Bangladesh J. Agric. Econs. XXXVII, 1&2 (2014, 2015) 55-68

NON-FARM EMPLOYMENTAND PRODUCTION EFFICIENCY OF FARM HOUSEHOLD IN

Shakila Salam1 Siegfried Bauer2

ABSTRACT This article attempts to examine the effect of non-farm employment on rural farm household’s technical efficiency in Bangladesh. Cobb-Douglas production function through stochastic frontier modelwith single stage estimation procedure is used to estimate farm efficiencies. The study was based on primary data collected from a cross-section of 153 farm households, drawn by multi-stage purposivesampling from three . Part-time farmers are less technically efficient than the full-time farmers which indicate negative influence of non-farm employment on farm efficiency. A mixed influence on technical efficiency is found when non-farm employment is broken down into different sources. Compared toother types of non-farm activities, self-employment increases the farm technical efficiency. Results indicate that numbers of active household members, household head age, farm size, and ownership of agricultural machines are important factors for reducing the inefficiency level of part-time agricultural households.

Key words: Non-farm employment; Technical efficiency; Stochastic frontier; Farm household; and Bangladesh

I. INTRODUCTION The movement of labour out of agriculture has significantly witnessed in many developing countries like Bangladesh. Participation in part-time farming or multiple jobholding is not a recent phenomenon in the world. Many developed and developing countries (e.g., Germany, Japan, Ireland, England, Korea, China, etc.) promoted part-time farming for structural transformation and development of the economy (Pfeffer, 1989; Johnston, 1962; Cawley, 1983; Zhou et al, 2001; and Kimhi, 2000). Part-time farming is commonly defined as a special case of multiple job-holding, which implies the combination of a small amount of farming practices along with an occupation outside of agriculture (Shishko andRostker, 1976; and Salter, 1936). The increasing nature of unemployment in Bangladesh is a result of high population growth rate, extreme landlessness and seasonal nature of agriculture. Besides, wide-spread use of modern technologies, especially in land preparation, irrigation and post- harvesting has also released a considerable amount of labour from agriculture. Moreover, less

1Assistant Professor, Institute of Agribusiness and Development Studies, Bangladesh Agricultural University, -2202. E-mail: [email protected] 2Professor, Department of Regional and Project Planning, Justus-Liebig University, Giessen, Germany. 56 The Bangladesh Journal of Agricultural Economics profitable farming business contributed to the deep pervasiveness of poverty in rural farm households. Therefore, rural households are losing their interest on farming and compelled to join in some sort of non-farm activities. Households are generally engaged in self- employment based activities (such as, business, cottage industry, transportation services, retail trading, etc.), wage based activities (non-agricultural labour work, service in government and non-government organizations, industrial labour and so on) and migrating to urban or overseas countries. As agriculture in Bangladesh is still characterized by subsistence farming, most of the farmers are not interested to leave farming as a whole. Therefore, part-time farming has emerged as an alternative livelihood strategy for surviving land-scarce poor farm households. However, participation in non-farm activities has direct and indirect influence on agricultural production. Existing studies on different countries, for instance studies on Bulgaria, Romania, Ukraine, Kosovo, Ethiopia, and China reveal that involvement in different types of non-farm activities might have a considerable impact on rural areas (Dittrich and Jeleva, 2009; Surd, 2010; Peacock, 2012; Sauera et al, 2015; Abebe, 2014; and Jin et al, 2014). Particularly, farm income of part-time farmer is insufficient and it is considered as one of the important threats to achieve production efficiency in agriculture (Jervell, 1999). Therefore, the consequence of non-farm employment on agricultural production efficiency is still under farm policy debate. This situation leads to an important query about the impact of non-farm activities on agricultural efficiency. This studyfocuses on the effect of non-farm income on farm technical efficiency in Bangladesh. Another objective of this research is to identify the factors causing variations in technical efficiency of part-time farming households. A Stochastic frontier model with single stage estimation procedure is used to address the objectives of the research. Moreover, the study contributes to the development literature by finding out the actual relation among non- farm income, part-time farming, and technical efficiency. The findings of Bojnec and Ferto (2011), Yue and Sonoda (2012), and Abebe (2014) confirmed the positive association between off-farm income and technical efficiency in case of Slovenian, Chinese, and Ethiopian farmers, respectively. On the other hand, Kumbhakar et al (1991) found that farmers without off-farm wage are more efficient than those with off-farm wage on Utah dairy farm households. Although there has been much research on the efficiency measurement, but very few research are done to find out this type of non-farm income effect on farm efficiency in Bangladesh. Therefore, analysing the impact of non-farm income on farm technical efficiency is of greater importance and aims to fill the gap in this area.

II. METHODOLOGY 2.1 Data Sources The required data for this study was derived from a cross-sectional primary data set, collected through a farm household survey from 3 districts, namely Mymensingh, and districts of Bangladesh. Among these districts, Comilla is a district where non-farm employment and migration rate is too high and 39.41 percent of households fully depend on Non-Farm Employment and Production 57 non-agricultural activity for their livelihood (BBS, 2011). According to latest available data, Mymensingh added the highest gross value to agriculture, while Comilla added comparatively lower value in 2005 (BBS, 2006). Bhaluka and Haluaghat under Mymensingh, BoruraUpazila under Comilla and BirolUpazila under Dinajpur district were selected as study areas. Finally, multi-stage purposivesampling procedure was employed to identify the 153 sample households from four villages. Sample households were categorized into farm households (income source is only agricultural activities), and farm and non-farm income earning households or part-time farming households (income source is both agriculture and non-farm activities) consisting of 59 and 94 households respectively (Appendices Table A1). Data were collected through a key information and questionnaire survey with farm households during July to November 2014. For gathering qualitative information, knowing the actual situation and crosschecking the data, Focus Group Discussion (FGD) with the farmers was conducted in each of the selected villages.

2.2 Empirical model The term technical efficiency (TE) measures the degree to which a producer maximize possible outputs using a given set of inputs, or producing a given level of output by using possible minimum inputs. The first definition leads to the term of output-oriented efficiency measures, whereas the next one indicates input-oriented efficiency measures (Coelli et al, 2005a). In this analysis, an output-oriented efficiency measure is used because it is mostly practiced in developing country’s agricultural production system.There are two approaches for measuring technical efficiency: (i) the parametric and (ii) the non-parametric approach. Like many other previous studies, this study considered parametric approach for estimating TE and its determinants in a single-equation procedure (Kumbhakar et al, 1991; Huang and Lui, 1994; Battese and Coelli, 1995; Kilic et al, 2009; Anik, 2012; andKabir et al, 2015). This single-equation approach is widely used and less objectionable from a statistical point of view, as it provides more efficient inference regarding included parameters. As based on a certain point of production frontier (where technical efficiency is zero) the ratio between observed (actual) output and potential output calculates the degree of technical efficiency of each agricultural household; stochastic frontier approach is best suited to this situation (Coelli et al, 2005b; and Kilic et al, 2009). Thus, the amount by which an agricultural household actual production level drops below the frontier level is measured by technical inefficiency. Mathematically, TE can be expressed as,

Yi TEi = …………………………………………….(1) vi f (X i ;β )*e

th Where, Yi and Xi indicate output and vector of inputs used in the production by i household v respectively, β is a vector of frontier parameters to be estimated and e i implies random shocks.This value of efficiency lies between zero and one. The efficiency score 1 implies achievement of potential production level and less than 1 indicates existence of technical inefficiency.

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Cobb-Douglas (CD) and Translog (TL) models are mostly used for frontier analysis in the previous studies. In this research, both of these models are specified and finally selected most relevant one by using log-likelihood ratio test. The test statistic is presented as:

LR = -2{ln [L(H0)/L(H1)]} = -2{ln[L(H0)] - ln[L(H1)]}……………...... (2) where, L(H0) and L(H1) are the values of the likelihood function under the null hypothesis (CD function) and alternative hypothesis (TL function) respectively. It is found in the LR test that the calculated LR value is 8.96 for the selected samples. As the critical LR at upper 5 C 2 percent level of significance and 15 degrees of freedom ( χ0.05,15 ) is equal to 24.99 (Kodde and Palm, 1986), null hypothesis cannot be rejected. Therefore, the Cobb-Douglas production function is more suitable for the sampled data.

Cobb-Douglas: ln(Yi) = β0 + βi ∑ln(X i ) + vi – ui……………….…...(3)

Where,Y is the total value of agricultural output; X indicates a set of input quantities; β0 refers the constant term; β’s are the unknown parameters to be estimated; i denotes an agricultural household. The error term, v is related to the omitted variables and uncontrollable factors, 2 which is independently and identically distributed as N (0,σ v ). Another term u is a non- negative random variable that measures the technical inefficiency of individual household. th Output-oriented TE of the i farmer is TEi= exp (− i) [0, 1] (Sossouet al, 2014). Therefore, the technical efficiency is inversely related to the inefficiency effect. The Cobb-Douglas production function is the most𝑢𝑢 universal∈ form of analysing production behaviour in the field of theoretical and empirical agricultural analyses till present day. The easy estimation procedure and relatively accurate result of current production and elasticity estimation makes this model more remunerable (Yabi et al, 2013; and Akhter, 2015). Moreover, the negatively skewed OLS residuals in the third moment indicate the maximum likelihood estimation method provides more steady estimates than ordinary least squares in this study(Appendices Figure A1). Generally, half-normal, exponential, truncated-normal, and gamma distributions along with their own assumptions and characteristics are assumed for the non-negative error term. Following the typical approaches of stochastic frontier literatures, the term u is assumed as a half-normal distribution with unknown mean and variance. Moreover, according to Deininger et al (2007), linear function of relevant explanatory variables can substitute for the term of technical efficiency for better understanding unobserved potentiality of agricultural household. The technical efficiency term therefore can be re-written as: ui = ω0+ ∑ωk zki + φi……………………………………………………………………….………..(4)

In the equation (4), zk indicates a set of relevant explanatory variables; φi implies the independently and identically distributed random variable with a positive half normal distribution. As β’s, these ω0 and ωk are the unknown parameters to be estimated. All of these unknown parameters are estimated together with the variance parameters, which are Non-Farm Employment and Production 59

2 2 2 2 2 expressed in terms of following two ways: σ = σ u −σ v and γ = σ u /σ . Here, the value of parameter γ varies between zero and one. A single stage estimation procedure can be obtained using maximum likelihood by substituting equation (4) into equation (3). Logarithm of non-farm incomes, generated from wage, self-employment and migration-based non-farm activities, are considered as an explanatory variable in equation (4) for assessing the impact of these incomes on farm technical efficiency. As it is not possible to use instruments for endogenous variables (non- farm income) in the single-equation estimation that jointly estimates the stochastic frontier and the determinants of technical efficiency, this problem of endogeneity is omitted in this estimation process. However, in this section, the production frontiers for both full-time and part-time farm are estimated separately.

III. RESULTS AND DISCUSSION The summary description of variables used in the Cobb-Douglas production function and technical inefficiency model are presented in Table 1. First part of this table presents the input uses in different crop production and produced total output of full-time and part-time farmers.In this model, farm production is used as a dependent variable and measured by monetary value that the selected farms earned from producing all crops. Though the production function indicates the relationship between inputs and output in physical terms, in this study it is measured in value terms. Prices of these crops are considered to make them equivalent in measurement. In a specific period of time, price is assumed as a constant factor and value of production can be represented as total crop output. It is found from the first part of Table 1 that full-time farm households are cultivated on an average 1 hectare of land which is much higher than all types of part-time farming households. Similarly, average per hectare farm production of part-time farm is also less than the full-time farm. The reasons behind this difference might be due to their fewer amounts of input uses and spending less time on farming activities. As the farmers of the selected areas are cultivated vegetable and it required more amounts of labour, sampled households have to employed more labour than the as usual rice or other crop producing farmers. Farm households produced different crops and also use different varieties of seed, even for a single crop. Thus, in this study, seed is measured in monetary value (BDT) and combined the cost for all types of seed together. Nowadays, major portion of the farmers are using power tiller or tractor for their land preparation which cost is indicated by the land preparation cost in the model. Second part of Table 1 shows that average non-farm income is highest for households with migrant compared with wage and self-employment earning households. As expected, the average rate of year of education is higher (12 years) for wage earning households. In general, part-time farmers are more educated than the full-time farmers. Moreover, on an average households involved in non-farm activities had less farm size but more non-agricultural assets compared to pure agricultural households.

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3.1 Estimation of production function of full-time and part-time agricultural households The estimated elasticities of output with respect to almost all selected input variables are found positive for both full-time and part-time farmers (Table 2). In case of full-time farming, cultivated land shows the highest elasticity for crop production among all the selected inputs. The estimated value of elasticity for cultivated land indicates that with a 1 percent increase of cultivated land, farm production is going to increase by 0.30 percent. Previous literature found that land elasticity of production is much higher than labour and capital elasticity of production in the context of Asia (Bardhan, 1973; Cornia, 1985; Ohkawa, 1972; and Kabir et al, 2015). Moreover, Cornia (1985) identified land scarcity as the main reason behind this high land elasticity, as he found land elasticity is relatively higher in land scarce countries and lower in land-rich countries.

Table 1. Descriptive statistics of variables used in the stochastic production frontier and technical inefficiency model Part-time agricultural households Full-time Variables agricultural Agriculture Agriculture Agriculture households & wage & self- & migration employment employment Variables used in the production function (per hectare) Cultivated land (ha) 1.00 (0.30) 0.77 (0.16) 0.82 (0.15) 0.70 (0.15) Family labour (man-days) 40.59 (9.37) 25.58 (3.55) 35.19 (8.96) 42.73 (6.42) Hired labour (man-days) 90.90 107.66 102.89 118.07 (13.33) (15.40) (15.99) (42.93) Seed ('000BDT) 3.79 (0.42) 2.57 (0.37) 2.74 (0.50) 3.62 (1.67) Land preparation ('000BDT) 6.51 (1.69) 5.54 (0.90) 6.27 (0.67) 5.17 (0.85) Irrigation ('000BDT) 8.52 (2.59) 6.10 (0.89) 7.74 (1.08) 6.24 (1.90) Fertilizer ('000BDT) 17.89 (13.57) 10.81 (2.07) 11.97 (1.61) 10.61 (2.47) Farm production ('000BDT/year) 191.65 155.18 173.71 141..54 (54.23) (24.08) (17.97) (25.48) Variables in the production inefficiency equation Non-farm income('000BDT/ year) - 99.88 (9.8) 109.95 145.34 (10.5) (12.13) No. of hh member below 6 0.77 (0.17) 0.69 (0.14) 0.58 (0.19) 0.77 (1.6) No. of hh member between 6-14 0.63 (0.17) 1.26 (0.15) 0.61(0.14) 0.63 (0.26) No. of hh member between 14.01-59 3.43 (0.30) 3.29 (0.32) 2.67 (0.26) 3.43 (0.11) No. of hh member more than 59 0.60 (0.14) 0.39 (0.12) 0.73 (0.15) 0.60 (0.34) Education of hh head (year) 5.00 (0.35) 12.48 (0.4) 8.82 (0.35) 5.30 (0.44) Age of hh head (year) 58.53 (2.09) 48.90(2.52) 45.23 (2.37) 52.15 (2.58) Ownership of ag.machinery (%) 43.60 33.33 36.48 24.24 Farm size (ha) 1.06 (0.19) 0.86 (0.17) 0.89 (0.13) 0.76 (0.14) Farming experience (year) 25.64 (2.42) 23.47(3.04) 17.02 (2.59) 28.79 (3.22) Value of non-agricultural asset 147.33 168.93 263.98 373.32 ('000BDT) (46.50) (43.20) (142.46) (130.06) Acceptance of credit (%) 37.74 36.67 69.70 64.52 Availability of infrastructure (%) 37.74 60.00 58.06 58.30 Note: Figure in the parentheses implies standard errors; BDT = Bangladeshi Taka Source: Author’s estimation based on field study, 2014 Non-Farm Employment and Production 61

Similar result is also found by Akhter (2015) that land elasticity for crop production is 0.394, which is the highest elasticity among all other inputs used in crop production. Like as full- time farms, family labours are found to be more efficient than hired labours in non-farm based agricultural households. Moreover, the significant effect of labour indicates the labour- intensive farming system in Bangladesh, though small-scale mechanization is now used but it is mainly used for some specific field activities. As expected, family labour has greater importance in case of households with non-farm activities compared toonly farm activities.

Table 2. Production functions of full-timeand part-time agricultural households Full-time agriculture Part-time agriculture Variables Coefficients SE Coefficients SE Cultivated land 0.30*** 0.08 0.30** 0.11 Family labour 0.21** 0.06 0.34*** 0.09 Hired labour 0.12*** 0.08 0.24* 0.14 Seed -0.07 0.07 0.13 0.11 Machinery for land preparation 0.14* 0.08 0.09 0.12 Irrigation 0.13** 0.06 0.19* 0.10 Fertilizer 0.10* 0.05 0.02 0.15 Constant 5.46*** 0.69 0.64*** 0.14 Technical inefficiency model Log of wage income -0.09 0.08 Log of self-employment income -0.14* 0.08 Log of income from migration 0.03* 0.06 No. of hh member below 6 0.01 0.33 No. of hh member between 6-14 0.15 0.34 No. of hh member between 14.01-59 -0.40** 0.20 No. of hh member more than 59 0.25 0.46 Age of hh head (year) -0.04* 0.04 Education of hh head (year of schooling) 0.05 0.15 Farm size (ha) -0.56*** 0.48 Agricultural machinery(Δ) -0.02* 0.01 Log value of non-agri. asset 0.03 0.03 Farming experience (year) -0.09 0.27 Acceptance of credit (Δ) -0.43 0.61 Availability of infrastructure (Δ) 0.16 0.71 Constant 0.87** 0.20 Model summary sigma_v 0.29*** 0.05 0.32 0.04 sigma_u 0.31*** 0.08 0.62 0.08 sigma2 0.18 0.49 0.08 lambda 1.07*** 0.12 1.97 0.12 gamma 0.80 Log likelihood -109.50 -113.73 Wald chi2 (7) 95.59 92.57 Prob> chi2 0.00 0.00 Mean TE 0.76 0.13 0.67 0.22 Number of observations 59 94 Note: SE = Standard Error; Δ = Dummy variable; Asterisks (***, **, *) denote significance at the 1%, 5% and 10% level respectively Source: Author’s estimation based on field study, 2014

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The model implies that an increase of the irrigation positively and significantly affects farm production of all farms. More specifically, a 1% increase in irrigation costs leads to 0.13% and 0.19% increase in total farm production of full-time and part-time farming households respectively. Similar results were also found in different studies mostly in the case of rice production, as Boro rice mostly required irrigation water (Anik, 2012; Kabir et al, 2015; Akhter, 2015; and Hasnain et al, 2015).However, modern technology, in terms of land preparation and fertilizer application purposes has also a positive impact on farm production for both full-time and part-time farm. On the other hand, seed has a negative but insignificant effect on production in full-time farming, which may be explained by the availability of seeds. The major portion of the sampled households is small and marginal farms, where these farmers normally use their own family supplied seed or purchase normal seed from the market (Rahmanet al, 2012). Therefore they can use sufficient amounts of seed but yields of these seeds are less than the high yielding varieties (HYVs) of seed. This also implies a lack of availability of quality and HYV seed in the study area.

3.2 Estimation of technical efficiency The technical efficiencies for both full-time and part-time agricultural households are presented in lower part of Table 2. The presence of inefficiency indicated by lambda (λ) is greater than zero and significant at 1 percent in half-normal model. The mean technical efficiency of full-time agricultural households is 0.76, which implies that on an average household are 76 percent efficient compared to the most efficient household in the corresponding model. This result is consistent with the previous studies (Haideret al, 2011; Rahmanet al, 2012; and Anik, 2012). In some studies of Boro rice production, this TE score was found higher (0.84 to 0.91) as they considered only one crop and used different practices (Miah et al, 2010; and Kabir et al, 2015). The value of Gamma (γ) represents whether there is technical inefficiency exists in the production process or not (Abebe, 2014). The closer the value of γ is to 1, the presence of inefficiency and suitability of the stochastic frontier model for the data is clearly identified (Piesse andThirtle, 2000). In this model the value of γ is found to be 0.80 for part-time farms, which confirms the existence of technical inefficiency in the farms. Therefore, it implies that technical inefficiencies of the selected non-farm based households are responsible for 80 percent of the total variation in their farm production. The mean technical efficiency of part- time agricultural household is found at 0.67, which indicates the presence of higher levels of inefficiency. According to Kilic et al (2009); Fernandez-Cornejo et al (2007); and Coelli andBattese (1996) the general behaviour of overall non-farm income is that farm efficiency decreases, as farmers are less concerned about farming activities at that time. This study also proves this behaviour by finding a smaller efficiency score for part-time farm households than pure agricultural households. Abebe (2014), Asefa (2012), and Tirkaso (2013) also found smaller technical efficiency value for off-farm and non-farm households. The maximum (0.95) and minimum (0.22) efficiency scores indicates a larger variation in technical Non-Farm Employment and Production 63 efficiency scores among households. The distribution of these technical efficiency scores is shown in Figure 1.

Figure 1. Distribution of technical efficiency scores

As shown in this figure, distribution of efficiency is highly concentrated (35.11 percent) around farm households with scores between 0.71 and 0.80. About 40.43 percent of sampled household have a TE score lower than the average TE value, which implies more possibilities to improve their efficiency. Moreover, 7.45 percent of agricultural households have the minimum level of efficiency ranging from 0.21 to 0.30 and 0.51 to 0.60, whereas higher efficiency scores (greater than 80) are obtained by only 17.02 percent of the non-farm households. Household’s higher level of inefficiency in resource utilization is clearly indicated by this type of wider variation in TE scores. 3.3 The effect of non-farm income and other determinants on technical efficiency of the farm This sub-section exhibits effects of different non-farm income and other explanatory variables on farm technical efficiency. The result is exhibited in the second panel of Table 2, where the positive coefficients resemble negative effect on technical efficiency. Though it is found in the previous sub-section that income from non-farm activities reduces farm’s overall technical efficiency, a mixed influence on technical efficiency is found when this income is broken down into three sources (Table 2). In this study, the estimated coefficient of migration income shows a negative and self-employment income shows a positive and significant association between non-farm income and technical efficiency. This implies that part-time farmer involved in self-employment activities increases farm’s technical efficiency more compared to other types of non-farm activities involvements. This type of positive association also found in some previous researches (Rizov et al, 2001; Bojnec and Ferto, 2011; Abebe, 2014; and Kabir et al, 2015). Generally, migration shows negative effect on efficiency, but its

64 The Bangladesh Journal of Agricultural Economics positive influence is also found in some empirical evidence in other countries (Wouterse, 2010). More non-farm income from migrant member may tend to have lower levels of technical efficiency presumably because they care less about farming activities and treat it as less prestigious work. In the case of wage income, the effect is found positive, but not significant. All of the households involved in non-farm activities have less family labour to invest in agriculture and have to depend more on hired labour. Therefore, it has a negative influence on their farm’s technical efficiency. An obtained positive impact could be interpreted that households with rural wage or self-employment income are more like to invest in agricultural technologies such as machinery for land preparation, irrigation, modern varieties of seed, fertilizer and so on (Table 1). In fact, at the times of high labour requirement, self-employed and wage-employed household members can help in farming activities. These facilities may offset the disadvantages of less family labour. Regardless of the source of non-farm income, the numbers of active household members (age group of 15-59), age of household head, and ownership of agricultural machines have positive and significant effects on the efficiency of part-time agricultural households. This implies that households with more active labour are technically more efficient. Younger household heads are less efficient than the older household heads supporting Coelli and Battese (1996), Dinar et al (2007), Anik (2012), and Rahman et al (2012), because through their experience they know how to optimize resources to produce better output. Similarly, the dummy for households that own machinery for crop cultivation shows they are technically more efficient than those households without these facilities. Moreover, education of the household head has a negative but insignificant impact on the efficiency argument that educated farmers are technically more inefficient than less educated or uneducated farmers. The coefficient of this variable failed to show the expected sign that is suggested in other studies. Generally, farmer’s involvement in non-farm activities increases with an increase of their education level. Therefore, this education may not exert any significant impact on their farming efficiency level. In fact, farmers are generally learnt from their day-to-day activities instead of learning through formal education. That’s why experience is also another important factor in reducing inefficiency. Kilic et al (2009) also found the same inverse relationship of education and efficiency among non-farm households in Albania. However, a negative and statistically significant impact of cultivated land on technical inefficiency implies that, farmers become more efficient by increasing their cultivated land. Obtaining external credit also found to decrease technical inefficiency. This result supports Dinar et al (2007), Sossou et al (2014), and Kilic et al (2009), where they argued that a lack of capital provoke farmers to make inefficient decisions. Finally, the coefficient of infrastructure (another dummy variable) is found positively (not significantly) associated with inefficiency. It could be interpreted that households living in a developed infrastructural area may have relatively better opportunities for involvement in non-farm activities. As for households living in backward infrastructural areas, such opportunities are limited and they can fully devote their all effort to production activities. This makes them more efficient compared to the farmers in developed regions.

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IV. CONCLUSION AND POLICY IMPLICATIONS Nowadays, a large portion of the rural households are involved in one or more non-farm activities along with farming which indicates part-time farming as an emerging livelihood strategy in the rural areas of Bangladesh. The central theme of this study has been to examine the role of non-farm income of rural farm households on farm production efficiency. From the Cobb-Douglas production function analysis, it is found that part-time farmers are less technically efficient than the full-time farmers. That means participating in non-farm activities causes to be farm technically more inefficient. Wider variation in TE scores of part-time farm might indicate higher level of resource utilization inefficiency of households. However, both pure and part-time farm’s efficiency scores indicate room for further improvement. Results also show that Land and labour are the most vital components for increasing farm production. Modern farm technologies, like machinery for irrigation and land preparation also have great influence on farm production. From the efficiency analysis, it is also found that income from self-employment activities increases the technical efficiency of farmers compared to other types of non-farm activities. Numbers of active household members, household head age, farm size, and ownership of agricultural machines are important factors for reducing the inefficiency level of non-farm income earning households. Though involvement in different non-farm activities reduces farm technical efficiency, further improvement of farm management practices using the existing technologies can be suggested through which farm production can still be substantially increased.

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ACKNOWLEDGEMENTS The authors are grateful to the funding organisation, German Academic Exchange Service (DAAD), for providing the fund for this research.

APPENDICES Table A1. Definition of sample householdscategories Categories Definition % of households Full-time agriculture Households generate income only from agriculture 38.56 Agriculture and wage- Households generate income from both agriculture & 20.26

employment wage based works in non-farm activities Agriculture and self- Households earn income from both agriculture & 20.26

- time employment self-employment in non-farm activities Agriculture and Both agriculture & remittances received from in- 20.92 Part households agricultural migration country and/ out-country migrant of household member(s) are the sources of income

Kernel density estimate 1.5 1 Density .5 0

-1.5 -1 -.5 0 .5 Residuals kernel = epanechnikov, bandwidth = 0.0843

Figure A1.Kernel density estimate of OLS residuals Source: Author’s calculation