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ECN 620: Applied Economic Analysis
创建日期：2024-04-20 16:53:55
所属分类：经济Economics
标签： ECN 620
Applied Economic Analysis

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ECN 620: Applied Economic Analysis
Introduction:
With today’s interconnected world, the ability to speak a second language is a vital skill
that gives you a competitive advantage over those who are unilingual. Thus, there are many
reasons for learning a second language. These reasons may be for cultural, intellectual, personal
identity, political, and economic purposes. The focus of this study is the economic reasoning for
learning a second language and how it positions you as a more valuable asset in the labour
market.
As a result of Canada’s two national languages, English and French, the language policy
poses a demand for bilingualism for economic purposes. Some of the economic incentives for
learning a second language is that it gives “an advantage for access to the job market and for
obtaining a higher income” (Savoie, 1997). This advantage shows employers you are a more
diverse job applicant which may lead to increased salary and a higher demand than unilingual
job candidates, meaning a lower chance of unemployment. Many studies have been done over
the past decades about the economic benefits and costs of Anglophone and Francophone
speakers in the workplace. Some of the findings of these papers conclude that “other languages
are useful because they serve to express various cultures” (Grenier, 1987), and “a bilingual
country will have a greater capacity to contribute to the advancement of knowledge than a
unilingual country” (Vaillancourt, 1992).
These past studies use numerous methodologies and data sets to conclude their findings.
Each past study is based on different sources and may not have the same control factors.
However, these studies show reoccurring evidence that in Quebec, bilingual male Francophone’s
have a higher economic advantage than unilingual male Anglophone’s. However, this trend is not
ECN 620 RESEARCH PROJECT 2 FINAL DRAFT 3
entirely consistent for bilingual female Francophone’s in Quebec. Additionally, it is important to
note that this advantage has a lesser effect for the other Canadian provinces outside of Quebec.
The empirical evidence from these past studies show a more significant bilingual earnings
premium for men compared to women residing in the province of Quebec, and in one case
entirely insignificant for women (Christofides and Swidinsky, 1998). Furthermore, evidence
from past studies show even less significance for the rest of Canada for both men and women,
ranging from 2% to 15%. The reason for these large ranges of earnings premiums from past
studies are a result of a high percent error. This error is due to a lack of control for various
attributes in the data, such as not taking into account the quality of ESL or FSL skills.
To add further to the research from previous works, this study will use data from the 2016
Census Public Use Microdata File. To showcase the economic implications of learning a second
language in Quebec and the rest of Canada, the sample data will be strictly observed from
individuals with attributes based on the knowledge and the use of their English and/or French
skills. Other restrictions also apply to the sample data and methodology of this research study,
which will be provided in the following section. These restrictions and information will allow the
results of this research to account for and control the attributes missing from past studies to
provide a more accurate and unbiased result.
Methodology:
In this study, data projections were found using the R-Studio statistical software, as well
as from Microsoft Excel. The 2016 Canadian census data was inputted into R-Studio to calculate
the sample distribution and average earnings for the different language groups for both males and
females. Using the R programming language within the R-Studio software to create our output, a
ECN 620 RESEARCH PROJECT 2 FINAL DRAFT 4
table was formed (Table 1), mimicking Christofides and Swidinsky’s table from past studies. To
ensure accurate results, certain data points that were deemed outliers or not-applicable were
removed from the dataset. Numerous dummy variables were created to see how various
economic incentive-affecting factors correlated with average earnings between males and
females. Examples of these dummy variables include marriage status, educational status,
language status, etc. These are the variables and restrictions that allow the results of this research
in order to account for and control the attributes missing from past studies to provide a more
accurate and unbiased result. These attributes were applied to both genders, and the data was
split into the two groups to show comparing results. With the results in an organized form, it is
easier to process the economic incentives for learning a second language. Following this step, a
log-linear regression code was executed to output the human capital earnings function for both
male and female samples (Table 2). The results recorded in this table show the coefficients and
absolute t-statistic values for the constant, along with the dummy variables. The following
section will analyze the meaning between the correlation of these variables and the average
earnings for males and females. Furthermore, this analysis will add to previous studies,
answering which characteristics and attributes give you the greatest advantage in terms of
economic incentive.
Analysis:
The sample distribution and average earnings results for different language groups are
presented in Table 1. As Table 1 shows, out of the total sample size of 30,934 males, 82.19% of
these individuals are unilingual English speakers, while only 17.81% are bilingual English and
French speakers. It is important to note there is a higher percentage of English and French
speaking work environments (BIL/MEFF) in Ontario than there are bilingual but mostly English
ECN 620 RESEARCH PROJECT 2 FINAL DRAFT 5
speaking work environments (BIL/MEFE). While bilingual speakers are the clear minority in
terms of language in Ontario, this group of individuals have a higher average wage than their
unilingual counterparts. The highest average earnings of $98,967.68 going to BIL/MEFE male
individuals. On the other hand, out of the total sample size of 26,131 females, 77.80% of these
individuals are unilingual English speakers, while only 22.20% are bilingual in English and
French. Just like the male sample, there is a higher percentage of BIL/MEFF females than there
are BIL/MEFE. It is important to note that bilingual females have an overall higher percent
differential in terms of average earnings than males. However, the average unilingual earnings
are approximately 25.8% less for unilingual females than unilingual males and approximately
16.3% less for bilingual females than bilingual males.
The log-linear regression results are depicted in Table 2. This data output will show in
more detail which variables have a stronger correlation to average earnings for both males and
females. As Table 2 shows, high school and trade/college students have the higher significance
compared to university and postgraduate students. However, high school students and
trade/college students have a negative correlation in terms of average earnings, while university
and postgraduate students have a positive correlation. This shows returns are higher for both
male and female individuals with a higher education level. In terms of language and language
skills, Table 2 shows that these variables have a significance on earnings. Male and female
individuals who are bilingual and use both English and French frequently at work earn a higher
wage than those who are BIL_MEFE and UNIL_ENGLISH. However, the least significance
goes to bilingual speakers who use mostly French at work. This is highly likely due to the fact
that Ontario is a mainly English speaking province in Canada. In terms of occupation, we see
that both male and female individuals have greater significance with respect to earnings in a
ECN 620 RESEARCH PROJECT 2 FINAL DRAFT 6
professional or management position compared to semi-professional and blue collar positions.
There is a positive correlation with earnings with a professional or management occupation and a
negative correlation with the other two. This is the same for males and females and the
coefficients for both are quite similar. With respect to industry, we see higher coefficient with
public and service industries than goods with both male and females. This is most likely due to
the fact that that the need for bilingual individuals in Ontario in these industries is higher, and
thus earns them a higher average wage. R2 is a measure of how close the data is to the fitted
regression line. Typically, a value closer to 100% means the model explains all the variability of
the data, however the value of R2 in this case is quite low. Males have a 0.1282 R-squared
value and females have a 0.1513 R-squared value. This is most likely because we are dealing
with human behaviour and psychology. As a result of the extensive pool of variable possibilities
that may affect human behaviour, it is very hard to get definite results. Finally, in terms of
marriage, married males have a higher coefficient than married females, and thus have a higher
average earning.
Conclusion:
The focus of this study is the economic reasoning for learning a second language and
how it positions you as a more valuable asset in the labour market. Specifically, this study is an
analysis of how certain characteristic traits and knowledge can earn you an economic advantage
in the workplace in Ontario, Canada. As stated in the introduction, the error in terms of large
ranges of earnings premiums from past studies is due to a lack of control for various attributes in
the data. The findings in this study prove to be more accurate and unbiased with the inclusion of
these missing attributes. These missing attributes include taking into account the quality of ESL
or FSL skills, education level, marriage status, etc. The analysis of this study is based on the
ECN 620 RESEARCH PROJECT 2 FINAL DRAFT 7
sample distribution and average earnings for different language groups, as well as the log-linear
regression using the human capital earnings function for both males and females. This data was
taken from the 2016 Census Public Use Microdata File. From Table 1, the analysis concludes
that there is a majority of unilingual individuals for both genders. Out of the sample of males and
females, on average, males earn a higher wage than females in Ontario. Furthermore, being a
bilingual speaker earns you an even higher wage. This premium is highest for individuals who
speak mostly English in the workplace (BIL/MEFE).
Next, from Table 2, the analysis concludes that a higher education level has a greater,
positive correlation in terms of average earnings. The same can be said in terms of occupation.
The more prestigious management and professional careers offer a greater, positive correlation
than semi-professional and blue collar careers. Public and service industries have more
significance than the goods industry, which is most likely due to the need of bilingualism in these
industries. The language variables, UNIL_ENGLISH, BIL_MEFE, BIL_MEFF, and
BIL_FRENCH seem to show the greatest significance related to wages. Male and female
individuals who are bilingual and use both English and French frequently at work earn a higher
wage than those who are BIL_MEFE and UNIL_ENGLISH. However, the least significance
goes to bilingual speakers who use mostly French at work. This is most likely due to the fact that
Ontario is a mostly English speaking province in Canada.
Although this study has shown major implications for the reasoning behind calculating
average earnings for males and females in Ontario, there are still other influences that contribute
to this formula. It is difficult to list all possible attributes contributing to average earnings as this
topic deals with human behaviour and psychology. Nonetheless, as a result of Ontario being
mainly English speaking, being another unilingual English speaker does not separate you from
ECN 620 RESEARCH PROJECT 2 FINAL DRAFT 8
the rest, and thus does not give you an advantage in the labour market. Therefore, bilingual
English and French speakers earn a higher average wage. Along with this, as shown in this study,
other variables such as higher education, marriage status, and occupation and industry status also
play a part in calculating average earnings. Some more than others. These attributes should be
taken into consideration when finding economic incentive in the labour market. Furthermore,
these results will most likely stay somewhat linear over time, assuming Ontario continues to be a
mainly English speaking province.
ECN 620 RESEARCH PROJECT 2 FINAL DRAFT 9
References
Christofides, L. N., & Swidinsky, R. (1998). Bilingualism and Earnings: A Study Based
on 1971, 1981 and 1991 Census Data. In New Canadian Perspectives: Economic
Approaches to Language and Bilingualism, Ottawa: Official Languages Branch,
Department of Canadian Heritage. 123-186.
Christofides, L. N., & Swidinsky, R. (2010). The Economic Returns to the Knowledge and
Use of a Second Official Language: English in Quebec and French in the Rest-of-
Canada. Canadian Public Policy, 36(2), 4–10. doi:10.3138/cpp.36.2.137
Grenier, G. (1987). Earnings by Language Group in Quebec in 1980 and Emigration from
Quebec between 1976 and 1981. The Canadian Journal of Economics / Revue
Canadienne d’Economique, 20(4), 774-791. doi:10.2307/135415
Savoie, G. (1997). The Comparative Advantages of Bilingualism on the Job Market:
Survey of Studies. In New Canadian Perspectives: Official Languages and the Economy.
Ottawa: Official Languages Branch, Department of Canadian Heritage. 65-88.
Vaillancourt, F. (1992). An Economic Perspective on Language and Public Policy in
Canada and the United States. In Immigration, Language, and Ethnicity, Canada and the
United States (ed. Barry R. Chiswick), Washington, D.C.: The AEI Press. 179-228.
ECN 620 RESEARCH PROJECT 2 FINAL DRAFT 10
Appendix
Table 1
Sample Distribution and Average Earnings for Different Language Groups in ON, Canada
SAMPLE
SIZE
% OF TOTAL
SAMPLE
% OF
BILINGUAL
ANNUAL
EARNINGS ($)
%
DIFFERENTI
AL
MEN
TOTAL
SAMPLE
30934 100.00% - 83685.00 -
UNILINGUA
L
25426 82.19% - 81436.29 -
BILINGUAL 5508 17.81% 100.00% 70114.29 -13.90%
BIL/MEFE 2248 7.27% 40.81% 98967.68 21.53%
BIL/MEFF 3138 10.14% 56.97% 91509.50 12.37%
BIL/FRENC
H
122 0.39% 2.21% 78673.08 -3.39%
WOMEN
TOTAL
SAMPLE
26131 100.00% - 62843.00 -
UNILINGUA
L
20330 77.80% - 60443.64 -
BILINGUAL 5801 22.20% 100.00% 58717.56 -2.86%
BIL/MEFE 2503 9.58% 43.15% 76323.36 26.27%
BIL/MEFF 3010 11.52% 51.89% 67862.88 12.27%
BIL/FRENC
H
288 1.10% 4.96% 72907.58 20.62%
ECN 620 RESEARCH PROJECT 2 FINAL DRAFT 11
Table 2
Log-Linear Regression Results using Human Capital Earnings Function
MEN WOMEN
Coefficient |t| stat. Coefficient |t| stat.
CONSTANT: 11.11489 66.245 11.047607 116.471
MARITAL STATUS:
MARRIED 0.46797 37.828 0.268026 23.709
SCHOOLING:
HIGHSCHOOL -0.48951 21.303 -0.462721 24.684
TRADE/COLLEGE -0.31561 14.376 -0.410334 24.197
UNIVERSITY 0.11749 5.353 0.127398 7.617
POSTGRAD 0.16891 8.987 0.15681 8.359
LANGUAGE:
UNIL_ENGLISH 0.15613 0.938 0.074767 0.829
BIL_MEFE 0.15981 0.968 0.036901 0.417
BIL_MEFF 0.1747 1.046 0.017125 0.188
BIL_FRENCH -0.03558 0.325 -0.051859 0.773
OCCUPATION:
MANAGEMENT 0.19543 13.424 0.197737 7.394
PROFESSIONAL 0.13839 8.26 0.137539 4.803
SEMIPROF -0.19862 11.925 -0.225837 8.164
BLUECOLLAR -0.19942 13.598 -0.23058 8.534
INDUSTRY:
PUBLIC 0.13351 8.447 0.091395 7.196
GOODS 0.07322 5.602 0.03076 2.359
SERVICE 0.12924 8.218 0.07501 4.28
ADJ. R SQUARED 0.1282 0.1513
# OF
OBSERVATIONS
30934 26131
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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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FN1024 MATH0094 MANG 6554 ECOS3003 ELEC6252 C2511C PHY328 EC1060 ECON32202 COMP4002-E1 MATH3063 EFIMM0124 QBUS2310 ECON4411 FUNDAMENTALS ACCT20001 18YX RPM COMP 2711 QBUS6600 BUSS5221 ST3189 FIT1047 MAT344 EGB375 MATH60013 MATH96011 COMP3557 MAST20029 ECM159 EAE1011 ENSCEN 105 SECTION A STA 141C SOSE 2022 PAM006 COMP0048 M40007 ISOM2500 CMPE 142 A06472 MCC150 ACST 8083 GEOG6087 ECON920 FINM7008 ENVENG 4110 COMPSCI 361 ENGRCEE 21 PHYS 1112 ACF2100 ECMM108 COMPS267F MAT00050M MAST30025 CSIT970 MSCI 224 INT202 PUBL0054 MSDM5004 BMAN 21020 BMAN21020 MGMT1002 MATH11150 LAWS7023 ACTL30002 CIVL3340 MATH1002 ECON 8069 COMPSCI 351 IBUS7302 STAT4528 CHEM1101 STAT3925 BUSN 7008 Comp 3120 ECOS2040 SWEN90010 ENGN6528 ECM158 ECON20022 COMP6452 Calculation CITS1401 S1889112 BUS 351 ACTL30008 FIT2091 ECON6002 IBUS6020 PHI 001 ECON30290 MATH7501 ECON10004 MFIN7017 ISYS90043 LAWS7012 ECON30025 COMP30023 COMM1190 ECOM20001 GSBS6009 STAT 337 EMET8002 ECOM90001 ECOM30001 FINC 2012 BUSINESS ENGCV512 ECO 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