Search results

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

In this chapter, the author considers a three-sector general equilibrium model in the context of a developing nation to find out the impact of an increase in foreign capital inflow on the welfare level of the nation. Comparative static analysis reveals that an increase in the inflow of foreign capital causes redistribution across the factors of production and a reallocation of resources, reflected through the change in output. Moreover, the author considers the case of technology transfer and proves that an increase in foreign capital inflow makes the country better off in terms of social welfare even if the foreign capital is fully repatriated. Hence, this work shows that in the absence of any trade distortion, a partial investment liberalisation causes a welfare gain for a small open economy.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

Relationships have changed dramatically in the last 50 years. Fewer couples are marrying, more are cohabiting. Reasons for this shift include more attractive labor market opportunities for women and changing social norms, but the shift may have consequences of its own. A number of models predict that those cohabiting will specialize less than those marrying. Panel data on time use – particularly housework time – as well as on the degree of specialization in more narrowly defined household tasks from the 2001–2019 waves of the Household, Income and Labour Dynamics in Australia survey are used to test this prediction.

The time use data for men provides only limited supporting evidence for specialization. The results for women are much stronger. Women who marry without first cohabiting increase their reported housework time more than those who enter cohabitations (by 3.7 hours versus 1.2 hours). The latter generally make up a third of the difference if they do marry. Expanding the analysis to other uses of time yields some further evidence of specialization.

Survey responses on the degree of specialization are more informative. The raw data show substantial intrahousehold specialization and further analysis reveals that on average married couples specialize more than cohabiting couples. Adding couple-specific fixed effects reveals that specialization increases when cohabiting couples marry. Interestingly, there does not appear to be a substantial tradeoff between tasks; partners who report specializing more on one task are more likely to report specializing on other tasks as well. Given the important roles couples have in family formation and the labor market, it is important to understand this intrahousehold behavior.

Hazel Kyrk’s contribution is the most advanced formulation of the economics of consumption as a social phenomenon, an approach to the analysis of consumption that, originated from Veblen’s theory, was developed in the US in the early 20th century. This approach was part of a wider stream of empirical analyses of consumption expenditure that had begun more than a century earlier.

Along with elements that can be traced back to the neoclassical tradition, in Keynes’ analysis of consumption, we find original elements. The dependence of consumption expenditure on the level of income, which is essential for asserting the principle of effective demand, can also be found in a long tradition of empirical studies. In qualifying this relationship, Keynes uses theoretical elements echoing key insights of the economics of consumption as a social phenomenon. There is no documentary evidence that Kyrk or the economics of the social relevance of consumption came to Keynes’ attention. It is possible, however, to develop reasonable speculative considerations to argue a link between Keynes’ elaboration and both the empirical literature on the determinants of consumption and the economics of consumption as a social phenomenon.