Abstract

1. Introduction

2. Literature Review

3. Materials and Methods

4. Hypotheses, Research Model and Methodology

5. Results

6. Conclusions

7. Limitations of the Proposal

8. Future Lines of Research

References

Abstract

Organizational culture (OC) is one of the key factors that enables the development of innovation. A great deal of research has analyzed the characteristics that make up an innovation-focused OC; however, none have unified them. This article analyzes and integrates the literature on the characteristics of an innovation OC. Its objective was to test whether there is a specific set of distinctive characteristics of an innovation-focused OC that, once identified, can be implemented and developed by firms. To this end, this study proposed and tested a model for measuring the innovation orientation of an OC. This study collected samples from small- and medium-sized companies from the oil-producing states of Mexico, obtaining information from 176 companies. To determine the group of characteristics that make up an organizational culture of innovation (OCI), the authors applied non-experimental, descriptive, transactional research with a quantitative approach. Results were obtained through the application of the following statistical techniques: Bonnet test, KMO index, Bartlett’s sphericity, Chi-square, confirmatory factor analysis, Cronbach’s alpha, principal component analysis and structural equations, which made it possible to measure the level of innovation of the OCI of these companies and to determine the characteristics that comprise it. The proposed model allowed us to identify the existence of a specific group of behavioral characteristics that emanate from a company’s personnel and another group of characteristics that arise from the company itself. Both integrated groups determine whether the OC is focused on innovation. The results also confirmed that the level of innovation of a company depends greatly on its personnel. The authors found no previous work that analyzed these characteristics from the joint perspectives of employees, middle managers and managers. This research considered the opinions of these agents, which confers greater veracity to the findings obtained. The limitations and implications are listed at the end of the study.

 1. Introduction

Due to the current trend of globalization, organizations are immersed in a changing environment, where competitive advantages are difficult to maintain. Therefore, it is important to identify, renew and combine resources and capabilities so that companies can consider alternatives that guarantee their survival and success [1], as well as the integration of permanent innovation as a strategy to face this unstable environment [2,3]. Therefore, the analysis and management of the development of innovation within companies are essential. In this sense, Schumpeter [4] emphasized the importance of organizations focusing on innovation, stating that a country without innovative organizations is condemned to technological and economic backwardness and, therefore, to poverty.

Research carried out in recent decades to determine the factors that focus a company on innovation have all assigned a prominent role to OC (organizational culture) [5,6]. Along these lines, Souto [7] affirmed that by focusing OC on innovation, companies can achieve success and, at the same time, surpass the competition in the development of opportunities.

A review of the literature shows that since the formal study of OCI (organizational culture of innovation) began in 1978 with the work of Roger Harrison [8], many researchers have focused on analyzing the behavioral characteristics that make it up [5,9]. However, there is still no epistemology regarding them [10].

Innovation is increasingly important in growth processes, in facing the economic crisis derived from market globalization and, even more so, in the process leading to economic development and social welfare. According to the National Survey on Productivity and Competitiveness of Micro, Small and Medium-Sized Enterprises [11], 97.6% of the approximately 4 million enterprises in Mexico are microenterprises, which account for 75.4% of the economically active population, followed by small enterprises, which account for 2.0% and 0.4% of workers, while medium-sized enterprises account for 13.5% and have 11.1% of the total number of current workers. Of all of them, only 35% invest less than 2% of their revenue into innovation initiatives, 32% between 2% and 5%, and 21% between 6% and 10% of the company’s revenues [12].

1- Introduction

2- Data and empirical methodology

3- Empirical results and analysis

4- Conclusions and policy implications

References

Abstract

Using a time-varying parameter structural vector autoregression with stochastic volatility (TVP-SVAR-SV) model, we decompose the structural shocks of oil price fluctuations into four types: oil supply shocks, global demand shocks, domestic demand shocks and oil-specific demand shocks. We then analyze the time-varying effects of these oil price shocks on China’s inflation at the import, production and consumption stages using monthly data from January 1999 to December 2016. The results show that the pass-through effects of the four types of oil price shocks on China’s inflation at each stage are time-varying and that there are significant differences at different time horizons and points in time. The analysis of the variance decomposition shows that the effects of oil price shocks on China’s inflation at each stage are incomplete and decrease along the price chain. The increase in oil prices driven by oil-specific demand shocks is the most important cause of China’s inflation at the import and production stages during the full sample period, while China’s inflation at the consumption stage is mainly affected by domestic demand shocks. In addition, the inflationary effects of oil price shocks have been dramatically weaker since the international financial crisis compared with before the crisis.

Introduction

As an important component of production and transportation costs, oil price plays an indispensable role in inflation (Prat and Uctum, 2011; Myers et al., 2018). In recent years, with the acceleration of China ’s industrialization and urbanization, the demand for crude oil has been very high, and China has become the second largest crude oil consumer after the U.S. , consuming 578.7 million tons , or approximately 13.1% of global oil demand , in 2016 (BP Statistical Review of World Energy, 2017). Therefore, China’s sustainable economic development is highly dependent on oil (Wen et al., 2016; Gong and Lin, 2017; Zhang et al., 201 7 ; Gong and Lin, 2018a). However, domestic production cannot supply the amount of oil required for economic growth, and China must import a large amount of crude oil from the international market. Thus, its dependence on crude oil imports is relatively high. In 2016, China imported 381 million tons of crude oil, and its dependence on foreign crude oil increased to 65% (China Petroleum Enterprise Association, 2017), exceeding the security limit of 50%. In addition, with the adjustment of the domestic oil price formation mechanism in 2001, China’s oil prices gradually integrate d with international oil prices (Li et al., 2017) .

1. Introduction

2. The data

3. Methodologies: the SVAR models and copula modeling

4. Results

5. Concluding remarks

Appendix A. Supplementary data

References

Abstract

Following the adoption of new techniques of shale and fracking by U.S. oil companies, a structural vector autoregression model (SVAR) complements studies on why Brent and WTI started to diverge around early2011. Using monthly data from 2000 to 2018, we decompose oil supply into: world oil (excluding U.S.), U.S. conventional (non-tight) oil and U.S. tight oil. We examine the variance decomposition of stock returns for the aggregate market (S&P 500), the S&P Energy sector and Chevron and Exxon Mobil oil companies, and we further identify differences between two subsamples from 2000 to 2010 and 2011 to 2018, respectively. We find that supply considerations (especially due to tight oil) become more important in the subsample after 2011, not only for individual oil companies but also for the aggregate market and energy sector: Supply shocks due to tight oil explain in our benchmark model between 29% (S&P 500) and 31% (S&P Energy) of the variance in stock returns after 24 months and between 28% and 29% for oil companies. None of these are statistically significant in the pre-2011 subsample. Among impulse responses, tight oil production responds positively to disruptions in world oil, and U.S. stock returns respond positively to oil price shocks and respond negatively to tight oil shocks which is a further finding while being consistent with the literature. Copula modeling uncovers stronger tail dependences in the second subsample for the interactions during downturns and upturns among global demand, crude oil prices and stock markets.

Introduction

In an influential paper, Kilian (2009) proposes a structural vector autoregression (SVAR) approach to global oil supply, an index of global real demand and real oil prices. The latter are typically calculated by oil prices denominated in U.S. dollars (such as the U.S. refiner cost of oil, crude WTI or Brent oil prices) deflated by the U.S. price index. Its main message is that supply shocks are relatively less important than demand shocks, themselves captured by a global index of commodity prices, which became known as “Kilian index”. Due to its impact in the academic literature, several extensions of Kilian (2009) have been developed, usually adding one series in the SVAR to consider the effects of supply and demand not only on real oil prices but also on other financial markets, including U.S. stock prices by Kilian and Park (2009), U.S. bond prices by Kang et al. (2014), exchange rates by Chen et al. (2016a), economic policy uncertainty by Kang et al. (2017) and U.S. consumer sentiment by Güntner and Linsbauer (2018). Another stream of papers has focused on misspecifications of the basic SVAR model to allow for inventories and speculative trading put forward by Kilian and Murphy (2014).

Some authors have examined the causes of why Brent and WTI started to diverge. This is very important because the two oil prices serve as major benchmarks for the world and the U.S. For example, Scheitrum et al. (2018, p. 463) aim to “explain what happened in 2011 when the spread diverged from historical levels, sending WTI to more than a $20 discount under Brent and resulting in the WTI being viewed as a broken benchmark for a time.” Fig. 1a illustrates the divergence between Brent and WTI using monthly data making clear the shift in the spread after 2011 when advances in oil production became evident, as shown in Fig. 1b which compares the world (excluding U.S., right axis), U.S. non-tight and U.S. tight oil production over our sample period. Interestingly, the oil spread started diverging from historical levels at a time when U.S. tight oil production started rising. In this paper we will therefore refer to the first subsample running from 2000 to 2010 and the second running from 2011 to 2018. This paper merges the SVAR literature on real oil prices and the divergence in crude oil prices. Our contribution is not on the causes of the divergence but on the implications of this divergence for stock returns. We believe this is of interest because both academic studies and the financial press have suggested that the vast increase in U.S. oil production may be responsible for the divergence in oil prices, which itself may impact how oil price shocks are transmitted into the financial markets.

In this work, the impact of oil price shocks on the stock exchanges of three countries in the Caspian Basin − Iran, Kazakhstan and Russia − was examined through a structural vector autoregression (SVAR) model. For the research, monthly data from the stock exchanges, the oil price, inflation, industrial production and exchange rates were collected between March 2005 and June 2018. According to the results of variance decomposition, in these three countries, the impact of negative oil price shocks on the stock exchanges was stronger than that of positive shocks, and constituted the largest source of changes in the three stock exchanges. In addition, according to the results of impulse response functions, the response of the stock exchanges in the three countries to negative oil shocks was highly significant. Consequently, these countries should avoid macroeconomic imbalances and falls in their stock exchanges due to the negative impact of the oil price, and should instead focus on industrial production that will contribute to exports. In this way, they can avoid the negative impact of oil price shocks on their stock exchanges.

Introduction

In this work, the main aim was to examine the asymmetric impact of Brent oil price shocks on the stock exchanges of three Caspian Basin countries through structural vector autoregression (SVAR). It is known that Caspian Basin countries are not globally strong in medium- and high-tech industrial production,1 and that they are highly dependent on oil production for export. Moreover, their own economies are susceptible to positive or negative impacts from oil prices. In this research, it is assumed that stock exchanges are largely dependent on fluctuations in the oil price in these countries. Oil price shocks are taken to be one variable that exerts a significant impact on their stock exchanges. In addition, it is assumed that the exchange rate and inflation can also influence the stock exchanges. Hence, the main question is how the oil price shocks, exchange rates, inflation and industrial production influence the stock markets. If there are significant impacts on stock exchanges, which variables show the strongest effects and how great are the impacts in these countries? Furthermore, as it is assumed that the oil price can have a significant impact on stock exchanges, then what about the impact of positive or negative shocks? In other words, which shocks have the stronger impacts on stock exchanges? This work seeks to answer these questions.

The decision over asset holding is traditionally premised on the double-edge objective of returns maximization and risk minimization. While the class of assets held by a typical investor depends on his attitude towards risks, an optimal investment portfolio requires a strategic combination of alternative assets (commodities, T-bills, stocks etc). To this end, our paper analyzes the role of crude oil prices in predicting stock returns, in addition to the traditional factors, particularly the returns on risk-free assets (such as, T-bills) as enunciated by the Capital Asset Pricing Model (CAPM). We also consider the possibility of nonlinearities in the nexus between crude oil prices and stock returns of nine major oil and gas companies that are currently listed on the Nigerian Stock Exchange over the period of January 2014 to November 2019. Our results show significant in-sample predictability of stock returns using crude oil prices, thereby affirming our argument that oil price matters in the predictability of stock returns for some listed oil and gas firms in Nigeria. We also offer evidence for the role of asymmetries in the predictability of stock returns for the majority of the listed oil and gas companies in Nigeria. By implication, the increasing exposure of the earnings, vis-a-vis, � the share prices of some major oil and gas companies to negative changes in global oil prices suggests the need for diversification of their scope of operations.

Introduction

The Nigerian economy is largely oil dependent, making it highly sensitive to movements in global crude oil prices. Oil price volatility matters for the investment decisions of prospective investors in Nigeria’s oil and gas sector, most especially. This in turn affects the profitability of firms and hence the values of their shares on the domestic stock market (see, for instance, Gupta, 2016 and Soyemi et al., 2017). In the words of Kayalar et al. (2016), changes in crude oil prices are believed to affect stock markets through the channel of expectations. Meanwhile, Basher and Sadorsky (2006) argued that the impact of falling oil prices on stock market differs from country to country depending on whether the country is an oil exporter or an oil importer. In an oil exporting country, an increase in oil prices improves the trade balance, leading to a higher current account surplus and an improving net foreign asset position. At the same time, a rise in oil prices tends to increase private disposable income in oil exporting countries. This in turn enhances corporate profitability, boosts domestic demand and push up stock prices, thereby causing exchange rate to appreciate. In oil importing countries, the process works broadly in reverse: trade deficits are offset by weaker growth and, overtime, real exchange rate depreciates and stock prices decline (Basher and Sadorsky, 2006). The extent to which stock prices are influenced by world oil price changes is explained by the theory of equity valuation, which defines the stock price as the sum of discounted values of expected future cash flows at different investment horizons (Jouini, 2013). Consequently, oil prices affect stock prices directly by impacting future cash flows or indirectly through an impact on the interest rate used to discount the future cash flows. In the absence of complete substitution effects between the factors of production, rising oil prices, for example, increases the cost of doing business, and for non-oil related companies, it reduces profits. Rising oil prices can also be passed on to consumers in form of higher prices, but this will reduce the demand for final goods and services and depress profits.

1. Introduction

2. Literature review

3. Problem description

4. Model

5. Numerical test

6. Conclusion

Acknowledgements

Appendix A. Supplementary data

Research Data

References

Abstract

Financial risks related to crude oil imports are certainly affected by crude oil price uncertainty. Our question is: How important is it to take also physical risks, such as the crude oil exporters’ political risks and transportation risks into account when controlling financial risks in line with the importer’s risk attitude when planning crude oil imports and transportation at a tactical level? In this paper, two-stage stochastic programming models are proposed to illustrate the problem, and a numerical test is conducted to better understand the effects of physical risks. The mechanism for controlling risk will be forward physical contracts. The results show that the real financial risk is much higher than the importer might believe if physical risks are not considered. Unless the importer is risk neutral, more forward crude oil will be imported when physical risks are considered, and the distribution of forward crude oil will depend strongly on correlations among risks.

Introduction

Crude oil is of strategic importance in all countries, and it is often crucial for a crude oil importer’s national economy. Every import country wants to secure an uninterrupted availability of crude oil at an affordable price. But many crude oil exporters, particularly those located in the Middle East, Africa and Latin America, suffer political instability or have a high risk potential (Vivoda, 2009). The unstable political situations in these regions will create supply disruption risks for importers. There are also correlations between political risks of different exporters in the same region, since they are impacted by some common risk factors (Li et al., 2009). For example, the war in Yemen, with Iran and Saudi Arabia involved, leads to instability in the Middle East. Disruptions in supply will lead to an imbalance between supply and demand in the international crude oil market, and global crude oil prices will fluctuate. Since the global crude oil market is highly integrated, the crude oil prices of other exporters will also be affected. In addition to the political risks and crude oil price fluctuations of exporters, there are also transportation risks. Most crude oil is transported by ship. But the straits and canals in the maritime transportation network are exposed to risks due to political instability in neighboring countries, as well as terrorism, piracy, conflicts and other extreme events (Emmerson and Stevens, 2012). For example, the political instability in the Middle East threatens the safety of the Bab el Mandeb and the Suez Canal. If certain straits or canals are closed, the crude oil transportation will be affected.

Due to the political risks of exporters, transportation risks of straits and canals, and oil price uncertainty, crude oil import and transportation decisions are very challenging for importers, who want to control financial risks. But this problem is underexplored. In previous studies, the focus has mostly been on crude oil price fluctuations, and different hedging strategies are proposed. Chen et al. employed geometric Brownian motion processes to model oil price fluctuations, and determined the optimal oil procurement. Operational and financial hedging strategies are proposed by Ji et al. (2015) to manage risks caused by oil price fluctuation. Chang et al. (2011) used a dynamic multivariate GARCH to obtain a crude oil hedging strategy. Sukcharoen and Leatham (2017) proposed the use of vine copulas to capture characteristics of price changes and estimate multiproduct hedge ratios for a refinery to manage risks. In these studies, there are no considerations of selection of exporters, so the political risks of exporters are not taken into account. In addition, the transportation risks are also not considered.

We evaluate the impact of changes in the price of crude oil on the United Kingdom (U.K.) real gross domestic product (GDP) growth rate by way of an out-of-sample forecasting analysis. We compare the performance of several nonlinear models and determine, which aspects of nonlinearities are most useful for obtaining forecast improvements. Likewise, our approach takes into account the possibility that relative predictive performance can vary over the out-of-sample period. Results based on quarterly data from 1974q1 through 2018q4 illustrate that our conclusions depend on the definition of forecast improvement and whether we rely on pairwise or multiple forecast comparison. For instance, it is very difficult tofind evidence that point forecasts exploiting crude oil price variables are statistically significant more accurate than point forecasts produced under the benchmark. On the other hand, the null hypothesis of no population-level predictability is borderline rejected for certain nonlinear crude oil price variables. We also observe notable differences between using real-time and ex-post revised GDP data with regards to local out-of-sample performance. The predictive power associated with the more successful crude oil price measures appears to concentrate in the early 1990s and around the onset of the Great Recession.

Introduction

The aim of this study is to evaluate the predictive impact of changes in the price of crude oil on the United Kingdom (U.K.) real gross domestic product (GDP) growth rate via an out-of-sample analysis. We are motivated to do so based on several interesting results reported in widely cited studies, such as Bachmeier, Li, and Liu (2008), Carlton (2010), Kilian and Vigfusson (2013), Ravazzolo and Rothman (2013) and Ravazzolo and Rothman (2016) . 1 The central question running through these studies is whether changes in the price of crude oil have a predictive impact on the real U.S. GDP growth rate out-of-sample.2 However, in the context of other countries relatively less is known about whether (a): Changes in the price of crude oil help forecast the country specific real GDP growth rate, (b): If the predictive impact of crude oil price changes on the real GDP growth rate is nonlinear and (c): If so what types of nonlinearities. In fact, in their seminal study, Kilian and Vigfusson (2013) argue that understanding the nonlinear/asymmetric predictive impact of changes in the price of crude oil on the real GDP growth rate deserves further study on non-U.S. data. Arguably, the U.K. can be considered as an interesting case because it is a developed, open economy, which has transitioned from being a net crude oil importer in the 1970s to a net exporter in the 1980s and early 1990s before returning to being a net importer again since the mid 2000s.3 This in turn, provides a very good opportunity to evaluate (a), (b) and (c). Likewise, studies such as Jim
enez-Rodríguez and S
anchez (2006), Harrison, Ryland, and de Weymarn (2011), Millard (2011) and Millard and Shakir (2013) focus on the causes and effects of crude oil price changes on the U.K. economy via examination of impulse response functions. However, they do not account for nonlinearities in the relationship between the price of crude oil and the growth rate. Similar to simulations provided by the National Institute for Economic and Social Research (NIESR) and the Bank of England they conclude that a fall in the price of crude oil leads to an increase in the U.K. GDP growth rate. To date, a comprehensive analysis on the predictive impact of crude oil price changes on the U.K. GDP real growth rate has not been conducted. The purpose of this study is to fill this gap. Finally, the increasing financialization of the crude oil market and the important role of crude oil in the context of a developed, open economy, such as the U.K. makes our analysis interesting to a broad audience.

23-1- Operating Cost Categories

23-2- Labor

23-3- Logistics and Transportation

23-3-1- Marine Vessels

23-3-2- Helicopters

23-4- Materials and Supplies

23-4-1- Chemicals for Water Treatment

23-4-2- Chemicals for Corrosion

23-4-3- Chemicals for Flow Assurance

23-4-4- Fuel, Water and Utilities

23-5- Repairs and Maintenance—Wells and Flowline

23-5-1- Maintenance

23-5-2- Stimulation

23-5-3- Well Failure

23-5-4- Flowline and Export Repairs

23-6- Repairs and Maintenance—Equipment and Structure

23-6-1- Regulatory Requirements

23-6-2- Inspection Schedules

23-6-3- Three Zones

23-6-4- Painting

23-6-5- Underwater Maintenance

References

ABSTRACT

Activity-based cost models apply work decomposition methods and knowledge of engineering and market conditions to estimate cost. The tasks required to be performed are identified, and the time and duration to perform each task are estimated. Activity-based costing is the most detailed and transparent method that can be applied in operating cost estimation and require a higher level of expertise to successfully apply. In this final chapter, work decompositionmethods are described to illustrate how the operating cost components of labor, logistics and transportation, materials and supplies, and repairs and maintenance are estimated. Examples are provided to develop analytic skills and intuition regarding the relative importance of cost components. Regional diving, helicopter, and marine vessel contracts and service markets are reviewed as part of this discussion.

23-1- OPERATING COST CATEGORIES

The primary cost categories for offshore oil and gas operations include the following:
• Salaries of operating personnel
• Transportation of products and people
• Materials and supply services
• Repair and maintenance of wells and flow lines
• Repair, maintenance, and inspection of equipment and structure
Cost may be incurred hourly, daily, monthly, or annually; be volume- or capacity-based; or be per person (Table 23.1). In activity-based costing, the tasks required to be performed are identified, and the time and duration to perform each task are estimated. If only a small number of facilities are evaluated, a detailed approach is feasible, but for more than a few structures, the resources required to complete an activity-based cost study are significant (Kaiser, 2019).

1- Introduction

2- The Model

3- Static Equilibrium

4- Dynamic Equilibrium

5- Dynamic Dutch Disease

6- Empirical Implications and Results

7- Conclusion

References

Abstract

In this paper we develop the first model to incorporate the dynamic productivity consequences of both the spending effect and the resource movement effect of oil abundance. We show that doing so dramatically alters the conclusions drawn from earlier models of learning by doing (LBD) and the Dutch disease. In particular, the resource movement effect suggests that the growth effects of natural resources are likely to be positive, turning previous growth results in the literature relying on the spending effect on their head. We motivate the relevance of our approach by the example of a major oil producer, Norway. Empirically we find that the effects of an increase in the price of oil may resemble results found in the earlier Dutch disease literature, while the effects of increased oil activity increases productivity in most industries. Therefore, models that only focus on windfall gains due to increased spending potential from higher oil prices, would conclude – incorrectly based on our analysis – that the resource sector cannot be an engine of growth.

Introduction

In 1969 Norway discovered off-shore oil in the North Sea. Since then, the oil sector has been one of the key drivers of growth, crowding in many oil related industries that, over time, have developed advanced knowledge on how to operate in deep waters under harsh climate conditions, see e.g. Bjørnland and Thorsrud (2016). Likewise, the recent North American shale revolution has turned the United States in the space of only a few years into the world largest oil producer. Technological developments in drilling and fracking since the turn of this century have unlocked the huge reserves that lie trapped in shale rock. The oil boom seems to have benefited local industries in the U.S., and there has been no crowding out of the manufacturing sector so far, see e.g. Allcott and Keniston (2018) for some recent empirical evidence. In contrast, in countries such as Angola and Venezuela, decades of oil abundance have led to lower productivity and income levels, see e.g. Mehlum et al. (2006). In this paper we aim to understand why, contrary to the traditional models of the Dutch disease, the effects of oil in countries such as Norway, and more recently the U.S., have been so favorable. We argue that incorporating the dynamic productivity consequences of petroleum related industries is key to understand this, and that existing theories of the Dutch disease have to be extended, as we discuss in the following. One of the most influential explanations of the Dutch disease is that of the Learning By Doing (LBD) models of van Wijnbergen (1984a), Krugman (1987) and Sachs and Warner (1995). According to these approaches, a country that discovers oil is essentially in receipt of a foreign exchange gift. The gift increases income, and with traded and non-traded goods both being normal goods, the increase in demand pushes workers out of the traded sector and into the non-traded sector; the increased demand for traded goods can be satisfied by using the foreign exchange gift, while the increased demand for nontraded goods can only be satisfied by having a larger share of the labour force producing non-traded goods. This structural transformation of the economy, however, means that labour is transferred from strong to weak LBD sectors. Economic growth is pushed down. While these theoretical approaches can explain the negative economic developments observed in countries such as Angola and Venezuela, they fail to describe what is going on in oil producing countries such as Norway. This can be illustrated by comparing the economic development of Norway with that of its neighbor Sweden, a major oil importer.

Introduction

The surge in oil and gas supply due to hydraulic fracturing or ‘fracking’ has transformed markets and industries with wide ranging effects impacting coal burning facilities’ retirement dates and the follow-through on nuclear power plant additions. Interestingly enough, though, oil and gas are not the primary outputs of this type of production – water is. At the nascent stages of modern unconventional extraction, circa 2007, onshore oil and gas wells contributed as much as 17.82 billion barrels of ‘produced water’ (Clark and Veil, 2009). This water is later separated from the oil and gas and re-injected into disposal wells that are often at greater depth than the water originated.1 Alongside the surge in U.S. oil and gas supply, and the disposal of produced water, there has been a staggering increase in the amount of earthquakes felt in areas where waste-water injection is taking place (Ellsworth et al., 2015). Although wastewater-induced earthquakes have been felt in other areas,2 the state of Oklahoma has witnessed a striking increase in earthquake activity. Figure one shows just how unprecedented the change in earthquake activity has been. In the top panel all earthquakes from January 1 2000 through the end of 2009 are plotted; in the bottom panel the amount of earthquakes witnessed through 2016 are shown. Clearly, there has been a distinct increase over these seven years. In this paper I discuss the economic drivers of induced seismicity and further explore how effective regional authorities have been in reducing the amount of included earthquakes. Seismicity in Oklahoma serves as a very unique case because the current earthquake rate is 300 times higher than the historical rate (Weingarten, 2015). In fact, the seismicity rate in Oklahoma has increased so drastically that it is now more common to have a magnitude 3.0 or larger earthquake in a single day than in entire years prior to 2008. Specifically, Weingarten (2015) shows that the rate of magnitude 3.0+ earthquakes was 11 2 per year prior to 2008, and 21 2 magnitude 3.0 + earthquakes per day after 2008. Linking now to the amount of produced water, the advent of hydraulic fracturing has significantly increased the amount of disposal because the targeted formations often have a large amount of ‘associated’ water that is high in salinity and is brought to the surface as a bi-product. For example, Nicot et al. (2014) find that there was a five fold increase in produced water disposal in the Barnett shale between 2000 and 2011 – from 8.8 thousand acre feet per year to 45.7 thousand acre feet per year. In Oklahoma, approximately 849 million barrels3 of produced water per month were injected into disposal wells at the beginning of the fracking boom. By 2014 this amount had grown to 1.54 billion barrels per month. For comparison, produced water in the state of Texas increased from 33.8 million barrels per month in 2007–81.1 million barrels per month in 2014 (Kuchment and Kuchment ()). For context, this means that nearly 19 times more produced water was injected within Oklahoma than Texas; even though Texas has more than three times the land area.