rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

The critical role of innovation in economic growth and economic development is uncontested. Innovation is the engine of the Schumpeterian process of “creative destruction” where innovative firms and entrepreneurs continuously drive allocative efficiency and productivity growth (Schumpeter, 1942). A large empirical literature, mainly based on data on OECD countries, documents a robust positive relationship between firm‐level innovation and productivity (Hall et al., 2009; Hall, 2011); innovation and employment (Harrison et al., 2008); R&D and productivity (Hall et al., 2010).

Yet very little is known about the extent and impact of firm level innovation in developing countries. The majority of existing studies are limited to either one country or a specific industry (Bogliacino et. al, 2012). Some exceptions have focused on the experience in Latin America, using cross‐country analysis (Turriago, 2003; Hall e Maffioli, 2008), highlighting the differences with the experience of European countries (Raffo et al., 2008), or the linkages between innovation and productivity (Crespi and Zuniga, 2012; Zuniga and Crespi, 2013; Crespi et al., 2014). Outside Latin America the evidence is even more limited. Cirera et al. (2015) have analyzed the impact of innovation on productivity in a large sample of countries in Sub‐ Saharan Africa, South Asia, Eastern and Central Europe and the Middle East. Other studies include a recent analysis of the impact of corruption on innovation (Paunov, 2016) and a study on the use of the Internet as a driver for inclusive innovation (Paunov and Rollo, 2016). This lack of evidence has greatly undermined the ability to design appropriate and evidence‐based policies to promote innovation and support economic growth in developing countries.

One of the main reasons for this lack of evidence is a lack of adequate data. Despite significant efforts in recent years by national and international agencies to implement innovation surveys in
a slight delay after the adoption of the law in September 1995. As discussed in the last section, this could suggest that the largest bulk of investment and export volumes were not achieved until some years later.

59. Similarly to the results of the sectoral analysis within South Africa, we find that the positive impact of the MIDP program lasts four to six years. In particular, the coefficient turns insignificant from 2002 onwards. This result supports the common finding that tax incentives may affect some business decisions particularly in the short run, but in general they are not a primary consideration for investors in the long run. The fading out of the tax incentives may also be related to the challenges facing general economic activities in South Africa. As noted in a number of investment climate surveys, these challenges to investing in South Africa include: a volatile exchange rate, a remote location, crime, shortage of skilled labor, inadequate infrastructure and an increasing regulatory burden.

60. Economic density and the terms of trade are significant in explaining automotive export performance. In particular, one unit increase in the economic density coefficient (measured as US$/sq m) increases, on average, the volume of automotive exports by 0.8 to 1 percent. The coefficient of the terms of trade enters the regression negatively, suggesting that for every 1 unit decrease in the terms of trade index, automotive exporters increase the volume of their auto exports by approximately 0.3 percent. This result may at first seem counterintuitive, as 21
reliable data sources (Robayo-Abril and Rude, 2023a). One important limitation is that monetary measures of energy poverty rely mainly on data gathered via household budget surveys (HBS), which often consist of small sample sizes and are only representative at high geographic levels. In addition, these surveys gather limited information on individual and household characteristics. Consequently, it is difficult to

> 2 In this case, energy poverty is the share of households unable to keep their homes warm, which is a non-monetary measure of energy poverty.

2
they adopted such rules. Nevertheless, there is evidence that some features of a second generation of fiscal rules—such as the use of cyclically adjusted targets and well-defined escape clauses, combined with strong legal and enforcement arrangements—have been associated with reduced procyclicality (Bova, Carcenac, and Guerguil 2014). The country cases examined in Section VI suggest that fiscal rules or SWFs are most effective in achieving their stated objectives when they are well-designed, closely linked to broader policy objectives, and supported by strong institutions and political commitment.

Armed with the insights from the correlates of fiscal procyclicality established above, the remainder of this section uses cross-country regressions to identify the main drivers of fiscal procyclicality in commodity exporters.<sup>11</sup> The dependent variable is the correlation between the annual percentage changes of real government spending and real GDP. The explanatory variables are intended to capture the four explanations for the existence of procyclical fiscal policy described above: capital account openness (measured by an index of financial openness); political economy factors (measured by an index of political constraints, which reflects the extent to which policy changes are inhibited by institutional and political factors); macroeconomic stability (measured by the standard deviation of output); and institutional quality (measured by an index of control of corruption).

The coefficients of each of these four variables are statistically significant and three of the four have the expected sign (Table 3). More open capital accounts and better control of corruption are estimated to have helped reduce fiscal procyclicality while greater output volatility has been associated with higher procyclicality. However, the coefficient of the political constraints index does not have the expected sign, suggesting either that the “voracity effect” did not hold or that the political constraints index fails to capture it. The variables are jointly significant when combined and explain about 18 percent of procyclical behavior across countries.<sup>12</sup>

**Robustness checks** : In line with Lane (2003), GDP per capita,
,508|1.32||0.14|
|Yemen|6|24,976|95.8|3,241,486|0.2|0.2|0.16|

Sources: MIX database, WDI, IFS 2008 data. „GLP‟: gross (microcredit) loan portfolio. Total credit: Domestic private credit to the real sector by banks.

6 Sanabel, 2010. These statistics do not include Mauritania and Sudan, which are included in Sanabel regional totals.
explicit treatment of domestic factors. To overcome these limitations we estimated country-specific VARs incorporating domestic real interest rates and real exchange rates as additional explanatory variables.

The paper is divided in three parts. The first section presents the quarterly GDP panel regression exercise used to determine the importance of external factors explaining the regional slowdown . Using quarterly data from 1992 to 1998 for the eight largest countries of the region, we estimated panel regressions, and using the 1997 exogenous external conditions as a benchmark, we found that at least 52% of the regional slowdown was due to the adverse behavior of external factors.

The second section summarizes the country-specific "stylized facts" about the relationship between events in the external capital markets<sup>2</sup> , terms of trade, domestic real interest rates, real exchange rates and industrial production growth, using monthly data

> 2 To capture the external capital markets shocks we use the Brady Bond sovereign spreads provided by JP Morgan. In the last section of the paper we discuss the inclusion of this "price" variable instead of a "quantity" indicator.
08|

_Notes:_ y: real output per worker (2000-2010); h: human capital based on school and learning estimates (2000-2010). We convert HLO units into standard deviations based on a cross-country standard deviation of 68 to correspond to the returns to schooling parameter value which is given per standard deviation. We assume rates of return based on the microeconomic literature. We take the value _r_ = .10 for the rate of return per school year, and _w =_ .20 per standard deviation increase in learning. The .20 value is based on U.S. data. However, we might expect that returns to skills will be higher in developing countries, where the supply of skills is lower, as is the case in the returns to schooling literature. Significant work has been done to identify this parameter value. For the purpose of this paper, our intention is not to provide a final result, but rather to motivate the use of the data for future use in the development accounting literature. To this end, we take parameter values as given and conduct sensitivity analyses with values _w_ = .15 and _w_ = .25. When _w = 0,_ our accounting de facto only includes schooling; for any value _w > 0_ , we include learning as well as !"#[%&'())] schooling. We include 131 countries in this development accounting exercise. We define the human capital share as . !"#[%&'(,)] _Source:_ Schooling data are from Barro-Lee (2013). GDP data are from PWT 9.0. Learning estimates are from our database. Literature estimates are derived from the referenced papers.

21
an  average  of  18%  in  1998.<sup>9</sup>

# **2.2.   Worker   data**

The  linked  employer–employee  data  are  from  the  Brazilian  Labor  Ministry,  which  requires by  law  that  all  formally-­‐registered  firms  report  to  the  ministry  on  all  workers  in  every year.<sup>10</sup> These  administrative  records  have  been  collected  in  the _Relação  Anual  de Informações  Sociais_ (RAIS)  database  since  1986.  In  this  paper,  we  use  information  from RAIS  for  the  years  1990  through  1998,  when  we  also  have  complementary  data  on  the export  status  of  firms  and  industry-­‐level  tariff  rates.

The  main  benefit  of  the  RAIS  database  is  the  ability  to  trace  individually-­‐identifiable workers  over  time  and  across  firms.  The  data  include  a  unique  worker-­‐identification number  (which  remains  with  the  worker  throughout  his  work  history),  the  tax  number  of the  worker’s  firm,  the  industrial  classification  of  the  worker’s  firm<sup>11</sup> ,  and  the  municipality  of the  worker’s  firm.  These  data  are  particularly  valuable  as  they  offer  variables  beyond  the available  information  in  many  other  firm-­‐level  databases,  often  used  in  studies  like  ours.  In particular,  the  data  contain  detailed  information  on  workers’  skill-­‐levels  (as  defined  by occupation<sup>12</sup> and  education),  which  are  paramount  to  this  analysis.  Other  variables  of 9  Trade  policy  reforms  coincided  with  gradual  foreign  investment  liberalizations  and  the privatization  of  state-­‐owned  companies,  both  of  which  contributed  to  attracting  substantial  capital inflows  over  this  time  period.  Meanwhile,  the  government’s  regional  development  plans  also included  export  promotion  policies  as  explicit  elements,  helping  to  boost  exports  beginning  in  1995. In  each  specification,  we  include  region-­‐specific  year  dummies  to  capture  the  impact  of  these  and other  general  macroeconomic  trends  on  wages.

10  The  process  for  firms  to  report  on  their  workers
Policy Research Working Paper 10202

# **Abstract**

Motivated by the deterioration in global food security conditions, this paper develops a parsimonious machine learning model to derive a multi-year outlook of global severe food insecurity from macro-economic projections. The objective is to provide forecasts that are internally consistent with wider economic assessments, allowing both food security policies and economic development policies to be informed by a cohesive set of expectations. The model is validated on holdout data that explicitly test the ability to forecast new data from history and extrapolate beyond observed intervals. It is then applied to the World Economic Outlook database of April 2022 to project the severely food insecure population across all 144 World Bank lending countries.

The analysis estimates that the global severely food insecure population may remain above 1 billion through 2027 unless large-scale interventions are made. The paper also explores counterfactual scenarios, first to investigate additional risks in a downside economic scenario, and second, to investigate whether restoring macroeconomic targets is sufficient to revert food insecurity back to pre-pandemic levels. The paper concludes that the proposed model provides a robust and low-cost approach to maintain reliable long-term projections and produce scenario analyses that can be revised systematically and interpreted within the context of available economic outlooks.

This paper is a product of the Development Data Group, Development Economics and the Agriculture and Food Global Practice. 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 author may be contacted at bandree@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
|2.0|7.5|8.5|5.9|
|Europe, Middle East & North Africa|2.2|8.2|11.3|7.2|-0.6|4.6|11.9|5.2|
|Sub-Saharan Africa|2.5|4.7|9.7|5.6|0.2|3.0|13.5|5.4|
|**Memo:**|||||||||
|BRICS 5 countries|10.5|10.4|18.6|13.1|11.1|9.9|17.4|12.8|
|OPEC 12 members|-2.4|6.7|7.3|3.8|-3.1|3.3|13.6|4.4|

Source: Based on World Bank World Development Indicator database.
To do this, we employ a unique dataset that merges firm-level financial data with meteorological information. Our analysis hinges on the concept of location-specific weather uncertainty for firms, measured via grid-level temperature uncertainty. We utilize financial records from the Orbis database, covering 30 million firms across 64 countries, alongside daily surface temperature data on a 1x1 grid from the Berkeley Earth Foundation dataset (BERKEARTH). The merging of these two datasets creates an extensive grid-level dataset, covering around 4,000 economically meaningful grids with firm presence from 2000 to 2019. Grid-specific temperature uncertainty is measured based on the maximum daily temperatures over a year. We examine whether this grid-specific temperature uncertainty is associated with the growth of firms located in the grid.

Our analysis yields three key findings. First, there is a strong negative correlation between weather uncertainty and firm growth. Our results indicate that for every one-degree increase in temperature uncertainty, measured by the standard deviation of deviations from expected temperatures, firms’ growth rate of fixed assets decreases by 0.77 percentage points.

Second, the impact of weather uncertainty on firm growth varies across industries with different degrees of investment irreversibility. Industries with higher investment irreversibility experience a more pronounced negative impact on firm size growth. Doubling capital intensity intensifies the negative effect of temperature uncertainty on firm size growth by roughly 0.72 percentage points.

Third, the impact of weather uncertainty on firm growth varies across countries with different income levels. Higher-income countries are more adversely affected by weather uncertainties in terms of firm behavior. As income levels double, the negative impact of temperature uncertainty on fixed asset growth becomes more severe by 0.63 percentage points.

This paper is among the first to examine firms’ behavior in response to location-specific temperature uncertainty at the grid level. While climate-related uncertainty is often studied using indirect, non-weather measures like the Climate Change News Index (Engle et al. 2020) and in climate policy uncertainty literature (Gulen & Ion 2016), recent studies have increasingly focused on direct measures of weather variability, as highlighted
The population in Pakistan in year , is an exogenous variable derived from UN population projections, while pollution concentration, , (expressed as <u>μE</u> ~~)~~ is derived endogenously as a PPVVKK,tt SS PPCCpp PPMM2.5 function of economic activity (see below). The theoretical minimum risk exposure level, , for PM 2.5 PPVVKK,tt CC ss<sup>3</sup> 5.9+2.4 is assumed to be uniformly distributed between 2.4 and 5.9 so that = 4.15 (GBD 2017 2 SSmmrrppll Risk Factor Collaborators 2018, Supplementary Appendix, page 73). We use <u>μE</u> p SSmmrrppll= as a parameter and calibrated to equal based on data from the year 2017. ddppS hss p rr ss3 rr p rrssCCmm The parameter value is calculated as follows. First Estimates of urban and rural deaths from air pollution . 80XX−06 for Pakistan were extracted from (World Bank 2020b) as = 56,205 and = 34,078 . These were combined with WHO (1987) estimates for average rrssrrbbEE pollution concentrations in urban ( = 56 ) and rural areas ( SSCCS ll AAPP ddppS hss = 52 ) . These were ssrrb CC SSCCS ll AAPP ddppS hss then combined with population data to derive deaths per unit of PM2.5 as ssrrb CC rrssrrbbEE PPMM2.5 PPMM2.5 <u>m3 m3</u> p ~~,~~<sup><u>yy</u></sup> uurruuSSmm ddppS hss p rr<sup><u>μ</u></sup> + rruurrSSll ddppS hss p rr<sup><u>μ</u></sup> The economic value of life is not calculated in the model. Instead we only report deaths related to ddppS hss p rr mm3<sup>μμyy</sup> rr p rrssCCmm= PPCCppPPVVKK,
land is expected to have displaced current cropland, 27 percent (21 percent) forests, and 12 percent (6 percent) grassland. Under SSP5, the land conversion proportions to urban expansion are expected to be 51 percent from cropland, 29 percent from forests, and 15 percent from grasslands. This projected conversion of cropland and grassland into urban land has important implications for crop and livestock production. Chen et al. (2020) show that urban expansion is projected to result in a 1.8– 2.4 percent reduction in global cropland by 2030, and 80 percent of this loss is likely to take place in Asia and Africa. As mentioned above, d’Amour et al. (2017) show that urban expansion is expected to take place on cropland that is 1.77 times more productive than the global average. Under SSP5, the significant increase in urban land demand and the resulting land conversion may lead to the greatest amount of crop production losses among the scenarios, due to the rapid urban expansion and the potential displacement of highly productive cropland.

# _SSPs and projected urban expansion data_

The urban expansion projection data (Chen et al. 2020) are calculated using panel data regression methods based on historical population, urbanization rate, and gross domestic product (GDP) data. Here, the urbanization rate is defined as the percentage of urban population to total population. The settlement extent data (i.e. all artificial cover and paved surfaces, not limited to urban) for the years 1975, 1990, 2000, and 2015 are from the Global Human Settlement Layer (GHSL), a dataset developed by the European Commission. The population and GDP data are from the World Bank and the United Nations. These data are fed into the built panel data regression model to project the built-up areas for each SSP scenario based on the predictions of future population, urbanization rate, and GDP from the SSP database. The projection outputs are then combined with other geographical information and inputted into the Future Land-Use
growth<br>control visit|Child participated in operation Timbang<br>Plus or other growth monitoring<br>government program|Yes|No|
|Health<br>interventions|NNS Form<br>4.2|**Age**<br>**appropriate**<br>**vaccinations**|**Child vaccinated according to WHO**<br>**schedule. BCG, Oral Polio, DPT (or**<br>**pentavalent), Measles (allowance for**<br>**delayed vaccinations)**|**Yes**|**Yes**|
|||Dewormed|Child received deworming medication<br>from government program|Yes|No|
|Nutrition<br>supplementation|NNS Form<br>4.2|Vitamin A<br>supplementation|Child participated once or twice in<br>Vitamin A supplementation government<br>program|Yes|No|

Nationally, skilled postnatal care for the mother, neonatal screening and up to date vaccinations, cover 78, 64 and 67 percent of the target population. With the exception of vaccinations, differences in coverage associated with family income and maternal education are wide. For example, 86 percent of children in the richest quintile underwent neonatal screening shortly after birth, compared to 45 percent of children in the poorest quintile. About 40 percent of children under-2 nationally show evidence of having access to all three sub-indicators of postnatal care. While differences in coverage of adequate postnatal care associated with income and mother’s education are wide, it is not significant between urban and rural areas.

# **_5.6 Household environment: Water, sanitation and hygiene infrastructure_**

Whether a child comes into contact with infectious diseases or other environmental pathogens is partly determined by the conditions of the dwelling and of the surrounding community. A set of 6 indicators from the NNS were identified as measures of the sanitary and hygienic
school for children aged 6-10 was 58 percent, enrollment at the middle school level for children aged 11-14 was 29 percentage points lower (Multiple Indicator Cluster Survey – MICS, 2003).

Education levels of females are low, both in absolute and compared to boys, especially in rural areas. At baseline, girls in program areas were less likely than boys to be enrolled in school. While enrollments in primary and middle school were very similar for boys and girls in nonstipend districts, large differences are observed in stipend districts – up to 8 percentage points (MICS, 2003). Such gaps in enrollment by gender are even wider when the samples are restricted to rural areas of Punjab. In terms of school attainment, numbers for 2001 (before the program started) show that women in Pakistan achieved on average 4.9 and 1.3 years of schooling in urban and rural areas, respectively – about 1.9 and 2.8 fewer years of education than men. These disparities by gender are also evident for Punjab, where the levels of enrollment and educational attainment are systematically lower for women in rural and urban areas (Table 1).

Finally, there are also marked differences in school enrollment and access between districts in Punjab covered and not covered by the GSP program. Enrollment levels in program districts were well below those of non-stipend districts. In 2003, for instance, enrollment in primary school for children aged 6 to 10 was 20 percentage points lower in stipend districts; similarly, only 20 percent of children 11-14 years old were in middle school compared to 34 percent in non-stipend districts (MICS, 2003). As shown in Table 2, access to school was also more difficult in stipend districts before the program began, where higher fractions of households live in villages without a school and, thus, their children had to travel longer distances to go to school (MICS, 2003; PSLM, 2004).

# **_The Female School Stipend Program_**

The FSSP was designed as part of a