rafmacalaba/datause-encoder-data huggingface dataset:
To study this issue, we take advantage of microdata from Senegal’s representative 201819 Harmonized Survey of Household Living Conditions ( _Enquête Harmonisée sur les Conditions de Vie des Ménages_ , EHCVM). The EHCVM covers 7,156 households and 36,746 individuals above the age of 15. An advantage of using this survey is that it includes an in-depth labor module that covers social security contributions at the individual level and non-agricultural enterprise characteristics. Then, following Alcaraz, et al. (2015), we estimate a model by maximum-likelihood to measure the size of involuntary informality and its underlying drivers.

The results consistently suggest that the desire of being formal is greater for workers with formal education, married and with a lower proportion of children under the age of 5 living in the household, while the individual's preference for the formal sector grows with age at a decreasing rate. Furthermore, our results also show that labor informality is mainly a voluntary phenomenon in Senegal. Our model estimates that the probability of obtaining a formal job for a worker who prefers a job in the formal sector is slightly below 0.50, which results in 30 percent of informal workers being involuntarily displaced into the informal sector. The magnitude of involuntary informality varies significantly across different types of workers, and it is the lowest for wage earners with positive incomes at 11 percent.

The main findings of the analysis are robust to different models restricting the sample to individuals working either 30 or more, or 35 or more hours per week. Results are also robust to models excluding from the sample 10.8 percent of salaried urban workers reporting zero earnings, as well as other groups such as unpaid workers. As an extra robustness check, we incorporate two alternative definitions of informality. The first is a more stringent legal definition that considers whether firms have a formal accounting system transmitting to the Tax Authority. The second, a more relaxed definition, merely requiring firm registration with the Commercial Registry of the Tax Authority, or just a formal accounting system.

This paper contributes to the literature by expanding the evidence based on the relative importance of self-selection versus exclusion of workers from formal jobs in SSA as in Alcaraz et al (2015), Gunther and Launov (2012), Falco and Haywood (2015) and Garcia (2017). This paper also contributes to the thin literature on the profile of informality in West Africa, where the share of informal employment is highest across SSA (ILO 2018).

2
|Communication|1.5|1.6|1.6|1.7|1.7|1.7|1.8|1.8|2.1|2.4|
|---|---|---|---|---|---|---|---|---|---|---|
|Recreation and Culture|1.2|1.4|1.5|1.5|1.5|1.6|1.6|1.6|1.6|1.4|
|Education|0.7|1.1|1.3|1.7|1.9|2.5|3.1|3.8|4.5|7.3|
|Restaurants and Hotels|1.6|1.9|1.9|1.9|2.0|2.9|2.2|2.3|2.6|3.3|
|Miscellaneous|5.2|5.4|5.4|5.4|5.6|5.5|5.5|5.8|5.9|6.5|
||100|100|100|100|100|100|100|100|100|100|

Source: Authors own calculation from PBS data. Note: D=Decile The evolution of inflation experienced by the top and bottom deciles between November 2018 and May 2022 is shown in Figure 4. The figure suggests that poor families experience higher inflation overall.

**Figure 4: Inflation Inequality across Income Deciles**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Decile 1 (Poorest) Decile 10 (Richest) YoY_Inflation<br>Inflation<br>Nov_18 Jan_19 Mar_19 May_19 Jul_19 Sep_19 Nov_19 Jan_20 Mar_20 May_20 Jul_20 Sep_20 Nov_20 Jan_21 Mar_21 May_21 Jul_21 Sep_21 Nov_21 Jan_22 Mar_22 May_22<br><!-- End of picture text --> Source: Authors’ calculations from PBS data.

The poorest households (decile 1), on average, experienced one percentage point higher inflation rates than the richest households (decile 10) over this period. The maximum difference of 3.50 percentage points between these two deciles was observed in July 2020. The highest levels of inflation inequality were observed between April 2020 and January 2021, when inflation was on the decline due to COVID-19. This gap can be explained by the fact that during this period, the average energy inflation was -2.4 percent and 0.5 percent for urban and rural regions, respectively (Figure 3).<sup>4</sup> The food inflation, however, was in double digits during the same period (Figure 2). Since food has the highest budget share for the poorest of the households, while utilities and transport are used the most by the top decile, the gap between these extreme

> 4 This was due to reduced oil and utility consumption resulting from the fall in transportation and industrial activities caused by COVID lockdowns.

8
-7-

where h is the derivative of the h'th household's utility with respect to the i'th price. Using Roy's identity we can write (2) as where Ah is the marginal utility of income of the h'th household.

Let us turn now to the revenue constraint of the government. Total commodity tax revenue, R, can be expressed as: (4) R { , tk Xk

-where Xk **Eh** 1 Xk and tk is the ad valorem tax rate applied to the k'th

commodity. Revenue neutrality requires that and by simple algebraic manipulation we can write equation (5) as where a The parameter a determine the rate of substitution between the changes in the two prices and captures the efficiency dimension of the tax substitution. It can be interpreted as the differential welfare cost of raising public funds by taxing the i'th commodity more heavily and using the proceeds to subsidize the J'th commodity. To see this interpretation

**Massllf4/W*lsr* Dominanoe** & **the** Design of **holeO** Tasztion In **the** Coto D'ZvoIre/WThirak:Ab/5/18/88
|(DMA) used to administer water|
|---|
|across the city, with 78 units across|
|Lusaka District) and categorized as|
|relating to water quality, water|
|supply,or sewerage.|

Figure 1 shows a schematic summary of the main components of the analysis and how they interrelate. The analysis proceeded in three main stages: (i) assembly and standardization of a suite of geospatial covariates, gridded at a resolution of 100m × 100m and representing a range of environmental, sociodemographic and infrastructure covariates potentially related to Cholera risk; (ii) use of these covariates along with data on cholera case locations in a Log-Gaussian Cox Process model to generate a predicted map of underlying cholera risk across the city; and (iii) the use of a counterfactual modeling approach to evaluate and compare the possible impact of different mitigation strategies. The data and analytical steps are now discussed in more detail.

# _Study area and Cholera case data_

Figure 2 shows the study area, consisting of the 34 Wards that make up the metropolitan District of Lusaka. Data on cholera cases were obtained from the Zambian National Institute of Public Health (ZNPHI) with the study dataset consisting of the 5,444 cases reported between October 2017 and May 2018, with each geopositioned by household location.

# _Constructing covariates and assessing their linear association with cholera risk_

A suite of geospatial covariates was constructed from the data sources listed in Table 1 to include a range of factors potentially related to cholera risk and explaining some fraction of the observed spatial variation in case incidence. All covariates were defined on a spatial raster grid at 100m × 100m resolution across the study area, and fell into one of three categories, as follows.

(i) Hydrology. A digital elevation model(NASA 2015) (DEM) was used to derive the path of main river and stream channels and a raster grid created denoting distance of each grid cell from those channels. Data on reported flooding at 128 point locations across the city were obtained from the Millennium Challenge Account – Zambia (MCA-Z), and these were converted into a raster grid denoting distance to flood prone areas. Raster data were also obtained on the vulnerability to pollution of the underlying groundwater aquifers across Lusaka, based on geological characteristics. This includes taking into account infiltration characteristics including consideration of rock type, composition, tectonic lineaments, catchments or drainage basins, groundwater flow, and surface vegetation (Nick, Mweene, and Baumle 2012).

(ii) Water and sanitation infrastructure. Digitized data were obtained from the Lusaka Water and Sewerage Company (LWSC) on the piped water and sewerage infrastructure that underly the city, and
factor markets as the expanding industrial sectors pull resources from the agricultural and 'traditional' sectors.<sup>4</sup> Third, supply-side sources of growth decompositions for countries that have followed an ELG strategy resemble more the pattern of developed countries than the pattern of other developing countries. For ELG countries, the contributions of capital accumulation and TFP growth to total output growth are higher than for other developing countries (indeed, than for developed countries).<sup>5</sup> By itself, these TFP results do not help "explain" differences between ISI and ELG patterns of development. Since the contribution to growth of TFP is calculated residually, the observed difference really amounts to a restatement of the problem.

Fourth, limited cross-country evidence using data for manufacturing sectors indicates a positive correlation between the role of export expansion and TFP growth at the rcwtordl level.<sup>6</sup> These results are consistent with the hypothesis that export expansion leads to higher TFP growth through exploiting econonies of scale, technology transfer, or increasing competitive incentives. There is also evidence that import substitution is correlated with low T1FP growth rates at the sectoral level. These results are consistent with the hypothesis that import substitution (liberalization) leads to lower (higher) TFP growth by reducing 4The classic two-sector development Ladel is Lewis (1954). Bruno (1968), Robinson (1971), and Feder (1986) present two-sector models in which they econometrically estimate the contribution of disequilibrium adjustment in the factor markets to growth. Syrquin (1986) presents some computations for archetype economies.

5rhe evidence is detailed in Chenery (1986).

6The evidence is from Nishimizu and Robinson (1984). The countries are Japan (1955-1970), Korea (19651973), Turkey (1953-1973), and Yugoslavia (1962-1972). The data cover 16 manufacturing sectors.

-4-
# **1. Introduction**

This paper analyzes the evolution of job tenure in Europe from 1995 to 2020. The focus on job tenure is motivated by two sets of arguments: one related to wellbeing and equity issues, and the other related to efficiency.

The first set of arguments considers the length of job tenure as a key measure of job stability <u>(Neumark 2000) and, in turn, job stability as one of the main drivers of job satisfaction and workers’</u> wellbeing <u>(Clark, Knabe, and Rätzel 2010; Origo and Pagani 2009). Long-lasting tenure is</u> particularly relevant in Europe, where the idea of a “job for life” was part of its citizens’ collective identity in the second half of the 20th century. In fact, many Europeans still set their expectations with that ideal job as their primary reference point,<sup>1</sup> even if fundamental changes in labor markets since the 1970s broke the implicit contract of long-term commitment between employers and workers <u>(Stone 2004). Concerns about an increase in job instability have been highlighted by Hollister (2011) and Standing (2011), among others, even if, as Manning and Mazeine (2022)</u> report, there is not much hard evidence on the _objective_ measures of job instability and its long term trends<sup>2</sup> and it is not clear that even _subjective_ indicators point to a worsening situation.<sup>3</sup> In terms of equity, even if at the aggregate average level, tenure has not changed much, some groups may experience a shrinking of their tenure. This uneven incidence of shorter job tenures could be a potential reason behind tensions in Europe’s social contracts (Bussolo and others 2018).

The second set of arguments motivating a study on the evolution of job tenure points out that, beyond being an indicator of job stability, tenure also reflects the efficiency of the job search process (Pries and Rogerson 2022). When workers and employees are satisfied with the job match, only a strong shock can sever the employment relationship, but bad job matches are more prone to dissolve quickly. Changes in the average length of job tenure can then indicate changes in how good job matches are on average in the economy. Tenure is also important for human capital

> 1 According to data from the 2015 round of the International Social Survey Programme, 59 percent of European respondents considered job security a “very important” and 33 percent considered it an “important” attribute of a job (ISSP 2017).

> 2 Some papers have focused on documenting the heterogeneity in _levels_ of tenure, but fewer have analyzed the time _trends_ of tenure (see infra the literature review section for more details).

> 3 <u>Manning and Mazeine (2022)</u> find that in the US, UK and Germany, subjective perceptions of the probability of job loss have remained stable over the past 30 years. They find some differences in perceptions between subgroups of the population, but there are no substantial differences in trends over time. Note also that Molloy, Smith and Wozniak <u>(2021)</u> show that subjective perceptions of job stability can be more aligned with changes in short tenure jobs than in long tenure jobs.

2
Table 6: Third-country effects of deep RTAs: Regulation-intensive products

||(1)|(2)<br>Export pa|(3)<br>rticipation|(4)|(5)|(6)<br>Export|(7)<br> entry|(8)|
|---|---|---|---|---|---|---|---|---|
|Exp Other...(t-1)|||||||||
|... RTA|0.018***|0.017***|0.005***|0.004***|0.009***|0.008***|0.004***|0.003***|
|... RTA x STC|(0.001)<br>-0.002**|(0.001)<br>-0.003***|(0.001)<br>0.000|(0.001)<br>-0.001|(0.001)<br>-0.001**|(0.000)<br>-0.001***|(0.000)<br>-0.000|(0.000)<br>-0.001|
|... Depth MFN|(0.001)<br>0.025***<br>(0.002)|(0.001)|(0.001)<br>0.008***<br>(0.002)|(0.001)|(0.001)<br>0.011***<br>(0.001)|(0.000)|(0.001)<br>0.006***<br>(0.001)|(0.000)|
|... Depth Pref|-0.021***||-0.005***||-0.010***||-0.005***||
||(0.002)||(0.002)||(0.001)||(0.001)||
|... Depth MFN x STC|0.014***<br>(0.003)||0.012***<br>(0.003)||0.005***<br>(0.001)||0.005***<br>(0.001)||
|... Depth Pref x STC|-0.008***<br>(0.002)||-0.008***<br>(0.002)||-0.003**<br>(0.001)||-0.003**<br>(0.001)||
|... SPSorTBT||0.001||0.001*||-0.000||0.000|
|||(0.001)||(0.001)||(0.000)||(0.000)|
|... SPSorTBT x STC||0.005***||0.003***||0.002***||0.001***|
|||(0.001)||(0.001)||(0.001)||(0.001)|
|... Border|||0.044***<br>(0.001)|0.045***<br>(0.001)|||0.016***<br>(0.000)|0.017***<br>(0.000)|
|... Language|||0.003***<br>(0.000)|0.003***<br>(0.000)|||0.002***<br>(0.000)|0.002***<br>(0.000)|
|... Colony|||-0.013***|-0.013***|||-0.003***|-0.003***|
||||(0.001)|(0.001)|||(0.000)|(0.000)|
|Observations|164,595,130|164,595,130|164,595,130|164,595,130|164,298,452|164,298,452|164,298,452|164,298,452|
|R-squared|0.338|0.338|0.340|0.340|0.156|0.156|0.156|0.156|
|Firm-prod-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Dest-prod-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Firm-prod-dest FE|YES|YES|YES|YES|YES|YES|YES|YES|

Robust standard errors, clustered at the firm-product level, are in parentheses. The dependent variable _yijp,t_ is a dummy variable equal to 1 if firm _i_ exported (or started exporting) product _p_ to country _j_ in year _t_ . STC=1 if a Specific Trade Concern (STC) on product _p_ has been raised in the WTO. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **5 Robustness analysis**

This section presents a series of robustness checks. First, we use different measures of products’ regulation intensity: continuous STC measure and FDA prior notice requirements. Second, we use an alternative measure of depth to provide further evidence that the positive spillover effects are driven by deeper forms of regional integration. Finally, we include additional fixed effects to control for firm-destination-year effects. All regressions in the robustness analysis include spatial exporting controls, although coefficients are not reported for brevity.

## **5.1 Continuous measure of regulation intensity**

The baseline specification uses a binary variable as a proxy for the regulation intensity of products. We can also construct a continuous measure from the STC data by counting the number of times each product was raised as a concern in the SPS/TBT committees 16
|**N.E Nigeria**|Host Community|0.107|(0.045, 0.169)|17.0%|62.7%|71.5%|
|---|---|---|---|---|---|---|
||IDP|0.139|(0.103, 0.175)|23.3%|59.8%|28.5%|
|**Somalia**|Non-IDP|0.286|(0.245, 0.327)|44.8%|63.8%|64.3%|
||IDP|0.396|(0.337, 0.454)|63.3%|62.5%|35.7%|
|**South Sudan**|Host Community|0.119|(0.057, 0.180)|19.5%|60.9%|86.7%|
||IDP|0.203|(0.004, 0.402)|34.1%|59.5%|13.3%|
|**Sudan**|Host Community|0.054|(0.033, 0.074)|9.4%|57.0%|67.5%|
||IDP|0.255|(0.232, 0.277)|43.6%|58.5%|32.5%|

Note: Own elaboration based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).

Another important trend concerns the Ethiopia and Sudan samples, which surveyed refugee and/or IDP households in camps, whereas the Nigeria, Somalia, and South Sudan data sets surveyed IDP populations distributed throughout the country. Of the five countries, Ethiopia and Sudan have the largest differences in MPI and indidence between the refugee/IDP populations and their host communities. Ethiopia has a nearly 33 percentage point difference in incidence between refugee households in camps and host community peers, while Sudan has a 34 percentage point difference in incidence between the IDP households in camps and their host community neighbors. Curiously, only in Ethiopia and Sudan do we see higher intensities of poverty among the refugee and IDP populations, whereas in Nigeria, Somalia and South Sudan, we observe, on average, a higher intensity of deprivations among the poor in the host community populations. More, the included areas of Ethiopia and Sudan also have the lowest rates of multidimensional poverty for the host communities, with incidences of 12.8% and 9.4%, respectively.

Delving further into the depth of poverty, Table 4 shows a breakdown of the poor population by intensity, ranging from 50% to 100%. In Sudan, 72% of the poor in host communities and 62% of the poor IDPs are in the lowest intensity band, relatively close to the poverty line. In Somalia, the country with the highest average intensity across both groups, the distribution is less concentrated, with only one-third of IDPs in the lowest band. Nearly 18% are deprived in 70% to 79% of the weighted indicators, and a further 6% of poor IDPs face deprivation in 80% or more of the weighted indicators.

_Table 4: Intensity of deprivation among MPI poor by displacement status (%)_

||**Eth**|**iopia**|**N.E Ni**|**geria**|**Soma**|**lia**|**South S**|**udan**|**Sud**|**an**|
|---|---|---|---|---|---|---|---|---|---|---|
|Intensity|Host|Refugee|Non-IDP|IDP|Non-IDP|IDP|Non-IDP|IDP|Non-IDP|IDP|
|**50-59%**|65.5|57.3|59.2|49.1|45.8|34.0|54.8|57.0|71.6|61.8|
|**60-69%**|31.0|34.2|35.0|35.7|33.6|42.4|21.4|36.7|21.6|29.7|
|**70-79%**|3.0|7.4|5.9|13.6|17.1|17.6|21.3|6.3|6.8|7.8|
|**80-89%**|0.5|1.1||1.7|3.1|5.6|2.6|||0.8|
|**90-100%**|||||0.3|0.4|||||

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Policy Research Working Paper 8152

# **Abstract**

The paper delves into the implications of a failure to account for rental regulation in the measurement of households’ welfare, poverty, and inequality when using household surveys. Exploiting previously unavailable data for the Egyptian case, the paper illustrates the long-lasting distortions in the rental market that the 1977 rental law has created. The paper finds evidence that earlier studies may have substantially underestimated households’ welfare in urban areas and overestimated urban poverty. National poverty rates show smaller corrections, as poverty is mainly a rural phenomenon in the Arab Republic of Egypt. An appropriate measure of welfare also led to downward corrections in inequality in urban Egypt, while increasing the inequality across regions. These effects counterbalance each other and nationwide inequality estimates are affected only slightly.

This paper is a product of the Poverty and Equity Global Practice 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 glaraibarra@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
combined expansion of Takaful and Karama and the other targeted cash assistance packages previously described. These results suggest that government responses helped dampen the immediate impact of the crisis to a large extent (counterbalancing about 45 percent of the labor income decline, on average). The significant expansion of Takaful and Karama is the most important transfer in counteracting income losses. Due to the program’s targeting rules, its expansion markedly compensated income losses in the poorest rural areas of Lower and Upper Egypt. A decomposition for changes in the income-based poverty headcount using the $3.65 international poverty line shows that, had cash transfers remained at their pre-COVID levels, the increase in poverty among Egyptian households would have been 3.3 percentage points, compared to the estimated 2.2 percentage points registered with the expanded cash transfers.

Figure 6: Odds of becoming poor due to COVID-19, households with specified characteristics <!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Head education Governorate<br>Head is male Head's age (mean) Household size Head is informal worker Head not working Basic education or lower Secondary ed. Tertiary ed. Metropolitan (Ref.) Lower Urban Lower Rural Upper Urban Upper Rural<br><!-- End of picture text --> Source: Authors’ calculations.

Note: Odds ratios from a logistic regression for becoming poor due to COVID-19. Reference group is all other households (always poor, exited poverty, and always non-poor); 95 percent confidence intervals in brackets.

Table 5: Change in household income per capita due to COVID-19 in FY20 (%)

||National|Metropolitan|Lower<br>Urban|Lower<br>Rural|Upper<br>Urban|Upper<br>Rural|
|---|---|---|---|---|---|---|
|Total income|-1.7|-2.3|-2.5|-1.5|-2.3|-0.3|
|Labor income|-3.1|-2.8|-3.3|-3.1|-3.2|-3.1|
|Takaful and Karama|1.0|0.2|0.5|1.1|0.6|2.4|
|Other non-labor<br>income|0.4|0.3|0.3|0.4|0.3|0.5|

Source : Authors’ calculations.

Note : Difference between household income per capita with and without COVID, FY20, percent.

14