Determinants of Technical Efficiency in Transition Agriculture ...

EU Phare ACE and The World Bank/ECSSD/DECRG A Working Paper Series on

Micro Economic Analysis of Rural Households and Enterprises in Transition Countries Working Paper No. 1 November 1999

Determinants of Technical Efficiency in Transition Agriculture: Evidence from Bulgaria and Hungary Erik Mathijs Liesbet Vranken

This working paper series is a joint effort of several organizations: the World Bank’s Environmentally and Socially Sustainable Development unit (ECSSD) and Development Economics Research Group (DECRG), the EU Phare ACE research projects “Farm Restructuring in Central and Eastern Europe: Causes, Efficiency and Policy Implications (1997-1999)” and “Micro-Economic Analysis of Farm Restructuring in Central and Eastern Europe” (1999-2000), and a group of academic and research institutions in the EU and CEE-FSU countries.* The objective of the series is to publish and distribute micro-economic studies of rural households and enterprises in transition countries based on data collected through farm- and household-level surveys. The findings, interpretations, and conclusions are the authors’ own, and should not be attributed to the World Bank or any World Bank member country. Submissions to the series should be sent to Erik Mathijs, Policy Research Group, Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, W. de Croylaan 42, 3001 Leuven, Belgium (tel. +32 16 321614 – fax +32 16 321996 – e-mail: [email protected]). The working papers can be downloaded from two sites: http://www.agr.kuleuven.ac.be/aee/clo/mart.htm http://wbln0018.worldbank.org/ECSSD/ECSSDPartner_DocLib.nsf

*These institutions include: • • • • • • • •

Research Institute of Agricultural Economics (VUZE), Prague, the Czech Republic; Research Institute of Agricultural Economics (VUEPP), Bratislava, Slovakia; Slovak Agricultural University, Nitra; Institute of Agricultural Economics, Bucharest, Romania; University of World and National Economics, Sofia, Bulgaria; University of Athens, Greece; Deutsche Gesellschaft für Technische Zusammenarbeit, GTZ Office, Tirana, Albania; Katholieke Universiteit Leuven, Belgium.

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DETERMINANTS OF TECHNICAL EFFICIENCY IN TRANSITION AGRICULTURE: EVIDENCE FROM BULGARIA AND HUNGARY Erik Mathijs and Liesbet Vranken1 ABSTRACT Based on survey data on Bulgarian and Hungarian crop and dairy farms and using Data Envelopment Analysis, a double-peaked distribution of technical efficiency is observed. Several factors explain differences in efficiency. Human capital matters not only through age and education, but also through gender as farms with a higher share of women are more efficient. Contracting with upstream processors increase efficiency through facilitating the adoption of technology and the access to credits. The superiority of family farms over corporate farms is confirmed for crops but not for dairy.

1.

INTRODUCTION

Farm restructuring in Central and Eastern Europe has been expected to bring about productivity and efficiency improvements, mainly as a result of improved incentives from competitive markets. Reform measures to liberalize prices, to abolish subsidies, and to create competitive markets should increase the competitive pressures in agriculture and push farms to the efficient frontier or drive them out of business (Sotnikov, 1998). There is a longstanding belief that only production units organized as family farms will survive, due to their transaction cost advantages in dealing with labor (Schmitt, 1993; Deininger, 1995). Studies based on country data suggest that partial and total factor productivity measures have indeed improved in countries where the shift from collective to individual tenure has been more profound—such as Albania and Romania—compared to countries where largescale corporate farms persist—such as the Czech Republic, Hungary and Slovakia (Macours and Swinnen, 1997; Mathijs and Swinnen, 1998). However, these studies primarily focus on the shift to individual farming and do not take into account other reform changes. Studies using firm-level accountancy data for East Germany and the Czech Republic seem to suggest that the successors to the large-scale state and collective farms are not necessarily less efficient than the newly established family farms (Mathijs and Swinnen, 1997; Thiele and Brodersen, 1997; Mathijs et al., 1999; Hughes, 1999). Unfortunately, all these studies have limited explanatory power as efficiency is affected by many more factors than organizational form, such as human capital and the farm’s environment. This paper uses survey data from Bulgarian and Hungarian farmers, which provide a rich set of variables to explain the pattern of technical efficiency. We proceed in two stages: first, non-parametric firm-level technical inefficiency measures will be calculated using Data 1

Respectively Assistant Professor and Research Associate at the Policy Research Group of the Katholieke Universiteit Leuven, Belgium. This research was undertaken with support from the European Union’s Phare ACE Programme 1996. The authors thank Tibor Ferenczi, Hamish Gow, Diana Kopeva, Alexander Sarris and Jo Swinnen for their input in the survey and for their comments on the paper. Contact author: Erik Mathijs, Policy Research Group, W. de Croylaan 42, 3001 Leuven, Belgium. E-mail: [email protected] 2

Envelopment Analysis (section 2) and second, these measures will be used as dependent variable in a regression analysis (section 3). Section 4 concludes the paper. 2.

MEASUREMENT OF TECHNICAL EFFICIENCY

To measure technical efficiency requires first, the specification of a frontier production function, and second, the measurement of the deviation or distance of the farms from the frontier, which is then a measure of technical inefficiency. For this, we use the technique of Data Envelopment Analysis (DEA), that constructs a convex hull around the observed data (Charnes et al., 1978). The advantages of this technique are (1) that no functional form needs to be specified for the frontier production function, (2) that the performance of every farm is calculated relatively to all other other farms in the population, and (3) that total technical efficiency can be decomposed into pure technical efficiency effects and size effects. A farm displays total technical efficiency if it produces on the boundary of the production possibility set, i.e. it maximizes output with given inputs and after having chosen technology. This boundary or frontier is defined as the best practice observed assuming constant returns to scale (CRS). Total technical efficiency can be further decomposed into pure technical efficiency and scale efficiency. To calculate pure technical efficiency, the production technology is assumed to display variable returns to scale (VRS). Scale efficiency is then the residual between total and pure technical efficiency. As a result, a farm that displays pure technical efficiency may not operate at an optimal scale, that is, its inputoutput combination may not correspond to the combination that would arise from a zeroprofit long-run competitive equilibrium situation (Färe et al., 1985). In the remainder of the paper we will only deal with measures of total technical efficiency to enable the comparison between family farms, most of which are relatively small, with successor farms of state and collective farms, which are all large. We follow the approach suggested by Coelli et al. (1998) who contend that “in a VRS model an inefficient farm is benchmarked against firms of similar size. In a CRS a firm may be benchmarked against firms which are substantially larger (smaller) than it”. As in Färe et al. (1985), we assume that production is characterized by a non-parametric piecewise-linear technology, so that simple linear programming techniques can be used to calculate efficiency. We further assume strong disposability of outputs and inputs and estimate the non-parametric deterministic frontier, expressed in terms of minimizing input requirements. Total technical efficiency can be estimated using the following linear program for each farm k that constructs the CRS frontier: {minλ,z λ subject to z Y ≥ Yk; z X ≤ λ Xk; z ≥ 0}, where Yk denotes the output of farm k, Xk is a vector of four inputs employed by farm k (capital, land, labor and other inputs), and z is a vector of k intensities that characterizes each farm. Data for the efficiency calculation are derived from a representative survey of Hungarian and Bulgarian farms carried out in 1998 collecting data for 1997. The Budapest University of Economic Sciences and the University of National and World Economics (Sofia) executed these surveys within the framework of a Phare-ACE project. The survey was stratified according to organizational form (family farms and corporate farms). The data sets contain detailed information on production structure, labor, land and other input use, capital, nonagricultural activities, investments, credits and external conditions. The Hungarian data include 1,618 family farms and 404 corporate farms (including cooperatives), while the 3

Bulgarian data set contains information on 1,411 households and 196 corporate farms (mostly cooperatives). A review of the data revealed some errors and farms for which errors could not be resolved were dropped. Further, farms for which information about physical production was missing were eliminated. We assumed that three inputs are essential in agricultural production: labor, land and capital. If information about these inputs was absent, the farm was also removed. The data used for the calculation of technical efficiency include gross output and data on land, labor, capital and other inputs. Concerning land use, figures were available for the total cultivated area in 1997. We could calculate the input ‘capital’ based on the estimated values of farm buildings, machinery, livestock and plantations. The available labor figures were converted into annual working units (AWU) in order to achieve comparable data. In the Hungarian sample, one AWU corresponds to 2,150 labor hours or the number of hours that a full-time worker can perform in one year. The labor numbers used in the Bulgarian efficiency calculations are based on the total amount of workers and not on the total hours worked. The surveys include also figures on other inputs, such as seeds, feed grains, roughage, concentrated feed, fertilizers, electric energy, gas, fuels and services. In the efficiency calculations we took into account the value spent on these inputs plus the value of the inventories. Output is physical production valued at fixed prices and corrected for own produced feed used for the breeding of the animals. Using fixed national prices was necessary to avoid that output would be effected by price differences. The prices used in the output calculations were based on price information available in the surveys. When looking for determinants of technical efficiency, it is best to study an homogeneous group of farms. In this paper we pay attention to two production sectors, crops and dairy production. To be classified as a crop or a dairy farm, the value of grain or cow milk production in total output had to be more than 50 %. As a result of the data cleaning and the omission of mixed farms, we retained 178 Hungarian crop farms (63 cooperatives, 40 companies, and 75 family farms), 77 Hungarian dairy farms (24 cooperatives, 13 companies, and 40 family farms), and 93 Bulgarian crop farms (45 cooperatives, 9 companies, and 39 family farms). Bulgarian dairy farms could not be included in the study since the available number of farms specialized in cow milk production is too small. The summary statistics per organizational form are given in table 1, respectively for Hungarian crop, Hungarian dairy and Bulgarian crop farms. Corporate farms are further subdivided into companies and cooperatives. Family farms include both farms registered as sole proprietors and unregistered farms. Corporate farms are subdivided into two groups. First, companies include both privatized state farms registered as joint stock companies, cooperatives that have turned themselved into joint stock or limited liability companies and limited partnerships that could have been established as a result of the break-up of a collective farm or de novo. Second, cooperatives are true production cooperatives (based on the one man = one vote principle). Table 1 reveals that the family farms in the data set are relatively small compared to the corporate farms. A comparison between companies and cooperatives shows that in crop production the cooperatives are on average larger than the companies, while the opposite is true for the dairy farms. Further, the summary statistics prove that the Bulgarian crop farms are on average smaller than the Hungarian ones.

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Table 1. Summary statistics Family farm Corporate farm Company Cooperative Average St.Dev Average St.Dev Average St.Dev HUNGARY Crop farms Output (× 106 HUF) Land (ha) Labor (AWU) Capital (× 10³ HUF) Inputs (× 10³ HUF)

1.605 26 1.2 2600 971

3.683 60 1 5031 2142

86.876 1282 26 48760 57490

Dairy farms Output (× 106 HUF) Land (ha) Labor (AWU) Capital (× 10³ HUF) Inputs (× 10³ HUF)

0.860 3 1.6 985 323

1.353 4 1.1 807 341

756.510 2339 137 340370 236150

1558.941 2031 134 392341 267217

606.860 2131 137 240367 163328

604.907 1512 103 147629 114425

BULGARIA Crop farms Output (× 106 BUL) Land (ha) Labor (AWU) Capital (× 10³ BUL) Inputs (× 10³ BUL)

4.871 6 1.2 17996 651

7.746 11 0.8 27619 1541

324.955 453 11 59343 19887

474.846 477 10 75384 19296

415.215 838 44 432768 53990

424.815 793 39 1880651 76920

78.345 974 23 47901 56890

106.583 1645 58 93841 69506

82.068 1085 41 83383 68305

Source: Own calculations Efficiencies were calculated for each of the three production sectors separately (Hungarian crops, Bulgarian crops and Hungarian dairy). To calculate the technical efficiency of the crop farms, we took into account the land quality of the cultivated area. In this manner efficiency differences are not affected by differences in land quality. Because land quality is much less important in cow milk production, we did not make these corrections in the efficiency calculations of the dairy farms. The distribution of technical efficiency of Hungarian crop farms is shown in figures 1a and 1b. Figure 1a shows that the majority of the farms reach an efficiency level between 30 and 60 %. Both family farms and corporate farms can be found on the frontier, but a considerably higher share of family farms is located on the frontier. The distribution of technical efficiency of cooperatives is shifted more to the left than those of companies and family farms. Figure 1b suggests that, while the three production structures can be technically efficient, on average cooperatives are less efficient than companies, while companies in turn perform worse than family farms. The average technical efficiencies confirm this idea: family farms reach an average of 58 %, companies of 50 % and cooperatives of 44 %. However, the differences are too small to be conclusive. Figures 2a and 2b display the distribution of the technical efficiency of the Hungarian dairy farms. A two-peaked distribution is shown: 45 % of the dairy farms reach a technical efficiency between 10 % and 20 % and a rather large amount of the farms is located on the frontier. Corporate as well as family farms are located on the frontier. The distribution of the 5

efficiencies of cooperatives is again shifted to the left. This is translated into a rather low average technical efficiency of 39 %. Companies perform somewhat better in terms of average technical efficiency: they reach an average of 43 %, due to the large amount of companies located on the frontier. The large spread of the efficiencies of family farms leads to an average efficiency of 45 %, which does not allow us to conclude that they are more technically efficient than companies or cooperatives. A two-peaked distribution of the technical efficiency of Bulgarian crop farms becomes clear in figures 3a and 3b. Corporate farms as well as family farms are able to become technically efficient. In comparison with the Hungarian crop farms, we find a relative large amount of inefficient production units. For example, more than 30 % of the cooperatives reach a technical efficiency lower then 20 %, while none of the Hungarian cooperatives specialized in crop production performed that bad in terms of efficiency. Companies reach an average efficiency of 51 % and thus perform better than family farms that reach an average efficiency of 44 %. It is important to take into account that the data contains only 9 Bulgarian companies specialized in crop production. This should be kept in mind when a comparison is made between companies and one of the other two production structures. The average technical efficiency of the cooperatives is 43 %. To conclude, huge inefficiencies are apparent in all three sectors. Organizational form seems to be playing in role in explaining some in the differences in inefficiency, but it is also clear that additional factors will play an important role. In the next section, we will explore the factors that determine technical (in)efficiency in more detail.

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40

Absolute frequency

35 30 25 20 15 10 5 0 0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Total technical efficiency

Figure 1a. Distribution of total technical efficiency of Hungarian crop farms.

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Absolute frequency

16 14 12 10 8 6 4 2 0 0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Total technical effciency Company

Cooperative

Family farm

Figure 1b. Distribution of total technical efficiency of Hungarian crop farms by legal form.

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20 18

Absolute frequency

16 14 12 10 8 6 4 2 0 0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Total technical efficiency

Figure 2a. Distribution of total technical efficiency of Bulgarian crop farms.

Absolute frequency

12 10 8 6 4 2 0 0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Total technical efficiency Company

Cooperative

Family farm

Figure 2b. Distribution of total technical efficiency of Bulgarian crop farms by legal form.

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25

Absolute frequency

20

15

10

5

0 0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Total technical efficiency

Figure 3a. Distribution of total technical efficiency of Hungary dairy farms.

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Absolute frequency

8 7 6 5 4 3 2 1 0 0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Total technical efficiency company

cooperative

family farm

Figure 3b. Distribution of total technical efficiency of Hungarian dairy farms by legal form.

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3.

EXPLANATION OF TECHNICAL EFFICIENCY

Owners, managers and employees combine their resources into a firm to create rents: owners provide capital, employees labour and skills and managers the ability to optimally combine all resources. A firm’s inability to achieve the highest level of production efficiency can be attributed (1) to a lack of quantity and quality of resources, (2) to problems in the acquisition of resources, and (3) to inefficient management and organization of resources. 3.1.

Quantity and quality of resources

The stock of resources in a farm may not be high enough to create sufficient synergies or to acquire additional resources to reach the technological frontier. In other words, when the production process is characterized by increasing returns to size and a firm does not have enough funds to acquire sufficient resources, it may be inefficient. This refers both to the quantity and quality of human and physical resources that are present in the farm. First, human resources are widely reported as a key resource for a farm in the context of the quality of the human and the social capital of a household. While human capital has many attributes, typically the age and the years of formal schooling of the household head are used as proxies. Most studies report age as having a negative effect through reduced ability to work or because older farmers are unwilling or unable to adopt technological innovations (Herdt and Mandac, 1981). However, it could be argued that older farmers are more experienced and can use their knowledge to use inputs more efficiently (Wilson et al., 1998). The effect of schooling should be unambiguously positive as better educated farm managers are expected to have more skills to run their farm efficiently (Kalirajan, 1990; Battese and Coelli, 1995; Parikh et al., 1995). Further, also the effect of gender on farm efficiency is not straightforward: women may be more motivated to work hard, but are also more constrained in their time allocation as they have to take care of other domestic tasks. Also the ownership of other resources can play an important role in farm efficiency. Farms with more assets are usually technologically more advanced and thus better equipped to reach the technological frontier. However, the hypothesis that big farms are more efficient than small farms has been clouded by the fact that small farmers often use their inputs more intensively leading to an inverse relationship between productivity and size. Empirical evidence on the relation between technical efficiency and size is hence inconclusive as there as many studies that find a positive relationship as there are claiming a negative or insignificant correlation. Finally, the concept of social capital, or the quantity and quality of associational life and the related social norms, is increasinly recognized as having an important impact on efficiency. Social capital enhances a person’s access to information, services, insurance and technology (Narayan and Pritchett, 1999). 3.2.

Investment in resources

The acquisition of resources may meet with high transaction costs due to the imperfection or the incompleteness of their markets. As a result, firms may be constrained in their expansion to attain an optimal size. This problem can be solved by the internalization of such markets, but only if sufficient resources are already available or can be combined. Whether households want to invest in human capital (education), social capital (networking, off-farm activities) or physical capital, substantial financial resources are required. Chavas and Aliber (1993) suggest that medium- and long-run debt financing has a positive effect on technical efficiency, as technological change is embodied in the newly acquired 10

assets. However, when debt financing is limited such as in transition economies, increases in technical efficiency have to be financed in other ways. Farmers in transition economies who cannot finance their investment through equity typically deal with liquidity problems by vertically integrating with suppliers or buyers. Gow and Swinnen (1998) report the positive effects on efficiency in transition economies through contracts offered to farmers by upstream processors, as the latter often provide advice, information and new technology, but also because such contracts facilitate the farmer’s access to credits through the intermediation efforts of the processor.2 3.3.

Organization of resources

The organization of resources may not create the proper incentives for owners, employees and managers to maximize their effort. Lack of a self-enforcing incentive structure leads to principal-agent problems among the three interest groups in the farm and increases the transaction costs of organizing its resources thus reducing its efficiency. Moreover, the combination of individual skills results in the creation of social skills or synergies which can be very specific to a certain aspect of the production process and which increase the transaction costs. Schmitt (1993) proposes the following efficiency ranking of governance structures in agricultural production. Cooperatives or worker controlled firms are the least efficient governance structure, as it is very costly to solve agency problems in a democratically organized farm. Managerially controlled or hierarchically organized farms are more efficient, particulary when such a farm is owned by outsiders. In general, outside ownership increases the access to capital, and thus to technology, and pushes management towards taking efficient actions.3 In addition, Konings (1997) shows that newly established or de novo firms are more efficient than privatized and transformed successor farms. Finally, family farms are the most efficient governance structure, as the problems concerning motivation of workers are limited. Household members fulfill all tasks associated with agricultural production and since they maximize family welfare rather than individual welfare, they will face no incentive to shirk (Pollak, 1985). Thus, the costs of monitoring and controlling effort are lower and this will have a positive influence on the efficiency level of family farms compared to corporate farms (Carter, 1984). However, to conclude that family farms are always more efficient than corporate farms is premature, as the impact of production organization on technical efficiency crucially depends on the nature of the production activity. The important role of production activity is a result of the difficulty to link output to labor effort and the ability to supervise labor directly and particularly of the role of the seasonality and biological nature of agricultural production therein (Alchian and Demsetz, 1972; Allen and Lueck, 1998). The seasonality and biological character of production not only increases the costs of labor monitoring, but also determines to what extent gains from specialization are important. The occurrence of sequential production stages limits the gains from specialization. If, however, the effect of nature can be eliminated or reduced, gains from specialization become more important and labor can be monitored more easily. In addition, as larger farms have better access to capital, removing the effect of nature will change the nature of the farm from family-based to corporate. 2

See Glover (1987) for an overview of the advantages, but also the disadvantages of contract farming. 3 See for example Jones (1997) for an overview of theoretical arguments and empirical evidence for industrial firms. 11

Hence, the extent to which the trade-off between moral hazard incentives on the one hand and gains from specialization and better access to capital on the other favors a certain organizational form depends on the influence of biological factors in the production process. Family farms will still dominate in sectors where this influence is high, such as in land intensive crop production. Corporate farms will prevail where the influence of nature is reduced through technological innovations (e.g., greenhouses, climatized stables), such as in capital intensive livestock production (Allen and Lueck, 1998). 3.4.

Results

We use a Tobit regression model to assess the sources of measured efficiencies as there are truncated.4 Farm-specific estimates of total technical efficiency are used as dependent variable. As information on potential explanatory variables differ by organizational forms, we carried out three sets of regressions: the first set including only family farms, the second only corporate farms and the third pools data on family farms and corporate farms to assess the impact of farm organization and product specialization on efficiency. Family farms Table 2 provides the summary statistics of the explanatory variables used in the regressions using data only for family farms: •

As proxies for the stock of resources, we use the average age of farm operators who spent most of their time on the farm (age), the average number of years of schooling of the most active farm operators (education), and the average share of women in adult household members (gender). On average, Bulgarian farmers (60 years) are older than Hungarian ones (51 years), while the level of education is about the same (9 to 11 years of schooling). We also introduce the variable purchased land, which is the share of newly purchased land in the total land holding of the household. This variable may be interpreted as reflecting the entrepreneurial ability of a household.

•

The resource flow is captured first of all by the dummy variable investment that equals one if the household has made any investment in the last year. Hungarian farmers have invested slightly more (58 to 65 %) than Bulgarian ones (44 %). Further, the dummy variable contract, that equals one when some sales where made on contract, is introduced to test whether contracting has facilitated the access to technology. While quite a high share of Hungarian farmers deliver their produce on contract (31 to 45 %), contracting is relatively unimportant for Bulgarian family farms (13 %). For Bulgarian crop farmers, we introduced an additional variable sales, which is a dummy that equals 0 if all production is consumed within the household and 1 if at least some products are sold. Table 2 reveals that 15 % of all Bulgarian crop farmers sold nothing at all.

•

Farm organization is captured by the following variables: specialization is the share of grains or milk in total output; feed production is a dummy that equals one if feed used for the breeding of animals was partly produced on the farm; and member/partner is a dummy that equals one when at least one household member is member of a cooperative or partner in a company. The summary statistics reveal

4

See also e.g. Burki and Terrell (1998) and Zheng et al. (1998). 12

that 75 % of all dairy farms produce their own feed. Further, only a small share of family farms has a direct link with a cooperative or company. Table 2. Summary statistics of explanatory variables, family farms Hungary, crops Hungary, dairy Bulgaria, crops Age 51 years 51 years 60 years Education 11 years 9 years 10 years Gender 29 % 36 % 40 % Land acquisition 13 % 14 % na Contract* Sales* Investment*

31 % na 65 %

45 % na 58 %

13 % 85 % 44 %

Specialization Feed production* Member/partner*

77 % na 19 %

73 % 75 % 25 %

78 % na 15 %

Landman-ratio 22 ha/AWU 2 ha/AWU Distance 0.79 km na * Share of family farms for which the dummy variable equals one.

10 ha/AWU na

Source: Own calculations The results of the regressions are shown in table 3. We also introduced the land-man ratio to account for the differences in natural environment and in the case of Hungarian crop farmers the distance to the nearest bus stop. The following conclusions can be reached: •

First, the results confirm the importance of human capital: the positive impact of education on technical efficiency is strongly confirmed both in dairy and crop production and in Bulgaria and Hungary. The effect of age, however, differs. While age has a positive impact on Hungarian crop farms, it has a negative effect on Hungarian dairy and Bulgarian crop farms. The share of women in the household always has a positive impact on efficiency, but only significantly so in the case of Hungarian crop farms. The variable land acquisition has a positive and significant effect on the efficiency of Hungarian farms and is probably a proxy for the entrepreneurial orientation of the household: households that buy land are more entrepreneurial, ceteris paribus, than households that do not. Economic size as measured by total output has a positive effect on efficiency in Hungary—not in Bulgaria—which suggest that larger family farms are more efficient than smaller ones. This is consistent with the general wisdom that the average cost curve in agriculture is L-shaped (Hallam, 1991).

•

Second, contracting has a positive and significant effect on efficiency, but more so in Hungarian crop farming than in Hungarian dairy farming. For the regression on Bulgarian crop farms we also introduced a variable sales to capture the effects of subsistence. This was not necessary in Hungary where all farms are commercial businesses. Sales have a positive effect on efficiency, which suggest that subsistence has a negative impact on technical efficiency. This confirms the finding of Parikh et al. (1995), who contend that subsistence prevents farmers from reaching the 13

efficiency frontier. When, in addition, products are being sold on contract, efficiency increases even more. Further, the negative effect of the investment dummy for Hungarian farms seems strange, as investment is mostly increasing efficiency though improved technology. However, crop farms invested in livestock rather than in assets enhancing crop productivity. Moreover, as the specialization variable shows, specialized farms are more efficient, which suggests that economies from specialization outweigh economies of scope. •

Third, membership in another agricultural enterprise has a negative effect for Hungarian crop farms, but a positive impact for Hungarian dairy farms and Bulgarian crop farms. Several factors play a role here. The traditional explanation is that offfarm work has a negative effect on efficiency, as less time is spent on managerial activities improving farm efficiency (Timmer, 1971; Parikh et al., 1995). However, Herdt and Mandac (1981) found a positive relationship, which suggests that spending time off the farm improves the farmer’s managerial skills through the acquisition of information. However, enterprise membership also reflects a better access to services, such as input provision, marketing and machinery. Particularly the latter plays an important role in Bulgaria.

Independent variables Stock Age Age^2 Education Education^2 Gender Land acquisition Output

Table 3. Tobit regression results for family farms Hungary, crops Hungary, dairy Bulgaria, crops coeff. prob. coeff. prob. coeff. Prob. 3.66 -0.03 5.67 -0.31 0.43 0.29 0.50

0.0001 0.0019 0.0134 0.0006 0.0001 0.0015 0.0090

-1.99 0.03 21.04 -0.98 0.14 0.36 0.08

0.0443 0.0022 0.0069 0.0228 0.1738 0.0001 0.0327

-2.78 0.02 12.82 -0.64 0.06 0.36

0.4793 0.5810 0.0048 0.0037 0.6996 0.5362

24.99 -8.84

0.0001 0.1149

11.86 -13.97

0.0169 0.0269

25.29 22.22 7.23

0.0821 0.0397 0.4401

Organization Specialization Feed production Member/partner

0.13 -13.42

0.3685 0.0270

0.68 0.43 17.04

0.0033 0.9496 0.0011

1.16 15.07

0.0003 0.2257

Landman-ratio Distance Intercept

-0.27 14.93 -94.57

0.0004 0.0001 0.0018

-3.44 -97.83

0.0184 0.0691

-27.01

0.7765

Flow Contract Sales Invest

Source: Own calculations

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Corporate farms The summary statistics of explanatory variables for cooperatives and companies are given respectively in tables 4 and 5. With respect to human resources, only gender and the share of workers older than 60 could be used (60+). Again, Bulgarian farmers are older than Hungarian farmers, while slightly more women are employed on Bulgarian farms. Contract and investment have the same meaning as for the family farms. Almost all Hungarian farms sell their produce on contract (100 % for dairy and 92-95 % for crops), while only a quarter to a third of the Bulgarian corporate farms sell on contract. While specialization and feed production have the same meaning as for family farms, several additional variables related to the property rights and governance structure of corporate farms are included: •

Insider: the share of insiders, i.e. people actually working on the farm, in the total number of members. Particularly Hungarian dairy companies are characterized by a high share of absentee landowners (79 % of all members are absentee). Cooperatives have generally less outsiders.

•

Joint venture: enterprises with shares in other enterprises or from which shares are owned by other enterprises. The numbers point to a relatively high integration of both Hungarian companies and cooperatives into down- or upstream firms. This is due to the privatisation process which allowed corporate farms to buy shares of food processors. A similar process did not happen in Bulgaria, where cooperatives are far less integrated.

•

Transfer: dummy variable equal to 1 when a member or partner is allowed to transfer his or her ownership rights to his or her children. Interestingly, up to 46 % of Hungarian dairy cooperatives report that property rights are restricted, a surprisingly high figure considering property rights should be strong.

•

Sell: dummy variable equal to 1 when a member of partner is allowed to sell his or her land.

•

De novo: dummy variable equal to 1 when the farm has not been established as a direct successor of a state or collective farm. Most cooperatives are successors, while half to two thirds of the companies were established de novo

•

Non-agricultural activities: dummy variable equal to 1 when the farm has nonagricultural activities. Particularly Hungarian cooperatives are characterized by having activities in addition to their farming operation.

The results of the three regression analyses are summarized in table 6 and allow to draw the following conclusions:

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60 + Gender

Table 4. Summary statistics of explanatory variables, companies Hungary, Hungary, dairy Bulgaria, crops crops 1% 1% 14 % 13 % 22 % 30 %

Contract* Investment* Insider Joint venture* Transfer of ownership rights to children* Sell* De novo* Specialization Feed production* Non-agricultural activities *

95 % 60 %

100 % 77 %

53 % 45 % 80 % na 68 % 78 % na 30 %

21 % 62 % 85 % na 46 % 72 % 85 % 46 %

Landman-ratio 67 ha/AWU 21 ha/AWU * Share of companies for which the dummy variable equals one.

33 % 78 % 80 33 67 78

% % % % na 93 % na 33 %

35 ha/AWU

Source: Own calculations

60+ Gender

Table 5. Summary statistics of explanatory variables, cooperatives Hungary, Hungary, dairy Bulgaria, crops crops 2% 1% 19 % 28 % 25 % 28 %

Contract* Investment*

92 % 65 %

100 % 71 %

Insider Joint venture* Transfer of ownership rights to children* Sell* De novo* Specialization Feed production* Non agricultural activities*

67 % 64 % 73 % na 6% 74 % na 70 %

75 % 63 % 54 % na 17 % 69 % 96 % 54 %

Landman-ratio 45 ha/AWU 18 ha/AWU Share of cooperatives for which the dummy variable equals one. Source: Own calculations

16

25 % 71 % 70 22 71 36

% % % % na 82 % na 16 % 23 ha/AWU

•

First, human capital has limited explanatory power to account for differences in technical efficiency among corporate farms. Both the share of labor force older than 60 years and the share of women in the labor force only have a positive impact on the efficiency of Hungarian dairy farms. Further, as for the family farms, economic size has also a positive impact on the efficiency of all corporate farms. This is a bit surprising, but the effect is smaller as reflected by the smaller size of the coefficients.

•

Second, contract production has a positive impact on the performance of Hungarian crop farms. We introduced an interaction term between investment and contract in the regression on Bulgarian crop farms. The positive impact of contract production is even more stressed: farms that invested in 1997 and did not have contract production are less efficient than farms that invested and did sell products on contract.

•

Third, the share of insiders has a positive influence on the technical efficiency of cooperatives. This can be ascribed to the fact that a high share of active members facilitates supervision. The effect of the share of insiders on the efficiency level of companies, as captured by the interaction effect company*insider, is negative which supports the idea that outside ownership increases the access to capital and encourages managers to improve technical efficiency. Companies perform significantly better than cooperatives, which suggests that principal-agent problems are more restrictive in the latter. Among Hungarian dairy farms, de novo farms are more efficient than direct successors of cooperatives or state farms.

•

Fourth, property rights matter as the variable transfer has a negative impact on technical efficiency of Hungarian crop farms. If members or partners of a corporate farm are able to pass on ownership rights directly to their children, the corporate farm herself is land insecure. Of course, this will have a negative effect on its efficiency. Nevertheless, this variable has an opposite effect on the efficiency level of Bulgarian crop farms. But here we introduced a dummy variable sell, which equals one if members or partners of a corporate farm could withdraw and sell their land. This variable also has a negative impact on the efficiency level. The variable sell was even more significant than the variable transfer. So, we can conclude that land insecurity has also a negative effect on the efficiency of Bulgarian crop farms.

•

Fifth, only the efficiency level of Bulgarian crop farms is significantly affected by the degree of specialization, as more specialized farms are more efficient. Farms that are engaged in non-agricultural activities do not perform wel. This confirms the idea regarding the efficiency advantages of specialized firms. In the crop sector, joint ventures are performing significantly better than other firms. This can be ascribed to the exchange of technological and managerial know-how.

All farms Regression results for all farms pooled together are summarized in table 7. Only a limited number of variables could be included (gender, age, output, specialization, feed production, contract, investment). The results seem to support the hypothesis put forward by Allen and Lueck (1998): in crop production, an extensive production sector, family farms are significantly more efficient than corporate farms, while in an intensive production sector as dairy, there are no significant differences between family farms and corporate farms.

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Table 6. Tobit Regression results for corporate farms Hungary, crops Hungary, dairy Bulgaria, crops coeff. prob. coeff. prob. coeff. prob. Stock 60+ Gender Output

-1.14 0.03 0.08

0.1758 0.8294 0.0080

5.58 1.74 0.06

0.0008 0.0001 0.0001

0.26 -0.26 0.03

0.2376 0.1882 0.0443

Flow Contract Invest Invest*contract

11.39 11.09 -

0.0892 0.0071 -

-6.68 -

0.1561 -

-18.10 -50.08 56.49

0.2300 0.0002 0.0010

0.15 27.63 -0.26 8.22 -16.55 -0.11 -14.89

0.0076 0.0001 0.0044 0.0489 0.0007 0.4288 0.0002

0.15 38.28 -0.51 -0.70 6.85 28.72 -0.12 72.85 -24.94

0.0871 0.0001 0.0001 0.8657 0.1631 0.0055 0.7255 0.0001 0.0001

0.21 73.79 -0.40 27.61 21.98 -40.64 1.25 -1.51

0.0298 0.0201 0.2703 0.0087 0.0094 0.00 0.0001 0.8654

-0.06 41.89

0.0425 0.0026

0.16 -103.36

0.5390 0.0001

-0.08 -38.25

0.0036 0.2376

Organisation Insider Company Company*insider Venture Transfer Sell De novo Specialization Feed production Non-agr.activities Landman-ratio Intercept

Source: Own calculations Independent variables Stock Gender 60+ Output Flow Contract Invest Invest * family farm Organization Company Family farm Specialization Feed production Intercept

Table 7. Tobit Regression results for all farms Hungary, crops Hungary, dairy Bulgaria, crops Coefficient prob. coefficient prob. coefficient prob. 0.26 -0.04 0.07

0.0017 0.5808 0.0463

0.05 0.18 0.04

0.7330 0.0368 0.0001

0.14 -0.14 0.02

0.3420 0.1504 0.0432

9.14 -27.10

0.0773 0.0010

-14.48 8.56

0.1388 0.4926

22.90 -

0.0068 -

16.99 49.21 -0.28 -

0.0004 0.0001 0.0168 -

18.24 16.33 1.50 10.13

0.0595 0.1945 0.0001 0.2249

-3.79 28.80 1.34 -

0.7477 0.0014 0.0001 -

51.08

0.0001

-79.83

0.0003

-74.56

0.0017

Source: Own calculations 18

5.

CONCLUSION

In this paper we used survey data on Bulgarian and Hungarian crop and dairy farms to meausure and explain farm-specific technical efficiency. Using Data Envelopment Analysis, a double-peaked distribution of efficiency can be observed, suggesting that most farms are far from the efficiency frontier. Hypotheses testing through Tobit regressions confirmed the superiority of family farms over corporate farms in crop farming, but rejected it in dairy farming. This result confirms the theory that family farms can only be more efficient in orientations where the influence of nature still plays a dominant role. An important policy implication is that farm restructuring towards individual tenures is only increasing efficiency in crop farming. The analysis further indicated that not only age and education play an important role, but also gender. Farms with a higher share of women are more efficient. This suggests that policies should be directed at women not only for equity reasons, giving women equal access to wealth, but also for efficiency reasons. Finally, a strong positive effect of contracts with upstream processors was found both for family and corproate farms. Contracts facilitate the adoption of technology and the access to credits. Governments should create an attractive environment for contracting and foreign direct investment, particularly as such contracts are introduced by foreign firms. Vertical coordination is an important strategy to tackle the situation of imperfect and missing markets that is so characteristic for transition economies. REFERENCES Alchian, A.A. and Demsetz, H. (1972). Production, Information Costs and Economic Organization. American Economic Review 62(5), 777-795. Allen, D.W. and Lueck, D. (1998). The Nature of the Farm. Journal of Law and Economics 41(2), 343-386. Battese, G.E. and Coelli, T.J. (1995). A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data. Empirical Economics 20(2), 325-332. Burki, A.A. and Terrell, D. (1998). Measuring Production Efficiency of Small Firms in Pakistan. World Development 26(1), 155-169. Carter, M.R. (1984). Resource Allocation and Use Under Collective Rights and Labor Management in Peruvian Coastal Agriculture. Economic Journal 94(376), 826-846. Charnes, A., Cooper, W.W. and Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operations Research 2, 429-444. Chavas, J.-P. and Aliber, M. (1993). An Analysis of Economic Efficiency in Agriculture: A onparametric Approach. Journal of Agricultural and Resource Economics 18(1), 1-16. Coelli, T., Prasada Rao, D.S. and Battese, G. E. (1998). An Introduction to Efficiency and Productivity Analysis. Kluwer Academic Publishers, Boston. Deininger, K. (1995). Collective Agricultural Production: A Solution For Transition Economies? World Development 23(8), 1317-1334. Färe, R., Grosskopf, S. and Lovell, C.A.K. (1985). The Measurement of Efficiency in Production. Kluwer Academic Publishers, Boston. Glover, D.J. (1987). Increasing the Benefits to Smallholders from Contract Farming: Problems for Farmers’ Organizations and Policy Makers. World Development 15(4), 441-448. 19

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