Anankastic conditionals are still a mystery

‘If you want to go to Harlem, you have to take the A train’ doesn’t look special. Yet a compositional account of its meaning, and the meaning of anankastic conditionals more generally, has proven an enigma. Semanticists have responded by assigning anankastics a unique status, distinguishing them from ordinary indicative conditionals. Condoravdi & Lauer (2016) maintain instead that “anankastic conditionals are just conditionals.” I argue that Condoravdi and Lauer don’t give a general solution to a well-known problem: the problem of conflicting goals. They rely on a special, “effective preference” interpretation for want on which an agent cannot want two things that conflict with her beliefs. A general solution, though, requires that the goals cannot conflict with the facts. Condoravdi and Lauer’s view fails. Yet they show, I believe, that previous accounts fail too. Anankastic conditionals are still a mystery

Immigration Policy and Self-Selecting Migrants

We explore the implications of migrants' self-selection for the determination of immigration policy in a simple model where incentives and resources to migrate vary with skills. We show how self-selection determines the response of potential migrants to immigration policy changes, which is crucial for predicting the effects of such policy in the receiving country. For example, restricting immigration when it is low skilled may worsen self-selection and thus the receiving country skill distribution. These selection effects may lead low skilled natives to support a more restrictive policy even though current immigrants are not harmful for them, and the receiving country government to impose significant restrictions even in a purely utilitarian world.Immigrant self-selection; immigration policy preferences; political economy of immigration.

Credit Constraints, Entrepreneurial Talent, and Economic Development

In this paper, we formalize the view that economic development requires high rates of productive entrepreneurship, and this requires an e¢ cient matching between entrepreneurial talent and production technologies. We …rst explore the role of …nancial development in promoting such e¢ cient allocation of talent, which results in higher production, job creation and social mobility. We then show how di¤er- ent levels of …nancial development may endogenously arise in a setting in which …nancial constraints depend on individual incentives to mis- behave, these incentives depend on how many jobs are available, and this in turn depends on the level of …nancial development. Such com- plementarity between labor market and …nancial market development may generate highly divergent development paths even for countries with very similar initial conditions.Credit constraints, allocation of entrepreneurial talent, productive and unproductive entrepreneurs, economic development.

I want to, but...

I want to see the concert, but I don’t want to take the long drive. Both of these desire ascriptions are true, even though I believe I’ll see the concert if and only if I take the drive.Yet they, and strongly conflicting desire ascriptions more generally, are predicted incompatible by the standard semantics, given two standard constraints. There are two proposed solutions. I argue that both face problems because they misunderstand how what we believe influences what we desire. I then sketch my own solution: a coarse-worlds semantics that captures the extent to which belief influences desire. My semantics models what I call some-things-considered desire. Considering what the concert would be like, but ignoring the drive, I want to see the concert; considering what the drive would be like, but ignoring the concert, I don’t want to take the drive

Credit Constraints, Entrepreneurial Talent, and Economic Development

In this paper, we formalize the view that economic development requires high rates of productive entrepreneurship, and this requires an efficient matching between entrepreneurial talent and production echnologies. We first explore the role of financial development in promoting such efficient allocation of talent, which results in higher production, job creation and social mobility. We then show how different levels of financial development may endogenously arise in a setting in which financial constraints depend on individual incentives to misbehave, these incentives depend on how many jobs are available, and this in turn depends on the level of financial development. Such complementarity between labour market and financial marketdevelopment may generate highly divergent development paths even for countries with very similar initialcredit constraints; allocation of entrepreneurial talent; productive and unproductive entrepreneurs; economic development

Liquidity, Risk, and Occupational Choices.

We explore which financial constraints matter the most in the choice of becoming an entrepreneur. We consider a randomly assigned welfare program in rural Mexico and show that cash transfers signi cantly increase entry into entrepreneurship. We then exploit the cross-household variation in the timing of these transfers and nd that current occupational choices are signi cantly more responsive to the transfers expected for the future than to those currently received. Guided by a simple occu- pational choice model, we argue that the program has promoted entrepreneurship by enhancing the willingness to bear risk as opposed to simply relaxing current liquidity constraints.insurance; entrepreneurship; Financial constraints; liquidity;

On the Complexity of Mining Itemsets from the Crowd Using Taxonomies

We study the problem of frequent itemset mining in domains where data is not recorded in a conventional database but only exists in human knowledge. We provide examples of such scenarios, and present a crowdsourcing model for them. The model uses the crowd as an oracle to find out whether an itemset is frequent or not, and relies on a known taxonomy of the item domain to guide the search for frequent itemsets. In the spirit of data mining with oracles, we analyze the complexity of this problem in terms of (i) crowd complexity, that measures the number of crowd questions required to identify the frequent itemsets; and (ii) computational complexity, that measures the computational effort required to choose the questions. We provide lower and upper complexity bounds in terms of the size and structure of the input taxonomy, as well as the size of a concise description of the output itemsets. We also provide constructive algorithms that achieve the upper bounds, and consider more efficient variants for practical situations.Comment: 18 pages, 2 figures. To be published to ICDT'13. Added missing acknowledgemen