rafmacalaba/data-use-mentions-extended huggingface dataset:
alter prices on a day-to-day basis.<sup>8</sup> Therefore, in all three tables of descriptive statistics, an observation is at the aggregated level.

There is a significant amount of variation in total catch, market prices, and cooperative prices (table 1). Comparing the second and third panels of table 1, the log of cooperative price and log of total catch show a marked difference between the exclusive and nonexclusive cooperatives, despite the fact that average market prices are similar. The log of cooperative price and the log of catch also have a higher coefficient of variation for the exclusive cooperative.

The final rows of each panel show the range of variation for the “Recruit Effect” variable and the “ONI X Recruit Effect” interaction. The majority of observations have a value of 0 for the “Recruit Effect” variable, but approximately 800 observations exhibit a positive recruitment effect, and more than 500 observations exhibit a negative effect (figure 2). This variation permits a test of the model’s third implication regarding population growth rates.

To understand the relationship between market prices in La Paz and market prices external to that fishing area, we use the daily transaction data to estimate a regression of La Paz prices on La Nueva Viga prices, separately for each species for which there are data from both sources. Of the 55,841 daily transactions across Abreojos, Sargento, and Pichilingue, the species that match across the logbooks and La Nueva Viga account for only 4,208 transactions. Moreover, some of these transactions have no information on La Paz market prices. In table 2, we run OLS regressions of the log La Paz price on the log La Nueva Viga price by species common name.

The results paint a mixed picture. For five of the nine common names, there is a positive and statistically significant relationship between La Nueva Viga prices and La Paz prices. For these, the R-squared ranges from 0.016 to 0.877, depending on the species.
49 Table A2.18: Shares of total household expenditure on LPG for all households and for purchasing households in Albania (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|**U**<br>**Rural**|**ser households**<br>**Urban**|<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|1.7|3.3|2.4|3.1|4.7|3.8|
|**2**|1.9|2.5|2.1|2.8|3.6|3.1|
|**3**|2.0|2.9|2.4|2.7|3.6|3.2|
|**4**|2.3|2.4|2.4|2.9|3.2|3.1|
|**5**|1.9|1.8|1.8|2.3|2.7|2.5|
|**All**|2.0|2.4|2.2|2.8|3.3|3.1|

_Source:_ Authors’ calculations.

# **Brazil (POF 2008–2009)**

The data are taken from the 2008–2009 Consumer Expenditure Survey (POF) administered by the Brazilian Institute of Geography and Statistics (IBGE). The fieldwork for the survey was carried out between May 2008 and May 2009.

The value of all non-purchased items was about 11 percent
*-0.46****|**9.33*****|**8.57*****|**-0.76*****|
||(0.15)|(0.15)|(0.21)|(0.18)|(0.18)|(0.25)|
|No. Observations|3192|3241|6433|3192|3241|6433|
|||**Estimate**<br>|**s of Mobility**|**fromQuadra**|**tic CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|9.33|8.51||**_IGME0 _**|0.093|0.35|
|**_ES6 _**|10.43|10.61||**_IGME6 _**|0.268|0.35|
|**_ES9_**|11.38|11.66||**_IGME9 _**|0.354|0.35|
|**_ES16_**|14.66|14.11||**_IGME16_**|0.558|0.35|

Notes: (1) The data used are CFPS 2016 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; *
proceeded as follows. First, we fi ltered the retail and wholesale fi rms’ data from the SIC 4- digit level to the North American Industrial Classi fi cation System (NAICS). Assuming that importers lobby according to the importance of each agricultural and manufacturing NAICS code in its production process, the 1997 U.S. Input-Output matrix provided by the Bureau of Economic Analysis (BEA) was used to determine the level of service fi rms’ contributions by each agricultural and manufacturing NAICS code. Finally, a concordance between the Input-Output codes and the HS 10-digit codes provided by the BEA was used to map the contributions to the international trade data system.

Political organization of importers for each agricultural and manufacturing ISIC 3-digit and HS 8-digit code was determined using thresholds following Goldberg and Maggi (1999) and Krishna, Gawande and Robbins (2004). The fi rst, second and third quartiles of the importers’ distribution of political contributions were used to determine which manufacturing and agricultural sectors present politically organized importers. For example, using the fi rst quartile of the distribution of political contributions to determine whether importers are organized or not means that those sectors with lower (higher) contributions than the one displayed by the fi rst quartile code are assumed to be not politically organized (are politically organized) so that Ik<sup>i= 0(I</sup> k<sup>i= 1) .</sup> Political Organization of Exporters The data set used to determine Latin American exporters political organization is the same one used in Kee, Olarreaga and Silva (2003). Latin American exporters contribution data were provided by the U.S. Department of Justice under the legislation known as the Foreign Agent Registration Act (FARA). First, the foreign lobbying expenditure data related to trade on agricultural and industrial goods were separated from those involving trade in services and other types of foreign lobbying. Kee, Olarreaga and Silva explain in details the procedure used to select contributions related to trade in goods. The
changed their access policy over time. For example, Tunisia initially did not share access to their 2015 survey, but later granted access as part of their engagement with the World Bank. Morocco did not share its microdata but decided in 2019 to publish online its most recent HBS. Importantly, there are instances where the World Bank provides substantial technical assistance to the National Statistics Office but where data are not shared, or where there are substantial delays before data are shared; for example, Morocco only granted access to its 2013 survey in 2018. Algeria granted access to the World Bank to group data only.

Table 3. Status of public and WB access to household budget surveys in MENA region as of August, 2019

|Public or licensed access/ partial or full|nopublic access|no WB engagement|
|---|---|---|
|Djibouti<br>Egypt, Arab Rep.<br>Iraq<br>Iran, Islamic Rep.<br>Yemen, Rep.<br>Tunisia<br>West Bank and Gaza<br>Morocco|Algeria<br>Jordan<br>Lebanon|Libya<br>Syrian Arab Republic|

Source: Authors’ compilation. Note: Licensed means the request to get the microdata should be cleared.

# <u>Comparability</u>

## _Regional picture_

In addition to needing recent HBSs to provide current welfare metrics, comparability of these surveys across time is important to estimate trends. Poverty estimates may not be comparable for different reasons. Non-comparability arises when surveys measure either the welfare aggregate (consumption or income) differently across different years following changes in survey design or instruments or because the statistical office changes the way it constructs welfare aggregates.<sup>10</sup> In the absence of at least two comparable data points, it is impossible to analyze poverty or shared prosperity across time.

Figure 7 shows the distribution of countries in each region that report shared prosperity in the most recent World
: Distribution of retail price differences (with respect to Delhi) across cities Figure 2: Distribution of retail absolute price differences (with respect to Delhi) across districts <!-- Start of picture text -->
-2 0 2 4 6 0 1 2 3 4 5<br>Proportional Retail Price Differences with Respect to Delhi Proportional Retail Price Absolute Differences with Respect to Delhi<br>Source: Authors’ calculations.  Source: Authors’ calculations.<br>2.5 4<br>2<br>3<br>1.5<br>2<br>1<br>Density Simple Differences Density Absolute Differences 1<br>.5<br>0 0<br><!-- End of picture text -->

The distribution of price differences is more spread out at the wholesale level. In this case, about 60 percent of district/product combinations display higher prices in Delhi than elsewhere in the country, with 90 percent of differences with respect to Delhi lying in the range of ‐44.4 percent to +41.6 percent (Figure 3). In absolute terms, the median price difference (with respect to Delhi) is greater than in retail at 17.2 percent, with 90 percent of the differences lying between 2.8 and 58 percent (Figure 4).

Figure 3: Distribution of wholesale price differences (with respect to Delhi) across districts Figure 4: Distribution of wholesale absolute price differences (with respect to Delhi) across districts

> 3 To measure price differences and to test for price transmission, it takes Delhi as a reference point. Apart from being the nation’s capital, and being economically crucial, Delhi’s geographical location, at the center of the northern landlocked land mass makes it a major redistribution hub for consumer products.

7
coefficient implies a decrease in the dependent variable. Overall, we find that firms facing adverse cash flow add to their cash balances during crisis and recovery periods, increase their capital expenditures and other investments, borrow long-term debt and issue equity while reducing their other financing. Specifically, when firms in our sample face a $1 shortfall in cash flow, they add 18.5 cents to their cash balances in 2006-07 (pre-crisis), 20 cents in 2008 and 23.6 cents in 2009 and 23.3 cents in 2010. Increases in capital expenditure are 1.8 cents in 2006-07 and 2008, 2.2 cents in

> 24 As described in the Appendix, the Other Investments variable is computed from Compustat (for firms following cash flow format 7) as Acquisitions + (Increase in Investments – Sale of Investments) – Changes in Short-term Investments – Other investing Activities. Thus it includes the cash flow associated with mergers and acquisition activity. When we re-estimate the system by stripping the Other Investments variable of Acquisitions and including it as an independent variable, we find that over 50% of the Other Investments/Cash Flow sensitivity is due to Acquisitions. We don’t use this in all our regressions since we are unable to separate out acquisitions in the Bloomberg sample on emerging market sample.

28
consolidation and to fight inflation, however, the effects of these reforms are not expected to be seen in the short-term.

Since 2019, Sudan has been experiencing soaring rates of inflation, which were partially driven by the higher energy and staple food prices (Figure 1). More recently, the Russia-Ukraine crisis that started in March 2022, has further exacerbated the country’s social and economic problems, through Sudan’s higher import costs of wheat,<sup>4</sup> given the high dependency of the country on imported wheat and food and the country’s trade connections to Russia and Ukraine.<sup>5</sup> In particular, Sudan is expected to face significant negative food security implications as food prices reach historic peaks due to the Russian invasion of Ukraine. All of the economic and social problems that have negatively impacted Sudan are expected to have increased the number of poor in the country, which represented about 61.1% of the population based on the latest available household survey in 2014/2015.

_Figure 1 – Evolution of inflation rate (%), 2019 - 2022_ <!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Inflation rate (%)<br>Percent<br>January-19 March-19 May-19 July-19 September-19 November-19 January-20 March-20 May-20 July-20 September-20 November-20 January-21 March-21 May-21 July-21 September-21 November-21 January-22<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on data obtained from the Central Bureau of Statistics (CBS) of Sudan, and World Bank Currently, there are efforts in Sudan to rollout a new Social Safety Net Program, which seeks to assist the most food insecure households in the most affected states and localities, by providing unconditional cash and food transfers to beneficiary families in 11 states of Sudan.
The data set uses plant-level information on the type of fuel used, the calorific value of the fuel mix and its emission and oxidation factors to calculate the total carbon dioxide emitted by the plant in a year. The annual tons of carbon dioxide divided by the total units of energy produced by a plant in a year gives a plant-level estimate of average tons of carbon dioxide per unit of energy generated. The social cost of carbon per unit of energy generated by a plant is estimated by multiplying the average tons of carbon dioxide per kWh by Rs. 2184 (Nordhaus (2018) cost estimate for a ton of carbon emission is $33.60; at approximately Rs. 65/USD this amounts to Rs. 2184 per ton of carbon dioxide). Finally, using the SLDC high-frequency data set on generator-wise energy dispatch at 15minute time intervals from Rajasthan’s system operator, the price of emission at each period<sup>26</sup> ( ) is calculated as: ccm ccggnn ii PP

> 24 The average marginal transmission loss estimate is comparable to the average annual transmission 3.95% reported by JVVNL in its FY2016-17 financial petition to the regulator: <u>http://energy.rajasthan.gov.in/content/dam/raj/energy/jaipurdiscom/pdf/tariff/2018/true/additional_submission.pdf</u> . In reporting, the average annual distribution loss, JVVNL pools commercial losses (that occur due to billing inefficiencies) with physical losses over the wire. For the estimation of social marginal costs, we are interested in measuring the physical losses that occur over the distribution network. This estimate can be found in a report by the Forum of Regulators (2012) that shows the average distribution losses over a three-month period at a representative feeder within JVVNL’s network to be 9.2%. This is lower than the 13.4% average marginal losses calculated using our methodology.

> 25 CO2 baseline database for the Indian power sector
**Figure 18. Expansion in access to water and sanitation has not keep pace with population growth and increases reliance on untreated surface water and open defecation, 2001-2006**

Population gaining access per year between 2001–06 <!-- Start of picture text --> a. Water b. Sanitation<br>6<br>6<br>5<br>5<br>4<br>4<br>3.5<br>3.5<br>3 2.9 2.8 3 2.9 2.8<br>2<br>2<br>1<br>1<br>0<br>0<br>Nationa l Urban Rural<br>National Urban Rural<br>‐1<br>‐1<br>‐2 ‐2<br>Flush Toilets<br>Piped Water Standposts<br>Wells/Boreholes Surface Water Improved Latrines<br>Traditional Latrines<br>Annualized Growth<br>Population gaining access per year (%)<br>Population gaining access per year (%)<br><!-- End of picture text --> _Source:_ WHO (2010) from the Demographic and Health Surveys 2001 and 2006.

# Power

## **Achievements**

_In the late 1990s, the Government of Uganda undertook a comprehensive power reform program that placed the power sector under private management operating on commercial principles_ . The sector has subsequently been unbundled, legal and regulatory reforms have been introduced, and the operation of the main generation and distribution assets has been turned over to the private sector under long-term concession agreements.

_There has been a doubling of generation infrastructure over the last few years_ . Following a serious drought and resulting power supply crisis in the mid-2000s, Uganda has made serious efforts both to expand its power generation assets and diversify its mix to become less reliant on hydropower. Generation capacity increased from 280 megawatts (MW)
Policy Research Working Paper 9947

# **Abstract**

Does the digitization of transactions in an economy increase tax compliance? This paper studies the effect of financial incentives on the adoption of electronic payment technology and on tax compliance by firms. Exploiting administrative data and policy variation from Uruguay, the paper shows that i) consumer value-added tax rebates for credit and debit card transactions trigger an immediate 50 percent increase in the number of card transactions, ii) firms’ use of card machines increases only on the intensive margin, and iii) tax compliance is unaffected. Endogenous card machine adoption and a low share of card sales in total reported sales can rationalize the findings.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at abrockmeyer@worldbank.org and msaenzsomarriba@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if
# Guiding Question 3: Why are some groups excluded? The _<u>why</u>_ question

The “how question” is relatively easier to answer compared to the “why question”. The former requires analysis of country data to establish the manner and extent to which certain groups face disadvantages in access to markets, services or spaces. The “why question” is more complex since it requires a granular understanding of processes, politics and the nature of vertical and horizontal stratification in societies.

World Bank (2013) highlights the example of tribal women in India and illustrates the nuance in the _how_ and _why_ questions. The National Family Health Survey of India 2004/05 reports that 80 percent of tribal women in India give birth at home, compared to 60 percent of all Indian women. When asked why they opt to deliver their children at home, 72 percent of . responses indicate that women do not consider it necessary to deliver in a health facility<sup>6</sup> This finding could lead health authorities to conclude that the seemingly low demand for institutional births among tribal women is a result of cultural factors and of ignorance. However, low demand may actually reflect complex phenomena that are not immediately discernible. For example, the tribal woman may be unable to deliver at a health center because of the distance to the facility, the absence of female health providers at the facility, the costs involved, or simply because she is not comfortable with the manner in which providers deal with her. She may for instance, feel disrespected because of her culture or ethnicity and way of life. The SCDs were accordingly read with the lens of whether or not they unpacked such processes that perpetuate exclusion of some groups.

Guiding Question 4: Does social inclusion feature as a priority and are there recommended actions? The _<u>what next</u>_ question The final question that guided the review was whether social inclusion was identified as a priority by the SCD and if
price for a service tends to expand access. Based on the limited evidence available access to water or power may cover several extra tens of percent of the population, if effective pricing schemes are implemented. This may require allowing free unregulated entry as discussed in the first section.

Some poor people may pay high unit costs for service, but they buy very little. For such small quantities it may not be economic to build out an infrastructure system. Finally, willingness-to-pay studies show that in many countries there are very poor people who still cannot afford modern service and continue to live with low quality provision, for example, walking many hours to fetch water of questionable quality.

Where possible, price discrimination is a way to charge rich people more and poor people less as in the quote from Adam Smith at the beginning of this section. Price discrimination is an efficient form of cross-subsidy. One way to discriminate prices is to offer populations with low willingness to pay a special and appropriate price-quality mix. For example, poor people may not be able to pay regularly, but when they have money. Water vending systems from buckets are a way of accommodating this. They may get simpler pipeline systems. They may get a flexible service offering and more appropriate quality standards – better than what they have, but less demanding than found in some of the most modern water or power systems. For example, it may be worth having access to water even if it is not fully treated. For many uses, such as flushing toilets treated water is not needed. Boiling may be the preferred way to make sure drinking water is safe .They may get more flexible payment terms. As discussed before, allowing entry of “unconventional” service suppliers can be helpful ranging from forprofit vendors to community-based organizations.

Governments may also provide subsidies to deal with affordability concerns. Yet, many subsidies do not effectively enhance access. For example, subsidies in an existing utility may simply help those already connected. The subsidies may then just benefit the better-off as
. But as countries began to reopen in May and June, the United States, Latin America and the Caribbean (LAC), and the Middle East and North Africa (MENA) have seen a resurgence of COVID-19 cases (Figure 3).

**The rapid increase in COVID-19 cases has caused a significant loss of life and overwhelmed many health systems.** The daily new cases of COVID-19 deaths peaked globally at 244 per million by the beginning of April, largely driven by Western Europe, LAC and Europe and Central Asia (ECA) (Figure 4). However, as strict lockdowns were adopted, countries managed to contain the mortality rate and, as a result, daily COVID-19 deaths dropped by 50% by the end of August (Figure 3). Most of these deaths could have been caused not by the disease itself, but by the further strain on health care systems. This is particularly true for countries where health systems were unable to cope with the surge in demand due to the lack of health care workers, medical equipment and supplies.

> 1 Data on cases and deaths used in our study, which covers the period January 1 to August 31, 2020, were obtained from Hale et al. (2020).

4
<!-- Start of picture text --> Map 1: A third of surveyed adults rely on agriculture by growing crops<br>or raising livestock as the main source of household income<br><!-- End of picture text --> Adults living in a household where growing crops or raising livestock is the main source of household income (%), 2017 <!-- Start of picture text -->
*<br>Less than 19%<br>MM 20-29%<br>ME 30-39%<br>MM 40-49%<br>HB 50-100%<br>No Data<br><!-- End of picture text -->

Source: Global Findex database. Note: Data are displayed only for economies in Sub-Saharan Africa.