Lerman, Zvi

English

Research Papers in Economics

םילשוריב תירבעה הטיסרבינואה 
The Hebrew University of Jerusalem         
 
 
להנמו תיאלקח הלכלכל הקלחמה 
The Department of Agricultural 
Economics and Management 
תיאלקח הלכלכב רקחמל זכרמה 
The Center for Agricultural 
Economic Research 
 
 
 
 
Discussion Paper No. 8.05 
 
 
Farm Fragmentation and Productivity 
Evidence from Georgia 
 
 
 
by 
 
 
 
 
Zvi Lerman 
 
 
םיאצמנ הקלחמה ירבח לש םירמאמ 
םהלש תיבה ירתאב םג: 
 
Papers by members of the Department 
can be found in  their home sites: 
http://departments.agri.huji.ac.il/economics/indexe.html 
 
ת . ד  . 12  ,  תובוחר 76100       P.O. Box 12, Rehovot 76100 
       
 Farm Fragmentation and Productivity: Evidence from Georgia 
Zvi Lerman 
Department of Agricultural Economics and Management, The Hebrew University 
Version: May 2005 
 
The following analysis is based on the survey of 2,520 rural households in 40 Georgian villages 
conducted by the Hebrew University of Jerusalem (HUJ) with USAID/CDR funding in March-
April 2003.  The villages were selected from four administrative districts: Dusheti, Mtskheta, 
Sagarejo, and Gardabani. While the survey was designed to explore general reform-related 
issues, it contained detailed questions on both farm fragmentation and farm production. The 
database of this HUJ 2003 survey thus provides a relatively unique opportunity for exploring the 
impact of farm fragmentation on productivity in a transition country. 
 
Farm fragmentation in Georgia is the outcome of a land-reform strategy that strove, back in 
1992, to endow all the rural population with land on an equitable basis. The land endowments 
distributed to rural households were intended to satisfy local subsistence needs and to ensure a 
fairly regular flow of surplus food commodities to urban markets in a time of general unrest and 
civil strife. The land distribution strategy has been often credited with enabling Georgia to avert 
widespread famine during the early years of disruption and civil war.  
 
The average farm in the HUJ 2003 survey had 1.6 hectares divided into 2.4 parcels. The average 
farm size is biased upward by a small number of relatively large farms. Thus, just 1% of the 
farms in the survey were larger than 10 hectares. As a result, the median farm size is much 
smaller than the mean size (0.75 hectares only).  
 
Judging by the number of parcels, farm fragmentation in Georgia is close to that in Moldova, 
where the average holdings is split into 2-3 parcels. However, there is one significant difference: 
the data for Georgia represent fragmentation of land use (“farm fragmentation”), whereas the 
available data for Moldova represent fragmentation of land ownership, which is distinct from 
land use. No data on land-use fragmentation are currently available for Moldova.  
 
For purposes of our analysis, farm fragmentation was expressed by three measures (Table 1): 
-- the number of parcels; 
-- the average distance to the parcels in each farm; 
-- the Simpson index, calculated as 1 minus the ratio of the sum of squared parcel areas to the 
squared area of the total farm (the Simpson index is 0 when the farm consists of a single parcel 
and approaches 1 for farms split into numerous plots of equal size).  
 
Farm productivity was represented by the partial productivity of land, calculated as the 
aggregated value of farm output per hectare. To determine the aggregated value of output, the 
production quantity of each commodity as reported in the survey (including both crops and 
livestock products) was multiplied by the median price of that commodity as estimated from the 
questions on commodity sales. Constant median prices were applied to all observations in the 
database because the large number of missing values in the sales section ruled out the option of 
using case-by-case prices to calculate the value of output. Table 2 and Figure 1 present the land 
productivity versus the number of parcels (from 1 to 8). The immediate visual impression is that farm productivity decreases with the increase in the number of parcels, although the pairwise 
differences are generally not statistically significant. Lack of statistical significance is clear from 
Figure 2, where the median productivity of each successive fragmentation category (the black 
horizontal strokes) generally falls within the interquartile range of the next category (the gray 
vertical bars). 
 
Table 1. Measures of farm fragmentation in Georgia 
  Mean  Median  Lower quartile  Upper quartile 
Farm size, ha  1.61  0.75  0.34  1.15 
Number of parcels  2.40  2  1  3 
Average distance of parcels 
from home, km 
1.37 1  0.25 2 
Simpson  index  0.36 0.43 0.00 0.56 
 
Table 2. Land productivity versus number of parcels 
Productivity, lari/ha  Number of 
parcels 
Number of 
observations  Mean   Median   Lower quartile  Upper quartile 
1   611  8.02  8.16  7.36  8.77 
2   895  7.53  7.58  6.94  8.25 
3   572  7.17  7.24  6.61  7.87 
4   232  6.95  7.11  6.49  7.62 
5   78  6.91  6.98  6.46  7.51 
6   51  6.88  6.97  6.38  7.49 
7   19  6.66  6.91  6.03  7.10 
8 8  6.94  7.07  6.59  7.34 
All farms  2466  7.47  7.51  6.81  8.23 
 
Land productivity vs. fragmentation
12345678
number of parcels
5
6
7
8
9
10
productivity, lei/haInstead of utilizing just eight observations on medians, as in Table 2 and Figure 1, we can 
exploit nearly 2,500 observations by regressing the raw productivity values on fragmentation.  
The regression coefficient of logged productivity on any of the three fragmentation measures 
(number of parcels, average distance, and Simpson index) was found to be negative and highly 
significant (p = 0.001; the corresponding results are presented as Models 1-3 in Table 3). This 
implies that land productivity indeed decreases with fragmentation, as initially suggested by 
Figure 1. The regression line demonstrating the decrease of productivity with increasing 
fragmentation (as measured by the number of parcels) is shown in Figure 2. However, the 
explanatory power of these regressions is very low (R
2 < 0.1), which means that productivity 
actually depends on additional variables, and not only fragmentation.  
 
 
Table 3. Land productivity and fragmentation: regression results with logged output per hectare as the 
dependent variable 
 Number  of 
parcels 
Average 
distance, km 
Simpson 
index 
Farm size 
(logged) 
Specialization 
(share of crop 
production, %) 
R
2
Model  1  -0.275       0.093 
Model  2   -0.210      0.098 
Model  3    -0.815     0.031 
Model  4  -0.040    -0.642  -0.013  0.438 
Model  5   -0.083   -0.619  -0.013  0.446 
Model  6    -0.215  -0.651  -0.013  0.438 
Model  7  -0.035 -0.081   -0.595 -0.013  0.447 
Model  8    -0.083 -0.214 -0.601 -0.013  0.448 
 
Since fragmentation may proxy for farm size (the larger the farm, the greater the number of 
parcels) or for diversification of production (which probably increases with the number of 
parcels), we augmented the regression model to include the total land used by the farm (in 
hectares) and the share of crop production in farm output (in percent). Farm size, like productivity, was transformed to logarithmic form, whereas the fragmentation and specialization 
variables remained unlogged. The results for the augmented models are presented by Models 4-8 
in Table 3. Models 4-6 present the regression results with each of the three fragmentation 
variables taken separately (together with farm size and specialization). Models 7-8 combine two 
of the three fragmentation measures in one model: average distance is included together with the 
number of parcels (Model 7) and with the Simpson index (Model 8). All coefficients in Table 3 
are significant at p= 0.05 (or better) and R
2 for the augmented models is greater than 0.4. The 
Simpson index in a certain sense is equivalent to the number of parcels (weighted by parcel 
areas), and the  models that include both these fragmentation variables are not shown in Table 3 
(the corresponding coefficients are not significant).  
 
The results in Table 3 consistently produce statistically significant negative coefficients for the 
fragmentation variables – taken on their own or controlling for farm size and specialization. The 
2003 farm survey in Georgia thus shows that fragmentation has a negative effect on 
productivity: land productivity declines as farm fragmentation increases.  
 
Another interesting finding is the effect of farm size: controlling for fragmentation and product 
mix, productivity decreases with the increase of farm size. On the face of it, this result is similar 
to the finding for Moldova, where small farms have been found to be more productive than large 
farms. In Moldova, however, small farms were in the range of 1-10 hectares, whereas large 
farms where in the range of 500-3,000 hectares. In Georgia, “small” and “large” farms are all 
basically less than 10 hectares, and we would expect a certain increase in productivity as very 
small farms consolidate into larger units. To investigate the farm size effect in Georgia by the 
same methodology as in Moldova, we proceeded to calculate Total Factor Productivity (TFP) 
from two-input production functions (i.e., models that include labor as well as land) estimated 
using the 2003 survey database.  
 
TFP for Georgian farms 
 
TFP is calculated as the ratio of the value of production to the value of aggregated inputs. The 
inputs are aggregated by applying the estimated production-function coefficients as the weights.  
 
Table 4. TFP calculation (n = 2418) 
 Sample  mean  Production  function 
coefficients* 
Weights 
Value of production, lari  2,009  6.73185   
Land use, ha  1.64  0.32401  0.394 
Number of workers**  2.89  0.49891  0.606 
Aggregated inputs, lari  2.39     
TFP, lari per unit of 
aggregated inputs 
914    
*Two-input Cobb-Douglas production function; coefficients significant at p < 0.0001; R
2 = 0.202. 
**Full time equivalents calculated by assigning the weight 1 to people who work the whole year on the farm and 0.3 
to part-time and seasonal workers. Includes family labor and hired workers.  
 
To capture the impact of farm size and fragmentation on total factor productivity, we regressed 
TFP (logged) on land area (logged), two fragmentation variables (number of parcels and average 
distance from home to the parcels), and a specialization measure (the share of crop production in total output). The regression results are presented in Table 5. Similarly to the productivity of 
land in Table 3, TFP was observed to decrease for higher fragmentation and for higher crop 
specialization: the corresponding regression coefficients were negative. However, contrary to the 
productivity of land, which decreased with farm size in Table 3, TFP was observed to increase 
with farm size: the corresponding coefficient was positive.  
 
Table 5. Impact of farm size and fragmentation on total factor productivity (n = 2250) 
  Linear model  Quadratic model 
Constant 7.198  (p < 0.0001)  7.245 (p < 0.0001) 
Farm size, ha  0.054 (p = 0.0148)  0.058 (p = 0.0083) 
Farm size squared  --  -0.023 (p = 0.0045) 
Number of parcels  -0.034 (p = 0.0496)  -0.044 (p = 0.0117) 
Average distance of parcels from home  -0.072 (p < 0.0001)  -0.071 (p < 0.0001) 
Specialization (share of crop production, %)  -0.012 (p < 0.0001)  -0.012 (p < 0.0001) 
R
2 0.183 0.186 
 
To capture possible nonlinear size effects, which initially drive the productivity up as farm size 
increases but eventually make it decline, we estimated a quadratic regression model with two 
farm size terms: land area and land area squared (last column in Table 5). In this quadratic 
model the linear size term had a positive coefficient, whereas the quadratic size term had a 
negative coefficient. This supports the hypothesis that initially productivity rises as farm size 
increases, but eventually it peaks out and starts decreasing. The farm size coefficients in Table 5 
indicate that the turnaround point falls near 3.5 hectares: TFP rises with increasing size for farms 
smaller than 3.5 hectares and decreases with increasing size for farms larger than 3.5 hectares 
(Figure 3). The impact of fragmentation and specialization is the same as in the linear model. 
 Additional evidence of the negative impact of fragmentation is obtained from technical 
efficiency results of the 2003 survey.
1  A statistically significant negative relationship is 
observed between the TE scores and the three fragmentation measures. Technical efficiency (like 
TFP) decreases as fragmentation grows. 
 
 
                                                 
1 The TE scores were calculated by Ofir Hoyman as part of his MSc thesis at the Department of Agricultural 
Economics and Management, HUJ (2005). The inputs in this TE analysis were  land, family labor, and the value of  
farm fixed assets; the output was sales-based value added, calculated by subtracting the cost of hired labor from 
sales revenue. The specific procedure used to generate the data for the TE analysis reduced the number of available 
observations with valid TE scores from the total of 2520 to 1390. PREVIOUS DISCUSSION PAPERS 
 
1.01  Yoav Kislev - Water Markets (Hebrew). 
 
2.01  Or Goldfarb and Yoav Kislev - Incorporating Uncertainty in Water 
Management (Hebrew). 
 
3.01  Zvi Lerman, Yoav Kislev, Alon Kriss and David Biton - Agricultural Output 
    and Productivity in the Former Soviet Republics. 
 
4.01  Jonathan Lipow & Yakir Plessner - The Identification of Enemy Intentions 
    through Observation of Long Lead-Time Military Preparations. 
 
5.01  Csaba Csaki & Zvi Lerman - Land Reform and Farm Restructuring in 
  Moldova:  A  Real  Breakthrough? 
 
6.01  Zvi Lerman - Perspectives on Future Research in Central and Eastern 
European Transition Agriculture. 
 
7.01  Zvi Lerman - A Decade of Land Reform and Farm Restructuring: What 
    Russia Can Learn from the World Experience. 
 
8.01  Zvi Lerman - Institutions and Technologies for Subsistence Agriculture: 
    How to Increase Commercialization. 
 
9.01  Yoav Kislev & Evgeniya Vaksin - The Water Economy of Israel--An 
Illustrated Review. (Hebrew). 
 
10.01  Csaba Csaki & Zvi Lerman - Land and Farm Structure in Poland. 
 
11.01  Yoav Kislev - The Water Economy of Israel. 
 
12.01  Or Goldfarb and Yoav Kislev - Water Management in Israel: Rules vs.  
  Discretion. 
 
1.02   Or Goldfarb and Yoav Kislev - A Sustainable Salt Regime in the Coastal  
Aquifer (Hebrew). 
 
2.02  Aliza Fleischer and Yacov Tsur - Measuring the Recreational Value of 
  Open  Spaces. 
 
3.02  Yair Mundlak, Donald F. Larson and Rita Butzer - Determinants of 
Agricultural Growth in Thailand, Indonesia and The Philippines. 
 
4.02  Yacov Tsur and Amos Zemel - Growth, Scarcity and R&D. 
 
5.02  Ayal Kimhi - Socio-Economic Determinants of Health and Physical 
    Fitness in Southern Ethiopia. 
 
6.02  Yoav Kislev - Urban Water in Israel. 
 
7.02  Yoav Kislev -  A Lecture: Prices of Water in the Time of Desalination. (Hebrew). 
 
  
8.02  Yacov Tsur and Amos Zemel - On Knowledge-Based Economic Growth. 
 
9.02  Yacov Tsur and Amos Zemel - Endangered aquifers: Groundwater management 
under  threats of catastrophic events.  
 
10.02  Uri Shani, Yacov Tsur and Amos Zemel - Optimal Dynamic Irrigation Schemes. 
 
1.03  Yoav Kislev - The Reform in the Prices of Water for Agriculture  (Hebrew). 
 
2.03  Yair Mundlak - Economic growth: Lessons from two centuries of American 
               Agriculture. 
 
3.03  Yoav Kislev - Sub-Optimal Allocation of Fresh Water. (Hebrew). 
 
4.03  Dirk J. Bezemer & Zvi Lerman - Rural Livelihoods in Armenia. 
 
5.03  Catherine Benjamin and Ayal Kimhi - Farm Work, Off-Farm Work, and 
    Hired Farm Labor: Estimating a Discrete-Choice Model of French Farm 
    Couples' Labor Decisions. 
 
6.03  Eli Feinerman, Israel Finkelshtain and Iddo Kan - On a Political Solution to 
    the Nimby Conflict. 
 
7.03  Arthur Fishman and Avi Simhon - Can Income Equality Increase Competitiveness? 
 
8.03  Zvika Neeman, Daniele Paserman and Avi Simhon - Corruption and 
Openness. 
 
9.03  Eric D. Gould, Omer Moav and Avi Simhon - The Mystery of Monogamy. 
 
10.03  Ayal Kimhi - Plot Size and Maize Productivity in Zambia: The 
  Inverse Relationship Re-examined. 
 
11.03  Zvi Lerman and Ivan Stanchin - New Contract Arrangements in Turkmen 
  Agriculture: Impacts on Productivity and Rural Incomes. 
 
12.03  Yoav Kislev and Evgeniya Vaksin - Statistical Atlas of Agriculture in 
  Israel - 2003-Update (Hebrew). 
 
1.04  Sanjaya DeSilva, Robert E. Evenson, Ayal Kimhi - Labor Supervision and 
  Transaction Costs: Evidence from Bicol Rice Farms. 
 
2.04  Ayal Kimhi - Economic Well-Being in Rural Communities in Israel. 
 
3.04  Ayal Kimhi - The Role of Agriculture in Rural Well-Being in Israel. 
 
4.04  Ayal Kimhi - Gender Differences in Health and Nutrition in Southern 
 Ethiopia. 
 
5.04  Aliza Fleischer and Yacov Tsur - The Amenity Value of Agricultural 
  Landscape and Rural-Urban Land Allocation. 
 6.04  Yacov Tsur and Amos Zemel – Resource Exploitation, Biodiversity and 
Ecological Events. 
 
7.04  Yacov Tsur and Amos Zemel – Knowledge Spillover, Learning Incentives 
And Economic Growth. 
 
8.04  Ayal Kimhi – Growth, Inequality and Labor Markets in LDCs: A Survey. 
 
9.04  Ayal Kimhi – Gender and Intrahousehold Food Allocation in Southern 
Ethiopia 
 
10.04   Yael Kachel, Yoav Kislev & Israel Finkelshtain – Equilibrium Contracts in 
The Israeli Citrus Industry. 
 
11.04  Zvi Lerman, Csaba Csaki & Gershon Feder – Evolving Farm Structures and 
  Land Use Patterns in Former Socialist Countries. 
 
12.04  Margarita Grazhdaninova and Zvi Lerman – Allocative and Technical   
             Efficiency of Corporate Farms. 
 
13.04  Ruerd Ruben and Zvi Lerman – Why Nicaraguan Peasants Stay in 
Agricultural Production Cooperatives. 
 
14.04  William M. Liefert, Zvi Lerman, Bruce Gardner and Eugenia Serova - 
  Agricultural Labor in Russia: Efficiency and Profitability. 
 
1.05  Yacov Tsur and Amos Zemel – Resource Exploitation, Biodiversity Loss 
and Ecological Events. 
 
2.05  Zvi Lerman and Natalya Shagaida – Land Reform and Development of  
Agricultural Land Markets in Russia. 
 
3.05  Ziv Bar-Shira, Israel Finkelshtain and Avi Simhon – Regulating Irrigation via 
Block-Rate Pricing: An Econometric Analysis. 
 
4.05  Yacov Tsur and Amos Zemel – Welfare Measurement under Threats of 
Environmental Catastrophes. 
 
5.05  Avner Ahituv and Ayal Kimhi – The Joint Dynamics of Off-Farm 
Employment and the Level of Farm Activity. 
 
6.05  Aliza Fleischer and Marcelo Sternberg – The Economic Impact of Global 
Climate Change on Mediterranean Rangeland Ecosystems: A Space-for-Time 
Approach. 
 
7.05  Yael Kachel and Israel Finkelshtain – Antitrust in the Agricultural Sector:   
            A Comparative Review of Legislation in Israel, the United States and the  
            European Union. 
 
8.05  Zvi Lerman – Farm Fragmentation and Productivity Evidence from Georgia. 

Farm Fragmentation and Productivity: Evidence from Georgia

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics