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Empirical Economics

, Volume 53, Issue 2, pp 669–694 | Cite as

Markups and bargaining power in tradable and non-tradable sectors

  • João Amador
  • Ana Cristina Soares
Article

Abstract

This article jointly estimates product and labour market imperfections for narrowly defined sectors in the Portuguese economy for the period 2006–2009, following Roeger (J Polit Econ 103(2):316–330, 1995), Crépon et al. (Ann Econ Stat (79/80):583–610, 2005), Dobbelaere (Int J Ind Organ 22(10):1381–1398, 2004) and Abraham et al. (Rev World Econ 145(1):13–36, 2009). In addition, we propose a criterion for the identification of tradable and non-tradable sectors based on the export-to-sales ratio and compare markups and workers’ bargaining power along this dimension. Our findings suggest that markups are higher in the non-tradable than in the tradable sector but workers’ bargaining power is similar. In addition, there is a significant level of heterogeneity across markets, particularly in the non-tradable sector. Moreover, the article confirms that, if labour market imperfections are disregarded, markups are significantly understated.

Keywords

Nash bargaining Market competition Portuguese economy 

JEL Classification

J5 L10 L60 O50 

1 Introduction

Competition in the product market is a key ingredient for an efficient allocation of resources in the economy and thus relevant for aggregate welfare. In addition, economic growth is driven by the adoption of new technologies and the emergence of new products, a process in which competition also plays an extremely relevant role. However, establishing robust measures of competition is a strong challenge both from theoretical and empirical points of view.

The empirical literature has used different approaches to estimate markups. The seminal contribution of Hall (1988) proposed a methodology based on the Solow (1957) residual, were both markups and technological progress can be identified. However, there is an endogeneity concern associated with the fact that technological progress and input growth are likely to be correlated. Alternatively, Roeger (1995) proposed a modified setting to solve such endogeneity concern that involves using the primal and dual maximization problems of the firm. The theoretical contribution by Blanchard and Giavazzi (2003) has pointed out that product and labour markets are not independent. In this context, empirical frameworks were extended to include imperfect labour markets as in Crépon et al. (2005), Dobbelaere (2004), Abraham et al. (2009) and Dobbelaere et al. (2015). Indeed, workers tend to receive wages above productivity, suggesting that their bargaining power is positive, i.e., workers keep a share of the rents of the firm. If these rents are not explicitly considered, firms’ market power appears to be lower than what it is in reality. Another important contribution was recently made by De Loecker and Warzynski (2012). Based on Hall (1988), they propose a new setting where markups are firm and time specific and no longer constant within the sector, though the role of labour market imperfection is disregarded. All in all, although there is a large body of empirical research documenting the non-competitive nature of labour markets, there is still limited work with respect to the joint estimation of markups and distortions in the labour market.

Another important question discussed in the empirical literature is the impact of exposure to international competition on market power, i.e., the commonly known pro-competitive hypothesis of trade. In this context, firms operating in non-tradable industries could sustain higher market power. Nevertheless, most empirical analysis conducted along this strand of research disregards the interactions between product and labour markets. Some exceptions are Abraham et al. (2009) and Boulhol et al. (2011). Using firm-level data for Belgium and UK, respectively, they find evidence that high import penetration rates reduce both markups and union bargaining power. However, empirical evidence including the set of non-manufacturing firms is virtually non-existent. In this context, the standard approach in the literature is to assume manufacturing as tradable and services as non-tradable. Nevertheless, recent technological progress in transports, information and telecommunications technologies have been increasing the relevance of trade in services, which strongly challenges the validity of such assumption.

The contribution of this article is two folded. Firstly, it contributes to the empirical literature on product and labour market competition in narrowly defined sectors (used as an approximation for the relevant market) including services. Using the methodology proposed by Roeger (1995) and the extension proposed by Crépon et al. (2005), Dobbelaere (2004) and Abraham et al. (2009), labour and product market imperfections are jointly estimated for the Portuguese economy in the period 2006–2009. Secondly, the article proposes a new definition of tradable and non-tradable sectors using firm-level data on exports-to-sales ratios, thereby taking into account the tradable nature of some services.

The article concludes that perfect competition is rejected for virtually all markets in the economy, though there is substantial heterogeneity in price-cost margin estimates across markets. In order to obtain results for the overall economy, markets were weighted according to their relevance in sales, gross value added and employment. Results obtained for the Portuguese economy suggest a price-cost margin between 25 and 28 %, depending on the variables used to weight each market. Our findings point also to a significant underestimation of firm’s market power if competitive labour markets are assumed. In fact, the price-cost margin for the overall economy is underestimated by around 11 p.p., though the underestimation can reach 35 p.p. in some markets. Similarly, perfect competition in the labour market is rejected in around 75 % of the markets. Workers’ average bargaining power in the Portuguese economy lies between 12 and 14 %, depending on the weighting variables considered for each market. Consistently with the results in the empirical literature, workers’ bargaining power is positive and strongly correlated with price-cost margins across markets in the economy. Finally, the distinction between tradable and non-tradable sectors uncovers interesting patterns. Market power is higher in the non-tradable than in the tradable sector but bargaining power has been found similar in the two sectors. Nevertheless, there is a significant dispersion across markets in both sectors, particularly in the non-tradable. Several non-tradable markets stand amongst those with highest estimates for the bargaining power and the price-cost margin.

The article is organized as follows. The next section shortly refers the methodology used in the estimation of price-cost margins under competitive and imperfect labour markets. Section 3 describes the data used in the article and presents a new definition of tradable and non-tradable sectors. The next section discusses the results obtained, focusing on the difference between tradable and non-tradable sectors. Section 5 presents some concluding remarks.

2 Methodology

The seminal contribution of Hall (1988) uses a standard growth accounting exercise to identify market power. The main problem is that technological progress is not observed and it is likely to be correlated with input growth rates. Hence, there is an endogeneity problem which implies that OLS is inconsistent. The solution proposed by Hall (1988) implies the use of instrumental variables. However, it is generally difficult to identify suitable instruments and results tend to be sensitive to their choice. The solution proposed by Roeger (1995) consists in using the dual problem of the firm to eliminate the parameter associated with technological progress, hence solving the endogeneity problem. Both frameworks have been extended to account for imperfect competition in the labour market (see Crépon et al. 2005; Dobbelaere 2004; Abraham et al. 2009). As derived in “Appendix 1”, the difference between the primal (SR) and dual Solow residual (\(\mathrm{SR}^\text {dual}\)) for firm i in year t are, under the following assumptions:
(i)
Perfect competition for both product and labour markets
$$\begin{aligned} \mathrm{SR}_{it}-\mathrm{SR}_{it}^\text {dual}=0 \end{aligned}$$
(1)
(ii)
Relaxing perfect competition in the product market
$$\begin{aligned} \mathrm{SR}_{it}-\mathrm{SR}_{it}^\text {dual}=\left( 1-\frac{1}{\mu }\right) \left[ (\varDelta p_{it}+ \varDelta q_{it})-(\varDelta r_{it}+ \varDelta k_{it})\right] + u_{it} \end{aligned}$$
(2)
(iii)
Relaxing perfect competition in both product and labour markets (efficient Nash bargaining)
$$\begin{aligned} \mathrm{SR}_{it}-\mathrm{SR}_{it}^\text {dual}= & {} \left( 1-\frac{1}{\mu }\right) [(\varDelta p_{it}+\varDelta q_{it})-(\varDelta r_{it}+ \varDelta k_{it})]\nonumber \\&+\,\frac{\phi }{(1-\phi )}(\alpha _{it}^{L}-1)[(\varDelta l_{it}+\varDelta w_{it})-(\varDelta r_{it}+\varDelta k_{it})] + u_{it}\qquad \end{aligned}$$
(3)
where
$$\begin{aligned} \mathrm{SR}_{it}-\mathrm{SR}_{it}^\text {dual}\equiv & {} (\varDelta p_{it}+\varDelta q_{it})-\alpha _{it}^{L}(\varDelta w_{it}+\varDelta l_{it})\\&-\,\alpha _{it}^{M}(\varDelta p_{it}^m+\varDelta m_{it})-(1-{\alpha _{it}}^{M}-{\alpha _{it}}^{L})(\varDelta r_{it}+ \varDelta k_{it}) \end{aligned}$$
where \(\mu \) refers to the markup output ratio over the marginal cost, \(q_{it}\) is the log of output, \(k_{it}\), \(l_{it}\) and \(m_{it}\) are the logs of inputs. In addition, \(p_{it}\) is the log of output price and \(w_{it}\), \(r_{it}\), \(p_{it}^m\) are logs of input prices, respectively. Finally, \(\alpha _{it}^{L}\) and \(\alpha _{it}^{M}\) stand for nominal shares of labour and intermediates on sales, respectively.

Assuming perfectly competitive product and labour markets, the primal and dual Solow residual exactly match. However, relaxing these assumptions creates a wedge between the primal and dual Solow residuals which can be used to identify market power. Equations 2 and 3 allow the identification of the price-cost margin \((1-\frac{1}{\mu })\) assuming perfect competition in the labour market and relaxing this assumption, respectively. This last equation allows the estimation of the workers’ bargaining power \((\phi )\). From Eq. 3, it can be seen that the exclusion of the term related to the labour market induces a downward bias in the coefficient for the price-cost margin as labour costs and revenue growth rates are likely to be positively correlated and \((\alpha _{it}^{L}-1)\) is expected to be negative.

3 Data and definitions

Our data are drawn from Informação Empresarial Simplificada (IES) which is jointly collected by Instituto Nacional de Estatística, Banco de Portugal, Ministry of Justice and Ministry of Finance since 2006. This database includes information on balance sheet and income statements items for virtually the universe of non-financial firms comprising around 350,000 firms per year. In order to ensure robust estimations, some observations were eliminated from the database. Firstly, firms reporting less than two consecutive observations were excluded. Additionally, only firms reporting strictly positive sales, labour costs, intermediate inputs and net capital stock (tangible and intangible) were considered. Secondly, observations associated with depreciation rates outside the [0,1] range were disregarded. The same approach was adopted for the share of labour costs and intermediate inputs in total sales. Moreover, observations below the 1st percentile and above the 99th percentile in the distribution of growth rates of sales, labour costs, intermediate inputs and tangible and intangible assets were removed. Finally, sectors as “Agriculture, Mining and Quarrying”, “Education” and “Health” were not considered given their low share in total gross value added (GVA) or its non-market nature.

Throughout the article, it is assumed that firms operating in the same market share the same technology and sell a homogenous good. Hence, Eqs. 2 and 3 are estimated separately for each market. To overcome the well-known difficulties in establishing relevant markets, the standard approach in the literature is to use an economic activity classification. Markets are defined at a low level of aggregation (3-digit level in NACE Rev. 1.1) as a way to capture technology and product substitutability, i.e., approximating the relevant market of the firms. Due to lack of degrees of freedom, markets associated with less than five observations for a given year were eliminated. Nonetheless, it should be acknowledged that these highly concentrated markets are particularly relevant from a competition perspective.

One of the main restrictions to firms’ market power is exposure to international competition. These markets are more likely to follow the law-of-one-price and are commonly classified as tradable. A rough proxy used in the empirical literature is to consider manufacturing markets as tradable and services as non-tradable. The problem with this proxy is that technological progress and trade liberalization brought international competition to many services, moving the line between tradable and non-tradable markets. The empirical literature on this issue is scarce. De Gregorio et al. (1994) use the export-to-production ratio as a measure of international exposure and set a threshold at 10 %. Under this approach, the use of manufacturing as a proxy for the tradable sector seems to be quite accurate, but the analysis was conducted at a high level of aggregation. Alternatively, Jensen and Kletzer (2010) use a methodology based on the idea that non-tradable goods are produced and consumed in the same location. Therefore, more concentrated industries suggest higher tradability. This approach uncovered a significant level of heterogeneity in US services, signaling the tradable nature of some services (e.g., information, scientific and technical services).
Fig. 1

Classification of tradable and non-tradable markets. a Distribution of export-to-sales ratio in 2006–2009. b Threshold sensitivity: accumulated distribution of non-manufacturing markets by export-to-sales ratio

The article relies on the export-to-sales ratio, at the sector level, to assess the tradable nature of non-manufacturing sectors. The ratio corresponds to the sum of exports of goods and services relatively to the sum of total turnover of the firms in the sector for the period under analysis. This ratio was computed for the average period of 2006–2009 in order to dismiss specific shocks at firm and sectoral level. Although this procedure is more accurate than the usual manufacturing and services division, some issues remain. First, we assume that firms in one market account for all the exports in that market which may not hold in the presence of multiproduct firms. Second, imports may also signal tradability. Although imports are available at firm-level, it is not possible to assign them to a specific sector due to their diverse nature.

Panel a of Fig. 1 plots the distribution of the export-to-sales ratio in Portuguese markets, distinguishing between manufacturing and non-manufacturing markets, for the average period of 2006–2009. The figure suggests that several non-manufacturing markets exhibit high export-to-sales ratio, hence considering them as non-tradable would be inaccurate. We define tradable sectors as the set of all manufacturing sectors including services sectors which report an export-to-sales ratio above 15 % (the vertical line in Fig. 1). The choice of a 15 % threshold for the exports-to-sales ratio is compatible with the empirical literature (Knight and Johnson 1997; Dixon et al. 2004) and robust for Portuguese data. Panel b of Fig. 1 illustrates that the percentage of non-manufacturing markets classified as tradable remains unchanged for thresholds between 14 and 19 %. Excluding sectors reporting less than five observations per year, there is a total of 156 markets, 108 of which are considered tradable and 48 non-tradable. In this sample, the non-tradable sector represents 56 % of GVA, 61 % of sales and 54 % of total employment in the period 2006–2009. Using this criterion, around 23 % of non manufacturing markets are considered as tradable. “Appendix 2” presents the share of tradable and non-tradable markets in each industry. Transport and Storage and Business Activities stand as the non-manufacturing industry with the highest share of markets classified into the tradable sector (41.7 %). Conversely, Wholesale Trade and Retail Trade show a small share of markets allocated to the tradable sector (10.5 %). Not surprisingly, Electricity and Water Supply, Real Estate, Post and Telecommunications are exclusively non-tradable, which is broadly compatible with the definition presented by Spence and Hlatshwayo (2012).

The set of variables required to estimate Eqs. 2 and 3 in Sect. 2 are defined as follows. Firstly, output corresponds to sales from goods and services. Secondly, labour costs are given by nominal wages and other benefits including social security contributions. Thirdly, shares of labour and intermediate inputs (\(\alpha ^{L}\) and \(\alpha ^{M}\)) consist of the ratios of labour costs and costs of goods and services to sales, respectively. In addition, it is also required information on the stock of capital and its rental price. The stock of capital includes both tangibles and intangibles (net of depreciations at book value), at odds with most related empirical studies. The inclusion of intangibles can be particularly important since these assets tend to assume an extremely relevant role in particular in non-manufacturing sectors. Following Jorgenson and Hall (1967), the rental price of capital of firm i in year t is defined as \(P_{Kit}= (r_{it} +\delta _{it})P_{t}^{I}\) where \(r_{it}\) is the real interest rate, \(\delta _{it}\) is the depreciation rate and \(P_{t}^{I}\) is the index of investment goods prices. The real interest rate (\(r_{it}\)) corresponds to a nominal interest rate (\(i_{it}\)) deducted of HICP inflation rate of the aggregate economy in period t. The index of investment goods prices (\({P_{t}^{I}}\)) was obtained from the national accounts. Unlike most studies in the literature, both depreciation rates and interest rates were calculated at firm-level in order to reduce measurement error. The depreciation rate was calculated as the ratio of total depreciations in year t to gross capital stock at book value in year \(t-1\) for each firm. The interest rate was defined as the ratio between interest and financial debt for each firm and year. Thus, the underlying assumption is that funding through equity is equivalent to funding through debt. This assumption may be restrictive with respect to the level of the interest rate. However, the estimation requires a set of variables defined in growth rates, which minimizes the restrictive nature of this assumption. In order to avoid a substantial loss of observations, the interest rate of the firms that report no debt, interest payments or ratios outside the [0, 1] range was considered equal to the average of the respective market in each year.

Table 1 reports some descriptive statistics, distinguishing between tradable and non-tradable sectors. The average growth rate of sales is approximately 4 %, without a clear distinction between tradable and non-tradable sectors. Labour costs increased around 4 % in the tradable sector and 3 % in the non-tradable sector in the period 2006–2009. In addition, intermediates and the stock of capital fell in both sectors. Finally, the figures for the depreciation rate are similar in both sectors (around 10 %) and in line with the ones used in similar studies. For example, Christopoulou and Vermeulen (2012) uses a rate of 8 % with longitudinal data, Boulhol et al. (2011) considers rates of 5 and 7 %, while Konings and Vandenbussche (2005) assumes a depreciation rate of 10 %.
Table 1

Descriptive statistics (average for the period 2006–2009)

 

Log difference of

Share on turnover of

Depreciation rate

Interest rate

Sales

Intermediate inputs

Labour costs

Stock of capital

Labour costs

Intermediate inputs

  

Non-tradable

Mean

0.04

\(-\)0.02

0.03

\(-\)0.06

0.22

0.66

0.10

0.02

Stdev.

0.35

0.40

0.31

0.58

0.16

0.20

0.10

0.01

p25

\(-\)0.11

\(-\)0.17

\(-\)0.07

\(-\)0.31

0.10

0.55

0.04

0.02

p50

0.02

\(-\)0.01

0.03

\(-\)0.09

0.18

0.70

0.08

0.02

p75

0.16

0.14

0.15

0.06

0.30

0.82

0.14

0.03

Tradable

Mean

0.04

0.00

0.04

\(-\)0.06

0.31

0.54

0.11

0.02

Stdev.

0.30

0.37

0.30

0.57

0.19

0.21

0.10

0.02

p25

\(-\)0.10

\(-\)0.17

\(-\)0.06

\(-\)0.31

0.17

0.39

0.05

0.01

p50

0.03

0.00

0.03

\(-\)0.10

0.28

0.56

0.08

0.02

p75

0.16

0.17

0.14

0.08

0.42

0.70

0.15

0.03

Overall economy

Mean

0.04

\(-\)0.01

0.04

\(-\)0.06

0.25

0.62

0.11

0.02

Stdev.

0.33

0.39

0.31

0.58

0.17

0.21

0.10

0.01

p25

\(-\)0.10

\(-\)0.17

\(-\)0.07

\(-\)0.31

0.12

0.48

0.04

0.01

p50

0.02

0.00

0.03

\(-\)0.09

0.21

0.65

0.08

0.02

p75

0.16

0.15

0.15

0.07

0.34

0.79

0.14

0.03

For econometric purposes, an additional set of observations was disregarded. Firms exhibiting negative operational profits were withdrawn, representing approximately 22 % of the observations in the database. The inclusion of these firms generates negative estimates for the price-cost margin for several markets. The presence of systematic negative operational profits is unlikely to hold in a setting of profit maximizing firms.1 Hence, instead of setting a cutoff value, all the firms reporting negative operational profits have been disregarded.

Equations 2 and 3 are estimated for each market separately by OLS with clustered errors in the benchmark estimation, fixed effects, random effects and two-step Heckman regressions are also estimated to ensure robust results.2 In particular, the two-step Heckman regressions are run to account for the potential sample selection bias associated with the exclusion of firms reporting negative operational profits.3 This estimation approach yields coefficients for the price-cost margin and the bargaining power that correspond to an average across the firms within the relevant market for the period under analysis.

4 Results

The perfect competition paradigm is widely rejected across Portuguese product markets. At a significance level of 5 %, estimated price-cost margins are statistically different from zero for virtually all markets considered. While perfect competition is always rejected in the non-tradable sector, the same is not true for the tradable sector. In particular, the perfect competition assumption is not rejected for around 7.4 % of tradable sectors.

Figure 2a ranks estimated price-cost margins from the highest to the lowest. Our findings point to a sizeable heterogeneity of estimated price-cost margins across narrowly defined sectors. More specifically, price-cost margins range between a minimum of 6 % and a maximum of 62 %. Highest price-cost margins can be found in markets where human capital is an important input, activities with a high level of product differentiation and activities related to construction (e.g., Real Estate; Construction; Renting activities; Research on sciences and humanities; Hotels). In contrast, the lowest price-cost margins are almost exclusively found in Manufacturing and Retail and Wholesale activities (e.g., Manufacture of motor vehicles; Basic iron and steel; Prepared animal feeds; Retail sale in non-specialized stores; Wholesale of food, beverages and tobacco). Even though tradable and non-tradable sectors are present in both top and bottom of the price-cost margin rank, competition in the non-tradable sector is, on average, less intense compared to the tradable sector. Unweighted price-cost margins are 29 and 26 %, respectively. This pattern is also visible using manufacturing and non-manufacturing aggregates. Furthermore, results for the aggregate economy point to an unweighted price-cost margin of 27 %.
Fig. 2

Price-cost margin across markets under imperfect labour markets (2006–2009). a Benchmark specification. b Alternative specifications. Note: Each market corresponds to a 3 digit level in NACE Rev. 1.1 classification. Black bars identify non-tradable markets. Grey bars correspond to coefficients not significant at a 0.05 significance level, in at least one specification. The benchmark specification corresponds to OLS estimations for each market with clustered errors

From a policy perspective, it is particularly relevant to ensure that the results obtained are robust across econometric specifications. Figure 2b reports price-cost margins estimated by fixed effects, random effects and two-step Heckman regressions for each market, sorted according to the benchmark specification. One striking aspect is that the rank obtained through the different specifications is largely unchanged, implying that the identification of markets associated with potentially less intense competitive environment is robust across econometric specifications. Furthermore, the null hypothesis of perfect competition is consistently rejected. In fact, the perfect competition paradigm is not rejected for only around 8 % of markets, regardless of the specification. These exceptions are mainly related to the manufacturing sector.4
Fig. 3

Workers’ bargaining power across markets (2006–2009). a Benchmark specification. b Alternative specifications. Note: Each market corresponds to a 3 digit level in NACE Rev 1.1 classification. Black bars identify non-tradable markets. Grey bars correspond to coefficients not significant at a 0.05 significance level, in at least one specification. The benchmark specification corresponds to OLS estimations for each market with clustered errors

The workers’ bargaining power \((\phi )\) for each market can be recovered from the estimate for \(\phi /(1-\phi )\) in Eq. 3. Figure 3a reports the workers’ bargaining power in each of the markets sorted in descending order. Similarly to the results found for the product market, the assumption of perfect competition in the labour market is widely rejected. In particular, at a significance level of 5 %, in 3 out 4 markets the perfect competition hypothesis is rejected. At this level, tradable and non-tradable sectors present distinct features. In fact, labour markets in the tradable sector are more likely to be characterized by perfect competition than non-tradable sectors. Perfect competition is rejected in 85 % of non-tradable markets and only in 72 % of the tradable.

Our findings suggest also a substantial difference in the size of the bargaining power across sectors. The highest workers’ bargaining power can be found in activities with high relevance of human capital, product differentiation and high fixed costs (e.g., Research and experimental development on natural sciences and engineering; Games and toys; Water transport). In contrast, the lowest bargaining power is found in Manufacture and non-tradable markets characterized by a low level of product differentiation and/or a low level of skill (e.g., Prepared animal feeds; Industrial cleaning; Labour recruitment and provision of personnel). Workers’ bargaining power is very heterogeneous, reaching values above 30 % in specific markets of “Transports” and “Real estate activities” but also very low figures in markets related to “Trade” and the manufacturing sector. Negative values are abnormal and associated to non-significant estimates, i.e., markets where it is not possible to reject the existence of perfect competition in the labour market. The unweighted average bargaining power for the overall economy stands at about 14 % and, interestingly, presents similar values for tradable and non-tradable sectors. To ensure robustness, alternative estimation strategies were also performed. Figure 3b overlaps estimates sorted according to the benchmark specification. The results are broadly consistent, though it can be seen that some estimates obtained using fixed effects differ from the benchmark.

One of the results found in the empirical literature is the substantial underestimation of the market power of the firm when labour markets are assumed to be perfectly competitive. In fact, by assuming perfectly competitive labour markets, labour costs are incorrectly assumed to translate the true productivity of workers. When part of the rents captured by the firm are transferred to its workers, the market power of the firm is perceived to be lower than what it is in reality. More specifically, our findings point to a sizeable underestimation of the coefficient for the price-cost margin once rent sharing between firms and workers is ignored.
Fig. 4

Price-cost margins under perfect and imperfect labour markets and underestimation bias. a Price-cost margins under perfect and imperfect labour markets (%). b Bias from assuming perfect labour markets (p.p.). Note: Each market corresponds to a 3 digit level in NACE Rev. 1.1 classification. The underestimation bias corresponds to the difference between the price-cost margin estimated assuming imperfect labour markets and the one obtained under perfect labour markets. Coefficients were obtained by OLS with clustered errors for each market

Figure 4a, b illustrate this result by comparing price-cost margins presented above with the ones obtained assuming perfect competition in labour markets and plotting the distribution of this bias by market. We have found some heterogeneity with respect to the underestimation of estimated price-cost margins across markets. The average underestimation is 11 p.p., though in some markets the bias reaches values above 35 p.p.. Even though, tradable and non-tradable sectors present similar figures. Results in the empirical literature have also pointed to a substantial underestimation. Bassanetti et al. (2010) refers an underestimation of 10 p.p., while Dobbelaere (2004) reports a higher underestimation, around 20 p.p., but only considering the manufacturing sector. Still, the correlation between estimated margins in both frameworks is very high (80 %). This result suggests that the markets previously identified as having a poor competition setting were not completely misidentified.

The comparison of estimated price-cost margins under the two frameworks yields interesting results. Considering the degree of rent sharing between firms and workers changes the shape of the price-cost margin distribution. In fact, dispersion increases substantially once the bargaining power is taken into account both for tradable and non-tradable sectors. On average, the non-tradable sector still reports less intense competition. Note however that in both frameworks, the right tail of the price-cost margin distribution and, to a lower extent the left tail, are heavier in the non-tradable sector. This result holds regardless of the assumption on the labour market structure (see Fig. 5).
Fig. 5

Price-cost margin distribution under competitive and imperfect labour markets (%). a Imperfect labour markets. b Competitive labour markets. Note: Each market corresponds to a 3 digit level in NACE Rev. 1.1 classification. Coefficients were obtained by OLS with clustered errors for each market

A striking result is the tight relation between the degree of imperfection in the product and labour market, which is consistent with the results found in the empirical literature. Our results point to a correlation between the price-cost margin and the bargaining power across markets of around 81 % (Fig. 6). For example, Estrada (2009) reports a correlation of 50 % for several EU countries in the period 1980–2004. Considering only the manufacturing sector, Boulhol et al. (2011) studied 20 markets in the UK in the period 1988–2003 and reports correlations of 71 and 53 % in different specifications, while Dobbelaere (2004) reports a correlation of 87 % for a set of Belgian firms in the period 1988–1995. The latter paper presents two different explanations for the positive correlation between price-cost margins and workers’ bargaining power. One explanation is that a high bargaining power leads to increased wages and a reduction of the rents kept to the firm. Consequently, some firms exit the market, thus reducing the intensity of competition in the product market. On the contrary, it can be argued that workers tend to exert less bargaining pressure if there is no surplus to be extracted from the firm, which is the case when there is strong competition in the product market. In this context, Blanchard and Giavazzi (2003) suggest a model that relates labour and product market imperfections.
Fig. 6

Product and labour market imperfection (%). Note: Each market corresponds to a 3 digit level in NACE Rev 1.1 classification. Coefficients were estimated by OLS estimations with clustered errors for each market

A question often discussed in the empirical literature is to what extent accounting price-cost margins, at sectoral level, can be used as a proxy for estimated price-cost margins. Our results suggest that estimated and accounting price-cost margins are positive and highly correlated but only if labour markets are assumed to be perfectly competitive.5 The correlation coefficient between accounting and estimated price-cost margins under competitive labour markets is 0.75 and it drops to 0.24 if this assumption is relaxed. In both frameworks, correlation coefficients are significant at 1 %.

So far, we have addressed market power of both firms and workers for individual markets and found a very substantial level of heterogeneity. However, to draw patterns across sectors and provide figures for the overall economy requires an aggregation of individual markets. Tables 2 and 3 reports estimated price-cost margins and workers’ bargaining power, respectively, aggregating markets into sectors considering several weights (markets, sales, GVA and employment).6 At sectoral level, high price-cost margins are still associated with high bargaining power. “Electricity” and “Construction” exhibit the highest price-cost margins (above 35 %) associated with workers’ bargaining power above other sectors of the economy (around 14 and 20 %, respectively). In contrast, the lowest price-cost margins are associated with “Trade” and to a lesser extent the manufacturing sector. In these cases, the bargaining power is also lower than in other sectors of the Portuguese economy. Furthermore, results obtained using several aggregation variables and alternative specifications are not substantially changed.
Table 2

Price-cost margins per sector (2006–2009) (%)

Sectors

Nb. markets (1)

Non-rejection of perfect competition (% of markets) (2)

Min

Max

Median

Average

   
      

Un-weighted

Weighted

       

Sales

GVA

Employment

Overall economy

156

5.1

6.1

61.7

25.2

26.6

24.9

27.7

25.7

(5.45)

(3.08)

(4.15)

(1.93)

Tradable

108

7.4

6.1

56.1

25.0

25.8

24.7

25.7

25.4

(6.16)

(4.81)

(3.99)

(2.58)

Non-tradable

48

0.0

7.7

61.7

26.9

28.5

25.1

29.3

25.9

(3.73)

(2.82)

(4.18)

(1.67)

Manufacturing

93

8.6

6.1

46.8

24.8

24.7

24.2

25.3

24.7

(6.36)

(5.47)

(4.64)

(3.04)

Non-manufacturing

63

0.0

7.7

61.7

27.8

29.5

25.3

28.8

26.2

(5.44)

(2.83)

(4.11)

(1.64)

Electricity and water supply

3

0.0

29.6

39.2

38.6

35.8

38.0

38.1

38.5

(6.64)

(6.57)

(6.58)

(6.68)

Construction

5

0.0

28.3

47.5

39.3

38.9

44.6

44.1

43.2

(2.81)

(0.69)

(0.70)

(0.71)

Trade

23

0.0

7.7

57.7

19.0

20.9

17.2

19.7

20.4

(1.77)

(0.90)

(0.93)

(1.02)

Transports and communications

10

0.0

21.4

56.1

27.8

31.7

26.8

26.3

27.5

(6.49)

(5.00)

(5.11)

(3.73)

Other services

22

0.0

9.2

61.7

34.0

34.4

32.8

30.3

21.8

(3.94)

(1.67)

(1.75)

(1.70)

(1) Each market corresponds to a 3 digit level in NACE Rev. 1.1. Coefficients were obtained by OLS with clustered errors, for each market. Standard errors, reported in parenthesis, were computed using the delta method (Greene 1993). (2) The non-rejection of the hypothesis of perfect competition is evaluated at a significance level of 5 %

Table 3

Workers’ bargaining power per sector (2006–2009) (%)

Sectors

Nb. markets (1)

Non-rejection of perfect competition (% of markets) (2)

Min

Max

Median

Average

   
      

Un-weighted

Weighted

       

Sales

GVA

Employment

Overall economy

156

23.7

\(-\)8.6

34.1

13.5

13.5

11.9

12.9

12.8

(5.23)

(2.56)

(3.41)

(2.20)

Tradable

108

27.8

\(-\)8.6

34.1

13.9

13.5

11.5

11.8

12.7

(5.57)

(4.99)

(4.05)

(2.51)

Non-tradable

48

14.6

\(-\)1.2

27.0

12.2

13.5

12.2

13.7

12.8

(3.70)

(2.14)

(3.25)

(2.10)

Manufacturing

93

30.1

\(-\)8.6

30.7

13.8

13.1

11.8

13.0

13.4

(5.84)

(5.65)

(4.44)

(2.91)

Non-manufacturing

63

14.3

\(-\)1.2

34.1

12.3

14.0

11.9

12.8

12.4

(5.22)

(2.18)

(3.32)

(2.05)

Electricity and water supply

3

66.7

7.6

25.7

8.6

14.0

9.7

10.5

16.0

(6.74)

(4.54)

(4.52)

(4.75)

Construction

5

0.0

16.0

24.7

19.1

20.6

23.4

23.2

22.8

(2.44)

(0.56)

(0.57)

(0.58)

Trade

23

4.3

4.7

27.0

10.0

11.4

9.4

10.9

11.6

(1.73)

(0.77)

(0.83)

(1.04)

Transports and communications

10

20.0

5.3

34.1

16.4

16.1

13.5

12.7

13.0

(4.99)

(4.36)

(4.53)

(3.21)

Other services

22

18.2

\(-\)1.2

30.3

14.5

14.2

11.6

9.7

6.0

(4.02)

(1.79)

(2.23)

(3.47)

(1) Each market corresponds to a 3 digit level in NACE Rev. 1.1. Coefficients were obtained by OLS with clustered errors, for each market. Standard errors, reported in parenthesis, were computed using the delta method (Greene 1993). (2) The non-rejection of the hypothesis of perfect competition is evaluated at a significance level of 5 %

Fig. 7

Sectoral contribution to overall price-cost margin and bargaining power (%). a and b Price-cost margin. c and d Bargaining power

As mentioned above, assuming perfect competition in the labour market significantly changes the estimate for product market imperfection. The overall economy price-cost margin for Portugal is around 15 % assuming competitive markets and 27 % under imperfect labour markets. At sectoral level, the bias is particularly relevant in “Electricity and water supply” and “Construction” where the underestimation is more than 15 p.p. across specifications, regardless of the variables used to weight individual markets. However, the patterns identified on the sectors assuming the highest and lowest price-cost margins are still unchanged. “Trade” and the manufacturing sector present the lowest price-cost margin and “Electricity and water supply”, “Construction” and “Other services” exhibit the highest.7

Similar studies on product and labour market competitive settings can be found for other countries. However, the papers may exhibit substantial differences in terms of sectors included, sample periods, database characteristics and methodological details, which limits comparability. Estrada (2009) uses industry data and reported price-cost margin estimates for Germany, Spain, Italy and France are 34.7, 25.3, 22.8 and 16.2 %, respectively, while estimates for the workers’ bargaining power stands at 20.2, 7.2, 12.6 and 14.2 %, respectively. Additionally, Moreno and Rodríguez (2011) use a sample of 2000 Spanish manufacturing firms in the period 1990–2005 and reported a price-cost margin under imperfect labour markets of 17.6 % and a coefficient for the workers’ bargaining power that lies between 13 and 15 %. Similarly, Dobbelaere (2004) and Abraham et al. (2009) report an average price-cost margin of 33–26 % for the Belgian manufacturing sector, along with a bargaining power of 24 and 12 %, respectively. Considering a set of French firms in the manufacturing sector, Crépon et al. (2005) reports a price-cost margin of 30 %and a high figure for workers’ bargaining power (66 %).

Finally, we break down market power of both firms and workers of the aggregate economy into main economic sectors and tradables and non-tradables aggregates (see Fig. 7). The non-tradable sector accounts for around 60 % of the overall price-cost margin and bargaining power in the economy using GVA weights. At sectoral level, “Transports and communications”, “Electricity and water supply” and “Construction” represent around 43 % of the price-cost margin and 42 % of the overall bargaining power.

5 Concluding remarks

This article contributes to the empirical literature by jointly testing perfect competition in Portuguese product and labour markets, following Roeger (1995), Crépon et al. (2005), Dobbelaere (2004) and Abraham et al. (2009). It discusses the role of labour market imperfection in the assessment of product market competition, while highlighting the differences between tradable and non-tradable sectors. In this context, we propose a new definition of tradable and non-tradable sectors based on export-to-sales ratios. This is particularly relevant because technological progress has changed the nature of several non-manufacturing sectors and the standard division between manufacturing and services became less meaningful.

The article concludes that perfect competition is rejected for virtually all markets in the economy, though a substantial heterogeneity is observed. Price-cost margins across markets range from 6 to 62 %, reaching figures between 25 and 28 % for the overall economy, depending on the set of weights considered. Perfect competition in the labour market is also rejected in three quarters of markets and workers’ bargaining power for the overall economy lies between 12 and 14 %, according to the aggregation variable. In addition, we find that market power is higher in the non-tradable sector but the bargaining power in tradable and non-tradable sectors is very similar. The non-tradable sector presents a weaker intensity of competition than the tradable, regardless of the assumption on the labour market setup. Our findings point also to a sizeable underestimation of the coefficient for the price-cost margin if the degree of rent sharing between firms and workers is ignored.

Consistently with results found in the empirical literature, the degree of imperfection in the product market is closely related to the imperfection in the labour market. Therefore, from a policy perspective, our results highlight the need to approach labour and product market reforms in an integrated way.

Footnotes

  1. 1.

    Predation strategies are outside the scope of the model presented in Sect. 2.

  2. 2.

    Fixed effects regressions were run to account for measurement error associated, for instance, to the use of a simplified version of the cost of capital.

  3. 3.

    The inverse Mills ratio is significant for around 30 % of the markets, at a 5 % significance level. The explanatory variables in the participation equation are firm’s age, sales and lagged total assets, in logarithm. Furthermore, the introduction annual dummies in the remaining econometric approaches does not affect the results, thus they were not included. The Hausman test was also performed for each market, and random effects were rejected in around 45 % the markets at a 5 % significance level.

  4. 4.

    For further details on estimated price-cost margins under alternative econometric specifications see “Appendix 3”.

  5. 5.

    Accounting price-cost margins were computed as the ratio of turnover deducted from intermediate inputs and labour costs to turnover for each sector.

  6. 6.

    The weights used are based on the average of the period 2006–2009.

  7. 7.

    For additional details on estimated price-cost margins under perfect competition in the labour market see “Appendix 3”.

  8. 8.

    Time and firm subscripts have been omitted for simplicity.

  9. 9.

    This framework allows testing the Nash bargaining model against the right-to-manage model. In the first case, workers can influence both wages and employment. In the second, workers bargain over wages in a first step and next employment is decided according to the labour demand.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Economics and Research DepartmentBanco de PortugalLisboaPortugal
  2. 2.Nova School of Business and EconomicsLisboaPortugal

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