rafmacalaba/data-use-mentions-extended huggingface dataset:
to access financial markets (i.e., the ability to get loans) and to access government support programs to improve productivity. Costs of informality often include expected costs of getting caught including associated fines (which would increase as enforcement increases) and the opportunity costs of not having access to the modern economy, including access to financial markets. The benefits of informality likely include increased flexibility (e.g., easier responsiveness to changing market circumstances, a greater ability to change product lines, etc.) as well as the ability to avoid tax, regulatory and harassment burdens.

Based on such a model, Ulyssea proposes three types of informal firms, based on complementary views for understanding informality. Those that correspond to the “De Soto view” (or “held-back entrepreneurs”), namely enterprises that would switch to formality if entry costs were reduced to zero. The net present value (NPV) of these firms is higher remaining informal than becoming formal but the difference is less than entry or registration costs. Relative to other informal firms, these firms have higher productivity. They are kept out of formality solely due to high regulatory costs. To the extent that these entry costs are due to non-welfare-improving regulations and associated onerous licensing procedures and red tape, eliminating them would be clearly desirable. Recent empirical evidence suggests that these costs are either lower than economists used to believe, and/or that their reduction does not really create big changes in incentives to register. Based on an estimated structural model and matched employeremployee data on formal and informal firms in Brazil, Ulyssea infers that these firms make up 9 percent of firms. The second group (the “parasitic view”, 42 percent of firms) refer to those that would remain informal even if entry costs were reduced to zero. These are enterprises that would still have positive NPV if they somehow switched to formality, but the NPV of informality is higher. They are productive enough to survive in the formal sector but choose to remain informal to earn higher
, 1994a; Guzanova et al., 1994b; Guzanova et al., 1994c; Guzanova, 1994; Pchelintsev, 1994; Abankina and Zyev, 1994. The technique for the study of the households' needs, plans and preferences as regards housing, as well as questions about the choice of the so-called search methods, were reproduced in many household surveys conducted in 1994-1996 in Russia.

- **_The_** _Moscow Longitudinal Household Survey, 1992-1996 (M1,HS)_ was conducted for the Urban Institute, Washington. The objective of the survey was to obtain annual data over a period of four years to track the changes in the housing sector in Moscow and particularly how they affect individual households. The first wave of the survey was completed in December 1992; the second wave, in December 1993; the third wave, in December 1994; and the fourth wave, in January 1996. A total of 2,002, 2,150, 2,247 and 2,220 households were interviewed, respectively. The primary sample was drawn randomly from a listing of residential telephone numbers provided by the Moscow Telephone Network. Units without telephones in new residential suburbs were added to the sample. The same households were polled each year, with the sample complemented by new households that moved to new dwellings over the year in question (proportionately to the newly built housing). Ninety-five to ninety-seven percent of all units in Moscow were included in the population from which the sample was drawn. The Moscow Longitudinal Household Survey (second wave, 1993) and the USAIDWorld Bank Household Survey, 1993, in Moscow were conducted within the framework of the same field study. This work uses mainly the third and fourth waves of MLHS (1994 and 1996) when examining the factors and social consequences of privatization. When analyzing the development of the housing market, the Section 2 uses waves two and four (1993 and 1996).

- **_The Vladitir_** _and Gorodets Longitudinal Household Survey on Housing Allowances_ was conducted for
so we consider analysis of this data as exploratory. In particular, 26% (22%) of the treatment (control) schools missed the baseline questionnaire, 13% (11%) the final paper-based questionnaire, and 59% (63%) the short-term online follow-up. However, the key results are robust to controlling for baseline responses and to dropping T3 schools, which had higher response rates.<sup>7</sup> Finally, the attrition rate is not correlated with the baseline responses, nor with student dropout rates at baseline or follow-up.

## **Qualitative analysis**

We also performed a series of focus groups (12 in total) to gather qualitative information on the challenges of program implementation. We interviewed almost 100 people involved in the program, including 63 teachers and principals from across treatment arms and 34 trainers in 4 different locations.<sup>8</sup> Short paper-based questionnaires were also completed during these focus group sessions.

# Descriptive statistics and balancing tests

Table 2 reports the average baseline characteristics for the students and their schools according to the administrative data available.<sup>9</sup> The table reports the averages for the control arm (column 1), the pooled treatment arms (column 2), and each treatment arm separately (columns 3-5). The administrative data includes detailed academic grades by subject (math, language, natural sciences, etc.), age, gender, school dropout rates, the average dropout probability for the cohort, and regional variables.

Overall treatment and control groups are relatively balanced. Most of the differences in the variables considered are statistically insignificant. The chi squared test after running seemingly unrelated regressions of each of these baseline variables on treatment assignment shows that the

> 7 These schools had higher response rates in online questionnaires likely because the reminders made schools more prone to see the survey link; furthermore, they explain all the difference on the response rates between treatment and control schools in the online survey used
be used in the event of a blackout. Recall as well that all firms have access to the simplest production technology: sewing by hand using a needle and thread. This paper finds no evidence of changes in capital composition in response to outages,<sup>20</sup> but supposes rather naturally that firms of both types shift, at least partially, to (existing) functional manual production technologies when blackouts render electric sewing machines obsolete. Though this study is limited in its ability to directly describe differential changes in production processes in firms with workers, it shows that these firms are able to more flexibly shift labor inputs to cope with outages. As seen in Table 4, paired days with any blackout are associated with additional worker hours relative to paired days with lights. Firms owners in firms with workers also significantly increase their own hours on days with electricity following a blackout day, suggestive evidence of potentially complex dynamic shifts in production processes and in the matching between capital inputs and labor inputs in firms with workers.

# **6 Conclusion**

Unreliable power supplies are a well-known obstacle to doing business across much of the developing world, yet surprisingly little is known about how frequent blackouts affect firms of different sizes, or workers of different skill levels. Previous analyses have focused on larger firms and capital-based coping strategies, uncovering interesting patterns using annual data, but leaving a space in the literature to explore how outages impact microenterprises which comprise the overwhelming majority of firms in lowincome countries and are a vital source of employment and income for poor and vulnerable households. This study begins to fill that gap by using high-frequency blackout, profit, and labor input data to estimate the effects of blackouts on microenterprises.

The analysis finds that single-person firms experience large reductions in revenues and profits, but fails to reject no effect of outages on the revenues and profits of firms with employees. Turning to laborbased coping strategies, the study documents that one-person firm owners reduce their labor supply in response to outages. Firms with employees adjust labor
# **7. CONCLUSIONS AND POLICY IMPLICATIONS**

The role of the state in markets is still at play and it can have implications for firm performance. This paper analyzes the performance differentials in terms of operational efficiency measured by labor productivity and financial performance through profitability, liquidity (current ratio and solvency ratio), and return on investments (ROA and ROE) between privately owned enterprises (POEs) and Businesses of the State (BOSs). The latter are defined as firms with state direct and indirect state participation of at least 10% following Dall'Olio et al. (2022). This paper uses two main data sources: firm-level data from the ORBIS financial module and the World Bank’s Businesses of the State (BOS) database.

This paper provides new evidence on the relationship between state ownership and firm performance studying 16 countries in the European and Central Asia region. Findings suggest important opportunity costs of state ownership as firms with state participation seem to achieve lower labor productivity outcomes and subpar financial performance in terms of profitability and return on investments. Yet, BOSs seem to be more financially leveraged and rely more on external debt compared to private peers suggesting potential soft budget constraints.

This paper suggests important trade-offs between state ownership and firm performance and highlights some policy implications that are relevant when designing and embarking on reforms.

First, the findings highlight that the private sector can play an important role in driving operational and financial performance. Firms with private shareholding seem to obtain better outcomes in terms of labor productivity as well as profit margins, and higher returns on assets and equity, and are less likely to support operations through external debt. Compared to firms in which the state is the sole owner, the results of this paper are indicative that private ownership can instill marketbased incentives to improve performance.<sup>29</sup> It has important policy implications. Reforms can explore alternatives to crowd-in private participation beyond full divestiture or privatization through public-private partnerships or joint ventures (Alfalter & Sanchez-Navarro, 2023)
**|_9,650_|_63.9_|_87.9_|_12.4_|_28.5_|

Note: (0,0) = no shocks to earnings and receivables. (30,30) = 30 percent shock to earnings, 30 percent shock to receivables. Vulnerable sectors = Aerospace & Defense, Oil & Gas Producers, Oil Equipment, Services & Distribution, and Travel & Leisure.

6. Corporate Balance Sheet Vulnerabilities and Corporate Insolvency Framework **The most vulnerable countries should prepare for a rise in defaults in the corporate sector, which may lead to an increase in bad loans as a result of the COVID-19 crisis.** To mitigate these negative effects and ensure a stable recovery, it is critical that countries rely on a strong legal and institutional framework for corporate insolvency. Indeed, the insolvency system can assist countries in responding to the COVID-19 crisis with a critical framework for collective action to save viable firms, maximize debt recoveries and preserve employment. The readiness of the insolvency system to deal with widespread default in the corporate sector is therefore essential in understanding the implications of the acute corporate vulnerabilities identified in our analysis. However, corporate insolvency institutions may weaken during the crisis due to the disruptions to basic operations of courts and insolvency agencies.

**We find that a significant fraction of countries in our sample exhibit both elevated corporate vulnerabilities and weaknesses in their corporate insolvency frameworks, setting the stage for a ‘perfect storm’ of firm failure.** While these vulnerabilities were already present by the end of 2019, COVID-19 related shocks may exacerbate these. For example, the insolvency system may become more inefficient due to case overload. Exhibit 9 presents country-level data on DaR1 and DaR2 and an index of corporate insolvency that measures the quality and efficiency of the insolvency system. Of the 56 countries in the figure, a significant fraction of countries has over 60 percent (15 percent) of corporate debt considered financially vulnerable according to at least one indicator (both indicators) and a corporate
, similar work for Nigeria—integrating household panel data and historical coverage maps—finds an 11.1 percent increase in total consumption and a 7.9 percentage point decline in extreme poverty after three years of coverage (Bahia et al. 2020). Using data from 14 rural and geographically isolated areas in the Philippines between 2016 and 2018, Blumenstock et al. (2020) also find that the introduction of a new phone tower led to an increase in household income of 17 percent, and increased household expenditures by 10 percent.

> 18 Data on 2G coverage are also provided in the Mobile Coverage Maps database and by the mobile operators.

> 19 The self-reported ownership of cellphone or Internet access is measured by a positive expenditure on cellphone or Internet subscriptions.

8
# **1. Introduction**

Poverty is heavily concentrated in rural areas, where 68.8 percent of Yemen’s total population resided in 2009. According to a poverty assessment undertaken by the World Bank in 2007, urban poverty rates in Yemen have declined while poverty reduction in rural areas is not robust (World Bank 2007d). The proportion of the rural population living on less than US$1 a day decreased only slightly between 1998 and 2005, from 42.5 percent to 40.1 percent. In urban areas, this proportion decreased more significantly, from 32.2 percent in 1998 to 20.7 percent in 2005 (figure 1.1). The GINI coefficient reflects worsening income inequality during this period, increasing from 33.4 in 1998 to 37.7 in 2005.<sup>1</sup> In addition to poverty and income inequality, the gender inequality index for Yemen is the worst of 146 countries and improved only slightly, from 0.853 in 2008 to 0.796 in 2011 (UNDP 2010; 2011).<sup>2</sup>

**<u>Figure 1.1 Poverty Ratios (Below $1 ppp per day) and GINI Index</u>**

<!-- Start of picture text --> 45 42.5<br>40.1<br>40<br>33.4<br>35 37.7<br>GINI index<br>30<br>32.3<br>25<br>Poverty headcount ratio at rural<br>20 poverty line (% of rural<br>20.7 population)<br>15<br>Poverty headcount ratio at urban<br>10<br>poverty line (% of urban<br>5 population)<br>0<br>1998 2005<br><!-- End of picture text --> _Source_ _<
and the Caribbean and Sub-Saharan Africa make up nearly a quarter of all available data. This provides the largest representation of developing countries to date in a learning database.

Our methodology leverages the growth of international assessments to construct globally comparable learning outcomes. These tests are derived from assessments conducted in the United States since the 1960s such as the SAT and the National Assessment of Educational Progress (NAEP). The tests are psychometrically designed, standardized assessments of cognitive skills. Since the 1990s, international assessments have been conducted by organizations such as the OECD. Two high profile examples are PISA and TIMSS which covered 71 and 65 countries in 2015. These assessments enable credible global comparison of learning across countries and over time. However, to date most analyses of these assessments focus mainly on OECD countries and cover few developing countries.<sup>7</sup> This limits the distribution of countries represented and has implications for our understanding of the link between human capital and economic development.

We include 164 countries, two-thirds of which are developing countries, by linking international assessments to their regional counterparts. Regional assessments cover much of Sub-Saharan Africa and Latin America, but have often been excluded from international comparisons. We employ methods to convert a regional test score to an international test score within subjects and schooling levels (primary and secondary) and within adjacent years. By including tests across the same testing round and at the disaggregated schooling and subject level, this minimizes the likelihood that test differences are a function of time, proficiency, schooling level, or data availability and are an accurate reflection of test difficulty. We then apply this conversion to a country that participates in regional test but not an international test to produce a comparable score (referred to as a Harmonized Learning Outcome (HLO) in the database). Means are also calculated for disaggregated groups such as by gender.

The success of this approach hinges on three key assumptions. First, linked tests must capture the same underlying population. This assumption is satisfied by using sample-based assessments representative
) in the final year. In contrast, for Indonesia, persistent inefficiency at 60.4% is higher than Australia’s, but Indonesia’s time-varying component went down from 26.4% to 7.7%, signaling an improvement over time.

# **7. Conclusion**

To assess a country’s performance in terms of a relevant set of development indicators, we need a benchmark against which these indicators can be compared. If the benchmark is measured as a feasible frontier, then departure from the benchmark can be viewed as inefficiency, slack, or potential for improvement. This paper focuses on international benchmarking for assessing a country’s realistic potential for improvement on various social and economic dimensions.

We use the stochastic frontier (SF) approach to estimate a country-, time-, and indicator-specific benchmark and (one-sided) inefficiency. Our approach contrasts with the literature that compare countries by looking at raw indicators without taking into account the structural endowments or predetermined conditions that constrain the maximum performance that countries can achieve in a policy-relevant horizon. The SF approach also improves on the literature that uses OLS regression residuals to measure performance. Although the residuals can give some idea about a country’s performance, they cannot be interpreted as inefficiency because they are mixed with noise and can take both positive and negative values.

To illustrate the SF methodology, we apply it to 10 indicators of economic and social performance, using panel data of 142 countries for the period 2005-2014. We compute benchmarks, as well as persistent and time-varying inefficiency measures. To facilitate the discussion of results, we introduce the concept of overall technical efficiency (OTE), the inverse of inefficiency. Not surprisingly, we find that countries’ overall technical efficiency varies across indicators, with no country or region dominating the rest. More interestingly, we find that the potential for improvement does not follow a simple relationship with 14
# **Non-technical Summary**

Maritime transport costs significantly impede international trade, and influence geographical patterns of production and income. Even though improvements in technology (notably containerization) have led to a significant decline in unit costs, there has not been a commensurate decline in maritime transport costs. We focus on two explanatory factors: restrictive trade policies of governments and private anti-competitive practices. The former include a variety of cargo reservation schemes, as well as the monopoly rights granted to providers of port and auxiliary services, such as cargo handling. The latter include primarily, but not exclusively, the rate fixing practices of maritime conferences, which enjoy an exemption from competition rules in major traders, like the United States and European Union.

Until recently, paucity of data has precluded a rigorous empirical examination of the role of these factors. An even greater barrier to empirical research has been the absence of comprehensive information on public policies and private practices. Both constraints were relaxed recently. We use a new database, created as part of the World Bank's Trade in Services Project, which contains information on both policy and private rate-fixing arrangements affecting maritime trade with the United States. The US Department of Transportation recently published data on waterborne transport charges, broken down for the first time by type of service-liner, bulk and tanker.

The econometric analysis presented in this paper confirms, first of all, the importance of all the standard determinants of transport prices, ranging from distance to technology. More interestingly, we find that both public policy and private practices exercise a significant influence on maritime transport prices. Of public restrictions, the cargo reservation policies which proliferated in the 1970s and 1980s seem to be largely ineffectual, but the restrictions on the provision of port services significantly raise prices. Most striking is the even more powerful effect that private anti-competitive practices have in keeping prices high. While trade liberalization would lead to an average reduction in transport prices by 9 percent and cost savings of up to $850 million, the breakup of private carrier agreements
# **3.3.2 Supply cost**

The supply cost of biodiesel produced from oil-bearing trees is relatively higher than that of diesel due to the high cost of feedstock (Table 8).

**Table 8: Typical cost and related assumptions of jatropha-based biodiesel in China**

|Items|Unit|Jatropha|
|---|---|---|
|**Feedstock cost**|**Yuan/tonne(ethanol)**|**3,000-6,000**|
|Assumed feedstock pricing mechanism||Cost-based pricing<sup>a</sup>|
|Assumed feedstock price|Yuan/tonne(feedstock)|1,000-2,000<sup>b</sup>|
|Assumed conversion efficiency|Tonne (feedstock) / tonne<br>(biodiesel)|3|
|**Non-feedstock Cost**|**Yuan/tonne(biodiesel)**|**2,000**<sup>**c**</sup>|
|Conversion technology||super critical fluid transesterification<br>without catalyst|
|Investment cost|Yuan/tonne(biodiesel)|2,300-2,500<sup>d</sup>|
|**Total production cost**||**5,000-8,000**|
|Share of feedstock cost|%|75|
|Designated biodiesel wholesale price<br>from January 1, 2008 to April 7, 2011|Yuan/tonne|See Table 5|

Note: a. there is no mature market for jatrapha in China, while there are some jatrapha planting contract between company and farmers with protect price range from 1000 to 2000; b. estimated by expert; c. data from field survey based on Hainan project; super critical fluid process is expensive on capital and operation costs compared with conventional acid/basic catalyzed process according to Hainan project; d. data