rafmacalaba/datause-encoder-data huggingface dataset:
projects out of about 300 will be sufficient to bring out valid general conclusions.”<sup>24</sup> Along similar lines, another Bank officer proposed focusing on a specific sector (e.g. power or roads), and implementing a thorough analysis of all the projects financed in that particular sector.<sup>25</sup> This second suggestion was not followed at that time, but it was not forgotten by the Bank: among the first studies sponsored in the early 1970s by the newborn Bank’s Operation Evaluation Division, was a thorough analysis of the Bank’s accomplishments in the power sector (World Bank 1972).

# **4. The Genesis of the Bank’s Evaluation Department**

The coming-together of the Bank and Hirschman, under the umbrella of the Brookings Institution’s research, was a timely joint venture. Like several other institutions, the World Bank was devoting increasing efforts to establishing and refining methods for evaluating the impact of its policies on less developed countries, often in collaboration with the Brookings Institution (see for example Fromm, ed. 1965 and Adler 1967 on transportation; specifically on roads, Wilson, Bergmann, Hirsch, and Klein 1966; de Weille 1966; Walters 1968; van der Tak and de Weille 1969; on power production, van der Tak 1966; the largest comparative coverage of World Bank’s development projects and their evaluation in the late 1960s is probably King 1967). These early reflections were progressively institutionalized within the Bank in an evaluation function, embodied first in a Programming & Budgeting Department (from 1970) and subsequently in an Operations Evaluation Department (from 1973).<sup>26</sup> The most ambitious outcome of the initial phase of evaluation were two reports that presented a full analysis of the Bank’s activity in one specific sector, electricity, and in one specific country, Colombia (World Bank 1972a and 1972b).<sup>27</sup> In subsequent years, the operations evaluation function struggled, not always successfully, to define and strengthen its role within the Bank.

> 24 P.M. Mathew to Mr. William Diamond, “Investment in Developing Countries – Effects, Expectations and Reality,” February 24, 1964, World Bank Hirschman Folders Vol. 1. 25 Robert F. Skillings to Mr. Syed S. Husain, “Professor Hirschman’s Forthcoming Study,” February 26, 1964, World Bank Hirschman Folders Vol. 1. 26 In December 2005 the Operations Evaluation Department, together with two other evaluation entities of the World Bank Group, was renamed Independent Evaluation Group.

> 27 Electricity was a typical infrastructural investment financed by the Bank; Colombia historically had a privileged relation with the Bank, since it was considered among the most dynamic and endowed developing countries. The first Bank’s general survey mission to a developing country was to Colombia in 1949. For an analysis of early Bank-Colombia relations see Alacevich 2009.

13
destination income and the other potentially endogenous covariates. Tables 5 and 6 report a number of diagnostic statistics for the first stage, which is common between them. Because we have no particular reason to believe that errors are homoskedastic, we use the heteroskedasticity-robust Kleibergen and Paap (2006) test statistics for under-identification and weak instruments. The Kleibergen-Paap LM statistic indicates that we can reject the null hypothesis that the endogenous regressors are unidentified. This leaves open the possibility that the instruments are only weakly correlated with the endogenous regressors, however. Stock and Yogo (2005) tabulate critical values for the Cragg-Donald (1993) F-statistic to use in testing the null that instruments are weak in the homoskedastic case. Because we are reluctant to assume homoskedasticity, we instead report the heteroskedasticity-robust Kleibergen-Paap (2006) Wald rk F-statistic. Although the appropriate critical values in the heteroskedastic case have not been tabulated in the literature (Mikusheva, 2013), common practice is to compare this statistic to the Stock-Yogo critical values. This comparison suggests that we _cannot_ reject the null of weak instruments. For this reason, below we report weak-instrument-robust test statistics and consider further the consequences of weakness of the instruments.

Keeping these caveats in mind, we now turn back to the IV estimates in Table 5. The IV estimates of the coefficient on the destination-income term are significantly positive and significantly greater than the OLS estimates. Given the positive cross-sectional correlation of export prices and destination income in cross section (Table 3), this result is perhaps not surprising, but it is nonetheless reassuring for our strategy. Because of the weak-instruments concern, we also report a Anderson-Rubin (1949) Wald test, which is robust to weak instruments. This is a test of the null that the coefficients on the excluded instruments are jointly zero when they are included in place of the endogenous covariates in the outcome equation. In the specification of Column 5, this is equivalent to a test of the hypothesis that the coefficient on the destination-income term is zero. The test decisively rejects the null. In Columns 6 and 7, where there are multiple endogenous covariates, the Anderson-Rubin test corresponds to the hypothesis that the coefficients on the endogenous covariates are jointly zero. Tests for subsets of endogenous regressors in weakly identified IV models is a frontier of research in econometric theory (Mikusheva, 2013), and the literature has not converged on a standard test in this setting. Here we satisfy ourselves with two simple observations. First, it is reassuring that the IV estimates of the destination-income coefficient are reasonably robust across specifications. Second, in settings with weak instruments, IV estimates are “biased towards” the corresponding OLS estimates (see e.g. Angrist and Pischke 14
||TableXVID|eterminantsofActiv|eJobSearch||
|---|---|---|---|---|
||Sample<br>proportions|**I**|I.<br>**1996/99**|**III**<br>**2000**|
|Searching|65.30|||l|
|Searchingactively|30.93||||
|Unemployed|64.0AA||||
|Outoflaborforce|35.99A||_|*|
|Registered(=1)|6 6<sup>.</sup>38<sup>A</sup>|-0.046|-0.238**|0.320|
|||(0.094?|(0.086)|(0.128)|
|Nounemployment|50.62|-0.208|-0.163|-0.394"|
|insurance(UI)||(0.091)|(0.073)|(0.125)|
|Reg*****NoUI|25.15|0.163|0.166|0.319"|
|||(0.113)|(0.098)|(0.108)|
|Past**Ul**|5.00|0.114|0.013|0.139|
|||(0.050)|(0.045)|(0.082)|
|Receivessocial|13.27|0.048|0.070|0.054|
|assistance/support||(0.033)|(0.034)|(0.068)|
|Female(=1)|43.75|-0.066|-0.037|-0.134|
|||(0.024)|(0.021)|(0.049)|
|Married(=1)|65.64|-0.053|-0.058|-0.009|
|||(0.027)|(0.026|(0.052)|
|Head|37.61|0.080|0.107|-0.001|
|||(0.033)|(0.032)|(0.067)|
|Spouse|26.66|0.042<br>(0.040)|0.052<br>(0.039)|0.048<br>(0.079)|
|**16-25**|20.17<br>[omitted]||||
|26-35|21.20|0.014|0.008|-0.002|
|||(0.029)|(0.026)|(0.060)|
|**36-45**|19.70|-0.010|-0.019|-0.018|
|||(0.033)|(0.027_)|(0.074)|
|**46-55**|21.37|-0.107*|-0.086"|-0.143"|
|||(0.031)*|(0.024)|(0.077)|
|**55-65**|17.56|-0.287*|-0.185|-0.356**|
|||(0.021)|(0.016)|(0.080)|
|Primary|18.20<br>[omitted]||||
|Apprent.|42.69|0.116|0.098*|0.117*|
|||(0.030)|(0.031)|(0.053)|
|Secondary|35.24|0.150|0.138|0.176**|
|||(0.031A|(0.033)|(0.053)|
|College(+)|3.87|0.120|0.162|0.109|
|||(0.074)|(0.084)|(0.108)|
|Region||YES|YES|YES|
|<br>Year||YES|YES|YES|
|Quarter||YES|YES|YES|
|Observedprob.||30.93|18.02|57.69|
|Predictedprob.<br> <br>|_|24.28|13.26|55.37|
|Prob**>**Chi<sup>2</sup>||0.000|0.000|0.000|
|Numberof**ind**|2,494|2,494|1,331|1,163|
|Numberofobs|5,033|5,033|3,395|1,638|

Source: Labor Force Survey 1996.1-1996.4, 1999.1-1999.4, and 2000.1-2000.4. ACorrespond to the first observation for each individual.

57
an unweighted average of $1.90, and a population weighted average of $1.93.<sup>54</sup> Using the “equivalent poverty lines” approach among the 15 countries that originally defined the $1.25 line – which would also satisfy the desire of keeping the poverty line “constant” (on average) in the world’s poorest countries - would give a simple average of $1.86 and a population weighted average of $1.88. The fact that all four of these numbers are either equal or very close to $1.90 provide additional support for the robustness of our approach, the results of which are largely unaffected by the identity of the original Ravallion et al. (2009) fifteen countries.

Not unexpectedly, Table 7 shows that given the regional differences in revisions of relative price levels from the 2011 ICP, the average “equivalent poverty lines” diverge regionally. Sub-Saharan Africa’s “equivalent poverty lines” give a simple average of $1.94, close to the overall mean, while LAC and ECA’s poverty equivalent lines are around $1.60, and EAP, MENA and SAR have averages above $2. This suggests that, on average, a line of $1.90 would lead to small changes in countries in Africa, higher poverty in countries in LAC and ECA, while for countries in EAP and SAR poverty would fall with the new PPPs. Of course, the precise changes to the regional aggregates will depend on population shares and the density of the distribution around the poverty lines in these countries, as well as other adjustments.

Another alternative approach to updating the $1.25 line would be to re-estimate the relationship between national poverty lines and national incomes (from the Ravallion et al., 2009, estimate shown in Figure 1) using the 2011 PPPs. Such a procedure could involve selecting a new sample of the poorest countries as the reference group for the international line. Klasen et al. (2015) suggest re-estimating the poverty line using the original, piecewise regression function similar to that used to identify the reference group of 15 countries that defined the $1.25 line. On that basis, they estimate an updated global line of $1.67-1.71. However, these estimates are significantly lower (to our knowledge) because they rely on CPIs from WDI, which, as discussed in preceding sections, underestimate inflation (and thus the conversion from 2005 to 2011 PPPs) for a number of countries. Alternatively, if we re-rank the countries by mean consumption denominated in 2011 PPPs, one country in the bottom 15 changes – and the mean of the lines in 2011 PPPs becomes $1.84. However, across these

> 54 These estimates differ from Kakwani and Son (2015) due to our larger sample size, and use of different CPIs for selected countries.

32
(horseshoe) crab. The Tiger Reserve was designated a UNESCO World Heritage Site in 1987.

Many households living within the Sunderbans are economically disadvantaged. Almost 40 percent are from poor scheduled castes and about half of the region’s 4.2 million people live below the poverty line. Literacy rates are below 35 percent and most communities lack safe drinking water and electricity. Most households (89 percent) rely on agriculture for income and most are either marginal farmers or landless laborers.

Traditionally, rice has been the region’s major crop; however shrimp exports have grown rapidly in recent years and many in the region are involved in aquaculture. An estimated 150,000 people depend on shrimp larvae collection in the Indian Sunderbans (Central Inland Fisheries Research Institute, 1999 and 2000). Moreover, in spite of the prevalence of poverty in the region, people have been migrating there because of the rich natural resources. The study looks at how decisions taken by local and state governments contributed to the growth of an export-led shrimp industry in West Bengal and at the consequences for local livelihoods and the local ecology.

# **3. An overview of the trade events studied**

The starting point for each of the case studies is a shift in the level or composition of trade that is of sufficient scale to affect the livelihood strategies of the local poor. As a practical matter, the research teams recognized that it is seldom possible to identify a single event and what is referred to in shorthand as “the trade event” is usually a sequence of policy decisions sometimes stretching over several years, or is, in some cases, a whole collection of (nearly) simultaneous actions. By way of example, while World Trade Organization (WTO) membership might be viewed as the driving trade event in both China and Vietnam, the case studies point out that the Doi Moi policies of Vietnam began in 1986 and that reform in China began with a partial liberalization of agriculture in 1978.

For purposes of the discussion, it is possible to use a two-way taxonomy of the cases, which constitutes (1) general trade liberalization of the country under study: Chile, China, India, Madagascar (horticulture), Vietnam; and (2) a trade event affecting mainly one product market: sugar in South Africa and maize Madagascar. But other classifications are possible. For example, the studies could be divided into those dealing with effects of external 12
**Table 1. Share of Exports and Imports in the World Market**

||**Power**<br>**genera**<br>**eqp.**|<br>**ting**|**Indust**<br>**machi**|**rial**<br>**nery**|**Office,**<br>**& telec**|**data**<br>**om**|**Electri**<br>**machi**|**cal**<br>**nery**|**Road**<br>**transpo**|**rtations**|**Other**<br>**transp**|<br>**ort**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**93-94**|**03-04**|**93-94**|**03-04**|**93-94**|**03-04**|**93-94**|**03-04**|**93-94**|**03-04**|**93-94**|**03-04**|
|**Exports**|||||||||||||
|Indonesia|0.1|0.4|0.1|0.1|0.6|0.7|0.4|0.6|0.2|0.1|0.4|0.1|
|Malaysia|0.8|0.3|0.6|0.5|4.3|3.6|4.8|3.0|0.1|0.1|1.2|0.4|
|Philippines|0.0|0.1|0.0|0.1|0.4|1.1|1.5|2.7|0.1|0.2|0.0|0.3|
|Thailand|0.6|0.8|0.6|0.6|2.0|1.7|1.4|1.6|0.3|0.5|1.0|1.6|
|_China_|_1.3_|_2.4_|_1.0_|_3.3_|_2.8_|_15.7_|_2.4_|_8.2_|_0.5_|_1.6_|_1.2_|_2.8_|
|Developing EA|2.8|3.8|2.3|4.7|10.1|22.8|10.5|16.0|1.1|2.6|3.8|5.1|
|**Imports**|||||||||||||
|Indonesia|1.8|0.6|2.0|0.7|0.3|0.2|0.8|0.2|0.7|0.3|1.2|0.6|
|Malaysia|1.7|0.8|2.4|1.0|1.6|1.2|6.3|4.9|0.4|0.3|2.4|0.9|
|Philippines|1.2|0.2|0.6|0.3|0.4|0.6|0.8|2.8|0.3|0.2|0.9|0.4|
|Thailand|1.9|1.0|2.4|1.3|1.3|0.9|2.5|2.0|1.1|0.4|2.6|0.6|
|_China_|_3.8_|_4.5_|_7.9_|_8.6_|_2.6_|_5.5_|_2.8_|_9.4_|_1.5_|_1.9_|_3.8_|_2.6_|
|Developing EA|10.4|7.0|15.3|12.0|6.1|8.3|13.1|19.4|4.0|3.0|10.9|5.0|

Note: Lightly (dark) shaded figures are products with increasing (decreasing) market shares. Source: UN-COMTRADE

> <sup>19</sup>
Policy Research Working Paper 8492

# **Abstract**

Using a rich data set of primary school students, this paper estimates the effects of immigrant concentration in the classroom on the academic achievement of natives. In contrast with previous contributions, it exploits rare information on age-atmigration to estimate separate spillover effects by duration of stay of immigrant classmates. To identify treatment effects, it uses cohort-by-cohort deviations in immigrant concentration within schools combined with attractive features of the Dutch school system. Overall, the paper finds no effect of the concentration of immigrant students on natives' test scores. However, although immigrant students who have been in the country for some time have virtually no effect on natives, the analysis finds a small negative effect of recent immigrants in the classroom on natives’ test scores. The effect is significant only for language test scores, but insignificant for mathematics test scores. When significant, effect sizes are quite small compared to other educational interventions and classroom peer effects estimated in other contexts..

This paper is a product of the Social Protection and Jobs Global Practice. 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/research. The author may be contacted at lbossavie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
For each country dataset we construct comparable covariates to measure household income, remittance behavior, family composition, and demographic characteristics. Remittances are typically measured at the household level, not the individual level. Our level of analysis is therefore the household and we define variables at this level whenever possible, for example by taking the highest level of schooling achieved by any migrant adult in the household. All financial values are reported in constant 2003 US$. In addition, we drop any observations where reported annual remittances are more than twice annual household income. We always use sample weights provided with the data. To pool the data, we post-stratify by country of birth and education so that the combined weighted observations match the distribution of developing country migrants to all OECD countries in the year 2000 (Docquier and Marfouk, 2005). See the data appendix for further details.

Table 2 presents summary statistics for each country survey and the pooled samples of all destination countries. Overall, 37% of the migrants in our database have completed a university degree, ranging from 4% in the Spanish NIDI survey to 59% in the Belgium IRSHS survey. The remainder of the table summarizes the covariates by the maximum educational attainment of all adult migrants in the household. The significance stars indicate that the mean of the variable is statistically different between university-educated and non-university educated households. Altogether, including both the extensive and intensive margins, more educated migrants send home an average of $874 annually, compared with $650 for less educated migrants. There are two opposing effects: a negative effect of education on the extensive margin, and a positive effect of education on the intensive margin. At the extensive margin, migrants with a university degree are less likely to remit anything than those without a degree: 32% of low-skilled migrants send any money home, compared with 27% of university-educated migrants. However, conditional on remitting (the intensive margin), highly educated migrants send more money back, sending about 9% more than less-educated migrants.

Table 2 also shows how characteristics which can affect remittance behavior differ between less- and more-educated migrants. Firstly, more skilled migrants are both more likely to live in a household where adults are working, as well as have a higher household income, than low skilled migrants. However, contrary to conventional wisdom, the household composition of the two types of migrants is not so different: on average, only 6% of low skilled migrants have a spouse outside the country, compared with 3% of high skilled migrants. Low skilled migrants are 10
prioritize expansion of public services to reach marginalized neighborhoods, so that basic services, such as waste collection, water supply, and electricity grids, reach even the most poor.

Accurate spatial data and city maps are a crucial ingredient to smart land use and city planning. Historically, shanty areas have often been omitted from city planning maps, making it difficult to address the improvements these neighborhoods need. When the master plans for many African cities were drafted, the current rates of growth and poverty were unforeseen.<sup>7</sup> By accurately identifying and including slums in city land maps, policy makers will be better equipped to properly guide the planning of inclusive urban development. Furthermore, precise and up‐to‐date maps of a city can help facilitate better coordination between transportation projects and current land use, and prioritize areas in need of housing upgrades.

Urban developers can leverage spatial data to not only improve planning, but also to better understand the interactions between firm locations, residential growth, and unplanned expansion. Advances in geographic information systems (GIS) and satellite imagery provide policy makers with new tools to analyze the changing morphology of cities. A better understanding of the composition of cities, and how land (residential, commercial, green space) changes throughout a city, can provide helpful insights for city planning. For example, In London city planners have relied upon GIS to plan and create more “livable streets” that encourage walking, cycling, and transit trips. Planners depend upon land use data and transport layers to target arterial roads and intersections that could be redesigned without significantly impacting the movement of motorized vehicles, but at the same time benefiting the greatest number of pedestrians.<sup>_8_</sup> In Madrid developers consult a GIS application before breaking ground on new construction. Following the national guidelines and regulations, the system identifies buildable land, ensures compliance with historical preservation rules, and informs users of land use restrictions.<sup>9</sup> These are just a few of many examples where accurate and timely spatial data is a crucial component to planning and decision making.

> 7 Watson, Vanessa and Batatunde Agbola. 2013. Who will Plan Africa’s Cities? Africa Research Institute: Understanding Africa Today. <u>http://www.africaresearchinstitute.org/publications/counterpoints/who‐will‐plan‐ africas‐cities/.</u>

> 8 UN Habitat. 2013. Streets as Public Spaces and Drivers of Urban Prosperity. Page 83.

> 9 ‐ SGGAGE. GIS Application: ESRI Solutions City Planning https://sggage.wordpress.com/2011/01/12/gis <u>application‐esri‐solutions‐city‐planning/.</u>

3