rafmacalaba/datause-encoder-data huggingface dataset:
||**Table**<br>|**10: The effe**<br>|**cts of barrier**<br>|**s on the labo **<br>|**r market by t**<br>|**ype of localit**<br>|**y**<br>||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Emp|(2)<br>Wage|(3)<br>W_days|(4)<br>W_hours|(5)<br>Emp|(6)<br>Wage|(7)<br>W_days|(8)<br>W_hours|
|_CP_<sup>_30min_</sup>|-0.0041***|-0.0719***|-0.0314***|0.0476***|-0.0030***|-0.0634***|-0.0322***|0.0394***|
||(0.001)|(0.014)|(0.009)|(0.011)|(0.001)|(0.016)|(0.010)|(0.013)|
|_REG_<sup>_30min_</sup>|-0.0004|0.0037|0.0027|-0.0023|0.0001|0.0020|-0.0025|0.0006|
||(0.000)|(0.004)|(0.002)|(0.003)|(0.000)|(0.004)|(0.002)|(0.003)|
|_CP_x net labor|0.0000**|0.0003**|0.0001**|-0.0000|||||
|import|(0.000)|(0.000)|(0.000)|(0.000)|||||
|_REG_x net labor|-0.0000***|-0.0001|-0.0000|0.0000|||||
|import|(0.000)|(0.000)|(0.000)|(0.000)|||||
|_CP_x tot eml|||||-0.0000*|0.0000|0.0000**|0.0000|
|p.|||||(0.000)|(0.000)|(0.000)|(0.000)|
|_RBO_x tot empl|||||-0.0000*|-0.0000|-0.0000|-0.0000|
|.|||||(0.000)|(0.000)|(0.000)|(0.000)|
|Observations|78,215|69,361|76,330|72,221|90,888|80,637|88,525|83,724|
|R-sq. within|0.111|0.034|0.023|0.012|0.110|0.034|0.022|0.011|
|Nr. workers|37,696|35,158|36,888|36,228|43,518|40,641|42,507|41,771|

_Robust standard errors (Huber-White method) clustered at individual level in parentheses; *significant at 10%; **significant at 5%; ***significant at 1%. All regressions include individual fixed effects, time effects, sector-time effects and a set of controls including years of schooling, age, age squared, marital status, tenure, urban area / refugee camp residence dummies, a set of work place dummies, Hebron district-time effects, locality’s number of fatalities in the preceding quarter, locality’s length of the West Bank wall, the locality’s share of workers employed in Israel the year before and sector-year effects._ 44
|P143650|2014|2019|12/12/2018|10 Closed|Gambia,The|
|---|---|---|---|---|---|
|P143650|2014|2019|6/28/2019|11 Closed|Gambia,The|
|P143650|2014|2020|12/29/2019|12 Closed|Gambia,The|
|P143652|2014|2014|5/29/2014|1 Closed|Benin|
|P143652|2014|2015|11/25/2014|2 Closed|Benin|
|P143652|2014|2015|5/18/2015|3 Closed|Benin|
|P143652|2014|2016|11/30/2015|4 Closed|Benin|
|P143652|2014|2016|5/19/2016|5 Closed|Benin|
|P143652|2014|2017|11/11/2016|6 Closed|Benin|
|P143652|2014|2017|5/12/2017|7 Closed|Benin|
|P143652|2014|2018|11/2/2017|8 Closed|Benin|
|P143652|2014|2018|5/4/2018|9 Closed|Benin|
|P143652|2014|2019|11/19/2018|10 Closed|Benin|
|P143652|2014|2021|7/20/2020|11 Closed|Benin|
|P143661|2013|2013||0 Closed|Pakistan|
|P143661|2013|2014|11/22/2013|1 Closed|Pakistan|
|P143661|2013|2014|3/3/2014|2 Closed|Pakistan|
|P143661|2013|2015|10/13/2014|3 Closed|Pakistan|
|P143661|2013|2015|4/28/2015|4 Closed|Pakistan|
|P143661|2013|2016|11/18/2015|5 Closed|Pakistan|
|P143689|2015|2016|12/8/2015|1 Active|Morocco|
|P143689|2015|2016|6/29/2016|2 Active|Morocco|
|P143689|2015|2017|1/14/2017|3 Active|Morocco|
|P143689|2015|2018|8/31/2017|4 Active|Morocco|
|P143689|2015|2018|4/18/2018|5 Active|Morocco|
|P143689|2015|2019|11/5/2018|6 Active|Morocco|
|P143689|2015|2019|5/9/2019|7 Active|Morocco|
|P143689|2015|2020|12/6/2019|8 Active|Morocco|
|P143689|2015|2020|6/18/2020|9 Active|Morocco|
|P143701|2013|2015|7/9/2014|1 Closed|Lebanon|
|P143701|2013|2016|12/21/2015|2 Closed|Lebanon|
|P143715|2013|2013||0 Closed|Yemen,Republic of|
|P143715|2013|2014|1/7/2014|1 Closed|Yemen,Republic of|
|P143715|2013|2015|7/7/2014|2 Closed|Yemen,Republic of|
|P143715|2013|2015|1/26/2015|3 Closed|Yemen,Republic of|
|P143715|2013|2020|5/2/2020|4 Closed|Yemen,Republic of|
|P143743|2013|2017|1/12/2017|1 Closed|Philippines|
|P143751|2015|2016|8/3/2015|1 Active|India|
|P143751|2015|2016|11/25/2015|2 Active|India|
|P143751|2015|2016|5/26/2016|3 Active|India|
|P143751|2015|2017|12/21/2016|4 Active|India|
|P143751|2015|2017|4/3/2017|5 Active|India|
|P143751|2015|2018|7/13/2017|6 Active|India|
|P143751|2015|2018|2/8/2018|7 Active|India|
|P143751|2015|2019|10/8/2018|8 Active|India|
|P143751|2015|2019|4/8/2019|9 Active|India|
|P143751|2015|2020|10/16/2019|10 Active|India|
# **1. Introduction and background**

South Africa’s economy continues to struggle with slow growth, high levels of inequality and unemployment. The South African economy grew by an annual average of 1.9 percent between 2010 and 2016, well below the levels required to address structurally high levels of unemployment (National Treasury, 2017). Exploring dynamics at the firm level is critical to strengthening our understanding of the performance of the private sector and its contribution to growth. In this study, administrative tax data, which are increasingly being used for empirical research, are used to explore firm, employment and productivity dynamics trends for South African formal businesses.

The level of detail available in administrative tax data allows for examination of sectors in unprecedented detail as analysis can proceed at the level of each individual firm. An analysis of total factor productivity (TFP) in South Africa using these data yielded the first firm-level productivity estimates for South Africa’s manufacturing sector (see Kreuser and Newman, 2018).<sup>2</sup> Other studies, such as Edwards et al. (2018), found that firms that engage in international trade employ more people, pay higher wages, have more capital, and exhibit higher levels of productivity.<sup>3</sup> This paper explores firm-level dynamics in South Africa by providing a detailed set of descriptive data along with regression analysis of employment, wage, productivity, and other firm dynamics. A key contribution is the international firm-level data comparisons used to benchmark South African data. These comparisons provide valuable insight into the structure, performance and peculiarity of the South African economy in comparison to selected advanced and developing countries.

## _Evolving policy context_

The South African economy developed around the mining industry, exploiting the country’s rich resource endowment. This led to the underdevelopment of downstream industries – as cheap energy provided by the state electricity utility allowed for the exploitation of South Africa’s mineral wealth and the development of capital-intensive upstream industries (Roberts and Rustomjee, 2009). The apartheid government’s interventions in telecommunications, mining, agriculture, and energy led to the creation of large firms and high levels of market

> 2 Two key findings were that: (i) productivity varies strongly by firm size, with larger firms being more productive; and (ii) firms with linkages to international markets, through importing or exporting goods, are more productive than other firms.

> 3 A special issue focusing on a collaborative effort between the National Treasury and the South African Revenue Service (SARS) to employ administrative record data from SARS for economic policy analysis was published by the South African Journal of Economics (https://onlinelibrary.wiley.com/doi/abs/10.1111/saje.12183).

2
Policy Research Working Paper 7810

# **Abstract**

The estimated duration of forced displacement situations is a key parameter in defining an adequate response to the crisis. Where the crisis is short, humanitarian aid may suffice; when it lasts, development interventions are required. Using data from the United Nations High Commissioner for Refugees, this paper proposes propose a new approach to estimate the mean and median durations of exile, and their variations over time. The analysis finds that people who were refugees at the end of 2015 have been in exile for an average duration of 10.3 years and a median duration of 4 years; the average duration of exile has varied between 10 and 15 years since the late 1990s. The number of people who are in protracted situations (over five years) has been steady at 5 million to 7 million since the mid-1990s, and currently stands at 6.6 million. For those people, the average duration of exile is as long as 21.2 years. All these estimates are very sensitive to two situations: Afghanistan, where the crisis has been ongoing since 1979 and increases all averages, and the Syrian Arab Republic, which is relatively recent and lowers the averages.

This paper is a product of the Fragility, Conflict and Violence Cross-Cutting Solutions Area and Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at abonfield@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
# **Figure 2**

# **Brazil**

<!-- Start of picture text --> Primary deficit and public infrastructure investment<br>(% GDP)<br>2.00% 3.50%<br>1.00%<br>3.00%<br>0.00%<br>2.50%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003<br>-1.00%<br>2.00%<br>-2.00%<br>1.50%<br>-3.00%<br>1.00%<br>-4.00% Primary deficit (left scale)<br>Infrastructure investment (right scale)<br>-5.00% 0.50%<br><!-- End of picture text --> Source: Afonso (2005)

# **Figure 3**

**Sub-Saharan Africa Overall deficit and public infrastructure investment (percent of GDP, average of 11 countries by period)**

Source: Estache (2004) 3
Policy Research Working Paper 7481

# **Abstract**

This paper examines poor households in the city of Mumbai and their exposure, vulnerability, and ability to respond to recurrent floods. The paper discusses policy implications for future adaptive capacity, resilience, and poverty alleviation. The study focuses particularly on the poor households, which tend to have greater exposure and vulnerability to floods and limited ability to respond given the constraints on physical and financial resources. The study seeks to understand the implications of the fact that poor households are more likely than non-poor households to be located in flood-prone areas. The study used the land use maps for the selected flood-prone areas to determine the extent and spread of poor and non-poor households and other types of assets and activities in areas with chronic and localized flooding. Primary data were obtained through detailed household surveys to understand the vulnerability and impacts of the extreme floods of July 2005, recurrent floods and the ability of households to respond and cope. The study examined the option of relocation to flood-free areas and identified factors that influence families’ decisions regarding relocation. The study finds that a significantly large proportion of poor households are located near areas with chronic and localized flooding. These households are either below the poverty line or have low incomes and reside in informal settlements or old and dilapidated structures. Future climate risks are likely to put greater burden on the poor and push them further into poverty unless well directed efforts are made to protect them.

This paper was commissioned by the World Bank Group’s Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank Group’s flagship report: “Shock Waves: Managing the Impacts of Climate Change on Poverty.” It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at archana.patankar09@gmail.com.

_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
In addition, there is good longitudinal evidence from the country‐sized Indian state of Andhra Pradesh (with a population of over 76 million). There, absence rates have been measured frequently since 2005 at a representative random sample of schools, as part of a series of experiments with interventions to improve school quality (Muralidharan and Sundararaman, 2008). A comparison with the findings from 2003, which were generated by one of the same researchers, shows no statistically significant improvements. Absence rates dropped only slightly in the control schools, from 25 percent in 2003 to 24 percent in 2005‐06. Surprisingly, even in the schools in which researchers introduced teacher performance bonus payments on an experimental basis, absence remained stagnant at 25 percent – even as the incentive payments improved teachers’ preparation and accelerated student learning.

# **_Benchmarking absence_**

How does the problem compare with levels of teacher absence in wealthier countries? It would be useful to know the answer to this question as a benchmark for developing‐country education systems. Unfortunately, no comparable survey of teacher attendance based on direct observation has been done in a developed country, to our knowledge. Officially recorded absence levels are typically on order of 5 to 6 percent (Ballou, 1996; Miller, Murnane, and Willett, 2007; Podgursky, 2003). It is possible that in some locations unrecorded absences are frequent enough to compare with Peru’s 11 percent overall absence level, but it seems impossible that unrecorded absence could match Indian or Ugandan levels.

# **_Comparison with administrative records_**

Earlier, we justified direct‐observation survey methods with the assumption that administrative records may be a poor indicator of actual teacher absence rates. By comparing reported absence rates with those directly observed by survey teams, it is possible to gauge whether this assumption is valid. Evidence from a number of studies suggests that it is. For example, in Ecuador, over one‐quarter of absent teachers were incorrectly reported by head teachers as being present (Rogers _et al_ , 2004). In the ongoing work conducted by Muralidharan and Sundararaman in Andhra Pradesh, the researchers find that the official absence rate recorded on the day prior to the unannounced visits was 18 percent, while the absence rate measured by direct physical observation on the day of the visit was 25 percent.<sup>8</sup> 8 Based on personal communication from Karthik Muralidharan.
<!-- Start of picture text --> m = TInt Ly<br>| |<br>| | Ps]<br><!-- End of picture text -->

<!-- Start of picture text --> Low case High case<br>ContributionGDP420% growthtotal toas(%) globalshare ContributionGDP420% growth total (%)toas global share<br>100 100<br>80 80<br>Dhigh-income<br>countries<br>60 Odeveloping 60<br>countries,<br>40 excl. China<br>China “<br>20 20<br>0 0<br>1960-1965 1970-1975 1060-1985 1900-1995 2000-2005 2010-2015 2020-2025 1960-1965 1969-1974 1978-1983 1987-1992 1996-2001 2005-2010 2014-2019 2023-2028<br><!-- End of picture text -->
# **Table A2.1: Gaps in survey mean and national accounts means, alternative weighting**

# _Panel A. Gap in levels between survey mean and HFCE mean_ - _all observations equal weights_

||||**All**|||**Co**|**nsumption**|||**Income**||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Group**|**Ob**|**s.**|**Gap**|**S.E.**|**O**|**bs.**|**Gap**|**S.E.**|**Obs**|**.**<br>**Gap**|**S.E.**|
|All|2020|(156)|-0.256 ***|0.018|947|(132)|-0.305 ***|0.022|1073|(72) -0.214 ***|0.020|
|1980s|118|(57)|-0.159 ***|0.028|52|(36)|-0.161 ***|0.039|66|(30) -0.157 ***|0.039|
|1990s|358|(113)|-0.190 ***|0.024|196|(92)|-0.199 ***|0.031|162|(54) -0.179 ***|0.032|
|2000s|747|(140)|-0.274 ***|0.019|368|(116)|-0.330 ***|0.023|379|(58) -0.220 ***|0.021|
|2010s|792|(142)|-0.285 ***|0.020|331|(116)|-0.363 ***|0.024|461|(61) -0.228 ***|0.024|
|Low income|198|(50)|-0.143 ***|0.038|189|(47)|-0.147 ***|0.039|9|(7) -0.054|0.100|
|Lower middle income|503|(87)|-0.267 ***|0.035|338|(74)|-0.329 ***|0.032|165|(23) -0.140 **|0.067|
|Upper middle income|524|(55)|-0.346 ***|0.032|238|(42)|-0.410 ***|0.030|286|(27) -0.292 ***|0.040|
|High income|768|(42)|-0.218 ***|0.022|168|(30)|-0.292 ***|0.028|600|(41) -0.198 ***|0.020|
|East Asia & Pacific|160|(18)|-0.250 ***|0.069|117|(14)|-0.303 ***|0.076|43|(6) -0.105|0.070|
|Europe & Central Asia|1072|(49)|-0.287 ***|0.025|484|(47)|-0.352 ***|0.030|588|(38) -0.233 ***|0.026|
|Latin America & Caribbean|392|(21)|-0.218 ***|0.044|29|(6)|-0.428 ***|0.084|363|(20) -0.202 ***|0.040|
|Middle East & North Africa|91|(14)|-0.178 ***|0.046|65|(12)|-0.198 ***|0.057|26|(3) -0.130|0.089|
|North America|81|(2)|-0.255|0.082|32|(1)|-0.335|0.000|49|(2) -0.203|0.098|
|South Asia|46|(7)|-0.340 ***|0.058|45|(7)|-0.345 ***|0.056||||
|Sub-Saharan Africa|178|(45)|-0.181 ***|0.037|175|(45)|-0.179 ***|0.038|3|(2) -0.297|0.057|
# **How important is the efficiency of government investment?**

# **The case of the Republic of Congo**

Hannah Nielsen and Hans Lofgren<sup>*</sup> JEL classification: C68, E22, H54 Keywords: Government investment, Efficiency, MAMS Sector Board: Economic Policy (EPOL)

* Hannah Nielsen is a Senior Economist in Africa PREM and Hans Lofgren is a Senior Economist in the Development Economics Prospects Group (DECPG). The authors would like to thank Edith Kikoni, Bienvenu Biyoudi, Israel OsarioRodarte and Dörte Dömeland for valuable input. The authors would also like to thank participants of a workshop on the Employment and Growth Study for the Republic of Congo in Brazzaville in March 2011 for helpful comments and suggestions. Parts of that study were undertaken with the financial support of the Diagnostic Facility for Shared Growth, which was instrumental to completing the work. The views, findings and conclusions expressed in this paper are entirely those of the authors and do not necessarily reflect those of the World Bank, its Executive Board, or member country governments. Corresponding author: Hannah Nielsen, email: hnielsen@worldbank.org.
The indicator allows us to track the severity of the pandemic across countries and over time in ways that complement previous efforts. Like the crude mortality rate, it provides a relative expression of mortality, which helps dimension severity. Unlike the crude mortality rate, it normalizes for past mortality patterns, which provides a sense of proportion relative to what was common. Unlike the excess death ratio, the indicator is globally comprehensive, as it does not rely on ‘nowcasting’ total deaths in the numerator, which typically is not possible for poorer countries due to lack of data. However, where such data are available, the relative severity ratio can incorporate adjusted COVID-19 mortality data based on excess deaths.

The second part of the paper questions the observed inequality in the mortality toll between rich and poor countries. Since COVID-19 exhibits a strong age gradient in the risk of death, our analysis zooms in on the interaction between epidemiological parameters of infectivity and fatality and demographic patterns of age structure. Specifically, we simulate what the global distribution of the death toll from COVID-19 would look like if plausible assumptions of infection prevalence and age-stratified fatality are applied to age distributions of World Bank income groups.

This approach builds on the literature in a number of ways. The strong age gradient of COVID-19 has been well established in many studies.<sup>4</sup> What has been examined less is the interaction of agestratified fatality ratios with population-level demographic structure. A seminal contribution in the context of COVID-19 was made by Dowd and others (2020), who apply common patterns of infectivity and fatality to the country-level age-distributions. This paper extends the approach to the global level.

Following Verity and others (2020), we impose uniform attack rates across age groups and apply age-stratified infection fatality ratios corrected for censoring and ascertainment biases due to lack of testing and other distortions that affect measurement. The simulations are subjected to robustness checks by utilizing other epidemiological parameters.<sup>5</sup> This procedure allows us to produce a baseline of the expected mortality toll on account of variation in demographic structure. We use the baseline as a backdrop to examine how other factors may influence the results.

> 4 Baud and others, 2020; Begley, 2020, Belligoni, 2020; Courage, 2020; Dylan, 2020; Huang and others, 2020; Mahase, 2020; Onder and others, 2020; Richardson and others, 2020, Soneji and others, 2020; Wu and McGoogan, 2020.

> 5 Istituto Superiore di Sanità, 2020; Korean Ministry of Health and Welfare, 2020; Spanish Ministry of Health, 2020.

5