Ethiopian Agricultural Cooperatives in an Era of

Journal of African Economies Advance Access published October 8, 2010

Journal of African Economies, pp. 1 –25 doi:10.1093/jae/ejq036

Ethiopian Agricultural Cooperatives in an Era of Global Commodity Exchange: Does Organisational Form Matter? Gian Nicola Francesconi 1,* and Nico Heerink 2 1

* Corresponding author: Gian Nicola Francesconi. E-mail: [email protected]

Abstract In Ethiopia, agricultural cooperatives are expected to play a key role in linking smallholder farmers to the recently established commodity exchange system. Recent research has found, however, that the commercialisation levels of cooperative members do not differ significantly from those of non-member farmers in Ethiopia. We argue though that the impact of cooperative membership on commercialisation may vary significantly depending on the type of cooperative organisations considered. Applying propensity score matching as well as regression analysis to a set of farm household living in rural areas where the commodity exchange system was to become operational, we consistently find significantly higher commercialisation rates, when compared with non-member famers, for farmers belonging to marketing cooperatives. Livelihood cooperatives, on the other hand, appear to have insignificant or negative impact on Ethiopian farmers’ commercialisation. We conclude that the selective inclusion of marketing cooperatives in the commodity exchange system has the potential to simultaneously reduce the rural poverty and maximise agro-commodity commercialisation in Ethiopia.

# The author 2010. Published by Oxford University Press on behalf of the Centre for the Study of African Economies. All rights reserved. For permissions, please email: [email protected]

Downloaded from jae.oxfordjournals.org by guest on October 9, 2010

Development Strategy and Governance division (DSGD), International Food Policy Research Institute (IFPRI), c/o UNOPS, Immeuble Ousseynou Thiam Gueye, Rue de Thies, Point E, BP 15702, CP 12524, Dakar, Senegal 2 Development Economics Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, The Netherlands

2

|

Gian Nicola Francesconi and Nico Heerink

1. Introduction


Ethiopia is the largest producer of maize and wheat in Africa, with domestic production more than double the volumes jointly produced by Kenya, Tanzania and Uganda in 2004–05 (Gabre-Madhin and Goggin, 2005). Ethiopia is also Africa’s largest coffee producer and the birthplace of the bean. Overall, grains, coffee and other agro-commodities are central to the Ethiopian economy, engaging almost 10 million smallholder farmers and related households in the production process. In Ethiopia, agro-commodity commercialisation has the potential to boost the economy and reduce poverty. Despite the downfall of the Derg regime in 1991 and subsequent policy reforms towards market liberalisation, numerous studies (Dadi et al., 1992; Lirenso, 1993; Dercon, 1995; Negassa and Jayne, 1997; Dessalegn et al., 1998; Gabre-Madhin, 2001) document that agro-commodity commercialisation in Ethiopia remains far below capacity. Why? High transaction costs and price volatility overshadow the benefits of market liberalisation reforms to a great extent. Consequently, subsistence and semi-subsistence farming remain dominant all over rural Ethiopia (Alemu et al., 2006; Alemu and Pender, 2007). In many countries facing similar problems, (inter)national donors and policy-makers have decided to return to policies promoting rural collective action (Collion and Rondot, 1998; World Bank, 2003). Collective action is increasingly regarded as a way to improve market access for the myriad of smallholder farmers in the developing world. Nonetheless, empirical evidence suggests varying levels of success for cooperative type of business in developing countries (Tendler, 1983; Attwood and Baviskar, 1987; Uphoff, 1993; Damiani, 2000; Sharma and Gulati, 2003; Chirwa et al., 2005; Neven et al., 2005). In Ethiopia, collective action is synonymous with cooperatives. According to the Ethiopian law (proclamation no. 85 from 1994), cooperatives are defined as ‘associations established by individuals on a voluntary basis, to collectively solve economic and social problems and to democratically manage them’ (FDRE, 1994). In the last two decades, cooperatives have been actively promoted by the Ethiopian government and its donors despite controversial historical evidence. Ethiopian cooperatives are managed by member-elected organs. Cooperatives can form unions and are based on the democratic principle of one member one vote. More recently, a quantitative study by Bernard et al. (2008) concluded that the membership of Ethiopian cooperatives provides no clear advantage for grain commercialisation. Their study finds that cooperatives do

Ethiopian Agricultural Cooperatives

|

3

2. Background

Obtaining more insights about the (lack of ) impact of cooperatives on agricultural commercialisation has become even more important with


provide better prices to their farmers, but price incentives are not sufficient to ensure greater market participation. Poorer members of cooperatives tend to sell less and consume more cereals, whereas richer members, who have larger supply elasticities and smaller income elasticities of cereal consumption, tend to sell more. In this study, we argue that beyond heterogeneity in membership there are also important differences at the organisational level that need to be taken into account in explaining the impact of Ethiopian cooperatives on agricultural commercialisation. Organisational forms are usually neglected in quantitative studies, because it is often problematic to distinguish whether organisational forms are the cause or the result of a certain performance (for instance agricultural commercialisation). In other words, this paper is an attempt to dig further into the impact, if any, of cooperative membership on agro-commodity commercialisation in Ethiopia. In particular, it examines alternative impact pathways associated with alternative organisational forms, defined according to modern agri-business theory (Sykuta and Cook, 2001; Cook and Chambers, 2007), distinguishing between: (i) market-oriented and livelihood-oriented cooperatives and (ii) between open and closed cooperatives. These alternative organisational forms are associated with trade-offs in mainstream collective behaviour, such as pro-active versus defensive, sustainable versus dependent, solidarity versus efficiency and inclusive versus elitist behaviour. The remainder of the paper is structured as follows: (i) in the next section, we provide background information on the dynamics of the agrocommodity market and rural cooperatives in Ethiopia, including a description of the recently established Ethiopian Commodity Exchange (ECX); (ii) next, we describe the data set used for the analysis, (iii) present the analytical model based on both propensity score matching (PSM) and parametric regression techniques, (iv) present and discuss the results of the empirical analysis, and contrast them with those presented by Bernard et al. (2007, 2008), (v) explore potential impact pathways under alternative collective (or organisational) behaviour and (vi) conclude with a summary of our main findings and a discussion of the main implications for policy and further research.

4

|



the establishment of the Ethiopian government Commodity Exchange (the ECX) in 2008, which aims at boosting the commercialisation of national agro-commodities on global scale. In 2006, the Ethiopian government and various international donors approved the proposal of the International Food Policy Research Institute (IFPRI) to establish and launch the first ECX by 2008. The ECX represents a milestone in the evolution of the Ethiopian agro-commodity market. The scope of the ECX is to promote the commercialisation of major agricultural commodities, such as grains, pulses and coffee. As explained by Gabre-Madhin and Goggin (2005), a commodity exchange is a central marketplace where sellers and buyers meet to transact in an organised fashion, with certain clearly specified and transparent ‘rules of the game’. In its wider sense, a commodity exchange is any organised marketplace where trade is funnelled through a single, well-defined mechanism. The ECX is expected to increase trust among buyers and sellers. Making use of modern information and communication technology, the ECX is also expected to increase the concentration of buyers and sellers over a single trading floor, improving effective market competition and reducing transaction costs. In brief, the ECX is an institutional response to the Ethiopian longstanding problem of ‘thin markets’, defined as markets in which there are few purchases and sales. The ECX will initially deal with six agricultural commodities: teff (the national staple cereal), wheat, maize, coffee, sesame seeds and pea beans. The initial structure of the ECX includes a central trading floor located in Addis Ababa, plus 20 terminal centres and 10 warehouses (see Figure 1). The ECX was designed in such a way as to build upon the preexisting agro-commodity market. Empirical evidence from before the ECX (Dercon, 1995; Jayne et al., 1998; Negassa, 1998; Gabre-Madhin, 2001; Gabre-Madhin and Megzebou, 2006) shows that the bulk of national agrocommodity production and consumption is indeed concentrated in surroundings of the 20 towns hosting the terminal centres of the ECX. It suggests also that agro-commodities flow from surplus markets, mainly confined in the western highland regions, through the central market of Addis Ababa and then towards the deficit markets of Dessie, Mekele, Asayta, Gonder, Dire Dawa and Harer (mainly in the eastern lowland regions). It also shows that variations in agro-commodity prices are significantly correlated across these markets, suggesting a significant level of market integration. The establishment of the ECX is expected to add an alternative floor for agro-commodity trade. However, there are widespread concerns about the


|

5

actual commercial capacity of the Ethiopian farmers. Ethiopia counts approximately 10 million rural households, producing grains, pulses and other agro-commodities mainly for their own subsistence. Lack of capital and remoteness form the barrier that keeps farmers far away from the market, and therefore from the potential benefits of the ECX. To help farmers overcome access-barriers to markets, the Ethiopian government is strongly promoting the formation of agricultural cooperatives and unions all over the national territory. According to the Ethiopian government, agro-commodity commercialisation through cooperatives should be favoured over individual commercialisation. Independent farmers are too vulnerable to traders’ and brokers’ opportunism, whereas the formation of cooperatives can allow farmers to scale up production and gain market power. According to Bernard et al. (2007), the share of kebeles with cooperatives went up from 10% in 1991, to nearly 35% in 2006.1 In 2002, cooperative governance was reinforced by the establishment of the Federal 1

A kebele is the smallest administrative unit, below the municipality-district level, in Ethiopia.


Figure 1: ECX Structure. Source: Ethiopian Strategy Support Program (ESSP), IFPRI.

6

|


3. Data

The farm household data used in this study were collected between May and July 2005, approximately one and a half years before the establishment of the ECX. The survey was jointly carried out by the IFPRI, the Ethiopian Development Research Institute (EDRI) and the Central Statistical Agency (CSA), with the direct involvement of the first author of this paper in all the phases of the survey. The survey focussed on smallholders’ commercialisation and covered all rural parts of the country, except the Gambela region and the non-sedentary population in the Afar (three zones) and Somali (six zones) regions. The sampling procedure adopted was based on the sampling scheme of the Annual Agricultural Survey carried out by the CSA in 2004 –05. The resulting sample includes more than 7,000 farm households. From the original sample, we created a sub-sample to be used in this specific study that comprises only farm households located in woredas (i.e., municipalities) hosting ECX centres (Figure 1). As discussed in the previous section, the bulk of national agri-commodity trade is


Cooperative Commission (FCC), a governmental body with the ambitious mandate to establish one cooperative per kebele (i.e., neighbourhood) in 70% of the national kebeles by 2010. Ethiopian cooperatives often serve as a preferential channel to access subsidised agricultural inputs (such as fertiliser, artificial insemination or improved live animals and seeds), training and donations from the state and from the NGOs (Spielman et al., 2008). Sometimes, they also provide basic services for output marketing, such as collection and sale of members’ supplies. Other services such as storage, transportation and manufacturing, are extremely rare. Following the classification proposed by Cook and Chambers (2007), Ethiopian cooperatives can be distinguished into marketing and livelihood organisations depending on whether members’ output is respectively commercialised through or outside the cooperative system, i.e., collectively or individually. Bernard et al. (2007) suggest that the number of marketing cooperatives is growing rapidly, but livelihood cooperatives, which depend mainly on external support to exist, are still the majority. An alternative classification, drawing from the pioneering work of Sykuta and Cook (2001), is between open and closed cooperatives. The former type focuses on promoting solidarity, whereas the latter’s goal is elite capture.


|

7


concentrated in these woredas. Moreover, collaboration among farmers through cooperatives is considered to be of crucial importance for making the ECX into a success. This re-sampling rationale leads to a drastic reduction in the number of available observations (from more than 7,000 to 417 observations); however, the resulting sample is expected to be more homogeneous in terms of exposure to the national agricommodity market. The study by Bernard et al. (2008) is based on a different sub-sample of the IFPRI-EDRI-CSA data set, namely the set of farm households located in ‘similar’ rural areas, defined on the basis of agro-ecological, demographic and infrastructure indicators. In contrast, we restricted our analysis to rural woredas (i.e., municipalities) whose similarity, and thus comparability is ascertained by established commercial linkages (Dercon, 1995; Jayne et al., 1998; Negassa, 1998; Gabre-Madhin, 2001; Gabre-Madhin and Megzebou, 2006), which induced policy-makers to use these woredas as terminal centres (hubs) for the ECX (see Figure 1). Owing to the fact that the survey did not cover three of the ECX woredas (Robe, Harar and Asella), our sub-sample comprises a total of 17 woredas: Mekele, Humera, Asayta, Gonder, Metema, Dessie, Bahir Dar, Bure, Nekempte, Jimma, Nazreth, Shashemene, Asosa, Hosaina, Awasa, Addis Ababa and Dire Dawa. In each woreda, 24 –25 farm households were surveyed, for a total sample size of 417 households. In this sub-sample, 88% of the farm households (368 farms) grew at least one of the six agrocommodities of interest to the ECX (teff, maize, wheat, coffee, sesame and pea beans). Given our scope to produce results that are relevant to improve the emerging ECX system, we excluded farm households that do not produce any of the ECX agro-commodities. It is also important to note that none of the 368 households considered grew sesame and green peas during the period investigated by the survey (previous 12 months), and therefore these specific ECX-commodities are not part of the following analysis. The woredas included in the sample represent both deficit and surplus markets. Out of the 368 farm households analysed, 24% are located in the five deficit markets represented by the woredas of Mekele, Asayita, Gonder, Dessie and Dire Dawa. Furthermore, 21% of the 368 farm households are enrolled in at least one cooperative, when compared with a 9% enrolment figure reported by Bernard et al. (2007) for the whole country. It is also important to note that cooperative members were not sampled from every ECX woredas. Our data show that cooperative members were only sampled in eight woredas. On the other hand,

8

|


2

The distinction between marketing and livelihood cooperatives could be made upfront using household data referring to the question: Does your cooperative purchase/collect your output to sell it in the market? On the other hand, the distinction between open and closed cooperatives could not be made at this stage since it is a distinction to be inferred from the comparison of impact analyses conducted with alternative sub-samples.


independent farmers, i.e., the farmers that are not members of a cooperative, were sampled from all the 17 woredas. Approximately, half of the cooperative farmers (11% of all farmers) indicate that they regularly embark on collective marketing, i.e., the collection and sale of members’ output. It is also interesting to note that while market-oriented cooperatives (i.e., cooperatives embarking in collective marketing) are almost absent in deficit woredas, members of livelihood cooperatives—i.e., cooperatives that embark in collective activities for social protection, mainly through advocacy and the provision of production subsidies—can be found in both surplus and deficit woredas. Table 1 compares the characteristics of independent and cooperative farm households, distinguishing further the cooperative cluster into farms engaged in marketing cooperatives and farms engaged in livelihood cooperatives.2 The last row of Table 1 shows the values of the so-called commercialisation index, defined as the ratio between the value of ECX-commodities sold and the total value of ECX-commodities produced (see Von Braun, 1995; Strasberg et al., 1999; and Alemu et al., 2006, for similar definitions). Hence, a value of zero indicates a farm household where teff, wheat, maize, sesame or coffee is produced exclusively for home consumption. The closer the index is to one, the higher is the level of commercialisation for these agro-commodities. Table 1 shows that the average degree of commercialisation is significantly higher for cooperative farmers in general, and for the members of marketing cooperatives in particular, when compared with independent farmers. The degree of commercialisation among members of livelihood cooperatives, however, is not significantly different from that of individual farmers. Farmers who belong to marketing cooperatives are also more likely to produce coffee, and less likely to produce grains (wheat, maize or teff ) than independent farmers. Farmers in livelihood cooperatives, on the other hand, are less likely to produce coffee and more likely to produce grains than independent farmers. All farmers belonging to livelihood cooperatives produce grains, whereas almost one-quarter of the farmers belonging to marketing cooperatives produce only coffee. Overall, cooperative


|

9

Table 1: Characteristics of Farm Households in the Sample (2005) Independent farmers (290 observations)

All cooperative farmers (78 observations)

Marketing cooperative farmers (42 observations)

Livelihood cooperative farmers (36 observations)

Number of household members Dependency ratio (no. of children/ no. of members) Age of household head (years) Dummy for male household head Education of household head (years) Fixed arable land (ha) Number of agrocommodities produced Production of coffee (dummy) Production of wheat, maize or teff (dummy) Deficit woredas (dummy) Commercialisation indexa

4.87 (2.23)

5.86 (2.23)**

5.71 (2.60)**

6.03 (1.73)**

1.09 (1.09)

1.36 (1.01)**

1.31 (1.12)

1.42 (0.87)**

43.9 (15.3)

43.4 (13.0)

44.1 (12.3)

42.5 (14.0)

0.77 (0.42)

0.91 (0.29)**

0.83 (0.38)

1.00 (0.00)**

3.02 (6.33)

5.90 (8.44)**

4.98 (7.52)*

6.97 (9.39)**

1.39 (1.31)

2.93 (2.85)**

3.58 (3.64)**

2.18 (1.12)**

2.68 (1.82)

3.32 (1.51)**

3.31 (1.83)**

3.33 (1.04)**

0.20 (0.40)

0.24 (0.43)

0.43 (0.50)**

0.02 (0.17)**

0.88 (0.33)

0.87 (0.34)

0.76 (0.43)**

1.00 (0.00)**

0.27 (0.44)

0.17 (0.38)**

0.02 (0.15)**

0.33 (0.48)

0.28 (0.38)

0.42 (0.37)**

0.56 (0.36)**

0.27 (0.33)

Standard deviations in parentheses. *Denotes mean is significantly different from the mean of independent farmers at 10% level. **Denotes mean is significantly different from the mean of independent farmers at 5% level. a The commercialisation index c is computed as the ratio of the value of ECX-commodities sold vs to the total value of ECX-commodities produced vy by a farm: c=

vs vy

with vy =

N n=1

yn p∗n and vs =

N

sn p∗n ,

n=1

where ECX-commodities (n ¼ 1,2, K, . . . ,N) include teff, wheat, maize, sesame and coffee, y indicates the volume produced, s the volume sold and p* the average sample price for teff (2.17 Birr), wheat (1.63 Birr), maize (1.15 Birr), sesame (5.08 Birr) and coffee (11.57 Birr), respectively.


(368 observations)

10

|


4. Analytical approach

The analytical method used in this study draws from the work of Ravallion (2001), Godtland et al. (2004) and Bernard et al. (2008). According to these authors, the main goal of impact assessment is to compute the average treatment effect on the treated (ATT), which in this case refers to the average effect of cooperative membership on the degree of commercialisation of cooperative members. The empirical problem we face in this case is the typical absence of data concerning the counter-factual: how would cooperative farmers have performed if they had not joined the cooperative? Our challenge is to provide the most accurate estimate, given the data available, of the causal effect of cooperative membership on farmer commercialisation. Hence, we need to identify a suitable comparison group of non-members whose commercialisation outcomes provide the closest possible estimates of the outcomes that cooperative members would have had in the absence of the cooperative. To do so, we need, first of all to minimise potential confounding factors, such as diffusion or spill-over effects between target and control groups. Following Godtland et al. (2004) and Bernard et al. (2008), we reduce the proximity between target and control households by excluding from the sample all the non-members located in woredas with a cooperative. Second, to isolate the effect of membership, we need to account for other differences that may possibly exist between the control group of independent farmers and the target group of cooperative farmers and that may


members produce significantly more agro-commodities (wheat, teff, maize and coffee) than independent farmers do. The land area of cooperative farmers is more than twice that of independent farmers on average. For farmers belonging to marketing cooperatives, it is more than 150% larger, while the land area of livelihood cooperative members exceeds that of individual farmers by more than 50%. Demographic characteristics also differ significantly between the four (sub-)groups of farmers in our sample. Farm households belonging to cooperatives have more household members, a higher number of children per adult, more educated household heads and more land. The age of the household head does not differ significantly between all groups, but the gender of the head does. All households belonging to livelihood cooperatives in our sample have a male head, whereas that is not the case for the other two categories of farmers.


|

11

affect commercialisation. Potential determinants of commercialisation for a household i can be specified as follows: di = f (hi , mi , ei , pi , ci )

(1)

3

Please note that the household samples taken from each woreda (25 household from each woreda) are not representative of woredas’ populations. The same problem is encountered even when regional (a region comprises several woredas) dummies are included in the model. Because of the non-representativeness of our sample at the woreda and regional level, we cannot control for local and regional fixed effects as these perfectly predicts successes and failures in cooperative membership.


where d measures the degree of commercialisation, h defines household characteristics, m measures market access, e controls for agro-climatic conditions, p stands for policy environment and governance regime, and c differentiates between cooperative members and independent farmers. By limiting the sample to 17 ECX woredas, we expect to reduce the heterogeneity in m (see Section 3). Given the highly centralised nature of the Ethiopian government and the national mandate of the FCC to establish one cooperative per kebele (a woreda is composed of several kebeles) in 70% of the national kebeles by 2010, we assume p to be more or less constant across woredas and households. Agro-climatic heterogeneity, e, will be captured on the basis of two major characteristics: farms producing coffee against farms producing cereals, and farms in surplus woredas versus farms in deficit woredas. Production of coffee is only possible under very specific agro-climatic conditions (high humidity and soil moisture and dense spontaneous vegetation). The distinction between surplus and deficit woredas reflects the agro-ecological differences found between the temperate and rainabundant highlands and the arid and hot lowland areas. Although dummy variables controlling for fixed effects at the woreda level could have helped to better control for local variability in m, p and e, these variables cannot be possibly included in the model since cooperative members were not sampled in some (9) of woredas considered. 3 Finally, as suggested by the result shown in Table 1, landholding size and household demographic characteristics (h) may also significantly affect commercialisation levels of the target and control groups. Therefore, to maximise the precision in the identification of the commercial impact attributable to cooperative membership, we specify a Probit model that predicts the propensity of each farmer to join a cooperative using the control variables presented in Table 1. Next, we match independent and cooperative farmers on the basis of their propensity scores,

12

|


5. Impact assessment

In Table 2, column (1) shows the Probit regression results for cooperative membership for the ‘restricted sample’, excluding independent farmers located in woredas with a cooperative. The impact of landholding size is found to follow a concave function. As land size increases the propensity to join a cooperative increases as well, until it reaches the threshold level at approximately 9 ha of land. Other variables that are statistically significant include the dependency ratio, household size, the degree of agrocommodity diversification and location in a deficit area. Farmers located in deficit areas have 19% lower propensity to join a cooperative, providing further support for the perception that cooperatives proliferate in areas with a high potential for agricultural production (see also Bernard et al., 2007). Tables A1 –A3 in the appendix show the estimated propensities of farmers to participate in cooperatives, using predicted values obtained for the Probit model in Table 2, column (1). In total, 35 observations were dropped in Tables A1 –A3, since these observations were found outside the area of common support.4 Tables A6 –A8, column (1) in the 4

The exclusion of these observations did not lead to any further reduction in the number of the woredas in the sample.


using two different matching (Kernel and Nearest Neighbour) techniques, and calculate the average difference (ATT) in the commercialisation index between cooperative farmers and their matches. This procedure is commonly referred to in the literature as PSM. In this case, it is deployed for ex-post-impact assessment, with data collected at one point in time (crosssection data). The robustness of the PSM estimations obtained with alternative matching techniques is further tested by contrasting them with the results obtained from parametric estimations using Tobit estimation. The explanatory variables in the Probit and Tobit models are intentionally overparameterised using quadratic terms so as to take into account possible non-linear relationships and maximise the predicting power of the model (see Godtland et al., 2004). Further, to ensure maximum comparability of the treatment and control groups, the sample used for matching in the PSM model is restricted to the common support region, defined as the values of propensity scores where both target and control households are found.


|

13

Table 2: Probit Model for Cooperative Membership (Reduced Sample, Marginal Effects) Explanatory variables

Marketing cooperatives (2)

Livelihood cooperatives (3)

0.18 (0.04)** 20.01 (0.00)** 0.01 (0.02)

0.10 (0.02)** 20.00 (0.00)** 20.01 (0.01)

0.09 (0.04)** 20.01 (0.01) 0.00 (0.01)

20.52 (0.24)* 0.16 (0.08)**

20.42 (0.30)* 0.03 (0.05)

20.00 (0.00) 0.11 (0.04)**

20.06 (0.02)** 0.02 (0.02)

20.01 (0.01) 0.02 (0.01)

20.04 (0.01)** 0.01 (0.01)

20.00 (0.00)

20.00 (0.00)

20.00 (0.00)

20.01 (0.04) 20.00 (0.00) 0.12 (0.07)

0.03 (0.06) 20.00 (0.00) 0.03 (0.04)

20.02 (0.04) 0.00 (0.00) 0.00 (0.00)

0.014 (0.08) 0.07 (0.05) 20.01 (0.01)* 20.19 (0.05)** 279 0.2497 2124.04

0.05 (0.05) 0.00 (0.02) 20.00 (0.00) 20.17 (0.03)** 243 0.3248 275.53

20.08 (0.03)** 0.12 (0.04)** 20.02 (0.001)** 20.02 (0.03) 237 0.2809 272.60

Standard errors in parentheses. *Denotes significance at 10% level. **Denotes significance at 5% level.

appendix shows the results of parametric estimation using Tobit analysis. Only the dependency ratio and the number of agro-commodities are found to be significantly associated with the commercialisation rate. The resulting average impact of cooperative membership, estimated with two alternative matching techniques as well as with a parametric Tobit estimator (see Table A4 in the Appendix), is shown in the first row of Table 3, columns (1), (3) and (5), respectively. The results confirm the findings of Bernard et al. (2008) that the level of commercialisation of cooperative members does not differ significantly from that of similar independent farmers.


Fixed arable land (ha) {Fixed arable land}2 Household size (no. of members) {Household size}2 Dependency ratio (children/ adults) {Dependency ratio}2 Education of household head (years) {Education of household head}2 Age of household head (years) {Age of household head}2 Male household head (dummy) Coffee production (dummy) No. of agro-commodities {No. of agro-commodities}2 Deficit woredas (dummy) No. of observations Pseudo R 2 Log-likelihood

All cooperatives (1)

14

|

Membership

Kernel matching

Nearest neighbour matching

Parametric estimator (Tobit)

Reduced sample (1) Full sample (2)



All cooperatives

0.03 (0.07) 78 members 142 independent

0.01 (0.09) 78 members 48 independent

0.02 (0.08) 0.13 (0.10) 78 members 78 members 50 independent 201 independent

Marketing cooperatives

0.11 (0.07)**

0.11 (0.14)*

0.26 (0.10)**

42 members 190 independent 20.08 (0.08)**

42 members 24 independent

42 members 42 members 31 independent 201 independent

20.14 (0.10)**

20.03 (0.11)



36 members 36 members 27 independent 201 independent

42 members 152 independent Livelihood cooperatives

20.10 (0.08)** 36 members 139 independent

0.03 (0.06) 78 members 255 independent 0.17 (0.07)**

0.33 (0.13)**

Average treatment effect of the treated (ATT) in bold. Standard errors in parentheses. Number of observations per target and control group in italics. *Denotes significance at 10% level. **Denotes significance at 5% level.

20.05 (0.13)

0.14 (0.10) 78 members 290 independent 0.29 (0.12)** 42 members 290 independent 20.02 (0.13) 36 members 290 independent


Table 3: Impact Estimates



|

15

It may also be noted that this finding contradicts the results presented in Table 1 above. A simple comparison of commercialisation rates between cooperative and independent farmers based on Table 1 suggests that members of cooperatives have a significantly higher commercialisation index. However, when we control for differences in observable farm household characteristics that affect commercialisation through PSM and parametric estimators, we find that the commercialisation index is not significantly different between the two groups. This is a concrete example of how a naive comparison can lead to biased results.

Our analysis reaches the same conclusion as Bernard et al. (2008), despite different sampling rationales and sample sizes, and thereby adds to the robustness and credibility of the zero impact hypothesis of cooperative membership in Ethiopia. However, our impact analysis, just like the analysis by Bernard et al. (2008), relies on the same debatable assumptions (see also Godtland et al., 2004): Assumption 1 Ignorability of treatment (i.e., membership) conditional on observed characteristics h, m, e, p, outcomes d0 (for non-members) and d1 (for members) and participation c are independent: E(d0 | h, m, e, p, c) = E(d0 | h, m, e, p)

(2)

E(d1 | h, m, e, p, c) = E(d1 | h, m, e, p)

(3)

and

This assumes that the estimation is not affected by hidden bias due to unobserved characteristics (motivations, preferences and skills) simultaneously explaining membership and commercialisation. Bernard et al. (2008) support the validity of this assumption by arguing that Ethiopia is a highly centralised state where cooperative membership is a preferential channel to obtain subsidised fertiliser and other agricultural inputs from the government. As such, the voluntary attribute of membership associated with farmers’ choice to self-select into a cooperative is negligible. Assumption 2 Non-ignorability of diffusion (or spill-over) bias between cooperative members and non-members. This assumes that there is diffusion or spill-over between members and neighbouring non-members and that


6. Impact pathways

16

|



the exclusion of neighbouring non-members (from woredas with at least a cooperative) from the sample will improve the identification of the impact of cooperative membership. In the following analysis, we attempt to test these two basic assumptions and examine how the estimated impact might vary with the alternative assumptions we make. This is what we define as impact pathways analysis. First of all, we argue that, despite highly centralised governance, the Ethiopia cooperative movement is far more complex and diverse than it is commonly portrayed by researchers and policy-makers. As put by Cook and Chambers (2007) ‘a cooperative is not a cooperative is not a cooperative is notK’, meaning that there is no such thing as two identical cooperatives. Heterogeneity in organisational forms is expected to be simultaneously associated with heterogeneity in collective behaviour and commercial impact. It follows that any assessment of the impact of cooperative membership based on non-experimental methods (like ours and that by Bernard et al., 2008) is potentially affected by hidden selection bias. Our alternative assumption is that heterogeneity in unobservable collective behaviour associated with membership choice can be broadly captured by organisational characteristics. Cook and Chambers (2007) distinguish between: (a) livelihood-oriented cooperatives, which are predominantly motivated by defensive purposes and aim to provide social protection through increased visibility and reduced transaction costs vis-a-vis aid agencies and government subsidies; and (b) market-oriented cooperatives, which are predominantly motivated by offensive entrepreneurial purposes and aim at generating income through collective marketing (i.e., collection, bulking and sale of members’ output). A second distinction can be made between ‘open’ and ‘closed’ cooperatives, as proposed by Sykuta and Cook (2001). When cooperatives are open, the impact generated by the formation of a cooperative is expected to trickle down to neighbouring non-members (spill-over or diffusion effect). This will generally lead to an underestimation of the impact, if the latter is positive, and to an overestimation if the impact is negative. On the other hand, no underestimation or overestimation is expected when closed cooperatives are in place. We correct for these two potential sources of bias in the following ways. First, we perform two new impact analyses to distinguish the impacts associated with membership in marketing and livelihood cooperatives. The results of the Probit analysis are presented in Table 2, columns (2) and (3), and the resulting ATTs in Table 3, second and third row (and the corresponding blocks of propensity scores are presented in the


|

17

7. Conclusions and implications

In 2008, Ethiopia witnessed the establishment of its first agro-commodity exchange. The ECX represents a great opportunity to boost the commercialisation of cereals, pulses and coffee on a global scale and alleviate longstanding rural poverty and malnutrition. It is believed that the ECX will


appendix [Tables A2 and A3]). They show that membership of marketoriented cooperatives is consistently associated with a significantly higher level of agro-commodity commercialisation. Depending on the method used, the average level of commercialisation for members of marketing cooperatives is found to be between 11 and 33% higher than that of independent farmers. Members of livelihood cooperatives, on the other hand, are found to have either a similar (Tobit estimator) or a 10 –14% lower level of commercialisation than independent farmers. In other words, our analysis points out that the insignificant impact of Ethiopian cooperatives on farmers’ commercialisation may indeed hide the co-existence of two opposite impact pathways, reflecting whether the organisation is market or livelihood oriented. Second, following a technique proposed by Godtland et al. (2004), we reintroduce all the independent farmers located in woredas with a cooperative into the sample. Then, we re-estimate the impact of membership (for all cooperatives and for marketing and livelihood cooperatives, respectively) with the full sample. The resulting ATTs are presented in columns (2), (4) and (6) of Table 3, while the results of the Probit analysis, the propensity score blocks and the Tobit model can be found in the appendix (Tables A5– A9). Again, we find that the rate of commercialisation does not differ significantly between members of cooperatives and independent farmers. And we find that members of marketing cooperatives have rates of commercialisation that are between 17 and 29% higher than those of independent farmers. The results obtained with the two PSM techniques suggest the presence of negative spill-over (i.e., periphery-core or elite-capture) effects, but parametric estimation technique does not. The ATTs for livelihood cooperatives confirm that members of such cooperatives have either similar or lower rates of commercialisation than individual farmers. The two PSM estimation techniques suggest that there may be positive spill-over (i.e., core-periphery or solidarity effect) effects in the case of livelihood cooperatives, but the results obtained with the parametric estimation technique do not indicate spill-over effects.

18

|



create a more transparent and competitive market. However, it is not yet clear how smallholder farmers will be involved and may benefit from the ECX. A widespread perception is that collective action could help smallholders overcome access barriers to the ECX. However, in line with the assessment previously performed by Bernard et al. (2008), this study finds that, on average, cooperative membership in Ethiopia is not associated with a significantly higher rate of agrocommodity commercialisation. We argue further that the organisational form adopted by an agricultural cooperative may matter for the degree to which their members are involved in output markets. Making a distinction between marketing- and livelihood-oriented cooperatives, we find that members of marketing cooperatives have significantly higher rates of commercialisation when compared with individual farmers, whereas members of livelihood cooperatives have commercialisation rates that are similar or lower than those of individual farmers. Our results suggest that collective commercialisation provide a competitive advantage to individual commercialisation, and that the selective inclusion of marketing cooperatives into the ECX system has the potential to simultaneously reduce poverty and expand agro-commodity flows. The importance of institutional links between marketing cooperatives and the ECX can be further stressed by evidence emerging from the comparison of primary and secondary data, and indicating that Ethiopian cooperatives tend to be concentrated in rural areas with high market potential, and especially in the rural areas linked to the ECX. It is important to emphasise, however, that the limitations of our data set (cross-section data collected at one point in time) prevent us from examining the direction of causality between alternative organisation forms and their commercial performance. Further research through, for example, experimental methods, such as simulation/games or natural experiments based on randomisation techniques, is required to gain more insights into the direction of the relationship. In addition to this, since the data available were collected before the establishment of the ECX, these findings do not take into account the potential effects that the ECX may have on agro-commodity commercialisation and on membership of cooperative organisations. Finally, it is also important to note that both our analysis and that performed by Bernard et al. (2008) indicate that cooperatives members are middle-level landholders, located in areas with high potential for agricultural commercialisation, and therefore cooperatives should not be seen as means to ensure the participation of the poorest among the


|

19

poor. In contrast, this study suggests that cooperatives are rather instruments to reinforce rural elites and the vested order, as they serve to either concentrate market power (in the case of marketing cooperatives) or distribute social welfare (in the case of livelihood cooperatives).

Acknowledgements

References Alemu, D.E. and J. Pender (2007) Determinants of Food Crop Commercialization in Ethiopia. Washington, DC: International Food Policy Research Institute. Mimeo. Alemu, D.E., E. Gabre-Madhin and S. Dejene (2006) ‘From Farmer to Market and Market to Farmer: Characterizing Smallholder Commercialization in Ethiopia’, ESSP Working Paper. Washington, DC: International Food Policy Research Institute. Attwood, D. and B. Baviskar, (1987) ‘Why Do Some Co-operatives Work but not Others? A Comparative Analysis of Sugar Co-operatives in India’, Economic and Political Weekly, 22 (26): A38– 45. Bernard, T., A.S. Taffesse and E.Z. Gabre-Madhin (2007) ‘Smallholders’ Commercialization through Cooperatives: a Diagnostic for Ethiopia’, IFPRI Discussion Paper 00722. Washington, DC: International Food Policy Research Institute (IFPRI). Bernard, T., A.S. Taffesse and E.Z. Gabre-Madhin (2008) ‘Impact of Cooperatives on Smallholders’ Commercialization Behavior: Evidence from Ethiopia’, Agricultural Economics, 39: 147– 61. Chirwa, E., A. Dorward, R. Kachule, I. Kumwenda, J. Kydd, N., Poole, C. Poulton and M. Stockbridge (2005) Walking Tightropes: Supporting Farmer Organizations for Market Access. Natural Resource Perspectives 99, London: Overseas Development Institute (ODI). Collion, M.H. and P. Rondot (1998) Background, Discussions, and Recommendations: Agricultural Producer Organizations, their Contribution to Rural Capacity Building and Poverty Reduction. Washington, DC: The World Bank.


This study was made possible thanks to the financial support of the International Food Policy Research Institute (IFPRI) and Ethiopia Strategy Support Programme (ESSP). The authors are especially indebted to Eleni Gabre-Madhin and Tanguy Bernard, who generously offered their support and knowledge to the benefit of this study.

20

|



Cook, M.L. and M. Chambers (2007) ‘Role of Agricultural Cooperatives in Global Netchains’, Working Paper for the Montpellier Workshop organised by INRA-MOISA and Wageningen University. Montpellier, France: INRA-MOISA. Dadi, L., A. Negassa and S. Franzel (1992) ‘Marketing Maize and Teff in Western Ethiopia: Implications for Policies Following Market Liberalization’, Food Policy, 17 (3): 201 – 13. Damiani, O. (2000) The State and Nontraditional Agricultural Exports in Latin America: Results and Lessons of Three Case Studies, Report for the Inter-American Development Bank. Washington, DC. Dercon, S. (1995) ‘On Market Integration and Liberalization: Method and Application to Ethiopia’, Journal of Development Studies, 32 (1): 112– 43. Dessalegn, G., T.S. Jayne and J.D. Shaffer (1998) ‘Market Structure, Conduct, and Performance: Constraints of Performance of Ethiopian Grain Markets’, Working Paper 9, Grain Market Research Project. Addis Ababa, Ethiopia: Ministry of Economic Development and Cooperation. FDRE (Federal Democratic Republic of Ethiopia) (1994) Proclamation no. 85/1994 Agricultural Cooperative Societies. Addis Ababa: Federal Negarit Gazeta. FDRE (Federal Democratic Republic of Ethiopia) (1998) Proclamation no. 147/ 1998 to Provide for the Establishment of Cooperative Societies. Addis Ababa: Federal Negarit Gazeta. Gabre-Madhin, E.Z. (2001) Market Institutions, Transaction Costs, and Social Capital in the Ethiopian Grain Market. Research Report 124. Washington, DC: International Food Policy Research Institute. Gabre-Madhin, E.Z. and I. Goggin (2005) ‘Does Ethiopia Need a Commodity Exchange? An Integrated Approach to Market Development’, EDRI-ESSP Policy Working Paper no.4. Addis Ababa, Ethiopia: Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI), Addis Ababa Office. Gabre-Madhin, E. and T. Mezgebou (2006) ‘Prices and Volatility in the Ethiopian Grain Market’, Paper presented at the IFPRI-ESSP Policy Conference 2006. Addis Ababa, Ethiopia. FIPRI, Washington, DC, USA. Godtland, E.M., E. Sadoulet, A. de Janvry, R. Murgai and O. Ortiz (2004) ‘The impact of Farmer-Field-Schools on Knowledge and Productivity: A study of Potato Farmers in the Peruvian Andes’, Economic Development and Cultural Change, 53: 63 – 92. Jayne, T.S., A. Negassa and R. Myers (1998) ‘The Effect of Liberalization on Grain Prices and Marketing Margins in Ethiopia’, MSU International Development Working Paper No. 68, East Lansing: Michigan State University. Kirsten, J.F., A.S.M. Karaan and A.R. Dorward (2009) ‘Introduction to the Economics of Institutions’, in J.F. Kirsten, A.R. Dorward, C. Poulton and N. Vink (eds) Institutional Economics Perspectives on African Agricultural Development. Washington, DC, USA, International Food Policy Research Institute (IFPRI).


|

21


Lirenso, A. (1993) ‘Grain Marketing Reform in Ethiopia’, Ph.D. Dissertation, University of East Anglia, Norwich, UK. Negassa, A. (1998) ‘Vertical and Spatial Integration of Grain Markets in Ethiopia: Implications for Grain Market and Food Security Policies’, Working Paper No. 9. Grain Marketing Research Project, Addis Ababa, Ethiopia: International Food Policy Research Institute (IFPRI), Addis Ababa Office. Negassa, A. and T.S. Jayne (1997) ‘The Response of Ethiopian Grain Markets to Liberalization’, Working Paper No. 6. Grain Marketing Research Project, Addis Ababa, Ethiopia: Ministry of Economic Development and Cooperation. Neven, D., T. Reardon and R. Hopkins (2005) Case Studies of Farmer Linking to Dynamic Markets in Southern Africa: The Fort Hare Farmers Group. East Lansing, MI: Michigan State University. Ravallion, M. (2001) ‘The Mistery of Vanishing Benefits: An Introduction to Impact Evaluation’, World Bank Economic Review, 15 (1): 115– 40. Sharma, V.P. and A. Gulati (2003) ‘Trade Liberalization, Market Reforms and Competitiveness of India Dairy Sector’, Markets, Trade and Institutions Division Discussion Paper 61, Washington, DC: International Food Policy Research Institute. Spielman, D.J., M.J. Cohen and T. Mogues (2008) ‘Mobilizing Rural Institutions for Sustainable Livelihoods and Equitable Development: A Case Study of Local Governance and Smallholder Cooperatives in Ethiopia’, IFPRI Working Paper, Washington, DC: International Food Policy Research Institute. Strasberg, P.J., T.S. Jayne, T. Yamano, J. Nyoro, D. Karanya and J. Strauss (1999) ‘Effects of Agricultural Commercialisation on Food Crop Input Use and Productivity in Kenya’, MSU International Department of Agricultural Economics Development, Working Paper no. 71, East Lansing, MI: Michigan State University. Sykuta, M.E. and M.L. Cook (2001) ‘Cooperative and Membership Commitment: A New Institutional Economics Approach to Contracts and Cooperatives’, American Journal of Agricultural Economics, 83: 1273 – 79. Tendler, J. (1983) What to Think About Cooperatives: A Guide from Bolivia. Rosslyn, VA: The Inter-American Foundation. Uphoff, N. (1993) ‘Grassroots Organizations and NGOs in Rural Development: Opportunities with Diminishing States and Expanding Markets’, World Development, 21 (4): 607 –22. Von Braun, J. (1995) ‘Agricultural Commercialization: Impact on Income and Nutrition and Implications for Policy’, Food Policy, 20 (3): 187– 202. World Bank (2003) ‘Reaching the Rural Poor, a Renewed Strategy for Rural Development’, Discussion Paper, Washington, DC: World Bank.

22

|


Appendix Table A1: Blocks of Propensity Scores for Independent and (all) Cooperative Farms (Reduced Sample; Only Observations within Common Support) Independent farm households

Cooperative farm households

Total

0.08 0.2 0.4 0.6 0.8 Total

63 52 20 5 2 142

10 27 18 10 13 78

73 79 38 15 15 220

Table A2: Blocks of Propensity Scores for Independent and Marketing-Cooperative Farms (Reduced Sample; Only Observations within Common Support) Blocks of propensity scores

Independent farm households

Marketing-cooperative farm households

Total

0.01 0.2 0.4 0.6 0.8 Total

109 37 2 4 0 152

11 11 9 1 10 42

120 48 11 5 10 194

Table A3: Blocks of Propensity Scores for Independent and Livelihood-Cooperative Farms (Reduced Sample; Only Observations within Common Support) Blocks of propensity scores


Livelihood-cooperative farm households

Total

0.01 0.2 0.4 0.6 0.8 Total

103 24 10 1 1 139

8 13 12 2 1 36

111 37 22 3 2 175


Blocks of propensity scores


|

23

Table A4: Results of Tobit Model for Commercialisation Rate (Reduced Sample) Explanatory variables





Table A5: Probit Model for Cooperative Membership (Full Sample and Marginal Effects) Explanatory variables

Fixed arable land (ha) {Fixed arable land}2 Household size (no. of members) {Household size}2 Dependency ratio (children/ adults) {Dependency ratio}2

Any cooperatives Marketing coop- Livelihood coop(1) eratives (2) eratives (3) 0.10 (0.02)** 20.00 (0.00)* 0.02 (0.01) 20.07 (0.24) 0.13 (0.05)**

0.05 (0.01)** 20.00 (0.00) 0.01 (0.01) 20.05 (0.15) 0.03 (0.03)

0.06 (0.02)** 20.00 (0.00)* 0.00 (0.00) 20.00 (0.00) 0.06 (0.03)**

20.04 (0.02)**

20.01 (0.01)

20.02 (0.01)**

(continued on next page)


Membership 0.13 (0.10) 0.33 (0.13)** 20.05 (0.13) Fixed arable land (ha) 0.06 (0.05) 0.03 (0.05) 20.15 (0.11) 20.00 (0.00) 20.00 (0.00) 0.03 (0.02)* {Fixed arable land}2 Household size (no. of members) 0.01 (0.02) 0.01 (0.02) 0.02 (0.03) 0.25 (0.34) 20.31 (0.33) 0.65 (0.43) {Household size}2 Dependency ratio 0.14 (0.12) 0.13 (0.13) 0.15 (0.13) (children/adults) 20.07 (0.04)** 20.07 (0.04)* 20.07 (0.04)* {Dependency ratio}2 Education of household head 0.04 (0.03) 0.02 (0.04) 0.04 (0.04) (years) 20.00 (0.00) 20.00 (0.00) 20.00 (0.00) {Education of household head}2 Age of household head (years) 0.00 (0.00) 0.00 (0.00) 0.00 (0.01) 20.00 (0.00) 20.00 (0.00) 20.00 (0.00) {Age of household head}2 Male household head (dummy) 20.05 (0.13) 20.06 (0.13) 20.04 (0.14) Coffee production (dummy) 0.16 (0.12) 0.13 (0.13) 20.04 (0.16) No. of agro-commodities 0.12 (0.06)** 0.15 (0.06)** 0.30 (0.07)** 20.00 (0.01) 20.01 (0.01) 20.02 (0.01)** {No. of agro-commodities}2 Deficit woredas (dummy) 20.19 (0.11)* 20.09 (0.13) 20.10 (0.01) No. of observations 279 243 237 0.0856 0.099 0.0888 Pseudo R 2 Log-likelihood 2251.58 2218.46 2209.26 Left-censored observations (¼0) 104 93 99 Right-censored observations 43 38 34 (¼1)

24

|


Table A5: Continued Explanatory variables

Any cooperatives Marketing coop- Livelihood coop(1) eratives (2) eratives (3) 0.01 (0.01)

0.01 (0.01)

20.00 (0.00) 20.00 (0.00) 0.00 (0.00) 0.00 (0.01) 20.00 (0.00) 20.00 (0.00) 0.07 (0.05) 20.01 (0.03) 20.04 (0.05) 0.02 (0.04) 0.06 (0.03)** 0.03 (0.02) 20.01 (0.00)** 20.00 (0.00) 20.10 (0.04)** 20.11 (0.02)** 368 332 0.2157 0.2498 2149.09 294.56

0.01 (0.01) 20.00 (0.00) 20.00 (0.00) 0.00 (0.00) 0.00 (0.00) 20.06 (0.02)** 0.08 (0.03)** 20.01 (0.00)** 20.01 (0.02) 326 0.2972 279.60

Standard errors in parentheses. *Denotes significance at 10% level. **Denotes significance at 5% level. Table A6: Blocks of Propensity Scores for Independent and (all) Cooperative Farms (Full Sample; Only Observations within Common Support Blocks of propensity scores


Cooperative farm households

Total

0.02 0.2 0.4 0.6 0.8 Total

162 65 22 5 1 255

13 35 15 10 5 78

175 100 37 15 6 333

Table A7: Blocks of Propensity Scores for Independent and Livelihood-Cooperative Farms (Full Sample; Only Observations within Common Support Blocks of propensity scores


Livelihood-cooperative farm households

Total

0.01 0.2 0.4 0.6 Total

141 29 8 2 180

10 16 8 2 36

151 45 16 4 216


Education of household head (years) {Education of household head}2 Age of household head (years) {Age of household head}2 Male household head (dummy) Coffee production (dummy) No. of agro-commodities {No. of agro-commodities}2 Deficit woredas (dummy) No. of observations Pseudo R 2 Log-likelihood


|

25

Table A8: Blocks of Propensity Scores for Independent and Marketing-Cooperative Farms (Full Sample; Only Observations within Common Support Blocks of propensity scores


Marketing-cooperative farm households

Total

0.03 0.2 0.4 0.6 0.8 Total

144 36 10 0 0 190

16 14 5 4 3 42

160 50 15 4 3 232

Explanatory variables

Membership Fixed arable land (ha) {Fixed arable land}2 Household size (no. of members) {Household size}2 Dependency ratio (children/ adults) {Dependency ratio}2 Education of household head (years) {Education of household head}2 Age of household head (years) {Age of household head}2 Male household head (dummy) Coffee production (dummy) No. of agro-commodities {No. of agro-commodities}2 Deficit woredas (dummy) No. of observations Pseudo R 2 Log-likelihood Left-censored observations (¼0) Right-censored observations (¼1)




0.14 (0.10) 0.55 (0.04) 20.00 (0.00) 20.00 (0.02) 0.28 (0.22) 0.13 (0.03)

0.29 (0.12)** 0.05 (0.04) 20.00 (0.00) 0.00 (0.02) 0.30 (0.21) 0.12 (0.11)

20.02 (0.13) 20.12 (0.09) 20.03 (0.02)** 0.01 (0.03) 0.40 (0.24)* 0.12 (0.12)

20.07 (0.03)** 0.03 (0.03)

20.07 (0.04)** 0.02 (0.03)

20.07 (0.36)** 0.03 (0.03)

20.00 (0.00) 0.01 (0.03) 20.00 (0.00) 0.09 (0.11) 0.31 (0.11)* 0.13 (0.06)* 20.00 (0.00) 20.06 (0.11) 332 0.092 77.003135 52

20.00 (0.00) 20.00 (0.00) 0.00 (0.00) 0.13 (0.11) 0.31 (0.13)** 0.22 (0.07)** 20.01 (0.01)* 20.07 (0.10) 326 0.0784 89.292141 48

20.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.10 (0.11) 0.34 (0.11)** 0.11 (0.05)** 20.00 (0.01) 20.12 (0.10) 368 0.083 433146 57



Table A9: Results of Tobit Model for Commercialisation Rate (Full Sample)