rafmacalaba/data-use-mentions-extended huggingface dataset:
employment based size distribution. Luttmer (2010) highlights the linearity of the right tail of the US establishment and firm size distribution across employment, as well as the stationarity of the distribution over time, . using various sources of US micro-data<sup>12</sup> For a given value of the productivity step size _h_ and a given value of the exogenous exit probability _δ_ , the slope of the right tail of the firm size distribution in the model is determined by _α_ . To ensure that we are capturing the linear portion of the size distribution, we focus on the slope implied

> 11 ˆ 2 2 2 The exact value for the variance is _Var_ � _L_ � = ( _ηh_ ) _−_ [ _ηh_ ] (2 _p −_ 1) . The approximation is exact in the case of a stochastic process with zero drift, namely _p_ = 0.5. We show below that our calibrated value of _p_ is 0.467, which allows us to qualify the approximated value of the variance as a close approximation. The sole advantage of it is that we can independently identify the values of the parameters of the stochastic process that match their counterparts in the data.

> 12County Business Patterns Database, statistics from the Small Business Administration and the Business Dynamics Statistics from the census.

19
on two variables: GDP per capita and Polity V. GPD per capita is measured in real terms and published by the Maddison Project, and serves as an alternative to splitting the sample by income group, as is often done in the literature. The Polity Project rates countries based on how democratic they are and chosen here as a proxy for institutional strength. The main index produced by the Polity Project score countries from -10 to +10, where the lowest score is attributed to countries that are strongly autocratic and the highest score to countries that are strongly democratic. Compared with other indicators that</mark>

> 13 <mark>See Ams et al. (2018) citing Moody’s (2018).</mark>

> 14 <mark>However, in some cases our default designation differs from that of credit agencies because we may have uncovered information unavailable to these agencies at the time – for example considering a debt or dispute not captured by the agencies – or because in our judgment there are important reasons to signal or not a default. Such as creditors’ unwillingness to “accelerate” the contract because they are engaged in conversations, the presence of a legal dispute, or the technical nature of a default not capturing a sovereign’s inability to honor its commitments. See Appendix A.1.3 for an example of how the default data was compiled (Farah Yacoub et al. 2021).</mark>

10
such as Somalia.

The data used in the first six methods are from the raw census data. The data for the last three methods are missing because of omissions in the underlying data and need to be filled. Therefore, varying percentages of observations in each decade are assigned by the methods described (table 4). In 1960, 59 percent of observations are directly assigned from the raw bilateral data or from one of the disaggregations of the aggregate raw data (the first six categories). By 2000, this proportion rises to 69 percent. However, these observations account for some 84 percent of the total number of international migrants in 1960 (table 5). This proportion rises to 86 percent by 2000 because a small number of corridors (cells) account for a large proportion of global migration stocks. The bulk of the remaining international migrants are assigned on the basis of interpolation.

15
and intensity with which agricultural activities of interest are performed. Some outcomes in agriculture (labor inputs, harvesting of some crops) that are dynamic over the agricultural season would benefit from more frequent data collection, some happen only during certain periods, while others are static within a given crop growing season and may well be collected only at one point during the season. Centered around the main crop growing season, agricultural surveys often visit farms at the end of the season. This approach can lead to respondents having to remember activities and outcomes many months in the past, and recall decay has been found to affect data quality in agricultural surveys (Beegle et al., 2012a; Wollburg et al., 2020). Increasing the frequency of data collection has been shown empirically to improve data quality in several domains of interest (Arthi et al., 2018; Gaddis et al., 2021; Kilic et al., 2021; Knippenberg et al., 2019).

Phone surveys offer the flexibility and cost advantage to conduct surveys at the appropriate time and more frequently. Agricultural labor inputs are used frequently and often in a manner that is difficult to track. Arthi et al. (2018) in Tanzania and Gaddis et al. (2021) in Ghana show experimentally that weekly phone surveys improve the quality of labor data relative to the standard end of agricultural season inperson modules. Weekly phone surveys have also shown to yield better harvest data quality for extended harvest crops, like tomatoes, roots and tubers (Ceballos et al., 2020; Kilic et al., 2021), which are often harvested over the course of the season rendering recall difficult. The Georgia Survey of Agricultural Holdings fielded an AGRIS production survey over the phone relying on quarterly data collection to shorten the recall period (50x2030 Initiative, 2020). Higher frequency data collection over the phone can also be suitable for agricultural input and output prices (Hermosilla, 2017; Hoogeveen et al., 2014), though community- rather than household-level data
# **4.2.Sensitivity to spatial deflation**

As mentioned above, the headline result that 80 percent of the extreme poor live in rural areas may be an overestimate. In Africa and much of South Asia, where the majority of the extreme poor live, welfare aggregates are not consistently spatially deflated when estimating international poverty rates. Since the cost of living is typically lower in rural areas, failure to account for regional price differences will overstate the share of poor that are rural.

How much would using nominal welfare aggregates affect the share of the poor that is rural? To get a sense of this, we analyze data from countries in South Asia.<sup>33</sup> For the purposes of this exercise, urban and rural India are considered to be separate countries, because each uses a different PPP exchange rate. As expected, using spatially deflated aggregates lowers poverty in rural areas, and within urban and rural India, reverses the urban-rural gap in the poverty rate (Figure 23). However, using deflated rather than nominal aggregates only moderately decreases the share of the poor in rural areas (Figure 24). In Bangladesh and Nepal, for example, deflating the welfare aggregate using regional poverty lines would cause the share of the poor that are rural to fall 6 to 7 percentage points.

**Figure 23: Sensitivity of urban/rural poverty rate to spatial price adjustment**

<!-- Start of picture text --> Extreme poverty rate (%)<br>60<br>Urban Nominal Urban Spatially deflated<br>51.6<br>50 46.3 Rural Nominal Rural Spatially deflated<br>40<br>29.9<br>30<br>24.8<br>23.4<br>20.8 20.018.9<br>20 17.0 16.3<br>11.7<br>10.29.1 8.9 10
In contrast, servitization motivated by consumers’ preference for bundling could be an avenue to maintain or expand manufacturing revenue. Case studies of GE IBM, Siemens and Hewlett Packard suggest that servitization helped stabilize overall revenues (through diversification), while increasing revenue from main manufactured products (Visnjic and Van Looy, 2009; Sawhney, 2004).<sup>5</sup> These bundled products are also less sensitive to price-based competition which can increase firms’ profits (Mallaret, 2006). This effect may be especially important for “smoothing” services like finance, insurance or highly customized services (e.g. Cusumano et al., 2015). Analysis of French firm-level data reveals that over the period 1997-2007, servitized firms increased their sales of goods by 3.6 percent and profitability between 3.7 and 5.3 percent (Crozet and Milet, 2015).

However, servitization may not always be successful in defending domestic manufacturing sales – an outcome referred to as the _servitization paradox_ (Neely et al., 2011). Some studies have alluded to the importance of reaching threshold levels of servitization before reaping superior financial returns (Fang et al, 2008). Others argue that firms need to have the capacity to handle the cultural and organizational shifts required with servitization (Gebauer et al, 2005). Servitization may require firms to incur fixed costs of servitization, which only the more productive firms may be able to afford.

Our work contributes to the literature on the servitization of manufacturing by conducting a rigorous firmlevel study in a developing country. Most existing studies either use a case study approach or, even when they rely on firm-level data, the samples are hardly representative (e.g. Neely et al., 2011). Although a few studies such as Crozet and Milet (2015) and Breinlich et al. (2014) study servitization using representative firm-level data from France and the United Kingdom, there is no previous firm-level analysis of servitization in a developing country. We fill this
combination of country-specific loan approval decisions from previous years with "typical" disbursement rates. I will use changes in this predicted disbursement series as an instrument for changes in government total spending.

The second major data requirement for this paper is data on government spending itself. My primary source for this is the total government expenditure series reported in the IMF's World Economic Outlook (WEO) database. While this is by far the most comprehensive single data source for government spending, its country-year coverage is nevertheless limited, particularly for low-income countries. Data on official creditor lending from DRS is available for 2804 country-years over the period 1970-2010 in my full regression sample. However, WEO data on government spending cover only 1732, or 62%, of these observations. To fill this gap, I substantially expand coverage of the government spending data by piecing together additional information from a variety of other published sources, including current and previous editions of the IMF's Government Finance Statistics, the African Development Indicators of the World Bank, and data published by the Fiscal Affairs Department of the International Monetary Fund.<sup>13</sup> The success of my identification strategy requires a strong correlation between fluctuations in government spending and fluctuations in predicted disbursements on loans from official creditors. This is unlikely to be the case in countries that do not rely significantly on official creditors as a source of financing for public spending. Accordingly, I restrict attention to those countries where disbursements on loans from official creditors are on average equal to at least one percent of GDP, averaging over the entire period 1970-2010. In addition, in order to have meaningful within-country time series variation for each country, I further restrict the sample to those countries that have at least 15 years of data on government spending.<sup>14</sup> This results in a core regression sample of 2804 country-year observations covering 102 countries listed in Table 2, and averaging 28 annual observations per country. Averaging

> 13 Specifically, I draw on the total government spending
**Summary**

The lack of private financial flows to Sub-Saharan Africa in recent years has made countries in the region increasingly dependent on official development assistance (ODA), in the form of either grants or highly concessional lending. Meanwhile, "aid fatigue" has become a common concern among many in the field of development, and aid flows have fallen in recent years as budget constraints in the major donor countries become increasingly tight.

Given the increasing demand from developing countries for additional aid flows on the one hand and the limited resources available from industrial countries on the other, aid effectiveness is a critical issue. In this context official donors have stepped up their efforts to provide larger amounts of aid (with partial or no repayment obligation) and to provide official debt forgiveness, bilaterally and through the Paris Club.

This paper evaluates the effect these initiatives had on recipient countries based on data accumulated during 1989-93. The analysis focuses on the potential tradeoff between pure grants and bilateral ODA debt forgiveness, in thirty-two poor African countries.

The analysis first compares countries that have received significant amounts of ODA debt forgiveness in recent years with those that have not, and then compares the two groups of countries during 1984-88 and 1989-93 (ODA debt forgiveness surged during the latter period). The effect of the two types of aid is measured by the recipient countries' import capacity.

There are five major conclusions of the paper:

- Grants have allowed recipient countries to significantly expand their import capacity over the past decade (1984-93). In fact, grants and import capacity-inclusive of international reserves accumulation-have been increasing since 1984. Yet the increasing share of concessional lending and debt relief in recent years has not allowed these countries to reduce their total indebtedness and solve their debtoverhang problem. In fact, arrears increased significantly during this period for all the countries in our sample.

- The 32 countries in the sample can be grouped according to the type of ODA debt relief they have received from donors. Of the three groups, the highest recipients have received
<mark>employment with those based on informal employment allows to tentatively assess the role of the informal labor market.</mark> Finally, this study contributes to the econometric literature on the labor market effects of GVC <mark>participation at the country-sector level. The majority of earlier sectoral studies focused on the effect of outsourcing, based on national input-output linkages, on the relative demand for workers or total labor demand in one specific country (see, e.g., the seminal study by Feenstra and Hanson 1996), whereas our analysis covers LMICs and also uses GVC measures based on international input-output linkages.</mark><sup>8</sup> <mark>This study also relates to the literature on measuring social upgrading through GVC participation, especially with regards to job creation. More research has been dedicated to studying the economic rather than the social impacts of trade and GVC participation. Empirical evidence across and within countries confirms that GVC participation fosters productivity, value-added, and growth (Constantinescu, Mattoo, and Ruta 2019; World Bank 2019; Stolzenburg, Taglioni, and Winkler 2019; Pahl and Timmer 2020). Research suggests, however, that social upgrading does not necessarily follow economic upgrading (Milberg and Winkler 2013; Kruse 2023), for instance due to high labor market adjustment costs (Hollweg et al. 2014).</mark> In section 2, we apply econometric analysis to estimate trade-employment and trade-income elasticities within sectors across countries. Labor demand models can be derived from a variable unit cost function<sup>9</sup> and have been applied widely in the literature at the country-sector-year level (see Appendix 1). The analysis is based on two different data samples: a sample of 48 HMICs covering mainly formal employment and a sample of 37 LMICs capturing both formal and informal employment (for details, see Appendix 2). In addition, the dataset of HMICs enables us to estimate both trade-employment and tradeincome elasticities and therefore examine which of these channels
# **7. Annexes**

## **_A. Details for measuring accessibility, availability and safety of the public_**

## **_transport network_**

In this annex we provide the technical details for the construction of measures of accessibility, availability, and safety.

### _a. Measuring accessibility_

#### **Definition**

Our first measure of the public transport network focuses on measuring the spatial accessibility of job opportunities throughout the city using either public transport or walking. More precisely, for each residential location, we compute the percentage of total jobs<sup>38</sup> that are accessible within 60 minutes during peak hours (8am to 9am). The measure of accessibility combines the shape of the transport network with the distribution of jobs within the city to measure accessibility— combining the time taken to walk to the public transport stop, time spent in the public transport vehicle, and any time needed to walk to a job opportunity. This approach has strong support in the transport and accessibility literature (see Dijst et al., 2002; El-Geneidy & Levinson, 2006; and Palacios Santana & El-Geneidy, 2022 for a theoretical discussion and Peralta Quiros et al., 2019 for an example of practical application).

#### **Data sources**

We use various sources of data to construct this index.

First, we use a layer of the street composition for each city. All street grid data is taken from OpenStreetMap. For the three cities, there are no pedestrian restrictions, meaning all streets and links in the network are considered accessible to pedestrians.

Second, we use the public transit network that has been collected. The collected frequency of departure, and speeds by which the vehicles travel on any route are used for the analysis (see 2.1. for details and maps about this data).

Third, we use the distribution of employment in each city. We base it on Barzin et al. (2022
**</sup>||||||||||**De**<br>**10**<sup>**th**</sup>|||||||||||
|**richest**|1|0|1|2|2|3|7|8<br>23|54|**richest**|0|1|2|2|4|5|8|10|20|47|

_Note: Deciles are for real consumption per capita. Each number in this table represents a percent of the nth decile population which either remained in the same decile three years later (diagonal) or moved to a new decile (upward mobility- green, downward- grey). All rows sum up to 100, the population of each decile. Source: own estimates based on HIECPS 2005-2008._ Panel A represents initial data showing observed actual transitions between deciles of distribution. Panel B shows the most extreme cleaning, when all observations are replaced by their predictions from 17
readiness. Data were collected from children, their caregivers and a variety of other respondents in 310 villages in 9 districts across Indonesia. Respondents were 11,263 caregivers who resided in these districts. Among those, 7,982 were mothers who fully reported on outcomes and attributes we employ in our models below, including location of her own birth. They represent 171 distinct districts of birth. Our analysis further restricts this sample to women in birth cohorts surrounding the time of the INPRES program implementation (those born between 1966 and 1975), leaving us with 2,118 mothers from 50 different districts of birth. Figure 1 presents the geographic distribution of contemporaneous district of residence (Panel A), as well as the spatial location of districts of birth for both the full sample (Panel B) and the working sample (Panel C).

Previous analysis has compared contemporaneous household level characteristics in the Indonesia ECED data to households observed in the rural subsample of Indonesia’s nationally representative National Socioeconomic Household Survey (SUSENAS). Hasan, Hyson, and Chang (2013) report that the education levels of the heads of households, the rates of asset ownership among households, and the quality of construction materials used in the home among the households in the ECED data are comparable to those observed in the SUSENAS. Similar analysis using a village level census reveals that the villages in the ECED data share many characteristics in common with the rest of rural Indonesia. As such, the underlying data from which our analytical sample is drawn share several characteristics in common with households and villages typically found in rural Indonesia.

Our identification strategy described below relies on both the year and location of birth, and this is because the INPRES program followed specific rules for allocation of schooling expansion funds across districts. Therefore, our second source of data is information on construction planned under INPRES reported in Duflo (2001), which identifies the number of schools to be built in each district after 1974. We merge this district-level data to our working sample of 2
Policy Research Working Paper 10670

# **Abstract**

Refugees face important barriers to participation in the formal market, which locks them in informal employment and makes them more vulnerable to shocks. Using data from Jordan, this paper compares the job finding and separation rates of Syrian refugees to those of their hosts before and after the onset of the COVID-19 pandemic. The findings show the change in these rates over time for Syrians to be similar to those of their Jordanian hosts prior to the pandemic, with a significant divergence after the start of the pandemic. Distinguishing between Syrians living in camps and those living in host communities shows that the Syrian disadvantage was entirely explained by living in camps.

This paper is a product of the Office of the Chief Economist, 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://www.worldbank.org/prwp. The authors may be contacted at wahby001@umn.edu and assaad@umn.edu _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
country legally must be sponsored by a prospective employer; the exception is the employer category (representing a mere 1 percent of non-citizens) which includes foreign investors seeking to set-up business in Malaysia.

The income implications of these employment status results are ambiguous at this stage. Milanovic (1998) finds that self-employment in Malaysia is much more common among the poor, and that the incidence of poverty is 32 percent for those primarily self-employed in agriculture, compared to 10 percent for the population at large. On the other hand, the greater share of own-account workers among Malaysians corroborates earlier assertions by Blau (1986) that workers in Malaysia tend to move

> The 1995 labor force survey yields somewhat different estimates for own-account workers: 19 percent for Malaysians, 9percent for non-Malaysians _(Malaysia: Labor Force Survey Report 1995)._