rafmacalaba/datause-encoder-data huggingface dataset:
as baseline characteristics and the 2018<sup>7</sup> data of WASSCE test outcomes as our main outcome variables in the analysis.

The third data source is a survey designed by the team, following the use of administrative data to build a control group. The survey collected data on students in grade 12 in both the program and control schools. The survey also gathered information on learning outcomes through written assessments in English and Math. The questions in the assessment were designed by teachers designated by MoBSE and based on the curriculum. We additionally gathered information on the socio-economic characteristics of students, teachers’ background, and school principal’s background. We also collected qualitative data to shed light on evaluation questions and topics that were not well-suited for quantitative measurement and analysis, using semi-structured interviews with program teachers, school headmasters and project beneficiaries. The survey allowed us to collect the unique identifier of students, assigned for the purpose of WASCEE 2018, and later used the identifiers to match the survey with students’ WASCEE performance. Additionally, we collected students' GABECE performance data to serve as the student level baseline performance. To allow for further matching at the student level, we oversampled two to three times as many students in the control groups compared to program students.

# 3.2. Identification

The identification and the causal interpretation of the findings rely on a combination of two main methods. First, the Ministry of Basic and Secondary Education used the following criteria to select the pilot schools: availability of electricity, availability of a sufficient number of science teachers, and the presence of a computer lab. As a result, a simple comparison of the program students and non-program students is ruled out. From Table 2, we see that PSI-PMI pilot schools are generally better off than the non-program schools but not across the board: the program pilot schools tend to be larger, with more enrolled students, but at a higher student-

> 7 The study team was able to obtain student-level WASSCE 2018 data for the 32 schools in the sample. We then used the WASSCE ID collected during the survey to match students’ WASSCE scores with other information. The match rate was 99.81%.

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The costs of building and running a general equilibrium model for tax policy analysis may usefully be assessed by considering the phases of a model's "life cycle". Although the phases are not disjoint, the following breakdown is convenient for our purposes: (1) selection of model structure, (2) compilation of a raw data base, (3) model implementation, (4) generation of a consistent data base, (5) model calibration and (6) experimentation with the model.

# Selection **of Model Structure**

The formulation of the problem at hand determines the structure of the model in a straightforward way. For instance, the evaluation of excise taxes requires a fairly disaggregated description of the production structure, while income tax enalysis may call for closer attention to be paid to income generation, transfer incomes and the determinants of factor supplies an(' demands. Studies of trade taxation naturally call for a detailed description of foreign trade.

# **Compilation of a Raw Data Base**

The structure of the model makes clear what data are required, whence the compilation of a raw data base may begin. This is a time consuming task. A typical model requires statistics on production, factor employment and income, demand patterns, foreign trade, government revenue and expenditure. The data comes from diverse sources. National accounts, aggregate foreign trade statistics and government revenues and expenditures are usually easily obtained. An increasing number of countries have household expenditure surveys and many have input-output tables that can be updated using other information. Employment and income statistics are harder to obtain, especially when sectorally disaggregated information is needed. In particular, it is normally not possible to get
had, however, an unintended effect: it increased the enrollment rate of the upper castes! Thus, aid targeted to Dalits did not, as policy-makers had expected, narrow the schooling gap between the Dalits and the rest of society. The increase in high-caste enrollment maintained the relative superiority of the high caste in years of education.<sup>11</sup> Finally, an observational study in Pakistan identifies a causal impact of high-caste dominance and low enrollment of low-caste children in school. This finding is consistent with our conjecture that cues to the caste order evoke a mental frame that impairs low-caste individuals’ ability or desire (or both) to achieve in the classroom. Jacoby and Mansuri (2011) analyze data from a survey of over 3000 households and 1000 elementary schools in rural Pakistan. They define a settlement as _high-caste-dominant_ if a high caste owns the majority of land in the settlement, giving them the power to enforce the traditional caste order. They show that low-caste children are deterred from enrolling in schools in high-caste dominant settlements. In fact, the very low enrollment of low-caste children<sup>12</sup> for whom the closest available school is in a high-caste-dominant hamlet can account for the entire enrollment gap favoring high-caste over low-caste children The following responses from low-caste women to the question, “Do children receive the same treatment from teachers,” illustrate the kinds of exclusionary norms imposed on low-caste individuals: “They let the daughters of [high castes] use the latrines, but tell our daughters to use the fields because you stink.” “The teachers make the daughters of Zamindar Zaats [high castes] sit inside the rooms, under the fans. Our poor children are outside, under the sun and dust” (Jacoby and Mansuri, p. 7).

In sum, these two observational studies —the only studies of which we are aware that examine the effect on achievement of increasing or decreasing the salience of the caste order— indicate that when one’s traditional status in the social order seems more fixed, both high- and low-caste individuals are less likely to enroll in school.

# **7.   Conclusion**

The experimental data show that being hard-working or clever is not a trait of the person, in the sense that it is always there, in a fixed manner. Instead, situational cues to one’s place in the

> 11 We thank Anjini Kochar for bringing her work to our attention.

> 12 Especially girls, for whom honor considerations restrict the freedom to travel outside their own hamlet.

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Figure iS **EC12:** SHARE OF INTRAREGIONAL TRADE, 1966-1995 <!-- Start of picture text --> 100<br>9 0<br>6 0<br>7 0<br>iO-<br>30<br>20<br>1 9685 1990 1995<br>AGRICULTURE<br>|-- MANUFACTURE<br><!-- End of picture text -->

Figure 17 APEC ASIA: SHARE OF INTRAREGIONAL TRADE, 1965-1995 <!-- Start of picture text --> 9 0 0<br>80-<br>70<br>' 50<br>40<br>30<br>2 0<br>1 0<br>1985 1970 1975 1980 1985 1990 1995<br>+.- AGRICULTURE<br>-in- MANUFACTURE]<br><!-- End of picture text --> 42
verify that the net impact of the cooperative price on catch,  0 , is positive. This is a necessary prerequisite to the model’s assumption that the cooperative can control its members’ effort by changing cooperative buying prices.

We use the following idea to address the identification concerns: Unlike in the model, changes in _Pct_ and changes in _X t_ and fishing costs do not happen at exactly the same point in time. We assume that in reality, when the cooperative changes _Pct_ , both individual catch _qict_ * and aggregate effort _Ht_ respond to the change immediately; however, the stock _X t_ and unobservable time-varying shocks to fishing do not respond immediately. Under this identifying assumption, if we can examine a narrow enough window around the price change, we can reasonably assume that the expected value of the stock _X t_ and unobservable fishing costs is similar on either side of the price change.

To operationalize this idea, we estimate equation 17 using fixed effects at the fishing teamspecies-month level and the fishing team-species-week level. The fishing team is not identified in the Sargento data, so we restrict the analysis to Pichilingue and Abreojos. Unlike in the model, in reality members may not fish for certain species at certain times. Consequently, some members have zero catch for a species in particular periods. The results below first examine the responsiveness of catch to prices including just the intensive margin, and then include both the intensive and extensive margin.

The first set of results appear in table 3, which examines fishers who catch a positive amount of a species. The first panel (columns 1–3) uses the Pichilingue sample, while the second panel (columns 4–6) uses the Abreojos sample. Both panels are structured analogously: the first column estimates equation 17 using fixed effects at the fishing team-species-month level and conventional standard errors, the second column does the same but clusters standard errors at the fishing-team-species month level, and the third column uses fixed effects and clustered standard errors at the fishing team-species-week level. Moving from the first to second column shows the effect of clustering, while moving from the second to third column shows the effect of focusing on a narrower time interval.<sup>21</sup> The coefficients on log cooperative price are generally positive and statistically significantly different from zero at conventional levels, though the P-value in column 2 increases to

> 21. For identification, the fixed effects model does not use fixed effect groups that only have one observation. For Pichilingue, 19–37% of groups have more than one observation (depending on the specification), and for Abreojos 70– 77% of groups have more than one observation.

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# **5.5 Quantifying Spillovers**

Given the potential for spillovers to women in the control group due to our individual-level randomization, we use baseline social network data to directly estimate spillovers. We find evidence of positive, significant network-based spillovers to women in the control group. Table 7 shows estimated spillovers at endline for our skills index, owning a business, earning positive profits, our extensive and intensive margin indices of business performance, employment, and wage work. Panel A shows spillovers estimated by accounting for any network link between a woman in the control group and a treated woman at baseline. Panel B shows spillovers for women connected at baseline through friendship links, and panel C shows spillovers for women connected at baseline through business links.

There is little evidence of positive spillovers in skills. None of estimated effects are statistically significant and they are all relatively small. However, we observe positive spillovers on a range of business outcomes. Having baseline business links with a treated woman increases the likelihood of owning a business by 5.7pp, 9%. It increases the likelihood of earning positive profits by 13% and the coefficient on the intensive margin business performance index is positive, though not statistically significant. Business links to treated women also reduce the likelihood of wage work by 32%, with no effects on overall employment. Strikingly, these effects are more than twice the size of the effects for women connected through friendship links at baseline, suggesting that there may be direct, positive economic spillovers between businesses.

Given that business links with treated women lead to positive spillovers, we adjust all estimated average treatment effects using spillovers specifically from business links.<sup>27</sup> At baseline, the average woman in the control group has 0.26 business links with a treated woman. We multiply the estimated spillovers by 0.26 and add them to our estimated average treatment effects (see Table A8, Table A5, Table A6, Table A7, and Table A9). Adjusted effects on skills and household outcomes do not change substantially when accounting for spillovers. However, extensive margin business effects increase by 7%–20% at endline and intensive margin effects increase by up to 38% when adjusting for spillovers. Savings and investment effects increase up to 31%. Our adjusted effects speak to substantial economic spillovers from the program.

> 27 We can provide tables showing estimated spillover effects from business links on all outcomes upon request.

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Source: Country data as detailed in Table 1 and authors’ calculation.

The potential productivity gains appear to decline over time. The result is encouraging as it suggests that economies have undertaken steps to reduce misallocation. This is consistent with the evidence from the Global Competitiveness Index (GCI) ( **Error! Reference source not found.** ). The GCI provides a summary measure of competitiveness based on three dimensions: basic requirements (i.e., institutions, infrastructure, macroeconomic environment, health and primary education), efficiency enhancers (i.e., higher education and training, goods market efficiency, labor market efficiency, financial market development, technological readiness, market size), and innovation and sophistication factors (i.e., business sophistication and innovation). Higher values are associated with more competitive economies.<sup>6</sup>

**Figure 11 Evolution of Global Competitiveness Index**

<!-- Start of picture text --> 5.5<br>5.3<br>5.1<br>4.9<br>4.7<br>4.5<br>4.3<br>4.1<br>3.9<br>3.7<br>3.5<br>Indonesia Malaysia Philippines Viet Nam<br><!-- End of picture text --> Source: Global Competitiveness Report 2017-2018 Within a narrow industry, highly productive firms are facing higher distortions (or “taxes”). In this framework, distortions are measured by TFPR (revenue TFP), while productivity is measured by TFPQ (quantity TFP). Figure 12 plots log TFPRi against log TFPQi ~~,~~ where TFPR̅̅̅̅̅̅̅̅ and TFPR̅̅̅̅̅̅̅̅ TFPQ̅̅̅̅̅̅̅̅ TFPQ̅̅̅̅̅̅̅̅ are the sector averages. In a frictionless world, firms with higher (lower) TFPR would grow (shrink) up to the point where TFPR are equalized (TFPRi = TFPR̅̅̅̅̅̅̅̅). For all ASEAN countries studied, we observe a positive relationship between revenue and physical productivity. The result is consistent with the evidence emerging from other developed and developing countries. It suggests that more productive firms (i.e. those that have larger TFPQ) face larger idiosyncratic distortions (i.e. larger TFPR). In other words, more productive firms are “taxed” at a higher rate (either explicitly or implicitly), hence capital and output wedges or taxes absorb

> 6 In 2018, the GCI ranged from 5.7 for the U.S. to 2.8 for Chad. The average for the 17 East Asia and Pacific economies considered by the GCI was 4.8, with Singapore and Mongolia on opposite sides of the spectrum.

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<!-- Start of picture text --> <s+y.+f,(b)pibs \ U()=V( ply)<br>° Ca(phy) poop seer ooo<br>> U()=V( p’,y)<br>< y.+f,(b)<br>Co (ply) prea r ance centr neces<br>i<br>| rN<br>i<br>Ci (p°.y)= Ci (p'y)<br>Cy<br><!-- End of picture text -->

<!-- Start of picture text --> Ci(p,<br>y+ EV") | - - +s 22---------- a=<br>)<br>Ci(p,y+EV") Ci(p.y+EV')<br>cy<br><!-- End of picture text -->
The literature examining the link between child labor and subsequent labor market outcomes is much more limited. Indeed, the only papers we are aware of are Beegle, Dehejia, and Gatti (2005) and Emerson and Souza (2007). Beegle, Dehejia, and Gatti (2005) find that child labor is associated with a significant increase in wages 5 years later; the wage increase is sufficiently large to offset the cost of displaced education. Emerson and Souza (2007) instrument for child labor and child schooling using the number of schools per child in the state, the number of teachers per school, and GDP per capita at age 12. They find that, even controlling for completed schooling, child labor has a negative effect on adult earnings. The strength of this paper is a large, nationally representative data set, but its limitations are that child labor is measured retrospectively (in adulthood) and only for the sample of adults who are working for a wage. In contrast with these papers, the present paper observes child labor as it occurs and is able to follow individuals over a 10-year horizon.

# **III. Data Description**

# _III.1 Data set_

The Kagera Region of Tanzania is located on the western shore of Lake Victoria, bordering Uganda to the north and Rwanda and Burundi to the west. The population (1.3 million in 1988, about 2 million in 2004) is overwhelmingly rural and primarily engaged in producing bananas and coffee in the north and rain-fed annual crops (maize, sorghum, cotton) in the south. This study uses baseline data from the Kagera Health and Development Survey (KHDS), a longitudinal socioeconomic survey conducted from September 1991 to January 1994 covering the entire Kagera region (World Bank, 2004). Because adult mortality of the working age population (15-50) is a relatively rare event and HIV/AIDS was unevenly distributed in Kagera, 5
Any of the above tariff structures may be complemented by a fixed monthly charge, and are then described as two-part electricity tariffs. The fixed charge can be used to cover the fixed administrative costs associated with serving a customer, and is sometimes also used to discriminate between customers based on other cost-related characteristics such as load served and network location.

# **a. Residential tariffs**

Two-thirds of the prevailing pricing schemes in Africa are IBTs, and the remaining third are linear (figure 1a). The use of linear rates is more common in countries with prepayment systems (Malawi, Mozambique, and South Africa) (table 1). The prevalence of IBTs is consistent with recent trends in power regulation. IBTs have often been put forward as a good tool for reconciling cost-recovery targets with distributional aims, although their success in doing so is critically dependent of the details of tariff design (Filipovid and Tanid 2009; Borenstein 2008). A more detailed description on residential tariff structures practiced in Africa can be found in Annex 2.

<!-- Start of picture text --> Figure 1. Prevalence of specific tariff schemes<br>a. Block tariff pricing b. Fixed charges<br>70% 60%<br>60% 50%<br>50%<br>40%<br>40%<br>30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0% 0%<br>Increasing Block Linear Rate Fixed No Fixed Fixed No Fixed<br>Charge Charge Charge Charge<br>(Linear) (Linear) (IBT) (IBT)<br>tariff schemes tariff schemes<br>schemes schemes<br>percentage of prevailing percentage of prevailing<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database.

About half of the sample countries have adopted two-part tariffs (figure 1b), combining fixed charges with block tariff pricing. Among countries practicing linear tariffs, the use of two-part tariffs is more common than where IBTs are applied. For African countries the fixed charge tends to be relatively high: between $1.00 and $3.00 per month (table 1). As a reference, the average fixed charge in Latin American countries is $0.70 (Foster and Yepes 2006).

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are included have more active firms and, hence, other things equal, we would expect these corridors to be more competitive. On average, across all corridors, the number of respondents is 8 and it varies between 2 in the Spain-China corridor and 18 in the US-Mexico corridor.

We also include a direct measure of competition among banks in both receiving and sending countries. The rationale for including this variable is that more competitive banking sectors are going to offer cheaper services, including remittances. This will create pressure for other providers to lower costs as well. Of course, this implicitly assumes that banks are significant players in the remittance business.

Following Panzar and Rosse (1982, 1987), we compute the _H-statistic_ , which measures the degree of competition by calculating the elasticity of the total interest revenue of banks with respect to input prices.<sup>16</sup> Under perfect competition, an increase in input prices raises both marginal costs and total revenues by the same amount and, hence, the H-statistic will equal 1. In a monopoly, an increase in input prices results in a rise in marginal costs, a fall in output, and a decline in revenues leading to an H-statistic less than or equal to 0. Panzar and Rosse (1987) show that when H is between 0 and 1 the system operates under monopolistic competition. We expect a negative relationship between the H-statistic in sending and receiving countries and the cost of sending remittances. We use data for the period 1994-2006 from Bankscope to compute the H-statistic. Among both remittance receiving and sending countries, the H-statistic averages close to 0.53. But as expected the standard deviation is larger for the latter.

As an alternative measure of market structure in the remittance industry, we include the share of bank respondents among all remittance service providers in the database. To the extent that, as some have argued, banks view remittances as a marginal product and are less likely to offer competitive prices for this product (Ratha and Riedberg, 2005), we expect to find a positive correlation between the share of bank respondents and the average cost of remittances. Across the 119 corridors the share of bank respondents varies from 0 in the Italy-Sri Lanka corridor to 100 in the South Africa-Swaziland corridor. On average, the ratio of bank respondents across corridors is 31 percent.

> 16 Other studies that use this methodology to estimate competition include: Bikker and Haaf (2002), Gelos and Roldos (2002), Claessens and Laeven, (2004), and Levy-Yeyati and Micco (2007).

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