rafmacalaba/data-use-mentions-extended huggingface dataset:
H. Moylan, J. Ilukor, C. Mtengula, and I. Pangapanga-Phiri (2021). Root for the tubers: Extended-harvest crop production and productivity measurement in surveys. _Food Policy 102_ , 102033.

- K ̈onig, M. D., D. Rohner, M. Thoenig, and F. Zilibotti (2017). Networks in conflict: Theory and evidence from the Great War of Africa. _Econometrica 85_ (4), 1093–1132.

- Kosmowski, F., A. Aragaw, A. Kilian, A. Ambel, J. Ilukor, B. Yigezu, and J. Stevenson (2019). Varietal identification in household surveys: Results from three household-based methods against the benchmark of DNA fingerprinting in southern Ethiopia. _Experimental Agriculture 55_ (3), 371–85.

- Lobell, D. B., G. Azzari, M. Burke, S. Gourlay, Z. Jin, T. Kilic, and S. Murray (2020). Eyes in the sky, boots on the ground: Assessing satellite- and ground-based approaches to crop yield measurement and analysis. _American Journal of Agricultural Economics 102_ (1), 202–19.

- Lundstad, E., Y. Brugnara, D. Pappert, J. Kopp, E. Samakinwa, A. H ̈urzeler, A. Andersson, B. Chimani, R. Cornes, G. Demar ́ee, J. Filipiak, L. Gates, G. L. Ives, J. M. Jones, S. Jourdain, A. Kiss, S. E. Nicholson, R. Przybylak, P. Jones, D. Rousseau, B. Tinz, F. S. Rodrigo, S. Grab, F. Dom ́ınguez-Castro, V. Slonosky, J. Cooper, M. Brunet, and S. Br ̈onnimann (2023). The global historical climate database HCLIM. _Scientific Data 10_ (1), 44.

- Maccini, S.
which the welfare costs of the pandemic arise in equal parts from greater poverty and mortality. Under that assumption ( _α_ = 10 ), Belgium (with ˆ _α_ = 3 _._ 6 ) would be the only country in our sample for which Covid-induced mortality is the dominant source of welfare losses from the current crisis. All things considered, it seems safe to say that, given individual and public policy responses, the poverty costs of the pandemic in these six countries are not dwarfed by its mortality costs. Indeed, for most plausible parameter estimates, the reverse is true (at least) in Pakistan and the Philippines.

# 3.2 The whole world

The ndings above suggest that the poverty eects of the pandemic are substantial, even in relation to its mortality eects. They also hint at a possible pattern where that cost ratio is much larger for developing countries relative to developed countries. To investigate those conjectures further, we go beyond our six-country sample, and extend the analysis to as many countries as possible.

11
**G. Beyond the firm: "The micro-foundations of macroeconomics"1 0**

The scope of firm-level surveys goes well beyond testing theories of the firm. As many researchers have suggested, firm-level data has become key to understand the macro economy. Macroeconomic analysis concentrates mainly on understanding the determinants of output, unemployment and inflation. Researchers have thus built macro-economic variables from industry-level data under the assumption of a "representative firm" within each industry. Recent studies in developed economies, however, demonstrate that seemingly similar firms in the same industry exhibit different behavior in terms of output, investment, and employment, suggesting that aggregation may obscure important phenomena. Though these findings do not justify rejecting the representative firm assumption, they highlight how we can more completely understand changes in macro variables by analyzing these variables at the firm level.

Firm-level data is also useful for macro-analysis.since it helps test robustness by complementing aggregate, official, sources of data. Robustness tests are especially relevant for transition economies, where pre-transition data is often not reliable and the process of collection of post-transition data is still under revision. Firm-level surveys can help reconcile seemingly contradictory aggregate results by disentangling contrasting effects and offering a different perspective on the issue in question.

Next, we provide a brief overview of the most important theoretical macro-economic issues, which can benefit from this micro approach to macro-analysis, and of the (limited) empirical testing done using World Bank surveys.

# **Growth and Investment**

An important aspect of macro-economic analysis is the link between growth and investment. In particular, recent studies suggest that the rate of investment is positively linked to the rate of growth. To better understand this link, researchers have focused on the determinants of the investment decision and found that this decision depends on financial constraints, the risks firms face, and the productivity of investment itself (Jenskin, 1998; Gyimah-Brempong and Traynor, 1999). This line of research however has used mainly aggregate (country-level) data.

> **10** This
Policy Research Working Paper 9445

# **Abstract**

This study takes advantage of a publicly salient policy sphere—road quality—in the Russian Federation’s capital city to explore the use of digital technologies as means of aggregating information and demonstrating government capacity and effectiveness. It focuses on the potential linkage between road quality based on citizens’ complaints and electoral outcomes in two rounds of Moscow mayoral elections in 2013 and 2018. The data on more than 200,000 online potholes’ complaints were collected and combined with local election data. The causal relationship between these two processes is established, making use of an arguably exogenous variation in the differences across local weather conditions during the heating season that differentially affects pothole creation but is uncorrelated with electoral outcomes. The results indicate that greater use of digital technologies (measured by pothole complaints) results in an increased number of votes and a higher margin of victory for the incumbent. They highlight digital technologies’ role as a tool to create participatory governance mechanisms and convey to the public an image of a transparent, responsive, and capable government.

This paper is a product of the Development Research Group, Development Economics. 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 jsteinbuks@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
The problem with the traditional approach of estimating wage regressions to test theories of wage determination is that, without independent measures of worker productivity, it is difficult to determine whether wage differentials associated with workers’ characteristics reflect productivity differential or some other factors. For example, typical wage regressions report positive coefficients on age (conditional on a variety of controls). These positive coefficients neither imply that older workers are more productive than younger ones, nor that wages rise faster than productivity. Similarly, without direct measures of the relative productivity of workers, factors associated to wage differentials cannot be established.

Verner (1999) analyzes the determinants of wages and productivity in Zimbabwe, using a matched employee-employer manufacturing sector data, the Regional Program on Enterprise Development (RPED) survey data from 1993. The number of firms and employees interviewed are 201 and 1609, respectively. In each firm, about 10 randomly selected workers from different occupational categories are interviewed to obtain the worker level data. Here workers are the unit of observation and firms are included in the individual vector of variables. The main conclusions from this study were that formal education, training, and experience impact wages and productivity positively, and that females were being paid less than male employees, despite that females are not measurably less productive. This analysis is innovative; nonetheless, this model uses firm-level productivity as a measure of the individual productivity, while the worker characteristics variables and the endogenous wage variable are at the individual level – regardless of the differences in education and training, all workers in the firm are assigned the same productivity measure. The problem is that the effect of worker characteristics on productivity will not be properly identified when there is no variation in the workers’ productivity within a firm but differences in workers’ characteristics exist.<sup>1</sup> Hellerstein, Neumark, and Troske (1999) use a unique plant-level dataset in the United States, the Worker Establishment Characteristics Database (WECD), which matches long-form respondents to the 1990 Decennial Census of Population to
6|49.8|
|R-squared<br>||0.669<br>||Fuel for Car|0.104|30.1|30.6|
|Constant||8.575||**Dwelling Characteristics**||||
|Observations||988||Wall material is Concrete|0.115|10|12.7|
|||||No Toilet<br>**Performance metrics**|-0.055|16|3.9|
|||||R-squared||0.582||
|||||Constant||8.091||
|||||Observations||2,986||

**Source** : World Bank estimations using 2019/20 IE-LFS and AWMS R3 surveys.

9
bank regulation**

Until Basel III becomes globally widespread and authorities admit the use of banks’ internal rating systems, regulatory frameworks for credit risk may not be completely aligned to the underlying risk of banks portfolios. However, loan loss provisions should be commensurate with expected or average losses, while capital is to cover unexpected losses. Bank capital acts as a buffer against unexpected bank risk therefore reducing the risk of bank failure and helps to control for moral hazard, mitigating the incentives to take excessive risk.

As long as PCRs contain sufficiently long historical data for the largest possible share of borrowers in the banking sector, the analysis performed with the information therein contained can assist in reshaping the prudential regulation for credit risk. By means of diverse data mining and econometric techniques, such as transition matrices and credit scoring models, and by deriving loss distributions from credit portfolio models, local authorities can better calibrate provisioning ratios and capital requirements to more closely reflect expected and unexpected losses for credit risk.

While banks may use their own internal rating systems to manage credit risk,<sup>13</sup> in many

> 11 For an analysis of the usefulness of credit bureau data in relation to Basel II see, for example, Trucharte Artigas (2004).

> 12 As of early 2009 the BCBS membership was expanded to include 27 countries: Argentina, Australia, Belgium, Brazil, Canada, China, France, Germany, Hong Kong SAR, India, Indonesia, Italy, Japan, Korea, Luxembourg, Mexico, the Netherlands, Russia, Saudi Arabia, Singapore, South Africa, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States.

> 13 For a review of the range of practice regarding internal rating systems of financial institutions from G10 countries, see Basel Committee on Banking Supervision (2000).

5
preferences using a gravity equation and a country-year indicator for whether preferences are in effect.<sup>7</sup> Our paper follows a more rigorous identification strategy to obtain a causal impact, combining a triple-differences specification, highly disaggregated country-product-year trade data, and treatment indicators identifying countries and products benefitting from GSP LDC and AGOA in each year.<sup>8</sup> Accounting for product heterogeneity in eligibility and hence for the extent of tariff cuts and preference margins is crucial to attribute positive trade outcomes to the preferences.

Using country-product-year data from 1998 to 2006, Frazer and Van Biesebroeck (2010) show that AGOA significantly increased African countries’ non-oil exports to the US and the impact grew over time from 2001 until 2006. We make four key innovations in our study relative to theirs. First, and most important, by considering the period until 2017 we examine the durability of the impact of trade preferences and test whether benefits survive the erosion of preferences due to the end of the MFA.<sup>9</sup> Second, by considering a period starting in 1992 we estimate separately the impact of GSP LDC from that of AGOA, whereas they attribute to AGOA an impact on nonapparel products for LDCs from 2001 onwards which was in fact an impact of GSP LDC from 1998 onwards. Third, and also crucial, our specification controls for treated-group specific time trends to account for the possibility that pre-treatment trends differ for treated and untreated product-country cells, which helps to ensure that causal impacts of AGOA and GSP LDC policy

> 7 The three studies use state-of-the-art gravity equation estimation which accounts for multilateral resistance terms, time-invariant and time-variant unobserved bilateral heterogeneity, and zero trade flows. Kassa and Coulibaly (2018) apply quasi-experimental synthetic control methods to bilateral gravity estimation of the impact of AGOA.

> 8 Our study’s nuanced view of the impact of trade preferences complements some gravity equation studies’ findings. Herz and Wagner (2011) obtain a negative long-run impact on exports of GSP beneficiary countries that they
and location,</sup><sup>_Lj_</sup> _i_<sup>, and wage rates in each location</sup><sup>_wi_, and given</sup> exogenous changes in trade costs _τni_<sup>_j_,weareabletocomputecounterfactualsforwages,</sup><sup>_w_ˆ</sup><sup>_i_,</sup> employment _L_<sup>ˆ</sup><sup>_j_</sup> _i_<sup>,tradeshares</sup><sup>_π_ˆ</sup> _ni_<sup>_j_,expenditure</sup><sup>_X_ˆ</sup> _i_<sup>_j_andprices.</sup>

The parameters needed to solve the model are reported in table 10. The table also reports the data used to calibrate the parameters and compute the counterfactuals. In the next section we describe more in detail how we use the data to produce estimates for the model.

# **6.2 Estimation of Trade Costs and Trade Shares**

For the West Bank, estimates of the trade flows between towns and villages are not available. However, we rely on our novel data collected with the survey instrument to obtain estimates for these flows. In this section, we describe how we proceed.

In the survey, we ask firms detailed information on the location of the trading partners. We combine these data with data from the Establishment Census and data on wages from the Labor Force Survey to estimate the gravity equation stemming from the model. This approach requires to obtain an estimate of the elasticity of trade flows to distance and an estimate of location- and industry-specific productivities. This section proceeds as follows. First, for the estimation of the elasticity of trade flows to distance, we estimate a logit of the probability that a firm interviewed in the survey, with establishment in location _o_ has a 25
canton<sup>5</sup> ) and international migration in Ecuador, but increase internal long-distance (between-canton) migration, pointing to highly heterogeneous impacts concerning the type of migration. Nawrotzki, Riosmena, and Hunter (2013) find a positive and statistically significant relationship between weather anomalies and international migration from Mexico to the United States. Liu et al.’s (2022) work on India also shows that increasing temperature can reduce internal rural-urban migration. Cottier and Salehyan (2021) employ temporally disaggregated data on the detection of unauthorized migrants at the EU’s external borders and report that droughts in origin countries do not increase international migration towards the European Union but, if anything, they reduce it, in particular for countries dependent on agriculture, whereas more rainfall increases migration. Their interpretation of the results is that international

> 5 Cantons are the second-level subdivisions of Ecuador, below the provinces.

8
.099|0.099|0.101|0.098|0.109|

_note: .01 - ***; .05 - **; .1 - *; Controls for Provinces included but not shown_ The converse is true for those localities with greater resident shares who are wage employed, and who are graduates of lyceum in the relevant academic year (table (5) column (5)). This demonstrates that the greater the share of the resident population that is wage employed (table (5) column 3), the lower the share of households that send a migrant abroad. A relevant statistic in this comparison is that about 96 percent of migrants identified in the L2CU phone-based panel between September and

> 5 This comparison is made against other households of similar size living in the same areas.

11
3 FOREIGN DIRECT INVESTMENT IN KENYA

# **3.2.4 Chinese firms employ a large share of local workers**

Contrary to the popular belief that Chinese companies only hire Chinese workers, 93 percent of companies report hiring Kenyan employees; private enterprises are more likely to hire locals than state enterprises. In addition, larger firms are more likely to hire Kenyans than smaller firms: 40 percent of micro enterprises and all small, medium and large enterprises hire Kenyans (SACE 2014). Of the companies surveyed, Kenyans represent 78 percent of full-time and 95 percent of part-time employees. The companies had an average of 360 local employees: 252 were part-time (70 percent) and 108 were full-time (30 percent) (SACE 2014). All foreign manufacturing companies in Kenya had 127.8 full time workers and 19.3 part-time workers on average. Manufacturing and construction companies are larger employers, hiring 762 employees on average compared to 45 in the services sector; all foreign manufacturing firms hire 158.3 full-time workers and 47.3 part-time workers on average (World Bank Enterprise Survey 2013). Chinese companies in the services sector hire 71 percent full-time employees, but the manfacturing and construction sectors hire only three percent full-time employees. 90 percent of manufacturing employees are local, and 82 percent of service sector employees are local. Chinese companies also hire more Kenyans over time: they had 102 full-time local employees upon establishment, and by the time of the BPI survey, they had hired 214 full-time local employees. 63 percent of Chinese companies said they had a policy of replacing Chinese employees with locals (SACE 2014). Larger firms were more willing to replace Chinese workers than smaller firms, and private enterprises were more willing to replace Chinese workers than state enterprises. A local technician is much cheaper than a Chinese technician because a work permit costs US $4,597. Local employees also receive basic insurance from Chinese companies: 44 of 68 companies noted that they offer basic insurance for all employees. Again,
., 2003), but has had limited success convincing mainstream economists of its importance. Chetty et al, Mitnik, Helso, and Bryant (Chapter 11) offer several compelling reasons for why studies of IOp have thus far struggled to gain broader recognition. To highlight a few, their chapter argues that IOp scholars (a) are not focusing on long-run income (something the literature on intergenerational mobility does), (b) prioritize theoretical properties over interpretability, (c) often only have data on an incomplete set of circumstances (interestingly, the intergenerational mobility literature conveniently avoids this issue by focusing on one circumstance to begin with), and relatedly (d) have to work with data on a different set of circumstances for different countries which complicates cross-country comparisons (see e.g., Ferreira et al., 2018). Chapter 11 addresses these concerns, which by itself offers a methodological contribution to the IOp literature.<sup>5</sup> In their empirical application using data for the United States and Denmark, the authors restrict the circumstances to gender and parental income rank to facilitate cross-country comparability, in the process showing

> different interest groups pertaining to housing – ‘renters’, ‘owners’ and ‘capitalists’ – finding that (a) across all categories there is wide variation in wealth trajectories between countries (because of their respective policy histories) and yet (b) renters in general (except in the UK) are most likely to be at the bottom of the wealth distribution.

> 3 For example, _World Development Report 2006: Equity and Development_ (World Bank 2006) and a series of high-profile papers by senior IMF staff led by Andrew Berg (e.g., Berg et al 2018).

> 4 Other dimensions on inequality – stemming from Sen’s (1980) important question ‘Equality of what?’ – include Rao and Walton’s (2004) argument for considering inequality of agency, given the large differences between the rich and poor
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **C. Roadmap**

Section 2 positions our paper in the literature and describes the country context. Next, section 3 outlines the engagement model to work with the PURA and the GBoS on access to CDR data and producing statistical information from CDR data in The Gambia. Section 4 presents the data and defines the methodology to analyze the CDR data, and Section 5 showcases key results. Section 6 summarizes lessons learned from the ongoing engagement in The Gambia, and Section 7 concludes and outlines the next steps.

# **2. Context and literature review**

# **A. Policy relevance of CDR data**

There is a rich and growing literature seeking to leverage the potential of CDR data to inform policy making. With mobile penetration rising in developing countries (GSM Association, 2020), researchers have demonstrated the use of CDR data to create poverty maps (J. Blumenstock, Cadamuro, & On, 2015), understand migration patterns, and estimate a household’s economic characteristics (J. E. Blumenstock, 2018).There have been sustained improvements in forecasting population density by combining high-resolution satellite data with powerful algorithms (Stevens, Gaughan, Linard, & Tatem, 2015). WorldPop trains its algorithms on historical census data and projects annual population density at 100-meter resolution in a publicly available data set (Stevens et al., 2015). However, these data sets rely on slow-moving indicators and are computationally intensive. Forward-looking projections are based on linear extrapolation and do not account for short- or medium-term population movement dynamics.

Researchers have harnessed mobile phone data to map population movements. Deville et al. (Deville et al., 2014) showed that the density of unique users in a cell tower’s catchment area scales with population density, and can be plotted on a logarithmic curve. Researchers can therefore extrapolate shifts in the number of unique users to predict shifts in