subsectors, sanitation explained 31%, water 17% and roads 14%, of the variance in growth over and above the benchmark model.

**Table 4: Augmented Solow model with infrastructure variables**

||Tele<br>|coms<br>|Elect<br>|ricity<br>|Ro<br>|ads<br>|San<br>|itation<br>|Wat<br>|er<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Ben<br>ch|st<br>Infra|Bench|st<br>Infra|Benc<br>h|rast<br>Inf|Ben<br>ch|t<br>Infras|Be<br>nch|st<br>Infra|
|Education|1.24|−.31|2.49|−.04|.73|.38|1.23|.28|1.02|.31|
|t-stat|3.65|−1.35|4.14|−.16|1.40|.92|2.73|.63|2.21|.62|
|)<br>Elast(β|1.24|.00|2.49|.00|.73|.00|1.23|.00|1.02|.00|
|Total Invst|.03|−<br>.83|.36|−<br>.35|.72|.43|−.39|−<br>.22|−.11|−<br>.67|
|t-stat|.08|−<br>3.64|.61|−<br>1.72|1<br>.40|.89|−.65|−.46|−.16|−<br>1.08|
|)<br>Elast(α|.00|−4.88|.00|.00|.00|.00|.00|.00|.00|.00|
|Infrast||.94||1.04||.55||1<br>.72||2.53|
|t-stat||10<br>.39||1<br>2.72||2<br>.53||3.64||2.57|
|γ)<br>Elast (||5.50||1.04||.55||1.72||2.53|
|R<sup>2</sup>|.29|.83|.41|.94|.24|.38|.20|.51|.14|.31|
|. Obs<br>No|42|42|23|23|28|28|23|23|25|25|

_Note_ : This Table shows the human capital augmented Solow models (denoted by Benchmark) and the infrastructure and human capital augmented Solow models (denoted by Infrast). Constants are not reported. In order to compare the Benchmark and the Infrast and avoid the effect of missing observations, Bechmark is estimated for the 5 different set of observations (each infrastructure variable has different missing observations). Total Invst stands for Total Investment minus population growth minus depreciation. Infrast stands for the infrastructure variable. Elast stands for the implied estimated elasticities of the economic model. Parameters are estimated by pooled OLS and standard errors estimators are heteroskedastic-consistent. _R_<sup>_2_</sup> stands for the adjusted goodness of fit.

Education has a positive and significant impact on growth across all subsectors in the benchmark model. However, education loses significance across all models once we include the infrastructure variables, implying that the omission of these infrastructure variables biases upward the impact of education on growth. Also noteworthy is the fact that total investment seems statistically unrelated to growth in the electricity, roads, sanitation and water subsectors (for both the benchmark and infrastructure models). It gains significance only when telecom is incorporated in the regression model, although somewhat counter-intuitively, it is then negatively associated with growth. In other words, Table 4 suggests that when considered alongside telecom infrastructure (which has a significant and positive impact on growth), non-telecom investments are negatively associated with growth. A possible reason for this finding could be that the configuration of telecom infrastructure and non-telecom investments is sub-optimal. To the extent that key infrastructure services, such as transport, energy and telecom, enter as complementary inputs in production and/or consumption processes, a bottleneck and negative elasticities may arise if one or more of these complementary inputs is (are) under-provided.

An important policy implication that emerges from Table 4 is that infrastructure subsectors across the board have a positive and significant impact on growth in East Asia, and are shown to be statistically significant engines of growth. Notably, education loses relevance to growth once 18