rafmacalaba/data-use-mentions-extended huggingface dataset:
respondents who say they were affected “a great deal” or “a fair amount” by the crisis received at least one of the four types of benefits. Differences in the coverage of poor households that were affected by the crisis can be explained in part by differences in the proportion of the population that already received universal benefits (regardless of the crisis) and the policy response. However, because the proportion of poor households among those that report being affected by the crisis varies by country, the subjective coverage of public safety nets (as measured in LiTS II) is not equivalent to objective measures of coverage of the poor (as measured with administrative and household data).

The four type of benefits considered in the LiTS II are generally targeted towards the poor, but provide little insurance against unemployment in non-EU countries. In most countries the poorest third of the population were more likely to have received these benefits than other people. However, targeting benefits at those most affected by the crisis is constrained by the fact that only a small proportion of the population in non-EU member states is covered by unemployment insurance. In some countries, this is because a large portion of the workforce is employed in the informal sector. Only in EU member states did the proportion of households applying for and receiving unemployment benefits in the previous 12 months (among households in which at least one member lost his or her job due to the crisis) exceed 10 percent.

# **_Private safety nets_**

Households mostly sought informal insurance mechanisms when attempting to raise a loan in the crisis. Over 60 percent of households tried to obtain loans from relatives and 48 percent did so from friends. Only 28 percent went to a bank. As expected, poorer households are even more likely to seek loans from relatives or friends, whereas those in the higher-consumption bracket are substantially more likely to borrow from a bank.

The crisis also had a negative impact on informal insurance networks. Thirteen percent of respondents from affected households and 9 percent of all respondents
quality of infrastructure data for SSA countries. However, these are the very same data that are extensively used in the literature and thus we rely on the same imperfect figures.

# **4. Results**<sup>**12**</sup>

In this section, we discuss the determinants of growth in WAEMU countries over 2011-2016. The analysis focuses on 2011-2016 because this period corresponds to WAEMU’s growth acceleration episode. Results for 2000-10 are also presented in tables and graphs for comparison (See Annex Figures 8 and 9). The period before 2000 was not included in the analysis as data were incomplete for some countries and variables. Figures 2-7 show the predicted growth contributions of the determinants based on the parameter values shown in Table 1 and the observed changes in the variables between the respective data points.

Growth in WAEMU during 2011-2016 was mainly driven by improvements in structural factors. Structural factors contributed about 1.5 ppts of the average annual per capita growth rate of 2.5 percent, i.e. about two-thirds of predicted growth (Figure 2). This reflects the considerable progress WAEMU countries have made in financial deepening, infrastructure development, and trade openness. Structural improvements in WAEMU exceeded the SSA average. A stable macroeconomic framework also supported growth while external factors were unimportant.

Financial deepening and infrastructure were the main structural determinants of growth. Increased private sector credit and infrastructure contributed roughly 0.7 ppts and 0.4 ppts respectively, together accounting for three-quarters of growth due to structural factors. The largest increases in private sector credit (in percent of GDP) were registered by Togo (by about 19 ppts between 200010 and 2011-16), Senegal (12 ppts), and Burkina Faso (10 ppts). Several countries experienced a substantial increase in private investment (percent of GDP), with cumulative gains ranging from 2- 4 ppts in 4 of the 8 countries to 11 ppts in Niger. As infrastructure coverage in WAEMU countries is low, the growth effect of
measure of interest displays high degrees of persistence. Hence, we further shed light on this issue by restricting our estimating sample to companies with no credit access in _t −_ 1. Even focusing on “switchers” only (i.e., firms with new loans that were disconnected in the previous waves), our results prove to be extremely robust (see Table A2). Moreover, unreported regressions linking past loan availability with current perception of informal competition show no correlation between the two, further suggesting that reverse causality is unlikely to drive our findings.<sup>9</sup> Most importantly, we employ an IV approach to further take care of endogeneity. We rely on a cellaverage method wherein we instrument informal competition with the proportion of all other firms that are constrained by informality and operate within the same 2-digit sector and geographical area of each company (at a given time). This approach, widely used in the literature (see, among many others, Distinguin et al., 2016; Dollar et al., 2006; Fisman and Svensson, 2007; Amin and Soh, 2021; Amin, 2021), allows us to capture an environmental component of the informal competition faced by a company that is, however, unrelated to

> 8Because of the structure of the dataset, we can only introduce firms’ fixed effects when dealing with loan availability and application. Notice that, in such case, identification is achieved exclusively exploiting data from Egypt, which is the only country for which we have three waves (as discussed in footnote 3). For expectations, the absence of a panel structure does not allow for such analysis.

> 9Notice that, while being largely insignificant (p value of 0.210), the coefficient of past loan availability on informal competition is even of opposite sign: positive rather than negative.

10
firms from insolvency even when facing higher drops in sales.

# **8.2 Policy interventions and uncertainty**

Public policies can also play an important role in reducing the uncertainty faced by firms. This is a potentially important channel that could influence recovery because high degrees of uncertainty are likely to adversely affect firm investments and incentives to innovate, by reducing the appetite for entrepreneurial risk taking (see e.g., Bloom et al. (2007)) and limiting jobs growth and reallocation. The survey provides some suggestive evidence that public policies could reduce uncertainty and improve growth expectations. The right panel in Figure 9 shows a negative correlation between the predicted probability that firms have access to public support programs and their uncertainty, measured as the average standard deviation of the firms’ sales prediction based on (Altig et al., 2019). This cross-sectional correlation though may be driven by a lot of different factors, and as shown by Figure 8 when we control for size, country, sector and timing of the survey (relative to the peak of the crisis), and the size of the experienced drop in sales, we find that uncertainty is not any different between firms that received support versus those that did not.<sup>21</sup> While public policies may play an important role in reducing uncertainty towards the future, it is possible that the large uncertainty associated with the duration of the pandemic is at play during the COVID-19 crisis, which could explain our results. Applying to some of these public programs is costly and often

> 20These figures are higher than seen in high-income countries. For example, the Federal Reserve Bank of Atlanta’ Survey of Business Uncertainty reports expected drops in sales of between 0 and 3 percent for March and April 2020. 21This pattern is identical across countries with different income levels, as well as different sizes and sectors as shown in our online appendix.

11
034|0.006|0.015|0.025|0.052|

The asterisks indicate the significance level: *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses. Regressions take sample design and household weights into account by using Stata’s svy command. Data: EMDHS 2014.

Note that access to water and toilet and education are also potential variables (as EA averages) in the imputation models, and the z-score was, of course, used to obtain the SAE estimates in the first place. However, for the purposes of the analysis above, these dependencies are not a concern.

12
|2.58|2.88|2.50|3.15|1160|0.87|
|Ukraine|4.14|1.72|2.88|1.71|730|7.62|
|Vietnam|3.66|3.57|2.87|3.43|1394|4.01|

_Note_ . Earnings have been converted to 2011 PPP-adjusted U.S. dollars. The sample includes 25- to 64year-olds in self-employment or wage work. Employers, unpaid workers, part-time workers, and the top 1 percent of earners are excluded from the sample.

_Source_ : STEP Surveys (2014).

We examine the unconditional wage gap at different percentiles of the distribution. These are presented in Figures 1, 2, and 3. The distributions show substantial variation throughout the wage distribution. Further, the pattern is not systematic across countries. In Bolivia and Kenya we observe higher gaps at lower ends of the wage distribution, whereas in Armenia and Colombia we find the biggest gaps at the top of the wage distribution, implying that women do well in the labor market up to a point, beyond which their prospects and growth are limited (Albrecht, Bjorklund, and Vroman, 2003). In Ukraine, the second and fourth quartiles of the wage distribution show the biggest gaps. These patterns will be further explored in the decomposition analysis.

> 14 It must be noted that the observed wage gap is for the urban population only and could be an over- or underestimate of the overall wage gap in the country.

13
percent in 2016, consistent with the notable increase in the age profile of the population pyramid. With high levels of emigration of working age population being an important contributing factor, since independence the average age in Moldova has been increasing more rapidly than in the ECA region overall (Cojocaru and Matytsin, 2018).

The income decompositions described above provide some insight into the contribution of certain parts of the fiscal system (notably, transfers) to inclusive growth. However, it is worth considering more rigorously, to what degree various components of the fiscal system, both taxes and transfers, alleviate poverty and inequality in Moldova. The answer to this question is important both in the context of creating fiscal space for growth enhancing spending while protecting poor and vulnerable groups, and also to be able to assess the distributional impact of the important policy changes such as the tax policy changes that were introduced in 2018. An investigation of this broad question – what is the distributional incidence of each element of the fiscal system, and of the system overall, and how effective (and efficient) each element of the fiscal system is in alleviating poverty and inequality – is the objective of this paper.

In order to answer this question, the paper employs the Commitment to Equity (CEQ) framework – a fiscal incidence analysis that focuses on the poverty and inequality implications of the country’s fiscal system (Lustig and Higgins, 2013). The methodology, described in detail in section 3 of this paper, relies on assigning to each household in a representative household survey sample, various taxes and social transfers (both direct and indirect), based on a set of income concepts that allow for a gradual incorporation of different components of the fiscal system into the household’s income. This setup then allows us to compute a set of poverty and inequality indices before and after the program intervention, which gives the marginal effect of a given program on poverty and inequality. In addition to the poverty and inequality effects, the CEQ setup also enables us
Larson. 1993. “Modern contraception use in Ethiopia: does involving husbands make a difference?” _American Journal of Public Health_ 83 (11): 1567-71.

Thaddeus, S., and D. Maine. 1994. “Too far to walk: maternal mortality in context.” _Social Science and Medicine_ 38 (8): 1091-110.

Thomas, D. 1990. “Intra-household resource allocation: An inferential approach.” _Journal of Human Resources_ 25 (4): 635-64.

Thomas, D. 1994. “Like Father, Like Son or Like Mother, Like Daughter: Parental Education and Child Health.” _Journal of Human Resources_ 29 (4): 980-89.

UNESCO Principal Regional Office for Asia and the Pacific (UNSECO PROAP). 2000. Increasing the number of women teachers in Rural Schools. Bangkok.

Vermeersch, C., and M. Kremer. 2004. _School Meals, Educational Achievement and School Competition: Evidence from a Randomized Evaluation in Kenya._ Harvard University Economics Department, Cambridge, MA.

36
of poor people suggests that increased access to productive assets could be an important avenue for poverty reduction. Given the imnportance of developing a diverse and less subsidy-dependent rural sector, a suitably adapted land reform could play an important role in the restructuring of South Africa's rural sector.

At the same time. the limited progress under the current program suggests that much of this potential is, as of yet, Linrealized. Our analysis points towards a number of clear lessons with respect to program design, as discussed earlier. Probably the most important conclusion is to increase beneficiary awareness and participation. Doing so would include shifting from a bureaucratic and centralized approval structure specifically designed for land distribution towards a more integrated vision that takes account of other

> '3 It would be very interesting to use the sample of private land transactions for case studies to illustrate specific factors that could act as barriers to entry into the land market - and ways in which institutions and capacity building efforts may be aligned to reduce their impact.

> _14_ Even though some of these credits may have been designed to complement government programs, the private sector takes some risk and therefore performs an important monitoring function in all of them.

17
|**Table 15: Dev**|**iations from five-**|**year means**<br>Percent|Deviation|
|---|---|---|---|
|City|Country|2015-19|2017-21|
|Greensboro|United States|21.78|-20.46|
|Warsaw|Poland|17.66|-37.90|
|Melbourne|Australia|-9.38|-14.70|
|Madrid|Spain|-14.72|-14.70|
|La Rochelle|France|-15.25|-14.89|
|Fayetteville|United States|-19.15|-22.01|

# **6.  Estimating Urban Emissions**

Our econometric analysis also enables estimation of emissions levels for selected regions or local areas. In our approach, emissions levels have two components. The first, predicted by our econometric model, is the expected value of emissions from an area, given its industry structure, fire occurrences, population, income per capita, climate and mass transit infrastructure. The second component is the deviation from a city’s expected value measured by its regression residual. Negative deviations identify areas whose emissions are lower than expected, while positive deviations identify areas with higherthan-expected emissions.

For both components, atmospheric concentration measures can be converted to estimated emissions with exogenous information that provides measurement benchmarks. For the computation of expected values, we employ standard global emissions estimates. For the translation of regression residuals to emissions deviations, we use the EDGAR gridded database of CO2 emissions estimated from sectoral activity data and standard emissions parameters (Crippa et al. 2020).

# **6.1 Mapping Global CO2 Emissions**

Our econometric exercise measures marginal effects on atmospheric concentration anomalies from CO2 emissions by industry, fires, and non-industrial population-related sources. Concentration anomalies are measured in parts per billion; we convert to emissions volumes using
grids, we use data at a spatial resolution of approximately 1 km.<sup>31</sup> While the _LandScan_ and _GHSPop_ data are available for the entire globe, the _WorldPop_ data are limited to 53 countries, including 27 LAC countries. One other key difference is that _LandScan_ provides estimates of ambient population (i.e. average population over 24 hours), while both _GHS-Pop_ and _WorldPop_ provide estimates of residential population.<sup>32</sup>

# _3.2  Travel time and settlement point data_

The travel time data used in the original AI analysis were based on “... _estimates of the time required to travel 1 km over different road and off-road surfaces...”_ derived from a cost surface that was constructed from a variety of GIS data layers (Uchida and Nelson, 2010). These layers included data on road and rail networks, navigable rivers and water bodies, travel delays for crossing international borders, roughness of terrain and foot based travel for off-road and paths.<sup>33</sup> The AI estimates generated for this paper are based on an updated version of this same cost surface layer that is taken from Berg, Blankespoor, Li and Selod (2017). This updated layer is derived from more recent (i.e. _circa_ 2010 versus _circa_ 2000) data on roads, railroads, and land cover.

In contrast to the population and travel time data, we use the same settlement point layer as Uchida and Nelson (2010) – i.e. GRUMP Settlement Points v 1.<sup>34</sup> Because these data relate to the year 2000, it means that the updated AI estimates of national urban population shares generated as part of this paper are likely to be biased downwards. This will be the case, in particular, to the extent that there are settlements that were below �<sup>�</sup> (i.e. the population threshold used to define
shareholders by the controlling shareholders, rather than that of restricting empire building by unaccountable managers. The scope for this conflict, and the resulting expropriation, depends on the wedge between cash-flow and control rights. The East Asian corporations, for which we have ultimate cash-flow and control information, provide a useful data set to study the evidence on minority shareholder expropriation.

# **3. Data Description**

# _A. Cash-Flow versus Control Rights_

The analysis is based on newly-assembled data for 2,658 publicly-traded corporations (including both financial institutions and non-financial institutions) in Hong Kong, Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore, Taiwan, and Thailand. As our starting point in the data collection, we use the Worldscope database which generally provides the names and holdings of large owners. The database has been used in previous studies of corporate ownership structure (La Porta et al. (1997), Lins and Servaes (1998)). We supplement the data with information from the Asian Company Handbook 1999, the Japan Company Handbook 1999, the 1997 Annual Reports of the Hong Kong, Jakarta, Seoul, Kuala Lumpur, and Manila Stock Exchanges, as well as with ownership data from the Korean Fair Trade Commission, the Securities Exchange of Thailand Companies Handbook, and the Singapore Investment Guide. We exclude companies which have proxy ownership that cannot be traced to a specific owner. In all cases, we collect the ownership structure as of December 1996 or the end of the 1996 accounting year. We end up with 2,658 companies for which we can trace the ultimate owners, and where stock market data are available. Balance sheet and segment data are available for 2,510 of these corporations.

9
2002. Source: Computations based on UN COMTRADE Statistics.

Bulgaria fits neatly into two different patterns of specialization that appear to have been emerging in Central Europe since 2000. First, like most CEEC-10 economies that ‘plugged-in’ automotive networks, it has been moving towards specialization in automotive parts. While the share of parts in Bulgarian automotive exports was 66 percent in 1999, it fell to 44 percent in 21 See Table 17 above on emerging top performers of electro-engineering industry.
value for this ratio means a lower share of investment in inventories that can be financed by ongoing revenue, suggesting a higher level of liquidity needs that have to be financed externally. The reason for focusing on inventories rather than the overall investment in working capital is to capture the technologically driven exogenous part of liquidity needs. The completion of final goods take longer in certain industries, requiring higher inventory values during the production process. This technological link is arguably weaker for other parts of liquid assets like cash (Raddatz 2006, p. 685).

Following Claessens and Laeven (2003), we measure _Asset intangibility_ as the ratio of the net value of intangible assets (Compustat item 33) to the net fixed assets (Compustat item 8).<sup>11</sup> In Compustat, the intangibles include the value of blueprints or building designs, patents, copyrights, trademarks, franchises, organization costs, client lists, computer software patent costs, licenses, and goodwill. The ratio thus measures for a representative firm in a given industry the relative importance of assets that are less suitable to serve as collateral due to their lack of physical manifestation as opposed to the easily collateralizable fixed assets.

The proxies for our two additional transmission channels also rely on Compustat data on large US firms. Following Fisman and Love (2003), we compute _Trade credit dependency_ as the ratio of accounts payable (Compustat item 70) to total assets (Compustat item 6). The ratio represents the share of total assets that is financed by trade credit, capturing the ability of a representative firm in a given industry to rely on informal credit from its business partners rather than on formal financing from financial markets and intermediaries. We measure _Global growth opportunities_ by the actual growth in real sales for the representative firm in a given industry in the US. This proxy is based on the argument outlined in Fisman and Love (2007) that large publicly traded US firms respond optimally to worldwide industry-specific shocks to growth opportunities, making the actual growth