Does Okun’s Law Hold for China? Some Empirical Evidence. [https://doi.org/10.3897/brics-econ.3.e95672]
BRICS Journal of Economics, 3(3), 173–182. [PDF]
Abstract: 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 GMM 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.
Keywords: Unemployment Rate, China, Okun’s Law.
JEL: C22, E24, E32.
Business Cycle Fluctuations in China Investigated: Aggregate Demand or Aggregate Supply Shock, What Changes post-Pandemic? [PDF]
Journal of Business Cycle Research, Revise & Resubmit
Abstract: This paper is a novel attempt to dissect the sources of business cycle fluctuations in the case of China since the year 2001, the year when China joined the WTO. Previous empirical efforts – using just two variables – in this direction, tend to place aggregate supply as the dominant source of fluctuations. In the light of rapidly unfolding economic scenario after pandemic, and also given the fact that “new normal” has been the subject of much focus among the Chinese elite policymakers since the great financial crisis, this paper is a fresh attempt to dissect in a relatively more granular framework the underlying mechanism of business cycle fluctuations in China. In the first scenario – which consists of three variable SVAR – it comes across that AS shock is indeed the dominant source of business cycle fluctuations. This appears to be the case for both pre and post pandemic. In the second scenario – which rightly assumes the US IIP as the dominant source of business cycle fluctuations in China – the aggregate demand appears to the dominant source of business cycle fluctuations. Hence, this is rather anomalous findings and tend to point that aggregate demand can indeed replace aggregate supply as the dominant source of business cycle fluctuations. One area, among others, in which this work stands out from others is using the Divisia monetary aggregates rather than simple-sum.
Assessing the Importance of Aggregate Demand Shock and Aggregate Supply Shock – Evidence for the Indian Economy [PDF]
This paper has been uploaded to Macroeconomics and Finance in Emerging Market Economies for publication.
Abstract: Fluctuations in aggregate demand under the presumption of the classical model that the LRAS curve is vertical will lead to permanent changes in prices and temporary changes in employment. However, the causal relationship between aggregate demand and aggregate supply runs in both the direction which the BQ (1989) did not conceptualize. This paper, following the empirical work of Cover et al. (2006), analyses the contemporaneous response of structural shock if the causality runs from aggregate supply to aggregate demand and vice versa. The paper finds that the value of the SRAS curve is 0.518 – which means that the short-run aggregate supply curve is inelastic. In essence, it implies that aggregate demand shock will have larger impact on output than prices. The contemporaneous response of aggregate demand shock to the aggregate supply shock is 0.47. Essentially, if the two structural shocks are correlated, this means that 0.47% of the variation in aggregate demand shock to the right comes from 1% shock to aggregate supply shock to the right. On the other hand, 0.343 of the variation in aggregate supply shock to the right comes from 1% shock to aggregate demand shock. The paper shows that in the second possibility when the aggregate demand shock is causally prior to aggregate supply shock, then the former – the aggregate demand shock – becomes very important.
Manhar Singh 