rafmacalaba/data-use-mentions-extended huggingface dataset:
The analysis presented here is based on a database put together as part of the Africa Infrastructure Country Diagnostic (AICD).<sup>2</sup> The database includes the basic institutional characteristics of African power systems as well as standard power sector indicators (performance, capacity, and so on). In addition the database documents the power-tariff regimes of 27 Sub-Saharan African countries in detail.<sup>3</sup> Together, these nations account for over 85 percent of the population and gross domestic product (GDP) of the region. They were carefully selected to represent the economic, geographic, cultural, and political diversity that characterizes Sub-Saharan Africa. They also represent the four Sub-Saharan power pools; include countries with small, medium, and large-scale generation; and constitute a representative mix of predominantly thermal and/or hydro power systems. As such, the sample can be considered a statistically representative basis for inferring tariff-setting trends in Sub-Saharan Africa.

The tariff data set includes one published tariff regime for each country in the sample. Results presented here are based on the latest published tariff regime available for each country during the AICD data collection period (2003–08). For most countries that year was 2006 (see annex 1).

Using the AICD database, we seek to characterize African power tariffs by (i) describing prevalent tariff structures, both residential and nonresidential; (ii) analyzing their ability to recover costs; (iii) assessing their economic efficiency against long-run marginal costs; and (iv) exploring their equitability and affordability vis-à-vis country-specific purchasing power.

# **What power tariff structures are prevalent in Sub-Saharan Africa?**

Most electricity tariffs—and Africa is no exception—are based on block tariff-pricing schemes; that is, the price of power is linked to the level of consumption.

Power tariffs are commonly structured around blocks. A block is a pre-determined range of power consumption; with the unit price of each kWh being fixed within the block. The relation between blocks and prices
insights on the distribution of property prices.<sup>5</sup> Despite these shortcomings, this database currently offers the most comprehensive data series on house prices.

Second, the International Comparison Program (ICP 2011), which collects prices for a range of goods and services that make up final consumption expenditure and gross capital formation, also captures housing expenditures. The ICP survey collects annual rental prices and dwelling stock data. Rents are either captured as actual or imputed rents (World Bank 2020). In the most recent ICP cycle (2017), participating economies collected rental data for 21 different dwelling types, ranging from one-bedroom apartments to single-family homes.

Third, a more regional-focused data source on property prices is provided by the Organization for Economic Co-operation and Development (OECD), which publishes nominal residential property price indices for OECD countries, as well as price-to-rent and price-to-income ratios.<sup>6</sup> The database particularly focuses on house price developments across regions and cities within countries to capture spatial price variation. For select countries, OECD also offers the number and value of housing transactions. While insightful for advanced economies, this database does not cover any emerging economies and mostly publishes indexed data to track price changes over time.

Fourth, institutions such as the International Monetary Fund (IMF) or the United States Federal Reserve Bank collate property price data from various national sources. IMF’s Global Housing Watch platform, for instance, tracks developments in housing markets across the world on a quarterly basis.<sup>7</sup> The database collates property price data from different sources (e.g., BIS, European Central Bank, Federal Reserve, and national source) for 63 countries – mostly advanced economies – to assess valuation in housing markets. Further, it provides metrics such as price-to-rent and price-to-income ratios. Similarly, the Dallas Federal Reserve Bank’s International House Price Database publishes quarterly house prices for 25 mostly developed economies by drawing on national public sources primarily from central
# **3 Appendix F: Follow-Up Results**

Table (F1) Impacts of the Program on Primary Outcomes

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Housing Quality Index|0.09|(0.19)|[0.47]|-0.01|(0.99)|1,321|
|Log total consumption (USD PPP)|-0.05|(0.10)|[0.47]|4.54|(0.78)|1,316|
|Depression (CESD, std)|0.26*|(0.15)|[0.13]|-0.01|(1.00)|1,285|
|Child SDQ score, std|-0.56***|(0.17)|[0.01]|-0.01|(1.00)|859|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Housing quality index is defined in table 2. Total Consumption is defined in table 3. Depression is defined in table 7 (higher here reflects more depression). Child SDQ score is defined in table 8 (higher here reflects more socioemotional difficulties).

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile,
triggering and aggravating East Asia's financial crisis, were thus already in existence in the early 1990s.

# **2. Data**

The data come from annual reports of the companies listed on the major stock exchanges in the region and come from Worldscope and Extel databases. The datasets are unbalanced, i.e., the number of observations varies from year to year. We have excluded companies, which report data less than three times over the period 1988-96. We have also excluded financial and banking institutions (SIC6000-6999). Finally, in any given year, we exclude companies which do not include all of the following variables - net sales, net income after taxes, cost of goods sold, total assets, and the value of common equity. The data set consists of 588 companies in Hong Kong, 317 companies in Indonesia, 2526 companies in Japan, 392 companies in Korea, 772 companies in Malaysia, 170 companies in Philippines, 348 companies in Singapore, 265 companies in Taiwan, and 564 companies in Thailand.

Several caveats apply to the data. First, the statistics we report do not attempt to correct for cross-country differences in industrial structure. If a country data set has many utility firms, for example, average leverage might be higher and profitability lower. A forthcoming companion paper breaks down the sample into sectors (based on two-digit SIC codes) to provide a more accurate comparison of company performance across countries. The data also cover mainly large firms-the median size of the _5550_ firms is 4273 employees, with the largest company employing more than 150,000 employees. This selection pattern arises since firms have to be listed on a stock exchange in order to enter the database, and listed companies tend to be large. The bias towards larger companies may be problematic if one were studying the effect of the Asian financial crises on the corporate sector. It does not pose a problem here, since we focus on the years preceding the crisis, when (as critics argue) large companies were at
On the other hand, flood in July assist sowing of wet season rice by watering the fields (Islam et al 2004, Quasem 1992). Flood in August may wash away transplanted seedlings and increase the demand for labor when the water recedes and farmers replant their fields (Ahmed et al 2001). The timing of normal flood may increase or depress agricultural labor demand; Banerjee (2007) finds long run wages are higher in the flood prone areas. While normal floods may increase agricultural productivity and wage employment opportunities, heavy flood such as those in 1998, 2004, and 2007 have immediate devastating effects on the households.

It is clear from the discussion above that flood water in the flood prone Upazilas is a substitute for monsoon rain during the planting season. As a result, Households in flood prone Upazilas are not as dependent on rain for their crop cultivation as compared with households in non-flood prone Upazilas.

# **3. Data**

This study combines data from several sources such as Bangladesh Household Income Expenditure Survey of 2010 (HIES 2010), historical rainfall recorded at weather stations, rainfall data generated by Climate Research Unit (CRU) of the University of East Anglia, and flood data from Bangladesh Water Development Board. The HIES 2010 was a nationally representative survey undertaken in the 12 month period between January 2010 and January 2011. The original survey includes both rural and urban areas; however, we focus on 7,840 rural households from 333 Upazilas. **Map 1** shows the location of the Upazilas included in the HIES 2010 survey.

The HIES 2010 collected detailed information about the economic activities of each members of the households it surveyed along with other household level information. To get to the sectoral focus and diversification of household members, we focus on the primary economic activity of working age (10-65) members reported for the past 12 months. Where the members reported multiple economic activities, the highest total man-hour for the economic activity was used to arrive at the primary economic activity of the
work carried out by dozens of firms in multiple countries. Because there is less need for co-location of engineers than in other technology-intensive sectors, it is relatively easy for ICT firms to engage in the twin strategies of outsourcing and offshoring. As a result, international sourcing is common. Business functions can be separated and

> 5 Local production in autos is also driven by high product variation, the heavy and bulky nature of some components, and the low inventory demands of “lean production” practices. 6 U.S. Bureau of Labor Statistics Current Employment Statistics program, <u>http://www.bls.gov/data/#employment, accessed Janurary 15, 2010.</u>

4
not necessarily causal. However, they motivate the possibility that informal redistributive arrangements could dampen labor supply levels.

**Figure 1:** Motivational Evidence: Redistribution and Hours Worked

<!-- Start of picture text --> Slope = -7.48<br>Slope = -4.17<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Provided any money to family or friends Provided any money to family or friends<br>Country-region-ethnicity-level regression. N = 1010 Region-ethnicity-level regression. N = 338<br>Source: LSMS surveys; y-axis is total hours worked among adults aged 18-65 in a household Source: LSMS surveys; y-axis is total hours worked among adults aged 18-65 in a household<br>(A) West Africa (B) Côte d’Ivoire<br>Slope = -1.91 Slope = -8.58<br>.4 .6 .8 1 0 .02 .04 .06 .08<br>Provided any economic support to family or friends Received any money from family or friends<br>Municipality-ethnicity-level regressions. State-race-year-level regressions. N = 1074<br>Source: IFLS Wave 5, adults aged 25-55 Source: SIPP 2014-2020, adults aged 25-55<br>80 75<br>70<br>75<br>65<br>60<br>70<br>55<br>Average hours worked per week Average hours worked per week<br>65<br>50<br>42<br>42<br>41<br>40<br>38 40<br>Average hours worked per week Average hours worked per week<br>36 39<br><!-- End of picture text -->

# **(C)** Indonesia

# **(D)** United States

_Notes:_ Binscatter plots of the relationship between transfers in all
proportions were equal to that of the population. The population weights for a tender in a particular stratum equals the ratio of the population of that stratum over the number of tenders collected from that stratum.

66. The standard errors of coefficients are calculated using bootstrapping (500 samples with replacement). The matching strategy and frequency weights have been incorporated into the bootstrapping design through the selection of the bootstrapped samples, where the probability of selection into the bootstrap sample is equal to the product of the matching and the frequency weights.28 Bootstrapping is preferable to traditional standard error calculations given the complex nature of our sample with e-GP data representing the full population and our sample of manual samples oversampling smaller PEs. (For a methodological description of bootstrapping methods, see for example Good, 2006 or Carpentel & Bithell, 2000.) 67. Data collection also included contract implementation information, however following a somewhat different procedure. While for all manual tenders sampled, we also collected contract implementation information, electronic tenders had to be sampled for additional data collection because electronic records did not contain information on contract implementation. A sample of about 1,500 tenders was selected from a population of about 6,500 electronically administered tenders from FY13-14 for data related to contract management indicators. A similar approach was followed for weighting the electronic tenders for which contract management information was collected as described above, that is the weighted tenders were representative of the population.

# 6. RESULTS

# 6.1 MAIN EFFECTS

68. First, we examine hypothesis one (H1) which posits that e-procurement improves access to public tenders (Table 3). Across the different models, we find consistent support for H1, using three different proxies for open access. The models have generally high albeit varying explanatory power (R2 falls between 15 percent and 38 percent, with logit models having generally lower explanatory power). In Models 1a (within PE matching) and 1b (within year matching), we find that there is a statistically significant, large impact of e-procurement on
- Hopenhayn, H. (2016): “Firm Size and Development,” _Econom ́ıa Journal_ , 17, 27–49.

- Hopenhayn, H. A. (1992): “Entry, Exit, and Firm Dynamics in Long Run Equilibrium,” _Econometrica_ , 60, 1127–1150.

- Hsieh, C.-T. and P. J. Klenow (2009): “Misallocation and manufacturing TFP in China and India,” _The Quarterly journal of economics_ , 124, 1403–1448.

- Hsieh, C.-T. and B. A. Olken (2014): “The Missing “Missing Middle”,” _Journal of Economic Perspectives_ , 28, 89–108.

- IBGE (2019): “Pesquisa Nacional por Amostra de Domic ́ılios Cont ́ınua,” .

- INDEC (2019): “Encuesta Permanente de Hogares, EPH,” Instituto Nacional de Estad ́ıstica y Censos, Rep ́ublica Argentina.

- INE Paraguay (2019): “Encuesta Permanente de Hogares Continua, EPHC,” Instituto Nacional de Estad ́ıstica, Paraguay.

- INE Uruguay (2019): “Encuesta Continua de Hogares,” Instituto Nacional de Estad ́ıstica, Uruguay.

- INEC (2019): “Encuesta Nacional de Hogares, ENAHO,” Instituto Nacional de Estad ́ıstica y Censos, Costa Rica.

- INEGI (2018): “Encuesta Nacional de Ingresos y Gastos de los Hogares,” Instituto Nacional de Estad ́ıstica y Geograf ́ıa, M ́exico.

- INEI (2019): “Encuesta Nacional de Hogares, ENAHO,” Instituto Nacional de Estad ́ıstica e Inform ́atica, Per ́u.

- Karabarbounis, L. and B. Neiman (2013): “The Global Decline of the Labor Share,” _The Quarterly Journal of Economics_ , 129, 61–103.

- Korea Labor Institute (2019): “Korean Labor & Income Panel Study, KLIPS,” Korea Labor
More recent rounds of the DHS also include questions on decision making and attitude toward domestic violence. The decision making questions include whether or not women are involved in decisions on their own health care, purchases, and visits to family. The decision making index is the sum of all the decision making items in which the woman is involved individually or jointly. A higher index suggests higher decision making power for women. The questions on attitude toward domestic violence include whether it would be justified for a husband to beat his wife if she goes out without telling her husband, neglects the children, argues with her husband, refuses sex, and burns the food. The index for the attitude toward domestic violence is the sum of all the items with which the woman agrees. A higher index suggests a higher acceptance of domestic violence.

# **2.2 Empirical strategy**

## **2.2.1 Estimation**

We use an event study approach based on sharp changes in women’s labor market outcomes observed around the time of marriage, _t_ = 0, for individuals at age _a_ observed in calendar year _y_ . Ideally, the estimation uses individual-level panel data, in which case relative time indicators for marriage and childbirth can be included simultaneously, along with individual and year fixed effects, in a single event-study regression where labor market participation is the outcome Kleven et al. (2023).

However, in the absence of such data, following Kleven (2022), cross sectional data can be used to create pseudo-panel data. For married individuals, we observe the age of marriage, and therefore their location the post-marriage event space, _t ≥_ 0. For unmarried individuals, we cannot observe their timing of marriage, and therefore only observe that they are in the pre-marriage event space, _t <_ 0, but not their precise location in this space. The pseudopanel approach matches married women to unmarried women with the same demographic characteristics to form a pre-marriage counterfactual estimate of the outcome, transforming
numbers in the last two columns indicate the ratios of the respective business- to economy-class footprints.

These “relative footprint” values are somewhat lower than their respective s values (as result of the weighting of the space and weight factors related to individual travelers, with relatively more weight to the former factor), but not dramatically lower. Another finding is that the footprint of economy-class passengers needs to be lower, and for business-class passengers higher, than the overall average footprint per traveler. This is also seen from the table; albeit the reduction (below unity) is small for economy-class (since this class constitutes a very large fraction of overall air travel in terms of traveled distance).

Our argument that weight-related arguments should make average footprint numbers less variable across travel classes, depends on passenger weight (including all weight factors that depend on individual passengers such as their luggage, and their seat) being independent of travel class on average. This is likely not the case. First, seats are heavier in premium classes, more so in first class than in business class. Secondly, passengers in premium classes are allowed to carry more luggage and may thus do so (although we do not have data for the average weight of passenger luggage by class). Thus, most likely, the impact of this factor will in practice be smaller than the numbers reflected in our calculations in this chapter. Put otherwise, the correct relative footprint numbers are closer to being proportional to space allocation, than those shown in Table 3.1.

# **4. Calculations of Relative Space Requirements by Travel Class**

# **4.1 Step 1: Relative Seat Configurations**

We will now go to the more practical footprint calculations, in part with basis in data for WBG travel in Calendar Year 2009 (CY-09). In calculating the carbon footprint from a given amount of air travel one first needs to find the class-specific per-passenger footprint. As noted, seat configurations in airplanes differ between travel classes, with premium-class
## _4.3 Could We Use a Bunching Estimator to Bound the MVPF?_

Our empirical strategy amounts to estimating the reduction in the mass of households bunching at the old notch and the increase in the mass of households bunching at the new notch as a result of the reform using a difference-in-difference strategy. But one may wonder whether we could have used standard bunching techniques (developed in Kleven and Waseem (2013) and Saez (2010)) to back out the changes in these masses. Of course, even using standard bunching techniques, one must account for time variation, i.e., one must estimate how many people would have bunched at the old notch and at the new notch in the post-reform period had the reform not occurred. As an example of how one might use standard bunching techniques while also controlling for time variation, one could first use these techniques to back out the counterfactual reported income distributions that would result if there were no notches in both the pre- and post-reform period (e.g., in June 2014 and June 2016). To do so, one would use the observed reported income distributions in the pre- and post-reform periods and make the following two assumptions (which are standard in the bunching literature): (a) the reported income distribution would be smooth in absence of the notch, and (b) the reported income distribution sufficiently far away from the notch is unaffected by the notch. One could then assume that the relationship that exists between the counterfactual and the observed reported income distributions in the pre-reform period would have stayed the same in the post-reform period in absence of the reform. This assumption combined with the estimated counterfactual reported income distribution in the post-reform period would then allow one to calculate the reported income distribution in the post-reform period had the reform not happened. A similar strategy is employed in Carril (2022). However, this strategy is unlikely to be feasible in our setting. As seen in Figure 17 in Appendix C.3, the
interpreted as a subjective measure of relative income. Respondents were asked to place their household on an economic ladder on which the poorest 10 percent of people in their country stood at the lowest step (step 1) and the richest 10 percent of the people were at step 10. Unlike the question about satisfaction with finances, the economic ladder question only asks the respondent to place the welfare of the household in their country’s

> 11 Accounts of subjective well-being measures are broadly categorized into _evaluative_ measures and _affective_ measures. In contrast with evaluative measures, affective measures such as those collected via the Experience Sampling Method or the Day Reconstruction Method, are generally concerned with _experienced utility,_ or the amount of affect experienced at any given moment (Kahneman and Krueger, 2006).

> 12 Likert measures are generally based on a symmetric scale that can measure disagreement (possible responses are strongly disagree, disagree, neither agree nor disagree, agree, and strongly agree) or satisfaction (e.g. very dissatisfied, dissatisfied, neither satisfied nor dissatisfied, satisfied, and very satisfied). Another type of evaluative measure is the Cantril ladder, based on a self-anchoring scale derived from the following description: “Please imagine a ladder with steps numbered from zero at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” It is used, for instance, in the Gallup Poll (see Deaton 2011).

5
>bank use|Far from<br>branch|Assets|Below median:<br>Patience|
|1-day delay|-178.377|-160.676|575.481|46.441|-238.399|
||(582.873)|(375.611)|(452.081)|(339.262)|(489.439)|
|8-day delay|-597.176|-812.426**|-673.182*|-367.269|-1026.894**|
||(539.522)|(349.030)|(348.521)|(333.372)|(421.200)|
|Observations|162|320|239|246|282|
|Mean of dependent variable in Immediate group|1317.84|1498.06|1222.05|1084.20|1694.44|
|R-squared|0.070|0.082|0.125|0.136|0.138|
|P-value: 1-day=8-day|0.275|0.024|0.001|0.129|0.013|
|P-value: equal efect of 1-day vs 0-day|0.828|0.768|0.076|0.702|0.606|
|P-value: equal efect of 8-day vs 0-day|0.825|0.635|0.887|0.226|0.225|
|P-value: equal efect of 1-day vs 8-day|0.571|0.869|0.040|0.378|0.427|

The top panel includes respondents from female-headed households and those with above-median values for number of transactions at NBS in the 90 days preceding the transfer, those who live above the median distance from the bank branch, those with above-median values of a principle-index of asset ownership, and those with above-median patience,