rafmacalaba/data-use-mentions-extended huggingface dataset:
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 9 Productivity We use the Defense Meteorological Satellite Programs - Operational Linescan System (DMSP-OLS) nighttime lights (NTL) data to outline urban areas. The NTL data report the recorded intensity of Earth’s surface lights. Nighttime lights products have high correlation to human activities (Hsu, Baugh, Ghosh, Zhizhin, & Elvidge, 2015), and have been previously used for regional and global analysis of urbanization (Cheng et al., 2016; Pandey, Joshi, & Seto, 2013; Sutton, Cova, & Elvidge, 2006; Zhang & Seto, 2011; N. Zhou, Hubacek, & Roberts, 2015; Y. Zhou et al., 2015), population modeling (Anderson, Tuttle, Powell, & Sutton, 2010; Lo, 2001), and economic performance (Cao, Wu, Kuang, Huang, & Wang, 2016; Forbes, 2013).

There are two different nighttime light products from DMSP-OLS that can be used to delineate urban areas: the stable or ordinary product (NTL), and the radiance-calibrated (RC) product. For this work we use the latter, given that such data include a correction for the saturation issue likely to be an important issue for most large cities in the region. Further, previous work has suggested that the RC information provides a better proxy for socioeconomic variables than the stable products (Hsu et al., 2015; Ma, Wu, Li, Peng, & Liu, 2014). Radiance-calibrated yearly composites for 1996, 2000 and 2010 obtained from NOAA National Centers for Environmental Information were used to delineate urban extents. These composites have a spatial resolution of 30 arc-seconds (about 1 km at the Equator).

Another known issue of the DMSP-OLS products is the “overglow” effect: dim lighting detected from light in surrounding areas of cities because the scattering of lights in the atmosphere (Wu
In this paper, we explore rates of access to infrastructure and social services among hosts and displaced persons, and related perceptions of quality of service delivery in Jordan, Lebanon, and KRI. The extent of competition with host communities depends on the extent to which the hosts relied on publicly provided services. But the addition of hundreds of thousands of people in a short time has unquestionably strained the public service delivery systems of host countries, and there is a need for more investment to expand the supply of services and delivery personnel.

The data used in the analysis stem from varied sources, including UNHCR’s registration database, publicly available casualty data, and primary data collected in the three host countries. Similar efforts to collect representative data on refugees in national household surveys are underway in many countries. Such efforts are critical to generating the evidence for the transition from short-term humanitarian response to long-term development response in countries affected by protracted conflict and displacement crises.

10
The procedure we are using improves the precision of estimates compared to conventional OLS growth regressions, so we actually find significant results on variables like openness that do not generally survive statistical tests in growth regressions. Overall, the explanatory power of the regression is 0.46 when investment is included and 0.35 when it is not.

# _2. Coefficient estimates_

For our purposes, the magnitudes of the parameter estimates are of central importance. In what folows, we compare our results (in the model where investment is included) with those found elsewhere in the growth literature. In particular, we focus on two recent studies that also relied on panel data -- those of Barro and Sala-i-Martin 1995 (henceforth B-SM) and Caselli, Esquivel, and Lefort 1995 (henceforth C-E-L). They are two of the best and most recent studies on growth using panel data. B-SM use a "random-effect" model, which assumes that the unobserved country-specific effects are uncorrelated with the explanatory variables. If as we believe, these country-specific effects are correlated with the regressors, B-SM's estimates will be biased (upwards if the partial correlation between the regressor and growth-enhancing specific effects is positive, and viceversa). C-E-L's estimation procedure (based on time-differencing and the application of the generalized method of moments) is very similar to ours. B-SM consider two ten-year periods: 1965-75 and 1975-85 (data for 1960-65 and 1970-75 are respectively used as instruments for the two ten-year periods); and C-E-L consider five five-year periods from 1960 to 1985. Both B-SM and C-E-L attempt to account for possible joint endogeneity by using previous observations of each endogenous regressor as instruments for it.

Beginning with the policy variables, we are able to compare our results with those in the previous studies for four of the five policy variables. We estimated the coefficient of the ratio of 13
; the ranking will <u>Source: WDI 2014 GDP (US$)/Workman 2015/Kemoe and Aymele 2016</u> remain unchanged — so Vietnam’s relative size is one. Using this method, the ranking is totally different from Box 1 (where each country was ranked based on its piano export earnings). In Box 2 Vietnam, which was number 16, is now number one, and China, which was leading is now number nine.

But more immediately, as cultural statistics get improved, it would be helpful to know, which country leads in the exportation of traditional instruments like drums. Is it Senegal or Mali? How about the ukulele? Is it Zimbabwe or Brazil? Even more important, what is the contribution of such sectors, to the employment of women, the young and the old formally and informally? In an attempt to consider how cultural commerce might better contribute to sustainable development, such questions deserve attention.<sup>17</sup> Also, since the issue of measuring the quality of life is

> 16 (I) The figures in column 3 are achieved by dividing the country’s piano exports figures by the total (piano exports) of the 16 countries provided (bottom of column one). E.g. for Vietnam, $13,390,000 ÷ 617,432,000 = 0.021686599. (II) The country size is calculated by dividing the country’s 2014 GDP with that of a reference county (in this case, Vietnam’s 2014 GDP). E.g. Vietnam’s 2014 GDP ($186.205 billion÷$186.205 billion) = 1, Honk Kong SAR, China ($291.23bn ÷$186.205 billion) = ~ 1.56403, etc. (III) The normalized share, which provides the rank, is the result of Step 1divided by Step II. E.g. for Hong Kong SAR,
entry regulation required for a significant impact on new firm registration. A priori, it is not clear what magnitude of reduction in costs (or other parameters such as days or procedures) is necessary to cause a significant impact on firm registration. In other words, what exactly constitutes a reform? Is a 20 percent reduction in the costs of registration sufficient or is a 50 percent reduction necessary to encourage a significant number of firms to register? We further examine the effect of simultaneous and sequential reforms across measures of entry regulation. Finally, we examine how the impact of reform depends on the level of regulation prior to the reform.

We use a new dataset that is uniquely suited for this purpose: a cross-country, time-series panel dataset on the number of newly registered limited liability firms. We supplement this dataset with information from Doing Business reports on the cost, days and number of procedures required for the registration of new companies. Importantly, both datasets focus comparably on only limited liability firms.

Our results show that the costs, days and procedures required for business registration are important predictors of new firm registration over time. However, on average, small reforms - in general less than 40 percent for procedures and 50 to 60 percent for costs and days - do not have a significant effect on new firm registration. In addition, reforms in multiple indicators (e.g. the cost and number of procedures required to register a business) have a larger impact on business registration and simultaneous reforms have a larger impact than sequential reforms. Furthermore, we find that a country’s initial conditions matter: Countries that start out with relatively higher initial costs need larger reforms to result in a significant increase in number of new registered firms.

3
# **_Annex 1 Explanatory variables_**

**<u>Table A1. Explanatory variables used in the analysis – definitions and data sources</u>**

|**_Name_**|<br>**_Abbreviation_**|<br>**_Source_ **<sup>c</sup>|<br>**_Description_**|**_Scale_**|
|---|---|---|---|---|
|Employment protection<br>legislation strictness|_EPL_2_|OECD and Muravyev<br>(2010).|Employment protection legislation index,<br>version 2.|0 to 6, higher =<br>more rigid|
|Government<br>Effectiveness<sup>b</sup>|_GOV_EFFECT_|World Governance<br>Indicators (Kaufmann<br>et al. 2010; World Bank<br>2010)|Captures perceptions of the quality of<br>public services, the quality of the civil<br>service and the degree of its independence<br>from political pressures, the quality of<br>policy formulation and implementation,<br>and the credibility of the government's<br>commitment to suchpolicies.|‐2.5 to 2.5,<br>higher = more<br>efficient|
|Rule of Law<sup>b</sup>|_RULE_LAW_|World Governance<br>Indicators (Kaufmann<br>et al. 2010; World Bank<br>2010)|Captures perceptions of the extent to which<br>agents have confidence in and abide by the<br>rules of society, and in particular the quality<br>of contract enforcement, property rights,<br>the police, and the courts, as well as the<br>likelihood of crime and violence.|‐2.5 to 2.
The classification of enterprises into HEs and MEs is done primarily based on information provided in the enterprises module. Usually, analysis only defines MEs, and does not include HEs as a sub-category. Here they are separated, in recognition of importance of the ability to hire workers from outside the household as an indicator of managerial ability and growth potential, and therefore the probability of a different response to a particular policy or program from MEs compared with HEs (see de Mel et al, 2008 and 2009).

We include enterprises in the study _regardless of registration status_ . In classifying an enterprise as _informal_ , standard practice (ILO, 2011) requires that it meet (i) an ownership criteria (unincorporated, owned by household members) an _d either_ (ii) a size criteria (below a specified level of employment, e.g. 5 or 10 employees depending on the country), and/or (iii) a legal status criteria (non-registration of the enterprise or its employees). We have adopted the first criteria, but not the legal status. We find that the rules on registration differ by country, and within countries there may be several levels of registration -e.g. national as well as sub national. And the meaning of registration is different by country. In some countries, (e.g. Uganda), registration implies a certain level of legitimacy from the state, but in other countries (e.g. Rwanda, Tanzania), it does not. In addition, in some countries it is legal to do business in one‟s own name _without_ registration. This means that the concept of registration is not defined or implemented consistently across sample countries, and so we do not use it as a sorting variable. On this point, our study differs from those using the ILO definition (Grimm et al, 2011; ILO, 2011; Nguyen et al, 2011).

The core analysis is based on nationally representative household survey data from the following countries:
where _yihcs_ are private inputs to the education of child _i_ in household _h_ from community _c_ and school _s_ . We consider three financial inputs (informal payments to teachers, tutoring, and transport expenditures) and two non-financial inputs if the child is enrolled in school and the frequency with which the caregiver spends time supporting the child in educational activities. The main explanatory variable of interest, Mig _hc_ is a household-level dummy variable taking the value one if the child lives in a migrant household and zero otherwise; _Xihcs_ is a vector of childand household-level control variables; and _εihcs_ is the error term.

Clearly, migrants are not a random population group but rather self-select into migration. Thus, it can be expected that they systematically exhibit distinct unobservable characteristics relative to non-migrants that might bias OLS estimates of equation (1). To overcome this problem, we estimate an instrumental variable approach by two-stage least squares (2SLS).<sup>19</sup> Our instrument for migration status is the interaction between pre-existing migration networks at the local-level and destination-specific economic conditions. Formally, we use the growth rate of per capita GDP for each destination country between 2004–2010 and weight it with the share of migrants that, by 2004, had migrated from the community to that destination.<sup>20</sup> The data for the migrant-destination share at the community level are derived from the 2004 Moldovan Census.<sup>21</sup> The variable has already been employed as an instrument for migration in other studies of the Moldovan context (e.g., <u>Lücke et al. 2012, Böhme et al. 2015). The rationale behind the use of</u> _Network-Growth_ is twofold. First, migrant networks are known to be very important in 19. The most common approach in the literature is instrumental variable strategies exploiting exogenous aggregate factors at the origin or destination: past migration rates (McKenzie and <u>Rapoport 2010, Antman 2011, Zhang et al. 2014
Lastly, the survey-to-survey imputation exercise uses household-level data on a range of non-monetary indicators from the GHS panel for Nigeria for 2010/11, 2012/13, 2015/16, and 2018/19. Each wave contains around 5,000 households, and is representative at the national, zone, and urban-rural level. Within each wave, data are collected in two distinct visits to the same set of households: the first “post planting” visit takes place sometime between August and October (of 2010 for the 2010/11 survey, for example) and the second “post-harvest” visit takes places sometime between January and April of the following year.<sup>11</sup> For the purpose of the survey-to-survey imputations, we use information on household demographic characteristics (dependency ratio), dwelling characteristics (main floors material, main source of cooking fuel, availability of toilet facility in the household), household head’s demographics (age, gender) and employment indicators (employment in non-farm activities and in wage-employment), household assets ownership (TV set, air conditioner, generator, microwave, computer, cars and other vehicles, microwave, washing machine), and _frequency_ of consumption – that is dummy variables for whether or not an item was consumed – for some food and non-food items (imported rice, beef, fresh fish, and recharge cards).

# 4. Methodology

This analysis uses two approaches to estimate a poverty trend for Nigeria for the period 2009-2019: backcasts and survey-to-survey imputations. This section describes these approaches in detail. One key contribution of the paper is that while the two methods described in this section are very different, they yield very similar results, as we show in section 5.

# _<u>4.1. Backcasting</u>_

For the backcasting exercise, we start with the full consumption vector in the 2018/19 NLSS then construct the consumption vector in each previous year by “rolling back” consumption for each household using sectoral real
Policy Research Working Paper 5500

# **Abstract**

This paper proposes a novel method of isolating fluctuations in public spending that are likely to be uncorrelated with contemporaneous macroeconomic shocks and can be used to estimate government spending multipliers. The approach relies on two features unique to many low-income countries: (1) borrowing from the World Bank finances a substantial fraction of public spending, and (2) actual spending on World Bankfinanced projects is typically spread out over several years following the original approval of the project. These two features imply that fluctuations in spending on World Bank projects in a given year are in large part determined by fluctuations in project approval decisions made in previous years, and so are unlikely to be correlated with shocks to output in the current year. World Bank projectlevel disbursement data are used to isolate the component of public spending associated with project approvals from previous years, which in turn can be used to estimate government spending multipliers, in a sample of 29 aid-dependent low-income countries. The estimated multipliers are small, reasonably precisely estimated, and rarely significantly different from zero.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at akraay@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,
WDI. The literature on financial development and its relationship to growth has grown extensively over the past decades (e.g., Buzzi and Russo 2019) and various studies and data sets on more detailed aspects have appeared (e.g., Svirydzenka 2016). Credit over GDP, however, has the clear advantage of wide availability across countries and over time and has been used by several seminal studies in the literature on finance and growth. Brunnermeier et al. (2021) confirm that credit expansion is rather a sign of financial deepening than the prelude for financial busts.

To gauge the effects of _financial crises_ , we rely on the database from Laeven and Valencia (2020), which has coverage until 2017. Our data set contains dummy variables for individual years for banking, currency, or sovereign debt crises and a

> 7 For similar reasons, we also do not include fiscal deficit variables, which are highly cyclical and hence tend to smooth out over the five-year averages.

> 8 We add 1 to this variable to avoid negative numbers, which cannot be translated into logs.

> 9 We also calculated deviations of the real exchange rate from an HP-filtered trend, as well as volatility measures, but the results did not provide additional insights.

-8-
. DB has been updated annually since its introduction in 2003. However, the ES have been repeated in only a few countries, generally with a 3-5 year lag. This section uses only those countries for which an ES was fielded, there was then a formal change in the regulation that manifests as a reduction in the formal time expected for the requirement to be met, and another ES fielded.

The frequency of reforms varies across the 3 indicators. Reforms were more frequent for getting goods through customs and getting operating licenses. However, there are only 3 instances of reforms of construction permits enacted between Enterprise Surveys.

24
We estimate one set of elasticities for 2014 using information on sectoral GDP and employment changes between 2007 and 2012 ( Table 2 ). Over the last 5 years, these parameters show a weak or inelastic response of employment to changes in output. For instance, manufacturing and construction grew at a high annual growth rate of 13 percent between 2007 and 2012, however, employment grew by only 4 percent annually. In the case of services, which employ more than 65% of total Iraqi workers, employment has been increasing annually at 2.4% in 2007‐12; less than half of the rate of growth of output in the services sector in the same period.

# **Population growth**

The simulation takes into account population growth and is particularly relevant in the Iraq case. The total population is projected to grow by 2.6% annually between 2012 and 2014, with the size of the working age population (from 15 ‐64 years old) growing by around 3.1% annually. We use official population projections by gender age groups and governorates provided by CSO. This adjustment is necessary to fully account for demographic changes that would affect the size and composition of the labor force and ultimately impact our estimates of the welfare measure (i.e. consumption or income per capita).

# **Changes in prices: Inflation**

All household and individual incomes from labor and non‐labor sources are expressed in real terms of the baseline year (constant in 2012 prices). Macroeconomic changes replicated on the micro data are also in real terms. This implies that in order to obtain projected poverty measures for 2014, the simulated household consumption will need to be compared against the poverty line of 2012.

In principle, there would be no need to adjust for prices given that all micro and macro components of the simulation are expressed in real terms. However, if food prices were to rise at a significantly different rate from the non‐food prices, the food basket to which
5 5 relative to purchases by firms and households, and highlight significant positive effects of PTAs on cross-border procurement, especially in services. Moreover, they identify that policies that are “multilateral” promote cross-border procurement the most, and EU membership has the largest effect on trade in government procurement.<sup>2</sup> Given that our empirical analysis focuses on domestic laws that apply equally to foreign firms from different countries, we draw on a similar gravity structural model as Mulabdic and Rotunno (2022), but consider the effects on total trade (in government procurement and private purchases).

A few studies have mobilized “micro” contract-level data to investigate the issue of home bias in government procurement. This work is normally on single countries or regions (e.g., the US, the EU, Norway and the Republic of Korea), and identifies cross-border procurement as contracts awarded to firms located abroad (or to a different region when home bias is defined locally).<sup>3</sup> Overall, the micro studies confirm the evidence from cross-country macro data. Using EU data and gravity-style estimation approach, Herz and Varela-Irimia (2020) find that government authorities award disproportionally more to firms located within their countries and their region. García-Santana and Santamaría (2021) confirm this result in a subsample of French and Spanish procurement contracts, and show that the public home-bias is driven by sub-national authorities.<sup>4</sup> A related strand of the literature highlights the impact of winning government contracts on firms’ outcomes, thus stressing the economic relevance of government procurement. Lee (2021) and Ferraz, Finan, and Szerman (2015) find that winning a procurement contract increases long-term firm growth in employment, sales and value-added in Korea and Brazil, respectively. Furthermore, receiving procurement contract allows small and financiallyconstrained firms to acquire credit (di Giovanni et al. 2023), and increase investments (Hebous and Zimmermann 2021). The firm-level studies confirm how crucial the allocation