General results for investment

Table II.2 contains the results for our base regressions. The first column shows the effect of conflict on investment without any control variables, although the fixed effects will already capture the effect of time invariant firm characteristics. The advantage of this approach is that we can use the maximum number of observations since we do not face the problem of additional missing values in our control variables.

Figure II.3: Scatterplot Battles vs. Investment

This means we can use data on 631 firms with a total of nearly 2600 firm-year observations.

In this specification the battle-count within 50km distance is highly significant and has a coefficient of -0.01. This suggests that one additional incidence of battle within 50 km of the

01234Investment rate

0 2 4 6 8 10

Battles50km

Firm observations Regression line

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town in which the firm is located will decrease the average investment rate by 1 percentage point. This is quite a sizable effect, considering that the mean investment is only 11 percent.

When we introduce control variables in column two, the coefficient for our conflict measure decreases slightly (by 0.003), but remains significant at the one percent level. The only control variable coming out significant - using standard significance thresholds - is the profit rate, showing that owners invest more in profitable firms. Due to missing values in the control variables we only observe data from 540 firms in this specification.

Table II.2: Regression results firm investment, basic models

Dep. Variable Investment Rate, Fixed Effects Regression Basic specifications

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

Since it could be argued that it is not so much current profit that makes owners invest, but rather profit in the last period, we use the first lag of the profit rate in the specification in column 3. By adding another lag we reduce our analysis sample to only about 380 firms. The conflict effect remains stable and significant at nearly the 5 percent level and the coefficient for the lagged profit is highly significant as well. Since using it does not seem to have any

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further advantage, but strongly reduces observations, we stick to the current profit as the control variable for all other specifications.

The specification in column 4 is equivalent to the specification in column 2 but it adds year dummies to the regression. This allows us to control for time fixed effects such as macro-economic developments that affects all firms in a given year (e.g., macro-economic growth). Our results still hold although the significance of our conflict indicator is slightly reduced. The reason for this is probably a systematic correlation between the battle count and the year dummies since some years were more conflict intensive than others.

In Table B.1 we control for the robustness of the conflict effect to the use of different distance measures (up to which distance battle events are counted). We use a count of battles within 30km in the first two columns and within 100km in the last two columns.

Using the 30km measure we see a slightly higher coefficient in the model with control variables (column two) compared to the main specification. With the 100km specification the coefficients are lower compared to the earlier regressions. All effects are statistically significant. This table could be interpreted as the conflict effect being stronger, the closer the conflict is to the firm’s location.

An important aspect of the investment reduction is whether the effect is lasting and whether the investment is completely cancelled or merely postponed. In order to get more information about this, we use the lagged conflict experience instead of the current one. The results for this can be found in Table B.2. The first column shows the original model as a baseline. In the second column we use the first lag of conflict because of which we lose some observations. The coefficient is negative, low and statistically insignificant. Adding control variables it turns significant, but further checks show that this is probably not due to the control variables, but rather to the different sample. The different sample is generated by the missing values in the control variables and from this we conclude that there is no robust relationship.²⁹

29 To check this we perform a regression without control variables on the same reduced sample and find the same negative significant effect. Including the current conflict alongside the lags (not reported) also turns the negative effect in column 3 insignificant but has no big effect on the other regressions.

There are no significant results for the second lag either as can be seen in columns 3 and 4. The lack of any significant effects implies two things. First, it appears that there is no long-term effect of conflict on investment. It really seems to be the current

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insecurity keeping people from investing during the same period. Second, if the investment was delayed by one or two periods, we might find a positive effect of the lagged conflict on investment. We do not find any evidence for such a delay in investment. This is however not conclusive, as most likely different firms would delay their investment for different time periods and then it would not show up clearly in the data.