rafmacalaba/data-use-mentions-extended huggingface dataset:
Impact evaluation does not escape this general trade-off between breadth and identification, but typically locates at the ―narrow‖ end of the spectrum since it identifies changes in performance measures that are directly attributable to the project. For instance, when evaluating a customs modernization program, the performance measure is likely to be something like container dwell time, even though less quantifiable dimensions of customs performance, like security at the borders, may also matter.

But identifying and documenting the chain of causality from program to ultimate outcomes can be challenging for some trade interventions. In trade facilitation programs, it is not always clear what are the micro-level mechanisms by which transport costs reductions influence firms and households and, more generally, economic activity.

In addition, the use of IE can affect incentives in the long run. The focus on narrow, immediate performance outcomes may well lead to measurement biases or, even worse, create perverse incentives when used for monitoring and evaluation. For one thing, it can focus attention on readily measured outcomes at the expense of less easily measured ones. Consider a customs modernization program. Using IE results to design reward schemes for customs officials might lead to over-emphasis on easy-to-measure reductions in clearance times, at the expense of the monitoring of suspect shipments. If, say, there is a low rate of smuggling illicit products, it may take time before the consequences of reduced monitoring get noticed — too long to show up in an IE.

# **5.2 How do we obtain the data?**

The feasibility of rigorous impact evaluation hinges critically on data availability. Whether the IE is based on experimental (RCTs) or quasi-experimental design, it needs to include a baseline survey and at least one follow-up survey. If quasi-experimental methods are used, the baseline survey must include a rich set of covariates to estimate a (first-stage) selection regression. One of the advantages of RCTs, especially in developing countries, is that they are less demanding in terms of data; however, even with randomization, firm-level covariates
using bootstrap with 200 replications. Standard errors are corrected for sampling weights and cluster correlation at the commune level. Village variables (kindergarten, distance to the nearest town, accessible by car) are available only for the rural sample. Thus, the number of observations in regressions using the interaction between child care attendance and these village variables is lower than other regressions. *** p<0.01, ** p<0.05, * p<0.1. <mark>Source: Estimation from VHLSS 2010, 2012, 2014 and 2016.</mark> 39
2011). Despite such claims, there appears to be no rigorous panel study investigating whether improvements in the official rankings improve FDI inflows.

This paper uses a panel data set of approximately 84 countries from 2006 to 2009 to show on average, improvements in the World Bank’s Ease of Doing Business Rankings (forthwith referred to as the Doing Business Rankings) do significantly improve FDI inflows. However, countries undertaking relatively more reforms as proxied by large improvements in the Doing Business rankings do not significantly attract greater FDI inflows than other countries. Further, when restricting the sample to 56 developing countries, the relationship between improvements in rankings and FDI inflows is insignificant.

This paper is structured as follows. Section 2 outlines the determinants of FDI, section 3 explains the data and methodology used in the analysis, section 4 presents the results and section 5 concludes.

# **2. Determinants of FDI**

Different researchers propose different models and variables when investigating the determinants of FDI (see for example Bergstrand and Egger 2007, Stein and Daude 2007 and Walsh and Yu 2010). Clearly the inclusion or exclusion of certain variables could materially influence the results of the analysis. As such, this paper considers various combinations of institutional variables and the key macroeconomic 2
# **Appendix 3a.  Variables and Definitions in the WFP Mobile Phone Survey**

|Variable|Definition|
|---|---|
|BorrowOrHelp|# ofdayshousehold using this coping strategy per week|
|Cereals|# of days household eating this food item per week|
|CSG==1|coping strategy group==poor|
|CSG==2|coping strategy group==borderline|
|CSG==3|coping strategy group==acceptable|
|Dairy|# of days household eating this food item per week|
|Eggs|# of days household eating this food item per week|
|FCG|food consumption group|
|FCS|food consumption score|
|Fruits|# of days household eating this food item per week|
|HouseType==Camp|prevalence-->where HouseHold is staying|
|HouseType==Other|prevalence-->where HouseHold is staying|
|HouseType==Own_home|prevalence-->where HouseHold is staying|
|HouseType==Public_building|prevalence-->where HouseHold is staying|
|HouseType==Rental|prevalence-->where HouseHold is staying|
|HouseType==Staying_with_someone_for_free|prevalence-->where HouseHold is staying|
|HouseType==Unfinished_building|prevalence-->where HouseHold is staying|
|IDP_YN==Y|prevalence-->household is IDP|
|LessExpensiveFood|# of days household using this coping strategy per week|
|LimitPortionSize|# of days household using this coping strategy per week|
|Meat|# of days household eating this food item per week|
|Pulses|# of days household eating this food item per week|
|rCSI|reduced coping strategy|
|ReduceNumMeals|# of days household using this coping strategy per week|
|RestrictConsumption|# of days household using this coping strategy per week|
|Veg|# of days household eating this food item per week|
|FoodAssistance_YN==Y|prevalence-
Canada also drops since Canada’s option value diminishes significantly.

While issues related to international transit migration receive relatively little attention in economics, they have a more prominent place in other fields, such as demography and sociology, with varying definitions and approaches. More specifically, the phrase ‘transit migration’ is used to describe the migration patterns emerging in Europe following restrictions on legal migration and border controls that were introduced in the 1990s. As a result, many migrants from Eastern Mediterranean and African countries moved to countries at the periphery of the European Union, such as those in North Africa and Eastern Europe, while waiting for a chance to move to the European Union countries (Collyer and de Haas, 2012; Collyer et al, 2012). The paths of most recent refugees from Syria, Iraq and other source countries follow similar transit routes, such as through Turkey, as they try to reach Western Europe. The phrase is also used extensively to refer to low-skilled and mostly undocumented migrants from Central America who travel through and temporarily live in Mexico before reaching the United States. In other words, transit migration has a strong affiliation with irregular and undocumented migrants and refugees as well as a strong sense of temporariness (see Duvell, 2012). Even among the few papers in the economics literature (such as Djajic, 2013), this is the general perception.

We use the expression ‘transit migration’ to describe any migrant who lives in a foreign country before moving to a second foreign country. Our data from the American Community Survey indicate that transit migrants, defined as those living in a country other than their birth country ‘one year’ prior to their move to the United States, are somewhat different than those the demography and sociology literatures focus on. First, transit migrants to the United States are more likely to have tertiary education as opposed to primary or secondary education. Second, most transit migrants are born in high-income European (and some African and East Asian) countries as opposed to most Latin American
# **1. Introduction**

Guinea has rich natural resource endowments, especially in mining, agriculture and hydropower. It has the world’s largest reserves of bauxite, the ore used to produce aluminum. Guinea’s bauxite reserves are estimated at more than 40 billion tons. Guinea also has one of the largest unexploited iron deposits known in the world, in the Simandou mountains, as well as large reserves of gold, diamonds, uranium, and some 20 other minerals. The country’s water potential is estimated at 27,000 m<sup>3</sup> per capita per year. Large rivers such as the Niger, Senegal and Gambia have their source in Guinea, making the country the “water tower” of West Africa. With such important endowment of water resources, Guinea can develop a hydroelectric potential estimated at more than 6,000 megawatts. Moreover, Guinea’s huge agricultural potential remains largely unexploited. Of 6.2 million hectares of arable land, only 25 percent is farmed and of 364,000 hectares for irrigation potential, less than 10 percent is developed.

Despite its enormous potential, Guinea remains one of the poorest countries in the world. Guinea’s GNI per capita was estimated at US$830 in 2018 (current US$, Atlas method). With a Human Development Index of 0.459, Guinea was ranked 175<sup>th</sup> of 189 countries in 2018 (UNDP, 2018). The country’s Human Capital Index (HCI) is estimated at 0.37, which means that a child born in Guinea today will grow up to be, at best, 37 percent as productive as she could be if she enjoyed complete education and full health. Based on the HCI, Guinea was ranked 141 of 157 countries in 2017.<sup>1</sup> Poverty has increased from 49 percent (national poverty line) in 2002 to 55 percent in 2012, according to the most
time?’, ‘What type of work/activity were you doing at your first employment?’, ‘Have you ever worked or having any economic activity?’, ‘What type of work did you do in your last job?’._ 5. How and what will we measure in a changing labor market? Recommendations and concluding remarks Demand for employment data as well as the type of data required to monitor and explain labor markets is changing. One reason is the SDGs, which put more emphasis on labor markets than its predecessor, the Millennium Development Goals (MDGs), and the new ILO‐resolutions concerning (unpaid) work and employment.

In this final section, we attempt to set an agenda to improve the collection of individual‐level employment data in LSMS‐ISA surveys in the short and medium term. The first part deals with short‐term objectives, i.e. actions that can be implemented in the near future and do not require (much) additional research. The second part, medium‐term objectives, discusses how to measure unpaid domestic and care work, and informality. These issues are part of the SDGs and methodological work is currently ongoing to develop (standardized) tools to capture those concepts.

We refrain from discussing long‐term objectives, such as the skills agenda, the issue of capturing (international) migration and its implications for local labor markets, the use of technology to collect individual‐level employment data or the integration of survey data with administrative data sets. These discussions are of foremost importance but go beyond the scope of this paper.

24
# _Methodology: Defining a Discovery_

With the 1990s HS data, we define a discovery as a product which was not sold abroad in a large amount at the beginning of the 1990s (exports were less than $10,000 in 1992, or in 1993 if 1992 data from the country were not reported), but by the end of the 1990s was consistently exported in a large quantity (exports over $1,000,000 in 2000, 2001 and 2002 or in 1999, 2000 and 2001 if 2002 data were not yet reported). These cutoff values are arbitrary, but the results were not sensitive to the choice. Nominal amounts are used so that, from a product level, the filter applies to each country in the same manner.

It is possible that increases in exports for some goods may be due largely to price effects rather than increased production. However, we cannot net out or deflate price effects with the data. Furthermore, as part of the valuable information that must be discovered is demand, new goods whose prices quickly rise represent more valuable discoveries than new goods whose prices stagnate.

In order to verify that this definition (which we will call Filter 1) does not capture goods that were exported in large quantities prior to 1992 and then fell temporarily in that year, we also employ a second filter (Filter 2) that only considers goods that were not exported for more than $10,000 in 1992, 1993 or 1994, but topped $1,000,000 in 2000 and 2001. Though more restrictive (it lowers the count of discovery events as it only considers discoveries in the second half of the 1990s), this filter rules out false identification of new products. All of the findings discussed below are found to be consistent across filters.

14
and increased output. The first two are consistent with the idea of skill-biased technological change embodied in new investment yet they account for only a small fraction of the acceleration in skill-upgrading between the 1980s and 1990s. The increase in output alone predicts almost half of that acceleration, which is really a surprise. Fast growing industries are upgrading their skill mix faster than slow-growing or stable industries.

To put the estimated capital-skill complementarity coefficients in context, compare them with U.S. estimates. For U.S. manufacturing the comparable capital-skill complementarity coefficient (estimated in a similar regression) in 1959-86 is 0.038 (see Berman, Bound and Griliches, 1994), and for the 1979-86 period is 0.064. Thus, these estimates indicate that capitalskill complementarity in Indian manufacturing increased from an estimated zero in the 1980s to a level in the 1990s comparable to the U.S. level for the 1960s and 1970s. We interpret this as evidence of skill-biased technological change for technology embodied in _new_ capital.

Is increased capital-skill complementarity in Indian manufacturing related to the pattern of skill-biased technological change evident in the manufacturing sectors of OECD and middle income countries? One way to look at this is to ask whether the same industries upgrade skills in different countries at the same time. This cross-country correlation approach has shown consistent patterns of skill upgrading in many industries across countries in the OECD in the 1980s (Berman, Bound and Machin, 1998). Those same industries generally underwent skillupgrading in middle-income countries in the 1980s as well (Berman and Machin, 2000).

Table 9 reports the result of a similar exercise for our Indian data, in which we ask if skill upgrading in Indian industries in the 1990s is predicted by upgrading in the same industries in previous decades in the U.S., after accounting for the effects of capital-skill complementarity and increased output.

26
estimated, which is guaranteed by Assumption 5, it follows that the estimator for _λd_ will be consistent. □ Finally, note that the sub-group inequality decompositions presented in Section 2.1 can readily be extended to accommodate the sub-division of the top tail into _D_ districts. (Note that the bottom segment can in principle stay as is, i.e. need not to be sub-divided into districts.) Let us denote the income share going to the top tail from district _d_ by _sd_ = _P_ 2 _,d_ ( _μ_ 2 _,d/μ_ ), where _μ_ 2 _,d_ = _E_ [ _Y |Y > τ, district d_ ]. Note that the population- and income shares corresponding to the bottom segment now solve 1 _−_<sup>�</sup> _d_<sup>_λd_and 1</sup><sup>_−_�</sup> _d_<sup>_sd_, respectively.Similarly, let us</sup> denote the Theil index or the mean-log-deviation for the top incomes from district _d_ by _T_ 2 _,d_ and _MLD_ 2 _,d_ , respectively. Using this notation, the decomposition of the Theil index and the mean-log-deviation into the 1 + _d_ sub-groups is seen to solve:

# **3 Data**

This paper uses two different types of data-sets: (1) Household Income, Expenditure and Consumption Survey (HIECS) data, and (2) listings of homes for sale derived from (large) real-estate databases. All data used in this study are for Egypt. The HIECS is from 2008/9. The house price data are slightly more recent, covering the period early 2013 to 2015, and come from two different real-estate firms. Details are given below.

## **3.1 Egyptian Household Income, Expenditure and Consumption Survey**

The Egypt HIECS 2008/9 is conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS). We were given a 50
*|31.5|
|Paved road traffic|Average annual daily traffic|1,341.1|**867.7**|3,797.7|
|Unpaved road traffic|Average annual daily traffic|38.5|**35.8**|74.7|
|Paved classified network condition|% in good or fair condition|86.2|**96.1**|82.0|
|Unpaved classified network condition|% in good or fair condition|55.8|**90.3**|57.6|
|Perceived transport quality|% firms identifying roads as major business constraint|27.6|**55.8**|18.2|
|Overengineering|% of main road network paved relative to low traffic|29.6|**25.9**|18.4|
|Underengineering|% of main road network paved relative to high traffic|13.5|**1.0**|20.0|

_Source:_ Gwillliam and others 2008. Derived from the AICD national database (http://www.infrastructureafrica.org/aicd/tools/data). _Note_ : GIS = geographic information system.

The selection of road standards matches traffic levels (figure 6), despite the fact that paved road traffic is very low when compared to the average for nonfragile LICs (table 5). Less than 1 percent of its classified road network is underengineered and only one-third is overengineered, but this is above the average for the region, at 27 percent.<sup>1</sup>

> 1 Here the definition of over- and underengineering is very basic. A segment of road is overengineered if it is paved and its daily traffic is below the 300-vehicles-per-day (vpd) threshold. On the contrary a segment of a road is considered underengineered if it is not paved and its traffic is over 300
# II – Data

The primary data used for this study come from the Listening to the Citizens of Uzbekistan (L2CU) study conducted by the World Bank together with the Development Strategy Center of Uzbekistan and other partners. The study collected information in four distinct modes:

- A comprehensive national survey conducted in-person with a representative sample of 4,010 households. Recipients of social protection benefits were oversampled using registration data maintained at the mahalla level, and sampling weights were adjusted for the inclusion of these households in the baseline results. Full data on household consumption, expenditure, income, remittances, and information on any current migrants were collected. A full module on well-being and views on local economic conditions was also included.

- Administrative data collected from mahalla officials in each of the selected PSUs of the national household survey. This included comprehensive data on all officially registered migrants, the demographic profile of migrants, the registered destination countries, local labor market information, social protection beneficiaries, and related data.

- A nationally representative panel survey conducted monthly over the phone with a randomly selected subsample of 1,503 households that participated in the baseline. The survey also collected comprehensive information on potential/intending/current and returning migrants.

- Qualitative data collected in focus groups. These data include discussions regarding hurdles to migration and the administrative procedures surrounding migration decisions.

The primary sampling units (PSU) for the L2CU baseline survey were _mahalla_ s, the lowest-level administrative unit in Uzbekistan. A total of 200 PSUs were randomly selected proportionate to size by World Bank staff using a full official list of mahallas provided by the National Mahalla Committee of Uzbekistan. An “omniscient” adult household member was interviewed for each household, preferably the individual with the most information about the household budget, and protocol allowed multiple people to contribute if the primary respondent did not have responses for questions about other household members (for instance, regarding incomes or work arrangements).

The design closely followed protocols applied to Living Standards Monitoring Surveys (LSMS) type surveys. The
Policy Research Working Paper 8014

# **Abstract**

The income generated from parental migration can increase funds available for children’s education. In countries where informal payments to teachers are common migration could therefore increase petty corruption in education. This hypothesis is tested by investigating the effect of migration on educational inputs. An instrumental variables approach is used on survey data and matched administrative records from the World Bank’s Open Budget Initiative (BOOST) from Moldova, one of the countries with the highest emigration rates. Contrary to the positive income effect, the strongest migration-related response in private education expenditure that is found is a substantial decrease in informal payments to public school teachers. Any positive income effect due to migration must hence be overcompensated by some payment-reducing effects. A number of potential explanations at the family level, school level or community level are discussed, several of these explanations ruled out and possible interpretations for future research highlighted.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at tobias.stoehr@ifw-kiel.de.

_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
Assessment**|
|---|---|---|---|---|
|Jalanidhi|87|87|87|90|
|KWA BM|22|35|35||
|Other Community Managed<br>Schemes|48|50|50|45|
|Total No of Schemes|157|172|172|135|
|Number of Observations|2,582|172|172|135|

Since the analysis is at the scheme level, data collected through household surveys were aggregated at the scheme level. The technical and financial audits as well as the institutional surveys collected data at the scheme level only. We used imputation technique to handle missing data due to non-response and lack of information. Data imputation at the household level was done only if at least 20 percent of the surveyed households from a particular scheme responded to the question. The missing value was predicted based on the information collected from the 20 percent (or more) households such as education and proxies for income and other characteristics of the schemes. To impute data at the scheme 10