Challenges in the Era of Globalization - iaabd

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Proceedings of the 12th Annual Conference © 2011 IAABD Is this optimism realistic? Therefore understanding the drivers of import demand for rice will inform help policy on imports and what needs to be put in place to meet the domestic demand. Table 1: Rice production, consumption and imports in Uganda (tonnes) Year Production Population Consumption Total consumption Milled rice (t) (Million) (t) 2008 163,150 28 8 224,000 60,850 2013 345,150 32.8 10.2 334,560 10,590 2018 509,600 38.4 13 499,200 10,400 Source: MAAIF, 2010. (t) Rice Imports The main objective of this paper is to analyze parameters influencing rice import demand. It is through knowing them that relevant policies can be adopted. The rest of the paper is covered as follows: The next section covers methods and data analysis followed by results and discussions. The last section gives the summary and conclusions Method and data analysis Nature, data sources and analysis Data used in this study was sourced from Uganda Bureau of Statistics (UBOS), Bank of Uganda, Uganda Revenue Authority, Ministry of Trade and Food and Agriculture organization (FAO). The study used time series data for a period of 1961 to 2009. The data set consisted of the following variables; quantity of rice imports IMP defined as the quantity of rice imported in various years, RICEPRO quantity of rice produced in tons, RIPRIC is real price of rice imported into the country, MAIPRO is quantity of maize produced in tons, POPN defined as midyear population of country, GDP defined as GDP per capita to represent real disposable income and time to represent change in tastes and preferences. Since they were time series data, they were transformed in logs and Cochrane-Orcutt procedure made to deal with autocorrelation. Since, this were time series data, a pretest for stationarity using the Augmenteg Dikey- FULLER test was done. A linear regression analysis was run with a dependent variable being quantity of imported rice in the country. The model Econometric estimation of import demand functions usually relate real imports (m) to real income (GDP) and the prices of the commodities(Deyak and Sawyer, 1988; and Carnoe, 1996).The implicit model was of the form; QM = f (Price of rice, Production of rice, population GDP per capita, time trend) In modeling the import demand function, the log-log linear model was preferable to the linear model. Therefore, the estimated empirical model had a log-log specification as follows. Ln(IMP) = β0 +β1lnricepro+ β2 lnripric+β3lnMaipro+ β4lnPopn+ β5lnGDP + β6time+ε Whereby (t) 264
Challenges in the Era of Globalization Edited by Emmanuel Obuah Lnimp is log of rice imports, lnripric is the log of domestic price of rice, lnMaipro is the quantity of maize produced domestically, lnPopn is the log of midyear population , lnGDP is log of GDP per capita time is the time trend , is an error term which is assumed to be normally distributed with a zero mean and a constant variance. Table 2: Independent Variables and Expected signs Variables Expected sign Real price of rice imported - Quantity of rice produced - Quantity of maize produced - Population + GDP representing real disposable income + Results and Discussions The regression results are presented on table 3. From the results, the adjusted R 2 is 0.946 which implies that variables included in the model explain 95% of variation in import demand. After correcting for autocorrelation, the D-W statistic of 2.17 was obtained implying that there is no problem. The regression estimates on own import price of rice, the population, consumption variables are statistically significant and carry the expected signs. However, the quantity of maize produced has a negative expected, but not statistically significant. The estimated coefficient on GDP per capita variable carries an unexpected negative sign and not statistically significant. The results show that a 1 % increase in import price of rice decreases the quantity of rice imports by 0.35% reduction in rice imports. This means that as the rice import price increases, it will have a positive impact on domestic rice production. Bamidele et al, (2010) found the same results for Nigeria. A 1 % increase in quantity of rice produced domestically will lead to decrease of 1.99%of domestic production. In terms of population, a 1% increase in population will lead to approximately 4.08% increase in rice imports. This implies that domestic rice production will have to be increased to meet the needs of the increasing population given that Uganda is experiencing one of the highest (3.2%) growth rate in Africa, if savings in foreign exchange have to be realized. Table 3: Estimated coefficients for rice import demand Variable Coefficients t-statistic P >t Constant -37.385 -7.94 0.000 Own import price -0.349 -2.07 0.046 Quantity of maize -0.028 -0.15 0.882 Quantity of rice produced -1.999 -7.49 0.000 Population 4.082 11.31 0.000 GDP per cap -0.024 -0.13 0.882 R 2 = 0.954 Adjusted R 2 = 0.946 Durbin- Watson 2.17 265
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Table of Contents Challenges in the
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Lennart Bangens, Goteborg, Sweden C
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Local Steering Committee Jacob Musi
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Abstract Challenges in the Era of G
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Abstract Determinants of Bank Lendi
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Table 3 Determinants of Competitive
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Abstract: Study on Africa Governmen
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Abstract Challenges of Oil Revenue
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Conclusion Challenges in the Era of
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Theoretical Framework Challenges in
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Single 18.1 Masters and above 35.3
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Source: Field survey, 2010 Table 3:
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Abstract Exploring Organisational R
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Defining the Organisational Culture
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Table 1: A Summary of Operating Ban
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Discussion and Conclusion Challenge
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Abstract What does Globalization me
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7. I see globalization as providing
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Research Methods approach Challenge
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Statement of the Problem Challenges
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Banks in Ghana Challenges in the Er
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References: Challenges in the Era o
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Relationship Commitment Total 1264.
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Conclusion Dissatisfied 6 3.4 3.6 7
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