rafmacalaba/data-use-mentions-extended huggingface dataset:
intend to provide a detailed analysis of such government interventions, an understanding of the nature and consequences of maturity choices is a step towards evaluating their potential effects and desirability.

The main objective of this paper is to provide a thorough investigation of various issues related to the maturity structure of firms' debt using panel data at the company level for the UK and Italy. We first review the insights on the determinants of the maturity composition of debt that can be obtained from the theoretical literature. We then document how different firms' characteristics are related to their maturity choices by estimating a maturity equation and interpret them in the light of the theoretical predictions. The next step is to analyze what are the effects of maturity on _firms'_ subsequent performance, in terms of profitability, growth, and productivity. To this end we present descriptive evidence on the average profit rate, the rate of growth in real sales, and the investment rate calculated over four year periods, when the firmyear observations are sorted by initial maturity. We also estimate a Cobb-Douglas production function and assess how total factor productivity depends upon the degree of leverage and the maturity composition of debt. Finally, we present empirical evidence on the effect of financial factors, including the maturity structure of debt, on firms' investment policies by estimating a simple accelerator model.

The advantage of investigating these issues both for the UK and Italy is related to the differences in the two countries financial structure. In the UK (as in the US) 3
real GDP in emerging markets (see for example seminal paper by Aguiar and Gopinath, 2007), a natural concern is the extent to which the findings of Figure 6 are robust to other detrending more “flexible” techniques. While most data for monetary poverty and HDI (and its components) start in mid-1990s to early 2000s, we have 32 countries in our sample with unemployment rate data starting in the year 1980 (which gives us almost 4 decades of continuous data). Using this data, we cannot reject the null hypothesis that the median contribution of the business cycle to the variance in the unemployment rate is the same across well-known more “flexible” detrending techniques. In particular, and based on a nonparametric K-sample test on the equality of medians, a contribution based on a linear trend (45.2 percent) is statistically the same as that based on a Hodrick-Prescott filter (38.5 percent) or a Christiano-Fitzgerald filter (42.2 percent).

10
Table 7: Heterogeneous effects of class compositions. Restricted Sample

||D|ependent vle: Te|st score in Spani|sh|
|---|---|---|---|---|
||Lower<br>(1)|Lower Middle<br>(2)|Upper middle<br>(3)|Upper<br>(4)|
|Prop peers in L||-.055<br>(.009)<sup>_∗∗∗_</sup>|-.114<br>(.017)<sup>_∗∗∗_</sup>|-.098<br>(.019)<sup>_∗∗∗_</sup>|
|Prop peers in LM|.039<br>(.009)<sup>_∗∗∗_</sup>||-.077<br>(.014)<sup>_∗∗∗_</sup>|-.044<br>(.015)<sup>_∗∗∗_</sup>|
|Prop peers in UM|.073<br>(.015)<sup>_∗∗∗_</sup>|.020<br>(.011)<sup>_∗_</sup>||.020<br>(.016)|
|Prop peers in U|-.002<br>(.016)|-1.12e-06<br>(.011)|-.062<br>(.016)<sup>_∗∗∗_</sup>||
|Obs.|107,523|130,721|50,303|44,720|
|e(N-g)|6,274|6,444|6,052
are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We thank Cristina Luna for her valuable assistance in obtaining and interpreting data from Banco of Spain and Bank of Italy on the Spanish and Italian banks’ foreign claims, respectively. We also thank both central banks for access to these data. We are grateful to Maria del Pilar Casal and Sarojini Hirshleifer for excellent research assistance and to Harald Anderson for providing us data. We received useful comments and suggestions from Gerard Caprio, Asli Demirgüc-Kunt, Enrica Detragiache, Patrick Honohan, Ross Levine, Jesus Saurina, Adrian Tschoegl, and participants at the Stanford Center for International Development (SCID) conference on _Latin American Financial Systems and the Challenges of Economic Growth_ and those at the World Bank and Journal of Banking and Finance conference on _Globalization and Financial Services in Emerging Economies._ Contact author: Maria Soledad Martínez Pería, World Bank, 1818 H St. N.W., Washington, DC 20433, email mmartinezperia@worldbank.org.
_δKt_ = _λ_ ( _Kt_<sup>_f−Kt−_1)</sup> (9) Here _Kt_ and _Kt_<sup>_f_representtheactualandoptimallevelsofcapitalattime</sup><sup>_t_,whilethe</sup><sup>_λ_</sup> parameter represents how much of the gap between these values is bridged in each time period. Lower values of _λ_ imply slower rates of adjustment and, thus, higher adjustment costs. As adjustment costs may differ for different levels of desired investment, Chow tests are conducted at different points along the investment function to test the continuity of its slope for each type of capital.

# **3 Data**

## **3.1 Overview**

We use a panel of firm-level data for manufacturing firms in the Republic of Ireland. These data are collected by the Irish Central Statistics Office (CSO) via the annual Census of Industrial Production (CIP). Response to the CIP is compulsory for firms operating in the Irish manufacturing sector with three or more persons engaged. The census collects data on various accounting measures such as sector of operation (at the NACE 4 digit level), location, sales, employment, intermediate inputs, capital acquisitions and trade. While all firms of three or more employees are surveyed, larger firms are asked to complete a more detailed questionnaire which includes, among other additional information, information on energy expenditure disaggregated by fuel type (smaller firms are asked only for aggregate energy expenditure). As we are interested in adjustment costs of fuel-using capital by type of fuel, we concentrate our analysis on these larger firms and for the period from 2004 to 2009, when these more detailed data were collected on an annual basis. Our final dataset contains approximately 8,600 firm-year observations.

The census does not ask firms to report a price for capital, therefore, the price of capital we use in our model is the market cost of capital as estimated for Irish manufacturing firms by ˇZnuderl and Kearney (2013). This
Potential impacts of the US-Vietnam FTA and loss of AGOA preferences on trade, growth and welfare were estimated using a global dynamic computable general equilibrium (CGE) model. This modeling framework allows for the incorporation of complex interactions between producers, consumers and the government. It captures productivity differences at the country, sector and factor levels, shifts in demand as income rises, as well changes in comparative advantage and trade flows following trade liberalization. The analysis includes 17 sectors and 35 trading partners (see Table 2) and simulates the impacts of policy changes up to 2030, including reductions in tariffs, NTMs, and regulatory barriers in trade in services. For this analysis the 2010 social accounting matrix (SAM) for Lesotho has been updated to 2011 and incorporated into the Global Trade Analysis Project (GTAP) database<sup>7</sup> version 9.2, benchmarked to 2011.<sup>8</sup> The key macroeconomic variables and trade flows were then updated to the most recent data available for Lesotho (primarily form 2015).

**Table 2: Sectors and countries/regions included in the global CGE model.**

|**Sectors**|**Countries/Regions**|
|---|---|
|**Agriculture**|Australia|
|**Natural resources / mining**|Brunei Darussalam|
|**Food, beverages, tobacco**|Canada|
|**Textiles**|Chile|
|**Wearing apparel and leather**|Japan|
|**Chemical, rubber,plasticproducts**|Malaysia|
|**Metals**|Mexico|
|**Transport equipment**|New Zealand|
|**Electronic equipment**|Peru|
|**Machinery and equipment**|Singapore|
|**Other manufacturing**|United States|
|**Utilities**|Vietnam|
|**Construction**|Brazil|
|
could not conduct their agricultural activities as usual in the reference agricultural season, and the reasons for disruption to agricultural activities.

Second, we collected detailed information on extensive (whether the household used any inorganic fertilizer) and intensive margin fertilizer use (how much inorganic fertilizer was used across all plots of the household; and how much was purchased). Depending on country and survey round, we also collected information on the different types of fertilizer applied. For intensive margin fertilizer use, respondents reported the total quantity of fertilizer used across all plots of the households in a unit of their choice. Wherever a quantity was reported in non-standard units, we use standardized conversion factors to express the quantity in kilograms. For those households that used no inorganic fertilizer, we collect information on their reasons. For those who used any fertilizer, we further ask whether households were able to acquire the full amount of fertilizer they desired. Those that responded that they could not acquire as much fertilizer as desired were asked how much more fertilizer (in kilograms or a unit of their choice) they would have liked to acquire. They were also asked about the reasons for the fertilizer shortfall and their coping strategies for having had less fertilizer than desired. We normalize all quantities reported (fertilizer quantity per hectare, purchased fertilizer quantity per hectare, fertilizer shortfall per hectare) by the total selfreported area under cultivation and winsorize at the 2.5 and 97.5 percentiles within each country.

Third, we collected information on fertilizer prices from two sources depending on the survey round. The first source of fertilizer price information comes from a dedicated prices module which, among others, collected information on the current price of inorganic fertilizer for all households reporting that fertilizer was available for sale in their community or a nearby location.<sup>4</sup> This question was not specific to any fertilizer type. The second source is the HFPS agriculture module which asked all respondents in crop-farming households to report the current price of inorganic fertilizer by fertilizer type.
<!-- Start of picture text --> FIGURE 2: SPEED AND SCALE OF E-PROCUREMENT ADOPTION12<br><!-- End of picture text --> <!-- Start of picture text --> 100% 100% 100% 100% 100% 100% 100% 100%<br>100% 90% 94% 96%<br>90% 87%<br>80%<br>70%<br>60% 55%<br>49%<br>50%<br>40% 37%<br>30%<br>20% 14%<br>10% 5%<br>0% 0% 0% 1%<br>0%<br>2011-2012 2012-2013 2013-2014 2014-2015 2015-2016 2016-2017 2017-2018<br>BWDB LGED RHD<br><!-- End of picture text --> Source: Data collected from procuring entities and e-procurement system

# 3. THEORETICAL FRAMEWORK

20. Well-functioning public procurement systems are meant to achieve value for money, ensure open and fair access for bidders and run efficiently. We consider these three distinct sets of outcomes—(i) economy; (ii) access and (iii) efficiency—separately, while recognizing their interdependence. In this section, we define these concepts and identify the main mechanisms through which e-procurement can affect them. In the following section, we will map these concepts to specific indicators.

# 3.1 OUTCOME DEFINITIONS

21. “Economy” or “value for money” for a given quantity of products refers to the quality of goods or works obtained for a given procurement price. Accordingly, economy can increase because a given quality is achieved at a lower price or because a higher quality is achieved at a given price or both.

22. “Access” in the context of public procurement captures the extent to which the procurement process de facto ensures open and fair competition among bidders. “Limited access” refers
In addition to these issues of specification, there are some measurement considerations. First, in our preferred regression models, we specify the dependent variables as the change in the prevalence of food insecurity across two successive periods. This approach is in contrast to most of the analogous growth-poverty literature, in which it is common to measure the dependent variable as a percentage change. However, taking the percentage changes of a prevalence rate can cause scaling problems and create outliers (Deaton 2006; Headey 2011c **[[There is no “Heady 2011c” listed in the references section. Do you mean 2011a or 2011b?]]** ).<sup>13</sup> The only significant advantage of using a percentage change is that it allows for the derivation of elasticities that can be directly compared to the literature that examines the impact of economic growth on poverty. Therefore, in some of our results, we also report these elasticities, although our preferred estimates focus on first differences.

A second issue pertains to measurement error in the Gallup data. Some apparent outliers are indicative of this measurement error. In figure 1, we consider potential outliers more systematically with scatter plots between changes in food security and various indicators of assumption that both right-hand side variables are strictly exogenous at all leads and lags, which is unlikely. Hence, we do not solely rely on the fixed effects estimator.

13 The problem with taking percent changes in prevalence rates can be illustrated with an example of a country with high food insecurity and a country with low food insecurity. In the food-insecure country, suppose that food insecurity decreases from 42 percent at time _t_ − 1 to 40 percent at time _t_ . This yields a first difference of two percentage points and a percent change of approximately −4.7 percent (that is, 2/40 × 100). However, an equally large reduction in malnutrition prevalence in the foodsecure country from 4 to 2 percent yields a percent change of 50 percent. Not only is a 50 percent
induce an attenuation bias in the least squares estimate of the elasticity.) In all our regressions we have also tried an Instrumental Variables (IV) estimator, in which the instruments excluded any variables derived from the same survey as the dependent variable. Our estimates also control for the use of mixed reference periods for some of the NSS rounds.

Table 3 gives our estimates of the elasticities of all three poverty measures with respect to three measures of economic growth based on: (i) consumption per person from the NSS; (ii) consumption per person as estimated by the NAS and population census; and (iii) NDP ("income" for short) per person, also from the NAS and census. In all cases, the elasticities are estimated by regressing the log poverty measure on the log mean consumption or income. We

> 42 This follows from the fact that the mean and the Lorenz curve fully specify the cumulative distribution function. 43 Analytic formulae for the partial elasticities are found in Kakwani (1993). On the conceptual distinction between the partial and total elasticities in this context, see Ravallion (2007).

15
Kamitonda and Kushimoto using the bypass is 49 minutes. The traffic volume between Kamitonda and Kushimoto is assumed to be the design traffic volume in the ordinary state, whereas the volume in the disrupted state is assumed to be zero, based on expert opinion. The expected time for recovery in the case of a disruption of the existing route is 255 hours (10.625 days), based on the road clearance plan for a tsunami disaster prepared by MLIT.

|Table 2. C<br>Indicators|ase 2 Resilience Indicator Data|||
|---|---|---|---|
|of LoF|Relevant Data|Data source|Content|
||Traffic volume per min under theordinarystate (x�)|Design traffic<br>Volume|5.14 pcu/min<br>(7,400 pcu/day)|
||Traffic volume per minwithoutthe project under thedisruptedstate (x�)||0 pcu/min|
|Ei|Travel time between representative OD (Kamitonda – Kushimoto)without<br>the project under theordinarystate (t<sup>�����</sup>)|Route search<br>system|60 mins|
|conomc<br>Loss|Travel time between representative OD (Kamitonda – Kushimoto)withthe<br>project under theordinarystate (t<sup>����</sup>)|Route search<br>system|49 mins|
||Travel time between representative OD (Kamitonda – Kushimoto)without<br>the project under thedisruptedstate (t<sup>�����</sup>)|Route search<br>system|119 mins|
||Travel time between representative OD (Kamitonda – Kushimoto)withthe<br>project under thedisruptedstate (t<sup>����</sup>)|Route search<br>system|49 mins|
||Population of coverage area
, Sandström and Bergström 2005).

However, despite growing global relevance, private school involvement as a part of any government’s official education strategy tends to be highly controversial. This is partially because private schools may exacerbate existing inequalities in education access - a particular concern if these schools skim off the higher-income or higher-performing students, leaving only the more disadvantaged students in the public system. Some have argued that even if private schools bring short-term benefits, they may undermine effective public schooling in the longer term. Also, cross-country data analysis finds no private school advantage in terms of student performance in a majority of countries, when controlling for other factors (Sakellariou 2017).

One possible solution that may minimize risks but maximize potential benefits of private schools is well-designed public-private partnerships. These are contracts between the government and a private sector provider, where the government acquires a service for a specified time period, for instance, with the aim to increase enrollment and minimize inequality (Taylor 2003). PPPs in education may be able to ensure increased access by harnessing the excess capacity, management efficiency, and creative potential of the private sector without sacrificing the equity goals typically associated with the public sector. PPP designs can address both supply- and demand-side constraints. The most prevalent demand-side mechanisms include vouchers and subsidies and supply-side mechanisms include private finance initiatives where the private sector is contracted to operate/maintain/manage schools or even to provide services like teacher training, curriculum design and textbook provision (Patrinos 2009).

Would PPPs in basic education be a viable strategy for Tanzania? This paper provides a first-stage empirical exploration of this question. It examines some important enabling conditions for potential PPPs in secondary education in Tanzania through different information sources and perspectives. We rely largely on in-depth data on public and private schools in the Morogoro region of Tanzania, buttressed by national statistics and a nationally representative parental SMS survey.

4
<u>Lightsact</u> Annual per capita growth = In|?oma ~~<u>n</u> Lightsact—1~~ Populationgct-1 t y

dc t

> Spatial Gini = ~~——*~~ n-1 ~~n (oe)f (2i-n—-1)*y;~~ n2y

l n n Vi x in ~~(eae)Lightsqct~~ Theilil- = ~~~—~~ Nju1 ~~%m~~ <u>5</u> In( ~~()z~~ Yi; y N MLD =_ ~~~i~~ nfe In (%* ~~Vi~~ )
a paybanding system that imposed high-powered performance incentives on supervisors, but not on nonsupervisory personnel. Using data from the 2002 Federal Human Capital Survey, the author showed that the incentive scheme crowded in intrinsic motivation at the lowest pay levels, and crowded out at the highest levels.

(World Bank 2001) used survey data from revenue departments in 14 low, middle and high income countries, and detailed case study evidence from 7 of those, to review the effectiveness of bonus and salary supplement systems as a means to enhance effectiveness in revenue departments. They concluded that the ―circumstantial evidence‖ suggests that bonus systems do indeed seem to have an impact on organizational effectiveness. They note that in a number of countries the introduction of bonus systems have had a measurable impact on recruitment and retention of employees. However, they note that the success of bonus systems relies heavily on ―legitimacy‖, i.e. the internal and external ―acceptance‖ of the bonus system.

A set of studies of performance incentives in a similar organizational context is that of the US Job Training Partnership Act (JTPA). Under the Act, 620 semi-autonomous training centers were responsible for implementing job training programs for the indigent, and were given financial incentives tied to labor market outcomes — employment status, earnings — of the trainees. These bonuses were given to the training centers thereby augmenting their budgets but could not be used to supplement staff salaries. (Courty and Marschke 2004) find evidence of the prevalence of gaming among the agency staff in the choice of termination date of the training for the participants, which while increasing organizational bonuses imposed a cost to the participants in terms of earnings. Similar effects have been found by related studies of the program (Heckman, Heinrich, et al. 1997).

An early quantitative observational study of performance pay in the public service was implemented by (Asch 1990), who collected data on the behavior of Navy recruiters, subject to a point-based performance system. The incentive consisted of a point-scheme for the quality
. 2021). We use data from the first four survey rounds, covering May 2020-March 2021. In addition, we construct measures for February 2020, before the first COVID-19 cases in Kenya, using recall questions from the first round.

The outcomes of interest are measures of labor supply.<sup>5</sup> The extensive margin is measured by participation in the last 7 days in three activities: employed/wage labor, household non-farm enterprise, and household agriculture. The intensive margin is captured using hours of work by activity in the last 7 days; an individual not working in a given activity is coded as working 0 hours. The survey also includes data on total child hours spent working in household agriculture.

Information on what grades children were enrolled in prior to the initial closures allows us to identify households affected by the partial reopening. We define ‘treatment’ households as those with children enrolled in grades 4 or 8 prior to the pandemic (eligible for the partial reopening)<sup>6</sup> while ‘control’ households have children in grades 3, 5, 6, 7, or 9, but not in grade 4 or 8.<sup>7</sup> We

> 4. See Appendix D for more detail.

> 5. We use the term labor ‘supply’ to refer to equilibrium outcomes, acknowledging that individuals may have been willing to supply additional labor but faced limited demand.

> 6. Few households report any children in grade 12; we test robustness of our results to including them.

> 7. Results are robust to including households with children in grade 2 as controls and to excluding households with children in grade 9 (in secondary school) or in grade 6 (to focus only on immediately adjacent grades).

6
km/1,000 km<sup>2</sup>of land area|88|**360**|278|
|Total road network density [1]|km/1,000 km<sup>2</sup>of land area|132|**385**|318|
|GIS Rural accessibility|% of ruralpopwithin 2 km from all-season road|25|**26**|31|
|Main road network condition[2]|% ingood or fair condition|72|**87**|86|
|Rural road network condition[3]|% ingood or fair condition|53|**39**|65|
|Classifiedpaved road traffic|AADT|1,131|**2,460**|2,451|
|Classified unpaved road traffic|AADT|57|**54**|107|
|Primarynetwork overengineering|% ofprimarynetworkpaved with 300 AADT or less|30|**12**|18|
|Primarynetwork underengineering|% ofprimarynetwork unpaved with 300 AADT or more|13|**26**|20|
|Perceived transport quality [4]|% firms identifying transport as major business<br>constraint|28|**22**|18|

_Source:_ AICD Road Sector Database on 40 Sub-Saharan African countries. _Note:_

[1] Total network includes the classified and estimates of unclassified and urban networks.

[2] Main network for most countries is defined as result of adding the primary and secondary networks. Uganda is only primary.

[3] Rural network is generally defined as the tertiary network and does not include the unclassified roads.

- <u>[4] Source: World Bank—IFC Enterprise Surveys on 32 Sub-Saharan African countries.</u>

18