Spatial research in the RDC environment:

challenges and opportunities

Robin Leichenko – Rutgers and Julie Silva – Univ. of Florida

New York Census Research Data Center 2nd Annual Census Workshop Series

Spatial Statistics and Spatial Research Using the Census RDCsMay 8, 2008

Background with RDC

‘Experienced’ users of the RDC– Export and firm size project (Leichenko)– International trade and rural economies

project (Leichenko and Silva)

But limited primarily limited to Economic Census based analyses, particularly the Longitudinal Business Database (LBD) in combination with BEA regional data and other public data sources

Spatial Research Background

Broad training: economic geography, GIS, spatial analysis, spatial econometrics, regional science

Types of research questions and issues:- drivers of economic growth and change across cities and regions- consequences of globalization for regions, cities and rural areas, and for firms and workers in spatially isolated regions

Econometric/spatial software: 'typical users’ of software such as SAS; RATS; Eviews; ArcGIS; SpaceStat or MatLab for cross-sectional and panel analyses and GIS

A Spatial Research Project at the RDC: International Trade and Rural

Economic ChangeAcknowledgements

U.S. Department of Agriculture: Funding support for this research was provided by the U.S. Department of Agriculture, Cooperative State Research, Education, and Extension Service under Agreement No. 00-35401-9204.

U.S. Census Bureau: This study reports the results of research and analysis undertaken while the author was a research affiliate at the Center for Economic Studies at the U.S. Census Bureau. Research results and conclusions expressed are those of the author and do not necessarily indicate concurrence by the Census Bureau.

Research plan• Comparison of 1997 export measures in the Census

of Manufacturing with published data (Criterion: Understanding and/or improving the quality of data produced through a Title 13, Chapter 5 survey, census, or estimate and Identifying shortcomings of current data collection programs and/or documenting new data collection needs)

• Construction of county-level import and export database (Criterion: Enhancing the data collected in a Title 13, Chapter 5 survey or census, for example by improving imputations for non-response or developing links across time or entities)

• Used of new database for the analytical pieces of the project (Criterion: Preparing estimates and characteristics of population as authorized under Title 13, Chapter 5)

Data RequirementsNon Public (at the RDC)• Manufacturing exports, shipments, and employment by 4-

digit sector (SIC system) aggregated to the county level for all U.S. counties, 1972-1994 (Center for Economic Studies Census of Manufacturing, Longitudinal Research Database)

Public (brought into the RDC)• U.S. exchange rate data by country (1972-1994)• U.S. industrial exports (4-digit SIC) by country of destination

(Feenstra 1997) • U.S. industrial imports (4-digit SIC) by country of origination

(Feenstra 1996) • BEA REIS – employment, earnings, population (county)• Rural-urban continuum codes to identify rural counties by

size, remoteness• State-level unionization

County trade database • Aggregated LBD data from the firm-

level to the county level

• Created measures of export involvement and import exposure by county

• Creates measures of export and import exchange rates by county

County Trade Measures: Lessons

• Think about appropriate spatial unit of analysis

• Allocate sufficient time to build spatial datasets

• Ability to 'look' at the data visually would be helpful

Analytical Research Questions I: Employment and Earnings Analysis1. What are the effects of international trade

on rural manufacturing employment and rural manufacturing earnings?

2. Do the effects of trade on rural counties differ from those in urban counties?

3. How do the effects of trade vary across major economic regions?

Empirical Models

1. County manufacturing employment = fn(endowments, agglomeration economies, trade exchange rates, trade orientation measures)

2. County manufacturing earnings = fn(endowments, agglomeration economies, trade exchange rates, trade orientation measures)

Model Estimation

• Panel regression with county and time period fixed effects

• All counties including for 1972-1994• Estimated by rural counties, urban

counties, and by Census region• Explanatory variables lagged by one

year• Estimated with one lag (in most cases)

Analytical Research Questions IIIncome Inequality Analysis

1. What has been the effect of changing exchange rates and industry trade orientation on income inequality across the contiguous United States, and the rural and urban portions of states?

2. Is there variation in trade-inequality linkages across the major census regions?

Empirical Models

Income inequality = fn(income measures, urbanization

measures, manufacturing measures, international trade measures)

Dependent Variables – two modifications of the Theil Index (Nissan and Carter 1996):

(1) Inequality across states

(2) Inequality within states

Model Estimation

• Panel regression (OLS) with state and time period fixed effects

• All contiguous states for 1972-1994 time period

• Estimated by all states, urban portions of states, and by rural portions of states

Analytical Research Questions III: Skill Differential analysis

1. What are the effects of changing exchange rates and trade orientation on the relative demand for skilled manufacturing workers in the United States?

2. Do high-tech industries respond differently to changing trade pressures than traditional manufacturing sectors?

Our Model

Demand for non-production workers = fn(income measures, urbanization measures, manufacturing measures, international trade

measures)

Dependent Variables:

(1) Non-production worker wage share

(2) Non-production worker employment share

Model Estimation

• Panel regression (OLS) with industry and county fixed effects

• All 4-digit SIC industries by all counties for 1972, 1977, 1982, 1987, 1992, and 1997

• Estimated by traditional and hi-tech manufacturing industry groupings, and for individual hi-tech sectors

Refereed papers

• Leichenko, R and J. Silva. 2004. International Trade, Employment, and Earnings: Evidence from Rural Counties. Regional Studies 38: 355-374.

• Silva, J. and R. Leichenko. 2004. Regional Income Inequality and International Trade. Economic Geography 80: 261-286.

• Silva, J. 2008. International Trade and the Changing Demand for Skilled Workers in High-tech Manufacturing. Growth and Change. 39: 225-249.

Limitations of Rural Trade Study within RDC environment

• Ability to control for spatial effects was limited to fixed effects and/or construction of spatial-lag variables

• Did not have access to software with advanced panel techniques in the RDC environment

• Tests for lag-length, unit roots, co-integration were done off site using only the public data (i.e. without the trade measures)

• Did not conduct spatial panel tests• Were not able to map new trade variables (or

other spatial variables) due to disclosure rules

Spatial Research Opportunities in RDC environment

Analysis of spatial units (deductive; theory testing)--unit of analysis is a city, county, state, census track etc. for cross-sectional and/or panel analysis --increasing recognition of the need control for spatial effects especially tests and controls for spatial autocorrelation and spatial lags

This type of spatial analysis is feasible at the RDC. Addition of GIS and spatial econometric software would allow controls for spatial effects for ‘typical’ users

Spatial Research Opportunities (cont’d)

Analysis of the role of spatial factors in influencing economic/social phenomena (deductive; theory testing)--agglomeration economies--spatial proximity/isolation--transportation/market access

This type of analysis is feasible at the RDC with GIS and spatial econometric software

Spatial Research Challenges

Mapping and analysis of spatial patterns (inductive; theory forming)-- Mapping of social and economic phenomena-- Search for spatial patterns and regularities

This type of work is more challenging in RDC environment– Our experience suggests there are significant

limitations on what can be mapped or tabulated for public release (we could not disclose a lower level of aggregation than what was publicly available)

– Inductive exploratory work is harder to justify within the RDC proposal/Census benefit statement format

Further information

Robin Leichenko: rleichen@rci.rutgers.edu

Dept. of Geography, Rutgers University

Julie Silva: silva@geog.ufl.edu

Dept. of Geography and Center for African Studies, Univ. of Florida