rafmacalaba/data-use-mentions-extended huggingface dataset:
priority areas currently not protected. In other words, these are areas with extremely high priority actions for biodiversity conservation that exclude existing protected areas. In addition, priority areas tagged as very high and high that do not overlap with protected areas are also identified. The size of these areas and their location are presented in Figure 3, along with federal unit lines. The drawing of politicaladministrative divisions was possible by blending the spatial information obtained in previous steps with information from the Brazilian Institute of Geography and Statistics (IBGE 2019).

We estimate firms’ indebtedness and geographic location by combining granular information of credit outstanding by federal unit and economic sector, with detailed non-financial information of all formal Brazilian firms. On the one hand, our credit outstanding data (as of March 2021) provides a disaggregation of the 27 Brazilian federal units and more than 1,300 subclasses categorized according to the Brazilian National Classification of Economic Activities (CNAE 2.0). On the other hand, the latest census of the Brazilian formal labor market, _Relação Anual de Informações Sociais_ <u>(RAIS 2019), provides information on 3.9 million corporate establishments</u>

> 17 IUCN categories are: (Ia) Strict Nature Reserve, (Ib) Wilderness Area, (II) National Park, (III) Natural Monument or Feature, (IV) Habitat/Species Management Area, (V) Protected Landscape/ Seascape, and (VI) Protected area with sustainable use of natural resources.

> 18 For a discussion of the SCP, see Fonseca and Venticinque (2018), Sarkar and Illoldi-Rangel (2010), and Margules <u>and Pressey (2000).</u>

> 19 See http://areasprioritarias.mma.gov.br/2-atualizacao-das-areas-prioritarias.

10
endline survey exercise included two phases: surprise visits to schools in January 2015, and announced visits made to schools in the sampled villages between January 2015 and March 2015.

27 Following analysis of the school endline survey data, the endline household survey was conducted for a sub-sample of 160 randomly selected baseline villages. Power calculations and budget limitations motivated the decision to reduce the endline household sample. Initial visits were conducted at the household level in January 2017 to obtain tracking data on household members and children, with the full survey conducted from March to June 2017.

28 The World Bank worked with local survey firms: Research Consultants (RCons) and Weitek Group on various rounds of data collection. The baseline and endline surveys were conducted by Weitek and RCons, respectively, to mitigate any risks associated with biased reporting in interviews by the same firm for both rounds.

28
where _Yht_ is our outcome variables from the LSMS-ISA, described above, for household _h_ in year _t_ and _Xht_ is a matrix of input variables from the LSMS-ISA.<sup>9</sup> We control for year fixed-effects ( _γt_ ) and include household fixed-effects ( _αh_ ) in some specifications. The function _fj_ ( _Wjht_ ) represents our weather variables of interest where _j_ represents a particular measurement of weather. Finally, _uht_ is an idiosyncratic error term clustered at the household-level.

From this general set-up, we estimate six versions of the model: three linear and three quadratic. For each model, a single weather variable is considered. For the linear specification: For the quadratic specification: All of the regression models are estimated for each permutation of the data (see Table 5). This is a substantial number of regressions, given the number of variables defined (14 rainfall, eight temperature variables), the number of countries (six), the number of remote sensing products (six rainfall, three temperature), the number of extraction methods (ten), and the number of outcomes (two). This gives us a total of 77,760 different regressions: each of our six models on the 12,960 different data sets.<sup>10</sup> By varying both specifications and data, we seek to define a robust set of outcomes by combining the multiple analysis approach of Simonsohn et al. (2020) with the multiverse approach of Steegen et al. (2016).

> 9Where relevant, all continuous variables from the LSMS-ISA are log transformed using the inverse hyperbolic sine.

> 10We also test a series of 51,840 linear combinations of temperature and rainfall data for each of the possible combinations of rainfall and temperature weather products (six data products for rainfall with three data products for temperature). We only test combinations of rainfall and temperature for the same extraction method. For the linear combinations, we estimate each with the linear specification, but estimate two linear
entrepreneurship.<sup>3</sup> However, it is unclear whether these programs reduce migration, or instead promote it by giving youth marketable skills that they can use at destination.

We launched these interventions in April and May 2019. Randomizing the placement of the baseline survey questions, to before, or after, the provision of the informational video and offer of the alternative treatments, enables us to measure the immediate impacts of the treatments on knowledge, beliefs, and intentions. We find all three interventions improved knowledge about backway migration, with those treated being more likely to be aware of how expensive it is, and of the possibility of being forced to work without pay. However, we find no significant impacts on beliefs about the riskiness of the journey or expressed intentions to migrate, although this may reflect our baseline measurement of probabilistic intentions not working as anticipated. The Dakar treatment offered increased knowledge about migrating to Senegal, and reduced interest in learning more about wages in Europe in a behavioral task measure.

We then measured impacts on migration intentions, steps, and actions through an endline survey that took place between September and November 2020, approximately 18 months after the baseline and information intervention. We were able to successfully reinterview 89 percent of the youth, and combined with proxy reporting and WhatsApp location sharing, obtain the current location for all but one of our 3,641 youth. We have three main results. First, the interventions had lasting impacts on migration knowledge, and on intentions to migrate in the next five years. Eighteen months after treatment, those given the information treatment were 5 percentage points less likely to say they are considering taking the backway. The Dakar treatment increased knowledge about migrating to Senegal, reduced the intention to migrate to Europe by 3 to 4 percentage points, and increased intentions to migrate to Senegal by 3 to 7 percentage points. The offer of vocational training reduced intentions to migrate via the backway and to Europe by 5 to 10 percentage points.

> 3 In The Gambia
generalized trust. As part of the survey, all parents and teachers were also involved in three behavioral games. In this paper we focus only on parents‟ behavior in a dichotomous public goods game.<sup>18</sup> After the games, we collected information about the existing social ties between the experimental participants.

# **3.2 The public goods experiment**

In order to generate a direct measure of individuals‟ propensities to coordinate and cooperate with others to solve collective action problems, we conducted a public goods experiment. Laboratory

> 16 The sample was drawn from the list of all public primary schools in Albania. There are 2691 public basic level schools in Albania, of which 12 percent are located in urban areas. Out of the 2691 primary schools, we defined as “eligible” those schools with five or more pupils in grade three, and ten or more pupils in grade seven. Only 1623 of the 2691 primary schools in the complete list met this eligibility criterion. However, only 9 percent of all Albanian public primary school pupils attend ineligible schools. The list of eligible schools was then divided into 36 strata according to district and either six, four or two schools was randomly sampled from each, the number depending on the number of pupils attending primary school in the district. Since the district of Tirana contained twice the percentage of pupils as well as twice the number of independent eligible schools, it was divided into two strata, Tirana Municipality and Tirana District, leading to a total of 37 strata. In all but two counties, the majority of surveyed schools where located in villages or hamlets with less than 3000 inhabitants. The average number of pupils per school is 327, although this number falls to 200 if we exclude the urban schools.

> 17 The MoES assisted in the implementation of the survey by  demanding the full cooperation of school directors and teachers in the sampled schools.

> 18 For a full description of the Albania School Stakeholder survey and behavioral experiments, see Serra
In telecom, the accelerated growth requirements of the order of some US$4 billion are very significant and it is not clear whether they can be met within the current industry and market structure set up. It will, therefore, need to be considered in the context of the broader regulatory reform of the sector to tap the potential efficiency gains from greater competition and higher investments.

# **Conclusion**

The following main conclusions arise from this and related analyses.

First, new investments in infrastructure are required in South Africa to reverse the investment decline from the past several years and build the new capacity required to support accelerated growth, competitiveness, poverty reduction and more rapid economic and social integration of the society.

Second, our previous work on international benchmarking of infrastructure performance (Bogetić and Fedderke (2005 a) suggests solid service at reasonable quality compared to relevant benchmarks and, in some cases, very competitive prices. But there remain significant shortfalls relative to benchmarks in all infrastructure sectors, largely related to limited access and less than expected quality, especially in rural areas where most of the poor reside. It also shows that access to electricity in urban areas, for example, in South Africa (84%) remains below that of its main comparator group of countries––upper middle income group (90%). Access in rural areas is rather low (37%) although higher than the average in the same group of countries (30%), reflecting recent major efforts towards electrification.

Third, we estimated a long-run dynamic model of demand for electricity and telecom using panel data on 52 countries for the period 1980-2002; the model performs well in characterizing historical demand. We use the model to project demand forward in the 2003-2010 period for South Africa in order to derive preliminary estimates of the cost of required new investments in electricity and telephone lines under two growth scenarios for South Africa’s economy.

Fourth, the estimates of required investments to meet demand for electricity and telephones under the current growth scenario of
quintile they are in. We thus assume that coverage rates by quintile are the same for subnational areas as they are for national areas. For the analysis we need to assign whether each household receives social protection or not, so households are randomly assigned social protection based on the probability of coverage.<sup>21</sup> This random assignment is implemented in each country to ensure that the survey populationweighted values from the simulated sample match the reported statistics for access to social protection in each quintile. Subnational values for each country are then calculated for this measure. The random assignment process is repeated 100 times to account for the heterogeneity of households in each subgroup. The averages of subnational indicators from the 100 simulations are used as the final estimates for each country. Given information on the education, water and electricity dimension is also present in the same survey used for poverty estimates, this process also informs the overlap between social protection and these non-monetary dimensions at household level.

For some countries the same survey is used for poverty and social protection data and these surveys are used to assess the accuracy of this approach. Appendix A1 includes the details of this comparison, and indicates that overall, the approach works well with a correlation of 80 percent between survey-based and simulated measures. However, lack of access to social protection tends to be over-estimated in the simulated results, potentially over-estimating vulnerability on this dimension. The method used to impute social protection for households in the GMD can indeed be improved beyond what is done here, but as a first estimate, the approach delivers reasonable results.

# Access to financial services

Lastly, we also consider whether a household has access to financial services. We use indicators from the Global Financial Inclusion (Global Findex) database, drawn from nationally representative survey data of about 150,000 people in 148 economies for 2011 and 128,000 people in 123 economies for 2021 as part of the Gallup World Poll.<sup>22</sup> However not all questions are available for all
> of the propensity score is in principle zero, since _P_  _X_ is a continuous variable. Various methods have been proposed in the literature to overcome this problem, and three of the most widely used are nearest neighbor matching, radius matching, and kernel matching.

Based on the literatures on the selection and implementation of matching estimators, we decided to analyze the effect of credit guarantee with kernel matching which showed good precision when the unsupported-to-supported ratio is high (Frolich 2004) and can avoid the criticism expressed by Abadie and Imbens (2006), which pointed out the problem of nonsmoothness in calculating standard errors with bootstrapping for the case of nearest neighbor matching or radius matching.

# **4 Data and Variables**

# 4.1 The Data

The statistical source used for this analysis is the World Bank‘s Enterprise Surveys (ES). The ES collect data from key manufacturing and service sectors in every region of the world. The Surveys use standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across the world‘s economies.

To generate internationally comparable data, the questions in the Core questionnaire are asked in all countries and for all industries where the survey is implemented. In addition to this Core instrument, the Manufacturing Module and Services Module questions are asked to establishments in the manufacturing and services sectors, respectively.

The Core instrument is comprised of eleven sections: the first eight sections contain qualitative questions, asking for the manager‘s opinion on the business environment and for his motivation for business decisions<sup>16</sup> . The last three sections of the questionnaire deal with facts and figures specific to the transactions businesses make in order to operate<sup>17</sup> . The Manufacturing Module contains questions about capacity (use of production capacity and hours of operation).

The sampling methodology of the World Bank‘s Enterprise Survey generates sample sizes appropriate for achieving two main objectives: (1) a sample representative of
<u>Table 18. Innovation by City Size (% of all firms in each category)</u>

|**Country**|**City Size**|**Product**|**Process**|**Product or Process**|**Organizational**|
|---|---|---|---|---|---|
|Bangladesh|Less than 1 mm|17%|12%|24%|18%|
||Over 1 mm|41%|62%|80%|24%|
|Pakistan|Less than 1 mm|0.2%|2%|2%|6%|
||Over 1 mm|10%|10%|18%|22%|
|India|Less than 1 mm|51%|69%|86%|51%|
||Over 1 mm|57%|52%|81%|51%|
|Nepal|Less than 1 mm|11%|11%|20%|59%|
||Over 1 mm|14%|9%|23%|18%|
|ECA|Less than 1 mm|19%|12%|26%|23%|
||Over 1 mm|17%|12%|24%|18%|
|Africa|Less than 1 mm|23%|28%|41%|32%|
||Over 1 mm|32%|29%|51%|39%|

Source: Author’s elaboration from Enterprise Survey (2014) In line with the behavior observed in the ECA and Africa regions, all countries of the South Asia region except Bangladesh display higher product innovation rates in main business cities compared to other cities (Table 19). By contrast, all countries except Pakistan show higher process innovation rates in other
# **Measuring Farm Labor: Survey Experimental Evidence from Ghana**<sup>+</sup>

Isis Gaddis Gbemisola Oseni World Bank and IZA World Bank Amparo Palacios-Lopez Janneke Pieters World Bank Wageningen University and IZA

**JEL:** C8, J22, O12, Q12

**Keywords:** Recall bias, measurement error, farm labor, agricultural productivity, gender, Ghana

> + This study received funding from the U.K. Department for International Development (DFID) under the “Minding the (Data) Gap: Improving Measurements of Agricultural Productivity through Methodological Validation and Research” project and from the William and Flora Hewlett Foundation under the “Improving the Measurement of Subsistence Agriculture in the Framework of ICLS 19 and Its Implications for Gender Statistics” project. The authors gratefully acknowledge the comments from participants at the GLM|LIC Research Network Conference, the Conference of the Centre for the Study of African Economies, the International Conference on Globalization and Development, the Royal Economic Society Annual Conference, the World Statistics Congress and seminars at the ETH Zurich and the World Bank. 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 and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The authors may be contacted at <u>igaddis@worldbank.org or apalacioslopez@worldbank.org.</u>
are most prevalent in the establishment (e.g. FDI in real estate), post‐manufacturing (maritime, road, rail, and air transport) and back‐office (accounting, legal) stages. These include barriers to firm entry and, to a greater extent, firm operations (which affect the ability of firms to grow).

**These findings suggest that restrictions on competition in services, particularly from foreign firms, may lead Turkish manufacturing firms to provide more services in‐house than would be optimal from a productivity perspective.** In other words, while it may be more efficient to outsource certain services (e.g. logistics, transport, accounting), Turkish firms may instead perform them in‐house due to lack of high quality, reasonably priced external service providers. This could help explain the high shares of revenue associated with post‐manufacturing and establishment services activities within manufacturing firms (Figure 3.8), which could divert these companies from their core manufacturing activities and dampen productivity. Similarly, manufacturing industries use and export levels of transport and real estate services value‐added that well‐exceed OECD averages. The high revenue and exported value‐added shares of these services also suggest that regulatory restrictions may be inflating prices, creating market inefficiencies.

**Given the negative relationship between services restrictions and firm performance, removing restrictions could boost the productivity of Turkish manufacturing firms.** Increased competition could improve the quality and reduce prices of services inputs, boosting Turkey’s performance in GVCs and the economy more broadly. Yet, increased competition is not a given. As the recent literature has shown (e.g. World Bank, 2016 and Van der Marel et al., 2016), regulatory reform processes work best when institutions are strong and promote effective competition in services. Some types of services can lag due to market failures, requiring special attention from competition agencies and domestic regulators. OECD competition policy indicators show that Turkey performs well vis‐à
We use mirror data for FDI in the same way as for bank loans and deposits. CDIS and UNCTAD report two values for FDI for a given country pair: (i) the value of FDI assets country _i_ holds in country _j_ , and (ii) the value of FDI liabilities country _j_ owes to country _i_ . Hence, FDI positions appear twice in each database whenever source and destination are both reporting countries (as FDI assets of country _i_ in country _j_ and as liabilities of country _j_ with respect to country _i_ ) _._ To maximize data coverage, we use the mirror data for each database separately. Whenever the value of FDI assets (liabilities) of country _i_ vis-à-vis country _j_ is available but its mirror value obtained from liabilities (assets) data is not, we use the value of assets (liabilities). Whenever both assets and liabilities are available and the corresponding values do not coincide, we use the maximum of the two. This methodology yields aggregate FDI assets, for the CDIS and the UNCTAD data, that are closer to the values reported in Lane and Milesi-Ferretti (2018).<sup>27</sup> Combining the two databases is not straightforward because each source can report different FDI values for the same bilateral connection, starting from different baseline values. We deal with these differences by adjusting the UNCTAD data so that they match the CDIS data when both are available. Specifically, we calculate the ratio of the CDIS’s FDI value to the UNCTAD’s FDI value for each country pair for 2009–12 (for the earliest year with data on both data sets).<sup>28</sup> We then use this ratio to multiply the UNCTAD data backward for 2001–08, bringing it to the same level as the CDIS data. For 2009–12, when the CDIS and UNCTAD data are available, we use the CDIS data when the value for a given bilateral link for a given year is positive or when the CDIS value
from the country (Sachs and Woo, 2000). This tendency should be more pronounced in private capital rather than public capital, as public capital often flows into a country in the form of international rescue package when a country faces financial crises. In this sense, operationally, capital flight statistics can be used as an indicator. The core of calculating capital flight is net errors and omissions plus non-FDI net private flows in balance of payments (Loungani and Mauro, 2001). One issue is whether to include current transfer in the current account, since this item includes private remittance (Caves, Frankel and Jones, 1996). The decision whether to include the current transfer sometimes makes significant differences on the crisis identification. For example, the inclusion of the current transfer cannot identify the 2001 Argentina as financial crises,<sup>(9)</sup> while the exclusion identifies the periods as financial crises. As the 2001 Argentina crisis is often characterized as abrupt capital outflows, the current transfer is excluded from capital flight.

# **2.4 Data and Methodology**

The main data sources are the IMF’s International Financial Statistics (IFS), the World Bank’s Global Development Finance (GDF), World Development Indicators (WDI) and CEIC Asian economic database. Seven crisis types (liquidity type banking, solvency type banking, balance of payments, currency, debt, growth rate and financial crises) in 15 emerging economies between Q1 1980 and Q4 2002 are examined on a quarterly basis based on the operational definitions. The fifteen countries are selected based on regional diversification, frequencies in appearing in existing crisis studies and data availability. These countries are: five East Asian (Indonesia, Korea, Malaysia, the Philippines and Thailand), one European (Turkey), six Latin American (Argentina, Brazil, Colombia, Mexico, Uruguay and Venezuela), two South Asian (India and Sri Lanka), and one African (Kenya).

Data processing and crisis period identification follow