rafmacalaba/data-use-mentions-extended huggingface dataset:
, and Business sophistication). The percentage contributions are calculated as Owen values for each group of enabling factor. While the goodness of fit (R2) decomposition does not have standard errors, bootstrapping is applied to help attach greater reliability to comparisons of importance. The analysis used all countries available in GII and WDI dataset Which enabling factors are more important to explain innovation output performance across all countries? To answer this question, a cross-country model is estimated where the innovation output index is regressed against GDP per capita and all innovation input sub components (as displayed in Figure 4) grouped under each enabling factor/pillar: _Institutions_ ; _Human capital and research; Infrastructure; Market sophistication; and Business sophistication_ .<sup>6</sup> Then, in order to identify which group of covariates presents higher importance to explain the variance of the innovation output index across countries, the estimated model’s goodness of fit (R2)<sup>7</sup> is decomposed according to the Owen values of each (exogenous) group of explanatory variables. Figure 7 displays the 90 percent bootstrap confidence intervals for the absolute Owen values for each set of variables, grouped under each enabling factor.<sup>8</sup> Not surprisingly, the “usual suspects” _Human capital and research_ and _Business sophistication_ stand out as key factors explaining the variation of innovation output performance across countries; results suggest that the supply of

> **6** Precisely, the following variables, besides GDP per capita,  are included in the regression analysis: political environment, regulatory environment and business environment (under Institutions); education, tertiary education and research and development (under Human Capital); ICTs, general infrastructure and ecological sustainability (under Infrastructure); credit, investment and trade, competition and market scale (under Market sophistication); and knowledge workers, innovation linkages and knowledge absorption (under Business sophistication).

> 7 Which turned out to be around 85 percent.

> 8 For a brief discussion about
we will estimate the regressions with the OECD definition of HGFs whereby a firm is high‐growth if it employs at least 5 employees at the beginning of the period and has an annualized growth rate of employment of at least 20 percent for a period of 3 years (OECD, 2010).

By definition, the share of Birch‐firms is 5 percent for the full sample in each year. There are some differences across industries, with the most HGFs in high‐tech manufacturing industries.<sup>8</sup> The OECD definition shows similar patterns across industries, but by construction it also varies across years, following the macro cycle (see Table 1).

Importantly, the data set allows us to exploit the detailed geographical information of each firm. The data on the municipality of the firms’ headquarters are used to construct our spillover measures. Municipalities are the smallest geographical unit of measurement in Hungary: they can be aggregated up to 174 microregions, where each microregion tends to include one city and the neighboring smaller

> 7 In spite of the conceptual differences between TFPR and TFPQ, Foster et al. (2017) use US data to show that these measures are highly correlated, have similar relationships with growth and survival, and show similar dispersion. These results cast doubt on the interpretation of TFPR as a proxy for distortions as in the Hsieh and Klenow (2009) framework.

> 8 We rely on Eurostat’s indicators on high‐tech industry and knowledge‐intensive services. Manufacturing industries are grouped in four categories: high‐tech, medium‐high‐tech, medium‐low‐tech and low tech. Services are organized in knowledge‐intensive services and other services.

5
Source: author calculations using Financial Times fDI Markets Database The manufacturing sector has traditionally attracted the largest share of greenfield investments in total, but the average size of a project is the largest in primary industries—including oil and gas extraction and mining—at $750 million per project (Table 1). The average size of a project in the manufacturing sector was $80 million, while for services it was $32 million. Hence to obtain a full picture of investment activity, it is important to also track the extensive margin.

**Table 1: Average greenfield deal size ($ millions)**

|Period|Manufacturing|Services|Primarysector|
|---|---|---|---|
|2019|79.8|31.8|748.8|
|2018|127.7|38|480.8|
|2017|77.7|28.1|287.7|

Notes: Financial Times fDi Markets Database.

Greenfield investments do not necessarily follow the same trend as FDI inflows measured by BoP. Our main data source for foreign investment, _fDi Markets_ data, traces firm level investment activity (which may not be recorded in BoP), and hence identifies the ultimate investment ownership. Thus, this firm level investment activity is not mis-measured by the phenomena of transshipping or roundtripping, where FDI from a given country may be routed to the destination through intermediate countries. In 2018 for example, the nominal value of greenfield investments and M&As (data which similarly traces firm level investment activity) spiked more sharply than the value of FDI recorded by BoP, suggesting that many countries in the region may have acted as a conduits for FDI ultimately directed outside Asia. Therefore, in addition to the fact that greenfield investment data is disaggregated at the sector level, it is also a more accurate measure of origin and destination of FDI flows. With this in mind, we turn to how this financing may impact productivity.
, 2020; Kalkuhl and Wenz, 2020).

We then turn our focus to the subnational analysis to interpret the estimation results. Specifically, Column (1) of Panel B illustrates that a 1<sup>◦</sup> C increase in temperature corresponds to a 7.2 percentage point increase in poverty, translating to a 61.5% increase in poverty given

> 14 We also check the robustness of our findings using alternative measures of poverty in the Appendix.

> 15 Alternatively, we also use country-level data from the World Development Indicators (WDI) database. Our findings remain consistent (see Table A4 in the Appendix).

> 16 For the long differences model, we measure poverty and weather variables as the differences between 2010 and 2019.

27
|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|*|0.861***|0.842***|-0.652|11.17*|24.40*|-32.92|
|ReformDistrust|(0.268)|(0.274)|(0.977)|(6.758)|(13.74)|(31.48)|
|Observations|108|108|108|96|84|96|
|R-squared|0.792|0.860|0.502|0.750|0.791|0.132|
|Number of regions|12|12|12|12|12|12|

Notes: All regressions include year and region fixed effects. The outcome variable in column 1 assigns new firms to the year they report as their registration year. The outcome variable in column 2 assigns new firms to the year when they first appear in the database. Employment, survival and growth in columns 3 and 6 are based on year of entry, not year of registration (since they are only observed once a firm enters the dataset). The growth rate in column 6 is calculated as emp� �emp��� growth� � �emp� �emp����/2 at the firm level. After that, we take the simple average of growth rates in a region-year cell. Bootstrapped standard errors clustered at the region level in parentheses; *** p<0.01, ** p<0.05, * p<0.1 <u>Sources: Data from Serbia’s Financial Accounts Registry and 2006 Life in Transition Survey</u>

> 6 As a robustness check, we also calculate an employment growth rate that is weighted
>Number of observations|34,894|35,641|36,722|36,722|
|Number of countries<br>_Note:_Standard errors (in par|95<br>entheses) are ro|96<br>bust to heteroske|96<br>dasticity and clust|96<br>ered on countries.|

_<mark>Note:</mark>_ <mark>Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries.</mark> Model: _EG_ = _b_ 0 + _b_ 1 _FC_ + _b_ 2Small* _FC_ + _b_ 3Medium* _FC_ + _b_ 4Large* _FC_ + _b_ 5Mature* _FC_ + _b_ 6Older* _FC_ + _b_ 7Multi + _b_ 8Manuf + _b_ 9Exporter + _b_ 10Foreign + _b_ 11Govt + Country Fixed Effects + _e_ . * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level.

_<mark>Source:</mark>_ <mark>Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).</mark> 29
# **2. Country context – macroeconomic indicators and recent poverty surveys in Tanzania**

Tanzania has experienced substantial improvements in macroeconomic indicators in recent years. At the national threshold, poverty headcount has decreased from 34.4% in 2007, to about 26.4% in 2018, and it stood at 44.9% in 2018 using the 2017 international poverty line of US$2.15. Poverty in Tanzania, like other countries in the region, is predominantly rural. Similar to other SSA countries, food constituted about 59.9% of the consumption basket in 2017 in Tanzania, about 4 percentage points increase from 2011, with rural and urban shares being <mark>63.2% and 52.6%, respectively</mark> (Ministry of Finance, NBS, and World Bank 2020). Food security is a major developmental challenge in Tanzania. The FAO (2021) estimates that about 34.4 million Tanzanians were moderately or severely food insecure in 2021, an increase from 25.1 million in 2014. As an agrarian economy, the agriculture sector continues to be critical to Tanzania, especially the rural poor. The sector contributed about 30% to GDP in 2021 (NBS 2022a), employed nearly 62% of the population in 2021 (NBS 2022b), and generated about 30% of foreign exchange earnings (Government of Tanzania 2017).

The apex government agencies, NBS in Mainland and OCGS in Zanzibar, are mandated with the production of official statistics, including poverty and inequality in the country. The NBS and OCGS have, in the past, independently published official poverty statistics for their jurisdictions using data from the HBS where food consumption is captured using diaries. Besides the HBS, the NBS and OCGS also implement the NPS as part of the Living Standards Measurement Study Integrated Survey on Agriculture (LSMS-ISA) project, which uses 7-day recall for food consumption data collection.

Both the NPS and HBS are multi-topic household surveys. Though the HBS and NPS are
Verluise 2022).

The patterns of Chinese collaboration have shifted recently. During the 2000s, Chinese innovation was heavily reliant on collaborations with U.S. inventors, which contributed to around 6% of Chinese patents granted in CNIPA and 33% of Chinese patents granted in EPO, USPTO, and under PCT (Figure 3, left panel) from 2002 to 2012. However, since 2012, there has been a noticeable change in the dynamics of U.S. and other foreign collaborations. The relative importance of U.S. collaborators has declined, with Chinese applicants increasingly turning to inventors from advanced East Asia and Europe (Figure 3, right panel). The importance of inventors from these regions has grown, especially after 2016, implying a diversification in China’s international collaboration network possibly in response to the intensified tension with the U.S.

# **3 Data**

## **3.1 Data Source and Sample**

We analyze the impact of being included in the Entity List on a firm’s innovation output. The primary data source used in our analysis is the PATSTAT Global 2022 Spring Version. PATSTAT categorizes patent applicants as government entities, companies, individuals, or unknowns. For applicants labeled as unknown, we employ firm-specific identifiers (such as “company”, “group”, “ltd”) in their name to determine their status as firms. For our analysis, we only retain entities identified as firms.

There are three major patent categories granted globally: invention, utility models, and industry design. We exclude industrial design patents from our analysis due to their limited scientific value. Given the varied grant requirements and the absence of utility models in certain patent offices (e.g., USPTO and Canada), our analysis focuses solely on invention patents. Furthermore, China implemented a significant reform in its intellectual property and patent system in 2006. To mitigate the potential impact of these policy changes on our 6
ticipate in a comprehensive household survey. In the vast majority of cases (84% of all surveyed households), the respondent was the actual borrower identified by the bank record, as well as the user of the loan and the household’s main financial decision-maker. When somebody else in the household was the financial decision-maker and the loan’s true user, we interviewed that individual instead. Household members other than the borrower identified on the beneficiary list were only interviewed, once the actual borrower had been located and this borrower confirmed that another household member was both the financial decision-maker and the actual user of the loan in question. This occurred in a small number of cases, where the loan was taken out in the father’s or wife’s name, who legally owned the land, but the son or husband was the true financial decision-maker and user of the loan.

There were two main sources of attrition. First, although considerable effort was made to locate all borrowers identified on beneficiary lists, this proved quite difficult for loans that were disbursed several years prior to the program. Second, because only imperfectly recorded and transliterated names were available from banks, many villages had multiple individuals with the same name, which sometimes created obstacles to the identification of individuals if no additional bank data on the beneficiary account was available. No interview was conducted if a borrower could not be identified with certainty based on bank records. To verify that attrition was balanced across treatment and control groups, Table D.2 (a) compares located households with households that could not be found and shows that the probability of a household being located was indeed independent of treatment status. As an additional test, Table D.2 (b) compares basic characteristics of located and non-located households (as available from the bank data). Tested jointly, balanced attrition cannot be rejected at conventional levels of significance ( _p_ = 0 _._ 24). Similarly, attrition is not systematically related to landholding or relief amount.

The average
43

## Is Ist gov. party special interest? GOVSPEC

Are any coalition parties special interest? COALSPEC

## Electoral Rules

Legislative Index of Political Competitiveness LIEC Executive Index of Political Competitiveness EIEC mean district magnitude (House) MDMH mean district magnitude (Senate) MDMS Number of senate/ (number of house + number of senate) $/S+H Plurality? (1 if yes, O if no) PLURALTY Proportional Representation? (1 if yes, 0 if no) PR If Plurality and Proportional Representation which governs the majority/all of the House seats? (1 if Plurality, O if Proportional) HOUSESYS If Plurality and Proportional Representation which governs the majority/all of the Senate seats? (1 if Plurality, O if Proportional) SENSYS What is the vote threshold for representation? THRESH Is the D'Hondt system used? (1 if yes, 0 if no) DHONDT Are closed lists used? (1 if yes, O if no) CL Candidate selection: national(1), sub-national(2), primary (3) SELECT Were vote fraud or candidate intimidation serious enough to affect the outcome of elections?’ FRAUD

# Stability and Checks & Balances

Longest tenure of a veto player TENLONG Shortest tenure of a veto player TENSHORT Tenure of system of government if democratic; tenure of chief executive otherwise TENSYS Index of Political Cohesion (based on Roubini, Sachs 1989) IPCOH Number of veto players CHECKS1 Checks? Check1, + 1 for each veto player whose orientation is closer to opp than gov CHECKS2 Maximum difference of orientation among Govt. parties (0-2) POLARIZ % veto players dropping from Govt., assume Senate changes STABS % veto players dropping from Govt., assume Senate doesn't change STABNS

## Federalism

Are there autonomous regions? AUTON Are the municipal governments locally elected? MUNI Are the state/ province governments locally elected? STATE Do Sub-national govts. have extensive taxing, spending or regulatory authority? AUTHOR .

Are the constituencies of the senators the states/provinces
|1998-2002|47%|
|---|---|
|2003-2007|57%|
|2008-2012|38%|

Table 3 presents the population coverage for each five-year time period. We find that for the initial surveys from 1993 to 1997, the population coverage of low- and middle-income countries in our analysis was 18%. However, we see population coverage increase over the years to 47%, 57%, and 38% for the periods 1998-2002, 2003-2007, and 2008-2012, respectively. This shows that population coverage improved, likely because the number of surveys across countries increased over the years. While we may not have sufficient data for precise aggregation at this point, as more surveys are conducted in the future, we will have greater population coverage and greater precision in future analysis.

<u>Table 4: Percentage of regional population covered in the malnutrition aggregations</u>

|Region:|1995|2000|2005|2010|
|---|---|---|---|---|
|East Asia and Pacific|0%|1%|5%|7%|
|Europe and Central Asia|40%|31%|57%|16%|
|Latin America & Caribbean|54%|22%|19%|20%|
|MENA|26%|26%|57%|43%|
|South Asia|12%|85%|85%|26%|
|Africa|32%|65%|86%|86%|

Similarly, in Table 4 we present the population coverage for the regional aggregations in Figure 3. As we can see, while South Asia and Africa are well-represented in several of the timewindows, the coverage for the other regions are generally well below 50%. This demonstrates that the regional coverage estimates need to be interpreted with caution. However, it is important to note that the purpose of this exercise is
# **6. The role of socio-economic characteristics in driving (the reduction in) forest fire occurrences**

Having established that the FIP program was effective in reducing end-of-the-agricultural cycle forest fires, the next question is whether the program was equally effective in all forest areas, or whether there are marked differences in the response to the program by the communities surrounding the forests. We use responses from the 2014 Living Standards Measurement Study (LSMS), elicited between January and March of that year, to characterize villages surrounding the forests. Next, we assign each forest grid cell to the “sphere of influence” of the geographically most proximate village (see Appendix D for details about the matching process) and define the set of grid cells in the same sphere as a “subforest”. By doing so, we assume that economic activities in a specific forest location are undertaken by inhabitants of the nearest village. We then calculate fire occurrences at the subforest level and relate the observed fire occurrences to the characteristics of the associated village.

Table 4 presents the socio-economic characteristics of the villages surrounding all 77 protected forests in Burkina Faso, as well as those of the subsamples of project and non-project forests. As shown in Column (1), the average village consists of about 175 households, nearly all households engage in agriculture, about half of them use inorganic fertilizers and the same holds for the use of organic fertilizers. The level of education is fairly low, household assets amount to FCFA 270,000 (or about US$ 400), the average distance from the village to the nearest protected forest is about 1.5 kilometers, and the average distance from a village to the main road is about 3 kilometers. More importantly, comparing Columns (2) and (3) of Table 4 we see that differences between the FIP and non-FIP villages tend to be small.

We implement two types of village-level analyses. First, we explore whether the characteristics listed in Table 4 are correlated with the subforest-level November forest fire
The datasets are based on stratified random samples of firms, which as much as possible are drawn from the universe of eligible firms. Stratification is by size, location, and sector. Extensive meta-data for each survey documents in detail the sampling frame and aspects of the survey implementation. In some countries, only firms in the manufacturing sector are sampled, but in most cases service sector firms are included as well. An important advantage of this source of information is that all the data (including meta-data) are easily accessible online. Surveys are conducted in several years for most countries which makes it possible to trace country-level patterns over time. The surveys include a large number of variables, making it possible to research a broad range of topics. One disadvantage is that, in contrast with the original RPED sampling frame that had an annual frequency, it is not possible to follow firms over time. As a result, it is not possible to estimate firm-level productivity controlling for the fundamental simultaneity problem of productivity and input choice (Van Biesebroeck, 2008).

In Table A.1 in the Appendix, we show the number of firms sampled in each survey. The average over all 94 surveys in sub-Saharan Africa is 382 observations. It ranges from a low of 75 in Lesotho in 2003 to a high of 2,676 in Nigeria in 2014. The earliest survey is in 2002 and every year new surveys are added to the database. In the average year, 5.5 surveys are organized, but they are not spread equally over time. In 2008 and 2012 not a single country was surveyed. When we started our work to harmonize the data, only a single survey for 2018 could be included.<sup>7</sup> While these surveys are not specifically aimed to be nationally representative, due to the three dimensions of stratification, these samples are likely to cover a substantial fraction of total exports. Freund and Pierola (2015) have documented the remarkable concentration of trade in very few enterprises for a
continue to have lower wages than in other sectors, real wages remained stable over this period and actually increased in the last year of the study period.

<!-- Start of picture text --> Figure 31. Cumulative change in log real wage, Figure 32. Real wage by overall sector,<br>2014-2022 (base year: 2010) 2014-2022 (thousand 2010 ZMK)<br>0.2 160.0<br>0.1 140.0<br>0 120.0<br>-0.1 100.0<br>-0.2 80.0<br>-0.3 60.0<br>-0.4 40.0<br>-0.5 20.0<br>-0.6 2014 2015 2016 2017 2018 2019 2020 2021 2022 -<br>2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Within-firm Between-firm Structural transformation<br>Entry* Exit* Total Agriculture, forestry, and fishing Mining and quarrying<br>Rest of industry Non-industrial services<br>Note: Wages are deflated by Consumer Price Index (CPI).<br>Source: World Bank and ZRA staff calculations based on PAYE database<br>Log-changes<br><!-- End of picture text -->

**Net formal job creation has generally been in lower paying subsectors.** At the ISIC 1-digit level, the largest net formal job creators have been agriculture, forestry and fishing (about 28,000 net new jobs between 2014 and 2022), retail trade (28,000), manufacturing (21,000), and other service activities (23,000),<sup>12</sup> all relatively low-wage sectors. By contrast, there has been limited new formal job creation in financial services (5,000 new jobs), the highest-wage subsector, and there has been net formal job destruction in mining (