Labor Productivity, Misallocation and Structural Transformation

Madhur Gautam and Mansur AhmedAgriculture Global Practice, The World Bank

March 20, 2019

The Agriculture Productivity Gap (APG)

Interpretations of APG in the Literature

Evidence – Africa, China and India: How much labor supplied to agriculutre?

Implications: How large is the APG?

But…Misallocation of labor?

Outline

Why is labor productivity in Agriculture so low?

•Direct relationship to income and poverty reduction

•Stylized ST trend – divergence/convergence of APG

•But “stunted” transformation?

•Reallocation → productivity growth (ST as SOG)

•“Poor but efficient” (Schultz) - why not move?

•Answers have huge policy implications

•Spawned a surge in recent literature

(Gollin, Parente and Rogerson 2007; Cordoba and Ripoll 2009; Restuccia, Yang and Zhu 2008; Vollrath 2009; Caselli and Coleman 2005; Herrendorf and Schoellman 2018; Hicks et al. 2017; Rogerson 2017; Hsieh and Klenow 2009; Restuccia and Rogerson 2008; Adamopoulos and Restuccia 2014, 2015; Adamopoulos et al. 2017; Restuccia and Santaeulalia-Llopis 2017; Volrath 2018; Young 2013; McCullough 2017, Gollin and Udry 2017)

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Europe &Central Asia

South Asia LatinAmerica &Caribbean

Middle East& North

Africa

East Asia Sub-SaharanAfrica

Services Industry

Macro statistics: Large Agriculture Productivity Gap(2011-15 average ratio, N-Ag/Ag)

Source: World Development Indicators, World Bank

Interpretations of APG: Misallocation

•Wedges/frictions: barriers to mobility

•Workhorse model: Cobb-Douglas

•Key assumption: Common technology (e.g., Gollin, Lagakos and Waugh, QJE 2014)

•But is test valid? (Rogerson 2017)

•If factor shares differ, even with efficient labor markets, APG ≠ 1

𝑌𝑌𝑖𝑖 = 𝐾𝐾𝑖𝑖𝛼𝛼𝑖𝑖𝐿𝐿1

(1−𝛼𝛼𝑖𝑖)

Ave. Prod.: 𝐴𝐴𝐴𝐴𝑖𝑖 = 𝑌𝑌𝑖𝑖𝐿𝐿𝑖𝑖

= 𝐾𝐾𝑖𝑖𝐿𝐿𝑖𝑖

𝛼𝛼𝑖𝑖

Marg. Prod.: 𝑀𝑀𝐴𝐴𝐿𝐿𝑖𝑖 = 1 − 𝛼𝛼𝑖𝑖𝐾𝐾𝑖𝑖𝐿𝐿𝑖𝑖

𝛼𝛼𝑖𝑖= 1 − 𝛼𝛼𝑖𝑖 𝐴𝐴𝐴𝐴𝑖𝑖

Hence: 𝐴𝐴𝐴𝐴1𝐴𝐴𝐴𝐴2

= 1−𝛼𝛼21−𝛼𝛼1

= 1

𝐴𝐴𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖: 𝛼𝛼1 = 𝛼𝛼2

Analytically Challenged?

Interpretations of APG: Measurement Errors

•Issue 1: Monolithic block of workers (large army toiling full time on tiny plots)

•RNFE literature: livelihood diversity long established

•Key Q: how much time “farmers” actually spend farming, and implication for APG?

•Simple but fundamental accounting issue

•Issue 2: Homogenous quality of labor (but unobserved ability, skills, motivation)

•“Worker selection” rather than “causal impact” at play (Hicks et al 2017)

•Micro-panel studies, different countries:→ “Switchers” no better off ito earnings

•Focus on wage-gaps (AWP)

Empirics: Country settings and Data

China

• Often cited as a country with well known policy “wedge” – the Hukou residency permit system

• Data: RCRE Fixed Point (i.e., Panel) surveys, 2003-2013

• Large nationally representative sample – about 20,000 households each year

India

• Also often cited as a country with restricted labor mobility – due to the caste system

• VDSA (ICRISAT) Village Surveys – 2010-2014

• 42 villages in nine states in the humid and semi-arid zones – India (Bangladesh data not used)

Africa – rely on McCullough (2017) – using LSMS-ISA surveys (Ethiopia, Malawi, Tanzania and Uganda)

Presentation Title5

How much labor is supplied to Agriculture?

Labor Supply of Rural Households: How Much Labor Supplied to Agriculture?

China: Allocation of labor days to farm and nonfarm activities, 2003–13 (%)

3928

2428

38 44

5139

2126

28 35

2003 2013 2003 2013

MALE FEMALE

AGRICUTLURENON-AGRIC. IN TOWNSHIPNON AGRIC. OUTSIDE TOWNSHIP

Africa (ET; ML; TZ; UG): Share of labor in Agriculture

Source: McCullough (2017).Note: The ‘‘Hours” measure generated from LSMS-ISA data. The ‘‘Part. indiv” measure is based on the primary occupation (most reported hours) of individuals in the dataset. The ‘‘Part. head” measure is based on the primary occupation of the household head. The ‘‘National account” measure is from the World Development Indicators database, and the ‘‘DHS” measure is based on DHS surveys, as described in the text.

Source: Own estimates using RCRE surveys, 2003–13.

India: Complex Livelihoods in Rural Households

Work days across activities –A typical month for an avereage adult rural worker

Number of Economic Activities HH Members Performed

Source: VDSA data Source: Own estimates using RCRE surveys, 2003–13.

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Own farm work Livestock work Farm labor Nonfarm labor

Business+Service work Salaried job Domestic work Other activities

Illness Labor-endowment

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Number of Economic Activities HH Members Performed

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Labor Supply of Rural Households: Complex Livelihoods in Rural India

Distinct seasonality in agriculture based work: Business/non-farm work counter-cyclical

Days worked per month by different “main” workers, Males and Females

Source: Own estimates using VDSA surveys, 2010-15 Source: Own estimates using VDSA surveys, 2010–15.

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Farmer Livestockworker

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Bussinessworker

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Farmlaborer

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Bussinessworker

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Own farm Work Livestock Work Farm Labor Non-Farm Labor

Business Salaried Job Endowment

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Farm Labor (left axis) Own farm Work (left axis)

Non-Farm Labor (right axis) Business and Service Work (right axis)

So, How large is the Agriculture Productivity Gap?

Africa: Much Lower AGP

Case studies: View from Macro Statistics – very large APG

Source: McCullough (2017)

View from Micro-data – significantly lower APG

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Ethiopia Malawi Uganda Tanzania

Per-person Per-hour

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ETHIOPIA INDIA CHINA MALAWI UGANDA TANZANIA

INDUSTRY SERVICES

Source: World Development Indicators, The Word Bank

China – Vanishing APG

Hukou holders (share of population): 44%

FTE share of labor (based on micro estimate): 14.7%

Agricultural labor force (NBS) in 2013: 31.4%

Ag. labor supply less than 50% NAs

AND – Non-Ag. Workforce underestimated

Ag labor productivity underestimated by 50%

AND Non-ag. overestimated by 24%

APG vanishes!

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2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Crop VA/Crop labor input Farm VA/Total agric worker time

China: Labor productivity at the micro level

India: Reversal of APG? By Type of Household

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Pure Farm HH (44%) Pure Non-Farm HH (18%) Mixed HH (38%)

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A. Household Income

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Pure Farm HH (44%) Pure Non-Farm HH (18%) Mixed HH (38%)

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B. Earnings per Worker

Agriculture Non-Agriculture

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Pure Farm HH (44%) Pure Non-Farm HH (18%) Mixed HH (38%)

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B. Earnings per Worker

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Pure Farm HH (44%) Pure Non-Farm HH (18%) Mixed HH (38%)

D. Earnings per Hour

Agriculture Non-Agriculture

Why do People Stay in Agriculture?

Annual labor earnings per worker Annual labor earnings per day Annual labor earnings per hour

Source: Authors’ Own Calculation from ICRISAT Data

Labor Productivity Differences by Farm Size

Source: Authors’ Own Calculation from ICRISAT Data

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R U R A L L A N D L E S S S M A L L F A R M E R

M E D I U M L A R G E

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Agriculture Non-Agriculture

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R U R A L L A N D L E S S S M A L L F A R M E R

M E D I U M L A R G E

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IAN

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Agriculture Non-Agriculture

Average earnings per worker by farm size, 2010–15 Average earnings per day by farm size, 2010–15 Average earnings per hour by farm size, 2010–15

Labor Productivity Differences by Education

Source: Authors’ Own Calculation from ICRISAT Data

Average earnings per worker by farm size, 2010–15 Average earnings per day by farm size, 2010–15 Average earnings per hour by farm size, 2010–15

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Misallocation of Labor?

China: Agriculture Wage Gap?

Trends in farm and nonfarm wages: Agricultural wage at a premium wage, and risen faster

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NONAGRICULTURAL ADULT MALE WAGE

NONAGRICULTURAL AVERAGE WAGE

AGRICULTURAL WAGE

China: What explains wage gaps between agriculture and non-agriculture?

For workers (15-65)Dep. Var. log(nonfarm wage/farm wage) Basic Extended Basic ExtendedConstant 0.29 0.26 0.29 0.27Age-gender (ref. male adult)

Female adult -0.27*** -0.25*** -0.265*** -0.254***Male youth -0.06*** -0.06*** -0.06*** -0.062***Female youth -0.24*** -0.25*** -0.243*** -0.243***Male old -0.35*** -0.28***Female old -0.59*** -0.53***Male child -0.22** -0.22**

Log (schooling years) 0.01* 0.009*

Labor-supply ratio (worktime/available days) 0.036** 0.036**Profession (ref. operation ag. labor)

Operation non-ag labor 0.037* 0.038*Hired labor -0.045*** -0.044***Other non-ag professions -0.04** -0.038**

Health category (ref. excellent)Good -0.019 -0.021*Moderate -0.053* -0.051*Bad -0.159** -0.168**Lost work capability -0.133 -0.132

Log (farm size) 0.006Year dummies (ref. 2004) (not reported)Observations 15,714 15,486 15,559 15,401Adj R-squared 0.126 0.128 0.126 0.127

India: Farm and Non-farm Wages, monthly

Household data: Ag and Non-Ag Wages, by gender

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Farm Wages at nearest market (trader)

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Adult Female - non-Ag Adult Female - Ag

Adult Male - Ag Adult Male - non-Ag

Village Level data: Farm and Non-Farm (nearest market)

India: Local Area Wage Differential, but Generlizable?

• ICRISAT Villages not nationally representative, but reasonable representation of rural village India

• Turning to national representative employment/unemployed surveys (NSSO):• 2011/12 (68th Round Survey):

Nominal wages Rural Urban Premium

Casual Males 149.3 178.8 1.20

Casual Females 103.3 108.8 1.05

Regular Males 322.3 469.9 1.46

Regular Females 201.6 366.2 1.82

Rangarajan Committee (Planning Commission)

Poverty Line (2011/12) 32 47 1.47

Conclusions and Implications

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Conclusions

• Potentially large systematic measurement errors in how APG calculated (national accounts)

• Agriculture does not appear to be as unproductive as might appear

• Farming appears attractive when considering the amount of effort going into it

• Misallocation? Questions efficacy of structural transformation for productivity growth (point strongly made analytically by Rogerson 2017)

• China: No evidence of a wage gap – labor markets work well, despite the infamous Hukou

• India: Farm wages actually higher than casual non-farm labor wage

Implications

• Refocus debate on innovation and productivity driven growth and job creation to raise wages and facilitate structural transformation – especially OFF FARM

• Seasonality means proximity and seasonal off-farm opportunities important for jobs

THANK YOU