rafmacalaba/datause-encoder-data huggingface dataset:
including differences in education and experience, hours worked, occupational segregation, and gender discrimination. As transition economies move towards contribution-based systems, these tendencies should result in lower pensions for women, on average, as their total lifetime income will be lower than men's. Finally, women have higher life expectancy at retirement age than men, so they receive old-age income in average over a longer period.

**3.8** Table 2 shows the current differences in labor market characteristics in the countries in our study. While male and female labor force participation rates were high and very close dring the previous regime, women's rates have fallen in most of the ex-communist countries of East-Europe and Central Asia since the beginning of the transition. In general, female rates fell even more than men did, and the fall has been heavily concentrated in the younger and older categories. (Barry and Reilly **1999).** Recorded unemployment, however, is not significantly higher for women, which may reflect the lower participation rates at the ages most likely to suffer unemployment.

|**Table2.L **|**aborMarketC**|**haracteristics**|||
|---|---|---|---|---|
||**Kazakhstan**|**KyrgyzRepublic**|**Latvia**|**Moldova**|
|**Laborforceparticipation**<br>**(formalsector)**|||||
|**_%_**_ofworking-agepopulation_<br>Female|42.8|**38.2**|**50.2**|45.3|
|Male|**_52.5_**|_45.6_|**_59.0_**|**_52.6_**|
|**Unemploymentrates**<br>**_%_**_oflaborforce_<br>Female|6.4|**7.5**|**16.3**|**...**|
|Male|**5.7**|**7.0**|15.4|**...**|
|**Wage gap**|||||
|**_%_**_ofmale wage_|||||
|Female|**72.3**|**81.6**|**79.9**|...|

_Source:_ ILO; **1996** Kyrgyz **LSMS; OECD-CEET 1997** database; **E.** Barry and M. Reilly, **1999;** World Bank data.

**3.9** Comprehensive information is not available on how these differences translate in total years of contribution for men and women. For the purposes of our analysis, we generated average lengths of service based on each country's labor force participation **by** age and

**14**
|Parana Market Towns Improvement Project||
|---|---|
|Parana Market Towns Improvement Project||
|Parana Market Towns Improvement Project||
|Parana Market Towns Improvement Project||
|Parana Market Towns Improvement Project||
|Credit and MarketingReform Project||
|Credit and MarketingReform Project||
|Credit and MarketingReform Project||
|Credit and MarketingReform Project||
|Credit and MarketingReform Project||
|Credit and MarketingReform Project||
|Credit and MarketingReform Project||
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Northeast Rural Development Project(01)- SERGIPE|Tulio Barbosa|
|Land Tenure Improvement Project||
|Land Tenure Improvement Project||
|Land Tenure Improvement Project||
|Land Tenure Improvement Project||
|Land Tenure Improvement Project||
|Land Tenure Improvement Project||
|Land Tenure Improvement Project||
|Development BankingProject(03)||
|Development BankingProject(03)||
|Development BankingProject(03)||
|Development BankingProject(03)||
|Development BankingProject(03)||
|Development BankingProject(03)||
|Development BankingProject(03)||
|Science and TechnologyProject|Robin Scott Horn|
|Science and TechnologyProject|Robin Scott Horn|
|Science and TechnologyProject|Robin Scott Horn|
|Science and TechnologyProject|Robin Scott Horn|
|Science and TechnologyProject|Robin Scott Horn|
|Science and TechnologyProject|Robin Scott Horn|
|Science and TechnologyProject|Robin Scott Horn|
|Science and TechnologyProject|Robin Scott Horn|
|Power Transmission Project - CHESF - FURNAS||
If we focus just on the WTO cases in Table 4 that involve administrative reviews, we have a sample of 45 dumping margins. Of this sample, in 35 of the 45 cases the margin would have been eliminated if zeroing were not employed. If one is willing to assume that this is a representative statistic for other cases, the evidence from the current WTO jurisprudence suggests that about 75% of review margins would be eliminated but for zeroing. This is consistent with the CATO study which also found the impact of zeroing at the review phase to be particularly significant.

We again urge caution in applying the WTO AB statistics to the overall sample of U.S. antidumping cases. As we discussed above, the margins for cases brought to the WTO AB are generally lower than those for other cases. It may simply be the case that the low margin cases give the complaining country the “biggest bang for the buck” and therefore are more likely to result in WTO challenges.<sup>33</sup> Moreover, given that non-challenged cases tend to have higher margins, it is uncertain what the impact of zeroing is on the trade volumes. That is, suppose the U.S. stopped zeroing in all cases. The elimination of zeroing may result in lower margins but nevertheless have little impact on trade. This would be the case, for instance, if the computed margin without dumping was still quite high. Suppose a firm has a dumping margin with zeroing of 80% and its margin without zeroing was 35%. It is not likely that a margin of 35% would result in a significantly different volume of imports than a margin of 80% – a duty can easily be prohibitive at 35%.

> 33 Bown (2005) argues this selection issue applies more generally in WTO disputes.

– 42 –
developing countries’in‡ows and out‡ows experience a steep fall in the 1980s, and recover thereafter. They peak in 2011, after the global ...nancial crisis, and decline subsequently.

Table 1 reports descriptive statistics. The ...gures in the table are group averages of the underlying country data. Like with the aggregates in Figure 1, advanced countries exhibit by far the largest gross ‡ows as percent of trend GDP. Moreover, as noted by Broner et al (2013), their in‡ows and out‡ows are highly synchronized –their average contemporaneous correlation exceeds 0.90.<sup>11</sup> The correlation between CIF and COD is smaller for emerging and, especially, developing countries. The individual-country data, shown in Table A3 in the appendix, reveal that all of the 19 advanced-country in‡ow-out‡ow correlations are signi...cantly positive.<sup>12</sup> This is also the case for 22 out of 28 emerging countries, but only for 10 out of 30 developing countries. On the other hand, advanced countries show the smallest degree of capital ‡ow variability, as measured by the coe¢ cient of variation, while developing countries show the largest, for both in‡ows and out‡ows.

# 4 Empirical results

To check the suitability of a latent factor model for characterizing the patterns of capital ‡ows, we ...rst assess their degree of international comovement. Table 2 reports cross-country correlations of gross capital ‡ows. The numbers shown in the top panel of the table are averages of individual-country ...gures. In each block, the diagonal entries correspond to within-group correlations (i.e., the average of all the intra-group pairwise cross-country correlations), while the o¤-diagonal entries are average between-group correlations.<sup>13</sup> In turn, the bottom panel of the table indicates what fraction of the total number of individual correlations underlying each of the averages in the top panel are signi...cantly di¤erent from zero.

All but one of the average within-group correlations are positive, with the negative entry corresponding to the developing country group’s in‡ow-out‡ow correlation. For both in‡ows and out‡ows, the highest average within-group correlation corresponds to advanced countries – they are the countries whose ‡ows co-move most strongly. The ...gures shown in the

> 11The correlation is similarly high (0.86) if ‡ows are expressed in ...rst di¤erences rather than ratios to trend GDP. In a disaggregated analysis of capital ‡ows by economic sector, Avdjiev et al (2017b) show that the positive correlation between in‡ows and out‡ows, especially in advanced countries, is primarily due to banks, although the in‡ows and out‡ows of corporates and government also show positive (but smaller) correlation. On this issue see also Davis and van Wincoop (2017).

> 12The standard error of a correlation r is approximated as q <u>1T� �r1</u><sup>2:</sup>

> 13The average in‡ow-out‡ow correlations in the table exclude the within-country correlation. On the other hand, all the qualitative conclusions in the text continue to hold if ‡ows are expressed as ...rst di¤erences rather than ratios to trend GDP.

10
<!-- Start of picture text --> Share of Share of Children Share of adults Share of working<br>rural residents (0-14 years) with no education adults in agriculture<br>EastPacificAsia & Extreme Poor (33% 67% = NB 7NNN «OMY51%<br>73%<br>Moderate Poo 27% = GOW a<br>82% HT 88%<br>Nonpoor 57%<br>CentralEurope& Extreme Poor 36%= 64% 5% 85%<br><!-- End of picture text --> <!-- Start of picture text --> Nonpocr 82% 0 80%<br>AmericaCaribbeanLatin & extremeModeratePoorPoor - |43842% === 58%57 % «== BTW)MBBS] SAeae48%32%<br>Nonpoor BZN 25%<br>South Asia ModerateExtreme PoorPoor 4%(34%75%50%0% SN «8TH(6%(52%)44%<br><!-- End of picture text -->

<!-- Start of picture text --> S ub-aharan Extreme Poor 50% = 50% [76%24%<br>Africa" Moderate Poor 44% = 56% 166% 34%<br>Nonpoor (34% 66% «= NPBOW «4%58%<br>0% 50% 100% 0% 50% 100% 0% 50% 100% 0% 50% 100%<br>lm Urban @ 15 or above lm Some education @ Non-agriculture<br>@ Rural m 0-14 li No education ® Aariculture<br><!-- End of picture text -->
Foroutan, Faezeh, Lant Pritchett. “Intra-Sub-Saharan African Trade: Is It Too Little,” World Bank wokring paper.

- Froot, Kenneth A. and Jeremy C. Stein, (1991) "Exchange Rates and Foreign Direct Investment and Imperfect Capital Market Approach," _Quarterly Journal of Economics_ , Nov, p. 1191 – 1217.

- Globerman, Steven, and Daniel Shapiro, 2002, “Global Foreign Direct Investment Flows: the Role of Governance Infrastructure,” Industry Canada.

- Griliches, Zvi, and Jerry Hausman. 1986. "Errors in Variables in Panel data" Journal of Econometrics 31(1), pp. 93-118.

- Haddad, Mona, and Ann E. Harrison. 1993. “Are There Positive Spillovers from Direct Foreign Investment?  Evidence from Panel Data for Morocco.” _Journal of Developing Economics_ 42, 51-74.

- Hallward-Driemeier, Mary, Scott Wallsten, Lixin Colin Xu. "Ownership, Investment Climate and Firm Performance," _Economics of Transition_ 14(4), 629-47.

- Haskel, Janathan; Sonia Pereira, and Mathew Slaughter. 2002. “Does Inward Foreign Direct Investment Boost the Productivity of Domestic Firms?”  NBER working paper No. 8724.

- Keller, Wolfgang, and Stephen Yeaple. 2003. “Multinational Enterprises, International Trade and Productivity Growth: Firm Level Evidence from the United States.” NBER working paper No. 9504.

- Konings, J. 2001. "The Effects of Foreign Direct Investment on Domestic Firms: Evidence from Firm-Level Panel Data in Emerging Countries," _Economics of Transition_ 9(3), 619-33.

- Hanson, Gordon, Matthew Slaughter, and Raymond Mataloni, 2001, "Expansion Strategies of U.S. Multinational Firms," in Dani Rodrik and Susan Collins, eds., _Brookings Trade Forum 2001_ , 2001: 245-282.

- Hausman, R., Dani Rodrik, Andres Velasco. 2005. “Growth Diagnostics.” in J. Stiglitz and N. Serra, eds., _The Washington Consensus Reconsidered: Towards a New Global Governance_ , Oxford University Press, New York.

- Javorcik, Beata. 2004. “Does Foreign Direct Investment Increase the Productivity of Domestic Firms?  In Search of Spillovers through Backward Linkages,” _American Economic Review_ 94, 605-27.

- Kee, Hiau Looi. 2010. “Uncovering Horizontal Spillovers: When Foreign and Domestic Firms Share Common Local Input Supplier.” Working paper, World Bank, Washington, D.C.

- King, Robert G and Ross Levine. 1993. “Finance, Entrepreneurship, and Growth: Theory and Evidence,” _Journal of Monetary Economics_ 32:3, pp. 513-42.

- Knack **,** Stephen; Rahman, Aminur. 2007. "Donor Fragmentation and Bureaucratic Quality in Aid Recipients," Journal of Development Economics 83(1), 176-97.

- Kremer, Michael. 1993. “The O-Ring Theory of Economic Development.” _Quarterly Journal of Economics_ 108, 551-575.

- La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer and Robert Vishny. 1997. “Legal Determinants of External Finance,” _Journal of Finance_ 52:3, pp. 1131-50.

22
between the buyers and sellers, so price discounts or buffers, where e.g., two units of carbon stored may only equal one unit to be sold on the market, could send negative signals to sellers indicating a lack of trust, thus reducing the effectiveness of the contract. Gong et al. (2010) recognize the importance of social capital such as strong cultural norms and trust, which may make up for incomplete contracts, such that a lack of full information or trust may result in unfulfilled contracts even if monetary benefits are positive. They conclude that establishing formal institutions, such as property rights, and tailoring contractual arrangements according to the degree of local social capital might improve the success and reach of CDM projects. This remains an important area of future research.

_The Costs of Measuring, Reporting, and Verification_ Measuring, reporting, and verification (MRV) of carbon will need to be handled both at the national level via policy, and on a contract-by-contract basis. National systems and policies addressing these issues will need to be established, but individual contracts that link sellers of carbon credits to buyers will also need to specify measuring, monitoring, and verification methods. For example, the Climate Action Reserve (CAR) established monitoring and reporting requirements in its Forest Project Protocol (Version 3.2) for projects in the United States. All enrolled projects must submit initial forest inventory reports estimating carbon stocks in the project area. Then, monitoring and reporting primarily consists of updating the forest inventories annually according the project monitoring plan. Forest inventory updates that include carbon stocks also require oversight by a Professional Forester (FPP, 2010).

A major concern for some forest projects is that MRV methods could be very costly. At the project level, estimates indicate that carbon measurement would cost in the range of $1 to $2 22
available. Using these data, and highly disaggregated information on import tariffs for the same period, we analyze how different trade policy interventions affect the ability of Indonesian exporters to adjust to sudden changes in foreign demand. Specifically, we focus on how exchange rate shocks in Chinese yuan affect Indonesian firms’ exports in Japan, the largest regional trading partner for Indonesia (after China) and the export destination for 8,353 Indonesian firms.<sup>1</sup> Indonesia provides a suitable context for the analysis for a number of reasons. First, it fits remarkably well the general pattern of trade policy in developing countries over the past three decades, namely import tariffs liberalization since the 1990s and a contemporaneous increase in NTMs. Second, many of these NTMs are applied on intermediate inputs and have been changed frequently over time, thus providing a rich source of variation for our analysis. Specifically, merging customs and policy information allows us to build a unique data set on the universe of exporters and their imported inputs with precise time-varying data on trade policy at a detailed product level. Finally, many Indonesian exporters compete with Chinese producers in third markets, particularly Japan. Importantly for our analysis, Indonesian exports are denominated in USD. These characteristics allow us to exploit the significant changes in the USD/yuan exchange rate in the 2010s as an exogenous demand shock to Indonesian exporters to Japan.

The demand shocks we use are monthly variations in exchange rates between the yuan and the USD. Such exchange rate movements—out of control of Indonesian firms—affect their exports by changing the relative prices of Indonesian and Chinese goods in Japan, where the largest share of imports are from China. Consider for instance a depreciation of the yuan versus the USD, which is the case for most of our sample period. One of the key strategies through which an Indonesian exporter could counter the increase in Chinese competition associated with this exchange rate shock is to lower its marginal costs. In the

> 1The largest regional trading partner is China. Since we use variations in demand arising from exchange rate shocks in yuan/USD, we do not study China as the destination market but as a competitor in the Japan market.

2
Changes in PM1 O emission sources and their contribution to air quality is even more unclear. Relevant sources of directly emitted particles include building and construction (for example road surface works), diesel engine vehicles (about 30 percent of all vehicles (SMA 1999)), forest fires, industry, and open-air refuse burning. Measurements of PM 1o since 1995 (the year in which continuous PM1 o air quality measuring started) do not indicate a trend, although in 1999 the number of days that standards were violated were the lowest in the fiveyear period. In the absence of any concrete trend data or integrated model on emission projections for 2010 for fixed and mobile sources, we assume the reference case in 2010 to be equal to the 1998 baseline air quality. We note the arbitrariness of 2010 baseline.

# _The Future Scenarios_

To examine the implications of different levels of pollution control we developed four alternative 2010 scenarios in addition to the 2010 reference scenario:

- a 10 percent air pollution reduction scenario;

- a 20 percent air pollution reduction scenario;

- an air quality standard compliance scenario assuming air quality would improve to the standard (50 JIg/M<sup>3</sup> for PMI 0 and 0.11 ppm 1-hour maximum for ozone) in all locations in the ZMVM-the AQS 1 scenario;

- an air quality standard compliance scenario superimposing the needed percentage decrease in concentrations in the most polluted areas (Xalostoc for PM1 O and Pedregal for ozone) across the ZMVM (68 percent and 47 percent reduction in ozone and PM1o concentrations, respectively)-the AQS2 scenario.

To enhance the potential plausibility of the scenarios, we compared them with air quality trends for the South Coast Air Basin of California (see box 3.1). The trends for this area show a decline in maximum ozone concentration of 24 percent in 10 years due to strict controls. Similarly, the PM1 O annual average decreased by 40 percent, but maximum PMlo concentrations remained the same. Hence, the first two scenarios proposed seem plausible but the third and especially fourth scenario would be very difficult, but not impossible, to achieve. We do not consider the policies needed to achieve the concentration reductions in this study.

_II_
22 as reflected in reductions in the poverty gap and the squared poverty gap. See Table 4, which is adapted from Table 4.3 in Fiszbein and Schady (2009).

_Educational outcomes_ : The evidence on the educational impacts of CCTs is both of higher quality (because it often relies on the internal comparison of outcomes for children randomly allocated to treatment or control groups) and more mixed. There is considerable evidence that the programs increased school enrollment and attendance, and lowered school drop‐out rates (Behrman at al., 2005, Britto 2004 and 2007, and Rawlings, 2005). This effect was particularly pronounced at the secondary school level – where enrollment rates were lower than in primary to begin with – and, in some countries, for girls. But there is much less evidence that the programs helped improve final educational outcomes – such as learning as measured by achievements in standardized examinations.<sup>22</sup> It would thus appear that the behavioral impacts of CCTs are stronger on (if not limited to) the immediate behaviors on which the transfers are conditioned, such as enrollment and attendance at schools. It has been hypothesized that further improvement in actual learning requires additional measures, including investments in the supply‐side of the educational system.<sup>23</sup> Such investments are all the more needed to the extent that the children being attracted to – or retained in – school as a result of CCTs are typically from poorer family backgrounds and may require additional support to achieve even the learning levels of their new peers.

_Health outcomes and nutrition_ : CCTs also appear to have increased the demand for health services (on which they are conditioned) although, once again, improvements in final health and nutritional outcomes have been modest. For instance, the evidence reviewed in Lomeli (2008) suggests that, as a result of the programs, take‐up of prenatal, natal, and postnatal care has increased in Peru, Honduras, Mexico, and El Salvador. Take‐up of child growth monitoring increased in Colombia, Honduras, Mexico, Nicaragua, and Peru, while vaccination rates increased in Colombia, Honduras, Nicaragua, and Peru. It is less clear, however, whether the programs have had long‐term impacts on health outcomes. Although positive effects on maternal mortality rates (in Mexico) and morbidity rates (Mexico and Colombia) have been reported in the literature, methodological and data shortcomings mean that the evidence remains less than conclusive.<sup>24</sup> There is nonetheless some robust evidence that PROGRESA has caused a

> 22 See Chapter 5 in Fiszbein and Schady (2009), and the references therein.

> 23  The original and highly successful PROGRESA program in Mexico, which was the precursor to _Oportunidades_ , did in fact implement such complementary supply‐side interventions. But they would appear to have been overlooked in most subsequent, large‐scale CCT programs.

> 24 See the discussion in Chapter 5 in Fiszbein and Schady (2009).
|Duration of activity squared|-0.0003|-0.0003|179.0|
|---|---|---|---|
||(-2.04)|(-2.03)||
|If the enterprise is registered|-0.134|-0.133|0.147|
||(-1.03)|(-1.02)||
|If the enterprise is home-based|-0.526|-0.503|0.136|
||(-4.03)|(-3.85)||
|Months of operation per year|-0.001|-0.0003|10.6|
||(-0.08)|(-0.02)||
|Share of hired labor among workers|-0.017|-0.006|0.097|
||(-0.08)|(-0.03)||
|Manufacturing and processing activities|0.763|1.063|0.121|
||(3.74)|(0.71)||
|Transport activities|0.734|5.554|0.104|
||(3.41)|(3.25)||
|Trading activities|0.417|(3.704|0.020|
||(1.16)|(1.40)||
|Service activities|0.379|1.903|0.670|
||(2.26)|(1.54)||
|Log running capital and value of enterprise|1.076|-|99.806.2|
|(Tk./year)|(24.32)|||
|Manufacturing and processing activities X|-|1.186|1.267|
|Log running capital and value of enterprise||(12.67)||
|(Tk./year)||||
|Transport activities X|-|0.750|1.072|
|Log running capital and value of enterprise||(5.98)||
|(Tk./year)||||
|Trading activities X|-|0.899|0.202|
|Log running capital and value of enterprise||(3.87)||
|(Tk./year)||||
|Service activities X|-|1.070|7.072|
|Log running capital and value of enterprise||(21.59)||
|(Tk./year)||||

43