rafmacalaba/data-use-mentions-extended huggingface dataset:
joie.12209</mark> Syverson, C. (2011). What determines productivity? _Journal of Economic literature, 49_ (2), 326365.

Toivanen, Otto, and Lotta Väänänen. 2016. “Education and Invention.” Review of Economics and Statistics 98(2): 382–96.

<mark>Tokman, Victor E., (1978), Competition between the informal and formal sectors in retailing: The case of Santiago,</mark> _<mark>World Development</mark>_ <mark>, 6, issue 9-10, p. 1187-1198.</mark> Valero, Anna, and John Van Reenen. 2019. “The Economic Impact of Universities: Evidence from Across the Globe.” _Economics of Education Review_ 68(1): 53–67.

<mark>Xu, Gang and Go Yano. (2017). How does anti-corruption affect corporate innovation? Evidence from recent anti-corruption efforts in China,</mark> _<mark>Journal of Comparative Economics</mark>_ <mark>, 45(3): 498-519.</mark> Wagner, J. (2007). Exports and productivity: A survey of the evidence from firm‐level data. _World Economy, 30_ (1), 60-82.

41
**<br>(0.150)|
|GVC|<br>0.156***|<br>0.349|<br>0.0524|<br>0.523*|<br>1.051***|<br>3.559|<br>1.320***|<br>6.101***|
||(0.059)|(0.476)|(0.036)|(0.296)|(0.354)|(2.831)|(0.232)|(2.029)|
|Observations|11,610|11,610|11,626|11,626|4,820|4,820|6,797|6,797|

Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Note: - All the regressions include country, year, and sector fixed effects. - The intercept is included.

# **6. Conclusion and Policy Implications**

This paper aims at examining the impact of GVCs on women’s trade participation as entrepreneurs and employees. We also analyze how this effect is moderated through external (gender provisions in trade agreements) and internal (investment climate variables) factors. To do so, we use firmlevel data for 154 developing economies and emerging markets with a special focus on the Middle East and North Africa region. Our main findings show that GVC integration increases the likelihood of being a female owner and the share of female employees, especially production ones. A less robust negative effect is found regarding the impact on being a female top manager. These effects are moderated by the inclusion of gender provisions in trade agreements and by some characteristics of the investment climate, namely corruption, access to finance and tax policy. These results remain robust after we
# **1.  Introduction**

Health shocks are common in the developing world, and frequently severe enough to impair a worker’s earning ability (Strauss and Thomas 1998). While formal sector contracts may insure workers against income loss due to health shocks (Gutierrez 2014), casual wage workers and the self-employed are not typically insured against such shocks. Furthermore, if the financial cost of treatment for injury or illness is high and few households have full insurance against these costs, then these income shocks occur at the same time that a typical household’s marginal utility of income has increased. Given that many households are constrained in the ability to borrow and save, households may compensate for lost income due to an unanticipated injury or illness of a worker by increasing the labor supply of other members.

This paper documents such labor supply responses to illness shocks using high-frequency data on labor outcomes that we collected in urban Ghana during a 10 week period from August to October, 2013. High frequency data are crucial to interpret the effects of illness shocks, because credit constraints may prompt households to respond to an income shock at precisely the time it occurs. Our survey design also allows us to isolate unanticipated shocks (i.e., weeks in which the individual was planning to work but did not), minimizing the possibility that our estimate of the ex post response to a health shock is confounded by the household’s ex ante risk mitigation behavior in response to anticipated shocks. We use individual and household-level fixed effects to control for possible correlation between the susceptibility to illness of an individual’s household members and the unobserved determinants of his or her labor market outcomes. Moreover, short-lived illness spells tend to be under-reported in recall data, particularly among poor households (Das, Hammer, and Sánchez-Paramo 2012), but highfrequency data are plausibly less susceptible to such recall bias.

2
regarding the role of the terms of trade in tariff determination (Bagwell and Staiger 1990, 1999). For example, Broda, Limão and Weinstein (2008) directly test the optimal tariff theory in a cross-section of hundreds of imported products for a set of countries that faced no internationallybinding external constraints in setting trade policy. Their evidence supports the theory that governments set higher tariffs when export supply is more inelastic. In a different setting, Bagwell and Staiger (2011) find broad empirical support for terms-of-trade models of international agreements through a cross-sectional examination of negotiated tariff reductions for countries acceding to the WTO. Finally, Bown and Crowley (forthcoming) analyze a panel of time-varying US trade policy 3
IMF’s Government Finance Statistics and International Financial Statistics. To account for varying degrees of capital market developments, we obtained the Financial Market Depth variable from the IMF’s Financial Development Index data set.

In the factor analysis in Section 4.7, we will compare extracted latent factors with observed global financial variables (see Figure 9, Appendix). We consider the following candidates.

1. Monthly _federal funds rate_ data are provided by the Federal Reserve Economic Data (FRED) database of the Federal Reserve Bank of St. Louis. It serves as a proxy for global liquidity conditions (Csonto et al., 2013). This is particularly relevant for exchange rate volatilities, for instance due to the US federal reserve large-scale asset purchase programs, which Gadanecz et al. (2018) demonstrate to have influenced local currency bond yields.

2. We include the _TED spread_ and the Chicago Board Options Exchange’s Volatility Index to control for global credit risk and volatility conditions (Hilscher et al., 2010).

3. To mirror equity market conditions in developed markets, we include end-of-month values of the _MSCI World Index_ .

4. For global bond market conditions, we also obtain end-of-month values of _J.P. Morgan Global Bond Index (GBI)_ for emerging markets and the European monetary union, which captures developments in local currencies.

5. Furthermore, we obtain end-of-month values of _J.P. Morgan Emerging Markets Bond Index (EMBI)_ suite to reflect bond market developments in foreign currencies.

# **3 Preliminary analysis**

When a country raises capital on bond markets, it needs to set the return on the bond. This return, or yield, will determine the market appetite for this debt issuance. To simplify and focus the subsequent discussion, we treat bond yields as the price of borrowing. Considering the importance of natural capital to the economy and thereby to its bonds, we ask: Is it worth investing into forest expansion, sustainable agriculture or national parks? Figure
gross enrollment rate rose from 20% in 1993 to 79% in 2004. Infant mortality also improved with lower rates than other countries of similar GDP per capita. Similarly, the country also invested heavily in infrastructure, and notably, the Export Processing Zones to facilitate development of light manufacturing as part of the country’s Growth and Transformation Plan, as well as hydroelectricity that builds upon the country’s relatively generous river resources as part of the country’s green development.

Fostering the accumulation of physical, human, and natural capital strengthens the root of an economy so that it can branch out into diversified products and external markets. It has been argued that successful integration into the world economy requires a complementary set of policies and institutions at home where the state plays a crucial role: the evidence suggests that the countries that have grown most rapidly since the mid-1970s are those that have invested a high share of GDP and maintained macroeconomic stability (Rodrik 1999). More recent evidence on a panel of countries over time argues that public investment has a positive effect on long-term growth and labor productivity, and increases the speed of convergence of catching-up countries (Fournier 2016).<sup>19</sup> Emerging evidence, such as from the Global Commission on the Economy and Climate, which was founded by seven countries including Ethiopia, has also demonstrated that investment in environmental sustainability is also complementary and essential for sustained economic growth (New Climate Economy 2018).

> 19 The paper suggests that the impact of public investment differs across sectors ( _i.e._ , more effective in areas such as health and research & development) and levels of capital stock ( _i.e._ , growth gains decline at higher levels of capital stock).
separate ‘mixed’ households with children in both grade groups from ‘treatment’ households as they might experience different effects when not all of their children in the relevant grade range return to school. The main analysis sample includes 335 treatment, 361 mixed, and 948 control households.

Prior to the partial reopening, 98.5% of sample households stated that their children would return to school after schools reopened, despite possible concerns about the pandemic or conditions leading some children to drop out. Close to 99% of eligible students are reported to have returned to school after October 12, 2020.<sup>8</sup> Across all grades, 97% of children enrolled prior to the pandemic closures returned to school after the full reopening in January 2021.

Finally, the data include questions on household childcare arrangements, including respondent childcare hours.<sup>9</sup>

# **2.3 Childcare Arrangements**

At least 93% of children at each age from 6-16 in the RRPS are reported to have been enrolled in school in February 2020. After the March closures these children were all home requiring care and supervision during the working day, representing a large and unexpected shock to household childcare needs. Children primarily stayed at home with a parent during the closures (Figure A1), including situations where parents were simultaneously working. Almost no households report their children spending time with childcare providers outside the home or with a maid/domestic helper at home, and this does not vary by rural/urban setting or change as pandemic restrictions were relaxed and case numbers fluctuated. Adults with schoolchildren at home will have faced trade-offs in their allocation of time across childcare, work in different sectors, and other activities given a limited time budget to accommodate increased childcare burdens.

Figure 2 Panel A presents how hours of childcare from different providers (excluding schools) vary with the number of household children, using data from after schools fully reopened.

Non-household members provide very little childcare on average—86%
Only 17 percent of adults in developing economies report having personally paid for health insurance, though the share is as low as 2 percent in low-income economies. Of adults working in farming, forestry, or fishing in developing economies, only 6 percent report having purchased crop, rainfall, or livestock insurance in the past year.

# **The Global Findex database fills an important gap**

A growing literature examines household finance and especially the borrowing and savings decisions of households.<sup>5</sup> Using evidence from the FinMark Trust (FinScope) surveys in 2009 in Kenya, one study shows that savings and credit services are used mostly for family-related purposes and less for business-related purposes.<sup>6</sup> This finding is consistent with another study showing that about half the volume of borrowing by poor households is for nonbusiness purposes, including consumption.<sup>7</sup> Still another study, conducting field experiments in Kenya, finds that people with access to savings accounts or simple informal savings technologies are more likely to increase productivity and income, increase investment in preventive health, and reduce vulnerability to illness and other unexpected events.<sup>8</sup> Yet because of the lack of systematic data on household use of financial services, empirical literature investigating the links between household access to finance and development outcomes remains scarce. The Global Findex database extends this literature by providing cross-country, time-series data on individuals’ use of financial services.

There have been earlier efforts to collect indicators of financial access from providers of financial services (financial institutions) as well as from the users (households and individuals). But those collecting individual- and householdlevel data have been limited and questions—and the resulting data—often are not consistent or comparable across economies. The Global Findex indicator on account penetration lends itself most easily to comparison. While the results are broadly consistent with those of earlier efforts, the correlation is imperfect and in a few cases there are nontrivial discrepancies.

These
22 Consequently, the country was unable to produce the goods at a cost advantage and therefore unable to compete in these industries.

**Table** **<u>3. The Economics of Unrealistic Ambitions</u>**

|**Country**|**Industry**|**Time**|**Main producer**<br>**at Time**|**Real GDP pc**<br>**Latecomer**<br>**Country**|**Real GDP pc**<br>**Leading**<br>**Country**|**Income Ratio**<br>**Follower versus**<br>**Leader**|
|---|---|---|---|---|---|---|
|China|Automobile|1950s|USA|577|10,897|5%|
|DRC|Automobile|1970s|USA|761|16,284|5%|
|Egypt|Iron,Steel,Chemicals|1950s|USA|885|10,897|8%|
|India|Automobile|1950s|USA|676|10,897|6%|
|Indonesia|Ships|1960s|Netherlands|983|9,798|10%|
|Senegal|Trucks|1960s|USA|1,511|13,419|11%|
|Turkey|Automobile|1950s|USA|2,093|10,897|19%|
|Zambia|Automobile|1970s|USA|1,041|16,284|6%|

Source: Author's calculations based on data from Maddison (1995).

To implement the comparative-advantage-defying strategy, developing country governments had to protect numerous nonviable enterprises in the priority sectors. Administrative measures to which governments resorted to reduce the investment and operation
accuracy in standard units of the farmerreported yields; (iii) in most of the cases, the imputation approach provides accurate results in the within-survey imputation framework and collecting crop-cut measurements on a sub-sample of plots can offer a cost-effective approach while still achieving reliable ML predictions of CC yields, but less so in the survey-to-survey framework especially when statistics are computed at a desegregated level. These results point to important cost savings in survey operations, but also highlight the stringent survey data requirements in terms of quality.

The paper is organized as follows. Section 2 describes data situations in which crop-cut yields are missing and how the data at our disposal allows us to investigate imputation frameworks that are suited to handle the situations. Section 3 describes the survey data. Section 4 presents the empirical approach. Section 5 discusses the results and section 6 concludes.

# **2 Data situations**

Contexts where yields data based on crop-cuts are unavailable at large scale are very common and it has led researchers to develop statistical methods to overcome such scarcity. Table 1 provides an overview of data situations as it pertains to crop-cut yield and the potential

> 1The Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) is a unique system of longitudinal surveys designed to improve the understanding of household and individual welfare, livelihoods and smallholder agriculture in Africa. Partners countries include: Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, and Uganda. For more information see: https://www.worldbank.org/en/programs/lsms/initiatives/lsms-ISA.

3
calibrate a growth model consistent with China’s economic transition. The authors show that private-run firms, which are more productive than government-run firms, must finance investments through internal savings due to financial distortions that favor state-owned firms. These distortions in turn cause allocative inefficiencies in the credit market.

More recently, Jurzyk and Ruane (2021) analyze the role of state ownership on real misallocation among listed Chinese firms, during the period 2002-2019. The authors find that SOEs are, on average, 30 percent less productive than private firms, with large differences in capital productivity explaining the performance gap between both types of firms. The paper also shows that government-run enterprises have higher leverage levels and lower effective interest rates than their private counterparts. A counterfactual reform that both closes the productivity gap and equalizes the average capital intensity of both groups of firms can generate aggregate real gains of 7.5 percent in China. In related work, Nigmatulina (2021) studies the contribution of state ownership and political connections to misallocation in the Russian Federation, employing a unique natural experiment, whereby U.S. and EU sanctions targeted Russian firms connected to government elites. The paper shows that sanctions and the consequent government’s response worsened allocative efficiency, as private-sanctioned firms maintained their relative size, while state-sanctioned ones gained additional government support.

De Haas et al. (2022) employ cross-country firm-level data from 89 countries from 2000 to 2019 to study the relationship between state ownership and corporate leverage. Their results show that state ownership, both at the extensive and intensive margins, is negatively related to firm leverage. Second, the negative relationship between state ownership and corporate leverage holds across most of the firm-size distribution, with the important exception of the largest firms. This relationship holds across most of the firm-size distribution, with the important exception of the largest companies, and is stronger in countries with weak political and legal institutions. Geng and Pan (2023) document that Chinese state-owned firms face lower credit costs than 8
0.27|
|Wife in paid work last week|-0.15**|-0.00|0.03|0.04|0.02|0.33|
||(0.07)|(0.02)|(0.02)||||

**Note:** Standard errors are shown in parentheses (List method estimates show robust standard errors).  *** p<0.01, ** p<0.05, * p<0.1. Columns 1-3 show the relevant covariate coefficients from separate regressions. Columns 4-6 show the p-values from a chi-squared test of the differences between these coefficients. The vulnerability index includes household economic shock experience, husband's trauma score, husband's alcohol consumption, and whether any household member has skipped a meal in the past week. The unequal gender norms index includes incentivized and unincentivized questions about perceived unequal gender attitudes held by others in their village. The relationship quality index includes measures of marital trust, empathy and communication. The gender engagement index includes questions on the wife's previous participation in rallies and workshops about family violence and gender equality. The gender attitudes index includes questions about respondent beliefs about gender roles and the acceptability of IPV.

by women above median vulnerability. There was no IPV reporting across the list and direct methods for the least vulnerable women.

More vulnerable women may face greater personal safety or economic security risks if they report that their husband perpetrates IPV and, for example, he discovered her answer or was jailed as a result. For such women, the list experiment’s anonymity may provide them with additional reassurance and safety, making them more willing to report IPV. These results demonstrate that the relationship between vulnerability and IPV can be very different if estimated using anonymous list experiment IPV data. In Rwanda, it also demonstrates that ACASI’s added privacy does not address the reasons vulnerable women are misreporting.

In Rwanda, we
# **1 Introduction**

Air quality poses a significant threat to health in most parts of the world. At current levels air pollution is responsible for approximately 10 million premature deaths each year.<sup>1</sup> While the number of deaths associated with indoor air pollution has declined in recent years, increases in deaths due to ambient pollution have offset this improvement and led to an overall increase in total air pollution deaths. More than half of the world’s population now lives in areas where ambient air pollution levels are increasing. The majority of those facing increasing levels of air pollution live in low- and middle-income countries (LMICs)<sup>2</sup> where more than 90% of air pollution-related deaths occur.<sup>3</sup> The health burden of air pollution is also unequally shared _within_ countries. With a few exceptions, low income populations in Europe are exposed to higher pollution levels than wealthy populations in the same country.<sup>4</sup> In the United States low income populations<sup>5–8</sup> and racial and ethnic minority populations<sup>8–13</sup> tend to be more exposed to ambient particulate pollution. These differences across groups have persisted over the past several decades despite universal reductions in pollution levels.<sup>14,15</sup> While the within-country relationship between income or wealth and air pollution has been well documented in Europe and the US, evidence from LMICs is scarce.<sup>16</sup> This scarcity of evidence in the parts of the world with the largest populations, the highest levels of ambient pollution, and where pollution levels are increasing fastest limits our understanding of the present and future patterns of vulnerability. Lack of evidence may hinder effective policy-making to reduce the harms of this exposure. Furthermore, with the exception of recent studies in Eastern Europe<sup>4</sup> and China,<sup
36 measured by stock market capitalization) than they do in countries that start with relatively weak financial systems. Similarly, using firmn-level data from 30 countries, Asli Demirguc-Kunt and Vojislav Maksimovic (1996b) argue that firms with access to more developed stock markets grow at faster rates than they could have grown without this access. Furthermore, when individual states of the United States relaxed intrastate branching restrictions, this boosted bank lending quality and accelerated real per capita growth rates even after controlling for other growth determinants (Jith Jayaratne and Philip Strahan 1995). Thus, using firm level- and industriallevel data for a broad cross-section countries and data on individual states of the United States, recent research presents evidence consistent with the view that the level of financial development importantly affects the rate and structure of economic development.

Not surprisingly, these empirical studies do not unambiguously resolve the issue of causality. Financial development may predict growth simply because financial systems develop in anticipation of future economic growth. Furthermore, differences in political systems, legal traditions (LaPorta, et al. 1996), or institutions (Engerman and Sokoloff 1996; Douglas North 1981) may be driving both financial development and economic growth rates. While these studies do not prove that finance causes growth, the body of evidence would tend to push many skeptics toward the view that the finance-growth link is a first-order relationship and that difference in financial development can alter economic growth rates over ample time horizons.

# C. Country-Case Studies

Country-case studies provide a rich complement to cross-country comparisons. For example, Rondo Cameron, Olga Crisp, Patrick, and Richard Tilly (1967) dissect the historical