rafmacalaba/data-use-mentions-extended huggingface dataset:
- How the firm sets its prices (e.g., based on production costs, competitors' prices, the target profit level, or willingness-to-pay by the consumers) and how it adjusts price for inflation (e.g., an index based on inflation, own costs, or dollars and exchange rates).

# **E. Transaction Environment**

According to transaction costs economics (Williamson 1989, 1993), incomplete information and the potential for opportunistic behavior prevent contracts from specifying outcomes under every uncertain contingency. Firms thus establish various types of relationships -including using contracts-with suppliers, dealers and customers. Maintaining many types of relationships entails distinct benefits and costs for firms. These myriad relationships require time and productive resources, but allow degrees of specialization and different levels of investments. Researchers therefore must study contractual arrangements that facilitate adaptation and dispute settlement such as the legal system, arbitration, long-term contract, and vertical integration.

Transaction cost economics suggest that to better understand the impact of these costs on firm's performance we should have at least the following information about the principal characteristics of firm's exchanges (Williamson 1989): (1) The frequency with which they occur, (2) The degree and type of uncertainty to which they are subject, and (3) The condition of asset specificity.

When asselt specificity is important, theory suggests that the transacting parties will arrange transaction structures-for instance, vertical integration, merger, long-term contracts-to economize transaction costs. Existing surveys pay only limited attention to such transaction characteristics. Future Bank surveys may become more valuable when this typie of information is collected.

An important determinant of transaction costs is the business and legal environment in which the firn operates. This set of considerations is especially important for transition economies and developing countries, where the legal and business environment is in flux and may seriously impact the firm's decisions. To investigate these issues, surveys should collect detailed information on the firns' exchanges. For example, what are the costs of establishing
of school who are in school at that level or the level above; (ii) the “net enrollment rate” (NER), measuring the fraction of children in the theoretical age range for a given level of school who are in school at that level; and (iii) the “gross enrollment rate” (GER), measuring the number of children of any age who are enrolled in a given level, as a fraction of the number of children in that age range.

To maximize country coverage, the following procedure populates the enrollment rate series used to calculate expected years of school:

- Available data for all four enrollment rates for all four levels of school were retrieved, combining information from the three external data platforms maintained by the UNESCO Institute of Statistics.  For pre‐primary, TNERs are not reported as there is no level below pre‐primary, and ANER is available only for the age corresponding to one year before the official start of primary school.

- Gaps in each enrollment rate for each level and country were filled by taking the most recently available data, going backwards up to 10 years for each country‐year observation.

- Within each level, the preferred enrollment rate available in the filled‐in data as of 2017 was chosen, in the following order of priority:  TNER, ANER, NER, and GER.  The filled‐in series for this enrollment rate was then used for all years for this level of school.  Note that in some cases differences in availability of enrollment measures means that different enrollment rates are used for different levels of school.  However, with a level for a given country, same type of enrollment rate is used.

- Data on repetition rates for primary, lower‐secondary, and upper‐secondary school were retrieved, and filled in with up to 10 years of lags in the same way as for enrollment rates. These are needed to adjust enrollment rates obtained in the previous step for repetition. Adjusting for repetition is important because failing to
**Table A1 Data sources**

|**Indicator**|**Years**|**Source**|
|---|---|---|
|Number of physicians and<br>nurse/midwives per 1000<br>population|1990‐2013|WHO Global Health Observatory|
|Total population, Population aged<br>65 or over|1980‐2030|United Nations, Department of Economic<br>and Social Affairs,Population Division|
|Real GDPper capita(2011 PPP$)|1995 ‐ 2030|See Annex B,estimation byPatrick Eozenou|
|Total health expenditures per<br>capita (PPP constant 2011<br>international $), Percent of total<br>health expenditures spent out‐of‐<br>pocket|1995‐2013|World Development Indicators, extracted<br>from the WHO Global Health Expenditure<br>database|

Data on worker density from the WHO Global Health Observatory database for 193 countries were first cleaned to remove obvious outliers due to misreporting. In each of the country‐year observations listed in Table A2, outliers were replaced with missing data so that estimated growth rates would not be unduly influenced by arbitrary substitution.

**Table A2 Workers per 1000 population data outliers**

|**Country**|**Years**|
|---|---|
|Bhutan|2012|
|Saint Lucia|1999|
|Swaziland|2000|
|Bahrain|2005,2011|
|Bolivia|2001|
|Cabo Verde|2004|
|Cameroon|2004|
|Central African Republic|2004|
|Congo|1998|
|Ghana|2004|
|Guinea‐Bissau|2004|
|Guyana|2004|
|Micronesia|2009|
|Samoa|1999|
|Sierra Leone|2004|
|Zambia|2004|
|India|1991|

Missing data points for physicians and nurses/midwives per 1,000 population between
Policy Research Working Paper 9991

# **Abstract**

This paper analyzes the initial impact and recovery of globally engaged firms from the COVID-19 crisis. It uses rich survey data of nearly 65,000 firm-year observations in 45 countries spanning three waves of data collection. The findings are organized in a series of stylized facts, which suggest that although the pandemic had an immediate adverse impact on most firms, the globally engaged ones are recovering faster, possibly due to their higher capabilities. Among globally engaged firms, those directly involved with international markets show better recovery than the ones that were indirectly involved. These results mask wide variation by firm traits, sectoral attributes, and country characteristics.

At the core of the recovery of globally engaged firms is their heightened response to the crisis by finding novel ways to adapt supply chains even in the presence of lockdowns and uncertainty. These firms swiftly digitalized, introduced new products and changed their markets and sources of inputs. Over and above their capabilities, global engagement cushions firms against shocks. Policymakers could therefore facilitate global linkages by providing information on potential markets and products, by making production flexible in terms of facilitating remote work, reducing the rigidity of contracts; and incentivizing financial institutions to issue instruments that reduce uncertainty risk.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and 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 at ineagu@worldbank.org, afernandes@worldbank.org, agrover1@worldbank.org, s.poupakis@gmail.com, and sreyesortega@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
Table 1 also provides the average number of nightly observations each quarter that is used to construct the nighttime light data. Nighttime lights of AEs, most of which are located in the northern part of the Earth, are affected by late sunsets in summer months and therefore have on average a lower number of effective observations than EMs. The average AE has about 26 nights of observations each quarter, compared with 29 in an average EM. LIDCs, many of which are situated in tropical areas, also have relatively low numbers of observations due to frequent cloud cover. Even at the 90th percentile, the number of nightly observations is just 39, and hence less than half of a quarter. The variation in the number of nightly observations across countries and over time can be used to quantify the measurement errors in nighttime lights, which allows us to identify the elasticity between nightlight growth and GDP growth.

A country’s annual nighttime lights averaged across the monthly data are very strongly correlated with those from alternative annual VIIRS data.<sup>15</sup> However, the correlation of the annual growth rates averaged across the monthly data with the direct annual growth rates is weaker, suggesting that elasticity estimates from annual and monthly data should not be used interchangeably.<sup>16</sup>

# **4.2 Cross-Sectional and Temporal Relationships**

The recording of nighttime lights is affected by extraterrestrial and atmospheric conditions. As a result, temporal fluctuations in the data can be large, making it problematic to use time-series changes in nighttime lights as a proxy for changes in economic activity directly. However, because the noise is often common to all countries at a point in time, the relationship between nighttime lights and economic activity is strong and relatively stable when the analysis controls for time-specific factors.

Table 2 examines the roles of country- and time-specific factors in the relationship between nighttime light growth and GDP growth. Without any fixed effects, the correlation coefficient of 0.32 is statistically significant only at the 10
5.314***|5.080***|5.255***|5.030***|5.170***|5.322***|
||(0.198)|(0.182)|(0.189)|(0.161)|(0.273)|(0.199)|
|Observations|7,045|7,164|7,164|6,464|3,356|7,045|
|R-squared|0.876|0.872|0.872|0.883|0.873|0.875|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

31
appear to be driving student learning, while others _within the same public education system_ are not. This suggests a role for government to encourage schools with weak operations management to follow best practice. As we observe from our mapping exercise, ‘strong’ operations management practices do two things, they: actively promote quality of delivery in the classroom (e.g. via personalization of learning, and encouragement to follow best educational practice); and put processes in place to review school performance and drive change (e.g. dialogue and meetings focused on continuous improvement, collection and use of student assessment data). Such practices could be adopted more widely in the public sector and, our analysis suggests, should improve both teacher selection and household incentives.

To be sure, our goal has not been to produce a global theory; given the diversity of real-world education systems, we have deliberately focused on one region, Latin America, and developed a theoretical framework for that context. One could adapt this framework to study different settings, for instance South Asia and parts of East Africa where there is a preponderance of ‘low-cost private schools’. Such a model would need to allow for the possibility of ‘queues’ for jobs in public schools and, as a result, to explicitly model _demand_ -side selection. To the extent that strong management practices enable public school principals to offer higher levels of compensation and then choose motivated and able teachers from the resulting queue, then qualitatively similar predictions would likely still apply. We hope that our new index will enable fertile ground for further research.

# **4 Conclusion**

Policy makers have begun to set ambitious, universal learning goals. To achieve these targets it will be necessary to understand why, within and across current education systems, some students are learning more in some schools than others. Although there are likely many factors at work, it has been suggested that part of this variation in learning might stem from differences in school management. To explore this issue and
round.

The information on attitudes toward smoking is available on households and individuals. In the family questionnaire are questions about how much households spend on cigarettes and how many packs were purchased during a recent period. This allows the unit values (individual prices) of cigarettes to be calculated for all the households that were involved in smoking. There are also questions on how much the respondent smokes and how much the respondent spends on cigarettes.

Another source of information is cigarette prices in communities. This information is also collected through the survey. Prices for four types of cigarettes are collected in this section of the questionnaire: brands that are domestic, imported, cheaper, or more expensive. However, the variation in prices among households is relatively low. This is why the information is not helpful in the analysis.<sup>8</sup> The analysis was run and is presented below by deciles that were defined using per capita expenditures (unless specified otherwise).

> 7 Sakharova, Antonov, and Salagay (2017) observe a 25 percent decrease in consumption in 2005–14 that correlates with government action on the tobacco epidemic.

> 8 As a robustness check, we tried a few modules in which the prices in communities were used as instruments. However, the results appeared to be statistically unstable.

4
by the expelled Muslim population. The RSC split the large estates of Muslim landlords ( _chiftliks_ ) into smallholdings and distributed them to the Greek refugees.<sup>8</sup>

**Infrastructure Works.** The RSC also expanded the cultivable land area by undertaking important and costly drainage and reclamation works in the valleys of the Northern provinces, often waterlogged and prone to malaria. As a result, the area of cultivated land doubled in Northern Greece (Kontogiorgi, 2006, pp. 277–290) and the drained marshlands hosted many new settlements of refugees.<sup>9</sup> In addition to distributing land and houses to refugees, the RSC also initiated a public utility program: it constructed roads, bridges, dispensaries, and schools (Pentzopoulos 1962, p. 108; Kontogiorgi 2006, p. 297). The decennial 1922–1932 total budgetary expenditures for the relief, housing and settlement of refugees amounted to a little more than 10 millions British pounds, with 9.1 millions spent for agricultural settlement and 1.3 millions for urban settlement (Kritikos

> 8By contrast, the refugees who settled in urban centers — about 50 percent of them — were given no means of providing for themselves, apart from accommodation (Kontogiorgi, 2006, p. 103).

> 9In total, the Greek regions of Macedonia, Thrace, Epirus, and Thessaly hosted more than 90 percent of the refugees’ agricultural settlements (uthors’ calculation using data from the 1930 Statistical Yearbook of Greece)

4
0.059|
|Super-area and sector dummies|0.129|Super-area and sector dummies|0.104|
|Number of observations|19,570|Number of observations|9,393|

_Notes: Stars indicate statistical significance at 5, 1, and 0.1 percent levels with robust standard errors. Subarea variables are aggregated across GN Divisions in Sri Lanka and villages in Tanzania. Area variables are populationweighted averages of sub-area variables aggregated to the are level. Super-area variables are district dummies in Sri Lanka and regional dummies in Tanzania. Sector dummies are a rural dummy in Tanzania and urban and estate sector dummies in Sri Lanka. See Table 2 for sources of remote sensing indicators._ For Tanzania, the lasso model selected several building footprints and built-up area measures, as well as measures of night-time lights that capture building and population density. Building counts, night- time lights, and built-up area are all positively associated with welfare. Several climactic variables were also selected, reflecting the dependence of rural areas on favorable rainfall patterns. Higher variance in the NDVI vegetation index, reflecting areas that contain a mix of built-up area and green space, is also positively related to welfare. The Sri Lanka model contains measures of built-up area, rainfall, as well as texture features such as the Fourier Transform and Line Support Region Mean at the subarea level, and other features such as the standard deviation of the Gabor filter and Histogram of Ordered Gradients at 22
systematically related to concentration in the banking sector, lack of financial depth, and exchange rate volatility. Policies that improve competition in the banking sector, that encourage financial development in the migrant’s home country, and that reduce exchange rate volatility will help lower official transactions costs.

The paper proceeds as follows. The next section provides background information on informal remittance flows; Section III surveys the literature on measuring informal flows; Section IV presents a simple model of the informal sector; Section V discusses the data; Sections VI and VII present results from cross-country data and household surveys, respectively; and Section VIII concludes.

# **II.  Informality in Remittance Flows**

The choice of provider for remitting money depends on a number of factors. The most prominent among these are: the cost of the transaction, speed, security of funds, geographic proximity/ accessibility, convenience in terms of familiarity and language. The attractiveness of formal and informal channels varies greatly across these factors.<sup>2</sup> Globally, studies indicate that informal channels are cheaper than formal ones. The pure monetary cost (transaction cost) of remitting money across borders using official channels is estimated at approximately 13 percent of the remittance value. Orozco (2003) estimates the cost of a Hawala / Hundi transaction to be less than 2 percent of the value of the principal. For the informal remittance channels as a whole, Sander (2003) reports the average cost of remitting at 3-5 percent globally, although they can be higher in specific cases. Swanson and Kubas (2005) report costs from less than 1 to 5 percent. Similarly, remittances through friends, taxi drivers, etc., are also low-cost

> 2 Pieke et. al. (2005) provide a survey of informal market definitions, costs, and systems.

4
the standard errors, but also the coefficients. Because of the presence of this heteroscedasticity in the original nonlinear gravity specification, the authors suggest estimating the multiplicative form of the model using nonlinear models, such as the Poisson pseudo-maximum likelihood (PPML), which also provides a natural way of dealing with zero co-patenting and the extreme skewness of the dependent variable, intrinsically heteroscedastic with variance increasing with the mean (Cameron and Trivedi 1998).<sup>5</sup> Thus, equation (1) is estimated by means of PPML using the fact that the conditional expectation of _COPATijt_ in (1) can be written as the following exponential function: (2) _E( COPATijt | X ijt )_ = _exp_ <u>[</u> _β0_ + _β1 ln DIASPORAijt_ + _γn ln Z ijt_ + _τi_ + _τ j_ + _δt_ <u>]+</u> _εijt_ .

# _Data_

# **Inventors’ International Migration**

A large part of the migration literature surveyed above has made use of census-based migration datasets becoming available during the last 15 years, broken down by skills— primary, secondary, and tertiary level of education (for a recent data contribution, see Özden et al. 2011).

In contrast, the present analysis is based on a dataset of inventors with migratory backgrounds applying for PCT patent applications between 1990 and 2010. The use of inventor data for migration analysis comes with two main advantages. First, patent data

> 5. Moreover, Santos Silva and Tenreyro (2010, 2011) use simulation techniques to show that the PPML estimator is generally well-behaved in the presence of a large proportion of zeros. As a robustness check, appendix S8 replicates the main estimations using alternative count data methods for zero-inflated models. No important differences with respect to the main results and conclusions emerge.

10
**Table A.10:** Bureaucratic Outcomes: Heterogeneity by Time of Appointment

||||**Bure**|**aucrat survey**|||
|---|---|---|---|---|---|---|
||Parent Head|Parent Served|Enthusiasm|Motivation|Interacts dailyw/ citizens|Freq. index|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||**Panel**|**A: Bureaucrats**|**appointed bef**|**ore last election**||
|New village head|-0.051|-0.104|0.252|21.931|0.347<sup>_∗∗∗_</sup>|0.544<sup>_∗∗∗_</sup>|
||(0.060)|(0.122)|(0.223)|(19.398)|(0.132)|(0.180)|
|Observations|531|531|529|527|529|529|
|Control mean|0.055|0.28|0.10|101.3|0.58|0.37|
|Robust p-value|0.237|0.427|0.175|0.218|0.00379|0.00106|
|MSE-opt. bandwidth|14.9|21.8|21.0|22.5|20.9|19.3|
|Effective obs.|214|279|269|287|269|256|
|New village head|-0.103|**Pane**<br>-0.389<sup>_∗∗∗
Carrillo, Paul, Mercedes Onofa, and Juan Ponce. 2011. “Information Technology and Student Achievement: Evidence from a Randomized Experiment in Ecuador.” IDB Working Paper No. 78. Washington, DC: Inter-American Development Bank. https://dx.doi.org/10.2139/ssrn.1818756 Conn, Katherine. 2014. “Identifying Effective Education Interventions in Sub-Saharan Africa: A MetaAnalysis of Rigorous Impact Evaluations.” PhD diss., Columbia University.

Cristia, Julián, Pablo Ibarrarán, Santiago Cueto, Ana Santiago, and Eugenio Severín. 2017. “Technology and Child Development: Evidence from the One Laptop Per Child Program.” _American Economic Journal: Applied Economics_ 9(3): 295–320. <u>https://doi.org/10.1257/app.20150385</u> Das, Jishnu, Stefan Dercon, James Habyarimana, Pramila Krishnan, Karthik Muralidharan, and Venkatesh Sundararaman. 2013. “School Inputs, Household Substitution, and Test Scores.” _American Economic Journal: Applied Economics_ 5(2): 29-57. <u>http://dx.doi.org/10.1257/app.5.2.29</u> de Melo, Gioia, Alina Machado, and Alfonso Miranda. 2014. “The Impact of a One Laptop per Child Program on Learning: Evidence from Uruguay.” IZA Discussion Paper No. 8489. Bonn, Germany: Institute for the Study of Labor.

de Ree, Joppe, Karthik Muralidharan, Menno Pradhan, and Halsey Rogers. 2017. “Double for Nothing? Experimental Evidence on an Unconditional Teacher Salary Increase in Indonesia.” _Quarterly Journal of Economics 133_ (2): 993-1039. https://doi.org/10.1093/qje/qjx040 Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2015. “School Governance, Teacher Incentives, and Pupil-teacher Ratios: Experimental Evidence from Kenyan Primary Schools.” _Journal of Public Economics_
the presence of a non-farm salaried worker in the household continues to have a robust positive effect on monthly per capita consumption for rural households, regardless of whether the district has a relatively high urban population, manufacturing workers, or public employment. We also find no evidence of heterogeneity in non-farm employment’s effect on rural consumption across district bank branches, suggesting a limited role for aggregate supply-side factors such as infrastructure.

# **3.4 Barriers to the Performance of Rural Non-Farm Enterprises**

Panel B of Table 1A shows that 60 percent of rural non-farm workers are either self-employed, or engaged as casual workers. This forms the motivation to briefly consider the performance of rural non-farm enterprises, and examine the impact of constraints faced by such enterprises on their performance. We exploit the rich enterprise level data provided by the Survey on Unincorporated Enterprises, undertaken in 2010-11 by the NSS. As described in Section 23