Economic Implications. The BPTA’s positive impact on economic relations between India and China cannot be overstated. A stable and peaceful border environment encourages increased economic cooperation, trade, and investment. Both nations stand to benefit from enhanced connectivity and collaboration, unlocking new opportunities for economic growth. The BPTA, therefore, serves as a catalyst for fostering a conducive environment for economic engagement between two of the world’s fastest-growing economies. Strategic Considerations: From a strategic perspective, the BPTA contributes to building trust and confidence between India and China. In an era marked by geopolitical uncertainties, the agreement provides a foundation for constructive engagement and cooperation. By preventing the militarization of the border areas and promoting transparency in military activities, the BPTA helps mitigate the risk of inadvertent confrontations that could have broader strategic implications.
Economic Implications. First, we examine and compare the dynamics across the two regimes through impulse response analysis. Figure 6 shows the im- pulse responses to three economic disturbances. The first column depicts the re- sponses to a markup shock under the “low-frequency” regime (in grey area) and the “high-frequency” regime (dotted red line). After a markup shock standard deviation, inflation and output follow the opposite direction while the nominal interest rate increases. The responses are remarkably similar across the two regimes. The second column depicts responses to a preference shock under the “low-frequency” regime (in grey area) and the “high-frequency” regime (dotted black line). The xxx- terns of each variable do not change dramatically across the regimes, except for in- flation. The response of inflation is more pronounced and persistent under the ”low- frequency” regime. As expected, inflation, output, and nominal interest rate increases for both regimes. Although there are small differences across the two regimes when analyzing mon- etary policy shocks, the macroeconomic variables follow similar pattern under both regimes. Therefore, the real effects of monetary policy shock stays the same. The 90 percent error bands overlap, which reinforce the results. Once again, the infla- tion reaction is much weaker under the “low-frequency” regime, corresponding to low-inflation environments. Overall, the transmission mechanisms appear to remain stable across the two regimes. The difference in the degree of nominal rigidities across regimes is not drastic enough to capture changes in the real effects of nominal shocks. A closer inspection is performed by looking the values of the slope of the NKPC, κ. This slope, describing the relationship between inflation and real marginal costs, is largely influenced by the parameter θp, which determines the degree of nominal rigidity in the economy. A priori, the smaller the slope, the larger the nominal rigidity and the impact of monetary policy on real activity. Table 2 reports the slope across the two regimes of the model Mfreq. As is clearly visible from this Table 1, the slope is very different across the two regimes of frequency of price adjustments. The estimated mode for κ(st = 1), under the “low-frequency” regime, is 0.0190 with tight probability intervals [0.0019; 0.0302]; whereas under the “high-frequency” regime, the estimated mode for
Economic Implications. According to the Farm to School Census, “the percent of local food purchased by schools increased by 55%” from the 2011-12 to the 2013-14 school year, and in 2013-14, local food accounted for $600 million of food purchases made by schools (USDA, 2015). Pathways to the potential economic improvement through FTS include increasing participation in the school lunch program and subsequent revenue increases for schools, and increased revenue for farmers selling to local schools. Previous studies have found modest economic benefits to farmers as a result of FTS, or as a combination of FTS and the subsequent increase in local food purchasing in the community (Xxxxxx, 2011; O’hara & Priog, 2013; Tuck et al., 2010), though some producers do report challenges in providing competitive prices to schools (Xxxxxx, 2013; UMD, 2012). Farm to school is associated with increased participation in the school lunch program (Joshi et al., 2006; Flock et al. 2003; Xxxx et al., 2018), which also has the potential to increase revenue for the school from students who pay for their lunch.
