Economic Mobility and Migration Economics 2333 Class 11 Spring 2014 Robert A. Margo

Outline Background Ferrie and Long Olivetti and Paserman Abramitzky, Boustan, and Eriksson (student presentation) Salisbury (if time)

Mobility Life-cycle and across generation Across generation: intergenerational transmission of inequality. Ln Y (child) = a + b ln Y (parent). B = intergenerational correlation coefficient. Role of geographic mobility: within and across countries “Assimilation” of immigrants: does second generation converge? Another important example: black-white income differences C19: Traditional view is that US offered more upward mobility than Europe. Upward mobility: higher wages, cheaper land, (possibly) more occupational upgrading.

Ferrie Numerous studies by sociologists and historians examining “occupational mobility” across generations in a particular place. In history, pioneered by Thernstrom. Link individuals over time or fathers-sons. Severe methodological problem: if studying a single community, outmigration may be selective. Joe Ferrie (Northwestern): census record linkage. Very popular at the moment, much cheaper because of computer linkage and ancestry.com Ferrie and Long: samples and methods to measure occupation mobility across generations. Compare over time and across countries.

Census Linkage Invented by historians but developed in economic history by Ferrie, Atack, and others. Create sample of census in year t. Find the same individuals in year t+ z, where z = 10 or more. Earliest year is 1850, latest is Matching can be by hand (slow) or by computer (fast). Matches subject to Type #1 and Type #2 error. Criteria for matching: last name, first name, place of birth, date of birth, other family members. Generally cannot be done for women because of name changes at marriage. Historical match rates generally low (can be much better for uncommon names but this may create selection bias).

Ferrie and Long Intergenerational mobility in C19: Was the US exceptional? Comparison is with England. Similar census-based samples. Base sample consists of young men living at home with fathers. Locate son in later census. Compare occupations of fathers and sons in an N x N contingency table. Develop statistical method for assessing which tables exhibit greater occupational mobility. Focus on occupational mobility comes from sociology and also data limitations (no C19 information on income). Could use IPUMS-type occupation status but presumably the argument is that the contingency table is more informative. No formal model, but discussion has the flavor of Becker and Tomes, and Solon. In BT, and Solon, intergenerational correlation reflects various factors:

The Data Samples of males linked across censuses from & in Britain, and males linked from & in the U.S. Linkage based on (i) name, (ii) year of birth, (iii) parish & county (Britain) or state (U.S.) of birth. Individuals were years old in the terminal year and were observed with their fathers in the initial year. Fathers’ & sons’ occupations observed at same life-cycle point.

Measuring Intergenerational Mobility The conventional approach: ln Y i Son = β ln Y i Father + ε i where β is intergenerational income elasticity But we’ve only got occupations, and they’re difficult to order unambiguously Paper: tinyurl.com/Migration2013 Slides: tinyurl.com/MigrationSlides2013

Measuring Mobility with Occupation Across Countries Occupational Classification White CollarProfessional etc., occupations (accountant, lawyer, doctor, clergyman) and Intermediate occupations (bookkeeper, manager, farm foreman) Skilled &Skilled occupations (blacksmith, Semi-Skilledtailor, weaver, craftsman and Semiskilled occupations (machinist, factory operative) FarmerFarm Operators Unskilled Unskilled Laborers (general laborer, servant, farm laborer) Paper: tinyurl.com/Migration2013 Slides: tinyurl.com/MigrationSlides2013

Measuring Intergenerational Mobility M P = 3/8 M Q = 7/10 M P = 3/8 M Q / = 5/8 Paper: tinyurl.com/Migration2013 Slides: tinyurl.com/MigrationSlides2013

Measuring Intergenerational Mobility Cross-Product Ratios: (3 x 2) / (2 x 1) = 3 for P (2 x 1) / (6 x 1) = 1/3 for Q Paper: tinyurl.com/Migration2013 Slides: tinyurl.com/MigrationSlides2013

Measuring Intergenerational Mobility Cross-Product Ratio for Q = CPR for Q' = 1/3 Paper: tinyurl.com/Migration2013 Slides: tinyurl.com/MigrationSlides2013

Measuring Intergenerational Mobility For tables > 2 × 2, use the “Altham statistic,” which uses all of the cross-product ratios: Measures distance between mobility in P and mobility in Q Also calculate d(P,J), d(Q,J): distance between P/Q and J, a matrix of 1’s representing perfect mobility Paper: tinyurl.com/Migration2013 Slides: tinyurl.com/MigrationSlides2013

Follow Up Ferrie-Long does NOT take account of British migrants to the US. More recent work does. Consider British migrants to the US. How much intergenerational mobility did this group experience? How did their mobility experience compare with that of non-migrants in both countries? Use 2 cohorts of British movers and stayers ( & ), observing (1) migrants before & after departure and (2) non-migrants before & after the migrants left

The Context Migration was completely unrestricted at this time (before the Quota System of the 1920s) Driven not by desperation (c.f. Irish Famine migrants) but by “normal” forces (e.g. relative wages) The British were a large fraction of the migrant stream (close to 40% in some years), but their share moved opposite the total volume of migration

The Context The Britain each cohort left behind was a decade or more ahead of the U.S. in its industrialization More opportunity in the U.S. for those squeezed out by changes (consolidation in farming, displacement of craft workers by factories and machines)

Additional Data For comparable data on migrants from Britain to the U.S., Ferrie-Long generated 2 new samples: British-born males age in the 1880 U.S. Census of Population linked back to the 1861 British Census British-born males age in the 1900 U.S. Census of Population linked back to the 1881 British Census

The Data Main challenge: Lack of specific birthplace info for migrants in U.S. censuses Requirements/Checks (1861  1880): – Unique record (name, age birthplace) in 1880 U.S. census and 1861 Br census – Not present in British 1881 census – Not present in U.S census index – If they were present in the 1870 U.S. census index, they were not also present in the 1871 British census index, and if they were present in the 1871 British census index, they were not also present in the 1870 U.S. census index. – Oldest U.S.-born child in 1880 was born after 1860 – Youngest Britain-born child in 1880 was born before 1862

The Data U.S. samples: 4,138 ( ) & 3,919 ( ) British samples:2,039 ( ) & 4,071 ( ) Migrant samples:1,176* ( ) & 1,144 ( ) * 2,174 linked; remainder awaiting occupational transcription

British Migrants vs Non-Migrants Non-migrants (“stayers”) Migrants (“movers”)

Mobility Measures

Visualizing Mobility Differences

Paserman and Olivetti Traditional estimates of intergenerational correlation coefficient (or contingency table) rely on fathers and sons. Daughters too difficult to link. PO advocate a new (and creative) methodology: use first names. Assumption is that first names carry economic information. Use two cross sections. In first cross section, regress income of fathers on full set of dummy variables of children’s names. In later cross section, regress incomes of adults with name j on predicted income of fathers who name their children j from the first regression. Advantages (a) no record linkage (b) can be done for daughters, sons-in- law, across multiple generations. Does this really work? Amazingly, YES. PO sketch simple model of intergenerational transmission that includes marriage. Yields estimating equation for son’s earnings and sons-in-law.

Data Analysis Rely on IPUMS, use OCCSCORE (1950 median income is the occupation weight). Assess sensitivity to alternative measures. Coding of names: benchmark ignores middle initials AND also treats nicknames as separate names (William vs. “Bill”). Again, extensive sensitivity analysis.

Salisbury Context: relationship between migration across US states in C19 and initial level of wages. Lebergott (1964) observed that migration tended to go in the direction of higher wages, but correlation was low. His conclusion: migrants “sub-optimized”. Simple problems (a) nominal, not real wages (b) population growth, not migration BUT there are other similar migration anomalies in US econ history. Salisbury: local labor markets offered two paths to higher income. Path #1: higher income. Path #2: greater occupation mobility. Migrants selected based on various factors not observable to the econometrician. One of these may be “ambition” or “ability” that is complementary to upward occupation mobility. Individuals with this characteristic will leave places where upward mobility is less likely and move to places where it is more likely. Hypothesis: skill premium (ratio of skilled to unskilled wages) is a useful, measureable signal. If skill differential is high, relative supply of skill is low and upward mobility may be more likely. So, high ambition/ability individuals are less likely to leave such places but more likely to move to them. Construct linked sample from IPUMS and match to data on unskilled wages and skilled (carpenter) wages from Margo (2000); this is from the census of social statistics.

Data Linked census samples and Basic data: white males ages who worked as an unskilled laborer or farm laborer in year 0. S1: wage data in home and destination counties. Includes migrants and non- migrants. S2: cross-county migrants only, must have wage data in destination county.

Basic Results Basic results in Tables 3-5. Table 3: people who left counties with low skill premiums and moved to counties with high skilled premiums tended to experience occupational upgrading. NOTE: on average, places with high skill premiums tended to have lower average real wages (more rural). Table 4: conditional on migrating, occupational upgrading is increasing in the skill premium of the county of destination. Table 5: similar pattern of results, although not very significant, if farmers are excluded.

Table 9 Table 9 runs the previous regression in reverse. Dependent variable is now the destination skill premium. Key independent variable is expected occupational upgrade. This is the actual upgrade, but 2SLS using demographic variables as instruments. Idea is that these help predict upgrading but are not obviously correlated with skill premium in destination. Results suggest that selection bias was present. Bottom line: some unskilled migrants went were the unskilled real wage was highest but others went where the possibility of occupational upgrading was highest. These places generally had a high skill premium but not necessarily a high unskilled wage. In the aggregate we get the pattern noted by Lebergott. VERY likely relevant to international migration, but not studied thus far.