A+| A| A-
Do Policies Targeting Poor Districts Work?
This paper investigates the impact of the Backward Regions Grant Fund on change in access to amenities for more than 5,000 subdistricts using data from the Census of India, 2001 and 2011. Using covariate balancing propensity score-weighted matching method, this paper shows that the BRGF programme failed to improve the public goods access in backward areas. The results also indicate that the non-BRGF subdistricts have performed better regarding public goods access. Further, using a nationally representative panel survey of rural households in India, the paper shows no significant improvement in household income and consumption expenditure due to the programme.
The author wishes to thank Sumit Mishra for his valuable suggestions and discussions in completing this paper. She also wishes to acknowledge Lakshmi Kumar, Jyothi Prasad Mukhopadhyay, Prijil Mathew, and participants at the 14th Annual Conference on Economic Growth and Development, ISI New Delhi, 14th Annual Conference on Public Policy and Management, IIM Bangalore and PhD Colloquium: CoRe 2019, IGIDR Mumbai, for their helpful comments and suggestions. Any remaining errors are the author’s own. Usual disclaimers apply.
Spatial and regional disparities in economic activities, income, employment, and social indicators are rising in many low- and middle-income countries (Kanbur and Venables 2007). Such differences have induced governments to design various place-based policies to reduce regional inequality (Kline and Moretti 2014b). The empirical literature has questioned the welfare aspects of such policies, pointing out that benefits received by targeted areas may come at the expense of other regions (Glaeser and Gottlieb 2008). The theory, however, suggests that market failures with spatial dimensions justify policies that target poor areas (Neumark and Simpson 2015; Austin et al 2018). Targeting a specific region may help overcome specific market imperfections and induce firms to move to those areas, taking advantage of the agglomeration economies and positive externalities (Moretti 2010; Kline and Moretti 2014b). Other possible explanations for place-based policies are knowledge spillover, industry localisation, spatial mismatch, network effects, and equity motivations (Neumark and Simpson 2015).
Empirical evidence, however, suggests that there exists a variety of measures ranging from enterprise zones (Lynch and Zax 2011; Neumark and Kolko 2010; Freedman 2013), discretionary grant-based policies (Bernini and Pellegrini 2011; Criscuolo et al 2012), cluster policies (Falck et al 2010), special economic zones (Alkon 2018; Wang 2013) and infrastructure investment or region-specific policies (Kline and Moretti 2014b; Glaeser and Gottlieb 2008). Studies on the United States (US) federal empowerment zone exhibit positive employment effects (Ham et al 2011; Hanson 2009). On the other hand, Neumark and Kolko (2010) find the opposite effect of employment growth on California enterprise zones. Similarly, Bernini and Pellegrini (2011) studied Italy’s Law 488/1992 and found a positive impact on output growth and employment. The Tennessee Valley Authority (TVA), one of US history’s most extensive infrastructure development programmes, had a more significant aggregate effect on agriculture and manufacturing (Kline and Moretti 2014b). Contrary to this, the empirical evidence suggests that place-based policies account for a severe problem of negative displacement effect. For example, agglomeration gains in the TVA region were offset by losses in other parts of the country, reflecting the negative spillover on these regions (Kline and Moretti 2014b; Neumark and Simpson 2015). Due to the better employment opportunity, employers move from one area to another to take advantage of the discretionary policies, reflecting negative spillovers in other areas.
Dear Reader,
Or
To gain instant access to this article (download).
(Readers in India)
(Readers outside India)
