Can the informal groundwater markets improve water use efficiency and equity? Evidence from a semi-arid region of Pakistan

Science of the Total Environment 666 (2019) 849–857

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Can the informal groundwater markets improve water use efficiency and equity? Evidence from a semi-arid region of Pakistan Amar Razzaq a, Ping Qing a,⁎, Muhammad Asad ur Rehman Naseer b, Muhammad Abid c, Mumtaz Anwar d, Iqbal Javed e a

College of Economics and Management, Huazhong Agricultural University, No. 1, Shizishan Street, Hongshan District, Wuhan, Hubei Province 430070, PR China Institute of Agricultural and Resource Economics, University of Agriculture, Faisalabad 38000, Pakistan Centre for Climate Research and Development (CCRD), COMSATS University, Park Road, Tarlai Kalan, Islamabad 45550, Pakistan d Department of Economics, University of the Punjab, Lahore, Pakistan e Department of Economics, University of Lahore - Sargodha Campus, Sargodha, Pakistan b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Due to the scarcity of surface water, informal groundwater markets are very common in Punjab, Pakistan • Water use efficiency of three groups of groundwater users i.e. self-users, selfusers cum sellers, and buyers is assessed • Informal groundwater markets improve efficiency, as WUE of buyers and sellers is higher compared to self-users • WUE is significantly associated with offfarm income, participation in the water market, extension, and power source • Government interventions are required to increase equitable distribution of water among water users

a r t i c l e

i n f o

Article history: Received 30 December 2018 Received in revised form 16 February 2019 Accepted 16 February 2019 Available online 19 February 2019 Editor: Huu Hao Ngo Keywords: Groundwater markets Water use efficiency Equity Data envelopment analysis Punjab Pakistan

a b s t r a c t Pakistani farmers are using groundwater at an increasing rate to supplement their irrigation needs. This practice has led to overexploitation of groundwater in the country, resulting in many negative externalities and increased resource costs. In response to the growing water shortage, the informal groundwater markets in the arid and semi-arid regions of Punjab have gradually emerged. These markets are believed to improve the fair distribution of groundwater and encourage more efficient use of agricultural water. This study aims to investigate these claims through conducting a field survey of 120 farmers that are further divided into three groups i.e. buyers, self-users cum sellers, and self-users (control group). Further, the study employed a Data Envelopment Analysis (DEA) approach to estimate the water use efficiency of all three type of groundwater actors. The study findings show that water buyers are mostly small farmers who do not own tube wells, hence buy water from tube well owners (large farmers). The study also found that groundwater markets improve the equity of water access to some extent, as water is transferred from large farmers to small farmers. The results of DEA analysis show water buyers and water sellers are more efficient in using water than the control group, making buyers the most efficient of all groups. Therefore, participation in water markets appears to be improving the WUE of farmers. The results of single bootstrapped truncated regression show that participation in water markets and access to extension services can improve WUE, while off-farm income and the diesel tube wells can reduce WUE in the study area. However, government could play an important role here through introducing groundwater

⁎ Corresponding author. E-mail addresses: [email protected] (A. Razzaq), [email protected] (P. Qing).

https://doi.org/10.1016/j.scitotenv.2019.02.266 0048-9697/© 2019 Elsevier B.V. All rights reserved.

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A. Razzaq et al. / Science of the Total Environment 666 (2019) 849–857

regulations and improving water use efficiency for sustainable and equitable distribution of water among water users. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The scarcity of irrigation water is one of the key challenges facing Pakistan's agriculture. Due to insufficient and uneven surface water supply, farmers are increasingly using groundwater to supplement their irrigation needs (Watto and Mugera, 2014). This phenomenon is common in many South Asian countries, where most of the groundwater is now used for agriculture. However, the socio-ecological returns of groundwater irrigation boom have declined over time, mainly in the form of groundwater shortages (Shah, 2007). Due to the increase in groundwater shortages, the informal groundwater markets have emerged in South Asia in the past few decades (Jacoby et al., 2004). Compared to formal water markets that serve large areas and are governed by state or federal regulations, informal water markets are developed locally and governed by rules developed at the community level (Easter and Huang, 2014). In Pakistan, the water markets are also informal and are mainly used for groundwater irrigation. These water markets exist in all provinces, particularly in the irrigated areas of Punjab, where nearly 30% of private tube well owners sell water to other farmers (Qureshi et al., 2003; Ashfaq et al., 2009). The increased participation in the groundwater market in Punjab is because these markets provide water to marginal farmers who cannot afford the high installation costs of wells. To some extent, the groundwater markets provide small farmers with the opportunity to increase agricultural productivity through timely irrigation (Shiferaw et al., 2008). The groundwater markets also provide economic benefits to tube well owners who invest in drilling new and deep wells. These farmers can use their surplus capacity by selling water. Therefore, water markets seem to be improving the overall welfare of society (Saleth, 2004; Saleth, 2014). Currently, there is no limit to the extraction of groundwater due to the informal nature of the groundwater markets in Punjab. Anyone can buy and sell as much water as they wish. The water trading mechanism is determined by social networks, where different types of informal water exchange arrangements exist. Farmers buy water from their relatives or neighboring farmers. The payment method can be crop share, labor, or cash. Almost all large farmers have their own private tube wells. Only resource-poor farmers do not own private tube wells (Ashfaq et al., 2009). However, the reduction in water volume due to falling groundwater levels and increased groundwater extraction costs has forced many tube well owners to purchase water. In some parts of Pakistan Punjab, the annual groundwater abstraction rate (60 km3) exceeds the annual natural recharge rate (55 km3), which makes it economically infeasible to use groundwater due to increased extraction costs (Watto and Mugera, 2015a). The increasingly scarce surface water and groundwater in the country's largest agricultural province pose challenges to agricultural sustainability (Ahmad, 2006; Mustafa and Qazi, 2007) Within this context, this paper examines whether informal groundwater can improve groundwater use efficiency in the study area. Previous studies show that the groundwater markets can induce participants (buyers and sellers) to use water more efficiently, given that the price signals conveyed by these markets reflect the scarcity of water. Delayed irrigation can have a negative impact on the efficiency of other inputs (fertilizers, seeds, chemicals). In this case, tube well ownership provides the advantage of reliable access to water, thereby increasing farmers' productivity (Jacoby et al., 2004; Manjunatha et al., 2011; Manjunatha et al., 2016). It is also believed that the groundwater markets improve water allocation by transferring water from large farmers to marginal farmers. However, these claims have not yet been

investigated in Punjab, Pakistan. The increasing participation of farmers in the Punjab water markets warrants an empirical study to determine the impacts of groundwater markets on water use efficiency. Most studies on established water markets only consider the economic benefits of groundwater markets (Brooks and Harris, 2008), or they focus on the operation of these markets (Chong and Sunding, 2006; Murphy et al., 2009). In addition, some studies on the groundwater markets use the simulation results to study the economic potential of these markets (Gómez-Limón and Martínez, 2006). However, the focus of this study is on the impact of groundwater markets on water use efficiency and groundwater distribution for different water users. Therefore, the main objectives of this paper are: (1) to study the impact of groundwater markets on water access in the study area; (2) to estimate and compare the water use efficiency of water market participants (water buyers, sellers, and self-users); (3) to identify the determinants of water use efficiency. We use a two-stage analytical procedure to analyze the impact of groundwater markets on water use efficiency. In the first stage, we used data envelopment analysis to estimate the sub-vector water use efficiency of groundwater market participants (buyers, sellers) and compared these efficiencies with the control group farmers (water self-users). Based on the results of previous studies, it is hypothesized that water buyers and sellers will use water more efficiently than the control group because water allocation is improved through the water markets (Srivastava et al., 2009; Manjunatha et al., 2011). In the second stage, we used a bootstrap truncated regression model to identify the determinants of water use efficiency. The remainder of this paper is organized as follows: The next section outlines the methodological framework for the study, including a description of DEA model used to estimate water use efficiency, and a bootstrapped truncated regression to identify the efficiency determinants. Section 3 provides a description of the study area, data collection procedures, and summary statistics for the variables used in the DEA model. Section 4 presents the results and discussion, and the last section provides the conclusions of the study. 2. Methodological framework 2.1. Efficiency measurement and data envelopment analysis Although the terms productivity and efficiency are not precisely the same measures, the two concepts are often used interchangeably. Simple productivity measures used in most studies related to efficiency differentials in agricultural production are easy to calculate, but they can be misleading because the output per cubic meter of water only considers water input and ignores other inputs, such as chemicals, labor, and fertilizer. These inputs can also explain the difference in efficiency between firms (Speelman et al., 2008). The concept of efficiency measures stems from the seminal work of (Farrell, 1957) who first introduced the concept of technical efficiency (TE). He suggested that a firm's TE could be calculated by comparing the inputs used by the firm with the output produced by the firm and the output of other firms in a given group. Two types of TE can be distinguished: (i) input-oriented (ii) output-oriented. Input-oriented measures consider the potential of firms to reduce their input use to produce a given amount of output, while output-oriented measure take into account the potential of firms to increase output at a given level of input use (Coelli, 1996; Coelli et al., 2005). Since our main objective in this study is to estimate water use efficiency, we use an inputoriented approach to calculate the efficiency of farmer groups.

A. Razzaq et al. / Science of the Total Environment 666 (2019) 849–857

Generally, efficiency is widely measured using two key methods, namely the parametric approach and the non-parametric approach (Lovell, 1996). In a parametric method such as stochastic frontier approach (SFA), the efficiency of any decision-making unit (DMU) is estimated technically, while in a non-parametric method such as data envelopment analysis (DEA), the efficiency of a DMU is described by the relationship between inputs and outputs on a linear piecewise frontier constructed by the DEA model (Charnes et al., 1978). This study uses the DEA method developed by Charnes et al. (1978) to estimate efficiency because it provides a straightforward way to estimate the efficiency gap between individual producer behavior and best practices. The main advantage of DEA approach compared to the SFA is that the former does not require assumptions about the functional form of the production function or the distribution of the error term (Coelli, 1996; Alsharif et al., 2008). In addition, DEA is a deterministic method that is essentially non-parametric. It makes it easy to incorporate multiple inputs and outputs. Furthermore, the DEA method has been successfully used in many countries to estimate the efficiency of agriculture, such as Australia e.g. (Fraser and Graham, 2005), in the United States e.g. (Lilienfeld and Asmild, 2007), in Pakistan e.g. (Watto and Mugera, 2007; Watto and Mugera, 2014; Ullah et al., 2016), and India e.g. (Manjunatha et al., 2011; Manjunatha et al., 2016).

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increase in crop volume is not necessarily obtained; one reason for the variable returns (VRS) to scale option is more appropriate for this study (Speelman et al., 2008). (Coelli et al., 2005) also pointed out that using constant returns to scale (CRS) to estimate technical efficiency is only appropriate when all firms are operating at optimal scale, which is unlikely in agriculture due to factors like financial constraints and imperfect competition among others. Such estimations result in measures of technical efficiency confounded by scale inefficiencies. The variable returns to scale (VRS) specification allows technical efficiency measures devoid of scale inefficiencies. However, in this study, we estimated the CRS and VRS efficiencies for better comparison. Therefore, we impose another restriction ∑k=1kλk ≤ 1 in Eq. (1) to calculate the TE under CRS. Solving the given linear programming problem (Eq. (1)) in n-times yields the technical efficiency index for each of the n DMUs. ∅0VRS = 1 indicates that the ‘0’ DMU is on the frontier and is technically efficient, while the value ∅0VRS b 1 indicates that the DMU is below the efficiency frontier and is technically inefficient. Solving this model also provides the optimum amounts of inputs to produce a given amount of output. We can estimate water use efficiency by comparing the optimal and actual water use (Srivastava et al., 2009). 2.3. Determinants of water use efficiency

2.2. Estimation of water use efficiency through DEA approach Let us consider n DMU that produces an output Y using X input. X is an n × k matrix of inputs, Y represents an m × k output row vector. By solving the following standard linear programming problem, we can calculate the input-oriented technical efficiency (TE) under variable returns to scale (VRS) for a given DMUk: Min ∅VRS k

n

o ∅VRS k ≥0

ð1Þ

Subject to: (i) (ii) (iii) (iv)

∑k=1kλkymk ≥ ym∗ ∑k=1kλkxnk ≤ ∅kVRSxn∗ ∑k=1kλk = 1, λk ≥ 0

We used a single bootstrap truncated regression model to identify the determinates of water use efficiency of farmers. Most previous studies used Tobit regression to identify the determinants of efficiency scores estimated by the DEA method. The authors of these studies considered the DEA efficiency scores to be censored values; therefore, they used Tobit regression in the second stage (Wadud and White, 2000; Dhungana et al., 2004; Speelman et al., 2008; Frija et al., 2009). However, recent studies have shown that the second stage of OLS produces more consistent results than the Tobit regression, and that a single bootstrap truncated regression produces better confidence intervals when used in the second stage (Simar and Wilson, 2007; McDonald, 2009; Simar and Wilson, 2011; Watto and Mugera, 2015b). Therefore, we use a single bootstrap truncated regression to identify the determinants of water use efficiency, as follows:

Y j ¼ ∝j þ The inputs of the kth DMU are multiplied by the parameter ∅kVRS to scale them down by the smallest possible factor subject to the constraint that these minimized inputs must still be able to produce the original output bundle. In other words, the goal is to construct a virtual DMU for each DMU in the sample using other DMUs in the sample. This virtual DMU is then compared to the real DMU to determine the difference between the two. The parameter ∅kVRS is the Farrell TE measure of the kth DMU under VRS, and λ is the (K × 1) vector of the weights attached to each DMU. The asterisk defines the DMU under investigation. The first constraint requires that the weighted average of the outputs of all DMUs (∑k=1kλkymk) less the output of the kth DMU (ym∗) is greater than or equal to zero. This means that the output of the virtual DMU being constructed must be at least (ym∗) units. Similarly, the second constraint requires the virtual DMU not use inputs that exceed the (xn∗) level (Mulwa et al., 2009; Manjunatha et al., 2011). The convexity constraint (∑k=1kλk = 1) ensures that inefficient firms are only benchmarked against firms of similar size, i.e. the projected point for that DMU on the DEA frontier is a convex combination of observed DMUs. This convexity constraint is not imposed in the model's constant returns to scale (CRS) specification. Therefore, in CRSDEA; a firm may benchmark against firms that are much smaller or larger than it (Coelli et al., 2005). In the case of agriculture, the increased amount of input does not increase the number of outputs proportionally. For instance, when the amount of irrigation water applied to the crop increases, a linear

n X

  β j Z j þ ε j ≥0; j ¼ 1; ……; N and ε j →N 0; σ 2 ;

ð2Þ

j¼1

where Yj is the water use efficiency, Zj is the set of explanatory variables for j = 1, …, 8, and εis the error term. 3. Study area and data 3.1. Study area Pakistan is an agricultural country with approximately 17.25 million hectares of agricultural land (GOP, 2012). Punjab accounts for the largest share of Pakistan's agricultural production, with an agricultural area of about 63% (or 10.94 million ha) (Naseer et al., 2016). Mixed farming is often practiced in Punjab as almost every farmer grows crops and raises dairy animals (Ashfaq et al., 2015a; Ashfaq et al., 2015b). By ecology, Punjab is located in an arid to semi-arid region, with an aridsemiarid ratio of 58:29 (Farooq et al., 2009). The semiarid areas of Punjab are mainly irrigated by canal water, but in some areas, farmers rely entirely on groundwater irrigation (Imran et al., 2018). In this paper, we used a case study methodology to select the study area. We have purposively chosen the Gujrat district of Punjab as it provides a suitable case study for groundwater market activities (Fig. 1). Most parts of Gujrat are characterized by semi-arid conditions. The main source of irrigation in most parts of the region is groundwater. In some areas, canal water is available during Kharif (April–September)

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Fig. 1. Map of the study area.

season. However, the percentage of seasonal canal water irrigation in the district is very small. Farmers have no choice but to rely on groundwater for irrigation. As a result, informal groundwater markets have emerged in the area. The main crops grown by farmers in the study area are wheat, sorghum, sugar cane and forage crops such as sorghum, barley, and berseem (Egyptian clover). Only a few farmers grow rice, most of which are tube well owners. Due to the small size of land ownership, most water buyers produce wheat only for domestic consumption, while the tube well owners manage to sell a certain percentage of the wheat crop after meeting their own consumption needs. 3.2. Data and variable definitions We collected primary data for the 2012–13 wheat crop through personal interviews. The wheat crop was chosen because it is the only crop grown by all farmers. Respondents were selected using a multi-stage random sampling procedure. In the first phase, we randomly selected two villages from the list of all the villages in the two tehsils1 of Gujrat. In this way, four villages were selected namely, the villages Mandeer and Chohan Kalan from tehsil Kharian, and the villages Goleki and Dheerke from tehsil Gujrat. The third tehsil of the district, Sara-iAlamgir, consists mainly of barren lands and hills called ‘pabbis’. We did not include this tehsil in the survey. In order to select respondents, we prepared a list of all groundwater users, namely water self-users, water sellers and water buyers in each sample village, and randomly selected 10 respondents in each category. As a result, the final sample included 30 respondents from each village. The survey was conducted by trained enumerators who were graduate students in Agricultural Economics at the University of Agriculture, Faisalabad. We used a structured survey instrument designed to obtain demographic information as well as input and output usage information related to the wheat crop. In order to assure the quality of data collection, different measures were adopted. First, the in-field training for the enumerators was conducted to improve their understanding of 1 Tehsil is an administrative unit used in Pakistan. A district usually divided into several tehsils.

the terminologies and types of questions. In addition, the questionnaire included cross questions to cross-check whether the respondent provided correct information. Further, a pre-survey was conducted to ensure that no important information was missed and that enumerators were trained before the final survey. We tried to reduce the potential for interviewer misconduct by ensuring that different interviewers selected the households and conducted interviews. Before collecting the data, the purpose of the study was described to respondents, and verbal consent was obtained from all the respondents. Participation in the survey was entirely voluntary. We assured participants that their data will be kept confidential and used anonymously. We interviewed a total of 120 farmers. The final sample included 40 farmers from each of the three farmer categories, namely self-users, water sellers, and water buyers. This sample size is consistent with the minimum sample size requirement for data envelopment analysis (Banker et al., 1984), i.e., the sample size should exceed the sum of the number of outputs and inputs multiplied by 3. In our case, the sum of the number of inputs and outputs of the wheat crop is 7. Therefore, in this study, the sample size is sufficient for DEA. The inputs considered for DEA analysis in this study are: (i) the amount of water used for irrigation (m3), the farm size (hectares), and expenditures on seeds, labor, fertilizers, and chemicals per-hectare of the wheat crop. We calculated irrigation water use for the wheat crop by obtaining data on the number of irrigations, irrigation duration, depth of bore, suction pipe diameter and engine horsepower. We estimated groundwater volume (in liters) using the following pre-tested estimation model (Eyhorn et al., 2005; Srivastava et al., 2009; Watto and Mugera, 2014): Q¼

t  129574 :1  BHP   2 d þ ð255:5998Þ  BHP2 =d  D4

ð3Þ

where, Q is the amount of groundwater extracted (liters), t is the total duration of irrigation (hours), d is the depth of the borehole (meters), BHP is the engine power of the pump (HP), and D is the suction pipe diameter (inches). The amount of water was converted to cubic meter to be included in the DEA analysis.

A. Razzaq et al. / Science of the Total Environment 666 (2019) 849–857 Table 1 Summary of inputs and outputs used in DEA model (per- hectare of the wheat crop). Variables

Self-users (Control group)

Sellers cum self-users

Mean

Mean

SD

Buyers SD

Mean

SD

3

Water (m ) 9215.39 4135.76 10,566.77 5146.57 6413.83 2299.75 Seeds (US$) 42.39 11.20 45.47 9.67 40.08 11.45 Chemicals (US$) 26.67 14.22 30.46 13.03 26.25 14.41 Fertilizers (US$) 177.24 45.24 193.09 56.46 149.23 38.65 Land (Hectares) 1.57 1.43 1.79 0.86 0.78 0.31 Casual Labor (US$) 49.50 44.97 53.44 51.29 38.12 44.37 Gross Returns (US$) 372.20 63.27 530.03 100.63 359.76 56.74

Table 1 shows a summary of the input and output variables for the wheat crop. These variables are used for DEA analysis. The results showed that the water consumption of water sellers and self-users (control group) was 65% and 44% higher than that of water buyers, respectively. This is a logical result because well owners (sellers and self-users) have greater control over groundwater irrigation water. A similar finding was also reported by (Meinzen-Dick, 1996). For water buyers, using less irrigation water is a rational act because they have to pay for additional water purchases. Although water prices in the region are higher than extraction costs, they may not reflect the actual cost of water shortages. The cost of groundwater irrigation faced by tube well owners is lower than that of water buyers. Therefore, water buyers use water more economically. Since the wheat production system has many other inputs besides water, we need to consider all these inputs to estimate the water use efficiency of the three farmer groups. We do this using DEA approach which can include multiple inputs in the analysis. Table 1 also lists the use of other inputs (seeds, chemicals, fertilizers, land, and casual labor). Water sellers use the highest of these inputs, followed by the control group. Water buyers use the least amount of inputs in wheat production. The reduced use of water and other inputs confirms the scarcity of resources faced by water buyers. This may also explain why they cannot make the necessary investments in installing tube wells. The gross profit margin of wheat production was the highest among water sellers, followed by the control group. Water buyers have the lowest profit margins. (Meinzen-Dick, 1996) also reported that water buyers have lower gross margins than tube well owners. 4. Results and discussion 4.1. Socioeconomic characteristics of groundwater actors The socioeconomic characteristics of sample groundwater actors indicate that tube well owners are older than water buyers (Table 2). (Meinzen-Dick, 1996) reported a similar finding of the relationship between the household head's age and tube well ownership. The overall

853

education level of the farmers is low, with only 8 years of schooling. The average household size of farmers is about 8 persons per household, and there is no significant difference between the three groups of farmers. The size of landholdings is an important determinant of the economic status of farmers (Ojiako et al., 2009). The results show that tube well owners are relatively large farmers with an average farm size of N2 ha. In particular, water sellers have the largest farm area of 2.49 ha. This means that large farmers in the region dominate water sales. Water buyers are resource-poor farmers with an average farm area of 1 ha. It may not be economically viable for water buyers to install tube wells for these small and fragmented plots. These results are consistent with the findings of (Meinzen-Dick, 1996) and (Manjunatha et al., 2011). Other studies report that water buyers in the informal groundwater markets are small farmers with limited access to credit and technology (Hadjigeorgalis, 2008). Therefore, these water markets where water is transferred from large farmers to small farmers are different from other countries, such as Australia, where the reverse pattern of water movement is observed (Bjornlund, 2006; Brooks and Harris, 2008). The results also show that about 20% of farmers receive off-farm income. However, the number of water buyers who receive off-farm income is only 10%. About 50% of the farmers in the sample can get farm extension services. A comparison of access to extension services for farmers' groups shows that 60% of water buyers report access to farm extension services, the largest of the other two. This result contrasts with a recent study that found a positive correlation between farm size and access to farm advisory services (Elahi et al., 2018b). Finally, the results show that 80% of the tube wells in the study area are operated by diesel tube wells.

4.2. Operation of groundwater markets in the study area Groundwater trading in the study area is governed by the physical relationship between the buyers and the seller, and the nature of groundwater market contracts. The major constraint reported by water buyers is that there are not a large number of water sellers in the study area. Unlined watercourses do not allow groundwater to be transported over long distances without significant conveyance losses. Therefore, water purchasers can only purchase water from sellers located near their fields. The average distance between the tube well and the purchasers' field in the study area was 300 m. This distance is less than the 600-m distance reported by (Meinzen-Dick, 1996) in Faisalabad, Punjab. Modern water conveyance technologies can increase the transport distance of water and enlarge the size of water markets. It also increases the amount of water the buyer receives and allows water to be sold to a wide range of buyers (Meinzen-Dick, 1996; Ashfaq et al., 2009; Saleth, 2014). However, we did not find the use of lined watercourses or underground pipes in the study area. Due to the physical limitations of water transport in the study area, a limited

Table 2 Socio-economic characteristics of farmer groups. Variables

Farmer's age (years) Education of the farmer (schooling years) Household Size (no.) Farm size (hectares) Off-farm income (0 = no, 1 = yes) Access to extension services (0 = no, 1 = yes) Tube well power source (0 = electric, 1 = diesel) Water sellers (0 = farmer does not sell water, 1 = farmer sells water) Water buyers (0 = farmer does not purchase water, 1 = farmer purchases water)

Total respondents

Self-users

Sellers cum self-users

Buyers

Mean

SD

Mean

SD

Mean

SD

Mean

SD

46.93 7.78 8.28 1.86 0.20 0.50 0.80 0.33 0.33

15.06 3.85 2.83 3.57 0.39 0.50 0.39

50.18 7.87 7.83 2.09 0.33 0.38 0.7

15.18 3.888 2.57 1.98 0.47 0.49 0.46

46.23 7.43 8.40 2.49 0.15 0.53 0.78

15.99 4.1 3.13 1.04 0.36 51 0.42

44.38 8.05 8.63 1.01 0.10 0.60 0.95

13.71 0.063 2.76 0.41 0.30 0.50 0.22

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sellers respectively irrigated 60% and 10% of the total farm area under smallholder category. The third farm size category includes relatively large farmers. Only a few water buyers (2.1%) fall into this category. Overall, water sellers irrigated the largest farm area in the sample. The irrigated area of the water purchasers accounted for 18% (39.9 ha) of the total area irrigated by the surveyed farmers. In the absence of groundwater markets, it is difficult for water buyers to use groundwater to irrigate their land. The groundwater markets provide convenient water access to these resource-poor farmers by transferring water from large farmers to small farmers. These findings confirm that the groundwater markets have a positive impact on groundwater distribution in the study area. These results are consistent with the findings of (Meinzen-Dick, 1996; Shah et al., 2008; Srivastava et al., 2009; Manjunatha et al., 2011). However, there may be an opportunity cost of becoming a water buyer in terms of lower control over the timing and reliability of irrigation water. It is worth noting that the irrigated area of sellers participating in the groundwater market is the highest of all three farmers.

Table 3 The price of water under different water market contracts, by type of pump. Type of contract/Tube well specification

Type of contract Flat charge per hour (US$) Share of crop (US$) Tube well specifications Diameter of outlet (inches) Engine capacity (hp)

Type of pump Electric

Diesel

$0.89 0

$1.85 0

2.5 3

3.21 13.57

number of water sellers is one of the major constraints to competition in the groundwater market. Another constraint to the development of competitive groundwater markets is the dependence of water sales on the social and kinship relationships between buyers and sellers. These relationships between water buyers and sellers limit the sale of water to relatives or people with whom they have other ties (Meinzen-Dick, 1996; Dubash, 2002; Saleth, 2014). However, our results indicate that sales are not limited to close relatives. Only 18% of groundwater market transactions were conducted between close relatives. Of these, the majority of cases (90%) were due to the fact that these relatives had the closest landholdings. Most water sales in the study area are determined by the physical distance between farmers. However, because the distance between these farmers' fields is very small, they may have some social relationship with their neighbors. The most common form of groundwater market contract in the study area is a flat charge for pumping per hour. This type of arrangement is common to electric and diesel tube wells. We also observed few cases of sharecropping contracts for water. In these contracts, the buyer does not pay any water charges. Prices under the hourly charging system depend on the engine capacity of the tube well and the amount of water per hour. The average water price per hour of the diesel tube well is twice that of the electric tube well (Table 3). This is because the diesel tube wells installed in the area have large pump and outlet sizes. The diameter of the outlet of the diesel pump is 3.2 in., while the diameter of the outlet of the tube well is 2.5 in. Most of the tube wells in the study are powered by diesel engines. However, the number of electric tube wells in the region is also increasing due to rising diesel costs and low initial investment requirements for electric tube wells.

4.4. Water use efficiency of groundwater market actors/farmers The water use efficiency (WUE) results of different farmers' groups showed the overall low WUE scores under the CRS model (0.68) and the VRS model (0.72) (Fig. 2). The WUE results indicate that while maintaining the same level of production, there is considerable potential for reduction in water use in the study area. Using the DEA approach, similar low water use efficiency scores were found by (Watto and Mugera, 2015a) for cotton growers in Pakistan, (Manjunatha et al., 2011) for irrigated agriculture in India, and (Speelman et al., 2008) for smallholder irrigators in South Africa. These results indicate that farmers in the study area are using excess water and they need to adjust agricultural practices to improve water use efficiency. According to the CRS and VRS specifications of DEA, water buyers have the highest WUE, followed by the water sellers and the control group. The small differences in CRS and VRS WUE scores indicated that scale effects are rather limited. This is logical because the average farm size in the study area is below 2 ha, and even the farm size of large farmers is b3 ha (Table 2). We used non-parametric tests (Kruskal-Wallis and Mann-Whitney U tests) to test differences in efficiency scores observed for the three farmer groups (Elahi et al., 2018a). The difference in WUE scores of farmer groups under CRS and VRS was statistically significant at the 5% and 1% significance levels, respectively. The significant difference in efficiency scores means that the water buyers' WUE is higher than the sellers and self-users. For water buyers, a higher WUE means that these farmers use groundwater more efficiently than tube well owners because they have to pay higher water fee in addition to extraction costs. Further, the WUE of the water sellers is also higher than the control group. Although water sellers use more water than the control group (Table 1), they seem to use it more efficiently. The expected economic incentives to sell the surplus water may induce water sellers to save more water. These results indicate that farmers involved in the groundwater markets (i.e., water sellers are water buyers) use water more efficiently than farmers who do not participate in these markets. Therefore, the study area provides a suitable groundwater market case

4.3. Access to groundwater irrigation One of the objectives of the study was to study the impact of groundwater markets on the equity of groundwater irrigation in the study area. Table 4 shows the results of different farmer groups' access to groundwater irrigation. Based on the size of the land holdings, we further divide the farmers' groups into marginal, small and large farmers. The results show that all farmers in the marginal farmers' category are water buyers. Under this category, water buyers irrigated 14.4 ha of farm area. Participation in the groundwater markets provides them with the opportunity to irrigate land by purchasing water. In the smallholder category, about 36% of farmers are water buyers who irrigate about 30% of the farm area under this category. Self-users and water

Table 4 Groundwater access and farmers' area under different water market regimes. Farm category

Marginal (0–1 ha) Small (1–2 ha) Large (N2 ha)

Sellers cum self-users

Buyers

HHa (%)

Self-users Farm area

HH (%)

Farm area

HH (%)

Farm area

HH (%)

Farm area

0 54.7 23.4

0 (0) 45.9 (60) 35.2 (27.2)

0 9.4 74.5

0 (0) 8.0 (10.4) 91.5 (70.6)

100 35.9 2.1

14.4 (100) 22.7 (29.6) 2.8 (2.2)

100 100 100

14.4 (100) 76.6 (100) 129.5 (100)

Note: Figures in parenthesis for farm area represent the percentage area under respective categories. a HH = households.

Overall

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855

(TE) and irrigation efficiency. However, (Manjunatha et al., 2016) reported a negative but insignificant impact of education on TE. Some studies have found that education does not affect irrigation water efficiency (Speelman et al., 2008; Tang et al., 2015). Therefore, the impact of education on efficiency measures is still inconclusive in the literature. The impact of household size is negative, though insignificant. This result indicates that the size of the household does not have much impact on efficiency measures, consistent with the results of (Speelman et al., 2008; Manjunatha et al., 2016). The impact of farm size is also negative but insignificant. This result is in contrast to (Watto and Mugera, 2014), which found that increasing farm size would reduce the irrigation efficiency of rice farmers. The outcome in our study is probably because most farmers in the study area are small farmers with an average farm size of b2 ha. Therefore, farmers' land ownership does not explain their efficiency differentials. The impact of off-farm income on WUE is negative and significant under both models. This outcome is probably due to the fact that the inflow of off-farm income may provide a buffer against the irregular flow of agricultural income reported by some poor farmers in the study area. Therefore, farmers receiving non-agricultural income may not have enough incentive to use inputs wisely. This outcome is consistent with the findings of (Chang and Wen, 2011; Larochelle and Alwang, 2013). However, it is in contrast to the results of some studies that report the positive impact of off-farm income on agricultural efficiency (Kilic et al., 2009; Pfeiffer et al., 2009; Bojnec and Fertő, 2013). For both models, the dummy variables of water sellers and water buyers are found to have a positive and significant impact on WUE. Under the VRS model, the coefficient estimate of water sellers (0.059) indicates that when a farmer producing under variable returns to scale sells water, his/her water use efficiency could be about 6% higher than the average WUE of the surveyed farmers. Similarly, the coefficient estimate of water buyers (0.136) indicates that when a farmer producing under variable returns to scale purchases water, his/her water use efficiency could be about 14% higher than the average WUE of the surveyed farmers. These results reaffirm the findings of (Manjunatha et al., 2016) who reported the positive impact of water sales and purchases on technical efficiency. However, these results are in contrast to the findings of (Watto and Mugera, 2014), which report the negative impact of water purchases on irrigation efficiency in rice fields. Overall, the positive impact of water sales and water purchases on farmers' water efficiency in both models suggests that participation in the water market can increase their WUE. We see that the dummy variable of the tube well power source has a significant negative impact on the farmers' WUE. This outcome indicates that the WUE of farms irrigated by a diesel-powered tube well is lower than the WUE of farms irrigated by an electric tube well. During the field survey, we observed that diesel tube wells mainly used 12horsepower or 16-horsepower engines, while electric tube wells were powered by small 3-horsepower motors (Table 3). It is possible that farmers using diesel-powered tube wells to irrigate their farms are more likely to use more water. This practice was confirmed by comparing the amount of irrigation water used by farmers using diesel and electric tube wells. The results show that the amount of irrigation water used on farms irrigated with diesel tube wells (1618 m3/ha) is about 60% more than that of electric tube wells (642 m3/ha). Finally,

Fig. 2. Water use efficiency (WUE) across different groundwater actors.

study in which informal water markets promote water use efficiency through its effective mechanisms. These results are consistent with the findings of (Bjornlund, 2007) and (Manjunatha et al., 2011). Further, we compare farmers' groups according to the WUE category. As shown in Table 5, according to the CRS model, a total of 70 farmers (58%) have a WUE of b70%, while only ten farmers (8%) have a WUE of 100%. On the other hand, according to the VRS specification, about 54 (45%) farmers have a WUE of b70%, and only 19 (15%) farmers have WUE of 100%. If we compare the three groups under CRS model, we can see that the WUE of all water buyers is N50%, while the WUE of 8 water sellers and 11 self-users is b50%, which means water buyers are more efficient than other groups. In the case of VRS, the number of water buyers with b50% is very small compared to water sellers and self-users. In addition, we can see that most water buyers (58% and 73% under CRS and VRS, respectively) and water sellers (40% and 58% under CRS and VRS, respectively) have N70% WUE, while only a few self-users (28% and 30% under CRS and VRS, respectively) have N70% WUE. These results indicate that when we move from buyers to selfusers, WUE drops substantially. 4.5. Determinants of water use efficiency We used a single bootstrapped truncated regression model to identify the determinants of water use efficiency (WUE). To this end, we regressed the bias adjustment efficiency scores on farmers' socioeconomic and farm characteristics. The regression results are shown in Table 6. The results show that various socioeconomic and farm characteristics have a significant impact on farmers' WUE. Farmers' age, education, household size, and farm size have no significant impact on WUE. However, off-farm income, participation in the groundwater markets, tube well power source, and access to extension services have a significant impact on farmers' WUE. The results show that impact of farmers' education on WUE is positive, though insignificant at conventional significance levels. Apparently, using water more efficiently does not require much education. However, previous studies have found positive and negative effects of education on efficiency. For example, (Watto and Mugera, 2014) reported a significant positive impact of education on technical efficiency Table 5 Frequency distribution of CRS and VRS WUE scores of different farmer groups. WUE (%)

Below 50 50–69 70–99 100

Constant returns to scale (CRS)

Variable returns to scale (VRS)

Buyers

Sellers

Self-users (control)

Overall

Buyers

Sellers

Self-users (control)

Overall

0 17 17 6

8 16 12 4

11 18 11 0

19 51 40 10

4 7 18 11

6 11 17 6

9 19 10 2

19 37 45 19

856

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Table 6 Bootstrapped truncated regression estimates for bias-adjusted WUE measures. Variables

Model intercept Age (years) Farmer's education (years) Household size (no.) Farm Size (hectares) Off-farm income dummy (0 = No, 1 = Yes) Water sellers (0 = farmer does not sell water, 1 = farmer sells water) Water buyers (0 = farmer does not purchase water, 1 = farmer purchases water) Tube-well power source (0 = electricity, 1 = diesel) Access to extension services (0 = No, 1 = Yes) Log Likelihood

WUE (CRS)

WUE (VRS)

Coefficient

Standard error

Coefficient

Standard error

0.731*** −0.000 0.002 −0.006 −0.002 −0.116*** 0.049* 0.152*** −0.115*** 0.115*** 77.45

0.001 0.001 0.003 0.005 0.007 0.027 0.030 0.037 0.038 0.030

0.807*** −0.000 −0.001 −0.005 −0.003 −0.114*** 0.059* 0.136*** −0.140*** 0.136*** 58.09

0.104 0.001 0.003 0.004 0.007 0.031 0.034 0.039 0.031 0.030

Note: *, **, and *** indicate significance at 10, 5, and 1%, respectively. Standard errors are derived from 5000 bootstrap replications.

the impact of access to the extension service on WUE is positive and significant in both modes. This result is consistent with the findings of (Karagiannis et al., 2003; Watto and Mugera, 2014). This outcome is probably because farmers who can access farm extension services may use agricultural inputs more efficiently. Studies have also shown that better access to extension and farm advisory services in Punjab can increase wheat productivity (Elahi et al., 2018b)

the sustainable use of groundwater, as the informal markets do not impose any restriction on the amount of water being extracted. In order to prevent overdrafts and ensure the sustainability of groundwater, farmers may be encouraged to use water-saving technologies. This can be achieved by increasing awareness through extensions services, as our results also indicate the positive impact of farm extension services on water use efficiency.

5. Conclusions and recommendations References Water markets are believed to improve water productivity by transferring water to the most efficient users to obtain the highest marginal returns. This effect will appear in the form of increased water efficiency for participants in the groundwater markets. In the context of Pakistan, the informal groundwater markets provide an additional advantage for resource-poor farmers who are unable to invest in their wells due to financial constraints. This study confirmed these benefits of groundwater markets. First, the descriptive statistics of respondents indicate that water buyers consist of small farmers who rely on informal groundwater markets to purchase irrigation water from relatively large farmers. Without groundwater markets, these farmers would be unable to irrigate their farms. Thus, the groundwater markets appear to be improving the equity of water distribution in the study area. Secondly, the water use efficiency (WUE) of water buyers, water sellers, and the control group is significantly different. The results show that the overall WUE of the surveyed farmers was lower (64% and 72% under the CRS and VRS models, respectively). However, WUE of water buyers and water sellers is higher than that of self-users (control group). The WUE difference between the water buyers and the control group can be explained by the fact that water buyers have to pay for water which encourages them to use water more efficiently. The difference in the WUE of water sellers and the control group may be due to the economic incentive of water sellers to save water. The results of second-stage bootstrapped truncated regression showed that participation in water markets and access to farm extension services have a positive impact on WUE. However, off-farm income and diesel tube wells have a negative impact on WUE. The confirmed potential of groundwater markets to increase equity of water access and water use efficiency of farmers participating in these markets requires the government to facilitate groundwater markets by strengthening water property rights and establishing a groundwater markets legal framework. Currently, these markets are informal, and the exchange of water depends on the physical distance between water buyers and sellers and the social network of farmers. However, due to the lack of clear water property rights, some farmers may be excluded from the water markets. Therefore, by increasing farmers' participation and reducing the insecurity of market participants, regularizing the water market can further improve farmers' WUE. However, it should be noted that groundwater markets do not automatically ensure

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