rafmacalaba/datause-encoder-data huggingface dataset:
these characteristics will not change _b_ substantively. In addition, we estimate the impact of actually receiving and using the matching grant (the local average treatment effect, LATE) by instrumenting the receipt of treatment with assignment to treatment. Since none of the control group received a grant, the LATE is also the treatment-effect-on-the-treated (TOT).

Given the logistical and time constraints on fielding the follow-up survey, we were limited in the number and complexity of questions we could ask. In addition, as noted above, the heterogeneity of firms limits our power for examining impacts on financial outcomes. We did try asking about sales at the end of the survey, but had 51 percent item-non-response amongst the firms that did answer the survey. As a result, we largely examine binary outcomes that help address the issue of additionality: did the grants get firms to undertake more innovative activities than they otherwise would have done, or did the grants just subsidize firms to do what they were going to do anyway?

# **4.1 Impacts on Innovative Activities**

Table 4 examines the impact of the matching grant on the likelihood the firm undertook different innovative activities. Panel A presents the ITT impacts with only a control for city (the randomization strata) and Panel B the same impacts after controlling further for the range of observable baseline characteristics in Table 1. We see the two rows yield very similar results in terms of magnitudes and statistical significance, which is in line with the evidence presented above that the follow-up sample appears balanced on baseline characteristics. Panel C then presents the LATE estimates.

Column 1 shows that the matching grants resulted in a sizeable and statistically significant increase in product innovation: firms receiving the grants were 30.3 percentage points (p.p.) more likely to introduce a new product, which is more than a doubling of the 26.5 percent rate in the control group. Column 2 shows the impact on process innovation is positive, but not statistically significant. Columns 3, 4 and 5 show that in accordance with what firms reported wanting the grants for, they do more marketing (19.8 p.p. increase), introduce new accounting systems (48.5 p.p. increase), and do more worker training (33.2 p.p. increase) with the grants – in each case approximately doubling the control group rates. We saw at baseline that it was incredibly rare for firms to get quality certification, and Column 6 shows that this continues to be the case, with only 4 percent of the control group getting a new quality certification and the matching grants having no impact on this rate. Column 7 shows those with a matching grant are 27.4 p.p. more likely to 10
# **APPENDIX**

# **A Data**

## **A.1 Construction of a State Panel for Synthetic Difference-in-Difference**

States that implemented the _Pink Slip program_ could exhibit dissimilarities from other states, including neighboring ones. To mitigate this concern, we utilize a synthetic difference-in-differences approach (SDID) proposed by Arkhangelsky et al. (2021). The SDID method re-weights the control units to align their time trends with those of the treated units and subsequently applies a DID analysis to the re-weighted panel. That is, this method establishes a synthetic counterfactual state for causal estimation. Since our treatment occurs at the state level rather than the household (or individual) level, we aggregate our household-level (or individual) data to the state level and then match states and their pre-trends using a set of variables discussed below We use six variables for the household-level data. The variable “size group of household” is a categorical variable that includes groups such as one member, three members, eight to ten members, and more than 15 members. It is based on the number of members in a household. The variable “age group of household” is a categorical variable that includes groups such as households dominated by children, households dominated by grown-ups, and balanced households with seniors. The variable “occupation group of household” is a categorical variable that includes groups such as wage laborers, selfemployed professionals, entrepreneurs, and farmers. The occupation group of a household is based on the distribution of members of a household by the nature of their occupation. The CMIE classifies households into different groups based on certain rules and not just based on the occupation of the head of the household. The variable “education group of household” is a categorical variable that includes groups such as all graduates, graduates majority, and some literates. The education group of a household is based on the distribution of members of a household by their education level. The variable “gender” is a categorical variable that includes groups such as only males, only females, female majority, and balanced. The last variable we use is the average new confirmed daily cases as a share of the state population. For each categorical variable, we then generate corresponding dummy variables, and these dummy variables after aggregating to the state level can be interpreted as the share of households having a certain 32
8 to the change in mean consumption holding the Lorenz curve constant is the growth impact. The impact of inequality or redistribution results from a change in the Lorenz curve, holding mean consumption constant. There is typically a residual R in this decomposition, which is therefore written as: P(>t2, nt2)-P(gtl, **7ctl) =** [P(IiC, ntl)iP(4tl, irtl)] + [P(Rtl, 7Ct2)-P(11tl, ntl)] + R (8) Thus the first two terms in (8) are the growth and inequality components. Below, we will present results in a slightly different way by giving the values of, respectively, P(1 t+ 1, nt) and P(Oit, 7tt+1).

# _111.3 Results_

The national, rural, and urban point elasticities of poverty to growth and inequality obtained using Kakwani's formulae in (4) and (6) and the MPRS trade-off in (7) are given for the survey year 1995-96 in Table 3 (see the appendix for estimates of these elasticities at the regional level). Table 4 gives the results of the decomposition (8) at the national, urban, and rural levels. The key findings are as follows: Elasticity of poverty to growth In general, elasticities are lower with the upper than the lower poverty lines, but the impact of growth on poverty tends to be similar because the poverty measures are higher with the upper poverty lines. For example, at the national level, a growth in the mean welfare ratio of one percent would generate a reduction in the headcount index of respectively 2.14 and 1.47 percent with the lower and upper poverty lines. This would correspond to a drop in the share of the population below the poverty line of 0.76 point (2.14 percent of the headcount of 35.55) with the lower poverty line, and 0.78 point with the upper poverty line (1.47 percent of 53.08). Note also that the elasticities are larger in urban areas than in rural areas with the lower poverty lines, but they are similar with the upper poverty lines. At the area level, elasticities differ substantially by area
unable to do this given our data.

We next decompose the aggregate composition and structural effects into the contributions of the individual covariates. We find that monthly hours drives the composition effect in Tanzania and Rwanda. More specifically, while gaps in monthly hours worked explains a significant portion of the income gap between low earning men and women, it explains much less of the gap between higher earning men and women. Industry drives the composition effect in the Republic of Congo and savings drives the composition effect in Ghana. Consistent with the aggregate decomposition results, the savings composition effect remains fairly constant for Ghana while the portion of the gap explained by industry differences decreases in importance for the Republic of Congo as we move to the right in the income distribution.

We conclude by highlighting the policy implications of our findings. Specifically, we argue that our results highlight the importance of observable determinants of – – income namely, hours and industry beyond standard human capital and demographic factors such as age and education. We also point out that our results from several countries suggest a glass ceiling effect, wherein a large portion of the income gaps between high-earning men and women cannot be explained by observable characteristics. We argue that these descriptive results can be tested in future research that causally identifies determinants of earning gaps between men and women.

# **2 Data and Summary Statistics**

For the main part of the analysis, we use nationally representative household survey data for the following countries (survey year in parenthesis): the Republic of Congo (2009, urban only), Ghana (2005), Rwanda (2005), Tanzania (2009) and Uganda (2009).

Our main dependent variable is the logarithm of monthly self employment income for the non-agricultural self-employed individuals. We calculate self-employment income as wages to the owner plus profits. Profits are calculated as the difference between revenues and expenses with adjustments as recommended in the recent literature (De Mel, McKenzie, and Woodruff (2009).<sup>2</sup> Since the survey provides minimal information, profit figures are less rigorous only in the case of Tanzania.

Concerning the explanatory variables, on the one hand, our main interest is on the role of the individual traits identified by the literature in explaining firm performance (see for example Minniti (2005)). On the other hand, to ensure consistency in the interpretation of the results across countries, we selected and harmonized as many variables as possible.

The main explanatory variables include marital status, age, education, number of children, average monthly hours worked, savings, and industry. Education consists

> 2Unfortunately, the surveys do not directly ask for profits, which is the recommended measure by De Mel, McKenzie, and Woodruff (2009)

3
# Therapy, Mental Health, and Human Capital Accumulation Adolescents in among Uganda

Sarah Baird<sup>1</sup> , Berk Ozler<sup>2</sup> , Chiara Dell’Aira<sup>2</sup> , Luca Parisotto<sup>3</sup> , and Danish Us-Salam<sup>4</sup> 1 Department of Global Health, George Washington University, Washington, DC, corresponding author; sbaird@gwu.edu

> 2Development Research Group, The World Bank, Washington, DC

> 3Department of Economics, Bocconi University, Milano

> 4Behavioural Consumer Finance Unit, Central Bank of Ireland

**Keywords:** adolescence, mental health, group interpersonal psychotherapy, cash transfers, COVID19

**JEL Codes:** O10, C93, I15, J16, J13
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25
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# **Table 2**

# **Testing for differences in financing features across small, medium-sized, and large enterprises (cont.)**

This table reports average values for small, medium-sized, and large enterprises and t-tests for differences between the three groups. Panel A presents averages across all banks, Panel B divides the sample between banks operating in developed and developing countries and Panel C divides the banks by ownership type. SE stands for small enterprises. ME stands for medium-sized enterprises. LE refers to large enterprises. *,**,*** denote significance at 10, 5, and 1 percent, respectively.

||**P**|**anel C: Sep**|**arating b**|**a**|**nks by ow**|**nership type**|||
|---|---|---|---|---|---|---|---|---|
|Variable|Ownership|SE Mean|ME Mean||LE Mean|<br>t-test SE vs. ME|t-test SE vs. LE|t-test ME vs. LE|
|Share of total loans (%)|Private|8.37|8.06||28.42|-0.09|3.05***|3.09***|
||Government|13.12|17.18||41.23|0.53|3.98***|2.82**|
||Foreign|11.32|14.48||31.53|0.85|3.99***|3.62***|
|Share of applications approved (%)|Private|73.45|81.50||78.96|1.11|0.63|-0.32|
||Government|82.50|89.89||60.45|1.07|-0.71|-0.96|
||Foreign|77.36|72.85||85.75|-0.54|1.25|1.68|
|Share of loans for investment (%)|Private|40.43|46.15||50.35|0.71|1.14|0.49|
||Government|56.36|45.07||53.50|-1.07|-0.30|1.03|
||Foreign|44.88|49.53||51.72|0.68|0.87|0.30|
|Share of secured loans (%)|Private|69.54|69.60||62.10|0.01|-0.82|-0.83|
||Government|77.84|87.96||85.07|1.20|0.78|-0.39|
||Foreign|85.91|85.07||72.95|-0.14|-1.79*|-1.75*|
|Loan fees (% of size of loan)|Private|1.32|1.07||0.83|-0.62|-1.18|-0.62|
||Government|0.71|0.81||0.78|0.36|0.28|-0.08|
||Foreign|1.13|0.94||0.69|-0.94|-2.30**|-1.40|
|Interest rates on best clients (%)|Private|11.81|10.06||10.01|-0.91|-0.92|-0.03|
||Government|10.31|10.64||10.05|0.19|-0.15|-0.33|
||Foreign|10.96|9.83||8.86|-0.71|-1.22|-0.58|
|Interest rates on worst clients (%)|Private|17.12|15.15||15.22|-0.90|-0.86|0.03|
||Government|13.28|13.38||13.19|0.05|-0.05|-0.10|
||Foreign|15.48|13.64||11.82|-0.85|-1.65|-0.84|
|Share of non-performing loans|Private|7.47|5.99||4.83|-0.54|-0.84|-0.50|
|(% of total loans)|Government|7.76|4.39||1.45|-1.15|-2.35**|-2.03*|
||Foreign|7.08|6.16||3.72|-0.41|-1.69|-1.53|

21
containment remains a challenge for Stanbic. This should be expected for an institution merging the operations of two large banks, while at the same time shifting its lending strategy. Moreover, the costs associated with rebranding are one-time, and thus we would expect Stanbic’s operating cost ratio to decline in the future. Third, profitability and credit growth are now on par with that of the typical Ugandan bank, a marked improvement over UCB’s performance prior to the acquisition. This suggests that the additional revenue streams associated with increased lending have exceeded the additional costs, at least so far. In the branch-level analyses in the next section, we examine what these changes have meant across locales (especially rural vs. urban) and economic strata.

# **_IV. Branch-Level Analysis_**

As summarized in the Appendix (Figures 8 and 9), from 2000 to 2005 the total number of bank branches in Uganda increased by 60 percent (from 123 to 197). However, 59 of the 74 additional branches are located in Kampala, which suggests that the benefits of increased competition were distributed unequally across geographic areas. That said, some of the districts with new branches were starting from a low base in 2000. In percentage terms, these new branches represented a substantial increase in potential competition. In a handful of other districts, however, the total number of branches actually declined. In the empirical exercises that follow, we exploit this variation to see whether the performance of Stanbic branches is in part explained by competitive pressures.

The variation in outcomes across Stanbic branches is substantial (Table 6). Although we only have branch-level data for three years (2002-2004), return on assets ranges from –20 to 52 percent. One fifth of the branch observations have ROA less than or equal to zero. Below, we test whether the least profitable branches are disproportionately located in areas with low per capita income and population density. The ratio of loans to deposits, which is an indicator of the extent to which local funds are re-lent within the community, also varies substantially.<sup>25</sup> The figure for the median

> 25 We acknowledge at the outset of this section that we don’t know for certain whether loans (deposits) attributed to a particular branch go to (come from) the district where the branch is located. Our assumption is that transaction costs make it highly likely that they are, but that is only an assumption.

22
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