The initial capital stock of the enterprise is strongly positively associated with the probability of investment, indicating that larger firms are more likely to invest. Similarly, the age of the firm is positively associated with the probability of investing (the effect is statistically significant from zero at the 5% level in the parsimonious specification and at the 10% level in the extended one). This could be because, as time goes, upgrading the capital stock becomes more important. Alternatively, it could be that young firms are faced with higher uncertainty regarding the prospects of the enterprise, which may lead to caution on in the investment side. Local demand, proxied by the WRSI variable, plays a role, though it appears the effect possibly operates with a one-year lag: good agricultural performance in 2005 is positively associated with investment in 2006. Further, there is strong evidence that enterprises linked to households that can raise 100 Birr in a week to deal with an emergency, invest more. Thus enterprises linked to poor households are not likely to invest. There is no evidence that investment behavior differs across managers with different characteristics, or across firms in different types of locations. There is also no evidence investment depends on the level of competition faced by the enterprise, or the measured quality of the local investment climate.

Of course, the investment climate variables are fairly crude measures of the true concept, and we would not argue that investment is unresponsive to the quality of the investment climate more generally. Household shocks are not generally associated with investment spending, though there is weak evidence that households who experienced the death of a household member invest more, while those who experience a disease shock invest less. The coefficients on the credit variables are either insignificant or, in one case, significant with the ‘wrong’ sign, except for the variable that measures the availability of emergency credit discussed above.

Now consider the regressions modeling the growth of the labor force and labor demand. The data at our disposal are of course cross-sectional, but as noted above contain some recall questions on the size of the enterprise at start-up, measured in terms of usage of labor, thus enabling us to construct growth rates. Of course, recall data have their problems and the results presented below should therefore be interpreted with caution. OLS results are shown in columns (3), and (4) of Table 17. For reasons already discussed, we consider labor days used, rather than number of workers, as our measure of labor input. The explanatory variables are the same as in the investment regressions.

The first finding here is that average annual growth of labor is negatively related to labor usage at start-up, hence initially small firms tend to record higher subsequent growth rates than initially large ones. This coefficient on initial log labor days is -0.28 in the parsimonious specification (and only marginally different in the extended one), which translates into an AR (1) coefficient equal to 0.72 for log labor, thus indicating moderate but not substantial persistence in the variable. Of course, measurement errors in the labor variable would yield spurious reversion towards the mean, and so the true degree of persistence is probably a bit higher (in other words, the difference in growth rates between small and large enterprises is probably exaggerated). Moreover, firms that start with a lot of capital tend to grow faster than firms that do not. There is weak evidence that crop shocks and

-17-