rafmacalaba/datause-encoder-data huggingface dataset:
provides a comparison of all eligible children and column 6 provides a comparison of just those children who registered for the lottery. In all cases, these children are very similar, making it reasonable to perform comparisons across localities.

# **B. Comparison at Baseline**

Given that the students who registered for the lottery are representative of all eligible children in the communities, we turn to checking whether or not the randomization succeeded in creating comparable treatment and control groups. This initial comparability is essential for us to be able to attribute future differences between the research groups to the respective treatments.

The comparisons for students who provided a baseline survey are presented in Table 3.<sup>11</sup> As in Table 2, each row displays the comparisons for the indicated demographic variable. Columns 1-4 compare students in San Cristobal and columns 5-8 compare students in Suba. In both localities, the differences are negligible. For San Cristobal, columns 2-4 display the simple differences (using Equation 1) between the basic treatment and the Control Group, the differences between the savings treatment and the Control Group, and finally, the difference between the two treatments, respectively. Almost all of the differences are statistically insignificant and those that are statistically significant (such as the fact that the basic treatment has 3 percent more girls than the savings treatment) are economically small.

The same is true for Suba. Columns 5 and 7 respectively show the average control group characteristics for the younger (grades 6-8) and older (grades 9-11) children, respectively. The younger children received the basic treatment, and those selected for the basic treatment are very similar to those in the control group (column 6). Similarly, the older children who received the tertiary treatment are similar to the older students who constitute the control group (column 8).

To check for differences in the distribution of children rather than just the mean, we also plotted the distributions. An example is shown in Figure 3. The figure contains a plot of the distribution of household income in the treatment and control groups – as shown in the plot, the distributions are very similar.

> 11 In un-presented results, we also make the same comparison for all students in the sample and for all students claiming to be registered at the 68 schools that were selected for the surveying and attendance collection.  The results for these samples are the same as for the data set of only the students providing a baseline survey.

- 18 -
In the first step, the regression equations we estimate are of the form: Firm Innovation = α + β 1 Log (GDP/Capita) + β 2 Firm Size Dummies + β 3 Age + β 4 Corporation Dummy + β 5 Number of Establishments + β 6 Capacity Utilization Dummies + β 7 Industry Dummies + β 8 Country Dummies + ε (1) All regressions are estimated using firm level data across 47 countries.<sup>14</sup> The dependent variable is either one of the three aggregate indicators (INDEX8, CORE, or INDEX2), or one of the ten underlying indicators of innovation based on firms’ responses to survey questions. Since the ten individual indicators of firm innovation are 0-1 variables, these regressions are estimated using a Logit probability model. For the aggregate indicators, we use Ordered Logit. All regressions report robust standard errors.

We use equation (1) as the baseline and build on it to examine the impact of governance (i.e. competition, ownership, management) and access to external finance. In the second step the regression equations we estimate are of the form: Firm Innovation = α + β 1 Log (GDP/Capita) + β 2 Firm Size Dummies + β 3 Age + β 4 Corporation Dummy + β 5 Number of Establishments + β 6 Capacity Utilization Dummies + β 7 Industry Dummies + β 8 Country Dummies + β 9 X + ε (2) where X is a variable or a vector of variables characterizing different aspects of the firm’s financing, governance, and competition environment.

# **5. Results**

Table 3 reports the estimated coefficients of baseline regression (1). The table shows that individual firm characteristics are an important determinant of the extent of innovation a firm undertakes. Probability of undertaking all types of innovative activities increases with firm size. Compared to small firms, medium and large firms are more likely to develop new product lines, upgrade existing product lines, introduce new technology, discontinue at least one product, open a new plant, close at least one existing plant, sign a new joint venture with a foreign partner, sign new licensing agreements, outsource a major activity and bring in-house a previously outsourced activity. The F-test

> 14 We prefer to include country dummies in the regression specification rather than clustering since clustering would assume the country effect to be a part of the composite error term and thus uncorrelated with the other regressors. However, we find that the amount of external financing varies significantly across countries necessitating the use of country dummies.

13
# _2.3 Other explanatory variables_

We control for several firm-level and country-level variables. The degree of crime a firm faces may depend on its size, the sector it belongs to, and its locale (Amin, 2009; Glaeser and Sacerdote, 1999). Big cities tend to provide higher pecuniary benefits for criminals and also criminal activity may involve lower costs due to a high probability of anonymity and low likelihood of arrest. We control for firm size using dummies for small and medium firms. A small firm is defined as a firm with less than 20 employees, while a medium firm has workers between 20 and 99. We also capture the economic sector of the firm by using a dummy variable for manufacturing firms. A priori it is not clear whether a manufacturing firm should have higher crime losses with respect to other sectors. We also include a city size dummy variable which takes the value of 1 if the city has a population of 250,000 of greater, or is a capital city, and 0 otherwise. There is also evidence that female-owned households as well as female-owned firms have a positive association with higher crime rates (Glaeser and Sacerdote, 1999; Islam, 2013; Kelly 2000). We thus include a dummy variable for whether a firm has at least one female owner. All these variables are from the Enterprise Surveys data set.

At the country level, we control for real GDP per capita growth, real GNI per capita, and the Gini index, given their prominence in the literature (Rogers 1989; Fajnzylber et al., 2002; Soares, 2004; Demombynes and Ozler, 2005; Dahlberg and Gustavsson, 2008). Increasing inequality typically increases the rewards of engaging in criminal activity due to a presence of well off potential victims and also increases the proportion of poor who may engage in criminal activity. For cases where the GINI index or GNI per capita information is missing for the year corresponding to the enterprise surveys data, we use the estimate of the most recent year available. The exact year of 10
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_Table 3. Logit regressions of unfairness perceptions (all unfair) and individual characteristics_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Gini coefficient<br>Age|0.456***<br>(0.059)|0.454***<br>(0.059)<br>0.004***<br>(0.000)|0.443***<br>(0.059)<br>0.004***<br>(0.000)|0.441***<br>(0.059)<br>0.004***<br>(0.000)|0.436***<br>(0.060)<br>0.004***<br>(0.000)|0.284***<br>(0.054)<br>0.005***<br>(0.000)|
|Age squared||-0.000***<br>(0.000)|-0.000***<br>(0.000)|-0.000***<br>(0.000)|-0.000***<br>(0.000)|-0.000***<br>(0.000)|
|Male dummy||<br>-0.013***|<br>-0.013***|<br>-0.012***|<br>-0.012***|<br>-0.012***|
|||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Married or civil union|||-0.001|-0.001|0.000|-0.000|
||||(0.002)|(0.002)|(0.002)|(0.002)|
|Literacy|||<br>-0.013***|<br>-0.013***|<br>-0.011**|<br>-0.014***|
||||(0.005)|(0.005)|(0.005)|(0.005)|
|Complete Primary|||0.009***|0.009***|0.010***|0.012***|
||||(0.003)|(0.003)|(0.003)|(0.003)|
|Complete Secondary|||<br>0.016***|<br>0.016***|<br>0.020***|<br>0.023***|
||||(0.004)|(0.004)|(0.004)|(0.004)|
|Complete Tertiary|||0.024***<br>(0.005)|0.025***<br>(0.005)|0.030***<br>(0.005)|0.034***<br>(0.005)|
|Economically active dummy||||-0.003|-0.003|-0.004|
|||||(0.003)|(0.003)|(0.003)|
|Unemployed dummy||||0.018***|0.019***|0.018***|
|||||(0.005)|(0.005)|(0.004)|
|Sewage|||||0.003|0.003|
||||||(0.003)|(0.003)|
|Computer|||||-0.000<br>(0.003)|0.001<br>(0.003)|
|Washing machine|||||<br>-0.017***|<br>-0.018***|
||||||(0.003)|(0.003)|
|Landline|||||<br>0.002<br>(0.003)|<br>0.000<br>(0.003)|
|Has access to a car|||||<br>-0.007***<br>(0.003)|<br>-0.007**<br>(0.003)|
|Observations|150,081|150,081|149,056|149,053|144,977|145,104|
|% Unfair|79.56|79.56|79.57|79.57|79.58|79.60|
|Pseudo R-squared|0.0674|0.0692|0.0695|0.0696|0.0702|0.070|

Note: This table presents estimates of the correlation between unfairness perceptions dummy variable that indicates if the individual believes income distribution is unfair or very unfair and individuals’ characteristics. Columns 1 to 5 coefficients presents the marginal effects at the mean values of the rest of the variables and were estimated through Logit regressions, weighting by individuals probability of being interviewed, while column 6 coefficients were estimated through Ordinary Least Squares regressions. All columns include country, subnational and year fixed effects. ***, ** and * denote significance at 10%, 5% and 1% levels, respectively. Heteroskedasticity-robust standard errors in parentheses.

20
average. We construct the upstreamness measure for 40 industries and 6 firm groups.<sup>16</sup> The second extension is that we relax the proportionality assumptions they make about the allocation of imports and exports in each industry pair. Specifically, our estimated IO coefficients already have imports taken out by explicitly including A<sup>)</sup> in our model. When dealing with exports from sector _i_ to sector _j_ by firm type, we use data on exported intermediate inputs from China’s customs and assign the bi-sectoral exports to different firm types based on their shares in each IO link in the domestic economy. See the appendix for details. Table A3 in the appendix report the 240 upstreamness measures, along with the industry upstreamness estimated based on the conventional IO table (without any split).

Table 7 reports the top 5 and bottom 5 industry upstreamess measures based on the conventional IO table. By construction, the upstreamness measure ranges between 1 and the maximum number of the industries in the country’s IO table. The top 5 most “upstream” industries (out of 40) are “Extraction of Petroleum and Natural Gas”, “Mining of Ferrous Metal Ores”, “Mining and Washing of Coal”, “Production and supply of Electricity and heat”, “Processing of Petroleum, Coking and Nuclear Fuel”. The values of upstreamness for these industries range between 4 and 5, meaning that these industries are on average 4-5 industries away before reaching final consumers. These raw material and energy industries sell intermediate inputs to many other industries, including other upstream industries. They are expected to rank high up in the domestic production network. The bottom 5 “upstream” industries are “Real Estate”, “Health and Social service”, “Education”, “Construction industry”, “Public administration and social organization”. They tend to sell final goods and services directly to customers.

# (Insert Table 7 here)

By using the split IO table, we can estimate the upstreamness measures for different firm groups. Consistent with the high indirect export ratio, SOEs, particularly the small ones, tend to have the highest upstreamness measure among all firms types within each industry, while SMEs tend to have the lowest upstreamness, particularly in the least upstream industries, among all firm types. Fig. 4 plots the SOEs’, FIEs’, LPs’ and SMEs’ upstreamness measures against the industry overall measures, which are estimated using the original aggregate IO table. Most measures for the SOEs (blue squares) are above the 45-degree line, suggesting that SOEs are often more upstream than other firm types within the same industry. SMEs, on the other hand, are often the most “downstream” within industries.

Another way to show that SOEs have a dominant position in the upstream industries is to examine the correlation between the share of SOEs in different aggregate outcomes and industry upstreamness. Fig. 5

> 16 The original IO table has 42 industries, but we dropped

28
_Role of NGOs in Heath Delivey Afra Health Policy Study_ _-22_ PHAM Is overwhelmingly staffed by expatriates, especially among senior professional staff, mainly due to the lack of qualified Malawian doctors. The government pays all Malawian staff of NGOs at the same salary scale as the government and has now begun to accept secondment of government staff to NGO hospitals. Because the salaries are low in Malawi, the PHAM tries to supplement low salaries with other benefits but btil faces & severe staff shortage. One problem appears to be t'e lack of career structure for Malawian staff.

PHAM has been operatng successfully for over 20 years, and has maintained avery constructive relationship with the Malawi Ministry of Health. The problems it has encountered are in many ways shared by government, such as low salaries and staff shortages.
Figure 4: Formal plant size distribution in the benchmark economy <!-- Start of picture text --> (a) Fraction of Plants (b) Employment Shares<br>data model data model<br><= 10 11-20 21-50 51-100 101+ <= 10 11-20 21-50 51-100 101+<br>1<br>.5<br>.8<br>.4<br>.6<br>.3<br>.4 .2<br>.2 .1<br>0 0<br><!-- End of picture text --> Source: INEGI Notes: This figure compares the formal plant size distribution in the benchmark economy with the data for different size categories.

cross-section of plants shows similarities with the data. In the model, the growth rates of formal plants in the cross-section are higher than their average growth rates, while the growth rates of informal plants in the cross-section are lower than their average growth rates, as in the data. Informal plants in the cross-section grow by 61.3% over their life cycle (from the 0-4 to the 35-39 age group) in the model, compared to a 583.5% growth rate for formal plants in the cross-section Finally, we discuss informal plants transitioning into formality over their life cycle before presenting the quantitative exercises. In the benchmark economy, no informal plants transition into formality during the last or the last two periods of their life cycle. Only 1.9% of plants begin their life as informal and switch to formality during their life cycle. Therefore, the occupation choices of agents are characterized by eight threshold ability levels in the benchmark economy.

# **5. Quantitative Findings**

The development literature mainly focuses on the role of reducing the burden of being formal and increasing enforcement on the prevalence of informality.<sup>21</sup> In this section, we first present the equilibrium effects of changes in the tax rate, _τ_ , and changes in 21Please see Kuehn (2014), Orsi et al. (2014), Leal (2014), Ulyssea (2018), Johnson et al. (1998), Fajnzylber et al. (2011), De Mel et al. (2013), Bruhn and McKenzie (2014), De Andrade et al. (2016) and Rocha et al. (2018) among many others.

21
effectively push some small entrepreneurs into the informal sector. Second, the costs resulting from corruption can be lowered by reducing the number of permits and licenses, and by limiting the discretionary power of bureaucrats. This can be done by specifying clear rules and transparent criteria which govern granting a permit or a license (Djankov et al., 2001).

# **Employment regulations**

Employment protection legislation is very stringent in Moldova. It is stringent even compared to other post-socialist countries, which all inherited labor laws granting workers generous rights. It is also more strict than in most EU countries, including those – such as Germany and France – where labor markets are heavily regulated (Table 6). Specifically, flexible forms of employment (e.g. fixed-term contracts) are restricted, working conditions are heavily regulated, and dismissals are difficult (e.g. the law requires trade union approval of dismissals).

**Table 6  Employment regulations indicators in selected transition and EU economies, 2002**

|Country|Alternative<br>Employment<br>Contracts<br>|Conditions of<br>Employment|Job<br>Security|Employment<br>Laws Index|
|---|---|---|---|---|
|**Transition economies**|||||
|Bulgaria|0.55|0.87|0.31|1.73|
|Czech Republic|0.33|0.63|0.35|1.31|
|Hungary|0.62|0.90|0.22|1.73|
|Latvia|0.56|0.81|0.41|1.77|
|Lithuania|0.62|0.86|0.34|1.82|
|**Moldova**|0.78|0.73|0.56|2.07|
|Poland|0.56|0.91|0.46|1.92|
|Romania|0.65|0.82|0.30|1.77|
|**EU economies**|||||
|_Low unemployment_|||||
|Denmark|0.56|0.22|0.12|0.90|
|Ireland|0.56|0.43|0.12|1.11|
|United Kingdom|0.56|0.31|0.08|0.94|
|_High unemployment_|||||
|France|0.74|0.54|0.31|1.59|
|Germany|0.72|0.40|0.50|1.62|
|Spain|0.83|0.86|0.50|2.19|

Note: higher values of the index indicate more restrictive regulations.

Source: The World Bank Doing Business database (http://rru.worldbank.org/DoingBusiness) 36
|P000806|1982|1991|10 Closed|Gambia,The|
|---|---|---|---|---|
|P000807|1984|1985|0 Closed|Gambia,The|
|P000807|1984|1986|0 Closed|Gambia,The|
|P000807|1984|1987|0 Closed|Gambia,The|
|P000807|1984|1988|0 Closed|Gambia,The|
|P000807|1984|1989|8 Closed|Gambia,The|
|P000807|1984|1990|10 Closed|Gambia,The|
|P000807|1984|1991|14 Closed|Gambia,The|
|P000807|1984|1992|16 Closed|Gambia,The|
|P000807|1984|1993|17 Closed|Gambia,The|
|P000808|1984|1985|0 Closed|Gambia,The|
|P000808|1984|1986|0 Closed|Gambia,The|
|P000808|1984|1987|0 Closed|Gambia,The|
|P000808|1984|1988|0 Closed|Gambia,The|
|P000808|1984|1989|5 Closed|Gambia,The|
|P000808|1984|1990|7 Closed|Gambia,The|
|P000808|1984|1991|8 Closed|Gambia,The|
|P000808|1984|1992|10 Closed|Gambia,The|
|P000808|1984|1993|12 Closed|Gambia,The|
|P000809|1986|1986|0 Closed|Gambia,The|
|P000809|1986|1987|0 Closed|Gambia,The|
|P000809|1986|1988|0 Closed|Gambia,The|
|P000809|1986|1989|8 Closed|Gambia,The|
|P000809|1986|1990|11 Closed|Gambia,The|
|P000809|1986|1991|13 Closed|Gambia,The|
|P000809|1986|1992|14 Closed|Gambia,The|
|P000810|1987|1989|3 Closed|Gambia,The|
|P000810|1987|1990|6 Closed|Gambia,The|
|P000810|1987|1991|7 Closed|Gambia,The|
|P000810|1987|1992|9 Closed|Gambia,The|
|P000810|1987|1993|11 Closed|Gambia,The|
|P000810|1987|1994|14 Closed|Gambia,The|
|P000810|1987|1995|16 Closed|Gambia,The|
|P000810|1987|1996|19 Closed|Gambia,The|
|P000812|1987|1987|0 Closed|Gambia,The|
|P000812|1987|1988|0 Closed|Gambia,The|
|P000812|1987|1989|6 Closed|Gambia,The|
|P000812|1987|1990|8 Closed|Gambia,The|
|P000812|1987|1991|11 Closed|Gambia,The|
|P000812|1987|1992|14 Closed|Gambia,The|
|P000812|1987|1993|17 Closed|Gambia,The|
|P000812|1987|1994|21 Closed|Gambia,The|
|P000812|1987|1995|22 Closed|Gambia,The|
|P000813|1987|1987|0 Closed|Gambia,The|
|P000813|1987|1988|0 Closed|Gambia,The|
|P000817|1989|1989|1 Closed|Gambia,The|
|P000817|1989|1990|2 Closed|Gambia,The|
We also provide several tests of robustness to rule out additional threats to our identification strategy, such as the role of selective migration, spatial spillovers, contemporaneous expansion in school infrastructure, and the definition of catchment areas of coffee mills.

Three main findings emerge from the paper. First, early life exposure to the coffee mills is associated with improvements in student performance in the medium term. Specifically, the aggregate test scores of students born in the catchment area of the coffee mills after the establishment of the mills are 0.09 standard deviation (4% relative to the mean) higher than comparable students without exposure to the coffee mills in early life. The effects persist across the various subject groups: STEM (math and science), and Non-STEM (social studies, English, and Kinyarwanda). Second, the positive effects of early life exposure to coffee mills pertain largely to students in rural communities. This provides suggestive evidence of the importance of income shocks in improving human capital accumulation in low-income environments where liquidity constraints are strictly binding. Third, we find suggestive evidence of improvements in household welfare (income), child health, and school attendance as plausible channels.

The effects are robust to several robustness checks, including measurement of early exposure, selective migration, spatial spillovers in exposure, parallel trends assumption, expansion in school infrastructure, and staggered treatment with heterogeneous treatment effects. For instance, evidence from an event study analysis using the Borusyak et al. (2024) estimator, provides support to the so-called "parallel trends" assumption, by showing similar trends in the evolution of test scores of students in treated and control villages before the establishment of coffee mills. However, following the arrival of the mills, the performance of students born in the treated villages begins to diverge from their counterparts in control villages.

This paper is connected to three strands of the literature. First, it builds on the literature that studies long-and medium-term effects of early life conditions on human capital in developing countries (Duflo, 2001; Edmonds et al., 2010; Adhvaryu et al., 2019; Carrillo, 2020; Adhvaryu et al., 2023). Adhvaryu et al. (2019) for instance show that early life exposure to income shocks via commodity price boom is associated with a lower likelihood of experiencing severe mental distress in adulthood. Similarly, Adhvaryu et al. (2023) find that adverse rainfall shocks at the time of birth are associated with lower educational attainment in the long run. However, the authors show that access to social protection programs such as cash transfers plays a key role in minimizing these adverse impacts: the effects on children from beneficiary households of the cash transfer program, PROGRESA, were much lower than those on children from non-beneficiary households. Results from our paper resonate with the prior results by demonstrating the importance of early life shocks on the cognitive development of children in the medium-to-long term.

3