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  1. Finite sample evaluation of causal machine learning methods
    guidelines for the applied researcher
    Erschienen: [2021]
    Verlag:  Tinbergen Institute, Amsterdam, The Netherlands

    The econometrics literature proposed several new causal machine learning methods (CML) in the past few years. These methods harness the strength of machine learning methods to flexibly model the relationship between the treatment, outcome and... mehr

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    The econometrics literature proposed several new causal machine learning methods (CML) in the past few years. These methods harness the strength of machine learning methods to flexibly model the relationship between the treatment, outcome and confounders, while providing valid inferential statements. Whereas numerous options are available now to the applied economics researcher, there is limited guidance on the most useful methodology for a particular applied setting. In this paper, we perform a comprehensive evaluation of the finite sample performance of recently introduced CML methods from the econometrics literature, under a wide range of data generating processes. We focus our analysis on data features that are relevant for causal inference such as varying degrees of: nonlinearity in the outcome and treatment equations, overlap, percentage of treated, alignment and heterogeneity in the treatment effect. We evaluate the methods that have received the most attention so far from the empirical economics literature: double machine learning, causal forest and the generic machine learning methods. Results on the bias, root mean squared error, coverage rates and interval lengths for the average treatment effect, group average treatment effects and individual treatment effects reveal information on the characteristics of the methods and the data features that affect their performance the most.

     

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    Sprache: Englisch
    Medientyp: Buch (Monographie)
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    Weitere Identifier:
    hdl: 10419/248774
    Schriftenreihe: Array ; TI 2021, 090
    Schlagworte: average treatment effect; causal inference; empirical Monte Carlo; heterogeneous treatment effects; individual treatment effects; machine learning
    Umfang: 1 Online-Ressource (circa 54 Seiten), Illustrationen
  2. The value of long-term relationships when selling to informal retailers - evidence from India
    Erschienen: [2021]
    Verlag:  [University of Toronto - Rotman School of Management], [Toronto]

    Attempts to distribute durable, life-improving goods to customers at the Base of the Pyramid (BoP) – the more than three billion customers who live on less than US$2.50/day – through traditional supply chains or e-commerce have struggled to succeed... mehr

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    Attempts to distribute durable, life-improving goods to customers at the Base of the Pyramid (BoP) – the more than three billion customers who live on less than US$2.50/day – through traditional supply chains or e-commerce have struggled to succeed at scale. One hypothesis for why distributors struggle to scale last-mile distribution is poor relationship management with small informal retailers, who are the primary source of retail purchases for BoP customers. These retailers are often embedded within communities, where local and long-term relationships are particularly important to business transactions. We provide empirical evidence for this hypothesis through an analysis of panel data from a distributor selling to 331 formal retailers and 493 informal retailers in India from April 2016-December 2019. Specifically, we study the role of long-term relationships in selling durable goods to informal retailers, by leveraging a staged natural experiment that allows us to examine the effect of a sales agent reallocation on subsequent orders placed by informal and formal retailers. Using two different quasi-experimental methods, we find that formal retailers experience an average performance decrease of at least 35.7% relative to predicted order value and then recover within three sales cycles of a sales agent reallocation; in contrast, informal retailers experience an average performance decrease of at least 70.4% relative to predicted order value, and do not experience sustained recovery within five sales cycles of a sales agent reallocation. This indicates that business relationships, and disruptions to these relationships, are particularly important when selling to retailers in informal markets

     

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    Schriftenreihe: [Rotman School of Management working paper ; no. 3792639]
    Schlagworte: Base of the Pyramid (BoP); causal inference; panel data; difference-in-difference
    Weitere Schlagworte: Array
    Umfang: 1 Online-Ressource (circa 25 Seiten), Illustrationen
  3. Essays in causal inference
    Erschienen: Frühjahrs-/Sommersemester 2021

    Universitätsbibliothek Braunschweig
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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    Technische Universität Hamburg, Universitätsbibliothek
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    Bibliothek der Hochschule Hannover
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    Zentrale Hochschulbibliothek Lübeck
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    Hochschule Magdeburg-Stendal, Hochschulbibliothek
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    Universitätsbibliothek Mannheim
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    UB Weimar
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    Quelle: Verbundkataloge
    Beteiligt: Rothe, Christoph (AkademischeR BetreuerIn)
    Sprache: Englisch
    Medientyp: Dissertation
    Format: Online
    Weitere Identifier:
    RVK Klassifikation: QH 234
    Schlagworte: causal inference
    Umfang: 1 Online-Ressource (ix, 173 Seiten), Illustrationen
    Bemerkung(en):

    Dissertation, Universität Mannheim, 2021

  4. Grounded in methodology, certified by journals
    the rise and evolution of a mainstream in economics
    Erschienen: [2021]
    Verlag:  Institut de recherche économiques et sociales, UC Louvain, Louvain-la-Neuve

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    Sprache: Englisch
    Medientyp: Buch (Monographie)
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    Weitere Identifier:
    hdl: 2078.1/249190
    Schriftenreihe: LIDAM discussion paper IRES ; 2021, 15
    Schlagworte: mainstream; neoclassical approach; experimental economics; causal inference; methodology
    Umfang: 1 Online-Ressource (circa 39 Seiten), Illustrationen
  5. Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment
    Erschienen: June 2021
    Verlag:  IZA - Institute of Labor Economics, Bonn, Germany

    We propose a method of retrospective counterfactual imputation in panel data settings with later-treated and always-treated units, but no never-treated units. We use the observed outcomes to impute the counterfactual outcomes of the later-treated... mehr

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    We propose a method of retrospective counterfactual imputation in panel data settings with later-treated and always-treated units, but no never-treated units. We use the observed outcomes to impute the counterfactual outcomes of the later-treated using a matrix completion estimator. We propose a novel propensity-score and elapsed-time weighting of the estimator's objective function to correct for differences in the observed covariate and unobserved fixed effects distributions, and elapsed time since treatment between groups. Our methodology is motivated by studying the effect of two milestones of European integration - the Free Movement of persons and the Schengen Agreement - on the share of cross-border workers in sending border regions. We apply the proposed method to the European Labour Force Survey (ELFS) data and provide evidence that opening the border almost doubled the probability of working beyond the border in Eastern European regions.

     

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    hdl: 10419/236503
    Schriftenreihe: Discussion paper series / IZA ; no. 14472
    Schlagworte: causal inference; cross-border employment; European integration; matrix completion; panel data
    Umfang: 1 Online-Ressource (circa 24 Seiten), Illustrationen
  6. Demand estimation using managerial responses to automated price recommendations
    Erschienen: June 2021
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    We provide a new framework to identify demand elasticities in markets where managers rely on algorithmic recommendations for price setting, and apply it to a dataset containing bookings for a sample of mid-sized hotels in Europe. Using non-binding... mehr

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    We provide a new framework to identify demand elasticities in markets where managers rely on algorithmic recommendations for price setting, and apply it to a dataset containing bookings for a sample of mid-sized hotels in Europe. Using non-binding algorithmic price recommendations and observed delay in price adjustments by decision makers, we demonstrate that a control-function approach, combined with state-of-the-art model selection techniques, can be used to isolate exogenous price variation and identify demand elasticities across hotel room types and over time. We confirm these elasticity estimates with a difference-in-differences approach that leverages the same delays in price adjustments by decision makers. However, the difference-in-differences estimates are more noisy and only yield consistent estimates if data is pooled across hotels. We then apply our control-function approach to two classic questions in the dynamic pricing literature: the evolution of price elasticity of demand over time as well as the effects of a transitory price change on future demand due to the presence of strategic buyers. Finally, we discuss how our empirical framework can be applied directly to other decision-making situations in which recommendation systems are used.

     

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    hdl: 10419/236669
    Schriftenreihe: CESifo working paper ; no. 9127 (2021)
    Schlagworte: big data; causal inference; machine learning; revenue management; price recommendations; demand estimation
    Umfang: 1 Online-Ressource (circa 59 Seiten), Illustrationen
  7. On the sensitivity of genetic matching to the choice of balance measure
    Autor*in: Oyenubi, Adeola
    Erschienen: November 2020
    Verlag:  Economic Research Southern Africa, [Cape Town]

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    Schriftenreihe: ERSA working paper ; 840
    Schlagworte: Genetic Matching; Balance measures; causal inference; Machine learning
    Umfang: 1 Online-Ressource (circa 17 Seiten)
  8. The value added of machine learning to causal inference
    evidence from revisited studies
    Erschienen: [2021]
    Verlag:  Tinbergen Institute, Amsterdam, The Netherlands

    A new and rapidly growing econometric literature is making advances in the problem of using machine learning (ML) methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these... mehr

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    A new and rapidly growing econometric literature is making advances in the problem of using machine learning (ML) methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.

     

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    hdl: 10419/229707
    Schriftenreihe: Array ; TI 2021, 001
    Schlagworte: machine learning; causal inference; average treatment effects; heterogeneous treatment effects
    Umfang: 1 Online-Ressource (circa 68 Seiten), Illustrationen
  9. The global economic impact of politicians
    evidence from an international survey RCT
    Erschienen: January 2021
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    We use the US presidential election on 3 November 2020 to examine how the US president influences economic expectations of international experts. We design a large-scale RCT among 843 experts working in 107 countries, asking about their expectations... mehr

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    We use the US presidential election on 3 November 2020 to examine how the US president influences economic expectations of international experts. We design a large-scale RCT among 843 experts working in 107 countries, asking about their expectations regarding GDP growth, unemployment, inflation, and trade in their country. The sample is split randomly in two subsamples. Half of the participants were surveyed closely before the election, the other half directly after Joe Biden had been called US president. Our results show that the election of Joe Biden increased growth expectations of international experts by 0.98 percentage points for the year 2021. We also find that (i) treatment effects materialize only in the short-run and (ii) experts’ uncertainty increased after the election. Our results suggest that exceptional politicians influence global economic outcomes.

     

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    hdl: 10419/232430
    Schriftenreihe: CESifo working paper ; no. 8833 (2021)
    Schlagworte: US presidential elections; politicians; economic expectations; economic experts; Randomized Controlled Trial; causal inference
    Umfang: 1 Online-Ressource (circa 59 Seiten), Illustrationen
  10. Effects of eligibility for central bank purchases on corporate bond spreads
    Erschienen: [2020]
    Verlag:  Banca d'Italia Eurosistema, [Rom]

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    Schriftenreihe: Temi di discussione / Banca d'Italia ; number 1300 (November 2020)
    Schlagworte: asset purchase programs; corporate bonds; causal inference
    Umfang: 1 Online-Ressource (circa 40 Seiten), Illustrationen
  11. COVID-19 and the labour market
    estimating the employment effects of South Africa’s national lockdown
    Erschienen: May 2021
    Verlag:  Development Policy Research Unit, University of Cape Town, Cape Town

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    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781920633899
    Schriftenreihe: DPRU working paper ; 2021, 07
    Schlagworte: South Africa; labour market; COVID-19; pandemic; employment; causal inference; quasi-experimental; inequality
    Umfang: 1 Online-Ressource (circa 32 Seiten), Illustrationen
  12. Way off: the effect of minimum distance regulation on the deployment and cost of wind power
    Erschienen: 2021
    Verlag:  DIW Berlin, Deutsches Institut für Wirtschaftsforschung, Berlin

    With the expansion of onshore wind power, countries increasingly consider the introduction of minimum distance regulations between wind turbines to nearby residential areas, to increase public acceptance. In 2014, the German federal state of Bavaria... mehr

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    With the expansion of onshore wind power, countries increasingly consider the introduction of minimum distance regulations between wind turbines to nearby residential areas, to increase public acceptance. In 2014, the German federal state of Bavaria introduced a minimum distance regulation that requires new wind turbines to be ten times their total height away from settlements (10-H regulation). This translates into a distance of 1,900 metres on average, which far exceeds national provisions on minimum distances. Using a difference-in-differences approach, we find that the introduction of the 10-H regulation led to a decline of the newly added wind power capacity in Bavaria of between 62 percent and 90 percent. Moreover, the legislation affected technological parameters of new wind turbines, with severe unintended consequences for the deployment and cost of wind power. The regulation triggered a reduction of the height of new turbines, which lowered energy yields and increased levelized costs of electricity (LCOE) by about 0.2 ct/kWh. Furthermore, lower energy yields also require a higher absolute number of turbines in the long term to achieve the expansion targets for onshore wind energy, counteracting the goal of increasing acceptance of wind power.

     

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    hdl: 10419/249163
    Schriftenreihe: Discussion papers / Deutsches Institut für Wirtschaftsforschung ; 1989
    Schlagworte: Onshore wind power; minimum distance regulation; separation distance; panel data; difference-in-differences; causal inference
    Umfang: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  13. Learning management through matching
    a field experiment using mechanism design
    Erschienen: June 2019
    Verlag:  Centre for the Study of African Economies, Oxford

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    Schriftenreihe: CSAE working paper ; WPS/2019, 10
    Schlagworte: field experiments; management practices; self-employment; causal inference; propensityscore
    Umfang: 1 Online-Ressource (44 Seiten), Illustrationen
  14. Estimating density ratio of marginals to joint
    applications to causal inference
    Erschienen: [2022]
    Verlag:  LSE, STICERD, London

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    Schriftenreihe: Econometrics papers / LSE ; STICERD ; paper number EM619
    Schlagworte: density ratio; causal inference; nonparametric estimation
    Umfang: 1 Online-Ressource (39 Seiten)
  15. Heterogeneous causal effects with imperfect compliance
    a Bayesian machine learning approach
    Erschienen: [2021]
    Verlag:  KU Leuven, Department of Economics, Leuven

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    Schriftenreihe: Discussion paper series / [KU Leuven, Department of Economics] ; DPS21, 13 (December 2021)
    Schlagworte: causal inference; instrumental variable; heterogeneous effects; interpretable machine learning; school funding; students' performance
    Umfang: 1 Online-Ressource (circa 41 Seiten), Illustrationen
  16. What does OLS identify under the zero conditional mean assumption?
    Erschienen: [2022]
    Verlag:  Department of Economics, Faculty of Business and Social Sciences, University of Southern Denmark, Odense, Denmark

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    Schriftenreihe: Discussion papers on economics ; no. 2022, 3
    Schlagworte: OLS; zero conditional mean error; causal inference
    Umfang: 1 Online-Ressource (circa 15 Seiten), Illustrationen
  17. What does OLS identify under the zero conditional mean assumption?
    Erschienen: $n[2022]
    Verlag:  Università di Siena, [Siena]

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    Schriftenreihe: Quaderni del Dipartimento di economia politica e statistica ; n. 872 (febbraio 2022)
    Schlagworte: OLS; zero conditional mean error; causal inference
    Umfang: 1 Online-Ressource (circa 15 Seiten), Illustrationen
  18. Effect or treatment heterogeneity?
    policy evaluation with aggregated and disaggregated treatments
    Erschienen: September 2022
    Verlag:  IZA - Institute of Labor Economics, Bonn, Germany

    Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that... mehr

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    Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that uncovers masked heterogeneity, avoids spurious discoveries, and evaluates treatment assignment quality. The estimation and inference procedure based on double/debiased machine learning allows for high-dimensional confounding, many treatments and extreme propensity scores. Our applications suggest that heterogeneous effects of smoking on birthweight are partially due to different smoking intensities and that gender gaps in Job Corps effectiveness are largely explained by differences in vocational training.

     

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    hdl: 10419/265801
    Schriftenreihe: Discussion paper series / IZA ; no. 15580
    Schlagworte: causal inference; causal machine learning; double machine learning; heterogeneous treatment effects; overlap; treatment versions
    Umfang: 1 Online-Ressource (circa 88 Seiten), Illustrationen
  19. Selection and parallel trends
    Erschienen: August 2022
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    One of the perceived advantages of difference-in-differences (DiD) methods is that they do not explicitly restrict how units select into treatment. However, when justifying DiD, researchers often argue that the treatment is "quasi-randomly" assigned.... mehr

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    One of the perceived advantages of difference-in-differences (DiD) methods is that they do not explicitly restrict how units select into treatment. However, when justifying DiD, researchers often argue that the treatment is "quasi-randomly" assigned. We investigate what selection mechanisms are compatible with the parallel trends assumptions underlying DiD. We derive necessary and sufficient conditions for parallel trends that clarify whether and how selection can depend on time-invariant and time-varying unobservables. We also suggest a menu of interpretable primitive sufficient conditions for parallel trends, thereby providing the formal underpinnings for justifying DiD based on contextual information about selection into treatment. We provide results for both separable and nonseparable outcome models and show that this distinction has implications for the use of covariates in DiD analyses. Building on our analysis of nonseparable models, we connect DiD to the literature on nonparametric identification in panel models.

     

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    hdl: 10419/265945
    Schriftenreihe: CESifo working paper ; no. 9910 (2022)
    Schlagworte: causal inference; conditional parallal trends; covariates; difference-in-differences; selection mechanism; time-invariant and time-varying unobservables; treatment effects
    Umfang: 1 Online-Ressource (circa 53 Seiten), Illustrationen
  20. Protectionism and economic growth
    causal evidence from the first era of globalization
    Erschienen: February 3, 2021
    Verlag:  Verein für Socialpolitik, [Köln]

    We investigate how protectionist policies influence short-run economic growth. Our empirical strategy exploits an extraordinary tax scandal that gave rise to an unexpected change of government in Sweden. A free-trade majority in parliament was... mehr

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    We investigate how protectionist policies influence short-run economic growth. Our empirical strategy exploits an extraordinary tax scandal that gave rise to an unexpected change of government in Sweden. A free-trade majority in parliament was overturned by a protectionist majority in 1887. We employ the synthetic control method to select control countries against which economic growth in Sweden can be compared. We do not find evidence suggesting that protectionist policies influenced economic growth and examine channels why. Tariffs increased government revenue. However, the results do not suggest that the protectionist government stimulated the economy by increasing government expenditure.

     

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    Weitere Identifier:
    hdl: 10419/242425
    Schriftenreihe: Jahrestagung 2021 / Verein für Socialpolitik ; 102
    Schlagworte: protectionism; economic growth; first era of globalization; synthetic control method; causal inference
    Umfang: 1 Online-Ressource (circa 36 Seiten), Illustrationen
  21. Political leaders and macroeconomic expectations
    evidence from a global survey experiment
    Erschienen: September 2022
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    Can one single political leader influence macroeconomic expectations on a global scale? We design a large-scale survey experiment among influential economic experts working in more than 100 countries and use the 2020 US presidential election as a... mehr

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    Can one single political leader influence macroeconomic expectations on a global scale? We design a large-scale survey experiment among influential economic experts working in more than 100 countries and use the 2020 US presidential election as a quasi-natural experiment to identify the effect of the US incumbent change on global macroeconomic expectations. We find large effects of Joe Biden's election on growth expectations of international experts, working through more positive expectations about trade. The electoral outcome particularly affected the expectations of Western allies and increased global economic uncertainty. Our findings suggest important political spillover effects in the formation of macroeconomic expectations.

     

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    Sprache: Englisch
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    Weitere Identifier:
    hdl: 10419/266009
    Schriftenreihe: CESifo working paper ; no. 9974 (2022)
    Schlagworte: US presidential elections; politicians; economic expectations; economic experts; survey experiment; causal inference
    Umfang: 1 Online-Ressource (circa 84 Seiten), Illustrationen
  22. Institutional and macroeconomic stability mediate the effect of auctions on renewable energy capacity
    Erschienen: December 2022
    Verlag:  Zentrum für Entwicklungsforschung (ZEF), Center for Development Research, Bonn

    Decarbonizing the global energy matrix through investments in renewable energy (RE) is considered a pathway to mitigate the effects of global climate change. Auctions have become an increasingly popular policy instrument for this purpose. In the last... mehr

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    Decarbonizing the global energy matrix through investments in renewable energy (RE) is considered a pathway to mitigate the effects of global climate change. Auctions have become an increasingly popular policy instrument for this purpose. In the last few years, auctions have been rapidly adopted by low- and middle-income countries due to their flexibility and several theoretical advantages to mitigate risks deriving from poor business environments. Previous research has used data from higher-income countries and two-way fixed effects models to estimate the effects of auctions on RE capacity, mostly with favorable results. However, none of these studies accounted for heterogeneous treatment effects across units to explore whether auctions are effective in countries with unstable business environments. Here we analyze if auctions can foster RE in countries facing macroeconomic instability or poor institutional quality. For this purpose, we have drawn from multiple publicly available databases to build a panel dataset covering 98 countries for the period 2000-2020. Our definition of RE includes solar, wind, and biomass sources. We show results for each RE source separately and all of them combined. We first cluster countries in terms of the quality of their business environment and then perform a differences-in-differences analysis considering staggered treatment adoption. Our results show that auctions positively affect RE capacity, but average treatment effects are higher for countries with better business environments. Thus, caution is needed in adopting this instrument, especially in countries exposed to macroeconomic or institutional instability. At the same time, dynamic treatment effects suggest that the policy needs time to show results.

     

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    hdl: 10419/268099
    Schriftenreihe: ZEF-discussion papers on development policy ; no. 323
    Schlagworte: renewable energy; auctions; policy evaluation; difference-in-differences; causal inference
    Umfang: 1 Online-Ressource (circa 49 Seiten), Illustrationen
  23. A unified framework for dynamic treatment effect estimation in interactive fixed effect models
    Erschienen: 11-2022
    Verlag:  Department of Economics, Queen's University, Kingston, Ontario, Canada

    We present a unifying identification strategy of dynamic average treatment effect parameters for staggered interventions when parallel trends are valid only after controlling for interactive fixed effects. This setting nests the usual parallel trends... mehr

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    We present a unifying identification strategy of dynamic average treatment effect parameters for staggered interventions when parallel trends are valid only after controlling for interactive fixed effects. This setting nests the usual parallel trends assumption, but allows treated units to have heterogeneous exposure to unobservable macroeconomic trends. We show that any estimator that is consistent for the unobservable trends up to a non-singular rotation can be used to consistently estimate heterogeneous dynamic treatment effects. This result can apply to data sets with either many or few pre-treatment time periods. We also demonstrate the robustness of two-way fixed effects imputation to certain parallel trends violations and provide a test for its consistency. A quasi-long-differencing estimator is proposed and implemented to estimate the effect of Walmart openings on local economic conditions.

     

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    hdl: 10419/281099
    Schriftenreihe: Queen's Economics Department working paper ; no. 1495
    Schlagworte: factor model; panel treatment effect; causal inference; fixed-T
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  24. ddml
    Double/Debiased Machine Learning in Stata
    Erschienen: February 2023
    Verlag:  IZA - Institute of Labor Economics, Bonn, Germany

    We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in... mehr

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    We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using DDML in combination with stacking estimation which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.

     

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    hdl: 10419/272590
    Schriftenreihe: Discussion paper series / IZA ; no. 15963
    Schlagworte: st0001; causal inference; machine learning; doubly-robust estimation
    Umfang: 1 Online-Ressource (circa 52 Seiten)
  25. Unemployment scarring effects
    a symposium on empirical literature
    Erschienen: March 2021
    Verlag:  Università Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali, [Ancona]

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    Schriftenreihe: Quaderni di ricerca / Università Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali ; n. 453
    Schlagworte: Unemployment scarring effects; state dependence; wage penalties; causal inference; literature review
    Umfang: 1 Online-Ressource (circa 49 Seiten), Illustrationen