rafmacalaba/data-use-mentions-extended huggingface dataset:
|Yes|Yes|Yes|
|Observations|11,714|11,714|11,730|11,730|4,861|4,861|6,864|6,864|
|R-squared|0.064|0.060|0.011|0.010|0.456|0.454|0.472|0.465|

Robust standard errors clustered by country and year in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: - We control for firms’ age, size, city of operation and the share of government ownership.

- Country dummies have been removed from panel (b) given the high collinearity between countries and provisions. Moreover, our sample drops when we focus only on the MENA region. - The intercept is included.

# **4.2.2. Internal Factors**

In addition to external factors, there is growing evidence that an adverse business environment impedes firms’ performance and hence negatively affect women’s participation. Thus, the impact of GVC on women can also be moderated through the characteristics of the investment climate. This can be explained by two main reasons.

Investment climate affects trade performance and GVC integration. Dovis and Zaki (2020) show that the number of days that are required to pay taxes, the number of procedures that are necessary to register property, and the time to export and to import have a significantly negative relationship with the likelihood of a firm’s integration into a GVC. In the same vein, Aboushady and Zaki (2019), using the WBES for Egypt, show that access to finance, tax payments and competition from the informal sector affect the firms’ decision to become exporters, which is a part of GVCs. Second, several constraints hinder women’s participation. Indeed, ILO (2021) argues that women’s access to finance and markets, their
# **I. Introduction**

What started as a crisis in the sub-prime mortgage market in the United States in late 2007 quickly became a full-fledged global financial crisis. The financial crisis led to a massive worldwide economic slowdown, with gross worldwide product shrinking by 2.1 percent in 2009.<sup>2</sup> Although all regions were affected, Eastern Europe and Central Asia (ECA) was hit harder than most. After growing at an annual rate of 7.1 percent in 2007 and 4.2 percent in 2008, GDP in the region contracted by 5.3 percent in 2009—a larger contraction than in any other region (World Bank, 2010). This was accompanied by a large drop in private inflows of capital. In 2007, $491 billion (16 percent of GDP) of private capital came into the region. By 2009, this had fallen to $84 billion. The combination of a drop in demand and a drop in the availability of external financing affected firms throughout the region.

This paper uses two sources of data to assess how the crisis affected different firms in the ECA region. The first, a panel of firms that were interviewed in 2002, 2005, and 2008-9 as part of the Business Environment and Enterprise Performance Surveys (BEEPS), allows us to document how financial constraints evolved over time and to see how firm and country characteristics affected these constraints during the crisis. The second dataset, from the Financial Crisis Surveys (FCS) that were conducted as follow-ups to the BEEPS in six countries in 2009, allows us to look at how changes in access to financing affected firm survival rates during the crisis.

Other papers have used BEEPS data to study access to and use of financial services within the ECA region (Brown et al., 2011; Ongena and Popov, 2010; and Popov and Udell, 2010). Brown et al. (2011), which is the most similar to our analysis, uses data from the 2005
by 3.6% in nominal tenns and 12.1% in real terms.<sup>7</sup> However, as Reyes et al. acknowledge, even these before-after comparisons are problematic for the Philippines due to non-comparability of the income and consumption modules across the two surveys (see below for details). The before-after comparisons also run into the problem of a misspecified counterfactual. Even for a systemic shock, "before" estimates may not be a good approximation of the estimates "in the absence of a shock".

A different approach has been used for Thailand and Korea (Kakwani 1998, Kakwani and Prescott 1999) where the counterfactual level of an indicator of interest is constructed by obtaining a predicted value from past trends of the indicator up to the crisis. Thus, if _y,_ is the value of, say, the poverty indicator in the crisis period t, and y, is its predicted value based on past trends, then a crisis index for the poverty indicator is defined as ((y, _/yt) - 1)_ and it measures the percentage change in poverty due to the crisis. This approach, though unimplementable for the Philippines for lack of comparable post-crisis distributional data, is also methodologically problematic on two counts. First, it is not clear over what period should one estimate trends prior to the crisis, particularly so for a country such as the Philippines which has had a checkered history of booms and busts (Lim 1998). In the end, the choice of the estimation period often becomes an arbitrary expedient of data availability. Second, since the counterfactual is constructed using unconditional trends, the approach attributes a 100% of the departure from trend to the crisis, thus making no allowance for changes in other non-crisis determinants of living standards.

7There were some studies done before the APIS data became available in 1999. For instance, Reyes and Mandap ( 1999) used an existing CGE model to simulate the likely impact of the crisis on incomes.
Most research on the division of household tasks has been conducted using timeuse surveys for the United States and Scandinavian countries. Bianchi, Milkie, Sayer, and Robinson (2000) attempt to disentangle the contributions of three previous theories in explaining the evolution of the gender division of household tasks. Using time-diary data from representative samples of U.S. adults, they find more support for the timeavailability and relative-resource models of household production than for the gender perspective, although there is some support for the latter perspective as well.

Evertsson and Nermon (2007) investigate the gender distribution of household tasks in Sweden using data from the Swedish Level of Living Survey for the years 1991 and 2000. The analysis shows that changes in spouses’ relative resources only resulted in a moderate change in women’s share of the housework between 1991 and 2000. The changes that do occur indicate that women’s share of housework decreases if their relative resources, in terms of education level and social status, increase during the time span analyzed. If a woman’s economic dependency on her spouse decreased during these two years, her share of the housework also decreased. The decrease in women’s share of the housework is mainly due to an increase in men’s time spent on housework. However, regardless of access to resources, Swedish men perform less housework than Swedish women.

# **<<A>>The Effect of Gender Policies on Men’s Behavior and Attitudes toward Women**

The evidence summarized in the previous section identifies an important role for men in the well-being of women. Men clearly influence women’s health outcomes by controlling reproductive methods and access to health care facilities. Men can damage women’s health through their violent behavior. There is also evidence that culture and public institutions that favor men and place women in a residual position (patriarchal societies and certain kinship norms) limit women’s autonomy in many ways. In developing countries, these social structures favor the persistence of son preference and have
# **3. Results and Discussion**

# **Disability Prevalence**

Disability prevalence estimates are shown in Figure 3.1 and Appendix 2. The top panel of Figure 3.1 gives the prevalence of any difficulty at the individual level from the lowest to the highest. The prevalence of any difficulty ranges from a low of 4% in the Philippines to a high of 28% in Papua New Guinea. The median among the 24 data sets stands at 10%. Functional difficulties tend to be more common in rural areas, affect older individuals more, as well as women more often than men (Appendix 6 and 7).<sup>4</sup> Figure 3.1: Prevalence of functional difficulties (%) <!-- Start of picture text --> Among Adults 15+<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Any Difficulty (yes/no) At least a lot of difficulty Some difficulty<br>Among Households<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Any Difficulty (yes/no) At least a lot of difficulty Some difficulty<br><!-- End of picture text --> 4 The case of South Africa will require a closer look as results from the Census and the General Household Survey are different in terms of prevalence, especially by rural/urban areas.

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# **Table 6 International Remittances**

||**Percentage**|
|---|---|
|Transferred money to someone living in an-<br>other country or in a U.S. territory within past<br>year|20.0|
|**Most frequent means used to transfer funds**||
|Wire thefunds|44.8|
|Mailamoney order|23.8|
|Carry the cashpersonally|4.5|
|Bank-to-banktransfer|4.4|
|Maila check|3.7|
|Use a courier|2.9|
|Other|4.6|

The households in the OCC survey reported using a variety of forms of consumer credit. As shown in Table 7, 56 percent of individuals with bank accounts had a major credit card but only 8 percent of those without banks accounts did. When banked individuals looked for a loan, the largest percentage reported applying to a bank. In the case of the unbanked, the largest percentage turned to a family member. In terms of their use of nonbank forms of credit, 16 percent of the banked reported that they had obtained a credit card cash advance at some time in the previous 3 years, 10 percent had used a layaway plan to purchase a consumer good, and 5 percent had obtained a loan against their expected tax refund. In the case of the unbanked, 10 percent reported that they had used a layaway plan and 4 percent had obtained a tax refund loan. It is likely that the unbanked make much less use of bank loans and credit cards because they would not be able to pass traditional risk screening procedures. Interestingly, when the OCC survey asked if the respondents could borrow $500 for 3 months if the need arose, 68 percent of the banked were confident that they could do so as were 51 percent of the unbanked. This suggests that about a third of the banked and half of the unbanked in the survey are
6%|60.4% 56.0% 62.8% 63.9%|
|Of which Manufacturing|33.2%|42.4%|39.5%|39.2%|47.0% 39.4% 44.9% 53.6%|
|Services|30.6%|38.4%|25.5%|30.6%|34.6% 38.1% 31.6% 31.9%|

_Source_ : Author calculation using data from WDI

> 5 The commune system inflicted great hardship especially in the less fertile areas of the country.  By the late 1970s, the strength of grass roots opposition forced a loosening of the commune imposed restraints in Anhui and Sichuan. In Anhui, the first steps from the team-based system back to household farming in the Fengyang region were supported by Party Secretary Wan Li. Zhao Ziyang backed similar moves to the household responsibility system in Sichuan (Hutchings 2001; Perkins and Yusuf 1984).

5
drugs excluded.

The collection of consumption data also varies from country to country. Consumption data are generally collected using two methods, (i) diary – where the household records all the consumption data over a certain period in a notebook, or (ii) recall – where the households list what they has consumed for the past few days from memory. While the diary may seem to be a better method, in practice, interviewers often assist in completing the diary, effectively blurring the line (Beegle, et al., 2012). Sri Lanka uses both diary and recall methods to collect consumption data, Maldives uses a diary for all items, while other countries use recall to collect the data.

The length of consumption recall is another source of differences. Respondents are asked to recall their food consumption for the day, last week, last two weeks, and last month. For food items, Bangladeshi enumerators spend two weeks in the primary sampling unit (PSU), visiting each household in the PSU seven times in a two-week period. During each visit, the household is asked about their food consumption in the past two days, so this covers a total of 14 days of food consumption. In addition, information about spice consumption in Bangladesh is collected once a week during those two weeks. On the other hand, Bhutan asks the respondents to recall the last week, last month and last one year of food consumption. India and Maldives ask the households 8
# **3 Estimating the Effect of Trade Policies on Green Goods**

In this section, we estimate the ad valorem equivalents of NTMs to assess which NTMs impose significant costs on imports of green goods, then assess the relationship between the NTMs derived from that analysis on workers.

## **3.1 Ad Valorem Equivalents of NTMs**

### **Methods**

To analyze the costs of NTMs, we estimate the tariff ad-valorem equivalent (AVE). AVEs of non-tariff measures (NTMs) involve estimating the uniform tariff that will result in the same trade impacts on the import of a product due to the presence of the NTMs. The AVE of an NTM is often interpreted as measuring the distortion imposed by the NTM on the domestic economy (Montfaucon et al., 2023).

As a methodology, we follow Looi Kee et al. (2009) and Montfaucon et al. (2023) to estimate the ad-valorem tariff equivalents (AVE) of NTMs on green goods in the Philippines over the period 2014-2020. They are estimated by comparing the trade effect of NTMs to the one from tariffs. Specifically, it is theorized that the total effect of NTMs is a product of trade elasticity and AVEs. The following regression specification is then estimated using the Philippines import data as follows: Where: _lnV kt_ is the logarithm of import value of good _k_ in year _t_ ; _τkt_ is the tariff rate on good _k_ in year _t_ ;<sup>1</sup> _NTMnkt_ is the dummy where 1 is the indicator variable denoting the imposition of a specific

> 1 _τkt_ = _ln_ (1 + _tariffkt_ )

16
**1.3**|**0.3**|**18.9**|**1.8**|**4.0**|**0.5**|**1.3**|**0.2**|

_Note: all measures in percentages, P0-headcount, P1-poverty gap, P2- severity of poverty (squared P1). Table uses actual household size in 2005 and 2008 to weight data together with households sampling weights. The frontier regions are excluded from regional disaggregation due to their small sample size, yet they are included in other national averages. Source: Authors estimates based on HIECPS 2005-2008._ Indices sensitive to distribution show less impressive poverty reduction. Poverty gap (P1), and severity of poverty (P2) capture the degree of poverty as experienced by the poor. Table 3 demonstrates that assessment using changes in P2 shows no reduction in poverty, with a clear worsening in rural areas. Indeed during this period the ranks of the extreme poor – those consuming less than the cost of the subsistence food basket - swelled by 1.1 million, entirely due to the sharp increase of incidence of extreme poverty in rural areas. Hence, growth in the period 2005-2008 was not beneficial to the extreme poor, i.e. it was not pro-poor in a strict sense.

Table 4 further illustrates the bias against the poor of the growth during 2005-2008 by listing several inequality indices and dissecting the distribution. This is done by presenting percentiles of distribution: for example, p25/p10 is ratio of consumption between those who are at the 25th percentile and those who are on the 10th percentile (that is, poorer). Table 4 shows a widening of distribution at the top (p75/p50 and p90/p50 measure) and at the bottom (p25/p10 measure) – the latter exclusively in rural areas.

11
higher than those of formal lenders, they are much lower than informal lending rates (Faruqee and Khalily 2011). Moreover, the rates of return for activities supported by MFIs are not necessarily lower than the average interest rates of microcredit loans (e.g., Khandker, Samad, and Ali 2013). In any case, concern over whether microcredit participants are trapped in poverty and debt merits more careful and objective scrutiny.

This study revisits the controversy surrounding the role of microfinance in poverty reduction and indebtedness using the most recent data from Bangladesh. Using a follow-up survey of respondents over 20 years, it examines whether the earlier findings of Pitt and Khandker (1998) and Khandker (2005) can be substantiated using a long panel survey and whether microcredit participants are indeed trapped in debt and poverty as many have argued. The study also examines whether the gender of program participants matters, as observed by Pitt and Khandker (1998) and Khandker (2005). The complete data set used in this study constitutes an unusually long panel of more than 20 years, providing a unique opportunity to examine whether households that participated in microcredit programs over a long period remained poor or graduated from poverty.

Using a fixed-effects (FE) method that controls for both time-invariant and time-varying heterogeneity, we estimate the net effect of program participation for an average participant and separately by gender of program participants. Contrary to the prevailing view about microfinance dependence over time, we find that participants in microcredit programs are generally neither trapped in poverty nor overly indebted. Our findings also confirm that the welfare effects are higher for women than for men when both genders participate, as observed in earlier studies using the first (Pitt and Khandker 1998) and second (Khandker 2005) rounds of this panel data.

The paper is organized as follows. Section 2 discusses the data, including household participation in microcredit and the microcredit loan portfolio. Section 3 presents a descriptive 4
Policy Research Working Paper 7967

# **Abstract**

Foreign banks can play an important role in facilitating international trade. Most research has focused on the financing role banks can play, but less is known about the information role. This paper estimates a gravity model using sub-regional data from Turkey between 2002 and 2010 to explore whether foreign banks promote trade through the information channel. The presence of a foreign banks from a trade partner promote the exports of the sub-region.

In addition, the analysis finds that the footprint of these foreign banks matters more for exports than the presence of these banks and the level of financial development of the sub-region. Banks from countries neighboring trade partners can also decrease exports. When taking into account financial conditions and examining the differential impacts during the recent financial crisis, the analysis finds that the footprint of foreign banks has the largest impact on trade.

This paper is a product of the Trade and Competitiveness Global Practice 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 authors may be contacted at swtan@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 of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
can be used to understand certain questions regarding refugee return. By leveraging these results, policymakers and program managers can augment current data sources and even supplant them in areas where contacts are not readily made or available.

To address biases, it is important to consider what populations are being reached with data collection and what populations may be left out, such as those without internet access. In addition, researchers should note the ‘public’ nature of social media, and thus choose projects for which public opinions and attitudes are salient. For example, research focusing on highly sensitive or personal issues may benefit from a different data source. Finally, just as in a survey, it is necessary to consider that an individual’s public expression of their views may differ from their personal actions and motives. Using a variety of sources can help mitigate some bias issues, as different sources have different policies on censoring violent material and thus may be used for different purposes.

Our research showcases the granularity at which social media can provide insights. For instance, as one would expect, we find that there is a clear, statistically significant difference in mentions of violence in areas with more return. This trend prevails even while looking at specific subdistricts, thus indicating the geographic granularity at which social media can provide a signal. Another interesting observation from our analysis is the role of Turkey and its mentions on social media. The high prevalence of topics related to Turkey, Kurdish politics, extremism and factions suggests that these are key issues impacting return in northern Syria. Social media data provides a window into discussions around violence and trust in these areas, including the role of foreign powers and the politics involved in the conflict.

Apart from security, discussion of governance, goods provision, and relief organizations are more prevalent in areas which people have returned to. An important difference in return and non return seems to be access to services as well as markets for goods in places of return. Thus, it is important to think of policies that stabilize
39. Another question of interest may be related to heterogeneity between domestic and international shipments. The literature points out an important difference in empty vehicle rate between domestic and international cargos in Europe. To analyze potential structural differences, the sample data is divided by the presence of border crossing, _bordercrossing_ . The results are somewhat mixed but consistently indicate that the estimated impacts of most of the determinants are slightly greater for domestic shipments than international cargos ( **Table 8** ). For instance, domestic shipments generally look more sensitive to shipping speed. The impact of cargo weight is also greater for domestic shipping. All of these effects may provide greater incentive for shipping carriers to consolidate more cargos, reducing empty trucks.

**<u>Table 8. OLS and fixed effects models by border crossing</u>**

||OLS w/o carrier FE|||Carrier F|E models||
|---|---|---|---|---|---|---|
||_crossborder_=0|_crossbo_|_rder_=1|_crossbord_|_er_=0|_crossborder_=1|
||Coef.<br>Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.<br>Std.Err.|
|ln_speed_|-0.437 (0.018) ***|-0.330|(0.026) ***|-0.055|(0.015) ***|-0.036 (0.009) ***|
|ln_dist_|-0.011 (0.005) **|-0.283|(0.009) ***|-0.050|(0.003) ***|-0.010 (0.005) *|
|ln_weight_|-0.677 (
TABLE 2. Number of Missing Census Rounds

|**Number of missing census**<br>**rounds**|**Number of destination**<br>**countries**|**Share of world**<br>**migration in 2000**<br>**(%)**|
|---|---|---|
|**0**|68|68|
|**1**|55|12|
|**2**|41|10|
|**3**|39|8|
|**4**|17|2|
|**5**|6|0|
|**Total**|**226**|**100**|

_Source:_ Authors‘ calculations based on data described in text.

37