Education, Skills and Earnings Evidence from India and Pakistan Monazza Aslam Anuradha De Geeta Kingdon Rajeev Kumar

Introduction Aim – examine role of education in labour market (i.e. economic) outcomes Education can affect labour market outcomes in 2 major ways: better occupational attainment higher earnings within any given occupation Our research looks at both these channels of effect from education onto economic well-being

Aim – examine role of education in labour market (i.e. economic) outcomes

Education can affect labour market outcomes in 2 major ways:

better occupational attainment

higher earnings within any given occupation

Our research looks at both these channels of effect from education onto economic well-being

Intro. Contd. Because interested in both effect of education on earnings and on occupational attainment, classify individuals as: OLF, unemployed, unpaid workers, agri workers, self employed and wage workers (among these into regular and casual); Objectives met in 2 parts of the paper – first part graphical analysis whether and how much education facilitates entry into lucrative occupations; Focus on second part – how education and skills affect people’s economic outcomes through earnings.

Because interested in both effect of education on earnings and on occupational attainment, classify individuals as: OLF, unemployed, unpaid workers, agri workers, self employed and wage workers (among these into regular and casual);

Objectives met in 2 parts of the paper – first part graphical analysis whether and how much education facilitates entry into lucrative occupations;

Focus on second part – how education and skills affect people’s economic outcomes through earnings.

The key features of this work Motivated by two main questions not satisfactorily answered on economic outcomes in developing countries: Past estimates on profitability of waged workers – small and often shrinking part of total labour market. This paper: wage employment, self employment and agricultural work; Learning matters to productivity – plug this gap by estimating returns to literacy and numeracy; In addition because of unique data can also do the following: Consider the pattern of returns to different levels of education – is it concave as conventionally believed? Compare labour market rewards of education/skills for men and women in particular looking at Does education lower the gender gap in earnings (via increasing women’s chances of waged work and/or via higher returns to education) Look at labour market returns to English language.

Motivated by two main questions not satisfactorily answered on economic outcomes in developing countries:

Past estimates on profitability of waged workers – small and often shrinking part of total labour market. This paper: wage employment, self employment and agricultural work;

Learning matters to productivity – plug this gap by estimating returns to literacy and numeracy;

In addition because of unique data can also do the following:

Consider the pattern of returns to different levels of education – is it concave as conventionally believed?

Compare labour market rewards of education/skills for men and women in particular looking at

Does education lower the gender gap in earnings (via increasing women’s chances of waged work and/or via higher returns to education)

Look at labour market returns to English language.

Data Similar RECOUP surveys MP and Rajasthan in India Punjab and NWFP in Pakistan Sample size in India – >1000 households, n people Sample size Pakistan – 1194 hh, 8750 people

Similar RECOUP surveys

MP and Rajasthan in India

Punjab and NWFP in Pakistan

Sample size in India – >1000 households, n people

Sample size Pakistan – 1194 hh, 8750 people

Table1: Distribution of the Labour Force in India & Pakistan, by gender (aged 15-60)

Key points Pak and India clearly very different in some respects – Pak data from 2/4 provinces and Indian from 2 of North Central States – great diversity hence numbers not representative of countries but illustrative; Larger proportion of individuals OLF in Pak – largely driven by twice as many women OLF than men (influence of culture/tradition); Occupational attainment very different; Gender divide more apparent in Pakistan.

Pak and India clearly very different in some respects – Pak data from 2/4 provinces and Indian from 2 of North Central States – great diversity hence numbers not representative of countries but illustrative;

Larger proportion of individuals OLF in Pak – largely driven by twice as many women OLF than men (influence of culture/tradition);

Occupational attainment very different;

Gender divide more apparent in Pakistan.

RECOUP Pakistan : Summary statistics by occupation All Out of Labour Force Unempl oyed Unpaid Family Labour Agri. Self employed Self employed Wage employed Annual Earnings (Mean) 61,835 - - - 53,575 63,875 62,356 Annual Earnings (Median) 48,000 - - - 42,000 48,000 52,000 Log Earnings (Mean) 10.7 - - - 10.4 10.6 10.8 Years of education 4.8 3.4 5.6 3.6 4.9 6.0 6.8 Observations 3960 1564 314 430 206 480 966 Earnings Observations 1530 0 0 0 170 458 902 Assigning unpaid workers to their respective occupation Annual Earnings (Mean) 43880 - - - 23,324 38,808 62,356 Annual Earnings (Median) 36000 - - - 0 18,000 52,000 Log Earnings (Mean) 10.7 - - - 10.3 10.6 10.8 Years of education 4.8 3.4 5.6 - 3.9 5.7 6.8 Observations 3827 1564 314 - 375 608 966 Earnings Observations 2018 0 0 0 375 608 902

RECOUP India : Summary statistics by occupation All Out of Labour Force Unemployed Unpaid Family Labour Agri. Self employed Self employed Wage employed Annual Earnings (Mean) 36,675 -- -- -- 40,792 58,413 29,092 Annual Earnings (Median) 21,600 -- -- -- 21,696 36,000 18,000 Log Earnings (Mean) 9.99 -- -- -- 10.03 10.56 9.81 Years of education 5.6 6.6 8.1 4.0 4.5 8.1 5.7 Age 31.9 28.2 23.7 31 42.7 36.6 31.6 Proportion men 53.3 25.5 55.2 32.6 88 84.5 74.4 Proportion urban population 31.5 55.1 59.7 10.5 1.2 57.0 30.7 SMaths 2.8 3.1 3.7 2.2 2.7 3.8 2.7 Sliteracy 2.3 2.8 3.4 1.7 2.2 3.3 2.1 English 4.6 6.2 7.7 2.9 2.9 7.8 4.3 Observations 3438 848 67 886 325 291 1021 Earnings Observations 1571 0 0 0 324 277 970

Economic Outcomes of Education - Earnings We have estimated Mincerian earnings functions to see the relationship between years of education (or cog. skills) and earnings; Dependent variable = log monthly earnings; Very parsimonious models; Coefficient on ‘years of schooling’ measures the economic rewards to schooling in terms of earnings.

We have estimated Mincerian earnings functions to see the relationship between years of education (or cog. skills) and earnings;

Dependent variable = log monthly earnings;

Very parsimonious models;

Coefficient on ‘years of schooling’ measures the economic rewards to schooling in terms of earnings.

OLS Earnings equations, by gender PAKISTAN INDIA Agriculture Self Employed Wage Worker Agriculture Self Employed Wage Worker Male Education 0.016 0.015 -0.017 -0.022 0.040 0.109 0.080 0.043 0.044 0.070 (0.50) (0.44) (-0.69) (-0.98) (8.66)** (8.00)** (6.22)** (4.94)** (5.15)** (10.7)** LnCapital 0.127 0.172 - 0.077 0.020 (4.04)** (3.73)** (7.16)** (2.32)** # N 168 162 356 355 768 285 285 235 235 723 Female Education 0.104 0.074 0.091 0.114 0.088 0.019 0.016 0.087 (3.91)** (3.08)** (4.77)** (1.7)* (1.52) -0.57 -0.42 (6.37)** LnCapital 0.130 - -0.049 -0.019 (5.04)** (-1.03) (-0.38) # N 98 88 177 39 39 42 42 245 All Education 0.013 0.012 0.018 0.005 0.045 0.108 0.082 0.039 0.040 0.076 (0.39) (0.36) (0.86) (0.25) (9.21)** (7.97)** (6.44)** (4.45)** (4.58)** (12.78)** LnCapital 0.130 0.160 - 0.068 0.016 (4.16)** (4.82)** (6.33)** (1.80)* Male 1.495 0.978 1.205 0.453 0.605 0.350 0.310 1.498 1.486 0.695 (1.29) (1.22) (8.61)** (2.36)** (6.18)** (2.11)** (1.77)* (9.8)** (9.58)** (12.47)**

Key Findings Pakistan – Males return to education in wage work only. Females – large returns in waged work and in self employment; ROR (women) > ROR (men) in waged could reflect scarcity premium – some jobs reserved for women such as primary school teaching; Large ROR (women) in self employment welcome news suggests edn plays productivity enhancing and poverty reducing role in potentially large and growing sectors. Gender pattern of returns provides women with strong economic incentives to invest in schooling.

Pakistan – Males return to education in wage work only. Females – large returns in waged work and in self employment;

ROR (women) > ROR (men) in waged could reflect scarcity premium – some jobs reserved for women such as primary school teaching;

Large ROR (women) in self employment welcome news suggests edn plays productivity enhancing and poverty reducing role in potentially large and growing sectors.

Gender pattern of returns provides women with strong economic incentives to invest in schooling.

Key Findings... India – Large returns to edn. in agri, contrary to notion (hitherto untested in India) that rewards to edn. must be lower in agri than in wage employment. Notions shaped by early work done in 1980s in Nepal and Africa but pattern could have changed; Agriculture a more ‘skill-rewarding’ activity in India than in Pak; Wage work – returns larger than in Pakistan – ‘skilled’ casual work rather than ‘unskilled’ Gender big thing in Pakistan – returns to education for women are higher than for men say in waged work – this suggests the extent to which you can ‘catch’ up to men is greater in Pak but women also face greater differential treatment as apparent from descriptive stats.

India – Large returns to edn. in agri, contrary to notion (hitherto untested in India) that rewards to edn. must be lower in agri than in wage employment. Notions shaped by early work done in 1980s in Nepal and Africa but pattern could have changed;

Agriculture a more ‘skill-rewarding’ activity in India than in Pak;

Wage work – returns larger than in Pakistan – ‘skilled’ casual work rather than ‘unskilled’

Gender big thing in Pakistan – returns to education for women are higher than for men say in waged work – this suggests the extent to which you can ‘catch’ up to men is greater in Pak but women also face greater differential treatment as apparent from descriptive stats.

Estimated marginal return to schooling India Pakistan Agri Self Wage Agri self wage Male Primary 10.0*** -- 7.5*** 10.1* -- 2.9** Lower Secondary 8.1*** 3.4** 7.9** -- -- 3.0*** Higher Secondary 19.6*** 3.4** -- -- -- 13.0*** Tertiary 0.0 8.9*** 23.2*** 33.3*** -- 3.8***

Although RETURNS to education high for women in both Pak and in India, earnings also much lower – coefficient on MALE. Hours worked? Still men earn a large premium... In other words – even though slope of earnings/education relationship twice as steep for women (waged work in Pakistan), intercept term large (see Figure 1)

Although RETURNS to education high for women in both Pak and in India, earnings also much lower – coefficient on MALE.

Hours worked? Still men earn a large premium...

In other words – even though slope of earnings/education relationship twice as steep for women (waged work in Pakistan), intercept term large (see Figure 1)

Figure 1 – Education and Earnings among Wage employed in Pakistan

Earnings equations, with cognitive skills PAKISTAN INDIA Agriculture Self Wage Agriculture Self Wage Male Smaths 0.081 0.034 0.033 -0.011 0.070 0.020 (0.68) (0.63) (1.38) (-0.20) (1.14) (0.77) Sliteracy 0.023 -0.012 0.060 0.165 0.029 0.119 (0.32) (-0.34) (3.81)** (3.57)** (0.61) (5.36)** English -0.000 0.002 0.017 0.059 0.021 0.040 (-0.00) 0.15 (4.90)** (4.99)** (3.15)** (7.73)** # N 157 157 335 335 515 514 262 262 201 201 493 493 Female Smaths 0.056 -0.002 0.171 0.240 0.065 (0.34) (-0.01) (1.06) (1.45) (1.77)* Sliteracy 0.197 0.226 0.039 -0.149 0.094 (2.44)** (2.82)** (0.29) (-1.46) (2.16)** English 0.067 0.047 --- 0.014 0.060 (2.73)** (2.77)** (0.69) (4.90)** # N 96 96 114 114 38 38 39 39 231 231

Main Findings Pakistan – consistent with pattern of returns to education, ROR skills (women) > ROR skills (men); No returns to agriculture but large returns to ‘basic’ literacy and English in waged work and in self employment; India – somewhat consistent with pattern before, large returns to literacy and English in agriculture – non-traditional for men and more traditional for women. Wage workers – substantial returns to skills for men and women

Pakistan – consistent with pattern of returns to education, ROR skills (women) > ROR skills (men);

No returns to agriculture but large returns to ‘basic’ literacy and English in waged work and in self employment;

India – somewhat consistent with pattern before, large returns to literacy and English in agriculture – non-traditional for men and more traditional for women. Wage workers – substantial returns to skills for men and women

What is the ‘size’ of these returns? The coefficients are taken from Tables 3 and Table 4 and computed as follows in Pakistan: for men in waged work, the coefficient on Education is 0.041. A 1 standard deviation (4.5 years) increase in schooling causes earnings to increase by 0.041*4.5=0.1845 i.e. roughly 18 %. For self employed women, the coefficient on Education (controlling for capital) is 0.074 and a 1 SD increase in schooling (4.5 years) raises earnings by 0.074*4.5=0.333 i.e. about 33 %. Computed only for cases where the coefficient on literacy or English etc. was not significantly different from zero. MALES FEMALES Agri Self Wage Agri Self Wage PAKISTAN Schooling (4.5) - - 0.18 Schooling (4.5) - 0.33 0.41 Literacy (2.24) - - 0.13 Literacy (2.06) - 0.41 0.47 English (7.45) - - 0.13 English (5.76) - 0.39 0.27 INDIA Schooling (4.35) 0.35 0.19 0.30 Schooling (4.63) - - 0.40 Literacy (2.03) 0.33 - 0.24 Literacy (2.05) - - 0.19 English (7.16) 0.42 0.15 0.29 English (5.82) - - 0.43

The coefficients are taken from Tables 3 and Table 4 and computed as follows in Pakistan: for men in waged work, the coefficient on Education is 0.041. A 1 standard deviation (4.5 years) increase in schooling causes earnings to increase by 0.041*4.5=0.1845 i.e. roughly 18 %. For self employed women, the coefficient on Education (controlling for capital) is 0.074 and a 1 SD increase in schooling (4.5 years) raises earnings by 0.074*4.5=0.333 i.e. about 33 %. Computed only for cases where the coefficient on literacy or English etc. was not significantly different from zero.

Concluding Remarks Good start made in understanding the rewards to education and to skills in labour markets in India and Pakistan Work in progress; small dataset New explorations – wage work shrinking sector and self employment expanding in several developing countries; In India and even in Pakistan (levels results), find returns to certain levels of education in agriculture – previous conclusions based on African and Nepalese studies from the 1980s and our findings suggest a possible changing pattern to more skill-rewarding agriculture

Good start made in understanding the rewards to education and to skills in labour markets in India and Pakistan

Work in progress; small dataset

New explorations – wage work shrinking sector and self employment expanding in several developing countries;

In India and even in Pakistan (levels results), find returns to certain levels of education in agriculture – previous conclusions based on African and Nepalese studies from the 1980s and our findings suggest a possible changing pattern to more skill-rewarding agriculture

Main Findings We found large differences in the way education affects wages Between India and Pakistan – e.g. men’s education is significantly better rewarded in India (7%) than in Pakistan (4%). This leads to Q: how the pattern of production and of demand for labour differs in the 2 countries, to lead to these differential rewards Between men and women – women’s education is twice as well rewarded (9%) as men’s (4%) in Pakistan, and is also better for women (9%) than men (7%) in India. This is good news because it shows that education helps to reduce the large gender gap in earnings in S Asia. But gender diffs in wages continue even at high education levels We have seen how the rewards of education differ by occupation – while RORE is generally larger in waged work than in other occupations, one surprise is the large return to education in agriculture in India and (when we allow for non-linearities) even in Pakistan. This suggests that certain parts of agriculture are modernized, requiring skills

We found large differences in the way education affects wages

Between India and Pakistan – e.g. men’s education is significantly better rewarded in India (7%) than in Pakistan (4%). This leads to Q: how the pattern of production and of demand for labour differs in the 2 countries, to lead to these differential rewards

Between men and women – women’s education is twice as well rewarded (9%) as men’s (4%) in Pakistan, and is also better for women (9%) than men (7%) in India. This is good news because it shows that education helps to reduce the large gender gap in earnings in S Asia. But gender diffs in wages continue even at high education levels

We have seen how the rewards of education differ by occupation – while RORE is generally larger in waged work than in other occupations, one surprise is the large return to education in agriculture in India and (when we allow for non-linearities) even in Pakistan. This suggests that certain parts of agriculture are modernized, requiring skills

Extra table : Earnings equations with education level, and the RORE (Pakistan) Earnings equations Marginal return to education Agri Self Wage Agri self wage 1. Male OLS OLS OLS Primary 0.505 -0.043 0.146 10.1* -- 2.9** (1.68)* (-0.32) (2.14)** Lower Secondary 0.058 -0.034 0.297 -- -- 3.0*** (0.18) (-0.21) (5.44)*** Higher Secondary 0.261 0.229 0.556 -- -- 13.0*** (0.70) (0.88) (6.78)*** Tertiary 1.261 -0.828 0.671 33.3*** -- 3.8*** (3.18)*** (-0.79) (7.16)*** # Individuals 168 356 768 2. Female Primary 0.388 0.534 -- -- -- (1.54) (1.07) Lower Secondary 0.862 0.836 -- 9.5** 6.0*** (2.51)** (3.16)*** Higher Secondary 0.336 1.018 -- -- 9.1*** (0.54) (3.16)*** Tertiary 1.763 1.340 -- 18.0*** 10.7*** (2.88)*** (4.32)*** # Individuals 98 117