rafmacalaba/datause-encoder-data huggingface dataset:
about 70% has a positive job finding rate and positive job separation rate; the second group of about 14% has a positive job finding rate and a zero job separation rate. The third group of about 16% has a zero job finding rate. Nevertheless, the parameters of the benefit variables hardly change and therefore our main conclusions remain as they are.<sup>7</sup>

# **5 Wages in post-unemployment jobs**

In addition to the effects on job durations we also analyze the effects of PBD reductions on post-unemployment wages. One of the problems with the analysis of the wages is that for many unemployed that found a job no information about the new wage (or the previous wage) is available. The records on earnings are based on merging the data from the Pension and Disability Fund with data from the Statistical Office of Slovenia, based on personal identification numbers (PINs). The merging is incomplete, because Pension and Disability Fund records sometimes only contain identifiers issued by the Fund, with PIN numbers missing. There is no reason, as far as we know, that such omissions are non-random. For new earnings, in addition, there are fewer matches because at the time of data collection (the end of 2002), some employers did not yet provide data for 2001. The deadline for submission is June of the following year, but in reality many employers submit data with a delay as large as one year.<sup>8</sup> Only for about 4000 males and females that have found a job, information is available about their earnings. Wages are calculated as earnings – divided by spell hours taking into account the amount of overtime and the fact that overtime working hours pay 20% more. To reduce the effects of errors in earnings and hours of work we limit the wage range from 20,000 to 150,000 Slovenian Tolars.<sup>9</sup> This reduces the sample to about 3000 males and females.

> 7The parameter estimates are available on request.

> 8The reduction of the sample size is as follows:

||Males|Females|
|---|---|---|
|Found job|6630|7245|
|Earnings info|3819|4088|
|20,000_<_wage_<_150.000|2973|3111|

> 9At the time 1 US dollar was approximately 180 Slovenian Tolars.

18
# **Table 4. Comparison of original and Census-based poverty headcounts**

||||Urban-rural|regressions|Stratum reg|ression|
|---|---|---|---|---|---|---|
||VLSS1998||with Ce|nsusdata|withCensus|data|
|Region|Poverty|Stderror|Poverty|Stderror|Poverty|Std.Error|
|Hanoi&HCMC|0.009|0.004|0.037|0.007|0.031|0.009|
|Otherurban|0.138|0.021|0.145|0.012|0.146|0.014|
|RuralNUplands|0.652|0.057|0.598|0.011|0.626|0.037|
|RuralRedRDelta|0.361|0.038|0.379|0.006|0.407|0.031|
|RuralNCCoast|0.488|0.058|0.513|0.011|0.490|0.036|
|RuralSCCoast|0.436|0.075|0.460|0.010|0.400|0.028|
|RuralCHighlands|0.524|0.097|0.533|0.016|0.525|0.046|
|RuralSoutheast|0.130|0.022|0.234|0.004|0.173|0.018|
|RuralMekongDelta|0.412|0.033|0.397|0.007|0.386|0.031|
|Total|0.374|0.016|0.365|0.012|0.365|0.011|

Source: Data from 1998 VLSS and 3% sample of 1999 Population and Housing Census Note: Poverty headcounts are expressed as fractions rather than percentages.

the VLSS estimates where the incidence of poverty is low (such as in the rural Southeast and in urban areas) and lower where the incidence is high (such as in the rural Northern Uplands). In every region except one (Hanoi and Ho Chi Minh City), the standard errors of the Census based estimates are substantially smaller than those of the VLSS estimates. Apparently, the gains in accuracy from using a larger sample exceed the losses due to estimating expenditure based on household characteristics.

According to the urban-rural regression results in Table 4, the rural Northern Uplands is the poorest region. In fact, it is significantly poorer than the other eight regions at the I percent confidence level (see Table 5). The rural Central Highlands and the rural North Central Coast are the next poorest regions, although there is no statistically significant difference between the two. Then follows the rural South Central Coast, the rural Mekong Delta, and the rural Red 20
# **Table** 7 **(continued)**

|Grupo Mava (B)|3'12/91<br>3,/15/91<br>9/20/91*<br>11/27/91*|Company relocation requested bv Citizens.<br>Governrent action: warning.<br>Citizens complaint.<br> (11/25/94): Citizens and ecologists complaint.|
|---|---|---|
|Tolteca|10/14/90|NGO's black list of air polluter.|
||2/13i92|Temporary and partial shutdown.|
|Met-Mex<br>Penoles (A)|3/22/91<br>6/4/91<br>819/91*<br>3/2/94<br>3/4/94<br>8/27/94|Citizens complaints.<br>Company pollution bad record pointed bv a Senator.<br>Government action: company temporarily shutdown.<br>Accident: citizens complaint.<br>Pollution control equipment investigation.<br>Relocation of 300 families living in the vicinity of the co.|
|Met-Mex<br>Penoles (B)|3/22/91<br>6/4/91*<br>3/4/94|Citizens complaints.<br>Company pollution bad record pointed by a Senator.<br>Pollution control equipment investigation.|
|Cementos de<br>Chiguagua<br>(GC3)|5/25/92|Government action: warning about environmental<br>performance.|
|Kimberly Clark|5/21/92 *|Government action: fine for water pollution.|
|Grupo Bimbo|3/19/92 *<br>2/14/93|Black list of air polluter.<br>Government action: initiate court action.|
|Telefonos de<br>Mexico|5/21/19<br>6/9/94|Government action: warning about tree cutting.<br>Government action: fine.|

Given the nature of these results, we have pooled together government and citizens' complaints and tested whether or not they had a statistically significant differential impact on market values when compared to all other negative events. Results in Table 8 indicate that they strongly do.

23
originating from SOEs. In 2007, these sectors include “Mining and Washing of Coal” (SOEs’ share in total sector’s VAX = 39.98%), “Extraction of Petroleum and Natural Gas” (49.56%), “Mining of Non-Ferrous Metal Ores” (32.50), “Processing of Petroleum, Coking and Nuclear Fuel” (44.16), “Smelting and Rolling of Metals” (36.67), “Production and Supply of Electricity and Heat” (52.05). These are obviously “upstream” sectors that provide essential inputs to downstream exporters. In the next section, we will conduct a systematic analysis on SOEs’ potential dominance in “upstream” sectors, using Antras et al.’s (2012) measures.

# (Insert Table 6 here)

While SOEs appear to have a dominant position in some sectors, they are not the firm group that has the highest VA shares for most sectors. It is the SMEs that often contribute more than 30% of VAX in most sectors. In fact, SOEs’ VA share exceeded 30% for only 13 sectors (out of 40) compared to 24 for SMEs. For example, SMEs’ shares of VAX in “Foods and Tobacco” and “Manufacture of Textile Products” are 60% and 52%, respectively. These findings suggest that SMEs have been playing an important role driving Chinese exports. This is consistent with the hypothesis that a lot of SMEs do not export directly, possibly because of high fixed export costs. Instead, they participate actively by supplying intermediate inputs and services to larger downstream exporters. In 2010, the number of sectors in which SOEs’ share in VAX exceeded 30% actually dropped from 13 to 11. However, in those sectors that SOEs had the highest VAX share in 2007, SOEs’ VAX shares have increased substantially. For example, in the “Mining and Washing of Coal” sector, SOEs’ VAX share was 40% in 2007, which increased to 56% in 2010.

# **4.2 Industry Upstreamness by Firm Type**

Table 3 shows a vast heterogeneity in indirect export shares across industries, consistent with the conventional view that non-tradable sectors do not export much and typically participate in exports indirectly. Table 6 further shows that SOEs seem to prevail in “upstream” sectors. These findings hint that SOEs and SMEs derive their large indirect exports through different channels. To analyze these channels more systemically, we use the method proposed by Antras et al. (2012) to measure industry upstreamness. We make two important extensions to the original method. First, given our split IO table, we can measure an industry’s upstreamness by firm size and ownership type. With these measures in hand, we can then examine whether within an industry, some firm types are relatively more upstream on 27
certain ranges of fundamentals. The expected devaluation introduces the motive to place assets abroad and smooth consumption to whatever extent possible given transactions constraints.

As is necessary to distinguish across models, alternative specifications must have at least one unique identifying exogenous variable in each case. These focus variables include measures of capital flight, net external debt and gross external liabilities. This is because, as demonstrated in sections 2 and 3, there is a need to consider changes in gross foreign asset and liability positions, gross foreign asset and liability flows and the term structure of gross liabilities. Gross debt is adjusted for forgiveness and arrears on interest and principal. Considering only net changes results in a loss of information as highlighted in the review of the "third-generation models" that are the focus of this paper.

# B. Contribution to the literature

(1) The construction of the data set allows me to be able to test across seemingly observationally equivalent theories of currency crises by distinguishing patterns in the data not previously available. The data includes gross asset and liability positions for all the countries in the sample based on which net foreign asset positions were constructed for a significantly larger number of countries than was available earlier. This allows a direct comparison of the validity of different indicators as sources of shifts in expectations.

(2) This new database enables us to distinguish between theories of crises more effectively than in previous studies. This is especially relevant in disentangling the various first and modified first generation theories. This is done by identifying at least one unique exogenous variable associated with each crisis mechanism.

(4) Since a crisis involves a fall in the demand for domestic assets, we can go beyond the simplification that domestic money is the only relevant "asset" and examine the role of volatilities **in** gross asset inflows and outflows in causing a crisis given the NFA database.

9
society), and the influence of various interest groups, clearly predetermine the extent to which a government can exploit a potential tax base.<sup>19</sup> The attitude of citizens toward the government, hence to taxes, may be influenced by religious and ideological tendencies, as well as by the quality of public services and by the efficiency with which tax revenues are spent. Moreover, lack of public confidence in govermnent and the national economy may cause capital flight and currency substitution, which further restrict government revenue (Heller and Diamond, 1990).

Also, war and crises seem to have a long-term tax effects: People accept, in a period of war, or depression, tax levels and methods of raising revenue that in quieter times they would have thought intolerable, and this acceptance remains long after the disturbance itself has disappeared (Peacock and Wiseman, 1961, and Oshima, 1957). Since the beginning of the century, and even in absence of major crisis, for example, during 1960-85, government expenditure and taxes (as well as deficits and public debt) in OECD countries were steadily rising (Tanzi, 1986). It has proven difficult to quantify what is the tolerable level of taxation (Peacock and Wiseman, 1961), or even to argue that such a 'tolerable maximum" exists.

# Empirical **Results for Revenue Sustainability**

Based on the indications provided by the literature, we ran a number of regressions to predict country capacity to raise revenue. The model is estimated from 1993/94 general government revenue (gr) figures of the same 47 countries, and applied on the transition countries. Our selected regressions (Box 2) use PPP-based per capita GDP (gdp), share of manufacturing in GDP (mn), share of exports in GDP (x), and share of urban population (urb), as proxies for the capacity to collect revenue.20 In addition, secondary education enrollment (sec) is to indicate the country constraint on the tax system sophistication. Regional dummies (eu and las) again proxy the historical-philosophical traditions, and so the public willingness to pay taxes. We also include the public debt (d) in percent of GDP to represent an exogenous pressure on the govermment effort to expand revenue (Tanzi, 1992). With respect to the literature above, we tested the 19 Peacock (1969), for example, suggested that the share of Christian population and years of national independence have a positive relationship with the tax ratio; and that, with respect to both the willingness to pay taxes and demand for government services, some index of political democracy or ideologies can be roughly constructed.

> 20 The past literature usually emphasized imports, rather than exports, as the direct source of potential revenues. As for the long-term revenues, however, exports predetennine the sustainable level of both imports and tariff revenues.

22
advance (February 5, 2009), that is, with even shorter notice than for the moderate phase.<sup>18</sup> – Equation (1) is estimated for the two-year symmetrical time windows (1997 1999 and 2008–2010).<sup>19</sup> First, the impact of PYP on CO is analyzed for the series containing hours of the day (hereafter, “all hours”) when traffic is most active (5:00 a.m.–9:00 p.m.). Although the moderate restrictions applied only to peak hours, this overall model provides insights into whether the program affected the average CO levels during the day. Second, to evaluate the effects of the program in restricted and nonrestricted periods, equation (1) is estimated for time subsamples: morning peak, evening peak, off-peak, and weekends.<sup>20</sup> Note that PYP might have affected driving not only in the restricted periods, as it might have displaced trips to unrestricted hours. All models include weather variables as a quadratic polynomial and their first lag. Standard errors are estimated taking serial correlation into account.<sup>21</sup> V. RESULTS 18. It is also reasonable to believe that people prefer to make decisions based on official messages, since it is not unusual that policy projects are abandoned due to public or political opposition.

19. CO and meteorological variables were found to be stationary according to the augmented Dickey Fuller (ADF) test. Hence, the models were estimated with the variables in levels.

20. Restricting the sample to time intervals was initially suggested by Davis (2008). For the moderate phase, the sample is restricted to 7:00 a.m.–9:00 a.m. (morning peak), 5:00 p.m.– 8:00 p.m. (evening peak), and 10:00 a.m.–5:00 p.m. (off-peak) during working days. For the drastic phase, the sample is restricted to 6:00 a.m.–9:00 a.m. (morning peak), 4:00 p.m.–7:00 p.m. (evening peak), and 10:00 a.m.–4:00 p.m. (off-peak) during working days. For weekends, the subsample includes the time slot 7:00 a.m.–11:00 a.m. for both the moderate and drastic phase. Indicator variables representing interactions between weekends and hour of the day are excluded in the subsample models.

21. Based on the autocorrelation function, in most cases residuals are serially correlated between three days and one week. In very few cases, correlation exceeds one week, e.g., the “all hours” model, although the magnitude and statistical significance of the autocorrelation coefficients decrease remarkably after that period. Therefore, to account for serial correlation, standard errors, in parentheses, are robust to heteroscedasticity and arbitrary correlation within one-week clusters.

12
across households and individuals, as the better educated and better informed may see the intrinsic value of having a property title for economic investments.

In the Indian state of Madhya Pradesh, the government has implemented slum regularization programs where slum dwellers have been provided with titles, called _pattas_ . On average, slum dwellers with titles spend about twice as much on home maintenance and upgrading housing quality compared to other slum dwellers (Lall et al. 2006). In addition to the influence of tenure security on private investment, property rights are likely to be associated with a higher degree of community participation (Lall et al. 2004). Tenure security provides incentives for individuals to invest in the community because the gains from improvements in services can be capitalized in the value of the home. Slum households with titles are more likely to participate in community based service provision—37.5% of the survey respondents with titles participate in community service provision, compared to 28.9% for households without titles. Similarly, in Ecuador, Lanjouw and Levy (2001) show that households with a stronger claim to their property are significantly more likely to participate in activities to improve the community.

_Why is risk often not priced into home values and mitigation often not done?_<sup>_12_</sup> As discussed in preceding sections, the evidence from hedonic analysis of risk mitigation in housing markets is mixed. There is some evidence that known hazard risk and vulnerable building quality lower housing prices. Yet, even in high-risk cities and neighborhoods, mitigation efforts are often scarce.<sup>13</sup> Economic and behavioral reasons provide some possible explanations.

> 12 The discussion of the principal-agent problem and possible solutions to it is based on Adhvaryu and Deichmann (2009).

> 13 Dr. Richard Sharpe (Earthquake Engineering New Zealand) reported on work in Istanbul. Many areas with highest potential ground acceleration in the likely event of a future earthquake are occupied by fivefloor apartment buildings.  Inspection of a sample of these buildings suggests that most would not be able to withstand a major earthquake (51% were at high risk, 28% at very high risk). Building collapse will likely lead to high mortality. Yet, structural retrofitting of these buildings is extremely rare at costs of external retrofitting solutions of 19% of reconstruction costs. (R. Sharpe, Presentation at the World Bank, 22 September 2008.).

32
- 14 -

# IV. Some Solutions to Tax Administration Problems in LDC's

# 1. Withholding

Despite the formidable problems of tax environment and the specific problems mentioned above, administrators in developing countries do have weapons at their disposal, some of them enhanced by modern technology. Probably the single most effective method of tax collection In industrial as vell as developing countries is withholding, I.e., the **idea** that tax **Is** withheld and turned over to the Governmaet before it **reaches** the hands of the taxpayer.

Withholding is most commonly applied to wage income, but it is also applied in some countries, such as Argentina, Senegal and Taiwan, to Interest and dividends. An effective withholding scheme, however, requires a relatively small number of easily identifiable payers ofincome. Withholding may thus be applied to wages, dividends and interest, but it in difficult to apply to rental income, income of professionals and income from small businesses. In these latter cases there are as many payers as receivers of such income.

Three systems of withholding may be distinguished. The U.S. system is not intended to withhold the precise amount of income tax liability due, although some effort is being made in that direction following the
**Figure 1: Number of Job Listings on Babajob and Average Number of Applciations per Listing by**

**Occupational Category, 2015**

<!-- Start of picture text --> Managers<br><!-- End of picture text --> <!-- Start of picture text --> Professionals<br><!-- End of picture text --> <!-- Start of picture text --> Clerical support workers<br><!-- End of picture text --> <!-- Start of picture text --> Skilled workers<br><!-- End of picture text --> <!-- Start of picture text --> machine operators, Assemblers,<br><!-- End of picture text --> <!-- Start of picture text --> Elementary occupations<br><!-- End of picture text --> <!-- Start of picture text --> Applicants per ad<br><!-- End of picture text --> Source: Authors’ calculations using Babajob data

# **5. Using Big Data to Analyze Labor Market Dynamics in India: the Case of Babajob**

A review of the existing literature and a thorough analysis of Babajob data revealed five ways in which online job-portal data can be used to formulate and refine labor market policies. These include (i) labor market monitoring and analysis, (ii) assessing demand for workforce skills, (iii) observing job-search behavior and improving skills matching, (iv) predictive analysis of skills demand, and (v) experimental studies.

# **5.1. Labor Market Monitoring and Analysis**

Online job-portal data can be used to monitor and analyze labor market trends in real time. Big data can complement official government statistics and other traditional forms of employment information-gathering, such as sample-based labor force and enterprise surveys, to provide richer and more granular data. Labor force surveys capture information on the personal characteristics, wages and experience levels of employees, as well as employer characteristics such as firm size and workforce composition. Enterprise surveys focus on formal-sector business and production activities and assess employee productivity. Both labor force and enterprise surveys tend to be conducted infrequently and focus on a small number of variables. A key advantage of online job-portal data is that it allows for a much larger sample to be monitored in real time, enabling analysts and policy makers to develop a more thorough and timely understanding of evolving conditions in the labor market.

8