lower than the bound tarrifs during the 1990s WTO 1998; WTO 2003. As data on tariff lines is not available for some years 1994, 1997, and 1998, we use infor-
mation from four three-year intervals 1993, 1996, 1999, and 2002 both in the pooled cross-section and in the district panel. We can consistently match the relevant
product categories to sectoral employment data derived from Susenas at the one- digit level.
The sectoral share of GDP per district that we use for constructing an alternative tariff weighting scheme is derived from the Regional GDP GRDP data of the
Central Bureau of Statistics in Indonesia BPS. The district GRDP are available from 1993 onward, and breaks down district GDP by one-digit sector, of which the
tradable sectors are agriculture, manufacturing, and miningquarrying.
Some districts have been dropped from the analysis. Districts in Aceh, Maluku, and Irian Jaya have not been included in the Susenas in some years due to violent
conflict situations at the time of the survey. In addition, the 13 districts in East Timor were no longer covered by Susenas after the 1999 referendum on independence.
Another problem is that over the period 1993 to 2002 some districts have split up over time. To keep time consistency in the district definitions, we redefine the dis-
tricts to the 1993 parent district definitions.
Since the Susenas rounds are representative for the district population in each year, we construct a district panel by pooling the four annually repeated cross-
sections. This yields a balanced panel of 261 districts, which reduces to 244 districts when we use the GRDP data. In addition to the pooled data, we also create a district
pseudo-panel by computing district-level means for each variable, weighted by sur- vey weights. The advantage of pooling the cross-section data is that we can work
with individual level data and can account for individual heterogeneity. For example, we are interested in the differential impact for high- and low-skill labor, urban and
rural areas, by birth order, and gender. On the other hand, in the pseudo-panel the observation unit is the district which allows us to investigate dynamic effects at the
district level.
11
Table 1 provides descriptive statistics. Pooling the four years of Susenas data yields a sample of 458,401 observations for children age 10–15. The top panel of
the table shows the outcome variables and the individual and household character- istics that we will use in the regressions. The bottom panel shows the descriptive
statistics for the different tariff measures after they have been merged to the indi- vidual data. The table also reports the district specific poverty head count ratio P
and poverty severity P
2
. The poverty measures are based on per capita expenditure data from Susenas and province-urbanrural specific poverty lines.
12
B. Regional Tariff Exposure
Following Topalova 2005 and Edmonds et al. 2010, tariff exposure measures are constructed by combining information on geographic variation in sector composition
11. In order to allow for heterogeneity in the district panel, we construct it not only for the whole sample but also for subsamples, divided by age, gender, the household head’s education, birth order, and for rural
and urban districts. 12. Details are described in Suryahadi, Sumarto, and Pritchett 2003.
Table 1 Descriptive statistics
Variables Observations
Mean Standard
deviation Minimum Maximum Pooled
Child market work 458,401
0.123 0.328
1 Child work hours
458,401 2.661
9.360 99
Work hours per child worker 49,166
24.81 16.35
1 99
Child domestic work 458,401
0.028 0.166
1 Female
458,401 0.486
0.500 1
Age 458,401
12.45 1.71
10 15
Female head 458,401
0.081 0.272
1 Household size
458,401 5.727
1.815 1
22 Birth order: First born
458,401 0.366
0.482 1
Birth order: Second born 458,401
0.293 0.455
1 Birth order: Third born
458,401 0.173
0.378 1
Birth order: Later born 458,401
0.125 0.331
1 Birth order: Other
458,401 0.043
0.203 1
Rural 458,401
0.668 0.471
1 Head’s ed.: none
458,401 0.381
0.486 1
Head’s ed.: primary 458,401
0.329 0.47
1 Head’s ed.: secondary
458,401 0.117
0.321 1
Head’s ed.: higher 458,401
0.174 0.379
1 Head works in agriculture
458,401 0.470
0.499 1
Head works in manufacturing 458,401
0.079 0.270
1 Head works in services
458,401 0.393
0.488 1
Head does else 458,401
0.058 0.234
1 Tariff weighted by labor shares
458,401 5.416
3.086 0.176
14.9 Tariff weighted by GRDP shares
432,161 4.441
2.356 0.160
13.85 Tariff weighted wo nontradables
458,401 10.46
4.004 4.188
17.33 District panel aged 10–15 years
Child market work 1,044
0.121 0.080
0.011 0.488
Average age 1,044
12.46 0.112
12.11 13.03
Female share 1,044
0.487 0.027
0.385 0.598
Rural share 1,044
0.646 0.317
1 Share of household heads without
education 1,044
0.376 0.160
0.028 0.848
Tariff weighted by labor shares 1,044
5.264 3.080
0.176 14.90
Tariff weighted by GRDP shares 976
4.278 2.314
0.160 13.85
Tariff weighted without nontradables
1,044 10.287
3.953 4.188
17.33 SSN scholarship coverage
1,044 0.023
0.037 0.259
Minimum wages 1,044
17.360 11.161
4.8 59.13
Total population Poverty headcount ratio P
1,044 0.268
0.171 0.871
Squared poverty gap P
2
1,044 0.017
0.019 0.155
Figure 4 Evolution of tariff protection
of the economy and temporal variation in tariff lines by product category. This yields a measure indicating how changes in exposure to tariff reductions vary by geographic
area over the period 1993 to 2002. For each sector h the annual national tariff lines T
ht
for the relevant product categories are weighted by the 1993 sector shares of active labor force L in district
k:
H
L
hk ,1993
L
T ⳱ ⳯T
1
kt
兺
ht
冢 冣
L
h⳱ 1
k ,1993
The evolution of tariff protection, weighted by employment shares, is shown in Figure 4.
13
This measure reflects how households are exposed to trade liberalization through local labor market dynamics. However, alternative measures of regional
exposure to tariff reductions can be constructed, and we will probe into the robust- ness of our findings to the choice of tariff measure.
First, regional difference in economic sector composition, and hence relative ex- posure to tariff reductions, also could be expressed in terms of total output, instead
of employment shares. Thus, we can define exposure by weighting tariff lines by the sectoral shares in GRDP:
13. During the analyzed time span, rice prices were regulated, as the national trading company BULOG had an import monopoly on rice, while export bans on rice also were effective. Given the government’s
control of rice import and export, we exempt rice production from tradable agricultural good production, and reduce the labor and GRDP shares in tradable agriculture by the share of rice fields in agricultural
plantations within each district. We compute this latter information from the 1993 village agricultural census PODES.
H
GRDP
hk ,1993
GRDP
T ⳱
⳯T 2
kt
兺
ht
冢 冣
GRDP
h⳱ 1
k ,1993
This exposure measure differs considerably from exposure based on labor shares, as agriculture typically has relatively high employment but low economic production
shares, while the opposite holds for manufacturing. This weighting scheme results in overall lower exposure since GRDP weights give a lower weight to agriculture
than its importance in terms of employment.
Second, we explore the sensitivity of our results to excluding nontradables in the weighting scheme. By assigning nontraded goods and services a zero tariff, as in
Topalova 2005 and Edmonds et al. 2010, our measure of tariff exposure will be relatively small in regions where the share of the nontradable and hence not directly
affected sectors is large. However, Hasan, Mitra, and Ural 2007 criticize this approach, finding very different results to Topalova 2005 when they weight tariff
changes across traded sectors only.
14
We therefore construct a similar measure of tariff exposure, based on labor shares of the tradable sectors only. Sensitivity to
excluding not-tradables would imply that our results hinge on the size of the non- tradable sector in the regional economy.
Because regionally representative data on the sectoral composition of households is usually available only at the one- or two-digit level, we cannot distinguish tariff
reductions on locally produced import-competing goods from tariff reductions on goods which are not produced locally. Instead, our focus lies on the interactions
between overall trade liberalization and the regional differences in economic struc- ture, which determine the extent to which a region might be negatively affected by
reductions in protection but also the extent to which it might be able to benefit from the efficiency gains associated with more competition in the local economy.
C. Identification
