rafmacalaba/datause-encoder-data huggingface dataset:
76 TABLE 17: Trends In the Volume of Agricultural Credit, 1983-88

|Millions of||||
|---|---|---|---|
|1980 Pesos|<br>(1)|(2)<br>(3)|(4)|
||BANRURAL|BANRURAL<br>FIRA-SN<br>EJldatarlos|C<br>TOTAL|
|1983|42,298|30,020<br>67,342|109,640|
|1984|49,489|36.262<br>77.411|126.900|
|1985|53,831|40,018<br>94,616|148,447|
|1986|50,423|37,913<br>69,783|120,206|
|1987|42,045|30,274<br>49,213|91,258|
|1988|62,760|46,483<br>75,352|138,112|
||Share of Banrural<br>credit in total|Share of<br>ejidatarlos In|Share of Banrural<br>credit to ejidatarlos in|
||Agricultural credit|all Banrural credit|total agricultural credit|
||(1)J(4) (%)|(2)/(1)(%)|(2)/(4)(%)|
|1983|38.6|71.0|27.4|
|1984|33.4|73.3|24.5|
|1985|36.3|74.3|27.0|
|1986|41.9|75.2|31.5|
|1987|46.1|72.0|33.2|
|1988|45.4|74.1|33.7|

SOURCE: Banrural, Informe de Autoevaktacion Sexenal, 1982-1988, Mexico D.F., 1989.
# **_Table F2: Child Age Breakdown: Impact of COVID-19 Related School Closures for Business Owners with Children aged 0-5years and 6-18years_**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||l|||Time Spent on( <br>|Hoursper day)...<br>|<br>||
||Busines<br>(1=Yes|s cosed<br>; 0=No)|Paid|Work|Care and<br>Respon<br>(Self/M|Domestic<br>sibilities<br>arried)|Care an<br>Respon<br>(Spouse|Domestic<br>sibilities<br>/Married)|
||_Children_<br>_0 to 5years_|_Children_<br>_6 to 18years_|_Children_<br>_0 to 5years_|_Children_<br>_6 to 18years_|_Children_<br>_0 to 5years_|_Children_<br>_6 to 18years_|_Children_<br>_0 to 5years_|_Children_<br>_6 to 18years_|
|Female Respondent|0.003|-0.002|-1.189***|-0.969***|2.286***|1.397***|-2.669***|-2.096***|
||[0.01]|[0.01]|[0.21]|[0.15]|[0.19]|[0.11]|[0.19]|[0.13]|
|School closure due to COVID-19|-0.008|0.002|0.544***|0.213|-0.080|0.130|0.205|0.586***|
||[0.01]|[0.01]|[0.20]|[0.15]|[0.13]|[0.10]|[0.19]|[0.14]|
|School closure due to COVID-19*Female|0.036**<br>[0.02]|0.034***<br>[0.01]|-0.454*<br>[0.26]|-0.072<br>[0.19]|0.973***<br>[0.23]|0.760***<br>[0.15]|-0.419*<br>[0.23]|-0.516***<br>[0.16]|
|Academic Break|-0.003|0.004|0.878***|0.492***|-0.228|0.018|-0.254|0.188|
||[0.02]|[0.01]|[0.23]|[0.19]|[0.16]|[0.12]|[0.22]|[0.17]|
|Academic Break*Female|0.026|0.015|-0.422|-0.148|0.693***|0.437***|0.330|-0.158|
||[0.02]|[0.01]|[0.28]|[0.21]|[0.25]|[0.16]|[0.26]|[0.17]|
|GDP per capita (Ln)|-0.012**|-0.019***|0.457***|0.461***|0.240***|-0.088*|-0.068|-0.233***|
||[0.01]|[0.00]|[0.09]|[0.07]|[0.07]|[0.05]|[0.08]|[0.06]|
|Constant|0.207***|0.270***|3.783***|4.027***|1.392*|3.868***|6.949***|6.989***|
||[0.06]|[0.05]|[0.92]|[0.72]|[0.72]|[0.53]|[0.78]|[0.59]|
|Observations|20413|31952|11764|18413|17025|24968|12927|18934|
|Adjusted R-squared|0.08|0.09|0.08|0.06|0.18|0.10|0.11|0.13|
|SC+SC*FEMALE(Coeff)|0.03|0.04|0.09|0.14|0.89|0.89|-0.21|0.07|
|<br>SC+SC*FEMALE(SE)|0.01|0.01|0.23|0.17|0.22|0.14|0.19|0.13|
|<br>SC+SC*FEMALE (P value)|0.05|0.00|0.70|0.42|0.00|0.00|0.27|0.60|
|Mean Male|0.15|0.14|7.76|7.90|4.39|3.97|7.05|5.86|
|Wave|Pooled|Pooled|Pooled|Pooled|Pooled|Pooled|Pooled|Pooled|
|<br>Notes:* significant at 10% level, ** significant at<br>[1] Female is a dummy variable where 1 = female-led<br>[2] OLS pooled regressions include Region of the worl<br>retail & recreation/workplaces); GDP Per Capita; and<br>[3] Sample is restricted to CHILD DEPENDENTS samp<br>dependents aged 6-18years (Columns (2), (4), (6), and|<br>5% level, *** sig<br>business and 0 if<br>d and Time Fixed<br>Demographic Cont<br>le and is split amo<br>(8)).|<br>nificant at 1% lev<br>male-led business. S<br>Effects and full set<br>rols (Married and Ty<br>ng those business|<br>el.<br>chool Closure due<br>of controls includin<br>pes of Dependents<br>owners with any ch|<br>to COVID-19 is a du<br>g Firm Controls (Se<br>).<br>ild dependents age|<br>mmy variable wher<br>ctor + Size, Age and<br>d 0-5years (Column|<br>e 1 = schools closed<br>Role in Business); M<br>s (1), (3), (5), and (7)|<br>in country at time<br>obility Controls (<br>) versus those with|<br>of survey wave.<br>Movement around<br>any child|

69
# **7.  Summary and Conclusions**

This paper has extended recent research on satellite-based CO2 measurement to an easily-updated template for tracking changes in CO2 concentration anomalies at local and regional scales. Using observations from NASA’s OCO-2 platform, we develop the template from the data filtering techniques and econometric analysis employed by Dasgupta, Lall and Wheeler (2022). For a large sample of urban areas, we compare alternative trend estimation models and conclude that the template can be constructed from a simple model that estimates trends directly from OCO-2 data that are prefiltered to isolate local concentration anomalies. We also investigate the impact of regional atmospheric circulation on local changes and develop a methodology for identifying urban areas whose changes are independent of regional circulation effects. We present illustrative applications for a long-period trend model and a short-period model that focuses on changes in the most recent year. In both cases we find striking patterns of variation, across regions, within regions, and over time.

We also use our estimation results to compute expected emissions for a large number of urban areas. Then we convert the regression residuals to emissions deviations using information from the EDGAR global database of estimated CO2 emissions. This enables us to quantify the extent to which urban emissions are greater or less than their expected values. Among 1,306 urban areas with populations greater than 500,000, we find a rough balance between cities with positive and negative emissions deviations. In the distribution of percent deviations, we find typically-higher deviations among cities whose emissions are below their expected values.

We conclude with an acknowledgment that even “simple” trend tracking and emissions estimation require software and hardware that are beyond the means of many interested stakeholders. For this reason, the World Bank’s Development Economics Vice Presidency (DEC) has established an open web facility, the XCO2 database,<sup>19</sup> that pre-filters the OCO-2 data and publishes monthly mean concentration anomalies for all terrestrial cells of a 25 km global grid (G25). The website will also publish annual change estimates for urban areas by statistical significance class and provide information that links G25 grid cell IDs to IDs for urban areas and national administrative units (levels 0, 1 and 2). We believe that this web facility will contribute to the global effort to reduce CO2 emissions as rapidly as possible. We provide more details in the Appendix.

> 19 Available online at https://datacatalog.worldbank.org/search/dataset/0062760.

34
area (in ‘000 hectares) and crop productions (in ’000 tons) and crop yields (in kg/ha), for the main crops in the country. This includes maize and rice. These allow us to create a measure of crop yields and shares.

# **Soils: NSS Land and Livestock Survey**

Recall that the NSS land and livestock survey collected information on the soil texture. These data are only available in Round 59 (which covers year 2002), but as texture tends to be stable over time, this suffices for our purpose of generating a district level control variable on soil conditions. Soils, at the field level, are classified into the following texture categories: sand - 1, loam - 2, silt - 3, light clay - 4, heavy clay - 5, others - 9. We use a district-level (weighted) vector which indicates the percentage of soils into each soil category.

# **Distance: Digitization of road maps and computation of the minimum travel time between districts**

We obtained scanned paper maps of India from 1996 and 2011 via the work of Allen and Atkin (2022). These maps included different types of roads which are distinguished by color. For instance, a highway has a different color than a local road. Different road types imply different travel times: for example, it would typically take less time to travel 1 km on a highway than on a local road.

Using GIS software, we superimposed the road maps onto district shape files of 2001 and 2011 (geo-coding, in GIS parlance). We then converted the road map into color-coded pixel data (raster data, in GIS parlance). Using information on the location of each district, and the roads connecting them, we computed a measure of the minimum travel time between districts.<sup>33</sup> This calculation attributed a “cost of distance” to each road type: the highways were attributed a cost of one, medium roads a cost of two, and small roads a cost of three per cell. That is, higher costs reflect slower average speeds. To compute the travel time along a given path, the distances/cells 33 We are grateful to Michael Norton (World Bank) for generating these calculations.

56
# **1 Introduction**

The Bangladesh-India border is the fifth-longest border in the world. It is so thick that it is more costly for Bangladesh and India to trade with each other than for either of them to trade with Germany. As a result of the thick border, bilateral trade represents only about 10 percent of Bangladesh’s trade and 1 percent of India’s trade. These figures compare poorly with East Asian and Sub-Saharan African economies, where intraregional trade accounts for 50 percent and 22 percent of total trade, respectively.

Lack of transport integration is an important contributor to the thickness of the border. Because trucks are not allowed to cross borders, cargo must be transloaded, adding to transport and trade costs. On average, crossing the border at Petrapole-Benapole, the most important border post between the two countries, takes 138 hours, including 28 hours spent transloading cargo. In contrast, the time to cross borders handling similar volumes of traffic in other regions of the world, including East Africa, is less than six hours.

Lack of transport integration between Bangladesh and India also means that Indian trucks are not allowed to transit through Bangladesh. As a result, India’s northeast states are connected with the rest of India only through the Siliguri corridor, a 27-kilometer wide tract of land commonly known as the ”chicken’s neck.” The transit restriction leads to long and costly routes between northeast India and the rest of India and the world. Goods from Agartala, for example, travel 1,600 kilometers through the Siliguri corridor to reach Kolkata Port instead of 450 kilometers through Bangladesh.

The Motor Vehicles Agreement (MVA) between Bangladesh, Bhutan, India, and Nepal (known as the BBIN countries), signed in 2015 but not yet implemented, seeks to facilitate the unrestricted cross-border movement of cargo, passenger, and personal vehicles between BBIN countries. Under the agreement, trucks carrying export-import or transit cargo can move inside the territories of other countries without transshipping to local trucks at border land ports.

This paper explores the aggregate and regional effects of changing current transport and trade barriers between Bangladesh and India on aggregate real income and on the distribution of population and real income across regions within both countries. It uses a spatial general equilibrium model calibrated to these two economies, along with road network travel time calculated using GPS data, to measure changes in economic outcomes given changes in trade costs across regions.

The paper examines three scenarios. The counterfactuals explore the potential benefits 2
difficult to estimate quarterly or monthly impacts due to the lack of macro projections at this level. Finally, the model does not require sectoral reallocation because only the agriculture sector was growing in some countries during the period under analysis, but the method would need to be adjusted in cases where there are substantial job gains in multiple sectors.

# **3.2 Results**

# _Projected income changes_

<mark>Figure 6 presents our estimations of changes in average income or consumption per quintiles of the initial per capita household income/consumption distribution between 2019 and 2020.</mark><sup>16</sup> <mark>In all countries analyzed there is a decline in the per capita household income/consumption across the entire distribution after the pandemic shock. The pattern of change –i.e., whether the bottom</mark> quintiles faced larger reductions than richer quintiles— differs on a country-by-country basis.

**Figure 6. Projected average income changes between 2019 and 2020 per quintiles of the initial** **<mark>per capita household income distribution. In percentages</mark>**

<!-- Start of picture text -->
-2.4%<br>-2.5%<br>Sri Lanka -2.4%<br>-2.0%<br>-1.6%<br>-1.1%<br>-0.5%<br>Turkey -0.5%<br>-0.4% Q1<br>-0.8%<br>Q2<br>-4.4% Q3<br>-7.1%<br>Philippines -7.3% Q4<br>-7.1%<br>-5.6% Q5<br>-1.8%<br>-3.9%<br>South Africa -7.4%<br>-9.1%<br>-6.5%<br>-11.9%<br>-13.0%<br>Brazil -11.6%<br>-10.0%<br>-8.8%<br><!-- End of picture text -->

Notes: Projections following the methodology detailed in section 3.1. Estimates based on reported employment data. Estimates do not include changes in non-labor income.

> 16 Table A3 in the Appendix presents the values of average per capita household income by quintiles.

17
# **1. Introduction**

Fuelwood for cooking and heating is generally the most important direct use of forest ecosystems in low‐income countries, with nearly half the world’s population relying on solid fuels, such as biomass, for cooking (IEA 2014). In Sub‐Saharan Africa, 68% to 90% of the population relies on biomass fuels (Rehfuess 2006, Smith et al. 2004). Without any major change in policy, the number of people relying on traditional biomass fuels in 2030 is expected to remain roughly the same as now, about 40% of the current world population (IEA 2012).

Deforestation and forest degradation related to extraction and use of biomass fuels is a significant part of global forest biomass loss<sup>1</sup> , which causes destruction of local ecosystem services and releases CO2 emissions into the atmosphere. Sub‐Saharan Africa has the highest rates of deforestation in the world and fuelwood use that leads to (net) deforestation is not carbon neutral. For example, in Ethiopia, about 88% of forest fuelwood harvests are considered nonrenewable (UNFCCC, 2012)<sup>2</sup> . Black carbon, another byproduct of burning biomass fuels for cooking and heating is believed to be another key contributor to climate change (Rosenthal 2009, Bond _et al._ 2013). Smith _et al._ (2000) find that, depending on the timeline examined, the global warming contribution of a meal cooked using biomass can be significantly higher than one cooked using fossil fuels.

Traditional cook stoves can be particularly dangerous to human health as well. Inefficient and poorly vented stoves create a hazardous indoor environment, as smoke often pollutes the insides of homes. According to the World Health Organization (2016), over four million premature deaths are caused each year from indoor air pollution. Traditional stoves also foster gender inequalities because women are typically the ones who spend hours collecting wood and who are exposed to smoke while cooking in the home. Furthermore, children are often expected to collect firewood, which can be time‐consuming and dangerous.

Replacing traditional stoves with fuel‐efficient and lower‐emitting improved stoves can help mitigate these problems (Anenberg et al. 2013, Bensch and Peters 2013, Bensch and Peters

> 1 Over 50% of global forests have been converted to human use since the advent of modern agricultural practices. Tropical forest area is decreasing at over 10 million hectares per year, with much of the deforestation occurring in developing countries (MEA, 2005, Pan et al., 2011).

> 2 Bailis et al. (2015) argue that the UNFCCC (2012) non‐renewable biomass estimates for low‐income countries like Ethiopia are too high.

2
_<mark>Source:</mark>_ <mark>Authors’ simulation based on expanded version of CKW 2017 model.</mark> When considering the status quo by sector, women’s contribution to the modern sector was USD 436.4 million or 10.8 percent of GDP while women represented 32.7 percent of its workforce with an average yearly wage of USD 1,582 (Table 3). In contrast, men’s total contribution to the modern sector was more than double that of the female contribution and amounted to USD 957.6 million or 23.65 percent of GDP. Men represented 67.3 percent of the workforce allocated to the modern sector and had average yearly wage of USD 1,686.

**Table 3. Labor Market Statistics for Baseline Scenario (2020)**

|**3. Decomposition by Labor Supply**|
|---|
|3.1. Total Labor Force<br>1,905,372|
|3.2. Female Labor Force<br>888,824|
|of which, modern (%)<br>20.69%|
|of which, traditional (%)<br>79.31%|
|3.3. Male LF<br>1,016,547|
|of which, modern (%)<br>37.25%|
|of which, traditional (%)<br>62.75%|
|**4. Decomposition by Earnings**|
|4.1. Modern-sector wage<br>$1,652|
|4.2. Modern-sector, female wage<br>$1,582|
|4.3. Modern-sector, male wage<br>$1,686|
|4.4. Traditional-sector wage<br>$1,030|
|4.5. Traditional-sector, female wage<br>$919|
|4.6. Traditional-sector, male wage<br>$1,153<br>Source:_Authors’ simulation_based on expanded version of CKW 2017 model.|

The contribution of women to the traditional sector was USD 647.9 million or 16 percent of GDP. The traditional sector employed 70.5 percent of the workforce in Liberia.<sup>18</sup> With an average annual wage of USD 1,030, the traditional sector generated 34.1 percent of total GDP. Women represented 52.5 percent of the traditional sector workforce and had an average annual wage of USD 919. In contrast, the contribution of men to the traditional sector was USD 957.6 million or 18.7 percent of GDP. Men represented 47.5 percent of the workforce in the traditional sector and had average yearly wage of USD 1,153. Women’s lower pay in both sectors, and their greater employment in the traditional sector underpins their smaller contribution to GDP than men’s. That said, women generated about one-quarter of Liberia’s output. Their contribution would have been even greater if gender gaps to productivity were smaller.

**5.2 Women’s Potential Contribution to GDP If Gender Gaps Narrow**

Closing the education gender gap would increase Liberia’s GDP by 8.4 percent (Figure 1). In Liberia, the female workforce aged 20-65 years old has an estimated educational attainment of 3.22 years on average,

> 18 A lack of consensus on the definition of the formal and informal sectors leads to significant differences in the point estimate for the modern/traditional shares of the workforce. For Liberia, the share of the labor force in the formal sector ranges from 13 percent (Johanssen da Silva, 2021) to 30 percent (ILO, 2020).

15
**Table 3 Key incentive Indicators, 1982-92**

_(Percentages and indices, 1982 -= 100)_

|_Indicators_|_: 982_|1984|_1986_|_1988_|_1990_|1992|
|---|---|---|---|---|---|---|
|_Exterryll sector_|||||||
|Effective exchange rates,|||||||
|Nominal|100.0<br>|97.4<br>|82.9<br>|73.6<br>|69.3<br>|55.1<br>|
|Real|100.0|101.9<br>|87.1<br>|72.5<br>|61.8<br>|60.9|
|Terms of tade|100.0|109.9|103.4|88.4|71.0||
|Average_import tariffs_(percent)|||||||
|Unweighted<br>|0.0<br>|4 1.0<br>|38.8<br>|41.3<br>|38.8<br>||
|Import-weighted"|0.0|0.0|24.3|27.3|22.0||
|Domestic_Economy_|||||||
|Real Wages,|||||||
|Private sector|100.0<br>|83.8<br>|83.9<br>|89.0<br>|83.7<br>||
|Public sector<br>|100.0<br>|85.3<br>|87.5<br>|88.3<br>|77.8<br>||
|Inflation (annual percent)'<br>|22.2|9.0|5.6|12.3|15.8|27.3|
|Real interest rates (annual percent)|||||||
|Deposits|10.0<br>|2.7<br>|5.6<br>|2.0<br>|-2.1<br>|-10.1<br>|
|Loans|7.7|5.3|8.4|2.7|3.0|-6.6|
|Not availabie.|||||||

a. IMF index.

b. Weighted by 1989/80 import values; estimates are for fiscal years (e.g. 1987 refers to 1987/88).

**c.** Upper-ends of average commercial bank 3 to 6 months<sup>deposits and unsecured</sup> loans and deposits.

d. Revised Nairobi consumer price index since 1987.

Source Statistical Abstracts, Economic Surveys (various issues), IMF, and<sup>World B.nk staff estimates.</sup>

**Table 4 Annual lkense requirements and costs**

|_Item_|Ov|erall|S|mall|Me|dium|La|te|
|---|---|---|---|---|---|---|---|---|
|Number, regular|4.5|(1.3)|2.9|(0.6)|4.0|(0.5)|9.7|(1.3)<br>|
|Number, specific (import/export)|10.2|(3.4)|1.2|(3.4)|3.8|(2.3)|38.9|(1.7)|
|Total|15.1|(2.4)|4.3|(1.2)|8.0|(1.2)|49.0|(1.4)|
|Full cost (thousands<br>KSh)|124.3|(3.1)|16.1|(3.7)|95.0|(2.8)|450.4|(1.6)|
|Transaction<br>cost(work hours)|223.0|(14.8)|69.0|(1.7)|89.7|(1.7)|816.0|(2.8)|
|Full cost/sales<br>(percent)|5.2|(3.0)|9,4|(2.3)|1.8|(1.7)|1.2|(2.6)|
|Licensemarkupa(percent)|66.0|(4.9)|30.5|(2.1)|102.3|(S.1)|84.6|(2.1)|

Note: The left entry in each cell is the mean value. The coefficient<sup>ovariation is provided in parentheses. Mean values reported in the table are</sup> statistically significant at the 05 level uniess othervwise noted a Defined as full cost/direct cost I Source: Worid Bank/GTZ firm level survey, 1992.

65
- ii -

- (iii) The links between teacher training, educational research institutions and rural education problems need to be strengthened. This should occur through involving teacher training and education research institutions in the continuous in-service training of extension field staff as well as other research, development and training activities.

4. The Training and Visit model can be aRplied to any of the three common education supervision aRproaches used in Bank proiects. However, certain key parameters need to be firmly established to make the system function effectively. These include the following:

- (i) Establish a limited span of schools for each field staff which permits at least one and preferably two monthly visits to each school.

- (ii) Assure that field staff spend approximately 2/3 of their time visiting schools and 1/3 in training activities organized for client teachers as well as training for field staff themselves.

- (iii) Establish a system of continuous in-service training for field staff and their supervisors that is linked to teacher training and educational research institutions.

- (iv) Provide extensive retraining for existing supervisor/inspectors in cases where an existing supervision/inspection system is to be the vehicle for establishing a rural education extension service.

# Recommendations

1. Include the Training and Visit System as a central component for an ongoing or future educational Droiect.

Because the Training and Visit system provides a well-defined, tight management model that addresses a number of key problems facing rural school supervision systems, it should be adopted as the management system for a rural education extension service of an existing or future education project. This should be done on a pilot basis to develop the necessary adaptation of the Training and Visit system to the rural education sector.

2. Carefully evaluate the Rilot project for determination of the scoRe for more extensive future apDlications of the Training and Visit System to the education sector.

The pilot application of T & V should be carefully evaluated with regard to its cost-effectiveness and approprtateness to the management of rural supervision systems, i.e. (i) helping to regularize visits and increase their frequency, (ii) building a cadre of field staff trained to help teachers with teaching problems and improvement of their pedagogic skills; (iii) shifting the time spent by field staff from bureaucratic to school-related tasks; (iv) increasing the time field staff actually spend in the field; and (v) improving the links between rural schools and educational research and teacher training institutions.