Mobile Phones and Farmers’ Welfare in Niger

Jenny C. Aker and Marcel Fafchamps

Center for the Study of African Economies (CSAE) Conference March 22, 2010

Motivation 

Costly information can make it difficult for market agents to engage in optimal arbitrage



Excess price dispersion for homogeneous goods is a common occurrence in developed and developing countries (Stigler, JPE 1961, Brown and Goolsbee, JPE 2002, Jensen, QJE 2007)

Motivation

Motivation

Motivation Alankoss 10 km~2 hours

15 km~3 hour

Bakin Birgi (Monday) Hawkin Sara

Motivation Alankoss

10 km~2 min

15 km~2 minutes 6

Zinder (Sunday)

Bakin Birgi (Monday) Hawkin Sara

Motivation “[With a mobile phone], no dishonest trader can cheat me when I buy and sell….” Farmer in Maradi, Niger

Motivation  Goal: Assess the impact of mobile phones on farm-gate price dispersion in Niger  Exploit

the quasi-experimental rollout of mobile phone

towers  Investigate the impact upon farm-gate price levels

 Three Datasets  Market-level

time series (monthly) farm-gate price panel

1999-2008  Mobile phone tower rollout between 1999 and 2008  Unique farmer panel collected between 2005-2007

Preview of Findings  The introduction of mobile phones leads to a decrease in farm-gate price dispersion  The

effect is stronger for cowpeas as compared to millet  The effect is stronger for markets that are in closer proximity, especially for millet

 Initial evidence suggests that this has not resulted in an increase in farm-gate price levels

Mobile Phone Rollout  Between 2001-2008, cell phone towers were phased-in throughout the country  Mobile phone companies (Celtel/Zain, Sahelcom, Telecel) intended to provide universal coverage by 2009  There were two criteria to prioritize the rollout: o o



Whether the town was an urban center Whether the town was located near a border (Benin, Burkina Faso, Mali and Nigeria)

Widespread coverage into rural areas between 2009 and 2010

Mobile Phone Coverage by Market and Year, 2001-2008

Number of Mobile Phone Subscribers and Landlines in Niger, 2001-08 600000 550000

Number of Landlines

Number of cell phone subscribers

500000 450000 400000

584,286

Number

350000 300000 250000 200000 150000 100000 50000 0 2001/2002

2002/2003

2003/2004

2004/2005

2005/2006

2006/2007

2007/2008

How will Mobile Phones affect Farmers’ Behavior?

 Direct channels (Aker and Mbiti 2010, Jensen 2010) o Reduce search costs and improve arbitrage o Reduce monsopsonistic market power

 Indirect channels o Increased supply o Reduced transportation o Reduced price variability

How will Mobile Phones affect Farmers’ Behavior?

 Direct channels (Aker and Mbiti 2010, Jensen 2010) o Reduce search costs and improve arbitrage o Reduce monopsonistic market power

 Indirect channels o Increased supply o Reduced transportation o Reduced price variability

Linking the Model to the Data Mobile phones reduce the per-search cost as compared to personal travel  Most farmers live within 10 km of their nearest principal market o 35 percent reduction in farmers’ (marginal) search costs  Therefore, the introduction of mobile phones will: • #1. Increase farmers’ reservation prices (unobserved) • #2. Increase the number of markets over which farmers’ search • #3. Reduce farm-gate price dispersion across markets



Linking the Model to the Data Mobile phones reduce the per-search cost as compared to personal travel  Most farmers live within 10 km of their nearest principal market o 35 percent reduction in farmers’ (marginal) search costs  Therefore, the introduction of mobile phones will: • #1. Increase farmers’ reservation prices (unobserved) • #2. Increase the number of markets over which farmers’ search • #3. Reduce farm-gate price dispersion across markets



Related Literature on the Impact of Mobile Phones in Agriculture • Fisheries in India (Abraham 2007, Jensen 2007) • Consumer price dispersion and traders’ behavior in Niger (Aker 2008) • Farmer participation in Uganda (Muto and Yamano 2009) • Internet kiosks and soybean prices in India (Goyal 2009)

First Dataset: Market-Level Panel

 Monthly millet and cowpea prices in 42 domestic and cross-border markets  State-level rainfall and agricultural production  Monthly gasoline prices  Estimated transport costs between markets  State-level population and urban status  Road distances, road quality and estimated travel  Criteria used by mobile phone companies for cell phone rollout  Date of mobile phone entry in each market

Second Dataset: Farmer Panel  Panel survey of traders and farmers collected between 2005-2007  395 traders and 205 farmers across 35 markets in 6 regions of Niger  Census of grain markets and grain traders on each market  Detailed data on farmers’ operations in 2005/2006 and 2006/2007, with retrospective questions for 2004/2005

Table 1. Description of Key Variables: Grain Trader and Market Baseline Characteristics Variable Name Sample Mean (s.d.) Panel A: Trader-Level Characteristics Socio-Demographic Characteristics Ethnicity Hausa Zarma Other Age

0.65 0.17 0.18 45.71(12.2)

Gender(male=0, female=1)

0.11(.32)

Education (0=elementary or above, 1=no education)

0.62(.48)

Trader type Wholesaler Semi-wholesaler Intermediary Retailer Years' of Experience

0.17 0.15 0.15 0.53 16.0(10.2)

Commercial Characteristics Engage in trading activities all year round

.94(.22)

Trade in agricultural output products only Engage in activities outside of trade Co-ownership of commerce

0.98(.02) 0.92(.28) .19(.40)

Changed "principal market" since he/she became a trader

.10(.31)

Number of markets where trade goods

4.42(2.84)

Number of markets where follow prices

3.87(3.0)

Number of days of storage

7.14( 9.8)

Own cell phone

.29(.45)

Own means of transport (donkey cart, light transport)

.11(.32)

Table 2. Description of Key Variables: Farmers Variable Name

Sample Mean (s.d.)

Panel A: Farmer-Level Characteristics Socio-Demographic Characteristics Household head

.915(.279)

Member of hausa ethnic group

.675(.469)

Age

49(16)

Gender(male=0, female=1)

.01(.09)

Education (0=elementary or above, 1=no education)

.85(.35)

Household size

12.6(7.92)

Own mobile phone Panel B. Agricultural Marketing Activities Sold millet in the past year

0.25

Sold cowpea in the past year

0.56

Purchased millet since the previous harvast

0.91

Number of hours walking to principal market

1.53

Access to a paved road

.269(.444)

Number of purchase and sales markets

1.46(.670)

Member of a producers' association

0.22

Sold to intermediary since the last harvest

0.45

Bought agricultural products on credit in the past year

0.41

Received payment in advance for harvest

0.16

Responsible for transport if sell product

0.64

Low Infrastructure Investment

Empirical Strategy  Assess the impact of the introduction of mobile phones on agricultural price dispersion across markets  “Treatment” defined as a mobile phone tower,  Use market-level time-series panel dataset

not adoption

 Exploit the quasi-experimental nature of the rollout of mobile phone towers  Pooled difference-in-differences estimation  Measure treatment effect heterogeneity over

time and space

Estimating the Impact of Mobile Phones at the Market Level Y jki ,t = β 0 + β1mobile jk ,t + X 'jk ,t γ + α jk + θ t + µ jk ,t absolute value of the log farm-gate price difference between market i and market j at time t* Mobile jk,t variable =1 if the market pair received cell phones in period t, 0 otherwise Mobileon ejk,t variable=1 if one market in pair receive mobile phone coverage, 0 otherwise Transportijt per unit/per km fuel * distance Droughtijt markets i and j at time t θt time effects (monthly or yearly) aij market-pair specific effects u ijt error with 0 conditional mean, E [u ijt|mobile ijt,Xijt, a ij, θt]=0 t time in months, t=1…111 N number of market pairs Yjk,t

Xij,t

*Alternative measures of price dispersion (correlation, covariance) and the treatment variable are also used **Include market-specific fixed effects and cluster by month - -in future will also correct for serial autocorrelation

Effects of Mobile Phones: Cowpeas Table 3. Impact of Mobile Phones on Farm-Gate Price Dispersion for Cowpea Dependent variable: |ln(Pit-)-ln(Pjt)| Mobile coverage both markets Mobile coverage one market

(1) (2) (3) -0.022*** -0.018*** -0.096*** (-1.704) (-5.05) (-18.88) -.011 (1.143)

-0.012*** (-3.02)

-0.030 (-6.92)

(4) -0.020* (-1.86)

(5) -0.026*** (-2.94)

-0.012 (-1.196)

0.002 (.265)

.0413*** (6.021)

Distance between markets

0.027*** (10.8)

Distance*mobile two markets

0.0093*** 0.011*** (2.072) (11.76)

0.010*** (11.36)

0.011 (1.070)

0.005 (.802)

Distance*mobile one market

0.0172*** 0.009*** (4.095) (8.812)

0.007*** (6.82)

0.011 (1.20)

-0.010 (-1.40)

0.131 (17.564)

0.217*** (170)

0.326*** (44)

0.124*** (15.83)

0.197*** (17.5)

Other covariates

Yes

Yes

Yes

Yes

Yes

Monthly fixed effects

No

No

Yes

No

Yes

Market pair fixed effects

No

Yes

Yes

No

Yes

Number of observations

41,070

41,070

41,070

13,646

13,646

Constant

Effects of Mobile Phones: Millet Table 4. Impact of Mobile Phones on Farm-Gate Price Dispersion for Millet Dependent variable: |ln(Pit-)-ln(Pjt)| Mobile coverage both markets Mobile coverage one market

(1) -0.022* (-1.70) -0.010 (-1.14)

(2) 0.000 (.016)

(3) 0.008** (1.98)

-0.019*** -0.017*** (-6.35) (5.18)

(4) -0.03*** (-3.16)

(5) -0.025*** (-4.17)

-0.008 (-.818)

-0.010 (1.56)

Distance between markets

.027*** (10.839)

0.046*** (8.76)

Distance*mobile two markets

0.009 (2.072)

-0.003*** (-4.25)

Distance*mobile one market

.017*** (4.09)

0.003*** (4.67)

Other covariates

Yes

Yes

Yes

Yes

Yes

Monthly fixed effects

No

No

Yes

No

Yes

Market pair fixed effects

No

Yes

Yes

No

Yes

Constant

.120*** (21.9)

.163*** (182)

.169*** (28.4)

.123*** (91)

.142*** (15.6)

Number of observations

41,070

41,070

41,070

13,646

13,646

-0.001 (-1.48)

.018*** (3.20)

0.004*** -0.021*** (5.59) (-2.71)

0.017*** (3.20) -0.006 (-.925)

Mobile Phones, Producer Price Dispersion and Distance Mobileij=0 |ln(Pit)-ln(Pjt) Mobileij=1

Distanceij

Threats to Identification  Selection bias  Hidden bias (conditional independence assumption)  Collusive behavior and entry and exit

Balance of Pre-Treatment Variables Table 5. Comparison of Observables by Mobile Phone and non-Mobile Phone Groups (1999-2001) Difference in Difference in Unconditional Mean Means Distributions

Pre-Treatment Observables

Mobile Phone

No Mobile Phone

Unconditional

Unconditional Kolmogorov-Smirnov Test

Mean (s.d.)

Obs

Mean (s.d.)

Obs

s.e.

D-statistic

p-value

|Pit-Pjt| of Millet Producer Prices (CFA/kg)

15.04(16.05)

4566

15.99(14.8)

3044

-.949(.787)

0.0672***

0

|Pit-Pjt| of Cowpea Producer Prices (CFA/kg)

29.46(28.5)

4566

29.57(27.44)

3044

-.115(1.452)

0.0423***

0.006

Distance between markets (km)

438.75(275)

561

413.86(247)

105

24.89(26.87)

0.0647

0.852

Road Quality between markets

.338(.47)

561

.390(.49)

105

-.052(.052)

0.0518

0.972

Drought in 1999 or 2000

.050(.22)

13464

.052(.22)

2520

-.002(.008)

0.002

1

Urban center(>=35,000)

.346(.476)

561

.305(.46)

105

.041(.049)

0.041

0.998

Transport Costs between Markets (CFA/kg)

12.57(7.3)

13464

11.91(6.6)

2520

.656(.708)

0.0513

0

Panel A. Market Pair Level Data

Balance of Pre-Treatment Variables Table 5. Comparison of Observables by Mobile Phone and non-Mobile Phone Groups (1999-2001) Unconditional Mean

Pre-Treatment Observables

Mobile Phone

No Mobile Phone

Difference in Means

Difference in Distributions

Unconditional

Unconditional Kolmogorov-Smirnov Test

Mean (s.d.)

Obs

Mean (s.d.)

Obs

s.e.

Dstatistic

p-value

99.47(27.8)

327

98.62(31.5)

93

.849(4.91)

0.1579*

0.054

150.91(43.5 9)

327

139.69(39.75)

93

11.22(7.32)

0.2324*

0.063

.588(.49)

34

.4(.49)

5

.188(.24)

0.1882

0.988

Market Size

88.58(80)

34

124(77)

5

-35.41(41.3)

0.4853

0.368

Drought in 1999 or 2000

.052(.22)

816

.025(.16)

120

.027(.026)

0.0277

1

Landline service 1999-2001 (1=Yes, 0=No)

.865(.34)

816

.5(.51)

120

.364(.362)

0.3649

0.962

Urban center(>=35,000)

.382(.486)

34

0(.00)

5

.382***(.084)

0.3824

0.55

Panel B. Market Level Data Millet Producer Price

Cowpea Producer Price Road Quality to Market (1=Paved, 0=Unpaved)

Tests of Conditional Independence Table 6. Tests of the Conditional Independence Assumption Dependent Variable: Price Dispersion in 1999-2001 (Pre-Treatment) Cowpea

Millet

Coeff(s.e.) Coeff(s.e.)

Unconditional difference in means

-.116 (1.45)

-.948 (.788)

Conditional difference in means

-.124 (1.43)

-.890 (.793)

Estimating the Impact of Mobile Phones on Farmers’ Welfare ln( priceijt ) = β 0 + β1mobile jt + X 'jt γ + α j + θ t + µ jt

Ln(price) j,t Mobile jt Xijt θt

u ijt t N

log of farm-gate price of commodity i in market j at time t variable =1 if the market received mobile phone coverage in period t, 0 otherwise vector of exogenous regressors of market j at time t time effects (year) error with 0 conditional mean, E[uijt|Zijt, Xijt, ai, aj, θt]=0 t=0,…11 number of markets, N=42

Table 7. Impact of Mobile Phones on Farm-Gate Price Levels for Cowpea

Dependent variable: ln(Pit)

(1)

(2)

(3)

.036 (.029)

.001 (.0178)

.036 (.030)

Lagged dependent variable

No

No

No

Other covariates

Yes

Yes

Yes

Monthly fixed effects

Yes

Yes

Yes

Market fixed effects

No

Yes

Yes

Distance to Nigerian border

No

No

Yes

41,070

41,070

41,070

Mobile coverage both markets

Constant Number of observations

Welfare Estimates  Less than 10 percent of villages had mobile phone access in 2007/2008  Mobile phone coverage and adoption increased during the period after this study (2008-2010)  While no evidence of collusion in consumer prices, perhaps market power differs in consumer and producer markets