You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 book_publishing_company
【Schema】
# Table: titleauthor
[
(au_id:TEXT, Primary Key, Examples: [172-32-1176, 213-46-8915, 238-95-7766]),
(title_id:TEXT, Primary Key, Examples: [PS3333, BU1032, BU2075]),
(au_ord:INTEGER, Examples: [1, 2, 3]),
(royaltyper:INTEGER, Examples: [100, 40, 30])
]
# Table: roysched
[
(title_id:TEXT, Examples: [BU1032, PC1035, BU2075]),
(lorange:INTEGER, Examples: [0, 5001, 2001]),
(hirange:INTEGER, Examples: [5000, 50000, 2000]),
(royalty:INTEGER, Examples: [10, 12, 14])
]
# Table: titles
[
(title_id:TEXT, Primary Key, Examples: [BU1032, BU1111, BU2075]),
(title:TEXT),
(type:TEXT, Examples: [business, mod_cook, UNDECIDED]),
(pub_id:TEXT, Examples: [1389, 0736, 0877]),
(price:REAL, Examples: [19.99, 11.95, 2.99]),
(advance:REAL, Examples: [5000.0, 10125.0, 0.0]),
(royalty:INTEGER, Examples: [10, 24, 12]),
(ytd_sales:INTEGER, Examples: [4095, 3876, 18722]),
(notes:TEXT),
(pubdate:DATETIME, Examples: [1991-06-12 00:00:00])
]
# Table: employee
[
(emp_id:TEXT, Primary Key, Examples: [A-C71970F, A-R89858F, AMD15433F]),
(fname:TEXT, Examples: [Aria, Annette, Ann]),
(minit:TEXT, Examples: [M, R, F]),
(lname:TEXT, Examples: [Cruz, Roulet, Devon]),
(job_id:INTEGER, Examples: [10, 6, 3]),
(job_lvl:INTEGER, Examples: [87, 152, 200]),
(pub_id:TEXT, Examples: [1389, 9999, 9952]),
(hire_date:DATETIME, Examples: [1991-10-26 00:00:00])
]
# Table: authors
[
(au_id:TEXT, Primary Key, Examples: [172-32-1176, 213-46-8915, 238-95-7766]),
(au_lname:TEXT, Examples: [White, Green, Carson]),
(au_fname:TEXT, Examples: [Johnson, Marjorie, Cheryl]),
(phone:TEXT, Examples: [408 496-7223, 415 986-7020, 415 548-7723]),
(address:TEXT, Examples: [10932 Bigge Rd., 309 63rd St. #411, 589 Darwin Ln.]),
(city:TEXT, Examples: [Menlo Park, Oakland, Berkeley]),
(state:TEXT, Examples: [CA, KS, TN]),
(zip:TEXT, Examples: [94025, 94618, 94705]),
(contract:TEXT, Examples: [0])
]
# Table: sales
[
(stor_id:TEXT, Primary Key, Examples: [6380, 7066, 7067]),
(ord_num:TEXT, Primary Key, Examples: [6871, 722a, A2976]),
(ord_date:DATETIME, Examples: [1994-09-14 00:00:00]),
(qty:INTEGER, Examples: [5, 3, 50]),
(payterms:TEXT, Examples: [Net 60, Net 30, ON invoice]),
(title_id:TEXT, Primary Key, Examples: [BU1032, PS2091, PC8888])
]
# Table: jobs
[
(job_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(job_desc:TEXT, Examples: [New Hire - Job not specified]),
(min_lvl:INTEGER, Examples: [10, 200, 175]),
(max_lvl:INTEGER, Examples: [10, 250, 225])
]
# Table: publishers
[
(pub_id:TEXT, Primary Key, Examples: [0736, 0877, 1389]),
(pub_name:TEXT, Examples: [New Moon Books, Binnet & Hardley, Algodata Infosystems]),
(city:TEXT, Examples: [Boston, Washington, Berkeley]),
(state:TEXT, Examples: [MA, DC, CA]),
(country:TEXT, Examples: [USA, Germany, France])
]
# Table: discounts
[
(discounttype:TEXT, Examples: [Initial Customer, Volume Discount, Customer Discount]),
(stor_id:TEXT, Examples: [8042]),
(lowqty:INTEGER, Examples: [100]),
(highqty:INTEGER, Examples: [1000]),
(discount:REAL, Examples: [10.5, 6.7, 5.0])
]
# Table: stores
[
(stor_id:TEXT, Primary Key, Examples: [6380, 7066, 7067]),
(stor_name:TEXT, Examples: [Eric the Read Books, Barnum's, News & Brews]),
(stor_address:TEXT, Examples: [788 Catamaugus Ave., 567 Pasadena Ave., 577 First St.]),
(city:TEXT, Examples: [Seattle, Tustin, Los Gatos]),
(state:TEXT, Examples: [WA, CA, OR]),
(zip:TEXT, Examples: [98056, 92789, 96745])
]
# Table: pub_info
[
(pub_id:TEXT, Primary Key, Examples: [0877, 1389, 1622]),
(logo:BLOB),
(pr_info:TEXT)
]
【Foreign keys】
titleauthor.au_id=authors.au_id
titleauthor.title_id=titles.title_id
roysched.title_id=titles.title_id
titles.pub_id=publishers.pub_id
employee.job_id=jobs.job_id
employee.pub_id=publishers.pub_id
sales.stor_id=stores.stor_id
sales.title_id=titles.title_id
discounts.stor_id=stores.stor_id
pub_info.pub_id=publishers.pub_id

    【Question】
    Calculate the average level difference between the Marketing editors hired by the US and non-US publishers? Marketing manager is a job description which refers to job_desc; US publisher refers publisher in the US where country = 'USA'; non-US publishers refers publisher not in the US where country! = 'USA'; job level refers to job_lvl; average level = AVG(job_lvl)

    【Evidence】
    Marketing manager is a job description which refers to job_desc; US publisher refers publisher in the US where country = 'USA'; non-US publishers refers publisher not in the US where country! = 'USA'; job level refers to job_lvl; average level = AVG(job_lvl)

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT (CAST(SUM(CASE WHEN T1.country = 'USA' THEN job_lvl ELSE 0 END) AS REAL) / SUM(CASE WHEN T1.country = 'USA' THEN 1 ELSE 0 END)) - (CAST(SUM(CASE WHEN T1.country != 'USA' THEN job_lvl ELSE 0 END) AS REAL) / SUM(CASE WHEN T1.country != 'USA' THEN 1 ELSE 0 END)) FROM publishers AS T1 INNER JOIN employee AS T2 ON T1.pub_id = T2.pub_id INNER JOIN jobs AS T3 ON T2.job_id = T3.job_id WHERE T3.job_desc = 'Managing Editor'