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8. A Appendix A Table A.1: Panel Attrition 2013-2017 (1) (2) (3) (4) (5) (6) 2013 2014 2015 2016 2017 #firms 206 32 47 52 47 #firms 380 347 292 252 216 attrition rate 0.09 0.23 0.34 0.43 Source: Survey on micro and small enterprises in Uganda, waves 2013-2017, own calculations. Numbers refer to the estimation sample.
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26. Column (2): Estimation controls for past RCTs conducted in April 2013 and April 2016. Column (3): Estimation is restricted to strictly positive investments in a given wave. Column (4): Dependent variable is dichotomous indicating whether someone made an investment in a specific wave or not. Standard errors in parentheses. Standard errors clustered at firm level. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
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47. Notes: The table provides an overview on how long firms stay in the sample, the number of firms in each year and on panel attrition The shaded areas refer to the waves in which a firm is observed in the sample. Based on all firms in the estimation sample in 2013, 206 firms stay 5 consecutive waves in the panel (Row 1), 32 for 4 waves (Row 2), 47 for 3 waves (Row 3), 52 for 2 waves (Row 4) and 47 for 1 wave only (Row 5). The row labelled with ’#firms’ gives the number of firms which are observed in the respective waves e.g. in 2013 there are 383 firms in the estimation sample.
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50. Source: Survey on micro and small enterprises in Uganda, waves 2013-2017, own calculations. Notes: Column (1) provides the correlation between all single constraints and investments, controlling for socio-economic characteristics of the business owner and characteristics of the firm. Based on the full model in Column (1), the following columns gradually remove the constraint with the highest p-value. Cells with grey background color indicate which specific variable has been removed in a specific column. Column (15) thus provides the final model with significant constraints. Standard errors in parentheses. Standard errors clustered at firm level. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
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51. Source: Survey on micro and small enterprises in Uganda, waves 2013-2017, own calculations. Notes: Columns (1)-(16) provide the correlation between each single constraint and investments. The estimation in Column (17) includes all constraints which have negative and significant effects on investments in the previous estimations where each constraint is included individually. In each Column it is controlled for the full set of socio-economic characteristics of the business owner and characteristics of the firm. Full estimation results are reported in Appendix Table A.4. Standard errors in parentheses. Standard errors clustered at firm level. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
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52. Source: Survey on micro and small enterprises in Uganda, waves 2013-2017, own calculations. Notes: The table provides a heterogeneity analysis by business characteristics. The estimates provided are for the sample divided by: industry (Columns (2) -(9)), whether the business owner is an own-account worker (Columns (10) -(11)), firm age (Columns (12) -(13)), and whether the business is registered (Columns (14) -(15)). Standard errors clustered at firm level. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
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53. Source: Survey on micro and small enterprises in Uganda, waves 2013-2017, own calculations. Notes: The table provides a heterogeneity analysis by socio-economic characteristics of the business owner. The estimates provided are for the sample divided by: marital status (Columns (2) -(3)), age (Columns (4) -(7)), cognitive ability (Columns (8) -(9), median split), whether the business owner plans an investment (Columns (10) -(11)), and by business experience (Columns (12) -(13), median split). Standard errors in parentheses. Standard errors clustered at firm level. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Table A.8: Heterogeneity Analysis: Business Characteristics (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) Dependent Variable: Investment Industry Own-account Firm age Registered All Services M-print M-text M-rem O
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54. Steinert, J. I., Zenker, J., Filipiak, U., Movsisyan, A., Cluver, L. D. and Shenderovich, Y. (2018). Do saving promotion interventions increase household savings, consumption, and investments in Sub-Saharan Africa? A systematic review and meta-analysis. World Development, 104, 238–256.

55. The last row gives the attrition rate. For example in 2014, 9 percent of firms observed in 2013 are not in the survey any more.
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56. The Republic of Uganda (2014). Ugandas employment challenge.
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57. Woodruff, C. (2018). Addressing constraints to small and growing businesses. Tech. rep., International Growth Centre.
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