Differential effects of conflict on investment

To gain more insight into the processes behind the investment reduction and to analyze who might be most affected by it, we interact the conflict variable with dummy variables that identify private firms, capital intensive firms and the industry it belongs to.³⁰

For the regressions reported in Table II.3, we classified a firm as capital intensive if the capital stock per worker was larger than 50,000 Birr. While this is an arbitrary value we have tried the same with other values (e.g., the median value) with the same results. 50,000 Birr per worker is about the 70th percentile of the distribution in our data and a little bit more than twice the median value. With this definition of capital intensive firms, we find that the effect of battle is nearly twice as large as in the base specifications and highly significant as reported in column 1. The interaction term between the high capital dummy and the battle count, which can be interpreted as the effect of conflict on firms with high capital intensity, is also highly significant and the coefficient is 0.015. Therefore the negative effect of conflict would be nearly offset for capital intensive firms or it would at least be significantly smaller (only about -0.004). Adding control variables lowers the absolute values of both coefficients and their significance but the tendencies remain unchanged. This implies that it is mainly the labour intensive firms that reduce their investment, while in general, firms with a high capital share in their production process react much less to increased insecurity around them.

Capital intensive firms are larger (on average they have twice as many employees), which could mean that they are better able to cope with risks. We do control whether larger firms are less affected by an interaction term between the conflict variable and firm size but it

30 The dummy variables are time invariant and hence cannot be separately included in the fixed effects regression. Therefore the main effect of the dummies is not visible but, due to the time invariance, it is captured by the fixed effect, allowing for the regular interpretation of the interaction term.

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does not turn out significant at conventional levels (regression results not reported).³¹ When introducing this interaction along with the capital intensity interaction nothing changes, the latter remains significant (at similar magnitude) and the former is not. So size cannot explain the differential effect of conflict on capital intensive firms.

Table II.3: Regression results firm investment for capital intensive and private firms Dep. Variable Investment Rate, Fixed Effects Regression

Control for differential effects on capital intensive or private firms

Battles 50km -0.019*** -0.013*** -0.012** -0.012** -0.020***

(-4.51) (-3.32) (-2.39) (-2.37) (-3.32)

No. obs. 2581 2060 2581 2060 2060

No. firms 631 540 631 540 540

R sq. 0.005 0.025 0.003 0.024 0.026

Robust standard errors; t-statistics in parentheses. ***,**,* denote significance at the 1%, 5%, 10% level respectively.

A possible explanation could be different risk attitudes between owners of capital intensive and labour intensive firms. Firm owners with a relatively large capital stock are most likely less risk-averse. A risk-averse person would probably rely on more labour intensive production processes in the first place, because workers are only paid per month and represent no more cost when fired if the firm terminates production. A large part of the capital investment on the other hand would be lost in such a case.

31 Since the distribution of firms’ size is skewed to the right we try the same with the logarithm of the number of employees but the results are not significant either.

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If the investment reduction is due to fear, then risk-averse firm owners will react more strongly as we suggest in our theory. So if the above assumption is correct then the result of a much stronger reaction for less capital intensive firms is what we would expect.

Another aspect could be that reducing the investment too much might cause major disruptions in a capital intensive firm. Capital intensive firms rely more heavily on capital goods as production factors and those might need a regular minimum investment for repair and replacement in order to continue operation. So the potential loss from not investing might outweigh the perceived risk from conflict.

As already mentioned in the theory section, not only firm owners but also their employees might react to greater perceived risk, e.g., by reducing their work hours. If this is the case it might also explain why more labour intensive firms show a stronger reaction to conflict.

Additionally, employees in capital intensive firms would receive higher wages and richer households are better able to insure against insecurity, which might make them react less strongly.

We also control for differences between public and private firms by including an interaction between conflict intensity and the private dummy. The interaction term for private companies is - as reported in columns three and four - positive, suggesting that they are less affected than public firms but it is not significant. In column five we include both interaction terms in the regression and the interaction with the private dummy nearly reaches the 10 percent significance level. When adding up the coefficients for both interaction terms we would find that a capital intensive, private firm would not lower investment due to increased conflict in their environment.

In the regressions reported in Table II.4, we control for the different industries by interacting each industry dummy with the conflict intensity measure. We leave out the interaction term with the food industry, which means that the non-interacted conflict measure represents the effect of conflict on this sector and the interaction terms represent differences to this main term for the other industries.

In column one we see a strong and highly significant effect of conflict on the food industry and no significant differences with the other sectors (the interaction term for the textile industry is however almost significant). When adding control variables in column two, the

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effect on the food industry increases. At the same time, the interaction effects for the textile and non-metal sectors turn significantly positive. For the textile sector, the interaction term’s coefficient is so high, that it completely offsets the negative main effect. This means that the textile industry would not be affected by conflict.

Table II.4: Regression results firm investment by industry

Dep. Variable Investment Rate, Fixed Effects Regression Control for differential effects by industry

Battles 50km -0.015*** -0.016*** -0.022*** -0.018** -0.028***

(-2.95) (-3.07) (-4.51) (-2.52) (-3.66)

Textile ind.*Battles 0.017 0.019* 0.013 0.019 0.020*

(1.59) (1.80) (1.18) (1.57) (1.66)

Non-metal ind*Battle 0.005 0.010* 0.004 0.004 0.008

(0.85) (1.75) (0.69) (0.74) (1.45)

Metal ind.*Battle 0.005 0.009 0.004 0.004 0.005

(0.63) (0.78) (0.45) (0.47) (0.42)

No. obs. 2581 2060 2581 2581 2060

No. firms 631 540 631 631 540

R sq. 0.004 0.026 0.006 0.004 0.028

Robust standard errors; t-statistics in parentheses. ***,**,* denote significance at the 1%, 5%, 10% level respectively.

Since the capital intensity seems to be a very important determinant it was added to the model. When controlling for it, by using the interaction term with conflict, it shows up positive and significant as in the earlier models. Without the other control variables however all industry interactions remain insignificant (again with the textile industry interaction having by far the highest t-value). With all control variables included the interaction terms for high capital intensity and textile industry show up significant and positive.

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While the results are not completely robust they do suggest that the textile industry is less affected than all the others but we cannot directly explain why.