Knowledge that Transforms

Dynastic models common in macroeconomics use a single parameter to control the willingness of individuals to substitute consumption both intertemporally, or across periods, and intergenerationally, or across parents and their children. This article defines the concept of elasticity of intergenerational substitution (EGS), and extends a standard dynastic model in order to disentangle the EGS from the EIS, or elasticity of intertemporal substitution. A calibrated version of the model lends strong support to the notion that the EGS is significantly larger than one. In contrast, estimates of the EIS suggests that it is at most one. What disciplines the identification is the need to match empirically plausible fertility rates for the U.S. We illustrate the potential role of the EGS in macroeconomics.

© The Author(s) 2018. Low adoption of agricultural technologies holds large productivity consequences for developing countries. Many countries hire agricultural extension agents to communicate with farmers about new technologies, even though a large academic literature has established that information from social networks is a key determinant of product adoption. We incorporate social learning in extension policy using a large-scale field experiment in which we communicate to farmers using different members of social networks. We show that communicator own adoption and effort are susceptible to small performance incentives, and the social identity of the communicator influences others' learning and adoption. Farmers appear most convinced by communicators who share a group identity with them, or who face comparable agricultural conditions. Exploring the incentives for injection points in social networks to experiment with and communicate about new technologies can take the influential social learning literature in a more policy-relevant direction.

Can we identify highly central individuals in a network without collecting network data, simply by asking community members? Can seeding information via such nominated individuals lead to significantly wider diffusion than via randomly chosen people, or even respected ones? In two separate large field experiments in India, we answer both questions in the affirmative. In particular, in 521 villages in Haryana, we provided information on monthly immunization camps to either randomly selected individuals (in some villages) or to individuals nominated by villagers as people who would be good at transmitting information (in other villages). We find that the number of children vaccinated every month is 22% higher in villages in which nominees received the information. We show that people’s knowledge of who are highly central individuals and good seeds can be explained by a model in which community members simply track how often they hear gossip about others. Indeed, we find in a third data set that nominated seeds are central in a network sense, and are not just those with many friends or in powerful positions.

We study the implications of including many covariates in a first-step estimate entering a two-step estimation procedure. We find that a first-order bias emerges when the number of included covariates is “large” relative to the square-root of sample size, rendering standard inference procedures invalid. We show that the jackknife is able to estimate this “many covariates” bias consistently, thereby delivering a new automatic bias-corrected two-step point estimator. The jackknife also consistently estimates the standard error of the original two-step point estimator. For inference, we develop a valid post-bias-correction bootstrap approximation that accounts for the additional variability introduced by the jackknife bias-correction. We find that the jackknife bias-corrected point estimator and the bootstrap post-bias-correction inference perform excellent in simulations, offering important improvements over conventional two-step point estimators and inference procedures, which are not robust to including many covariates. We apply our results to an array of distinct treatment effect, policy evaluation, and other applied microeconomics settings. In particular, we discuss production function and marginal treatment effect estimation in detail.

The majority of innovations are developed by multi-sector firms. The knowledge needed to invent new products is more easily adapted from some sectors than from others. We study this network of knowledge linkages between sectors and its impact on firm innovation and aggregate growth. We first document a set of sectoral-level and firm-level observations on knowledge applicability and firms’ multi-sector patenting behaviour. We then develop a general equilibrium model of firm innovation in which inter-sectoral knowledge linkages determine the set of sectors a firm chooses to innovate in and how much R&D to invest in each sector. It captures how firms evolve in the technology space, accounts for cross-sector differences in R&D intensity, and describes an aggregate model of technological change. The model matches new observations as demonstrated by simulation. It also yields new insights regarding the mechanism through which sectoral fixed costs of R&D affect growth.

In this article, we use cross-state panel and cross-U.S. commuting-zone data to look at the relationship between innovation, top income inequality and social mobility. We find positive correlations between measures of innovation and top income inequality. We also show that the correlations between innovation and broad measures of inequality are not significant. Next, using instrumental variable analysis, we argue that these correlations at least partly reflect a causality from innovation to top income shares. Finally, we show that innovation, particularly by new entrants, is positively associated with social mobility, but less so in local areas with more intense lobbying activities.

This article proposes a strategic framework to account for fertility choices in polygamous households. It uses unique data on fertility histories of a representative sample of co-wives in Senegal to estimate a duration model of birth intervals with individual baseline hazards. Exploiting entries and exits of co-wives as well as gender of births, empirical tests show that children are strategic complements. One wife raises her fertility in response to an increase by the other wife, because children are the best claim to resources controlled by the husband. This result is the first quantitative evidence that the competition between co-wives drives fertility upwards. It suggests that polygamy undermines the fertility transition in Sub-Saharan Africa by incentivizing women to want many children. This article is also one of the few attempts to open the black box of non-nuclear families, placing strategic interactions at the heart of household decision-making.

We develop a model explaining how criminal organizations strategically use pre-electoral violence as a way of influencing electoral results and politicians' behaviour. We then characterize the incentives to use such violence under different levels of electoral competition and different electoral rules. Our theory is consistent with the empirical evidence within Sicily and across Italian regions. Specifically, the presence of organized crime is associated with abnormal spikes in violence against politicians before elections-particularly when the electoral outcome is more uncertain-which in turn reduces voting for parties opposed by criminal organizations. Using a very large data set of parliamentary debates, we also show that violence by the Sicilian Mafia reduces anti-Mafia efforts by members of parliament appointed in Sicily, particularly from the parties that traditionally oppose the Mafia.

We document that the spread of the Mafia in Sicily at the end of the 19th century was in part caused by the rise of socialist Peasant Fasci organizations. In an environment with weak state presence, this socialist threat triggered landowners, estate managers, and local politicians to turn to the Mafia to resist and combat peasant demands. We show that the location of the Peasant Fasci is significantly affected by a severe drought in 1893, and using information on rainfall, we estimate the impact of the Peasant Fasci on the location of the Mafia in 1900. We provide extensive evidence that rainfall before and after this critical period has no effect on the spread of the Mafia or various economic and political outcomes. In the second part of the article, we use this source of variation in the strength of the Mafia in 1900 to estimate its medium-term and long-term effects. We find significant and quantitatively large negative impacts of the Mafia on literacy and various public goods in the 1910s and 20s. We also show a sizable impact of the Mafia on political competition, which could be one of the channels via which it affected local economic outcomes. We document negative effects of the Mafia on longer-term outcomes (in the 1960s, 70s, and 80s) as well, but these are in general weaker and often only marginally significant. One exception is its persistent and strong impact on political competition.

We conduct an empirical analysis of consumer preferences and search costs on an Internet platform. Using data from a major French platform (PriceMinister), we show descriptive evidence of substantial price dispersion among adverts for the same product, of consumers often not choosing the cheapest advert and sometimes choosing an advert dominated in price and non-price characteristics by another available advert. We consider a sequential search model where consumers sample adverts in an endogenous order based on their preferences and search costs. We show that the optimal search-and-purchase strategy can be characterized by a set of inequalities which can feasibly be tested on transaction and advert data. This allows us to estimate, for each transaction, a set of preference and search cost parameter values, thus allowing for flexible consumer heterogeneity in preferences and search costs. The estimated model can then describe a wide range of consumer search and purchase behaviours. We find that the model can explain almost all transactions in the data and requires non-zero preferences and search costs for at least 50% and 22% (respectively) of observations. We also find evidence of heterogeneous and sometimes substantial search costs.