rafmacalaba/data-use-mentions-extended huggingface dataset:
are especially useful for addressing development challenges in lagging regions, where poverty tends to be spatially concentrated. In addition, because poorer farmers are more likely to live in poorer regions, assessing the broad contours of the spatial distribution of subsidies can give some indication of a subsidy scheme’s overall progressiveness or regressiveness at household level.

Our analysis finds that output subsidies are spatially progressive in 10 countries and spatially regressive in six. We also find that if countries were to repurpose their output subsidies from rice—a very waterintensive crop responsible for exacerbating harmful externalities such as water scarcity, salinization, and carbon dioxide emissions—to maize, their subsidies would become significantly more spatially progressive. This finding stems from rice being produced in richer regions, probably because it is more input-intensive.

For input subsidies, of the 23 countries examined, we find that 11 have spatially progressive subsidies, nine have spatially regressive subsidies, and three have spatially neutral ones. As well as distributional impacts, we examine environmental consequences. Using spatially disaggregated data on fertilizer use, we find a very close, positive relationship between input subsidies and fertilizer use, particularly in richer regions. We also show that subsidies are significant contributors to soil saturation of nitrogen, a sign of fertilizer overuse and an indicator of increased environmental degradation.

The remainder of the paper is as follows. Section 2 briefly describes the literature on the impact of agricultural subsidies on distributional and environmental outcomes. Section 3 explains the main datasets we use and our strategy for estimating the spatial distribution of subsidies across regions. Section 4 describes our strategy of using Lorenz curves for estimating the progressiveness of subsidies. Section 5 presents our findings on the distribution of output subsidies, and section 6 on the distribution of input subsidies and their impact on fertilizer use and nitrogen concentrations in soils. Section 7 contains a discussion of the results and our conclusions.

# **2 Literature**

Much of the literature examining the distributional impacts of agricultural subsidies has focused on developed countries, and specifically the EU’s Common
countries: Evidence from the living standards and measurement study surveys. _Economics of Education Review_ , _49_ , pp.69-90; and 42 Psacharopoulos, G., and Patrinos, H.A. 2018. Returns to investment in education: a decennial review of the global literature. _Education Economics_ 26(5): 445-458: <u>https://www.tandfonline.com/doi/full/10.1080/09645292.2018.1484426.</u>

> 43 <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-</u> - <u>lending groups</u>

44 Psacharopoulos, G., and Patrinos, H.A. 2018. Returns to investment in education: a decennial review of the global literature. _Education Economics_ 26(5): 445-458: <u>https://www.tandfonline.com/doi/full/10.1080/09645292.2018.1484426</u> 45Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., and Liu, J. 2020. Projecting the potential impacts of COVID-19 school closures on academic achievement. EdWorkingPaper: 20- 226. Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/cdrv <u>yw05</u> 46 Azevedo, J.P., Hasan, A., Goldemberg, D., Iqbal, S.A., and Koen, G. 2020. Simulating the Potential Impacts of COVID-19 School Closures on Schooling and Learning Outcomes: A Set of Global Estimates. Policy Research Working Paper No. 9284. World Bank, Washington, DC <u>https://openknowledge.worldbank.org/handle/10986/33945.</u> 47 Azevedo, J.P., Hasan, A., Goldemberg, D., Iqbal,
# **1. Introduction**

The COVID-19 crisis represented an unprecedented shock for the private sector, affecting businesses through many channels at an uncontrolled speed and spanning a duration of more than two years. In the process, it unveiled the underlying vulnerabilities of firms, sectors, and countries. These types of crises pose pressing questions not only about the magnitudes and responses to economic shocks in general, but also about the heterogeneity of their effects. For instance, evidence from the COVID-19 shock and recovery indicates that small firms were disproportionately affected (Apedo-Amah et al., 2020; Cirera et al., 2021). Given that most informal firms tend to be small, it is crucial to understand not only the effects of a shock on informal firms, but also if those effects are explained by external characteristics such as firm size or by other internal attributes of informality.

_A priori_ , given the characteristics of informal firms, the magnitude and direction of the impact of crises and, in particular, of the COVID-19 shock are not entirely clear. On the one hand, initially, informal firms are expected to exhibit a worse performance due to their higher vulnerability, including the lack of access to finance from formal sources and the difficulty of public support reaching these firms. Specific to the COVID-19 crisis, these firms are concentrated in sectors requiring more face-to-face interactions and were, therefore, more exposed to the lockdown restrictions. On the other hand, informal firms can avoid certain taxes and regulatory burden and have more flexibility in terms of employment (including lower hiring and firing costs), location, resource management, and market competition (Loayza, 2018). Therefore, informal firms and jobs can more easily recover given their lower organization capital and entry costs (Alfaro et al., 2020).

Using several rounds of Business Pulse Survey (BPS) data of the World Bank for six countries in South Asia, this paper quantifies the heterogeneity in the impact of the COVID-19 crisis and the recovery of firms by their informality level
|0.44|0.61|0.35|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

34
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data

# **1 Introduction**

_"All Cretans are liars."_ ( _Epimenides, Philosopher from Knossos, Crete, circa 600 BC)_ Over the past decades, firm-level data have become widely available for economic research and are used in manifold and vibrant lines of research. This is especially true for research on firm behavior in developing countries. The World Bank ‘Regional Program on Enterprise Development’ (RPED) was the first large-scale effort to gather firm-level survey data in developing countries using a multipurpose survey instrument. The survey was executed in eight Sub-Saharan countries in the early 1990s and the resulting survey data have been extensively used in research on firm investment, export behavior, rent- and risk-sharing (Teal 1996, Bigsten, et al. 2003). With the success of the RPED surveys, more large-scale firm-level surveys in developing countries followed, such as the ‘World Business Environment Surveys’ (WBES), ‘(Productivity and) Investment Climate Surveys’ ((P)ICS), and the ‘Business Environment and Enterprise Performance Surveys’ (BEEPS), organized by the World Bank, European Bank for Reconstruction and Development (EBRD) and/or other (multilateral) institutions. Also these recent firm-level surveys have attracted the attention of economic researchers and have been analyzed intensively (Dollar, HallwardDriemeier and Mengistae 2005, Cull and Xu 2005).<sup>4</sup> Of course, researchers have been well aware that firms may have an incentive to misreport their activities, for reasons such as high marginal tax rates, corruption, and crime (Johnson, et al. 2000, Dabla-Norris and Koeda 2008, Gatti and Honorati 2008). Firm-level studies on underreporting of firm sales and/or output often rely on self-reported measures of 2 A comprehensive review of the literature using firm-level data from developing countries is beyond the scope of this paper.

2
proposed to be identified universally and updated regularly. The rural access indicator, one of the five proposed headlines, is a good example in this regard. The indicator has been developed for inclusion in the set of indicators for International Development Association (IDA-14) results measurement system. The transport sector headlines are derived from the set of transport Recent Economic Developments in Infrastructure indicators.

At the national and sub-national level, the transport sector Recent Economic Developments in Infrastructure indicators are included in the more comprehensive Recent Economic Developments in Infrastructure "Snapshot" and "Diagnostic" indicator sets, as proposed by the Infrastructure Action Plan. They are derived from the sets of core measures which have been established for each of the transport sub-sectors.

23 ____________________________________________________________________________________________________________
disasters, and the desert locust crisis of 2020 to name a few. These shocks have had devastating impacts on the lives and wellbeing of Yemenis. Many of these crises are inter-related and are rooted in the ongoing conflict. This includes an unprecedented scale of internal displacement caused by the fighting. To date, 4.3 million Yemenis have been displaced within the country. Overall, the economic conditions of Yemenis are very precarious, and the latest Humanitarian Needs Overview (HNO) indicates that 23.4 million Yemenis are in need of humanitarian assistance, 19 million of whom require food assistance.<sup>1</sup> It is estimated that prior to the conflict around 13 percent of the Yemeni workforce was unemployed, but these numbers were substantially higher for youth and women, and masked the inactivity of those who were out of the labor force (ILO 2015). While the male labor force participation rate was 65.4 percent prior to the conflict, this was at 6 percent for women, one of the lowest in the MENA region (ILO 2015). Moreover, most employed women were working in the agriculture sector, engaged in unpaid work, or were economically inactive because they were fully engaged in household chores (Al-Ammar and Patchett 2019). A rapid damage needs assessment conducted by the ILO at the start of the war documented the effects of the conflict on women’s participation in the economy, reporting job losses estimated at an average of 28 percent among female respondents, compared to 11 percent among male respondents (ILO 2016).

Women have also faced further setbacks throughout the war. A World Bank phone survey asked respondents about coping strategies adopted to ease financial distress (World Bank 2023b). Alarmingly, 3 percent of respondents said that female children were married to ease financial stress over the last three months alone (Figure II.1). This was almost double for displaced households, and higher in Houthi areas than those under IRG control. Child marriage has been a concern in Yemen
it still accounts for about a half of all capital expenditure by SOEs (Statistics South Africa 2021).

Among oil exporters, investment growth also slowed significantly after 2014 in Angola, Chad, and Nigeria, and turned negative in Equatorial Guinea, where oil production fell by nearly 60 percent from 2014 to 2021. The effects of the sharp decline in oil prices in the mid-2010s were exacerbated by combinations of weak business environments, new capital and foreign exchange controls (Angola, Nigeria), austerity measures to offset falling commodity revenues (Angola, Chad, Nigeria), and deteriorating security situations (Nigeria, Chad). Together, these weighed heavily on investor sentiment. Falling capital spending in the SSA oil sector also reflected a secular decline in oil production because of aging oil fields and increasing production costs; investment was further depressed in 2020 by pandemic-related stoppages, supply chain problems, and maintenance delays (Cherif and Matsumoto 2021). Fiscal space also diminished considerably for many of the region’s oil producers, with sharp declines in tax revenues from the oil sector, which constrained public investment. Even so, in some countries (Cameroon, Gabon) large infrastructure investment programs continued, boosting investment growth despite declining oil industry investment.

As in SSA oil exporters, investment growth in other commodity-exporting countries slowed sharply in 2015-17. Strong economic growth during 2011-14 had been accompanied
(T+R)|6,339,517|
|Megafon|May2020|~2,000,000|<br>1(T+R)|2,000,000|
||Total|5,533,860||82,657,737|

Alongside the Mobile Engage project, the Government of Tajikistan also endorsed an expansion of the existing Listening to Tajikistan (L2T) survey, another World Bank initiative, to collect individual and household data on the social and economic impacts of the COVID-19 pandemic. L2T is a continuous monthly household panel survey conducted over the phone with a nationally representative sample of about 1,200 households. The L2T survey was first launched in 2015, and because interviews are conducted over the phone, continued operation without interruption during the COVID-19 pandemic when in-person interviews were not possible. This study uses the national panel survey data to assess the impact of the Mobile Engage project on reported risk reducing behaviors.

The L2T survey was organized around two primary components. The first was a comprehensive inperson baseline survey, conducted in 2015, and supplemented with a second similar comprehensive in-person survey completed in 2017. The baseline sample design used a stratified two-stage clustered approach, with a target of national as well as regional representativeness for household consumption and expenditure. The sample frame was the national census of Tajikistan conducted in 2010. In the first stage, 150 clusters (census tracts) were randomly selected from the census, with a probability of selection proportional to size. In the second stage, 3,000 households were randomly selected from

> 8 TCell, Megafon, and Babilon M

5
workforce and adequate infrastructure (Butollo 2021; Arunyanart et al. 2021).

As discussed in Kilic Celik, Kose, and Ohnsorge (2023), potential output growth is expected to slow in many EMDEs in the coming decade amid unfavorable demographics and slowing investment and productivity growth. One way in which policymakers in EMDEs can boost long-term growth of output and productivity is by promoting trade integration through measures to reduce trade costs.

Our study examines the following questions. First, what is the link between trade growth and long-term output and productivity growth? Second, what are the prospects for trade growth in the coming decade? Third, how large are trade costs? Fourth, what are the correlates of trade costs? And fifth, which policies can help to reduce trade costs?

Our study contributes to the literature in a number of ways. First, it expands on World Bank (2021b) with a new, comprehensive review of the theoretical and empirical literature on the links between trade and output growth. Second, it presents an event study of the evolution of trade in goods and services through global recessions, including the pandemic-induced global recession of 2020.

Third, our study revisits an earlier literature that reported estimates of trade costs and their correlates (Arvis et al. 2016; Novy 2013; World Bank 2021b). It uses estimates of the costs of goods trade for up to 180 countries (29 advanced economies and 151 EMDEs) from the World Bank/UNESCAP database for 1995-2019. The drivers of the costs of goods trade, which accounts for about 75 percent of world and EMDE trade in goods and services, are estimated econometrically. Our study also quantifies the contribution of one type of services trade—logistics and shipping services—to the costs of goods trade. In addition, our study goes further than previously published research in assessing the role of trade policy—tariffs, participation in trade agreements—in trade costs.

Fourth, our study builds upon its analytical findings to discuss policy options for lowering trade costs.
|Description|ERA5 climate<br>reanalysis gives a<br>numerical<br>description of the<br>recent climate,<br>produced by<br>combining models<br>with observations.<br>Total precipitation is<br>the accumulated<br>liquid and frozen<br>water, comprising<br>rain and snow, that<br>falls to the Earth’s<br>surface. It is the sum<br>of large-scale<br>precipitation and<br>convective<br>precipitation.|The CHIRPS data<br>archive is a quasi-<br>global (50S–50N)<br>gridded 0.05°<br>resolution times series<br>covering precipitation<br>from 1981 to near-real<br>time.|The SPEI is a multi-<br>scalar drought index<br>based on climatic data.<br>It is a ratio of actual to<br>potential<br>evapotranspiration.|This spatially explicit<br>index is an indicator of<br>crop performance<br>based on the<br>availability of water to<br>the crop during a<br>growing season.|The Soil Water Index<br>quantifies the<br>moisture condition at<br>various depths in the<br>soil. It is mainly driven<br>by precipitation via<br>the process of<br>infiltration. Various t-<br>values are available: 1,<br>5, 10, 15, 20, 40, 60,<br>100. The T- value<br>simulates the<br>infiltration time into<br>deeper soil layers and<br>hence indicates<br>qualitatively the depth<br>of the SWI estimate.<br>T40 was selected as<br>being generally<br>representative
simulation must be summed up. Throughout the analysis, we decomposed trade effects into trade creation and trade diversion. Trade creation in SMART is defined as the direct increase in imports following a reduction of the tariff imposed on good _i_ from country _j_ . If the tariff is a preferential reduction then imports of good _i_ from country _j_ will increase due to the substitution away from imports of good _i_ from countries that become relatively more expensive as a result of the preferential tariff. SMART defines this as the trade diversion effect.

To perform the simulations we used the Harmonized Commodity Description and Coding System (HS) product classification at the 6-digit level using bilateral import data from United Nations Commodity Trade Statistics Database (UN Comtrade) for Russia, Belarus, and Kazakhstan. Although Russia’s bound tariff commitments are specified at the HS 10-digit level, averages are taken across HS 6-digit product groups. Whenever information on applied MFN tariff rates was missing at the 6-digit level,<sup>6</sup> MFN rates based on the average of the HS 4-digit group to which 6-digit products belong were used as proxies for missing values. Information on the applied CET and Russia’s schedule of accession commitments (tariff bindings) was taken from Shepotylo and Tarr (2012). Information on Russia's tariff preferences on imports from CIS countries and GSP beneficiaries was taken from the United Nations Conference on Trade and Development (UNCTAD) Trade Analysis and Information System (TRAINS).

The study makes use of two baselines. A baseline of 2012-20 is used for the analysis of Russia’s tariff

> 6 This is the case in 3,047 partner-product flows representing 2.87 percent of total observations in a given year.

RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 5
in their agreements by negotiating mutual recognition agreements in professional services, we focus on a range of possible policy options in professional services. These are presented in section VIII. Conclusions are presented in section IX. In appendix A, we discuss the trade and tariff data in some detail. We document the calculation of ownership shares by sector and region in appendix B. How we obtained estimates of the Dixit-Stiglitz elasticities in goods is described in appendix C. The explanation of the estimation of the ad valorem equivalents of barriers in professional services, done by Nora Dihel and Josaphat Kweka, is explained in appendix D.

# **II. Overview of the Kenyan Service Sectors**

In this section, we summarize the key institutional and policy issues in telecommunications, banking, insurance and transportation. This discussion is based on several policy notes written on the Kenyan business services sectors.

## **Transportation**

One bright spot in the Kenyan transportation network is its air transportation services. In recent years, Kenya allowed private sector development (both Kenyan and foreign) to develop the air transportation links. The efficient air transportation services facilitate the important tourism sector and have been instrumental in the development of the Kenyan cut flower industry, which in turn has contributed to growth and poverty reduction.

However, Kenya’s port, rail and road transportation facilities are significant problems for transportation of its goods and for the competitiveness of its exports (for details see Helu, 2007; Ochieng, 2007; and World Bank, 2007). Its principal port, Mombassa, is plagued by poor infrastructure and complicated bureaucratic procedures. As a result, it takes an average of two weeks to clear a container at the port and more than four weeks for over 5 percent of the containers. The cost of importing a container into Kenya exceeds USD 1300, while it is under USD 1,000 per container in Tanzania and South Africa 8
|(0.173)|(0.126)|(0.104)|(0.114)|(0.134)|(0.0980)|||
|Financial Openness Index|-0.410|0.0331|-0.0658|-0.194|..|..|..|..|..|..|-0.186|-0.154|
|_(Chinn-Ito index)_|(0.451)|(0.778)|(0.640)|(0.558)<br>|||||||(0.564)|(0.589)|
|Remittances<br>_(% GDP received)_|0.153<br>(00939)|0.460<br>(0357)|0.198<br>(0145)|0.230*<br>(0133)|0.237<br>(0170)|0.358<br>(0243)|0.133<br>(00860)|0.185*<br>(0102)|0.409*<br>(0223)|0.240*<br>(0145)|0.365<br>(0222)|0.284<br>(0200)|
|_,_<br>International Tourism|.<br>0.0180|.<br>-0.0543|.<br>-0.0330|.<br>0.0217|.<br>-0.00229|.<br>-0.0335|.<br>-0.00291|.<br>0.0299|.<br>-0.0545|.<br>-0.00455|.<br>-0
March 2011.

HEIS includes both demographic and income information but its main focus is on expenditures. Surveys collect expenditure information on more than 1,000 items. The recall period of expenditures for most items is the last month. For durables, some education expenses, insurance expenditures, investment in housing and farms, and income components, the recall period is the last 12 months. Like in most surveys of such type, income and expenditure data are self-reported. HEIS does not distinguish between how much is spent and how much is consumed and reports only expenditures on a particular item. For items, which were not purchased, an estimated value is provided.

The food component of the welfare aggregate includes purchases or the estimated value of all food products bought or received for free. Food expenditures in restaurants and dining facilities are also included in food. The non-food component aggregates the following groups of expenditures: tobacco, miscellaneous, hotel, leisure, communication, transport, furniture, housing, clothing, actual and estimated rent, education, and health insurance.

As discussed above, there are no clear rules on whether education and health expenditures should be included or excluded from the welfare aggregate and the decision depends mainly on the country context. Following Deaton and Zaidi (2012), the elasticity of education and health expenditures with respect to total expenditure per capita has been estimated to determine whether education and health expenditures are closely associated with wellbeing. The point is to keep expenditures with high elasticities to total expenditures. As is often observed in other countries, elasticity of education expenditures is higher than

> 6 Data points were downloaded in January 2016.

5