Why do countries differ in total factor productivity?

Theoretical and empirical literatures on sources of economic growth emphasized on factor accumulation and factor productivity as two major sources of growth. Though factor accumulation can explain a significant part of economic growth, it can’t explain the sustained long run economic growth, as sustained long run economic growth is attributable to growth in productivity. Productivity is the cornerstone of economic growth. Increases in productivity allow firms to produce greater output for the same level of input, and thus result in higher Gross Domestic Product (GDP).

We should make clear the difference between labor productivity, which is output per worker, and Total Factor Productivity (TFP), which is the ‘ability’ with which all factors are combined to produce outputs. TFP is the part of output which is not explained by the amount of inputs used in production. Essentially, its level is determined by how efficiently and intensely the inputs are used in the production process. TFP growth is usually measured by the Solow residual. TFP plays a key role in economic fluctuations, economic growth and cross-country differences in per capita income.

The scatter-plot using the data for 110 countries in 2011 shows a very interesting association between TFP and log of per capita GDP. The TFP data are derived from the Penn World Table version 8.1 (PWT 8.1) with some required adjustments and extensions. Here the TFP level of USA is considered as 1 and other countries’ TFP levels are indexed against USA’s TFP level. For example, among the South Asian countries, the TFP levels of Bangladesh, India, Nepal, Pakistan and Sri Lanka in 2011 were 0.15, 0.48, 0.11, 0.28 and 0.42 respectively. Similarly, TFP levels of Malaysia and Thailand were 0.47 and 0.65 respectively. Singapore’s TFP level (1.1) was higher than that of USA. The trend line shows a very strong positive association between TFP and log of per capita GDP (the correlation coefficient is 0.9). Nepal and Bangladesh, though on the trend line, are at the lower end of the association. A straightforward policy suggestion for these countries is that a rise in the TFP level is required to raise their per capita GDPs.


Why do countries differ in TFP? How to improve the level of TFP? In order to answer these questions, we have run fixed effect cross-country panel regressions for 110 countries for the period 1995-2011 considering log of TFP as the dependent variable. The explanatory variables include log of human capital (an index of human capital per person which is linked to the average years of schooling and the return to education), log of public expenditure on health as % of GDP, and log of trade-GDP ratio. The data source of human capital is the PWT 8.1, and the data of health expenditure and trade-GDP ratio are taken from the World Bank WDI. The logic behind the formulation of this model is that we want to explore how cross-country differences in statuses of education, health and openness affect the cross-country differences in the TFP. The regression results suggest that all three explanatory variables are statistically significant with expected signs. 1% rise in the human capital index is associated with 0.39% rise in the TFP. Also, 1% rise in the ratio of public health expenditure to GDP is associated with 0.03% rise in the TFP. Finally, 1% rise in the trade-GDP ratio is associated with 0.03% rise in the TFP.

In extended models, we have found that the ratio of FDI to GDP is positively and significantly associated with the TFP. 1% increase in the FDI-GDP ratio is associated with 0.01% rise in the TFP. Furthermore, institutional variables like bureaucracy quality and investment profile (from the PRS database) are positively associated with the TFP with statistical significance. Improvements in the bureaucracy quality and investment profile by one unit are associated with the rise in TFP by 0.03% and 0.01% respectively.

The aforementioned analyses point to some very obvious policy suggestions. Countries like Bangladesh need to attach decisive emphasis on improving their currently low levels of human capital. This can happen by enhancing investing on education and health quite a lot in order to increase the efficiency in using inputs in the production process thus raising the level of TFP. Also, larger trade and FDI orientations and improvement in the quality of institutions are indispensably important.

Published at the Thinking Aloud on 1 May 2016

Published at The Financial Express on 10 May, 2016


Does export orientation lead to higher productivity? Firm-level evidence from Bangladesh

Selim Raihan, Nafiz Ifteakhar and Mir Tanzim Nur Angkur

For long, empirical studies on the role of exports in promoting growth in general, and productivity in particular, used data at the country or industry level to test whether exports promote productivity growth or vice versa. However, a series of empirical studies since early 1990s started using firm level data to look at differences between exporters and non-exporters in various dimensions of firm performance, including productivity.

Two alternative but not mutually exclusive hypotheses can be mentioned why exporting firms can be expected to be more productive than non-exporting firms. The first one relates to the fact that firms which are considered to be more productive than others are likely to participate in export markets – the so called ‘self-selection’ of the more productive firms into export markets. The second one relates to the notion of ‘learning by exporting’ hypothesis which suggests that after entering the export market, firms are able to acquire new knowledge and adopt new expertise which eventually leads to higher level of productivity. Though there is sizeable evidence that exporters perform better than non-exporters, the issue of the direction of the causality between exports and productivity is still debated. While in the contexts of more advanced countries most studies find evidence that the export premium is due to a self-selection process, a number of recent studies on less developed countries tend to endorse the learning effect.

Against this backdrop, this paper explores how export orientation affects firm-level productivity by looking at the range of determinants of productivity of manufacturing firms in Bangladesh. Review of empirical studies suggest that there could be several factors, i.e. firm size, firm age, share of firm’s output in the industry, export orientation measured as percentage of total firm’s output that is exported, which may affect firm’s productivity. Our measure of firm’s productivity is the total factor productivity (TFP) which is derived using the Cobb-Douglas production function framework. Specifically, we have regressed log of output (calculated as total sales of firms) on log of capital (measured as netbook value of fixed assets of the firms) and log of labor (measured as total number of employees) to get the output elasticity of capital and labor which are then used to estimate the total factor productivity (TFP).  To get unbiased estimates of those elasticities in the presence of industry fixed effects, we have included industry dummies in the above regression. We have used the dataset of “The World Bank, Enterprise Survey-Bangladesh” for 2007 and 2013 and have only considered firms belonging to the manufacturing sector. Table 1 shows the industry descriptions along with the distribution of firms for both 2007 and 2013. We have estimated TFPs of firms separately for 2007 and 2013 by following the same procedure described above.


In order to explore the effect of export orientation on the productivity of firms we have run cross-section regressions for 2007 and 2013. For both years we used the same model and Table 2 shows the estimated results. The dependent variable of our model is total factor productivity. The main explanatory variable is the export orientation while the set of control variables include firm size, firm age, firms’ output share and internet connection. In the regression models, export orientation of the firm is represented by a dummy variable, where the dummy variable takes the value of 1 if the firm exports 25% or more of its total output.


For firm size, we have also taken three dummies- large, medium and small based on the number of employees. For capturing the effect of technology on productivity, we have taken internet connection dummy. Internet connection dummy will take the value of 1 if the firm communicates by e-mail.

The cross section regression result of 2007 suggests that firm age has positive and significant effect on productivity, while the result of 2013 indicates no such relationship. For 2007, it is estimated that an increase in firm age by one year would lead to a rise in productivity by 1%. For both years, firm size has an effect on productivity. In particular, both medium and small sized firms tend to be less productive than large firms. The firm’s output share is found to have a positive and significant effect on productivity for both 2007 and 2013 respectively. For 2007, one percentage point increase in firm’s output share would lead to a rise in firm’s productivity by 31%, while for 2013 such productivity rise would be by 12%. Now considering the effect of internet connection on productivity, firms with internet connections are found to be more productive than firms with no internet connection for both 2007 and 2013.

Our variable of interest is the export orientation which is found to have a positive and significant effect on productivity for 2007 and 2013. For 2007, we have found that on average productivity of a firm that exported 25% or more of its output was 156% higher than a firm that exported less than 25% of its total output. Such productivity difference was however reduced in 2013, as productivity of a firm that exported 25% or more of its output was 112% higher than a firm that exported less than 25% of its total output.

From the aforementioned analysis, it can be said that larger firms are more productive as compared to small and medium sized firms. Larger firms, due to economies of scale, are able to reap some benefits which help them to utilize resources more efficiently. Firms which started earlier in an industry also tend to be more productive than firms which entered in the industry later. This is due to the fact that already established firms have advantages over new firms in case of production, marketing, etc.

Also output share of the firm belonging to an industry (measured by the proportion of sales of firms in total industry sales) may influence the firm’s productivity. Firms with higher output share can positively affect productivity, as dominant firms hold the necessary resources and technical skill and expertise as compared to firms with low output share. It is also found that firms which have access to internet connections can benefit from lower communication cost and can also communicate with its clients and suppliers timely and thus leads to higher productivity.

Finally, the regression results confirmed that the exporting firms in Bangladesh are more productive than their counterparts. There could be several reasons for this. The learning process may work through technical supports from external buyers, and/or through the exposure to competition in the international markets, which can result in knowledge, technology, and efficiency gain from exporting.

Published at the Thinking Aloud on 1 May 2016

Published at The Financial Express on 4 May, 2016