rafmacalaba/datause-encoder-data huggingface dataset:
percent of the reduction in unemployment observed during the 2003-2012 period.

Subsequently, they assess the contribution of several factors in generating the observed decline in informality. Overall, the composition of the labor force, productivity and demand parameters are, together with the minimum wage, the main determinants of the changes observed in the Brazilian labor market during this period. The change in the composition of the labor force appears as the main driving force behind the reduction in informality. The model predicts that without an increase in the share of skilled workers, informality would have gone up instead of declining.

Moreover, positive productivity and demand shocks contributed to reducing informality. In their model these shocks beneted mainly the formal rms and induced their expansion. This in turn increased the formalization of labor through the intensive margin. In particular, the sizable improvement in terms of trade associated with the commodity boom may have contributed to labor formalization. In Section 5 we will discuss some evidence that the credit expansion of the 2000s may have also played an important role.

In the model, an increase in the relative supply of skilled workers makes their labor market less tight. Since formal rms are more intensive in skill workers, the improvement in the skill composition of the labor force induces an expansion of formal rms. Moreover, due to the complementarity between skilled and unskilled workers, the productivity of unskilled workers increases. Both forces end up reducing informality through both the intensive and extensive margins. There is a reallocation of labor to formal rms and marginal informal rms choose to become formal. show that these model predictions are consistent with the Brazilian evidence.

# 4.2.2 Minimum wage

Between the mid-1990s and the mid-2010s, Brazil has seen a remarkable decline in income inequality.<sup>19</sup> Over the same period, Brazil's real minimum wage more than doubled. This fact has motivated several studies about the eects of the minimum wage and other variables on earnings inequality. However, very few papers addressed the eects of the minimum wage increase on the eciency of labor allocation among rms.

Engbom and Moser (2021) combine rich administrative and survey data with an equilibrium model of the Brazilian labor market and quantify the eects of the increase in the minimum wage in Brazil from 1996 to 2012 on inequality and employment. They show that the rise in the minimum wage accounts for a large part of the decline in earnings inequality in Brazil during this period. The paper focus on the role played by heterogeneous rms

> 19Firpo and Portella (2019) analyze the decline in wage inequality in Brazil since the mid-1990s.

18
# **4 SVAR Performance in the Framework of Chari, Kehoe, McGrattan (2008)**

So far, we have shown that the NAMS, Spectral and Limited Spectral identifications either perform as well as, or exceed the performance of the Max-Share and longrun identification SVARs. However, our framework is a simple non-structural twovariable system designed for ease of interpretation. In this section, we use the model of Chari et al. (2008) to show that our methodologies outperform the long-run identification SVAR, and perform comparably with the Max-Share identification in their RBC DSGE framework. Chari et al. (2008) use this model to demonstrate the difficulties faced by long-run restricted SVARs in capturing technology shocks as other non-technology confounding shocks drive an increasing share of the variance in the model. We therefore test our proposed identification methodologies against this known DGP with which long-run restrictions are shown to have difficulty.

In this model, there is no advantage of using the new specifications above the advantages offered by the Max-Share identification, as the technology shock has a unit root, and has no impact on the _growth_ of productivity. However, we demonstrate that compared to the long-run restriction, all other specifications show lower degrees of bias. Therefore, in contrast to the findings of CKM, using the right identifications, SVARs can prove useful in identifying the impacts of technology shocks in a range of DGPs.

Consumers’ utility functions are given by where _c_ is consumption, _l_ is per capita labor, _β_ is the discount rate and _γ_ the population growth rate. Consumers maximize utility subject to the budget constraint _ct_ + (1 + _τx_ )[(1 + _γ_ ) _kt_ +1 _−_ (1 _− δ_ ) _kt_ ] = (1 _− τlt_ ) _wtlt_ + _rtkt_ + _Tt_ . Where _τx_ is a tax on investment, _k_ is the capital stock, _δ_ the depreciation rate, _w_ wages, _r_ the rental rate on capital and _T_ a lump sum transfer. Firms face a resource constraint _ct_ + (1 + _γ_ ) _kt_ +1 = _yt_ + (1 _− δ_ ) _kt_ .

The technology and non-technology shocks evolve according to This RBC model contains a unit root for technology, but also a highly persistent non-technology shock _τ_ , where _ρl_ is 0.95 in the standard calibration. Chari et al. (2008) note that two sources of bias exist for the long-run SVAR methodology: non-technology shocks will increase IRF bias as they drive a larger proportion of the variance of output; and a lag-truncation bias, where limited VAR lags result in a bias due to the true specification of the VAR having an _∞_ -representation.

23
12 Figure 1 Average import weighted trade cost reductions as a result of BRI investments under upper bound BRI related infrastructure investment scenario BRIUBD (percentages).

# Bilateral Trade Costs Reduction

<!-- Start of picture text --> Kyrgyz Republic<br>Thailand<br>Tanzania<br>Malaysia<br>Ethiopia<br>Cambodia<br>Kenya<br>Iran<br>China<br>Laos<br>Pakistan<br>Turkey<br>USA<br>Rest of East Asia<br>Kazakhstan<br>Vietnam<br>Rest of South Asia<br>Rest of Central Asia<br>Indonesia<br>India<br>Philippines<br>Rest of High-inc.<br>Rest of E. Europe<br>Rest of MENA<br>Bangladesh<br>Rest of W. Eur.<br>Russia<br>Sri Lanka<br>Egypt<br>Lat. Am. & Car.<br>Nepal<br>Rest of SSA<br>EU ECA<br>Poland<br>Non BRI<br>BRI<br>World<br>0 2 4 6 8 10<br>BRI Non BRI<br><!-- End of picture text --> _Source: de Soyres et. al. (2018)._ BRI-related infrastructure investments will naturally also reduce the costs of trading inside the countries. The estimated ad valorem equivalents of internal trade costs reductions from de Soyres et. al. (2018) are applied to input-output coefficients of trade services and all transport services. The domestic trade cost reductions range from Poland with a trade cost reduction of 0.9% for BRI countries and of 0.2% for non-BRI countries, to Kyrgyz Republic, the country that has the highest trade cost reductions, with reductions of 10.2% for BRI countries and 5.9% for non-BRI countries. To capture improvements in internal logistics and transportation, the diagonal element of the estimated improvements is assumed to apply to the domestic trade and transport services used to produce individual commodities. For example, if the domestic, i.e. diagonal, estimate of improvements in trade and transport is 5%, intermediate demand for the relevant trade and transport commodities in each sector is assumed to decline by 5% relative to output, i.e. the inputoutput coefficient is adjusted downwards by 5%. In the current configuration of the input database,

- 6 -
- 41 -

Despite the decreasing effectiveness of monetary policy, fiscal policy was not used to contain overheating during the period 1993-96, and indeed may have added to demand pressures. As discussed above, Thailand had succeeded in eliminating fiscal dleficits following the fiscal consolidation of the mid-1980s. The underlying fiscal policy position remained strong in Thailand during 1988-1996, and Thailand recorded surpluses every year except 1996/97. Although fiscal surpluses declined during the period 199296-as the focus of fiscal policy shifted towards addressing infrastructure bottlenecksthey remained around 2 percent of GDP during 1992-94. In 1995, the fiscal surplus rose to 2.5 percent of GDP. However, the fiscal stance which had been expansionary in 1994 (and hence had been counter-cyclical given that the economy was not experiencing overheating pressures) turned slightly more expansionary in 1995 (so that the fiscal impulse was expansionary) when excess demand pressures emerged. In 1996, the fiscal surplus declined to 1.6 percent of GDP, entailing a strong expansionary impulsealthough the growth of the economy had only slowed slightly and demand pressures remained very high as measured by the output gap and the current account deficit. In both years, therefore, fiscal policy was procyclical. The procyclical nature of fiscal policy during this period is also reflected in the correlation between the fiscal impulse and excess demand pressures in Thailand which turned positive over 1993 to 1996. In sum, while the fiscal stance remained conservative in Thailand over 1988-1996, fiscal policy imparted a positive impulse to the domestic pressures in the critical 1993-96 period, thereby aggravating the pressure on interest rates (Figure 16).

**Figure 16. Fiscal Impulse**

<!-- Start of picture text --> 4.0%<br>3.0%<br>2.0%<br>1.0%<br>4 0.0%<br>-1.0%<br>-2.0%<br>-3.0%<br><!-- End of picture text --> <!-- Start of picture text --> 1988/89 1989/90 1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97<br><!-- End of picture text -->

*G Central Govemment i General Govemment

# **d. Exchange Rate Policy**

From 1985 to mid-1997, the nominal exchange rate in Thailand remained fairly constant. Indeed, after a 10 percent devaluation in mid-1981, and another 18 percent
# **References**

- **Adukia, Anjali, Sam Asher, and Paul Novosad** , “Educational Investment Responses to Economic Opportunity: Evidence from Indian Road Construction,” 2017. Working paper.

- **Aggarwal, Shilpa** , “Do Rural Roads Create Pathways out of Poverty? Evidence from India,” 2017. Working Paper.

- **Ali, Rubaba** , “Impact of Rural Road Improvement on High Yield Variety Technology Adoption: Evidence from Bangladesh,” 2011. Working paper.

- **Alkire, Sabina and Suman Seth** , “Identifying BPL Households: A Comparison of Methods,” _Economic and Political Weekly_ , 2013, _48_ (2), 49–57.

- **Anderson, Michael L.** , “Multiple Inference And Gender Differences In The Effects Of Early Intervention: A Reevaluation Of The Abecedarian, Perry Preschool, And Early Training Projects,” _Journal of the American Statistical Association_ , 2008, _103_ (484), 1481–1495.

- **Atkin, David, Azam Chaudhry, Shamyla Chaudry, Amit K. Khandelwal, and Eric Verhoogen** , “Markup and Cost Dispersion across Firms: Direct Evidence from Producer Surveys in Pakistan,” _The American Economic Review_ , 2015, _105_ (5). Working Paper.

- **Banerjee, Abhijit, Esther Duflo, and Nancy Qian** , “On the Road: Access to Transportation Infrastructure and Economic Growth in China,” 2012. NBER Working Paper No. 17897.

- **Baum-Snow, Nathaniel, Loren Brandt, J. Vernon Henderson, and Matthew A. Turner** , “Roads, Railroads and Decentralization of Chinese Cities,” _Review of Economics and Statistics_ , 2017.

- **Benjamini, Yoav and Yosef Hochberg** , “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,” _Journal of the Royal Statistical Society. Series B (Methodological)_ , 1995, _57_ (1), 289–300.

- **Binswanger, Hans P., Shahidur R. Khandker, and Mark R. Rosenzweig** , “How Infrastructure and Financial Institutions Affect Agricultural Output and Investment in India,” _Journal of Development Economics_ , 1993, _41_ (2).

- **Blimpo, M. P., R. Harding, and L. Wantchekon** , “Public Investment in Rural Infrastructure: Some Political Economy Considerations,” _Journal of African Economies_ , 2013, _22_ (AERC Supplement 2).

- **Brueckner, Markus** , “Infrastructure, Anocracy, and Economic Growth: Evidence from International Oil Price Shocks,” 2014. Working paper.

26
Against the backdrop of the existing literature discussed above, the main contribution of our paper is in terms of testing the validity of an _ex ante_ tool for evaluating the impacts of CCT programs (the BFL model) that is operationally useful, both in terms of ease of implementation and data requirements. Compared to the dynamic _ex ante_ models suggested by Attanasio et al and Todd and Wolpin (see above), the BFL model makes fewer demands on data requirements and do not rely on the availability of panel data, as it employs a reduced-form approach that imposes simplifying assumptions on household behavior. If the predictions of the BFL model with all its simplicity were to compare well with the impacts measured by rigorous _ex post_ evaluations, it would be a practical and credible _ex ante_ tool for policymakers to employ during the design and planning phase of CCT programs.

# **III.** **_Ex Ante_ Simulation with the BFL Model**<sup>**9**</sup>

This section presents a summary description of the BFL model, used as the _ex ante_ evaluation tool in this paper (a more detailed discussion of the model can be found in the original BFL paper). Rather than constructing a complete structural model of demand for schooling and intra-household labor allocation, the BFL model aims to obtain reasonable orders of magnitude for the likely effects of cash transfers that are conditional on school attendance. The structural aspects of modeling are thus kept to the minimum necessary to capture the main effects of the program. There are four key underlying assumptions. Firstly, occupational choice is assumed to reflect (in reduced form) the end result of whatever decision-making process about a child’s time allocation unfolds in a household, regardless of _how_ the decision is made. Secondly, the decision to send a child to school is assumed to be made _after_ all occupational decisions by adults within the household have been made, so that the schooling decision does not affect those earlier decisions. Thirdly, the model does not allow for decisions about the occupational choice of multiple siblings in a household to be made simultaneously or jointly. Fourthly, the composition of the household is taken as exogenous. While each of these assumptions is an

> 9 A toolkit for running the BFL model was prepared by the World Bank. The toolkit provides a how-to guide in the application of the BFL micro-simulation model that enables users to analyze and compare several alternative scenarios on the basis on representative household survey data. It can thus be used in selecting the most cost-effective design or it can be used for sensitivity and cost-benefit analysis. The toolkit can be downloaded at www.worldbank.org/safetynets under _toolkit_ or by clicking on the following hyperlink: BFL model toolkit

9
<!-- Start of picture text --> Legend© — High Schools AN ‘ = 7<br>° os<br>Road Network ' 4 ;<br>————— SecondaryPrimary ‘ tT os2 fe pt?<br>Tertiary Sut 3 =<br>-<br>Travel Time ( (in min ) &&el )<br>EE < <0 4Bae gil a(ae, A ~~<br>:<br>I 20-60 rales ro Gad<br>[5 5 tv Se Qsvets Sees<br>60- 90 2 Cy<br>copy wa w =] 4 !<br>I k ee, ve aie " \ 2e<br> > 120 i peas * fre 9 +7<br>lo 1020 40 60 280 pitasa cot. aoF WY. i D4,Re,yo ><br>Kilometers es . «<br><!-- End of picture text --> <!-- Start of picture text --> Legend© — Hospital AN le<br>Road Network i ny<br>—————— SecondaryPrimary F BS if oe:<br>i on oS id<br>Tertiary: on y a ah<br>aeTravel«>Time (in min)| ~ eral Fr\ ,ie ye<br>HE 20-60 Pay giain/4,<br>Re =e ewe ale<br>60-90 ve wey LS ee) »<br>* ‘ee \ oe<br>HNC1p12 00- > 120 120«0 «o onit. KoAyy wey?- ‘eenCETAte. 7VAiy D ONG> an Pity% gee=<br>Kllarieters (<br><!-- End of picture text -->
from 0.46 to 0.39 between 1992/93 and 2006, whereas it increased slightly for men from 0.38 to 0.41 over the same period.

The inclusion of neighborhood fixed effects reduces the estimates of intergenerational correlations between parents and children also. But compared with sibling correlations, the magnitudes of the reductions are much smaller. For instance, estimates for men (brothers) declined from 0.52 to 0.48 in 1992/93 and 0.49 to 0.45 in 2006. After controlling for the neighborhood fixed effects, the estimates of intergenerational correlations indicate that more than 20 percent of variations in total schooling of children and nearly a third of sibling correlations in education can be explained by parent‘s education alone.

To provide a sense of relative importance of different factors in explaining the variations in children‘s education, we use estimates from Table 3 and plot them in Figure 1. Figure 1 show that in 2006, individual effort and other idiosyncratic factors explain about 40 percent of variations in education outcomes of men, and about 30 percent for women, the rest are due to common factors experienced by siblings. The sibling correlation can be decomposed into three components: (i) parental education, (ii) geographic location (i.e., neighborhood effect), and a residual family environment shared by siblings which presumably capture the parental child rearing skills, among other things. The neighborhood effect and parent‘s education together can explain more than 70 percent of sibling correlations. The common neighborhood factors and parental education are particularly important for sisters (women): their share in sibling correlations is large -- 0.81 in 1992/93 and 0.78 in 2006. For men, contribution of these two factors to sibling correlations decreased from 0.78 in 1992/93 to 0.69 in 2006 due mainly to decrease in the neighborhood correlations (Figure1). For women, intergenerational persistence has declined but neighborhood correlations remain nearly unchanged.

# **(4.2) Geography of Educational Mobility: Evidence from Alternative Partitioning of the Data**

The evidence on strong neighborhood effects in sibling and intergenerational correlations discussed above brings the focus on geographic location as an important factor in understanding educational mobility in post-reform India. This raises the question whether the levels, time trends and gender patterns of sibling and intergenerational correlations differ significantly across different geographic areas; for example, are there any significant differences between rural and urban areas, between less developed and more developed states? The recent academic literature and reports in popular press in India give a strong impression that the rural areas and certain lagging states such as 19
engineering, and technical education. Results reveal that for each additional hour (1⁄4 hour) of training, students exposed to VR training score 3 percent higher in technical (cognitive) learning assessments, when compared to students exposed to the same curricular content delivered through traditional training methods. Results also indicate that students who complete a VR training course report, on average, 30 percent higher scores in socio-emotional skills assessments.

Table 2 presents the observed average effects of VR training on learning value added, as proxied by the % difference between posttests and pretests of students exposed to VR training. These results are indicative of the capacity of VR training to positively improve students’ skills. The effects are calculated based on information of a total of 27 available experiments, and account for the experiments’ sample size and VR exposure time. Results indicate that, on average, VR training contributes to gains in technical (practical) skills that average 17 (8) percent per hour (1⁄4 hour) of training. Moreover, students who completed a VR training course, report on average, gains averaging 20.5 percent in self-reported socio-emotional skills.

**Table 2. Average effects of VR training on student learning value added**

||% Difference b<br>|etweenposttests andprete<br>|sts(VR only) <br>|
|---|---|---|---|
||**Technical skills**<br>(effect per hour of<br>training)|**Cognitive skills**<br>effect per 1⁄4 hour of<br>training)|**Socio-emotional skills**<br>(effect per training<br>course)|
|Average impact|17.3|8.0|20.5|
|Standard Deviation|20.9|31.3|26.1|
|Number of experiments|10.0|7.0|10.0|
|Registering a positive effect|9.0|7.0|7.0|
|Registering negative effect|0.0|0.0|0.0|
|Registering no effect|1.0|0.0|3.0|
|Average VR exposure time in hours|3.5|2.42|0.24|
|Average experiment size|23.4|89.0|39.4|

Source: Author’s elaboration.

Average results hide important patterns that arise when assessing the effects of VR training by education type of skill and education field. Results in the next subsections are organized by the type of skill assessed by the available experiments (technical, cognitive, and socio-emotional) and, within each skill, if there are any observed patterns, there is brief discussion of the impacts of the training by education field.

- _4.1. Impacts of VR training on technical skills_

Technical skills are often measured using ability tests, whereby students are required to perform specific task and are graded based on how their performance fares against predetermined 17
Table 3b: Detailed input counts for industries by <u>gender in unorganized sector, 2000</u>

|||Unorga|nized sect|or,male|Unorgan|ized secto|r,female|
|---|---|---|---|---|---|---|---|
|NIC|IndustryDescription|Mean|SD|Median|Mean|SD|Median|
|15|Food products and beverages|2.58|1.60|2.00|2.53|1.41|2.00|
|16|Tobacco products|1.67|0.85|1.00|1.20|0.77|1.00|
|17|Textiles|1.74|1.03|1.00|1.30|0.70|1.00|
|18|Wearing apparel; dressing and dyeing<br>of fur|3.24|1.68|4.00|2.13|1.19|2.00|
|19|Leather; luggage, handbags, saddlery,<br>harness and footwear|3.43|1.39|4.00|2.77|1.49|2.00|
|20|Wood and wood products, except<br>furniture; straw and plating|1.56|0.98|1.00|1.29|0.62|1.00|
|21|Paper and paper products|2.48|1.28|2.00|1.75|0.92|2.00|
|22|Publishing, printing and reproduction of<br>recorded media|2.50|1.36|2.00|2.71|1.45|2.00|
|23|Coke, refined petroleum and nuclear<br>fuel|1.64|0.88|1.00|2.00|0.00|2.00|
|24|Chemicals and chemical products|3.31|1.42|3.00|3.47|1.36|3.00|
|25|Rubber and plastic products|2.07|1.28|2.00|1.77|1.03|1.00|
|26|Other non-metallic mineral products|2.07|1.14|2.00|2.01|1.16|2.00|
|27|Basic metals|2.04|1.34|2.00|1.67|0.80|1.00|
|28|Fabricated metal products, except<br>machinery and equipments|2.06|1.27|2.00|2.22|1.42|2.00|
|29|Machinery and equipment, n.e.c.|2.58|1.46|2.00|2.77|1.58|3.00|
|30|Office, accounting and computing<br>machinery|3.30|1.83|4.00||||
|31|Electrical machinery and apparatus,<br>n.e.c.|3.30|1.49|3.00|2.80|1.57|3.00|
|32|Radio, television, and communication<br>equipment and apparatus|3.74|1.40|4.00|2.43|0.53|2.00|
|33|Medical, precision and optical<br>instruments, watches and clocks|2.62|1.21|2.00|1.67|0.52|2.00|
|34|Motor vehicles, trailers and semi-<br>trailers|2.63|1.50|2.00|2.40|1.76|2.00|
|35|Other transport equipment|2.31|1.48|2.00|3.00|1.47|3.00|
|36|Furniture, manufacturing n.e.c.|2.46|1.45|2.00|1.96|1.09|2.00|
||Traditional|2.43|1.52|2.00|1.89|1.17|2.00|
||Modern|2.63|1.49|2.00|2.49|1.45|2.00|

Notes: See Table 3a.