rafmacalaba/datause-encoder-data huggingface dataset:
agglomeration economies which we focus on in the next section. Even in the field of international marketing, only four out of 50 studies reviewed by Zho and Stan (1998) and only six out of 52 studies reviewed by Sousa et al. (2008) address domestic market characteristics, which are measured at the national rather than regional level. This study aims to contribute to this lack of studies by incorporating regional determinants of exporting.

# **1.2 Agglomeration Economies and Exporting**

Our study includes three measures of regional agglomeration economies, namely (i) a region’s number of firms as percentage of a country’s total number of firms (urbanization effects), (ii) a region’s number of exporters<sup>3</sup> as percentage of a country’s total number of exporters (export spillovers), and (iii) a region’s number of firms within the same industry as percentage of a country’s total number of firms within the same industry (localization effects).

Agglomeration economies can have a particularly favorable influence on a firm’s propensity to export as they can reduce the sunk entry costs of exporting (Aitken et al. 1997). Agglomeration economies can lower (i) production costs through sharing of resources, mainly social and physical infrastructure, and (ii) transportation and transaction costs through increased interaction between suppliers and customers on site (Malmberg et al. 2000). Hence, an agglomeration can increase the probability of self-selection into exporting.

On the other hand, agglomerations may be characterized by congestion costs (Krugman, 1991) which can increase (i) production costs through the sharing of resources (e.g. power outages), and (ii) transportation and transaction costs through increased waiting times (e.g. for intermediate inputs or licenses). These effects can counterbalance the gains from agglomerations as described above. The net effect may therefore be ambiguous.

Studies in the field of international marketing have long acknowledged the role of agglomeration economies on exporting, and international trade studies have followed to recognize their importance. Malmberg et al. (2000) analyze the impact of localization and urbanization economies on export performance for Swedish firms in 1994. The authors find that traditional scale economies combined with urbanization economies have a much larger positive effect on export performance than localization economies. Mittelstaedt et al. (2006) confirm that urbanization and localization economies increase the probability of exporting for small manufacturing firms in the

> 3 Exporters include direct exporters and “indirect exporters”, defined as exporters who sell domestically to third parties that then export the products or services without modification. Goods sold domestically as intermediates to an exported product are not considered indirect exports.

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# **2.3 Estimating the Impact of Economic Shocks**

Using this set of baseline results, we can consider how these numbers will evolve due to shocks such as those resulting from higher food prices and a growth contraction.

The first thing that food price changes affect in our model is the Engel curve for calories. As can be seen from Figure 1, higher prices means one will need to spend more for the same amount of calories which in turn implies, ceteris paribus, that a higher fraction of the population is going to fall below the affordability mark<sup>3</sup> . So the Engel curve shifts “down” when prices go up for calories. The amount by which the Engel curve shifts depends on the elasticity of calories to prices and this elasticity in turn depends on the extent to which alternate caloric sources are available and affordable at any given level of income as well as the degree of substitutability between these sources. Unfortunately compared to the amount of work on the estimation of income elasticity of calories holding prices fixed, there is less evidence on the estimation of price elasticity of calorie demand holding income fixed is relatively scarce. Waterfield (1985) has an excellent review of the work in this area. He shows that Timmer and Alderman (1979) using data from 54000 households in Indonesia find that the average price elasticity of calories from all foods is -0.514; Gray (1982) finds it to be -0.477 for rice in Brazil and Pitt (1983) finds the calorie price elasticity in Bangladesh from rice to be -0.529 for the 25<sup>th</sup> percentile and -0.484 for the 90<sup>th</sup> percentile. Using this review Senauer and Sur (2001) argue that elasticity of total calorie consumption to the price of the primary staple food for the poorest households is in the range of -0.5. Having reviewed this evidence we also use this estimate though we also present results of the impact of higher food prices using elasticity values between -0.4 and -0.6 in Annex Table 2.

The manner in which the price elasticity of calorie demand (ε) enters our model is as follows. For every price shock we consider (5%, 25%,. _...,p_ ), the new Engel curve is estimated by lowering the (calorie) purchasing power of real income by _p_ . _ε_ from the previously estimated

> 3 In reality of course, calories are derived from a variety of sources some of which are cheap and some are more expensive. This will generate significant variation in relative prices within food categories inducing a lot of substitution between classes. However, we abstract from that and consider the composite price of food as being isomorphic to the price of calories.  To the extent that there are substitutions that mitigate the impact of price increases such as these, our estimates can be considered an upper bound of the impact on undernutrition.
# **Figure 1: Nigeria: Indicators of Oil Dependence**

<!-- Start of picture text --> Nigeria: Oil Dependence Nigeria: Revenue Dependence on Oil<br>120 60<br>100 60<br>100 50 50<br>80<br>80 40 40<br>60<br>60 30 30<br>40<br>40 20 20<br>20<br>10<br>20 10<br>0 0<br>0 0<br>Oil Revenue (% of Total Revenue)<br>Oil exports (% of total exports) Oil GDP (% of GDP, right scale)<br>Oil Price, Bonny Light (US$/bbl), right scale<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 197019731976197919821985198819911994199720002003<br><!-- End of picture text -->

# _II.1 Policy response to the oil windfall of the 1970s._

After an initial lag in 1973 and 1974, when large surpluses were saved and invested abroad, consolidated public expenditure accelerated rapidly: by 1976 it absorbed the entire oil windfall (Figure 2). By 1977 combined federation and states capital expenditure increased six fold over their 1970 level. Public capital expenditure accelerated so strongly that it alone accounted for the spending of more than half of the entire oil windfall.

However, Nigeria's continued failure to improve its ranking in measures of educational success or infrastructure quality suggest that much of this expenditure was conceived too hastily and ended up largely leading to waste and corruption. Weak institutions and poor governance have contributed substantially to Nigeria’s public debt problems, as the majority of projects financed by public borrowing during the late 1970s and 1980s failed to generate an adequate rate of return needed to improve the repayment capacity of the country.<sup>1</sup> Thus during the 1970s, public expenditure was primarily financed from oil revenues, made possible by the high oil prices in the 1970s, some domestic borrowing, and relatively modest external borrowing. At the time of the second oil shock in 1980, when oil prices jumped to almost $40/bbl, the

> 1 A study carried out by the Federal Ministry of Finance in 1996 of commercial external loans from bilateral and commercial creditors (amounting to about 70 percent of external debt outstanding in 1996) has documented problems encountered by externally financed projects. Many of these projects have not been completed, owing to cost overruns or lack of finance. Of these projects completed, many are inoperative owing to a lack of power or other missing links in the manufacturing sector. Furthermore, the maintenance of completed infrastructure assets has often been inadequate. In some cases, imported goods, financed by loans, could not be located. Finally, no significant project that was completed and documented by this study appears to have generated foreign exchange revenues.

4
<!-- Start of picture text --> 0 .05 .1 .15 .2 .25 .3 .35 .4 .45 .5 .55 .6 .65 .7 .75 .8 .85 .9 .95 1<br>5000<br>4000<br>3000<br>Frequency<br>2000<br>1000<br>0<br><!-- End of picture text -->

# **Figure 4. Distribution of the non-deposit funding share.**

The non-deposit funding share is the share of non-deposit short-term funding in total deposits & short-term funding. The plot shows the frequency distribution of the non-deposit funding share in our dataset. The horizontal axis represents intervals of the non-deposit funding share. Data are from Bankscope.

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12 A third objective is to enhance the transparency of cross-subsidies embedded in pricing schemes. The HCPM is likely to be the most accurate of the approaches, since it incorporates economic optimization and thus will calculate more accurate estimates of the costs of networks delivering subsets of the services provided by a complete network (stand-alone costs). Such computations are necessary for determining the presence and amount of cross-subsidies. Once again, we present a table where both the HCPM and the accounting methodologies are shown together.

|**Policy**<br>**Objectives**|**Accounting**<br>**Approach**|**HCPM**|
|---|---|---|
|Benchmarking|Accounting models do<br>not set independent<br>benchmarks.|All sides can<br>recognize<br>independent<br>benchmarks.|
|Information<br>Control|Determined by<br>accounting rules.|Determined by public<br>data inputs.|
|Cross-Subsidy<br>Transparency|Accounting models do<br>not enable calculation<br>of stand-alone costs;<br>thus, subsidies cannot<br>be calculated.|Greater optimization<br>improves estimate of<br>cross-subsidies.|

# **Theoretical validity**

The credibility of any model proposed for policymaking is determined by the adherence of the model to theoretical precepts. Such precepts include the realism of the model's treatment of customer locations, the incorporation of economic optimization, and the usefulness of the model for calculating true long-run incremental costs.

The HCPM accepts fully disaggregated customer location data and "builds" plant to individual customer locations, as mentioned in earlier sections of this paper. This characteristic helps in convincing skeptics who feel that cost models build "fantasy" networks, and is likely to result in more realistic cost estimates. Accounting models have no provision for handling customer locations on a prospective basis, since they can only model costs that have already been incurred. Moreover, they do not incorporate any
<!-- Start of picture text --> Figure 6: Fiscal Balance/GDP - Poland, Russia, Turkey (2000-2010)<br>15<br>ae<br>0oN iNN<br>SE Bi Oe ~_<br>-15<br>—— Poland —— Russia —— Turkey<br><!-- End of picture text --> <!-- Start of picture text --> Figure 7: Total Outlays/GDP - Poland, Russia, Turkey (2000-2010)<br>50<br>45 ae<br>35 7 “—__ Ee<br>OO<br>25 —“A_FEHEHRHReMAeOSTTES<br>20<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>—— Poland —— Russia —— Turkey<br><!-- End of picture text -->
Table 9 Treatment Effects: Externalities from Technology Adoption

|||_Dependent _|_variable:_||
|---|---|---|---|---|
|||Extra cha|rge = 1||
||(1)|(2)|(3)|(4)|
|Low adoption area|0.151<sup>_∗∗∗_</sup><br>(0.048)||0.151<sup>_∗∗∗_</sup><br>(0.049)|0.056<br>(0.034)|
|Low skill||0.443<sup>_∗∗∗_</sup><br>(0.115)|0.443<sup>_∗∗∗_</sup><br>(0.115)|0.387<sup>_∗∗∗_</sup><br>(0.111)|
|Low adoption area _×_ Low skill||||0.118<br>(0.075)|
|Worker fxed efects|X|X|X|X|
|Week fxed efects|X|X|X|X|
|Observations|504|504|504|504|
|Clusters (# neighborhoods)|24|24|24|24|
|R-squared|0.142|0.145|0.171|0.173|
|Mean dep.var|0.310|0.310|0.310|0.310|

Notes: The table reports treatment effects from the audit study. The dependent variable in all regressions is an indicator equal to 1 if the customer was charged an illicit fee for receiving assistance with the transaction. “Low skill” is a treatment indicator for the low skill audit script assignment. “Low adoption area” is an indicator equal to one if the transaction takes place in a neighborhood with below median payroll account penetration, measured by the share of firms that have adopted payroll accounts. Robust standard errors, clustered by neighborhood, in parentheses. * _p <_ 0.1, ** _p <_ 0.05, *** _p <_ 0.01.

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implying that trade liberalizing reforms have contributed to periods of REER depreciation.

# _Relative productivity differentials_

Perhaps the best known explanation for the behavior of long-run ‘equilibrium’ exchange rates is relative productivity differentials, the so-called Balassa-Samuelson effect. The Balassa-Samuelson effect rests on the assumption that productivity gains in the tradables goods sector are greater than productivity gains in the nontradables sector. The effect can be understood to work both within and across countries, with the same resulting impact on the real exchange rate. Within a country, an increase in productivity in the traded goods sector will lead to an increase in wages in both the tradable and nontradable sectors. Consequently, there is an increase in nontradables prices and an appreciation of the real exchange rate. If PPP in tradable goods holds across countries, stronger productivity gains in the tradables sector in one country relative to another implies a stronger increase in the relative price of nontradables. The result is an appreciating real exchange rate for the country with the higher relative productivity gain.

A striking feature of the Tanzania growth experience over the last thirty years is that it appears to be uncorrelated with the **Figure 7: Relative Productivity and Real Effective** investment rate (World **Exchange Rate** Bank 2002). Indeed, 300 300 several periods were characterized by high 250 250 investment rates and low 200 200 growth, implying declining productivity of 150 150 investment. Devarajan, 100 100 Easterly and Pack 1999, note that value added per 50 50 worker in the 0 0 manufacturing 1970 1975 1980 1985 1990 1995 2000 2005 experienced a sustained WPI-based REER Relative Productivity decline between 1975 and 1990. Poor productivity <u>Source: Authors’ calculations</u> performance relative to its key trading partners is consistent with the observed long-term trend decline in Tanzania’s REER (Figure 7). However, recent reforms aimed at increasing efficiency and investment appear to be bearing fruit as relative productivity in Tanzania appears to have halted its long-term decline.10

> 10 There are a number of measures that proxy relative productivity, but due to data limitations we use the ratio of real GDP per capita in the seven largest trading partners to real GDP per capita in Tanzania. An area for further work would be to develop some measures for productivity in different sectors of the economy to proxy the relative productivity growth differentials between tradables and nontradables within Tanzania.

11
Liu, Jing, Nico van Leeuwen, Tri Thanh Vo, Rod Tyers, and Thomas Hertel. 1998a. “Disaggregating Labor Payments by Skill Level in GTAP,” GTAP Technical Paper No. 11, Purdue University, West Lafayette, Indiana.

Liu, Jing, Nico van Leeuwen, Tri Thanh Vo, Rod Tyers, and Thomas Hertel. 1998b. Aziz Elbehri, and Truong P. Truong. _Global Trade, Assistance and Protection: The GTAP 4 Data Base_ , Center for Global Trade Analysis, Purdue University, West Lafayette, Indiana.

McCallum, J. (1995). National borders matter: Canada-US regional trade patterns. The American Economic Review, 85(3), 615-623.

OECD (2013), “Interconnected Economies: Benefiting from Global Value Chains”, OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/9789264189560-en OECD (2015) “Participation of developing countries in global value chains – implications for trade and trade-related policies”.

OECD (2015a), “Contribution of Trade Facilitation Measures to the Operation of Supply Chains”, forthcoming OECD Trade Policy Paper, <u>http://www.oecd-ilibrary.org/trade/oecd-trade-policyworkingpapers_18166873</u> OECD (2015b), “Developing countries participation in global value chains and its implications for trade and trade related policies”, OECD Trade Policy Papers, No. 179, OECD Publishing, Paris. DOI:http://dx.doi.org/10.1787/5js33lfw0xxn-en Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A., &Pietronero, L. (2012). A new metrics for countries' fitness and products' complexity. Scientific reports, 2, 723.

Timmer et al. (2012), The World Input-Output Database (WIOD): Contents, Sources and Methods, WIOD Background document available at <u>www.wiod.org.</u> Timmer, Marcel P., Abdul Azeez Erumban, Bart Los, Robert Stehrer, and Gaaitzen J. de Vries (2014) "Slicing Up Global Value Chins" Journal of Economic Perspectives, 28 (2): 99-118.

Tri Thanh Vo and Rod Tyers. 1996. “Splitting Labor by Occupation in GTAP: Source and Assumptions,” Australian National University.

UNCTAD (2013), World Investment report 2013: Global Value Chains: Investment and Trade for Development, United Nations Conference on Trade and Development, United Nations Publication ISBN 978-92-1-112868-0.

Wang, Z., S.-J. Wei, X. Yu, and K. Zhu. 2017. “Measures of Participation in Global Value Chains and Global Business Cycles.” Working Paper 23222, National Bureau of Economic Research, Cambridge, MA World Bank Group; IDE-JETRO; OECD; UIBE; World Trade Organization. 2017. Global Value Chain Development Report 2017: Measuring and Analyzing the Impact of GVCs on Economic Development. Washington, DC: World Bank. © World Bank. <u>https://openknowledge.worldbank.org/handle/10986/29593</u> License: CC BY 3.0 IGO WTO (2018), Trade in value-added and global value chains profiles - explanatory note.

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33 studies also suggest that the rate of TFP and MFP growth has gone down during the last decade, although it is impossible to know if this new trend will continue and, if so, how strong it will be.<sup>58</sup> While we would expect falling returns on real investment, other factors are likely to have positive effects on GDP growth, such as the faster restructuring and privatization of the SOEs, the gradually higher R&D spending (starting from a low level), increased competition as a result of joining the WTO, and better economic integration of various geographical areas within China itself.

On the basis of existing evidence, it is reasonable to conclude that (except mainly for the sector of foreign firms) economic growth in China has largely been _resource extensive_ in the sense of heavily relying on capital accumulation, inputs of raw materials and energy, and the depletion of environmental resources.

Naturally, both the public discussion and the scholarly literature have emphasized the huge regional differences in per capita income growth – with the eastern (coastal) provinces as the leaders, and the mountainous provinces in the west, along with the “rustbelt” areas in the north as the laggards; see, for instance, Démurger et al. (2002). Official statistics suggest that while GDP has grown by about 11.5 percent per year during the reform period in the most successful provinces (Fujian, Guangdong, Jiangsu and Zhejiang), the growth rate has been about half of that in the least successful province (Quinghai); see, for instance, Wei and Liu (2004). However, such extreme comparisons give an exaggerated picture of the _overall_ dispersion of the distribution of growth rates across provinces. The standard deviation of the per capita growth rate across provinces during the period 1980-2002 seems to have been 1.5 percentage point, which, however, is enough to generate large differences in levels of per capita income across provinces.<sup>59</sup> The _level_ of per capita GDP is currently reported to be about seven times higher in the most developed than in the least developed province, even if we exclude the very poorest province (Guizhou).

> 58 For instance, OECD (2005a, Table 1.4) reports a fall in multifactor productivity growth from 3.4 percent per year during 1993-1998 to 1.3 percent per year during 1998-2003. Angang and Zheng (2004) report a fall from 3.7 percent during 1991-95 to 0.6 percent during 1996-2001.

> 59 According to Blanchard and Giavazzi (2005, p. 6), the standard deviation of the _level_ of income per capita across provinces increased by 72 percent between 1998 and 2003.
# **Transparency, Trade Costs, and Regional Integration in the Asia Pacific**

Matthias Helble, Ben Shepherd and John S. Wilson<sup>1, 2</sup> The World Bank 1818 H St. NW Washington D.C. 20433

**Keywords:** Transparency, Uncertainty, Trade Costs, Non Tariff Barriers, APEC Economies **JEL Codes:** F 13, F 14, F 15, O 24, R 58

1 The first author is with the World Health Organization, second is a Consultant and third author a Lead Economist, in the Development Research Group—Trade, The World Bank. This paper is based on a report prepared for the Asia-Pacific Economic Cooperation, and is part of a broader “Transparency and Competitiveness” project supported through a Trust Fund established by the Australian Department for Foreign Affairs and Trade and the Australian Agency for International Development (AUSAID). The work here is aligned with and has benefited from a project on Trade Costs and Facilitation at the Bank with support of the U.K. Department for International Development. We are grateful to David Laborde, Alina Mustra, Alessandro Nicita, and Jerzy Rozanski for assistance with data. We benefited from helpful discussions and comments from Bernard Hoekman, Gary Hufbauer, and Beata Smarzynska Javorcik. Witold Czubala provided very capable research assistance. Comments to: <u>mhelble@worldbank.org, bshepherd@worldbank.org or jswilson@worldbank.org.</u> 2 The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.