rafmacalaba/datause-encoder-data huggingface dataset:
In the final robustness check, we aim to ensure that endogeneity is not a concern for our FDI variables. We instrument for foreign presence in Czech services industries by taking advantage of the fact that two neighboring countries, Slovakia and Hungary, were also expected to liberalize services industries in preparation for their EU accession, which took place on May 1, 2004. We use the EBRD index capturing progress in their reforms, weighted by the Czech input-output coefficients, to instrument for foreign presence in Czech services industries: The estimation results, presented in table 8, suggest that these instruments are good predictors of foreign presence in services industries when the capital share definition is used but less so in the case of the threshold definition. The test of excluded instruments and the overidentification test confirm the validity of the instruments.

TABLE 8. Instrumenting for foreign presence in services industries Productivity in domestic manufacturing firms and reform in upstream services sectors

|Dependent Variable: ln(T|FP)|OLS Pro|ductivity|Olley-Pakes|Productivity|
|---|---|---|---|---|---|
|||I|II|III|IV|
|Services input|FDI (share)|9.150**||8.593**||
|linkages||(4.162)||(4.216)||
||FDI (threshold)||11.515*||10.481|
||||(6.883)||(7.041)|
|Manufacturing|FDI (share)|-0.045||0.231||
|input linkages||(0.775)||(0.829)||
||FDI (threshold)||0.265||0.402|
||||(0.955)||(1.010)|
||MFN Tariff|-0.477**|-0.922*|-0.409*|-0.809|
|||(0.233)|(0.559)|(0.235)|(0.580)|
|Within sector|FDI (share)|-0.073||-0.224||
|effects||(0.329)||(0.334)||
||FDI (threshold)||-0.368||-0.392|
||||(0.532)||(0.538)|
||MFN Tariff|0.238**|0.434*|0.218**|0.394|
|||(0.108)|(0.251)|(0.109)|(0.261)|
||Hansen J statistic|0.49|0.14|0.78|0.30|
||p-value|0.48|0.71|0.38|0.59|
|Coefficients on<br>|Weighted EBRD index for|0.111***|0.086|0.111***|0.086|
|instrumental variables in<br>stage I|Slovakia|(0.027)|(0.056)|(0.027)|(0.056)|
||Weighted EBRD index for|0.048|-0.082|0.048|-0.082|
||<br>Hungary|(0.233)|(0.503)|(0.233)|(0.503)|
||F-statistic|13.78|2.36|13.78|2.36|
||p-value|0.00|0.12|0.00|0.12|
||Shea R<sup>2</sup>|0.35|0.05|0.35|0.05|
||R<sup>2</sup>|6875|6875|6875|6875|

The Inputs Linkage variables are weighted by the respective I/O coefficient. All regressors are lagged one year. All models include firm and year fixed effects. Robust standard errors clustered on industry are presented in parentheses. *, **, *** indicate statistical significance at the 10, 5 and 1% level.
banking sector flows. The left-hand panel shows changes on restrictions to residents and the right-hand panel changes to non-residents.

# **Fact 4: Liberalization of outflows slowed after the global financial crisis, while the liberalization of inflows picked up**

The pace of liberalization of capital inflows and outflows has evolved differently before and after the global financial crisis. Liberalization of resident and non-resident inflows and outflows followed a significant and positive trend until the crisis. However, after the crisis, only liberalization of non-resident inflows maintained a significant trend; and its coefficient was somewhat larger, suggesting that the pace of liberalization of non-resident inflows accelerated after 2008.

**Table 6: Trends in CFM Before and After the Global Financial Crisis**

||R<br>|esident<br>|No<br>|n-resident<br>|
|---|---|---|---|---|
||Inflows|Outflows|Inflows|Outflows|
||0.043*|0.153***|0.043***|0.028**|
|Pre-crisis trend|[1.95]|[4.67]|[2.79]|[2.05]|
|Observations|1,692|1,692|1,692|1,692|
|R-squared|0.05|0.27|0.09|0.05|
|# of countries|47|47|47|47|
||0.052|0.045|0.062***|0.007|
|Post-crisis trend|[1.62]|[1.57]|[2.92]|[0.69]|
|Observations|1,410|1,410|1,410|1,410|
|R-squared|0.04|0.03|0.11|0.00|
|# of countries|47|47|47|47|

Note: The table shows the coefficient of regression of the respective cumulative indices on a linear time trend. The first row regresses a cumulative index that takes the value 0 in 1999:Q1, on a time trend from 1999:Q1 until 2007:Q4 (precrisis trend). The second row regresses a time trend that takes the value 0 in 2009:Q1, on a linear time trend from 2009:Q1 to 2016:Q2 (post-crisis trend). All regressions include country fixed effects. Robust t-statistics are reported in parenthesis. ***, ** and * indicate statistical significance at the 10%, 5% and 1% level.

# **Fact 5: Large cross-country and regional heterogeneity in the use of CFMs**

The results presented so far mask significant heterogeneity across regions and countries. Not all countries use capital account policies regularly—some countries have not liberalized inflows by non-residents at all; and more than half of the countries in the sample never tightened restrictions on inflows by residents (see Charts 3 and 4).<sup>15</sup>

> 15 Contrary to some perceptions, India stands out as a country that has consistently liberalized its capital flows starting from a very closed capital account (see Gupta, 2016). For the most part, it has been due to the liberalization of inflows by

13
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another chance to the factor endowments perspective discussed in section 2, but as before, the results were unaffected by unobserved industry heterogeneity.

**<u>Table 7: Investigating Market Failures- GDP Included</u>**

|||**7a: H**|**S 4-Digit**|||
|---|---|---|---|---|---|
||**Model I**|**Model II**<br>**var1: lnAvgEduc**<br>**var2: TertEnroll**|**Model III**<br>**var1: lnJournals**<br>**var2: lnPatents**|**Model IV**<br>**var1: lnBusStart**|**Model V**<br>**var1:**<br>**PSCredit**|
|lnGDP|8.45497***<br>(4.51)|6.93744***<br>(3.54)|6.12783***<br>(3.46)|.98569***<br>(4.18)|8.63670***<br>(4.40)|
|lnGDP squared|-.16187***<br>(-4.51)|-.136567***<br>(-3.70)|-.11368***<br>(-3.42)|-.19695***<br>(-4.27)|-.16363***<br>(-4.37)|
|lnPopulation|.38661*<br>(1.87)|.53342**<br>(2.02)|.27843<br>(1.07)|.68491***<br>(2.86)|.38050*<br>(1.84)|
|lnInitialExports|-.48945**<br>(-2.55)|-.43369**<br>(-2.27)|-.34634**<br>(-2.01)|-.32430*<br>(-1.65)|-.60389***<br>(-2.83)|
|ExportGrowth|5.82809**<br>(2.49)|2.32391<br>(0.88)|4.31084<br>(1.50)|2.32404<br>(0.85)|6.74057***<br>(2.59)|
|var1||.40268<br>(0.96)|-.50299**<br>(-2.01)|.10416<br>(0.88)|.00450<br>(1.32)|
|var2||.79929<br>(-1.06)|-.50299<br>(-0.33)|||
|F-Test: var1<br>and var2||.4497|.0836*|||
|Sample Size|49|44|44|42|47|
|||**7b: H**|**S 6-Digit**|||
||**Model I**|**Model II**<br>**var1: lnAvgEduc**<br>**var2: TertEnroll**|**Model III**<br>**var1: lnJournals**<br>**var2: lnPatents**|**Model IV**<br>**var1: lnBusStart**|**Model V**<br>**var1:**<br>**PSCredit**|
|lnGDP|7.81598***<br>(5.84)|8.12994***<br>(5.99)|7.88057***<br>(5.37)|11.0404***<br>(6.98)|7.61322***<br>(5.61)|
|lnGDP squared|-.15779***<br>(-6.37)|-.16112***<br>(-6.48)|-.15431***<br>(-5.88)|-.22291***<br>(-7.38)|-.15189***<br>(-6.07)|
|lnPopulation|.49978***<br>(3.26)|.42693**<br>(2.14)|.38255*<br>(1.69)|.66011***<br>(4.07)|.44651***<br>(2.93)|
|lnInitialExports|.02533|-.03281|.04210|.12792|-.02375|
||(0.19)|(-0.25)|(0.31)|(1.05)|(-0.15)|
|ExportGrowth|9.26602***<br>(5.23)|8.66202***<br>(4.63)|9.69250***<br>(4.27)|7.30837***<br>(3.97)|9.29990***<br>(4.77)|
|var1||.32125<br>(0.23)|-.13946***<br>(4.27)|.15472**<br>(2.01)|-.00022<br>(-0.09)|
|var2||-.83852<br>(-1.57)|.81854<br>(0.77)|||
|F-Test: var1<br>and var2||.2816|.2088|||
|Sample Size|49|44|44|42|47|

Note: Parentheses indicate z-statistics. Significant at 10% level: *, 5% level: **, 1% level: *** See Appendix II for sample composition of each estimation. Source: Author’s Calculations.

33
23 Figure 3 Real effective exchange rate, CPI deflated (1993-95 =100) <!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>85.1 86.3 88.1 89.3 91.1 92.3 94.1 95.3 97.1 98.3 '00.1 '01.3<br><!-- End of picture text --> Whether to use consumer or producer (especially wholesale) prices for obtaining real exchange rates is a matter of debate (Cline, 1995, pp. 33-35). The main point, however, is that in terms of international competitiveness, the rise in the real exchange rate under the Convertibility Plan was considerably less than implied by relative consumer prices alone.

Direct international price comparisons provide an alternative gauge of possible overvaluation of the Argentine peso in the mid-1990s. In purchasing power parity (ppp) estimates based on surveys referring to 1993, the World Bank/ United Nations International Comparison Programme placed the 1999 ratio of Argentina’s nominal GNP to its ppp GNP at 0.67, about the same as for Brazil (0.70) but somewhat higher than the average for Mexico and Chile (0.56) and for all upper-middle-income economies as a group (0.59; World Bank, 2001, pp. 274-75). The ppp comparisons might suggest relative overvaluation on the order of 15 percent (i.e. 0.67/0.59), but not of much more extreme magnitudes.

In any event, the central test of exchange rate competitiveness is the revealed performance on external current account balance, taking into account the level of employment and activity. As indicated in figure 4, through most of the 1990s Argentina’s external current account deficit was smaller relative to GDP than in the late 1980s. Moreover, export performance was strong. In the period 1990-99, real exports of goods and services grew at an annual rate of 8.7 percent, far better than the 3.8 percent rate in 1980-90 (though not as high as the 10.8 percent average for upper-middle-income countries as a group, which includes the East Asian economies; World Bank, 2001, pp. 294-95). Even so, by 1999-2001 the moderate current account deficit masked an underlying weakness in competitiveness because it reflected weak import demand in the face of recession. The cyclically-adjusted current account deficit would have been
Lee, Y., Parish, W. L., and Willis, R. J. (1994). Sons, Daughters, and Intergenerational Support in Taiwan. _American Journal of Sociology,_ 99(4), 1010-1041.

Lee, Y., and Xiao, Z. (1998). Children's Support for Elderly Parents in Urban and Rural China: Results from a National Survey. _Journal of Cross-Cultural Gerontology, 13(1998), 39-62._ Lei, X., Strauss, J., Tian, M., Zhao, Y. (2011). Living Arrangements of the Elderly in China: Evidence from CHARLS.

Lei, X., Zhang, C., and Zhao, Y. (2001). Old-Age Support and Protection in China, CCER, Peking University.

Lillard, L. A., and Willis, R. J. Motives for Intergenerational Transfers: Evidence from Malaysia. _Demography,_ 34(1), 115-134.

McGarry, K., and Schoeni, R. F. (1995). Transfer Behavior in the Health and Retirement Study: Measurement and the Redistribution of Resources Within the Family. _The Journal of Human Resources, Special Issue on the Health and Retirement Study:_ Data Quality and Early Results, 30(30), S184-S226.

Nugent, J. (1985). The Old Age Security Motive for Fertility, _Population and Development Review_ , 11(1):75-97.

Rosenzweig, M. R., and Wolpin, K. I. (1993). Intergenerational Support and the Life-Cycle Incomes of Young Men and Their Parents: Human Capital Investments, Co-residence, and Intergenerational Financial Transfers. _Journal of Labor Economics,_ 11(1), 84.-112.

Secondi, G. (1997). Private Monetary Transfers in Rural China: are Families Altruistic? _Journal of Development Studies,_ 33(4), 487-509.

Smith, J., McArdle, J. J., Willis, R. (2010). Financial Decision Making and Cognition in A Family Context. _The Economic Journal_ , 120 (November), F363-F380.

Zhao, Y., Strauss, J., Park, A., and Sun. Y. (2009). China Health and Retirement Longitudinal Study, Pilot, User ' s Guide. _National School of Development, Peking University_ .

23
12 to reduce oil intensity than net exporters. Upper middle-income countries had the largest proportion of countries, one-third, in which declining oil intensity had offset one-fifth or more of the oil price effect through consumption.

Among net oil exporters, nearly half had declining oil production between 1999 and 2008. Despite rising oil prices, oil exports as a share of GDP declined in one-fourth of the exporters—that is, the oil price effect through output had been offset by a fall in oil production combined with the effect of the inverse of GDP.

Although the full set of data to carry out decomposition analysis is not yet available, preliminary data are available from the U.S. Energy Information Administration (EIA) to compute vulnerability for 2011, when the average price of oil reached the highest level in history in real terms; the average price in 2012 was slightly lower. The data available as of March 2013 were used to calculate vulnerability in 2011 as well as the change in vulnerability between 2009 and 2011, during which period the world price of oil in U.S. dollars increased by more than 60 percent in real terms. The EIA database is periodically revised and further revisions are expected; hence these results should be viewed as preliminary. Table 2 provides summary statistics by income, region, and oil trade status. The results by country can be found in appendix B. More than two-thirds of the countries in the sample reduced oil intensity between 2009 and 2011. The median vulnerability for net importers in 2011 was 5 percent of GDP, and that for net exporters was -16 percent. The median increase in vulnerability for net importers between 2009 and 2011 was 1.5 percent of GDP, and the median decrease in vulnerability for net exporters was 3.1 percent of GDP.

Table 2: Vulnerability in 2011 and Change in Vulnerability 2009 to 2011 by Income, Region, and Oil Trade Status (% of GDP)

|**Classification**|**Oil trade status**|**No. of countries**|**Median V**|**Median**∆**V**|
|---|---|---|---|---|
|All income categories|Importers|132|5.0|1.5|
||Exporters|40|-16|-3.1|
|Low income|Importers|30|5.7|1.9|
||Exporters|3|-2.0|-3.3|
|Lower-middle income|Importers|35|5.2|1.5|
||Exporters|12|-3.9|-2.5|
|Upper-middle income|Importers|33|6.7|2.2|
||Exporters|14|-16|-2.8|
|High income|Importers|35|3.4|1.2|
||Exporters|11|-32|-3.6|
# **5. The Physical Effect of Air Pollution**

This section deals with assessing the effect of improvements in air quality in the ZMVM on human health and environmental contingencies. We begin with an introduction to the health effects of ozone and PM10. Then we discuss the relationships between public health and air quality (exposure-response relations) and quantify the health benefits of the air quality improvement scenarios in physical terms. The economic benefits of these health benefits and of contingencies will be quantified in section 6.

# **Effects of Ozone and PM1o on Health**

Not all air pollutants have the same capacity to damage human health. The differences in toxicity are due to the physical and chemical properties of the components of pollution. First, we briefly discuss the types of health effects caused by exposure to air pollution. Then we describe the properties of PM1 o and ozone as they relate to toxicity. More details can be found in Cesar and others (2000). In the second section we discuss the development of the exposure response functions that are used in this study in more detail.

# **_Health Effects Due to Short- and Long- Term Exposure to Air Pollutants_**

Susceptibility to air pollution exposure varies greatly among individuals. Individual risk is determined by genetics, age, nutritional state, presence and severity of respiratory and cardiac conditions, and the use of medications. The variability in the estimates found in epidemiological studies may reflect these differences in the populations studied. A good example of variation in individual risk occurs in the evaluation of maximum expiratory flow in healthy children, children with minor respiratory disease and those with asthma, with and without pharmacological treatment, and all exposed to various environmental pollutants. The results show an association between exposure and disease only in children with asthma under pharmacological treatment, in other words, those children who are most seriously ill (Roemer and others 1999). Genetic susceptibility is another factor that could be associated with respiratory diseases (Moller, Schuetzle, and Autrup 1994). Age is an important factor as well, with preadolescents (<13 years) and the elderly (>65) at greatest risk (Wilson and Spengler 1996, Ghio and others 1999).

Toxic effects attributable to short-term exposure to high levels of air pollution (hereafter "acute effects and acute exposure") vary widely. Episodes of high pollution and the associated increases in diverse respiratory and heart diseases and death have been reported since the beginning of the industrial revolution. The most serious acute effect is mortality. Many reports describe an increase in total mortality (not including accidental death) associated mainly with exposure to particulate matter (PM), ozone, and sulfates. (Schwartz 1994a, Wilson and Spengler 1996).

Many studies report increases in mortality due to respiratory complications, and this relationship can obviously be related to exposure to air pollution. Many reports also claim an increase in death due to cardiovascular disease, which would also imply an indirect effect _18_
**Table 3: Finance Characteristics of SMEs, by Ethnicity**

||**Ke**|**nya**|**Tan**|**zania**|**Za**|**mbia**|**Zim**|**babwe**|
|---|---|---|---|---|---|---|---|---|
||**_African_**|**_Minority_**|**_African_**|**_Minority_**|**_African_**|**_Minority_**|**_African_**|**_Minority_**|
|**_Pct. receiving_**<br>**_Supplier Credit_**|13.8|43.5***|9.9|16.3|10.8|32.3***|29.1|92.4***|
|**_Avg. years of_**<br>**_supplier relation_**|6.5|10.0***|6.9|10.5*|6.7|11.3***|8.6|16.0***|
|**_Pct with title to_**<br>**_property_**|13.8|56.5***|23.9|69.4***|33.3|70.8***|34.5|49.4***|
|**_Pct. rec. any_**<br>**_bank loan at_**<br>**_startup_**|12.6|34.2***|6.6|12.2|10.7|12.3|3.6|16.5**|

_Source: Enterprise Surveys, 1990s. Regional Program on Enterprise Development, World Bank_ *** Differences are significant at 99% confidence level; ** Significant at 95% confidence level; *Significant at 90%; Table 4 shows the educational attainment of entrepreneurs managing SMEs in these four African countries. Differences are evident between ethnic minority entrepreneurs and indigenous-African entrepreneurs, particularly in the level of higher education attained. Many more ethnic-minority entrepreneurs have university degrees and secondary school educations.

**Table 4: Highest Educational Attainment of Entrepreneurs (percent)**

||**Primary**|**Secondary**|**Technical Ed.**|**Univ**|
|---|---|---|---|---|
|**_African_**|36|28|24|12|
|**_Asian_**|12|38|15|35|
|**_European_**|2|32|33|33|
|**_Kenya_**|28|36|14|22|
|**_Zambia_**|13|31|33|23|
|**_Zimbabwe_**|16|26|32|26|
|**_Tanzania_**|39|32|12|17|

_Source: Enterprise Surveys 1990s, Regional Program on Enterprise Development, World Bank_ 14
Source: World Bank's Development Prospects Group web data.

# <u>Conclusions from this annex</u>

1. The United States, like other Members, has applied SSG measures to only a few of the tariff lines on which they – legally speaking – might have applied them.

2. Even on these tariff lines they have applied SSG measures to only a small percentage of shipment.

3. In 2006, the only year for which we have compared trigger prices with import unit values, trigger prices were in large part irrelevant – unit values were substantially above them.

4. In years in which agricultural prices were below their 1986-88 level (the base for the trigger prices) members applied hardly more SSG measures than when they were below that level.

5. The SSG formula provide Members with legal discretion to apply additional import restrictions, Members have not used them as guidelines as to when it makes sense to apply import restrictions.

67
~~————————eee~~ <!-- Start of picture text --> We use FutureBrand’s proprietary Hierarchical Decision This model offers insights that help us assess how well-<br>model (HDM) to determine how key audiences—including developed a brand is as well as key challenges that need<br>residents, investors, tourists and foreign governments—telate to be addressed. Our HDM also allows for a measurement<br>to a country brand, from mere awareness to full advocacy. of a country’s relative performance and progress. In short,<br>the HDM takes seven areas into consideration:<br>AWARENESS: FAMILIARITY: ASSOCIATIONS: PREFERENCE: CONSIDERATION: DECISION / VISITATION: ADVOCACY:<br>Do key audiences know How well do people know What qualities come to mind How highly do audiences Is the country considered To what extent do people Do visitors recommend<br>that the country exists? the country and its offerings? BRYubesws Su ems esteem the country? for a visit? follow through and visit the the country to family,<br>How top-of-mind is it? country? Here, we look at Does it resonate? What about for investment country or establish friends and colleagues?<br>five association dimensions: or to acquire or consume its a commercial relation?<br>Value System, Quality of Life, products?<br>Good for Business, Heritage<br>and Culture and Tourism.<br>Source: THE FUTUREBRAND 2011-2012 COUNTRY BRAND IN © All rights reserved.<br><!-- End of picture text -->