Tracking the State Economies at High Frequency: A Primer

Tracking the business cycle at the level of state economies and discerning the impact of fiscal and regulatory policies – as well as nationwide policies – on state economies has been hampered by a limited number of available sets of high frequency indicators. In this paper, we review the data sources and series available for a cross-section of states, with discussion of the associated advantages and disadvantages. We provide estimates of the correlation and comovement of various indicators with state-level real GDP, and how different indicators define turning points in state economies. Finally, we illustrate the usefulness of high frequency state-level GDP by evaluating whether a particular state economy is performing in line with expectations.

The New Fama Puzzle

We re-examine the Fama (1984) puzzle – the finding that ex post depreciation and
interest differentials are negatively correlated, contrary to what theory suggests – for
eight advanced country exchange rates against the US dollar, over the period up to
February 2016. The rejection of the joint hypothesis of uncovered interest parity (UIP)
and rational expectations – sometimes called the unbiasedness hypothesis – still occurs,
but with much less frequency. Strikingly, in contrast to earlier findings, the Fama
regression coefficient is positive and large in the period after the global financial crisis.
However, using survey based measures of exchange rate expectations, we find much
greater evidence in favor of UIP. Hence, the main story for the switch in Fama
coefficients in the wake of the global financial crisis is mostly – but not entirely – a
change in how expectations errors and interest differentials co-move, though the risk
premium also plays a critical role for safe haven currencies (Japanese yen and Swiss

Money matters: The role of yields and profits in agricultural technology adoption

Despite the growing attention to technology adoption in the economics literature, knowledge gaps remain regarding why some valuable technologies are slow to be adopted. This paper contributes to our understanding of agricultural technology adoption by showing that a focus on yield gains may, in some contexts, be misguided. We study a technology in Ethiopia that has no impact on yields, but that has nonetheless been widely adopted. Using three waves of panel data, we estimate a correlated random coefficient model and calculate the returns to improved chickpea in terms of yields, costs, and prots. We nd that farmers’ comparative advantage does not play a signicant role in their adoption decisions and hypothesize that this is due to the overall high economic returns to adoption, despite the limited yield impacts of the technology. Our results suggest economic measures of returns may be more relevant than increases in yields in explaining technology adoption decisions.

The impact of One Acre Fund’s small farm program

This report presents the results from our independent analysis of One Acre Fund’s (1AF) randomized control trial in the Teso region of Kenya. The main aim of this study is to assess program impacts on maize yields and profits from maize and beans. The results show that 1AF participation leads to statistically and economically significant increases in both yields and profits: 1AF participation resulted in a roughly 34 percent increase in maize yields per farmer, and almost 20 percent increase in maize and bean profits. We also investigate whether 1AF participation has persistent impacts for farmers even once they have stopped participating, but we found no differences between control group farmers who were former clients and those that had never participated. Finally, using a quasi-experimental approach using data from the four surrounding districts in which 1AF operates, we can see that results from propensity score analysis yields impact estimates that are somewhat larger in magnitude than those from the experimental analysis. These differences could be driven by differential program impact across districts, or due to methodological differences between the experimental and quasi-experimental approaches.