rafmacalaba/data-use-mentions-extended huggingface dataset:
telecommunications services (Adamou 2009). Prices for fixed, mobile, and Internet services are all above the regional average (table 1). Estimations suggest that to achieve universal mobile coverage, around 14 percent of the population would need to be subsidized (figure 17). Improving the administration of the universal service fund will efficiently fill coverage gaps in rural areas that are not likely to be met by commercial services.

In the regional context, Niger has the lowest mobile penetration among the ECOWAS countries, even though Guinea-Bissau, Liberia, and Sierra Leone all have lower per capita incomes (table 15).

Internet penetration is low, at just over 1 percent of the population. Nevertheless there are signs that the market is beginning to develop, including recent sharp increases in international connectivity (figure 18a) and wireless broadband service offerings, and a reduction in retail Internet prices. Meanwhile, Niger’s Internet penetration and connectivity still lags most of its ECOWAS peers (figure 18b). The price of a fixed ADSL<sup>17</sup> subscription has fallen by more than one half (in local currency) since 2008. The incumbent offers a CDMA<sup>18</sup> -based wireless service with an option of unlimited monthly Internet access at

> 15 Only around 10 percent of households with electricity.

> 16 Based on gross national income (GNI) per capita using the Atlas method. Data from the World Bank’s World Development Indicators (http://data.worldbank.org/indicator/NY.GNP.PCAP.CD). 17 Asymmetric digital subscriber line.

> 18 Code division multiple access.

32
**Table 4.  Comparator Countries: Financial Soundness Indicators, 2007 (%)**

|**Country**|**CAR**|**NPLs Ratio**<sup>**4**</sup>|**Return on Assets**|**Return on Equity**|
|---|---|---|---|---|
|Brazil<sup>5</sup>|17.4|4.4|2.3|24.5|
|Indonesia<sup>6</sup>|19.2|16|2.6|28|
|Malaysia<sup>7</sup>|12.7|8.7|1.3|14.1|
|Nigeria|18.6|7.7|1.8|13.8|
|South Africa<sup>8</sup>|12.7|1.1|1.4|18.6|
|Kenya|18.0|10.9|3.0|27.5|

Sources: Central Bank of Kenya, Central Bank of Nigeria (CBN), and IMF, GFSR.

Stress tests confirm the resilience of Kenya’s banking system. Table 5 below shows stress tests conducted using individual bank data from December 2008 to assess the impact of credit risk using two shocks. The first evaluates a deteriorating loan portfolio that would result in an increase in provisions by 50% and 100%, while the second assesses the impact of migrating 25% and 50% of performing loans to the substandard category. The results indicate that on the whole, banks appear resilient to the shocks measured **.** However, the most significant impact would be felt if a riskier client base resulted in a 100% increase in provisions. Such a significant shock would result in 17 banks (accounting
5 simply deducted from the household’s bills. In May 2019, the government started “monetizing” the subsidies, which means that the households actually receive them as cash transfers.

The HUS design provides interesting insights, as it combines two targeting elements not typically applied jointly to target vulnerable customers with high energy burden—income assessment and the expected volume of energy consumption. In this paper we examine the effect of this energy assistance program, as well as program design trade-offs that may be helpful with future tariff reforms and energy markets. We ask three research questions. First, did the assistance program offset the tariff hikes and provide relief for the most vulnerable segments of the population? Second, did the assistance program end up helping heavy (and potentially wealthier) consumers, namely those that were already taking advantage of the artificially low price of natural gas before the tariff reforms? Third, are there alternative, more targeted designs for the HUS that perform better in terms of cost to the government and welfare effects? Should the HUS be paired with social tariffs to make it more sustainable?

We answer these research questions using up to five waves of Ukraine’s Household Budget Survey. Attention is restricted to natural gas consumption in households that use natural gas for space heating. These are almost half of the households in Ukraine, and this share is likely to grow in the future as more municipalities switch away from district heating. We use three main metrics. Specifically, we examine the gas bills as a share of household income and/or expenditure, the share of the population that experience fuel poverty, and the consumer surplus change from removing the HUS or redesigning it. We pay special attention to how these metrics vary across the joint distribution of income and gas consumption. A key feature of our analyses is that while the original, artificially low natural gas tariffs provided _untargeted_ subsidies to all
their productive needs, because their general skill level does not enable them to understand how they could productively use available apps, because the available quality of service is so poor that access to internet is not useful to them, or due to some other reason. The most important conditional correlates of smartphone and computer adoption among available variables are related to having a loan, having electricity, having business linkages with big firms as customers (being embedded in value chains that require upgrading), and relevant skills (managers having vocational training). More policy research work is needed to better understand what policies may be effective and appropriate to increase adoption and more intensive productive use of DTs across SSA countries.

As was emphasized in the introduction, while the data set used in this paper provides a wealth of information difficult to find elsewhere on microenterprises, the fact that it consists of a single cross section prevents causality from being inferred from the presented results. Importantly, it precludes any analysis of firm dynamics. Repeated implementation of the same survey, by following the same enterprises over time, would increase the amount of information for causal inference of the adoption decision, the impact of DTs on performance and firm growth, as well as on determinants of firm exit, and the differential impacts for formal versus informal enterprises. Given that one of the major problems faced by microenterprises in SSA is that few of them grow into competitive mid-sized or large companies, such a panel data set would provide valuable information to understand why this is so and whether the more widespread use of DTs for productive purposes can be effectively used to circumvent the associated barriers. Providing support for such future additional surveys would constitute an 33
in Annex 2. We first simulate the evolution of the distribution of income up to 2030 to create the baseline and then compare the results of the PTAs to this baseline.

Extreme poverty in Indonesia has steadily declined since the mid-1980s. The percentage of people living with less than PPP$1.90 a day fell from 70.3% in 1984 to 9.3% by 2013.<sup>17</sup> During this period, Indonesia successfully managed to keep the long-term pace of poverty reduction vis-à-vis other developing countries. Recent data show that Indonesia’s poverty rate was, nonetheless, 5.8

> 17 Data from PovcalNet <u>http://iresearch.worldbank.org/PovcalNet/povDuplicateWB.aspx# Poverty headcount ratio at</u> PPP$1.90 a day in 2014 is 8.3 percent.

22
example, if students learned 0.25 _σ_ per year in an intervention in South Africa, and students typically learn 0.25 _σ_ in a given year in South Africa, impacted students will have learned a year’s worth of schooling in South Africa. A few options exist for possible status-quo learning trajectories: for example, we might use the national average learning trajectory, or the learning gains in the control group of the same evaluation from which effect sizes are drawn. Alternatively, we could use an average learning profile across all countries being compared. This choice of status quo will affect our interpretation. If we choose the experimental or quasi-experimental control group, then the resulting figure for equivalent years of schooling gained is relevant to the study sample only. If we choose national average learning trajectories, we can interpret the figure as the number of equivalent years of schooling gained at the national level, with the embedded assumption that the standard deviations are comparable. In the main analysis reported above, we use national-level learning trajectories; in this section, we explore the alternative of using the study’s control group to measure status-quo learning.

An advantage of using control-group status-quo learning is that this estimate is drawn from the same sample as the learning gains from the intervention are, so the two are directly comparable. If the study sample is not representative of the nation, however, then when we later apply a national-level learning adjustment to compute LAYS in globally comparable terms, the adjustment will be less reliable. We can test the assumption of representativeness of study samples by examining the degree of variation within a country. If variation within a country is large, this means that any given study sample is likely to diverge from the national average. We test this assumption using a uniform test, EGRA. Two advantages of EGRA data are that (1) EGRAs are included in the World Bank Harmonized Learning Outcomes database and (2) they are often used to assess the impact of interventions and policies. In Appendix Figure B7,
placed bonds with an original maturity of more than one year.<sup>13,14</sup> Given that SDC Platinum does not collect data on the debt issues with maturity shorter than one year, our data set does not include commercial paper. To classify security issuances as domestic or international, we consider the main exchange where the issues are listed and compare it to the issuing firm’s nationality. The issues taking place in Hong Kong SAR, China or Taiwan, China are considered foreign issues for both China and India. For offerings that take place in more than one market, we consider the issues in each market as separate issues.

The data on equity capital raisings in domestic and international markets cover the period from 1991 to 2011. The coverage for bond issuance in international markets starts in 1991, while the coverage for domestic market activity is more limited and starts in 2000. Therefore, for bond financing activity we restrict our sample to the period 2000-2011. Our data set includes 18,085 security issuances <mark>, out of which 6,929 are bond issues and 11,156 are equity issues. This data set covers i</mark> ssues by 3,884 firms from China and 6,483 firms from India.

To analyze the characteristics and performance of the firms that issue and of those that do not issue in domestic and foreign capital markets, we match the data on security issuances from SDC Platinum with firm-level balance sheet data from the Bureau van Dijk’s Orbis database over the 2003-2011 period. Our sample covers only publicly listed companies, which gives us a more homogeneous sample of firms relative to using all firms. By not analyzing non-listed firms in our sample, we exclude relatively small firms for which it is probably very costly to issue bonds and equity and which are likely to have different accounting standards and to be informal (thus less

> 13 SDC Platinum collects data on security issuances mostly from filings with local regulatory agencies and stock exchanges. These data are
Figure 4. Work stoppages in Mongolia and Myanmar <!-- Start of picture text --> Mongolia<br>Myanmar<br><!-- End of picture text --> Note: 95% confidence intervals for difference from Q1 are shown (rather than from zero). Including controls for respondent characteristics do not qualitatively change results. See Appendix Table 2 for regression tables with controls. Source: HFPS Lao PDR, PNG, and Vietnam – After a short decline in mobility in March to April 2020, Lao PDR, PNG, and Vietnam experienced relatively normal mobility throughout the rest of the year and into 2021. Yet, in Lao PDR and PNG, poorer workers found it harder to resume work in 2020 and thus were more likely to be out of work compared to their wealthier counterparts: In 2020, the poorest quintile in Lao PDR was twice as likely as the wealthiest quintile to stop working, while in PNG, the poorest quintile was 2.3 to 2.9 times more likely to be out of work that the top quintile (Figure 5). While the prevalence of work stoppages was generally very low in Vietnam, a different gradient is evident, with wealthier workers marginally more likely to have stopped working. This result, however, is explained by two factors: 1) differences in sectoral participation across welfare quintiles; and 2) differences in residential area areas across quintiles. As with many other middle-income EAP countries, agriculture was one of the least affected sectors in Vietnam in terms of work stoppages. However, among the sample of countries, participation among bottom 20 workers in agriculture is relatively high<sup>19</sup> in Vietnam (74 percent), and differences across quintiles in agricultural employment are among the largest. Nevertheless, data on work stoppages is also available over a longer reference period in Vietnam (not only the week prior to the survey), which shows a clear regressive gradient, with households in the poorer quintiles being significantly more
not targeted to receive the program. As a result, the average distance between untargeted households in treatment and control clusters and the closest targeted household was 0.5 km and 1.4 km, respectively, (p-value of t-test of equality of means is 0.000).

Panel A of Figure A1 shows a map with the location of all study households while Panel B zooms in on a map of two study provinces with the location of study households in different colors depending on their treatment status.

Figure A2 describes the timeline of data collection and interventions. The baseline data was collected in-person between October 2019 and February 2020, with a total of 1,192 households. The preferred respondent was the head of household, and if unavailable, the primary holder of the Prosoli beneficiary card.<sup>3</sup>

> 2The program was implemented in the provinces of Santo Domingo, San Cristóbal, Santiago, Monte Plata, Barahona and Baoruco.

> 3The initial list used beneficiary data from 2018 and contained 2,172 households. By the time of the

4
represents a vector of associated characteristics. Event types include transactions (call, SMS, or data use), device switches, and geographic movement (coordinates of current tower). Characteristics derived from the raw transaction data include variables capturing socioeconomics (the handset model, the country of the recipient), timing (time until the credit is granted, day of the week, time of day, whether it was a holiday), and management of expenses (whether the sender or receiver had pre- or post-paid account, whether the transaction occurred during a discount time band, or at the discontinuity of a time band).

Second, for each individual _i_ , event type _e_ , and characteristic _k_ , we compute a vector with the sum of events of each potential value of the characteristic: This generates, for example, the count of calls by time of day, the number of minutes spoken with each contact, the number of SMS to pre- and post-paid accounts, and the total duration of calls immediately before and after the start of a discount time band.

Finally, for each vector we compute a set of summary statistics. For sequences, these include measures of centrality (mean, median, quantiles), dispersion (standard deviation, interquantile ranges), and for ordinal sequences, change (slope) and periodicity (autocorrelation of various lags, and fundamental frequencies—which correspond to the periods of the strongest repeating temporal patterns. For counts by category, we compute the fraction in each category and overall dispersion (Herfindahl-Hirschman Index). For geographic coordinates, we compute the maximum distance between any two points, the distance from the centroid to several points of interest, and use a clustering algorithm to identify important places (Isaacman et al., 2011). We also compute statistics that summarize pairs of sequences, including correlations, ratios, and lagged correlations (e.g., the correlation of minutes spoken with SMS, which may indicate whether a person coordinates in bursts of activity).

11
# Inequality, poverty and the intra-household allocation of consumption in Senegal

_Philippe De Vreyer and Sylvie Lambert_

**JEL classification codes:** C81, D13, D31, I32, O12, O15

**Keywords:** Inequality, Poverty, Household surveys, Intra-household allocation, Senegal.

Philippe De Vreyer is professor at Université Paris-Dauphine, PSL Research University, CNRS, IRD, LEDa, DIAL (philippe.devreyer@dauphine.psl.eu). Sylvie Lambert (corresponding author) is professor at Paris School of Economics, INRAE (sylvie.lambert@psemail.eu). We thank IDRC (International Development Research Center), INRA-Paris, and CEPREMAP for funding data collection, UNICEF for supporting preliminary work, and the NOPOOR program of the European Union for research support. This work has also been supported by the French National Research Agency, through the program Investissements d'Avenir, ANR-10--LABX_93-0.1. We thank Momar B. Sylla, whose insights proved invaluable to gain a better understanding of the context. For comments and suggestions, we are especially indebted to Denis Cogneau, Martin Ravallion, Dominique van de Walle and Stephan Klasen. Three anonymous referees are gratefully acknowledged for constructive comments. We are also thankful to participants in the 2016 EUDN workshop and 2017 CSAE conference, and seminar at Nova, Lisboa. We thank Anne-Sophie Robilliard for providing us access to the ESPS database.
||||||0.053|
|||||||(0.058)|
|Constant|0.825<sup>∗∗∗</sup>|0.912<sup>∗∗∗</sup>|0.708<sup>∗∗∗</sup>|0.749<sup>∗∗∗</sup>|0.825<sup>∗∗∗</sup>|0.737<sup>∗∗∗</sup>|
||(0.142)|(0.123)|(0.098)|(0.105)|(0.015)|(0.219)|
|Province FE|No|No|No|No|No|Yes|
|Observations|1227|1227|1123|1102|1232|1007|
|_R_<sup>2</sup>|0.000|0.001|0.003|0.001|0.004|0.030|

_Notes:_ Authors’ analysis based on data described in the text. The table reports OLS estimates. Selective attrition has been tested for the log of dependent variables in 2006. Consumption and spendings are per adult equivalent. Sampling weights are accounted for. Clustered-robust standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 36
project monitoring and information gathering to be done “offsite”. Thus, we foresee that this variable will have a negative impact on the share of local to total foreign claims.

We measure “economic” proximity between the home and the host country by including a measure of bilateral trade between these economies. This information comes from the IMF _Direction of Trade Statistics_ .<sup>12</sup> Finally, we measure home-host country “cultural” and “institutional” distance by including, respectively, a dummy that equals one if countries share a common language with the lender country and a dummy that equals one if countries have a common legal origin (which will then result in similar institutions). Data on common language and legal origin come from Glick and Rose (2002) and the CIA _World Factbook_ .

Government intervention in a country’s financial sector via regulations and/or taxation might affect the share of local to total foreign claims countries receive in different ways, depending on the kind of intervention. For example, controls on cross-border activity will negatively affect the level of cross-border claims and, therefore, will increase the share of local claims. Similarly, restriction on on-shore foreign currency lending, might also lead to a rise in cross-border claims to the degree that banks prefer to extend claims in foreign currencies. On the other hand, limits on “banking freedom”, in particular restrictions on bank activities and controls that affect foreign entry and foreign ownership of banks, will have the opposite effect.

In our estimations, we control for regulations affecting the volume of cross-border and local claims, respectively. First, we include a dummy that equals one if the country has controls on cross-border financial credits. Second, we control for restrictions on on-shore foreign currency lending by including a dummy for the countries where such restrictions are in place. Data for both variables come from the IMF _Annual Report on Exchange Arrangements and_

> 12 Ideally, we would have liked to control as well for the share of non-financial FDI from the
# **4. Intersectoral Linkages and Sectoral and Firm Performance**

In the previous section, we presented evidence on the role of the mining output shock in shaping regional differences in intersectoral linkages. We now aim to understand the mechanisms that link production networks and structural transformation but at a more granular level. We use 2003 NIC and 2014 IBES data to examine (1) how the positive mining employment shock relates to employment growth in other sectors across districts and (2) how the positive mining employment shock across time and geographic areas affects the entry of new firms and the growth of firm employment in different sectors.

# **District-Level Analysis**

We first examine the sensitivity of changes in employment in other sectors to changes in employment in mining across districts. Propagation of the mining employment shock to other sectors is identified based on the assumption that employment growth in sectors that have stronger linkages to mining (for example, heavy manufacturing industries) predominantly takes place in mining districts, that is, districts with at least one mining firm. The regression model is as follows: s ∆ log(Empd)03 − 13 = α +σ∆ log(MEmpd)03 − 13 +φ∆ log(MEmpr ,d ′≠ d)03 − 13 + β 1 ∆ log(IFirmd)93 − 03 + β 2 ∆ log(IFirmd)83 − 93 + β 3 ∆ log(SFirmd)93 − 03 + (1) β 4 ∆ log(SFirmd)83 − 93 + ε d , where ∆ log( _Empd_ )03<sup>_s_</sup> − 13 and ∆ log( _MEmpd_ )03 − 13 measure changes in log employment in sector _s_ in district _d_ between 2003 and 2013 and changes in the mining sector in district _d_ between 2003 and 2013, respectively. To capture the effect of mining activities in neighboring and other districts within the same region, we control for changes in log mining employment in region _r_ less mining employment in district _d_ denoted