rafmacalaba/datause-encoder-data huggingface dataset:
17 Urban texture variables indicate that LAC cities have a quite dense texture, with values of intersection density (Figure 5e) and street density (Figure 5f) that are rather high. The _Circuity_avg_ values (Figure 5d) indicate that the street network layout in LAC cities is very regular: they are very close to 1, and 75% are below 1.05, meaning that most of their street networks are about only 5% longer than if they were all composed of straight lines. This regularity is in line with the findings by Huang et al. (2007). However, a general small increasing trend is observed in _Circuity_avg_ values from 1996 to 2010, which might be due to recent occupation of rugged terrain in mountainous areas.

The land use variable, sprawl (Figure 5g), measures how uniform is the spatial distribution of population within the urban extents. In general, this variable shows medium values with a small decrease from 1996 to 2010. This could be a weak trend towards a more uniform distribution of the population within urban extents.

The diversity in climate and topographic conditions across the LAC region translates into a high diversity in the shape that urban areas have taken. We look at the 2010 data to highlight country level differences in urban areas (Figure 6). _nExchange_ boxplots from Figure 6 show that Venezuelan cities tend to be more elongated than most of the other cities in the region, while Bolivian cities tend to be more rounded. Bolivian cities tend to have more smoothed perimeters (high values of _nPerimeter_ ), and the cities with less smoothed perimeters are in México, Brazil, Argentina and the República Bolivariana de Venezuela. _Fullness_ boxplots show that lower values occur more frequently in Colombia, Ecuador and Peru than in other countries of the region, and the highest values are in Bolivia and Guatemala. Urban texture plots show that more regular networks are often denser than the organic ones: in general, the República Bolivariana de Venezuela and Guatemala have the cities with more organic street networks in the region (higher values of _Circuity_avg_ ), and they have also the lowest street and intersection densities. With respect to land use, the República Bolivariana de Venezuela and Peru exhibit higher values of _Sprawl_ , which indicates less uniform distribution of urban population in their urban areas and more intra-urban differences, while Bolivia and Cuba exhibit lower values, which indicate a more uniform distribution of the population within their cities.
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pilot areas were chosen because the National Smallholder Farmers Association of Malawi (NASFAM) had farmer clubs located near meteorological stations with reliable precipitation data. Additionally, the relatively good rain patterns for Malawi standards made the pilot scheme more feasible there. The most vulnerable Malawian farmers, located in more drought-prone areas are currently excluded from this scheme.

In November 2005, through their NASFAM clubs, 892 smallholder farmers bought the weather insurance that allowed them to access a loan package for 32 Kg of improved groundnut seed – enough to cultivate 0.405 ha (one acre). Before the rainy season, participating farmers receive improved agricultural inputs through a contract that specifies (i) an index-based weather insurance component, in which the premium is calculated based on the probability of a payout estimated using the entire available rainfall record (regardless of ENSO), and (ii) a loan component - at the end of the season the farmer will owe the lending institution an amount equal to the cost of agricultural inputs plus insurance premium plus interest and taxes. If rains are good (as measured in a nearby weather station operated by the meteorological service), then the insurance company keeps the premium and farmers repay the loan with proceeds from the (presumably good) harvest. If measured rains are below certain trigger values (based on critical stages of the groundnut growing season), then the insurance company pays part or all of the loan directly to the bank. For a more detailed description of the contract design, see UNDESA (2007). The 0.405 ha bundled package is the only option offered to eligible farmers.

Since the farmers targeted by this scheme typically do not have legal title to their land, the insurance is used to guarantee the loan by requiring the farmer to purchase insurance so that the maximum liability is equal to the loan size including interest.

7
share of intermediate goods that the country of "origin" exports to the country of "destination". The variable _share_ 1 accounts for a country’s importance in Global Value Chains as an exporter. The second variable, denoted _share_ 2, is the share of intermediate inputs that the country of "origin" is importing from other countries that are part of the same regional trade agreement. The variable _share_ 2 accounts for the importance of the regional component of GVC to the imports of a given country.

As we will discuss in the next section on our empirical approach, trade policy is endogenous. Countries that are natural trade partners may be more inclined to sign a trade agreement (Krugman (1991)). Figure 3 provides empirical support to this hypothesis by showing that on average, upon signing an agreement, a country-pair that just signed an agreement trades roughly twice as much as a country-pair where only one country only joined the trade agreement. More precisely: for each country and agreement, we normalized to 100 the trade flows between two countries signing a trade agreement, on the year the agreement is put into force. We can then compare this number (for the same year) to the trade flows between a member country to a non-member country. On average across all countries and trade agreements, we find that trade with non members is only 55.94% of trade between members, on the first year of the agreement.<sup>5</sup>

# **3 Regional trade agreements and trade flows**

Difference-in-differences is the natural identification strategy for a panel setting in which a policy affects groups. Our identification of the effects of trade agreements comes from comparing trade flows, for the relevant country-pairs, before and after a trade agreement comes into effect, for the 227 trade agreements that were signed between 1958 and 2015. Importantly, our specification controls for country-pair and country-time fixed effects.<sup>6</sup> The country pair fixed effects not only accounts for standard time invariant covariates of gravity models such as geography, institutions, and culture, but also for unobserved time invariant heterogeneity. Country-time

> 5In order to avoid outliers, we keep 92.5% of the observations.

> 6The directional trade data we use is rich enough that we could expand on the country-time fixed effects to control for country-time by exporter and importer status. Country-time fixed effects by exporter and importer status would however absorb all the variations that we use to separately identify the effects of trade agreements on inflows and outflows.

8
16. R. Khamsi, If a coronavirus vaccine arrives, can the world make enough? _Nature_ **580** , 578-80 (2020).

17. WHO, WHO calls for equitable access to future COVID-19 vaccines in Africa (2020); <u>https://www.afro.who.int/news/who-calls-equitable-access-future-covid-19-vaccines-africa.</u> 18. "PERC: Brief on Public Health and Social Measure Implementation in Africa," Africa CDC Tech. Rep. (2020); <u>https://africacdc.org/download/perc-brief-on-public-health-and-socialmeasure-implementation-in-africa/.</u> 19. A. Khodr, With schools closed due to COVID-19, Ethiopia grapples with providing distance education to children. (UNICEF _._ Addis Ababa, 2020); <u>https://www.unicef.org/ethiopia/stories/schools-closed-due-covid-19-ethiopia-grapplesproviding-distance-education-children.</u> 20. Proclamation 3/2020 - A State of Emergency Proclamation Enacted to Counter and Control the Spread of COVID-19 and Mitigate Its Impact. (Addis Ababa, 2020); <u>https://www.ilo.org/dyn/natlex/natlex4.detail?p_isn=110046&p_lang=en.</u> 21. Address by H.E. Muhammadu Buhari, President of the Federal Republic of Nigeria on the COVID-19 pandemic. (Abuja, 2020); <u>https://www.nigerianembassy.ru/index.php/address buhari-about-covid19.html.</u> 22. More guidelines on COVID19, preventive measures & the need for a shut down - President Museveni. (Kampala, 2020); <u>https://www.statehouse.go.ug/media/pressreleases/2020/03/30/more-guidelines-covid19-preventive-measures-need-shut-downpresident.</u> 23. Speech by President Mutharika on 21-day Covid-19 lockdown. (Lilongwe, 2020); <u>https://malawi.un.org/en/46797-speech-president-mutharika-21-day-covid-19-lockdown.</u> 24. Republic of Malawi in the High Court of Malawi, Lilongwe District Registry, Judicial Review Cause No. 22 of 2020, Before Honorable Justice Kenyatta Nyirenda, “Order granting permission to apply for judicial review and interlocutory order of injunction,” (Lilongwe, 2020); https://perma.cc/MML4-8FNU.

25. ILO, ILO Monitor: COVID-19 and the world of work. 6th edition. (ILO, Genève, 2020).

26. F. Ahmed, N. Ahmed, C. Pissarides, and J. Stiglitz, Why inequality could spread COVID-19. _Lancet Public Health_ **5** , e240 (2020).

27. D. Laborde, W. Martin, R. Vos, Poverty and food insecurity could grow dramatically as COVID-19 spreads (IFPRI, Washington, D.C., 2020); <u>https://www.ifpri.org/blog/poverty-andfood-insecurity-could-grow-dramatically-covid-19-spreads</u> 28. _The Millennium Development Goals Report 2015_ (United Nations, New York, 2015).

**Acknowledgments:** We gratefully acknowledge the research assistance provided by Ann Furbush and Joshua Brubaker. We also owe a great debt to the individuals involved in the design, implementation and dissemination of high-frequency phone surveys on COVID-19, specifically the World Bank Living Standards Measurement Study (LSMS) team, and the phone survey

10
interpolated from the W.H.O. life tables for Ethiopia for the year 2000.

Where the growth of total factor productivity (TFP) is concerned, we proceed as follows. The growth rate of A1,t, denoted by gA1,t, is set at a level such that the resulting rates of income and consumption growth in agriculture, together with that of ruralurban migration, remain plausible. If, at first glance, the annual rate of 1% (i.e., 22% over 20 years) looks rather low, it should be recalled that the accumulation of human capital will also contribute to the growth of individual productivity. In the urban sector, we choose, parsimoniously, gA2,t = 0. This apparently startling assumption does not rule out the growth of individual productivity, even if physical capital per head stays constant. For, as shown below, the parameters of the educational technology allow unbounded growth of individual human capital when all children are fully educated, and the production technology is Cobb-Douglas.

There remains the (exogenous) quantity d<sup>u</sup> in eq. (10). Since (expected) ‘utils’ are not observable and the purist position that d<sup>u</sup> = 0 is a useful benchmark, even when many factors have been omitted from the model, we sought results with d<sup>u</sup> as close to zero as the algorithm would permit when given a day or two in order to converge. With d<sup>u</sup> = 0.5, convergence became very slow indeed, and there we left it – provisionally. As it turns out, the resulting value of c1,1 would have to be about 17% larger in order that a young rural adult be indifferent between the two locations, which seems quite modest and so justifies the decision to proceed with d<sup>u</sup> = 0.5.

As a final preliminary, we calculate the rate of growth in the steady-state setting of Section 2.4. With the parameter values adopted above, eq. (11) becomes, for D = 1, whose relevant characteristic solution is λ1,t = λ1,0(1.139)<sup>t</sup> . Hence, the sector grows asymptotically at the rate 0.315 (= 0.222+0.667 × 0.139) per generation, or 1.38% p.a.

21
|tot_treated|0.0271***|0.0209**|0.0269***|0.0246**|0.0265***|
|---|---|---|---|---|---|
||(0.00997)|(0.00939)|(0.00958)|(0.00995)|(0.00981)|
|temp_shock|0.0799***|-0.0529***|-0.0589***|0.00812**|0.0168***|
||(0.00270)|(0.00394)|(0.00260)|(0.00318)|(0.00514)|
|did_var|-0.00383|0.00280|-0.00476|0.00122|-0.00428|
||(0.00570)|(0.00482)|(0.00411)|(0.00545)|(0.00687)|
|pop_HRSL_dens_km||||||
|2|0.00639***|0.00648***|0.00646***|0.00634***|0.00629***|
||(0.000899)|(0.000926)|(0.000887)|(0.000863)|(0.000860)|
|Constant|0.122***|0.175***|0.234***|0.228***|0.213***|
||(0.0129)|(0.0129)|(0.0128)|(0.0126)|(0.0128)|
|Observations|16,880|16,880|16,880|16,880|16,880|
|R-squared|0.772|0.772|0.775|0.764|0.759|
|Treatment Obs|9684|9684|9684|9684|9684|
|Control Obs|7196|7196|7196|7196|7196|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 1<sup>st</sup> to 99<sup>th</sup> percentile (0.02 to 2.7 nanoWatts/sr/cm<sup>2</sup> )

#

#

According to EM-DAT & CRED / UCLouvain (2023), Nigeria suffered flooding during the period of July 1, 2022 to October 31, 2022. The first row of Figure 9 presents the flooded pixel during each month and over the whole period, The second row shows flooded pixel during each month but not flooded during the other months. During the first couple of months of the flooding season, most flooded pixels are located in the south. During the second part of the period, the floods mainly occur along the Benue and Niger Rivers in the center part of the country. Given the length of the flooding period and the potential for adaptation and population displacement after the first flooding events, we focus on the ‘unexpected’ first floods. Therefore, we select the pixels that were flooded in July but not in August, September or October.

18
- 58 -

**Table** 4,12b: TRENDS OF INTERGOVERNMENTAL TRANSFERS - Variation Index, 1975 **-** 100 -

||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE|100|99|108|118|128|152|134|154|128|133|
|MARKET ECONOMIES|100|105|110|116|118|118|109|106|106|106|

Sourcei IMF, Government Finance Statiatics Yearbook, various years.

4.13 Borrowing. Direct external borrowing is usually not allowed to subnational governments, which makes good sense in exchange short countries, but "onlending" arrangements are often adopted between the centrt government and subnational entities, especially regarding officially approved projects or programs bearing international financing. Since the exchange risk is usually carried by the central authorities and the same repayment of the subloans not infrequently forgiven, such concessionary lending can be considered as just another form of intergovernment transfer. Presently, the access of LAC subnational authorities to domestic canital markets is merely nominal. This is not only due to the fact that the money available is little, short-term and very costly, but also to the existence of regulatory constraints which were designed to prevent local authorities from incurring debt and possibly making unwise investment decisions. This restraints usually involve lenghty procedures of loan review and approval, unwieldy ratios of debt-service to revenues, matching funds contributions, short maturities and cumbersome disbursement procedures. The current financial and regulatory environment also discourages the so called "autonomous" intermediaries from tapping private savings on behalf of municipalities by issuing bonds or debentures, which are rarely used to finance local investment. In practjice, the central banks or the financial authorities set annual ceilings to domestic credit for the whole public sector with very low priority to subnationral goveren.tsorg competing ofictial borrowers.

4.14 In most countries, different forms of institutional credit are being used to substitute for the scant development of commercial borrowing. In this fashion, substantial public resources are channeled to departmental enterprises and to regional and local governments (Table 4.14), mainly on a concessionary basis. Subnational authorities are often allowed to cover budget deficits with overdrafts or to undertake short term loans and other stop-gap arrangements with local banks (a feature which in a disrupted situation as that of Brazil led many State Banks to catastrophic results). Official credit 'windows' and/or Municipal Development Funds (MDF's) have been established nearly everywhere, capitalized through loans or deposits by the central banks or by publicly controlled pension and insurance schemes. External funding has also been used occasionally to
66

# **B.4 Economic and Institutional Regime Variables**

In section 7.4 we have seen that when economic openness and institutional quality variables, together with education, innovation and ICT variables, are included as regressors, the majority of the knowledge variables produce estimated coefficients that are statistically not significant. In this section, we allow economic openness and institutional quality to enter separately into the regressions so as to assess the individual effects of economic openness and institutional quality on economic growth, as well as their effects on the explanatory power of the rest of the knowledge variables.

## <u>Economic Regime Only</u>

Tables A4 and A4a presents the regression results in which the growth rate of real GDP and real GDP per worker are regressed against variables indicating economic openness, education, innovation and ICT infrastructure. As in the previous sections, Table A4 presents the results when the non-per-worker specification (Equation 7) is estimated and Table A4a presents the results when the per-worker specification (Equation 8) is estimated.

The economic openness variable is included as an additional regressor in Regs A4.2 through A4.11 and Regs A4a.2 through A4a.11. For convenient comparison, Regs A4.1 and A4a.1 present the results of regressions where economic openness variable was not included, which we have already examined in detail in the preceding sections. Regs A4.2 through A4.6 and Regs A4a.2 through A4a.6 employ the human capital stock measures based on the population aged 15 years or older with various innovation and ICT indicators. Regs A4.7 through A4.11 and Regs A4a.7 through A4a.11 re-estimates the regressions with the human capital based on average educational attainment of persons 25 years and older.

With the inclusion of the economic openness variable as an additional regressor, we see that the R squared value increases by less than 2 percentage points. Also, we note that the estimated coefficient of the economic openness variable is consistently positive and statistically significant. In addition, it can be seen that with the inclusion of the openness variable, a number of the knowledge indicators have estimated coefficients that are no longer statistically significant. For example, all of the estimated coefficients education or human capital variables, while still positive, are no longer statistically significant. Similarly, the ICT variables phones and Internet users also have estimated coefficients that in most cases are not statistically significant. On the other hand, all of the coefficients of the innovation variables maintain their positive signs and statistical significance, indicating that innovation variables are perhaps the most robust of all of the knowledge indicators.
**Table 2.1 Indicators for assessing financial intermediation in 24 focus countries and comparators**

|Country|Total assets of financial<br>intermediaries as % GDP /a|Private credit by deposit<br>money banks as % GDP|Longest maturity terms<br>available for loans (years)|Average lending rate<br>(%)|
|---|---|---|---|---|
||_end-2006 or most recent_|_end-2006_|_end-2006_|_end-2006_|
|Benin|21.6|16.1|10+|—|
|Burkina Faso|21.0|19.2|10+|—|
|Cameroon|12.9|9.2|—|15|
|Cape Verde|107.3|63.7|5|12|
|Chad|5.9|2.8|—|15|
|Congo, Dem. Rep.|3.0|2.1|3|67|
|Côte d’Ivoire|21.4|14.9|10+|—|
|Ethiopia|14.2|23.3|—|7|
|Ghana|46.9|19.9|20|26|
|Kenya|52.4|25.1|10+|14|
|Lesotho|26.7|7.5|20|12|
|Madagascar /b|16.2|10.9|See note b|30|
|Malawi|23.3|9.0|5|29|
|Mozambique|21.5|11.3|10+|19|
|Namibia|165.5|61.5|20|11|
|Niger|10.7|9.0|10+|—|
|Nigeria|25.7|12.6|5+|17|
|Rwanda|25.1|13.9|10 +|16|
|Senegal|32.2|26.7|10+|—|
|South Africa|192.1|76.5|20|11|
|Sudan|17.0|13.8|—|11|
|Tanzania|22.9|11.7|5 +|15|
|Uganda|23.5|8.1|20|19|
|Zambia|17.8|8.1|20|23|
|Chile|156|71|25|8.0|
|Malaysia|199|118|—|6.5|

Sources: International Monetary Fund International Financial Statistics; central banks and finance ministries. — = Not available. a. Due to asset data limitations for pension systems and insurance sectors in several countries, the reported figures may be under- or overestimates. Total deposit money bank assets data in the Democratic Republic of the Congo, Rwanda, and Nigeria are current for end-2005. b. In Madagascar, the seven commercial banks offer only very basic savings and credit vehicles to select clients; bank loans to the 10 largest corporate clients comprised nearly one-quarter of the banking sector’s total corporate loan portfolios (IMF, 2006e).

# Assets of financial intermediaries

The total amount of domestically available funds, as indicated by the total assets of financial intermediaries in the country, provides a theoretical maximum that these entities could possibly invest in infrastructure. Depending on the particular national regulatory environment (for example, regulations governing institutional investor investments of their assets and specific restrictions on asset allocation), some proportion of these funds could be invested in infrastructure (see chapter 4).

8