rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> Occupational Composition of Formal and Informal Employees<br>In European Countries, by Region. 2004-2009<br>Non-Manual, High-Skilled Non-Manual, Low-Skilled<br>IL<br>GR<br>East North South West East North South West<br>Formal Informal Formal Informal<br>Manual, High-Skilled Manual, Low-Skilled<br>FR<br>E S FR<br>IL<br>East North South West East North South West<br>Formal Informal Formal Informal<br>60 60<br>40 40<br>20 20<br>0 0<br>in Employment, %<br>Share of Occupation<br>60 60<br>40 40<br>20 20<br>0 0<br>in Employment, %<br>Share of Occupation<br><!-- End of picture text --> Formal: Employee with a contract; Informal: Employee without a contract. Self-employed excluded Data not weighted by countries' population: A worker from any country is equally likely to be sampled Source: Calculation with ESS data

# **Figure 10 Shares of manual and non-manual occupations among formal and informal workers in European Regions**

Recall (see, e.g. European Commission, 2008, p.104: Table 8 in Annex to Chapter 2) that a worker with higher education holding a manual or low-skilled non-manual occupation, is considered _over-qualified_ ; secondary-educated workers employed in elementary occupations are also over-qualified. Figure 11 provides a cross-country comparison of prevalence of over-qualification among formal employees and two categories of shadow workers. Overall, there is a quite strong positive correlation between the three overqualification rates. However, in countries such as Ukraine, Russia, Slovenia, Denmark, Norway, Spain and Switzerland, employees without contracts are much more likely than their formally employed colleagues to have education beyond the level their job require. This could indicate either a lack of
1): 1-18. Connelly, Rachel, Roberts, Kenneth and Zheng, Zhenzhen, 2010. "The Impact of Circular Migration on the Position of Married Women in Rural China." _Feminist Economics_ 16 (1): 3-41.

Datta, Namita. 2006. “Joint Titling – A Win-Win Policy? Gender and Property Rights in Urban Informal Settlements in India.” _Feminist Economics_ 12 (1 & 2): 271-298.

Deaton, Angus S. 1989. “Looking for Boy-Girl Discrimination in Household Expenditure Data.” _World Bank Economic Review_ 3 (1): 1-15.

36
".,icy **RFSEARCH WORKING PAPER** 2072

# Sumumary **findings**

Most empirical work on how growth affects poverty and incquality has been based on international panel data sets. Panels can also be used within a country, if the analysis is carried out at the regional level.

Wodon does this for Bangladesh, where regional panel estsmates indicate that growth reduces poverty in both urban and rural areas. Growth is associated with rising inequality only in urban areas.

Simulations based on these estimates **indi** Cate how much poverty reduction could increase in tlhe next 10 years if growth were prormoted in rural aels rather than urban areas.

T his paper-a product of the Poverty Reduction and EconomicManagementSector Unit, South Asia Region -was written as part of background work for the Bangladesh poverty assessment. Copies of the paper are available free frmo the World Bank, 1818 H Street NW, Washington, DC 20433. Please contactJillian Badami, room MC10-336, telephone 202-4580425, fax 202-522-2428, Internet address jbadami@worldbank.org. Policy Research Working Papers are also posted on the Web at http:/,/wwwv.worldbank.org/html/dec/Publicarions/Workpapers,/home.html.The author roay lb contacted at qwodon@worldbank.org. March 1999. (40 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage thre exchange o_ **_f_** _; a's about developmest issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fullv ;(./S,i The papers canry the names of the authors and should be cited accordingly. The findings, interpretations, and conclsl,ns eic this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Execative D. .:,,Ds or the countries they represent._ Produced by the Policy Research Dissemination Center
determinants of decision‐making as time‐invariant and suggests that other large changes in circumstances that are voluntary in nature (e.g. marriage, childbearing, retirement) may also be unlikely to lead to changes in preferences and beliefs.

The remainder of the paper is structured as follows: Section 2 describes the context in which this migration flow is occurring, and describes the survey and estimation strategy; Section 3 reports on impacts on risk and time preferences; Section 4 covers the impact on pro‐market beliefs; Section 5 reports on the impact on interactions with partners and on household efficiency; Section 6 discusses statistical power and external validity issues, and Section 7 concludes.

# **2. Context and Survey**

# **2.1 Country Context**

The Kingdom of Tonga is an archipelago of islands in the Pacific, with a resident population of just over 100,000. It has a gross national income (GNI) per capita of $5,069 (in 2011 PPP$), slightly less than that of India, and ranks 132<sup>nd</sup> out of 188 countries.<sup>1</sup> Average income in New Zealand is approximately six times as high, at $32,689. Both countries rank 32 places higher in the Human Development Index than they do in terms of per capita GNI.

Tonga has been and continues to be a less market‐oriented economy than is the case in New Zealand. The Index of Economic Freedom ranks New Zealand third in the world, as a free, modern, and competitive economy, while Tonga is ranked as “mostly unfree” at 95<sup>th</sup> in the world, between Swaziland and the Kyrgyz Republic (Heritage Foundation, 2016). Tongan society has historically been highly stratified, with a monarch and aristocracy controlling resources and a largely subsistence‐oriented economy. The extended family plays an important role, with the kinship system expected to provide
||Host Country|Number|Origin Country|Number|
|---|---|---|---|---|
|1|Turkey|2,715,873|Syrian Arab Rep.|4,914,718|
|2|Pakistan|1,566,950|Afghanistan|2,784,373|
|3|Lebanon|1,079,864|Somalia|1,059,490|
|4|Iran, Islamic Rep.|979,410|South Sudan|772,394|
|5|Ethiopia|732,018|Sudan|624,205|
|6|Jordan|680,952|Congo, Dem. Rep.|497,941|
|7|Kenya|555,291|Myanmar|481,467|
|8|Uganda|500,192|Central African Rep.|469,801|
|9|Germany|484,748|Iraq|381,491|
|10|Congo, Dem. Rep.|376,144|Eritrea|372,309|

Table 1: Top 10 host and origin countries by number of refugees at the end of 2015 UNHCR data show that there are 33 major refugee situations today which have been lasting five years or more (protracted situations). Yet, forced displacement situations are inherently dynamic and there are often significant variations in the number of refugees during a displacement period. This is often the reflection of the circumstances that cause refugee movements. To illustrate the extent to which UNHCR refugee statistics capture such evolving circumstances, Figure 2 displays refugee statistics for three situations: Somalis, Afghans, and Congolese refugees.

Panel 2a shows the number of Somalis who are hosted in Kenya. Refugees started to arrive massively around 1990, after the fall of the Siad Barre regime and the ensuing civil war. Their number declined slightly around the end of the United Nations intervention (1992-1995) as military clashes between factions became shorter,
>11</sup> Although some countries have missing observations, the majority of countries have the full panel set and this ensures that all regions of the world are well represented.

In order to understand how the variance of public investment is affected by institutional quality, we define volatility of public investment as the absolute value of the percentage change in the deviation of the ratio of public investment to GDP and total investment, , from the trend component extrapolated using the Hodrick-Prescott (HP) filter<sup>12</sup> , , minus the same deviation at time , normalized by the trend at time :

> 10 Keefer and Knack (2007) use the IMF’s Government Finance Statistics (GFS) central government capital expenditure data (not including investments by state-owned enterprises). The limited temporal coverage of GFS does not permit the authors to take into account the longitudinal dimension, thus they perform an average crosssectional analysis over the period 1974-1998. Other data sources include the IMF Article IV country reports, which unfortunately are not available on a regular basis.

> 11 As a robustness check public investment has also been normalized by general government total expenditure.

> 12 For the HP filter the smoothing parameter, λ, has been set to 6.25.

7
are more dependent on it.

The remainder of the paper is organized as follows. Section 2 provides background on the development of PTAs with strong intellectual-property chapters and overviews their scope and coverage, relying on World Bank data. Section 3 discusses the empirical framework and presents estimates of the effects of IP-related PTAs on aggregate and bilateral imports and exports at the sectoral level. Section 4 offers concluding remarks.

# **2. Background**

5
to unravel the mystery further through a study of multi-variate correlations. We do this first by using the Fryer-Levitt approach.

# **3 Regression Approach I: Fryer-Levitt**

We are now ready to investigate the secret a bit further by deepening our analytical approach beyond a mere comparison of means. We adopt a simple methodology that is easy to understand and interpret. Our approach closely follows [Fryer and Levitt, 2004] who sought to explain the black-white achievement gap in the first two years of schooling for children in the United States. For the results presented in this section, we pool the student level data from Vietnam and Dev7 countries. Recall that Dev7 stands for the seven developing countries in the 2012 PISA dataset with a per capita income below the cut-off 19
,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>Rainfall index Rainfall and Irrigation index<br><!-- End of picture text --> Source: Author-constructed based on meteorological data (Meteorological Department of Sri Lanka) and MCM data (Irrigation Department of Sri Lanka).

25 Based on Huruluwewa tank, from Irrigation Department, Ministry of Irrigation and Water Resources Management, Sri Lanka.
and subsidies are considered, the poverty rate for the corresponding income concept (consumable income) increases, leading to a global poverty increase for the fiscal system.

**Figure 10. Effect of Fiscal Policy on Poverty in Mali and Niger, 2014**

<!-- Start of picture text --> 47.02<br>44.54 44.46<br>42.99<br>40.59 40.42<br>Mali Niger<br>Market Income Disposable Income Consumable income<br>Source: Authors.<br><!-- End of picture text --> Direct taxes (−0.01 in Mali and Niger) and transfers (0.02 in Mali, 0 in Niger) have little effect on poverty because the richest households pay most of the direct taxes, and direct transfers are relatively small. In addition, in Niger, direct transfers are not well targeted at the poor. In contrast, **− −** indirect taxes have a strong impoverishment effect ( 3.66 in Mali, 2.44 in Niger) that is, in absolute value, greater than the poverty reduction effect (0.79 in Mali, 0.02 in Niger) of indirect subsidies (Figure C.4). Of indirect tax categories, VATs have the strongest impoverishment effect **− −** ( 1.79 in Mali, 0.94 in Niger). As a result, poverty headcount measured according to consumable income is far above poverty headcount measured according to market income, confirming that fiscal policy is impoverishing in Mali and Niger.

Calculation of the poverty indexes before and after application of the fiscal policy shows that the increase in poverty is due to taxation in the two countries, but it does not show the proportion of households that have become poorer or richer because of the various fiscal interventions. Although payment of taxes impoverishes households, the benefits from public spending enrich them, and the net effect
# **The Impact of Ethnic Fractionalization on Labor Productivity: Does Firm Size Matter?**

Mohammad Amin<sup>1</sup> and Usman Khalid<sup>2</sup>

**Keywords:** Ethnic fractionalization, Labor productivity, Growth, Firm size **JEL Codes:** Z13, O12, L25, D24

1 Senior Economist, Enterprise Analysis Unit, DECIG, World Bank, Washington DC, USA. <u>mamin@worldbank.org</u> 2 Corresponding author. Department of Innovation in Government and Society, United Arab Emirates University, Al Ain, UAE. E-mail: usman.khalid@uaeu.ac.ae, <mark>We thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the data available.</mark> 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.

_Data availability statement_ : The firm-level survey data used in this study are publicly available at https://www.enterprisesurveys.org/en/enterprisesurveys
Policy Research Working Paper 9679

# **Abstract**

This paper uses computable general equilibrium model-based simulations to assess the gender dimensions of the impact of COVID-19 on economic outcomes, that is, labor force participation, employment, wages, and earnings. It leverages the 2020 High-Frequency Phone Survey in Chad to assess the impact of COVID-19 on female-headed households, which comprise 23 percent of the country’s households. The findings show that the COVID-19 pandemic will have a disproportionately higher negative impact on women in urban areas. The simulation results suggest that more women than men working in paid jobs might lose their jobs. Although the paper focuses on the impact of COVID-19 in 2020, the findings can be generalized as the hysteresis effects might be deeper and more prolonged if the pandemic is more prolonged. The situation is potentially dire, especially in service sectors, where most women are employed in urban areas. Moreover, the High-Frequency Phone Survey shows that COVID-19 has notably impacted the households’ income from enterprises and suggests that this negative impact is more prevalent for female-headed households. Although male- and female-headed households are using common coping strategies during the pandemic, female-headed households in rural and urban areas have been more reliant on aid from family and friends and less reliant on savings, credit, or the sale of assets.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 kkabir@worldbank.org, hdudu@worldbank.org, and ftchanatchana@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
(Shrestha, 2018) have developed concepts of criticality and redundancy to explain the impact of seasonal disruptions on road network performance within a network analysis. Because these network-scale analyses approach seasonal impacts in terms of binary open-closed changes in roads’ status they obscure quantitative changes in network and road-scale performance from reduced speeds over damaged but still-functioning roads. Many are also data intensive and difficult to replicate in rural, developing world contexts. Where seasonal access is considered by social sciences researchers, they tend to rely on survey data, as in the model of seasonal market access in Ethiopia by Hirvonen et al. (2017). In Nepal, Huber (2015) adjusts speeds for major roads and airports for the monsoon based on expert guidance.

Walking travel must be modeled in rural areas of the developing world where lack of roads or simple poverty compel many to travel long distances on foot to reach services or transport networks. They can therefore represent the entire travel time to a service or be integrated with road transport for a multi-modal accessibility model. Representations of walking speeds vary in sophistication: while the hiking functions of Tobler (1993) and Van Wagtendonk and Benedict (1980) provide foundations for modeling walking speeds, they translate better to cost-time modeling, which treat walking cells like any other, than network analysis, which ignores off-network travel. In practice planners and researchers tend to ignore or simplify walking times in their analysis of rural roads given roads’ primacy in most economic and transport systems. In Nepal, Devkota et al. (2012) modeled walking as a primary transport modality when recommending rural trail bridge investments. To the authors’ knowledge however, the model we adapt here (from Banick and Kawasoe (2019)) remains the only published, scalable model of travel times which incorporates monsoon impacts and walking speeds in a rugged and sometimes high-altitude context.

# 2.3. Traditional rural transport valuation

Since available budgets for rural road building rarely match the observed needs, transportation planners and policy makers must choose the most productive subset of
in Table 3A; Annex 2 provides discussion and details. Moreover, for dependent informal employment, the model with instrumented Rule-of-Law is very similar to model {1a}, with the only exception that the coefficient on tax wedge is smaller and not significant. Hence we keep specification {1} as is, with non-instrumented contemporary (to account for an almost immediate impact of tax morale on informality) Rule-of-Law. The results are similar (and available on request) if the lagged values of _RULE_LAW_ are used for countries/years where ESS field work has started earlier than in September of respective year, although these models have lower R-squared; if _RULE_LAW_ is lagged for all cases in the sample, the results are also similar but do not capture the tax wedge effect (and R-squared declines further).

However, as we focus on institutional factors rather than the persistency effect, our preferred specification is {2}, in which all variables can be treated as strictly exogenous or

> 17 We refer to _past_ values here taking into account the time lag between field work of the country-specific surveys underlying WGI (for the items in question, these are the and World Economic Forum Global Competitiveness Survey, with field work performed early in the calendar year to which the data refers) and ESS surveys (in most cases, late in the year for Eastern and Southern European countries, see Table 1).

14
which the individual was living, whether the individual speaks English, reason for emigrating, municipality and city of birth, address of residence after repatriation, occupation and whether the subject has any criminal records. For repatriations that took place between 1995 and 2010, this detailed interview was not in place, thus, we observe only information on the reason and date of deportation, intended place of residence after repatriation, and date and place of birth.

This data set of Salvadoran returned migrants is the only source of information on all individuals who were returned to El Salvador, shedding light on their motive to flee the country, and the situation they may face once back in the country. Since the registration and interview is mandatory for all returnees, the data set includes information on the total number of individuals returned in each municipality.

Descriptive statistics of our sample are presented in Table 2. Around 64 percent of returnees have primary or no education—88 percent of those from which we have education data— and around 80 percent of them lived less than 1 month abroad—i.e., they were likely apprehended and repatriated when entering the United States.

Figure 5 presents the geographic distribution of the intensity of repatriations as a share of the mean municipal population between 2011 and 2016. There is an important variation in the share of repatriations across municipalities. The mean repatriation share for all the municipalities is 0.05, suggesting that, on average, all Salvadoran municipalities received approximately 5 percent of their population due to forced repatriation between 2011 and 2016. There are also some municipalities that saw a repatriation share of 67 percent, suggesting that their population was almost doubled by returnees. The figure also shows that the share of returned migrants to the northwest of the country was of relatively low intensity.

3. Additional municipal controls: other municipal covariates employed in our analysis include population and crime variables. Information on crime comes from the National Civilian Police of El Salvador ( _Polic ́ıa Nacional Civil_ , PNC) and socioeconomic and demographic
Policy Research Working Paper 6254

# **Abstract**

This paper develops a framework for the quantitative analysis of individual income dynamics, mobility and welfare. Individual income is assumed to follow a stochastic process with two (unobserved) components, component representing measurement error or transitory income shocks and an Autoregressive (AR(1)) component representing persistent changes in income. The analysis uses a tractable consumption-saving model with labor income risk and incomplete markets to relate income dynamics to consumption and welfare, and derive analytical expressions for income mobility and welfare as a function of the various parameters of the underlying income process. The empirical application of the framework using data on individual incomes from Mexico provides striking results. Much of measured income mobility is driven by measurement error or transitory income shocks and therefore (almost) welfare-neutral. A smaller part of measured income mobility is due to either welfare-reducing income risk or welfare-enhancing catching-up of low-income individuals with high-income individuals, both of which have economically significant effects on social welfare. Decomposing mobility into its fundamental components is thus seen to be crucial from the standpoint of welfare evaluation.

This paper is a product of the Macroeconomics and Growth 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 author may be contacted at Wmaloney@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