Land Misallocation and Productivity

Diego RestucciaUniversity of Toronto

Raul Santaeulalia-LlopisWashington University in St. Louis

Universitat de Valencia

ECAMA, June 2015

1/22

Motivation

I Question: Why is Malawi (and many other poorcountries) so much less productive in agriculture?

Agriculture matters a lot in accounting for income percapita differences between rich and poor countries:

I Poor countries are much less productive in agriculture thanin non-agriculture relative to rich countries

I Low productivity in agriculture means more people inagriculture to satisfy minimum subsistence consumption

I Answer: Misallocation of factors of production acrossfarms (in particular, connected to largelyunderdeveloped land markets) has an important roleexplaining low productivity in poor countries

2/22

What We Do and Find

1. Measure farm-level Total Factor Productivity usingunique micro data from household-farms in Malawi (ISA).

2. Assess the quantitative impact of misallocation foragricultural productivity with a simple efficient framework:If factors are efficiently reallocated agriculturalproductivity increases by 3.6 times its value.

3. Implementing an efficient allocation of resources throughrental markets→ Maximum output and Inequality goesdown!

4. Implementing an efficient allocation of resources impliesbig structural transformation for Malawi.

3/22

The Micro Data: Malawi 2010 ISA: A Snapshot

Key: ISA provides detailed high quality data on agriculturaloutput (all crops) and inputs at the plot level.

I Agricultural Production: Info for all crops, plots and seasons. Maizerepresents more than 80% of total land cultivated (de Magalhaes andSantaeulalia-Llopis, 2015)

I Land: Plot size accurately measured via GPS. Info for all cultivatedhousehold plots. The sum of all operated plots is household land size.

I Capital: Full array of agricultural capital equipment and structures (atselling price).

I Hours: Most households members work in the field (householdsize=4.57). Info on extensive and intensive margins of labor supply foreach household member: (i) weeks worked, (ii) days/week, and (iii)hours/day by plot & by agricultural activity:

I land preparation/ planting,I weeding/fertilizing, andI harvesting and by season (rainy, dry and permanent).

4/22

Relevant Context

I Extremely Small Farm Operational Scale. Avg of 0.83 Ha.In Belgium (similar land endowment), avg. size is 16.1 Ha, in the US187 Ha.

I Largely Underdeveloped Land Markets.

I 83.4% of farms operate only non-marketed land.I 10.4% of farms operate land that is 100% marketed.I Disaggregating marketed land: 3.0% rent land informally

(borrowed for free), 9.5% rent land formally, 1.8%purchased land w/o a title, and 1.3% purchased titled land.

5/22

Measuring Farm Productivity

We measure household farm productivity as the unobservablesi in:

yi = siζikθki (qi li)

θl

where θx are input factor shares.

I ζi represents rain shocks. Remove this variation of ouptutwith rain deciles that delivers a concave relationship.

I qi is an index of land quality. Remove this variation withslope and elevation (and their interaction) & dummies for 9discrete quality variables available at the plot level.

6/22

Land Quality Index qi , Malawi ISA-2010/11

7/22

Rain Shocks, ζi

8/22

Farm Productivity, Malawi ISA-2010/11

9/22

Table: Dispersion of Farm and Plant Level Productivity

Farms Manufacturing PlantsMalawi USA USA China India

Statistic 2010 1990 1977 1998 1987

SD 1.19 0.80 0.85 1.06 1.1675-25 1.15(3.2) 1.97 1.22 1.41 1.5590-10 2.38(10.8) 2.50 2.22 2.72 2.77N 8,009 AR(2014) 164,971 95,980 31,602

Note: Data for USA, China, and India refer to manufacturing establishments fromHsieh and Klenow (2009). SD is the standard deviation of log productivity; 75-25 is the log difference between the 75 and 25 percentile and 90-10 the 90 to10 percentile difference in productivity. N is the number of observations in eachdataset.

10/22

Fact: Land Size is Unrelated to Farm Productivity si

Notes: The correlations b/w land size and s(ζi ,qi ) is .04, s(0,0) is .01, s(ζi ,0) is .09, and s(0,qi ) is -.07.

11/22

Fact: Yield Increases with Farm Productivity

Notes: The correlation is . 77 (N .70, C .71, S .81).

12/22

Assessing Productivity Effect of Misallocation

I Planner chooses allocations to maximize agricultural outputgiven fixed amounts of capital and land:

Y e = max{ki ,li}

∑i

si (kαki lαl

i )γ, subject to K = ∑i

ki , L = ∑i

li .

I Efficient allocation that equates marginal products impliesthat optimal farm-level land (and capital) should be strictlyrelated to farm-level productivity:

kei =

zi

∑ziK , lei =

zi

∑ziL.

where zi ≡ s1/(1−γ)i ,

13/22

The Source of Low Agricultural Productivity:Misallocation at Work

14/22

Main Reallocation Result

I Reallocation Results – Output Loss (Y a/Y e)

Full Sample

Nationwide .2788

I That is, a nationwide efficient reallocation of land andcapital would increase aggregate agricultural output bya factor of 3.6.

Bstrap simulations imply high accuracy, 5th pct of .2455 and 95th of .3211.

15/22

Reallocation in Narrower Geographies and Institutions

Reallocation Results – Output Loss (Y a/Y e)

Average Median Min MaxGeographic Areas:

Regions .2943 .2921 .2757 .3485Districts .3410 .3568 .1409 .4821Enum. Areas .6077 .6245 .0911 .9624

Institutions:Traditional Authority .3213 .2872 .1791 .7220Language .2980 .2576 .1681 .8646

16/22

Direct Effects of Land Markets

Reallocation Results – Output Loss (Y a/Y e)

By Marketed Land Share By Marketed Land TypeNo Yes All Rented Purchased

(0%) (> 0%) (100%) Informal Formal Untit. Titled

Output (Productivity):Losses .2411 .5081 .6378 .5809 .5782 .1951 .7192Gains 4.146 1.968 1.567 1.721 1.729 5.125 1.390

Observations 5,962 1,189 746 215 682 126 97Sample (%) 83.4 16.6 10.4 3.0 9.5 1.8 1.3

17/22

Further Robustness Results

Our result are robust to:

I Human capital gropus: (a) schooling groups and (b)terrain-roughness skills.

I Other sources of Transitory Risk: Health risk, death risk,food security risk, marital status, distance to markets, otherincome sources, etc.

I Crop types: Maize seems to be marginally better allocatedthan non-maize crops (e.g., tobacco).

18/22

Rental Markets and Income Inequality

I Keep ownership and allow for perfectly competitive rentalmarkets for capital and land.

I Given the competitive rental rates of capital and land in thisdecentralized solution, endowment income is:

endowment income = rl(la− le) + rk (ka−ke) + ye

where (ka, la) are actual allocations which we take asendowment, (ke, le) are efficient allocations, rl and rk arethe prices that support the efficient allocation as acompetitive equilibrium, and ye is efficient output.

19/22

Inequality of Operational Scale vs. EndowmentIncome

Malawi ISA 2010/11 Productivity Partition

Bottom 20% Top 20% var(lnx)

Output (Operational Scale):Actual .14 4.78 1.824Efficient .00 25.06 6.782

Endowment Income (w/ Rentals):Actual .14 4.78 1.824Efficient 4.28 14.65 1.228Ratio: Actual/Eff. 23.70 3.88 –

Rental markets→ max. output and inequality goes down!

20/22

Broader Impact of Reallocation: Two Sectors

I A 2-sector model with non-homothetic preferences implies thefollowing allocation of agricultural labor:

Na =

(a

ZLγ

) 11−γ

.

I We find the GE effects of a 3.6-fold increase in agricultural TFPimply a big structrual transformation of Malawi:

Benchmark IncreasedProductivity

Productivity in Agriculture (Z ) 1.0 3.6Yield (Ya/L) 1.0 1.0Share of Employment in Ag. (Na) 0.65 0.04Labor Productivity in Ag. (Ya/Na) 1.0 16.2Average Farm Size (L/Na) 1.0 16.2

21/22

Conclusions

1. We measure Farm-Level Total Factor Productivity. Highquality ISA data is key for this measurement.

2. An efficient reallocation of land (and capital) increasesagricultural output (and productivity) by a factor of 3.6-fold.

3. Land markets are crucial: Output loss in farms with nomarketed land (83.4% of farms) is 0.24; in farms with onlymarketed land (10.4% of farms) is less than half 0.64.

4. Opening rental markets implies that overall inequalitydeclines and benefits the poorest farmers the most.

5. This reallocation can trigger a substantial structuraltransformation in Malawi: Share of employment goes down to4%, agricultural labor productivity and average farm size go upby a factor of 16.2. (Almost catching up with Belgium!)

22/22