rafmacalaba/data-use-mentions-extended huggingface dataset:
the current period. The median length of holding periods for easing phases also jumped, to almost 15 months during the 2008-19 window, but then dropped sharply after the pandemic. After the COVID-19-related easing, economies were on hold for an average of only 3 months from the last rate cut or end to asset purchases to the first rate hike.

Chart 18 Duration and Share of Holding Periods (Periods with no change in policy interest rates or asset purchases) <!-- Start of picture text --> Hold Duration (median)<br>15 15<br>10 10<br>1970-2020<br>5 5<br>0 0<br>Tightening Phases Easing Phases Highly Other<br>Synchronized Tightening<br>Tightening Phases<br>Holding Share of Phase (median)<br>45 45<br>30 30<br>1970-2020<br>15 15<br>0 0<br>Tightening Phases Easing Phases Highly Other<br>Synchronized Tightening<br>Tightening Phases<br>1970-1984 1985-1998 1999-2007<br>2008-2019 2020-2024(May)<br><!-- End of picture text --> Sources: Authors’ calculations based on data from January 1970 through May 2024. Notes: Chart shows the median duration of holding periods at the end of a phase before a turning point (top panel) or the share of the phase in the holding period (bottom panel). A holding period is the number of months after the last rate increase during a tightening phase or after the last rate decrease and end of any government QE programs. Movements in the policy interest rate <0.1 percentage point do not qualify as a change. Right side of the chart shows the same statistics across all Highly Synchronized Tightening periods before 2022 (in yellow) or in all tightening phases that are not Highly Synchronized.

These differences between tightening and easing phases—and the unusual duration of holding patterns since the pandemic—are even more
and sentiment are derived from Arabic tweets, and the measures are normalized to have unit variance. Trade openness is measured as the value of exports plus imports as a percentage of GDP. Institutional quality is the average of six measures from the World Governance Indicators database. Standard errors are clustered at the country level and papered in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 48
we also hypothesize that the joint training with partners is fostering gender dialogue and empowering women participants (H1.3) which, if this latter hypothesis is true, should in turn stimulate their IGAs (H1.4).

The second series of hypotheses recognizes that relaxing financial and human capital constraints may be insufficient because women face gender-specific constraints, including high risk of expropriation by other household members (De Mel et al. 2009; Jakiela and Ozier 2016; Bernhardt et al. 2019; Riley 2020). In line with the findings of Bernhardt et al. (2019), we acknowledge that focusing on women’s employment and income is insufficient because it ignores possible indirect effects on the household as a whole. For example, the cash grants to women could actually be invested in the IGAs of other household members. The interventions could therefore have positive effects beyond recipient women and directly impact household IGAs and household well-being (H2.1). If the joint training with partners increases women’s agency, the effect of the cash grant and training program on the IGAs of other household members is expected to be lower in households that participated in the joint training (H2.2). The program could also encourage migration if cash grants relax liquidity, credit, and risk constraints to migration (Gazeaud et al. 2021a) (H2.3). Jendouba—the governorate where this experiment is taking place—is one of the primary migrant-sending regions in Tunisia, with an out-migration rate of 13.2% in 2014 (Zuccotti et al. 2018).

# **4 Empirical strategy**

The empirical strategy is based on a pre-analysis plan, which can be accessed <u>here</u> (Gazeaud et al. 2021b). Supplementary analyses are described in Appendix G.

## **4.1 Data**

Baseline quantitative data on the 2,000 female participants were collected between April 2016 and January 2017, which is 12 to 18 months before the distribution
This study‘s primary objective is to explore the benefits for trade and economic growth that could be derived from enhanced market access for Bangladesh in India, and improved physical connectivity between the two countries. It studies the impact of market access on trade in goods, an area of particular interest to Bangladesh. It also analyzes trade facilitation and transit issues between the two countries, and examines a set of emerging challenges for the region, setting out a road map for deepening bilateral economic relations.

## **Data and Methodology**

The study is based on secondary data.<sup>2</sup> A computable general equilibrium (CGE) model was used to address market access issues and to assess the impact of, say, a free trade agreement (FTA) on potential exports. The indirect effects of even a single tariff reduction may be quite complex, and this complexity increases with the number of trade policies and markets involved. As FTAs cover multiple sectors and various trade reforms, they are often simulated using CGE modeling. The CGE model relies on standard microeconomic theory for rigor and consistency and on computer algorithms for model-solving.

An augmented gravity model was also used to assess the effect of trade facilitation and trade cost elements on bilateral trade. Estimated results from this partial equilibrium model would help to estimate the potential for trade, whereas those from the CGE model would provide directions to the way forward.

# **2. Quantifying the Benefits of Trade Liberalization and Facilitation between India and Bangladesh: Brief Literature Review**

In trade theory, welfare effects of any regional trade agreement (RTA) are analyzed using two concepts: trade creation and trade diversion.<sup>3</sup> The fundamental arguments for regionalism rest on evidence that suggests RTAs are predominantly trade-creating.<sup>4</sup> The reasoning is that most RTAs are likely to entail relatively low welfare losses resulting from trade diversion, since the countries involved are often neighbors and hence already engaged in substantial trade;
- Garnsey Elizabeth and Stam, Erik and Heffernan, Paul New firm growth: Exploring processes and paths. Industry and innovation, 2006. - Vol. 13.

- Greenaway David and Girma, Sourafel and Kneller, Richard Does exporting lead to better performance? a microeconometric analysis of matched firms. University of Nottingham, GEP Working Paper, 2002.

- Hainmueller Jens Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political analysis, 2012. - Vol. 20.

- Hannan Michael T and Freeman, John Structural inertia and organizational change. American sociological review, 1984.

- Leonard-Barton Dorothy Core capabilities and core rigidities: A paradox in managing new product development. Strategic management journal, 1992. - Vol. 13.

- McMillan M. Rodrik D., Verduzco-Gallo I. Globalization, Structural Change, and Productivity Growth, with an Update on Africa. World Development, 2014. - Vol. 63.

- Mensah, J. T. (2024). Jobs! Electricity shortages and unemployment in Africa. Journal of Development Economics, 167, 103231.

- Moyo Busani Power infrastructure quality and manufacturing productivity in Africa: A firm level analysis. Energy Policy, 2013. - Vol. 61.

- Neuenkirch Matthias and Neumeier, Florian The impact of US sanctions on poverty. Journal of Development Economics, 2016. - Vol. 121.

Rodrik Dani An African growth miracle? Journal of African Economies, 2018. - Vol. 27.

- Steinbuks Jevgenijs and Foster, Vivien When do firms generate? Evidence on in-house electricity supply in Africa. Energy Economics, 2010.

- Van Biesebroeck Johannes Exporting raises productivity in sub-Saharan African manufacturing firms. Journal of International economics, 2005. - Vol. 67.

- Wagner Joachim Exports and productivity: A survey of the evidence from firm-level data. World Economy, 2007. - Vol. 30.

16
-28-

To prepare the stage, let us think about the ways in which a firm can finance a loss. It would have basically three options, or a combination thereof: sell assets (or reduce liquid assets), increase equity or increase debt. If the rate of growth of assets is positive, as in the CMB se-mple for both groups of firms, then a loss with a contemporaneous increase in leverage would suggest that the loss has been financed by more debt than equity.

Table 8 shows that the increase in the debt to asset ratios of profit making firms was statistically insignificanc (P2 value of 12%). The movements in the leverage of loss making firms, on the other hand, is significanc. In 1980, the leverage of observations with losses was on average 6 percentage points higher than that of the firms in the other group, and the difference increased to 16 percentage points in 1982 and 15 percentage points in 1984. Furthermore, this increase in the difference was significant. Since the rate of growth of assets was positive in this period for both groups of firms, these results suggest that firms financed their losses by borrowing relatively more, in exactly the same period when cost of borrowing increased substantially. These results also lead to the following rather surprising conclusion: the gap in the average profitability between the two groups of observations is primarily to be explained by the increases in the leverage of loss-making observations during a period of higher financial costs.

Everything else constant, an increase in the cost of borrowing is expected to induce firms to hold less debt. The apparent higher borrowing in the present sample must have occurred due to a shift in the demand curve for loans rather than a movement along it.

What might have caused such a shift in the demand for loans is a question that requires further research. One variable that the present analysis
;

2. Three regional models based on common set of indicators from the SWIFT and the updated VM model (SEVAT) that was devised on the 2018 MCNA survey (hereafter SEVAT).

Means and standard deviations of the variables common to the national model and the old VM model are reported in Table 1. A single national VM model was devised in 2016, hence, the variables for all three regional models are the same. In total, 5 variables are in common between the two sources: household employment rate, dependency ratio, indicator for female headed household, household size, and a dummy to indicate households with IDP member/s. Table 2 reports the indicators selected for the new CLCI eligibility criteria, SEVAT, that were also considered for the SSN formula. Only 3 variables, home ownership, household size and employment rate, from the new Kurdistan and Center-South MCNA models can be duplicated in the national model. However, an additional variable, access to public network water tap, can be reproduced from the northern SEVAT model.

_Table 1. Common variables between the SWIFT and the 2016 VM model_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Iraq**|**Kurdistan**|**North**|**Center-**<br>**South**|
||Mean [SD]|Mean [SD]|Mean [SD]|Mean [SD]|
|Employment rate (emp/hhsize)|0.22|0.21|0.21|0.23|
||[0.15]|[0.15]|[0.14]|[0.15]|
|Dependency ratio|0.42|0.40|0.43|0.43|

8
important to firms, no matter their sector. Having a loan has the largest effect on employment growth for firms in the manufacturing sector. Being credit constrained has a negative effect on firms in all sectors, with the largest effect in the manufacturing and sales sectors. Having external finance matters in all sectors, and the effects are of similar magnitude.

Columns 12–15 present the estimation results across regions. Having a loan and external finance matters to firms in different regions, with the largest effects in Latin America. Being credit constrained has a significant effect on firm employment growth only in Europe and Central Asia and Latin America. From these results, together with the results in tables 7–10, we find that the interaction between financial access variables and firms’ size or age is significant in explaining firms’ employment growth.

# **8. Firm Size Distribution**

Because a firm’s size plays a significant part in determining its employment growth, we further assess the relationship between firm size and financial constraints. The survey data allow the creation of a variable showing which firms are credit constrained, which we identify as those that applied for a 15
al., 2013). At the local or national level, for instance, the flood maps from the global model can be replaced by results from local analyses at higher resolution, including flash floods and small basins. Local data on flood protection and better exposure data can often be mobilized (Aerts et al., 2014). And socioeconomic characteristics can be refined, accounting for instance for the institutional capacity to scale‐up social protection beyond what a global database can reasonably aim at providing (Pelham et al., 2011). But in spite of all these limits, our global approach may contribute to the monitoring of country and global progress in terms of resilience, and our findings already provide insights into promising policy options, such as adaptive and well‐targeted social protection, and show that “good development” increases resilience, especially if it reduces poverty and improves social safety nets.

# Discussion

Our socioeconomic resilience remains an imperfect metric, in the sense that it does not include all the dimensions discussed in the resilience field (Barrett and Constas, 2014; Engle et al., 2013; Keating et al., 2014). Our framework looks at the socioeconomic resilience, but disregards direct human and welfare effects (death, injuries, psychological impacts, etc.), cultural and heritage losses (e.g., the destruction of historical assets), social and political destabilization, and environmental degradation (for instance when disasters affect industrial facilities and create local pollution). The framework proposed here is for socioeconomic resilience, not for a broader concept of resilience.

Issues related to conflicts and government stability are not explicitly recognized, even though they indirectly influence our results since fragile governments usually provide little social protection and have 20
Policy Research Working Paper 9442

# **Abstract**

Several studies have documented a positive and causal relationship product or process innovation—and labor productivity. Given the links between labor productivity and wages, a likely implication of this positive relationship is that innovation is associated with higher wages of more productive firms. This paper explores the relationship between innovation and wages using Brazil’s employer-employee census and a novel measure of innovation derived from the share of technical and scientific occupations of workers in the firm. The results show a robust and positive wage premium associated with innovative firms. The decomposition of this innovation-related wage premium suggests a series of important stylized facts: (i) the innovation wage premium is larger for manufacturing but also positive and significant for agriculture and services; (ii) it is larger for large firms, but also positive and significant for all firm size categories including micro firms; and (iii) it is larger for medium- and low-skill occupations, although this depends on the use of firm fixed effects. More importantly, the paper explores the causality between innovation and wages and finds empirical support for the ideas that “self-selection”—firms that innovate already pay higher wages before becoming innovators—and increases in wages associated with starting innovation activity, which are persistent for three years after firms start innovating.

This paper is a product of the Finance, Competitiveness and Innovation 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 xcirera@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
test the impact of 4G technologies on consumption and poverty. The results are reported in appendix A, Table A.7. The finding is that 4G coverage is positively correlated with consumption and negatively with poverty, but this relationship is not robust to the inclusion of demographic and geographical controls.

> 13 This wealth index is based on whether the household has the following: durable walls; durable roof; durable floors; access to toilet; access to water toilet; and electric or gas cooking fuel. Based on household responses to these questions, the index was calculated using a weighted average, with weights derived from principal component analysis. The index is used because including the different components as separate variables introduces multicollinearity in the regression. 14 Data on nighttime light, road density, and elevation are derived from NASA VIIRS (Visible Infrared Radiometer Suite), Meijer et al. (2018), and SRTM 90m DEM Digital Elevation Database, respectively. Positive expenditure on electricity bills is taken as a measure of access to electricity in both rounds of the survey because there is no other direct measure of household electricity access.

6
find _a significantly positive effect of school size_ in almost all their specifications. They also find a positive influence of school competition (and accordingly a negative effect of the Herfindahl index) on students‟ test scores.

> 12 This country included the same eight transition countries as the previous ones – the countries for which TIMSS data were available.

> 13 The few studies on Ukraine are covered in the next section which focuses on the educational system in Ukraine

9
covering a<br>global sample.<br>Natons in Transit: 29<br>economies.|Annual. Used in<br>WGI since 1996<br>(Freedom in the<br>World and<br>Natons in<br>Transit) and<br>since 2019<br>(Freedom of the<br>Net).|Expert/NGO|Freedom in the World: An overview of the Freedom House<br>Freedom in the World methodology is availablehere.A<br>detailed descripton of the scoring criteria for the 2024 report<br>is availablehere.<br>Freedom of the Net: A detailed descripton of the<br>methodology, including scoring criteria, is availablehere.<br>Natons in Transit: A detailed descripton of the methodology,<br>including scoring criteria, is availablehere.<br>Note: between 1996 and 2017 the WGI also included data<br>from the Freedom House Freedom of the Press which was<br>discontnued in 2017. Data and methodology for this report is<br>availablehere.|
|GCB|Transparency<br>Internatonal|Global<br>Corrupton<br>Barometer|Varies with survey<br>rounds, latest round<br>covering Pacifc Islands<br>covered 10 countries.|Every 2-3 years<br>covering<br>regionally<br>rotatngsample.|Survey/HH|Household survey of corrupton perceptons. Documentaton<br>of survey methodology is provided separately for diferent<br>rounds of the surveyhere.For a link to the 2017 round<br>methodology,clickhere.|

39
# **Disclosure Requirements**

Recent research has focused on the value of disclosure requirements and effective securities regulation to protect minority shareholders. A natural experiment that allows us to observe the value of this protection is the cross-listing of firms from countries with weak protection on stock markets from countries with high investor protection. As Doidge, Karolyi and Stulz (2001) explain, cross listing premiums cannot be explained by the typical assumption that listing in the U.S. lowers the costs of raising capital by enlarging the shareholder base and allowing companies to tap previously unattainable sources of finance. If this were true, all companies with perceived benefits from lower capital costs sufficient to pay for the costs of listing (investment-bank fees, accounting certification etc...) would list in the U.S. The number of firms that actually list is much lower. Also, the argument of tapping previously unavailable sources of finance would imply that listing in the U.S. would have been more attractive in the past than now and would be more attractive for countries with less integrated capital markets than for those with capital markets that are highly integrated to the United States or other strong markets. Both of these assumptions are found to be incorrect. Cross border listings have increased substantially in the past 10 years and firms that receive the highest cross-listing premiums come from counties with well developed and integrated capital markets. Finally, the standard asset pricing models cannot explain why the premium for cross listing is larger for exchange listings than for private placements.

Doidge, Karolyi and Stulz (2001) find that firms cross-listed in the US trade at an average Tobin q that is a 16.5% larger than those that do not. Using information on 955 cross19
, but are otherwise excludable from a growth equation, and in particular are uncorrelated with _any_ institutions that may be related to subsequent growth. The next best thing is to trace the correlation of income inequality through various _observable_ institutional channels that affect growth. While we previously attempted this (Benjamin, Brandt, and Giles, 2006), we were unable to find any robust linkages between inequality and measured institutions at the outset of reforms. Again, this problem is thorny to solve, as initial inequality in a village in 1987 will reflect not just the immediate institutional structure of the reform period, but the entire history of the village throughout the occasionally tumultuous post-1949 period, e.g. land reform, collectivization, the Great Leap Forward, the Cultural Revolution, as well as its pre-1949 socio-economic structures. Moreover, “village” inequality will reflect “local” conditions beyond the village, at the township and even county-level, making it difficult to pin down the precise institutional mechanism.

> 2 Ferreira (2010), among others, has highlighted the importance of using micro-level data to shed light on those mechanisms driving the growth-inequality relationship that cannot be addressed by the cross-country macroeconomics literature.

_Inequality and Growth in Rural China_ , Page 2