rafmacalaba/data-use-mentions-extended huggingface dataset:
Roberts, 2016) notes that this evolution reflects a more general process of _in situ_ urbanization across much of the region.

Two characteristics of this feature bear emphasis. First, this morphing of places is not a phenomenon that is happening on the periphery of existing large urban areas. Only about a third of the new CTs are close to existing class I towns (urban areas of 100,000 or more), the rest being either isolated or part of a group of smaller rural settlements. Second, understanding this phenomenon is important not only for understanding urbanization, but also for rural poverty alleviation. A critical element of economic growth is the rise in non-farm employment and incomes. Himanshu et. al. (2011) find that rural non-farm diversification is more rapid when neighboring urban centers grow and this association is stronger if the urban center is a small town as compared to a large city.

In order to do this, it is not sufficient to study the small statutory towns. While the share of small towns (i.e., towns with a population of less than 100,000) in total urban population has actually increased marginally over 2001-11, the share of CTs has almost doubled from 7.4% to over 14%, and within small towns, it rose from 19% to 36%.

The aim of this paper is to contribute to a better understanding of the dynamics at play behind the sharp increase in the number of CTs observed in the 2011 census, and its potential implications in terms of policies aimed at supporting economic development and poverty alleviation in these small urban centers and their surrounding areas. The paper also tries to understand whether these settlements are connected to the broader urban economic expansion of the region or whether they do develop as independent nodes of localized urbanization process. The process of governance of these centers and their economic and spatial impacts are also addressed in this paper.

The paper is organized into six sections. Following this introduction, section II provides a reading of the existing literature
categorical variable that takes the</mark></sup> their size and engage in economies of scale which, in turn, allows them to enjoy lower costs of production and might hire more women. Appendix 2 summarizes the definition of different variables. Given that we pool data for different countries and years, we include year, country, and sector fixed effects ( and ) to control for unobservables. i i is the disturbance term. Our estimations are run using a pooled Ordinary Least Squares (OLS) estimation method (when the ii ii ii ii dependent variable is a continuous variable, namely share of females’ workers) and a Linear ζζ , γγ θθ δδ Probability Model (when the dependent variable is binary, which is the case of a female manager or a female owner).

We extend the analysis in three ways. First, we investigate whether the impact of firms’ integration into GVCs on female participation is conditional on some external factors such as gender provisions in RTAs. The literature on the efficiency of gender provisions in regional trade agreements (RTAs) is relatively scarce. Hence, there is little empirical evidence that supports the idea that gender-related provisions or labor provisions with clauses related to gender equality significantly promote gender equality and women’s empowerment in the workplace. In that vein, López Mourelo and Samaan (2018) run a difference-in-differences model using the Cambodian Socioeconomic Survey (CSES) conducted by the National Institute of Statistics of the Ministry of Planning over the period 1993-2012 in order to assess the average effect of the 1999 CambodiaUS Bilateral Textile Agreement (CUSBTA) on the gender wage gap. The CUSBTA encloses labor provisions, which mainly aim to improve working conditions through two main pillars. First, the elimination of discrimination between employees and especially those based on gender basis. Second, the reduction of gender-wage gap. The results of the study show that labor provisions decrease gender wage gap and gender discrimination in workplace only in the textile sector. However, the gap is still increasing in the other manufacturing
Second, we find empirical evidence that is broadly consistent with the hypothesis that crypto-assets are regarded as a risk asset. Global financial conditions were extraordinarily loose during our sample period. Real U.S. 10-year Treasury yields turned negative driven by the pandemic’s impact and the policy response to support financial markets and stimulate the economy (e.g., quantitative easing). In this context, we find that countries’ crypto volumes increase when real U.S. 10-year Treasury yields fall as it also tends to loosen global financial conditions and increase risk appetite. We further find that crypto volumes fall when our proxies of global risk aversion rise (i.e., the VIX index and the U.S. BBB corporate bond spread). Related and consistent with earlier literature (e.g., Liu and Tsyvinski 2018), we document that volumes typically respond positively to crypto-assets price momentum, suggesting speculative motives are relevant as well. Some emerging financial industry analyses indicate that crypto-assets may be a useful addition to a balanced investment portfolio, also as correlations with traditional asset classes appear relatively low (at least prior to the COVID-19 pandemic), but their short and volatile history makes it too soon to draw conclusions (e.g., Goldman Sachs, 2021a and Citi, 2021). However, others have concluded that crypto-assets are not a viable investment for a diversified portfolio (e.g., Goldman Sachs, 2021b).

Cross-sectional OLS regressions on 2020 crypto volumes for all available countries show that financial development and economic freedoms do not robustly explain the cross-country variation in crypto-assets activity associated with both all transaction sizes and smaller transactions only. However, we find tentative support that crypto activity is higher in less economically developed countries and in countries with stronger ICT penetration, broadband subscriptions in particular. We also find that crypto activity is higher in countries where personal remittances play a more important role. This finding is consistent with Graf von Luckner et al (2021) who study “off chain”
_Panel B: Inequality_

|Dependent variable: Gini<br>index|4-year<br>average|5-year<br>average|Adding<br>time-<br>invariant<br>covariates|Adding<br>temperature<br>interaction<br>term|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|∆Temperature|0.309***|0.339***|0.421***|0.977**|
||(0.045)|(0.042)|(0.052)|(0.455)|
|Temperature||||0.333<br>(0.388)|
|Temperature squared||||-0.027***<br>(0.007)|
|Temperature*∆Tempearture||||-0.464***<br>(0.070)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Subnational FE|No|No|No|No|
|Year FE|No|No|No|No|
|Number of countries|90|90|90|90|
|Observations|1,019|1,019|1,019|1,019|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Inequality is measured by Gini index. Control variables in Column (9) are taken from Kalkuhl and Wenz (2020) which include cumulative oil gas, distance to coast, distance to river, and altitude. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0
# **9. References**

Altman, M., Gostner, K., Lee, D., Tregenna, F., van der Heijden, T. & Onyango, D. (2005) Review of employment & remuneration trends for selected sectors in the SA economy (Prepared for Office of the Presidency).

Bartelsman, E., Haltiwanger, J, and Scarpetta, S. (2009). Measuring and analysing crosscountry differences in firm dynamics, in Producer Dynamics: New evidence from micro data (Dunne, J., and Roberts, eds), NBER / University of Chicago Press.

Centre for Competition, Regulation and Economic Development. (2016a). Competition, barriers to entry and inclusive growth: Telecommunications Sector Study. [online] Available: http://www.competition.org.za/s/Barrierstoentry_TelecommunicationSectorStudy.pdf [Accessed 23 April 2018].

Centre for Competition, Regulation and Economic Development (2016b). Competition, barriers to entry and inclusive growth: Fruit and Veg City Case Study. [online] Available: http://www.competition.org.za/s/BTEFruitVegChisoroDasNair290216.pdf [Accessed 23 April 2018].

Cutts, M. and Kirsten, J. (2006). Asymmetric price transmission and market concentration: an investigation into four South African agro-food industries. _South African Journal of Economics_ , 74 (2): 323–333.

das Nair, R., and Chisoro, S. (2015). ‘The expansion of regional supermarket chains: changing models of retailing and the implications for local supplier capabilities in South Africa, Botswana, Zambia, and Zimbabwe’. WIDER Working Paper 2015/114. Helsinki: UNU-WIDER.

Edwards, L. and Lawrence, R. (2008). South African Trade Policy Matters: Trade Performance & Trade Policy _. Economics of Transition_ , 16(4): 585-608.

Edwards, l., Cassim, R., and Van Seventer, D
1 presents a summary of the FDI data to the country as released by the Agency for the Promotion of Foreign Investments (FIPA, 2019, 2017, 2016, 2015, 2014). Foreign inflows have substantially declined since 2008. The trend has slightly reversed from 2017 but still about 14% below its level in 2008. It is worth noting that the surge in FDI (excluding energy parts) in 2012 was mainly explained by the privatization and transfer to international investors of the state’s shares in a domestic telephone operator.

**Table 1** . _FDI inflows in millions of Tunisian dinars (TND) (2008-2018)_

|**_Years_**|**2008**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|**2018**|
|---|---|---|---|---|---|---|---|---|---|---|
|**_FDI_**|3127|2165|1616|2504|1813|1806|1967|1901|2131|2742|
|**_FDI_**(_excl.energy_)|1124|848|553|1618|737|913|995|1097|1318|1832|

_Source : FIPA (2019, 2017, 2016, 2015, 2014)_
and thus may be affected at lower concentrations than healthy adults (U.S. EPA, 2009, Ch. 8). Our results suggest that the mechanism linking low levels of air pollution to labor supply is the increase in demand for caregiving: healthy adults reduce hours of work to take care of sick dependents. However, at higher concentrations, the link is more direct: pollution directly harms the health of those who participate in the workforce. To corroborate these findings, we use auxiliary data from the Demographic and Health Survey (DHS) on health outcomes for the same city and period. We find that PM2 _._ 5 is associated with an increase in respiratory diseases among small children.

Finally, we examine whether households respond to this shock by re-allocating caregiving

> Muney, 2010; Graff-Zivin and Neidell, 2012). Other studies focus on NO _x_ (Deschenes et al., 2012), SO2 (Hanna and Oliva, 2015) and aerosols (Jayachandran, 2009). Among the few studies focusing on PM2 _._ 5 are Zweig et al. (2014), Chang et al. (2014), Adhvaryu et al. (2014), Lavy et al. (forthcoming) and Li et al. (2015).

3
sensing data from the Google Earth Engine, particularly, data on nighttime lights, rainfall, land surface temperature, impervious surface, cropland, and normalized difference vegetation, snow, and water indices. In contrast to WEO and WDI, the remote sensing data are both more granular spatially and more frequent temporally. To fit into our framework, they need to be aggregated to the country-year level. We first aggregate them to annual data by calculating the mean, max, min, and standard deviation of each location over a year. Afterwards, we aggregate spatially by taking the mean, max, min, and standard deviation of the annual data for a country. This gives 16 features for each type of variable. Some of these combinations will not be relevant for global poverty 13
information on the initial capital stock is also needed to construct the capital series, which is rarely available. Researchers use several different techniques for fixing, or seeding, the initial value. They are often forced to choose among competing seeding techniques based on criteria such as whether the methods generate negative initial values (see for instance, Nehru and Dhareshwar, 1993). This choice would not be necessary if the investment series were sufficiently long (large _T_ ) because the productivity of old capital goods is low and their contribution to the current stock is small.

Therefore, our approach is to generate lengthier investment time series when they do not exist. We do this by regressing the logarithm of the investment-output ratio on time for the study period. We then use this regression to estimate past values of the investment-output ratio and apply them to the published output data to generate the needed missing investment values. If the output values are not available, we can estimate them from a regression of output on time.<sup>11</sup> The data series on fixed capital stock are constructed in constant local prices. To facilitate cross-country comparisons, the series need to be valued in comparable units. We value the series in current prices in local currency using national GDP deflators. Using “market exchange rates” from the International Monetary Fund, we convert the values to current US dollars. The series is then deflated by the US agricultural GDP deflator to obtain the capital stock series in constant (1990) US dollars.<sup>12</sup>

# **Livestock capital**

A considerable amount of agricultural capital is embodied in livestock, which are used to perform various roles – such as breeding and draft power. According to the United Nation’s accounting practices, animals that are not used for slaughter are included as fixed capital investments.<sup>13</sup>

> 11 Researchers at the FAO are introducing a more sophisticated method of estimating missing investment data, where they account for the possibility
than one employee (i.e., self-employed). Workers with a secondary job or in no job but in irregular economic activities are not included. In the LFS, informal employment includes workers that are employed but not at an enterprise in their primary job, workers employed without a written contract in their primary job, and selfemployed workers without registration. In the LiTS, informal employment is defined as working without a written contract in the main job. In the ESS, informal employment is defined as working without a contract in the main job. Rosstat’s official measure of informal employment is estimated using the LFS and includes workers that are employed but not at an enterprise in their primary or secondary job. All estimates are from 2016 surveys and weighted.

Informal Employment and Worker’s Wellbeing in Russia / June 2019 34
intermediation and the appropriability of returns are the most important constraints to growth. Private returns are reduced through the high cost of complex regulations and inefficient government services as well as the cost of innovation and exploration. He further suggests that the recent pick-up in growth was unrelated to efficiency improvements in the financial sector.

# **B. ANALYSIS OF SUB PERIODS**

The two decades since the early 1990s can be roughly divided into three sub-periods, according to the international capital flows: a first period of capital inflow in the early 1990s following the adoption of economic reform (1991-1998); a second period of capital outflow (1999-2004); and a third period of capital inflow (2005-2008).<sup>3</sup> Tables 1.a to 1.d describe policies and outcomes during the three sub-periods. This section shows the commonalities and differences across the sub-periods.

GDP growth is procyclical with the external capital inflow/outflow, though domestic reform also follows a pattern of the external cycle. Following the adoption of the Economic Reform and Structural Adjustment Program (ERSAP) in 1991, GDP grew at an average rate of almost 4 percent in the first period, peaking at 5.5 percent in FY97. The ERSAP aimed at resolving the macro imbalances through market-oriented reforms, namely, increasing the private sector participation, boosting international trade and privatizing many state-owned companies. A large fiscal adjustment brought the fiscal deficit down from 15 percent to 1.2 percent of GDP between FY91 and FY95 (Ikram, 2006). Yet, as a result of reform fatigue, a lack of fiscal discipline, and a shock to external capital flows due to the Russian default and Long Term Capital Management Crisis in the United States in 1998, Egyptian growth reached a trough of 2.4 percent in FY02. Macroeconomic reforms resumed in FY05, the external environment improved and growth picked-up to 6.4 percent between FY05 and FY08 from an average of 4.1 percent in the previous period.

The increase in net foreign
rated MU- for IP and DO in their last ISR. Slightly less, 57 percent, would have been picked up by the problem project module alone. By age group, the projects in their closing years seem overrepresented compared with first-year projects (48 percent versus 19 percent), or even with the remaining group (37 percent), but several of these projects will not close and will be extended into the following year. It also seems sensible to be more cautious for the exit year than for the first-year projects.

The problem project, flag-based module and also a module of projects with delays to signing and effectiveness were used to create a watch list of EAP projects based on July 1, 2014 data. A review methodology was designed based on IEG ICR review criteria, i.e., relevance of objectives and design, efficacy, and efficiency. The reviews were prepared in batches and forwarded by the regional vicepresident to the task team leaders and their practice managers. Once the projects on the watch list 13
6 in terms of monetary policy independence they observe that domestic nominal interest rates are less exposed to foreign interest rate innovations. For emerging market economies, in particular, this achievement is more important and increasing when economies achieve stationary targets. For a more complete analysis of early assessments of IT effects, see also the works compiled by Loayza and Soto (2002), Fraga et al. (2003), and Mishkin and Schmidt-Hebbel (2007b).

More recently, a number of authors have discussed the effects of IT in different types of economies. Roger (2009) offers a comprehensive review, which starts with IT adoptions New Zealand and Canada in 1990 and 1991, respectively. Also Brito and Bystedt (2010), studying the effects of IT on emerging markets using dynamic panel data methods, find no evidence that IT improves the performance of inflation and output growth. Although in their work there seems to be some indication that IT reduces inflation, this reduction is not robust to alterations in the control group of non-IT countries. They even report lower output growth after IT adoption. Agénor and Pereira da Silva (2013) discuss a variety of factors relevant to the developing world within the IT framework. Also, Canuto and Cavallari (2013) address the implications of monetary policy’s neglect of asset price booms and busts, as well as how to integrate macro prudential factors into central bank policy.

Mollick, Cabral and Carneiro (2008, 2011) blended the economic growth model with IT and examined the effects of IT on output per capita growth using the database of Lane and Milesi-Ferretti (2007) with samples of industrial and emerging economies facing increasing trade and financial integration for the period 1986- 2004. In those studies, the authors adopted a real output growth perspective and introduced controls for factors such as globalization (trade and capital flows) as well as IT. They found that the adoption of a fully-fledged IT regime results in higher output and income per capita.
# **1. Introduction**

Despite decades of retrenchment, the entrepreneurial state’s footprint is notable across many countries’ economies. Such prevalence is not just concentrated in sectors where government intervention is rationalized on the grounds of market failures, coordination problems, and increasing returns to scale. By contrast, it is widespread across all sectors of the economy, with 70% of government-owned enterprises operating in competitive industries, such as food, construction, and hospitality, typically dominated by private sector firms. Even when operating in sectors subject to market failures, state-owned enterprises (SOEs) may crowd out resources from more efficient private-owned firms (POEs). Thereby reducing aggregate productivity and output.

In this paper, we study one particular source of interference from governmentowned enterprises: the competition with private firms for external financing in capital markets. There are several well-understood mechanisms through which less efficient firms with a large government stake could access external financing more favorably than private counterparts, thereby distorting finance, innovation, investment, and resource allocation. These range from lower debt issuance costs due to the federal government’s backing of corporate debt to direct budget support from the government in the form of internal equity. While the mechanisms are well acknowledged, there is less clear understanding of their quantitative significance. Do capital markets favor SOEs with lower debt and equity financing costs? If so, how large is their financial premium? And what are the aggregate implications on productivity?

To study these questions, we appeal to Whited and Zhao’s (2021) methodology for inferring financial distortions from firms’ balance sheet data and apply it to 24 European countries from 2010-2016. Mirroring the real misallocation literature (Hsieh and Klenow, 2009), the methodology delivers a distribution of firm-specific financial distortions resulting from deviations in the rates of marginal revenue products of debt and equity across firms, the properties of which we characterize across state- and private-owned firms. We find that firms with public authorities as direct shareholders (SOEs) get, on average, subsidized
# **Table 1 Classification of extended labor force (economically active and marginally attached population) based on European Social Survey data**

|No.|Category|Definition|
|---|---|---|
|1|**_Formal employment_**|Formal employment includes the following two groups:<br>(i) Employees holding an employment contract;<br> (ii)Persons workingfor own family‘s business and havinga contract.|
|2|**_Formal self-employment_**|Formal self-employment includes the following two groups:<br>(i) Employers with more than 5 workers;<br>(ii)Self-employed workingasprofessionals(without workers)|
|3|**_Informal self-employment_**|Informal self-employment includes the following two groups:<br>(i) All non-professional self-employed operating solely;<br>(ii)Employers with 5 or fewer workers|
|4|**_Family workers_**|Persons workingwithout a contract for own family‘s business|
|5|**_Informal dependent_**<br>**_employment_**|Employees (persons in a dependent employment relationship)<br>without a contract or who is uncertain of their contract.|
|6|**_Unemployed willing to_**<br>**_work_**|Persons which during the reference week did not work, were not<br>temporarily absent form a job, and were either actively looking for a<br>job or wantingajob but not activelysearching.|

_Notes:_ This classification is fully applicable to ESS data starting from round 2. In round 1, it was not possible to distinguish between categories 1 and 5. Moreover, due to data limitations, the classification is applicable to French data only starting from round 3, and to Romanian data - from round 4.

# **Table 2 Prevalence of informal employment in Europe, by region. 2008-2009**

_Per cent_