rafmacalaba/datause-encoder-data huggingface dataset:
3

# Labor Market Institutions and the Industrial Relatiols System

In this chapter we describe the principal features of the industrial labor market and the industrial relations system. The modem anJ traditional sectors differ widely in terms of worker earnings and the governmental regulations concerning hiring, layoffs, employment contracts and working conditions. The modem sector is, to a large extent, unionized, and it is subject to a relatively rigid system of regulations; the traditional sector is informal, operating on the basis of market forces and traditions.

The modem-sector labor market institutions are in many respects similar to those of other French-speaking African countries. The trade union movement is weak and closely linked to the government.' Industrial relations are to a large degree governed by Senegal's 1961 Labor Code and several subsequent legal provisions.<sup>2</sup> The 1961 code closely resembles the _1952_ French Overseas Labor Code and grants considerable authority to the government and its agencies. The code greatly limits employers' discretioin over hiring, layoffs, form of employment contracts, and working conditions. In the case of trade unions, it imposes permissible bounds on their activities. The code also provides general guidelines for the process of wage determination.

The code is supplemented by the National Collective Agreement (NCA) of May 27, 1982, which was signed by the main trade union federation-Confederation Nationale des Travailleurs du Senegal (CNTS)--the principal employer associations at the time-Union Syndicale des Industriels (UNISYNDI), Syndicat des Commercants, Importateurs et Exportateurs de l'Ouest Africain

1. The weakness of unicns derives inter alia from their fragmentation, poverty, inefficienqc of operation, corruption of middle-level officials, and the gulf between the leaders and the workers See Pfeffermann (1968, ch. 5) and Martens (1982).

2. The references in the text are to the May 1984 edition of the Labor Code.
- 14 -

29. Road space is found to be an important determinant of land value. The more road space there is, the higher is land value. This is understood to mean that people value transport infrastructure because of less traffic congestion and better mobility, and possibly as an element of spatial amenity. In fact, amenity areas, such as cultural facilities, commercial zones, hotels and restaurants, are preferred: The coefficient of _AMENITY_ is significantly positive.

30. All-weather connectivity to the commune capital is also important. The coefficient of _ALLWEARD_ is always significantly positive. This is another piece of evidence supporting the importance of transport infrastructure for land values. Access to taxi-be services is also likely to contribute to increase land values. It is interpreted to mean that taxi-be is an essential transport means for people, which is consistent with and reflects the reality in Antananarivo.

31. With respect to other coefficients, not surprisingly, flood-prone areas are undesirable: The vulnerability has a negative impact on land prices. The coefficient is found to be always significantly negative. On the other hand, the impact of rice fields looks inconclusive. The coefficient is found to be negative as expected, but statistically insignificant. Thus, the land prices may be underestimated because of the presence of massive rice fields in the city. But further investigation is needed to determine the implication of possible changes in land use.

**<u>Table 2. OLS estimation results</u>**

||Coef.|Std.Err.|Coef.|Std.Err.|Coef.<br>|Std.Err.|
|---|---|---|---|---|---|---|
|ln_MIN_|-0.314|<br>(0.118) ***|||-0.283|(0.134) **|
|ln_JAI_|||0.131|<br>(0.048) ***|0.063|(0.063)|
|ln_POPDEN_|0.111|<br>(0.034) ***|0.123|<br>(0.032) ***|0.101|(0.034) ***|
|ln_CSB_|-0.049|<br>(0.036)|<sup>-0.045</sup>|<br>(0.037)|<sup>-0.046</sup>|(0.037)|
|_VUL_|-0.048|<br>(0.007) ***|-0.047|<br>(0.007) ***|-0.048|(0.007) ***|
|_RD_|2.923|<br>(0.717) ***|3.086|<br>(0.774) ***|2.852|(0.717) ***|
|_ALLWEARD_|0.214|<br>(0.072) ***|0.249|<br>(0.065) ***|0.209|(0.071) ***|
|_RICE_|-0.121|<br>(0.153)|<sup>-0.168</sup>|<br>(0.160)|<sup>-0.158</sup>|(0.157)|
|_AMENITY_|2.270|<br>(1.388) *|2.731|<br>(1.489) *|2.312|(1.392) *|
|_TAXIBE_|-0.004|<br>(0.082)|0.019|<br>(0.084)|<sup>-0.012</sup>|(0.083)|
|_SUBU_|-0.003|<br>(0.069)|<sup>-0.030</sup>|<br>(0.070)|<sup>-0.010</sup>|(0.070)|
|Constant|9.408|<br>(0.659) ***|7.719|<br>(0.279) ***|9.209|(0.759) ***|
Table E.4. Women’s Marriage (Dropping Flood Districts) Post the Gujarat Riots of 2002

||(1)<br>Age at<br>Marriage|(2)<br>Married Before<br>Age 18|
|---|---|---|
|Post X Riot|-0.346<sup>_∗∗∗_</sup>|0.068<sup>_∗∗∗_</sup>|
||(0.089)|(0.016)|
|Observations|91781|93416|
|R-squared|0.219|0.151|
|Control Average|18.63|0.388|
|District Fixed Efects|Yes|Yes|
|Year Fixed Efects|Yes|Yes|
|Controls|Yes|Yes|

_Note_ : This table reports coefficients from specification 1 only including Gujarat and its neighboring states. Standard errors are clustered at the state-year level. Control Variables include indicators for religion, sc/sct/obc, rural residence, wealth quintiles, and male household head.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

43
# **Fiscal Incidence in Belarus: A Commitment to Equity Analysis**

Kateryna Bornukova, Gleb Shymanovich, Alexander Chubrik<sup>*,**</sup> _JEL Codes_ : H22, H5, D31, I3 _Keywords_ : Fiscal incidence, social spending, inequality, poverty, Belarus

> * Kateryna Bornukova is academic director of BEROC Center for Economic Research, e-mail: bornukova@beroc.by; Gleb Shymanovich is economist at the IPM Research Center, e-mail: shymanovich@research.by; Alexander Chubrik is director of the IPM Research Center and fellow at CASE – Center for Social and Economic Research, e-mail: chubrik@research.by.

> ** We thank Alexandru Cojocaru, Mikhail Matytsin, participants of the World Bank CEQ workshop and referees for the excellent inputs, useful comments and suggestions. All errors are ours. We acknowledge the financial support from the World Bank.
# **Do Immigrants Shield the Locals?**

# **Exposure to COVID-Related Risks in the European Union**

Laurent Bossavie Daniel Garrote Sanchez Mattia Makovec Caglar Ozden

## **World Bank**

Originally published in the <u>Policy Research Working Paper Series on</u> _December 2020._ This version is updated on _March 2021._ To obtain the originally published version, please email prwp@worldbank.org.

## JEL codes:  F22, J28, J61

Keywords: Immigration, COVID-19 pandemic, labor market vulnerability, occupational choice _Acknowledgments:_ 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 of Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the countries they represent. We are also thankful to the Knowledge for Change Program, the World Bank Research Support Budget and the Multi-Donor Trust Fund on Trade for financial support. All errors are our responsibility.
# **Table 17. First Stage Regressions of Aggregate Consumption**

# **Dependent Variable: Covariate Violent Shock**

|**_Dummy_**<br>**Violent**<br>**Shock**|**_Dummy_**<br>**Violent**<br>**Shock**|**_Dummy_**<br>**Violent**<br>**Shock**|
|---|---|---|
|0.164**<br>(0.075)<br>_Dummy_Problems in the community do to water access|0.165**<br>(0.074)|0.186**<br>(0.078)|
|0.107**<br><br>Average time to community urban center|0.106**<br>|0.095*<br>|
|(0.053)<br>***<br>|(0.053)<br>***|(0.055)<br>***|
|0.063<br>(0.020)<br><br>Rural District Price Index|0.063<br>(0.020)<br>|0.066<br>(0.020)<br>|
|-0.105*<br>Lik I Sh f Tl Aill I|-0.103*|-0.094*|
|(0.056)<br>vestoc ncome as are o ota grcutura ncome|(0.056)|(0.053)|
|0.013<br>_Dummy_Participation in Goverment Program Familias in|0.015|0.017|
|(0.023)<br>Acción (1 if has acces to the program; 0 otherwise)|(0.023)|(0.023)|
|0.031<br>(0.027)<br>_Dummy_Wealth Index Quintile 2|0.031<br>(0.028)|0.028<br>(0.028)|
|0.058*<br>(0.034)<br>_Dummy_Wealth Index Quintile 3|0.056<br>(0.035)|0.056<br>(0.034)|
|0.065*<br>_D_Wlth Id itil 4|0.065*|0.060*|
|(0.033)<br>_ummy_ea nex Qune|(0.033)|(0.032)|
|0.074**<br> lh d iil 5|0.073**|0.071**|
|(0.035)<br>_Dummy_Weat Inex Qunte|(0.036)|(0.035)|
|0.004<br>A|0.003|0.004|
|(0.004)<br>ge|(0.005)|(0.004)|
|-0.000<br>(0.000)<br>Age Squared|-0.000<br>(0.000)|-0.000<br>(0.000)|
|0013<br>|0012|0012|
|.<br>(0.008)<br>Education (years)|.<br>(0.008)|.<br>(0.008)|
|-0.002<br>Size of the Housholds Farm in Ha.|-0.002|-0.002|
|(0.001)<br>|(0.001)|(0.001)|
|-0.011<br>_Dummy_Female Household Head (1 female head of|0.008|-0.005|
|(0.022)<br><br>household , 0 otherwise)|(0.027)<br>|(0.028)<br>|
|0.001<br>(0.015)<br>Number of children under 5 years of age|0.001<br>(0.015)|0.001<br>(0.015)|
|-0.003<br>(0.008)<br>Number of children between 5 and 18 years of age|-0.004<br>(0.008)|-0.003<br>(0.008)|
|0.018<br>|0.018|0.013|
|(0.019)<br>Number of members 65 or older|(0.019)|(0.019)|
|-0.002<br>Number of members of extended famil|-0.001|-0.001|
|(0.005)<br>y|(0.005)|(0.006)|
|% of time workin inside the household farm (Female)|-0.012|-0.025|
|g|(0.069)<br>|(0.072)<br>|
|% f i ki iid h hhld f Ml|0.078|0.067|
|o tme worng nse te ouseo arm (ae)|(0.062)|(0.066)|
||0.138|0.197**|
|% of time working in other farms (Female)|(0.084)|(0.084)|
||0.043|0.043|
|% of time working in other farms (Male)|(0.048)|(0.053)|
|Daily Wage||-0.000<br>(0.000)|
|Dummy Production Problems due to Land Quality (1||-0.108|
|Problems in the Rural District, 0 otherwise)||(0.082)<br>|
|Pl Si Hhld Ci ddid b i||0.008|
|ot ze at ouseo reaton (staanarze y regon)||(0009)|
|Constant<br>-8.363***|-8.334***|.<br>-8.746***|
|(2.653)|(2.654)|(2.699)|
|Observations<br>3513|3,513|3,344|
|R-squared<br>0.216|0.217|0.224|
|F-Test(Kleibergen-Paap)<br>5.045|4.984|4.929|
|Cluster by community|||
|*** p<0.01, ** p<0.05, * p<0.1|||
|Source: CLS-WILL: first wave – community and household questionnaire.<br>|||

47
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-46-
and nominated women yields the policy parameter of interest for the analysis of women’s promotions on the household since both groups fit the profile of candidates that factories would consider for a promotion in the absence of an intervention. Without any intervention, factories – as any profit-maximizing firm – would not choose supervisors at random, but would instead aim to award promotions based on expected supervisor performance and availability. As Uckat and Woodruff (2020) shows, factories may not always be well informed about all factors driving supervisor performance. However, we would expect them to choose female candidates for supervisory positions with higher than average skills, more experience and – given women’s limited autonomy in Bangladesh – a supportive family.

# **3.5 Empirical strategy and supporting evidence**

## **3.5.1 Promotion analysis**

The selection process for the promotion program described in Section 3.1 allows me to compare outcomes of selected women to nominated runners-up who were not included in the program because the factory did not have enough vacancies available. Recall that, after the factories provided us with a ranked list of candidates fulfilling pre-set criteria, we conducted extensive screening of all nominees and excluded those who failed literacy and numeracy tests. Half of all factories were able to re-rank their candidates in the shortlist once after reviewing the diagnostic tests, whereas the shortlist for the remaining factories remained the same. Out of the operators in the final shortlists, only those with a rank that was better than the factory-specific cutoff were selected and participated in the promotion program. The nominated women who passed all diagnostic checks but ranked below the cutoff continued to work as production-line operators and represent the comparison group.

I argue that the assignment to the selected and the nominated groups is quasi-random because factories have limited ability to predict which women would be successful as a supervisor among the eligible women. The factories had historically nearly always promoted men, and therefore had large numbers of competent female line operators from which to choose new supervisors. Our extensive eligibility checks meant that only skilled and experienced line operators were considered for the program. While it might be easy for factories to exclude operators who would **not** make a good supervisor in their factories, it is much harder to decide the ranking among a sample of women who had **all** been judged to be suitable after this extensive screening. This argument is similar in spirit to the literature on microenterprises, recruitment and venture capitalism, which shows that humans are often inept at predicting which firms, workers or investments will be successful among an already select sample (McKenzie and Sansone, 2017; Hoffman et al., 2018; Nanda, 2016).

Three pieces of evidence support this strategy. First, the results from the selection experiment in Uckat and Woodruff (2020) show that the nominees’ original rank – before a potential re-ranking 15
Policy Research Working Paper 10976

# **Abstract**

Since 2008, the World Bank’s Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS-ISA) program has supported the collection of nationally representative, longitudinal, multi-topic household survey data to inform researchers and policy makers of living standards in Sub-Saharan Africa. The surveys maintain a distinct focus on the agricultural sector, collecting detailed plot-level data and information about agricultural activities, while measuring socioeconomic conditions of thousands of smallholder farmers and households across multiple countries. This paper presents a harmonized panel dataset (HP) from LSMS-ISA surveys from 2008 to 2021 in seven Sub-Saharan African countries: Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, and Uganda, from 2008 to 2021. It includes more than 200,000 agricultural plot observations, more than 400,000 individuals, and about 59,000 households. The HP allows for in-depth analysis of farm, household, and individual dynamics over time and across countries. It is ideal for researchers interested in studying the dynamics between agriculture, economic development, and welfare outcomes in Sub-Saharan Africa.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at tbentze@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
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18 countries but high elsewhere. The alternativ **e** stimates are shown in Table 9 taken from Cochrane and Zachariah.

The effect of education on fertility is probably the most frequently suggested alternative method of reducing fertility. Estimates of the cost of averting a birth through five years of primary school for girls are shown in Table 10. Where the fertility of those with 4 to 6 years of school are lower than the unschooled the calculation of these costs are fairly straight forward and the costs of 5 years of schooling (capital and recurrent) are divided by the reduction in the total fertility rate that accompanies that increase in schooling. Table 10 summaries these costs. In cases where those with 4-6 years of schooling have higher fertility than those with no schooilng, the costs per birth averted is infinite. In those cases and in cases where 4-6 years of schooling has only small effects I have also calculated the costs of a birth averted by examining the fertility of those with 7 or more years of schooling and assuming the costs of this additional schooling to grade 8 or 9 on average is approximately equal to the first five years of school. These costs are shown in the last column of Table 10. Comparing the costs of births averted through family planning and education, one finds that in only one case, Nepal, is the cost of a birth averted through education ( 8 to 9 years of school) less than the cost through the family planning program. In the other country with very high costs of averting a birth through family planning, Kenya, the costs through education are even higher because of the small effects of education on fertility. There are two countervailing consideration that apply here. First the costs of births averted through education may be overestimated because the external effect through community level of education are not included. It
|||households to capture<br>impacts||
|---|---|---|---|
|| What is the asset and<br>income loss associated<br>with recurrent floods?| Sampling for households<br>in flood‐affected wards<br> Administering<br>questionnaires to sample<br>households to capture<br>asset and income loss| Primary data collected<br>from 200 households as<br>part of 2014 household<br>survey|
|| Are the impacts<br>heterogeneous| Examine the impacts on<br>poor and non‐poor<br>households separately<br> Examine welfare impacts<br>such as health,<br>productivity and savings<br>in asset that get exposed<br>to floods easily| Census 2011 data on<br>Mumbai households<br> Primary data collected<br>from 200 households for<br>the 2014 household<br>survey|
|| What are the<br>adaptation options at<br>the household level| Identify household<br>adaptation options, their<br>costs, source of finance<br>and decision‐making<br>process| Primary data collected<br>from 1168 households<br>under 2010 household<br>survey<br> Primary data collected<br>from 30 housing societies<br>under 2014‐2015 housing<br>society survey|
|**Ability to respond**| What is the role of<br>government in<br>facilitating private<br>adaptation| Role of local government<br>in facilitating private<br>adaptation<br> Community organizations<br>working with local<br>government| Secondary data and<br>reports from MCGM|
|| To what extent can<br>households respond<br>effectively to floods<br>and what are the<br>barriers to adaptation| Short to medium term<br>effectiveness of<br>household adaptation<br> Factors restricting the<br>ability to cope| Primary data from 30<br>housing societies under<br>2014‐2015 housing<br>society survey<br> Primary data from 2014<br>household survey|

9