# **Table 2: Examples of studies analyzing the relationship between physical infrastructure and economic variables**

|**Dependent**<br>**variable**|**Study**|**Level of**<br>**aggregation**|**Period**|**Specification**|**Infrastructure**<br>**variable**|**Method**|**Findings**|**Limitations in the**<br>**methodology used**|
|---|---|---|---|---|---|---|---|---|
||Garcia-Mila and<br>McGuire (1992)|US States|1970<br>to<br>1983|Cobb-<br>Douglas; Log<br>levels|Highway|OLS|Two publicly provided inputs – highways and education<br>have significant and positive effects on output. Estimated<br>effect of highway is 0.045***|Endogeneity; Reverse<br>causality; Common<br>trends|
||Rives and<br>Heaney (1995)|US<br>Community|1990|Levels|Index of sewer<br>capacity, water<br>plant capacity, and<br>highways|OLS|A composite measure of the level economic development<br>(index as a function of population, employment, property<br>values, and income) is affected positively by physical<br>infrastructure (0.205**)|Endogeneity; Reverse<br>causality|
||Lewis (1998)|Kenyan<br>municipalities|1994|Levels|Roads and water|OLS<br>2SLS|Impact of public infrastructure in the roads and water<br>sectors on municipal incomes is significant using OLS<br>estimator (0.009*), but becomes insignificant employing<br>2SLS (0.026)|Endogeneity|
||Lall 1999|Indian States|NA|Cobb-<br>Douglas; Log<br>levels|Public investments<br>in physical<br>infrastructure|OLS<br>FE<br>SUR|OLS estimate indicates that an increase in economic<br>infrastructure investment has positive effect on regional<br>output. However, FE, SUR and 2SLS models indicate<br>negative relationship. Results are robust across lagging,<br>intermediate and leading states|Measurement error;<br>Reverse causality|
|||||||2SLS|||
|**P/Income**|Estache,<br>Speciale, and<br>Veredas (2005)|Sub-Saharan<br>African<br>Countries|1976<br>to<br>2001|Cobb-<br>Douglas; Log<br>levels|Telecoms, roads,<br>electricity, and<br>water|GLS|All infrastructure sub-sectors, are shown to be statistically<br>significant engines of growth (0.19*** to 0.57***)|Relying on assumptions;<br>Aligned with the<br>weaknesses of Solow<br>model|
|**AGD**|Zou et al. (2008)|Chinese<br>Provinces|1994<br>to<br>2002|Cobb-<br>Douglas; Y-<br>Log levels;<br>X-Levels|Road density|FE|Higher growth level in East and Central China comes<br>from better transport infrastructure. Estimated coefficient<br>for road is 4.224***|Reverse causality|
||Banerjee et al.<br>(2012)|Chinese<br>Counties|1986<br>to<br>2003|Log levels|Distance of<br>transportation lines|FE|Proximity to transportation networks have a moderate<br>positive causal effect on per capita GDP levels across<br>sectors. Elasticity between the distance to the line and per<br>capita GDP is -0.0672***|Endogeneity|
|||Indian<br>National|1970<br>to<br>|||OLS<br>2SLS|||
||Sahoo and Dash||2006|||DOLS|||
||(2009,2010,2012)|South Asian<br>Countries|1980<br>t|Cobb-<br>Douglas; Log|Index of<br>infrastructure|OLS|Physical and social infrastructures have a significant<br>positive impact on output (0.18* to 0.46**). Further,<br>|NA|
|||South Asian<br>Countries|o<br>2005|<br>levels|stocks|FMOLS|infrastructure development contributes significantly to<br>output growth||
||Sahoo, Dash, and|Chinese<br>National|1975<br>t|||ARDL|||
||Nataraj (2012)|Chinese<br>National|o<br>2007|||GMM|||

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