rafmacalaba/data-use-mentions-extended huggingface dataset:
|88.4|11|11|10|14|
|8441|Shirts, men's, of textile fabrics|72.7|12|12|16|11|
|7492|Taps, cocks, valves etc.|69.7|13|13|15|50|
|8219|Other furniture and parts|59.7|14|16|21|48|
|8434|Skirts, women's, of textile fabrics|58.0|15|18|20|24|
|8429|Other outer garments of textile|56.3|16|17|18|21|
|8422|Suits, men's, of textile fabrics|54.0|17|15|23|34|
|6744|Sheets & plates, rolled >4.75mm|49.4|18|19|17|5|
|7731|Insulated, elect. wire, cable, bars|49.4|19|50|104|61|
|8424|Jackets,blazers of textile fabrics|48.8|20|23|26|17|
|Number|of items that were beyond top twenty of|the 2003 list|0|3|5|7|
|Share in|total EU-oriented exports||49%|48%|52%|46%|

Source: Based on EU Data from UN COMTRADE Statistics.

Yet, this conclusion may not be warranted. In fact, Bulgaria’s export expansion did not consist of merely exporting more of the same products. The evidence abound. First, as discussed earlier, the levers of export growth during the current phase have been medium to high technology products, with their share in manufactured exports rising 22 percent in 2000 to 27 percent in 2003. Under these conditions, it would appear that rather a considerable reshuffling in Bulgaria’s export offer has been taking place.

Indeed, the analysis of the group of
.e. the degree to which the maize price increased or decreased more than the price of the basket of consumption goods represented in the CPI. Each household is assigned the prices of the closest market, measured by geographical distance.

- Water Requirement Satisfaction Index (WRSI) calculated from satellite rainfall data for each pixel using a maize crop model calibrated to the growing seasons across Uganda. Specifically, the geoWRSI v 3.0 was used with the global PET and RFE<sup>5</sup> v2 (2001-2014) time series.  The WRSI is an indicator of crop performance based on the availability of water for the crop during the growing season. The index ranges from 0 to 100, where 100 means there was no deficit in the water needed, and each household was assigned the average between the main and short seasons for the period for which crop income data were collected.<sup>6</sup> These data have been merged into the UNPS using the Geographical Information System (GIS) coordinates of households.

- As a proxy of the incidence of violence and conflict, we use the number of fatalities per year in a 25-kilometer vicinity of each household, obtained from the Armed Conflict Location Event Dataset (ACLED).

Table 3 provides a description of the type of crops grown by the households for the latter two years of the analysis. Maize, beans, matooke and cassava are the four most important crops as a share of total crop income. Maize and beans are universally important—comprising 10 percent or more of crop incomes in all regions in all years. Matooke is important in all regions except the Northern region,

> 5 Potential evapotranspiration (PTE) and satellite gauge rainfall estimate (RFE).

> 6 For those households in Uganda living below the equator, the main season runs from February to July and the short season runs from August to January. For households living in bimodal areas above the equator, the main season runs from August to January and the short season from
solar panels allow mobile shallow-tube irrigation systems to meet rural farmer irrigation needs in Bangladesh and other countries that face limited electricity supply and make heavy use of groundwater.

Source: Popp (2011) on cooking stoves (using a combination of keyword and patent classification searches on the Delphion on-line patent database, and using a keyword search of abstracts and titles in the Web of Knowledge database); web searches and interviews for others.

Besides BoP innovation, the adaptation of existing technologies to local conditions is a growing area of green innovation in developing countries. Box 2 provides some recent examples of technology adaptations by innovative developing country companies that solve limitations of resources, labor and infrastructure. They create important co-benefits including more sustainable company cultures.

11
**Figure 7:** Relationship between HCI and health expenditure (as a percent of GDP), stratified by share of individuals above 65

<!-- Start of picture text --> Share of population over age 65yrs<br>Lowest quartile Second quartile Third quartile Highest quartile<br>0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15<br>Domestic government health expenditure (% of GDP)<br>1<br>.8<br>.6<br>HCI<br>.4<br>.2<br><!-- End of picture text --> One potential explanation for the non-linear relationship between HCI and spending is that, above a certain amount, government spending is crowding out private spending. When we look at the relationship between HCI and private spending per capita (from WHO-GHED), we find a pattern that is remarkably similar to the one observed for public spending (Figure 8). We also see no evidence for government spending crowding out private spending (or vice-versa) (Figure A.4).

17
46.7|
|Carriers passing IATA/IOSA Audit|%|0|0|0|28.6|0|0|
|FAA/IASA audit status||No audit|No audit|Failed|No audit|No audit|No audit|

_Source_ : Bofinger 2008. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data _Note_ : Herfindahl-Hirschmann index a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly, while a lower the HHI the more diluted is the market power as exerted by one company/agent. All data as of 2007 based on estimations and computations of scheduled advertised seats, as published by the Diio SRS Analyzer. This captures 98 percent of world-wide traffic, but a higher percentage of African traffic is not captured by the data

# **Challenges**

Despite the recent turnaround, capacity and connectivity remains low in relative terms owing to poor infrastructure and small market size. The domestic market for air transport in Benin is especially thin and served by a number of small operators such as Aero Benin, Trans Air Benin, and Benin Golf Air. In addition, there are a number of failed airlines,such as Afrique Airlines and Zircon Airways Benin. Compared with West African peers, Benin reported low numbers of seats for international travel within Africa and intercontinental travel, and its seat capacity per capita is one of the lowest in the region (table 7).

The type and condition of the country’s air transport infrastructure imposes a big challenge for expansion of the market. Airport infrastructure consists of the international airport in Cotonou, and basic and rudimentary airstrips in remote towns. The deterioration of Cotonou’s airport runway surface and its need to be reinforced is a major challenge. Users identify the main infrastructure problems affecting Cotonou airport as: (i
Table 6: Medium-term impacts of _Mais Educação_ on educational outcomes

|Dependent variable|Dropout rates|Test score Math|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Impa|cts on fifth-grade|variables||
|Dummy_Mais Educação_* year 2011|1.265|−3.374*|−2.086|
||(1.522)|(1.725)|(1.568)|
|_R_ <sup>2</sup>|0.686|0.817|0.823|
|Observations|1,992|1,992|1,992|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Impa|cts on ninth-grade|variables||
|Dummy_Mais Educação_* year 2011|−0.292|−3.821*|−0.664|
||(1.265)|(2.011)|(1.898)|
|_R_ <sup>2</sup>|0.749|0.792|0.798|
|Observations|1,730|1,730|1,730|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, Prova Brasil, School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3
al. (2018) estimate that 10.3 percent of all employment in the US were in “directly green” occupations while "indirectly green" occupations made up another 9.1 percent of all employment. Valero et al. (2021) estimate the share of green employment in the UK to be between 17 percent and 39 percent in 2019. The higher share of green jobs for the UK as compared to other countries is partly due to data challenges when cross-walking between the O*NET which uses the US 8-digit standard occupational classification and the UK labor force surveys which is based on the 4-digit ISCO-08 classification.

27 Introduced in 2011, the REIP4 used a competitive procurement program to increase electricity generation through renewable energy sources. Investing companies needed 40 percent local ownership, leading to increased partnerships between foreign and South African companies, including knowledge and skill sharing and green jobs (ILO 2018). The increase in the demand for solar PVs may have increased the demand for solar panel installers and assemblers, explaining the increase in potentially green jobs between 2013 and 2014. However, the stalling of the REIP4 initiative after 2014 meant that investment in renewable energy technology also stalled corresponding to the flattening of the trend.

28 Although we use the same methodology as Posadas and Granata (2024), there are a few reasons that could explain the differences in the estimated share of green jobs in South Africa and Indonesia. First, the Granata and Posadas (2024) analysis excludes agricultural occupations while we include the agricultural occupations in the sample. Second, it is possible that the South African economy differs from the Indonesian economy both in structure and in the profile of workers. For example, the mining sector, which will be substantially affected by the green transition, contributes to a significant share of employment in the South African economy and for this reason, our green occupations list includes occupations such as mining managers and mining engineers, occupations which are not included in Granata and Posadas’ (2024) green occupations list.

15
children. The indicators most frequently used are height-for-age, weight-for-age and weight-for-height. These indicators are standardized according with age and gender specific child growth standards provided by WHO and are universally comparable.<sup>12</sup> An insufficient height-for-age (or being stunted) or an insufficient weight-for-height (or being wasted) indicate acute malnutrition. Conversely, weight-for-age is a measure of underweight. In this study, I use height-for-age as an indicator of a child nutritional status standardized by age and sex according to WHO growth standards (henceforth HAZ). The reason is that HAZ is a better indicator of long term health. Indeed, it is an indicator of cumulative deficient growth and it is proved to be associated with diet, hygiene, infection and feeding practices. Conversely, weight-for-age is likely to be severely affected by temporary shocks due to morbidity and illnesses.

I study HAZ during early childhood. Focusing on early malnutrition is important because empirical evidence suggests that nutritional status during early childhood is a major predictor of health conditions and economic outcomes in further stages of life (Case et al., 2005; Gertler and Boyce, 2001). This is the reason why early policy interventions during childhood have been found to be particularly cost effective, with higher rates of return than later interventions (Heckman, 1995 and 2000).

# **4.2  The risk factors of child malnutrition**

Empirical evidence suggests that child malnutrition is caused by a lack of physical and human resources. There is a growing literature supporting the idea that social capital can act as a protective factor against socio-economic disadvantages. In this section I discuss the main risk factors associated with child malnutrition and define how they are measured in this study.<sup>13</sup> Table A1 in the Appendix shows descriptive statistics of the variables used in the empirical analysis.

## _Maternal social capital_

The YL survey contains a module to measure multiple aspects of maternal social capital. It is based on the _Short Adapted Social Capital Assessment Tool_ (SASCAT)
Identifying common variables between the surveys and the census The Small Area Estimation (SAE) process entails constructing a statistical model for predicting the outcomes, which in this case are per capita consumption when estimating monetary poverty and height for age Z scores when estimating stunting. Ultimately, this model, which is trained using survey data to forecast monetary poverty, is utilized for poverty prediction within the census dataset. However, this is only feasible if there is a shared set of explanatory variables for the relevant outcome in both the survey (EPM21-22), DHS, and the 2018 census. Therefore, the initial step in the SAE process involves recognizing common variables suitable for prediction. In this context, "common" denotes variables measuring the same indicators and thus should exhibit similar distributions across these two data sources.

Identifying common variables involved three primary stages. Firstly, we sought variables that gauge the same phenomena. This was achieved through a careful review of the questionnaires from EPM2122, DHS 2021, and the census to verify if the wording of the questions matched. Secondly, we compared the response categories for categorical variables to determine their alignment. In cases where differences were observed, we assessed if these categories could be merged for comparability. The third step involved comparing the means of the common variables. Drawing from previous SAE estimations, we categorized the prediction variables into three main groups:

- Demographic characteristics of the household head, including age, gender, marital status, and education level.

- Dwelling characteristics, encompassing materials used for the roof, walls, floor, etc.

- Household assets, which includes whether the household possesses common assets like bicycles, cars, radios, etc.

For instance, in the context of education, the first candidate variable is whether the household head attended school, a question posed similarly in both the census and EPM21-22. Thus, no significant transformation was necessary. The second candidate variable regarding education pertained to the highest level of education attained. However, there was a distinction in the response categories,
Start of picture text --> 10<br>9.1<br>9<br>8<br>7 6.8<br>6<br>5.3<br>5<br>4.2<br>4 3.9 3.6 3.8<br>3 2.8<br>2<br>1<br>0<br>All Post Cold War Low-income Post-Cold War & Low-<br>Income<br>Non-oil states Oil states<br><!-- End of picture text --> _Source:_ calculated from conflict data in Gleditsch et al. (2002).

_Notes:_ The figures represent the annual conflict rate for oil-producing states (black bars) and non-oil states (gray bars). The data are for 1960-2006, except the ̳Post-Cold War‘ period, which is 1992-2006. Low income countries are those with incomes below $5000 per capita. All of the differences between oil and non-oil states are statistically significant in t-tests.

2
>Total Health Exp as % GDP<br><!-- End of picture text -->

> * Data refer to 2001 the latest year for which complete data are available. The predicted figures are from a regression of the natural logarithm of total health expenditure (expressed as a share of GDP) on the natural logarithm of GDP per capita. Data are from the 2005 World Health Report annexes in the cases of Japan, Korea and Singapore. The per capita income data for Hong Kong (China) and Taiwan (China) are from the World Bank’s World Development Indicators. Data on health spending are from <u>http://www.hwfb.gov.hk/statistics/download/dha/en/table1.pdf in the case of Hong Kong (China) and from http://www.doh.gov.tw/ufile/doc/200411_Statistic%20of%20Expenditure%20for%20Health,%201991-2003.pdf</u> in the case of Taiwan (China).
Landsat specifications have high prediction power for the smallest communes. One potential reason is that Landsat does well at capturing the built-up area. There is similar decay of prediction power with increasing commune size where the full Landsat band specification dominates the sum of light approach for the lower half of the sample. The simple Landsat indices specification however performs less well than the nightlights beyond the smallest communes.

The analysis concludes that, consistent with earlier studies, nightlights perform poorly at predicting economic activity at very small geographic units. This drives the overall prediction power of typical nightlight regressions down, suggesting that the nightlight-economic activity relationship is unstable. The prediction of Landsat instead is poor for very large communes. For practical purposes, the results suggest that Landsat is a good predictor if researchers are interested in studying formal growth in urban areas, while nightlights are superior for the purpose of measuring economic activity in rural areas. Regardless of which setting is more important for the practitioner, both products can be a useful complement to each other and, if available, should both be used increase prediction power for economic activity.

# **4 Evaluating Changes in the Time Series**

The paper next examines the predictive power of satellite imagery in the time series and look at changes in economic activity over time.

## **4.1 Econometric setup**

We continue to use a simple linear prediction model and estimate differences in the economic outcome variables on changes in the remotely sensed satellite data. For that the estimating equation in (1) is differenced to arrive at 19
for Inclusive Urbanisation in China”, OECD Working Paper, Paris.

- Jorgenson D.W. (1991), “Productivity and Economic Growth”, in (eds) Berndt E. and J. E. Triplett, _Fifty Years of Economic Measurement: The Jubilee of the Conference on Research in Income and Wealth._ University of Chicago Press

- Kendrick J. (1956), “Productivity Trends: Capital and Labor”, in (ed) J. Kendrik, _Productivity Trends: Capital and Labor_ , National Bureau of Economic Research.

Liu, Ta-Chung (1968), “Quantitative Trends in the Economy” in Eckstien et al (1968).

- Liu, Ta-Chung and Kung-Chia Yeh, (1963), _The Economy of the Chinese Mainland: National Income and Economic Development_ , The Rand Corporation for the United States Air Force.

- National Bureau of Statistics (2016), “Notice of the National Bureau of Statistics on the revision of the R & D expenditure accounting method to revise the accounting data of the gross domestic product”, July 5<sup>th</sup> , Beijing.

- Perkins, Dwight H. (1988), “Reforming China’s Economic System”, _Journal of Economic Lite_ rature, June, pp. 601-645.

- Perkins, Dwight H. and Thomas G. Rawski (2008), “Forecasting China’s Economic Growth to 2025”, in ed. Brandt L. and T. G. Rawski _(2016), China’s Great Economic Transformation_ , , Cambridge University Press.

- Wu H.X., Xiniming Yue and George G. Zhang (2015), “Constructing Annual Employment and Compensation Matrices and Measuring Labor Input in China”, RIETI Discussion Paper Series 15-E-005

- Wu, Harry X. (2015), “Constructing China's Net Capital and Measuring Capital Services in China, 19802010”, _REITI Discussion paper Series 15-E-006_ , The Research Institute of Economy, Trade and Industry, Government of
income was found to be somewhat less satisfactory at the upper end of the distribution (Hill 1959, Cowell 1977), specifically the top 3-4 percentiles (Airth 1985). This prompted attempts to fit more complex functional forms -- displaced and/or truncated versions of the lognormal density (Metcalf 1969, Salem and Mount 1974) or alternative functional specifications (Fisk 1961, Salem and Mount 1974, Singh and Maddala 1976, McDonald 1984). Both strategies pose a tradeoff between goodness-of-fit and analytical tractability (Metcalf 1969), as well as interpretability of the parameters (Lawrence 1988), which explains the continuing popularity of the lognormal specification.<sup>6</sup> More recently, Bourguignon (2003) has offered an indirect reassessment of the empirical validity of the lognormal approximation. He reports OLS regression estimates in a framework explaining the observed change in a selected poverty measure on the

> 4 Specifically, Gibrat (1931) argued that the good empirical performance of the lognormal density could be rationalized under the following three conditions: (i) in each period the distribution of income is derived from that of the previous period by assuming that the variable corresponding to each member of the distribution is affected by a small proportionate change; (ii) such proportions differ for different members of the distribution; and (iii) these differences are determined randomly according to a given frequency distribution.

> 5 Rutherford performed single-country estimations using data for the UK in 1949; USA in 1947 and 1948; Canada in 1947, Australia in 1951, Sri Lanka (Ceylon at the time the paper was written) in 1950 and Bohemia (modern Slovakia and the Czech Republic) in 1932.

> 6 For example, Dollar and Kraay (2002) resort to the assumption of lognormality in order to complete their data sample by generating quintile shares from Gini coefficients for those observations for which only the latter is available.

-3-
Table 7: Intervention impact on toilet usage

||(1)<br>Open <br>All HH<br>members|(2)<br> Defecation<br>Any HH<br>member|
|---|---|---|
|SL|-0.107<sup>_∗∗∗_</sup>|-0.104<sup>_∗∗∗_</sup>|
||(0.0245)|(0.0243)|
|Cluster-robust p-value|[0.0000]|[0.0000]|
|Romano-Wolf p-value|[0.0000]|[0.0000]|
|Strata FE|Yes|Yes|
|Interviewer FE|Yes|Yes|
|Household covariates|Yes|Yes|
|Ratio sample clients/GP size|Yes|Yes|
|Control mean|0.603|0.610|
|N|2821|2821|

_Notes_ : SL equals sanitation loan arm. Standard errors clustered at the village level are shown in round parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level, referring to Romano-Wolf p-values. These are obtained from a step-down procedure to adjust for multiple hypothesis testing performing 1000 bootstrap replications as proposed by Romano and Wolf (2005). Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size. HH stands for household, OD for open defecation. Data source: household survey.

uptake indicator with the treatment assignment. This exercise assumes that changes in toilet ownership induced by the intervention happen only through the loan uptake itself. A potential alternative channel includes salience (i.e. the pure offering of the loan product making clients more inclined to make this investment). However, we show below that knowledge about the benefits of sanitation have not changed