How financial incentives induce disability insurance recipients to return to work

Using a local randomized experiment that arises from a sharp discontinuity in Disability Insurance (DI) policy in Norway, we provide transparent and credible identification of how financial incentives induce DI recipients to return to work. We find that many DI recipients have considerable capacity to work that can be effectively induced by providing financial work incentives. We further show that providing work incentives to DI recipients may both increase their disposable income and reduce program costs. Our findings also suggest that targeted policies may be the most effective in encouraging DI recipients to return to work

Are Lone Mothers Responsive to Policy Changes? Evidence from a Workfare Reform in a Generous Welfare State

There is a heated debate in many developed countries about how to design a welfare system that moves lone mothers off welfare and into work. We analyze the consequences of a major Norwegian workfare reform of the generous welfare system for lone mothers. The reform imposed work requirements and time limits on welfare receipt, while raising in-work benefits. Our difference-in-differences estimates show that the reform was successful in improving labor-market participation and in increasing the earnings of lone mothers. However, the reform was associated with income loss and increased poverty among a sizeable subgroup of lone mothers, who were unable to offset the loss of out-of-work benefits with gains in earnings. © The editors of The Scandinavian Journal of Economics 2012

Peer Effects in Program Participation

We estimate peer effects in paid paternity leave in Norway using a regression discontinuity design. Coworkers and brothers are 11 and 15 percentage points, respectively, more likely to take paternity leave if their peer was exogenously induced to take up leave. The most likely mechanism is information transmission, including increased knowledge of how an employer will react. The estimated peer effect snowballs over time, as the first peer interacts with a second peer, the second peer with a third, and so on. This leads to long-run participation rates which are substantially higher than would otherwise be expected

Family welfare cultures

Strong intergenerational correlations in various types of welfare use have fueled a long standing debate over whether welfare dependency in one generation causes welfare dependency in the next generation. Some claim a culture has developed in which welfare use reinforces itself through the family, because parents on welfare provide information about the program to their children, reduce the stigma of participation, or invest differentially in child development. Others argue the determinants of poverty or poor health are correlated across generations, so that children's welfare participation is associated with, but not caused by, parental welfare use. However, there is little empirical evidence to sort out these claims. In this paper, we investigate the existence and importance of family welfare cultures in the context of Norway's disability insurance (DI) system. To overcome the challenge of correlated unobservables across generations, we take advantage of random assignment of judges to DI applicants whose cases are initially denied. Some appeal judges are systematically more lenient, which leads to random variation in the probability a parent will be allowed DI. Using this exogenous variation, we find strong evidence that welfare use in one generation causes welfare use in the next generation: when a parent is allowed DI, their adult child's participation over the next five years increases by 6 percentage points. This effect grows over time, rising to 12 percentage points after ten years. Using our estimates, we simulate the total reduction in DI participation from a policy which makes the screening process more stringent; the intergenerational link amplifies the direct effect on parents at the margin of program entry, leading to long-run participation rates and program costs which are substantially lower than would otherwise be expected. The detailed nature of our data allows us to explore the mechanisms behind the causal intergenerational relationship; we find suggestive evidence against stigma and parental investments and in favor of children learning from a parent's experience with the DI program

Confidence Sets for Ranks with Applications to Intergenerational Mobility and Neighborhoods

It is often desired to rank different populations according to the value of some feature of each population. For example, it may be desired to rank neighborhoods according to some measure of intergenerational mobility or countries according to some measure of academic achievement. These rankings are invariably computed using estimates rather than the true values of these features. As a result, there may be considerable uncertainty concerning the rank of each population. In this paper, we consider the problem of accounting for such uncertainty by constructing confidence sets for the rank of each population. We consider both the problem of constructing marginal confidence sets for the rank of a particular population as well as simultaneous confidence sets for the ranks of all populations. We show how to construct such confidence sets satisfying desired coverage properties under weak assumptions. An important feature of all of our constructions is that they remain computationally feasible even when the number of populations is very large. We apply our theoretical results to re-examine the rankings of both neighborhoods in the United States in terms of intergenerational mobility and developed countries in terms of academic achievement. The conclusions about which countries do best and worst at reading, math, and science are fairly robust to accounting for uncertainty. By comparison, several celebrated findings about intergenerational mobility in the United states are not robust to taking uncertainty into account

Measuring long-term inequality of opportunity

In this paper, we introduce a new family of rank-dependent measures of inequality and social welfare consistent with the equality of opportunity (EOp) principle. The proposed framework can be used to measure long-term as well as short-term EOp, depending on whether we let permanent income or snapshots of income form the basis of the analysis. Furthermore, it allows for both an ex-ante and an ex-post approach to EOp. There is long-term ex-post inequality of opportunity if individuals who exert the same effort have different permanent incomes. In comparison, the ex-ante approach focuses on differences in the expected permanent income between groups of individuals with identical circumstances. To demonstrate the empirical relevance of a long-run perspective on EOp, we exploit a unique panel data from Norway on individuals' incomes over their working life span. This allows us to examine how well analysis of opportunity inequality based on snapshots of income approximate the results based on permanent income

Inference for ranks with applications to mobility across neighborhoods and academic achievement across countries

It is often desired to rank different populations according to the value of some feature of each population. For example, it may be desired to rank neighborhoods according to some measure of intergenerational mobility or countries according to some measure of academic achievement. These rankings are invariably computed using estimates rather than the true values of these features. As a result, there may be considerable uncertainty concerning the rank of each population. In this paper, we consider the problem of accounting for such uncertainty by constructing confidence sets for the rank of each population. We consider both the problem of constructing marginal confidence sets for the rank of a particular population as well as simultaneous confidence sets for the ranks of all populations. We show how to construct such confidence sets under weak assumptions. An important feature of all of our constructions is that they remain computationally feasible even when the number of populations is very large. We apply our theoretical results to re-examine the rankings of both neighborhoods in the United States in terms of intergenerational mobility and developed countries in terms of academic achievement. The conclusions about which countries do best and worst at reading, math, and science are fairly robust to accounting for uncertainty. The confidence sets for the ranking of the 50 most populous commuting zones by mobility are also found to be small. However, the mobility rankings become much less informative if one includes all commuting zones, if one considers neighborhoods at a more granular level (counties, Census tracts), or if one uses movers across areas to address concerns about selection

Finite- and Large-Sample Inference for Ranks using Multinomial Data with an Application to Ranking Political Parties

It is common to rank different categories by means of preferences that are revealed through data on choices. A prominent example is the ranking of political candidates or parties using the estimated share of support each one receives in surveys or polls about political attitudes. Since these rankings are computed using estimates of the share of support rather than the true share of support, there may be considerable uncertainty concerning the true ranking of the political candidates or parties. In this paper, we consider the problem of accounting for such uncertainty by constructing confidence sets for the rank of each category. We consider both the problem of constructing marginal confidence sets for the rank of a particular category as well as simultaneous confidence sets for the ranks of all categories. A distinguishing feature of our analysis is that we exploit the multinomial structure of the data to develop confidence sets that are valid in finite samples. We additionally develop confidence sets using the bootstrap that are valid only approximately in large samples. We use our methodology to rank political parties in Australia using data from the 2019 Australian Election Survey. We find that our finite-sample confidence sets are informative across the entire ranking of political parties, even in Australian territories with few survey respondents and/or with parties that are chosen by only a small share of the survey respondents. In contrast, the bootstrap-based confidence sets may sometimes be considerably less informative. These findings motivate us to compare these methods in an empirically-driven simulation study, in which we conclude that our finite-sample confidence sets often perform better than their large-sample, bootstrap-based counterparts, especially in settings that resemble our empirical application