rafmacalaba/datause-encoder-data huggingface dataset:
The most prominent fact of the labor market in the decade of the 2000s is a reversal in the negative trend seen in the 1990s. In effect, following a deterioration during the last half of the decade of the 1990s, which ended in 2002 with an unemployment rate which peaked at 21.5% in Argentina and 17% in Uruguay. As a consequence of the economic crisis in Argentina in that year and its spill-over effect on Uruguay, employment showed a very fast rate of growth, led especially by full-time employment.

Such change was a consequence of the configuration of a new macroeconomic stage, based on a type of competitive and stable change during the first post-crisis years in the case of Argentina, an in the significant increase in the price of commodities, the principal goods exported by both countries. From the dawn of this new context, the employment rate in both countries shows an upwards tendency, arriving in 2015 at 52.3% and 59% of the total population in Argentina and Uruguay, respectively. In the same way, the unemployment rate shows a significant fall throughout the period under scrutiny, arriving in 2015 at historical lows equivalent to 7% and 7.8% in Argentina and Uruguay respectively.

Figure 2 shows the variation in intensity of tasks required in a job according to whether these are non-routine cognitive (analytical or interpersonal), routine cognitive, routine manual or nonroutine manual.

Figure2. Intensity of the tasks performed in a job. a) Argentina. 1998 - 2015 <!-- Start of picture text --> 0.2<br>0.15<br>0.1<br>0.05<br>0<br>1998 2003 2015<br>-0.05<br>-0.1<br>-0.15<br>-0.2 Non-routine cognitive (analytical) Non-routine cognitive (interpersonal)<br>Routine cognitive Non-routine manual<br>Routine manual<br><!-- End of picture text -->

- 12 -
American Economic Review, vol. 102(6), pages 2540-69, October.

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Davis, Benjamin, Stephania Di Giuseppe and Alberto Zezza, 2014. "Income Diversi...cation Patterns in rural Sub-Saharan Africa: Reassessing the evidence," mimeo, World Bank.

Emran, M Shahe, and Forhad Shilpi (2014), “Agricultural Productivity and Structural Change in Rural Non-farm Economy: Evidence from Bangladesh”, Working Paper, World Bank and IPD, Columbia University.

Foster, A. and M. Rosenzweig, 2004, “Agricultural development, industrialization and rural inequality," mimeographed, Harvard University.

Goletti, F., Ahmed, R., and Farid, N. (1995). Structural Determinants of Market Integration: The Case of Rice Markets in Bangladesh. The Developing Economics, 33(2), 185-202.

Griliches, Z. 1963. The sources of measured productivity growth: U.S. agriculture,1940– 1960. Journal of Political Economy, 81 (4): 331–46.

Hossain, Mohammad Ismail, and Wim Verbeke. 2010. “Evaluation of Rice Markets Integration in Bangladesh.”Lahore Journal of Economics 15 (2): 77–96.

Laitner, J. (2000) "Structural Change and Economic Growth," Review of Economic Studies, 2000, 67, pp. 545-561.

Lanjouw, Peter and Rinku Murgai, 2009."Poverty decline, agricultural wages, and nonfarm employment in rural India: 1983-2004," Agricultural Economics, vol. 40(2), pages 243-263, 03.

34
25 Raiser, et al. (2001) analyzed transition countries and found that social capital in the form of civic participation and trust in public institutions has a significant impact on growth. Weakness of the legal system is a major problem in the transition process. Levin and Satorov (2000) stress that after the collapse of socialism, “judicial weakness left a legal vacuum that remains unfilled” (p. 120). Thus, as the transition process provides the opportunity to build new trustworthy institutions, increased attention should be given to developing credibility so that taxpayers feel comfortable with paying taxes. Our results indicate that tax administration and government are forced to drastically change their structures and their relationship with taxpayers.

The post-accession trajectory of several Eastern European countries cannot be fully understood without focusing on the problem of corruption (Andreev, 2009). Citing a Financial Times report from 2008, Andreev (2009, p. 378) concludes that the fight against corruption “is the key to everything that is going on now in Romania”. Similarly, Noutcheva and Bechev (2008) argue that in Romania and Bulgaria corruption grew exponentially over time which led to the period 2000-2005 where Romania focused on the formation of institutions dealing with the prevention and prosecution of corruption. Bulgaria, on the other hand, concentrated on improving the legislation. However, Noutcheva and Bechev (2008) point out: All this does not necessarily mean that the EU can claim success. It is doubtful whether the EU-driven measures have been effective, especially in the short term. Even in Bulgaria where public perceptions of the presence of administrative corruption improved, the record is mixed (p. 138).

Corrupt bureaucracy will not award the services to the most efficient producers, but rather to the producer who offers the largest bribes. Thus, corruption reduces the efficiency of allocation and produces delays in transactions when additional payments are sought (see, e.g., Rose-Ackerman, 1997; Jain, 2001). In many former centrally planned economies, the government and the administration still maintain strong discretionary power over the
Along these lines, Lesotho’s historical and continued dependence on migration highlights the need for national data to have better migration indicators collected by the Bureau of Statistics (see Annex 1). Improved measures would allow for more precise estimates of the prevalence and trends of migration, as well as the characteristics of migrants and their households.

# _Dynamics of Women’s Employment_

In a context with limited employment options for men or women, the growth of the export garment industry in Lesotho has been a boon for women. It offers one of the few opportunities for formal employment. Despite being based in Lesotho, these opportunities do not lessen the link between employment and migration, with the great majority (80%) of textile workers being internal migrants<sup>49</sup> . As confirmed through qualitative interviews, these migrant women do not see returning home as an option, so, even if they are to lose their jobs in the textile industry, they are likely to stay in Maseru and turn to informal employment to sustain themselves and their families. Finding ways to support women in informal self‐employment as well as support their children to stay in school will be essential.

This study offers insight into informal women’s employment through the experience of domestic workers. We found a higher than anticipated degree of two‐way movement between textile and domestic work, though most respondents showed a clear preference for the formality, higher pay, and independence of textile work. Informal employment is characterized by risk and hardship, including lack of basic protections and increased vulnerability to HIV, in addition to the security risks for all migrants associated with low‐income housing in urban areas. The other main occupational category dominated by women is informal business, particularly cross‐border trade, which evidence suggests to entail a high degree of risk including sexual harassment and theft.<sup>50</sup> Our findings also support the ISGA’s recommendation that more diversification in women’s employment opportunities is needed.

Also important to note are the things we did not find. An increasing reliance on female migrants as breadwinners brings with it the potential for changes, both good, if women’s bargaining power and control over assets improve, and bad, if household conflict increases as male traditional roles and identities are upended. Though we probed in our interviews with female migrants, we did not uncover such changes. The lack of such impacts could be due to the evolving nature of marriage in Lesotho (see

> 49 DHS 2009.

> 50 LMDA (2015).

52
**Table 3 Design of cases or (sensitivity analysis) to calculate LCOE varying values of all input variables**

|Data source|Solar PV<br>- Utility|CSP|Wind<br>Onshore|Wind<br>Offshore|Gas CC|Gas GT|Geother<br>mal|Hydro|Coal|Nuclear|Biomass|
|---|---|---|---|---|---|---|---|---|---|---|---|
|DR(r)||||||3%,5%,7%||||||
|Overnight|618|3,707|1,100|2,350|673|<br>536|2,680|1,282|1,200|4,000|1,968|
|construction|1,153|5,352|1,692|3,798|917|700|3,892|2,456|2,772|5,956|4,018|
|cost(US$/kW)|1,688|7,000|2,255|5,246|1,161|899|5,104|3,630|4,344|7,911|6,068|
|Capacity|15%,|25%|<br>25%|25%|75%|75%|80%|45%|75%|80%|65%,|
|availability|20%,|to|to|to|80%|80%|85%|50%|80%|85%|70%,|
|Factor(%)|30%|60%|45%|45%|85%|85%|90%|55%|85%|90%|75%|
|Economic Life<br>(Year)|25,30|25,30|25,30|25,30|20,30|20,30|25,35|35,40|35,40|40,50|20,30|
|Fuel<br>price|||||||||1.5, 2.0,|0.7,0.81,|0.9, 1.0,|
|(US$/GJ)|||||3.3,|7.5,12|||3.5|1.0|1.2|
|HR (MJ/kWh)|||||6.5, 6.8,<br>7.5|8.0,<br>8.4,10.3|||7.5,<br>9.1,12.3|10.5,<br>11.0,11.5|11,<br>13,14|
|Fixed<br>O&M||||||||||||
|costs<br>(%<br>of|0.9%|1.47%|2.54%|2.20%|1.54%|1.4%|3.6%|1.39%|1.3%|1.7%|2.9%|
|OC)|1.23%|1.71%|2.73%|2.69%|2.34%|1.7%|4.38%|1.7%|1.7%|2.0%|4.2%|
|No of LCOE|216|216|216|216|648|648|216|216|216|648|648|

Capacity factors: CSP -- 55%, 60% 65% for plant with $7000/kW capital cost; 35%, 40%, 50% for plant with $5352/kW capital cost; 25%, 30%, 35% for plant with $3707/kW capital cost. Wind: 30%,35%, 45% for higher capital cost plants and 25%, 30%, 35% for plants with lower and middle values of capital costs. The higher capacity factors for CSP are due to availability of storage facilities.

Heat rate: Gas CC -- 6.5, 6.8, 7.5 for high, middle, and low capital cost plants, respectively; Gas GT -- 8.0, 8.4, 10.3 for high, middle, and low capital cost plants, respectively; coal -- 7.5, 9.1,12.3 for high, middle, and low capital cost plants, respectively; nuclear -- 10.5, 11.0, 11.5 for high, middle, and low capital cost plants, respectively; biomass -- 11, 13,14 for high, middle, and low capital cost plants.

Variable O&M costs are the same as in Table 1.

15
WPS 3293

# **On the Conservation of Distance in International Trade**

Matias Berthelon University of Maryland and Pontificia Universidad Católica de Valparaíso Caroline Freund World Bank Abstract: The volume of world trade has grown more than twice as fast as real world income since 1980. Surprisingly, the effect of distance on trade has increased during this period. It could be that countries are trading greater volumes of goods that are highly sensitive to distance. An alternative explanation is that distance has become more import for a significant share of goods. Using highly disaggregated bilateral trade data, we find that adjustment in the composition of trade has not influenced the way in which distance affects trade. In contrast, for about 25 percent of industries, distance has become more important. This implies that the increased distance sensitivity of trade is a result of a change in relative trade costs that affects many industries, as opposed to a shift to more distance-sensitive products. We also find that homogeneous products are twice as likely to have become more distance sensitive as compared with differentiated goods. This is consistent with the hypothesis that falling search costs, resulting from improvements in transport and communications, are relatively more important for differentiated goods. The results offer no evidence of the “death of distance,” rather they suggest that distancerelated relative trade costs have remained unchanged or shifted in favor of proximate markets.

# World Bank Policy Research Working Paper 3293, May 2004

_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. Policy Research Working Papers are available online at http://econ.worldbank.org._ ___________________________________ We are grateful to Mary Amiti, Andrew Bernard, Carolyn Evans, James Harrigan, David Hummels, Maurice Schiff, Ravi Yatawara and seminar participants at the Empirical Investigations in International Trade conference at Purdue, the Federal Reserve Board, the University of Delaware, and the World Bank for comments on an earlier draft of this paper.
end-of-period exchange rates (the "timing effect"). Second, Tl stocks change not only due to quantity changes but also due to endof-period exchange rate fluctuations (the "revaluation effect").

# Revaluation effects

We will discuss the general solution of the problem of revaluation effects using the reserves of the Central Bank as an example. All other foreign currency denominated assets are treated in the same way.

The change in the domestic currency equivalent of reserves d(Ee.RESc) can be decomposed as: (A1.6) d(Ee.RESc) = Ee*dRESc + dEe.RESc(t-1) Ee is the end of period exchange rate while E is the period average rate. Adding and subtracting E*dRESc, we obtain: (A1.7) di(Ee.RESc) = E*dRESc + (Ee - E).dRESc + dEe.RESc(t-l) Note that we have already decomposed E*dRESc in dollar terms into the cross-currency effect and the balance of payment flows in equations A1.2 through A1.4. Here we translate this into Tl by multiplying with the average exchange rate: (A1.8) E.dRESc = E-K dRESc + E-CC dRESc The remaining two terms on the right hand side of equation A1.7 constitute the "timing effect" ((Ee-E).dRESc) and the "revaluation effect" (dEe.RESc (t-l1). We denote the sum as: 18
Second, we need a measure of potential (per-capita) child income, _yc_ . However, we do not observe potential incomes for those children in school. We therefore predict child income (for all children) using earnings data from children reporting earning an income. One may be concerned with selection bias in such a procedure; however, as shown in Attanasio et al (2011), there is no evidence of selection effects on children’s earnings, likely due to the fact that the jobs children perform in these rural villages are very lowskilled. Thus, our predicted measure of potential child income, _yc_ , is simply a function of child age, child gender, and area characteristics (see Appendix B.3 for details on how we estimate potential child incomes).

Using data from our sample of eligible, treated 12-15 year-old children for the years 1998 and 1999, and using our estimate of E � _PI_ � from Table VII above, we solve Equation (14) for _γ_ . Our results are presented in Table VIII below. Our curvature estimate is _γ_ = 1 _._ 05. Interestingly, this estimate is very similar to that estimated in Chetty (2006) who finds a value of _γ ≈_ 1, i.e., _u_ ( _c_ ) = _log_ ( _c_ ) (similarly, Layard and Mayraz (2008) find _γ_ = 1 _._ 26). When we vary mothers’ earnings from 0% to 100% as much as fathers’ earnings, we find that _γ_ ranges from [0 _._ 72 _,_ 1 _._ 13].<sup>32</sup> If we instead assume mother’s income is equal to 80% of the mean value of father’s income, we find _γ_ = 1 _._ 15.

TABLE VIII: Curvature of Utility

|E<br>�_I_<br>_P_<br>�|0.21|
|---|---|
|_γ_|1.05|

_Note: γ_ is estimated using Equation (14) and data on our sample of treated, eligible children aged 1215 years in 1998 and 1999. All monetary values used in these calculations are in pesos, per-capita, per-week and are inflation adjusted to 1998 values. Parental income is measured as 1.8 times father’s income. Finally, our estimate of E � _PI_ � are taken from Table VII above.

# VI Quantifying the Targeting Trade-off for Progresa

In this section, we quantify the targeting trade-off for Progresa households. First, we estimate the size of the targeting benefit relative to the exclusion cost under the observed, average Progresa grants. Second, we calculate the optimal CCT/UCT mix based solely on this targeting trade-off. Finally, to conclude the section, we expand our framework beyond just a targeting framework and incorporate the possibility that parents in Progresa villages misperceive the returns to education and consequently under-invest in their children’s schooling. Using this augmented framework, we conduct a back-of-the-

> 32. In order to rationalize the observed behavioral response (i.e., in order to rationalize the observed E � _PI_ �), as we increase parental income, we must increase curvature. Thus, _γ_ is increasing as we increase mom’s share of income from 0% to 100% of dad’s income.

35
About 38-43 percent of sub-sovereign bonds had the same rating as sovereign rating and 36-46 percent had lower rating than the sovereign rating—adding up to 74-89 percent of sub-sovereign bonds rated _at or below_ the corresponding sovereign rating. This confirms that the majority of sub-sovereign bonds are subject to a sovereign ceiling. The share of sub-sovereign bonds rated _above_ the sovereign rating is relatively small at 11-26 percent of all bonds (12.3 percent for S&P, 25.9 percent for Moody’s and 11.0 percent for Fitch), with average distance above the sovereign rating of 2.4 to 4.0 notches on our rating scale.

[ _Insert Table 2 around here_ ]

# **_2.2 Control variables_**

We use a range of global, country-level, and bond-level variables to account for the factors that can influence the sovereign and sub-sovereign rating relationship. The summary statistics for these are presented in table 3. The panel of charts in Figure 2 shows the evolution of the annual average of global and country-level macroeconomic variables since the early 1990s, together with the average distance between sovereign and sub-sovereign ratings. Pairwise correlations between the distance and macroeconomic variables are provided in the chart.

# _Global and country-level variables:_

Standard set of global and macroeconomic factors are included as determinants of the credit risk of sub-sovereign entities. First, the global financial environment affects the performance of subsovereign corporations by changing the financing cost for private firms. Riskiness of the global financial market is measured by the Chicago Board Options Exchange’s Volatility Index (VIX index), also known as the Fear Index (Whaley 2009). Higher global risk can narrow the distance as firms may become dependent on the sovereign during times of turmoil in global financial markets.

The first chart in figure 2 shows co-movement of global risk and the average distance between sub-sovereign and sovereign ratings, suggesting a negative relationship over time. The distance narrowed sharply during a period of high global risk and recurrent emerging market crises (Asian financial crisis in 1997-98, Russian debt default in 1998, and Argentinian crisis in 2000-2001). As global risk subsided during the boom years of 2002-2007, the average distance rose. It then fell sharply during the global financial crisis in 2008-09. In the post-crisis years, as global risk 6
instruments are constructed as ( _Zi − Z_<sup> ̄</sup> _i_ ) _ν_ ˆ _i_ . Lewbel (2012) notes that a subset of or the full vector of _X_ can be used as _Z_ . One worry about the approach is that when heteroskedasticity is not strong enough, the generated instruments will be weak and the IV estimates would be of little value. We use indicators of cohort (fathers age and its square) and of geographic location (province fixed effects) as _Z_ to increase the amount of heteroskedasticity and thus avoid weak identification. The choice of the subset is determined by an inspection of the determinants of heteroskedasticity in the selection equation.

# **(5) Data**

We use data from the 2002 round of Chinese Household Income Project (CHIP 2002) for our analysis. CHIP survey was collected by Chinese Academy of Social Science and a group of international researchers.The CHIP 2002 data have some important advantages for understanding the role played by family background in educational inequality in rural China. First, unlike the standard household surveys in many developing countries, we have the relevant data on education and occupation on three generations: grandparents-parentschildren. This allows us to understand whether the “children’s generation” who grew up mostly during the reform period face significantly different educational opportunities compared to their parents who mostly grew up before the reform. Second, the data do not suffer from selection due to coresidency restriction standard in the household surveys in developing countries.<sup>29</sup> For the parents generation (i.e., the grandparents-parents sample), the CHIP 2002 survey includes all of the grandparents irrespective of their residency. Thus the grandparents-parents sample does not suffer from any coresidency bias.

The sample on parents-children from the rural survey is also much richer compared

> 29The biases due to coresident sample in estimating the effects of parental characteristics on children’s human capital have been widely noted; see, for example, Behrman (1999). For a recent analysis that shows that the estimates of intergenerational regression coefficient are significantly biased downward in a coresident sample, see Emran and Shilpi (2015).

20
Table 2:6

|Null|Alternative|Lag: 4|95%|90%|
|---|---|---|---|---|
|Hypothesis|Hypothesis|With Constant|Critical|Critical Value|
|Ho: rank = r|||Value||
|i tracetest<br>r=0|r > 0|45.84|35.10|31.88|
|r <I|r> 1|13.48|20.17|17.79|
|r<2|r>2|4.02|9.10|_7.50_|

Rejection at the 5% level of significance _Source:_ Authors' calculations.

The estimates of trace test statistics, X ua, which test the hypothesis of less than or equal to r cointegrating vectors are reported in Table 2. The number of cointegrating vectors is determined by starting at the top of Table 2 and moving down until Ho cannot be rejected. Since the trace-test statistics for the null hypothesis of no-cointegration ( X **tra,e** = 45.84) exceeds its 95%-critical value of 35.10, but the null hypothesis of rT I cannot be rejected, there appears to be evidence for one cointegrating relationship between the industrial, the agricultural and the service sectors.<sup>7</sup> The Ecuadorian economy has been subjected to frequent and substantial external (e.g., oil boom, debt crisis and natural disasters) and internal shocks (e.g., changes in the development model) during the 1965 to 1998 period. Therefore, empirical evidence in favour of one stable long-run relationship describing the sectoral growth dynamics of Ecuador would be a surprising result.

To address the issue of stability of the cointegration relationship over time, we perform a recursive cointegration analysis, where the trace-statistics for the hypothesis of less than one cointegration vector is estimated for different sample periods.

Operationally, the data from 1965:Ql to 1982:Ql (roughly the first half of the sample) is used as a base period for the calculation of the first test statistic and the sample

**6** This result comes about by starting at the top of Table I and moving downwards until Ho cannot be rejected. As this is the case in the second row, the analysis maintains the Ho of zero cointegrating vectors, this implies the existence of exactly one cointegrating vector in the data.

The World Bank Latin American and Caribbean Regional Database contains data on annual real GDP from 1965-98 for Ecuador. Since this data base could have been used for our cointegration analysis, we perforn additionally a cointegration analysis using this data set (see Appendix 2). This allows us to contrast the results of a cointegration analysis based on annual data with the results of a cointegration analysis using a higher frequency. Since cointegration is a long-run property, the frequency of the data should not matter and we would expect to find similar results. As Appendix 2 shows, this appears to be the case.

9