rafmacalaba/datause-encoder-data huggingface dataset:
**TABLE 7: EMPLOYMENT GROWTH BY SECTOR AND SECTORAL SHARES IN EMPLOYMENT AND**

**EMPLOYMENT GROWTH**

||**Annual Employment**<br>**Growth, 1995-2004**|**Contribution to**<br>**Employment Growth,**<br>**1995- 2004**|**Share of Total**<br>**Employment, 2004 (%)**|
|---|---|---|---|
|Agriculture|1.9|36.4|51.1|
|Fishing|1.4|1.2|2.3|
|Industry|2.6|12.2|13.1|
|Construction|3.5|2.6|2.1|
|Commerce|5.9|38.2|19.1|
|Services|3.7|10.8|8.2|
|Public Administration|1.7|2.6|4.2|
|**Total**|**2.8**|**100.0**|**100.0**|
|Formal Sector|1.1|2.7|6.2|
|Informal Sector|2.9|97.3|93.8|

Source World Bank (2007) The decomposition of GDP growth in Table 3 shows that commerce contributed most to GDP growth during 1995-2005. Decomposition of employment growth during the same period, presented in Table 7, also indicates that commerce contributed the most to employment growth durig the period. Table 8 below provides information on the level of skills in the sectors, showing that 90 percent of workers in the commerce sector completed not more than primary education. The information provided in the tables is complementary, showing that growth during 1995-2005 was inclusive and employment elastic; the growth driver sector coincided with the employment driver sector, and both educated and uneducated workers had opportunities in the growing economy. However, by 2011, the growth pole had shifted to “other services,” where the proportion of workers that completed not more than primary education increased by only 0.60 percent between 2001 and 2011. Thus, the likelihood that this particular worker category will find jobs in the growing service economy might have decreased by about 20 percent.

**Table 8: Employment by activity and instruction level (in %)**

||Without<br>education|Primary|Middle|Secondary|Post<br>secondary|Total|
|---|---|---|---|---|---|---|
||||**2001**||||
|**Agriculture**|83.90|13.05|2.07|0.57|0.41|100|
|**Industry**|59.45|28.10|6.68|2.23|3.53|100|
|**Commerce**|73.68|15.98|6.36|2.44|1.54|100|

18
Table 1: Final database of robust studies of learning loss due to COVID-19

||**Source**|**Country**|**Closure**<br>**length**<br>**weeks**<sup>**1**</sup>|**Average**<br>**learning**<br>**losses (SD)**|**Equity**<br>**impact**|**Sample size**|
|---|---|---|---|---|---|---|
|1|Ardington et al 2021|South Africa|22|-0.22|Yes|5,810|
|2|Arenas and Gortazar 2022|Spain||-0.05|Yes|41,000|
|3|Asakawa and Ohtake 2021|Japan|11||Yes|5,400|
|4|Bielinski et al 2021|<br>United States||-0.14|N|not reported|
|5|Birkelund and Karlson 2021|Denmark|8|0|Yes|200,000|
|6|Borgonovi and Ferrara 2022|Italy|||Yes|850,000|
|7|Chen et al 2021|United Arab Emirates|||N/A|1,920|
|8|Clark et al. 2021|China|7|-0.22|N/A|<br>1,835|
|9|Contini et al 2021|Italy|15|-0.19|Yes|1,539|
|10|Domingue et al 2021|United States||-0.05|N|100,000|
|11|<br>Engzell et al 2021|Netherlands|8|-0.08|Yes|<br>350,000|
|12|EPI 2021|England||-0.09|Yes|180,000|
|13|Gambi and De Witte 2021|<br>Belgium||-0.23|Yes|213000|
|14|Gore et al. 2020|<br>Australia|8|0|N|4,800|
|15|Haelermans et al 2021|Netherlands|10|-0.17|Yes|201,819|
|16|Halloran et al 2021|United States|||Yes|<br>11,700|
|17|Hevia et al 2021|Mexico|48|-0.55|Yes|3,161|
|18|Hicks and Faulk 2022|United States|||Yes|not reported|
|19|Jakubowski 2022|Poland|29|-0.30|N/A|<br>4,581|
|20|Kogan and Lavertu 2021|United States||-0.23|Yes|124,700|
|21|<br>Korbel and Prokop 2021|Czech Republic||-0.11|N/A|2,234|
|22|Kuhfeld et al 2022|United States||-0.19|Yes|5,400,000|
|23|Lichand et al 2021|Brazil||-0.32|Yes|7,000,000|
|24|Locke et al 2021|United States||-0.15|No|<br>55,000|
|25|Ludewig et al 2022|Germany||-0.14|N/A|4,290|
|26|<br>Maldonado and Witte 2022|<br>Belgium|9|-0.18|Yes|1,300|
|27|Philadelphia 2021|United States|||N/A|65,000|
|28|<br>Pier et al 2021|United States||-0.08|N/A|100,000|
|29|Schult et al 2021|Germany|10|-0.08|N/A|<br>80,000|
|30|Schuurman et al 2021|<br>Netherlands|8|-0.09|Yes|886|
|31|Skar et al 2021|Norway|7|-0.24|N/A|2,453|
|32|Spitzer, Musslick|<br>Germany|8||N/A|2,500|
|33|<br>Tomasik et al 2020|<br>Switzerland|8|-0.20|N/A|<br>28,685|
|34|van der Velde|Netherlands|8||N/A|133,450|
|35|Chaban et al 2022|Russian Federation|8-20|-0.27|No|165,740|

> 1 We report the number of weeks of school closures as reported in each study.  When the study did not report the length of school closures, we also do not report it in Table _1_ or use it in our analyses.

9
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TABLE 5. Estimation in first differences using Olley-Pakes productivities: Productivity in domestic manufacturing firms and reform in upstream services sectors

|Dependent Vari|able: Δln(TFP)|I|II|III|IV|V|VI|VII|
|---|---|---|---|---|---|---|---|---|
|Services input|EBRD|1.138***|||||0.853*|1.183***|
|linkages||(0.425)|||||(0.452)|(0.443)|
||FDI (share)||7.627**||||6.171*||
||||(3.254)||||(3.388)||
||FDI (threshold)|||1.983||||2.544*|
|||||(1.515)||||(1.471)|
||Concentration||||0.905||3.278|2.572|
||||||(3.572)||(3.653)|(3.642)|
||Privatization (share)|||||18.931***|||
|||||||(7.149)|||
|Manufacturing|FDI (share)|0.156|0.557||-0.022|0.169|0.401||
|input linkages||(0.590)|(0.634)||(0.662)|(0.570)|(0.641)||
||FDI (threshold)|||0.11||||0.193|
|||||(0.500)||||(0.537)|
||MFN Tariff|-0.199*|-0.364*|-0.218|-0.14|-0.206|-0.366**|-0.320*|
|||(0.116)|(0.188)|(0.193)|(0.120)|(0.130)|(0.180)|(0.184)|
|Within sector|FDI (share)|-0.19|-0.389||-0.091|-0.159|-0.33||
|effects||(0.262)|(0.286)||(0.297)|(0.266)|(0.287)||
||FDI (threshold)|||-0.022||||-0.092|
|||||(0.214)||||(0.235)|
||MFN Tariff|0.091*|0.161*|0.091|0.061|0.08|0.171**|0.145*|
|||(0.053)|(0.087)|(0.090)|(0.056)|(0.060)|(0.083)|(0.084)|
||No. of obs.|4197|4197|4197|4197|4197|4197|4197|
||R<sup>2</sup>|0.02|0.02|0.018|0.018|0.02|0.02|0.02|

The Inputs Linkage variables are weighted by the respective I/O coefficient. All regressors are differenced one year and then lagged one year. All models include region and year fixed effects. Robust standard errors clustered on industry-year combinations are presented in parentheses. *, **, *** indicate statistical significance at the 10, 5 and 1% level.

The results presented in tables 6 and 7 also demonstrate that opening services sectors to FDI appears to be the key channel though which services liberalization benefits manufacturing firms. When proxies for foreign presence in services industries are included, the other measures of services liberalization fail to reach conventional significance levels.

17
13 study by Brittan (1972) of the payroll tax as a financing vehicle for the wider social security system in the United States.<sup>6</sup> / What underlies this argument is the following. The tax on the payroll of the firm is treated as a total tax which is paid by labor independently of whether or not taxes are paid by employers or employees. With low labor supply elasticities, labor, being the fixed factor, will bear the burden of the tax. Figure 1 illustrates this outcome. If the supply of labor is perfectly inelastic the only effect of a payroll tax is to reduce the wage received by labor from the gross-of-tax wage (wg) to the net-of-tax wage (wn) with the difference between the two being given by the tax. There is no impact of the tax on labor supplied, nor is there any impact on the costs of firms. In this case, the payroll tax is borne exclusively by labor.

This simple result, (which we shall argue is relevant to developing countries) is, however, almost certainly not applicable to developing countries in quite the same way. This is for a number of reasons. Firstly, the labor market in developing countries is not of the same form as operates in industrialized countries. Secondly, the payroll tax in developing countries is effectively restricted to urban employment since agriculture is excluded.

6 Brittain result that firms do not bear the burden of a payroll tax but shift it entirely onto labor, has been repeated in further research (e.g. Vroman 1974). Other studies, however, show a less than full shifting onto labor in the short run, e.g., 75 percent for the U.K. (Parkin, Sumner and Ware, 1976), 50 percent for Ireland (Hughes 1985) and for Sweden (Holmlund 1983). One limitation of the Brittain result, in the present context, is that it was unable, statistically, to distinguish between backward and forward shifting.
is the key source of error in the 2005 PPP data resulting in an overestimation of the price levels in Africa, Asia and Western Asia by 20 to 30 percent.

Findings from Ravallion (2014) lead to almost the opposite interpretation, suggesting that there are potentially significant concerns about the 2011 PPP data. He argues that the downward drift in prices observed for much of Asia (but not China) is in contrast to what would be expected given the observed rate of economic growth. A part of his interpretation of the data rests on the dynamic Penn Effect (Ravallion, 2013) which suggests that the ratio of the PPP index to the market exchange rate rises with economic growth. Ravallion offers a hypothesis that over time the bundle of goods used for the PPP index has become more heavily weighted towards internationally traded goods (for which prices exist) and this has led to a downward shift in price levels relative to market exchange rates (conditioning on growth rates).

Inklaar and Rao (2014) start from noting that the concern over the 2011 PPP data is primarily driven by the large difference between estimated average income levels based on the 2011 data and the same averages based on extrapolations of the 2005 PPP data. Indeed, income and consumption levels are about 24 percent greater on average when using the 2011 PPP data, suggesting that the world is much richer than extrapolations of the 2005 PPP data led us to expect. Their objective is to try to understand how much of this difference is due to the underlying data and how much is the result of the methodology used to create the PPP index. They note that the ICP introduced many initiatives to improve the data quality with the 2011 exercise, including the changed approach for handling ring countries.

Using the unprocessed, unit-record price data, Inklaar and Rao re-construct the 2005 PPP index following the same methodology that was used for the 2011 PPP index. This step alone reduces the divergence between mean consumption from about 24 percent to about 18 percent.

7
Table 29

|Dependent variable: Fi|scal adjustm|ent when|needed bas|ed on debt|/revenue||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.006***|0.006***|0.006***|0.005***|0.007**|0.010|-0.006|0.008|
|(% of GDP)|(0.001)|(0.001)|(0.002)|(0.002)|(0.003)|(0.008)|(0.006)|(0.007)|
|**Economic factors**|||||||||
|GDP growth (t-1)||0.017||-0.210|-0.190|-0.201|-0.215|-0.203|
|||(0.266)||(0.480)|(0.481)|(0.479)|(0.480)|(0.480)|
|Inflation (t-1)||-0.170||-0.405|-0.332|-0.402|-0.357|-0.404|
|||(0.239)||(0.273)|(0.259)|(0.272)|(0.257)|(0.271)|
|U.S. Interest rate||-0.017*||-0.028**|-0.030**|-0.030**|-0.034**|-0.028**|
|||(0.010)||(0.013)|(0.013)|(0.013)|(0.013)|(0.012)|
|**Political factors**|||||||||
|Gini|||0.002|0.002|0.002|0.005|0.002|0.002|
||||(0.002)|(0.002)|(0.002)|(0.005)|(0.002)|(0.002)|
|Rule of law|||0.023|0.027|0.020|0.026|-0.033|0.027|
||||(0.019)|(0.020)|(0.022)|(0.021)|(0.037)|(0.020)|
|Margin of Majority|||-0.191|-0.189|-0.183|-0.184|-0.177|-0.119|
||||(0.130)|(0.126)|(0.125)|(0.123)|(0.122)|(0.245)|
|Election's year|||-0.037|-0.042|-0.040|-0.042|-0.039|-0.042|
||||(0.029)|(0.029)|(0.030)|(0.029)|(0.030)|(0.030)|
|**Interactions**|||||||||
|EMEs|||||0.067<br>(0.142)||||
|EMEs * Rigidity|||||-0.005<br>(0.005)||||
|Gini * Rigidity||||||-0.000<br>(0.000)|||
|Rule of law * Rigidity|||||||0.002*<br>(0.001)||
|Majority * Rigidity||||||||-0.004<br>(0.010)|
|Observations|1,121|1,081|771|755|755|755|755|755|
|Country FE|No|No|No|No|No|No|No|No|
|r2_p<br>Robust standard error<br>*** p<0.01, ** p<0.05|0.0296<br>s clustered<br>, * p<0.1|0.0353<br>by country i|0.0533<br>n parenthe|0.0676<br>ses|0.0705|0.0681|0.0726|0.0678|

- 58 -
Moreover, the kink in the budget constraint at the point when benefits lapse in the model suggests that one should observe a much larger job-finding rate just before the benefits lapse. On the other hand, it is conceivable that higher unemployment benefits increase the job-finding rate. Increased benefits may decrease the worker's costs of future layoffs if actuarial return exceeds the costs (when the state also contributes to the unemployment fund, for example), and may also provide resources for a more effective job search.

Although the evidence pointing to disincentive effects is mounting, Atkinson and Micklewright (1991) insist that it is institutional aspects that are critical to the economic impact of the benefits.<sup>4</sup> They show that when unrealistic assumptions underlying a standard job search model are relaxed, the theoretical predictions about the disincentive effects of unemployment insurance may be reversed. It is institutional details that matter, and so it is the matter of empirical analysis to find out how the system works in a particular economy.

Following this institutional approach, I argue -- and show empirically below -- that the Slovenian Ul system is particularly likely to negatively affect the rate of jobtaking. Similar forces are at work in other transition economies. In these economies, the trade-off between income from (formal) employment and insured unemploymen **t** hat underlies predictions of Moffit and Nicholson is particularly skewed in favor of the insured, "formal" unemployment. In fact, many of the unemployed workers in transition economies are engaged in a kind of "double dipping: " they collect unemployment compensation or assistance **and** work at the same time under informal employment (employment paid in cash and without a formal contract, thus avoiding taxes and contributions levied on the payroll), or even under formal employment, as described above for Slovenia. By doing that, the unemployed may even not violate a law. Through such an arrangement, both the worker and the employer are better off: the unemployed receives double payments, and the employer avoids paying

> 4 The same reference provides a recent survey of disincentive effects of Ul systems.

8
23

# **References**

Ades, A., R. Misah, and D. Tenengauzer. 1998. "A New Framework for Predicting Financial Crises in Emerging Markets." GS-Watch - Economic Research, Goldman Sachs, December 18.

Andrews, D.W.K., and E. Zivot. 1992. "Further Evidence on the Great Crash, the OilPrice Shock, and the Unit-Root Hypothesis." _Journal of Business & Economic Statistics,_ 10: 251-270.

Barro, R. 2000. "Inequality and Growth in a Panel of Countries." _Journal of Economic Growth, 5:_ 5-32.

Beck, T., R. Levine, and N. Loayza. 1999. "Finance and the Sources of Growth." Policy Research Working Paper 2057. The World Bank.

Bernake, B., M. Gertler, and S. Gilchrist. 1998. "The Financial Accelerator in a Quantitative Business Cycle Framework." in J. Taylor and M. Woodford, eds. _Handbook of Macroeconomics._ Amsterdam, North Holland.

Bonds, S., and J. Cummins. 2000. "The Stock Market and Investment in the New Economy: Some Tangible and Intangible Fictions." Brookings Papers on Economic Activity, No.1: 61-108.

Campbell, J. Y. 2000. "Asset Pricing at the Millennium." _The Journal of Finance, 55:_ 1515-1567.

3 A. W. Lo, and A. C. McKinlay. 1997. _The Econometrics of Financial Markets._ Princeton University Press, Princeton.

, and R. J. Shiller. 1987. "Cointegration and Tests of Present Value Models." _The Journal of Political Economy, 95:_ 1062-1088.

Canova, F., and G. De Nicolo. 1995. "Stock Returns and Real Activity: A Structural Approach." _European Economic Review,_ 39: 981-1015.

Chen, N. 1991. "Financial Investment Opportunities and the Macroeconomy." _The Journal of Finance, 46: 529-554._ Conley, J., and W. Maloney. 1995. "Optimal Sequencing of Credible Reforms with Uncertain Outcomes." _Journal of Development Economics,_ 48: 151-166.

Diba, B. T., and H. I. Grossman. 1988. "Explosive Rational Bubbles in Stock Prices?" _American Economic Review,._ 98: _746-54._
State), and Nyala (South Darfur State) (see Box 1). Due to security issues on the ground and some technical issues faced by the survey firm, the fieldwork was conducted in two parts, with the main work carried out between April and June 2019, complemented by additional data collection in February 2020. Annex 1 provides a detailed description of the methodology followed for the qualitative data collection.

|**Table 1. Br**|**ief descripti**|**on of samp**<br>**l**|**le characte**<br>**l**|**ristics and**<br>**Poverty**<br>|**references use**<br>**b f**|**d in the report**|
|---|---|---|---|---|---|---|
|||**Popuation**|**Popuation**|**rate**|**Laor orce**||
|**Location**|**Type of**<br>**community**|<br>**projections**<br>**2020**<br>(millions<sup>a</sup>)|<br>(women<br>and youth,<br>% of total<sup>a</sup>)|(US$3.20<br>per capita<br>per day<br>PPP<sup>b</sup>)|**participation**<br>**rate**<br>(male/female<sup>b</sup>)|**Nature of economic activity**|
|Khartoum,<br>Khartoum<br>State|Urban|7.3<br>(state)|W: 47%<br>Y: 16%<br>(state)|40.2%<br>(state)|M: 70.3%<br>F: 25.1%<br>(state)|Has the highest concentration of industries<br>and is the headquarters of most of the<br>major business establishments in Sudan.<br>Based on industrial growth, the thriving<br>service industry is also established in the<br>city. Services include building and<br>construction, trade, hotel and restaurants,<br>transport and communications, finance,<br>insurance and real estate, and government<br>services.Also, Khartoum is a key transit<br>point for those migrating through Sudan,<br>mainlyto Europe.|
|Madani,<br>Al-Gezira<br>State|Rural|4.7<br>(state)|W: 51%<br>Y: 20%<br>(state)|39.7%<br>(state)|M: 75.7%<br>F: 22.6%<br>(state)|Has the second largest industrial sector after<br>Khartoum, which accommodates several<br>factories includingoil,textile,and cigarettes.|
|Nyala,<br>South<br>Darfur<br>State|Rural|3.7<br>(state)|W: 48%<br>Y: 24%<br>(state)|62.8%<br>(state)|M: 79.2%<br>F: 66.5%<br>(state)|Agriculture is the main source of income for<br>most households. Most common type is<br>small-scale rainfed crop production.<br>Sorghum and millet are the main crops|
|<br>_Note:_a. Ce<br>on NHBPS<br>_References_<br>or ‘working<br>For more d|ntral Bureau<br>2014/15.<br>_._Throughou<br>’ if they rep<br>etailed infor|of Statistic<br>t this repor<br>orted being<br>mation,se|s Report on<br>t, male and<br>engaged in<br>e Annex 1.|Populatio<br>female res<br>any incom|n Projections 2<br>pondents from<br>e-generating a|<br>grown here.<br>009–2020; b. World Bank (2020b), based<br>the FGDs are identified as ‘having a job’<br>ctivity at the time of the research.|

6
# Table I Average Government Size By Region"

|_____________________________Price|Government Consum<br>1985 International<br>s|ption/GDP**in**%b<br>O<br>1987 US dollars|bservations|
|---|---|---|---|
|All Countries||||
|1964-73|16.5|13.8|93|
|1974-83|18.4|15.5|113|
|1984-93|19.1|16.2|124|
|High Income OECD||||
|1964-73|13.4|16.9|22|
|1974-83|14.3|17.8|22|
|1984-93|14.3|17.7|22|
|All Developing Countries||||
|1964-73|17.5|12.5|67|
|1974-83|19.8|14.7|82|
|1984-93|20.6|15.6|93|
|Sub-Saharan Africa||||
|1964-73|19.9|13.1|29|
|1974-83|23.1|15.3|33|
|1984-93|_25.5_|17.4|38|
|Latin America and Carribean||||
|1964-73|14.2|10.5|20|
|1974-83|16.6|13.0|24|
|1984-93|17.7|13.7|26|
|East Asia and Pacific||||
|1964-73|16.0|15.4|10|
|1974-83|18.2|16.8|12|
|1984-93|16.3|14.6|14|
|South Asia||||
|1964-73|21.9|11.0|3|
|1974-83|19.6|9.7|3|
|1984-93|22.5|-<br>11.2|3|
|Middle East and North Africa||||
|1964-73|19.4|16.4|4|
|1974-83|20.3|19.5|10|
|1984-93|21.0|20.5|11|
|Europe and Central Asia||||
|<br>1964-73|15.0|13.7|5|
|1974-83|12.0|11.2|7|
|1984-93|11.4|12.0|8|

Source: PWT5.6 and World Bank b Govemment size in 1985 intemational prices only available up to 1992; unless otherwise noted, all regional breakdowns exclude high income countries; E. Asia and Pacific includes high income countries that are not OECD; Middle East and North Africa includes one high income country.

9
# **M. Regulatory substance- quality of supply index for Vietnam and comparators, 2015**

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchmark**|
|---|---|---|---|---|---|
|**Quality Regulation**|100%|100%|92%|71%|75%|
|**Quality of Service Standards**|100%|100%|100%|75%|82%|
|Requirement to meet quality of service standards|||||100%|
|Specific quality of service standards are formally<br>written and publicly available for- quality of the<br>product, quality of the service and customer<br>service|||||97%|
|Performance on quality of service standards is<br>public|||||71%|
|Fines for failing to meet quality of service<br>standards|||||59%|
|**Quality of Service Enforcement**|100%|100%|83%|67%|68%|
|Requirement to report technical data on a<br>periodic basis|||||100%|
|Regulator specifies how to collect technical<br>performance data|||||71%|
|Regulator reviews or validates technical<br>performance data|||||47%|
|Automated information management systems<br>are required to measure the quality or reliability<br>of the power supply|||||71%|
|Measurements of the quality or reliability of<br>power supply are made public|||||65%|
|Financial incentives to meet customer service<br>standards or increase customer satisfaction|||||53%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 51