Rwanda Green Leaf Price Reform Impact Evaluation Baseline...

Rwanda Green Leaf Price Reform Impact Evaluation

Baseline Report

March 2015

Investment Climate Impact Program

Contents

1. Overview ................................................................................................................................................. 1

1.1 Introduction ............................................................................................................................ 1

1.2 Tea in Rwanda ......................................................................................................................... 1

1.2.1 Use of evidence base by Rwanda authorities .................................................................... 3

1.3 World Bank Group Support of the Tea Sector Reforms .......................................................... 4

1.4 Objectives................................................................................................................................ 5

1.4.1 Objectives of the impact evaluation ........................................................................... 5

1.4.2 Policy perspective ....................................................................................................... 6

2. Methodology ........................................................................................................................................... 7

2.1 Randomization and Sample Design ........................................................................................ 7

2.2 Sample Size and Strategy ........................................................................................................ 8

2.3 Instruments for Data Collection and Data Quality Assurance ................................................ 8

2.4 Data Storage, Management, and Access Policy ...................................................................... 9

3. Sample Representativeness and External Validity of the Survey ........................................................... 9

3.1 Comparison of the 2013 and 2004 Tea Sector Surveys .................................................... 10

4. Findings of the Tea Sector Baseline Survey .......................................................................................... 13

4.1 Demographic Profiles of Tea and Non-tea Farmer from 2013 Baseline Survey ............... 13

4.2 Land Holdings and Size ...................................................................................................... 15

4.3 Livestock Ownership, Crop Activity, and Labor Usage of Farmers .................................... 16

4.4 The Pattern of Baseline Tea Prices .................................................................................... 19

4.5 The Pattern of Baseline Tea Production ............................................................................ 20

4.6 Conclusion ......................................................................................................................... 21

5. Internal Validity of the Study ................................................................................................................ 21

5.1 Intra-correlation Coefficients and Design Effects ............................................................. 22

5.2 The Measurement of Land Size ......................................................................................... 23

5.3 Respondent Responses for the Non-heads of Household ................................................ 24

6. Other Descriptive Findings from Baseline Survey: Requested Sector Evidence by GoR ...................... 24

6.1 Household Income Distributions ..................................................................................... 24

6.2 Household Income Determination of Rwandan Farmers in the Tea-growing Areas ....... 29

6.3 Tea Reforms and the Non-tea Producers ........................................................................ 33

6.4 Subjective Measures of Household Welfare for Rwandan Farmers ................................ 37

6.5 Some Concluding Remarks .............................................................................................. 40

7. Recommendations and Suggestions for Follow-up Survey .................................................................. 41

Annex 1: Tea Growing Districts ........................................................................................................ 43


Annex 2: Tea Growing Sectors Covered by the Survey .................................................................... 44

Annex 3: Mean Differences between Co-op and non-Co-op Thé Villageois Farmers...................... 45

Annex 4: Household Income Determinants for Female Farmers .................................................... 46

Annex 5: Household Income Determinants for Farmers with Health Disability .............................. 48

Annex 6: Probit Models Explaining “Yes” Outcomes in Table 6.8 ................................................... 50

List of Figures:

Figure 1 Tea Production in Rwanda (1961–2012) …………………………………………………………………………….. 1

Figure 2 Tea Area Harvested (1961-2012) .............................................................................................. 1

Figure 3 Tea Yield in Rwanda vs. in Kenya and Uganda .......................................................................... 3

Figure 4 Estimated Revenues for Smallholder Crops on 0.25 ha (RWF ‘000s, 2011) ............................. 4

Figure 5 Theory of Change for Rwanda’s Tea Sector Price Reform ........................................................ 5

List of Tables:

Table 3.1: Main Economic Activity of Head of Household by Tea Sector Survey ................................. 10

Table 3.2: Demographic Profiles of Head of Household by Tea Sector Survey .................................... 10

Table 3.3: Total Land Size Holdings of Farmers (in Hectares) ............................................................... 12

Table 3.4: Household Incomes for Rwanda Farmers (RWF at 2010 Prices) .......................................... 12

Table 4.1: Summary of Economic Activity of the Head of Household .................................................. 13

Table 4.2: Summary of Main Occupational Activity of the Head of Household ................................... 14

Table 4.3: Demographic Profile of Tea and Non-tea Farmers (Main Occupation) ............................... 14

Table 4.4: Land Size Profile for Tea and Non-tea Farmers .................................................................... 16

Table 4.5: Livestock Ownership of Farmers 12 Months Prior to Interview .......................................... 17

Table 4.6: Crop Activity of Farmers 12 Months Prior to Interview ....................................................... 18

Table 4.7: Labor Usage by Farmers ....................................................................................................... 19

Table 4.8: Average Baseline Price per Kilogram for Green Leaf Tea ..................................................... 19

Table 4.9: Baseline Price per Kilogram by Tea Holding Type ................................................................ 20

Table 4.10: Baseline Tea Production Yield ............................................................................................ 21

Table 5.1: Intra-cluster Correlations for Selected Variables for the 2013 Tea Survey ......................... 22

Table 5.2: Propensity Score Matching for Main Plot Size Measured in Log Hectares .......................... 23

Table 6.1: Household Income and Wealth Profile of Tea and Non-tea Farmers .................................. 25

Table 6.2: Real Household Incomes in the Tea Growing Areas of Rwanda 2004 and 2013 ................. 29

Table 6.3: Determinants of Log Annual Household Income for Tea and Non-tea Farmers ................. 31

Table 6.4: Quantile Regression Estimates for Tea Farmer Effect ......................................................... 32

Table 6.5: Attitudes & Perceptions of Tea Reforms among Non-tea Farmers ..................................... 34

Table 6.6: Minimum Green Leaf Price per Kilogram Required to Encourage Non-tea Farmers to

Cultivate Tea ......................................................................................................................................... 34

Table 6.7: Probit Model for Non-tea Farmers’ Reluctance to Cultivate Tea ........................................ 34


Table 6.8: Factors Preventing Engagement of Non-tea Farmers in Tea Cultivation ............................. 36

Table 6.9: Subjective Perceptions of Farmer Well-being ...................................................................... 37

Table 6.10: Ordered Probit Model of Farmer Satisfaction with Living Standards ................................ 38


Executive Summary

The Government of Rwanda (GoR) views the tea sector as central to the country’s economic

development across a number of key dimensions, including its potential to raise smallholder

farmer incomes (and thus reduce poverty), provide good returns and investment

opportunities for private investors, and assist the country in meeting its balance of

payments targets. Indeed, the tea sector is now the third largest employer in Rwanda and

tea is one of the country’s principal exports. In response to the constraints identified in the

first Tea Sector Strategy, specifically those related to farmers’ perceived low level of

remuneration for their green tea leaves, the GoR reformed the pricing mechanism for green

leaf from a factory-cost basis to a more transparent pricing mechanism that directly links

the prices paid to farmers to the international market price for “made tea.”

The World Bank Group (WBG) is conducting an evaluation study with the Ministry of

Agriculture and Animal Resources (MINAGRI), its National Agriculture Export Board (NAEB),

with the assistance of the National Institute of Statistics (NISR). The study aims to rigorously

evaluate the effects of the reform of the green leaf team pricing mechanism on tea

production, tea quality, and the living standards of Rwandan smallholder tea farmers. The

study exploits household survey data—baseline and follow-on in late 2015—to inform a set

of research questions around the impact of the reform. The baseline household survey was

conducted in October 2013 and this report summarizes the process and outcomes of the

baseline survey as well as additional data analysis on determinants of observed baseline

patterns and farmer perceptions of a number of key variables flagged by MINAGRI.

Key findings of the 2013 Rwanda Tea Sector Baseline Survey for the impact evaluation focus

on a comparison of profiles and outcomes between tea farmers and non-tea farmers to

provide a sense of differences or commonalities across these two groups. Analysis of the

baseline survey data suggests that Rwandan tea farmers are, on average, a more affluent

group compared to their non-tea growing counterparts, in terms of household income,

household assets, livestock holdings and land holdings. Tea farmers also tend to be more

diversified in terms of their cropping activities and they use 20 percent more hired labor

than non-tea counterparts. The majority of tea farmers are in cooperatives and there are

sizeable variations in revenues across these cooperatives in the data. There is a strong

gender dimension to tea revenues with female–headed households receiving 24 percent

less than male-headed households, on average. The analysis looks at some of the

determinants of these observed patterns and also examines farmer perceptions of the tea

sub-sector and the reforms. A key finding is the lack of any knowledge of the price reforms

among the overwhelming majority of non-tea farmers. The majority of non-tea farmers

surveyed are reluctant to engage in tea cultivation at any price, citing access to land, a lack

of tea production expertise, and other more profitable crops as key explanations for their

reluctance. Finally, the baseline report also yields key recommendations for the 2015 follow

on survey on which the final impact evaluation will be based.

1

1. Overview

1.1 Introduction The impact evaluation study aims to rigorously evaluate the effects of the reform of the

green leaf team pricing mechanism in Rwanda on tea production, tea quality, and the living

standards of Rwandan smallholder tea farmers. The evaluation will assess whether, and to

what extent, the tea pricing reforms recently implemented in Rwanda have impacted tea

revenues, farmer incomes, farmer wealth assets, productivity, and expansion. The study

exploits household survey data to inform a set of research questions relevant to the above

themes. The baseline household survey was conducted in October 2013 and will be

complemented by an end-line survey in December 2015.

1.2 Tea in Rwanda Tea cultivation was introduced in Rwanda in 1952. Since then, production has increased

steadily from a mere 160 tons in 1961 to 22,503 tons in 2012, with the exception of a

production decline during the 1994 genocide. Growth in production mirrors growth in areas

under cultivation, as well as moderate yield gains over this same period (FAOSTAT) (Figures

1 and 2). Over 90 percent of production is exported, making tea one of the country’s

principal exports. The tea sector is now the third largest employer in Rwanda, behind coffee

and the public sector, and currently employs about 60,000 people. The Government of

Rwanda (GoR) views the tea sector as central to the country’s economic development

across a number of key dimensions, including its potential to raise smallholder farmer

incomes (and thus reduce poverty), provide good returns and investment opportunities for

private investors, and assist the country in meeting its balance of payments targets.

Figure 1 Tea Production in Rwanda (1961–2012) Figure 2 Tea Area Harvested (1961-2012)

Source: FAOStat

Despite its importance to the economy, Rwandan tea accounts for only a small share of the

total volume traded globally, making Rwanda a price taker on the international market.

Currently, tea is grown on 14 estates comprising a total area of approximately 14,000

hectares. An estate is a tea-producing unit including a factory, a plantation, private tea

plots, and an associated forest to provide fuel wood to the factory for tea processing.

-5

0

5

10

15

20

25

30

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n (

00

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Tea Production (000' Tonnes)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

1961 1968 1975 1982 1989 1996 2003 2010

Area harvested (000' Ha)

Source: FAOstat

2

However, not all estates have all of these components; for instance, some do not own

plantations. All tea plantations are located near factories because of the perishable nature

of tea, which requires processing within a few hours of harvesting. Tea production is

primarily organized into three types of farming systems:

1. Industrial blocks: these are large plantations integrated into a processing plant. In total, they account for 31 percent of the area under tea cultivation. They employ wage labor;

2. Coopthés: These are also plantations, but owned by cooperatives. They employ a mixture of family and wage labor. They account for 8 percent of cultivated area. Cooperatives play a key management role in the tea cultivation and production processes. They distribute fertilizers, collect and deliver tea leaves to the factory, pay pluckers and the growers themselves, and redistribute surplus earnings to members;

3. Thé villageois: These are smallholder tea plots essentially relying on family labor. They account for 61 percent of the total area under tea cultivation. The majority of private smallholders are also organized under cooperatives. As with the coopthés, these cooperatives organize importation and distribution of fertilizers, and they facilitate access to credits and transportation of tea leaves. Production decisions, however, reside completely in the hands of the individual smallholder households.

Prior to 2003, all tea factories and the associated plantations were owned by the state.

OCIR-Thé, a parastatal directly responsible for the production, processing, and marketing of

tea, was in charge of managing the 10 state-owned factories. An eleventh estate and

factory, established in 1972, has always been privately owned by SORWATHE.

Since 2003, the GoR has gradually privatized the tea industry and restructured OCIR-Thé.

Two new (private) factories were established. OCIR-Thé assumed a new institutional role,

and many of its capacity constraints have been addressed. By 2007, the industry had

succeeded in raising productivity, particularly through improved fertilizer application. During

the same year, the size of the tea industry increased to $32 million compared to $23 million

in 2003, representing a 39 percent increase.1 The privatization process was finally

completed in 2012 with the privatization of the two remaining state-owned factories.

Some of the problems facing the sector prior to privatization, however, remained relevant.

Key challenges flagged by the first Tea Sector Strategy for Rwanda (2003), still remained

unresolved by the end of the privatization in 2012. In general, the industry was considered

to have remained in a low-quantity-low-quality trap. The factors underpinning this were

identified as:2

Tea farmers’ perception that they were not adequately remunerated for the quality of their green leaf delivered to the tea factories;

Tea farmers’ production of low-quality green leaf because of poor farming practices;

1 Ministry of Agriculture and Animal Resources. 2008 . A Revised Tea Sector Strategy for Rwanda –

Transforming Rwanda’s Tea Industry (MINAGRI: Kigali). 2 Ibid.

3

Tea farmers’ limited funds to invest in working capital for fertilizers and transportation;

Low capacity in tea husbandry and plantation management; Tea factories at maximum processing capacity, both qualitatively and quantitatively; Lack of investment in marketing for direct sales and in diversification of Rwanda’s

tea product portfolio.

The issue of price incentives to farmers formed the primary motivation for the

government’s reform of the green leaf tea pricing policy—the object of this impact

evaluation. At the time of the baseline report, the remainder of the above challenges still

remain, although to differing degrees. For instance, the yield in Rwanda remains low

compared to other producing countries in Africa, as shown in Figure 3.

Figure 3 Tea Yield in Rwanda vs. in Kenya and Uganda

1.2.1 Use of evidence base by Rwanda authorities The Ministry of Agriculture and Animal Resources (MINAGRI) has an interest in, and is

committed to, evidence-based policy formation. This is clear from the depth and variety of

impact evaluations conducted for the agricultural sector in Rwanda. MINAGRI is currently

conducting rigorous evaluations of agricultural financial products, extension services, feeder

roads, and it uses impact evaluation methodologies to measure the causal effects of its large

Land Husbandry, Water Harvesting, and Hillside Irrigation project. Most of the impact

evaluations are ongoing, but lessons learned and preliminary results have been taken into

account in project scale-up decisions, which have been shared with other stakeholders in

the sector. Furthermore, MINAGRI was very keen to use the baseline data of this impact

evaluation on the green leaf tea price reforms, to inform their current tea expansion

strategy. This led to the survey instrument including questions on farmer perceptions of the

reforms and their responses to different levels of price incentive. MINAGRI further

requested cross-sectional analysis based on these data to feed into its policy decisions

around the tea sector strategy in 2014, and that analysis forms a part of the deliverable to

the government, in addition to this baseline report.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

19

61

19

65

19

69

19

73

19

77

19

81

19

85

19

89

19

93

19

97

20

01

20

05

20

09

Yie

ld (

ton

s/ h

a)

Kenya Rwanda Uganda

Source: FAOstat

4

1.3 World Bank Group Support of the Tea Sector Reforms There have been four distinct phases in IFC Advisory Services’ support of the tea sector in

Rwanda, as part of broader World Bank Group (WBG) support of the sub-sector (tea) and

the sector (agriculture) at large: (1) an investor conference in November 2010; (2) the

privatization strategy note for the two publicly owned factories remaining in 2011; (3) the

green leaf pricing reform of July 2012; and (4) the collaboration with the World Bank on a

WBG concept note in support of the GoR’s further tea sector expansion program,

envisioning 18,000 additional hectares of tea cultivation under private management. The IFC

interventions were delivered under the second phase of the Rwanda Investment Climate

Reform Program, which aims to improve agribusiness market efficiency through market,

legal, and regulatory remedies. This second phase of the program drew on the recent gains

from extensive Doing Business reforms,3 and it is a part of a broader WBG program to

increase exports and investment in Rwandan agribusiness. These efforts include

considerable World Bank support to the GoR’s investments in productivity, capacity, and

irrigation infrastructure across the country. The impact evaluation looks specifically at the

green leaf tea pricing reforms for which WBG assistance was requested by the GoR.

In response to the constraints identified in the first Tea Sector Strategy, specifically those

related to farmers’ perceived low level of remuneration for their green tea leaves, MINAGRI

undertook to reform the pricing mechanism for green leaf. Up to that time, the price for

green leaf was determined annually, and the amount paid to farmers for their green leaf tea

was based on the self-declared costs of a factory or cooperative. The system was generally

viewed as cumbersome and opaque. The price paid to farmers, furthermore, was not linked

to the market price and was low by comparison to the benchmark provided by other tea-

producing countries. This had the effect of discouraging farmers from planting and

harvesting tea and investing in their plots and the quality of their output, given higher

returns could be obtained from cultivating alternative crops (Figure 4).

Figure 4 Estimated Revenues for Smallholder Crops on 0.25 ha (RWF ‘000s, 2011)

Source: FAOStat; Rwanda National Agricultural Export Board (tea)

3 In FY10, under Phase 1, Rwanda became the global top Doing Business reformer and made Doing Business

reform history by improving 76 ranks in one year. In the 2014 report, Rwanda is ranked 67th

of 183 countries. In addition to ease of doing business, impressive gains were made in important areas such as reducing the time and cost to trade, obtaining business licenses, registering a business, and the overall strengthening of the legal, regulatory, and policy framework for special economic zone developments in the country.

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400

600

800

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5

The GoR requested technical assistance from the WBG’s IFC Advisory Services to inform and

structure the reform of the green leaf pricing mechanism. In July 2012, the GoR passed a

radical reform to the pricing mechanism with this technical support. The reform consists of a

more transparent pricing mechanism that directly links the prices paid to farmers to the

market price for “made tea.” The made-tea price is now based on a weighted average price

of all Rwandan tea sales on the market, of which farmers receive a fixed percentage

(currently 30 percent), as opposed to the factory-cost model described above. These prices

are now set every six months rather than on an annual basis as previously. In addition, in

order to encourage farmers to improve the quality of their harvested green leaves, farmers

also receive a bonus depending on whether or not their tea exceeds a quality threshold.

Thus, the policy is designed to increase both the quantity and quality of tea produced by

farmers. The key implementers are MINAGRI and the Rwanda National Agricultural Export

Board (NAEB).

1.4 Objectives 1.4.1 Objectives of the impact evaluation

The objectives of the impact evaluation are linked to the objectives of the reform. The green

leaf price reform was undertaken by the GoR with the objectives of increasing farmers’

incomes and enhancing incentives to raise the productivity and the quality of the raw

material utilized by the tea factory. In addition to the government’s rural poverty alleviation

objectives, price reform was seen as critical to improving the efficiency and competitiveness

of Rwandan tea factories and increasing tea export revenues (MINAGRI, 2012).4

The purpose of the impact evaluation study is to evaluate the impact of tea price reform

measures on tea production, farmer tea revenues, and a range of farmer welfare metrics.

The impact evaluation is conducted for the introduction of a market-based green leaf pricing

mechanism for farmers, which was passed in Cabinet in July 2012 and became effective in

September 2012 after clearing the legislative process. The reform is expected to have the

intermediate outcomes of improving prices, quality, productivity, and output volumes. The

intermediate outcomes in turn are expected to lead to higher farmer revenues, growth in

exports and jobs, and greater food security (Error! Reference source not found., in black,

evaluation aims).

Figure 5 Theory of Change for Rwanda’s Tea Sector Price Reform

4 MINAGRI. 2012. Cabinet Briefing Paper: New Green Leaf Tea Pricing Model to Tea Farmers in Rwanda.

Activities

•green leaf price reform

Intermediate Outcomes

•improved prices

•improved quality

•improved productivity

•increased output

Higher Order Outcomes

•higher farmer revenues

•growth in exports

•growth in jobs

•greater food security

6

The green tea price reform activities are expected to result in intermediate outcomes and

ultimately, higher order outcomes by driving the following impacts:

Direct impact: Assuming that the price reform is implemented as planned, it will have a

direct impact in increasing farmer revenues by raising the prices received by farmers. The

assumption is that the old factory-cost model suppressed farmer returns and also isolated

farmers from market signals.

Indirect impacts: Increased tea prices following the reform are expected to affect revenue

indirectly through the incentive effect, encouraging greater on-farm investment.

Investments in tea farms (for example, in labor, land husbandry, improved inputs) are in

turn expected to improve farm productivity, thereby impacting farmer revenues through

increased quantities produced. Furthermore, the fact that prices are now made to reflect

differences in quality is expected to incentivize farmers to invest more in quality

improvement. This in turn is expected to bring additional higher prices for farmers, thus

providing a third channel to increasing farmer revenues. The final channel is through the

incentive effect anticipated that higher prices will induce new farmers to start producing

tea.

While the direct impact of the price reform is expected to affect all types of farmers in the

same manner, the indirect impact may work differently for independent smallholder

farmers (thé villageois) and farmers under cooperatives (coopthés). The structure of

production decision-making is different across these two groups of tea farmers, and the

response to incentives may differ as well. As part of the cross-sectional analysis promised to

the GoR from the baseline data, the report also informs on differences in price and

production by tea-farming system. Given the hypothesis of change and resulting impacts

following the reforms, and the additional analysis requested by GoR, the impact evaluation

study aims to answer the following specific questions:

- Are farmers actually being paid higher prices compared to the price levels before the reform, and to what extent have these raised household revenues?

- Are there any changes in production levels before and after the reform? Are these due to improvements in productivity or expansion in cultivation, or both?

- Did the price reform lead to improvements in productivity? - Are productivity improvements due to the expected increase in on-farm

investments?

The design of the survey instrument and the impact evaluation study to answer these

questions is discussed in the next section.

1.4.2 Policy perspective The study will contribute to GoR’s objectives for the tea sector (as well as resource

allocation) by supporting evidence-based policy making. The results of the impact

evaluation of Rwanda’s tea sector reforms are intended, at the government’s request, to

inform the GoR’s agricultural policy around pricing regulation and incentives for different

commodities. This is true of the final results, as well as for the immediate baseline study. As

mentioned, further to the impact evaluation questions, the baseline data will help to inform

7

key questions posited by the GoR in its current tea policy work. These include: what is the

level of awareness of reforms among farmers; what price level would induce farmers to stop

producing tea; what price level would induce them to increase production; what other

factors will motivate farmer participation in a potential tea expansion; and, are there

existing cross sectional differences between farmers according to type of farming system or

cooperative arrangement that affect incentives even at baseline? Results on the

demography of tea producing households and their use of labor through the production

cycle can inform on rural labor markets effects, impacting broader policies in the sector.

Beyond Rwanda, the outcomes of the impact evaluation can inform and motivate similar

price mechanism reforms in agricultural commodity markets where outmoded mechanisms

persist. The vast scope for reform in agricultural prices has been extensively documented,

with a long history of price distortion in every region of the world, spanning from farm gate

to export prices. 5 The factory-cost model reformed in Rwanda is common to many

regulated commodity price regimes. The results of the impact evaluation can therefore add

to the evidence basis for the reform of similar types of pricing regulations elsewhere in the

world.

2. Methodology

2.1 Randomization and Sample Design The study relies on a random sample of households within the tea-growing areas of

Rwanda. As each household within the target population had an equal chance of selection,

the sample is representative of the underlying population in the tea-growing areas

(including both tea and non-tea producers).

The study used the 2004 Rwanda tea sector survey Enquête Quantitative de Base auprѐs des

Ménages des zones Théicole (EQBT) as the template in designing the sampling strategy for

the 2013 baseline survey. Accordingly, the baseline closely follows the geographic coverage,

sample size, and sampling strategy of the 2004 survey. The target population is all rural

households (both tea-producing and non-tea producing) in tea-growing areas within the

production radius of Rwanda’s 14 tea factories. The EQBT covered 12 of these 14 factories.

The two new tea growing areas correspond to the two new tea factories opened after 2004

(see Annexes 1 and 2).

Multi-stage cluster sampling was used to randomly select 2,062 households. To start, all tea-

growing administrative sectors corresponding to each of the 14 tea factories were

purposively selected based on a list provided by NEAB, for a total of 71 sectors of which 63

(or 89 percent) were drawn randomly.6 Within each of these sectors, cells, villages and

households were selected using multi-stage randomization. In the first stage of the

randomization, administrative cells were selected randomly from each sector, such that the

number of cells is proportionate to the size of the sector (on average, two cells were 5For a recent landmark study, see Kym Andersen, ed. 2009. Distortions to Agricultural Incentives: A Global

Perspective, 1955–2007. (Washington, DC: Palgrave-MacMillan and The World Bank). 6 The plan was to cover the entire list of 71 tea-growing sectors provided by NEAB; however, the size had to be

reduced to 63 to accommodate logistical considerations. Still this covers about 89 percent of the total list of tea-growing sectors provided by NEAB.

8

selected from each sector). In the second stage, two administrative villages were randomly

selected from each of the selected cells. In the third stage, eight households were selected

randomly from each of the selected villages. Household selection was done using a walking

pattern where, in a given village, the supervisor assigned a specific walking direction to each

enumerator in the team by using a random reference point in the village. Each enumerator

then used five intervals in selecting a household. Within this walking pattern, specific

protocols were provided for replacing households where either respondents were not

available or refused to be interviewed.

In 55 of the selected cells, both villages randomly selected were found not to grow tea. In

these cases, field teams worked with village officials to categorize all villages in the cell as

either tea-growing or non-tea-growing. One village was then randomly selected from each

list, to replace each of the two originally sampled villages.

2.2 Sample Size and Strategy The overall sample size for the current study was based on power calculations detailed in

Section 5, below. Given the clustered sampling design described above, 2,000 households is

more than sufficient to statistically distinguish effects in the outcomes in which the

evaluation is most interested. Unsurprisingly, given the deliberate similarities in sampling

frame and desired outcomes, the overall sample size is similar to that of the EQBT. (The

implications of the design effects associated with the sample size are explored in more

detail in Section 5.)

2.3 Instruments for Data Collection and Data Quality Assurance The time frame for the analysis will span the effective introduction of the price reforms in

October 2012 to end-2015, when a follow-up survey is envisaged. The Cabinet decision of

July 2012 only cleared the legislative process in September 2012, with implementation

following shortly thereafter. This implied a one-year recall period for the surveyed

households for impact evaluation baseline conducted in 2013. The 2013 baseline survey was

specifically designed to collect recall data from the period before the reform

implementation started. A one-year lag in recall is common in annual agriculture surveys

(which require recall over the past seasons), and research shows little evidence of recall bias

affecting data quality.7 However, the impact evaluation will also use factory-level data on

quality, quantities, and prices to triangulate the survey data described here.

The questionnaire was designed to ensure that adequate information was collected from

each household to facilitate the empirical analysis for the impact evaluation, but that the

length was short enough to minimize interviewee fatigue. The questionnaire comprised

eight separate sections. The first section contained household and interview identification

information necessary for a follow-up survey. The second section contained household

roster information. A third section contained questions on household location, housing

quality, and household assets. A fourth section asked questions on household income from

7 Beegle, Kathleen; Carletto, Calogero; Himelein, Kristen. 2011. Reliability of recall in agricultural data. Policy Research

working paper ; no. WPS 5671. Washington, DC: World Bank. http://documents.worldbank.org/curated/en/2011/06/14286199/reliability-recall-agricultural-data

9

different sources and household expenditure activity, both for the last month and the last

year. Section five solicited information on household land holdings and agricultural

activities. Section six only applied to tea farmers and contained questions on various

aspects of their tea production activity. Section seven asked questions of all farmers on the

recent privatization and price reforms, while a final section asked respondents for an

assessment of their household living standards, both currently and in terms of changes over

the last 12 months. The average duration of interviews for non-tea-famers was about 45

minutes and 75 minutes for tea farmers.

The data were collected by the interviewers electronically using hand-held phones. This

ensured a high degree of quality control in that this method eliminates transcription errors

and ensures the correct sequence of questions is being asked by the interviewer. The

resultant data were transmitted back to the survey team coordinators at the end of each

day. The research team was forwarded data collected in the field on a weekly basis, and

conference calls were scheduled regularly to review any quality or other issues that

emerged in the data collection process.

2.4 Data Storage, Management, and Access Policy All household survey data collected for the impact evaluation will be filed in the World Bank

Microdata library (microdata.worldbank.org), in accordance with the World Bank’s Open

Data Policy. All personally identifying information will be removed from the datasets before

publication to the Microdata library. Once filed, the data will be publicly accessible.

3. Sample Representativeness and External Validity of the Survey

The 2004 EQBT and this baseline survey relied on the same sample frame, all smallholder

tea farmers in the sectors associated with tea factories.8 Comparisons between the two

surveys therefore yield useful descriptive evidence on changes in the sector which help to

inform the analysis. In addition, comparisons of selected household demographic profiles in

the tea-growing areas allow an assessment of the extent to which they are either

comparable or have evolved. Thus, while the EQBT is not directly exploited as part of the

impact evaluation for the price reforms, it does provide some valuable external information

against which our data can be compared and selected outcomes validated. In particular, it

can be used to validate the study’s use of recall data as a baseline, because there are few

observable changes between the two surveys that can’t be explained by general

demographic shifts in Rwanda.

To test the comparability of the populations, we first examine the degree to which the

sampling design described in section 2.1 yielded a comparable share of tea farmers to that

obtained in 2004. Second, we assess the size of landing holdings reported in the tea growing

areas. As the size of such holdings has moved glacially in Rwanda overall, little change would

be anticipated across these two surveys. Third, we investigate how farmers’ real incomes

have evolved over the last nine years and assess how this compares with the national trends

in agricultural incomes reported for Rwanda over the same period. Finally, we use selected

8 Essama-Nassah et al. (2008) provides a description of the EQBT survey. The design effects of the two surveys are

compared in section five.

10

outcomes from the 2004 survey to compare with some preliminary descriptive analysis

undertaken in section six.

3.1 Comparison of the 2013 and 2004 Tea Sector Surveys Table 3.1 reports the proportion of the sample across three aggregate economic activity

categories for the two years. The more recent sample comprises a slightly larger share of

households that are directly engaged in tea sector activities, broadly defined.

Table 3.1: Main Economic Activity of Head of Household by Tea Sector Survey

Main Activity of the Household Head Tea Sector Survey 2004

Tea Sector Survey 2013

Tea Related Agricultural Activity 0.185 0.234

Non-tea Related Agriculture Activity 0.545 0.610

All Other Activities 0.270 0.156

Sample Sizes 2,062 2,108 Notes to table 3.1: ‘All Other Activities’ in 2004 include ‘not in work’, which represents more than one-half of this category.

This category did not feature in the 2013 survey.

Table 3.2 compares selected demographic profiles of the heads of household across the two

surveys. The average household size has remained relatively stable over the two years. The

average head of household is 1.4 years older in the later compared to the earlier year and

the average differential is statistically significant (|t| = 3.0). The proportion of female-

headed households has fallen by almost five percentage points, with the reduction again

found to be statistically significant (|z| = 3.4). The marital status pattern has also altered

sharply with fewer heads now reported as widowed. In addition, the proportion of

household heads reporting no education at all has fallen by nine percentage points over the

relevant period. The differences noted in these average outcomes most likely reflect

changes in outcomes with respect to these variables within the underlying population rather

than sampling procedures. In particular, the average age, gender, and marital status

patterns reported are to be anticipated given the country’s recent history of genocide, while

the growth in human capital levels is also reflective of the widening access to education

evident in Rwanda in more recent decades.

Table 3.2: Demographic Profiles of Head of Household by Tea Sector Survey

Characteristics Tea Sector Survey 2004


Age in years of Head of Household (HoH) 45.7 (15.8)

47.14 (15.3)

HoH Female 0.289 0.241

Marital Status of HoH:

Married 0.697 0.779

Widowed 0.257 0.179

Other 0.046 0.042

Educational Background of HoH:

11

Characteristics Tea Sector Survey 2004


No Education 0.424 0.334

Complete or Some Primary Education 0.491 0.561

Other (including Secondary & Higher) 0.095 0.105

Household Demographics:

Number of Household Members 5.16 (2.26)

5.40 (2.26)

Sample Sizes 2,062 2,108

Table 3.3 reports the total landholdings for farmers who own their own land measured in

hectares. The 2013 survey validated respondent estimates with information from land title

deeds, which contain accurate information on plot sizes, in about one-half of the

interviews.9 The analysis reported in section 5.2 below provides some tentative evidence of

a tendency for respondents to over-estimate their land holdings so those measurements not

triangulated by the land documentation may be over-estimated. Land sizes reported in the

2004 survey were entirely self-reported estimates provided by respondents, so that the

current baseline survey employed more robust data collection on this variable. It should be

noted, however, that in the case of 15 percent of famers, no size holdings for any plots were

reported and this is reflected in the reduced sample sizes reported in Table 3.3, below.

Nevertheless, there is close concordance in land holding patterns across the two surveys. In

particular, average and median land size holdings remained fairly stable over this period.

The average farm in both years had approximately three-quarters of a hectare, while the

median was 0.4. There does appear to have been a decline in the inequality of land holdings

among farmers with both the upper/lower decile ratio falling. However, the difference is

not found to be statistically significant. Therefore, on balance, there appears to have been

little movement in either average land holding size or its dispersion.

9 Approximately half of the respondents did not provide interviewers access to land title deeds.

12

Table 3.3: Total Land Size Holdings of Farmers (in Hectares)

Mean & Quantiles Tea Sector Survey 2004


Average 0.773 (2.44)

0.760 (1.26)

10th Percentile 0.050 0.077





90th/10th Percentiles 30.0 22.8

Gini Coefficient 0.621 (0.031)

0.593 (0.013)

Sample Sizes 1,463 1,443 Notes to table 3.3: Standard deviations/errors are reported in parentheses.

The household income measure used for our comparative analysis is the sum of gross

incomes earned by the household in the last 12 months from (i) own farm activity, (ii)

providing labor services to other farms, and (iii) non-farm enterprise activity (including from

any salaried employment). We can directly compare this gross income measure across the

two surveys. There has been a real increase in this income measure of about 20 percent

over this nine-year period (almost 2 percent growth per annum). This aligns with similar

estimates by the Thematic Report on Income for Rwanda10 from 2012, which reported an

increase of about 23 percent nationally in agricultural incomes between 2005/6 and

2010/11 [see National Institute of Statistics of Rwanda (2012)]. The magnitude of income

inequality observed in the income data in Table 3.4 appears to have remained relatively

stable over this nine-year period.

Table 3.4: Household Incomes for Rwanda Farmers (RWF at 2010 Prices)



Average 146515.3 (325301.8)

174607.3 (259850.1)

10th Percentile 13834.6 17086.1

25th Percentile 40133.3 42715.3

50th Percentile 92783.1 102516.8

75th Percentile 173248.9 211868.0

90th Percentile 306069.2 375895.9

90th/10th Percentile Household Income 22.1 22.0

Gini coefficient for Household Income 0.554 0.550

10

The recent Thematic Report on Income for Rwanda based on the EICV3 conducted in 2010/11 gives more complex net income estimates, but proved to be very data intensive for the baseline survey purposes. The baseline gross income estimates do not include any estimate of the home consumption of food and non-food products as reported in the EICV3 report. Therefore, the income estimates reported here are not directly comparable to those contained in the above report.

13



(0.066) (0.029)

Sample Sizes 1,463 1,604 Notes to Table 3.4: (a) The household income are all expressed in 2010 prices using the Rwanda consumer price index; (b)

the sample is comprised of all farmers in both years; (c) standard deviations/errors are reported in parentheses; (d) the

sample sizes are different relative to Table 3.3 given the number of missing values for land size.

4. Findings of the Tea Sector Baseline Survey

This section summarizes some of the key findings of the 2013 Rwanda Tea Sector Baseline

Survey for the impact evaluation. The emphasis in this section is on a comparison of profiles

and outcomes between tea farmers and non-tea farmers to provide a sense of any

differences or commonalities across these two groups. As mentioned, the survey was

conducted after the introduction of the green-leaf price reform and relies on recall data to

describe household-level and other outcomes which pre-date the reform. These are either

outcomes that are immutable over such a short period of time or relate to time periods

prior to the implementation of the price reforms.

The next sub-section provides demographic profiles of these two farmer types (4.1), which

is followed by five sub-sections that focus in turn on land holdings (4.2), agricultural activity

profiles (4.3), baseline (pre-reform) tea prices both overall and by co-operative (4.4),

baseline tea production yields (4.5), and a summary of conclusions (4.6).

4.1 Demographic Profiles of Tea and Non-tea Farmer from 2013 Baseline Survey Table 4.1 provides the frequency distribution for the main economic activities of the heads

of household as reported in the 2013 baseline survey. Over 86 percent of surveyed

households report the head as engaged in agricultural activities either on their own or

someone else’s farm. This compares to about 90 percent nationally as reported in the EICV3

conducted in 2010/11. The sample sizes for the responses in the other non-agricultural

activities are negligible.

Table 4.1: Summary of Economic Activity of the Head of Household

Main Economic Activity of the Head of Household Sample Proportion

Number of Observations

Works on Own Farm 0.7676 1618

Works on Someone Else’s Farm 0.0977 206

Self-employed Vendor or Trades Person 0.0237 50

Salaried Employment in the Public Sector 0.0304 64

Salaried Employment in the Private Sector 0.0209 44

All Other Activities 0.0597 126

Total 1.0000 2,108

Table 4.2 reports the main occupational activities of these heads and reveals that the

majority working in agriculture are farmers (94 percent) with one-quarter of these recorded

as tea farmers.

14

Table 4.2: Summary of Main Occupational Activity of the Head of Household

Main Occupational Activity of the Head of Household Sample Proportion

Number of Observations

Farmer Engaged in Tea Cultivation 0.2035 429

Tea Plucking 0.0242 51

Casual Labourer on Tea Farms 0.0066 14

Farmer Engaged in Non-tea Cultivation 0.6106 1287

Working on Someone Else’s Farm 0.0066 14

Working in Non-agricultural Sectors 0.1485 313

Total 1.0000 2,108 Notes to Table 4.2: The non-agricultural sectors include those working in manufacturing, mining, construction,

transportation, commerce and retail trade, education and health, public sector administration, and other sectors.

The sample of all farmers provides the basis for computing the descriptive statistics

informing the demographic outcomes reported in Table 4.3 below. Given their immutable

nature over the short period between the price reforms and the survey implementation,

these profiles are taken to represent the baseline demographics obtaining prior to these

reforms.

The profiles are delineated across tea and non-tea producing farmers to provide insights on

the extent of any differences across these two groups. On average, tea farmers are almost

seven years older than non-tea farmers and are six percentage points more likely to report a

physical disability. About one-quarter of households are female-headed in both sub-

samples, with more widows reported among tea farmers. Approximately one-third of all

farmers report having no formal education, and the distribution of educational attainment

between the two samples (tea and non-tea farmers in the baseline) is not found to be

statistically different on the basis of a goodness-of-fit chi-squared test. The number of

household members is statistically higher for the sub-sample of tea farmers, although the

number of children under five in the household is statistically lower than that of non-tea

farmers. This latter finding is unsurprising given the older age profile of the tea farmers. The

overwhelming majority of farmers own their residential dwellings, though the ownership

rate is statistically higher for the tea farmers. The physical attributes of residential

properties, however, exhibit no statistical differences across the two sub-samples in terms

of either the number of bedrooms or the presence of electricity.

Table 4.3: Demographic Profile of Tea and Non-tea Farmers (Main Occupation)

Characteristics Tea Farmers Non-tea Farmers

Test of Difference

Age of Head of Household (HoH) in years 53.2 (14.5)

46.3 (14.9)

|t| = 8.2; pv=0.00

HoH Female (=1) 0.2634 0.2595 |z| = 0.2; pv=0.87

HoH Physical Health Disability (=1) 0.2004 0.1445 |z| = 2.7; pv=0.00


Married – monogamous (=1) 0.6760 0.7312 ‡

Married – polygamous (=1) 0.0629 0.0350 ‡

15

Widowed (=1) 0.2214 0.1888 ‡

Divorced (=1) 0.0210 0.0256 ‡

Single (=1) 0.0186 0.0194 ‡

χ2

4 = 9.0; pv=0.05


No Education (=1) 0.3263 0.3675 ‡

Some Primary Education (=1) 0.3357 0.3364 ‡

Complete Primary Education (=1) 0.2564 0.2222 ‡

Some Secondary Education (=1) 0.0536 0.0490 ‡

Complete Secondary Education (=1) 0.0047 0.0062 ‡

Other (including Higher) Education (=1) 0.0233 0.0186 ‡

χ2

5 = 3.8; pv=0.58



5.28 (2.15)

|t| = 3.5; pv=0.00

Number of Female Adults Aged 15+ 1.94 (1.08)

1.61 (0.93)

|t| = 6.2; pv=0.00

Number of Male Adults Aged 15+ 1.65 (1.10)

1.38 (0.98)

|t| = 4.7; pv=0.00

Number of Children Aged < 5 0.67 (0.89)

0.87 (0.90)

|t| = 3.9; pv=0.00

Number of Children Aged 6 to 14 1.50 (1.44)

1.45 (1.34)

|t| = 0.7; pv=0.47

Dwelling Characteristics:

Own House (=1) 0.9790 0.9594 |z| = 2.2; pv=0.00

The Number of Bedrooms (=1) 2.86 (0.95)

2.79 (1.04)

|t| = 1.3; pv=0.19

Electricity (=1) 0.0606 0.0816 |z| = 1.4; pv=0.16

Sample Sizes 429 1287 Notes to Table 4.3: (a) The summary statistics reported here relate only to households whose main activity is reported as

farming; (b) Standard deviations are reported in parentheses; (c) Goodness-of-fit chi-squared tests are used to test for the

differences in variables with mutually exclusive groups; (d) |z| denotes the absolute z-score, |t| denotes the absolute t-

value, and pv denotes prob-value.

4.2 Land Holdings and Size Table 4.4 reports the number of plots and the size of land holdings by tea and non-tea

farmers at baseline. The average number of plots owned by tea farmers is about 1.5 times

higher than that for non-tea farmers. The average tea farmer allocates 1.5 plots to tea

cultivation, which represents about one-third of the average. Both the average and median

tea farmer plot sizes, as measured in hectares, are almost twice the corresponding figures

for the sample of non-tea farmers, with the point differentials well determined in all cases.

The size distribution of land holdings is more dispersed among non-tea farmers.

Table 4.4 also reveals that the average land size devoted to tea cultivation is about 0.6

hectares, though the median is about one-half of this, suggesting that a small number of the

larger sized holdings are impacting the mean. Overall, the average and median tea farmers

devote roughly about one-half of their holdings to tea cultivation. There is a greater

dispersion in the distribution in the land size used by tea farmers for tea production

16

compared to their non-tea activities with the Gini land coefficient computed at 0.56 for the

latter (not reported in Table 4.4) compared to 0.62 estimate reported in this table.

Table 4.4: Land Size Profile for Tea and Non-tea Farmers

Tea Farmers

Non-tea Farmers

Test of Difference

Average Number of Plots Owned 5.232 (3.873)

3.555 (2.647)

|t| = 9.9; pv=0.00

Average Number of Tea Plots Owned 1.527 (1.347)

‡ ‡

Average Total Land Holdings (Hectares) 1.128 (1.592)

0.607 (1.053)

|t| = 7.3; pv=0.00

10th Percentile Land Holdings (Hectares) 0.174 0.060 |t| = 11.4 pv=0.00

25th Percentile Land Holdings (Hectares) 0.358 0.133 |t| = 14.5;pv=0.00

50th Percentile Land Holdings (Hectares) 0.680 0.305 |t| = 11.7; pv=0.00



90th/10th Percentile Land Holdings 13.69 22.53 ‡

Gini Coefficient for Land Holdings 0.529 (0.027)

0.601 (0.014)

|t| = 2.4; pv=0.00

Average Tea Holdings (Hectares) 0.586 (1.005)

‡ ‡

10th Percentile Tea Holdings (Hectares) 0.054 ‡ ‡





90th/10th Percentile Tea Holdings 22.37

Gini Coefficient for Tea Holdings 0.620 (0.025)

‡ ‡

Sample Size for Plots 427 1221 ‡

Sample Size for All Land Holdings 405 1090 ‡

Sample Size for Tea Land Holdings 348 ‡ ‡ Notes to Table 4.4: (a) The data used are based on those who own their own land and where households where farming is

the main activity; (b) standard deviations are reported in parentheses; (c) the statistical tests for the different quantiles are

based on the t-test for a tea-farmer dummy in quantile regression; (d) ‡ denotes not applicable for calculation; (e) the

differing sample sizes relates to missing values for the measured land variable.

4.3 Livestock Ownership, Crop Activity, and Labor Usage of Farmers Following recall methodology of agricultural surveys, Table 4.5 summarizes the nature of

livestock ownership and crop activity for both farmer types as of 12 months prior to the

interview (thus before the implementation of the price reforms). The table reports the

baseline ownership rate and number for seven separate livestock categories. In general, the

livestock ownership rate is statistically higher for tea compared to non-tea farmers for all

but one livestock group (rabbits). The sample averages, conditional on livestock ownership,

only differ statistically for the case of cattle. In this case, the tea farmers own a slightly

higher average number of cattle.

17

There are three growing seasons in Rwanda. These span September to January (Season A),

February to June (Season B), and July to August (Season C). The crop growing activities in

Seasons A and B cover September 2012 to June 2013, prior to the full implementation of the

green leaf price reform. These two seasons are the most intensive growing seasons for

Rwandan farmers with only 47 percent of all farmers in the survey reported as engaged in

any kind of cultivating activity in Season C. This is unsurprising because only farmers with

access to valley bottom plots have sufficient water to cultivate in Season C, and most

farmers have hillside plots.

Table 4.5: Livestock Ownership of Farmers 12 Months Prior to Interview

Tea Farmers

Non-tea Farmers

Test of Difference

Own Cattle (=1) 0.684 0.549 |z| = 4.8; pv=0.00

Average Number of Cattle (conditional on ownership)

1.77 (1.17)

1.60 (0.99)

|t| = 2.2; pv=0.02

Own Goats (=1) 0.464 0.350 |z| = 4.2; pv=0.02

Average Number of Goats (conditional on ownership)

2.42 (1.40)

2.30 (2.02)

|t| = 0.7; pv=0.48

Own Sheep (=1) 0.288

0.220 |z| = 2.8; pv=0.00

Average Number of Sheep (conditional on ownership)

2.22 (1.55)

2.06 (1.36)

|t| = 1.1; pv=0.29

Own Pigs (=1)

0.330 0.231 |z| = 4.1; pv=0.00

Average Number of Pigs (conditional on ownership)

1.65 (1.54)

1.81 (2.08)

|t| = 0.8; pv=0.44

Own Poultry (=1) 0.391 0.297

|z| = 3.6; pv=0.00

Average Number of Poultry (conditional on ownership)

3.16 (2.51)

3.45 (4.39)

|t| = 0.8; pv=0.21

Own Rabbits (=1)

0.145 0.118 |z| = 1.4; pv=0.15

Average Number of Rabbits (conditional on ownership)

4.94 (4.28)

4.61 (4.25)

|t| = 0.6; pv=0.58

Notes to Table 4.5: (a) standard deviations are reported in parentheses for continuous variables; (b) the statistical tests for

the continuous variables are based on the t-test, while the z-score is used for the proportional variables; (c) pv denotes the

prob-value.

Table 4.6 reports the average number of crops grown in each of the A and B Seasons by

farmer type. Given tea farmers possess both a larger number of plots and larger sized

holdings compared to their non-tea counterparts, it is not surprising that the average

number of crops grown in each of the two seasons is also, on average, statistically higher.

Tea farmers grow an average of one more crop than the non-tea producing farmers in

18

Season A, which is the most intensive growing season. The averages are also statistically

higher for the sample of tea farmers in Season B.

The four most popular crops grown in each of the two seasons are climbing beans, maize,

sweet potato and Irish potato. In all cases other than climbing beans in Season A, tea

farmers record statistically higher activity rates in the other three crops. For Season B, the

rates across all four crops are comparable. Overall, tea farmers appear more diversified

across crops and have higher levels of engagement than non-tea farmers in the four most

popular crops produced in the tea growing areas of Rwanda.

Table 4.6: Crop Activity of Farmers 12 Months Prior to Interview

Tea Farmers

Non-tea Farmers

Test of Difference

Crop Activity:

Average Number of Crops Cultivated (Season A)

5.012 (3.144)

4.063 (2.769)

|t| = 5.9; pv=0.00

Average Number of Crops Cultivated (Season B)

4.255 (2.469)

3.666 (2.535)

|t| = 4.1; pv=0.00

Most Popular Crop Type – Season A:

Climbing Beans (Haricot) 0.749 0.753 |z| = 0.2; pv=0.88

Maize 0.700 0.603 |z| = 3.6; pv=0.00

Sweet Potato 0.515 0.460 |z| = 1.96; pv=0.05

Irish Potato 0.424 0.333 |z| = 3.4; pv=0.00

Most Popular Crop Type – Season B:

Climbing Beans (Haricot) 0.562 0.536 |z| = 0.92; pv=0.88

Maize 0.522 0.553 |z| = 1.1; pv=0.26

Sweet Potato 0.451 0.482 |z| = 1.1; pv=0.26

Irish Potato 0.375 0.337 |z| = 1.4; pv=0.16

Sample Size

427 1,221

Notes to Table 4.6: (a) standard deviations are reported in parentheses for continuous variables; (b) the statistical tests for


prob-value.

Table 4.7 reveals there is a high reliance on household members for harvesting and non-

harvesting chores by both farmer types throughout Seasons A and B. The average number

of days worked by household members is also statistically higher for tea farmers reflecting,

among other things, the larger average number of household members for this group. In

contrast, the use of hired labour is statistically higher for tea farmers across both seasons.

In A and B almost one-half of tea farmers hired in outside labour, while the figure for non-

tea farmers is less than one-third. This may be attributable to the fact that tea farmers own

more plots and have larger average holdings than non-tea farmers, as well as the labor

intensive nature of tea as a crop (plucking). The average number of days worked by hired

labour on farms owned by tea farmers is about 20 percent higher compared to the group of

19

non-tea farmers in Seasons A and B. The median number of days worked by hired labour

across the two seasons is 25 percent higher.

Table 4.7: Labor Usage by Farmers

Tea Farmers

Non-tea Farmers

Test of Difference

Use Household Labour Season A (=1) 0.972 0.971 |z|=0.14; pv=0.88

Use Household Labour Season B (=1)

0.972 0.960 |z|=0.27; pv=0.61

Average Days Worked (Seasons A & B) [Conditional on using HH members)

138.8 (69.9)

121.6 (71.9)

|t|=4.16; pv=0.00

Median Days Worked (Seasons A & B) [Conditional on using HH members)

140 121 |z|=4.4; pv=0.00

Use Hired Labour Season A (=1) 0.491 0.319 |z|=6.44; pv=0.00

Use Hired Labour Season B (=1)

0.456 0.300 |z|=5.92; pv=0.61

Average Days Worked (Seasons A & B) [Conditional on using Hired Labour]

44.4 (49.2)

37.2 (46.7)

|t|=1.66; pv=0.09

Median Days Worked (Seasons A & B) [Conditional on using Hired Labour]

22.5 18 |z|=2.9; pv=0.00

Sample Sizes 421 1218 Notes to Table 4.7: (a) standard deviations are reported in parentheses for continuous variables; (b) the statistical tests for


prob-value.

4.4 The Pattern of Baseline Tea Prices This section examines the pattern of tea prices received by tea farmers prior to the green

leaf price reform. Table 4.8 reports the average price per kilogram of green leaf tea paid to

farmers during a pre-reform period (averaged over the 12-month period prior to the

implementation of the government’s price-reform). The average price per kilogram received

by tea farmers prior to the reforms was RWF 55.8. However, there was substantial variation

in the average price paid across the different co-operatives, with the average differences

found to be statistically significant across the fifteen that feature here. However, the small

sample sizes available for some of the co-operatives merits some interpretational caution

when focussing down on some of the individual co-operatives.

Table 4.8: Average Baseline Price per Kilogram for Green Leaf Tea

Co-operative Sample Proportion

Price per Kilogram in RWF

Overall Average 1.00 55.8 (22.5)

20

Of which:

COOP_1 0.013 26.0

COOP_2 0.096 55.7

COOP_3 0.022 71.3

COOP_4 0.132 39.2

COOP_5 0.041 56.9

COOP_6 0.013 71.3

COOP_7 0.039 52.1

COOP_8 0.080 64.2

COOP_9 0.106 73.5

COOP_10 0.124 66.6

COOP_11 0.013 100.7

COOP_12 0.078 39.0

COOP_13 0.072 35.9

COOP_14 0.017 76.0

COOP_17 0.026 63.7

Non-member 0.128 55.2

F-test for Differences in Co-op Means ‡ 16.25 pv=0.00

Sample Size ‡ 431 Notes to Table 4.8: (a) the baseline prices are the average in the 12 months prior to the reform; (b) pv denotes prob-value;

(c) the F-test for a difference in cooperative means is based ANOVA; (d) ‡ denotes not applicable.

Table 4.9 re-examines this issue from a different perspective through dividing the sample

between Thѐ villageois producers and those producers on co-operative lands. The pre-

reform price received by the former is about 20 percent higher with the difference

statistically significant. This differential is intuitive given that tea producers on co-operative

lands incur deductions for inputs used and services provided on these lands.

Table 4.9: Baseline Price per Kilogram by Tea Holding Type

Tea Producer Type Sample Proportion

Average Price per Kilogram in RWF

Cooperative Lands 0.20 48.3 (24.8)

Thѐ Villageois 0.80 57.6 (21.7)

Test for Difference ‡ |t| =3.5 (pv=0.00)

Sample Size ‡ 431 Notes to Table 4.9: (a) the baseline prices are the average in the 12 months prior to the reform; (b) |t| denotes the

absolute t-value for a difference in means; (c) pv denotes prob-value; (d) ‡ denotes not applicable.

4.5 The Pattern of Baseline Tea Production The baseline figures for farmer tea production are based on the output produced by tea

farmers in the full year prior to the survey based on the factory determined weight. Table

4.10 reports the baseline yield figures. The average yield generated per hectare is 6,394.7

kilograms of tea. However, this masks differences across the distribution with the median

21

reported at a more modest 3,362 kilograms per hectare. In addition, the decile ratio is

about 47, suggesting fairly substantial variation in yield between those producers at the

bottom and the top end of the yield distribution. Finally, Table 4.10 also reports yield per

tea plot, with the average plot producing 1,227.9 kilograms of tea, though the median is

reported at 780.5 kilograms.

Table 4.10: Baseline Tea Production Yield

Mean & Quantiles Kilograms per Hectare Kilograms per Tea Plot

6,394.7 (7902.7)

1,227.9 (1880.8)

10th percentile 347.6 147.2

25th percentile 1,063.1 320.75

50th percentile 3,361.7 780.5

75th percentile 8,498.2 1,465.75

90th percentile 16,297.0 2,519.3

90th/10th percentile ratio 46.9 17.1

Sample Size 272 272 Notes to Table 4.10: (a) standard deviations are reported in parentheses; (b) the calculations are based on all tea farmers

with a maximum of five tea plots; (c) the data are trimmed at the top end to exclude the impact of a small number of

extreme outliers on the calculations reported here. The data trimming comprised about 2% of the sample of tea

producers.

4.6 Conclusion These baseline data provide us with a platform to assess the research questions articulated

in Section 1. In conjunction with the data to be obtained from the planned follow-up survey

in 2015, the impact evaluation will investigate how the green leaf tea price reform has

impacted farmer productivity, revenues, the area under tea cultivation, and farmer non-

income wealth assets including livestock holdings and so on. The impact evaluation of the

price reforms on farmer incomes presents a greater challenge. However, the availability of

benchmark tea revenue data will allow us re-calibrate tea farmer incomes to reflect the

baseline levels that prevailed prior to the reforms and use this baseline for the purposes of

the impact evaluation on farmer incomes. Finally, since all green leaf has to be processed in

factories, the factory level data reviewed by the impact evaluation team provides baseline

and impact data to triangulate quantities (production) and factory-adjudicated quality.

5. Internal Validity of the Study

This section reviews a number of issues related to the internal validity of the baseline study.

The first sub-section explores the magnitude of the sample design effects ex-post. A

subsequent sub-section exploits propensity score matching techniques to get a sense of the

potential problem associated with the measurement of the land size variable. The final sub-

section covers some observations on potential quality differences depending on whether

the head of household or someone deputed by the head of household was the primary

interviewee.

22

5.1 Intra-correlation Coefficients and Design Effects The sampling undertaken for this survey was described earlier in section 2 above and

exploited randomization at a cluster level. The final number of administrative villages

clusters selected within the tea-growing areas of Rwanda was 126 and the average number

of interviewed households within each cluster was approximately 16 households. Clustered

samples are not as statistically efficient as those drawn from simple random sampling. In

particular, similarities among units in clusters (intra-cluster correlation, ) can reduce the

variability of responses from a cluster compared to that drawn from a simple random

sample.

Specifically, a positive correlation between units in the same cluster inflates the variance of

the sample mean. In the extreme theoretical case where = 0, and there is no correlation

in responses within clusters, then all of the variation is within clusters and the sampling is

equivalent to simple random sampling. In the alternative extreme case where = 1, all

responses within a cluster are identical and the effective sample size is simply the number of

clusters. In addition, the design effect, which allows computation of the effective sample

size, is positively related to both the intra-cluster correlation coefficient and the number of

units sampled in each cluster.

In order to examine this issue for the current baseline survey we compute intra-cluster

correlations for the sample of farmers for a selected array of continuous variables in the

survey. The purpose of this exercise is largely illustrative and is intended to demonstrate the

possible magnitude of design effects present in these data. Table 5.1 reports the intra-

cluster correlation coefficients for four separate continuous variables for the sample of

farmers in the survey.

Table 5.1: Intra-cluster Correlations for Selected Variables for the 2013 Tea Survey

Variable Intra-cluster Correlation

Sample Size Number of Clusters

Household Income 0.0563 1716 126

Land Size Holdings 0.0387 1496 126

Household Size 0.0505 1716 126

Age of Head of Household 0.0146 1703 126 Notes to table 5.1: The number of units per cluster is 16.

In general, the reported correlation coefficients are modest. As a further illustration, we

compute the design effect using the household gross income variable introduced in the last

section. The following formula can be used: D = 1)ρ - (n + 1 k where nk is the number of

sampled units per cluster. We assume nk = 16 for this calculation. In empirical analysis

using the income (or land size) measure, for example, standard errors are likely to be 1.36

(1.26) times higher than they would be if we had used a simple random sampling approach.

The foregoing calculations provide sufficient confidence that we have adequate power to

detect any effects in regard to the income (or land size) variable.

23

5.2 The Measurement of Land Size An issue of concern for the study relates to the size of land holdings and the self-reported

estimates provided by respondents. A primary objective of the survey was to ensure that

interviewers consulted land title deeds to obtain an accurate measurement for the size of a

farmer’s land holdings. Only in about one-half of the cases, however, where title deeds

were held by farmers, was the interviewer able to consult the land title deeds.

In order to investigate the implications of this issue further we focus down on the size of the

respondent’s main plot and separate the sample into those for whom a land title deed was

available and seen by the interviewer and all others. We exploit a propensity score matching

(PSM) technique to match those farm households with and without the land title deed

information. Once we have matched the households, using an array of characteristics, we

compute the difference in reported plot sizes in order to get a sense of the magnitude of

differences between these two groups for the main plot. For this exercise, farmers who had

and showed the land title are considered the ”treatment” group, with the remaining

farmers providing the “control” group.

The variables used for both the propensity score ( the probability of the ”treatment”) and

the outcome ( the size of the farm’s main plot11) include: head of household characteristics

(for example, age, gender, marital status, human capital level, and health), the log of

household income and household size, agricultural activity (for example, the number of

crops cultivated in each season, whether the farmer is a tea farmer, the number of different

livestock), the location of the plot ( top or bottom of a hill), and the district within which the

farm is located. A logit model used for the treatment assignment equation validates the

methodology: the constructed propensity score is balanced across treatment and

comparison groups, as are the covariates.

Table 5.2 reports the average treatment effect for the treated in this case. There were 803

treated farms in the common support and these were matched with 707 from the control

group. The ATT estimate suggests the average size of those main plots for which the size

was obtained directly from the title deed is about 20 percent smaller than those for which

this was not the case.12 Therefore, the main plots, where title deeds were used to provide

the plot size measurement, appear to be systematically smaller, on average, suggesting the

area size of the main plots for which self-reported estimates were supplied (and not

obtained from the title deed directly) is over-estimated by close to one-fifth, on average.

Table 5.2: Propensity Score Matching for Main Plot Size Measured in Log Hectares

Number of Treated Number of Controls Average Treatment of the Treated (ATT)

t-ratio

803 707 -0.222 (0.073)

-3.053

11

The outcome variable, size of the farm’s main plot, is expressed as log of hectares and relies on a kernel matching method based on the Epanechnikov density. 12

Bootstrapped standard errors were computed for the average treatment effect using 100 replications.

24

Notes to Table 5.2: The standard error in parentheses is bootstrapped using 200 replications.

Although the magnitude of this finding is best interpretable as suggestive, it does highlight a

potential caveat for the conduct of empirical work using the land size data reported in this

survey given some measurements provided are based on self-reporting. For example, the

work of Carletto, Gourlay and Winters (2013) emphasize the problems associated with self-

reported estimates of land area for empirical work. It is clear that additional work is

required to try and address the informational deficit on land size and this is discussed

further in the recommendations section.

5.3 Respondent Responses for the Non-heads of Household Another issue encountered by interviewers was the fact that about one-quarter of the

interviewees for the farming households were not the head of household. This appears to

have had implications for access to important information regarding the land title deeds (as

already discussed in section 5.2 above), which was seen as crucial in securing accurate

information on the size of farmer land holdings as well as other household characteristics.

About 15 percent of farm households reported having no land title deeds for their main

plot. In only 65 percent of cases where respondents reported having land title deeds were

the documents viewed by the interviewer. There was a large differential in interviewer

viewing rates between respondents who were heads of household and those who were not.

The viewing rate for the former was 68.9 percent and 57.4 percent for the latter. The

differential is found to be statistically significant with a z-score of 4.29 (pv=0.00).

A similar problem emerged for tea farmers in regard to access to the farmer’s tea factory

notebook recording the volume of green tea leaves delivered to the factory and the prices

paid. Only 18 percent reported not having such a book. The viewing rate by interviewers

was 72.9 percent when heads of households were present for interview and 61.4 percent

when not. Again the differential is statistically significant with a z-score of 2.05 (pv=0.02).

Overall, the foregoing highlights the informational benefits to interviewing heads of

household rather than those deputed by the head to provide such information.

6. Other Descriptive Findings from Baseline Survey: Requested Sector Evidence by GoR

6.1 Household Income Distributions The household income measure used for this part of the analysis, as described earlier, is the

sum of gross annual income earned by the household in the 12 months prior to the

interview date (hence, prior to the reform) from (i) own farm activity, (ii) providing labour

services to other farms, and (iii) non-farm enterprise activity (including from any salaried

employment). No imputations for own consumption or deductions for inputs are

undertaken for this measure.

Table 6.1 provides more detail on the distributional differences in household income

between the two sub-samples of tea and non-tea farmers. The average household income

for tea farmers is 60 percent higher to that of non-tea farmers, while the median is 85

percent higher. Annual household income is greater (and statistically so) at all reported

percentiles of the distribution. The income gap is widest for the poorest farmers (230

percent at the 10th percentile) and narrowest for the wealthiest farmers (39 percent at the

25

90th percentile). The average tea farmer’s gross household income level is higher than

approximately 82% of non-tea farmer households.

Table 6.1 also reveals a wide income distribution across the sample, i.e. there is a large

difference between the lowest and highest income households for both tea farmers and

non-tea farmers. The income dispersion is considerably more pronounced among the non-

tea farmers. The Gini coefficient, a commonly used measure of income inequality, is nearly 7

points higher for the non-tea farmers, and the difference is statistically significant. Similarly,

the ratio of income between the 90th and 10th percentile of non-tea farmers is double that

of tea farmers.

Table 6.1: Household Income and Wealth Profile of Tea and Non-tea Farmers

Tea Farmers

Non-tea Farmers

Test of Difference

Average Household Income (RWF) 282568.5 (381388.0)

176291.6 (265730.4)

|t| = 6.2; pv=0.00

10th Percentile Household Income (RWF) 50000.0 15000.0 |t| = 10.3 pv=0.00

25th Percentile Household Income (RWF) 95311.5 40000.0 |t| = 11.3; pv=0.00




Average Log Household Income 12.0340 (1.0807)

11.3571 (1.2916)

|t| = 9.6; pv=0.00

90th/10th Percentile Household Income 11.1 26.7 ‡

Gini coefficient for Household Income 0.514 (0.025)

0.583 (0.013)

|t| = 2.5; pv=0.00

PCA Wealth Index 0.258 (1.501)

-0.178 (1.441)

|t| = 5.3; pv=0.00

Correlation Coefficient between Household Income and Wealth Index

0.526; pv=0.00

0.459; pv=0.00

‡

Sample Sizes 424 1180 Notes to Table 6.1: (a) The data used are based on non-zero income for those households where farming is the main

activity; (b) standard deviations are reported in parentheses; (c) the statistical tests for the different quantiles are based on

the t-test for a tea-farmer dummy in quantile regression; (d) ‡ denotes not applicable for calculation.

Figure 5 plots the income distribution for all farmers in the sample and reveals a left-skewed

distribution. Figure 6 shows the income distributions by farmer type and reveals that the

central location of the distribution for tea farmers lies to the right of the non-tea farmer log

income distribution, suggesting both a higher mean and median income level for the sample

of tea farmers.

26

Figure 5 Kernel Density of the Log of Annual Farmer Household Income

Figure 6 Kernel Density of the Log of Annual Farmer Household Income by Farmer Type

0.1

.2.3

.4

De

nsity

6 8 10 12 14 16The Log of Annual Household Income

0.1

.2.3

.4

De

nsity

6 8 10 12 14 16ly

Tea-farmers

Non-tea farmers

kernel = epanechnikov, bandwidth = 0.2493

Kernel density estimate

27

An asset index, based on the household’s non-income wealth, is also constructed from the

survey data using Principal Components Analysis (PCA). The assets include those relating to

housing quality (i.e., the number of bedrooms, whether or not the residence has electricity),

the number of radios, the presence of a television set, mobile phones, bikes and motorbikes

owned by the household, and the numbers of different livestock owned by the household

(i.e., the number of cattle, sheep, goats, poultry). The distribution of the asset index broadly

follows the same pattern as household income with tea farmers possessing more assets

than non-tea farmers (Figures 7 and 8). On the basis of Table 6.1, the average PCA value for

tea farmers is about 0.44 of a standard deviation higher than the average value reported for

the non-tea farmers, with the differential between the two groups statistically significant.

Figure 7 Kernel Density of the PCA Index of Household Assets – All Farmers

0.1

.2.3

De

nsity

-5 0 5 10kernel = epanechnikov, bandwidth = 0.2638

28

Figure 8 Kernel Density of the PCA Index of Household Assets by Farmer Type

Figure 9 plots the PCA and the log household gross income (expressed as a standardized

variable) for all farmers and reveals a reasonably close relationship between the two

distributions. The Pearson product moment correlation coefficient computed for the

household income and the wealth index provides a statistically significant coefficient of

0.487 suggesting a reasonable degree of correlation between the two measures and

comparable to what is generally found in the literature for developing countries. The

estimated correlation coefficient is found to be higher for the tea farmers (0.53) compared

to the non-tea farmers (0.46).

0.1

.2.3

.4

De

nsity

-5 0 5 10Scores for component 1

Tea-farmers

Non-tea farmers



29

Figure 9 The Kernel Densities for Log Household Income and the Wealth Index

We now briefly draw again on the data from the 2004 survey to place the gap in income

between tea and non-tea farmers in a recent historical context. Table 6.2 below reveals

that in 2004 tea farmers already enjoyed a sizeable income advantage over their non-tea

growing counterparts. However, this had widened sharply by 2013. In 2004, the average

tea farmer’s income was 33 percent higher than non-tea farmers and by 2013, this gap had

almost doubled. Thus, at first blush, tea farmer incomes appear to have risen significantly

over this period but the explanation for this requires further and more detailed empirical

analysis.

Table 6.2: Real Household Incomes in the Tea Growing Areas of Rwanda 2004 and 2013

2004 2013

Ratio of Average Tea Farmer to Non-tea Farmer Household Income

1.34 1.60

Sample Size 1,505 1,604

6.2 Household Income Determination of Rwandan Farmers in the Tea-growing Areas We now explore the key determinants of annual gross household income of farmers in the

2013 sample. The logarithm of annual gross income introduced in section 6.1 provides the

metric for the dependent variable and the OLS procedure is used in estimation. The sample

is pooled across both farmer types. The reported goodness-of-fit of the regression model is

adequate and suggests that almost 30 percent of the variation in the log of household

income is explained by the set of covariates (including controls for 12 districts) in the

specification reported in Table 6.3.

The log of household income is found to be insensitive to the age of the head of household.

Incomes in households headed by women are subject to a penalty of almost 29 percent

0.1

.2.3

.4

De

nsity

-5 0 5 10lyx

Log Income

Wealth Asset



30

compared to those headed by men, on average and ceteris paribus, while a physical

disability to the household head is associated with a 22 percent lower household income.

There are significant effects for the formal human capital measures included, with

households headed by those without any formal education receiving around 58 percent less

than those with a head reporting secondary level or better. Household income is also

positively related to the number of household members (both adults and children), and an

additional member engaged in paid work (in the previous month) raises the household

income by 11 percent, on average and ceteris paribus.

The estimated income elasticity with respect to land size holdings is well determined, but

fairly inelastic. In particular, a 10 percent increase in land holdings raises household income

by a modest 2 percent.13 There is a positive relationship between the number of crops

grown in season A (September to January) and household income. An additional crop grown

in this season, relative to the average, raises annual household income by about 5 percent,

on average and ceteris paribus, while a one crop increase in the number grown in season C

(July and August) actually reduces household income by approximately 3 percent. Recent

infrastructural improvements in the local roads accessing markets are associated with

higher household incomes. In particular, those households where such improvements have

occurred in the last five years enjoy a 12.8 percent income premium compared to

households in areas that have not benefitted from such recent infrastructural

enhancements, on average and ceteris paribus.

As discussed, Table 6.1 revealed a wide disparity in household income between tea and non-

tea farmers. The ceteris paribus income differential reported in Table 6.3 is also found to be

sizeable. Specifically, farmers engaged primarily in the production of tea receive, on

average, 68 percent more in household income compared to their non-tea counterparts. In

addition, the quantile regression estimates reported in Table 6.4 confirm the income

advantage across selected quantiles of the conditional log household income distribution as

originally exhibited in the raw data in Table 6.1. The pattern reported in Table 6.4 is one of

a declining differential across the conditional distribution with a statistically significant

difference recorded between the 90th and the 10th percentiles. However, the magnitude of

these estimated effects are attenuated with the inclusion of other covariates. Nevertheless,

the quantile regression findings confirm that tea farmers at the bottom end of the

conditional distribution fare significantly better in income generation terms than their non-

tea counterparts also located at this part of their corresponding distribution.

13

An inelastic response is also found if the number of plots is used instead of the total land size. The estimated elasticity in this case is found to be 0.248, which is dimensionally comparable to the land elasticity reported in table 6.3.

31

Table 6.3: Determinants of Log Annual Household Income for Tea and Non-tea Farmers

Characteristics Sample Mean

OLS Estimates

Constant

1.0000 11.9308*** (0.3558)

Age of Head of Household (years) 48.27 (14.93)

0.0002 (0.0126)

Age of Head of Household Squared (years) 2553.1 (1545.8)

-0.0001 (0.0001)

HoH Female (=1) 0.2493 -0.2542** (0.1268)

HoH Physical Health Disability (=1) 0.1652 -0.2422*** (0.0855)


Married – monogamous (=1) 0.7263 -0.0562 (0.1349)

Married – polygamous (=1) 0.0417 0.1439 (0.1780)

Divorced (=1) 0.0201 -0.2050 (0.1995)

Single (=1) 0.0201 -0.5272* (0.2853)

Widowed (=1) 0.1918 ‡


No Education (=1) 0.3355 -0.8604*** (0.1333)

Some Primary Education (=1) 0.3384 -0.6987*** (0.1299)

Complete Primary Education (=1) 0.2442 -0.4552*** (0.1322)

All Other Education Greater than Primary (=1) 0.0819 ‡


Number of Adult Household Members 3.18 (1.55)

0.0824*** (0.0230)

Number of Children Household Members 2.31 (1.68)

0.0442** (0.0195)

Number of Household Members Engaged in Paid Work (Last Month)

0.82 (0.89)

0.1052*** (0.0406)

Land Holdings & Agricultural Activities:

Log of Total Land Size (Hectares) -0.9743 (1.2154)

0.2016*** (0.0272)

Number of Crops Grown in Season A 4.31 (2.89)

0.0499*** (0.0163)

Number of Crops Grown in Season B 3.79 (2.48)

0.0052 (0.0191)

32

Number of Crops Grown in Season C 1.23 (1.84)

-0.0291* (0.0177)

Non-tea Farmer (=1)

0.7126 ‡

Tea Farmer Primary Activity (=1) 0.2874 0.5194*** (0.0667)

Tea Farmer Secondary Activity (=1) 0.0151 0.0577 (0.2230)

Community Attributes & District Controls:

Improved Roads to Markets in the Last 5 Years (=1) 0.7055

0.1209* (0.0711)

Twelve District Controls Included

not reported

Yes

Adjusted R-Squared

n/a 0.2809

Sample Size 1,392 1,392 Notes to Table 6.3: (a) standard deviations are reported in parentheses for the sample means; (b) robust standard errors

are reported in parentheses for the OLS estimates; (c) ***, **, and * denote statistical significance at the 0.01,0.05 and 0.1

level of statistical significance respectively; (d) ‡ denote base group in OLS estimation; (e) n/a denotes not applicable.

Table 6.4: Quantile Regression Estimates for Tea Farmer Effect

10th Percentile

25th Percentile

50th Percentile

75th Percentile

90th Percentile

Tea Farmer 0.7949*** (0.1504)

0.6842*** (0.0916)

0.5076*** (0.0856)

0.4022*** (0.0785)

0.1564*** (0.0867)

Notes to Table 6.4: (a) The regression model contains all the variables included in the specification reported in table 6.3; (b) bootstrapped standard errors are computed using 200 replications; (c) ***, **, and * denote statistical significance at the 0.01,0.05 and 0.1 level of statistical significance respectively.

The empirical analysis reported in table 6.3 was developed further to investigate whether there was a gender dimension in the income returns to tea-growing activity. A comparable specification to the one reported in table 6.3 was re-estimated using only the sub-sample of female farmers. The results of the exercise are reported in Annex Four. A key finding of this analysis is that female tea farmers enjoy a sizeable income advantage over female non-tea farmers. Specifically, on average and ceteris paribus, the household income advantage is of the order of about 88%. However, the advantage enjoyed by female tea farmers relative to their non-tea farmer counterparts is not statistically different to the income advantage earned by male tea farmers relative to their male non-tea farming counterparts. The computed t-ratio for this latter differential is 0.87 with a prob-value of 0.38.

Finally, we also investigated whether there was evidence of differences in the income rewards for those tea farmers with health disabilities compared to non-tea farmers with such disabilities. In order to examine this issue we again estimated a comparable specification to that reported in table 6.3 but restricted the sample to those farmers reported to have a physical disability. The results of this exercise are reported in Annex Five but interpretational caution is required here given the small sample size of 230 farmers used here for the analysis. Table 6.3 revealed that, on average, farmers with a physical disability

33

had statistically lower household incomes than their able-bodied counterparts. The estimates from Annex Five suggest that tea farmers with a physical disability have household incomes that are, on average and ceteris paribus, 112% higher compared to non-tea farmers with such disabilities. This differential is also found to be statistically higher than that enjoyed by able-bodied tea farmers compared to their able-bodied non-tea counterparts at the 10% level of significance using a two-tailed test. The computed t-ratio is 1.75 in this case with a prob-value of 0.080. Thus, acknowledging the caveats regarding the sample size used here, the effect of physical disability on income determination is mitigated for the sub-sample of tea farmers.

6.3 Tea Reforms and the Non-tea Producers The future growth of the small farmer tea sector in Rwanda will require, among other

things, attracting existing non-tea famers into tea-growing activities as well as increasing the

amount of land cultivated by current tea farmers. Both will depend on incentives, among

other determinants. This sub-section examines some of the further challenges and

difficulties facing this type of transition as revealed in the survey responses provided by the

large sub-sample of non-tea farmers. This analysis is informed by the questions included at

the request of the GoR on farmer awareness, perceptions and conjectured responses to the

price reform.

We first investigate the awareness of non-tea farmers regarding both the factory

privatization programme and the recent green leaf tea reforms, and their attitude to

engaging in tea cultivation. Table 6.5 reports non-tea farmer awareness of the two most

recent tea sector reforms. Approximately one-quarter of non-tea farmers knew of the

privatization program but only 17 percent were aware of the recent price reforms.

Furthermore, as Table 6.6 reveals, no price, however high, would induce about two-thirds of

non-tea farmers to engage in tea cultivation. In other words, their reservation price to

engage in tea cultivation is set to infinity in this case.

In order to explore this particular issue further, we use a probit regression model to

interrogate the determinants of this reluctance among non-tea farmers. The dependent

variable assumes a value of one for the two-thirds of non-tea farmers who would not

engage in tea production at any price, and zero otherwise. Table 6.7 provides a description

of the explanatory variables, their sample average values, and the corresponding probit

marginal and impact effects.

The gender and health status of the head of household emerge as the most important

determinants. Female headed households are 13.3 percentage points more likely to be

reluctant to engage in tea production at any price, on average and ceteris paribus. If the

head of household is afflicted by a physical disability, the reluctance probability increases by

9.6 percentage points. There are no statistically significant effects for the key marital status

variables and human capital effects are also found to be absent. In addition, none of the

three welfare or asset wealth metrics included in the specification register statistical

significance at a conventional level and this result is invariant to excluding either land,

34

income or the wealth index in turn. In addition, recent improvements in road infrastructure

also fail to yield a statistically significant effect in this regression model.

On the other hand, farmers who have past experience cultivating tea, or know of either the

privatization or the price reform are significantly less likely to be reluctant to cultivate tea at

any price. A former tea farmer is 24.2 percentage points less likely to be reluctant to engage

in tea production compared to a farmer who has never engaged in tea production, on

average and ceteris paribus. In addition, knowledge of the factory privatization programme

reduces the probability of being reluctant to engage in tea cultivation at any price by 7.7

percentage points. However, the strongest effect is reserved for those non-tea farmers who

are aware of the price reform policy. These farmers are 15.5 percentage points less likely to

be reluctant to engage in tea production activity, on average and ceteris paribus.

Table 6.5: Attitudes & Perceptions of Tea Reforms among Non-tea Farmers

Question Yes (%)

‘Are you aware that tea factories were recently privatized?’

26.5

‘Are you aware of the government’s recent green leaf price reforms?’

16.9

Sample Size 1,168

Table 6.6: Minimum Green Leaf Price per Kilogram Required to Encourage Non-tea Farmers to Cultivate Tea

Question Responses (%)

‘What is the minimum price per kilogram (in RWF) that would encourage you to engage in tea production?’

Between 100 RWF and 160 RWF 8.0



Greater than 230 RWF 18.2

No price would encourage tea production 67.3

Sample Size 1,287

Table 6.7: Probit Model for Non-tea Farmers’ Reluctance to Cultivate Tea


Marginal/ Impact Effects


0.0016 (0.0014)

HoH Female (=1) 0.2503 0.1326** (0.0575)

HoH Physical Health Disability (=1) 0.1504 0.0960** (0.0823)

35


Married (=1) 0.7745 0.0696 (0.0751)

Divorced (=1) 0.0196 -0.2215* (0.1354)

Single (=1) 0.0206 -0.0250 (0.1355)

Widowed (=1) 0.1853 ‡


No Education (=1) 0.3471 -0.0755 (0.0703)

Some Primary Education (=1) 0.3357 -0.0778 (0.0691)

Complete Primary Education (=1) 0.2379 -0.0116 (0.0684)




-0.0143 (0.0131)


-0.0224** (0.0105)

Household Income & Wealth Measures:

Log of Annual Household Income 11.38 (1.31)

-0.0057 (0.0148)

Wealth Index based on Principal Components Analysis (PCA)

-0.06 (1.48)

0.0081 (0.0140)



-0.0085 (0.0144)


-0.0057 (0.0102)


0.0121 (0.0112)


-0.0386*** (0.0104)

Former Tea Farmer (=1) 0.0412 -0.2422*** (0.0881)

Knowledge of Recent Tea Sector Policy Reforms

Aware of Tea Factory Privatizations (=1) 0.2873 -0.0766* (0.0463)

Aware of Green Leaf Price Reform (=1) 0.1812 -0.1549*** (0.0533)



0.0331 (0.0386)

36

District Controls Included

n/a Yes

McFadden R-Squared n/a 0.1512

Sample Size 971 971 Notes to table 6.7: (a) maximum likelihood (asymptotic) standard errors are reported in parentheses; (b) ***, **, and *

denote statistical significance at the 0.01, 0.05 and 0.1 level of statistical significance respectively.

In addition to green leaf tea price, the non-tea farmers were also asked questions on their

perception of other constraints that impact their willingness to engage in tea production.

Table 6.8 provides responses from the sample of non-tea farmers for the three most

popular constraints cited. Over two-thirds cite the absence of adequate land, while close to

one in five flag a lack of expertise in tea growing. A reasonable number (14 percent) of non-

tea farmers also identify the higher profitability of other cash crops as a reason for not

engaging in tea production. A lack of either non-labor inputs, extension services or credit

was quoted by only 2 percent of farmers in each case as representing constraints on their

engagement in tea production.

Table 6.8: Factors Preventing Engagement of Non-tea Farmers in Tea Cultivation

Constraint Yes (%)

(1) Lack of Adequate Land 65.8

(2) No Expertise in Tea Production 18.8

(3) More Profitable to Cultivate Other Crops 14.1

Sample Size 1,287

In an accompanying piece of empirical analysis (not reported here, but see Annex 3 for the

table summarizing results), we separately modelled the three most popular responses as

reported in Table 6.8 again using a probit model. The dependent variables were coded one

for a “yes” response and zero for a “no.” The model included the set of covariates

contained in the specification reported in Table 6.7 in addition to some other variables

depending on the outcome modelled. In the case of the question regarding more profitable

crops, the key crops grown by non-tea farmers found to increase the probability of a ”yes”

response for this question were Irish potatoes and climbing (or haricot) beans. The former

increased the probability of a “yes” response by almost 10 percentage points, while the

latter by a more modest 1.4 percentage points. Both of these crops have exhibited a sharp

rise in popularity among farmers in Rwanda in recent years with the area used for

cultivating the latter rising nationally by 50 percent between 1997 and 2007. There has

been an even sharper increase of about 170 percent over the same period in the total area

used to cultivate Irish potatoes. This crop has shorter production cycles (four months)

compared to many others in Rwanda and generally enjoys high yields. It also currently

boasts strong market demand both domestically and in Uganda. Our survey data do not

permit a comparative analysis of input costs for competing crops and tea production, so the

above only represents a very partial analysis of the role of competitor crops in deterring

non-tea farmers from engagement in tea production.

37

In regard to the question on the absence of adequate land, a significant negative estimate

was registered for the log of land holdings, though the effect was numerically small.

Specifically, the marginal effect suggested that a 5 percent increase in land holdings reduces

the probability of a “yes” response by 0.12 of one percentage point, ceteris paribus and

relative to a mean value of 0.64. However, the quality rather than the quantum of land is

likely to be the more important consideration in animating a “yes” response to this

particular question.

In regard to the final constraint on the lack of expertise in cultivating tea, a gender effect

emerges as statistically significant with a female headed household ten percentage points

more likely to acknowledge a lack of expertise compared to a male headed one. Not

surprisingly, former tea farmers are 14 percentage points less likely to cite a lack of such

expertise as a constraint to engaging in tea production.

6.4 Subjective Measures of Household Welfare for Rwandan Farmers We now explore the responses of farmers to a question posed regarding current level of

satisfaction with their household’s standard of living. Table 6.9 reports the responses to this

question on a five-category ordinal scale. The responses are delineated across tea farmers

and non-tea farmers. There is a very small number of responses in the two extreme

categories with only 7% of all farmers reporting themselves as ‘very dissatisfied’ and 2.5% as

‘very satisfied’. The responses suggest that about one-third of all respondents express

themselves as either ‘satisfied’ or ‘very satisfied’ with their household’s standard of living.

However, there is a sharp contrast in responses between tea farmers and non-tea farmers

with 44.5% of the former placing themselves in these top two categories compared to just

30% for the latter. In addition, the difference in the frequency distribution across

satisfaction categories by farmer type is found to be statistically significant at a conventional

level with a chi-squared value of 55.1 and four degrees of freedom.

Table 6.9: Subjective Perceptions of Farmer Well-being

Question Tea Farmers (%)

Non-tea Farmers (%)

‘How satisfied are you with your household’s standard of living today?’

‘Very dissatisfied’ 3.6 8.1

‘Dissatisfied’ 21.7 36.1

‘Neither satisfied nor dissatisfied’ 30.3 26.2

‘Satisfied’ 41.7 27.0

‘Very satisfied’ 2.8 2.6

Sample Size 429 1287

In order to investigate the determinants of these responses in more detail we employ a five-

category ordered probit regression model. The maximum likelihood (ML) estimates are

reported in Table 6.10. Neither a head of household’s age nor formal human capital levels

matter in determining satisfaction outcomes but both gender and health do. In particular, a

38

female headed household is less likely to report being in the ‘satisfied’ category compared

to a male headed household by six percentage points, and a head of household with a

physical disability is less likely to be reported in this category than an able-bodied one by

four percentage points, on average and ceteris paribus. The marital status of the head of

household exerts no independent effect on reported satisfaction but the number of

household members (both children and adult) is found to lower it. For instance, an

additional adult (child) member reduces the probability of the household being reported in

the ‘satisfied’ category by 3.5 (1.2) percentage points. Further, recent infrastructural

improvements in road access to markets in the last five years is also found to enhance

satisfaction levels.

The three variables included to capture the scale of household wealth along three different

dimensions all yield statistically significant positive effects on household living standard

satisfaction levels. The implicit trade-offs inherent in these particular point estimates

suggest that a 10% increase in household income would be required to off-set a 15%

reduction in land holdings to ensure that an average household’s satisfaction (or utility)

level remained constant.14 In addition, a 10% increase in annual household income would

be required to off-set a 0.08 standard deviation reduction in the wealth asset index.

We now turn to the estimate of the impact of being a tea farmer on satisfaction. This is

found to be positive and extremely well determined. In particular, controlling for the array

of included variables, a tea farmer is eight percentage points more likely, on average, to be

‘satisfied’ compared to a non-tea farmer, and 1.3 percentage points more likely to be ‘very

satisfied’. Thus, as noted in Table 6.9, tea farmers appear more satisfied overall than their

non-tea producing counterparts.

Table 6.10: Ordered Probit Model of Farmer Satisfaction with Living Standards


ML Estimates


-0.0038 (0.0024)

HoH Female (=1) 0.2493 -0.2031* (0.1183)

HoH Physical Health Disability (=1) 0.1652 -0.1330* (0.0823)


Married – monogamous (=1) 0.7263 -0.0733 (0.1299)

Married – polygamous (=1) 0.0417 -0.2081 (0.1903)

Divorced (=1) 0.0201 -0.1741 (0.2152)

Single (=1) 0.0201 0.1953

14

However, the proposition of a unitary relationship cannot be rejected by the data in this case with an absolute asymptotic t-ratio of 0.67 reported for this test.

39

(0.2247)

Widowed (=1) 0.1918 ‡


No Education (=1) 0.3355 0.1451 (0.1218)

Some Primary Education (=1) 0.3384 0.1080 (0.1183)

Complete Primary Education (=1) 0.2442 0.0806 (0.1188)




-0.1117*** (0.0219)


-0.0377** (0.0185)



0.1166*** (0.0280)

Wealth Index based on Principal Components Analysis (PCA)

0.03 (1.49)

0.1484*** (0.0255)



0.0768*** (0.0265)

Non-tea Farmer (=1)

0.7126 ‡


Tea Farmer Secondary Activity (=1) 0.0151 0.0171 (0.2379)



0.1352* (0.0678)


n/a Yes

Threshold Parameters:

1

θ n/a -1.1395***

(0.4043)

2

θ n/a 0.2244

(0.4033)

3

θ n/a 1.0372***

(0.4039)

4

θ n/a 2.6792***

(0.4088)

McFadden R-Squared n/a 0.0519

Sample Size 1392 1392

40

Notes to table 6.10: (a) maximum likelihood (asymptotic) standard errors are reported in parentheses; (b) ***, **, and *

denote statistical significance at the 0.01, 0.05 and 0.1 level of statistical significance respectively.

6.5 Some Concluding Remarks The contemporary profile of Rwandan tea farmers emerging from both the analysis

undertaken in the current section and earlier in section four suggests, on average, a more

affluent group compared to their non-tea growing counterparts. This affluence is revealed

along a number of different dimensions including household income, household assets, and

land holdings. In addition from section four, we find that tea farmers also tend to be more

diversified in terms of their cropping activities. Overall, they also report themselves as

considerably more satisfied with their current standard of living as opposed to their non-tea

farming counterparts.

The majority of tea farmers are in cooperatives and although some sizeable variations in

revenues across these cooperatives were detected, the cooperative “top-slice” per unit

price for the services provided was modest, being of the order of 20 percent, on average.

The majority of non-tea farmers are reluctant to engage in tea cultivation at any price, citing

access to land, a lack of tea production expertise, and more profitable alternative crops as

key explanations for such reluctance. In particular, climbing beans and Irish potatoes

represent the more popular alternatives and both currently enjoy relatively high market

prices. Furthermore, they both require less demanding and intensive husbandry skills than

tea, though both are more vulnerable to disease, given the lack of adequate crop rotation,

and require a higher usage of pesticides compared to tea. However, such cash crops

compete with tea and clearly represent part of the challenge in persuading farmers to

engage in tea production.

Finally, a key finding from the econometric evidence here is the lack of knowledge of the

price reforms among the overwhelming majority of non-tea farmers. Thus, it is possible that

an information campaign on the nature of the price reforms and the scale of the more

recent and future potential benefits accruing to tea farmers might better inform and

educate non-tea farmers about the relative advantages associated with engagement in tea

production.

41

7. Recommendations and Suggestions for Follow-up Survey

There are four key recommendations for the follow on survey that emerge from the

baseline analysis. These are:

(a) It is important to minimize the use of non-heads of households in providing relevant

household data in the follow-up survey. In particular, it is imperative that the follow-up

survey envisaged under this project increases the viewing rate of both land title deeds and

farmer tea factory books by interviewers in order to enhance the quality of the responses

for these particular questions. There is clearly an enumerator training issue that needs to

be addressed here for the future. An emphasis on using the head of household should also

enhance the prospects of addressing some of the current problems in regard to existing

gaps that that emerged in the 2013 survey in terms of the responses on land size.

(b) The impact evaluation will use a separate factory survey to obtain the historical and

current data maintained by factories on the quantities of tea supplied and the prices given

to the tea farmers that feature in this baseline survey. This is desirable to triangulate the

survey results on quantity and yields, as well as being the only source of quality data (one of

the aspects of the theory of change for the price reform).

(c) It would be useful to investigate the possibility of accessing information held at Rwanda’s

national land registry as an additional way of dealing with the current informational deficit

in regard to land size information in the current survey data. The national land registry

should hold copies of the land title deeds. However, the research benefits of such an

exercise would need to be weighed against the costs of such an exercise, which would not

be trivial.

(d) Given the putative prospect of financial benefits accruing to tea farmers consequent on

the price reforms, an additional module required for the follow-up impact survey should

focus on eliciting detailed information on the investment behaviour of tea farmers in non-

tea related activities in the post-price reform era.

42

References

Carletto, C, Gourlay, S., and P. Winters (2013), Land Area Measurement and Implications for

Agricultural Analysis, Policy Research Working Paper Number 6550, The World Bank,

Development Research Group.

Essama - Nassah .B, Ezemenari Kene and Korman Vijdan, Reading Tealeaves on the Potential

Impact of Privatization of Tea Estates in Rwanda, Policy Research Working Paper, 4566, The

World Bank, 2008

Haisken-DeNew, J.P. and Schmidt, C.M. (1997), Inter-industry and Inter-region Differentials:

Mechanics and Interpretation, Review of Economics and Statistics, Vol. 79, No. 3, pp. 516-

21.

Kym Andersen, ed. (2009), Distortions to Agricultural Incentives: A Global Perspective, 1955-2007, The World Bank, 2009.

National Institute of Statistics of Rwanda (2012), EICV3 Thematic Report: Income, Kigali,

Rwanda.

Ministry of Agriculture and Animal Resources (2008), A Revised Tea Strategy for Rwanda –

Transforming Rwanda’s Tea Industry.

MINAGRI. 2012. Cabinet Briefing Paper: New tea green leaf pricing model to tea farmers in

Rwanda. Kigali, Rwanda.

Krueger, A.B. and Summers, L.H. (1988), Efficiency Wages and the Inter-industry Wage

Structure, Econometrica, Vol. 56, No. 2, pp. 259-93.

43

Annex 1: Tea Growing Districts

44

Annex 2: Tea Growing Sectors per District Covered

District # Tea Growing Sectors

Covered

Rulindo 6

Nyabihu 6 Burera 1

Nyaruguru 6

Nyamagabe 4

Gicumbi 11

Rusizi 6

Nyamasheke 5

Rubavu 3

Ngororero 5

Rutsiro 3

Karongi 7

45

Annex 3: Mean Differences between Co-op and non-Co-op Thé Villageois Farmers

Characteristics Co-op Member

Non-Co-op Member

Test of Differences

Age of Head of Household (HoH) (years) 53.1 (0.81)

51.6 (1.92)

pv = 0463

HoH Female (=1) 0.249 0.232 pv = 0.785

HoH Physical Health Disability (=1) 0.195

0.304 pv = 0.067


No Education (=1) 0.31 0.30 pv=0.963



1.11 (0.16)

pv=0.163



11.79 (0.17)

pv=0.019

Wealth Index based on Principal Components Analysis (PCA) 0.333 (0.08)

0.262 (0.20)

pv=0.749

Land Holdings

Number of Tea Plots 1.59 (0.08)

1.14 (0.05)

pv=0.029

Sample Size 300 54 Notes to table: (a) Standard deviations are reported in parentheses for continuous variables only; (b) pv denotes the prob-

value for the test of differences in means; (c) sample sizes may differ slightly from those reported in the final row across

some characteristics given missing values.

46

Annex 4: Household Income Determinants for Female Farmers


OLS Estimates

Constant

1.0000 10.5046*** (0.7392)


0.04252* (0.0126)


-0.0004** (0.0002)

HoH Physical Health Disability (=1) 0.2046 -0.2758* (0.1574)


Married – monogamous (=1) 0.1643 0.1388 (0.2055)

Married – polygamous (=1) 0.0057 -0.6705** (0.3031)

Divorced (=1) 0.0720 -0.2050 (0.1995)

Single (=1) 0.0489 -0.5409* (0.3194)

Widowed (=1) 0.7091 ‡


No Education (=1) 0.5591 -1.1238*** (0.2512)

Some Primary Education (=1) 0.2478 -1.0677*** (0.2720)

Complete Primary Education (=1) 0.1354 -0.4639*** (0.1322)




0.0732*** (0.0461)


-0.0012 (0.0561)


0.71 (0.86)

0.1395*** (0.0758)



0.2200*** (0.0474)


0.0281 (0.0277)


0.0121 (0.0490)


0.0273 (0.0429)

Non-tea Farmer (=1) 0.7003 ‡

47




0.1225 (0.1329)


not reported

Yes

Adjusted R-Squared

n/a 0.3290

Sample Size 347 347 Notes to Annex 4: (a) standard deviations are reported in parentheses for the sample means; (b) robust standard errors are

reported in parentheses for the OLS estimates; (c) ***, **, and * denote statistical significance at the 0.01,0.05 and 0.1


48

Annex 5: Household Income Determinants for Farmers with Health Disability


OLS Estimates

Constant

1.0000 11.1374*** (1.1481)


0.0073 (0.0364)


-0.0002 (0.0003)

HoH Female (=1) 0.3087 -0.0998 (0.3147)


Married – monogamous (=1) 0.6696 0.2895 (0.3504)

Married – polygamous (=1) 0.0391 0.6408 (0.4155)

Divorced (=1) 0.0217 -0.6047** (0.2554)

Single (=1) 0.0261 -0.6051 (0.4315)

Widowed (=1) 0.2435 ‡


No Education (=1) 0.4956 -0.4473 (0.4095)

Some Primary Education (=1) 0.2696 -0.2397 (0.4353)

Complete Primary Education (=1) 0.1783 -0.0948 (0.4545)




0.0622 (0.0489)


-0.0253 (0.0493)


0.71 (0.86)

0.0522 (0.1092)



0.2053*** (0.0695)


0.0888* (0.0450)


-0.0322 (0.0515)


0.0106 (0.0496)

Non-tea Farmer (=1) 0.6435 ‡

49




0.1478 (0.1614)


not reported

Yes

Adjusted R-Squared

n/a 0.4109

Sample Size 230 230 Notes to Annex 5: (a) standard deviations are reported in parentheses for the sample means; (b) robust standard errors are

reported in parentheses for the OLS estimates; (c) ***, **, and * denote statistical significance at the 0.01,0.05 and 0.1


50

Annex 6: Probit Models Explaining “Yes” Outcomes in Table 6.18

Characteristics (1) (2) (3)

Age of Head of Household (years) -0.0050*** (0.0014)

0.0007 (0.0010)

-0.0011 (0.0009)

HoH Female (=1) -0.0722 (0.0649)

0.1004** (0.0545)

-0.0052 (0.0449)

HoH Physical Health Disability (=1) 0.0033 (0.0465)

0.0102 (0.0356)

-0.1019*** (0.0224)


Married (=1) 0.0013 (0.0696)

0.0429 (0.0454)

0.0018 (0.0514)

Divorced (=1) 0.0137 (0.1230)

-0.1182 (0.0442)

-0.0722 (0.0569)

Single (=1) -0.0660 (0.1282)

0.0066 (0.0839)

-0.0078 (0.0835)

Widowed (=1) ‡ ‡ ‡


No Education (=1) 0.0782* (0.0433)

-0.0639** (0.0287)

0.0117 (0.0301)

Some Primary Education (=1) -0.0277 (0.0412)

-0.0706** (0.0276)

0.0257 (0.0277)

All Other Education Education Categories (=1) ‡ ‡ ‡


Number of Adult Household Members -0.0020 (0.0126)

0.0051 (0.0094)

-0.0030 (0.0088)

Number of Children Household Members -0.0078 (0.0106)

-0.0064 (0.0078)

-0.0017 (0.0069)


Log of Annual Household Income -0.0141 (0.0147)

-0.0168 (0.0106)

0.0217*** (0.0098)

Wealth Index based on Principal Components Analysis (PCA) -0.0455*** (0.0140)

0.0031 (0.0103)

0.0004 (0.0086)


Log of Total Land Size (Hectares) -0.0246* (0.0141)

0.0041 (0.0100)

0.0017 (0.0092)


-0.0007 (0.0076)

0.0203*** (0.0064)

Number of Crops Grown in Season B -0.0004 (0.0111)

0.0039 (0.0084)

-0.0015 (0.0082)

Number of Crops Grown in Season C -0.0071 (0.0105)

0.0184** (0.0077)

-0.0134 (0.0089)

Former Tea Farmer (=1) -0.1923** (0.0846)

-0.1392*** (0.0267)

-0.0335 (0.0454)

Knowledge of Recent Tea Sector Policy Reforms

Aware of Tea Factory Privatizations (=1) 0.0843* (0.0448)

-0.0679** (0.0321)

-0.0049 (0.0454)

Aware of Green Leaf Price Reform (=1) 0.0502 (0.0507)

-0.0184 (0.0399)

0.0628* (0.0391)


Improved Roads to Markets in the Last 5 Years (=1) 0.0011 (0.0375)

-0.0247 (0.0281)

-0.0077 (0.0238)

Cultivates Irish Potato in Season A n/a n/a 0.0066 (0.0265)

51

Characteristics (1) (2) (3)

Cultivates Irish Potato in Season B n/a n/a 0.0434 (0.0292)

Cultivates Irish Potato in Season C n/a n/a 0.0993** (0.053)

Cultivates Haricot Beans in Season A n/a n/a -0.0246 (0.0292)

Cultivates Haricot Beans in Season B n/a n/a 0.0106 (0.0242)

Cultivates Haricot Beans in Season C n/a n/a 0.0143*** (0.0242)


Yes Yes No

McFadden R-Squared 0.0870 0.0994 0.0908

Sample Size 992 992 992 Notes to table: (a) column (1) contains probit marginal/impact effects for the model where the dependent variable is

coded 1 if the responses was ‘ yes’ to the question on inadequate land; column (2) contains probit marginal/impact effects

for the model where the dependent variable is coded 1 if the responses was ‘ yes’ to the question of no tea expertise;

column (3) contains probit marginal/impact effects for the model where the dependent variable is coded 1 if the responses

was ‘ yes’ to the question that it was more profitable to cultivate other crops; (b) maximum likelihood (asymptotic)

standard errors are reported in parentheses; (c) ***, **, and * denote statistical significance at the 0.01, 0.05 and 0.1 level

of statistical significance respectively. (d) ‡ denotes base group in estimation; (e) denotes n/a in estimation; (f) the sample

size are different relative to Table 6.11 given missing values on the included explanatory variables; (g) district level

dummies were not included in column (3) specification because of a high correlation with the included crops variables.

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