El precio implícito de la erosión en Uruguay: una estimación...

El precio implícito de la erosión en Uruguay: una estimación con “derrames

espaciales” 1

Marcelo Caffera (a)

Felipe Vásquez (b)

Daniel Rodríguez Anza (a)

José Ignacio Hernández (b)

Leonidas Carrasco-Letelier (c)

Mariela Buonomo (d)

"La Economía del cambio climático: impacto y políticas de mitigación y adaptación”

Pontificia Universidad Católica Argentina

23 de abril de 2018

(a) Universidad de Montevideo, Uruguay

(b) Universidad del Desarrollo, Chile

(c) Instituto Nacional de Investigaciones Agropecuarias, Uruguay

(d) Ministerio de Ganadería, Agricultura y Pesca, Uruguay

(1) Authors gratefully acknowledge financial support from the Latin American and Caribbean Environmental Economics Program (LACEEP). Daniel Rodríguez Anza gratefully acknowledges financial support from the National Bureau of Research and Innovation (ANII).

What do we do - Objective

• Estimate implicit price (cost) of soil erosion

5/14/2018 Marcelo Caffera 2

Why - Contributions

• Policy arena: important input for the evaluation of the soil conservation policy implemented in Uruguay since 2013

• We use a direct measurement/estimation of erosion, based on an (official) anthropogenic erosion map (scale 1:500,000) • Most available studies use proxies for soil erosion (King and Sinden, 1988;

Boisvert et al., 1997; Donoso and Vicente, 2001)

• Others use measurements of potential erosion, i.e. as in USLE equation (Miranowski and Hammes, 1984; Palmquist and Danielson, 1989; Mendelsohn, Nordhaus and Shaw, 1994)

• Only exception: Gardner and Barrows (1985)


Why - Contributions (cont.)

• We estimate include spatial spillover effects between land transactions • Maddison (2009) and Mena et al. (2014) study land value determinants using

spatial econometrics, but no erosion

• We distinguish between sheet and gully erosion

• We use a large (+1,000 obs.), nationwide database

• Less developed country


Methodology – the Hedonic Approach • Hedonic price equation :

𝑝𝑖,𝑡 = 𝑓(𝐸𝑖 , 𝑆𝑖 , 𝐿𝑖 , 𝑃𝑖,𝑡 , 𝑇𝑡 , 𝐵𝑖) (1)

• 𝑝𝑖,𝑡 : per-hectare-price of farm i in quarter t, • 𝐸𝑖 : level of erosion of farm i, • 𝑆𝑖 : set of other relevant physical characteristics of the farm’s soil (topsoil thickness,

water-retaining capacity, forestry priority zone, Natural Conservation zone, lot’s aptitude for rice cultivation),

• 𝐿𝑖: set of location characteristics of farm i (distance to relevant city/town, port and closest national route, jurisdiction/department)

• 𝑃𝑖,𝑡 : set of values of relevant market prices: agricultural commodities (soybean, cellulose pulp, meat) and other commodities (iron ore, gold)

• 𝑇𝑡 is a set of year and quarter indicators • 𝐵𝑖: Buyer natural person


The Dataset – the Erosion Variables

• We calculate for each polygon of iso-erosion, parcel and lot:

• % of topsoil lost to sheet erosion = 𝑚3 𝑠ℎ𝑒𝑒𝑡 𝑒𝑟𝑜𝑠𝑖𝑜𝑛

𝑚3 𝑡𝑜𝑝𝑠𝑜𝑖𝑙

• Presence of deep gullies (i.e. depth exceed topsoil depth)

• (i.e. if any of the polygons comprising the lot presents gullies deeper than topsoil, then the dummy takes value 1 for the whole lot)

• Extension of sheet erosion



• Note: we take the upper bound of the extension and intensity categories to perform the above calculations

• i.e. if lot presents moderate erosion (25-75% of topsoil lost) with frequent extension (11-25% of lot affected), we use 75% of topsoil lost in 25% of the lot.


Summary Statistics Summary Statistics

Variable Mean Std. Dev. Min. Max. Variable Mean Std. Dev. Min. Max.

Price per hectare, in logs (PRICE) 7.205 0.955 4.094 9.426 Soybean price (SOYBEAN) 332.708 46.374 257.797 435.808

% of topsoil lost to sheet erosion (SHEET) 26.906 23.913 0 88.750 Pulp price (PULP) 662.835 118.942 465.818 847.968

Presence of gully deeper than topsoil, dummy (GULLY)

0.824 0.381 0 1 Beef price (BEEF) 114.256 8.954 95.734 133.945

Topsoil thickness (THICKNESS) 28.675 9.061 9 74 Gold price (GOLD) 867.141 129.97 704.395 1093.38

Share of lot surface affected by sheet erosion (EXTENSION)

0.514 0.357 0 1 Iron ore price (IRON) 104.991 31.378 60.139 177.517

Water-retaining capacity (MM) 112.043 30.893 20.3 194.8 Year 2007, dummy 0.11 0.313 0 1

Forestry priority, dummy (FOREST) 0.408 0.492 0 1 Year 2008, dummy 0.182 0.386 0 1

Lot within protected area, dummy (SNAP) 0.009 0.095 0 1 Year 2009, dummy 0.107 0.309 0 1

Aptitude for rice cultivation, dummy (RICECULT)

0.075 0.263 0 1 Year 2010, dummy 0.128 0.334 0 1

Distance to closest relevant town or city (DISTURBAN)

21.807 13.855 1.222 93.85 Year 2011, dummy 0.141 0.348 0 1

Distance to closest relevant port (DISTPORT) 176.65 112.229 9.643 441.363 Year 2012, dummy 0.129 0.336 0 1

Distance to closest national route: less than 10 km, dummy

0.838 0.368 0 1 Year 2013, dummy 0.116 0.32 0 1

Distance to closest national route: 10 to 20 km, dummy (DISTROUTE_1020)

0.126 0.332 0 1 Quarter 1 (January to March), dummy 0.229 0.42 0 1

Distance to closest national route: 20 to 30 km, dummy (DISTROUTE_2030)

0.024 0.152 0 1 Quarter 2 (April to June), dummy 0.265 0.441 0 1

Distance to closest national route: more than 30 km, dummy (DISTROUTE_30+)

0.004 0.061 0 1 Quarter 3 (July to September), dummy 0.261 0.439 0 1

Lot buyer’s nationality: Uruguayan, dummy (ACQUY)

0.667 0.471 0 1 Quarter 4 (October to December), dummy 0.263 0.44 0 1

Lot buyer’s nationality: not Uruguayan, dummy

0.333 0.471 0 1 Maldonado and Lavalleja departments

(MALDLAV) 0.12 0.325 0 1

Lot buyer’s legal condition: natural person, dummy (ACQNP)

0.755 0.43 0 1 Southwest littoral departments (SOUTHLIT) 0.261 0.439 0 1

Lot buyer’s legal condition: not a natural person, dummy

0.245 0.43 0 1 Rest of departments 0.619 0.486 0 1

Methodology – Spatial Spillovers

• Note from 𝑝 = (𝐼𝑛−𝜌𝑊)−1 𝑋𝛽 + (𝐼𝑛−𝜌𝑊)

−1(𝐼𝑛−𝜃𝑊)𝜀 that the conventional interpretation of estimates as marginal impacts on the dependent variable no longer holds

• “Direct effect”: 𝜕𝑝𝑖

𝜕𝐸𝑖, includes the “feedback loops” of 𝐸𝑖 affecting 𝑝𝑗 and 𝑝𝑗 affecting 𝑝𝑖

and longer paths (through 𝑝𝑘)

• “Indirect effect”: 𝜕𝑝𝑗

𝜕𝐸𝑖, Spatial spillover

• Total effect = direct + indirect effects



• Our weighting matrix W

• We truncate it spatially and temporally in a way similar to Maddison (2009):

• We estimate separately • An inverse distance matrix

• A contiguity matrix (each element wij is 1 if distance between lots i and j ≤50-km and 0 otherwise, and

• A “temporal contiguity” matrix (in which elements 𝑤𝑖𝑗 take value 1 if lot 𝑗 was traded in the same quarter or in a period of up to eight quarters before lot 𝑖),

• then multiply the three matrices to get 𝑊

• We row normalize W (i.e. each element 𝑤𝑖𝑗 is divided by the row sum 𝑤𝑖) since it simplifies calculations and computing time


SARMA Estimation

Variable SARMA

Coefficient Std. error

Share of topsoil lost to sheet erosion (SHEET) -0.002*** 0.001 Presence of gully deeper than topsoil, dummy (GULLY) 0.029 0.026

Topsoil thickness (THICKNESS) 0.000 0.001 Share of lot surface affected by sheet erosion (EXTENSION) 0.304*** 0.066

Water-retaining capacity (MM) 0.002*** 0.000 Forestry priority, dummy (FOREST) -0.053** 0.021

Lot within protected area, dummy (SNAP) -0.065 0.067 Aptitude for rice cultivation, dummy (RICECULT) -0.019 0.037

Distance to closest relevant town or city (DISTURBAN) -0.002*** 0.001 Distance to closest relevant port (DISTPORT) -0.001*** 0.000

Distance to closest national route: 10 to 20 km, dummy (DISTROUTE_1020) -0.105*** 0.025 Distance to closest national route: 20 to 30 km, dummy (DISTROUTE_2030) -0.116** 0.054

Distance to closest national route: more than 30 km, dummy (DISTROUTE_30+) -0.058 0.152 Lot buyer’s nationality: Uruguayan, dummy (ACQUY) -0.424*** 0.026

Lot buyer’s legal condition: natural person, dummy (ACQNP) -0.070** 0.027 Soybean price (SOYBEAN) 0.000 0.000

Pulp price (PULP) 0.000 0.000 Beef price (BEEF) 0.001 0.003 Gold price (GOLD) -0.001 0.001

Iron ore price (IRON) 0.000 0.000

Maldonado and Lavalleja departments (MALDLAV) 0.107*** 0.034

Southwest littoral departments (SOUTHLIT) 0.026 0.032 Rho 0.578*** 0.061

Theta 0.233*** 0.078 Constant 3.326*** 0.568

Year dummies Yes

Quarter dummies Yes

Wald test of spatial terms: chi2(2) = 322.17 Prob > chi2 = 0.0000

*p<0.10, **p<0.05, ***p<0.01

Estimation Results - SARMA • Sheet erosion and

extension significant and with expected sign

• Gully erosion dummy still not significant:

• Rest of controls have expected sign and are, in general, significant • Exceptions: topsoil depth,

lot within protected area and aptitude for rice cultivation)

SARMA Total, Direct and Indirect Effects

Variable SARMA

Total effect Std. Error Direct effect Std. Error Indirect effect Std. Error

Share of topsoil lost to sheet erosion (SHEET) -0.005*** 0.002 -0.002*** 0.001 -0.003** 0.001

Presence of gully deeper than topsoil, dummy (GULLY) 0.070 0.065 0.030 0.027 0.040 0.039

Topsoil thickness (THICKNESS) 0.000 0.003 0.000 0.001 0.000 0.002

Share of lot surface affected by sheet erosion (EXTENSION) 0.721*** 0.160 0.306*** 0.066 0.415*** 0.115

Water-retaining capacity (MM) 0.004*** 0.001 0.002*** 0.000 0.002*** 0.001

Forestry priority, dummy (FOREST) -0.125** 0.050 -0.053** 0.021 -0.072** 0.032

Lot within protected area, dummy (SNAP) -0.154 0.159 -0.065 0.067 -0.088 0.093

Aptitude for rice cultivation, dummy (RICECULT) -0.046 0.087 -0.019 0.037 -0.026 0.050

Distance to closest relevant town or city (DISTURBAN) -0.005*** 0.002 -0.002*** 0.001 -0.003** 0.001

Distance to closest relevant port (DISTPORT) -0.002*** 0.000 -0.001*** 0.000 -0.001*** 0.000

Distance to closest national route: 10 to 20 km, dummy (DISTROUTE_1020) -0.249*** 0.069 -0.106*** 0.025 -0.143*** 0.049

Distance to closest national route: 20 to 30 km, dummy (DISTROUTE_2030) -0.274** 0.134 -0.116** 0.055 -0.158* 0.083

Distance to closest national route: more than 30 km, dummy (DISTROUTE_30+) -0.138 0.360 -0.059 0.153 -0.080 0.207

Lot buyer’s nationality: Uruguayan, dummy (ACQUY) -1.004*** 0.152 -0.426*** 0.026 -0.577*** 0.145

Lot buyer’s legal condition: natural person, dummy (ACQNP) -0.165** 0.067 -0.070** 0.027 -0.095** 0.042

Soybean price (SOYBEAN) 0.000 0.001 0.000 0.000 0.000 0.000

Pulp price (PULP) 0.000 0.001 0.000 0.000 0.000 0.001

Beef price (BEEF) 0.002 0.006 0.001 0.003 0.001 0.004

Gold price (GOLD) -0.002 0.002 -0.001 0.001 -0.001 0.001

Iron ore price (IRON) 0.000 0.000 0.000 0.000 0.000 0.000

Maldonado and Lavalleja departments (MALDLAV) 0.253*** 0.085 0.107*** 0.034 0.145*** 0.056

Southwest littoral departments (SOUTHLIT) 0.062 0.072 0.026 0.032 0.036 0.041

Year dummies Yes

Quarter dummies Yes

*p<0.10, **p<0.05, ***p<0.01

Estimation Results - SARMA

• The total effect of sheet erosion should be interpreted as a semielasicity (price is expressed in logs)

• Our sheet erosion variable is the share of topsoil lost to erosion (1-100%): it is the same to analyze a unit change and a 1% change

• Then, an additional 1-percentage loss of topsoil to sheet erosion causes a 0.538% drop in price per hectare

• Indirect effects account for more than half of the total effects in most cases


• Average price per hectare in sample: USD 1,957.15

• Then, a 0.538% drop in price per hectare (i.e. the impact of 1% of topsoil loss)

represents a USD 10.53 drop

• A 1% loss of topsoil ≈ 37.95-ton loss per hectare

• Per hectare average volume of topsoil in sample: 2,875 m3. Average density of soil in

Uruguay: 1.32 ton/m3 (Califra, Beretta and Del Pino, 2014). Thus, per hectare average

weight of topsoil about 3,795 tons = 2,875 m3 * 1.32 ton/m3

• Average value of a lost ton: USD 0.277 =10.53 𝑈𝑆𝐷

37,95 𝑡𝑜𝑛


• Mean share of topsoil loss in our sample is 26.9%: 1,020.86 tons per hectare

• Then, according to our calculations above, the average lot has been traded for about USD 282.78 less per hectare on average, if we compare with a hypothetical case of no sheet erosion

• Land price levels within 2007-13 remained between USD 1,500-2,500 per hectare in average (at 2000 prices, adjusted by Uruguayan inflation in dollars)


Motivation

• Soil is a fundamental non-renewable natural resource: provides fundamental ecosystems functions and services (FAO, 2015)

• Erosion may affect soil’s ability to provide those services and their quality by altering its attributes (Durán and García Préchac, 2007)

• Climate change may increase soil erosion by changing a key erodibility factor: precipitation patterns (Nearing, Pruski and O’Neal, 2004; Mullan, 2013; Farell, Abatzoglou and Brooks, 2014)

• Under this scenario, adequate soil management practices become even more important.


Motivation

• To evaluate in economic terms private soil management practices (induced by soil conservation policies or not) we need the value of avoided erosion

• Lack of recent estimations of the implicit market price of soil erosion

• In this paper we contribute to fill this gap


Background and policy context

• Increase in agricultural commodities prices during 2006-2013 + export restrictions in Argentina = never-seen-before increase in agricultural production in Uruguay • 2000: 17,000 hectares dedicated to soybean

• 2011: 930,000 hectares (2011 General Agricultural Census, OPYPA)

• zafra 2014/2015: 1.334.000 hectáreas (Pauletti, Terra and Perranchon, 2016)

• Permanent soybean cultivation substituted conventional rotation schemes

• Agribusiness also changes: • Area under tenants (instead of landowners) increased by 20%;

• tenancy with sharecropping grew by 96% (2011 General Agricultural Census, OPYPA)

• Emergence of sowing pools



• All of these factors alerted public officials declaring soil erosion “the main environmental problem in Uruguay linked to agricultural activities”

• The Ministry of Agriculture (MGAP) policy response: a nation-wide soil conservation program called Responsible Soil Use and Management Plans



• Responsible Soil Use and Management Plans • Producer has to present a crop rotation schedule; approved if estimated

erosion is less than an acceptable level (7 ton/ha de suelo perdido por año, medida a través de la USLE calibrada para Uruguay).

• Experimental in 2011 • Compulsory in the winter of 2013 for landowners and land tenants planting

more than 100 hectares of wheat and barley • Extended to sorghum, corn, soybean and sunflower in the summer of 2013-

2014 • Extended to dairy producers in the Santa Lucía River basin in the summer of

2013-2014 (Episodes of algae bloom in 2013 and 2015) • 2016: a todos con más de 50 ha de agricultura


The Dataset – the Land Transaction Database

• From Directorate of Agricultural Statistics (DIEA, Ministry of Agriculture)

• From this database we get • Price per hectare (in 2000 US dollars, adjusted for Uruguayan inflation in dollars)

• Lot’s size (in hectares)

• buyer’s nationality (1 if the buyer is a Uruguayan national)

• buyer’s legal personality (1 if the buyer is a natural person)

• DIEA “agricultural land transactions”: • at least 10 hectares of land,

• total value of at least USD 1,000 and

• a per-hectare value between USD 50 and USD 30,000

• 16,254 observations originally, from January 2007 to December 2013


The Dataset – the Land Transaction Database

• We further trim the dataset from: • lots within the Metropolitan Area of Montevideo and suburban areas

surrounding major towns and cities

• lots within touristic areas (coastline and sierra) • lots in Canelones, Maldonado and Rocha departments located between the coastline and

the closest parallel national route

• lots in Lavalleja department smaller than 25 hectares

• Final: 10,717 transactions



• 2004 Anthropic Erosion Map (Ministry of Agriculture) codifies sheet and gully erosion for “landscape units” of iso-erosion (scale 1:500,000)

• Sheet erosion codification • Type of erosion: null, wind, water, other • Intensity of erosion:

• Mild: less than 25% of A-horizon (topsoil) lost • Moderate: 25% - 75% lost • Severe: 100% lost • Very severe: more than 100% lost

• Extension of erosion: • Infrequent: 1-5% of land tract • Common: 6-10% • Frequent: 11-25% • Very frequent: 26-50% • Dominant: more than 50%



• Gully erosion codification • Depth:

• Shallow: 0.2m – 0.5m

• Moderately deep: 0.5m – 1m

• Very deep: more than 1m

• Extension: • Isolated: less than 1% of land tract

• Common: 1% - 10%

• Frequent: 11% - 50%

• Dominant: more than 50%



• Intersections of parcels and landscape units produce “polygons” of iso-erosion

• Red lines represent parcels and black lines represent landscape units (QGIS screenshot of a rural zone near Rocha, southeast Uruguay)



• Potential sources of spatio-temporal dependence: • value of other farms traded in the recent past in the vicinity could be a source of

information of the value of farm i

• Presence of spatial dependence causes bias and inconsistency (Anselin, 1988; LeSage, 1999)


• Spatial correlation among observations confirmed by the Global Moran’s I-statistic

• Lagrange Multiplier tests locates the source of spatial correlation both in the dependent variable and the error term

• We thus incorporate spatial dependence in our analysis in the form of a SARMA model (GS2SLS estimation; Kelejian and Prucha, 2010)

Testing for spatial correlation in OLS model

Test Statistic

Global Moran's I-statistic 0.053***

Spatial dependence due to error term

Lagrange Multiplier test 596.36***

Robust Lagrange Multiplier test 35.942***

Spatial dependence due to dependent variable

Lagrange Multiplier test 595.37***

Robust Lagrange Multiplier test 34.952***

Lagrange Multiplier test for SARMA 631.32***

*p<0.10, **p<0.05, ***p<0.01

Estimation Results – Testing for Spatial Dependence


• Spatial autoregressive moving average model (SARMA):

𝑝 = 𝜌𝑊𝑝 + 𝑋𝛽 + 𝑢 (2.a)

𝑢 = (𝐼𝑛−𝜃𝑊)𝜀 (2.b)

with 𝑝 is land values, 𝑋 the vector of explanatory variables defined in (1) and 𝜀~𝑁(0, 𝜎2𝐼𝑛)

• Solving for 𝑝, we get the data generation process (LeSage and Pace, 2009):

𝑝 = (𝐼𝑛−𝜌𝑊)−1 𝑋𝛽 + (𝐼𝑛−𝜌𝑊)

−1(𝐼𝑛−𝜃𝑊)𝜀 (3)


• We estimate seven alternative specifications to check the robustness of our findings: • removing department dummies

• including an interaction term between sheet erosion and concentration of sheet erosive process

• removing gully erosion dummy

• including a spatial lag of sheet erosion (SDEM)

• removing gold price and including 1-year US Treasury bond rate as the alternative asset we take as a reference

• including port cities (Montevideo, La Paloma, Nueva Palmira) in our distance to closest city/town variable

• including total area of traded lot as a control

Estimation Results – Robustness Checks


• In general, robustness checks results do not present notorious changes when compared to our central model (neither signs/significance nor magnitude of marginal effects)

Robustness Check #1: SARMA (without dept. dummies)

Variable SARMA (without dept. dummies)

Coefficient Std. Error Total effect Std. Error Direct effect Std. Error Indirect effect Std. Error

Share of topsoil lost to sheet erosion (SHEET) -0.00242*** 0.001 -0.00583*** 0.002 -0.00244*** 0.001 -0.00339*** 0.001

Presence of gully deeper than topsoil, dummy (GULLY) 0.038 0.026 0.091 0.067 0.038 0.027 0.053 0.041

Topsoil thickness (THICKNESS) -0.000 0.001 -0.001 0.004 -0.000 0.001 -0.000 0.002

Share of lot surface affected by sheet erosion (EXTENSION) 0.296*** 0.065 0.714*** 0.154 0.298*** 0.066 0.415*** 0.109

Water-retaining capacity (MM) 0.002*** 0.000 0.004*** 0.001 0.002*** 0.000 0.002*** 0.001

Forestry priority, dummy (FOREST) -0.046** 0.020 -0.110** 0.048 -0.046** 0.020 -0.064** 0.030

Lot within protected area, dummy (SNAP) -0.061 0.067 -0.148 0.162 -0.062 0.067 -0.086 0.096

Aptitude for rice cultivation, dummy (RICECULT) -0.007 0.037 -0.018 0.089 -0.007 0.037 -0.010 0.052

Distance to closest relevant town or city (DISTURBAN) -0.002*** 0.001 -0.005*** 0.002 -0.002*** 0.001 -0.003** 0.001

Distance to closest relevant port (DISTPORT) -0.001*** 0.000 -0.002*** 0.000 -0.001*** 0.000 -0.001*** 0.000

Distance to closest national route: 10 to 20 km, dummy (DISTROUTE_1020) -0.103*** 0.025 -0.247*** 0.070 -0.103*** 0.025 -0.144*** 0.049

Distance to closest national route: 20 to 30 km, dummy (DISTROUTE_2030) -0.120** 0.055 -0.288** 0.136 -0.120** 0.055 -0.167* 0.086

Distance to closest national route: more than 30 km, dummy (DISTROUTE_30+) -0.060 0.153 -0.143 0.368 -0.060 0.154 -0.084 0.214

Lot buyer’s nationality: Uruguayan, dummy (ACQUY) -0.426*** 0.026 -1.026*** 0.151 -0.429*** 0.026 -0.597*** 0.145

Lot buyer’s legal condition: natural person, dummy (ACQNP) -0.064** 0.027 -0.154** 0.068 -0.064** 0.027 -0.090** 0.043

Soybean price (SOYBEAN) 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.001

Pulp price (PULP) 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.001

Beef price (BEEF) 0.001 0.003 0.002 0.006 0.001 0.003 0.001 0.004

Gold price (GOLD) -0.001 0.001 -0.002 0.002 -0.001 0.001 -0.001 0.001

Iron ore price (IRON) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Rho 0.585*** 0.059

Theta 0.238*** 0.074

Constant 3.310*** 0.559

Year dummies Yes

Quarter dummies Yes

*p<0.10, **p<0.05, ***p<0.01

Wald tests for the spatial terms were omitted for reasons of space


• Robustness check #1: we drop department dummies

• Qualitative results (sign and significance of total effects) hold

• Magnitude of some coefficients change slightly • Drop in price per hectare due to an additional 1% of topsoil lost to sheet erosion is

0.583% (from 0.538% in our central model)

• Similar to what happens in OLS models (comparing specifications with and without dept. dummies)


Robustness Check #2: SARMA (with interaction)

Variable SARMA (with interaction)


Share of topsoil lost to sheet erosion (SHEET) -0.00216** 0.001 -0.00522** 0.002 -0.00218** 0.001 -0.00305** 0.001


Topsoil thickness (THICKNESS) 0.000 0.001 0.000 0.004 0.000 0.001 0.000 0.002


Interaction: sheet erosion and concentration (SHEET_EXT) -0.00005*** 0.000 -0.00013** 0.000 -0.00005*** 0.000 -0.00007** 0.000

Water-retaining capacity (MM) 0.002*** 0.000 0.004*** 0.001 0.002*** 0.000 0.002*** 0.001 Forestry priority, dummy (FOREST) -0.053** 0.021 -0.127** 0.052 -0.053** 0.021 -0.074** 0.033

Lot within protected area, dummy (SNAP) -0.065 0.067 -0.157 0.162 -0.066 0.067 -0.092 0.096 Aptitude for rice cultivation, dummy (RICECULT) -0.018 0.037 -0.045 0.089 -0.019 0.037 -0.026 0.052

Distance to closest relevant town or city (DISTURBAN) -0.002*** 0.001 -0.006*** 0.002 -0.002*** 0.001 -0.003** 0.001 Distance to closest relevant port (DISTPORT) -0.001*** 0.000 -0.002*** 0.000 -0.001*** 0.000 -0.001*** 0.000






Soybean price (SOYBEAN) 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.001 Pulp price (PULP) 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.001 Beef price (BEEF) 0.001 0.003 0.002 0.006 0.001 0.003 0.001 0.004 Gold price (GOLD) -0.001 0.001 -0.002 0.002 -0.001 0.001 -0.001 0.001

Iron ore price (IRON) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Maldonado and Lavalleja departments (MALDLAV) 0.106*** 0.034 0.256*** 0.087 0.107*** 0.034 0.150** 0.058

Southwest littoral departments (SOUTHLIT) 0.025 0.032 0.060 0.074 0.025 0.032 0.035 0.042 Rho 0.586*** 0.061

Theta 0.225*** 0.078 Constant 3.269*** 0.568

Year dummies Yes Quarter dummies Yes

*p<0.10, **p<0.05, ***p<0.01 Wald tests for the spatial terms were omitted for reasons of space


• Robustness check #2: we include an interaction term between sheet erosion and concentration of the sheet erosive process (SHEET_EXT=SHEET/EXTENSION)

• Negative and significant direct effect of the interaction term • Decrease in price caused by sheet erosion is not linear in the extension of erosion

throughout the land tract

• In more concentrated sheet erosive processes, the effect of sheet erosion on price is stronger

• The total effect of an additional 1% loss of topsoil to sheet erosion on price barely changes: from 0.538% to 0.547%


Robustness Check #3: SARMA (without gully erosion)

Variable SARMA (without gully erosion)


Share of topsoil lost to sheet erosion (SHEET) -0.00229*** 0.001 -0.00495*** 0.002 -0.00230*** 0.001 -0.00264** 0.001



Water-retaining capacity (MM) 0.002*** 0.000 0.003*** 0.001 0.002*** 0.000 0.002*** 0.001

Forestry priority, dummy (FOREST) -0.052** 0.021 -0.113** 0.046 -0.053** 0.021 -0.060** 0.027



Distance to closest relevant town or city (DISTURBAN) -0.003*** 0.001 -0.005*** 0.002 -0.003*** 0.001 -0.003*** 0.001






Lot buyer’s legal condition: natural person, dummy (ACQNP) -0.071*** 0.027 -0.154** 0.061 -0.072*** 0.027 -0.082** 0.036

Soybean price (SOYBEAN) 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000

Pulp price (PULP) 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000

Beef price (BEEF) 0.001 0.003 0.001 0.006 0.001 0.003 0.001 0.003

Gold price (GOLD) -0.001 0.001 -0.002 0.002 -0.001 0.001 -0.001 0.001


Maldonado and Lavalleja departments (MALDLAV) 0.111*** 0.034 0.238*** 0.079 0.111*** 0.035 0.127** 0.049

Southwest littoral departments (SOUTHLIT) 0.035 0.033 0.076 0.068 0.035 0.033 0.040 0.036

Rho 0.536*** 0.064

Theta 0.272*** 0.080

Constant 3.637*** 0.582

Year dummies Yes

Quarter dummies Yes

*p<0.10, **p<0.05, ***p<0.01

Wald tests for the spatial terms were omitted for reasons of space


• Robustness check #3: we remove the gully erosion dummy

• Qualitative results hold, magnitude of direct effects show some minor changes • The drop in price consequence of an additional topsoil loss to sheet erosion of 1%

reduces from 0.538% to 0.495%

• Sheet erosion does not change in a drastic way when gully erosion is excluded, and gully erosion is not significant when included: sheet erosion may be capturing some effect of gullies by construction?


Robustness Check #4: SDEM (with spatial lag of SHEET)

Variable SDEM (with spatial lag of SHEET)

Coefficient Std. Error Total effect Std. Error Direct effect Std. Error Indirect effect Std. Error Share of topsoil lost to sheet erosion (SHEET) -0.00206** 0.001 -0.00600** 0.003 -0.00208** 0.001 -0.00392 0.002







Distance to closest relevant town or city (DISTURBAN) -0.002*** 0.001 -0.006*** 0.002 -0.002*** 0.001 -0.003** 0.001 Distance to closest relevant port (DISTPORT) -0.001*** 0.000 -0.002*** 0.000 -0.001*** 0.000 -0.001*** 0.000








Maldonado and Lavalleja departments (MALDLAV) 0.105*** 0.034 0.254*** 0.086 0.106*** 0.034 0.148** 0.057


Gamma -0.043 0.111 Theta 0.224*** 0.079

Constant 3.290*** 0.569


Wald test of spatial terms: chi2(3) = 318.96 Prob > chi2 = 0.000

*p<0.10, **p<0.05, ***p<0.01


• Robustness check #4: we add a third spatial lag, assuming spatial spillovers caused by sheet erosion • Rationale: erosion data was collected at a higher-than-lot scale

• Thus, our SARMA model becomes SDEM

• Qualitative results hold, though sheet erosion total effect presents some changes in magnitude • An additional 1% loss of topsoil to sheet erosion on price increases to 0.6%:

although not drastic, it is the biggest change we observe among robustness checks with respect to our central model


• Should we be wary of our previous results in light of this check? • The three spatial lags are jointly significant (Wald)

• Nevertheless… • spatial lag of sheet erosion is not individually significant

• increase in sheet erosion’s total effect is due to an increase in its indirect effect… which is not individually significant

• In any case, even relying in a total effect of 0.6%, the implicit price of a one-ton loss of soil does not change radically (from USD 0.277 to 0.309)


Robustness Check #5: SARMA (with 1-yr US Treasury Bond rate)

Variable SARMA (with 1-yr US Treasury Bond rate)









Distance to closest relevant town or city (DISTURBAN) -0.002*** 0.001 -0.005*** 0.002 -0.002*** 0.001 -0.003** 0.001

Distance to closest relevant port (DISTPORT) -0.001*** 0.000 -0.002*** 0.000 -0.001*** 0.000 -0.001*** 0.000 Distance to closest national route: 10 to 20 km, dummy (DISTROUTE_1020) -0.104*** 0.025 -0.248*** 0.069 -0.105*** 0.025 -0.143*** 0.049 Distance to closest national route: 20 to 30 km, dummy (DISTROUTE_2030) -0.114** 0.054 -0.271** 0.134 -0.114** 0.055 -0.156** 0.083




Soybean price (SOYBEAN) 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 Pulp price (PULP) 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 Beef price (BEEF) 0.005** 0.002 0.012** 0.005 0.005** 0.002 0.007** 0.003


1-yr US Treasury Bond interest rate (TREASURY) -0.145*** 0.035 -0.345*** 0.095 -0.146*** 0.035 -0.199*** 0.067

Maldonado and Lavalleja departments (MALDLAV) 0.110*** 0.034 0.262*** 0.085 0.111*** 0.034 0.151*** 0.057 Southwest littoral departments (SOUTHLIT) 0.025 0.032 0.060 0.073 0.025 0.032 0.035 0.041

Rho 0.580*** 0.061 Theta 0.233*** 0.079

Constant 3.548*** 0.572




• Robustness check #5: we change the asset we take as an alternative to land, from gold to 1-year US Treasury bond

• Treasury bond rate and beef price become statistically significant, with expected sign; the rest of the results remain the same in qualitative terms

• Magnitudes of total effects change only slightly

• In particular, an additional 1% loss of topsoil to sheet erosion on price changes to 0.547% (from 0.538%)


Robustness Check #6: SARMA (including Montevideo, Nueva Palmira and La Paloma in distance to closest town variable)

Variable SARMA (including Montevideo, Nueva Palmira and La Paloma in distance to closest town variable)









Distance to closest relevant town or city (DISTURBAN2) -0.002*** 0.001 -0.006*** 0.002 -0.002*** 0.001 -0.003*** 0.001






Lot buyer’s legal condition: natural person, dummy (ACQNP) -0.070** 0.027 -0.165** 0.067 -0.070** 0.027 -0.095** 0.042 Soybean price (SOYBEAN) 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000

Pulp price (PULP) 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.001

Beef price (BEEF) 0.001 0.003 0.002 0.006 0.001 0.003 0.001 0.004 Gold price (GOLD) -0.001 0.001 -0.002 0.002 -0.001 0.001 -0.001 0.001


Maldonado and Lavalleja departments (MALDLAV) 0.107*** 0.034 0.253*** 0.085 0.107*** 0.034 0.145*** 0.056


Theta 0.233*** 0.078 Constant 3.329*** 0.569




• Robustness check #6: we include Montevideo, Nueva Palmira and La Paloma among the reference cities for our distance to closest town/city variable • We consider “relevant” towns those with a Banco República branch and

ANCAP gas station (main bank and fuel provider in Uruguay, respectively)

• Nevertheless, as the above-mentioned are port cities, in our original model they were only taken into account for our distance to closest port variable

• Results reflect virtually no changes respect to our original model


Robustness Check #7: SARMA (with total area of transaction)

Variable SARMA (with total area of transaction)






Water-retaining capacity (MM) 0.002*** 0.000 0.004*** 0.001 0.002*** 0.000 0.002*** 0.001 Forestry priority, dummy (FOREST) -0.057*** 0.021 -0.134*** 0.051 -0.057*** 0.021 -0.077** 0.033



Total area of traded lot (AREA) 0.000*** 0.000 0.000*** 0.000 0.000*** 0.000 0.000*** 0.000 Distance to closest relevant town or city (DISTURBAN) -0.003*** 0.001 -0.006*** 0.002 -0.003*** 0.001 -0.003*** 0.001



Distance to closest national route: 20 to 30 km, dummy (DISTROUTE_2030) -0.128** 0.054 -0.302** 0.135 -0.129** 0.055 -0.174** 0.085



Lot buyer’s legal condition: natural person, dummy (ACQNP) -0.058** 0.027 -0.137** 0.066 -0.058** 0.027 -0.079* 0.041



Maldonado and Lavalleja departments (MALDLAV) 0.111*** 0.034 0.262*** 0.085 0.111*** 0.034 0.151*** 0.057


Theta 0.236*** 0.078 Constant 3.281*** 0.567




• Robustness check #7: we include total area of traded lot (in hectares) as a control • There could be potential profits from dividing or fusing plots for land sellers

(Maddison, 2000; Maddison, 2009)

• Qualitative results hold; quantitative results show only slight changes • In concrete, an additional 1% loss of topsoil to sheet erosion on price changes to

0.540% (from 0.538%)

• Total area of lot results positive and significant, but its effect looks negligible • A 100-hectare increase in lot’s total area induces just a 1.4% increase in price per

hectare

• Thus, there seems to be almost no potential profits from fusing lots


• We have performed an estimation of the market price of soil erosion for the case of the late 2000s in Uruguay • In light of Global Moran’s I-statistic and Lagrange Multiplier tests, we chose a

spatial autoregressive moving average (SARMA) model

• We relied on Kelejian and Prucha’s GS2SLS estimator under heteroskedasticity.

• Spatial spillovers matter • By comparing OLS and SARMA specifications, we observe the consequences of

attenuation bias in the former

• Indirect effects are huge: over half of total marginal effect of regressors in most cases

Concluding Remarks

• Soil productivity has a sound positive impact on land values • Water-retaining capacity is systematically positive and significant; forestry priority

(which identifies relatively poorer soils) is systematically negative and significant

• Distances to markets, services and institutions have a solid impact in land values • Distances to relevant urban centers, ports and routes resulted negative and

significant in every case

• Uruguayan nationals and natural persons tend to have lower willingness to pay (in line with stylized facts of agriculture in Uruguay within the late 2000s)

Concluding Remarks

• Sheet erosion is systematically significant and negative: markets internalize into land prices, at least up to some extent, the loss of soil properties that erosion represents

• Gully erosion dummy did never result significant • When we drop it from the model, we get similar estimates

• This might be suggesting that our sheet erosion variable captures some effect of gullies, by construction

• Extension of erosion over the tract is positive and significant • All things equal, the less concentrated erosion is (i.e. the more spread erosion is),

the less it harms productive properties of soil

• Consequently, for a given level of soil erosion, the less concentrated the erosive process is in the lot, the more the buyer is willing to pay for it.

Concluding Remarks

• We find that, in average, an additional 1% of topsoil lost to sheet erosion leads to a 0.538% fall in price per hectare • What is the same, a one-ton topsoil loss should be valued in about USD

0.277

• This means that the average lot in the period was traded by about USD 282.78 less per hectare than its potential price if it had no erosion at all

• In four of seven alternative specifications carried out as robustness checks, the figures remained between 0.53% and 0.55%

• In the three remaining, the figures kept between 0.495% and 0.6%, what means that a one-ton topsoil loss remains between USD 0.255 and USD 0.309 in all cases

Concluding Remarks

The Dataset – Variable Definitions and Sources of Data Variable Definitions and Sources of Data

Variable Definition Source Variable Definition Source

PRICE Per hectare price of farmland transaction, in logs (US dollars,

adjusted for Uruguayan inflation in dollars) DIEA / DGR

DISTROUTE_1020

Dummy: 1 if distance to closest national route is between 10 and 20 km, 0 otherwise

Own calculation

SHEET Ratio: volume of topsoil lost due to sheet (soil layer) erosion to

total volume of topsoil RENARE

DISTROUTE_2030

Dummy: 1 if distance to closest national route is between 20 and 30 km, 0 otherwise

Own calculation

GULLY Dummy: presence of severe gully (deeper than topsoil) RENARE DISTROUTE_30+ Dummy: 1 if distance to closest national route is more than 30

km, 0 otherwise Own calculation

THICKNESS Topsoil thickness (cm) INIA ACQUY Dummy, 1 if the acquirer (buyer) of the lot is Uruguayan, 0

otherwise DIEA/DGR

EXTENSION Share of the lot affected by any level of sheet erosion (%) RENARE ACQNP Dummy, 1 if the acquirer (buyer) of the lot is a natural person,

0 otherwise DIEA/DGR

MM Average net potentially available water in subsoil (mm) Molfino (2009) SOYBEAN

Quarterly average soybean price (Chicago Soybean futures contract, first contract forward, No. 2 yellow and par), in 2000

US dollars/metric ton, adjusted for Uruguayan inflation in dollars

IMF

FOREST Dummy: 1 if at least one of the parcels involved in transaction

is considered under forestry priority by law, 0 otherwise RENARE PULP

Quarterly average pulp price (Chicago wood pulp - Index NBSK), in 2000 US dollars/metric ton, adjusted for Uruguayan

inflation in dollars INSEE

SNAP Dummy: 1 if at least one of the parcels involved in transaction

is under conservation (integrated in the National System of Protected Areas), 0 otherwise

DINAMA BEEF Quarterly average beef price (Australia and New Zealand 85%

lean fores, CIF U.S. import price), in 2000 US dollar cents/pound, adjusted for Uruguayan inflation in dollars

IMF

RICECULT Dummy: 1 if at least one of the parcels involved in transaction is considered potentially apt for rice cultivation, 0 otherwise

Molfino (2013), Carrasco-Letelier and Beretta-Blanco (2017)

IRON

Quarterly average iron ore price (Brazil, VALE (formerly CVRD) Carajas sinter feed, contract price, f.o.b. Ponta da Madeira; prior to year 2010 annual contract prices), in 2000 US dollar

cents/dtmu, adjusted for Uruguayan inflation in dollars. Unit dry metric ton unit (dmtu) stands for mt 1% Fe-unit.

WB

DISTUC Distance to closest relevant town (km, see discussion in text) Own calculation GOLD Quarterly average gold price (UK, 99.5% fine, London

afternoon fixing, average of daily rates), in 2000 US dollars/toz, adjusted for Uruguayan inflation in dollars

WB

DISTPORT Distance to closest relevant port (km, see discussion in text) Own calculation MALDLAV Dummy, 1 if the transaction took place in Lavalleja or

Maldonado departments DIEA/DGR

SOUTHLIT Dummy, 1 if the transaction took place in Río Negro, Soriano,

Colonia, San José or Flores departments DIEA/DGR

El precio implícito de la erosión en Uruguay: una estimación...

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