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Trapped in Agriculture? Credit Constraints, Investments in Education and Agricultural Employment

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Abstract

The basic neo-classical model implies that agricultural employment responds positively to increases in farm income. This argument is assumed by proponents and critics of agricultural subsidy programs in their discourse in favor of or against government support for farmers. However, empirical evidence on the relationship between agricultural employment and farm income (and subsidies) is mixed, and some studies find evidence that an increase in farm income has a negative impact on agricultural employment. This article proposes a new explanation for this puzzle. When farm income increases, part of the additional income is invested by credit-constrained farmers in their children’s education and educated children are less likely to become farmers themselves. We provide a theoretical model and empirical evidence supporting this argument.

Abstract

Le modèle néo-classique de base part du principe que l’emploi agricole réagit positivement à l’augmentation des revenus fermiers. Cet argument est proposé par les défenseurs et les critiques des programmes de subventions agricoles, lorsqu’ils se positionnent pour ou contre le soutien gouvernemental aux fermiers. Cependant, les preuves empiriques sur la relation entre emploi agricole et revenus fermiers (et subventions) sont mitigées, et quelques études ont trouvé qu’une augmentation des revenus fermiers a un impact négatif sur l’emploi agricole. Cet article propose une nouvelle explication à ce problème. Lorsque les revenus fermiers augmentent, une partie de ces revenus supplémentaires est investie par des fermiers, limités par les contraintes de crédit, dans les études de leurs enfants. Les enfants ayant fait des études sont moins susceptibles de devenir eux-mêmes des fermiers. Nous fournissons un modèle théorique et des preuves empiriques pour appuyer cet argument.

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Notes

  1. For explanations of the causes of this pattern, see, for example, Swinnen (1994) and Anderson et al (2013).

  2. It is also well known that the income effects of subsidies are only partial, because of a variety of factors, such as targeting problems and factor market adjustments. There is a large literature showing that these effects differ with the nature of the subsidies, with different income effects of price support, direct payments and so on, and the nature of the output and factor markets. While these arguments are obviously important in designing optimal policies, they do not affect the arguments in our paper. The only assumption that we use (implicitly) in our arguments is that subsidies have some (positive) income effect (OECD, 2001).

  3. There are few representative data on this. Whatever information is available suggests that this factor is very important. Data from some EU countries, such as Belgium and the Netherlands, indicate that less than 25 per cent of farmers older than 50 years have a successor.

  4. Similar conclusions follow from a comparison of subsectors within the agricultural sector. For example, data for Belgium, the Netherlands, Portugal and Greece show that in the subsectors where agricultural subsidies were higher, agricultural labor outflow was stronger in the period 1990–2007 (Swinnen and Van Herck, 2010).

  5. The literature on the effect of agricultural subsidies on agricultural employment can be divided into two approaches. The first and most popular approach uses the occupational choice model developed by Todaro (1969) and Harris and Todaro (1970). Empirical models using this approach are usually based on aggregate (country- or region-level) data. In general, long time series are available, allowing for the use of panel regression techniques (for example, Dries and Swinnen, 2002; Breustedt and Glauben, 2007; Petrick and Zier, 2011; Olper et al, 2012). The second approach uses household models to analyze the impact of agricultural subsidies on agricultural employment (Becker, 1965). Empirical models using this approach usually employ farm-level data (for example, Mishra and Goodwin, 1997; Dries and Swinnen, 2004; Mishra et al, 2004). These data are often more time constrained, but may include more detailed and specific information than aggregate data and are therefore used in depth analyses, such as with respect to farm succession (for example, Kimhi, 1994; Kimhi and Bollman, 1999; Stiglbauer and Weiss, 2000). In our model we follow the latter approach, using household-level data.

  6. Attributes such as intelligence and non-cognitive skills are implicitly included in the income the child can earn in each sector. A more sophisticated model would include interactions between various human capital attributes, jointly determining educational and occupational choices. This is beyond the scope of this article.

  7. On the basis of a sample of high school graduates in the United States, Orazem and Mattila (1991) find that the returns to schooling are higher for non-agricultural occupations than for agricultural employment. Using data from a large sample of different countries, Psacharopoulos (1994) finds that these results hold in a more global perspective. Middendorf (2008) uses 2001 data to estimate the returns to schooling in different EU countries (including the countries used in our econometric specification). He also finds that returns to education are higher in the industrial and service sectors compared with the agricultural sector. Galor et al (2009) propose that in general individuals in agricultural economies have a lower level of education because there is a lower complementarity between human capital and land as compared with that between human capital, physical capital and technology.

  8. Introducing an altruism rate/discount factor different from 1 would not alter our qualitative results.

  9. Eighty-nine per cent of the households answered yes on at least one of the following six questions in the survey: (i) Can the household afford to keep its home adequately warm?; (ii) Can the household pay for a week’s annual holiday away from home?; (iii) Can the household afford replacing any worn-out furniture?; (iv) Can the household buy new, rather than second-hand, clothes?; (v) Can the household afford to eat meat, chicken or fish every second day, if desired?; and (vi) Can the household afford to have friends or family over for a drink or meal at least once a month?

  10. Exceptions analyzing ex-post (actual) succession decisions using panel data are studies by Kimhi (1994), Kimhi and Bollman (1999), Stiglbauer and Weiss (2000) and Väre et al (2010).

  11. In addition to the recursive simultaneous bivariate probit model, we have also estimated an instrumental probit model. In this model specification, HIGH_EDU is considered to be a continuous variable, while in reality this is a binary variable. This is different from the recursive simultaneous bivariate probit model, where both HIGH_EDU and LEAVE are binary variables, which makes the latter model better suited to test our hypotheses. Nevertheless, the main results remain robust across the two specifications, and the results of the instrumental probit model specification are available upon request from the authors.

  12. We choose the educational level ‘Basic or lower secondary education (ISCED 0-2)’ according to the ISCED classification level as the base category since in all countries in our sample this is the minimum compulsory educational level (Murtin and Viarengo, 2008).

  13. For example, in case a child was in the educational system in 1994 and finished their education in 1996, we use the natural logarithm of the average self-employed farm income of the household in the period 1994–1996 as the dependent variable. If the same child finished their education in 1999, we use the natural logarithm of the average self-employed farm income in the period 1994–1999. This will reduce potential measurement error and the effects of temporary income shocks.

  14. One could argue that while occupational choice depends on the expected farm income, educational choice depends rather on total household income, including both self-employed farm income and income from wage labor and other sources. Therefore, as a robustness check, we included in the educational equation the natural logarithm of the average total household income in the period between 1994 and 1999 that the child was in the educational system, while in the occupational choice equation we included the natural logarithm of the average self-employed farm income of the household in the period between 1994 and 1999 that the child was in the educational system. However, this did not change our main results, and the results are available upon request to the authors.

  15. Note that we also run a model in which we included the cubic form of FARMINC in the educational equation as suggested by Proposition 3. However, this term appeared to be insignificant, which may indicate that in our sample we only observe a selected farm income range compared with the full income range discussed in the theoretical framework. It is possible that very high farm incomes are not included in the sample, which is plausible given that we have only a limited number of observations (109) from countries where there are many poor farmers (Portugal, Spain, Italy and Ireland).

  16. OFFFARM and SOCIAL are two dummy variables that take a value of 1 if the farmer or the spouse received, respectively, off-farm income or social payments during the years that the child was in the educational system in the period 1994–1999 and 0 otherwise. AGR is the average share of self-employed agricultural income in total household income during the years that the child was in the educational system in the period 1994–1999.

  17. The use of the regional availability of higher education as an explanatory variable would be problematic if the educational decisions of farmers’ children were the main driver of this variable. However, the regional availability of higher education is calculated based on the educational level attained by all children in a region, independently of the occupation of their parents. As a result, this variable has been calculated based on a much larger sample (5483 observations) than the sample of farmers’ children used in our analysis (109 observations). Therefore, it is unlikely that the observations in our sample of farmers’ children drive the outcome for the regional availability of higher education. This approach is similar to the one followed by Key and McBride (2008), who use contract availability as an instrument to identify the effect of contract use on farm productivity.

  18. The covariance ρ between the random errors ɛ and μ is found to be significant, indicating that the two dependent variables are jointly determined and the recursive simultaneous bivariate probit model is the appropriate estimation technique.

References

  • Acemoglu, D. and Pischke, J.S. (2001) Changes in the wage structure, family income and children’s education. European Economic Review 45 (4): 890–904.

    Article  Google Scholar 

  • Anderson, K. and Hayami, Y. (1986) The Political Economy of Agricultural Protection: East Asia in International Perspective. Boston, London and Sydney: Allen and Unwin.

    Google Scholar 

  • Anderson, K. (2009) Distortions to Agricultural Incentives: A Global Perspective, 1955–2007. London; Washington DC: Palgrave Macmillan; World Bank.

    Book  Google Scholar 

  • Anderson, K., Rausser, G. and Swinnen, J. (2013) Political economy of public policies: Insights from distortions to agricultural and food markets. Journal of Economic Literature 51 (2): 423–477.

    Article  Google Scholar 

  • Barkley, A.P. (1990) The determinants of the migration of labour out of agriculture in the United States, 1940–85. American Journal of Agricultural Economics 72 (3): 567–573.

    Article  Google Scholar 

  • Becker, G.A. (1965) Theory of the allocation of time. Economic Journal 75 (299): 493–517.

    Article  Google Scholar 

  • Bentolila, S., Michelacci, C. and Suarez, J. (2010) Social contacts and occupational choice. Economica 77 (305): 20–45.

    Article  Google Scholar 

  • Blancard, S., Boussemart, J.P., Briec, W. and Kerstens, K. (2006) Short- and long- run credit constraints in French agriculture. American Journal of Agricultural Economics 88 (2): 351–364.

    Article  Google Scholar 

  • Breustedt, G. and Glauben, T. (2007) Driving forces behind exiting from farming in Western Europe. Journal of Agricultural Economics 58 (1): 115–127.

    Article  Google Scholar 

  • Briggeman, B.C., Towe, C. and Morehart, M.J. (2009) Credit constraints: Their existence, determinants and implications for US small farm agriculture. American Journal of Agricultural Economics 91 (1): 275–289.

    Article  Google Scholar 

  • Cawley, J., Heckman, J. and Vytlacil, E. (2001) Three observations on wages and measured cognitive ability. Labour Economics 8 (4): 419–442.

    Article  Google Scholar 

  • Chen, X., Shi, Y., Mo, D., Chu, J., Loyalka, P. and Rozelle, S. (2013) The impact of a senior high school tuition relief program on poor junior high school students in rural China. China and World Economy 21 (3): 80–97.

    Article  Google Scholar 

  • Ciaian, P., Falkowski, J., Kancs, D. and Pokrivcak, J. (2011) Productivity and Credit Constraints: Firm-Level Evidence from Propensity Score Matching. Factor Markets Working Paper 3, Centre for European Policy Studies, Brussels.

  • Dong, F., Hennessey, D.A. and Jensen, H.H. (2010) Contract and exit decisions in finisher hog production. American Journal of Agricultural Economics 92 (3): 667–684.

    Article  Google Scholar 

  • Dries, L. and Swinnen, J. (2002) Institutional reform and labour reallocation during transition: Evidence from polish agriculture. World Development 30 (3): 457–474.

    Article  Google Scholar 

  • Dries, L. and Swinnen, J. (2004) Foreign direct investment, vertical integration, and local suppliers: Evidence from the polish dairy sector. World Development 32 (9): 1525–1544.

    Article  Google Scholar 

  • Dries, L. and Bojnec, S. (2004) Causes of changes in agricultural employment in Slovenia: Evidence from micro-data. Journal of Agricultural Economics 56 (3): 399–416.

    Google Scholar 

  • European Commission (2010) Communication on the CAP towards 2020: Meeting the food, natural resources and territorial challenges of the future. COM(2010) 672 final, European Commission, 18 November, Brussels.

  • European Parliament (2010) On the Future of the Common Agricultural Policy after 2013. EP 439.972, Committee on Agriculture and Rural Development, Rapporteur: George Lyon, Brussels.

  • Färe, R., Grosskopf, S. and Lee, H. (1990) A nonparametric approach to expenditure-constrained profit maximization. American Journal of Agricultural Economics 72 (3): 574–581.

    Article  Google Scholar 

  • Foltz, J.D. (2004) Entry, exit, and farm size: Assessing an experiment in dairy price policy. American Journal of Agricultural Economics 86 (3): 594–604.

    Article  Google Scholar 

  • Galor, O., Moav, O. and Volrath, D. (2009) Inequality in landownership, the emergence of human capital promoting institutions and the great divergence. Review of Economic Studies 76 (1): 143–179.

    Article  Google Scholar 

  • Galor, O. (2011) Income Inequality, Human Capital Formation and the Process of Development, NBER Working Paper 17058, Cambridge, USA: National Bureau of Economic Research.

  • Glauben, T., Tietje, H. and Weiss, C.R. (2004) Intergenerational succession in farm households: Evidence from upper Austria. Review of Economics of the Household 2 (4): 443–461.

    Article  Google Scholar 

  • Glauben, T., Tietje, H. and Weiss, C.R. (2006) Agriculture on the move: Exploring regional differences in farm exit rates in Western Germany. Review of Regional Research 26 (1): 103–118.

    Article  Google Scholar 

  • Goetz, S.J. and Debertin, D.L. (1996) Rural population decline in the 1980s: Impacts of farm structure and federal farm programs. American Journal of Agricultural Economics 78 (3): 517–529.

    Article  Google Scholar 

  • Goetz, S.J. and Debertin, D.L. (2001) Why farmers quit: A county-level analysis. American Journal of Agricultural Economics 83 (4): 1010–1023.

    Article  Google Scholar 

  • Greene, W.H. (1998) Gender economics courses in liberal arts colleges: Further results. The Journal of Economic Education 29 (4): 291–300.

    Article  Google Scholar 

  • Greene, W.H. (2003) Econometric Analysis. Upper Saddle River, NJ: Pearson Education.

    Google Scholar 

  • Harris, J.R. and Todaro, M.P. (1970) Migration, unemployment and development: A two-sector analysis. American Economic Review 60 (1): 126–142.

    Google Scholar 

  • Heckman, J.J., Stixrud, J. and Urzua, S. (2006) The Effects of Cognitive and Non-cognitive Abilities on Labour Market Outcomes and Social Behavior. NBER Working Paper 12006, Cambridge, USA: National Bureau of Economic Research.

  • Hennessey, T. and Rehman, T. (2007) An investigation into factors affecting the occupational choices of nominated farm heirs in Ireland. Journal of Agricultural Economics 58 (1): 61–75.

    Article  Google Scholar 

  • Hoppe, R.A. and Korb, P. (2006) Understanding U.S. Farm Exits. Washington DC: U.S. Department of Agriculture, ERS Economic Research Report No. 21, June.

  • Jones, A. (2007) Applied Econometrics for Health Economists: A Practical Guide. Oxford, UK: Radcliffe Publishing.

    Google Scholar 

  • Johnson, D.G. (1973) World Agriculture in Disarray. Revised edition. London: St Martin’s Press.

    Book  Google Scholar 

  • Key, N. and Roberts, M.J. (2006) Government payments and farm business survival. American Journal of Agricultural Economics 88 (2): 382–392.

    Article  Google Scholar 

  • Key, N. and McBride, W.D. (2008) Do production contracts raise farm productivity? An instrumental variables approach. Agricultural and Resource Economics Review 37 (2): 167–187.

    Article  Google Scholar 

  • Kimhi, A. (1994) Optimal timing of farm transferal from parent to child. American Journal of Agricultural Economics 76 (2): 228–236.

    Article  Google Scholar 

  • Kimhi, A. and Bollman, R. (1999) Family farm dynamics in Canada and Israel: The case of farm exits. Agricultural Economics 21 (1): 69–79.

    Article  Google Scholar 

  • Lee, H. and Chambers, R.G. (1986) Expenditure constraints and profit maximization in US agriculture. American Journal of Agricultural Economics 68 (4): 857–865.

    Article  Google Scholar 

  • Maddala, G.S. (1983) Limited-Dependent and Qualitative Variables in Economics. New York: Cambridge University Press.

    Book  Google Scholar 

  • Middendorf, T. (2008) Returns to Education in Europe: Detailed Results from a Harmonized Survey. Ruhr Economic Paper 65, RWI, Essen.

  • Mishra, A. and Goodwin, B. (1997) Farm income variability and the supply of off-farm labour. American Journal of Agricultural Economics 79 (3): 880–887.

    Article  Google Scholar 

  • Mishra, A., El-Osta, H. and Johnson, J. (2004) Succession in Family Farm Business: Empirical Evidence from the U.S. Farm Sector. Selected Paper for Presentation at the AAEA Meeting, 1–4 August, Denver.

  • Murtin, F. and Viarengo, M. (2008) The Convergence of Compulsory Schooling in Western Europe: 1950–2000. CEE Discussion Papers CEEDP0095, Centre for the Economics of Education, London School of Economics and Political Science, London.

  • OECD (2001) Market Effects of Crop Support Measures. Paris, France: OECD.

  • OECD (2008) Agricultural Policy Design and Implementation: A Synthesis. Paris, France: OECD.

  • OECD (2013) Agricultural Policy Monitoring and Evaluation 2013. Paris, France: OECD.

  • Olper, A., Raimondi, V., Cavicchioli, D. and Vigani, M. (2012) Does the Common Agricultural Policy Reduce Farm Labour Migration? Panel Data Analysis across EU Regions. Factor Markets Working Paper 28, Centre for European Policy Studies, Brussels.

  • Olson, M. (1965) The Logic of Collective Action. New Haven, CT: Yale University Press.

    Google Scholar 

  • Orazem, P. and Mattila, J. (1991) Human capital, uncertain wage distributions, and occupational and educational choices. International Economic Review 32 (1): 103–122.

    Article  Google Scholar 

  • Petrick, M. and Zier, P. (2011) Regional employment impacts of common agricultural policy measures in Eastern Germany: A difference-in-difference approach. Agricultural Economics 42 (2): 183–193.

    Article  Google Scholar 

  • Pietola, K., Väre, M. and Oude Lansink, A. (2003) Timing and type of exit from farming: Farmers’ early retirement programs in Finland. European Review of Agricultural Economics 30 (1): 99–116.

    Article  Google Scholar 

  • Psacharopoulos, G. (1994) Returns to education: A global update. World Development 22 (9): 1325–1343.

    Article  Google Scholar 

  • Swinnen, J. and de Gorter, H. (1993) Why small groups and low income sectors obtain subsidies: The ‘altruistic’ side of a ‘self-interested’ government. Economics and Politics 5 (3): 285–296.

    Article  Google Scholar 

  • Swinnen, J. (1994) A positive theory of agricultural protection. American Journal of Agricultural Economics 76 (1): 1–14.

    Article  Google Scholar 

  • Swinnen, J. and Van Herck, K. (2010) Compensation payments in EU agriculture. In: G. Porto and B.M. Hoekman (eds.) Trade Adjustment Costs in Developing Countries: Impacts, Determinants and Policy Responses, Centre for Economic Policy Research. Washington DC: The World Bank, pp. 361–381.

    Google Scholar 

  • Swinnen, J. (2010) The political economy of agricultural and food policies: Recent contributions, new insights, and areas for further research. Applied Economic Perspectives and Policy 32 (1): 33–58.

    Article  Google Scholar 

  • Stiglbauer, A. and Weiss, C.R. (2000) Family and non-family succession in the Upper-Austrian farm sector. Cahiers d'économie et sociologie rurales 54: 5–26.

    Google Scholar 

  • Thomson, H. (2013) The Impact of Agriculture and Farm Produce Prices on Human Capital Formation: Education Decisions of Young Americans in Agricultural Areas Before and During the Food Crisis 2000–2010, Plan B Paper Submitted in Partial Fulfillment for the MS in Applied Economics, University of Minnesota.

  • Todaro, M.P. (1969) A model of labour migration and urban unemployment in less developed countries. American Economic Review 59 (1): 138–148.

    Google Scholar 

  • Väre, M., Pietola, K. and Weiss, C.R. (2010) The irrelevance of stated plans in predicting farm successions in Finland. Agricultural and Food Science 19 (1): 81–95.

    Article  Google Scholar 

  • Wilde, J. (2000) Identification of multiple equation probit models with endogenous dummy regressors. Economic letters 69 (3): 309–312.

    Article  Google Scholar 

  • Yi, H. et al (2012) Dropping out: Why are students leaving junior high in China’s poor rural areas? International Journal of Educational Development 32 (4): 555–563.

    Article  Google Scholar 

  • Zhang, L., Yi, H., Luo, R., Liu, C. and Rozelle, S. (2013) The human capital roots of the middle income trap: The case of China. Agricultural Economics 44 (1): 151–162.

    Article  Google Scholar 

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Acknowledgements

The authors of the paper thank the partners in the FP7 Factor Markets project, participants of the different conferences in which this paper was presented, and the anonymous reviewers for comments and suggested improvements to the paper. This research was financially supported by the FP7 Factor Markets project [245123 CP-FP] and the KU Leuven Research Fund (Excellence Finance and Methusalem Programme).

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Authors and Affiliations

  1. LICOS – Centre for Institutions and Economic Performance, KU Leuven, Leuven, Belgium

    Ruxanda Berlinschi, Johan Swinnen & Kristine Van Herck

  2. Department of Economics, Hogeschool Universiteit Brussel, Brussels, Belgium

    Ruxanda Berlinschi

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  1. Ruxanda Berlinschi
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Correspondence to Kristine Van Herck.

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Berlinschi, R., Swinnen, J. & Van Herck, K. Trapped in Agriculture? Credit Constraints, Investments in Education and Agricultural Employment. Eur J Dev Res 26, 490–508 (2014). https://doi.org/10.1057/ejdr.2014.30

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