Abstract: This paper uses age-related productivity gaps to analyze the Lewis turning point in China. The age-related productivity gap implies that under same wage rate, older and less productive rural laborers working in cities will earn less than the reservation wage. Thus, they may elect instead to return to the countryside. Therefore, this paper argues that while the supply of younger, high-productivity migrant workers fails demand and Lewis turning point emerges, there still exists a high volume of lower-productivity rural surplus labor.
Key words: Lewis turning point, productivity, age difference, migrant workers
JEL: J61, J64, O15
1. Introduction
Since 2004, shortages of rural migrant workers (hereafter referred to as “migrant workers”) have become a regular phenomenon in the Chinese economy. These shortages do not merely imply that, regardless of how high wages rise, employers cannot find labor; rather, they occur under current working, living, and wage conditions.
After the first eruption of such shortages, the public first pointed fingers at employers for underpaying migrant workers. Real wages, however, did not change substantially between the 1990s and 2004. Why, then, did the first widespread shortage of migrant workers take place in 2004? In 2007, Cai Fang attempted to explain this phenomenon using the Lewis turning point. According to Cai Fang’s explanation, in most developing countries there is a surplus of agricultural labor, and the shift from an unlimited supply of migrant labor to a shortage of urban labor is referred to as the Lewis turning point. As an economy approaches the Lewis turning point, given that the marginal product of labor in agricultural industries is zero, removing labor from agriculture will not reduce output (Cai Fang, 2010).
Other scholars define the Lewis turning point differently. In addition to the first Lewis turning point―to which Cai Fang refers--there is a second, later Lewis point at which marginal productivity of an economy’s non-capitalist, agricultural sector equals marginal productivity of its capitalist, non-agricultural sector (Liu Wei, 2008; Zhao Xianzhou, 2010). Cai Fang himself does not disagree with this definition but stresses, however, that the arrival of the first Lewis turning point causes real wage levels in non-agricultural sectors to rise. Hence, when Cai references the Lewis turning point in his research, he refers exclusively to the first Lewis turning point (Cai Fang, 2010). Liu Wei (2008) also stated that Cai Fang and other scholars who performed similar research were, in fact, studying the first Lewis turning point. Considering the ambiguity surrounding the definition of the Lewis turning point in China it is worth noting that when this paper discusses the Lewis turning point, it references the first Lewis turning point.
According to Cai, there are at least two indicators that China’s economy has reached the Lewis turning point: first, there is a universal shortage of labor in cities and in the countryside; second, rising wage levels for migrant workers have caused labor costs to rise (Cai Fang, 2007c). Cai and others argue that actual wages for migrant workers have been surging over the past few years: from 2002 to 2008, actual wages rose annually by 2.3 percent, 5.6 percent, 7.6 percent, 8.6 percent, 8.2 percent, 7.2 percent and 19.6 percent, respectively (Cai Fang, 2010). The only explanation for these increases must be the undersupply of migrant laborers and the arrival of the Lewis turning point.
Other scholars, however, disagree. According to their research, China’s countryside still harbors hundreds of millions of surplus laborers (Sun Ziduo, 2008; Liu Wei, 2008; Zhang Zongping, 2008; Jia Xianwen, et al., 2010; Zhao Xianzhou, 2010). This implies that China will not face a universal shortage of migrant workers and that the Lewis turning point is still years away. In short, factors other than a shrinking labor pool have caused wage levels to rise in recent years.
Cai, likewise, had difficulty explaining the phenomenon of rural surplus labor, admitted that at the onset of the labor shortages in 2004, the countryside still held more than 100 million surplus laborers (Cai Fang, 2007a).
How, then, can one explain the coexistence of labor shortages in urban areas and a labor surplus in rural areas? This question has been raised by both domestic and international scholars who study the Chinese economy (Knight et al., 2010).
Different explanations have been offered regarding this puzzle. Cai argued that China is a large country and that “regional differences in resource endowment and industrial structure ultimately led to heterogeneousness of development across regions” (Cai, 2010). According to Knight et al. (2010), institutional constraints in the areas of employment, housing, and public services have prevented migrant workers from bringing family to live with them in cities. Furthermore, a major reason why certain migrants cannot work in cities is that they are either too old, cannot find jobs suited to their age and skills, or are obliged to take care of aging parents and young children. Finally, according to research from Analysis and Forecast of China’s Macroeconomic Situation conducted by Renmin University (2010), workers who migrate to cities but leave family in rural areas cannot assist with farm work, take care of aging parents, or raise children. If workers continue to migrate from rural areas to urban ones, many rural households would have no labor left at home. The industrial sector must compensate for such a loss of family utility with an incentive of higher wage levels. Therefore, despite the existence of surplus labor in the countryside, labor supply no longer has a perfect elasticity due to the urgency of needs at home.
I attempt to explain the coexistence of the shortage and surplus of rural labor in China from another perspective. I consider that there is some oversight regarding surplus labor in the countryside, i.e. the presumption that productivity of migrant workers is not differentiated, so that migrant workers, regardless of age and gender, are perfectly interchangeable. Much of my previous papers has touched upon differentiated productivity of migrant workers in labor-intensive sectors, which can be summarized as follows: most migrants are employed in labor-intensive industries--that is, simple and repetitive work that requires little skill is easy to learn. Such jobs, however, require higher levels of physical strength, agility, and/or fine motor movement, and as a result, young migrant workers have higher productivity. As non-skilled migrants enter into middle age, these physical traits decline, causing productivity to decrease gradually. Given that these jobs are usually paid based on the quantity produced, i.e. at a piece rate, declining productivity finds expression in a declining wage for aging migrants (Zhang Zheng et al., 2005; 2007; 2008). Hence, in the same vein as the “heterogeneity” discussed by Cai Fang, we should also examine how age differences among migrant workers influence productivity. As for the three primary reasons why rural surplus labor is unable to move to cities as mentioned by Knight et al. (2010), the first cause (too old) and the second cause (cannot find job in cities) can be explained fully and partially, respectively, by age-related productivity gaps.
Part II of this paper will examine the impact of age difference on productivity and the Lewis turning point using mathematical economics. This section aims to explain how a large surplus of rural labor can coexist with rising actual wages. Part III proves the existence of an age-related productivity gap using indirect evidence. Part IV uses demographic data to explain the emerging undersupply of high-productivity, young migrant workers. Finally, Part V offers conclusions.
2. The Age-Related Productivity Gap and the Lewis Turning Point
The impact of the age-related productivity gap on the Lewis turning point can be explained using mathematical economics.
Rural surplus labor is divided into three age groups, with group 1 representing the youngest labor demographic and group 3 representing the oldest. Quantity of rural surplus labor among the three age groups is expressed as Q1, Q2 and Q3.
We assume the same productivity for each age group at the time of migration. Average productivity for each group is expressed as AP1, AP2 and AP3. Because productivity drops with age, AP1>AP2>AP3.
Reservation wage refers to the minimum wage level acceptable to an employee for a given job. In order to simplify, we presume that reservation wage includes only essential, everyday spending for migrant workers working outside of the home. We also presume that at the time of migration, rural laborers are willing to accept the same reservation wage, denoted as RW.
Urban employers’ demand for labor is denoted as QD. The piece wage rate is denoted as wr.
When QDAP2>AP3, income for the second and third age groups is lower than reservation wage. In other words, these workers will not earn sufficient income to cover living expenses. Hence, despite being part of the surplus rural labor pool, these two age groups will not enter the market under the standard wr1 piece wage. The so-called “unlimited” supply of rural labor refers only to the highest-productivity workers in the first age group and of quantity Q1.
As the economy develops, demand for migrant labor QD exceeds Q1. Assuming piece wage wr1 is kept unchanged there will be a shortage of the highest-productivity workers in the youngest age group. Additionally, workers in age groups 2 and 3 cannot migrate due to an insufficient piece rate wage. As a result, while large numbers of surplus laborers exist in rural areas, there appears to be a labor shortage in cities.
Under such conditions, urban employers must begin to consider attracting surplus labor from the second age group. Compensation, in turn, must reach reservation wage. Hence, urban employers are forced to increase piece wage from wr1 to , up
.
The increase in piece wage from wr1 to wr2 affects each age group differently. For young migrants in the first age group, urban income rises from reservation wage RW to . They now receive higher pay for the same work. For the second age group, on the other hand, urban wages reach RW, which enables them to migrate to cities for work. Finally, for the third group, income becomes , still an insufficient inventive to leave rural areas and migrate to cities for work.
The above analysis suggests that the presence of an age-related productivity gap has a major impact on the emergence of the Lewis turning point. First, the Lewis turning point is not necessarily preceded by the exhaustion of rural labor whose marginal agricultural productivity is zero. Instead, the Lewis turning point may emerge after the exhaustion of rural labor with high productivity in non-agricultural sectors. Second, while the Lewis turning point will cause real wages to increase, its appearance does not imply the exhaustion of rural surplus labor with low non-agricultural productivity. The Lewis turning point and rising real wages can coexist with rural surplus labor.
3. Does An Age-Related Productivity Gap Really Exist in China?
The existence of an age-related productivity gap in China can be discerned from the following indicators:
First, the relationship between age and migrant worker income shows that migrant wages decrease with age after middle age. Two examples follow:
Table 1 shows selected results of a survey of non-local Dongguan residents (mainly migrants) conducted in July 2009 by the Social Development Research Institute at the Dongguan University of Technology (in collaboration with the Dongguan Academy of Social Sciences). The survey distributed 600 questionnaires and collected 586 valid ones1. Results showed that the average monthly income of male migrant workers over 46 in Dongguan is 25.2 percent lower than the average monthly income of migrant workers between 26 and 35. Likewise, average monthly income of female migrant workers over 46 is 18.3 percent lower than their 26-35 year-old counterparts.
Another example comes from research conducted by Lai Fulin in 2009. Lai conducted a survey of migrants in the Yangtze River Delta in May 2006 and in July 2007. The May 2006 survey distributed more than 2,000 questionnaires in five cities in the Yangtze River Delta and collected almost 1,000 valid questionnaires. The July 2007 survey surveyed migrants in eight additional cities and collected 1,521 valid questionnaires. Lai concludes: “Migrant worker wages increase with age up to middle age; after middle age, however, income declines with age. The relationship between income and age is in an inverted V-shape”. In short, wages peak around age 33.
An age-related productivity gap, however, may not fully explain these findings. Other scholars have attempted to measure the impact of age on income for migrants after excluding other factors2.
Liu Linping et al. (2007) surveyed migrant workers in the Pearl River Delta in July and August of 2006, distributing 3,100 questionnaires and collecting 3,086 valid questionnaires. Excluding the impact of education and work experience (through total working years and years spent at the current employer), Liu found that “age has a significant negative effect” on migrant workers’ wages.
Scholars have also observed that industries such as apparel, leatherworking (including shoes), and toy manufacturing are all labor-intensive manufacturing3 industries where private and foreign-funded companies enjoy significant advantages over state-owned enterprises. How does one explain such an advantage?
Table 2 shows that for the above three sectors (apparel, leather manufacturing, and toy manufacturing), foreign-funded companies do not have higher proportions of engineering technicians than state-owned enterprises, nor do they have a higher percentage of employees with higher education levels (e.g. junior college and above, vocational school, and technical and high schools). Foreign-funded companies, however, do show a higher proportion of young employees: employees ages 35 and below account for between 59.4 percent and 62.6 percent of employees at SOEs, while they account for between 83.4 percent and 89.6 percent of employees at foreign-funded firms, a 20-30 percent gap. Zhang Zheng et al. (2005) also estimated the composition of employees in the three labor-intensive manufacturing sectors shown in Table. For SOEs in 1995, the number of middle-aged employees (aged 36 and above) per 100 employees under 35 were: 59.7 for the apparel industry; 68.4 for leather (shoe) manufacturing; and 64.2 for toy manufacturing. For foreign-funded companies, these figures were: 19.9, 11.6, and 14.4, respectively. In other words, assuming that both types of companies hire the same number of young employees, foreign-funded companies would have 66-83 percent fewer middle-aged employees than SOEs. Likewise, assuming both types of companies hire the same number of middle-aged employees, foreign-funded companies would have three to six times the number of young employees that an SOE has. Unlike SOEs, foreign-funded companies depend on maximizing profits to survive. The high proportion of young workers is explained by their higher productivity.
We should note that the figures from Table 2 are from the Third National Industrial Census, conducted in1995. At that time, state-owned enterprises had not yet conducted the massive rounds of layoffs that primed them to compete in the global market; most SOE employees enjoyed the “iron rice bowl,” guaranteed life-long employment. This promise of job stability meant that SOEs had a competitive disadvantage, especially compared to private companies whose flexible employment structures allowed them to hire more young workers. In order to compete with foreign-funded and private companies, SOEs and collective enterprises sacrificed job stability (at least in labor-intensive sectors), replacing less productive middle-aged workers with more productive young workers. According to Huang Yasheng (2010), the collapse of SOEs in the 1990s and the corresponding job loss for workers aged 40 to 60 is partially the result of an influx of young, migrant workers. I personally agree with this view.
It should be noted that the above data constitute indirect measures of an age-based productivity gap. Additional research should be done to obtain productivity statistics for employees of different ages across industries.
4. Are Young Migrant Workers Currently in Short Supply?
Part II argued that China may experience the Lewis turning point when it exhausts the supply of the youngest, most productive migrant workers. In other words, rising wages will go hand in hand with less productive rural surplus labor. This begs the question: are young migrant workers currently in short supply?
In order to use existing data to analyze supply and demand of migrant workers, we must first deal with three statistical methodological issues.
(1) “Resident” and “Hukou” population
According to The Second China National Agricultural Census Information Collection: Farmers, rural population and labor statistics for “resident”(changzhu, in Chinese) and “Hukou” populations are defined separately, yet there is only one statistical approach―using the “Hukou” population--for “rural migrant workers” or “non-local employment of rural labor”.
In order to give a more precise account of how many people live and work in China’s rural areas, national statistical authorities study the resident population, rather than the population of those who have completed an official household registration. Because, however, there is no resident-based data for rural migrant workers, national statistical authorities can only release the number of rural migrant workers who are registered with the official household registration system. For instance, Communique of Major Statistics for the Second National Agricultural Census (No.5) did not explain the statistical approach for rural labor in 2006. Upon comparison with the Second China National Agricultural Census Information Collection: Farmers, we found that total number of rural labor resources and rural employees in the statistics of National Bureau of Statistics were based on the “resident” approach, yet “rural migrant workers” were based on the “Hukou” approach.
These different statistical approaches present huge disparities in the statistics. In 2006, China’s rural “Hukou labor resources” reached 608.64 million, which is 77.64 million more than “resident labor resources”. Likewise, rural “Hukou employees” reached 555.11 million, which is 76.59 million more than “resident employees”4. Subtracting the number of resident “rural employees” from the number of Hukou “rural migrant workers in China” greatly underestimates the quantity of rural surplus labor.
Hence, rural labor statistics must be adapted from the “resident” to “Hukou” approach in order to analyze and forecast of supply and demand of rural workers, including rural migrant workers.
(2) “Labor resources” and “employees”
In this paper, “non-agriculture going-out ratio” is used to mean the ratio of rural employees who move to a non-agriculture sector. This paper attempts to calculate the non-agriculture going-out ratio for different age groups of rural employees in 2010. If statistics for rural employees and rural workers were available by age for the year 2010, this ratio could be calculated directly. However, rural population data from 2006 are currently available. Demographic analysis can be used to estimate population statistics for 2010 using population and mortality data from 2006 broken down by age. The result, however, is only the total number of rural labor resources by age group. Hence, in estimating the supply and demand of rural workers, labor resources must be converted to the number of employees according to the share of rural employees of different age groups in total labor resources of corresponding age groups.
(3) “Rural employees,” “rural workers” and “rural migrant workers”
In studies of rural migrant workers, there is an implicit presumption that 100 percent of rural employees will move to non-agricultural sectors. Statistics show, however, that even under shortage conditions, no more than 80 percent of young rural employees will move to non-agricultural sectors. For instance, in 2004 the highest going-out ratio for Guangdong province was 75.6 percent for 18-25 year-olds, and the second highest was 60.0 percent for 26-35 year-olds (Huang Dan, 2005). In addition, the highest going-out ratio for Hubei province of 2007 was 78.11 percent (18-25); the second highest was 75.14 percent (21-25); and the third highest was 68.17 percent5 (26-30). It is unrealistic to assume that 100 percent of young rural labor will become migrant workers.
Why is 20 percent to 30 percent of young rural labor unable to move out of the agricultural sector? I would suggest the following reasons: first, as mentioned by Economic Situation Analysis Project Team of the State Council Development Research Center (2010), there are a considerable number of women (including young women) in rural areas whose family responsibilities keep them from migrating to cities, e.g. childbirth, child rearing, child education, and parental care. In rural areas, limited natural resources or underdevelopment make suitable non-agricultural industries close to home unlikely. For women in rural areas, farm work becomes the only choice. For this reason, the number of female migrant workers declines after entering marriage and child-rearing age.
In Guangdong province, for instance, where female migrant workers account for a relatively high share of young migrant workers, the sex ratio (number of males per 100 females) in 2000 was 50.78 percent for migrant workers aged 15-19; 78.87 percent for workers aged 20-24; 117.41 percent for workers aged 25-29; and 141.98 percent for workers aged 30-34 (Guangdong Demographic Census Office, 2005). Unless whole families are relocated to urban areas, the share of women among migrant workers declines rapidly with age. Women alone do not account for a going-out ratio below 100, however.
Second as mentioned by the Economic Situation Analysis Project of the State Council Development Research Center, the larger pool of young rural laborers also includes ethnic minority laborers who cannot become migrant workers and “workers suffering chronic diseases or handicapped persons and disabled persons.”
Third, according to the definition of migrant workers used in the Report on the Monitoring of Rural Workers in 2010, rural workers include only “rural migrant workers who work outside their hometowns for more than six months a year and local rural workers who work in non-agriculture sectors for more than six months in a year”. Workers who work outside their hometowns or in non-agricultural sectors fewer than six months a year are not defined as rural migrants.
Fourth, rural labor resources may also include those who have homes in suburban areas and coastal regions (especially the Pearl River Delta), earn high incomes from property rents, and thus willingly give up employment but claim to be “unable to find suitable jobs”. Hence, when statistics show that more than 70 percent of rural employees in an age group become rural migrant workers, it seems plausible that available labor resources have already been depleted.
It should also be noted that not all non-agricultural workers in rural area choose to become migrant workers, with some choosing local employment. According to the author’s statistics, even under labor shortage conditions, no more than 2/3 of young rural employees opt to seek non-agriculture work outside their hometowns. For instance, in 2004 Chongqing’s highest going-out ratio was 66.6 percent (21 to 30 year-olds), and the second highest was 55.4 percent (16 to 20 year-olds) (Liu Qiyi et al., 2005). In the same year in Jiangxi province the highest going-out ratio was 66.3 percent (21 to 25 year-olds); the second highest going-out ratio was 65.8 percent (under 20); and the third highest going-out ratio was 56.9 percent (26 to 30 year-olds) (Liu Wenfeng, et al., 2005).
With the above three statistical questions resolved, we conducted an analysis on the supply and demand of rural workers in 2010 using the approach below:
First, we assume that the actual number of rural workers (including rural migrant workers and local rural workers) equaled demand. The composition of rural workers may be obtained from the Report on the Monitoring of Rural Migrants in 2010, published by National Bureau of Statistics. In this report, the populations of rural workers by age group are calculated with two different approaches, that is, first, direct calculation, and second, by-group calculation and sum. As Table 3 show, there is an error of 1 percent to 8 percent between figures with two approaches. Please refer to Table 3 for specific numbers.
Second, we calculated supply of rural workers using population and mortality statistics for all age groups among China’s rural population. These, in turn, were obtained from sample demographic data in the China Population and Employment Statistics Yearbook 20076. Zhang Zheng et al. (2008) estimated rural labor resources by age group between 2007 and 2022 (resident population)7. Using this same methodology, we estimated the rural Hukou population by age using Hukou and resident population data from the Second China National Agricultural Census Information Collection: Farmers8. All data was from 2006.
The number of rural employees by age group for the year 2010 was then calculated using the demographic method and following the statistical approach of Hukou population (calculated, as previously stated, from rural labor resources and rural employees among the Hukou population9 released in 2006 from the Second China National Agricultural Census Information Collection: Farmers). Given the rapid development of intermediate vocational education (high school education) opportunities for the rural Hukou population between 2006 and 201010, rural employees statistics for the 16-20 year-old age group in 2010 is higher than it should be, and the non-agriculture conversion rate is lower than in reality. Numbers for other age groups are more reliable.
Our results are as follows:
First, in 2010, there were 118.12 million rural employees between the ages of 21 and 30, based on the Hukou population. According to Table 3, the non-agriculture conversion rate for this age group was 73.6 percent for direct calculation and 77.8 percent for aggregation after calculation by group. Additionally, the non-local, non-agriculture conversion rate for this age group was 64.4 percent. These figures suggest that for the year 2010, no more labor of this age group were available to cities.
Moreover, the number of rural workers between ages 16 and 20 was approximately 15.18 to 15.74 million. If we consider this age group as part of the high-productivity, young rural worker demographic, we may conclude that the countryside had no more high-productivity labor in this age group available to supply in 2010.
Third, the number of rural employees between ages 31 and 40 (using Hukou population data) was 122.82 million in 2010. According to the number of rural workers provided in Table 3, the non-agriculture conversion rate of this age group was 46.3 percent when calculated directly and 46.7 percent when aggregated after calculation by group. The non-local non-agriculture conversion rate for this age group was 29.3 percent. If we consider the difference in family conditions for rural employees in different age groups (a higher proportion of rural employees between the ages of 31 and 40 are married, and they generally have more dependents), and if we adjust the non-agriculture conversion rate for this age group to 65 percent and the non-local non-agriculture conversion rate to 50 percent, we find that there are still rural laborers in this group who have the potential to become rural workers or rural migrant workers.
Fourth, we assume that after a shortage of rural workers appeared, labor-intensive businesses were forced to recruit older employees. We also assume that rural labor resources aged 31 to 40 who became rural workers or rural migrant workers were depleted gradually, with young labor depleting first. We also assume that the non-agriculture conversion rate for rural employees of a certain depletion age (such as age 31 to 32) to be 65 percent and that the non-local non-agriculture transfer rate is 50 percent. Non-agriculture conversion rate or non-local non-agriculture conversion rate for rural employees above depletion age (such as the 33 to 40 age group) is calculated using the average rate for those between the ages of 41 and 50. Then, using the non-agriculture conversion rate, the depletion age for rural employees is 32.3 years of age (using direct calculation figures from Table 3) or 32.8 years of age (using figures aggregated by group from Table 3). If we use the non-local non-agriculture conversion rate, the depletion age for rural employees is 34.6 years. Given that the shortage of rural workers appears primarily in regions where most of the labor force is not local, we argue that 34.6 years is a more precise figure.
Using the above methodology, we calculated the number of rural employees (using officially Hukou population statistics) to be 551.37 million in 2010. Subtracting 242.23 million rural migrant workers and 180 million rural laborers from these figures gives us 130 million rural surplus laborers. This means that the countryside still has a large number of surplus labors. At the same time, as far as supply and demand is concerned, the most productive labor resources below the age of 35 were almost depleted in 2010. Thus, we see how rural surplus labor and a shortage of migrant workers coexist in the Chinese economy.
5. Conclusion
This paper suggests that given the existence of an age-related productivity gap, rural workers of different age groups may not represent a homogenous labor force. When more productive, young rural labor resources are near depleted, China will face the Lewis turning point and real wages for migrant workers will rise despite the existence of less productive rural middle-aged surplus labor. The question is, for various labor-intensive sectors, which is the first age group to experience declining productivity?
While this question must be studied further in order to determine how productivity declines in different industrial sectors, according to current statistics, productivity in labor-intensive manufacturing businesses declines rapidly after age 35. If the shortage of female migrant workers over the age of 20 is ascribed to their family utility hypothesis proposed by the China Macroeconomic Situation Analysis and Forecast Project Team, the age-related productivity gap hypothesis offers the most explanatory power. In other words, the age-related productivity gap is the most important cause of the emergence of the Lewis turning point in China.
When he first proposed the theory of the Lewis turning point, Lewis did not consider worker age. Scholars who do not believe that China is facing the end of the Lewis turning point thus use overall working age population statistics to argue that China still has surplus labor. They argue, furthermore, that the shortage of migrant workers under 35-40 years old neither indicates an overall shortage of labor nor indicates the end of China’s Lewis turning point (Zhang Zongping, 2008).
However, the question is that analyses by Lewis and other foreign scholars are based on the precondition of permanent relocation of rural labor and their family to cities. In their view, the migration of labor from farms to cities is a “historic migration”, while the reverse migration from cities to the countryside (such as what happened in the United States during the Great Depression of 1932) is an exceptional phenomenon against this historic migration (Todaro, 1969).
China’s Lewis turning point, on the other hand, appeared against a backdrop of young rural labor entering cities while middle-aged rural labor returned to the rural areas to engage in agriculture. In foreign countries, because permanent relocation from countryside to cities is the norm, the low productivity of middle-aged labor is taken into account when determining wage rates. In China, the age-related productivity gap is the very cause of rural labor working in cities only when they are young. For this reason, the age-related productivity gap cannot be overlooked when analyzing the Lewis turning point in China.
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