rafmacalaba/datause-encoder-data huggingface dataset:
Real wage changes, in turn, can be positive or negative, and in our Mexican data substantial declines in the real wage are quite common. More specifically, Mexico experienced very high inflation rates during our sample period with annual declines in the _aggregate_ real wage as high as 25 percent during this time (see, for instance, Hanson (2003)), implying that the wage rates of some individual workers declined by an even larger amount. Thus, despite the often cited downward rigidity of wages, our sample includes large numbers of workers whose _real_ wages declined dramatically.

# _II.5. Results_

As described before, we have individual income data for the time period 1987-1998 covering 21 different manufacturing sectors in Mexico. Using the methodology outlined above, we estimate the risk parameters _σε_<sup>2and</sup><sup>_σ_</sup> _η_<sup>2foreachquarterandeachmanufacturingsector.In</sup> Tables II and III we provide the average estimate of _σε_<sup>2and</sup><sup>_σ_</sup> _η_<sup>2for each year (averaged across</sup> industries) and for each industry (averaged over time) respectively.<sup>20</sup> The mean value (across industries and over time) of the quarterly variance of the persistent shock, _σε_<sup>2,isestimated</sup> to be 0.008, or 0.032 annualized (i.e., _σε_ , is estimated to have a mean quarterly value of 0.09 and a mean annualized value of 0.18).<sup>21</sup> As expected, given the extent of measurement error in the income data (see our discussion in Section II), the estimated variances of transitory shocks are much larger in magnitude. More precisely, the mean value of the annualized variance of transitory shocks is 0 _._ 2 (an annual standard deviation of 45 percent), which is

> 20The averages presented in Tables II and III are merely summary descriptions and do not allow for any direct inferences regarding the relationship between trade policy and income risk.

> 21Given that in Section III we seek to uncover the relationship between trade policy and income risk using our estimates of the income risk parameters _σε_ , it is also interesting to investigate to what extent these estimates differ across industries and over time after making some adjustment for the fact that there is estimation error. To quantify this variation, we use the methodology of Krueger and Summers (1988). More specifically, we compute a measure of the “adjusted standard deviation” of the point estimates of the income risk parameters. In turns out that this number (0.018) is over twice the mean value of _σε_ in our sample _−_ indicating that the variation in _σε_ across industries and over time is indeed significant in our exercise.

15
(Elec: 30 to 33) and Transport equipment (Tra: 34, 35). _Source:_ Author’s calculations based on World Input-Output Database, April 2012, updated to 2011.

Aggregate EU27 specialization patterns hide substantial variation within the European Union. In Table 3 we present the RCA for member states, calculated as above, to track particular specialization patterns. Major new member states particularly improved their positions in GVCs of transport equipment, in 2008 all five having RCAs higher than one. RCAs for non-durables, traditionally a stronghold for these countries, declined in all countries and provide no longer a comparative advantage in Czech Republic, Hungary and the Slovak Republic. Instead they developed comparative advantage in electrical and non-electrical equipment. Across the old EU 15 it seems that specialization patterns have been reinforced in those industries for which the possibilities for international fragmentation are the highest, and for those countries that grasped the opportunities. Germany specialized further in activities in the transport equipment and non-electrical machinery manufacturing; The Netherlands and Ireland in chemicals; Austria and Sweden in non-electrical machinery; and Finland in electrical and non-electrical machinery. Specialization patterns in other countries have changed much less during this period. For example, Italy maintained its strong position in non-durables, the UK in chemicals and France in transport equipment, but they did not increase it. Italy’s particular strong position in activities in the production of non-durables (textiles, wearing apparel and footwear) might be surprising, given the perceived low-skill intensive nature of the production process of these products, and the massive increase in exports from Asia. But this basically suggests a shift of Italy in the non-durable value chains away from low-skill assembly and production activities towards higher skill activities, such as pre- and post-production services.

26
# **Results**

**Though limited by the occupations covered, the SDS can offer some general insights into the kinds of skills that are currently in demand in Vietnam** . The primary objective of the SDS is to provide detailed profiles of the tasks, skills, and education and experience requirements of specific occupations in Vietnam. The following section includes an example of these profiles. This section summarizes the results of the SDS to provide a general picture of the kind of insights available from the SDS. Importantly, however, these summary results refer to the 30 occupations sampled and so are not representative of Vietnam’s labor market at large.

**<mark>Education</mark>** <mark>. Forty-three percent of occupations require at least a secondary degree but not higher education (</mark> **<mark>Figure 2</mark>** <mark>). These are occupations that are more technical in nature (for example, technicians, assemblers, machine operators, heavy trucks drivers). A third of occupations require less than a secondary degree. These occupations are related to sales (for example, shopkeepers and stall and market salespersons), require operating simple machines (for example, sewing machine operators, motorcycle drivers), or involve performing elementary tasks (for example, building construction laborers, freight handlers). Twenty-three percent of occupations require bachelor’s degree or above. All of these occupations are professional occupations (for example, engineers, teachers, analysts). Still, educational requirements vary significantly within occupational groupings (</mark> **<mark>Figure 3</mark>** <mark>). For example, fairly large shares of Plant and Machine Operators and Assemblers report needing secondary education or vocational training (about 25 percent each), though most report needing only less than secondary education. About a third of occupation require certifications (</mark> **<mark>Figure 2</mark>** <mark>). These occupations are mostly high-skilled (e.g., mechanical engineers, software developers, teachers, associate professionals), although some are semi-skilled machine operators (for example, drivers who need driver’s licenses).</mark>

**Figure 2: Education requirements of the SDS’s 30 occupations**

_Share of occupations with at least half of workers reporting the level of education or certification is required (%)_ <!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Less than secondary Secondary Bachelors or more Certification<br><!-- End of picture text --> Source: SDS.

11
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Caselli, F., and J. Reynaud. 2020. "Do Fiscal Rules Cause Better Fiscal Balances? A New Instrumental Variable Strategy." _European Journal of Political Economy_ 63: 101873.

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<mark>Davoodi, H., P. Elger, A. Fotiou, D.M. Garcia, A. Lagerborg, R. Lam, and S. Pillai. 2022. "Fiscal Rules Dataset: 1985-2021." International Monetary Fund, Washington, D.C.</mark> <mark>Debrun, X., L. Moulin, A. Turrini, J. Ayuso-i-Casals, and M. S. Kumar. 2008. “Tied to the Mast? National Fiscal Rules in the European Union.”</mark> _<mark>Economic Policy</mark>_ <mark>23 (54): 298–362.</mark> <mark>Debrun, X., and M. Kumar. 2008. "Fiscal Rules, Fiscal Councils and All That: Commitment Devices, Signaling Tools or Smokescreens?"</mark> _Proceedings of the 9th Banca d’Italia Workshop on Public Finance_ . Rome: Banca d’Italia.

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Eichengreen, B., R. Hausman, and J. von Hagen. 1999. “Reforming Budgetary Institutions in Latin America: The Case for a National Fiscal Council." _Open Economies Review_ 10(4): 415-442.

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20
# **5. Discussion and Further Results**

# **5.1 Enrollment vs. Retention**

We found relatively large effects of the program on enrollment – especially for girls – in 2012 and 2013. How much of the program effect on total enrollment was driven by new students enrolling versus students being retained?

To answer this question, we match student enrollment records in 2011, to 2012 and 2013 and identify three samples of students: _Baseline_ , _Newly Enrolled_ (students not enrolled in grades three or four in 2011 or 2012, but enrolled in grades four or five in 2012 or 2013, respectively), and _New Cohort_ (students enrolled in grade three in either 2012 or 2013). To match students across 2011, 2012 and 2013 enrollment records, we use information regarding each student’s school, village, gender, age, last and first name, and father’s name. In some cases – for example, when a student name is common or when we are missing demographic information – there are multiple possible matches. Any student who matches to subsequent enrollment records (uniquely or otherwise), is coded as retained to the relevant year.<sup>17</sup> Appendix Table A7 estimates Equation (1) on enrollment numbers, disaggregating total enrollment by sample of student. In 2012, we find that a portion of the treatment effects on enrollment are driven by baseline sample girls – a coefficient of 0.28 (Panel A, Column 3), but the majority of the effects are coming from newly enrolled fourth and fifth grade girls – a coefficient of 1.1 (Panel A, Column 6). We detect no enrollment effects on new third graders (Panel A, Columns 7-9). In 2013, there are positive coefficients on the treatment effect on enrollment for

> 17 Matching baseline students to 2012 yields 97.1 percent unique matches (99.1 percent in the treatment and 95.1 in the control). Similarly, there are 97.6 percent unique matches from the baseline to 2013 (99.3% in the treatment and 95.7% in the control). The difference in the match rate is statistically significant in both years, p=0.001 in 2012, p=0.002 in 2013. The difference in the match rates across treatment and control schools is due entirely to enrollment rosters missing students’ father’s name: 40 schools have 100 percent missing father’s names, of these 37 are control schools.

22
**S**

## Figure 11

# GHANA Area under cereals

<!-- Start of picture text --> Thousand hewtares<br>1400<br>1200<br>1000<br>800<br>600 .I. .. ..<br>400<br>200<br>19.0 1972 1974 1976 1978 1980 1982 1984 1986<br>Maize Rice LI Millet Guinea Corn<br><!-- End of picture text -->
19 As a result, while most countries allow banks to own non-financial companies (only 9 countries out of 51 industrialized and emerging market economies surveyed by the Institute of International Bankers do not allow any investments), they stipulate some limits--related to the capital of the financial institution-and many require supervisory consultations to limit perverse incentives and assure risk diversification.

Many of the problems associated with ownership of financial institutions by corporations and other links arise however, from a lack of competition in the financial sector, lack of supervision, or generally distorted real or financial sectors. Examples include the financial conglomerates in Chile and other Latin American countries in the late 1970s/earlyl980s; financial industrial groups in Russia; and the merchant banks in Korea which were owned by chaebols but weakly supervised. Many countries have then also no or few restrictions on ownership of financial institutions by non-financial firms-apart frorn supervisory review/approval-as they have competitive financial systems and good market and supervisory oversight. Others do but find these restrictions tricky to enforce. Yet in countries with concentrated ownership structures, it may be warranted to break ownership links between nonfinancial corporates and financial institutions to curb connected lending more effectively (i. e. minimize the risk that non-financial companies use their financial subsidiary as financing mechanisms) and introduce a paradigm shift.
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22
**Table 6 -- Summary statistics on labor market indicators by county in Croatia (2002-04)**

||County|Active<br>population<br>rate|Participa-<br>tion rate|Employ<br>-ment<br>rate|Unemploy<br>-mentrate|Monthly<br>earnings<br>(kuna)|Years of<br>school|
|---|---|---|---|---|---|---|---|
|1|County of Zagreb|65.36%|62.27%|52.91%|14.78%|3319|9.63|
|2|County of Krapina-Zagorje|63.89%|82.04%|69.62%|4.89%|3012|8.68|
|3|County of Sisak-Moslavina|62.94%|64.07%|50.70%|18.98%|3084|9.10|
|4|County of Karlovac|61.30%|70.73%|57.12%|15.53%|2967|9.13|
|5|County of Varaždin|66.19%|73.31%|64.22%|8.20%|2573|9.57|
|6|County of Koprivnica-Križevci|63.89%|74.51%|62.85%|10.15%|2604|8.77|
|7|County of Bjelovar-Bilogora<br>County of Primorje-Gorski|63.04%|75.44%|63.86%|10.91%|2603|8.68|
|8|kotar|64.65%|63.89%|56.63%|11.21%|3697|10.56|
|9|County of Lika-Senj|58.48%|88.10%|69.03%|8.68%|3033|8.51|
|10|County of Virovitica-Podravina|62.45%|70.59%|58.09%|13.96%|2341|8.59|
|11|County of Požega-Slavonia|61.26%|65.36%|53.52%|13.90%|2670|8.49|
||County of Slavonski Brod-|||||||
|12|Posavina|60.96%|56.91%|46.45%|15.73%|2662|8.75|
|13|County of Zadar|63.04%|62.47%|49.38%|18.90%|3426|9.77|
|14|County of Osijek-Baranja|65.68%|62.08%|47.43%|22.89%|2958|9.57|
|15|County of Šibenik-Knin|56.91%|65.79%|45.91%|28.16%|3262|9.01|
|16|County of Vukovar-Sirmium|61.39%|56.75%|42.57%|24.07%|3106|8.67|
|17|County of Split-Dalmatia|63.22%|60.53%|48.14%|19.64%|3617|10.17|
|18|County of Istria|65.51%|64.15%|58.70%|8.38%|3330|9.91|
|19|County of Dubrovnik-Neretva|62.67%|65.34%|53.88%|17.42%|3519|10.22|
|20|County of Međimurje|65.95%|67.60%|58.98%|12.27%|2999|9.60|
|22|Cityof Zagreb|66.38%|64.06%|57.08%|10.71%|3895|11.49|

There also exist heterogeneities within many counties (table 7). For example, the distribution of earnings for the individuals who are employed are the most unequal in the county of Lika-Senj and many counties in the Central Region and Eastern Region, where the average earnings level is low. However, the two counties in the Zagreb Region, where the average earnings level is the highest, have the most equal distribution. Graph 4 shows that, for example, in the County of Lika-Senj, the level of earnings of the individuals who are employed clusters at two separate low levels, which is the so-called twin-peak distribution in econometrics; however, in the County of Zagreb, the earnings concentrates at a relatively higher level. The large inequality in distribution of earnings might aggravate the difficult situation that poor people face in the County of Lika-Senj.

10
**Table 8. Odds ratios from logit model of firm exit, by pair of waves, ELMPS 1998-2018**

||**1998-2006**|**2006-2012**|**2012-2018**|**2012-2018**|**Pool**|
|---|---|---|---|---|---|
|**Formality (informal omit.)**||||||
|Formal|0.811|0.625*|0.919|0.915|0.957|
||(0.243)|(0.117)|(0.159)|(0.158)|(0.193)|
|**Wave (1998-2006 omit.)**||||||
|2006-2012|||||1.481*|
||||||(0.232)|
|2012-2018|||||1.745***|
||||||(0.286)|
|**Interactions between formality**<br>**and wave**||||||
|Formality*2006-2012|||||0.710|
||||||(0.161)|
|Formality*2012-2018|||||0.873|
||||||(0.196)|
|**Ent. size (one worker omit.)**||||||
|2|0.950|0.885|0.714|0.722|0.863|
||(0.259)|(0.159)|(0.131)|(0.134)|(0.099)|
|3-4|1.090|0.830|0.677|0.687|0.853|
||(0.351)|(0.198)|(0.180)|(0.185)|(0.129)|
|5-24|1.417|0.743|2.043|2.096|1.275|
||(0.699)|(0.289)|(0.817)|(0.845)|(0.309)|
|**Industry (wholesale/retail omit.)**||||||
|Manufacturing and related||||||
|<br>trades|0.477|1.313|1.661*|1.668*|1.202|
||(0.197)|(0.294)|(0.427)|(0.430)|(0.180)|
|Construction|0.563|2.099*|4.624***|4.624***|2.107***|
||(0.353)|(0.656)|(1.492)|(1.490)|(0.436)|

53
# **E.4 Effect on Patient Deaths**

**Table E.4:** Effect on Patient Deaths

||_Dependent variable:_|
|---|---|
||Patient Deaths|
||(1)<br>(2)|
|Total Cases in Last 2 Weeks|0.245<br>0.247<br>(0.021)<sup>_∗∗∗_</sup><br>(0.021)<sup>_∗∗∗_</sup>|
|Pooled|0.063<br>(0.032)<sup>_∗∗_</sup>|
|Total Cases in Last 2 Weeks_×_Pooled|_−_0.098<br>(0.043)<sup>_∗∗_</sup>|
|CM|0.116<br>(0.037)<sup>_∗∗∗_</sup>|
|Total Cases in Last 2 Weeks_×_CM|_−_0.149<br>(0.046)<sup>_∗∗∗_</sup>|
|NFA|_−_0.007|
||(0.025)|
|Total Cases in Last 2 Weeks_×_NFA|_−_0.019<br>(0.032)|
|Control Mean|0.149<br>0.149|
||(0.49)<br>(0.49)|
|Observations|5,280<br>5,280|

_Notes_ : Standard errors clustered on section. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Significance: * p _<_ 0.10, ** p _<_ 0.05, and *** p _<_ 0.01.

**Table E.5:** Effect on Delays between Symptom Onset and Reporting

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Delay: Symptom Onset and Reporting|4.729|0.218|0.583|-0.066|0.649|160|
||(3.229)|(0.51)|(0.62)|(0.579)|(0.626)||

_Notes_ : Treatment effects estimated using OLS including matching-triplet fixed effects. Significance: *p < 0.10, ** p < 0.05, and *** p < 0.01. Delays greater than 60 days were removed to limit the influence of outliers.

A35
controlling such effects using the value added in manufacturing as a proportion of GDP ( _Manufacturing VA_ ), and value added in agriculture as a proportion of GDP ( _Agriculture VA_ ). The data source for these variables is WDI, World Bank. As above, we use average values of the variable taken over the last five years prior to the year the ES was conducted in the country.

Cultural and institutional factors are important factors that determine women’s engagement in the labor market. Studies show that social and cultural attitudes towards women and their market activity are correlated with women’s participation in the labor market, although the evidence on the issue is rather mixed (see for example, World Bank 2011, Contreras and Plaza 2010, Hajj and Panizza 2009). For culture or social attitudes, we follow the literature and use two sets of proxy measures. First, the proportion of population that is Catholic, Protestant and Muslim (the residual category of other religions is the omitted category). Second, a dummy variable equal to 1 if the legal origin of the country is Socialist and 0 otherwise. The data source is La Porta et al. (1999).

Laws on maternity leave are not the only laws that impact women’s employment. Other gender-specific laws could also impact female relative to male employment (see for example, Amin et al. 2016, Gonzales et al. 2015, Branisa et al. 2013, Islam et al. 2018). Thus, our main results concerning maternity leave could suffer from omitted variable bias if the other laws happen to vary systematically with the length of maternity leave across countries. To guard against such possibility, we control for various gender-specific laws all taken from the WBL data and for the year 2016. Specifically, we control for an overall measure of gender disparity in the laws ( _WBL disparity_ ).<sup>6</sup> Additionally, following Amin et al. (2016), we control for a dummy variable equal to

> 6 The _WBL disparity_ variable is an overall measure of Legal Gender Disparities based on WBL data (for 2016) and is obtained from Iqbal et al. (2016). It is constructed as follows. Fifty‐one gender disparities in laws are considered. If a law treats men and women differently (a legal gender disparity) then a score is assigned. If the disparity applies to only married women, a score of 1 is assigned. If it applies to both married and unmarried women, a score of 2 is assigned. If the law treats both men and women the same, then a score of 0 is assigned. The summation over all the scores provides the _WBL disparity_ variable.

14