rafmacalaba/datause-encoder-data huggingface dataset:
**Graph 7: Correlation between road allocation and support to PF**

<!-- Start of picture text --> Western<br>Northern<br>Eastern<br>NS o uthernrth Western<br>Lusaka<br>Central Luapula<br>Copperbelt<br>0 .2 .4 .6 .8<br>support to PF<br>roads Fitted values<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Notes:_ Y axis is the province road commitments over total road commitments (measured in percentage). X is the support to PF in 2008 (measured by the number of PF seats over total province number of seats).

In assessing for correlations with political support, the construction of the Mongu-Kalabo road in Western Province, launched in October 2010 by the MMD government, is a provocative case study. Although the contract distance is only 34 km, the route crosses the Zambezi flood plain and is expected to cost at least USD 260 million, making it one of the most expensive roads per kilometer in the world<sup>12</sup> . The cost of the project over four years exceeded the annual road budget for 2009. One potential explanation for the government’s decision to procure such a high cost road in the Western province was the incumbent, MMD government’s desire to secure the Western province’s loyalty. At the time, the Western province was considered a swing province and the ruling MMD party was concerned that the Western province’s political loyalty could shift to the PF or to UPND. MMD’s fears were in fact realized <mark>in the 2011 polls when two parliamentary seats shifted to PF and six to UPND (out of 14).</mark>

# **4. Procurement analysis**

Procurement is at the core of potential governance issues. Using a database of contracts for the rehabilitation and upgrade of works, we present descriptive statistics to identify the main trends occurring in procurement during the period 2008-2011.

## **The distribution of projects by funding source**

The largest contribution for road allocation (about 48%) comes from external sources; these are loans and grants from the donors (e.g. Development Bank of Southern Africa (DBSA), European Union, Exim Bank of China, KFW, and the World Bank). This is not surprising since Zambia is still highly dependent on 12 ‘On average the cost of building a tarred road in the rest of the country is ZKw 5 billion per kilometre, here because of the complicated terrain it will cost ZKw 60 billion per kilometre’. President Rupiah Banda quoted at the groundbreaking ceremony (Times of Zambia, 18 October 2010 quoted in Raballand and Whitworth, 2011).

14
directly.<sup>13</sup> Instead, we proxy education by a literacy indicator about whether or not a member of the household reads the newspaper.

Appendix tables A1 and A2 present data on the differences in various village and household characteristics between participants in community forestry and non-participants. The typical participant in community forestry is a poor household that lives on the fringes of forests (see Table A.2). Over half the participating households are agricultural laborers, while only 3% are headed by females. Eleven percent of these households have at least 1 literate member. Some 38% of participants belong to scheduled tribes, 7% to scheduled castes and another 39% to other backward classes. Participant households, on average, own less than 1 ha of land, but 22% own an asset such as a radio or TV or telephone.

# **4.  Methodology**

Our paper seeks to answer two key questions: a) What are the determinants of participation at the village and household levels in community forestry? and, b) What are the impacts of participation on household fuelwood consumption?

We treat domestic consumption of fuelwood as an indicator of dependence on forest resources for subsistence as well as a measure of household welfare. Household and village participation in CF affect fuelwood use. This relationship exists because in some cases community forestry restricts forest use, and, in other cases, it results in forest management activities that increase access to fuelwood. Further, households that are dependent on scarce fuelwood are more likely to participate. Thus, we model jointly household fuelwood consumption and participation in CF.

In the following paragraphs, we first discuss the determinants of village level participation in community forestry. This is followed by a discussion of household participation and impacts on fuelwood consumption. We use three methods to assess impacts – simple comparison of means, maximum likelihood estimation, and propensity score matching.

## **A.  Probit Model of Village Participation**

There is considerable theoretical and empirical literature on conditions for durable commons institutions and why cooperation in managing natural resources occurs in some cases and fails in others. Agrawal (2001), for example, identifies 24 conditions (see Table 1) based on three synthesis books by Wade (1988), Ostrom (1990) and Baland and Platteau (1996). This literature has greatly improved current understandings of common property institutions and helped promote decentralization in natural resource management. We have built on this literature in our model of why a village may choose to participate in community forest management. Our analysis is in the tradition of many other empirical papers that use forest groups as a unit of analyses to understand cooperative behavior (Heltberg 2001, Varughese and Ostrom 2001).

> 13 The 54th round of National Sample Survey of India included two household questionnaire modules that were asked to different households.

9
2014), the Africa Sentinel Sites soil database (Vagen et al., 2010), the GlobeLand30 land cover database, and the SoilGrids 1km predicted values, are joined through the use of 3D regression kriging founded on random forests modeling (Hengl et al., 2015). A layer of geospatial data is produced for each soil property at anywhere from one to six different soil depths.

Because MAPS georeferenced each agricultural plot, it is possible to join the AFSIS data with the center point of the agricultural plot and extract the point estimates of each soil property of interest, at the soil depths of interest. The following soil properties were extracted from AFSIS and utilized in the soil suitability analysis: cation exchange capacity, electrical conductivity, organic carbon, and pH. Each of these properties were available in depths of 0-5cm, 5-15cm, and 15-30cm. Because MAPS soil samples were taken at depths of 0-20cm and 20-50cm, we use a weighted average of the AFSIS 0-5cm and 5-15cm values for comparison with the MAPS 0-20cm samples. Subsoils, MAPS 20-50cm or AFSIS 15-30cm depths, are not utilized. Table 2 provides summary statistics for key soil properties derived from both the MAPS and AFSIS sources.

_Table 2_ . Comparison of Key Soil Properties Across Data Source

||MAPS|AFSIS|Test<br>of<br>Difference|
|---|---|---|---|
|pH|6.42|5.75|***|
|Cation exchange capacity (CEC, cmol/kg)|13.52|15.15|***|
|Organic carbon (%)|1.47|1.78|***|
|Electrical conductivity (salinity, dS/m)|0.06|0.10|***|
|N|840|840||

Notes: Two-sided p-values reported: *** p<0.01, ** p<0.05, * p<0.1

# **4. Empirical Approach**

Various approaches to agricultural productivity are used in the agricultural literature, depending on research objectives and data availability. Average measures of productivity, including partial and total factor analysis, can be used to create a single statistic but the methods require high quality crop price data for the monetization of reported production that are often hard to come by in rural agricultural contexts with thin markets. Alternatively, marginal productivity analysis can be conducted with more direct policy-related takeaways. Cobb Douglas functions, and variations of Cobb Douglas, are commonly used (Deininger et al., 2007; Sherlund et al., 2002). The limitation of a simple linear production function in this context is that it assumes all farmers to be performing at optimal levels, without explaining the deviations between the observed and attainable (predicted) output levels. In order to allow for the analysis of the heterogeneity in production potential conditional on crop-specific suitability, one of the main objectives of the paper, we use stochastic frontier analysis, which allows for a better understanding of the aforementioned deviations.

9
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE about 39 percent. Looking ahead, the relative burden is expected to decrease from the 2009 level to about 30 percent in 2010, as the projected nominal 2010 GDP is expected to continue rebounding. Even then, the burden is comparatively very high.

Thus, Zimbabwe‘s ―ideal‖ investment scenario may lie out of reach for the time being. Therefore, two alternative scenarios were developed in this case (table 16).

**Table 16. AICD annual spending needs estimates over a 10-year period**

|||Scenarios||
|---|---|---|---|
|$ million per year|Ideal|Intermediate|Minimalist|
|**Total**|2,009|1,729|1,219|
|Power|1,242|1,156|665|
|WSS|427|342|313|
|Transport|218|110|187|
|ICT|75|75|36|
|Irrigation|47|47|18|
|**New investment**||||
|Power|577|491|0|
|WSS|115|48|0|
|Transport|31|16|0|
|ICT|39|39|0|
|Irrigation|29|29|0|
|**Rehabilitation**||||
|Power|257|257|257|
|WSS|171|171|171|
|Transport|108|24|108|
|ICT|0|0|0|
|Irrigation|17|17|17|
|**Maintenance**|||0|
|Power|408|408|408|
|WSS|142|123|142|
|Transport|79|69|79|
|ICT|36|36|36|
|Irrigation|1|1|1|

_Source_ : Mayer and others 2009; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others, AICD 2009. Derived from models that are available online at www.infrastructureafrica.org/aicd/tools/models. _Note:_ WSS = water supply and sanitation; ICT = information and communication technology.

The ―minimalist‖ scenario assumes that only maintenance and rehabilitation needs should be met, thus reducing the total annual needs by 40 percent from $2.009 billion to $1.219 billion. But not making any new investments would have a negative impact on Zimbabwe‘s growth and economic sustainability and may slow down the overall recovery. Therefore, an ―intermediate‖ scenario is also considered, which, in addition to maintenance and rehabilitation needs, allows for a modest level of new investment, including greater adoption of appropriate technologies for the transport and water sectors. For example, 43
Round that are expected to reduce tariffs in the industrial sector (known as NAMA, for nonagricultural market access) are likely to result in the erosion of the DR-CAFTA market access preference for textile and clothing. Low, Eiermartini, and Richtering (2005) argue that Honduras will be one of the developing countries most severely affected by the erosion preferences in NAMA, indicating that an important part of the erosion would come from the textile and apparel sector.

# Annex 2: Identifying Employees in the _Maquila_ Industry from Honduras’s Household Surveys (EPHPM)

Before 2006 the Honduras household survey (EPHPM) did not include information on maquila labor participation. This Annex shows how workers were classified into maquila versus nonmaquila for years prior to 2006.

In 2006, the EPHPM shows that firms within the maquila regime and part of the textile and apparel industry are highly concentrated in few departments (or provinces) in Honduras.<sup>19</sup> In all years, the EPHPM reports information on employees working in the manufacture of textiles, knitting mills, or manufacture of wearing apparel, except footwear, as classify by the UN International Standard Industrial Classification of all Economics Activities, Third Revision (ISIC Rev. 3).

For years other than 2006, when the question about working in a _maquila_ was not included, a worker was classified as being part of the maquila industry when the following conditions were satisfied: currently employed, employed by the private sector, working in the textile and apparel industry, working in an establishment with 10 or more workers, and located in departments where _maquila_ operate.

In order to evaluate the “goodness of fit” of these criteria of classification, workers were classified into maquila and non-maquila in 2006 following the procedure described above and compared with the observed classification. In 98 percent of the cases, this procedure correctly identified workers in maquila sector also part of the textile and apparel industry giving us a high level of confidence in the approach.

> 19 The departments with a high concentration of maquila firms are: Cortes, Atlántida, Francisco Morazán, Yoro, Santa Bárbara, Comayagua.

19
concepts mean that effectively only five WTO members would be required to reduce actual support, based on 2001 notifications: Australia, EU, Iceland, Norway, and US (Jensen and Zobbe, 2006). Export subsidies are the one area where bold cuts (full elimination) are on the table, and we assume this outcome in our Doha scenario. When it comes to developing countries (see column three) domestic subsidy bindings are cut by 40 percent. In this case, Jensen and Zobbe (2006) estimate that only Thailand’s subsidies would be affected.

Agricultural tariffs in the rich countries are reduced using a tiered formula, with marginal cuts changing at 15 and 90 percent bound tariff rates. The marginal cuts are 45 percent on the first 15 percentage points of the tariff, 70 percent for the range between 15 and 90 percent, and 75 percent on the remainder.<sup>6</sup> For developing countries, the inflection points are placed at 20, 60 and 120 percent bound tariff levels in agriculture, with marginal cuts of 35, 40, 50 and 60 percent, respectively.

Of course, cross-sector trade-offs are at the heart of the WTO negotiations, so we also consider the impact of non-agricultural elements of a prospective Doha Development Agenda on both rich and poor countries. Given the importance of non-agricultural income to farm households in many of the rich countries, this also could have a direct bearing on farm household welfare. In the case of poverty impacts in developing countries, improved access to rich country manufactures markets, as well as access to the markets of other developing countries can have an important impact on the demand for unskilled labor, and hence poverty rates.

> 6 For example, a tariff of, say, 100% is cut by 66.95%: = [15%*0.45 + (90-15)%*0.70 + (100-90)%*0.75]. By applying the cuts at the margin we avoid the discontinuities implied by the July Framework.

24
data cover a large sample that varies between 62 and 112 countries depending on the specification, from 1962-2004. The evidence appears to support both the cumulative knowledge function and the R&D expenditure function.

Following RBR our second exercise is also close to Branstetter (2001) and Griffith et al. (2006) in many respects, particularly regarding the notion of technological distance. Our results show that knowledge spillovers affect the marginal, conditional and joint probability of product-quality upgrading and product innovation. However, the spillover effects decline with a firm’s productivity distance from the innovators. We find that the economic impact of technology transfer is, for most of the cases, larger for the introduction of new products than quality upgrading. A one standard deviation increase in quality-spillovers generates, for firms similar to innovators, an increment of 70% points in the conditional probability of doing quality up-grading and raise by 5.3% the probability of innovating only in this dimension. By contrast, a one standard deviation increment in variety-spillovers delivers, for firms close to innovators, an increment of 160% in the conditional probability of introducing a new good and raise 246% the probability of innovating only in this dimension. All these effects are lower for firms that are far from innovators as the estimated coefficients corresponding to the interaction between quality upgrading and product innovation by the most productive firms are negative and statistically significant. Indeed, international knowledge spillovers could be negative for firms far away from the technological frontier.

The rest of this paper is structured as follows. The next section briefly reviews the literature on knowledge production functions and inter-firm spillovers. Section 3 provides a formalization of the theoretical framework. Section 4 presents our empirical approach for our research questions. Section 5 discusses the data sources and the data main characteristics. Section 6 presents our empirical results and section 7 concludes.

# **2 Related Literature**

The existing attempts to test endogenous growth models have not been successful in supporting the theory, thus motivating important critiques. One of the main critiques concerns the scale effect, which should be observable because if (local) existing stocks of knowledge are affect innovation then large countries should growth faster than small economies. Backus et al. (1992) pointed out that larger countries did not grow faster in the postwar era. Jones (1995) used time-series to establish that there have been substantial increases in R&D inputs with no corresponding increase in growth rates. More recently, Bottazzi and Peri (2007) and Yasser and Joutz (2006) using co-integration techniques, indirectly tested endogenous growth theories by testing the implied time series properties of patents, R&D, and Total Factor Productivity (TFP). Bottazzi and Peri (2007) also found evidence against the endogenous growth scale effect in a sample of 15 OECD countries. However, from the 3
# **2.2. The Participants**

The recruitment of a total number of 700 participants took place between March 2010 and August 2011. Five individuals had to be excluded from the trial because of a missing consent form, becoming pregnant, developing cancer, and internal documentation problems.<sup>9</sup> The last participant finished the follow‐up phase by the end of July 2013.

Table 1 provides some descriptive statistics of the study population. The average body weight at the start of the experiment (at the end of the rehabilitation stay) was 113.0 kg (249.4 lbs.) or 37.6 in terms of BMI (Figure A1 shows the distribution of the BMI over time). About 68 percent of the participants are men and 21 percent have a migration background.<sup>10</sup> For most patients of the cooperating clinics, medical rehabilitation is paid by the German pension fund, whose predominant goal is to avoid work disability and early retirement. Since there are many obese in the overall population that are already retired, our study population oversamples persons that are available for the labor market.

As already noted, four rehabilitation clinics, located in different towns, have been involved in the trial. About 42 percent of the participants were recruited by the clinic in Bad Mergentheim, 33 percent in Bad Kissingen, 18 percent in Isny, and roughly 7 percent in Glottertal. The clinics in Bad Mergentheim and Isny primarily focus on orthopedic interventions, while the clinics in Bad Kissingen and Glottertal are specialized on gastroenterology as well as endocrinology and patients with psychosomatic disorders, respectively. Many participants came to the clinics because of diagnoses other than adiposity although their symptoms are related to their body weight. All participants are medically indicated to lose weight.

# **3. Hypotheses and Methods**

In our empirical analysis, we aim to estimate the causal effects of financial incentives on body weight. While short‐term effects are analyzed in detail by Augurzky et al. (2012), the present paper concentrates on the medium‐ and long‐term effects. Two main hypotheses are analyzed: (i) financial incentives for weight loss have an effect on body weight after their removal, i.e., post‐ intervention effects, and (ii) monetary rewards for maintaining a previously achieved target weight prevent weight regain during the intervention and after the intervention has ended. In addition to the percentage change of the body weight of the participants as the main outcome

> 9 Results are robust with respect to treating these individuals as dropouts in sensitivity checks described in Section 3.

> 10 These shares are substantially lower than the corresponding averages for obese individuals in Germany, which we obtained using the German Socioeconomic Panel (SOEP) – a representative panel of German households. The mean age of the study population (48 years) lies about ten years below the average age of obese in Germany, while the share of employed participants (82 percent) is almost twice as large. Only the share of married participants does not deviate considerably from the respective share of obese in Germany.

8
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_

# **Appendix: Tables**

**Table 1: Manufacturing, value added (% of GDP)**

|||||**Year**|||
|---|---|---|---|---|---|---|
|||2002|2003|2004|2005|2006|
||Botswana|4.2|4.3|4.1|3.7|3.7|
||Gambia|5.7|5.3|5.3|5.2||
|**one**|Ghana|9.0|9.0|8.8|8.3|7.6|
|**ph**|Kenya|11.3|11.2|11.2|11.5|12.1|
|**glo**|Namibia|11.1|12.4|13.5|13.1|12.9|
|**An**|Swaziland|37.9|38.4|37.9|37.2|36.8|
||Tanzania|7.3|7.2|7.0|6.8|6.9|
||Uganda|10.0|9.3|9.2|9.2|8.6|
||Benin|8.6|8.5|7.6|7.5||
||Burkina Faso|12.3|12.9||||
||Burundi|8.4|8.5|8.5|8.8||
||Cameroon|20.6|20.2|19.2|18.7|18.5|
|**one**|Cape Verde|6.1|8.0|||4.8|
|**oph**|Congo, DR|5.4|5.4|6.3|6.6|6.5|
|**nc**|Guinea|4.1|3.9|3.7|3.7|3.7|
|**Fra**|Madagascar|12.5|13.7|14.2|14.0|13.4|
||Mali|3.2|2.8|3.4|3.2|3.1|
||Mauritania|6.2|6.0|5.9|5.0||
||Rwanda|11.2|8.9|8.0|8.2|8.5|
||Senegal|12.5|11.7|11.5|10.9|10.2|

Source: ADI (2007)

**Table 2: Estimates of returns to education using firm survey data**

|Authors|Countries|Years|Returns|Increasing|Comments|
|---|---|---|---|---|---|
|Mazumdar and<br>Mazaheri (2002)|Cameroon, Cote d'Ivoire,<br>Ghana, Kenya, Tanzania,<br>Zambia, Zimbabwe|early<br>1990s|positive,<br>significant|yes||
|Bigsten_et al._<br>(2000)|Cameroon, Ghana,<br>Kenya, Zambia,<br>Zimbabwe|early<br>1990s|positive,<br>significant|yes|Returns to human capital are<br>much lower if measured at<br>the firm level via the<br>production function.|
|Teal (in progress)|Ghana|1993-95|positive,<br>significant|yes|cited in Appleton_et al._<br>(1996)|
|Söderbom_et al._<br>(2006)|Kenya, Tanzania|1993-<br>2001|positive,<br>significant|yes|Find also cohort effects<br>(young vs. old). Returns are<br>higher for older workers.|

The studies use manufacturing firm and worker data from the RPED dataset. Their dependent variable is worker wages.

30
19 * <!-- Start of picture text --> PERCENT 30%-<br>OF<br>HOUSEHOLDS<br>25-<br><!-- End of picture text --> <!-- Start of picture text --> C RAINY SEASON<br>O3 DRY SEASON<br><!-- End of picture text --> <!-- Start of picture text --> 2OX -<br>lox -<br>0<br>ICN1 Nl-4 NS-9 N10-24 N25-49 N50-99 No1<br>MONTHLY EXPENDITURE ON WATER<br><!-- End of picture text -->

> FIGURE 6: **FREQUENCY DISTRIBUTION OF HOUSEHOLDS' MONTHLY ON WATER (ONITSHA, NIGERIA)**