OCS Study 

MMS 2002-072 

 

Coastal Marine Institute 

Effect of the Oil and Gas Industry 

on Commuting and Migration Patterns 

in Louisiana: 1960-1990 

Cooperative Agreement 

Coastal Marine Institute 

Louisiana State University 

U.S. Department of the Interior 

Minerals Management Service 

Gulf of Mexico OCS Region 
U.S. Department of the Interior Cooperative Agreement 

Minerals Management Service Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University 

OCS Study 

 MMS 2002-072 

 

Coastal Marine Institute 

Effect of the Oil and Gas Industry 

on Commuting and Migration Patterns 

in Louisiana: 1960-1990 

Authors 

Natusumi Aratame 

Joachim Singelmann 

December 2002 

Prepared under MMS Contract 

14-35-0001-30660-19939 

by 

Departments of Sociology and Rural Sociology 

Louisiana State University 

Baton Rouge, Louisiana 70803 

Published by 
DISCLAIMER 

This research was supported by the Minerals Management Service (MMS) under MMS Cooperative 

Agreement No. 14-35-0001-30660. This report has been technically reviewed by MMS and approved 

for publication. Approval does not signify that the contents necessarily reflect the views and policies of

the Service, nor does mention of trade names or commercial products constitute endorsement or 

recommendation for use. It is, however, exempt from review and compliance with MMS editorial 

standards. 

REPORT AVAILABILITY 

Extra copies of the report may be obtained from the Public Information Office (Mail Stop 5034) at the 

following address: 

U.S. Department of the Interior 

 Minerals Management Service 

Gulf of Mexico OCS Region 

Attention: Public Information Office (MS 5034) 

 1201 Elmwood Park Boulevard 

 New Orleans, Louisiana 70123-2394 

 Telephone Number: 1-504-736-2519 

 1-800-200-GULF 

CITATION 

Suggested citation: 

Aratame, N. and J. Singelmann. 2002. Effect of the Oil and Gas Industry on Commuting and 

Migration Patterns in Louisiana: 1960-1990. U.S. Dept. of the Interior, Minerals Management 

Service, Gulf of Mexico OCS Region, New Orleans, LA. OCS Study MMS 2002-072. 43pp. 

iii 
v 

Table of Contents 

List of Figures ...............................................................................................................................vii 

List of Tables.................................................................................................................................vii 

1. Overview................................................................................................................................. 1 

1.1. The Oil and Gas Production and Fiscal and Employment Structure in Louisiana .........1 

2. Employment Pattern by Coastal and Non-Coastal Parishes ................................................... 2 

2.1. Coastal and Non-Coastal Parishes: 1960-90 Censuses...................................................2 

3. Commuting.............................................................................................................................. 4 

3.1. Commuting Patterns 1960-90 ......................................................................................... 4 

3.2. Commuting Patterns by Industry .................................................................................... 5 

4. Commuting Networks and Commuting Distance ................................................................... 5 

4.1. Commuting Distance ...................................................................................................... 6 

4.2. Commuting from Adjacent Parishes............................................................................... 7 

4.3. Commuting Distance by Industry................................................................................... 8 

5. Migration................................................................................................................................. 9 

5.1. Migration Patterns 1960-90 ..........................................................................................10 

5.2. Migration Patterns in Coastal Parishes.........................................................................10 

6. Predicting Commuting and Migration .................................................................................. 11 

6.1. Migration and Commuting Models: Three Approaches............................................... 11 

6.2 Specification of the Model............................................................................................14 

7. Estimation of the Volume of Commuting and Migration ..................................................... 15 

7.1. Commuting Model: 1960-1990 .................................................................................... 15 

7.2. Commuting Model: 1980-1990 .................................................................................... 17 

7.3. Migration Model: 1960-1990 .......................................................................................19 

7.4. Migration Model: 1980-1990 .......................................................................................20 

Bibliography.................................................................................................................................. 43 
vii 

List of Figures 

Figure 1. Commuters in Coastal Parishes (%), 1980 ................................................................... 23 

Figure 2. Commuters in Non-Coastal Parishes (%), 1990 ........................................................... 23 

List of Tables 

Table 1. Employment in Mining Industry, 1960-90 ................................................................... 27 

Table 2. The Number of Parishes by Percentage of Workers in Mining Industry, 1960-90 ..... 28 

Table 3. Commuting by Coastal and Non-Coastal Parishes, 1960-90........................................ 28 

Table 4. Growth of Commuting and Percent Commuting by Coastal and Non-Coastal 

Parishes, 1960-90.......................................................................................................... 28 

Table 5. Commuters and Total Workers by Industry and Coastal/Non-Coastal Parishes, 

1980-90 ......................................................................................................................... 29 

Table 6. Percent Commuters by Industry and Coastal/Non-Coastal Parishes, 1980-90 ............ 30 

Table 7. Average Commuting Distance: Coastal and Non-Coastal Parishes, 1960-90............. 30 

Table 8. Commuting in Coastal and Non-Coastal Areas, 1960-90 ............................................ 31 

Table 9a. Commuting Patterns by Sending States, 1960.............................................................. 32 

Table 9b. Commuting Patterns by Sending States, 1970.............................................................. 32 

Table 9c. Commuting Patterns by Sending States, 1980.............................................................. 33 

Table 9d. Commuting Patterns by Sending States, 1990.............................................................. 33 

Table 10. Commuting Patterns to Coastal Parishes, 1960-90....................................................... 34 

Table 11. Average Commuting Distance by Industry .................................................................. 35 

Table 12. Commuters by Travel Distance, 1980 and 1990 (%) ................................................... 35 

Table 13. Distribution of Commuters by Travel Distance by Industry in Coastal and NonCoastal Areas, 1980 (%)............................................................................................... 36 
Table 14. Distribution of Commuters by Travel Distance by Industry in Coastal and NonCoastal Areas, 1990 (%)............................................................................................... 37 

Table 15. Commuting Patterns Related to Mining Industries in Coastal Parishes, 1980 and 

1990 .............................................................................................................................. 38 

Table 16. Migration to Coastal And Non-Coastal Parishes, 1960-90 .......................................... 39 

Table 17. Migration to Coastal and Non-Coastal Areas by Origin State, 1960-90...................... 40 

Table 18. Percent of Population Migrating from Other States to Coastal Parishes, 1960-90 ...... 41 

Table 19. Migration to Coastal Parishes, 1990............................................................................. 42 

viii 
1. Overview 

The oil and gas industry comprises a very important part in the history and economy of coastal 

states in the Gulf of Mexico (GOM) region. In Louisiana in particular, where the impact of the 

oil and gas industry is the greatest, the history of oil and natural gas extraction dates back to the 

turn of the present century. After the discovery of the Heywood well six miles from Jennings in 

1901, oil wells have been drilled in many parts of Louisiana, including Caddo, Webster, 

Claiborne and Union parishes in the north, La Salle parish in the central part of the state, and in 

most coastal parishes in the south and in the GOM off the Louisiana coast. The Exxon refinery 

in Baton Rouge, which is the largest oil refinery in the United States, went on-stream in 1909. 

While drilling in Louisiana's coastal marsh areas became an important source of production in 

the 1920s, the advancement of new technology in the 1940s lead to the drilling in the Outer 

Continental Shelf (OCS). The first oil well off the Louisiana coast, located 45 miles south of 

Morgan City, was brought on-line in 1947. The production of oil and gas soared with more 

discoveries of oil fields in the GOM. Since the 1970s, however, the production gradually 

declined with the drop in reserves. Most recently, however, a large-scale exploration is under 

way in the deep water of the GOM. This push further off the Louisiana coast has been made 

possible by two major developments: first, more accurate 3-D mapping technology that has 

sharply lowered the number of dry wells, thus reducing the cost of exploration; and second, 

construction of taller platform structures suitable for deep-water drilling. 

1.1. The Oil and Gas Production and Fiscal and Employment Structure in 

Louisiana 

The oil and natural gas industry has been an important source of revenue and employment in 

Louisiana throughout these periods. The industry contributes a significant proportion in State 

revenue through the severance tax which, in turn, is used to finance various social and economic 

activities throughout Louisiana. A significant part of the severance tax (sometimes up to 90 

percent) has been used to finance the public school system, with the remaining proportion given 

back to the parishes. The strong impact of the industry on Louisiana's labor force is reflected in 

employment changes in the extractive industry that mirror the fluctuation of oil and gas 

production. 

While oil and gas production declined continuously from the 1970s to the early 1990s, the 

proportion of the severance tax, the number of workers employed in this industry, and the 

number of wells display more irregular patterns. 

The production of oil and gas peaked in 1969-70 and declined consecutively until 1990 due 

largely to declining reserves. The severance tax, on the other hand, peaked twice (in 1974 and 

1981) before it declined to much lower levels in the 1980s. The increase in the severance tax in 

the 1970s and 1980s resulted from an increase in oil prices that compensated for the decline of 

production after 1970. The first "oil shock" in 1973-74 and decontrol of oil prices that started 

after 1979 raised the oil prices dramatically and continuously since 1974, from $3.89 (bbl) in 

1 
1973 to $31.8 in 1981. Oil prices, however, plunged once again after 1981, which resulted in a 

declining severance tax. 

The number of wells in the GOM region increased rapidly in the 1960s when production was 

also rising, but they did not drop with the decline of oil/gas production. The number of active 

wells remained relatively stable after 1984, despite the general downward trend in production. 

The number of workers employed by the oil and gas industry does not follow the pattern in 

production or the number of wells before 1970. The employment peak came in the early 1980s, 

approximately 10 years after the peak in the production of oil and gas. It is possible that the 

rising oil prices and declining oil reserves in the late 1970s motivated the industry to bring in 

more workers in order to find more producing wells. Employment in the oil and gas industry 

declined substantially as oil prices declined during the 1980s. 

To summarize, the impact of the oil and gas industry on revenues and employment is explained 

by (1) the amount of production and (2) oil prices, with the latter affecting the revenue and 

employment structure more strongly after 1974 in particular. Until 1973, when oil prices were 

stable, the amount of oil and gas production, the severance tax, the number of wells and 

employment only show a weak association. The contribution of the severance tax to state 

revenue sharply increased to 40 percent in 1974 but declined to 14 percent in 1990, as the price 

of oil declined. The number of workers employed in this industry essentially followed the 

movement in the price of oil. Employment reached its peak when the price of oil was highest but 

declined thereafter. 

2. Employment Pattern by Coastal and Non-Coastal Parishes 

The employment effect of the oil and gas industry at the parish level differs from the general 

pattern described above. Some parishes have been more affected by the general fluctuation in 

the industry than others; the effect may also have changed over time. For example, as the 

reserve went lower, once booming sites may have curtailed their operation independent of the 

demand for oil and rising or declining oil prices. As drilling technologies advanced, once 

inaccessible oil reserves became accessible, benefiting coastal parishes. Below, we will briefly 

describe the employment pattern over time. 

2.1. Coastal and Non-Coastal Parishes: 1960-90 Censuses 

Table 1 shows how the total number of workers and the number of workers in the mining 

industry grew between 1960 and 1990, based on the decennial censuses. We refer here to the 

mining industry instead of the oil and gas industry, because the breakdown of the mining 

industry into its component categories before 1980 is not available at the county level. 

Before discussing Table 1, we call attention to two properties of the data. First, since the data 

are collected at the place of residence rather than the place of work, some workers counted in 

non-coastal areas may be working in coastal areas, and workers enumerated in coastal areas may 

have jobs in non-coastal areas. Since the differences in data collected at the place of work and 

2 
place of residence suggest the presence of commuting, the very subject of this research, we will 

look into this problem more carefully in later sections of this report. 

Second, the mining industry includes metal, coal, and nonmetallic mining in addition to oil and 

gas extraction. However, this aggregation is a less serious problem in Louisiana where more 

than 95 percent of workers in the mining industry are engaged in oil and gas extraction. For 

1980 and 1990, a special tabulation by the Census office with more detailed industrial categories 

supplements our analysis. 

Table 1 indicates that, as was shown in earlier figures, employment in mining steadily increased 

between 1960 and 1980; it especially increased between 1970-80 when the price of oil rose, and 

it declined between 1980 and 1990, when the price fell. While both total employment and 

mining employment rose, the latter grew by a much larger margin between 1970 and 1980. 

During the period 1980-90, however, the number of mining workers decreased by more than 35 

percent on average, while total employment maintained the same level as before. 

The differentiation of employment changes in the mining industry by coastal and non-coastal 

parishes reveals that, somewhat contrary to our expectation, the number of workers in mining 

doubled in the non-coastal parishes during the period 1970-80 (105 percent), while it increased 

in coastal parishes by only 46 percent. In the subsequent period, the difference in the rate of 

decline between two areas is much smaller (-40 and -30 percent, respectively). 

The greater increase in mining-related employment in non-coastal areas between 1970 and 1980 

is also observed in Table 2, which shows the number of parishes by the extent of oil dependency 

for the period 1960-90. Between 1970 and 1980, the number of parishes generally increased in 

those categories that have a greater proportion of mining employment. For example, the number 

of parishes in the 10 percent and more employment in mining increased from 9 to 13 between 

1980 and 1990. Similarly, the number of parishes in the 2.5-5.0 percent category increased from

19 to 26, whereas the number of parishes in the category with the lowest proportion of mining 

employment (0-2.5 percent) decreased from 27 to 15. The breakdown by coastal and non-coastal 

parishes shows that the majority of the increase in the categories with a higher proportion of 

mining employment took place in non-coastal parishes. 

There was a change in employment in the mining industry from 1970-1980. Some parishes had 

growth rates of mining employment higher than the state average (74.6 percent). The category 

with the highest rates of growth in mining employment is an indicator of the parishes that grew 

twice as fast as the state average. Except for St. Tammany (which we classify as a coastal 

parish), all areas shaded darkest are clearly located in non-coastal areas. All coastal areas except 

Vermillion, on the other hand, showed slower growth than the state average. 

The growth of mining employment is a reflection of the economic boom and bust in the 

respective parishes that affect the industries. The comparison of mining and total employment 

growth indicates the relative growth of mining is greater in non-coastal parishes than in coastal 

areas. 

3 
3. Commuting 

It was noted above that the growth of employment in the mining industry during the period 

1960-90 in general, and during the boom years during the 1970s in particular, took place most of 

all in the non-coastal areas. This fact suggests that commuting and migration may have 

increased more in non-coastal areas than in coastal areas during the same period, to the extent 

that the magnitude of commuting and migration often reflects the growth of employment. 

In analyzing the effect of employment on commuting and migration, however, it is important to 

recognize that what appears to be the effect of employment might reflect the effects of respective 

time periods, locational characteristics, or differences in industrial composition. In this section, 

we will briefly investigate the commuting pattern related to (1) period pattern: the period 1960-

1990; (2) locational characteristics: coastal and non-coastal areas; and (3) industrial 

composition: the relative share of employment in the mining industry versus all other industries. 

 We expect to find that the volume of commuting in coastal and non-coastal areas is highest in 

those areas with the largest share of employment in mining. We further expect that commuting 

plays a larger role in the mining industry in coastal areas as compared to non-coastal areas. 

3.1. Commuting Patterns 1960-90 

In general, the proportion of commuters among workers in Louisiana has steadily increased 

during the period 1960-90 as the number of commuters grew faster than that of workers: The 

number of commuters has increased four-fold during this period (from 104,485 to 412,605 

persons), while the number of total workers almost doubled (from 943,217 to 1,630,341) (see 

Table 3). In terms of growth rates, the number of commuters increased substantially during the 

periods 1960-1970 (83 percent) and 1970-1980 (99 percent), and they maintained a modest 

growth between 1980 and 1990 (9 percent). Total employment, in comparison, had the largest 

increase between 1970 and 1980 and showed practically no growth during the period 1980-90 

(Table 4). As a result, the proportion of commuters among total workers increased from 11.1 

percent in 1960 to 24 percent in 1980, and it continued to increase to 25 percent despite a decline 

in the growth rates in commuting during the recent period. 

Throughout this period, the eleven coastal parishes in Louisiana consistently received more 

commuters than the state's 53 non-coastal parishes combined. The proportion of commuters 

among workers, as a result, has been much higher in coastal areas than in non-coastal areas. In 

1960, 16 percent of all workers in coastal areas were commuters, compared to only 7.8 percent in 

non-coastal areas. By 1990, while the difference between coastal and non-coastal areas slightly 

narrowed, 32 percent of workers in coastal areas were commuters versus 21 percent in noncoastal areas. The smaller difference between coastal and non-coastal areas in the percentage of 

commuters resulted mostly from the differential growth in commuting during the period 1970-

80; during his period, total and mining-related employment grew more in non-coastal areas than 

in coastal areas, as noted previously. 

In summary, commuting has been an important source of workers in coastal areas during the 

period 1960-90. Commuting appears to be also sensitive to the growth of the economy as 

4 
reflected in the growth of employment. The importance of commuting as a source of labor 

increased during this period, but the gap between the coastal and non-coastal areas somewhat 

diminished, owing to a greater increase of commuting in non-coastal areas in response to a 

greater employment growth between 1970 and 1980. 

3.2. Commuting Patterns by Industry

The importance of commuting in coastal areas is generally in line with our prediction that 

commuting is an important source of workers for coastal parishes that relay on (offshore) mining 

activities. However, the data presented above do not allow us to conclude that the mining 

industry per se attracts more commuters than other industries; it may be other industries that are 

actually attracting these commuters. Consequently, it is necessary to disaggregate the 

commuting flows in terms of industry. For 1980 and 1990, the Journey-to-Work data from the 

1980 and 1990 Census provide a further breakdown of commuting workers in terms of the major 

industry categories (one-digit industrial classification). 

Tables 5 and 6 show the number and percentage of commuters by industry in coastal and noncoastal areas for 1980 and 1990. The breakdown clearly shows that mining (which combines 

agricultural services, forestry, fisheries and mining) includes the highest proportion of 

commuting workers for both periods. In 1980, 39 percent of workers in mining were 

commuters; only the construction industry had a comparable percentage of commuters in that 

year. In 1990, the proportion of commuting in mining industry increased to 43 percent. In 

comparison, workers in trade, public and private services and government employees are more 

likely to be residents of the area where they work. A further breakdown by coastal and noncoastal areas reveals that for all industry categories, workers in coastal areas are more likely to 

be commuters than is the case for workers in non-coastal areas. This applies especially to 

mining: about one-half of mining employment in coastal areas (47 percent in 1980 and 52 

percent in 1990) is made up by commuters. 

These findings clearly demonstrate that (1) coastal areas depend heavily on commuting workers 

as a source of employment, and (2) the mining industry especially attracts workers who commute 

from elsewhere. The assessment of the impact of non-resident workers in coastal areas, 

however, must include the areas from which workers are recruited; of particular interest here is 

the distance of commuting as an indication of the geographical spread of the attraction of mining 

employment in coastal areas. 

We therefore carried out an analysis of commuting networks to find out (1) how far the impact of 

commuting involving coastal areas extends in general and (2) which parishes in particular are 

involved in commuting networks for sending mining-related workers. 

4. Commuting Networks and Commuting Distance 

In this section, we present commuting data in several different ways to find out how coastal and 

non-coastal areas are joined by commuting networks. First, for the period 1960-90, we describe 

how commuting networks extend from coastal areas compared with non-coastal areas, to be 

5 
followed by a more detailed description of individual coastal parishes. Since commuting 

networks might differ by industry, we also investigate the industry differential of commuting for 

the period 1980-90 (which is the only period for which a breakdown of commuting flows by 

industry is available). 

4.1. Commuting Distance 

As shown above, coastal areas, regardless of industry, rely much more on commuting workers 

than do non-coastal areas. It is possible that a substantial portion of commuters observed in 

coastal areas originate in a few non-coastal areas that are adjacent to coastal parishes. However, 

it is more likely that a greater number of non-coastal parishes are involved to meet a demand for 

commuting workers in coastal areas as the pool of potential workers in adjoining parishes is 

exhausted. In view of the particular work schedule of OCS activities requiring the presence of

workers on the platforms for 1-3 weeks at a stretch (to be followed by at least one week off 

work), we expect many more workers in coastal areas to originate in distant places than in noncoastal areas, for such a work schedule allows for much longer-distance commuting than would 

be feasible with a daily 8-5 work schedule. 

Table 7 shows the average commuting distance for the period 1960-90 by coastal and noncoastal parishes. Distance is measured from the center of one parish to the other. The data 

presented in Table 7 demonstrate that coastal parishes recruited workers from slightly more 

distant parishes in every period than did non-coastal parishes. The difference between coastal 

and non-coastal areas during this period increased from 3.9 miles in 1960 to 12 miles in 1990. 

Thus, not only do coastal parishes recruit workers from more distant places than do non-coastal 

parishes, this difference has increased over time. 

The same data are tabulated in terms of a 10-mile radius to investigate the distribution of 

commuters, which is another important aspect of the spatial dimension of commuting (see Table 

8). This table shows the proportion of commuters by 10-mile distance categories for coastal and 

non-coastal parishes. Since there are no parishes adjacent to coastal parishes whose distance to 

coastal parishes is less than 20 miles, the frequency for the first category (10-19 miles) is zero 

for coastal parishes. 

We expect, given the very definition of "commuting," that short-distance commuting dominates 

a majority of all commuting takes place within 30 miles. Table 8 indicates that during the period 

1960-90, about 60-70 percent of all commuters traveled from residences that were less than 30 

miles away from their place of work. 

In 1960 and 1970, the coastal parishes attracted a slightly larger percentage of commuting 

workers from parishes within 30 miles compared with non-coastal areas, but in 1980 and 1990, 

they attracted fewer workers from nearby parishes than did non-coastal areas. This pattern 

becomes even more pronounced when longer commuting distances are considered (see Table 8). 

 In 1960, slightly more workers in non-coastal areas than coastal areas were commuting from 60 

miles and beyond (4.0 percent vs. 3.8 percent). Since 1970, however, this pattern has reversed: 

the percentage of commuting beyond this range in coastal areas compared with non-coastal areas 

6 
is 6.2 percent versus 3.5 percent (1970), 12.4 percent versus 10.1 percent (1980) and 10.0 

percent versus 6.9 percent (1990). Thus, while long-distance commuting has gained more 

popularity in general in both areas, the coastal areas since 1970 have always topped the noncoastal areas in terms of proportion of workers travelling long-distance. This difference in 

commuting distance accounts for the greater increase in average commuting distance in coastal 

areas shown previously in Table 7. 

Finally, Tables 9a-9d indicate the parishes (or counties) of origin in terms of source states 

present for all 11 coastal parishes. As expected from the above analysis, most commuting takes 

place within Louisiana. Exceptions include Cameron parish bordering Texas and St. Tammany 

and Orleans which are close to Mississippi. 

4.2. Commuting from Adjacent Parishes 

While commuting distance expressed in terms of the average straight distance provides an 

insight into the extent of commuting networks, it might be misleading since a straight distance is 

often shorter than the actual travel distance measured along the highways. Since a majority of 

moves take place between parishes located nearby, Table 10 describes the characteristics of 

commuting in terms of the proportion of commuters in coastal areas from adjacent and 

nonadjacent parishes. 

In general, the proportion of commuters from non-adjacent parishes increased during the period 

1960-90, from 17.3 percent in 1960 to 35 percent in 1990. This finding is consistent with the 

previously shown results showing that commuting distance has increased in general during this 

period. Most of the shift from adjacent to nonadjacent parishes (or counties) took place during 

the period 1960-80, for the proportions of commuting from adjacent and nonadjacent parishes 

remained almost unchanged during the 1980s. 

Among coastal parishes, some parishes received far more commuters than others, and proportion 

of commuting in each coastal parish also changed substantially over time. St. Bernard and St. 

Mary parishes, in particular, have consistently attracted many more commuters from distant 

parishes than other coastal parishes during the past 30 years: Seventy-two percent in St. Mary's

and 52 percent in St. Bernard's workers in 1990 are commuters from non-adjacent parishes. 

Cameron and St. Tammany parishes are two other parishes that attracted a large number of 

commuters from non-adjacent parishes, although the reliance on commuters in Cameron 

increased after 1970 and that in St. Tammany fluctuated between 1970 and 1990. These four 

parishes were selected to demonstrate the variation in the source counties of commuters to 

coastal-area parishes. Some coastal parishes draw commuters mostly from adjacent parishes, 

whereas other coastal parishes attract commuters from much farther away. 

It is not be surprising to find that Cameron and St. Mary attract so many commuters, since major 

drilling sites are off their coast. St. Bernard, on the other hand, is not a major site, although it 

does contribute a non-negligible amount of severance tax to the state. Since St. Bernard is 

developing as part of the New Orleans Metropolitan Area, non-mining related employment 

opportunities probably induced a significant amount of long-distance commuting. 

7 
The distinction of source areas in terms of adjacency provides only a crude measure of the 

geographic spread of commuting networks. The analysis based on total commuting flows may 

be different from the commuting flow brought about by the mining industry. Thus, we will 

explore how differences in industrial structure account for differences in commuting patterns 

between coastal and non-coastal areas for the period 1980-90 for which data on commuting by 

industry categories are available. 

4.3. Commuting Distance by Industry

The findings presented above suggest that coastal parishes receive commuters from more distant 

counties than do non-coastal parishes. Since coastal areas are characterized by mining-related 

activities, at least part of long-distance commuting can be ascribed to the mining industry. Table 

11 shows average distance in commuting by industry for coastal and non-coastal areas. The 

industry "mining" in this table includes, as before, agricultural services, forestry, fisheries and 

mining. The data in Table 11 show that between 1980 and 1990, the average commuting 

distance slightly shortened, although the difference between coastal and non-coastal areas in 

commuting distance increased. 

The differentiation of commuting by industry shows that in both 1980 and 1990, mining attracted 

the longest-distance commuters, and mining workers to coastal parishes traveled much longer 

distances than those to non-coastal areas. For example, mining workers, regardless of 

destination, commuted an average of 64.2 miles in 1980 and 59.1 miles in 1990, which is over 20 

miles more than traveled by construction workers who also tend to be longer-distance 

commuters. Furthermore, the comparison of coastal and non-coastal areas reveals that while 

mining workers commuted the longest distance in both types of areas, their commute to coastal 

parishes was even longer than to non-coastal parishes. 

Tables 12-14 show the distribution of commuters in terms of a 10-mile radius by industry and by 

coastal and non-coastal area destinations. Table 12 presents the data in terms of mining and nonmining industry for coastal and non-coastal areas for 1980 and 1990. Tables 13 and 14 

disaggregate the non-mining industry into more detailed industrial classification. These tables 

together explain why mining workers to coastal areas, on average, travel a much longer distance 

than do workers to non-coastal areas. 

Table 12 shows that a greater proportion of mining workers than non-mining workers in 1980 

and 1990, and in both coastal and non-coastal areas, come from areas located beyond 90 miles 

(see also Figures 14-15 that graph the data presented in Table 12). In addition, the comparison 

of coastal and non-coastal parishes indicates that in coastal areas, 27.8 percent of mining workers 

in 1980 and 25.8 percent in 1990 originate in this farthest location. Less than three percent of 

coastal-area commuters in all other industries combined came from residences that far away. A 

differentiation by industry (Tables 13 and 14) shows that the extreme commuting distance is 

specific to the mining industry in coastal parishes, for no other industry received more than 8 

percent of commuters from areas beyond 90 miles away. 

8 
Thus, all data presented in the preceding tables clearly indicate that commuting patterns in 

general, and long-distance commuting in particular, are affected strongly by the combination of 

two factors: (1) the industry effect, whereby the mining industry attracts relatively more 

commuters than does any other industry, and (2) location effect, whereby coastal parishes attract 

relatively more commuters than do non-coastal parishes. Coastal parishes with a heavy 

dependence on mining, as a result, attract many more long-distance commuters than does mining 

in non-coastal areas, or than other industries in either type of area. 

Finally, Table 15 presents the proportion of mining-related commuters to the 11 coastal parishes 

from adjoining and nonadjoining parishes. As was shown in Table 10 (for all industries 

combined), certain parishes within the coastal area attract more commuters than others; these 

include especially St. Mary, St. Bernard, Cameron, and St. Tammany. The data in Table 15, 

with the focus on mining employment, clearly show that many more workers are commuting into 

these parishes from distant locations. On average, more than half of mining-related employment 

is commuters from non-adjoining parishes, which is very different from the pattern contained in 

Table 10. Out of 11 coastal parishes, three parishes (St. Bernard, Lafourche, and St. Mary) 

recruited more than 70 percent of their mining workers from non-adjoining areas, and this 

proportion is more than 60 percent in Jefferson and Plaquemines. Three parishes, however, 

received less than 30 percent of their mining workers from non-adjoining places (Iberia, 

Vermilion and St. Tammany), although this could be a function of the relatively small sample 

size. 

5. Migration 

Migration, just like commuting, can be viewed as an individual response to changing social and 

economic conditions in the place of origin and destination. Past research has shown that the 

demographic characteristic of a potential migrant (as well as those of the household in which 

s/he resides) are likely to be associated with the propensity to migrate. When making the 

decision to migrate, however, s/he also takes into account the social and economic opportunities 

available in the area of destination relative to the place of origin. Furthermore, the distance 

between two places also affects the propensity to migrate. 

The opportunity structure that comprises the environment for potential movers varies from area 

to area, and it could change over time. The discussion above related to commuting patterns 

suggests that (1) location factor and (2) economic characteristics have independent effects that 

act simultaneously. It was found that coastal-area location attracts more commuters in general, 

and more commuters from distant areas in particular. It was also found that, independent of 

geographic location, an employment structure that relies heavily on mining industry is conducive 

to a greater number of commuting workers. These findings comprise the theoretical and 

empirical frame of reference below in which migration patterns are analyzed. 

The analysis of migration patterns, however, is more limited in scope due to a lack of several key 

pieces of information that were available for commuters, such as (1) the occupation of migrants 

in the place of destination, and (2) migration flow data at county level prior to 1980. 

Consequently, the analysis of migration patterns prior to 1980, in particular, will be based on 

9 
net-migration data alone. Migration throughout this report refers to the movement of persons 5 

years and older across county lines. 

5.1. Migration Patterns 1960-90 

Migration generally connects much more distant places than commuting does. This is not 

surprising, for migration is a (more or less) permanent move of residence, in contrast to daily, 

weekly, or monthly commuting. Partly because accurate information about distant places is 

harder to obtain, migration is expected to be less sensitive to a change in social and economic 

conditions in the sending and receiving areas. Consequently, migration between the areas i and 

j, once established, is expected to be more stable than that of commuting. 

Table 16 indicates that the percentage of migrants in Louisiana has fluctuated within a rather 

narrow range. The total number of migrants increased from the period 1955-60 to 1975-80 and 

declined during the period 1985-90. The patterns in coastal and non-coastal areas are similar, 

although the proportion of migrants in the coastal areas peaked during the period 1965-70, 

whereas the peak for the non-coastal areas occurred during the period 1975-80. The difference 

between the two areas in terms of their rates of migration, however, is rather small. The 

migration rates in the two types of area never differ by more than 2 percent, which is much 

smaller than the difference observed for commuting (see Table 16). 

Migration, compared with commuting, involves a greater number of people from other states. 

Table 17 disaggregates the migration flow data presented in Table 16 in terms of proximity to 

the current place of residence. Since detailed migration flow data showing the exact county of 

origin are not available prior to 1980, the distance is shown only in terms of migration 

originating in the same state and in other states. 

The percent of migration from the same state and other states are fairly evenly divided during the 

period 1960-1980; in 1990 migration from other states fell to the lowest level in the past 30 

years. A similar pattern in trend and level of migration is observed for both the coastal and noncoastal areas which, again, differs from the case of commuting that showed substantial 

differences between the two types of areas. The overall similarity between coastal and noncoastal areas, however, does not suggest that all parishes in Louisiana experience the same levels 

of migration; just like commuting, some parishes attract more migration than do others. 

5.2. Migration Patterns in Coastal Parishes 

Table 18 shows the proportion of out-of-state migrants in the eleven coastal parishes. Four 

parishes (Jefferson, Orleans, Plaquemines and St. Tammany) attract much more inter-state 

migrants than do other coastal parishes. While these four parishes are part of the coastal areas 

that rely so much on oil-related activities, migrants are also likely to be attracted to social and 

economic opportunities available in metropolitan areas. Jefferson, Orleans and St. Tammany 

belong to the New Orleans Metropolitan Statistical Area (MSA). Part of the out-of-state 

migrants to Plaquemines, which borders Orleans, should be also ascribed to the "pull" of the 

larger metropolitan environment. 

10 
Table 19 classifies the place of origin of migrants during the period 1985-90 in terms of 

adjacency to the current parish of residence. The tabulation in terms of adjacency is only 

possible for this one period because it requires information on the previous county of residence 

that is not available for the earlier periods. 

Generally, the proportion of migrants from distant counties (non-adjacent areas) is much higher 

than that of commuters in general (see Table 10). This is an expected result, although the 

difference becomes much smaller and reversed in some parishes when compared with miningrelated commuters (see Table 15). 

Five parishes (Orleans, Plaquemines, St. Mary, St. Tammany and Terrebonne) receive migrants 

from rather distant counties (i.e., non-adjacent counties). Part of the reasons for their reliance on 

migration is explained by the fact that they either border or are located close to Mississippi. The 

high proportion of migrants in St.Tammany, in particular, is clearly a function of its closeness to 

Mississippi. However, as Cameron parish (which borders Texas) shows, closeness to other 

states alone does not explain why migrants are attracted more to these five parishes, for Cameron 

parish has a lower than average proportion of migrants. We already noted that of the five 

parishes with above-average proportions of migration from nonadjacent parishes (or counties), 

Orleans and St. Tammany belong to the New Orleans MSA, and Plaquemines is located just 

south of Orleans. Terrebonne belongs to the Houma-Thibodaux MSA, which is a small MSA 

with a population of less than 200,000. Only St. Mary does not belong to any MSA. 

In summary, compared with commuting, coastal and non-coastal areas do not differ much in 

terms of magnitude of migration and the distance to origin areas. Within the coastal areas, the 

proportion of long-distance migration appears to be associated with general social and economic 

opportunities resulting from the proximity to metropolitan areas. 

6. Predicting Commuting and Migration 

The descriptive analyses presented above clearly indicated that while commuting is closely 

linked to locational and industrial characteristics, migration is more strongly affected by general 

economic conditions that are present in the sending and receiving areas. The objective in this 

section is to quantify the importance of factors considered in the previous sections in a 

multivariate framework while controlling for the effects of other potentially important factors 

that may simultaneously affect the pattern of commuting and migration. We will first discuss 

commuting and migration models, followed by a discussion of the independent variables. The 

results of the estimates and further discussion will conclude this section. 

6.1. Migration and Commuting Models: Three Approaches 

Commuting and migration both represent a movement of a person from area i, the place of 

origin, to j, the destination. However, commuting and migration differ in terms of travel distance 

and permanency of the movement. Commuting refers to the travel between a person's place of 

residence and his or her place of work, whereas migration refers to a change in place of 

residence across county boundaries. The motivation behind the decision for commuting over 

11 
migration may be quite different, however. While commuting, by definition, is a job-related 

movement, migration, unless specified as labor migration, may include movements for many 

non-economic purposes, such as educational and familial movements. Yet commuting and 

migration are affected by many similar factors (although not to the same extent), and we 

therefore will discuss both commuting and migration at the same time. We would like to 

caution, however, that in reality, the decision-making process for commuting may be taking 

place in a different social and economic context than that of migration. 

There are many reviews of migration theories and models, including Greenwood (1975, 1985), 

Clark (1986), and Cadwallader (1992). However, to our knowledge, few explicit attempts exist 

for theorizing commuting patterns, even though the impact of commuting on the demographic 

composition of sending and receiving areas may be as strong as that of migration. 

Partly because migration is a subject of concern in many disciplines, various approaches have 

been put forward in the past several decades regarding the specification of the model and the 

factors included in the model. In general, past research can be summarized in terms of three 

general models reflecting different disciplinary concerns: geographic models, economic models, 

and sociological models. The geographic models emphasize the importance of distance in 

affecting the volume of migration; economic models emphasize the wage equalizing function of 

migration between regions as a reflection of an individual's rational behavior to maximize utility; 

and sociological models emphasize the importance of social networks and social institutional 

settings that help or hinder movements. These different concerns lead to an emphasis of 

different factors to be included in the models, and they also resulted in different specifications of 

the models. 

The preoccupation by geographers with spatial effects has led to the adoption of a gravity model 

which posits that the magnitude of migration is proportional to the product of population in the 

origin and destination areas and inversely proportional to the distance between them. This type 

of model is generally specified as: 

Pb1

iPb2j

Mij = a ——— (1) 

Db3

ij

where Mij is the amount of migration between area i and j, Pi and Pj are the populations of area i 

and j, Dij is the distance between i and j, and a, b1, and b2 are parameters to be estimated. 

The equation is often linearized by taking the natural logarithm on both sides of equation, and it 

is estimated using OLS: 

LogMij = log a + b1logPi + b2 logPj - b3 logDij

Past research utilizing this model has consistently found strong evidence supporting the 

theoretical argument of the gravity model (Cadwallader 1992; Greenwood 1975, 1985; Massey 

1989). While the statistical impact of each factor in the gravity model is consistent with its 

expectations, the interpretation of the results is not clear cut. What do distance and population in 

12 
origin and destination really mean for an individual decision-maker? And what are the 

underlying processes that make the product of population in sending and receiving areas have a 

positive effect on the volume of migration between the two areas? 

Distance might represent both economic and psychic costs; it might also lessen the information 

available to potential migrations about the area of destination and, thereby, reduce the likelihood 

of migration. Similarly, population may, in fact, reflect social, cultural and economic attraction 

in the origin and destination. These variables, therefore, are considered to be crude proxies for 

underlying factors that affect the individual decision-making process. For these reasons, 

attempts have been made by both economists and geographers to extend the gravity model. That 

extension generally took the form of explicitly adding economic variables that reflect the 

economic advantages and disadvantages associated with the areas of origin and destination. The 

most famous of these extended models, the Lowry model, substituted population with factors 

related to economic conditions in the place of origin (Lowry 1966). Many other versions of his 

model have appeared since then. To incorporate the hypothesis that potential migrants compare 

the characteristics of origin and destination, variables are often expressed either as a ratio or a 

difference of the value of the variable in the areas of origin and destination. The model is 

usually expressed as a multiplicative function of origin, destination and distance variables: 

Pi

 b1 Yi b3

Mij = a Db3

ij –––– • –––– (2) 

Pj

 b2 Yj b4

where Mij, Pi, Pj and Dij are the same as in equation (1), and Yi and Yj are the economic variables 

for sending (i) and receiving (j) areas, such as unemployment rates, percent of workers employed 

in manufacturing, etc. When linearized, it becomes: 

LogMij = log a + b1logPi - b2logPj + b3logYi - b4logYj – b5 logDij

Instead of using a multiplicative model, a linear-additive model is used occasionally where the 

model is simply specified as: 

Mij = a + b1Pi + b2Pj + b3Yi + b4Yj + b5 Dij. 

It is found, however, that a multiplicative model generally performs better than the linearadditive model. 

Finally, sociological models recognize that migration is affected not only by economic factors 

but also by social conditions such as the presence of family members, relatives and friends in the 

place of origin and destination which help potential migrants. They create a network of 

information and social support that tends to positively affect the likelihood that an individual 

decides to migrate. 

Pi

 b1 Yi b3 Si b5

Mij = a Db3

ij —– • —— • —— (3) 

Pj

 b2 Yj b4 Sj b6

13 
where Mij, Pi, Pj, Dij, Yi and Yj are the same as above and Si and Sj are the social variables for 

sending (i) and receiving (j) areas. When linearized, it becomes: 

LogMij = log a + b1logPi - b2logPj + b3logYi - b4logYj – b5logSi – b6logSj + b7 logDij

The existence of such social networks often make it easier for migrants to find a job and a place 

to live, because these networks often provide information related to jobs, housing and other 

aspects of the social environment. The assistance may be direct or indirect: direct when the 

networks share part of the expenses such as providing relocation costs or allowing migrants to 

stay at their home for free while searching for jobs and housing. The assistance might be 

indirect if the networks merely provide information regarding jobs and housing. In addition, the 

presence of such social networks significantly reduces the psychic costs for migrants, i.e. the 

psychological anxiety when leaving a place in which they are surrounded by familiar faces for a 

location where they are unknown. 

The above description is a rather stylized summary of the migration models and their 

disciplinary association. Recent models often include all three elements. What they point out is 

that spatial, social and economic elements are all needed to fully explain the process of migration 

decision-making if individual-level data are available, and to explain the volume of migration if 

only aggregate data are available. 

6.2 Specification of the Model 

While more sophisticated models are available, unavailability of more detailed data restricts our 

analysis of migration/commuting carried out below to the most enduring, single-equation model. 

The past research reviewed above indicates that our model of migration and commuting should 

include the following variables: distance and factors related to the probability of employment, 

wage rates, and social networks. The descriptive analyses in earlier sections have also indicated 

that volume of commuting in the Gulf of Mexico region is affected by proximity to the coastal 

area. It was further suggested that the fluctuation in oil industry caused by a change in oil prices 

affected the employment structure after 1974. 

In other words, to fully account for the variation in migration and commuting, we should 

consider spatial and period effects, in addition to three factors above. In the analyses below, we 

will include factors related to these five aspects as much as possible, although the migration 

models are much simpler than the commuting models because of a difference between migration 

and commuting in the availability of detailed data. All models are specified as a multiplicative 

model as shown in equation (3), and they are estimated using OLS after linearizing the equation. 

14 
7. Estimation of the Volume of Commuting and Migration 

7.1. Commuting Model: 1960-1990 

Dependent variable:

The dependent variable is the number of persons commuting from area i to j between 1969-70, 

1979-80 and 1989-90 regardless of the types of work they are engaged in in the place of work. 

This type of variable has been normalized in several ways in past migration research (cf. 

Greenwood and Sweetland 1972). For example, some researchers have normalized it as Mij/Pj, 

Mij/Pi, or Mij/PiPj. These methods of normalization implicitly assume that the population 

parameters are constrained to one. For example, since 

ln(Mij/Pj) = lnMij-lnPj

in a logarithmic form, the regression equation that uses ln(Mij/Pj) as the dependent variable is 

equivalent of 

lnMij = ln a + 1.000lnPj + b1lnYi

Since there is no a priori reason to expect population parameters to be one, we will enter them

unconstrained on the right-hand side of the equation for our analysis. The dependent variable in 

our model, therefore, is the number of (gross) commuters in respective census periods. 

Independent variable:

The following independent variables will be considered in the estimation. All the variables 

except the coastal area location and period dummies are expressed in logarithms: 

• The straight distance in mileage between the origin (place of residence) and destination 

county (place of work). 

• Location in coastal parishes in Louisiana: 1 if located in coastal parishes, and 0 otherwise. 

• The number of civilian labor force in origin and destination in 1970, 1980 and 1990. 

• Percentage of workers in mining industry in 1970, 1980 and 1990. 

• Ratio of per capita income in the origin and destination in 1970, 1980 and 1990. The ratio 

variable is used, instead of entering the per capita income in origin and destination 

separately, to reduce the magnitude of multicollinearity. 

• Unemployment rates in the origin and destination in 1970, 1980 and 1990. 

15 
• The number of commuters that took place between the origin and destination in the previous 

period. 

• Period dummy variables to capture the effect of being in 1990 and 1980. The 1970 period is 

the reference period against which the effect of other periods are compared. 

Past migration research has noted the danger of simultaneity bias when the end of (migration) 

period value is used as the value for independent variables (Greenwood and Sweetland 1972; 

Greenwood 1975). For example, if the 1980 population is used to predict migration that took 

place between 1975 and 1980, the possibility exists that the 1980 population is the consequence 

of migration, rather than the cause of migration. In the analysis of commuting, the use of values 

close to the time of commuting not only poses fewer problems but may even be desirable. The 

best solution for this task would be the use of simultaneous equation modeling (SEM); however, 

the data available for estimating commuting are not suitable for the use of SEM methodology. 

The second best solution to estimate parameters with less bias would be the use of 

contemporaneous values, such as the 1980 population, to predict the number of commuters in 

1980. To the extent that commuting is measured in terms of a short time-span, the end-of-period 

value should be very close to what commuters have responded to when they made a decision to 

commute. On the other hand, the further the date at which the independent variables are 

measured (even if they precede commuting), the less realistic they would reflect the conditions 

under which commuting took place. Thus, unless specified otherwise, the independent variables 

below are generally collected from the same census year in which the data on commuting is 

reported. 

Estimation results:

Table 16 shows, as hypothesized in the previous sections, importance of distance, coastal area 

location and period effects. The distance, as expected from past research, clearly has a negative 

effect on the number of commuters. For a one percent increase in mileage between the place of 

residence and place of work, the number of commuters decreases by 1.1 percent. The location 

in the coastal region is also shown to increase the number of commuters. The periods effect are 

also significant. The 1980 period in particular and the 1990 period to a lesser degree increase the 

number of commuters compared with the 1970 period while controlling for the effect of other 

variables. As pointed out in Section 3.1, it reiterates the importance of considering the unique 

effect associated with each period. 

The potential commuters not only consider the distance and location but, as economic models 

argue, compare the economic opportunities in the place of residence and work. Four variables, 

the percentage of mining, the percentage of manufacturing and construction, unemployment rates 

and the per capita income, intend to capture the effect of such variables. We expected that the 

percentage of mining would capture the effect of reliance on mining industry, a peculiar 

industrial structure in Louisiana, while the percentage of manufacturing and construction would 

reflect how the difference in the level of more general economic development affect the number 

of commuters. The effects of both variables, however, are not present. The unemployment rates, 

which represent the condition in the labor market, are more in line with expectation: a higher 

unemployment rate in the place of residence sends out more commuters (but not statistically 

16 
significant) and a higher unemployment rate in the place of work attract less commuters 

(significant). The only economic variable that has a clear impact on the number of commuters is 

the ratio of per capita income: a higher income level in the place of work (compared with the 

place of residence) attracts more commuters. 

Lastly, as the sociological model predicts, the presence of friends and relatives who have 

commuted before is expected to affect the decision of potential commuters. The “stock” variable 

is at best a crude proxy of the function of social networks. We do not know if previous 

commuters are still engaged in commuting and are helping the current commuters in the place of 

work, or they are no longer engaged in commuting (or commuting to different places) but have 

provided information about the place of work to current commuters. Despite this ambiguity in 

measurement, the volume of commuters in the previous periods exerts a strong influence on the 

number of commuters in the current period. 

The above results generally confirm the expectation developed in previous sections in a 

multivariate framework where the effect of a variable is obtained while controlling the effect of

others. 

It appears, however, that the variables depicting the industrial structure is not very effective for 

predicting the number of commuters regardless of their work. In addition, without incorporating 

the effect of industry in estimation, we cannot conclude whether or not the difference in 

commuting distance by industry observed in Section 4 is real. We will address these questions 

below using more detailed data from the 1980 and 1990 Censuses. 

7.2. Commuting Model: 1980-1990 

Dependent variable:

The dependent variable is the number of persons commuting from area i to j between 1989-90. 

The information about the type of industry engaged by commuters is included in this data. 

Independent variable:

In addition to the variables included in the previous section, we will include the six industry 

dummies and the interaction variables of distance and industry dummies. The definition of 

industry variables are: 

• Agricultural services, forestry, fisheries and mining. 

• Construction and mining. 

• Transportation, communications, and public utilities. 

• Wholesale and retail trade. 

• Finance, insurance, and real estate. 

17 
• Services. 

Since agricultural services, forestry and fisheries are insignificant in Louisiana, the first industry 

category largely captures the effect of employment in the mining industry. The reference 

category to these industry dummy variables is federal, state and local government employees. 

Self-employed persons, farmers, and persons in military services are excluded. Per capita 

income in the place of work and residence are entered separately in this exercise, rather than as a 

ratio variable. Finally, since the dependent variable refers to only one period, the period effect is 

not included. 

Estimation results:

Table 17 shows once again the importance of distance and coastal area location. One percent 

increase in travel distance reduces the number of commuters by 1.6 percent while coastal area 

location increases the commuters by 0.3 percent. The interaction of distance and industry 

dummies demonstrates that the effect of distance indeed varies from industry to industry as 

observed in Section 4. Among six industries, only the mining industry has a positive effect in 

the interaction with distance (transportation, communication and public utilities also have a 

positive coefficient but they are insignificant). Consequently, for a one percent increase in 

commuting distance, the effect of distance for workers in the mining industry is to decrease 

commuting by only 0.73% (-1.54 + 0.84) while the effect for workers in the construction and 

manufacturing industry is to decrease commuting by 2% (-1.57 - 0.44). 

Among economic variables, the effect of per capita income is clear where higher income levels 

in the place of work attract more commuters as found in the previous model. The effect of 

industrial composition (percent worker in mining and construction/mining), with the presence of 

industry dummies and industry-distance interactions, become more complex. For example, the 

percentage of mining workers in the place of residence and work are both positive and 

significant. The positive effect in the destination confirms our hypothesis since we expected that 

a greater share of mining industry in terms of employment attracts more commuters. However, a 

positive effect in the place of residence suggests that a greater share in this industry will actually 

send out more commuters. Similarly, the effect of construction and mining industry is not only 

opposite in sign but insignificant. The effect of unemployment rates also has a wrong sign. 

The estimation based on more detailed data suggests, therefore, that (1) the commuting networks 

are geographically more spread for mining workers compared with workers in other industries 

and (2) the location in coastal areas adds to that effect, but (3) the effect of industrial structure is 

more mixed. Among economic variables, it is primarily the condition in the labor market 

(unemployment rates) and the difference in the levels of income in the place of residence and 

work that are important. The effect of social networks remains strong. 

18 
19 

7.3. Migration Model: 1960-1990 

Dependent variable:

The first model of migration analyzes the pattern of migration during the period 1960-90. Since 

migration flow data are not available at county level, the dependent variable in this model is the 

net migration (in-migration minus out-migration) for the period 1965-70, 1975-80 and 1985-90. 

Migrants are defined as persons aged 5 years and over whose residence 5 years ago is different 

from the current residence. 

Independent variable:

Similar variables as described in commuting models are used below. However, since the 

analysis does not distinguish the origin and destination of migrants, all the independent variables 

refer to social and economic conditions at current parish of residence in Louisiana and distance 

between the origin and destination counties could not be entered. 

Estimation results:

Several specifications were tested and Table 18 shows the result from the final model. Due to a 

high multicollinearity between the migration stock variable and the size of labor force, and per 

capita income and period dummy variables, the lag and period variables had to be dropped from 

the final model. 

Unlike the results in the commuting models, the location in coastal areas is not associated with a 

greater number of migrants; it actually decreases the migration by 0.25%, although it is not 

statistically significant. Among economic variables, only the per capita income follows our 

expectation where an increase in income by one percent results in an increase in net-migration 

by 3.2%. The proportion of workers in the mining industry, in the construction and 

manufacturing industry and unemployment rates are all significant but to the opposite direction; 

while a greater share in mining, construction and manufacturing decreases the net-migration, a 

higher unemployment rate actually increases it. 

The discrepancy between the expectations and this result may be ascribed to several factors. 

First, since this particular migration data include population aged 5 years and over regardless of 

their work status, the causes of migration due to non-economic reasons (e.g. marriage, 

education) may not have been captured by included variables. Second, even if migration occurs 

for economic reasons, the general labor market conditions such as the area unemployment rates 

and the proportion of workers in mining and construction/manufacturing industry may not have 

captured the conditions in a particular industry in which job search is conducted. The final model 

below intends to address some of these questions. 
20 

7.4. Migration Model: 1980-1990 

Dependent variable:

The dependent variable in the last model is taken from the migration flow files between counties 

for the period 1985-90. Since information about the age of migrants is included, in this analysis, 

the sample is restricted to persons aged 25 years and over whose residence in 1990 is different 

from 1985 so that children and migration for educational reasons are, more or less, excluded. 

Independent variable:

In this final analysis, a similar set of variables as used for the analysis of commuting between 

1960-90 is used. Since the analysis refers to only one period, the period dummy is not used. In 

addition, since the migration flow data for the previous period (1975-80) are not available, the 

lag variable used below is the number of net-migrants that were observed between 1975 and 

1980 regardless of their origins. 

Estimation results:

The results are generally in line with the findings from the commuting models. It is found that 

distance, as expected, has a clear negative association with the number of migrants while the 

location in coastal areas increases migration. Among economic variables, per capita income in 

destination has a positive and stronger effect than it has in origin, suggesting that a better living 

standard in the destination attracts more migrants. Similarly, a higher unemployment rates in 

origin send out more migrants while a higher rates in destination results in lesser migration. 

The effect of industrial structure on migration is a little more complex to understand since the 

parameters for origin and destination variables are both negative. The fact that the effect of 

construction/mining industry in origin is negative and significant while it is not significant in 

destination suggests that a higher share in these industries works as a deterrent of migration for 

the sending communities while the “pull” effect from the destination is largely absent. For the 

mining industry, the negative effect in both origin and destination indicates that a greater share 

of workers in this industry discourages migration not only out of origin but into destination 

communities. It appears that a presence of mining (and other primary) industry in the destination 

lowers the attractiveness of the communities. 

Finally, the lag variable, although this is a crude proxy of the effect of prior migration, is again 

found positive and significant. 

In general, as found in the commuting models for 1960-90 and 1980-90, the effect of distance, 

coastal area location and previous migration have all expected results. The effect of level of 

living as represented by per capita income in particular and area unemployment rates to a lesser 

degree affect the migration decision in an expected way. However, the effect of industrial 

structure does not seem to explain the volume of migration very well. The findings with respect 

to industrial structure in this respect are, at best, mixed ones.
Figures 
Figure 1. Commuters in Coastal Parishes (%), 1980. 

Figure 2. Commuters in Non-Coastal Parishes (%), 1990. 

23 
Tables 
27 

Table 1. Employment in Mining Industry, 1960-90 

 Workers 16 yrs+ Workers Ever Worked Workers 16 yrs+ Employment and Area Mining Oil/Gas Total  1960 1970 1980 1990 1975-80 1985-90 1960 1970 1980 1990 Number of Workers:  Total 36,335 46,584 81,327 52,329 89,910 62,457 1,007,812 1,158,245 1,639,394 1,641,614  Non-coastal areas 17,942 22,434 46,167 27,836 52,580 33,874 608,776 686,561 993,006 1,014,405  Coastal areas 18,393 24,150 35,160 24,493 37,330 28,583 399,036 471,684 646,388 627,209 % Workers in Mining:  Total 3.6 4.0 5.0 3.2 4.2 2.8  Non-coastal areas 2.9 3.3 4.6 2.7 4.0 2.4  Coastal areas 4.6 5.1 5.4 3.9 4.5 3.4 %Growth Between Census Periods:  Total 28.2 74.6 -35.7 -30.5 14.9 41.5 0.1  Non-coastal areas 25.0 105.8 -39.7 -35.6 12.8 44.6 2.2  Coastal areas 31.3 45.6 -30.3 -23.4 18.2 37.0 -3.0 Ratio of Growth Rates of Mining and Total Employment:  Total 1.9 1.8 -263.3 -6.6  Non-coastal areas 2.0 2.4 -18.4 -5.5  Coastal areas 1.7 1.2 10.2 -14.4 Source: Decennial censuses 
28 

Table 2. The Number of Parishes by Percentage of 

Workers in Mining Industry, 1960-90 

Area %Mining 1960 1970 1980 1990 

 0.0 - 2.5% 29 24 13 29 

Non- 2.5 - 5.0% 15 18 24 15 

Coastal 5.0 -10.0% 7 9 9 7 

Areas 10.0%- 2 2 7 2 

 Total 53 53 53 53 

 0.0 - 2.5% 3 3 2 3 

Coastal 2.5 - 5.0% 1 1 2 1 

Areas 5.0 -10.0% 1 0 1 3 

 10.0%- 6 7 6 4 

 Total 11 11 11 11 

 0.0 - 2.5% 32 27 15 32 

 2.5 - 5.0% 16 19 26 16 

Total 5.0 -10.0% 8 9 10 10 

 10.0%- 8 9 13 6 

 Total 64 64 64 64 

 

Source: Decennial censuses 

Table 3. Commuting by Coastal and Non-Coastal Parishes, 

1960-90

Employment Area 1960 1970 1980 1990 

 Coastal 59,709 115,499 196,473 208,513 

Commuters Non-coastal 44,776 75,604 183,405 204,092 

 Total 104,485 191,103 379,878 412,605 

Total Coastal 370,304 425,670 644,705 642,623 

Workers Non-coastal 572,913 612,732 961,290 987,718 

 Total 943,217 1,038,402 1,605,995 1,630,341 

 Coastal 16 27 30 32 

%Commuters Non-coastal 7.8 12.3 19.1 20.7 

 Total 11.1 18.4 23.7 25.3 

Source: Decennial Censuses 

Table 4. Growth of Commuting and Percent Commuting by Coastal and 

Non-Coastal Parishes, 1960-90 

 1960-70 1970-80 1980-90 

 COAST 93.4 70.1 6.1

COMMUTERS NONCOAST 68.8 142.6 11.3

 TOTAL 82.9 98.8 8.6

TOTAL COAST 15.0 51.5 -0.3

WORKERS NONCOAST 7.0 56.9 2.7

 TOTAL 10.1 54.7 1.5

 

Source: Decennial Censuses 
29 

Table 5. Commuters and Total Workers by Industry and Coastal/Non-Coastal Parishes, 

 Total Workers 1980 Total Workers 1990 Commuters 1980 

Industry Total Coast Noncoast Total Coast Noncoast Total Coast Noncoast TTotal 1,442,845 591,045 851,800 1,449,889 583,414 866,475 339,641 178,448 161,193 3Agri. services, forestry, fisheries, Mining 76,756 39,017 37,739 54,920 30,432 24,488 29,809 18,369 11,440 Construction 119,335 43,529 75,806 81,040 29,305 51,735 45,820 18,721 27,099 Manufacturing 214,691 72,682 142,009 188,300 57,946 130,354 65,533 27,946 37,587 Transp, communication, pub. utils. 106,352 56,053 50,299 94,574 44,014 50,560 33,822 21,128 12,694 Wholesale, retail trade 304,780 129,773 175,007 325,054 136,140 188,914 55,620 31,663 23,957 Finance, Insurance, real estate 72,486 31,673 40,813 80,270 35,421 44,849 14,430 9,746 4,684 Services 255,674 116,078 139,596 342,120 153,115 189,005 45,863 28,366 17,497 Federal government 51,370 23,071 28,299 49,031 21,845 27,186 13,196 8,088 5,108 State, local government 241,401 79,169 162,232 234,580 75,196 159,384 35,548 14,421 21,127 Source: Decennial Censuses 
30 

Table 6. Percent Commuters by Industry and Coastal/Non-Coastal Parishes, 

1980-90 

 %Commuters 1980 %Commuters 1990 

Industry Total Coast Non-coast Total Coast Non-coast 

Total 23.5 30.2 18.9 25.2 32.3 20.3 

Agri. services, forestry, fisheries, Mining 38.8 47.1 30.3 42.8 52.4 30.8 

Construction 38.4 43.0 35.7 38.7 42.7 36.4 

Manufacturing 30.5 38.4 26.5 34.6 41.3 31.7 

Transp, communication, pub. utils. 31.8 37.7 25.2 34.8 43.3 27.3 

Wholesale, retail trade 18.2 24.4 13.7 20.8 26.8 16.4 

Finance, Insurance, real estate 19.9 30.8 11.5 22.8 33.8 14.1 

Services 17.9 24.4 12.5 21.6 29.3 15.4 

Federal government 25.7 35.1 18.1 28.7 39.8 19.9 

State, local government 14.7 18.2 13.0 16.1 19.9 14.3 

 

Source: Decennial Censuses 

Table 7. Average Commuting Distance: Coastal and 

Non-Coastal Parishes, 1960-90 

 (Mile) 

Area 1960 1970 1980 1990 

Total 32.3 32.1 42.3 38.6 

Non-coastal area 30.1 28.5 36.9 32.5 

Coastal area 34.0 34.5 47.4 44.5 

%Growth in Commuting Distance 

 1960-70 1970-80 1980-90 

Total -0.6 31.8 -9.0 

Non-coastal area -5.2 29.4 -12.0 

Coastal area 1.5 37.6 -6.2 

 

Source: Decennial Censuses 
31 

31 

Table 8. Commuting in Coastal and Non-Coastal Areas, 1960-90 

1960 Miles Area 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+ ONon-coastal N 10,853 17,967 9,646 3,406 1,123 470 473 170 668 

 % 24.2 40.1 21.5 7.6 2.5 1.1 1.1 0.4 1.5 

Coastal N 0 41,200 9,886 3,201 3,170 865 358 234 699 

 % 0.0 69.0 16.6 5.4 5.3 1.5 0.6 0.4 1.2 

1970 Miles 

Area 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+ ONon-coastal N 18,760 31,194 16,514 4,329 2,151 1,173 830 525 128 

 % 24.8 41.3 21.8 5.7 2.9 1.6 1.1 0.7 0.2 

Coastal N 0 78,626 16,230 8,866 4,666 1,177 1,123 3,177 1,634 

 % 0.0 68.1 14.1 7.7 4.0 1.0 1.0 2.8 1.4 

1980 Miles 

Area 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+ ONon-coastal N 38,519 75,018 36,937 9,458 4,871 3,294 2,649 3,096 9,563 

 % 21.0 40.9 20.1 5.2 2.7 1.8 1.4 1.7 5.2 

Coastal N 0 118,054 28,409 17,468 8,199 4,088 2,824 1,803 15,604 

 % 0.0 60.1 14.5 8.9 4.2 2.1 1.4 0.9 7.9 

1990 Miles 

Area 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+ ONon-coastal N 43,510 87,850 42,006 11,812 4,809 3,460 2,679 2,199 5,767 

 % 21.3 43.0 20.6 5.8 2.4 1.7 1.3 1.1 2.8 

Coastal N 0 124,914 30,865 21,955 8,846 4,056 2,944 2,087 12,833 

 % 0.0 59.9 14.8 10.5 4.2 2.0 1.4 1.0 6.2 

Source: Decennial Censuses 
32 

32 

Table 9a. Commuting Patterns by Sending States, 1960 

Sending States 

Receiving Louisiana Alabama Florida Mississippi Texas Other states Parishes N % N % N % N % N % N % Total 57,949 97.1 95 0.2 0 0.0 1,141 1.9 346 0.6 178 Cameron 654 91.0 0 0.0 0 0.0 0 0.0 65 9.0 0 Iberia 937 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 Jefferson 9,912 98.0 0 0.0 0 0.0 163 2.0 0 0.0 0 Lafourche 1,215 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 Orleans 35,710 96.3 95 0.3 0 0.0 811 2.2 281 0.8 178 Plaquemines 2,247 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 St. Bernard 2,620 99.0 0 0.0 0 0.0 32 1.0 0 0.0 0 St. Mary 2,240 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 St. Tammany 524 80.0 0 0.0 0 0.0 135 20.0 0 0.0 0 Terrebonne 1,210 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 Vermilion 680 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 Source: Decennial Censuses 

Table 9b. Commuting Patterns by Sending States, 1970 

Sending States 

Receiving Louisiana Alabama Florida Mississippi Texas Other states 

Parishes N % N % N % N % N % N % 

Total 113,045 97.9 417 0.4 0 0.0 1,668 1.4 369 0.3 0 Cameron 419 87.0 0 0.0 0 0.0 0 0.0 65 13.0 0 Iberia 1,899 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 Jefferson 30,546 99.0 0 0.0 0 0.0 241 1.0 0 0.0 0 Lafourche 1,418 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 Orleans 63,714 97.0 417 1.0 0 0.0 1,105 2.0 304 0.0 0 

Plaquemines 3,657 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 St. Bernard 3,330 99.0 0 0.0 0 0.0 31 1.0 0 0.0 0 

St. Mary 3,411 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 

St. Tammany 2,081 88.0 0 0.0 0 0.0 291 12.0 0 0.0 0 Terrebonne 1,910 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 

Vermilion 660 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 

Source: Decennial Censuses 
33 

33 

Table 9c. Commuting Patterns by Sending States, 1980 

Sending states 

Receiving Louisiana Alabama Florida Mississippi Texas Other sta

Parishes N % N % N % N % N % N % 

Total 183,330 93.3 1,284 0.7 1,266 0.6 8,074 4.1 1,437 0.7 1,082 0.6 Cameron 2,168 82.0 54 2.0 0 0.0 97 4.0 268 10.0 48 1.8 Iberia 5,417 100.0 0 0.0 21 0.0 0 0.0 0 0.0 0 0.0 Jefferson 41,131 95.0 260 1.0 330 1.0 1,428 3.0 140 0.0 133 0.3 Lafourche 3,133 95.0 35 1.0 13 0.0 15 0.5 47 2.0 40 1.2 Orleans 93,716 94.0 462 0.5 363 0.0 3,283 3.0 839 0.8 861 0.9 Plaquemines 9,294 90.0 190 2.0 56 1.0 738 7.0 0 0.0 0 0.0 St. Bernard 5,119 95.0 0 0.0 0 0.0 250 5.0 0 0.0 0 0.0 St. Mary 10,419 89.0 196 2.0 317 3.0 712 6.0 122 1.0 0 0.0 St. Tammany 2,736 70.0 16 0.0 45 1.0 1,134 29.0 0 0.0 0 0.0 Terrebonne 7,537 92.0 71 1.0 121 1.0 413 5.0 21 0.0 0 0.0 Vermilion 2,660 100.0 0 0.0 0 0.0 4 0.0 0 0.0 0 0.0 Source: Decennial Censuses 

Table 9d. Commuting Patterns by Sending States, 1990 

Sending states 

Receiving Louisiana Alabama Florida Mississippi Texas Other states 

Parishes N % N % N % N % N % N % 

Total 195,544 93.8 968 0.5 937 0.4 9,037 4.3 1,338 0.6 689 0.3 Cameron 1,988 82.0 16 1.0 61 2.0 75 3.0 278 12.0 0 0.0 Iberia 4,774 99.0 20 0.0 25 1.0 0 0.0 0 0.0 0 0.0 Jefferson 52,387 96.0 193 0.0 130 0.0 1,594 3.0 314 1.0 15 0.0 Lafourche 3,840 95.0 17 0.0 56 1.0 126 3.0 0 0.0 0 0.0 Orleans 97,353 94.0 395 0.0 385 0.0 3,623 4.0 639 1.0 674 0.7 Plaquemines 9,631 90.0 141 1.0 67 1.0 751 7.0 61 1.0 0 0.0 St. Bernard 3,616 92.0 0 0.0 0 0.0 299 8.0 0 0.0 0 0.0 St. Mary 8,452 92.0 117 1.0 123 1.0 474 5.0 26 0.0 0 0.0 St. Tammany 4,143 70.0 27 1.0 0 0.0 1,734 29.0 0 0.0 0 0.0 Terrebonne 6,941 94.0 42 1.0 90 1.0 310 4.0 20 0.0 0 0.0 Vermilion 2,419 98.0 0 0.0 0 0.0 51 2.0 0 0.0 0 0.0 Source: Decennial Censuses 
34 

Table 10. Commuting Patterns to Coastal Parishes, 1960-90 

 

 

Commuters 

Area 1960 1970 1980 1990 

 N % N % N % N % 

All Parishes 

 Total 59,709 100.0 115,499 100.0 196,473 100.0 208,513 100.0 

 Adj. Parishes 50,637 82.8 93,290 76.1 141,618 65.8 142,106 65.0 

 Non-Adj. Parishes 9,072 17.3 22,209 23.9 54,855 34.3 66,407 35.0 

Cameron 

 Total 719 100.0 484 100.0 2,635 100.0 2,418 100.0 

 Adj. Parishes 659 91.7 388 80.2 1,249 47.4 1,315 54.4 

 Non-Adj. Parishes 60 8.3 96 19.8 1,386 52.6 1,103 45.6 

Iberia 

 Total 937 100.0 1,899 100.0 5,438 100.0 4,819 100.0 

 Adj. Parishes 926 98.8 1,780 93.7 4,692 86.3 4,364 90.6 

 Non-Adj. Parishes 11 1.2 119 6.3 746 13.7 455 9.4 

Jefferson 

 Total 10,075 100.0 30,787 100.0 43,422 100.0 54,633 100.0 

 Adj. Parishes 8,562 85.0 23,206 75.4 31,607 72.8 35,356 64.7 

 Non-Adj. Parishes 1,513 15.0 7,581 24.6 11,815 27.2 19,277 35.3 

 La Fourche 

 Total 1,215 100.0 1,418 100.0 3,283 100.0 4,039 100.0 

 Adj. Parishes 1,083 89.1 1,237 87.2 2,434 74.1 3,067 75.9 

 Non-Adj. Parishes 132 11.0 181 13.0 849 26.0 972 24.0 

Orleans 

 Total 37,075 100.0 65,540 100.0 99,524 100.0 103,069 100.0 

 Adj. Parishes 32,422 87.4 56,348 86.0 78,065 78.4 75,184 72.9 

 Non-Adj. Parishes 4,653 12.5 9,192 14.0 21,459 21.6 27,885 27.1 

Plaquemines 

 Total 2,247 100.0 3,657 100.0 10,278 100.0 10,651 100.0 

 Adj. Parishes 1,935 86.1 3,149 86.1 7,883 76.7 7,452 70.0 

 Non-Adj. Parishes 312 13.9 508 13.9 2,395 23.3 3,199 30.0 

St. Bernard 

 Total 2,652 100.0 3,361 100.0 5,369 100.0 3,915 100.0 

 Adj. Parishes 1,795 67.7 2,195 65.3 2,818 52.5 1,867 47.7 

 Non-Adj. Parishes 857 32.3 1,166 34.7 2,551 47.5 2,048 52.3 

St. Mary 

 Total 2,240 100.0 3,411 100.0 11,766 100.0 9,192 100.0 

 Adj. Parishes 1,136 50.7 1,783 52.3 2,593 22.0 2,533 27.6 

 Non-Adj. Parishes 1,104 49.3 1,628 47.7 9,173 78.0 6,659 72.4 

St. Tammany 

 Total 659 100.0 2,372 100.0 3,931 100.0 5,904 100.0 

 Adj. Parishes 578 87.7 1,085 45.7 2,648 67.4 3,642 61.7 

 Non-Adj. Parishes 81 12.3 1,287 54.3 1,283 32.6 2,262 38.3 

Terrebonne 

 Total 1,210 100.0 1,910 100.0 8,163 100.0 7,403 100.0 

 Adj. Parishes 940 77.7 1,567 82.0 5,562 68.1 5,435 73.4 

 Non-Adj. Parishes 270 22.3 343 18.0 2,601 31.9 1,968 26.6 

Vermilion 

 Total 680 100.0 660 100.0 2,664 100.0 2,470 100.0 

 Adj. Parishes 601 88.4 552 83.6 2,067 77.6 1,891 76.6 

 Non-Adj. Parishes 79 11.6 108 16.4 597 22.4 579 23.4 

Source: Decennial Censuses 
35 35 

Table 11. Average Commuting Distance by Industry 

 (MILE)  1980 1990 

Industry Total Coast Non-coast Total Coast Non-coast Total 42.3 47.4 36.9 38.6 44.5 32.5 Agri. services, forestry, fisheries, Mining 64.2 70.8 53.5 59.1 68.0 40.3 Construction 41.5 46.7 37.9 38.0 41.8 35.5 Manufacturing 33.8 37.3 31.2 30.7 34.9 28.3 Transp, communication, pub. utils. 37.0 39.6 32.8 34.2 38.4 28.5 Wholesale, retail trade 32.4 34.0 30.3 30.6 33.0 27.8 Finance, Insurance, real estate 29.5 29.4 29.6 28.3 30.3 24.5 Services 32.9 33.3 32.3 30.5 32.4 27.5 Federal government 29.9 29.8 30.1 39.3 47.7 25.8 State, local government 32.0 33.0 31.2 30.4 32.3 29.1 Source: Decennial Censuses 

Table 12. Commuters by Travel Distance, 1980 and 1990 (%) 

1980 Miles Area Industry 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 Non- Mining 11.9 32.8 19.6 6.8 4.1 2.7 2.8 3.3 Coast Others 23.0 43.3 20.1 4.2 2.0 1.4 1.1 1.5 

 Mining 0.0 38.3 8.8 8.9 6.7 3.3 3.4 3.0 

Coast Others 0.0 65.4 15.7 9.1 3.8 1.8 1.0 0.5 1990 Non- Mining 10.4 39.8 26.3 5.9 3.3 1.2 2.8 2.0 Coast Others 23.7 45.7 20.5 4.8 1.5 1.2 0.8 0.8 

 Mining 0.0 37.9 9.8 9.9 6.2 3.6 3.0 3.7 

Coast Others 0.0 64.9 15.8 10.9 3.8 1.6 1.1 0.5 Source: Decennial Censuses 
36 36 

Table 13. Distribution of Commuters by Travel Distance by Industry in Coastal and Non-Coastal A
37 37 

Table 14. Distribution of Commuters by Travel Distance by Industry in Coastal and Non-Coastal A
38 

Table 15. Commuting Patterns Related to Mining Industries in Coastal 

Parishes, 1980 and 1990 

 1980 1990 

 N % N % 

All Parishes 

 Total 18,369 100.0 15,960 100.0 

 Adj. Parishes 8,950 48.7 7,056 44.2 

 Non-Adj. Parishes 9,419 51.3 8,904 55.8 

Cameron 

 Total 1,401 100.0 905 100.0 

 Adj. Parishes 438 31.3 378 41.8 

 Non-Adj. Parishes 963 68.7 527 58.2 

Iberia 

 Total 879 100.0 781 100.0 

 Adj. Parishes 701 79.7 602 77.1 

 Non-Adj. Parishes 178 20.3 179 22.9 

Jefferson 

 Total 2,373 100.0 2,029 100.0 

 Adj. Parishes 917 38.6 654 32.2 

 Non-Adj. Parishes 1,456 61.4 1,375 67.8 

 La Fourche 

 Total 330 100.0 527 100.0 

 Adj. Parishes 158 47.9 146 27.7 

 Non-Adj. Parishes 172 52.1 381 72.3 

Orleans 

 Total 6,066 100.0 5,476 100.0 

 Adj. Parishes 3,806 62.7 2,580 47.1 

 Non-Adj. Parishes 2,260 37.3 2,896 52.9 

Plaquemines 

 Total 1,886 100.0 1,860 100.0 

 Adj. Parishes 1,006 53.3 733 39.4 

 Non-Adj. Parishes 880 46.7 1,127 60.6 

St. Bernard 

 Total 36 100.0 117 100.0 

 Adj. Parishes 0 0.0 25 21.4 

 Non-Adj. Parishes 36 100.0 92 78.6 

St. Mary 

 Total 2,984 100.0 2,169 100.0 

 Adj. Parishes 659 22.1 631 29.1 

 Non-Adj. Parishes 2,325 77.9 1,538 70.9 

St. Tammany 

 Total 56 100.0 69 100.0 

 Adj. Parishes 56 100.0 49 71.0 

 Non-Adj. Parishes 0 0.0 20 29.0 

Terrebonne 

 Total 1,666 100.0 1,444 100.0 

 Adj. Parishes 729 43.8 828 57.3 

 Non-Adj. Parishes 937 56.2 616 42.7 

Vermilion 

 Total 692 100.0 583 100.0 

 Adj. Parishes 480 69.4 430 73.8 

 Non-Adj. Parishes 212 30.6 153 26.2 

 

Source: Decennial Censuses 
39 

Table 16. Migration to Coastal And Non-Coastal Parishes, 1960-90 

 

 Area 1955-60 1965-70 1975-80 1985-90 

 Coast 161,463 219,886 241,363 190,147 

Migrant Noncoast 290,247 353,874 435,309 372,554 

 Total 451,710 573,760 676,672 562,701 

 Coast 1,029,490 1,237,070 1,424,033 1,418,101 

Total Noncoast 1,785,332 2,027,816 2,379,212 2,438,054 

 Total 2,814,822 3,264,886 3,803,245 3,856,155 

 Coast 15.7 17.8 16.9 13.4 

%Migrant Noncoast 16.3 17.5 18.3 15.3 

 Total 16.0 17.6 17.8 14.6 

 

Note: Migrants exclude persons from abroad. 

Source: Decennial Censuses 
40 

Table 17. Migration to Coastal and Non-Coastal Areas by Origin State, 1960-90 

 1960 1970 1980 

Total N % N % N % N Non-migrants 2,363,111 84.0 2,691,125 82.4 3,126,573 82.2 3Migrants 451,710 16.0 573,760 17.6 676,672 17.8  From the same state 248,763 8.8 269,736 8.3 352,006 9.3  From other states 202,947 7.2 304,024 9.3 324,666 8.5 Total 2,814,822 100.0 3,264,886 100.0 3,803,245 100.0 3Coastal Area Non-migrants 868,027 84.3 1,017,184 82.2 1,182,670 83.1 1Migrants 161,463 15.7 219,886 17.8 241,363 16.9  From the same state 90,037 8.7 109,139 8.8 121,468 8.5  From other states 71,426 6.9 110,747 9.0 119,895 8.4 Total 1,029,490 100.0 1,237,070 100.0 1,424,033 100.0 1Non-Coastal Area Non-migrants 1,495,084 83.7 1,673,942 82.5 1,943,903 81.7 2Migrants 290,247 16.3 353,874 17.5 435,309 18.3  From the same state 158,726 8.9 160,597 7.9 230,538 9.7  From other states 131,521 7.4 193,277 9.5 204,771 8.6 Total 1,785,332 173.4 2,027,816 163.9 2,379,212 167.1 2Note: Migration excludes persons from abroad. Source: Decennial Censuses 
41 

Table 18. Percent of Population Migrating from Other States 

to Coastal Parishes, 1960-90 

 

Area 1960 1970 1980 1990 

Cameron 6.3 8.1 8.0 3.6 

Iberia 5.7 3.3 4.6 2.4 

Jefferson 9.1 9.3 9.8 5.2 

Lafourche 3.5 2.9 4.0 2.0 

Orleans 6.7 7.4 8.1 6.5 

Plaquemines 10.6 8.9 11.3 7.2 

St. Bernard 5.0 4.1 2.5 2.1 

St. Mary 6.7 6.1 6.8 3.1 

St. Tammany 7.0 13.6 15.9 10.1 

Terrebonne 6.1 5.0 6.7 2.5 

Vermilion 2.7 2.2 2.9 1.9 

 

Source: Decennial Censuses 
42 

Table 19. Migration to Coastal Parishes, 1990 

 

 Migrants 

Area N % 

All Parishes 

 Total 190,147 100.0 

 Adj. Parishes 60,374 39.0 

 Non-Adj. Parishes 129,773 61.0 

Cameron 

 Total 1,160 100.0 

 Adj. Parishes 672 57.9 

 Non-Adj. Parishes 488 42.1 

Iberia 

 Total 4,814 100.0 

 Adj. Parishes 2,184 45.4 

 Non-Adj. Parishes 2,630 54.6 

Jefferson 

 Total 62,908 100.0 

 Adj. Parishes 28,866 45.9 

 Non-Adj. Parishes 34,042 54.1 

 Lafourche 

 Total 7,884 100.0 

 Adj. Parishes 4,103 52.0 

 Non-Adj. Parishes 3,781 48.0 

Orleans 

 Total 53,587 100.0 

 Adj. Parishes 11,808 22.0 

 Non-Adj. Parishes 41,779 78.0 

Plaquemines 

 Total 4,048 100.0 

 Adj. Parishes 1,595 39.4 

 Non-Adj. Parishes 2,453 60.6 

St. Bernard 

 Total 6,321 100.0 

 Adj. Parishes 3,243 51.3 

 Non-Adj. Parishes 3,078 48.7 

St. Mary 

 Total 4,453 100.0 

 Adj. Parishes 1,113 25.0 

 Non-Adj. Parishes 3,340 75.0 

St. Tammany 

 Total 33,973 100.0 

 Adj. Parishes 2,415 7.1 

 Non-Adj. Parishes 31,558 92.9 

Terrebonne 

 Total 7,431 100.0 

 Adj. Parishes 2,713 36.5 

 Non-Adj. Parishes 4,718 63.5 

Vermilion 

 Total 3,568 100.0 

 Adj. Parishes 1,662 46.6 

 Non-Adj. Parishes 1,906 53.4 

 

Note: Excludes persons from abroad. 

Source: 1990 Census 
43 

Bibliography 

Cadwallader, M. 1992. Migration and Residential Mobility: Macro and Micro Approaches. Madison: 

The University of Wisconsin Press 

Clark, W. A. V. 1986. Human Migration. Beverly Hills: Sage Publications. 

Greenwood, M. J. 1975. "Research on Internal Migration in the United States: A Survey." Journal of 

 Economic Literature 13: 397-433. 

Greenwood, M. J. 1985. "Human Migration: Theory, Models, and Empirical Studies." Journal of 

Regional Science 25: 521-544. 

Greenwood, M. J. and D. Sweetland. 1972. "The Determinants of Migration between Standard 

Metropolitan Statistical Areas." Demography 9(4): 665-681. 

Lowry, I. 1966. Migration and Metropolitan Growth: Two Analytical Models. San Francisco: 

Chandler. 

Massey, D. S. 1990. "Social Structure, Household Strategies, and the Cumulative Causation of 

Migration." Population Index 56(1): 3-26. 
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