rafmacalaba/data-use-mentions-extended huggingface dataset:
Section 3 outlines the empirical model. The results from the main analysis and the heterogeneity analysis are presented in Section 4. Section 5 concludes with policy implications.

# **2 Data**

## **2.1 Data sources**

The main source of data for this study is the commercial database ORBIS compiled by the Bureau van Dijk. The data set I use contains the main financial indicators for firms operating in Russia in the period 2007-2016. It also contains very detailed information on ownership, which is however limited to the last available update. To obtain ownership information for the earlier points in time, I enhance the data set with two additional ‘historical’ exports from ORBIS. This includes a data export from 2012 (with ownership updated mostly in April and September of that year) and a data export from 2015 (with ownership updated mostly in July of that year) that are merged with the main data based on a unique firm identifier.

I combine the ORBIS data for manufacturing firms with detailed information on exports at the level of firm, 10-digit HS product, destination country and year, available for 20122016 from the Russian Customs. I am able to merge the export data with the ORBIS data based on a unique firm tax reference number, common to both data sets.

I also use the Russian input-output table for 2011. The input-output table follows the 3-digit NACE classification, while the ORBIS data include 4-digit NACE codes. This allows me to match the firm-level data with the information from the input-output table.

## **2.2 Variable**

### **2.2.1 Product quality**

I use two proxies for the quality of exports. The first proxy is the unit value (i.e., the monetary value divided by the physical weight). I focus on a very narrow product definition, 5
# Table 1: Sources of Weather Data

|dataset|Length of record|Resolution|Time step|Data|Units|
|---|---|---|---|---|---|
|**Precipitation**<br>||||||
|-Africa Rainfall Climatology version 2 (ARC2)|1983-current|0.1 deg|daily|total precip|mm|
|-Climate Hazards group InfraRed Precipitation with Station data<br>(CHIRPS)|1981-current|0.05 deg|daily|total precip|mm|
|-CPC Global Unifed Gauge-Based Analysis of Daily Precipitation|1979-current|0.5 deg|daily|total precip|mm|
|-European<br>Centre<br>for<br>Medium-Range<br>Weather<br>Forecasts<br>(ECMWF) ERA5|1979-current|0.28 deg|hourly|total precip|m<br>|
|-Modern-Era Retrospective analysis for Research and Applica-<br>tions, version 2 (MERRA-2) Surface Flux Diagnostics|1980-current|0.625x0.5 deg|hourly|rain rate|kg m<sup>2 </sup>s<sup>1</sup>|
|-Tropical Applications of Meteorology using SATellite data and<br>ground-based observations (TAMSAT)|1983-current|0.0375 deg|daily|total precip|mm|
|**Temperature**||||||
|-CPC Global Unifed Gauge-Based Analysis of Daily Temperature|1979-current|0.5 deg|daily|min, max temp|C|
|-European<br>Centre<br>for<br>Medium-Range<br>Weather<br>Forecasts|1979-current|0.28 deg|hourly|mean temp|K|
|(ECMWF) ERA5<br>31||||||
|-Modern-Era Retrospective analysis for Research and
depends on GDP per capita (Jetter et al. 2015) and how mature the democracy is (Rock 2009). Notwithstanding the mixed nature of these findings, the possibility of a systematic correlation between corruption and democracy cannot be ruled out. If the level and quality of democracy also varies systematically with country size, our main results could be spuriously affected. We guard against this possibility by controlling for the quality of democracy in the country using the “Polity” measure from the Polity IV database.

Next, for civil society to play an important role in the fight against corruption,<sup>9</sup> an enabling institutional environment is needed in terms of freedom of press, ability of citizens to voice their dissatisfaction against the government without any fear of political oppression and unjust imprisonment (see for example, Themudo 2013). Similarly, the quality of bureaucracy and its independence from political pressures, the quality of policy formulation and implementation, the credibility of the government's commitment to such policies can have significant effects on the level of corruption. If the level of regulation also varies systematically with the quality of bureaucracy or protection against political pressure and unjust imprisonment, our main results could suffer from the omitted variable bias problem. Thus, we control for the quality of bureaucracy using the Government Effectiveness measure from Worldwide Governance Indicators. For protection against political pressure and unjust imprisonment, we control for a subcomponent of the Rule of Law measure from Freedom House’s Freedom in the World data set. The sub-component aims to capture if there is protection from political terror, unjustified imprisonment, exile, or torture, whether by groups that support or oppose the system, and if there is freedom from war and insurgencies (Rule of Law (Protection)).<sup>10</sup>

> 9 For the role of civil society in combatting corruption, see for example OECD (2003), World Bank (1997), Themudo (2013), Lambsdorff (2005) and McCoy and Heckel (
outcomes for the representative households<br>into a separate module that contains additional information about each household.|
|**Data/information:**<br>**s**|RH models require a SAM and distributional data describing the representative household groups or,<br>more specifically, a household survey.|
|**Time:**<br>**ement**|Only a few days to generate a base solution if data and skills are available. Between six months and a<br>year to collect data and work with the simulations.|
|**Skills:**<br>**quir**|Experienced modelers with substantial prior exposure to CGE models are required.|
|**Supporting**<br>**software:**<br>**Re**|Excel, EViews, Gauss.|
|**Financial cost:**|US$25,000–75,000 depending on the data that exist.|
|**Limitations:**|In the absence of a CGE model to feed into the RH module, the model is data intensive and difficult to<br>master.|
|**References and**<br>**applications:**|�<br>For an overview, see Löfgren, Robinson, and El-Said (2003).<br>�<br>Robilliard, Bourguignon, and Robinson (2001) on Indonesia.<br>�<br>Coady and Harris (2001) on Mexico.<br>�<br>Löfgren,Harris,and Robinson(2002).|

64
_100 km_<sup>_2_</sup> enters positively and significantly in the branch and ATM penetration and deposit indicator regressions, but not in the two loan indicator regressions. _Telephone Mainlines per Capita_ enters significantly in all regressions except for the loan-income ratio regression.

While these correlations and regressions are suggestive of economic relationships between banking system outreach and other country characteristics, they have to be interpreted with caution. In the absence of a more structural model, we are silent on whether our results reflect the effects of demand or supply factors and on the causality chain between banking system outreach and other country characteristics.

# **6.  Banking Sector Outreach and Financing Obstacles of Firms**

This section shows that the outreach indicators introduced in this paper are significantly associated with cross-country variations in firm-level survey indicators of financing obstacles. Specifically we show that: (i) our indicators of outreach capture important dimensions of financial sector development beyond financial depth; and (ii) banking system outreach is associated with lower levels of financing obstacles for firms. Given the literature that establishes the importance of relaxing financing obstacles for firm growth,<sup>19</sup> these results also suggest that broader financial sector outreach matters for economic development. Below we introduce the firm-level survey data and the methodology before discussing our empirical findings.

# **6.1.  Firm Survey Data**

To assess the relationship between the outreach indicators and firms’ financing obstacles, we use data from the World Business Environment Survey (WBES), a unique database of firm-

> 19 See for example Beck, Demirguc-Kunt and Maksimovic (2005).

19
In most of these other studies, as well the authors generally find that the inclusion of second order effects has only a marginal impact on the results that are found from considering only the firstorder terms.

Other studies have sought to identify the impact of food price spike on consumption measures. For example, in a study focused on Ethiopia (Alem & Söderbom, 2012), the authors focus on estimating the effect of food prices on household consumption of food, and self-reported measures of the effect of the shock on the quantity of food consumed and overall effect as perceived by the household head using three rounds of panel data. Their results indicate that those households with low levels of assets are likely to be more vulnerable to food price shocks.

In other related work Verpoorten, Arora, Stoop, & Swinnen (2013) examine the impact of the price rise using self-reported food insecurity measures from the Afrobarometer data set for two periods – 2005 and 2008. Their data set covers numerous countries including Mozambique. The authors argue that these self-reported measures provide a better picture of the impact of food price changes on food security rather than consumption data from household surveys, which might be prone to measurement error. Other studies have pointed towards this issue, among other potential problems that arise when using survey reported measures of consumption to measure welfare impact (Headey & Martin, 2016). Using the self-reported measures for Africa Verpoorten et al. find that there was only a small increase in the incidence of food insecurity and decreases in the depth of food insecurity, contrary to the conclusions widely drawn in other studies.

# **2.3 Food price impacts in Mozambique**

There is a previous study focusing on the effect of the 2007-08 food and fuel price spike on household welfare in Mozambique (Arndt et al., 2008). The authors use data from the 2002/03 National Household Consumption Survey (Inquérito aos Orçamentos Familiare (IOF) 2002-03) which provides information on income from the sale of agricultural output and on own consumption
Policy Research Working Paper 8758

# **Abstract**

There is a growing body of literature exploring the skill content of jobs. This paper contributes to this research by using data on the task content of occupations in developing countries, instead of U.S. data, as most existing studies do. The paper finds that indexes based on U.S. data do not provide a fair approximation of the levels, changes, and drivers of the routine cognitive and nonroutine manual skill content of jobs in developing countries. The paper also uncovers three new stylized facts. First, while developed countries tend to have jobs more intensive in nonroutine cognitive skills than developing countries, income (in growth and levels) is not associated with the skill content of jobs once the analysis accounts for other factors. Second, although adoption of information and communications technology is linked to job de-routinization, international trade is an offsetting force. Last, adoption of information and communications technology is correlated with lower employment growth in countries with a high share of occupations that are intensive in routine tasks.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at hwinkler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those
<mark>Demographic and Health Survey (DHS) data, Young (2012) shows that since 1990, real material consumption in Sub-Saharan Africa has been rising at a rate of 3.4 to 3.7 percent per year. This is 3 to 4 times higher than the number usually reported by international data sources like the Penn World Tables (PWT) and the UN: .9 - 1.1 percent. Non-African developing countries in the DHS sample exhibit a growth rate of 3.8 percent. When the data are examined by product group, Young (2012) finds greater Sub-Saharan growth in durable goods (5.6 percent) but lower growth in housing (1.8 percent). Although various sources claim Sub-Saharan Africa is growing at less than half the pace of other developing countries, DHS data suggest African growth is easily comparable to that of other economies. McMillan and Harttgen (2014) explore the reason behind the African growth and fill in some of the explanatory gaps in Young (2012).  The African “miracle” can be traced to a decline in the share of the labor force that engages in agriculture. The paper links improvements in living standards found by Young (2012) to sectoral changes: between 2000 and 2010 the share of the labor force employed in agriculture in sub-Saharan Africa declined by approximately 10 percentage points. With this came a 2-percentage point increase in manufacturing employment and 8-percentage point increase in services.  Between 2000 and 2010, structural change contributed 1 percentage point to labor productivity growth. Essentially the shift of employment from agriculture to more productive sectors explains some of the growth in real consumption documented by Young (2012).</mark>

3
increase in food commodity prices is that Americans tend to consume highly processed foods. When U.S. consumers purchase foods from supermarkets, convenience stores, or restaurants, a large fraction goes to cover labor associated with preparing, serving, and marketing the food.<sup>5</sup> Similar patterns are observed in other developed countries. This is not the case in developing countries. The poor spend a larger fraction of their income on food, whereas the typical American spends slightly less than 14% of total expenditures on food.<sup>6</sup> In contrast, Africans spend 43% of their expenditures on food,<sup>7</sup> and those subsisting on less than one dollar per day in Sub-Saharan Africa may dedicate as much as 70% of their expenditures to food.<sup>8</sup> We need a statement that the global food index assigns greater weight to raw grains unlike the U.S. CPI where the basket is processed foods. And we should refer to rich nations in general

> _5USDA, http://www.ers.usda.gov/Data/FarmToConsumer/Data/marketingbilltable1.htm 6U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, 2006. ftp://ftp.bls.gov/pub/special.requests/ce/share/2006/income.txt. Typical American refers to an individual at the median income level._

> _7Federal Reserve Board Staff calculation, IMF, and World Bank._

> _8The International Food Policy Research Institute 2020 Discussion Paper No. 43, “The World’s Most Deprived.”_

6
- Fernandez, R. (2007). “Women, work and culture.” _Journal of European Economic Association_ , 5(2), pp. 305-333.

- Ferrant, G., L.M Pesando, and K. Nowacka. (2014). “Unpaid Care Work: The Missing Link in the Analysis of Gender Gaps in Labour Outcomes”. OECD Development Centre.

- Floro, M. S., and M. Miles (2003). Time Use, Work and Overlapping Activities: Evidence from Australia. _Cambridge Journal of Economics_ , 27, pp. 881–904.

- Floro, M. S, and H. Komatsu. (2011). “Gender and Work in South Africa: What Can Time-Use Data Reveal?”. _Feminist Economics,_ 17(4): 33–66.

- Folbre, N. (2006). “Measuring Care: Gender, Empowerment, and the Care Economy.” _Journal of Population Economics_ , 7(2), pp. 183–199.

- Folbre, N., and J. Yoon (2007a). “What Is Child Care? Lessons from Time-use Surveys of Major English-speaking Countries.” _Review of Economics of the Household_ , 5, 223–248.

- Folbre, N. and J. Yoon. (2007b). “The Value of Unpaid Child Care in the U.S. in 2003.” In _How Do We Spend Our Time? Recent Evidence from the American Time-Use Survey_ , J. Kimmel. Kalamazoo (ed.), Michigan: W. E. Upjohn Institute for Employment Research.

- Folbre, N., J. Yoon, K. Finnoff, and A. Sidle Fuligni. (2005). “By What Measure? Family Time Devoted to Children in the United States.” _Demography_ 42(2), pp. 373–390.

- Foster, G., and L.S. Stratton. (2017). “Do Significant
|Fraction of poor HHs that have some welfare-relevant assets|33%|68%|
|---|---|---|
|Fraction of poor HHs that have a private car/motorcycle or bike|15%|24%|
|Fraction of poor HHs that have any washing machine (electric, semi-<br>automatic, or automatic)|16%|53%|
|Fraction ofpoor HHs that have a smartphone|8%|26%|

**Source** : Authors’ calculations based on HIECS 2015.

**Note** : “Low education” includes all illiterate individuals. “Currently not working” includes the unemployed and people not actively looking for a job. “Dependent” is any individual who is 65 years old (or older) or 18 years old (or younger).

# **3.2 Poverty figures in 2017/18**

Table 3 presents our estimated poverty rates for all regions in 2017/18. Our estimate of the national rate in 2017/18 is 31 percent. As in 2015, the two regions with the highest poverty rates are Upper Urban and Upper Rural Egypt, while the two regions with the lowest poverty rates are Lower Urban Egypt and the Metropolitan Areas.

_Table 3: Contrasting 2017/18 poverty estimates based on total expenditures with official poverty rates_

|Statistic|National|Metropolitan|<sup>Lower</sup><br>Urban|Lower<br>Rural|Upper<br>Urban|Upper<br>Rural|Borders|
|---|---|---|---|---|---|---|---|
|% of the population*|100%|6%|8%|30%|14%|39%|3%|
|Per capita total<br>expenditures*|7366|9626|8560|6886|7507
# **Appendix D. Variable Descriptions and Definitions, Gallup World Poll**

|**Variable**|**Question**|**Scale**|
|---|---|---|
|SWB|Please imagine a ladder, with steps numbered from 0 at the<br>bottom to 10 at the top. The top of the ladder represents the<br>best possible life for you and the bottom of the ladder<br>represents the worst possible life for you. On which step of the<br>ladder wouldyou say youpersonallyfeelyou stand at this time?|0 (worst possible<br>life) - 10 (best<br>possible life)|
|Age & age<sup>2</sup>/100<br>||Age inyears<br>|
|Gender||Male/female|
|Has a partner|Dummy taking the value of 1 if the respondent is married or has<br>a domesticpartner,and 0 otherwise.|1=yes; 0=no|
|Has children under 15|Dummy taking the value of 1 if the respondent has children<br>under 15 livingin one’s household,and 0 otherwise.|1=yes; 0=no|
|Immigrant|Wereyou born in this country,or not?|1=no;0=yes|
|Religious|Is religion an importantpart ofyour dailylife?|1=yes;0=no|
|Has health problems|Do you have any health problems that prevent you from doing<br>anyof the thingspeopleyour age normallycan do?|1=yes; 0=no|
|Had physical pain<br>yesterday|Did you experience the following feelings during a lot of the day<br>yesterday? How aboutphysicalpain?|1=yes; 0=no|
|Lives in a rural area|The respondent’s self-reported type of settlement: (
however, the richest perceived considerably greater improvements than the poorest. Indeed, those belonging to the bottom four classes only saw a marginal increase in the share of “Improved”, while the largest differences are observed only in the upper classes (+17 percent more). On the positive side, there is a sharp decline in the share of those whose situation worsened, so much so that the share of those in the lower classes declaring they faced improved conditions is about the same as the share of those declaring a decline; in addition, the further up we progress in welfare distribution, the more the share of “Declined” declines against the share of “Improved”.

Finally, it is very important to look at the trend of those declaring “Not changed”. This trend is also monotonic and declining as in the share of households declaring a worsening, but its interpretation deserves particular attention: after a not particularly rewarding decade, the fact that the lower classes were declaring no change has a completely different meaning than for those of the upper classes declaring the same thing. This may be a positive outcome for households belonging to the top classes but represents a negative outcome for those belonging to the bottom classes: in the latter case, nothing has changed, even after a long and sustained period of growth and rapid poverty reduction.

It is also interesting to compare the “objective” social status (class) with the perceived class. Unfortunately, this specific section of the questionnaire was only included in the 2006 survey, so we could only compare it to the results of the 2013 survey. We focused our attention on the question: “In what social level do you rank your household in comparison with what prevails in your social environment ( _douar_ or neighborhood)?” 15
Policy Research Working Paper 6066

# **Abstract**

Despite their increasing prominence in policy debates, little is known about gender inequities in nonagricultural labor market outcomes in rural areas. Using matched household-enterprise-community data sets from Bangladesh, Ethiopia, Indonesia and Sri Lanka, this paper documents and analyzes gender differences in the individual portfolio choice and productivity of nonfarm entrepreneurship. Except for Ethiopia, women are less likely than men to become nonfarm entrepreneurs. Women’s nonfarm entrepreneurship isn’t strongly correlated with household composition or educational attainment, but is especially prevalent amongst women who are the head of their household. Female-led firms are much smaller and less productive on average, though gender differences in productivity vary dramatically across countries. Mean differences in log output per worker suggest that male firms are roughly 10 times as productive as female firms in Bangladesh, three times as those in Ethiopia and twice as those in Sri Lanka. By contrast, no significant differences in labor productivity were detected in Indonesia. Differences in output per worker are overwhelmingly accounted for by sorting by sector and size. They can’t be explained by differences in capital intensity, human capital or the local investment climate, nor by increasing returns to scale.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at brijkers@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors