15 1991,the sector with the most respondents was mechanical engineering.The industries meeting our two conditions for being FDI exposed vary over time,with sectors such as instrument engineering and business services being added to the list. 3.2 Econometric Models By matching each BHPS observation with the relevant industry FDI information,we examine how self-assessments of economic insecurity relate to FDI exposure.We formalize the determinants of economic insecurity as follows, Insecurity =a;+BFDI+Z+(1) where the subscript i indexes individuals;the subscript t indexes years;Insecurity is our measure of economic insecurity;FDI,,is one of our measures of FDI exposure;the vector Z,includes dichotomous indicators for each year and,in many specifications,the control regressors discussed above;ai,B,and y are parameters to be estimated;and s,is an additive error term. The coefficient estimates of B in Equation(1)indicate whether and to what extent individual perceptions of economic insecurity are correlated with FDI exposure.Exposure to FDI activity is increasing in each of our three FDI variables,and we expect this to be positively correlated with the dependent variable Insecurity.This is the central hypothesis of our empirical analysis. Thus,our null hypothesis is that B=0,with the alternative B>0. The panel nature of the BHPS data is indicated in (1)by the i and t indexes.Pooling individuals across years has obvious advantages but generates a number of estimation issues regarding individual heterogeneity.It is likely that observations over time for the same individual will be more similar than observations across different individuals.This might be due to persistence in or unmodeled characteristics of individual perceptions of economic insecurity. This is particularly pertinent to our analysis because,as discussed above,there are good reasons15 1991, the sector with the most respondents was mechanical engineering. The industries meeting our two conditions for being FDI exposed vary over time, with sectors such as instrument engineering and business services being added to the list. 3.2 Econometric Models By matching each BHPS observation with the relevant industry FDI information, we examine how self-assessments of economic insecurity relate to FDI exposure. We formalize the determinants of economic insecurity as follows, it i it Zit it Insecurity =α + βFDI +γ +ε (1) where the subscript i indexes individuals; the subscript t indexes years; Insecurityit is our measure of economic insecurity; FDIit is one of our measures of FDI exposure; the vector Zit includes dichotomous indicators for each year and, in many specifications, the control regressors discussed above; αi, β, and γ are parameters to be estimated; and εit is an additive error term. The coefficient estimates of β in Equation (1) indicate whether and to what extent individual perceptions of economic insecurity are correlated with FDI exposure. Exposure to FDI activity is increasing in each of our three FDI variables, and we expect this to be positively correlated with the dependent variable Insecurity. This is the central hypothesis of our empirical analysis. Thus, our null hypothesis is that β = 0, with the alternative β > 0. The panel nature of the BHPS data is indicated in (1) by the i and t indexes. Pooling individuals across years has obvious advantages but generates a number of estimation issues regarding individual heterogeneity. It is likely that observations over time for the same individual will be more similar than observations across different individuals. This might be due to persistence in or unmodeled characteristics of individual perceptions of economic insecurity. This is particularly pertinent to our analysis because, as discussed above, there are good reasons