Table 6.
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Impact of Nationalization on Network Access Controlling for First-order Autoregressive Disturbances

VariablesPooled FGLSPooled OLSWithin EstimatorWithin Estimator D-K
Private provision−0.003−0.019−0.007−0.024
(0.019)(0.037)(0.048)(0.038)
Renationalized provision0.163***0.159***0.168***0.168***
(0.026)(0.048)(0.066)(0.059)
Education0.024***0.023***0.019**0.032**
(0.003)(0.008)(0.008)(0.011)
(log) Household per capita income0.159***0.157***0.164***0.137***
(0.013)(0.035)(0.035)(0.037)
(log) Accumulated precipitation0.017***0.0290.0230.042*
(0.007)(0.018)(0.016)(0.021)
Observations432432414432
AR(1)0.3290.2700.2090.06
Sample period1986–2009
VariablesPooled FGLSPooled OLSWithin EstimatorWithin Estimator D-K
Private provision−0.003−0.019−0.007−0.024
(0.019)(0.037)(0.048)(0.038)
Renationalized provision0.163***0.159***0.168***0.168***
(0.026)(0.048)(0.066)(0.059)
Education0.024***0.023***0.019**0.032**
(0.003)(0.008)(0.008)(0.011)
(log) Household per capita income0.159***0.157***0.164***0.137***
(0.013)(0.035)(0.035)(0.037)
(log) Accumulated precipitation0.017***0.0290.0230.042*
(0.007)(0.018)(0.016)(0.021)
Observations432432414432
AR(1)0.3290.2700.2090.06
Sample period1986–2009

Notes: All columns report estimates using panel data estimators controlling for first-order autoregressive (AR(1)) disturbances. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include individual effects, year effects, and time trends by city. Treatment Unit (one city): Maldonado; Control Units (17 cities): Artigas, Canelones, Colonia, Durazno, Florida, Fray Bentos, Melo, Mercedes, Minas, Paysandú, Rivera, Rocha, Salto, San José de Mayo, Tacuarembó, Treinta y Tres, and Trinidad.

Source: Authors' estimation using ECH and National Meteorology Office data.

*, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.

Table 6.
Open in new tab

Impact of Nationalization on Network Access Controlling for First-order Autoregressive Disturbances

VariablesPooled FGLSPooled OLSWithin EstimatorWithin Estimator D-K
Private provision−0.003−0.019−0.007−0.024
(0.019)(0.037)(0.048)(0.038)
Renationalized provision0.163***0.159***0.168***0.168***
(0.026)(0.048)(0.066)(0.059)
Education0.024***0.023***0.019**0.032**
(0.003)(0.008)(0.008)(0.011)
(log) Household per capita income0.159***0.157***0.164***0.137***
(0.013)(0.035)(0.035)(0.037)
(log) Accumulated precipitation0.017***0.0290.0230.042*
(0.007)(0.018)(0.016)(0.021)
Observations432432414432
AR(1)0.3290.2700.2090.06
Sample period1986–2009
VariablesPooled FGLSPooled OLSWithin EstimatorWithin Estimator D-K
Private provision−0.003−0.019−0.007−0.024
(0.019)(0.037)(0.048)(0.038)
Renationalized provision0.163***0.159***0.168***0.168***
(0.026)(0.048)(0.066)(0.059)
Education0.024***0.023***0.019**0.032**
(0.003)(0.008)(0.008)(0.011)
(log) Household per capita income0.159***0.157***0.164***0.137***
(0.013)(0.035)(0.035)(0.037)
(log) Accumulated precipitation0.017***0.0290.0230.042*
(0.007)(0.018)(0.016)(0.021)
Observations432432414432
AR(1)0.3290.2700.2090.06
Sample period1986–2009

Notes: All columns report estimates using panel data estimators controlling for first-order autoregressive (AR(1)) disturbances. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include individual effects, year effects, and time trends by city. Treatment Unit (one city): Maldonado; Control Units (17 cities): Artigas, Canelones, Colonia, Durazno, Florida, Fray Bentos, Melo, Mercedes, Minas, Paysandú, Rivera, Rocha, Salto, San José de Mayo, Tacuarembó, Treinta y Tres, and Trinidad.

Source: Authors' estimation using ECH and National Meteorology Office data.

*, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.

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