rafmacalaba/data-use-mentions-extended huggingface dataset:
an additional 1.19 days in _every 2-week paycycle_ .<sup>29</sup> In addition, these effects are not simply reflecting a substitution away from other income-generating activities outside of the factory jobs: we find no treatment effects on earnings outside the factory. This is consistent with the fact that, at baseline, 89% of workers report having no earnings outside of their factory job, and on average, 93% of total income comes from factory earnings. Consequently, these treatment effects reflect an increase in total earned income.

In Cols. (4)-(5), we verify that our Phase 2 treatment effects are not driven by Phase 1 treatment status. Specifically, since we cross-randomize treatment assignment across the two Phases, one potential concern is that treatment effects may be influenced by workers who were assigned to Private accounts in Phase 1 and then lost access to these accounts in Phase 2. In Col. (4), we exclude from the analysis workers who were assigned to Private accounts in Phase 1, so that the sample includes only workers who were in the Control group in Phase 1 or workers who were not in the Phase 1 sample at all. The results are nearly identical: among such workers, the Private treatment increases output by 214 FCFA or 12.8% ( _p_ =0.034). Moreover, even if we exclude all workers in the Phase 1 sample, and only examine effects on the 120 workers who only joined the study in Phase 2, the treatment effects are similar and remain significant (Col. 5, _p_ =0.020). Moreover, we cannot reject that the treatment effects estimates across Cols. (3), (4), and (5) are the same ( _p_ =0.427); in other words, the effects across these three samples are statistically indistinguishable. In addition, we verify that our treatment effects are robust to alternate empirical specifications, including using only intervention period data to examine
illustrated in Figure 1. The evaluation data includes sociodemographic variables, participation in formal schooling and training, labor-market outcomes, and detailed information on participation in the MVTP. Attrition reached 5.4 percent at baseline, 9.6 percent at the first follow-up survey, and 15 percent at the second follow-up survey. As shown in Table 1, and relative to similar interventions with similar timespan for the measurement of treatment effects, the attrition rate in this study is thus moderate.<sup>8</sup> Appendix Tables S1.1 and S1.2 provide results of tests for differences in baseline variables across treatment and control units for the six- and eighteen-month follow-up samples conditional on response status. For most baseline covariates, no systematic differences are observed between individuals in the treatment and control groups after conditioning on response status. Only for a small number of baseline covariates is the null of equality of means by treatment status rejected, after conditioning on response status. Finally, Appendix Table S1.3 reports the overall rate of attrition by treatment status. The null hypothesis that the overall attrition rate is of equal magnitude between treatment and control groups cannot be rejected.

Mean covariate balancing tests for the two experimental designs are shown in Table 2, ne for the allocation of training slots (left panel) and one for the allocation of weekly newsletters to individuals who received treatment (right panel). Baseline data show that the typical applicant is twenty-three years old, a woman (65 percent), and poor (82 percent lived in gers.<sup>_9_</sup> Almost half of the participants are married (45 percent), have children (42 percent), and live with their parents (47 percent). On average, they show high levels of formal

> 8 Table 1, Column 9, shows attrition rates that range from 18.5 (Card et al., 2011) to 46 percent (Cho et al. 2013). Alzua et al. (2016) reported
of some 30%. This is plausible as these data are known to exclude farms not formally registered as well as household plots. With 40, 25, 73 and 24 dt/ha for cereals, sunflower, maize and soybean, respectively, yields in 2018 were uniformly above those in 2017, a year in which precipitation was well below the long-term average.

# **3.3 Econometric approach**

Indexing crops by _j_ and village councils by _v_ , we characterize a field’s crop rotation history by the crops that precede crop _i_ to assess the impact of different predecessor crops on yield of this crop via an OLS regression of the form log i i i log i i (1) ii jj,ii−1 jjii,ii−1 ii,ii−1 ,ii−1 ii,ii−2 ,ii−2 ii ii ii (YY ) = αα+ �ββ SS + γγ MM + γγ MM + δδ (FF ) + εε jj≠ii where i is the yield of crop _i_ in village council _v_ at time _t_ , is the share of area planted with crop _i_ in _t_ ii that was under predecessor crop _j_ in _t-1_ ; i and jjii,ii−1i are the share of area with crop YY SS _i_ that had been cropped with the same crop consecutively for two or three years; ,ii−1 ,ii−2 i is the average MM MM farm size for crop _i_ in village _v_ to adjust for suitability and i is a random error term. Coefficients ii FF of interest are , the effect of having crop _i_ preceded by _j_ as well as ii and , the effect of εε planting the same crop jj,ii−1 _i_ for two and three consecutive years, respectively. Parameters are estimated ii,ii−1 ii,ii−2 ββ γγ γγ separately for 2017 and 2018. As data on crop cover are available only for 2016-2018, the effect of monocropping is estimated for two consecutive years in
balance, account fees, documentation requirements to open an account, characteristics of accounts (limits on no. of withdrawals); factors influencing branch location; for different categories of loans: paperwork requirements, collateral requirements, factors influencing credit decision and collateral decision, use of credit scoring, rates of interest, fees; cost and speed of different forms of retail payments transaction. Fairly full responses have been received from 23 countries. It is intended to expand the coverage to 90 countries.

The raw results of both of these surveys are currently being processed, and further provider-based sources of information are being explored by researchers at the Bank and the Fund.

A lot of information does reside with providers. They know about the pricing, production costs and frequency of use of different services and products (including, for example, the possible cost efficiencies of new modes of service delivery to low income persons). They should also have this information by geographic location of the point of service. Where they may not have much information is on the characteristics of their customers. Indeed, many banks still do not integrate information on different accounts held by the same customer and as such do not know how many different customers they have.

# _Credit registries_

Nevertheless, increasing computerization of banks’ administrative records and the growth of public or private credit registries allows the creation of usable databases. Importantly, these databases can include time series information about rather small firms otherwise costly to survey. For example, A large Indian bank provided a detailed data set to researchers Banerjee and Duflo (2003), containing information about its lending relationships with about two hundred small and medium-sized firms in the period 19962002. More than half of the firms were in the “small” category, having less than $150,000 worth of machinery and equipment assets. This data covered firm characteristics such as turnover, asset size, profits as well as the credit line, utilization and interest rate.

Credit registry-based data allow multiple banking relationships of a single borrowing firm to be
# **Box 2: Authority, Information and Motivation Go Together**

A series of studies in Europe looked explicitly at the role of setting targets in national health policies within a scheme of good governance (Wismar et al 2006). Such targets “are viewed as a means of defining and setting priorities, creating high-level political and administrative commitment to particular outputs, and providing a basis for follow-up and evaluation.” (European Observatory 2006).

Targets can play an important role in defining, generating and collecting useful information for governing the health sector. However, without concomitant changes in authority and motivation, it is not clear what impact targets can have.

England‟s experience with setting health service targets in the mid-1990s was combined with changes in authority and motivation. For one thing, the National Health Service‟s targets were defined in terms of outcomes rather than operational activities. In addition, these targets were introduced with a range of associated financial incentives (motivation) and increased autonomy (authority). Performance on targeted measures of care improved in England compared to Scotland and Wales where such policies were not implemented and even though spending levels are lower in England.

The case study in Hungary demonstrated how target-setting has been accompanied by improved data collection over a 10-year period, including the ability to disaggregate health status data by important social and geographical categories. An important advance in this process came with the coordination of data collection between the country‟s national statistical office and its national public health system. While data collection and reporting continued to improve, it was not possible to demonstrate any impact on health policy, health service performance, or health outcomes. While the use of such data in national policy debates may eventually have some impact, this case suggests that efforts to improve information without accompanying changes in authority and motivation may not bear the desired results.

Source: European Observatory 2006.

In light of this definition of completeness for governance determinants, the share of private sector contracting is not a very complete measure. It
156 86 Obs.

|**_Panel D. Ecuador_**|||
|---|---|---|
|HEI is public|0.346***|0.372***|
|Obs.|543|483|
|Total graduates|20.2**|19.9**|
|Obs.|465|405|
|Annual tuition cost|-387.2|-107.4|
|Obs.|333|291|
|Program is taught online|0.058*|0.052|
|Obs.|543|484|
|**_Panel E. Peru_**|||
|Technical program|-0.027|-0.026|
|Obs.|387|383|
|Professional program|0.027|0.026|
|Obs.|387|383|
|Annual tuition cost (in SOL)|1061.9|1460.4|
|Obs.|387|383|
|Program has high quality<br>accreditation|-0.002|-0.007|
|Obs.|387|383|
|Face-to-face program|0.033|0.030|
|Obs.<br>_Source_: Own estimations using WBSCPS.<br>_Notes_: This table summarizes the results from represe|387<br>ntativeness checks performed|383<br>using available program-|

_Notes_ : This table summarizes the results from representativeness checks performed using available programlevel administrative data, by country. In each column, we present estimated coefficients from the following model: yp = α0 + βDp + S+ εp for Brazil and Colombia and yp = α0 + βDp + εp for the rest of countries. In this model, yp is the characteristic of program _p_ (e.g., whether the program has high quality accreditation). We estimate a model for each characteristic available in the
violence, and by a factor of 3 for emotional violence. There are several possibilities that explain this discrepancy. Firstly, these poor, rural women may have a higher experience of violence than the national average. Secondly, and, perhaps, most importantly, more women may have felt comfortable revealing their experience of violence using our ACASI interviewing technique rather than the in-person approach used in the national survey.

Summary statistics on non-IPV GBV also indicate higher levels than reported on the national survey. Between 7% and 12% of respondents reported having experienced physical or sexual violence, either inside the home with a non-partner or from someone outside the home (Table 3). These numbers are at least twice as high as the national prevalence rates reported in the Lao National Survey on Women’s Health and Life Experiences, for physical and sexual violence from a non-partner: at 5.1% and 5.3%, respectively.<sup>11</sup> We note that due to the sensitive nature of these questions, non-response is high, as is typical for surveys on IPV (UN Department of Economic and Social Affairs Statistical Office 2014). For our IPV variables, non-response reached over 40% for some of the questions regarding controlling behavior, while the non-

> 11 The Lao National Survey on Women’s Health and Life Experiences does not collect data on exposure to nonpartner physical and sexual violence over the last 12 months.

11
free in public health facilities (including CSComs) to ensure that cost is not a barrier to accurate diagnosis.<sup>9</sup> Artemisinin combination therapies (ACTs) are recommended for simple malaria, while severe malaria cases should be treated with injectable artesunate followed by a dose of ACTs once the patient is stable.<sup>10</sup> These policies are in line with WHO recommendations (WHO, 2014) and are designed to limit the spread of drug resistance, which is hastened by overtreatment. Limiting overuse of antimalarials is particularly important, as drug resistance has rendered past generations of antimalarials ineffective across much of Sub-Saharan Africa and Asia (Arrow, Panosian and Gelband, 2004). This externality is one of the largest (potential) costs to overtreatment, though overtreatment also strains government budgets for drug subsidies, wastes resources of poor families, causes side effects, and can slow diagnosis and treatment of non-malarial illnesses.

In public facilities, ACTs are meant to be free for children under five and subsidized for older individuals, but there are no subsidies for other components of severe malaria treatment (PMI, 2016). In our study area, the ACT subsidy policy is only partially enforced, as evidenced by the fact that 37 percent of children under five receiving ACTs in our control group paid a positive price.

# **1.2 Status Quo Malaria Treatment in the Study Sample**

We use data from our Control group to quantify overuse of malaria treatment at the 60 clinics that participated in our study. Here we give a brief overview of the data; we provide additional detail on the sampling frame and data collection protocols in section 3. We stationed surveyors at each clinic for Previously, presumptive treatment of any fever cases was the main approach to malaria control (Kon ́e et al., 2015).

> 9In our data, RDTs were free of charge 70 percent of the time, while microscopy tests were free less than 3 percent of the time.

> 10Artesunate
466*** †|-0.325***|-0.511*** ‡|-0.354***|
||[0.111]|[0.145]|[0.0364]|[0.0561]|[0.0609]|[0.0626]|
|Common<br>language|-0.136***|-0.0924***|-0.183***|-0.153***|-0.146***|-0.254*** ‡|
|Observations|642|538|632|134|382|408|

Source: World Bank.

Note: * p<0.05, ** p<0.01, ***p<0.001,. Robust standard errors are shown in parentheses. † indicates statistically significant difference of the coefficient estimate from the coefficient estimate for a sample of advanced economies only. † indicates statistically significant difference of the coefficient estimate from the coefficient estimate for a sample of commodity importers only. The table shows estimated coefficients from a gravity panel regression estimated for up to 95 countries using annual data for 2007-18 where the dependent variable is the log of bilateral trade costs. The regression includes time fixed effects. Standard errors are clustered by country pairs.
<mark>The independent variables included vary by country and round, since the sampling frame and survey instrument vary by country and round. The basic information document for each phone survey provides specifics of what variables are included.</mark> <mark>5. We predict the probability of response (i.e. propensity score) and create ten equal groups (deciles) for this variable. Within each decile d, we compute the mean value and take the reciprocal as the phone survey attrition correction factor for observations in each decile</mark> (acD=d): <mark>where</mark> phonei is the predicted value <mark>(i.e. propensity score) for each observation i. N is the total number of individuals in each decile. We then apply the attrition correction factor to the adjusted household sampling weight as derived in step #3.</mark> <mark>6. We winsorize the resulting weight (</mark> wi,ac <mark>) by replacing the top two percent of observations, with the value at the 98</mark><sup>th</sup> <mark>percentile cut-off point.</mark> <mark>7. Finally, we post-stratify weights to reduce standard errors and to match the projected population totals at the highest spatial resolution possible, ranging from region to district, based on the data availability in each country. Population projections come from national statistic bureaus, often as part of census data. The post-stratification weights (</mark> wps <mark>) are calculated as the weighted total number of observations from the data divided by the census projections:</mark> <mark>where</mark> Mr <mark>is the total population in the region, or whatever is the highest spatial resolution available, and</mark> population is the national statistic bureau’s projected population for that region. The final weight is then calculated
and the cost-eectiveness of simple interventions to reduce these compliance gaps. In this sense, our study is a complement to Brockmeyer and Hernandez (2016) who conduct a systematic anatomy of compliance in Costa Rica, uncovering substantial compliance gaps on the extensive margin. The role of third-party information in enhancing tax compliance has featured prominently in the literature (Kleven et al. 2011; Kleven, Kreiner, and Saez 2015). Third-party reporting has been shown to enhance tax compliance at the intensive margin for the value-added tax (Pomeranz 2015; Naritomi 2015), but was also found to generate evasion shifting to the cost margin, when applied to income taxation (Carrillo, Pomeranz, and Singhal 2016, Slemrod et al. 2015). Our study focuses on the role of third-party reporting in enhancing compliance on the extensive margin, showing that information is both a tool for strengthening deterrence interventions and a by-product of better ling compliance.

Second, this experiment adds to other communication experiments to increase tax compliance, as reviewed in Dwenger et al. (2016) and Hallsworth (2014). Our study uses a cost eective delivery method - emails - and maximizes message impact by combining dierent message elements that have proven successful in other contexts. As expected, the observed treatment eect of our emails is substantially larger than treatment eects in most other studies. Besides, our study is one of few in this literature to focus on rms, including corporations, as opposed to individual taxpayers, and the rst with Kettle et al. (2016) to focus on tax ling.<sup>11</sup> Indeed, most studies in this literature have focused on correct reporting of liabilities or on the payment of already assessed liabilities, such as property tax liabilities. Furthermore, we extend the literature by using rich administrative data to measure a variety of outcomes. Contrary to compliance crowd-out, we nd positive impacts on compliance in prior tax periods, ling of informative declarations, and a persistence
The levels of Internet adoption in the exporter and importer have a different effect on the intensive and extensive margins. Internet adoption in the exporter has a larger impact on the extensive margin than the intensive margin, while it is the reverse for Internet adoption in the importer.<sup>6</sup> The result suggest that higher Internet adoption allows the exporter to find more overseas buyers for their products and increases the range of products exported. A 10 percent increase in Internet adoption in the exporter increases the extensive margin by 1.5 percent and the intensive margin by 0.4 percent. Riker (2015) also finds a similar result where higher Internet adoption in the exporter increases the probability of product entry into the U.S. market. Conversely, higher Internet adoption in the importer increases the intensive margin but decreases the extensive margin. A 10 percent increase in Internet adoption in the importer increases the intensive margin by 0.7 percent and decreases extensive margin by 0.15 percent. While the negative effect of the Internet on the extensive margin is puzzling, the aggregate trade data do not allow us to explore this further. Our intuition is that there is a substitution effect: higher Internet adoption in the importing country reduces the search and communication costs so buyers are able to purchase from a larger set of exporters, thus the number of products being exported between a given bilateral pair reduces.<sup>7</sup> This supports the intuition laid out by Freund and Weinhold (2004), where they infer that higher Internet adoption allows the importer to find more sellers and the equilibrium amount that each exporter will sell to the importer decreases.

**Table 3: Intensive and Extensive Margins and Internet Adoption**

|VARIABLES|(1)<br>ln (Total<br>exports)|(2)<br>ln (Average<br>value of<br>exports)|(3)<br>ln (Number of<br>product
profiles are as expected, although we have to realize that it is choice of households, so we observe only revealed usage and not potential access (given costs, quality, ease, etc., many do not bother to seek access from formal financial institutions). Socioeconomic characteristics such as income, wealth and education play the largest roles in explaining access. Financial exclusion is thus often part of a broader exclusion in terms of education, jobs, formal training, etc. Households that use credit have a different profile from those with banking account and savings use, and the profile is more affected by income and wealth characteristics, as it tends to be the richer that borrow.

The comparison between the US and Latin America countries indeed shows some similarities between otherwise very different countries in terms of why people do not (want to) bank (Table 3). Conveniences, trust and savings are important considerations for households to seek financial services from banks. Unbanked households in the US and Mexico also display very similar characteristics, two countries otherwise at different levels of development, with the exception of home ownership (Table 4). The costs of being unbanked do vary considerably, however, as the alternative means are much fewer and more costly in Mexico. The costs in the USA for being unbanked are estimated to be only 2.5% of median income in the lowest income segment, whereas in Mexico they are estimated at 5% (Solo et al. 2004).

Although also weak and often neither comparable, some data on firm access to financing have more recently become available. Specifically, Table 5 reports the percentages of firms that say access to financing presents major or severe obstacles to the operation and growth of their business. The data come from the World Bank Investment Climate Assessments (ICA) that have been conducted in the last few years. About a quarter of the firms on average complain about the lack of external financing, with large variations though, from less than seven percent for Latvia and Lithuania,