metadata: authors: 0: author: Iimi,Atsushi
1: author: Gericke,Ben
count: Zambia
docna: 0: docna: Output- and performance-based road contracts
            and agricultural production : evidence from Zambia
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Rural Roads & Transport,Roads & Highways,Inequality,Transport Services
teratopic: Agriculture,Transport
abstracts: cdata!: Rural access is among the most important
            infrastructure elements to stimulate economic growth in
            rural and remote areas. The sustainability of feeder road
            maintenance is a challenge in many developing countries.
            Many feeder roads are unpaved and need to be maintained
            frequently, but they are often neglected under budget
            pressure. Output- and performance-based road contracts are
            an instrument to ensure the sustainability of road
            maintenance. Contractors are required not only to improve
            roads, but also to maintain them. Using micro data from
            household surveys in Zambia, the paper examines the impacts
            of output- and performance-based road contracts on
            agricultural production. It shows that the contracts have a
            significant impact on crop production, especially maize and
            groundnuts, two major crops grown in the study area. The
            paper also finds that the measured impacts are associated
            with actual road maintenance works, regardless of
            contractual methods. Any road work can improve people's
            connectivity, even if it is not an output- and
            performance-based road contracts. The impact of the
            contracts is catalytic: more road works were implemented on
            contract roads than non-contract roads, holding everything
            else constant. This is an important contribution to the
            sustainability of road maintenance. Finally, road
            improvement works are found to facilitate farmers'
            market participation, but the impact seems weak. There may
            be other constraints. Transport service costs are found to
            have a negative impact on farmers' market sales. Thus,
            although roads are improved, transport services may be not
            available or too expensive, which still hamper farmers'
            market participation.
geo_reg_key: 517195,517191
repnme: repnme: Output- and performance-based road
            contracts and agricultural production : evidence from Zambia
display_title: Output- and performance-based road
            contracts and agricultural production : evidence from Zambia
listing_relative_url: /research/2017/09/27993836/output--performance-based-road-contracts-agricultural-production-evidence-zambia
topicv3: Crop Production m1326627 174,Transport m1326661 135,Poverty m1326687 695,Rural Roads m1504494 4125,Road Maintenance m1505354 4031
publishtoextweb_dt: 2017-09-20T00:00:00Z
historic_topic: Agriculture,Transport
no_of_pages: 32
author_keys: 0: author_key: 914707
model_extractions: WPS8201 Policy Research Working Paper 8201 Output- and Performance-Based Road Contracts and Agricultural Production Evidence from Zambia _Atsushi Iimi Ben Gericke_ Transport and ICT Global Practice Group September 2017
Policy Research Working Paper 8201

# **Abstract**

Rural access is among the most important infrastructure elements to stimulate economic growth in rural and remote areas. The sustainability of feeder road maintenance is a challenge in many developing countries. Many feeder roads are unpaved and need to be maintained frequently, but they are often neglected under budget pressure. Output- and performance-based road contracts are an instrument to ensure the sustainability of road maintenance. Contractors are required not only to improve roads, but also to maintain them. Using micro data from household surveys in Zambia, the paper examines the impacts of output- and performance-based road contracts on agricultural production. It shows that the contracts have a significant impact on crop production, especially maize and groundnuts, two major crops grown in the study area. The paper also finds that the measured impacts are associated with actual road maintenance works, regardless of contractual methods. Any road work can improve people’s connectivity, even if it is not an output- and performance-based road contracts. The impact of the contracts is catalytic: more road works were implemented on contract roads than non-contract roads, holding everything else constant. This is an important contribution to the sustainability of road maintenance. Finally, road improvement works are found to facilitate farmers’ market participation, but the impact seems weak. There may be other constraints. Transport service costs are found to have a negative impact on farmers’ market sales. Thus, although roads are improved, transport services may be not available or too expensive, which still hamper farmers’ market participation.

This paper is a product of the Transport and ICT Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Output- and Performance-Based Road Contracts and Agricultural Production: Evidence from Zambia**

Atsushi Iimi,<sup>¶</sup> Ben Gericke Transport and ICT Global Practice The World Bank Group Key words: Rural access, Agricultural production, Transport connectivity.

JEL classification: C21, R41, R42

> ¶ Corresponding author.
- 2 -

# **I. INTRODUCTION**

Rural access is among the most important infrastructure elements to stimulate economic growth in rural and remote areas. According to the Rural Access Index developed by Roberts et al. (2006), in Africa only 34 percent of the rural populations have access to an all-season road within an approximate 2-km walking distance. Many farmers and local businesses are still not well connected to the road network and do not have good access to markets. Rural access is particularly important from the poverty reduction point of view. The majority of the rural populations are living below the poverty lines, simply engaging in subsistence farming with few advanced inputs used. Rural access is also critical for people’s access to social facilities, such as schools and hospitals.

The literature is generally supportive of the impacts of rural accessibility. In the short term, transport costs and travel time can be reduced by improved road conditions (e.g., Lokshin and Yemtsov 2005; Danida 2010). Over the longer term, agricultural productivity would likely be increased (Khandker et al. 2009; Bell and Van Dillen, 2012), and firms would become more profitable with more jobs created (Mu and van de Walle, 2011). Poverty could then be alleviated (Dercon et al., 2008; Khandker and Koolwal, 2011).

Road financing is a matter of particular concern in many developing countries. Despite the fact that there are significant unmet needs for road infrastructure, available resources are limited. In Africa, it is estimated that about US$12.7 billion would be needed to rehabilitate, upgrade and maintain the road network to meet minimum connectivity standards (AICD, 2009). Maintenance of rural roads, of which the traffic level is low at less than 200 vehicles per day, tends to be neglected under fiscal pressure. On the other hand, many rural roads in Africa are gravel or earth roads, which require frequent maintenance. The current shortfall in road maintenance spending is estimated to cost Africa US$1.9 billion a year (World Bank, 2010).
- 3 -

Performance-based contracting is a useful instrument to ensure sustainability of road maintenance. The literature has long been discussing an incentive problem in transport projects: About 90 percent of transport projects experienced significant cost overruns (Flyvbjerg et al., 2002). Africa’s road projects were delayed by 10 months on average (Alexeeva et al., 2008). And each year of delay would likely cost on average US$4.6 million for a US$100 million transport infrastructure project (Flyvbjerg et al., 2004). In theory, output- and performance-based road contracts (OPRC) can tighten contractors’ incentives to avoid cost overruns, while delivering outputs as agreed.<sup>1</sup> Under the OPRC framework, contractors are responsible for initial rehabilitation works and following maintenance works for an agreed period of time. Their performance is normally not measured by used inputs, such as bitumen and concrete, but by certain performance criteria, such as surface degradation and road user comfort.

Despite the fact that OPRC are being used widely, there is little evidence showing their effectiveness. On the cost implication side of OPRC, Lancelot (2010) shows that performance-based contracts were 20-35 percent cheaper than traditionally procured projects in Brazil. In Europe, it is found that the cost of public-private partnerships, which is relevant to but more advanced than OPRC, is 24 percent higher in the road sector. This is possible because of the risk premium of anticipated cost overruns (Blanc-Brude et al., 2009).

To the best of our knowledge, the current paper is one of the first attempts to examine the impacts of OPRC on the user side. Using data from a rural road project in Zambia, it will examine the possible impacts of OPRC on agricultural production. The following sections are organized as follows: Section II provides an overview of the road sector in Zambia. Section III develops our empirical strategy, and Section IV describes our data. Section V presents main results and discusses policy implications. And Section VI concludes.

> 1 Theoretically, Bajari and Tadelis (2001) show that the incentive mechanism can be strengthened to improve the performance of contractors, while minimizing public procurement costs.
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# **III. EMPIRICAL STRATEGY**

The current analysis relies on two household surveys carried out in Chipata and Katete before and after the ADSP. The project areas were selected based on local agricultural potential. Eastern Province is among the most important agricultural areas in Zambia, producing cotton, soybean, groundnuts and tobacco as well as maize. Chipata and Katete produce more than 20 percent of the nation’s total groundnuts. Based on agricultural productivity, about 200 km of feeder roads were selected in each of the districts ( **Table 1** ). These account for about 24 percent of total feeder roads in Chipata, and 38 percent in Katete. Before the project, almost all feeder roads were in poor condition in these two districts.

Under the project, 5-year OPRCs were made from March 2009 to March 2014. The awarded contractors were required to rehabilitate selected road links in the first year and maintain them by spot improvements as well as emergency works in the following 4 years. In the five years, a total of 385 km of roads were improved with gravel surface, box culverts and stone patching. Note that the OPRCs were targeted at a specific set of roads, but they did not prevent other road works elsewhere in the districts. Regardless of the OPRCs, the local governments actually implemented some other traditional road improvement and maintenance works wherever needed.
- 9 -

**<u>Table 1. Selected project roads under ADSP in Chipata and Katete</u>**

||Chipata<br>Road wor|ks (km)||Katete<br>Road wor|ks (km)|
|---|---|---|---|---|---|
|Link name|Planned|Actual|Link name|Planned|Actual|
|D128|23.4|23.4|RD410|4.2|4.2|
|RD595-R280|11.0|11.0|RD411|38.1|38.1|
|R348-RD596|27.7|26.7|RD412|18.8|18.8|
|RD116|48.4|48.4|U20|23.2|23.2|
|RD117|19.9|19.9|U22|1.5|4.5|
|R266|13.3|13.3|RD687|19.9|18.0|
|RD694|34.2|30.5|RD592-1|6.0|6.0|
|RD697|16.6|16.6|RD592-2|12.1|12.1|
||||R300|11.2|11.2|
||||R306|15.3|15.1|
||||U29|17.8|17.8|
||||R291-R292|26.6|26.6|
|Total|194.6|189.9||194.6|195.5|

Source: RDA (2014).

To assess the impacts of road works under OPRC on agricultural production, the following agricultural production function is considered: where _V_ is the total value of crops produced by household _i_ at time _t_ . The analysis only takes into account 7 major crops that are grown in the survey area: _j=_ { _maize, cotton, cassava, vegetable, soybean, tobacco, groundnuts_ }. The vast majority of farmers in the area grow maize, and the second most popular crop is groundnuts. Some farmers also produce other crops, such as cotton. To aggregate different types of crops, regional producer prices ( _pj_ ) are used from FAOSTAT.<sup>4</sup>

> 4 Average producer prices are used from three countries: Kenya, Rwanda and South Africa, where comprehensive data are available. The prices of maize, cotton, cassava, tobacco, vegetable, soybean and groundnuts are assumed to be $0.22 per kg, $1.34, $0.25, $2.66, $0.16, $0.47 and $0.97, respectively.
- 10 -

Our identification strategy is based on the difference-in-differences (DID) method with other covariates taken into account. _OPRC_ is a dummy variable for the adoption of OPRC. There are two periods of time: before ( _t=_ 0) and after ( _t=_ 1) the project. The impact of OPRC is expected to be captured by _β_ 3 in the equation. An advantage of the DID estimator is that it allows to control unobserved heterogeneity between the treatment and control groups and mitigate the self-selection bias, as long as they are time-invariant. However, as often discussed, it is a strong assumption that no time-variant factor affects the outcome indicators. To mitigate this risk, time-variant household characteristics are included on the right-hand side of the regression equation (e.g., Jalan and Ravallion 1998).

Following the agricultural economics literature (e.g., see Gyimah-Brempong (1987) and Bravo-Ortega and Lederman (2004)), six inputs are considered for _xk_ : _k_   _L_ , _H_ , _S_ , _F_ , _P_ , _TR_  , where _L_ and _H_ denote labor and land, respectively. Three advanced inputs are included: improved seeds ( _S_ ), fertilizer ( _F_ ), and pesticides ( _P_ ). _TR_ is the amount of spending on transportation services to take outputs to market.

Agricultural production must depend on underlying agro-climatic conditions. _POT_ is an estimate of potential crop production based on agro-ecological and soil conditions. It is defined by  _j p j H ijtY j_ where _Hijt_ is household _i_ ’s land area used for production of crop _j_ at time _t_ . _Yj_ is a yield estimate (kg/ha) of crop _j_ at this location under the rainfed high inputs assumption. This comes from a global spatial data set, Global Agro-Ecological Zones (GAEZ).

Other observable household characteristics, such as access to utility services and household assets, are also included in _zm_ , and _u_ is an error term.

One empirical challenge is that many input variables are likely to be zero, when analyzing data from developing countries. As will be seen below, only half of the farmers actually use fertilizer (both basal and surface dressing included) in Zambia. About 15 percent and less
- 11 -

than 10 percent use pesticides and transport services, respectively. The market participation rate is only 35 percent in our sample data.

One traditional approach to deal with this problem is to add a small positive number to avoid taking the logarithm of zeros. However, this may cause significant bias in estimation results (Battese, 1997). To remove that bias, the following analysis uses the Battese’s specification, which introduces dummy variables, _D_ , for the incidence of zero inputs: where Another empirical challenge is endogeneity: By construction, _OPRC_ is endogenously determined because the OPRC scheme was applied to places where agricultural potential is presumably high. Thus, regardless of the actual road works, agricultural production may be greater along the OPRC roads. This is a general endogeneity problem of infrastructure placement. Some areas are more productive than others because of better infrastructure, but at the same time, governments often invest more where productivity is inherently high no matter for what reason.

To address this issue, the instrumental variable technique is used. Three instrumental variables are constructed: _ELEV_ , _SLOP_ and _KMT4._ The first two variables represent geographic conditions of each road location: _ELEV_ is the level of elevation in meters, and _SLOP_ is the slope angle of the terrain. These are likely to be relevant to the necessity of road works (but less relevant to agricultural production), because the main challenge in rural areas of Eastern Province is accessibility during the rainy season. Many rural roads tend to be
- 12 -

inundated and inaccessible because of the poor condition of roads. Thus, _ELEV_ and SLOP are expected to explain potential needs for road works to a certain extent. The data come from a global spatial data set, the Shuttle Radar Topography Mission (SRTM), which provides 90m resolution elevation data of the world. Under the ADSP, about 340 pipe culverts and 20 box culverts were installed. Still, five clusters of emergency works were carried out to repair washed away embankments in 2010, 2012 and 2013 (RDA 2014).

In addition, remoteness from a main trunk road, which is T4 in Eastern Province, is also considered important to predict project areas. On one hand, roads are generally supposed to form an integrated network. Since the primary road network has already been well maintained in Zambia, feeder roads close to it are more likely to be improved as well. On the other hand, the project aimed at providing transport connectivity to remote areas. If this is the case, more remote areas would receive more road works. To capture this effect, the straightline distance from each project site to T4 is calculated (denoted by _KMT4_ ). Note that as discussed above, agricultural potential is also an important instrument to determine the OPRC roads. Our model has already included _POT_ as an independent variable. The validity of the instruments will be ex post examined by conventional tests, such as exogeneity and overidentifying restriction tests.

An important policy question is why the OPRC intervention could increase agricultural production. It is obvious that nothing would happen unless significant road improvement works are actually carried out, even though a performance-based contract is made. When the follow-up survey was carried out, some rehabilitation works do not seem to have been completed.<sup>5</sup> Then, no impact would have materialized even though roads were under the OPRC.

> 5 Although the RDA status reports show that all the rehabilitation works were completed when the follow-up survey was carried out, the perception of road users was sometimes different. Some respondents along the treatment roads indicated that roads were still under rehabilitation, for example, D128 in Chipata District and U29 in Katete District (RDA, 2031).
- 13 -

On the other hand, agricultural production could be increased by any road improvement work, even though it is implemented under the traditional input-based contracts. For instance, RD585 and D60 in Katete were selected as paired roads in the control group but improved outside of the ADSP OPRC. Based on the users’ perspective, in total, about 62 percent of the roads received significant rehabilitation works in both treatment and control groups (RDA 2013).

To examine this impact, the following equation is considered: where _WORK_ is equal to one if actual road work is carried out. To deal with the endogeneity issue, the same instruments are used. In addition, _OPRC_ is also included as another instrument. It is believed that the existence of OPRCs could affect the likelihood of receiving road improvement and maintenance works, because the contractors were obliged to keep the roads in good condition.

# **IV. DATA**

Micro data were collected though two household surveys carried out before the project in 2009 and after the project in August to November 2012. The follow-up data were collected before the full completion of the OPRCs, which started in March 2009 and ended in March 2014. Thus, the impacts measured by the surveys are presumably associated with the initial rehabilitation works in the first year of the contracts. However, some emergency and maintenance works were also implemented in not only treatment but also control areas before the follow-up survey. Hence, the measured impacts may also reflect these effects.
- 14 -

The OPRC roads were not selected randomly but chosen based on existing agricultural potential. For each of the OPRC roads, a comparable road was selected in the same and adjoining districts, based on socioeconomic characteristics, proximity to social facilities, such as schools and health clinics, as well as access to the main trunk road ( **Figure 7** ). For each road (both treatment and control), at least 20 households were surveyed in each of the standard enumeration areas (SEAs) that the road passes. In total, the baseline survey covers about 650 households in Chipata ( **Table 2** ) and about 780 households in Katete ( **Table 3** ).

**<u>Table 2. Sample size for Chipata</u>**

|||Treatment|Obs.|Control|Obs.||
|---|---|---|---|---|---|---|
|Road p|air||_t=_0|_t=_1|_t=_0|_t=_1|
|A1||R128 up to RD596|120|119 RD403|120|120|
|A2||R128 first part|39|<br>39 D131|40|<br>40|
|A3||R348-RD596||U33|||
|A4||R348-RD596|40|<br>40 U33|20|<br>20|
|A5||RD116/B28/R267|40|<br>40 RD709, RD710|40|<br>40|
|A6||RD116/B28/R267|20|<br>20 D12-R273, RD121<br>R269, RD268, RD270-|20|<br>24|
|A7||R266-RD166|60|<br>60<br>RD118|60|<br>58|
|B1||RD596|20|20D130inChadiza|20|20|
|||Total|339|338|320|322|

# **<u>Table 3. Sample size for Katete</u>**

||Treatment|Obs.|Control|Obs.||
|---|---|---|---|---|---|
|Road pair||_t=_0|_t=_1|_t=_0|_t=_1|
|A1|U29|20|20 RD585|40|<br>41|
|A2|R291|40|39 D583-RD409|40|<br>0|
|A3|RD410|20|19 D60-R296|20|<br>20|
|A4|R306|40|40 RD703-R308|40|<br>39|
|A5|U20-RD412|40|60 RD704, RD705, RD135|<br>40|<br>40|
|B1|RD411-R303, RD687|140|119 RD582 in Petauke|12|<br>11|
|B2|U20-RD412|80|100 RD582 in Petauke|60|<br>62|
|B4|RD412|40|39 RD413 in Petauke|41|<br>42|
|B6|U29|41|43R286inChadiza|39|35|
||Total|461|479|332|290|
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- 16 -

**<u>Table 4. Comparison of household characteristics between treatment and control group</u>**

||_OPRC=_0|_OPRC=_1|Differen|ce|
|---|---|---|---|---|
||Mean|Mean|Mean|Std.Err.|
|HH size|5.24|<br>5.42|<br>0.18|0.13|
|Male HH head|0.80|<br>0.79|<br>-0.01|0.02|
|HH head age|42.64|<br>41.57|<br>-1.06|0.85|
|HH head educational|4.23|<br>4.33|<br>0.11|0.19|
|attainment|||||
|Concrete floor of the|0.11|<br>0.11|<br>0.00|0.02|
|house|||||
|Borehole use as a main|0.650|<br>0.586|<br>-0.064|0.024 ***|
|source of water supply|||||
|Tap water as a main<br>|0.008|<br>0.021|<br>0.013|0.006 **|
|source of water supply|||||
|Toilet use (pit latrine,|0.481|<br>0.447|<br>-0.034|0.024|
|ventilated, or flush)|||||
|Electricity or gas use as a<br>main source of lighting<br>HH assets:|0.065|<br>0.050|<br>-0.015|0.011|
|Solar panels|0.062|<br>0.080|<br>0.018|0.013|
|Vehicle|0.002|<br>0.006|<br>0.004|0.003|
|Motorcycle|0.024|<br>0.023|<br>-0.001|0.008|
|Bicycle|0.618|<br>0.635|<br>0.018|0.024|
|Mobilephone|0.256|<br>0.275|<br>0.019|0.022|

The summary statistics are shown in **Table 5** . The average value of crop production is US$801 or about ZKW4,000 per household. Recall that in Zambia about 78 percent of the rural population lives below the national poverty line.<sup>6</sup> In Chipata and Katete District, the poverty rates are estimated at 72.6 percent and 82.9 percent, respectively (World Bank 2015). The agro-climatic potential exceeds US$4,300, which is five times more than currently produced. The currently dominant production system is clearly subsistence farming, with little fertilizer and few pesticides. Average land cultivated is about 1.6 ha. Only about one-third of the households participated in market transactions.

> 6 In Zambia, the income distribution is highly skewed. According to the Government of Zambia, the threshold of poverty is defined by the nation’s 2010 food basket to meet minimal nutritional requirements, which is ZKW96,366 per household (6 household members). This is significantly higher than many households’ incomes. ZIPAR (2013), analyzing the income distribution in the country, indicates that the threshold of household income for the lower income group is ZKW510 in 2012. This group is estimated to account for about 24 percent of the total population.
- 17 -

## **<u>Table 5. Summary statistics</u>**

|Variable|Abb.|Obs|Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Total value of 7 crops produced (maize, cotton,<br>cassava, tobacco, vegetable, soybean, groundnuts)|_V_|2,049|<br>801|<br>2,306|9|<br>80,359|
|Total value of 7 crops sold|_SALE_|749|<br>749|<br>1,212|11|<br>19,408|
|Dummy variable for roads under OPRC|_OPRC_|2,049|<br>0.563|<br>0.496|0|<br>1|
|Dummy variable for roads where actual work was<br>implemented|_WORK_|1,895|<br>0.563|<br>0.496|0|<br>1|
|Dummy variable for the period after the project|_t_|2,049|<br>0.474|<br>0.499|0|<br>1|
|Total value of 7 crops that could potentially be|_POT_|2,046|<br>4,354|<br>17,919|173|<br>776,212|
|produced given agro-climatic conditions|||||||
|Number of 16 household members aged 16 or older|<br>_L_|2,030|<br>2.984|<br>1.723|1|<br>17|
|who do not have wage job|||||||
|Total land used (ha)|_H_|2,046|<br>1.632|<br>4.304|0.063|<br>162.279|
|Total seeds used (kg)|_S_|2,038|37.194|<br>35.829|0.500|<br>411.600|
|Total fertilizer used, including both basal and top<br>dressing (kg)|_F_|1,071|279.94|<br>431.20|1|<br>6000|
|Total spending on pesticides (ZKW)|_P_|330|110,488|<br>100,232|5,000|1,500,000|
|Total spending on transport services to bring<br>produce to market (ZKW)|_TR_|172|39,214|<br>75,080|2|<br>440,000|
|Size of household|_SIZE_|2,049|<br>6.025|<br>2.667|1|<br>19|
|Dummy variable for male household head|_MALE_|2,049|<br>0.785|<br>0.411|0|<br>1|
|Age of household head|_AGE_|2,049|43.829|<br>15.240|1|<br>94|
|Years of education attained by household head|_EDU_|2,049|<br>4.507|<br>3.836|0|<br>71|
|Dummy variable for concrete|_CONC_|2,049|<br>0.136|<br>0.343|0|<br>1|
|Dummy variable for|_TOIL_|2,049|<br>0.516|<br>0.500|0|<br>1|
|Dummy variable for household possessing<br>motorcycle|_MOTO_|2,049|<br>0.019|<br>0.137|0|<br>1|
|Dummy variable for household possessing bike|_BIKE_|2,049|<br>0.679|<br>0.467|0|<br>1|
|Dummy variable for Chipata District|_CHIPATA_|<sup>2,049</sup>|<br>0.502|<br>0.500|0|<br>1|
|Slope angle of terrain (degree)|_SLOP_|2,049|<br>2.779|<br>3.585|0.220|<br>22.733|
|Elevation (meter)|_ELEV_|2,049|<br>1,049|<br>115|823|<br>1,437|
|Distance to a main trunk road,T4(km)|_KMT4_|2,049|15.282|<br>8.624|1.297|<br>36.111|

# **V. MAIN RESULTS**

The instrumental variable (IV) regression is performed to estimate Equation (2). First of all, the IV approach is found to be appropriate with our data. The exogeneity test statistics are estimated at 30.51 and 50.98 with the Battese specification and the small-positive number specification ( **Table 6** ). Both are well above the conventional significance value. Thus, it is confirmed that the selection of OPRC roads is not exogenous. On the other hand, the overidentifying restriction tests indicate that our selected instruments are valid. The estimated
- 18 -

test statistics are well below the critical value: The hypothesis that the instruments are not correlated with _V_ cannot be rejected.

Second, from the empirical specification point of view, the Battese model fits the data better than the conventional small-positive number model. According to the Wald statistics, the overall goodness of fit is much greater at 845.3 in the Battese model. In addition, the hypothesis that all coefficients of d( _S_ ), d( _F_ ), d( _P_ ) and d( _TR_ ) are zero can be rejected easily with a test statistic of 22.41. Therefore, the Battese specification can be accepted. As discussed above, the small-positive-number model generated the possibly underestimated impacts of production factors that have a number of zero observations. See Battese (1998) for more discussion.

For the impact of OPRC, it is found that the impact is statistically significant. The coefficient of _OPRC_ * _t_ is positive at 4.57 under the Battese specification. The marginal effect is estimated at 1.30. Thus, it can be concluded that the OPRC increases agricultural production. The estimation result also indicates that everything else assumed constant, total agricultural production declined significantly between 2009 and 2012. The coefficient of _t_ is estimated at -2.59, which is also significant. This may have been attributed to the relatively poor rainfall in 2012.

Regarding other variables in the estimated production function, land ( _H_ ), seeds ( _S_ ) and fertilizer ( _F_ ) are found productive. The coefficients of ln _H_ , ln _S_ and ln _F_ are all significantly positive. Particularly, the result indicates that the elasticity of land is particularly high: This must reflect the current production system in Zambia, which is highly land-intensive. On the other hand, labor ( _L_ ) does not seem to be an important production factor. The coefficient is still positive but not significant, possibly indicating the fact that labor is abundant. There is little use of pesticides and transportation services for market sales. These impacts are not significant.
- 19 -

Overall, the estimated production function exhibits constant returns to scale: The sum of the coefficients of the six production factors is estimated at 1.081 with a standard error of 0.155. The null hypothesis of constant return cannot be rejected.

**<u>Table 6. Instrumental variable estimation: OPRC effect</u>**

||Battese's sp<br>Coef.|ecification<br>Std.Err.|Small-posi<br>Coef.|tive-number model<br>Std.Err.|
|---|---|---|---|---|
|_OPRC_*_t_|4.574|<br>(2.777) *|6.902|<br>(3.248) **|
|_OPRC_|-1.595|<br>(1.356)|-2.323|<br>(1.573)|
|_t_|-2.590|<br>(1.536) *|-3.953|<br>(1.776) **|
|ln_POT_|0.014|<br>(0.042)|-0.552|<br>(0.217) **|
|ln_L_|0.021|<br>(0.082)|0.022|<br>(0.114)|
|ln_H_|0.418|<br>(0.084) ***|1.058|<br>(0.275) ***|
|ln_S_|0.334|<br>(0.090) ***|0.009|<br>(0.037)|
|ln_F_|0.180|<br>(0.065) ***|0.020|<br>(0.006) ***|
|ln_P_|0.119|<br>(0.096)|0.051|<br>(0.013) ***|
|ln_TR_|0.009|<br>(0.031)|-0.004|<br>(0.013)|
|d (_S_)|1.657|<br>(0.437) ***|||
|d (_F_)|0.540|<br>(0.310) *|||
|d (_P_)|0.402|<br>(1.058)|||
|d (_TR_)|0.164|<br>(0.308)|||
|ln_SIZE_|0.050|<br>(0.083)|0.124|<br>(0.111)|
|d (_MALE_)|0.033|<br>(0.082)|0.056|<br>(0.115)|
|ln_AGE_|0.145|<br>(0.103)|0.210|<br>(0.142)|
|ln_EDU_|0.070|<br>(0.037) *|0.008|<br>(0.006)|
|_CONC_|0.047|<br>(0.101)|0.175|<br>(0.138)|
|_TOIL_|0.167|<br>(0.081) **|0.279|<br>(0.106) ***|
|_MOTO_|0.436|<br>(0.219) **|0.636|<br>(0.309) **|
|_BIKE_|0.043|<br>(0.075)|0.051|<br>(0.105)|
|_CHIPATA_|0.276|<br>(0.133) **|0.336|<br>(0.170) **|
|Constant|3.068|(1.264)**|10.544|<br>(1.662)***|
|Obs|2049||2049||
|R-squared|-0.456||-2.127||
|Wald chi2|845.43||338.76||
|Exogeneity test:|||||
|C statistic (chi2)|30.541|***|50.977|***|
|Overidentifying res|triction:||||
|Hansen's J|2.098||0.490||
|statistic (chi2)|||||
|Marginal effect of<br>_OPRC_|1.301|<br>(0.790) *|1.964|<br>(0.924) **|

The dependent variable is the log of total crop production value, _V_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.
- 20 -

One might be concerned about the magnitude of the estimated marginal effect of OPRC. One possible reason is that the estimated coefficient may include the effect of agro-climatic potential. Recall that the OPRC roads were selected based on agricultural potential. In addition, the underlying crop production patterns may have changed significantly during the project implementation period.

To investigate this, the simple average DID estimator is considered. Note that our data are cross-sectional at two periods of time, not panel data. The double difference statistics need to be adjusted with the weight, _w_ , given by kernel matching (Heckman, Ichimura and Todd, 1997): While maize production in the sample area increased between 2009 and 2012, other crops, including groundnuts, which is another important crop produced in the survey area, were harvested much less ( **Table 7** ). This is consistent with the national agriculture statistics: Zambia experienced a significant drop in agriculture production in 2012/2013. Particularly, cotton production in 2012/13 decreased by nearly 50 percent because of unfavorable market prices in the Southern African region. In our data, no cotton production was recorded in the follow-up survey.

Together with these drastic changes in production patterns, the significant increase in maize production is likely to explain the measured large impact of OPRC on the total production value. Maize production in the OPRC areas increased from 1,385 kg to 2,208 kg, which is more than 50 percent growth. Although production in the control group also increased, the net impact is significant at 209 kg per household. The net impact on groundnuts production is also significant at 40 kg per household, a more than 10 percent increase.
- 21 -

**<u>Table 7. Simple average DID estimator</u>**

||Before<br>Treatment|Control|After<br>Treatment|Control|Double<br>Coef.|difference<br>Std.Err.||
|---|---|---|---|---|---|---|---|
|Production (kg)||||||||
|Maize|1,385|1,049|2,208|1,511|361|209|*|
|Cotton|1,905|1,215||||||
|Cassava|850|1,025|325|500||||
|Tobacco|885|468||||||
|Vegetable|700|499|447|1,038|-793|1,085||
|Soybean|285|384|397|571|-76|192||
|Groundnuts|366|452|296|271|111|40|***|

*, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

To answer the question of why OPRC has a positive impact on agriculture production, Equation (4) is estimated using the same set of independent variables as above. This also addresses a likely question of whether this is because of OPRC or road maintenance works in general regardless of contractual arrangements. The results are broadly unchanged: The selected instruments are valid, and the impact of _WORK_ is positive and significant ( **Table 8** ). This is the main reason for the observed OPRC impact. Not surprisingly, the statistical significance of _WORK_ is much higher than that of _OPRC_ . Holding everything else constant, crop production was increased where road improvement works were actually implemented regardless of contractual arrangements. This is obvious: Without actual works, people’s connectivity would not be improved. The marginal effect is estimated at 0.95. Notably, however, this does not mean that OPRC has no effect on connectivity. It has an important indirect impact (see further discussion below).

Technically, the measured impact can be associated with all kinds of works implemented, such as rehabilitation, gravel roads, emergency works, installation of culverts and road signs. Given the timeline of the project implementation and follow-up survey, it is likely to be the impact of initial rehabilitation works under OPRC. But other possibilities cannot be excluded.<sup>7</sup>

> 7 To answer this question, more detailed and frequent data would be needed on both project implementation and farmers’ behavior (i.e., household surveys).
- 22 -

One difference from the previous estimation result is that the production function with _WORK_ exhibits increasing returns to scale. The degree of returns to scale is estimated at 1.236 with a standard error of 0.122. However, the production system, again, looks highly land-intensive: The coefficient of land, _H_ , is the largest at 0.55 among production factors. Labor is positive and weakly significant, but its elasticity is much smaller than land.

**<u>Table 8. Instrumental variable estimation: Actual work effect</u>**

||Battese's<br>|specificati<br>|on|Small-positiv<br>|e-number<br>|model|
|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||
|_WORK_*_t_|3.424|(1.306)|***|4.147|(1.470)|***|
|_WORK_|-1.183|(0.685)|*|-1.211|(0.764)||
|_t_|-1.916|(0.717)|***|-2.336|(0.783)|***|
|ln_POT_|0.028|(0.026)||-0.497|(0.179)|***|
|ln_L_|0.131|(0.070)|*|0.160|(0.085)|*|
|ln_H_|0.551|(0.076)|***|1.054|(0.197)|***|
|ln_S_|0.268|(0.086)|***|0.037|(0.037)||
|ln_F_|0.136|(0.043)|***|0.010|(0.006)|*|
|ln_P_|0.192|(0.089)|**|0.043|(0.008)|***|
|ln_TR_|-0.041|(0.031)||0.019|(0.006)|***|
|d (_S_)|1.409|(0.454)|***||||
|d (_F_)|0.500|(0.254)|**||||
|d (_P_)|1.340|(0.982)|||||
|d (_TR_)|-0.610|(0.274)|**||||
|ln_SIZE_|-0.018|(0.066)||0.043|(0.079)||
|d (_MALE_)|0.001|(0.067)||0.022|(0.078)||
|ln_AGE_|0.069|(0.068)||0.139|(0.086)|*|
|ln_EDU_|0.104|(0.036)|***|0.010|(0.005)|**|
|_CONC_|0.114|(0.089)||0.275|(0.106)|***|
|_TOIL_|0.013|(0.065)||0.083|(0.075)||
|_MOTO_|0.188|(0.228)||0.300|(0.269)||
|_BIKE_|0.084|(0.064)||0.125|(0.074)|*|
|_CHIPATA_|0.518|(0.126)|***|0.640|(0.147)|***|
|Constant|3.104|(1.195)|***|9.775|(1.618)|***|
|Obs|1891|||1891|||
|R-squared|-0.456|||-0.408|||
|Wald chi2|1142.21|||639.14|||
|Exogeneity test:|||||||
|C statistic (chi2)|33.342|***||44.399|***||
|Overidentifying restriction:|||||||
|Hansen's J statistic (chi2)|1.328|||5.603|*||
|Marginaleffect of_WORK_|0.949|(0.362)|***|1.149|(0.407)|***|

The dependent variable is the log of total crop production value, _V_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.
- 23 -

The first stage regression can provide more insight into the relationship among the OPRC, actual road works and the resultant agricultural production growth. From the IV estimation with OPRC, it is found that the coefficient of _SLOP_ is negative and significant ( **Table 9** ). This can be interpreted to mean that more road improvement and maintenance works are needed where the slope of the terrain is less steep, and therefore, more drainage capacity is needed. The coefficient of _KMT4_ is also found to be significant. It is consistent to the view that the project was focused on remote and isolated areas.

The second column model of the table shows that the use of OPRC contributed to more frequent road works. Recall that regardless of the project, local governments and communities can implement some other road maintenance or emergency works, whenever needed. But since the government has already been committed to the 5-year maintenance under the OPRC, the treatment roads were more likely to receive necessary road works in a timely manner. The coefficient of _OPRC_ is significantly positive at 0.18, meaning that the OPRC roads have an 18 percentage point higher probability of receiving road works than other non-OPRC roads. There seems to be a systematic difference in share of households with actual work between OPRC and non-OPRC areas ( **Figure 8** ). As discussed above, this is the most important indirect effect of OPRCs.

This result is consistent with another model to test the placebo effect. No significant impact of OPRC is found when the data are limited to cases where no road improvement works were carried out. The sub-sample size is 828, and the estimated coefficient of _OPRC_ * _t_ is -14.28 with a standard error of 14.72 ( **Table 10** ). Thus, the OPRC can have an impact only if actual work is implemented. This is considered as the same effect as the placebo effect.
<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>OPRC areas Non-OPRC<br>m@ Actual work No work<br><!-- End of picture text -->
- 25 -

**<u>Table 10. Instrumental variable estimation with limited samples where no actual works took place</u>**

||Battese's speci<br>|fication<br>|Small-positi<br>|ve-number mod<br>|el|
|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.||
|_OPRC_*_t_|-14.283|(14.719)|-19.312|(17.122)||
|_OPRC_|7.306|(7.479)|10.176|(8.852)||
|_t_|6.157|(6.450)|8.214|(7.515)||
|ln_POT_|0.044|(0.093)|-1.251|(0.913)||
|ln_L_|0.502|(0.535)|0.714|(0.650)||
|ln_H_|0.324|(0.298)|2.103|(1.389)||
|ln_S_|0.566|(0.317)|*<br>-0.131|(0.271)||
|ln_F_|-0.176|(0.382)|-0.043|(0.059)||
|ln_P_|0.640|(0.756)|-0.006|(0.032)||
|ln_TR_|-0.109|(0.185)|0.101|(0.073)||
|d (_S_)|6.477|(3.608)|*|||
|d (_F_)|-0.409|(1.253)||||
|d (_P_)|7.378|(8.963)||||
|d (_TR_)|-2.607|(2.710)||||
|ln_SIZE_|-0.345|(0.502)|-0.529|(0.693)||
|d (_MALE_)|0.156|(0.392)|0.120|(0.460)||
|ln_AGE_|-0.215|(0.442)|-0.277|(0.594)||
|ln_EDU_|-0.007|(0.206)|-0.002|(0.033)||
|_CONC_|0.607|(0.704)|0.715|(0.682)||
|_TOIL_|-0.260|(0.511)|-0.310|(0.605)||
|_MOTO_|-0.724|(1.165)|-1.044|(1.580)||
|_BIKE_|0.233|(0.320)|0.352|(0.446)||
|_CHIPATA_|-0.168|(0.465)|-0.398|(0.616)||
|Constant|-2.523|(7.780)|14.447|(7.231)|***|
|Obs|828||2049|||
|R-squared|-10.247||-19.669|||
|Wald chi2|46.76||17.72|||
|Exogeneity test:||||||
|C statistic (chi2)|20.394|***|31.318|***||
|Overidentifying restriction:||||||
|Hansen's J statistic<br>(chi2)|0.108||0.245|||

The dependent variable is the log of total crop production value, _V_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

Finally, the impact of OPRC on market sales is examined: The total value of crops sold in the market is regressed on the same independent and instrumental variables as above. No significant impact of OPRC is found. The coefficient of _OPRC_ * _t_ is not significant.
- 26 -

On the other hand, the impact of actual road improvement is found to be positive, but the observed impact seems to be relatively weak: The marginal impact is 0.273, and it is only significant at the 10 percent level. All the indications are that although OPRCs or road improvements in general are conducive to increasing crop production, people still may not benefit from possible market opportunities. Their market participation is limited. This is generally consistent with a view that transport services may not be available or too expensive, even though roads are improved. The coefficient of transport spending, of which the available data are limited to less than 90 households at each round of the surveys, is negative and significant, meaning that high transport service prices are constraining farmers’ market sales.

In addition, feeder roads should form one road network, but there are still many roads that are in poor condition in the study area. Finally, other complementary facilities and services, such as warehouses and processing mills, may also be needed to facilitate people’s market participation.

**<u>Table 11. Instrumental variable estimation of total crop sales</u>**

||Battese's<br>|specificatio<br>|n|Battese's s<br>|pecificatio<br>|n|
|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||
|_OPRC_*_t_|-2.545|(1.834)|||||
|_OPRC_|1.895|(0.647)|***||||
|_WORK_*_t_||||2.087|(1.111)|*|
|_WORK_||||0.100|(0.474)||
|_t_|0.465|(1.019)||-2.000|(0.606)|***|
|ln_POT_|0.254|(0.118)|**|0.292|(0.124)|**|
|ln_L_|0.190|(0.226)||0.204|(0.201)||
|ln_H_|0.823|(0.310)|***|0.868|(0.322)|***|
|ln_S_|-0.734|(0.629)||-0.901|(0.619)||
|ln_F_|0.129|(0.072)|*|0.136|(0.068)|**|
|ln_P_|0.222|(0.188)||0.332|(0.182)|*|
|ln_TR_|-0.027|(0.045)||-0.092|(0.039)|**|
|d (_S_)|-54.199|(60.434)||-55.077|(58.760)||
|d (_F_)|0.994|(0.408)|**|1.092|(0.392)|***|
|d (_P_)|2.080|(2.089)||2.852|(2.001)||
|d (_TR_)|-0.834|(0.406)|**|-1.172|(0.336)|***|
|ln SIZE|0.092|(0.142)||0.222|(0.131)|*|
|d (_MALE_)|-0.237|(0.333)||-0.230|(0.313)||
|ln_AGE_|0.126|(0.162)||0.259|(0.162)|*|
- 27 -

|ln_EDU_|0.036|(0.080)|0.092|(0.073)||
|---|---|---|---|---|---|
|_CONC_|0.190|(0.136)|0.284|(0.131)|**|
|_TOIL_|0.120|(0.112)|-0.011|(0.114)||
|_MOTO_|0.215|(0.389)|0.374|(0.388)||
|_BIKE_|0.193|(0.252)|0.269|(0.236)||
|_CHIPATA_|-0.028|(0.213)|0.453|(0.273)|*|
|Constant|2.107|(1.784)|1.676|(1.680)||
|Obs|753||697|||
|R-squared|-1.995||-2.012|||
|Wald chi2|373.99||388.95|||
|Exogeneity test:||||||
|C statistic (chi2)|19.888|***|15.052|***||
|Overidentifying restriction:||||||
|Hansen's J statistic (chi2)|0.000||0.016|||
|Marginaleffect|-0.419|(0.302)|0.273|(0.145)|*|

The dependent variable is the log of total crop sales, _SALE_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

# **VI. CONCLUSION**

Rural access is among the most important infrastructure elements to stimulate economic growth in rural and remote areas. Many rural farmers and local businesses are still likely faced with a crucial constraint to have access to input and output markets. The literature is generally supportive of the importance of feeder road connectivity in rural areas. Without the feeder road network maintained in good condition, it is difficult to improve agricultural productivity and encourage farmers to particulate in market transactions.

One of the most important challenges lies in maintenance of feeder roads. It is often neglected under budget pressure given limited available resources. Particularly, unpaved feeder roads require more frequent maintenance. Output- and performance-based road contracting (OPRC) is considered as an important policy instrument to ensure sustainability of road maintenance after the road rehabilitation. It normally requires contractors to not only improve roads but also maintain them through routine and emergency works.

Using the instrumental variable technique, the paper examined the impact of the recent OPRC experience in Chipata and Katete Districts, Zambia. By comparing the two areas: one
- 28 -

area along the feeder roads under the OPRCs, and another area where socio-economic characteristics are broadly similar and there are comparable feeder roads but there are no OPRCs, it is found that the OPRC has a significant impact on agricultural production. This is mainly attributed to the positive impact on production of maize and groundnuts, two major crops grown in the study region.

Most of the measured OPRC impacts are associated with actual road improvement works regardless of contractual arrangements. This is obvious: Without actual works, people’s connectivity would not be improved. On the other hand, any road work can improve people’s connectivity if it is not OPRC. The most important effect of the OPRCs is that the OPRC can increase the probability of receiving necessary road works whenever needed. The government has already been committed to the 5-year maintenance under the OPRCs, and the contractors are explicitly obliged to maintain those roads to meet the agreed performance criteria. Regardless of OPRCs, the government will carry out road improvement and maintenance works, whenever necessary, and it actually did. However, road works on nonOPRC roads seem to be more vulnerable to budget pressure. Without OPRCs, the probability of receiving road works is 18 percentage points lower than OPRC roads.

Road improvement works can also contribute to facilitating farmers’ market participation. The impact of actual road improvement is found to be positive and significant, though relatively weak. People’s market participation is generally limited because of a number of constraints. Of particular note, the impact of transport service costs is found negative on farmers’ market sales. It is consistent with the view that transport services may not be available or too expensive, even though roads are improved.