Inclusive Growth

Global Housing Watch

Forecasting Forum

Energy & Climate Change

Who Paid Los Angeles’ Minimum Wage? A Side-by-Side Minimum Wage Experiment in Los Angeles County

Who pays when minimum wage hikes come through the drawn-out demand-supply legislative processes?

This is precisely the question taken up by researchers Christopher Esposito of the University of Chicago and Edward Leamer and Jerry Nickelsburg of UCLA in an interesting working paper series. Drawing on a unique set of mandated wage hikes in the Los Angeles area, they present evidence that minimum wage changes led area restaurants to raise prices, change menu items, obtain lower rents in the high wage areas and, in some cases, caused eateries to shut down.

Results from the paper suggest that policymakers face an important dilemma when designing minimum wage policies to redistribute income while minimizing job loss. So, on one hand, restaurants in high-income neighborhoods studied by the authors passed on the full incidence of the minimum wage differential to their customers suggesting that minimum wages should be set relative to local income levels. The price passthrough channel for income redistribution is optimized when minimum wages are set uniquely for fine-grained spatial units, such as neighborhoods, within which the elasticity of demand for restaurant meals is homogenous. However, on the other hand, their findings also indicate that customers’ demand for restaurant meals can spill across jurisdictional borders with different minimum wages. Therefore, different minimum wages across fine-grained spatial units have the potential to move customer demand, jobs, and tax revenue out of jurisdictions that enact higher minimum wages. A universal minimum wage increase is not sensitive to this heterogeneity in the elasticity of demand, while minimum wage increases enacted at the neighborhood scale may cause restaurants to relocate out of higher-wage areas. The optimal spatial scale for setting minimum wages must balance these two offsetting forces.

In addition to these policy considerations, the study also raises the possibility that some of the incidence of minimum wage increases falls on landlords. The theoretical model predicts that land rents in regions subject to larger minimum wages will decrease, particularly at locations close to areas with lower minimum wages. This proposition is further strengthened because restaurant properties have specific use characteristics which are costly to change.

Click here to read the full explainer article/ full paper.

Source: Esposito et al. (2021). NBER. Who Paid Los Angeles’ Minimum Wage? A Side-by-Side Minimum Wage Experiment in Los Angeles County.

Who pays when minimum wage hikes come through the drawn-out demand-supply legislative processes?

This is precisely the question taken up by researchers Christopher Esposito of the University of Chicago and Edward Leamer and Jerry Nickelsburg of UCLA in an interesting working paper series. Drawing on a unique set of mandated wage hikes in the Los Angeles area, they present evidence that minimum wage changes led area restaurants to raise prices, change menu items,

Read the full article…

Posted by at 11:00 AM

Labels: Inclusive Growth

Forecasting Macroeconomic Variables in Emerging Economies

A new paper by Le HaThua & RobertoLeon-Gonzalezb

“Forecasting macroeconomic variables in rapidly changing emerging economies presents a number of challenges. In addition to structural changes, the time-series data are usually available only for a short number of periods, and predictors are available in different lengths and frequencies. Dynamic model averaging (DMA), by allowing the forecasting model to change dynamically over time, permits the use of predictors with different lengths and frequencies for the purpose of forecasting in a rapidly changing economy. This study uses DMA to forecast inflation and growth in Vietnam, Thailand, Philippines, Sri Lanka and Ghana. We compare its forecasting performance with a wide range of other time-series methods. We find that the size and composition of the optimal predictor set changed, indicating changes in the economic relationships over time. We also find that DMA frequently produces more accurate forecasts than other forecasting methods for both inflation and economic growth in the countries studied.”

A new paper by Le HaThua & RobertoLeon-Gonzalezb

“Forecasting macroeconomic variables in rapidly changing emerging economies presents a number of challenges. In addition to structural changes, the time-series data are usually available only for a short number of periods, and predictors are available in different lengths and frequencies. Dynamic model averaging (DMA), by allowing the forecasting model to change dynamically over time, permits the use of predictors with different lengths and frequencies for the purpose of forecasting in a rapidly changing economy.

Read the full article…

Posted by at 7:08 PM

Labels: Forecasting Forum

Comparing forecasting performance in cross-sections

New Paper by Ritong Quc, Allan Timmermanna & Yinchu Zhub

“This paper develops new methods for pairwise comparisons of predictive accuracy with cross-sectional data. Using a common factor setup, we establish conditions on cross-sectional dependencies in forecast errors which allow us to test the null of equal predictive accuracy on a single cross-section of forecasts. We consider both unconditional tests of equal predictive accuracy as well as tests that condition on the realization of common factors and show how to decompose forecast errors into exposures to common factors and idiosyncratic components. An empirical application compares the predictive accuracy of financial analysts’ short-term earnings forecasts across six brokerage firms.”

New Paper by Ritong Quc, Allan Timmermanna & Yinchu Zhub

“This paper develops new methods for pairwise comparisons of predictive accuracy with cross-sectional data. Using a common factor setup, we establish conditions on cross-sectional dependencies in forecast errors which allow us to test the null of equal predictive accuracy on a single cross-section of forecasts. We consider both unconditional tests of equal predictive accuracy as well as tests that condition on the realization of common factors and show how to decompose forecast errors into exposures to common factors and idiosyncratic components.

Read the full article…

Posted by at 7:04 PM

Labels: Forecasting Forum

Examining the role of Social Identity, Skills, and Personality in determining Labor Market Mobility in India

This study, by Michiels, Nordman, and Seetahul, combines behaviorist and structuralist views to understand the extent to which individual skills and personality traits facilitate labor market mobility of disadvantaged groups in the presence of constraining social structures.

Based on a rural India case study, results from this paper show that personality traits are important determinants of labor market mobility but also emphasize a strong rigidity of the socioeconomic structure of the Indian labor market in terms of gender and caste, and its relative stillness over time. While for women, literacy, emotional stability, and openness to new experiences appear to allow income gains, these benefits are limited by the labor market structure, maintaining them in low-skilled and casual occupations. For Dalits, emotional stability and agreeableness seem to play an important role in relative income mobility. These interesting findings highlight the segmented nature of the Indian labor market, which is still strongly organized by diverse forms of domination.

Source: Michiels et al. (2021). Many Rivers to Cross: Social Identity, Cognition, and Labour Mobility in Rural India. Institute of Labor Economics.

Click here to read the full paper.

This study, by Michiels, Nordman, and Seetahul, combines behaviorist and structuralist views to understand the extent to which individual skills and personality traits facilitate labor market mobility of disadvantaged groups in the presence of constraining social structures.

Based on a rural India case study, results from this paper show that personality traits are important determinants of labor market mobility but also emphasize a strong rigidity of the socioeconomic structure of the Indian labor market in terms of gender and caste,

Read the full article…

Posted by at 9:28 AM

Labels: Inclusive Growth

Housing View – November 5, 2021

On the US:   

  • 2022 home prices will keep rising at or near double digits, predicts the analyst who called the current housing boom – American Enterprise Institute
  • Flattening the curve: Pandemic-Induced revaluation of urban real estate – Journal of Financial Economics
  • Supporting Philadelphia’s Black Homeowners in the Aftermath of the COVID-19 Crisis – Philadelphia Fed
  • Introducing the Brookings and Ashoka Collaborative Innovation Challenge: Valuing Homes in Black Communities – Brookings
  • Affordable Housing as a Pathway to Economic Opportunity – Harvard University
  • Desperate for Housing Options, Communities Turn to Ballot Initiatives. Cities and counties will vote on measures, like tax increases and curbs on Airbnb, aimed at creating more affordable housing. – New York Times
  • In a Supertall Tower, How Much Affordable Housing Is Enough? The only residential building at the World Trade Center will be 25 percent affordable — a real accomplishment, supporters say. Others insist it should be 100 percent. – New York Times
  • ‘Palm Beach is sold out’ after frenzied pandemic property sales. Prices are rising as millionaires are priced out by billionaires, pushing them to nearby West Palm Beach – FT
  • As Boomers Downsize, Competition Grows for Simpler—but Not Always Smaller—Homes. Smaller houses, desired by aging seniors and young couples, are among the toughest to find – Wall Street Journal
  • ‘Gimme Shelter’: Meet California’s housing chiefs – Los Angeles Times
  • States under time crunch to provide housing assistance: How to fix it – Brookings
  • ‘Don’t Buy Zillow Homes’: A Tale of Failure, Mistrust and Hot Housing Markets. Twitter and TikTok users are raging against the real estate firm as it looks to sell 7,000 homes for $2.8 billion following the failure of a much-vaunted business. – Bloomberg
  • Could Zillow buy the neighborhood? iBuyers want to be the Amazon of real estate. What does that mean for the rest of us? – Vox
  • Zillow May Have Stumbled, But iBuying Is Here to Stay – Barron’s
  • Strip Malls to Homes: An Analysis of Commercial to Residential Conversions in – Terner Center for Housing Innovation
  • AEI housing market indicators, October 2021 – American Enterprise Institute
  • Institutional Investors Have a Comparative Advantage in Purchasing Homes That Need Repair – Urban Institute
  • Eviction and Voter Turnout: The Political Consequences of Housing Instability – Princeton University


On China

  • China’s long wait for a tax everyone loves to hate. The government will at last roll out a property tax – The Economist
  • The strongest weapon in Xi Jinping’s common prosperity armoury is a property tax – Quartz
  • How China’s property crackdown is being felt in a remote city steeped in Communist Party lore. Beijing’s push to reduce excessive borrowing in the property sector and tame house prices is hurting regional finances. The policy tightening could be related to the current political cycle ahead of the 20th Party Congress, some analysts say – South China Morning Post
  • Why China May Not Bail Out Evergrande. A complicated political calculus goes into which businesses Beijing cares about most. – Foreign Policy
  • Why Beijing will endure property tax’s drag on housing market – South China Morning Post


On other countries:  

  • [Australia] Australia’s Hot Housing Market Shows Signs of Cooling Momentum – Bloomberg
  • [Australia] Borrowers rush to lock in low interest rates amid expectations of RBA rise. Rising house prices and a resurgent economy could nudge the Reserve Bank to raise rates for first time in 11 years – The Guardian
  • [Brazil] Estimating the Economic Value of Zoning Reform – NBER
  • [Brazil] How to improve access to housing for the low-income population? – IADB
  • [Canada] Bank of Canada’s early lift-off warning may dampen housing boom fanned by speculators – Reuters
  • [Hong Kong] What Hong Kong must do to solve the city’s perennial housing and land shortage problems – South China Morning Post
  • [Israel] Israel Plans AirBnB Restrictions, Taxes to Curb Housing Prices – Bloomberg
  • [Italy] Living on my own: The impact of the Covid-19 pandemic on housing demand – Bank of Italy
  • [New Zealand] Growing supply will bring down New Zealand house prices, says RBNZ’s Orr – Reuters
  • [United Kingdom] Homeowners face biggest hike in mortgage costs since 2008. Data from government’s independent forecasting unit suggests interest payments could increase by 13% in 2023 – The Guardian
  • [United Kingdom] Surge in mortgage lending prompted by stamp duty deadline. The end of the tax break in September fuelled lending but the outlook for approvals remains ‘resilient’ – FT
  • [United Kingdom] Rising Interest Rates Expected to Cool U.K. Housing Market – Bloomberg

On the US:   

  • 2022 home prices will keep rising at or near double digits, predicts the analyst who called the current housing boom – American Enterprise Institute
  • Flattening the curve: Pandemic-Induced revaluation of urban real estate – Journal of Financial Economics
  • Supporting Philadelphia’s Black Homeowners in the Aftermath of the COVID-19 Crisis – Philadelphia Fed
  • Introducing the Brookings and Ashoka Collaborative Innovation Challenge: Valuing Homes in Black Communities – Brookings
  • Affordable Housing as a Pathway to Economic Opportunity – Harvard University
  • Desperate for Housing Options,

Read the full article…

Posted by at 5:00 AM

Labels: Global Housing Watch

Newer Posts Home Older Posts

Subscribe to: Posts