Transcript Spatial analysis in the RDC environment: challenges and

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 RDCs
May 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 Change
Acknowledgements
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 Requirements
Non 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 firmlevel 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 Analysis
1. 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 II
Income 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: 355374.
• 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 Hightech 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: [email protected]
Dept. of Geography, Rutgers University
Julie Silva: [email protected]
Dept. of Geography and Center for African
Studies, Univ. of Florida