Can Network Theory-based Targeting Increase Technology Adoption?

In order to induce farmers to adopt a productive new agricultural technology, we apply simple and complex contagion diffusion models on rich social network data from 200 villages in Malawi to identify seed farmers to target and train on the new technology. A randomized controlled trial compares these theory-driven network targeting approaches to simpler strategies that either rely on a government extension worker or an easily measurable proxy for the social network (geographic distance between households) to identify seed farmers. Our results indicate that technology diffusion is characterized by a complex contagion learning environment in which most farmers need to learn from multiple people before they adopt themselves. Network theory based targeting can out-perform traditional approaches to extension, and we identify methods to realize these gains at low cost to policymakers. Keywords: Social Learning, Agricultural Technology Adoption, Complex Contagion, Malawi JEL Classification Codes: O16, O13Comment: 61 page

Nonparametric tests of conditional treatment effects

We develop a general class of nonparametric tests for treatment effects conditional on covariates. We consider a wide spectrum of null and alternative hypotheses regarding conditional treatment effects, including (i) the null hypothesis of the conditional stochastic dominance between treatment and control groups; ii) the null hypothesis that the conditional average treatment effect is positive for each value of covariates; and (iii) the null hypothesis of no distributional (or average) treatment effect conditional on covariates against a one-sided (or two-sided) alternative hypothesis. The test statistics are based on L1-type functionals of uniformly consistent nonparametric kernel estimators of conditional expectations that characterize the null hypotheses. Using the Poissionization technique of Giné et al. (2003), we show that suitably studentized versions of our test statistics are asymptotically standard normal under the null hypotheses and also show that the proposed nonparametric tests are consistent against general fixed alternatives. Furthermore, it turns out that our tests have non-negligible powers against some local alternatives that are n−½ different from the null hypotheses, where n is the sample size. We provide a more powerful test for the case when the null hypothesis may be binding only on a strict subset of the support and also consider an extension to testing for quantile treatment effects. We illustrate the usefulness of our tests by applying them to data from a randomized, job training program (LaLonde, 1986) and by carrying out Monte Carlo experiments based on this dataset

Optimal Uniform Convergence Rates for Sieve Nonparametric Instrumental Variables Regression

We study the problem of nonparametric regression when the regressor is endogenous, which is an important nonparametric instrumental variables (NPIV) regression in econometrics and a difficult ill-posed inverse problem with unknown operator in statistics. We first establish a general upper bound on the sup-norm (uniform) convergence rate of a sieve estimator, allowing for endogenous regressors and weakly dependent data. This result leads to the optimal sup-norm convergence rates for spline and wavelet least squares regression estimators under weakly dependent data and heavy-tailed error terms. This upper bound also yields the sup-norm convergence rates for sieve NPIV estimators under i.i.d. data: the rates coincide with the known optimal $L^2$-norm rates for severely ill-posed problems, and are power of $\log(n)$ slower than the optimal $L^2$-norm rates for mildly ill-posed problems. We then establish the minimax risk lower bound in sup-norm loss, which coincides with our upper bounds on sup-norm rates for the spline and wavelet sieve NPIV estimators. This sup-norm rate optimality provides another justification for the wide application of sieve NPIV estimators. Useful results on weakly-dependent random matrices are also provided

Bias in Estimating Multivariate and Univariate Diffusions

Published in Journal of Econometrics, 2011, https://doi.org/10.1016/j.jeconom.2010.12.006</p

Uncertain growth and the value of the future

For environmental problems such as global warming future costs must be balanced against present costs. This is traditionally done using an exponential function with a constant discount rate, which reduces the present value of future costs. The result is highly sensitive to the choice of discount rate and has generated a major controversy as to the urgency for immediate action. We study analytically several standard interest rate models from finance and compare their properties to empirical data. From historical time series for nominal interest rates and inflation covering 14 countries over hundreds of years, we find that extended periods of negative real interest rates are common, occurring in many epochs in all countries. This leads us to choose the Ornstein-Uhlenbeck model, in which real short run interest rates fluctuate stochastically and can become negative, even if they revert to a positive mean value. We solve the model in closed form and prove that the long-run discount rate is always less than the mean; indeed it can be zero or even negative, despite the fact that the mean short term interest rate is positive. We fit the parameters of the model to the data, and find that nine of the countries have positive long run discount rates while five have negative long-run discount rates. Even if one rejects the countries where hyperinflation has occurred, our results support the low discounting rate used in the Stern report over higher rates advocated by others.Comment: 8 pages, 4 figure

Mediation and peace

This paper applies mechanism design to conflict resolution. We determine when and how unmediated communication and mediation reduce the ex ante probability of conflict in a game with asymmetric information. Mediation improves upon unmediated communication when the intensity of conflict is high, or when asymmetric information is significant. The mediator improves upon unmediated communication by not precisely reporting information to conflicting parties, and precisely, by not revealing to a player with probability one that the opponent is weak. Arbitrators who can enforce settlements are no more effective than mediators who only make non-binding recommendations

Rural-Urban Migration, Urban Employment and Underemployment, and Job Search Activity in LDCs

[Excerpt] In this paper, we shall present a formal theoretical mode with which to analyze the equilibrium allocation of the labor force between labor markets. Our basic premise is that the same kinds of forces that explain the choices of workers between the rural and urban sectors can also explain their choices between one labor market and another within an urban area and are probably made simultaneously. The decision-makers -- be they individuals or family units are presumed to consider the various labor market opportunities available to them and to choose the one which maximizes their expected future income

Monte Carlo Confidence Sets for Identified Sets

In complicated/nonlinear parametric models, it is generally hard to know whether the model parameters are point identified. We provide computationally attractive procedures to construct confidence sets (CSs) for identified sets of full parameters and of subvectors in models defined through a likelihood or a vector of moment equalities or inequalities. These CSs are based on level sets of optimal sample criterion functions (such as likelihood or optimally-weighted or continuously-updated GMM criterions). The level sets are constructed using cutoffs that are computed via Monte Carlo (MC) simulations directly from the quasi-posterior distributions of the criterions. We establish new Bernstein-von Mises (or Bayesian Wilks) type theorems for the quasi-posterior distributions of the quasi-likelihood ratio (QLR) and profile QLR in partially-identified regular models and some non-regular models. These results imply that our MC CSs have exact asymptotic frequentist coverage for identified sets of full parameters and of subvectors in partially-identified regular models, and have valid but potentially conservative coverage in models with reduced-form parameters on the boundary. Our MC CSs for identified sets of subvectors are shown to have exact asymptotic coverage in models with singularities. We also provide results on uniform validity of our CSs over classes of DGPs that include point and partially identified models. We demonstrate good finite-sample coverage properties of our procedures in two simulation experiments. Finally, our procedures are applied to two non-trivial empirical examples: an airline entry game and a model of trade flows

Causes of the Financial Crisis

[Excerpt] The current financial crisis began in August 2007, when financial stability replaced inflation as the Federal Reserve’s chief concern. The roots of the crisis go back much further, and there are various views on the fundamental causes. It is generally accepted that credit standards in U.S. mortgage lending were relaxed in the early 2000s, and that rising rates of delinquency and foreclosures delivered a sharp shock to a range of U.S. financial institutions. Beyond that point of agreement, however, there are many questions that will be debated by policymakers and academics for decades. Why did the financial shock from the housing market downturn prove so difficult to contain? Why did the tools the Fed used successfully to limit damage to the financial system from previous shocks (the Asian crises of 1997-1998, the stock market crashes of 1987 and 2000-2001, the junk bond debacle in 1989, the savings and loan crisis, 9/11, and so on) fail to work this time? If we accept that the origins are in the United States, why were so many financial systems around the world swept up in the panic? To what extent were long-term developments in financial markets to blame for the instability? Derivatives markets, for example, were long described as a way to spread financial risk more efficiently, so that market participants could bear only those risks they understood. Did derivatives, and other risk management techniques, actually increase risk and instability under crisis conditions? Was there too much reliance on computer models of market performance? Did those models reflect only the post-WWII period, which may now come to be viewed not as a typical 60-year period, suitable for use as a baseline for financial forecasts, but rather as an unusually favorable period that may not recur? Did government actions inadvertently create the conditions for crisis? Did regulators fail to use their authority to prevent excessive risk-taking, or was their jurisdiction too limited and/or compartmentalized? While some may insist that there is a single cause, and thus a simple remedy, the sheer number of causal factors that have been identified tends to suggest that the current financial situation is not yet fully understood in its full complexity. This report consists of a table that summarizes very briefly some of the arguments for particular causes, presents equally brief rejoinders, and includes a reference or two for further reading. It will be updated as required by market developments