rafmacalaba/datause-encoder-data huggingface dataset:
aggregate productivity.

The authors analyze the eects of both static and dynamic types of misallocation. First, the existence of a public earnings premium inuences the occupational decision of agents. The main idea is that such premium might attract high-productivity entrepreneurs who would not apply for a job in the public sector in the absence of such a public wage premium. Moreover, there may be negative long-run consequences, since investment decisions depend on the agents ́ occupational choices. The results show that an overpaid public sector has sizable productivity losses. They show that a decrease in the public wage premium from 19 percent to 15 percent can produce a signicant positive eect on long-run aggregate output (7 percent increase) without any substantial decrease in public infrastructure. They also show that the reallocation of factors of production accounts for about 19 percent of the aggregate change in output. The rest is due to changes in factor accumulation. The authors also argue that pension reforms can have similar aggregate eects, but such reforms change more the incentive of agents to invest in nancial assets and most of the impact on output is due to changes in factor accumulation. Changes in the legislation law which decrease job stability in the public sector also produce important quantitative eects on aggregate output and productivity. They nd that when the job destruction rate in the public sector increases from 0 percent to 1 percent the number of applicants to public sector jobs decreases by nearly 15 percent and output increases by more than 3 percent.

# 4.3 Subsidies to national innovation and low management quality

In addition to a more ecient allocation of resources across rms, one important source of productivity gains is innovation. In the last decades Brazil has implemented several innovation policies, most of them involving scal incentives. For instance, the Lei de Informática (Informatics Law), instituted in 1991 and renewed in 2001, 2004 and most recently in 2014, promotes increased local content of ICT hardware and related electronics assembly plus investments in local R&D operations. Similarly, the Lei do Bem (Fiscal Incentives Law), which was instituted in 2007 and replaced a prior 2005 law, expanded incentives for investments in R&D, authorizing companies that invest in R&D and meet certain requirements to claim tax incentives automatically for certain types of spending.

Several papers show that the incentives for innovation and R&D provided by the Lei de Informática have not been eective.<sup>21</sup> In particular, Kannebley Júnior and Porto (2012) show that it has been ineective in stimulating productivity-enhancing R&D. Although the incentives have contributed to leading global ICT hardware rms to produce locally, the ben-

> 21See Bank (2017b) for a review of the eect of innovation policies on productivity in Brazil.

20
# **7 Conclusion**

This paper aims to contribute to the ongoing debate on ways to improve the accuracy and timeliness of welfare statistics in developing countries experiencing severe budget constraints. Only a few developing countries have the capacity to collect annual data on income or expenditure, therefore, indicators such as inequality or poverty rates can be computed in these countries only when Household Budget Surveys are available, about every four to five–and sometimes as many as seven–years. To overcome this problem, methods have been developed to compare these indicators over time from surveys that are little comparable. These techniques (SSITs) have proven effective at predicting poverty indicators, but are much less accurate when used for inequality indicators.

To illustrate this limitation, we conducted several simulations based on data from Moroccan Household Budget Surveys and Labor Force Surveys. Our results indicate that the predicted inequality measures are, at best, one-third smaller than those directly estimated from the data. Other regression methods were implemented, but none seems to significantly reduce the negative bias in the Gini, Theil, and HCR indices. Our theoretical explanation for this points to two main limitations of the Standard SSIT models based on linear regressions: the overly stringent assumption of the residuals’ normality distribution and the expectation that regression-based models predict distribution compressed around the mean and with thin tails. Unfortunately, the shape of the tails is crucial to correctly estimate inequality. Thus, almost by design, these models tend to produce estimates that are far below the correct values.

The method we propose is based on an algorithm that minimizes the sum of the squared differences between a certain number of direct estimates of an index and its empirical version obtained from the predicted values. With this algorithm, we reduce the bias and obtain results that are not systematically biased. When the estimated results are compared with those directly estimated on the original sample, the bias is negligible. Furthermore, the estimates of inequality indices for the years in which only labor force data are available seem to be consistent with Moroccan economic trends.

In the future, it would be interesting to explore the implementation and estimation of HCR for two or more areas where the poverty line stands at 60% of the national median. An alternative approach would be to identify the starting points to estimate the parameter and its variance. Other distributions of consumption should be tested in order to reduce the margin of error. The authors are working on the log-student-t distribution. For this paper, it is evident that bias reduction, realistic rebuilding of the values, and the economic interpretation of results represent a good starting point.

16
# **Table 4: Returns to Innovation with Regulatory Quality**

|Variables<br>∆ln(|sales)|∆ln(sale|s/ labor)|
|---|---|---|---|
|Innovation<br>-0.848|-0.975*|-0.339|-0.360|
|(0.621)|(0.572)|(0.348)|(0.312)|
|RegQual*Innovation<br>0.0172*<br>(0.00865)|0.0177**<br>(0.00802)|0.00846<br>(0.00521)|0.00857*<br>(0.00472)|
|New Top Manager|-1.222***<br>(0.424)||-1.432***<br>(0.414)|
|Exporter|0.0445<br>(0.0975)||0.0222<br>(0.162)|
|More Competitors|-0.903***<br>(0.170)||-0.697***<br>(0.0991)|
|Small Size|-1.485***<br>(0.320)||-0.542**<br>(0.214)|
|Medium Size|-1.141***<br>(0.166)||-0.558***<br>(0.158)|
|Constant<br>1.746*|4.461***|1.725|3.711***|
|(0.941)|(1.203)|(1.027)|(1.295)|
|Year*Country FE<br>yes|yes|yes|yes|
|Industry FE<br>yes|yes|yes|yes|
|No of Obs<br>1,879|1,874|1,870|1,870|
|R-Squared<br>0.458<br>*** p< 0.01, ** p< 0.05, * p< 0.1<br>Clustered robust standard errors by country in|0.495<br>parentheses|0.450|0.462|

# **4.3 Return to Innovation with property right protection**

Since rule of law and regulatory quality are still too general to have specific policy recommendations, this section zooms in one particular component of institutions that we think are more obvious in affecting the return to innovation. It is property right protection. For our analysis, we use the property right index by the Heritage Foundation to proxy for property right protection. Property right protection assesses the extent to which private economic activity is facilitated by an effective legal system and rule-based governance structure in which property and contract rights are reliably respected and enforced.

The property rights here include both intellectual property rights (IPR) and more general property rights. While IPR laws and enforcements provide necessary protection to the fruits of R&D (patent, copyrights, trademarks, etc.), broader property rights protection and contract enforcements protect 15
Table 7: Eect of a direct SLM training on contact farmers' maize production

|(7)<br>T+TÖ<br>Dry Year<br>.<br>.<br>908.249<br>[431.334]<br>537.443*<br>[237.038]<br>1930.829<br>[977.493]<br>1635.694*<br>[664.713]<br>ation data.<br>le of the 1981-2013<br> maize yield and<br> by a Wald test.|
|---|
|(6)<br>R2<br>0.089<br>0.147<br>0.121<br>0.152<br>0.097<br>0.157<br>0.124<br>0.156<br> Precipit<br>st quarti<br> Year on <br>termined<br>re.|
|(5)<br>N<br>347<br>347<br>347<br>347<br>347<br>347<br>347<br>347<br>1/2013<br> the r<br>4.<br>h Dry <br>t is de<br>=hecta|
|(4)<br>TÖ<br>Dry Year<br>866.412*<br>[354.536]<br>528.280<br>[257.942]<br>1185.839**<br>[359.641]<br>1370.718**<br>[453.943]<br> 1/2x 1/2198<br>y season is in <br>dels in Table <br>nteraction wit<br> additive eec<br>Metacais; Ha|
|(3)<br>Dry Year<br>178.182<br>[451.867]<br>133.582<br>[440.298]<br>19.641<br>[1014.573]<br>-213.874<br>[996.793]<br>2013; NASA <br>n in the rain<br>iables as mo<br>t T and its i<br>cance on the <br>Mozambican <br>ritical level.|
|(2)<br>T<br>326.399<br>[319.157]<br>182.322<br>[121.077]<br>1137.593<br>[721.392]<br>722.089<br>[438.057]<br>41.837<br>[399.865]<br>9.163<br>[152.561]<br>744.990<br>[632.045]<br>264.976<br>[310.816]<br>rvey 2012, <br>precipitatio<br>anatory var<br>he treatmen<br>ets. Signi<br>nt; MZN=<br>10 percent c|
|(1)<br>Control Mean<br>[SD]<br> controlling for precipitation<br>Original data<br>1441.540<br>[1417.638]<br>Winsorize at 1%<br>1441.540<br>[1417.638]<br>Original data<br>4408.667<br>[4687.363]<br>Winsorize at 1%<br>4408.667<br>[4687.363]<br>ling for precipitation<br>Original data<br>1441.540<br>[1417.638]<br>Winsorize at 1%<br>1441.540<br>[1417.638]<br>Original data<br>4408.667<br>[4687.363]<br>Winsorize at 1%<br>4408.667<br>[4687.363]<br> farmer survey, 2010; Household su<br>is a dummy indicating cumulative <br>. All models include the same expl<br>col 7) presents the total eect of t<br>ociated standard errors are in brack<br> SLM=sustainable land manageme<br>dicate signicance at the 1, 5, and|
|Panel A: Without<br>Revenue per Ha<br>[MZN/Ha]<br>Total revenue<br>[MZN]<br>Panel B: Control<br>Revenue per Ha<br>[MZN/Ha]<br>Total revenue<br>[MZN]<br>Sources: Contact <br>Notes: Dry Year <br>historical average<br>T+TÖDry Year (<br>revenue. The ass<br>Ö=multiplied by;<br>***, **, and * in|

38
44 TIM PA"R _OF_ INVLATIO MtD _TO PSA OF_ MVALUATIOl (SUZ) <!-- Start of picture text --> (2 Per lbnth)<br>3.<br>80/2 61/1 82/1 83/1 84/1 8$/1 86/1 87/1 88/1<br>Inflation: -- valuatlon (Baket):<br><!-- End of picture text --> <!-- Start of picture text --> TIE RATE OF INFLATION AMD THtE tATB Or ClANG! OF CONOLLED PRICES<br><!-- End of picture text --> <!-- Start of picture text --> (2 Par Ibnth)<br>5.-<br>4-<br>3_<br>20-<br>80/2 81/1 82/1 83/1 84/1 85/7 86/1 87/1 87/1<br>Inflation: RPte of Change of Controlled Price ...<br>Tltl RATE OF INFIATION MtD MONET (63) GRWNm<br><!-- End of picture text --> <!-- Start of picture text --> (2 Per Mboth)<br>40<br>3.<br>20 . .-<br>80/2 81/l S2/1 83/1 84/1 85/7 86/1 87/1 88/1<br>Inflation: i3 Growth: - - - - - - -<br><!-- End of picture text -->
women who cooked with LPG or coal briquette. PAH is an organic compound chronic or longterm exposure to which could decrease immune function, damage kidney and liver and ause breathing problems, asthma-like symptoms, and lung function abnormalities. It is also carcinogenic.

Studies in India and Nepal reveal that non-smoking women exposed to biomass smoke have death rates from chronic respiratory disease comparable to those of heavy smokers who are males (Modi et al., 2005). Based on 2003-2004 district level household survey, Lakshmi et al. (2013) finds that biomass and kerosene cooking fuels are associated with stillbirth among married women aged 15-49, representing about 12% of stillbirths in India. In Gambia, girls under the age of five carried by their mothers while cooking have a six times greater risk of lung cancer than if their parents smoked cigarettes and were not exposed to HAP from cooking (Gaye 2007).

Dix-Cooper et al. (2012) investigated whether early life chronic exposure to wood smoke is associated with children's neurodevelopmental and behavioral performance in San Marcos, Guatemala and find that children’s neuropsychological tests were significantly associated with mothers’ 3rd trimester CO exposures during her pregnancy. The neuropsychological tests included visuo-spatial integration, short-term memory recall, long-term memory recall, and fine motor performance and these findings persisted even if they were adjustment for child sex, age, visual acuity, and socio-economic status.

Pokhrel et al. (2010) investigates, in Nepal, the relationship between HAP exposure and tuberculosis with the cases of 125 female patients of 20 - 65 years’ age, who visited TB clinics and who had been newly diagnosed with active pulmonary TB by chest X-ray and positive active sputum smears. The study finds that use of indoor biomass and kerosene fuels are associated with TB in women.

Lakshmi et al. (2013) analyzed the data from 188,917 ever-married women aged 15–49 included in India's 2003–2004 District Level Household Survey-II to investigate the association between household use of cooking fuels (liquid petroleum gas/electricity, kerosene, biomass) and the risk of stillbirth. The study finds that women who cook with firewood were more likely to have experienced a stillbirth than those who cook with LPG/electricity. Moreover, kerosene lamp use was also associated with stillbirths compared to electric lighting.

Based on the latest information available from the World Health Organization (WHO, 2016), HAP causes 4.3 million deaths each year (Figure 6). Of the total, 34% is caused by stroke, 20
magnitude of the coefficient is small and it is not statistically significant.

Note that although the entry costs we have estimated are sizable, they are an order of magnitude smaller than estimates obtained with a similar model in ACM. The main reason is that here we have allowed for the sector-occupation aggregate preference shocks _ηt_<sup>_iks_, which</sup> were absent in ACM. They are not of independent interest, but their estimated values are presented in Table 7.

# **4 Simulations**

We can now turn to simulation of a trade liberalization. For this, we need to complete the general-equilibrium model and calibrate it. We specify production functions for each sector below, and assume a spot market for labor in each sector that clears each period given the number of workers in each cell as of the beginning of the period. We will also specify trade policy that determines the prices of all tradeable goods for each date, and assume that all workers know that sequence. In addition, for this exercise, we suppress the shocks ( _ηt_<sup>_iks_)to</sup> preferences (see (1)), since they are a distraction from our interest in the effects of a trade shock. We also take the mean value of the parameters ( _Ct_<sup>1</sup><sup>_is_)’s, (</sup><sup>_C_</sup> _t_<sup>2</sup><sup>_ls_)’s,</sup><sup>_C_</sup> _t_<sup>3</sup><sup>_,k,l,s_</sup> ’s, and ( _Ct_<sup>4</sup><sup>_s_)’s</sup> and hold them constant for the simulation.

Given this structure, an equilibrium can be described as follows. (i) Consider a sequence _V_ of matrices of cell payoffs _Vt ≡{Vt_<sup>_iks_</sup> _}i_ =1 _,...I,k_ =1 _,...K_ from _t_ = 0 to _∞_ and an initial allocation of workers across the cells given by the matrix _L_ 0 _≡{L_<sup>_iks_</sup> 0<sup>_}i_=1</sup><sup>_,...I,k_=1</sup><sup>_,...K_for</sup><sup>_s_=</sup><sup>_c, n_.(ii) Given</sup> _L_ 0 and product prices at _t_ = 0, marginal value products of labor for each type of labor in each cell and hence period-0 wages can be computed, and given expected next-period values _V_ 1, the gross-flows matrix can be computed from (2). Therefore, the next-period allocation of labor _L_ 1 and next-period wages can also be computed. Proceeding in this way, the whole infinite sequence of labor allocations and wages can be computed, and from (1), the implied sequence of cell payoffs can be computed, and can be denoted _V_<sup> ̃</sup> . (iv) The value sequence _V_ is, then, an equilibrium with initial allocation _L_ 0 if and only if _V_ = _V_<sup> ̃</sup> . Existence and uniqueness of equilibrium are proven for a very similar model in Cameron, Chaudhuri and McLaren (2007), and a slight modification of the proof would ensure the same result here. Computational details are discussed in Artu ̧c, Chaudhuri and McLaren (2008).

20
Using data from the American “The High School and Beyond”, Arum and Shavit (1995) find that although vocational education inhibits students’ likelihood of attending college and subsequently of finding employment in the professions and managerial occupations, it reduces the overall risk of unemployment. They conclude that for students who are unlikely to continue on to college, vocational education is a safety net that reduces the risk of failing to achieve employability.

The somewhat contradictory conclusions reached in the existing literature are to some extent related to the methodologies employed in the various studies. There are two common limitations in existing empirically based studies: The first limitation is when only terminal education level is used for sample selection. Thus, the samples used usually include only students who conclude their education at secondary level (whether in the academic track or vocational track), disregarding students who pass through secondary education to achieve a higher level of educational attainment. If achieving a higher level of educational attainment leads to better labor market outcomes, the data censoring could lead to biased results by overestimating the benefit of vocational education. The second limitation is related to the endogeneity of being in academic or vocation track of education. These two limitations are usually related to the lack of longitudinal information.

In some cases, longitudinal information is used for analyzing the effect of vocational versus general schooling, but only for observing changing labor market outcomes over time, such as in and out of employment status and trend in labor market earnings (Campbell et al 1987; Ziderman 1989). Seldom is longitudinal data available in tracking the household decision of choosing vocational or general track of schooling; nor is it used for correcting data censoring bias. In the meantime, other areas of empirical research have shown that treatment for data censoring and endogeneity of schooling choice can indeed bring improvement in econometric estimates. Examples include Evans and Schwab (1995) on the effect of attending catholic schools on high school graduation and college entry correcting for schooling choice endogeneity, and Van de Ven and Praag (1981) on demand for deductibles in private health insurance correcting for data censoring due to health insurance coverage.

4
**Figure 4. Unemployment insurance benefit recipients and new monthly claimants**

<!-- Start of picture text --> 300,000<br>2017 2018 2019 2020<br>280,000<br>260,000<br>240,000<br>220,000<br>200,000<br>180,000<br>160,000<br>140,000<br>120,000<br>100,000<br>80,000<br>60,000<br>40,000<br>20,000<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>120,000<br>2017 2018 2019 2020<br>110,000<br>100,000<br>90,000<br>80,000<br>70,000<br>60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>Job seekers paid UIB<br>Initial Claims for Unemployment Benefits<br><!-- End of picture text --> Source: OAED Monthly Reports. Authors’ calculations.

# **5.3 Labor market flows**

The employment registry, ERGANI, provides a unique data source since employment levels and flows in and out of employment can be tracked on a daily basis. Figure 5 presents the day-by-day employment for 2020, from the start of the year to the end of June. The first COVID case and the introduction of the workplace restrictions are marked with vertical lines on the chart so that the employment trend can be observed with reference to these key dates. In order to provide perspective on the 2020 numbers, a counterfactual trend line is included which estimates what the 2020 20
Policy Research Working Paper 9047

# **Abstract**

This paper addresses the identification of low-frequency macroeconomic shocks, such as technology, in Structural Vector Autoregressions. Whilst identification issues with long-run restricted VARs are well documented, the recent attempt to overcome said issues using the MaxShare approach of Francis et al. (2014) and Barsky and Sims (2011) has its own shortcomings, primarily that they are vulnerable to bias from confounding non-technology shocks. A modification to the Max-Share approach and two further spectral methods are proposed to improve empirical identification. Performance directly hinges on whether these confounding shocks are of high or low frequency. Applied to US and emerging market data, spectral identifications are most robust across specifications, and non-technology shocks appear to be biasing traditional methods of identifying technology shocks. These findings also extend to the SVAR identification of dominant business-cycle shocks, which are shown will be a variance-weighted combination of shocks rather than a single structural driver.

This paper is a product of the Equitable Growth, Finance and Institutions Global Practice. 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 gkindberghanlon@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