Modeling Technology and Technological Change in Manufacturing: How do Countries Differ?

In this paper we ask how technological differences in manufacturing across countries can best be modeled when using a standard production function approach. We show that it is important to allow for differences in technology as measured by differences in parameters. Of similar importance are time-series properties of the data and the role of dynamic processes, which can be thought of as aspects of technological change. Regarding the latter we identify both an element that is common across all countries and a part which is country-specific. The estimator we develop, which we term the Augmented Mean Group estimator (AMG), is closely related to the Mean Group version of the Pesaran (2006) Common Correlated Effects estimator. Once we allow for parameter heterogeneity and the underlying time-series properties of the data we are able to show that the parameter estimates from the production function are consistent with information on factor shares.Manufacturing Production; Parameter Heterogeneity; Nonstationary Panel Econometrics

A Common Factor Approach to Spatial Heterogeneity in Agricultural Productivity Analysis

In this paper we investigate a ‘global’ production function for agriculture, using FAO data for 128 countries from 1961-2002. Our review of the empirical literature in this field highlights that existing cross-country studies largely neglect variable time-series properties, parameter heterogeneity and the potential for heterogeneous Total Factor Productivity (TFP) processes across countries. We motivate the case for technology heterogeneity in agricultural production and present statistical tests indicating nonstationarity and cross-section dependence in the data. Our empirical approach deals with these difficulties by adopting the Pesaran (2006) Common Correlated Effects estimators, which we extend by using alternative weight-matrices to model the nature of the cross-section dependence. We furthermore investigate returns to scale of production and production dynamics. Our results support the specification of a common factor model in intercountry production analysis, highlight the rejection of constant returns to scale in pooled models as an artefact of empirical misspecification and suggest that agro-climatic environment, rather than neighbourhood or distance, drives similarity in TFP evolution across countries. The latter finding provides a possible explanation for the observed failure of technology transfer from advanced countries of the temperate ‘North’ to arid and/or equatorial developing countries of the ‘South’.

Aggregation versus Heterogeneity in Cross-Country Growth Empirics.

The cross-country growth literature commonly uses aggregate economy datasets such as the Penn World Table (PWT) to estimate homogeneous production function or convergence regression models. Against the background of a dual economy framework this paper investigates the potential bias arising when aggregate economy data instead of sectoral data is adopted in macro production function regressions. Using a unique World Bank dataset we estimate production functions in agriculture and manufacturing for a panel of 41 developing and developed countries (1963-1992). We employ novel empirical methods which can accommodate technology heterogeneity, variable nonstationarity and the breakdown of the standard crosssection independence assumption. We then investigate the potential for biased estimates due to aggregation and empirical misspecification, relying on both theory and Monte Carlo simulations. We confirm substantial bias in the technology coefficients using data for a stylised aggregate economy made up of agricultural and manufacturing sectors and a matched PWT dataset.

Econometrics for Grumblers: A New Look at the Literature on Cross-Country Growth Empirics

Since the seminal contribution of Gregory Mankiw, David Romer and David Weil (1992), the growth empirics literature has used increasingly sophisticated methods to select relevant growth determinants in estimating cross-section growth regressions. The vast majority of empirical approaches however limit cross-country heterogeneity in production technology to the specification of Total Factor Productivity, the 'measure of our ignorance' (Abramowitz, 1956). The central theme of this survey is an investigation of this choice of specification against the background of pertinent data properties when the units of observations are countries or regions and the time-series dimension of the data becomes substantial. We present two general empirical frameworks for cross-country productivity analysis and demonstrate that they encompass the approaches in the growth empirics literature of the past two decades. We then develop our central argument, that cross-country heterogeneity in the impact of observables and unobservables on output is important for reliable empirical analysis. This idea is developed against the background of the pertinent time-series and cross-section properties of macro panel data.Cross-Country Empirical Analysis; Nonstationary Panel Econometrics; Parameter Heterogeneity; Common Factor Model; Cross-section Dependence

A Common Factor Approach to Spatial Heterogeneity in Agricultural Productivity Analysis

In this paper we investigate a `global' production function for agriculture, using FAO data for 128 countries from 1961-2002. Our review of the empirical literature in this field highlights that existing cross-country studies largely neglect variable time-series properties, parameter heterogeneity and the potential for heterogeneous Total Factor Productivity (TFP) processes across countries. We motivate the case for technology heterogeneity in agricultural production and present statistical tests indicating nonstationarity and cross-section dependence in the data. Our empirical approach deals with these difficulties by adopting the Pesaran (2006) Common Correlated Effects estimators, which we extend by using alternative weight-matrices to model the nature of the cross-section dependence. We furthermore investigate returns to scale of production and production dynamics. Our results support the specification of a common factor model in intercountry production analysis, highlight the rejection of constant returns to scale in pooled models as an artefact of empirical misspecication and suggest that agro-climatic environment, rather than neighbourhood or distance, drives similarity in TFP evolution across countries. The latter nding provides a possible explanation for the observed failure of technology transfer from advanced countries of the temperate `North' to arid and/or equatorial developing countries of the `South'.agriculture; cross-country productivity analysis; nonstationary panel econometrics; factor models

What can explain the Chinese patent explosion?

We analyse the ‘explosion’ of patent filings by Chinese residents both domestically and in the United States during the early 2000s, employing a unique dataset of 374,000 firms matching patent applications to manufacturing census data. Our analysis reveals that patenting is highly concentrated among a small number of firms, operating in the information and communication technology sector. Although increases in patent filings by these companies are partly driven by increased R&D intensity, our analysis suggests that the explosion of patent filings at the Chinese patent office is driven by factors other than underlying innovative behavior, including government subsidies that encourage patent filings directly

How Does Democracy Cause Growth?

Recent empirical work has established that 'democracy causes growth'. In this paper, we determine the underlying institutions which drive this relationship using data from the Varieties of Democracy project. We sketch how incentives and opportunities as well as the distribution of political power shaped by underlying institutions, in combination with the extent of the market, endogenously form an 'economic blueprint for growth', which likely differs across countries. We take our model to the data by adopting novel heterogeneous treatment effects estimators, which allow for non-parallel trends and selection into institutional change, and run horse races between underlying institutions. We find that freedom of expression, clean elections, and legislative executive constraints are the foremost drivers of long-run development. Erosion of these institutions, as witnessed recently in many countries, may jeopardise the perpetual growth effect of becoming a liberal democracy we establish for the post-WWII period

Nonlinearities in the relationship between debt and growth: (no) evidence from over two centuries

I revisit the popular concern over a nonlinearity or threshold in the relationship between public debt and growth employing long time series data from up to 27 countries. My empirical approach recognises that standard time series arguments for long-run equilibrium relations between integrated variables (cointegration) break down in nonlinear specifications such as those predominantly applied in the existing debt-growth literature. Adopting the novel co-summability approach my analysis overcomes these difficulties to find no evidence for a systematic long-run relationship between debt and growth in the bivariate and economic theory-based multivariate specifications popular in this literature