rafmacalaba/datause-encoder-data huggingface dataset:
Ventura, Jaume, “Towards a Theory of Current Accounts,” _World Economy,_ 26 (2003), 483– 512.

Vissing-Jorgensen, Annette, “Limited Asset Market Participation and the Elasticity of Intertemporal Substitution,” _Journal of Political_ Economy, 110 (2002), 825–853.

Watcher, Jessica, “Portfolio and Consumption Decisions under Mean-Reverting Returns: An Exact Solution for Complete Markets,” _Journal of Financial and Quantitative Analysis,_ 37 (2002), 63–91.

Watcher, Jessica, and Missaka Warusawitharana, “Predictable Returns and Asset Allocation: Should a Skeptical Investor Time the Market?” NBER Working Paper No. 13165, 2007.

Yogo, Motohiro, “Estimating the Elasticity of Intertemporal Substitution when Instruments Are Weak,” _Review of Economics and Statistics,_ 86 (2004), 797–810.

45
to the maximum level (160 percent), fiscal policy is countercyclical, and the reverse happens in countries close to the minimum. The threshold level for switching from procyclical to countercyclical is 68%, which is significantly higher than the median of 21% for developing countries.

The external credit availability, measured by the Chinn and Ito (2006) capital openness variable (column 6) was not significant, similar to previous findings for African countries (Konuki and Villafuerte, 2016), but different from studies with broader samples which include developed economies (Frankel et al., 2013).

The role of the quality of institutions is proxied by two variables which capture the impact of the (perceived) corruption in both the political sphere and in the public services (columns 7 and 8). Higher indices of perceived corruption are associated with higher degrees of procyclicality, in line with previous literature (Alesina et al., 2008). Fiscal policy is procyclical when the perceived corruption indexes exceed .53 (in the political sphere) and .66 (in the public services) threshold levels, as summarized in Table 2. Other papers that explore the role of quality of institutions aggregate different variables to measure the quality of institutions, which has the benefit of parsimony in the analysis but does not allow granularity (Frankel et al., 2013). We also examine the role of institutions by including variables related to the rule of law, and the sign is the expected one (Table 3, column 9), with countries with higher scores in law and order having lower procyclicality bias.

**Table 2: Thresholds to reach a countercyclical policy, whole sample (2000-2016)**

|Conditional effects|Threshold level|Median in sample|
|---|---|---|
|Development level|3.47|2.00|
|Revenue.ratio (%)|22.84%|23.64%|
|Credit.ratio (%)|82.31%|25.99%|
|Pol.corruption (0-1)|0.53|0.37|
|Pub.service.corruption (0-1)|0.66|0.71|
|Law and order (1-6)|4.94|3.00|
|Resource.distribution (0-1)|0.80|0.59|
|Ethnicandrel.stability (0-6)|2.55|4.25|

**<u>Notes</u>** : Median values are computed on the sample of the respective regression, not the whole sample of 116 countries.

10
Figure 1: Vulnerability and capital flows in developing countries <!-- Start of picture text --> .6 600<br>.5<br>400<br>.4<br>.3<br>200<br>.2<br>1990 1993 1996 1999 2002 2005 2008 2011 0<br>1990 1993 1996 1999 2002 2005 2008 2011<br>External sector Fiscal sector<br>Overall economy and institutions Overall index FDI ODA Remittances Private debt & portfolio eq.<br>(a) Vulnerability Index (b) Capital flows<br>current USD bln<br>Vulnerability index (Dabla Norris & Bal Gunduz 2012)<br><!-- End of picture text --> Source: World Development Indicators and International Debt Statistics (developing countries are defined as low and middle income countries), The World Bank, for the left panel. Data for the right panel are elaboration on the Vulnerability Index data set. See Dabla-Norris and Bal-Gunduz (2012) for details on how the index and its sub-components are constructed.

empirical debate on the correlation between remittances to developing countries and the business cycle in the source and recipient economies.

Related to this, our data on remittances cover the periods before and after the 2007-08 financial crisis, allowing an analysis of the response of remittances to economic conditions in source and recipient economies during the global financial crisis. This is particularly relevant because the global financial crisis affected jointly the migrants’ home and host countries, with an _a priori_ ambiguous aggregate effect on remittances. On the one hand, the downturn in the home country might induce a positive change in remittances driven by altruism or insurance. On the other hand, the recession in the host country would reduce the income of migrants, including in particular temporary workers employed in the construction sector.<sup>1</sup> Second, we deal with the possible endogeneity of the business cycle in the recipient country more satisfactorily than previous studies. Reverse causality from remittances to output may significantly bias estimates, since remittances often represent a large share of developing countries’ GDP, and they have been found to affect both output growth and financial development (Gupta _et al._ , 2009; Giuliano and Ruiz-Arranz, 2009; Bettin and Zazzaro, 2012). In some cases, most of the transfers come from a limited set of origin countries, so that even bilateral remittances represent a significant share of GDP, potentially leading to reverse-causality issues in cross-country bilateral data. According to the data in Lueth and Ruiz-Arranz (2008), during 2002-2004 remittances from Russia to Tajikistan equaled on average almost 12 percent of Tajikistan’s GDP, while remittances from the USA to the Philippines represented almost 5 percent of the Philippines’ GDP.<sup>2</sup> To the

> 1According to the Italian National Institute of Statistics, the unemployment rate for foreign-born workers increased from 10.2 percent in 2005 to 12.1 percent in 2011. The figures for native workers were respectively 7.6 percent in 2005 and 8.0 percent in 2011.

> 2We thank Marta Ruiz Arranz for sharing the data on bilateral remittances.

3
services liberalization can generate much larger welfare effects than goods liberalization is probably robust. However, as stressed by Konan and Maskus (2006), to be more informative, CGE analyses need to be able to draw on empirical research that determines the effects of policies on markups and costs.

An interesting conclusion emerging from Konan and Maskus (2006) is not only that gains from services liberalization may be greater than from goods liberalization, but the adjustment costs associated with service-sector reforms may be lower because of the absence of corner solutions: services will continue to be produced locally and thus generate demand for labor. While this is intuitive, less so is their finding that comprehensive reforms spanning both services _and_ goods trade will generate less need for factors to be reallocated across industries than just goods liberalization alone. Given that in practice the focus of trade reforms has been on goods, not services, an implication is that past reform programs undertaken by many developing countries may have generated excessive adjustment, insofar as subsequent services liberalization will generate factor flows that may go in the opposite direction. Thus, their analysis has important implications for the sequencing of liberalization – it may be best to proceed on a broad front, targeting both goods and services markets.

Rutherford, Tarr and Shepotylo (2005) also employ a static CGE model to assess the impact on Russia of accession to the WTO. Their analysis is innovative in that all 55,000 households distinguished in the Russian Household Budget Survey are incorporated into their model, allowing assessments of the impacts on income distribution and the poor. Their analysis also includes FDI (mode 3) and incorporates Dixit-Stiglitz endogenous productivity effects in both the trade and poverty analysis. They conclude that in the medium term virtually all households would gain from liberalization, with increases in real incomes in the range of 2 to 25 percent of base year household income. These estimates are decisively affected by liberalization of FDI in business services sectors and endogenous productivity effects in business services and goods. The gains from FDI liberalization in services alone are 5.3 percent of the value of Russian consumption, and represent more than 70 percent of the total value of the potential gains from WTO accession-related reforms. The welfare gains from Russia’s tariff reductions and better access to markets abroad would be equivalent to only 2 percent of consumption. Thus, as was found by Konan and Maskus for Tunisia, the most important component of potential welfare gains from liberalization are removal of barriers against FDI in services sectors. However, they also find that many households may lose in the short term, making it important to put in place effective safety nets to protect the poorest members of society during the transition.

25
explain the smaller number eventually making it to the city, compared to the towns.

We use household survey and census data from Tanzania to explore our key question, why many more migrants move to seemingly suboptimal towns. As in other settings documented in the literature, the data confirm the existence of rising living standards across the urban spectrum, from rural areas over towns to cities. That urban gradient is visible both in terms of assets and housing wealth as well as amenities. Overlaying the wealth distributions, we find that living standards of the 90th percentile in rural areas still lie below those of the 10th percentile in urban areas.

This is problematic for the basic Todaro (1969) model, which assumes that the rural wage needs to equal the weighted average of the high and low urban wage, with the weights being the likelihood of getting each. Kanbur et al. (2019) consider an extension of the basic model that introduces migration costs.<sup>1</sup> These can bring the Todaro model back in line with the empirical observation in our setting that the rural wage is lower than both the lower and higher urban wage. Their second extension to the model is to consider multiple destinations (town or city), each characterized by different formal and informal destination wages (Kanbur et al., 2019).<sup>2</sup> Guided by this model, we use a novel data set on migrants for whom we have pre-migration and post-migration survey data to explore how migration costs and expected income at destination affect destination choice. At baseline in the early 1990s, the respondents all lived in Kagera, a large, remote and primarily rural region in the northwestern part of Tanzania. By 2010 about half of those who were still alive had moved out to other villages, towns or cities. We use the pre-migration survey data to create a dyadic data set of all possible migrant-destination pairs, among which the migrant will have chosen one and not chosen all others. We then use census data to construct measures of the basic Todaro building blocks (expected wages and migration costs), allowing us to test the relative importance of each.

Particularly, we proxy migration costs with distance. The use of distance as a proxy for

> 1Migration costs are broadly defined here, including transport costs but also the risk associated with the move as well as setup and job search costs at the destination, and so forth. They typically increase with distance.

> 2Similarly, Lagakos et al. (2020) emphasize the importance of heterogeneous migration costs in understanding observed migration patterns and returns across destinations (and individual attributes).

2
holds without phones are ineligible for benefits. After presenting these main results, we compile data from several existing targeting programs to give an indication of the substantial reduction in marginal costs associated with CDR-based targeting.

These results connect two distinct strands of prior work. The first is the literature on program targeting, which studies the effectiveness of different mechanisms for identifying program beneficiaries. In LMICs, research has focused on the performance of proxy means tests (PMTs) (Grosh & Baker, 1995; Filmer & Pritchett, 2001; Brown et al., 2018), community-based targeting strategies (CBTs) (Alatas et al., 2012; Fortin et al., 2018), and related approaches (Banerjee et al., 2007; Karlan & Thuysbaert, 2019; Premand & Schnitzer, 2020). A meta-analysis by Coady et al. (2004), which includes 8 PMTs and 14 communitybased programs, finds little difference in targeting accuracy between the two methods — but notes that targeting is regressive in a quarter of programs reviewed. In addition to issues with targeting accuracy, the current methods available for poverty targeting in LMICs are time- and resource-intensive, and may be infeasible in fragile or conflict-affected areas or in contexts where social interaction is limited, such as during a pandemic.

The second body of work explores the extent to which non-traditional sources of data, in conjunction with machine learning, might help address data gaps in LMICs (e.g., Blumenstock, 2016; Burke et al., 2021). Much of this work focuses on estimating the geographic distribution of poverty at fine spatial granularity, using data from satellites (Jean et al., 2016; Engstrom et al., 2017), mobile phones (Blumenstock et al., 2015; Hernandez et al., 2017), social media (Fatehkia et al., 2020; Sheehan et al., 2019), or some combination of these data sources (Steele et al., 2017; Pokhriyal & Jacques, 2017; Chi et al., 2022).

Most relevant to our current analysis, two prior papers investigate whether mobile phone use can approximate the wealth of individual mobile subscribers. Blumenstock et al. (2015) show that CDR data are predictive of an individual-level asset-based wealth index among a nationally representative sample of 856 Rwandan mobile phone owners ( _r_ = 0.68). Blumenstock (2018b) finds similar results with a sample of 1,234 male heads of households in the Kabul and Parwan districts of Afghanistan. While these results show that phone data can be used to _predict_ poverty levels, they do not evaluate whether those poverty estimates are of sufficient quality for real-world policy applications.

Our paper connects these two literatures by rigorously assessing the extent to which phone-based estimates of poverty can help with program targeting (Blumenstock, 2020). We believe the analysis will be especially relevant to the increasing number of interventions that rely on mobile money to distribute cash payments (Gentilini et al., 2020), and the growing 3
area. Half of SuperVia’s passengers are women,<sup>6</sup> and one in six to eight carriages are reserved for women, depending on the train length.<sup>7</sup> Thus only a fraction of all women riders could ride in the reserved space at times of high congestion. Male compliance with the reservation rule is enforced by platform officers who patrol to ensure the overall safety of the boarding process without having any policing power. However, their presence varies substantially across time and stations. This foreshadows substantial differences in the _de facto_ effectiveness of the reservation rule across space and time.

# **3 Data**

We generate two main types of data.<sup>8</sup> First, we use a crowdsourcing app to task regular women and men commuters to repeatedly report on riding conditions. Second, we administer a platform survey and IATs on a random sample of men and women commuters. Appendix B describes the construction of the variables used in our analyses. Appendix C describes the measures the research team took to ensure the study followed ethics guidelines.

## **3.1 Crowdsourced Rider Experiences**

Women and men riders were recruited and invited to report on their commuting experiences through a smartphone application for a monetary payment. The application allows us to vary the assigned location (reserved vs public space), pay-out, and data collection task across rides. This setup is used to 1) elicit women commuters’ revealed preferences for a reserved space, and 2) introduce exogenous variation in which space to ride. These are described in Sections 5 - 6. We use the same application to task male riders with the collection of data on transit conditions throughout the network. This allows us to capture granular variation in the ride environment, independent of women riders’ choice of space during their commute.

> 6Source: SuperVia administrative data.

> 7The location of the reserved cars is stable for a given train composition.

> 8The analysis dataset is available on the World Bank’s Microdata Catalogue under the reference code BRA ~~2~~ 015-2016 ~~D~~ SS ~~v~~ 01 ~~M~~ (Kondylis et al., 2020).

8
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# Does Informality Depress Investments and Job Recovery? Firm-Level Evidence from the COVID-19 Crisis in South Asia<sup>∗</sup>

Arti Grover<sup>†</sup> & Mariana Pereira-Lopez<sup>‡</sup>

## **JEL classification** : D22, D25, L20, L25, O17

: **Keywords** COVID-19, Crisis, Firms, Informality, Jobs

> ∗Acknowledgments: The authors are grateful to the South Asia Regional Trade Facilitation Program (SARTFP) for funding support and Denis Medvedev for his strategic guidance during the inception of the project. The authors thank several members of the World Bank teams for their efforts in data collection, including Amila Indeewari Dahanayake, Besart Avdiu, Hosna Ferdous Sumi, Mauricio Alejandro Pinzon Latorre, Mohammad Sulaiman Akbari Ojashwi Samser JB Rana, Peter Mousley, Rafay Khan, Ruchita Manghnani and Subika Farazi. The team is grateful to Franklin Maduko, Sarah Hebous, and Reyes Aterido for their careful work in data harmonization. The team thanks Maurizio Bussolo and Siddarth Sharma for their helpful feedback.

> †International Finance Corporation, World Bank Group, email: agrover1@ifc.org

> ‡International Finance Corporation, World Bank Group, email: mpereiralopez@ifc.org
compared to male entrepreneurs (de Mel, McKenzie, and Woodruff, 2009; Fafchamps, McKenzie, Quinn, and Woodruff, 2014). Accordingly, we expect the aspirations of female entrepreneurs to be lower than the aspirations of their male counterparts.

Third, we hypothesize that aspirations change over the life cycle of an entrepreneur. Since aspirations adapt to the outcomes realized (Dalton, Ghosal, and Mani (2016)), we expect aspiration gaps to be higher for younger entrepreneurs. However, previous literature on the relationship between age and individual aspirations is inconclusive. In a study set in rural Myanmar, Bloem, Boughton, Htoo, Hein, and Payongayong (2017) find that aspirations generally show no association with age. Older respondents do, however, express higher aspirations for remittances. This finding ties into results of a study using household-level data from rural China. Measuring income aspirations as the _minimum income need_ for the household to be sustained for a year, Knight and Gunatilaka (2012) find households with older members to have greater requirements for minimum income. The opposite association may also be true, as consistent with increased risk-aversion, older individuals may not aspire to more change but to more resources to sustain the current state.

A fourth important determinant of aspirations is educational attainment. The existing literature today concerns itself largely with educational aspirations. It shows that mothers with higher educational attainment have higher educational aspirations for their children (Serneels and Dercon, 2014; Bloem, Boughton, Htoo, Hein, and Payongayong, 2017). Closest to our study, Bernard, Dercon, Orkin, and Taffesse (2014) find aspirations to be higher for individuals with more formal education in a cross-section from rural Ethiopia. On a related note, Knight and Gunatilaka (2012) show that the minimum income needs of rural Chinese households increase as a positive function of education. Building on this, we expect entrepreneurs with more formal education to aspire to higher minimum income and measurably demonstrate higher aspirations for business growth.

Fifth, we investigate the relationship between aspirations and the entrepreneur’s business ability as measured by the set of best practices as per McKenzie and Woodruff (2017). Although this paper provides first evidence on this relationship, we build on previous research on business practices and management. As per McKenzie and Woodruff (2017), beyond changes in productivity, business practices may engender further effects. While marketing practices can affect the demand faced by the firm, the use of record-keeping may be better suited to hone in on more efficient procedures and to bring down costs in order to streamline the production process. Consistent with this mechanism, Anderson, Chandy, and Zia (2018) find that, in a sample of small-scale businesses in South Africa, firms treated with marketing training adopt a growth focus on higher sales while those treated with finance training adopt an efficiency focus on lower costs. Therefore, in our sample of small-scale 7