores would reduce the geographic proximity to consumers of grocery retailers.

14. The proposed measures could significantly improve conditions in the retail sector. However, relaxing the planning rules also for larger grocery stores would boost productivity gains more, and contribute more substantially to lower prices and a broader choice for consumers (European Commission, 2016). The productivity gains of retail trade deregulation can be significant and even higher than those from deregulation of network industries. Copenhagen Economics (2013a, b, and c) estimate that the productivity gap between the Danish retail sectors and peers from 1995 to 2010 corresponds to a loss of DKK 12.5 billion—equivalent to about 1 percent of the average GDP over that period. They find that zoning regulation is the regulatory factor contributing most to poor productivity growth and that productivity in the grocery retail subsector alone could have been higher by 13 percent over that period without such regulation..

C. Data and Empirical Specification

15. The economic impact from product market reforms could go beyond firms in regulated markets. The degree of regulation in network industries affects productivity of other firms through the quality and price of production inputs, producing ripple effects throughout the economy. Similarly, regulation in retail trade affects other sectors that rely on retail services as part of their production and distribution process though retail costs and (hence) final product prices. Indeed, a growing body of the literature shows that benefits from reducing anti-competitive regulation in network or retail industries extend beyond the immediate sector being liberalized.12

16. We measure both the direct and indirect burden from PMR for all sectors in the Danish economy. The OECD’s PMR indicators for seven network sectors and for retail trade are used as measures of the direct regulatory burden to firms in these sectors (Box 1). In the analysis, regulation in those industries also indirectly affects firms in the downstream sectors. For example, a manufacturer that relies more extensively on the use of electricity and gas would bear a heavier burden from regulation in the electricity and gas sectors, either through paying higher prices or enduring sub-optimal quality of services. We measure the indirect regulatory burden on all sectors that we henceforth refer to as upstream PMR, which is the downstream effect of product market regulation in upstream sectors. Upstream PMR combines the PMR indicator with an indicator of the intensity of network and retail usage calculated from Denmark’s input-output table (Box 1). Thus, unlike the PMR indicators that cover the seven network industries and retail trade only, upstream PMR affects the entire economy to the extent that other sectors use inputs from network industries and retail trade.13 In line with the literature where the effect of deregulation is found to differ across firms of different size, we also allow for the estimated burden from PMR to vary by firm size.14

17. We calculate different measures of firm productivity using firm-level data from Orbis. Orbis is a commercial database, compiled by Bureau Van Dijk, which provides worldwide financial data at the firm level on value added, number of employees, and fixed assets, among other variables, allowing for the computation of firm-level productivity.15 The Denmark sample consists of about 29,000 public and private firms and covers the period between 2010 and 2014, resulting in 71,374 firm-year observations.16 We compute both labor productivity (i.e., real value added per worker) and three TFP measures for each firm using three different methodologies (Box 2).

18. The following empirical specification is used to investigate the correlation between PMR and firm productivity:

ist=β*PMRst+γXist+Zt+Ds+Dr+ɛist

Where Yist refers to the natural logarithm of firm productivity (either labor productivity or TFP), PMRst denotes either the direct sector-level PMR indicator in the regulated sectors or the calculated indirect sector-level upstream PMR across all sectors, Xist is a vector of firm-level control variables (e.g., leverage defined as the ratio of total debt to total assets and company age17), Zt is the output gap to capture the economy’s cyclical condition, and Ds and Dr are sector and region fixed effects. We run the regressions by firm size class (i.e., micro, small, medium, and large) to allow for the impact of deregulation to vary for firms of different sizes. 18

Measuring Direct and Indirect Regulatory Burdens

The OECD indicators of PMR are used to measure regulatory provisions in retail trade and the seven network sectors covered in the analysis (air transport, electricity, gas, post, rail, road transport, and telecom) over the sample period. The evaluation of the network sector-specific PMRs follows a bottom up approach, aggregating data on entry regulation, public ownership, vertical integration, market structure, and price controls. Retail trade sector regulation is similarly assessed by compiling evaluations of six dimensions, i.e. entry regulation, restrictions on shop size, protection of existing firms, regulation of shop opening hours, price controls, and promotions or discounts. The scale of the PMR indicators ranges from 0 to 6, with higher values indicating more regulation. The indicators are provided on a yearly basis for network industries but they are only available every five years in 2003, 2008, and 2013 for retail trade. The regulated network sectors and retail trade account for about 10 percent of total output in the economy.

From the Danish input-output table for 2012, we extract information on the use of inputs for each of the 87 NACE Revision 2 sectors as well as their output. The variation in input usage across industries called input intensity allows us to extend the direct regulatory burden in network sectors and retail trade to the entire economy, thereby capturing the indirect regulatory burden from upstream sectors on all firms. Using both the PMR indicators and input intensities, we follow Bourles and others (2013) to measure the indirect regulatory burden from regulation in network and retail industries on downstream sectors, which we refer to as Upstream PMR. More specifically, we aggregate PMRs and input intensities (from upstream regulated sectors) for each two-digit level sector in the economy as follows:

pstreamPMRdt=Σu=17PMRut*Intensitydu

PMRut is the direct regulatory burden for regulated sector u at time t, and Intensitydu refers to sector-specific input intensities of downstream sector d from the regulated sector u, measured as the units of regulated product u that are needed to produce one unit of final output in sector d. Thus, UpstreamPMRdt measures the indirect regulatory burden that sector d is subject to at time t, calculated as the weighted average of the direct regulatory burden in regulated sectors and the sector-specific input intensities. The text figure below illustrates the level of upstream PMR from the seven network sectors and retail trade for selected two-digit-level downstream sectors in Danish economy. With varying input dependency on products from regulated sectors, all sectors in the economy are subject to upstream product market regulation from the seven network sectors and retail trade that ranges from 0.002 to 0.23.

A04ufig4

Upstream PMR (network industries and retail trade), selected sectors

(In percent)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A004

Sources: Statistics Denmark, OECD and Fund staff calculations.

Measures of Firm-Level TFP

Three measures of firm TFP are computed for the analysis. First, an index number-based TFP measure is calculated as the Solow residual from a Cobb-Douglas production function with labor and capital as factors of production. For each 1-digit NACE sector, the labor and capital shares are obtained from the OECD STAN database. The Cobb-Douglas production function has the general form:

Aist=ist/[ListαsKist1αs]

Where Aist denotes TFP of firm i in sector s in year t, Yist is real value added, List is the number of employees, Kist is the firm’s value of real fixed assets, and αs denotes labor share in sector s. Thus, the assumption of constant returns to scale in every sector is made.

Second, a production function of the following form is estimated using OLS for each NACE sector:

InYist=βs+αsLInList+αsKInKist+γt+ɛist

Year fixed effects are included to capture time-varying common shocks to all sectors. We obtain the labor and capital shares from the regressions no longer assuming constant returns to scale, and use them to compute firm TFP as before.

Third, we estimate the same production function but using the Levinsohn-Petrin (LP) methodology of instrumenting for the unobserved productivity shock (Levinsohn and Petrin, 2003). The idea is that more productive firms tend to hire more inputs, thus rendering input uses correlated with productivity and causing the OLS coefficients to be inconsistent and biased. In line with the literature, we use as instrument the firm’s working capital (defined as the difference between current assets and current liabilities), in the absence of good data on intermediate inputs.

The three measures of firm TFP are highly and significantly correlated with each other. The simple correlations range from 0.54 to 0.8. Average firm TFP (using the LP method) is relatively higher in manufacturing and services sectors, whereas it is lower and with more variation across firms in agriculture, forestry and fishing, as well as mining and quarrying.

A04ufig5

Distribution of TFP in Selected Sectors, 2014

(Mean and 2-standard deviation band, in logs)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A004

Sources: Orbis and Fund staff calculations.Note: TFP calculated using Levinsohn-Petrin method.

D. Results and Policy Experiments

19. Our analysis indicates that product market regulation lowers productivity in the directly regulated industries.19 The analysis suggests that firms operating in sectors that are more heavily regulated tend to experience lower productivity levels, measured by either labor productivity or TFP (Table 1).20 However, while the results are statistically significant for small firms and medium-sized firms, there is no firm evidence that micro and large firms are affected. The adverse impact from the direct regulatory burden is economically significant: for example, a one-standard-deviation reduction in the PMR indicator correlates with higher average level of TFP by 10 percent for small firms and 7 percent for medium firms.

Table 1.

Denmark: Direct Effect of PMR on Firm Productivity

article image
Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.Source: Fund staff calculations.

20. More importantly, we find robust evidence that PMR in network and retail sectors indirectly affects firm productivity in other sectors. The results for the indirect regulatory burden are robust to multiple specifications and productivity measures, and they point to a negative and significant correlation between upstream PMR and firm productivity in all sectors (Table 2). Firms operating in sectors that rely more heavily on inputs from the regulated industries are likely to be less productive than others. Our results also suggest that the impact of PMR on firm productivity varies by firm size, similar to the findings in the literature, probably due to variation in sensitivity to input costs among firm size classes. The productivity reducing effect of regulation is most pronounced for micro and small firms, whereas there is no evidence that large firms are affected. A one-standard-deviation reduction in PMR implies 6 percent higher TFP for micro and small firms but only 3.5 percent higher for medium-sized firms.21 The results are on the same order of magnitude as those estimated for Italy (Lanau and Topalova, 2016). The magnitude of the coefficients is similar for labor productivity and TFP, but the explanatory power of the regressions is higher for TFP.

Table 2.

Denmark: Indirect Effect of PMR on Firm Productivity

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Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.Source: Fund staff calculations.

21. Regulation in retail industries impacts downstream sectors more than that in network sectors. We repeat the analysis of indirect regulatory burdens, separating the upstream PMR indicators for network and retail sectors (Appendix III). Whereas the coefficients on upstream PMR are negative and highly significant for both types of regulation, they are larger for retail regulation, implying that productivity gains from relaxing regulation in the retail industry are likely higher than those from relaxing network regulation. Further, network upstream PMR has the largest impact on micro and small firms (Appendix III, Table A3.1). The estimated impact of retail upstream PMR is more evenly distributed across firm sizes though the coefficients remain slightly larger for small to medium size firms (Appendix III, Table A3.2).

22. Closing half of the PMR gap with the European OECD frontier would generate sizable productivity gains. In a simple policy experiment, we use the estimated coefficients from Tables 5 and 6 to calculate the average change in steady-state firm TFP from reducing Denmark’s upstream PMR indicator for network or retail sectors such that half of the distance between Denmark and the frontier is closed. Our calculations suggest that, for example, closing half of the gap in network regulation would increase the average level of firm TFP in Denmark by roughly 12 percent, with the largest benefits accruing to smaller downstream firms. The productivity gains from reducing regulation in the retail sector are even larger, at about 20 percent of TFP on average. In contrast to network deregulation, relaxing regulation in retail services would also benefit large firms.

A04ufig6

Relaxing Half of the Regulatory Gap between Denmark and the European Frontier on Average Firm TFP

(Percent change)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A004

Sources: Fund staff estimates.1/ Results not significant for large firms.

23. The productivity boost from deregulation varies across different downstream industries, depending on how intensively they use inputs from the regulated industries. We examine whether productivity gains from product market deregulation differ for firms in different sectors. For example, manufacturing firms would benefit slightly more from network deregulation than firms in services, possibly as manufacturers tend to employ more network inputs for the production (e.g., use of electricity and gas) and distribution (e.g., through rail and road infrastructure) of their products. On the other hand, firms in the services sector would benefit substantially more from retail deregulation, possibly reflecting the large productivity gap in the services sector, but also the intensive use of retail input among services firms given the customer-oriented nature of services.

A04ufig7

Relaxing Half of the Regulatory Gap between Denmark and the European Frontier on Average Firm TFP

(Percent change)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A004

Source: Fund staff estimates.Note: Results not significant for large firms.
A04ufig8

Relaxing Half of the Regulatory Gap between Denmark and the European Frontier on Average Firm TFP

(Percent change)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A004

Source: Fund staff estimates.

24. These results should be interpreted with the usual caveats. Our estimates of productivity gains from further deregulation of product markets for Denmark are broadly in line with estimates in the literature (e.g., Copenhagen Economics, 2013b for Denmark, Lanau and Topalova, 2016 for Italy). Nevertheless, the results should be interpreted with care—notably in view of the crude nature of the OECD PMR indicators—and can only be indicative of potential productivity gains. As is well known, it is an empirical challenge to isolate the impact of any structural reform from that of other reforms that may be implemented at or around the same time. In addition, some degree of regulation in certain sectors may be justified by other policy considerations or societal preferences. The paper does not pass judgment on whether lower scores on the individual PMR indicators are necessarily optimal from other than strictly economic criteria.

E. Conclusion

25. There is scope for significantly improving productivity in Denmark from reforming the product markets. Sizable regulation remains in some network sectors and retail industries, which hinders competition and firm performance. Moreover, the impact pertains not just to the regulated industry per se but also to downstream sectors that use network and retail inputs. Our analysis suggests that product market deregulation in network and retail sectors could significantly raise labor productivity and TFP for Danish firms.

Appendix I. Data Sample

We retrieve data on all firms in Denmark for which value added and the number of employees are available. Our data comes from the Orbis database provided by Bureau Van Dijk over the period 2005–2014, resulting in a total of 139,063 firm-year observations. Our selection of companies focuses on unconsolidated financial statements where available and consolidated statements otherwise, excluding subsidiaries to avoid double-counting of firms. Most companies are located in Hovedstaden (36 percent), Midtjylland (22 percent), Syddanmark (19 percent), Sjaelland (12 percent), and Nordjylland (7 percent), and the remaining firms are from three smaller regions in Denmark.

We apply a number of filtering rules to the original sample. In line with the corporate literature, we drop all firms in the financial services industry because their high leverage is not an indication of distress and they also hold liquidity to meet regulatory requirements and not to undertake positive net present value investment projects (Fama and French, 1992; Bates and others, 2009).1 We also delete observations with negative values for key variables—such as current assets, fixed assets, total assets, leverage, shareholder funds, sales, and cost of employees; we drop the bottom and top 5 percent of the distribution of return on assets and return on equity, and keep only data as of 2010 due to scarce firm representation for prior years. Our final sample includes close to 71,374 firm-year observations over 2010–2014 distributed across 29,000 firms in 17 sectors. The majority of firms belongs to wholesale and retail trade, followed by construction, manufacturing, professional services, and information and communication sectors. Transportation and storage sector, however, generates the largest value added share in the economy along with manufacturing and wholesale and retail trade, with these sectors also employing the largest share of employees.

Table A1.1.

Value Added and Employment by Sector of Economic Activity

article image
Source: Fund staff calculations.

The majority of firms in Denmark are very small privately-held firms. We group firms in different size categories using the number of employees. Micro firm employ less than 10 employees (62 percent of the sample), firms with employees less than 50 but more than 10 are labeled as small (27 percent of total), medium firm have between 50 and 250 employees (9 percent of total), and above that are large firms (2 percent). The overwhelming majority of firms (99.8 percent) are privately-held and a mere 0.2 percent is listed. These figures suggest that focusing on large or listed firms only is likely to provide an incomplete picture of economic activity in Denmark. Also, 90 percent of firms are active, and the rest is ether dissolved, merged, or bankrupt. We keep both active and inactive firms in our sample to capture the dynamics of the market in terms of not just entry but also exit.

Firms across different size classes have different asset composition and more similar funding structure. In Denmark, very small firms invest much less in fixed assets than medium and large firms, which could be due to the type of sector that they operate in and which requires more investment in working capital than in tangible assets. Noteworthy is that the capital structure of firms across different size classes does not exhibit large variations, suggesting that access to financing for the smallest firms may not be an impediment. Also, standard profitability indicators do not exhibit great variability across firms of different size, albeit higher return on assets and return on equity for small firms. Finally, 8 percent of firms in our sample are start-ups (established less than 5 years ago), 49 percent are young (between 5 and 10 years of operations), 37 percent are mature (between 10 and 35 years of age), and the remaining 7 percent have been in the market for more than 35 years.

Table A1.2.

Asset Composition and Funding Structure

article image
Source: Fund staff calculations.

Appendix II. Variables Definition and Sources

Description and sources of all variables entering the regressions appear in Table A2.1.

Table A2.1.

Variables Definition and Sources

article image
Source: Fund Staff calculations.

Summary statistics on the key variables entering the empirical specification appear in Table A2.2. Since we keep both active and inactive or dissolved firms, the latter typically may have negative equity and hence the debt-to-assets ratio that exceeds 100 percent.

Table A2.2.

Descriptive Statistics for Key Variables

article image

Labor Poductivity and TFP variables are in logs; PMR variables are indices (0-6); Downstream PMR, Firm Leverage, and Output Gap variables are in percent.

Source: Fund staff calculations.

Appendix III. Additional Results

The results for the direct and indirect effect of PMR on firm productivity in the baseline are robust to alternative specifications.

First, we replace the upstream PMR for all regulated sectors with either the upstream PMR for network or retail sectors (Tables 5 and 6). Similar to the results using all regulated sectors’ PMR, there is a negative and significant correlation between upstream PMR in either network or retail sectors and the productivity of micro, small, and medium firms across all sectors. Unlike the results based on aggregated upstream PMR, retail PMR also has significant indirect effects on large firms’ productivity in the economy. Moreover, the robustness check reveals the differentiated magnitude of the positive downstream effects from the liberalization of network sectors and retail trade. The average indirect impacts of retail trade PMR on firm productivity are three times larger than that of all regulated sectors’ PMR, whereas that of the network sector PMR is slightly lower.

Table A3.1.

Indirect Effect of Network PMR on Firm Productivity

article image
Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.Source: Fund staff calculations.
Table A3.2.

Indirect Effect of Retail PMR on Firm Productivity

article image
Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.Source: Fund staff calculations.

Second, the results for both the direct and indirect regulatory burdens are robust to using alternative productivity measures. In addition to the results for labor productivity and the Levinsohn-Petrin measure of TFP reported in Box 3, we test the sensitivity of our results to using two alternative TFP measures (described in Box 2). The baseline results for both direct and indirect impact of PMR hold in both robustness checks.

Table A3.3.

Direct Effect of PMR on Firm TFP

article image
Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.Source: Fund staff calculations.
Table A3.4.

Indirect Effect of PMR on Firm TFP

article image
Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.Source: Fund staff calculations.

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1

Prepared by Nan Geng, Giang Ho, and Rima A. Turk. We would like to thank Mico Mrkaic (IMF, 2016a) for sharing with us the code to extract Orbis data and calculate firm productivity.

2

Network sectors include air transport, electricity, gas, post, rail, road transport, and telecom.

3

The retail sector in Denmark can be divided into five subsectors: grocery, department stores, clothes and shoes, pharmacies, and others and repairs (Copenhagen Economics, 2013c).

4

For instance, the electricity and gas sectors in Denmark have been liberalized, and the gap suggested by the OECD indicator seems possibly too large—although significant scope for further efficiency gains in these sectors remains.

5

The OECD frontier is calculated as the average indicator of the best three countries for each sector, all of which are from the EU.

6

The OECD PMR indicator for the network sectors covers five areas of regulation: public ownership, vertical integration, entry regulation, market structure, and price controls.

7

In addition to the five areas listed, the OECD PMR indicator for the retail sectors includes promotions/discounts.

8

Regulation may be in place to support other key policy objectives, e.g., the generation of electricity from renewable sources.

9

As in many other EU countries, the passenger rail system is mainly dominated by a state-owned company in Denmark, although the country has some experience with tendering since a few lines are operated by another supplier.

10

Prices are highest in the EU for categories of food, footwear and consumer electronics, the second highest for clothing and household appliances (European Commission, 2015).

11

Foreign companies are only represented in the discount segment of groceries market and account only 12 percent of that sector (European Commission, 2015).

12

A number of papers have documented the presence of adverse effects from upstream inefficiencies using I-O linkages in a single country context (Arnold and others, 2011; Forlani, 2012; Correa-López and Doménech, 2014; Lanau and Topalova, 2016) and across OECD countries (Barone and Cingano, 2011; Bourlès and others, 2013).

13

Although downstream spillover effects from product market deregulation in network sectors are likely to be higher than those deriving from deregulation in retail sectors, there is potential for improving both competition and productivity in retail if regulatory barriers are reduced. Whereas the channel from retail trade to other sectors is less clear, potential productivity gains could be sizable due to the high contribution of the retail sector to the economy and its relatively large productivity gap (see also Copenhagen Economics, 2013a).

14

Forlani (2012) finds that small firms are most harmed by uncompetitive behavior in network industries in France, whereas the findings of Correa-López and Doménech (2014) for Spain indicate that large firms benefit most from deregulation.

15

See Appendix I for a description of the sample and the procedure we implement to prepare the Orbis data for analysis.

16

Data for pre-2010 years are sparse and thus are not used in the analysis.

17

We classify firms across four age classes: start-ups, young, mature, and well-established (Appendix I).

18

We classify firms into four size classes: micro = 10 employees or fewer, small = 10 to 50 employees, medium = 50 to 250, and large = more than 250. Instead of running regressions by firm size class, we also try controlling for the logarithm of total assets; the results are qualitatively unchanged.

19

The reported results for direct and indirect regulatory burdens use the Levinsohn-Petrin measure of TFP (our preferred measure) and an aggregate indicator of PMR (covering both the network and retail sectors) and upstream PMR for all sectors. For results using other TFP measures and when considering separately the effect of PMR in network and retail and sectors, see Appendix III.

20

All regressions control for firm age, firm leverage, and the output gap. They also include region fixed effects as well as sector fixed effects, and robust standard errors are clustered at the sector-year level. The estimation sample for the direct PMR effect contains only of firms operating in the seven network industries and in retail trade, whereas the sample for indirect upstream PMR contains firms in all sectors of the economy.

21

To calculate the average effect on firm productivity across all sectors from reducing Upstream PMR, we keep input use intensity across all sectors constant at the average level.

1

We also exclude companies in public administration and defense.

Denmark: Selected Issues
Author: International Monetary Fund. European Dept.
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    Denmark: Labor and Total Factor Productivity in Selected Countries

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    Product Market Regulation: Change 2003-13. 1/

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    Product Market Regulation in 2013, Denmark vs. OECD 1/

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    Intensity of Local Competition, 2015

    (Score range: 0-100; 141 economies)

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    Upstream PMR (network industries and retail trade), selected sectors

    (In percent)

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    Distribution of TFP in Selected Sectors, 2014

    (Mean and 2-standard deviation band, in logs)

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    Relaxing Half of the Regulatory Gap between Denmark and the European Frontier on Average Firm TFP

    (Percent change)

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    Relaxing Half of the Regulatory Gap between Denmark and the European Frontier on Average Firm TFP

    (Percent change)

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    Relaxing Half of the Regulatory Gap between Denmark and the European Frontier on Average Firm TFP

    (Percent change)