The Unassuming Economist

From a VoxEU post by Barbara Biasi:

“Rates of intergenerational mobility vary widely across the US. This column investigates the effects of reducing differences in revenues and expenditures across school districts within each state on students’ intergenerational income mobility, using school finance reforms passed in 20 US states between 1986 and 2004. Equalisation has a large effect on mobility, especially for low-income students. The effect acts through a reduction in the gap in inputs and in college attendance between low-income and high-income districts.

There are large differences in intergenerational income mobility across US states and local labour markets. The probability that a child born into a family in the bottom quintile of the national income distribution will reach the top quintile during adulthood is 14.3% in Utah, but only 7.3% in Tennessee (Chetty et al. 2014).

But we do not know much about which factors make a place successful at generating high income and intergenerational mobility. High-mobility places tend to have:

• low income and racial segregation,
• low inequality,
• high social capital, and
• better schools (as proxied by test scores; see Chetty et al. 2018).

These patterns suggest that institutions and public policies have a role in promoting mobility. This cannot, though, be interpreted as a causal relationship.

The first step to mitigating these differences and improving mobility is to nderstand the role of public policies. Recently I examined the causal role of school finance equalisation – a reduction in the differences in public school revenues and expenditures across school districts in a state – on the intergenerational mobility of students exposed to different types of funding plans while in school (Biasi 2019).

US schools were historically funded mostly from local levies such as property taxes, and so wealthier districts (with a larger tax base) were able to spend more per pupil than poorer districts. A funding formula expresses each district’s revenues as a combination of state funds and local levies, and it allocates state aid to each district. In an attempt to equalise expenditure and guarantee equal opportunities to all children, over the past 40 years states have reformed their school finance schemes through changes to these funding formulas.

School finance equalisation reforms have varied across states and over time, although sharing a common objective, As a result, reforms in the same state, implemented under the same name, and with the same objective have had different effects on both the level and the distribution of school expenditure across districts.”

Continue reading here.

From a new post by Timothy Taylor:

“Between the summer of 1830 and the summer of 1832, riots swept through the English countryside. Over no more than two years, 3,000 riots broke out – by far the largest case of popular unrest in England since 1700. During the riots, rural laborers burned down farmhouses, expelled overseers of the poor and sent threatening letters to landlords and farmers signed by the mythical character known as Captain Swing. Most of all, workers attacked and destroyed threshing machines.”

“Here’s a figure showing locations of the Captain Swing riots. The authors [Bruno Caprettini and  Joachim Voth (2018)] collect evidence about where threshing machines were being adopted based on newspaper advertisements for the sale of farms–which listed threshing machines at the farm as well as other property included with the sale. They show a correlation between the presence of more threshing machines and rioting. But as always, correlation doesn’t necessarily  mean causation. For example, perhaps areas where local workers were already more rebellious and uncooperative were more likely to adopt threshing machines, and the riots that followed only show why local farmers didn’t want to deal with their local workers.

Thus, the authors also collect evidence on what areas were especially good soil for wheat, which makes using a thresher more likely, and what areas had water-power available to run threshers. it turns out that these areas are also where the threshers were more likely to be adopted. So a more plausible explanation seems to be that the new technology was adopted where it was most likely to be effective, not because of pre-existing local stroppiness.”

From VoxEU post by Paul Johnson and Chris Papageorgiou:

“The recent wave of growth in several developing economies has led to many analysts to claim that poorer countries are catching up with advanced economies. This column argues that, with the exception of a few countries in Asia which exhibited transformational growth, most of the economic achievements in developing economies have been the result of removing inefficiencies which are merely one-off level effects. While these effects are not unimportant and are necessary in the process of development, they do not imply ongoing economic growth.

In the past 50 years or so, the gap between average living standards in the poorest countries of the world and those in the richest countries has grown markedly. This unwelcome change is contrary to the proposition that less advanced economies ought to be able to catch up to more advanced economies through capital accumulation and technology transfer. Although the proposition wasn’t new, Abramowitz’s (1986) and Baumol’s (1986) examinations of its veracity as a negative correlation between initial per capita income levels and subsequent growth in per capita income, initiated a vast literature testing the so-called convergence hypothesis.¹ Following Barro and Sala-i-Martin (1990), the catch-up approach to the convergence hypothesis became known as β-convergence.²

Table 1 provides some insight into what has driven the widening gap between the poorest countries and the richest countries. It shows decadal average per capita GDP growth rates for low, upper-middle, and high-income countries (LICs, MICs, and HICs respectively) over the 50-year period from 1960 to 2010.

Table 1 Decadal average per capita GDP growth (%) by income

Notes: This table is an abridged version of Table 2 in Johnson and Papageorgiou (2018) where data sources and other details are given. There are 29 HICs, 68 MICs, and 51 LICs. Appendix Table A.1 in Johnson and Papageorgiou (2018) lists the countries in each group.

Despite the slowdown in their average growth rates over this period, the HICs have typically grown more quickly than the MICs, which in turn have grown more quickly than the LICs. These differences are the exact opposite of the pattern required for catching up to be observed, and so it is hardly a surprise that the dispersion of per capita GDP across countries has grown markedly since 1960. This growth is documented in Figure 1, which plots the standard deviation of the cross-country distribution of GDP per capita for a constant group of countries from 1960 to 2010 and shows that, apart from a small decline in the very late 2000s, the dispersion has risen steadily since 1960.

In addition to lagging that of the HICs and MICs, the growth experience of the poorest countries from 1960 to 2010 has been heterogeneous, both across countries and over time. Table 1 shows that the LICs experienced a continuous decline in growth rates in every decade from the 1960s to the 1990s, with negative growth rates in the 1980s and 1990s, before the surprising and unprecedented resurgence of growth in the 2000s. The MICs saw a similar resurgence in the 2000s although their slowing in the 1980s did not produce the negative growth rates experienced in the LICs.

Table 1 also makes the distinction between what are commonly called fragile and non-fragile LICs. Fragile states are those facing political frailty, characterised by weak institutional capacity, poor governance, corruption, and conflict. This distinction illuminates the marked differences in the growth experiences of the fragile LICs and the non-fragile of LICs, most notably in the 1990s and 2000s when the annual average growth rates differ by over 2 percentage points. That is, while there is a lot of optimism over the most recent growth acceleration in LICs, aggregating their experience masks the fact that only about half of the LICs are contributing to the resurgence while the rest are stagnant.”

Continue reading here.

From a new INCLUDE report:

“While most African countries have registered high economic growth, a large number of people remain excluded from the benefits of this progress. INCLUDE envisages that inclusive development aims to reduce poverty, both in income and non-income dimensions, and inequality, through improved redistribution on these dimensions. Inclusive development is increasingly recognized as a must, since inequality is rising and the evidence base of the detrimental effect of high levels of inequality on economic growth and social and political stability is increasing. INCLUDE identified six policy domains that are key to reduce poverty and inequality:

• Economic growth with structural transformation of the economy.
• Productive employment; i.e. more jobs with good working conditions, remuneration and stability.
• Social protection for resilience, poverty reduction and sustainable economic growth.
• The provision of basic services (education, health, finance, infrastructure, housing, water, etc.).
• Territorial development and spatial equity (e.g. between rural and urban areas).
• Quality and inclusive governance, especially for poor minorities and other marginalized groups.

These policy domains are clearly interlinked and it is important to identify and empower strategic actors for effective and inclusive design and implementation of policies. Working with strategic actors and investing in these policy domains is however not sufficient. To move beyond pro-poor and pro-growth approaches towards inclusive approaches with more inclusive outcomes requires the consideration of three key areas:

• Equality: besides absolute improvements, policymakers should consider the distributional consequences of their policy choices. Evaluation mechanisms should therefore recognize the added
  value of reducing inequality.
• Diversity: considering distributional consequences helps to understand the heterogeneity in access to an realization of development outcomes. To move beyond the mere recognition of heterogeneity towards inclusive action, policymakers are encouraged to develop integrated policies where interventions complement each other to not only create improvements on average, but to also decrease inequality, even if this creates an ‘extra mile needed to reach the more difficult to reach.
• Context: to move beyond one-size-fits-all solutions, policies need to be aligned with local or regional contexts. Policies that fail to integrate with horizontal and vertical policy frameworks, and fail to identify and include strategic actors, are likely to promote exclusion rather than inclusion. “

A new paper “revisits the old standing issue of the stability of labour shares. […] Empirical results indicate diverse patterns in labour share movements, the most preponderant being a downward deterministic trend with break(s). Upward trends are observed in a limited set of economies (Belgium, Luxembourg and the Netherlands).  Overall, the stability of the labour share hypothesis appears to find only weak support. Exploratory analysis demonstrates that most of the structural breaks are economically significant and relate to the recent economic and political history of individual economies. The nature of labour share dynamics, as a country-specific and (to a large extent) policy and political phenomenon, is emphasized.”