rafmacalaba/data-use-mentions-extended huggingface dataset:
road network data they collected is reasonably recent (2015) and by far the most complete data set available within Nepal. Moreover, IRI ratings may not capture the precise state of the road in a given moment, but they do suggest the priority given by local and regional governments to maintaining a particular road. Therefore, for modeling purposes they are sufficient to indicate generic conditions, with some acceptance of error.

# Calculation for off-road travel

Land cover type is a classifying factor for off‐road, off‐path walking speeds. Initial land cover data was taken from ICIMOD and additional water bodies from the OSM river network data were rasterized and merged into this data set to produce a final land cover raster. The average walking speed from the household survey (distance to the closest road / time to the closest road) was around 4.3 km per hour. We determined the walking speed over a flat surface for each landcover category with reference to similar studies such as Nelson (2000) and Black (2004) (Table 6).
# **S14 What land cover type does urban replace?**

The Globcover data shows 6,956 300-meter pixels that transitioned to Urban at some point from 1992 to 2018. Table S17 shows the frequency of land cover classes that transitioned to urban areas. Cropland is the most frequent land cover class that transitioned to urban; considering all cropland classes (which includes (1) cropland, rainfed, (2) mosaic cropland ( _>_ 50%), (3) Cropland, rainfed - Herbaceous cover, and (4) Cropland, irrigated or post-flooding), cropland makes up 52.6% of landcover that transitioned to urban during the study period.

**Table S17:** Summary Statistics of Dependent Variables

|Land Cover Class|N|Proportion|
|---|---|---|
|Cropland, rainfed|1825|0.262|
|Mosaic cropland (_>_50%); natural vegetation (tree, shrub, herbaceous cover) (_<_50%)|1540|0.221|
|Mosaic natural vegetation (_>_50%); cropland (_<_50%)|1429|0.205|
|Shrubland|731|0.105|
|Grassland|584|0.084|
|Cropland, rainfed - Herbaceous cover|151|0.022|
|Cropland, irrigated or post-fooding|148|0.021|
|Mosaic herbaceous cover (_>_50%); tree and shrub (_<_50%)|124|0.018|
|Mosaic tree and shrub (_>_50%); herbaceous cover (_<_50%)|123|0.018|
|Tree cover, broadleaved, deciduous, open (15-40%)|111|0.016|
|Bare areas|44|0.006|
|Consolidated bare areas|41|0.006|
|Tree cover, fooded, saline water|33|0
study we define two scenarios. The first scenario considers the implementation of biofuel use targets consistent with what countries already have announced (hereafter “AT” scenario). The second scenario generally considers a doubling of the

> 7 The base year for calibrating the model is 2004, the year for which the social accounting matrices, the main database for the model, were constructed. Although our base year is 2004, the model is calibrated to replicate major historical statistics until year 2009.

> 8 Impacts of removal of subsidies and import duties have been analyzed in detailed in a separate paper. 9 A module that can represent both conventional and un-conventional oil and gas reserves and production would be ideal; however, the model used here does not have that capacity. Hence, we used energy price forecasts from other sources instead of generating them endogenously in the baseline.

11
the firm was<br>formally registered. Variable takes a value<br>of zero if firm was registered when they<br>started operations|World Bank Enterprise Surveys|
|Bribery depth|% of public transactions where a gift or<br>informal payment was requested|World Bank Enterprise Surveys|
|Bribery incidence|Firms experiencing at least one bribe<br>payment request Y/N|World Bank Enterprise Surveys|
|Informal payment/gift requested for water<br>connection Y/N|Dummy variable that takes the value of 1<br>if the firm applied for a water connection<br>over the last 2 years before the survey and<br>an informal payment or gift was expected<br>or requested|World Bank Enterprise Surveys|
|Proportion of public transactions (excluding<br>water) where a gift or informal payment<br>required|Proportion of public transactions<br>(excluding water) that required a gift or<br>informal payment for firms that applied for<br>a water connection in the last 2 years|World Bank Enterprise Surveys|
|Firm experienced at least one public<br>transactions (excluding water) where a gift<br>or informal payment required|Dummy variable that takes the value of 1<br>if the firm was asked for an informal<br>payment or gift was expected or requested<br>for any public transaction excluding water<br>(this is only for firms that applied for a<br>water connection)|World Bank Enterprise Surveys|

30
are particularly motivated to invest in a toilet joining the MFI to obtain a sanitation loan. We note that this design allows us to estimate the impact of introducing sanitation loans over and above any other ongoing, parallel activities promoting sanitation. This is important in this context given the Government of India’s SBM-G scheme. An accompanying paper (Augsburg et al., 2019) analyses the complementarity between the credit intervention and SBM-G policy.

Finally, we check the robustness of our findings to multiple hypothesis testing using the step-down procedure proposed by Romano and Wolf [2005]. The adjusted p-values are reported in each table testing hypotheses tested within that table and we also test jointly all outcomes considered in this paper in Table C.1 in Appendix C.

# **6 Results**

In this section we report our main results. We start with discussing take-up of the newly introduced sanitation loan, followed by an analysis of sanitation investments made, leading to a discussion on sanitation loan to new toilet conversion. Thereafter, we consider impacts on sanitation behaviour, which is a policy relevant outcome when considering the aim of reducing environmental contamination through toilet construction. Finally, we study impacts on business investments and overall household borrowing.

## **6.1 Sanitation Loan Uptake**

We begin by studying the pattern of sanitation loan uptake in the study areas using the administrative data from our micro-finance partner. The exhaustive administrative data on any loan disbursements, sanitation or otherwise, that took place in the study branches since the start of this impact evaluation, provides us with an objective measure of sanitation credit uptake. The evolution of loan uptake is displayed in Figure 2 which shows the cumulative number of sanitation loans per client disbursed (yaxis) since the introduction of sanitation loans in treatment areas in February 2015 (x-axis). The Figure shows a clear upward trend in sanitation loan disbursement.

By the time of the endline survey, about 20 percent of client households in treatment areas had taken a sanitation loan. A small number of
24|4.16|-0.98|-2.76|1.25|4.01|
|Georgia|1.86|0.23|2.39|-0.53|0.15|1.78|1.64|
|Ghana|-0.56|0.47|1.23|-1.79|-0.77|-0.95|-0.18|
|Guatemala|3.15|0.32|4.02|-0.87|0.15|3.04|2.89|
|Guinea|3.15|0.84|5.90|-2.75|-1.57|2.46|4.03|
|Guinea-Bissau|3.10|1.62|6.88|-3.78|-3.09|0.71|3.79|
|Indonesia|1.57|0.24|1.87|-0.30|0.48|1.77|1.28|
|Iraq|1.33|0.25|1.79|-0.46|0.57|1.48|0.91|
|Jordan|4.49|0.56|4.95|-0.46|1.38|5.07|3.69|
|Kenya|2.54|1.11|6.63|-4.09|-2.18|1.39|3.57|
|Kyrgyz Republic|1.97|0.32|2.77|-0.79|0.18|2.02|1.84|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of country’s own agricultural import tariffs.

31
their decisions and form their own expectations about the future.<sup>5</sup> In this regard, the WBES dataset represents the only available survey providing forward-looking expectations on firms’ future sales for MENA regions. This is a critical piece of information that made our study possible and allows to shed light on the mechanism at stake. We employ a continuous measure for firms expected sales growth in the next year (E( _Sales growth_ )), as well as an ordinal measure capturing increasing, decreasing, or stable expected earnings (respectively, E( _Sales growth_ ): _Positive_ , _Negative_ , or _Stable_ ).

We match all this information with a wide set of financial characteristics of the company. First of all, we exploit data on the availability of outstanding loans or credit lines ( _Loan availability_ ) at the time of the survey, which is a synthetic measure for firms’ access to bank finance. Because we are interested in the

> 3The original number of waves for countries in the MENA region ranges between two and five, from 2007 to 2020. However, only in more recent years (i.e., the global dataset) firms are attached to a consistent panel identifier across waves that allows for longitudinal analyses. For most countries, two waves with panel identifiers are available: 2013–2019 for Jordan, Lebanon, Morocco, West Bank and Gaza, and 2013–2020 for Tunisia. The only exception is Egypt, for which three waves are available: 2013, 2016, and 2020. We account for differences across countries and timing with the inclusion of country-specific time fixed effects that purge the model from economy-wide factors that vary over time.

> 4We collapsed the two answers because there is no clear ranking between the available options and they are both identifying a significant perceived obstacle for the company. As a robustness check, we employed a categorical measure with unchanged results. The exact formulation of the questionnaires is provided in Table B1 of the Data Appendix.

> 5We also control for
treatment; Standard errors clustered at the club level in parentheses; Estimates at endline are weighted to account for the probability of selection for intensive tracking; Columns 1-2 show results for the primary outcomes, indicators for whether the PHQ-8 score is _≤_ 4 or the GHQ-12 score is _<_ 3; Columns 3-7 show results for the secondary outcomes, the PHQ-8 and GHQ-12 raw scores, the Rosenberg Self-Esteem scale, the Child and Youth Resilience Measure-Revised, and Locus of Control, all in terms of their raw scores; The rows marked as _H0:..._ show the p-value for a t- test of the indicated null hypothesis on the regression coefficients and the corresponding q-values adjusting for the false discovery rate within primary and secondary outcomes and round, as per Benjamini and Hochberg (1995); Sample size differences across outcomes are due to shorter tracking questionnaires being administered to difficult-to-reach subsets of CRs.

37
# **6 Conclusion**

This paper studies how rules on SPS and TBT in PTAs signed by Chile, Colombia, and Peru affect their firms’ export performance along the firm size spectrum. We combine novel data on the content of SPS and TBT provisions in trade agreements with customs data covering the universe of exporting firms in each country over the 1996-2015 period. After accounting for the overall depth of the PTA and product-specific bilateral tariffs, our firm gravity equations show that firms’ exports increase significantly to destination markets with PTAs including a larger number of SPS and TBT provisions and the effect is stronger for smaller firms. We further provide evidence that provisions for the harmonization of SPS regulations in PTAs benefit more the exports of smaller firms and so do PTAs that include stronger transparency provisions of SPS and TBT regulations. We find that the rising exports of smaller firms ensuing from PTAs with SPS and TBT provisions are partly explained by those smaller firms’ higher entry into new product-markets. This finding shows that PTAs, and especially the SPS and TBT provisions they include, are a useful tool for making the benefits of trade liberalization reachable by small firms.

# **References**

- Amiti, M. and Khandelwal, A. K. (2013). Import Competition and Quality Upgrading. _The Review of Economics and Statistics_ , 95(2):476–490.

- Anderson, J. E. and Yotov, Y. V. (2020). Short run gravity. _Journal of International Economics_ , 126:103341.

- Baier, S. L. and Bergstrand, J. H. (2007). Do free trade agreements actually increase members' international trade? _Journal of International Economics_ , 71(1):72–95.

- Baier, S. L., Bergstrand, J. H., and Feng, M. (2014). Economic integration agreements and the margins of international trade. _Journal of International Economics_ , 93(2):339–350.

24
where A denotes ordinary wealth, Y L denotes net of tax labor income in the ...nal preretirement year and SSW denotes social security wealth de...ned as the present value of retirement bene...ts.

Note that � measures the e¤ect of the stock of pension wealth on the stock of ordinary wealth. This should be taken into account when comparing the results with time series studies that usually measure the e¤ect of the stock of pension wealth on the ‡ow of saving. A value of � = :66, for example, would be consistent with the results obtained in Feldstein (1996).

The most pure life cycle model suggests that � = 1 if net social security wealth is equal to zero. Relaxing the assumptions of the pure life cycle model, � can take any value from greater than unity to less than zero. Using The Survey of Financial Characteristics of Consumers for 126 married couples, the point estimates indicate that � is signi...cantly di¤erent from zero but not signi...cantly di¤erent from one. In other words, the estimates generally show that � is somewhat less than unity.

By exploiting a sample of more than 2,000 observations obtained from a series of surveys of male household heads aged 45-59 conducted by the Bureau of Census, Kotliko¤ (1979) used a similar econometric speci...cation to the one employed by Feldstein and Pellechio (1979). He also included other controls in the regression such as: dummies for marriage, race, and heads who are separated, widowed, or divorced, the head and the wife’s ages and the square of their ages as well as the number of family members. His results are in line with those that were obtained by Feldstein and Pellechio 17
and effects or of complex causality, but they do provide an alternative measure of effect that does not depend on service delivery statistics. Moreover, contraceptive prevalence surveys have often been able to supplement information on current use with information about knowledge, accessibility and sources of supply, all of which permit a more complete assessment of program impact. Information from regular and effective prevalence surveys may help amid the kind of problems that arise with service statistics. Bank support for prevalence surveys has varied from country to country. Support for these surveys seems to have come from USAID in most cases. This is an appropriate division of labor, but it is important that Bank supervisory missions make use of this data and emphasize its importance to leaders.

49
The Effect of Household Wealth on Educational Attainment Around the World: Demographic and Health Survey Evidence'

# _Introduction_

][n this paper we are interested not just in countries' average educational enrollment and attainment, for which there has been a great deal of examination both from official and academJ[c sources, but in how educational attainment differs by household wealth _within_ countries.<sup>2</sup> How much schooling are children from poor households India, Brazil, or Kenya receiving, both absolutely and relative to the rich in the same country?

Answering this question, especially in a way that produces valid comparisons across countries is hampered by the limited availability, difficulty of use, and comparability of household survey data. The Demographic and Health Surveys (DHS), having applied essentially the same survey instrument in 35 countries potentially overcomes these problems. One potential limitation of the DHS is that it lacks questions on household income or consumption expenditures, which are conventionally used as indicators of households' economic status. However, in a separate methodological paper (Filmer and Pritchett, 1998a) we shoNv that an index constructed from the questions asked in the DHS about household assets and housing characteristics (e.g. construction materials, drinking water and toilet facilities) works as well, and arguably _better,_ than consumption expenditures as a proxy for household long-run wealth. This finding allows us to use a comparable _method,_ principal components, in

> ' This work is the result of research developed jointly with Jee-Peng Tan, and it has greatly benefited from her input. We would also like to thank Emiliana Vegas and seminar participants for helpful comments and suggestions. This research was funded in part through a World Bank Research support grant (RPO 682-1 1).

> 2 Several recent estimates of the stocks of schooling years in many countries have been produced based on the UNESCO Yearbook series enrollment rates and labor free and census surveys (Nehru, Swanson and Dubey, 1993; Barro and Lee, 1993; Dubey and King,
PSM, 2SLS, and ITTs, we find that the _Mahila Samakhya_ program empowers women in a variety of ways, via both direct and indirect routes. In addition to directly increasing access to outside employment, political participation, and physical mobility, our results suggest the program generates significant spillovers that allow non-participants to benefit via greater access to outside employment. The program also empowers participants who do not work (and therefore only benefit from an increased reservation wage) to participate in village council meetings. Finally, we find that participants who do not have access to off-farm work still have greater physical mobility and political participation than untreated women.

# **8 Conclusion**

This paper uses primary data from the north Indian state of Uttarakhand to study the impact of a community-level rural women’s empowerment program called _Mahila Samakhya_ . _Mahila Samakhya_ aims to empower women through education and information, taking a grassroots approach to its implementation. We conceptually disentangle the effect of the program into a direct component that works through access to outside employment and an increased reservation wage, as well as an indirect component that works through information spillovers. This distinction between direct and indirect effects extends beyond the _Mahila Samakhya_ program to other programs operating at the community-level that may have spillover effects. We find evience that Mahila Samakhya both directly and indirectly succeeds in helping empower rural women.

Our empirical approach addresses two sources of potential bias. First, the program may be placed in villages and districts that are inherently different from those without the program, which might bias our estimates. Second, the characteristics of women who participate in the program may be different from those who choose not to participate, again, biasing our results. We first consider program placement, and find that the characteristics of the women are similar across treated and untreated districts. We further formally estimate bargaining power outcomes for non-participants in treated districts and their matched counterparts in untreated villages and find no significant 22
Table 2: Round 1 characteristics - Households survey

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||ULGSP LGA|Control LGA|Dif|p-value|N|
|Household size|4.54|5.04|-0.51|0.00|2,998|
|Literate (all respondents)|0.84|0.75|0.10|0.00|2,998|
|Education: None|0.10|0.16|-0.06|0.00|2,998|
|Education:Less than complete Primary|0.13|0.20|-0.06|0.00|2,998|
|Education:Less than complete lower Secondary|0.50|0.53|-0.03|0.07|2,998|
|Education:Less than complete upper Secondary|0.23|0.10|0.12|0.00|2,998|
|Education:College degree|0.04|0.01|0.03|0.00|2,998|
|Sector of Work: Agriculture|0.47|0.76|-0.29|0.00|2,743|
|Sector of Work: Small Business|0.32|0.15|0.16|0.00|2,743|
|Sector of Work: Other|0.22|0.09|0.13|0.00|2,743|
|Owns a car|0.11|0.03|0.08|0.00|2,998|
|Owns TV|0.45|0.18|0.
The striking results here are the high numbers for Latin America (60 to 94 percent), EasternmSouthern Africa (excluding Ghana, 75 to 92 percent) and East Asia (85 and 90 percent),. In these countries, the main explanation for why children do not complete primary school _is not that_ they don't start school, it is the fact that they drop-out. In the Western/Central African and South Asian countries, dropout explains less than half the shortfall from universal primary education for the poor, with values ranging from 10 percent in India to 49 percent in Comoros (which although it lies off the Eastern coast has the attainment patterns of Western/Central Africa).

,Of course this approach can only address the question of the physical _availability_ of schools, not true _access_ to education. In particular it is possible that children did attend first grade, but in classes of 100 or more, with no materials, indifferent (or worse) teaching, and deteriorating buildings. Not surprisingly there will be high drop-out, due not to physical availability but to access to an education, which when properly defined, includes quality.

A second approach to the impact of school availability would be to look directly at the relationship between the presence of schools and enrollment. For example, using the DHS data from India, Filmer and Pritchett (1998b) found in state by state regressions that there was only a weak relationship between the availability of schools and enrollment rates. In this paper we don't replicate the analysis country by country but, as a heuristic indication, we report the proportion of "Rich Rural Males" (RRM) who have completed at least grade 1. Since poverty is not completely regionally concentrated this group is likely to suffer from a similar lack of physical access to schools as less socially favored groups (the poor and females). If RRM have high enrollment this provides some evidence on the degree to which other groups are falling short due to their status, not school availability.
As biofuels are not a proper sector in the original GTAP 7.0 database, we modified it in a way that allowed us to introduce biofuels sectors in our CGE model. This was done according to the following procedure. Based on detailed information collected on production, consumption and trade, a total of seven new sectors<sup>5</sup> have been created by splitting existing GTAP sectors. The Splitcom<sup>6</sup> software developed by Horridge (2005) was used to process the splits and keep the global social accounting matrix balanced. In the next step, the energy bundle of the model was augmented in order to integrate the new biofuel and fossil fuel sectors (see Figure 1c). A detailed description of the biofuel module used here can be found in Timilsina et al. (2010).

Land use changes are incorporated into the model via a CET representation of land supply for each country/region and for eighteen Agro-Ecological Zones (AEZs). The land data are based on the GTAP 7.0 database and were derived analogously to Lee et al. (2008). Following Huang et al. (2004) and Banse et al. (2008), we develop a land use module based on a nested structure. In line with Birur et al. (2008), at the top level of this module, total available land area is allocated to forest land, pasture and crop land. At the second level, crop land is further divided into four different categories: rice, sugar-crops, grains and oilseeds, and fruits and vegetables. Finally, the grains and oilseeds category is partitioned into wheat, corn, other coarse grains, and oilseeds (see Figure 1b). This nested structure seems reasonable as, in reality, not all crops stand in direct competition. For instance, rice typically does not compete directly with other crops for available land.

> 5 Corn, gasoline, diesel and biofuels (three ethanol and one biodiesel) sectors are