rafmacalaba/data-use-mentions-extended huggingface dataset:
Moreover, the region exhibits the world’s highest loan concentration ratio, reflecting a bias towards lending to large firms and excluding young and small firms (World Bank, 2011). Thirtyseven percent of MENA firms report that access to finance is a major constraint to doing business, higher than all regions except for Sub-Saharan Africa (Figure 1).

Stimulating trade in the MENA region is not only an economic challenge, but also a governance challenge. Many empirical works find a strong relationship between poor governance and stunted growth in MENA.<sup>10</sup> World Governance Indicator (WGI) scores for countries in the MENA region are much lower relative to other countries, and along certain dimensions of governance, MENA scores are lower than even lower-middle-income economies. Firms in MENA report being significantly more constrained along regulatory factors than in any other region. For example, 25.9 percent of MENA firms reported that business licensing and permits are a major constraint to their business operations, which is nearly double the rate in any other region (Figure 1). In addition to low governance ratings, political issues impede the movement of goods in the region. For example, Jordan neighbors the West Bank, which is largely excluded from the global market.

Figure 1. Share of Firms Identifying Business Enviroment Factor to be a Major Constraint <!-- Start of picture text --> 50<br>43.0<br>40 36.6<br>33.4<br>30.8<br>30 26.6 25.9<br>22.3<br>20 19.7 17.8 17.1 19.1 15.9 15.1 18.3<br>11.8<br>8.4 7.6 9.2<br>10<br>0<br>Customs and Trade Regulations Access to Finance Business Licensing and Permits<br>East Asia & Pacific Eastern Europe & Central Asia
Slovenia they account for 22%. In order to explore these differences, in Figure 1 we plot the changes in the extensive and intensive margins of exports within each country during the 2002-2013 period. On the y-axis we plot the changes from 2002 and 2009, while on the x-axis we plot the changes between 2009 and 2013. Observing scatter dots concentrated in the first (third) quadrants is a sign of a boom (bust) in exports that continued during the entire sample period. Conversely, scatter dots in the second (fourth) quarter indicate that exports fell (rose) in 2013-2009 after an initial increase (decline) in 2002-2009. We find that many ECA countries are in the fourth quadrant: there was a decline in the extensive (share of exporters) and intensive (share of exports) components of the changes in trade in 2002-2009, and an increase in 2009-2013. During the period 2002-2009, the share of exporting firms and exports declinedby almost 15% and 30% respectively across most ECA countries. Macedonia, FYR experienced the largest fall in the share of exports and in the number of exporters. Some of these countries increased the intensive and extensive margins in 2009-2013. Conversely, the share of exporters and exports in Turkey increased over both periods, and substantially during the second period.

# **3.1 Empirical methodology**

We test the relative importance of demand shocks and credit constraints of firm _i_ in country _j_ at time _t_ on the ability to export (extensive margin) in Equation 1 and the relative extent of exports (intensive margin) in Equation 2 by estimating the following equations: The dependent variables ( _Prob_ ( _Exportijt >_ 0) and _Exportijt_ ) are constructed from the BEEPS survey question: “What percentage of your firm’s sales are sold domestically, exported directly, or indirectly through a distributor?” Firms that report a positive value for either direct or indirect exports are identified as exporters, with the dummy variable _Prob_ ( _Exportijt >_ 0) taking a value
<!-- Start of picture text --> 3<br>3<br>8,5<br>2<br>15<br>2 4 6 8<br>Household asset index<br><!-- End of picture text --> <!-- Start of picture text --> 28<br>26<br>=<br>23 24<br>$<br>=<br>©35 22<br>25<br>2<br>18<br>° 2 4 6 8 10<br>Household asset index<br><!-- End of picture text -->
as self-reported perceptions, such as of personal “wellbeing”, government, politics, and religion. The 2009 round of the poll added several new questions in SSA in order to measure financial inclusion. The measurement was done along the following three dimensions: use of bank accounts, credit allocation and risk mitigation. We utilize this data for SSA countries in our evaluation of the role of microfinance in business-creation. We report data averaged by country over 2009 and 2010 since some countries only have data available for one of the two years and while other have data for both years.

# **_3.2 Evidence on Microfinance Usage and Awareness_**

In the previous section, we briefly examined the current literature on microcredit and its role in creating and expanding businesses. Since most studies show less than expected utilization of microcredit, it is useful to first look at perceptions and uses of various sources of financing. The Gallup World Poll asks what sources of financing individuals would consider using to start a new business. The data show that 42.3 percent of all responders in SSA state family as the primary source of funds for potential business needs (Figure 1). Community savings groups (also known as ROSCAs – Rotating Savings and Credit Associations), through which groups of people save money together, are a popular mode of savings in parts of Africa and are cited as a source of funds by about 10 percent of those surveyed. Importantly, commercial banks are reported by about 20 percent of responders (though not all respondents necessarily have the collateral and/or credit history to access formal bank financing).

Notably, in none of the Sub-Saharan countries surveyed, does the proportion of people willing to borrow from MFIs exceed 17 percent, and the mean for all SSA countries is a meager 4.3 percent. Not all of this difference between the borrowing rates from community networks and 9
achieving the target, which indicates “sorting” of reported figures around the target. This, combined with increasingly frequent adjustments of official GDP figures, is suggestive of potential manipulations of annual GDP growth rates. Following Henderson, Storeygard, and Weil (2012),<sup>2</sup> we then estimate the city-level true economic growth rates and the level of manipulation with nighttime lights data. We find large variations in the level of manipulation (i.e., the difference between the reported GDP growth and the predicted true growth) across cities and years during 2001-2013.<sup>3</sup> To understand the motivations behind bureaucrats exaggerating GDP performance, we examine how it is related to their career incentives. We first show whether career incentives related to political

> 1 The evidence on GDP manipulation in China is inconclusive. On the one hand, many have cast doubt on the credibility of Chinese official GDP data, offering evidence of manipulation (Adams and Chen 1996; Rawski 2001; Maddison and Wu 2006; Ma et al. 2014; Lyu et al. 2018; Chen et al. 2019). On the other hand, there are also studies that fail to find evidence of data manipulation, and claim that the Chinese data are accurate (Perkins and Rawski 2008; Mehrotra and Pääkkönen 2011; Holz 2014).

> 2 Researchers now often use nighttime luminosity as a measure of real economic activity. For example, Martinez (2019) demonstrates that night-lights elasticity of GDP is systematically larger in non-democratic countries relative to democratic countries, suggestive of manipulation. See Donaldson and Storeygard (2016) for a comprehensive discussion. 3 However, as detailed in section 4, due to its derivation from a regression-based framework, our measure of manipulation should be interpreted in _relative_ terms. We do not focus on the absolute extent of GDP over-reporting or under-reporting; we instead focus on the relative extent of GDP manipulation across regions, and the underlying incentives.

3
May 2021 (Table A2). Our analysis is, therefore, based on the panel of households interviewed in the pre-war and post-war-onset phone surveys between April 2020 and May 2021 (Table A4). While the pre-pandemic (and pre-conflict) sample of the LSMS-ISA survey in Ethiopia is randomly selected from urban and rural households in each district, the follow-up phone surveys are subject to two levels of non-random selection issues. First, the phone sample may differ in systematic ways from the original 2019 sample, partly because of ownership of mobile phones may be correlated with wealth (see Table A4). Second, phone surveys in conflict hotspot areas are likely to suffer from pervasive non-response, partly because of inaccessibility and network problems. The likelihood of being contacted in the phone surveys is likely to be greater for those who are relatively better off economically, as well as those located in areas with better access to telecommunication services (due to better infrastructure or less destruction by the war). In line with this, Table A3 shows that sample attrition increases with the presence of armed conflicts because the response rate for all regions is higher than for Tigray. This can be explained by the disruption in telecommunication and electricity services in the region. As the war continued, telecommunication and electricity services were suspended or intermittent in areas under the control of the Tigray region government. In areas that came under the control of the federal government and provisional regional government in Tigray, telecommunication and electricity services were restored, which allowed access to some households by phone.

To account for these systematic non-responses in the phone surveys, we constructed inverse probability sampling weights which we used in our subsequent analysis. Our final sample consists of those households appearing (at least once) in the pre-war and post-war-onset phone survey rounds, implying that the weights need to be constructed considering attrition and nonresponses in both phases.<sup>14</sup> We use a rich set of household and location characteristics collected in the 2019 survey to characterize and predict the (joint)
Policy Research Working Paper 10083

# **Abstract**

Hardly any evidence exists on the effects of mental illness on refugee labor outcomes. This paper offers the first study on this topic in the context of Australia, one of the host countries with the largest number of refugees per capita in the world. Analyzing the _Building a New Life in Australia_ longitudinal survey, the paper exploits the variations in traumatic experiences of refugees interacted with post-resettlement time periods to causally identify the impacts of refugee mental health. The findings show that worse mental health, as measured by a one-standard-deviation increase in the Kessler mental health score, reduces the probability of employment by 14.1 percent and labor income by 26.8 percent. There is also evidence of adverse impacts of refugees’ mental illness on their children’s mental health and educational performance. These effects appear to be more pronounced for newly arriving refugees and those without social networks, but they may be ameliorated with government support.

This paper is a product of the Development Data Group, Development Economics and the Fragility, Conflict and Violence Global Theme. 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 hdang@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
<mark>economic and living conditions of a sample of Syrian refugees living in the Al Azraq and Zaatari camps and refugees and Jordanian citizens living in the surrounding governorates.</mark> <mark>I retrieved the following data from the survey: characteristics of the refugees' household head, household income per capita, household dwelling, access to services such as electricity and water, assets accumulation, and overall life experience. The survey also has some</mark> retrospectiv <mark>e information on pre-crisis characteristics, such as t</mark> he household head's economic status in Syria, household earnings in 2010 before the crisis, household assets in Syria, and the number of years the household has been living in Jordan. The estimations in this paper use this information as control variables.

# 4.2. Identification strategy

Using an immigration survey to evaluate causal impacts often faces identification threat (Borjas, 2018). In other words, refugees who are surveyed in camps may be different from refugees who are surveyed in the cities, leading to selection bias. I identify three reasons selection bias can arise in this research. Refugees may self-select to live outside of the camp because they:

- (a) have social networks in the cities who can accommodate them.

- (b) have more access to finance to pay for the rent, or

- (c) because of their economic desire for access to jobs (Malaeb & Wahba 2019).

I confront refugees' self-selectivity by employing the difference-in-difference (DiD) method and the propensity score matching method. The DiD method is used to estimate the effect of a given instrument on a treatment group and a control group over time, attributing any differences to the effect of treatment membership (Athey & Imbens, 2006; Lechner, 2010). While the DiD controls for baseline differences in the outcome of interest, it holds a parallel trend assumption, for which it is difficult to account. This parallel trend assumption requires that the difference between
# **1. Introduction**

The pandemic-induced economic crisis resulted from a chain reaction effect of global lockdowns imposed to contain the COVID-19 virus, which reverberated through demand, supply, logistics and uncertainty shocks around the world (Apedo-Amah et al., 2020). For firms, the use of digital technologies became a key pathway to mitigate the economic losses that resulted from these shocks.

There is ample evidence that illustrates the role of digitalization in improving the performance and resilience of firms during the COVID-19 pandemic. In a cross-country setting, Doerr et al. (2021) find that firms headquartered in jurisdictions with better digital infrastructure generated relatively higher revenue during the pandemic. The authors find that improving a country’s digital technology capability by one standard deviation is associated with a relative increase in revenues of the average firm by around 4%. In the United States, Oikonomou et al. (2023) find that unemployment increased less in states with greater IT adoption before the pandemic. Similarly, Bellatin and Galassi (2022) find that cities in the United States with more job postings related to digital technologies before the pandemic were characterized by more jobs during the pandemic.

The relationship between digitalization and firm performance has also been analyzed using data on technology use at the industry- and firm-level. Using data from 24,000 firms in 75 countries, Copestake et al. (2022) find that firms in industries that are more digitalized experienced lower revenue losses following the COVID-19 recession. Using data from the Middle East and Central Asia region, Abrardi et al. (2022) find that digitally-enabled firms faced a lower decline in sales by about 4 percentage points during the pandemic compared to digitally-constrained firms. Cirera et al. (2022) use firm-level survey data from Brazil, Senegal, and Viet Nam to show that pre-COVID 19 digital technology sophistication helped firms mitigate the initial negative impact of the pandemic. Further, there is evidence that the COVID-19 shock accelerated the use of digital platforms and investment in digital solutions among businesses (Avalos
not receive any of the components. The program lasted for 12 months from the time of asset transfer. A follow-up survey was conducted from July to October 2018, approximately 1 year after program completion and 2 years after the asset transfer. In this study, we present estimates of the impact of the program on UP households, comparing the treatment and control groups at the first follow-up. Figure 4 provides a graphical representation of how the final study sample was generated.<sup>13</sup>

> 13 To avoid the potential for data mining generating spurious relationships, we registered the trial in the American Economic Association Randomized Controlled Trial registry and prepared a pre-analysis plan, pre-specifying all primary and secondary outcomes and measurement approaches: <u>https://www.socialscienceregistry.org/trials/2665. All de-</u> identified data will be made available on the World Bank’s Microdata Catalog and coding files used to generate the results in this report will be available on GitHub. The study also has the Institutional Review Board (IRB) clearance from Princeton University and the Ministry of Public Health in Afghanistan.

13
population share became insignificant in some specifications. The inclusion of R&D spending, which is available only for a much smaller sample, and urbanization also do not materially change the results.

Potential labor supply is defined as the product of the working-age population and the fitted value of age- and gender-specific regressions of labor force participation rates (lfpra,g,t) in percent on their structural determinants (Xa,g,t) and controlling for cohort effects, fixed effects, and the state of the business cycle—defined as the deviation of the logarithm of real GDP from the Hodrick-Prescott-filtered trend. The vector Xa,g,t includes gender-specific education outcomes (secondary and tertiary completion rates in percent of the population over the age of 25 and enrollment rates in percent of population of the age group that officially corresponds to the level of education, age-specific fertility rates (births per woman), and life expectancy (in years). These are interacted with a dummy variable Demde which takes the value of 1 for EMDEs. The vector Ca,g,t includes all the control variables:<sup>19</sup> lfpra,g,t = _α_ a,g + _β_ a,g Xa,g,t + _γ_ a,g Xa,g,t * Demde + _δ_ a,g Ca,g,t + _ε_ a,g,t .

Data on the working-age population comes from the UN Population Statistics Database. Data for age- and gender-specific labor force participation rates are available from Key Indicators of the Labor Market (KILM) of the ILO Population Statistics Database for 1990-2019, which is spliced by Labour Force Statistics of the OECD for 1960-2020 for 33 advanced economies and 16 EMDEs. This produces data for age- and gender-specific labor force participation rates for 1960-2020 for up to 38 advanced economies and 142 EMDEs.<sup>20</sup> Completion rates of secondary and tertiary education are from Barro and Lee (2013) and the World Bank’s
Africa|1.8|94|6|
|Timmer et al. (2014)|1990-2010|Asia|3.6|85|15|
|||Latin America|0.9|113|-13|

Source: Authors’ calculations based on data from DOSM, McMillan et al. (2014), Timmer et al. (2014) and Martins (2019) Note: This study and Timmer et al. (2014) use the average annual growth of output per worker, while McMillan et al. (2014) and Martins (2019) use the compound annual growth rate of output per worker. Annual growth in output per worker reported for this study excludes the mining sector, consistent with the findings of the decomposition that are reported above.

# **4.2. Sectoral Decomposition of Economic Growth**

Building on the aggregate decomposition of economic growth, this subsection implements a series of more detailed Shapley decompositions that decompose the growth in Malaysia’s output per capita from 1987 to 2018 into the contributions from each of the seven sectors of the one-digit national account classification. In addition, the subsection discusses how these contributions relate to within-sector productivity growth, betweensector labor reallocation, changes in sectoral employment as a share of the working age population, and demographic change. Finally, it takes a closer look at the effects of between-sector shifts on output per worker in each of the seven sectors.

Table 12 decomposes the growth in output per capita from 1987 to 2018 into the contributions from each of the seven sectors and how these relate to the different components of growth. For the sake of conciseness, the contribution of changes in the demographic structure to growth in output per capita is omitted from the table. Overall, the manufacturing and trade services sectors were the largest contributors to the growth of output per capita between 1987 and 2018. Both sectors contributed 23 percent to aggregate growth. The contribution of the manufacturing sector can almost entirely be attributed to improvements in within-sector productivity while for
# **1 Introduction**

The role of women in the labor force has undergone a profound transformation over the past century, evolving from a largely marginalized position (Boserup, 1975) to one of increasing representation, influence, and leadership in the global workforce (Goldin, 2024). In the 1970s, for instance, there were, on average, fewer than four women for every ten men in professional and managerial positions across a large cross-section of countries. By the 2010s, this number increased to more than eight women for every ten men. This growing representation of women in the labor market not only reflects progress toward gender equality, but also has significant implications for countries’ aggregate employment structures, productivity, and economic growth.<sup>1</sup> Over the same period, economies around the world have undergone fundamental structural changes, with employment transitioning from agriculture to manufacturing and services (Kuznets, 1973).

In this paper, we study the evolution of gendered labor market opportunities, economic growth, and structural change empirically and theoretically. First, we use micro-data from 91 countries at different stages of development and over five decades to document new evidence on the link between economic development and gendered labor market outcomes. We then propose a model to study these patterns. Our framework integrates two broad explanations for the observed evolution of gendered labor market outcomes and economic growth: first, economic mechanisms such as sector- or occupation-specific technological change, income effects in consumption, and improved education; and second, gender-specific distortions arising from discrimination and social norms. We structurally estimate our model to six major economies, which offer high-quality gender-disaggregated data on _both_ wages and employment over five decades. We then use the estimated model to study the effects of changing gender distortions on structural change and aggregate growth in these countries. We find that gender distortions are important and have declined in most countries. On average, the decline in gender distortions in our sample countries accounts for a substantial share of overall economic growth and reallocation