Application of Quasi- Experimental Approaches to U.S. Farmland Research

Transcription

1 Application of Quasi- Experimental Approaches to U.S. Farmland Research Dr. Wendong Zhang Assistant Professor, Department of Economics Nanjing Agricultural University June 22, 2016

2 A Quick Introduction: Grown up in a rural county in Shandong Province BSc. in Environmental Science from Fudan 2009 Ph.D. in Ag Econ in 2015 from Ohio State U Exchange student in U of Hong Kong 2007 fall 2012 summer intern at USDA-ERS on farm economy and farmland values Research interests: land value, land ownership, agriculture and the environment, China Ag Teaches Econ 364: Rural Property Appraisal

3 Outline of this Talk Overview of Quasi-Experimental Approach Several Applications of Quasi-Experimental Approaches in my research related to U.S. farmland, especially farmland values Propensity score matching and instrumental variables: ethanol Accounting for spatial auto-correlation Regression discontinuity design Panel Data Analysis Some comments on how to do empirical research Don Fullerton s guidelines on writing research papers Angrist: Mostly Harmless Econometrics Some observations on current U.S. agricultural credit and finance conditions

4 Natural Experiments and Quasi-Experiments

5 Some definition: randomized experiment RCT: randomized control trials

6 Natural Experiment Vietnam War lottery draft as instruments for schooling, random assignment of cases to judges Geological formation of bedrocks which affect construction costs

7 Quasi-Experiment Propensity score matching selection on observables Instrumental variables Difference-in-difference Regression discontinuity design

8 Propensity Score Matching and Instrumental Variables: The Expanding Ethanol Market and Farmland Values

9 Motivation The rise of U.S. ethanol industry supported by federal energy policies The Energy Policy Act of 2005 The Energy Independence and Security Act of 2007 Source: CERES

10 Motivation U.S. farmland values did not see a dramatic decline, despite the significant downturn pressure imposed by the recent residential housing market bust and subsequent Great Recession

11 Research question and hypothesis Common wisdom of the effect of expanding ethanol market: help the farm sector and farmland values withstand the downturn pressure due to the housing market bust (Nickerson et al. 2012) Question: How does the ethanol market expansion supported by federal energy policies in mid-2000s impact the surrounding farmland values? This paper provides the first formal test

12 Research question and hypothesis Specific Hypotheses: We expect the increase in the demand for biofuels were capitalized into surrounding farmland values, which led to a declining price gradient over the distance to agricultural market channels such as ethanol plants after its construction Newly constructed ethanol plants may become new competitors for traditional agricultural markets like agricultural output terminals

13 Conceptual framework The capitalization formula parcel-specific attributes affecting agricultural productivity Ait such as soil quality natural amenities variables Nit such as proximity to surface water unban influence variables Uit such as surrounding urban population, access to highway agricultural market influence variables Mit such as proximity to ethanol plants, grain elevators and agricultural output terminals

14 , Hedonics and its identification problem Hedonic price of farmland values Two types of agricultural markets Ethanol markets Agricultural output terminals Interaction term: D_post = 1 if parcel is sold after the construction of nearest ethanol plant Ethanol plants: the significance and magnitude of the spatial effects of proximity to ethanol plants following construction of these plants. Agricultural output terminals: structural change in its capitalization effects before and after construction of new ethanol plants

15 Hedonics and its identification problem Identification problem due to the nonrandom location process of an ethanol plant The availability of feedstock nearby, access to highways/railroads, access to sewer/natural gas pipeline, abundant corn supply, business incentives (Lambert et al. 2008) 1) sample selection on observables: agricultural parcels closer to ethanol plants may have better soil quality and access to transportation network 2) endogeneity of distance to nearest ethanol plant due to omitted variables

16 Source and direction of potential bias Could be upward bias OLS overestimates the effects of proximity to ethanol plant by misattributing better soil quality and easier access to transportation network to ethanol plant Could be downward bias Managers of ethanol plants may select areas with lower corn basis to minimize land purchase costs (Towe and Tra 2013) Selection of a specific ethanol plant may intentionally avoid spatial competition with other existing agricultural markets and select areas with low competitive pressure Both are possible depending the relative magnitude of various factors

17 Econometric strategy: Matching + Instrumental Variables Matching addresses sample selection due to observables How propensity score matching works Propensity score = prob(being treated/ covariates) Estimated using Logit model with dependent variable = 1 if treated Treatment: Close to ethanol plant [e.g. distance to nearest ethanol plant < 10 miles] Covariates: parcel attributes and other location characteristics, including parcel size, soil suitability, proximity to nearest employment center, proximity to nearest highway, surrounding land uses and neighborhood population density.

18 Econometric strategy: Matching + Instrumental Variables Two-step matching 1 to 4 nearest neighbor matching 1) Trimming based on timing Treated =1 if parcel is sold after the construction of nearest ethanol plant 2) Matching on location Treated if within a certain distance cutoff from the agricultural market Ethanol plant: 10 miles Grain elevator: 5 miles Agricultural output terminal: 15 miles Cutoff distances determined based on semiparametric regressions and overall covariate imbalance test in matching Match separately for ethanol plants, grain elevators and agricultural terminals

19 Econometric strategy: Matching + Instrumental Variables Potential endogeneity of distance to nearest ethanol plants are addressed using two instruments based on the idea of spatial competition: avoid competition to max market area Plant 1 Plant 2

20 Econometric strategy: Matching + Instrumental Variables Two instruments: capacity weighted distances to other ethanol plants capacity weighted distances to agricultural terminals Following Bayer and Timmins (2007) Residential location sorting model Objective: distinguish effects of local spillovers from natural advantages Endogenous variable: fraction of individuals/firms choosing a specific location that is correlated with the unobserved quality of the location Instruments: Exogenous characteristics of other locations that influence the sorting equilibrium (and, thereby, the share of individuals who choose a given location), but have no direct effect on utility

21 Econometric strategy: Matching + Instrumental Variables 2SLS 1 st stage: Endogenous variable (Mit): distance to nearest ethanol plant 2 nd stage: regression on the matched sample Interpretation: the significance and magnitude of the spatial effects of proximity to ethanol plants following construction of these plants.

22 Data armslength farmland sales not within MSA counties ethanol plants all constructed in late 2006-early agricultural output terminals 129 grain elevators

23 Unit Mean Std. Dev. General Parcel Attributes Sale price Dollars Sale price per acre Dollars Assessed land value Dollars Assessed improvement value Dollars Assessed land value % of total assessed % SUMMARY STATS Total acres Acres Sale year Year Agricultural Productivity Variables National Commodity Crops Productivity Index Number Cropland % of parcel % Soil class 1 area % of parcel % Soil class 2 area % of parcel % Soil class 3 area % of parcel % Soil class 4 area % of parcel % Steep slope (>15 degrees) Binary

24 Urban Influence Variables Building area % of parcel % Distance to urbanized area of over 25,000 people Kilometers Total urban population within 25 miles Thousands Distance to highway ramp Kilometers Distance to nearest city Kilometers Distance to nearest railway access point Miles Gravity index using three nearest cities Number Agricultural Market Influence Variables Distance to nearest ethanol plant Kilometers Production capacity of nearest ethanol plant Mgal Number of ethanol plants within 25 miles Number Total production capacity of ethanol plants within 25 miles Mgal Distance to nearest grain elevator Kilometers Distance to nearest agricultural terminal Kilometers Capacity-weighted distance to other ethanol plants Kilometers Capacity-weighted distance to other agricultural terminals Kilometers SUMMARY STATS

25 Number of farmland sales by year Distribution of farmland sale prices by year

26 Results 1) Why matching? Within close proximity to ethanol plant = 1 if Distance to nearest ethanol plants < 10 miles

27 Results 2) Hedonic estimates on unmatched vs. matched samples Nominal farmland values ($/acre) Unmatched sample Matched sample Coef. Robust SE Coef. Robust SE Distance to nearest ethanol plant Dist_Ethanol * Post construction dummy Assessed land value % of total assessed *** *** Total acres *** *** 3.07 Total acres squared 0.013*** *** 0.01 Crops Productivity Index Prime farmland Distance to highway ramp ** Distance to nearest city *** Incremental distance to second nearest city *** ** Distance to nearest grain elevator Distance to nearest agricultural terminal ***

28 Results 3) Instrumental Variables Regressions on the matched sample Nominal farmland values ($/acre) Ethanol Plant Agricultural Terminal Coef. SE Coef. SE Distance to nearest ethanol plant Dist_Ethanol * Post construction dummy ** Distance to nearest agricultural terminal *** Dist_Terminal * Post construction dummy 18.12** 7.86 OLS

29 Validity, validity, and robustness of the IV approach Validity: instruments satisfies the test of overidentifying restrictions Relevance: instruments rejects weak identification test 1 st stage results: (I) Dist_Ethanol* Post (I) Dist_Ethanol Nominal farmland values ($/acre) construction dummy Coef. Std. Err. Coef. Std. Err. Capacity-weighted distance to other ethanol plants *** *** Capacity-weighted distance to other terminals *** *** Avg_Dist_Ethanol * Post construction dummy -7.04E-06* E-05*** Avg_Dist_Terminal * Post construction dummy *** The negative correlation between instruments and endogenous distance to nearest ethanol plant confirms the spatial competition argument

30 Ethanol Plant Agricultural Terminal Coef. Std. Err. Coef. Std. Err. Panel I: 1 to 2 nearest neighbor matching Panel II: 1 to 1 nearest neighbor matching Panel III: Mahalanobis metric covariate matching Panel IV: Kernel matching Dist_Ag Market *** Dist_Ag Mkt * Post_Dummy *** ** 9.22 Number of observations Adjusted R Dist_Ag Market *** Dist_Ag Mkt * Post_Dummy *** ** Number of observations Adjusted R Dist_Ag Market * Dist_Ag Mkt * Post_Dummy * Number of observations Adjusted R Dist_Ag Market *** 8.21 Dist_Ag Mkt * Post_Dummy *** *** 6.08 Number of observations Adjusted R Robustness check: Alternative Matching algorithms

31 Accounting for Spatial Interactions and Spatial Auto-Correlation: Fixed Effects and Spatial Error Model

32 Spatial autocorrelation explained

33 Some equations spatial fixed effects, spatial econometrics

34 Criticism of spatial error model and spatial econometrics

35 Decline in urban premium

36 Robustness checks Spatial fixed effects: Census track, township, block group, block Specifications: Log-log, semi-log, Box-cox Spatial econometrics 2,4,6,8 nearest neighbor spatial weight matrix Inverse-distance spatial weight matrix Falsification test: assume housing bust happened in 2005

37 Alternative ways to account for spatial autocorrelation Special issue of Journal of Regional Science 2010 Nonparametrics, semiparametrics Exploit spatial variations through natural or quasi-natural experiment Construct IV based on spatial equilibrium or historical lagged Spatial first difference Construct spatial W matrix through auxiliary survey of social network

38 Panel Data Analysis using Individual Responses to Iowa Land Value Survey

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40 % Change in Iowa Farmland Values % 30.0% 20.0% 10.0% 0.0% -10.0% -20.0% -30.0% -40.0%

41 Iowa Land Value Survey Annual survey of farm real estate market professionals (e.g., farm managers, appraisers, real estate brokers, assessors, etc.)

42 Empirical strategy Use a panel of repeated responses to measure how estimated values evolve over time Examine the influence of prior error (deviation between individual and county aggregate) Simple model based on important factors, similar to net present value of farmland Income Interest rates

43 Current Estimate vs. Last Year s Estimate

44 Empirical model Define annual change at time tt for respondent ii in county jj as: yy ii,jj,tt = yy ii,jj,tt yy ii,jj,tt 1 The model takes the form: yy ii,jj,tt = αα ii + γγ yy ii,jj,tt 1 yy jj,tt 1 + ββ 1 rr tt + ββ 2 pp tt + εε ii,tt Annual change in estimate Individual fixed effects Deviation between previous estimate and county level average Change in interest rate Change in commodity prices All variables expressed in natural logs Farm Real Estate interest rate obtained from Chicago Fed Corn price serves as proxy for commodity price Residual

45 Explaining changes in land value estimates over time Variable Simple Full yy ii,jj,tt *** *** (0.058) (0.055) rr tt *** (0.041) pp tt *** (0.012) RR Individual fixed effects (n = 826, N = 3,545) Robust standard errors in parentheses ***α 0.01, **α 0.05, *α 0.10 Respondents influenced by prior deviation from survey averages A 1% increase in deviation from previous county-level average is associated with a 0.84 decrease in next year s estimate Responses reflect changes in interest rates and corn prices Respondents more sensitive to interest rate changes χχ 2 =

46 Regression discontinuity design Other examples of RDD exploiting spatial boundary discontinuity - Effect of school quality on housing (Black 1999) - Farmland tenure and ownership (anti-corporate farming laws in U.S. Midwest)

47 Resources on How to Conduct Empirical Research and Write Research Papers

48 Don Fullerton s Guidelines

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50 How to write research paper

51 Outline for good research paper

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53 Mostly Harmless Econometrics (book)

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56 Scott Long: The Workflow of Data Analysis using Stata

57 Current U.S. Agricultural Credit and Finance Conditions

58 Credit Conditions Have Gradually Deteriorated Source: Nathan Kauffman, Fed Reserve. Kansas City

59 Rising agricultural debt

60 Source: Nathan Kauffman, Fed Reserve. Kansas City

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62 Iowa Farmland Ownership and Transfer Trends USDA TOTAL 2014 Tenure, Ownership and Transfer of Agricultural Land 2014

63 Distribution of Iowa Farmland Acres by Land Ownership Type % 90% 80% Percent of Farmland Acres (%) 70% 60% 50% 40% 30% 20% 10% 0% Sole owner Joint tenancy Tenancy in common Partnership Estates Trusts Corporations LLC Government/institution Source: Ag DM PM1980

64 Distribution of Iowa Farmland Acres by Tenure % 90% 80% Percent of farmland acres (%) 70% 60% 50% 40% 30% 20% 10% 0% Year Owner-operated Cash rent lease Crop share lease Other type of lease Source: Ag DM PM1980

65 Rent, Expense, Asset, Debt by Landlord Ownership Types 2014 Rent Expenses Value of Land Total Debt Received and Buildings Operator Landlords Non-operator Landlord Individual & Partnership Corporation & Trust Other Multiple* Iowa Total Illinois Total Midwest Total U.S. Total , Source: USDA NASS TOTAL

66 % of Farmland by Age and Life Stage of Owners 100% 90% 80% 70% Percent of Farmland 60% 50% 40% 30% 20% 10% 0% Year < > 74 Source: Ag DM PM1980

67 Method of Anticipated Transfer % 90% 80% Percentage of Farmland 70% 60% 50% 40% 30% 20% 10% 0% Will to family Will to others Give to family Give to others Sell to family Sell to others Put in trust Other

68 Iowa Farmland Value Portal

69 Iowa Farmland Value Portal

70 Iowa Farmland Value Portal ww.card.iastate.edu/ farmland

71 Thank You! Wendong Zhang Assistant Professor and Extension Economist 478C Heady Hall Iowa State University