Methods

To examine the effect of regional exposure to tariff reductions on inequality and poverty, we first estimate the following model:

Yp,t = α + βTariffp,t + X'p,t γ + δp + λr,t + εp,t (2.4)

where Yp,t are province-level dependent variables (Gini, the share of expenditure of the

top 20%, the share expenditure of the least 20%, and poverty rate) in province p, year t. TLp,t is the province exposure to the labour-weighted tariff. Xp,t is a set of the average

time variant province characteristics (regional gross domestic product per capita, sectoral value added share, government spending, government own revenue generation share, average years of schooling and infant mortality rate). We also include interactive major island-year fixed effects, λr,t , to control for shocks over time that affects trade across all

provinces but may vary across different major island groups within Indonesia.17 _The

coefficient of interest, β, captures the average effect of trade reform on regional outcomes related to inequality and poverty. We estimate equation (2.4) as a balanced panel both for inequality and poverty estimations using ordinary least square (OLS) estimation with province fixed effects, δp, to control for unobserved province-level heterogeneity.

Fixed effects method is efficient when the unobserved effects are serially uncorrelated. However, when we expect the unobserved factors that change over time to be serially correlated, it is better to use the first difference method.18 _{It is also the case when T is}

relatively large, and N is not very large. We then exercise a first difference approach by

17 _{There are five main island dummies: Sumatera, Java, Kalimantan, Sulawesi and the other islands.} 18 _{Jeffrey M. Wooldridge (2009) argues that if unobserved factors are serially correlated first difference}

is more efficient than fixed effects. Moreover, first differencing has the benefit of swifting an integrated time series process into a weakly dependent process. We can also claim to the central limit theorem even in the case where T is larger than N.

44 rearranging Equation 2.4 which can be rewritten as a first difference specification as follows:

ΔYp,t = α + βΔTLp,t + ΔX'p,t γ + I'pθ + λr,t + Δεp,t (2.5)

After differentiating manufacturing output tariffs (TO) and input tariffs (TI), we then estimate the following model:

ΔYp,t = α + β1ΔTOp,t + β2ΔTIp,t + ΔX'p,t γ + I'pθ + λr,t + Δεp,t (2.6)

Likewise the fixed effect method, first difference specification removes province fixed effects and eliminates the unobserved heterogeneity that might be instigated by the initial province sectoral structure in employment and industry output. Furthermore, it eliminates potential bias due to endogenous national tariffs by controlling for country variation over time and by limiting variation only at the province level.

However, if any unobserved time variant confounders exist, the first difference approach can still be biased. The potential cofounders may include structural change, economic performance and any policies related to initial province sectoral structure and urban-rural differences. To deal with this problem, we incorporate a vector of initial conditions, Ip,,

which includes the 1976 sectoral labour shares (aggregated to one-digit sectors), 1976 rural population shares and, in some specifications, the initial level of the respective dependent variable.

Next, we conduct a placebo test by regressing changes in independent variables on future changes in tariff measures,19 with the null of no confounding patterns rejected if the future tariff coefficient is not statistically significant. This is to test whether the tariff measures

19 _{That is, we first regress y}

45 are endogenous to inequality or poverty measurements, or if they seize differential trends in inequality or poverty between provinces. We would also expect inequality and poverty to be correlated with future changes in province tariff exposures.

Since our data cover the period of the financial crisis in 1997- 1998, there may be concern that our results are affected by the crisis. One way to deal with this potential relationship is to re-estimate equation 2.2 using pre-crisis data, thus ruling out the crisis effects. However, this could lead to a decrease in sample size, so we decided to use the full sample and interact the tariff measures with a dummy to represent the crisis – it is one for the years 1997 and 1998 and zero otherwise.

Lastly, we experiment with alternative, longer difference periods. We re-examine equations (2.5) and (2.6) using three-year differences20 and compare the results with the one-year difference described earlier. Lastly, to further exploit the longer time series, we also include period dummies based on combinations of economic episodes21 and the tariff data. We find concordance with episodes developed by Basri and Hill (2004) and by grouping average tariff rates based on quintile ranking.22 _{The episodes are the periods of}

1977-84, 1984-90, 1990- 96, the crisis period of 1997- 1998, 1999- 2002 and the period after 2002.23 We incorporate the episodes dummy as a tariff-episode interaction variable for all tariff measurements and episodes combinations into equation (2.5) and (2.6) for each dependent variable of interest

20 _{Starting from 1980, that is: 1980, 1983, 1986, 1989, 1992, 1995, 1998, 2001, 2004, 2007, and 2010.} 21 _{This is based on the trade episodes used in Hill (1997), Hill (2000), Basri (2001), Basri and Hill (2004) and}

Miranti (2010).

22 _{The quintiles groups of tariff rank: 1977-1983 (average tariff of 9.5%), 1984-1990 (average tariff of 7.1%),}

1991-1998 (average tariff of 3.9%), 1999-2000 (average tariff of 1.8%), and 2001-2012 (average tariff of 1.5%)

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