Agricultural productivity, shadow wages and off-farm labor decisions in Nicaragua

Economic Systems 43 (2019) 99–110

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Agricultural productivity, shadow wages and off-farm labor decisions in Nicaragua Alexandre N. Almeidaa, , Boris E. Bravo-Uretab,c, ⁎

a b c

T

⁎⁎

Department of Economics, Administration and Sociology, University of Sao Paulo/ESALQ, Brazil Department of Agricultural and Resource Economics, University of Connecticut, USA Department of Agricultural Economics, University of Talca, Chile

ARTICLE INFO

ABSTRACT

JEL Classification: Q12 D13 J16 J22

The objective of this study is to analyze the decision to work off-farm by male and female farmers in Nicaragua using a three-year unbalanced panel dataset. Shadow income and shadow wages are also estimated. Moreover, to mitigate biases from unobserved individual and farm time-invariant characteristics as well as from sample selection, we apply a semiparametric approach for panel data. Our main findings suggest that shadow wages play a major role in off-farm labor decisions for both males and females. This implies that less labor is allocated to off-farm activities as the opportunity cost for agricultural work goes up. In addition, as the on-farm marginal productivity of households (i.e., shadow income) rises, both males and females reduce the hours they allocate to off-farm activities.

Keywords: Shadow wages Farm productivity Semiparametric models Labor supply Nicaragua

1. Introduction After 43 years of military dictatorship by the Somoza Dynasty (1936–1979) and 10 years of civil war under the Sandinista political regime (1980–1990), the Nicaraguan rural sector still exhibits a complex and challenging socioeconomic structure (IFAD, 2009). Approximately 42% of the total population of 6.2 million live in rural areas, and 71% of the rural population are subsistence farmers below the poverty line (ECLAC, 2015).1 These farmers face distorted labor and credit markets and a highly unequal distribution of land ownership; a significant number of farmers is landless or nearly landless (Deininger et al., 2003). Most of Nicaragua’s rural poor live in the vast dry central region, where natural resources are limited, and the high population density has led to overexploitation of these resources (IFAD, 2009; ECLAC, 2015). In addition, lacking money to invest in their farms and unable to produce enough income from agricultural activities to meet basic household needs, poor farmers in developing countries are often forced to explore opportunities to sell their labor in off-farm markets (Lanjouw and Lanjouw, 2001). This off-farm income can be a significant source of cash to purchase inputs and make on-farm investments, which can lead to improved yields, more profitable farms and poverty reduction (Huffman, 1980; Oseni and Winters, 2009; McCarthy and Sun, 2009; Davis et al., 2009; Covarrubias et al., 2012; Dethier and Effenberger, 2012). Off-farm labor income contributes up to 60% of total rural household income in Indonesia and Vietnam, and 50% in Latin America (Reardon et al., 2001; Haggblade et al., 2007; World Bank, 2008). Equally important is the participation of women in the Corresponding author at: Av. Pádua Dias, 11 Piracicaba, São Paulo, 13418-900, Brazil. Corresponding author at: 1376 Storrs Road, W.B. Young 303B, Storrs, CT, 06269-4021, USA. E-mail addresses: [email protected] (A.N. Almeida), [email protected] (B.E. Bravo-Ureta). 1 The most recent figures for indigence and poverty line indicators in Nicaragua are for the year 2009 (ECLAC, 2015). ⁎

⁎⁎

https://doi.org/10.1016/j.ecosys.2018.09.002 Received 11 November 2017; Received in revised form 21 July 2018; Accepted 4 September 2018 Available online 06 December 2018 0939-3625/ © 2018 Elsevier B.V. All rights reserved.

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labor market, which plays a major role in agricultural development by enhancing their bargaining power and status, while improving overall household welfare (Newman and Canagarajah, 2000; IFPRI, 2000; Haggblade et al., 2007; FAO, 2011). According to the World Bank (2008), about 25% of adult females in rural areas worldwide work off-farm. Many societies that traditionally have not allowed women to work either on-farm or off-farm markets have been liberalizing this restriction gradually (World Bank, 2008; FAO, 2011). From a policy perspective, it is also critical to determine whether agricultural and rural off-farm sector activities are complementary or substitutes; particularly, if the potential of an agricultural region is low, then policies designed to promote non-farm activities might be a desirable and effective poverty reducing strategy (Matshe and Young, 2004; Dethier and Effenberger, 2012). By contrast, if higher returns from agricultural production are expected, the expectation is that more hours are spent on farming activities and less off-farm (Mathenge and Tschirley, 2015). The aim of this paper is to investigate the participation of farm household males and females in off-farm activities in Nicaragua using an unbalanced panel dataset from the World Bank Living Standards Measurement Study (LSMS) for the years 1998, 2001 and 2005. Two key features distinguish this study from others. (1) The empirical strategy addresses the well-known Heckman sample selection bias stemming from the opportunity cost of labor by using a semiparametric approach developed by Kyriazidou (1997) along with panel data. (2) Following the pioneering work of Jacoby (1993) and Skoufias (1994), we measure the impact of shadow wages and shadow income associated with on-farm activities on the decisions of men and women aged over 15 years to work offfarm. More specifically, shadow wages and shadow income are estimated from a production function and we account for the opportunity cost of working on-farm, which is determined by the household rather than by market forces, and might affect the hours of labor supplied off-farm (Jacoby, 1993). Here we do not test if off-farm labor supply decisions between adult males (husbands) and females (spouses) are jointly determined, as done by Huffman (1980), Huffman and Lange (1989), Abdulai and Delgado (1999); Ahituv and Kimhi (2006) and Bjørnsen and Biørn (2010). The reason is that, to the best of our knowledge, there does not appear to be an empirical econometric approach designed specifically for panel data that is able to address joint decisions between males and females while also correcting for sample selection bias. Moreover, in this study we use the terms nonfarm and off-farm interchangeably to refer to all income derived from wage-paying and self-employment activities executed outside the household’s farm (FAO, 1998). Thus, the income generated from off-farm includes all labor activities taking place either on other people’s farms or in other economic enterprises in rural or urban areas. This definition is important and should not be confused with the definition of Rural Nonfarm Income (RNI), which includes all economic activities taking place outside the agricultural sector (for a broad review of RNI, see Haggblade et al., 2007). The literature covering nonfarm work in developing countries is quite rich, but analyses focusing on gender differences are limited. Studies of farmer’s participation in off-farm activities in many developing countries suggest that not only do women earn less than men, but they also work more (Abdulai and Delgado, 1999; Canagarajah et al., 2001; Matshe and Young, 2004; Atamanov and Van den Berg, 2012). In addition, off-farm labor, irrespective of gender, is positively associated with community participation, diminishing returns to farm inputs, market failures in land and labor markets, population density, higher education, access to credit and off-farm work experience. It is furthermore negatively associated with age, land productivity, tenure security, family size and being located in more remote areas or living without any household infrastructure (e.g., electricity and water services) (Lanjouw and Lanjouw, 2001; Barrett et al., 2001; Goodwin and Holt, 2002; Jolliffe, 2004; Zezza et al., 2007; Mccarthy and Sun, 2009; Bhaumik et al., 2011; Mathenge and Tschirley, 2015; Kumar et al., 2017). There are only a few quantitative studies analyzing rural nonfarm income in Central America. Lanjouw (2001) analyzed data from 1994 and 1996 for El Salvador and found that women tend to earn around 30% less than men from nonfarm employment at a given level of education. In Honduras, the nonfarm sector employed 35.8% of the workers and provided 31.3% of the total income of rural households in 1998 (Isgut, 2004). In Nicaragua, Corral and Reardon (2001) and Malchow-Moller and Svarer (2005) examined rural nonfarm income using Living Standard Measurement Survey (LSMS) data for 1998. They found that in areas with a relatively high population density where households have access to electricity, water and paved roads, 41% of farm household income comes from nonfarm activities. Moreover, most nonfarm income is earned from wage employment and declines as the farm size rises. Finally, younger and more educated individuals, as well as those who face land insecurity, were more likely to participate in off-farm labor than older, less-educated individuals or farmers with a secure title to their land. The studies focusing on Central America provide useful insights but do not distinguish between the decisions made by males and females regarding their participation in off-farm labor either as wage workers or in self-employment. This study contributes to the literature by accounting for factors that influence the decisions, by males and females, to engage in off-farm employment in addition to on-farm labor. The analysis has implications for strategies to reduce gender inequality that can promote changes in rural economic conditions in poor countries like Nicaragua. The rest of this paper is organized as follows: The next section briefly presents the economic background and the semiparametric approach adopted, while Section 3 describes the data used. Section 4 presents the results, followed by concluding remarks. 2. Theoretical background and econometric strategy The theoretical and empirical framework for this study draws on the classical agricultural household models of Gronau (1977) and Rosenzweig (1980), and refinements made by Jacoby (1993) and Skoufias (1994). In summary, the traditional static neoclassical approach establishes that farm household production and consumption decisions are independent of each other. Thus, under certain conditions, farmers first maximize profits from crop production and then decide how much income – conditional on farm profits – is 100

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allocated to the consumption of goods/services produced on the farm or purchased outside and to leisure (Barrett et al., 2008). This is known as the separability assumption (Skoufias, 1994). In the literature, especially in studies of developing countries, there is no consensus on the validity of the separability assumption, because this traditional approach also assumes that there is no disutility associated with working off-farm, that markets (for agricultural inputs and outputs) are perfectly competitive, and that family and hired labor are perfect substitutes. Therefore, market wages would provide an exogenous measure of the value of time of labor, irrespective of whether farmers work on- or off-farm (Rosenzweig, 1980; Deolalikar and Vijverberg, 1987; Benjamin, 1992). One of the most important contributions of Jacoby (1993) and Skoufias (1994) is the method they propose to derive shadow wages and shadow income irrespective of market failures. Specifically, if labor markets are not complete, then the opportunity cost (shadow wage) of the farmer is expected to equal his/her marginal product of on-farm labor, and a market wage is no longer needed to approximate the shadow value of time (Jacoby, 1993). Thus, the shadow wage for on-farm work (wˆ ) can be generated from the estimation of a farm production function. Using the shadow wages of males (wˆ 1) and females (wˆ 2 ), a set of structural labor supply equations, derived from the utility maximization problem of the household, is defined as:

p1* = p1 (wˆ 1, wˆ 2, vˆ; z ) and p2* = p2 (wˆ 1, wˆ 2, vˆ; z )

(1)

where p1* and p2* is the total hours of work performed by males and females, respectively, in market work, farm production and the production of home goods. The variable vˆ stands for shadow income and includes “shadow” farm profits (e.g., from cultivating land) and the “profit” from household work (e.g., childcare, cooking, etc.), while z is a vector that includes household and individual characteristics assumed to be exogenous (e.g., number of adults, number of children, age, sex, etc.). A useful graphical representation and details of Jacoby’s (1993) model can be found in Skoufias (1994). Therefore, the quantity of labor supplied is a function of the incentives the household faces, in particular the relative returns (shadow wages) and risks of farm and nonfarm activities, and the household’s capacity (e.g., farm, education, household size, access to markets, etc.) to undertake these activities (Corral and Reardon, 2001). For practical reasons, one important difference between the expressions in Eq. (1) and traditional labor supply models is that shadow wages and shadow income are jointly determined with labor supply (Jacoby, 1993). Following Jacoby’s (1993) approach, the first step in our econometric strategy is to estimate the marginal value product of labor for on-farm activities for males and females using an agricultural production function model. The total value of farm output (TVFO) is the dependent variable and the regressors of interest are hours of on-farm work for all family members except for children and teens up to 14 years old. In the second step, the estimated marginal value products of labor for males and females of age 15 or older are used to compute the on-farm shadow wage (discussed in detail below) for household members in the same age range but who are only working off-farm. In other words, to estimate the off-farm labor supply equations, if a farm has males and/or females working onfarm, then their shadow wages are included along with the other socioeconomic characteristics for members of the same household, males and females, working off-farm. Thus, it is assumed that for any individual working outside the farm for a wage or selfemployment, his/her choice of supplying off-farm work depends not only on market earnings but also on farm shadow wages and income derived from those in the same household who are working on-farm (Jacoby, 1993). As discussed above, this two-step structural framework is based on Jacoby (1993) and Skoufias (1994), and more recent extensions by Abdulai and Regmi (2000), Barrett et al. (2008) and Le (2009). These authors estimate male and female labor supply functions (equation 1) for Peru, rural India, Nepal, Côte d’Ivoire and Vietnam considering total hours spent in: (1) off-farm employment; (2) on-farm work; and 3) home activities. An important limitation of our study is that the total number of hours spent on home activities is not available in the LSMS database. Thus, we include shadow wages and shadow income associated with on-farm work along with other demographic and socio-economic drivers and focus only on an individual’s decision to engage in off-farm employment in our labor supply models. In addition, when off-farm labor supply equations are estimated using Ordinary Least Squares (OLS) and from individuals who are only participating in the off-farm labor market, estimates will be biased due to self-selection (Greene, 2008). Heckman procedures are often applied to correct for this selectivity bias, particularly when cross-sectional data are used (Cameron and Trivedi, 2005). These methods are likely to provide biased results if inadequate or weak instruments are used to control for unobserved factors. However, if panel data are available, as in our case, and managerial skills, motivation and related unobserved variables are assumed to be timeinvariant, then traditional fixed effects estimates can mitigate such biases (Baltagi, 2008). The novelty in our analysis is the use of semiparametric techniques that account for heterogeneity from unobservable variables. The semiparametric approach proposed is based on Kyriazidou (1997), who developed a panel data estimator correcting for selectivity bias while also controlling for other sources of bias that arise from time-invariant unobserved individual characteristics. The major reason for using semi- or non-parametric methods is to avoid restrictions or misspecifications that can stem from specific functional forms, which can produce biased estimates (Yatchew, 2003). Semiparametric models can be helpful in identifying the true underlying structure of the data; indeed, one of the main advantages of using such models is that they “let the data speak for itself” (Eubank, 1999). The application of semi- or non-parametric models remains limited, but has been increasing in several fields of economic research (Henderson and Parmeter, 2015). Now consider the following econometric model as the empirical counterpart of Eq. (1) focusing only on off-farm labor as follows:

hit = dit hit* = dit (x it* +

* i

+

* it )

= x it +

i

+

it ;

i = 1, …, n; t = 1, …T . 101

(2)

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A.N. Almeida, B.E. Bravo-Ureta

and

dit = 1{rit +

it

+ uit

(3)

0}; i = 1, …, n; t = 1, …T .

where hit* is off-farm hours of labor supplied by individual i in period t, x it* is a vector of explanatory variables (e.g., individual and farm household characteristics), i* is a time-invariant individual component, and it is the residual term. Selectivity bias occurs because the variable hit* in the participation Eq. (2) is observable only when the indicator variable dit = 1 in Eq. (3), i.e., when a farm member is engaged in off-farm activities. As in the two-step Heckman procedure, the vector of explanatory variables rit (e.g., individual and farm household characteristics) in Eq. (3) cannot all be the same as in x it* (Kyriazidou, 1997). The i term in Eq. (3) is also a time-invariant individual component and uit is the residual term. The other Greek letters in Eqs. (2) and (3) are parameters to be estimated. Following the traditional fixed effects approach, the time-invariant individual components i* and i and the error terms * it and uit are allowed to be correlated with x it and rit , respectively. The strategy is to estimate the off-farm labor supply in hours (equation 2) by time-differencing pairs of observations. For such observations, differencing will not only eliminate the fixed effect, but also the selectivity effect (Charlier et al., 2001). Hence, Kyriazidou (1997) suggests the following two-step procedure: Step 1. Get estimates for by using a conditional Logit model (Askildsen et al., 2003); and Step 2. Use the ˆ estimates from step 1 to construct kernel weights, and estimate β in equation (2) by the traditional weighted ordinary least squares (WLS) method. Thus, the Kyriazidou estimator to handle endogeneity of βs (e.g., shadow and market wages) in equation (2) is given by:

ˆ=

1

n

ˆin (x it i=1

where x = (z it

x is ) (x it

n

ˆin (xit

x is ) dit dis

xis ) (hit

his ) dit dis

i=1

z is ) is a vector of instruments, ˆin =

1 k bn

(

(rit

ris ) ˆn bn

),

(4)

ˆin is a kernel weight declining to zero as the difference

ris n | increases, and bn is the bandwidth tending to zero as n→∞ (Charlier et al., 2001). n One important feature of the Kyriazidou estimator is that the distribution of all unobservables is left unspecified when estimates from the selection equations in the first step are used to construct nonparametric kernel weights to be used in the second step for the estimation of participation equations. However, one of the disadvantages of this method is that the choice of the bandwidth parameter bn is more important than the choice of the kernel function, since the bandwidth regulates the trade-off between variance and bias in the estimates (Henderson and Parmeter, 2015).

|rit

3. The LSMS survey As already indicated in the introduction, the data used in this study are from the Living Standard Measurement Study (LSMS) for the years 1998, 2001 and 2005. The LSMS is a nationwide household survey carried out mostly by the Nicaraguan Statistical Service (INIDE) with technical assistance from the World Bank.2 The LSMS covers a wide range of topics, such as household composition, health, education, income and expenditures, occupation, agricultural production, credit, and savings.3 The Nicaraguan LSMS is very useful for research purposes because it is designed to follow the same households and individuals over time. In order to construct the dataset used in this study, we extracted survey results representing all households that had (1) access to land (owned, borrowed, or rented), and (2) a total farm output with a value greater than zero for the three years of the survey. Data points for households that did not meet these criteria along with a few outliers were excluded from the sample. The final unbalanced panel is made up of 5466 data points containing 1,310, 1387 and 2769 observations for the years 1998, 2001 and 2005, respectively. Table 1 shows the definitions for all variables used in the analysis, including their means and standard deviations. All monetary values were converted from Córdoba (C$) to US real dollars (US$) using the official exchange rate and deflated by the Consumer Price Index (CPI 2005 = 100). The official exchange rate and the CPI were both extracted from the World Development Indicators (World Bank, 2016). The periods of information gathering for the 1998, 2001 and 2005 LSMSs were April–August, May–August and July–October, respectively. The most prevalent annual crops were maize, rice, beans, sorghum, potatoes and cassava (not reported in Table 1), while cultivation of permanent crops was also common (e.g., mangoes, citrus fruits, bananas and coffee). The period of sowing and growing for the main crops (termed “lean period” by FAO) is from May to August.4 On average, males and females aged 15 or older were in their thirties and the average level of schooling was generally low (3 years), but slightly higher for females than males. From 1998 to 2005, the participation of males in any type of off-farm activities (wage labor or self-employment) was around 30%–37%, while the participation of females was lower, around 12%–13%. For those farmers who earned income outside their own farm, the yearly average contribution from nonfarm income to total value of farm output was 30% in 1998, 25% in 2001, and 29% in 2005. Note that there is a difference in the earnings from off-farm work between women and men over 15 years old. The data indicates that average total off-farm earnings for males ranged between US $111–124 per month over the survey period while the figure for females is US $138–145. Between 1998 and 2005, men worked off-farm 2 The data can be accessed at www.worldbank.org/lsms. We are grateful to the World Bank and the Instituto Nacional de Información de Desarollo (INIDE) (www.inide.gob.ni) in Nicaragua for making these data available. 3 See Basic Information Document (1998); Ficha Técnica (2001) and Informe de Metodología and Operaciones (2006). 4 http://www.fao.org/docrep/014/am890e/am890e00.pdf.

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Table 1 Sample statistics: Variable definitions, means and standard deviations. Source: Own computation from 1998, 2001 and 2005 LSMS surveys. Variables Farm Characteristics TVFO1 Land D1 Inputs D2 Hired labor expenditures Hired labor D3 Landrec D4 Payland Title Credit Training Organiza Household Characteristics HHsize Males age > 14 Females age > 14 Males/females (age 5-14) Farm size per adult

Description

1998

Total value of farm output (crops and livestock) in dollars/year Owned and rented land in manzanas (1 Maz. = 0.7 ha) 1 if paid for seeds, fertilizers, etc. Total expenditure on variable inputs (crops and livestock) in dollars/year (seeds, fertilizers, etc.) 1 if paid for hired labor Total expenditure on hired labor in dollars/year Total hours of males and females on hired labor /year 1 if received payment for rented land Payment received for rented land (dollars/year) 1 if paid for rented land Payment paid for rented land in dollars/year 1 if farm has legal title to at least some of the land farmed 1 if farm receives credit 1 if farm receives technical assistance 1 if farm participates in farmer organizations

Number of household members Number of males with age > 14 Number of females with age > 14 Number of males and females with age 5 and 14 Farm land divided per household members > 14 years old Child5 1 if farm has at least one child less than 5 years old On-farm labor males (6–14) 1 if there are males (6 to 14 years old) working on-farm On-farm labor females (6–14) 1 if there are females (6 to 14 years old) working on-farm Remit Remittances in dollars/month Renda_off Share (%) of off-farm income on total value of farm output Male (> 14 years old) Characteristics Sex 1 if male Age – male Age Educ – male Years of schooling Work – male 1 if worked Work Off-farm – male 1 if worked off-farm Wage Off-farm - male Monthly off-farm wage in dollars Hours of on-farm male labor Monthly hours worked on-farm Hours of off-farm male labor Monthly hours worked off-farm Female (> 14 years old) Characteristics Age -female Age Educ-female Years of schooling Work - female 1 if worked Work Off-farm – female 1 if worked off-farm Wage Off-farm - female Monthly off-farm wage in dollars Hours of on-farm female Monthly hours worked on-farm labor2 Hours of off-farm female Monthly hours worked off-farm labor2 Geographic Characteristics Managua 1 if farm is located in Managua region Pacifico 1 if farm is located in Pacífico region Central 1 if farm is located in Central region Atlantico 1 if farm is located in Atlántico region

2001

2005

Mean 1,259.83

SD 2,805.36

Mean 1,589.24

SD 2,761.07

Mean 2,207.88

SD 3,448.89

24.92 0.81 92.98

66.12 0.39 331.73

19.20 0.83 104.49

42.05 0.38 215.20

19.46 0.81 355.79

38.71 0.39 12,447.30

0.25 329.25 201.36 0.02 64.44 0.11 51.49 0.50 0.14 0.19 0.26

0.44 2,193.15 1,219.33 0.15 68.8 0.31 69.96 0.50 0.34 0.44 0.63

0.19 273.50 277.65 0.02 112.21 0.11 59.99 0.53 0.07 0.11 0.05

0.39 2,219.38 1,957.02 0.15 165.45 0.31 77.88 0.50 0.26 0.32 0.22

0.28 249.85 260.41 0.03 223.13 0.10 69.48 0.49 0.23 0.05 0.23

0.45 1,661.38 1,516.71 0.17 351.81 0.3 164.82 0.50 0.42 0.22 0.42

7.29 2.26 2.05 2.46 5.18

3.24 1.22 1.14 1.35 17.21

7.35 2.35 2.08 2.42 4.22

3.2 1.33 1.13 1.29 14.55

7.06 2.29 2.02 2.31 5.17

3.08 1.25 1.12 1.26 28.78

0.67 0.22 0.04 9.40 30.09

0.47 0.42 0.19 38.47 66.06

0.62 0.23 0.04 7.76 25.20

0.48 0.42 0.21 65.35 62.80

0.56 0.23 0.04 8.31 29.10

0.5 0.42 0.20 40.33 200.36

0.52 34.43 2.84 0.87 0.30 111.84 45.73 46.51

0.50 17.29 2.9 0.34 0.46 211.1 19.81 19.47

0.53 35.06 3.08 0.89 0.35 128.19 45.76 55.01

0.5 17.95 2.99 0.32 0.48 237.92 19.42 39.21

0.53 35.11 3.32 0.90 0.37 124.6 44.24 51.11

0.50 17.71 3.11 0.29 0.48 154.55 17.39 26.2

32.99 3.04 0.27 0.12 138.4 27.69

15.93 3.08 0.44 0.32 133.71 17.89

33.95 3.30 0.33 0.13 172.53 26.46

16.47 3.16 0.47 0.34 170.94 16.83

34.76 3.42 0.34 0.13 145.00 27.78

16.56 3.22 0.47 0.33 133.66 17.53

37.12

17.45

39.93

20.67

37.33

15.18

0.04 0.28 0.42 0.25

0.20 0.45 0.49 0.43

0.02 0.25 0.49 0.25

0.12 0.43 0.50 0.43

0.01 0.14 0.44 0.40

0.10 0.35 0.50 0.49

1

There was no information for the total value of farm output for 1998 so we included total sales of farm output as an alternative. In male equivalent units, a teen and adult female older than 16 was considered equivalent to 0 0.75 adult male. Source: Agricultural Censuses and Gender Considerations - Concept and Methodology, FAO 1999 (Available at: http://www.fao.org/docrep/003/x2919e/x2919e00.htm). 2

46–51 h per week, and women for 26–27 h per week. Thus, on average women earn more and work less than men. Similar results were found by Corral and Reardon (2001) for Nicaragua, who showed that men tended to engage in very low-wage (e.g., construction and commerce) self-employment activities (e.g., rural day laborer and unskilled labored), while women tended to work in the formal labor market at higher average wage rates on jobs requiring more education (e.g., teaching). Table A.1 in the Appendix contains further details regarding on- and off-farm activities for males and females aged 15 or older by months worked in each of the three years covered by the data. It includes all individuals who declared their primary (1st) and 103

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secondary (2nd) jobs during the week prior to the survey as well as any job during the preceding 12-month period (12months). In our study, it is important to capture seasonal employment effects—that is, specific months worked—in both farm production and off-farm activities among males and females.5 4. Model implementation and results 4.1. Estimating shadow wages and shadow income As stated in the methodology section, to estimate the off-farm labor supply functions, it is first necessary to derive shadow wages and shadow income from farming activities for males and females over the age of 15.6 For this purpose, a Cobb–Douglas production function was estimated, with Total Value of Farm Output (TVFO) as the dependent variable and a set of exogenous regressors as farm inputs. As mentioned before, a critical advantage of panel data is the ability to account for time-invariant non-observable effects, such as skills and motivation (Skoufias, 1994). In addition, one concern related to our estimation of the production function was the potential loss of observations. For example, to calculate separate shadow wages for males and females of a given household, we would have to limit our data to farms including at least one male and one female working on-farm, thereby reducing the total number of farm observations from 5466 to 1315. As an alternative scenario, and keeping other farm inputs in the production function constant, we calculated the marginal product of onfarm family labor without distinguishing hours of on-farm work between family male and female laborers. This allowed us to use the total sample of 5466 farms. The results for the Cobb–Douglas models are displayed in Table 2. Specification I includes 1315 farm observations and hours of on-farm labor of males and females are estimated as separate farm inputs, while specification II uses 5466 observations with no distinction between genders in the same household. Random effects and fixed effects using the within transformation were used to estimate the production functions. We find that the contribution of hired female workers appears to be relatively small in Nicaragua, so we do not differentiate hired workers by gender for estimation purposes. Another consideration is that some inputs (e.g., hired labor and expenditure on variable inputs) are reported at the zero level in a number of cases, which presents a problem when using the Cobb-Douglas functional form. To deal with these zeroes we follow the procedure introduced by Battese (1997), which consists of adding a dummy variable equal to one if the value of a particular input is positive and zero otherwise along with a variable that has a zero for non-users and the natural logarithm of the continuous value for users. Overall, Hausman tests favored the fixed effects at the 1% level for both specifications I and II. Translog models (not reported) were also estimated for all specifications; however, the coefficient for female labor, which is of key interest here, exhibited a negative and non-significant value and therefore we opted for the Cobb–Douglas specification. The fixed effects results for specification I show a higher impact for male labor (0.42) than for females (0.13). These results suggest that labor productivity in Nicaraguan farms varies by gender. In addition, our findings are similar to the results reported by Jacoby (1993) and Abudlai and Regmi (2000), but different from those of Skoufias (1994); Barrett et al. (2008) and Le (2009), who did not observe labor productivity differences by gender. By comparison, in specification II, which aggregates hours of on-farm working of males and females over 15 years old in a single regressor, the value of the corresponding coefficient turned out to be the second highest (after input expenditures) in the production function in the fixed effects model, with an elasticity of 0.19. Our results also provide some evidence that off-farm work executed by men and women farmers is a substitute for on-farm work, regardless of the number of months worked off-farm. In all production functions, most of the coefficients have negative signs; however, not all are statistically significant. The number of males and females working off-farm for 4 months is an exception. Its coefficient is positive and statistically significant only in the fixed effects estimation of specification I. As stated, following Jacoby’s (1993) method, the estimates of the CD fixed effects model are used to calculate agricultural shadow wages for the ith male or ith female laborer separately with specification I, and jointly with specification II. Similarly, these models are used to calculate the agricultural shadow income for the jth farm in year t using the following expressions derived from Jacoby (1993) and Skoufias (1994): ˆ jt TVFO ˆhour _ ONFam , and (A) Shadow wage: wˆ ijt = hour _ ONfamijt

ˆ jt (B) Shadow income: Iˆjt = TVFO

ijt

hiredlaborjt inputsjt paylandjt + landrecjt + remitjt where the ˆhour _ onfarm in (A) is the on-farm labor coefficient(s) from the estimated production function, and TVFO ˆ t is the fitted value of output ijt for the jth farm in the tth time period based on the estimated coefficients of the production function including fixed effects. The use of predicted instead of observed TVFOt is based on the hypothesis that farmers face production uncertainty primarily due to weather and ˆ t is market conditions, determinants embedded in the error term and not controlled by the researcher (Le, 2009). Le argues that TVFO the best approximation the econometrician has to reflect farmer’s output expectations. All variables needed to calculate shadow wages and shadow income are defined in Table 1. In the absence of market failures, the effective wage received by family members who participate in the off-farm market should be equal to the marginal productivity of work on the family farm according to the assumption that households maximize utility (Abdulai and Regmi, 2000). Jacoby (1993) proposes a test for the validity of this perfect market assumption using both shadow and observed 5 6

wˆ ijt (hour _ONfarmijt )

We thank one of the anonymous reviewers for suggesting the analysis of labor seasonality. Fifteen years is typically the minimum age for legal employment according to the International Labor Organization (ILO, 2008). 104

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Table 2 Agricultural production function estimates. Dep. Variable Total Value of Farm Output (TVFO)

Specification I (1) Fixed Effects

(2) Random Effects

(3) Fixed Effects

(4) Random Effects

Ln of land

0.22*** (0.06) −0.39 (0.39) 0.21*** (0.07) 0.38 (0.32) −0.02 (0.05) 0.42*** (0.09) 0.13* (0.07)

0.40*** (0.03) −0.77*** (0.13) 0.34*** (0.03) −0.05 (0.16) 0.06** (0.03) 0.38*** (0.04) 0.06** (0.03)

0.17*** (0.03) −0.49*** (0.11) 0.25*** (0.03) −0.07 (0.13) 0.07*** (0.02)

0.37*** (0.01) −0.80*** (0.07) 0.36*** (0.02) −0.23*** (0.09) 0.11*** (0.02)

0.19*** (0.02)

0.17*** (0.01)

0.005 (0.16) 0.95** (0.43) 0.05 (0.12) −2.57*** (0.76) 1.45 (1.27) 0.11 (0.16) Yes

−0.28* (0.15) −0.09 (0.20) −0.13 (0.10) −0.54 (0.37) −0.04 (0.32) −0.02 (0.08) Yes

−0.23 (0.17) 0.11 (0.14) −0.07 (0.12) 0.20 (0.25) −0.11 (0.19) −0.02 (0.06) Yes

−0.16 (0.11) −0.13 (0.09) −0.07 (0.09) −0.12 (0.15) −0.05 (0.10) −0.09** (0.04) Yes

Yes Yes Yes Yes Yes Yes Yes 78.08*** 1315 0.33 6.95***

Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes 167.13*** 5466 0.40 28.87***

Yes Yes Yes Yes Yes Yes Yes

D1 D1Ln of inputs D2 D2Ln hours of hired labor Ln of hours of on-farm male labor Ln of hours of on-farm female labor† Ln of hours of on-farm male + female† labor Number of Males and Females working for 1-2 months off-farm working for 3-4 months off-farm working for 5-6 months off-farm working for 7-8 months off-farm working for 9-10 months off-farm working for 11-12 months off-farm Constant Dummies On-farm labor males aged 6 -14 On-farm labor females aged 6 -14 Training Organization Title Credit Region Hausman χ2 N R2 F(fixed effects)/Wald(Random effects)

Specification II

1315 0.55 1611.64***

5466 0.49 5728.34***

*

p < 0.10, ** p < 0.05, *** p < 0.01. † In male equivalent units (See note 2 of Table 1).

market wages. The procedure is to regress ln (wˆ ijt ) = + ln (wage _ offfarmijt ) + ijt , where wˆ ijt is the estimated shadow wage7 and wage _offfarmijt is the observed off-farm market wage. Thus, the null hypotheses to be tested is H0: α = 0 and = 1. The results of this test are displayed in Table 3. The regressions were estimated using the time-differencing method as proposed by Kyriazidou (1997), and robust standard errors to account for possible heteroscedasticity across individuals. The results suggest the rejection of H0, for males and females, both when taken separately and together as one regressor (columns 1, 2 and 3). These findings suggest that imperfections in the rural labor markets are present and lend indirect support to the non-separability of farm production and consumption decisions (Skoufias, 1994; Abdulai and Regmi, 2000). 4.2. Off-farm selection equations Table 4 (columns 1 to 3) reports the results for the selection equations estimated using conditional random effects Logit models for three different samples (Baltagi, 2008). The first and second samples include 2363 males and 2425 females over 15 years of age, 7 Given that shadow wages and shadow income are estimated and not observed, bootstrapping techniques have recently been adopted to calculate standard errors (Barrett et al., 2008). We did not find much difference between the standard and bootstrapping results.

105

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Table 3 Test of the equality of estimated marginal productivity (shadow wage) of on-farm labor and market wages: Kyriazidou Generalized Method of Moments (Kyriazidou-GMM) estimates1. Dep. Variable: Ln of shadow wage

(1) Males

(2) Females

(3) Males and females

Ln of off-farm wagesa

−1.210*** (0.186) −0.016 (0.145) 42.37*** 141.40*** 5.62 771

−1.417*** (0.429) 0.720*** (0.250) 10.91*** 39.18*** 6.27 250

0.048*** (0.005) −0.001 (0.006) 106.07*** 4109.89*** 8.96 4,521

Constant Wald test F testΔ Hansen's J χ2 N

Standard errors in parentheses. Note: 1. Due to possible measurement errors in market wages it is necessary to have instruments in the regressions (Le, 2009). The instruments for the K-GMM regressions are: ln of TVFO, payment received for rented land in dollars/year, log of farm size per adult, education, age, the square of these two variables plus remittances in dollars per month, and dummies for years. The Hansen’s J χ2 confirms the validity of the instruments. * p < 0.10, ** p < 0.05, *** p < 0.01. a Potentially endogenous. Δ Test under the null hypothesis H0: α = 0 and ρ = 1.

respectively, for a total of 1315 farms. The third sample includes all 18,530 individuals—males and females–over 15 years old from all 5466 farms. The variables incorporated in these models follow Jacoby (1993) and Skoufias (1994) as presented in Section 2, and standard models of household employment in the non-farm sector (Corral and Reardon, 2001; Haggblade et al., 2007). In the three models, the dependent variable is equal to 1 if the individual worked off-farm (1st, 2nd or 12months) in any of the three years surveyed and 0 if they did not. The fixed effects Logit procedure was also considered (Baltagi, 2008); however, convergence during the estimation of male and female models was not achieved because of a significant loss of individual observations due to the unbalanced structure of the panel dataset. This computational failure precludes the use of the Hausman test to check if fixed effects would be more suitable than random effects, and in such cases it is desirable to use a random effects model rather than the fixed effects approach (Baltagi, 2008). Overall, the results suggest that, as land endowment per worker in the household increases, the probability of males working offfarm decreases. Our findings are aligned with Corral and Reardon (2001), who show that land scarcity is a driving force in nonfarm employment participation in Nicaragua. Similar and statistically significant results are observed when males and females are considered together; however, the relationship is not statistically significant when only females are included. Our results are also consistent with earlier observations for the production function estimates; namely, that rural off-farm sector activities are substitutes for farm activities. We find that irrespective of months worked on-farm (1st, 2nd or 12months), all these workers are less likely to work off-farm. The coefficient for a dummy variable for gender in the third sample, equal to 1 if the worker is male and 0 if female (column 3, Table 4), indicates that the probability of males over the age of 15 of engaging in off-farm labor is higher than for the corresponding female group. The results for the three samples also reveal that the coefficient for household size is not statistically significant. Moreover, as long as there are children under 5 years of age, households with both men and women (column 3) are more likely to engage in nonfarm activities. The rationale for including children is that in particular women with pre-school or primary school age children would be less likely to have time to engage in market activities (Abdulai and Regmi, 2000). However, there is some evidence that elder siblings in large families can take care of younger siblings when mothers are working outside the home (Yamauchi, 2008). Therefore, the sign of the parameter for children on adult labor decisions can be either positive, as is the case here, or negative. The level of education, age, the square of these two variables, and dummies for years and regions were also included as additional regressors (see note 1 in Table 4). Our main results for the three samples (not shown) indicate that education and age play an important role in an individual’s participation in nonfarm activities, which corroborates the findings reported for male and female workers in Nicaragua by Corral and Reardon (2001) and Malchow-Moller and Svarer (2005). We find that more educated males are less likely to pursue nonfarm activities than females. When education for males and females is combined, only the higher levels of education matter for those participating in nonfarm markets. In addition, as males and females get older, they are more likely to participate in nonfarm work, but at a decreasing rate. 4.3. Off-farm labor market participation equations Table 4 (columns 4–6) reports estimates for the off-farm labor supply equations for males, females, and males and females together (over 15 years old) based on the second step proposed by Kyriazidou (1997), along with robust standard errors. The dependent variable is the natural logarithm of monthly hours worked off-farm regressed on estimated shadow income and shadow wages, observed monthly market wages, and a set of control variables such as age, education, the square of these two variables and dummies for the regions where the farms are located. The number of males and the number of females aged 15 or older and the 106

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Table 4 Selection equations (conditional random effects logit) and off-farm labor supply equations (Kyriazidou-GMM)1,2,3. Conditional RE Logit

Kyriazidou-GMM

Dep. Variable: 1 if works off-farm

Dep. Variable: Ln monthly hrs. worked off-farm

(1) Males

(4) Males

(2) Females

(3) Males and Females

a

Ln of shadow income

a

Ln of male shadow wages

Ln of female shadow wagesa Ln of males and female shadow wagesa Ln of male off-farm wagesa Ln of female off-farm wagesa Ln of male and females off-farm wagesa

0.018 (0.019) −0.099 (0.113)

0.056 (0.058) 0.375 (0.370)

2.647*** (0.104) 0.005 (0.008) 0.113** (0.0527)

−0.053*** (0.015) −0.298 (0.249) −0.182 (0.204) −0.402*** (0.141) −0.914*** (0.200) −1.222*** (0.216) −2.072*** (0.141) Yes 2363

−0.042 (0.035) −2.918*** (0.800) −3.125*** (0.859) −1.859*** (0.563) −1.623* (0.899) −2.068** (0.805) −3.977*** (0.538) Yes 2425

−0.047*** (0.005) −0.283** (0.140) −0.196* (0.109) −0.231*** (0.077) −0.611*** (0.103) −1.132*** (0.117) −2.477*** (0.103) Yes 18,530

357.40***

95.46**

811.92***

Sex Household size Children age < 6 Number of males age > 14 Number of females age > 14 Number of males and females (age 5 & 14) Farm size per adult = 1 if works for 1-2 months on-farm = 1 if works for 3-4 months on-farm = 1 if works for 5-6 months on-farm = 1 if works for 7-8 months on-farm = 1 if works for 9-10 months on-farm = 1 if works for 11-12 months on-farm Constant N R2 Wald test Hausman χ2 Hansen's J χ2

(5) Females *

(6) Males and Females

−0.025 (0.020) −0.165* (0.092) 0.169* (0.088)

−0.022 (0.012) −0.179 (0.126) 0.111 (0.110)

−0.022 (0.034)

0.782*** (0.016) −0.002 (0.017)

−0.003 (0.009) 0.695*** (0.026)

−0.053 (0.047) 0.055* (0.029) 0.004 (0.021)

−0.023 (0.035) −0.007 (0.025) −0.029* (0.015)

−0.039 (0.025) 0.034 (0.035) 0.004 (0.019)

Yes 771 0.94 3516.30*** 14.16 9.52

Yes 250 0.90 1794.63*** 34.95*** 3.26

Yes 4,521 0.84 5985.20*** 23.50* 7.67

−0.559*** (0.312)

0.775*** (0.016) 0.196*** (0.051)

Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. Notes: 1. Specifications (1) to (3) include the following additional regressors: education, age, the square of these two variables, dummies for years and regions. 2. Specifications (4) to (6) include the following additional regressors: education, age, the square of these two variables, and dummies for regions. 3. The instruments for the K-GMM regressions (4), (5) and (6) are: Ln of TVFO, payment received for rented land in dollars/year, log of farm size per adult, education, age, the square of these two variables plus remittances in dollars per month, and dummies for years. a Potentially endogenous.

number of males and females aged 6–14 are also included in the labor supply equations. To account for endogeneity of both shadow and off-farm wages (Jacoby, 1993), the Generalized Method of Moments (GMM) approach (Kyriazidou-GMM) is used to estimate the participation equations. Hansen’s J χ2 tests for overidentifying restrictions were performed for each regression (Cameron and Trivedi, 2009), and the results confirmed that the instruments employed are valid. In addition, the F/Wald statistics are significant at the 10% level (or better) for the three models estimated. A Hausman-type test did not reject the null hypothesis of no selectivity for the male equation (column 4, Table 4). For the combined male-female equation (column 6, Table 4), the null hypothesis is rejected at the 10% level of statistical significance, while 107

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the Hausman-type test rejected the null hypothesis of no selectivity for the Kyriazidou-GMM female equation at the 1% level. The test of the selection hypothesis consists of comparing the kernel-weighted regression with the same regression without kernel weights. It is important to remember that these weights are also used in the estimation of the participation equations and are generated from the estimation of the selection equations (Step 1 of the Kyriazidou approach) (Charlier et al., 2001). A Gaussian kernel is used to construct the weights and the choice of the optimal bandwidth as a fixed value is based on Silverman’s rule (Salgado-Ugarte et al., 1995).8 In the selection equations (Table 4) the coefficient for the natural logarithm of the own-shadow wage, which is of particular interest, is negative (-0.559) when male and female labor are combined (column 6) and when male labor (-0.165) is treated separately (column 4). These statistically significant and negative signs for shadow wages imply that less labor is allocated to nonfarm activities as the opportunity cost for agricultural (shadow wages) work goes up, all else being equal. The parameter for the female cross-shadow wage is positive (0.169) and significant, which implies that when the opportunity cost for agricultural work for females increases, the off-farm labor activities engaged by males increase as well. However, the reverse is not true; the effect of the male cross-shadow wage (-0.179) on off-farm work by females is negative but not statistically significant. We also find that females work less in off-farm activities when the shadow income goes up. In addition, monthly observed earnings, which are used as a proxy for market wages, are included. The positive elasticities obtained, 0.782 for males, 0.695 for females and 0.775 for males and females combined, suggest that if wages rise, individuals work more off-farm, and that males are more responsive to changes in the marginal returns to their labor than females. Negative elasticities, which cannot be ruled out, would be evidence of a backward-bending labor supply curve meaning that if wages rise beyond a certain point, individuals work less; thus, the income effect dominates the substitution effect (Rosenzweig, 1980). Our findings also corroborate the hypothesis that there is a difference between males and females in terms of hours worked off-farm in Nicaragua. When a distinction is made between males and females (column 6, Table 4) through a dummy variable for sex (1 for males), a positive and statistically significant parameter is obtained. We also find (not shown in the paper) that working hours for men go down with age at a decreasing rate, while in all three equations (columns 4–6, Table 4) the coefficients of education level are not statistically significant. The square of education level when males and females are estimated together (column 6) is an exception. The effect of the number of males and females over 15 and the number of children and teens between 6 and 14 years old is also examined. Table 4 (columns 4–6) shows that family composition can have some influence on off-farm labor supply decisions for both males and females. In summary, we find that the hours of male off-farm labor increase with the number of adult females in the household. A possible explanation for this finding is that off-farm wage employment is more likely when other members in the household have similar employment, probably because they can share the off-farm experience (Isgut, 2004). We also observed that female off-farm labor hours decrease with the number of children and teens, most likely because of childcare responsibilities (Abdulai and Regmi, 2000; Barret et al., 2008). 5. Concluding remarks This paper contributes to the literature by focusing on the incentives for off-farm labor supply in the rural sector of Nicaragua using panel data for males and females. We depart from the approach introduced by Jacoby (1993) and Skoufias (1994) to estimate shadow wages and shadow income and incorporate both sets of shadow values in off-farm labor decisions. Our work also innovates by applying a semiparametric panel data approach to mitigate biases not only from unobserved individual time-invariant characteristics, but also from sample selection. It appears that only a few semiparametric studies have considered potential biases from sample selection when panel data is used in the analysis of labor supply, and we found no such studies in the agricultural economics literature. One of our major results is that shadow wages and shadow income for both males and females play a major role in the supply of labor to nonfarm activities. Specifically, when the marginal productivity of on-farm agricultural work rises, the time allocated to nonfarm activities declines, particularly for male farmers. Moreover, we have shown that when off-farm market wages go up, more time is allocated to off-farm work. These findings are consistent with the literature that farmers engaged in non-farm activities tend to exhibit higher farming inefficiency because they have less time available to participate in technical training from extension services or other sources, and to adopt new technologies and apply existing ones successfully, actions that contribute to good management (Abdulai and Eberlin, 2001; Chavas et al., 2005). We also find that controlling for the seasonality of off-farm work by household members affects the production function estimates. In addition, on-farm labor affects the odds of individuals working off-farm, corroborating the trade-off between on- and off-farm activities. These findings lend support to the premise that policy efforts designed to increase farm productivity and output growth among peasant farmers may be effective in alleviating rural poverty and reducing incentives for rural to urban migration (World Bank, 2008). For instance, in densely populated rural communities located in highly degraded areas, such as parts of Nicaragua and much of Central America, public support for agricultural research and extension should be seen as a means to (1) promote productivity growth and poverty alleviation; (2) reduce labor market inequities between men and women; and (3) develop more environmentally sustainable production processes in order to improve the quality of life and livelihoods for rural communities (Bravo-Ureta et al., 2011; Dethier and Effenberger, 2012). These are important issues left for future study.

8

Not reported but available upon request. 108

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Appendix

Table A1 Sample characteristics of on- and off-farm labor workers, aged 15 or older1. Source: Own computation from 1998, 2001 and 2005 LSMS surveys. Over the past 12 months how long have you worked

Working on-farm 1st Job

Until 2 months Between 3 and 4 months Between 5 and 6 months Between 7 and 8 months Between 9 and 10 months Between 11 and 12 months Total Observations Until 2 months Between 3 and 4 months Between 5and 6 months Between 7 and 8 months Between 9 and 10 months Between 11 and 12 months Total Observations

Males (%) 1.91 3.55 15.35 9.31 7.82 62.06 100 6,647 Females (%) 5.51 6.44 12.38 4.52 5.45 65.72 100 1,616

Working off-farm

2nd Job

12months Job

1st Job

2nd Job

12months Job

7.97 13.05 26.10 6.81 4.97 41.11 100 866

10.62 23.58 40.23 11.61 8.19 5.76 100 1,111

8.53 10.82 11.60 6.19 4.34 58.53 100 2,052

12.11 24.21 23.97 8.96 4.36 26.39 100 413

21.83 31.61 29.91 8.87 4.89 2.89 100 1,003

16.67 6.67 13.33 6.67 13.33 43.33 100 30

13.56 11.86 47.46 8.47 8.47 10.17 100 59

11.69 9.28 9.28 4.07 7.62 58.07 100 787

– – 50.00 – – 50.00 100 2

24.24 33.33 18.18 6.06 9.09 9.09 100 66

Note: 1) Includes information for all individuals who had one (1st) or two (2nd) jobs for the week prior to the survey and during the preceding 12month period (12monhts).

References Abdulai, A., Delgado, C.L., 1999. Determinants of nonfarm earnings of farm-based husbands and wives in northern Ghana. Am. J. Agric. Econ. 81 (1), 117–130. Abdulai, A., Eberlin, R., 2001. Technical efficiency during economic reform in Nicaragua: evidence from farm household survey data. Econ. Syst. 25, 113–125. Abdulai, A., Regmi, P.P., 2000. Estimating labor supply of farm households under nonseparability: empirical evidence from Nepal. Agric. Econ. 22 (3), 309–320. Ahituv, A., Kimhi, A., 2006. Simultaneous estimation of work choices and the level of farm activity using panel data. Eur. Rev. Agric. Econ. 33 (1), 49–71. Askildsen, J.E., Baltagi, B.H., Holmas, T.H., 2003. Wage policy in the health care sector: a panel data analysis of nurses’ labour supply. Health Econ. 12 (9), 705–719. Atamanov, A., Van den Berg, M., 2012. Participation and returns in rural nonfarm activities: evidence from the Kyrgyz republic. Agric. Econ. 43 (4), 459–471. Baltagi, B.H., 2008. Econometric Analysis of Panel Data, 4th ed. . John Wiley & Sons, West Sussex. Barrett, C.B., Reardon, T., Webb, P., 2001. Nonfarm income diversification and household livelihood strategies in rural Africa: concepts, dynamics, and policy implications. FDLIs Food Drug Policy Forum 26, 3151–3331. Barrett, C.B., Sherlund, S.M., Adesina, A.A., 2008. Shadow wages, allocative inefficiency, and labor supply in smallholder agriculture. Agric. Econ. 38 (1), 21–34. Basic Information Document, 1998. 2002. Nicaragua: Living Standards Measurement Study Survey. The World Bank, Washington, DC, USA. Battese, G.E., 1997. A note on the estimation of Cobb-Douglas production function when some explanatory variables when some explanatory variables have zero values. J. Agric. Econ. 48, 250–252. Benjamin, D., 1992. Household composition, labor markets, and labor demand: testing for separation in agricultural household models. Econometrica. 60, 287–322. Bhaumik, S.K., Dimova, R., Nugent, J.B., 2011. Off-farm labor supply and labor markets in rapidly changing circumstances: bulgaria during transition. Econ. Syst. 35 (3), 378–389. Bjørnsen, H., Biørn, E., 2010. Interrelated Labor decisions of farm couples: a censored response analysis of off-farm work. Agric. Econ. 41 (6), 595–610. Bravo-Ureta, B.E., Almeida, A.N., Solís, D., Inestroza, A., 2011. The economic impact of Marena’s investments on sustainable agricultural systems in Honduras. J. Agric. Econ. 62 (2), 429–448. Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge, UK. Cameron, A.C., Trivedi, P.K., 2009. Microeconometrics Using STATA. The Stata Press, USA, College Station, TX. Canagarajah, S., Newman, C.B., Bhattamishra, R., 2001. Non-farm income, gender, and inequality: evidence from rural Ghana and Uganda. FDLIs Food Drug Policy Forum 26 (4), 405–420. Charlier, E., Melenberg, B., Van Soest, A., 2001. An analysis of housing expenditure using semiparametric models and panel data. J. Econom. 101 (1), 71–107. Chavas, J., Petrie, R., Roth, Michael, 2005. Farm household production efficiency: evidence from Gambia. Am. J. Agric. Econ. 87 (1), 160–179. https://doi.org/10. 1111/j.0002-9092.2005.00709.x. Corral, L., Reardon, T., 2001. Rural nonfarm incomes in Nicaragua. World Dev. 29 (3), 427–442. Covarrubias, K., Davis, B., Bakouan, A., Di Giuseppe, S., 2012. Household Income Generation Strategies. Available at:. The World Bank, Washington, DC, USA. https://openknowledge.worldbank.org/handle/10986/12133. Davis, B., Winters, P., Reardon, T., Stamoulis, K., 2009. Rural nonfarm employment and farming: household-level linkages. Agric. Econ. 40 (2), 119–123. Deininger, K., Zegarra, E., Lavandez, I., 2003. Determinants and impacts of rural land market activity: evidence from Nicaragua. World Dev. 31 (8), 1385–1404. Deolalikar, R.E., Vijverberg, W., 1987. A test of the heterogeneity of Family and hired labour in Asian agriculturae. Oxford Bull. Econ. Statist. 49, 291–305. Dethier, J.J., Effenberger, A., 2012. Agriculture and development: a brief review of the literature. Econ. Syst. 36 (2), 175–205. ECLAC, 2015. CEPALSTAT: Bases De Datos Y Publicacionnes Estadisticas. Available at:. Economic Commission for Latin America and the Caribbean, United Nations. http://estadisticas.cepal.org/cepalstat/WEB_CEPALSTAT/Portada.asp.

109

Economic Systems 43 (2019) 99–110

A.N. Almeida, B.E. Bravo-Ureta

Eubank, R.L., 1999. Nonparametric regression and spline smoothing, 2 ed. Statistics: A Series of Textbooks and Monographs Vol 157 CRC Press. FAO, 1998. Rural Non-farm Income in Developing Countries. Agriculture Series n. 31. Available at:. FAO, Rome, Italy. http://www.fao.org/docrep/w9500e/ w9500e12.htm. FAO, 2011. The State of Food and Agriculture 2010-2011 Women in Agriculture: Closing the Gender Gap for Development. Available at:. FAO and Agriculture Organization of the United Nations. FAO, Rome, Italy. http://www.fao.org/docrep/013/i2050e/i2050e.pdf. Goodwin, B.K., Holt, M.R., 2002. Parametric and semiparametric modeling of the off-farm labor supply of agrarian households in transition Bulgaria. Am. J. Agric. Econ. 84 (1), 184–209. Greene, W., 2008. Econometric analysis. Pearson Prentice Hall, New Jersey, 6th ed. Upper Saddle River, NJ, USA. Gronau, R., 1977. Leisure, home production, and work—the theory of the allocation of time revisited. J. Polit. Econ. 85 (6), 1099–1123. Haggblade, S., Hazell, P.B.R., Reardon, T., 2007. Transforming the Rural Nonfarm Economy: Opportunities and Threats in the Developing World. The John Hopkins University Press, Baltimore, MD, USA. Henderson, D.J., Parmeter, C.F., 2015. Applied Nonparametric Econometrics. The Cambridge University Press, Cambridge, MA, USA. Huffman, W.E., 1980. Farm and off-farm work decisions: the role of human capital. Rev. Econ. Stat. 62 (1), 14–23. Huffman, W.E., Lange, M.D., 1989. Off-farm work decisions of husbands and wives: joint decision making. Rev. Econ. Stat. 71 (3), 471–480. IFAD, 2009. International Fund for Agricultural Development. IFAD: Rural poverty in Nicaragua, Rome, IT. IFPRI, 2000. Women: the Key to Food Security. International Food Policy Research Institute, Washington, DC, USA. ILO, 2008. Promotion of Rural Employment for Poverty Reduction. Report IV: Fourth Item on the Agenda, 97th Session. International Labour Organisation, Génova, IT. Informe de Metodología and Operaciones, 2006. Nicaragua: Encuesta Nacional De Hogares Sobre Medición De Nivel De Vida 2006. The World Bank, Washington, DC, USA. Isgut, A.E., 2004. Non-farm income and employment in rural Honduras: assessing the role of locational factors. J. Dev. Stud. 40 (3), 59–86. Jacoby, H.G., 1993. Shadow wages and peasant family labour supply: an econometric application to the Peruvian sierra. Rev. Econ. Stud. 60 (4), 903–921. Jolliffe, D., 2004. The impact of education in Rural Ghana: examining household labor allocation and returns on and off the farm. J. Dev. Econ. 73 (1), 287–314. Kumar, A., Mishra, A.K., Saroj, S., Joshi, P.K., 2017. Institutional versus non-institutional credit to agricultural in India: evidence on impact from a national farmer’s survey. Econ. Syst. 41, 420–432. Kyriazidou, E., 1997. Estimation of a panel data sample selection model. Econometrica 65 (6), 1335–1364. Lanjouw, P., 2001. Nonfarm employment and poverty in rural El Salvador. World Dev. 29 (3), 529–547. Lanjouw, J.O., Lanjouw, P., 2001. The rural non-farm sector: issues and evidence from developing countries. Agric. Econ. 26, 1–23. Le, K.T., 2009. Shadow wages and shadow income in farmer’s labor supply functions. Am. J. Agric. Econ. 91 (3), 685–696. Malchow-Moller, N., Svarer, M., 2005. Wage-labor activities by agricultural households in Nicaragua. J. Dev. Stud. 41 (7), 1221–1246. Mathenge, M.K., Tschirley, D.L., 2015. Off-farm labor market decisions and agricultural shocks among rural households in Kenya. Agric. Econ. 46, 603–616. Matshe, I., Young, T., 2004. Off-farm labour allocation decisions in small-scale rural households in Zimbabwe. Agric. Econ. 30 (3), 175–186. McCarthy, N., Sun, Y., 2009. Participation by Men and Women in Off-farm Activities: an Empirical Analysis in Rural Northern Ghana. International Food Policy Research Institute, Washington, DC, USA. Newman, C., Canagarajah, S., 2000. Gender, Poverty, and Nonfarm Employment in Ghana and Uganda. Policy Research Working Paper Series n. 2367. World Bank, Washington, DC, USA. Oseni, G., Winters, P., 2009. Rural nonfarm activities and agricultural crop production in Nigeria. Agric. Econ. 40 (2), 189–201. Reardon, T., Berdegue, J., Escobar, G., 2001. Rural nonfarm employment and incomes in Latin America: overview and policy implications. World Dev. 29 (3), 395–409. Rosenzweig, M., 1980. Neoclassical theory and the optimizing peasant: an econometric analysis of market family labor supply in a developing country. Q. J. Econ. 94 (1), 31–55. Salgado-Ugarte, I.H., Shimizu, M., Taniuchi, T., 1995. Practical rules for bandwidth selection in univariate density estimation. Stata Tech. Bull. 27, 5–19. Skoufias, E., 1994. Using shadow wages to estimate labor supply of agricultural households. Am. J. Agric. Econ. 76 (2), 215–227. Ficha Técnica, 2001. Nicaragua: Living Standards Measurement Study Survey 2001. The World Bank, Washington, DC, USA. World Bank, 2008. World Development Report 2008. The World Bank, Washington, DC, USA. World Bank, 2016. World Bank Development Indicators. Available at:. The World Bank, Washington, DC, USA. http://data.worldbank.org/data-catalog/worlddevelopment-indicators. Yamauchi, R., 2008. Early childhood nutrition, schooling, and sibling inequality in a dynamic context: evidence from South Africa. Econ. Dev. Cult. Chan. 56 (3), 657–682. Yatchew, A., 2003. Semiparametric regression for the applied econometrician. In: Philips, P.C.B. (Ed.), Themes in Modern Econometrics. The Cambridge University Press, Cambridge, MA.USA. Zezza, A., Winters, P., Davis, B., Carletto, G., Covarrubias, K., Quinones, E., Stamolis, K., Karfakis, T., Tasciotti, L., DiGiuseppe, S., Bonomi, G., 2007. Rural Household Access to Assets and Agrarian Institutions: a Cross Country Comparison. ESA Working Paper 07-17. FAO, ESA, Rome.

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