rafmacalaba/datause-encoder-data huggingface dataset:
WPS4118

# **Self-selection patterns in Mexico-U.S. migration: The role of migration networks**<sup>*****</sup>

## **David McKenzie**<sup>**a**</sup> **and Hillel Rapoport**<sup>**b**</sup>

**a World Bank, Development Economics Research Group b Department of Economics, Bar-Ilan University and CReAM, University College London**

## **Abstract:**

This paper examines the role of migration networks in determining self-selection patterns of Mexico-U.S. migration. We first present a simple theoretical framework showing how such networks impact on migration incentives at different education levels and, consequently, how they are likely to affect the expected skill composition of migration. Using survey data from Mexico, we then show that the probability of migration is increasing with education in communities with low migrant networks, but decreasing with education in communities with high migrant networks. This is consistent with positive self-selection of migrants being driven by high migration costs, as advocated by Chiquiar and Hanson (2005), and with negative self-selection of migrants being driven by lower returns to education in the U.S. than in Mexico, as advocated by Borjas (1987).

Keywords: Migration, migration networks, educational attainments, self-selection, Mexico JEL codes: O15, J61, D31.

World Bank Policy Research Working Paper 4118, February 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Corresponding author: David McKenzie, MSN MC3-300, World Bank, 1818 H Street N.W., * Washington D.C., USA 20433. Email: <u>dmckenzie@worldbank.org. We thank Gordon Hanson and</u> participants at the 2006 meeting of the European Society for Population Economics, Verona, for helpful comments.
context that caused their displacement in the first place.

The qualitative focus groups provide some important insights for international actors. International definitions of social cohesion may not match local realities. International humanitarian actors should keep abreast of the dimensions of social cohesion beyond simply the limiting of violence. The qualitative and participatory exercises described in this paper could be re-purposed for development aid, which may not fit local needs (Ferguson 1990).

In addition, the findings show that the relationship between hosting displaced persons and perceptions of social cohesion is not necessarily altogether negative. Instead, refugees and IDPs are associated with host community perceptions of social cohesion differently; refugees in general correspond to lower levels of social cohesion than IDPs. We also find that hosting in cities and general population exhibit some important differences. The backlash frequently observed in refugee studies may not be present in situations of internal displacements and such an effect can be mediated by contextual factors of displacement.

The results suggest that programming that seeks to address the needs of host communities in contexts of forced displacement may require fundamentally different approaches than those that are used in refugee or camp-based displacement scenarios. Programmatic decisions should focus on supporting host communities even in informal contexts but remain cognizant and avoid uneven distributional consequences. Programming that seeks to address the needs of displaced persons in such scenarios must consider how multiple displacement or hosting during protracted conflict presents different challenges than single, long-term displacements.

Humanitarian actors must additionally remain cognizant of gender. The analysis suggests that hosting may disproportionately and negatively impact women, in situations with IDPs in cities. Female respondents in cities were more likely to have negative perceptions of in group and out group relationships and were less likely to participate with other ethnic groups if they had IDPs in their communities. In contrast, hosting IDPs was associated with improved perceptions of social cohesion among men for all sub dimensions other than access to basic needs. International hu33
where _Capital i_ refers to the average _HR_ or _L_ of country _i_ during the three years —1995, 2000 and 2005 _;_ and  ˆ is the estimated average elasticity associated with the _k kth_ disaster (from Tables 4-7).<sup>9</sup> _Affected i_ , _kt_ and _Affectedi_ , _kt_  _n_ denote the average number of people affected by a _kth_ disaster in country _i_ at time _t_ and _t-n_ , respectively. _Affected All_ , _kt_<sup>refers to the average number of affected</sup> people across the entire sample at time _t_ ; while _Affected All_ , _kt_  _n_ is similarly defined although it corresponds to time _t-n_ . These four latter variables are also expressed in per capita terms for sensitivity analyses.

# <u>Human and Intangible Capital (HR)</u>

As an example, we estimate equation 13 using the human and intangible capital (HR) and total impacts of earthquakes events lasting _two years_ . Table 8 and Table 9 present the estimated losses in HR of selected countries due to earthquakes, with respect to the number of people affected and people affected per capita, respectively.

Equation (13) shows that the losses in capital are dependent on two factors ― the relative magnitude of impacts of a natural disaster (in terms of people affected) and the value of capital. Hence, a relatively higher number of people affected does not necessarily translate to greater losses in the value of human and intangible capital. For instance, in Table 8, the HR losses of Japan (US$246 per capita) are higher compared to that of China ($82 per capita) even though China has a greater number of people affected. Similarly, in Table 9, Japan has higher losses of HR (US$138 per capita) relative to the losses in Turkey (US$34 per capita) and Colombia (US$24 per capita) although Japan has a lower number of affected people per capita.

The upper and lower limits of HR losses can be obtained by calculating the value of losses _plus or minus_ the standard error. For example, in Table 8, the HR losses in Mexico ranges between US$19.07 and US$42.11 per capita (i.e., US$30.59 per capita  US$11.52); while in Table 9, the lower and upper bounds of HR losses are, respectively, US$2.88 and US$10.04 per capita (i.e., US$6.46 per capita  US$3.58).

> 9 We divide this by 100 because the elasticity is expressed as percentage.

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**_<u>________________________________________________________</u>_**

indicators that are closely followed in the specialized transport and economic press and by transporters who need to follow the various national trends affecting their operations. GDP forecasts are based on the latest forecasts of the EU statistical office (Eurostat) in its "Quarterly National Accounts ESA". In those countries where forecasts are not available, trend analysis and average growth of neighboring countries are used to make the forecasts. Registration forecasts are based on time series analyses, taking into account other factors such as the development of transport volumes and GDP. Transport volumes forecasts are made using NEAC Transport Simulation System which quantifies the relationship between production sectors and the transport they generate, using various economic scenarios, to estimate future transport volumes in and between 176 regions.

# _<u>United Nations Economic and Social Commission for Asia and The Pacific (UNESCAP)</u>_

The report _Transit Transport Issues in Landlocked and Transit Developing countries (2003)_ includes average and maximum waiting times at the border for several landlocked Asian countries: LAO PDR, Mongolia, Nepal, Uzbekistan and Kazakhstan. The intention is to extend the study to border crossings along the Trans Asian corridors.

For individual countries, the _<u>Moroccan Government</u>_ provides detailed data for several years on clearance through Customs. Recent DTIS studies and Trade and Transport Facilitation Audits carried out in select countries are also good sources of information.

“ _–_ _<u>Trade and Transport Facilitation An Audit Methodology</u>_ ”, published by the World Bank in April 2000, the toolkit has incorporated changes to reflect new developments such as recent antiterror security requirements, important trade facilitation initiatives. The toolkit seeks to link audit results, through careful analysis, to appropriate remedial Action.

_<mark>World Bank Trade and Transport Facilitation Audits (TTFAs)</mark>_ are being carried out to identify the obstacles associated with the cross-frontier movements of a routine assignment and means of related payment by both exporters and importers. These standardized audits should: (i) enable more systematic cross-country and historical benchmarking; (ii) be readily reusable by multiple institutions (through an integration of their requirements in surveys/instruments); (iii) improve effectiveness of reviews; (iv) reduce the present duplication of efforts; and (v) enable countries to conduct their self assessment.

Based on interviews, TTFAs focus on the effectiveness of procedures and costs of services available to international trade encompassing:

- Inland, sea, and air transport costs related to movements of both inputs and outputs;

- Quality and range of logistics services and infrastructure;

- Trade finance and sources of commercial risk;

- Procedural and documentary requirements;

- Information exchange and coordination between private and public parties.

20 __________________________________________________________________________________________________________
**Figure 1: Commodity price indices**

<!-- Start of picture text --> Index, constant US$ (2010 = 100)<br>140<br>120<br>100<br>80<br>60 Metals<br>40<br>20<br>Energy<br>0<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Source : World Bank<br><!-- End of picture text -->

**Figure 2: Commodity consumption per unit of GDP**

<!-- Start of picture text --> Index, 1965 = 100<br>120<br> Metals, world Energy, world<br> Metals, world ex. China Energy, world ex. China<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->

**Source** : BP Statistical Review, World Bureau of Metal Statistics, and World Bank.

_— 21 —_
|**Manufacture of electrical**<br>**and optical equipment**|0.06%|94.8%|19,100|
|---|---|---|---|
|**Manufacture of transport**<br>**equipment**|0.01%|97.7%|48,400|
|**Manufacturing n.e.c.**|0.05%|95.7%|7,630|

Source: Author’s calculations using RUSLANA

# _ANALYSIS OF EXITS, SLUMPS, SURGES AND COMPETITION_

For firm-level data for Russian firms, the study exploits a rich, firm-level dataset called RUSLANA.<sup>32</sup> However, the same method is used to calculate sector-level<sup>33</sup> surges and slumps in RUSLANA. To measure productivity, value added per worker is used.<sup>34</sup> The current assets of the firm are used along with value added per worker to approximate firm-level estimates of total factor productivity. The price cost margin (PCM) measures of competition.<sup>35</sup> Controls are used for regressions: an interaction between 2-digit- industry codes and years to control to time-varying sector specific shocks, and controls for location fixed effects using 85 regions dummies. The controls for years account for overall macroeconomic shocks as well as timevarying industry-level factors such as political economy, demand shocks or technological shocks, while the location controls adjust for region-specific characteristics that are time-invariant. The regressions are also clustered sectors and years as the main explanatory variables do vary at the level of sector and year.<sup>36</sup> Finally also included is the age of the firm and dummies for size<sup>37</sup> as additional controls. Clearly, there are other firm-level time varying shocks which could influence the results that not captured here. However, to the extent that these are uncorrelated with other key variables (surge/slump, competition) conditional on the covariates the results are unbiased.

> 32 As with the growth rate calculation, operating revenue is used to calculate surges and slumps. Operating revenue was summed at the level of 4-digit NACE 1.1 for every year to calculate surges and slumps.

> 33 4-digit NACE 1.1 is used to define sectors.

> 34 The ideal measure of firm productivity would be total factor productivity (TFP) however data limitations do not allow to estimate it. The main drawback of labor productivity is that it is influenced by the capital intensity of the company. In other words, two companies that have exactly the same productivity measured using TFP but differ in their capital intensity will appear to have different labor productivity as the one with more capital will be able to produce more value added per worker. In order to address this problem, all regressions include the stock of capital (assets) of the company.

> 35 PCM=(Operating revenue - Cost of goods sold)/Cost of goods sold. A higher PCM means a lower the level of competition.

36 In this case the variables of interest are at the firm level while some of the main regressors vary at the sector-year level, hence clustering of standard error at the sector-year level is required as suggested by Moulton(1990)

> 37Refer to this link for definitions for micro, small and medium in Russia. - <u>http://www.gks.ru/free_doc/new_site/business/inst preob/obsled/mal_bisnes.htm</u>

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WPS3675

# **BUSINESS ENVIRONMENT, CLUSTERING, AND INDUSTRY LOCATION: EVIDENCE FROM INDIAN CITIES**

# Somik V. Lall and Taye Mengistae

# Abstract

How do differences in the local business environment influence location of industry within countries? How do the benefits of a good business environment compare to those from good market access and agglomeration economies from industry clustering? We examine these questions by analyzing location decisions of individual firms. Using data from a recently completed survey of manufacturing firms in India, we find that both the local business environment and agglomeration economies significantly influence business location choices across cities. In particular, excessive regulation of labor and of other industrial activities reduces the probability of a business locating in a city. Our findings imply that in order to attract industrial activity, smaller or remoter cities need to offer even more attractive policy concessions or reforms in order to offset the effects of their relatively adverse (economic) geography. Our methodology pays special attention to the identification of agglomeration economies in the presence of unobserved sources of natural advantage.

Keywords: Business environment, agglomeration and clustering, firm location, Indian industry.

JEL Classification: L5, L6, R12, R3 World Bank Policy Research Working Paper 3675, August 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1
42

**Table A.2. Revenues collected by Ho Chi Minh City from each district in 2010 and the resulting estimate of economic value in each district.**<sup>**27**</sup> **f**

||2010 Revenues<br>(billion VND)|Percent of Total<br>Ho Chi Minh City<br>Revenue|Economic Value<br>(billion USD)|
|---|---|---|---|
|_Ho Chi Minh_<br>_City Total_|_11,225_||_24.5_|
|Binh Thanh|470|4.5%|1.10|
|District 1|187|6.9%|1.70|
|District 10|232|2.9%|0.70|
|District 3|189|2.7%|0.65|
|Go Vap|548|5.4%|1.33|
|Phu Nhua|175|2.3%|0.57|
|Tan Binh|430|5.9%|1.44|

# **_Modeling Vulnerability_**

The vulnerability of people or economic assets can depend on a number of factors related to flooding – the flood depth, duration, rate, etc. Many vulnerability functions found in the literature are depth-damage relationships, mapping a certain level of depth to a certain percent of impact or loss. We use depth-damage curves in this study as well, given that SWMM provides us with information about inundation depth but not other flood characteristics.

Depth-damage curves for the population and economic assets in Vietnam or for comparable countries are not well developed, though recent efforts have begun to gather data that could support the development of such functions (Phi _et al._ 2012). Given the lack of good data on vulnerability, we have developed candidate depth-damage curves, but we treat key parameters of this relationship as uncertain and include them in the exogenous factors varied in the analysis. Importantly, as our analysis in Chapter 5 reveals, although vulnerability is deeply uncertain, the comparative performance of the strategies we consider is not strongly affected by this uncertainty. Other factors play a much more important role. Thus, uncertainty regarding vulnerability turns out not to be decision-relevant in this study.

## **Population Vulnerability**

In the absence of other information, we have chosen a sigmoid function to describe the population depth-damage relationship shown in Figure A.5. Sigmoid functions have _________

> 27 Tax revenue data are from the Ho Chi Minh City Statistics Book, Table 3.11.
the US where the size-age ratio is eight times (Hsieh and Klenow, 2012), there is clear evidence that older firms in the formal manufacturing sector are larger than younger firms.

We do find stark differences in firm lifecycle in the formal and informal sectors: Older firms in the unorganized manufacturing sector in India employ fewer people than firms younger than 5 years old. This is consistent with recent findings in La Porta and Shleifer (2008) who show that informal firms look very different from formal firms in terms of size, productivity, and education level of managers and find little evidence that growth occurs by informal firms eventually becoming large formal firms.

Our results also contribute to the large literature on firm size and age (Dunne, Roberts, and Samuelson, 1989; Davis, Haltiwanger, and Schuh,1996; Cabral and Matta, 2003, Foster, Haltiwanger, and Syverson, 2012). Given the idiosyncratic distortions in the business environment, it is not clear what type of size-age gap to expect in developing countries. A recent paper by Hsieh and Klenow (2012) suggests that in India and Mexico, in contrast to the case of the U.S., the lifecycle of manufacturing plants is relatively flat - that in fact on average the surviving manufacturing plants in India shrink in size between ages of 5 and 35. The dual economy view in development economics (originally associated with Harris and Todaro, 1970) predicts that informal firms should look very different from formal firms in terms of size, productivity, wages paid, and the industries/markets they operate in. Recent studies such as La Porta and Shleifer (2008) also show that while informal firms account for a large portion of economic activity in developing countries, growth and development comes from the creation of highly productive formal firms. Strikingly, Hsieh and Klenow find that plants in both the formal and the informal sector share the same trajectories. Our results using data for the same year 1994/95 as in their paper stand in sharp contrast with their findings on the formal sector. Given 7
# **II. Earlier debates on labor markets and social cohesion**

Previous scholarship has taken multiple approaches to the interaction between social cohesion and labor markets. First, what I will call the ‘social atomization’ view in sociology, has focused on processes of labor market stratification and social segregation that undermine individual opportunities and mobility experiences. This view highlights the potentially divisive consequences of increased professional specialization and the division of labor. Other contributions emphasize potentially positive consequences of work-related interactions and identities. This includes the type of ‘bridging social relationships now emphasized in policy debates on social cohesion and jobs, as well as political and institutional solutions for conflict resolution often linked to more inclusive labor market and social protection policies.

Sociological research of work-relations has traditionally been dominated by the social atomization view. Marx famously predicted that workers who are solely in command of their labor would inevitably rise to secure a larger share of the economic surplus. Marx’s ideas of societies that are divided between economically-determined classes still play an important role in the work of some contemporary analysts of capitalist relations and politics (see for instance Wright 1979, 1997). Durkheim and Weber, often considered as the founders of ‘modern sociology’, also struggled with the question how functionally integrated societies could maintain their internal coherence through the capitalist transformation. Weber in particular noted that the rise of capitalist relations would undermine traditional socio-political orders, replacing them with new orders of social stratification and rational-legalistic systems of bureaucratic rule (for an overview of classical sociological literature see Norton and de Haan 2012).

More recent sociological work has elaborated the atomization argument against the background of increased international economic integration and rising wage inequalities (Norton and De Haan 2012). A large literature on ‘social exclusion’ in Europe argued that macro-economic transformations, such as slowing economic growth and a shift out of manual work would diminish economic opportunities of lower-skilled workers and undermine formal and informal systems of solidarity (Alphonse et al. 2008, Byrne 1998, Castells 1996, Hills et al. [eds.] 2002, Paugam 1996, Silver 1994). In the US and Latin America similar arguments have been made in the context of growing trends to residential and racial segregation around deprived inner-city neighborhoods (Wilson 1987, see also Anderson 1999 Borjas 1995, Caldeira 2000, Katzman et al. 2004, Loury 2002).

5
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