rafmacalaba/datause-encoder-data huggingface dataset:
Although it is rare to have data that both contain detailed information on firms and the workers they employ and are as comprehensive in coverage over the span of a crisis period, some limitations of the SI data should be kept in mind. First, the census only includes firms with 20 or more employees and is consequently not representative of the entire manufacturing sector. Moreover, ̳entry‘ is defined as entry into the survey, which happens when a new establishment with 20 or more employees is started or when a pre-existing establishment passes this threshold. Similarly, ̳exit‘ could be due to firm closure or dropping below the threshold. Any temporary lapses in reporting are not counted as exits.<sup>13</sup> Second, only net changes in employment at the firm-level are observed. This could understate the total amount of churning that takes place. Moreover, we do not know whether a reduction in firm size is accompanied by forced layoffs or achieved by natural attrition, that is, whether workers leave voluntary or involuntary. A related concern is that the definition of the employment variable changed slightly in 2001, with unpaid workers no longer being reported separately. However, the number of such workers had not been large, and the results are robust to excluding the period 2001-2004.<sup>14</sup> Moreover, our occupational categorization of workers in to blue-and white collar categories is crude and we may consequently fail to document subtler gender differences occurring at finer levels of aggregation.

In addition, the data do not contain information on individuals. For example, we have no information on the number of hours they work, their age, their tenure and experience. Consequently, changes on the intensive margin cannot be identified and it is not possible to control for workers‘ observed and unobserved personal characteristics.

Appendix B presents supporting evidence on the evolution of employment and wages in the broader manufacturing sector based on repeated representative cross-sectional labor force surveys, the SAKERNAS data. This analysis corroborates the results presented below and, suggests that in manufacturing, gender differences in crisis induced adjustment in hours worked were small.

> results, however, is robust to doing so, regardless of whether we impute the capital stock for all observations for which it is missing or whether we only use observations for which information on the capital stock is available. These results are omitted to conserve space, but available from the authors upon request.

> 13 To examine the sensitivity of our results to the 20-person cut-off, we re-estimated the regressions presented in this paper using a 30, 50 and 100-people cutoff instead. The resulting pattern of results did not change qualitatively. Our results do not appear to be driven by the exclusion of firms with fewer than 20 employees. Results are not presented to conserve space, but are available from the authors upon request.

> 14 These results are omitted to conserve space, but available from the authors upon request.

9
_nomics and Human Biology_ , 7(1): 1–6.

- **Galiani, Sebastian, Paul Gertler, and Ernesto Schargrodsky.** 2005. “Water for Life: The Impact of the Privatization of Water Services on Child Mortality.” _Journal of Political Economy_ , 113(1): 83–120.

- **Grantham-McGregor, Sally, Yin Bun Cheung, Santiago Cueto, Paul Glewwe, Linda Richter, and Barbara Strupp..** 2007. “Developmental Potential in the First 5 Years for Children in Developing Countries.” _Lancet_ , 369(9555): 60–70.

- **Grantham-McGregor, Sally M., Ernesto Pollitt, Theodore D. Wachs, Samuel J. Meisels, and Keith G. Scott.** 1999. “Summary of the scientific evidence on the nature and determinants of child development and their implications for programmatic interventions with young children.” _Food and Nutrtion Bulletin_ , 20(1).

- **Guerrant, RL, JB Schorling, JF McAuliffe, and MA de Souza.** 1992. “Diarrhea as a cause and an effect of malnutrition: Diarrhea prevents catch-up growth and malnutrition increases diarrhea frequency and duration.” _American Journal of Tropical Medicine and Hygiene_ , 47(1): 28–35.

- **Hammer, Jeffrey S., Nazmul Chaudhury, Soma Ghosh Moulik, and Atul Pokharel.** 2007. “Hygiene and Health: An evaluation of the Total Sanitation Campaign in Maharashtra.” working paper, World Bank WSP.

- **Hanushek, Eric A. and Ludger Woessmann.** 2008. “The Role of Cognitive Skills in Economic Development.” _Journal of Economic Literature_ , 46(3): 607–688.

- **Humphrey, Jean H.** 2009. “Child undernutrition, tropical enteropathy, toilets, and hand–

- washing.” _The Lancet_ , 374: 1032 35.

- **Joint Monitoring Programme for Water Supply and Sanitation.** 2012. _Progress on Drinking Water and Sanitation: 2012 Update_ : WHO and UNICEF.

- **Knudsen, Eric I., James J. Heckman, Judy Cameron, and Jack P. Shonko.** 2006. “Economic, Neurobiological, and Behavioral Perspectives on Building America’s Future Workforce.” _PNAS_ , 103(27): 10155–10162.

31
A more appropriate subset of the population for a comparison across data sources, albeit far from perfect, is “men of working age who migrate.” The 2001 Census reports the male migration rate as 17 percent (Srivastava, 2012). In the IHDS, working-age male migrants (15 years or older) constituted 4.6 percent of the population in 2011-12, up from 1.6 percent in 2004-05. Total male migrants constituted 5.1 percent of the population in 2011-12, up from 1.8 percent in 2004-05. The male migration rate from rural areas, at around 6 percent, approximates that in the 2007-08 NSSO survey. Table 1 provides a summary of labor migration stock estimates, based on the different data sources and related definitions. Further, Box 1 reviews the current main data sources and methods, highlighting potential gaps, suggestions for improvement in both questionnaire design and frequency, as well as potential “big data” sources.

# **<u>Table 1: Internal Labor Migration in India, Definitions and Stock Estimates</u>**

|<br>Source|<br>Definition of labor migrant|<br>Stock<br>(milli|<br>of labor<br>on)|<br>migrant|s|
|---|---|---|---|---|---|
|||2001|2004-<br>05|2007-<br>08|2011|
|Census|A labor migrant is one residing in a place other<br>than his or her place of birth or one who has<br>changed his or her usual place of residence<br>(residence for six months or more), having<br>moved primarily for economic reasons.|33|||51|
|NSSO|A labor migrant is identified by the change in<br>usual place of residence definition, as described<br>above, with the principal reason for moving<br>being employment-related.|||32||
|Cohort-<br>based<br>Migration<br>Metric|Net labor migration is the percentage change in<br>population between the 10-19-year-old cohort in<br>an initial census period and the 20-29-year-old<br>cohort in the same area (state or district) a<br>decade later, after correcting for mortality<br>effects.||||80|
|IHDS|Labor migrants are non-resident household<br>members who are identified through household<br>responses to: “Does any woman in the household<br>have a husband who lives outside the<br>household”? (Working age male migrants)||16||60|

Note: These numbers do not adequately capture circular migrants. In the case of the IHDS, the number of circular migrants was estimated at an additional 200 million in 2011-12.

6
<!-- Start of picture text --> Cost of increasing the RAI by 1% for MAR Cost of increasing the RAI by 1% for BOL<br>a 400<br>350<br>400<br>e<br>300<br>§ 300 5 250<br>EE<br>“e “ 200<br>% 200 e° a<br>8 e 8 150<br>e® e e@ . 100<br>100 ee? °<br>$ 50<br>ee<br>0<br>35 40 45 50 55 60 22 24 26 28 30<br>RAI (%) RAI (%)<br>Cost of increasing the RAI by 1% for BGR<br>250 e<br>200<br>S 150<br>=<br>U% 100 A)<br>oe? e<br>°<br>50 @<br>é e<br>e<br>0 e<br>40 45 50 55 60 65<br>RAI (%)<br><!-- End of picture text -->
**Figure 14:** Welfare gains from increased oversight

_Notes:_ The figure shows the mean welfare gains from increased oversight (by lowering the monitoring fee) as a function of initial debt and for different levels of hidden debt. These gains are unconditional averages over the _y_ and _ε_ states.

# **7 Conclusion**

This paper is the first to systematically measure the degree of public debt under-reporting in a large sample of developing and emerging market countries. We use our novel data to motivate and numerically discipline a state-of-the-art quantitative model of sovereign debt and default with hidden debt, endogenous recovery rates, and investor monitoring. Our results from the model indicate a sizeable effect of hidden debt revelations on equilibrium spreads, default incentives, and welfare. We find that eliminating the uncertainty associated with hidden debt and its dynamics delivers substantial welfare gains equivalent to a permanent increase of 5.5% in consumption.

Our results have important implications for both practitioners and academic researchers. Analysts in both asset pricing and country surveillance should take into account that debt statistics tend to increase after their initial publication. For researchers, our findings and data open exciting new avenues for studying information acquisition and expectation formation in sovereign debt markets with asymmetric information.

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seeking, elite capture, and clientelism; and of the design to respond to the governance and political economy constraints.

Among an overall sample of 180 operations drawn from 344 newly approved operations in fiscal year 2008, the report included 17 public sector governance (PSG) operations (5 DPLs and 12 ILs). These were found to be somewhat more frequently responsive to GPE dimensions than the sampled Bank operations overall (see Table 4).

**Table 4: Operations included in the QAG GAC review and responsiveness on the GPE dimension**

||Responsive t<br>all reviewe<br>(N=<br>#|o GPE factors<br>d operations<br>180)<br>%|PSG<br>(N<br>#|total<br>=17)<br>%|PSG<br>DPLs<br>(N=6)<br>#|PSG<br>ILs<br>(N=11)<br>#|
|---|---|---|---|---|---|---|
|Very responsive (1 &2)|32|_18_|6|_35_|_4_|_2_|
|Somewhat responsive (3)|50|_27_|4|_24_|_1_|_3_|
|Limited effort (4)|56|_32_|6|_35_|_1_|_5_|
|Unresponsive (5 & 6)|42|_23_|1|_6_|_0_|_1_|

This paper compares the operations‘ level of GPE responsiveness at entry with their most recent status or outcome rating to explore any relation between the two.

Table 5 summarizes the relationship between the QAG panel GPE ratings and status or outcome ratings. It shows that operations seen as ̳somewhat responsive‘ or better to GPE issues outperformed those with limited effort or being unresponsive to GPE issues.

**Table** **<u>5: Summary – QAG attention to GPE and project performance ratings</u>**

||**Implementation or outcome**<br>**rating MS or better**|**Implementation o**<br>**rating MU or**|**r outcome**<br>**worse**|
|---|---|---|---|
|_Very or somewhat responsive to GPE_<br>_total PSG_|9|1||
|of which<br>DPLs ILs|5<br>4|0|1|
|_Limited effort or unresponsive to GPE_<br>_total PSG_|4|3||
|of which<br>DPLs ILs|1<br>3|0|3|

The robustness of this finding is limited given the small sample size, sub-sectors of public sector reforms covered, as well as the variance in the status of the ratings (some operations are ongoing, others have been closed).<sup>44</sup> Within these limitations, the data indicates that ignoring GPE issues is associated with a higher risk of poor project performance and that when greater attention is

> 44 More continuous monitoring is probably valuable – including additional years and operations – to generate a more robust sample over time, and one that potentially allows analysis also for specific lending instruments and subsectors.

36
27

**Table 4.17: Contribution of Poverty by Age Group of Head of Household, 1985-93 (Poverty Line=N395.41)**

||||_RURAL_||||
|---|---|---|---|---|---|---|
|||**1985/86**|||**1992/9**|**3**|
|**Age Group**|**P0**|**P,**|**P2**|**P0**|P,|**P2**|
|16 to 25|1.7|2.3|1.3|1.9|1.5|2.0|
|26 to 35|14.2|8.5|10.0|14.6|8.9|8.2|
|36 to 45|30.2|23.5|27.7|27.8|27.7|27.7|
|46 to 55|26.5|29.4|29.3|27.0|29.2|28.7|
|56 to 65|17.6|26.8|19.5|19.5|22.5|22.6|
|66 and Over|9.6|10.3|12.2|10.1|9.5|9.9|
|_Al/ Rural_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|
||||_URBAN_||||
|||**1985/86**|||**1992/93**||
|**Age Group**|**P0**|**P,**|**P2**|**P0**|**P,**|**P2**|
|16 to 25|1.2|_0.5_|0.4|2.0|0.0|0.6|
|26 to 35|17.6|6.1|9.6|13.1|10.1|10.9|
|36to45|29.9|35.8|28.8|31.9|31.9|33.1|
|**46**to 55|28.6|24.5|32.2|28.4|27.9|25.9|
|56 to 65|14.0|20.9|20.4|15.3|17.8|16.7|
|66 and Over|8.8|11.7|8.6|9.2|11.4|12.5|
|_All Urban_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|
|Source: NCS, 1|985/86 and 19|92/92.|||||

Table 4.18 shows the incidence of poverty by gender of head of household. Taking the rural areas first, in 1985 the headcount for male and female headed households was 50.5 percent and 42.1 percent respectively. In 1992 the values were 37.9 percent and 21.8 percent for male and female headed households respectively. There was a decline of 12.6 percentage points among male headed households and a much larger decline of 20.3 percentage points among the female headed households in the rural areas. There was a 2.7 percentage points decline in the depth of poverty among rural male headed households, compared to 4.8 points decline among rural female headed households. The table also shows that there was 0.4 percentage points increase in the severity of poverty among male headed households in rural areas, compared to 2.5 percentage points increase in urban areas. In the urban areas, there were 0.9 percentage points decline in headcount, 1.5 percentage points increase in depth and 2.5 percentage points increase in severity among male headed households within the period of our study. Looking at the urban female headed households, there were 7.2 percentage points decline in headcount, 2.0 and 0.3 percentage points decline in depth and severity respectively, during the period of our study. The poverty measures for female headed households show that they fared much better than the male headed households in terms of all three measures of poverty. However, it should be recalled that female headed households make up only 10 percent of all households in Nigeria.
<!-- Start of picture text --> Government Securities/Total Assets<br>G<br>0.6<br>O<br>0.5 Stanbic<br>0.4 UCB<br>0.3<br>UCB+Stanbic<br>0.2 (Virtual)<br>Sector<br>0.1<br>0<br>1996 1998 2000 2002 2004<br><!-- End of picture text -->

**Figure 6**

Increased Stanbic lending to agriculture does not necessarily imply a shift in the sectoral composition of its portfolio. It could be that Stanbic shifted from holding government securities to lending, but that all sectors have benefited proportionally. Figure 6 shows that Stanbic’s ratio of government securities to total assets declined from over half just after privatization to about 40 percent at the end of 2004. The pattern is reminiscent of that of the first UCB privatization, in which the share of government securities fell from 43 to 32 percent in the year after the transaction. In both cases, cleaning the asset portfolio of UCB to facilitate sale resulted in abnormally high holdings of government securities. Lending is likely to comprise a larger share of Stanbic’s portfolio, not only due to the natural tendency to shift away from government securities after privatization, but also because Stanbic’s government securities share hovered near only 10 percent throughout the preprivatization period.

As noted, the rapid increase in Stanbic’s holdings of government securities in 2001 and 2002 was also driven by the increase in the stock of short-term government debt and the resultant hike in short-term interest rates. Although all banks availed themselves of this profit opportunity Figure 6 does indicate that Stanbic’s share of government securities was increasing more rapidly than that for other banks. We note however that the share of government securities is included as an explanatory variable in 20
60 In Reg A1.2 and Reg A1a.2, we include the human capital stock measured using the exponentially compounded by the product of the average years of schooling of persons, at 15 and older, with the global average Mincerian rate of return of 9.5 percent (See Equation 9).

We see that the estimated coefficients of human capital variables are positive, which is theoretically consistent, but are statistically not significant. These results are consistent with earlier papers that attempted to assess the effects of human capital on economic growth. The estimated coefficients of the control variables continue to possess their theoretical expected signs and be statistically significant. However, note that the R squared values of the regressions actually fall with the inclusion of these educational attainment variables.

We mentioned above that proper construction of the human capital stock requires accounting for differences in the quality of the education systems in different countries. As such, we include educational quality as an additional regressor in regressions Reg A1.3 and Reg A1a.3. As mentioned above, our educational quality variable is taken from Hanushek and Kimko (2000) and Bosworth and Collins (2003). We see that in both cases, the estimated coefficient of the educational quality variable is positive, but is not statistically significant. Also, the human capital measure remains positive and not statistically significant.

For the next pair of regressions, Reg A1.4 and A1a.4, we include a quadratic term for the human capital variable to capture the possible existence of strong diminishing returns to human capital accumulation. We see that the estimated coefficient of the squared human capital variable is negative and statistically significant, indicating evidence of diminishing returns. In addition, the estimated coefficient of the human capital variable remains positive but turns statistically significant. Hence, accounting for second-order effects leads to results that suggest that human capital accumulation is a significant determinant of economic growth. Note that the educational quality variable still positive but not statistically significant. However, this could be due to the data not being able to accurately measure quality differences, rather than educational quality not being important in explaining differences in long-term economic growth across countries<sup>51</sup> . As such, we continue to include the educational quality variable as an educational pillar regressor, even though it is not statistically significant.

Next, we seek to improve our estimates by accounting for country differences in Mincerian rates of return to education. As such, we employ the human capital stock measure that again uses exponential compounding of the average years of schooling, but multiplied by country specific rates of return (See Equation 16).

51 Recall that more than 50 percent of the data from Hanushek and Kimko (2000) on educational quality were in fact predicted values from a regression with various schooling inputs as the independent variables.
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# **Annex 2: Summary of observations based on fieldwork and international statistical data**

|Category of goods|SITC code|Goods observed<br>at the Algerian<br>border|Goods observed<br>at the Libyan<br>border|Gap<br>between<br>Tunisia<br>and<br>Libya<br>(%<br>of<br>import value)|Year<br>last<br>gap<br>between Tunisia<br>and Libya noted|Gap<br>between<br>Tunisia<br>and<br>Algeria (% of<br>import value)|Year<br>last<br>gap<br>between Tunisia<br>and<br>Algeria<br>noted|
|---|---|---|---|---|---|---|---|
|Vegetables|054|No|No|***|2011||2011|
|Fruit and nuts (excluding oil<br>nuts)|057|Yes|Yes|***|2010|66%|2011|
|Tea|074|No|Yes|*|*|*|*|
|Tobacco(processed)|122|Yes|No|*|*|*|*|
|Petroleumproducts|334|No|Yes|81%|2009|4%|2011|
|Rubber tires|625|Yes|Yes|***|2010|15%|2011|
|Floor coverings|659|No|Yes|315%|2009|269%|2003|
|Iron and steel bars|676|Yes|No|815%|2010|***|***|
|Television receivers|761|Yes|Yes|***|2009|45%|2007|
|Electricalpower machinery|771|Yes|Yes|***|2009|10%|2010|
|Men's or boys' coats,etc.|843|Yes|Yes|***|2011|***|2011|
|Footwear|851|Yes|Yes|***|2011|***|2011|

* Not observed

*** Data not counter-balanced Source: Authors

33