rafmacalaba/datause-encoder-data huggingface dataset:
Looking at Panels B and C of Table 3, we see that quality ratings information works to both increase the number of providers chosen with the highest score of 5 (by 7 to 11 percentage points), as well as to reduce the number of bottom-ranked firms (by 9 to 15 percentage points). Thus it does not seem to be a story of firms using these rankings to simply screen out really bad providers and then other information such as location or how attractive they find the company’s logo and tagline to then choose providers, but rather than there is a demand for the highest quality as well as just for avoiding low quality.

# **4.4 Impact on Subsequent Use of Business Service Providers**

We conducted a short follow-up survey approximately six months after the information intervention. This survey took place between March 30 and May 14, 2020. It began just as the COVID-19 pandemic was starting, and as a result had to be conducted by telephone. We were able to re-interview 697 of the 1,054 firms (66%), with the response rates not statistically different (p=0.888) for those receiving the information treatment (65.5%) and the control group (67.7%). Appendix Table C1 shows that female business owners were more likely to attrit than male owners, but that other characteristics of the firms are similar between attritors and non-attritors. Moreover, the information treatment and control group remain balanced on baseline observables among the non-attriting firms.

We use this follow-up survey to estimate the impact of providing firms information and quality ratings on their knowledge and use of this marketplace for business service providers. We run the following treatment regression to measure the impact of being given any form of information through the business services marketplace: Where _Y_ is an outcome measuring knowledge or use of the market for business service providers, _InfoTreatment_ is a dummy variable taking value one for those assigned to the information treatment, and _Lagos_ is a dummy variable for being in Lagos versus Abuja (since randomization was stratified on city). We then also examine whether the impacts differ by the type of information treatment assigned.

13
# Policy Makers Can Increase Account Ownership and Use in Various Ways

Governments and businesses could help dramatically reduce the number of unbanked adults by moving routine cash payments into accounts. Such payments could include public sector wages, public pensions, and government transfers of social benefits. Globally, digitizing such payments could reduce the number of unbanked adults by up to 100 million (Demirgüç‐Kunt and others 2018). Many unbanked adults have the basic technology to receive payments digitally. Of the 60 million unbanked adults worldwide who receive government transfers in cash, two‐thirds have a mobile phone.

Digital payments have played an important role in boosting financial inclusion in ECA. About 1 in 6 account owners opened their first account with a financial institution to receive digital government payments. The share of adults in the region receiving government wages, pensions, or social benefits is twice the developing world average—and two‐thirds of recipients receive their payments digitally.

More can be done. Nearly 25 million unbanked adults in ECA receive government payments in cash— and 75 percent of them have a mobile phone. Moving public sector pension payments into accounts would reduce the number of unbanked adults in the region by up to 20 million, including 8 million in Russia alone. In the private sector, about 19 million unbanked adults receive their wages in cash—and more than 90 percent of them have a mobile phone. About 15 million unbanked adults receive agricultural payments in cash—and more than 90 percent of them have a mobile phone.

Although financial inclusion starts with providing the unbanked with an account, the benefits come from actively using the account for saving, managing risk, borrowing, and making or receiving payments. The Global Findex database reveals many opportunities to help account owners make better use of their accounts. These benefits are considerable in ECA, given the ample room for promoting account use. For example, 80 million adults in ECA are banked but still pay utility bills in cash—and 95 percent of them own mobile phones. Digitizing these payments would improve usage.

# Future Directions of Our Work

Our analysis of financial inclusion across the region reveals many interesting issues deserving of future research.

First, how can the trust issue—which impedes financial inclusion, particularly the low levels of formal savings—be addressed?

Second, what explains the differences across ECA countries in recent advances in financial inclusion? Why, for example, have Armenia, Georgia, the Kyrgyz Republic, Moldova, and Tajikistan shown significant increases in account ownership over the past three years while their neighbors, Azerbaijan and Uzbekistan, have not? What role do digitalization and technology play in explaining the different experiences?

Third, how can we explain and address the observed disparities in financial inclusion across subregions within countries, such as Romania? Could credit unions and cooperative banks play a role in reaching remote rural areas? Again, what role can technology play?

27
24 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE Second, to compute � _τij_ , we combine equations (3), (4), (25) and (27) to get (with appropriately defined fixed effects) Armed with estimates of _F_<sup>�</sup> _ij_ and ( _σ −_ 1) _τ_ � _ij_ , we can compute their correlation and check whether � _Fij_ increases or decreases with distance (demeaned by origin and destination fixed effects).

These empirical exercises require estimates for _σ_ ̄ _φ_ as well as _Ni_ for every country. We use Bento and Restuccia (2015) (henceforth BR) data to estimate a value for _Ni_ for all the countries in our sample.<sup>26</sup> We acknowledge slippage between theory and data in that we obviously do not have a measure of the entry level _Ni_ , but (at best) only for the number of existing firms, which in theory would correspond to (1 _− Gi_ ( _φ_<sup>_∗_</sup> _ii_<sup>))</sup><sup>_Ni_(our approach in the next subsection avoids this</sup> problem). We use the QQ-estimation proposed by Head et al. (2014) (henceforth HMT) to obtain estimates of _σφ_ and _μφ,i_ for every _i_ (see Online Appendix C for a detailed description).

# **Full Melitz-lognormal model**

The previous section has shown that a model with a lognormal distribution of firm productivity is capable of generating a positive intensive margin elasticity conditional on fixed costs. However, the model we considered had two very stark predictions. First, fixed trade costs that are common across firms lead to the prediction that sales of the least productive exporter from _i_ to _j_ are equal to _σFij_ . In the data we observe many firms with very small export sales (sometimes as low as $1) which implies unrealistic fixed trade costs. Second, as shown by Eaton et al. (2011), the model implies a perfect hierarchy of destination markets (i.e., destinations can be ranked according to profitability, with all firms that sell to a destination also selling to more profitable destinations) and perfect correlation of sales across firms that sell to multiple markets from one origin. None of these predictions holds in the data.

In this section we consider a richer model with firm-specific fixed trade costs and demand shocks that vary by destination. This is similar to the setup in Eaton et al. (2011). We assume that firm productivity, demand shocks (denoted by _αj_ ) and fixed trade costs (denoted by _fj_ ) are

> 26Using census data as well as numerous surveys and registry data, BR compiled a dataset with the number of manufacturing firms for a set of countries. Unfortunately, the sample in BR has missing observations for a number of countries in the EDD. We impute missing values projecting the log number of firms on log population. There is a tight positive relationship between log number of firms in the BR dataset and log population with an elasticity of 0.945, as reported in Table 10 and in Figure 9.
Table 1: Means of Quality Variables, Which Suggest that Private Pre-Primary Education is of Higher Quality than Public

|Measure of Quality|Private|Public|Diference|
|---|---|---|---|
||(1)|(2)|(3)|
|Pre-primary students per pre-primary teacher|15.3<br>(0.062)|18.3<br>(0.045)|3.1<br>(0.083)**|
|Fraction of pre-primary teachers with post-secondary education|0.231<br>(0.002)|0.197<br>(0.001)|-0.035<br>(0.002)**|
|Fraction of pre-primary institutions with own school building|0.979<br>(0.001)|0.949<br>(0.002)|-0.030<br>(0.002)**|
|Fraction of pre-primary institutions with electricity|0.997<br>(0.001)|0.903<br>(0.001)|-0.095<br>(0.002)**|
|Fraction of pre-primary institutions with indoor bathroom|0.960<br>(0.001)|0.799<br>(0.001)|-0.161<br>(0.003)**|
|Fraction of pre-primary institutions with library|0.623<br>(0.003)|0.197<br>(0.001)|-0.427<br>(0.003)**|
|Fraction of pre-primary institutions with computer|0.849<br>(0.002)|0.370<br>(0.002)|-0.479<br>(0.003)**|

_Notes:_ Standard errors are in parentheses. ** indicates p _<_ .01.

_Sources:_ Author’s calculations based on school- and teacher-level data from the 2008 Census of Schools (Censo Escolar).

Table 2: Number of Municipalities, by Tercile of Income Per Capita and Tercile of Net Per-Child Receipts (Net Per-Child Benefits) from FUNDEF Education Finance Reform, 1998

||Lowest Receipts|Middle Receipts|Highest Receipts|
|---|---|---|---|
|Poorest|51|372|710|
|Middle|664|695|369|
|Richest|638|286|273|
|**Total**|1353|1353|1352|

_Notes:_ The total number of municipalities in the table is smaller than the national total, due to missing data on per capita municipal income or per-capita FUNDEF receipts data. _Sources:_ Author’s calculations based on data from Tesouro Nacional and IBGE (1998).

41
# _<u>Simulation</u>_

The combination of age-specific saving rates, population projections and inequality levels is used for projections of the weighted average of the saving rate. 2006 is used as the initial year for the projections based on IBGE estimates for Families’ Saving. The levels of the saving rates are used in the simulations but for the final analysis, the percentage changes in the saving rate are used to project the aggregate family saving rate as it was reported by IBGE at 4.84 percent in 2006. This is because the levels are based on a sample of households in Brazil and may not necessarily be representative to the whole population of non-poor and poor households.

In the simulations, the new rich — the share of the population that would still have been poor if the poverty headcount rate did not fall — are not assumed to save with the full amount as the rich population segment. Instead, we assume that the simple average of the age-specific saving rates for the rich and poor population segments applies to this “intermediate” group. Therefore, it is assumed that the transition from high to low saving rates is phased in line with improvements in the poverty headcount.

By combining the three types of projections: (i) projections for age-specific saving rate; (ii) projections for age-specific population changes; and (iii) projections for the share that is non-poor and poor, it is possible to project the weighted aggregate saving rate in Brazil. The main finding is that the saving rate may increase slightly from 4.84 to 7.49 percent in 2050 if the poverty headcount follows it trend (as illustrated in Figure 4a). If the poverty headcount remains unchanged, the trajectory of the saving rate will take a parallel downward shift and reach 6.92 percent in 2050.

**Figure 4a. Poverty has Fallen from 1995 to 2007;** **<u>Two Scenarios for Future Poverty Headcount Ratio</u>**

**Figure 4b. Household Saving Rate May Increase** **<u>by almost 3 Percentage Points by 2050</u>**

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Source: Author’s projections for the saving rate depend on scenarios for poverty and the exclusion or inclusion of public pensions in income.

13
# **Data**

The main data sources are the 2008-09 and 2010-2011 rounds of the National Panel Survey, a large multi-topic household survey conducted in Tanzania by the National Bureau of Statistics with support from the World Bank. 3,200 households were interviewed in 2008-09, and these households, plus any new households that split off from the original sample, were re-interviewed in 2010-2011. The main focus of the survey was household consumption, poverty, and agricultural production, but there was also a governance module, which was asked of one single randomly selected adult household member. Helpfully for this paper’s identification strategy, the timing of survey visits were randomized. The 2008-2009 LSMS survey documentation notes that “within each zone, each district and each region were visited at 3 separate (randomly assigned) points during the year, so as to account for seasonal fluctuations.” Communication with LSMS survey staff confirms that this randomization into early or later survey dates was maintained as much as possible in the 2010/2011 follow up survey.<sup>10</sup> This randomization, combined with the staggered roll out of the bed net campaigns, enables an “as-if” random RD design. Dates of the ITN distribution campaigns in each zone are obtained from program documents provided by one of the implementing NGOs, Mennnonite Economic Development Associates, and from Bonner et al. (2011) and Renggli et al. (2013).<sup>11</sup> I use outcome variables from this governance survey module, which asked voters for their level of approval of 8 public officials: village chairman, village executive officer, ward executive officer, ward councilor, headmaster, extension officer, police officer, and member of Parliament. I create binary outcome variables for each political leader, excluding the sectoral functionaries (extension officer, headmaster and policeman), and create standardized versions of the continuous

> 10The “cycle” variable, which denotes whether a respondent was interviewed in the earlier or later survey cycle, correlates at 0.83 across the two rounds.

11It is worth noting that randomization of survey dates is not required for the validity of the RD identification strategy, which is specifically designed to recover causal estimates of the effect of endogenous independent variables.

12
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To complete the poverty map we associate the estimated parameters from the second stage with the corresponding characteristics of each household found in the census. This enables us to predict the log of per capita expenditure and the simulated disturbances. Since the disturbances have a complex structure which makes the computation of the variance of the imputed welfare indices intractable, bootstrapping techniques are used to get a measure of the dispersion of the imputed welfare index in the Census. From the previous stage, a series of coefficients and disturbance terms are drawn from their corresponding distributions. We then, for each household found in the census, simulate a value of the welfare index ( _y_ ˆ _chr_ ) based on the predicted values and the disturbance terms: That process was repeated 100 times, each time redrawing the full set of coefficients and disturbances terms. The means of the simulated welfare index become our point estimate and the standard deviation of our welfare index is the standard errors of these simulated estimates.

The strata-specific regression results are available upon request. The ultimate choice of the independent variables was based on a backward stepwise selection model. A check of the results confirmed that almost all of the coefficients were of the expected sign. Note that the models are not meant to indicate causality. Instead, they are purely predictive models. The regressions attempt to control for location effects by incorporating the cluster level averages of some of the independent variables. We also ran a series of regressions using the base model residuals as dependant variables to correct for heteroskedasticity.

The R<sup>2</sup> s of the main regressions vary from 0.25 to 0.49. Although this may appear to be on the low side, these statistics are typical of survey-based cross-section regressions and can are comparable with results from other poverty maps. The relatively low R<sup>2</sup> s for some of

> multicollinearity problems in our econometric models.

8
# **The Impact of Digital Technologies on Routine Tasks: Do Labor Policies Matter?**<sup></sup>

Rita K. Almeida _The World Bank and IZA_ Jennifer P. Poole<sup></sup> _American University and IZA_ Carlos H. L. Corseuil _IPEA_

**Keywords:** Information Technology; Skills; Jobs; Enterprise development; Labor Regulations. **JEL:** J24; O3.

>  This paper is a product of the Education GP. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. This work has benefitted from funding by the World Bank’s Latin America and Caribbean’s Chief Economist Office, under the regional study “Digital Technology Adoption, Skills, Productivity, and Jobs in Latin America”. Carlos Corseuil acknowledges financial support from CNPq. Ivette Contreras, Jon Mallek, Nadine Neumann, and Katcha Poloponsky provided excellent research assistance. We thank Miriam Bruhn, Lourenço Paz, and Diego VeraCossio for helpful discussions, as well as workshop participants at American University, the World Bank, the American Economic Association annual meetings, IPEA, NBER Summer Institute 2017, and the Midwest International Economic Development Conference.

>  Corresponding Author: 4400 Massachusetts Avenue, NW, School of International Service, American University, (202) 885-6689, poole@american.edu.
_4_ _WATER WHEN IT COUNTS_ ior can be substantial. In our context, tailored recommendations rely on the collection of high-frequency behavioral and water allocation data. Technical expertise is required to translate observed behavior into recommendations. Both factors may preclude the scalability of this type of feedback mechanism as an extension modality. In contrast, highlighting the relative water requirements over the life cycle of primary crops has the benefit of reaching a broader base of beneficiaries, since the main cost of producing the information is deciphering the primary crops grown in the region.

The competing demands of scalability and direct relevance for individual farmers create conflicting pressures on agricultural extension systems. Information disseminated through public extension must be cheap enough to generate that it can be scaled up and made available to farmers across remote areas. Yet, if generalized information is not being used by farmers for lack of relevance, providing general information alone may not be a panacea.

To test these ideas, we run a field experiment in which we assess whether individual feedback on usage of irrigation water is necessary to influence behavior beyond general, lower cost reminders. In practice, we examine the role of (1) providing farmers with general recommendations on water requirements, and (2) juxtaposing individual use relative to the general recommendations in curbing the inefficient water allocation patterns observed at baseline. Use of high-frequency water data permits an event-study design to quantify the effect of any feedback on water sufficiency. Within this event-study design, we randomize a subset of farmers to receive individualized feedback to assess any additional benefit of tailoring recommendations on water allocations.

Two months after feedback provision, we observe a marked drop in both water-related conflicts and plot-level water scarcity. Specifically, the share of plots receiving insuffi-