objectives - iowa state universitynehring 2003 semi-log 1997 pop acc, soil stress wiebe book nehring...

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The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

Hedonic Analysis of Land Quality: Postscript on other Capital inputs in

OECD Agriculture Richard Nehring*, V. Eldon Ball*, and Jarrett Hart*

Wallace Huffman TributeAmes, IA, August 1, 2014

The views expressed here are not necessarily those of Economic Research Service or the U.S. Department of Agriculture.

Affiliation *ERS


Disregarding quality differences generates biased estimates of the land input and thus of productivity.

In this PPT we present an example of techniques and data sets used to quality-adjust values for land

United States, Canada, Australia, Japan, and 14 European countries using price and quantity data for 2005.

Objectives

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Hedonic Studies of the Effect of Various Characteristics on Farmland Values --------------------------------------------------------------------------------------------Author Year Method Data Key Characteristics --------------------------------------------------------------------------------------------Palmquist et.al. 1989 Box-cox 79-80 Erosion, tobacco quota Land Econ Miranowski et.al.1984 Linear 74-79 Topsoil depth, PH AJAE Maddison 2000 Linear 94 Popden, milk quota Land Econ Roka et. al. 1997 Various 94-95 Popden, prime farmland Land Econ ------------------------------------------------------------------------------------------------------------


Hedonic Studies of the Effect of Various Characteristics on Farmland Values --------------------------------------------------------------------------------------------Author Year Method Data Key Characteristics --------------------------------------------------------------------------------------------Nehring 2001 Semi-log 1997 Pop acc, Soil stress JPA Nehring 2003 Semi-log 1997 Pop acc, Soil stress Wiebe Book Nehring et al. 2006 Box-Cox 98-01 Pop acc, Soil stress AJAE Ball et al. 2007 Box-Cox 1992 Pop acc, Soil stress Applied Econ ------------------------------------------------------------------------------------------------------------

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Land

To estimate the stock of land in each country, we construct time series price indexes of land in farms

The stock of land is then constructed implicitly as the ratio of the value of land in farms to the time series price index

Differences in the relative efficiencies of land across countries prevent the direct comparison of observed prices

To account for these differences, indexes of relative prices of land are constructed using hedonic methods


Methodology

Box-Cox Model

P(1 ) = Xt(2) + D +

P(1 ) is the Box-Cox transformation of land price

X(2 ) is the Box-Cox transformation of RHS Continuous variables

D are dummy variables

is value used to transform continuous variables

is the error term

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Under the hedonic approach, the price of land is a function of the characteristics it embodies

Therefore, the hedonic function may be expressed as W=W(X,D), where W represents the price of land, X is a vector of characteristics, and D is a vector of other variables

Characteristics include soil acidity, salinity, and moisture stress, among others

In areas with moisture stress, agriculture is not possible without irrigation, hence irrigation is included as a separate variable

Because irrigation mitigates the negative impact of acidity on plant growth, the interaction between irrigation and soil acidity is also included in the hedonic regression


In addition to environmental attributes, we also include a “population accessibility” score for each region in each country

These indexes are constructed using a gravity model of urban development, which provides a measure of accessibility to population concentrations

A gravity model accounts for both population density and distance from that population center

The index increases as population increases and/or distance from the population center decreases

Other variables (denoted by D) include country dummy variables which capture price effects other than quality

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Most empirical studies adopt the semilog or double-log form of the hedonic price function

However, economic theory places few if any restrictions on the functional form of the hedonic price function

We adopt a generalized linear form where the dependent variable and each of the continuous independent variables are represented by the Box-Cox transformation

This mathematical expression can assume both linear and logarithmic forms, as well as intermediate non-linear forms


Figure 1. Stress Categories in the United States; Data from World Resources group NRCS

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Figure 2. Stress Categories in Europe; World Resources Group NRCS


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Appendix Figure 2. Texture Index By ASD

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Table 1. Definition of variables in the data set

Variable Unit Definition Land price Local currency per

hectare Price of agricultural land

Land area Hectares Total land area Population density Index A measure of the size and proximity of nearby population centers Ice cover Percent of total land

area Covered by ice

Ocean “ Covered by ocean Inland water “ Covered by lakes or rivers Low temperature “ Having soils with mean annual temperature < 0oC and mean summer temperature < 10oC Salinity “ Having soils with pH > 9.0 (i.e. where the salt concentration is so high that it prevents

plant growth) Acidity “ Having soils with pH < 5.2 (i.e. where soil acidity reduces root growth and prevents

nutrient uptake) Moisture deficit “ Experiencing soil moisture stress for 4 or more months in a year Moisture stress “ Experiencing continuous soil moisture stress Low water storage “ Having soils with low ability to store moisture Excess water “ Having soils saturated with water during long periods of the year High organic matter “ Having peats or organic soils Low nutrients “ Having sandy soils or soils with clays with a low capacity to hold nutrients High shrink swell “ Having soils dominated by a mineral that causes soils to crack during the dry season High anion exchange “ Having volcanic soils where phosphate is made unavailable to plants Irrigation “ Irrigated Few constraints “ Having soils with few or no major soil-related constraints and a generally temperate

climate Source: World Soils Group, Natural Resource and Conservation Service.

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Table 2. Hedonic regression results for land price as a function of productivity-related characteristicsVariable Coefficient t-statistic Variable Coefficient t-statisticD1 (United States) 5.929887*** 75.73 Ice cover -0.210585 -0.61D2 (Canada) 6.223239*** 55.89 Ocean -0.306224 -0.53D3 (Australia) 4.98466*** 28.6 Inland water -0.612973*** -3.5D4 (France) 7.935627*** 23.16 Low temperature 0 .D5 (Finland) 8.644438** 2.27 Salinity 0.458457*** 5.49D6 (England) 6.587544*** 6.76 High organic matter -0.310311 -0.64D7 (Ireland) 7.243259*** 3.57 Excess water 0.002775 0.02D8 (Belgium) 10.69653*** 6.02 Moisture deficit -0.445639*** -3.49D9 (Denmark) 7.605018*** 3.31 Moisture stress -1.013411*** -4.55D10 (Luxembourg) 11.216754 0.64 Acidity -0.102152** -2.02D11 (Netherlands) 8.48386*** 3.37 Low water storage 0.804731*** 2.59D12 (Japan) 12.040995*** 61.93 High shrink/well -0.287112** -2.11D13 (Germany) 7.916417*** 19.88 High anion exchange 0.126939 0.26D14 (Italy) 15.163847*** 15.62 Acidity* irrigation 0.030052*** 2.89D15 (Spain) 12.104318*** 16.08 Few constraints -0.057805 -1.25D16 (Greece) 13.188315 1.27 Accessibility 0.196948*** 16.59D17 (Portugal) 11.01414*** 4.23 Irrigation -0.03737*** -4.36D18 (Sweden) 6.870508*** 4.48

λ-Moisture deficit 1.148484*** 3.82Number of observations 1807 λ-Moisture stress 1.326057*** 5.09Log Likelihood -1404 λ-Acidity* irrigation 0.078327* 1.72AIC 2889 λ-Acessibility 0.026969 0.55Schwarz Criterion 3109 λ-Irrigation 0.577739*** 16.15


We find that prices of land of constant quality in European countries relative to the United States are significantly different than what would be derived by equating land prices with exchange rates.

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Table 3. Land Prices and Purchasing Power Parities 1990--------------------------------------------------------------------------------------------------Country Land Price Purchasing Power Paity PPP/EX Nominal Quality-Adj---------------------------------------------------------------------------------------------------

U.S 1,650 893 1.00 ----U.K. 3.673 2,334 2.61 1.46Ireland 3,709 2,812 3.15 1.90Belgium 444,616 176,052 197.25 5.90Denmark 50,000 16,721 18.73 3.02France 19,883 11,390 12.76 2.34Germany 33,639 14,495 16.24 10.02Greece 1,476,553 1,430,450 1,602.66 10.11Italy 6,894,000 4,370,901 4,897.11 4.09Netherlands 44,814 6,824 7.65 4.20----------------------------------------------------------------------------------------------------


Map showing the Harmonized World Soil Database by data Sources

(Source: Nachtergaele et al.,2012)

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Table 4. Definition of variables in the data set

Variable Unit Definition Land price Local currency per

hectare Price of agricultural land

Land area Hectares Total land area Population density Index A measure of the size and proximity of nearby population centers Irrigation Percent of total land

area Irrigated

Aluminum “ Soils with aluminum toxicity Calcareous “ Soils with calcareous reactions Sulfidic “ Sulfidic soils Moisture stress “ Experiencing continuous soil moisture stress Aridic torric “ Aridic or torric soil moisture regime too dry to grow a crop without irrigation Leaching “ High leaching potential Waterlogging “ Soils experiencing waterlogging Phosphorus “ High phosphorus fixation Alkalinity “ Soil alkalinity Salinity “ Soil salinity Cryic frigid “ Cryic and frigid (12% organic C to a depth of 50 cm or more (histosols and histic groups) Clayey top Loamy top Clayey sub Loamy sub Rock Sandy top Sandy sub

“ “ “ “ “ “ “

Clayey topsoil >50% (dummy) Loamy topsoil >50% (dummy) Clayey subsoil Loamy subsoil Rock or other hard root-restricting layer within 50 cm Sandy subsoil Sandy topsoil

Source: World Soils Group, Natural Resource and Conservation Service.


Table 5. Hedonic regression results for land price as a function of productivity‐related characteristicsVariable Coefficient t‐statistic Variable Coefficient t‐statisticD1 (United States) 8.340205*** 40.56 Aridic/Torric Soil dummy ‐0.854886*** ‐21.98

D2 (Canada) 8.512836*** 48.79 High Leaching Potential dummy ‐0.949345*** ‐3.39

D3 (Australia) 8.661371*** 26.64 Waterlogging dummy 0.042464** 1.98

D4 (France) 10.610257*** 47.15 High Phosphorous Fixation dummy 0.052681 0.49

D5 (Finland) 10.187115*** 11.29 Alkilinity dummy 0.015342 0.40

D6 (England) 8.988770*** 6.94 Cryic/Frigid Soil dummy 0.035078 0.94

D7 (Ireland) 8.861795*** 6.95 Permafrost dummy 0.009510 0.10

D8 (Belgium) 13.677157*** 12.33 Cracking Clays dummy ‐0.028223 ‐0.56

D9 (Denmark) 10.279499*** 9.83 Volcanic Soils dummy ‐0.021171 ‐1.08

D10 (Luxembourg) 15.413769*** 4.31 Loamy Subsoil dummy ‐0.053426 ‐1.54

D11 (Netherlands) 10.989803*** 10.25 Organic Soil dummy ‐0.016017 ‐0.43

D12 (Japan) 13.309955*** 22.14 Rock/Hard‐Root Layer dummy 0.023379 0.97

D13 (Germany) 10.356365*** 15.86 Irrigation Percentage 0.070615*** 6.13

D14 (Italy) 13.064757*** 28.92 Clayey Topsoil 6.719910** 2.07

D15 (Spain) 14.063024*** 15.35 Loamy Topsoil 0.178910** 2.21

D16 (Greece) 9.720294*** 2.73 Population Density 0.378472*** 0.378472

D17 (Portugal) 9.369802*** 6.87 Soil Moisture Stress ‐2.492817 ‐3.74

D18 (Sweden) 10.133151*** 7.95 Clayey Subsoil ‐0.063501 ‐1.35

Aluminum Toxicity dummy 0.190107*** 8.57 Sandy Topsoil 0.002949*** 2.87

Calcareous Reaction dummy 0.366795*** 2.91 λ‐Clayey Topsoil 9.217902** 2.37

Salinity dummy ‐0.051443 ‐0.52 λ‐Loamy Topsoil 0.060192 0.30

Number of Observations 3598 λ‐Irrigation Percentage 1.183951*** 9.21

R‐square 0.9941 λ‐Clayey Subsoil 0.184569 0.55

Adjusted R‐square 0.9941 λ‐Population Density 0.069458*** 3.53

F Value 14350 2 λ‐Soil Moisture Stress 5 748127*** 3 98

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The estimation results as illustrated, figure to follow,

indicate that the quality adjusted land prices tend to be highest in a number of eastern Corn Belt areas

producing high value crops for urban centers and in Corn Belt states traditionally known to possess high quality land, ie; Iowa and Illinois.


Conclusions and Future Research

• Quality‐adjusted values for land are estimated for OECD countries.

• These values can be translated into purchasing power parities providing information on land prices.

• The quality‐adjusted land input allows an unbiased estimate of TFP when conducting international comparisons of agricultural productivity.

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Conclusions and Future Research• In future work we will add Argentina, Brazil, China, and India to the land project.

• And we include the land input along with other capital inputs in a farm sector comparison of 17 OECD countries as summarized in the following appendix

• Appendix work builds on work in Applied Economics 2008. “Capital as a factor of production in OECD agriculture: measurement and data.

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V. Eldon Ball, W. A. Lindamood, and Richard Nehring

Economic Research Service

U.S. Department of Agriculture

1800 M Street, NW

Washington, DC 20036-5831

USA

Carlos San Juan Mesonada

Department of Economics

Universidad Carlos III de Madrid

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Capital as a Factor of Production: Measurement and Data

Overview

• We construct capital accounts for the agricultural sector in fourteen OECD countries

• In doing so we make a distinction between declines in efficiency of an asset and economic depreciation

• The efficiency of a used asset relative to that when new is defined as the marginal rate of substitution of the used asset for the new one

• Economic depreciation is the decline in price of a capital good with age, so that estimates of depreciation depend on the relative efficiencies of assets of different ages

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• Economists frequently assume that relative efficiency declines geometrically

• Under geometric decline in efficiency, depreciation is also geometric and occurs at the same constant rate

• No distinction need be made when measuring capital

• In general, however, efficiency decline and depreciation are different

• This results in two distinct but related measures of capital--capital as a factor of production and as a measure of wealth

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Capital as a Factor of Production

• The starting point for construction of a measure of capital input is the measurement of capital stock for each asset type

• For depreciable assets, the perpetual inventory method is used to derive capital stock from data on investment in constant prices

• We estimate the stock of land implicitly as the ratio of the value of land in farms to the price index of land

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• The next step in developing measures of capital input is to construct estimates of prices of capital services

• Implicit rental prices for each asset are based on the correspondence between the purchase price of the asset and the discounted value of future service flows derived from that asset

• Our estimates of capital input incorporate the same data on relative

efficiencies of capital goods into estimates of both capital stock

and capital rental prices, so that the requirement for internal consistency of measures of capital input is meet

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Concluding Remarks

• Our objective is to provide a farm-sector comparison of levels of capital input among OECD countries

• This comparison begins with estimating the capital stock and rental price for each asset class for each country

• The same patterns of decline in efficiency are used for both capital stock and the rental price of each asset, so that the requirement for internal consistency of measures of capital input is met

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• In order to compare levels of capital input among countries, we require conversion factors that reflect the comparative value of their currencies

• We make use of purchasing power parities for investment goods output, taking into account the flow of capital services per unit of capital stock

• These conversion factors are used to express the value of capital service flows in each country in a common unit

• As a final step, we form indexes of relative prices of capital input

among countries by taking the ratio of the purchasing power parity

and the exchange rate

• This allows us to decompose the values of capital services into

price and quantity components

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