Market Participation Decisions, Commercialization Intensity, and Structural Transformation in Maryland’s Goat Sector: An Empirical Analysis

Authors

  • Dharma R. Katuwal Department of Agriculture, Food, and Resource Sciences, USA
  • Lila B. Karki Department of Agriculture, Food, and Resource Sciences, UMES Extension, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA
  • Anuj Dhakal Department of Agriculture, Food, and Resource Sciences, USA
  • Enrique N. Escobar Department of Agriculture, Food, and Resource Sciences, UMES Extension, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA

DOI:

https://doi.org/10.54536/ajebi.v5i2.7324

Keywords:

Fixed-Effects Estimation, Herd Size, Market Participation, Small-Scale Goat Farming

Abstract

Although goat production accounts for a small share of the total U.S. livestock output, it plays a crucial role in diversified and small-scale farming systems. Despite representing less than 1% of the U.S. goat inventory and sales, Maryland provides valuable insights into the commercialization of small-scale livestock systems. However, limited empirical evidence exists on the drivers of goat commercialization at the state level, particularly in distinguishing between market growth driven by expansion within existing farms and that driven by the entry of new producers. This study examines the determinants of goat market participation and commercialization intensity using county-level panel data from the USDA Census of Agriculture for 2002–2022 (92 county–year observations). A Two-Way Fixed Effects (TWFE) and Poisson Pseudo-Maximum Likelihood (PPML) frameworks are employed to isolate within-county variation over time. Results show that increases in herd size are positively and significantly associated with goat sales (TWFE: β = 0.844, p < 0.05; PPML: β = 1.204, p < 0.01), while changes in the number of farms are not statistically significant. Although market participation increased until 2017 and declined by 2022, the overall pattern indicates emerging structural constraints in commercialization. These findings demonstrate that commercialization in small-scale goat production systems is primarily driven by intensification among existing producers rather than by new farm entry. By focusing on within-county variation, this study provides a clearer understanding of scale effects in small-scale livestock systems and contributes to the literature on structural transformation. The results suggest that policies focusing on improving market access, processing infrastructure, and support for herd expansion among existing farmers are more effective than policies aimed at increasing the number of producers.

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References

Aguilar-Loyo, J. (2025). A comparative analysis of two-way fixed effects estimators in staggered treatment designs. Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2025.106059

Alexandre, G., & Mandonnet, N. (2005). Goat meat production in harsh environments. Small Ruminant Research, 60(1–2), 53–66. https://doi.org/10.1016/j.smallrumres.2005.06.005

Arkhangelsky, D., Imbens, G. W., Lei, L., & Luo, X. (2024). Design-robust two-way fixed-effects regression for panel data. Quantitative Economics, 15(4), 1467–1511.

https://doi.org/10.3982/QE1962

Arouna, A., Adegbola, P. Y., Zossou, R. C., Babatunde, R., & Diagne, A. (2017). Contract farming preferences of smallholder rice producers in Benin: A stated choice model using mixed logit. Tropicultura, 35(4).

Abate, D., Addis, Y., & Gonzalez-Redondo, P. (2021). Factors affecting the intensity of market participation of smallholder sheep producers in northern Ethiopia: Poisson regression approach. Cogent Food & Agriculture, 7(1), Article 1874154.

https://doi.org/10.1080/23311932.2021.1874154

Aditya, A., & Acharya, R. (2011). Export diversification, composition, and economic growth: Evidence from cross-country analysis. Review of International Economics, 19(5), 959–992. https://doi.org/10.1080/09638199.2011.619009

Baldwin, K. L., DeVeau, V., Foster, K., & Marshall, M. (2008). Traits affecting household livestock marketing decisions in rural Kenya. Selected paper presented at the American Agricultural Economics Association Annual Meeting, Orlando, FL. https://ageconsearch.umn.edu/record/46991

Berisso, O. (2024). Endogeneity, heterogeneity, and determinants of inefficiencies in crop-producing farmers: Empirical evidence from the central highlands of Ethiopia (Version 1) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-4087178/v1

Blanc, E., & Schlenker, W. (2017). The use of panel models in assessments of climate impacts on agriculture. Review of Environmental Economics and Policy, 11(2), 258–279.

https://doi.org/10.1093/reep/rex016

Bowen, H. P., & Wiersema, M. F. (2004). Modeling limited dependent variables: Methods and guidelines for researchers in strategic management. In Research methodology in strategy and management (Vol. 1, pp. 87–134). Emerald Group Publishing Limited. https://doi.org/10.1016/S1479-8387(04)01104-X

Buckley, Y. M. (2015). Generalized linear models. In G. A. Fox, S. Negrete-Yankelevich, & V. J. Sosa (Eds.), Ecological statistics: Contemporary theory and application (pp. 131–148). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199672547.003.0007

Chiu, A., Lan, X., Liu, Z., & Xu, Y. (2025). Causal panel analysis under parallel trends: Lessons from a large reanalysis study. American Political Science Review, Advance online publication, 1–22. https://doi.org/10.1017/S0003055425000243

Chung, S. (2013). Empirical essays on globalization and international trade (Doctoral dissertation). Southern Methodist University. ProQuest Dissertations & Theses Global. (Publication No. 3607694)

Conceicao, P., & Galbraith, J. K. (2000). Constructing long and dense time-series of inequality using the Theil index. Eastern Economic Journal, 26(1), 61–74.

Conceicao, P., Galbraith, J. K., & Bradford, P. (2001). The Theil index in sequences of nested and hierarchic grouping structures: Implications for the measurement of inequality through time, with data aggregated at different levels of industrial classification. Eastern Economic Journal, 27(4), 491–514. https://www.jstor.org/stable/40326064

Devicienti, F., Fanfani, B., & Maida, A. (2019). Collective bargaining and the evolution of wage inequality in Italy. British Journal of Industrial Relations, 57(2), 377–407.

https://doi.org/10.1111/bjir.12444

Dubeuf, J.-P., Morand-Fehr, P., & Rubino, R. (2004). Situation, changes, and future of the goat industry around the world. Small Ruminant Research, 51(2), 165–173.

https://doi.org/10.1016/j.smallrumres.2003.08.007

Durham, C. A., Wechsler, L. J., & Morrissey, M. T. (2015). Using a fractional model to measure the impact of antioxidant information, price, and liking on purchase intent for specialty potatoes. Food Quality and Preference, 45, 1–7. https://doi.org/10.1016/j.foodqual.2015.07.007

Escareno, L., Salinas-González, H., Wurzinger, M., Iniguez, L., Solkner, J., & Meza-Herrera, C. (2012). Dairy goat production systems. Tropical Animal Health and Production, 45, 17–34. https://doi.org/10.1007/s11250-012-0246-6

Evren, A., Tuna, E., Ustaoglu, E., & Sahin, B. (2021). Some dominance indices to determine market concentration. Journal of Applied Statistics, 48(13–15), 2755–2775.

https://doi.org/10.1080/02664763.2021.1963421

Frome, E. L. (1983). The analysis of rates using Poisson regression models. Biometrics, 39(3), 665–674. https://doi.org/10.2307/2531094

Gbur, E. E., Stroup, W. W., McCarter, K. S., Durham, S., Young, L. J., Christman, M., West, M., & Kramer, M. (2012). Analysis of generalized linear mixed models in the agricultural and natural resources sciences. American Society of Agronomy, Crop Science Society of America, & Soil Science Society of America. https://doi.org/10.2134/2012.generalized-linear-mixed-models

Hart, S., Merkel, R., & Gipson, T. (2019). Current situation and future prospects of the U.S. goat industry. Professional Agricultural Workers Journal, 6(2), 25–39.

https://doi.org/10.22004/ag.econ.319688

Imai, K., & Kim, I. S. (2021). On the use of two-way fixed effects regression models for causal inference with panel data. Political Analysis, 29(3), 405–415. https://doi.org/10.1017/pan.2020.33

Katuwal, D. R., & Karki, L. B. (2026). Market dynamics of U.S. goat meat (chevon) in reference to the global market. American Journal of International Trade, Business Analytics, and Economics, 1(1), 17–29.

Kibona, C. A., & Yuejie, Z. (2021). Factors that influence market participation among traditional beef cattle farmers in the Meatu District of Simiyu Region, Tanzania. Plos One, 16(4), e0248576. https://doi.org/10.1371/journal.pone.0248576

Kropko, J., & Kubinec, R. (2020). Interpretation and identification of within-unit and cross-sectional variation in panel data models. Plos One, 15(4), e0231349.

https://doi.org/10.1371/journal.pone.0231349

Kumar, S. (2007). Commercial goat farming in India: An emerging agri-business opportunity. Agricultural Economics Research Review, 20, 503–520.

Llano-Verduras, C., Perez-Balsalobre, S., & Rincon-Aznar, A. (2021). Market fragmentation and the rise of sub-national regulation. Annals of Regional Science, 67, 765–797. https://doi.org/10.1007/s00168-021-01066-x

Lomi, A. (1995). The population ecology of organizational founding: Location dependence and unobserved heterogeneity. Administrative Science Quarterly, 40(1), 111–144. https://doi.org/10.2307/2393702

Lu, C. D., & Miller, B. A. (2019). Current status, challenges and prospects for dairy goat production in the Americas. Asian-Australasian Journal of Animal Sciences, 32(8), 1244–1255. https://doi.org/10.5713/ajas.19.0256

Malakar, K., & Mishra, T. (2017). Application of Gini, Theil, and concentration indices for assessing water use inequality. International Journal of Social Economics, 44(10), 1335–1347. https://doi.org/10.1108/IJSE-01-2016-0017

Mazhangara, I. R., Chivandi, E., Mupangwa, J. F., & Muchenje, V. (2019). The potential of goat meat in the red meat industry. Sustainability, 11(13), 3671. https://doi.org/10.3390/su11133671

Melo, J., & Portugal-Perez, A. (2013). Preferential market access design: Evidence and lessons from African apparel exports to the US and the EU (Policy Research Working Paper No. 6357). World Bank. https://openknowledge.worldbank.org/handle/10986/12183

Miller, B. A., & Lu, C. D. (2019). Current status of global dairy goat production: An overview. Asian-Australasian Journal of Animal Sciences, 32(8 Suppl), 1219–1232.

https://doi.org/10.5713/ajas.19.0253

Millimet, D. L., & Bellemare, M. F. (2023). Fixed effects and causal inference (IZA Discussion Paper No. 16202). IZA Institute of Labor Economics. https://www.iza.org/publications/dp/16202

Misango, V. G., Nzuma, J. M., Irungu, P., & Kassie, M. (2022). Intensity of adoption of integrated pest management practices in Rwanda: A fractional logit approach. Heliyon, 8(1), e08735. https://doi.org/10.1016/j.heliyon.2022.e08735

Monteiro, A., Costa, J. M., & Lima, M. J. (2017). Goat system productions: Advantages and disadvantages to the animal, environment, and farmer. In G. M. Rosa (Ed.), Goat science. IntechOpen. https://doi.org/10.5772/intechopen.70002

Morand-Fehr, P., Boutonnet, J.-P., Devendra, C., Dubeuf, J.-P., Haenlein, G. F. W., Holst, P., Mowlem, L., & Capote, J. (2004). Strategy for goat farming in the 21st century. Small Ruminant Research, 51(2), 175–183. https://doi.org/10.1016/j.smallrumres.2003.08.013

Mthethwa, K. N., Ngidi, M. S. C., Ojo, T. O., & Hlatshwayo, S. I. (2022). The determinants of adoption and intensity of climate-smart agricultural practices among smallholder maize farmers. Sustainability, 14(24), 16926. https://doi.org/10.3390/su142416926

Mudiwa, B., & Mudiwa, B. (2011). A logit estimation of factors determining adoption of conservation farming by smallholder farmers in the semi-arid areas of Zimbabwe. (AgEcon Search Working Paper). https://doi.org/10.22004/AG.ECON.198516

Mullahy, J. (2010). Multivariate fractional regression estimation of econometric share models (NBER Working Paper No. 16354). National Bureau of Economic Research. https://www.nber.org/papers/w16354

Nair, M. R. R., Sejian, V., Silpa, M. V., Fonseca, V. D. F. C., Devaraj, C., & Bagath, M. (2021). Goat as the ideal climate-resilient animal model in tropical environment: Revisiting advantages over other livestock species. International Journal of Biometeorology, 65, 2229–2240. https://doi.org/10.1007/s00484-021-02179-w

Nguyen, V. D., Nguyen, C. O., Chau, T. M. L., Nguyen, D. Q. D., Han, A. T., & Le, T. T. H. (2023). Goat production, supply chains, challenges, and opportunities for development in Vietnam: A review. Animals, 13(15), 2546. https://doi.org/10.3390/ani13152546

Nyariki, D. M. (2009). Price response of herd offtake under market liberalization in a developing cattle sector: Panel analysis applied to Kenya’s ranching. Environment and Development Economics, 14(2), 263–280. https://doi.org/10.1017/S1355770X08004610

Peleckis, K. (2022). Determining the level of market concentration in the construction sector—Case of application of the HHI index. Sustainability, 14(2), 779. https://doi.org/10.3390/su14020779

Rashid, H. & Simon, H.M. (2025). Trade agreements and export performance of Bangladesh: A gravity model analysis. Asian Journal of Economics, Business and Accounting 25(7), 215–230. https://doi.org/10.2139/ssrn.5379852

Sande, D. N., Houston, J. E., & Epperson, J. E. (2005). The relationship of consuming populations to meat goat production in the United States. Journal of Food Distribution Research, 36(1), 156–160.

Santos Silva, J. M. C., & Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88(4), 641–658. https://doi.org/10.1162/rest.88.4.641

Schwiebert, J., & Wagner, J. (2015). A generalized two-part model for fractional response variables with excess zeros. In Beiträge zur Jahrestagung des Vereins für Socialpolitik 2015: Ökonomische Entwicklung – Theorie und Politik – Session: Microeconometrics (No. B04-V2). ZBW – Leibniz Information Centre for Economics. https://hdl.handle.net/10419/113059

Sorensen, B. B., Estmann, C., Sarmento, E. F., & Rand, J. (2020). Economic complexity and structural transformation: The case of Mozambique (WIDER Working Paper No. 2020/141). United Nations University World Institute for Development Economics Research. https://www.wider.unu.edu/publication/economic-complexity-and-structural-transformation-case-mozambique

Stephen, W., Waititu, A., Salifu, D., Mwalili, S., Karanja, E., Adamtey, N., Tonnang, H., Matheri, F., Mwangi, E., Bautze, D., & Tanga, C. (2022). Modeling the incidence of maize spotted stem-borer (Chilo partellus) infestation under long-term organic and conventional farming systems. International Journal of Data Science and Analysis, 8(6), 169–181.

https://doi.org/10.11648/j.ijdsa.20220806.11

Tapson, D. R. (1990). The overstocking and offtake controversy reexamined for the case of KwaZulu. South African Journal of Economics, 58(1), 1–23.

U.S. Department of Agriculture, National Agricultural Statistics Service. (2002). Census of agriculture, 2002: Maryland state and county data.

https://www.nass.usda.gov/AgCensus/archive/census_parts/2002-maryland/index.html

U.S. Department of Agriculture, National Agricultural Statistics Service. (2007). Census of agriculture, 2007: Maryland state and county data.

https://www.nass.usda.gov/AgCensus/archive/census_parts/2007-maryland/index.html

U.S. Department of Agriculture, National Agricultural Statistics Service. (2012). Census of agriculture, 2012: Maryland state and county data.

https://www.nass.usda.gov/AgCensus/archive/census_parts/2012-maryland/index.html

U.S. Department of Agriculture, National Agricultural Statistics Service. (2017). Census of agriculture, 2017: Maryland state and county data.

https://www.nass.usda.gov/Publications/AgCensus/2017/Full_Report/Census_by_State/Maryland/index.php

U.S. Department of Agriculture, National Agricultural Statistics Service. (2022). Census of agriculture, 2022: Maryland state and county data.

https://www.nass.usda.gov/Publications/AgCensus/2022/Full_Report/Census_by_State/Maryland/

Wajdi, N., Adioetomo, S. M., & Mulder, C. H. (2017). Gravity models of interregional migration in Indonesia. Bulletin of Indonesian Economic Studies, 53(3), 309–332.

https://doi.org/10.1080/00074918.2017.1298719

Westerlund, J., & Wilhelmsson, F. (2009). Estimating the gravity model without gravity using panel data. Applied Economics, 41(6), 641–649. https://doi.org/10.1080/00036840802599784

Wooldridge, J. M. (2025). Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. Empirical Economics, 69, 2545–2587. https://doi.org/10.1007/s00181-025-02807-z

Weber, M. A., Mozumder, P., & Berrens, R. P. (2012). Accounting for unobserved time-varying quality in recreation demand: An application to a Sonoran Desert wilderness. Water Resources Research, 48(5), W05511. https://doi.org/10.1029/2010WR010237

Wu, H., Han, M., & Shen, Y. (2023). Technology-driven energy revolution: The impact of digital technology on energy efficiency and its mechanism. Frontiers in Energy Research, 11, 1242580.

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Published

2026-07-29

How to Cite

Katuwal, D. R. ., Karki , L. B. ., Dhakal, A. ., & Escobar, E. N. . (2026). Market Participation Decisions, Commercialization Intensity, and Structural Transformation in Maryland’s Goat Sector: An Empirical Analysis. American Journal of Economics and Business Innovation, 5(2), 131-146. https://doi.org/10.54536/ajebi.v5i2.7324

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