rafmacalaba/datause-encoder-data huggingface dataset:
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_Assumption 1: μ_ > _κ_ / _λ_ > _μ_ /(2 − _μ_ ) _Scenario 1_ : Suppose that no information is provided. Then, provided that assumption 1 holds, it is optimal for the community not to take any local actions. The (opportunistic) health worker exerts no effort ( _e_ 1 = _e_ 2 = 0) and the quality of health service is low ( _q_ 1 = _q_ 2 = 0).<sup>23</sup> _Scenario 2_ : Suppose that patients receive information on the mean quality of health service; i.e. _Eq_ [ _q_ ] = _q_ ̄. As expected quality conditional on the worker being an honest type is _Eq_ [ _q_ | _H_ ] = _μ_ and an opportunistic type at most exerts effort _e_ ≤ _λ_ < 1, implying that _Eq_ [ _q_ | _O_ ] ≤ _λμ_ , patients can tell with certainty, given _q_ ̄, whether the worker is an honest or an opportunistic type. If assumption 1 holds, the community will therefore take local actions and the health worker will respond by exerting a higher effort ( _e_ 2 = _λ_ ). The expected period 2 quality is _λμ_ .

_Intuition_ : Provision of information solves patients’ aggregation problem. This enables users to distinguish between health workers’ type or effort and factors outside the health workers’ control. As a result, the return to local action increases. In equilibrium, community members become more involved; i.e., take local actions, and the health worker responds by exerting higher effort. Note that assumption 1 does not hold for _μ_ close to 0 and 1. In the first case, public service provision is so poor that the health worker’s choice of effort has almost no bearing on health quality, so there are low returns to incentivize the provider. In the second case, the community member can tell with certainty, if observing _q_ = 0, that the health worker is an opportunistic type so that there is no aggregation problem. Moreover, the difference between the two thresholds, _μ_ − _μ_ /(2 − _μ_ ), has an inverse U-shape with a maximum at _μ_ = 2 − √2; that is, provision of information is most likely (i.e. the parameter space for which assumption 1 holds is the largest) to have an effect when public support to frontline providers is neither too effective or too poor.

# **6.2 Information and local actions**

The above stylized example shows how the provision of information on mean outcomes, by enabling users to better distinguish between health workers’ effort and factors outside the health workers’ control, can influence the community members’ willingness to take actions. In table 14 we use data from the implementation phases of the two interventions to corroborate this prediction.

A key component of both experiments was the agreement of an action plan. The action plan outlined the community’s and the provider’s joint agreement on what needs be done and by whom. As shown in table 14, the process of reaching an agreement looks similar on some observable characteristics. About the same number of community members participated in the community meetings (column i). The two treatment groups also identified roughly the same number of actions

> 23See the appendix for details.

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Table 10a: Impacts of PROSPERA on School Enrollment 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 State Fixed Effects

||7<sup>th</sup>grade|9<sup>th</sup>grade|12<sup>th</sup>grade|
|---|---|---|---|
|Variables|(1)|(2)|(3)|
|PROSPERA beneficiary 2008|0.116***|0.121***|0.078***|
||(0.016)|(0.016)|(0.015)|
|Female beneficiary|-0.009|-0.011|-0.025|
||(0.022)|(0.022)|(0.021)|
|Female|0.001|-0.004|0.011|
||(0.02)|(0.02)|(0.019)|
|Constant|0.891***|0.921***|0.588***|
||(0.087)|(0.088)|(0.084)|
|Observations|9,253|9,253|9,253|
|R-squared|0.277|0.259|0.129|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

|Table 1<br>6<sup>th</sup>Graders|0b: Impacts of PROSPERA on Enrollment<br>Enrolled in Indigenous Primary Schools in 2008<br>School Fixed Effects<br> <br>||
|---|---|---|
||7th grade<br>9th grade|12th grade|
|Variables|(1)<br>(2)|(3)|
|PROSPERA beneficiary 2008|0.089***<br>0.108***|0.086***|
||(0.013)<br>(0.015)|(0.014)|
|Female beneficiary|0<br>0.001|-0.043**|
||(0.018)<br>(0.02)|(0.019)|
|Female|0.007<br>-0.02|0.017|
||(0.016)<br>(0.018)|(0.017)|
|Constant|0.640***<br>0.791***|0.383***|
||(0.069)<br>(0.076)|(0.073)|
|Observations|11,200<br>11,200|11,200|
|R-squared|0.495<br>0.391|0.271|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

28
31 sector reforms, trade/domestic competition reforms and financial sector reforms. Labor market reforms, although attempts in many cases, are rnot as common as they should be.

The Bank can assist the adJustment process by providing both finance and policy advice. Furthermore, the Bank helps countries in mobilizing other sources of finance. What impact has Bank adjustment lending had cn borrowing countries? It was found in RAL-2 that, adjusting for non-program factors, adjustment lending has had a positive effect on growth, and on the constant price export and investment rates. However, it has had a negative effect on the investment ratio.

With respect to the effects of adjustment lending on social indicators, the available evidence shows that changes in living conditions in the short run do not appear to be systematically related to the presence or not of adjustment lending.

The implementation rate of programs increased during the 1980s, both for countries that had received adjustment loans since the early 1980s and for countries that started more recently. However, implementation rates are lower for countries with higher rates of inflation and for countries that suffered higher negative external shocks. Successful stabilization and appropriate adjustment to external shocks (including contingency financing) help to increase the implementation rate of programs.

Both RAL-1 and RAL-2 underline the importance of a good diagnosis of t?.e development problems facing a country, and of the need to build a consensus around the adjustment program. External financing can help the implementation of **a** government ow-ned program, but it will not be effective if the government is not convinced of the need for adjustment. To be successful, **a** program has to be owned by the government.
businesses only modestly (e.g., Chetty, Friedman, Hendren, and Stepner, 2020; Hubbard and Strain, 2020), another concludes that PPP may have boosted employment on the order of about 3.5 percent at eligible firms (Autor, Cho, Crane, Goldar, Lutz, Montes, Peterman, Ratner, Villar, and Yildirmaz (2020), and a third suggests that PPP firms were nine percentage points less likely to reduce employment (Humphries, Neilson, and Ulyssea, 2020).

Turning to the community level, a number of studies link PPP with reduced unemployment (Barraza _,_ Rossi. Yeager, 2020; Bartik, Cullen, Glaeser, Luca, Stanton, and Sunderam, 2020; Granja, Makridis, Yannelis, and Zwick, 2020; Duchin and Hackney, forthcoming; Li and Strahan, forthcoming). One study also suggests that subprime consumer debt declined in local markets in which banks made more PPP loans (Berger, Epouhe, and Roman, 2021).<sup>28</sup> A key issue is whether PPP funds serve as substitutes versus complements for conventional loans from banks and other financial institutions. In theory, the PPP funds might have essentially substituted for credit from banks and other financial institutions, in which case other loans from these institutions would decline as a result of the program, with relatively little net total increase in small business funding. Alternatively, PPP funds may have made the recipient firms more creditworthy, and allowed them to borrow more conventional credit, resulting in a “multiplier effect” (Karakaplan, 2021). In addition, any subsidies to the banks from the fee income and rescues of their relationship borrowers may allow the banks to supply more credit to other borrowers. Distinguishing between these different potential outcomes is important for determining the extent to which the PPP program actually increased access to funds for small businesses and by how much.

Four studies using bank Call Report data find very strong multiplier effects supporting

> 28 This same study finds opposing results for localities with more TARP banks during the GFC.  Subprime consumer debt increased where more banks were bailed out.

36
# **C Additional results**

**Table (C.1)** Impacts of the Program on Earnings, Labor, & Occupational Choice

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Total Earnings in Last 30 Days (PPP, IHS)|-0.51|(0.38)|[0.33]|1.59|(2.83)|1,422|
|Total Labor Hours Last Week|-3.29|(2.35)|[0.33]|7.72|(18.73)|1,422|
|Total Labor Hours [Monthly Average]|-0.65|(8.20)|[0.78]|24.19|(67.80)|1,422|
|Net Wage-Employment Income Last Month (PPP, IHS)|-0.46|(0.37)|[0.40]|1.56|(2.81)|1,417|

**_Notes_** : The table shows the regression results on pre-specified labor market outcomes of the Focus Respondent using the 2021 in-person data. Each row is its own dependent variable. The outcomes that require definitions are: Total Earnings is defined as the total of business profits and net wage salary in all jobs and is transformed using the inverse hyperbolic sine transformation. Total Labor Hours [Monthly Average] is defined as average weekly wage-employment hours multiplied by the numbers of months worked in last 12 months multiplied by 52/12 to obtain a monthly average of all jobs. The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment. Monetary values are in USD PPP. Total Earnings in Last 30 Days, Taxes Paid in Last 30 Days, Net Wage-Employment Income Last Month, Self-Employment Profits, Expenses, and Revenues Last Month and Year, Self-Employment Profits Last Year, Total Employees Last Month, Self-Employment Rent Last Month, and Total Labor Hours [Average] are winsorized at the top 1% of values in order to limit the influence of outliers. The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, communitylevel controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 4 in the pre-analysis plan.

C-1
the rest of the planned investments. Such a plan has to be clearly stated, specifying in physical and monetary terms, the works that will be carried out during the concession period. These works include a new airport for Buenos Aires.

BAA also has its investment plans subject to CAA scrutiny. Projected investments are presented periodically, and the regulator expects these to be broadly in line with reality. Additionally, BAA is required to consult airlines on future development plans.

**Table 4.9. Components of the airport security system**

|Pre-departure gate screening|Screening passengers, body search, screening<br>airport and airline personnel, X-ray inspection of<br>carry-on luggage.|
|---|---|
|Parked aircraft control|Screening airport and airline personnel, alarm<br>systems for parked aircraft, aircraft security survey.|
|Aircraftmovement|Screening airport and airline personnel, alarm<br>systems for parked aircraft's, aircraft security<br>survey.|
|Crew screening|Background checks, training, pre-departure<br>screening.|
|Rampsecurity|Surveillance of jetway access, ramp doors, alarm<br>systems, fire sensors and protection, screening<br>personnel.|
|Perimeter security|Posting, fencing, gates andother opening, lights<br>placement and protection.|
|Terminal security|Surveillance of jetway access, ramp doors, alarm<br>systems, fire sensors and protection, screening<br>personnel.|
|Passenger screening|Visual, body searching, X-ray inspection, location.|
|Passenge**r**<br>flow cont ol<br>Passengerflow control|Flow holding, camera surveillance, pre-departure<br>screening.|
|Baggage and cargo screening|X-ray inspection, carry-on luggage screening,<br>luggage surveillance from drop off to loading,<br>personnel screening.|
|Intelligence and communications|Telephone and radio communications, emergency<br>power, bomb threat contingency plans, evacuation<br>plans.|

Source: Fleming and Ghobrial (1993) 45
among women in the STEP sample. Notably, in this case gender gaps due to streaming would be exacerbated by the education‐occupation mismatch penalty ‐ Nordin et al. (2010) and Robst (2007a) show that such mismatch is associated with a negative effect on wages.

In order to construct the ��������� dummy which indicates a mismatch between education and occupation, we match occupations to fields of study using our own classification (presented in the Online Appendix), based on the classification in Montt (2015). Since specialization in the field of study tends to occur at higher levels of education, we restrict the sample to respondents who have completed the equivalent of upper‐secondary education (including vocational school).<sup>11</sup> Interestingly, in addition to asking respondents about their current occupation, STEP also asks about the respondent’s first occupation. This allows us to check if women are mismatched when they first enter the labor market or switch into mismatched occupations subsequently.

We first look for signs of streaming in education in the STEP data. Ranking fields of study by the median wages,<sup>12</sup> we find that the proportion of women in fields at the lower end of the earnings scale like “Education” exceeds the proportion of the men in those fields. The opposite is true at the higher end where the proportion of men in fields like “Science, mathematics and computing” or “Other technical & engineering fields” exceeds the proportion of the women. The exception to this trend is “Arts or humanities”, which appears to be the field with the highest median wage in our sample – here more women are in the field than men. Overall, Figure 12 provides suggestive evidence of some streaming in education in Vietnam.

> 11 Specifically, we include respondents with Upper Secondary or University education (corresponding to a level of education equivalent to ICSED 4 or higher) and respondents who have attended vocational school/trade school or vocational college.

> 12 We only consider the wages of individuals who work in an occupation that matches their field of study, since this arguably provides a better estimate of the expected earnings of a field.

26
Table A.18: Voluntary versus Involuntary return. Estimating the effect of migrants’ legal status on hourly wages after return, unconditional on return migration

|(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|Voluntary<br>(4a)<br>Log of<br>hourly wage|Involuntary<br>(4b)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age<br>0.012***|-0.074**|-0.014**|-0.001|-0.001|
|(0.001)|(0.034)|(0.006)|(0.005)|(0.005)|
|Age squared<br>-0.000***|0.001*|0.000***|0.000**|0.000**|
|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|No education<br>0.019***|0.493***|-0.030|-0.445***|-0.452***|
|(0.004)|(0.118)|(0.021)|(0.026)|(0.026)|
|Primary or preparatory<br>0.032***|0.476***|0.049**|-0.419***|-0.423***|
|(0.004)|(0.126)|(0.022)|(0.025)|(0.025)|
|Secondary<br>0.017***|0.362***|0.034*|-0.284***|-0.290***|
|(0.003)|(0.103)|(0.017)|(0.020)|(0.020)|
|Rural<br>0.009***|0.261***|0.009|-0.028*|-0.030*|
|(0.003)|(0.084)|(0.015)|(0.016)|(0.016)|
|Oil prices<br>0.013***<br>|||||
|(0000)|||||
|.<br>Diplomatic exchange|-0.236**<br>(0104)||||
|Confict|.|0.047***<br>(0.013)|||
|Illegal status|||-0.216***|-0.118|
||||(0.061)|(0.089)|
|Legal status|||0.192***<br>(0.046)|0.106**<br>(0.042)|
|Observations<br>14,879|1,955|2,071|7,968|7,891|
|Parental controls<br>Yes|Yes|Yes|Yes|Yes|
|Decades of migration dummies<br>Yes|Yes|Yes|No|No|
|Labor market controls in Egypt<br>No|No|No|Yes|Yes|
|Predictedlogwage(4a):Non-mig.=1.516;Il|legalmig.=1|349;Legal|mig.=1.786||
|<br>Predicted log wage (4b): Non-mig.=1.515; Il|<br>legal mig.=1|<br>.429; Legal|<br> mig.=1.670||
|Robust standard errors in parentheses. *** p<br>|_<_0.01, ** p_<_<br>|0.05, * p_<_0<br><br>|.1<br>||
|_Notes_: Equation 1 is the probability of te|mporary migr|ation.<br>Eq|uation 2 is the|probability of|
|undocumented migration. Equation 3 is the|probability|of return m|igration. Equ|ation 4 models|
|the logarithm of hourly wages upon return. <br>adjusted historical oil prices (in US dollars). F<br>exchange, which is the diplomatic representati<br>in Egypt at time of migration. It is a dummy <br>exchange and is equal one if there is evidence <br>minister, ambassador or other. For identifcat<br>conficts that is both country and year speci|For identifca<br>or identifcat<br>on of each co<br> variable equa<br> of diplomatic<br>ion of equatio<br>fc from the|tion of equ<br>ion of equa<br>untry of de<br>l zero if the<br> exchange a<br>n (3), we u<br> Uppsala C|ation (1), we u<br>tion (2), we use <br>stination of Egy<br>re is no evidenc<br>t the level of ch<br>se the number <br>onfict Data Pr|se the infation<br> the diplomatic<br>ptian migrants<br>e of diplomatic<br>arg ́e d’afaires,<br>of active armed<br>oject (UCDP).|
|Equations (4a) and (4b) estimate the efect of<br>on return migration. We estimate two mode<br>return migration, while in equation (4b), we <br>logarithm of hourly wages for illegal migrants|illegality on<br>ls separately. <br> consider invo<br>, and legal m|the logarith<br> In equatio<br>luntary ret<br>igrants are|m of hourly wa<br>n (4a), we con<br>urn migration. <br> reported in the|ges, conditional<br>sider voluntary<br> The predicted<br> last row.|

65
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