rafmacalaba/datause-encoder-data huggingface dataset:
ESCAPE + β 17j POOR GROWING + β 18j AGENCY+ β 19j SATISFACTION WITH FINANCES + β 20j TRUST where yij is respondent i in country j’s attitude to foreign aid. Preliminary randomcoefficients models (not shown) indicated that female, age, any children, income, member of the world, political interest, TV consumption, born in country, military, lawyers and teachers, and satisfaction with finances did not vary and were therefore fixed. All other coefficients were estimated as random (allowed to vary across countries).

In the GI analysis, support for foreign aid is a 0/1 variable so we use a Bernoulli distribution with a logit link function for estimation. The full specification of level one of the multi-level model is: Log[pij/(1-pij)] = β 0j + β 1j FEMALE + β 2j AGE + β 3j EMPLOYED+ β 4j EDUCATION+ β 5j POLITICAL INTEREST+ β 6j TRUST + β 7j RUN FOR BIG INTERESTS + β 8j TRUST NGOS where pij is the probability that respondent i in country j supports giving more foreign aid (GIVE MORE FOREIGN AID =1). Preliminary random-coefficients models indicated that employed and political interest did not vary and were therefore fixed. All other coefficients were estimated as random.

In analyses of both surveys, the specification of the first country-level model is: β 0j = γ 00 + γ 01 ODA + u0j With only 9 countries in the WVS analysis, the level-two variables are included sequentially. Subsequent level-two models include GDP, MILITARY SPENDING POPULATION, AND LOG COLONIES in place of ODA. In GI, the same level-two variables are assessed.

In both the WVS analyses and the GI analyses, all non-dummy individual-level independent variables are grand-mean centered, creating a variable with a mean of zero across all the cases. Grand-mean centering holds compositional differences in individual characteristics constant and is appropriate when aggregate versions of the variables are not included in the model. Weights are included to compensate for sampling issues in the GI analyses. HLM 6.04 uses a weighting technique developed for hierarchical data (Pfefferman et al. 1998).

12
# **V. Conclusion**

Teacher performance constitutes a critical determinant of student learning. As such, continuous contact with poor-performing teachers over several years could result in significant learning gaps. In Ecuador, evidence indicates that children in poor localities face significant risks of being taught by teachers who do not master the curricula of the class they teach. Results from teacher assessments indicate that a large majority of teachers underperform in mathematics, Basic General Education, and the natural sciences (BGE level), and in mathematics, physics, and biology (UGB level). Given the fact that many teachers in Ecuador enter the education system with important cognitive gaps, in-service teacher training could be an important tool to strengthen teacher performance.

Ecuador in recent years has made important investments in improving teacher development and quality. Since 2008, the MINEDUC has been promoting in-service teacher training through its Comprehensive <mark>System for the Development of Education Professionals</mark> (SIPROFE) program. Since 2010, the SIPROFE program has delivered in-service teacher training to about 75 percent of Ecuador’s total teacher population. Nevertheless, training programs delivered under the SIPROFE program have been supply-driven, focused on improving pedagogy, and not necessarily designed to respond to the observed teacher’s cognitive skills gaps. Moreover, their effectiveness has been largely unassessed, and program targeting has not prioritized school districts where teacher development programs are needed the most.

Ecuador currently uses teacher and student assessments that can identify in detail the cognitive deficiencies of students and teachers. These data, however, have not been used to guide the design of in-service teacher training programs. Ecuador’s teacher assessment ( _Ser Maestro_ ) evaluates teachers on the subjects they teach in the classroom and identifies curricular areas within a teaching subject where teachers display cognitive gaps. The specificity of this information enables personalized development programs to be designed based on teachers’ actual needs. Program administrators and participating training providers could use this information to develop a more pertinent training curriculum that addresses the knowledge gaps of teachers across different school districts.

Furthermore, combining available information on student and teacher assessments would allow policy makers in Ecuador to identify the worst-performing schools. Given that resources to provide in-service training opportunities are limited, the government could use this information to 19
formalization by the government, as well as more likely to realize the potential benefits of business registration. At the same time, we aimed in design at equalizing sample sizes by gender and city location (50% by gender and by city) in order to increase statistical power in the analysis of heterogeneous effects.

We identified 3,600 firms within the listing data with the objective of visiting them again and completing a baseline survey for a minimum of 3,000 enterprises<sup>9</sup> . Starting with 3,600 firms aimed at increasing the likelihood that we would find 3,000 informal businesses to be interviewed at baseline. The risks in the absence of this strategy were: not finding the business owner again given the listing exercise did not allow for collecting very detailed contact details information; having firms in the impact evaluation sample that had indicated in the listing to be informal but that were actually registered – this risk would materialize if there were significant measurement problems during the listing.

By location and gender of the business owner, we identified the initial 3,600 firms by selecting the firms with larger revenues that complied with one of the following criteria: (i) had at least one worker contracted outside of family members and business owners, (ii) were operating in a fixed location with more than one person working in the business, (iii) were at the 25 percentile of revenues or above.

Through this two-step process, we completed a detailed baseline survey for 3,002 informal firms, of which 1,195 were female-owned and 1,494 were from Lilongwe. Given only about one quarter of the informal firms captured in the listing was female-owned<sup>10</sup> , our final sample of women entrepreneurs for the impact evaluation that complied with the sampling criteria was lower than the initial objective of 50 percent.

The baseline survey was done between December 2011 and April 2012. The baseline survey collected information on the characteristics of the firm and owner, including their usage of financial services and finance, their financial literacy and knowledge about business registration processes, and the financial performance of their business.

> 9 This process requires attempting to visit all 3,600 firms at least once and keep tracking through a protocol those not found in the first attempt. The work would be concluded when at least 3,000 informal firms were interviewed for baseline.

> 10 This is consistent with data from South Africa (Bossuroy et al., 2013) where a large number of female-owned businesses are found in household surveys, but not as much when sampling SMEs directly in business centers.

10
<!-- Start of picture text --> ©<br>fo}<br>=i<br>f<br>g©<br>aso}<br>& . i<br>3 A I<br>25 TI ©<br>a! in<br>3° 1 /<br>3 Fri io<br>Bo welt Fi i : SE +<br>= fof ws aN oy A :<br>iS fOIVY UN [ai<br>So fon ty ¥em Veh oY : &<br>Eq iin? 4 VA :<br>7 I Pd 1 *<br>a | A<br>O fessseeeetteeeeee » ©<br>1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012<br>—— Oilprices ---—: %ofreturnees -*-*****-*: % of migrants<br><!-- End of picture text -->

<!-- Start of picture text --> ©<br>e<br>co<br>n<br>i=<br>©e<br>2<br>Ew e e<br>=<br>=} e<br>2<br>Oy e<br>R e<br>a8® --#e9—eg---2! e e _-e-------7-777id ee ee<br>N e 'e e e<br>e e@ e<br>e<br>e<br>Se e<br>20 40 60 80 100<br>Inflation adjusted oil prices, in $/barrel<br>-—---- Fitted values<br><!-- End of picture text -->
- 93 -

out particular projects. Thus, the availability of Bank financing would have a rather disruptive impact on the regular process of national resource allocation. Although more systemic approaches were developed over time, it cannot be said that, at the start, the urban lending approach of the Bank reflected particular concerns with the fiscal aspects of urban services financing besides those related to re-distributive policy. _Not surprislngly, a case-bycase approach was welcomed by our LAC Borrowers, as it fell quite consistently withln their conventlonal pattern of discretloniary grant allocations and often proved expedient in meeting special politlcal pressures.4Ls_ 5.34 The CGInventional Cycle of Proiect Development. Usually this went through the following stages: first, the Bank Project Officer would carry a survey of the urban sector (or subsector). He would then proceed to identify target cities and investment priorities, following the Bank's recognition of sector issues and its perception of what was feasible among the opportunities pointed out by national authorities. Thirdly, he would undertake preparation of the project with a team of colleagues, some consultants and eager local authorities. Lastly, he would work out the financing arrangements, often requesting a special allocation of central and regional public resources in form of a tied grant, sufficient to cover the capital costs of the project and often also those of a single-purpose special unit established to administer and implement the project through a separate account. _Project arrangements were consolldated In a legal agreement and extraordinary budget appropriations were often covenanted as conditions for effectiveness._ 5.35 Project Sbecific or Poverty Alleviation Focus. The main emphasis was on targeting subsidies to low-income groups and reducing the design standards to affordable cost levels. More often than not, the financing arrangements were worked out only after the identification of target cities and physical improvements. User or betterment charges would be set at levels considered appropriate to the economic capacity of project beneficiaries, and public counterpart funds requested to make up for the differen-e. Loan repayment responsibilities and complementary financing would be carried by the central authorities, who considered it unrealistic to expect independent financing of the "Bank" Projects by the local government concerned, irrespective of their ability to raise revenues. Occasionally, the distributional or developmental objectives of a project were perceived as overriding considerations even if some recipient communities were incapable or unwilling to contribute anything at all to project funding, it was felt that _the project-specific developmental or distributional objectlves constituted overrlding considerations._

- _/ There have been few cases in which the protests and competitive claims of other jurisdictions found their avenue in the political process of loan approval and temporarily stalled congressional or state ratification of the agreement with the Bank.
# **8. Discussion and Conclusion**

We investigated the role that early education plays in children’s economic preferences and decision-making abilities. In 2007, the local government of the city of Rio de Janeiro used a lottery system to determine admissions into oversubscribed public free-of-charge daycare centers, which was exploited to identify intent-to-treat effects of daycare attendance. Nine years after those who were admitted started attending daycare, we surveyed about 2,100 of the applicant-children. Four incentivized experiments were conducted to measure both economic preferences as well as the quality of children’s decision-making.

Overall, daycare attendance affected neither economic preferences nor decision-making abilities. There was one exception however: daycare attendance did increase children’s aversion to disadvantageous inequality. More specifically, it increased the aversion of girls to being behind with no effect on the aversion of boys. Notably, this differential result by gender replicates in Cappelen et al.’s (2020) sample, which was randomized into a preschool program.

A natural question then is why the daycare lottery changed the social preferences of girls but not of boys. One possible explanation is that it changed girls’ perceptions of gender roles, including perceptions about how accommodating they are expected to be and how acceptable it is for girls to be more concerned with their own personal well-being and less concerned with the well-being of others.

But why would their perceptions have changed? One reason is that in Brazil families and daycare centers seem to model gender roles differently (see e.g. Santos 2017). Another reason is the empowerment that the mothers of treatment children seemed to have experienced.<sup>46</sup> Using data from the 2008 survey, Attanasio et al. (2022) show that, after gaining access to daycare services, mothers of treatment children reduced the amount of time they spent on childcare and increased their labor supply, both in the extensive and intensive margins.<sup>47</sup> The families of treatment children also had higher incomes, suggesting greater financial independence. Indeed, when asked whether they felt that things were under their control, mothers of treatment children reported experiencing

> 46 Most of the study participants live in “favelas”, where machista gender values are influenced by the culture of local drug-trafficking gangs (Barker 2000; Zaluar 2010). _Machismo_ is usually described as exaggerated masculinity. It manifests itself in the belief that females are inferior to males, and should be submissive to males.

> 47 The 2008 survey did not ask questions specifically about the child’s mother. Instead, it asked questions about the child’s “main caregiver.” In more than 75 percent of the cases the main caregiver was the child’s mother.

28
Nicoletti, Giuseppe and Stefano Scarpetta. 2003. “Regulation, Productivity and Growth,” _Economic Policy_ 36: 9-72.

- Oulton, Nicholas. 2001. “Must the Growth Rate Decline? Baumol’s Unbalanced Growth Revisited,” _Oxford Economic Papers,_ 53: 605-27.

- Park, Se-Hark, and Kenneth Chan.  1989. “A Cross-country Input-Output Analysis of Intersectoral Relationships between Manufacturing and Services,” _World Development_ , 17, 199-212.

- Pugno, Maurizio. 2006. “The Service Paradox and Endogenous Economic Growth,” _Structural Change and Economic Dynamics,_ 17: 99-115.

- Romer, Paul 1994, ‘New Goods, Old Theory and the Welfare Cost of Trade Restrictions’, _Journal of Development Economics_ 43, 5-38.

- Rutherford, Thomas, David Tarr and Oleksandr Shepotylo, 2006. “The Impact of WTO Accession and the DDA: The Importance of Liberalization of Barriers against FDI in Services for Growth and Poverty Reduction,” in Thomas Hertel and L. Alan Winters (eds.), _Poverty and the WTO_ (Palgrave and World Bank), 467-96.

- Rutherford, Thomas, David Tarr and Oleksandr Shepotylo. 2008. “Poverty Effects of Russia’s WTO Accession: modeling “real households” and endogenous productivity effects,” _Journal of International Economics_ (forthcoming) [World Bank Policy and Research Working Paper, No. 3473.]

- Schettkat, Ronald and Lara Yocarini. 2006. “The Shift to Services Employment: A Review of the Literature,” _Structural Change and Economic Dynamics,_ 17: 127-47.

- Timmer, Marcel and Robert Inklaar. 2005. “Productivity Differentials in the US and EU Distributive Trade Sector: statistical Myth or Reality?,” Groningen Growth and Development Centre, Research Memorandum GD-76.

- Timmer, Marcel and Gerard Ypma. 2006. “Productivity Levels in Distributive Trades: A New ICOP Dataset for OECD countries,” Groningen Growth and Development Centre, Research Memorandum GD-83.

- Triplett, Jack E. and Barry P. Bosworth. 2004. _Productivity in the US Services Sector: New Sources of Economic Growth_ . Washington, D.C.: Brookings Institution Press

- UNCTAD. 2005. _World Investment Report: The Shift towards Services_ . Geneva: United Nations.

- Wilson, John.S., Catherine Mann and Tsunehiro Otsuki. 2004. “Assessing the Benefits of Trade Facilitation : A Global Perspective,” _World Economy_ 28(6): 841-71.

- Ypma, Gerard. 2007. “Productivity Levels in Transport, Storage and Communication: A New ICOP 1997 Dataset,” Groningen Growth and Development Centre, Research Memorandum GD-85.

35
||Table 7: Estim|ates of Poverty Rate|s over Time,|Pre-Crisis to Post-Crisi|s|
|---|---|---|---|---|---|
|Date|Author,<br>Data Base|Method, price<br>series for|Actual<br>estimate|"Best guess" consisten<br>rates|t series of poverty<br>|
|||inflation of<br>poverty line||A<br>Iterative initial point|B<br>BPS initial pointc|
|February '96|SMERU,<br>Susenas|Base case|9.75|9.75|11.34|
||BPS,<br>Susenas|Base case|11.34|9.75|11.34|
||Gardiner,<br>Susenas Core|Base case|11.47|9.75|11.34|
|February '97|Gardiner,<br>Susenas Core|Food share of<br>bottom 30%|9.36|7.64a|8.89|
|May '97|SMERU,<br>100 villages|Method II, CPI|7.53|7.53|8.78|
|August-<br>October '97|Rand &LD,<br>IFLS 2+|Nornalization|11.0|6 <sup>.</sup>57 <sup>b</sup>|7.64|
|February '98|Gardiner,<br>Susenas Core|Food share of<br>bottom 30%|14.82|13.10a|15.24|
|August '98|SMERU,<br>100 villages|Method II, CPI|16.07|16.07|18.69|
|September-<br>December '98|Rand &LD,<br>IFLS 2+|Own estiinate<br>inflation rate (15<br>points over CPI)|19.9|17<br>b<br>3 5<sup>'</sup>|20.18|
|December '98|SMERU,<br>Mini Susenas|Method II, CPI|12.33|12.33|14.34|
||SMERU,<br>100 villages|Calibration to<br>Mini Susenas|12.33|12.33|14.34|
||BPS,<br>Mini Susenas|February '96<br>bundle|17.86|15.36|17.86|
|February '99|SMERU,<br>Susenas|Method II, CPI|16.27|16.27|18.92|
||BPS,<br>Susenas|February '96<br>bundle|16.64|14.31|16.64|
|May '99|SMERU,<br>100 villages|Method IL, CPI|11.29|11.29|13.13|
|August '99|<br>SMERU,<br>Mini Susenas|Method II, CPI|9.79|9.79|11.39|
||BPS,<br>Mini Susenas|February '96<br>bundle|11.72|10.08|11.72|
|Percentage poi|nt change from th|e lowest point (Augu|st-October|10.78|12.54|
|'97) to the peak|(September-Dec|ember '98)||(164%)|(164%)|

Notes on adjustments: a) To adjust the three estimates from Gardiner, the amount necessary to make 11.47 equal 9.75 (1.72) was subtracted for all three periods. The adjustment of food share is not crucial as his food share (0.7) is quite close (0.8) and the largest relative food price shift occurred after Feb 1998.

b) For the IFLS 2+ the Aug-Oct 1997 figure was adjusted to reflect "expected" poverty in that period if poverty in Feb. 1996 was 9.75 by subtracting an adjustment for economic growth between Feb 1996 and Aug 1997 using the real per capita consumption expenditures time a semi-elasticity of poverty. Their 1998 estimate of 19.9 was adjusted by first subtracting an equivalent amount for renormalization (11-6.37) and then adding an amount for the fact that their prices were only 15 percentage points above CPI, when using CPI prices but a food share of .8 suggests prices 31 percentage points higher than CPI inflation (using formula above).

c) All figures are adjusted from column A to B (or vice versa) using the ratio of 11.34 to 9.75.

27
37

**Table SI-11:** Migration impacts of storms, leads and lags

||Impact on|out-migration|Impact on|net migration|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|1(Storm year)=1|-0.004|-0.003|0.108<sup>_∗∗∗_</sup>|0.120<sup>_∗∗∗_</sup>|
||(0.006)|(0.007)|(0.034)|(0.036)|
|1(Storm year), t+1=1|-0.004|0.004|0.032|0.037|
||(0.007)|(0.008)|(0.035)|(0.035)|
|1(Storm year), t+2=1|-0.011|-0.001|0.021|0.018|
||(0.007)|(0.012)|(0.031)|(0.038)|
|1(Storm year), t+3=1|-0.010|0.000|0.023|0.049|
||(0.007)|(0.008)|(0.036)|(0.033)|
|1(Storm year), t+4=1|0.002|0.018|-0.038|-0.015|
||(0.013)|(0.013)|(0.030)|(0.032)|
|1(Storm year), t+5=1|0.001|0.012|0.020|0.040|
||(0.008)|(0.010)|(0.035)|(0.029)|
|1(Storm year), t-1=1||0.010||0.081<sup>_∗_</sup>|
|||(0.010)||(0.041)|
|1(Storm year), t-2=1||0.015<sup>_∗_</sup>||0.034|
|||(0.008)||(0.038)|
|1(Storm year), t-3=1||-0.018||0.060<sup>_∗_</sup>|
|||(0.013)||(0.030)|
|1(Storm year), t-4=1||-0.006||0.024|
|||(0.012)||(0.035)|
|1(Storm year), t-5=1||0.046<sup>_∗_</sup>||-0.004|
|||(0.025)||(0.046)|
|N|52,514|40,579|52,448|40,528|
|Fixed Efects:|||||
|County|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|State_×_Year|Yes|Yes|Yes|Yes|

Notes: All columns report the results of a fixed effects specification with the IHS transformation of the number of out-migrants from a county as the outcome in columns 1-2 and the IHS transformation of net migration as the outcome in columns 3-4. Net migration is defined as in-migration minus out-migration so that negative net migration indicates a declining population. Counties are defined as exposed to a storm if at least one hurricane resulted in a flood warning during the year or the county experienced wind speeds of at least 21 m/s - the speed at which structural damage begins to occur - during a hurricane in the year. 1(Storm year), t-# indicates that the county experienced a storm # years previously.<sup>_∗_</sup> p=0.1,<sup>_∗∗_</sup> p=0.05,<sup>_∗∗∗_</sup> p=0.01.
> <sup>11</sup>

drive down enrollment. One piece of evidence is reported above: teacher and student absenteeism move together. Bommier and Lambert (2000) find, in addition, that students enroll earlier in Tanzanian schools where school quality is higher. In the estimations below, therefore, gross secondary school enrollment, from _World Development Indicators_ , is taken as an indicator of incentives to provide public rather than private and targetable goods.

# **_Measuring public or non-targeted good provision:  government ownership of newspapers_**

The final indicator of public good provision is related to government policy towards citizen information. Since citizen information is a public good, government policies that expand or restrict citizen access to information should be influenced by the same forces that drive public good provision more generally – including the credibility of political promises. One such policy is the market share of government-owned newspapers as a fraction of the market share of the top five newspapers in a country, collected by Djankov, et al. (2003). Where the market share of government newspapers is high, citizens have less access to a diversity of information sources. Elected governments can more easily deny citizens this public good if public goods are not electorally relevant, as when political competitors cannot make credible promises to a large fraction of citizens.

It may of course also be true that elected governments with no intention of surrendering power might also foreclose citizen access to information. Such a government would pass from the category of “competitively elected”, eventually, when it suppressed an election or won an election with a large super-majority, to the non-competitively elected category, creating an association, by definition, between government ownership and the continuous years of elections. In this case, however, the highest government share of the newspaper market should be found where elected leaders have the longest tenure. There is, in fact, a strongly positive relationship between the tenure of elected executives and the