Archive for October 1st, 2019

Estimating global poverty in R and Stata

October 1, 2019

World Bank has put up the poverty data which can be easily plugged into R and Stata.

WB’s data blog reports:

The World Bank’s global poverty measures published through the PovcalNet website can now be accessed directly from within R and Stata. The R package povcalnetR and the Stata command povcalnet offer the same functionality as the website, namely the estimation of poverty at any poverty line for individual countries, groups of countries, or entire regions. Accessing PovcalNet directly from within R and Stata is a major improvement to the usability of the tool; for example PovcalNet results can be directly merged with any other R/Stata dataset. In this blog, we show how the commands can be downloaded and illustrate their use with an example. We will illustrate the use of the commands with a series of blog posts over the next few weeks. A more detailed description with more examples can be found in the github pages (R and Stata), as well as Castaneda et al. (2019) for the Stata command. We encourage users to send us comments and suggestions, and to report any bugs in the github issues pages (R and Stata).

PovcalNet reports the World Bank’s official global, regional and country-level poverty estimates, as well as a range of inequality statistics. It is managed jointly by the Data and Research Groups within the World Bank’s Development Economics Division, and draws heavily upon a strong collaboration with the Poverty and Equity Global Practice, which is responsible for gathering and harmonizing the underlying household survey data. The website is based on a web API, which was documented in more detail as part of the September 2018 PovcalNet update (see Zhao, 2018). This means that every query to the PovcalNet website (e.g. estimate the poverty headcount ratio at $2 per day in Bangladesh in 2016) generates a URL that returns the results in a machine-readable format. The R and Stata packages query the PovcalNet API and read the results directly into R/Stata.

The R package povcalnetR can be installed from github with

install.packages(c(“devtools”, “httr”))

devtools::install_github(“worldbank/povcalnetR”)

and will soon be available in CRAN.

The Stata povcalnet command can be installed from SSC by typing:

ssc install povcalnet

The development version, which includes the latest updates and features, can be downloaded from github by using the github Stata command (developed by E. F. Haghish):

net install github, from (“https://haghish.github.io/github/”)

github install worldbank / povcalnet

It is important to understand that PovcalNet reports estimates for two types of years: the survey year, which is the year for which the welfare variable was collected, and the reference year, which is the year for which global poverty estimates are produced. The reference year estimates make additional assumptions to align household surveys, that may be conducted at infrequent intervals, to a common year for which poverty can be estimated for as many countries around the world as possible. Currently, the available reference years are 1981, 1984, 1987, 1990, 1993, 1996, 1999, 2002, 2005, 2008, 2010, 2011, 2012, 2013 and 2015. Inequality estimates are only available for the survey-years.

The next line of code queries the global and regional estimates of extreme poverty at the international poverty line of $1.9 per day for all the reference years:

R: df <- povcalnetR::povcalnet_wb()

Stata: povcalnet wb, clear

With a few additional lines of code (R and Stata), the changing geographic distribution of extreme poverty can be easily graphed. In 2015, Sub-Saharan Africa accounted for more than half of the global poor, and together with South Asia for more than 85 percent. It is clear that the reduction in global poverty was driven by rapid progress in East Asia. In recent years, the number of poor has also fallen steadily in South Asia. This stands in sharp contrast with Sub-Saharan Africa, where the total number of poor people has actually been increasing over time. As discussed in this report, this shift in the geography of global poverty from high-growth (East Asia and recently South Asia) to low-growth (Sub-Saharan Africa) regions also implies a likely slowdown in the future reduction of global poverty (see this blog for projections beyond 2015 to 2030).

 

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Mario Draghi interview: He reflects on the 8 years as ECB President

October 1, 2019

Mario Draghi steps down as ECB President at end of this month.

In this FT interview, Draghi discusses and reflects his term:

(more…)

The Puzzling Lure of Financial Globalization

October 1, 2019

Arvind Subramanian and Dani Rodrik in this Proj Syndicate piece argue that financial globalisation continues to excite economists:

Although most of the intellectual consensus behind neoliberalism has collapsed, the idea that emerging markets should throw their borders open to foreign financial flows is still taken for granted in policymaking circles. Until that changes, the developing world will suffer from unnecessary volatility, periodic crises, and lost dynamism.

Further:

After holding off for decades, China has finally embraced financial globalization, announcing recently that it would eliminate capital controls to allow unfettered short-term foreign inflows (so-called hot money). By contrast, after decades of boom-bust cycles, Argentina is facing another a macroeconomic crisis, and has finally imposed capital controls to prevent a catastrophic decline in its currency.

Both of these episodes reveal the intellectual hold that financial globalization still has on policymakers, despite its history of failure. Why, after all, would China abandon capital controls now, and what took Argentina so long to adopt such obviously necessary measures?

The Chinese economic miracle has many sources. In addition to the turn to markets, China has benefited from exports and foreign investment, internal migration, and the Maoist legacy of a public education and health system. It is also the civilizational heir to a strong, effective state with an enlightened, albeit ruthless, leadership. Its people collectively crave stability. But an important factor in China’s rise was the decision not to open the economy to capital flows

Consider the following counterfactual history. In the late 1990s, when China’s economic miracle was becoming evident, it could easily have succumbed to the prevailing orthodoxy on financial globalization. Had it done so, the likely outcome would have been a surge in foreign capital chasing high Chinese returns, rapid appreciation of the renminbi, slower export growth, and lost dynamism. China’s export machine would not have become the juggernaut that it is, and its economy may well have suffered through much more volatility as a result of the fickleness of foreign capital. In fact, Argentina – with its periodic macroeconomic volatility and recurring financial crises – offers a perfect illustration of these downsides.

Nearly every major emerging-market financial crisis of the past few decades has been preceded or accompanied by surges in capital inflows. That was true of Latin America in the 1980s, India in 1991, Mexico in 1994, and East Asia and Russia in the late 1990s. It was also true of Brazil, Turkey, and Argentina in the early 2000s; the Baltics, Iceland, Greece, and Spain in the late 2000s and early 2010s; and the “Fragile Five” emerging-market economies (Brazil, India, Indonesia, South Africa, and Turkey) in 2013. And it is true of Argentina today.

To be sure, capital flows have often reflected deeper policy problems or imbalances within a given emerging market. But they are also usually the necessary transmission mechanism for crises, and thus have magnified the eventual costs to those economies. Although most tenets of the neoliberal consensus – privatization, deregulation, trade integration, immigration, fiscal discipline, and the primacy of growth over distribution – are now being challenged or outright rejected, financial globalization remains a glaring exception.

Well, there is too much money and too many careers are at stake and they all try their best to keep the circle going..


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