rafmacalaba/data-use-mentions-extended huggingface dataset:
them as possible both at the country level and the region level. We need to make a judgment call to decide the relevant cleavage along which groups are identified. In most cases, language forms the most politically relevant cleavage, but in other cases religion or other another cleavage dimension is more politically salient so we focus on those.

Our preferred way of coding regional distinctiveness is the following: for each region, identify the dominant group (A) as the largest group in the region that is not the national majority and collect information on what share of the country-wide population of group A lives in that region as opposed to other regions of the country; as well as information on other (non-A) national minority groups in the region. A region is coded as sufficiently ―different‖ if (i) most its inhabitants belong to group A (a _national_ minority) and (ii) most of A’s members live in the region. The intuition behind this ―double-requirement‖ approach is clear: if almost all people belonging to an ethnic group live in one region, and they represent a strong majority there, such a region will be very distinct from the rest of the country. Ideally the data would fill a matrix with one dimension giving ranges of the share of group A inside the region and the other dimension giving ranges of A’s share of the regional population. The intensity of demands for sovereignty should be proportional to the degree of regional difference.

Data to fill such a matrix are available for a few countries using census data, other government documents, and secondary sources. Switzerland, for example, has good data. It also has several regions that are dominated by an ethnic minority: Fribourg, Geneva, Jura, Neuchatel, Valais (Scion), and Vaud (Lausanne) are all dominated by French-speakers, with regional population percentages in the 60-88% range. However, French speakers are not concentrated in any one of those regions, so the degree of ―regional difference‖ from the
This calls into question the treatment of “foreign investors” as a unified investor category; not only do EM funds themselves have different investment mandates, but there are multiple sub-classes of foreign investors, who may have widely differing attitudes to risk, and behaviors. For the debt manager, it is likely to be increasingly important to differentiate in a systematic way amongst foreign investors, since assessing the category as a whole may not yield accurate insights into the behavior of different classes of offshore participants in the debt market.

Amongst asset managers benchmarked to EM domestic debt indices such as the JP Morgan GBI-EM, there is a spectrum of approaches running from index-tracking, passive investors, to active managers with large permitted index tracking errors. A notable recent development has been the growth of exchange-traded funds (ETFs) tracking EM local currency bond indices. Two examples are the iShares Emerging Markets Local Currency Bond Fund, launched in October 2011 and benchmarked against the Barclays Emerging Markets Broad Local Currency Index, and the SPDR Barclays Capital Emerging Markets Local Bond ETF, launched in February 2011 and benchmarked against the Barclays EM Local Currency Government Diversified Index. Both funds are listed on the NYSE. ETFs such as these provide investors, including retail investors, with exposure to an entire EM bond index at low cost, potentially significantly boosting investor access and interest.

Beyond EM-dedicated investment funds, including ETFs, a first additional foreign investor category to consider is official sector investors, comprising:

- i. Central banks. Global central bank reserves remain heavily concentrated in US dollars and Euros, but as reserves continue to grow and some diversification continues, demand from central banks for EM local currency debt instruments (including bonds) looks likely to rise. Total reserves allocated in “other” (non-traditional reserve) currencies, recorded by the IMF’s COFER database, stood at US$294 billion as of Q1-2012. This is an order of magnitude higher than the US$22 billion recorded in 1999, but still represents just 5% of the total allocated amount (
The shift from agriculture to manufacturing can in theory raise labor productivity in several ways: growth in labor productivity in the agricultural sector, growth in labor productivity in the manufacturing sector, and growth in labor productivity from the structural change between lowerproductivity agriculture and higher-productivity manufacturing. In the context of growth in African nations in the last two decades, it turns out that labor productivity growth outside agriculture is almost entirely from structural change, not from within the manufacturing or service sectors. In this section, we provide evidence for this claim; in the next section, we discuss how the patterns of Africa’s manufacturing growth between smaller informal firms and larger formal firms helps to explain what has been happening.

To describe these patterns, we use the recently released Economic Transformation Database (ETD) (de Vries et al. 2021). There are 18 Sub-Saharan African countries included in the ETD; in 2018 these countries accounted for about 74% of Sub-Saharan Africa’s GDP and 64% of its population. It includes the two most populous countries, Ethiopia and Nigeria, the two richest countries, Botswana and Mauritius (measured using GDP per capita in 2018) and two of the poorest countries in Africa, Malawi and Mozambique (author’s calculations using the World Development Indicators). For the most part, economywide value-added per worker in these countries is extremely low. For example, in 2018 employment-weighted value added per worker was only 4,689 current U.S. dollars; however, the value for 10 of the 18 countries fell well below this average. Following Diao et al. (2019), we examine the period 2001-2018—which corresponds most closely to the beginning of the “growth boom” in many African countries.

The ETD data includes value added, employment and price deflators for 51 countries for the period 1990-2018. Notably, reliable data on capital inputs per sector are not available, precluding calculations of total factor productivity growth. In any case, our preferred outcome measure here is value added per worker
in the event study further relieves endogeneity concerns for the instrument. In addition, we probe the robustness of our results using an alternative identification strategy based on a Bartik instrument exploiting the differential exposure to regulated product markets across 274 commuting zones. Such design minimizes the potential endogeneity of changes to the NIL and labor market power in a given product market, thus providing further reassurance against possible endogeneity biasing our results.

> 3 Indonesian manufacturing data have been extensively used in previous work (e.g. Amiti and Konings, 2007; Javorcik, 2004; Arnold and Javorcik, 2009; Hallward-Driemeier and Rijkers, 2013).

3
These data are available for the period between 1992 and 2012. For each pixel, the digital number of calibrated light intensity ranges from 0 to 63, which is often referred to as the nightlight intensity (we use the “stable lights” band, where ephemeral events, such as fires, have been discarded). We inter-calibrate the data to account for the lack of on-board calibration between satellites (see annex 2).

However, there are well-known limitations to DMSP-OLS data (see, for example, Donaldson and Storeygard (2016)). These include saturation effects, in which the amplification of light detection to capture low levels of light leads to right censoring in detection in highly-lit areas (for example, city centers); and blooming effects (or the “diffusion of light”), in which light emitted in one pixel or over an area is detected in nearby pixels, making highly lit areas appear to be larger than they are.

For this paper, a grid of hexagons, each covering 260 square kilometers (20 kilometers from vertex to vertex), was created. The nighttime lights intensities at the pixel level were summed by hexagon to represent an overall value of light.<sup>14</sup> A global artificial hexagon data set was created to allow a standardized level of analysis for all projects across the globe. Hexagons are appropriate and efficient for management and analysis of geospatial data (Tong, Ben, and Wang 2010) and are increasingly popular as bases for discrete spatial simulations (Sahr, White, and Kimerling 2003). Hexagonal tessellation has been adopted by previous studies in the remote sensing domain (Potere 2009; Goldblatt et al. 2018). Using hexagons is advantageous over square pixels because of their higher symmetry, sampling efficiency, equidistance,

> 12 We cannot use alternative measures, such as the World Bank Country Policy and Institutional Assessment scores, because the series starts only in 2005.

> 13 Obvious candidates for capturing complementary policies and institutions are variables related to doing business
. (2018). We use estimates from the year 2015 since these are the most recent available estimates.

# **Quantile Delta Mapping Post-Processing for CMIP6 Simulations**

We post-process the Earth system model simulations using a quantile mapping method, which corrects systematic distributional biases in climate model output data by removing historical biases relative to observations. Specifically, we apply the Quantile Delta Mapping (QDM) method (Cannon et al. 2015) in order to preserve the trends in the climate model simulations in such a way as to ensure that the climate sensitivity (i.e., the temperature response to greenhouse gas forcing) of the model is not affected by bias correction. QDM adjusts the distribution of simulated climate data to more closely match the distribution of observed (or reanalysis) climate data, while 8
subnational empirical work on Ethiopia. When we began this project, relatively little subnational data was collected, the data was often of poor quality, and few attempts were made to systematize the results into any obviously

> 10 Questions have been raised about the quality of administrative data in Ethiopia, and discrepancies do exist between data published by the independent Central Statistical Agency (CSA) and line ministries. Some discrepancies can be explained by the way line ministries collect data through establishment reporting; also, in the case of the Ministry of Health, the use of cluster surveys rather than household surveys using more representative census-based samples.  Various initiatives have addressed such discrepancies.  Recently, an inter-ministerial committee charged the CSA to carry out Data Quality Assessments (DQAs), funded by external donors, to assess data quality and make recommendations for improvement. Administrative data has seemed to improve since.

14
<!-- Start of picture text --> Fig. 2a: India's spatial mismatch, 1989-2005<br>0.35<br>0.30<br>0.25 Plants<br>0.20<br>Employment<br>0.15<br>0.10<br>0.05 Notes: Figure plots spatial mismatch in plants<br>and employment each year using the Ideal<br>Metrics for both the organized and unorganized<br>sectors combined together.<br>0.00<br>1989 1994 2000 2005<br>Urban spatial mismatch index<br><!-- End of picture text -->
|Remittances from abroad within the same enumeration area (EA)|-0.047|-0.180**|0.066|0.056**|0.069|
|---|---|---|---|---|---|
||(0.058)|(0.091)|(0.061)|(0.022)|(0.067)|
|Remittances from family within the same enumeration area (EA)|0.099*<br>(0.051)|0.23***<br>(0.09)|-0.097<br>(0.068)|0.009<br>(0.007)|-0.046<br>(0.053)|
|Observations|881|311|311|141|237|
|Note. Results are presented in margins and clustered standard errors in parent<br>at:<br>***<br>p<0.01,<br>**<br>p<0.05,<br>*<br>p<0.1.<br>In Ethiopia, the number of youths who expressed a desire to relocate to rural<br>individuals in these categories respectively. Consequently, several observa<br>presence of perfect collinearity with the dependent variable. We also contr<br>community that has your dream job, Years of education, Own account wo<br>Remittances from abroad. Results for these variables were either not statist<br>available under request.|heses. Individ<br>Enumeratio<br>areas and to a<br>tions had to b<br>olled for the<br>rker, Quintile<br>ically signific|ual-level surve<br>n<br>areas
_14</sup> It is important to note that for now (i.e., when evaluating the evidence

> 11See Coibion (2012) for an excellent review and a discussion of the limitations of this approach for the case of the monetary policy in the United States.

> 12While the empirical monetary literature in the United States and other industrial countries has mostly relied on the use of monthly data (typically using industrial production as a proxy for economic activity), this is not the dominant approach when focusing on developing countries (e.g., Disyatat and Vongsinsirikul, 2003; Le, 2009). First, for many developing countries monthly industrial production is unavailable or available only very recently. For example, while Argentine monthly industrial production is available at best since early 2000s, quarterly real GDP is available since 1990. Even when available, the quality and/or relevance of industrial production monthly data, in particular as a proxy for economic activity, is doubtful. For example, while both quarterly and annual data indicate that Argentina grew 4.1 percent in 2008, monthly data suggest a drastic fall of 12.7 percent.

> 13For the IR we use the monetary policy rate published by the central bank in the case of Argentina, the SELIC rate in the case of Brazil, and the money market rate in the case of Uruguay. See Appendix 8 for details.

> 14We use four lags. Our results for the case of eight lags remain almost the same as in the four-quarter

15
Policy Research Working Paper 5889

# **Abstract**

A number of recent survey articles express hope that new data from enterprise surveys would shed new light on corruption complementing the corruption perception index by Transparency International. The paper explores this using the World Bank’s Enterprise Survey data globally and not just the data on Eastern Europe and Central Asia that have been used before. The authors find that in general the Enterprise Survey data provide aggregate views on corruption that are similar to the corruption perception index. However, massive differences exist for key countries, such as China and India. This suggests that idiosyncratic, country-specific biases are at work in one or both data sources. The authors use the Enterprise Survey data and relate them to measures of bureaucratic complexity from the World Bank’s Doing Business data, finding that more red tape is associated with higher corruption. The data are also consistent with the view that bribe payments reduce the burden of red tape. Finally, the paper looks at corruption in infrastructure. It has been suggested that the natural monopoly characteristics of infrastructure provide the lever to extract bribes. However, based on data on pricecost gaps, the authors find that infrastructure ventures in power and water typically charge prices below cost in developing economies, not anywhere near monopoly prices. Furthermore, the Enterprise Surveys do not suggest that infrastructure-related bribe payments are more significant than those, for example, related to tax payments or various forms of licensing. Existing sources on bribery surrounding specific projects suggest that the value of bribe payments may not be the biggest problem but the choice of uneconomic and inefficient projects. If infrastructure ventures were entirely dependent on revenue from user fees, they could not afford to pursue inefficient projects, thus reducing the cost of corrupt activity to society. Monopoly pricing would be better than the typical current pricing policy.

This paper is a product of the Finance, Economics and Urban Department, Sustainable Development Network. It is part of a larger effort by the World Bank to provide open access to its research and make
in comparison with their neighbors who were not expelled. They found (i) that a former refugee household consumes on average 7% less compared to a non‐refugee household and (ii) that it takes 8 to 10 years after return before the welfare of a former refugee household is at the same level of its non‐refugee neighbor. Key data are reported in Table 5, where formerly displaced households are over‐represented among the very poor.

**Table 5: the welfare of returnees (formerly displaced) and non‐displaced households in Burundi**

|Forced<br>|Le<br>|vel of Pov<br>|erty<br>|Totals|
|---|---|---|---|---|
|Displacement|Very poor|Poor|Non- Poor||
|Never|1104|492|583|**2179**|
||50.67|22.58|26.76|100%|
|At least once|2533|983|1005|**4521**|
||56.03|21.74|22.23|100%|
|_Total_|**3637**|**1475**|**1588**|**_6700_**|
||54.28|22.01|23.70|100%|

**Notes** –  Pearson Chi Square (2)=  20.96 [p-value=0.000] Data source: Core Welfare Survey for Burundi (2006).

**5.3 Some Key Findings from Case Studies**

29
.4|58.8|68.7|76.0|82.8|92.1|96.4|0.61|0.27|

a/ Measured relative to a value of 100 which corresponds to a system in which no pupils drops out before completing the cycle. b/ Measured relative to a value of 100 which corresponds to a system with no repeaters.

Note: The R<sup>2</sup> corresponds, in column (1), to a regression of the indicator concerned to the natural logarithm of the per capita GNP and its square, based on data for the whole sample; and in column (2), to the same regression based on data for countries with a per capita income below $4,000 in 1993.

The last line in the table pertains to the efficiency of student flow from grade 1 to grade 8, using as an indicator the percentage of school leavers who exit from the system in the interval _between_ the primary and lower secondary cycles rather than _within_ either cycle. The larger the percentage, the more children complete their studies rather than abandon them halfway, and the more efficient the pattern of student flow. An index of 100 means that all selection takes place between the two cycles.'<sup>6</sup> As countries grow richer, the efficiency of intercycle student selection improves: the share of pupils leaving between cycles is only 46.4 for countries at $200 in per capita, compared with 96.4 for countries at $20,000. The R<sup>2</sup> statistic is 0.61 for the whole sample, suggesting the efficiency of student selection rises in a predictable fashion as incomes increase. Among countries with per capita income below $4,000, however, the R<sup>2</sup> statistic drops to only 0.27, indicating much greater diversity in the efficiency of student selection, as well as
them suitable for phone surveys but these should be considered carefully in light of the objectives of a given survey. One approach is asking questions at a more aggregated level of observation, such as the farm or the household instead of the plot or the individual. In this approach, instead of enumerating a plot roster and eliciting inputs and outputs at the plot-level, questions are asked in aggregate, e.g. _“What is the total area of land, summing all parcels, you operate?”,_ This approach was used in some of the LSMSsupported HFPS. A similar kind of aggregation was done in the IPA’s RECOVR surveys (Innovations for Poverty Action, 2020) but with respect to household composition and demographic characteristics. This kind of aggregation may save time, though some aggregation questions, such as total area of land summing all parcels, may require respondents to do computations which increases respondent burden and introduces potential for error. Also, aggregation of individual- or plot-level outcomes at the household-level necessarily entails a loss of information.

Another strategy to simplify complex questions, such as those involving land or harvest quantifications, is posing such questions in relative terms, as compared to a previous period. This makes responses easier because farmers only need to roughly compare two periods, without the need of an exact quantification. For example, IFPRI’s vegetable value chain phone surveys in Ethiopia ask questions like _“Do you use more or less fertilizer currently than normally?”_ with responses _“1. The same; 2. Lower; 3. Higher”_ (Hirvonen et al., 2021b; Minten et al., 2020) _._ This simplification strategy is useful for before-and-after comparisons when no baseline quantification exists for comparison, as was the case in many of the COVID-19 phone surveys. However, responses may be biased positively or negatively depending on the sources of bias at play (for example, social desirability bias or perceived benefits to indicating poor outcomes). Moreover, the loss
several programs are based either on simulated scenarios (such as in Brown et. al 2018) or on actual programs, but without fully harmonized features (such as in Coady et al., 2004 and Devereux et al., 2017).

Relying on harmonized household-level data from nine programs implemented in the Sahel region, this paper contributes to the academic and policy discussion on targeting by (i) providing comparable evidence on the performance of targeting methods implemented in practice; and (ii) showing the role of different measurement choices in targeting performance. We analyze targeting schemes in six countries (Burkina Faso, Mali, Niger, Senegal and Chad, and Northern Cameroon) that were implemented between 2014 and 2018.<sup>1</sup> Our study focuses on low-income countries where evidence is scarce and where targeting raises specific questions in contexts that are characterized by high poverty rates, low budgets relative to needs, and low administrative capacities. We analyze data sets that include information on proxy means testing

> 1 Each targeting scheme was evaluated under a different study, currently part of the gray literature (Table A1). A review of these reports and papers was conducted by the authors and is available upon request. Taken together, these studies represent a wide range of methodologies for measuring performance, making comparisons difficult.

2
. As a point of reference, the World Bank KNOMAD surveys (2015) show that Vietnamese workers in the manufacturing sector send, on average, US$200/month to their family members back home.

- (iv) We exclude observations with transaction amounts of RM5,000 or more (equivalent to about three months’ salary of an ordinary foreign worker). The World Bank Greenback 2.0 surveys find that foreign workers send money home at least once every two months.

# **Step 2. Identify transactions undertaken on behalf of others in each month and record them as**

**transactions done by different individuals.** We treat the following transactions as those done by another individual:

- (i) The third or subsequent transactions by an individual within a month, assuming that foreign workers send money home, on average, twice a month at most, as each transaction entails remittance costs and they are sensitive to these costs.

- (ii) Second or subsequent transactions by an individual on the same day.

- (iii) Transfers to a country that is different from the remitter’s nationality.

- (iv) Transfers made by a Malaysian to various destination countries. For example, most of these cases specify that the purpose of the transfer is “family maintenance” and the source of funding as “employee salary” or “others’ income.”

8