The Dynamics of Child Poverty in Sweden

The purpose of this paper is to study (empirically) the dynamics of child poverty in Sweden, the quintessential welfare state. We find that 1 out of every 5 children is disposable income poor at least once during his or her childhood, while only 2 percent of all children are chronically poor. We also document a strong life-cycle profile for child poverty. Just over 20 percent of all children are born into poverty. The average poverty rate then drops dramatically to about 7.5 percent among 1-year old children. After which, it declines (monotonically) to about 3.9 percent among 17-year olds. Children in Sweden are largely protected (economically) from a number of quite serious events, such as parental unemployment, sickness and death. Family dissolution and longterm unemployment, however, do push children into poverty. But for most of these children, poverty is only temporary. Single mothers, for example, are overrepresented among the poor, but not among the chronically poor. Children with immigrant parents are strongly overrepresented among the chronically poor; as are children whose parents have unusually low educations. We argue that information about the dynamics of child poverty may help policy makers to construct more salient policies for fighting child poverty.child poverty; chronic poverty; poverty dynamics

Capital-Skill Complementarity and Inequality in Sweden

Income inequality increased in Sweden during the 1980’s and 90’s as did the returns to higher education. The main conclusion of this study is that increased income inequality between high and low skilled workers is demand driven and is due to the presence of capital-skill complementarity in production. Increased investments in new, more efficient capital equipment, together with a slowdown in the growth rate of skilled labor, have raised the ratio of effective capital inputs per skilled worker, which, in turn, has increased the relative demand (and market return) for skilled labor through the capital-skill complementarity mechanism.capital-skill complementarity; inequality; relative wages; skill premium; university wage premium

Life-Cycle Variations in the Association between Current and Lifetime Income: Country, Cohort and Gender Comparisons

This study applies Haider and Solon’s (2005) generalized errors-in-variables model to Swedish income tax data in order to produce estimates of the association between current and lifetime income. Our estimates of this association demonstrate strong life-cycle patterns. This implies that the widespread use of current income as a proxy for lifetime income (following the standard errors-in-variables model) leads to inconsistent parameter estimates (a.k.a. life-cycle bias). Estimates for comparable cohorts of Swedish and American men demonstrate surprising similarities. There are, however, significant gender and cohort differences in this association which, in turn, lead to statistically significant and quantitatively meaningful differences in life-cycle biases. The results from this study can aid the applied researcher in analyzing and correcting for life-cycle bias.errors-in-variables model; life-cycle bias; lifetime income

Is the Swedish Central Government a Wage Leader?

Is the Swedish central government a wage leader? This question is studied empirically in a vector error-correction model using a unique, high quality data set. Private sector salaries are found to be weakly exogenous to the system of equations. This means that the private sector is the wage leader in the long-run model. We also find that salaries in these two sectors do not converge to a common salary in the long-run and that changes in central government salaries do not Granger cause changes in private sector salaries. Together, these findings clearly demonstrate that the central government is not placing undue pressure on salaries in the private sector. The central government is not acting as a wage leader.public sector wages; Sweden; vector error-correction model; wage leadership.

What More Than Parental Income? An Exploration of What Swedish Siblings Get from Their Parents

Sibling correlations are used as overall measures of the impact of family background and community influences on individual outcomes. While most correlation studies show that siblings are quite similar in terms of future achievement, we lack specific knowledge of what it is about family background that really matters. Studies on intergenerational income mobility show that parental income matters to some extent, but they also show that more than half of the family background and community influences that siblings share are not even correlated with parental income. In this paper, we employ a data set that contains rich information about families in order to explore what factors in addition to parental income can explain why siblings tend to have such similar outcomes. Our results show that measures of family structure and social problems account for very little of sibling similarities in adult income above and beyond that already accounted for by parental income. However, when we add a set of indicators for parental involvement and attitudes, the explanatory power of all our variables increased from about a third (using only traditional indicators of socio-economic status) to just over half. Interestingly, indicators of parents' patience, i.e., propensity to plan ahead and willingness to postpone benefits to the future, are particularly important.family background, intergenerational mobility, parents, siblings, long-run income

Removal of flowmeter bearings from blind cavities

Bearings are removed by the application of a simple hydraulic principle using beeswax in place of a liquid

Monitoring Global Forest Land-Use and Change

Earth’s forests contain nearly three-fourths of the World’s floral and faunal diversity, function as a large carbon sink capable of mitigating the effects of global climate change, affect local and regional physical and chemical cycles and provide wood and non-wood products. However, humans are now capable of modifying their environment in ways more impactful and at rates faster than at any other time in history. Consistent and comparable estimates of global forest land-use and change are critical for monitoring human impacts on the Earth system. International treaties and reporting requirements aimed at safeguarding the delivery of forest-related ecosystem services depend on such estimates for measuring progress against their stated goals. Many existing studies have estimated tree cover and change at a variety of spatial scales from local to global. However, this existing research focuses largely on land cover classification, but generally lacks ecological context for estimating true human land use. The objective of this dissertation is to address this gap by exploring how forest land use can be mapped and monitored using medium spatial resolution optical satellite imagery in order to estimate forest land use change over time for large geographic areas. First, the effects of clouds, cloud shadows and missing data were analyzed to determine the amount of moderate spatial resolution, optical satellite data needed to detect and map land cover changes over large, spatially continuous areas on frequent time intervals. Second, an alternative method to spatially exhaustive mapping was developed and tested for estimating land cover and land use change globally employing object-based image analysis and a sample-based estimation approach. The method facilitated expert human intervention to identify true land use change in an operational way. Finally, these methods were applied to a globally distributed sample of remotely sensed data for the time periods 1990, 2000 and 2005. The results of this research produced the first consistent and comparable global time-series dataset of forest land-use estimates

Data Mining in Electronic Media Usage Statistics: A Case Study of Knowledge Discovery in Databases

As databases grow larger, analysts are turning to computers to help them analyze the massive amounts of data their computers have collected. As the difference between having data and having useful information becomes more clear, different methods of using computers to analyze data are becoming available. Knowledge Discovery in Databases (KDD) is a general methodology for preparing the data, using software algorithms to discover new patterns or relationships in the data, and integrating the results back into the system. The KDD methodology is explained and hypothetically applied to usage statistics generated by the CSB/SJU Libraries Internet resources. Examples are drawn from that source and from other industries to clearly illustrate the properties of Knowledge Discovery and decide if KDD is an appropriate methodology for the Libraries to use in this situation