نابرابری های دستمزد فضایی: مسائل مرتب سازی!
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|16129||2008||20 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Urban Economics, Volume 63, Issue 2, March 2008, Pages 723–742
Spatial wage disparities can result from spatial differences in the skill composition of the workforce, in non-human endowments, and in local interactions. To distinguish between these explanations, we estimate a model of wage determination across local labour markets using a very large panel of French workers. We control for worker characteristics, worker fixed effects, industry fixed effects, and the characteristics of the local labour market. Our findings suggest that individual skills account for a large fraction of existing spatial wage disparities with strong evidence of spatial sorting by skills. Interaction effects are mostly driven by the local density of employment. Not controlling for worker heterogeneity leads to very biased estimates of interaction effects. Endowments only appear to play a small role.
In many countries, spatial disparities are large and a source of considerable policy concern. In this paper we propose a new approach to account for spatial wage disparities. We implement it on a large panel of French workers. To explain large spatial wage disparities, three broad sets of explanations can be proposed. First, differences in wages across areas could directly reflect spatial differences in the skill composition of the workforce. There are good reasons to suspect that workers may sort across employment areas so that the measured and unmeasured productive abilities of the local labour force vary. For instance, industries are not evenly distributed across areas and require different labour mixes so that we expect a higher mean wage in areas specialised in more skillintensive industries. Such skills-based explanations essentially assume that the wage of worker i is given by wi = Asi , where si denotes individual skills and A, the productivity of labour, is independent of location.Consequently, the average wage in area a is the product of average skills, sa, by the productivity of labour: wa = Asa.2 The second strand of explanations contends that wage differences across areas are caused by differences in local non-human endowments (hereafter endowments). For instance, workers in some areas may have a higher marginal product than in others because of geographical features such as a favourable location (like a port or a bridge on a river), a climate more suited to economic activity, or some natural resources. Arguably, local endowments cannot be restricted to natural features and should also encompass factors of production such as public or private capital, local institutions, and technology. More formally, this type of argument implies that in area a with endowments Ea affecting positively the productivity of labour, the wage is given by wa = A(Ea).3 The third family of explanations argues that some interactions between workers or between firms take place locally and lead to productivity gains. Interactionsbased explanations have a wealth of theoretical justifications. Following Marshall (1890), denser input–output linkages between buyers and suppliers, better matching of workers’ skills with firms’ needs in thicker labour markets, and technological externalities resulting from more intense direct interactions are frequently mentioned (see Duranton and Puga, 2004 for a review).4 A key issue is whether these benefits stem from the size of the overall market (urbanisation economies) or from geographic concentration at the industry level (localisation economies). Stated formally, these arguments imply that the mean wage in area a and industry k is given by wa,k = A(Ia, Ia,k), where Ia and Ia,k are two vectors of interaction variables to capture urbanisation and localisation economies.5 We are not aware of any work using individual data considering these three strands in a unified framework. This is the main purpose of this paper. In our specification, we allow skills, endowments, and interactions to determine local wages. More formally, our model implies that in equilibrium the wage of worker i in area a(i) and industry k(i) is given by wi = A(Ea(i), Ia(i), Ia(i),k(i))si . A unified framework encompassing skills-, endowments-, and interactions-based explanations should provide us with a sense of magnitudes about the importance of these three types of explanations in determining wage disparities across areas. These magnitudes are crucial to inform policy and to guide future theoretical work. Unfortunately, a unified framework also imposes formidable data requirements. More specifically, to deal properly with skills-based explanations we must control for unobserved worker heterogeneity, which requires a panel of workers. In our empirical analysis, we use a large panel of French workers. We develop a two-stage approach. The first stage of the regression allows us to assess the importance of skills-based explanations against those highlighting true productivity differences across areas (i.e., betweenindustry interactions and endowments-based explanations). Formally, we regress individual wages on timevarying worker characteristics, a worker fixed effect, an area-year fixed effect, an industry fixed effect, and a set of variables relating to the local characteristics of the industry (to capture local interactions within industries). The area-year fixed effects can be interpreted as local wage indices after controlling for observed and unobserved worker characteristics and industry effects. Our main result is that differences in the skill composition of the labour force account for 40 to 50% of aggregate spatial wage disparities. This occurs because workers sort across locations according to their measured and unmeasured characteristics: The correlation between the local mean of worker fixed effects and de-trended area fixed effects (which are computed controlling for worker fixed effects) is large at 0.29. This suggests that previous approaches, which typically do not pay much attention to the sorting of workers across areas, are likely to suffer from an important omitted variable problem. In the second stage of the regression, we use the area fixed effects estimated in the first stage and regress them on a set of time dummies, several variables capturing local interactions between industries, and some controls for local endowments.We use a variety of panel data techniques and instrumental variables approaches to deal with estimation concerns. Our findings point first at substantial local interactions despite the importance of sorting. Urbanisation economies (measured by the density of local employment) play the most important role. Market access plays a less important part, while endowments play a weak role. Second, controlling for sorting halves standard estimates of the intensity of agglomeration economies. Our favourite estimate for the elasticity of wages with respect to employment density is at 3%. Third, after controlling for skills and interactions, residual spatial wage disparities are smaller than disparities in mean wages by a factor of around three. This result is consistent with a major role for skillsbased explanations, a moderate role for interactions, and a weak role for endowments. The rest of the paper is structured as follows. We first document wage disparities between French employment areas in the next section. Then, in Section 3 we propose a general model of spatial wage disparities. In Section 4, this model is estimated on individual data to assess the importance of skills-based explanations. In Sections 5 and 6, we discuss the issues relating to endowments- and interactions-based explanations and assess their importance. In Section 7, we reproduce our regressions using aggregate data. Finally some conclusions are given in Section 8.
نتیجه گیری انگلیسی
investigate the sources of wage disparities across local labour markets: skills, endowments and within- and betweenindustry interactions. This framework unites different strands of literature that were so far mostly disjoint. It shows that the research about the ‘estimation of agglomeration economies’ is closely intertwined with those dealing with ‘regional disparities’, ‘local labour markets’ and ‘migration’. Empirically, the main novelty of the paper is to use a very large panel of workers and a consistent approach to exploit it. This allows us to assess precisely the effects of unobserved worker heterogeneity. We find that the effect of individual skills is quantitatively very important in the data. Up to half of the spatial wage disparities can be traced back to differences in the skill composition of the workforce. Workers with better labour market characteristics tend to agglomerate in the larger, denser and more skilled local labour market.We believe more work is now needed to understand the nature of this sorting.We also pay considerable attention to the issues of simultaneity. When correcting for possible biases, our estimates for economies of density, at around 3.0%,are lower than in previous literature. Nonetheless, economies of density still play an important role in explaining differences in local wages. We find that the market potential also matters. The evidence on other types of local interactions such as those taking place within particular industries is more mixed. They are significant but do not matter much quantitatively in explaining local wages disparities. Our approach also suggests at best a modest direct role for local non-human endowments in the determination of local wages.