rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Key household and individual characteristics**

||**Total**|**1999/2000**|**2007/08**|**Control**|**Treated**|
|---|---|---|---|---|---|
|||**Panel A**|**: Household charac**|**teristics**||
|**Characteristics**||||||
|Household size|6.35|6.94|5.71|6.35|6.34|
|Head female|0.06|0.04|0.07|0.05|0.06|
|Head married|89.32|89.81|88.8|89.65|89.09|
|Head’s age|51.41|50.68|52.19|50.98|51.71|
|<br>Head’s education|4.07|4.16|3.97|3.88|4.2|
|Max. education|6.98|7.61|6.31|6.98|6.99|
|**Welfare & assets**||||||
|Inc. p.c. (Rs./a)|12746|10141|15553|14292|11655|
|Cons. p.c (Rs/a)|5649|5087|6256|6271|5210|
|Poverty ratio|0.37|0.43|0.31|0.31|0.41|
|<br>Land owned in acres|5.18|5.6|4.72|5.35|5.06|
|of which irrigated|58.3|57.17|59.52|63.63|54.54|
|<br>of which inherited|88.13|82.07|95.09|89.06|87.46|
|Owns any bullocks|95.58|94.16|97.11|95.48|95.65|
|<br>if yes value|9540.43|7971.59|10999.49|8801.93|9941.73|
|No. of observations|6,468|3,356|3,112|2,677|3,791|
|||**Panel B: Wa**|**es and individual c**|**haracteristics**||
|**Wages (if working)**||||||
|Average wage|50.03|46.07|53.98|57.05|45.57|
|Male|55.34|51.60|58.69|63.24|49.96|
|Female|37.88|35.52|40.94|40.68|36.36|
|agricultural|42.92|41.12|44.71|46.92|40.68|
|Male|47.19|46.12|48.15|51.89|44.46|
|Female|36.12|34.17|38.41|38.48|34.88|
|nonagricultural|61.77|55.89|65.86|71.90|54.64|
|Male|64.17|59.45|67.13|74.30|56.60|
|Female|47.28|41.24|55.09|52.71|44.69|
|**Characteristics**||||||
|Female|48.96|48.15|49.76|48.37|49.44|
|Age|35.27|34.4|36.11|35.08|35.42|
|<br>Educ. (years)|4.34|4.68|4.01|4.27|4.4|
|**Engaged in**||||||
|Agric. self-empl.|30.6|28.38|32.78|32.75|28.85|
|<br>Non-ag. self-empl.|3.67|2.97|4.36|3.67|3.67|
|Agr. casual labor|10.44|9.66|11.21|8.81|11.77|
|<br>Non-ag. casual|8.25|6.07|10.39|7.71|8.7|
|Salaried work|4.12|4.69|3.57|4.46|3.85|
|Domestic work|24.76|25.63|23.91|24.66|24.85|
|School/college|9.21|7.46|10.93|9.9|8.65|
|Unemployed/not in labor<br>force|12.81|15.15|10.53|12.23|13.29|
|<br>No. of observations|38714|19147|19567|17398|21316|
||,|, <br>**Panel C: Village**|, <br>**level characteristi**|, <br>**cs (8 states only)**|,|
|No of households|508||508|704|400|
|.<br>MNREGS. days total/hh|<br>21.39||<br>21.39|<br>6.53|<br>38.79|
|<br>MNREGS spdg tot (Rs/hh)|5762||5762|2312|9734|
|<br>MNREGS. days irrig/hh|9.34||9.34|2.72|17.11|
|MNREGS spdg irr. (Rs/hh)|2347||2347|1278|3513|
|No. of obs.|94||94|40|54|

_Source:_ Own computation from REDS 1999/00 and 2007/08. Village level data are available for 2007-08 only. _Note:_ Irrigation related days are those relating to work related to irrigation facilities (including micro and minor irrigation works); renovation of traditional water bodies (including de-silting of tanks); water conservation and water harvesting (digging/maintenance of ponds, watersheds, etc.); and drainage works (cleaning and digging of new main and field drains). Works not included were those for drought proofing (including afforestation and tree plantation); land development/improvement activities; flood control or protection; rural connectivity (all-weather access); and building/maintenance of school buildings or community centers.

16
**<u>Table 4(continued)</u>**

|**Case Number**|**Case Name**|**Company**|**AD Duty**<br>**(with zeroing)**|**AD Duty**<br>**(w/o zeroing) **<sup>**a**</sup>|
|---|---|---|---|---|
|DS322 ‐ No. 7|(AR) Spherical Plain Bearings and Parts Thereof From<br>Japan|NTN Corporation|2.78%|Negative (‐26.06%)|
|DS322 ‐ No. 8|(AR) Ball Bearings and Parts Thereof From Japan|Koyo Seiko Co.,Ltd.|10.10%|Negative(‐5.51%)|
|||NTN Corporation|9.16%|Negative(‐15.21%)|
|||NSK Ltd.|4.22%|Negative(‐20.76%)|
|DS322 ‐ No. 9|(AR) Cylindrical Roller Bearings and Parts Thereof From|Koyo Seiko Co.,Ltd.|5.28%|Negative(‐11.70%)|
||Japan|NTN Corporation|16.26%|Negative(‐8.08%)|
|DS322 ‐ No. 10|(AR) Spherical Plain Bearings and Parts Thereof From<br>Japan|NTN Corporation|3.60%|Negative (‐10.31%)|
|DS322 ‐ No. 11|(AR) Ball Bearings and Parts Thereof From Japan|NSK Ltd.|6.07%|Negative(‐18.78%)|
|||Asahi Seiko Co.,Ltd.|2.51%|Negative(‐26.83%)|
|||NTN Corporation|9.34%|Negative(‐12.17%)|
|DS322 ‐ No. 12|(AR) Ball Bearings and Parts Thereof From Japan|NTN Corporation|4.51%|Negative(‐25.99%)|
|||NSK Ltd.|2.68%|Negative(‐29.90%)|
|DS322 ‐ No. 13|(AR) Ball Bearings and Parts Thereof From Japan|Koyo Seiko Co.,Ltd.|5.56%|Negative(‐10.83%)|
|||NTN Corporation|2.74%|Negative(‐25.86%)|
|||NSK Ltd.|2.46%|Negative(‐29.61%)|
|DS344 ‐ No. 1|(OI)Stainless Steel from Mexico)|ThyssenKruppMexinox S.A. de C.V.|30.85%|Lower|
|DS344 ‐ No. 2|(First AR)Stainless Steel from Mexico|ThyssenKruppMexinox S.A. de C.V|2.28%|Negative|
|DS344 ‐ No. 3|(Second AR)Stainless Steel from Mexico|ThyssenKruppMexinox S.A. de C.V|6.15%|Lower(1.83%)|
|DS344 ‐ No. 4|(Third AR)Stainless Steel from Mexico|ThyssenKruppMexinox S.A. de C.V|7.43%|Lower(4.96%)|
|DS344 ‐ No. 5|(Fourth AR)Stainless Steel from Mexico|ThyssenKruppMexinox S.A. de C.V|5.42%|Lower(1.54%)|
|DS344 ‐ No. 6|(Fifth AR)Stainless Steel from Mexico|ThyssenKruppMexinox S.A. de C.V|2.96%|Negative|

a This column indicates what would have been the outcome if zeroing were not applied; “lower” simply means the margin would have been lower; “negative” means the margin would have been negative (i.e., no dumping) and as a result the case would have been terminated (for original investigations) or no duties would have been paid (for administrative reviews).

“ _De minimis_ ” means the margin is too small to be subject to an order. “OI” indicates original investigation whereas “AR” indicates administrative review.

Source: Compiled by the authors from the public documents submitted as part of each AB dispute; case information available from the WTO website.

– 39 –
Table 2 Durable Ownership Share Engel Curve Regressions

||(1)<br>Car|(2)<br>Motorcycle|(3)<br>Air Conditioner|(4)<br>Refrigerator|
|---|---|---|---|---|
|log(income)|0.139***|0.0259|0.123***|0.129***|
||(0.0213)|(0.0204)|(0.0203)|(0.0365)|
|Constant|0.162***|0.318***|0.0713***|0.511***|
||(0.0188)|(0.0175)|(0.0147)|(0.0316)|
|Mean Y|0.3583|0.1608|0.14|0.6214|
|Nation/Year Fixed Effect|Yes|Yes|Yes|Yes|
|Countries Included|84|79|70|84|
|Observations|775|595|540|680|
|R-squared|0.951|0.980|0.926|0.949|

The unit of analysis is a nation/year/income quintile. The dependent variable is the share of households who own the durable. Robust standard errors in parentheses. The standard errors are clustered by country. *** p<0.01, ** p<0.05, * p<0.1 The regressions are weighted by the nation's population in the sample year.

25
production function assumed. Our preferred specifications in Columns 3-4 estimate (2) to account for heterogeneous impacts based on the export intensity of the sector for the theoretical reasons outlined previously. Under our preferred specifications, we find a negative and statistically significant coefficient on FDIs,t−1 , regardless of whether a Cobb-Douglas or translog production function is assumed. In other words, greater FDI intensity is associated with decreases in markups in a given sector. This result is largely in line with the findings of Chung (2001), Sivadasan (2009), Forte and Sarmento (2012) and Bottasso and Sembenelli (2001). Specifically, we find the point estimate implies that a 10-percentage point increase in FDI presence is associated with a 1.5 to 2.6 percentage point reduction in the ratio of price to marginal cost in sectors that sell entirely to domestic markets. Similarly, the reduction on markups due to an increase in FDI of the same magnitude in the median sector of the export distribution is between 0.04 to 1.2 percentage points. The coefficient on FDIs,t−1 is also negative across estimations of (1) that exclude the interaction term, although they are not always statistically significant.

_Table 2. Impact of FDI presence on markups_

|VARIABLES|(1)<br>Markup<br>(CD)|(2)<br>Markup<br>(TL)|(3)<br>Markup<br>(CD)|(4)<br>Markup<br>(TL)|
|---|---|---|---|---|
|FDIs,t−1|-0.0339|-0.111**|-0.153*|-0.261**|
||(0.0446)|(0.0459)|(0.0796)|(0.0955)|
|EXPs,t−1|-0.116|-0.245*|-0.323*|-0.505***|
||(0.101)|(0.127)|(0.157)|(0.155)|
|FDIs,t−1∗EXPs,t−1|||0.371*|0.464|
||||(0.199)|(0.281)|
|MNEi,s,t|0.0860***|0.125***|0.0861***|0.125***|
||(0.0273)|(0.0263)|(0.0273)|(0.0261)|
|Constant|1.144***|1.260***|1.203***|1.335***|
||(0.0308)|(0.0459)|(0.0466)|(0.0523)|
|Firm fixed effects|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Observations|100,956|97,823|100,956|97,823|
|R-squared|0.810|0.824|0.810|0.824|

Robust standard errors clustered at the industry level in parentheses. Dependent variable displayed at the top of each column: CD denotes markups derived assuming a Cobb-Douglas production function, while TL denotes markups derived assuming a translog production function. *** p<0.01, ** p<0.05, * p<0.1 Our results provide some evidence that the impact of foreign presence is lower in export-intensive sectors. The coefficients on the interaction term between FDI intensity and export intensity are generally positive in Table 2 and marginally statistically significant in Column 3, while the coefficient in Column 4 just misses the threshold for statistical significance. In addition, the coefficient on the export intensity term is negative across all specifications and at least marginally statistically significant in all but one specification. Taken together, these results suggest that FDI is associated with reduced markups primarily in domestic market-oriented sectors, potentially because firms in export-oriented sectors already face competition from producers around the world, making the contribution of MNE entrants to competition more negligible.

11
# FIGURE 1

# INDUSTRIAL LAND USE AND JOB DENSITY PATTERNS

Figure l.a <!-- Start of picture text --> MOSCOW - LAND USE<br>Percentage of Industrial Land within Built-up Area<br>70%-<br>950% V<br>40%-<br>30%-<br>0 20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1718S 19 2021<br>Dlatt,c* from Ciy CnerW (km) h: f m<br>Date Eour.: Inat.tute ot The Mat*r Plan ot Mo..ow<br><!-- End of picture text --> Figure 1.b <!-- Start of picture text --> MOSCOW - LAND USE ANALYSIS<br>Job Density<br>25<br>200- -.---. ------*<br>g1o0 -. -.<br>50<br>Dgtwice fron City center (kn)<br>-ED- 3rOSS Job danity M Dendty I Bt* |<br>Source: Institute of Master Plan of Moscow 1992<br><!-- End of picture text -->
Table 7: Conditional convergence 1970-2010, PPP-measures productivity

|||_D_|_ependent varia_|_ble:_||
|---|---|---|---|---|---|
||1970-80s<br>(1)|10-year log <br>1980-90s<br>(2)|change in labo<br>1990-2000s<br>(3)|r productivity<br>2000-2010s<br>(4)|Panel<br>(5)|
|**_All economies_**||||||
|Initial productivity|-0.129**|-0.134**|-0.170***|-0.150***|-0.169***|
|_Convergence rate PPP (annual)_|_1.38%_|_1.43%_<sup>_∗∗_</sup>|_1.86%_<sup>_∗∗∗_</sup>|_1.62%_<sup>_∗∗∗_</sup>|_1.79%_<sup>_∗∗∗_</sup>|
|_Convergence rate MER (annual)_|_1.06%_<sup>_∗∗∗_</sup>|_1.29%_<sup>_∗∗∗_</sup>|_1.24%_|_1.46%_<sup>_∗∗∗_</sup>|_1.33%_<sup>_∗∗∗_</sup>|
|Schooling (years)|0.007|0.025|0.022|0.014|0.016*|
|Economic complexity|0.093*|0.135*|-0.024|-0.021|0.034|
|Commodity exporter|0.030|-0.065|-0.124|-0.052|-0.077*|
|Trade (% GDP)|0.134*|0.123|-0.033|-0.009|0.055|
|Investment (% GDP)|-0.208|-1.109**|2.133***|1.010***|-0.153|
|Law and Order|0.033|0.010|0.052|0.011|0.046***|
|Observations (All)|61|69|78|79|287|
|_Note:_|||_∗_p_<_|0.1; _∗∗_p_<_0.05;|_∗∗∗_p_<_0.01|

Note: Decade dummies are used in the panel specification, but no fixed effects are included. Initial productivity is the average of log productivity over the 10-years in the preceding decade. Productivity growth calculated as the change in average log-productivity between the two decades (10-year average growth). For the decade beginning in 2010, productivity is assumed to grow at its average rate between 2010-7 for the final two years of the decade, 2018 and 2019. All conditioning variables are lagged decadal averages. Market exchange rate results are also provided for comparison.

# **PPP-effects on productivity growth**

To establish the effects of differences in relative prices on the level of output across economies, the Penn World Tables draw on multiple years of data from the World Bank’s ICP, with data beginning in 1970. Because their PPP-adjustments are updated across multiple years, their impact is not just on the level of output per worker but the growth rate of output per worker. The PPP-adjusted growth rate of output in each economy often differs substantially from the nationalaccounts growth rate used in the market-exchange rate approach (where exchange rates are fixed in one year). This occurs for two reasons, both of which cause the prices recorded by the ICP to 41
intervening in the early years. Second, it shows that children regardless of socio-economic status are at risk for effects of arsenic poisoning. Third, it reports a clear does-repose relationship, where increasing levels of arsenic increase the likelihood for developmental impairment. Finally, it confirms that children are sensitive to even small doses of arsenic that are less than the current Bangladeshi national standard of 50 ppb.

There are, however, limitations to this analysis that should be considered when interpreting the results. Some limitations surround the validity of how arsenic exposure is defined in the analysis. As reported in the methodology, arsenic was only tested randomly for 5 households per cluster in the original MICS data set. However, in order to increase the sample size, the result of the five arsenic test is applied to the rest of the households in that cluster. Although it is likely that households in close vicinity and with the same water source would have similar risks of arsenic exposure, it is still an assumption to believe all households in the same cluster would have the same arsenic concentration in their respective water sources. To reduce the impact from this assumption, households with dissimilar types of water sources from the tested household in the cluster were eliminated from the sample (i.e. if the tested household had a tube well, then households that had piped water in the cluster were not included in the sample). Yet, it is possible that even households with the same type of water source have different concentrations of arsenic levels.

Another limitation is the assumption that children’s exposure to arsenic equates the concentration of arsenic in their household’s water source. Although children aged 3-5 are most likely regularly drinking from their household’s water source (i.e. are no longer breastfeeding), they could be consuming arsenic from other sources such as soil and food. The analysis also cannot account for past exposure such as from in utero or if a child lived in a different area. Biological specimens such as urine, toenails, or blood could have been a better proxy for actual exposure levels, but the data were limited in this regard.

The ECDI measurement also has some limitations. The ECDI is a mother-reported measurement of child development, which can introduce some bias. The ECDI may also not be able to capture the severity of disability, as mothers are only able to give yes or no responses. Nevertheless, the ECDI enables a better national-scale approximation of early childhood development than was previously possible. To our knowledge, only one other study utilizes the ECDI to measure population effects on child development (Miller, et al., 2015).

Additionally, as in the case of most cross-sectional analysis, attribution of causality can only be made with caution due to the non-temporal nature of the data set. However, since arsenic is a naturally 13
## **Table 4 First difference OLS regression:**

**Change in consumption ranking on change in household incomes**

||H<br>|H added HE<br>|<br>|H<br>|H stopped H<br>|E<br>|HH changed re<br>|porting of HE<br>|
|---|---|---|---|---|---|---|---|---|
||All<br>HE|Prim<br>HE|Sec<br>HE|All<br>HE|Prim<br>HE|Sec<br>HE|Secondary to<br>Primary|Primary to<br>Secondary|
|National|7.3***|17.7***|-0.50|2.50|2.70|5.20|32.7***|11.0*|
|Urban|4|10.3***|-2.50|4.20|2.00|15.4*|29.6***|8.40|
|Rural|8.7***|23.3***|1.00|-3.90|-4.40|-2.00|34.3***|5.90|
|rciles in<br>1|7.8**|17.8***|-0.10|7.00|6.70|10.90|22.9***|17.0***|
|mption te<br>2003<br>2|11.9***|20.6***|6.20|13.1**|20.4***|-3.70|8.20|9.20|
|Consu<br>3|6.6**|16.5***|-0.90|4.70|6.00|5.30|32.6***|12.3**|
|head in<br>No completed|7.1**|19.7***|-1.80|6.90|8.00|8.10|37.5***|19.0*|
|l of HH<br>03<br>Lower primary|10.0**|16.7**|3.10|-6.60|-6.60|-6.20|25.0***|-1.70|
|tion leve<br>20<br>Upper primary|7.4|11.30|6.40|0.20|0.30|9.40|6.50|14.4*|
|duca<br>Lower secondary|-0.9|||-1.00|||||
|E<br>or above||17.4**|-14.50||3.80|-6.40|12.50|25.3*|

_Source:_ NPS panel and authors calculations. * Significant a 1% level, ** at 5% level. Standard errors take survey design and clusters into account. Table shows regressions results for variables on interest. Regressions also control for change in number of adults in household, change in number of children, change in income from agriculture, and change in income from wage. Terciles are defined based on consumption per capita in 2003. First line within each category shows a combined regression of all HE activity. Second line shows HE activity broken into primary and secondary activity. Means of variables are found in table A8

# _Can HEs alleviate poverty?_

This is the question often asked by policy makers. To assess this in more detail we impose a relative poverty line for the bottom 50 percent of the population in each year. The bottom 50 percent roughly corresponds to Mozambique’s national poverty estimates in 2003 and 2008 (Ministry of Planning and Rural Development, 2010). With all households defined as poor and non-poor in both 2003 and 2008 the sample can be divided into following four categories representing possible changes in welfare: 1) Always poor, 2) Never poor, 3) Moved out of poverty, and 4) Fell into poverty. We can associate these states in 2008 with whether a household started, sustained or closed an HE.

Considering only HEs as the primary activity for the owner, Figure 7 shows that rural HE start-up households were much more likely to move out of poverty than Never HEs, while there does not seem to be an impact on poverty for urban households. Rural area households that started up an HE were 17 | P a g e
<!-- Start of picture text --> Equatorial Guinea: Government Revenues<br>(% of GDP)<br>50<br>45<br>40<br>35<br>30<br>25 Domestic Revenues<br>20 Oil revenues<br>15<br>10<br>5<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>% of GDP<br><!-- End of picture text --> Source: Equatoguinean authorities <!-- Start of picture text --> SSA Oil Producing Countries: Overall Fiscal Balance (excluding grants)<br>1997 - 2006<br>25<br>20<br>15<br>1997-2001<br>10<br>2002<br>5 2003<br>0 2004<br>2005<br>-5<br>2006<br>-10<br>-15<br>-20<br>Angola Cameroon ChadCongo, Rep of Cote d’ivoireEquatorial Guinea Gabon Nigeria<br>% of GDP<br><!-- End of picture text --> Sources: IMF, African Department database, March 2007; and World Economic Outlook 2007.

12
# **Appendix Table I. 14: Organized Sector Log (Real Output) of Small, Medium-sized and Large Plants**

||(1)<br>Small Orga|(2)<br>nized Plants|(3)<br>Medium-siz|(4)<br>ed Organized|(5)<br>Large Orga|(6)<br>nized Plants|
|---|---|---|---|---|---|---|
|Horizontal Output Spillovers||_Oth_|_er-sector Empl_|_oyment Spillov_|_ers_||
|from Unorganized Sector|0.1240***|0.0502***|0.0876***|0.0288++|0.0931***|0.0445**|
||(0.0178)|(0.0166)|(0.0220)|(0.0216)|(0.0207)|(0.0207)|
|Forward Output Spillovers from|||||||
|Unorganized Sector|0.2881|-2.0992++|0.8798**|-1.4907|-0.3317|3.4666**|
||(0.2687)|(1.4854)|(0.3514)|(1.6106)|(0.3783)|(1.4285)|
|Backward Output Spillovers from|||||||
|Unorganized Sector|-0.1073|-1.2351*|-0.4614*|-1.0559+|-0.2407|-0.3563|
||(0.1999)|(0.7437)|(0.2362)|(0.7263)|(0.2533)|(0.8015)|
|||_Oth_|_er-sector Prod_|_uctivity Spillov_|_ers_||
|Average TFP of Horizontal-|||||||
|linked Unorganized Plants|0.2006*|0.1719+|0.0552|-0.1059|0.0677|-0.0786|
||(0.1138)|(0.1143)|(0.1821)|(0.1878)|(0.1784)|(0.1763)|
|Average TFP of Backward-linked|||||||
|Unorganized Plants|0.8635***|1.9586***|0.2653|0.8576|0.5693+|-0.2585|
||(0.2598)|(0.6389)|(0.3584)|(0.7742)|(0.3509)|(0.8280)|
|Average TFP of Forward-linked|||||||
|Unorganized Plants|-0.4941|-2.0036|-0.1491|0.7540|-0.2016|-4.5119**|
||(0.3999)|(1.5956)|(0.4917)|(1.8889)|(0.4738)|(1.7790)|
|||_Ow_|_n-sector Empl_|_oyment Spillov_|_ers_||
|Forward Output Spillovers from|||||||
|Organized Sector|2.6764***|16.0094***|2.0100**|12.6001***|3.7832***|8.7808***|
||(0.7685)|(2.9738)|(0.9160)|(3.3854)|(1.0057)|(2.3892)|
|Backward Output Spillovers from|||||||
|Organized Sector|1.1532***|2.6564+|1.1018**|1.4958|1.7622***|1.5102|
||(0.4409)|(1.7085)|(0.5206)|(1.6860)|(0.5631)|(2.0008)|
|Constant<br>|-85.3306***|-361.7262***|-74.0800***|-275.3983***|-112.3907***|-284.3498***|
||(18.1552)|(57.2583)|(19.3961)|(65.0484)|(22.5967)|(61.3908)|
|Observations|5888|5888|5888|5888|5888|5888|
|R-squared|0.0717|0.2265|0.0289|0.1555|0.0367|0.1672|

_Note: The dependent variables are constructed using the total employment by organized plants in a state-industry cell. Small plants are defined as those with less than 50 employees, medium-sized plants have 51-150 employees and large plants are defined as those with more than 151 employees. All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Columns 2, 4 and 6 contains state-year and industry-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 67
# **Figure 3 International Financial Integration**

# **China and India**

<!-- Start of picture text --> 300%<br>250%<br>200%<br>150%<br>100% China<br>50% India<br>0%<br>East Asia and G7<br>300%<br>250%<br>G7<br>200%<br>150%<br>East Asia<br>100%<br>50%<br>0%<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1998 1999 2000 2001 2002 2003 2004<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text -->

**Eastern Europe and Latin America**

<!-- Start of picture text --> 300%<br>250%<br>200%<br>150%<br>Eastern Europe<br>100% Latin America<br>50%<br>0%<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Sum of foreign assets and liabilities expressed as a ratio to GDP. East Asia is the average of Indonesia, Korea, Malaysia, and Thailand. G7 is the average of Canada, France, Germany, Italy, Japan, United Kingdom, and United States. Latin America is the average of Argentina, Brazil, Chile, and Mexico. Eastern Europe is the average of Czech Republic, Hungary, and Poland. The series for the regions are weighted averages where the weights are the countries' GDPs as a fraction of the region's GDP. Source: Authors' calculations drawing on the dataset constructed by Lane and Milesi-Ferretti (2006).