Riccardo Curtale (p), Filipe Batista e Silva

Carlos Mendez

Socioeconomic projections are essential tools for policy-making that are typically available at coarse geographical aggregations, which is often insufficient for policy domains with a strong territorial dimension. The disaggregation, or downscaling, of socioeconomic projections to finer geographical resolution, is often required to assess future impacts of scenarios on climate, environment, natural hazards, urbanization and other issues. The most simple calculation rule for downscaling is based on keeping observed regional shares constant through time. Such an assumption however would be futile, as it simply replicates the status quo of a given observed moment in time by keeping the regional variability constant, thus ignoring ongoing or expected processes of divergence/convergence of regions. To overcome this limitation, we propose a novel regionalisation model to regionalise long-term, country-level socioeconomic projections in an integrated manner. The model relies on a set of linked equations that integrate assumptions regarding future regional growth, and estimate regional levels of GDP, employment and population dynamically and recursively, while ensuring consistency with any set of given socioeconomic projections at the country level. The model is structured in a flexible way that allows to expand or substitute any of its components by alternative approaches, and to generate different regionalisation scenarios based on the same set of country-level projections. A first pilot of the model is conducted to downscale country-level socioeconomic projections to NUTS3 regions in Europe. An accuracy assessment of the model is currently ongoing, aiming at the comparison of observed historical data with projections from a null model assuming invariant growth rates within each country and data generated from the regionalization model.

Tsubasa Shibata (p), Takashi Yano

Riccardo Curtale

The rate of birth is declining worldwide. Declining fertility, once only a common phenomenon in high-income countries, has been observed in middle-income countries as well in recent decades, among which is Thailand. The total fertility rate, which was 1.8 in 2009, dropped to 1.5 in 2021, well below the population replacement rate (the level at which the population is maintained between generations) of 2.0. In the society of Thailand, the declining fertility rate, coupled with longevity, results in a seriously low fertility and aging population.
Demographic change due to declining the rate of birth and an aging population means that the working-age population will decline as the proportion of the population and a shift to elderly workers in the age distribution in the working-age population leading to lower productivity growth. A slowdown in economic growth and depression of the economy will exacerbate wages, the employment rates, and other labor conditions, further affecting households’ fertility selection behavior, which will be reflected in a further birthrate decline. Future economic growth in Thailand depends on how population changes are strategically addressed. Therefore, it will be essential to implement policies with a perspective that comprehensively understands the economic conditions and social trends surrounding the declining birthrate and aging society. In this regard, this study constructs a model linking demographic, a fertility, and multisector models based on an input–output (IO) table. We then evaluate the medium- to the long-term impact of the declining birthrate and aging population on the Thai economy by applying the model and conducting simulations focused on potential population-related policies.

Federico Fantechi (p), Ugo Fratesi

Tsubasa Shibata

The agglomeration forces of territories have long been acknowledged, in the urban and regional economic literature, as strong drivers of economic growth. In developed countries, economic activities are strongly agglomerated, and such geographical proximity produces externalities that are usually recognized to play a major role in the process of knowledge creation and diffusion. The literature identifies two types of externalities having a primary role in territorial growth: "specialization externalities", which operate mainly within a specific industry and "diversity externalities" which work across sectors. While points in support of each type of externality can easily be made, both suffer from the same methodological issue: they are measured over large territorial aggregates, usually administrative areas, while the impact of such externalities - if any - operates at a quite smaller geographical proximity. Moreover, such territorial aggregation also masks a large part of the territorial heterogeneity, present within such administrative units by, e.g., pooling together urban and rural territories.

This paper proposes a novel methodology measuring such externalities at territorial level, rather than administrative level, using firm-level data. This is done by applying Frenken et al. (2007) entropy measures to firms' level data aggregated onto a regular grid measuring "small-level" related and unrelated variety (diversity externalities) and Sectoral Location Quotients to measure "small-level" specialization (specialization externalities).
Using these measures for Italy between 2011-2019, allows to test the role played by these externalities on both employment labour productivity and growth of firms and territories in their specific context and allows to study the heterogeneity of the effect in different sub-regional territorial contexts (e.g., urban areas-peripheral areas). Results show that Jacobs' externalities are closely related to growth in employment especially in urban contexts, while specialization externalities play a bigger role in supporting the productivity of firms in non-urban areas.

Damiaan Persyn (p)

Federico Fantechi

This paper considers biases that may occur when statistical analysis is conducted using aggregated data (eg at the national level), when the underlying data is generated at a lower (e.g. regional) level. Two types of bias are examined. A first bias occurs when aggregating underlying non-linear relationships, as described by Lewbel (1992, ReStud). The second bias occurs when aggregating dynamically heterogeneous relationships, as described by Pesaran and Smith (1995, JEconom.). As an example, I compare wage rigidity estimation conducted at the national and regional level. A monte-carlo study suggests that in this application, wage curve elasticities estimated using national data may be upward-biased by 100%.

Jonas Westin (p)

Damiaan Persyn

The purpose of the paper is to discuss experiences from the ongoing project for estimating and validating interregional trade in the new multiregional input-output (MRIO) tables at Statistics Sweden. In the paper we investigate a novel method for estimating interregional trade flows in sectors where no or little survey data of trade patterns are available.

The project is part of a quality assurance initiative to develop interregional input-output tables for research, policy assessment and planning. For this effort to be successful, it is necessary to add further resources for the collection of interregional trade statistics and the consistent modelling of interregional trade using a combination of survey and non-survey tools.

There are many techniques for updating or estimating multiregional input-output tables using non-survey methods. A common method for estimating trade matrices is the gravity-RAS approach where unobserved trade flows are estimated using a gravity model in combination with the RAS-algorithm for fitting the estimated matrix to total production and consumption in each region. This is the method used in both previous Swedish MRIO-projects as well as in the process for estimating Production-Consumption-matrices for the Swedish National Freight Transport model SAMGODS.

A drawback of the used method is that it requires survey data on regional commodity flows, which can be expensive and difficult to collect. The estimation procedure uses survey data to estimate parameters in a gravity model. This model is then utilized to generate à priori matrices that is fitted to data on regional production and consumption using RAS-balancing.

In this paper, we investigate an alternative method for estimating the parameters in the gravity model using an error function that penalizes errors in the marginal constraints. This way, we can use regional data already available to find the most likely gravity model trade patterns that fits the data.

Comparisons of estimated gravity models using historical trade flows for Sweden have shown that the method, in many situations, produces results that are similar to more traditional survey based estimations techniques. In this paper we investigate the properties of this new method further in a simulation study where different ways of estimating MRIO-matrices are compared using Monte Carlo simulations.

Carlos Mendez, Harry Aginta (p), Masakazu Someya

Jonas Westin

This paper investigates the regional Okun's law across 514 districts of Indonesia over the 2011-2020 period. To address the endogeneity issue, we use regional temperature as instrument for economic growth. Our results show that regional growth becomes statistically significant only after endogeneity is taken into account. Furthermore, we show that Okun's law is geographically heterogeneous. Only in the more industrialized western regions of Indonesia does unemployment have a significant relationship with GDP growth.