rafmacalaba/data-use-mentions-extended huggingface dataset:
--|3 10.35<br>|2,279.4|21,465.|9 10.62|
|Total in our sample|101,404.|1 788,550.|7 12.86|15,422.2|86,645.|4 17.80|

Source: Derived from tables 3.7, 3.10 and Censo Industrial. Includes adjustment for quality differences in the motor vehicles industry.

TABLE 3.12 **Quantities (Value Added. US Census Concept). Mexico/USA. 1975**

|------------------------------------------------------------|at M<br>|exican<br>"pri<br> <br>|ces"<br>|__---------------__--<br>at<br>|-----<br>US "pric<br> <br>|es"<br>|
|---|---|---|---|---|---|---|
||Mexico<br>1975<br>(1975 P|<br>USA<br>1975<br>s. million|Mexico/<br>USA<br>)(X)|Mexico<br>1975<br>(1975 U|<br>USA<br>1975<br>SS million)|Mexico/<br>USA<br>(%)|
|Grain Mill Products|2,591.8|<br>20,842.0
is problematic. Another is that the demographic characteristics of the eBay users may be online-specific and not representative of the offline world. A further shortcoming is that international search costs may be very different from those within the US. Hence their sample may not be fully appropriate to study the “death of distance” in international trade.<sup>1</sup> Our dataset allows us to overcome these criticisms and compare the distance effect on eBay and offline trade considering the same set of countries and goods. It covers all eBay transactions, disaggregated into 40 product categories, between 62 countries (representative of 92% of total world trade) during 2004-2007. To create the best-possible comparison groups, we match eBay product categories to product descriptions from the 6-digit level HS classification to build a comparable basket of goods. We also drop from our eBay data all transactions that were concluded via auctions (60 percent of eBay traded value), as well as those sold by consumers, so that our eBay data reflect offline practices. Prima-facie evidence (Figure 1) indicates that the relationship between trade flows and distance is indeed more flat-sloped on eBay (left panel) than offline.

To identify as precisely as possible the effect of distance we use a gravity framework (Anderson and van Wincoop 2004), controlling for other standard gravity trade costs such as the absence of a common language, a common legal system, a border, a colonial history, or a free-trade agreement. We find the distance effect to be 65 percent smaller online than offline. This difference in distance coefficients is statistically significant at the 99 percent level, robust to using OLS or Poisson pseudo-maximum likelihood estimations as well as to various aggregations of goods online and offline, including using disaggregated data. We also run our gravity model by product category and show that distance matters less online for all products.

This supports the prediction of Chaney (2011), namely that in a world where search costs are greatly reduced, the role of distance in explaining trade flows is smaller
Figure 2: Co-movement of international and domestic coffee prices <!-- Start of picture text --> 2006m1 2008m1 2010m1 2012m1 2014m1<br>Jimma Gimbi Yirgachefe<br>Dila Bedele Awasa<br>International price<br>100<br>5000<br>80 4000<br>60<br>3000<br>40<br>Domestic price(birr/kg) 2000 International price index<br>20<br>0 1000<br><!-- End of picture text --> Note: This figure plots international coffee price index (adjusted for nominal exchange rate depreciation) along with domestic coffee price at major coffee production centers. Data Source: International Coffee Organization (for international coffee price index) and Retail Price Survey (for domestic coffee prices). Our retail price data covers only until 2014.

First, coffee-producing households have lower total adult equivalent consumption than non-coffee farmers, even though this is statistically significant only at 10%. However, looking into the components, food and non-food consumption are not significantly different across the two groups. There are statistically significant differences across the two groups of households in the means of the following variables: household size, female on-farm labor supply, spouse on-farm labor supply, probability of receiving assistance/transfer and the size of assistance/transfer, and rainfall. Besides these differences, the mean of the variables across the two groups are not statistically significantly different.

Panel B of table 1 provides descriptive statistics of child anthropometric measures. It shows an average Ethiopian child has poorer anthropometric measures compared to 2006 WHO child growth standards, as indicated by negative values of weight-for-age z-score (WAZ), 8
- Dang, T.H., Nguyen, T.A., Van, M.H., Santin, O., Tran, O.M.T., Schofield, P. (2021). Patient-Centered Care: Transforming the Health Care System in Vietnam With Support of Digital Health Technology. _Journal of Medical Internet Research_ , 23(6): e24601.

- Deaton, A. (1988). Quality, Quantity, and Spatial Variation of Price, _American Economic Review_ , 78: 418-430.

- Deaton, A. (1990). Price elasticities from survey data: extensions and indonesian results. _Journal of Econometrics_ , 44: 281-309.

- Deaton, A. and J. Muehllbaur (1980). An Almost Ideal Demand System, _American Economic Review_ , 70: 312-326.

- Filippini M, Pachauri S (2004) Elasticities of electricity demand in urban Indian households. _Energy Policy_ , 32: 429–436.

- Gibson, J. and Kim, B. (2019). Quality, quantity, and spatial variation of price: Back to the bog, Journal of Development Economics, 137: 66–77.

- Gibson, J., Kim, B. (2013a). Quality, quantity, and nutritional impacts of rice price changes in Vietnam, _World Development_ , 43(1): 329–340.

- Gibson, J., Kim, B., (2013b). Do the urban poor face higher food prices? Evidence from Vietnam. _Food Policy_ , 41: 193–203.

- Gibson, J., Romeo, A. (2017). Fiscal-food policies are likely misinformed by biased price elasticities from household surveys: evidence from Melanesia. _Asia & the Pacific Policy Studies_ , 4(3): 405– 416.

Hausman, J. A. (1978). Specification tests in econometrics. _Econometrica_ 46: 1251–1271.

- Hien, P.D. (2019). Excessive electricity intensity of
in off-budget aid over time. In Fiji and Vanuatu, 70% of all aid is off-budget aid (Feeny, 2007). In Malawi, approximately 40% is off-budget aid (Fagern ̈as and Schurich, 2004), and in Liberia, approximately 75% is off-budget aid (Republic of Liberia Ministry of Finance, 2009).

6For example, suppose that in the absence of any health aid, a recipient government spends 100 million dollars in the health sector. If a donor provides 200 million dollars of off-budget health aid, then full fungibility would entail that the recipient government reduces its own health expenditures at an approximately one-to-one rate (i.e., the recipient government reduces its health expenditure by 200 million dollars). However, the government cannot implement such a reduction because health expenditure would need to decrease below zero. The most that this government can do is to reduce its health expenditure by 100 million dollars; in this situation, health aid is only partially fungible.

7Many of the papers in this body of literature disaggregate aid into grants and loans, multilateral and bilateral aid, or by aid modality, but not by sector.

> 8The studies that are referenced in this paragraph estimate the degree of fungibility using panel data for either a large (Feyzioglu et al., 1998; Devarajan et al., 2007) or small (Heller, 1975; Feeny, 2007) number of countries, or they report country-specific estimates of fungibility (all other studies referenced in this paragraph).

> 9I describe the OECD’s aid databases as they were when I began to construct the sectoral aid data (December 2006). Since then, the CRS and DAC Directives have been updated, and the databases have undergone minor changes (see OECD, 2007a,b).

10 _RY_ denotes recipient-year, _agg_ denotes aggregate aid, and _comm_ denotes commitments. No superscript is used for disbursements. 11The data in DAC5 are a mix of disbursements and commitments. To account for this, I scale the DAC5
|0.89|0.86|0.80|0.84|0.91|0.89|
|Number of children under 18|1.55|1.47|1.40|1.40|1.47|1.59|
|Years of Education|10.16|10.34|10.51|10.51|10.56|10.53|
|Risk-seeking score|6.81|6.87|6.53|6.73|6.82|6.75|
|Digitspan Recall|6.00|6.04|6.01|6.03|5.93|6.06|
|Raven test score|2.58|2.75|2.68|2.76|2.59|2.81|
|Total household income from all sources|17192|18245|17595|16422|16690|16393|
|Wealth index (principal component)|0.01|0.05|0.28|-0.09|-0.12|-0.11|
|Household has a fridge|0.45|0.53|0.51|0.39|0.41|0.43|
|Household has a sewing machine|0.56|0.60|0.60|0.51|0.54|0.55|
|Household has an oven|0.08|0.08|0.12|0.09|0.05|0.08|
|Household has a gas cooker|0.25|0.23|0.30|0.28|0.24|0.24|
|Age of Firm (years)|6.47|6.88|6.35||||
|Ever had a loan from financial institution|0.23|0
, the scope for intensification of existing operations is limited. Labor supply through migration from other regions is likely to be inelastic in the short to medium term so that intensification of land use would require some mechanization and larger farm sizes. Trends towards larger operational units may be reinforced by high capital outlays to clear land or establish necessary infrastructure and the production of unfamiliar new crops which can place a premium on skills and entrepreneurship that may not be available locally.<sup>4</sup> To gauge how relevant this may be in practice, data on potential supply of land for rainfed cultivation is needed. We use the global agro-ecological zoning (GAEZ) methodology developed by the International

> 4 Historically these areas have been cultivated through **s** ettlements from more densely populated areas of the country or from abroad (e.g., North America and Australia). Settlement may be state supported or spontaneous in response to land pressures in the origin area. Large state-supported schemes were generally costly and less productive than expected as inexperienced farmers have to adjust to new crops and environments or nonfarmers attempt to learn farming (Kinsey and Binswanger 1993). Conflicts, with local land users have been common. Ethiopia’s resettlement program from high density highlands to the ‘virgin’ lowlands in the 1980s under the communist regime has been revamped in 2004-06 to resettle 600,000 people under strict guidelines. But, lack of respect for local rights was still a major source of conflict (Pankurst and Piguet 2009). 16
similar to that using IHDS data, but more precise given the larger samples in DLHS-3 data.

The main analysis focuses on how religion differentially affects households of that religion, but an important caveat is that religion may also influence average sanitation practices within locations. For example, Hindu religious customs could influence sanitation practices of nearby Muslims and Hindus, such that influence of religion becomes absorbed into location fixed effects. While the study focuses on whether Hindu households have different sanitation practices than nearby Muslim households, it also explores how religion influences average sanitation practices in locations (i.e., the location fixed effects). The analysis estimates using LASSO that average sanitation practices in locations are more predicted by locations’ rural status and female literacy than by locations’ religion shares.<sup>18</sup>

# **IV.B Analysis of Migrant Households**

The prior analysis found that religion-specific gaps in latrine ownership were smaller when controlling for characteristics of household locations or including fixed effects by household location. To further disentangle religion from the local economic environment, migrant households are now examined.

The analysis focuses on Hindu and Muslim migrants, who have moved from rural areas and converged in similar urban locations that allow the analysis to hold constant the available sanitation infrastructure and other features of an urban area. Another advantage to analyzing rural-to-urban migrants is that they have similar generational exposure to urban areas, which is important giving the long history of Muslim urbanization in India (see Figure 1). An important caveat to this analysis is that migrant households may be less religious, on average, than non-migrant households. Thus, differences between Hindu migrants and Mus-

> 18In practice, location fixed effects are estimated by regressing latrine ownership on household religion and PSU fixed effects. LASSO is then used to select which variables most predict variation in the fixed effects. Following the procedure suggested by Belloni, Chernozhukov and Hansen (2014), the analysis includes the household characteristics and location characteristics (from Table 2) along with their pair-wise interactions
30 that education is an important determinant of the choice to work for the government/public sector in both countries.

26 World Bank, Country Assistance Strategy for Arab Republic of Egypt. Report No. 16533-EGT (Washington, DC: May 1997), page 8.

27Jonathan Morduch and Terry Sicular, "Rethinking Inequality Decomposition, With Evidence from Rural China," Development Discussion Paper No. 636 (Cambridge, MA: Harvard Institute for International Development, 1998).

23As Morduch and Sicular (ibid) note, one drawback of incorporating regression analysis into the decomposition is that the role of variables that are constant for all observations is not estimable.

**29** While the Gini coefficient for per capita income in this study is 0.532, the Gini coefficient of landownership (including households with no land) is 0.899.

3OJames Heckman, "Sample Selection Bias as a Specification Error," Econometria 47 (April 1979): 153161.

31 In the agricultural income equation, the variable mean age squared generates a larger positive income share than the land owned variable. However, the net sum of the age and the age squared variable yields a negative income share (-44.7).

32 For an interesting effort to use cross-national data to examine the nature of the casual relationship between income and land in a sample of 28 developing countries, see Nguyen Quan, "Concentration of Income and Land Holdings: Prediction by Latent Variables Model and Partial Least Squares," Journal of Development Economics 31 (July 1989): 55-76..

33In rural Jordan income from nonfarm unskilled labor represents an inequality-increasing source of income. As discussed in the text, this outcome may be due to the small share of nonfarm income coming from unskilled labor.
Vessels that fished inside a potential closure the day before closure announcement have a much smaller total treatment effect (0.5% increase in total juvenile catch) than vessels that did not (87.2% increase in total juvenile catch).<sup>31</sup> The treatment effect for vessels that fished inside a potential closure the day before closure announcement is not different from zero (delta method standard error is 3.3%), whereas the treatment effect for vessels that did not fish inside a potential closure the day before closure announcement is highly statistically significant (standard error is 5.8%). The difference in treatment effects between the two information groups is also statistically significant (difference is 86.7% with a standard error of 6.7%).<sup>32</sup> The information mechanism also operates at the firm-level. Vessels who had a different member of their firm fish inside a potential closure the day before closure announcement would occur have a much smaller treatment effect (19.4% increase in juvenile catch) than vessels who did not (78.4% increase in juvenile catch). Both treatment effects are different from zero (standard errors are 3.8% and 5.4%), as is the difference in treatment effects (difference is 59% with a standard error of 6.6%).

The results in this section support Proposition 3 that vessels that receive a stronger information shock from closures have larger treatment effects. The information provided by closures is a mechanism underlying the policy’s large spillover effects. The vessel-level result in Figure 8 also explains why vessels underreport percentage juvenile in the raw electronic logbook data (Section II) even though closures provide valuable information (Section V). Closures only provide information to vessels who were not already fishing in the area, so vessels might underreport percentage juvenile to avoid triggering a closure in the area they are already fishing.<sup>33</sup>
<!-- Start of picture text --> Figure 5: Among crop farming households borrowing for agricultural<br>inputs, most relied on supplier credit in the past 5 years<br><!-- End of picture text --> <!-- Start of picture text --> Adults living in a household where growing crops is the main source of household income<br>(%), 2017<br>Central African Rep. iif<br>Congo, Dem. Rep. |<br>Cote d'ivoire ||<br>Kenya | a<br>Malawi :<br>Mozambique : UY<br>Rwanda LL.<br>Tanzania ll]<br>Togo<br>Uganda |<br>ania<br>0% 10% 20% 30% 40% 50% 60%<br>@ Used bank loan for inputs @ Used bank loan and credit for inputs<br>@ Used credit for inputs Did not use a bank loan nor credit for inputs<br><!-- End of picture text -->

Source: Global Findex database. Note: The total length of the bar represents the share ofadults living in a household where growing crops is the main source of income. The survey asked respondents their source for purchasing agricultural inputs in the past five years.
# **Figure 2: Frequency and cost of government intervention over time**

## **A: Number of government interventions by year B: Cost of government intervention (% of total tax revenue)**

<!-- Start of picture text --> 8 50<br>45<br>40<br>6<br>35<br>30<br>4 25<br>20<br>15<br>2<br>10<br>5<br>0 0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Equity NPE acquisitions Debt Equity NPE acquisitions Debt<br>Number of government interventions<br>Government expenditures (% of Government revenues)<br><!-- End of picture text --> Source: Own elaboration using data from the National Bank of Kazakhstan, National Wealth Fund Samruk-Kazyna, the Kazakhstan Sustainability Fund, public news reports, and the World Bank. Note: In Panel B, we report the cumulative government expenditures on bailouts in a given year.

Quasi-fiscal entities played a crucial role in the structuring of bank bailouts. The most recent purchases by the Problem Loan Fund (PLF) of bank non-performing loans much above their market price and often at 100 percent of their nominal value—while their market price can be often lower than 10-20 cents on the dollar<sup>h</sup> —have been the costliest bailout options for the general government without any claim on the future profits of banks received later in the form of dividends (Figure 4). The recoveries from the NPE purchases have been minuscule and the prospects for further recoveries deteriorating with time. The procedural steps in a bailout using the PLF purchases of bank NPEs are described in table A1.B in the annex.

The use of debt bailouts through NBK’s Kazakhstan Sustainability Fund has been
– across countries in LAC. Taking Brazil as an example, Leste-Urban Area has the lowest moderate poverty line ($3.5 per person per day) in LAC, which is considerably lower than the moderate poverty line of Region Pampeana in Argentina ($4.8 per person per day). By contrast, the metropolitan Area of Porto Alegre in Brazil has a moderate poverty line of $5.5 per person per day, which is above the median moderate poverty line in LAC ($5.3 per person per day) and considerably higher than moderate poverty lines in subnational areas of Argentina. Second, there are highly populated subnational regions that contribute substantially to the overall population in LAC. For instance, Mexico and Brazil, which are characterized by larger circles in the figure, represent roughly 60 percent of LAC’s population. Third, there is large heterogeneity in terms of the number of subnational poverty lines by countries.

> 26  The annual average was chosen because the PPP figures reflect prices for the entire calendar year.

11
representative sample of Mexican female entrepreneurs taken from the 2023 ENOE, and to a sample of CREA’s in-person clients from Iacovone et al. (2018). Online recruitment was successful in scaling across a wide geographic range. The businesses are based in all 32 states of Mexico, with only 28% coming from Mexico City and the neighboring State of Mexico. 8% of the women are in Guatemala. The businesses are small in size, with only 59% having any employees, and an average of 1.5 employees, and average monthly profits of approximately 2,000 pesos ($100). Firms were required to be in operation for at least a year to join the program, and the average years of operation is 4 years. Firms cover a heterogeneous mix of industries, but the majority involve women making and selling some sort of product, while 30 percent are in services. The most common sectors are baked and prepared food, beauty, handicrafts, and clothes and accessories. The women running these businesses average 40 in age, and 48 percent have some university education. At baseline there was plenty of scope to improve their business practices: while 65% said they kept written accounts, they were only doing 38 percent of the marketing practices, 27 percent of the accounting practices, and 17 percent of the planning practices that the training was intended to cover.

We see that the women who are recruited for Zoom training are on average younger and more educated than the average Mexican female entrepreneur, and more likely to have an employee in their firm. They are more similar to the typical in-person clients of CREA in age, but also more educated. In-person clients tend to be concentrated in a few states, whereas online recruitment gives a sample more geographically representative. Household income levels are similar to those of the average microentrepreneur and profits and sales are substantially lower than for in-person clients, although this could reflect the impact of the COVID-19 pandemic on demand.

# **2.2 Random Assignment
# III. Data & Methods

Our study is based on a pilot experiment designed and implemented jointly with the General Directorate of Taxation (GDT) in early 2020 based on 5,423 registered firms (and over 8,000 targeted employees) across select occupation groups, identified by the authorities as having previously declared suspiciously low pay for a subset of employees on their payroll. This risk assessment is carried out on a quarterly basis by the Risk Management Directorate of the GDT based on average wages defined for select occupations. There is evidence of _bunching_ of wages at or near the minimum wage, and this tendency carries over into professions where the minimum wage is substantially below the expected (market) wage. As such, the GDT assesses these tendencies and communicates with firms and their employees about this suspicious reporting.

## Setting & taxpayer sample

The sample of taxpayers included in our study are not a random sample of firms but rather a subset of firms subject to the personal income tax (PIT) that were assessed to be at risk of under-declaring wages by the Risk Management Directorate of the GDT. The risk assessment is carried out on a quarterly basis, and the assessment to identify the sample of firms in our study was carried out in early 2020. This exercise identified individual taxpayers (employees) with a declared wage substantially below the market wage (defined through tax records from the entire universe of taxpayers) for a given sector and occupation based on monthly declarations in the fourth quarter of 2019. The exercise is carried out by the GDT and evaluates if the difference between the reported and expected (market) wage are large enough to generate suspicion, and these suspicions are validated with additional information on employees and their firm. The assessment targeted a total of 21 professions ranging from non-technical (e.g., van driver) to technical (e.g. pharmacist) occupations, none of which had been targeted in previous campaigns.<sup>3</sup> The risk assessment resulted in the identification of