The Unassuming Economist

From a paper by Rasmus Bisgaard Larsen, Søren Hove Ravn, and Emiliano Santoro:

“We present aggregate and regional evidence showing that U.S. house prices increase persistently in response to positive shocks to fiscal spending. In sharp contrast to this, house prices decline in conventional dynamic general equilibrium models, where shocks that have short-lived effects on the shadow value of housing inevitably generate negative comovement between households’ marginal utility of consumption and house prices (see Barsky et al., 2007). In response to an increase in government spending, the negative wealth effect exerted by the simultaneous increase in the present-value tax burden increases the marginal utility of consumption. Even overcoming the consumption crowding-out puzzle is not sufficient to resolve this shortcoming. To tackle this problem, we extend an otherwise standard model embedding a lender-borrower relationship with alternative—yet, potentially complementary—propagation channels that leverage the expansion in total factor productivity stemming from a positive shock to fiscal spending, so as to contrast the negative wealth effect of higher taxes. This class of models succeeds in generating a persistent expansion in house prices, although the propagation required to match the data is stronger—in some cases significantly so—than what is typically found in the literature. The positive interplay between house prices and productivity finds support in both aggregate and regional data.”

From a paper by Remi Jedwab, Elena Ianchovichina, and Federico Haslop:

“Census data for 7000 cities – three fourth of the world’s urban population – reveal that cities of the same population size in countries with similar development levels differ substantially in terms of their employment composition, especially in the developing world. Using these data, we classify cities into production cities with high employment shares of urban tradables (e.g., manufacturing or business services), consumption cities with high employment shares of urban non-tradables (e.g., retail and personal services), or neutral cities with a balanced mix of urban tradables and non-tradables. After establishing stylized facts regarding the sectoral distribution of employment in our global sample of cities, we discuss the various paths by which developing nations may urbanize through production cities – via industrialization or tradable services – or consumption cities – via resource exports, agricultural exports, or deindustrialization. Country and city-level data corroborate our hypotheses. Results on the construction of very tall buildings also provide suggestive evidence on the relationship between resource exports and consumption cities. Importantly, consumption cities seem to present lower growth opportunities than production cities, diminishing the role of cities as “engines of growth.” Understanding how sectoral structure mediates the urbanization-growth relationship and how consumption cities become production cites is thus highly relevant for policy.”

1. Housing View – February 2, 2024
2. Housing View – January 5, 2024
3. Housing View – January 19, 2024
4. Housing View – March 1, 2024
5. Housing View – July 26, 2024
6. Housing View – February 23, 2024
7. Housing View – January 26, 2024
8. Housing View – January 12, 2024
9. Housing View – August 23, 2024
10. Housing View – March 22, 2024

From a paper by Zaid Tahat:

“This thesis investigates the dynamic linkages among financial, industrial, service, and general
indices of the Amman Stock Exchange (ASE) in Jordan from 2000 to 2020 using a vector
autoregression (VAR) model and by using daily data. The main aim is to provide a
comprehensive understanding of the interrelationships among these key sectors over the 21-year
period. The objectives are to examine both short-term and long-term dynamic linkages, assess
the model’s explanatory power for variations in sector indices, and derive insights for investors
and policymakers.

The study employs a VAR methodology to capture the dynamic interactions among the sector
indices. Daily data on sector indices is analyzed using Granger causality tests, impulse response
functions, and variance decomposition to quantify the linkages.

The findings reveal significant dynamic linkages among ASE sector indices. The VAR model
exhibits high explanatory power, with R-squared and adjusted R-squared values above 99% for
all sectors. Granger causality tests indicate bi-directional causality between the financial and
general indices and between the service and industrial indices. Impulse response functions show
that shocks to each sector have significant effects on the other sectors that persist over several
days. Variance decomposition analysis attributes 27-38% of forecast error variance in each
sector to innovations in other sectors, affirming the importance of intersectoral relationships.
The empirical evidence can inform portfolio diversification and risk management strategies for
investors. For policymakers, the findings underscore the importance of considering spillover
effects in regulatory frameworks governing the financial sector and capital markets.

To mitigate systemic risk and promote stability, policymakers could consider implementing
macroprudential policies such as countercyclical capital buffers, exposure limits, and liquidity
requirements that account for the interconnectedness of sectors. Enhancing transparency through
disclosure requirements and stress testing that incorporate intersectoral linkages could also help
monitor and manage systemic risk. Coordination among regulators overseeing different sectors
may be warranted to address cross-sector vulnerabilities. Overall, a holistic approach that
recognizes the dynamic linkages among sectors is recommended to foster a resilient financial
system.”

From a paper by Nasser Khiabani  and Solaleh Tavassoli:

“This study reviews the evolution of national and regional housing models that developed and received much attention in the housing economics literature. From this point of view, first, we focus our attention on the econometric modeling of national housing markets and discuss their limitations in twofold: inferring individual-level relations from aggregate-level data or aggregate shocks, and assuming spatial homogeneity in all regions. These two problems will be addressed precisely in the newly developed regional housing market models by identifying the sources of cross-sectoral dependence, namely, spatial and temporal dependence. Spatial dependence refers to how spatial factors influence economic processes. It is measured through a spatial weighting matrix. Cross-sectional dependence stemming from common factors is attributed to economy-wide shocks that affect all individuals with different intensities coming from different macro shocks, such as interest rates, oil prices, and technology shocks.”