The effects of elite public colleges on primary and secondary schooling markets in India

Abstract

We present the first estimates of the effects of higher education investments on lower levels of schooling. Using the roll-out of elite public colleges in India, we show that investments in higher education increased educational attainment among school-age children. Private schools entered districts with new elite public colleges, and students switched from public to private schools. In addition, elite public colleges crowded in investments in electricity, roads, and water services. We find suggestive evidence that public investments in infrastructure may have reduced setup costs for private schools, and consequently, travel costs for school-going children. ☆ We thank Sam Asher and Wilima Wadhwa for sharing data. We thank Achyuta Adhvaryu,

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

“…Widely cited early studies from two different research teams noted that DMSP data are noisy, but in a wide range of contexts [6,7], or alternatively, just in data-poor environments [8,9], DMSP data could add value to conventional economic statistics. In contrast to earlier studies focused particularly on comparing regions, a theme in recent studies by economists is using NTL data to track fluctuations in local economic activity in response to various shocks such as disasters [10][11][12], or certain policy interventions [13,14]. This use of NTL as a proxy for changes in local economic activity, plus ongoing cross-sectional use as a proxy for variation in economic performance, raises the question of how predictive NTL data are for studying differences in economic activity between areas and the temporal changes in activity within areas.…”

“…However, applied researchers who draw support from validation studies to justify their use of NTL data as an economic activity proxy have increasingly focused on smaller and lower level spatial units [16]. Several studies have used DMSP data at the third sub-national level, which includes counties, sub-districts, and NUTS3 regions [10,[17][18][19][20], with some studies for even lower level spatial units such as villages [14], micro-grids [21], and even pixel-level [11,22]. A mismatch exists between the spatial level of validation studies and the spatial level of applied studies that use NTL data to proxy for economic activity matters because flaws in DMSP data such as spatial imprecision and blurring [23,24] make the predictive performance far worse for lower level spatial units such as the third sub-national level than for more aggregated units such as the national or first sub-national level [25].…”

Factors Influencing Parents’ Awareness of Children’ Education Investment: A Systematic Review

“…2020 Bai, Sun, and Chiu [36] Focuses on improving the efficiency of China's higher education input-output to improve the efficiency of the transformation of higher education investment. 2020 Jagnani and Khanna [37] Analyses the impact of educational investment at the primary school level.…”

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

“…Widely cited early studies from two different research teams noted that DMSP data are noisy, but in a wide range of contexts [6,7], or alternatively just in data-poor environments [8,9], DMSP data could add value to conventional economic statistics. In contrast to earlier studies focused especially on comparing regions, a theme in recent studies by economists is using NTL data to track fluctuations in local economic activity in response to various shocks, such as disasters [10][11][12], or certain policy interventions [13,14]. This use of NTL as a proxy for changes in local economic activity, plus ongoing cross-sectional use as a proxy for variation in economic performance, raises the question of how predictive are NTL data for studying differences in economic activity between areas and temporal changes in activity within areas.…”

“…Yet applied researchers who draw support from validation studies to justify their use of NTL data as an economic activity proxy increasingly focus on smaller and lower level spatial units [16]. Several studies use DMSP data at the third sub-national level that includes counties, sub-districts and NUTS3 regions [10,[17][18][19][20], with some studies for even lower level spatial units such as villages [14], micro-grids [21], and even pixellevel [11,22]. A mismatch between the spatial level of validation studies and the spatial level of applied studies that use NTL data to proxy for economic activity matters because flaws in DMSP data, such as spatial imprecision and blurring [23,24], make predictive performance far worse for lower level spatial units, such as the third sub-national level, than for more aggregated units, such as the national or first sub-national level [25].…”