Julian Reynolds, James Owen and Bob Gilhooly
This post examines how policy in China supported the Chinese economy prior to the Covid-19 pandemic, drawing on a newly developed toolkit. This topic is particularly important for China, where economic developments have a significant impact on the rest of the global economy, but where assessing the full spectrum of policy – monetary, regulatory and fiscal – is difficult. Policy levers in China have evolved alongside a rapidly changing economy, and there is still some uncertainty surrounding which levers are being pulled – and how hard – at any given point in time. This post attempts to paint a clearer picture of Chinese policy by assessing key policy levers and their effects on growth.
Monetary policy and financial conditions
Assessing the stance of monetary policy is complicated by the Peoples’ Bank of China’s (PBoC) wide remit and the large number of instruments at its disposal. Monetary policy is in transition towards a more market-based system, but continues to operate via both prices (a range of interest rates) and quantities (the levels of credit and liquidity).
Building on work by European Central Bank staff (Lodge and Soudan, 2019), we construct a ‘Monetary Policy Index’ (MPI), essentially an aggregate measure of monetary policy. The MPI weights together different policy instruments based on ‘equivalent’ changes in policy, so that MPI shocks of the same magnitude have the same impact on growth, regardless of which policy lever is pulled. In this set-up, 100bp cuts in the Loan Prime Rate (Benchmark Rate prior to August 2019), the seven-day Reverse Repo Rate, the three-month Central Bank Bill Rate or the interest rate on banks’ excess reserve holdings are equivalent to a 200bp reduction in the Reserve Requirement Ratio (RRR), and a 65bp cut in the household deposit rate.
We exclude other measures whose primary purpose is liquidity provision, or other instruments related to credit policy and transmission (such as the targeted medium-term lending facility). The inclusion of some liquidity measures can lead to periods of implausible volatility and counterintuitive changes in the MPI which do not align with the broader stance implied by other monetary policy levers.
The MPI (Chart 1, red line) tracks the major qualitative changes in monetary policy. The greater the value of the MPI above zero, the more the overall stance of monetary policy has tightened since the previous quarter; conversely a more negative value denotes a larger loosening. The MPI suggests that monetary policy loosened between the start of 2018 and late 2019. We believe that this was mostly driven by RRR cuts and in 2019 Q3 by limited cuts to the loan prime rate. In contrast, the more material loosening in 2015 reflected significant cuts to the benchmark lending rate and the seven-day reverse repo rate.
Chart 1: Broad stance of Chinese policy
Sources: CEIC, Datastream, WIND and Bank calculations.
We complement the MPI with a Financial Conditions Index (FCI), (Chart 1, purple) which captures a broader range of policy levers that affect the economy through the financial system. For example, regulatory steps taken in 2017 to de-risk the financial system were associated with a notable tightening of financial conditions which were not driven by a tightening of monetary policy. We construct the FCI by taking the first principal component of a range of financial market variables (primarily spreads). The FCI is therefore a level relative to its average since 2007, with a positive/negative value reflecting relatively tight/loose financial conditions.
To an even greater extent than with monetary policy, assessing fiscal policy is complicated by the authorities’ broad set of policy instruments. Central government fiscal announcements give only a loose steer on how the fiscal stance is likely to evolve, and represent just a small part of the support which fiscal actions provide. There is uncertainty about how the aggregate fiscal stance is evolving in real-time, and there is also continuing debate about where the boundaries of the government sector (the ‘fiscal perimeter’) are.
We build on the approach taken by the IMF in constructing annual estimates of China’s ‘augmented fiscal deficit’, to produce a broad measure of fiscal actions based on quarterly data. In addition to the official central government deficit, this ‘augmented deficit’ measure also includes other central government borrowing; local government borrowing; and asset sales and other items. The official deficit (Chart 2, blue bars) makes up only around a fifth of this broader measure of the deficit; local government borrowing (red) accounts for the bulk of fiscal stimulus; this is supplemented by other central government borrowing (grey), while other ‘below the line’ items (yellow) – for example spending that is financed through receipts from land sales – also push up spending (without adding to the official deficit).
Chart 2: Composition of augmented deficit
Sources: CEIC, WIND and Bank calculations.
We can use the quarterly change in the estimated augmented deficit (as a percentage of GDP) to assess the stance of fiscal policy: an increase in the deficit denotes fiscal expansion and vice versa. Overall, our estimates suggest that the augmented deficit has increased notably since 2015 (Chart 1, blue). And based on official announcements, and estimates of other items using available data, we estimate that the 2019 augmented deficit rose by almost 2pp. These results are broadly in line with the IMF’s findings.
The impact of policy on growth
The literature has not yet produced a consensus on the effects of Chinese policy instruments on growth. Estimates of policy multipliers on GDP are wide ranging, and struggle to account for the full spectrum of policy levers and their changing use over time.
We estimate a Structural Vector Auto-Regression (SVAR) model at a quarterly frequency – this is an improvement over other studies which consider on-a-year-ago growth, as it avoids statistical problems associated with embedding the serial correlation inherent in annual growth rates. GDP growth on-a-quarter-ago is the primary variable of interest and the model includes the MPI, FCI, change in the augmented deficit, an estimate of private sector credit and the GDP deflator. Short-run restrictions identify the SVAR, with policy variables affecting GDP with a one quarter lag.
The resulting impulse response functions (IRFs) suggest that a tightening of the MPI by 1 percentage point (pp) leads to a peak fall in GDP growth (oqa) of almost –0.1pp, three quarters after the initial shock (Chart 3, red line). The implied MPI multiplier for annual growth (around 0.3) is broadly in line with estimates from other studies. This tightening of monetary policy is also associated with a reduction in private sector credit growth, and a tightening of the FCI.
A tightening of financial conditions of 1 point is only slightly less powerful than a monetary shock, leading to a peak fall in GDP growth of -0.07pp, two quarters after the initial FCI shock (Chart 3, purple). This too implies an annual growth multiplier of around 0.3.
Chart 3: Impulse response functions
Source: Bank calculations.
Finally, a fiscal contraction equating to a 1pp reduction in the augmented deficit leads to a 0.2pp peak fall in GDP growth, one quarter after the initial fiscal shock (Chart 3, blue). This gives an implied annual fiscal multiplier of 0.7, which is also broadly consistent with other estimates (although multipliers span anywhere from 0.3 to 2.8 in the literature). Infrastructure spending should have a high multiplier; hence, our results appear consistent with this being a major component within fiscal expenditure.
We can bring together these estimates with changes in policy to illustrate how policy has been affecting growth in recent years (Chart 4). According to our model, policy easing from April 2018 to December 2019 represented a significant stimulus: fiscal easing is estimated to have provided the bulk of policy support to GDP growth, on top of incremental support from monetary policy (mostly RRR cuts). But the boost to activity was noticeably smaller than policy support in 2015-16. Also, this easing of policy followed a period when policies aimed at reducing medium-term financial stability risks likely tightened financial conditions, weighing on growth.
Chart 4: Estimated impact of policy on growth
Sources: CEIC, Datastream, WIND and Bank calculations.
Policy support could be weaker or stronger than our estimates imply. Tax cuts typically have smaller effects on growth than infrastructure spending, hence fiscal stimulus packages where tax cuts comprise a larger proportion than usual – including the 2019 fiscal package – might be less supportive than our fiscal multiplier would suggest. On the other hand, we only indirectly capture currency moves in our regression framework – via the reaction of interest rate spreads in the FCI – and so we may be missing an important channel: for instance, the renminbi depreciation over 2018 and 2019 almost matches the rise in tariffs since trade tensions with the US escalated.
Chinese policy operates across a varied menu of monetary, fiscal and regulatory instruments which – combined with a rapidly changing economy – make judging the stance and impact of policy difficult. Using summary measures of the authorities’ policy tools across the three main policy areas allows us to estimate their individual impacts on GDP growth. And while this analysis does not cover the period spanned by the Covid-19 pandemic, the techniques outlined in this post should help assess the stance and impact of Chinese policy during this latest economic downturn.
Julian Reynolds and James Owen work in the Bank’s International Directorate. Bob Gilhooly contributed to this post when working in the Bank’s International Directorate.
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