The Unassuming Economist

From a new VOX post:

“As countries such as the US face increasingly ageing populations, policymakers face the question of whether to encourage workers to work beyond historical retirement age. Using strategic survey questions, this column gauges whether older Americans stop working due to their lack of interest in working longer or due to lack of opportunity, and finds that it may be the latter. The revealed strong willingness to work implies that job opportunities with flexible schedules are hard for older Americans to find. ”

“The survey responses reveal a strong willingness to work among older Americans who are currently not working. Even when the hypothetical job opportunity requires them to work exactly the same number of hours as in their previously-held reference job, about 40% of the VRI sample that are current not working report that they would accept the offer (Figure 1, blue bars). The acceptance rate is slightly higher for those who had a bridge job after leaving the career job.⁴ Some of them are even willing to accept a significant wage reduction to go back to work. More than 20% of non-workers are willing to take a 10% reduction in wages and more than 10% are willing to take 20% reduction in wages.”

“Motivated by the recent evidence that part-time options are relatively more common among post-career bridge jobs (Maestas 2010, Rupert and Zanella 2015, and Ramnath et al. 2017), the strategic survey questions also included a scenario where the job opportunity allows respondents to choose the number of hours worked. The survey responses reveal strong preferences for a flexible work schedule among older Americans. The acceptance rate for the hypothetical job opportunity is substantially higher under a flexible work schedule than a fixed work schedule (yellow bars in Figure 1 indicate the increases in the acceptance rate in the flexible schedule compared to the fixed schedule). Perhaps the most striking finding, more than half of the current non-workers would be willing to work again if they could choose the number of hours worked and earned the same hourly wage as in their most recent job. About 40% of them would be willing to take a 10% reduction in hourly wage, and about 20% would be willing to take a 20% reduction in hourly wage, to work under a flexible schedule if other conditions were similar to their most recent job. “

From a new post by Timothy Taylor:

“Remittances are money sent back to a home country by emigrants. On a global basis, remittances to developing countries topped $400 billion in 2017, far exceeding foreign aid to those countries, similar in size to flows of loans and equity investment in those countries, and beginning to approach the level of foreign direct investment in those countries.”

These inflows of funds are clearly helpful to the recipient families, helping to boost and to smooth their consumption. But do they help to boost overall economic growth for the recipient country? Ralph Chami, Ekkehard Ernst, Connel Fullenkamp, and Anne Oeking raise doubts in “Is There a Remittance Trap? High levels of remittances can spark a vicious cycle of economic stagnation and dependence,” published in Finance & Development (September 2018, pp. 44-47). This short and readable article draws on insights from their IMF working paper, “Are Remittances Good for Labor Markets in LICs, MICs and Fragile States? Evidence from Cross-Country Data” (May 9, 2018).

The authors point out that at a big picture level, countries that receive more remittances (as a share of GDP) don’t seem to grow faster. They offer the intriguing example of Lebanon”

Continue reading paper here.

From a new ILO working paper:

“The current wave of technological change based on advancements in artificial intelligence (AI) has created widespread fear of job losses and further rises in inequality. This paper discusses the rationale for these fears, highlighting the specific nature of AI and comparing previous waves of automation and robotization with the current advancements made possible by a wide-spread adoption of AI. It argues that large opportunities in terms of increases in productivity can ensue, including for developing countries, given the vastly reduced costs of capital that some applications have demonstrated and the potential for productivity increases, especially among the low-skilled. At the same time, risks in the form of further increases in inequality need to be addressed if the benefits from AI-based technological progress are to be broadly shared. For this, skills policy are necessary but not sufficient. In addition, new forms of regulating the digital economy are called for that prevent further rises in market concentration, ensure proper data protection and privacy and help share the benefits of productivity growth through a combination of profit sharing, (digital) capital taxation and a reduction in working time. The paper calls for a moderately optimistic outlook on the opportunities and risks from artificial intelligence, provided policy-makers and social partners take the particular characteristics of these new technologies into account.”

A new VOX post “reviews the evidence on the main causes of the secular decline in employment since the turn of the century. Labour demand factors – notably import competition from China and the rise of industrial robots – emerge as the key drivers. Some labour supply and institutional factors also have contributed to the decline, but to a lesser extent.”

“Among the effects for which we were able to construct an estimate, increased import competition from China is the single largest contributing factor to the decline in employment. A great deal of empirical evidence links import competition from China to the decline in manufacturing employment. These import pressures also had negative employment effects on ‘upstream’ intermediate goods industries, as well as other non-manufacturing industries (Acemoglu et al. 2016, Autor et al. 2015). Estimates from Acemoglu et al. (2016) are the basis for our estimate that the 302% increase in Chinese imports (measured in 2007 US dollars) from 1999 to 2016 led to displacement of approximately 2.65 million workers, a 1.04 percentage point shift in the employment-population ratio.”

“The employment effects from industrial robots are most clearly documented by Acemoglu and Restrepo (2017), who find that each additional robot per thousand workers between 1993 and 2007 reduced employment by 5.6 workers. This estimate implies that the rise in the stock of robots between 2007 and 2016 displaced 0.95 million workers, equivalent to a 0.37 percentage point decline in the employment-to-population ratio.Although improvements in computing technology have been if anything even more ubiquitous, Autor et al. (2015) find that competition from computing technology affects only routine task-intensive occupations, and that any employment losses in those occupations were offset by employment gains in abstract and manual task-intensive occupations.”

Labour supply factors as a group have been considerably less important than labour demand factors in driving the decline in employment.The claim that expanded safety net support through SNAP (food stamps) or Medicaid led to sizable decreases in employment is hard to square with either the institutional features of these programmes or the evidence on causal linkages. Careful work finds little to no labour supply effects of these programmes, and as a practical matter, they offer very little by way of income support to able-bodied childless adults (see, for example, Hoynes and Schanzenbach 2016 on SNAP, and Leung and Mas 2016 on Medicaid.)

That said, the availability of lifelong disability insurance benefits through the Social Security Disability Insurance (SSDI) programme and the Veterans Affairs Disability Compensation (VADC) programme has contributed modestly to falling employment rates. Applying age-group specific causal labour supply estimates from Maestes et al. (2013) to the growth in the SSDI caseload over and above that due to the population becoming older yields an estimated 0.14 percentage point decline in the e/pop ratio over our time period owing to growth in this programme. Applying the labour supply elasticity from a recent study of an exogenous policy change to VADC program eligibility (Autor et al. 2016) to an estimate of the excess VADC caseload implies that programme’s growth led to an e/pop decline of perhaps 0.06 percentage points.

One might expect the tremendous rise in incarceration in the US to have been a significant driver of declining employment, but because so many incarcerated individuals had low levels of labour force attachment even before their prison term, we estimate only a modest aggregate effect. Applying the causal estimates from Mueller-Smith (2015) to rough estimates of the number of former prisoners by length of time served and prior earnings history, our very rough estimate is that perhaps 0.13 percentage points of the decline in e/pop between 1999 and 2016 can be attributed to policy-induced increases in incarceration. Minimum wage increases probably also had a small but non-negligible impact, especially among younger, less-skilled workers. Taking account of the range of estimates produced by credible study designs, we estimate that increases in state and local minimum wages might have contributed 0.10 percentage points to the e/pop decline.

Other plausible factors driving the decline in employment are the sharp rise in occupational licensing (Kleiner and Krueger 2013), the decline in geographic mobility (Molloy et al. 2011), and increased difficulties securing affordable, high-quality child care. We do not attempt to assign a magnitude to these factors because we lack sufficient evidence to establish the causal impact of these factors or, in the case of child care, to assess how much the factor itself has changed. Another open question is to what extent anecdotes about worsening mismatch between the skills workers possess and those that employers need are borne out in the data.

Scholars have noted the connection of both increased leisure time (including time playing video games) (Aguiar et al. 2017) and increased opioid use (Krueger 2017, Currie et al. 2018) with non-employment, but whether one is causing the other or vice versa, or they are all manifestations of other societal changes, is not easy to disentangle.”

From a new article by Jessie Romero and Felipe F. Schwartzman at the Richmond Fed:

“Inequality in the United States has an important spatial component. More-skilled workers tend to live in larger cities where they earn higher wages. Less-skilled workers make lower wages and do not experience similar gains even when they live in those cities. This dynamic implies that larger cities are also more unequal. These relationships appear to have become more pronounced as inequality has increased. The evidence points to externalities among high-skilled workers as a significant contributor to those patterns.”

“A large body of research has identified several key facts about inequality across and within cities. First, larger cities have a greater concentration of high-skilled workers. In the Fifth District, for example, the share of the population over age twenty-five with a bachelor’s degree is 45 percent in the most urban areas, compared with 16 percent in the most rural areas. In the United States as a whole, the proportion ranges from 35 percent in the most urban areas to 17 percent in the most rural areas.”

“Second, nominal wages are higher in larger cities and in cities with a larger proportion of high-skilled workers. In the most urban areas of the Fifth District, average annual pay in 2016 was nearly $64,000; in the most rural areas, it was less than $35,000. Nationwide, workers in the most urban areas earned about $60,000 on average in 2016, while workers in the most rural areas earned about $36,000. (See Figure 2 above.) In recent research, Nathaniel Baum-Snow, Matthew Freedman, and Ronni Pavan find that nominal wages increase 0.065 percent for every percentage point increase in city size (based on data from 2005–07). They also find that the relationship between city size and wages has strengthened over time and that the wage gap between urban and rural areas has increased”