econfin.massey.ac.nzeconfin.massey.ac.nz/school/documents/seminarseries... · Abstract Competition between firms is important for a well-functioning , efficient economy. Aside from

Competition and Firm Markups in New Zealand

Research proposal for the fulfilment of the requirements for the degree of

Doctor of Philosophy in Economics

Reece Pomeroy

Supervisors:

Professor Martin Berka

Dr. Syed Hassan

School of Economics and Finance

Massey University

Palmerston North

March 2020

Competition and Firm Markups in New Zealand Reece Pomeroy 18043836

Abstract Competition between firms is important for a well-functioning, efficient economy. Aside from keeping prices and costs low, competition impacts upon many aspects of the economy such as productivity, innovation, and resource allocation. This thesis shows that all productive sectors of the New Zealand economy exhibit imperfect competition, shown by high markups – i.e. prices above marginal costs. We show that since the global financial crisis (GFC), markups have decreased or stayed the same for the majority of the industries. Three out of 39 industries show greater markups following the GFC.

In essay two, we will explore the relationship between productivity and competition to see if low competition explains the poor productivity growth experienced in New Zealand.

In essay three, we will use the markups estimated in essay one, along with firm and industry characteristics to explore markup determinants.


Chapter 1 - Introduction Background and motivation Competition is vital and has a major impact on many aspects of our lives and economy. Competition between firms helps prices and costs stay low, motivates firms to innovation and use resources efficiently, improving productivity. The more that can be produced with limited resources increases welfare of both consumers and producers.

Economics argues that, under a number of assumptions, the state of highest social welfare is achieved when firms are perfectly competitive. In this state, prices of products and services are set are their marginal cost, i.e. the cost to produce an additional unit. On the contrary, in an environment of low competition, firms can charge prices above the perfectly competitive equilibrium price, thereby extracting excess economic surplus from consumers while also providing a suboptimal quantity of goods and services. By distorting prices, imperfect competition also influences allocative and productive efficiencies, innovation, and welfare. These distortions caused by an imperfectly competitive market have large implications on the economy as a whole. Estimating markups allows us to understand which sectors of the economy may face distortions and help with the understanding the causes and magnitude of misallocation of markups, which is important from a policy perspective.

Welfare As a result of high markups1, equilibrium prices are higher. This is usually associated with a lower quantity of output. While the producers enjoy higher profits, this is at the expense of consumers and the overall welfare. In an influential recent paper, Edmond et al. (2018) show that welfare costs from markups are large. Using US sales data, they estimate that the average consumer would gain 7.5% in consumption-equivalent terms if they did not face the distortions from markups. They decompose these welfare costs of markups into three channels: an aggregate markup; misallocation of factors of production; and an inefficiently low rate of entry. Edmond et al. find that the aggregate markup contributes three quarters of the welfare cost, about one quarter for the misallocation, and negligible costs due to inefficient entry.

When the aggregate markup (weighted average of firm-level markups) shows that prices are higher than optimal, the aggregate markup acts like a uniform output tax. This relationship is due to higher prices reducing quantity produced and sold, influencing producer and consumer welfares, along with a dead-weight loss for a loss in total welfare. With quantity lower than optimal, employment and investment are both below optimal levels as well. So imperfect competition in the product market causes distortions that flow onto other parts of the economy. The aggregate markup is an important indicator of the overall trend in markups, but the aggregate can hide compositional movements within industries (De Loecker et al., 2020; De Loecker & Eeckhout, 2018). De Loecker et al. (2020) found that the median markup had remained constant over time, while the upper percentiles went up considerably. The rise in average markups they found has been attributed to the reallocation of market share towards higher markup firms.

Imperfect competition causes resources to be allocated inefficiently to firms. This is due to firms utilising below optimal amounts of labour and capital to produce less in exchange for the higher prices. Part of this effect comes from the fact that more productive firms are larger, and therefore face relatively less elastic demand and can then charge higher prices (Edmond et al., 2018; Haltiwanger, 2011). High-markup firms are also getting larger (Baqaee & Farhi, 2020). So larger firms (some of whom are and some of whom are not more productive), are employing too few resources. Remaining

1 Ratio of prices over marginal cost


resources are then under-utilized elsewhere (often by less productive firms). Edmond et al. (2018) state that because of the relatively inelastic demand, the productive firms are effectively ‘near-satiation’, with little room for increasing output due to strongly diminishing returns for the individual firm. Imperfect competition also allows some firms to survive due to the lack of pressure on their costs and prices. By surviving, these ‘zombie’ firms mean that resources are allocated in unproductive activities which depresses average productivity (Adalet McGowan et al., 2018). In the presence of market power, there is less need for firms to improve their efficiency (productivity) as there is less pressure to maintain market share from other companies. So, resources (labour and capital) get allocated to firms that are less efficient. This limits the capacity of the economy to grow and improve welfare and living standards. Baqaee & Farhi (2020) find that total factor productivity would rise roughly 15% if the misallocation from markups was removed.

Firm dynamism – firm births and deaths – plays a big part in the competitive nature of an industry. Barriers to entry that prevent new firms from entering an industry mean that there is less pressure on the current firms to innovate in order to retain market share. The cycle of firm births and deaths is vital to productivity gains and allocative efficiency improvements. When new firms enter, they provide opportunities for new ideas and innovations that can increase productivity and jobs. New entrants are also more productive than incumbent firms (Adalet McGowan et al., 2018; Chappell et al., 2018). An inefficient rate of entry of new firms reduces welfare in a multiple of ways. First, each additional firm increases pressure of current firms to keep prices and costs low, i.e. reduce markups, as to retain market share. Second, consumers have a love of variety, so more firms mean more variety. Therefore, barriers to entry reduce the competitive pressure faced by an industry and reduce the variety. Firm deaths on the other hand free up these resources so they can transfer to newer, more productive firms. The firms that die are usually those who are unprofitable, less productive, and cannot compete anymore. Creative destruction is where firm births and deaths work together to enable resources to move from the failing firms to the newer entrants. Two features here impact on the firm dynamism: 1) entry barriers; 2) firm survival. First, in the presence of entry barriers, it is harder for new firms to start up and target consumers in an industry. These could be physical barriers (such as capacity issues that prevent new firms from gaining supply), to monopolistic competitors that will outprice any new entrances until they fail. Secondly, while it is important that new firms have opportunities to thrive, so they can create more jobs and increase productivity, it is also important that old unproductive (zombie) firms are not unnecessarily kept alive through artificial means. Recently, due to low interest rates, firms (across the world) are managing to survive in hard times, when in previous recessions they would have failed, which would have allowed resources to transfer to other firms. This flow of resources from old (unproductive) to new (productive) firms is vital to a productive, competitive economy.

Optimal markup Conventional wisdom might suggest that the optimal level of competition should be with zero markups. However, studies have shown that positive markups may beneficial. There are trade-offs with markups: higher markups can provide incentives to innovate and support the flow of resources to higher productive firms, whereas intensive competition can stifle innovation. Aghion et al. (2005) found an inverted U-shaped relationship between innovation and competition. At low levels of competition, firms lack incentive to innovate as there is no or little pressure on market share from competitors. Innovation is costly and involves risk, therefore there is little incentive to invest in a potentially risky venture for little market gain (or retention). Increasing competition (e.g. by lowering barriers to entry) increases the potential for innovation from the new firms by increasing the payoffs (Aghion et al., 2005; Gardiner, 2017). But high levels of competition decrease the returns from innovation, thereby decreasing the incentive to innovate (Aghion et al., 2005).


Edmond et al. (2018) show that size-dependant policies that aim to increase competition in the form of reducing market concentration may in fact end up reducing welfare further. While these policies might also decrease aggregate markups, they also increase misallocation and reduce aggregate productivity due to the larger firms being more productive. So, efforts to reduce concentration (reduce market share by the larger firms), can backfire by propping up the less productive smaller firms, increasing misallocation and reducing aggregate productivity.

Firms and industries are heterogenous, a one-size-fits-all policy will not work. While low markups may increase welfare today, it might be harmful to future gains in welfare from productivity growth. Industries that have higher productivity growth require higher markups to maximise their labour productivity growth (Ciriani & Jeanjean, 2019). Edmond et al. (2018) model markups as the returns on past sunk investments. In this case, high markups in an industry may not be a symptom of poor competition if these technology intensive industries require sufficient markups to recoup investments (Ciriani & Jeanjean, 2019; Edmond et al., 2018; Oliveira Martins & Scarpetta, 1999). Therefore, care must be used in comparing competition measures across industries; an industry with markups double that of another industry does not necessarily indicate that competition is less intense for the industry with higher markups.

Market structure and impact on competition Competition analysis requires a multi-faucet approach. Looking at just one measure and trying to improve competition using just that measure can have the opposite effect. Depending on how an industry or market is structured, it will change how a measure should be interpreted and compared. There are various factors that influence competition, and therefore the level of markups, such as: barriers to entry, product differentiation, the extent of market segmentation, level of regulation, international exposure.

Consumers gain welfare from not just low prices, but also from the diversity of firms (number of firms) [ref?]. The size of the market is important for competition; each firm adds to the competitive pressure on the market and existing firms, helping keep prices low. Barriers to entry or small market size can limit the number of firms, which means that there may lack sufficient number of firms for competition to keep prices down (Hoekman et al., 2001). The nature of competition and therefore policy type and implementation can vary depending on the size of the economy (Gal, 2003). Hoekman et al. (2001) show that due to differences in economy size and scale, large and small economies face different effects from barriers to competition. For small economies, barriers to import competition is relatively more important, while local firm entry regulation is more important for larger economies. For the smaller economies, industries may lack sufficient internal demand to support sufficient firm numbers for competition to keep prices down. Therefore, for smaller economies, import penetration is an important factor for competition. However, for larger economies, their internal markets should be sufficiently large to enable the competitive pressures to keep a lid on prices. For these larger economies, the barriers to internal firm creation are important.

International trade can have a large impact on competition. First, opening to trade means that for exporters (or potential exporters), the market size increases, allowing for greater economies of scale. This greater market size also increases the payoffs from innovation, increasing the incentives for local firms to innovate, thereby improving their productivity (Aghion et al., 2015). Second, if the world price is cheaper than the local price, this will push local prices down through the import competition from cheaper international producers. This means that import competition can act as market-discipline when cheap imports restrict the ability of local producers to have high markups (Harrison, 1994; Levinsohn, 1993). The competitive pressure faced by local producers from the import competition forces producers to innovate in order to differentiate themselves from the imported product


(Fernandes & Paunov, 2010). Local tradability is also important. An industry may appear to be decentralised (small average firm size), with no one firm having market power nationally, but due to the low ability to trade geographically, the firm has effect market power locally.

Markup trends While there are various papers looking at markups and trends in markups around the world, it can be complicated to try and compare the studies. Due to data differences (collection, quality and missing data/variables), it makes it hard to compare studies. While some collections of data (EU KLEMS, Compustat, OECD, etc.) combine data from various sources, they do so with the intention of creating comparable data. A consequence of this is that they often have to choose methodology that is second best, due to a lack of data coverage in some countries.

There is evidence of increasing firm markups internationally. De Loecker et al. (2020) analyse firm markups using US data and find that the average markup increased from 21% above marginal costs to 61% from 1980 to 2016. While the majority of the increase occurred before the 2000’s when markups stagnated, US markups experienced a sharp rise in the period post 2010. Hall (2018a, 2018b) uses industry level KLEMS data for 60 industries in the US and finds that Lerner indexes grew moderately between 1988 and 2015. This data is based on actual output, rather than accounting data. Hall’s studies supports De Loecker & Eeckhout (2017) rise in market power, but with smaller increases. Hall (2018a) looks at the impact of the rise of mega-firms (10,000 or more workers) on markups. He finds no evidence of employee concentration is associated with market power, but some evidence with the increase in mega-firms is related with market power.

Reallocation between firms is also causing a widening gap between macro and micro studies (De Loecker & Eeckhout, 2018). Hall (2018b) prefers to use industry data, due to the higher quality data that is available compared with firm-level data. While in some circumstances, industry level data may be better quality, it lacks the heterogeneity and distributional aspect of firm-level data to be able to see how markups are changing amongst firms. This is especially important in the face of rising concentration, with an ever-increasing market share allocation going towards higher productivity and higher markup firms. Whether this is bad is debatable. It all depends on the interpretation of what a markup represents. If a markup represents the payback of past investments, then firms that have a greater level of investment would naturally require a greater markup to compensate them.

The within industry reallocation of market share is impacting on a wide variety of issues. Evidence shows that industries are concentrating market share to larger, more productive firms (Autor et al., 2020). Larger firms also tend to have higher markups. Some of the trends in the aggregate markup and productivity are hidden by the lack of accounting of the heterogeneity of firms and the reallocation of market share and resources amongst them (De Loecker & Eeckhout, 2018). More productive firms are larger and therefore employ a large share of the workforce. However, they are also more profitable and have larger markups, this means that the labour share of GDP is lower. So, as these firms grow and increase in market share, the weighted average labour share will fall, and the weighted average firm markup, and firm productivity will rise. A major contributing factor to the reallocation occurring is due to the rise of the “superstar firm” (Autor et al., 2020). Autor et al. (2020) finds small changes in the unweighted mean of the labour share. This means that little has changed for the average firm. This means that a lot of the changes in labour share, markups, and productivity are due to the reallocation towards the superstar firms, who are more productive, profitable, larger and who use relatively less labour.

It is important to note that rising markups are not necessarily a consequence of a decrease in competition (or excessive market power). As markups are the relationship between price and marginal


cost, a markup may increase from either an increase in price, or a decrease in cost. Due to productivity and efficiency improvements, a firm’s cost structure may decrease, thereby increasing the markup. This cost reduction may also involve a decrease in price, but if the price decrease is less than the cost decrease, markups will have increased. In this case, it would appear that competition is worsening due to the increase in markups, but there would be an increase in welfare for both consumers and producers.

The New Zealand Context Competition impacts across many facets of the economy; innovation, productivity, reallocation of resources, firm dynamism. The situational effects mentioned earlier in the literature may be different for NZ. A lot of the earlier literature on markups focuses on large economies, such as the USA. However, the path for improvement may be different for a small economy such as NZ (Conway, 2016; Gal, 2003). NZ faces different challenges to growth than typical OECD countries because of its geographical isolation, low population density and scale (Conway, 2018). NZ faces a paradox: while our competition policies compare favourably with international standards, our productivity is relatively poor (Gardiner, 2017; McCann, 2009). NZ’s GDP per capita is currently around 90% of the OECD average and while it has improved from the low it reached in the 1980s and 1990s, productivity growth is still lacking (Conway, 2018). All these considerations further highlight the importance of understanding the geographic and firm-level drivers of competition in NZ for the future of our economic wellbeing.

New Zealand once experienced relatively high living standards (GDP per capita), due to the close (colonial) relationship with the United Kingdom (Conway, 2018). From the 1970’s, New Zealand’s relative living standards began to fall: GDP per capita fell from around 125 per cent of OECD average to around 80 per cent in the early 1990’s (Conway, 2018). While New Zealand’s GDP per capita has improved since the low’s in the 1990’s and is currently performing well, productivity growth is still poor and New Zealand is experiencing a productivity slowdown (Alan, 2018; Nolan et al., 2019). Productivity growth is currently averaging around 1 per cent, compared with 1.3 per cent before the GFC. Productivity growth means that we can produce more with the same level of output, or using less to produce the same level of output. Conway et al. (2015) show that real wages increase alongside productivity growth, so a lack of productivity growth will slow growth in real wages.

Productivity growth is highly associated with innovation and technology improvements. Conway (2016) lists three forces for productivity growth: pushing out the technology frontier, technology diffusion, and reallocation of resources between firms. The technology frontier (or productivity frontier) represents the most productive firms in an industry. Evidence shows that along with overall productivity growth, New Zealand’s domestic productivity frontier is also lagging behind the global frontier (Conway, 2016). The causes for lagging behind the global frontier are not necessarily related to what is moving forward the domestic frontier (Aghion et al., 2015). For lagging countries, imitation of the global frontier is important, while as countries approach the global frontier, policies and institutions that enable a strong rule of law, an open economy, low entry barriers, and research education are important (Aghion et al., 2015).

Technology diffusion represents the transfer of knowledge from more productive (and innovative) firms to less productive firms. By diffusing knowledge, and reducing the gap, the overall productivity level can be increased. The distribution between frontier and lagging firms, known as the technology gap, increases with the degree of competition (Aghion et al., 2005, 2015). With a low technology gap, firms operate neck-and-neck and an increase in competition increases the incumbent firms to innovate by increasing the post-innovation returns (Aghion et al., 2005). However, with a large technology gap, lagging firms are discouraged from innovating due to there being little prospects of


them catching up. New Zealand’s innovation rate is lagging behind international averages (Wakeman & Conway, 2017). One reason is likely due to New Zealand’s small market size which limits the returns to innovation from firms (Aghion et al., 2015; Wakeman & Conway, 2017).

Ideally, capital and employment would flow from unproductive (or less productive) firms to high productivity firms. This happens as more productive firms can out compete other firms, so their market share rises. As the market share rises, the firm grows, requiring more capital and employment. The reverse happens for less productive firms, as they lose market share, they will shrink, or die, causing their resources to free up, hopefully to be absorbed by the more productive firms. This is called creative destruction, where new firms cause older firms to die or shrink. However, as firm productivity increases, the labour requirements do not necessarily increase as well. While more productive firms may not increase their labour demanded, as aggregate productivity increases, this increases aggregate incomes, thereby providing an indirect increase in growth of labour demanded (Autor & Salomons, 2018). The reallocation of resources relies on the creative destruction that occurs when old firms die from the emergence of newer, more productive and competitive firms (Aghion et al., 2015). As New Zealand is experiencing a decline in firm dynamism , whether that is due to a lower birth rate or a lower death rate, possibly due to historically low interest rates that are allowing these ‘zombie’ firms to survive. These resources are held in the relatively low productive firms.

New Zealand’s population is widely dispersed and relatively low dense. New Zealand is also located far from international markets. Geographical isolation effects both sides of trade. Being far from export markets increases the physical cost of sending goods to the consumer, while also making supporting the sales harder and more expensive too (flight costs to consumer market, etc). Improving trade openness increases the access to the international market for intermediate goods can improve local product quality by incorporating better (and potentially cheaper) intermediate goods into the production chain (Fernandes & Paunov, 2010). New Zealand is also geographically dispersed, meaning that the already small market nationally is fractured further into regional markets by the thinly spread population.

While many of the earlier studies exploring markups focused on large economies such as the USA, the effects might not be directly comparable with smaller economies (Hoekman et al., 2001). For small economies like New Zealand, industries are generally more densely concentrated than larger economies (Gal, 2003). When looking at competition for a small economy, it is more important to focus on total welfare than just consumer welfare. Trying to keep concentration low in an industry to keep market power down may have the consequence of a lack of scale economies (Gal, 2003). This means that an industry gets stuck in a high cost scenario.

Another point of difference for small economies is the relative importance of domestic entry barriers and foreign entry barriers (Hoekman et al., 2001). The market size in a large economy is usually has sufficient number of firms for competitive pressures to keep markups low. However, for small economies, the market size may be too small to support enough firms. While the market may be too small for new domestic firms to enter, foreign established firms may still be able to sell locally, therefore applying competitive pressure from the outside to keep markups low. So, for a large economy, barriers to entry for local firms are important, while barriers to foreign entry are more important for small economies.

New Zealand’s competition policies and regulatory settings compare favourably to international standards (Gardiner, 2017). While it is relatively easy to start a business in New Zealand, for a small economy, barriers to local entry are less important than barriers to international firms competing in New Zealand. This is due to the small market size in small economies restricting the number of firms


and competitors that there lacks sufficient competition internally to keep prices low. Therefore, it is more important for smaller economies to reduce barriers to foreign entry. While New Zealand is an open economy with few tariffs, there are still barriers to foreign entry because of the distance of New Zealand to international markets and competitors. New Zealand is relatively isolated and far from international markets and this increases the costs for foreign firms wanting to compete in New Zealand.


Chapter 2 – Essay One (Firm-level markups in NZ) Introduction Competition among firms is what helps keep prices and costs low and improvements in efficiency and innovation which helps improve upon societies’ welfare. This study strives to improve our understanding of the competitive nature of industries in New Zealand. Imperfect competition occurs when firms exhibit market power that allows them to increase prices above marginal costs (the markup). With market power, firms have less incentives to improve their practices regarding productivity and efficiency. This study improves upon previous work on markups in New Zealand by using a method by Roeger (1995) that estimates the relationship between price and marginal cost, rather than using variable cost from accounting data. Using firm-level data, I evaluate the markups of 39 industries, which form the majority of New Zealand’s market economy, for the period 2001 to 2017. This study uses firm-level observations from the newly updated productivity database from StatsNZ’s Longitudinal Business Database (Fabling & Maré, 2019). Analysis shows that all industries exhibit positive markups, but with differing trends over time.

Competition effects the incentives to innovate. Where firms sit in the productivity distribution in an industry changes the paybacks to innovation, and the ratio of leading vs lagging firms impacts on how competition effects innovation and therefore growth (Aghion et al., 2015).

There is evidence of wide heterogeneity in the levels competition throughout New Zealand, both across and within industries (MBIE, 2016). Understanding how different measures of competition impact upon the different industries is important in discovering how each industry is performing and developing policy that enables an optimal competition level that balances welfare and productivity.

The main measure I use is the markup – the ratio between prices and marginal cost. In a perfectly competitive market, a firm should have a markup of one, i.e. prices equal marginal cost. However, in the presence of market power, a firm (or industry) would have a markup greater than one. Firm markups represent just one way of competition measurement. Various other measurements are available such as the Herfindahl-Hirschman Index (HHI) which focuses on market structure, and Profit Elasticity (PE) and the price-cost margin (PCM) which focus on market outcomes. Understanding the nature of competition can be different for each industry, depending on the circumstances. Therefore, to fully examine competition, one needs to identify where each industry fits. For example, an industry that has high markups may not necessarily reflect low competition, but a necessity for firms to recoup investment costs. Also, a major concern with these competition measurements is that they sometimes offer conflicting information. For example, markups might be decreasing, but concentration is increasing.

The Herfindahl-Hirschman Index (HHI) is the sum of squares of the market shares for all firms in a market. The HHI is a measure of market structure. It measures the concentration and dominance of firms in an industry. A low HHI (close to zero) represents high competition, while an HHI of 1 represents a monopoly. While HHI is relatively simple to measure, it has a few limitations when it comes to interpreting competition intensity. Firstly, HHI is usually measured at the national level and what might look competitive nationally with many small to medium firms having small market shares, locally, these firms may lack any competitors. HHI is also limited to firms operating in NZ; foreign competitors that sell direct to NZ consumers (e.g. Amazon, Aliexpress) do not show up in the HHI measure, so even if there is one or very few firms operating in the particular industry, they may in fact face extreme competitive pressures from foreign firms.


The price-cost margin (PCM) measures the ratio of (price – cost) over price. While PCM would ideally be calculated using marginal costs, it can be difficult to calculate these, as most studies have limited data on prices and costs. In most studies, they are limited to data derived from accounting data, and therefore instead of marginal costs, they use variable costs. While variable costs are easy to measure, using (price-variable costs)/price understates the (price-marginal costs)/price when fixed costs are present (Hall, 2018a). Using variable costs in the PCM also leaves out capital costs from the equation. The absence of capital can lead to quite large errors in the calculation of the PCM (Fisher, 1987).

Profit elasticity measures the responsiveness of profit to changes in marginal costs and was developed as a new measure to help overcome the issues faced with the accounting PCM (using variable costs rather than marginal costs) which fails to show the correct changes in competition intensity in cases of reallocation between firms to more efficient firms (Boone, 2008). In this situation, profit elasticity can be more robust and PCM fails to show that even though PCM has risen, price has fallen due to the efficiency (Boone et al., 2013). Like the accounting PCM, most studies fail to account for capital costs by calculating PE with variable costs. Both PCM and PE are more robust to issues with the market definition; import competition or poorly defined markets (e.g. industry groupings or national vs local markets) do not impact upon the PCM or PE results (Maré & Fabling, 2019).

Care is needed when comparing the absolute values of competition measures due to the potential differences in the structures of the industries (Schiff & Singh, 2019). For example, an industry with a PCM that is double of another industry does not necessarily imply that competition in less intense for the industry with the higher PCM. It might just reflect the fact that some industries require larger outlays of investment, and therefore require a higher PCM to recover these costs. But trends over time are still important, they still reflect changing competition within an industry (or reallocation).

Competition literature in New Zealand Stevens (2009) provides an overview of competition in New Zealand by examining competition through several different measures. He is one of the first to use the LBD, which contains effectively the whole population of economically significant firms, to examine competition broadly by calculating the competition measures at the 4-digit industry level, and then aggregating them up to the 2-digit industry level. He finds considerable heterogeneity in the competition intensity in both within and between industries. In comparing competition measures, Stevens finds little evidence of correlations between PCM and the concentration indices they use. Stevens estimations lack any time trend, likely due to the limited timeframe of the data (2000 to 2007).

Conway & Zheng (2014) explore the impact of tradability on competition. They find some evidence that industries with lower domestic tradability tend to experience higher price-cost margins. They suggest that some service-sector firms are relatively insulated from domestic and international competition compared with other parts of the economy.

MBIE (2016) examines competition in New Zealand across a broad range of industries using firm level data. MBIE uses the newer profit elasticity2 method to measure competition over previous methods such as the PCM (calculated using accounting data). They examine 309 4-digit industries over the 2000-2010 period. They find the manufacturing and the construction industries to have the most intense average competition, while the agriculture, forestry & fishing, and the finance & insurance industries are the least competitive. They find there is considerable variation in the degree of competition within sectors. While the profit elasticity measure that MBIE estimates the relationship

2 The percentage fall in profits due to a 1% increase in marginal costs.


between profits and marginal costs, due to the data limitations, they have to use average variable costs instead, which ignores any capital costs. The absence of capital costs in the markup calculation skews markups upwards [ref]. This is especially true for capital intensive firms, which require higher markups to compensate for the greater outlay [ref].

Maré & Fabling (2019) update MBIE (2016) using the newly updated productivity dataset produced by Fabling & Maré (2019). Unlike MBIE (2016), Maré & Fabling examine competition by comparing many measurements, along with creating composite indicators. Using these indicators, they cluster industries into four distinct groups based on differing competitive environments. Alongside profit elasticity, dominance index, HHI, they also calculate markups using the accounting PCM method. Where MBIE (2016) found decreasing competition in the period 2000-08, Maré & Fabling have found the opposite. Maré & Fabling suggest that this is likely due to the earlier dataset containing greater measurement error. In comparing the time trends between the different measurements, they find differing changes in competition depending on which measure you look at. PE with fixed effects shows that competition has intensified, while the PCM shows decreasing competition. While they look for broad overall changes in competition, they leave more detailed examination of industry competition to future research.

Schiff & Singh (2019) use the results from Maré & Fabling to explore competition trends more narrowly, choosing specific industries to examine. Tracking where industries fall within the dispersion of the competition measures, they find 6 industries are consistently in the top quartile: Horticulture and Fruit Growing; Food, Beverage and Tobacco Product Manufacturing; Wood and Paper Products Manufacturing; Furniture and Other Manufacturing; Building Construction; and Construction Services. They also found 5 industries that were consistently in the lower quartile (weakest competition): Mining; Supermarket, Grocery Stores and Specialised Food Retailing; Financial and Insurance Services; Auxiliary Finance and Insurance Services; and Rental and Hiring Services (except Real Estate).

This study adds to the current literature by using a method by Roeger (1995) that estimates firm-level markups (PCM) that accounts for capital costs. As the markups are estimatable at the firm-level, they can also provide distributional and allocational changes within industries. This method also allows markups to be estimated at the regional level due to the ability to use nominal data, rather than relying on price indices.

While the PCM method still works fine in the presence of poor market definition (e.g. industry), looking at the distribution and reallocational effects may pose issues. If the firms within a grouping are not related, or are not in competition with each other, it is meaningless to discuss the reallocation between firms.

Method We focus on the measurement of price-cost margin, also known as the Lerner index ((P-C)/P). It is related to the markup which measures the price over marginal cost ratio (P/C). Ideally, these would be calculated using marginal costs, but marginal costs are usually not directly observable (especially with the production data usually available). The literature normally uses average costs, which are easy to measure, but understate the price/marginal cost ratio in the presence of fixed costs (Hall, 2018a). Hall (1988) presents a method to calculate markups by estimating marginal cost using the idea that in the presence of imperfect competition, the Solow residual measures the weighted sum of technological change and the growth rate of the output-capital ratio (Christopoulou & Vermeulen, 2012), rather than solely productivity.


The method3 starts with a standard production function under the assumption of Hicks neutral technological change: 𝑄𝑄𝑡𝑡 = 𝐴𝐴𝑡𝑡𝑓𝑓(𝑁𝑁𝑡𝑡 ,𝐾𝐾𝑡𝑡 ,𝑀𝑀𝑡𝑡). Qt represents output, Nt is labour, Kt is capital input, Mt is intermediate inputs, At is technology, t is the time subscript. Following Solow (1957), productivity growth is represented by the “Solow” residual:

∆𝑄𝑄𝑡𝑡 − 𝜀𝜀𝑁𝑁𝑡𝑡∆𝑁𝑁𝑡𝑡 − 𝜀𝜀𝑀𝑀𝑡𝑡∆𝑀𝑀𝑡𝑡 − 𝜀𝜀𝐾𝐾𝑡𝑡∆𝐾𝐾𝑡𝑡 = 𝜃𝜃𝑡𝑡

(Error! Bookmark not defined.1)

where ∆𝑄𝑄𝑡𝑡 is output growth, ∆𝑁𝑁𝑡𝑡 is labour growth, ∆𝐾𝐾𝑡𝑡 is capital growth, ∆𝑀𝑀𝑡𝑡 is intermediate input growth, 𝜃𝜃𝑡𝑡 is the technological change (productivity term), 𝜀𝜀𝑁𝑁𝑡𝑡 , 𝜀𝜀𝑀𝑀𝑡𝑡 , 𝜀𝜀𝐾𝐾𝑡𝑡 are output elasticities with respect to labour, intermediate input, and capital, respectively. When competition is perfect, these elasticities equal the nominal shares of output, 𝜀𝜀𝐽𝐽 = 𝛼𝛼𝐽𝐽 (J = N, M, K). Hall (1988) shows that under imperfect competition with prices above marginal costs, that the relationship between the elasticities and the nominal share in output depends on the markup 𝜀𝜀𝐽𝐽 = 𝜇𝜇𝛼𝛼𝐽𝐽, where 𝜇𝜇 is the markup, which is represented by the price over marginal cost ratio. Therefore, the Solow residual under imperfect competition becomes:

∆𝑄𝑄𝑡𝑡 − 𝛼𝛼𝑁𝑁𝑡𝑡∆𝑁𝑁𝑡𝑡 − 𝛼𝛼𝑀𝑀𝑡𝑡∆𝑀𝑀𝑡𝑡 − 𝛼𝛼𝐾𝐾𝑡𝑡∆𝐾𝐾𝑡𝑡 = �1 −1𝜇𝜇𝑡𝑡� (∆𝑄𝑄𝑡𝑡 − ∆𝐾𝐾𝑡𝑡) +

1𝜇𝜇𝑡𝑡𝜃𝜃𝑡𝑡


The left-hand side is still the same as above, this represents the Solow residual SR. So now the SR represents not just productivity, but the weighted sum of the productivity growth, and the output to capital growth rate. This Solow residual shows that output growth is now disproportional to input growth in the presence of imperfect competition. With perfect competition, the markup equals 1, and the Solow residual becomes 𝜃𝜃𝑡𝑡. Assuming constant returns to scale, 𝛼𝛼𝐾𝐾𝑡𝑡 becomes (1 − 𝛼𝛼𝑁𝑁𝑡𝑡 −𝛼𝛼𝑀𝑀𝑡𝑡), which gives this equation:

∆𝑄𝑄𝑡𝑡 − 𝛼𝛼𝑁𝑁𝑡𝑡∆𝑁𝑁𝑡𝑡 − 𝛼𝛼𝑀𝑀𝑡𝑡∆𝑀𝑀𝑡𝑡 − (1 − 𝛼𝛼𝑁𝑁𝑡𝑡 − 𝛼𝛼𝑀𝑀𝑡𝑡)∆𝐾𝐾𝑡𝑡

= �1 −1𝜇𝜇𝑡𝑡� (∆𝑄𝑄𝑡𝑡 − ∆𝐾𝐾𝑡𝑡) +



Hall (1988) used his version of this equation to calculate markups for manufacturing industries in the US. However, due to the endogeneity issues, the productivity term and (∆𝑄𝑄𝑡𝑡 − ∆𝐾𝐾𝑡𝑡) are positively correlated. Therefore, Hall uses instruments in place of 𝜃𝜃𝑡𝑡. The choice of instruments for the productivity term can be a challenge, as potential instruments are also likely to be correlated as well (Görg & Warzynski, 2006; Roeger, 1995).

Roeger (1995) solves these issues by showing that markups can be estimated the difference between the primal (quantity based) and dual (price based) Solow residuals. A positive “Net Solow Residual” shows the ability of firms to collect rent by paying factors of production below their productivity (Thum-Thysen & Canton, 2015). In addition to the Solow Residual given by Hall (1988), Roeger develops the price-based equivalent:

∆𝑝𝑝𝑡𝑡 − 𝛼𝛼𝑁𝑁𝑡𝑡∆𝑤𝑤𝑡𝑡 − 𝛼𝛼𝑀𝑀𝑡𝑡∆𝑝𝑝𝑚𝑚𝑡𝑡 − �1 − 𝛼𝛼𝑁𝑁𝑡𝑡 − 𝛼𝛼𝑀𝑀𝑡𝑡�∆𝑟𝑟𝑡𝑡

= �1 −1𝜇𝜇𝑡𝑡� (∆𝑝𝑝𝑡𝑡 − ∆𝑟𝑟𝑡𝑡) −


(Error! Bookmark

3 Hall (1988) and Roeger (1995) use value added for their output, and therefore only use labour and capital. However, this study uses gross output and so also includes intermediate costs.


not defined.4)

where ∆𝑝𝑝𝑡𝑡 is the growth in output prices, ∆𝑤𝑤𝑡𝑡 is the change in wages, ∆𝑝𝑝𝑚𝑚𝑡𝑡 is the change in intermediate input prices, ∆𝑟𝑟𝑡𝑡 is the user cost of capital. The left-hand side is now defined as the negative of the price-based Solow residual (-SRP). The Net Solow residual (NSR), the difference between the SR and the SRP is given by:

𝑁𝑁𝑁𝑁𝑁𝑁𝑡𝑡 ≡ 𝑁𝑁𝑁𝑁𝑡𝑡 − 𝑁𝑁𝑁𝑁𝑆𝑆𝑡𝑡 = �1 −1𝜇𝜇𝑡𝑡� [(∆𝑝𝑝𝑡𝑡 + ∆𝑄𝑄𝑡𝑡) − (∆𝑟𝑟𝑡𝑡 + ∆𝐾𝐾𝑡𝑡)]


where

𝑁𝑁𝑁𝑁𝑁𝑁𝑡𝑡 ≡ (∆𝑝𝑝𝑡𝑡 + ∆𝑄𝑄𝑡𝑡) − 𝛼𝛼𝑁𝑁𝑡𝑡(∆𝑤𝑤𝑡𝑡 + ∆𝑁𝑁𝑡𝑡) − 𝛼𝛼𝑀𝑀𝑡𝑡�∆𝑝𝑝𝑚𝑚𝑡𝑡 + ∆𝑀𝑀𝑡𝑡� − (1 − 𝛼𝛼𝑁𝑁𝑡𝑡

− 𝛼𝛼𝑀𝑀𝑡𝑡)(∆𝑟𝑟𝑡𝑡 + ∆𝐾𝐾𝑡𝑡)


The NSR cancels out the productivity term, removing the need for instrumental variables, and thus provides a consistent estimator. The NSR also allows the estimation of markups using nominal variables, removing the need to use deflators. The NSR consists of (∆𝑝𝑝𝑡𝑡 + ∆𝑄𝑄𝑡𝑡) the nominal output growth, (∆𝑤𝑤𝑡𝑡 + ∆𝑁𝑁𝑡𝑡) the wage bill growth, �∆𝑝𝑝𝑚𝑚𝑡𝑡 + ∆𝑀𝑀𝑡𝑡� the intermediate consumption growth, and (∆𝑟𝑟𝑡𝑡 + ∆𝐾𝐾𝑡𝑡), which is the capital costs growth.

For estimation purposes, the equation can be transformed to:

∆ ln �Gross output𝑐𝑐𝑐𝑐𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐

� − 𝛼𝛼𝑁𝑁∆ ln �𝑤𝑤𝑐𝑐𝑤𝑤𝑤𝑤𝑐𝑐

𝑐𝑐𝑐𝑐𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐�

− 𝛼𝛼𝑀𝑀∆ ln �𝐼𝐼𝐼𝐼𝑐𝑐𝑤𝑤𝑟𝑟𝐼𝐼𝑤𝑤𝐼𝐼𝑐𝑐𝑐𝑐𝑐𝑐𝑤𝑤 𝐼𝐼𝑐𝑐𝑐𝑐𝑤𝑤𝑟𝑟𝑐𝑐𝑐𝑐𝑐𝑐s

𝑐𝑐𝑐𝑐𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐�

= �1 −1𝜇𝜇𝑡𝑡� ∆ln �

Gross output𝑐𝑐𝑐𝑐𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐

�


and

∆𝑦𝑦𝑖𝑖𝑡𝑡 = 𝛽𝛽𝑖𝑖𝑡𝑡∆𝑥𝑥𝑖𝑖𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑡𝑡


where the change in natural logs is used for growth rates, 𝛽𝛽𝑖𝑖𝑡𝑡 = (1 − 1 𝜇𝜇𝑖𝑖𝑡𝑡⁄ ) and 𝜀𝜀𝑖𝑖𝑡𝑡 is the error term. The 𝛽𝛽𝑖𝑖𝑡𝑡 coefficient can be rearranged to acquire the markup estimate, however, the 𝛽𝛽𝑖𝑖𝑡𝑡 is still useful to interpret on its on as it represents the price-cost margin. 𝜇𝜇𝑖𝑖𝑡𝑡 is assumed to be constant over time due to potential measurement errors restricting the ability to calculate year-to-year markups (Christopoulou & Vermeulen, 2012; Roeger, 1995).

This study estimates the model using firm fixed effects, along with year fixed effects. Following Roeger (1995), I apply White’s (1980) heteroskedasticity robust standard errors.

Data My study uses firm-level data from Productivity Dataset based on the Longitudinal Business Database (LBD)4 from StatsNZ (Fabling & Maré, 2015, 2019). The LBD represents anonymised administrative and tax data for all firms in New Zealand. Fabling & Maré (2015, 2019) combine the various administrative

4 This data is only available in secure Datalab environments, managed by StatsNZ


and tax data to create a dataset that has comparable and consistently measured variables that enable productivity research. This approach removes Government and not-for-profit firms, along with those without sufficient quality data for productivity analysis. The productivity dataset allocates firms into one of 39 industries, based on the firm’s prominent activity. Further disaggregation is available at the four-digit (ANZSIC’06) industry level. Due to missing data and the fact that some data sources lack certain data, imputation is required to fill-in some missing information for firms. The coverage rate of the productivity dataset covers on average 65% of firms and 80% of employment of applicable firms (Fabling & Maré, 2019). The coverage rate for employing firms is slightly higher. The resulting dataset covers the period 2001 to 2017 and contains an unbalanced panel of firms covering the 39 industries from the productivity dataset that make up the measured sector of New Zealand. The yearly data represent the firm-level data adjusted to fit the April-March financial tax year5.

The methodology requires only four nominal variables, which are already available in the dataset. The variables required are: gross output, employee remuneration, intermediate consumption, and capital services. Employee remuneration also includes related party expenses for working proprietors. Rental, leasing & rates expenses form part of capital services, rather than intermediate consumption to increase the consistency of capital use across firms. The user cost of capital component of capital services is calculated based on a constant value of 10%, to reflect the average business lending rate (Fabling & Maré, 2015). Capital services is used instead of capital stock to reflect the flow of services produced by capital assets, rather than a value point in time (Biatour et al., 2007; OECD, 2001). Capital services, as a flow, is also better suited in for productivity measurement in growth accounting models (Biatour et al., 2007).

Outlier exclusion

While the data from the Productivity Dataset has already been cleaned to an extent, further cleaning is required to remove outliers that are too extreme and have a high likelihood of missing or incorrect data. The percentile cut-offs for outlier exclusion vary widely in studies examining firm-level markups. Görg & Warzynski (2003) excluded the top and bottom 5 percentile observations from the composite ∆𝑦𝑦𝑖𝑖𝑡𝑡 and ∆𝑥𝑥𝑖𝑖𝑡𝑡 variables. Soares (2019) goes further by excluding based on the component variables below the 10th and above the 90th percentiles for each year, sector, and country. While De Loecker, Goldberg, Khandelwal, & Pavcnik (2016) use a different method, they trim their input variables below and above the 3rd and 97th percentiles.

In the data cleaning, I removed firms that used no labour component, as they would not fit the method, and the data quality of some of these small firms can be dubious [ref?]. In examining the differences between the various cut-offs (1st & 99th, 3rd & 97th, 10th & 90th), I found that the latter cut-offs did not improve the results compared with the 1st and 99th percentile cut-off. The differences were not statistically significant. Although, all cut-offs improved the results over the initial uncleaned data. In the end, I decided to exclude based on the four component variables (gross output, wage bill, intermediate costs, and capital services), the two composite variables ∆𝑦𝑦𝑖𝑖𝑡𝑡 and ∆𝑥𝑥𝑖𝑖𝑡𝑡 below the 1st percentile, and above the 99th percentile for each industry and year. Due to the first-difference nature of the method, further observations were lost due to missing data in some firms. In total, 30% of observations were either dropped from cleaning or unable to be used due to missing data. This left around 1.2 million observations across 156,000 firms for analysis.

5 Data from firms with alternative balance dates are shifted to fit the standard financial year period


Returns to scale Oliveira Martins et al. (1996) show how returns to scale can bias Roeger’s (1995) markup equation. This provides an adjusted 𝛽𝛽:

𝛽𝛽′ = �1 −𝜆𝜆𝜇𝜇𝑡𝑡�

where 𝜆𝜆 represents the scale elasticity. In the presence of decreasing returns to scale, the markup estimation is downward biased, while increasing returns to scale biases the estimation upward.

In order to ascertain whether the data fits the CRS assumption and if any biases might result, I estimated the returns to scale6. I run the estimation by industry. Out of the 39 industries, 25 rejected constant returns to scale. 20 industries had varying degrees of decreasing returns to scale, while 5 industries had mildly increasing returns to scale. For the majority of the industries, the potential bias is small. Dobrinsky et al. (2004) adapt Kee (2002) method for adjusting markup estimates for returns to scale. They first calculate the returns to scale estimate from the production function. Then they calculate the markup estimate. Finally, for the groups of firms that fail constant returns to scale, they use the returns to scale estimate to adjust the markup estimate.

Descriptive statistics Table 1 shows the number of observations (firms) per year that is available in the productivity dataset from the LBD (Fabling & Maré, 2019). Roughly half of the firms in the productivity dataset are working proprietor only firms. Due to these firms lacking reliable labour data, they are excluded from our analysis. This leaves around 1.7 million observations, roughly averaging 100,000 observations each year.

6 measured as the sum of the Cobb-Douglas coefficients (De Loecker et al., 2020)


Table 1 Firm counts

Year Firms WP-only Employers 2001 181,005 94,836 86,169 2002 181,647 94,086 87,561 2003 187,008 94,863 92,145 2004 191,505 95,676 95,832 2005 194,523 95,643 98,880 2006 198,798 97,566 101,241 2007 200,964 98,061 102,903 2008 205,248 100,137 105,108 2009 202,677 99,897 102,777 2010 200,139 100,224 99,915 2011 202,464 102,363 100,101 2012 201,867 101,580 100,290 2013 203,949 102,537 101,412 2014 208,395 104,022 104,367 2015 206,982 101,748 105,231 2016 202,635 100,251 102,381 2017 202,680 97,707 104,976 Total 3,372,480 1,681,197 1,691,289 Note: WP (working proprietor). Authors’ calculations based on StatsNZ LBD data

Table 2 shows the observations across the whole period by industry. This table shows that there is a lot of variation in the percentages of employing firms (and therefore data coverage) across industries. These will be accounted for by weighting for missing observations [Not yet done]. Tables 3 and 4 show that there is also a lot of variation within the firm size and the four main variables: gross output; wages; intermediate consumption; and capital services. Table 3 shows that although the WP firms represent 48% of firms in 2017, they only represent 4.8% of the gross output. Table 3 also shows that large firms (employing at least 50 people) account for over half of gross output and employment, while only representing 1.2% of the firms in the data.


Table 2 Industry

Total N Employing firms % employing

Agriculture, Forestry and Fishing AA 728,052 299,895 41%

Mining BB 4,371 3,090 71%

Manufacturing CC 259,872 167,862 65%

Electricity, Gas, Water and Waste Services DD 9,993 5,880 59%

Construction EE 551,703 249,591 45%

Wholesale Trade FF 167,154 111,066 66%

Retail Trade and Accommodation GH 499,506 361,170 72%

Transport, Postal and Warehousing II 154,011 63,081 41%

Information Media and Telecommunications JJ 35,892 14,853 41%

Financial and Insurance Services KK 54,798 27,858 51%

Rental, Hiring and Real Estate Services LL 35,049 17,688 50%

Professional, Scientific, Technical, Administrative and Support Services MN 623,604 232,287 37%

Arts, Recreational and Other Services RS 248,472 136,971 55%

Note: Authors’ calculations based on StatsNZ LBD data

Table 3 Firm size 2017

FTE size Firms Gross Output

($mil) Labour (FTE)

WP 97,707 48.2% 14,826 4.8% 0 to 5 79,284 39.1% 42,517 13.7% 126,114 13.2% 6 to 9 12,516 6.2% 20,531 6.6% 87,771 9.2%

10 to 19 6,918 3.4% 22,326 7.2% 95,646 10.0% 20 to 49 3,825 1.9% 28,645 9.2% 116,205 12.1% 50 over 2,433 1.2% 181,772 58.5% 531,105 55.5%

Total 202,683 100.0% 310,618 100.0% 956,841 100.0% Authors’ calculations based on StatsNZ LBD data


Table 4 Overall Distribution

($) Gross Output Wages Intermediate Consumption Capital Services

Mean 1,196,975 248,958 677,192 159,627

Std. Dev. 33,301,423 4,340,673 25,605,069 4,578,633

Median 136,191 27,441 52,480 21,108

p1 0 0 687 370

p99 11,069,026 2,687,261 6,163,288 1,203,302 Authors’ calculations based on StatsNZ LBD data. Data from 2001 to 2017.

Price-cost margin in New Zealand We start by estimating equation (8) for all firms in the sample in order to estimate a reference of the average PCM for New Zealand firms. This to provide some descriptive information on the general trends for firms. The results are shown in column (1) of Table 5. The results are estimated controlling for unobserved firm level heterogeneity with a fixed effects specification. We report the estimated coefficients of 𝛽𝛽𝑖𝑖𝑡𝑡 and the implied markup 𝜇𝜇𝑖𝑖𝑡𝑡. The regression results show that the average New Zealand firm rejects the hypothesis of perfect competition with an average PCM of 0.222, which is similar to the weighted average accounting PCM given in Maré & Fabling (2019). This gives an implied markup of 1.285. This means that prices minus marginal costs make up 22% of prices, or prices are 29% above marginal costs.

The estimation so far assumes that PCM is constant over time. We relax this constraint in two ways, first by adding an interaction dummy for post GFC, presented in column (2), second by interacting year dummies with ∆𝑥𝑥𝑖𝑖𝑡𝑡 from equation (8), presented in column (3). Column (2) shows that there has been an overall drop in the average firm PCM following the GFC. The yearly estimates from column (3) are shown together in Figure 1. Figure 1 shows the estimated yearly trend in the average PCM for all firms. There is an initial rise in PCM, followed by a decrease until the GFC. PCM rose for two years after the GFC, before falling once again and roughly stabilising after 2012. The F-test for joint significance of the yearly terms shows the interaction terms are jointly significant. The trend presented here suggests that markups may be countercyclical in New Zealand.

These estimations so far assume all firms and industries exhibit the same PCM. To further explore the heterogeneous nature of the various industries in New Zealand, equation (8) is run separately for each of the 39 productivity industries. These results are shown in Figure 2. The primary industries have on average greater PCMs, but the services industries have the highest standard deviation within industries. Auxiliary Finance and Insurance Services industry has the highest PCM (markup) at 0.503 (2.01), which means that their prices are more than double their marginal costs. The most competitive industry is Building Construction with a PCM (markup) of 0.07 (1.08). The industry regressions are further relaxed as above with the addition of an interaction term for the GFC and then yearly interaction terms. While the overall trend for the data shows a trend of decreasing PCM, with a brief rise following the GFC, the trends for the 39 industries experience a lot of variability. Table 8 and Table 9 show the industry regressions which include the GFC interaction term. These show that three industries (AA12, AA13, CC5) have an increase in PCM following the GFC, while 11 industries (EE11, EE13, GH13, GH21, II11, II13, KK1_, KK13, MN11, RS11, RS21) experience a decline in PCM following the GFC. The remaining 25 industries had no significant change in the PCM.


The figures 3, 4, and 5 included a yearly interaction term with the PCM and were calculated for each industry. These show the 19 industries for which had jointly significant year trend interactions. The remaining industries had no significant trend and are therefore not shown. The full regression tables are shown in the appendix [not yet]. Due to the reduced sample size when estimating the yearly PCM at the industry level, the standard errors are much higher, but these 19 industries still exhibit a significant trend.


Table 5 Average price cost margins

(1) (2) (3) Overall GFC Yearly

x 0.222*** 0.235*** 0.240*** (0.000880) (0.00148) (0.00413)

Implied µ 1.285 1.307 1.316 post GFC * x

-0.0198***

(0.00167)

Y03 * x

0.0243*** (0.00477)

Y04 * x

-0.000316 (0.00498)

Y05 * x

-0.0101* (0.00502)

Y06 * x

-0.0124* (0.00500)

Y07 * x

-0.0248*** (0.00505)

Y08 * x

-0.00516 (0.00489)

Y09 * x

0.0131** (0.00489)

Y10 * x

-0.00678 (0.00487)

Y11 * x

-0.0132** (0.00485)

Y12 * x

-0.0385*** (0.00479)

Y13 * x

-0.0478*** (0.00480)

Y14 * x

-0.0434*** (0.00476)

Y15 * x

-0.0460*** (0.00473)

Y16 * x

-0.0372*** (0.00480)

Y17 * x

-0.0347*** (0.00490)

N 1,169,145 1,169,145 1,169,145 Firms 156,285 156,285 156,285

R-squared 0.213 0.2138 0.2163 F Test

55.09

P-value

0.0000 Heteroskedasticity-consistent standard errors in parentheses. * significant at 5%; ** significant at 1%; *** significant at 0.1%. F-test for joint significance of time interaction terms. µ is the estimated markup ratio from equation (8), calculated as 𝛽𝛽𝑡𝑡 = (1 − 1 𝜇𝜇𝑡𝑡⁄ ).


Figure 1

Note: Points represent estimated marginal effect for each year with 95% confidence intervals shown


Figure 2

Note: Graphed estimates from tables 6 and 7.

0 0.1 0.2 0.3 0.4 0.5 0.6

Horticulture and Fruit GrowingDairy Cattle Farming

Poultry, Deer and Other Livestock FarmingSheep, Beef Cattle and Grain Farming

Fishing and AquacultureMining

Forestry and LoggingAg., Forestry and Fishing Support and Hunting

Petroleum, Chemical, Polymer and Rubber…Non-Metallic Mineral Product Manufacturing

Machinery and Other Equipment ManufacturingElectricity, Gas, Water and Waste Services

Transport Equipment ManufacturingTextile, Leather, Clothing and Footwear…

Metal Product ManufacturingFood, Beverage and Tobacco Product…

Furniture and Other ManufacturingHeavy and Civil Engineering Construction

Wood and Paper Products ManufacturingPrinting

Construction ServicesBuilding Construction

Auxiliary Finance and Insurance ServicesWholesale Trade

Other Store-Based Retailing and Non Store…Supermarket, Grocery Stores and Specialised…

Motor Vehicle and Motor Vehicle Parts and Fuel…Arts and Recreation Services

Rental, Hiring and Real Estate ServicesFinancial and Insurance Services

Professional, Scientific and Technical ServicesTelecommunications, Internet and Library Services

Road TransportRail, Water, Air and Other TransportAccommodation and Food Services

Postal, Courier Transport Support, and…Information Media Services

Other ServicesAdministrative and Support Services

Primary Secondary Services


Table 6 - Primary and Secondary PCM

Industry ANZSIC06 Overall N Firms R-squared

Horticulture and Fruit Growing AA11 0.440*** (0.00763) 26,007 3,489 0.392

Sheep, Beef Cattle and Grain Farming AA12 0.365***

(0.00321) 70,869 9,225 0.314

Dairy Cattle Farming AA13 0.323*** (0.00321) 62,214 8,979 0.429

Poultry, Deer and Other Livestock Farming AA14 0.375***

(0.0122) 9,189 1,263 0.318

Forestry and Logging AA21 0.195*** (0.0123) 4,596 627 0.219

Fishing and Aquaculture AA31 0.227*** (0.0139) 4,326 612 0.337

Ag., Forestry and Fishing Support and Hunting AA32 0.246***

(0.00528) 21,588 3,012 0.211

Mining BB11 0.290*** (0.0207) 1,914 234 0.246

Food, Beverage and Tobacco Product Manufacturing CC1 0.179***

(0.00570) 18,375 2,418 0.146

Textile, Leather, Clothing and Footwear Manufacturing CC21 0.208***

(0.00710) 9,624 1,191 0.158

Wood and Paper Products Manufacturing CC3 0.194***

(0.00502) 13,569 1,581 0.160

Printing CC41 0.0765*** (0.00680) 8,058 984 0.133

Petroleum, Chemical, Polymer and Rubber Product Manuf CC5 0.164***

(0.00965) 8,298 921 0.245

Non-Metallic Mineral Product Manufacturing CC61 0.174***

(0.0112) 4,077 507 0.223

Metal Product Manufacturing CC7 0.174*** (0.00445) 20,868 2,352 0.188

Transport Equipment Manufacturing CC81 0.239***

(0.00853) 7,737 924 0.162

Machinery and Other Equipment Manufacturing CC82 0.154***

(0.00481) 19,542 2,223 0.222

Furniture and Other Manufacturing CC91 0.205***

(0.00617) 12,267 1,446 0.155

Electricity, Gas, Water and Waste Services DD1 0.150***

(0.0130) 4,047 528 0.194

Building Construction EE11 0.135*** (0.00161) 58,587 8,154 0.081

Heavy and Civil Engineering Construction EE12 0.163***

(0.00630) 8,175 972 0.202

Construction Services EE13 0.157*** (0.00155) 122,007 15,864 0.154

Heteroskedasticity-consistent standard errors in parentheses. * significant at 5%; ** significant at 1%; *** significant at 0.1%.


Table 7 – Services PCM

Industry ANZSIC06 Overall N Firms R-squared

Wholesale Trade FF11 0.283*** (0.00372) 80460 9780 0.325

Motor Vehicle and Motor Vehicle Parts and Fuel Retailing GH11 0.185***

(0.00703) 22134 2643 0.259

Supermarket, Grocery Stores and Specialised Food Retailing GH12 0.324***

(0.00700) 28119 4032 0.250

Other Store-Based Retailing and Non Store Retailing GH13 0.309***

(0.00395) 90489 11724 0.283

Accommodation and Food Services GH21 0.240***

(0.00307) 90123 14196 0.146

Road Transport II11 0.228*** (0.00387) 26370 3531 0.252

Rail, Water, Air and Other Transport II12 0.503***

(0.0173) 3750 525 0.116

Postal, Courier Transport Support, and Warehousing Services.

II13 0.211*** (0.00833) 11910 1659 0.178

Information Media Services JJ11 0.140*** (0.00860) 7785 1047 0.139

Telecommunications, Internet and Library Services JJ12 0.200***

(0.0214) 2145 327 0.172

Auxiliary Finance and Insurance Services KK1_ 0.340***

(0.00781) 14472 2055 0.202

Financial and Insurance Services KK13 0.185*** (0.0250) 4098 606 0.331

Rental, Hiring and Real Estate Services LL11 0.191***

(0.00916) 11568 1650 0.206

Professional, Scientific and Technical Services MN11 0.163***

(0.00234) 1E+05 16557 0.212

Administrative and Support Services MN21 0.162***

(0.00354) 40875 5988 0.124

Arts and Recreation Services RS11 0.194*** (0.00807) 12906 1908 0.244

Other Services RS21 0.257*** (0.00266) 82080 10557 0.128



Table 8 - Primary and Secondary PCM with GFC interaction

Industry ANZSIC06 x x * GFC N Firms R-sq Horticulture and Fruit Growing AA11 0.447***

(0.0115) -0.0117 (0.0141) 26,007 3,489 0.392

Sheep, Beef Cattle and Grain Farming AA12 0.286***

(0.00452) 0.0584*** (0.00592) 70,869 9,225 0.317

Dairy Cattle Farming AA13 0.311*** (0.00500)

0.0916*** (0.00618) 62,214 8,979 0.435

Poultry, Deer and Other Livestock Farming AA14 0.351***

(0.0185) 0.0224

(0.0222) 9,189 1,263 0.318

Forestry and Logging AA21 0.230*** (0.0199)

-0.00509 (0.0231) 4,596 627 0.219

Fishing and Aquaculture AA31 0.314*** (0.0202)

-0.0382 (0.0255) 4,326 612 0.338

Ag., Forestry and Fishing Support and Hunting AA32 0.198***

(0.00881) -0.00417 (0.00994) 21,588 3,012 0.212

Mining BB11 0.216*** (0.0380)

0.0436 (0.0446) 1,914 234 0.248

Food, Beverage and Tobacco Product Manufacturing CC1 0.170***

(0.00907) -0.0104 (0.0109) 18,375 2,418 0.146

Textile, Leather, Clothing and Footwear Manufacturing CC21 0.172***

(0.0112) 0.00342 (0.0139) 9,624 1,191 0.158

Wood and Paper Products Manufacturing CC3 0.164***

(0.00764) -0.0172

(0.00931) 13,569 1,581 0.161

Printing CC41 0.166*** (0.0103)

-0.0248 (0.0130) 8,058 984 0.134

Petroleum, Chemical, Polymer and Rubber Product Manuf CC5 0.207***

(0.0131) 0.0489** (0.0168) 8,298 921 0.248

Non-Metallic Mineral Product Manufacturing CC61 0.212***

(0.0174) -0.00632 (0.0211) 4,077 507 0.223

Metal Product Manufacturing CC7 0.184*** (0.00710)

-0.0141 (0.00834) 20,868 2,352 0.188

Transport Equipment Manufacturing CC81 0.180***

(0.0151) -0.000840 (0.0169) 7,737 924 0.162

Machinery and Other Equipment Manufacturing CC82 0.200***

(0.00797) 0.00772

(0.00909) 19,542 2,223 0.223

Furniture and Other Manufacturing CC91 0.157***

(0.00912) 0.0107

(0.0110) 12,267 1,446 0.156

Electricity, Gas, Water and Waste Services DD1 0.224***

(0.0220) -0.0413 (0.0253) 4,047 528 0.196

Building Construction EE11 0.0825*** (0.00304)

-0.00826* (0.00334) 58,587 8,154 0.0815

Heavy and Civil Engineering Construction EE12 0.151***

(0.0106) 0.00967 (0.0123) 8,175 972 0.202

Construction Services EE13 0.144*** (0.00278)

-0.0134*** (0.00309) 122,007 15,864 0.154



Table 9 – Services PCM with GFC interaction

Industry ANZSIC06 x x * GFC N Firms R-squared

Wholesale Trade FF11 0.333*** (0.00572)

0.0106 (0.00685) 80460 9780 0.325

Motor Vehicle and Motor Vehicle Parts and Fuel Retailing

GH11 0.282*** (0.0105)

0.000276 (0.0130) 22134 2643 0.259

Supermarket, Grocery Stores and Specialised Food Retailing

GH12 0.322*** (0.0112)

-0.0197 (0.0134) 28119 4032 0.250

Other Store-Based Retailing and Non Store Retailing GH13 0.336***

(0.00596) -0.0182* (0.00734) 90489 11724 0.284

Accommodation and Food Services GH21 0.215***

(0.00525) -0.0450*** (0.00621) 90123 14196 0.149

Road Transport II11 0.222*** (0.00638)

-0.0413*** (0.00753) 26370 3531 0.254

Rail, Water, Air and Other Transport II12 0.181***

(0.0301) 0.0165

(0.0354) 3750 525 0.117

Postal, Courier Transport Support, and Warehousing Services.

II13 0.225*** (0.0137)

-0.0583*** (0.0157) 11910 1659 0.182

Information Media Services JJ11 0.165*** (0.0131)

-0.00340 (0.0165) 7785 1047 0.139

Telecommunications, Internet and Library Services JJ12 0.199***

(0.0269) 0.00175 (0.0369) 2145 327 0.172

Auxiliary Finance and Insurance Services KK1_ 0.256***

(0.0120) -0.0431** (0.0143) 14472 2055 0.203

Financial and Insurance Services KK13 0.586***

(0.0365) -0.158*** (0.0457) 4098 606 0.339

Rental, Hiring and Real Estate Services LL11 0.240***

(0.0160) -0.000932 (0.0188) 11568 1650 0.207

Professional, Scientific and Technical Services MN11 0.231***

(0.00408) -0.0292*** (0.00460) 1E+05 16557 0.213

Administrative and Support Services MN21 0.149***

(0.00694) -0.0138

(0.00759) 40875 5988 0.124

Arts and Recreation Services RS11 0.292*** (0.0144)

-0.0514** (0.0164) 12906 1908 0.246

Other Services RS21 0.182*** (0.00449)

-0.0296*** (0.00517) 82080 10557 0.130


[include a scatterplot of pre and post PCM - later]


Figure 3 – Industry PCM trends

Note: Full regression tables are shown in the appendix. Y-axis not consistent. Points represent estimated marginal effect for each year with 95% confidence intervals shown







Firm Level The analysis so far provides estimates at the industry level, representing the “average” firm. While informative, this lacks any insight into changes within industries. Due to the heterogenous nature of firms, it is important to examine any distributional effects happening within the data. Trends can be misinterpreted by the failure to examine how the reallocation between firms (De Loecker et al., 2018). To examine the distribution of markups, I estimate equation (8) for each firm, first for the whole period, and then splitting the period into two to identify the change in firm markups after the GFC. To improve the estimation, we restricted the estimation to firms that had at least three observations in the relevant estimation period. We remove the top and bottom 1% firm level markups to reduce the impact of measurement error. Out of the 156,285 firms in the dataset, 29,103 were in the first period, but not the second, 73,596 were in the second period only, 47,913 were in both periods, while 5,676 firms were short-lived, only appearing in the overall period, but not the first or second7.

The firm-level results shown in Table 10 reaffirm the earlier results that the average New Zealand firm has a PCM greater than zero, indicating imperfect competition. The firm level PCM’s also provide further evidence of an average decrease in markups and PCM following the GFC. The change following the GFC is also shown by the kernel density in Figure 6, which shows that there has been a decrease in the variance, with the upper tail thinning. This is in contrast to the change in dispersion found in De

7 This is due to these firms having more than three observations overall, but less than three in either period.


Loecker et al. (2020) with US data which shows an fattening in the upper tail. These are also shown in Table 10 which shows a decrease in PCM across the spectrum.

Figure 6 – kernel density plot – distribution of PCMs pre and post GFC

PCM are unweighted

Table 10 - Firm PCM percentiles mean median p1 p5 p10 p25 p75 p90 p95 p99 Overall 0.26 0.21 -0.37 -0.37 -0.05 0.06 0.43 0.66 0.80 1.03 01-07 0.27 0.23 -0.46 -0.46 -0.06 0.05 0.47 0.71 0.86 1.12 08-17 0.25 0.20 -0.41 -0.41 -0.06 0.04 0.43 0.67 0.81 1.04 Note: Unweighted

The firm level results can also be aggregated at the industry level to explore differences and trends across and within industries.

Robustness The regressions were also run using a restricted sample of only those firms which had data for every year. While this gives a balanced panel, it largely reduces the sample size.

Weighting The earlier industry level results treat all observations as equal, to represent the average firm. While this might still show a trend in the average markups charged by the average firm, it might not represent the average markup faced by the consumer. The average markup felt by the consumer


depends on the market share of each firm. In order to account for this, I weight the observations by the average market share (gross output) across the whole period.

Conclusion Using Roeger (1995) method to estimate PCM, we find that all 39 industries of the measured sector in New Zealand exhibit imperfect competition and have a PCM significantly greater than 0. We show that since the global financial crisis (GFC), markups have decreased or stayed the same for the majority of the industries. Three out of 39 industries show greater markups following the GFC, while 11 industries experienced a decline. The trend in markups in New Zealand differs from the experience of the United States, where markups have increased following the GFC. The dispersion of markups in New Zealand also differs from the United States experience with the dispersion in New Zealand decreasing.


Appendix Industry regressions with yearly interactions

Table 11 AA11 AA12 AA13 AA14 AA21 AA31 AA32 BB11

x 0.444*** -0.0269

0.300*** -0.0108

0.282*** -0.0129

0.340*** -0.0385

0.226*** -0.0385

0.347*** -0.0557

0.208*** -0.0247

0.217* -0.0898

3.yr#c.x 0.00764 (-0.0328)

-0.00687 (-0.0149)

-0.0577** (-0.0184)

0.0439 (-0.0523)

0.124* (-0.0528)

0.057 (-0.0691)

0.0346 (-0.0299)

0.15 (-0.102)

4.yr#c.x 0.0149 (-0.0352)

-0.0383** (-0.0141)

0.0453** (-0.0169)

0.0206 (-0.0561)

-0.0489 (-0.0608)

-0.0485 (-0.0748)

-0.0168 (-0.0292)

0.00713 (-0.107)

5.yr#c.x -0.0128 (-0.035)

-0.0109 (-0.0147)

-0.0014 (-0.0165)

0.0173 (-0.0602)

-0.0193 (-0.0506)

-0.0759 (-0.0613)

-0.019 (-0.0294)

-0.00974 (-0.107)

6.yr#c.x -0.00204 (-0.0368)

-0.0252 (-0.0147)

-0.0608*** (-0.0173)

-0.0128 (-0.0604)

0.0322 (-0.0575)

-0.121 (-0.068)

0.00106 (-0.0298)

-0.0378 (-0.124)

7.yr#c.x -0.00161 (-0.0438)

-0.0288 (-0.0153)

-0.0147 (-0.0169)

-0.0116 (-0.0562)

-0.0237 (-0.0625)

-0.0252 (-0.0664)

-0.0444 (-0.0309)

-0.00719 (-0.108)

8.yr#c.x 0.0205 (-0.0329)

0.00827 (-0.0155)

0.0536** (-0.0165)

0.0205 (-0.0559)

-0.064 (-0.0525)

-0.0227 (-0.062)

-0.0163 (-0.0297)

0.032 (-0.1)

9.yr#c.x 0.0132 (-0.0366)

0.0332* (-0.0158)

-0.0425* (-0.0181)

0.0215 (-0.0533)

0.0888 (-0.0669)

0.000902 (-0.065)

-0.0275 (-0.0288)

0.151 (-0.102)

10.yr#c.x 0.0193 (-0.0346)

0.0156 (-0.015)

0.104*** (-0.0179)

-0.0268 (-0.056)

0.00722 (-0.0555)

-0.00302 (-0.0715)

0.00392 (-0.0308)

0.0378 (-0.101)

11.yr#c.x -0.03 (-0.0375)

0.0704*** (-0.0151)

0.0931*** (-0.0171)

0.0648 (-0.0646)

0.0677 (-0.0497)

-0.0958 (-0.0696)

0.00385 (-0.0296)

0.146 (-0.111)

12.yr#c.x -0.00509 (-0.0341)

0.0445** (-0.0158)

-0.000849 (-0.0183)

-0.0453 (-0.0583)

0.00734 (-0.0502)

-0.103 (-0.0653)

-0.0122 (-0.028)

0.0158 (-0.102)

13.yr#c.x -0.0857* (-0.036)

0.0146 (-0.0165)

-0.0267 (-0.0171)

0.033 (-0.0514)

-0.0566 (-0.056)

-0.103 (-0.0695)

-0.0557* (-0.0284)

0.0134 (-0.101)

14.yr#c.x 0.0424 (-0.0362)

0.0115 (-0.016)

0.0377* (-0.0167)

-0.00487 (-0.0599)

-0.0416 (-0.0539)

-0.138 (-0.0731)

-0.000129 (-0.0292)

-0.0206 (-0.152)

15.yr#c.x -0.034 (-0.0356)

0.028 (-0.0172)

-0.0142 (-0.0167)

0.115* (-0.0516)

-0.0327 (-0.0513)

-0.0156 (-0.0651)

-0.0264 (-0.0278)

0.0528 (-0.113)

16.yr#c.x -0.0156 (-0.0356)

0.0395* (-0.0179)

0.023 (-0.0182)

0.0326 (-0.0494)

-0.00804 (-0.0505)

-0.0925 (-0.0645)

-0.0159 (-0.0295)

-0.0293 (-0.171)

17.yr#c.x -0.0308 (-0.0387)

0.0550** (-0.0198)

0.105*** (-0.0181)

0.135* (-0.0544)

-0.0106 (-0.0532)

-0.142 (-0.0854)

-0.0132 (-0.0292)

0.0994 (-0.103)

N 26007 70869 62214 9189 4596 4326 21588 1914

N_g 3489 9225 8979 1263 627 612 3012 234

r2 0.3951 0.3239 0.4992 0.3249 0.2316 0.3569 0.216 0.2654

F_diff 1.55 7.36 20.17 1.31 1.71 2.01 1.54 1.26

p_diff 0.0808 0 0 0.1899 0.0451 0.013 0.0843 0.2305

Heteroskedasticity-consistent standard errors in parentheses. * significant at 5%; ** significant at 1%; *** significant at 0.1%. Yearly terms and constant not shown


Table 12 CC1 CC21 CC3 CC41 CC5 CC61 CC7 CC81

x 0.196*** -0.0225

0.191*** -0.0265

0.144*** -0.0184

0.185*** -0.0312

0.199*** -0.0293

0.163*** -0.0368

0.157*** -0.0188

0.185*** -0.0368

3.yr#c.x -0.0285 (-0.0277)

-0.0174 (-0.0359)

-0.0113 (-0.0251)

0.00615 (-0.0434)

0.0217 (-0.0437)

0.00884 (-0.0458)

0.0566* (-0.0233)

-0.0259 (-0.0495)

4.yr#c.x -0.0304 (-0.0283)

-0.00768 (-0.0335)

0.0277 (-0.0227)

-0.0147 (-0.0385)

-0.0447 (-0.0382)

0.0889 (-0.0524)

0.034 (-0.0242)

-0.0323 (-0.047)

5.yr#c.x -0.0421 (-0.0307)

-0.0186 (-0.0365)

0.0433 (-0.0245)

-0.00868 (-0.0357)

0.0101 (-0.0406)

0.108* (-0.053)

0.02 (-0.0222)

-0.0365 (-0.0473)

6.yr#c.x -0.0297 (-0.029)

-0.0266 (-0.034)

0.0254 (-0.0267)

-0.0153 (-0.0384)

0.0288 (-0.0403)

0.0531 (-0.0539)

0.0106 (-0.0237)

0.0479 (-0.0471)

7.yr#c.x -0.0147 (-0.0289)

-0.0457 (-0.033)

0.0233 (-0.0251)

-0.0525 (-0.0379)

0.031 (-0.0407)

0.0322 (-0.0496)

0.0287 (-0.0247)

0.00689 (-0.0494)

8.yr#c.x -0.00183 (-0.0294)

-0.00673 (-0.0326)

0.0125 (-0.0244)

-0.0509 (-0.0424)

0.0803 (-0.042)

0.0334 (-0.0507)

-0.00662 (-0.0228)

0.0423 (-0.0469)

9.yr#c.x -0.00528 (-0.029)

-0.02 (-0.0355)

-0.0105 (-0.0269)

0.00384 (-0.0391)

0.0629 (-0.0438)

0.113* (-0.0519)

0.0201 (-0.0235)

-0.0651 (-0.0466)

10.yr#c.x -0.0369 (-0.0285)

0.0134 (-0.0399)

0.0105 (-0.0254)

-0.0800* (-0.0399)

0.0413 (-0.0377)

0.0537 (-0.0519)

0.0483* (-0.0224)

-0.0467 (-0.0422)

11.yr#c.x -0.0776** (-0.0276)

0.0134 (-0.0371)

0.0123 (-0.0247)

0.00812 (-0.038)

0.0375 (-0.0415)

0.094 (-0.0483)

0.0397 (-0.0237)

0.0208 (-0.044)

12.yr#c.x -0.0528 (-0.0296)

-0.0348 (-0.0375)

-0.0193 (-0.0265)

-0.0228 (-0.0382)

0.0857 (-0.044)

0.0528 (-0.0495)

0.00254 (-0.023)

0.0267 (-0.0458)

13.yr#c.x -0.0608* (-0.029)

-0.0597 (-0.0363)

-0.0232 (-0.0264)

-0.00964 (-0.0407)

0.0475 (-0.0386)

0.0545 (-0.0471)

-0.0155 (-0.0235)

-0.0156 (-0.0428)

14.yr#c.x -0.0444 (-0.0307)

-0.0219 (-0.0384)

0.0045 (-0.026)

-0.0791* (-0.0386)

0.0467 (-0.0484)

0.0152 (-0.0444)

0.0115 (-0.0238)

-0.0055 (-0.0451)

15.yr#c.x -0.0532 (-0.0284)

-0.024 (-0.0351)

0.00316 (-0.0242)

-0.044 (-0.0375)

0.0338 (-0.0427)

-0.0111 (-0.0517)

0.0119 (-0.0221)

0.0153 (-0.0446)

16.yr#c.x 0.00131 (-0.0299)

-0.0339 (-0.0344)

-0.0134 (-0.0259)

-0.0467 (-0.0402)

0.0629 (-0.0435)

0.0255 (-0.0519)

0.00493 (-0.0234)

-0.0181 (-0.0472)

17.yr#c.x -0.0331 (-0.0319)

0.0152 (-0.0363)

0.0306 (-0.0263)

-0.142*** (-0.0402)

0.0853 (-0.0521)

-0.0221 (-0.0655)

0.00179 (-0.0228)

0.0212 (-0.0514)

N 18375 9624 13569 8058 8298 4077 20868 7737

N_g 2418 1191 1581 984 921 507 2352 924

r2 0.15 0.1613 0.1645 0.1445 0.2524 0.2359 0.192 0.1722

F_diff 1.71 0.79 1.28 2.75 1.3 1.47 1.93 1.35

p_diff 0.042 0.695 0.2068 0.0004 0.1957 0.1118 0.0169 0.1638



Table 13 CC82 CC91 DD1 EE11 EE12 EE13 FF11 GH11

x 0.210*** -0.0187

0.153*** -0.0209

0.248*** -0.0538

0.0742*** -0.00837

0.125*** -0.0234

0.127*** -0.00703

0.331*** -0.0152

0.294*** -0.0259

3.yr#c.x -0.0104 (-0.0273)

-0.0544* (-0.0275)

-0.0178 (-0.0608)

0.000467 (-0.0118)

0.0317 (-0.0351)

0.017 (-0.00976)

0.0178 (-0.0183)

-0.00873 (-0.0354)

4.yr#c.x -0.00654 (-0.0255)

0.0198 (-0.027)

-0.0634 (-0.0633)

0.016 (-0.0112)

0.0660* (-0.0298)

0.0280** (-0.00923)

0.00167 (-0.0182)

-0.0168 (-0.0314)

5.yr#c.x -0.00173 (-0.0252)

0.00731 (-0.027)

0.0469 (-0.0628)

0.00691 (-0.0108)

-0.000935 (-0.034)

0.0229* (-0.00905)

0.00332 (-0.019)

-0.0295 (-0.0347)

6.yr#c.x -0.0095 (-0.0239)

0.0188 (-0.0307)

-0.0447 (-0.0963)

0.0134 (-0.0104)

0.0611 (-0.0314)

0.0190* (-0.009)

0.000499 (-0.0188)

-0.00361 (-0.0332)

7.yr#c.x -0.0302 (-0.0249)

0.0173 (-0.028)

-0.0462 (-0.0705)

0.0073 (-0.0104)

0.012 (-0.0279)

0.0109 (-0.00876)

-0.0156 (-0.0188)

-0.0151 (-0.0333)

8.yr#c.x 0.000829 (-0.0235)

-0.0104 (-0.0272)

-0.0446 (-0.064)

0.00956 (-0.0102)

0.000662 (-0.0282)

0.0205* (-0.00849)

0.00961 (-0.0187)

0.0253 (-0.0343)

9.yr#c.x 0.0131 (-0.0245)

0.0254 (-0.0277)

-0.068 (-0.0831)

0.00739 (-0.0101)

0.0414 (-0.0277)

0.0215* (-0.00841)

0.0464* (-0.019)

-0.023 (-0.0333)

10.yr#c.x 0.0133 (-0.0232)

0.0193 (-0.0288)

-0.0764 (-0.0727)

0.00691 (-0.0101)

0.0614* (-0.0308)

0.0202* (-0.00835)

0.0515** (-0.0191)

0.00812 (-0.0339)

11.yr#c.x 0.0078 (-0.0245)

0.0315 (-0.031)

-0.0721 (-0.0641)

0.000221 (-0.00994)

0.0822* (-0.0328)

0.00881 (-0.00856)

0.00893 (-0.0188)

0.0154 (-0.0336)

12.yr#c.x -0.00219 (-0.0249)

0.00217 (-0.0294)

-0.0501 (-0.0629)

-0.000686 (-0.00995)

0.0276 (-0.029)

-0.00489 (-0.00838)

0.0223 (-0.0189)

0.012 (-0.0341)

13.yr#c.x -0.016 (-0.0224)

0.0341 (-0.0285)

-0.0401 (-0.061)

-0.00927 (-0.00969)

0.0463 (-0.0297)

-0.0064 (-0.00842)

-0.00371 (-0.019)

-0.046 (-0.0335)

14.yr#c.x -0.0247 (-0.0234)

-0.00394 (-0.0264)

-0.0283 (-0.0673)

-0.00469 (-0.0095)

0.0298 (-0.03)

-0.00913 (-0.00817)

-0.00636 (-0.0184)

-0.0285 (-0.038)

15.yr#c.x -0.0148 (-0.0228)

0.00271 (-0.0294)

-0.086 (-0.0617)

-0.0059 (-0.00939)

-0.00374 (-0.0361)

-0.00931 (-0.00815)

0.00112 (-0.0188)

-0.0256 (-0.0356)

16.yr#c.x -0.00981 (-0.0238)

0.00286 (-0.0293)

-0.101 (-0.058)

0.00113 (-0.00963)

0.0041 (-0.0307)

-0.00166 (-0.00829)

-0.0121 (-0.0195)

-0.0399 (-0.0333)

17.yr#c.x 0.000817 (-0.025)

0.0401 (-0.0311)

-0.0605 (-0.0668)

0.000383 (-0.00983)

0.0421 (-0.0313)

0.00208 (-0.00833)

-0.0166 (-0.0201)

-0.0109 (-0.0365)

N 19542 12267 4047 58587 8175 122007 80460 22134

N_g 2223 1446 528 8154 972 15864 9780 2643

r2 0.2243 0.1591 0.2025 0.0833 0.2134 0.1563 0.3274 0.2645

F_diff 0.59 1.23 0.93 1.47 1.64 6.01 2.5 0.8

p_diff 0.8859 0.242 0.532 0.1063 0.0574 0 0.0011 0.6783



Table 14 GH12 GH13 GH21 II11 II12 II13 JJ11 JJ12

x 0.325*** -0.0269

0.317*** -0.0145

0.235*** -0.0142

0.213*** -0.0146

0.253*** -0.0677

0.271*** -0.0325

0.194*** -0.03

0.135** -0.0481

3.yr#c.x 0.0285 (-0.0367)

0.0336 (-0.0178)

0.00987 (-0.0177)

-0.00132 (-0.0198)

-0.000561 (-0.0873)

-0.071 (-0.0407)

-0.0497 (-0.0489)

0.0624 (-0.0755)

4.yr#c.x -0.00275 (-0.0361)

0.0376 (-0.0195)

-0.0156 (-0.0179)

0.0169 (-0.0202)

-0.154 (-0.0874)

-0.0602 (-0.0438)

-0.0269 (-0.0411)

0.0484 (-0.0777)

5.yr#c.x -0.00525 (-0.0381)

-0.0023 (-0.0192)

-0.0244 (-0.0175)

0.0244 (-0.0184)

-0.105 (-0.108)

-0.00755 (-0.041)

-0.0117 (-0.041)

0.176* (-0.0769)

6.yr#c.x -0.0178 (-0.0345)

0.0148 (-0.0188)

-0.0278 (-0.0182)

0.0192 (-0.0195)

-0.102 (-0.0884)

-0.0726 (-0.0429)

-0.042 (-0.0366)

0.0586 (-0.0717)

7.yr#c.x -0.00616 (-0.0339)

0.0283 (-0.0194)

-0.0378* (-0.0181)

-0.00991 (-0.0188)

-0.0161 (-0.0897)

-0.058 (-0.0399)

-0.0322 (-0.04)

0.0234 (-0.0864)

8.yr#c.x 0.0223 (-0.0361)

0.0434* (-0.0196)

-0.031 (-0.0182)

-0.0502* (-0.0205)

-0.094 (-0.082)

-0.0557 (-0.046)

-0.00454 (-0.0411)

0.121 (-0.122)

9.yr#c.x 0.0131 (-0.0376)

0.035 (-0.019)

-0.0287 (-0.0175)

-0.026 (-0.0193)

0.0479 (-0.107)

-0.0937* (-0.0476)

-0.0346 (-0.0428)

0.0432 (-0.0628)

10.yr#c.x -0.018 (-0.0355)

0.0329 (-0.0184)

-0.0257 (-0.018)

-0.0137 (-0.0199)

0.0235 (-0.0975)

-0.110** (-0.0407)

-0.00507 (-0.0393)

0.0767 (-0.0652)

11.yr#c.x -0.0307 (-0.0361)

0.0375* (-0.019)

-0.0666*** (-0.0175)

-0.025 (-0.019)

-0.104 (-0.0958)

-0.117** (-0.0398)

0.0335 (-0.0413)

0.011 (-0.0578)

12.yr#c.x -0.0501 (-0.0357)

0.00894 (-0.0195)

-0.0671*** (-0.0172)

-0.0262 (-0.0189)

-0.0287 (-0.0788)

-0.119** (-0.0386)

-0.0749 (-0.0397)

0.0792 (-0.0658)

13.yr#c.x -0.0457 (-0.0334)

-0.00393 (-0.0188)

-0.0733*** (-0.0172)

-0.0647** (-0.0199)

-0.234 (-0.121)

-0.102** (-0.0361)

-0.0409 (-0.0384)

0.15 (-0.0781)

14.yr#c.x -0.044 (-0.0337)

-0.00533 (-0.0193)

-0.0913*** (-0.017)

-0.0269 (-0.0195)

-0.0654 (-0.0756)

-0.116** (-0.0372)

-0.0176 (-0.0439)

-0.0265 (-0.0756)

15.yr#c.x -0.0495 (-0.0337)

-0.0591** (-0.0191)

-0.0968*** (-0.0168)

-0.0322 (-0.0183)

-0.109 (-0.0801)

-0.115** (-0.0395)

-0.0216 (-0.0433)

-0.0248 (-0.0685)

16.yr#c.x -0.0147 (-0.0358)

-0.0480* (-0.0199)

-0.0749*** (-0.0172)

-0.0114 (-0.0189)

-0.0064 (-0.0735)

-0.131** (-0.0399)

-0.0978* (-0.0382)

0.266** (-0.092)

17.yr#c.x -0.011 (-0.0357)

-0.0323 (-0.0198)

-0.0865*** (-0.0176)

-0.0405* (-0.0199)

-0.0433 (-0.0873)

-0.0961** (-0.0372)

-0.0571 (-0.0476)

-0.0492 (-0.0774)

N 28119 90489 90123 26370 3750 11910 7785 2145

N_g 4032 11724 14196 3531 525 1659 1047 327

r2 0.2531 0.2867 0.1517 0.2576 0.1373 0.1862 0.1459 0.2067

F_diff 0.99 5.23 8.33 3.21 1.19 1.86 1.34 2.21

p_diff 0.4577 0 0 0 0.2786 0.023 0.1725 0.0061



Table 15 KK13 KK1_ LL11 MN11 MN21 RS11 RS21

x 0.281*** -0.0288

0.745*** -0.13

0.198*** -0.0396

0.240*** -0.0111

0.177*** -0.0181

0.276*** -0.0409

0.206*** -0.0113

3.yr#c.x -0.00293 (-0.0334)

-0.157 (-0.114)

0.075 (-0.0503)

-0.00249 (-0.0139)

-0.00602 (-0.0243)

0.0206 (-0.0603)

-0.00474 (-0.015)

4.yr#c.x -0.00464 (-0.0409)

-0.171 (-0.144)

0.031 (-0.0511)

0.00199 (-0.0133)

-0.0182 (-0.0237)

0.0437 (-0.0469)

-0.0173 (-0.0151)

5.yr#c.x -0.0333 (-0.0366)

-0.205 (-0.148)

0.0831 (-0.0507)

-0.0191 (-0.0141)

-0.0207 (-0.0219)

0.0097 (-0.05)

-0.026 (-0.0148)

6.yr#c.x -0.0356 (-0.0383)

-0.237 (-0.154)

0.0265 (-0.0547)

-0.00587 (-0.0137)

-0.0167 (-0.022)

-0.000533 (-0.0531)

-0.0162 (-0.0147)

7.yr#c.x -0.0642 (-0.0366)

-0.161 (-0.15)

0.0475 (-0.0553)

-0.0222 (-0.0135)

-0.0764** (-0.0244)

0.00557 (-0.0498)

-0.0391** (-0.0147)

8.yr#c.x -0.00118 (-0.0375)

-0.401** (-0.149)

0.0252 (-0.0539)

-0.0176 (-0.0137)

-0.0192 (-0.022)

0.00565 (-0.0499)

-0.0457** (-0.0139)

9.yr#c.x -0.0202 (-0.0396)

-0.23 (-0.159)

-0.0201 (-0.054)

-0.0148 (-0.0133)

-0.0292 (-0.0211)

0.0192 (-0.0497)

-0.0395** (-0.014)

10.yr#c.x -0.0418 (-0.0397)

-0.25 (-0.149)

0.0248 (-0.0497)

-0.00878 (-0.0132)

-0.0414* (-0.0206)

-0.0383 (-0.0457)

-0.0536*** (-0.0143)

11.yr#c.x -0.053 (-0.0394)

-0.148 (-0.152)

0.0528 (-0.0469)

-0.017 (-0.0132)

-0.0348 (-0.0208)

-0.026 (-0.0484)

-0.0458** (-0.0147)

12.yr#c.x -0.066 (-0.0359)

-0.355* (-0.159)

0.0814 (-0.0499)

-0.0438*** (-0.0129)

-0.0382 (-0.0204)

-0.0534 (-0.0474)

-0.0522*** (-0.0141)

13.yr#c.x -0.127*** (-0.0355)

-0.272 (-0.151)

0.073 (-0.0507)

-0.0578*** (-0.0131)

-0.0699*** (-0.0207)

-0.0603 (-0.0477)

-0.0646*** (-0.0137)

14.yr#c.x -0.0892* (-0.0368)

-0.462** (-0.149)

0.0832 (-0.0464)

-0.0559*** (-0.0128)

-0.0474* (-0.0203)

-0.0687 (-0.0484)

-0.0562*** (-0.0139)

15.yr#c.x -0.104** (-0.0393)

-0.3 (-0.156)

0.0188 (-0.0457)

-0.0652*** (-0.0128)

-0.0455* (-0.0206)

-0.0571 (-0.0468)

-0.0540*** (-0.0141)

16.yr#c.x -0.111** (-0.0387)

-0.302* (-0.151)

0.0536 (-0.0486)

-0.0496*** (-0.0131)

-0.0491* (-0.0209)

-0.0291 (-0.0464)

-0.0640*** (-0.0144)

17.yr#c.x -0.130*** (-0.0353)

-0.480*** (-0.134)

0.0082 (-0.0537)

-0.0515*** (-0.0132)

-0.0443* (-0.0213)

-0.0446 (-0.0535)

-0.0470** (-0.0147)

N 14472 4098 11568 123915 40875 12906 82080

N_g 2055 606 1650 16557 5988 1908 10557

R-squared 0.2102 0.3502 0.2131 0.2155 0.1279 0.2537 0.1328

F_diff 3.22 2.95 0.97 8.57 2.36 1.58 3.96

p_diff 0 0.0001 0.4867 0 0.0022 0.0718 0 Heteroskedasticity-consistent standard errors in parentheses. * significant at 5%; ** significant at 1%; *** significant at 0.1%. Yearly terms and constant not shown


Chapter 3 – Essay 2 (Productivity and Markups) Introduction Competition impacts productivity across many facets of the economy; innovation, reallocation of resources, firm dynamism. While competition between firms helps keep prices and costs low, it also pushes firms to innovate, improving themselves to retain or improve market share. Low competition, however, means that there is little need for firms to change and invest in potentially risk ventures. This means that productivity growth will be lower when competition between firms is low. Productivity growth is useful to enable income and real wages to grow and potentially for more leisure time (Nolan et al., 2018). Productivity growth means more output per input, reducing the environmental cost for some production (Nolan et al., 2018).

Maré & Fabling (2019) estimate the relationship between productivity and competition by combing various competition measures into composite indicators. They find some evidence that the tail of unproductive firms might be truncated with greater competition. Overall, they find limited evidence for a direct relationship between competition and productivity.

Lee & Choi (2012) examine export intensity, markups, and productivity in Manufacturing firms in South Korea. They found that while there was exporters did have higher markups, the gap between exporters and non-exporters reduced over time. Their finding suggest an inverted U-shaped relationship between both productivity, markup and export intensity.

Baqaee & Farhi (2020) develop a theory for aggregation in inefficient economies which can accommodate distortions such as markups and frictions to resource misallocation. Using US data between 1997 and 2015, they find that 50% of aggregate TFP growth is accounted for by greater allocative efficiency. They suggest that removing the misallocation that is resulting from the markups would raise aggregate TFP by about 15%.

Productivity in New Zealand Productivity growth in New Zealand is lacking (Conway, 2018). While New Zealand once experienced a relatively high living standard (measured in GDP per capita), New Zealand’s GDP per capita is currently around 90% of the OECD average. New Zealand’s previously high living standard relied on the colonial ties with the United Kingdom, which gave preferential trade for New Zealand exports. Once the UK joined the European Community in 1973, living standards fell from about 125% of OECD average to 80% by the early 1990’s (Conway, 2018). Productivity growth is still poor, averaging around 1 percent currently (Nolan et al., 2019).

The main question of this essay is do markups explain poor productivity growth in firms?

Method Similar to essay one, except after calculating firm markups, you put back the markups into one of the equations to solve for firm productivity. This will be compared with the standard approach of estimating productivity growth using the LBD.


Chapter 4 – Essay 3 (Markup Determinants) Outline Essay 3 will examine the markup determinants in New Zealand using the firm-level and industry-level markups (PCM) estimated in essay one. Our goal is to explain the variation in markups using firm and industry characteristics. Ponikvar & Rant (2007) examine the firm specific determinants of markups for Slovenian manufacturing firms. They find that a firm’s markup can be explained mostly by a firm’s productivity, capital and labour costs, along with its market power and organisational characteristics.


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Timeline Schedule Task January 2020 Ph.D. confirmation February 2020 – June 2020 Complete essay one July 2020 – January 2021 Complete essay two February 2021 – November 2021 Complete essay three November 2021 Submit draft copy of the dissertation February 2022 Submit final bound copy of dissertation

I do not foresee any risks to successful completion of my PhD.

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econfin.massey.ac.nzeconfin.massey.ac.nz/school/documents/seminarseries... · Abstract Competition between firms is important for a well-functioning , efficient economy. Aside from