Study context and data collection

The study was conducted in Kilosa District in the Morogoro region in Tanzania in 2010. The district has an area of 14 245 km² and had a population of 488 191 in the latest (2002) census.

Agriculture is the main income generating activity, employing about 85% of the labor force (URT 2007).

The area of land under forest cover in Kilosa district is approximately 52%¹ (URT 1997). All villages in our sample have some level of community rights to harvest forest resources from forests within their village boundaries, either by statutory or customary laws, but user rules and regulations exist. Both commercial and subsistence uses of timber are regulated, as well as commercial use of NTFPs. Subsistence use of NTFPs are allowed in all villages, except in certain areas that are protected as state forest reserves.

Our data set is part of the Global Comparative Study on REDD+ (GCS-REDD) conducted by the Center for International Forestry Research (CIFOR) and its partners. Kilosa is one of the

1 The exact number is unknown (URT 2007), and different estimates occur in the literature. Our number is estimated based on numbers from 1997 (URT 1997).

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six study sites in Tanzania². Three of the villages are included as pilot projects of the global effort aimed at reducing emissions from deforestation and forest degradation (REDD+) implemented by a national NGO, and were randomly selected from all villages included in this project. The two last villages were selected as control villages from a pool of other villages in the district, based on how well they matched on a to a set of village level variables, such as market access and tenure rights (Sunderlin et al. 2010).

We use data from a sample of 150 randomly selected households in the five villages. Detailed information on household characteristics, asset holdings and incomes was recorded through household surveys in July and August 2010. If possible, both the head of household and the spouse were present if the head of household was married. A one year recall period was used.

Total income is defined as the sum of cash income, subsistence income and net gifts/transfers of cash or in-kind. The accounting methods from different sources of incomes draw primarily on Cavendish (2002) and the PEN survey (Angelsen et al. 2011).

Some goods, particularly environmental goods, are for self-consumption and not traded in a market. The goods sold are typically traded only locally. We used own reported values to get a more realistic estimate of the real price (value to the household) rather than inflated prices in a far-way regional market. To calculate income from each source we deduced the cash costs of inputs and hired labor from the value (price * quantity collected or produced). Due to lack of data we do not deduct the depreciation of farm implements. The value of family labor is not included in the costs, and should not be based on the standard definition of income. For all agricultural, forest and livestock products, we used a test to identify outliers and checked total values and prices for each product by unit combination, and reviewed outliers manually. In the case of a missing value of an item, the mean village value was used. Given restrictions on the harvest in protected areas or of certain products, such as woody material for production of charcoal, some activities are illegal and may be underreported in the household surveys. We are not able to test or control for this

potential bias in our data, but tried to limit this during data collection by underscoring to respondents that none in our group of field workers were linked to government or any environmental NGO and that the households’ information would remain confidential.

The income variables are divided by adult equivalent units (AEU). There is a range of methods to account for differences in household composition and thereby different consumption needs across households (Deaton 1997). We adapt a version of the OECD scales, whereby adults aged 15-64 are given full weight while dependents (below 15, above 64) are given half a weight (Atkinson et al. 1995). As a measure of the level of land scarcity, operational agricultural land

2 For more details about the project, see http://www.cifor.org/gcs/global-comparative-study-on-redd.html

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holding is also divided by AEU. Crop income is by far the main component of the total household income, accounting for some two thirds of the income in the study villages, followed by forest income (Table 1)³. The mean forest reliance – the share of forest environmental income on total household income - among the households in the sample is 13%. The single most valuable forest product is firewood.

The households’ liquid assets include livestock, business capital stock and household and farm implements. The value of these items were determined by asking for the current sale price of the item, taking into account the age and condition of the asset. We did not collect information about cash savings, but assume (based on non-systematic information) that most savings are in livestock and other assets rather than cash.

3 We also recorded negative income for some households, e.g. were they have had large input costs for crops but experienced a crop failure.

91 Table 1 Descriptive statistics

Variable Obs Mean Std. Dev. Min Max % share¹

Household income per AEU (in TSH*)

Total household income 150 189 675 213 410 -46 900 1 886 057

Crops 150 127 182 153 149 -87 500 1 042 000 67.4

Business 150 22 167 107 982 0 1 192 000 5.3

Forest 150 16 099 20 797 0 144 000 12.7

Firewood 150 10 252 10 363 0 60 000 8.9

NTFPs 150 2 787 8 810 0 73 234 2.1

Timber prod, incl. charcoal 150 3 061 15 825 0 133 333 1.8

Salary 150 10 983 55 117 0 637 500 5.7

Non-forest environmental 150 8 267 24 102 0 258 462 6.1

Livestock 150 2 732 12 891 -60 667 97 500 1.6

Miscellaneous 150 2 245 7 076 0 51 429 1.2

Human capital assets

Household size (#) 150 5.09 2.09 1.00 14.00

AEU (#) 150 3.98 1.61 1.00 9.50

Age household head (years) 150 45.56 14.41 20.00 98.00 Education household head (years) 150 4.43 2.94 0.00 11.00 Illness household head (days) 150 11.63 22.90 0 182

Illness spouse (days) 150 7.45 14.61 0 90

Female headed household (0-1) 150 14%

Salary income (0-1) 150 25%

Household business (0-1) 150 19%

Productive and liquid assets

Agricultural land per AEU (ha) 150 0.51 0.37 0.08 2.23 Hh./farm implements (TSH) 150 55 347 95 055 0 840 000

Large livestock (#) 150 0.03 0.41 0 5

Medium livestock (#) 150 0.70 2.73 0 24

Chicken chicken (#) 150 9.11 10.60 0 64

Contextual variables

Distance to village center (min) 150 92.98 87.57 0.00 360.00

*Exchange rate July 1st 2010: 1 USD = 1599 TSH (From: http://www.exchangerates.org.uk/USD-TZS-exchange-rate-history-full.html). ¹The mean income shares are calculated by taking the mean income shares for the households

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Methods

2.1 Observed versus predicted income

Most forest-poverty studies use observed one-year income, both to classify the households into poverty groups and to calculate forest reliance. The commonly observed pattern is a negative correlation between total household income and forest reliance and a positive correlation between absolute forest income and total income. Most studies therefore conclude that the poorest

households are the most forest reliant (Mamo et al. 2007; Vedeld et al. 2007; Babulo et al. 2009;

Heubach et al. 2011; Nielsen et al. 2012; Rayamajhi et al. 2012).

Cross-sectional studies typically do not take into account that incomes fluctuate greatly from year to year. Panel data studies of poverty have found that households that are classified as poor in one period may not be in another period (and vice versa) due to random fluctuations in crop yields and prices, and irregular earnings from casual labor, remittances etc. (Carter and Barrett 2006).

Thus, household identified as poor in the survey year may not be persistent poor over time.

Similarly, some of the households with high observed income might have been lucky in the year of the survey, but that will again be among the low-income households next year. In a study from Ethiopia, Dercon and Krishnan (2000) found that one third of the households identified as poor in the first year in a two-year panel data set were different from the households identified as poor the second year.

Nielsen et al. (2012) present a simple framework to take some of the dynamics aspects of poverty into account the when studying poverty-environment relations in cross-section data based on the observed household income and liquid asset holdings. A limitation of their approach is that they use only a subset of assets. Agricultural crop income is the dominant income source in our sample, similar to most rural households in Africa (Davis et al. 2010; Angelsen et al. 2014), and thus the amount of land and labor are important productive assets. Human capital assets, such as health and education are potentially important for household income as well as income

diversification. We therefore use an augmented asset approach to predict household income, including liquid and non-liquid assets, human capital assets as well as contextual variables, and argue that the resulting predicted income is a better measure for the poverty status of a household.

Also, the regression analysis gives an estimate of the relative importance of various assets and it avoids the problem of converting all assets into monetary value.

93 2.2 Estimating predicted income

To predict household income, we estimate the following log-linear⁴ regression:

(1) The absolute income data is measured in Tanzanian Shillings (TSH) and log-transformed to account for non-normality in the distribution and to reduce the impact of outliers. lnY is the logarithmic transformation of household income⁵. Householdcharacteristics is a vector of household characteristics which are expected to be correlated with household income: number of adult males, adult females, elderly, young and children. These variables are indicators of the labor available in the household, and we expect households with more adults to have higher total income. The number of children may have a negative effect on household income, as this might require more time set aside for care and other non-productive activities. A dummy variable for gender of the household head is included in the vector, along with the age of the household head. We also include a squared value of age, to accommodate any non-linear effects of this variable. The

number of years the household head has been in school is included, and we expect more educated household heads to have higher incomes due to better agricultural skills, better access to

information and off-farm income generating activities. A dummy variable for business is included, taking the value 1 if any of the household members have a family business. We expect this variable to be positively correlated with household income. In addition, a dummy indicating whether or not any household member receive salary is included as a proxy for off-farm income opportunities.

Charcoal production is particularly profitable, and a dummy variable is included taking the value 1 if any in the household are engaged in this activity. Finally, health is expected to affect total

household income, and the number of days the household head and spouse were ill in the previous 12 months are included as proxies of health, and expected to be negatively correlated with total income.

Assets is a vector of productive, liquid and non-liquid assets, all expected to be positively correlated with household income. Crop income is the main source of income for most households, and agricultural land is an important productive asset. Size of land measured in hectares (ha) that the household had access to cultivate at the time of the survey, and includes owned land not rented out and rented in land. The total value of farm and household implements (in TSH 1000) is included, as well as the number of large, medium and small livestock.

4 The main purpose of predicting income is to classify households into poverty categories, and as a robustness test we predict income based on a model where all continuous variables are log-transformed.

5 One household has negative total net income because of high costs related to agricultural production; this household is not included in the regression.

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Distance is the walking time from the residence to the village center (in minutes), and we expect remote households to have lower total incomes. To control for village specific variations in

income, Village dummies are included. When transforming the income variable back from log to the level variable to estimate the predicted income, we adapt the smearing estimate developed by Duan (1983) to avoid the retransformation bias and underestimation of predicted income.

2.3 Forest reliance

The share of forest income (FI) relative to the total income (TI) of the household is the most commonly used measure of forest reliance (FR) or forest dependence, i.e. FR = FI/TI. Given the yearly fluctuations in income, this is not necessarily an accurate measure for long term or structural forest reliance. If the degree of fluctuation varies across income sources, and forest incomes are more stable over time compared to non-forest income sources, a potentially better measure for the structural forest reliance is the observed forest income as a share of the predicted total income. The critical assumption for this to be a preferred measure is that forest incomes are more stable over time compared to non-forest income sources. There are good arguments to believe so. Much of the forest income is for subsistence, and it is therefore more sheltered against price fluctuations in the markets (Angelsen et al. 2014). Compared to crop production, the supply of forest products are – in general – also less sensitive to climate variability (e.g., rainfall). Lack of good panel data sets makes it hard to find good empirical evidence for this, but one of the few panel data sets from Malawi supports our assumption (Chilongo and Angelsen 2014).

The second possibility is not only to predicted total household income, but also to use

predicted forest income and predicted forest reliance. We do this by estimating the same equation as (1), except using forest income⁶ or forest reliance as the dependent variable. The predicted forest income is divided by predicted total income as an alternative measure of structural forest reliance. Similarly, we predict the household specific forest reliance by estimating Equation 1 as a linear model, using the observed forest reliance as the dependent variable.

2.4 Categorizing Households

The first approach is to divide the sample into five quintiles based on either observed or predicted income. Although most households in our sample are probably poor in a macro context, the focus of this paper is inter- and intra-village variation, and households are categorized from poor to rich relative to the other households in the sample. As a measure of the poverty profile of forest income, Vedeld et al. (2004) suggest using the Kuznets Ratio; the ratio between the mean

6 17 households did not harvest forest resources. In order to be able to include these households in the estimation, we added 1 to forest income when taking the log of this variable.

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forest income going to the 20% highest-earning households and the forest income going to the 40% lowest-earning households. If the ratio is below 1, low-income households have higher mean forest income. We calculate the both the Absolute Kuznets Ratio (absolute forest incomes) and the Relative Kuznets Ratio (forest income shares).

The second approach is to categorize households based on both observed and predicted total income. Carter and May (2001) distinguish between stochastic and structural poverty. Following their categorization, households are defined as structurally poor, stochastically poor, stochastically rich or structurally rich based on their level of observed household income and predicted income.

Households in the lower observed income quintiles but in the higher predicted income quintiles are expected to be able to earn a higher income in a future or in a normal year. Either they were unlucky this year and thereby achieved a low income or they have not been able to make the full use of their productive resources. Thus these households are categorized as stochastically poor. The households with high reported income but low predicted income, on the other hand, are expected to earn less in the future, and their high income this year might be explained by luck or they have sold off some of their assets over the year. These households are categorized as

stochastically rich. Characteristics of households are compared across quintiles and the household categories. Means of variables are compared and one-way ANOVA with Bonferroni and Kruskal-Wallis tests are applied to assess the statistical significance of the results.