rafmacalaba/data-use-mentions-extended huggingface dataset:
>|3,736|3,736|3,736|3,736|3,736|3,736|
|Adj._R_<sup>_2_</sup>|0.27|0.18|0.11|0.21|0.14|0.11|

Note: All specifications estimated with ordinary least squares, with commune fixed effects. Robust _p_ -values, with standard errors clustered at the village level, reported in parentheses. Coefficient estimates of the following controls are insignificant and not reported: ethnic dummies (Adja, Fon, Bariba, Dendi, Yoa/Lokpa, Peulh, Goua/Otamari, Yoruba); age and gender of household head; and whether the respondent is Islamic. The following village controls are insignificant and not reported: whether the village chief has a primary education; whether a paved road serves the village; the distance to the closest urban center; the number of functional private schools; and the presence of a secondary school.

Table 4 reports six estimates of equation (1). The first looks at the effect of radio access on the total number of bed nets reported by households; the second focuses only on free, governmentprovided bed nets; and the third examines only the number of government bed nets that the households paid for (did not receive for free). Columns four through six repeat the analysis, but focus only on bed nets pre-treated with insecticide. All specifications contain the same control variables, including commune fixed effects, and are estimated using ordinary least squares. The first row of Table 4 reports the effects of access to non-commercial private (community) radio.

Taken together, the community radio coefficients across the six specifications support the hypothesis that radio access increases both demand for bed nets and a particular supply response – the diversion of free bed nets to the paid market by local officials. Columns 1 and 4 indicate that radio access has no significant influence on the total number of bed nets in the
interpolate using estimates of equation (4).<sup>21</sup>

> Overall, our FE estimates are effectively based on approximately one third of the number of observations used in the pooled OLS estimates implying a substantial loss in efficiency.

> 20  Price data are not reported for the Pwani region which forms the hinterland to Dar es Salaam. We use Dar es Salaam prices for Pwani households.

> 21 The procedure for estimating seasonal gaps is robust against the presence of missing values avoiding the need for interpolation at that stage. However, in constructing the average staples prices we have the choice between dropping missing observations and interpolating. In the latter case, we can interpolate using estimates of equations (4). The results are qualitatively similar and in what follows we report the results using interpolated prices. We use equation (4) to construct interpolated prices for region _r_ over a gap of _k_ months as

19
. Gilmore. 2008. “Economics of Tobacco Taxation in Russia.” International Union against Tuberculosis and Lung Disease, Paris.

- Sakharova, Galina, Nikolay Antonov, and Oleg Salagay. 2017. “Tobacco Control: A Comprehensive Approach at Country Level in the Russian Federation.” Regional Office for Europe, World Health Organization, Copenhagen.

- Stefler, Denes, Aytalina Azarova, Darja Irdam, Gabor Scheiring, Michael Murphy, Martin Mckee, Larry King, and Martin Bobak. 2018. “Smoking, Alcohol, and Cancer Mortality in Eastern European Men: Findings from the Privmort Retrospective Cohort Study.” _International Journal of Cancer_ 143 (5): 1128– 33.

- Verguet, S., C. L. Gauvreau, S. Mishra, M. MacLennan, S. M. Murphy, E. D. Brouwer, R. A. Nugent, K. Zhao, P. Jha, and D. T. Jamison. 2015. “The Consequences of Tobacco Tax on Household Health and Finances in Rich and Poor Smokers in China: An Extended Cost-Effectiveness Analysis.” _Lancet Global Health_ 3 (4): e206–e216.

- Wang, Yingying, Wei Zhang, Hong-lan Li, Jing Gao, Yu-ting Tan, Yu-tang Gao, Xiao-Ou Shu, Wei Zheng, and Yong-Bing Xiang. 2015. “Population Attributable Risks of Cigarette Smoking for Deaths of All Causes, All Cancers, and Other Chronic Diseases among Adults Aged 40–74 Years in Urban Shanghai, China.” _Chinese Journal of Cancer Research_ 27 (1): 59–65.

- WHO (World Health Organization). 2009. Tуберкулез и Tабак [Tuberculosis and Tobacco] [in Russian], WHO, Geneva.

http://www.who.int/tobacco/resources/publications/ru_tfi_tb_tobacco_factsheet.pdf.

- ———. 2017a. “WHO Tobacco Fact Sheet.” WHO, Geneva. http://www.who.int/mediacentre/factsheets/fs339/en/
Unified Beneficiary Registry data containing household geocoordinates. In this context, the Chi et al. (2021) estimates may perform poorly because they come from a model trained to wealth while the benchmark reference indicator is per capita consumption; these are conceptually different measures of welfare that tend to diverge more in rural areas and among the extreme poor (Ngo and Christiaensen, 2019). For the standard geospatial poverty map based on the survey, the low R<sup>2</sup> is also due to the paucity of survey data, as there are only 9
the studies that did include CEA covered DHIs for clients (n=119 IEs and n=11 SRs) and health care providers (n=45 IEs and n=8 SRs). Only three IEs and no SR covering DHIs for health system managers reported economic outcomes. Moreover, none of the studies on DHIs for data services included any cost data (See Figure 4).

The economic outcomes reported by the studies are further organized into simple, intermediate and summary/impact based on the DHI’s TOC causal pathway. The simple economic outcomes refer to reporting of cost data without any reference to health or wellbeing outcomes (e.g., cost incurred/saved). However, some refer to outputs such as cost per user or persons reached. Studies reporting simple economic outcomes are generally variations on forms of costing analyses. Intermediate and summary economic outcomes link intervention cost data to select health outcomes. The difference between the two is the level of aggregation and finality of the health outcome. Intermediate economic outcomes focus on immediate or natural outcomes such as cost per infection averted whereas the summary outcomes invoke ultimate health outcomes such as cost per life saved or cost per quality adjusted life-year gained. Highly aggregated outcomes, such as net benefits calculated under a cost-benefit analysis framework, would also be classified as a summary outcome, however no cost-benefit analyses were identified in the included studies.

<!-- Start of picture text --> Cost only/Cost analysis 85 8<br>Cost minimization analysis 1<br>Cost consequence analysis 2<br>Cost effectiveness analysis 55 8<br>Cost utility analysis 26 2<br>Cost benefit analysis 0<br>0 10 20 30 40 50 60 70 80 90 100<br>Impact evaluation Systematic review<br><!-- End of picture text -->

**Figure 4 Economic Outcomes reporting from the DHI Evidence base**

# 3.1.7 Evaluation methods

Most studies (n=583, 91
|1.9|4.1|88|94|79|95|79|
|Sierra Leone|5.1|2.9|5.5|2.9|5.5|61|90|56|90|56|
|South Africa<sup>a</sup>|1.2|1.0|1.5|1.0|2.2|98|99|97|99|95|
|Swaziland|1.9|0.9|2.4|—|—|94|100|92|—|—|
|Tanzania|1.9|0.9|2.1|0.9|2.1|95|100|94|100|94|
|Togo|6.9|3.6|9.0|3.6|9.0|54|81|37|81|37|
|Uganda|4.4|1.7|4.7|2.0|4.7|71|97|69|96|68|
|Zambia|4.1|1.0|4.6|1.2|4.9|71|100|66|99|63|
|Median|2.2|1.1|2.4|1.2|3.0|93|99|92|99|81|

_Source_ : World Bank staff calculations based on utility information and household survey data. _Note_ : — = Grid connection status not available in the survey. a. These numbers are for Johannesburg. Depending on eligibility, poor households are provided with 50, 100, or 150 kWh of free electricity a month in Johannesburg.

In Angola (effective unit charge of US$0.012/kWh), Ethiopia (US$0.022), Ghana (US$0.069), Mali
exposure and segmented labor markets. Using the Enterprise Census (EC) from 2017 and the Labor Force Survey (LFS) from the same year, we show in Figure 8 that regions with a higher number of exporting firms (Panel A) are also the regions with major employment concentration (Panel B). Although the EC does not allow us to track importing firms, exporting firms heavily rely on imported raw materials and inputs for domestic production and exports. Around 75% of imported goods are raw materials, intermediate goods, and fuel necessary for both production and exports. Thus, we argue it is a good proxy for importing firms (Zaki et al., 2018).

Similarly, in Figure 9, we observe that informality rates, mean real wages, and female labor force participation are highly distinct between regions at the beginning and end of our study period (2009-2017). In fact, although all indicators have increased, most of the regional differences persist after eight years. For example, the four regions with the highest informality rates, FLFP, and mean wages in 2009 remain the top four in 2017. The same pattern is maintained for the four regions with the lowest labor market indicators. Barriers to labor mobility seem to be stringent. The structure of occupations in Egypt could be a potential explanation since it is skewed 10
% increase in the travel time between Colombian cities reduces the value of trade between them by about 7%. To gauge whether within-city roads affect trade, he constructs a municipality-level road index summing up the log of the mileage of principal roads and the log of the number of exists from these roads. A 10% increase in a city’s road index increases the value of its exports by about 4%, suggesting that congestion may be an important factor for transport costs.<sup>7</sup> In some developing countries, comprehensive domestic trade flow data may not be available. All countries, however, collect customs level international trade data. In many cases, these records contain information about exporting/importing firms, their locations in the country, and the port of export/import. Linking these two provides researchers with a snapshot of trade flows within the country, despite the restricted and

> 7A similar data source from India— _Inter-State Movement/Flows of Goods by Rail, River and Air_ —does not contain information about motorized shipments on roads.

13
not with productivity. In contrast, the Global Competitiveness indicator of Corporate Ethics<sup>16</sup> is significantly and negatively associated with firm growth—and thus in this case firm corruption is associated with higher firm growth. The contradictory findings on corruption between local indicators and national indicators suggest that corruption does not have a robust association with firm growth. Given that the Enterprise Survey indicator of corruption captures local variations better, and previous studies have found negative association/effects of corruption on firm growth (Fisman and Svensson 2007; Xu 2011), we place slightly more weight on the findings based on the Enterprise Survey data—that is, at least at the local level, corruption is negatively related to firm growth.

Basic physical safety as captured by the Enterprise Survey indicator of a lower share of a firm’s sales spent on security expenses (Security Costs) is positively associated with a higher productivity level. Lowering it by one standard deviation is associated with increasing labor productivity by 8 log points and TFP by 4 log points. In contrast, the cross-country counterpart of the Global Competitiveness Index, Police System, does not show a significant association with firm performance. Again, with its ability to ascertain local variations, the Enterprise Survey indicator is better at capturing the effects of basic physical safety than the corresponding Global Competitiveness indicator.

_Infrastructure_ . Electricity provision is associated with a higher productivity level and firm growth. A higher Power Outage measure (from the Enterprise Survey) is associated with lower firm growth; a higher Getting Electricity measure (from _Doing Business_ ) is associated with a higher productivity level. Reducing Enterprise Survey Power Outage indicators by one standard deviation is associated with a 0.4 percentage point increase in employment growth, while increasing nationwide electricity quality (captured by the _Doing Business_ Getting Electricity indicator) by one standard deviation is associated with a 9-percentage point increase in labor productivity. The Global Competitiveness indicator of Electricity Quality, which is perhaps a narrower measure of the quality of the power supply system, is
8|17%|
|**SAR**|7|4|57%|3|43%|5,125|1|14%|2|29%|0|0%|
|**ECA**|54|18|33%|22|41%|4,395|1|2%|3|6%|5|9%|
|**MNA**|21|6|29%|14|67%|5,607|0|0%|3|14%|1|5%|
|**N. America**|3|1|33%|2|67%|1,750|2|67%|0|0%|0|0%|

**Notes:** Criteria are not mutually exclusive, i.e. countries with multiple criteria are listed in more than one of the columns (Source: authors’ analysis of data from UN, 2014)

# _2.2. Approaches to the consistent measurement of urbanization_

A growing recognition of the problems posed for cross-country analyzes of urbanization and development by the use of national definitions of urban areas has led to several proposed

> 16 The fact that LAC countries tend to count smaller settlements as ‘urban’ may be related to the history of human settlement in the region. Unlike, for example, Asia and Europe, which experienced more organic urban growth, most of Latin America’s cities were purposefully and ‘abruptly’ founded by European colonizers (Morse, 1962; World Bank, 2009). Perhaps due to these origins, Latin American countries might consider these settlements to be ‘urban’ regardless of how small or sparsely populated they are, whereas an Asian village that has expanded and densified over time might still be considered ‘rural’. However, this is a speculative hypothesis which would need further investigation to substantiate.

6
To estimate the impact of the treatments we estimate the following regression analogous to equation 1 above:

- (2) _Yij = δ + α_ 1 _Ordinaryj + α_ 2 _Ord_PrivRafj + α_ 3 _Ord_PubRafj_

_+ α_ 4 _Commitmentj + α_ 5 _Com_PrivRafj + α_ 6 _Com_PubRafj + β_ ’ _X_ ij _+ εij_ _Yij_ is the dependent variable of interest for farmer _i_ in club _j_ . The savings treatment indicators _Ordinaryi_ and _Commitmenti_ and the respective interactions with the raffle treatment indicators _Ord_PrivRafj_ , _Ord_PubRafj\_ , _Com_PrivRafj_ and _Com_PubRafj_ are defined as in equation 1. **_X_** ij is a vector that includes stratification cell dummies and control variables measured in the baseline survey, prior to treatment (the 17 baseline variables in Table 3). Following closely the interpretation of equation 1 above, the coefficients on the treatment indicators ( _α_ 1 and _α_ 4) reflect the impact on the dependent variable of the ordinary treatment and the commitment treatments, respectively, without additional raffle treatments vis-à-vis the control group, as well as the differential impacts of the savings treatments when combined with the private raffle ( _α_ 2 and _α_ 3) or public raffle treatments ( _α_ 5 and _α_ 6).

We focus on intent-to-treat (ITT) estimates because not every club member offered account opening assistance decided to open the account. We do not report average treatment on the treated (TOT) estimates. It is plausible that members without accounts are influenced by the training script itself or by members who do open accounts in the same club, either of which would violate SUTVA (Rubin, 1974).

# **4.** **<u>Empirical results: impact of treatments</u>**

To understand the impacts of access to formal savings, we first study the extent to which funds flowed into and out of the savings accounts in the pre-planting and planting periods. Then we examine impacts on agricultural inputs, farm output, household expenditures and other household outcomes.

# **A. Savings transactions (deposits and
did real credit to the private sector resume and was aggregate credit growth positive in real terms?

# **II. Cross Country Experience**

**_Sample Selection and Information Sources._** While setting up centralized asset management companies has become a popular component of banking distress resolution strategies in the recent East Asian financial crises-Indonesia, Malaysia and Korea all set up centralized AMC structures-they were a less frequently used tool in the past. According to a survey of 26 banking crises<sup>1 5</sup> , centralized AMC structures were only set up in nine cases. AMCs proved to be particularly popular in Africa as four out of the nine AMCs were launched in Africa.,<sup>6</sup> Out of these nine, seven cases, where sufficient data was publicly available, were selected for more detailed analysis. In four out of the seven cases (Mexico 1994; Philippines 1981-86, Spain 1977-85; US 1984-91); the governments set up rapid asset disposition vehicles, and in three cases (Finland 1991-94; Ghana 1982-89; Sweden 1991-94) restructuring agencies were established. As data sources, published reports, Annual Reports of AMCs if available or World Bank reports, or interviews with experts familiar with the individual cases were employed.

> **14** Some facilities also pursued the explicit objective to minimize fiscal costs. However, as we do not have information as to the counterfactual, we cannot evaluate whether AMCs have achieved that objective. **15** Caprio/Klingebiel 1996.

> **16** Benin, Cote d' Ivoire, Ghana and Senegal all set up centralized AMCs as part of their bank restructuring mechanisms. The other cases were Finland, Philippines, Spain, the US, and Uruguay.

8
|Eastern Europe<br>and Central Asia|1,718,000|1,588,000|567,000|27,000|4,000|2,000|15,000|23,000|
|---|---|---|---|---|---|---|---|---|

## **2.4. Prioritization of rural investments**

We built a model to prioritize rural road investments based on two simple criteria: (i) maximizing the RAI increase per kilometer paved; and (ii) candidate roads for upgrade must be connected to the existing primary and secondary network. The only investment option available in our model is to upgrade existing roads labeled as either ‘tertiary’ or ‘track’ in OSM to an all-season road where those roads are directly adjacent to the network of current primary and secondary roads. The analysis was done in 19 countries in which the potential for increasing the RAI is high, and for which the data were complete enough.<sup>3</sup> The algorithm for investment prioritization takes a two-step approach, which can be described as follows:

**Step 1** : The OSM road data are processed to identify the largest current connected network of primary, secondary, tertiary roads and tracks (the largest connected sub-graph of the network). In many countries, a significant number of road segments are disconnected or misaligned in OSM. A quick fix is to generate a buffer of 200 meters around each road and remove the roads that are not connected to the network outside the buffer. In addition, all roads with a length of less than 500 meters were omitted, as well as the roads serving less than 0.01% of the total rural population.

**Step 2** : The network of connected roads is then overlaid with the population layer, and the number of people living
benefit of formalization, HE-SE are not registered due to lack of information on how to register.

Employers are older firms that managed to grow and create jobs though firm productivity is modest. By definition, the key defining characteristic of employers is that they employ an average of 2.5 paid workers, as compared to no paid workers in other firm types. A majority of employers are low-educated adult men (55.5 percent). They are more active in services or manufacturing than are the self-employed. They have the lowest use of good business practices, such as not mixing business accounts with household expenses, but slighter better use of financial services, including mobile money, than the self-employed. They tend to live in less dense areas that are accessible to other urban neighborhoods. While employers have the highest total revenues and profits, their per capital revenues and profits are on par with LE-SE enterprises.

# **9. Conclusions**

Informality strongly shapes Sub-Saharan Africa’s labor markets. This paper uses data from 26 harmonized household and labor surveys (GMD) and three Informal Enterprises (IES) to sketch a profile of the urban informal sector in Sub-Saharan Africa.

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concentrated right after the harvest. This is the channel emphasized by Khandker (2012) and Bellemare, Barrett and Just (2013) in studying the welfare loss from food price seasonality and volatility.

An indicative answer to the question of price induced seasonality in food consumption is given by the seasonal gap in consumption obtained from estimating (9), at least to the extent that the estimated seasonality related to the monthly fluctuations in consumption can be attributed to movements in the prices of food staples. Controlling for possible demand shifters such as the religious holidays in the estimation of the seasonal factors makes this plausible. Nonetheless, it cannot be excluded that other factors, unrelated to staple prices, are driving the results. Also, the very limited number of observations for each month, remains a concern. If unobserved common factors have affected food consumption in a particular year across an entire sample or sub‐sample, this may give rise to misleading seasonality estimates when samples are short. Examples of such factors in the Tanzanian data include increasingly severe drought conditions during the 2008‐09 LSMS‐ISA wave and severe flooding which may have impacted December and January food consumption in the 2010‐11 wave. Inclusion of household fixed effects does not control for the effect of such unobserved time variant factors.

To address these difficulties the effect of staple prices on food consumption is estimated directly, conditioning on the month of interview and religious holidays (i.e. conditioning on other potential sources of seasonality in food consumption). In particular, equation (9) is extended to: The price‐based seasonal factors for food consumption are then given by the seasonal factors in the average price series _p_<sup>_av_</sup> multiplied by the estimate of the price elasticity . Given that the price seasonal factors are obtained from longer time series market data (19 years in the case of Tanzania), they will be less subject to the short sample bias problems which potentially affects the consumption seasonality estimates of equation (8).
clusters of habitations or hamlets; though most residents would also refer to these as "villages", we will call them "settlements." Among the 165 PHRS-II villages, for example, our village census records 1031 named settlements. However, villages of North Punjab typically are less geographically dispersed and have much fewer settlements than those of Southern Punjab and Sindh (see Table 2 below).

> 3Beyond primary school, strong gender-region differences in dropout rates re-emerge. Conditional on having completed primary school, 81% of 9-15 year-old boys are still enrolled in school compared to 78% of girls, with region-specific gender gaps of 1, 15, and 22 percentage points, respectively. Since post-primary school enrollment, conditional on reaching that decision point, is driven more by the location of middle schools than of primary schools, we will have little more to say about it in this paper.

4