rafmacalaba/datause-encoder-data huggingface dataset:
17 Comparisons of city data over time indicate that average population densities are falling in virtually all high income cities as both employment and residences decentralize. Overall urban population growth occurs through expansion of the urban perimeter by annexation of surrounding municipalities with lower densities than the core city.

When city effects are controlled for in first difference estimates, many of the relations differ from the cross-city panel estimates (unlike at the national level, where the panel and first difference results are broadly similar). In particular, the first difference estimates suggest that urban road length barely increases with per capita income over time; the income elasticity of urban road length is around 0.1 (table 4). Using the techniques pioneered for vehicle ownership, it is possible to estimate the saturation level of urban road provision, which is 23 kilometers of road length per square kilometer, a level very close to the 24.9 kilometers observed in Tokyo in 1960.

These results indicate that urban road length is increasing much more slowly over time than urban vehicle ownership. A 1 percent increase in income produces a 1 percent increase in the number of urban vehicles and a 0.1 percent increase in urban road length. Relatively little new road length is being constructed in urban areas, presumably because the cost of new rights of way is high in both economic and political terms. Most increases in urban road length come from annexation of areas contiguous to the city which are the sites of new urban growth and have lower land costs, more open space, and less congested roads. Hence, most new road capacity in urban areas comes from spreading development over space and not by increasing the density of roads in existing built up areas.

# **6. Producing motor vehicle transport services**

Thus far the analysis has focused on vehicles and roads, but what consumers and firms actually seek or demand are transport services-that is, the movement of goods and passengers from one point to another. How do we relate our information about vehicles and roads to these transport services that are the object of demand? Motor vehicle transport services are produced by combining vehicles with roads (and other factors), much as labor and land are combined to produce agricultural products or as labor and machinery are combined to produce manufacturing goods. Economics has long used production functions to relate inputs to outputs, and this approach is used here to analyze how economies combine vehicles and roads to produce transport services.33 In the context of a typical production function, the cost-minimizing solution is for the ratio of inputs to depend on their relative prices and on technological factors that may differ across countries. The question is whether assumptions of competitive behavior and efficiency are relevant in motor vehicle transport service production. What really matters in this framework is government provision of roadways. Vehicle owners have an incentive 33 This section draws heavily on Ingram and Liu, "Motorization and the Provision of Roads," and "Vehicles, Roads, and Road Use."
forest vintage _v_ sequesters _μ_<sup>_w_</sup> _v_<sup>tCO2e.Youngforestvintagesgrowquicklyand</sup> sequester carbon at a rapid rate. Older vintages grow slowly and eventually cease to sequester carbon. As the unmanaged forest land (both reserved and non-reserved) comprises mainly the older tree vintages, its potential to sequester additional GHGs is small, and may be ignored (Odum 1969). However, the potential for GHG releases when these trees are cut down and burned or left as slash (Fearnside 2000, Houghton 2003) is large. Harvesting managed forests results in emissions of (1 _− φ_ ) _μ_<sup>_h_</sup> _v_<sup>tCO2eperhectareoflandharvested,where</sup> _μ_<sup>_h_</sup> _v_<sup>isthecarbonstockassociatedwithharvestedtreevintage</sup><sup>_v,_and</sup><sup>_φ_isthe</sup> share of permanently stored carbon in harvested forest products. We ignore the annual sequestration of carbon by agricultural product, as those crops are harvested and subsequently consumed in the form of food or bioenergy.

Based on the above, the equations describing net GHG ows in the economy are and Equation (25) describes the composition of GHG emissions ows. Equation (26) shows net GHG emissions from deforestation, food and livestock production, and forest sequestration.

Finally, we consider institutional control of GHG emissions' ows, which foresees their gradual reduction and the stabilization of atmospheric carbon stocks. Specically, we assume that at any point of time net GHG emissions from deforestation, application of fertilizers, and forest sequestration cannot exceed the emissions' quota, _<u>z</u>_<sup>_~~L~~_</sup> _._ We do not impose the emissions' constraints on GHG emissions from fossil fuels' combustion because they are exogenously determined. Rather we assume that emissions control instruments are reected in exogenous prices of fossil fuels, which aect the demand for fossil fuels. Finally, because biofuels provide a renewable alternative to fossil fuels, we credit the emissions' quota, _<u>z</u>_<sup>_L_</sup> _,_ by the fraction of fossil fuels' emissions displaced by the biofuels.<sup>12</sup>

> 12This doesn't necessarily mean that biofuels are 'greener' than fossil fuels. That will depend on the emissions associated with agricultural production and natural land conversion.

17
the 2017Q1 sensor recalibration (see section 4). First, we include a dummy for the period before the recalibration and column (4) shows that the results remain nearly unchanged. Next, we exclude all observations before the recalibration and even then, the changes are minimal and all conclusions hold. We also checked whether controlling for emissions per capita changes the findings, but they are not statistically significant even at the 10 percent level and have no impact on the other results.

Table 9: Differentiated Impact of COVID-19 at the Subnational Level

|||Night|time light gr|owth||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Avg. nighttime light per capita|0.47*|0.78*|0.20|0.31|-0.40|
||(0.27)|(0.40)|(0.32)|(0.33)|(0.57)|
|Avg. nighttime light per capita _×_ COVID||-1.71|-11.7***|-12.2***|-11.9***|
|||(1.61)|(1.36)|(1.45)|(1.57)|
|Population density|||0.31|0.35|0.15|
||||(0.21)|(0.22)|(0.34)|
|Population density _×_ COVID|||-11.9***|-12.2***|-12.1***|
||||(0.83)|(0.89)|(0.98)|
|Constant|8.77***|8.84***|0.63|0.70|-4.78***|
||(0.88)|(0.89)|(0.91)|(0.90)|(1.12)|
|Country fxed efects|Yes|Yes|Yes|-|Yes|
|Date fxed efects|Yes|Yes|Yes|Yes|Yes|
|2017Q1 adjustment|-|-|-|Yes|-|
|Starting period|full sample|full sample|full sample|Full sample|_≥_2017Q1|
|Obs|39176|39176|39176|39176|23301|
|Adjusted _R_<sup>2</sup>|0.27|0.27|0.32|0.33|0.41|

_Note_ . The sample includes 2165 first administrative regions of 116 EMDEs between 2020Q1 and 2020Q4. Nighttime light growth is in percent. Both average nighttime light per capita and population density are in logs. Throughout, country and date fixed effects are included. Standard errors are clustered at the country level and in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

# **8 Conclusion**

Annual DMSP nighttime light data have already contributed greatly to deepening our economic understanding (Donaldson and Storeygard, 2016). With the availability of monthly VIIRS data at an even finer spatial grid and with greatly improved measurement of low-light areas, nighttime lights are becoming an even more important source of information.

In this paper, we initially provided some stylized facts about global VIIRS nighttime light data at quarterly frequency. We documented the need to consider a few technical details when aggregating the data at the country level, including its ge27
# **Contents**

|Acr|onyms and Abbreviations ..................................................................................................................................... i|
|---|---|
|1.|The Value and Uses of Cotton By‐Products ................................................................................................ 2|
|2.|Cotton Oil in the Context of the Global Edible Oil Market .................................................................... 9|
|3.|Issues in the Use of Cotton Oil for Biofuels ............................................................................................. 14|
|4.|The Cotton By‐Products Industry in Sub‐Saharan Africa ................................................................. 17|
|5.|Conclusions and Policy Implications ......................................................................................................... 22|
|Ap|pendix A: Uganda ................................................................................................................................................... 28|
|Ap|pendix B: Tanzania ................................................................................................................................................ 32|
|Ap|pendix C: Benin ....................................................................................................................................................... 39|
|Ap|pendix D: Burkina Faso ....................................................................................................................................... 43|
|Ap|pendix E: United States ....................................................................................................................................... 57|
|Ap|pendix F: Questionnaire for Cottonseed Processing Companies ...................................................... 61|

**_Page_ ii**
Mundaca, L. & B. Damen. (2015). Assessing the effectiveness of the ‘Green Economic Stimulus’ in South Korea: Evidence from the energy sector. In 38<sup>th</sup> International Association for Energy Economics (IAEE) International Conference.

- Muro, M., Tomer, A., Shivaram, R. & Kane, J. (2019). Advancing Inclusion through Clean Energy Jobs. Metropolitan Policy Program at Brookings. April 2019.

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- Nickel, C. & Tudyka, A. (2014). Fiscal Stimulus in Times of High Debt: Reconsidering Multipliers and Twin Deficits. _Journal of Money, Credit and Banking_ , 46(7), 1313–1344.

OECD. (2009). Policy Responses to the Economic Crisis: Investing in Innovation for Long Term Growth.

- Owyang, M. T., Ramey, V. A., & Zubairy, S. (2013). Are Government Spending Multipliers Greater during Periods of Slack? Evidence from Twentieth-Century Historical Data. _American Economic Review_ 103(3): 129–34.

- Pereira, A. M. (2000). Is all public capital created equal? _The Review of Economics and Statistics, 82_ (3), 513-518.

- Pereira, A. M. & de Frutos, R. F. (1999). Public capital accumulation and private sector performance. _Journal of Urban Economics, 46_ (2), 300-322.

Perotti, R. (2004). Public Investment: Another (different) Look. Mimeo, Università Bocconi.

- Popp, D, Vona, F., Marin, G. & Chen, Z. (2020). The Employment Impact of Green Fiscal Push: Evidence from the American Recovery Act, _National Bureau of Economic Research Working Paper_ working paper No w27321.

Puig, J. (2014). Multiplicador del Gasto Público en Argentina. _Económica_ 60, 188‐210.

- Qiang, C. Z. (2010). Broadband infrastructure investment in stimulus packages: Relevance for developing countries. _Info (Cambridge, England), 12_ (2), 41. doi:10.1108/14636691011027175

- Ramey, V. A. (2011). Identifying government spending shocks: It's all in the timing. _The Quarterly Journal of Economics, 126_ (1), 1-50.

- Ramey, V. A. (2019). Ten years after the financial crisis: What have we learned from the renaissance in fiscal research? _Journal of Economic Perspectives_ , Vol. 33, No. 2. 89-114.

- <mark>Ramey, V. A. & Zubairy, S. (2018). Government spending multipliers in good times and in bad: Evidence from U.S. historical data.</mark> _<mark>Journal of Political Economy</mark>_ <mark>, vol. 126, pp. 850– 901.</mark>

- Richarson, D. (1998). Asian Financial Crisis. Current Issues Brief 23 1997-98. https://www.aph.gov.au/About_Parliament/Parliamentary_Departments/Parliamentary_Library/Publi cations_Archive/CIB/CIB9798/98cib23 (Last accessed in February 4, 2021).

- <mark>Robins, N., Clover, R., & Singh, C. (2009).</mark> _<mark>Taking stock of the green stimulus</mark>_ <mark>. 23 November 2009. HSBC Global Research, New York.</mark>

35
provides estimates of the time-varying effects of credit on the same set of behaviors, and the role of increasing level of market saturation and MFI competition on household welfare.

The results of the basic model unequivocally show that group-based credit programs have significant positive effects in raising household welfare including per capita consumption, household non-land assets and net worth. Microfinance increases income and expenditure, the labor supply of males and females, non-land asset and net worth as well as boys’ and girls’ schooling. Microfinance, especially female credit, also reduces poverty. The results using long-panel data thus confirm most of the earlier findings that microfinance matters a lot, and more for female than for male borrowers.

The results support the view that microcredit effects change over time, and show that the effects of new borrowing are different from that of old borrowing. For example, past credit has a higher impact on income and expenditure than current credit. There is also a sign of village diseconomies due to market saturation with higher village-level participation in microfinance programs. For example, with higher village-level aggregate current male borrowing, the marginal effect of male borrowing on per capita income gets lower.

The results also show certain patterns in participants’ inclination to borrow from multiple sources and to diversify income. Membership in multiple programs has grown steadily from none to 33 percent in 2010/11. However, it does not have any effects on the household outcomes. However, microfinance competition (captured by the number of MFIs operating in a village) seems to have beneficial effects, especially on household non-land asset and net worth growth.

The results of the panel household analysis indicate the presence of market saturation with possible village diseconomies and diminishing returns. This is due in part to credit expansion without much technological breakthrough in local economies. In fact, our data suggests that more than two-thirds of activities supported by microcredit programs in rural Bangladesh are in the trade sector, and this pattern of loan portfolio even increased over the study period of 1991/92-2010/11.

28
ranges between 1.2 and 2.5 percent. In other words, the BRI is expected to contribute to a decrease in shipping times of 7 to 15 hours across all country‐pairs in the world. It is important to note that this average is taken with respect to all possible country pairs in the world, some of which are not or very little affected at all by the BRI.

To have a better sense of the consequences of the BRI we aggregate the results across pairs of regions.<sup>13</sup> Looking at the figures in Table 6, one can notice several interesting patterns. First, as expected, the East Asia and Pacific region is experiencing the largest decrease in shipping times vis‐à‐vis all the other regions (first column in Table 6). The highest gains are between the East Asia and Pacific and South Asia, with the average gains reaching 4.67 percent when aggregating across all country pairs. Second, while not all pairs of regions are expected to have gains (there is no change in shipping time between North America and the Latin America), every single region will benefit with at least some of their partners. As an example, it is worth noting that the Latin America and Caribbean region is expected to decrease the shipping time by 1.15 percent with countries in the South Asia region.

At a more disaggregated level, the results also feature significant heterogeneity when looking across country‐pairs (Figure 4 and Figure 5). For example, among the top ten countries experiencing the largest decrease in shipping time with China, there are countries in Asia (Kirghizstan, Bangladesh, Malaysia) but also countries in Africa (Tanzania, Mauritius, Kenya). These results show why it is important to use a network approach in order to understand the interconnections of all planned infrastructures: the gains in Kenya are the results of the infrastructure in both Kenya and in several countries in Asia. The benefits from the Lamu port and LAPSSET rail corridor in Kenya are magnified by the rails in Myanmar, Thailand and Pakistan as well as the port projects in Kyaukpyu (Myanmar) and Gwadar (Pakistan).

_Table 6: Proportional decrease in shipment time, aggregated by regions – Lower Bound._

||East Asia &<br>Pacific|Europe &<br>Central Asia|Latin America<br>& Caribbean|Middle East<br>& North<br>Africa|North<br>America|South Asia|Sub‐Saharan<br>Africa|
|---|---|---|---|---|---|---|---|
|East Asia &<br>Pacific|**2.01**|||||||
|Europe &<br>Central Asia|**2.70**|**1.15**||||||
|Latin America<br>& Caribbean|**0.71**|**0.75**|**0.00**|||||
|Middle East<br>& North<br>Africa|**2.99**|**0.38**|**0.05**|**0.14**||||
|North<br>America|**1.06**|**0.74**|**0.00**|**0.18**|**0.00**|||
|South Asia|**4.67**|**0.88**|**1.15**|**0.70**|**1.49**|**3.59**||
|Sub‐Saharan<br>Africa|**2.54**|**1.06**|**0.55**|**0.91**|**0.47**|**2.59**|**0.97**|

_Note: Results are from the Lower Bound analysis and averaged over all country‐pairs in each region‐pair._

> 13 Regions are defined using the World Bank classification.

15
Table 1: Distribution of PTAs by <u>geographic</u> region and income level

||EAP|ECA|LAC|MNA|SAS|SSA|North|All|
|---|---|---|---|---|---|---|---|---|
|Number of countries|32|36|39|21|8|48|25|209|
|_North-South bilateral_|||||||||
|Countries in at least one PTA|4|12|6|10|0|2|10|44|
|PTAs per country, average|2|1|2|1||1|4|2|
|PTAs per country, maximum|4|4|4|3|0|1|24|24|
|_All others_|||||||||
|Countries in at least one PTA|24|22|33|20|8|47|10|164|
|PTAs per country, average|2|6|8|5|4|4|8|5|
|PTAs per country, maximum|3|12|17|12|9|9|15|17|
|_Total_|||||||||
|Countries in at least one PTA|26|26|35|20|8|48|11|174|
|PTAs per country, average|2|6|8|5|4|4|11|5|
|PTAs per country, maximum|7|12|19|13|9|9|29|29|

_<u>Source:</u>_ <u>Author’s estimates using published and unpublished WTO data, as well as print and web media</u> searches.

_Note:_ Bilateral agreements are defined as PTAs with two members. South is defined as all regions except North. South region names follow World Bank definitions: EAP is East Asia and Pacific, ECA is East Europe and Central Asia, LAC is Latin America and the Caribbean, MNA is Middle East and North Africa, SAS is South Asia, and SSA is Sub-Saharan Africa. North is the 24 members of the Organization for Economic Cooperation and Development (OECD) and Liechtenstein. Appendix A provides country membership detail of each of these aggregates.

also have a bilateral FTA. For the world as a whole, approximately 44 percent of country pairs in our PTA database participate in “perfectly overlapping” PTAs, although this number underlies a significant degree of regional variation. Of course, this finding does not necessarily imply that a large number of PTAs are in effect duplicates. There may be some logic in “nesting” PTAs in the sense that different sets of issues are more easily addressed in different forums. Thus, for example, sectorspecific liberalization commitments or border transit facilitation may be most efficiently handled in a bilateral context, while mutual recognition of standards, competition policies, or movement of labor may be more easily dealt with through regional negotiations. Nonetheless, if trade flows between a large number of PTA members fall under the provisions of more than one agreement, the aggregate effect of PTAs on trade could be less than the sheer number of agreements may indicate. Of course, it is not possible to discern the role of PTAs in world trade by simply considering the number of countries that have entered into these agreements. The next section will show that the PTA members’ tariff structure can have a significant effect on the share of intra-PTA trade that may be meaningfully considered “preferential.” 15
<!-- Start of picture text --> 100%<br>14%<br>90% 22% 26% 25% 26%<br>31% 31% 33%<br>80% 40% 37%<br>6%<br>Services 70% 5% 4% 6%<br>8% 6% 7%<br>Public administration 60% 9% 8%<br>13% 11%<br>5% 8% 7% 7% 25%<br>Construction 50%<br>12% 12%<br>Utilities 40% 21% 7%<br>69%<br>4%<br>30% 60%<br>57%<br>Manufacturing 51% 50% 52%<br>20% 37% 38%<br>Extractive industries 28% 29%<br>10%<br>Agriculture, forestry<br>and fishing 0%<br>Oriental<br>Khénifra<br>Béni Mellal- Drâa-Tafilalet Fès-Meknès Settat Souss-Massa Hoceima<br>Grand Casablanca- Marrakech-Safi Rabat-Salé-Kenitra Southern Regions Tanger-Tetouan-Al<br><!-- End of picture text -->

**Source:** Estimates based on the Enquête Nationale sur l’Emploi (ENE; Labor Force Survey)

The regions with the lowest female labor participation rates are also those with the highest unemployment (Oriental and Southern regions). The informality rate also varies across regions, with the southern regions having the lowest driven by high employment share in public administration. It is not surprising that the informality rate in Drâa-Tafilalet is highest since most jobs there are in agriculture.

# **3. Gravity Analysis**

The gravity model of international trade is the tool economists use to estimate the contribution of various factors to bilateral international trade flows. Tinbergen (1962) proposed this model to illustrate the asymmetry of global trade flows. Trade flows between two countries are modeled as functions of each country's size (GDP or GDP per capita), the distance between them, and trade costs. Trade costs might include a wide range of variables, including bilateral, multilateral, and regional trade agreements. Comparing change in trade after a particular trade agreement goes into effect is one way to estimate either the effectiveness of trade agreements or the relative importance of policies and trade costs not included in an agreement (Chaney, 2018).

The most common method in gravity model estimation for many years applied Ordinary Least Squares (OLS) to a log-linearized form of the gravity model. More recently, academic advances focus on possible biases in OLS parameters, the presence of heteroskedasticity, 10 | P a g e