rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendices**

# **A Measures of vulnerability and matching with FDI data**

The _FDI Markets_ database the FDI projects into very broad recipient sectors, which are loosely aligned with 1987 U.S. SIC codes. We match the manufacturing broad sectors to the corresponding three-digit ISIC codes (rev.2) reported in Rajan and Zingales (1998) and Kroszner et al. (2007); when the _FDI Markets_ categories covered several sectors, we used the median value of the financial vulnerability measure for these sectors.<sup>39</sup> Table 3 indicates how the matching was done.

Table 3:

|Broad_FDI Markets_Sectors|Corresponding<br>ISIC code in RZ/KLK|ED median<br>value|TANG median<br>value|
|---|---|---|---|
|Beverages|313|0.08|0.40|
|Food & Tobacco|311+314|-0.16|0.28|
|Textiles|321+322+323+324|-0.03|0.14|
|Wood Products|331+332|0.26|0.30|
|Paper, Printing & Packaging|341+342|0.19|0.32|
|Alternative Energy, Biotechnology,||||
|<br>Chemicals, Pharmaceuticals|352|0.22|0.27|
|Rubber|355|0.23|0.36|
|Plastics|356|1.14|0.38|
|Ceramics & Glass, Building & Construction Materials|361+362+369|0.06|0.42|
|Metals|371+372+381|0.09|0.32|
|Business Machines & Equipment,||||
|Engines & Turbines, Industrial Machinery,||||
|Equipment & Tools, Space & Defence|382|0.45|0.22|
|Communications, Consumer Electronics,||||
|Electric/Electronic Components, Medical Devices,||||
|Semiconductors|383|0.77|0.21|
|Aerospace, Automotive OEM, Automotive Components,||||
|Non-Automotive Transport OEM|384|0.31|0.23|
|_Average_||_0.28_|_0.30_|
|_Standard deviation_||_0.35_|_0.08_|

Notes: ED: external dependence. TANG: asset tangibility. RZ: Rajan and Zingales (1998), KLK: Kroszner et al. (2007).

> 39We always use the ED value for the three-digit broad ISIC sectors. In some cases, these broad sectors may not include data on subsectors, for which Rajan and Zingales (1998) and Kroszner et al. (2007) provide four-digit level specific ED values.

23
For the importing countries, the reduction in the world price to p2 resulting from the safeguard requires a second-round increase in the safeguard duty on top of that shown in Figure 1. For each country, the decline in the world price it faces is not just the initial reduction from p0 to p1 but that from p0 to p2 shown in Figure 2. Average world prices decline, since the measure sometimes increases—and never reduces—duties. Another key impact of the widespread use of a safeguard in importing countries is an increase in the _volatility_ of world prices (see Tyers and Anderson 1992, p264).

An important implication of the analysis in Figure 2 is that it may not be enough, when analyzing the impacts of introducing a safeguard covering all developing countries, to simply consider experience in the absence of a safeguard. Once the safeguard is introduced in a number of important markets, the volatility of world prices is likely to be greater than would otherwise be the case. If this effect is large, it will increase the probability that the safeguard will be triggered in any period.

As noted in Fraser and Martin (2008) and in Valdés and Foster (2005), the implications of a quantity-based safeguard depend heavily upon the source of the shock. If the cause is a decline in world prices of the type shown in Figure 1, for instance, imports rise from (q0- d0) to (q1- d1). If this decline is large enough to trigger the volumebased safeguard, then a volume-based safeguard could also be used as an alternative to a price-based safeguard. If the same additional duty were generated by either safeguard, then there would be no effective difference. Because the link between the size of the price decline and the tariff imposed under the Q-SSM is weak, this safeguard may permit a larger response than the price-based measure, and may even cause the domestic price to rise when the import price falls.

If the world price does not decline, but imports increase, then the volume safeguard can be triggered even though the price-based safeguard is not. In this situation, it is very important to _examine the cause of the increase in imports_ . In the case of agriculture, such an increase is likely to be due to either a shift in the domestic supply curve, such as a decline in the harvest associated with poor weather conditions— although increases in demand, as considered by Sen (1981), may also be important on occasion. The South Centre (2009, p2) concludes that over 85 percent of import surges 8
13 can be added to create a SAM/CGE.

A potential problem is the data and time requirements: construction of a SAM typically requires an input-output table, national income accounts, and information from a household survey on patterns of consumer spending. An analysis of fiscal revenue and expenditure incidence is needed to account for changes in the government budget that accompany most adjustments. Additional data are required for parameter estimates. If these data are not available and estimates are made, then the model may move away from the reality of the country. Furthermore, if large structural changes are to be simulated then the exogenous parameter values based on historical estimates may not be relevant; behavioural parameters estimated under the old regime may not reflect behaviour in the economy following structural adjustment.

Although some CGE models do not explicitly start with a SAM, the data requirements are implicitly the same. Data are required for the estimation of parameters and exogenous variables, to replicate a historical set of data. When data are lacking, CGE models often use "borrowed" parameter values, parameters estimated in similar income countries. Results can be sensitive to this calibration. If sensitivity analyses are performed, they are usually done on only one parameter at a time and can result in changes in the optimal policy rankings for objectives such as growth.<sup>16</sup> If all possible combinations of parameter values are simulated, the output results could have a large range of 16For example, de Janvry, Fargeix, and Sadoulet (1989) estimate a model for Ecuador and include sensitivity analyses. They assess how the optimum policy for growth is affected by a high and low effect of inflation on investment, a rigid or more flexible financial sector, and so on, relative to a base run. Changing only one parameter relative to the base run, alters the long-run optimal policy for growth in every scenario. They do not simulate combinations of parameter changes, such as a high impact of inflation on investment combined with a flexible financial sector.
# **5. Conclusions and implications**

Dropout at the USE level has been an issue of increasing concern to Latin America during the past decade due to its vast economic and social implications. This study implements a synthetic cohort approach made possible by pooling data from 234 household surveys for the region to trace schooling trajectories over the life cycle of different generations over time and document an apparently puzzling fact— that, in spite of increasing overall school enrollment rates across the region and a much more favorable macroeconomic and labor market environment during the 21<sup>st</sup> century, dropout rates at USE ages have not only remained at high levels after about 20 years, but have even increased markedly in several countries.

The present study reveals that while dropout rates in lower secondary age and in transition to USE declined in the region during the past 15 years, USE dropout rates increased. The latest data show that USE is the segment of the education system where higher dropout rates are observed. Countries with particularly large increases of dropout at USE ages include Ecuador, Guatemala, Honduras, Mexico, Nicaragua and Peru, all with increases above 6 percentage points.

By looking at the differences in the specific moment in the life cycle when dropout takes place, we classify countries in three broad categories: early dropout, dropout in transition to USE, and late dropout. The classification can be useful to direct policy efforts to different segments. For countries with high early dropout, interventions that enhance school enrollment at lower secondary are still necessary in order to increase the number of USE entrants. For the second group, policies focused on supporting youth when they shift between LS and USE would be a priority, while for the third group, programs and actions to increase retention at USE would be central –since higher rates of permanence at LS and transition to USE are already achieved.<sup>39</sup> Apart from documenting the aforementioned stylized facts, we present an exploration of the possible causes of stable –and increasing in some cases- dropout at USE ages in the region, by focusing on three groups of potentially important factors. The first is the increasing demographic pressure on USE services arising from higher proportions of youth completing LS, and particularly, greater shares of lower income youth joining the education sector and obtaining the academic requirements for entering USE. According to our estimates these increasing shares have in fact been significantly associated with higher USE age dropout across the region. The result is robust to different estimation techniques, and it applies to males and females separately, as well as to the three country categories.

> 39 As shown by Bentaouet Kattan and Székely (2014) there may also be high within country variability in the patterns for school dropout, so these approaches may apply also to different regions within any given country independently from its classification in the three aforementioned groups.

24
food insecure or worse using the FIES and zero otherwise as the dependent variable. Approximately 56.0 percent of the non-poor population is moderately food insecure or worse, which is more than double the figure in Column (1); and the share for poor Nigerians was 3.2 percentage points larger. Although the difference between poor and non-poor populations is statistically different from zero, the magnitude of the difference is a small share of the population and is very low relative to the large share of the population that is moderately food insecure or worse according to the FIES. Furthermore, we can still rule out the possibility of the share being much larger for the poor population, where one can reject the hypothesis that the share is more than 5.5 percentage points larger at standard significance levels.

The results for the FIES are in stark contrast to the strong alignment between the FCS and monetary poverty. Column (3) re-estimates Specification (1) using an indicator equaling one if the household had a poor or borderline FCS and zero otherwise as the dependent variable. Only a small share of the non-poor population had poor food access according to the FCS, and the difference in the share between the poor and non-poor populations was large. Specifically, only 7.2 percent of the non-poor population had poor food access according to the FCS; and the share among the poor population was 19.4 percentage points larger than among the non-poor population.<sup>28</sup> Not only is the magnitude of the simple percentage point difference larger for the FCS than for the FIES (19.4 versus 1.4 and 3.2), the resulting share of the poor population with poor food access is very large relative to the share of the non-poor population. Specifically, using the FCS, the prevalence of poor food access for the poor population is 3.7 times that of the non-poor population. Alternatively, the share of the population that is either moderately food insecure or severely food insecure using the FIES is nearly identical between the poor and non-poor populations.

Importantly, the lack of a difference in the share with poor food access using the FIES between the poor and non-poor population extends to the ends of the welfare distribution. Table 4 reports estimates from Specification (2), which quantifies the difference in the

> 28See Appendix 2 for estimates from a specification that directly compares the alignment of the FIES and monetary poverty to the alignment of the FCS and monetary poverty.

19
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_ declined precipitously, reaching a cumulative loss of about 15 percent in the period 19992003, wiping out the post-devaluation gains on the income and poverty front. Poverty increased from 38 1⁄2 percent in 2002 to about 42-44 at end-2003, the highest in a decade (Figures 4 and 5).

<!-- Start of picture text --> Figure 4: Cote d'Ivoire: Poverty and Real GDP, 1993-2003 Figure 5: Cote d'Ivoire: CAB and<br>(Poverty in percent, left scale; Output index 1998=100, right Real effective exchange rate, 1991-2002<br>scale) (% of GDP, left scale ; Index 1992=100, right scale)<br>50 105<br>44 4.0 120<br>45 100 2.0<br>40 36.8 38.4 0.0 100<br>35 32.3 33.6 95 -2.0 80<br>90 -4.0<br>30 -6.0 60<br>25 85 -8.0 40<br>-10.0<br>20 80 -12.0 20<br>15 75 -14.0<br>-16.0 0<br>Poverty Real per capita GDP Linear (Poverty ) Current account balance to GDP<br>Real effective exchange rate<br>19931994199519961997199819992000200120022003<br>REER<br>CAB/GDP<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Source: World Bank staff live database, and IMF and Bank staff estimates. Notes to Figure 1.2: The linear line is a simple linear trend of the poverty series. Poverty figure for 2003 is an estimate based on growth elasticity of poverty and the estimated number of internally displaced people (see Table 1.2 below in section on the social impact of the crisis).

**Figure 6: Côte d’Ivoire - Macroeconomic Impact of the Crisis, 2002**

<!-- Start of picture text --> 10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>-2.0<br>Growth (%) Inflat. (%) Budg. (% GDP) CAB (% GDP)<br>Pre-Crisis 3.0 2.0 -0.5 -1.7<br>Post-Crisis -1.8 3.1 -1.5 9.3<br><!-- End of picture text --> _Source_ : IMF and World Bank staff estimates.

5
Policy Research Working Paper 5291

# **Abstract**

Is there evidence from China’s pre-WTO accession period that newly imposed U.S. or EU import restrictions deflect Chinese exports to third markets? The authors examine this question by drawing on a newly constructed data set of U.S. and EU product-level import restrictions on Chinese trade imposed between 1992 and 2001 and estimate their impact on Chinese exports to 38 alternative markets. There is no systematic evidence that the import restrictions imposed during this period resulted in Chinese exports surging to such alternate destinations. To the contrary, there is weak evidence of a chilling effect on China’s exports to third markets.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to evaluate the impact that international institutions have on the market access. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbown@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 2: Year fixed effects

|_Dependent Variable:_||Migratio|n Flows||
|---|---|---|---|---|
||WGI CC <br>(1)|WGI GE<br>(2)|CPI<br>(3)|ICRG<br>(4)|
|WGI CC in _o_|0.15***<br>(0.02)||||
|WGI CC in _d_|–0.34***<br>(0.01)||||
|WGI GE in _o_||0.29***<br>(0.02)|||
|WGI GE in _d_||–0.44***<br>(0.02)|||
|CPI in _o_|||0.11***<br>(0.02)||
|CPI in _d_|||–0.39***<br>(0.01)||
|ICRG in _o_||||0.09***<br>(0.01)<br>|
|ICRG in _d_||||–0.36***<br>(0.01)|
|Constant|5.17***<br>(0.11)|5.06***<br>(0.13)|5.72***<br>(0.11)|5.83***<br>(0.12)|
|Year FE|Yes|Yes|Yes|Yes|
|Adj. R-Square|0.09|0.09|0.11|0.13|
|N|6890|6890|6594|5248|

Robust standard errors clustered by county in parenthesis. ***, **, and * indicate statistical significance at the 1 _%_ , 5 _%_ , and 10 _%_ levels, respectively.

16
21. Chart 5 presents the average share of taxes compared to total disposable income by decile groups in each country, as ranked by their level of equivalized disposable income—an upward sloping curve therefore shows that taxes as a share of disposable income are relatively greater for higher income groups. The tax systems in all EU8 countries are quite progressive, as illustrated by the upwardsloping tax incidence curves for each country. Even in the flat-rate tax countries of Estonia, Lithuania, and Slovakia, it seems the exemption threshold for incomes helps introduce a fairly strong element of progressivity in the overall system. In general, however, the degree of progressivity varies considerably across EU8 countries: for instance, the tax-incidence curve for Slovenia is considerably steeper than in other countries.

**Chart 5: Tax incidence curves**

<!-- Start of picture text --> Estonia Lithuania Slovakia Poland<br>0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10<br>Decile Decile Decile Decile<br>Czech Republic Hungary Slovenia<br>0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10<br>Decile Decile Decile<br>50 50 50 50<br>40 40 40 40<br>30 30 30 30<br>Tax share Tax share Tax share Tax share<br>20 20 20 20<br>10 10 10 10<br>50 50 50<br>40 40 40<br>30 30 30<br>Tax share Tax share Tax share<br>20 20 20<br>10 10 10<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on data from the 2006 EU Survey of Income and Living Conditions 22. In an analogous manner, Chart 6 presents the benefit-incidence curves for EU8 countries—in this case, the degree of progressivity of the benefit system is indicated by magnitude of the negative slope of the curves for each country. Several interesting points are evident from these graphs. First, the benefit-systems in all countries are considerably more progressive than the respective tax systems, not too surprising given that income redistribution (rather than revenue generation) is their main objective. Second, despite their high-progressivity, even households in the top income decile receive a non-negligible share of incomes in the form of benefits, which in turn helps explain why these programs enjoy such widespread public support in all countries. Third, the benefit-systems in Hungary and Slovenia are considerably more generous than in other EU8 countries for all income levels, indicating that the overall tax-benefit systems in these countries are in fact much more similar to their EU15 continental counterparts rather than to the Baltic countries or Poland. Finally, Poland and Slovakia stand out as the only countries where the share of benefits accruing to the poorest income decile constitutes less than 40 percent of their total disposable incomes.

12
<!-- Start of picture text --> Figure 1 . Impulse Responses of GDP to Various Shocks: Small countries, by population<br>reer tby rrate_us<br>.003 .003 .001<br>.002 .002 .0005<br>.001 .001 0<br>0 0 −.0005<br>0 5 10<br>gdp govexp<br>.015 .006<br>.01 .004<br>.005 .002<br>0 0<br>rrate gdp_us<br>.002 .004<br>.001 .003<br>0 .002<br>−.001 .001<br>0<br>−.002<br>0 5 10 0 5 10<br>step<br>95% CI Orthogonalized IRF<br><!-- End of picture text --> First, the direct effect of higher US interest rates is to trigger portfolio reallocation into higher yielding US assets. Such outflows can be strongly contractionary as shown in Calvo, Leiderman and Reinhart (1993) and Gavin, Hausmann and Leiderman (1995), at least in the short run. The second effect relates to the cost of international borrowing. When the US interest rates rise, they likely increase the cost of borrowing in the international financial markets, which again can have contractionary effects on the economies integrated in the world financial markets. The third effect arises through the relationship between world interest rates and commodity prices. For instance, Frankel (2008) argues that higher world real interest rate would lead to lower commodity prices by reducing speculative demand for commodities. In addition, higher interest rates make it costlier to hold commodity inventories, pushing their prices further down. Similarly, Calvo (2008) shows that low interest rates lead to a portfolio reallocation away from liquid assets and can drive commodity prices up. The effects of commodity prices on GDP then depend on whether a country is a net importer or a net exporter of commodities. Of course, commodity prices will also likely feed back into interest rates as well. By passing-through into domestic prices and/or affecting inflationary expectations, higher commodity prices could lead to higher inflation and interest rates.

Overall, this discussion makes clear that the relationship between the world interest rates and GDP in large and small countries is likely to be complex and determined by the relative strength of the effects described above. Our empirical findings indicate that smaller countries on net benefit from the US interest hikes, while larger countries tend to experience contractions after the same shocks. This could be a consequence of the fact that smaller countries are less integrated into the world financial system and thus are less subject to portfolio rebalancing and borrowing cost effect, 13
:13 -'' 3 What made SOEs adiust?

There was every reason to expect destructive rnangement behavior in Poland's SOEs after the collapse of communism in 1990. With Central authoritY in disarray and in the absence of a centralized treasury, the Government as equity owner was unlikely to be interested in or capable of,management control. And corporate debt was typically held in newly-created banks with no experience of Western style banking behavior. Yet by and large the state sector has belied all predictions and adjusted remarkably well to changing circumstances. We saw in the previous section that a survey of SOEs conducted over the period 1990-1993 shows adjustment patterns and management behavior remarkably in line with what one would expect from profit oriented forward looking entrepreneurs.

This immediately raises the question of why managers behaved in the way they did in spite of the absence of regular mechanisns of corporate control and without any a priori obvious incentives to do so. Did any of the formal owners force such behavior onto management, and if so why and how? Or did management respond to incentives that were not obvious to outside observers at the ouaset?'T The answers to these questions are of obvious importance o the design and implementation of enterprise andfinancial sector reform programs..

The starting point of the answer is the ownership strucmre of the enterprises involved- Except for three privatized SOEs. all enterprises in the sample are statewned, and only a subset had already. been corporatized. Thus the Government was the equity owner, de jure for the corporatized enterprises. and de facto for the rest. At the same time. the government also is the senior debt holder in most enterprises through the existence of tax arrears. Since loss-making enterprises by definition do not pay Income tax, those arrears were largely incurred on dividend and Popiwek tax liabilities.

inom Ehs arer :a wer -in.u

> **to An** early discussion of **incentive** issues is contained in Frydman and Wellisz (1991). A more recent discussion of corporat **governance** and privatization is **contained** in Frydman and Rapaczynski (1994)..