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«The Effectiveness of Industrial Policy in Developing Countries: Causal Evidence from Ethiopian Manufacturing Firms Tewodros Makonnen Gebrewolde, ...»

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Given our sample is relatively large, and contains the universe of ˜ ˆ manufacturing firms, it is reasonable to expect β ∆ = Λ − β 0 if there were no firm entry. Thus β ∆ 0 (conversely, β ∆ 0) implies entering firms are more (less) productive than existing firms. Standard 8 Ifthe productivities did not vary over time then the instrument relevance assumption would also be violated.

errors are obtained via the bootstrap. Column 2 of Table 4 presents the results and shows that the productivity impact of firm entry to be negative and significant as suggested by the theory. To see this, note that ∆ ∆ ∆ βτ 1 + βτ 2 + βτ 3 = −2.53, which is significant at all conventional levels.

Interestingly, firms entering only due to the geographical treatment are more productive on average than other new firms. Whether, this reflects a positive effect of the policy or some other factor is unclear. Notably, a calculation of the the average impact of the geographical treatment across both treated and untreated sectors shows it to be equal to −0.75, suggesting an overall negative effect of the treatment. Note, that whilst productivity has fallen, output has increased. In the long-run, the presence of additional low-productivity firms may eventually impede growth, but the associated increase in output may be important in the short-run. However, the cost estimates presented in Section 7 suggest that this output increase has come at a substantial fiscal cost.

Indirect Increases in Productivity Due to Spillovers

As described in Section 2, one margin on which IP might improve productivity is through spill-over effects. That is, if there are more or larger companies operating in an industry there may be more innovations to emulate, downwards pressure on input prices, for instance, thus leading to faster TFP growth. This is particularly true at Ethiopia’s current level of development where production techniques often lag significantly behind those used in richer countries but are improving rapidly. Thus, the importation of new techniques and innovation may both be expected to be common. The framework in Section 2 suggests that if φ 3 then ∂A 0 and the policy would thus raise the productivity of both treated ∂τ and untreated firms approximately equally, meaning our DDD strategy would identify no effect.

To estimate the extent of agglomeration externalities we consider the effects of the presence of treated firms on nearby firms in untreated sectors. We do this by exploiting the rich geographic detail of the data and contrast productivity in untreated sectors in those Zones with few treated firms to those with more. We generate a geographic proximity variable for each non-treated firm measured by the number of treated firms in the same Zone, Nzt. For ease of interpretation Nzt is standardized, and thus the coefficient κ describes the percentage impact on productivity of a standard deviation increase.We include Zone and year fixed effects, ψt and φz, and a vector of controls Xit as in (6). Thus κ is identified using the within variation of firm productivities as the number of treated firms with their zone varies. Our regression model is

then:

yit = κNzt + βXit + φz + ψt + µi + (11) it We find, as Column 1 of Table 4 reveals, that there is evidence for a small but positive effect on existing firms in untreated sectors.

Specifically, a 1 standard deviation increase in the number of treated firms increases the productivities of firms in other sectors by 0.15%.

However, when we also consider entering firms in Column 2, this effect is smaller and less precise.9 Thus, whilst there is some evidence of agglomeration externalities for existing firms, there is no evidence that these would be sufficient to offset the deleterious effects of the policy on average productivity. There are several reasons why spill-over effects may not be larger.10 Ferracci et al. (2014) find evidence, in the rather different context of the French labour market, for the opposite mechanism – that large-scale training programmes in a given local labour market may lead to crowding-out effects. Here, an equivalent explanation might focus on the limited ability of local markets to absorb additional production. Alternatively, it could be that the low density of manufacturing in Ethiopia itself limits the scale of agglomeration effects. Evidence for these explanations would not alter our finding of the policies ineffectiveness, but might suggest that a similar policy might work better in a more industrialised country.

9 These findings are robust to a variety of alternative specifications of (11).

10 Blonigen (2015) considers the effect of IP targeting steel-producers on users of steel and finds a negative effect on their export competitiveness. Here, however, the treated sectors are by design those that use largely bulky agricultural products, producing products for domestic consumption so we should not necessarily expect this form of negative spillover.

Decreases in Productivity Due to Diversification

One important way in which firms grow is through diversification (Berry, 1971). In Table 4 the coefficient on diversification is consistently positive, that is more diversification is associated with lower productivity. However, one reading of the model is that tax-breaks will lead to additional diversification, and that this will lower productivity within existing firms. The model in Section 2 does not describe multi-product firms specifically, but note that instead of a continuum of potential firms, we can imagine the specification describing one firm potentially producing a continuum of individual products.





Then, our expectation is that the IP will have induced firms to diversify, and thus that this is one way in which the policy led to lower average productivity. Column 3 reports the results of estimating a similar specification as in (6) except now we move our diversification measure to the LHS. We find that overall effect of the policy is negative, that is it increased diversification. Specifically, τ2 is significant at the 10% level and τ3 is insignificant but relatively precisely estimated.

Testing the joint significance of τ we are able to reject the null of no overall effect at all levels.

6.1 Effects on Capital We have now seen that the policy was unsuccessful in encouraging productivity growth. We also seen that this is because as predicted by the theory, the new firms were less productive, and there were insufficient spillovers to offset this. We now consider the key mechanism by which firms were to be affected – cheaper capital. One might be contented, as governments often are in rich countries, with a policy that was at least successful in increasing capital levels and employment rates.

We now see that the policy was also unsuccessful when judged on these criteria. Whilst, the provision of tax-breaks and subsidised loans did indeed increase capital levels, we find that this increased capital was normally used for investments other than new machinery necessary for greater or more efficient production, but rather in buildings or vehicles.

Furthermore, we show that this can be understood as a hedge against inflation and changes in market conditions given rampant inflation and a dynamic but challenging business environment. We then show, that as suggested by the theory, the lack of investment in productive assets limited employment growth due to the policy.

Direct Increases in Capital Due to Subsidies

Both our intuition, and Section 2 suggest that treated firms should increase investment as the policy lowers the cost of capital. Column 4 of Table 5 reports the results of again estimating (6); but, now with firms’ total book-capital on the left-hand side. The results suggest that firms in the treated sectors increased their capital levels, and that those treated by both arms of the policy did by slightly more;

but, the geographical component of the treatment was associated with lower than average capital accumulation. This latter finding suggests that owners of firms preferred to take additional profits rather than reinvest. This might explain the results in Column 2 of Table 4 – that the tax-break discouraged capital accumulation. This suggests that the subsidised loan programme that was a large part of the sectoral treatment was more effective at increasing capital levels than the taxbreaks. Testing the overall effect of the policy we can rule out that the policy did not increase capital levels, and thus on this basis may be judged as successful.

Increases in Capital are Not Invested in Machinery

Column 5 reports that despite the increases in Capital there were no overall effects on the Marginal Product of Capital; this is surprising as we would expect that a large increase in the capital stock should be reflected in a decrease, other things equal, in the marginal product.11 Column 6 reports estimates with the ratio of machinery to overall capital on the left hand side and documents that the sectoral treatment, led to a decrease in this ratio. This implies that new investments occasioned by the policy were in other forms of capital such as buildings and vehicles.

11 This results also suggests that the policy is not encouraging growth by reallocating capital. If it were we would expect a large positive and significant coefficient here.

–  –  –

We do not adjust for quantities sold of these products to avoid potential endogeneity bias due to responses in production decisions due to changes in prices or vice-versa. We then estimate the following

regression:

12 As discussed by Lencho (2008), Ethiopian Law does provide a Bankruptcy procedure; but, the law has rarely been applied since 1960, and most lawyers are unfamiliar with it.

ln(machinery) = βln(bookcapital) + γln(T oT ) + µi + (13) it.

The results are reported in Column 7. In line with our hypothesis we find that the ratio of capital in machines, etc., to total book capital is higher when the ‘terms of trade’ of a particular firm are higher. This highlights the challenges in designing successful IP – this behaviour is the upshot of several interrelated features of the particular context.

Firstly, the high-growth high-inflation environment means that firms will seek to avoid holding cash whilst being willing to incur debt.

Second, entrepreneurs will be more risk-averse due to the lack of effective bankruptcy protection. Finally, the absence of a well-developed financial services sector means that firms are unable to diversify, through acquisition, for example; thus, we get the accumulation of unproductive capital. However, these three factors are not unique to Ethiopia and neither, therefore, are the difficulties they suggest in the encouragement of investment.

6.2 Effects on Employment The final outcome variable we consider is employment. The theoretical framework discussed above suggests that the firm-level effects of the IP on employment will depend on the relative magnitudes of the substitution and scale effects. Column 8 of Table 5 shows that there was no overall effect of the policy on employment. Again, we observe a negative effect of the geographical treatment, whether this reflects the failure of the tax-breaks to lead to additional capital accumulation is unclear. But, the positive and significant coefficient on (log) Total Book Capital suggests that this may be the case.

7 The Cost of the Policy Rigorous policy evaluation techniques are by now routinely applied to assessing the effectiveness of different forms of aid at both a macroeconomic level, and also at the level of individual policies. Many development agencies and charities are committed to funding projects only based on evidence that they represent value for money. This suggests that IP should be evaluated on a similar benefit–cost basis.

Given that we find little evidence of any positive effects of the policy, we could assume the policy had no benefits and focus on its costs. Instead, more conservatively, we prefer to assume the policy had the maximum plausible impact – the maximum of the 99% confidence interval of each of τ1, τ2, τ3. Thus, we evaluate the policy on the premise, that contrary to our results, it achieved an 83% increase in TFP. We also take into account the increase in the tax base due to additional entry of firms due to the policy. We do this by comparing the number of firms that entered in treated sectors to untreated sectors and use the difference as the number of firms caused by the policy. Again conservatively, we assume that all of the additional new firms in treated sectors are because of the policy.

Following the the arguments in Section 2, and the results in the previous section, we assume that the least productive entrants are those induced by the policy. Thus, following the notation in Section 2, the profit of firm i is Πi. Denote the set of existing firms as X and the set of additional

entering firms as E benefits in year t, Bt are given by:

–  –  –

where T1 is the tax rate for firms treated by the policy and T0 is the taxrate without it. We take a similarly conservative approach to the costs of the policy. We focus only on the loss of tax-revenue although this focus will understate the cost of the policy substantially as it ignores the costs of concessionary loans and the investment in sector specific training and technology transfer programmes. In particular, the costs of the loans will be substantial, given real interest rates were far below zero. We ignore both of these other costs as the cost of the loans will depend on future delinquency rates as well as future inflation, and there is no data on the costs of training and technology transfer. Costs are given by the

loss of tax revenues on existing firms:

–  –  –

Figure 3 plots the lost tax receipts due to the policy – the blue line – and the additional tax due to TFP growth and firm entry – the red line –by year. The cost ranges from $39.4 Million (358 Million Birr) to over $121 Million (1100 Million Birr). Put differently, the average cost over the period was 0.5% of GDP or 5% of total Government spending.



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