rafmacalaba/datause-encoder-data huggingface dataset:
Slovenia they account for 22%. In order to explore these differences, in Figure 1 we plot the changes in the extensive and intensive margins of exports within each country during the 2002-2013 period. On the y-axis we plot the changes from 2002 and 2009, while on the x-axis we plot the changes between 2009 and 2013. Observing scatter dots concentrated in the first (third) quadrants is a sign of a boom (bust) in exports that continued during the entire sample period. Conversely, scatter dots in the second (fourth) quarter indicate that exports fell (rose) in 2013-2009 after an initial increase (decline) in 2002-2009. We find that many ECA countries are in the fourth quadrant: there was a decline in the extensive (share of exporters) and intensive (share of exports) components of the changes in trade in 2002-2009, and an increase in 2009-2013. During the period 2002-2009, the share of exporting firms and exports declinedby almost 15% and 30% respectively across most ECA countries. Macedonia, FYR experienced the largest fall in the share of exports and in the number of exporters. Some of these countries increased the intensive and extensive margins in 2009-2013. Conversely, the share of exporters and exports in Turkey increased over both periods, and substantially during the second period.

# **3.1 Empirical methodology**

We test the relative importance of demand shocks and credit constraints of firm _i_ in country _j_ at time _t_ on the ability to export (extensive margin) in Equation 1 and the relative extent of exports (intensive margin) in Equation 2 by estimating the following equations: The dependent variables ( _Prob_ ( _Exportijt >_ 0) and _Exportijt_ ) are constructed from the BEEPS survey question: “What percentage of your firm’s sales are sold domestically, exported directly, or indirectly through a distributor?” Firms that report a positive value for either direct or indirect exports are identified as exporters, with the dummy variable _Prob_ ( _Exportijt >_ 0) taking a value of one. The sum of direct and indirect exports yields the value of total export as a percentage of total sales used to estimate Equation 2. For robustness checks, we use alternative definitions of the dependent variables. First we consider excluding exports sold indirectly through a distributor. Second, we measure the dependent variable in Equation 2 as the total value of exports, allowing for a distinction between small and large firms.<sup>5</sup> There is an objective and a subjective measure of the variable _CreditConstraintijt_ . The sub-

> 5Total value of exports is not directly recorded in the BEEPs survey. It is calculated by multiplying the share of exports in total sales and the value of total sales. While export value is a preferable dependent variable to the share of exports in total sales, there are inconsistencies in the way total sales are recorded across the countries and time periods. We cannot dismiss the potentially considerable measurement error and related bias. Hence, we use this measure only for robustness checks.

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# **Ignorance of bulk discounts**

Is it possible that many people in Tanzania simply do not know of the available bulk discounts? We are doubtful. When we conducted informal interviews with individuals in the study area, everyone was well aware of bulk discounts for a wide range of consumption items. Furthermore, many households personally experience non-linear prices. Column 5 of Table 3 shows the results of item-level regressions of unit price on quantity, with household fixed effects. Even _within household_ , bulk discounts are present. The members of households that purchase items multiple times—exactly those that are foregoing potential consumption by not buying in bulk—are surely aware of the available discounts.

# **Rationing consumption**

Could it be that people avoid buying in bulk as a way to limit their consumption? A sophisticated but present-biased agent would forego bulk purchasing in order to prevent her future self from over-consuming (Laibson, 1997; O’Donoghue and Rabin, 1999). Relatedly, shoppers may not trust other household members to control their consumption, and so may limit stocks as a form of rationing. In focus-group discussions we heard numerous variations on this theme.<sup>18</sup> If rationing is present, it is most likely to occur on items that are “temptation goods”—goods which, if held in stock, are likely to be consumed too quickly relative to one’s _ex ante_ plan. Because temptation goods are culturally specific, we conducted a survey in Tanzania to measure the temptingness of the study items. We invited Tanzanian staff members from recent survey projects to rank the study items on a five-point scale (1 = not at all tempting; 5 = tempting for essentially everyone who consumes the item). These respondents implement consumption surveys and speak regularly with households about their economic choices—in essence, they constitute a panel of experts. We asked respondents to

> 18The story of one respondent in Bukoba can be paraphrased as follows: “We know that we need 1 kilogram of maize flour each evening. But if we buy a 50 kilogram bag, we may find that it is gone at the end of one month, because we use too much each day. So it is better to buy smaller amounts.” Despite this anecdote, in the forthcoming analysis maize was not deemed to be “temptation goods” in our survey (see next paragraph). This underscores the subjectivity of temptation and the importance of grounding any analysis of temptation in data sourced from more than one person.

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# **6.  Global Trends for High-Priority GGUs**

In this section, we extend our sampling and modeling approach to global scale. Among 51,976 Global Geographic Units, we have identified 1,131 GGUs that account for 50% of global methane emissions in the EDGAR database. After limiting our exercise to GGU sampling areas with at least 20 Sentinel-5P observations per year, we estimate trend models (1) and (2) by fixed effects for 775 high-priority GGUs.

# **6.1 Overall Results**

Table 10 provides a summary that aggregates the econometric results into four categories: No Trend (statistically-insignificant results for both models); Mixed (statistically-insignificant results for one model or oppositely-signed but significant results for two models); Decreasing (significant negative results for both models); and Increasing (significant positive results for both). We find No Trend for 56 GGUs because neither model attains classical significance (p=0.05). Results are Mixed for 150 GGUs, Decreasing for 106 and Increasing for 453. In summary, a substantial majority of 775 GGUs have experienced unambiguous increases in CH4 emissions during the past four years, whether judged by the overall trend or change in the most recent year. The same picture emerges if we weight the regression results by EDGAR emissions.

**Table 10:  GGU estimation results (fixed effects)**

||||Emissions-<br>Weighted|
|---|---|---|---|
|Results|Count|%|%|
|No Trend|56|7.2|6.5|
|Mixed|160|20.6|23.3|
|Decreasing|106|13.7|13.1|
|Increasing|453|58.5|57.1|

# **6.2 Sectoral Results**

Table 11 explores the sectoral distribution of results weighted by EDGAR emissions. Soils and Livestock exhibit enormous disparities between Increasing and Decreasing cases (90.2 % vs. 0.6% for the former; 63.6% vs. 4.6% for the latter). The fuels sectors reveal striking differences, with a huge imbalance toward Increasing for Coal (66.7% vs. 5.1%) while Gas and Oil are both dominated by Decreasing (44.7% vs. 25.3% for the former; 23.8% vs. 10.6% for the latter). The two waste sectors have large imbalances toward Increasing (52.4% vs. 18.0% for Landfills; 61.0% vs. 18.3% for Wastewater). In summary, Oil and Gas exhibit more Decreasing results but the two sectors only account for 27.2% of EDGAR emissions. The remaining sectors all exhibit huge dominance for Increasing results.

26
27 average of the financial protection and service coverage scores. The year against the country’s name is the average of the years of the surveys from which the data come: for example, if half the indicators are from 2005, and half are from 2007, the year indicated against the country’s name is 2006. The lower panel of Table 2 shows the underlying data.

A couple of points are important to keep in mind in interpreting the charts and table. First, with the exception of impoverishment, the indicators are adjusted for inequality: for a given mean, a country’s score is pulled down if the indicator is higher among the better off than among the poor; the larger the pro-rich inequality, the larger the ‘penalty.’ Second, the financial protection indicators are normed with reference to global maxima for the two underlying indicators: an absence-ofcatastrophic-payment score of 0 corresponds to a catastrophic payment rate of 25%, while an absence-of-impoverishing-payment score of 0 corresponds to an impoverishment rate of 15%; by contrast, absence-of-catastrophic-payment and absence-of-impoverishing-payment scores of 80 correspond to catastrophic and impoverishing payment rates of 5% and 3% respectively.

We see a cluster of five countries with overall UHC scores between 79 and 84, namely Brazil, Colombia, Costa Rica, Mexico, and South Africa. Brazil and Colombia do worse than the others on financial protection, but compensate by doing better on service coverage. Countries also reach their dimension-specific scores in different ways. For example, Costa Rica and South Africa achieve similar service coverage scores. Costa Rica outperforms South Africa on all prevention indicators, 27
3 The intervention is the ‘healthy villages’ component of the DRC’s Healthy Villages & Schools program, co-led by the Ministries of Public Health, and of Primary, Secondary, and Professional Education, with support from the United Nations Children’s Fund (UNICEF). Since 2008, nearly 9 million people in almost 11,000 villages have been reached with WASH services through the program. Healthy Villages & Schools was the largest WASH program implemented by UNICEF globally and comprised 90% of total external funding committed to rural WASH in the DRC from 2005 to 2020 [25]. Our goal was to estimate the effect of Healthy Villages & Schools on diarrhea prevalence, child length-for-age, and WASH institutions.

# Methods

## Study design and participants

The intervention of interest is a DRC government-run program that began several years before our study. For our study, the government agreed to randomly assign the next phase of the program (i.e. the next group of villages to receive the intervention). In 2018, we randomly assigned groups of villages to intervention or control (details below). Since we did not collect any data prior to randomization, we did not yet register the study. We collected the first round of data in late 2019, about 5 months after the intervention was implemented (implementation took about one year in each group of villages) [26].

<mark>We pre-registered the analysis of that first round of data collection (5 month follow-up), before any of the authors saw the data, in the American Economics Association (AEA) Registry</mark> (AEARCTR-0004648) (https://www.socialscienceregistry.org/trials/4648).

<mark>In Feb 2021, we registered plans for additional data collection in the Pan-African Clinical Trials Registry (PACTR202102616421588;</mark> <u><mark>https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=14670). In April 2023, before the PIs had</mark></u> <mark>seen any data for the 3.6-year follow-up (i.e., the data for the current manuscript), we updated the registration with our primary and secondary outcomes. In April 2023, we also posted our preanalysis plan for the 3.6 year follow up on the AEA Registry (see "Three Year Follow up PreAnalysis Plan" at https://www.socialscienceregistry.org/trials/4648). All planned studies from this project are now registered and any future work will be registered prospectively.</mark> This study is reported as per CONSORT guideline (S1 Text).

We worked with intervention implementers to design a cluster-randomized trial in rural villages in five DRC provinces: Kongo Central, Kasai, Kasai Central, North Kivu, and South Kivu. The implementers identified 403 candidate villages in which the intervention could be launched during the study period, based on the established criteria for the intervention: that the village was located in a secure and accessible Health Area that was not already served by the WASH Consortium, the Health Area staff were dynamic and interested in participating, and there was a problem of diarrhea, cholera, and/or malnutrition. Among these villages, 34 already had program activities in process before research activities began, leaving 369 eligible villages.
income countries perform notably better on the Statistical Performance Indicators and Index (SPI) – the World Bank’s new official tool to measure country statistical capacity and is being added to the SDG indicators under SDG17 (Dang et al. 2023).

One explanation for this paradox on reporting SDGs and statistical strength overall is that richer countries may have been slow to report SDGs compared with low- and middle-income countries that have experience in engaging with the Millennium Development Goals (MDGs) (MacFeely 2018). A second explanation lies in the specific focus of some SDGs. This difference in the overall performance of national statistical systems and the reporting on gender SDGs might be explained by presence in the latter of indicators which relate to phenomena that are arguably infrequent or rare for high income countries (or perceived as such). For example, data on child marriage and on female genital mutilation (covered in SDG target 5.3) are rarely collected in OECD countries. OECD (2022) describes the extra lengths needed to get such data from alternate sources in order to be able to report on this SDG.

A third explanation, related to the second one above, is the presence of systematic and large-scale data collection under the Demographic and Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS) programs, which are focused on low-income countries (and often financed with non-national resources). These surveys are often the source of gender-related data, especially in the domains of female health and empowerment. To assess this, we examine the main data sources for the 50 SDG gender indicators. The DHS or MICs is the source for at least one country data point for 13 out of 50 indicators but only extensively (well over half of the data points) for 5 indicators.<sup>7</sup> We find very slight evidence that the DHS/MICs data source explains lower

> 7 For the 13 indicators where the DHS/MICs is ever the source, the share of data points which are from DHS/MICs are: 2% (indicator 4.1.1), 4% (indicator 5.b.1), 14% (indicator 5.a.1), 16% (indicator 3.7.2), 24% (indicator3.1.2),

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_Page 10_ Figure 9: R & D Expenditure <!-- Start of picture text --> (% of GNP)<br><!-- End of picture text --> <!-- Start of picture text --> 3.5<br>3<br>2.5-<br>2 _<br>1.5 -<br>I<br>0.<br>Brazil Chile Mexico Korea Taiwan Italy Sweden Let Amer Asia Europe<br><!-- End of picture text --> <!-- Start of picture text --> Sources UNESCO<br><!-- End of picture text --> Figure 10: Research Personnel (Per million population) <!-- Start of picture text --> thousands<br>7e<br>6t<br>5-<br>4<br>2<br>Brazil Chile Mexico Korea Taiwan Italy Sweden Lat Amer Asia Europe<br><!-- End of picture text -->

**Source. UNESCO**
Corn products are the core of basic food consumption for a large portion of the population (especially among the poor) above other cereals such as wheat or rice (see Table 2). In developing countries, the production of corn products typically escapes monopolistic controls in the first chain of the production since several small farms produce maize. However, the products of these small farms tend to converge into a small number of specialized firms for the stages of storage, processing, and commercialization. At this level, corn product manufacturers can exert market power.

**Figure 4. Consumer price index of selected corn products**

<!-- Start of picture text --> 150<br>140<br>130<br>120 Total<br>Maize<br>110<br>Maize Tortilla<br>100<br>90<br>Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11 Jan-12 May-12 Sep-12 Jan-13 May-13 Sep-13 Jan-14 May-14 Sep-14 Jan-15 May-15 Sep-15 Jan-16 May-16 Sep-16 Jan-17 May-17 Sep-17 Jan-18 May-18<br><!-- End of picture text --> <!-- Start of picture text --> Source: INEGI 2018.<br><!-- End of picture text --> Table 2 shows the expenditure shares on different cereal products in Mexico. The expenditure shares on corn products are relatively high among the poorest households, where they range from about 13.4 percent for the poorest to about 1.0 percent for the richest decile. Among cereal products, corn products are the most highly consumed, followed by wheat products.

**Table 2. Expenditure shares on cereal products**

||**All**|Poo|rest||Househ|old inc|ome dec|iles|||Richest|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Category**||1|2|3|4|5|6|7|8|9|10|
|**TOTAL**|**3.37**|**13.37**|**10.05**|**8.38**|**7.09**|**5.89**|**5.13**|**4.28**|**3.48**|**2.52**|**1.04**|
|**CORN**|**1.72**|**7.51**|**5.84**|**4.95**|**4.08**|**3.23**|**2.76**|**2.31**|**1.70**|**1.13**|**0.33**|
|_Grain corn_|_0.11_|_1.93_|_0.61_|_0.48_|_0.27_|_0.15_|_0.10_|_0.06_|_0.03_|_0.02_|_0.00_|
|_Maize flour_|_0.03_|_0.33_|_0.12_|_0.07_|_0.07_|_0.05_|_0.03_|_0.03_|_0.01_|_0.01_|_0.00_|
|_Mass of corn_|_0.05_|_0.29_|_0.21_|_0.19_|_0.13_|_0.11_|_0.08_|_0.06_|_0.05_|_0.03_|_0.01_|
|_Corn tortilla_|_1.46_|_4.82_|_4.78_|_4.06_|_3.49_|_2.81_|_2.42_|_2.06_|_1.51_|_1.00_|_0.28_|
|_Toast_|_0.06_|_0.10_|_0.08_|_0.11_|_0.09_|_0.08_|_0.10_|_0.09_|_0.08_|_0.06_|_0.03_|
|_Other maize products_|_0.02_|_0.03_|_0.04_|_0.02_|_0.03_|_0.02_|_0.03_|_0.02_|_0.02_|_0.02_|_0.01_|
|**WHEAT**|**1.32**|**4.58**|**3.27**|**2.74**|**2.41**|**2.14**|**1.93**|**1.6**|**1.46**|**1.11**|**0.55**|
|**RICE**|**0.15**|**0.93**|**0.61**|**0.44**|**0.32**|**0.25**|**0.21**|**0.17**|**0.14**|**0.09**|**0.04**|
|**OTHER**|**0.18**|**0.36**|**0.34**|**0.26**|**0.28**|**0.26**|**0.23**|**0.2**|**0.18**|**0.18**|**0.13**|

_Source:_ World Bank estimates using ENIGH 2014. _Note_ : Deciles based on household expenditure per capita.

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# **Table 2:  Probit estimation results (controlling for birth month) Dependent variable:  Child death before age 2 months**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**Full**|**Cluster Dis**|**tance Bound (km)**||
||**Sample**|**40**|**30**|**20**|
|**Observations**|**39,150**|**8,183**|**4,847**|**2,072**|
|**Pre-natal salinity**||**0.020**|**0.023**|**0.024**|
|**(month before birth)**||**(2.21)***|**(2.07)***|**(1.52)**|
|**Female child**|**-0.025**|**-0.024**|**-0.062**|**-0.083**|
||**(1.09)**|**(0.43)**|**(0.86)**|**(0.74)**|
|**Male household head**|**0.123**|**0.400**|**0.462**|**0.484**|
||**(2.71)****|**(3.10)****|**(2.58)****|**(1.71)**|
|**Age of mother**|**-0.012**|**-0.014**|**-0.011**|**-0.005**|
||**(6.10)****|**(2.97)****|**(1.79)**|**(0.54)**|
|**Mother education (years)**|**-0.019**|**-0.007**|**-0.010**|**-0.016**|
||**(5.63)****|**(1.05)**|**(1.02)**|**(0.97)**|
|**Wealth - top 40%**|**-0.001**|**-0.183**|**-0.188**|**-0.206**|
||**(0.05)**|**(2.50)***|**(1.94)**|**(1.36)**|
|**Sanitation (no toilet facility**|**excluded)**||||
|**Flush toilet**|**-0.100**|**-0.226**|**-0.188**|**-0.095**|
||**(2.14)***|**(1.95)**|**(1.26)**|**(0.41)**|
|**Pit latrine**|**-0.061**|**-0.106**|**-0.069**|**-0.012**|
||**(2.06)***|**(1.43)**|**(0.71)**|**(0.08)**|
|**Water source (tap and pipe**|<br>**d water exclud**|<br>**ed)**|||
|**Tubewell**|**0.069**|**-0.041**|**-0.065**|**-0.084**|
||**(1.85)**|**(0.48)**|**(0.58)**|**(0.49)**|
|**Spring**|**0.386**|**0.425**|**0.482**|**0.503**|
||**(5.01)****|**(2.69)****|**(2.38)***|**(1.62)**|
|**Surface**|**0.235**|**-0.187**|**-0.168**|**-0.101**|
||**(2.32)***|**(0.72)**|**(0.52)**|**(0.22)**|
|**Cooking fuel (dirty fuels exc**|**luded)**||||
|**Electricity or gas**|**-0.067**|**0.196**|**0.204**|**0.202**|
||**(1.23)**|**(1.66)**|**(1.36)**|**(0.88)**|
|**Constant**|**-1.459**|**-1.803**|**-1.957**|**-2.158**|
||**(17.31)****|**(8.52)****|**(6.94)****|**(4.89)****|

**Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1%**

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Until 2003–04, the government’s allocation for both seasonal and year-round safety-net programs was fairly low and about the same, after which time the allocation to seasonal programs rose considerably, reaching Tk. 6 million per year by 2007–08, more than 10 times the amount allocated in 2003–04. Over the same period, allocation to year-round programs increased by only half (Figure 2). Thus, the relative importance of short-term versus longer-term programs is a point worth exploring.

# **3. Survey and Data Description**

This study is based on a large household survey conducted in the greater Rangpur region in 2006–07 by the Institute of Microfinance (InM), with support from the Palli Karma Shahayak Foundation (PKSF), Bangladesh’s wholesale outlet for microfinance. The InM survey aimed to identify the hard-core poor, the group most vulnerable to monga, and to design and implement appropriate mitigating interventions.<sup>1</sup> The survey covered roughly 70 percent of rural residents in the greater Rangpur region,<sup>2</sup> and was conducted immediately following the 2006 monga season (September– November). A total of 482,928 households were surveyed, representing 23 sub-districts ( _upazilas_ ) and some 2,300 villages; after data cleaning, 480,918 households were retained for the purpose of this paper’s analysis. Survey data on the extent of seasonal food deprivation and coping mechanisms adopted by the poor to mitigate it were limited.

The survey characterized poor households in the greater Rangpur region by a high dependency ratio (0.63), a high rate of wage employment (54 percent), and a low rate of selfemployment (16 percent) (Table 5).<sup>3</sup> Since most of the poor are wage employed in the agriculture

> 1 In the InM survey, the hard-core poor are defined as those households that own less than half an acre of land, whose monthly incomes do not exceed Tk. 1,500 (equivalent to US$22), or who sell labor for a daily wage.

> 2 The InM survey covered all five of the greater Rangpur region’s districts: Kurigram, Gaibandha, Nilphamari, Lalmonirhat, and Rangpur.

> 3 The dependency ratio is measured by a household’s proportion of non-working members.

11