rafmacalaba/data-use-mentions-extended huggingface dataset:
## 2. Development implications of migration for the origin countries

Migration is a decision that impacts the welfare of the household, the home community, and in the end the whole economy in various ways (Azam and Gubert 2006). The welfare implications of migration on the origin country are most often, though not always, sizable and positive. The main channels through which migration alleviates poverty are increased incomes from remittances, ability to smooth consumption, access to finance for starting a new business, as well as tapping on to the knowledge and resources provided by the international community of the migrant diaspora. Besides pure monetary gains, migration and remittances allow for higher investment in health care and education. Yet, not all impacts are positive: exploitation of migrants by unscrupulous recruiters or employers is reportedly widespread; separation from family can be stressful for migrants; and large scale immigration can pose serious challenges to a nation’s identity and sovereignty.

# **_2.1 Economic implications for the country of origin_**

While migration has economic, social, and cultural implications for the sending and host societies, remittances the migrants send home are perhaps the most tangible and least controversial link between migration and development (Ratha 2007). According to the official estimates, migrants from developing countries sent over $315 billion to their origin countries in 2009, three times the size of official development assistance (Ratha _et al._ 2010) _._ The true size of remittances including unrecorded flows through formal and informal channels is likely to be even higher. While remittances to developing countries declined modestly in 2009 because of the global financial crisis, these flows have remained resilient compared to private capital flows, and have become even more important as a source of external financing in many developing countries.

Migration and remittances have both direct and indirect effects on the welfare of the population in the migrant sending countries.<sup>2</sup> A cross-country study of 71 developing countries found that a 10 percent increase in per capita official
, not in terms of services. Reaching further back, Donner (1972) published a map showing path lengths in units of porter days. All the above studies use incomplete and partial transportation data sets, particularly of pathways, implying a degree of inherent error.

Researchers in Nepal not working off custom models tend to use reported travel times from household surveys, especially the Nepal Living Standards Survey (e.g. Jacoby 2000, Fafchamps and Hill 2005). There are departures from this approach. Thapa and Shively (2017) estimate the relationship between accessibility and agricultural and food security indicators using paved road densities (per km2). Elsewhere, the International Labor Organization (ILO) recommends the use of Integrated Rural Accessibility planning in Nepal (ILO 2005). This is a participatory approach to assessing access and planning rural infrastructure development accordingly. On a local level Devkota et al. (2012) built a gravity model of interactions over trail bridges using network analysis to indicate access to various services and optimum locations for additional bridges. The model is promising for small‐scale applications but too reliant on rich local data to easily scale.

The government of Nepal’s treatment of remoteness varies considerably between ministries. Traditionally, ministries and development actors capture such metrics using self‐reported travel times from surveys or administrative questionnaires sent to local officials (MOE and UNESCO 2015). For instance, the Ministry of Health’s Second Long Term Health Plan (2007, pg. 10), called for:

- _“Essential Health Services at the District...[to be] available to 90 percent of the population living within 30 minutes travel time”_

Similarly, the Ministry of Education repeatedly references the number and types of children within 30 minutes walking distance to primary schools and 1 hour to secondary schools in its Consolidated Equity Strategy (MOE 2014).

The Department of Roads (inconsistently) embraces more sophisticated approaches to measuring accessibility when planning new infrastructure. This is largely to comply with the Government of Nepal
2020.

Individuals are randomly selected for a transition to a different labor market status based on transition shares calculated from administrative information and the European Labour Force Survey, both provided by EUROSTAT. Both types of transitions – to unemployment and a monetary compensation scheme - are based on quarterly transitions in order to approximate annual overall transition rates in 2020 (an overview of transition shares foreach category in each country is provided in Appendix B). The ‘out of employment’ status is based on detailed quarterly LFS data for net changes in employment broken down by sex, age, sector of activity and type of contract. The break-down differs in some countries due to small sample sizes. Net changes are applied due to sample differences between LFS and EU-SILC data. For those transitioning to unemployment, their labor market incomes are adjusted based on the number of months left in employment and labor market characteristics are changed in order make the ‘newly’ unemployed eligible to unemployment benefits if they fulfil the eligibility criteria of the benefit. Transitions to monetary compensation schemes are based on monthly administrative data collected by EUROSTAT from EU Member States and measures the stock of workers supported by governments. For employees who transit to monetary compensation schemes, employment income and the number of months in employment are adjusted proportionally, considering the number of months in compensation schemes. For self-employed who transit to monetary compensation schemes, self-employment income and the number of months in self-employment are adjusted proportionally, considering the reduction in the number of months worked.

> 2 <u>https://ec.europa.eu/eurostat/web/experimental-statistics/income-inequality-and-poverty-indicators</u> [accessed on 23 May 2022]

6
Engel and Rogers (1996) examine the relative price variability of similar goods among cities in Canada and the United States. They find that relative price variability between cities is significantly greater when a border is crossed.

2Wei (1996) develops a technique to estimate the domestic bias in trade using input-output tables to estimate internal trade for countries without regional data. He assumes that internal trade distances are one-quarter the distance between a country and its nearest trade partner, to estimate the border effect. One problem with this method is that the border estimate is proportional to the assumed internal trade distance.

3
# **Table 6. The Investment Dynamics**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable for various post-event periods. The independent variable is the averaged investment (capital expenditure divided by PP&E) for a subsample period between 2013 and 2017 net of investment between 2010 and 2012. Column (1) presents parameter estimates associated with the average investment between 2013 and 2014. Column (2) presents parameter estimates associated with the average investment between 2015 and 2016. Column (3) presents parameter estimates associated with the average investment of 2017. Titling Firm is a dummy variable that is 1 if the firm has allocated land before the land title scheme in 2012 and 0 if otherwise. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||∆<br>|(Investment/PP&E)<br>||
|---|---|---|---|
||Year 2013/2014|Year 2015/2016|Year 2017|
||(1)|(2)|(3)|
|Titling Firm|0.130*|0.262***|0.304***|
||(1.888)|(3.395)|(3.443)|
|ROA|0.002**|0.001|0.002*|
||(1
This paper contributes in two aspects. First, it shows that geographical targeting using woreda levels of undernutrition is a viable strategy, as it explains a large share of the variation in undernutrition levels, even after controlling for other known correlates at the household level that could be used as a targeting mechanism. Second, almost all available undernutrition studies on Ethiopia note the importance of location in identifying, understanding and addressing child undernutrition. The paper addresses this shortage of updated evidence on levels of undernutrition at the woreda level by estimating levels for 2014, using SAE.

The rest of the paper is organized as follows: Section 2 describes the data, Section 3 presents the SAE methodology, Section 4 shows the results, while Section 5 concludes.

# **2. Data**

The primary data source of undernutrition in this study is the Ethiopia Mini Demographic and Health Survey (EMDHS) 2014 [2]. The EMDHS 2014 is a stratified nationally representative survey, including 5,579 <mark>children under five years of age.</mark> The survey follows the Demographic Health Surveys [23] standard survey design, although some sections, such as HIV and immunization, were not included. Detailed information about the sampling is available in the survey’s report [2].

Undernutrition is measured as share of children moderately undernourished (below two standard deviations from the mean) based on z-scores for stunting (height for age), underweight (weight for age) and wasting (weight for height). The z-scores were calculated using EMDHS and the 2006 WHO growth standards [24].

To obtain estimates of undernutrition rates at the woreda level, EMDHS is combined, through SAE, with the 2007 Census. The 2007 census has two formats – a long and a short format. The long format is richer in terms of data and, in addition to demographic information, it includes information on assets, housing characteristics, education, fertility, and mortality. A randomly selected 20 percent of the households received the
reorganization proceedings. Half of this index measure is based on the implemented regulatory framework within which firms operate and therefore seems relevant for TFiS. Moreover, the general issue of “resolving [investment-related] issues” relevant for Mode 3 has also been identified as relevant by experts.

A third relevant database for potential TFiS measures is the World Bank’s **_Governance Indicators_** database. This database does not measure the quality and effectiveness of any regulatory proceedings themselves, but rather measures the quality and effectiveness of the institutions or the institutional framework as such. In that regard, the institutional dimension of this database sets it apart from the Regulatory Governance database as discussed above. To the extent that potential TFiS measures are related to the importance of the institutional strength and soundness of national regulatory bodies, two areas of this database seems of particular importance, which are **(a) Regulatory quality** and **(b) Government effectiveness.** Regulatory quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Compared to the previous two databases, all the elements are broad in nature and, admittedly, their definitions seem close to the regulatory governance issues described above. However, one should note that the two indicators really measure the _capacity_ of the government to effectively formulate and implement sound policies (see Kaufmann et al., 2010), not so much the process or implementation of the regulations as measured with the Regulatory Governance database. The two indicators are therefore closer to measuring items such as whether regulators are independent from any external political influence, have a clear mandate, expertise (i.e. skills) and resources to operate.<sup>10</sup
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE significantly higher than that of Sudan for similar destinations. South Sudan benefits from good connectivity with East African regional hubs namely Addis Ababa and Nairobi. (annexes 2 and 3).

**Figure 11. Evolution of seats and city pairs in South Sudan**

<!-- Start of picture text --> a. Seats b. City pairs<br>600,000 10<br>9<br>500,000<br>8<br>7<br>400,000<br>6<br>300,000 5<br>4<br>200,000<br>3<br>100,000 2<br>1<br>- 0<br>2001 2004 2007 2009 (Est)<br>200 1 Total 2004 2007 2009 (est)<br>Total<br>International<br>International<br>Intercontinental excluding flights between NA and SSA Intercontinental<br>Between Sudan and South Sudan<br>Between Sudan and South Sudan<br>Domestic<br>City pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems. NA = North Africa; SSA = Sub-Saharan Africa.

21
The average size in the sampling frame is 62 employees, whilst in our survey it is 83 employees (See Table 1). Likewise, the average age in the sampling frame is 12.3 years, whilst in our survey it is 21.7 years. Our survey has 28 percent of firms reporting to have exported in 2017 as compared to the observed 9.5 percent in the sampling frame when matching Ruslana information with customs data for 2016.<sup>12</sup> However, part of the difference could also be indicative of a selection bias.<sup>13</sup> To understand this, Annex Table A.1 presents a regression of respondent dummy on a host of firm characteristics to show that relative to all firms in the sample frame, respondents are more likely older, larger and exporters. Given our finding that exporters have better managerial capabilities (see section 3), we use this information to infer that the overall management score in Russia may be over-

> 11 Small firms are defined as those with no more than 100 employees and/or 800 million RUB in sales; medium firms are defined as those with no more than 250 employees and/or 2,000 million RUB in sales.

> 12 Customs data are another source of trade data that record each transaction by all exporting firms. These data are then matched to the Ruslana sample frame. The share of exporters in these matched data could be biased downward partly due to mismatches between customs data and survey responses and upward because the sample frame is not the full universe of firms. Moreover, it should also be noted that the percentage of surveyed firms that reported to be exporting in 2016 is 15%, which is 13 percentage points lower than that reported by respondents in 2017.

> 13 Annex table A.2 presents the results of an OLS regression of the probability of conducting a successful interview against a series of firm characteristics. Size, age and having exported in 2016 according to customs data
-|---|---|---|
|||Index of|Number||Index of|Number||Index of|Number||Index of|Number||Index of|Number|
|_Dependent variables:_|Profits<sup>α</sup>|profits<br>and sales|of emplo‐<br>yees<sup>α</sup>|Profits<sup>α</sup><br>|profits<br>and sales|of emplo‐<br>yees<sup>α</sup>|Profits<sup>α</sup>|profits<br>and sales|of emplo‐<br>yees<sup>α</sup>|Profits<sup>α</sup>|profits<br>and sales|of emplo‐<br>yees<sup>α</sup>|Profits<sup>α</sup>|profits<br>and sales|of emplo‐<br>yees<sup>α</sup>|
|_Impact of Formalization on dep. var. for het_|_erogeneou_|_s variable_|_=0_|||||||||||||
|Formalized (Gufe data)|‐16,140|0.039|0.29|‐15,696|‐0.225*|‐0.09|‐47,233|0.015|‐0.46|‐22,447|0.111|‐0.44|‐11,342|‐0.097|‐0.05|
|(Instrumented by treatment assignment)|(19,827)|(0.177)|(0.49)|(12,898)|(0.124)|(0.34)|(29,063)|(0
|---|
|Age|28.914|31.461|23.776|23.646|
||(19.606)|(19.099)|(3.326)|(3.299)|
|Male|0.489|0.504|0.512|0.382|
|Currently Employed|0.290|0.375|0.346|0.378|
|Currently Searching|0.102|0.151|0.302|0.984|
|_<_ complete secondary school|0.737|0.612|0.429|0.011|
|Complete secondary school|0.184|0.255|0.449|0.610|
|_>_ complete secondary school|0.072|0.120|0.116|0.379|

Table compares the sample of workseekers in this study (column 4) to several external benchmarks: the country (column 1), the province of Gauteng where the study takes (column 2), and people in Gauteng in the eligible age range for the study (column 3). National and provincial statistics are calculated from the Quarterly Labour Force Surveys (QLFS), averaging over all 2016 waves and using post-stratification weights supplied by Statistics South Africa. QLFS data are not available by city but the greater Johannesburg metropolitan area where the study is conducted accounts for over half the population of the Gauteng province. Standard deviations are shown in parentheses for all continuous variables.

50
2

# **1.  Introduction**

The financial crisis that emerged in the United States in mid-2007 quickly transformed into a global credit crunch that sharply reduced global trade flows. Many countries were affected by the severe global recession that followed, and millions of individuals throughout the world lost jobs, leading to increased informality and working poverty. In the aftermath of the crisis, policymakers throughout the world are questioning whether existing labor market regulations successfully balanced the competing goals of maintaining flexibility and protecting workers.

While discussions over the scope of labor market regulations are often heated, views are typically based either on the predictions of theoretical models or anecdotes. The lack of evidence is particularly acute for developing countries, where hard data are rare and mechanisms for enforcing regulations tend to be limited. Lack of enforcement is a particularly serious concern, given that significant portions of workers report earning wages that fall below the statutory minimum.<sup>2</sup> No systematic evidence, however, exists regarding compliance with other labor regulations, making it difficult to make an informed assessment of the costs and benefits of regulatory reform.

This paper contributes new evidence on the compliance of firms with severance pay regulations in Indonesia, and the extent to which changes in severance pay regulations could affect employment rigidity. This evidence sheds light on a vigorous policy debate that arose in Indonesia prior to the onset of the global financial crisis, following the passage of a 2003 labor law that increased employment protection and severance pay. The debate pitted the business community, which warned that increases in severance pay regulations would reduce employment, against organized labor, which argued that severance pay makes an important contribution to income security.

The reaction to proposed changes to severance pay regulations in 2006 exacerbated this debate. Following persistent lobbying by the business community, as well as firm surveys that showed that high severance pay obligations were a serious concern, the easing of severance pay requirements became a major part of investment-

> 2 See, for example, World Bank (2011) finds
POLICY RESEARCH WORKING PAPER 2194

# Summary findings

Dethier, Ghanem, and Zoli analyze whether political freedom and civil liberties help or hinder economic liberalization, using panel data from 25 postcommunist countries of Central and Eastern Europe and the former Soviet Union between 1992 and 1997.

Building on arguments and counterarguments put forth in recent literature, they identify the channels through which political freedom affects economic liberalization during the transition. Then they test the arguments empirically with an econometric framework that takes into account possible problems with simultaneity between the economic and political transitions.

Their empirical findings clearly reveal that democracy has facilitated economic liberalization in countries of Central and Eastern Europe and the former Soviet Union.

This conclusion is confirmed under various model specifications, for both ordinary and two-stage least squares procedures and using two different measures of liberalization.

The econometric results reveal that the existence of a vibrant civil society at the start of the transition has the most explanatory power in the authors' regressions.

This paper -a product of the Poverty Reduction and Economic Management Sector Unit, Europe and Central Asia Region - was presented at a seminar at the World Bank in April 1999. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Hafez Ghanem, room H4-201, telephone _202-458-5557,_ Internet address hghanem@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/html/dec/Publications/Workpapers/home.html. The other authors may be contacted at jdethier @worldbank.org or ezoli@worldbank.org. October 1999. (30 pages) _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
) (Loayza and Pennings 2018, and

> 2 <u>http://www.worldbank.org/en/news/feature/2016/04/16/spending‐more‐and‐better‐essential‐to‐tackling‐the‐infrastructure‐gap</u>

> 3 The relevant data and parameters for all countries are already pre‐loaded into the LTGM‐PC spreadsheet.

2
|
|Numberofcountries|27|17|27|17|27|17|
|Observations|156,850|107,922|156,850|107,922|156,850|107,922|
|R-squared|0.218|0.318|0.384|0.384|0.075|0.137|

Source: Own estimations using GMD. Notes: Linear probability models estimated by Ordinary Least Squares. Omitted categories: 15-24 years old, no education, being household head, agriculture, low-skilled occupations. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.

# **6. Profiling Urban Informal Firms**

We now turn to the firm data to understand the nature of informal jobs from a firm perspective. We rely on the IES, keeping in mind that the harmonized data are from only three countries: Zambia, Zimbabwe, and Mozambique.

The IES sample differs from the GMD sample in a few important ways. The IES sample has fewer female firm owners in comparison to the share of women in our GMD sample, informal firm owners are more likely to be in the prime-age group than they were in the GMD sample, and informal firm owners are less educated than in our GMD sample. Though 72 percent of urban informal firms in the IES data are owned and operated by one person (selfemployment) who has a low level of education, (Figure 12), the share of employers is much larger (25 percent) than in the GMD sample. This suggests that the findings based on worker analysis in the above section might underestimate the level of informality in urban Africa, since that analysis had assumed all employers were in the formal sector.

27