Make or Buy: Offshoring of Services Functions in Manufacturing

Between 25 and 60% of employment in manufacturing is in services functions such as transport, marketing, IT, R&D, management, maintenance, repair, cleaning, and training (Miroudot and Cadestin 2017). Digitisation and trade liberalisation have significantly decreased transaction costs in services, which has opened the opportunity to outsource such supporting services. Following the rise of India as a major exporter of computer services, scholars took interest in the offshoring of services, which is sometimes referred to as “trade in tasks” (Grossman and Rossi-Hansberg 2008). Trade in tasks is portrayed as the latest turn in the spiral of ever-deepening specialisation and fragmentation of production.

According to business surveys in Europe and the Americas, however, firms tend to outsource services functions rather than individual tasks.¹ Furthermore, the surveys reveal that most firms outsource locally. When they buy services from abroad, the source is most often countries in the same region with similar production costs as in their own country. Multinationals are more likely to offshore than are local firms, and the former often offshore from their own subsidiaries. The story of deepening globalisation through the slicing of the value chain into ever thinner slivers seems not to tally with the insights from these surveys.

This paper proposes an empirical approach to study the make-or-buy decision that is embedded in the offshoring literature in a multi-country setting. It exploits new information on employment in services within manufacturing firms and the 2016 release of the World Input Output Database (WIOD) to map the services functions that are produced inside the manufacturing sector and services that are provided by outside suppliers. I next use this mapping to create a new measure of narrow outsourcing and offshoring, which makes it possible to analyse rigorously the make-or-buy decision for services in the offshoring context in a similar manner as for goods.

The empirical literature on offshoring distinguishes between narrow and broad offshoring. The former includes imported intermediate inputs from the importing industry only, while broad offshoring comprises imports of any intermediate inputs. Broad offshoring captures all imports of intermediate inputs—not only inputs that are commonly produced in-house—and empirical offshoring studies therefore prefer to analyse narrow offshoring. From this perspective, offshoring of services is excluded. Given the importance of services functions inside manufacturing firms, however, narrow offshoring should capture not only the fabrication activities in manufacturing but also the supporting services functions.

Consider for example a computer engineer who is employed in the IT department of a car manufacturer. The market for her skills is IT departments in any sector, including special computer services firms. Similarly, she and her colleagues in the IT department face direct competition from computer services firms that may offer to take over the IT functions of the car manufacturer on a contractual basis rather than facing competition from car producers abroad.

The main contribution of this paper is, first, to create a measure of narrow offshoring that matches the services functions inside manufacturing to services that are provided by outside suppliers. Second, it analyses the relationship between market and product characteristics as well as the policy environment on the one hand and the outcome of the make-or-buy decision on the other. The contribution in this regard is to create sector-specific policy and technology indicators and explore how the location of services functions is conditioned on the policy and technology framework.

The data reveal that the aggregate share of services inputs in manufacturing has not changed much during the period 2000–2014. There is however substantial variation across manufacturing sectors and countries. Furthermore, the share that is offshored and the share that is produced inside manufacturing have both increased slightly at the expense of local services suppliers.

Tentatively, our results suggest that on average offshoring does not have a large impact on in-house employment. However, the average conceals large variations across sectors and functions. Furthermore, the marginal impact of offshoring on internal manufacturing employment strongly depends on the characteristics of the sector and the policy environment. Narrow offshoring affects manufacturing employment mainly in ICT-mature sectors. Narrow offshoring tends to replace in-house functions in short and simple value chains, but narrow offshoring complements in-house functions in long and complex value chains. The function that is most affected by offshoring is R&D, which seems to be completely hived off when offshored. Finally, narrow offshoring complements internal employment when policy barriers to entry and investment are high.

The rest of the study is organised as follows: The data and stylised facts are portrayed in Sect. 2. Section 3 positions the paper in the literature and describes the analytical framework. Regression results are presented and analysed in Sect. 4, while Sect. 5 concludes.

Previous studies that relate offshoring to changes in overall employment found no or limited effects (Hijzen et al. 2011; Liu and Trefler 2008). Nevertheless, there is evidence of quite significant effects on the skills composition of employment and relative wages (Geishecker and Görgy 2013; Hijzen et al. 2005).⁹ Little is known about the impact of offshoring on the functional composition of employment, but there is some evidence that offshoring of material inputs from the US to China is associated with a net decline in low-skilled production workers, which has been more-than-offset by a net increase in employment of non-production workers (Wright 2014).

Analysis of micro data is suitable for gaining insights on job creation and job separation across firms within sectors and sometimes even within firms, which can allow distinguishing between firm and worker characteristics. It is, however, difficult to study trade and regulatory policy drivers of labour market outcomes in country-specific microanalysis, since firms and workers within a country face the same policy environment. Furthermore, it is well known from the classical work of Hecksher, Ohlin, Vanek, and others that trade leads to changes in relative prices, which drive a reallocation of resources to their most efficient use. These changes affect all firms and workers in the economy—not only those who are directly engaged in trade. Therefore, cross-country analysis may be more suitable for policy analysis and studies on the overall impact of trade.

The WIOD and the TiVA databases have been instrumental for cross-country analysis of trade and jobs at the industry level. They confirm that overall employment is mostly determined by factors other than trade and trade policy. There is evidence that services offshoring has contributed to making hiring and firing more sensitive to changes in wages and other market conditions (Foster-McGregor et al. 2013; Hijzen and Swaim 2010), and thus may have had an impact on job security.¹⁰ Finally, cross-country analyses support the finding that offshoring may have contributed to the polarisation of earnings: where the medium-skilled workers have seen their share of the total wage bill decline to the benefit of high-skilled workers and—to a less extent—to low-skilled workers (Foster-McGregor et al. 2016).

I start with the estimation of capital-constrained unconditional aggregate labour demand while introducing services outsourcing and offshoring as shift parameters. This very basic regression is to confirm that the WIOD 2016 version exhibits the predicted relationship between employment and the core variables in the labour demand function. The result is reported in Appendix Table 9. As expected: Labour demand is downward sloping in wages; investment generates employment; higher prices of intermediate inputs tilt the make-or-buy decision towards make; local outsourcing of services supports employment in manufacturing; while the offshoring of services does not have a statistically significant impact on overall manufacturing employment.

I next classify employment into the seven functions that were presented in Table 1 and run the same regressions for each function, using seemingly unrelated regressions (SUR) where all variables are in 5-year differences, and I use the broad measures of outsourcing and offshoring. Table 2 reports the results.

The own- and cross-price elasticity of labour demand by function reveals complementarities and substitutability among business functions. First, and reassuringly, all own-price elasticities are negative, and all are statistically significant, except for the management function. The core operations function is complementary to management and R&D (negative cross price elasticity) and a substitute for transport (positive cross-price elasticity). These results are intuitively appealing as the number of managers and supervisors tend to follow the number of shop-floor workers. It also makes sense that higher wages for shop-floor workers is associated with more R&D, which in turn is likely to be associated with higher productivity when targeting process innovation or higher output prices when targeting product innovation. Finally, one could envisage that higher wages for core operators may raise the demand for transport workers. This occurs if (e.g.) machine operators initially do some internal transport. A wage rise could then trigger the transfer of internal transport to a specialised transport division. Presumably internal transport workers are less skilled and earn lower wages than do machine operators.

Marketing substitutes for management and R&D. The latter may suggest that firms have two ways of product differentiation: product development, or targeting certain market segments through marketing. R&D complements all other business functions except “other”.

We note that, as in the aggregate, outsourcing to local suppliers goes hand-in-hand with higher labour demand for management, R&D and “other” functions, but is negatively associated with labour demand in core operations and marketing. Offshoring on the other hand is associated with higher employment in core operations and the management function, and less employment in the R&D and “other” functions. Thus, we note that outsourcing and offshoring core operations, R&D and “other” have opposite effects on in-house labour demand. This is further discussed below.

With respect to narrow offshoring, labour demand in each of the seven business functions is regressed on matched local- and foreign-sourced services as indicated in Table 1. The results are reported in Table 3. The impact of narrow outsourcing and offshoring is strikingly different from broad outsourcing and offshoring. Fewer functions are affected, but the marginal impact of those concerned is larger.

Only marketing and R&D are significantly related to outsourcing, while offshoring is significantly associated with core processing, R&D, and “other” functions. Interestingly, the marginal impact is quite strong for R&D—with opposite signs for outsourcing and offshoring. Recall, however that R&D is mainly done in-house (see Fig. 6), so that a small change in absolute numbers will translate into a large percentage change.

Remarkably, while outsourced R&D increases labour demand in in-house R&D, offshored R&D has the opposite effect with a similar but less precisely estimated parameter in absolute value. A possible story behind these results is that R&D—particularly the research part of it—requires a minimum efficient scale and highly specialised skills. Only large and innovative companies can recover the cost of research. Smaller and less innovative companies resort to licensing technology, which tends to be an arms-length transaction. The development part of R&D in contrast often involves in-house teams that are supported by external services providers who work close to the operation activities. Frequent face-to-face interaction is needed in this kind of activity, which is therefore more likely to be sourced locally. Furthermore, such collaboration requires substantial in-house capacity to make full use of external support (Bengtsson and Dabhilkar 2009; Muller and Zenker 2001).

The analytical framework that explains the make-or-buy decision emphasises the nature of the product and production process as important determinants. As noted, complex production processes and products beget complex contractual relationships. Complex contractual relationships in turn may require more in-house effort to monitor and enforce the contracts. We use the length of the value chain as an indicator of complexity and analyse to what extent it makes a difference with respect to the in-house employment response to outsourcing and offshoring.

Thus, do offshored services function require more in-house support and monitoring in long value chains? Or conversely, are business functions more likely to be completely hived off in long value chains? We explore this question by entering the length of the value chain together with an interaction term with offshoring in the labour demand system.

A potential problem is that outsourcing, and offshoring may result in longer value chains, which could constitute an endogeneity problem. The length of the value chain and offshoring are indeed correlated but except for the core operations function, the correlation is weak (see Appendix Table 10). If the length of the value chain is partly determined by offshoring, the estimate of the marginal effect of offshoring conditioned on the length of the value chain would be upward-biased. The marginal impact of offshoring on in-house labour demand by function at different lengths of the value chains are reported in Table 4, while the full regression results are presented in Appendix Table 11.

Bearing in mind the caveat that the estimates could be upward-biased, we first notice that the marginal impact on labour demand in manufacturing is statistically significant for all business functions except “other”. There are also fundamental differences among the six remaining business functions. Operations, transport, marketing, and management depict a similar pattern, while the IT and the R&D functions display different and distinct features.

With respect to transport, marketing, and management, the result indicates that when value chains are short and simple, offshoring is associated with a large reduction in in-house employment in the same function. Within our analytical framework of heterogeneous firms, the result suggests that the firms that offshore these business functions completely hive them off—possibly retaining an internal interface with foreign suppliers. With increasing complexity, however, the marginal impact of offshoring on in-house employment in the same function turns positive, which supports the prediction that complex processes require more in-house support. In addition, the finding is consistent with dynamic offshoring and automation models where tasks and functions are standardised, digitised, and offshored or automated, while in-house workers move on to new functions that cannot easily be offshored or automated.¹⁶

IT and R&D follow a different pattern. As was shown in Figs. 5 and 6, IT is by far the smallest services function in manufacturing, and there are fewer observations for this function than for the other six. There may therefore be more noise in the data for this function than the others. With this caveat in mind, it appears that narrow offshoring of IT functions is strongly complementary to in-house employment when supply chains are short and simple. With lengthening value chains, the positive effect on in-house employment declines sharply. Similar to the business functions that were discussed above, this finding is also consistent with a dynamic offshoring model where mature tasks and functions are automated and offshored while new tasks and functions are performed in-house. For IT functions the job creation effect is stronger than the destruction effect in short value chains, while the balance shifts towards the job destruction effect as the value chain becomes longer and more complex.

Finally, the offshoring of R&D is complementary to in-house employment when supply chains are short but turns negative when they get longer and more complex. Furthermore, offshoring replaces internal R&D functions with a higher marginal effect the longer the value chain. As was discussed above, R&D may not be modular. It is therefore less likely to be offshored; but when it is, the entire function may be offshored. This may particularly refer to research, where external sourcing is often in the form of arms-length licensing.

Another important factor that characterises the product and production process is technology—particularly ICT. Most studies find that ICT is skills-biased: It pushes up the demand and relative wages for skilled workers. This paper explores a different aspect: whether ICT affects the sensitivity of labour demand to offshoring. For this I use the OECD ICT maturity index: It is sector-specific but unfortunately does not vary across countries and over time. The best I can do to study the different marginal effects conditioned on ICT maturity is to divide manufacturing into different ICT maturity categories and run the labour demand regressions separately for each category.

I sort manufacturing industries into three categories: high, medium and low ICT maturity and run the same SUR regressions that were reported in Table 3 for each category. High ICT maturity is defined as sectors with an ICT maturity index that is greater than one standard deviation above the mean; low ICT maturity refers to a score that is more than one standard deviation below the mean; and medium ICT maturity represent the scores between high and low ICT maturity.¹⁷

Table 5 reports the results. In the interest of space, I report the coefficients on narrow offshoring only.

We recall that narrow offshoring is significantly related to labour demand only for core operations, R&D, and “other” in the pooled regressions for all manufacturing sectors. When I split the sample into the three categories, the same functions are significantly affected by offshoring, but it appears that the results are largely driven by the high ICT maturity sectors. In this category, R&D offshoring has a particularly large and negative impact on labour demand for the in-house R&D function. There is also a relatively large impact on “other” in the high ICT maturity category. For core operations, in contrast, only low ICT maturity sectors are affected by narrow offshoring. We finally note that there is a large but not very precisely estimated impact of offshoring of IT functions in medium ICT mature sectors.

With respect to the relative cost of the different outcomes of the make-or-buy decision, trade and investment restrictions as well as burdensome domestic regulation are important determinants of such costs. Unfortunately, information on the fixed and variable costs that are related to the four outcomes of the make-or-buy decision is not available. As was discussed in the data section, the only relevant policy indicator that covers the sectors, countries, and years that correspond to the WIOD data is the OECD PMR indicators, which capture the regulatory burdens that face the key services inputs to manufacturing.

In the same manner as for the length of the value chain, the PMR and an interaction term between the PMR and offshoring are introduced into the regressions to explore whether the marginal effect of offshoring on the internal provision of services functions varies with the level of regulation.

The marginal effects are reported in Table 6, and the full regressions are in Appendix Table 12.

Product market regulation is significantly associated with offshoring and employment in core operations, transport, marketing, management and “other”. With respect to the core operations function, offshoring complements in-house employment, which is consistent with the results that were reported in Table 3. Furthermore, the marginal impact is stronger when the regulation is more burdensome. A similar pattern is observed in the transport function: Since transport is location-specific, offshoring of the transport function must be related to exports of final output and imports of intermediate inputs.¹⁸ More research is needed on the interface between own-account transport and transport that is sourced from external providers in an international trade context to explain this result.

The offshoring of marketing supports internal employment when the regulatory burden is low but replaces in-house marketing functions when the regulatory burden is high. One can only speculate what might explain this finding: A possibility is that modern marketing is highly information-intensive, is subject to substantial scale economies, and relies heavily on efficient logistics, distribution, and telecommunications inputs. With a high regulatory burden falling on these inputs, they are likely to be expensive, and only large and highly productive manufacturers have sufficient scale to benefit from doing marketing themselves. Furthermore, the interface between offshored and in-house marketing may become more distinct and marketing may become more arms-length with weaker infrastructure services.¹⁹

In-house administration complements offshored administration when product market regulation is considered, although the marginal effect is independent on the level of regulation. Finally, “other” follows a similar pattern as core operations: Offshoring the “other” function is associated with more complementary in-house employment the more burdensome is regulation.

This study has analysed the employment impact of offshoring and outsourcing services functions from manufacturing: I match the services functions that are performed inside manufacturing to services that are sourced from outside. Previous studies have defined narrow offshoring as imports of materials from the same sector, which ignores the fact that services workers inside manufacturing are also exposed to the offshoring of the activities that they perform. Given that on average about 40% of employment in manufacturing is in services functions—with a slightly increasing trend over time—shedding light on the relationship between services workers and service offshoring in manufacturing is important.

As is true of previous studies I find that offshoring has a relatively small impact on overall labour demand in manufacturing. However, the average conceals large differences across functions. The function that is most sensitive to offshoring is clearly R&D: Offshoring replaces in-house employment irrespective of the characteristics of the sector or policy environment. It is, however, worth noticing that offshoring accounts for a very small share of expenditure on R&D, and the relatively large marginal effects are from a small base. The IT function is also highly sensitive to narrow offshoring, but in this case offshoring complements and strengthens in-house employment.

As is predicted by trade and industrial organisation theory, the labour demand response to offshoring depends on sector characteristics as well as the policy environment in which manufacturing operates. Complexity—as captured by the length of the value chain—is the most important factor that shapes labour demand dynamics in manufacturing. Apart from R&D, in-house employment is less sensitive to offshoring when the value chain is longer. Furthermore, apart from R&D, narrow offshoring is associated with lower in-house employment in short value chains, but as the value chain lengthens, the effect turns positive and narrow offshoring strengthens in-house employment in long value chains. It appears, however, that the employment effect of services offshoring is largely confined to high ICT maturity sectors.

To conclude, the employment impact of narrow offshoring of services functions in manufacturing is small on average but with substantial differences across business functions and when conditioned on technology, market structure, and regulation. Previous studies of labour market implications of offshoring have emphasised that the relevant measure of offshoring is narrow offshoring, and most have limited the analysis to manufacturing. However, as services functions gain prominence, the employment effect of narrow offshoring cannot be fully understood if services are ignored. This paper has contributed to filling this gap, but better information and much more research is needed to understand the policy dimension of this phenomenon.