5 resultados para differences in response

em Repositório digital da Fundação Getúlio Vargas - FGV


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This paper investigates cross-country productivity convergence among Mercosur members plus associates (Chile and Bolivia) and Peru, during the period 1960-1999. The testing strategy is based on the definitions of time-series convergence by Bernard and Durlauf (1995), and applies sequentially the multivariate unit root tests proposed by Sarno and Taylor (1998), Flôres, Preumont and Szafarz (1995) and Breuer, Mc Nown and Wallace (1999). The last two tests allow to identify the countries that converge. Our results show evidence of convergence among the four Mercosur countries, using either Argentina or Brazil as benchmark. Weaker evidence of convergence is also found with Bolivia. The results point out that monetary union among the Southern Cone economies, though a far objective, is not without sense.

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With more and more business being done between Brazil and Norway we are seeing an increasing number of expatriates from Norway moving to Brazil!to work. Most!are!related!to!the!oil!and!gas!industries.!With!the!two!cultures,! countries!and!backgrounds!being!so!different!what!are!some!of!the!issues!and! challenges!that!arise? Using!existing!literature!as!well!as!faceRtoRface!interviews!of!Norwegian! expatriates!working!in!Rio!de!Janeiro!this!thesis!tries!to!compare!the!two! countries!and!at!the!same!time!explore!some!of!these!potential!issues.!The! research!focuses!on!differences!in!trust,!negotiation!style,!planning,!organization,! conflict!as!well!as!general!cultural!challenges. Analysis!of!the!responses!shows!that!for!some!issues!there!are!no!clear!problems! or!challenges!but!for!others!there!are.!Specifically!the!treatment!of!time!and!the! general!timeliness!in!Brazilian!society!seems!to!be!a!challenge!for!Norwegian! expatriates.!Secondly!and!equally!as!challenging!is!the!hierarchical!way!of! organizing!businesses!in!Brazil!compared!to!the!flatter!structure!found!in! Norwegian!businesses.!With!the!hierarchy!comes!also!bureaucracy,!another! factor!that!the!subjects!in!this!thesis!found!to!be!difficult!to!deal!with. The!thesis!is!divided!into!6!chapters!starting!with!“introduction”,!followed!by! chapter!2!“Literature!review”.!Chapter!3!is!“Research!Methodology”!followed!by! chapter!4!“Data!presentation”.!Finally!the!results!are!discussed!in!chapter!5! “Analysis!and!discussion” and!concluded!in!chapter!6.

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The research topic of this paper is focused on the analysis of how trade associations perceive lobbying in Brussels and in Brasília. The analysis will be centered on business associations located in Brasília and Brussels as the two core centers of decision-making and as an attraction for the lobbying practice. The underlying principles behind the comparison between Brussels and Brasilia are two. Firstof all because the European Union and Brazil have maintained diplomatic relations since 1960. Through these relations they have built up close historical, cultural, economic and political ties. Their bilateral political relations culminated in 2007 with the establishment of a Strategic Partnership (EEAS website,n.d.). Over the years, Brazil has become a key interlocutor for the EU and it is the most important market for the EU in Latin America (European Commission, 2007). Taking into account the relations between EU and Brazil, this research could contribute to the reciprocal knowledge about the perception of lobby in the respective systems and the importance of the non-market strategy when conducting business. Second both EU and Brazilian systems have a multi-level governance structure: 28 Member States in the EU and 26 Member States in Brazil; in both systems there are three main institutions targeted by lobbying practice. The objective is to compare how differences in the institutional environments affect the perception and practice of lobbying, where institutions are defined as ‘‘regulative, normative, and cognitive structures and activities that provide stability and meaning to social behavior’’ (Peng et al., 2009). Brussels, the self-proclaimed "Capital of Europe”, is the headquarters of the European Union and has one of the highest concentrations of political power in the world. Four of the seven Institutions of the European Union are based in Brussels: the European Parliament, the European Council, the Council and the European Commission (EU website, n.d.). As the power of the EU institutions has grown, Brussels has become a magnet for lobbyists, with the latest estimates ranging from between 15,000 and 30,000 professionals representing companies, industry sectors, farmers, civil society groups, unions etc. (Burson Marsteller, 2013). Brasília is the capital of Brazil and the seat of government of the Federal District and the three branches of the federal government of Brazilian legislative, executive and judiciary. The 4 city also hosts 124 foreign embassies. The presence of the formal representations of companies and trade associations in Brasília is very limited, but the governmental interests remain there and the professionals dealing with government affairs commute there. In the European Union, Brussels has established a Transparency Register that allows the interactions between the European institutions and citizen’s associations, NGOs, businesses, trade and professional organizations, trade unions and think tanks. The register provides citizens with a direct and single access to information about who is engaged in This process is important for the quality of democracy, and for its capacity to deliver adequate policies, matching activities aimed at influencing the EU decision-making process, which interests are being pursued and what level of resources are invested in these activities (Celgene, n.d). It offers a single code of conduct, binding all organizations and self-employed individuals who accept to “play by the rules” in full respect of ethical principles (EC website, n.d). A complaints and sanctions mechanism ensures the enforcement of the rules and addresses suspected breaches of the code. In Brazil, there is no specific legislation regulating lobbying. The National Congress is currently discussing dozens of bills that address regulation of lobbying and the action of interest groups (De Aragão, 2012), but none of them has been enacted for the moment. This work will focus on class lobbying (Oliveira, 2004), which refers to the performance of the federation of national labour or industrial unions, like CNI (National Industry Confederation) in Brazil and the European Banking Federation (EBF) in Brussels. Their performance aims to influence the Executive and Legislative branches in order to defend the interests of their affiliates. When representing unions and federations, class entities cover a wide range of different and, more often than not, conflicting interests. That is why they are limited to defending the consensual and majority interest of their affiliates (Oliveira, 2004). The basic assumption of this work is that institutions matter (Peng et al, 2009) and that the trade associations and their affiliates, when doing business, have to take into account the institutional and regulatory framework where they do business.

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Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.

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Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).