Research Article |
Corresponding author: Manhar Singh ( manhar_singh@outlook.com ) Academic editor: Marina Sheresheva
© 2022 Manhar Singh, Ibrahim Nurudeen.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits to copy and distribute the article for non-commercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Singh M, Nurudeen I (2022) Does Okun’s Law Hold for China? Some Empirical Evidence. BRICS Journal of Economics 3(3): 173-182. https://doi.org/10.3897/brics-econ.3.e95672
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This paper seeks to estimate the applicability of Okun’s law to the situation in China between 1991 and 2020. A defining and most significant feature of this paper is that China’s unemployment rate has been proxied by youth unemployment and urban unemployment. The stochastic properties test reveals that all the three variables follow I(1) process. The paper uses this knowledge to build data generating process (DGP), which is an outstanding contribution to international research into the steady state growth. Many researchers have pointed out that the poor countries catch up faster and, consequently, their growth rate should have a trend component to it. The applied regression model has proxied the trend when estimating the operation of the Okun’s law. The inclusion of trend, strongly factual, is accounted for and reveals that Okun’s law is valid for China. Apart from the OLS estimator for testing the Okun’s law, the generalized method of moment estimator has also been used as another estimator with the first lag of both unemployment and GDP as instrumental variables. Empirical evidence supports the proposition that Okun’s law is indeed valid in the case of China contrary to the conclusion of some studies.
Unemployment Rate, China, Okun’s Law.
The underlying principle of the Keynesian revolution is that there always exists an excess capacity that should be used to create additional employment by pumping up aggregate demand. The government’s role is to find ways of using this excess capacity (Klein, 2016). Active involvement of government in economic activity would inevitably lead to the creation of greater employment opportunities, which in turn should spur growth.
The rate of output growth in the Chinese economy since its liberalization has been around 10 percent. In the year 2014, China overtook the United States as the largest economy in terms of Purchasing Power Parity and is expected to surpass it in nominal terms by the end of this decade (Morrison, 2019; Lin, 2020). The question of factors and policies behind such a remarkable transformation has occupied the attention of economists over the last few decades. Stripped to its essentials, the excess capacity existed in China, due to the country’s massive size, huge population and inadequate infrastructure; since 1978, the opening up of many sectors and expanding manufacturing bases were some of the key elements that provided the impetus for growth through efficient exploitation of the excess capacity organized by the Chinese policymakers. Most of the excess capacity, that has been mentioned, has been utilized. It is important to point out, however, that in 2014 growth in China started to decelerate (Lanteigne, 2015). Does it mean that the dwindling excess capacity will lead to reduction in the rate of employment generation in China?
The negative relationship between output and cyclical unemployment, first documented empirically by Arthur Melvin Okun in 1962, is known as Okun’s law (
The answer to the above question will therefore depend on the nature and strength of the Okun’s coefficient in the case of China. Many factors might be involved in holding out the relationship between growth and employment, for instance, the role played by the labor market institutions
This study builds on the work of the previous studies (Huang, 2003; Ball et al., 2012; Gazi & Prieto, 2016; Feng, 2018; Abubakar & Nurudeen, 2019). The underlying conclusion is that the relationship between unemployment and rate of economic growth is stable and positive. In 2021, China was the only major economy that did not witness recession, in spite of being in a lockdown in some parts of the country. The unemployment rate, globally, increased drastically. During the pandemic, the underlying relationship between growth and unemployment has become a matter of intense speculation but the question of Okun’s law needs to be explored.
From the political standpoint, the character of the relationship between growth and employment in the case of China is rather unique. Generating great employment opportunities in a democratic country is the basis on which many elections are held and often won. For the general public, having means and opportunities to get employment is of paramount concern. In a democracy, the political party that can do a better job at delivering such an outcome wins the election. As a one-party rule, the legitimacy of the Chinese Communist Party hinges on the existing positive linkages between growth and employment. In this context, the paper contributes to revealing the true relationship between output and rate of unemployment in the Chinese economy by exploiting the stationarity properties of the output that has grown exponentially. It is sometimes difficult for policy makers to control or exert the necessary degree of influence on the rate of unemployment. As they need to determine the degree of control over economy, the relationship between growth and unemployment rate is undeniably important from policy perspective.
The rest of the paper is as follows. Section 1 presents the literature review, which summarizes the previous theoretical and empirical research. The methodology is explained in Section 2. Section 3 discusses the results obtained in the course of the research. Then a conclusion is drawn and future research agenda proposed in the Conclusions section.
There are two approaches to the estimation of the Okun’s law. The first presupposes the use of level series, also called the GAP model. The second approach involves the first difference of both series (Karadzic et al., 2022; Porras & Martín-Román, 2022). It is important to point out that unemployment can also be an explanatory variable and output or income can be a dependent variable in both cases. To understand the level version of the model, consider equation (1) below,
where U and U* are the realized unemployment rate and the natural unemployment rate. Similarly, Y and Y* are the actual output that is produced and potential output or natural level of output. In the same vein, to understand the differenced version of the model, consider equation (2), shown below,
∆Ut = ξ(∆Y) (2)
where ∆ is the change or a differenced series. By the rule of thumb, a 1 percent increase/decrease in unemployment will lead to 2 percent increase/decrease in output. In other words, 1 percent increase in unemployment will lead to a deviation of GDP from its long run path.
The empirical evidence for the existence and validity of the Okun’s law in the case of Chinese economy is mixed. Several socio-economic reasons, unique to the Chinese economy, are perhaps the reason why the Okun’s law is not found to be holding in some empirical studies (Yin & Zhou, 2010; Wu-Liu, 2012).
In developing countries, the higher rate of growth of the country leads to an increase in the rate of urbanization, which in turn gets captured by the rate of growth of registered and formal employment. By many reckonings, China is still a developing country but has seen rapid and vast migration of people from rural to urban areas in the last two decades. Nonetheless, the level of urbanization is nowhere near the level that prevails in advanced countries. The use of the Okun’s law without factoring in the rate of urbanization is bound to lead to the conclusion that the relationship between (un)employment and growth is not holding in the case of China. The original Okun’s law is perhaps not so applicable in the case of China where the level of urbanization is still very low compared to advanced economies (
It is essential to point out that the Chinese economy started to slow down in 2014. Since that time, no large-scale increases in employment have been observed. Basically, it is the rural to urban migration that is affected as a result of slowing growth rather than the jobs in the urban China. In the short-term, the transfers of labor from agricultural areas are correlated significantly with the economic cycles (Feng, 2018).
On the other hand,
The empirical literature on the inclusion of youth unemployment in the Okun’s law in BRICS countries is available for India. The study that empirically examined the relationship between unemployment and output in India confirmed that although Indian output was growing, the growth was jobless because it was not up to the threshold level that ensures a decline in unemployment (Abubakar & Nurudeen, 2019). There is some evidence that the youth employment rate is the most affected during downturns since the early entrants to the job markets are the first to lose their jobs when there is a slump in economic activity.
However, before the results that has been estimated is put forth, it is useful to reveal the relationship between growth and employment in China.
A novel attempt, from the methodological standpoint, was made by including the trend in the output growth. The growth theory according to
The growth rate of the nominal GDP has been used as a proxy for the output. Both the youth unemployment and the urban unemployment rate were used for estimating the Okun’s law. The data was extracted from the economic research by the Federal Reserve Bank of St. Louis and all the series spanned from 1991 to 2020. The Augmented Dickey Fuller test (Dickey & Fuller, 1979) henceforth, (ADF) and Dickey Fuller generalized least square test (
The series will be considered to be stationary if the probability value is negative and less than 5 percent.
Yt = adt + Ut (4)
where Ut = Ut – 1 + et and et follow a normal distribution and adt is thus the deterministic component of the model. The null hypothesis will be accepted in favor of unit-root if a = 1 and if a < 1 then the alternative hypothesis is accepted. Using both these tests would help to determine the stochastic properties of the series.
The estimation of the relationship between output and unemployment was conducted using the OLS estimator. The specification of the regression used to estimate the parameters of the constant, represented by a1, trend which is represented by a2 has been shown in (5).
Yt = a1 + a2t + a3Xt + et (5)
Where Yt is the dependent variable and Xt is the independent variable, a1 and a3 measures the average and slope of the relation, and a2 is the deterministic component of the model while et is a white noise assumed to be independent and identically distributed with zero mean and a constant variance. The Generalized Method of Moment estimator (GMM) was also used. The first lag of both the unemployment rate and GDP were used as instrumental variables.
Table
Variables | ADF Statistics | DF-GLS | Decision | ||
Level | First Difference | Level | First Difference | ||
Unemployment | -2.437 | -3.295*** | -2.309 | -3.359** | I(1) |
GDP | -1.871 | -4.122*** | -2.048 | -3.453** | I(1) |
Youth Unemployment | -1.339 | -3.950*** | -2.131 | -3.918* | I(1) |
The estimated linear regression model has been presented in table 2, shown below. The underlying assumption of the data generating process (DGP) favors intercept and trend for all the series in the unit-root test. The regression model included intercept as well as the trend in the estimation. The regression model is therefore specified and estimated using equation (3) above. The estimation testifies the significance of both constant and trend. Keeping in line with the objective of the paper, GDP was taken as the endogenous series to find out if the GDP’s targeted growth of 8.5 percent can be achieved given the World Bank’s position. This is firstly assessed by a linear regression model using an ordinary least square (OLS) estimator. The results have been presented in table 2, shown below. Additionally, the generalized method of moment (GMM) estimator was employed in order to check and assess the robustness of the regression result, reported in table 3. The regression result shows that 1 percent increase in unemployment leads to 3.96 percent decrease in Chinese GDP. This is further assessed with a GMM estimator, and it shows quantitatively, that 1 percent increase in unemployment will decrease Chinese GDP by 4.68 percent; both OLS and GMM estimates are significant at 1 percent level of significance. Given the targeted growth of GDP by 8.5 percent pronounced by the World Bank, our regression model showed that the rate of unemployment would decline by 0.05 percent; this implies that unemployment would turn out to be 4.95 percent by the end of 2021 considering 5 percent unemployment rate existed in 2020. While our GMM model showed that 8.5 percent growth rate would instead increase unemployment by 1.591 and the rate of unemployment would turn out to be 6.59 by the end of 2021 fiscal year. Youth unemployment for the ages of 16-24 is higher in China than for the age group beyond 24 years.
Variables | Coefficient | Standard Error | T-Statistics | Probability Value |
Constant | 8.72 | 1.26 | 6.920 | 0.0001 |
Trend | 8.35 | 3.84 | 21.72 | 0.0001 |
Unemployment Rate | -3.96 | 4.31 | -9.20 | 0.0001 |
The estimate of the regression points to Okun’s law holding in the case of China. The inclusion of trend, for which there is a strong theoretical justification due to
Variables | Coefficient | Standard Error | T-Statistics | Probability Value |
Constant | 8.22 | 1.25 | 6.55 | 0.0001 |
Trend | 9.78 | 5.71 | 17.12 | 0.0001 |
Unemployment Rate | -2.01 | 2.30 | -8.79 | 0.0001 |