The Unassuming Economist

From a new IMF policy paper:

“This paper reviews the experience with the fiscal space assessment framework that was piloted during 2017–18. In 2016, staff proposed an operational definition of fiscal space and a new four-stage framework for its assessment. These were discussed informally by the Board in June, and a Board paper “Assessing Fiscal Space: An Initial Consistent Set of Considerations”incorporating Directors’ views was published in December. Fiscal space was narrowly defined as the room for undertaking discretionary fiscal policy relative to existing plans without endangering market access and debt sustainability. The framework was developed in response to the need to provide a more systematic approach to assessing fiscal space in the Fund’s surveillance. It was designed as a tool to inform the availability of fiscal space over a 3 to 4 year horizon for discretionary action, as opposed to the optimality of its use. Indeed, it was stressed that the availability of space does not necessarily mean that it should be used or should not be further expanded. The framework was piloted in the Article IV consultations of 34 advanced economies and emerging markets, comprising almost 80 percent of global GDP in PPP terms.”

David Mihalyi and Tommy Morrison at NRGI created a World Economic Outlook Forecast Tracker that enables users to see how IMF economic projections have evolved over time. On it, you can select from an expansive list of countries and country groupings to track how IMF forecasts evolved year-to-year for dozens of economic indicators, such as GDP growth, government revenues and the budget deficit as well as the price of various commodities. The app shows an animated plot of the forecasts and historical values over 10 years, as well as providing a data download and a plot download (example attached).

The IMF’s latest report on Lithuania says that:

“Household credit remained strong, rising by 7 percent thanks to solid wage growth, and coincided with a surge in housing activity. The number of transactions in the real estate market rose sharply and neared the pre-crisis peak. Moreover, housing prices, especially in major urban centers, rose sharply, prompting the Bank of Lithuania (BoL) to raise the countercyclical capital buffer by 0.5 percentage points in December 2017. Nonetheless, the stock of credit is still modest at 41 percent of GDP, well below the pre-crisis peak of 68 percent of GDP. Similarly, housing prices remain significantly below their 2007 peak, especially when adjusted for inflation. Financial soundness indicators remain strong. Lithuania’s banking system is well capitalized, liquid, and profitable despite the low interest rate environment. Nevertheless, spillovers from real-estate related vulnerabilities in Nordic parent banks, which control most of Lithuania’s financial sector, remain a risk.”

 

The report also says:

“An analysis of Lithuania’s credit, housing price, and output cycles during 1995–2017Q3, reveals that housing price cycles are more frequent, but shorter-lived than the other two with credit cycles being the most volatile. The analysis finds strong synchronization among them in Lithuania, particularly between the credit and housing price cycles.

Lithuania’s cycles are highly synchronized with those of other Baltic and Nordic countries. This is particularly true for credit due to the close links of Lithuania’s financial system to parent bank developments. Housing price cycles are the least synchronized possibly because real estate markets are mostly affected by local conditions.

An econometric exercise shows that housing price booms are the key determinant of credit upturns. Other factors causing a credit upturn include the negative impact of the global financial crisis, bank profitability, deposit growth, interest rates, and private sector indebtedness. The presence of an economic boom does not seem to be a significant determinant of a credit upturn, suggesting that other, potentially external, factors play a more significant role.

A panel VAR that includes other variables potentially influencing credit demand and supply shows that Lithuania is more vulnerable to shocks than the region as a whole, and that credit and real GDP shocks in Lithuania have a particularly strong impact on Lithuania’s credit. Credit, housing price, and output shocks in other Baltic and Nordic countries on average also have a strong impact on Lithuania’s credit.”

From Natural Resource Governance Institute: 

“Fiscal rules—permanent quantitative constraints on government finances—are an important tool to help mitigate the macroeconomic challenges associated with managing natural resource revenues. Partly inspired by successes in managing resource revenues in countries such as Chile, Peru and Norway (countries that have established fiscal rules and have abided by these budgetary constraints for over a decade), more countries have been adopting such rules.

The authors of this paper reviewed the use of fiscal rules across countries assessed in the Resource Governance Index (RGI). For each of the 34 RGI countries with fiscal rules, they reviewed the evidence on the rule’s characteristics, the compliance with the rule, and oversight of this compliance. They analyzed levels of compliance in 2015 and 2016, the years directly following the commodity price crash. The research provides new insight into how these fiscal rules performed during serious economic shocks.

The analysis sheds light on large gaps in compliance and oversight of fiscal rules, and the paper provides policy recommendations on how fiscal rules can be further strengthened.”

Continue reading here.

A new IMF working paper finds “striking differences in the evolution of labor force participation across countries, and even more across groups of workers. While the heterogeneous timing and pace of the demographic transition can explain part of the divergent trends, other factors, including policies and differential exposure to the global forces of technological progress, are also at play.”

“The findings of this paper suggest that many countries so far successfully counteracted the negative forces of aging on aggregate labor force participation by strengthening the attachment of specific groups of workers to the labor force. Changes in labor market policies and institutions, together with structural changes and gains in educational attainment, account for the bulk of the increase in the labor force attachment of prime-age women and older workers in the past three decades. Conversely, technological advances, namely automation, while beneficial for the economy as a whole, weighed on labor supply of most groups of workers, and can partially explain declining prime-age male participation. Individual-level evidence confirm the significant impact of vulnerability to routinization, and that detachment from the labor force is significantly more likely among individuals whose current or past occupations are more vulnerable to automation. But encouragingly, higher spending on education and active labor market programs, and access to more diverse labor markets, tend to attenuate this negative effect, at least for prime-age workers.”