Appendix 2

SOUTH ASIA VACCINATES

In general, there is a good opportunity—through not a very long opportunity— to take advantage of the recovery from the crisis to rebuild. The pandemic has brought to light the fact that events previously considered remotely possible— so-called tail risks—will occur more often, and South Asia is particularly vulnerable to them. The region should, therefore, take the current state of affairs as an opportunity to build more resilience for the future. Given its levels of income, it has stepped up to the formidable challenge of vaccinating its population with boldness, as will be discussed in the next chapter.

Appendix 2

Derivation of synchronization measure and cyclicality estimation

Synchronization: to consider co-movements between the variables across the region, we rely on a standard synchronization measure from the GDP growth literature (Morgan et al. (2004); Giannone et al. (2008); Kalemli-Ozcan et al. (2013), Cesa-Bianchi et al. (2019)). Synchronization indicator is defined as the absolute bilateral differential in variables’ growth rates:

Sij,t =−|∆xi,t −∆xj,t|, (1)

where ∆xi,t and ∆xj,t are the growth rates of variables to be analyzed in the country i and j at time t. According to its definition, S increases with the degree of synchronization, with negative values closer to zero between countries that are more synchronized.

Cyclicality: to assess a stabilization effect of fiscal policy in the region, we estimate the response of fiscal balance to changes in economic activity (Furceri and Jalles (2018)):

bi,t =αi,t +βi,t ∆yi,t +εi,t

βi,t =βi,t−1 + vi,t , vi,t ~ N (0,σi 2) (2a)

(2b)

where b is the fiscal balance-to-GDP ratio, ∆y stands for the GDP growth as a proxy of changes in economic activity, whereas β measures the degree of fiscal countercyclicality, with larger values of the coefficient implying higher countercyclicality. Furthermore, we assume that the regression coefficients α and β may vary over time, with the conditional expected values equal to their past values, reflecting the fact that policy changes are slow and depend on the immediate past. Our

precArious outlook

dataset comes from Macro Poverty Outlook and covers six South Asia economies13 over the period 1990 – 2019. We rely on Bayesian methods and Gibbs sampling algorithm (Carter and Kohn (1994)) to estimate the model.

In addition, to evaluate the features of government consumption in the region, we estimate the following panel regression with country fixed-effects and country-clustered standard errors (Fatas and Mihov (2003, 2006); Afonso et al. (2010); Agnello et al. (2013)):

∆gi,t =θi +λ∆gi,t−1 +γ∆yi,t +δ∆di,t−1 +ΓXi,t +εi,t (3)

where ∆g is the growth rate of real government consumption, ∆y is the real GDP growth, ∆d is the change in real government debt, while X is a set of other controls, including inflation and time trend. Coefficients λ and γ represent the measures of government consumption persistence and responsiveness, respectively.

Dependent variable λ γ δ Observations

Government consumption growth -0.2 1.2** -0.1 144

Multiplier estimation

We use the Local Projection method (Jorda (2005)) to estimate expenditure multipliers within the region. It provides certain advantages over the traditional structural VAR methodology (Auerbach and Gorodnichenko (2012, 2013)). It estimates sequential regressions of the endogenous variable shifted several steps ahead instead of recursive use of the initial set of estimated coefficients and is more robust to potential misspecifications. Additionally, it is more suitable in capturing potential nonlinearities in the dynamic response that may be impractical in a multivariate SVAR context (an important feature in our interaction exercise). Our specification broadly follows Duval and Furceri (2018) and Izquierdo et al. (2019):

yt+k,i − yt−1,i = ci +τt +βk l Si,t F(ei,t)+βk h Si,t(1− F(ei.t))+θXi,t−l +εi,t

F(ei,t)=

exp(−γei,t) _ 1+exp(−γei,t) ,γ > 0 (4a)

(4b)

where y is the log of real GDP, β stands for the cumulative response of y in each k year after changes in expenditure (S), whereas c and τ denote country and timefixed effects, respectively. Additionally, X indicates the set of control variables that

13 We consider Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka in our sample.