rafmacalaba/datause-encoder-data huggingface dataset:
- _Shared payments systems platforms_ : Greater investment in open architecture payments system information technology is required. Recipients should increasingly be able to receive remittances from any state or private bank branch or ATM machine, whether or not they hold an account with that institution.  The potential for nonbank financial institutions, such as post office networks and money transfer businesses, to connect to banks’ branches and technological advantages promises great potential for expedient, cost-effective remittance networks and services.

- _Public-private partnerships_ : Strategic partnerships between the public and private sectors across infrastructure, products, and services are necessary for countries in the region that seek the types of gains attained elsewhere.

- _Cross-selling_ : It is essential that, as the profitability of the conventional formal financial sector remittance business model continues to decline, formal financial institutions invest in cross-selling complementary financial services and products. Encouraging recipients to open and maintain bank accounts that might lead to short- and long-term auto and housing loans, for example, should be part of a business strategy for banks.

In the long run, strengthening the formal financial sector for remittance purposes will facilitate the move away from informal remittance systems, which, though beneficial to the direct users, may not have as effective an impact on the macro level as formal remittance transfers.

High transaction costs, long delays in transferring remittances, foreign currency controls, and overly bureaucratic polices and procedures for simple money transfers have no place in a vibrant and still-growing remittance industry.

# **Notes/References:**

1 Remittance trends in Sri Lanka are similar to worldwide trends. Globally, remittances remained the secondlargest financial flow to developing countries in 2003 after foreign direct investment, more than double the size of net official development assistance. Remittance volumes continue to increase, despite expectations to the contrary based on weak labor markets and the tightening of border controls in the industrial countries after the terrorist attacks of September 11, 2001.

2 The relatively stable nature of remittances suggests that countries with access to significant remittance inflows may be less prone to damaging fluctuations, whether in output, consumption, or investment. In extreme cases, remittances might reduce the probability of financial crises. Such considerations are strengthened by the fact that remittances, unlike capital inflows, are unrequited transfers, which do not create future debt servicing or other obligations (IMF 2005).

3Based on the 2001/2003 survey, total remittances constituted nearly 3 percent of income of the average household.

> 4 The Middle East region has been the primary destination, having received almost 90 percent of total Sri Lankan contract workers.

26
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Figure 2 **:** Poverty **H** eadcount **REDUCTION BY SOCIAL PROTECTION** , **BY REGION AND INCOME LEVEL** ( **A** t $1.25/ **D** ay/ **P** erson) <!-- Start of picture text --> 100% 12%<br>10%<br>75%<br>8%<br>50% 6%<br>4%<br>25%<br>2%<br>0% 0%<br>Low Lower Mid Upper Mid SSA SAR EAP MNA LAC ECA All<br>Income Income Income Developing<br>World<br>Poverty headcount reduction as % of pre-transfer poverty Poverty headcount reduction as % of population<br>Poverty reduction% population<br>Poverty Reduction as % of pre-transfer FGT0<br><!-- End of picture text --> SSA: Sub-Saharan Africa, MNA: Middle East and North Africa, SAR- South Asia, EAP- East Asia and Pacific, LAC- Latin America and Caribbean, ECA- Eastern Europe and Central Asia Source: Authors based on ASPIRE, see Annex Turning now to differences across **_countries_** we see quite a large spread in terms of poverty reduction, even across countries belonging to the same income group or the same region: dollar a day poverty is reduced by 100 percent (it is eliminated) in several upper middle income countries in ECA (such as Romania or Poland), and in some low income country like Mongolia in EAP (see Annex 4). Figure 3 shows that typically it is in the poorest countries that the impact of social protection on poverty is the smallest, and arguably this is where it is needed the most. But at the same time countries that are close in poverty incidence achieve very different results in terms of poverty reduction: Georgia and Bolivia and next to each other on the graph, but without social protection Georgia would have a much higher poverty rate.

**FIGURE 3: GRAPH OF POVERTY (POST-TRANSFER) AND POVERTY HEADCOUNT REDUCTION BY SOCIAL PROTECTION**

13
_Handbook of Social Economics Volume 1B_ , ed. by J. Benhabib, A. Bisin, and M. Jackson, pp. 1053–1163. Elsevier.

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39
weaknesses in providers’ effort, which is a crucial dimension of quality of care. _Effort_ can also be considered an indicator of governance performance, as it is influenced by incentive arrangements (such as fees for service or performance bonuses), although other factors like intrinsic motivation also affect it.

In education, there are corresponding measures of the quality of teaching, gathered through _classroom observation_ studies. One such indicator is _time on task_ —the total amount of time that teachers are actively engaged in teaching and students are actively learning (for example, Abadzi 2006). Most observers will agree that schooling is likely to be more effective when teachers are able to devote less time to maintaining discipline or carrying out administrative tasks and more time to teaching. However, a concern with time-on-task studies is the amount of variation by enumerator. It is difficult for researchers to agree on how to categorize how teachers spend their time in the classroom. Beyond time on task, if we are willing to make assumptions about the most effective pedagogy (for example, by prioritizing ―active learning‖), then the time-on-task metrics could be made more detailed by measuring the amount of time spent on good pedagogy. These metrics could be used to construct more direct _assessments of the quality of teaching_ as indicators of quality of education services.

# **Subjective Quality Measures**

A second type of indicators is measures of satisfaction of the clients or recipients of health and education services. These subjective measures can proxy for measuring actual quality of services and can also measure the responsiveness of services to the needs and preferences of clients. In the case of health services, we can survey patients about their experience, for example 32
The infrastructure condition is represented by two variables. First, we measure the simplicity of water flow direction. When the flow directions are relatively simple, a manager can easily monitor water volume and effectively implement a water distribution schedule with WUG members. We expect that the motivation to remain in a WUG and to then hire a manager will rise with this variable. Utilizing GIS data, we develop a variable that is the proportion of the area with the same water flow direction as a single continuous area over the entire irrigated area of the WUG.<sup>13</sup> The second infrastructure-related variable is the length of the earthen part of the irrigation canal. In a surface irrigation system, seepage takes place along the canal, and seepage increases as the length of the earthen canal increases; however, seepage is negligible along a lined canal, regardless of its length. Seepage loss occurs when the soil in an earthen canal is dry and becomes minimal once the water starts flowing along the canal and the soil becomes saturated. Hence, the loss occurs only at the beginning of the irrigation practice as a fixed startup cost of the irrigation that follows. This fact introduces economies of scale and provides an incentive to remain in the group, rather than moving toward individual or small group irrigation. Hence, we expect seepage to have a positive impact on remaining in the WUG.

To measure WUG exit options, we introduce the number of ponds or pumps per farmer in the WUG.<sup>14</sup> Because these variables can change as a result of institutional choice, we utilize their levels before _fei gai shui_ to avoid simultaneity bias.

The exit to a new smaller group is determined based on the characteristics of the farmers in the surrounding plots, as a new group’s management will be easier when strong personal ties exist or when high-bonding social capital is endowed among would-be members. We use the following variables: the number of surrounding parcels and its squared term, the average number of years that farmers have known the owners of surrounding plots, and the proportion of 13 As a first step, we estimate the direction of water flow at each pixel in the WUG’s GIS map (see examples of two WUGs shown in figures S2 and S3 in the supplemental appendix). Using that information, the largest single area is measured as the aggregated size of the continuous pixels that have the same flow direction. Figure S2 is an example of a simple water flow direction group (index value = 0.23). The largest single area (brown tiles or pixels in the center) relative to the group size is large. Meanwhile, figure S3 is an example of a complicated water flow direction group (index value = 0.07). See Rala et al. (2009) for more details about the data-processing algorithm.

14 We employ these WUG-level variables rather than the household-level ownership of ponds and pumps because even non-owners can purchase water from ponds and/or rent pumps.

15
On the revenue side, we analyze the two main components of state government income—taxes and federal transfers—representing 80 percent of their total revenue.

The third data set is the quarterly GDP of Mexico’s 32 states, which started being collected in 2014by INEGI. In addition to the total GDP, we also use information on the GDP contributed by each state’s primary sector—mining and agriculture—secondary sector—manufacturing and construction—and tertiary sector, mainly services.

The fourth data set consists of state-level panel data at the biennial frequency on a series of poverty indicators on even years from 2010 through 2018. Data are collected and compiled by the National Evaluation Council (CONEVAL). CONEVAL created a methodology to evaluate poverty in a state according to the following indicators: income per capita, average educational attainment, access to health services, access to social security, quality and housing spaces, access to nutritious and quality food, and access to paved roads.

The fifth data set, which we refer to as the loan-level data, consists of credit registry data containing bank commercial loans in Mexico to private firms and government-backed entities from July 2014 to June 2018. Its coverage includes the universe of bank loans issued to governmentbacked entities and all bank loans issued to a nationally representative 10 percent random sample of private firms. Loans to government-backed entities include federal, state, and municipal governments, as well as firms owned by the government (federal, state, or municipal). The data are obtained from regulatory reports monthly submitted by every commercial bank to the bank regulator (CNBV). Reports are mandatory, updated electronically, and include detailed characteristics of all new and continuing loans made to all firms. All business loans, regardless of their size, are reported. For each loan, we use information on the issuing bank, the outstanding amount, the interest rate, loan guarantees, and the type of financing (i.e., whether the loan is intended to finance working capital or investment). There is also descriptive information about each borrower, such as its location, industry, and number of employees when the loan started. In the case of privately-owned (henceforth private) firms, we adopt a similar approach to La Porta et al. (2003) and aggregate individual loans at the _firm-bank-month_ level, as some borrowers have more than one loan issued by the same bank at a given point in time. For the same reason, we aggregate all state-government loans at the _state-bank-month_ level. Loan characteristics such as 12
# Table 11: Social outcomes

||(1)<br>Participation in<br>groups or associations<br>(# groups)|(2)<br># times received<br>fnancial support<br>(last 12 mths)|(3)<br># times provided<br>fnancially support<br>(last 12 mths)|(4)<br>Participation in<br>community works<br>(# times in<br>last 12 mths)|(5)<br>Participation in<br>social activities<br>(# times in<br>last 12 mths)|(6)<br>Trust<br>Index<br>(z-score)|(7)<br>Victimization<br>(confict)<br>Index<br>(z-score)|(8)<br>Insecurity<br>(perception)<br>Index<br>(z-score)|
|---|---|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates|||||||||
|Treatment (ITT)|0.16***|0.21**|0.29**|0.03|0.15|0.03|-0.04|0.04|
||(0.05)|(0.10)|(0.12)|(0.03)|(0.36)|(0.06)|(0.04)|(0.06)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|1.19|0.88|1.31|61.10|6.59|0.00|0.00|0.00|
|Observations|2,620|2,620|2,620|2,620|2,620|2,374|2,614|2,617|
|Panel B. Treatment Arm Estimates|||||||||
|VSLA (T1) (ITT)|0.18***|0.15|0.27*|0.04|0.28|0.05|-0.01|0.03|
||(0.07)|(0.13)|(0.14)|(0.04)|(0.45)|(0.08)|(0.06)|(0.07)|
|Cash Grant with repayment (T2) (ITT)|0.14**|0.12|0.31**|0.03|-0.27|0.00|-0.05|0.03|
||(0.07)|(0.11)|(0.14)|(0.03)|(0.41)|(0.07)|(0.05)|(0.07)|
|Cash Grant (T3) (ITT)|0.16**|0.53**|0.28|0.01|0.84|0.04|-0.10*|0.07|
||(0.08)|(0.23)|(0.19)|(0.04)|(0.51)|(0.11)|(0.05)|(0.11)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|1.19|0.88|1.31|61.10|6.59|0.00|0.00|0.00|
|p-value T1=T2|0.59|0.84|0.78|0.69|0.20|0.53|0.58|0.99|
|p-value T2=T3|0.81|0.09|0.90|0.64|0.03|0.72|0.35|0.70|
|p-value T1=T3|0.82|0.13|0.94|0.45|0.28|0.93|0.17|0.71|
|p-value T1=T2=T3|0.87|0.23|0.96|0.75|0.07|0.81|0.37|0.92|
|Observations|2,620|2,620|2,620|2,620|2,620|2,374|2,614|2,617|

Robust standard errors clustered at locality level. Z-scores are centered in the control group. The _Trust index_ is based on 16 variables measuring different forms of trust (economic, financial, security) and trust in different types of groups or institutions (neighbors, same and other ethnic groups, leaders, foreigners). The _Victimization index_ is based on 2 variables, measuring if the respondent reports having been victim of an attack or involved in a conflict over the past 12 months. The _Insecurity (perception) index_ is based on 6 variables, measuring the insecurity perception of the respondent, including the perceived likelihood of a robbery, attack or shootings, a subjective assessment of the peacefulness of the locality, fear of conflicts, and knowledge of past conflicts in the locality. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
is critical to understand how they view and enact important behaviors for the development of the children they teach.

# **4 Results**

There are three questions that have important policy and research implications: (i) in what areas of early childhood development is educator knowledge most lacking? (ii) what do these gaps look like for children with disabilities, who often need special attention to promote their development? (iii) what are the largest constraints to addressing these gaps? In order to answer these questions, we conducted two surveys with teachers and head teachers across ECED centers in eastern Nepal. We measured teacher understanding of the five most important domains of development: cognitive, social, emotional, language, and physical development. We also measured teacher understanding and behaviors in promoting these types of development. We also looked at barriers such as teacher job satisfaction, teacher motivation, lack of resources, and teacher training.

_A. Gaps in Knowledge_ We answer our first question by looking at areas with the largest gaps in knowledge amongst the five domains of development. In our initial surveys, we asked teachers a variety of questions to learn about their understanding of the five different realms of early childhood development: physical development, language development, social development, emotional development, and cognitive development. The results are shown in tables 1 and 2. We first asked teachers which domains of development they believe occur during early childhood. Most teachers understand the types of development that occur during a child’s first five years of life. Over 95% of teachers report that each domain occurs during early childhood. Most teachers in our sample have learned about ECD from three sources: books, training programs, and literacy programs. Still, over half report learning about ECD from friends, neighbors, and society. Very few report having learned anything about ECD from radio or 6
suggesting it was the less profitable control firms that were more likely to attrit. Second, we examine dynamic selection into attrition in Table A3, comparing firms which answered both the in-person and phone surveys, to those which answered only one, and to the attritors. We see that the firms that were harder to get to respond were ones that were less likely to be still operating after two years, and that were more likely to have below median profits. The attritors therefore appear to be less successful entrepreneurs on average, making our estimates of long-term treatment effects likely to be lower bounds (since we are missing more less-successful control firms than less-successful treated firms). We additionally show the results are robust to different sets of robustness controls and to different bounding assumptions about the self-employment status and profits of these attritors (Tables A4 and A5). Finally, we note that the attrition rates for the traditional business training group are similar to those of the personal initiative group, and so comparisons between these two groups should be less affected by attrition.

# **D. Estimation**

We estimate the following panel data equation for firm _i_ in randomization strata _s_ at time _t_ , using our 4 short-term rounds and combined fifth long-term follow-up round _:_ our 4 short-term rounds and combined fifth long-term follow-up round _:_ s s T ii,tt ii llss ii ii yy = αα+ ββ 1(tt≤4) ∗P + ββT 1(tt= 5) ∗PS<sup>+ ρρ</sup> + γγ<sup>+ δδ</sup> 1(tt≤4) ∗T<sup>(1)</sup> llss ii<sup>+ ρρyy</sup> ii,0 LLSS<sup>yy</sup> ii,0 tt<sup>+ μμ</sup> ss<sup>+ εε</sup> ii,tt Where and +γγ 1(tt= 5) ∗T are indicator variables for the short-run and long-run survey rounds respectively, _PI_ denotes assignment to the personal initiative training, _TRAD_ denotes assignment 1(tt≤4) 1(tt= 5) to traditional business training, is the baseline value of the dependent variable, are survey round fixed effects, are randomization strata by short- or long-run fixed effects, and the standard ii,0 tt yy δδ errors are clustered at the firm level.ss<sup>2</sup> s then gives the short-run effect of personal initiative μμ training averaged over the four initial survey rounds, and ii,tt the long-run effect at 7 to 7.5 years εε ββ post-training (with s and defined likewise as the short-run and long-run effects of traditional llss ββ business training). We then test llss s to test whether the impact of personal initiative training γγ γγ is constant over time, and can also test llss to test whether personal initiative training has the ββ = ββ llss llss ββ = γγ

> 2 We use this saturated formulation which allows the strata and coefficient on the lagged variable to vary with shortand long-run, so that the estimated short- and long-run treatment effect coefficients are the same as those which would be obtained by estimating this model separately for the two time periods.

6