rafmacalaba/datause-encoder-data huggingface dataset:
# 1. Introduction

A stated goal of China’s Belt and Road initiative is to strengthen economic integration and policy coordination in the broad Eurasia region. The initiative includes a series of transportation infrastructure projects, which are proposed along two pillars: the Silk Road Economic Belt and the 21st Century Maritime Silk Road. Specifically, the “Belt” links China to Central and South Asia and onward to Europe, while the “Road” links China to the nations of Southeast Asia, the Gulf countries, East and North Africa, and on to Europe. Six economic corridors have been identified: (1) the China‐Mongolia‐Russia Economic Corridor; (2) the New Eurasian Land Bridge; (3) the China–Central Asia–West Asia Economic Corridor; (4) the China–Indochina Peninsula Economic Corridor; (5) the China‐Pakistan Economic Corridor, and (6) the Bangladesh‐China‐India‐ Myanmar Economic Corridor.

This paper offers a first assessment of the impact of the new and improved BRI transport infrastructure on trade flows of BRI countries.<sup>2</sup> Specifically, we examine the potential trade effects of the BRI using a combination of geographic and econometric analysis. First, we assess the impact of the BRI on trading times using a new database on transport projects linked to the BRI (de Soyres et al., 2018; Reed and Trubetskoy, 2018). Georeferenced data and geographical information systems (GIS) analysis are used to compute the bilateral trade time between capitals, ports and major cities (>100K) in the Belt and Road countries before and after the proposed interventions.<sup>3</sup> Second, we use a gravity model (Head and Mayer, 2014) to estimate the total effect of bilateral trade time on bilateral exports and a comparative advantage model à la Nunn (2007) to estimate the impact of bilateral time to trade on the export pattern in time sensitive products and in products that rely on time sensitive inputs.

To assess the trade impact of the BRI, the GIS analysis and econometric estimates are combined. These results are also used to investigate whether there is complementarity between infrastructure improvements and policy reforms that promote trade facilitation, market access

> 2 There is no official list of “BRI countries”. In this paper, we focus on a list of 71 economies, including China, that are geographically located along the “Belt” and the “Road” (see Table A. 1).

> 3 The list of proposed interventions can be found in de Soyres et al. (2018).

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focuses on the period of economic reform beginning in 1978 and is largely concerned with inequality of income and factors that generate this inequality.

We address a series of timely, important, and serious questions. How well can China’s income inequality be measured? Can discrepancies in the evidence from alternative sources be explained? How and why has wealth inequality increased? What are the dimensions and components of increasing income inequality? How do the different components help to explain the remarkable rise in income inequality? What is the relationship of inequality to poverty? Why and how is income inequality of concern to people and to the government? Can China’s past and likely future income inequality be interpreted in terms of the inverted-U of the Kuznets curve? What are the implications for Chinese policy? Are there lessons for research and for other developing countries?

# <<A>>Measuring Inequality

Some knowledge and understanding of the data sources and their limitations is necessary. There are two main sources of information on income inequality over time: the annual national household income and expenditure surveys of the National Bureau of Statistics (NBS) and the periodic national household surveys of the China Household Income Project (CHIP). The NBS surveys contain many observations but a limited number of questions. They cannot be used as a panel (namely, longitudinal study over two or more points in time), and they are generally not available to researchers at disaggregated household and individual levels. Therefore, measures 3
||EarlyASEANEconomic Initiatives||
|---|---|---|
|Initiative|Purpose|DegreeofSuccess|
|ASEANIndustrial Joint<br>Venture(AIJV)<br>1983|AprivatesectorversionofAlPs.Same<br>privilegesasenjoyedunderAIPsbut<br>hasmoreflexibility.Investorscan<br>locateprojectsinanyparticipating<br>country.Opentonon-ASEANnations<br>aslongasmajority ownershipbyat<br>leastone member.Emphasisonequity<br>andmarketsharing,unlikeAIC's<br>reciprocity.|Veryfewprojects thatwere<br>proposedwereeverimplemented.<br>Mosthadforeignequity<br>participationand fewwere<br>ASEAN-wideincoverage.<br>Reasons for lackofsuccess:<br>*<br>Delays duetobureaucratic<br>processes.<br>*<br>Members unwilling to<br>participateinjointventures<br>with preferential tariffs<br>becauseofthreat toown<br>industries.<br>*<br>Members unwillingtoimport<br>productsofprojectsunless<br>own productswereimported<br>byothermembers.|
|TheBrand-to-Brand<br>Complementation(BBC)<br>1988|ReplacedAIC.Similarto theAIC<br>schemeexceptthatBBCencourages<br>economiesofscaleinautomotive<br>production throughexchangeof<br>componentparts forspecifiedbrands.<br>Atthispoint,ASEANeconomic<br>strategycompletelyshifted from import<br>substitutiontoexportpromotionwith<br>foreign capital investment.|<br>ASEANcountriestried toprotect<br>their ownstrategicindustriesand<br>economicinterestsandplaceda<br>banorconsiderable impediments<br>upon the importation of<br>completedvehicles.Indonesia did<br>notevenparticipateinBBC<br>schemeuntil1995.|

Sources: Tongzon (1998: 59-65), Thongpakde (2001: 50), and Tan (2000: 249-257).

This industrial-type cooperative effort (which is a different approach from the PTA effort, also included in the above table) would involve resource pooling and market sharing and, thus, would allow ASEAN economies to take advantage of economies of scale and a large combined market. These schemes would enable individual countries to specialize in manufacturing specific components as a means of developing large-scale industries (e.g., automobiles). This should increase complementarity among regional economies and eventually increase intraregional trade and investment. Finally, industrial cooperation would shift the focus of industrialization from a national level to a regional level. As regional industrialization grows, regional cooperation should increase leading to further industrialization and so on. 167 Theoretically, this plan should have worked, but in reality it did not. As outlined in the above table, the various industrial cooperative efforts were largely unsuccessful due to problems related to implementation, financing, and private sector involvement. ASEAN's early attempts at economic cooperation did not contribute to the remarkable economic performance of the ASEAN countries at the time. This was rather the result of ASEAN's trade and investment links with the rest of the world.

While ASEAN's early initiatives were rather ad hoc and piecemeal, they did give ASEAN members an introduction to economic cooperation. The next step was to embark on the path to forming a free trade area.

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an increase of just under 47 percent in 8 years.<sup>5</sup> Significantly, nonoil (oil) exports rose (fell) as a share of GDP, from 5.4 (18.5) percent to 19.1 (11.6) percent. This has been led by the private sector, which now accounts for 92.8 percent of all nonoil exports from the country. The composition of nonoil exports has also moved from raw materials toward intermediate and consumer goods, with the latter two forms accounting for more than half of all nonoil exports in 2007.

At the most superficial level, the number of Syrian exports has grown significantly over the past decade. The number of merchandise products exported in 2006 has more than doubled from the low of 60 product lines in 1999, with these export numbers displaying a solid upward trend; in addition, this trend exceeds both the regional as well as lowermiddle income country average, of which Syria is part (Figure 1).

<!-- Start of picture text --> Number of<br>300<br>products<br>exported<br>250<br>200<br>Middle East and<br>North Africa<br>150<br>Lower Middle<br>Income<br>100<br>50 Syria<br>0<br>1995 1997 1999 2001 2003 2005 2007<br>Source: World Trade Indicators (2010)<br><!-- End of picture text --> Figure 1: Number of products exported, 1995–2008, Syria, regional average, and lowermiddle income country average. Syrian data not available after 2006. Number of merchandise products calculated at 3-digit SITC level, and includes only products whose value exceeds USD $100,000 or 0.3 percent of the country’s total exports, whichever is smaller.

This trend is most pronounced in the period following the start of reform in 2001, coinciding with the launch of Syria’s 9th FYP. Over the 6-year period between 2001 and 2006, the number of products exported grew by 144.7 percent, compared to -35.9 percent in the preceding 6-year period. This corresponds to an effective (average) annual growth rate of 16.1 percent, or the addition of about a dozen new exports lines per annum. Trade flows have surged as well, with exports doubling (in absolute terms) from SYP 243.1 billion ($5.05 billion, or 23.9% of GDP) to SYP 505.0 billion ($10.92 billion, or 29.6% of GDP) over the same period.

> 5Trade subsequently fell as a result of the global crisis and recession to 59.6 percent in 2008.

7
debate here beyond stating that our findings are in line with those found elsewhere."<sup>8</sup> The most robust finding is that of a band across the North-Western States of India (and which extends into several provinces in Pakistan) in which there are large disparities in child mortality rates. The high gender disparities in Northern India and Pakistan have been pointed out before, perhaps most notably in Miller (1982, 1989, 1993), Murthi et al (1995), and Agarwal (1997) in which the focus was on juvenile sex ratios. Moreover, Miller (1982) points out the persistence of the disparity, going back to evidence from the 1872 census. Miller (1982) attributes the mortality differential to the relative neglect of girls in the allocation of food, medical care, and "love and walmth." Others have focused more specifically on the role of the quantity or quality of medical care (e.g. Das Gupta, 1989, Wyon and Gordon, 1971). In our results, however, there is a puzzle as to how these differentials come about: although we find that female disadvantage in absence of health care is higher in South Asia than elsewhere, neither differences in absence of health care nor differences in nutritional status (as reflected by wasting and stunting) show nearly as pronounced a band across the Northern States as mortality.<sup>19</sup> Our finding that gender disparities are not systematically decreasing with income is also in line with other studies (see Murthi et al, 1995, for a description of this debate), although the result is perhaps not surprising given that gender disparities are observed to be A useful summary of the claims and counter-claims and an assessment of these can be found in Murthi et al (1995).

9 As pointed out before, the latter of these non-findings might be due to the fact that one is essentially looking at the tails of distributions of which may be hard to get reliable estimates.

20
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**Table 7. Wage Premium Regressions, Short and Long Run**

||Cam|bodia<br>|Sri L|anka<br>|
|---|---|---|---|---|
||1|2|3|4|
|Female dummy|-0.198***|-0.311***|-0.557***|-0.555***|
||(0.019)|(0.030)|(0.023)|(0.024)|
|Hours|0.015***|0.015***|0.012***|0.012***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Age|0.042***|0.043***|0.148***|0.149***|
||(0.004)|(0.004)|(0.006)|(0.006)|
|Education|0.026***|0.026***|0.046***|0.045***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|T&G dummy|0.389***|-0.102|0.065***|0.084***|
||(0.026)|(0.063)|(0.010)|(0.013)|
|1999-2004 dummy||-0.024||0.062***|
|||(0.023)||(0.008)|
|2005 and above dummy|0.284***|0.267***|0.077***|0.104***|
||(0.014)|(0.023)|(0.008)|(0.008)|
|T&G* (1999–2004 dummy)||0.623***||-0.051**|
|||(0.076)||(0.020)|
|T&G* (2005 and above dummy)|-0.226***|0.350***|-0.027|-0.050**|
||(0.033)|(0.074)|(0.023)|(0.024)|
|Female* (1999–2004 dummy)||0.157***||-0.010|
|||(0.033)||(0.013)|
|Female* (2005 and above<br>dummy)|-0.015|0.099***|-0.040***|-0.043***|
||(0.022)|(0.032)|(0.013)|(0.015)|
|Hazard ratio|0.002|0.001|0.679***|0.682***|
||(0.051)|(0.051)|(0.045)|(0.045)|
|Constant|10.388***|10.316***|4.808***|4.784***|
||(0.125)|(0.128)|(0.151)|(0.151)|
|Observations|31,768|31,768|153,155|153,155|

_Source:_ Authors’ calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011, and Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.

The grand mean effects of the industries are calculated; post-MFA is a dummy equal to 1 for years 2005 and later; additional controls include age squared, industry, and occupation dummies. Sri Lanka Household Income and Expenditure Survey was excluded from the analysis as hours worked were not available in this survey. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as logarithm of real monthly individual labor income.
Figure A.4. Refugee Literacy Rate by Gender <!-- Start of picture text --> .8<br>.6<br>.4<br>.2<br>0<br>Year<br>Adult literacy rate female Adult literacy rate male<br>Adult literacy rate 15-24 female Adult literacy rate 15-24 male<br>1979 1985 1991 1997U.S Entry2003 2009 2015U.S. Withdrawal<br>% of Literacy Rate<br><!-- End of picture text --> _Source:_ The data comes from the The United Nations Educational, Scientific and Cultural Organization (UNESCO).

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# **Table A5: Other Predictors**

|||Non-Win<br>|ners<br>|||Winne<br>|rs<br>||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Survival|(2)<br> <br>Employment|(3)<br> Sales|(4)<br>Profits|(5)<br>Survival|(6)<br> <br>Employment|(7)<br> Sales|(8)<br>Profits|
|**Panel A: Reason for Going**|**into Busine**|**ss**|||||||
|Necessity Entrepreneur|-0.0757*|<br>-0.8930*|-0.8349|<br>-0.5946|0.0314|-0.3666|0.6762*|<br>0.5239|
||(0.0384)|<br>(0.3808)|(0.5440)|<br>(0.5018)|(0.0239)|<br>(0.6538)|(0.3343)|<br>(0.3618)|
|Sample Size|1,078|1,078|1,059|<br>1,059|1,062|1,062|1,047|<br>1,047|
|R<sup>2</sup>||.0175|.0412|.0375||.0127|.0163|.0145|
|**Panel B: Firm is Named Aft**|**er the Own**|**er**|||||||
|Eponymous Firm (Narrow)|||||-0.0262|-1.1963|0.1178|<br>-0.4042|
||||||(0.0322)|<br>(1.0055)|(0.7134)|<br>(0.8179)|
|Sample Size|||||1,051|1,051|1,036|<br>1,036|
|R<sup>2</sup>||||||.0121|.0136|<br>.0124|
|Eponymous Firm (Broad)|||||0.0198|0.0461|0.5464|<br>0.3037|
||||||(0.0262)|<br>(0.8512)|(0.3768)|<br>(0.4190)|
|Sample Size|||||1,051|1,051|1,036|<br>1036|
|R<sup>2</sup>||||||.0116|.015|.0126|
|**Panel C: Time of Registrati**|**on for the C**|**ompetition**|||||||
|Late Entrant to Competition|0.0024|-0.4967|-0.0396|<br>-0.1541|-0.0606**|2.3158|-1.3276*|<br>-0.8912|
||(0.0440)|<br>(0.4364)|(0.6013)|<br>(0.5623)|(0.0185)|<br>(1.7820)|(0.5524)|<br>(0.5171)|
|Sample Size|1,078|1,078|1,059|1,059|1,062|1,062|1,047|<br>1,047|
|R<sup>2</sup>||.0152|.0391|.0364||.0172|.0211|<br>.0162|

Robust standard errors in parentheses. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively. Necessity entrepreneur defined as saying that difficulty finding a job is a very important or important reason for wanting to start a business. Eponymous firm (narrow) requires surname of owner to be in firm name. Eponymous firm (broad) requires partial match between firm name and owner's first name, surname, or initials. Late entrants are those who enrolled after the original deadline.
- Dixit, A. K. and J. E. Stiglitz (1977), “Monopolistic Competition and Optimum Product Diversity,” _American Economic Review_ , 67: 297–308.

- Ethier, W.J. (1982), “National and International Returns to Scale in the Modern Theory of International Trade,” American Economic Review, 72(2):389-405.

- Fernandes, Ana M. and Caroline Paunov (2012), “Foreign direct investment in services and manufacturing productivity: evidence for Chile,” _Journal of Development Economics,_ Vol. 97 (2), March, 305-321.

- Findlay, Christopher and Tony Warren, eds., (2000), _Impediments to Trade in Services: Measurement and Policy Implications_ , London: Routledge.

- Francois, Joseph F. (1990), “Trade in Producer Services and Returns due to Specialization under Monopolistic Competition,” _Canadian Journal of Economics_ , 23:109-124.

- Francois, Joseph and Bernard Hoekman (2010), “Services Trade and Policy,” _Journal of Economic Literature_ , Vol. 48 (September), 642-692.

- Frankel, J. A., and A. K. Rose (2005), “Is Trade Good or Bad for the Environment? Sorting Out the Causality,” _The Review of Economics and Statistics_ , 87 (1): 85-91.

- Gentry, B.S. (1999), “Foreign Direct Investment and the Environment: Boon or Bane?” in: _Foreign Direct Investment and the Environment_ , OECD proceedings, Paris, 21-46.

- Grossman, G.M. and A. B. Krueger (1993), “Environmental impacts of a North American free trade agreement,” In: Garber, P.M. (Ed.), _The U.S.-Mexico Free Trade Agreement_ . MIT Press, Cambridge, USA.

- Helpman, E. (1981), “International Trade in the Presence of Product Differentiation, Economies of Scale, and Monopolistic Competition: A Chamberlin–Heckscher–Ohlin Model,” _Journal of International Economics_ , 11: 305–340.

- Hummels, David and Volodymyr Lugovsky (2005), “Trade in Ideal Varieties: Theory and Evidence,” <u>http://www.nber.org/papers/w11828, December.</u>

- IFC (2008), _Energy Efficiency in Russia: Untapped Reserves_ . International Finance Corporation.

- Ivanova, Nadezhda (2005), “Estimates of Own and Cross-Price Elasticities of Disaggregated Imported and Domestic Goods in Russia,” www.worldbank.org/trade/russia-wto, September.

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2004), “The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization,” World Bank Policy and Research Working Paper No. 3391. Available at: www.worldbank.or/trade/russia-wto.

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2007), “The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization,” _Review of Development Economics_ 11: 482-506.

- Kang, Joog-Soon (2000), “Price Impact of Restrictiveness on Maritime Transportation Services,” in Findlay, Christopher and Tony Warren (eds), _Impediments to Trade in Services: Measurement and Policy Implications_ , London: Routledge.

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**Table 9:  Productivity Effect of Services Liberalization, Structural Break Approach. Differential Effect of Services Liberalization on Foreign Firms. Ackerberg et al. TFP Measure**

|Banking Break 2001|2.376***<br>(0.667)|||||2.318***<br>(0.592)|
|---|---|---|---|---|---|---|
|Banking Break 2001 *Foreign|0.649*<br>(0.384)|||||-0.179<br>(0.376)|
|Rajan-Zingales Break 2001||0.449***<br>(0.085)|||||
|Rajan-Zingales Break 2001*<br>||0.097**|||||
|Foreign||(0.046)|||||
|Telecom Break 2002|||6.145**|||4.962*|
||||(2.670)|||(2.626)|
|Telecom Break 2002*Foreign|||5.484***|||3.418|
||||(1.965)|||(2.256)|
|Insurance Break 2002||||3.558*||1.934|
|||||(2.122)||(1.818)|
|Insurance Break 2002*Foreign||||4.884***||3.266**|
|||||(1.184)||(1.369)|
|Transport Break 1997|||||7.983***|8.433***|
||||||(2.640)|(2.640)|
|Transport Break 1997*Foreign|||||1.306***<br>(0.481)|0.989**<br>(0.471)|
|Tariffs1|0.000|0.003|0.000|0.000|-0.000|0.001|
|(t-)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Inut Tariffs|-0.004|-0.004|-0.003|-0.003|-0.010|-0.009|
|p(t-1)|(0.009)|(0.009)|(0.009)|(0.009)|(0.007)|(0.007)|
|Forein Dumm|0.019|0.012|0.018|0.019|-0.013|-0.025|
|g y|(0.017)|(0.018)|(0.017)|(0.017)|(0.024)|(0.024)|
|Observations|22,558|22,558|22,558|22,558|22,558|22,558|
|R-squared|0.030|0.034|0.030|0.029|0.032|0.037|
|Number of firms|3771|3771|3771|3771|3771|3771|

Notes: The dependent variable is the log TFP estimated using the Ackerberg et al. method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry-year level, are reported in parentheses. *** denotes significant at the 1 percent level, ** at the 5 percent level, * at the 10 percent level 44
level grow at the same rate, thus maintaining the existing structure of employment by education. Assume also that skill mismatch occurs only between educational levels. i.e., there are no skill mismatches within educational levels.<sup>22</sup> Under such best-case scenario, where the number of vacancies equals the number of job seekers, about 22 percent of all unemployed will not find a job because of skill gap, that is, because their skills fall short of employers requirements.<sup>2 3</sup> Expectedly, the extent of the skill gap is more pronounced among the long-term unemployed than among the short-term unemployed, although the difference is relatively small, smaller than in other countries. Given the current rate of unemployment of about 17 percent, the estimated skill gap implies some 4 percent unemployment rate caused by skill gap. This is a lower bound estimate due to optimistic assumptions underlying the calculations. In reality, the problem of skill gap is likely to be even more pronounced.

The problem of the skill gap-the inadequacy between skills demanded by employers and those possessed by the unemployed-seems to be more pronounced in Lithuania than in some other transition economies of Central Europe. For example, in both Bulgaria and Poland the skill gap is smaller, accounting for about 17 and 14 percent of unemployment, respectively.<sup>24</sup> Thus, the inadequate skill level of the unemployed, especially of the long-term unemployed, are likely to be a key factor behind relatively limited outflows from unemployment to work in Lithuania. Poor skills prevent a substantial fraction of the unemployed to effectively compete for jobs, and can lead to their marginalization on the labor market.

The apparent importance of the skill mismatch problem in Lithuania has two policy implications. First, it indicates the need for greater wage flexibility, especially at the lower end of the wage distribution, in order to encourage the creation of low skilled jobs and improve the employment prospects of poorly educated unemployed. Second,

> **21** This is an optimistic scenario, since in reality due to the skill biased technological change high skilled jobs grow at a faster pace than low skilled jobs.

> **22 This is** again an optimistic assumption as after all skill mismatches do exit **within** educational groups. **_L_**

> **23** The formula to calculate the skill gap is: _sg_ = _(u - et)_ for u, _> e,_ where ui and _e 1_ are percentage shares of the

> i-th educational level in unemployment and employment, respectively, and L is the number of educational levels.

> **24** See Rutkowski (1998) and Rutkowski (1999) for the estimates of the skill gap for Poland and Bulgaria, respectively.

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