rafmacalaba/datause-encoder-data huggingface dataset:
an episode and in which no event occurs (column 2). Coefficients in the table with an asterisk indicate that differences in initial conditions are statistically significant at the 5% level.

Consistent with the results demonstrated in Table 3, countries that experience unemployment reduction episodes have significantly higher initial unemployment, notably 14.7% on average, than countries in which episodes do not occur, with average unemployment at 11.6%. These numbers may seem high, but recall that we have confined the sample to country-year observations experiencing unemployment levels of at least 6%.

In spite of this differential in average initial unemployment, countries embarking on episodes of unemployment reduction are remarkably similar in other macroeconomic domains to those that do not.<sup>7</sup> They neither have significantly different initial GDP levels, nor record significantly different growth rates at the time of onset. They also do not have a significantly higher propensity to export, import and to receive FDI inflows. Moreover, they do not differ from those that do not embark on episodes in terms of average inflation and government spending as a share of GDP. In addition, they are not significantly more or less democratic on average.

Countries that have unemployment reductions also do not score higher on indicators of the overall regulatory framework. Our preferred proxy for overall regulatory quality is the economic freedom index of the Fraser Institute, as it is time-varying. Since this indicator and its subcomponents are only available every 5 years from 1980 until 2010, we linearly interpolate scores for the intervening years. No statistically significant differences between countries that embark on episodes and ones that do not are detected in any of the sub-components or the aggregate index itself.

We complement these broad indicators with specific indicators of labor regulation and financial openness from the IMF, as well as with data on governance and regulation from the _Worldwide Governance Indicators_ (Kaufmann et al., 2010) and the _Doing Business Indicators._ Since these last two data sets are only available from 1996 and 2004 onwards, respectively, we follow Collier and Goderis (2008) and extrapolate them backwards for years in which they were not available. Note that this procedure implicitly assumes that these policies do not vary over time, whereas in reality they may well have changed in response to (the absence of) unemployment reduction events. In other words, they are potentially endogenous. Collier and Goderis (2008), however, argue that because these indicators tend to capture structural policies that change only slowly over time, the

> 7 We also examined differences in average debt, savings, real interest rates, and the terms of trade and did not find any statistically significant differences in these either.

8
POLICY **RESEARCH WORKING PAPER** 2528

# Summary findings

Rao examines the paradox of very poor households spending large sums on celebrations. Using qualitative and quantitative data from South India, Rao demonstrates that spending on weddings and festivals can be explained by integrating an anthropological understanding of how identity is shaped in Indian society with an economic analysis of decisionmaking under conditions of extreme poverty and risk.

Rao argues that publicly observable celebrations have two functions: they provide a space for maintaining social reputations and webs of obligation, and they serve as arenas for status-making competitions.

The first role is central to maintaining the networks essential for social relationships and coping with poverty. The second is a correlate of mobility that may become more prevalent as incomes rise.

Development policies that favor individual over collective action reduce the incentives for the networking function and increase the incentives for status-enhancing functions-thus reducing social cohesion and increasing conspicuous consumption.

Market-driven improvements in urban employment, for example, could reduce a family's dependence on its traditional networks, could reduce incentives to maintain these networks, and could reduce social cohesion within a village and thus its capacity for collective action. In contrast, microfinance programs and social funds try to retain and even build a community's capacity for collective action.

This paper-a product of Poverty and Human Resources, Development Research Group-is part of a larger effort in the group to study the relationship between poverty and collective action. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Sader, room MC3-556, telephone 202-4733902, fax 202-522-1153, email address psader@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The author may be contacted at vrao@worldbank.org. January 2001. (25 pages) _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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
|Reallocation<br>(6)<br>.027<br>(.027)<br>-.106<br>(.081)<br>-.014<br>(.013)<br>-.018<br>(.017)<br>-.0006<br>(.0004)<br>.001<br>(.0007)<br>-.005<br>(.016)<br>.017<br>(.022)<br>11993<br>.015<br>ates market-share-<br>variance between<br>r dummies. Each<br>stered at the state-<br>edian in granting|
|---|
|Reallocation<br>(5)<br>.013<br>(.020)<br>-.071<br>(.090)<br>-.028<br>(.011)_∗∗_<br>-.009<br>(.017)<br>-.0003<br>(.0003)<br>.0004<br>(.0007)<br>.015<br>(.015)<br>.003<br>(.022)<br>11993<br>.015<br>Aggregate” indic<br>” indicates the co<br>l, and include yea<br>ard errors are clu<br>te is above the m|
|Average<br>(4)<br>-.088<br>(.048)_∗_<br>-.483<br>(.146)_∗∗∗_<br>.046<br>(.021)_∗∗_<br>.032<br>(.022)<br>.0007<br>(.0007)<br>-.0009<br>(.001)<br>-.008<br>(.023)<br>-.040<br>(.031)<br>11993<br>.086<br>each column. “<br>d “Reallocation<br>te-industry leve<br>ears, and stand<br>o one if the sta|
|Average<br>(3)<br>-.087<br>(.038)_∗∗_<br>-.482<br>(.153)_∗∗∗_<br>.070<br>(.019)_∗∗∗_<br>.011<br>(.024)<br>.0006<br>(.0006)<br>-.0009<br>(.001)<br>-.045<br>(.020)_∗∗_<br>-.006<br>(.032)<br>11993<br>.087<br>n at the top of<br>oductivity, an<br>yses at the sta<br>ry across all y<br>riable equal t|
|Aggregate<br>(2)<br>-.062<br>(.037)_∗_<br>-.588<br>(.139)_∗∗∗_<br>.031<br>(.018)_∗_<br>.015<br>(.021)<br>.0001<br>(.0005)<br>.00009<br>(.001)<br>-.013<br>(.019)<br>-.024<br>(.031)<br>11993<br>.087<br>e names are give<br>eighted mean pr<br>xed-effects anal<br>the state-indust<br> is a dummy va|
|Aggregate<br>(1)<br>-.074<br>(.038)_∗∗_<br>-.553<br>(.138)_∗∗∗_<br>.043<br>(.018)_∗∗_<br>.002<br>(.022)<br>.0003<br>(.0005)<br>-.0005<br>(.001)<br>-.029<br>(.019)<br>-.003<br>(.031)<br>11993<br>.087<br>endent variabl<br>ndicates unw<br>fcations are f<br>ber of frms in<br> of Closure”) <br>(close).|
|Final Goods Tariff<br>Input Tariff<br>FDI Reform<br>Delicensed<br>Ease of Layoff X Final Goods Tariff<br>Ease of Layoff X Input Tariff<br>Ease of Layoff X FDI Reform<br>Ease of Layoff X Delicensed<br>Ease of Closure X Final Goods Tariff<br>Ease of Closure X Input Tariff<br>Ease of Closure X FDI Reform<br>Ease of Closure X Delicensed<br>Obs.<br>_R_2<br>Each observation is a state-industry. Dep<br>weighted mean productivity, “Average” i<br>market share and productivity. All speci<br>observation is weighted by the total num<br>industry level. “Ease of Layoff” (“Ease<br>requests by large frms to lay off workers|

48
# **Introduction**

Rights matter. They matter in themselves. The question here is what is the evidence that they matter for other outcomes of interest – namely the ability to pursue economic opportunities, and health and education outcomes? Existing research shows that strengthening women’s economic and legal rights has a real and positive impact on women’s labor force participation, investment and agricultural productivity. However, most of the literature focuses on a particular country, examining differences in property rights across groups of individuals or looking at a particular reform episode. The “50 Years of Women’s Legal Rights Database” offers an opportunity to expand the analysis – with comparable indicators across 100 countries and covering 50 years.

The legal rights focused on here can be grouped into three broad categories. The first regards the ability to access and own property. This can mean land, but it also means other types of assets more generally. The second is legal capacity, whether women have the same ability as men to enter into legal agreements in their own name. The last is the approach to principles of equality and non-discrimination within the constitution. Such principles can be recognized in general, or with enumerated groups (e.g. non-discrimination based on gender or sex). But in some countries laws governing property, marriage and inheritance can be explicitly exempt from these principles in the constitution.

The database highlights the extent to which gender gaps in these three areas have been widespread, recognized in all regions and across all income groups. But the time dimension also shows that there has been change. Not only countries, but entire regions have eliminated the constraints examined here. Today’s rich countries of the OECD had their share of gender gaps in the 1960s and 1970s. However, today, they have removed all of the ones covered in this 2
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 6. Benchmarking port indicators**

||Matadi<br>(DRC)|Boma<br>(DRC)|Luanda<br>(Angola)|Douala<br>(Cameroon<br>)|Pointe<br>Noire<br>(Congo)|Apapa<br>(Nigeria)|Durban<br>(South<br>Africa)|
|---|---|---|---|---|---|---|---|
|**Traffic**||||||||
|Container cargo throughput (TEU/year)|—|10,000|377,208|190,700|—|336,308|1,899,065|
|Container-handling capacity (TEU/year)|200,000|—|400,000|270,000|150,000|500,000|1,450,000|
|General cargo throughput (tonnes/year)|—|—|4,000,000|3,800,000|3,300,000|3,400,000|—|
|General cargo-handling capacity<br>(tonnes/year)|1,700,000|500,000|4,000,000|6,500,000|5,000,000|4,000,000|7,900,000|
|**Efficiency**||||||||
|Average container dwell time in terminal<br>(days)|25.0|—|12.0|11.5|18.0|42.0|4.0|
|Average truck-processing time for receipt<br>and delivery of cargo (hours)|18.0|—|14.0|12.0|12.0|6.0|5.0|
|Average container crane productivity<br>(containers loaded-unloaded per crane<br>hour)|6.5|—|6.5|18.5|6.5|12.0|15.0|
|Average general cargo crane productivity<br>(tonnes loaded-unloaded per crane<br>working hour)|6.0|5.0|16.0|12.0|7.5|9.0|25.0|
|**Tariffs**||||||||
|Container-handling charge, ship to gate<br>($/TEU)|120|—|320|220|140|155|258|
|Average general cargo-handling charge,<br>ship to gate ($/tonne)|10.0|10.0|8.5|6.5|5.5|8.0|8.4|

_Source_ : Mundy and Penfold 2008. — = data not available.

In the longer term, the DRC will need to secure access to a deep-sea port. While an improved Port of Matadi will be able to service the southwest DRC for some years to come, in the longer term additional capacity will need to be found. The DRC would probably also benefit from having direct land access to a deepwater port that receives direct calls from major international shipping lines. To achieve this goal, the DRC faces two strategic options.

One option is to further develop the Port of Banana and (by means of major dredging works) convert it into a deep-sea port. The establishment of such a port, however, would cost around $2 billion and take 10 years to complete. Even once established, it is not clear whether the port would handle the kind of traffic volumes needed to attract direct calls from major shipping lines, or whether it would continue to rely on transshipment services from Pointe Noire.

The other option involves strengthening land links with the Republic of Congo to facilitate access to the Port of Pointe Noire. The poor quality of service provided by the Congolese rail operator Chemin de Fer Congo-Ocean (CFCO) and the total deterioration of the road corridor from Brazzaville to Pointe Noire have essentially ruled out this option at present. But there are efforts underway in the Republic of Congo to rebuild the road corridor and concession both the railway and container port terminal. Once 15
# **Table 6: Ordered Probit and Probit Regressions**

Financial Vulnerability Regressions (Marginal Effects)

|||**arrears**|**2**<br>|||**arrears**|**3**<br>|||**himedc**|**ost**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|
|_New EU Countries_|||||||||||||
|Czech Republic|0.0067|0.0312 ***|0.17|-4.46 ***|0.0091|0.0512 **|0.10|-2.88 ***|-0.0009|-0.0001|0.14|-1.50|
|Estonia|0.0086|0.0117|0.10|-0.20|0.0154|0.0393 **|0.15|-1.03|-0.0005|0.0076 **|0.19|-1.19|
|Hungary|0.0294 **|0.0537 ***|0.13|-1.39|-0.0271 *|0.0136|0.09|-1.67 *|0.0070 *|0.0046|0.09|0.42|
|Latvia|0.0371 **|0.0475 ***|0.10|-0.45|0.0265|0.0321|0.09|-0.19|0.0399 **|0.0395 ***|0.15|0.04|
|Lithuania|0.0081|0.0190|0.09|-0.31|-0.0037|0.0219|0.07|-0.77|-0.0009|-0.0067|0.13|0.77|
|Poland|0.0437 *|0.0216 **|0.09|0.87|0.0119|0.0401 ***|0.07|-0.90|0.0149|0.0053|0.10|0.67|
|Slovak Republic|0.0014|0.0424 ***|0.11|-3.89 ***|-0.0125|0.0109|0.09|-2.54 **|-0.0021|0.0047|0.20|-2.62 ***|
|Slovenia|0.0436|-0.0043|0.08|1.61|-0.0422|0.0327|0.06|-1.21|||||
|_EU-15 and Cyprus_|||||||||||||
|Austria|0.0054|0.0051|0.13|-0.07|0.1047 *|0.0440|0.17|0.97|0.0019|0.0007|0.07|0.47|
|Belgium|0.0154 **|0.0489 ***|0.16|-4.17 ***|0.0121|0.0246 *|0.16|-1.42|0.1137 ***|0.1810 ***|0.29|-0.89|
|Cyprus|-0.0154|0.0057|0.07|-1.24|-0.0236|0.0153|0.10|-1.05|-0.0003|0.0080|0.10|-0.70|
|Denmark|-0.0157 ***||0.28||-0.0294 ***||0.20||-0.0006||0.18||
|Finland|0.0320 ***|0.0175 **|0.10|1.99 **|0.0120|0.0526 ***|0.14|-3.27 ***|0.0014|0.0030 *|0.12|-1.04|
|France|-0.0128 *|0.0215 **|0.12|-5.03 ***|0.0027|0.0296 **|0.09|-2.72 ***|0.0043|0.0107 ***|0.14|-1.75 *|
|Greece|0.0234|0.0924 ***|0.28|-3.28 ***|0.1153 **|0.0929 **|0.11|0.42|0.0134|0.0208 **|0.08|-0.58|
|Ireland|0.0011|0.0428 ***|0.26|-3.40 ***|0.0000|0.0072|0.19|-1.03|0.0033|0.0023|0.39|0.35|
|Italy|0.0659 ***|0.0725 ***|0.12|-1.31|0.0163|0.0351 *|0.09|-0.86|0.0177 ***|0.0224 ***|0.09|-1.10|
|Luxembourg|-0.1392 **|-0.0907 *|0.20|-1.48|-0.0060 ***||0.28|||0.0016 **|0.34||
|Netherlands|-0.0208 ***||0.13||-0.0442 **||0.14||0.0000||0.27||
|Portugal|0.0046|0.0190 *|0.10|-1.26|0.0129|0.0285 *|0.12|-0.95|0.0350 **|0.0616 ***|0.09|-1.56|
|Spain|0.0191 ***|0.0244 ***|0.07|-0.49|0.0265 *|0.0581 ***|0.06|-1.53|||||
|Sweden|-0.0047|0.0152|0.13|-3.98 ***|0.0098|0.0753 **|0.16|-7.94 ***|-0.0016|0.0039|0.15|-3.64 ***|
|United Kingdom|0.0074|0.0482 ***|0.17|-5.80 ***|-0.0003|0.0519 ***|0.15|-6.36 ***|||||

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2007 13
for computing the ES-implied correlation do not have a significant impact on the results. Consider the efficient frontier between US and Canada. The ES-implied correlation equals 0.7920 using equal weights 50% and 50% at 5%-quantile. Assuming the annual risk-less rate is 3%, the tangency weights, which provide the highest Sharpe ratio, are 55.81% in the US equity index and 44.19% in the Canadian equity index. We can update this result. Using this newly updated tangency weights, the ES-implied correlation is 0.7924. The difference in the ES-implied correlation, 0.0004, is trivial. This new ES-implied correlation then leads to a new efficient frontier and a new set of tangency weights. I repeat this process until the difference in the ES-implied correlation is less than 10<sup>−7</sup> . It appears that the ES-implied correlation converges after three iterations. The final tangency weight set is (55.28%,44.72%) and the corresponding ES-implied correlation is 0.7923. In this case, the starting weight vector (50%, 50%) is very close to the final optimal tangency weights. Even if the starting weight vector is (20%, 80%), the tangency weights still converge to the optimal weights after three iterations. Thus, not using the optimal weights to calculate the ESimplied correlation affects little of finding the optimal weights. Similar to section 4.3, I employ the _H_ and _AH_ statistics to test the impact on the ES-implied correlation between using and not using the optimal weights. Table 5 reports the difference in the implied correlation from using the optimal tangency weights and using equal weights. There is a significant difference in the VaR-implied correlation between the US and Germany, and between the US and Japan at 10% significance level but no discernible difference in the ES-implied correlation.

# **5 Conclusion**

Tail-based dependence measures play a central role in risk management and asset allocation. It is well known that the linear correlation provides a poor indicator of the co-movement between financial assets under extreme circumstances, particularly during market crashes. Other dependence measures, the exceedance correlation and tail dependence coefficient, have conditioning biases and can not be used to measure correlation asymmetry directly. An alternative measure, the VaRimplied correlation, does not have a conditioning bias; however, it has a number of disadvantages, including the fact that the VaR is not coherent, disregards the data beyond it, and the VaR-implied correlation does not work around the center of distributions.

A development of the VaR-implied correlation, the ES-implied correlation, can address the shortcomings of the VaR-implied correlation. Simulations indicate that the estimated ES-implied correlation is as accurate as the estimated linear correlation when the estimated linear correlation is appropriate, but is much more accurate than the estimated linear correlation when the estimated linear correlation is inappropriate with respect to average bias, standard deviation and root-meansquare errors. The VaR-implied correlation is much less stable than the ES-implied correlation in 22
growing sectors would be located within the TPP-RCEP block, excluding the United States, the European Union, and China.

<!-- Start of picture text --> Figure 10. Sectoral decomposition of exports in 2030<br>(percentage point differences with respect to baseline)<br><!-- End of picture text --> <!-- Start of picture text --> EU-IDN FTAAP TPP-15<br>Agriculture<br>Natural resources / mining<br>Food, beverages, tobacco<br>Textiles<br>Wearing apparel and leather<br>Chemical, rubber, plastic products<br>Metals<br>Transport equipment<br>Electronic equipment<br>Machinery and equipment nec<br>Other manufacturing<br>Utilities<br>Construction<br>Trade and transport<br>Finance and other business services<br>Communication and business services nec<br>Social services<br>-5 0 5 -5 0 5 -5 0<br>USA Other TPP TPP/RCEP joint members China Other RCEP members EU Rest of World<br><!-- End of picture text -->

# Productivity kick

The results described above do not incorporate gains in productivity due to trade liberalization and increased openness as the analysis only captures the reallocation of production to the most efficient sectors. However, several studies (e.g., Topalova, 2004; Amiti and Konings, 2007; Yu, 2015) show that lower import tariffs for final goods increase firms’ productivity by inducing tougher import competition, while cheaper intermediates can improve productivity via learning, variety and quality effects. In particular, Amiti and Konings (2007) find that a 10 percentage point fall in input tariffs leads to a productivity gain of 12 percent for Indonesian manufacturing firms that import their inputs. For Chinese manufacturing firms, Yu (2015) illustrates that a 10 percentage point fall in output (input) tariffs leads to a productivity gain of 9.2% (5.1%). Moreover, the author concludes that import trade liberalization contributes at least 14.5% to economy-wide productivity growth in China.

To illustrate an upper bound of the possible gains from different PTAs, we run the same scenarios with additional productivity gains based on trade-weighted average reduction of tariffs over the FTA members under each scenario (see Table 3 for details). In particular, we follow one of the 21
and the institution providing the training. Compared to students in non-MDIC classes, students in MDIC courses are slightly older, more likely to be male, and more likely to have been unemployed at the time of registration, and exhibit higher course completion rates.

Because all records contain unique national identification numbers, we are able to link the students in _Pronatec-MDIC_ courses with national administrative data from the Ministry of Labor containing information on all formal sector employment. In the following section we describe the employment data and how we linked them to trainees in firm-requested courses.

# **3.2 Monthly employment records**

Our employment data come from the Rela ̧cao Anual de Informa ̧c ̃oes Sociais (hereafter RAIS). RAIS is an annual administrative dataset containing employment and earnings information collected primarily for the purpose of administering social welfare programs such as unemployment and retirement benefits. Our data contain full details on the monthly employment and wage earnings of all formally employed workers in Brazil from calendar years 2013 to 2015. RAIS contains employer-reported records of a worker’s hours, earnings, hiring/dismissal dates and reasons (if applicable), as well as the firm’s industrial classification, the worker’s occupational classification, and the worker’s sex, education level, and age. The data importantly contain unique identifiers (i.e., tax IDs) for both workers and employing firms, which are the standard identification provided to and used by firms and workers for their various dimensions of interaction with the government. These fields are used to link the student information to their employment records as well as to identify employment at requesting firms versus other businesses. We deflate earnings using a standard monthly consumer price index (IBGE, 2016).<sup>6</sup>

> 6For workers who have multiple records within a given month, or who worked only part of the month (based on precise hiring and firing dates), we add all deflated earnings across jobs, and construct a monthly wage rate based on the share of the month worked.

9