Abstract Regulation often takes the form of a standard that can be met through the implementation of any of a number of different policies. This paper examines how the authority to set the standard and the authority to choose the combination of policies to meet the standard should be allocated between a central government and local governments. In the context of the United States, for example, should standards regarding such public goods as the environment or education be set and implemented by the federal government, by individual state governments, or by both? Because decisions about setting and/or meeting the standard can be non-contractible, an incomplete contracting approach is used. A central finding is that "conjoint federalism" (the central government sets the standard while the local governments meet the standard), which is the regulatory structure often used in federations such as the United States and the European Union, can be the least efficient form, while a reverse form of delegation, in which local governments choose their own individual standards which the central government then decides how to collectively meet, can be the most efficient.
Abstract. At the onset of the Great Depression, John Maynard Keynes made some startlingly optimistic predictions about the economic possibilities his grandchildren might face a century later. Within the next 100 years, Keynes proclaimed, technological progress would solve the economic problem facing human beings, individuals would devote themselves to noneconomic pursuits instead, and no one would continue to strive for relative goods. Was Keynes right? This article examines each of Keynes's three main prophecies and concludes that, even though over 75 years have transpired since they were made, and even though they were founded in part on the belief held by most classical economists in the value of technological progress and economic growth, not one of Keynes's predictions has been realized.
This paper uses a structural econometric model to analyze the investment timing game in offshore petroleum production that ensues on wildcat tracts in U.S. federal lands off the Gulf of Mexico. When individual petroleum-producing firms make their exploration and development investment timing decisions, there are two types of externalities that they do not internalize: an information externality and an extraction externality. The model I develop enables me to estimate the structural parameters governing each firm's investment timing decisions and therefore to assess the net effect of these externalities. According to my results, the extraction externality appears to dominate the information externality. Moreover, decreasing the lease term may increase ex ante tract value and hence government profits. The econometric methodology presented in this paper can be employed to analyze any problem of dynamic multi-stage strategic decision making in the presence of externalities.
In order to maximize profits, petroleum-producing firms make decisions that are dynamic and strategic in nature, and that take into account constraints imposed by the regulatory and institutional environment. This paper describes our research modeling, estimating and analyzing the efficiency of the decisions of petroleum-producing firms in the Gulf of Mexico and Alaska, and examining the impact of government policy on these decisions. Petroleum production is a multi-stage process involving sequential investment decisions. The first stage is exploration: when a firm acquires a previously unexplored tract of land, it must first decide whether and when to invest in the drilling rigs needed to begin exploratory drilling. The second stage is development: after exploration has taken place, a firm must subsequently decide whether and when to invest in the production platforms needed to develop and extract the reserve. Because the profits from petroleum production depend on market conditions such as the oil price that vary stochastically over time, an individual firm producing in isolation that hopes to make dynamically optimal decisions would need to account for the option value to waiting before making either irreversible investment. After investments in drilling rigs and production platforms have been made, the third stage of production is extraction. The dynamic decision-making problem faced by a petroleum-producing firm is even more complicated when its profits are affected not only by exogenous market conditions, but also by the actions of other firms producing nearby. When firms own leases to neighboring tracts of land that may be located over a common pool of reserve, there are two types of externalities that add a strategic (or non-cooperative) dimension to firms' investment timing decisions and may render these decisions socially inefficient. The first type of externality is an information externality: if tracts are located over a common pool or share common geological features so that their ex post values are correlated, then firms learn information about their own tracts when other firms drill exploratory wells or install production platforms on neighboring tracts. The information externality is a positive one, since a firm benefits from its neighbors' information. A second type of externality is an extraction externality: when firms have competing rights to a common-pool resource, strategic considerations may lead them to extract at an inefficiently high rate. The extraction externality is a negative one, since it induces a firm to produce inefficiently. Owing to both information and extraction externalities, the dynamic decision-making problem faced by a petroleum-producing firm is not merely a single-agent problem, but rather can be viewed as a multi-agent, non-cooperative game in which firms behave strategically and base their exploration and development policies on those of their neighbors. In this paper, we summarize the previous work of Lin (2007) on whether a firm's investment timing decisions and profits in the Gulf of Mexico depend on the decisions of firms owning neighboring tracts of land. Do the positive information externalities and negative extraction externalities have any net strategic effect that may cause petroleum production to be inefficient? We then describe our ongoing research analyzing the efficiency of petroleum production in Alaska.
In order to maximize profits, petroleum-producing firms make decisions that are dynamic and strategic in nature, and that take into account constraints imposed by the regulatory and institutional environment. This paper describes our research modeling, estimating and analyzing the efficiency of the decisions of petroleum-producing firms in the Gulf of Mexico and Alaska, and examining the impact of government policy on these decisions. Petroleum production is a multi-stage process involving sequential investment decisions. The first stage is exploration: when a firm acquires a previously unexplored tract of land, it must first decide whether and when to invest in the drilling rigs needed to begin exploratory drilling. The second stage is development: after exploration has taken place, a firm must subsequently decide whether and when to invest in the production platforms needed to develop and extract the reserve. Because the profits from petroleum production depend on market conditions such as the oil price that vary stochastically over time, an individual firm producing in isolation that hopes to make dynamically optimal decisions would need to account for the option value to waiting before making either irreversible investment. After investments in drilling rigs and production platforms have been made, the third stage of production is extraction. The dynamic decision-making problem faced by a petroleum-producing firm is even more complicated when its profits are affected not only by exogenous market conditions, but also by the actions of other firms producing nearby. When firms own leases to neighboring tracts of land that may be located over a common pool of reserve, there are two types of externalities that add a strategic (or non-cooperative) dimension to firms' investment timing decisions and may render these decisions socially inefficient. The first type of externality is an information externality: if tracts are located over a common pool or share common geological features so that their ex post values are correlated, then firms learn information about their own tracts when other firms drill exploratory wells or install production platforms on neighboring tracts. The information externality is a positive one, since a firm benefits from its neighbors' information. A second type of externality is an extraction externality: when firms have competing rights to a common-pool resource, strategic considerations may lead them to extract at an inefficiently high rate. The extraction externality is a negative one, since it induces a firm to produce inefficiently. Owing to both information and extraction externalities, the dynamic decision-making problem faced by a petroleum-producing firm is not merely a single-agent problem, but rather can be viewed as a multi-agent, non-cooperative game in which firms behave strategically and base their exploration and development policies on those of their neighbors. In this paper, we summarize the previous work of Lin (2007) on whether a firm's investment timing decisions and profits in the Gulf of Mexico depend on the decisions of firms owning neighboring tracts of land. Do the positive information externalities and negative extraction externalities have any net strategic effect that may cause petroleum production to be inefficient? We then describe our ongoing research analyzing the efficiency of petroleum production in Alaska.
In order to maximize profits, petroleum-producing firms make decisions that are dynamic and strategic in nature, and that take into account constraints imposed by the regulatory and institutional environment. This paper describes our research modeling, estimating and analyzing the efficiency of the decisions of petroleum-producing firms in the Gulf of Mexico and Alaska, and examining the impact of government policy on these decisions.Petroleum production is a multi-stage process involving sequential investment decisions. The first stage is exploration: when a firm acquires a previously unexplored tract of land, it must first decide whether and when to invest in the drilling rigs needed to begin exploratory drilling. The second stage is development: after exploration has taken place, a firm must subsequently decide whether and when to invest in the production platforms needed to develop and extract the reserve. Because the profits from petroleum production depend on market conditions such as the oil price that vary stochastically over time, an individual firm producing in isolation that hopes to make dynamically optimal decisions would need to account for the option value to waiting before making either irreversible investment. After investments in drilling rigs and production platforms have been made, the third stage of production is extraction.The dynamic decision-making problem faced by a petroleum-producing firm is even more complicated when its profits are affected not only by exogenous market conditions, but also by the actions of other firms producing nearby. When firms own leases to neighboring tracts of land that may be located over a common pool of reserve, there are two types of externalities that add a strategic (or non-cooperative) dimension to firms' investment timing decisions and may render these decisions socially inefficient.The first type of externality is an information externality: if tracts are located over a common pool or share common geological features so that their ex post values are correlated, then firms learn information about their own tracts when other firms drill exploratory wells or install production platforms on neighboring tracts. The information externality is a positive one, since a firm benefits from its neighbors' information.A second type of externality is an extraction externality: when firms have competing rights to a common-pool resource, strategic considerations may lead them to extract at an inefficiently high rate. The extraction externality is a negative one, since it induces a firm to produce inefficiently.Owing to both information and extraction externalities, the dynamic decision-making problem faced by a petroleum-producing firm is not merely a single-agent problem, but rather can be viewed as a multi-agent, non-cooperative game in which firms behave strategically and base their exploration and development policies on those of their neighbors.In this paper, we summarize the previous work of Lin (2007) on whether a firm's investment timing decisions and profits in the Gulf of Mexico depend on the decisions of firms owning neighboring tracts of land. Do the positive information externalities and negative extraction externalities have any net strategic effect that may cause petroleum production to be inefficient? We then describe our ongoing research analyzing the efficiency of petroleum production in Alaska.
In order to maximize profits, petroleum-producing firms make decisions that are dynamic and strategic in nature, and that take into account constraints imposed by the regulatory and institutional environment. This paper describes our research modeling, estimating and analyzing the efficiency of the decisions of petroleum-producing firms in the Gulf of Mexico and Alaska, and examining the impact of government policy on these decisions.Petroleum production is a multi-stage process involving sequential investment decisions. The first stage is exploration: when a firm acquires a previously unexplored tract of land, it must first decide whether and when to invest in the drilling rigs needed to begin exploratory drilling. The second stage is development: after exploration has taken place, a firm must subsequently decide whether and when to invest in the production platforms needed to develop and extract the reserve. Because the profits from petroleum production depend on market conditions such as the oil price that vary stochastically over time, an individual firm producing in isolation that hopes to make dynamically optimal decisions would need to account for the option value to waiting before making either irreversible investment. After investments in drilling rigs and production platforms have been made, the third stage of production is extraction.The dynamic decision-making problem faced by a petroleum-producing firm is even more complicated when its profits are affected not only by exogenous market conditions, but also by the actions of other firms producing nearby. When firms own leases to neighboring tracts of land that may be located over a common pool of reserve, there are two types of externalities that add a strategic (or non-cooperative) dimension to firms' investment timing decisions and may render these decisions socially inefficient.The first type of externality is an information externality: if tracts are located over a common pool or share common geological features so that their ex post values are correlated, then firms learn information about their own tracts when other firms drill exploratory wells or install production platforms on neighboring tracts. The information externality is a positive one, since a firm benefits from its neighbors' information.A second type of externality is an extraction externality: when firms have competing rights to a common-pool resource, strategic considerations may lead them to extract at an inefficiently high rate. The extraction externality is a negative one, since it induces a firm to produce inefficiently.Owing to both information and extraction externalities, the dynamic decision-making problem faced by a petroleum-producing firm is not merely a single-agent problem, but rather can be viewed as a multi-agent, non-cooperative game in which firms behave strategically and base their exploration and development policies on those of their neighbors.In this paper, we summarize the previous work of Lin (2007) on whether a firm's investment timing decisions and profits in the Gulf of Mexico depend on the decisions of firms owning neighboring tracts of land. Do the positive information externalities and negative extraction externalities have any net strategic effect that may cause petroleum production to be inefficient? We then describe our ongoing research analyzing the efficiency of petroleum production in Alaska.
ANPCyT, Argentina ; YerPhI, Armenia ; ARC, Australia ; BMWFW, Austria ; FWF, Austria ; ANAS, Azerbaijan ; SSTC, Belarus ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ; NSERC, Canada ; NRC, Canada ; CFI, Canada ; CERN ; CONICYT, Chile ; CAS, China ; MOST, China ; NSFC, China ; COLCIENCIAS, Colombia ; MSMT CR, Czech Republic ; MPO CR, Czech Republic ; VSC CR, Czech Republic ; DNRF, Denmark ; DNSRC, Denmark ; IN2P3-CNRS, CEA-DRF/IRFU, France ; SRNSFG, Georgia ; BMBF, Germany ; HGF, Germany ; MPG, Germany ; GSRT, Greece ; RGC, Hong Kong SAR, China ; ISF, Israel ; Benoziyo Center, Israel ; INFN, Italy ; MEXT, Japan ; JSPS, Japan ; CNRST, Morocco ; NWO, Netherlands ; RCN, Norway ; MNiSW, Poland ; NCN, Poland ; FCT, Portugal ; MNE/IFA, Romania ; MES of Russia, Russian Federation ; NRC KI, Russian Federation ; JINR ; MESTD, Serbia ; MSSR, Slovakia ; ARRS, Slovenia ; MIZS, Slovenia ; DST/NRF, South Africa ; MINECO, Spain ; SRC, Sweden ; Wallenberg Foundation, Sweden ; SERI, Switzerland ; SNSF, Switzerland ; Canton of Bern, Switzerland ; MOST, Taiwan ; TAEK, Turkey ; STFC, United Kingdom ; DOE, United States of America ; NSF, United States of America ; BCKDF, Canada ; CANARIE, Canada ; CRC, Canada ; Compute Canada, Canada ; COST, European Union ; ERC, European Union ; ERDF, European Union ; Horizon 2020, European Union ; Marie Sk lodowska-Curie Actions, European Union ; Investissements d' Avenir Labex and Idex, ANR, France ; DFG, Germany ; AvH Foundation, Germany ; Greek NSRF, Greece ; BSF-NSF, Israel ; GIF, Israel ; CERCA Programme Generalitat de Catalunya, Spain ; Royal Society, United Kingdom ; Leverhulme Trust, United Kingdom ; BMBWF (Austria) ; FWF (Austria) ; FNRS (Belgium) ; FWO (Belgium) ; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; FAPERGS (Brazil) ; MES (Bulgaria) ; CAS (China) ; MoST (China) ; NSFC (China) ; COLCIENCIAS (Colombia) ; MSES (Croatia) ; CSF (Croatia) ; RPF (Cyprus) ; SENESCYT (Ecuador) ; MoER (Estonia) ; ERC IUT (Estonia) ; ERDF (Estonia) ; Academy of Finland (Finland) ; MEC (Finland) ; HIP (Finland) ; CEA (France) ; CNRS/IN2P3 (France) ; BMBF (Germany) ; DFG (Germany) ; HGF (Germany) ; GSRT (Greece) ; NKFIA (Hungary) ; DAE (India) ; DST (India) ; IPM (Iran) ; SFI (Ireland) ; INFN (Italy) ; MSIP (Republic of Korea) ; NRF (Republic of Korea) ; MES (Latvia) ; LAS (Lithuania) ; MOE (Malaysia) ; UM (Malaysia) ; BUAP (Mexico) ; CINVESTAV (Mexico) ; CONACYT (Mexico) ; LNS (Mexico) ; SEP (Mexico) ; UASLP-FAI (Mexico) ; MOS (Montenegro) ; MBIE (New Zealand) ; PAEC (Pakistan) ; MSHE (Poland) ; NSC (Poland) ; FCT (Portugal) ; JINR (Dubna) ; MON (Russia) ; RosAtom (Russia) ; RAS (Russia) ; RFBR (Russia) ; NRC KI (Russia) ; MESTD (Serbia) ; SEIDI (Spain) ; CPAN (Spain) ; PCTI (Spain) ; FEDER (Spain) ; MOSTR (Sri Lanka) ; MST (Taipei) ; ThEPCenter (Thailand) ; IPST (Thailand) ; STAR (Thailand) ; NSTDA (Thailand) ; TAEK (Turkey) ; NASU (Ukraine) ; SFFR (Ukraine) ; STFC (United Kingdom ; DOE (U.S.A.) ; NSF (U.S.A.) ; Marie-Curie programme ; Horizon 2020 Grant (European Union) ; Leventis Foundation ; A.P. Sloan Foundation ; Alexander von Humboldt Foundation ; Belgian Federal Science Policy Office ; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) ; F.R.S.-FNRS (Belgium) ; Beijing Municipal Science & Technology Commission ; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic ; Hungarian Academy of Sciences (Hungary) ; New National Excellence Program UNKP (Hungary) ; Council of Science and Industrial Research, India ; HOMING PLUS programme of the Foundation for Polish Science ; European Union, Regional Development Fund ; Mobility Plus programme of the Ministry of Science and Higher Education ; National Science Center (Poland) ; National Priorities Research Program by Qatar National Research Fund ; Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu ; Programa Severo Ochoa del Principado de Asturias ; EU-ESF ; Greek NSRF ; Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand) ; Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; Welch Foundation ; Weston Havens Foundation (U.S.A.) ; Canton of Geneva, Switzerland ; Herakleitos programme ; Thales programme ; Aristeia programme ; European Research Council (European Union) ; Horizon 2020 Grant (European Union): 675440 ; FWO (Belgium): 30820817 ; Beijing Municipal Science & Technology Commission: Z181100004218003 ; NKFIA (Hungary): 123842 ; NKFIA (Hungary): 123959 ; NKFIA (Hungary): 124845 ; NKFIA (Hungary): 124850 ; NKFIA (Hungary): 125105 ; National Science Center (Poland): Harmonia 2014/14/M/ST2/00428 ; National Science Center (Poland): Opus 2014/13/B/ST2/02543 ; National Science Center (Poland): 2014/15/B/ST2/03998 ; National Science Center (Poland): 2015/19/B/ST2/02861 ; National Science Center (Poland): Sonata-bis 2012/07/E/ST2/01406 ; Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu: MDM-2015-0509 ; Welch Foundation: C-1845 ; This paper presents the combinations of single-top-quark production cross-section measurements by the ATLAS and CMS Collaborations, using data from LHC proton-proton collisions at = 7 and 8 TeV corresponding to integrated luminosities of 1.17 to 5.1 fb(-1) at = 7 TeV and 12.2 to 20.3 fb(-1) at = 8 TeV. These combinations are performed per centre-of-mass energy and for each production mode: t-channel, tW, and s-channel. The combined t-channel cross-sections are 67.5 +/- 5.7 pb and 87.7 +/- 5.8 pb at = 7 and 8 TeV respectively. The combined tW cross-sections are 16.3 +/- 4.1 pb and 23.1 +/- 3.6 pb at = 7 and 8 TeV respectively. For the s-channel cross-section, the combination yields 4.9 +/- 1.4 pb at = 8 TeV. The square of the magnitude of the CKM matrix element V-tb multiplied by a form factor f(LV) is determined for each production mode and centre-of-mass energy, using the ratio of the measured cross-section to its theoretical prediction. It is assumed that the top-quark-related CKM matrix elements obey the relation |V-td|, |V-ts| « |V-tb|. All the |f(LV)V(tb)|(2) determinations, extracted from individual ratios at = 7 and 8 TeV, are combined, resulting in |f(LV)V(tb)| = 1.02 +/- 0.04 (meas.) +/- 0.02 (theo.). All combined measurements are consistent with their corresponding Standard Model predictions.