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 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 Outline Motivation and research question 1 Theory 2 Gap in the literature and hypothesis 3 Specifications and methodology 4 Data and summary statistics 5 Results 6 Final remarks 7 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 and research question 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 Theory: Ricardian hedonic framework Farmers maximize profits by using specific plots and choosing x ∗ . Farmer bid for a parcel: θ ( z , p , π D , α ) = π ∗ DV ( p , z , α ) − π ∗ D (1) A landowner maximizes profits by renting plots and choosing ˜ z ∗ . The landowner offer function is : z , P , σ, π s ′ ) = π s ′ + C (ˆ φ (ˆ z , ˜ z , ˜ z , P , σ ) (2) The equilibrium condition indicates that: z , P , σ, π s ′ ) = θ ( z , p , α, π D ) = R ( z ) φ (ˆ z , ˜ (3) The Hedonic rental price equation is as follows (reduced form): R ( z ) = R ( z 1 , ..., z n ) (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 Gap in the literature and hypothesis 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 Ricardian Hedonic Model Loglinear specifications and marginal values of the cross sectional Ricardian equations (reduced forms) : Ln ( π ) = β 0 + β 1 F + β 2 F 2 + β 3 H + u (5) ∂π = [ β 11 a + 2 β 12 a E [ f a ] + β 13 b E [ f b ]] ∗ E [ π ] (6) ∂ f a and, Ln ( R ) = β 0 + β 1 F + β 2 F 2 + β 3 H + u (7) ∂ R = [ β 11 a + 2 β 12 a E [ f a ] + β 13 b E [ f b ]] ∗ E [ R ] (8) ∂ f a 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 Data and summary statistics 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 -105,056.00 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 Data and summary statistics 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 -4.00 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