rafmacalaba/data-use-mentions-extended huggingface dataset:
. How Much Should We Trust Difference-inDifferences Estimates? _Quarterly Journal of Economics_ Feb. 2004: 249-275.

- Bertrand, Marianne, Antoinette Scholar and David Thesmar (2007). “Banking Deregulation and Industry Structure: Evidence from the French Banking Reforms of 1985.” _Journal of Finance_ 62(2): 597-628.

- Besley, Timothy and Robin Burgess (2004). “Can Labor Regulations Hinder Economic Performance? Evidence from India.” _Quarterly Journal of Economics_ 119(1): 91-134.

- Bollard, Albert, Peter J. Klenow and Gunjan Sharma (2010), India‟s Mysterious Manufacturing Miracle, mimeo.

- Bosworth, Barry, Susan  Collins, and Arvind Virmani, (2006).  “Sources of Growth in the Indian Economy.” Brookings Working Paper.

- Bosworth, Barry and Susan Collins, (2008).  “Accounting for Growth:  Comparing India and China.” _Journal of Economic Perspectives_ ,  22(1):  45-66.

- Burgess, Robin and Rohini Pande (2005). “Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment.” _American Economic Review_ 95(3): 780-795.

- Central Statistics Office, Ministry of Statistics and Programme Implementation, Government of India (2005). _National Accounts Statistics._

- Chari, Anusha and Nandini Gupta (2008) “Incumbents and Protectionism: Firm level Evidence from India,” _Journal of Financial Economics_ 88(3): 633-656.

- Department of Public Enterprises, Ministry of Heavy Industries and Public Enterprises, Government of India, _Public Enterprises Survey 2002-03_ Vol.1.

Department of Telecommunications (2008). Indiastat, 2008.

- Directorate General of Civil Aviation, Government of India (2005). “Air Transport Statistics 2004-2005.” <u>http://dgca.nic.in/reports/stat-ind.htm.</u>

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of uncertainty, particularly uncertainty deriving from more predatory practices such as unconstrained corruption and weak property rights (Gehlbach 2008; Gehlbach and Keefer 2012).

Because RuFIGE does not include questions about corruption, these firm-level data were supplemented with regional level data collected from the RR BEEPS. This survey was conducted between August 2011 and June 2012 as part of the fifth round of the Business Environment and Enterprise Performance Survey (BEEPS), a joint initiative of the World Bank Group (WB) and the European Bank for Reconstruction and Development (EBRD). The main objective of the survey was to gain an understanding of firms’ perception of the environment in which they operate and it was the first BEEPS survey to provide representative, albeit small, sub-national samples. The survey covered 4,223 firms in 37 Russian regions.

Regional-level independent variables, which we discuss in detail below, were derived from the RR BEEPS data for three main reasons. First, because BEEPS was conducted several years before RuFIGE, the probability that the investment patterns we observe are driving corruption outcomes in the regions somewhat diminishes. Second, by using regional level data generated by a group of firms outside of our primary sample (RuFIGE), the probability that any relationship observed is due to the peculiarities of the RuFIGE sample also somewhat diminishes, and our regional variables data is independent of our firm-level data. Finally, we also note that using BEEPS measure provides a fuller and more complete measure of regional level variables, as it is representative for a wider range of sectors than RuFIGE.

An addition, we also use a measure of violent corporate raiding in Russian regions introduced in Kazun (2015) to capture uncertainty over property rights enforcement. This indicator is calculated using a publicly accessible database of complaints to the NGO “Business Against Corruption” from entrepreneurs claiming to have faced unlawful criminal prosecution ( _c.f._ <u>http://www.nocorruption.biz/?cat=6</u> _)_ . The data from
<sup>14</sup> and their relationship with worldwide standards. The database provides an inventory of the “stock” of active standards<sup>15</sup> issued by the European Committee for Electrotechnical Standardization (CENELEC) and their link with standards issued by the International Electrotechnical Commission (IEC). Product standards are classified according to the International Classification of Standards (ICS) and the database covers the period 1990-2007.<sup>16</sup> See Portugal-Perez et al. (2010) for a full description of the EUESDB and its construction.

An important contribution of my analysis is the creation of a new set of industry level measures of EU product standards for the Electronic sector. There is currently no official concordance

> 13I do not present data for 2002 because the change in industry classification does not allow me to observe entry of new firms into sic4 industries for that year. This issue is not problematic for the empirical application since the model does not predict any role for product standard harmonization in domestic firm entry and exit.

> 14This sector refers to electrical, electronic and related technologies. More information can be found at www.iec.ch.

> 15The primary variable of interest is the total number of standards with which an exporter should comply during a particular year.

> 16A list of the ICS codes can be found at www.iso.org/iso/ics6-en.pdf

11
rounds of COV-ES and include individual firm fixed effects to eliminate selection bias stemming from time-invariant characteristics of firms. We also add a set of COV-ES variables to capture time-varying aspects of firms’ operations, reducing endogeneity concerns. To alleviate concerns about selection bias, we re-estimate panel regressions on a restricted sample. For El Salvador, this means that we drop firms that state the following reasons for not receiving government support: not aware, not eligible, no need, or applied but did not receive it. In the context of the pandemic, these firms likely faced fewer hardships than others, so we expect to reduce negative selection bias by excluding them from the control group. This same information is not available in the Georgia surveys, so to restrict the sample, we include only the firms that have not yet received support but are expecting in the next three months.

We think that some concerns about selection bias – which can be positive as well as negative, with positive selection bias likely reduced in the context of panel regressions – and endogeneity likely remain, so we refrain from commenting on the estimated size of the effectiveness of government support and only focus on whether it is estimated to be above zero.

We find nearly no statistically significant positive effect of government support on any of our four outcome variables in either country, with only one exception. Our panel regressions suggest that government support helped firms avoid reducing their total hours of operations per week in El Salvador (Table 11). COV-ES data includes questions asking firms to specify whether they received the following types of support: cash transfers, deferral of payments, access to new credit, fiscal relief, and wage subsidies (with the additional general “other” category also asked), with multiple types possible at the same time. Among these questions, we added to the respective panel regression the ones with at least 50 Yes responses. This exercise suggests that the link between government support and total hours of operations in El Salvador is through cash transfers (Table 13).
# 4. Data, classification, and descriptive statistics

## 4.1. Brazil Consumer Expenditure Survey

Our research relies on the comprehensive Consumer Expenditure Survey (Pesquisa de Orçamento Familiar, POF) released in 2020, encompassing consumption data collected during 2017-18. The POF is a comprehensive survey conducted by the Brazilian Institute of Geography and Statistics (IBGE) that collects data on the purchasing habits and dietary intake of households across the country. It classifies food items into different groups based on their primary purpose and intended use, such as staple foods, beverages, dairy products, meats, and processed foods. Notably, the POF dataset offers extensive disaggregated data on a wide array of products, including over 8,000 food items, facilitating our identification of food groups based on the NOVA classification. In addition, this survey collects information on various aspects of household living standards, including consumption patterns, non-food expenditures, income, and socioeconomic variables.

To extract consumption data, we employed two distinct questionnaires from the POF. The first one is the " _Caderneta coletiva_ " questionnaire, which captures shared household consumption and provides information on total expenditure and quantities consumed. The second one is the " _Despesa Individual_ " database, which focuses on individual consumption, provides data on total expenditure for a limited number of items and does not include information on quantity. Food consumption is only included in the former. However, as we are also interested in the total consumption, we merge the two databases to obtain total household consumption, adding the shared component with the individual consumption of each household member.

To present the profile of the individuals in the database, Table 1 presents demographic statistics by consumption decile. These variables are important for the analysis, as they are included as controls in the estimation of price elasticity, described in section 5.1. The demographic statistics of POF are aligned with other household surveys, such as the Continuous National Household Sample Survey ( _PNAD Contínua_ ), showing that the higher the consumption decile, the higher the incidence
20 Figure 7: “WTO +” and “WTO x” provisions in PTAs <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>WTO+ WTO‐X<br>Included Legally enforceable<br>No. of Agreements<br>FTA Industrial FTA Agriculture AD CVM TBT GATS TRIPs SPS Competition Policy IPR Investment Movement of Capital Industrial Cooperation<br><!-- End of picture text --> Source: WTO dataset on content of PTAs.

While the above summary statistics, which use coverage and/or frequency ratios, are helpful in understanding the incidence of NTMs, it should be noted that they need to be treated with caution. In addition to the data collection limitations discussed above, the analysis of NTMs faces a number of conceptual problems related to the nature of NTMs themselves.

First, _imprecision_ : as discussed in the introduction NTMs are often qualitative in nature which complicates measuring the severity of a regulation. In rare cases an individual NTM may provide an exact measure of stringency, say the maximum level of a certain chemical in a product. However, in general data on non-tariff measures are bound to be simply summary indicators, such as a dummy indicating the presence of a regulation, or an imprecise measure of trade restrictiveness
records and thereby access financing that male-owned businesses may already access. This study aims to empirically test the key hypothesis of whether the relationship between mobile money use and investment varies by the gender of the business owner.

# **3. Data and Summary Statistics**

The main data source used in this paper consists of cross-sectional firm-level surveys for 16 economies across Sub-Saharan Africa conducted by the World Bank’s Enterprise Surveys (ES). Table A2 presents the list of economies. Most of the surveys were conducted between 2015 and 2017 apart from four economies (Ghana, Tanzania, Uganda, and Zambia) for which data were collected in 2012. The ES collect information on a representative sample of formal (registered) private firms with at least five employees operating in manufacturing or services sectors. The ES data are fully comparable across countries and are collected via face-to-face interviews with business owners or top managers by using a global methodology.<sup>4</sup> The data have been widely used by several studies to explore the private sector in developing economies (Paunov, 2016; Besley and Mueller, 2018; Chauvet and Ehrhar, 2018; Hjort and Poulsen, 2019; Falciola et al., 2020).

The ES global methodology includes a consistent definition of the universe of inference, a standardized survey instrument, a uniform methodology of implementation, and a standard sampling methodology. The selection of firms in each country is done by stratified random sampling with three levels of stratification: sector of activity, firm size, and location within the country. Sampling weights are used to correct for unequal probability of selection as well as for ineligibility and non-response.<sup>5</sup> Supervisors and enumerators attend formal training sessions to ensure the best practices are deployed. Several quality control checks are implemented to guarantee the quality of the data throughout the data collection process. Consistency checks are employed for 10 percent and 50 percent batches of the data during the survey so as to allow quick callbacks to respondents to be undertaken
||s-era5-single-<br>levels?tab=overview|||https://earlywarning.us<br>gs.gov/fews/product/1<br>28|||
|---|---|---|---|---|---|---|
|Data set<br>reference/citati<br>on|Hersbach, H., B. Bell,<br>P. Berrisford, G.<br>Biavati, A. Horányi, J.<br>Muñoz Sabater, J.<br>Nicolas, C. Peubey,<br>R. Radu, I. Rozum, D.<br>Schepers, A.<br>Simmons, C. Soci, D.<br>Dee, and J.-N.<br>Thépaut. 2018.<br>“ERA5 Hourly Data<br>on Single Levels<br>from 1979 to<br>Present.” Copernicus<br>Climate Change<br>Service (C3S) Climate<br>Data Store (CDS).|Funk, C.C., P. J.<br>Peterson, M. F.<br>Landsfeld, D. H.<br>Pedreros, J. P. Verdin,<br>J. D. Rowland, B. E.<br>Romero, G. J. Husak, J.<br>C. Michaelsen, and A.<br>P. Verdin. 2014. “A<br>Quasi-Global<br>Precipitation Time<br>Series for Drought<br>monitoring.” US<br>Geological Survey Data<br>Series 832, ftp://chg-<br>ftpout.geog.ucsb.edu/<br>pub/org/chg/products/<br>CHIRPS-<br
designated as the ‘parent’ household and the others as ‘split-off’ households.<sup>6</sup> For example, consider a husband and wife cohabiting in round 1, but who are now living in separate households in round 2. The parent household will typically be the one occupying the same dwelling as in round 1 or the household in a dwelling nearest the location of the round 1 dwelling. The other household(s) would be designated as a split-off household(s). The split-off households are then, ideally, tracked and interviewed in their new location. Often the designation between the two is somewhat arbitrary and one of convenience for field work.

Again, due to cost and logistics considerations, it may not be possible to track all split-off households. In this case, eligible movers are stratified and a sub-sample is selected for tracking. It is necessary to integrate this selection into the weight calculations. The rules on which movers to track need to be established (and well-enforced) based on the expected analytical primary uses of the data.

For example, in the Tanzania National Panel Survey (TZNPS), all regular adult household members were deemed as eligible tracking targets. This excluded children under the age of 15, servants, and guests. In the Ugandan National Panel Survey (UNPS), two households within each EA were randomly assigned to have all split-offs tracked, regardless of the age of the member who resided in a new household. Since this selection was done _ex ante_ at the statistics bureau headquarters, it was possible that some households selected for tracking did not have any members who formed a split-off, or that no one from the household was located in round 2.

In the TZNPS, there are no additional calculations necessary at this step because the tracking rules were defined at the individual level and therefore all households were eligible for tracking. In the UNPS, the sub-sampling of households complicates the weighting calculations. Ideally, the parent household would receive a tracking weight of 1,
MONGOLIA'S INFORMAL SECTOR - Abstract

# **Abstract**

The explosion of informal entrepreneurial activity during Mongolia's transition to a market economy represents one of the most visible signs of change in this expansive but sparsely populated Asian country. In an attempt to deepen our understanding of the informal sector during transition, this paper takes an eclectic approach, merging the anecdotal experience of numerous qualitative interviews with the hard data provided by a survey of 770 informals in Ulaanbaatar, by a national household survey, and by official employment statistics. The varied sources of data and information are used to generate rudimentary estimates of the magnitude and trends of informal activity in Mongolia, estimates that are surprisingly consistent with each other. The reasons for such rapid growth of informal activity attending the transition are then explored and evaluated. In a broader sense, this paper paints a portrait of the character of the informal sector in Mongolia: what it means to be informal, how informals perceive their employment status, the levels of incomes, the regulatory environment, the attitudes of officials toward the informal sector, the degree of coverage under social and health insurance, and the contribution of micro enterprise to the battle against poverty.
emissions and other macroeconomic outcomes including output. Data from the next round of PIAAC would help provide comparable measure of how cognitive skills have changed since 2012.

The policy implication of our work is that carbon pricing that is accompanied by improvements in education quality will result in better environmental and economic outcomes when carbon pricing is used to reduce emissions. The resulting motivation for investing in education quality is not to change values or behaviors of consumers but rather to enable technological change that can reduce emissions, and this is a different motivation than the reasoning behind a lot of policy literature on how education can help benefit climate change. Indeed, the EU’s Green Deal includes investment in human capital as a complement to emissions targets; our model suggests that this human capital investment should be utilized to improve cognitive skills and help enable technological change to mitigate the costs and enhance the benefits of increased carbon pricing.

32
are modeled against a set of country characteristics such as per capita income, economic structure, and model-specific variables such as the ”work for an employer” index from the Gallup World Poll. Second, the models estimate the evolution of these employment category shares based on the economic cycle and population demographics. The application of these shares on the estimated number of people employed gives the estimated number of individuals engaged in each employment category, which is the main input we use. Among other limitations of these data to estimate the number of businesses by country is that it does not provide information on firm characteristics, such as size distribution, which is available in firm-level establishment censuses and representative surveys.

# **2.2 Establishment censuses, economic censuses, and surveys**

Economic censuses are statistical surveys conducted on the full set of economic units belonging to a given population. In the US, the Economic Census serves as the statistical benchmark for current economic activity, feeding into the calculation of GDP and the Producer Price Index (PPI). The Census provides information on employment, payroll, revenue, sales, and industry classification. Economic censuses are also commonly used to update business registries.

> 4See table A1 in the Appendix for the underlying data sources used by ILO.

5
01|0.00|0.01|0.02|0.04|0.20|1.23|1.54|
|Total|15.52|2.62|2.17|7.30|3.07|13.04|12.69|3.80|37.13|2.66|100.00|

_source: own calculations, using GTAP8 data._ For Korea, commercial services accounted for only 3.7 percent of Korean exports on a gross value basis, and 8 percent on a direct value added basis, but for 22.4 percent of all value added contained in exports when embodied exports are included. China proves a notable example as well. While China has opened up and become a dominant exporter of manufactured goods (especially machinery), the contribution of services to total export value added has actually decreased. Indeed, there is an interesting dynamic to China’s service intensity. As China experienced a dramatic increase in exports of manufactured goods, the initial stages used for production saw a drop in total domestic value added in exports, with a rising share of manufactured goods. However, as China has progressed to higher stages of processing, with machinery replacing textiles and clothing as a major export category, there has been a recovery in the total service intensity of China’s exports, from a low of 8.2 percent in 1997 to 13.3 percent in 2007.<sup>9</sup> In this section, we have highlighted the importance of services as inputs to manufacturing, and how this relates to the activity content of exports. In terms 9China has seen a dramatic drop in share terms for services, but this is not something linked to the level of services exports per se, but rather to the very dramatic increase in manufacturing exports since 1992. In 1992, almost half of exports were textiles and clothing, while services were a large share of a small base
Jul-Larsen, E. and P. Mvula. 2007. "Security for Many or Surplus for the Few? Customary Tenure and Social Differentiation in Southern Malawi." _CMI Working paper 2007:9_ , Christoph Michelsen Institute, Bergen.

Kaarhus, R. and S. Dondeyne. 2015. "Formalising Land Rights Based on Customary Tenure: Community Delimitation and Women's Access to Land in Central Mozambique." _The Journal of Modern African Studies_ , **_53_** (02), 193-216. Kydd, J. and R. Christiansen. 1982. "Structural Change in Malawi since Independence: Consequences of a Development Strategy Based on Large-Scale Agriculture." _World Development_ , **_10_** (5), 355-375. Lawry, S., et al. 2016. "The Impact of Land Property Rights Interventions on Investment and Agricultural Productivity in Developing Countries: A Systematic Review." _Journal of Development Effectiveness_ , 1-21. Lea, N. and L. Hanmer. 2009. "Constraints to Growth in Malawi." The World Bank, Policy Research Working Paper Series: 5097. Lewin, P. A., M. Fisher and B. Weber. 2012. "Do Rainfall Conditions Push or Pull Rural Migrants: Evidence from Malawi." _Agricultural Economics_ , **_43_** (2), 191-204. Linkow, B. 2016. "Causes and Consequences of Perceived Land Tenure Insecurity: Survey Evidence from Burkina Faso." _Land Economics_ , **_92_** (2), 308-327. Lovo, S. 2016. "Tenure Insecurity and Investment in Soil Conservation. Evidence from Malawi." _World Development_ , **_78_** , 219229. Lunduka, R. 2009. "Land Rental Markets, Investment and Productivity under Customary Land Tenure Systems in Malawi." _PhD Thesis_ , Norwegian University of Life Sciences, Aas. Lunduka, R., S. T. Holden and R. Oygard. 2009. "Land Rental Market Participation and Tenure Security in
activity were chosen.<sup>2</sup> The questionnaire was applied only to women who are business owners or partners, i.e. who take managerial, financial and marketing decisions, and the sample included firm with no more than 5 employees or sales that are less than 4 million Mexican pesos per year (which is the definition of a

> 2These areas with higher concentration of commercial activity were identified relying on the 2009 Economic Census data.

3