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Top of FormBottom of Form | Journal of International Business and Economics, Dec 2011 v11 i4 p142(5) An analysis on the long-term effects of rural labor migration in China based on the Markov chain method. Huang Ningyang; Charles Corcoran; Gong Meng. Author's Abstract: COPYRIGHT 2011 International Academy of Business and Economics The migration of rural labor to non-agricultural sectors helps solve three agriculture issues in China: dated agrarian methods, an overpopulation of rural areas, and an excess of supply of farm labor. But a large population and fast economic development may impede the process. The proportion of rural labor is large and the labor force participation rate is high, while education levels are low. Further, the elasticity of employment is low, while economic growth is much faster than rural labor employment growth. Based on questionnaire data investigating rural labor markets, and utilizing the Markov Chain Method, a forecast is made of when the rural labor shift will stabilize, along with the three employment states' probabilities: (1) working and residing on the home farm, (2) leaving home but working near their hometown, and (3) leaving home and working far from their hometowns. It is projected that the rural labor shift will stabilize in 24 years. The probabilities of the three employment states mentioned above will be 5.9%, 45.4%, and 51.2%, respectively. These findings may contribute to policy decisions regarding city growth patterns, industrial development, and population migration. Keywords: rural labor stability, rural labor migration, labor force participation rate, employment elasticity, deviation of employment structure, Markov chain Full Text: COPYRIGHT 2011 International Academy of Business and Economics 1. INTRODUCTION With China's continued industrialization and modernization, rural workers are increasingly finding employment in urban areas. The Third Plenary Session of the Fourteen Central Committee noted that the migration of rural laborers to cities and towns should be encouraged (The Third Plenary Session, 1994). The Fifteenth National Congress stated that China's large rural population worked mostly in agricultural sectors that depended heavily on manual labor. in an increasingly industrial and modern economy. The Sixteenth National Congress further pointed out that it was an inevitable consequence of industrialization and modernization that surplus rural labor gravitates to non-agricultural industries in urban areas. The migration of rural labor into urban industrial settings continues to be an important source of economic growth during this period of economic reform and openness. However, the process of rural labor transfer will take a long period of time. It took approximately 80 to 100 years for the proportion of agricultural labor force to decline from 50% percent to 10% in Europe and the United States. In Japan, it took 38 years for the agricultural proportion to decline from 51.6% in 1947 to 8.8% by 1985. This is a 1.13% average annual reduction. In China, the transition will, likewise, occur over a protracted time period. This is due to a large numbers of rural laborers, an already high labor force participation rate, and the relatively low education level of rural laborers. With the passage of time, economic and societal developments transpire and a new order becomes the norm. Unsettled labor markets stabilize, as happened in developed countries. But when will labor market stability prevail in China? Using the Markov Chain forecasting method, Deng Dasong, et. al. (2008) built two models, forecasting when stability would occur in different industries and in different regions of China. His research result was that the main flow of rural labor remains to coastal areas and inner-provincial areas. Using data from Lin Qu Town, Lin Qu County, Wei Fang City, Shandong Province, and using the Markov Chain principle, Mi Rui Hong etc (2005) built two models to forecast the trend of rural labor migration in different industries as well as in different regions of developing areas. Her conclusion was that stability will occur in nineteen years in different industries and thirteen years in different regions. Wei Dan, et. al. (2008) built a Markov-based model forecasting rural labor stability in about twenty-one years. Three states of rural labor stability include: (1) remaining at home and working in farming; (2) leaving home but working near their hometowns; (3) leaving home and working far from their hometown. Based on these three outcomes, a questionnaire was designed. Using the forecast principle of the Markov chain and the data investigated from more than one-thousand questionnaires, a model was built to forecast the stability stage for these three scenarios. 2. ANALYSIS OF LONG-TERM RURAL LABOR MIGRATION The population of China is approximately 1.33 billion (2008). The rural population of 0.721 billion accounts for 54.2% of the total. (1) The population of employment in primary industry, secondary industry and tertiary industry were 0.307billion, o.211billion and 0.257billion, respectively. Stated in percentages, that's 39.6%, 27.2% and 33.2%, respectively. (2) Both the rural labor and its population were so large that it would be a long-term process for them to flow toward non-agricultural industries or cities and towns in China. The labor force is defined as follows; (1) at least 16-years old; (2) physically capable; and (3) seeking employment opportunities. The labor force participation rate refers to the percent of working age people in the labor force. On the basis of data from the International Labor Organization, the Chinese labor force was 780 million in 2006, accounting for 25.4% of global employment. The labor force participation rate was 81.7%, 11.76% higher than the world average level of 71.0%. (3) Employment pressure was increased because the labor participation rate was higher than that of low-income, medium-income and high-income countries. The Chinese labor market has experienced a mismatch between labor pool skills and labor force needs. The education level of the labor force has been low relative to that of key trade partners. The education level was far below developed countries, as well other BRIC developing countries, Brazil, India, and Russia. (4) Employment elasticity is the relation between the economic growth and employment levels. It refers to the rate of employment growth to the rate of economic growth. The employment elasticity in China is averaging about 0.1, while the developed countries it is about 0.3 to 0.4. (5) Usually, employment elasticity shows a gradually diminishing trend when economic growth has matured. The reduction in employment elasticity suggests fewer labor inputs are necessary to produce a given production output. (6) The employment elasticity of primary industry tends to diminish and become negative. This may suggest the agricultural sector is poised for diminished employment opportunities. Employment elasticity of both secondary and tertiary industries is unstable. The tertiary sector of the economy (also known as the service sector or the service industry) is one of the three economic sectors, the others being the secondary sector (approximately the same as manufacturing) and the primary sector (agriculture, fishing, and extraction such as mining). Tertiary industry employment elasticity is relatively high, supporting higher employment levels. This trend is consistent with many countries. Deviation of employment structure is the employment proportion of industry, less the proportion of output value of industry. It is an important standard of measuring the degree of fit between industrial structure and employment structure. Generally speaking, the relationship between deviation of employment structure and productivity is an inverse ratio. When deviation of employment structure is above zero (positive deviation), the proportion of employment is larger than the proportion of output value, and this implies that the productivity of the industry is very low. (7) Contrarily, negative deviation implies that productivity of industry is high. There is emigration potential in a positive deviation industry, while there is immigration potential in a negative deviation industry. If every industry was open without barriers to entry, and there was complete competition, the marketplace would be the final arbiter of determining human resource allocation. Deviation of employment structure would gradually trend to zero. (8) Deviation of employment structure of primary industry is more than zero, implying that a large amount of labor would emigrate from primary industry. (9) Deviation of employment structure of secondary and tertiary industries was negative. Asymmetry of the output value structure and the employment structure imply that there was a protracted term for Chinese rural labor transfer. Economic growth has exceeded employment growth. According to the data from China statistics yearbooks, before 1994 economic growth increased at a high level, while rural labor employment grew by about 2%. The national employment growth rate was about 4%. After 1994, economic growth declined dramatically and trended to stabilization. Since 1994, economic growth grew annually by approximately 9%, and the growth of secondary industry, including manufacturing and construction, exceeded 10%. Tertiary industry grew by 11%. Average annual employment growth, however, was only 4%. (10) At the same time, the rural employment growth rate declined to zero. In fact, rural employment contracted between 2002 and 2007. (18) The lack of job opportunities was a key factor to affect the rural labor migration to urban centers. It will take a long time to complete the labor shift from rural areas to urban centers. The proportion of rural labor is large and the labor participation rate is very high, while the education level of the population is low. Moreover, elasticity of employment is low, while the deviation of employment structure is large. Economic growth in urban and coastal regions well exceeds employment growth levels for rural laborers. These factors suggest a protracted period of adjustment in Chinese labor markets. 3. METHODOLOGY A questionnaire identifying the current state of rural laborers was created. The three states include "at home working on the farm," at home but working elsewhere," and "living and working far from home. Fourth, the time of three situations was half one year or above. Rural laborers from six largely rural provinces were queried--Anhui, Hubei, Henan, Jiangxi, Sichuan and Hunan. The questionnaire was distributed to 1,500 workers, 1,280 were returned, and 1,194 were usable. See outcomes in Table 1. There were nine investigation indicators in the questionnaire. They were as follows: (1) engaging in agriculture both in 2007 and 2008; (2) engaging in agriculture in 2007, but working near their hometowns in 2008; (3) engaging in agriculture in 2007, but living and working far from their hometowns in 2008; (4) working near their hometowns in 2007, but being at home and engaging in agriculture in 2008; (5) working near their hometowns both in 2007 and 2008; (6) working near their hometowns in 2007, but living and working far from their hometowns in 2008; (7) living and working far from their hometowns in 2007, but engaging in agriculture in 2008; (8) living and working far from their hometowns in 2007, but working near their hometowns in 2008; (9) living and working far from their hometowns both in 2007 and 2008. See Table 3 for the probability of different situations. Three conditions are specified--at home and engaging in agriculture, at home and working nearby their hometowns, and living and working far from their hometowns. These conditions have an a-periodic nature, and they fit the no-cyclicity and ergodic property of the Markov chain. (11) The probability limit according to the Markov ergodic property is: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] From the table 4, a probability matrix, including the nine outcomes of rural labor, is created. It is denoted by P(1), [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] A probability matrix P(2) may be created, according to P(1). P(2) is as follows, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] All the elements in P(2) are more than zero. A linear equation is then calculated, from which the limit distribution of rural labor stability is predicted, pi.. [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] The limit probability was solved using Matlab7.0. Result is as follows: II = (0.0589, 0.4541, 0.5118). The limit distribution probability is, respectively: [P.sub.1]=0.0587; [P.sub.1]=0.4403; [P.sub.3]=0.5010. They are Markov chain limit probabilities of the three states, respectively, when they are stable. Pj is not dependent on initial state. This indicates, [for all] [epsilon] > 0, [there exists] N, when n > N, always have [P.sub.ij.sup.(n) [member of] ([[pi].sub.j] - [epsilon], [[pi].sub.j] + [epsilon]). When n is equal to 24 (years), the limit probability is [P.sub.1]=0.0589; [P.sub.2]=0.4541; [P.sub.3]=0.5118, respectively. Term to stability is 24, and the error, [epsilon], is smaller (0.01) than that of the Markov chain limit probability. 4. CONCLUSION Given initial conditions of living at home and engaging in agriculture, or working near their hometowns, or living and working far from their hometowns, large scale labor migration to employment opportunities will tend stabilize in 24 years in rural China. The probability of the three states, including living at home and engaging in agriculture, or working near their hometowns, or living and working far from their hometowns, is 5.89%, 45.41% and 51.18%, respectively. It remains popular for rural laborers to venture far from their hometowns. This phenomenon will continue in the future, as the population engaged in agriculture continues to diminish. REFERENCES: | |

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