rafmacalaba/data-use-mentions-extended huggingface dataset:
4 mechanism, the change in estimated elasticities is smaller and not statistically significant (although a qualification is that value added trade data are available for a shorter period and fewer countries).

We try to assess the Smithian concern by focusing on the growth implication of a slowing pace of GVC growth. We do this in two steps. First, we report estimates from our related work (Constantinescu, Mattoo and Ruta, 2016b) of the impact of vertical specialization on productivity growth. These estimates indicate that increasing backward specialization has a positive impact on labor productivity growth, as predicted by the theory (e.g. Grossman and Rossi-Hansberg, 2008). Second, we use these estimates to quantify the growth in labor productivity due to the growth in backward vertical specialization. While this share is not large, as productivity growth is explained by many factors beyond vertical specialization, its contribution has decreased by half in recent years, suggesting that the trade slowdown is a contributing factor of the decrease in productivity growth.

The rest of the paper is organized as follows: Section 2 reviews the evidence on the trade slowdown and its explanations; Section 3 presents the evidence on the consequence of the trade slowdown, focusing on the demand and the supply channels separately; Section 4 concludes.

# **2. Why is global trade slowing down?**

World trade growth has been persistently sluggish since the aftermath of the Global Financial Crisis. World trade (total trade volumes) grew at 2.8 percent in 2012 and 3.4 percent in both 2013 and 2014. Data indicate a further slowdown in world trade in 2015 (Constantinescu,
................. 20_**|
|**_Appendix 1: Container tracking data from TradeLens .................................................................. 22_**|
|**_Appendix 2: Postal data from the Universal Postal Union (UPU) .................................................. 30_**|
|**_Appendix 3: Air cargo tracking data from Cargo iQ ..................................................................... 34_**|
|**_Appendix 4: Container ship tracking datasets ............................................................................. 36_**<br>MDS Transmodal ...............................
standards (Figure 2). The losses, though gradually declining, were estimated at 35 percent in 2009. Despite some recent improvements, the Albania’s system is still losing about one-third of total energy produced (and imported).

On paper, the meter penetration rate is already high in Albania. But the remaining deficiency has long been persistent, negatively affecting the whole governance in the electricity sector. According to the ERE data, about 100,000 electricity customers or about 11 percent of total

> 5 In Africa, the quasi-fiscal deficits in the electricity sector were estimated at almost zero to 4.7 percent of GDP with an average of 0.84 percent (Briceno-Garmendia _et al._ , 2008). In the Europe and Central Asia (ECA) region, the deficits were estimated at nearly zero to 1.3 percent of GDP in 2003 (World Bank, 2006).
real exchange rate depreciations and capital outflows relative to the cases of no policy and no learning. This is because agents are less uncertain about the future and save less for precautionary reasons. When this mechanism is strong enough, imposing capital controls could result in welfare losses relative to the decentralized equilibrium.

The key assumption in my model is that people learn about economic fundamentals from capital controls. In the empirical section, I provide some evidence of this assumption from an event study of capital control announcements in Brazil and from an exercise nowcasting Brazilian real GDP. Event studies using high-frequency data show that expectation of economic fundamentals in Brazil respond meaningfully to major capital control announcements. Specifically, I use daily survey data of market participants’ median expectation of quarterly real GDP growth collected by the Central Bank of Brazil and I consider six major capital control announcements in the country between 2008 and 2013. On average, a tightening(loosening) announcement is associated with a downward(upward) revision of real GDP growth forecast of 0.23%.

Since the survey data averages many forecasters’ expectations constructed with different methodologies, and hence can be hard to interpret in greater details, I provide a second set of empirical evidence by taking a stand on how to form expectations of fundamentals. Specifically, I put myself in the position of a forecaster who employees a commonly-used econometric model and uses all types of available macroeconomic data to produce nowcasts<sup>3</sup> of Brazilian quarterly GDP growth rates in real-time. Including capital control announcements in the forecaster’s information set helps improve the precision of her nowcast. The magnitude of such improvement measured in the nowcasting model provides a basis for the calibration of key model parameters related to Bayesian updating and learning in my quantitative model earlier.

# **2 Literature Review**

This paper contributes to several strands of the international macroeconomics literature. First, it adds to the recent theoretical literature of macro-prudential policies and 3A nowcast is a forecast of the present or the very-near future when data have
are really providing “better” aid, in the absence of more evidence on how using country systems affects either the quality of systems or development outcomes.

The current study builds on the preliminary empirical tests reported in Knack and Eubank (2008), using a much-expanded dataset. Unlike the earlier study, it addresses potential sample selection and endogeneity problems in detail. There is a time dimension to the dataset used in this study, allowing us to rule out omitted variable or simultaneity bias in certain key results. While the earlier study corrected standard errors for clustering only by donors or by recipients, this update reports more conservative standard errors, corrected for two-way clustering for donors and recipients. In addressing endogeneity and other potential problems more thoroughly, we have chosen to report results only for one dependent variable: use of country public financial management (PFM) systems. Results in general hold for the additional dependent variables in Knack and Eubank (2008), namely use of direct budget support and use of program-based approaches (which include but are not limited to direct budget support). Finally, in the attempt to minimize sources of endogeneity and other bias, the current study measures quality of country systems using widely-known governance indicators that mostly reflect views of experts outside aid agencies; Knack and Eubank (2009) relied heavily on indicators reflecting the judgments of World Bank staff.

Before turning to the empirical analysis, the remainder of this section presents the theoretical arguments from Knack and Eubank (2009) in condensed form. To preview, a donor‟s trust in a recipient‟s aid management systems is determined in our framework by three broad considerations:

1. Confidence it will reap sufficient benefits from investing in recipient country systems;

2. Trustworthiness of those systems, as measured for example by corruption ratings; 4
# **1. Introduction**

The past decades have witnessed remarkable progresses for women on most fronts. According to the World Development Indicators for 187 countries (World Bank, 2019), while the labor participation rate for males has declined from 76.2 percent in 1990 to 72.3 percent in 2018, that for females has increased from 48.5 to 51.8 percent. In 2016, the average share of females (over the age of 25) with a bachelor’s degree or above was 19.7 percent, surpassing that for males (i.e., 18.3 percent). Similarly, based on harmonized learning outcome (HLO), the latest cross-country indicator of comparable human capital from the World Bank, girls out-perform boys on average in the world (Angrist et al., 2019). The advancement of women, however, is not uniform. In leadership jobs, women continue to lag significantly behind (Goldin et al., 2006; Blau and Kahn, 2017; Fairlie et al. 2017; OECD 2019). In this paper, we focus on the determinants of female leadership in businesses, and in particular, the role played by foreign direct investment (FDI), around the world.

Understanding female entrepreneurship is important. Entrepreneurship, by turning ideas into business ventures and new products, is a key driver of economic growth (Schumpeter, 2017). The body of research on entrepreneurship, however, does not say much about what determines female entrepreneurship. With increasing participation of women in economic life, and with their relatively faster growth in human capital, commonly viewed by economists as the driver of growth (Lucas, 1988; Romer, 1990), understanding female entrepreneurship around the world gains urgency, and could be a key area to facilitate development, especially among countries where women traditionally do not play important roles in working in and/or running formal firms.

FDI could be an important determinant of female entrepreneurship. In less than two decades
toward work. At endline, women were asked whether they intended to look for a job in the subsequent 6 months, and about 30 percent of women, regardless of treatment status, expressed this intension. Similarly, in India—a setting with very low female labor force participation—McKelway (2023) finds a temporary uptake in employment among women in response to an information intervention for women’s family members fading after one year. In Croke et al.’s (2024) study on high-quality community social services jobs for women that offered full-time work for 1-1.5 years in rural Egypt, and another setting with low female labor force participation, they do not find lasting effects on women’s employment after the programs concluded. This finding is also consistent with <mark>Gehrke and Hartwig (2018) and Bagga et al.’s (2023) reviews which find t</mark> hat public works for both men and women does not result in long-run employment effects.

# **5.3. Impact on time use**

Table 5 presents the time use data results for both the female beneficiaries and their husbands. The results are the calculated average of data from two 24-hour measurements (the day before the midline survey for weeks 1 and 3, and the day preceding the three weekly endline surveys). The time use across the 7 categories in Table 5 refers to the time spent by the female beneficiaries and their husbands on the main activity reported for each hour. We compute caregiving as the number of minutes spent caring for the youngest child if caregiving was either the primary or secondary activity of that hour, since caregiving is often performed simultaneously with other activities, such as chores.

Figure 3 also shows a subset of the results for time use—namely, women in the control group allocate 67 percent of daily time to household tasks and to caring for other household members. Working time makes up, on average, only 8 percent of women’s daily time, which
**As discussed, minimum wages target individual workers with low wages, rather than families with low incomes; in Kosovo, a large fraction of minimum wage workers are not in poor households.** Specifically, Figure 12 shows that the percentage of poor<sup>23</sup> in Kosovo was higher among workers who would be affected by the policy (16.2 percent) compared with workers who would not (8.3 percent), but about 83.9 percent of minimum wage workers were not in poor families.<sup>24</sup> As a result, a large share of the higher income from increases in the minimum wage would benefit households at the top of the distribution This suggests that the impact on poverty may be ambiguous, but likely small. Moreover, the percentage of poor is the highest among non-employed individuals who cannot be directly affected by the policy (19.9 percent).

**Figure 12. Decomposition of population by poverty level, aged 15-64**

<!-- Start of picture text --> Not affected not employed 19.91% 80.09%<br>Not affected workers 8.28% 91.72%<br>Affected 16.15% 83.85%<br>Poor Non-poor<br><!-- End of picture text --> _Source: Own estimates using 2017 Kosovo HBS._

# **Given this analysis, we conclude the following potential effects of the minimum wage policy:**

1. The increase in the minimum wage might be effective in reducing the gender pay gap, given that women are overrepresented in low-wage occupations. For the same reason, however, women are also more likely than men to lose their jobs. Because the policy directly affects the active working-age population only, and females have low labor force participation rates, few women are likely to be affected by the policy.

2. The majority of potentially affected workers are unskilled workers
containerized freight rates to the increasing input costs, such as fuel, ship prices, and port costs. From the market structure perspective, the industry has significant entry lags due to the time it takes to build a new ship. Bridgman (2021) attributes the lack of a dramatic fall in freight rates at least in part to the strong labor unions in ports. Due to their strong bargaining power, port-related labor costs did not fall much even though containerization resulted in enormous labor productivity gains. Finally, the demand for international trade and transportation services is constantly increasing (see Fig. 1), which may allow maintaining high freight rates in the highly concentrated industry with the legalized collusion.

**Stylized Fact 10.** _Between 1970 and 2004, containerized freight rates did not exhibit a decreasing trend. The non-decreasing trend, despite some technological advances, can be blamed on the increasing input costs, market structure, and constantly increasing demand._

# **3 Transport Costs Measures and Data Sources**

over We consider three measures of transportation costs: (i) approximation by distance, (ii) the c.i.f. f.o.b. ratio, and (iii) the direct (either specific or ad-valorem) measures. This section discusses the advantages and disadvantages of each measure in the context. We also describe available datasets 1. and provide their summary in Table

## **3.1 Transportation Costs Approximated by Distance**

Understanding the size and nature of transport costs requires detailed data on freight rates. These data are sparse. Researchers have widely used physical distance as a proxy for transport cost to overcome the paucity of transport cost data.<sup>14</sup> This tendency in empirical literature has been in part motivated by a robust to many specifications and periods ln( _Distance_ ) coefficient of ( _−_ 1) in the _a_ ggregate trade gravity regressions initially introduced by Tinbergen (1962). In these regressions, 14An example of the
A small literature examines the role of the IFC as an investor and development institution. Dreher et al. (2019), investigating the link between IFC loan allocation and board membership in the institution, found a positive relationship between political influence and lending decisions, and Taussig and Delios (2015), using data from the IFC’s investment in private equity funds to examine the role of local expertise and performance, found local expertise to improve performance more in countries with weak contract enforcement institutions. Kenny et al. (2018), analyzing the countries targeted by IFC investment between 2001 and 2016, noted a shift in allocation from lowto middle-income countries. Although none of these papers report on returns obtained by the IFC, Desai et al. (2017) studied the relationship between IFC project returns and ESG risk factors since 2005, finding no evidence that better ESG performance is linked to improved financial performance.

**Potential Selection Bias** The IFC equity portfolio data enable us to avoid two forms of selection bias that typically hamper analysis of the performance of an asset class. A first source of selection bias is survivorship bias, that is, when successful investments or funds are more likely than failures to appear in a data set (Carhart et al. 2002) discuss this in the context of the mutual fund industry). Because the data include all of the IFC’s equity investments, including write-offs, comparisons of investments within the portfolio will not be affected by such bias. Another version of survivorship bias would be if its performance influenced the probability of the IFC disclosing its returns. The IFC, however, has had a longstanding policy of disclosing performance in annual reports.

A second potential source of bias is infrequent valuation, as when an investor values an investment only upon a successful initial public offering (IPO) or after a company completes a successive round of fundraising (Cochrane 2005 discusses this in the context of venture capital). Because valuations may be positively correlated with these events, ignoring investments that have
of table (1) draws on the 1991 population census to describe the long run effects of the exemption by comparing outcomes of exempt and non exempt youths belonging to the two cohorts under analysis ten years after the exemption. The census also includes some retrospective information as of 1986. Ideally, we would like to observe the same individuals in both data sources,

> 12We exclude the 1962 cohort from the comparisons as its members were 19 when the exemption was passed, an age when it is hard to make assumptions about whether they were still in high school or already beyond it.
data on value‐added. Normally value‐added at the firm is defined as sales minus the value of inputs, which includes materials, services and energy. In the Amadeus data set, unfortunately only operating net revenue and material costs are reported, which we hence use to compute value‐added. Moreover, materials are not reported either for any firm in Cyprus, Denmark, UK, Greece, Ireland, Lithuania and Malta. Since this forms a substantial number of countries, we proxy material inputs for these countries. The way we choose to do so is based on Basu _et al_ . (2009) in which the authors compute materials as operational revenue minus operational profits and wages taking into account depreciation. Since this is of course a less precise measure of material inputs, we therefore check whether a strong correlation exists between both measures: the direct and proxied approach for all countries that report both types of methods. Table A6 report the regressions as correlations of the indirect measure on materials reported directly for these countries. The result shows that correlations are very strong with a high R2. Once we use this proxied method, the number of firms increases by 11 percent.<sup>10</sup> The production functions themselves are estimated using the standard approach of Cobb‐Douglas in logarithmic form, as shown in the following equation:

> 9 An interesting minor detail is that the frequency share of firm observations between services and goods closely follows their value‐added composition in GDP which in 2014 for goods was 24.3 percent, while for services this figure was 74 percent according to the World Development Indicators.

> 10 Note, however, that Malta only has 39 observations and are not included in the final data set. Furthermore, in Amadeus firms from Greece, Cyprus and Lithuania have neither reported nor proxied materials, and are therefore excluded from any further analysis in this paper.

9
eciaries have not been able to produce internationally competitive ICT products. Regarding the Lei do Bem, the evidence suggests that the program had a positive but modest impact on innovation. For instance, Devereux and Guceri (2015) nd that the average realized R&D intensity is relatively low. They argue that this is because the legislation design favors incumbent, older and larger rms and it does not reach most small or new companies. This is due to the fact that Lei do Bem excludes rms that declare income tax returns based on their presumed prot, which includes most of the young rms.<sup>22</sup> To the extent that Lei do Bem favors incumbent rms, it may have contributed to slow down the reallocation process from low-growth incumbents to high-growth young rms. A recent paper by De Souza (2021) studies a Brazilian innovation program implemented in 2001 that taxed the leasing of international technology to subsidize national innovation. The Brazilian Technology Substitution Program (TSP) created a 10 percent marginal tax rate on the payments of any international intellectual property. The revenue raised by the tax was used to subsidize innovation projects by rms in targeted sectors. The author uses a novel rm-level dataset with information on international technology leasing, patent applications, and employment of Brazilian rms and calibrates a model to analyze the eects of the innovation policy. He shows that the program led rms to replace technology licensed from developed countries with in-house innovations, which led to a decline in employment and an increase in the share of low-skilled workers. He nds that rms more exposed to the TSP program had an increase of 4.47 percentage points in the probability of having at least one patent, in comparison to the control rms, while drastically reducing the probability of leasing technology from developed countries.

Another important source of eciency gains at the level of the rm is through an increase in management quality.<sup>23</sup> Bank (2017b) provides evidence on the average management quality of
36

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