rafmacalaba/data-use-mentions-extended huggingface dataset:
# <u>Box 1. Innovation Activities in the Enterprise Survey</u>

|Innovation or knowledge inputs are activities that are associated with the development of innovation at<br>the firm level.<br>|
|---|
|They are grouped into 5 categories:<br>i. Research and development|
|The firms were surveyed on the source of R&D (internal vs external) as well as expenditure.<br>ii. Capacity building<br>|
|The firms reported on the training provided as a result of new innovations introduced as well as<br>expenditure.|
|iii. Purchase/licensing of inventions or other knowledge forms<br>The firms reported on the expenditure on the purchase of inventions or intellectual property that<br>helped them come up with innovations.<br>iv. Acquisition of business intelligence|
|The interviewed firms reported on what were the key sources of information and ideas for their<br>innovative activities.|
|v. Intellectual property|
|On this, firms reported on whether they applied for patents, utility models, trademarks, copyright<br>design or registered an industrial design.|

# <u>Box 2. Innovation Outputs from the Enterprise Survey</u>

|Similar<br>Survey<br>i.<br>ii.<br>iii.<br>iv.|to the innovation surveys based on the Oslo Manual, the innovation module of the Enterprise<br>measures the following types of outcomes:<br>Product innovations<br>Process innovations<br>Organizational innovations<br>Marketing innovations|
|---|---|
|Produc<br>context<br>a. <br>b. <br>c.|t innovations are essentially new, redesigned, or substantially improved goods or services. In the<br>of the survey, there are 3 metrics used
. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data come from the most recent round of phone survey data available. This is round 2 in Ethiopia, Malawi, and Nigeria, and round 1 in Uganda. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

57
incentive effect that arises because strong operations management practices encourage both students and parents to increase their inputs. We also find evidence of these mechanisms in our PISA data for Latin America. Principals in public schools with higher PISA-based operations management scores are less likely to report experiencing teacher shortages and also report higher levels of teacher motivation, teacher effort and household effort, compared to principals in public schools with lower PISA-based operations management scores.

While this is not definitive causal evidence, this combination of theory and descriptive empirical analysis offers a novel insight into why management matters in schools and we therefore move on to consider policy implications. People management practices such as performance pay, while common in the private sector, may not be possible in public schools. But there would seem to be fewer barriers to conducting assessments to judge teacher effectiveness, and letting such appraisals lead to changes in public recognition, opportunities for professional development, likelihood of career advancement, and/or greater responsibilities. That is, these people management practices help to attract, develop and reward good performers, and, our analysis suggests, should improve both teacher selection and incentives.

There is also substantial variation in the strength of operations management practices _within_ the public sector. This suggest a role for government to encourage principals in public schools with weak operations management to follow best practices. Specific areas suggested by our analysis include processes that facilitate: personalization of learning; dialogue among staff, students and parents focused on continuous improvement; and collection and use of student assessment data.

**Related literature.** Our first contribution—a new approach to measure management practices in schools—relates to two bodies of work. The first is the literature that has evolved since the creation of the WMS dataset first described in Bloom and Van Reenen [2007]. The WMS methodology has been adapted to a range of public sector institutions, including schools and universities [Bloom et al., 2015a, McCormack et al., 2014], healthcare facilities [Bloom et al., 2015b
L, Mealli F., and F. C Rosati  (2010) “Household vulnerability and child labor: the effect of shocks, credit rationing, and insurance” Journal of Population Economics January 2010, Volume 23, Issue 1, pp 169-198

- Handa, S., Natali, L., Seidenfeld, D., Tembo, G. and the Zambia Cash Transfer Evaluation Team (2015). “The Impact of Zambia’s Unconditional Child Grant on Schooling and Work: Results from a large-scale social experiment”, Innocenti Working Paper No.2015-01, UNICEF Office of Research, Florence.

- Hoddinott, J., D. O. Gilligan, and A. S. Taffesse. (2009). “The Impact of Ethiopia’s Productive Safety Net Program on Schooling and Child Labour.” Working Paper.

- ICF (2013) “Impact Evaluation: Combating Worst Forms of Child Labor by Reinforcing Policy Response and Promoting Sustainable Livelihoods and Educational Opportunities in Egypt, 2011–2012” Final Report (available at

<u>http://www.dol.gov/ilab/iclre/evaluations/egyptEval.htm)</u>

- International Labour Organization (2013) “Global Child Labour Trends 2008 to 2012”. International Labour Office, International Programme on the Elimination of Child Labour (IPEC) – Geneva.

- IEG (2011) “Evidence and Lessons Learned from Impact Evaluations on Social Safety Nets” The World Bank, Washington DC.

- IPEC (2007) “Prevention of child recruitment and reintegration of children associated with armed forces and groups: Strategic framework for addressing the economic gap” Geneva, International Labour Office.

- Jafarey, S. and Lahiri, S. (2002) “Will trade sanctions reduce child labour?: The role of credit markets” _Journal of Development Economics_ 68(1): 137-156

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Friedman, J. and Gibson, J. 2012. Methods of household consumption measurement through surveys: Experimental results from Tanzania. Journal of Development Economics, vol. 98(1), pages 3-18.

Benstead, J.L. (2014). Does interviewer religious dress affect survey responses? Evidence from Morocco. Politics and Religion, vol. 7, Issue 4, pp. 734-760 Bischoping, K., and Schuman, H. (1992). Pens and polls in Nicaragua: An analysis of the 1990 pre-election Surveys. American Journal of Political Science, vol. 36 (2), 331-350 Blair, G. and Kosuke, I. (2012). Statistical analysis of list experiments. Political Analysis, 20 (1), 40-77.

Blair, G., Fair, C., Malhotra, N., Shapiro, J. N. (2012). Poverty and support for militant politics: Evidence from Pakistan. American Journal of Political Science, vol. 57, 30-48 Blaydes, L. and Gillum, R. M. (2013). Interviewer effects in the Islamic world: Evidence from a randomized survey experiment in Egypt. Politics and Religion, 6 (3), 459-485 21
Policy Research Working Paper 8641

# **Abstract**

A variety of approaches to delineate metropolitan areas have been developed. Systematic comparisons of these approaches in terms of the urban landscape that they generate are however few. This paper aims to fill this gap. The paper focuses on Indonesia and makes use of the availability of data on commuting flows, remotely-sensed nighttime lights, and spatially fine-grained population, to construct metropolitan areas using the different approaches that have been developed in the literature. The analysis finds that the maps and characteristics of Indonesia’s urban landscape vary substantially, depending on the approach used. Moreover, combining information on the metro areas generated by the different approaches with detailed micro-data from Indonesia’s national labor force survey, the paper shows that the estimated size of the agglomeration wage premium depends nontrivially on the approach used to define metropolitan areas. .

This paper is a product of the Social, Urban, Rural and Resilience 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 authors may be contacted at mroberts1@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
8.84 percentage point if method I is employed and 11.39 percentage point by method II.

Table 6: Changes in Poverty Rates Using Various Food Shares and Prices (%)

|Share of food in inflation|Base<br>Case|Usin|g CPI|Using Su<br>Prices|senas Unit<br>for Food|
|---|---|---|---|---|---|
||1999|1996|Change|1996|Change|
|Method I: Actual share of<br>food in consumption<br>expenditures, at povertyline|27.13|19.95|7.18|18.29|8.84|
|Method II: Using the share<br>of food in the poverty basket|27.13|17.55|9.58|15.74|11.39|

Notes: Working backward - start with poverty rate in February 1999 resulted from the iterative method.

Since the iterative method in 1999 produces a much higher level of poverty, these absolute changes are smaller percentage changes. Take for example the method II with CPI prices. Using the 1996 base case, poverty rate increased from 9.75 percent to 16.27 percent, which is 6.52 percentage points or 66.9 percent.

Meanwhile, using the 1999 base case, poverty rate rose from 17.55 percent to 27.13 percent, which is 9.58 percentage points but only 54.6 percent.

# IV) A **consistent set of estimates of poverty over the course of the crisis**

Over the course of the crisis there have been a number of estimates of poverty rates, using different large scale (but not necessarily nationally representative) household surveys. Unfortunately, each of those used a different and noncomparable _base_ for the "pre-crisis" poverty rate and a different method of deflation for the changes in the poverty
0.12<br> available (N=59,424). One<br>d only for municipalities with<br> and TSE.|
|---|
|**Variable**<br>Fraction of 0-6 population enrolled in a municipal pre-primary school<br>Municipal pre-primary students per pre-primary teacher<br>Fraction of municipal pre-primary teachers with post-secondary education<br>Pre-primary teacher quality index, frst PC from 2-factor PCA<br>Fraction of municipal pre-primary institutions with own school building<br>Fraction of municipal pre-primary institutions with electricity<br>Fraction of municipal pre-primary institutions with an indoor bathroom<br>Fraction of municipal pre-primary institutions with a library<br>Fraction of municipal pre-primary institutions with a computer<br>Pre-primary school infrastructure quality index, frst PC from 5-factor PCA<br>Education spending as fraction of revenue<br>Education spending per capita, 100s 2005 Reais<br>Infrastructure and urban development spending as fraction of revenue<br>Infrastructure and urban development spending per capita, 100s 2005 Reais<br>Revenue per capita, 100s 2005 Reais<br>Simulated revenue per capita, 100s, 2005 Reais<br>Average income per capita, 10,000s of 2005 Reais<br>Median income per capita in 2000, 10,000s of 2005 Reais<br>Gini coefcient in 2000<br>Population, 100,000s<br>Population aged 0-6, 100,000s<br>Population aged 7-15, 100,000s<br>Municipality had participatory budgeting at some point during 1995-2008<br>Municipality has a socialist party mayor<br>Fraction of population that lived in urban areas in 2000<br>Labor force participtaion rate (share of population economically active)<br>Racial fractionalization index in 2000<br>_Notes:_ Data are summarized over 1995-2008 and over all municipalities for which data are <br>exception is the dummy for participatory budgeting during 1995-2008, which is summarize<br
and Scarpetta, 2009, 2013; Hsieh and Klenow, 2009; Restuccia and Rogerson, 2008, 2017). This paper contributes to this literature in two primary ways. First, it is the first paper to our knowledge to apply the Olley-Pakes decomposition methods to African data. To carry out this decomposition, we have generated new longitudinal data for Ethiopia (2012-2014) and Tanzania (2008-2012) and gained access to new panel data from Côte d’Ivoire.<sup>1</sup> In the case of Ethiopia, our dataset adds to the existing panel which covers the period 1996 to 2009.<sup>2</sup> Plus, we have generated a short panel for Bangladesh that covers every other year between 1995 and 2001. Using these data, we examine the sources of manufacturing productivity growth including the relative importance of factor reallocation for African countries.

Several interesting findings emerge from this analysis. First, there are clear differences in the size and sources of productivity growth across both countries and plants within countries. In terms of country performance, Ethiopia outperformed both Côte d’Ivoire and Tanzania on nearly every metric that we estimated. For example, aggregate manufacturing productivity increased by 75 percent in Ethiopia between 2006 to 2016 while it increased by 6 percent in Côte d’Ivoire over a similar period (2004 to 2016). Aggregate manufacturing productivity fell in Tanzania over the period 2008 to 2012. Second, we find several similarities between Ethiopia’s pattern of productivity growth in recent years and that of Bangladesh during its early years of industrialization. For example, Bangladesh’s aggregate manufacturing productivity increased by 52 percent between 2001 and 2012 which is comparable to that achieved by Ethiopia between

> 1 We’d like to thank Nouhoum Traore for sharing the Ivorian panel data with us.

> 2 In addition, we have establishment-level data for 2010, 2011, 2015, and 2016.  We hope to match the establishments in those years to our existing dataset so that we have a panel covering the
For interventions, we begin with the sample of impact evaluation studies in the areas with the largest number of measured learning outcomes in a systematic review (Snilstveit et al. 2015): structured pedagogy (e.g., introducing new lesson content and providing teachers with training on how to teach), computer‐assisted learning, and school based management. We only include studies with any measure of the three building blocks of the STEP literacy assessment – word meaning, sentence processing and basic passage comprehension – to ensure the comparability to the extent possible, and sort them by standardized effect size.<sup>12</sup> Forty‐nine of the total 63 reading outcomes analyzed in this paper are from impact evaluations that employ randomized controlled trials (RCTs), and the rest are from studies using either difference‐in‐differences (DID) or multivariate regression evaluation designs. In order to estimate the potential increase in average annual income associated with an intervention, we use the GNI per capita, PPP (current international $) combined with the labor share of national income of the country where the intervention was implemented (World Bank 2017; Neiman and Karabarbounis 2013). To compare all the interventions in the same time frame, we assume that all interventions began in 2015 (Buchmann et al. 2016).

For cost‐effectiveness analysis, a recent study by J‐PAL (2014) provides standardized effect sizes and program costs of 27 education interventions with student learning outcomes across Sub‐Saharan Africa and Asia. Our analysis includes 14 of those 27 programs with a significant impact at the 10% level. We estimate the equivalent years of schooling per $100 and the rate of return on investment. The cost data from J‐PAL in general consider costs to the implementer such as administrative and monitoring costs and the opportunity costs of the beneficiary and their family (see Dhaliwal et al. (2013) for details on methodology). To follow the time frame of J‐PAL’s data
countries, it risks making the classification substantially less useful to users. More importantly, it is unlikely to be found acceptable for operational purposes, thereby leading to an income classification that departs significantly from the operational guidelines.

One pragmatic approach is to use a hybrid based on an average of the two conversion factors: adjusted market exchange rates and PPPs. This would reduce the impact on the classification of switching to a PPP-based method, and—depending on the use to which the classification is put—there may be some justification for maintaining exchange rates in the formula. For example, market exchange rates provide a better measure of a country’s ability to service its international obligations and may, therefore, better reflect its creditworthiness. But there are several disadvantages, including that the PPP-based method would still need to use annual estimates based on extrapolation from benchmark years, it would still be subject to substantial periodic revision, and any method of combining the two series may be seen as an arbitrary choice.

It is also worth noting that the ranking of countries using either conversion method is very similar. Using data for 2010 (extracted from _World Development Indicators_ on May 30, 2014), the rank correlation coefficient is 0.99. And, as noted earlier, correlations between selected social indicators and GNI per capita using either market exchange rates or PPPs are relatively high.

The discussion leads us to the conclusion that market exchange rates, with appropriate adjustments to mitigate the impact of short-term volatility, continue to provide a reasonable and practical basis for preparing comparable GNI per capita estimates for use in classifying countries. We also conclude that the use of PPPs should be considered only when consistent annual estimates of PPPs are produced by the ICP.

# **6. Adjusting classification “cutoffs” over time**

The initial choice of the three threshold levels used in the income classification appears to have been made largely on pragmatic grounds. The cutoffs for defining the low and lower middle income thresholds were already in use in the World Bank’
and number of items previously asked. Unfortunately, we cannot estimate such a response bias in the available data. However, implementation of the rapid approach with an enhanced design with different optional modules varying in their comprehensiveness of items can in general shed light on this bias. Comparison between responses for the same item in a comprehensive and a non-comprehensive list can also indicate a lower bound for response bias. Assuming that the context of a comprehensive list is a better estimate, the response bias could be corrected for. However, it is expected that this type of response bias is very small in comparison to general measurement and estimation errors.

The main source of bias for the rapid approach is created by the assumption of zero co-variance between optional modules. Further research can help to estimate co-variance between modules within the survey and adjust the consumption estimates accordingly. Using a random assignment of items to optional modules, the co-variance between groups of items within an optional module can potentially be used to estimate the co-variance between optional modules. Or administering optional modules that share items might also be helpful to estimate the co-variance between modules.

The rapid approach reduces administering time considerably. While this creates opportunities to include additional questionnaire modules on different topics (e.g. remittances or health), it also has the potential to reduce the non-response rate (A. Deaton and Grosh 2000). The KIHBS 2015/16 survey suffered from a high non-response rate specifically in wealthier areas. For example, the capital city Nairobi had a response rate of 77 percent compared to 92 percent in the rest of the country. The highest non-response rates were specifically observed in clusters in wealthier areas within Nairobi. A high correlation of non-response with welfare status can lead to biased poverty estimates. The considerably shorter pilot survey – which not only used the rapid approach for consumption but generally shortened the questionnaire across modules and was carried out using tablets rather than paper – did not show the same pattern of lower response rates in wealthier areas. The pilot response rate of 99 percent in
wage growth may influence the change in exports, as a rise in wages may hamper export expansion. In the latter case, the change in Export Index and the error term would be negatively correlated, causing a downward bias in the coefficient estimate for the change in Export Index.

Whatever the source of endogeneity may be, I address the potential endogeneity problem using the province-specific measure of the U.S. tariff reduction as an instrumental variable. The U.S. tariff cuts that each province faces are calculated using a methodology similar to the one developed by Topalova (2010) and applied to Vietnam by McCaig (2011) and Coello (2009). Topalova (2010) examines the 1991 Indian trade liberalization episode and finds that rural districts, in which production sectors more exposed to import liberalization were concentrated, experienced slower reduction in poverty in India. Following Topalova (2010), McCaig (2011) constructs a provincial measure of U.S. tariff reductions and finds that provinces that were more exposed to the U.S. tariff cuts in the aftermath of the BTA experienced faster decreases in poverty. My approach differs from the previous studies in terms of explicitly modeling the rise in labor demand induced by exports as a channel through which exports influence wage growth.<sup>39</sup> In order to overcome potential endogeneity problems, I use the U.S. tariff cut measure as an instrument. Furthermore, I extend the tariff cut measure developed by Topalova by including a

> 39 McCaig (2011) also examines the impacts of the BTA on provincial wages, but for different education groups. McCaig (2011) shows that the increase in the U.S. tariff cut measure is associated with an increase in the mean provincial hourly wage growth for workers with “at most a primary education” whereas the relationships are not statistically significant for the other education groups. His findings are consistent with a rise in labor demand for unskilled workers, but the study does not show or model a mechanism