China’s Labor Market in the “New Normal”

Contributor Notes

Authors’ E-Mail Addresses: wlam@imf.org; xliu3@imf.org; aschipke@imf.org

As China implements reforms under the “new normal,” maintaining stability in the labor market is a priority. The country’s demography and labor dynamics are changing, after benefitting in past decades from ample cheap labor. So far, the labor market appears to be resilient, even as growth slows, driven in part by expansion of the services sector. Migrant flows and possible labor hoarding in overcapacity sectors may also help explain this. Yet, while the latter two factors help serve as shock absorbers— contributing to labor market stability in the short term—if they persist, they may delay the needed adjustment process, contributing to an inefficient allocation of resources and curtailing productivity gains. This paper quantifies to what extent structural trends and the reform pace affect employment growth under the new normal. Delays in reform implementation would weaken growth prospects in the medium term, running the risk that job creation will fall below policy targets, leading to labor market pressures in the future. In contrast, successful transition might require faster reforms, including in the overcapacity and state-owned enterprise sectors, supported by well targeted social safety nets.

Abstract

As China implements reforms under the “new normal,” maintaining stability in the labor market is a priority. The country’s demography and labor dynamics are changing, after benefitting in past decades from ample cheap labor. So far, the labor market appears to be resilient, even as growth slows, driven in part by expansion of the services sector. Migrant flows and possible labor hoarding in overcapacity sectors may also help explain this. Yet, while the latter two factors help serve as shock absorbers— contributing to labor market stability in the short term—if they persist, they may delay the needed adjustment process, contributing to an inefficient allocation of resources and curtailing productivity gains. This paper quantifies to what extent structural trends and the reform pace affect employment growth under the new normal. Delays in reform implementation would weaken growth prospects in the medium term, running the risk that job creation will fall below policy targets, leading to labor market pressures in the future. In contrast, successful transition might require faster reforms, including in the overcapacity and state-owned enterprise sectors, supported by well targeted social safety nets.

I. Introduction

China has embarked on the comprehensive, third-plenum reform blueprint. Its objective is to move toward more inclusive and sustainable growth through better allocation of credit and resources and improved social welfare. In this context, stable labor markets are a priority. The National People’s Congress 2015 work report highlighted that China has begun transition toward a “new normal” as economic reforms progress. Under it, priority is on maintaining stable growth and ensuring ample employment while pursuing reforms (State Council, 2015).

Labor market conditions appear to be holding up well, despite slower growth. Newly created urban jobs have exceeded official targets by a significant margin, while the registered unemployment rate remains stable at about 4 percent.2 Average wages have grown in line with nominal GDP, and the urban–rural income gap has not widened. High-frequency purchasing managers’ indices (PMIs) on employment have softened somewhat, but the labor market remains resilient overall.

Structural trends—in addition to unique buffers from migrant flows and labor hoarding in state-owned enterprises (SOE)—tend to support labor market resilience, despite slowing growth. China is at a demographic turning point, part of which includes a decline in surplus rural labor, which could dampen the negative pressures on employment as economic growth slows. At the same time, an expansion of the more labor-intensive services sector is generating more jobs. Unique features in China’s labor market—such as migrant flows and the employment of excess labor among SOEs and overcapacity sectors—also buffer employment against adverse shocks. However, even though this labor hoarding by SOEs may mitigate negative impact on employment as the economy slows, prolonged reliance on it could reduce labor flexibility, leading to its inefficient allocation, limiting productivity gains.

Migrant flows are key to understanding China’s labor market conditions. The number of migrant workers is significant, at about 270 million in 2013, or a third of the total labor force (Meng, 2012) and half of urban employment. These migrant flows are closely related to GDP growth and better reflect short-term dynamics in labor markets than do unemployment rates. Our estimates further suggest that the urban-rural income gap and economic growth are key determinants of flows. However, hukou restrictions and the lack of social services for migrants could weaken long-term labor market flexibility.

Empirical analysis suggests that the long-term resilience of labor markets hinges on the progress of reform implementation. A scenario analysis to quantify the effects on employment of reforms across sectors finds that delays in their implementation could cause further distortions, which would weaken medium-term employment prospects. It demonstrates that new employment levels risk falling below the current official job target. In contrast, faster reforms in overcapacity sectors and SOEs may, in the near term, release excess labor and push up the interim unemployment rate by ½–¾ percentage point, but facilitate structural transition—such as urbanization and services sector expansion—to more sustainable growth and job creation in the medium term.

The key policy implication of this analysis is that stronger labor market flexibility will facilitate China’s economic transition to the new normal. First, labor market stability during economic restructuring can be achieved more effectively with policies that foster the reallocation of surplus labor through effective, on-budget social policies. This is rather than by relying solely on inherent buffers against cyclical shocks (such as the employment of excess labor among SOEs noted earlier). Third, steadfast implementation of reforms will facilitate migrant flows and structural trends, which in turn will help generate jobs and urban employment in the medium term. This includes opening up the services sector and reforming hukou regulations to enhance labor market flexibility (Whalley and Zhang, 2007). At the same time, fiscal reforms on taxation, pension portability, and higher social spending will help narrow the urban–rural income gap (Lam and Wingender, 2015). Finally, broadening the coverage and timeliness of data, especially related to migrant flows, will facilitate policy design and assessment.

The paper is structured as follows. Section II discusses recent labor market developments, and Section III helps explain why labor markets have been resilient, despite slower growth, in light of migrant flows and some signs of labor hoarding in SOEs and overcapacity sectors. Section IV discusses the recent development of migrant flows and analyzes the key determinants of the movement of migrant workers across provinces. Section V uses a scenario analysis to quantify the effects on labor markets when China implements reforms and transits to the new normal. Section VI discusses the policy implications and Section VII concludes.

II. Labor Market Developments

Until recently, labor market conditions appeared resilient, despite slower growth (Figure 1).

  • Employment is holding up well. Newly created urban jobs reached 13.6 million in 2014, exceeding the official target of 10 million.3 New jobs reached 3.2 million in the first quarter of 2015, slightly lower than 2014:Q1, but still estimated to exceed the target this year. In fact, during the past decade, new jobs have always surpassed annual policy targets and with significant margins.4 Demand in urban labor markets has also outpaced supply since the global financial crisis across regions in China, suggesting some tightness in the labor market. Over the past few years, the official registered unemployment rate has been stable at about 4 percent; the official surveyed unemployment rate has also held steady, at about 5 percent. Tracking employment is difficult because of data shortcomings (Annex 1). High-frequency indicators such as the purchasing managers’ indices (PMI) show some softening signs. Both the manufacturing and services PMIs for employment—available on a monthly basis—fell below 50 in 2014 (indicating a contraction). And the PMIs on employment seem to correlate with year-over-year growth in urban job creation, a key policy target.

  • Wage growth has slowed, but it outperformed output growth. Average wage growth for urban and migrant workers has slowed, but has remained higher than nominal GDP growth and labor productivity in recent years. The average monthly income of migrant workers grew 9.5 percent in 2014, higher than nominal GDP growth of 8.2 percent. But migrant wages have stayed at about 60 percent of urban workers’ wages over the past few years, after significant convergence during the late 1990s and early 2000s.

Figure 1.
Figure 1.

Labor Market Developments

Citation: IMF Working Papers 2015, 151; 10.5089/9781513570693.001.A001

III. Explaining Labor Market Resilience

Structural trends, such as changing demography and expansion of the services sector, tend to support labor market resilience during the current growth slowdown. Specifically:

  • Demography. China may now be at a demographic turning point (often termed as the Lewis turning point), with less surplus labor from rural areas (Das and N’Diaye 2013; Zhang, Yang, and Wang, 2011). A decline in surplus labor could also dampen new pressures on employment, which partly explains why labor markets have held up well as the economy slows (Figure 2). How demography will affect labor markets going forward is less certain. On the one hand, China’s population is aging. The fertility rate remains low and the dependency ratio will climb. The working-age population will soon begin to contract.5 And these demographic headwinds may reduce growth and wage prospects. On the other hand, the labor force participation rate remains near 80 percent, one of the highest globally.6 Plans to raise the retirement age could also boost the shrinking labor force (Zhang and Zhao, 2012; Gruber, Milligan, and Wise, 2009). Average labor productivity is likely to rise because incoming cohorts have, on average, more years of schooling than those exiting the labor force.

  • Expansion of the services sector. The growing services sector is often cited as a key reason for labor market resilience amid slowing growth. It tends to be more labor intensive and low skilled, on average, and is thereby able to absorb surplus labor. For instance, jobs created from a 1 percentage point increase of the services sector share in GDP could offset the employment loss from a 0.4 percentage point decline in GDP growth (Ma and others, 2014). Both employment in and output of the services sector have expanded rapidly, particularly after 2008 (text figure). Services sector employment accounted for about 40 percent of the labor force in 2014, and value-added from the services sector reached 48.2 percent in 2014, surpassing that of the manufacturing sector (Figure 3). The contributions of the services sector to total employment are large, often exceeding half in most provinces.7 Meanwhile, while employment may remain firm, labor productivity in the services sector is, in general, lower than that in manufacturing.

Figure 2.
Figure 2.

Demography in China

Citation: IMF Working Papers 2015, 151; 10.5089/9781513570693.001.A001

A01ufig1

Annualized Growth in Employment by Sector

(in percent; bubble size scaled by total urban employment 1/)

Citation: IMF Working Papers 2015, 151; 10.5089/9781513570693.001.A001

Sources: CEIC and authors’ estimates.1/ between 2002 and the latest year available.
Figure 3.
Figure 3.

China: Services Sector Expansion

Citation: IMF Working Papers 2015, 151; 10.5089/9781513570693.001.A001

At the same time, unique features in China’s labor market—such as migrant flows and surplus workers in SOEs—buffer against adverse shocks, but come at a cost (Figure 4).

  • Rural–urban migrant flows, which for the most part are not fully reflected in unemployment statistics, have acted as a shock absorber. Migrants seek opportunities in urban areas (which account for about 35.5 percent of total employment and 50.9 percent of nonagricultural employment). During an economic downturn or a temporary slowdown from the implementation of structural reforms, declining job opportunities in cities may keep rural workers from migrating, and migrants in cities return to rural areas. Migrant worker jobs, largely in the private sector and in low-skill industries, are usually more vulnerable to a growth slowdown than are urban workers’ jobs. Rural–urban migrant flows start to slow before the unemployment rate rises. For instance, when the global financial crisis hit in mid-2008, it was reported that about 20–45 million migrant workers returned to their rural homes, helping mute the impact on urban unemployment (Meng, 2012).

  • SOEs also provide buffers against adverse shocks by hoarding excess labor instead of laying off workers during downturns (Friedman, 1996; Bidani, Goh, and O’Leary, 2002; Dong and Putterman, 2001 and 2003). SOEs favor a gradual adjustment through relocation, buyouts, and severance pay. Although their share of total employment has declined, SOEs are often concentrated in overcapacity sectors in which excess labor is more common (text chart).8 Data on the size of excess labor among SOEs are limited, though anecdotal evidence suggests the scale may be large for individual SOEs (see Annex 2).

Figure 4.
Figure 4.

Short-Term Buffers in Labor Markets against Adverse Shocks

Citation: IMF Working Papers 2015, 151; 10.5089/9781513570693.001.A001

While these buffers may temporarily mitigate the impact on employment of an economic slowdown, if they persist for a prolonged period of time, they could delay the reforms necessary for economic transition. For instance, limited migrant flows could imply inefficient allocation of labor that limits productivity gains, while having SOEs hold on to excess labor delays the unwinding of overcapacity sectors.

IV. Empirical Analysis on Migrant Flows

Migrant flows are key to understanding China’s labor market conditions.9 Migrant flows are closely related to GDP growth and better reflect short-term dynamics in labor markets than unemployment rates (Lu, Liu, Jiang, and Zhang, 2014). In fact, migrant flows also grew more mildly in 2014 (year-over-year), in line with the growth slowdown (Figures 4 and 5). The correlation between GDP growth and migrant flows is 0.8, relative to 0.4 for the unemployment rate.10 There were about 270 million migrant workers in China in 2013, about a third of the total labor force (Meng, 2012) and half of urban employment.11 Increasingly, migrants have stayed close to local areas—perhaps because local job prospects are improving and firms are relocating inland. At the same time, migrant flows also contributed to urbanization in China. The urbanization rate, now at 54.8 percent, is expected to rise to about 60 percent by 2020. Urban employment has more than doubled during the past two decades to about 393 million, and for the first time, in 2014, exceeded rural employment (Hu, 1998; Young, 2003; Liu and Lu, 2014).12 The annual increase in urban employment has been broadly in line with the increase in nonagricultural employment, except the latter is more volatile.

Figure 5.
Figure 5.

Summary of Conditions for Migrant Workers

Citation: IMF Working Papers 2015, 151; 10.5089/9781513570693.001.A001

Table.

Characteristics of Migrant Workers

in percent unless otherwise stated

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Sources: CEIC, Meng (2012), labor survey (2009).

Measured in calendar year and subject to some selection bias.

Even after moving to the cities for work, migrant workers often have limited access to social welfare and services there. The hukou restrictions and the lack of social services discourage migrants from staying permanently in cities (Gruber, Milligan, and Wise, 2009). The participation rate and employment rate for migrant workers was very high (nearly 95 percent), mostly in manufacturing and the unskilled services sector, but migrants were only earning slightly more than half of urban workers’ income (text table). Migrant workers’ wages have also increased in line with urban workers in recent years, partly driven by expansion of the services sector and the rise of minimum wages.13 Nonetheless, migrant workers still account for most of the employment in the informal sector.

A. Okun’s Law Estimates

Migrant flows, rather than the unemployment rate, are closely related to growth fluctuations. The typical specification Okun’s law uses growth (or the output gap) as the dependent variable, while the unemployment rate (or gap with the nonaccelerating inflation rate of unemployment) is the independent variable, or vice versa (Okun 1962). Taking the features of China’s labor market into consideration, the estimation model conducted for this paper is given in equation (1):

gyt=β0+β1Δut+β2Dt(k)+β3[Dt(k)×Δut]+β4Migt+εt(1)

in which gyt is the real GDP growth rate, ut is either the official registered urban unemployment rate or the estimated unemployment rate based on Urban Household Survey data from 1989–2009, variable Dt is a dummy for the year of urban employment reform, k is the year of structural reform in the labor market, and Migt denotes the annual change in the migrants as a share of total employment.14 The empirical results suggest a correlation between the fluctuations of output and the cyclical conditions of China’s labor market. The Chow test implies the structural break occurred in 1993 (F-statistic is 3.67 with p-value of 0.047 when using the Urban Household Survey urban unemployment rate; the F-statistic is 2.79 with p-value of 0.092 when using the official registered rate). The Okun coefficient is β1 before the structural reform and β1 + β3 afterward.

Estimates suggest the registered unemployment rate has little relationship with GDP growth, while the estimate using unemployment rates from surveys shows a negative and significant relationship (Table 1).15 For instance, a 1 percentage point increase in unemployment after 1993 is associated with a reduction in the growth rate by about 0.8–1.0 percentage point. Moreover, the inclusion of the migrant share in employment also improves the overall fit of the regression. Growth in migrant flows is strongly correlated with GDP growth. A 1 percentage point increase in migrant flows is associated with GDP growth of nearly 2 percentage points. Migrant workers have a closer link to economic fluctuations, possibly because they are more vulnerable to job losses. These estimates suggest that migrant flows may better reflect labor market conditions.

Table 1.

Estimation of Okun’s Law for China

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1/ Dummy variable (1993) for year 1993 to reflect thestructural change related to reform.s2/ Standard error is in parentheses.*, **, *** indicatesstatistical significance at10 percent, 5 percent, and 1 percent levels, respectively Dependent variables in columns (O.2) and (S.2)are authors’ calculations based on Urban Household Survey datawhile others are from NBS.Sources: NBS, Urban Household SurveyIMF staff calculations.

B. Determinants of Migrant Flows

Cross-province analysis finds that the urban–rural income gap and GDP growth are key determinants of migrant flows. The empirical analysis uses provincial-level panel data. The sample period begins in 1992, the year that marked the start of a series of reforms after Deng Xiaoping’s famous southern tour. The dependent variable, migrant flows, is based on the annual change in the rural labor force net of agricultural employment. In that context, it is assumed that the rural labor force in the agricultural sector is fully employed.

Core, cross-province explanatory variables include (1) the urban-rural income gap (measured as the gap between urban household income and rural household net income per capita); (2) GDP growth rate; (3) infrastructure level (proxied by road density); (4) total factor productivity (TFP, estimated using provincial panel data on industrial output, net values of fixed assets and labors with system GMM estimation methods) and (5) agricultural labor productivity (measured as the ratio of total agricultural capital use to agricultural employment). In addition, a set of control variables is included, such as the degree of openness (proxied by the ratio of foreign direct investment to GDP and the ratio of trade to GDP), share of SOE output in total industrial output, financial sector size (loans-to-GDP ratio), and per capita public expenditure on education. Other potential variables are included in the third specification, including the urban unemployment rate (both registered and surveyed), the rate of return on capital (ratio of profits to net fixed assets for industrial enterprises), and inflation rate (Table 2).

Table 2.

Descriptive Statistics

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Data sources: China Statistical Yearbooks, China Compendium of Statistics 1949-2008, China Compendium of Statistics in Agriculture 1949-2008, Provincial Statistical Yearbooks, Provincial Traffic Statistical Yearbooks, and the official websites of Provincial Department of Transportation, and CEIC. Urban surveyed unemployment rate is estimated using micro data of urban household survey. The regression sample spans from 1992 to 2010. Due to data missing, the numbers of observations are not equal for all variables.

In consideration of the spatial correlation of the migrant flows and corresponding explanatory variables across provinces, two spatial econometric models are used in our regression analysis. Urban–rural income gaps as well as infrastructure may have varying spatial impacts on migrant flows across provinces (Xu and Wang 2010; Luo 2010; Zhang, Hong, and Chen, 2013). The spatial correlation of economic variables may come from explanatory variables or from the unexplained residual terms. As a result, the analysis considers both a spatial autoregressive model (SAR) and a spatial error model (SEM) using maximum likelihood estimation to account for potential different sources of the spatial correlation effects. Specifically, the regression can be expressed as:

SAR:Yi,t=ρWi,tYi,t1+βXi,t+εi,t(2)
SEM:Yi,t=βXi,t+ui,t,ui,t=λWi,tui,t+εi,t(3)

in which Y is migrant flows, X is a matrix of explanatory variables listed above, W is the spatial weighting matrix, with coefficients ρ and λ, respectively. The weight is selected as 1 for neighboring provinces, and 0 otherwise, and the weight matrix is then standardized in the estimation as in Luo (2010) and Zhang, Hong, and Chen (2013).

The regression results show that the coefficients mostly have the expected signs. The urban–rural income gap is a key driver of migrant flows across provinces. A larger urban–rural income gap would encourage migrants to move to cities for nonagricultural jobs. Higher GDP growth is associated with shifting labor out of the agriculture sector and encouraging the shift of workers to urban areas. Infrastructure is also statistically significant, suggesting that better developed infrastructure would help reduce migrant mobility costs.

Table 3.

Determinants of Migrant Flows

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The estimation results for other variables are also broadly in line with our expectations: the higher share of SOE employment in a province would be associated with lower migrant flows. It could possibly be that under the hukou systems, migrant workers rarely work in SOEs. At the same time, as the share of SOE employment decreases (possibly due to structural reforms that led to massive layoffs in the mid-1990s and early 2000s), private enterprise increases, and laid-off workers would seek opportunities as migrant workers outside their local rural areas. The negative coefficients on TFP seem counter-intuitive. But since the regression includes GDP growth, the TFP coefficients may capture the replacement effect between capital and workers, especially when the technology is capital oriented. Public expenditure on education is negative and significant, indicating that the increase in public education expenditure is not conducive to improving the productivity of the agricultural labor force. It is because current public expenditures on education are seriously biased toward urban households, which further reduces the competitiveness and employment opportunities of the rural labor force. Size of the provincial financial sector and agricultural labor productivity are generally correlated with migrant flows. Returns to capital also have a strong positive effect on migrant flows, likely suggesting complementarities of capital and labor inputs when China was opening up. The inflation coefficient is not significant, possibly because variation between provinces is fairly small, with free movement of workers and goods. Unemployment rates also do not have a strong effect, perhaps due to data shortcomings in these indicators.

V. Scenario Analysis on the Labor Market under the New Normal

Implementation of the reform blueprint will have long-lasting effects on the labor market. Measures in the third-plenum reform blueprint (State Council, 2013) will affect economic growth over the medium term. Moreover, other reforms such as hukou reforms and expanding coverage of social security and raising the minimum wage will have direct effects on labor markets (He, Lei, and Zhu, 2015). At the same time, reform implementation may well reinforce the course of structural trends, which in turn will affect labor market conditions.

The scenario analysis shows that a steady implementation of reforms is crucial for the resilience of labor markets. Our approach first obtains historical estimates on the relationship between employment and growth across sectors (subsection A below). Using cross-country experience, the speed of services sector expansion—important for employment—was estimated based on panel regression on per-capita income (subsection B). The design of the scenarios in subsection C is identical to that in the IMF staff report on China (2015) and Lam and Maliszewski (2015). The simulation is based on the Flexible System of Global Models (Andrle and others, 2015), which is widely used in simulating policy responses. In areas related to labor market conditions, the scenario incorporates key elements of the reform blueprint, including financial, fiscal, SOE, and hukou reforms. Hukou reforms will improve labor mobility and support urbanization (Annex 3). The reform plan commits to raising the urbanization rate to about 60 percent by 2020 (about 1 percentage point per year). This paper complements those studies, which do not directly consider responses in labor markets in the model framework.

A. Elasticity between Employment and Growth across Sectors

The elasticity measures the extent to which employment in a sector will increase if growth in that sector rises by 1 percentage point. We estimate the average elasticity over the sample period between 1993 and 2013 for the agriculture, manufacturing, and services sectors (Table 4).

Table 4.

Elasticity of Employment in China across Sectors

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Estimated based on data from 1993-2013. Standard errors are in parentheses.

*, **, *** indicates significances at 10 percent, 5 percent and 1 percent level respectively

Based on the estimated aggregate elasticity, a 1 percentage point increase in employment is associated with GDP growth of 0.08 percentage point, on average. The elasticity declined to about 0.04 after the global financial crisis, about half its historical level. The elasticity for the primary sector is negative because rural workers moving to nonagricultural employment would likely boost growth. The elasticity of the services sector tends to be about 0.1 percentage point higher than elasticity of manufacturing, suggesting that the services sector is more labor intensive and has lower labor productivity.16 The result seems consistent with the observation that labor markets have held up well despite the slowdown in growth, driven in part by an expansion of the services sector.

B. Estimation of Services Sector Share

An international comparison may help estimate how much the services sector share of economic output could expand in China (Guo and N’Diaye 2009). There is a close, positive linkage between per capita income and services sector employment. Countries at a similar development stage as China often experience a continual expansion of services as income rises. For instance, estimates suggest that a 1 percent increase in per capita GDP would drive up the services sector share of employment and output by 0.09 and 0.06 percentage points, respectively (text chart and Table 5). The economic transformation in China that aims to lift per capita income therefore will further raise services sector employment (Song, Storesletten, and Zilibotti, 2011).

Table 5.

The Relation between Service Sector Development and Income Level

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Source: National Bureau of Statistics of China.Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
A01ufig2

Per-capita Income and Share of Employment in Services Sector

(in percent and in constant 2005 USD)

Citation: IMF Working Papers 2015, 151; 10.5089/9781513570693.001.A001

Sources: World Development Index and authors’ estimates.

C. Scenario Analysis

  • The baseline scenario assumes gradual yet steady progress in implementing reform (IMF 2015). Growth slows in the near term as a reduction in unsustainable demand—needed to reduce vulnerabilities—weighs on activity. This includes slower credit growth to address debt overhang and a multiyear residential real estate adjustment to bring down excess housing inventories. Growth thus falls to 6¼ percent in 2016 and 6 percent in 2017, cushioned by productivity gains from structural reforms. Starting in 2018, overall growth picks up modestly as those productivity gains begin to dominate.17

  • Slow reform scenario. This scenario assumes inadequate progress in advancing reforms and containing vulnerabilities. The unsustainable pattern of growth will persist if progress is too slow, and vulnerabilities will continue to rise. Over the medium term, the likelihood of China falling into a period of protracted weak growth would rise considerably, and a risk of a sharp and disorderly correction would also increase as the existing buffers—a still relatively healthy public sector balance sheet and large domestic savings—would diminish quickly.

Scenario simulations will give rise to a GDP growth path over the medium term (IMF, 2015). The estimated elasticity—estimated in subsection A—is used to determine the impact on employment in the manufacturing and services (nonagriculture) sectors for each scenario.18 The simulated growth path also allows us to derive per capita income growth to pin down—based on estimates in subsection B—the services sector share of employment and migrant flows, as well as the underlying unemployment rate using Okun’s law estimates. The path for the urbanization rate would help cross-check the estimated change in urban employment. We use the annual increase in urban employment or nonagricultural employment as proxies for the official job targets.19

D. Simulation Results across Scenarios (Figure 6).

  • Baseline scenario. The baseline growth forecast would slow from 6.8 percent in 2015 to about 6 percent by 2017 before picking up to about 6.3 percent by 2020. Implementation of reforms would initially slow growth, but productivity gains would later lift growth to a more sustainable trajectory. In the baseline scenario, the services sector continues to expand to nearly 52.4 percent of output and 46 percent of employment by 2020 (text charts). The unemployment rate, while rising by about ½ percentage point, would remain stable in the medium term. The net increase in urban employment—a proxy for new urban jobs, an official job target—just exceeds 10 million people each year.

  • Slow reforms. Although investment-led measures can support near-term growth, the likelihood of a sharp slowdown heightens as vulnerabilities build up in the medium term. Migrant flows would slow as the services sector expansion stalls and hukou restrictions pose obstacles. The net increase in urban employment would decline, at times about 10 million workers a year, while the unemployment rate would spike from initially stable levels.

Figure 6.
Figure 6.
Figure 6.

Scenario Analysis of Economic Transition under the New Normal

Citation: IMF Working Papers 2015, 151; 10.5089/9781513570693.001.A001

The scenario analysis is subject to several caveats. First, the effects on labor markets are based on elasticity estimates that rely on the long-term relationship between growth and employment. The elasticities could evolve as China’s economy is transformed. Second, if aggregate productivity were to fall short of expectations, it could risk that the rise in urban employment falls short of target, or even if the employment target is met, GDP and wage growth are much lower because of stagnant productivity. A sensitivity analysis shows that if the increase in urban employment stays the same as in the baseline, but without reform-led productivity gains in the services sector, then GDP growth could slow by 0.2–0.4 percentage point (Table 6).

Table 6.

Different Scenarios of Productivity Gains and Real GDP Growth

(In percent)

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Source: authors’ estimates.

VI. Policy Implications

The key policy implication of our analysis is that the elimination of impediments to labor market flexibility with on-budget and targeted social safety nets will facilitate to economic transition to the new normal in China.

Strengthen labor market flexibility rather than relying too much on buffers to shocks in the medium term. Although these buffers—for instance, migrant flows and SOEs’ capacity to hoard labor—can temporarily lessen unemployment pressures during an economic downturn, they hinder reform efforts. Smaller migrant flows would imply lower productivity gains, whereas allowing SOEs to hold onto excess labor would delay the necessary adjustments. Policies such as retraining for work in the services sector could strengthen labor market flexibility while enhancing productivity.

Structural reforms are key to a strong labor market in the medium term. As seen in the scenario analysis, slow reforms would lead to significant downside risks for growth and employment in the medium term. The priorities should be to continue reforms to contain vulnerabilities and move China toward a more sustainable growth path.

  • Fiscal reforms, including revenue reforms and pension portability, will support labor mobility across provinces. Broadening the value-added tax can help services sector expansion by removing the cascading effects on investment. Social security reforms, including pension portability, would significantly increase labor mobility, while also strengthening social safety nets. On-budget targeted social safety nets and retraining programs may facilitate labor market flexibility. Higher social spending could further narrow the urban–rural income gap while lifting the quality of the labor force (Lam and Wingender, 2015).

  • Opening up the services sector will contribute to the sector’s expansion by encouraging entry and competition. Although increased competition may hurt individual workers and firms, the overall productivity gains will generate ample benefits by creating jobs and raising income.

  • Hukou and rural land reforms help remove labor mobility obstacles and clarify property rights, which will speed up urbanization and encourage gainful employment of migrant workers in urban areas, where they will receive better social benefits (Annex 3).

Policy design and assessment would require timely and comprehensive data. Data shortcomings should be addressed to better reflect the underlying momentum. For instance, wider coverage of surveyed unemployment and the public release of labor and household surveys would significantly improve transparency, accountability, and policy research. Better data collection and coverage of migrant flows will go a long way toward improving the understanding of China’s labor markets. The authorities are taking steps to improve data quality, including their intention to subscribe to the Special Data Dissemination Standard and the plan to expand coverage of the unemployment rate from 65 large cities to all prefecture-level cities at a monthly frequency.20

VII. Conclusions

Maintaining stability in the labor market as China implements structural reforms will be important. So far, labor market conditions have been holding up quite well despite the economic slowdown. However, there are signs of increased labor hoarding in overcapacity sectors. At the same time, migrant flows between rural and urban employment rather than measured unemployment are more correlated with growth. While labor hoarding absorbs some of the shock in the short term, if sustained, it can undermine needed adjustment and hence the more efficient allocation of resources and stronger productivity growth.

Changes in rural–urban migration and the growing services sector will have a profound impact on labor markets in China. Empirical estimates find that economic growth is a key contributing factor toward the structural trends of a growing services sector and rural–urban migrant flows. This would imply that managing the growth slowdown will be important for stabilizing labor markets as structural reforms continue.

Quantitative analysis shows that delays in reforms could lead to a weakening of labor market conditions over the medium term. In particular, it would give rise to a sustained increase in the unemployment rate and could cause job creation to fall short of policy targets. For a successful economic transition toward sustainable growth, it is critical that labor is reallocated to new growth sectors. Labor market mobility and increased productivity should therefore be prioritized. In particular, government should support labor market mobility through on-budget, targeted social safety nets and retraining programs and the acceleration of hukou reforms, with less reliance on hoarding labor in overcapacity sectors.

China’s Labor Market in the “New Normal”
Author: Mr. Waikei R Lam, Xiaoguang Liu, and Mr. Alfred Schipke