Knowledge that Transforms

The conditions under which young workers find their first real post-graduation jobs are important for their future careers and insufficiently documented given their potential importance for young workers welfare. To study these conditions, and in particular the role played by social ties, we use a Swedish population-wide linked employer–employee data set of graduates from all levels of schooling that includes detailed information on family ties, neighbourhoods, schools, class composition, and parents' and children's employers over a period covering years with both high and low unemployment, together with measures of firm performance. We find that strong social ties (parents) are an important determinant for where young workers find their first job. The effects are larger if the graduate's position is “weak” (low education, bad grades), during high unemployment years, and when information on potential openings are likely to be scarce. On the hiring side, by contrast, the effects are larger if the parent's position is “strong” (long tenure, high wage) and if the parent's plant is more productive. The youths appear to benefit from the use of strong social ties through faster access to jobs and by better labour market outcomes as measured a few years after entry. In particular, workers finding their entry jobs through strong social ties are considerably more likely to remain in this job, while experiencing better wage growth than other entrants in the same plant. Firms also appear to benefit from these wage costs (relative to comparable entrants) starting at a lower base. They also benefit on the parents' side; parents' wage growth drops dramatically exactly at the entry of one of their children in the plant, although this is a moment when firm profits tend to be growing. Indeed, the firm-side benefits appear large enough for (at least small) firms to increase job creation at the entry level in years when a child of one of their employees graduates.

This article provides evidence that schools can influence parents' involvement in education, and this has causal effects on pupils' behaviour. Furthermore, it shows how the impact of more involved parents on their children is amplified at the class level by peer group interaction. We build on a large-scale controlled experiment run in a French deprived educational district, where parents of middle-school children were invited to participate in a simple program of parent–school meetings on how to get better involved in their children's education. At the end of the school year, we find that treated families have increased their school-and home-based involvement activities. In turn, pupils of treatment classes have developed more positive behaviour and attitudes in school, notably in terms of truancy and disciplinary sanctions (with effects-size around 15% of a standard deviation). However, test scores did not improve under the intervention. Our results suggest that parents are an input for schooling policies and it is possible to influence important aspects of the schooling process at low cost.

We provide a solution to the informed-principal problem in the independent private values setting with monetary transfers. The principal's private information creates signaling considerations that may distort the implemented allocation. We show that there is no distortion: all principal types implement an allocation that is optimal for the principal ex ante, before he/she learns his/her type. As an application, we consider settings with linear utility. For bilateral exchange in which the principal is one of the traders, the solution is a combination of a participation fee, a buy-out option for the principal, and a resale stage with posted prices.

Large-scale transport infrastructure investments connect both large metropolitan centres of production as well as small peripheral regions. Are the resulting trade cost reductions a force for the diffusion of industrial and total economic activity to peripheral regions, or do they reinforce the concentration of production in space? This article exploits China's National Trunk Highway System as a large-scale natural experiment to contribute to our understanding of this question. The network was designed to connect provincial capitals and cities with an urban population above 500, 000. As a side effect, a large number of small peripheral counties were connected to large metropolitan agglomerations. To address non-random route placements on the way between targeted city nodes, I propose an instrumental variable strategy based on the construction of least cost path spanning tree networks. The estimation results suggest that network connections have led to a reduction in GDP growth among non-targeted peripheral counties. This effect appears to be driven by a significant reduction in industrial output growth. Additional results present evidence in support of a trade-based channel in the light of falling trade costs between peripheral and metropolitan regions.

Pay distributions fan out with experience. The leading explanations for this pattern are that over time, either employers learn about worker productivity but productivity remains fixed or workers' productivities themselves evolve heterogeneously. We propose a dynamic specification that nests both employer learning and dynamic productivity heterogeneity. We estimate this model on a 20-year panel of pay and performance measures from a single, large firm. The advantage of these data is that they provide us with repeat measures of productivity, some of which have not yet been observed by the firm when it sets wages. We use our estimates to investigate how learning and dynamic productivity heterogeneity jointly contribute to the increase in pay dispersion with age. We find that both mechanisms are important for understanding wage dynamics. The dispersion of pay increases with experience primarily because productivity differences increase. Imperfect learning, however, means that wages differ significantly from individual productivity all along the life cycle because firms continuously struggle to learn about a moving target in worker productivity. Our estimates allow us to calculate the degree to which imperfect learning introduces a wedge between the private and social incentives to invest in human capital. We find that these disincentives exist throughout the life cycle but increase rapidly after about 15 years of experience. Thus, in contrast to the existing literature on employer learning, we find that imperfect learning might have especially large effects on investments among older workers.

While employed, workers learn their comparative advantage and eventually choose occupations that best match their abilities. This learning process is consistent with a number of key facts about occupational mobility, such as the offsetting worker flows across occupations, the non-random patterns of occupational transitions, and the decline of occupational switching with age. We illustrate how search frictions delay learning and lead to mismatch, thereby reducing worker productivity. Moreover, we explore how different workers perform in different occupations. Are the best workers in one occupation also the best workers in another occupation (one-dimensional model of ability)? Or are some workers good at one occupation and other workers good at a different one (comparative advantage model)? The calibration favours the model of comparative advantage, as opposed to the widely used one-dimensional ability model. We use the calibrated model to investigate how the level of unemployment benefits affects worker productivity.

We estimate the effect of interstate highways on the level and composition of trade for US cities. Highways within cities have a large effect on the weight of city exports with an elasticity of approximately 0.5. We find little effect of highways on the total value of exports. Consistent with this, we find that cities with more highways specialize in sectors producing heavy goods.

We study information acquisition in a flexible framework with strategic complementarity or substitutability in actions and a rich set of externalities that are responsible for possible wedges between the equilibrium and the efficient acquisition of information. First, we relate the (in)efficiency in the acquisition of information to the (in)efficiency in the use of information and explain why efficiency in the use is no guarantee of efficiency in the acquisition. Next, we show how the acquisition of private information affects the social value of public information (i.e. the comparative statics of equilibrium welfare with respect to the quality of public information). Finally, we illustrate the implications of the results in a monetary economy with price rigidities and dispersed information about productivity shocks.

International audience

We propose robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances. We allow for the number of regressors to be larger than the sample size. To make informative inference feasible, we require the model to be approximately sparse; that is, we require that the effect of confounding factors can be controlled for up to a small approximation error by including a relatively small number of variables whose identities are unknown. The latter condition makes it possible to estimate the treatment effect by selecting approximately the right set of regressors. We develop a novel estimation and uniformly valid inference method for the treatment effect in this setting, called the “post-double-selection†method. The main attractive feature of our method is that it allows for imperfect selection of the controls and provides confidence intervals that are valid uniformly across a large class of models. In contrast, standard post-model selection estimators fail to provide uniform inference even in simple cases with a small, fixed number of controls. Thus, our method resolves the problem of uniform inference after model selection for a large, interesting class of models. We also present a generalization of our method to a fully heterogeneous model with a binary treatment variable. We illustrate the use of the developed methods with numerical simulations and an application that considers the effect of abortion on crime rates.