rafmacalaba/data-use-mentions-extended huggingface dataset:
cash; if the transfer is paid in-kind, one would require an estimate of the average WTP for $1 worth of the in-kind good to apply an analogue to Proposition 3.

Lastly, we discuss the advantages of bounding the welfare impacts (as opposed to exactly characterizing the welfare impacts) of a notch reform. First, because we cannot apply envelope conditions in our setting, exactly characterizing welfare impacts requires one to take a stance on which margins households are responding, the functional form of their utility function, the sorts of frictions they face, etc., whereas bounding welfare impacts requires very little structure on the household optimization problem. In fact, we prove in Appendix A.4 that the bounds in Proposition 3 are as tight as possible without making additional assumptions on primitives. Second, our bounds on the welfare impact of a notch change are expressed in terms of estimable reducedform objects, whereas exactly characterizing welfare impacts would require one to estimate a potentially large number of structural parameters.

We now turn to our empirical application: estimating the number of bunching and jumping households for the June 2014 Bolsa Fam ́ılia reform, and, in turn, calculating bounds for the welfare impact of this reform. We begin by describing the structure of the Bolsa Fam ́ılia program, our data, and the June 2014 reform.

# 3 The Bolsa Fam ́ılia Program

The Brazilian anti-poverty program Bolsa Fam ́ılia (BF) provides cash transfers to poor households based on their reported, monthly per-capita income. BF was implemented in October 2003 and is administered by the social development ministry (Minist ́erio do Desenvolvimento Social, or MDS). BF is one of the world’s largest cash transfer programs with around 14 million households receiving benefits in 2014 (Gazola Hellmann, 2015). Applicants report their information, including information on household income, expenditures, assets, socioeconomic characteristics, and demographic characteristics, to interviewers at Cadastro Unico<sup> ́</sup> agencies, which are program offices spread across Brazil
century: Gender equality, empowerment and growth_ (The World Bank).

- UNESCO. 2016 'Leaving no one behind: How far on the way to universal primary and secondary education?', _Policy Paper 27/Fact Sheet 37_ .

- Unterhalter, Elaine, Amy North, Madeleine Arnot, Cynthia Lloyd, Lebo Moletsane, Erin Murphy‐Graham, Jenny Parkes, and Mioko Saito. 2014. "Interventions to enhance girls’ education and gender equality." In.

World Bank. 2017. 'World Development Indicators '.

18
peer comparisons and adaptation to circumstances as an integral part of the “happiness function”.

In addition, there is no systematic evaluation of historical life events and current emotional state in reported life-satisfaction, except in some laboratory experiments. The importance of retrospective events on current mental state has been proposed by psychologists and economists (Easterlin, 2003; Lucas, et al., 2004; Kahneman and Krueger, 2005). Some of the recent work on life events includes Wu (2001) and Lucas (2005) on adaptation to marriage and divorce, Oswald and Powdthavee (2005) on adaptation to illness and Lucas et al. (2004) on adaptation to unemployment. Di Tella et al. (2005) find that a large share of the effect of income increases on happiness vanishes. They note that the effect after four years is only about 42% of the effect after one year. In another interesting study, Burchardt (2005) finds evidence of greater adaptation to rises in income than to falls in income. In one of the earlier works, Inglehart and Rabier (1986) documented that aspirations adapt to circumstance, such that, in the long run, stable characteristics do not affect well-being. There is lack of consensus among researchers whether the strength of adaptability depends on the nature of an event (say job loss versus marriage) or time (nothing matters in the long run)<sup>1</sup> .

There is evidence of third party evaluation on respondents’ life satisfaction to validate their answers (Diener and Lucas, 1999). Possible choices could include family members, friends or the interviewer. As documented by Kahneman and Krueger (2006), measures of temperament and personality typically account for much more of the variance of reported life satisfaction than do life circumstances, but empirical research on this issue is very scant.

In this paper we look at life-satisfaction outcomes within transition countries using Life in Transition Survey (LiTs) data for 27 transition countries<sup>2</sup>
) in addition to financial impacts, but many of these social and environmental impacts are difficult to monetize. Fourth, there is a desire to balance analytical efficiency, derived from standardization, with policy and political responsiveness, derived from selection of decision criteria.

To the last point, IPF recognizes that the selection of infrastructure projects cannot be divorced from the political economy of project selection. Particular projects may be chiefly valued by governments and other stakeholders due to key policy goals which are non‐economic in nature or due to considerations that objective indicators cannot measure, such as promoting social cohesion, honoring culture, or redistributing wealth to the poor. But selection can be responsive to policy without becoming altogether political. As such, this support framework explicitly accommodates policy responsiveness in two ways: through criteria selection and by leaving a degree of freedom in decision‐making through multiple references for judgment (i.e., two indices).

In addition to building space for political deliberation, consultation, and professional judgment, the following design ideals were incorporated, based on a survey of international best practice:

- The strategic relevance of a project must be determined at the sector level as well as within the appropriate tier of government;

9
|(DMA) used to administer water|
|---|
|across the city, with 78 units across|
|Lusaka District) and categorized as|
|relating to water quality, water|
|supply,or sewerage.|

Figure 1 shows a schematic summary of the main components of the analysis and how they interrelate. The analysis proceeded in three main stages: (i) assembly and standardization of a suite of geospatial covariates, gridded at a resolution of 100m × 100m and representing a range of environmental, sociodemographic and infrastructure covariates potentially related to Cholera risk; (ii) use of these covariates along with data on cholera case locations in a Log-Gaussian Cox Process model to generate a predicted map of underlying cholera risk across the city; and (iii) the use of a counterfactual modeling approach to evaluate and compare the possible impact of different mitigation strategies. The data and analytical steps are now discussed in more detail.

# _Study area and Cholera case data_

Figure 2 shows the study area, consisting of the 34 Wards that make up the metropolitan District of Lusaka. Data on cholera cases were obtained from the Zambian National Institute of Public Health (ZNPHI) with the study dataset consisting of the 5,444 cases reported between October 2017 and May 2018, with each geopositioned by household location.

# _Constructing covariates and assessing their linear association with cholera risk_

A suite of geospatial covariates was constructed from the data sources listed in Table 1 to include a range of factors potentially related to cholera risk and explaining some fraction of the observed spatial variation in case incidence. All covariates were defined on a spatial raster grid at 100m × 100m resolution across the study area, and fell into one of three categories, as follows.

(i) Hydrology. A digital elevation model(NASA 2015) (DEM) was used to derive the path of main river and stream channels and a raster grid created denoting distance of each grid cell from those channels. Data on reported flooding at 128 point locations across the city
indicators. By carefully revising the questionnaires, we can ensure that each LSMS‐ISA survey can serve as an instrument to monitor the SDGs. This requires, for instance, that a question is added to the 2015/16 Ethiopia LSMS‐ISA survey on ‘job attachment for those that are temporarily absent from work’ in order to correctly identify the employed population; that a question is added to the 2014 Mali LSMS‐ISA survey to capture the NEET‐rate; and that questions are added to the LSMS‐ISA surveys in Burkina Faso and Ethiopia to distinguish formal from informal employment. Aligning the surveys with the SDG indicators not only increases the value of the surveys for data users and policy makers interested in monitoring SDGs, but also for users that leverage the multi‐topic nature of LSMS‐ ISA surveys to explain labor markets. It will, for instance, facilitate cross‐country comparisons.

Adapt the questionnaires to the new ILO standards and definition of work and employment using the new module tested by the ILO. The 19<sup>th</sup> ICLS defined worked and narrowed the definition of employment to work for pay or profit. This requires distinguishing own‐use producers from market producers. In practice, this distinction is hard to make in most developing countries as the boundary between production mainly intended for own‐use or mainly intended for the market is blurred. This definition is therefore likely to remain subject to debate. LSMS‐ISA surveys collect detailed data on crops sold on the market and consumed by the household, which could be used to determine whether the household mainly produces for own consumption or for the market. Such a calculation is, however, cumbersome, requires multiple 25
15 (estimated) inventories and liquid assets, ASS 1. This is consistent with the idea that firms tend to match the maturity structure of assets and liabilities, as implied by the conventional wisdom and as predicted by the more formal model by Hart and More (1994). It is also consistent with the hypothesis that fixed assets may be a better form of collateral for long term debt, so that their availability is associated with longer maturity of debt. Finally the growth of real sales, GYREAL 1, has a positive but not significant effect on maturity.

One important issue that deserves to be investigated is whether changes have occurred over time in the determinants of access to long term credit and, conditional on having access, on the maturity structure of debt. In particular, we would like to know whether financial liberalization has introduced any change in the allocation mechanism of long term debt. We have used the longer SC2 sample to obtain some evidence on this **8** issue. In Table 11 we have reported the results obtained estimating the sample selection model for the SCI sample. After some experimentation, we have allowed the coefficients on lagged operating profits as a proportion of total assets (PROFI) and on the lagged value of the logarithm of real assets (LRTA1) to vary before and after liberalization, both in the access and maturity equation (in Table 12 LD is a dummy variable that equals one from 1989 onward).<sup>9</sup> The results suggest that the probability of having access to long term debt before financial liberalization is positively related to size and leverage and negatively related to profits. These results are similar to those obtained for the SCI 8 In order to investigate the effect of financial liberalization, it would have been desirable to be able to link the two samples in order to cover a longer data period. both pre and post liberalization. As we have already explained this is not possible.

> 9 Although financial liberalization started in 1984. it has been a
a lead firm, we are unable to distinguish between GVC and non-GVC trade in the LACEX due to its sectoral level of aggregation. Instead, we focus on important GVC sectors globally: motor vehicles, textile and apparel, electronic equipment, and food products. We assess how South Africa’s level of GVC participation in these sectors has changed over the last decade, and link this to changes in the direct and indirect labor intensity (Figure 18), labor content (Figure 19) and the skills mix (Figure 20 and Figure 21) in South Africa’s GVC exports.

South Africa’s participation in these GVC-oriented sectors has been varied. Using data from the UNCTADEora GVC database, between 2001 and 2011 South Africa’s participation in the automotive GVC— measured by foreign content embodied in South Africa’s exports as a buyer and South Africa’s content embodied in foreign exports as a seller—more than tripled and GVC participation remained the second highest for manufacturing. In contrast, South Africa remained weakly integrated in the wearing apparel GVC, and GVC participation fell to one of the lowest for manufacturing. More generally, domestic apparel manufacturing has been in decline over the past 15 years due to increased import competition, a process that accelerated significantly when South Africa acceded to the WTO’s Agreement on Textiles and Clothing in 1994 (van der Westhuizen 2006). Nevertheless, it is classically one of the most GVC-intensive sectors globally, and an important sector for unskilled labor.

A high import content of exports is a key feature of GVC participation as a buyer, and as expected the labor intensity of motor vehicle exports declined sharply in South Africa. Automotive exports contributed around $37 of domestic labor per $100 of exports in 2001, and declined to below $30 in 2011, well below that of Brazil and China, where the labor content instead increased, and India, where it remained stable. Enhanced participation in the automotive GVC—as both a buyer and a seller—fueled rapid growth
We use corporate income tax records to confirm firms’ turnover and hence their eligibility for the POS rental subsidy.

Figure A.9 shows that the number of VAT filers has increased steadily over time, with a mild slowdown in the growth rate in 2014 and 2015. There is thus no indication that the introduction of the VAT rebates motivated previously informal firms to register. Table A.3 provides summary statistics for the full sample of VAT filers, and for retail and wholesale firms, the treated and control firms for part of our analysis. Retail firms are very similar to wholesale firms in terms of the distribution of their annual sales and VAT liability, except at the top of the distribution, where wholesale firms are larger. The key distinction between the two groups is that 52 percent of retail firms used a POS terminal even in 2013, before the reform, but only 16 percent of wholesale firms did. In both sectors, POS usage increases with firm size.

# **4 Use of Electronic Payment Technology**

We begin our analysis by evaluating the impact of VAT rebates on the use of electronic payment technology. As the rebates became available to all consumers nation-wide on the same day, we examine the effect of the rebates on aggregate outcomes. We use a regression discontinuity estimation in time around August 1, 2014, when the rebates became available. In the following sections, we present our empirical strategy, the results and robustness tests.

## **4.1 Empirical Strategy**

We use the following variables to measure the use of electronic payment technology on the extensive and intensive margin: the aggregate number of card transactions, the volume of card transactions, the number of POS in use, and the number of firms with at least one POS. Figure 1, Panel A, plots the raw time series of these outcomes between January 2010 and June 2016. Some of the series, especially the number and volume of transactions, exhibit seasonal variation with peaks in December and during the spring holiday
In fact, our preferred model explains only about 25% of the out-of-sample variation in growth in mean welfare. The models using 1,000+ variables perform at about the same level or slightly better. In other words, most of the variation we are trying to predict cannot be predicted by any information we are utilizing.

Why is poverty so hard to predict by any model? We hypothesize five possible answers. First, the available features may not be well-suited to predict growth in welfare. Though we rely on predictors from different databases including remote sensing data, it is possible that the most important features are contained in other data sources, such as mobile phone data or proprietary remote sensing data. Second, it may be that other algorithms or ensemble learning might perform better. Third, this analysis has only considered data at the country level, and it is possible that models specified at the subnational level may perform better.

Fourth, the reason for the seemingly poor performance of all methods may be due to measurement error and measurement differences in welfare aggregates across countries. Issues like whether a recall or diary is used, number of consumption items asked about, the treatment of rent and durables, and differential non-response to surveys vary between and within countries and can have significant bearings on poverty rates (Beegle _et al._ , 2012; Jolliffe, 2001). This affects both poverty rates in the cross-section as well as changes. Accounting for these issues would require a detailed database on the methodology used to construct welfare aggregates, which is not currently available. Finally, the fact that the distributions of many countries have a lot of mass around the international poverty line means that even small changes to the distribution of welfare can yield large changes to the poverty rate. This makes it difficult to predict the poverty rate with great precision.

Regardless of which, if any, of these hypotheses can explain why no model is able to explain a significant fraction of the variation in the growth in welfare, a very simple model utilizing
is Emotionally Violent|0.14|0.11|-0.03**|
|Husband is Physically Violent|0.33|0.21|-0.12***|
|Makes Health Decisions|0.78|0.78|-0.01|
|Employed|0.90|0.86|-0.04***|
|Controls Own Income|0.84|0.79|-0.05***|
|Makes Household Purchase Decisions|0.78|0.79|0.02*|
|Makes Decisions About Visits|0.78|0.81|0.02*|
|Men’s Age at Marriage|22.35|21.40|-0.95**|
|No. of women married before 2002|174,226|6,104||
|No. of women married after 2002|227,593|7,518||

_Note_ : This table reports simple differences in means of the characteristics of women living in Gujarat and other Indian states using data from the NFHS-IV. The total sample represents the observations used for the age at marriage and education analysis, and the sub-sample represents observations used for analyzing women’s socioeconomic status. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

26
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 3. Road indicators, benchmarked against Sub-Saharan African fragile and low-income countries**

|Indicator|Unit|Fragile<br>states|CAR|Low-<br>income<br>countries|
|---|---|---|---|---|
|Road network density [1]|km/1000 km<sup>2</sup>of land area|145|41|132|
|GIS rural accessibility|% of rural pop within 2 km from all-season road|30|58|23|
|Classified paved road network condition [2]|% in good or fair condition|80|62|86|
|Classified unpaved road network condition|% in good or fair condition|72|2|56|
|Classified paved road traffic|AADT|843|200|1,288|
|Classified unpaved road traffic|AADT|31|14|39|
|Primary network overengineering|% of primary network asphalted with 300 AADT or less|47|61|30|

_Source:_ AICD Road Sector Database. [1] Total network includes the primary, secondary, and tertiary networks. [2] Classified roads are those that have been included in the roads legislation as public roads. GIS = geographic information system; AADT = average annual daily traffic; the CAR = Central African Republic.

The CAR relies heavily on its regional corridors for the efficient movement of goods and people, but its neighbors are not prioritizing the maintenance of their portions of the corridors. The Douala-Bangui and Point Noire–Brazzaville–Bangui corridors are still not completely paved. Some sections of the Douala-Bangui corridor (about 250 km in Cameroon and 210 km in the CAR) are being upgraded as part of the Economic and Monetary Community of Central Africa (CEMAC) Transport Transit program. Meanwhile, all of Cameroon’s segment of the Point Noire–
# Introduction

## The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Uganda. The results have been presented in reports covering different areas of infrastructure—information and communication technology (ICT), irrigation, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Uganda over the period 2001 to 2006, allowing the country’s infrastructure situation to be benchmarked against that of its African peers using data collected over the same period, and providing a baseline against which to assess Uganda’s subsequent progress in the infrastructure sectors. Uganda is a low-income state that has recently discovered oil; therefore, both low-income and resource-rich benchmarks will be used to evaluate its performance. Detailed comparisons will also be made with immediate regional neighbors in the East African Community (EAC) in some cases.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2003 to 2009. Most technical data presented are for 2009 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Data on benchmark countries, however, typically relate to the somewhat earlier period 2001 to 2006. Second, to make comparisons across countries, it was necessary to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

## Country context

Despite conflict, political unrest, and various geographical disadvantages, Uganda has sustained relatively high and robust growth rates over two decades. Annual gross domestic product (GDP) growth rates averaged 7 percent in the 1990s and accelerated to more than 8 percent over the first
Williamson 1979; Grossman and Hart 1986). An alternative to training an entrepreneur to develop finance and marketing skills herself is to hire someone who already has these skills, either as an employee within the firm (insourced worker) or as an outside service provider (outsourced professional). Figure 1 uses application data from more than 8,000 Nigerian firms who applied to government programs to show how the likelihood of an entrepreneur doing the finance or marketing activities of the firm varies with the number of workers in the firm. Although the entrepreneur is the main person in charge of financial accounts and marketing when the firm has only a couple of workers, by the time firms reach 10 to 20 workers, half of them have someone other than the entrepreneur implementing these business practices. This suggests that if the goal is to help small firms grow, rather than training entrepreneurs in finance and marketing, it may be 2