rafmacalaba/data-use-mentions-extended huggingface dataset:
product (78%), and developing a new process (78%), while developing a new industrial design was the least common form (41%). In-Tech funding has small, and not statistically significant, impacts on all of these measures, suggesting that it did not lead to an overall increase in these types of innovative activities.

The overall summary index of research and innovation shows a 0.13 to 0.31 standard deviation increase in research and innovation. This impact is only statistically significant when we use the local mean specification within the optimal bandwidth, and not using a local linear specification. The bottom right panel of Figure 5 shows why this variation in treatment effect arises – the research

> 11 We use 10 or more publications, since the distribution of reported number of publications in the last 5 years is heavily skewed, with a 25<sup>th</sup> percentile of 3, median of 12, mean of 41, and 90<sup>th</sup> percentile of 100. The treatment effect is also insignificant for the number of publications, with an extremely wide confidence interval of (-32.8, 32.0) as a result.

20
the credit program because many mega firms, who did not participate in the program, either chose not to, or were unable to, roll over their existing debt maturing in 2020, resulting in a decline in outstanding non-program debt.<sup>22</sup>

# **3 Data**

We use three administrative data sets from various sources in Chile. These data sets cover the entire formal private sector in Chile in rich detail, including credit flows and balances, default history, and terms of individual transactions. The firm-level data contain financial statements, input use, and sales collected monthly, along with the industry and location of firms. Below we describe the data sources, sample selection, and key variables.

First, we use granular confidential bank-to-firm information compiled by the Financial Market Commission (the financial supervisory agency) for all firms using the entire banking system. We have information on stocks and flows of credit. For stocks (i.e., loans outstanding), we have data on the amount of debt each firm has with each bank in the system every month. We also know the number of days each loan in the system is past due. For flows, we have transaction-level data on each loan received by each firm, including information on the loan amount, interest rate, and loan maturity. We complement the bank data with unique data on the credit program we analyze, including detailed information on loan applications by firms (such as the amount requested) as well as banks’ decisions (such as whether a loan request is approved or rejected and the approved amount). These data allow us to measure selection and disentangle supply and demand factors in the allocation of credit across the whole economy.

Second, we use confidential administrative tax records from Chile’s tax authority (Servicio de Impuestos Internos). These data contain monthly, firm-level information, including sales, materials expenditure, value added, number of workers, wage bill, net worth, age, industry, and
be associated with growth only in ‘low-income’ regions. In those regions, the relationship is very robust (significant at the 0.1 percent level), but it is relatively weak in impact: a doubling of the number of foreign investments per 100,000 inhabitants is associated with a 1.1 percent increase in GDP per capita growth. For example, a region with an average per capita income of €4,000 and 2 foreign investments per 100,000 inhabitants, would find on average that doubling the number of investments to 4 per 100,000 inhabitants is associated with an increase in per capita income of €44. This should not discourage low-income regions, since the relationship with FDI is still robust and positive, but indicates that increases in GDP per capita for the whole region are associated only with a dramatic increase in the number of foreign investments.<sup>32</sup>

> 32 Some readers will question why the number of investments is used, rather than the value of investments or the jobs created in the host region through inward FDI. Both values are available from the same _fDi Markets_ database. However, they are generally regarded

22
- 1 −βn: share of payment to labor in value added by country.

- jk

- • γn : input-output coefficients, consumption of materials from sector k in gross output in sector j.

- j

- • αn: share of sector j in total final consumption in country n.

- j j

- • wnLn + rnHn: value added by country and sector.

- j

- • Xni: bilateral trade flows across countries for each sector (including all countries in the sample and a constructed rest of the world).

- j

- • Xnn: domestic sales, constructed as gross output minus total exports.

- j

- • tni: bilateral tariffs across countries for each sector (including all countries in the sample and a constructed rest of the world).

- Gn : spending in infrastructure by country estimated in the subsequent section.

- j

- • κ̂ni: proportional changes in trade costs associated with BRI transport projects, for each origin-destination-sector, estimated in de Soyres et al (2018), and discussed below.

We use the sectoral trade elasticities θ<sup>j</sup> from Caliendo and Parro (2015) which were estimated for 20 tradeable sectors and which we map to our 31 sectors (Table 1). Their estimations are performed using trade and tariff data, without assuming bilaterally symmetric trade costs as is standard in the literature. Moreover, their method is consistent with any trade model that delivers a gravity-type trade equation.<sup>13</sup>

> 13 We assume an elasticity 4.0 for the Oil, Gas and Coal industry to account for the fact that it takes time to renegotiate energy contracts and that some countries may not be able to source energy from alternative suppliers due to infrastructure constraints such as existing gas pipelines.

14
Over this sample period, the authors present evidence consistent with the idea that countries may be less likely to impose protection against partners undergoing a _simultaneous_ contraction. While we present evidence of a similar result for the period covering the Great Recession in section 4.2 below, here we examine whether such a relationship existed in the pre-Great Recession period. As column (6) indicates, we fail to find evidence of this relationship in data from _prior to_ the Great Recession; i.e., countries were no more or less likely to impose new bilateral import restrictions during a domestic downturn if that was synchronized with the trading partner’s downturn.

18 We anticipate that this issue does not matter much across these particular industrialized economies as they each have applied MFN tariff rates relatively close to their WTO tariff bindings. By design, this institutional similarity influenced the focus of this paper to these five policy-imposing economies. Put differently, one contributing explanation as to why we deliberately chose not to include a number of emerging economies in the empirical analysis is because there is substantial variation across those countries, products, and time as to the extent to which WTO commitments discipline their trade policies. We speculate that a much more serious treatment of the choice set of available trade policy instruments is required to analyze the impact of macroeconomic shocks on the import protection for such policy-imposing countries; thus we leave it to future research.

19 The variable is constructed from HS-06 data on WTO maximum tariff bindings and applied MFN tariffs deriving from WITS; the data are available from only 1995-2010. Note that we define this variable as equal to _or above_ (as opposed to simply equal to) because the early WTO period involved many of these economies phasing in reductions to their applied tariffs to levels that were only eventually at or below their bindings. We have also considered other modifications to the definition of this variable – e.g., defining it more flexibly as a product’s applied tariff being within a 1, 5 or 10 percentage point cutoff of
a wide range of outcomes, we construct a novel dataset, comprising all published electronic public procurement tenders and data extracted from a large sample of paper-based procurement records from there major GOB agencies, which are responsible for the bulk of public works projects.

87. We rely on a range of fixed effect regression models combined with coarsened exact matching estimators, to address potential threats to causal identification. We find overwhelming positive impacts of e-procurement on a wide range of outcomes.

33
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Achievements**

Installed electricity generation capacity in Sudan tripled between 2005 and 2007. Sudan’s installed capacity increased from 801 MW (AICD 2005) in 2005 to around 2,687 MW in 2009 (Platts 2009).<sup>6</sup> Compared to its African peers, Sudan’s installed capacity far exceeds what lowincome countries have put in place but lags behind that of resource-rich countries. In 2005 around 70 percent of the electricity was generated via thermal generation and around 20 percent using hydropower resources. But the composition mix seems to have changed since 2005; with the construction of new hydropower plants, the generation mix has now shifted to 55 percent hydropower and 45 percent thermal.

**Table 12. Relatively high usage of modern fuels for cooking in Sudan**

|Usage of modern fuels||
|---|---|
||All households<br>Richest households|
|**Sudan (2009)**|**36.10**<br>**64.90**|
|Kenya (2003)|3.45<br>16.52|
|Nigeria (2003)|1.19<br>5.51|
|Uganda (2001)|0.87<br>4.34|
|Ethiopia (2005)|0.31<br>1.48|
|South Sudan (2009)|0.30<br>0.80|
|Tanzania (2005)|0.24<br>1.22|
|_Source:_World Bank 2010|b and c DHS various years.|

The National Energy Corporation (NEC) enjoys healthy bill collection. The utility reports that all of the billings are routinely collected by the utility. These rates are higher than all other African peer groups (table 13).

Sudan boasts a relatively high use of modern fuels (gas and electricity)
Policy Research Working Paper 5992

# **Abstract**

Croatia began to implement case-based provider payment reforms in hospitals beginning in 2002, starting with broad-based categories according to therapeutic procedures. In 2009, formal diagnostic related groups were introduced, known locally as _dijagnostičko terapijske skupine._ This study examines the efficiency and quality impacts of these provider payment reforms globally on the Croatian health system by analyzing data on five procedures in acute health care for 10 years, between January 2000 and December 2009. The five procedures are cataracts, pneumonia, coronary bypass, appendectomy, and hip replacement. Using data from the Croatian Institute for Health Insurance, this study finds that both broad-based and detailed case-based payment systems have improved efficiency as measured by a reduction in average length of stay, with little impact on the number of cases. These provider payment reforms have had no adverse impact on quality as measured by readmissions. While it is still too early to quantify the impact of Croatia’s introduction of formal diagnostic related groups, it appears that the introduction of both broad and detailed case-based payment systems has improved efficiency in acute hospital care.

This paper is a product of the Human Development Sector Unit, Europe and Central Asia Region.. 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 eyeh@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
a panel of states.” _American Journal of Agricultural Economics_ 86.5: 1315-1321.

**Bandyopadhyay, D. (2009).** “Lost opportunity in Bihar.” _Economic and Political Weekly_ : 12-14.

**Banerjee, Abhijit (2004).** “Who is getting the public goods in India? Some evidence and some speculation.” _India’s Emerging Economy_ : 183-213.

**Banerjee, Abhijit, and Lakshmi Iyer (2005).** “History, institutions, and economic performance: The legacy of colonial land tenure systems in India.” _American Economic Review_ 95.4: 1190-1213.

**Bansil, Puran Chand (2011).** “Bihar agriculture: A perspective.” _Concept Publishing Company_ , Delhi, India.

**Barro, Robert J., Xavier Sala-i-Martin, Olivier Jean Blanchard, and Robert E. Hall (1991).** “Convergence across states and regions.” Brookings Papers on Economic Activity: 107-182.

**Barro, Robert J., and Xavier Sala-i-Martin (1992).** “Convergence.” _Journal of Political Economy_ 100.2: 223251.

**Baumol, William J. (1986).** “Productivity growth, convergence, and welfare: What the long-run data show.” _American Economic Review_ , 1072-1085.

**Bhalla, G. S., & Singh, G. (2010).** “Growth of Indian agriculture: A district level study.” _Planning Commission_ , Government of India.

> 11 By regressing the TFP growth on yield growth, I first obtain the slope and intercept coefficients to predict TFP growth for Bihar districts. Then, under the assumption that relative yields in the initial year approximate relative TFPs, it is straightforward to construct TFP series using estimates of TFP growth.

19
# **1. Introduction**

Arbitron, the radio ratings company, recently switched from surveys to 'Portable People Meters', an electronic device that directly records the listening habits of their large sample of listeners.<sup>1</sup> The move from self-reports to electronic recording resulted in a 10.7% drop in the estimate of the market share of classical music. Men proved to be listening to soft-rock much more than they had previously reported. Evidently when selfimage is involved, survey respondents are less than candid in their responses and candor differs across groups. This anecdote alone should sensitize researchers to the problems arising when surveying value-laden activities such as corruption. At the very least, if the proportion of candid respondents varies across groups, comparisons of responses to corruption questions across groups can be misleading. Cross-country comparisons will be similarly affected.

The purpose of the present paper is to examine the prevalence and consequences of such problems by using the responses from the World Bank sponsored Nigeria Enterprise Survey. This survey was fielded in two waves in 2008 and 2009, covering a total of 5422 firms. The survey posed questions on a wide range of aspects of business operations, such as financing, organization of production, economic performance, reactions to regulation, and obstacles to current operations. A significant number of questions focused on corruption, but these questions were by no means dominant in the survey. It seems unlikely that any respondent would have concluded that this was a corruption survey, _per se_ .

The obvious difficulty in assessing respondent candor is that there is nothing analogous to the 'Portable People Meters' for corrupt activities. What is needed is a technology that identifies those who are not candid and that relies solely on survey responses. This has been a long-sought element of the methodology of surveyors. Recently, Azfar and Murrell (2009) (AM) proposed such a methodology, which in a

> 1 "Never Listen to Céline? Radio Meter Begs to Differ" By Stephanie Clifford. _New York Times_
with special focus on the matching of WBES data with bank-level data and on the construction of three latent variables aimed at measuring the presence of credit constraints, innovation, and green activities. Sections 3 and 4 describe our empirical strategy and present regression results. Section 5 concludes.

# 2. Data

This paper uses 3 types of data: (i) bank-level data from Fitch Connect; (ii) firm survey data from the World Bank Enterprise Survey, and (iii) macroeconomic data from the IMF-WEO database and from the World Bank World Development Indicators. We start by describing the bank-level dataset, we then move to WBES and describe how we matched these two datasets, and conclude by describing the construction of three latent variables aimed at measuring credit constraints, innovation, and green activities.

5
321|
|Number of evictions|0.21**|(0.10)|[1.00]|0.19|(0.57)|1,321|
|Shares dwelling {0,1}|0.00|(0.01)|[1.00]|0.00|(0.05)|1,321|
|Number of families (if shares dwelling=1)|0.00|(0.01)|[1.00]|0.00|(0.05)|1,321|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Housing quality index is an standardized index of the following: Indicator for high-quality floors, Indicator for high-quality roof, Indicator for high-quality walls, Indicator for reliable electricity, people per room. Housing material quality index is a standardized index of indicators for high quality floors, roof and walls. Has access to grid is an indicator for having access to grid electricity, zero if no electricity or from a generator, etc. Generator is an indicator equal to one if the household uses a generator for electricity, zero otherwise. Number of rooms in dwelling is the total number of separate rooms excluding toilets and store rooms, including rooms shared with other households if applicable. Number of occupied rooms only includes rooms occupied by the household. Household size is the number of individuals of any age in the household. Owns house is an indicator equal to one if the household owns their dwelling. Change in rental debt is the total amount of rental debt accumulated minus total amount of rental debt repaid in the past month. Outstanding rental debt is the total amount of unpaid rent from all months that the landlord expect to be repaid. Moved between dwellings is an indicator if the household has moved from one dwelling to another in the past 12
Figure 10: Average number of students, by level <!-- Start of picture text --> 856<br>571<br>442<br>327<br>Public Private Public Private<br>Primary Secondary<br><!-- End of picture text --> Source: Authors' calculations using 2015 Morogoro data.

Consequently, average primary and secondary class sizes are significantly smaller in private schools than public schools. Note that both public and private schools are gender-balanced.

Based on national data, in primary grades, private schools are at 70 percent of their student capacity while public schools are currently at 130 percent of capacity (MoEST, 2016). In lower secondary schools, private schools have even greater excess capacity; private schools are only 57 percent filled while public schools are at 107 percent of capacity.

# 6.2 Teachers

Public school teachers in our 2015 Morogoro data sample have higher levels of education. Among the total teachers in the 142 schools surveyed, 62 percent are public school teachers and 38 percent are private school teachers. Nearly 42 percent of public school teachers have a bachelor’s degree compared to 22 percent of teachers in private schools. This is driven largely by the high number of private pre-primary teachers and high number of public lower secondary teachers in the sample. Looking exclusively at lower secondary schools, the percentage of teachers with a bachelor’s degree is not statistically significant between public and private schools, at 67 and 71 percent, respectively. Also, private schools are significantly more likely to have uncertified teachers. Nearly, 21 percent of the private schools have full time teachers without certification whereas the number is 13 percent for public schools. Accordingly, teacher salaries in private schools are significantly lower, possibly driven by the lower qualifications required for pre-primary teachers.

Based on the 2013 IE data, we find that compared to public schools, teachers are less likely to be absent and more likely to be in class and teaching in private schools. As
those without a high school diploma – also referred to as “dropouts” in the analysis.

These wage premiums are estimated by means of a Mincer wage regression based on individual worker’s microdata. The Mincer equation consists of a regression of the logarithm of the hourly wages on dummies for educational levels, a potential experience variable (constructed as age minus years of education minus 6) and its powers up to the order of 4, and a series of regional and urbanrural controls. The remuneration for each input (ln _wkt_ ) is computed from this regression as a weighted average of the returns to education of each of the educational levels belonging to the corresponding input. The wage premiums are then obtained as the difference of these remunerations. These regressions have the following form: where _w_ is the wage for worker _i_ at time _t_ , the _D_ variables are indicators for the level of educational attainment (college complete and incomplete, secondary complete and incomplete, and primary complete – primary incomplete is the omitted category) with their corresponding _β_ coefficients, and the _X_ variables represent a set of individual characteristics, which include years of experience, region of residence and urban/rural status (when available).

37
.01, * p<0.05, + p<0.1; (3)<br>Missing observations in CAP data are treated as zero; (4) Luxembourg and Cyprus Republic are not being analyzed here;(5) CAP data is missing for 21 regions in Croatia and 2 regions in Spain (with 1 more region missing<br>for Pillar 2 data); (6) Standard errors are clustered at country level<br><!-- End of picture text --> Table 6 above indicates that, overall, the CAP funds seem to be reaching poorer areas within the EU. Starting with total CAP payments, a positive and significant relationship with poverty rate is observed. Further disaggregating the total payments into the two CAP pillars supports this result, as both pillars, when individually analyzed, remain significantly associated to poverty rates. When analyzing pillar 1 by its individual components, the decoupled payments remain significantly and positively associated with poverty rate. These results remain when analyzing each of these components on a per capita level. Therefore, CAP funds do tend to reach regions where higher poverty rates prevail. Nevertheless, it is important to keep in mind that CAP payments reaching poor areas do not imply they are necessarily reaching the poorest households within those areas.

We continue our analysis by assessing the relationship between CAP payments and poverty, but now turning to the share of the countries’ poor as the measure for poverty. Table 7 below presents the results obtained, which are similar to the ones previously presented using the poverty rate. Total CAP payments are significantly and positively associated with the share of the country’s poor. Similarly, for the specifications including each of the CAP’s pillars, we observe the same significant and positive association. In this case, both components of pillar 1, coupled and decoupled payments, seem to be reaching areas where there is a high share of the country’s poor population.

**Table 7.** CAP