Bilingual Competency in U.S. Occupations: Resetting Expectations About Language in American Society

In the United States today, there is a persistent popular belief of businesses needing employees who can serve customers not only in English but in foreign languages as well. American companies import and export globally, suggesting doing business in customers’ languages could confer competitive advantages. This reasoning implies there should be a wage premium for foreign language competencies in U.S. occupations. However, earlier research largely examined non‑English speaking immigrants’ returns to English proficiency, finding higher English skills associate with better job prospects and earnings. A study by Fry and Lowell (2003) found no higher wage return to bilingual skills after controlling for human capital, though they cautioned such premiums might exist in occupations with extensive customer contact in areas with limited English proficiency. Subsequent nursing studies were mixed: Kalist (2005) reported up to 7% wage premiums for bilingual RNs depending on local Spanish-speaking populations, while Coombs and Cebula (2010) found no premium. Chiswick and Miller (2007a) reported native-born bilingual Americans earned less than monolingual English speakers at home. Despite such evidence, the idea that learning a foreign language raises wages remains prevalent in books, professional magazines, and peer‑reviewed articles, and many parents believe bilingual competency yields labor market advantages. The current study contributes by addressing the importance and the level of bilingual competency U.S. occupations demand in the workplace, shifting focus from worker self-reported supply to employer-rated occupational demand.

Prior studies relied on worker self-reports of language proficiency (e.g., English Very Well/Well/Not Well/Not at All; foreign language Fluent/Not Fluent), thus reflecting supply rather than employer-defined occupational demand and lacking measurable, standardized proficiency levels. This limited interpretability and comparability of language requirements across occupations. Current study’s departure: It uses employer-rated importance and standardized proficiency benchmarks for both English and foreign languages through a nationwide occupational data system, framing bilingual competency from a demand perspective and enabling practical benchmark interpretations. Theoretical framework: Marschak (1965) introduced the economics of language, considering language in terms of value, utility, costs, and benefits. Subsequent human capital theory posits language learning as an investment affecting productivity and wages, with exposure, efficiency (age, education, linguistic distance), economic incentives, and personal wealth shaping proficiency acquisition (Chiswick & Miller). Agirdag (2014) proposed that linguistic human capital can transform into higher wages directly (unique job duties bilinguals can perform), indirectly (as part of academic qualifications), and via social capital (access to networks). However, these models often do not specify task-level language use, occupational contexts (e.g., U.S. vs. foreign-owned firms), or specific foreign languages demanded. Guiding research questions: (1) To what extent is there demand for English and foreign language skills in U.S. occupations, as measured by employer-rated importance? (2) To what level do U.S. occupations require English and foreign language proficiency, as measured by standardized benchmarks? The questions arise amid evidence that bilingual competency has not consistently yielded wage premiums and the possibility that supply exceeds demand or that required workplace proficiency is too rudimentary to merit premiums.

Data sources: The Occupational Information Network (ONET), maintained by the U.S. Department of Labor’s Employment and Training Administration, provided employer- and worker-based occupational ratings. Version 19.0 datasets in SAS format (OCCDATA, n=1110; KNOWLEDGE, n=942) were merged by SOC (Standard Occupational Classification) 7-character codes to yield 942 occupation records with knowledge descriptors including English and foreign language. Employment counts by SOC came from the Bureau of Labor Statistics (National Occupational Employment and Wage Estimates). Merging ONET and BLS by SOC produced the final analytic sample of 764 detailed occupations. Measures: SOC code served as the nominal independent descriptor of occupations. For both English and foreign language, O*NET collects two outcomes via ongoing surveys: (a) Importance (5-point ordinal scale: 1=Not Important to 5=Extremely Important; study used rounded means to nearest integer) and (b) Level/proficiency (7-point scale collected only if importance ≥ Somewhat Important; coded 0 if Not Important). Benchmark anchors: English level 2 = write a thank you note; 4 = edit a feature article in a local newspaper; 6 = teach a college English class. Foreign language level 1 = say "please" and "thank you" in a foreign language; 3 = ask directions in a foreign city; 5 = write in English a review of a book written in a foreign language. The distinction between importance and level is critical: a skill may be important across occupations but required proficiency can vary substantially. Analytic approach: Primarily descriptive statistics summarized distributions of importance and level for English and foreign languages across occupations and employment. Inferential analyses included chi-square tests comparing proportional distributions (English vs. foreign language) by importance and level, Pearson correlations among job educational requirements (Job Zone), required English level, and required foreign language level for occupations where foreign language was at least Important. Ratios were computed to contextualize demand versus supply. All analyses were conducted in SAS 9.4.

English requirements:

• No occupation rated English as Not Important; all U.S. occupations require some English proficiency.
• Average required English level across occupations: Mean=3.63 (SD=1.11), near the benchmark "edit a feature article in a local newspaper" (median=4).
• Distribution by English importance (n=764 occupations; 131.76M employees): Somewhat Important 6.5% of occupations (2.7% of employees) at about level 2; Important 40.1% (48.0% of employees) near levels 2–3; Very Important 47.3% (46.3% of employees) around level 4; Extremely Important 6.2% (2.9% of employees) around level 6.
• By level, 47.5% of occupations (55.1% of employees) required English just below level 4, and 52.5% (44.9% of employees) required level 4 or higher; distribution approximately normal (skew=0.18, kurtosis=−0.31).

Foreign language requirements:

• Importance distribution differs markedly from English (chi-square by importance X^2(4, N=1528)=1266.9, p<0.0001). Nearly half of occupations (46.7%; 38.0% of employees) rated foreign language Not Important (level=0). Another 50.4% (60.5% of employees) rated it Somewhat Important, corresponding to very rudimentary skills (mean level=1.35, SD=0.48), slightly above the ability to say "please" and "thank you."
• Only 2.9% of occupations (n=22; ~1.6% of employees ≈2.01M) rated foreign language as Important or higher; required levels span from level 3 (ask directions) to 5+ (write an English review of a foreign-language book), with a few at levels 6–7.
• Overall foreign language level across all occupations: Mean=0.79 (SD=0.91), median near level 1 (below "please/thank you"); excluding Not Important cases, mean level=1.47 (SD=0.75). Distribution highly right-skewed (skew=1.49, kurtosis=4.44).
• By level: 46.7% of occupations (38.0% of employees) required no foreign language (level 0); 32.9% (50.8% of employees) required level 1; 17.5% (9.8% of employees) required level 2; only 2.9% (1.5% of employees) required level ≥3.

Occupations with notable foreign language demand (n=22): include interpreters/translators (foreign level 7), foreign language/literature professors (level 6), area/ethnic/cultural studies and several social science and education roles (levels 3–5), select service roles (e.g., reservation agents, flight attendants) with level 3–4. Correlations: Job Zone strongly correlated with required English level r(20)=0.81, p<0.0001; Job Zone vs. required foreign language level r(20)=0.18, p=0.437 (ns); English vs. foreign level r(20)=0.32, p=0.142 (ns).

Demand-supply context and ratios:

• Void in demand: 97.1% of occupations (≈129.7M employees) require no foreign language or only "memorized proficiency" (levels 0–1), versus ≈2.01M employees in occupations where foreign language importance is Important or higher, a ratio of ~64.5:1 (reciprocal ≈15.5 per 1,000).
• Potential surplus: From 2011 Census, ~29.5M workers spoke a language other than English at home (~21%). Subtracting ~14.9M employed in jobs requiring foreign language level ≥2 yields an estimated surplus of ~14.6M bilingual workers, roughly 1 surplus bilingual per employed bilingual in such jobs (~1.02:1).
• Job-finding likelihood: Workers are ~18 times more likely to find occupations requiring intermediate/superior English proficiency (n=401) than occupations requiring intermediate/advanced foreign language (n=22). English-centric occupations hire ~29.4 times more workers (≈59.11M) than foreign-language-required ones (≈2.01M).

Findings directly address the research questions by showing that employer demand in U.S. occupations is overwhelmingly concentrated on English proficiency, with required levels typically in the intermediate-to-superior range, while foreign language demand is sparse and generally limited to rudimentary, "memorized" skills. This helps explain prior null or negative wage premia for bilingualism: (1) required foreign language levels are too basic to be valued with wage premiums, and (2) supply of bilingual workers appears to exceed demand for jobs requiring nonrudimentary foreign language, exerting downward pressure on wages. The data reveal that as importance increases, required levels rise for both English and foreign languages, but the overall foreign language importance and level remain low in most occupations. Policy and education implications include prioritizing development of high-level English skills across reading, writing, speaking, and listening for broader labor market returns, while recognizing niche occupational clusters where nonrudimentary foreign language proficiency is valued. The results also suggest employers may favor bilingual applicants ceteris paribus without corresponding wage differentials, contributing to potential overqualification and undercompensation unless language is central to job tasks. These insights support resetting societal expectations: strong English proficiency is generally more consequential for employment and earnings in the U.S., while foreign language skills offer targeted occupational value and broader non-economic benefits.

Within the study’s limitations, the analysis indicates that learning a foreign language does not generally translate into higher wages in U.S. occupations because employer demand for nonrudimentary foreign language proficiency is limited. However, bilingualism confers meaningful non-economic returns, including cognitive reserve benefits in older age, social benefits, and private cultural and interpersonal rewards. The study underscores prioritizing advanced English proficiency for widespread labor market advantages while acknowledging select occupations where stronger foreign language skills are required. Future research should integrate wage data with detailed language requirements and explore task-level language use, specific languages, and sectoral contexts to more precisely identify when and how bilingual competencies yield economic returns.

• Measurement/context limitations: O*NET defines occupations using cross-job descriptors, not task-level content, limiting insight into how language is used for specific job tasks. The language benchmarks serve as proxies rather than task-specific proficiency measures.
• Descriptive design: Reliance on descriptive statistics constrains causal inference and generalization beyond U.S. occupational contexts; language effects may be confounded with factors such as race/ethnicity, generation, tenure in the U.S., education, immigration status, gender, age, location, and bias.
• Language specificity: The study did not target specific foreign languages; benchmarks are general and do not capture socio-linguistic or genre-specific competencies (e.g., technical vocabulary, differential oracy vs. literacy demands).
• Scope: Findings pertain to the U.S.; demand patterns may differ in other countries or global business contexts.
• Wages not analyzed: No direct wage analysis was conducted; future work should regress wage outcomes on language importance/levels controlling for other O*NET knowledge descriptors and human capital factors.