rafmacalaba/datause-encoder-data huggingface dataset:
section and/or longitudinal sources.<sup>8</sup> The scarcest data sources, but very helpful in clarifying migrant self-selection issues, are those that identify information about US immigrants in their origin country.<sup>9</sup> Economists use either earnings or occupational placement to proxy performance, i.e. the way immigrants deal with the complex and time consuming process of adapting to the host country’s environment. While focus on occupational placement increased over time, most studies evaluating and explaining migrants’ performance in the destination countries rely on earnings as a measure of performance.

The leitmotiv of discussions about immigrants’ performance is the concept of “assimilation” i.e. the impact of time spent on the US labor market, other factors being constant. The evaluation of “assimilation” involves two approaches: assessing how migrants perform at a certain point in time after migration versus their performance around the time of arrival; and assessing how the pattern of migrants’ performance over time relates to that of natives.

# **_IDENTIFICATION OF ASSIMILATION PATTERNS_**

Immediately after arrival in the US, and keeping individual characteristics such as age, education, race etc. constant, migrants are shown to earn less than natives. This finding was interpreted using the human capital model (Ben-Porath, 1967) as a consequence of the absence or poor transferability of specific skills valued on the US labor market. The initial human capital investment necessary to acquire some of these skills depresses wages even further. As migrants overcome their disadvantages relative to natives, earnings start to grow, mirroring what literature refers to as “the skill transferability” mechanism, first described by Chiswick (1978).

Using multivariate analysis on data from the US Census 1970, Chiswick (1978) predicted that, because migrants are positively self-selected from their countries of origin with respect to ability and motivation<sup>10</sup> , their earnings’ growth actually exceeds that of natives, a situation which makes it possible for immigrants’ earnings to equal and eventually overcome natives’ earnings within 10 to 15 years since arrival. Chiswick’s 1978 article inspired a significant number of subsequent studies, which confirmed its results in different empirical settings.<sup>11</sup> A challenge to this line of research came in 1985 when Borjas showed that cross-section analyses of migrants’

> 8 Hu (2000) uses the Health and Retirement Study (HRS) linked to the Social Security Earning data, while Lubotsky (2007) combines Current Population Survey (CPS) and the Survey of Income and Program Participation (SIPP).

> 9 McKenzie and Rapoport (2007)

> 10 Unless “push” factors, such as political conflicts, weaken or reverse the positive selection implied by the theory.

> 11 Carliner (1980), DeFreitas (1980), Long (1980), Bojas (1982), Borjas and Tienda (1985)

5
<!-- Start of picture text --> Figure 5.7: Comparisons between LAYS and Schoellman (2012)’s returns‐based measures<br>Returns to schooling in the US labor market Human capital derived using Schoellman’s<br>for immigrants from different countries approach applied to Barro‐Lee data versus<br>(Schoellman 2012) versus learning‐per‐year LAYS<br>relative to Singapore<br>0.5 0.6 0.7 0.8 0.9 1.0<br>TIMSS: Learning per year rel. to Singapore 0 5 10 15<br>LAYS using TIMSS<br>Source: Authors’ calculations, using US labor‐market returns data from Schoellman (2012) and LAYS analysis<br>using TIMSS.<br>Notes: For ln(h), see text for details. For LAYS, numeraire country is Singapore. Illustration for case where<br>learning starts at Grade 0 for TIMSS. For relative learning per year: Correlation coefficient is 0.23 and Spearman<br>rank correlation is 0.29. Excluding just three countries (Kuwait, South Africa, Rep. of Korea) increases these<br>correlations to 0.65 and 0.64, respectively. For LAYS: Correlation coefficient is 0.61 and Spearman rank<br>correlation is 0.68. Excluding the same three countries increases these to 0.84 and 0.85, respectively.<br>0.15 3.5<br>3.0<br>0.10 2.5<br>2.0<br>0.05 1.5<br>ln(h) using Schoellman (2012) approach<br>1.0<br>Returns to schooling in US labor market (Schoellman 2012)<br>0.00<br>0.5<br><!-- End of picture text --> Schoellman (2012) also derives a measure of the stock of human capital in a country, built on his measure of quality-adjusted years of schooling, and based on the following equation: where S� is years of schooling in country _c_ , and Q� is schooling quality in country _c_ and is implemented as the wage increment to an additional year of schooling of those educated in that country while working in the US. The parameter η is included to account for the fact that years of schooling acquired themselves might be higher because of school quality; based on his empirical estimates, Schoellman sets this parameter equal to 0.5. To compare our _LAYS_ approach to that of Schoellman (2012), we calculate his estimate of ln�h�� using Barro-Lee 36
|Exlanator variables||Depend|ent variable is t|hegrowth ofpe|r capita expendit|ure(LogYt– L|ogYt-1)||
|---|---|---|---|---|---|---|---|---|
|py|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|
||(0.010)|(0.011)|(0.073)|(0.011)|(0.011)|(0.010)|(0.011)|(0.010)|
|Lagged share of urban population|0.002***|0.002***|0.002***|-0.002|0.002***|0.003***|0.002***|0.002***|
||(0.001)|(0.001)|(0.001)|(0.005)|(0.001)|(0.001)|(0.001)|(0.001)|
|Lagged share of population with high-school<br>diloma|0.175|0.163|0.063|0.203|-3.784*|0.102|0.145|0.177|
|p|(0.156)|(0.155)|(0.138)|(0.146)|(2.155)|(0.135)|(0.146)|(0.159)|
|Lagged share of ethnic minority population|0.000|-0.000|-0.000|0.000|0.000|-0.006**|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.003)|(0.000)|(0.000)|
|Lagged Gini index|-0.887***|-0.902***|-0.873***|-0.882***|-0.934***|-1.008***|-1.924|-0.789***|
||(0.133)|(0.148)|(0.155)|(0.149)|(0.143)|(0.156)|(2.299)|(0.175)|
|Lagged poverty rate|-0.135**|-0.183**|-0.117|-0.145**|-0.127*|-0.073|-0.116*|1.312**|
||(0.062)|(0.075)|(0.077)|(0.071)|(0.067)|(0.071)|(0.069)|(0.600)|
|Lagged share of wage income|0.280***|0.302***|0.333***|0.291***|0.267***|0.326***|0.300***|0.353***|
||(0.098)|(0.100)|(0.097)|(0.099)|(0.095)|(0.098)|(0.096)|(0.101)|
|Lagged share of non-farm income|0.217**|0.203**|0.141|0.151|0.161*|0.147|0.141|0.213**|
||(0.097)|(0.091)|(0.097)|(0.094)|(0.086)|(0.096)|(0.090)|(0.092)|
|Constant|5.718***|6.568***|4.252***|4.582***|4.439***|4.257***|4.547***|4.416***|
||(1.534)|(1.522)|(0.585)|(0.610)|(0.567)|(0.536)|(0.772)|(0.550)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|567|567|567|567|567|567|567|567|
|R2/ Chi2|6983|7747|8511|9686|7710|8503|7664|6568|
|Number of provinces|63|63|63|63|63|63|63|63|
|Arellano-Bond test for AR(1)|-6.628|-6.502|-6.668|-6.661|-6.734|-6.800|-6.654|-6.715|
|Arellano-Bond test for AR(2)|4.146|4.120|4.194|4.309|4.235|4.299|4.293|4.109|
|Sargan test|198.4|186.1|225.8|214|210.9|209.5|222.8|219.9|

Note: i) Authors’ estimation from VHLSSs using province GMM models, ii) Robust standard errors in parentheses clustered at the province level, iii)*** p<0.01, ** p<0.05, * p<0.1.

50
**ORURO**

<!-- Start of picture text --> 5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>RIBERALTA<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>RURRENABAQUE<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br><!-- End of picture text --> <!-- Start of picture text --> PUERTO SUAREZ<br><!-- End of picture text --> <!-- Start of picture text --> 5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br><!-- End of picture text -->

**ROBORE**

<!-- Start of picture text --> 7.0<br>5.0<br>3.0<br>1.0<br>-1.0<br>-3.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>SAN IGNACIO DE VELASCO<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br><!-- End of picture text --> 21
assume that the influence function is stable across the units being compared. In other words, the same level of citations implies the same level of influence in each of the publication records. This assumption is implicit in past citation comparisons, such as those using the _h_ -index. Here we will only consider comparisons within a given discipline. However, stability can in principle be relaxed by introducing an explicit scaling factor for inter-disciplinary differences in the influence function.

A second assumption about the influence function is monotonicity, meaning that the function is strictly increasing, in that a higher citation count for a publication implies greater influence. This is a natural assumption (though, as we have noted, the _h_ -index need not order records consistently with that assumption).

We will say that there is citation-curve dominance when two citation curves do not overlap; one curve is somewhere above the other and nowhere below. When combined with stability, montonicity implies that a robust comparison of aggregate influence can be deduced from citation-curve dominance. The publication record with the higher citation curve will have had greater aggregate influence, as assessed by any stable increasing influence function.

However, this does not get us far given that citation curves will often intersect. We can still draw conclusions about the aggregate influence of the publication record, but only by making stronger assumptions about the influence function. A powerful, but potentially contentious, additional assumption is diminishing marginal influence, whereby the first citation to a given publication has the highest impact, followed by the second, and so on. This is equivalent to assuming that the influence function is concave.

A simple example of a measure of the overall influence of a publication record satisfying these three assumptions—stability, monotonicity, and concavity—is the quadratic-influence <u>index</u> ( _qi_ -index), which we define more precisely in the next section. This is the special case of the general class of indices satisfying our assumptions; the key characteristic of this special case is that the marginal influence of extra citations is linear in citations.

We show in the next section that a concave influence function (along with other technical assumptions) implies robust orderings in terms of aggregate influence if the area under the inverse citation curve of one record is everywhere greater than that of the other.<sup>5</sup> Our empirical

> 5  The inverse citation curve is obtained by flipping the ordinary citation curve—swapping the axes.

6
# **1. Local participatory development and political change**

There are fundamental reasons to see the World Bank's report _Localizing Development_ , by Ghazala Mansuri and Vijayendra Rao, as one of the most important books on development in recent years. Trade and investment for economic growth depend on basic public services and infrastructure which must be provided by government, and the quality of government is determined by political leaders who hold recognized authority in every community. When markets fail to provide gainful employment for people's talents and resources in a poor country, the market failure may be due to a government failure, which in turn may be due to a failure of civil society to hold political leaders properly accountable. Thus, successful economic development may require the strengthening of local political institutions that empower people to demand better services from their government.

In _Localizing Development_ , Mansuri and Rao provide a comprehensive overview of the theory and evidence for development strategies that are based on local community empowerment. An important contribution of their book is its critical survey of the evidence on effectiveness of participatory development strategies (see particularly chapters 4-6 of Mansuri and Rao, 2013). When the goal of development assistance includes broad political change, the desired results are hard to summarize in a few quantitative measures, and comparisons that span different countries and different kinds of public goods inevitably involve a great deal of noise. Still, Mansuri and Rao find broad evidence that community involvement can improve the quality and sustainability of public investments in several areas.

But they find that the political relationship between the center and localities matters greatly, as do the incentives of local politicians under democratic decentralization, and theory is essential to help us identify the potentially important institutional variables. Mansuri and Rao work to provide a broad theoretical framework for such analysis. In this note, I want to support and extend their theoretical argument by focusing on the role of local government as a vital source for new leadership.

# **2. Educating people or opening new paths for leadership?**

Mansuri and Rao lay the theoretical groundwork for their analysis in chapters 1-3. They cite great social theorists going back to Jean-Jacques Rousseau and John Stuart Mill who have argued that participatory institutions of local government can be a training ground for 2
classification methodology will need to be introduced carefully, perhaps alongside existing methods.

Other classification schemes have been proposed, for example using cluster analysis techniques, or using methods based on the construction or use of appropriate indices to replace or supplement the use of GNI per capita. For example, Nielsen (2011) and Vázquez and Sumner (2012 and 2014) consider the use of measures of poverty, inequality, and human development. Other candidates proposed include the Human Development Index of the United Nations Development Program and the Multidimensional Poverty Index of the Oxford Poverty and Human Development Initiative. However, some of these composite indicators and methods also attract criticism, including the arbitrariness in weighting patterns, the implicit trade-offs between components, and their practicality when based on indicators with poor geographic coverage and update frequency. Analyses of these alternatives have not been attempted here, though it can be argued that they can also produce abrupt or inexplicable changes in classifications from one period to the next. It is also important to note that classifications based on such approaches would “decouple” the analytical classification of countries from the Bank’s operational guidelines.

Different classification schemes are already in use by other international agencies; selected groupings are presented in Annex 2, including those used in the Human Development Report of UNDP, the World Economic Outlook of the IMF, and the World Economic Situation and Prospects report of the United Nations. The UN statistical convention of developing and developed regions and the UN operational categories of Least Developed Countries, Land Locked Development Countries, and Small Island Developing States are also listed, since these are commonly used.

# **8. Conclusion**

This paper reviews the methodological details of the current income classification of the World Bank, highlighting its pros and cons. A classification based on GNI per capita covers almost all countries in the world and can be updated on an annual basis. While critics argue that the thresholds of the Bank’s income classification are dated and yet used by many for policy purposes, it should be emphasized that many also utilize the main benefit of the analytical 31
the revolution in retailing brought about by Wal-Mart via its substantial investment in ICT-based methods, subsequently spread to other ICT-using manufacturing industries. As such, ICT is considered the preeminent ‘General Purpose Technology’ of the past two decades as it has driven economy-wide growth by spreading over a range of sectors and prompting them to further innovate (Bresnahan and Trajtenberg, 1995; Helpman and Trajtenberg, 1996).

# **_III.2 Data_**

Our analysis is based on firm-level data from the Business Environment and Enterprise Performance Survey (BEEPS) collected by the World Bank and the European Bank for Reconstruction and Development in 28 countries in the ECA region in 2002 and in 2005.<sup>6</sup> The samples consist of cross-sections of 6667 firms in 2002 and 9655 firms in 2005. We also construct a panel sample of 2892 firms which were surveyed both in 2002 and in 2005.<sup>7</sup> The surveys cover a large range of manufacturing and services industries and are designed to be representative of the universe of firms according to the industry and location within each country.<sup>8</sup> In addition to information on ISOcertification and web-use, the surveys collect information on firm ownership structure, size, composition of labor force, participation in trade, R&D expenditures, access to credit, and various measures of the constraints faced in conducting business. Appendix Table A1 defines all the variables used in the analysis while Appendix Tables A2-A3 show the composition of the samples across countries and sectors. Appendix Table A4 provides summary statistics for all the investment climate variables.

Figure 1 shows the shares of firms that adopted advanced technology by country and sub-region within ECA. In 2002, the share of firms across the ECA region that is ISO-certified is 13.6%, with the highest share being (not surprisingly) displayed by the EU-8 countries (15.9%) and the lowest by Turkey (7.6%). The share of firms that use the web in 2002 is 58.2% across the ECA region, but ranging from 37.1% in the CIS outside Russia and Ukraine (labeled as ‘Other CIS’) to 76.4% in the EU-8 countries. In 2005, 12.5% of firms in the sample are ISO-certified, with the lowest share being displayed by the ‘Other CIS’ region (8.9%) and the highest being displayed by the EU-8 countries

> 6 The information collected in the BEEPS surveys is based on a 1.5-2 hours interview with the firm manager. Detailed information on the surveys can be obtained at http://go.worldbank.org/RQQXYJ6210 . Throughout the paper we will refer to firms as being the unit of analysis, although the surveys collect data on plants.

> 7 This panel sample has been used by Safavian and Sharma (2007) to study the determinants of firm access to finance. 8 However, we should emphasize that the 2002 sample and the panel sample have a relatively larger share of firms in services industries (61.4% and 62% respectively) while the 2005 sample is split evenly between manufacturing and services (49.6% and 50.4% respectively).

- 7 -
# **Wider Economic Benefits of Transport Corridors:**

# **_Evidence from International Development Organizations_**<sup>**_*_**</sup>

Muneeza Alam<sup>§</sup> Matias Herrera Dappe Martin Melecky Ran Goldblatt _New Light World Bank World Bank World Bank Technologies_

**Keywords** : Transport Investment Projects; International Development Organizations; Project Design and Implementation; Project Meta-data; Difference-in-Differences Methodology; Nighttime Lights Intensity

**JEL Classification** : R42, H54, O19

> * The authors thank Nathalie Barboza, Nicolle Konai, Ruifan Shi, and Akib Khan for excellent research support, and Robin Carruthers for his contributions with project reviews. The authors thank Martin Rama, Arjun Goswami, Jay Menon, Akio Okamura, Takayuki Urade, Duncan Overfield, Mark Roberts, Marianne Fay, Bill Maloney, Forhad Shilpi, and Hatem Chahbani for suggestions and comments on earlier drafts of the paper. The authors also thank Trevor Monroe and Dharana Rijal of the World Bank Big Data Group for assistance with obtaining the geocoded granular data on nighttime lights intensity and human settlements. The authors also thank Brad Bottoms from New Light Technologies for excellent research support. Financial support from the South Asia Regional Trade Facilitation Program (SARTFP) is gratefully acknowledged.

> § Corresponding author, e-mail: malam5@worldbank.org.
Although total production of biodiesel around the world remains small in comparison to ethanol, its growth is higher than that of ethanol, at an average annual growth rate of 50% between 2004 and 2008. This growth from 2.3 billion liters in 2004 to 14.7 billion liters in 2008 is illustrated in Figure 2. Germany, France and Italy are the biggest producers in the EU, but the US passed France to become the second biggest producer of biodiesel after Germany in 2007 (OECD, 2008).

**Figure 2: World Biodiesel Production**

<!-- Start of picture text --> Other Italy Brazil Argentina France USA Other EU27 Germany<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008<br>Source: EBB (2009); EIA (2009); REN21 (2009)<br>Note: Data for Argentina and Brazil unavailable for 2007.<br><!-- End of picture text --> Worldwide biodiesel production grew by 43% between 2005 and 2007 despite slow growth within the EU, the traditional center of biodiesel production (OECD, 2008). This growth in other countries, especially the US, led to a decline in the EU’s share of global biodiesel production, which had been more than 90% until 2004 to less than 60% in 2007 (F.O. Licht, 2008; EBB, 2008). In recent years, some countries outside Europe and the US have begun to produce biodiesel. For example, Brazil opened its first biodiesel plant, which uses a mixture of vegetable oil and sewage as feedstock, in March 2005 (OECD-FAO, 2008). Indonesia and Malaysia have recently begun producing biodiesel for the European market, and Argentina started biodiesel production in 2007 (OECD, 2008).