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Journal articles on the topic 'Urban bias'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Mourmouras, Alexandros, and Peter Rangazas. "Efficient urban bias." Journal of Economic Geography 13, no. 3 (2012): 451–71. http://dx.doi.org/10.1093/jeg/lbs014.

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2

Bezemer, Dirk, and Derek Headey. "Agriculture, Development, and Urban Bias." World Development 36, no. 8 (2008): 1342–64. http://dx.doi.org/10.1016/j.worlddev.2007.07.001.

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3

Varshney, Ashutosh. "Introduction: Urban Bias in perspective." Journal of Development Studies 29, no. 4 (1993): 3–22. http://dx.doi.org/10.1080/00220389308422293.

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4

Bates, Robert H. "‘Urban bias’: A fresh look." Journal of Development Studies 29, no. 4 (1993): 219–28. http://dx.doi.org/10.1080/00220389308422300.

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5

Pi, Jiancai, and Yanwei Fan. "Urban bias and wage inequality." Review of Development Economics 23, no. 4 (2019): 1788–99. http://dx.doi.org/10.1111/rode.12603.

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6

Grabowski, Richard. "Urban Bias, Villages and Economic Development." European Journal of Development Research 6, no. 2 (1994): 160–81. http://dx.doi.org/10.1080/09578819408426619.

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7

Menaldo, Victor Alexander. "The fiscal roots of urban bias." Business and Politics 18, no. 4 (2016): 435–65. http://dx.doi.org/10.1515/bap-2016-0008.

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Why do some countries indulge in urban bias, potentially harming economic development in the process, while others promote a vibrant agricultural sector? Two main explanations have been put forth. On the one hand, market failures, due to information asymmetries, mean that farmers who dearly require credit to succeed are shut out of lending markets, even if lenders could potentially benefit from making loans more readily available. On the other hand, political failures, due to state capture, mean that farmers will be subject to implicit taxes as a way of generating rents for politically powerfu

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8

Jones, Gareth A., and Stuart Corbridge. "The continuing debate about urban bias." Progress in Development Studies 10, no. 1 (2010): 1–18. http://dx.doi.org/10.1177/146499340901000101.

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9

Oi, Jean C. "Reform and Urban Bias in China." Journal of Development Studies 29, no. 4 (1993): 129–48. http://dx.doi.org/10.1080/00220389308422297.

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10

Majumdar, Sumon, Anandi Mani, and Sharun W. Mukand. "Politics, information and the urban bias." Journal of Development Economics 75, no. 1 (2004): 137–65. http://dx.doi.org/10.1016/j.jdeveco.2003.08.003.

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11

Wegren, Stephen K. "Democratization and Urban Bias in Postcommunist Russia." Comparative Politics 34, no. 4 (2002): 457. http://dx.doi.org/10.2307/4146948.

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12

McMillen, Daniel P., and John F. McDonald. "Selectivity Bias in Urban Land Value Functions." Land Economics 65, no. 4 (1989): 341. http://dx.doi.org/10.2307/3146802.

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13

Hidayat, Nasrullah. "Fenomena Migrasi Dan Urban Bias di Indonesia." JURNAL GEOGRAFI 12, no. 01 (2020): 245. http://dx.doi.org/10.24114/jg.v12i01.16236.

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AbstactPenulisan ini bertujuan mengkaji feneomena migrasi di Indonesia. Maraknya urbanisasi dalam artian migrasi pun menyebabkan kerugian baik untuk desa maupun untuk kota, hanya dalam waktu 20 tahun penduduk kota bertambah hampir 50%, sebaliknya setengah penduduk desa sekarang mengalami perubahan menjadi penduduk kota. Pada tahun 2005 jumlah penduduk kota sebesar 47,9% sementara pada tahun 2025 jumlah penduduk kota diprediksikan akan meningkat menjadi 68% Meningkatnya urbanisasi membuat kemiskinan terkonsentrasi di kota,akan tetapi hal ini terjadi karena adanya pandangan masyarakat desa bahwa

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14

Lipton, Michael. "Urban bias: Of consequences, classes and causality." Journal of Development Studies 29, no. 4 (1993): 229–58. http://dx.doi.org/10.1080/00220389308422301.

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15

Marcus, Anthony, and Sulikah Asmorowati. "Urban Poverty and the Rural Development Bias." Journal of Developing Societies 22, no. 2 (2006): 145–68. http://dx.doi.org/10.1177/0169796x06065800.

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16

Gonzalez, Josue M. "School Meanings and Cultural Bias." Education and Urban Society 25, no. 3 (1993): 254–69. http://dx.doi.org/10.1177/0013124593025003004.

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17

Varghese, N. V., and Jinusha Panigrahi. "Concentration of Institutions and Urban Bias in India." International Higher Education, no. 99 (September 17, 2019): 20–21. http://dx.doi.org/10.6017/ihe.2019.99.11661.

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Higher education development in India shows signs of concentration and urban bias. As in many countries, the permeation of market processes and proliferation of private higher education institutions seem to have contributed to increased regional inequalities. Relying on the concentration ratio, a measure developed by a CPRHE/NIEPA research study, this article discusses the nature and extent of regional inequalities in the current provision of higher education and identifies locations to be prioritized for establishing new institutions to level off regional inequalities in the future.

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18

Arnott, Richard J., and Mark Gersovitz. "Social Welfare Underpinnings of Urban Bias and Unemployment." Economic Journal 96, no. 382 (1986): 413. http://dx.doi.org/10.2307/2233124.

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19

Bryceson, Deborah Fahy. "Urban Bias Revisited: Staple Food Pricing in Tanzania." European Journal of Development Research 4, no. 2 (1992): 82–106. http://dx.doi.org/10.1080/09578819208426572.

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20

Riddell, Barry. "Sierra Leone: Urban-elite bias, atrocity & debt." Review of African Political Economy 32, no. 103 (2005): 115–33. http://dx.doi.org/10.1080/03056240500121032.

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21

Karshenas, Massoud. "Dynamic economies and the critique of urban bias." Journal of Peasant Studies 24, no. 1-2 (1996): 60–102. http://dx.doi.org/10.1080/03066159608438631.

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22

Zaidi, S. Akbar. "The urban bias in health facilities in Pakistan." Social Science & Medicine 20, no. 5 (1985): 473–82. http://dx.doi.org/10.1016/0277-9536(85)90362-4.

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23

Epperson, David L., Jerry M. Davis, Peter Bloomfield, Thomas R. Karl, Alan L. McNab, and Kevin P. Gallo. "Estimating the Urban Bias of Surface Shelter Temperatures Using Upper-Air and Satellite Data. Part II: Estimation of the Urban Bias." Journal of Applied Meteorology 34, no. 2 (1995): 358–70. http://dx.doi.org/10.1175/1520-0450-34.2.358.

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Abstract A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986&#x96;89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data&#x97;the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness d

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24

Sechele, Latang. "Urban Bias, Economic Resource Allocation and National Development Planning in Botswana." International Journal of Social Science Research 4, no. 1 (2016): 44. http://dx.doi.org/10.5296/ijssr.v4i1.8536.

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Michael Lipton formulated a theory of urban bias to account for the poverty and inequalities that rack many developing countries today. The theory proposes that development planning in less developed countries is biased against rural areas in that most of the economic resources are allocated to the urban areas than the rural ones making the poor to get poorer. This article seeks to apply the theory to Botswana’s development planning process. Data was obtained from the analysis of the first nine out of the ten national development plans published since independence which clearly show a distinct

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25

Pugh, Cedric. "'Urban Bias', the Political Economy of Development and Urban Policies for Developing Countries." Urban Studies 33, no. 7 (1996): 1045–60. http://dx.doi.org/10.1080/00420989650011492.

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26

Chrun, Elizabeth, Daniel Berliner, and Aseem Prakash. "Stakeholder scrutiny, urban bias, and the private provision of public goods." Business and Politics 20, no. 2 (2017): 273–300. http://dx.doi.org/10.1017/bap.2017.33.

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AbstractWhile many scholars have studied “urban bias” in public policy, the potential for bias in the private provision of public goods has received little attention. Private certification is a mechanism encouraging private provision of environmental public goods. We show that within countries, there are often wide disparities in certification rates between firms located in urban and non-urban areas. However, these disparities can be mitigated if there is a countervailing force: scrutiny of firms' practices by key stakeholders. We suggest that the presence of strong civil society, independent

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27

Del Giudice, D., M. Honti, A. Scheidegger, C. Albert, P. Reichert, and J. Rieckermann. "Improving uncertainty estimation in urban hydrological modeling by statistically describing bias." Hydrology and Earth System Sciences Discussions 10, no. 4 (2013): 5121–67. http://dx.doi.org/10.5194/hessd-10-5121-2013.

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Abstract. Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and computationally efficient methodology (i) to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii) to separate prediction uncertainty into its components. Our approach ac

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28

Del Giudice, D., M. Honti, A. Scheidegger, C. Albert, P. Reichert, and J. Rieckermann. "Improving uncertainty estimation in urban hydrological modeling by statistically describing bias." Hydrology and Earth System Sciences 17, no. 10 (2013): 4209–25. http://dx.doi.org/10.5194/hess-17-4209-2013.

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Abstract. Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and relatively efficient methodology (i) to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii) to separate prediction uncertainty into its components. Our approach acknowl

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29

London, Bruce, and David A. Smith. "Urban Bias, Dependence, and Economic Stagnation in Noncore Nations." American Sociological Review 53, no. 3 (1988): 454. http://dx.doi.org/10.2307/2095652.

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30

Weede, E. "Urban Bias and Economic Growth in Gross-National Perspective." International Journal of Comparative Sociology 28, no. 1-2 (1987): 30–42. http://dx.doi.org/10.1177/002071528702800103.

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31

Ballard-Rosa, Cameron. "Hungry for Change: Urban Bias and Autocratic Sovereign Default." International Organization 70, no. 2 (2016): 313–46. http://dx.doi.org/10.1017/s0020818315000363.

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AbstractWhat drives autocrats to default on their sovereign debt? This article develops the first theory of sovereign debt default in autocracies that explicitly investigates survival incentives of political actors in nondemocracies. Self-interested elites, fearful of threats to their tenure because of urban unrest, may be willing to endure the long-term borrowing costs that defaulting creates rather than risk the short-term survival costs of removing cheap food policies for urban consumers. I test my main claims that both urbanization and food imports should be associated with greater likelih

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32

Karl, Thomas R., and Philip D. Jones. "Urban Bias in Area-averaged Surface Air Temperature Trends." Bulletin of the American Meteorological Society 70, no. 3 (1989): 265–70. http://dx.doi.org/10.1175/1520-0477(1989)070<0265:ubiaas>2.0.co;2.

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33

Johnson, Gregory L., Jerry M. Davis, Thomas R. Karl, et al. "Estimating Urban Temperature Bias Using Polar-Orbiting Satellite Data." Journal of Applied Meteorology 33, no. 3 (1994): 358–69. http://dx.doi.org/10.1175/1520-0450(1994)033<0358:eutbup>2.0.co;2.

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34

Weede, Erich. "Urban Bias and Economic Growth in Cross-National Perspective." Comparative Sociology 28, no. 1 (1987): 30–42. http://dx.doi.org/10.1163/156854287x00031.

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35

Danielle Cireno Fernandes and Jorge Alexandre Neves. "Urban Bias in Development and Educational Attainment in Brazil." Journal of Developing Areas 43, no. 2 (2010): 271–88. http://dx.doi.org/10.1353/jda.0.0057.

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36

Gruver, Gene W., and Lester A. Zeager. "ECONOMIC INCENTIVES FOR AN URBAN BIAS IN DEVELOPMENT POLICIES." Bulletin of Economic Research 42, no. 1 (1990): 55–62. http://dx.doi.org/10.1111/j.1467-8586.1990.tb00291.x.

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37

Cordes, Vojislava Filipcevic. "Planning Interventions: Urban Bias, Social Reform and the City." Planning Practice & Research 34, no. 1 (2018): 84–102. http://dx.doi.org/10.1080/02697459.2018.1548954.

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38

Braverman, Avishay, and Ravi Kanbur. "Urban bias and the political economy of agricultural reform." World Development 15, no. 9 (1987): 1179–87. http://dx.doi.org/10.1016/0305-750x(87)90186-0.

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39

Prutzman, Priscilla. "Bias-Related Incidents, Hate Crimes, and Conflict Resolution." Education and Urban Society 27, no. 1 (1994): 71–81. http://dx.doi.org/10.1177/0013124594027001008.

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40

Walle, Nicolas Van De. "Rice Politics in Cameroon: State Commitment, Capability, and Urban Bias." Journal of Modern African Studies 27, no. 4 (1989): 579–99. http://dx.doi.org/10.1017/s0022278x00020450.

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It has become the common wisdom among students of sub-Saharan Africa since the publication of the so-called Berg Report that the poor performance of agriculture in the continent is a result of the economic policies pursued by most governments.1 Their intervention in the economy, according to several authors, has systematically favoured those living in the towns and cities at the expense of the vast majority in the rural areas. Urban bias is allegedly the consequence of the inability of the state to resist pressure from urban constituencies. Robert Bates, in particular, has been influential in

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41

WEI, Houkai. "The Administrative Hierarchy and Growth of Urban Scale in China." Chinese Journal of Urban and Environmental Studies 03, no. 01 (2015): 1550001. http://dx.doi.org/10.1142/s2345748115500013.

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Chinese cities are characterized with administrative hierarchy. According to administrative hierarchy and government seat, Chinese cities can be divided into seven levels including municipality, vice-provincial city, general provincial capital, general prefecture-level city, county-level city, county town, and general designated town. As the analysis result shows, urban scale and growth is closely related to their administrative hierarchy, and with the upgrading of administrative hierarchy, both the urban population and land scale present a tendency of exponential increase. From the perspectiv

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42

Demont, Matty, Pieter Rutsaert, Maimouna Ndour, and Wim Verbeke. "Reversing Urban Bias in African Rice Markets: Evidence from Senegal." World Development 45 (May 2013): 63–74. http://dx.doi.org/10.1016/j.worlddev.2012.11.011.

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43

Connell, John. "A new inequality? Privatisation, urban bias, migration and medical tourism." Asia Pacific Viewpoint 52, no. 3 (2011): 260–71. http://dx.doi.org/10.1111/j.1467-8373.2011.01454.x.

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44

Downey, Greg. "Being Human in Cities: Phenotypic Bias from Urban Niche Construction." Current Anthropology 57, S13 (2016): S52—S64. http://dx.doi.org/10.1086/685710.

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45

Kim, Sung Eun, and Johannes Urpelainen. "Democracy, Autocracy and the Urban Bias: Evidence from Petroleum Subsidies." Political Studies 64, no. 3 (2015): 552–72. http://dx.doi.org/10.1111/1467-9248.12200.

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46

Gallo, Kevin P., and Timothy W. Owen. "Satellite-Based Adjustments for the Urban Heat Island Temperature Bias." Journal of Applied Meteorology 38, no. 6 (1999): 806–13. http://dx.doi.org/10.1175/1520-0450(1999)038<0806:sbaftu>2.0.co;2.

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47

Goodridge, James D. "Urban bias influences on long-term California air temperature trends." Atmospheric Environment. Part B. Urban Atmosphere 26, no. 1 (1992): 1–7. http://dx.doi.org/10.1016/0957-1272(92)90032-n.

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48

Suryanarayana, M. H. "PDS: beyond implicit subsidy and urban bias — the Indian experience." Food Policy 20, no. 4 (1995): 259–78. http://dx.doi.org/10.1016/0306-9192(95)00025-9.

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49

Maddah, Hisham. "Strategies to Overcome Diversity Challenges between Urban/Rural Students in Universities." International Journal for Innovation Education and Research 7, no. 1 (2019): 45–54. http://dx.doi.org/10.31686/ijier.vol7.iss1.1280.

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The urban-rural imbalance between university students raises diversity challenges in many educational institutions. University leaders favor urban students’ admission because of the misconception that rural students have lower academic performance than their urban counterparts. Here, strategies to overcome diversity challenges between urban and rural students in universities have been proposed. A diversification workshop project was suggested to be delivered for the current and/or incoming students at KAU in Rabigh to improve students attitudes towards their peers (student-to-student) and facu

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50

Wang, Xiaoming, Sukun Wang, and Warren Kindzierski. "Eliminating systematic bias from case-crossover designs." Statistical Methods in Medical Research 28, no. 10-11 (2018): 3100–3111. http://dx.doi.org/10.1177/0962280218797145.

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Case-crossover designs have been widely applied to epidemiological and medical investigations of associations between short-term exposures and risk of acute adverse health events. Much effort has been made in literature on understanding source of confounding and reducing systematic bias by reference-select strategies. In this paper, we explored the nature of bias in the ambi-directional and time-stratified case-crossover designs via simulation using actual air pollution data from urban Edmonton, Alberta, Canada. We further proposed a calibration approach for eliminating systematic bias in esti

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