rafmacalaba/data-use-mentions-extended huggingface dataset:
- <mark>de Bellefon, Marie‐Pierre, Pierre‐Philippe Combes, Gilles Duranton, and Laurent Gobillon. 2018. “Delineating Urban Areas with Building Density.” Paper presented at 8th European Meeting of Urban Economics Association, Düsseldorf, Germany, May 11.</mark>

- <mark>Denis, Eric, and Kamala Marius‐Gnanou. 2011. “Toward a Better Appraisal of Urbanization in India: A Fresh Look at the Landscape of Morphological Agglomerates.”</mark> _<mark>Cybergeo: European Journal of Geography</mark>_ <mark>.</mark>

- <mark>Donaldson, Dave, and Adam Storeygard. 2016. “The View from Above: Applications of Satellite Data in Economics.”</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>30 (4): 171–98.</mark>

- <mark>Duranton, G., and D. Puga. 2004. “Micro‐foundations of Urban Agglomeration Economies.” In</mark> _<mark>Handbook of Regional and Urban Economics</mark>_ <mark>, Vol. 4, edited by J. V. Henderson and J. F. Thisse,  2063–117. Amsterdam: North‐Holland.</mark>

- <mark>______. 2015. “Urban Land Use.” In</mark> _<mark>Handbook of Regional and Urban Economics,</mark>_ <mark>Vol. 5, edited by</mark> Gilles Duranton, J. Vernon Henderson <mark>, and W</mark> illiam Strange <mark>, 467–560. Amsterdam: North‐Holland.</mark>

- <mark>Elvidge, Christopher D., Benjamin T. Tuttle, Paul C. Sutton, Kimberly E. Baugh, Ara T. Howard, Cristina Milesi, Budhendra Bhaduri,
panel structure of the LSMS-ISA data allows us to make some progress in addressing the endogeneity problems facing existing related studies that are based on observational data (see the survey papers provided by Birkhaeuser et al., 1991; Evenson, 2001), in particular through our ability to include farm fixed effects and proxies for agricultural shocks. Yet, – obvious endogeneity concerns remain, due to the possibility that time-varying shocks other than those that we can control for – affect both demand for extension and outcomes, and the results should be interpreted as suggestive correlations in the data. Results are reported in Appendix D. We find that access to extension services is positively associated with a number of relevant farm outcomes. This particularly holds for agricultural advisory services that are provided at farmers’ own request. The quantitative magnitudes of our estimates imply that receiving agricultural advice is associated with a 7% larger value of harvest and 8% higher profits (defined as value of harvest net of costs for seeds, fertilizer, and agrochemicals).<sup>30</sup> Thus, as long as farmers’ beliefs are consistent with the outcomes observed in the data, there is no reason to assume that farmers abstain from requesting extension services because they believe that the provided advice is not useful (also recall that more than 80% of farmers that received advice rate it as useful or very useful in our sample; see Table 1).

In addition to the ability to distinguish between solicited and unsolicited advice, the data also allow an analysis of potential channels through which extension might affect farm outcomes. We find a significant link between extension services and use of modern farm inputs (mainly fertilizer and improved seeds), which are commonly argued to be important determinants of agricultural productivity (Evenson and Gollin, 2003; Morris et al., 2007; Duflo et al., 2011). Another concern might be that the observed correlation between shocks and received extension services may be driven by supply-side effects rather than the proposed mechanism through increases in farmers
necessary for the transformations.<sup>11</sup> In order to check on the results of this work, we compared the resulting values from our database with the IMF GDP per capita PPP data and we also verified the consistency of results across countries and years.

We use two different Gini as alternative measures of _income inequality_ ( _I_ ) _._ The first is calculated by country and year using the income variable already described present in the database we use (Gini WVS for short). The second is the Gini coefficient taken from the UNU-WIDER database on inequality (Gini WIDER for short).

The Gini WIDER puts together country estimates of the Gini coefficient calculated from a variety of income and consumption measures. For this reason, we opted to use two forms of the Gini WIDER. The first form is constructed with different types of income or consumption measures giving priority to disposable income, other forms of income and consumption in this order.<sup>12</sup> This allows us to cover all country/year data points available in the World and European Values surveys. The second form is the Gini WIDER estimated using disposable income only. This restricts the usable sample to two-thirds of the original size but provides more precise estimates for income inequality. Given that we use two different samples for the Gini WIDER we also present results for the Gini WVS for both samples. Thus, all tables will report four sets of results (two Gini for each of the two samples).

_Relative income_ ( _R_ ) is measured as income divided by mean income within each country and year. In happiness research this variable is often used in conjunction with income and/or income inequality to test the importance of the relative income position as opposed to absolute income in explaining satisfaction. Relative income has been found to have a significant and positive effect on satisfaction but the sign and significance of this variable may be affected by collinearity with other variables such as income or income inequality. It is important therefore to test
decarbonization technologies and services with pro-development characteristics (OECD, 2017). For example, understanding countries’ productive capabilities to recycle and maintain solar panels could help generate labor demand in small and medium sized enterprises. Second, exploring how countries’ policy space relates to their strengths in decarbonization technologies is another open question. Do countries with competitive exports in decarbonization technologies have lower trade costs, more ambitious nationally determined contributions (NDCs) and decarbonization targets, benefit from domestic subsidies and low regulatory barriers to FDI or other policies? Finally, diffusion of decarbonization technologies beyond production hubs is critical to help countries transition to a more low-carbon economy. This requires a better understanding of the structural and policy drivers behind countries’ and firms’ adoption of decarbonization technologies.

# 4.) Methods

The analysis in this paper involved (i) developing a new mapping of global value chain products for solar PV, wind turbines and EVs, (ii) merging this mapping with export data, and (iii) constructing and calculating several metrics.

## 4.1) Mapping global value chains of key decarbonization technologies

We used the 6-digit product classification of the Harmonized System (HS) to identify tradeable products associated with the value chains of solar PV, wind turbines and EVs. The 6-digit HS classification is the most granular, internationally harmonized classification of products. While many existing classifications of environmental goods have been based on the 6-digit HS coding classification<sup>4</sup> , it has some limitations, notably dual use and product specificity.

> 4 <mark>Steenblik, R. (2005). Environmental goods: A comparison of the APEC and OECD lists (No. 2005/4). OECD Publishing; Sugathan, M. (2013). Lists of environmental goods: an overview. International Center for Trade and Sustainable Development.</mark>

11
Policy Research Working Paper 9035

# **Abstract**

This paper examines the effects of large inflows of internally displaced persons (IDP), who are primarily absorbed by the informal sector, on the behavior of formal manufacturing firms in Colombia. To identify causal effects, the analysis employs annual, firm-level panel data between 1995 and 2010 and exploits that when conflict intensifies, forcefully displaced individuals tend to migrate to municipalities where people from their origin locations settled earlier. The paper finds that large inflows of IDP induce sizable, negative effects on the intensive and extensive margins of production of formal firms. These effects are stronger for firms operating in sectors that face a stronger competition from the informal economy.

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/prwp. The authors may be contacted at hwinkler@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
## **Data source**

The data used for the construction of this indicator include the same street grid data and transit network data as used for the indicator on accessibility.

## **Methodology**

We first compute for each household the number of public transport ‘runs’, that is the number of vehicle departures at all stops available within a 10 minute walking time over the course of an hour. We use the same 8:00 am to 9:00 am window to be consistent with the accessibility indicator and to account for the peak services during morning commutes. The walking distance are analyzed using the road-grid distance, using 3.6 km/h as walking speed and based on the same network as used for the accessibility analysis. A public transport vehicle that can be stopped continuously, is assumed to stop every 100 meters.

To give an example, a household that is within a 10-minute walk of three different bus stops, which have a bus arriving – to any destination - once an hour, every thirty minutes (i.e. 2 per hour) and every five minutes (i.e. 12 per hour) between 8 am and 9 am will have a value of 1+2+12=15 for this metric.

Once we have this number of runs, we normalize the number to obtain an index between 0 and 100%. In practice, we divide by 60 and truncate the values equal to or over 1 to be equal to 100%. This means that we have an index of an average number of bus reachable each minute between 8 am and 9 am, at a maximum distance of 10-minute walk, with the maximum being 1 bus per minute (all locations that have a higher availability will report an index=100%).

# _c. Measuring safety_

## **Definition**

The third and last indicator of the public transport network looks at a different aspect of transport, that can affect the decision to use public transport
of respondents, (iv) eliciting posterior beliefs, and (v) self-reporting of consumption plans for all respondents. In this section, we describe each component of the intervention in turn.

# **4.3.1 Baseline Information**

We begin with a set of standard questions on the respondent’s socio-economic background, including employment status, highest level of education attained, marital status, and dependents. We do not ask about gender, age, or income as this information is available in the administrative records obtained from the partner bank. We also include one question about the expected economic conditions in the country over the next 12 months, for which the possible responses are “better off,” “about the same,” and “worse off.” The language in this question, and all other questions about expectations, closely follows the wording used in the most widely used surveys of consumer expectations, such as the University of Michigan’s Survey of Consumers and the Federal Reserve Bank of New York Survey of Consumer Expectations (see, for example, Bachmann, Berg, and Sims, 2015; Fuster et al., 2020).

# **4.3.2 Elicitation of Prior Beliefs**

Next, we elicit participants’ inflation and exchange rate expectations at two points in time: immediately before the treated individuals receives information from the experimenter ( _prior beliefs_ ) and after a randomly chosen subset of respondents is provided with an inflation or exchange rate forecast ( _posterior beliefs_ ). The wording in both rounds is closely modeled on that used in standard surveys of consumer expectations, and was adjusted to the context of our study through qualitative interviews and an online pilot.

We elicit beliefs about the future inflation rate and exchange rate. To avoid artificially making one belief more salient than the other, we randomized the order of these two questions. To elicit inflation expectations, the surveyors first provide a definition of inflation by explaining that “[...] inflation is the measure of
, as specified in equation (1), with the variables introduced above. The “small model” in column (1) captures fewer variables, which allows for a larger sample size (~1,500 observations from 168 countries). At each column of table 2, the model is sequentially extended, shrinking the sample accordingly.

The results in table 2 can be summarized as follows: (i) First, the autoregressive parameter is in the expected range, hovering around 0.8. Note that this parameter is likely downward biased, as further discussed in section 5.4.

(ii) Second, a number of correlations is quite robust: infrastructure and trade are positively correlated with income per capita levels, financial crises, inflation, and inequality changes negatively. All those correlations are statistically different from 0 across all specifications (at conventional levels of significance).

(iii) Third, a number of correlations are in line with expectations and show reasonable standard errors but cannot be reliably distinguished from a 0-correlation in most models. This concerns the negative coefficient estimates for temperature variability, terms of trade changes, and growth volatility, as well as the positive parameter estimate for credit/GDP, FDI, human capital, and the non-linear U-shaped relationship with export concentration. The latter suggests that medium specialization (around the sample mean) is associated with the lowest income levels and growth strategies can either be successful through diversification (e.g., United States, Netherlands) or specialization in goods with outstanding comparative or factor advantage (e.g., Saudi Arabia, United Arab Emirates).

> 18 For this reason, we plan to include data from pip.worldbank.org in future updates of the dataset instead.

-13-
2020. “Short‐ and Long‐run Asymmetric Effect of Oil Prices and Oil and Gs Revenues on the Real GDP and Economic Diversification in Oil‐ dependent Economy.” _Energy Economics_ , <u>https://www.sciencedirect.com/science/article/pii/S0140988320300190</u>

- Christensen, B.V. 2016. “Challenges of Low Commodity Prices in Africa.” BIS Paper no. 87. Bank of International Settlements, Basel, Switzerland.

- Clark, C. 1940. _The Conditions of Economic Progress._ Macmillan, London.

- Cleveland, C.J., and M. Ruth. 1998. “Indicators of Dematerialization and the Materials Intensity of Use.” _Journal of Industrial Ecology_ 2(3): 15‐50.

- Considine, T.J. 1987. “Understanding Trends in Metals Demand.” _Materials and Society_ 11(3): 349‐370.

- Cooper, R.N., and R.Z. Lawrence. 1975. “The 1972‐75 Commodity Boom.” _Brookings Pa‐ pers on Economic Activity_ 3: 671‐723.

- Costantini, V., and C. Martini. 2010. “The Causality Between Energy Consumption and Economic Growth: A Multi‐Sectoral Analysis Using Non‐stationary Cointegrated Panel Data.” _Energy Economics_ 32 (3): 591‐603.

- Crompton, P. 2015. “Explaining Variation in Steel Consumption in the OECD.” _Resources Policy_ 45: 239‐246.

- Crowson, P. 2017. “Intensity of Use Reexamined.” _Mineral Economics_ 31 (1‐2): 61‐70.

_— 25 —_
question was asked within a regular cycle of the World Bank Enterprise Surveys (WBES, described in more detail in the next subsection). The form and phrasing of the question were therefore framed to fit the approach of the WBES, an important element of which is asking busy top managers or owners of firms a large set of questions on a collection of heterogenous topics. This means that questions must be terse and understood in a natural way by businesspeople, leading to budget constraints on words and concepts embodied in questions.<sup>27</sup> Question design begins with the choice to use 'agreements', and not 'contracts', since contracts are only one type of agreement. Then one must decide whether to ask firms about their agreements with transactional partners in general, or to ask about one specific partner, since the transactioncost and contract-theory literatures suggest that relationships may differ across different partners. Focusing on a specific partner is conceptually and practically complicated. First, the question must suggest to respondents which specific partner they should focus upon.<sup>28</sup> A possible solution is asking respondents to choose a partner when answering questions, as when Mike and Kiss (2019) suggested choosing a 'typical' supplier and buyer.<sup>29</sup> But this solution invites selection bias: Mike and Kiss (2019) found that respondents equated typical business partners with those having longstanding ties, implying that the resultant data set characterized relationships in which

> 27 Terseness in wording has benefits for the whole survey. Longer questions cause problems because they place a greater cognitive burden on respondents (Yan and Tourangeau 2008). This affects respondent comprehension (Holbrook et al. 2006), adding complexity without altering the distribution of responses (Shaeffer et al. 2005), leading to respondent satisficing and decreased measurement reliability (Alwin and Beattie 2016). It also leads to a potential bias as respondents with more cognitive resources will answer complex questions differently than those with fewer (Oberski 2016
Finally, two additional attribute variables of individual countries are used. In particular, the number of PCT patents per country, for five-year annually repeated timewindows. This variable controls for the size of country innovation systems, which clearly determines a country’s capacity to collaborate with foreigners, as well as its capacity to attract inventors from abroad or send them to other locations. In addition, it includes GDP per capita, from the World Development Indicators (World Bank), expressed in US$ 2005 at PPP, in order to capture the market potential of countries as well as their capacity to innovate. Table 1 contains summary statistics of the variables included in the models, and appendix S6 the correlation matrix.

TABLE 1. Summary Statistics

||Observations|Mean|St. Dev|Min.|Max.|
|---|---|---|---|---|---|
|Collab. inv_i- inv_j|26,160|1.26|10.55|0|678|
|Collab. app_i- inv_j|26,160|1.73|14.87|0|708|
|Diaspora size|26,160|22.29|447.18|0|26,661|
|Distance|26,160|6,889.27|4,539.38|59.62|19,629.50|
|Contiguity|26,160|0.01|0.10|0|1|
|Common language|26,160|0.09|0.29|0|1|
|Lang. similarity|26,160|0.16|0.18|0|0.89|
|Colonial links|26,160|0.04|0.18|0|1|
|Religion similarity|26,160|0.13|0.19|0|0.90|
|EXP+
than the more holistic approach looking at the financial services industry as a whole, and the distribution of the characteristics of its small-scale and low-income users.

Of course, similar questions arise in regard to borrowing customers and insurance, and to various forms of small value payments services.

Even if we had a credible measure of the penetration of financial services as a proportion of the population, it would not satisfy our curiosity for long. We also want to know how many _poor_ people have access, we would want to explore different _types_ of each service (residential mortgages _vs._ six-month crop loans) and ask about the _price_ at which they are available to different classes of people.

There is some potential for extracting more and better data from financial service providers and their regulators (and this is reviewed in Section 4 below), but the difficulties already mentioned underline the need for seeking other sources of information such as household and enterprise surveys (Sections 2 & 3). It is also clearly essential to ensure that any such efforts start out with questions that are both answerable in a way that is comparable across countries, and contribute to building a systematic picture of access to financial services allowing private and public sector interests to be served. In short, there must be a coherent conceptual framework.

# **_1.2 The conceptual framework for data collection_**

_Framework underlying other international databases_ The most successful international data collection exercises on national economic conditions have been driven by the attempt to quantify powerful underpinning economic models.

Most prominent among the international economic databases is the System of National Accounts (http://unstats.un.org/unsd/sna1993/introduction.asp), originally built on the Keynesian IS-LM model and requiring the classification of aggregate national economic transactions into income and expenditure, consumption and savings, investment and the balance of payments. Monetarist developments of the same Keynesian system underlie the depository corporations survey (formerly known as the monetary survey - cf. the IMF’s Monetary and Financial Statistics Manual
and refers to 1989.

Central Government employment, Education and health employment are from Zambia Public Expenditure Review of April 20, 1995 and relates to 1994. Local Government employment is from the same report but relates to 1993. Education and Health correspond to civil servants in the Education scales and Medical scales.

GDP and Wages and Salaries of Consolidated Central Government estimate are from IMF Government Finance Statistics Yearbook 1995 and relate to 1993.

Zimbabwe Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Data on Central, Non-central Education and Health employment is also from Ame Disch, AF1MI and relates to 1996 'Local Government employees are estimated as follows: The 22 urban councils have budgets which together are close to 10% of central Government's. The average cost would be lower than for Central government's, but you can safely assume at least the same labor recurrent cost ratio as in central government. Accordingly, an estimate of 25,000, including provincial and rural districts is probably close. Health employees are 23,000, of which 6400 civil servants and 16400 other health employees, largely funded from ministry budgets, though local authorities, particularly urban councils in the larger cities contribute a lot". Data on military employment do not include employment in paramilitary units, such as the Zimbabwe Republic Police Force (19,500), Police Support Unit (2,300) and the People's Militia (1,000).

Wages and Salaries as percent of GDP and Average wage is from IMF Report No. SM/96/32 of February 7, 1996 and relates to 1995. Central Government wage bill is a Staff estimate from Ame Disch (AFIMI), currently working on a Public Sector Review in Zimbabwe. The data relates to the 1995/96 fiscal year. GDP is from IMF Staff Country Report and relates to 1994. Calculations of Average Public Sector wages and Average wage as percentage of GDP result from this data. Data on