surprising, since the production of electronics relies more on specialized ma- chines than on simple office equipment. Expenditures for the training of the workforce are significant for Subsector 39 only. There is a positive effect for exporting firms and none per se for foreign ownership on TFP growth. This provides evidence in favor of the learning-by-exporting hypothesis and is consistent with earlier work. For instance, Greenaway and Kneller (2004) found small effects and only temporary effects of export market entry by firms in the United Kingdom on subsequent productivity growth. Moreover, the work of Greenaway, Gullstrand, and Kneller (2005) and Greenaway and Kneller (2007) points to the heterogeneity of exporting effects on a firm’s own productivity growth. According to the survey by G¨ org and Greenaway (2004), empirical evidence on productivityspilloversfrom foreign ownership using paneldata is rare and ambiguous, especially, for economies in transi- tion. Different reasons for non-positive externalities of foreign ownership may arise from competition or business stealing effects (see Haddad and Harrion, 1993; Aitken and Harrison, 1999). Furthermore, foreign-owned firms may have a bigger incentive to protect their knowledge and to avoid spillovers than other firms (see Perri and Andersson, 2012), especially, in knowledge- scarce countries (see De Faria and Sofka, 2010). From this perspective, we would not have strong priors towards an unambiguously positive effect of for- eign ownership on spillovers in the strongly knowledge-dependent electronics industry, especially, in a country of transition such as China.
Diverging slightly in terms of the choice of survey, Wang, et al. (2015) use cross-sectional datafrom the 2006 Chinese General Social Survey (CGSS) to examine the relationship between income inequality and SWB. They find evidence of an inverted-U shaped relationship between income inequality and SWB such that an increase in income inequality is associated with lower levels of SWB until a threshold beyond which further increases in inequality is associated with an increase in SWB levels. More recently, Yan and Wen (2019) add to the income inequality-wellbeing literature by incorporating corruption into the discourse. Using datafrom 2013 CGSS, Yan and Wen (2019) examine the role of corruption in the inequality-wellbeing relationship, and conclude that corruption is an important channel through which income inequality influences wellbeing. Other studies that have used the CGSS to examine the inequality-wellbeing relationship include Zhao (2012) and Wu and Li (2017), although Wu and Li (2017) add on to the literature by providing a perspective that draws on repeated cross-sections from the 2003 to 2010 surveys, rather than inferences from a single year. Other datasources that have featured in the literature that examines the relationship between income inequality and SWB in China include the China Labor-force Dynamics Survey (CLDS) (Huang, 2019), single wave from the China Family Panel Studies (CFPS) (Lei et al., 2018), and other surveys (Smyth & Qian, 2008). Huang (2019) examines the role of distributive justice beliefs in shaping the inequality- wellbeing relationship. Using multi-level modelling, they find that lower inequality is associated with higher SWB. Lei et al. (2018) use the 2012 wave of the CFPS but focus on the role of expenditure inequality on SWB. Smyth and Qian (2008) use cross-section datafrom 31 cities across China. Their study emphasises on the relationship between inequality and SWB in urban China. They report heterogeneous effects of inequality across high and low income individuals.
The data used for the current study are mainly from the Annual Report of Industrial Enterprise Statistics compiled by the State Statistical Bureau of China during the period 1998-2001. The data set covers nine two-digit industries, including food processing, food manufacturing, beverage production, garments and other fibre products, medical and pharmaceutical products, ordinary machinery manufacturing, transport equipment manufacturing, electric machines and apparatuses, and electronic and telecommunications equipment. Due to entry and exit and ownership restructuring, the number of firms in operation is changing over time. In this study, the same firms have been identified based on their identifiers to produce a balanced set of 15,761 firms for each year. The data are then cleaned via extensive checks for nonsense observations, outliers, coding mistakes, and the like. In addition, only firms with at least three years of data for value added, output, capital stock, intangible assets, exports and total sales are kept. These finally leave us with a balanced panel of 13,250 firms.
Looking beyond the individual firm, anecdotal evidence tends to suggest that the presence of foreign MNEs may have an impact on the productivity of domestic firms. There are two commonly cited sources of positive externalities associated with Foreign Direct Investment (FDI). In the first instance, foreign subsidiaries can increase competi- tion in the domestic market (Barrell and Pain, ). In the second instance, the literature suggests the existence of potential indirect benefits that spill over from foreign to domes- tic firms, both in terms of technology transfer (Keller ), and upgrading skills in the local labour market via inter-firm labour mobility (Driffield and Taylor, ). Empirical studies, using aggregated and disaggregated UK data, have found positive impacts as- sociated with intra-industry, inter-industry and spatial agglomeration effects (Girma and Wakelin , Harris and Robison ). However, the vast majority of research on the productivity of foreign MNEs in Britain, including those particular studies, has fo- cused on manufacturing. It is therefore important to see if their conclusions about FDI can also be extended to other dynamically growing sectors like retailing, and study the mechanism through which ownership advantages and knowledge transfer may affect the regional performance and distribution of domestic firms.
As shown in Table 1, provincial level studies are not able to distinguish domestic firms from foreign invested firms. Thus, both of the papers listed in the top panel of the table (Huang, 2004; Cheung and Lin, 2004) suffer from an upward aggregation bias. 10 Three out of four studies using industry level data, summarized in the second panel of Table 1, do separate domestic firms from foreign invested firms, and two out of the three such studies, Li, Liu, and Parker (2001) (LLP2001) and Buckley, Clegg, and Wang (2002) (BCW2002) find positive FDI spillover effects. Both studies explore the manufacturing sector using the 1995 Third Industrial Census of China. While Li, Liu, and Parker (2001) only explore the FDI effects on labor productivity, Buckley, Clegg, and Wang (2002) also study the potential FDI spillovers on other measures of firm performance (including high-tech and new product development as well as export performance) and finds positive spillover effects. However, neither study addresses the endogeneity of FDI. Buckley, Clegg, and Wang (2002) use ordinary least squares, while Li, Liu, and Parker (2001) uses three–stage least squares to address the endogeneity of value added of firms with different ownership types. Thus, both of these studies potentially suffer from an upward bias due to endogeneity of FDI.
Although researchers have long contemplated the Kuznets pattern between migration and inequality in the sending communities – that is, inequality rises in the beginning of the migration process and drops after migration becomes more established – the literature has little to offer in terms of solid empirical evidence. Earlier studies treated remittance income as an exogenous transfer, and compared income inequality with and without the inclusion of remittance income. More recently, remittances are treated as a potential substitute for home earnings and the observed income distribution with remittances are compared to a counterfactual scenario in which no migration takes place but includes an imputed level of home earnings. Although the earlier approach is unrealistic in assuming that remittance-earning migrants are separate entities from their households in rural areas, the improvement that the counterfactual model provides is limited because the selection into migration is difficult, if not impossible, to model. In addition to these methodological challenges, the usefulness of earlier literature is also tempered by the cross- section nature and small sample sizes of the sources of their data. The lack of paneldata at the community level seriously limits the researchers’ ability to quantify the temporal dimension of migration and inequality.
In this paper, I seek to investigate the impact of trade on productivity by using newly constructed paneldata on formally registered and unregistered establishments in Cambodia. For an empirical strategy, a key issue is an endogenous relationship between trade and productivity. 2 This paper exploits a natural experiment from the EU’s reform in rules of origin (ROO) under the EU generalized-system-of-preferences (GSP) scheme: a policy shock involving Cambodia’s garment exports to the European Union (EU). The European Union (EU) granted Cambodia with duty-free and quota-free access under the Everything But Arms (EBA) scheme in 2001, and simplified restrictive origin requirements for the EU GSP after January 2011. After the EU’s reform, garment exporters could use imported fabric from any third country and still maintain preferential treatment. Consequently, garment exports from Cambodia to the EU markets increased sharply after 2011, which coincided with a surge in textile imports to Cambodia fromChina. The policy shock was substantial for the Cambodian economy as the garment industry accounted for 77.7% of total commodity exports in 2014 (UN COMTRADE). Thus, I seek to identify the productivity effects of trade by exploiting a positive export shock to the garment industry and a negative import shock to the textile industry.
One main component of China’s enterprise reform has been compensation reform. The rigid compensation system employed by pre-reform Chinese enterprises was one of the features that set these enterprises apart from firms in other countries. Before the economic reforms started in the late 1970s, compensation levels were completely based on seniority and job assignment in all Chinese SOEs and most other Chinese enterprises. Between 1950 and 1978, there was virtually no link between the employee’s pay level and his or her productivity. In addition, for many employees the compensation level was kept fixed for long periods of time, sometimes for decades. After 1978, along with reforms in other areas, different forms of compensation reform have been experimented in order to promote better economic performance of SOEs. Beginning in 1994-1995, the compensation reform policy finally gave publicly listed companies virtually complete discretion in setting compensation levels for their employees. 17
Finally, consider two more issues which are harder to deal with: aggregation and training stocks vs. training flows. Estimation at the three digit industry level has advantages but also disadvantages relative to micro-level estimation. The production function in equation (1) at the firm level describes the private impact of training on productivity. However, many authors, especially in the endogenous growth literature (e.g. Aghion and Howitt, 1998), have argued that there will be externalities to human capital acquisition. For example, workers with higher human capital are more likely to generate new ideas which may spill over to other firms 17 . If spillovers are industry specific this implies that there should be additional terms added to equation (5) representing training in other firms (e.g. the mean number of trained workers in the industry). In this case the coefficient on training in an industry level production function should exceed that in a firm level production function 18 . Secondly, grouping by industry may smooth over some of the measurement error in the micro data and therefore reduce attenuation bias.
Second, at the aspect of data set, either the cross-sectional or paneldata sets are used in these studies, and these data sets are either on a firm level or on an industry level. So this is a two dimension issue. For the cross-sectional data set, the major problem is that it tends to overestimate the magnitude of technology spillovers. As found in Gorg & Stroble’s (2001) studies, using cross-sectional data set finds systematically more technology spilloversfrom FDI than those studies using paneldata set. One reason is that there is usually reverse causality fromproductivity to FDI, i.e. not only the presence of FDI may increase the productivity of domestic firms, but also FDI often tends to flow into the industry with higher productivity. The solution to this reverse causality problem is either to find a instrumental variable, which seems rather difficult in reality in that the variables that are correlated with FDI usually are also correlated with the productivity, or to use simultaneous equation system, which is not used by all previous studies that employ a cross-sectional data set. In contrast, in addition to the usual advantages, such as it can increase the degree of freedom and reduce the multi-collinearity problem (Hsiao 2003), the paneldata set can also accommodate the reverse causality problem easily, for example by using the lagged FDI as the instrument. Besides, the technology spillover itself has a dynamic nature, i.e. the technology spillovers usually happen through time. This means the cross-sectional data set may not be able to capture all relevant aspects of technology spillovers. In this sense, the paneldata set is preferable to the cross-sectional data set.
It has long been recognized that worker wages and possibly productivity is higher in large firms. Moreover, at least since Schumpeter (1942) economists have been interested in the relative efficiency of large firms in the research and development enterprise. This paper exploits paneldata on inventors in the pharmaceutical and semiconductor industries, two industries that are prolific generators of innovations and patents, to examine the relationship between firm size and the productivity of workers specifically engaged in innovation. We use patents and patent citations as measures of inventor productivity. We link the inventors to firms in these industries through U.S. patent records, and obtain additional information on both the inventors and their employers from secondary sources. We find that in both industries, inventors’ productivity increases with firm size. This result holds across different specifications and even after controlling for inventors’ experience, educational level, the quality of other inventors in the firm, and other firm characteristics.
European countries using firm-level data. The paper explores the empirical regularities of firm productivity distribution across countries. In particular, we assess the degree of persistence of firm relative productivity and consider its effect on aggregate productivity improvements. Moreover, the paper analyses the impact of the competitive forces on aggregate productivity growth by disentangling the role of firm learning and market selection. Finally, we estimate the relationship between labour productivity growth and firm-specific factors such as size, age and capital intensity across countries. The paper uses annual account data over the period 1993-2003 from Amadeus dataset (Bureau van Dijk) for a balanced panel of manufacturing firms. In line with previous evidence, our analysis shows that firm relative productivity levels are both highly heterogeneous across firms and very persistent over time in all the countries in the sample. With reference to aggregate productivity growth, we find that both labour productivity and total factor productivity changes are mostly driven by firm learning, i.e. within-firm productivity improvements, in most European countries. Conversely, the reallocation of resources spurred by the competitive selection process is found to play a minor role in fostering aggregate productivity growth. Finally, in line with macroeconomic trends, gains in productivity seem to be associated with capital deepening, but also with employment losses.
This essay investigates the contributions of different factors to regional economic growth in China. A statistical analysis on a provincial paneldatafrom 1978 to 2007 confirms the increasing regional inequality in China can be understood as different patterns of regional economic growth, which are affected by factors such as capital and labour inputs, education and institutional variables. We base our study on a growth accounting model with a Cobb-Douglas production function. We find that there are significantly positive associations between education and GDP per worker, total factor productivity and wage. In particular, senior secondary schooling is most important for China’s productivity and wage, especially for economic growth. University and above only improves the economic growth, suggesting that government should pay more for the university and above than individuals. The effect of education on economic growth appears to be much stronger after 1994 and mainly occurs in the coastal region. Moreover, institutional variables, such as FDI and openness affect positively, while inflation rate and birth rate have negative effect. The impact of semi-private and private units, fiscal expenditure on education and industrialization on the three productivity proxies are mixed. We conclude that investing in human capital with further market reform will be an effective policy to promote economic growth as well as a remedy to increasing regional inequality.
Developing countries are eager to host foreign direct investment to receive positive technology spillovers to their local firms. However, what types of foreign firms are de- sirable for the host country to achieve spillovers best? We address this question using firm-level paneldatafrom Vietnam to investigate whether foreign Asian investors in downstream sectors with different productivity affects the productivity of local Viet- namese firms in upstream sectors differently. Using endogenous structural breaks, we divide Asian investors into low-, middle-, and high-productivity groups. The results suggest that the presence of the middle group has the strongest positive spillover ef- fect. The differential spillover effects can be explained by a simple model with vertical linkages and productivity-enhancing investment by local suppliers. The theoretical mechanism is also empirically confirmed.
Attracting inward foreign direct investment (FDI) is high on the agenda of many governments, be it in developing or industrialised countries. One reason for this is the expectation of positive external effects of inward FDI fuelling growth of the domestic economy. The evidence to support this policy approach comes mainly from two literatures. Considering the relationship between FDI and growth at the macro level, recent studies find that there is a positive link only if countries have certain characteristics, such as high levels of human capital or developed financial systems. When considering the relationship between inward FDI and domestic firm-level productivity at the micro level, evidence is much more mixed. While some recent paneldata studies for industrialised countries support the notion that domestic firms benefit from horizontal spilloversfrom inward FDI, there is some evidence that what is more important is spilloversfrom FDI in vertically related sectors, through input-output linkages. Research showing the importance of vertical linkages generally use micro level data for one particular country. It is therefore difficult to generalise from these particular case studies.
An important strand of Robert Basmann’s early work was devoted to the estimation of simultaneous equations (see Basmann, 1957, 1959, 1961, or 1963, to mention a few). Ex- amples of simultaneous systems of equations in economics are the study of the domestic and foreign demand of outputs across ﬁrms, of the consumption of diﬀerent goods and services across households, or the behavior of diﬀerent workers within ﬁrms or sectors. A host of economic problems at various levels of aggregation involves paneldata and systems of equations. Two econometric problems which complicate the analysis con- siderably are the following: The data broadly speaking may be missing in the sense of censoring or truncation and is most likely cross-sectionally dependent (e.g., see Pinkse and Slade, 1998; McMillen, 2002; Smith and LeSage, 2004; Pinkse, Slade, and Shen, 2006; Klier and McMillen, 2008; Smirnov, 2010; Conley and Topa, 2007; Case, 1992; Wang, Iglesias, and Wooldridge, 2013; for approaches towards estimating problems with cross-sectionally dependent binary outcome variables; or LeSage, 2000; Flores-Lagunes and Schnier, 2012; Xu and Lee, 2015a; Xu and Lee, 2015b; LeSage and Pace, 2009; for cross-section approaches towards a wider range of models with cross-sectionally depen- dent, censored or truncated and other limited dependent variables). While the literature on spatial and social-interaction models has formulated and analyzed models for systems of equations (see, e.g., Cohen and Morrison Paul, 2004, 2007; Kelejian and Prucha, 2004; Wang, Li, and Wang, 2014; Baltagi and Deng, 2015; Wang, Lee, and Bao, 2015), in these approaches the structural form of the model is linear in parameters, and, except for Co- hen and Morrison Paul (2004) and Baltagi and Deng (2015), the approaches are designed for an analysis of cross sections of units.
Several recent studies have empirically investigated vertical spillovers. The most frequently cited of those is Javorcik (00) who develops the idea that spillovers are more likely to occur through vertical relationships, rather than horizontally as has been the predominant view in the literature. Using firm level paneldata for Lithuania for 99 – 000 she finds evidence consistent with her conjecture. Domestic firms in sector j increase their productivity following the presence of multinationals in industries which are being supplied by j. She refers to this as spillovers through backward linkages. While the evidence on such backward linkages is robust to a number of amendments, there is no robust evidence that domestic firms benefit from horizontal spilloversfrom multinationals. Blalock and Gertler (00) also find results suggesting positive productivityspillovers through backward linkages in their analysis of Indonesian plant- level paneldata. They do not find evidence for horizontal spillovers, however. Furthermore, Girma et al. (005), using UK firm-level data, find that vertical linkages are important for spillovers, and that there are substantial differences in such spillover benefits, depending on whether multinationals are export or domestic-market oriented.
the relevant sector, a different picture emerges – at both the 2- and 4-digit NACE sector levels, the coefficient of employment is positive and significant. The difference in the results suggests that we need to look in more detail at what it is we believe actually leads to the spillover, and whether the absolute rather than the relative size of the MNC sector is imp ortant. In the Irish case the 1990s saw a rapid increase in the presence of multinationals (32% change in employment), but since the LC sector was also growing quickly, there is relatively small variation in the FDI share of employment. This may explain in part the difference in the results obtained here from those obtained by Kearns (2000), who using the employment share measure to capture foreign presence finds evidence of spillovers in the Irish case. The difference may be due to the fact that his analysis is based on data that are more limited in terms of plant coverage, but it may also be due to the fact that the data cover a longer time period, during which there was more variation in the share of FDI employment. (The change in the share of employment accounted by foreign firms between 1984 and 1998 was nearly 26%, whereas this change is only 7% for the period 1991-1998)
Abstract. This paper investigates the impact of knowledge capital stocks on total factor productivity through the lens of the knowledge capital model proposed by Griliches (1979), augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on total factor productivity (TFP) in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen and Diewert (1982). This index describes how efficiently each region transforms physical capital and labour into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy regional knowledge capital stocks for N=203 regions over the 1997- 2002 time period. In estimating the effects we implement a spatial paneldata model that controls for the spatial autocorrelation due to neighbouring regions and the individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and add an important spatial dimension to the discussion, by showing that productivity effects of knowledge spillovers increase with geographic proximity.
tradition and high endowment of skilled labor. From 1990, the Czech Republic has been trying to catch-up to more developed countries. It has a highly open economy that received the highest inflow of FDI per capita out of all transitional Eastern European countries during the 1990s. Figure 1 and Figure 2 in the Appendix present, respectively, FDI inflows in manufacturing between 1993 and 2004 and the territorial structure of the stock of FDI as of December 31, 2004. One of possible reasons why Javorcik and Spatareanu (2005) might not find any evidence for spillovers is that they work with data for 1998-2000. Figure 1 in the Appendix reveals that there was a surge in FDI inflow to the Czech Republic in 1998 and 1999. If it takes more time before spillovers through linkage manifest themselves, one should focus on the period after 1999. To check whether a focus on the later time period leads to a different conclusion, I used the existing methodology and tested for spillovers at the industry level with data for 2000-2002. Javorcik and Spatareanu (2005) used balance sheet datafrom the commercial database Amadeus. I made use of a paneldata set designed by the Czech Statistical Office specifically for the purpose of this exercise. It contains balance sheet information on all manufacturing firms (NACE 15 – 36) above 100 employees and on a sample of firms with less than 100 employees from 2000 to 2002. However, despite using different dataset and focusing on later time period, I did not find any evidence in favor of spillovers through backward linkages at the aggregate level either. 5 These results sharply contrast with findings of this study. Here, using conceptually correct, i.e. firm-level measures of linkages, I find econometric evidence consistent with productivityspilloversfrom multinationals to their local suppliers. It shows that observation of a neutral or even a negative spillover effect at the aggregate level does not preclude the possibility of a positive impact at a more detailed level.