rafmacalaba/data-use-mentions-extended huggingface dataset:
should occur almost simultaneously. Third, our analysis of heterogeneity is consistent with corruption mediating the effect of aid on wealth in havens: we find larger estimates in more corrupt countries although the difference is not statistically significant. In any case, since money is fungible we cannot distinguish between direct diversion of the funds disbursed by the donor and diversion of other public funds freed up by the aid disbursement.

Some alternative interpretations exist, but we find them harder to reconcile with all the patterns in the data. Most importantly, firms directly involved in aid-sponsored projects may receive payments in quarters with aid disbursements and deposit some of these funds with foreign banks. One set of results provides some support for this explanation: we find larger estimates in countries with less private credit suggesting that foreign banks serve as a substitute for inefficient domestic banks. However, this mechanism does not explain our finding that money only flows to places like Zurich, the global center for bank secrecy and private wealth management (Zucman, 2017), and not to other international banking centers like New York, London and Frankfurt. The dynamics in haven deposits, a permanent level shift at the time of aid disbursements, also seems more consistent with personal wealth accumulation than firm cash management.

There are other mechanisms that we can more confidently rule out. First, firms in developing countries have been shown to engage in aggressive tax avoidance by shifting profits to low-tax affiliates (Johannesen et al., 2016), but such profit shifting cannot explain our results because of the way the BIS statistics are constructed: deposits belonging to the Bermuda subsidiary of a Tanzanian company are assigned to Bermuda rather than Tanzania. Second, aid may increase income more broadly in the economy by stimulating aggregate demand and may therefore indirectly increase evasion of personal income taxes through havens; however, our model accounts for aggregate income shocks by conditioning on GDP growth and the sharp increase in haven deposits in the disbursement quarter does not mirror the typically protracted expansionary effect of economic stimulus (Kaplan and Violante, 2014)
on school attendance, but rather that after 4 years of a conflict in which there is broad economic suffering in locations that are not necessarily affected by violence (e.g., Tandon and Vishwanath 2020), school attendance does not vary in response to additional changes in violence in the district of residence.

However, it is possible that other factors correlated with violence or shocks causing violence and school attendance to co-move are obscuring the relationship. Furthermore, another reason that there is potentially little correlation between school attendance and the location violence is that other conflict-related shocks are more important to school attendance in this context. Investigating these possibilities, we further investigate the impact of sudden conflict-related shocks on school attendance using the WFP mobile phone survey. In particular, we investigate the impact each of the four conflict-related shocks described above on school attendance- three of them are sudden and unexpected changes in the severity of violence, and another was caused by the conflict but was not related to any specific violent incidents.

# **Section 5. Response of Attendance to Each Type of Conflict-Related Shock**

The basic empirical strategy is illustrated in figure 4, which presents the evolution of school attendance in regions affected by each of the four shocks and indicates when the shock occurred. We estimate the change in school attendance immediately after the shock happened in the affected regions and compare this change to the change at the same time in all the districts of Yemen that were unaffected by any of the four major shocks analyzed.

The first three panels of figure 4 demonstrate that there was little change in the share of households where all school-aged children attended school following the secessionist capture of regions in the south from the IRG, the onset of the partial ceasefire,27 or the capture of Al Jawf from the IRG by DFA forces. However, the fourth panel illustrates how school attendance significantly worsened following the beginning of the teacher strike in the governorates fully controlled by the IRG.28 Formalizing this comparison between shock-affected and unaffected districts, we estimate 29 the following
19.7|22.6|0.2|0.2|-|2.1|0.8|0.2|45.8|
|Sub-Saharan Africa|18.5|5.0|1.3|0.4|0.3|0.4|2.0|0.2|28.1|
|Total|355.9|225.7|43.0|13.2|30.3|22.6|72.8|43.6|807.4|

(*) Numbers might not exactly add up, due to rounding Source: World Bank, PPI Project Database

# **5. Progress on policy reform**

The 1990s saw an impressive array of reforms in the way the sectors were being managed ranging from corporatization to full divestiture. They also included many efforts to promote competition whenever possible and to create independent sector regulators when necessary. Reliable data on infrastructure policies are, however, no easier to come by than data on access and quality. Still, available indicators show that infrastructure policy and regulation have been improving—though much remains to be done. The picture varies enormously across regions, countries, and sectors.

A 1998 “scorecard” compared the electricity sectors in 115 countries by asking experts in each country whether a variety of reforms had been completed, including commercialization, restructuring, regulation, legal reform, and private investment. The experts were also asked to grade reforms on a scale of 1 to 5 (from low to high progress).

21
the local fish market that their husbands are unable to sell to merchants.

Boat owners’ daily gross average earnings are INR 1,076. Since the survey includes a few questions about the amount of their daily expenses on kerosene and laborers’ wages, we can get a rough estimate of their net daily income at INR 310 (roughly USD 6.50). Boat owners go fishing 73 percent of week days excluding Sundays or approximately 4 days a week.<sup>18</sup> In the analysis of Section 4 we use three different samples of boat-owner-weeks. The first (baseline sample) corresponds to all boat-owner-weeks when the boat owner went fishing at least one day during that week.<sup>19</sup> The second corresponds to all boat-owner-weeks

> 15They did not keep track of prices for other types of Indian fish because according to Mercabarna, the volume of trade was not significant enough.

> 16Fish Information and Services (FIS), a widely recognized consulting firm for global seafood industry information, reported that the vast majority of exported Indian fish (calamari, octopus, and cuttlefish) is sold in Europe. The rest of the exports, consisting mainly of tuna, is exported to East Asia and particularly Japan. (We tried to collect price data but we were unsuccessful.)

> 17Sources: US Energy Information Administration (fuel prices) and International Monetary Fund (Consumer Price Index and Exchange Rate).

> 18The following days are considered holidays: the first Friday of every month, January 1st and 6th, Good Friday (during Easter), August 15th (Independence Day), December 3rd, 21st and 25th.

> 19Sundays are excluded because there is no fish market and boat owners never go fishing. The sample also

8
equivalent to regressing investment disbursement on lagged PTA variables, which is one of the common techniques used when there is concern about endogeneity. Another advantage of using the fDi Markets database is the availability of company level investment data. This allows us to address endogeneity concerns related to the fact that large investment decisions may determine the adherence to certain disciplines in PTAs by host countries, which would imply that it is large investments that determine PTAs rather than the converse.<sup>13</sup> To tackle that endogeneity concern, in the final part of our analysis we remove the largest investments from the sample, as well as the sectors that spend the most resources on lobbying for specific contents in PTAs, to check the robustness of results to that potential source of endogeneity.

Overall, the data covers more than 60,000 companies from 177 source countries investing in 162 destination countries from 2003 to 2015. Each company’s project is classified according to 39 industries and 18 business activities. Table A2 shows how the different sectors and activities overlap in the data. Business activities are defined as the actual function of a project, whereas sectors are based on the FDI recipient company’s core business area. Examples of project announcements are “Wiseway Group (Australia – a transportation services provider) is investing in China in the Transportation sector in a project related to Sales, Marketing and Support activity”; “ProLogis (United States – a leasing and property management company) is investing in Sweden in the Real Estate sector in a construction project”. This means that an industry traditionally classified as manufacturing may receive investments to develop a service-related activity and vice versa. In the data for example, 65 percent of the investment received by the “consumer electronics” sector is dedicated to manufacturing activities but 16 percent falls in retail, 5 percent in research and development, 4 percent in marketing, 2 percent in logistics, distribution and transportation, and so on (all service-related activities). We think that these activities

> 13 For
enterprises on average, female-owned enterprises may be further away from the margin of formalizing and less likely to respond to the assistance.

To draw the sample for the baseline, we stratified the listing data by location and gender of the business owner and identified 3,600 firms that complied with one of the following criteria: (i) had at least one worker contracted outside of family members and business owners, (ii) were operating in a fixed location with more than one person working in the business, (iii) were at the 25th percentile of revenues or above. These criteria were developed in collaboration with government officials to focus on firms that were larger and more likely to both be the target of government formalization efforts, as well as potentially more likely to benefit from formalizing. We were targeting 3,000 firms for the baseline but started with 3,600 given the risk of not finding firms (the listing exercise collected limited contact information) or finding that they registered in the meantime or were incorrectly listed as not registered.<sup>11</sup> Through this process, we completed a detailed baseline survey for 3,002 informal firms, of which 1,195 were female-owned and 1,494 were from Lilongwe.

The baseline survey was done between December 2011 and April 2012. The baseline survey collected information on the characteristics of the firm and owner, including their usage of financial services and finance, their financial literacy and knowledge about business registration processes, and the financial performance of their business.

# **3.2 Summary Characteristics of the Sample by Gender**

Appendix Table 1 compares and further discusses the baseline characteristics of our sample by gender. Over 70 percent of the firms in our sample were in the retail sector, including selling groceries (21 percent of total), selling agricultural produce (10 percent), selling animal produce (10 percent), and hardware shops (8 percent). The focus on retail was particularly pronounced for men, while women were more
Assaad, R., Krafft, C., & Sieverding, M. (2021a). _Survey of Young People in Jordan—UNICEF ACSS Dataverse V1.0_ (V1.0) [dataset]. ACSS Dataverse.

Assaad, R., Krafft, C., & Sieverding, M. (2021b). _Youth Transitions to Adulthood in Jordan: High Aspirations, Challenging Realities_ (Issue November).

Assaad, R., Krafft, C., & Yassin, S. (2018). Comparing retrospective and panel data collection methods to assess labor market dynamics. _IZA Journal of Development & Migration_ . https://doi.org/10.1186/s40176-018-0125-7 Assaad, R., & Salemi, C. (2019). The Structure of Employment and Job Creation in Jordan: 2010-2016. In C. Krafft & R. Assaad (Eds.), _The Jordanian Labor Market Between Fragility and Resilience_ . Oxford University Press.

Bartik, A. W., Bertrand, M., Lin, F., Rothstein, J., & Unrath, M. (2020). Measuring the labor market at the onset of the COVID-19 Crisis. _Brookings Papers on Economic Activity_ , _Special Edition_ , 239–268. https://doi.org/10.1353/eca.2020.0010 Beaujouan, J., & Rasheed, A. (2020). The Syrian Refugee Policy of the Jordanian Government.

In _Syrian Crisis, Syrian Refugees_ (pp. 47–63). https://doi.org/10.1007/978-3-030-35016-1 Betts, A., Bloom, L., Kaplan, J. D., & Omata, N. (2017a). _Refugee economies: Forced_ _displacement and development_ (First edition). Oxford University Press.

Betts, A., Bloom, L., Kaplan, J., & Omata, N. (2017b). _Refugee Economies: Forced_ _Displacement and Development_ (First edit)
the household model as the coverage rate falls to 59 percent, as opposed to 82 percent for the subarea model and 67 percent for the area-level model.

Figure 4 shows the boxplot for relative bias. All estimates are slightly biased downward. This is because they are estimated using 2016 data, which is also used for the benchmarking procedure, and then compared against 2015 estimates generated using the 2014 sample data. Therefore, it is not surprising that estimates based on 2016 would be systematically below the benchmark estimates, reflecting the overall trend of poverty decline. Nonetheless, the results confirm that the household-level model generates less biased results than the sub-area and area-level models in sample, which each give less biased results than the direct estimates. Meanwhile, the householdlevel model does at least as well as – if not slightly better than – the sub-area and area-level models out of sample. When using the 2016 sample data to estimate the model, the estimates produced by the household model are less biased than those produced by sub-area and area level models and represents a considerable improvement in accuracy and efficiency over the direct estimates. However, the uncertainty appears to be substantially underestimated, which is not as true for the sub-area estimates. Thus, while accuracy is best for the household-level model, the sub-area model outperforms in terms of correctly estimating uncertainty.

# _6.5 Including predictors at various levels._

The baseline specification includes AGEB-level variables, municipal-level variables, and statelevel dummies as candidate predictors. To better understand the sensitivity of the household model to including variables at different levels, we estimate five additional models, representing different combinations of including or excluding AGEB, municipal, and state dummies. Two models use only AGEB or only municipal candidate predictors without state dummies, two more only AGEB or only municipal candidate predictors with state dummies, and one uses AGEB and municipal predictors without state dummies. In all cases, we use plug-in LASSO to select a model from the set of candidate predictor variables.

Table 8 reports the results from this exercise. The in-sample
POLICY RESEARCH WORKING PAPER NO. XXXX

# Ethiopia’s Infrastructure: A Continental Perspective

## _Vivien Foster and Elvira Morella_

### **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably Kenichi Ohashi (country director), Jeeva Perumalpillai-Essex (sector leader), Yoshimichi Kawasumi (transport), Luiz Maurer (power), Mark Williams (ICT), and Yemarshaet Yemane (local consultant).
# **National Trends in Sectoral Employment and Value-Added Shares**

Figure 2 depicts trends in each sector’s share of employment and value-added, which reflect structural transformation, based on ETD statistics. Ghana did not exhibit any strong signs of structural transformation until 2000. Between 2000 and 2010, structural transformation gained momentum, but the changes were mostly limited to agriculture’s share of employment declining and services’ share of employment increasing. Industry’s share of employment grew from 16 percent in 2010 to 20 percent in 2018, driven primarily by manufacturing. Meanwhile, agriculture’s share of employment declined from 55 percent to 33 percent, and services’ share of employment grew from 30 percent to 47 percent over the same period.

The sectoral shares of value-added followed somewhat different trends. Between 1990 and 2018, services’ share of value-added hovered at about 50 percent. Both agriculture’s and industry’s shares of value-added remained close to 25 percent until 2010. Since then, the two sectors’ shares of value-added diverged, with industry’s share rising above that of agriculture. Within industry, mining’s share of value-added increased from 3 percent in 2000 to 12 percent in 2018 (9 percentage points). Construction’s share of industry value-added has increased moderately since 2005, whereas manufacturing’s share has been declining since the mid-2000s.

# **Regional Trends in Industry and Services in the North and the South**

Trends in employment and value-added shares at a more detailed sectoral level are not available in the ETD. As an alternative, we use 2014 IBES summary statistics from GSS (2016) on the number of firms in industry and services, categorized by district and year of establishment, to describe the evolution of sectoral activities at the regional level. We follow the classification of Ghanaian regions prior to the 2018 referendum, which expanded the number of regions from 10 to the 16 in effect today. We define the north as comprising the Brong Ahafo, Northern, Upper East, Upper
A recent exception is Gertler, Shelef, Wolfram, and Fuchs (2016), which uses household survey data in several large developing countries to document an S-shaped relationship between the income distribution and both consumer durable ownership and energy use, in the cross-section and over time as income grows. They then make use of a cash-transfer program-generated exogenous shock to incomes in Mexico to establish that such relationship is indeed causal. In a related paper, Davis and Gertler (2015) analyze energy consumption using Mexican household data, looking at both the extensive margin (whether households adopt air conditioning or not) and the intensive margin (the dollar amount of electricity consumption).

Lee, Miguel, and Wolfram (2016) use an experiment to derive the demand for electricity connections among poor rural households in Kenya, and find that consumer surplus is far less than total costs at all price levels, suggesting that residential electrification may reduce social welfare—at least in the absence of externalities.

Our contribution is first to systematically document infrastructure access and asset ownership across a panel of 14 countries in Latin America over 20 years, in the four main infrastructure sectors. The SEDLAC database provides a harmonized sample of surveys, allowing in particular for the comparison of households at similar levels of income across countries of the region. While the household surveys used here are not following the same households over time, we are able to construct a pseudo-panel by grouping households by income quantiles and looking at the within-quantile evolution of infrastructure access over time. This then allows for the estimation of income elasticities of infrastructure access in a dynamic setting.

This enables us to document extensive inequality of access, within countries across the income distribution, and across countries in Latin America for households at similar levels of income. In addition, the scope of the dataset allows us to test the relevance of country fundamentals vs. policies in explaining these differences, and to provide comparative estimates of the time needed to reach universal coverage.

This paper focuses exclusively on the extensive margin, i.e.
br>28.8<br><!-- End of picture text --> <!-- Start of picture text --> 66.6<br>33.4<br><!-- End of picture text --> <!-- Start of picture text --> 65.3<br>34.7<br><!-- End of picture text --> <!-- Start of picture text --> 66.6<br>33.4<br><!-- End of picture text --> Source: Authors’ own estimate based on NSSO data.

We find that the association between marital status and FLFP did vary across location. In rural areas, the FLFP rate of married women was distinctly above that of unmarried women in all time 18
WRITING<br>4 LETTERS AND FOLDING)**|
|40||NEW|Can copy a pattern of 4 bottle tops<br>**END IF FAILS 4 BOTTLE<br>TOPS**|
|41||NEW|Can copy a pattern of 6 bottle tops<br>**END IF FAILS 6 BOTTLE<br>TOPS**|
|42||NEW|Beans and beads|
|43||43||

<u>Table 2: Comparison of original MDAT Language items with Western Kenya MDAT</u>

|Item|Language/Hearing|Item|Western Kenya MDAT|
|---|---|---|---|
||Startles or jumps/responds to<br>sounds|||
|**1**|Happy vocalising or making<br>sounds–not crying|||
|2|Laughs/chuckles|||
|3|Turns to voice – if you are out of<br>sight, does she/he look in the<br>directionofyourvoice orsound|||
when the GUO25 is used as an SOE identifying variable even though it has 25% participation of the state.<sup>31</sup> In other words, the variable GUO25 misses all the firms owned by a state or a government at 25% or more when other shareholders have higher shares.

- **GOU25 and GOU50 suffer from the lack of completeness of ORBIS data, which results in a large share of firms being missed.** The GOU variable suffers from omission and measurement errors resulting from the completeness and accuracy of ORBIS data. In the former, it refers to companies that are operating in the markets but are not covered by ORBIS, whereas the measurement error is linked to firms that are incorrectly classified as private.

To fully capture the comprehensive list of directly and indirectly owned SOEs (i.e., SOE subsidiaries) we developed and implemented a new methodology which uses the information collected by ORBIS, complements it with other sources and elaborates it to fully capture the ownership of the state in the corporate sector. The steps for the identification of SOEs detailed in this methodology yield a more accurate count of SOEs globally. A detailed description of the functions of the algorithm is presented in section 5.

Even adopting a 25% threshold for state participation, our analysis shows that the GUO variable tends to significantly underestimate the real presence of the state as a shareholder in most of the countries analyzed. When comparing the number of SOEs directly owned by a government at 25% or above with those identified through the GUO25 variable in ORBIS for the same set of 60 countries, we find that on average, the Global BOS database quadruples the number of firms with state participation per country (see

> 30 Depending on the threshold (25 or 50) used in the GUO variable in ORBIS, it can lead to different “ultimate” owner entities. On the contrary, our methodology captures the full ownership structure of ownership using a lower threshold that allows to identify all paths with state participation to