Search arXivSearch

arXiv · 1807.07051

A New Index of Human Capital to Predict Economic Growth

Abstract

The accumulation of knowledge required to produce economic value is a process that often relates to nations economic growth. Such a relationship, however, is misleading when the proxy of such accumulation is the average years of education. In this paper, we show that the predictive power of this proxy started to dwindle in 1990 when nations schooling began to homogenized. We propose a metric of human capital that is less sensitive than average years of education and remains as a significant predictor of economic growth when tested with both cross-section data and panel data. We argue that future research on economic growth will discard educational variables based on quantity as predictor given the thresholds that these variables are reaching.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Henry Laverde, Juan C. Correa, Klaus Jaffe. 2018-07-18. A New Index of Human Capital to Predict Economic Growth. https://arxiv.org/abs/1807.07051

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Summary Indices in Treatment Effect Estimation

This paper studies the practice of combining multiple outcomes into a summary index to estimate a causal effect. For common estimators and index constructions, the estimate equals a weighted sum of the estimated effects on the components, with weights that are implicit and rarely reported. The paper derives the weights and shows that, for inverse-covariance-weighted indices, they can be negative and unrestricted in magnitude, so the index effect can have the opposite sign to every component effect. The paper proposes two procedures for valid inference on the index effect: a variance estimator that accounts for the data-dependent weights, and a shifted t-test that requires no such correction. Conventional t-tests of the null of no effect remain valid. Contrary to common claims, summary indices do not generally improve power. Three published studies illustrate the results.

econ.EM

The "Rough" HAR Model

This paper proposes discrete-time approximations to rough continuous-time models of realized variance (RV). The leading rough models can be viewed as autoregressive processes driven by fractional Gaussian noise. We show that the Wold representation of this noise concentrates its dependence at the first lag when the Hurst parameter is below one half. Augmenting the autoregressive (AR) and heterogeneous autoregressive (HAR) models with a first-order moving-average (MA(1)) component therefore approximates the roughness, and the MA coefficient maps almost linearly into the Hurst parameter. We refer to these extensions as the "rough" AR and "rough" HAR models. Estimating them on the log RV of ten ETFs, we find negative MA coefficients for every asset, and the implied Hurst parameters align closely with the estimates from the continuous-time models. In the HAR literature, the negative MA(1) component is a significant feature that has been largely overlooked. In out-of-sample comparisons, the "rough" models outperform their classical counterparts for nearly every asset and horizon, with the largest gains at short horizons, and their accuracy is comparable to that of the rough continuous-time models but much easier to estimate by standard off-the-shelf software.

econ.EM

Match forecasts in UEFA club competitions: Elo ratings versus Transfermarkt valuations

The pre-season strengths of European football clubs are usually measured by two proxies in the literature. Football Club Elo Ratings provide strictly performance-based Elo ratings from the early days of the European Cups, while Transfermarkt valuations are crowd-based estimates of squad market values. This paper compares them by evaluating their ability to forecast the results of matches played in the UEFA Champions League and the UEFA Europa League between the seasons 2020/21 and 2024/25. The two indicators yield almost identical out-of-sample accuracy when used separately. Combining the two measures leads to a modest improvement, but the best aggregation procedure is sensitive to the forecast target. Our results suggest that seeding based on Elo ratings would be (closely) optimal.

econ.EM