Using panel data to estimate risk effects in seemingly unrelated production functions

In this paper, production functions in the form of seemingly unrelated regressions (SUR) with errors that are heteroscedastic and that contain cross-section and time components are proposed. These functions are distinguished from others in that they allow the risks (indicated by variances) of outputs to change in any direction in response to input changes. The SUR are then applied in the analysis of cross-section time-series data for rice, wheat and maize production in China.     

Threshold effects in panel data stochastic frontier models of dairy production in Canada

One of the most enduring problems in econometrics is how to properly account for heterogeneity among firms. Threshold regression models are intuitively appealing methods to deal with this issue. We consider a fixed-effect panel data stochastic frontier model (Schmidt and Sickles, 1984; Martin-Marcos and Suarez-Galvez, 2000) and, relying on Hansen (1999, 2000a), we propose an estimator that accommodates multiple thresholds. Our model assumes absence of any unmeasured time invariant heterogeneity across firms as in Greene (2005, p. 277). Slope and threshold parameters can be estimated using a within estimator combined with a grid search over the threshold parameters. Testing for threshold effects is problematic because threshold parameters are not identified under the null hypothesis, a case of the so-called Davies' problem. We apply the bootstrap procedure proposed by Hansen (1999, 2000a) to test for the presence of thresholds. An asymptotic confidence set for the threshold parameter can be obtained by inverting an LR test, using the distribution result presented in Hansen (1999, 2000a). Our empirical application features a panel of Quebec dairy farms. We use farm size as the threshold variable. The presence of a trend in the specification matters for the determination of the number of thresholds. Technical efficiency scores and rankings of farms estimated from competing model specifications are highly correlated and do not vary significantly across groups of farm sizes defined by the threshold parameter values.     

Nonlinear models with panel data

Panel data play an important role in empirical economics. With panel data one can answer questions about microeconomic dynamic behavior that could not be answered with cross sectional data. Panel data techniques are also useful for analyzing cross sectional data with grouping. This paper discusses some issues related to specification and estimation of nonlinear models using panel data.JEL Classification: C230The research behind this paper was supported by the National Science Foundation, the Gregory C. Chow Econometric Research Program at Princeton University, and Danish National Research Foundation (through CAM at the University of Copenhagen). The author thanks Ekaterini Kyriazidou, Hong Li, Marina Sallustro, and the editors for helpful suggestions.     

Dynamic panel data models: a guide to micro data methods and practice

This paper reviews econometric methods for dynamic panel data models, and presents examples that illustrate the use of these procedures. The focus is on panels where a large number of individuals or firms are observed for a small number of time periods, typical of applications with microeconomic data. The emphasis is on single equation models with autoregressive dynamics and explanatory variables that are not strictly exogenous, and hence on the Generalised Method of Moments estimators that are widely used in this context. Two examples using firm-level panels are discussed in detail: a simple autoregressive model for investment rates; and a basic production function.JEL Classification: C23This paper is prepared for a special issue of the Portuguese Economic Journal. My understanding of this subject has benefitted immeasurably from the input of colleagues, notably Manuel Arellano, Richard Blundell, Costas Meghir, Steve Nickell and Frank Windmeijer. I thank Joao Santos Silva, Frank Windmeijer and other participants at the Cemmap/ESRC Econometric Study Group workshop in London on 22 February 2002 for detailed comments on an earlier draft. Financial support from the ESRC Centre for the Microeconomic Analysis of Public Policy is gratefully acknowledged.     

Income and religion: a heterogeneous panel data analysis

A recent empirical literature has addressed the relationship between income and religion, but most of the studies are based on microdata. Macroeconomic analysis of the issue has largely ignored the potential heterogeneity between countries. Using retrospective data on church attendance rates for a panel of countries between 1925 and 1990, we apply heterogeneous panel data estimators and reveal that the effect of participation in religious activities on income per capita is mostly non-significant. This is consistent with some of the recent research that casts doubt onto the influence of religion on income, once causality is taken into account.     

Determinants of international tourism: a three-dimensional panel data analysis

International tourism is a fast growing industry generating half a trillion dollars in annual revenues and accounting for almost 10%?of total international trade, and almost half of total trade in services. Yet, it has so far failed to receive the attention it deserves from mainstream economics. This paper attempts to provide an initial understanding of the determinants of international tourism. This paper claims that international tourism, as other forms of trade in services, is driven by unique factors of production, and may be better dealt with in a single industry study rather than in a general equilibrium trade model. In order to understand these determinants the world is viewed as a market of differentiated products, and a discrete choice estimation technique is applied to a large three-dimensional data set of tourist flows. It is shown that a relatively simple estimation technique, combined with a rich data set, can deliver reasonable substitution patterns. It is found, among other things, that political risk is very important for tourism, and that exchange rates matter mainly for tourism to developed countries. These have exchange rate elasticity of about one.     

Health expenditure models: A comparison using panel data

In this article, we focus on the estimation of outpatient expenditures with panel data. We model the logarithm of expenditures and consider five different models. The first two are two-part and sample selection cross-section models. Two-part panel data models turn out to be inappropriate for dealing with expenditures. We thus estimate sample selection models with panel data: one without a lagged dependent variable and two with a lagged dependent variable. These two latter models differ in their assumptions on the variance of the residuals. Modelling heteroscedasticity may indeed be important to avoid the bias due to the retransformation problem. We show that lagged dependent variables are important factors for heteroscedasticity. For the models with state dependence, we provide a new solution to the initial conditions problem by controlling for generalised residuals. We establish that panel data models highly improve the correlation explained by the model in the time-series dimension without damaging the fit in the cross-section dimension. For all indicators of fit, the model with state dependence and heteroscedasticity seems to dominate the others.     

The macroeconomy and microfinance outreach: a panel data analysis

This study examines the effects of macroeconomic and institutional factors on microfinance institution (MFI) outreach. To capture the performance of the macroeconomy, we include proxies for institutional quality, economic growth, FDI inflow, unemployment rate, inflation and the prevalence of wage-earning jobs. Further, we examine effects on outreach along the dimensions of outreach depth and outreach breadth. Using data on 1526 MFIs from 99 countries over the period 2000 to 2015, our results suggest that environments characterised by high levels of economic performance and good institutions tend to hinder outreach performance. These results suggest that MFIs depend on a poor economy to thrive given the informal nature of microfinance.     

Entrepreneurship and income inequality: a spatial panel data analysis

The main objective of this paper is to empirically analyze the relationship between entrepreneurship and income inequality. We use a spatial panel data analysis for both 33 high-income countries and 39 middle- and low-income countries over a period of 11 years. Estimation results and rigorous diagnostic analysis suggest that: (i) there is a strong support for the existence of an inverted U-shaped relationship between entrepreneurship and income inequality espoused by the Kuznets Curve hypothesis; (ii) the relationship between entrepreneurship and income inequality is negatively moderated by country’s level of economic development; (iii) regardless of income inequality levels, entrepreneurship has a non-linear relationship with income per capita; (iv) gross domestic expenditure on research and development exhibits significant negative impacts on entrepreneurship; (v) significant mixed effects on the likelihood of entrepreneurial activity are observed with governance, globalization, population growth rate, and competitiveness variables; (vi) there are significant mixed feedback effects on entrepreneurship; and (vii) there are statistically significant, positive as well as negative spatial spillovers to country-level entrepreneurial activity.     

Technological progress and scientific indicators: a panel data analysis

Total factor productivity (TFP) is generally interpreted to be a proxy for technological advancement. In this paper, we use stochastic frontier analysis to decompose the growth in TFP into three components: technological progress, scale effect and change in technical efficiency. Then, we conduct a comprehensive panel data analysis using the technological progress component of the TFP growth and several scientific and technological indicators using data from 160 countries over the period from 1960 to 2009. Our results generally show that the technological progress component of the TFP growth properly reflects certain dimensions of actual scientific and technological progress. However, we also find that this result is somewhat sensitive to different econometric specifications and assumptions.     

Spatial analysis of municipal water demand: a panel data approach

While the municipal water demand literature is well developed, one area that is understudied is the impact of spatial effects. After controlling for factors shown to impact demand, this study applies spatial econometric methods via a spatial weights matrix to a panel municipal water consumption data set. While diagnostics suggest the presence of spatial lag and spatial error, thus indicating the potential usefulness of spatial empirical methods, several important pitfalls must be acknowledged. First, the application of spatial weights in a panel setting is computationally intensive, especially when the number of time periods or observations is large, and perhaps necessitates aggregation. Second, because most users in a municipality are likely to be subject to similar utility action, climate, etc., a spatial lag signal may be spurious. Third, because premises served by the utility may enter or exit the data set through time, the requirement of balanced panels requires careful consideration. Fourth, if the option to use premises-level (or similar) data or aggregated data is available, it is typically advisable to use premises-level data despite the possible presence of spatial effects.     

Understanding wage determination in a multi-level bargaining system: a panel data analysis

This article presents an estimation of the elasticity of actual wages to industry-level collective bargaining thereby quantifying empirically the role of industry-level bargaining on wage determination. For this purpose, we use a unique employer–employee panel dataset covering the entire Belgian employment population over 9 years (1998–2006). Like several other European countries, e.g. Germany, France, Italy and the Netherlands, Belgium has a relatively centralised wage bargaining system, with the industry level playing the most important role. Regression results confirm that wage increases collectively decided at the industry level are, on average, fully passed on to individual wages. In addition to industry-level bargaining, we are interested in the supplementary wage increases granted at the firm level, referred to as wage drift or wage cushion in the literature. Our estimates show that wage drift is affected by company size, by the economic performance of the industry and to a much lesser extent by labour market tensions as measured by the local unemployment rate. Interestingly, our results show that industry-level bargaining also takes most of these features into account.     

Output growth and investment dynamics in Finland: a panel data analysis

This study analyzes the relationship between output growth and investment in a panel of 20 regions of Finland over the period 1975–2007. This regional study uses Granger non-causality and error-correction models. The most important finding of this study is a unidirectional causality which runs from investment to output growth. The study also verifies the existence of a positive association between growth and investment for the panel of 20 regions of Finland. These findings reconcile with the capital fundamentalists.     

Market share instability in Brazilian industry: a dynamic panel data analysis

The paper investigates market share instability in the context of Brazilian industry for the 1986–1998 period. The paper proposes the use of panel data unit root tests to access market share instability for a sample of industrial firms from different sectors and therefore generalizes related time series unit root tests proposed by Gallet and List (2001 Gallet, CA and List, JA. 2001. Market share instability: an application of unit root tests for the cigarette industry. Journal of Economics and Business, 53: 473–80.  ). The results mostly indicate that one cannot reject the hypothesis of market share instability and therefore there exist some degree of market rivalry in the Brazilian case.     

Deep habits in consumption: a spatial panel analysis using scanner data

Using scanner data from a large European retailer, this paper empirically assesses deep habit formation in consumption. Deep habit formation constitutes a possible source of price stickiness and helps to mimic procyclical labour and real wage dynamics that are present in macrodata. To gauge the existence and the extent of deep habits in consumption, we estimate a dynamic time–space simultaneous model for consumption expenditure at different levels of product aggregation. This spatial panel model enables us to test for both internal and external deep habit formation at the same time. The former captures inertia or persistence in consumption and is included in the empirical specification as a time lag. The latter captures preference interdependence across households and is captured by a spatial lag. Our results show mixed evidence with respect to internal habit formation, whereas the external habit effect is almost always positive and significant.     

Purchasing power parity across Mexican cities: a panel data analysis

This article presents three different unit root tests for panel data, the main objective is to find the level of internal integration market through the purchasing power parity (PPP) evidence, based in the Balassa–Samuelson approach. Thus, eight kinds of markets, as tradable and nontradable goods for 16 main Mexican cities during a 21 year period have been contrasted. While two tests showed the PPP validity for seven markets, the other rejected it. The results obtained moreover feed the present controversy about which test is most appropriate to test the PPP, as soon as it is highlighted new elements emerge to explain the half-life analysis.     

Structural change in Finnish manufacturing: The theory of the aggregation of production functions and an empirical analysis with a plant-level panel

In this paper, structural change in the Finnish manufacturing industries is studied using the theory of the aggregation of production functions and longitudinal plant-level data for the period from 1980 to 2005. To characterise the nature of structural change in 12 industries, we examine the invariance of aggregate production functions over time. Aggregate production functions need not be estimated because, according to the theory of the aggregation of production functions, the invariance can be analysed by investigating the stability of capacity density functions, which describe the distribution of value added in these industries. Even though the shapes of aggregate production functions alter over time in most industries, there are differences in timing and in the degree of turbulence across industries. The analysis confirms that in some industries (e.g., the paper industry) the late 1980s marked the beginning of a period of relatively strong structural change. The food and communications equipment manufacturing industries are examples of industries for which the 1990s was a period of turbulence.     

Estimation and specification analysis of models of dividend behavior based on censored panel data

Dividends move in discrete jumps. Moreover, some companies pay dividends, others do not. Both these aspects necessitate the use of limited dependent variable models in the analysis of dividend behavior. Models of dividend behavior usually ignore these problems and treat dividends as a continuous variable. The present paper analyzes dividend behavior using panel data on 649 firms for 12 years (1976–1987). The model used is a censored regression model which allows for firm-specific and time effects. It is estimated using the maximum likelihood method under three different error covariance specifications. Based on specification tests, it is argued that it is important to allow for the zero observations, industry effects, and firm-specific and time effects in the estimation of models of dividend behavior.     

Three-dimensional panel data models with interactive effects: Estimation and simulation

This paper considers three-dimensional panel data models with unobservable multiple interactive effects, which are potentially correlated with the covariates. We propose an iterated OLS (IOLS) approach for the estimation of the static model and an iterated GMM (IGMM) method for the dynamic model. Monte Carlo simulations illustrate that our methods perform well in finite samples.     

The productivity-wage and productivity-employment nexus: a panel data analysis of Indian manufacturing

This article investigates the long-run relationship between labour productivity and employment, and between labour productivity and real wages in the case of the Indian manufacturing sector. The panel data set consists of 17 two-digit manufacturing industries for the period 1973–1974 to 1999–2001. We find that productivity-wages and productivity-employment are panel cointegrated for all industries. We find that both employment and real wages exert a positive effect on labour productivity. We argue that flexible labour market has a significant influence on manufacturing productivity, employment and real wages in the case of Indian manufacturing.