Sangli is in Haryana, where Green Revolution techniques (high yielding seed varieties, chemical fertilizers and pesticides, and agricultural machinery like tractors and threshers) were adopted early on. It also happens to be close to the industrial belt that extends from the national capital Delhi to its surrounding districts, where foreign capital has congregated in the neoliberal era. This makes it an interesting place to study processes of generation and re-investment of agrarian surpluses, and to peer into the relationship between “modernized” agriculture and neoliberal industrial and urban growth that has dwarfed the rural economy.
Brazil is in a crisis again. The COVID-19 pandemic has spread across the country and political incompetence has led to a massive health crisis. Investment outflows have been rapid and the Brazilian real has depreciated dramatically. The Brazilian economy is set to contract again after three years of weak positive growth.
Brazil’s development bank Banco Nacional de Desenvolvimento Econômico e Social (BNDES) has announced some measures to deal with the financial instability caused by the COVID-19 pandemic. However, these measures are being criticised for being insufficient. Rather than being a temporary policy mistake that can be corrected easily, BNDES’ passive response is linked to the bank’s structural retreat from the economy over the past five years.
During the 2000s, BNDESwasacclaimedas a catalyst ofthe country’s economic growth. Globally, developing countries such as Indonesiasaw the rise of BNDES as something favourableand sought to mobilise their own national development banks.
At the OECD’s origin, we find the 1947 Marshall Plan that re-industrialised a war-torn Europe. At the very core of the Marshall Plan was a profound understanding of the relationship between a nation’s economic structure and its carrying capacity in terms of population density. We argue that it is necessary to rediscover this theoretical understanding now, in the mutual interest of Africa and Europe.Read More »
In two previous posts on this blog, I’ve discussed the issue of premature deindustrialization and some of its possible consequences. In a recent paper, I, along with co-author Bret Anderson of Southern Oregon University, explored the potential consequences of premature deindustrialization further by examining the possible connections between premature deindustrialization and the defeminization of industrial employment. Premature deindustrialization is a situation in which the shares of manufacturing value added and employment begin to shrink at per-capita income levels much lower than those of the early industrializers, along with manufacturing employment peaking at lower levels. The scarce manufacturing jobs that do remain, however, are likely to be relatively high paying jobs that countries and workers compete for. In our work, we assessed whether premature deindustrialization is a feminizing or defeminizing force in industrial employment. By examining 62 countries from 1990 to 2013, we find that premature deindustrialization is likely to amplify the male bias of industrial upgrading.Read More »
In a previous post, I wrote about the global trend of premature deindustrialization; the trend towards lower levels of industrial employment, and a shift away from industrial employment at lower levels of per capita income, and how the effects on human well-being of these trends are not yet clearly understood. An important question in understanding the impact of these changing structural patterns on individuals’ well-being is to whether either a lifting of the living standards of those not in formal employment, or the generation of employment to replace the manufacturing employment, is taking place.
In a recent working paper, I illustrated how combining a household level indicator of well-being with decomposition of change analysis can shed light on these questions by focusing on two specific episodes of growth; South Africa from 1996 to 2007 and Brazil from 1991 to 2010. Using Census data from IPUMS, I created indices of well-being on a scale of 0-100, using indicators such as child survival rate, access to clean water and electricity, and educations levels, culled from census data. Next, each household was assigned to a “type” based on sectoral employment of the household head and urban/rural location, and average household scores were calculated for each type. A decomposition of change analysis was then used to assign improvement in well-being to improvement within the types and shifts in population between these types.Read More »