rafmacalaba/data-use-mentions-extended huggingface dataset:
a $1 increase in cash flow is associated with reduction in short-term debt of 4.5 cents, increase in long-term borrowing of 7.6 cents, increase in equity issuances of 13.9 cents and increase in share repurchases of 4.3 cents. In 2008, a $1 increase in cash flow is associated with increase in equity issuances of 9.8 cents, increase in share repurchases of 8.8 cents and decrease in other financing of 1.7 cents for manufacturing firms. For non-manufacturing firms in 2008, $1 increase in cash flow is associated with increase in long-term debt of 6.5 cents, decrease in short-term debt of 6.1 cents, increase in equity issuances of 10.2 cents, decrease in other financing of 1.2 cents and increase in share repurchases of 3.8 cents. In 2009, the non-manufacturing firms reduce their short-term debt by 4.5 cents, increase their equity issuances by 10 cents and reduce share repurchases by 3.2 cents while manufacturing firms reduce short-term debt by 3.7 cents and increase equity issuances by 6 cents. During the recovery period in 2010, manufacturing use their positive cash flow shocks to repay long-term debt (10 cents), repay short-term debt (2.7 cents) and issue new equity (15.6 cents) while non-manufacturing firms use their positive cash flow shocks to repay short-term debt (2.3 cents), reduce other financing (1.7 cents) and issue new equity (6.9 cents) without any significant change in their long-term debt positions.

As explained before, the interpretation of the coefficients in the negative cash flow sample is such that a negative sign indicates an increase and a positive sign indicates a decrease.

Focusing on the firms facing adverse cash flow shocks in B1 and B2, we find that both manufacturing and non-manufacturing firms have increases in long-term debt and equity issuances over the entire period. They also have substantial addition to their cash balances and increases in capital expenditures
Harttgen (2009) model implemented on the household survey. The predicted rates and the rankings by department produced by both methods are comparable, providing confidence in the robustness of the small area application to vulnerability (figure 2).

**Figure 2. Vulnerability estimates of survey implementation using two methods, by department**

<!-- Start of picture text --> 1<br>Tambacounda<br>0.9 Kaffrine<br>Kolda Sedhiou<br>0.8<br>0.7 Saint-Louis Louga Fatick Matam<br>0.6 Diourbel<br>0.5 Thies Ziguinchor Kedougou<br>Koalack<br>0.4<br>0.3<br>0.2<br>Dakar<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Vulnerability rate (%)<br>Survey Günther and Harttgen method<br>Vulnerability rate (%)<br>Poverty Map estimates<br><!-- End of picture text --> _Sources:_ Calculations using EHCVM (Enquête Harmonisée sur le Conditions de Vie des Ménages 2018–2019; Harmonized Survey on Household Living Standards 2018–2019), Living Standards Measurement Study, World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/4292; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics and Demography, Dakar, Senegal, https://www.ansd.sn/enquete-et-etude/recensement-general-de-la-population-et-de-lhabitat-delagriculture-et-de-lelevage.

_Note:_ A household is considered vulnerable if it exhibits a probability of falling into poverty above 29 percent in one year (50 percent in two years
an electric grid by 10 km marginally increases employment by 4 percentage points in the younger age group and 15 percentage points in the older age group in the LSMS sample; the corresponding estimates for the DHS sample are 9 percentage points for the younger age group and -2 percentage points for the older age group. The joint effect of roads and electricity is positive and statistically significant for both age groups in the LSMS sample, with a greater magnitude for the older group. By contrast, in the DHS sample, the effect is significant only among those in the younger group.

The OLS results are reported in Table 13 and largely mirror the findings from the IV results, with a few differences. However, overall, both the OLS and the IV estimates show that expanded access to roads and electricity affect employment mostly among young and middle-aged individuals. These findings therefore suggest that younger individuals have a greater ability to benefit from the expansion of these types of infrastructure, most likely because people in these groups can more easily change occupations.

# **7 Conclusions**

Using two sources of geo-referenced household surveys and spatial data on road and electricity expansion in sub-Saharan Africa, we analyze the average and heterogeneous impacts on job creation that stem from investments in roads and electrification. We examine the impacts that stem from expanding access to road networks and electric grids, both in isolation and in combination. We find that significant, positive complementarity effects emerge between both types of investments. The positive impacts are heterogeneous across countries, locations, gender, and age groups. The gains from investing in roads and electricity grids tend to be greater in more-advanced economies, where the complementarities are greater. Within countries, the benefits of expanding the reach of such infrastructure differ between urban and rural areas. Employment gains from roads accrue to a greater degree in rural areas. Moreover, the complementarity between the two types of infrastructure is much stronger in rural areas. At the same time, the expansion of such infrastructure leads to a decline in low-skilled employment,
3.5 Economic evaluation and the World Health Survey The numerous problems involved in measuring the impact of investments in health have not been presented to dismiss the need for such evaluation, but to identify areas where the WHS could contribute most to improving methodology. It would obviously be best to focus on areas where amelioration is most possible and likely to result from the WHS.

Comparing investments in health with investments in other sectors is beyond current methodology and seeking to improve CBA techniques is not a top priority. As noted, the problems of measurement of health impact and valuation undermine the use of CBA in the health sector. Even if the WHS focused on some valuation issues (eg.tracing the labour time benefits of health programmes through data about the relationship between health and available labour at the household level), other problems of valuation and of measurement would still remain.

The key measurement problem for CEA (and CBA) of demonstrating the link between investment and health outcome is difficult to address in the context of a world health survey. Improvements in methodology could not be generated by data collection using standardised survey questions, as they could not hope to capture the differences between countries which have a direct bearing on the health impact of investment. Even if the survey's questions were made countryspecific, the detailed research required to investigate this link could only feasibly be undertaken within the context of the WHS for a few interventions with clearly definable coverage and for which confounding variables can be controlled for statistically at the individual or household level. Certainly controlled intervention trials are not needed routinely. Indeed, if the benefits of the intervention were well established, this would be ethically questionable. It is more feasible to organise such evaluations as 'ad hoc' activities tailored to the nature of the intervention and circumstances in which it i, made and taking advantage of opportunities for the application of innovative methods such as case control studies. It would be more appropriate to undertake such 21
factor taxes. The data for import tariffs are replaced with collected tariff rate data for the year 2006.

Given that Tanzania participates in preferential trade areas with the East Africa Customs Union and the South African Development Community, it was necessary to make further adjustments. That is, since, in principle, tariff rates should be zero within these preferential trade areas, we set tariff collections on imports from SADC and EACU (our Africa region) at zero. We

> 19 For the year 2006, aggregate data from Tanzania show that tariff collections are 1.47 percent of GDP.

33
The remainder of the paper is as follows: Section 2 briefly summarizes existing literature on list randomization; Section 3 describes the list randomization question used in our study and the four surveys in which it was added; Section 4 gives our main results; and Section 5 provides discussion and conclusions.

# **2. List Randomization**

List randomization (also referred to as list experiments, the item-count technique, and the unmatched count technique) was introduced by Miller (1984) as a way of eliciting information about sensitive behaviors (in her case, illegal drug use). It has been used in a number of papers in sociology, political science, and psychology to measure incidences of sensitive behaviors as well as attitudes that might otherwise be misreported because of social desirability pressures.

The use of this method typically leads to higher reports of sensitive behaviors than is obtained through direct questioning. For example, LaBrie and Earleywine (2000) find college students to have higher incidences of having had sex without a condom, and having had sex without a ‐ condom after drinking via the list randomization method than in an anonymous self report survey; Gonzalez-Ocantos et al. (2012) use it to find higher incidences of people reporting they had been promised a gift or service in exchange for their vote in elections in Nicaragua; Tsuchiya et al. (2007) find a higher proportion of shop-lifters than by direct questioning; and Wimbush and Dalton (1997) find higher incidences of reports of employees engaging in theft. This technique has seldom been used in economics, but a recent exception is Karlan and Zinman (2012) who find microfinance clients to be more likely to report spending their loans on things other than business uses when asked via list randomization than through direct response. In contrast, actions and behaviors which have not had social stigma or illegality associated with them have resulted in similar estimates via list randomization as from direct elicitation. Examples include asking college students whether they have gotten drunk (LaBrie and Earleywine, 2000) and asking about
# **2.2 Foreign aid**

We first construct a project-level database of aid disbursements from the World Bank through its two principal institutions, the International Development Association (IDA) and the International Bank for Reconstruction and Development (IBRD). From the World Bank Project Database (so-called API data), we obtain information on approval date, commitment amount, sector and instrument type for each project. We combine this dataset with monthly project-level information on disbursements from Kersting and Kilby (2016).<sup>7</sup> Next, we draw on this database to construct our main aid variable, _Aidit_ , which aggregates disbursements from the World Bank across all projects in a given country _i_ in a given quarter _t_ . By construction, this variable does not include aid from other sources such as humanitarian assistance and development aid from individual countries. It also excludes debt relief. The main reason to focus on aid from the World Bank is that we observe the timing of disbursements within the year, which is important for empirical identification because the main effect shows up at quarterly frequency. Data on other sources of aid, including the leading aggregate measure of development aid Official Development Assistance (ODA), is typically available only at the annual frequency. Further, the fact that Word Bank disbursement data is available at the level of individual projects allows us to implement an instrumental variables strategy (see below).

Our main sample consists of the 22 countries that, on average over the sample period 19902010, receive annual disbursements from the World Bank equivalent to at least 2% of GDP.<sup>8</sup> As shown in Table 1, annual aid disbursements from the World Bank are almost 3% of GDP on average whereas development aid from all sources exceeds 10% of GDP on average. Foreign aid is thus a major source of income within this sample. The distribution of our main explanatory variable, the ratio of quarterly aid disbursements from the World Bank to annual GDP, is displayed
online appendix

# **A.1.2 EBM data construction**

Our analysis of the EBM data is built on a dataset of prices and quantities at the buyer sub-District-by-firm-product-by-month level. We construct the following outcomes from this data:

**Product description** Each item in a receipt is accompanied by a free-fill description entered by the firm (e.g., “Coca-cola 1.5l”). Firms enter these descriptions to EBM II software when they receive new stock. EBM II subsequently prints the description of each item at the point of sale.

**Value and quantity** To account for cancellations in the data, we aggregate over values and quantities of receipt items on the same receipt, with the same item description, the same price and the same product code. For the example in Figure A1 this creates a quantity and value of the third and fourth item will be 1 + -1 = 0 and 1800 + -1800 = 0, respectively. We remove products from our dataset which were voided or cancelled.

**Price** We take the following steps to improve measurement of prices. First, as described in Section 3.1.2, we employ firm-product fixed effects in all analysis of prices to ensure that we are appropriately comparing mask prices adjusting for quality differences across firm-products. Second, we note that the units in which retailers report a sale of masks within firm-product are sometimes imperfectly measured in the data. We therefore winsorize prices within firm-product at the 5th and 95th percentiles.

We identify products using string descriptions. To identify masks and paracetamol, we string match masks and paracetamol, respectively, in English, French, and A6
/sup> and its representativeness. However, a more conventional sampling frame would not have permitted us to collect sensitive information like the incidence of wifeabuse, as we would not have established the level of trust necessary to gather such information with reasonable accuracy.

**14** The model suggests that some of these women are in bad "separated" marriages where links between the husband's and wife's families have been severed, but where violence is absent. NVhile we have anecdotal evidence from a sub-sample of 40 interviews suggesting that about fifteen per cent of marriages fall into this category, the survey instrument did not include questions on the quality of the links between the two families.

**_15_** The questions on the extent and severity of violence were answered by only 70 per cent of the sample. Even when the questions were answered the responses had strong interviewer effects with some interviewers being able to elicit more complete and consistent responses than others. On other the hand, the dichotomous question on whether a woman had ever been physically assaulted by her husband was answered by all the respondents without significant interviewer effects.

**16** This sample size is comparable to the widely analyzed ICRISAT sample, the core of which consists of 120 households from three villages.

17
Second, while price indices present equilibrium outcomes of housing markets, they do not cover details about, broadly, the quantity of housing. They often only include a particular type of housing for which prices are tracked. Whereas in high-income economies, the latter may remain relatively stable in the short term, in EMDEs, with rapidly expanding formal housing markets, quantity and type of housing are important elements to capture. They provide context to changes in prices as the sample over which prices are indexed changes, and as price and quantity and type of housing supplied are highly interrelated. Also, they have important policy implications in the context of markets’ ability to supply homes for different market segments, and formal developers’ ability to go reach lower income groups. The measurement of these dimensions of the housing markets is absent across EMDEs.

Third, primary data collection for actual, non-indexed house prices is still somewhat limited and, if available, almost exclusively covers advanced economies. With a few notable exceptions, there is a severe lack of contributions in the literature on property price estimations in EMDEs. Property price data for EMDEs are virtually non-existent – both for within-country assessments, and even more so for crosscountry comparison. Most studies on house prices obtain data from readily available sources such as land registries, real estate agencies, or commercial banks, or tap into established indices. Given the significant effort to collect original data on house prices, there are very limited efforts. Since the scope and focus of these studies differ or as they purely rely on price indices, comparing property prices across studies is not feasible.

Fourth, efforts to investigate property prices in EMDEs mainly converge to analyze the determinants of _mean_ house prices. Distributional efforts in property price collection for different income segments within emerging economies are largely absent. CAHF is unique in its effort to approach the house price estimation from the perspective of low-cost developers. Yet, CAHF takes it to the other extreme. It only collects the _cheapest_ price of a house built by a formal developer in
6 better in terms of the number of items, separating outpatient and inpatient care, and the appropriateness of the recall period.

## 2.3. <u>Survey changes</u>

For the 2018 HEFPI database, 1,707 surveys (or adaptations thereof) were analyzed and 575 were retained in the data set covering 142 countries. For the 2019 database, 1,846 surveys were analyzed and 650 were retained covering 149 countries. Of the 650 surveys retained in the 2019 database, 120 were not in the 2018 database. Not all the 575 surveys (or adaptations thereof) retained in the HEFPI 2018 database were retained in 2019 database: 46 were replaced by different ones. This was based in part on comparisons between the old and new surveys of the quality-check indicators outlined in Wagstaff et al. (2018). Sometimes, however, the decision was based on other factors: for example, we learnt that the public release of the US Consumer Expenditure Survey is top-coded which is an issue given we are interested in especially large out-of-pocket expenditures. We also realized that our estimates had not exploited the panel nature of the data set. We ended up favoring instead our (new) estimates from the US Current Population Survey.

# **3. Health Equity Indicators**

On the health equity side, the 2019 HEFPI database includes 9,930 data points – 197 points more than the 9,733 points in the 2018 version. This increase is the result of two major counteracting enhancements of the database: on the one hand, we added 535 entirely new points, of which 493 come from the addition of all available fifth and sixth wave surveys of the MICS and several new DHS; on the other hand, we conducted a major quality review of all data points for eight health equity indicators for which we examined the country time-series and dropped 338 previously included outlying points for which we could not find a policy or epidemiological explanation.
al. (2024) provided more current evidence on AI use, which includes generative AI, based on the Business Trends and Outlook Survey (BTOS). They found that the AI use rate rose from 3.7% in September 2023 to 5.4% in February 2024, with an expected rate of 6.6% by early fall 2024. AI users often utilize AI to substitute for work tasks and equipment or software, though few firms report reductions in employment due to AI use yet. IBM Global AI Adoption Index 2023 is among the few studies assessing firms’ AI adoption around the world. Globally, 42% of enterprises with more than 1000 employees reported having actively deployed AI in their business. India (59%), the United Arab Emirates (58%), Singapore (53%), and China (50%) are leading the way in AI usage. Companies in the financial services industry, industrial equipment, and telecommunications are most likely to be using AI.

While most firms are still trying to figure out how AI can add value for their businesses, individuals and employees have been embracing the technology much faster. Pew Research Center has conducted two rounds of surveys among US adults to shed light on ChatGPT usage by individuals. By Feburary 2024, 23% of US adults report ever using ChatGPT, up from 18% in July 2023. Users are concentrated in those aged 18-29 and highly educated. Most users use ChatGPT for tasks at work, learning, and entertainment (McClain 2024). The 2024 Work Trend Index Annual Report by Microsoft and LinkedIn highlights that 75% of knowledge workers in 31 surveyed economies use AI at work in 2024. Four out of five AI users bring their own AI tools to work, and this is even more common at small and medium-sized companies. Individuals in administrative and support services, real estate and retail industries report the highest generative AI adoption. Reported generative AI usage is the highest in India, Thailand, Indonesia and China (above
index (Calderón and Servén 2010; see Timilsina, Hochman, and Song 2020 for a review). We follow this approach and combine the logs of phone lines, mobile phones, internet connections, electricity access, and secure internet connections, all taken from the WDI.<sup>14</sup> Each of those five infrastructure variables is first Pearson standardized to a sample mean of 0 and a sample standard deviation of 1. Thereafter, they are weighted by the number of available original observations in each year, divided by the total number of original observations from all five series in the same year. For example, if 150 original observations for log phone lines were available in 1970, and 50 observations were available for electricity, the former gets a weight of 3/4, and the latter gets a weight of 1/4, while the remaining three infrastructure variables are not part of the composite index in 1970. This weighting reflects two aspects: statistical reliability and economic relevance. Fewer observations of a variable for a given year mean more predicted values for that variable. Statistically, less weight should be attributed to the variable in this case, as predicted values are less reliable than original observations. Having fewer observations also reflects that the indicator has less relevance. For example, secure internet connections or mobile phones were simply not available or not as important in the 1970s; hence, their availability is not recorded in the data. This constitutes an economic reason for attributing less weight to series with fewer original observations.

# 3.2.5 Political violence

We include data on major episodes of _political violence_ from Systemicpeace.org. The variable _actotal_ in the original data set is the total summed magnitude of all the societal and interstate episodes of political violence, with the magnitude of each episode ranging between 1 (lowest) and 10 (highest).<sup>15</sup> The link from conflict, political violence, and fragility to growth is well established (e.g.,
ECCAS<br>Figure 2.1a. The condition of ECCAS‘s regional road network|6<br>8|
|Figure 2.1b. Traffic on ECCAS‘s regional road network<br>Figure 2.2. ECCAS‘s main regional road corridors|9<br>12|
|Figure 2.3. Time required to import goods from Douala to Ndjamena via road and rail<br>Figure 2.4. Cost of importing goods from Douala to Ndjamena via road and rail|15<br>15|
|Figure 2.5. Time required to import goods by road through alternate gateways*|16|
|Figure 2.6. Cost of importing goods by road through alternative gateways|17|
|Figure 2.7.ECCAS‘s regional railway network|19|
|Figure 2.8. Traffic densities on African railways|20|
|Figure 2.9.Ratio of current demand to reported capacity|22|

iii