A Mexican Ricardian analysis: land rents or net revenues? Saul - - PowerPoint PPT Presentation
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks A Mexican Ricardian analysis: land rents or net revenues? Saul Basurto Hernandez
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
Saul Basurto Hernandez
4th Annual Nottingham-Birmingham-Warwick DTC student conference SXB1022@bham.ac.uk
October 28, 2015
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
1
Motivation and research question
2
Theory
3
Gap in the literature and hypothesis
4
Specifications and methodology
5
Data and summary statistics
6
Results
7
Final remarks
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
Motivation:
Under 4 different scenarios (2081-2100 relative to 1986-2005): global temperature ⇑ 0.3 and 4.8◦ C, precipitation patterns (IPCC, 2014) The agriculture sector is very vulnerable to CC: source of house- holds income, employment and food supply
Research question: Do the implicit land attributes (climate) values differ by using land rental prices or net revenues as indicators of land productivity?
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
Farmers maximize profits by using specific plots and choosing x∗. Farmer bid for a parcel: θ(z, p, πD, α) = π∗DV (p, z, α) − π∗D (1) A landowner maximizes profits by renting plots and choosing ˜ z∗. The landowner offer function is: φ(ˆ z, ˜ z, P, σ, πs′) = πs′ + C(ˆ z, ˜ z, P, σ) (2) The equilibrium condition indicates that: φ(ˆ z, ˜ z, P, σ, πs′) = θ(z, p, α, πD) = R(z) (3) The Hedonic rental price equation is as follows (reduced form): R(z) = R(z1, ..., zn) (4)
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
Contributions:
1 rental prices have not been used within the Ricardian frame-
work
2 a comparison between rental prices and net revenues as (an-
nual) measures of land productivity Hypothesis: Although direct rental prices are subject to long leases, these measures improve the RHM estimations because rents are determined before the crop year and are not sensitive to annual weather
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
Loglinear specifications and marginal values of the cross sectional Ricardian equations (reduced forms): Ln(π) = β0 + β1F + β2F 2 + β3H + u (5) ∂π ∂fa = [β11a + 2β12aE[fa] + β13bE[fb]] ∗ E[π] (6) and, Ln(R) = β0 + β1F + β2F 2 + β3H + u (7) ∂R ∂fa = [β11a + 2β12aE[fa] + β13bE[fb]] ∗ E[R] (8)
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
VARIABLES Source Observations Mean Standard Deviation Minimum Maximum Rent per hectare $/ha ENA 2012 2,750 7,539.20 17,653.09 0.11 36,000.00 Net revenue per hectare $/ha ENA 2012 2,750 11,745.21 291,513.50
96,087.00 Winter Temperature C Worldclim 2,750 15.95 5.06 0.00 27.42 Spring Temperature C Worldclim 2,750 20.90 5.00 0.00 31.23 Summer Temperature C Worldclim 2,750 25.57 6.49 0.00 32.47 Autumn Temperature C Worldclim 2,750 22.00 6.01 0.00 28.83 Winter Precipitation mm. Worldclim 2,750 197.41 58.58 0.00 547.00 Spring Precipitation mm. Worldclim 2,750 246.20 55.06 0.00 421.67 Summer Precipitation mm. Worldclim 2,750 424.66 143.18 0.00 1,163.70 Autumn Precipitation mm. Worldclim 2,750 336.96 136.09 0.00 1,071.00 Winter Diurnal Temperature C Worldclim 2,750 16.27 3.62 0.00 21.97 Spring Diurnal Temperature C Worldclim 2,750 17.46 3.97 0.00 22.43 Summer Diurnal Temperature C Worldclim 2,750 12.98 3.25 0.00 21.59 Autumn Diurnal Temperature C Worldclim 2,750 14.16 3.39 0.00 20.16 Winter storm days CLICOM 2,750 0.34 0.89 0.00 12.46 Spring storm days CLICOM 2,750 0.51 1.21 0.00 12.54 Summer storm days CLICOM 2,750 3.29 5.66 0.00 47.93 Autumn storm days CLICOM 2,750 1.42 2.61 0.00 23.50 Winter cloudy days CLICOM 2,750 2.39 4.38 0.00 51.77 Spring cloudy days CLICOM 2,750 1.70 3.67 0.00 49.77 Summer cloudy days CLICOM 2,750 1.93 5.10 0.00 53.37 Autumn cloudy days CLICOM 2,750 2.30 5.12 0.00 55.15 Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
VARIABLES Source Observations Mean Standard Deviation Minimum Maximum Acrisol (proportion over total land) INEGI 2,750 0.0034 0.0554 0.0000 1.0000 Andosol (proportion over total land) INEGI 2,750 0.0061 0.0767 0.0000 1.0000 Cambisol (proportion over total land) INEGI 2,750 0.0693 0.2426 0.0000 1.0000 Castanozem (proportion over total land) INEGI 2,750 0.0115 0.1027 0.0000 1.0000 Chernozem (proportion over total land) INEGI 2,750 0.0004 0.0191 0.0000 1.0000 Feozem (proportion over total land) INEGI 2,750 0.1010 0.2910 0.0000 1.0000 Fluvisol (proportion over total land) INEGI 2,750 0.0032 0.0523 0.0000 1.0000 Litosol (proportion over total land) INEGI 2,750 0.0134 0.1089 0.0000 1.0000 Luvisol (proportion over total land) INEGI 2,750 0.0216 0.1397 0.0000 1.0000 Planosol (proportion over total land) INEGI 2,750 0.0353 0.1814 0.0000 1.0000 Regosol (proportion over total land) INEGI 2,750 0.0951 0.2859 0.0000 1.0000 Rendzina (proportion over total land) INEGI 2,750 0.0056 0.0704 0.0000 1.0000 Solonchak (proportion over total land INEGI) 2,750 0.0294 0.1519 0.0000 1.0000 Vertisol (proportion over total land) INEGI 2,750 0.3444 0.4581 0.0000 1.0000 Xerosol (proportion over total land) INEGI 2,750 0.2045 0.3915 0.0000 1.0000 Yermosol (proportion over total land) INEGI 2,750 0.0136 0.1107 0.0000 1.0000 Irrigated area (over total land) ENA 2012 2,750 0.7599 0.4094 0.0000 1.0000 Ejidal area (over total land) ENA 2012 2,750 0.6121 0.4538 0.0000 1.0000 Nearest city kilometers ENA 2012-GIS tools 2,750 7.2006 8.4463 0.00000 74.8439 Nearest river kilometers ENA 2012-GIS tools 2,750 4.8582 4.6717 0.00000 46.4238 Nearest water body kilometers ENA 2012-GIS tools 2,750 16.1770 12.7468 0.0000 84.6661 Total area hectares ENA 2012 2,750 127.8120 822.9572 0.2000 31,000 Electricity ENA 2012 2,750 0.3927 0 .4884 0.0000 1.0000 Altitude meters above the sea level INEGI 2,750 683.03 837.59
3,224.00 Farmer age years ENA 2012 2,750 52.06 12.19 18.00 90.00 Farmer education years ENA 2012 2,750 10.34 5.17 0.00 26.00 Farmer gender ENA 2012 2,750 0.9480 0.2221 0.0000 1.0000
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
VARIABLES All Ejidal Ejidal-Private Private Small Large Irrigated Irrigated-Rainfed Rainfed Winter Temp. 0.294***(0.10) 0.496***(0.13) 0.538**(0.26)
0.265**(0.11) 0.407***(0.11) 0.435(0.32)
Winter Temp. Sq. 0.004(0.01) 0.009(0.02)
0.005(0.02)
0.016*(0.01) 0.036(0.02) 0.023(0.04) 0.001(0.01) Spring Temp.
0.060(0.26) Spring Temp. Sq. 0.001(0.01)
0.081(0.09)
0.148(0.10)
Summer Temp. 0.088(0.09) 0.250**(0.12) 0.578**(0.26)
0.062(0.21) 0.070(0.10) 0.201**(0.10) 0.146(0.34)
Summer Temp. Sq. 0.002(0.00) 0.002(0.00)
0.002(0.00) 0.004(0.00) 0.004(0.00)
0.009(0.01) Autumn Temp. 0.006(0.11)
0.338(0.33) 0.132(0.30)
1.087**(0.50) Autumn Temp. Sq.
0.039**(0.02)
0.000(0.00) 0.026(0.03)
Winter Prec.
0.002(0.01)
0.001(0.01)
Winter Prec. Sq. 0.000**(0.00) 0.000(0.00)
0.000(0.00)
0.000***(0.00) 0.000*(0.00) 0.000(0.00) 0.000(0.00) Spring Prec. 0.018**(0.00) 0.011*(0.01)
0.031*(0.02) 0.001(0.01) 0.011**(0.01) 0.015**(0.01)
0.002(0.01) Spring Prec. Sq.
0.000(0.00)
0.000(0.00)
0.001(0.00)
Summer Prec.
0.000(0.00) 0.001(0.00)
0.003(0.00) Summer Prec. Sq. 0.000**(0.00) 0.000**(0.00) 0.000*(0.00) 0.000(0.00) 0.000(0.00) 0.000*(0.00)
0.000(0.00) Autumn Prec. 0.000(0.00) 0.000(0.00) 0.003(0.00) 0.003(0.00) 0.002(0.00) 0.001(0.00)
0.004(0.00) 0.001(0.00) Autumn Prec. Sq.
0.000(0.00)
0.000(0.00) 0.000(0.00)
Winter Temp.*Prec.
0.002(0.01)
0.002(0.00)
0.001(0.00) Spring Temp.*Prec. 0.001(0.00) 0.003(0.00)
0.006(0.00)
0.003(0.00) 0.002(0.00)
0.002(0.00) Summer Temp.*Prec.
0.000(0.00)
0.000(0.00) 0.001(0.00)
Autumn Temp.*Prec. 0.000(0.00) 0.000(0.00) 0.000(0.00)
0.000(0.00)
Winter Diurnal
0.052(0.13)
0.370**(0.18)
0.046(0.12)
0.329*(0.20) Spring Diurnal
0.040(0.14) 0.010(0.14)
0.038(0.07) 0.058(0.08)
Summer Diurnal
0.175(0.22) Autumn Diurnal 0.211*(0.11) 0.195(0.15) 0.414(0.29) 0.192(0.33)
0.292**(0.13) 0.137(0.14) 0.518(0.35)
Irrigated area 1.064***(0.08) 0.918***(0.09) 1.049***(0.21) 1.010***(0.19) 0.741***(0.11) 1.231***(0.10) 0.445(0.29) Ejidal area 0.129**(0.05) 0.133(0.17)
0.031(0.06) 0.051(0.05)
0.208(0.14) Nearest city
0.618(0.91)
0.175(0.44)
1.500(1.13)
Nearest water body
Total area
Electricity 0.217300***(0.05) 0.256***(0.06) 0.156*(0.09) 0.195(0.13) 0.263***(0.10) 0.196***(0.05) 0.125**(0.05) 0.388**(0.16) 0.458***(0.14) Constant 7.398***(0.20) 7.342***(0.25) 7.302***(0.44) 8.121***(0.46) 8.995***(0.37) 7.075**(0.23) 8.587***(0.18) 8.626***(0.68) 6.963***(0.55) Observations 2,750 1,421 467 614 944 1,806 1,963 245 542 R-squared 0.386 0.393 0.517 0.446 0.250 0.504 0.161 0.441 0.432 Soil types, storm and cloudy days and farmer characteristics parameters are not displayed in this table Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
Table: Implicit values of land attributes using rental prices
VARIABLES All Ejidal Ejidal-Private Private Small Large Irrigated Irrigated-Rainfed Rainfed Temperature Winter Temp. $/C 1,076.17 (14.27) 1,288.45 (17.32)
686.05 (8.19) 1,941.75 (18.47) 1,703.00 (28.45) 1,951.04 (22.52)
Spring Temp. $/C
2,854.06 (36.88)
2,337.89 (58.89) 1,134.22 (22.34) Summer Temp. $/C 483.15 (6.41) 1,083.36 (14.57)
2.437.98 (23.19) 365.69 (6.11) 1,763.06 (20.35)
Autumn Temp. $/C
9,348.83 (120.82) 917.72 (10.96)
2,295.99 (57.83) 3058.98 (60.25) Precipitation Winter Prec. $/mm
254.76 (3.29)
425.75 (4.05)
Spring Prec. $/mm 31.10 (0.41) 27.31 (0.37) 15.45 (0.20) 135.75 (1.62) 14.69 (0.14) 92.62 (1.55) 9.96 (0.11)
Summer Prec. $/mm
5.48 (0.07) 1.77 (0.02)
9.10 (0.18) Autumn Prec. $/mm 8.90 (0.12) 14.94 (0.20)
3.53 (0.06)
Average rental price per hectare Rent $/ha. 7,539 7,438 7,738 8,373 10,512 5,985 8,665 3,970 5,077 Percentages in parentheses
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
Figure: Annual implicit values of an additional Celsius degree (-55 to 69%) Figure: Annual implicit values of an additional mm. (-9 to 5%)
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
VARIABLES All Ejidal Ejidal-Private Private Small Large Irrigated Irrigated-Rainfed Rainfed Winter Temp.
0.003(0.00)
0.003**(0.00) 0.003(0.00) 0.002(0.00)
Winter Temp. Sq.
0.000(0.00)
0.000(0.00)
0.000(0.00)
Spring Temp. 0.005(0.00) 0.000(0.00)
0.037(0.03) 0.056**(0.03)
0.036(0.03) Spring Temp. Sq. 0.001(0.00)
0.001(0.00)
0.002(0.01) 0.000(0.00) 0.000(0.00)
Summer Temp.
0.000(0.00)
0.000(0.00)
Summer Temp. Sq. 0.000(0.00)
0.000(0.00) 0.001(0.00) 0.001(0.00) 0.000(0.00) 0.000(0.00) 0.000(0.00) 0.002(0.00) Autumn Temp. 0.005(0.00) 0.000(0.00) 0.002(0.00) 0.018(0.01) 0.017(0.02)
0.003(0.01) 0.017(0.02) Autumn Temp. Sq.
0.000(0.00)
Winter Prec.
0.000(0.00) 0.002(0.00)
Winter Prec. Sq.
0.000(0.00)
0.000(0.00) 0.000(0.00) 0.000(0.00)
Spring Prec. 0.000(0.00)
0.000(0.00)
0.000**(0.00) 0.000(0.00) 0.000(0.00)
Spring Prec. Sq. 0.000(0.00)
0.000(0.00)
0.000(0.00) 0.000(0.00) 0.000(0.00)
0.000(0.00) Summer Prec. 0.000(0.00) 0.000(0.00) 0.000(0.00) 0.000(0.00) 0.000(0.00) 0.000(0.00) 0.000(0.00)
0.000(0.00) Summer Prec. Sq.
0.000(0.00) 0.000(0.00) 0.000(0.00) Autumn Prec.
0.000(0.00)
0.000(0.00)
0.000(0.00) 0.000(0.00)
Autumn Prec. Sq. 0.000(0.00) 0.000*(0.00)
0.000(0.00) 0.000(0.00)
0.000(0.00)
Winter Temp.*Prec. 0.000(0.00) 0.000(0.00)
0.000(0.00) 0.001(0.00)
0.000(0.00)
0.000(0.00) Spring Temp.*Prec.
0.000(0.00)
0.000(0.00)
0.000(0.00)
Summer Temp.*Prec.
0.000(0.00) 0.000(0.00)
Autumn Temp.*Prec.
0.000(0.00)
0.000(0.00)
0.000(0.00) 0.000(0.00) Winter Diurnal
0.001(0.00)
Spring Diurnal 0.004(0.00)
0.003(0.00) 0.010(0.01) 0.013(0.02) 0.002**(0.00) 0.001(0.00) 0.002(0.00) 0.007(0.01) Summer Diurnal
0.001(0.00)
Autumn Diurnal 0.005(0.00)
0.004(0.00) 0.027(0.02) 0.018(0.03) 0.002(0.00) 0.004(0.00) 0.001(0.00) 0.019(0.02) Irrigated area 0.010*(0.01) 0.002**(0.00)
0.040(0.03) 0.055(0.04) 0.001(0.00) 0.000(0.00) Ejidal area
0.025(0.02)
0.000(0.00)
Total area 0.000(0.00)
0.000(0.00)
0.000***(0.00)
Electricity
0.000(0.00)
0.003(0.00)
Extreme events
0.002(0.00)
Constant 16.019***(0.01) 16.018***(0.00) 16.020***(0.01) 16.031***(0.02) 16.052***(0.07) 16.016***(0.00) 16.013***(0.00) 16.016***(0.00) 16.069***(0.03) Observations 2,750 1,152 801 517 374 2,376 1,731 585 434 R-squared 0.034 0.045 0.034 0.090 0.141 0.016 0.024 0.061 0.127 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
Table: Implicit values of land attributes using net revenues
VARIABLES All Ejidal Ejidal-Private Private Small Large Irrigated Irrigated-Rainfed Rainfed Temperature Winter Temp. $/C 76.83 (0.65)
149.61 (0.44) 236.45 (0.85) 108.30 (0.45) 48.99 (0.28) 4.33 (0.02)
799.38 (2.97) Spring Temp. $/C
64.95 (0.19)
22.92 (0.12)
Summer Temp. $/C 129.83 (1.11)
143.36 (0.42) 325.64 (1.17) 137.97 (0.57) 47.64 (0.27) 28.64 (0.15)
1,113.77 (4.14) Autumn Temp. $/C
2.34 (0.02)
32.78 (0.17)
Precipitation Winter Prec. $/mm 6.42 (0.05) 2.61 (0.02)
31.30 (0.11) 40.99 (0.17)
0.82 (0.00) 31.43 (0.12) Spring Prec. $/mm
0.41 (0.00) 2.99 (0.01) 16.49 (0.06)
Summer Prec. $/mm
0.84 (0.01) 0.95 (0.00)
0.07 (0.00)
4.60 (0.02) Autumn Prec. $/mm
0.99 (0.00) 12.98 (0.05) 8.03 (0.03) 0.63 (0.00) 0.45 (0.00) 2.06 (0.01) 12.82 (0.05) Average net revenue per hectare Rent $/ha. 11,745 13,943 34,184
17,378 18,737 19,710
Percentages in parentheses
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
The main contributions of this study: A comparison between direct rental prices and net revenues: long- term land attributes are not relevant for net revenues. The implicit values are not stable across both equations (Chow tests). Future research plan:
1
Explore alternative specifications for the net revenues equation
2
Explore the ENA 2014 and validate these results
3
Compute a Multinomial Logit model for farmers decisions about crops and animals options
Saul Basurto Ricardian analysis
Motivation and research question Theory Gap in the literature and hypothesis Specifications and methodology Data and summary statistics Results Final remarks
I would like to thank the Economic and Social Research Council (ESRC) for its invaluable support.
Saul Basurto Ricardian analysis