rafmacalaba/datause-encoder-data huggingface dataset:
_Inside US Trade_ , April 26, 2002.

- Joskow, P. and M. Baughman (1976), The Future of the U.S. Nuclear Energy Industry,” _Bell Journal of Economics_ , 7 (Spring), 3-32.

- Khramov, Vadim, Larisa Korableva and Anna Kovaleva (2008), “Export Taxes on Russian Timber Case of Finland and Sweden,” New Economic School International Trade Policy term project paper, Moscow, May 5, mimeo.

- Liu, Ben-Chieh (1983), “Natural Gas Price Elasticities: Variations by Region and by Sector in the USA,” _Energy Economics 10_ (3), 195-201.

Nemtsov, Boris and Vladimir Milov (2008), Putin and Gazprom http://www.docstoc.com/docs/1603180/Nemtsov-White-Paper-Part-II----Gazprom

- Oi, Walter (1971), “A Disneyland Dilemma: Two-Part Tariffs for a Mickey Mouse Monopoly” _Quarterly Journal of Economics, 85_ (1), February: 77-96

Phlips, Louis (1981), _The Economics of Price Discrimination_ , Cambridge: Cambridge University Press.

- Ramirez, Juan C. and Juan Rosellon (2002), “Pricing Natural Gas Distribution in Mexico,” Energy Economics, 24(3), May: 231-248.

Scherer, F.M. (1980), _Industrial Market Structure and Economic Performance_ , Chicago: Rand McNally

- Sherman, Roger and MichaelVisscher (1982), “Rate-of-Return Regulation and Two-Part Tariffs,” _Quarterly Journal of Economics_ , 97(1), February: 27-42.

- Smith, Keith (2008), “Russian Energy Policy and  its Challenge to Western Policy Makers,” Center for Strategic and International Studies,” March. Available at: <u>http://csis.org/files/media/csis/pubs/080407_helsinki.pdf</u>

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

- Taylor, L.D. (1977), “The Demand for Energy: A Survey of Price and Income Elasticities,” in E.D. Nordhaus (ed.) , _International Studies of the Demand for Energy_ , Amsterdam: North Holland.

18
where the parameter _ξ_ 1<sup>_P_capturesthecurvatureoftheliquidfossilfuelcost</sup> function. We calibrate the values of the parameter _ξ_ 1<sup>_P_tomatchobservedex-</sup> traction rates of oil and natural gas in the rst decade of the 21st century. We obtain the extraction quantities of oil and natural gas from the BP Statistical Review of World Energy database (2013).

## B.1.1.3 Other Primary Resources

The initial values for the production of other primary inputs come from GTAP v.7 database and are dened as the value of output from labor (skilled and unskilled), capital, and natural resources. We set the growth rate of other primary resources as the dierence between global GDP growth and total factor productivity growth rates from Jorgenson and Vu (2010) projections based on econometric estimates for 122 economies over the 1990 - 2008 period (see Figure B.1, panel b).

# B.1.2 Intermediate Inputs

## B.1.2.1 Petroleum Products

The rening of petroleum, _x_<sup>_p_</sup> _t_<sup>_,_isachemicalengineeringprocessthatcanbe</sup> described by a linear production function: where _θ_<sup>_p_</sup> is the rate of conversion of fossil fuels to petroleum products. We set _θ_<sup>_p_</sup> based on the conversion factor supplied by the U.S. Energy Information Administration.<sup>2</sup> We obtain the expenditures on rening fossil fuels into petroleum products from GTAP v.7 database (sectors 16, 17, and 32), and normalize them by the total expenditures on other primary resources. To compute the normalized average cost of rening fossil fuels, _c_<sup>_o,p_</sup> _,_ we divide normalized expenditures on the amount of petroleum products produced in 2004, which we obtain from BP Statistical Review of World Energy database (2013).

> 2See http://www.eia.gov/tools/faqs/faq.cfm?id=24&t=7.

12
# **I. Introduction**

,Crude estimates suggest that the informal production sector is large, accounting for 20 to 50 percent of employment in many developing countries (Portes, 1994). Yet progress towards consensus on the sector's origins, operations and even definition has been hampered by two problems.' First, the lack of comprehensive data has prevented accurately establishing the basic characteristics of informal production beyond conjecture and casual observation. Second, whereas, the literature on informal finance -- i.e. unregulated financial intermediation -- has a broad theoretical underpinning,2 the literature on informal production tends toward ad hoc characterizations and lacks a comparably broad foundation.<sup>3</sup> In general, these frameworks rely on an institutional distortion such as a binding minimum wage, evasion of government regulation and taxation, or differences between firms in worker monitoring ability to generate the informal sector.<sup>4</sup> This paper makes two contributions. First, it offers systematically collected data on a broad cross-section of urban firms in Mexico with details on compliance with or participation in a number of different societal institutions. The data are derived from a nationally representative sample of all such firms, a significant improvement over pre-existing case study data sets. We are thus able to move beyond anecdotal analysis and establish some definitive stylized facts about irnformal production for the first time.

Second, it offers a theoretical framework to motivate the analysis of the data. The 'A large body of literature equates informality with the low-wage, low-productivity segment of a dual labor market (for example, Lewis, 1954, and Harris and Todaro, 1970). An equally sizeable literature equates informality with unregulated self-employment (for example, Hart, 1972, and de Soto, 1989). See Thomas (1992) and Portes (1994) for excellent overviews. Our approach in this paper equates informality and noncompliance with societal norms such as tax obligations, labor protections, census enumerations, business guild participation, _etc._ In line with both Thomas' and Portes' characterizations, we are concemed with unregulated/umnonitored activities that are ostensibly legal, not those that are truly illegal (criminal).

^ See Besley (1995) for an excellent overview.

See Thomas (1992) and Portes (1994). Exceptions include Esfahani and Salehi-Isfahani (1989), Rauch (1991), Loayza (1995), and Banerji and Jain (1996).

4 For the remainder of the paper we will use "informal" exclusively to characterize the production and distribution of goods processes.
investment grade suppliers will offer longer terms. For any level of buyer market power, one could argue that a supplier for whom providing credit costs less will be squeezed for more. Similarly, in order to signal commitment to quality, an investment grade supplier who can raise finance at lower cost will offer longer terms.

Next, we examine the sample of contracts that include an early payment discount. The view that discounts are used as a screening mechanism to gauge buyer default risk would suggest that smaller and non-investment grade buyers, where default risk tends to be higher, would more likely receive discounts. To the extent that it is easier to absorb and diversify default risk for large firms, this view would also suggest that small suppliers are more likely to extend discounts. On the other hand, if there are fixed costs in screening, one would expect that large suppliers are more likely to offer discounts.

Table 9 shows logit regressions of determinants of early payment discounts for the subsample of contracts that offer early payment discounts. The dependent variable takes value 1 if the contract includes a discount (two-part contract), and 0 otherwise. As before, the first two columns present results for regressions with buyer clustered standard errors, the next two columns present results for regressions with buyer fixed effects, and the final two columns present results for regressions with supplier fixed effects. In the second of each of the regression pairs, we drop observations from buyers that never receive discounts, to abstract from the possibility that such firms are systematically different from firms that receive discounts.

We find that discounts are less common for large and investment grade buyers, consistent with the screening hypothesis according to which early payment discounts offer suppliers a screening mechanism to gauge buyer default risk.

The buyer fixed effects regressions in columns 3 and 4 indicate that early payment discounts are more common from small suppliers. This is consistent with the market power hypothesis according to which large buyers demand discounts from small suppliers, despite the fact that two-part contracts are generally considered to be more expensive to administer. This result is also in line with Burkart, Ellingsen, and Giannetti (2008) who find using U.S. survey data that firms that have more buyer market power receive larger early payment discounts. This finding is also consistent with the screening view that stipulates that smaller suppliers are more 14
i.e, an ARMA (p, q) with constant ,u and linear trend t, with p 5 2 and q S 2. For d=1, (1- j L- 2 L<sup>2</sup> )Ay, = + Pit + (1- OIL - 02L<sup>2</sup> )e, i.e. an ARIMA (p, 1, q) with constant p and linear t, and p 5 2 and q • 2. Models in Table 3B correspond to the same forms, with no linear trend included.

On the basis of the prediction error variance, one would choose an ARIMA (2, 1, 2) frum Table 3B (&2=0.0109) and either an ARIMA (1,1,2) with trend 2= 0.0107) or an ARIMA (2, 1, 2) with trend (2= 0.0108) from Table 3A. However, since the "t-statistics" associated with the coefficients are too low, we would drop some of the coefficients, and choose models where all the estimated parameters are significant. In the ARIMA (1, 1, 2) with trend in Table 3A, the estimated ,B is not significant. By dropping it, we get the ARIMA (1, 1, 2) in Table 3B with &2 =0.116. Conversely, if we drop the second-order lag MA parameter, we get **p** an ARIMA (1,1,1) with trend, but this turns out to have a non-invertible MA polynomial (the same happens if we exclude the linear trend).

A slightly better fit could be obtained by dropping the time trend from the ARIMA (1,1,2) and estimating it in levels, i.e. with an ARMA (2,2). This gives a 62= 0.115. No other ARMA model without tr.nd fares better. Conversely, a better **p** fit can be obtained with an ARMA (2, 1) with trend (&2 = 0.112) but, still, the MA coefficient is barely significant. Thus, if we stick so the significance of all the estimated parameters the best we can get is, for the models with no trend, an ARMA (0, 2) (but &2 =0.0162) or an ARMA (1,0) (with C2 = 0.0123, but _ai_ is not significant.). An ARMA (2, 1) with trend gives C32=0.0112 (but 0, is barely significant) whereas an ARMA (1, 0) with trend yields 2 =0.C1 14, with all the coefficients highly significant.

25
China’s Exports in million USD <!-- Start of picture text --> total value added in exports gross export value<br>1000000 1400000<br>a00c0d Utilities 1200000<br>rae m@ Energy and Mining<br>cn 1000000<br>TM Primary and Foods<br>SOD<br>TM Textiles and Clothing 800000<br>amen & Chemicals saansie<br>400000 @ Metals<br>alee<br>TM Machinery 400000<br>200000 .<br>100000 @m@ otherServicesF Manufacturing 200000<br>0 0<br>1992 1995 1997 2001 2004 2007 1992 1995 1997 2001 2004 2007<br><!-- End of picture text -->
Policy Research Working Paper 10792

# **Abstract**

Accurate trade data remain central for empirical investigations of international trade and informed formulation of trade policies. However, discrepancies in trade reporting, stemming from reasons such as logistics to deliberate misclassification, pose challenges to obtaining an accurate representation of trade activities. This paper provides a systematic examination of these discrepancies by using the Discrepancy Index, a measure of bilateral asymmetry in trade reporting. First, the paper proposes a rich set of country- and product-level indicators that capture both the frequency of misreporting and its impact on the overall recorded trade value. Second, it demonstrates how the Discrepancy Index database can aid analysis and resolve data reliability issues in international trade. This comprehensive data set is used to analyze the general trends in trade data reporting and its reliability, providing empirical insights into the nature and extent of reporting discrepancies. Finally, the paper demonstrates the practical application of the developed discrepancy database and aggregate indicators through case studies of Senegal and the trade relationship between Madagascar and France, shedding light on reporter-specific instances. The paper seeks to equip trade analysts and researchers with tools and resources to make informed decisions on the use of reported trade data and their mirror. In doing so, the study contributes to the broader endeavor of enhancing the reliability of international trade data, thereby contributing to a more accurate empirical investigation of global trade patterns and their policy ramifications.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at skaushik@worldbank.org and smitikj@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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competition policy rating from 2 to 3. Our results suggest that this change (a 50% improvement) will increase Internet intensity by approximately 30%.

**Table** 2: **Regression Results for Equation** (5)

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a I/T = Total Internet subscribers/total mainline connections b Minimum Internet connections set to 1 unit in 1990 c t-statistics in parentheses; White heteroskedasticity-consistent standard error and covariance estimates

** Significant at .05 or greater by the standard criteria

The income parameter estimate contrasts strongly with the policy result: Economic development does not have a significant impact on Internet intensity. Indeed, African, Asian and Latin American countries appear (ceteris paribus) to have higher Internet intensities than their counterparts in the OECD. This result is obviously not consistent with the conventional view of the digital divide, which highlights the role of the human resource gap and other development-related variables. A possible counter-argument is

**5**
two more consecutive quarters. Households are subsequently dropped from the sample. This feature is important as it allows us to generate a panel of individuals over time. Each survey round after 1998 contains approximately 1,300 households with 15,000 individuals aged 15 years and above residing in the West Bank. Our sample in the analysis below consists of more than 45,000 individuals over the period considered.

We restrict the sample from the PLFS to individuals in the labor force between the ages of 15 and 64 and surveyed during the 44 quarters between quarter one of 2000 and quarter four of 2009. The rounds of the survey prior to 2000 are not considered as the data on the barriers for that period are available only for the year 1995, when the methodology of the PLFS sample design was substantially different.<sup>13</sup> The PLFS data includes information on various individuals’ personal characteristics, such as age, marital status and education, labor market variables, including employment status, daily wage, number of days worked in the previous month, hours worked in the previous week, occupation and industry, as well as the households’ locality of residence and the locality of work place of individuals. The latter geographical data is a key element to identify the effects of the closures on the labor market variables. Localities represent the smallest spatial unit for which economic data is available in the West Bank and provide a very refined spatial scale for our analysis. There are 660 localities defined by the PCBS in the West Bank with an average size of 8.5 km<sup>2</sup> . Of these localities we exclude the 17 localities part of the Jerusalem district with unrestricted access to West Jerusalem and the rest of Israel, as the barriers do not affect their access to the Israeli labor market. In addition many localities have very little Palestinian population no labor market data is available for them. In the end our sample consists of 321 localities, encompassing virtually the entire Palestinian labor force.

Yearly data on the physical barriers for the years 2000, 2001, 2003, 2006, 2008 and 2009 come from ARIJ. We complement it with data from OCHA for the remaining years in the period 2000-2009, except for the year 2002 for which data are not available due to the severe unrest occurring in the West Bank in that year.<sup>14</sup> ARIJ has been collecting this data using the same type

> 13 In 1995 the survey was conducted in one quarter only and it was an experimental sample.

> 14 We do not have the date in which the barriers were placed in each year so we assume that each barrier has been there since the beginning of the year if observed in that year. This limitation of our data may give rise to some

13
What matters for economic growth are the spillovers to other firms withiin and across industries. Evidence on this is much less robust. The case study literature has argued that positive spillovers are significant. It has also documented the importance of the availability in local skills and in-house technological capacity in adapting and using techniques developed elsewhere (Lall, 1992; Evenson and Westphal, 1995). Conversely, recent micro-econometric studies using panel datasets of enterprises have come to more ambiguous conclusions. Some analysts have found a statistically significant negative relationship between the size of foreign investment in an industry or economy and the productivity performance of domestic firms (e.g., Harrison, 1996; Haddad and Harrison, 1993).

This paper investigates the impact of foreign investment on productivity performance of firms in the Czech Republic during the initial post-reform period (1992-96).i We distinguish between firms that established partnerships with foreign firms-either through a joint venture or through direct sale of a majority equity stake-and those that did not, and ask whether total factor productivity (TFP) growth rates of these groups of firms differ. TFP is used as an indirect measure of technology transfer. Data constraints prohibit using more direct measures, such as R&D effort or the turnover of managers and highly skilled labor. Our results suggest that TFP growth is higher in firms with foreign partnerships, and that there is a clear hierarchy: firms that have been acquired by foreign owners have the highest TFP growth, followed by firms with joint ventures. Firms without foreign partnerships have the lowest TFP growth as a group. This result

> ' A separate but related literature on technology diffusion has focused largely on two issues: (i) analysis of the determinants of the number of firms or the proportion of industry output produced by a new technology (aggregate diffusion); and (ii) analysis of the determinants of the time at which a firm adopts a new technology relative to other firms (so-called duration models) (See e.g., Ray, 1964; Karshenas and Stoneman, 1994). Data constraints prohibit analysis of the types of questions asked in the diffusion literature as it is not possible to identify specific technologies in our data set.

2