rafmacalaba/datause-encoder-data huggingface dataset:
- Frankel, Jeffrey A. & David H. Romer (1999). “Does Trade Cause Growth?” _American Economic Review_ 89(3) (June): 379–399

- Freedom House (2009). _Freedom in the World: The Annual Survey of Political Rights and Civil Liberties_ . Rowman & Littlefield, Lanham, MD

- Grossman, Gene M. & Elhanan Helpman (1991). _Innovation and Growth in the Global Economy_ . Cambridge, MA: MIT Press

- Hadi, Ali S. (1992). “Identifying Multiple Outliers in Multivariate Data”. _Journal of the Royal Statistical Society, Series B_ 54(3): 761–771

- Harrison, Ann E. & Andr ́es Rodr ́ıguez-Clare (2009). “Trade, Foreign Investment, and Industrial Policy for Developing Countries”. _NBER Working Paper_ 15261, Cambridge, MA: National Bureau of Economic Research

- Hesse, Heiko (2009). “Export Diversification and Economic Growth”. In Richard S. Newfarmer, William Shaw & Peter Walkenhorst (editors), _Breaking into New Markets: Emerging Lessons for Export Diversification_ , pp. 55–80. Washington, DC: The World Bank

- Jansen, Marion (2004). “Income Volatility in Small and Developing Economies: Export Concentration Matters”. _WTO Discussion Paper_ 3, Geneva, Switzerland: World Trade Organization

- Jones, Benjamin F. & Benjamin A. Olken (2008). “The Anatomy of Start-Stop Growth”. _Review of Economics and Statistics_ 90(3) (August): 582–587

- Kose, M. Ayhan, Eswar S. Prasad, Kenneth S. Rogoff & Shang-Jin Wei (2009). “Financial Globalization: A Reappraisal”. _IMF Staff Papers_ 56(1) (April): 8–62

- Kose, M. Ayhan & Raymond G. Riezman (2001). “Trade Shocks and Macroeconomic Fluctuations in Africa”. _Journal of Development Economics_ 65(1) (June): 55–80

- Melitz, Marc J. (2003). “The Impact of Trade on Intra-Industry Allocations and Aggregate Industry Productivity”. _Econometrica_ 71(6) (November): 1695–1725

- Raddatz, Claudio E. (2007). “Are External Shocks Responsible for the Instability of Output in LowIncome Countries?” _Journal of Development Economics_ 84(1) (September): 155–187

- Ramey, Garey & Valerie A. Ramey (1995). “Cross-Country Evidence on the Link between Volatility and Growth”. _American Economic Review_ 85(5) (December): 1138–51. URL `http://ideas.repec.org/ a/aea/aecrev/v85y1995i5p1138-51.html`

- Rodr ́ıguez, Francesco R. & Dani Rodrik (2000). “Trade Policy and Economic Growth: A Skeptic’s Guide to the Cross-National Evidence”. In Ben S. Bernanke & Kenneth S. Rogoff (editors), _NBER Macroeconomics Annual_ , volume 15, pp. 261–338. Cambridge, MA: National Bureau of Economic Research

- Rodrik, Dani (1997). _Has Globalization Gone Too Far?_ Washington, DC: Institute for International Economics. URL `http://bookstore.piie.com/book-store/57.html`

31
Table A5: Heterogeneity - within group transfers

|||Amoun|t transferre|d (std)||
|---|---|---|---|---|---|
||1|2|3|4|5|
|Cooperation|0.387<sup>_∗∗∗_</sup>|0.254<sup>_∗∗_</sup>|0.135|0.251<sup>_∗∗_</sup>|0.245|
||(0.105)|(0.118)|(0.135)|(0.114)|(0.160)|
|Cooperation*Republican|-0.490<sup>_∗∗_</sup>||||-0.512<sup>_∗∗_</sup>|
||(0.200)||||(0.204)|
|Cooperation*High age||-0.032|||0.090|
|||(0.180)|||(0.181)|
|Cooperation*Female|||0.194||0.188|
||||(0.180)||(0.180)|
|Cooperation*High income||||-0.039|0.014|
|||||(0.186)|(0.186)|
|Republican|0.171|-0.074|-0.078|-0.073|0.177|
||(0.147)|(0.102)|(0.102)|(0.103)|(0.147)|
|High age|-0.026|-0.014|-0.028|-0.030|-0.068|
||(0.090)|(0.125)|(0.091)|(0.091)|(0.124)|
|Female|0.019|0.012|-0.084|0.013|-0.074|
||(0.090)|(0.091)|(0.126)|(0.090)|(0.126)|
|High income|-0.136|-0.155<sup>_∗_</sup>|-0.159<sup>_∗_</sup>|-0.136|-0.147|
||(0.093)|(0.093)|(0.092)|(0.131)|(0.129)|
|Constant|0.798<sup>_∗∗∗_</sup>|0.875<sup>_∗∗∗_</sup>|0.932<sup>_∗∗∗_</sup>|0.875<sup>_∗∗∗_</sup>|0.867<sup>_∗∗∗_</sup>|
||(0.092)|(0.096)|(0.095)|(0.096)|(0.107)|
|Observations<br>|508|508|508|508|508|
|_R_<sup>2</sup>|0.034|0.021|0.023|0.021|0.036|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 _Note:_ The table reports OLS regressions on transfers when the two workers belong to the same group. The dependent variable is the standardized amount transferred to the unlucky worker. “Cooperation” is an indicator for the spectator being assigned to a treatment where the workers cooperate within group. “Republican” is an indicator for the spectator self-reporting voting Republican. “High age” is an indicator for the spectator being above median age in the sample. “Female” is an indicator variable for the spectator being female. “High income” is an indicator for the spectator having an income above the median income in the sample. Standard errors in parentheses, where * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

19
that, although many covariates contributed in a statistically significant way to the final model fits, their interpretation is not as straightforward as in a non-spatial model, because much of the variation in observed indicator values is accounted for via the random effect component.

**<u>Table 1</u>** <u>Parameter estimates for fixed effects (covariates)</u>

||**Percentile**|**EVI**|**Aridity**|**LST**|**NTL**|**Time to**<br>**waterpoint**|
|---|---|---|---|---|---|---|
||2.5<sup>th</sup>|-0.699|-2.290|-4.184|-0.064|**-0.033**|
|Basic water|50<sup>th</sup>|-0.041|-0.765|-2.066|-0.020|**-0.028**|
||97.5<sup>th</sup>|0.615|0.758|0.051|0.024|**-0.022**|
|Improved water|2.5<sup>th</sup>|**-1.597**|-2.226|-4.409|-0.084|**-0.050**|
||50<sup>th</sup>|**-0.869**|-0.506|-2.061|-0.036|**-0.044**|
||97.5th|**-0.143**|1.209|0.282|0.011|**-0.039**|
||2.5th|-0.353|-2.181|**-4.438**|**-0.104**|-0.005|
|Improved water on premises|50th|0.248|-0.782|**-2.497**|**-0.064**|0.000|
||97.5th|0.849|0.611|**-0.559**|**-0.024**|0.005|
||2.5th|**1.251**|-1.699|-2.541|**0.012**|**0.014**|
|Piped water on premises|50th|**1.723**|-0.618|-1.055|**0.042**|**0.017**|
||97.5th|**2.197**|0.456|0.432|**0.072**|**0.021**|
||2.5th|**4.083**|**-3.472**|-1.945|**0.151**|**0.014**|
|Oen defecation|50th|**4.728**|**-1.899**|0.121|**0.191**|**0.018**|
|p|97.5th|**5.373**|**-0.324**|2.187|**0.231**|**0.023**|
||2.5th|-0.628|-1.857|-3.760|**-0.096**|-0.005|
|Improved sanitation|50th|0.012|-0.343|-1.692|**-0.054**|0.000|
||97.5th|0.652|1.168|0.377|**-0.012**|0.005|
||2.5th|**1.908**|**-3.598**|**-5.114**|**0.013**|**0.012**|
|Sewerage connection|50th|**2.320**|**-2.650**|**-3.803**|**0.039**|**0.015**|
||97.5th|**2.731**|**-1.697**|**-2.489**|**0.064**|**0.018**|

_Note_ : EVI, enhanced vegetation index; LST, land surface temperature; NTL, brightness of nighttime lights. In a Bayesian model, each coefficient is fitted as a probability distribution function, and this is summarized here by the median and 95% credible interval range. Coefficients statistically different from zero (“significant” with 95% confidence) are highlighted in gray.

# **Model validation**

The predictive performance of the model for each indicator is assessed via out-of-sample crossvalidation. A fourfold hold-out procedure was implemented whereby 25% of the data points were randomly withdrawn from the data set, the model was run in full using the remaining 75% of data, and the predicted values at the locations of the hold-out data were compared with their observed values. This was repeated four times without replacement such that every data point was held out once across the four validation runs. Standard validation statistics were computed as measures of 12
Table 2.4.

Cross Tabulation<sup>ofPopulation</sup> with Net Internal<sup>Migration</sup>

||||Rural|areas|197<br>Urban|5<br>areas|Tota|l|
|---|---|---|---|---|---|---|---|---|
||||||OOO|ercent|OOOs|percent|
||||000s|percent|s|p<br>|<br>||
|'|Rural areas<br>|008<br>lpRural<br>|_16,415_<br>careast.<br>_<br>|-4,103<br>98.8/.0_<br>_-8551_||-<br>2<br>32.4/20.<br>-8748|0,519<br>0<br>-|70.1/100<br>-<br>|
|i~<br>P1965|~1965<br>Urban areas<br>Urban areas|0819<br>OPct.|<br>-|_,_<br>1.2/2.3|-|67-6/97-7<br>|<sup>_</sup><br>|29.9/100|
||Total|O00Q<br>pct.|16,612<br>-|-<br> <br>100/56.8<br>|12,654<br> <sup>-</sup><br>|-<br> <br>100/43.2|9,267<br> <sup>-</sup>|-<br>100/100|
|1965-<br>1975|Net rural-urban<br>migration|0009<br>pct.||3,|906<br>13.3||||

Percents<sup>below/ add upto 100 horizontally.</sup> Percents Notice;<sup>Percents above/add up to 1oO vertically.</sup> Yearbook<sup>of Turkey,1968and 1985, and Timur.Kurano "intermal</sup> Sources:<sup>My computations</sup> Migration: The Unorganized based on<sup>Statistical</sup> Urban Sector and Income<sup>Distribution</sup> in Turkey, in Turkey,<sup>Prenceton,</sup> 1963-1973", N.J., in Ozbudub 1980w Table<sup>and</sup> Ulusun, ed.s The Political Economy<sup>of Income DIstribution</sup> 11.1 and<sup>p. i6Z.</sup>
6. The RWMA in Poznań covers the Warta River basin from its source to its mouth at the Oder River.

7. The RWMA in Szczecin covers the Oder River basin from the mouth of the Nysa Łużycka River to the mouth at the sea, excluding the Warta. It also includes the basin of the west littoral rivers.

On behalf of the central government, RWMAs perform IWRM planning and coordinating functions within river basins, overseeing the actions of voivodeship and local governments and private users for compatibility with basin water management plans, and maintaining specified water works and state-owned reservoirs and other facilities. Tasks related to these functions include:

- monitoring and forecasting surface water and groundwater demand conditions,

- determining the conditions of use of integrated water resources in the basin,

- acting as a party in legal proceedings in cases regarding the specific use of inland waters and in other administrative proceedings pertinent to water management and protection,

- formation of, and communication with, Regional Councils of Water Management (RCWMs) composed of representatives of water users and other organizations, to have opportunities to comment on water management plans and environmental impact assessments,

- collecting charges and fees for navigation and sluice operation, for materials extracted from surface waters, and for leasing of the shore or river bank areas and use of facilities,

- providing opinions on designs and siting of developments that may appreciably affect water conditions and facilities located along river banks,

- cooperating on water management with, among others, the Inspectorates of Environmental Protection, the Institute of Meteorology and Water Management, maritime offices and regional forestry boards,

- cooperating on transboundary waters in compliance with the effective legal provisions and international agreements,

- keeping water resource databases (cadasters) and GIS,

- initiating studies and research on the development and protection of surface and underground waters,

- developing plans and programs for water resources management, water protection, and flood and drought prevention, and

- participating in flood emergency operations.

RWMAs have a legally recognized role in the water use and discharge permitting procedures that are carried out by voivodeship or local (poviat or starost) offices. This allows RWMA staff to be aware of activities and investments in environmental protection and improvement in the basin, and to object to permit applications that the staff concludes will harm basin conditions. RWMA staff also can identify and try to discourage developments that may threaten surface or groundwater quantity or quality. The RWMA can also request modification of permits.

14
17 Equation (6) shows that if p > t, importers have an incentive to overinvoice, Vf > Vt. If p < t, the incentive operates in the reverse direction. According to Pryor (1988, p. 37) Madagascar's black market premium ranged from 50 to 70 percent during 1982-84. Appendix Table 2 gives tariff rates for different imports.

Since as equation (6) shows, the relative level of tariffs and the black market premium determine the direction of incentives for false invoicing, data on the level of Madagascar's tariffs and related import charges were drawn from an UNCTAD (1987) report. This source gives nominal import duty averages for many SITC (Revision 2) products down to the five-digit level. Using these statistics, items were ranked by decreasing tariffs and two groups selected. The first was composed of high tariff items (import duties for this group ranged from 40 to 131 percent--see Appendix Table 2) while the second consists of products with relatively low (15 percent or under) tariffs and special import charges.<sup>18</sup> Next, Madagascar's average relative import price was computed for each group before and after PSI was adopted. The results are reported in Table 6.

18The complete list of products included in these two groups along with their corresponding SITC (Rev. 2) codes and nominal tariffs is presented in Appendix Table 2. Over the period covered by these tests Madagascar's black market premium and tariffs changed in ways that would be offsetting. In 1987 and 1988 .mport tariffs were simplified and reduced and quantitative restrictions eliminated. According to estimates by Pryor (1988, p. 37) government policies restored economic growtn and cut the black market exchange rate by about onethird from its 1978-80 level.
- 20 -

The applicability of these estimates to other regions is evident. Our sample analyzed above may represent relatively more developed developing countries. In Africa, for instance, the quality of infrastructure measured in the same way is much poorer than the ECA region. The frequency of power outages reaches as many as 80 days per year in several African countries (Figure 19). There is no such case in Europe and Central Asia, except for Albania. Similarly, many countries in Africa are suffering from more than 20 days of water suspensions a year (Figure 20). Again, these are fairly exceptional in other regions. Much greater efforts toward infrastructure quality improvements are called for in particular in Africa.

Figure 19. Quality of Electricity Infrastructure Services in Africa <!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Source: BEEPS.<br>Figure 20. Quality of Water Infrastructure Services in Africa<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Source: BEEPS.<br>Days of power outages a year<br>Congo, Dem. Rep. Burundi Guinea-Bissau Eritrea Benin Angola Kenya Madagascar Malawi Mauritania Zambia Senegal Swaziland Lesotho Botswana Niger Namibia Cape Verde Mali Cameroon Burkina Faso Mauritius South Africa EAP ECA LAC MENA SA<br>Days of water suspensions a year<br>Congo, Dem. Rep. Burundi Mauritania Kenya Angola Eritrea Guinea-Bissau Lesotho Malawi Zambia Mauritius Benin Swaziland Cape Verde Cameroon Madagascar Burkina Faso Senegal Namibia South Africa Mali Niger EAP ECA LAC MENA SA<br><!-- End of picture text --> Figure 20. Quality of Water Infrastructure Services in Africa

# **IV. CONCLUSION**

Infrastructure is one of the most important factors for growth. Good quality infrastructure services are essential for firms to strengthen competitiveness. However, few studies have
Moor, G.M. and Nieuwoudt, W.L. 1998 Tenure security and productivity in small-scale agriculture in Zimbabwe: Implications for South Africa _Development Southern Africa_ 15(4), 609-620.

Moyo, Sam 1998, The Land Acquisition Process in Zimbabwe (1997/8), UNDP Harare Persson, Torsten; Tabellini, Guido, Is Inequality Harmful for Growth?, _American Economic Review;_ 84(3), June 1994, pages 600-621.

Piketty, Thomas 1999, Theories of Persistent Inequality and Intergenerational Mobility, Chapter 6 of Handbook of Income Distribution (A. Atkinson, and F. Bourguignon, Eds.) PIR, 1998: _Poverty and Inequality in South Africa,_ Poverty and Inequality Report to the Office of the Deputy President, Praxis Publishing, Durban.

SA-PPA 1997: _The Experience and Perceptions of Poverty: The South African Participatory Poverty Assessment,_ Praxis Publishing, Durban.

South Africa Labor and Development Research Unit (SALDRU) 1994, "South Africans Rich and Poor: Baseline Household Statistics." Cape Town: University of Cape Town.

SPP, 1983: _Forced Removals in South Africa, Vol. 4, Natal,_ The Surplus People's Project, Cape Town.

The Economist, July 17, 1999, Land reform in Brazil A plot of our own Tomlinson, Mary R. 1999, From Rejection to Resignation: Beneficiaries' Views on the South African Government's New Housing Subsidy System, _Urban Studies_ 36:8, 1349-59 van Rooyen, Johan, Jan Groenewald, Simphiwe Ngqangweni, and Tamas Fenyes Eds. 1998, Agricultural policy reform in South Africa, Africa Institute for Policy Analysis and Economic Integration, Cape Town van Zyl, **J.,** J. Kirsten, and H.P. Binswanger 1996, _Agricultural Land Reform in South Africa: Policies, Markets, and Mechanisms,_ Oxford University Press, Cape Town Wilson, F., and M. Ramphele, _Uprooting Poverty: The South African Challenge_ Cape Town: David Philip, 1989.

22
the World Development Indicators (WDI) dataset for GDP and population data, as well as the OECD-CRS for aid flows.

From WITS, we include 115,230 positive trade flows for 167 importers (reporters) and 172 exporters (partners) for the years 1990 to 2005. Tariff data for effective applied rates (simple average) are included from the TRAINS database where available, and where the data are unavailable, the closest year’s data are chosen with older data chosen in the event of a tie. Information is included for each EU member as an individual country, as well as the EU as a whole. The CEPII data on distances was simply joined to the trade data.<sup>27</sup> Yearly data from the WDI on GDP (in USD at then current price levels) and population as well as data from CEPII about other country-specific data (such as area or language) was added to the corresponding countries and then also joined to the trade data.

# **III.2. Methodology**

Our methodology is the popular gravity model equation, which has only gotten more popular since the micro-foundations proposed by Anderson and van Wincoop (2003, 2004). From basic microeconomic principles, they derive a gravity-like model of exports from country _i_ to country _j_ in sector _k_ ( _X ijk_ ): (1) log  _Xijk_  log  _Ekj_  log  _Yik_  log  _Y k_  1  _k_  log  _tijk_  1  _k_  log  _Pjk_  1  _k_  log  _ik_  _ijk_ Where<sup>_Y_</sup> _i k_<sup>is the output of country</sup><sup>_i_in sector</sup><sup>_k_;</sup> _E kj_ stands for the expenditure of country _j_ in sector _k_ ;<sup>_Y_</sup> _t k_<sup>denotes aggregate (world) output in sector</sup><sup>_k_;</sup>  _k_ is the elasticity of substitution in sector _k_ ;<sup>_t_</sup> _ijk_<sup>are trade costs facing exports from country</sup><sup>_i_to country</sup><sup>_j_in</sup> _k k_ sector _k_ ;  _i_ , _j_ = Country _i’s_ / _j’s_ output share in sector _k;_ and finally  _ij_ stands for the random error term, satisfying the usual assumptions. The main innovation in Anderson and van Wincoop’s work is the inward and outward resistance terms. The inward resistance term  _Pjk_  1  _k_   _N_  _i k_  1  _ik_  _tijk_ 1  _k_ takes into _i_  1 account that _j_ ’s imports from _i_ depend on trade costs across all exporters. The outward resistance term  _ik_  1  _k_   _N Pj_  _k_  1  _kj_  _tijk_ 1  _k_ describes, by analogy, that the exports from _i j_  1 to _j_ are affected by the trade costs across all importers.

The data requirements for the multilateral resistance terms exceed what is generally available, so the standard solution is to use fixed-effects estimation. Fixed-effects could be country-specific (for each importer and exporter) or bilateral fixed effects (for each bilateral country pair in the sample). Bilateral fixed effects capture many of the standard

> 27 In order to create distance data for the EU, we simply used Luxembourg’s geographical data. We consider this assumption as appropriate taking into account that Luxembourg is centrally located within Europe and very close to the geographic center of the EU15.

9
Table 2.1. Real GDP Growth Rate (Percent per year)

||1981-90|1991-97|1998|1999|2000-01|
|---|---|---|---|---|---|
|Argentina|-1.3|6.7|3.9|-3.4|-2.6|
|Bolivia|-0.4|4.3|5.0|0.4|1.8|
|Brazil|2.3|3.1|0.1|0.8|3.0|
|Chile|4.0|8.3|3.9|-1.1|3.6|
|Colombia|3.4|4.0|0.6|-4.1|2.0|
|CostaRica|2.4|4.8|8.4|8.2|1.6|
|Ecuador|:2.1|3.2|0.4|-7.3|4.0|
|Mexico|1.5|2.9|5.0|3.6|3.2|
|Peru|0.0|5.3|-0.5|0.9|1.7|
|Venezuela|0.3|3.4|0.2|-6.1|3.0|
|Average|1.4|4.6|2.7|-0.8|2.1|

Source: World Development Indicators Database and the Unified Survey.

Figure 2.1. Poverty, Inequality, **and** Unemployment <!-- Start of picture text --> 45 - - | Poverty Headcount Ratio (left scale) 42.04 0.5<br>41- . -Gini coeficlent (right scale) _<br>40 -. ¢- ^-Unemployment (left scale) 0.49<br>35--<br>30.40 30.0 .4 290 0.48<br>30--<br>25 - - 24.10 0.47<br>21~~~ ~ ~~~ 21t8 21.607-<br>20 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~0.46<br>15- 17.<br>15 14.1 ~~~~~~~~~~0.45<br>10-1211.<br>-dP 8.7 ~~~~~~~~~~~~~~~~~~~~~~0.44<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br><!-- End of picture text -->

**Data for urban areas. Sources: Llnemployment from INDEC, Gini Coefficient and Poverty Head Count ratio from World Bank Poverty Assessment Reporl (2000) except for the 1999-2002 ratios which are estimated using the trend observed for the Greater Buenos Aires headcount ratios from INDEC.**

Unemployment kept a slightly increasing trend up to the Tequila crisis, when it jumped sharply (Figure 2.1). The fact that unemployment was rising even when the economy was growing at full steam reflects a combination of rising participation rates _5_
emerging market countries maintain their current pace of growth, a similar pattern and opportunity will arise.

# **4.2 Significant changes in wages**

China‘s labor cost is rising rapidly, and the structure of its industries, exports and employment is changing. Many Chinese economists argue that China has absorbed its surplus labor, and approached the _Lewisian_ turning point (Cai, et al 2009, Huang and Jiang 2010). Recent data indicates that wages in China‘s manufacturing sector grew rapidly, rising from just over 150 dollars per month in 2005 to around 350 dollars in 2010 (about $4,200 per year—see Figure 4.1).<sup>22</sup> More precisely, the wage gap between China and other high middle-income countries is closing and likely to continue with certainty over the coming decade.<sup>23</sup> China‘s 12<sup>th</sup> Five-Year Plan projects that during the period 2011-2015, the Chinese economy will grow on average at 7 percent per year. It also proposes that real wages will grow as fast as GDP. Both growth rates are likely to be achieved and imply that real monthly wages will double from around $350 per month to $700 per month over the next decade. When combined with continued currency appreciation, China‘s real wages could _approach $1,000 per month_ within a decade or the level of some of the higher middle-income countries (Turkey, Brazil), and $2,000 per month by 2030 or the level of Korea, and Taiwan, China today.

**Figure 4.2: Labor Productivity and Average Wage Rates in Chinese Manufacturing** **<u>(USD)</u>**

<!-- Start of picture text --> 70000 3500<br>60000 3000<br>50000 2500<br>40000 2000<br>30000 1500<br>20000 1000<br>10000 500<br>0 0<br>Labor Productivity in Manufacturing Wages in USD<br>Sources: UNIDO and China Statistical Yearbook 2010.<br>Chinese labor productivity (USD) Average annual wage - USD<br>1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text -->

> 22 <mark>Source: Oxford Analytica, March 28, 2011. In 2010-2011, China‘s minimum wage for 30 municipalities rose by 25% or more.</mark>

> 23 One study puts China‘s hourly labor compensation costs in manufacturing at only 4 percent of those in the United States (Banister and Cook 2011). However another study, incorporated the rising social insurance cost, has put China‘s total labor cost on par with that of Thailand. Source: Chris Devonshire-Ellis, ―China now has the third highest labor cost in Emerging Asia,‖ January 19, 2011.

44