rafmacalaba/data-use-mentions-extended huggingface dataset:
44 percent of non-account-holders in that country cite cost as a reason for not having a formal account. Analysis finds a significant relationship between cost as a self-reported barrier and an objective measure of costs.

But fixed fees and high costs of opening and maintaining accounts also often reflect lack of competition and underdeveloped physical or institutional infrastructure. These issues seem to be particularly important in Sub-Saharan Africa and Latin America and the Caribbean, where improvements that reduce costs could potentially increase the share of adults with a formal account by up to 24 percentage points.<sup>17</sup> Lack of trust in banks can be a difficult barrier to overcome. This distrust can stem from cultural norms, discrimination against certain population groups, past episodes of government expropriation of banks, or economic crises and uncertainty. In Europe and Central Asia 31 percent of non-account-holders cite lack of trust in banks as a reason for not having an account—a share almost three times that in other regions on average.<sup>18</sup> Religious reasons for not having a formal account are most commonly cited in the Middle East and North Africa and South Asia. In these regions, developing financial products compatible with religious beliefs (Islamic finance) could pay off—potentially increasing the share of adults with a formal account by up to 10 percentage points in the Middle East and North Africa and by up to 5 percentage points in South Asia.

Global Findex data suggest that indirect use of an account is most common in South Asia: 34 percent of adults in the region without a formal account cite another family member already having one as a reason, compared with a global average of 23 percent. Women tend to be more likely to be indirect users as well: in South Asia and the Middle East and North Africa there is a gender gap of about 10 percentage points in citing this reason. A recent study shows that lack of account ownership (and personal asset accumulation) limits women’s ability to pursue self-employment opportunities.<sup>
- Costello, C., S. Polasky, and A. Solow. 2001. “Renewable Resource Management with Environmental Prediction.” _Canadian Journal of Economics/Revue Canadienne d_ ’ _Économique_ 34 (1), 196–211.

- Cota-Nieto, J. 2010. Descripción história y reciente de las pesquerías artesanales de punta abreojos B.C.S., México. periodo 2000–2007., Thesis, Universidad Autónoma de Baja California Sur.

- Deacon, R. T. 2012. “Managing Fisheries by Assigning Rights to Harvester Cooperatives.” _Review of Environmental Economics and Policy_ 6 (2), 258–77.

- Deacon, R. T., D. P. Parker, and C. Costello. 2008. “Improving Efficiency by Assigning Harvest Rights to Fishery Cooperatives: Evidence from the Chignik Salmon Co-op.” _Arizona Law Review_ 50 (2).

- ———. 2013. “Reforming Fisheries: Lessons from a Self-Selected Cooperative.” _Journal of Law and Economics_ 56 (1).

- DeWalt, B. R. 2001. “Community Forestry and Shrimp Aquaculture in Mexico: Social and Environmental Issues.” _presentado en la Latin American Studies Association, Washington DC_ 6.

- Dong, D., B. W. Gould, and H. M. Kaiser. 2004. “Food Demand in Mexico: An Application of the Amemiya-Tobin Approach to the Estimation of a Censored Food System.” _American Journal of Agricultural Economics_ 86 (4), 1094–107.

- Eberhardt, L. 1977. “Relationship between Two Stock-Recruitment Curves.” _Journal of the Fisheries Research Board of Canada_ 34 (3), 425–28.

- Edmonds, E. V. 2002. “Government-Initiated Community Resource Management and Local Resource Extraction from Nepal’s Forests.” _Journal of Development Economics_ 68 (1), 89– 115.

- Foster, A. D. and M. R. Rosenzweig. 2003. Agricultural Development, Industrialization and Rural Inequality
the gap outlined above. Following the COVID-19-induced recession, women’s employment and labour market outcomes have been disproportionately reduced (Titan et al. 2020; AdamsPrassl et al. 2020; Farre et al. 2020; Forsythe 2020; Blundell et al. 2020; Andrew et al. 2020; Benzeval et al. 2020; Collins et al. 2021; Hupkau and Petrongolo 2020; Hipp and Buenning 2021; Alon et al. 2021; Dang and Nguyen 2021). While the impacts of the pandemic on the labour force of developed economies have been widely studied, research on the gender gap in developing countries has been less explored. For the MENA region, the nascent literature provides insights into the employment gap in services and in the care work sectors, where women are disproportionately overrepresented compared to manufacturing (Assaad et al. 2022; Barsoum and Majbouri 2021; Krafft et al. 2022, Marouani et al. 2022; Kraft et al. 2022). It also highlights the lockdown’s effect on the increased burden in female unpaid housework activities (ESCWA 2020; Alon et al. 2022; UN Women Jordan 2020; Kraft et al. 2022). Nevertheless, there has been no equivalent work on the evolution of female employment in manufacturing post-COVID. This paper fills this gap.

We proceed in two ways. First, we look at the evolution of the gender employment gap post COVID-19. Then, we examine the heterogeneous effects of firm characteristics and other controls on the ensuing gap. In doing so, we use a unique firm-level dataset, namely the Egyptian Industrial Firm Behavior Survey 2020/21 (EIFBS). The dataset gives detailed information on employment levels pre and post the COVID-19 breakout, and their gender distribution. Two sets of factors may affect the gender gap. We distinguish between ‘status variables’ or ‘innate characteristics’, and those which are shaped by the behavior of the industrial firm such as managerial practices, investment in innovation or worker training and the adoption of advanced technology. ‘
,710|12,710|
|R-squared|0.281|0.233|0.108|
|Pairwise contrasts||||
|Sequence:||||
|[II] - [I]|0.0558**|0.0861***|0.0284|
|[III] - [II]|0.182***|0.134***|0.0660**|
|[IV] - [III]|0.171***|0.196***|0.09|

Note: Robust standard errors clustered at the village level in parentheses. All regressions include child characteristics (age, grade, gender, and stunting), family characteristics (household wealth, mother’s education, parenting practices), ECED characteristics (average quality, average fees), and district fixed effects.

*** p<0.01, ** p<0.05, * p<0.1

30
US$300/month or less, but with a long right tail stretching into monthly profits that number in the thousands (Figure 8). The average monthly profit reported by the surveyed firms is US$860, which amounts to 30 percent of total monthly revenue. This figure lines up reasonably well with the average reported annual (2010) profit of US$12,396 which works out to just over US$1,000 per month. It is also consistent with the entrepreneurs’ expected profits for the entire 2011 (approximately US$1,090), a somewhat curious result given the general lack of optimism about enterprise growth prospects expressed by entrepreneurs during the focus group sessions (most entrepreneurs complained about growing costs, higher taxes, and increased levels of competition).

**Figure 8: Distribution of monthly profits**

<!-- Start of picture text --> 15<br>10<br>5<br>0<br> 10 30 100 300 850 3,000 10,000 30,000<br>Log of self-reported monthly profits (x-scale in US$)<br>Percent<br><!-- End of picture text --> _Source_ : Authors’ calculations with EMES data.

# **3. How do we model firm profitability?**

The emphasis of this paper is on modeling profitability rather than productivity because the former is the main determinant of firm survival. From the firm owner’s perspective, decisions are normally made based on their impact on (expected) profitability rather than productivity, which most small firm owners do not observe directly. Moreover, while profitability and productivity are obviously linked, more productive firms are not necessarily more profitable, and vice versa. Katayama et al (2009) note that “success ultimately depends upon profits rather than efficiency or product quality,” a view echoed by Foster et al (2008) who show that “selection [for survival] is on profitability, not
|89,265|83,553|
|**Jobs per $1 mil. of exports**|||||
|Direct<br>11|13|9|6|5|
|Total<br>24|29|19|13|11|
|_Whole sample_|||||
|**Jobs per $1 mil. of exports**|||||
|Direct<br>14|17|11|7|6|
|Total<br>31|38|27|16|14|
|No. of countries<br>36|48|73|65|66|

Note: Based on export-weighted data for 32 countries that are present in the database every year since 1997, including: Australia, Austria, Belgium, Bulgaria, Canada, Switzerland, Czech Republic, Germany, Denmark, Spain, Estonia, Finland, Greece, Bosnia and Herzegovina, Hungary, Ireland, Italy, Korea, Rep., Lithuania, Luxembourg, Latvia, Mexico, Netherlands, New Zealand, Philippines, Poland, Portugal, Romania, the Russian Federation, the Slovak Republic, Slovenia, and Sweden.

Table 4 presents various indicators of the job content of exports but for the 15 richest and 15 poorest individual countries in our database in 2011 based on GDP per capita.<sup>18</sup> While the first two columns show the number of jobs aggregated across all sectors directly and indirectly embodied in exports (in thousands), the third column shows the ratio of the two and thus the relative importance of direct employment compared to the employment generated by backward linkages. The fifth and sixth columns show the total number of jobs per $1 million of gross exports and the share in the economy-wide employment (JobD), respectively. Thus these two columns allow comparisons across countries in terms of the job intensity of exports and how important exports were for generating jobs in total employment.

The data suggest that in
readily-available monthly data on such series as international trade, production, or agricultural output? This would make it easier for other countries to build models – based on occasionallyavailable household survey data – that could then be used, along with easy-to-find data series, to track the effects of external shocks on major groups in society, such as the poor, or women, or older individuals. We note, however, that past efforts to derive welfare effects from a small number of readily-available, or easily-collectable, variables have not been successful (Ravallion 1996; Haughton and Khandker 2009). Certainly, the strong seasonal rhythm of agricultural production (see Figure 9) makes it difficult to use monthly agricultural data to help track the patterns of household consumption.

<!-- Start of picture text --> 250 14<br>12<br>200<br>10<br>150 8<br>6<br>100<br>4<br>50<br>2<br>0 0<br>2005 2006 2007 2008 2009 2010 2011 2012 2005 2006 2007 2008 2009 2010 2011 2012<br>Paddy rice production, m tonnes<br>Agricultural production index (2005 = 100)<br><!-- End of picture text -->

**Figure 9.  Index of Agricultural Production (left) and Volume of Rice Output (right), Thailand, 2005-2012**

Source: Reported by Bank of Thailand, based on data from Office of Agricultural Economics, Ministry of Agriculture and Cooperatives.

The approach we take here is to regress real consumption per capita (for each household), from the socio-economic survey data for 2007 through 2010, on a variety of household level variables (the “survey regression” variables listed in Table 8) and on a number of data series that are published monthly and are readily available (“public data regression” variables in Table 8). If there is a close fit between the public data and per capita
evolution of such disagreements has been shown to affect the probability distribution of forecast errors (Bachman, Elstner and Sims 2012; Patton and Timmerman 2010). Three risk indicators are used in this exercise:<sup>2</sup>

- **Equity prices.** Equity prices futures—especially the Standard and Poor’s S&P500 Index—are positively correlated with prospects for the U.S. and global economy. Their implied volatility signals changes in global investors’ risk aversion (Adrian and Shin 2014, Bekaert, Hoerova and Lo Duca 2013). The implied volatility of the S&P 500 returns at 6- and 18-months-ahead option contracts at 100 percent moneyness is obtained from Bloomberg and used as a proxy for equity market uncertainty.

- **Term spreads.** Term spreads (difference between long and short-term nominal interest rates) embed both inflation and real equilibrium interest rate expectations, both of which are tightly connected to growth prospects (Cieslak and Povala 2014). A rapid decline in term spreads is seen as a predictor of increased recession risks.<sup>3</sup> The dispersion of term spread forecasts therefore captures uncertainty surrounding growth prospects while a left hand shift in their distribution signals a predominance of downside risks. Current and nextyear term spread forecasts are compiled from monthly surveys published by Consensus Economics from January 2006 to March 2016. A global term spread is proxied by GDPweighted (at 2010 prices and exchange rates) term spreads across G20 economies.

- **Average of Brent and WTI crude oil forward prices.** Abrupt changes in oil prices make growth prospects more uncertain. A supply-driven decline in oil prices tends to improve global growth prospects (Baffes et al 2015; Kilian 2014). As in the case of S&P 500, the implied volatility at 100 percent moneyness of Brent and WTI crude oil prices at 6- and 18-months-ahead option contracts are obtained from Bloomberg and used as a proxy for crude oil market uncertainty. Crude oil prices
**China − US − Other (unweighted)**

<!-- Start of picture text --> Avg. Country−pair including China<br>Avg. Country−pair including USA<br>Other Country−Pairs<br>1992 2000 2008<br>0.6<br>0.4<br>0.2<br>Corr(GDP−HP)<br>0.0<br>−0.2<br><!-- End of picture text -->

**Figure 2.** Source: WDR 2020 team, based on World Bank’s World Development Indicators (database). Note: The blue line represents the average of all pair-wise GDP correlation taken over all country-pairs that include China. The orange line represents the average taken over all countries that include the United States. The grey line represents country-pairs that contain neither the United States nor China. The date corresponds to the midpoint of a 10-year rolling window.

# **3.2 Link Between Trade and GDP Co movement: overview**

In figure 3, we show the linear relationship (in cross-section) between total trade intensity and GDP correlation. An increase in trade intensity is significantly associated with more GDP co movement. Figure 4 refines this analysis and captures the changes in intermediate input trade as opposed to total trade. As a first pass, and without controlling for country-pair specific and time window specific characteristics, we observe a positive relationship between trade in intermediate inputs and GDP synchronization. The objective of the following analysis is to test, in a more formal and rigorous way, the empirical relationship between trade proximity (separated between final goods and intermediate inputs) and GDP co-movement, controlling for unobserved heterogeneity (through fixed effects), financial integration, sectoral composition and trade network (through third country indexes).

11
260 USD) per month in 2015 to 1300 TL (about 350 USD) in net terms.<sup>10</sup> With an inflation rate of 8.8 percent in 2015, the increase represented a rise of about 24 percent in real terms. By contrast, the minimum-wage increases in previous years had ranged from 5 percent to 8 percent annually in nominal terms, levels that were roughly aligned with consumer expectations and realizations about inflation (Figure 2). To alleviate possible negative employment effects of the 2016 minimum-wage increase, the government introduced a subsidy of 100TL of employers’ social security contributions for employees whose wages were at a level below twice the minimum wage of 2015. As a result, the cost of minimum wage to employers increased by about 26 percent in January 2016.

The increase in the minimum wage was largely politically motivated and driven by <mark>the electoral competition in 2015 (Akgunduz et al., 2019). A</mark> ll political parties promised a sharp increase in the minimum wage in their campaigns before the November 2015 elections, and they competed with one another over the magnitude of the raise promised. The ruling Justice and Development Party (AK Party), which was re-elected, promised a net minimum wage of 1300 TL. Consequently, the new minimum wage was set at this level on January 1<sup>st</sup> , 2016. The political process that led to the new minimum wage suggests that the change was largely <mark>exogenous to the internal dynamics of the economy, and instead was mainly driven by the exogenous political competition.</mark> The 2016 minimum wage thus appears to constitute a valid experiment to study the causal effects of the minimum-wage increase on firms and employment.<sup>11</sup>

# **3. Data**

## 3.1. The Turkish Enterprise Information System

The primary data source used in this paper is the Turkish Enterprise Information System (EIS), an
Policy Research Working Paper 10662

# **Abstract**

While the travelers’ gender has not been a central consideration driving urban mobility planning, increasing evidence points to gender-differentiated mobility preferences and behaviors. This paper explores this topic in the context of the Buenos Aires Metropolitan Area, aiming to identify policy relevant differences between the mobility of women and men. It does so by leveraging mobile phone–based data, combined with existing household travel survey data and an original large-scale interception survey implemented in late 2021 and early 2022. The paper provides descriptive analysis of key spatial and temporal mobility patterns as well as implements statistical analysis to identify whether gender represented a key determinant of mode choice in the context of the pandemic. The analysis finds that women in the Buenos Aires Metropolitan Area travel less, tend to have shorter individual trips, and are much more likely than men to travel during off-peak hours, including due to disproportionately taking on “care mobility” responsibilities. In terms of mode choices, women represent the majority of public transport users and are more likely than men to say they would cycle. However, women’s share among public transport users and their actual cycling and walking appear to be spatially correlated with, respectively, the availability of public transport services and dedicated, safe infrastructure. The travel responses to the pandemic documented in the original survey data also suggest that women are more likely than men to switch from public transport to private motorized modes as soon as their incomes allow them to unless appropriate health safety measures are put in place.

This paper is a product of the Transport 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/prwp. The authors may be contacted at astokenberga@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
# **Appendix 1** (continued)

|**Variable**|**Description**|**Source**|
|---|---|---|
|Lending rate|Lending interest rate (percent) 2007|IFS|
|Market capitalization/GDP|Market capitalization of listed companies as percent of GDP, 2007|World Development Indicators|
|Net interest margin|Net Interest Margin, 5‐year average (2002‐2007)|Financial Structure Database|
|Number of outstanding loans|Regulatory agency provided the latest estimate for the total number of|Financial Access Database|
|to SMEs|outstanding loans to SMEs|(2010)|
|Number of SMEs with<br>outstanding loans|Regulatory agency provided the latest estimate for the total number<br>of SMEs with outstanding loans|Financial Access Database<br>(2010)|
|Offshore financial center|Equals one if the country was defined by the IMF as an offshore center in<br>2008|IMF|
|Overhead costs/total assets|Bank Overhead Costs divided by Total Assets, 5‐year average (2002‐2007)|Financial Structure Database|
|Periodic reporting|If regulatory agency monitors the level of lending to SMEs through<br>periodic surveys of financial institutions|Financial Access Database<br>(2010)|
|Regular reporting|If regulatory agency monitors the level of lending to SMEs through regular<br>reporting|Financial Access Database<br>(2010)|
|Reporting from credit registry|If regulatory agency monitors the level of lending to SMEs using estimates<br>from credit registry|Financial Access Database<br>(2010)|
|Self‐employed|Self‐employed as percent of total employed, 2007|World Development Indicators|
|Share of rural population|Rural population (percent of total population) 2007|World Development Indicators|
|SME definition ‐ loan size|Regulatory agency uses loan size
remittances (Pooled waves)**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcomes as % i|n household inco|me)||
|selected|2_._21*|0_._13|0_._27|0_._78*|5_._13***|
||(1_._14)|(0_._59)|(0_._53)|(0_._46)|(1_._50)|
|N|350|109|350|100|349|
|Nominated mean|5_._71|1_._53|4_._31|1_._37|6_._31|
|Nominated SD|4_._52|1_._63|3_._58|1_._51|9_._39|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from wife’s demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. The sample in Panel C, Column 2 (Column 4) is limited to those respondents with a daughter (son), or a female (male) child in the household.

**Table C.23: Phone surveys—Effects on women’s decision-making and expenditures**

||(1)<br>Involvement in<br>decisions(Index)|(2)<br>Women’s and girls’<br>expenditures<br>(Taka