rafmacalaba/data-use-mentions-extended huggingface dataset:
mean of the real interest rate is positive) than in developing countries (where it is negative).

5 The sources of central government data are different from those of public sector data. The former cover many more countries but fewer years (from 1970 on only). For comparability, all the regressions in the table use the same set of countries, and hence the central government-based saving regressions possess fewer observations. © The second exception is that M2/GNP has a positive and significant coefficient only in the regressions corresponding to the central government definition of the public sector. *7 Using instead the variance of the forecast error to measure uncertainty yielded similar results. *8 Other year dummies, when added to the regressions, were likewise insignificant. ° Other experiments using instead the Hodrick-Prescott decomposition yielded qualitatively similar results: larger coefficients on the temporary than on the permanent components of both variables (the latter was in fact negative for the terms of trade), but in this case all significantly different from zero.
A large section of the questionnaire is dedicated to the topic of finance. This section asks about sources of financing for day-to-day operations, purchases of assets, and sources of financing of these purchases, along with multiple questions regarding access to credit. Questions about the use of financial services such as mobile money are also included. Data is also collected on fixed assets used by informal businesses (whether owned or not) along with costs and even profits from the business. These more delicate questions are placed towards the end of the interview following standard practices of questionnaire design.

A sizeable portion of the interview is devoted to the topic of informality itself. In particular, the questions examine potential reasons for the business to be in the informal sector, along with a battery of questions exploring potential costs and benefits of formalization as perceived by the owner or manager of the informal business.

The topic of business practices is also covered in the questionnaire, with the wording of the questions closely following the work of McKenzie and Woodruff (2017) and aligned broadly with studies on management practices among formal firms in developed economies (e.g., Bloom and Van Reenen 2007, 2010 and Bloom et al. 2013). The standard set of questions on management practices used in the study by McKenzie and Woodruff (2017) was adapted for simplified and less formal wording to be more accessible to the target respondents of the Informal Sector Enterprise Surveys. Further, the battery was also considerably shortened to accommodate the implementation of these questions together with the rest included in the Informal Sector Enterprise Surveys within the average of 25 minutes allotted to the entire interview. Principal components analysis was used to determine which of the questions to include from the original set developed by McKenzie and Woodruff (2017). In addition to the topic of management practices, the interview also covers informal businesses' relations with their suppliers and customers.

# **2.4 Construction of Databases**

This paper combines the data collected under the methodology described in Section 2.1
some interesting patterns and trends that could yield insights for education and labor policies in the region in several areas.

First, the note provides new information on the size of the nini problem. In the 15 countries for which we have harmonized household survey data through the extended SEDLAC database, 18.2 million youth between the ages of 15 and 24 were living as ninis in 2010. Scaling up this surveybased estimate to cover all the countries in the region yields an estimated 20.8 million ninis for the Latin American and Caribbean region as a whole in 2015.

Latin America is not an outlier when it comes to the issue of ninis, however. Globally, there are close to 260 million youth both out of school and out of work, spread across regions. The share of ninis in Latin America, at about 19 percent, is roughly equal to the global average of 22 percent. The region with the highest share of ninis is the Middle East and North Africa, where one in three youth is out of school and out of work, while the lowest share is in high-income countries (11 percent).

Second, we provide information on the time trends of the nini phenomenon in Latin America. Increased prosperity over the past two decades has not solved the problem of ninis. For LAC as a whole, the percentage of youth who are ninis declined gradually between 1992 and 2010, but because the region’s youth population has increased, the _number_ of ninis has actually grown slightly. Unsurprisingly, nini trends have varied across the region, with some countries showing sharp declines in the percentages of ninis over the 1992-2010 period and others seeing small increases.

Third, we describe the characteristics of ninis. In gender terms, the nini phenomenon in LAC is largely a female phenomenon, but it is becoming more of a male problem over time. Women make up over two-thirds of ninis in LAC, but the number of male ninis jumped 46 percent between 1992 and 2010. As a result, males account for the
Our models include country and year effects and allow income and governance to have nonlinear effects in the same model. The usual reason economists are reluctant to include country effects in panel models is concern that governance indicators exhibit too little within-country variation to permit identification of their effects. The indicators in our sample exhibit substantial variation within countries, and this enables us to identify highly significant effects of both income and governance, not just one or the other, in several models. As mentioned earlier, these effects evidently differ between developing and developed countries, which is important given that floods have a disproportionate impact in developing countries. These differences are broadly consistent with concerns raised by hydrologists about flood management practices in developed countries, especially the United States.

# **2. Models**

Income and governance might affect flood fatalities through a variety of indirect and direct pathways (Table 2). They might affect it indirectly through an impact on the number of floods, by facilitating the construction, maintenance, and retrofitting of bridges, dams, levees, and other flood-management infrastructures and by facilitating the adoption and monitoring of zoning regulations, as land use can affect flooding risks (upper left-hand cell in Table 2). For example, regulations that reduce the expansion of impervious surfaces could result in less frequent flooding. Income and governance might also influence the effectiveness and celerity of flood management actions that affect the magnitude of flooding, through dam releases and the use of sandbags (lower left-hand cell). The DFO data enable us to estimate models for both the frequency and magnitude of floods.

5
status change in the 18 Food and Agriculture Organization (FAO) Regions that span the world’s oceans? We will follow the fisheries science literature, where biological overfishing occurs when fishing pressure ( _F_ ) is higher than the fishing pressure that would produce the maximum sustainable yield ( _Fmsy_ ). Then, the relevant metric is the ratio _F/Fmsy_ , where _F/Fmsy >_ 1 indicates overfishing, _F/Fmsy_ = 1 indicates biologically sustainable fishing, and _F/Fmsy <_ 1 indicates fishing pressure could increase without threatening the long-term biological size of the stock. We refer to this ratio as overfishing status.

# **6.1 Calculating changes in overfishing status**

We calculate the change in overfishing status that would result from a reduction in Chinese distant water fuel subsidies. Specifically, we consider three policy changes that would reduce the subsidy by 10%, 30%, and 50%. We follow the same approach as McDermott et al. (2019), who calculated the changes in overfishing status that result from changes in fishing effort<sup>27</sup> using catch-weighted mean _F/Fmsy_ values by FAO Region. Our values of overfishing status are for 4,713 fish stocks and they come from Costello et al. (2016).<sup>28</sup> We take fishing hours observed during 2020, the most recent publicly available year of data, as the baseline level of fishing activity currently occurring in the world’s oceans. We then aggregate fishing hours by Chinese and all other vessels to the level of FAO Region and calculate the fraction attributable to Chinese distant water vessels (Figure A10). Since we only analyze the subsidies granted to China’s distant water fishing fleet, we assume that only Chinese fishing outside Chinese waters responds to changes in subsidies.<sup>29</sup> For each policy scenario, we use our estimate of the elasticity of fishing by Chinese distant water vessels with respect to fuel subsidies (Column
Zhao, and Xia Zhao** , “A Harmonized Global Nighttime Light Dataset 1992–2018,” _Scientific Data_ , 2020, _7_ (168).

- **Litan, Robert and Alice Rivlin** , “Projecting the Economic Impact of the Internet,” _American Economic Review_ , May 2001, _91_ (2), 313–317.

- **Manacorda, Marco and Andrea Tesei** , “Liberation Technology: Mobile Phones and Political Mobilization in Africa,” _Econometrica_ , 2020, _88_ (2), 533–567.

- **Mastrobuoni, Giovanni** , “Crime is Terribly Revealing: Information Technology and Police Productivity,” _The Review of Economic Studies_ , 2020, _87_ (6), 2727–2753.

21
A one standard deviation increase in the overall business regulation index leads to a 3.4 percent productivity increase for the average manufacturing firm and a 1.7 percent increase for the average service firm. A one standard deviation increase in the tax index correlates to a 3 percent increase in manufacturing and 2 percent in services. A one standard deviation increase of the trade index leads to 3.16 and 2.10 percent increases for manufacturing firms and service firms, respectively. A one standard deviation increase in the employment regulation index raises labor productivity by 1.52 in manufacturing and 1.42 percent in services.<sup>42</sup>

# **5.3 EU15 South**

While the majority of the EU15 countries experienced positive productivity growth from 2003 to 2008, the southernmost countries—Greece, Italy, Portugal, and Spain—experienced a contraction. Given the results of the previous section on the importance of firm size, foreignaffiliation, and outward FDI for productivity, the answer may lie in their mix of firms.

**Figure 4 Share of Foreign Firms (% of total)  in EU15, 2008**

<!-- Start of picture text --> EU15 North EU15 Continental EU15 South<br>Sweden Finland Norway Belgium France Italy Spain<br>Firm<br>9 5 4 13 6 2 2<br>population<br>Employment 40 14 10 37 26 10 8<br>Value add 33 20 14 57 35 11 12<br>Value add<br>11 8 5 37 16 7 -9<br>growth<br><!-- End of picture text --> _Source_ : World Bank staff calculations based on Amadeus.

Amadeus data show that less than 3 percent of Italian and Spanish firms are global headquarters (Figure 4). The distribution of firm size in the EU15 South is skewed towards microenterprises; very small, family-operated firms play a much greater role in the economies of Southern Europe than in
-36-

In table 3.2 the figures for Brazil and Mexico are converted into US dollars at the average exchange rates for the year 1975 as given by IMF (8.13 cruzeiros and 12.50 pesos to the US dollar respectively). These can be compared with 1975 output values for the United States as derived from the Annual Survey of Manufactures.

TABLE 3.2

|Grors Value of Output in Brazil,<br>Mexico an<br>at official<br>exchange<br>rates<br>(1975 U<br>...)______|d the USA in<br>S dollars)<br>____-__________|1975<br>_______________|
|---|---|---|
|Brazil<br>(million<br>dollars)|Mexico<br>(million<br>dollars)|USA<br> <br>(million<br> <br>dollars)|
|Total Manufacturing<br>Output<br>96,272.8|38,403.9|1,039,377.4|
|Grain Mill Products<br>913.7|729.9|6,469.3|
|Sugar & Sugar Products<br>1,493.5|52.7a|4,490.8|
|Malt and Malt Beverage<br>421.8|877<sup>.</sup>9<sup>a</sup>|6,232.2|
|Tobacco and Tobacco Products<br>752.6|307<sup>.</sup>8<sup>a</sup>|8,059.9|
|Textiles<br>3,618.8|1,432.8|15,770.8|
|Footwear and Leather Products<br>1,265.9|515.8|5,
**Score 1 (2013)**

**Score 3 (2013)**

**Score 5 (2013)**

**Topic Ghana 2013 questions**

# **Table A4 Continued: Ghana 2013 Management Survey**

**Management Practice**

|**CS-Other**<br>**Staffing**|Examines the emphasis put on<br>attracting talented applicants<br>and applicants with particular<br>skills|The organization does not go<br>out of its way to recruit talented<br>applicants or applicants with<br>particular skills, so hiring is<br>driven entirely by staff numbers<br>and availability of funds.<br>Training opportunities are not<br>actively sought, and when<br>available are not allocated<br>based on capacity and/ or<br>need.|The organization occasionally<br>tries to recruit applicants with<br>particular skills, but<br>identification of needed skills is<br>not systematic and recruitment<br>is only occasionally based on<br>identified skill needs. The<br>organization rarely goes out of<br>its way to recruit talented<br>applicants. Training<br>opportunities are occasionally<br>sought, but driven mostly by<br>availability.|The organization actively<br>recruits talented applicants and<br>applicants with needed skills,<br>based on regular assessments<br>of skill needs. Training<br>opportunities are sought and<br>allocated to the staff who can<br>most benefit from them.|
|---|---|---|---|---|
|**Targeting**|Examines whether the<br>organization defines an<br>appropriate range of<br>meaningful objectives and/ or<br>targets to operationalize its
|Energy poverty incidence rate (absolute)|2,952|0.106|0.308|0|1|
|---|---|---|---|---|---|
|Energy poverty (> 10 percent)|2,952|0.530|0.499|0|1|
|Electricity (share)|2,952|0.0762|0.0792|0|3.063|
|Natural gas and town gas (share)|2,952|0.00132|0.00942|0|0.170|
|Liquefied hydrocarbons/butane/propane (share)|2,952|0.00111|0.00428|0|0.0630|
|Liquid fuels (share)|2,952|3.00e-05|0.00104|0|0.0519|
|Coal (share)|2,952|0.00329|0.0352|0|1.625|
|Other solid fuels (share)|2,952|0.0374|0.0790|0|1.075|
|Heat energy (share)|2,952|0.0118|0.0292|0|0.243|

_Notes: The indicators in this table are at the household level (N=2,952). We weigh each observation by household survey weights. The energy poverty incidence rate (share) is the 2M energy poverty indicator. The energy poverty incidence rate (absolute) is the M/2 energy poverty indicator. All variables are reported as shares._ _Source: Own estimates based on HBS (2019)._

# **Lower welfare quintiles report higher energy expenditure shares and higher energy poverty rates.**

Figure 1 demonstrates that those households that report the lowest average income spend a higher share of their overall household expenditure on energy (18.1 percent
advantage. The draft Orissa Revenue Administration Bill is intended to simplify, consolidate and replace the various laws that currently govern land administration. However, the extent to which legislative and organizational improvements in land administration will enhance access to land for the rural poor, depends to a large extent on the degree of transparency with which land administration is conducted in practice. Access to information and public awareness of rights appear to be critical factors. The recent initiative of the Revenue Department, Government of Orissa, to disseminate a local-language 'how to' manual on matters of land transfers and access to land records, is a most welcome contribution in this area, and one from which other states could learn.

- _Land consolidation:_ the involvement of local NGOs in the formation of village committees for land consolidation may help to overcome some of the resistance to consolidation, where consolidation is genuinely demanded by particular villages, but where the government-initiated process is perceived to be insufficiently participatory. NGOs could be encouraged to undertake mass-awareness generation by using informal media techniques (puppetry, folk theatre, etc.) and create a basis for negotiated and participatory land consolidation at village level. The government's role would be confined to the correction of existing land records, demarcation of common lands, assistance in land measurement, and preparation of the new set of land records. To the extent that both poorer and better-off farmers wish voluntarily to consolidate their holdings in the interests of raising productivity, the most effective public interventions are likely to be those that reduce transaction costs in the land market.

- **_E_** _ncroachment:_ the most promising avenues for protecting rights of access to common land for the rural poor are through efforts to raise public awareness and access to information. Some NGOs in Orissa have been successful in pursuing public interest litigation to defend tribal land rights. Following their lead, the strengthening of local panchayats could make a vital contribution towards promoting the watchdog function of civil society institutions. Only with strong civil society institutions will there be effective
fraction of freight hauled by trucks annually in India, the geographical coverage of the data set is quite extensive.

# 3 **Transport costs and their determinants: Descriptive Analysis**

We start with summary statistics from the pre-pandemic period trucking data set which are reported in Table 1. On average, a truck in our data set travels about 924 km and spends a little over 17 hours on the trip. The truck capacity varies from light (1 to 10 tons) to heavy-duty trucks with carrying capacity up to 35 tons. The average truck size is around 18 tons. The share of trips taken by light trucks is about 22 percent which is consistent with the national average (21 percent). The average number of trips between origin-destination is about 286 though about 8 percent of origin-destination pairs have 5 or fewer trips. We utilize the trucking data to establish key features of trucking costs in India. Table 2 reports several regression results for freight rate per ton-km as a dependent variable. The regressions are used to detect correlations between freight rate and economic density and should not be interpreted as causal relationships. We take total night light luminosity as a measure of economic density in a district. The basic regression specification controls for the log of the distance between origin and destination districts, types of vehicles used (container, trailer or open bedded) and commodity transported, size of the truck, and appropriate location fixed effects. The standard errors are clustered at the route level. In the following, we report the main stylized facts.

## **_Stylized Fact 1: The freight rate per ton-km is relatively low in India._**

The average freight rate is about Rs.3.92 per ton-kilometer (mt-km, for short) (Table 1) which translates into about 5 cents in US $. This is much lower than about 13 cents/mt-km in the United States, and 20.5 cents/mt-km in Colombia (Allen et al, 2020) before the pandemic. Gasoline price in
# **Table A1: Variable Definitions, 1/5**

|**Variable name**|**Variable defnition and data source(s)**|
|---|---|
|**A. Additional control variables**||
|Familiarity|Log of 1 plus the number of declarations registered by a given broker and cleared<br>by a given inspector in the six preceding months. Defned at the inspector-broker-<br>semester level. Source: Madagascar customs.|
|Congestion|Log of the number of declarations assigned to the inspector in a given month.<br>Defned at the inspector-month level. Source: Madagascar customs.|
|**B. Auxiliary variables**||
|Internal reference prices (IRP)|Median of unit prices (ratio of value to quantity in kilograms) of a given HS 6-digit<br>product from a given country of origin computed across all import declarations<br>in Madagascar customs data in each year. Defned at the country-HS 6-digit<br>product-year level. Source: Madagascar customs.|
|Initial [fnal] average internal refer-<br>ence price|Weighted average of the IRP for all items included in the import declaration with<br>weights being the initially submitted weights by the importer or his representative<br>[fnal weights retained by customs] for each item. Defned at the declaration level.<br>Source: Madagascar customs.|
|External reference prices (ERP)|Unit price (ratio of value to quantity in kilograms) of a given product being<br>exported by a given trading partner to Madagascar. Defned at the country-HS<br>6-digit product-year level. Source: UN COMTRADE.|
|Initial [fnal] average external refer-<br>ence price|Weighted average of the ERP for all items included in the import declaration with<br>weights being the initially submitted weights by the importer
Helmy, Imane, Hebatallah Ghoneim and Khalid Siddig (2019). Implementing Cash Transfer Programmes in Egypt Differently: An Economic Impact Analysis. International Conference on Economic Modeling and Data Science (EcoMod2019), Portugal held on July10-12, 2019.

<u>https://www.researchgate.net/publication/334710756_Implementing_Cash_Transfer_Pro grammes_in_Egypt_Differently_An_Economic_Impact_Analysis</u> (Downloaded on October 8, 2022).

International Energy Agency (IEA). (2022). Fossil fuel subsidy database. <u>https://www.iea.org/reports/fossil-fuels-consumption-subsidies-2022.</u> Jiang, Z., X. Ouyang and G. Huang (2015). The distributional impacts of removing energy subsidies in China. China Economic Review 33: 111–122.

- Lofgren, H. (1994). Egypt's Experience from CGE Modeling: A Critical Review. Working Paper. Economic Research Forum for Arab Countries, Iran and Turkey. <u>https://www.researchgate.net/publication/254411235_Egypt%27s_Experience_from_CG E_Modeling_A_Critical_Review</u> (Downloaded on 30 Sept. 2022).

- Mosa, A. A. (2019). Non-agricultural activities and household time use in Ethiopia: a computable general equilibrium model analysis.

- Nicholls, R.J., P.P. Wong, V.R. Burkett, J.O. Codignotto, J.E. Hay, R.F. McLean, S. Ragoonaden and C.D. Woodroffe (2007): Coastal systems and low-lying areas. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 315-356.

Rausch, Sebastian, Gilbert E. Metcalf and John M. Reilly
of low emissions from using very low capital exceeds the non-compliance effects. Other effects come into play when non-CO2 emissions are considered.

Overall, the findings using informal employment shares in developing countries illustrate that emissions per value added in the informal sector are higher than in the formal sector when aggregate regressions are conducted. The sector-level regressions showing that larger informal employment is associated with lower emissions only in manufacturing and other services imply that emissions per value added can be larger or lower in these sectors depending on the differences in labor productivity between the formal and informal firms. Instead, for all other sectors, the non-compliance of informal workers with environmental regulations leads to higher emissions. The latter also holds for non-CO2 emissions when conducting country-level regressions and this positive relationship between informality measured in 20