Spring water stress in Scots pine

Water use and net carbon assimilation during spring was examined on Scots pine trees exposed to different soil warming dynamics in the field. Sap flow, needle water potential and net carbon assimilation were measured on trees that were exposed to a wide range of soil temperature regimes caused by manipulating the snow cover on tree-scale soil plots. This made it possible to quantify the sensitivity of water uptake and recovery of gas exchange by Scots pine in the critical transition from winter dormancy to the growing season, which can be influenced by silvicultural practices. A part of the study was to find a tool for estimating the coupled effect of belowground and aboveground climate on transpiration, as well as to adapt this tool to the harsh climate of the boreal forest. Combining the results of field experiments on tree susceptibility to water stress with a physically based SVAT model as well as a model for estimating the recovery of photosynthesis helped to predict spatial and inter-annual variability of snow depths, soil warming, water uptake and net primary productivity during spring within different Scots pine stands across the landscape. This could provide a better basis for a more frostconscious forest management. The studies have confirmed the importance of low soil temperatures in combination with aboveground climate for root water uptake and net carbon assimilation during spring, when soil warming occurs after the start of the growing season. The studies have also confirmed that earlier, controlled laboratory studies on the inhibiting effects of low soil temperature on water relations and gas exchange for seedlings or saplings also hold true on mature trees in the field. The experimental data served well as the basis for model analyses of the interaction between belowground and aboveground conditions on water use and net photosynthesis. The results of the field studies and model analyses suggest that the effect of soil temperature on tree water uptake and net photosynthesis during spring, in conjunction with aboveground conditions, are factors that need to be considered in forest management in areas susceptible to soil frost and low soil temperatures

On Omitted Variable Bias and Measurement Error in Returns to Schooling Estimates

Lam and Schoeni (1993) consider an equation where earnings are explained by schooling and ability. They assume that ability data are lacking and that schooling is measured with error. The estimate obtained by regressing earnings on schooling thus contains omitted variable bias (OVB), which is positive, and measurement error bias (MEB), which is negative. Adding a family background variable to proxy for ability is claimed to: i) decrease the OVB towards, but not below, zero and ii) make the MEB even more negative. This note claims that while ii) is true, even in the context of multiple family background variables, i) is in general incorrect. The OVB may increase in magnitude and/or change sign. Conditions are provided under which i) holds. A simulation procedure is suggested that will yield consistent estimates of the total bias and its components, conditional upon values on the true return and the measurement error variance

Proxying ability by family background in returns to schooling estimations is generally a bad idea

A regression model is considered where earnings are explained by schooling and ability. It is assumed that schooling is measured with error and that there are no data on ability. Regressing earnings on observed schooling then yields an estimate of the return to schooling that is subject to positive omitted variable bias (OVB) and negative measurement error bias (MEB). The effects on the OVB and the MEB from using family background variables as proxies for ability are investigated theoretically and empirically. The theoretical analysis demonstrates that the impact on the OVB is uncertain, while the MEB invariably increases in magnitude. The empirical analysis shows that the MEB generally dominates the OVB. As the measurement error increases and/or more family background variables are added, the total bias rapidly becomes negative, driving the estimated return further and further away from the true value.Missing data; proxy variables; measurement error; consistent estimates of omitted variable bias and measurement error bias

A General FIML Estimator for a Certain Class of Models that are Non-Linear in the Variables

Most econometric multi-equation models estimated are assumed to be linear in both the variables and the parameters. One reason is that, in general, methods of linear algebra cannot be applied to nonlinear systems. In this paper a certain class of nonlinear models is defined, however, the members of which can be formulated in matrix terms. Particular interest is focused upon nonlinearities in the variables. An algorithm for full information maximum likelihood (FIML) is described, including the linear model as a special case. Neither the likelihood function presented, nor its first order derivates are overly complicated relative to the usual (linear) FIML case. The latter makes the suggested approach particularly attractive compared to 'derivative-free' methods when dealing with systems containing many parameters. It is also shown how the efficiency in the actual computations can be greatly increased by exploiting certain properties of the involved matrices

Is Human Capital the Key to the IT Productivity Paradox?

Unlike previous analyses, we consider (i) that IT may affect productivity growth both directly and indirectly, through human capital interactions, and (ii) possible externalities in the use of IT. Examining, hypothetically, the statistical consequences of erroneously disregarding (i) and (ii) we shed light on the small or negative growth effects found in early U.S. studies, as well as the positive impacts reported recently. Our empirical analysis uses a 14-industry panel for Swedish manufacturing 1986-95. We find that human capital developments made the average effect of IT essentially zero in 1986 and steadily increasing thereafter, and, also, generated large differences in growth effects across industries. IT-human Capital Complementarity; New Growth Theory; Applied Econometrics

Human capital is the key to the IT productivity paradox

Unlike previous analysis, we consider (i) possible externalities in the use of IT and (ii) IT and human capital interactions. Examining, hypothetically, the statistical consequences of erroneously disregarding (i) and (ii) we shed light on the small or negative growth effects found in early studies of the effects of IT on productivity growth, as well as the positive impacts reported more recently. Our empirical analysis uses a 14-industry panel for Swedish manufacturing 1986–95. We find that human capital developments made the average effect of IT essentially zero in 1986 and steadily increasing thereafter, and, also, generated large differences in growth effects across industries.IT productivity paradox; applied econometrics

Do regional payroll tax reductions boost employment?

Using a Difference-in-Differences approach we evaluate the effects of a 10 percentage points reduction in the payroll tax introduced in 2002 for firms in the northern part of Sweden. We find no employment effects for existing firms and can rule out that a 1 percentage point payroll tax reduction would increase employment with more than 0.2 percent. We do, however, find that tax reductions have significantly positive effects on the average wage bill per employee. These are likely to be driven by higher average wages, but might also be due to more hours worked. As a sensitivity check we investigate if reduced payroll taxes affect the likelihood of firm entry and exit, and find some support for a net firm inflow. Our attempts to assess concomitant effects on employment indicate that payroll tax reductions might yield increases in employment through the start-up of new firms.Payroll tax; Labour demand; Incidence; Firm entry/exit; Difference-in-Differences

The Multi-Dimensional Nature of Labor Demand and Skill-Biased Technical Change

Investigating the robustness of the skill-biased technical change hypothesis, this analysis incorporates two novel features. First, effective labor is modeled as the product of a quantity measure - number of employees with a given level of education - and a quality index. The quality index, depending on, i.a., demographic characteristics and fields-of-study, makes it possible to control for other time-varying factors, beside technical changes. Second, low-skilled labor is more disaggregated than in earlier studies, making it possible to investigate aggregation bias with respect to the low-skilled. A full structural model is used, containing equations for four categories of labor, two types of capital and intermediate goods. Short run and long run effects are distinguished by allowing structure capital (plants) to be fixed in the short run. The empirical application is based on a panel of 24 industries in the Swedish manufacturing sector 1985-1995. As a by-product of the specification of effective labor, improved wage instruments are obtained, through the joint estimation of the instrumental variables and the parameters in the labor quality indexes. Compared to earlier analyses, the skill-bias is further corroborated in this study: controlling for changes in labor quality does not render the bias insignificant. Technical change hurts the demand for low-skilled labor and it hits harder the lower the level of education. Positive effects are found only for university educated workers. Substantial differences are, however, found among two groups of workers both of whose levels of education are below upper secondary school. Thus, when considering policy measures, low-skilled workers should not be treated as a homogenous group.

The multi-dimensional nature of labor demand and skill-biased technical change

Investigating the robustness of the skill-biased technical change hypothesis, this analysis incorporates two novel features. First, effective labor is modeled as the product of a quantity measure - number of employees with a given level of education - and a quality index, depending on, i.a., demographic characteristics and fields-of-study. Second, low-skilled labor is more disaggregated than in earlier studies. A fully specified structural model is used, containing demand equations for four categories of labor, two types of capital and intermediate goods. The empirical application covers 24 industries in the Swedish manufacturing sector 1985-1995. The skill-bias is further corroborated: it is confirmed although the specification of effective labor is supported. Substantial differences are, however, found among the low-skilled.Robustness test; Labor quality; Heterogenous low-skilled; Manufacturing application