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on Investment |
| By: | Andrea Ciaccio (Department of Economics, University Of Venice CÃ Foscari) |
| Abstract: | This paper provides novel evidence on the impact of a cost-containment measure first introduced in Italy in 2007 – Piani di Rientro sanitari (PdRs) – on the quality and efficiency of Regional Health Services (RHSs). Thus far, ten out of twenty-one RHSs have undergone at least one round of PdRs – three managed to exit, but seven are still treated – raising the question of whether cost reduction has had any unintended negative effect on the quality of treated RHSs. I answer this question using the Two-way Mundlak approach. Compared to the classic Two-way Fixed Effects, this method explicitly models the staggered nature of the policy by allowing me to analyze how the treatment effect varies along different dimensions. Further, it allows the estimation of the long-run impact of PdRs. Overall, I find that Piani di Rientro managed to reduce costs. However, cost reduction was not followed by a boost in the efficiency of RHSs and the appropriateness of care provided, as expected by the policymaker. Conversely, reduced budgets made available to regions only resulted in an unintended deterioration in the quality of healthcare services. Results also hold in the long run and are robust to a set of bounded-variations assumptions. |
| Keywords: | Recovery plans, Health outcomes, Variation in treatment timing, Treatment effect heterogeneity, Two-way Mundlak, Bounds |
| JEL: | C14 C21 I10 I18 J38 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2023:24&r=inv |
| By: | Shevelova, Anastasia; Machukha, Ielyzaveta; Motliuk, Mark; Kulinich, Volodymyr |
| Abstract: | Labour productivity is an essential economic indicator, offering insights into a nation's hourly economic output. Understanding a country's performance is pivotal for assessing policy effectiveness and shaping new strategies. This study aims to identify the primary determinants of labour productivity and analyze their impact. Employing data from the World Bank and ILOSTAT, the linear regression method was used for analysis to uncover significant insights. The findings reveal a positive correlation between urbanization and labour productivity, while employment in agriculture, as expected, exerts a negative influence. Furthermore, a direct relationship was observed between a country's income level and labour productivity, with higher incomes associated with increased productivity. Notably, the unemployment rate exhibits a positive association with labour productivity, and this effect intensifies as income levels decrease. |
| Keywords: | Labour productivity, Country performance, Determinants of labour productivity, Linear regression analysis |
| JEL: | J0 J01 O4 O40 |
| Date: | 2023–07–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:118622&r=inv |
| By: | YAMAGUCHI Kazuo |
| Abstract: | By using the data from the 2000-2018 Japanese General Social Surveys linked with U.S. occupational skill data provided by O*NET Online of the US Ministry of Labor , this paper clarifies, with Analysis 1, the determinants of the attainment of occupations with (1) high SK (science and technology) skill, (2) high SS (social service) skill, and for comparison, (3) managerial/administrative positions. In particular, differences in the determinants of (1) and (3) demonstrate that effective policies to promote women’s empowerment in labor markets will be quite different between the promotion of women in the STEM-related occupations and the promotion of women in managerial/administrative positions. With Analysis 2, this paper clarifies the effects of the two kinds of occupational skills on wage and, in particular, how these effects are related to gender wage gap and the underutilization of occupational skills among irregular employees. The decomposition of the gender gap in the attainment of high SK-skill occupations provides policy guidance for the promotion of women in STEM-related occupations. This paper also clarifies how irregular employment and its expansion over time worked against the force of increasing higher education levels during the years 2000-2018, which could have otherwise generated greater increases in the proportion of people who attain occupations with higher skills and consequently higher wages despite being based on indirect evidence. Theoretically, although quantitative analyses of the effects of human capital on wage, represented by Jacob Mincer and his colleagues, have mostly ignored the role that occupational attainment plays as an intervening variable, this paper suggests that the introduction of occupational skills as intervening variables may introduce a new method of assessing the role that matching between occupation and human resources plays in determining wages. This paper also urges the construction of occupational-skill measures based on Japanese data, especially for the future evaluation of the government’s reskilling policies. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:eti:rdpsjp:23033&r=inv |
| By: | Lence, Sergio H.; Plastina, Alejandro |
| Abstract: | A very large number of productivity analyses have focused on Total Factor Productivity (TFP), the volume of aggregate output produced per unit of aggregate input, as the measure of choice. For example, industry-level TFP data series have been widely used to investigate many important economic issues, including whether productivity gains have been concentrated in a few industries and whether such gains were linked to the use of information technology (Stiroh 2002), whether automation is labor-displacing (Autor and Salomons 2018), whether the recent rise in the capital share can be attributed to increasing automation (Aghion, Jones, and Jones 2019), how GDP growth has been impacted by sectoral trends in TFP and labor growth (Foerster et al. 2022), the contributions of individual industries to U.S. aggregate TFP growth (Jorgenson, Ho, and Samuels 2019), and the reasons for the productivity gap between Europe and the United States in the late 1990s and early 2000s (van Ark, O’Mahony and Timmer 2008). Recently, growing concerns about environmental degradation and climate change have spurred interest in “environmentally-adjusted” TFP indicators, which take into account the production of undesirable by-products and externalities, as well as how intensely natural resources are used (OECD 2020b). For the agricultural sector in particular, studies based on TFP have analyzed public investments (Fuglie, Wang, and Ball 2012; Fuglie 2018; Ortiz-Bobea et al. 2021), international trade (Garcia-Verdu et al. 2019; Yuan et al. 2021), and the design of policies aimed at decoupling productivity growth from environmental pressure (OECD 2020a), among other issues. In the United States, agricultural TFP measures have been extensively used to evaluate returns to public investments (Fuglie and Heisey 2007; Alston et al. 2011; Jin and Huffman 2016), identify the drivers of productivity growth (Capalbo 1988; Schimmelpfennig and Thirtle 1999; Huffman and Evenson 2006; Alston et al. 2010; Andersen, Alston and Pardey 2012; O’Donnell 2012, 2014; Plastina and Lence 2018), evaluate convergence in productivity across states (McCunn and Huffman 2000; Ball, Hallahan, and Nehring 2004; Poudel, Paudel, and Zilberman 2011), assess spillovers between agriculture and other sectors of the economy (Lence and Plastina 2020), and gauge the impact of weather and climate on aggregate productivity (Njuki, Bravo-Ureta, and O’Donnell 2018; Sabasi and Shumway 2018; Chambers and Pieralli 2020; Ortiz-Bobea, Knippenberg, and Chambers 2018; Plastina, Lence, and Ortiz-Bobea 2021; Ortiz-Bobea et al. 2021). Given the vast literature that has applied TFP to analyze issues concerning productivity, it is not surprising that significant efforts have been devoted to the development of proper measures of the individual components of TFP (OECD 2001; Fuglie, Wang, and Ball 2012; Fuglie 2015; Shumway et al. 2017; USDA-ERS 2021), as well as to the evaluation of the relative merits of alternative aggregation methods (Szulc 1964; Eltetö and Köves 1964; Jorgenson and Griliches 1967; Caves, Christensen, and Diewert 1982a, 1982b; Bjurek 1996; Balk and Althin 1996; O’Donnell 2012, 2016; Färe and Zelenyuk 2021). Contrastingly, there has been a dearth of studies exploring the quality of real-world TFP data series. Interestingly, studies analyzing productivity usually rely on a single source of TFP data, even in cases where more TFP sources are available. Typically, no robustness analyses are conducted to assess the extent to which inferences hold using alternative TFP data sources. Implicitly, such studies assume that the underlying TFP data being used is of sufficiently high quality to yield valid inferences. However, Alston (2018) and Andersen, Alston, and Pardey (2011) --among the few studies analyzing more than a single TFP source-- provide evidence that calls this assumption into question. The lack of studies concerning the quality of real-world TFP series provides the main motivation of the present investigation. We contribute to the literature by examining the industry-level TFP series for the United States obtained from three alternative sources, namely, (1) Jorgenson, Ho, and Samuels (JHS), (2) the U.S. Bureau of Labor Statistics (BLS), and (3) the U.S. Bureau of Economic Analysis (BEA). These three sources are of special interest because they are highly regarded and their series have been used extensively by researchers to analyze productivity (e.g., Stiroh 2002, Autor and Salomons 2018, Aghion, Jones, and Jones 2019, Foerster et al. 2022, Jorgenson, Ho, and Samuels 2019, van Ark, O’Mahony and Timmer 2008). Besides providing an empirical assessment of the relative quality of the aforementioned series, our study contributes to the literature by proposing a general method to examine the quality of alternative time series reportedly measuring the TFP of a particular entity or sector. The main goal of our study is to spur interest in the exploration of the quality of real-world TFP data series, with the aim of finding ways to enhance them and uncovering series whose quality may be deemed questionable. Our preliminary results show that, out of the 61 industry series for which TFP data from different sources are being compared, between 34 (for JHS vs. BEA) and 46 (for BEA vs. BLS) industries have inconsistent series across sources. In other words, only 31% to 64% of the industries have TFP data consistent between source pairs. These results strongly suggest that empirical analyses based on a single data source may not be sufficiently robust to draw strong inferences and implications. The results also demonstrate the need to devote greater attention to improving the reliability of TFP data. |
| Keywords: | Productivity Analysis, Research Methods/ Statistical Methods |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae23:338535&r=inv |
| By: | Mateo-Berganza Díaz, María Mercedes; Näslund-Hadley, Emma; Cabra, Margarita; Vélez Medina, Laura Felizia |
| Abstract: | In this article we experimentally evaluate Colombia’s Think Equal program, which teaches socioemotional skills to children ages 3 to 6. Given the context of COVID-19, the original design was adapted as a hybrid model, alternating in-person and remote instruction and engaging families in the implementation of the curriculum. We found that the program had positive effects on children’s prosocial behavior, self-awareness, and cognitive learning. The intervention also had an impact on education centers personnel (community mothers) and caregivers implementing the activities. Treated community mothers had higher levels of empathy, lower negative health symptoms, better pedagogical practices, and a closer relationship with the children’s caregivers compared with those in the control group. Treated caregivers had better stimulation practices and lower negative health symptoms compared with those in the control group. These findings suggest that a well-designed intervention has the potential to develop socioemotional skills in children at an early age and, at the same time, to develop capacities in those who implement the activities. Our results have important implications for the design, implementation, and evaluation of early childhood socioemotional learning programs and provide novel evidence about the challenges faced by interventions combining face-to-face and remote learning. |
| Keywords: | Preschool learning;socioemotional learning;early childhood development;parent engagement;randomized controlled trial |
| JEL: | C93 I20 I24 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:idb:brikps:12829&r=inv |
| By: | Aguirre, Emilio; Garcıa Suarez, Federico; Sicilia, Gabriela |
| Abstract: | Since the Second World War, the primary source of U.S. agricultural output growth has come from lifting productivity (Wang et al., 2015). Long-term investments in agricultural R&D appear as the predominate driver of those productivity gains (Alston and Pardey, 2021). Public research plays a critical role in the U.S. agricultural innovation process. From 1970 to the early 2000s, public research spending in the U.S. was nearly equal to private research spending, each amounting in 2002 to just under $6 billion (Wang et al., 2015, p. 41). However, Wang et al show that since 2002 when world commodity prices started climbing, a stark divergence between the two developed; by 2010, real public U.S. research spending fell to ~$5 billion and private research spending spiked to ~$9 billion. In the late 1990s and early 2000s, a new approach to funding U.S. innovation emerged: venture capital (VC) began to support newly-created firms to move promising inventions and business ideas from inception to commercialization (Kortum & Lerner, 2000; Arque-Castells, 2012). In agriculture, VC funding helps firms overcome high entry costs resulting from long-term research risk, spatial heterogeneity for applications, and economies of scale characteristic of many agricultural markets. In 2010, total VC investment in U.S. startups focused on farm production technologies was ~$400 million. By 2018, investment in VC-backed agricultural startups had grown to over $7 billion (Graff et al., 2020). In 2020, that investment was over $15 billion (AgFunder, 2021). Scholars hypothesize that VC investors became attracted to agriculture following the 2002 climb in commodity process, which increased farmers’ abilities to adopt new technologies and signaled to input suppliers that global demand may soon exceed supply (Fuglie, 2016). Others suggest a shift towards cleantech and biofuels in the 2000s introduced VC investors to agriculture amid an economy-wide surge in the financing of VC funds (Graff et al., 2020). It could be that the culmination of various general-purpose technologies (e.g., cloud computing, satellite imagery, vehicle automation, gene editing) opened technological opportunities in agriculture, as investors maximized economic benefits across multiple sectors of application (Olsson, 2005), including agriculture, given its historically high rates of return on research (Hurley et al, 2014). We explore the relationship between technological opportunity and the large exogenous shock in VC funding of agricultural startups. Specifically, we investigate the agricultural startup life-cycle. Within the cycle of firm birth, venture investment, and investor exit, what is the relationship between patents and firm financials? Do firms that patent have more successful financings and exits than those that patent little or not at all? In which industries/subsectors were technological opportunities pronounced? What are observed characteristics of the technological opportunity in agriculture? To investigate these issues, we began with a unique dataset of privately-held agricultural startups founded between 1977 and 2019. These unique startups were obtained from four commercial databases: Venture Source (now CB Insights), Crunchbase, Pitchbook, and CapitalIQ. Following a careful matching process, we identified 4, 681 firms from PitchBook (49.26% of the sample), 3, 399 from Capital IQ (35.77%), 1, 312 firms from Crunchbase (13.81%), and 111 from VentureSource (1.17%). From these 9, 503 firms, we narrowed to 7, 287 distinct startups founded in the United States on or after 1987. Of these agricultural startups, we matched 6, 084 to at least one establishment in the National Establishment Time Series (NETS) database, an 83.5% match rate. The NETS database is the most comprehensive source of establishment-level economic information for U.S. firms. Next, we matched the same set of agricultural startups to assignees listed in the USPTO’s pre-grant publication (PG Pub) and granted patent databases. Of those 6, 084 agricultural startups matched to economic information in NETS, we find 10% (634 startups) have one or more published patent application or grant, and 36% (2, 214 startups) have reported financing deals. Of the 634 startups with patent filing activity, 72% (458) report financing deals. We find a strong increase in the number of agricultural startups, both with and without VC investments, over the 1989-2019 period. Startups with VC grew, in terms of employment and sales, faster than startups without VC. We find substantial increases in patenting by the agricultural startups over time. Importantly, there has been great diversification of technology fields in which the startups patent, as well as of industry classifications in which startups operate, evidence of startups pursuing technological opportunity in agriculture. Among industries, we find the greatest increase of patenting by startups primarily classified in the manufacturing and professional, scientific, and technical services. Startups classified in these industries patented in Ag & Food, but also in biotech, chemicals, physics, electricity, and climate-change related new technologies. Next steps include detailing the timeline of firm birth, investment, and exit, and exploring causal and correlative relationships between patenting and VC-funded startups. REFERENCES AgFunder, 2021. AgFunder AgrifoodTech Investment Report. Available from: https://agfunder.com/research/2021-AgFunder-agrifoodtech-investment-report/ Alston, J., and P. Pardey, 2021. The Economics of Agricultural Innovation. In Handbook of Agricultural Economics, Eds., C. Barrett and D. Just. Vol. 5, Chapter 75, Elsevier Publishing. Arque-Castells, 2012. How Venture Capitalists Spur Invention in Spain: Evidence From Patent Trajectories. Research Policy (41): 897-912. Fuglie, 2016. The Growing Role of the Private Sector in Agricultural Research and Development World-wide. Global Food Security (10): 29-38. Graff, et al., 2020. Venture Capital and the Transformation of Private R&D for Agriculture. NBER Working Paper. Heisey and Fuglie, 2018. Public Agricultural R&D in High Income Countries: Old and New Roles in a New Funding Environment. Global Food Security (17): 92-102. Hurley, T., X. Rao, and P. Pardey, 2014. Re-Examining the Reported Rates of Return to Food and Agricultural Research and Development. American Journal of Agricultural Economics 96 (5): 1492-1504. Kortum, S., and J. Lerner, 2000. Assessing the Contribution of Venture Capital to Innovation. The RAND Journal of Economics (31): 674-692. Olsson, O., 2005. Technological opportunity and growth. Journal of Economic Growth 10: 35-57. Wang, S.L., P. Heisey, D. Schimmelpfennig, and E. Ball, 2015. Agricultural Productivity Growth in the United States: Measurement, Trends, and Drivers. Economic Research Report 189, Economic Research Service, U.S. Department of Agriculture. July. |
| Keywords: | Livestock Production/Industries, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae23:338554&r=inv |
| By: | Arkema, Katie; Bailey, Allison; Guerrero Compeán, Roberto; Menéndez Fernandez, Pelayo; Reguero, Borja |
| Abstract: | This chapter describes tools and a methodology to model wind and flood risks from tropical storms under present and future climate accounting for natural infrastructure. Wind forcing provide a crucial link to hydrodynamic models that can be used in risk assessments to estimate extent of and damages from flooding and erosion. Further, such flood risk models can then include the effects of ecosystems, such as mangroves, to model the effects on risk of conservation and restoration outcomes but also individual nature-based projects to reduce risks. The chapter describes hazard modeling techniques and presents simple applications to (1) assess the effect of climate change in the Caribbean, by estimating wind fields for tropical cyclones for present and future climate scenarios, (2) address the limited observations in hurricane data by using existing tools to derive synthetic storms and readily use them in coastal models, and (3) compare modeling approaches and datasets to provide recommendations for assessing flood attenuation of mangroves. The results and data developed in these applications is available with this chapter to be used in other local applications, or to infer damages from wind or in flood hazard models. |
| Keywords: | Disaster risk assessment;tropical cyclones;flood hazard modeling;mangroves |
| JEL: | C63 Q20 Q54 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:idb:brikps:12941&r=inv |
| By: | Jakub Micha\'nk\'ow; Pawe{\l} Sakowski; Robert \'Slepaczuk |
| Abstract: | This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We introduce a completely novel approach to diversification activity not on the level of single assets but on the level of ensemble algorithmic investment strategies (AIS) built based on the prices of these assets. We employ four types of diverse theoretical models (LSTM - Long Short-Term Memory, ARIMA-GARCH - Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity, momentum, and contrarian) to generate price forecasts, which are then used to produce investment signals in single and complex AIS. In such a way, we are able to verify the diversification potential of different types of investment strategies consisting of various assets (energy commodities, precious metals, cryptocurrencies, or soft commodities) in hedging ensemble AIS built for equity indices (S&P 500 index). Empirical data used in this study cover the period between 2004 and 2022. Our main conclusion is that LSTM-based strategies outperform the other models and that the best diversifier for the AIS built for the S&P 500 index is the AIS built for Bitcoin. Finally, we test the LSTM model for a higher frequency of data (1 hour). We conclude that it outperforms the results obtained using daily data. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.15640&r=inv |
| By: | Linda Cai; Josh Gardner; S. Matthew Weinberg |
| Abstract: | Despite having the same basic prophet inequality setup and model of loss aversion, conclusions in our multi-dimensional model differs considerably from the one-dimensional model of Kleinberg et al. For example, Kleinberg et al. gives a tight closed-form on the competitive ratio that an online decision-maker can achieve as a function of $\lambda$, for any $\lambda \geq 0$. In our multi-dimensional model, there is a sharp phase transition: if $k$ denotes the number of dimensions, then when $\lambda \cdot (k-1) \geq 1$, no non-trivial competitive ratio is possible. On the other hand, when $\lambda \cdot (k-1) |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.14555&r=inv |
| By: | Matevosova Anastasia (Department of Economics, Lomonosov Moscow State University) |
| Abstract: | In 2022, Russian economy faced unprecedented sanctions pressure from the collective West. Against this background, the government and the Central Bank need to constantly monitor the economic situation in the Russian Federation in order to take timely and effective measures. A high-frequency indicator of inflation expectations based on big data can help in the solution of this problem. The article presents significant shortcomings leading to the inapplicability of existing common approaches to the assessment of inflation expectations under sanctions. Based on the constructed high-frequency indicators of inflation expectations, contribution of sanctions to the formation of inflation expectations and sanctions concerns, the impact of sanctions on the inflation expectations of Russian population is analyzed. The method of the assessment inflation expectations based on big data has confirmed its effectiveness in the conditions of sanctions. This method proved the impact of sanctions on the formation of inflation expectations of the Russian population. |
| Keywords: | sanctions, inflation expectations, high-frequency indicator, inflation |
| JEL: | F51 E31 D84 C55 C82 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:upa:wpaper:0057&r=inv |
| By: | Mohamed Boly (World Bank Group); Jean-Louis Combes (LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne, UCA - Université Clermont Auvergne); Pascale Combes Motel (LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne, UCA - Université Clermont Auvergne) |
| Abstract: | We econometrically assess how elections affect environmental performance, namely climate policy, using a sample of 76 democratic countries from 1990 to 2014. Three key results emerge from our system-GMM estimations. First, CO2 emissions increase in election years, suggesting that incumbents engage in fiscal manipulation through the composition of public spending rather than its level. Second, the effect has weakened over recent years and is present only in established democracies. Third, higher freedom of the press and high income that can proxy high environmental preferences from citizens reduce the size of this trade-off between pork-barrel spending and the public good, namely environmental quality. Deteriorating environmental quality can bring electoral benefits to politicians. |
| Keywords: | CO2 emissions, Electoral cycles, Environmental policy, Panel data, 2 emissions Electoral cycles Environmental policy Panel data JEL Codes D72 E62 O13 Q54, 2 emissions, Panel data JEL Codes D72, E62, O13, Q54 |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04209496&r=inv |
| By: | Elizabeth Fussell; Nora Schwaller; Todd Gardner; Narayan Sastry |
| Abstract: | The Census Bureau collects demographic data on Puerto Rico in the decennial census and the Puerto Rico Community Survey. These cross-sectional data capture year-to-year change in the size and composition of the population of Puerto Rico. Given Puerto Rico’s status as a US territory and Puerto Rican’s citizenship status, many residents move between the US states and Puerto Rico, contributing to demographic change in Puerto Rico (and in the US states). This technical report explores the opportunity to increase information on demographic change in Puerto Rico by using the Person Identification Validation System (PVS) to link data sets and create longitudinal data on the 2000 and 2010 decennial population cohorts of Puerto Rico residents. We report person record linkage rates for the two decennial cohorts with: (1) the Master Address File Auxiliary Reference File (MAFARF) and Master Address File (MAFX) and (2) the Puerto Rico and American Community Surveys (PRCS and ACS, respectively). These linked data sets have the potential to provide variables that can be used to study residential mobility and a range of other social and demographic outcomes. We find that linkage rates to the MAFARF and MAFX are non-existent before 2007 and very low thereafter, limiting opportunities to examine individual residential histories. Investments in standardizing address formats in Puerto Rico may improve the MAFARF and MAFX data and support migration research. We find that linkage rates of the 2000 and 2010 Puerto Rico decennial census cohorts to repeated cross-sections of the PRCS and ACS are adequate to support research on a wide range of topics. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:cen:tnotes:23-15&r=inv |
| By: | Khanna, Gaurav |
| Abstract: | Roodman (2023) (henceforth R23) re-evaluates Khanna (2023) (henceforth K23). R23 is able to replicate K23's results, highlighting no mistakes in K23's analysis. R23 argues that K23's results may be sensitive to recreating part of the underlying district-level sample, using a subset of K23's datasets. In this reply, I show that despite concerns with R23's sample construction, K23's results are robust to evaluating R23's sample as given. R23 raises other secondary questions, which this reply answers. I also address R23's misinterpretations of K23's general equilibrium model. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:i4rdps:71&r=inv |