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FDI and Inequality in Latin American Middle-Income Economies
Unformatted Document Text:  12 presence of this country effect, LSDV is the main technique used for analysis of panel data when time is dominant over units, 16 producing unbiased and also efficient ß (given the small number of units in the analysis). The estimated models assume that the disturbances across panel are heteroscedastic. I also assume first-order autocorrelation within panels (an AR1 process). 17 In the presence of unit effects, the alternatives to a LSDV model are two: to lag the dependent variable and estimate an OLS regression with panel corrected standard errors, 18 or estimate the GLSE ‘random effect’ model. 19 In spite of the fact that a shortcoming of LSDV ‘fixed effects’ for time dominant designs, in comparison to GLSE ‘random effect,’ is that in the presence of time invariant regressors, these will be collinear with LSDV dummies (Beck, 2001). In the concrete case of my models, the slow moving regressors included vary more across time than across units. LSDV model results should be considered conservative in the sense that low significance relationships among regressors and the inequality measure could be masked. Nevertheless, in the world of model alternatives and assumption violations, a type II error is preferable to model misspecification. Table 3 displays the analysis results for the Step 1 models. Model 1 summarizes the analysis of the food manufacturing sector FDI effect on employment in manufacturing, and includes the major macroeconomic and domestic economic factors and welfare variables emphasized in previous studies. As expected, Food manufacturing FDI has a significant (p ≤ .05) negative effect on manufacturing employment. This result supports 16 One of the most important critiques to LSDV model is that it removes degrees of freedom. In this case, however, the analysis of seven countries (units) will have a cost of 6 degrees of freedom. 17 Heteroskedasticity and AR1 are not tested given the presence of unit effect and the choice for LSDV model. 18 I opted not to lag the dependent variable precisely because, as Achen (2000) and others warn against, LDVs tend to dominate the regression equation, generating downwardly biased coefficient estimates on the explanatory variables. When lagging, both in step 1 models (employment by sector as dependent variable) and in step 2 models (Gini as dependent variable) the LDV included explains most of the variance of the former, affecting the significance of the other ß estimates. From a substantive point of view, this leads to a useless auto-deterministic explanation of inequality. 19 The decision to use fixed effects over random effects laid both on theoretical and methodological considerations. With respect to the substance, random-effects would be more appropriate if the sample is drawn from a substantially larger population, factor that in the case of cross-country studies favors the fixed-effects model. On top of this, the ‘random effects’ model assumption of no correlation of unit specific errors with model’s independent variables is a too strong one for country panel data.

Authors: Bogliaccini, Juan.
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12
presence of this country effect, LSDV is the main technique used for analysis of panel
data when time is dominant over units,
16
producing unbiased and also efficient ß (given
the small number of units in the analysis). The estimated models assume that the
disturbances across panel are heteroscedastic. I also assume first-order autocorrelation
within panels (an AR1 process).
17
In the presence of unit effects, the alternatives to a
LSDV model are two: to lag the dependent variable and estimate an OLS regression with
panel corrected standard errors,
18
or estimate the GLSE ‘random effect’ model.
19
In spite of the fact that a shortcoming of LSDV ‘fixed effects’ for time dominant designs,
in comparison to GLSE ‘random effect,’ is that in the presence of time invariant
regressors, these will be collinear with LSDV dummies (Beck, 2001). In the concrete
case of my models, the slow moving regressors included vary more across time than
across units. LSDV model results should be considered conservative in the sense that low
significance relationships among regressors and the inequality measure could be masked.
Nevertheless, in the world of model alternatives and assumption violations, a type II error
is preferable to model misspecification.
Table 3 displays the analysis results for the Step 1 models. Model 1 summarizes the
analysis of the food manufacturing sector FDI effect on employment in manufacturing,
and includes the major macroeconomic and domestic economic factors and welfare
variables emphasized in previous studies. As expected, Food manufacturing FDI has a
significant (p ≤ .05) negative effect on manufacturing employment. This result supports
16
One of the most important critiques to LSDV model is that it removes degrees of freedom. In this case,
however, the analysis of seven countries (units) will have a cost of 6 degrees of freedom.
17
Heteroskedasticity and AR1 are not tested given the presence of unit effect and the choice for LSDV
model.
18
I opted not to lag the dependent variable precisely because, as Achen (2000) and others warn against,
LDVs tend to dominate the regression equation, generating downwardly biased coefficient estimates on the
explanatory variables. When lagging, both in step 1 models (employment by sector as dependent variable)
and in step 2 models (Gini as dependent variable) the LDV included explains most of the variance of the
former, affecting the significance of the other ß estimates. From a substantive point of view, this leads to a
useless auto-deterministic explanation of inequality.
19
The decision to use fixed effects over random effects laid both on theoretical and methodological
considerations. With respect to the substance, random-effects would be more appropriate if the sample is
drawn from a substantially larger population, factor that in the case of cross-country studies favors the
fixed-effects model. On top of this, the ‘random effects’ model assumption of no correlation of unit specific
errors with model’s independent variables is a too strong one for country panel data.


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