rafmacalaba/data-use-mentions-extended huggingface dataset:
years; (ii) the distribution of growth rates is left-skewed during pandemic years (reflecting negative outcomes) while that of non-pandemic country years is right-skewed.<sup>7</sup> Panel evidence from 210 countries from 1960 to 2018 shows that the global impact of a pandemic on economic activity is negative and persistent.<sup>8</sup> For instance, the average output loss is about 2.6 percent for all countries at the onset of the pandemic, and it is still 3 percent below the prepandemic level five years later. Among the five previously identified epidemic episodes, the global output loss from H1N1 is somewhat larger (4 percent in the year of the outbreak, and 4.3 percent after five years).<sup>9</sup> Note that these estimates tend to be surrounded by uncertainty from the unknown timing, spread, and severity of the virus.

McKibbin et al. (2020) quantify the potential global economic costs of COVID-19 under different possible scenarios. They also show the economic benefits of globally-coordinated policy responses to tame the virus using the global macroeconomic model (G-Cubed) based on McKibbin and Triggs (2018). The model has six sectors and 24 countries, which is extended from McKibbin and Wilcoxen (1999, 2013). There are short-term and long-term policy responses for the COVID-19 pandemic. In the short term, central banks need to support the economies during the ongoing

> 5 See Correia, Luck and Verner (2020).

> 6 The five episodes were identified by Jamison et al. (2017).

> 7 See Ma, Rogers, and Zhou (2020a).

> 8 Furceri et al. (2020) show that modern pandemics—although smaller in the magnitude of infections and deaths than the COVID-19 pandemic—have led to greater income inequality with higher Gini coefficients, and lower employment-to-population ratio for people with basic education as opposed to those with higher education. 9 See Ma, Rogers, and Zhou (2020b).
Policy Research Working Paper 7965

# **Abstract**

This paper studies migration choices in the presence of liquidity constraints and varying costs of migration. This paper presents a simple theoretical framework that analyzes migration response to both push and pull factors in such settings. This framework implies that a shock to the push factors in the origin leads to differential observed response to migration to various destinations, as the shocks affect different parts of the wealth distribution. The implications of this framework are tested in the context of international migration from Nepal, using a panel of 452 villages observed at three periods in the 2000s. The analysis uses rainfall shocks and deaths due to conflict as “push” shocks and growth in manufacturing and construction in destination countries as “pull” shocks. The findings show that a rainfall shock that increases household income by US$ 100 increases migration to India by 54 percent but has no effect on migration elsewhere. An increase in conflict, which reduces consumption and amenity of the wealthier more, increases migration abroad, especially from urban areas. An increase in demand from the destination countries, especially the Gulf countries and Malaysia, has strong effects on migration to those destinations. These findings are consistent with the theoretical framework, and suggest the presence of large liquidity constraints. An increase in income can boost migration to India, whereas a reduction in the cost of migration might increase profitable migration elsewhere. The responsiveness to “pull” shocks suggests that households are willing to take advantage of these opportunities.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mshrestha1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues
more than unskilled men, down from 2.6 times more in 2004. Note that despite the drop in the skill premium, in all countries wages are higher for men relative to women and for skilled relative to unskilled workers. Unskilled women are always at the bottom of the wage distribution, while skilled men are at the top.

The general downward trend in the skill premium is often mirrored by an upward

> 7Our results are robust to keeping these observations.

> 8The rises in wage skill premia and job polarization are common trends associated with skillbiased technologies that have been observed in advanced economies. Maloney and Molina (2016) find no evidence of job polarization for developing countries.

> 9Maps for all 7 countries documenting the skill premium’s evolution by region are available on request.

7
sector precedes or occurs at the same time as the recovery in output of the corporate sector? Do firms experience Phoenix Miracles where their sales recover without a recovery in external credit? We then compare and contrast the experience of the emerging market firms to that of US firms during the 2008 US financial crisis and investigate if the US recovery process is a Phoenix Miracle as suggested by recent macro studies.

To answer these questions, we explore if the micro mechanism of the recovery process detailed in Calvo et al. (2006a, b) is borne out in our sample of publicly listed firms.<sup>2</sup> To explain the existence of Phoenix Miracles, Calvo et al. (2006b) propose a partial equilibrium model with financial frictions, where firms in developing countries can obtain short-term credit from banks for working capital used to finance inventory accumulation or the wage bill. In their model, bank credit dries up in a crisis leading to a drop in firms’ holdings of short terms assets that are financed by bank credit. The short term assets/output ratio does not fully adjust to permit the firm to produce at the same level, leading to a drop in output and investment. Over time, the stock of short term assets is restored by discontinuing investments and out of self-generated cash flows. Thus, output recovers even while bank credit hasn’t fully recovered, giving rise to the Phoenix Miracle.<sup>3</sup> To test the model predictions and reconcile the macro evidence with micro data, we first use firm-level data from Bloomberg database for 9 emerging market 3S episodes over the 1990s – Argentina, Indonesia, Korea, Malaysia, Mexico, Russia, Thailand, and two episodes in Turkey.<sup>4</sup> Each of the emerging market crisis episodes has also been previously identified as being a Phoenix Miracle, where there is a recovery in output while credit still stagnates. Developing countries are most likely to the meet the conditions for the existence of Phoenix
network that is more locally connected and spatially less concentrated. We further provide evidence for the implications of extrapolating from formal sector data about the aggregate impact of economic shocks, i.e. ignoring the presence of the informal sector. Simulations of the pass-through of output shocks using the revised network reveal that formal sector data leads us to underestimate the impact of domestic output shocks in regions and sectors with high informality. Conversely, we may overstate the local output effects of international trade shocks in sectors and regions with a high incidence of informality. This is because formal sector data places more weight on formal firms with stronger links to international markets, when in fact the overall economy has weaker ties to import markets.

Our findings are applicable to a variety of contexts with high informality across the world. Informality matters given its systematic occurrence as it is not randomly distributed across sectors and geographies. Our proposed approach for incorporating informal firms is useful for settings where data on the links of informal firms are not available with sufficient information on heterogeneity across key dimensions (e.g. sector and geography) and researchers therefore have to rely on secondary sources to recover the structure of the broader network. Our findings highlight the value of complementing administrative firm-to-firm data with auxiliary data on informality to draw inferences about the aggregate implications of economic policies.

An important question for future research, beyond the scope of this paper, is whether the observed spatial concentration of formal sector firm networks is a result of market frictions or a feature of structural transformation (Gollin, 2008). Understanding its drivers can inform policy recommendations about the optimal distribution of formal economic activity across space.

36
# **6 Conclusion**

The scarcity of empirical evidence on sectoral private equity returns in EMDEs has left critical questions unanswered for international investors seeking to diversify their portfolios and assess risks effectively. This study comprehensively examines sectoral private equity returns in EMDEs using an extensive dataset from the International Finance Corporation. By decomposing returns into firm, sectoral, and macroeconomic components, we highlight the dominance of firm-specific factors in explaining return variability. This finding aligns with existing literature on idiosyncratic risk and underscores the critical role of firm-level dynamics in investment performance. Our analysis also reveals the heterogeneity in returns across sectors, with high-growth industries such as technology and finance demonstrating superior returns and higher incidences of ”home run” investments. Conversely, more mature sectors showed lower, albeit more stable, performance. Macroeconomic factors, particularly real exchange rate movements, emerge as critical determinants of returns. Real exchange rate depreciation negatively impacts dollar-denominated returns, emphasizing the need for robust risk management strategies in currency-sensitive investments. Furthermore, the role of GDP growth in boosting returns varies by sector and investment type.

Our analysis has several limitations. The data do not account for certain variables, such as an investee’s reliance on exports or the currency in which it generates revenue, which will likely influence the sensitivity of equity returns to currency depreciations. These are important considerations for future research, as understanding these factors could provide insights into the relationship between currency movements and returns.

Future research on private equity returns could also benefit from deeply exploring how real and financial components influence industry cycles, particularly in competitive sectors. Hoberg and Phillips (2010) highlight that firms in competitive growth industries often face negative cash flows and abnormal stock returns after periods of high industry financing and overvaluation. This indicates that private equity investors may encounter greater downside risk in these environments, underscoring the importance of understanding how macroeconomic conditions, such as financing booms, interact with firm valuations and long-term returns. Additionally, Zhang (2005) points out that growth-oriented firms, especially in their early stages,
_Table 1 - The list of available surveys and their sample size. Prior to 2010, the nationality of households cannot be identified in HEIS. HH and INDV refer to the number of households and individuals._

|Persian|Year||Labor For<br>|ce Survey<br>||Household E<br>Income<br>|xpenditure &<br>Survey<br>|
|---|---|---|---|---|---|---|---|
|year||Afg|han|A|LL|Afghan|ALL|
|||HH|INDV|HH|INDV|HH|HH|
|1390|2011-12|1415|6641|153812|580694|121|38513|
|1391|2012-13|1430|6994|151366|560725|157|38192|
|1392|2013-14|1888|8870|197257|693770|102|38316|
|1393|2014-15|1960|9087|202942|699952|120|38275|
|1394|2015-16|2059|9482|205153|701430|141|38252|
|1395|2016-17|2104|9422|204868|695690|147|38146|
|1396|2017-18|2323|10428|206349|696032|194|37962|
|1397|2018-19|2495|11147|212324|709939|214|38960|
|1398|2019-20|2519|11215|212910|709288|179|38328|

22
DRC is well integrated in the global economy, with total trade reached 95 percent of GDP by 2014. Despite a slight drop in 2008, foreign direct investment (FDI) remained around 5 percent of GDP in (Figure 1). Mining is the most integrated sector with the global economy, as its output is almost completely designated for exports. Correspondingly, when demand falls, this sector contracts, with dire consequences for suppliers and DRC’s economy as a whole.

Figure 1 DRC: FDI Inflow in Mining and Non-mining Sectors, 2007-14 <!-- Start of picture text --> 2,000<br>1,800<br>1,600<br>1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014<br>Others sectors Mining sector<br><!-- End of picture text --> Source: Author’s calculation based on Government, World Bank, and IMF Database DRC’s economic structure makes it more prone to exogenous shocks, reflected in high terms of trade volatility (Figure 2). Thanks to their low marginal costs, large mines remained open in 2009 when copper and cobalt prices collapsed. The sharp decline in the 2009 growth rate is due to a contraction in artisanal mining, since miners could no longer sell their products to small-scale smelters. The marginal cost of smelters was around US$4,000 per ton and they were closed when the price dropped well below US$3,000 per ton. In sum, while large-scale 6
cycle.<sup>2</sup> The first dimension is informative to identify whether it is the policies affecting the functioning of financial and labor markets that are a more binding constraint for the economy, or if it is the case that policies affecting capital and labor equally, such as monopoly power and other product market frictions, are more prevalent. We find that output distortions are more strongly correlated with firms’ physical productivities than capital-labor ratio distortions and, thus, are relatively more important in accounting for that total gains from efficient reallocation. In terms of the firms’ life-cycle pattern of growth and the role distortions play in shaping it, we find the growth of employment over time, conditional on survival, is remarkably flat. This is consistent with evidence documented by Hsieh and Klenow (2014) for India and Mexico. We also find that the flat pattern of life-cycle growth is mostly accounted for by the life-cycle evolution of physical productivity, with a minor role played by an age-dependent component in the distribution of distortions.

We conclude the paper with an econometric exercise aimed at further tightening the connection between observed misallocation and actual policies and distortions that increase the cost of doing business. Appealing to quantitative measures of the quality of the business environment captured in the World Bank’s Enterprise Surveys, such as accessibility to credit or fees and payments to government officials; and exploiting sub-national variation of these costs, we find suggestive evidence that some of these business environment indicators have a significant correlation with the observed distortions that we find in the data. Nonetheless, the lack of variation in the data at the level of the firm as well as many potential confounding factors in the identification raise a word of caution in interpreting the size and direction of our results. Our view, which is shared by similar attempts at connecting distortions to

> 2To clarify the distinction between capital/labor and revenue wedges, the former refers to distortions that interfere with the optimal capital to labor ratio in the firm, while the latter refers to distortions that
.123|0.153|‐0.188|0.074|‐0.011|
||(0.204)|(0.388)|(0.257)|(0.304)|(0.197)|(0.055)|
|Group Treatment|0.242|0.238|0.210|0.276|0.241|0.066|
||(0.203)|(0.350)|(0.266)|(0.286)|(0.175)|(0.049)|
|**Panel E: Baseline Controls + Ancova**|||||||
|Individual Treatment|0.072|0.233|0.168|‐0.160|0.133|‐0.009|
||(0.199)|(0.394)|(0.252)|(0.299)|(0.199)|(0.055)|
|Group Treatment|0.267|0.335|0.232|0.296|0.214|0.068|
||(0.214)|(0.372)|(0.276)|(0.302)|(0.163)|(0.048)|
|Sample Size|50|50|50|50|50|46|
|Control Mean in 2016 of outcome|2.88|2.89|3.24|2.96|2.51|0.53|
|Control S.D. in 2016 of outcome|0.65|1.13|0.76|0.90|0.56|0.14|

Notes: Each panel represents treatment impacts from a separate regression. 70 of the 159 firms were given the WMS survey in
</sup> The extent to which international e-commerce is interconnected with postal services was pointed out by a McKinsey report<sup>11</sup> published in March 2022, specifying that the UPU is responsible for handling about two-thirds of all letter-parcel (up to 2 kilograms) deliveries across borders. The Universal Postal Union (UPU) also manages international express shipments, which account for about 5%-10% of international e-commerce volumes, and the rest is delivered by commercial operators. Therefore, information collected by UPU is a source of comprehensive data for over 190 member countries, and probably the best source of information on e-commerce trade.

UPU develops and maintains technical standards and Electronic Data Interchange message specifications used in exchange of electronic information between postal services. UPU’s databases of EDI messages contain detailed information on volumes, frequencies, key cross-border activities and other tracking data on postal items. This information is available via the EMSEVT (Express Mail Service

> 8 https://www.mckinsey.com/industries/travel-logistics-and-infrastructure/our-insights/signed-sealed-and-delivered-unpacking-the-cross-border-parcel-markets-promise

> 9 https://unctad.org/news/global-e-commerce-jumps-267-trillion-covid-19-boosts-online-sales 10 Hristova et al. 2016

> 11 https://www.mckinsey.com/~/media/mckinsey/industries/travel%20logistics%20and%20infrastructure/our%20insights/the%20endgame%20for%20postal%20net-

> works%20how%20to%20win%20in%20the%20age%20of%20e%20commerce/the_endgame_for_postal_networks_how_to_win_in_the_age_of_e-commerce.pdf

4
|2,343|2,116.53|1,979.93|0.832|0.478|

Baseline survey characteristics of children and households who were visited at endline. The sample includes all baseline children with cognitive tests at follow-up and all households interviewed at follow-up. Children born after the baseline are not included. P-values are of the test of difference between control and treatment, after controlling for stratification variables (one-to-last column), or without any controls (i.e. unconditional, last column). P-values based on standard errors clustered by community.

35
- Grosh, M., Leite, P., & Wai-Poi, M. (in press). _A New Look at Old Dilemmas: Revisiting Targeting in Social Assistance_ . The World Bank.

- GSMA. (2020). _Mobile economy._ `https://www.gsma.com/mobileeconomy/wp-content/ uploads/2020/03/GSMA` ~~`M`~~ `obileEconomy2020` ~~`G`~~ `lobal.pdf` .

- Hanna, R., & Olken, B. (2018). Universal basic incomes versus targeted transfers: Antipoverty programs in developing countries. _Journal of Economic Perspectives_ , _32_ , 201-226.

- Hernandez, M., Hong, L., Frias-Martinez, V., & Frias-Martinez, E. (2017). Estimating poverty using cell phone data: evidence from Guatemala. , _World Bank Policy Research Working Paper Series No. 7969_ .

- Hu, X., Yuan, M., Yao, J., Deng, Y., Chen, L., Yang, Q., . . . Zeng, J. (2015). Differential privacy in telco big data platform. _Proceedings of the VLDB Endowment_ , _8_ (12), 1692– 1703.

- Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016, August). Combining satellite imagery and machine learning to predict poverty. _Science_ , _353_ (6301), 790–794. Retrieved 2016-10-07, from `http://science.sciencemag.org/content/353/ 6301/790` doi: 10.1126/science.aaf7894

- Jerven, P. (2013). _Poor numbers_ . Cornell University Press.

- Karlan, D., Lowe, M., Osei, R., Osei-Akoto, I., Roth, B., & Udry,
- **b. Middle-income countries experience a hump-shaped emigration response in which emigration steadily rises and then falls off**

_Sources_ : Original estimations based migration data from Özden et al. 2011 and UN DESA 2020; and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure traces the emigration trajectory for low-income (panel a) and middle-income (panel b) countries as they have developed. The horizontal axis represents the increase in incomes and the vertical axis represents the increase in migration stock relative to the baseline year. Each lightly colored line represents the trajectory for a particular country, with a few examples highlighted. The solid lines show the locally linear fit between increases in incomes and changes in emigration rates compared to the baseline year. The baseline year, for each country, is defined as the year in which per capita GDP in constant 2017 US dollars in purchasing power parity terms, (2017 PPP$) was at its lowest level since 1960. Low-income countries were among the bottom third circa 1960 in terms of per capita GDP (2017 PPP$), and middle-income countries were among the middle third. Estimates exclude six outliers in terms of changes in emigration rates: three low-income countries/economies (Bosnia and Herzegovina, Grenada, and West Bank and Gaza) and three middle-income countries (Antigua and Barbuda, Dominica, and Saint Kitts and Nevis). CI = confidence interval.

Again, there is substantial variation in the experiences of individual countries. For instance, Guatemala and the Philippines experienced steady increases in migration as their incomes more than tripled between 1960 and 2020. Azerbaijan experienced the highest levels of migration in 2000 following the economic collapse in the early 1990s. As the economy recovered, migration rates subsided. For large, populous countries such as the Islamic Republic of Iran and Nigeria, migration rates barely changed even though their economies faced large ups and downs.

The emigration response observed here is a gradual process spanning decades
about half of the cases it is reported that the student attended a private school. As most students in primary and secondary education attend public schools, this suggests that attendance of private schools increases the likelihood of attending university (and this is confirmed in the econometric analysis on Mexican census data presented in Section 5). About half of the SOFES-recipients in Bachelor-programs are female, and 41% of the SOFES-students at the Master-level are female. The average educational attainment of the father is high school.

> 6 See, for instance, the documentation provided at <u>http://cursos.itam.mx/rodrigo/encuestas/enigh/2000/.</u> 7 As the information is double-checked by SOFES, the data quality in the SOFES database is high. There are however a few peculiarities and we cleaned the data in the following way. We dropped missing cases on study field, students in a Ph.D.-program, extreme observations on grade point averages (lower than 6 and larger than 10), extreme reported income levels (lower than 1,000 pesos per month and larger than 50,000 pesos per month), very low reported credit levels (lower than 0.2), very low reported levels of the socio-economic stratification index (lower than 2.1), and very high reported levels of additional financial support (more than 30,000 pesos). Finally, we only include people who are studying at the moment of data collection (June, 2003). The data used in this paper is a random sample of the total SOFES database.

7