rafmacalaba/datause-encoder-data huggingface dataset:
# **4 Data**

The structural estimation outlined in the previous section is conducted with data from Labor Force Surveys (LFS) from Brazil, and Mexico. A common feature of these surveys is a rotative panel structure that permits the construction of employment transition matrices. For both countries, we restrict the sample to individuals between 15 and 65 years of age. Again, the industries are aggregated into five sectors: i) Agriculture, Mining, Construction and Utilities, ii) Manufacturing, iii) Commerce, iv) Hotels and Restaurants, and v) Other Services. Each sector is further divided into formal and informal. A worker is considered formal if she/he is registered in the social security system. Additionally, there is a residual sector that captures individuals who are either unemployed or out of the labor force, the aforementioned “inactive” sector.

The “Encuesta Nacional de Ocupacion y Empleo” (ENOE) is used to compute the transition matrices for Mexico. The ENOE is a household survey that collects detailed information on labor force status, wages, occupational and demographic characteristics. It is collected quarterly since 2005, and it is representative at both the national and state levels. The sample size is around 120,260 households in each quarter. Each household is interviewed in five consecutive quarters. In each quarter, one fifth of the sample (e.g., households in their fifth interview) is replaced. We construct the transition matrix by looking at the first and fifth interview. We derive six year-on-year transition matrices, the first matrix reflecting transitions between 2005 and 2006 and the most recent between 2010 and 2011.

The “Pesquisa Mensal de Emprego” is the Brazilian LFS. The survey collects detailed information of Brazil’s labor market and is representative of only six metropolitan (urban) regions. Each wave of the survey has approximately 120,000 individuals. The panel structure consist on each household being interviewed 12 times over an 18 months period. In each wave of the survey, we identify individuals in their first interview and we follow them a year later at 12
PRE Working Pager Series

|IL|A nhor|A|Contact<br>for paper|
|---|---|---|---|
|WPS498 The Build, Operate, and Transfer<br>("BOT) Approach to Infrastructure<br>Projects in Developing Countries|Mark Augenblick<br>B. Scott Custer, Jr.|August 1990|D. Schein<br>70291|
|WPS499 Taxing Foreign Income in Capital-<br>Importing Countries: Thailand's<br>Perspective|Chad Leechor<br>Jack M. Mintz|September 1990|A. Bhalla<br>37699|
|WPS500 Projecting Fertility for All Countries|Eduard Bos<br>Rodoifo A. Bulatao|September 1990|V. Altfeld<br>31091|
|WPS501 Tax Systems in the Reforming<br>Socialist Economies of Europe|Cheryl W. Gray|September 1990|L. Lockyear<br>36969|
|WPS502 Patents and Pharmaceutical D.ugs:<br>Understanding the Pressures on<br>Developing Countries|Julio Nogues|September 1990|M. T. Sanchez<br>33731|
|WPS503 Household Production, Time<br>Allocation, and Welfare in Peru|John Dagsvik<br>Rolf Aaberge|September 1990|M. Abundo<br>36820|
|WPS504 Applying Tax Policy Models in<br>Country Economic Work:<br>Bangladesh, China, and India|Henrik Dahl<br>Pradeep Mitra|September 1990|A. Bhalla<br>37699|
|WPS505 Creating the Reform-Resistant<br>Dependent Economy: The CMEA<br>International Trading Relationship|Arye L. Hillman<br>Adi Schnytzer|September 1990|CECSE Staff<br>37176|
|WPS506 Changes in Food Consumption<br>Patterns in the Republic of Korea|Merlinda D. Ingco|September 1990|A. Daruwala<br>33713|
|WPS507 Poverty in Poland, Hungary, and<br>Yugoslavia in the Years of Crisis,<br>1978-87|Branko Milanovic|September 1990|A. Bretana<br>37176|
|WPS508 A RMSM-X Model for Chile|Luis Serven|September 1990|S. Jonnakuty<br>39074|
|WPS509 The Childbearing Family in<br>Sub-Saharan Africa: Structure,<br>Fertility, and ...e Future|Odile Frank|September 1990|B. Rosa<br>33751|
|WPS510 Public Expenditure Reviews for<br>Education: The Bank's Experience|Antoine Schwartz<br>Gail Stevenson|October 1990|C. Cristobal<br>33640|
|WPS51 1 The Macroeconomic Underpinnings<br>of Adjustment Lendinrg|Fred Jaspersen<br>Karim Shariff|October 1990|A. Oropesa<br>39075|
<!-- Start of picture text --> 8<br>woN<br>oN<br>©<br>°<br>o<br>oSPREEWY Dok OO WENSOON, DKO06D MP<i DNVHPO PAPHOS2% 0 ASD<br>GEN GVC-Outp Forw «= HEE.-sGC--Outtp Backw<br>GE GVC-Outp Mix<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>$<br>SooO<br>&<br>o2<br>o SPREEoN, Dok ONWMHSOON, DK HDDOG <i DNVHPO PAPHOS2% ASD<br>GN GvVC-Trade Forw «=:HNNJ“s GC--Trade Backw<br>GR GVC-Trade Mix<br><!-- End of picture text -->
with occupation. We calculate the change in employment separately for low-paid and highpaid occupations. Low-paid occupations are those that pay below the median on average and high-paid occupations are those that pay above the median on average. The table shows that employment increased by a similar amount in both low-paid (0.28 percentage points) and high-paid (0.24 percentage points) occupations. Furthermore, there is clear heterogeneity within both low-paid and high-paid occupations: virtually all the employment change comes from low-TFP firms. Columns (5) and (6) also highlight that the employment elasticity is similar in low-paid and high-paid occupations. At low-TFP firms it is close to -0.50, while at high-TFP firms it is close to zero within both occupation groups.

Finally, in Panel C we study worker heterogeneity by education. Since we do not observe education directly, we again rely on occupation information in our data. First, we use the Hungarian Labor Force Survey<sup>27</sup> that has detailed information on education and occupation for the same individuals for a large sample of workers. We calculate the mode of the education level for each four-digit occupation. Then we assess the employment change by the modal education-level in each occupation.

The table shows that the employment increase mainly comes from the lowest-skilled workers with primary or lower-secondary education. There is also a slight increase in employment for workers with tertiary education and no change workers with upper-secondary education. When we look at employment changes within an education group, we find clear indication for firm heterogeneity in all cases. Employment at low-TFP firms increased within every group and the elasticities vary between -0.22 and -0.69 (see column 5). These elasticities are statistically significant in all cases at the 5% level. At the same time, there is no evidence for significant employment change at high-TFP firms in any education group. The employment change is close to zero in all cases and the elasticities are statistically insignificant at the conventional levels. Overall, these findings highlight that the firm heterogeneity is present even if we focus on a group of workers with the same skill level and so our main results reflect firm heterogeneity and not only worker heterogeneity.

# **4.5 Effect on Worker Transitions and Firm Dynamics**

The estimated employment change can come from two sources: (1) workers who have been employed previously and keep their jobs at higher rates (incumbents) or (2) workers who were unemployed/inactive before and become employed (new entrants). Panel A of Table 5 decomposes our main employment effect into these two groups. We define incumbent workers as those who had a job in the previous 12 months (between _t −_ 1 and _t −_ 13) and

> 27The Hungarian Labor Force Survey (Hungarian LFS) is very similar to the Current Population Survey in the United States.

23
# **Annex 7. Robustness Checks: Results using the 5 clusters based on ascending digital occupation levels without merging clusters 2 and 3**

Table A.7.1 and figures A.7.1. and A.7.2. show that the results presented in Tables 6 and Figures 6 and 7 are robust to merging the clusters based on ascending digital occupation levels.

## **Table A.7.1. Conditional correlations between clusters based on ascending digital occupation levels and cognitive and socioemotional skills requirements (replication of Table 6)**

|||||Clus<br>|ters based o<br>|n ascendin<br>|g digital oc<br>|cupation le<br>|vels<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||2 vs. 1|3 vs. 1|4 vs. 1|5 vs. 1|3 vs. 2|4 vs. 2|5 vs. 2|4 vs. 3|5 vs. 3|5 vs. 4|
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Cognitive**|Thinking|0.51|**0.82***|**1.35*****|**2.78*****|0.06|**0.78****|**1.43****|0.14|0.38|0.01|
|||(0.26)|(0.40)|(0.27)|(0.53)|(0.31)|(0.24)|(0.47)|(0.11)|(0.39)|(0.15)|
||Communication|0.21|**0.91***|0.18|-0.12|0.33|-0.19|-0.43|-0.25|-0.52|0.01|
|||(0.16)|(0.42)|(0.27)|(0.21)|(0.18)|(0.46)|(0.71)|(0.15)|(0.35)|(0.33)|
||Organization|-0.11|0.30|-0.27|-0.81|0.26|0.16|1.25|**-0.35****|-0.43|0.12|
|||(0.19)|(0.37)|(0.27)|(0.55)|(0.22)|(0.35)|(0.76)|(0.12)|(0.34)|(0.10)|
|**Socioemotional**|Emotions|0.30|-0.35|**1.30*****|1.08|-0.33|0.78|0.04|**0.49*****|0.47|-0.30|
|||(0.16)|(0.33)|(0.25)|(0.54)|(0.19)|(0.44)|(0.66)|(0.11)|(0.33)|(0.21)|
||Relationships|**-0.43****|-0.12|**-1.44*****|-0.89|0.26|-0.60|-1.00|-0.39|**-0.80***|0.08|
|||(0.14)|(0.25)|(0.30)|(0.57)|(0.26)|(0.54)|(0.53)|(0.19)|(0.30)|(0.38)|
||Personal growth|**-0.32***|0.09|**-1.09*****|-0.73|-0.05|-0.76|-0.92|-0.13|0.22|**0.36***|
|||(0.13)|(0.30)|(0.29)|(0.54)|(0.31)|(0.48)|(0.68)|(0.17)|(0.31)|(0.18)|
||Constant|1.48***|1.17***|2.49***|1.98**|2.22***|2.90***|2.69***|3.79***|4.11***|4.22***|
|||(0.15)|(0.26)|(0.33)|(0.72)|(0.13)|(0.36)|(0.65)|(0.11)|(0.61)|(0.22)|
||N|58|59|72|51|41|54|33|55|34|47|
||R-sq|0.34|0.50|0.67|0.44|0.36|0.34|0.26|0.55|0.36|0.13|

_Sources_ : Authors’ calculations based on SEAD data for Malaysia.

_Note_ : The regressors for the outcome are dummy variables taking the value of one if the skills count of a skills category of an occupation is above the mean. The 2017 Malaysian labor force survey does not report any workers in one of the 127 occupations from our occupational skills profile (paramedical practitioners, ISCO code 224). See subsection 3.2. for details on the clustering.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 60
# **6. Forecasts and Policy Scenarios**

The purpose of this section is to simulate some possible policy scenarios and thus predict the CPI over December 2008 to June 2010 for illustrative purposes. The empirical models used are Model 1 and Model 4; we need to estimate both models simultaneously to predict the error correction term, which is endogenous.

Making forecasts is a challenge since there are many uncertainties, so the numbers reported should not be taken at face value. As Elliot and Timmermann (2008) show, even the best of models face significant difficulties when predicting the future. The major uncertainties here are the future evolution of world food prices, exchange rate and monetary policy, and domestic agricultural production, which are the three key determinants of the CPI according to our inflation model for Ethiopia. Moreover, to evaluate the role of policies, we would need to estimate a structural model or at least ensure that the policy variables are super exogenous to avoid the Lucas critique.

We use projections from the Development Prospects Group of the World Bank (DECPG) for the annual grain index for 2009 and 2010 to forecast world food prices. The accuracy of the predications can be discussed because historical comparisons show that commodity forecasts are surrounded with a large degree of uncertainty (Deaton, 1999). This is also evident from most recent projections. Even though they were made in late 2008, the monthly grain index had a lower value in November 2008 than the projected average mean value for 2009, in spite of the fact that the projection shows a decline in grain prices. Thus, we simply assume that the monthly index is constant over the period 2008:12 – 2010:6.

We use two scenarios for the exchange rate, assuming there is no substantial change in the Dollar-Euro exchange rate during the period 2009-2010. In the first case the Birr-Euro exchange rate is constant at the November 2008 rate. In the second case, it depreciates by 20 percent in 2008:12 and then stays constant. The depreciation is a bit larger than the change in the Birr-Euro rate during last year, 2007:11-2008:11, when the Birr depreciated by 15 percent.

We also use two alternative scenarios for agricultural growth. In the first case, agricultural growth is assumed to follow a path similar to the one in 2003 from the beginning of 2009, when according to official data it grew by above 10 percent. The growth peaks in mid-2010 and then declines. In the second case, we assume an output gap is similar to the drought in 2002/03, which corresponds to a contraction in 35
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Policy ReseaRch WoRking PaPeR 4404

# **Abstract**

Using a recent household survey for two cohorts of married women, this paper examines norms about gender equality in education for children and adults. Among the main findings are that gender education gap norms have changed: younger generations of women are more positive about female vs. male education, both as pertaining to child and adult education outcomes. Perhaps the strongest result is that Bangladeshi women are more likely to espouse attitudes of gender equality in education for their children and less so about gender equality among spouses. It is also easier to explain norms regarding children’s education and more difficult to explain norms about equality in marriages. The authors believe that question on relative education of boys and girls captures the value of education per se, while the question on educational equality in marriage captures the norms regarding marriage and the relative worth of husbands and wives. The effect of education in determining norms is significant though complex, and spans own and spousal education, as well as that of older females in the household. This indicates sharing of education norms effects or externalities arising from spousal education in the production of gender education gap norms within marriage as well as arising from the presence of older educated females in the household. Lastly, the authors also find associations between gender education gap norms and household poverty, information processing and religion, though the evidence here is more mixed.

This paper—a product of the South Asia Sustainable Development Department (Social Development Unit)—is part of a larger effort in the department to undertake empirical work on issues of equity and inclusion. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mdas@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
- 32 -

Table 7: Welfare Effects of a Switch to Free Trade and Liberalization of Domestic Control.

|Year|Producer<br>Welfare|Consumer<br>Government<br>Welfare<br>Revenue*|Economic<br>Surplus|
|---|---|---|---|
||-----------|---------<br>(1984 US$ Millions)-----|---------------|
|1985|-199.70|214<br>179|193.30|
|1986|-336.79|310<br>185|158.21|
|1987|7.66|75<br>203|285.66|
|1988|177.36|-11<br>176|341.86|
|1989|619.18|-287<br>162|494.18|
|1990|531.70|-192<br>192|531.70|
|1991|99.65|62<br>202|363.65|
|1992|471.74|-161<br>190|500.74|
|1993|2,057.74|-1,083<br>162|1,136.74|
|1994|2,222.30|-1,012<br>233|1,443.30|
|1995|680.46|-170<br>235|745.46|
|1996|-35.23|162<br>239|365.77|
|1997|184.72|26<br>244|454.72|
|1998|809.95|-403<br>248|654.95|
|1999|1,097.18|-595<br>263|765.18|
|2000|946.52|-425<br>275|796.52|
|2001|1,403.71|-664<br>186|925.71|
|2002|1,666.20|-760<br>362|1,286.20|
|2003|1,294.90|-556<br>339|1,077.90|
|2004|1,112.00|-470<br>333|975.00|
|Mean|740.56|-314<br>230.40|656.96|
|Benefit per$||||
|of transfer to||||
|consumers|-2.35|1<br>-0.73|-2.09|

* From elimination of profits and losses of exports and imports and from the elimination of subsidies to mills.

Source: IECCO, World Bank.
Although many Mexican patterns are similar to those in developed countries, we find that worker flows - but not job flows - increased during the late 1980s, which was a period of significant policy reform. Small firms suffered disproportionate employment declines during the December 1994 peso crisis, during which the credit market essentially shut down. Worker and job flows increased between 1997 and 2001, as the economy recovered from the peso crisis and additional reforms were implemented. Unlike during the peso crisis, the data suggest that the 2001 recession hit large manufacturing establishments along the U.S. border especially hard.

We also find differences between workers with different demographic characteristics. Workers between the ages of 60 and 65 are hired at rates that are only slightly lower than those for younger workers. Although these older workers separate from their firms at substantially higher rates, many of these people attempt to find new employment. Furthermore, while worker and job flow statistics are quite similar for men and women, establishments are born mainly with men. Establishments quickly move to hire women in their early years.

Our results reveal a wide variety of opportunities for policy makers that go beyond established reasons why understanding worker and job flows are important.<sup>4</sup> Continuous turnover imposes substantial risk on workers and may justify an expansion of unemployment insurance. Adjustment may be less costly than in developed countries, but adjustment costs are still significant for workers and firms.<sup>5</sup> Changes in these flows may be linked to policy reforms, which would provide a way to evaluate the impacts of these reforms. Secondly, understanding these flows can be used to enhance the efficiency of policies designed to aid dislocated workers. Mexico's training program PROBECAT ( _Programa de Becas de Capacitactión para Trabajadores_ ), for example, uses a nonlinear selection mechanism to determine eligibility [see Revenga, Riboud, and Tan (1994) for more details], which could be tailored to reflect 2
Note that, if the normalized citation curves intersect we may still be able to establish a robust ordering in terms of average influence by imposing declining marginal influence. By a similar argument, the corresponding second-order dominance condition for this case is that _GA_ ( _c_ )  _GB_ ( _c_ ) for all _c_ , with strict inequality for some _c_ . If this holds then one can conclude that the publication record of A has greater average influence per publication for any influence function satisfying both our core and extra assumptions.

# **Empirical examples**

We illustrate these ideas using comparisons of the economics publications records of selected universities and the World Bank.

We rely on <u>Google Scholar</u> for citation counts. Compared to other bibliographic databases, Google Scholar casts a broad net, including citations by books, working papers, conference proceedings, open-access journals, new and less well-established journals. Google Scholar is also more “global” in its reach, as it includes research outputs from everywhere in the world and all languages. Google Scholar is also timelier than the bibliographic databases.

No time limit was set for the data on citations, since all are old institutions; in practice the records we use date back to 1964, but only five percent of them are before 1980. We searched in SCOPUS for articles where any author was affiliated with one of the institutions we consider; multiple records are therefore possible in the dataset where collaboration has taken place across two or more of the institutions. Only articles in the discipline “Economics, Econometrics and Finance” were included in our analysis. Google Scholar citation data were then obtained for each article in our database.

Table 1 gives the summary statistics on citations for Berkeley, Harvard, London School of Economics (LSE), Oxford, Princeton and Yale, plus the World Bank. The _h_ -index ranking is Harvard (first), Berkeley, World Bank, Princeton, Yale, LSE, and Oxford. This is exactly the same ranking as our _qi_ -index and the two series have a correlation coefficient of 0.98. The ranking in terms of mean citations per article is Harvard, Berkeley, Princeton, World Bank, Yale, LSE, Oxford, which is similar to the ranking in terms of our normalized _h_ -index.

However, not all these rankings are robust to allowing all possible influence functions satisfying our core assumptions. Figure 2 shows the citation curve; panel (b) enlarges the area around the origin to show more clearly the relative positions of the curves. There are a number of 12
# **Table 2.  Cross-Country Comparison of Key Vulnerability Ratios**

|**Country**|**Jordan**|**Egypt**|**Israel**|**Morocco**|**Turkey**|
|---|---|---|---|---|---|
|Return on Assets|6.1%|12.4%|1.8%|10.7%|4.2%|
|Total Debt (estimated) to Assets|24%|40%|45%|27%|35%|
|Total Liabilities to Total Assets|30.1%|47.6%|49.0%|40.3%|45.9%|
|Current Liabilities to Total Liabilities|81.5%|76.4%|67.9%|79.5%|73.4%|
|Short-term Debt to Total Debt|67.9%|43.6%|47.2%|69.9%|65.4%|
|Quick Ratio|1.92|1.13|2.80|.|1.71|
|Interest Coverage|8.96|7.03|3.60|73.15|3.39|
|Interest Coverage Less than 0.75|0.0%|0.0%|23.4%|0.0%|23.6%|
|Altman Z-score|5.46|4.61|3.02|.|3.62|
|PercentFloat|52.7%|35.6%|45.8%|28.7%|36.4%|
|N obs(based on total debt)|18|39|149|14|168|

Source: Worldscope Data Quick Ratio is a measure of liquidity, which plays a role of a buffer against shocks to financing and cash flows. It’s defined as the ratio of current assets, minus inventories, over current liabilities. The quick ratio measures a company's ability to meet its short-term obligations with its most liquid assets. The higher the quick ratio, the better the liquidity of the company. Jordan reports a fairly high quick ratio of 1.92, which suggests that companies have nearly twice the amount of liquid assets to liquid liabilities and are well positioned to cover liquid liabilities with liquid assets. This is above Egypt and Turkey, while below Israel.

An important measure of corporate ability to repay its debt obligations is Interest Coverage Ratio (ICR), defined as EBITDA – Earnings before Interest, Taxes, Depreciation and Amortization – over interest expense. Interest Coverage shows the ability to cover interest expenses using the current period earnings. The average interest coverage in Jordan is relatively high – 8.96, which is the highest (except Morocco, which contains a number of outlier observations with very low interest payments).

While the average interest coverage is useful for determining how vulnerable the average firm is, it is also important to know how many firms have dangerously low interest coverage, i.e. statistically speaking, what is the low end of the distribution of interest coverage. The interest coverage below one might indicate that the firm will find it difficult to cover interest payments with current earnings, and may fall into arrears. However, the firm might still be able to cover interest by using some liquid assets. Therefore the ratio of interest expense below 0.75 is a more 5