1. Javier Oyakawa Center for Community and Business Research Institute for Economic Development University of Texas at San Antonio Paper presented at the 2015 User’s Conference, Austin, Texas, May 20, 2015 Energy Prices and the Eagle Ford Region

2. ABOUT MYSELF Economics researcher at the Center for Community and Business Research (CCBR). Studied economics at the PhD degree level at the University of Texas at Austin and worked on computational general equilibrium models for analyzing trade and fiscal policies. In early 2005, together with Dr. Mark Hager and Robert McKinley, UTSA Associate Vice- President for economic development, the CCBR began its activities. In early 2011, together with Dr. Dominique Halaby, I designed, developed, and implemented the first Eagle Ford Shale economic impact study, and continued to be the lead investigator in all following studies. Later in that year, I was the Interim Director of the CCBR, between the months of May and October while preparing the second report for the Eagle Ford Shale. I have taught economics courses at several universities in Texas and Perú. In 2000-2001 I was full-time instructor at Trinity University in San Antonio, and in 2002 I was a full-time professor at the Catholic University in Perú.

3. I.Introduction. II. Economic Impacts and Official data III. Hydraulic fracturing and horizontal drilling IV. Comparing costs conventional and shale gas wells V. Oil, gas prices different effects in different plays VI. Some Eagle Ford Shale developments VII. Community issues, the “boomtown” framework VIII. Community issues, the “resource curse” framework IX. Comments on Mexico’s energy reform. Presentation topics:

4. I.INTRODUCTION

5. Four shale oil and gas plays in Texas

9. II. Economic Impacts and Official Data

10. Official employment data and employment impact studies show different aspects of the processes and need to be understood in context.

11. Source: Texas Workforce Commission, the Quarterly Census on Employment & Wages (QCEW) data for the fourth quarter of 2009 and 2011.

12. Source: Oyakawa et al. (2012)

13. The self-employed

15. Hotels and the oil and gas industries

16. Source: Office of the Governor of Texas.

17. RV PARKS NUMBERS FOR THE EAGLE FORD AREA

19. III. HYDRAULIC FRACTURING AND HORIZONTAL DRILLING

20. http://biomass.scienceblog.com/2012/02/20/what-we-should-know-about-fracking-and-carbon-capture-and-sequestering/

21. http://www.dallasfed.org/research/energy11/barnett.cfm

22. http://www.eia.gov/todayinenergy/detail.cfm?id=2170

24. IV. COMPARING COSTS AND PRODUCTIVITY: VERTICAL AND HORIZONTAL DRILLING

25. In the initial years of the combined use of fracking and horizontal drilling: Cost ratio of horizontal versus vertical wells is approximately 2 to 1, but varies by well. Production ratio for horizontal wells versus vertical wells is approximately 3.2 to 1. Also varies by well. http://www.horizontaldrilling.org/

26. But over time technology has become more efficient

27. http://info.drillinginfo.com/escondido-resources-conventional-vs-unconventional/

28. V.OIL AND GAS PRICES. DIFFERENT EFFECTS ON DIFFERENT PLAYS

34. Why did this happen?

35. Eagle Ford Shale produces oil and natural gas liquids in large quantities. Natural gas producers moved from the Barnett Shale to the Eagle Ford Shale as the price of dry natural gas declined. Drilling permits in the Eagle Ford soared while in the Barnett Shale collapsed.

37. http://www.eia.gov/todayinenergy/detail.cfm?id=16191

38. What is the effect of a decrease in the price of natural gas in the Eagle Ford after 2008?

39. The final estimates included the price of oil, the price of gas, and a dummy variable (D): Parameter Estimates VariableDFEstimate Standard Error Intercept11.89181.19101.590.1181 lnPoil1 0.83470.26333.170.0025 lnPgas1 -0.6775 0.1644-4.120.0001 DmlnPoil1-0.05480.0273-2.010.0497 THE LOWER THE PRICE OF GAS THE MORE DRILLING ACTIVITY IN THE EAGLE FORD SHALE Source: Economic Impact of the Eagle Ford Shale. CCBR study Sept 2014

40. AGAIN: THE LOWER THE PRICE OF GAS THE MORE DRILLING ACTIVITY IN THE EAGLE FORD SHALE AFTER 2008. DIFFERENT FROM WHAT HAPPENED IN THE BARNETT SHALE.

41. VI.SOME EAGLE FORD SHALE DEVELOPMENTS

42. Employment Change by County County Total EmploymentPercent Change (Annualized) 20012006201020132001-20062006-20102010-2013 Atascosa9,1939,1699,34613,021-0.050.4811.69 Bee8,4448,4508,7589,9240.010.904.25 DeWitt6,8686,9366,5187,3880.20-1.544.26 Dimmit2,6962,6933,0835,727-0.023.4422.93 Frio4,0194,2064,8596,0870.913.677.80 Gonzales5,8826,5706,4156,7772.24-0.601.85 Karnes4,0113,8563,7164,768-0.79-0.928.66 La Salle1,2621,6211,8273,2585.133.0421.27 Live Oak2,8622,9173,0154,4280.380.8313.67 Maverick11,32014,05216,18816,9124.423.601.47 McMullen251203256572-4.165.9730.73 Webb70,55984,50785,40492,8313.670.262.82 Wilson5,3836,2506,4907,0723.030.952.90 Zavala2,7272,8462,9522,5530.860.92-4.73 EFS64,91869,76973,42388,487 1.451.286.42 Texas 9,350,770 9,922,313 10,182,150 11,031,9071.190.652.71

43. County Population Change by CountyAnnualized Change Percent 20012006201020132001-20062006-20102010-2013 Atascosa39,82843,05944,95847,0931.571.081.56 Bee31,69531,97731,90232,7990.18-0.060.93 DeWitt20,06620,10820,04720,5030.04-0.080.75 Dimmit10,0309,97210,03210,897-0.120.152.80 Frio16,31516,72017,23318,0650.490.761.58 Gonzales18,71419,63319,82820,3120.960.250.81 Karnes15,34014,98514,86515,081-0.47-0.200.48 La Salle5,9346,5496,8987,3691.991.312.23 Live Oak12,07111,55911,54611,867-0.86-0.030.92 McMullen819765712764-1.35-1.782.38 Maverick47,59450,95154,47755,9321.371.690.88 Webb200,347229,307251,320262,4952.742.321.46 Wilson33,40839,00743,08345,4183.152.521.77 Zavala11,59611,64211,72712,1560.080.181.20 EFS263,410276,927287,308298,256 1.010.921.25 Texas21,319,62223,359,58025,245,17826,448,1931.841.961.56

44. County Offenses per 100,000 PopulationPercent Change (Annualized) 20052008201020132005-20082008-20102010-2013 Atascosa2,0332,3102,5442,4464.344.95-1.30 Bee *698594606351-5.220.96-16.65 DeWitt2,4041,9451,4862,761-6.82-12.5822.93 Dimmit3,9983,7422,0444,203-2.17-26.0927.16 Frio2,9792,0072,0992,275-12.332.262.72 Gonzales2,8642,4262,6803,353-5.385.117.75 Karnes1,6172,1932,9863,00410.6916.700.19 La Salle1,5131,7141,0616794.23-21.32-13.84 Live Oak9898718922,343-4.131.1937.97 McMullen1,0141361,1245,236-48.83187.5767.03 Maverick3,1933,7983,0992,5215.95-9.66-6.65 Webb6,2206,7275,2064,5762.65-12.03-4.21 Wilson1,4001,2001,2601,387-5.012.473.25 Zavala2,7322,9081,6141,7192.10-25.502.13 EFS27,43425,84423,49532,276 2.37-9.4911.16 Texas4882450042193654-2.68-3.17-4.68 * Only included Bee SO

45. Eagle Ford Capital Expenditures 2013-2015  Expected Capital expenditures budget for 2015: $16 Billion (Shale Experts estimate)  Capital expenditures for 2014: $25.4 Billion (Shale Experts estimate)  Capital expenditures for 2013: $28 Bill (McKenzie estimate)

46. Total impact in three scenarios 2023 in millions Low estimate Moderate estimate High estimate Economic impacts Output $37,105$106,394$230,734 Employment, full-time 55,328150,793361,974 Payroll $3,174$9,636$20,806 Gross regional product $19,561$61,816$139,539 Estimated local government revenues $1,131$3,742$8,849 Estimated state revenue, incl. severance taxes $1,131$3,774$8,854 Source: Economic Impact of the Eagle Ford Shale. CCBR study Sept 2014

47. Comparisons of population growth rankings The counties that had faster growth during the 2001- 2006 period also had faster growth during the 2006- 2010 period. The ranking of counties population growth during the 2010-2013 period has no resemblance to the previous two periods.

48. County Population Rank of Percent Change 2001-20062006-20102010-2013 Atascosa676.5 Bee101311 DeWitt121415 Dimmit13111 Frio985 Gonzales8914 Karnes141516 La Salle363 Live Oak1512 McMullen16 2 Maverick7413 Webb228 Wilson114 Zavala1110 EFS559 Texas436.5

49. Population Rankings Spearman Correlation Coefficients N=16Prob>|r| under H0: Rho=0 2001-20062006-20102010-2013 2001-20061.00000.92650.2355 <.00010.3800 2006-2010 0.9265 1.00000.2649 <.0001 0.3214 2010-20130.23550.26491.0000 0.38000.3214

50. Comparisons in employment growth rankings There is no relationship in the employment growth rankings among counties during the periods of 2001-2006 and 2006-2010. When comparing the employment growth ranking between the period 2001-2006 and 2010-2013, a NEGATIVE relationship emerges. Counties that grew faster during the 2001-2006 period, had slower growth in the 2010-2013 period, and vice-versa.

51. County Employment Rank of Percent Change 2001-20062006-20102010-2013 Atascosa14125 Bee12810 DeWitt11169 Dimmit1342 Frio827 Gonzales614 Karnes15 6 La Salle153 Live Oak1094 Maverick2315 McMullen1611 Webb31312 Wilson4611 Zavala9716 EFS5108 Texas71113

52. Ranking Employment Growth Spearman Correlation Coefficients N=16Prob>|r| under H0: Rho=0 2001-20062006-20102010-2013 2001-2006 1.00000.0912-0.4912 0.73700.0534 2006-2010 0.09121.00000.2647 0.73700.3218 2010-2013 -0.4912 0.26471.0000 0.0534 0.3218

53. VII. COMMUNITY ISSUES, THE “BOOMTOWN” FRAMEWORK

54. “By the late 1970s, a so-called “boomtown model” or “social disruption model” emerged as a prominent framework among researchers to describe the rapid growth that overwhelms local governments and permanently alters social relationships. The body of evidence tended to find a mix of positive and negative economic impacts to local residents, contrasted with highly negative social impacts.” Jeffrey Jacquet “Energy Boomtowns & Natural Gas: Implications for Marcellus Shale local governments & rural communities” The Northeast Regional Center for Rural Development The Pennsylvania State University. Rural Development Paper No. 43, January 2009

55. http://www.cdu.edu.au/sites/default/files/research-brief-2013-3.pdf

56. Need to be careful when forecasting impacts to avoid inflating numbers creating confusion. The CCBR prepares estimates under three scenarios, based on three different price projections from the EIA. The low estimates scenario assumes prices in the mid $60s for most of the decade ahead. Just in November last year, six months ago, some forecasts were extremely optimistic about production growth in the Eagle Ford this year.

57. Employment data and employment impacts. The case of McMullen county with official population in 2012: 726 people

58. Source: Texas Workforce Commission, the Quarterly Census on Employment & Wages (QCEW) data for the fourth quarter of 2009 and 2011. Author elaboration

59. Source: Oyakawa et al. (2012). Workforce Analysis for the Eagle Ford Shale

60. How many new jobs are really created?

61. Source: Jacquet et al. (2011). http://www.greenchoices.cornell.edu/downloads/development/shale/Workforce_Development_Challenges.pdf

62. Need for community and regional planning

63. Source: Jacquet, J “The Boomtown Impact model from oil & gas drilling” http://www.visionwestnd.com/documents/TheBoomtownImpactModelfromOilandGasDrilling.pdf

64. Projections: population, employment before the Eagle Ford Developments

65. 14-County Population Estimates YearPopulation 2010525,317 2011533,754 2012542,162 2013550,672 2014559,345 2015568,032 2016577,119 2017586,395 2018595,708 2019605,108 2020614,645 2021624,111 2022633,793 2023643,426 Source: Texas State Demographer

66. 14-County Employment Forecast 2012-2022 YearTotal 2012168,553 2013172,255 2014176,040 2015179,927 2016183,907 2017187,973 2018192,152 2019196,296 2020200,816 2021207,996 2022210,503 Source: CCBR estimations based on TWC WDA employment forecasts

67. PUMS DATA ImmigrateNon-Immigrate SectorTotalCountPercentageCountPercentage 06 - Goods-Producing Industries 82213816.7968483.21 10 - Natural Resources and Mining 1391611.5112388.49 NAICS 1133—Logging 1133200.002100.00 Sector 21—Mining 211371611.6812188.32 20 - Construction 4097718.8333281.17 Sector 23—Construction 234097718.8333281.17 30 - Manufacturing 2744516.4222983.58 Sectors 31, 32, 33—Manufacturing 31781114.106785.90

69. The NEDAM model North Dakota Economic-Demographic Assessment Model (NEDAM) by Leistritz et al. (1982) developed an extensive analysis and “designed a model to ascertain the impacts of major industrial and resource- based projects. Their model divides employment into four broad types: major project-related construction, project-related permanent, project-related temporary, and non-project employment which they termed “baseline.”

70. For population projections, they used a cohort-survival demographic model, and for employment forecasts they used information on direct employment from the project itself and on indirect and induced impacts from input-output multipliers applied to the project’s direct impacts

71. VIII. COMMUNITY ISSUES, THE “RESOURCE CURSE” FRAMEWORK

72. Some studies found that energy industries only impacted Personal Income growth but had no impact on employment and even had negative impact on population growth. These results are very different from the traditional “boomtown model.” But they try to emphasize the problems with the natural resource dependence or “resource curse”

73. CONTROVERSY ON THE IMPACTS OF NATURAL RESOURCES Several studies indicate that: there is an inverse relationship between the rate of economic growth and natural resource dependence at the international level. (Sachs and Warner 1995, 1999, 2001) Other papers show that: the “natural resource curse” is present at an even more disaggregated county level, as highlighted by a brief case study of Maine and Wyoming (James and Adland, 2010) On the contrary, other studies show that: for counties in the U.S., an increase in natural gas production has not been a “natural resource curse” for local economies. (Jason Brown, 2014)

74. With respect to negative “crowding out” effects of “natural resource curse” Jeremy G. Weber 2014 found no evidence of the curse. “Combined, these statistics imply that natural gas extraction was associated with higher average educational attainment among the local adult population, with high school graduates replacing high school non-graduates.”

75. In 2012, I developed a research using input-output tables and occupational matrices with similar results to Jeremy Weber

76. IX. MEXICO’S ENERGY REFORM

77. PowerPoint presentation by Cacheaux, Cavazos & Newton “Mexico’s Energy Reform” Feb 25, 2015, San Antonio, TX

79. THANK YOU! Javier Oyakawa The University of Texas at San Antonio | Institute for Economic Development 501 W César E. Chávez Blvd | San Antonio, TX 78207 210-458-2036 | Javier.oyakawa@utsa.edu Javier.oyakawa@utsa.edu