世界経済・社会統計 = World development indicators

计量经济分析 = Econometric analysis

We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

https://www.econstor.eu/bitstream/10419/81822/1/02-28.pdf

Economic growth and income inequality

Guns, Germs, and Steel: The Fates of Human Societies

We document a strong positive relationship between natural resource exports and urbanization in a sample of 116 developing nations over the period 1960–2010. In countries that are heavily dependent on resource exports, urbanization appears to be concentrated in “consumption cities” where the economies consist primarily of non-tradable services. These contrast with “production cities” that are more dependent on manufacturing in countries that have industrialized. Consumption cities in resource exporters also appear to perform worse along several measures of welfare. We offer a simple model of structural change that can explain the observed patterns of urbanization and the associated differences in city types. We note that although the development literature often assumes that urbanization is synonymous with industrialization, patterns differ markedly across developing countries. We discuss several possible implications for policy.

/pdf/urbanization-with-and-without-industrialization-2aw9imnyam.pdf

Urbanization with and without industrialization

https://marroninstitute.nyu.edu/uploads/content/Demography,_Urbanization_and_Development_Remi_Jedwab.pdf

Demography, urbanization and development: Rural push, urban pull and … urban push?

Standard models explain urbanization by rural-urban migration in response to an (expected) urban-rural wage gap. The Green Revolution and rural poverty constitute rural push factors of migration. The Indus- trial Revolution and the urban bias are urban pull factors. This paper offers an additional demographic mechanism, based on internal urban population growth, i.e. an urban push. Using newly compiled historical data on urban birth and death rates for 7 countries from Industrial Europe (1800-1910) and 33 developing countries (1960-2010), we show that many cities of to- day's developing world are "mushroom cities" vs. the "killer cities" of In- dustrial Europe; fertility is high, while mortality is much lower. The high rates of urban natural increase have then accelerated urban growth and ur- banization in developing countries, with urban populations now doubling every 18 years (15 years in Africa), compared to every 35 years in Industrial Europe. This is further found to be associated with higher urban congestion, possibly mitigating the benefits from agglomeration and providing further insights into the phenomenon of urbanization without growth. Both migra- tion and urban demographics must be considered in debating urbanization.

Rural Push, Urban Pull and... Urban Push? New Historical Evidence from Developing Countries

Land plays a critical role in both economic and environmental accounting. As an asset, it occupies a unique position at the intersection of the System of National Accounts (SNA), the System of Environmental-Economic Accounting Central Framework (SEEA-CF), and (as a spatial unit) SEEA Experimental Ecosystem Accounting (SEEA-EEA), making land a natural starting point for developing natural capital accounts more generally. We develop a pilot set of national and subnational land accounts for the United States that are consistent with the SEEA-CF and SNA principles, quantified in both physical and monetary terms. The physical accounts utilize detailed land use (National Land Use Database) and land cover (National Land Cover Database) datasets, which provide insights into how land cover in the U.S. is changing over time. To provide aggregate estimates of land values, we use a hedonic approach that exploits fine-grain microdata (“big data” from Zillow) that contains detailed information from hundreds of millions of property transactions and their corresponding physical characteristics covering much of the U.S. Methodologically, we show that it is feasible to produce monetary accounts for land that can be directly linked to and integrated with physical land cover/use. Overall, U.S. land cover has shown declines in forests, cropland, and pasture with increases in barren, scrub/shrub, and developed classes, which are particularly concentrated in the U.S. Southeast. Nominal land values in the U.S. fell about 28% ($7 trillion) from the boom to bust periods in the prior decade, albeit with substantial regional variation, and have subsequently experienced a nearly full recovery in recent years. We estimate private land in the contiguous 48 states to be worth approximately $25.1 trillion in 2016.

Accounting for land in the United States: Integrating physical land cover, land use, and monetary valuation

Developing a national account-based measure of the distribution of income from the commonly used census-based concept of money income has been the subject of earlier research. We use publicly available survey and administrative data to construct a distribution of personal income after enhancing the top income distribution in the Current Population Survey (2007 and 2012). We show that inequality measures are fairly sensitive to the definition of income contemporaneously and across time. This work helps bridge the gap between micro data and macro statistics and informs about results from other studies, such as Piketty et al. (2018).

/pdf/improving-the-measure-of-the-distribution-of-personal-income-4hpcp7morr.pdf

Improving the Measure of the Distribution of Personal Income

Historically, residential housing services or “space rent” has made up a substantial portion (approximately 10%) of U.S. GDP final expenditures. In this study, we develop estimates valuing housing services based on a user cost approach and detailed microdata from Zillow (ZTRAX), a “big data” set that contains detailed information on hundreds of millions of market transactions. This approach directly incorporates market prices into the estimates and uses these prices for hedonic imputations based on rich property-level information, leveraging detailed data for markets that vary extensively by region and locality. We compare our estimates to the corresponding BEA series, which is based on a rental-equivalence method and traditional, survey-based data sources. Since 2002, initial results (from our default user cost specification) track aggregate home price indices more closely than the current BEA estimates, yielding much higher estimates of housing services during the housing boom with more similar estimates during the subsequent recovery. The study demonstrates the potential benefits of large data sources, highlights challenges associated with the user cost approach, and identifies ways that detailed microdata can improve detail in regional estimates.

Marina Gindelsky

Papers

Demography, urbanization and development: Rural push, urban pull and … urban push?

Rural Push, Urban Pull and... Urban Push? New Historical Evidence from Developing Countries

Accounting for land in the United States: Integrating physical land cover, land use, and monetary valuation

Improving the Measure of the Distribution of Personal Income

Valuing Housing Services in the Era of Big Data: A User Cost Approach Leveraging Zillow Microdata