rafmacalaba/datause-encoder-data huggingface dataset:
The consequence is that Osow youth rates remain below 5 percent until age 15 and remain below 8 percent thereafter. This is an interesting result that shows that even in a more favorable context, some individuals may decide to remain in the Osow status (presumably) by choice.

If Figure 6 were actually the image of the life-cycle trajectory of cohort 2 under a scenario of lower restrictions, the conclusion would be that in general, Latin American households prefer to continue investing in the education of the majority of their school age members until around age 20, after which the majority enters to the labor market, with labor force participation rates of more than 80 percent at age 24 and almost 90 percent at age 27. About 8 percent choose to remain Osow through these ages. If this were the case, the Osow youth phenomena would not necessarily be of concern due, on the one hand, to its magnitude, and on the other, to its causes.

As a result, following the conceptual framework, deviations from what is observed in Figure 6 would be due to constraints that households face in their decision making process –including the budgetary constraint preponderantly- and due to the implications of the family, community, and macro environment. Section 3 below explores some of these factors empirically.

# **2. Out of School and Out of Work Youth Patterns**

As shown in the figures above, each cohort has its own trajectory over the life cycle. Since our household survey database includes observations starting in 1980, the oldest cohorts for which a full trajectory can be followed from the first year of school age into adulthood are those born in 1974-1976, which are expected to enter the schooling system precisely in 1980-1982. These individuals can be observed subsequently until they are about 34 to 36 years old by 2010. All cohorts born during the 9 previous years will be observed first in 1980 when they are already in late Primary or Lower Secondary age. For these individuals it will be possible to trace time use choices between continuing in school, entering the labor market, or the Osow status, starting at the ages from 15-17 until reaching between 34 to 45 years of age, which is well into the typical labor force participation years. Cohorts born earlier will start being observed when they are already 18 years of age or older, so their time choices –especially for those born before 1960- will be most likely restricted to the work or Osow options.

On the other hand, the youngest cohorts that are included in our analysis are those born between 1994 and 1996, and that are expected to enter Primary education during the years 2000-2002. These cohorts can be followed during their school trajectory up to the age of attending Upper Secondary education around 9 years later, so it is likely that the majority will not yet have been confronted with the decision of entering the labor market or the Osow status. All cohorts born 12
# **Table A6: Panel B – Nested Logit Regressions, 2006**

Log Odds ratio between Owning with and without a Mortgage

|**Country**|**parttime**|**unemp**|**retired**|**urban**|**suburban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|
|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_||||||||||
|Czech Republic|-0.32|-0.26|0.36|-0.84 ***|||0.48||0.20|
|Estonia|-0.18|0.83|1.53 **|0.16|||0.16||-0.18|
|Hungary|0.00|0.18|0.68 ***|0.00|-0.06|0.15|0.28|-0.22|0.20|
|Latvia|-0.12|0.10|0.02|-0.33 ***||-0.01|0.58|-0.48|-0.20|
|Lithuania||-1.61||-0.14|||0.65||-0.89 **|
|Poland|-1.00 **|0.02|-0.48|-0.27 *|-0.13|0.30|0.76|-0.71 **|-0.61 *|
|Slovak Republic|-0.11|0.04|-0.15|-0.56 ***|-0.39 ***|-0.04|-0.31|0.53|0.70|
|_EU-15 and Cyprus_||||||||||
|Austria|-0.94 **|-0.51|-0.05|-0.08|-0.25 ***|0.77|0.36|0.19|0.09|
|Belgium|-0.74 ***|0.19|1.01 ***|0.07|0.28|0.10|-0.09|0.69 ***|1.23 ***|
|Cyprus|-0.45|-0.58|0.27|-0.54 ***|-0.48 ***|-0.83|-1.46 ***|0.96 ***|0.73 ***|
|Denmark|0.43|0.76 **|1.30 ***|0.09|||0.96 ***||1.31 ***|
|Finland|0.01|-0.23|0.07|-0.01|-0.12|0.45|1.07 **|0.18|0.67 ***|
|France|-0.46|-0.60 *|-0.79 ***|-0.23 **|-0.22 *|1.00 **|0.97 **|0.47 **|0.77 ***|
|Greece|1.03 *|0.89 *|0.72 ***|0.12|0.25|-0.65|-1.23 **|-0.16|0.08|
|Ireland|-0.83 ***|-0.77 *|-0.14|-1.03 ***|-0.79 ***|-0.60|0.28|0.48 **|0.60 ***|
|Italy|-0.93 ***|-0.38|-0.21|-0.58 ***|-0.47 ***|-0.74 ***|-0.45|0.24|0.50 ***|
|Luxembourg|-0.94 **|-1.00|0.11|-0.53 **|-0.36 *|1.85 **|1.60 *|1.07 ***|1.10 ***|
|Netherlands|-0.07|1.26 *|0.87 ***|-0.22 **|||0.77 *||0.78 ***|
|Portugal|-0.48|-0.53|-0.47|-0.34 ***|0.18|-0.42|0.57|0.03|0.30|
|Spain|0.00|-0.35|0.41 **|-0.25 ***|-0.40 ***|-0.32|-0.25|0.51 ***|0.45 ***|
|Sweden|-1.47 **|-1.28 **|-0.25|0.26|-0.13|0.66|1.71|0.01|-0.11|
|United Kingdom|0.25|0.06|1.77 ***|-0.26 ***|||1.24 ***||0.64 ***|

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

Source: EU Statistics on Income and Living Conditions (SILC) database, 2006 42
below -60 percent as genuine, reflecting seasonality and the high degree of idiosyncratic variability facing microenterprise owners.

As a result of the high variability and low autocorrelation in economic outcomes like firm profits, income, and expenditure among poor households, there is much to be potentially gained by taking multiple measures of these outcomes at relatively short intervals and averaging over them when estimating treatment effects. This is the approach used in de Mel et al. (2008), who use a baseline and 8 quarterly follow-up waves of business profits and in Fafchamps et al. (2010) which uses two survey rounds before randomization and a further 4 quarterly follow-up surveys.<sup>1</sup> However, this approach seems to be the exception rather than the rule. The premise of this paper is that many field experiments are making a suboptimal choice of how many rounds of surveys to collect, and that the default choice of a single baseline and single follow-up study is unlikely to be optimal in many cases. This paper aims to combine insights from the medical literature on repeated measurement, new analysis of the formulae underlying power calculations, and experience from these studies using multiple measures in economics to provide a practical guide for researchers designing experiments. While our discussion will be in terms of experiments, many of the same issues and therefore lessons also apply for design of non-experimental impact evaluation designs, such as matched difference-in-difference estimation.<sup>2</sup> An additional contribution of the paper is to draw to the attention of empirical researchers in economics the large improvement in power that can arise when estimating treatment effects via an Analysis of Covariance (ANCOVA) estimation compared to using the more common difference-in-difference specification. The improvement in power is greatest when the autocorrelation is low – intuitively when the baseline data have little predictive power for future

> 1 Bloom et al. (2010) provides an example of very large T, with 114 weekly observations on firm output, quality, and inventory levels.

> 2 Gibson and McKenzie (2010) provide an example where multiple rounds of follow-up data are averaged to get more precise measurements of consumption and income in a matched difference-in-differences impact evaluation of a seasonal migration program. One key difference with non-experimental evaluations is that the presence of a baseline becomes more valuable to allow one to control for baseline differences across individuals; whereas randomization ensures these baseline differences are balanced on average. In addition, multiple rounds of pretreatment data are also of additional use in non-experimental studies to show parallel trends in difference-indifferences analysis, or to rule out or control for the presence of pre-program effects like an Ashenfelter dip. Multiple pre- and post-treatment data can also be combined to conduct overidentification tests to distinguish between different data generating processes (Ashenfelter and Card, 1985).

3
Rural - Western Region Urban <!-- Start of picture text --> 0.9 —@-S.1 Baseline 0.9 44<br>0.8 =&-S.2 Soil productivity 0.8<br>0.7 ~te~S.3 Green revolution 0.7<br>0.6 0.6<br>0.5 05<br>123 45 67 8 9 10 1234567 8 910<br>Year Year<br><!-- End of picture text --> <!-- Start of picture text --> Rural - Western Region Urban<br>1.00 1.00<br>0.75 0.75<br>=—®-S.1 Baseline<br>0.50 “-S.2 Soil productivity 0.50<br><!-- End of picture text --> <!-- Start of picture text --> ie S.3 Green revolution<br>0.25 0.25<br>0.00 0.00<br>123 4 5 6 7 8 9 10 1234 5 6 7 8 9 10<br>Year Year<br><!-- End of picture text -->
21 Despite the obvious relevance of a ‘learning‐oriented’ approach, governments are often reluctant to talk in these terms. Officials in many countries (including many developed democratic states) remain horrified at the thought of independent appraisals of the effectiveness of policy measures and government operations. Politicians worry that calling a policy approach ‘experimental’ may give the impression it has not been thought through. There is pressure to come up with firm ‘answers’ to policy problems. _But explaining why we can not have all the answers today, and why it is sometimes good to trial policy innovations, and then draw lessons from the experience, is part of the public education challenge confronting governments._ It is not just about the cause and potential impacts of climate change that citizens require education. They also need a better appreciation of the science/policy interface, of the need to adopt sound policy in a climate of pervasive uncertainty, and of the importance of a ‘learning‐oriented’ public policy.

The Netherlands provides a number of examples of ‘learning oriented’ environmental policy. The National Environmental Policy Plans NEPP I‐IV, from 1989 to 2002) established an iterative process that drew lessons from each round of policy making to develop a progressively more comprehensive and coherent approach to managing environmental burdens (NEPP4 2002). The Netherlands Environmental Assessment Agency (PBL 2008) provides independent assessment of social trends and policy impacts in the area of environment, climate change and sustainability. Such assessments are routinely used to adjust policy orientations, and have been sought out by government officials and politicians. Moreover, the ‘transition management’ approach (Rotmans, Kemp and M. van Asselt 2001) adopted since NEPP4 in 2002 is explicitly centered on the notion of transition experiments to accelerate movement towards sustainability (see Section 4c below).

With respect to ‘reflexivity’, this is best understood as a product of the interaction of diverse societal institutions and ongoing processes of debate and lesson drawing in the public sphere. Critical elements for its development are: a broad and vibrant civil society capable of independent evaluation of policy performance and substantial self organization; transparency of policy making, and of the climate performance of corporations and public bodies (for example through compulsory reporting requirements); and continuing openness to reconsider not just the _means_ for policy attainment but also the _goals_ of policy.

# **4. Some examples of recent governance innovations**<sup>2</sup>

2 This section draws material directly from a scoping paper recently completed for the Policy Research Initiative in Canada: James Meadowcroft and Francois Bregha, ‘Governance for sustainable development: meeting the challenge ahead, Ottawa, 2009).

21
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APPENDIX B: A Survey of **Indexation** ProwLsions **In** 8 Countries Argentina: Both depreciation deductions and capital gains are adjusted for inflation through the automatic annual revaluation of assets. A General Wholesale Price Index is used in making the adjustments. Net income is adjusted by adding to it the product of the inflation rate and net monetary liabilities. For this purpose, inventory is considered a monetary rather than a real asset. No adjustment of exchange loss deductions is in place. Bolivia: Depreciation allowances are based on historic cost, nominal interest is deductible, capital gains are estimated on the basis of historic cost and taxed as income. Not clear if inventory system approximates FIFO or LIFO. Asset revaluation occurs but is valid only for the financial report of the firm. Revenues are indexed by stating tax debts in U.S. dollars.

Brazil: Inflation adjustment applies to permanent and fixed assets, to depreciation allowances, and to net worth items. Inventory items are considered monetary assets for the purpose of this adjustment. The asset and net worth values are adjusted by the percentage change in the value of Treasury Bonds (OTNs, whose cruzado value is adjusted with inflation) and the balance sheet reflects these new values. The asset adjustment amount is added to taxable income while the net worth adjustment is deducted from profit. While revaluation of indexed or foreign currency denominated debts (debt claims) is not a specific part of the inflation adjustment, such items must be adjusted and the corresponding values must be deducted (as part of 'financial results') in calculating taxable income. Exchange losses are indexed to the change in the value of OTNs. Depreciation allowances are indexed, as noted earlier, as is capital gain which is calculated on the basis of inflation-adjusted acquisition cost. Since the decline in the
Policy Research Working Paper 8859

# **Abstract**

This paper examines the growth effects of different dimensions of international trade integration—notably, volume, diversification, and natural resource dependence—in Sub-Saharan Africa. First, the paper documents the recent trends in these foreign trade dimensions for the region and the traditional sources of growth. Second, it empirically estimates the impact of trade integration on growth per worker and the sources of growth; that is, growth of capital per worker and total factor productivity growth. To accomplish this task, the analysis uses a sample of non-overlapping five-year period observations for 173 countries from 1975 to 2014. The econometric evidence shows that increased trade openness, greater export production diversification, and reduced export dependence from natural resources will have a positive causal impact on economic growth. These effects will be mainly transmitted through faster capital accumulation or enhanced total factor productivity growth. Finally, the paper finds that, despite the progress exhibited in trade openness and diversification over the past decade, there are still potential benefits that can be accrued if countries were to deepen their integration to world trade.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ccalderon@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
wage premiums. Similarly, changes in Brazil’s labor market institutions, such as the level and coverage of minimum wages, and the degree of enforcement of formal employment contracts, may have played a role. Indeed, in a recent paper using administrative matched employer-employee data from Brazil’s formal sector (the RAIS dataset), Engbom and Moser (2016) claim that the rise in the real value of the minimum wage, including indirect spillover effects along the distribution, may account for as much as 70 percent of the reduction in the variance of log earnings in essentially the same period we study here (1996-2012).

The commodity boom that benefited Brazil during the 2000s may have triggered sectoral reallocation of employment, with additional effects on skill premiums and inequality, to the extent that different sectors have different intensities of demand for skills. Finally, there were also changes in employment and wage gaps across spatial areas: both rural versus urban, and across the country’s five main geographical regions.

We have therefore grouped the factors that may have affected earnings inequality in Brazil into five major categories: human capital, demographics, institutions, geography and sectoral distribution. Before describing our methodological approach in more detail in Section 4, and presenting results in Section 5, the remainder of this section looks descriptively at each of the five groups of candidate explanatory factors in turn. We first describe changes in the distribution of the relevant variables, and then report—again, descriptively – on changes in the partial correlation between labor earnings and each variable.

# **3.1 Changes in the Distribution of Candidate-Proximate Factors**

We begin with human capital. Figure 2 shows the cumulative distribution functions for years of schooling in the working age population (18-65), at three points in time: 1995, 2003 and 2012. These distribution functions illustrate an impressive expansion in the supply of years of schooling in the labor force. The proportion of the working age population with at least 10 years of schooling, for example, doubled from 25 percent to 50 percent between 1995 and 2012.<sup>12</sup>

## [Insert Figure 2 around here]

In the absence of information on actual experience per worker, we look at age and potential experience, defined as age minus years of schooling minus six, for each worker. As life expectancy increased over this period, the proportion of the working-age population aged

> 12 This increase in the supply of educated workers reflects educational policy changes dating back to the late 1980s, but also the subsequent decentralization of basic education funding from the state level to municipal level, as well as changes in the funding system with the creation of FUNDEB ( _Fundo Nacional para o Desenvolvimento da Educação Básica_ ) to reallocate funding according to demand. See Cruz and Rocha (2016).

9
Given that export participation has a positive effect on the provision of technical upgrading within firms, an important complementary question is the extent to which the corresponding trainees benefit from these additional skills in the form of higher labor earnings. To examine this question, we exploit detailed information on training records and earnings trajectories at the worker-firm level. Because entry in export markets may affect wages via several forms of rent sharing (Egger and Kreickemeier, 2009; Egger, Egger and Kreickemeier, 2012; Davis and Harrigan, 2009; Helpman, Itskhoki and Redding, 2010), we restrict our attention to firms that were always exporters over the period of analysis. Using models with worker-firm fixed effects to account for unobserved heterogeneity of workers and firms, we document a robust positive relationship between completion of technical upgrading courses and hourly wages within exporting firms. This relationship holds within small, medium and large exporting firms.

In addition to the work cited above, this paper complements and extends other strands of existing research. In particular, it adds to a growing empirical literature using workerfirm data to examine the link between exporting, skills and wages (Fr ́ıas, Kaplan and Verhoogen, 2012; Helpman, Itskhoki, Muendler and Redding, 2012). An important question in this literature has been the extent to which higher wages among exporting firms reflect sorting on individual ability or wage premia due to rent sharing. By suggesting that export participation leads to skill upgrading of firms’ _existing workforce_ via job training, and that this training leads in turn to higher wages, we uncover a related but still distinctly different channel by which exporting influences skill utilization and labor earnings. In doing so, our paper adds to a small literature on how other aspects of globalization influence job training. In particular, Hogrefe and Wrona (2015) develop a theoretical model examining the impact of offshoring on individual skill upgrading through on-the-job training. The model predicts that offshoring, by scaling up workers’ wages, creates previously unexploited skill upgrading possibilities and, thus, leads to more on-the-job training. Using individual-level survey data from Germany, they document a positive relation between an industry-level measure of offshoring and job training.<sup>1</sup>

> 1More broadly, our paper is also related to the literature on labor market adjustment to globalization;

4
**Table 6: Estimates of dynamic random effects probits of current employment status, Wooldridge's estimator, 1997-2004 and 1994-2000**

|||Current e<br>1997-2|mployment sta<br>004|tus=Y<br>it||1994-2|000||
|---|---|---|---|---|---|---|---|---|
||Unemployed|Informal|Self-|Public|Unemployed|Informal|Self-|Public|
|||sector|employed|sector||sector|employed|sector|
|||worker||worker||worker||worker|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Y t-1|1.092|1.277|1.637|1.618|1.383|1.549|2.039|1.873|
||(9.92)**|(12.08)**|(11.93)**|(11.29)**|(6.86)**|(7.46)**|(6.09)**|(5.78)**|
|Y 1994|1.202|1.340|1.256|1.357|1.925|1.906|1.632|1.945|
||(10.38)**|(12.29)**|(8.86)**|(9.16)**|(9.39)**|(9.13)**|(4.85)**|(5.95)**|
|Female|-0.060|0.085|0.134|0.023|-0.074|0.098|0.161|0.050|
||(0.97)|(1.34)|(1.75)|(0.30)|(1.17)|(1.51)|(2.04)*|(0.61)|
|Married|-0.531|0.240|0.125|0.110|-0.515|0.418|0.249|0.008|
||(3.24)**|(1.60)|(0.76)|(0.67)|(2.40)*|(2.11)*|(1.11)|(0.03)|
|Age 30-44|-0.257|0.067|-0.090|0.558|-0.135|-0.045|-0.218|0.667|
||(1.96)*|(0.51)|(0.56)|(3.76)**|(0.81)|(0.26)|(1.02)|(3.36)**|
|Age 45-54|-0.373|-0.165|-0.175|0.613|-0.277|0.004|-0.121|0.747|
||(1.59)|(0.80)|(0.77)|(2.73)**|(0.88)|(0.02)|(0.40)|(2.44)*|
|Age 55-64|-0.003|-0.037|0.020|0.331|-0.098|0.067|0.101|0.759|
||(0.01)|(0.13)|(0.07)|(0.94)|(0.20)|(0.18)|(0.25)|(1.70)|
|**Completed scho**|**oling (referenc**|**e group=no**|**formal educ**|**ation)**|||||
|Primary|0.071|-0.026|-0.339|-0.143|-0.312|0.053|-0.360|0.144|
||(0.50)|(0.21)|(2.29)*|(0.88)|(1.77)|(0.35)|(1.85)|(0.64)|
|Secondary|0.512|-0.485|-0.183|-0.150|0.487|-0.733|-0.291|-0.210|
||(3.27)**|(3.31)**|(1.00)|(0.87)|(2.48)*|(3.88)**|(1.15)|(0.85)|
|Some tertiary|-0.037|-0.833|-0.719|0.465|0.547|-0.839|-1.078|0.108|
||(0.15)|(3.47)**|(2.46)*|(2.05)*|(1.74)|(2.75)**|(2.84)**|(0.33)|
|Ethinicity/ religion|dummies<br>Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Location dummie|s Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Time (or group)**|**means of**||||||||
|**time varying ch**|**aracteristics**||||||||
|Marital status|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age groups|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Schooling|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Occupation|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|-1.497|-0.855|-1.587|-2.497|-1.957|-1.435|-2.149|-2.659|
||(9.25)**|(5.41)**|(9.13)**|(13.12)**|(11.74)**|(8.75)**|(11.84)**|(12.92)**|
|Observations|4484|4484|4484|4484|7125|7125|7125|7125|
|Individuals)|2242|2242|2242|2242|2375|2375|2375|2375|
|Log likelihood|-1132|-1130|-799|-775|-1005|-1003|-698|-609|
|estimate of σ|a 9.1*10<sup>-4</sup>|9.1*10 <sup>-4</sup>|9.1*10 <sup>-4</sup>|9.1*10 <sup>-4</sup>|9.1*10 <sup>-4</sup>|9.1*10 <sup>-4</sup>|9.1*10 <sup>-4</sup>|9.1*10 <sup>-4</sup>|
|[s.e.(σ a )]|0.052|0.070|0.087|0.089|0.063|0.065|0.083|0.095|

Absolute value of z statistics in parentheses

* significant at 5%; ** significant at 1%

33