rafmacalaba/datause-encoder-data huggingface dataset:
relevant EU-funded program. These programs helped industries in the CEECs to restructure in order to prepare them for accession – in particular to prepare them for competition from EU producers – and helped establish trade facilitating infrastructure such as certification and quality control systems in the CEECs. A comparable instrument could prove very useful for Ukraine.

**Table 2-2: Development of trade in food and live animals (SITC 1) of the EU-27 with Hungary (1995-2007, in million €)**

||Imports by the<br>EU|Exports to<br>Hungary|Total trade|Annual change|Balance|
|---|---|---|---|---|---|
|1995|874|346|1220||-528|
|1996|950|289|1239|1.5%|-660|
|1997|922|359|1281|3.4%|-563|
|1998|951|377|1328|3.7%|-574|
|1999|998|341|1339|0.8%|-657|
|2000|1062|433|1495|11.7%|-629|
|2001|1200|528|1728|15.6%|-673|
|2002|1303|595|1898|9.9%|-708|
|2003|1325|629|1953|2.9%|-696|
|2004|1381|835|2216|13.4%|-546|
|2005|1605|1175|2780|25.4%|-429|
|2006|1779|1379|3158|13.6%|-401|
|2007|2117|1588|3705|17.3%|-530|

_Source: Comext Database_ The total volume of trade between Hungary and the EU increased substantially in the second half of the 1990s, by about 25 % per annum. However, this substantial growth slowed after the millennium, and recovered around the date of accession in 2004. The last column indicates EU exports to Hungary grew slower than imports from Hungary, so that the EU’s trade balance with Hungary is now negative. However, the balance is small relative to the overall volume of trade and changes from year to year. It is, however, striking that total trade picked up considerably after accession even although the Europe Agreements had already granted full access to the EU prior to accession. This highlights the importance of deep integration beyond pure tariff reduction.

The agricultural trade data in Table 2-2 reinforces this notion. Agricultural trade grew only insignificantly until 2000, picked up speed in 2000 and 2001, lost momentum once more, and picked up after accession. The agricultural trade balance between the EU and Hungary has remained constantly in surplus for the EU. The accelerated growth after accession is again probably caused by the elimination of non-tariff barriers to trade. Another likely cause is connected to the steady improvements in trading relations, which now take place under a largely harmonized framework.

12
# **1 Introduction**

International trade costs have fallen drastically in the last decades. However, intra-national (i.e. within-country) trade costs still remain high in low- and middle-income countries. Quantifying trade costs and their components is crucial since lower trade costs can increase efficiency, improve the welfare of households and firms, and drive structural transformation through increased trade opportunities (Sotelo, 2019; Fajgelbaum and Redding, 2014; Costinot and Donaldson, 2016). By understanding the costs associated with domestic trade, policy makers can identify barriers hindering market access and formulate targeted policies to reduce these obstacles.

Low- and middle-income countries are often characterized by many barriers affecting the free flow of goods within their borders, such as poor road conditions, old transport fleet, and poor logistics. These affect trade flows and limit the gains of reduced international trade costs and increased regional integration. Remote regions tend to be especially isolated often due to high travel costs, including high travel time. In turn, higher transportation costs are often passed on to consumers in the form of increased prices. Therefore, households and firms in these areas face higher spatial price gaps, which limits the variety of goods they can choose from and reduces disposable income (Aggarwal, 2018; Martin, Mayneris and Theophile, 2020).

Despite the recent isolated attempts to study the extent of intra-national trade costs,<sup>1</sup> there is a gap in the literature on the cross-country comparison of within-country trade costs.

In this paper we estimate intra-national trade costs for six low- and middle-income countries in Africa and Eastern Europe: Kenya, Madagascar, Nigeria, Rwanda, Tanzania and Georgia. We exploit microdata collected by countries’ national statistical offices (NSO) for consumer price index (CPI) calculation purposes.

There are two main estimation-based approaches in the literature to estimate trade costs, the gravity approach and the price differential approach (Co ̧sar, 2022). The gravity approach looks at the impact of varying travel distance or travel time on trade flows, which are inversely proportional to each other.<sup>2</sup> Using gravity estimation to estimate the impact of travel distance on trade between Colombian cities, Duranton (2015) estimates that increasing the travel distance between them by 10% decreases trade flows by 7% and weight by 6%.

The price differential method combines data on prices from different markets or cities with variables that are thought to affect the cost of shipping goods between locations. Using these data, one can compute price differences across space (spatial price gaps) and assess how different cost shifters affect these differences. Distance between where the

> 1See, for example, Atkin and Donaldson (2015) on the estimates for Ethiopia, Nigeria, and the U.S., and Bougna, Ewane, Jones and Kondylis (2020) on Rwanda.

> 2Head and Mayer (2014) summarize the literature on gravity equations and provide an extensive overview of best-practice methods and a step-by-step cookbook.

2
The rest of the paper is organized<sup>as follows.SectionIIdescribesour unique datasetand</sup> some cross-sectional characteristics of<sup>oursample. SectionEIIreports the resultsof the empirical</sup> analysis. Section IV provides<sup>a summary and conclusions.</sup>

# II. Data

Our primary data is collected from<sup>IndianIPOfilingsbetweenJanuary1999andApril</sup> 2001.2 The sample includes all 152<sup>IPOs issued duringthe period.IPOsfromall majorsectors</sup> including financial, media, telecom,<sup>high-tech,andindustrialarerepresented.</sup> These filings include information on firm characteristics,<sup>IPO characteristics,and ownershipinformation.IPO</sup> characteristics include float percentage,<sup>number of days between opening and closing of the IPO,</sup> gross proceeds, initial return, exchange<sup>listing, and whethertheleadunderwriterwasforeignor</sup> domestic. Firm characteristics include<sup>balance sheetand incomestatementitems,suchas sales,</sup> leverage, earnings before income<sup>andtaxes(EBIT)andgrossmargin.</sup> Information is also included on the age of the firm,<sup>theexistenceof a bankingrelationshipwith a foreignbank,and</sup> the industry of the issuer. Since we<sup>havethenames of all shareholdersand lenders,wecanalso</sup> identify firms with equity and lending<sup>relationships with domesticand foreign banks.</sup> The main focus of our paper<sup>istoanalyzeownershipcharacteristicsthatinclude</sup> ownership by foreign and domestic<sup>institutionsand also ownershipbyinsidersof the firm.The</sup> institutions studied include corporations,<sup>financial institutions,andventure capitalists.</sup> The corporate governance proxies used<sup>intheanalysisaretheexistenceofanemployeestock</sup> ownership plan and whether the<sup>foundercontinuedas theCEOof thefirm.</sup> We also have information on whether the firm<sup>iscloselyheldandby whom.IndiaCapital,aprivateIndian</sup> financial information vendor, provided<sup>informationonthe nationalityand"type"of allequity</sup> 6
Thomas Hertel and L. Alan Winters (eds.), _Putting Development Back into the Doha Agenda: Poverty Impacts of a WTO Agreement_ , New York: Palgrave McMillan and the World Bank. Reprinted in: L. Alan Winters (ed.), _The WTO and Poverty and Inequality_ , An Elgar Reference Collection, Cheltenham, UK: Edgar Elgar Publishing, 2007.

- Rutherford, Thomas F., David G. Tarr and Oleksandr Shepotylo (2005), ―Poverty Effects of Russia‘s WTO Accession: modeling ―real‖ households and endogenous productivity effects,‖ World Bank Policy and Research Working Paper No. 3473, February.

- Sachs, J. D. and A. Warner (1995), ―Economic Reform and the Process of Global Integration,‖ in: _Brookings Papers on Economic Activity_ , W. C. Brainard and G. L. Perry (eds.), (Washington, D.C.), 1-117.

- Schiff, Maurice and Yanling Wang (2006), ―North-South and South-South Trade Related Technology Diffusion: An Industry Level Analysis of Direct and Indirect Effects,‖ _Canadian Journal of Economics_ , Vol. 39 (3), August, 831-844.

- Schott, P. (2004), ―Across-Product versus Within-Product Specialization in International Trade,‖ _Quarterly Journal of Economics_ 119, 647-678.

- Shepotylo, Oleksandr and David G. Tarr (2008), ―Specific tariffs, tariff simplification and the structure of import tariffs in Russia: 2001-2005,‖ _Eastern European Economics_ , Vol. 46, 49-58.

- Shepotylo, Oleksandr and Volodymyr Vakitov (2012), "Impact of services liberalization on productivity of manufacturing firms: evidence from the Ukrainian firm-level data," World Bank Policy and Research Working Paper Number 5944, January.

- Stibora, Joachim and Aldert de Vaal (1995), _Services and Services Trade: A Theoretical Inquiry_ , Amsterdam: Tinbergen Institute.

- Tarr, David G. (2010), ―The Economic Impact of Export Restraints on Russian Gas and Raw Timber,‖ in _The Economic Impact of Export Restrictions on Raw Materials_ , OECD, Paris: OECD, 131-154.

- Tarr, David G. (2010), The Crucial Role for Competition in the Russian Gas Market: Implications for Russia and Europe,‖ _IAEE Energy Forum_ , Fourth Quarter,  31-35.

- Tarr, David G. (2007), ―Russian Accession to the WTO: An Assessment,‖ _Eurasian Geography and Economics_ , Vol. 48, (3), May-June, 306-319.

- Tarr, David G. editor, (2006), _Trade Policy and WTO Accession for Development in Russia and the CIS: A Handbook_ , in Russian, Moscow: Ves Mir.

- Tarr, David G. and Peter Thomson (2004), "The Merits of Dual Pricing of Russian Natural Gas," _The World Economy_ , Vol. 27, Issue 8. August, 1173-1194.

91
profession) refused to participate in our survey on the ground that they viewed themselves as having little or no basis for providing correct or qualified answers.

Still, we will argue, the results from our Delphi exercises can be helpful. The greatest help might be not in terms of providing accurate _valuation levels_ , but rather by indicating how WTP for Amazon forest protection is likely to _vary across countries at different income levels_ . The elasticity estimates of WTP per household with respect to national per-household GDP levels then represent a key, and interesting, set of results. From Tables 5-8, these elasticities are, rather consistently, in the range 0.7-0.9 with respect to regular GDP levels per capita; and (also consistently) even more consistently close to unity when measured with respect to PPP-adjusted GDP levels per capita, and not significantly different from unity when results are pooled across all countries in the sample. We view these findings as encouraging, and consistent with similar results found for other environmentally-related goods and features (e.g. for assessments of environmentally-related premature deaths using Value of Statistical Life estimates; see Lindhjem et al 2011; OECD 2012).<sup>26</sup> The already cited expert elicitation study by León et al (2003) seems, as noted, to provide some (limited) support to the idea that experts’ WTP answers can be used constructively in this way. Such an assessment is based on their conclusion that experts’ relative evaluations of alternative projects appear to be far more precise than their absolute-level valuations of individual projects; and the former is the crucial aspect in predicting how WTP is likely to vary with average per-capita incomes across countries.

There are few existing studies against which the numbers coming out of our exercise can be compared. The only reasonably similar existing study cited above, Horton et al. (2003), gave average annual per-household WTP of about $60 among Italian and U.K. households in 1999 for an Amazon forest protection plan that was, admittedly, less comprehensive than that presented to our experts. The equivalent expert-based figures for these two countries from our survey (see Table 1b) were closer to $25-30. This might indicate that experts’ assessments in our survey are too low. Alternatively, Horton et al.’s values could be high (despite having been collected 15 years ago). But note that Horton et al. surveyed not random population samples but rather convenience samples of users of outdoor recreation sites, who might have stronger

> 26 In particular, if an elasticity estimate of 1 for PPP-adjusted per-capita GDP is appropriate as an approximate global value, an approximate aggregate global WTP value can be obtained from a smaller set of national surveys, by scaling the aggregate, global WTP value up proportionately to the PPP-adjusted GDP values by country.

30
# **Table 4:** Egypt – Financing for Development – General Government Space and Private Source Space.

|**Indicator**|**Actu**|**C**<br>**al**|**omparator Pr**|**ojection**|**Pe**|**rcentile ra**|**nking**|
|---|---|---|---|---|---|---|---|
||**2000 **|**Recent**|**Recent**|**2030**|**2000**|**Recent**|**Change**|
|**General G**|**overnment**|**Space**||||||
|_General government revenue_||||||||
|Total revenue (% of GDP)||22.2|31.4|||11.3||
|Tax revenue (% of GDP)||12.6|17.2|13.9||12.5||
|Grants revenue (% of GDP)||1.0|0.6|0.5||75.0||
|Net ODA (% of GNI)|1.4|0.7|1.7|0.4|31.1|31.1|0.0|
|Net ODA received per capita (current US$)|19.6|26.5|46.5||45.4|28.5|-16.9|
|Other revenue (% of GDP)||8.5|5.1|||83.8||
|_General government expenditure_||||||||
|Total expenditure (% of GDP)||32.9|33.1|||30.4||
|Total current expenditures (% of GDP)||30.4|27.1|32.3||34.2||
|Consumption (% of GDP)|11.2|11.4|14.6||28.2|22.2|-6.0|
|Compensation of employees (% of GDP)||8.3|8.9|||32.1||
|Interest expense (% of GDP)||7.4|1.0|||2.5||
|Net/gross investment in nonfinancial assets (% of GDP)||2.5|4.5|1.9||58.0||
|Subsidies (% of GDP)||6.2|0.5|||3.8||
|_Governance and government efficiency_||||||||
|Government Effectiveness: Percentile Rank|48.2|27.9|28.1|35.1|69.2|41.4|-27.8|
|Government Effectiveness: Estimate|-0.2|-0.7|||69.2|41.4|-27.8|
|**Private**|**Source Spa**|**ce**||||||
|_Domestic private_||||||||
|Domestic credit to private sector, % of GDP|49.4|26.3|30.5|34.4|11.6|60.3|48.8|
|Gross domestic savings (% of GDP)|12.9|5.8|12.8|8.0|46.9|26.5|-20.4|
|Market capitalization of listed domestic companies (% of GDP)||9.9|21.5|15.0||5.9||
|_External private_||||||||
|Exports (% of GDP)|16.9|11.3|29.9|16.2|18.0|6.0|-12.0|
|Goods exports (% of GDP)|7.1|5.7|18.1||11.0|8.0|-3.0|
|Service exports (% of GDP)|9.8|5.6|7.0||60.0|38.0|-22.0|
|Imports of goods and services (% of GDP)|22.8|19.6|41.0||20.0|8.8|-11.2|
|Foreign direct investment, net inflow s (% of GDP)|1.2|2.1|2.9||33.1|36.2|3.1|
|Net current transfers from abroad (% of GDP)|4.7|5.0|4.0|3.8|56.4|53.0|-3.4|
|Portfolio investment (% of GDP)|-0.3|0.0|||22.1|52.6|30.5|
|_External debt sustainability_||||||||
|External debt (% of GNI)|29.0|14.3|38.4||12.4|6.2|-6.2|
|Debt service as a proportion of exports of goods and services|8.5|6.0|4.3||55.9|37.3|-18.6|
|Short-term external debt stocks, % of reserves|29.8|27.9|15.1||61.4|37.7|-23.7|
|Short-term external debt stocks, % of total|14.1|9.3|5.9||26.9|51.3|24.4|
|Total external debt stocks, % of GDP|27.8|14.4|37.8||87.3|91.5|4.2|
|_Government debt sustainability_||||||||
|General government gross debt, % of GDP||88.5|45.0|||0.0||
|General government gross debt, % of average tax revenues||583.0|308.3|||5.3||

17
Estimates suggest that about 10 percent of the Bangladeshi male workforce are international migrant workers.

Consequently, the remittances sent by these workers have become an important source of national income. Between 2000 and 2017, remittance inflows increased by almost seven times with an annual growth of about 12 percent. At its peak, between 2008 and 2012, remittances made up one-tenth of the national GDP, making them a significant source of national income. In 2015, Bangladesh was the 10<sup>th</sup> largest remittance-receiving country globally. Remittances from workers abroad have been one of the key drivers of poverty reduction in Bangladesh, and they continue to be a large share of household income for poorer households (Hill and Endara, 2019; World Bank, 2013, 2015).

Despite the importance to the economy, the process of migration is far from straightforward. Lowskilled migrants depend on a layer of agents for intermediation. Over 900 recruitment agencies in Bangladesh provide intermediation services for low-skilled jobs abroad (Das et al. 2018). Recruitment agencies in Bangladesh employ extensive layers of middlemen ( _dalals_ ) to identify aspiring migrants, provide documentation services, and then place workers in jobs abroad.<sup>3</sup> This is a complex undertaking: a worker seeking to migrate must procure a national identity certificate, their birth certificate, a passport, bank account, contract, visa, a smart card containing identity documents, and a medical checkup; this list is not exhaustive (ILO, 2015).

The services of these middlemen are therefore essential to aspiring migrants, but the recruitment practices have raised some concerns. Anecdotes of fraudulent middlemen cheating migrants abound (Das et al. 2018). Recruitment agents often falsify documents and do not provide proper information about jobs abroad, which leads to many migrants finding the work abroad to be very different from what they had expected. In addition, migration costs in Bangladesh, often exceeding $4,500 (3.2 times GDP per-capita), are notoriously high compared to other sending countries (World Bank 2013; Farole and Cho, 2017). Most migrants borrow, often at high interest rates, to finance the high costs of migration. High indebtedness combined with fraudulent recruitment practices make migrants vulnerable to long-term indebtedness and further exploitation.

Such malpractices and concerns often translate to drastic and sub-optimal policy response from destination countries. As **Figure 1** shows, a steady outflow of migrant workers to Saudi Arabia

> 3 The layered intermediation process and the associated services are a common feature of low-skilled migration form other South Asian countries as well (see for example Shrestha 2019, 2020).

7
Ali-Yrkkö Public R&D funding from the Finnish Technology Agency OLS and instrumental-variable (value of funds that are (2005) (Tekes). Analyze how public R&D financing impacts the potentially awardable to firm) regressions of R&D labor demand of companies employment on subsidies. Non-experimental. Loof and Evaluates whether firms receiving public funding have on Non-experimental matching method Nearest neighbor. Heshmati average higher R&D intensity compared to those not (2005) receiving any such support Czarnitzki Estimate the impact of public R&D grants on firms' R&D Non-experimental, matching. Probit Regression for and Licht and innovation input Programme Participation; All Firms. Subsample of (2006) Supported Firms vs. Firms Permanently Performing R&D. Double-diff (participation and time) Racic et al. To what extent and in which ways can an innovation Non-experimental. Treatment effects of selected policy (2007) policy facilitate innovative activities and contribute to instruments and determinants of innovation activities by restructuring, technological advancement and economic using a probit model. Complements with qualitative growth in Croatia assessment using European Innovation Scoreboard Aerts and Investigate whether public R&D funding in Belgium Non-experimental matching using probit and second Czarnitski crowds-out the private investment in the business sector. stage GLS. (2004) Hanel (2004) Government programs supporting R&D and innovation 3SLS-Ordered logit regressions estimating the probability by Canadian manufacturing firms and the relationship that a firm uses a particular government program. two between the support received and the R&D and stage logit regressions estimating the probability that a innovation performance.(R&D tax credits and R&D firm introduces a more rather than a less original subsidies) innovation. In the last section are results of ordered logit regressions estimating the impact of government programs on the share of product innovations in total sales.

- David et al. This paper explores the degree in which R&D subsidies Structural model, Endogeneity biases correction, IV, (2000) and/or tax breaks are susbtitute of complementary to latent variables effects firm's outcomes. Provides a literature review and strong theoretical framework to evaluate additionality

48
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