rafmacalaba/datause-encoder-data huggingface dataset:
Child Wasting Maternal Anemia <u>Std. Logit Robust DHS Cluster Std. Logit Robust DHS Cluster</u> Survey Month (January NA; dummy for February excluded

|March|0.312|0.312|0.312|-0.105|-0.105|-0.105|
|---|---|---|---|---|---|---|
||(1.23)|(1.22)|(1.23)|(0.90)|(0.90)|(0.61)|
|April|0.270|0.270|0.270|-0.489|-0.489|-0.489|
||(1.07)|(1.06)|(1.07)|(4.18)**|(4.18)**|(2.84)**|
|May|0.532|0.532|0.532|-0.324|-0.324|-0.324|
||(2.11)*|(2.09)*|(2.11)*|(2.78)**|(2.78)**|(1.89)|
|June|0.686|0.686|0.686|-0.468|-0.468|-0.468|
||(2.72)**|(2.70)**|(2.73)**|(4.01)**|(4.01)**|(2.72)**|
|July|0.705|0.705|0.705|-0.535|-0.535|-0.535|
||(2.79)**|(2.76)**|(2.79)**|(4.55)**|(4.55)**|(3.10)**|
|August|0.562|0.562|0.562|-0.534|-0.534|-0.534|
||(2.20)*|(2.18)*|(2.19)*|(4.39)**|(4.39)**|(3.00)**|
|September|0.402|0.402|0.402|-0.631|-0.631|-0.631|
||(1.57)|(1.55)|(1.57)|(5.16)**|(5.17)**|(3.51)**|
|October|0.388|0.388|0.388|-0.570|-0.570|-0.570|
||(1.51)|(1.50)|(1.50)|(4.62)**|(4.62)**|(3.16)**|
|November|0.131|0.131|0.131|-0.165|-0.165|-0.165|
||(0.49)|(0.49)|(0.48)|(1.11)|(1.11)|(0.79)|
|December|-0.143|-0.143|-0.143|-0.396|-0.396|-0.396|
||(0.52)|(0.51)|(0.52)|(2.17)*|(2.17)*|(1.65)|

Birth Month (dummy for January excluded)

|February|-0.003|-0.003|-0.003||||
|---|---|---|---|---|---|---|
||(0.05)|(0.05)|(0.05)||||
|March|-0.019|-0.019|-0.019||||
||(0.34)|(0.34)|(0.34)||||
|April|-0.004|-0.004|-0.004||||
||(0.07)|(0.07)|(0.07)||||
|May|-0.049|-0.049|-0.049||||
||(0.86)|(0.86)|(0.86)||||
|June|-0.024|-0.024|-0.024||||
||(0.42)|(0.42)|(0.41)||||
|July|-0.131|-0.131|-0.131||||
||(2.37)*|(2.37)*|(2.41)*||||
|August|-0.161|-0.161|-0.161||||
||(3.08)**|(3.08)**|(3.04)**||||
|September|-0.163|-0.163|-0.163||||
||(2.99)**|(3.00)**|(3.00)**||||
|October|-0.144|-0.144|-0.144||||
||(2.72)**|(2.72)**|(2.72)**||||
|November|-0.176|-0.176|-0.176||||
||(3.24)**|(3.24)**|(3.17)**||||
|December|-0.023|-0.023|-0.023||||
||(0.43)|(0.43)|(0.42)||||
|Constant|-1.330|-1.330|-1.330|-0.904|-0.904|-0.904|
||(4.90)**|(4.85)**|(4.80)**|(6.84)**|(6.83)**|(4.84)**|
|Observations|45,199|45,199|45,199|109,817|109,817|109,817|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 12
# **3.1.2 Grammatical Gender Data**

Data on the gender structure of languages comes from a range of sources. One of the best known is the World Atlas of Language Structures (WALS), which characterizes the noun classification system of 525 languages (Corbett 2013a, Corbett 2013b, Corbett 2013c). Unfortunately (from our perspective), many of these are indigenous languages from Papua New Guinea, Australia, and the Americas with very few living speakers.<sup>13</sup> Additional sources of data include: George L. Campbell’s _Compendium of the World’s Languages_ (Campbell 1991), which characterizes several hundred of the world’s most widely spoken languages; George Abraham Grierson’s eleven-volume _Linguistic Survey of India_ (Grierson 1903a, 1903b, 1904, 1905, 1907, 1908, 1909, 1916, 1919, 1921), which was compiled between 1891 and 1921 and covers more than 300 South Asian languages and dialects; and the UCLA Language Materials Project (UCLA Language Materials Project 2014), which provides detailed descriptions and learning materials for 116 languages. Additional data on the grammatical gender structures of languages comes from academic articles and teaching materials focused on individual languages. We also collected first-person accounts from native speakers for a small number of relatively undocumented languages (e.g. Fiji Hindi and Rohingya). Detailed information on the full range of sources (including the quotes used to characterize each language’s grammatical gender) is provided in our Data Construction Appendix.

# **3.1.3 Identifying Gender Languages**

For each mother tongue in the _Ethnologue_ database, we attempt to code two variables characterizing the language’s grammatical gender structure. First, we create an indicator

> 13Data from the WALS must be used with caution because they were compiled by the linguist Greville Corbett; as discussed above, Corbett advocates the use of a non-standard definition of grammatical gender that includes systems of anaphoric pronominal agreement (Corbett 1991). This is particularly problematic when one combines the WALS with other data sources that do not classify systems of pronominal agreement based on the gender of the referent as examples of grammatical gender. We address this by excluding WALS data on languages that are classified as “strictly semantic” (i.e. agreement class can always be inferred from the meaning of the noun) since Corbett considers pronominal agreement an example of such a system. We rely on other sources to classify those languages. Languages that are classified in the WALS as either lacking a grammatical gender system of having a system that is “semantic and formal” are unambiguous.

14
Some details of product coverage: <u>EEC price-based: The price-based list includes sugar and molasses plus poultry</u> meat and poultry meat products. Sugar and molasses appear on the ‘made operational’ list each year. Chicken meat has been on the list every year, turkey carcasses and pieces were added in 2001/2002. (Year by year coverages are compared in Table A1.11.) <u>EEC volume-based: Vegetables and fruits make up the list for which the EEC</u> volume-based SSG system has been operational. The same products; e.g., tomatoes, cucumbers, apples, lemons, oranges; are on the list each year. Each product is on the ‘made operational list for the same period each year; e.g., oranges, 1 December 2002 to 31 May 2003 for the 2002/2003 marketing year; a parallel period, 1 December to the next 31 May for other marketing years. Many products are on the ‘made operational’ list for the entire year, e.g., tomatoes and cucumbers. (Year by year coverages are compared in Table A1.12.) <u>United States: The list of products on which the United States reports price-</u> based actions is likewise much the same from one year to the next. The list includes sugar and products containing sugar, milk and milk products such as cheeses and yogurt. Some years peanuts and cotton are on the list, others not. The United States has reported volume-based actions in only two years; 1998: on sheep meat, 6 tariff lines, on 3,709 tones; 2002: on Cheese and curd, 2 tariff lines, 2,071 kg.

<u>Japan: Japan’s reporter actions have been more volume-based than price-based.</u> Again the list of products varies little from year to year; products on the list include swine and swine meat, milk and milk products, beans of various types, rice and wheat and flours and starches of wheat and rice.

<u>Korea: The list includes groundnuts; wheat, buckwheat along with their starches</u> and other vegetable starches and meals; peas and beans; ginseng and products. As with other countries, the list changes little from year to year.

<u>Chinese Taipei</u> has continuously (2002-2007) applied SSG measures on chicken parts, liquid milk, peanuts, oriental pears and red beans. Measures were applied on sugar 2002-2005, but there are no more recent notifications of SSG measures on sugar. (As of 09 January 2009).

Tables at the end of this annex list the year-to-year product coverage of SSG measures by major users.

49
o Internet contribution to reduce inventory costs is: (1=no improvement, 7=huge improvement)

# **References**

Anderson, James E. (1979). “A Theoretical Foundation for the Gravity Equation.” _American Economic Review_ 69: p.106-116.

Anderson, James E. and Eric van Wincoop (2003). “Gravity with Gravitas: A Solution to the Border Puzzle.” American Economic Review v93, n1: 170-92.

Asia Pacific Economic Co-operation (APEC) (1999). _Assessing APEC Trade_ _Liberalization and Facilitation: 1999 Update, Economic Committe_ e, September 1999. APEC: Singapore.

Asia Pacific Foundation of Canada (1999). _Survey on Customs, Standards and Business Mobility in the APEC Region._ APF Canada: Vancouver.

Balistreri, Edward J. and Russell H. Hillberry (mimeo). “Trade Friction and Welfare in the Gravity Model: How Much of the Iceberg Melts?” U.S. International Trade Commission, Washington, D.C.

Clark, Ximena, David Dollar and Alejandro Micco. (2002). “Maritime Transport Costs and Port Efficiency.” World Bank Working Paper Series # 2781. The World Bank: Washington, D.C.

Djankov, Simeon, Caroline Freund, and Cong S. Pham (2006), “Trading on Time.” World Bank mimeo.

Dollar, David and Aart Kraay (2001). “Trade, Growth, and Poverty” World Bank Working Paper Series #2615. The World Bank: Washington D.C.

Fink, Carsten, Aaditya Mattoo and Cristina Ileana Neagu (2002a). “Trade in International Maritime Services: How Much Does Policy Matter?” World Bank Economic Review v16, n1 (2002): 81-108.

20
# **Annex 2.  Financial Analysis: Prices and Financial Statements**

## **Table 7.  Shadow Prices**

|_Particulars_|_1998-99_|_1999-00_|_2000-01_|
|---|---|---|---|
|Inflation rate|11|6|3|
|Inter-bank lending rate|9|8|7|
|Prime rate paid by commercial||||
|bank borrowers|13|12.5|12|
|Marginal commercial rate||||
|Available to SHGs|15|15|15|
|Exchange rate(Rupees/US$)|42.5|43.5|46|

All rates in annual percent

## **Table 8.  Actual Prices in SHGs**

|||_KVK_|||_SPMS_||
|---|---|---|---|---|---|---|
|_Particulars_|_1998-99_|_1999-00_|_2000-01_|_1998-99_|_1999-00_|_2000-01_|
|Interest paid on savings|12|12|12|12|12|12|
|Cost of loans from bank|12.5|12.5|12.5|12.5|12.5|12.5|
|Cost of loans from federation|18|18|18|18|18|18|
|Cost of funds for housing|13.5|13.5|13.5|15|15|15|
|Interest charged to members on<br>general loans|24|24|24|24|24|24|
|Interest charged to members on|||||||
|housingloans|15|15|15|18|18|18|

All rates in annual percent

- 30 -
**<u>Table 5: Plot productivity per acre: OLS and IV estimates</u>**

||**OLS**|**IV**|
|---|---|---|
|Cultivated land size in acres (Log)|-0.478**|-0.428**|
||(7.58)|(5.62)|
|Family size (Log)|0.076<br>|0.145<br>|
||(0.86)|(1.41)|
|Use of chemical inputs (Dummy)|0.486**<br>|0.626**<br>|
||(2.61)|(2.75)|
|Conflict pending on plot (Dummy)|-0.334*|-2.360*|
||(2.31)|(2.11)|
|Reason for conflict: exceeding boundaries (Dummy)|-0.120|0.552|
||(0.88)|(1.46)|
|Reason for conflict: tenancy (Dummy)|0.144|1.164*|
||(0.88)|(2.01)|
|Reason for conflict: use of public land (Dummy)|0.625<br>|1.899*<br>|
||(1.61)|(2.31)|
|Household had plot for free (Dummy)|0.011|0.045|
||(0.10)|(0.37)|
|Household have a house on plot (Dummy)|-0.091|-0.040|
||(0.76)|(0.28)|
|Wife and husband cultivate plot (Dummy)|0.347**|0.266*|
||(3.29)|(2.15)|
|Household have coffee on plot (Dummy)|0.240|0.140|
||(1.83)|(0.89)|
|Household have mangoes on plot (Dummy)|-0.141|-0.196|
||(1.20)|(1.45)|
|Household have bananas on plot (Dummy)|0.067|0.135|
||(0.56)|(0.96)|
|Number of cattle (Log)|0.425**|0.349**|
||(3.86)|(2.71)|
|Number of goats (Log)|-0.112|-0.171|
||(1.06)|(1.35)|
|Number of poultry (Log)|0.121|0.091|
||(0.93)|(0.61)|
|Number of pigs (Log)|0.697**|0.590**|
||(5.13)|(3.70)|
|Bad soil quality (Dummy)|-0.265*|-0.222|
||(2.50)|(1.81)|
|Tenure status: plot rented (Dummy)|-0.336|-0.280|
||(0.64)|(0.48)|
|Households with access to credit in village (Percentage)|0.031|0.249|
||(0.20)|(1.22)|
|East (Dummy)|-0.766**|-0.383|
||(4.29)|(1.45)|
|Center (Dummy)|-0.769**|-0.448*|
||(4.95)|(2.00)|
|West (Dummy)|<br>4.177**|<br>3.938**|
||(15.94)|(12.82)|
|Constant|-0.478**|0.552|
||(7.58)|(1.46)|
|No. of Observations<sup>+</sup>|818|800|
|R-squared|0.24|0.05|

<u>+ 43 plots with no output or owned land were excluded (51.35% of these plots have no conflicts pending)</u> Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 23
# Table 4. Average Yields of Major Crops, 1990 (kg/ha)

||Wheat|Corn|Barley|Rice|Sugar beet|Sunflower|
|---|---|---|---|---|---|---|
|Bulgaria<br>|3,947|4,000|3,889|n/a|22,519|1,731|
|Czechoslovakia|5,153|6,144|4,679|n/a|34,845|2,352|
|GDR|5,732|n/a|5,222|n/a|28,650|n/a|
|Hungary<br>|4,927|4,091|4,594|n/a|47,961|1,879|
|Poland<br>|3,727|n/a|3,333|n/a|34,008|n/a|
|Romania<br>|3,333|3,172|4,079|3,211|25,577|1,674|
|USSR<br>|2,264|3,852|2,153|4,592|29,456|1,504|
|World|2,586|3,653|2,477|3,557|35,573|1,394|

00 n/a = Not applicable.

_Source: Agriculture and Food Processing, 1990,_ Hungarian Ministry of Agriculture, Budapest.
Other examples of questions that could be answered using `icio` for the analysis of value-added trade are:

- _Which part of a country’s total exports is home produced, i.e. is domestic GDP?_ `icio, exporter(deu) output(dva)`

- _Which part of a country’s total exports can be traced back to other countries GDP?_ `icio, exporter(deu) output(fva)`

- _Where the foreign value-added in German exports is produced?_ `icio, origin(all) exporter(deu) output(fva)`

- _Considering the bilateral exports from Italy to Germany, where the Italian GDP (domestic VA) re-exported by Germany is absorbed?_ `icio, exporter(ita) importer(deu) destination(all) output(dva)`

- _How can be obtained the complete breakdown by origin and destination of the value-added (both domestic and foreign) for Chinese exports to the US?_ `icio, origin(all) exporter(chn) importer(usa) destination(all) output(va) save(CHN_to_USA.xls)`

- _How can the (corrected) Koopman et al. (2014) decomposition be retrieved using icio?_

``` icio,exporter(deu)perspective(world)approach(sink) ```

- _Which is the Chinese GDP that at any point in time, passes through a certain bilateral trade flow, say Chinese exports to the United States? In other terms, what is the Chinese GDP potentially exposed to US tariffs on imports from China?_

``` icio,exp(chn)imp(usa)persp(bilat)output(dva) ```

- _Which is the German GDP potentially exposed to US trade barriers on all imports?_

``` icio,origin(deu)imp(usa)persp(importer)output(va) ```

- _Which is the German GDP that could be affected by US tariffs on imports in sector 20?_

``` icio,origin(deu)imp(usa,20)persp(sectimp)output(va) ``` 27
2021). These sources are inconsistent due to differences in spatial and radiometric resolution, spectral responses, the spread function of the sensors, local overpass time at night, radiance range, and on-board calibration (Li et al. 2017; Sahoo et al. 2020; Chiovelli et al. 2023, 2026). Hence, we harmonize the night-light series for 1992–2021 following the process outlined by Li et al. (2020, 2017). Figure C.1 shows the spatial distribution of night-light density in 2014 (right after the oil price decline) and in our last period of analysis (2021). It illustrates a stark reduction in night-light density over a remarkably short period of time.<sup>13</sup> To measure spatial inequality, we construct a novel spatial Gini index that corresponds to the traditional Gini formula estimated for each municipality and year, using the night light of each _parroquia_ (county) within each municipality as the unit of observation. Tables B.1 and B.2 show that our measure correlates with the traditional measure of income inequality constructed using population censuses for Venezuela (1990) and Colombia (1993 and 2005).

We also use other remote-sensing variables as controls that are listed in Table C.1. These capture baseline economic characteristics and their linear trajectories. They include annual tree-cover loss from Global Forest Watch (2023) and data on urban coverage and areas covered by water from MODIS Land Cover (2023) (see Appendix B for details). All variables were recorded in 2001, the first year for which they were available.

# **3.3 Electoral Data**

**Presidential elections.** We web-scrape information to construct longitudinal data at the municipal-election-year level for the five presidential elections from 2006 to 2024. The data come from the Consejo Nacional Electoral (2023) and the Venezuela 360 (2023) project. Specifically, we analyze two outcomes: turnout (total votes divided by the electoral census) and opposition support (votes for candidates other than Ch ́avez or Maduro as a percentage

> 13We confirm the validity of this measure as a proxy for economic growth and income inequality in Tables B.1, B.2, and B.3, and test the sensitivity of our main results to controlling for oil production in order to account for concerns related to biases due to brightness from gas flares in the satellite data.

18
_Central America and NAFTA_ 8 previous section. Mexico and Costa Rica which are about in the same point in the resource triangle have about the same pattern of trade, although Mexico has substantial receipts from petroleum exports. Both have substantial earnings from exports of laborintensive manufactures. Both export tropical agricultural products. El Salvador, with its great abundance of labor but scarcity of capital is not quite able to export the laborintensive manufactures and has earnings heavily concentrated on tropical agricultural products. Honduras which has about the same level of land per worker as Costa Rica has much less capital, and therefore has no earnings from labor-intensive manufactures and depends very heavily on exports of tropical agricultural products. Nicaragua has about the same capital per worker as Honduras, but more land, and is even more dependent on exports of agricultural products.

30. The trade diagrams also include the two other primary actors in our drama: China and the U.S. China in 1965 did export the labor-intensive manufactures, but trade per worker was very small in all categories. Chinese trade in all categories increased enormously, and net exports per worker of labor-intensive manufactures in particular grew from US$0.5 per worker to US$22.0 per worker. The U.S. market readily accepted these changes with net imports per worker growing from US$20.7 in 1965 to US$434.9 in 1988.

31. Panama, with an enormous appetite for machinery, is quite unusual. Panama's imports of labor-intensive manufactures is about the same as the U.S. with net imports exceeding US$400 per worker. The U.S. has an apparent trade deficit in merchandise trade with cereals and chemicals net exports not great enough to offset imports of all otlher items.

# **Changing Patterns of Trade in Labor-intensive Manufactures**

32. The action in Central America is in the labor-intensive manufactures. The same **kinid** of action is occurring in Asia. Figures E.7a and b display the net export data of this commodity aggregate for a large number of countries, comparing 1965 with 1988. Figure E.7a has the data in normal scale and Figure E.7b uses a logarithmic scale to allow as many of the data to be seen as possible.' It should be noted that this scale greatly compresses extreme values. If there were no changes in comparative advantage fromii 1965 to 1988 these data would all lie on a straight line. Also, a lot of the 'noise' in the logarithmic graph occurs for small values of net exports per worker which are scaled to appear large. Clearly, comparative advantage in labor-intensive manufactures (arppar el and footwear) is very much in turmoil with a large number of countries shifting **tromii** being importers to being exporters. In response, France, the UK, Austria and even Hong Kong switch in the opposite direction from exporting to importing. Japan and Belgium. although still having positive net exports in this category, are substantially reducing their export dependence on this product. falling from the list of top ten net exporters to well back in the pack.

S!n(x) lo2(1 ±+abs(x))
guidance as to which road segments to invest first based on their importance in maintaining decent travel times for many commuters under wet conditions.

Finally, we hope to point out that given climatic uncertainties in the future, the frequency and magnitude of floods in Kinshasa and other regions around the world will likely increase. The 10-year flood scenario has the lowest exposure level among all five flood return periods that we examined in this study. As climate change impacts unfold over time, the potential impact of floods on transportation is likely to increase dramatically. It is possible to imagine that the economic cost we report for the current 50-year flood event, for example, will occur more frequently in the future, thereby increasing the returns from resilient transport infrastructure planning and design in Kinshasa.

A limited number of uncertainty areas deserve some discussion. First, because of the difficulty of surveying the entire public transport network during the narrow window when floods are still affecting travel conditions, only a share (approximately 25%) of routes were mapped under wet conditions. While we implemented strategies to overcome this data incompleteness and were able to draw valuable and reasonable insights from the sampled data, future studies could provide a more complete picture of public transport during heavy rain episodes in developing country cities and could help establish causal chains between floods, vehicle frequencies, rerouting and decreased travel speeds more clearly. Ongoing work on the city of Kigali, Rwanda, which has a smaller population, urban footprint and public transport network, will include a survey of the entire transit system in both dry and wet conditions, and will provide a more complete and precise picture of public transport disruptions from floods. Second, in our study, we used mosaics of the maximum value of each raster pixel from fluvial and pluvial flood map products to approximate flood depth and extent induced by the combination of both types of floods. Although this provides a useful estimate that captures the extremes of both types of floods, such operation lacks considerations of the hydrodynamics of flood processes and may introduce error and uncertainties in flood depth and extent estimations.

25
20

|Indicator|Definition|Main Data Source|
|---|---|---|
|Prevalence of obesity among men,<br>BMI (% of males ages 18 and<br>older)|Percentage of males aged 18 and older with BMI above 30|ECHP, EHIS, ISSP,<br>STEPS, WHS|
|Prevalence of obesity among<br>women, BMI (% of females ages<br>18 and older)|Percentage of females aged 18 and older with BMI above 30|ECHP, EHIS, ISSP,<br>STEPS, WHS|
|Prevalence of obesity among<br>women, BMI (% of females age<br>15-49)|Percentage of females aged 15-49 with BMI above 30 (excludes currently pregnant women and women<br>having given birth in the three months preceding the survey)|DHS|
|Mean diastolic blood pressure,<br>adult population (mmHg)|Mean diastolic blood pressure (mmHg) in adult population (age-range may vary)|DHS, STEPS|
|Mean systolic blood pressure,<br>adult population (mmHg)|Mean systolic blood pressure (mmHg) in adult population (age-range may vary)|DHS, STEPS|
|High blood pressure or being<br>treated for high blood pressure<br>(% of adult population)|Percentage of adult population with high blood pressure or on treatment for high blood pressure (age-<br>range may vary)|DHS, STEPS|
|Mean fasting blood glucose, adult<br>population (mmol/L)|Mean fasting blood glucose (mmol/L) in adult population (age-range may vary)|STEPS|
|Impaired fasting glycaemia (% of<br>adult population)|Percentage of adult population with impaired fasting glycaemia (age-range may vary)|STEPS|
|Mean cholesterol, adult<br>population (mmol/L)|Mean cholesterol (mmol/L) in adult population (age-range may vary)|STEPS|
|High cholesterol or on treatment<br>for high cholesterol (% of adult<br>population)|Percentage of adult population with high cholesterol or on treatment for high cholesterol (age-range<br>may vary)|STEPS|