rafmacalaba/datause-encoder-data huggingface dataset:
For the majority of beneficiaries, at or shortly after six months, the wage paid by employers exceeds the sum of the salary and incentive they accepted at the start of the job. By this time, the incentive may thus not be necessary to keep people in their job.

# **3.3 Data sources**

For our main employment outcomes, we use monthly social security data (IMSS). In Mexico, employers are required to register contributions on behalf of formally employed workers. IMSS data are reported monthly and include information on the type of contract the worker holds (temporary or permanent) and the daily salary reported by the employer. The administrative data are comprehensive of private sector employees, but exclude public sector employees since their social security contributions are managed through a different agency. Given that the public sector is small in San Luis Potosi (the study region) and only 2% of the study sample report holding a public sector job in our endline survey, this omission is unlikely to affect our estimated program impacts. We consider the use of administrative data as an important contribution of this paper. Most existing studies from developing countries in this literature rely on self-reported employment data, which are prone to surveyor demand effects and recall biases (McKenzie, 2017). High-frequency administrative data also allow us to accurately estimate cumulative treatment effects on work experience and income required to conduct a cost-benefit analysis.

We complement administrative data with two rounds of in-person data collection: a baseline survey prior to the workshop and a follow-up survey completed in June 2021, two years after the treatment. Both surveys are administered through a professional survey firm.<sup>20</sup> For the endline survey, we successfully surveyed 75% of participants. The overall attrition rate is slightly lower for treatment participants: 23.2% for the treatment and 27.2% for the control group. In addition, we administered an SMS survey in October and November 2021 to collect data on people’s time preferences and labor market beliefs.

> 20All the instruments are written in Spanish; they have been translated to English for IRB review purposes and fidelity of translation can be attested by the principal researchers who are bilingual. For the endline survey, we successfully surveyed 79.8% at home, 17.2% via phone and an additional 2.9% via email that we were not able to reach in-person due to COVID-19 concerns.

18
Panel B and Panel C). This lack of improvement is not surprising: we have repeatedly shown that the government continues to play a very important role in interacting with firms.

Larger firms face lighter government expropriation on average, and so do politically connected firms (i.e., Party membership for firm owners)—the sign tends to be consistent, though the coefficient is often insignificant (see Table 3). Interestingly, female-headed firms experience higher fee/sales, and the coefficients on other expropriation outcomes are also positive though insignificant. This is consistent with an emerging literature from firm-level and cross-country evidence that women are less involved in and more ill-adapted to corruption (Dollar et al. 2001; Swamy et al. 2001; Hanousek et al. 2017).

# **Evolution of entrepreneurs and firm characteristics**

Here we rely on the nation-wide private enterprise survey data on small- and medium-sized firms, and the CSMAR data on listed (and thus large) firms.

_Evolution of entrepreneur characteristics_ **.** On average, Chinese private entrepreneurs are 43 year old. The age profile remains stable over time. The percentage of female entrepreneurs has risen steadily from 11% in 1991 to 17% in 2012, indicating declining disadvantage for women to run businesses. Most significantly, the education of private entrepreneurs has improved significantly, from fewer than 9 years in 1991 to around 15 years in 2012. According to panel A of Table 4, only 5.2% of private entrepreneurs finished college in 1991, whereas 65% of them did so in 2012. The education levels of private entrepreneurs in larger firms are higher and have improved faster than those in smaller firms (see panel B of table 4). On average, the number of years of schooling is higher by 1.5 years among the top-quartile-asset firms than among those in the bottom-quartile-asset firms. The fast improvement in the education level of entrepreneurs indicates that Chinese talent allocation is increasingly shifted to the productive private sector, mostly from the government and the state sector. Since talent allocation between the government and the private sectors is crucial for long-term development (Baumol 1990, Murphy, Shleifer and Vishny1991), this trend bodes well for China’s future.

The social background of private entrepreneurs in China has changed substantially over the decades (Table 6). In the early 1990s, the largest proportion of private entrepreneurs came from the state sector (i.e., the government, state-owned enterprises, or the army). By 2012, 19
**Table 6 Government spending and commodity prices**

||**Depe**<br>|**ndent variab**<br>|**le: Real govern**<br>|**ment spendin**<br>|**g**<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||EMDEs||||Full sampl|e|
|Commodity export<br>price index|0.061*|0.08*|0.061*|-0.123***|-0.072|-0.121***|
|Terms of trade||-0.060|||-0.072||
|GDP growth (t-1)|||0.688***|||0.625***|
|Commodity export<br>price index x EMDE||||0.184***|0.158***|0.183***|
|_F_-test||||***|**|***|
|Observations|276|269|276|415|341|413|
|Countries|11|11|11|15|15|15|

_Source_ : World Bank.

_Note:_ Panel least squares with country fixed effects. All variables are in log-differences. The estimates in columns (1)-(3) are based on the sample of emerging market and developing economies (EMDEs) only while those in columns (4)-(6) are based on the full sample that includes advanced-economy commodity exporters. _F_ -test in columns (4)-(6) evaluates the joint significance of commodity export price index and the interaction of the index with the dummy variables for EMDEs. *, **, and *** indicate statistical significance at 0.10, 0.05, and 0.01 percent, respectively. GDP = gross domestic product.

50
after livestock, time in self-employed work, household industry (such as embroidery), or working as a salaried worker. Children’s needs are described as bathing or feeding.<sup>13</sup> Looking at the unadjusted difference-in-differences estimate of the effect of the program, I find that there are differences in the time use patterns of eligible households from the treatment district relative to those in the comparison districts. Specifically, comparing the time use data from round 1 (the baseline) with the data from round 3 (one year after the program began), mothers from stipendeligible households in the treatment district report spending roughly 82 additional minutes per typical school day on housework compared to mothers from eligible households in the comparison districts. Similarly they report spending 30 fewer minutes on paid work, 78 fewer minutes on children’s needs and report getting slightly approximately 20 fewer minutes of sleep relative to those in the comparison districts.

When I compare the baseline data to that from round 4, I find that the unadjusted effect of the program on time spent on housework, paid work and children’s needs is qualitatively and quantitatively unchanged and that the direction of the effect stays the same –housework goes up and paid work and time spent on children’s needs goes down. The effect on sleep stays practically small and is now positive.<sup>14</sup>

# **b. Methodology**

In order to see if these trends are robust to the inclusion of controls, I estimate the following empirical specification:

> 13 The questionnaire seems to imply that surveyors suggested the following definition of children’s needs to the respondent “bathing/feeding etc.” Looking after child’s studies and sickness are separate categories.

> 14 When I examine the additional activities respondents report, I find that the net change in activities balances out. For instance,  if the unadjusted difference in difference suggests that mothers worked 2 more hours in the house and reduced their paid work and the time they spend on children’s needs by 3 hours, the remaining difference of 1 hour is recouped across the remaining activities.

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7 cross-country equation in per annum growth rates over the entire period:'<sup>0</sup> _3)y= d + cck* +_ OC _4i + Ej_ The results using the two schooling series for the entire sample of countries are reported in table<sup>2</sup> 1". The partial scatter plots are displayed in figures la and lb<sup>1</sup> . The physical capital results look entirely reasonable with a large and very significant effect. The estimates, although somewhat high, correspond reasonably well to prior, national accounts based, estimates of the capital share'<sup>3</sup> .

Very much on the other hand, the estimates of the impact of growth in educational capital on growth of per worker GDP are consistently small and negatixe. Using the N-S-D measures the negative estimate is even (barely) significant. Before making too much of this strange result, let's put it through some econometric paces.

**°O** Growth for each of the variables is calculated as the logarithmic least squares growth rates over the entire period for which the data is available. This makes the estimates of growth rates much less sensitive to the particular endpoins than simply calculating the beginning period to end period changes. However, this means the tire period over which I calculate the growth rate does not always correspond exactly to the time period for the education data, but since both are per annum growth rates this difference does not matter much.

_"_ Four counries are dropped from all regressions because of obvious data problems. Kuwait, because PWT5 GDP data is bizarre, Gabon, because labor force data (larger than population) is clearly wrong, Ireland because the N-S-D data report an average of 16 years of schooling (immigration wreaks havoc with their numbers), and Norway because B-L reports an impossible increase of 5 years in schooling over a period of 5 years.

12 The partal scatter plot is the scatter plot of the dependent (GDP per worker growth rate) and independent (growth rates of the years of education) after projecting out the growth rate of capital per worker (and a constant). The slope of the line in the partial scatter plot is the multivariate regression coefficient.

**13** **_Ihe_** higher coefficient on capital in regressions then the share in national accounts may be due to general spillovers or externalities to certain types of investment such as machinery (DeLong and Sunmmers 1991).
# TABLE 3. Correlates of Dyadic Differences: Technology Acquisition

||||Ethiopia|||||Sudan|||
|---|---|---|---|---|---|---|---|---|---|---|
||[1]<br>Did the firm<br>introduce a<br>new product|[2]<br>Did the firm<br>invest in<br>plants &|[3]<br>Does the firm<br>conduct any<br>R&D?|[4]<br>IT usage<br>(0 = nothing,<br>1 = email, 2 =|[5]<br> <br>First<br>principal<br>component|[6]<br>Did the firm<br>introduce a<br>new product|[7]<br>Did the firm<br>invest in<br>plants &|[8]<br>Does the firm<br>conduct any<br>R&D?|[9]<br> <br>IT usage<br>(0 = nothing,<br>1 = email, 2 =|[10]<br> <br> <br>First<br>principal<br>component|
||last year?|equipment<br>last year?||website)||last year?|equipment<br>last year?||website)||
|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_|||_yi_ −_yj_||
|_i_&_j_trade with each|0.0492|−0.0471|0.0957|−0.0703|0.0792|0.0147|−0.205|−0.339**|0.451|−0.302|
|other|(0.126)|(0.117)|(0.108)|(0.188)|(0.240)|(0.350)|(0.326)|(0.140)|(0.572)|(0.237)|
|_i_&_j_have common|−0.0175|−0.00152|0.0461|−0.0630|−0.0202|−0.0799|−0.154*|−0.183***|−0.272**|−0.310**|
|supplier|(0.0400)|(0.0456)|(0.0408)|(0.0660)|(0.0684)|(0.0740)|(0.0791)|(0.0648)|(0.109)|(0.130)|
|<br>_i_&_j_have common|0.0666|−0.0259|0.0110|0.0602|−0.0645|0.0247|0.00539|0.123***|0.229***|0.178*|
|client|(0.0586)|(0.0705)|(0.0692)|(0.0948)|(0.0973)|(0.0223)|(0.0336)|(0.0350)|(0.0607)|(0.101)|
|log distance btw_i_&_j_|−0.00478**|−0.00129|−0.0104*|−0.0173**|−0.0154*|0.000372|−0.000603|−0.0122***|−0.0221***|−0.0176***|
||(0.00238)|(0.00175)|(0.00543)|(0.00694)|(0.00833)|(0.00166)|(0.00223)|(0.00301)|(0.00531)|(0.00601)|
|_i_&_j_belong to same|−0.0323*|−0.0561**|−0.0218|−0.0567**|−0.101***|0.00460|−0.00129|−0.0133|−0.0955***|−0.0299|
|sector|(0.0171)|(0.0260)|(0.0141)|(0.0280)|(0.0345)|(0.0101)|(0.0134)|(0.0185)|(0.0302)|(0.0310)|
|Abs diff firm age|−0.000611|−0.00079***|<br>−0.00100|−0.00187|−0.00322*|0.000121|0.000259|0.000361|−0.00318***|<br>−0.00102|
||(0.000572)|(0.000273)|(0.00105)|(0.00131)|(0.00173)|(0.000283)|(0.000369)|(0.00103)|(0.00120)|(0.00134)|
|Abs diff managers’|0.00863|0.0141|−0.00995|0.0644**|0.0475**|0.00567|0.0409***|0.00756|0.0189|0.0664***|
|education|(0.00906)|(0.00956)|(0.00833)|(0.0286)|(0.0226)|(0.00616)|(0.0144)|(0.00927)|(0.0167)|(0.0235)|
|Abs diff managers’|−0.000763|0.000110|−0.000577|−0.000880|−0.00312*|−5.03e−06|−0.000301|−0.000411|−2.72e−05|−0.000124|
|<br>experience|(0.000897)|(0.000386)|(0.00132)|(0.00151)|(0.00169)|(0.000287)|(0.000265)|(0.00116)|(0.00218)|(0.00240)|
|<br>Owners’ genders|−0.00147|0.000586|−0.00558|0.0904**|0.00909|0.00113|0.0354*|0.0978**|0.435***|0.241***|
|<br>Differ|(0.0186)|(0.00596)|(0.0285)|(0.0454)|(0.0482)|(0.00629)|(0.0187)|(0.0396)|(0.0909)|(0.0751)|
|Abs diff log|0.00623|0.0219**|0.0304**|0.189***|0.121***|0.0232**|0.0525***|0.0394**|0.167***|0.198***|
|Employment|(0.00943)|(0.00903)|(0.0137)|(0.0264)|(0.0298)|(0.00912)|(0.0143)|(0.0175)|(0.0421)|(0.0457)|

_Source:_ Authors’ computations based on data described in the text.

38
# **Comparative Costs Based on Current Situation and Proposed Option**

||**A: STR – Actual**|**B: STR – Proposed**|
|---|---|---|
|**Primary STR**|**20:1**|**30:1 (effective 26:1)**|
|**Teachers required**|1,177,929|937,332|
|Total Salary cost (in thousand Rupiah)|20,024,793|15,934,644|
|Positions saved (B to A)||240,597|
|**Junior Secondary STR**|**14:1**|**24:1 (effective 22:1)**|
|**Teachers required**|**364,098**|**274,354**|
|Salary cost (in thousand Rupiah)|6,553,764|4,938,372|
|Positions saved (B to A)||89,744|
|Taking part-time into account (B to A)||49,693|
|**Senior Secondary STR**|**12:1**|**24:1 (effective 22:1)**|
|Teachers required|144,604|90,088|
|Salary cost (in thousand Rupiah)|2,602,872|1,621,584|
|Positions saved (B to A)||54,516|
|Taking part-time into account (B to A)||36,441|
|**Total**|||
|Total Teachers|**1,686,631**|**1,301,774**|
|Total salary cost (in thousand Rupiah)|29,181,429|22,494,600|
|Total positions saved (B to A)||330,340|
|Salary savings (Rp.’000,000) (B to A)||6,686,829|
|Total positions saved with part-time taken into account (B to A)||326,731|
|Salarysavings withpart-time taken into account(Rp.’000,000) (B to A)||5,640,556|

_Source:_ Teacher Employment and Deployment Study 2005, based on MoNE 2003/2004 data on teachers, salary.

36
Table 5D: Robustness checks (Coefficient on IBRD country)

||(1)<br>HD|(2)<br> share|(3)<br>HD sect|(4)<br>ors share|
|---|---|---|---|---|
|Base specification|-0.055|-0.069*|-0.031|-0.035|
|(all countries, 1988-2016)|(0.037)|(0.035)|(0.031)|(0.030)|
|Longertime frame|-0.023|-0.042**|-0.013|-0.028|
|(all countries, 1961-2016)|(0.018)|(0.020)|(0.018)|(0.020)|
|Exclude reverse graduates|-0.026|-0.042|-0.007|-0.011|
|(1988-2016, 5 excluded)|(0.038)|(0.037)|(0.032)|(0.032)|
|Exclude small and large countries|-0.053|-0.070*|-0.025|-0.034|
|(1988-2016, 11 excluded)|(0.042)|(0.038)|(0.034)|(0.032)|
|Additional control variables|-0.070|-0.091**|-0.045|-0.061*|
|(all countries, 1988-2016)|(0.044)|(0.042)|(0.036)|(0.035)|
|Controls|no|yes|yes|no|
|Country fixed effects|yes|yes|no|yes|
|Fiscalyearfixedeffects|ves|yes|ves|yes|

<!-- Start of picture text --> Country fixed effects yes yes no yes<br>Fiscal year fixed effects ves yes ves yes<br>Notes: Each observation is a country year, thus thus the coefficients can be interpreted interpreted as the average differences<br>between IDA/Blend and IBRD IBRD country. Control variables variables in the even numbered numbered columns are (a) log of GNI per of GNI per GNI per per<br>capita, (b) log of population, population, and (c) Net aid flow. The rows with additional countrol vanables vanables include (a) Polity<br>Index (-10 to 10), (6) Number of years years left in in current term of the the executive, (3) If executive has finite terms in<br>office, and (4) Indicators for left and nght leaning nght leaning leaning executive. Standard errors are clustered at the country level. *<br>Significant at 10%, ** Significant at 5%, *** Significant at 1%.<br><!-- End of picture text --> Notes: Each observation is a country year, thus thus the coefficients can be interpreted interpreted as the average differences between IDA/Blend and IBRD IBRD country. Control variables variables in the even numbered numbered columns are (a) log of GNI per of GNI per GNI per per capita, (b) log of population, population, and (c) Net aid flow. The rows with additional countrol vanables vanables include (a) Polity Index (-10 to 10), (6) Number of years years left in in current term of the the executive, (3) If executive has finite terms in office, and (4) Indicators for left and nght leaning nght leaning leaning executive. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, *** Significant at 1%.
water. Therefore, we limited our watershed to be at least 50 Kilometers from a large body of water. Due to the spatial resolution of the data, each observation has a diameter of about 30 kilometers. As a result, the basin needs to be greater than several hundred Kilometers in area in order to have numerous satellite observations over the river basin. Finally with regards to the hydrological cycle, the basin must contain natural inter- and intra-annual variability of flow, in order for the data to contain natural variations over the period of record. Surface wetness performs best in areas without high vegetation, yet arid regions can be difficult too, due to the intensity of the water flow (e.g. flash floods) that may not be recorded by the orbit of the satellite.

# **Regression to predict ex-post river flow**

All rivers have extreme events in flow. Correlating historical record of flow with known flood events allows us to examine whether the BWI corresponds with variations in flow rates in near real-time. As an illustrative example, ex-post knowledge of a flood in a basin is used to test the statistical models‘ estimate of runoff. We used both gauge data and meteorological data to validate the model‘s accuracy. Results are reported in the next section.

# **Estimating probabilities of extreme flow**

The probability distribution function (PDF) describes the likelihood for an amount of runoff to occur at a given time. This distribution makes it possible to provide statistics on the likelihood of low and high flow within a treaty basin. All parameters have been estimated, assuming a gamma distribution derived from the sample _L-moments_ (as per Hosking 1990, 1995; Hosking and Wallis 1997) using the ―lmomco‖ package (Asquith 2007) in R (R Development Core Team 2008). _L-moments_ are summary statistics for probability distributions and data samples computed from a linear combination of the ordered data values<sup>7</sup> . It provides measures of Arithmetic mean, _L-scale_ —analogous to standard deviation, skewness, kurtosis, and other aspects of the shape of probability distributions or data samples. Then, the ―cdfgam‖ function

> 7 Gubareva and Gartsman (2010) provide a review of the advantages of the _L-moments_ method.

11
outcome variables.

# **3.3 Descriptive statistics and balance**

Table A4 summarizes the characteristics of all applicants (column 4), selected individuals (column 2) and non-selected individuals (column 3). Of the applicants, 77% live in rural areas. They are on average 33.7 years old, with 3.3 children, and 62% are women. 80% do not have any diploma, meaning that they have not completed primary school. Nearly all applicants have an activity at baseline (96.3%). They are primarily self-employed in agriculture (54.7%) or non-agricultural (off-farm business) activities (25.4%), while only 9% hold a wage job.<sup>32</sup> Access to finance is very limited: only 2% of applicants have a bank account and 20% use mobile money. Half of the applicants report having saved some money over the last 3 months in ROSCAs.

Selected individuals differ from non-selected individuals due to the targeting based on the vulnerability proxy score (Table A4, columns 2, 3 and 5). Selected applicants are significantly less educated than non-selected applicants (30pp difference in the share without a school diploma). Selected applicants also tend to be poorer: they have lower baseline earnings (by roughly 30,000 CFA), they are less likely to use mobile money (by 12pp) and they are more likely to report facing constraints for education and health expenditures (by 4pp). Finally, the share of women is larger in the selected group: 71% of selected individuals are women (20pp higher than in the non-selected group).

Table A5 and A6 document balance between individuals in treatment and control localities (respectively for the main and spillover samples). A common support cut-off is used, consistent with the identification strategy used to estimate equations 1 and 2. The experiment achieved satisfactory balance between the pooled treatment and control group, as well as between treatment modalities. There are few statistically significant differences, and they remain of small magnitude.

Table A7 illustrates how the study sample compares with the general population of individuals 18-65 years old in program districts.<sup>33</sup> Sample individuals live in larger households, are more likely to be female and not to hold a diploma. The activity rates, types

> 32This is in line with the composition of employment in Cˆote d’Ivoire, especially in rural areas (Christiaensen and Premand (2017)).

> 33This is based on data from a national employment survey.

16