EFFECT OF EDUCATION ON TECHNOLOGY ADOPTION AND AGGREGATE CROP OUTPUT IN BANGLADESH

Transcription

1 Bangladesh J. Agric. Econs. XIX, 1 and 2 (1996): 1-15 EFFECT OF EDUCATION ON TECHNOLOGY ADOPTION AND AGGREGATE CROP OUTPUT IN BANGLADESH Uttam Kumar Dev Mahabub Hossain ABSTRACT The study examines the contribution of education on regional technology adoption and aggregate crop output. The results indicate that education has a positive effect on the adoption of irrigation, biological (MV) and chemical fertilizer technology. Infrastructural facilities (road) has significant positive impact on MV adoption. Higher the road availability per hectare of cropped area, higher the adoption of MV. The study reveals that to increase the technology adoption level as well as its utilization to the fullest extent, we need to expand agricultural knowledge oriented education system in the rural areas of Bangladesh. I. INTRODUCTION There is a general concensus that education has a positive effect on agricultural output and technology adoption. It is believed that the farm's capacity to produce more output from a given bundle of resources depends to a large extent on the knowledge stock of the farmer. Education increases the information acquisition ability and adjustment ability of the farmer, thereby providing awareness about real world possibilities and rational expectation for decision making. Thus, education helps farmers to decide to adopt modern technology and, thereby, increase output. In short, conventional economic theories treat education as a catalyst of production. However, new theories (endogenous growth models-romer 1986, 1990; Lucas 1988) have given more emphasis on the knowledge stock of human capital. A recent paper on economic growth theory, Romer (1990), mentions that education (human capital) acts as an input for production. He stresses to take four factors of production into account: physical capital, unskilled labor, human capital (for instance, schooling years), and an index of the level of technology. In other words, an aggregate crop production function can be written as equation (1). The authors are Scientific Officer, Agric. Econs. Division, Bangladesh Rice Research Institute, Gazipur, Bangladesh; and Economist and Head, Social Sciences Division, IRRI, P. O. Box. 933, Manila, Philippines respectively. This paper is derived from the first author's Ph. D. thesis submitted to the University of the Philippines at Los-Banos. Authors are grateful to the anonymous referees of the Journal and Dr. Shamsul Alam for their useful comments on an earlier draft of the paper and editing to the present form. However, the authors alone are responsible for the caveats remain.

2

3

4

5

6

7

8

9

10

11 Effect of Education on Technology Adoption : Dev and Hossain 11 MV technology adoption level also depends on the availability of new MVs. In a particular point in time the supply of MVs are fixed, but over a period of time it varies. Increase on the availability of MVs are agricultural research. Research, inturn, depends on the resources allocated to the research system. To reflect this supply side effect on technology adoption, we tried to measure the contribution of research on MV adoption level by incorporating the investment in agricultural research as a variable. We found a significant positive contribution of agricultural research on MV adoption at 1 percent level of significance. This indicates that increase in agricultural research investment increases the possibility of MV adoption significantly. Higher the expenditure on agricultural research higher the level of MV adoption. Similarly, extension expenditure per farm may be a proxy for the effort of extension people-knowledge dissemination-which inturn may lead to higher technology adoption. To see this, we have taken per farm extension expenditure as a variable into the MV adoption function. Our estimated coefficient shows that it has positive effect on MV adoption, as expected, but statistically it is not significant. To see determinants of fertilizer use (taka/ha) we ran OLS regression (following equation 4). The estimates of the regression equation are presented in table 8. The proportion of irrigated area (PIRGA) has significant positive effect on per hectare fertilizer adoption level of regions. This implies that higher the proportion of irrigated area, higher the level of fertilizer consumption per hectare. Effectiveness of the fertilizer depends on the soil moisture and the variety cultivated. MVs are more fertilizer responsive than local varieties. On the other hand, adoption of MVs depend to a large extent on irrigation facilities. Therefore, proportion of irrigated area has a highly significant effect on per hectare fertilizer use. Technology index (TECH) has significant positive contribution to fertilizer use at 1 percent level of significance. This indicates that higher the level of technology adoption, higher the level of fertilizer consumption. Since MVs are more fertilizer responsive, then it is obvious that increase in MV leads to the increase in per hectare fertilizer use. Rural education (EDN) has significant positive effect on per hectare fertilizer adoption at 1 percent level of significance. Our estimated results show that one additional year of average schooling of rural adult male population increases the per hectare fertilizer consumption by 184 taka. Significant contribution of farmers' education on per hectare fertilizer use is also supported by a farm level analysis (Deb 1995). Infrastructural facilities (ROAD) has significant positive effect on per hectare fertilizer use at 1 percent level of significance. This indicates that infrastructurally developed regions use higher doses of fertilizer per hectare compared to the infrastructurally underdeveloped regions. Land availability per worker has a significant negative contribution to fertilizer use level.this implies that higher the availability of land per worker, lower the fertilizer use rate.

12

13

14 14 The Bangladesh Journal of Agricultural Economics pushes them to earn more from non-farm activities rather than trying to manage agriculture efficiently. Due to the non-availability of non-farm income data at the regional level, we have not been able to substantiate these arguments with numerical data but these observations are supported with estimates by Deb (1995) where he performed household non-farm income determinant analysis based on household level data. Land (total cropped area-ha) had significant positive contribution to the aggregate crop output at 1 percent level of significance. Elasticity of aggregate output with respect to land (X 1 ) is This indicates that 1 percent increase in total cropped area leads to 0.76 percent increase in aggregate crop output. Labor has significant positive contribution to the aggregate crop output at 1 percent level of significance. Elasticity of aggregate output with respect to labor (X2) is 0.18 implying that 1 percent increase in labor increases the aggregate crop output by 0.18 percent. Draft power has significant positive effect on aggregate crop output at 1 percent level of significance. Elasticity of aggregate output with respect to draft power (X3) is This indicates that 1 percent increase in draft power leads to an increase in aggregate crop output by.07 percent. Chemical fertilizer has a significant positive effect on aggregate crop output at 1 percent level of significance. Elasticity of aggregate output with respect to fertilizer (X 4 ) is 0.05 indicating that 1 percent increase in fertilizer use increases the aggregate crop output by 0.05 percent. Technological index has significant positive effect on aggregate crop output at 1 percent level of significance. Elasticity of aggregate output with respect to technology (TECH) is One percent increase in MV adoption increases the aggregate crop output by 0.53 percent. Region dummies constructed on the basis of agroecological conditions are also significant, implying that agroecological and climatic differences across regions also determine variation in aggregate crop productivity. The sum of elasticities of material inputs (land, labor, capital) represented is 1.06 which is close to one. Therefore, we can say that constant returns to scale prevails in material inputs. On the other hand, the sum of elasticities of all input including technology and rural education is 1.52 which is significantly greater than one. This means that there is increasing returns to scale when technology and rural education are also included as inputs. This explains the underlying forces responsible for the increased aggregate crop output over time. IV. CONCLUSIONS Rural education contributed to the adoption of irrigation, MV, and chemical fertilizer technology. Technology index has extremely high coefficient to the aggregate crop output. High elasticity of aggregate crop output with respect to technology indicates that output

15