rafmacalaba/data-use-mentions-extended huggingface dataset:
influenced by domestic firm productivity. We therefore additionally instrument for the interaction term of foreign output share with the _inp_ variable in column 9, using the sectoral average number of days to clear imports through customs in a country (in natural logarithms) from the Enterprise Surveys Indicator Database as an inverse instrument. The argument for this choice of instrument would be that longer waiting times at customs may motivate foreign firms to source more inputs from the domestic market. The IV 2SLS regressions show that the interaction term with _inp_ has a positive coefficient sign (columns 8 and 9), while it is negative in the OLS regression (column 3). Both interaction terms are insignificant. The interaction term with sectoral FDI technology intensity in a country ( _tech_ ) has a negative but insignificant impact in both regressions (columns 4 and 10), rejecting the findings of a positive impact (e.g. Buckley et al. 2007; Lin et al. 2009).

Unfortunately, many results fall short of the thresholds of statistical significance, which could be related to heterogeneous productivity levels of domestic firms (Girma and Görg 2007). We therefore examine the dynamics of productivity at different points of the distribution in a next step.

# **_The Role of Domestic Firm Productivity_**

We split our sample into three productivity groups: LP <= 33.33 percentile (low-productivity firms); 33.33 percentile < LP <= 66.67 percentile (medium-productivity firms); and LP > 66.67 percentile (high-productivity firms). The IV 2SLS regression results by productivity level are shown in Appendices 5 to 7. We detect clear differences across the three types of domestic firms. The foreign output share variable mostly shows negative coefficient signs for all types of firms, but is rarely significant. However, the coefficients decline with firm productivity, suggesting that higher productivity reduces negative FDI spillovers. Capital intensity shows a constantly positive effect on labor productivity, but the impact is strongest for high-productivity firms, followed by lowproductivity firms and
Shrestha, M. (2020). Get Rich or Die Tryin’: Perceived Earnings, Perceived Mortality Rates, and

- Migration Decisions of Potential Work Migrants from Nepal. _The World Bank Economic Review_ , 34 (1), 1-27.

- Shrestha, M., Mobarark, A. M., and Sharif, I. (2019). _Migration and Remittances: The impacts of a government intermediated international migration program_ . Washington, DC: The World Bank.

- United Nations, Department of Economic and Social Affairs. Population Division (2019). International Migrant Stock 2019 (United Nations database, POP/DB/MIG/Stock/Rev.2019).

- Westfall, P. H., and Young, S. S. (1993). _Resampling-based Multiple Testing: Examples and Methods for P-Value Adjustment_ (Vol. 279). New York: John Wiley & Sons.

- Wickramasekara, P. (2016). _Review of The Government-to-Government Mechanism for the Employment of Bangladeshi Workers in the Malaysian Plantation Sector_ .  Global Migration Policy Associates, International Labor Organization.

- World Bank. (2013). _Bangladesh Poverty Assessment: Assessing a Decade of Progress in Reducing Poverty for the Period 2000-2010_ . Washington, DC: The World Bank.

- World Bank. (2015). _Bangladesh – More and Better Jobs to Accelerate Shared Growth and End Extreme Poverty: A Systematic Country Diagnostic_ . Washington, DC: World Bank Group.

- World Bank. (2020). The World Development Indicators. Online. Accessed Feb 11, 2020. Website: <u>https://databank.worldbank.org/source/world-development-indicators</u>

- Yang, D. (2008). International Migration, Remittances and Household Investment: Evidence from Philippine Migrants’ Exchange Rate Shocks. _The Economic Journal_ , 118(528), 591–630. https://doi.org/10.1111/j.1468-0297.2008.02134.x

- Yang, D.
microdata to Compensation of Employees; we match Mixed Income in the microdata to its equivalent in national accounts; and distribute taxes on production proportionally to total income, so it has no bearing on inequality measures. For distribution of capital income, we use a more detailed disaggregation. Using WID decomposition, capital income can be separated in property income of households; net operating surplus (NOS) of the households; corporate profits; and property income of the government. Similarly to taxes on production, we distribute the (small) property income of the government proportionally to households. Following Alvaredo _et al._ (2020), we match NOS to total imputed rent

> 2In Figure C1, we show that the growth of NNI closely matches the growth of Gross Domestic Product in the period. The levels of GDP available at WID match those in other sources such as the World Bank’s World Development Indicators.

> 3We note that not all subcomponents are available in the WID data in recent years, so we impute some of the aggregates. Specifically, no information for the four components of NNI are available for 2019, so we use the same shares observed in 2018 to recover levels in 2019, multiplying imputed shares by the NNI value in that year. Similarly, the subcomponents of capital income are not available between 2016-2019, so we impute the shares observed in 2015 and use them to recover levels of each of them in the latter period.

51
does not lower the tax rate on cash-on-hand, we should see no decline in transfers to others.

**Table 5:** Transfers to Network Members

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Panel A: Likelihood of transfer**|To anyone|To family|To non-family|From anyone|
|Private (vs. non-private)|0.0419|0.0540|-0.00523|-0.0780|
||(0.0464)|(0.0582)|(0.0583)|(0.101)|
||[0.37]|[0.35]|[0.93]|[0.44]|
|Non-private mean|0.784|0.510|0.405|0.314|
|**Panel B: Amount transferred**|To anyone|To family|To non-family|Net amount|
|Private (vs. non-private)|4998.2|3918.2|1080.0|3261.5|
||(2532.5)|(2398.1)|(951.8)|(3244.8)|
||[0.049]|[0.10]|[0.26]|[0.32]|
|Non-private mean|13128.8|9966.7|3162.1|4848.4|

_Notes:_ Data from in-person endline survey, covering transfers in past 3 months (in Phase 2). Observations at worker level. In Panel A, dependent variable is a binary indicator for providing any transfer outside the household to individuals in the given category in Cols. (1)-(3) and receiving any transfer in Col. (4). Panel B dependent variable
Long-term effects of the 1923 mass refugee inflow on social cohesion in Greece<sup>_∗_</sup>

# Elie Murard<sup>_†_</sup>

_University of Alicante, NOVA SBE, IZA, LEAP_ _JEL Classification Codes_ : O15, J62 ,N34, N44 _Keywords_ : Refugees, social cohesion, integration, historical persistence, immigration

> _∗_ This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts.” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

> _†_ I thank Sara Tonini for her very helpful suggestions and Chrysovalantis Vasilakis for sharing the crime data.
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*</sup>||-0.098|-0.294<sup>***</sup>|
||(0.066)||(0.064)|(0.070)|
|Refugee - Sudanese|<br>-0.651<sup>***</sup>||-0.413<sup>***</sup>|-0.463<sup>***</sup>|
||(0.043)||(0.052)|(0.052)|
|Number of Obs.|2002<br>2002|2002|2002|2002<br>2002|

Notes: The dependent variable is participation in employment. All regressions include camp fixed effects. Robust standard errors are reported in parentheses. Asterisks indicate statistical significance at 1% ***, 5% **, and 10% * levels.

Overall, the results suggest that Somali refugees have better employment opportunities than other refugee groups in Ethiopia. As noted above, refugees and hosts in the Somali region share both language and culture. This has allowed a peaceful coexistence and greater integration compared with other displacement settings, and better opportunities for socio-economic integration (Betts et al., 2019). Moreover, some Somali refugees are also benefited from crop irrigation schemes in Melkadid, which was implemented in line with CRRF pledge with the aim of benefiting refugees and hosts on equal basis (UNHCR. 2020b).

We check the robustness of our results by expanding the sample to include all households in the SPS. Table A4 shows that when we include all households, our estimated results remain robust.

18
In the absence of distortions, TFPR should not vary across firms within each sector. In other words, in a frictionless environment, more capital and labor should be allocated to firms with higher physical productivity (TFPQ) up to the point where their higher output results in a lower price, Psi, which translates in the TFPRsi equalizing across firms _i_ . As such, any dispersions of TFPR across firms within a sector imply distortions. A firm with TFPR higher than the sector average suffers disproportionately more from the effects of distortions. On the contrary, it is common for TFPQ to vary across firms because different firms may have different productivity levels.

From the revenue data, we can also derive TFPQsi as: This shows that TFPQ is calculated from Psi, which contains elements of distortions, and κ is normalized to 1.<sup>9</sup> Hsieh and Klenow (2009) choose the elasticity of substitution, σ=3,<sup>10</sup> and R=10, assuming a real interest rate of 5% and a depreciation rate of 5%. Capital share, αs, and labor share, (1 −αs), are taken from the United States manufacturing sectors, where firms are assumed to operate in an environment of minimal distortions. Therefore, the shares of capital and labor of firms in the United States represent an efficient utilization of resources. Any deviation from the US capitallabor shares suggests distortions.

Distortions represented by the output and capital wedges can be measured by: where firm _i'_ s wage bill is represented by wsiLsi , and PsiYsi represents the firm's value added. Both values are taken from the data. Rewriting this equation shows that the relative utilization of 1−αs factors will be affected by distortions in the capital market and ~~,~~ the labor-capital ratio in the αs less distorted (United States) environment.

If firm _i_ 's actual labor-capital ratio wsiLsi is higher than the less distorted labor-capital ratio, this RKsi implies
poverty line was considered an appropriate metric, also offering the ability to make international comparisons. Hence, the $1.90 USD PPP (2011) poverty line was first converted into current SSP and adjusted to reflect South Sudanese purchasing power using the South Sudan PPP conversion factor for 2011. It was then adjusted for inflation up to October 2016 using the national CPI calculated by the National Bureau of Statistics, resulting in a value of approximately 65 SSP (October 2016).

# 4. Results from the HFS

In 2016, more than 4 in 5 South Sudanese people in the seven states covered in the HFS lived under the international poverty line of US$1.90 PPP (2011) per capita per day. The poverty headcount was equal to 83 percent in 2016, with a 95 percent confidence interval from 81 to 85 percent. These levels of poverty place South Sudan among some of the poorest countries in the world. South Sudan’s poverty headcount ratio is much higher than the average estimates of other countries at similar levels of development (Figure 3). The estimated poverty headcount ratio is not particularly sensitive to the choice of poverty line, since average consumption levels are so low that the poverty line lies at a point where the slope of the cumulative distribution of consumption tapers off (Figure 5). The deterioration of economic conditions has driven many poor households down to hardship conditions (Figure ). The poverty gap, which measures poor households’ average deficit in consumption relative to the poverty line, is equal to 47 percent in 2016. The average poor household is therefore consuming about one‐half of the poverty line in 2016 (US$ 1.00

> 32 The international poverty line was first introduced in the 1990 World Bank World Development Report with the intent of measuring poverty across countries in a consistent manner. This international poverty line used data on 33 national poverty lines for the 1970s and 1980s and represented the predicted poverty line for the poorest country in the sample, equal to about $0
|**Total**|
|2002|14,747|||||||||14,747|
|2003|63%|5,435||||||||14,701|
|2004|49%|57%|4,397|||||||14,730|
|2005|42%|44%|55%|3,633||||||14,565|
|2006|36%|36%|41%|54%|3,628|||||14,696|
|2007|33%|32%|36%|42%|66%|3,250||||15,076|
|2008|30%|28%|32%|36%|53%|57%|3,057|||15,358|
|2009|28%|26%|29%|32%|46%|45%|56%|2,982||15,676|
|2010|26%|24%|27%|29%|41%|40%|44%|58%|2,846|15,925|

Notes: The top of each row provides the count of new exporters entering the market in the given year. The last column provides the sum total of exporters in each year.

Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

Table 9 summarizes the rates of survival by cohort. For instance, in 2003, 5,435 exporters start exporting. By 2004, only 57% of the original cohort manages to survive, i.e. 43% of firms exit the market and no longer export. After 7 years, i.e. by 2010, 24% of the original cohort still 35
are proportional to (imputed) rental values (as the household expenditure survey contains data on rents only, and we rely on the survey to identify the relationship between house value and household income),<sup>17</sup> (c) it is assumed that the Pareto tail index of the income distribution has been stable between 2009/10 (the time of the survey) and 2013/14 (the time of the house price database), (d) it is assumed that one house constitutes one household (the fact that top income households could be associated with multiple houses may lead us to under-estimate inequality) and that all houses are domestically owned, and (e) we will only be using house price data for Cairo and Alexandria to estimate the top tails of their respective income distributions. For the rest of urban Egypt the entire income distribution will be estimated using the HIECS. The latter decision is motivated by the fact that: (i)

> 17Under a non-arbitrage condition, house prices and rental values are expected to move in parallel, see e.g. Himmelberg et al. (2005).

16
estimated impact of drought on consumption declines may be upwardly biased.

These sources of estimation bias are difficult to take into account with cross-sectional data. However, as proposed in Datt and Hoogeveen (2003), a potential solution is the use of instrumental variables (IV) estimation. Instrumental variables are constructed as community means of shock variables leaving out the current household. These instruments are valid if households are more likely to report a shock when neighbours also report that shock, although neighbours affected by the shock do not influence other household’s economic well-being in another way than through the household’s self-reported shock.

# **4.2.Data**

The vulnerability and food security survey was conducted in Haiti in October and November 2007 on approximately 3,000 households living in 228 rural communities. This survey has been realized by the National Coordination of Food Security Unit with the partnership of the World Food Program. A community-related component was added to the household component of the survey, in connection with infrastructures and accessibility to basic social services. So, this survey contains quantitative information on household consumption expenditures, production, income and assets as well as a good deal of qualitative information on perceived shocks, coping strategies and other hazards. Our empirical study will thus try to assess vulnerability by using both sets of data –quantitative and qualitative.

26
remittance flows will therefore exceed the sum of official recorded flows and informal flows by the extent to which misreporting is important. To account for under-recording, we also include net errors and omissions as a percent of GDP in the panel exercise.

Provided misreporting is not correlated with the transaction cost variables, our estimates of the size of the informal sector will still be unbiased.

In addition, misrecording implies that part of the dramatic increase in official remittances in recent years owes to improved data collection and not just a shift from the informal to the formal sector. For example, Mexican remittances increased by 95 percent from 2000 to 2004. According to the central bank, improvements in central bank recording were a key factor: only after 2002 did Mexico begin recording transactions from money transfer providers (de Luna Martinez 2005).

We also collected data on the transaction costs associated with sending remittances to specific countries. These transaction costs include both explicit service fees, and the implicit exchange rate spreads (defined as the difference between the exchange rate offered by the remittance service provider, and the central exchange rate as quoted by Bloomberg). The total transaction costs, the service fees, and the exchange rate spreads were all computed assuming a remittance size of USD 200, and are quoted as a percentage of this amount. The transaction cost data were collected from Western Union, refer to transfers from the United States or the United Kingdom to the relevant country, and are only available for the year 2005. We use US costs for countries with large stocks of migrant workers in the United States, UK costs for countries with large migrant stocks in the United Kingdom, and an average of costs from the two countries 13
Policy Research Working Paper 6148

# **Abstract**

The paper provides basic guidelines and tools for simulating subsidy reforms with Stata using a single cross-section survey. Simulations are discussed under a partial equilibrium and medium-term framework using a marginal approach. The paper distinguishes between _single priced products,_ such as fuel or bread, and _multiple_ _priced products,_ such as household utilities. Part I provides basic instructions for carrying out subsidy analyses. Part II outlines economic theory and formulae for the two types of products considered. Part III illustrates the use of the Stata codes, which are downloadable from the Internet.

This paper is a product of the Poverty Reduction & Economic Management Department, Middle East and North Africa Region. 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 Araar.Abdelkrim@ecn.ulaval.ca and pverme@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
, and are those more directly related to our work. Mulabdic et al. (2017) and Dhingra et al. (2021) exploit the World Bank Deep Trade Agreements 1.0 ( _horizontal depth_ ) Database<sup>5</sup> (Hofmann et al., 2017) to measure the depth of PTAs, while Dhingra et al. (2018) exploit data from DESTA. All three works extract data on goods, services and value-added trade from the

> 5This database is a precursor to that used in this paper, which is the World Bank Deep Trade Agreement 2.0 ( _vertical depth_ ), by Mattoo et al. (2020)

10
demographic characteristics with units of analysis as individuals. For the purpose of this paper, we use the LFS for the years 2007, 2012, and 2018.

Our analysis includes the following labor force variables: real wages, informality status and female labor force participation. Over the period 2007 and 2018, several changes were introduced in the Egyptian LFS together with updates in concepts and definitions used which are standardized to make key labor market outcomes, administrative geographies as well as industry classifications comparable over time.

Table 1 in annex 1 provides an overview of the main labor force variables’ summary during the study periods. The data reveals a notable rise of 8.2 percentage points in informal employment within Egypt from 2007 and 2018, reaching a substantial 59 percent in 2018. While variables like real wages and female labor force participation demonstrated positive growth from 2007 to 2012, they experienced a decline by 2018.

Measurement and Construction of Export Exposure Using Input-Output Linkages Any changes in the foreign export demand for products of a particular sector will have dual effects. First, it will lead to a direct increase in demand for output in that sector. Secondly, it indirectly affects the upstream sectors that supply inputs to the directly impacted sector. Not accounting for these linkages will lead to an underestimation of the export exposure at the district level, as some districts may not have a concentration of industries directly exporting but still be supplying to exporting sectors. The literature represents this chain of responses using Leontief inverse of an input-output production matrix for an economy, which clearly tracks the use of intermediate inputs by each sector (Goutam et al. 2017; Acemoglu et al. 2016; Acemoglu et al. 2012).

To explore potential effects of exports through domestic inputs, we employ the 2008 Egypt Input-Output table to calculate the input shares of each industry. These shares are determined by dividing the input usage by the gross output (which includes the value added in the own sector with own sector inputs). The resulting shares are then multiplied by the exports