rafmacalaba/datause-encoder-data huggingface dataset:
46 Table 8: Natural Resources, (De-)Industrialization & Urban Primacy, Country-Level

|**Dependent Variable:**|Urban Pri|macy (%)|Urb|an Primacy|(%) in t (Pa|nel)|
|---|---|---|---|---|---|---|
||in 2020 (L|ong-Diff.)|3 Lag|s Incl.|4 Lag|s Incl.|
||(1)|(2)|(3)|(4)|(5)|(6)|
|MFGSERV ((3)-(6): sum of lags)|-0.19|-0.2|-0.10|-0.05|-0.09|-0.05|
||[0.13]|[0.14]|[0.10]|[0.13]|[0.10]|[0.13]|
|NRXGDP ((3)-(6): sum of lags)|-0.11||0.02|0.05|||
||[0.15]||[0.07]|[0.09]|||
|DEINDU ((3)-(6): sum of lags)|-0.08|-0.11|-0.09|-0.05|-0.1|-0.05|
||[0.17]|[0.17]|[0.18]|[0.19]|[0.18]|[0.18]|
|FMXGDP ((3)-(6): sum of lags)||-0.09|||0.06|0.08|
|||[0.16]|||[0.08]|[0.11]|
|AGXGDP ((3)-(6): sum of lags)||-0.46|||-0.11|-0.11|
|||[0.36]|||[0.18]|[0.20]|
|NRXGDP - MFGSERV|0.08||0.12||0.10||
||[0.18]||[0.09]||[0.14]||
|DEINDU - MFGSERV|0.11|0.09|0.01|-0.01|0.00|0.00|
||[0.20]|[0.18]|[0.24]|[0.23]|[0.28]|[0.27]|
|FMXGDP - MFGSERV||0.11||0.15||0.13|
|||[0.20]||[0.10]||[0.16]|
|AGXGDP - MFGSERV||-0.27||-0.01||-0.06|
|||[0.36]||[0.19]||[0.21]|
|Country, Year FE|N|N|Y|Y|Y|Y|
|Observations|115|115|462|346|462|346|

_Notes:_ Robust SE (clust. at the country level in (3)-(6)) in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
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disabilities.<sup>69</sup> For example, advances in cataract surgery has returned the gift of sight to millions, ivermectin is helping to control blindness resulting from onchocerciasis, praziquantel can turn back the rising tide of schistosomiasis infections, while surgery, azrithromycin, and face washing are reducing the ravages of trachoma (Medlin and others 2006).<sup>70</sup> Polio has been almost eradicated, there are effective treatments for CVD and for several cancers, both malaria and AIDS are being tackled with some glimmers of success already, and treatments for dementia are on the horizon. But the steady trickle of victories cannot hide the fact that the magnitude of the health care challenge is being whittled down very gradually. The urban population in low and middle income countries remains exposed to a variety of ailments against which remedies are not being found fast enough or against which the potency of existing drugs is being eroded by resistant strains. For example, malaria, TB, diarrhea, dengue fever,<sup>71</sup> pneumonia, and Chagas disease, not to mention AIDS, continue to exact a frightening toll. The pain and costs inflicted by these infectious diseases are being compounded by chronic diseases and by widespread debilitating mental health conditions.

Prevention by means of safe sex, better sanitation and hygiene practices,<sup>72</sup> breastfeeding and avoidance of smoking, is a significant part of the answer, but we should not discount the contribution of more potent drugs and vaccines which can be produced cheaply and in sufficient volume.<sup>73</sup> This requires research on a large scale and the building of production capabilities in industrializing countries, many of which are today dependant upon imports.

> 69 See for instance the findings reported by Lichtenberg (2003; 2006a; 2006b).

> 70 Merck is working with the WHO to supply praziquantel at one half of its current price.  Even at 50 cents per dose, the price might be too high ("Manufacturers Urged" 2006; "Merck in Bilharzia Scheme" 2006).

> 71 Recent outbreaks in northern India and Pakistan suggest that the battle against dengue is not going well. 72 An especially useful hygiene practice is hand washing with soap. Studies have identified that this practice can lead to 47% reduction in diarrhea incidence (Cairncross and Valdmanis 2006; World Bank 2003).  However, to be effective, this translates into 32 hand washes in a day which can consume as much as 20 liters of water and quantities of soap.  Access to a plentiful supply of water is the key (Keusch and others 2006).

> 73 Based on the US data from 1982 to 2001, Lichtenberg (2006a) estimates that the cost of extending life by one year through AIDS treatment was $17,715.  While this is reasonably cost-effective in the United States, the cost is high, especially for low- and middle-income countries.  In these countries, preventions can be more cost-effective (Canning 2006). A number of policy options exists to contain the spread of HIV/AIDS such as broad-based campaigns for changing (sexual) behavior (Canning 2006) and male circumcision ("Male Circumcision" 2005).

35
by the Earth Observation Group at the Colorado School of Mines.<sup>5</sup> To measure pollution, we use Nitrogen Dioxide (NO2) concentrations measured by Sentinel 5-P satellites.<sup>6</sup> We expect that NO2 levels increase in response to human activity, namely burning fossil fuels and biomass, which may have fallen during the crisis. EVI was taken from the Landsat 8 Collection 1 Tier 1 composite.<sup>7</sup> The Google search index at subnational levels reflects the relative popularity of any given search term. The total search volume for each term is divided by total Google search volume for that week and subnational region. The search index for each term is rescaled such that the week-subnational region observation with the highest relative search score, for all weeks in that country, is assigned a value of 100 with all other weeks expressed relative to this peak. The baseline (weeks with a value of 100) itself is a mechanism for rescaling search term data such that values are comparable across regions and over time.<sup>8</sup> We aggregate data into monthly averages, taking the average of the weekly data points for each subnational district.

Google restricts the quantity of data that can be downloaded in one batch. Therefore, the datasets are extracted in multiple rounds to cover all subnational districts for all countries with HFPS data. This process requires indexing all downloads against a common administrative district which serves as a reference point. The reference district is included in all rounds of downloads such that the resulting search terms are comparable regardless of whether they are pulled in the same round. This approach is based on Abay et al. (2020).

The Google mobility index shows how visits to places are changing over time in each geographic region. Google mobility data is measured against a baseline established by Google

> 5 https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG

> 6 “Sentinel-5P NRTI NO2 : Near Real-Time Nitrogen Dioxide”, Google Earth Engine. <u>https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2</u>

> 7 https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_8DAY_EVI

a where G t t NNt at <u>,ww,tt,tt</u> represents the share of all search volume in a particular week w (in month m for region r) for that term, and ,tt,tt<sup>represents the subnatiuonal google search index for a term in month m and subnational region r.</sup> Max is the maximum, across regions and weeks, in country c. NN aa,ww,tt,tt 9
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# **Conclusions**

The potential and pitfalls of linking smallholder farmers to higher value markets are growing themes within economic research and policy planning associated with developing country agriculture. For example, the World Development Report (WDR) 2008 notes the potential of the “emerging new agriculture”. This is “led by private entrepreneurs in extensive value chains linking producers to consumers and including many entrepreneurial smallholders supported by their organizations”. Dynamic opportunities exist in domestic, regional, and international markets.

Yet, the WDR also identifies frequent constraints on smallholder participation in these more dynamic markets, emphasizing weaknesses in infrastructure, mismatches in delivery scale or scope when supplying modern procurement systems (that is of domestic supermarkets) and the challenges associated with compliance with emerging food safety and other standards (especially for exports). For the present study, respondent practitioners similarly downplayed production level constraints and emphasized the challenges of efficient transaction‐making between (African) smallholder farmers and the downstream gate‐keepers of higher value supply chains. Responses from these practitioners did, however, highlight one frequently constraining factor of production; that is, farmer knowledge about market opportunities and requirements and about effective and market‐acceptable production techniques.

The existing literature provides numerous examples of where smallholder participation in higher value markets has been associated with higher levels of income and/or employment. However, without detailed and longer term studies examining patterns of participation and non‐participation/exit, variability of outcomes over time, and a broader range of impacts, it is difficult to draw definitive conclusions. The respondents to our practitioner survey were most optimistic about the ‘learning’ benefits (e.g. acquisition of technical and business skills) from smallholder participation in higher value supply chains. They were also positively inclined regarding income gains and other economic impacts. With regard to environmental and health impacts there was greater ambivalence, with the sample as a whole having a neutral position, while a considerable minority noting potential adverse impacts.

The practitioner survey brings out an interesting contrast between the sets of indicators typically used to monitor and evaluate the efficacy of interventions to better link smallholders with higher value markets, and those indicators which respondents indicate ‘should’ be used in order to best gauge impacts. The most frequently used measures relate to ‘inputs’ (for example number of people trained; number of groups 21
# **Table A 3: Balance Table for the Village Selection Model: Jharkhand**

|**Variables**|**Me**|**an**|||
|---|---|---|---|---|
||**Treated**|**Control**|**Percent**<br>**Bias**|**P-**<br>**value**|
|Agricultural Credit SocietyStatus – Census 2011|0.07|0.04|17.40|0.24|
|Tube Wells/Borehole Status – Census 2011|0.24|0.21|7.30|0.63|
|Proportion of Scheduled Tribe Population – Census 2011|0.77|0.76|2.20|0.86|
|Proportion of Scheduled Caste Population – Census 2011|0.04|0.05|-17.70|0.19|
|Logof Total Geographical Area – Census 2011|5.75|5.76|-1.90|0.89|
|Proportion of Forest Area – Census 2011|0.14|0.17|-19.70|0.11|
|Proportion of Government SectorJobs Households– SECC 2013|0.02|0.01|10.90|0.42|
|Proportion of Public SectorJobs Households– SECC 2013|0.02|0.00|45.50|0.00|
|Proportion of Private SectorJobs Households– SECC 2013|0.07|0.00|45.10|0.00|
|Proportion of Manual Scavengers Households– SECC 2013|0.00|0.00|19.50|0.14|
|Proportion OwningMobile OnlyHouseholds– SECC 2013|0.71|0.61|31.80|0.02|
|Proportion OwningMotorized FishingBoat Households– SECC 2013|0.01|0.00|18.40|0.18|
|Proportion OwningIrrigated Land Households– SECC 2013|0.15|0.30|-44.70|0.00|
|Proportion OwningUnirrigated Land Households– SECC 2013|0.25|0.52|-76.50|0.00|
|Proportion OwningOther Irrigated Land Households– SECC 2013|0.16|0.35|-56.00|0.00|
|Percentage of households with main source of light as electricity– Census 2011|0.33|0.18|50.40|0.00|
|Percentage of households with latrine facilities within thepremises – Census 2011|0.03|0.03|2.20|0.86|
|Logof Nearest StatutoryTown Distance – Census 2011|3.34|3.38|-7.10|0.60|
|Proportion OwningIrrigation Equipment Households– SECC 2013|0.08|0.06|12.80|0.38|
|Proportion HavingKisan Credit Card Households– SECC 2013|0.04|0.01|36.00|0.01|
|DensityPrimarySchool Numbers – Census 2011|0.97|0.90|5.70|0.66|
|DensitySecondarySchool Numbers – Census 2011|0.04|0.03|5.90|0.66|
|DensityMiddle School Numbers – Census 2011|0.31|0.30|1.70|0.90|
|DensitySenior SecondarySchool Numbers – Census 2011|0.00|0.01|-10.20|0.50|
|Proportion of Total Population Female – Census 2011|0.50|0.50|5.10|0.70|
|Proportion of Literates Population Person – Census 2011|0.46|0.47|-9.30|0.51|
|Proportion of Non-WorkingPopulation Person – Census 2011|0.51|0.49|24.50|0.07|
|Agricultural MarketingSocietyStatus – Census 2011|0.09|0.12|-9.20|0.51|
|Observations||37|8||

29
27 Table 2.4. Moving plans, by demographic categories, 1996 (% of each group)

|DS<br>eoBa<br>hic<br>oa|Moscow|Vladimir|Gorodets|
|---|---|---|---|
|--Peno.<br>.s:...||||
|single|5.2<br>|4.6<br>|1.9<br>|
|husband and wife|6.5|1.7|2.3|
|single wkigagpele||||
|childless|18.5|17.6|-|
|childless with parents|13.7|12.5<br>|8.7|
|with children|21.2|31.0<br>|-<br>|
|withchildren+parents<br>|14.3|24.1|11.1|
|... .<br>w k<br>ag.ee..........p........||||
|childless|41.0|23.1<br>|18.8|
|childless with parents|29.3<br>|0.0<br>|-<br>|
|with children|25.9<br>|40.4<br>|25.8<br>|
|with children + parents|26.1|46.2|10.0|
|.....................<br>..............<br>.<br>.<br>.**.**.**....**. .**....**<br>_ .**...**<br>Other<br>|2<br>|1316<br>|30.4<br>|
|,<br>........<br>..<br>. .. ...Average<br>.<br>.|~~~~~|29-5:.23,5|14.7|

Sources: The Moscow Longitudinal Household Survey, January 1996; Vladinir and Gorodets Longitudinal Household Survey,

## Decemnber 1996.

At the other pole are pensioner households (20-30% of all urban households), who are largely satisfied with their housing situation. Only 2-6% of these households are planning to move in the near future. The rest (single people, one-parent families) occupy an in-between stand.

In all three cities, similar trends were identified in the effect of the household's demographic type on potential housing mobility. The type of household, which is correlated both with the level of the household's provision with housing and the household average per capita income, is a strong determining factor in the decision to purchase housing.

# 2.2.3 Sources of fmancing housing purchases

In November 1996, only 11.4% of households in Vladimir and 7.9% in Gorodets were able to specify the sources of money for the purchase of housing (Table 2.5); this is much less than the proportion of households having such plans. Surveys conducted in 1994 (Urban Institute, 1995; Guzanova, 1994) indicate that the share of such households was noticeably higher then (for instance, 27.5% in Bamaul and 19.3% in Nizlmy Novgorod) and the range of sources was broader, reflecting the fact that the financial situation of many households has deteriorated in the past few years.

Table 2.5. Sources of financing for future improvement of housing conditions as identified by households

|Sources|Vladimir|Gorodets|
|---|---|---|
|Loans|4.3|2.6|
|Own firm, enterprise|3.2|1.3|
|Sale of apartment (house)|1.6|1.3|
|Help from relatives|1.0|2.0|
|Sale of property|1.0|0.3|
|Savings|0.8|0.7|
|Other|1.6|0.7|
|None|36.0|32.2|
|No need|52.6|59.9|

Source: Vladimir and Gorodets Longitudinal Household Survey, December 1996.

Despite this deterioration in household financial standing, 16.8% of Vladimir and 11.4% of Gorodets households would like to get long-term loans for the purchase or construction of housing. When the housing problem is very pressing, the proportion of such households increases sharply to reach 36% in
# **Borrow with Sorrow?**

# **The Changing Risk Profile of Sub-Saharan Africa’s * Debt**

César Calderón and Albert G. Zeufack<sup>1</sup>

**Key Words:** Debt sustainability, debt composition, bond issuances **JEL Codes:** E62, F34, H63

> * This paper builds on, and updates, the systematic fiscal and public debt analysis undertaken in the Chief Economist’s Office for Africa at the World Bank (AFRCE). Some of the findings have been reflected in the “Africa’s Pulse” (Volumes 15 through 18), and various issues of the “Africa Macroeconomic and Financial Report”. We would like to thank Sebastian Essl for his invaluable insights and assistance with the data. The views expressed in this paper are those of the authors, and do not necessarily reflect those of the World Bank or its Boards of Directors.

> 1 E-mail: Calderón: ccalderon@worldbank.org; Zeufack: azeufack@worldbank.org.

1
- 16 -

Sen 1983). It has been noted (Lele 1988) that contrasts in experiences have been overlooked in policy recommendations. For example, while evidence has accumulated on the responsiveness of production and exports to price and exchange rate adjustments, the influence of weather, infrastructure and institutions remains vital in Africa. The supply response of individual crops was high, but that of agriculture in the aggregate was low in many African countries. Moreover, non-price factors deserve greater attention and they include emphasis on management, infrastructure, research and extension. Price and non-price factors are complementary, yet it should also be noted that from a policy viewpoint they may turn out to be substitutes: price and nonprice incentives can have opposing budgetary implications, for example in situations where direct taxation is administratively difficult and the government needs to resort to trade taxes to generate revenues to finance infrastructure and irrigation.

Social costs of adjustment are being increasingly addressed in the literature and in the Bank's lending (World Bank 1986c, Demery and Addision 1987, Pinstrup-Anderson 1987, Cornea, Jolly and Stewart 1987). come country examples also have illustrated the disproportionate costs of adjustment for the poor. The assignment of costs to already existing structural problems and to remedial adjustment measures, however, is far from satisfactory. The question should also be addressed why the effects of a deceleration of growth would trickle down more readily than would the effects of an acceleration of growth. An adequate evaluation of the poverty effects of adjustment should also consider the costs of alternative strategies. But the poverty aspects
Holden, Paul and Vassili Propenko, “Financial Development and Poverty Alleviation: Issues and Policy Implications for Developing and Transition Countries”, IMF Working Paper, Washington, D.C., 2001.

- Jacob, Katy, Malcolm Bush, and Dan Immergluck. “Rhetoric and Reality: An Analysis of Mainstream Credit Unions’ Record of Serving Low-Income People” A report issued by the Woodstock Institute, February 2002.

- Kennickel, Arthur B., Martha Starr-McCluer and Annika E. Suden, “Family Finances in the U.S.: Recent Evidence from the Survey of Consumer Finances”, _Federal Reserve Bulletin_ January 1997 pp. 1-20

- King, Robert G. and Ross Levine, “Finance and Growth: Schumpeter Might be Right”, Quarterly Journal of Economics, 108, pp 717 -737, August, 1993.

- Martell, Javier, Paul Panichelli, Rich Strauch, and Sally Taylor-Shoff. “The Effectiveness of Scoring on Low-to-Moderate Income and High-Minority Area Populations,” A report issued by Fair, Isaac, and Company (San Rafael, CA) 1999.

- Orozco, Manuel. “Attracting Remittances: Markets, Money, and Reduced Costs,” A report commissioned by the Multilateral Investment Fund of the Inter-American Development Bank, January 2002.

- Richter, Lisa and Christopher Tan. “Retail Financial Services Initiative: A Report on Innovative Products and Services for Low-income and Unbanked Customers,” A report issued by the National Community Investment Fund, April 2002.

- Robinson, M.S _., The Microfinance Revolution, Sustainable Finance for the Poor,_ ISBN: 0-82134524-9, World Bank, Washington D.C., 2001.

- Rutherford, Stuart, “ _The Poor and Their Money_ ”, University of Manchester, Institute for Development Policy and Managemet, Manchester, 1999

- Schreiner, Mark, Margaret Clancy, and Michael Sherraden.  “Saving Performance in the American Dream Demonstration: A National Demonstration of Individual Development Accounts,” A report issued by the Center for Social Development at Washington University, October 2002.

- Schreiner, Mark and Gary Woller. “Microenterprise Development Programs in the United States and in the Developing World,” _World Development_ , Vol. 31, No. 9, 2003.

- Sherraden, Michael. _Assets and the Poor: A New American Welfare Policy_ , (Armonk, NY: M.E. Sharpe) 1991.

- ______________.  “On Costs and the Future of Individual Development Accounts,” A paper issued by the Center for Social Development at Washington University, October 2000.

59
# **What makes some cities more economically successful than others?**

Good salaries, high productivity, and vibrant innovation are highly concentrated, not uniformly distributed over space.<sup>2</sup> To get a more precise sense of how large the geographical differences in the level of economic activity can be within a country, consider table 1. The table shows average salary levels among U.S. metropolitan areas with the highest percentage of local workers with a college degree.<sup>3</sup> The list includes such large metropolitan areas as Austin, Boston, MinneapolisSt. Paul, Raleigh-Durham, San Francisco, San Jose, Seattle, and Washington, DC—as well as smaller cities with large universities, such as Ann Arbor, Fort Collins-Loveland, Iowa City, Lincoln, and Madison. In these cities, almost half the labor force is college educated, and a significant fraction has a postgraduate degree. Table 2 shows the metropolitan areas with the smallest percentage of workers with a college degree. It includes Flint, Michigan; VinelandMillville-Bridgeton, New Jersey; Visalia, California; and Yuma, Arizona. In these cities, only 1 in 10 workers has a college degree, and there is virtually no high-tech presence.

**Table 1. Metropolitan areas with the largest percentage of college graduates**

|**Top metro areas**|**Rank**|**Percentage**<br>**with**<br>**college**<br>**degree**|**Salary of**<br>**college**<br>**graduates ($)**|**Salary of high**<br>**school**<br>**graduates ($)**|
|---|---|---|---|---|
|Stamford,CT|1|56|133,479|107,301|
|Washington,DC/MD/VA|2|49|80,872|67,140|
|Boston,MA-NH|3|47|75,173|62,423|
|Madison,WI|4|47|61,888|52,542|
|San Jose,CA|5|47|87,033|68,009|
|Ann Arbor,MI|6|46|65,452|55,456|
|Raleigh-Durham,NC|7|44|63,745|50,853|
|San Francisco-Oakland,CA|8|44|77,381|60,546|
|Fort Collins-Loveland,CO|9|44|57,391|47,007|
|Seattle-Everett,WA|10|42|68,025|55,001|
|Trenton,NJ|11|42|81,914|64,299|
|Lexington-Fayette,KY|12|41|55,238|44,915|
|Austin,TX|13|41|62,289|48,809|
|Portland,ME|14|40|57,366|48,080|
|Minneapolis-St. Paul,MN|15|40|69,955|57,187|
|Denver-Boulder,CO|16|39|64,488|50,097|
|New York-Northeastern NJ|17|38|79,757|59,797|
|Lincoln,NE|18|38|50,401|41,837|
|Santa Cruz,CA|19|38|64,801|48,186|
|Tallahassee,FL|20|38|59,380|46,715|
|Worcester,MA|21|37|60,723|48,465|

_Source:_ Author’s calculations based on U.S. Census Bureau (2008).

2