rafmacalaba/datause-encoder-data huggingface dataset:
The migrants interviewed in the VMS originated in all Vietnam’s 64 provinces. Whereas about two thirds (63.7 percent) of them resided in urban<sup>23</sup> areas after their migration in this sample, about 78.3 percent of them came from rural areas. The “distance”<sup>24</sup> of re-location, which is approximated by the distance between the capitals of a migrant’s source province and destination province, averaged around 563 kilometers for the entire sample. About 79.8 percent of those who work for foreign firms came from rural areas, traveling long distances (771 kilometers on average). The migrants to the Central Highlands also tended to come from remote provinces at an average “distance” of 874 kilometers. In contrast, the “distance” of re-location for those who work for the state sector is relatively small at 226 kilometers on average.<sup>25</sup> Table 2 presents some key economic and demographic indicators by urban/rural locations and by Vietnam’s two biggest cities and eight regions, which are computed from the VHLSS 2004 and the _Enterprise Survey_ data.<sup>26</sup> Evaluating the standard of living by per capita expenditure, Table 2 reveals disparity between urban and rural areas and across regions. In 2004, the per capita average monthly expenditure in urban areas (.66 million dong) was more than double that in rural areas (.32 million dong). By this measure, HCMC, followed by Hanoi, and the Southeast (other than HCMC) attained the highest standard of living, while people living in the mountainous areas in the Northwest, Northeast and Central Highlands, as well as those residing in the North Central Coast, lagged behind.

> 23 Destination or origin areas are referred to as “urban” if migrants reported that they reside(d) in “large city”, “small city”, or “town”; areas are referred to as “rural” if migrants reported living in “countryside.”

> 24 Geographical distance between origin and destination areas is typically used as a proxy for the cost of migration in the literature, assuming that the latter variable captures transportation costs, psychological costs, and costs of acquiring information (Lucas, 1997).

> 25 The shorter distance may partly reflect a large migration flow to Hanoi and other northern cities from neighboring provinces, and partly a governmental practice of transferring personnel on a local basis (GSO/UNDP, 2001).

> 26 Whereas the variables in the empirical section are computed at the province and urban/rural level, Table 2 shows them at a more aggregated level to facilitate the presentation. See notes in Table 2 for the definitions of each variable.
Like for stunting, we randomly select individuals that are affected by malaria, based on current prevalence and estimates of future change due to climate change in various world regions from (Caminade et al. 2014). Then, we assume that these people are affected between 0.1 and 2 times per year, and lose income as presented in Table 4.

_Table 4 Malaria impacts on households in our model_

||Min impact|Max impact|
|---|---|---|
|Cost of treatment|$0.7|$6|
|Number of days out of work|1|5|
|Number of occurrencesperyear|0.1|2|

# _Diarrhea_

As the third leading cause of death in low‐income countries, diarrhea is an important risk for poor households due to easy contamination pathways resulting from unsatisfactory hygiene conditions and high exposure (WHO 2008). Reduction in diarrhea incidence may be undermined by climate impacts that damage urban infrastructure and reduce the overall availability of water through water resource depletion.

Here we use data by (Hutton, Haller, and others 2004) for the number of cases per country today and the cost of treatment (Table 5). According to (Kolstad and Johansson 2010) the prevalence of diarrhea could increase by 10% by 2030 because of climate change (in all regions), and we use this assessment in our scenarios.

_Table 5 Diarrhea impacts on households in our model_

||Low‐impact scenario|High‐impact scenario|
|---|---|---|
|Cost of treatment|$2|$4|
|Days out of work (for the sick or the caregiver)|3|7|
|Number of occurrencesperyear|1|8|

To account for development, as for stunting, we use DHS data to explore the relationship between the income of the households and its exposure to diarrhea (Figure 12). These data do not show a dramatic decrease with income and diarrhea persists at high income levels. In practice, we use a 10 percent prevalence as a reference level, beyond which diarrhea has economic impacts, and we use a linear regression to estimate the income level at which prevalence decreases below 10 percent. We find a threshold at $15,600 per year, and we assume that only households with income below this level are affected by the climate change effect on diarrhea.

Further, we assume that fast progress in access to water and sanitation in the _prosperity_ scenario halve the number of cases, consistently with the assessment in India by (Andres et al. 2014). Of course, this assumes that the new water and sanitation infrastructure are adapted to changing future climate conditions and can continue to perform well in 2030 and beyond. This would require to account for the 25
indicate the robustness of these findings through comparison of the change in primary completion rates for the 10- to 18-year-old group for districts with varying degrees of conflict intensification between the 2001 and 2006 DHS (equation _(2R)_ ). The last two columns include controls for rural location and the educational attainment of the household head.

The results in the first column indicate that areas with more fighting witnessed a larger increase in female primary education attainment. Casting this result in terms of the distribution of conflict violence, an increase in violence of one standard deviation of the district-level distribution of casualties during the conflict (0.98 casualties per 1,000 inhabitants) increases female primary schooling attainment by 5.6 percentage points. This is roughly the effect of a move from the 5th to the 75th percentile of the district-level conflict distribution of total casualties. The sign and order of magnitude of this effect is confirmed when comparing cohorts born only five years apart but exposed to very different levels of conflict using equation _(2R)_ . Across all specifications in table 2, conflict exposure does not appear to significantly affect male primary schooling completion, although the gender difference in the conflict effect is only statistically significant in columns (3) to (6).

These results are robust to including controls for household characteristics, suggesting that the results are not driven by a change in household composition due to, for example, selective mortality or migration (columns (5) and (6)). Table S2 presents specifications similar to those in table 2 but replaces the indicator for primary schooling completion with years of education completed. Similar results are obtained, indicating that an increase in violence of one standard deviation increases female educational attainment by 0.6 years.

Table S3 presents three different specifications to further check the robustness of the baseline results in the first two columns of table 2 to the following changes in specification: restricting birth year fixed effects to be identical across the five development regions of Nepal, changing the control cohort, and replacing the number of casualties with its natural logarithm. The results in table S3 confirm that primary education progressed more rapidly during the conflict in 20
completion rates are 55 percent, 35 percent, and 5 percent, and females from poor households at 21 percent, 10 percent, and 1 percent.

**Figure 4: Educational attainment profiles for ages 1!5-19 by gender and wealth (Vertical axis is proportion of children who have completed grade).**

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Harrison (2003) found no evidence that abatement costs affected incoming foreign investment in four developing countries: Cote d’Ivoire, Morocco, Mexico, and Venezuela. Cole and Elliott (2005) studied outbound investment from the US to Brazil and Mexico. They cite evidence that pollution intensive industry is on average capital intensive and so chose Brazil and Mexico as host countries because they have weaker environmental regulation than the US but are also relatively abundant in capital. They found some evidence that industries with high abatement costs in the US were more likely to have high outbound investment to Brazil and Mexico. Both the Eskeland and Harrison, and Cole and Elliot studies lacked data on abatement costs in the host countries and so used US abatement cost data as their measure of policy stringency. Elliot and Shimamoto (2008) studied outbound Japanese foreign direct investment to Malaysia, Indonesia and the Philippines in 10 manufacturing industries. They used Japanese abatement costs as their measure of the stringency of pollution regulations. They found no evidence that higher Japanese abatement costs increased outbound investment to any of the three host countries, and instead found that all else equal, high abatement costs in Japan had a negative effect in investment in the Philippines (contrary to the pollution haven hypothesis). As with the Eskeland and Harrison (2003) study, Elliot and Shimamoto did not have data on abatement costs in the host countries.

There is still much to learn more about the effects of environmental policy on international competitiveness in newly industrializing countries. Since much of the empirical work has been based on the experience of OECD countries, it is important to investigate potential channels through which effects may be different in a developing economy. For example, one important question is whether the competitiveness effects of tightening up environmental policy are larger or smaller in developing countries than in developed countries. On the one hand, _marginal_ abatement costs could be relatively lower in developing countries because of weaker pre-existing regulation – this is the "low-hanging fruit" story. And rapidly growing economies have the advantage that it is easier to implement environmentally friendly technologies when designing and building 12
The emergence of Fast Payment Systems (FPS)—also known as instant or real-time payments— has accelerated the digitalization of many of these transactions. FPS enables funds to be transferred instantly, are available 24/7, and often use features like QR codes, payment requests, and phone numbers instead of account numbers. FPS are now available in around 110 jurisdictions and the World Bank’s Project FASTT (Frictionless Affordable Safe Timely <u>Transactions)</u> is supporting low-and middle-income countries in enhancing and implementing fast and secure digital payment systems.

# **4. The benefits of digitalizing different payment types**

Several types of payments bring distinct development benefits to payers and recipients. These include payments that individuals receive, such as government transfers, wages, agriculture payments, and remittances, as well as payments they make, like utility bills and merchant payments.

# **4.1 The development impact of digital government payments**

Eighty-five million unbanked adults receive government payments in cash, including 45 million women, and 40 million adults in the poorest 40 percent of households in their economy (Global Findex, 2021). Digitizing these government payments has the potential to produce several positive outcomes for both governments and recipients. These include motivating greater account ownership, reducing administrative costs, and decreasing the time it takes for people to receive their funds.

# **4.1.1 Government payments can motivate account ownership**

Eighteen percent of adults in developing economies opened their first financial institution account to receive a payment from the government (Global Findex 2021). Even before the COVID-19 pandemic, when the volume of government payments increased across the world, 140 million women globally had opened their first account to receive digital government payments. This association between government payments and first account opening persists even in some high-income countries. In Argentina for example, 20 percent of women account owners opened their first account to receive digital government payments (Miller et al., 2020). By digitalizing payments, governments can incentivize recipients to open accounts, a first step in delivering the financial benefits associated with account ownership.

7
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39
25

# Footnotes

- 1/ Quotas in the MFA and in American dairy products are quite precisely defined whereas VERs on autos, steel and footwear have been negotiated on broader categories of products -- i.e. uumber of autos, number of pairs of leather shoes.

- 2/ With adjustment costs, profit maximizing firms will equate per unit rents after deducting adjustment costs. If effective, nontransferability of quota tickets (prevalent among ASEAN countries (see Hamilton, 1986) will preclude quality mix adjustment.

- 3/ Yoffie (1983) documents that Taiwanese and Korean negotiators for the footwear negotiations with the US were primarily concerned with minimization of foreign exchange earnings loss. As indicated by Bark and de Melo (1988), the allocation of quota tickets for Korean footwear (and other products subject to VERs) is consistent with Yoffie's observation.

- 4/ This section draws on Hamilton (1989).

- 5/ See Winters (1988). An import quota was imposed on Taiwan in August 1977 and was converted into a VER in January 1981; the VER with Korea was instituted in January 1979. In 1985 the British footwear industry let the VERs with Taiwan expire as it was manifestly over-shipped.

- _61_ West Germany, France, Italy, Netherlands. Other EC countries (e.g. Ireland) had bilateral restrictions.

- 7/ Expression (2) also holds for the bilateral index with ln Pij instead of ln P*

- 8/ Aw and Roberts (1988) multilateral comparison used 13 commodity groups.

- 9/ At this level of aggregation, the number of missing observations (pairs of country-commodity flows with zero values) is reduced to 27 out of a total of 1,092 observations.

- l0/ We also tested whether individual dummies for each of the restr!cted years (for both the US and the UK) were significant as a group and found them not to be statistically significant. We also ran regressions including dummy variables for Canada during the restricted period, yielding no discernable effect of the NTH.

- 11/ We exclude rubber overshoes (6401.3), women's shoes of artificial leather (6402.26), shoes of wood and cork (6405.0), gaitirs, spats, etc. (6406.0), all of which have very small and erratic exports, and parts (6405.0), for which upgrading is not an issue.
29 Tabb 3: GDP GCw Pobtntal from _Swvie_ In th USSR: Counterfatul Eubae for 19

|-- -<br>-----------<br>-----------|-------<br>-----|,<br>21- ...<br>_Adiftomat_|_)_<br>_Add*UoeuaDP as_|
|---|---|---|---|
|_Setor_|_Euauonieaidw_|_aDPW _|_N%_<br>_ofpmt_<br>aaP**_in_**_Sactor_|
|OECD Eauatioms||||
|**Tobtl**Serve|-10.3|120.9|43.3|
|Trarapouation|3.2|.34.0|-32.4|
|Commiuaicon|40.3|3.4|30.4|
|Trade|-5.9|63.5|76.0|
|Bang g<br>hnran|-2.8|30.3|1033.6|
|Conmer Savioes|.6.1|65.9|67.6|
|PublicService|-1.4|15.1|14.0|
|D<br>isrpay b/|2.4|-23.2|.1145.9|

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**Information, Loss Framing, and Spillovers in Pay-for-Performance Contracts**<sup>∗</sup>

Sebastian Bauhoff<sup>†</sup> Eeshani Kandpal<sup>‡</sup>

**JEL Codes:** C93; I11; I15; J41; J45; M52; O15 **Keywords:** pay-for-performance; effort; crowd out; health workers; field experiment

> ∗We are grateful to counterparts at the Nigerian Federal Ministry of Health and National Bureau of Statistics, Samuel Adebayo, Emmanuel Meribole, and Florence Oke as well as World Bank colleagues Opeyemi Fadeyibi, Benjamin Loevinsohn, Ayodeji Oluwole Odutolu, and Elina Pradhan for their advice and collaboration. We thank Harold Alderman, Jishnu Das, Jonathan de Quidt, Damien de Walque, Quy-Toan Do, Karen Eggleston, David Evans, Madhulika Khanna, Jason Kerwin, Yusuke Kuwayama, Kenneth Leonard, Berk Ozler,<sup> ̈</sup> Owen Ozier, Antonn Park, Bob Rijkers, Ellen Van de Poel, Tom Van Ourti, Adam Wagstaff, and participants of the ASHEcon, MeasureDev, and iHEA conferences for their comments. We appreciate Kevin McGee and Lena Nguyen’s support of experiment design and implementation. Mark Conlon provided excellent research assistance. Financial support was provided by the World Bank’s Health Results Innovation Trust Fund. The experiment was approved by the Health Media Lab Institutional Review Board (IRB #00001211) and registered with the AER RCT Registry as study AEARCTR-0002482. The findings in this paper are the opinions of the authors, and do not represent the opinions of the World Bank, its Executive Directors, or the governments they represent. All errors and omissions are our own.

> †Department of Global Health and Population, Harvard T.H. Chan School of Public Health and Center for Global Development. Email: sbauhoff@hsph.harvard.edu

> ‡Development Research Group, World Bank. Email: ekandpal@worldbank.org
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# **Figure 2.3: Returns to School Attainment, International Adult Literacy Survey**

<!-- Start of picture text --> 0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>Mincer returns Adjusted Mincer returns<br>USA Chile Poland HungaryCzech Rep ItalyDenmark FinlandGermanyNetherlands NorwaySwitzerland Sweden<br><!-- End of picture text --> Source: Hanushek and Zhang (2006).

The literacy tests in IALS are designed to measure quite basic skills only, and yet the differences are strongly associated with higher earnings. These results, from a broad age spectrum across a number of countries, reinforce the importance of quality.

The sample of countries for the IALS unfortunately contains just one developing country – Chile. Nonetheless, it is suggestive that the returns both to quantity of schooling and quality exceed those found across the countries with the exception of the United States. In the three transition economies (Czech Republic, Hungary, and Poland), the returns to school attainment are also near the top of the sample, but the returns to educational quality are noticeably lower – perhaps reflecting institutional aspects of their labor markets.

# **_2.5 Causality_**

The focus on causality of estimated returns to school attainment has not been duplicated for the estimated impacts of cognitive skills. The potential problems are, however, considerably different. The tests, with the exception of the IALS sampling, are uniformly given at a date before the labor market 16