2. Literature Review
2.8. Political Economy of the US Construction Industry
true if one were to assume perfect competition. However, in practice it appears to be much less the case as marketplaces tend not to enjoy perfect competition at all. Prices may be impacted by market conditions, but inevitably they are determined by the firm whether it is a producer, wholesaler, or retailer. The costs of products are strategically determined as opposed to cost-determined which furthermore asserts that the cost of products are influenced in concordance with their prices. While the cost of products may be determined by market forces, the final price is determined by the firm.
Importantly, these final pries set by the firm occur almost always at a markup. This is where profit-taking occurs. Such is the appropriate contribution from Post-Keynesian price theory. Beyond this though, the usefulness of the theory recedes.
2.8. Political Economy of the US Construction Industry
Increasingly in today’s politically polarized world, businesses are more and more being impacted by domestic politics. The tariff trade war President Trump waged against the Chinese is the most notable example of how political economy impacts markets in spite of its limited impact on equipment prices. Political risk analysts are greatly concerned with the relationship between market forces and public policy decisions. In fact, some have even argued that the US political system is one of the greatest threats to global free-trade in the history of the post-World War II era. Without a doubt, governments whether they are free-market capitalist or one-party state communist regimes are increasingly inserting themselves into marketplaces. Even without this line of argument, governments around the world and in the US have for decades been involved in the appropriation of funds towards public works projects which provide funding for commercial activities. Just examining the US, the government provides contracts to private companies for the construction of publicly funded roads, bridges, and other projects. Without a doubt, there appears
‧
to be a relationship between public construction spending (PCS) and GPD as can be seen in Figure 2-6.
Different political parties have different political motivations for supporting different appropriations. For instance, Democrats are more likely to increase public spending on healthcare subsidies or social welfare programs. This is most evident with President Obama’s increase in healthcare spending by almost $1 trillion US dollars (Rector, 2012). Republics on the other hand are more likely to decrease taxes which is effectively a spending stimulus since it decreases the amount of money being ushered into the government’s coffers as could be seen by President Trump’s tax cut legislation (Furman and Summers, 2019). Of course, there are likely instances where social welfare spending increased under Republicans and tax cuts were enacted under Democrats, but overall these are the party platforms. This begs the question of what could be the impact of political party governance on equipment prices and construction spending.
Before diving into these questions though, it seems clear that there is a strong and positive linear relationship between PCS and NRCS (see Figure 2-5). The logical directionality of this relationship should be that the public sector impacts the private sector, but it may be the other way around as was evidenced by the VAR Granger causality tests conducted on the matter. Moreover, there is a positive linear relationship between NRCS and CEPs in addition to NRCS and GMPs (see Figure 2-4 for the latter relationship). Therefore, it should be the case that increasing PCS or NRCS would in turn increase CEPs and GMPs. Naturally, when more demand is created for these products via the influx of expenditures and project availability in the marketplace, then capital is likely to rise.
‧
國立 政 治 大 學
‧
N a
tio na
l C h engchi U ni ve rs it y
Figure 2-4 GMPs and PCS One-Year Differenced
‧
國立 政 治 大 學
‧
N a
tio na
l C h engchi U ni ve rs it y
Figure 2-5 NRCS and PCS One-Year Differenced
Clearly, government expenditures may have an impact on prices. Yet, what does party affiliation do to these prices? Can one particular party be responsible for more expenditures than another? Common knowledge of the Republican/Democrat paradigm might insinuate that Republicans are inclined to avoid all public expenditures. Indeed, research found that the number of Democratic voters in a district determines the degree to which federal money flows to that district (Levitt and Snyder, 1995). Moreover, President Obama passed a large infrastructure spending bill worth $305 billion in 2015 which was dolled out over the course of five years (Berman, 2015). Yet, without the negotiation between his administration and the Republicans in the Senate, the bill would have never been passed. Indeed, Republicans under the leadership of
‧
國立 政 治 大 學
‧
N a
tio na
l C h engchi U ni ve rs it y
President Bush in 2005 passed a bill for infrastructure spending worth $286 billion (McNamara, 2012). At the time, congress was controlled by Republicans in the House and the Senate.
Figure 2-6 PCS and GDP One-Year Differenced
Political economy tends to force the analyst to dive deep into extremely complex and oftentimes qualitative conclusions. Keynesianism asserts that when the economy is running too slow the government should spend more and when it is running too hot spending should limit spending (Wildavsky, 1980, pg. 37). Figure 2-6 suggests that at least with construction spending the US government is clearly not adhering to this philosophy at least in the fiscal sense.
What then accounts for not only the acceptance, but the full-throated embracement of a fiscalism that runs contrary to one of the most widely accepted economists’ worldviews is elusive.
‧
Different legislation that is passed is sometimes partisan and sometimes bi-partisan. Sometimes, lawmakers allow for certain legislation to pass due to specified ‘kickbacks’ or components of the bill that allots certain expenditures towards their district if in the US House of Representatives or state if in the US Senate. Other times, lawmakers may actually be voting based on their publicly announced ideology while other times they may go against this built-up persona to vote on their own conscious for the sake of patriotism and the country. Overall, voting motivations likely does not follow some sort of set rules.
Chapter 3 3. Methodology
The method of this thesis includes three series of multivariate ordinary least squared (OLS) regressions and a number of vector autoregressions (VARs). The reason behind using these methods is primarily due to the fact that they are the most commonly used statistical methods in analyzing the relationship between variables when holding time constant as the OLS models do in this thesis and for time series respectively. For the OLS models, three primary dependent variables were used in this research. The first is CEPs extracted from the Federal Reserve Bank of St. Louis (2019). The second dependent variable in the OLS models was general machinery prices (GMPs) and the third was excavator, cranes, and dragline pries (ECDPs) both of which were one-year differenced. Both of these variables were also extracted from the Federal Reserve Bank of St.
Louis.
The datapoint figures were monthly from either January 1993 until October 2019. One of the variables – data processing CPI – only had data available from December 2001 until October 2019 and another variable – NRCS – had data from January 2003 until October 2019. Because of this time series difference, various regressions were conducted holding these variables constant and included to determine the impact of the decrease in the size of the sample since it the decrease
‧
accounted for around 30 percent of the entire dataset or specifically 108 observations. Because this difference was so large between the variable counts, it is important to keep in mind that the factor of the difference between sample sizes of the variables may have impacted the regression results since the regressions can only include datapoints where all variables include data and therefore months of data are simply dropped for all of the variables. Using regressions with these variables constant and then included is also useful in determining the degree to which variables are correlated including and excluding data from before the 2001 Tech Bubble. The reasons for this are described in the analysis section.
The reasoning behind using three primary dependent variables lays within the measurement of the various types of equipment PPI. Primarily, the methodology for measuring equipment PPI or what constitutes ‘construction equipment and machinery’ is at the discretion of the Bureau of Labor Statistics (2015). This is the organization which collects the PPI data that is used by the Federal Reserve Bank of St. Louis (2019). Primarily, the index of construction machinery and equipment only includes a certain number of construction equipment pieces and excludes many common pieces of construction equipment. Specifically, the index includes prices of non-agricultural tractors such as mounting machinery, shovel loaders (such as skid steers, crawlers, and integral design backhoes), dozers, and self-propelled skidders (Ibid. pg. 93). This index therefore does not include many pieces of equipment that are important components to construction processes such as pile driving equipment, draglines, excavators, cranes, pile boring machinery, pavers, compactors, or others. This research attempts then to resolve this problem by including various indexes of equipment prices.
The second dependent variable used was one-year differenced GMPs. The justification for using this variable as a comparison to CEPs is that it is a measurement of machines of which many
‧
are used in the construction industry such as overhead lifting cranes and equipment, pallet moving equipment, transportation equipment, and aerial work platform lifts. Although this equipment is not directly related to the physical construction of infrastructure, they are oftentimes critical components of the construction process. Because of this, determining correlates of general-purpose machinery can provide information on correlates of construction-related equipment.
The third dependent variable used was one-year differenced ECDPs. Since most of the projects which require either the first or second set of machinery also require the set of machinery, it is difficult then to conduct an analysis which only focuses on one primary component of machinery while ignoring the others. Such an analysis would be incomplete and too narrowly focused. Another option would have been to create a separate index which uses the average of all three of the equipment PPIs. However, an index like this would then not truly reflect any of the indexes at all and would be a statistically biased response variable. Moreover, regressing all three variables separately demonstrates interesting differences in the determinants of price action on specific types of machinery.
The justification for using 11 different models is primarily to test for spuriousness across categorizations of variable typologies in conjunction with testing or spuriousness across differentiated time periods. The variables used to test differences in time periods includes data processing CPI and NRCS. In the first series of regressions, Model 1 tests technological regressors against the dependent variable while Model 2 implements differentiated time periods. Model 3 adds macroeconomic regressors while Model 4 implements differentiated time periods. The efficacy of Model 5 is similar to that of Model 4. Model 6 excludes the time differential variables while including the recession dummy variable while Model 7 then includes the time differential variables. Model 8 then introduces commodity variables to the totality of the model while
‧
excluding the time sensitive variables after which Model 9 includes the time sensitive variables.
Model 10 technological variables and time variables and Model 11 excludes technological variables and commodity variables while including both time variables. The reason why Model 10 and Model 11 do not follow the same pattern as Model 1 through Model 9 is due to the fact that results did not change between Model 10 and a model which included time variables. This model was excluded in the results because it did not add any value to the analytical process since the results were the same as Model 10. The same was the case for a model similar to Model 11 which did not include time sensitive variables and so it too was excluded while Model 11 with time sensitive variables was included. The same exact pattern for Regression Table 1 is used for Regression Table 2. The reason the same model is not used for Regression Table 3 is because the dependent variable in the third series of regressions is already time sensitive in that the inclusion of variables within different models does not change the number of observations in the regressions.
3.1. Descriptive Statistics and Properties of Variables
The different models using the ordinary least square method is constant in that all independent variables are the same, again though with the last model using one-month differenced data. The rationalization of the framework is deeply embedded within the previous work conducted in the academic literature. Since most of the literature does not directly address correlates of CEPs, the model used to determine the correlates of CEPs is prefaced on the literature surrounding the correlates of equipment prices in general. Primarily, this based upon the propensity for the literature to focus on technology, macroeconomic indicators, and the inputs associated with the cost of production.
‧
Table 3-1 One-year Differenced Variable Codes and Descriptions
Variable Code Variable Description
equipppi Construction equipment prices (CEPs)
excadargcr~e Excavator, crane, and dragline prices (ECDPs)
generalmac~y General machinery prices (GMPs)
nasdaq The NASDAQ Composite
dataproces~g Data processing CPI
rdemplyscali R&D Employees in California
effectivf~s Effective federal funds rate
recesdummy Recession dummy
techcpi Technological hardware CPI
ironsteel Iron and steel CPI
ratioinven~s Ratio of inventory over sales for manufacturers
ipbusequip Industrial production of business equipment
gdp Gross domestic product (GDP)
diesel Diesel CPI
dxy Trade weighted dollar index
nonrescons~g Non-residential construction spending
There are three primary groups of independent variables. The first group of variables that was regressed included technological variables. These included the NASDAQ Composite, R&D employment in California, technological hardware CPI, and data processing CPI. The NASDAQ Composite acts as a proxy for technological investment. R&D employment acts as an important
‧
國立 政 治 大 學
‧
N a
tio na
l C h engchi U ni ve rs it y
variable in quantifying the degree to which technological advancement is being pursued beyond monetary expenditures. In other words, it can be used to quantify or as a proxy of the abstract concept of human capital or investment in human capital, both of which plays key roles in technological advancement while also suffering from the difficulty of quantification.
Technological hardware CPI simply quantifies the cost of the materials used to produce technological equipment. Finally, data processing CPI is also another variable which attempts to quantify the impact of human capital on equipment prices through human capital and technological advancement.
Table 3-2 Descriptive Statistics of Ordinary Least Square Regression Variables
The second set of variables in the model are all associated in some form or another with in general macroeconomic indicators, supply and demand, monetary policy, and overall economic conditions. These include the effective federal funds rate, the ratio of equipment inventory over equipment sales by private companies, the industrial production of business equipment, the OECD’s monthly measurement of US GDP, nonresidential construction spending, and a US recession dummy variable.
‧
The effective federal funds rate is not only a fundamental component to monetary policy and can proxy as the government’s impact on market forces, but interest rates are also crucial in borrowing costs, investment expenditures, and acts as the cost of capital (equipment prices). The ratio of equipment inventory over equipment sales, while not a traditional measurement, provides information on the demand of construction equipment and the speculative appetite of commercial actors who purchase goods from producers. Since companies that purchase equipment directly from producers tend to sell this equipment to contractors or other companies which intend to use equipment, the ratio of inventory to sales of provides insights on the relationship between speculative demand and market demand in reference to the price in which the equipment is being quoted. The industrial production of business equipment quite simply measures the supply of construction equipment. GDP, while having been quoted as being correlated with CEPs in the academic literature, also likely impacts the price of construction equipment, but in a counterintuitive way given the lagging nature of equipment prices and the state of the business cycle. Nonresidential construction spending is chosen over all construction spending since residential construction is much more speculative in nature over the past few decades in the US, does not reflect the demand of the construction market, and suffered a dramatic bubble in 2008 which was the result of dramatic market distortions. Finally, a recession dummy is used to test if any variables were spurious and also to determine what the direct relationship between equipment prices and the business cycle is.
‧
國立 政 治 大 學
‧
N a
tio na
l C h engchi U ni ve rs it y
Table 3-3 Correlation Coefficient Matrix of One-Year Differenced OLS and VAR Regression Variables
‧
Manufacturing unemployment is held constant primarily due to the fact that machines are primarily not produced by individuals, but are rather produced by other machines. Because of this, employment in the manufacturing industry likely plays a very small role in the price of the production of construction equipment in the United States. Taking this factor into consideration, the model is based upon the more recent impact of technological progress which has over the past few decades increased at greater speeds than prior years past. Moreover, the regressions use a form of employment, R&D employment, which is strongly and positively correlated with other forms of employment. Therefore, another employment figure or proxy variable more directly related to the production of construction equipment would have been redundant and unnecessary.
One question that arises within the variance of the variables which will be seen in the results section is the issue of why do financial markets impact equipment prices so consistently and so strongly compared to other variables. This is especially the case for the NASDAQ Composite which enjoyed a strong negative relationship with equipment prices of all kinds in the OLS models, but also enjoyed strong relationships with all types of equipment prices in in the VAR models. On the other hand, commodity prices such as the DXY, diesel prices, and iron and steel prices also were shown to have very strong and consistent impacts on equipment prices (mostly positive except for the DXY which was positive in the short-term and negative in the long-term).
Why is this the case? First of all, it may be due to the nature of the data and the impact of transformation. Financial markets tend to be much more volatile than macroeconomic data points.
Since financial markets are constantly spitting out new data points every second with data points sometimes measured in less than one second increments, these markets can suffer from dramatic outliers. Oftentimes, these outliers correct themselves and are retraced rather quickly. A good example of the rapidity of financial markets is what is known as flash crashes where a financial
‧
國立 政 治 大 學
‧
N a
tio na
l C h engchi U ni ve rs it y
instrument can drop by an extremely large amount, but recover in a matter of minutes or seconds occurring in a ‘flash’. Even in the event that flash crashes do not occur, financial markets at a daily or weekly level can change dramatically. This inevitably means there may be more extreme examples of outliers.
At any rate, the leptokurtotic nature of differenced variables is more pronounced in financial markets as can be seen in Figure 3-1. The first histogram of CEPs clearly shows a more
At any rate, the leptokurtotic nature of differenced variables is more pronounced in financial markets as can be seen in Figure 3-1. The first histogram of CEPs clearly shows a more