rafmacalaba/datause-encoder-data huggingface dataset:
of bankruptcy without access to finance.

In this paper, we examine how access to organizational resources and the pandemic control policy affects the underlying mechanisms behind firm performance, and the explanatory power of the mechanisms for firm performance during the pandemic. We ask, what resources, such as size, ownership, or CEO’s tenure, help firms survive and grow? What strategies did a firm choose that affect its survival and growth around the world? Is it the choice of stabilizing demand? Is it securing input supply? Or is it the effort to reduce uncertainties in finance? We also examine how the impact of the pandemic differs as the pandemic moves along, as captured by two different postpandemic surveys for the same set of firms. We also pay attention to how economic development level and COVID-19-related policies affect firm performance.

<mark>The rest of the paper is organized as follows. We develop our hypotheses in Section 2. Section 3 introduces the data and variables in the study. Section 4 presents our empirical results. We discuss our findings and conclude in Section 5.</mark>

# 2. Hypotheses

## 2.1. Strategic Choices and Performance during COVID-19

The existential threat of the pandemic has resulted in unprecedented uncertainties for firms. The strategies that firms would adopt to confront such challenges become matters of life and death. The drop in income and the rise in unemployment during the pandemic drastically reduce demand. Firms then have to utilize whatever resources are at their disposal to stabilize the demand for their products. The pandemic also drastically disrupts the supply of inputs. Various policies have been adopted to restrict domestic and international mobility, and financial access has also become more difficult. The factors would surely disrupt both imports and domestic supplies, and firms have to use their resources to restore and stabilize their input supplies. The pandemic also severely hampers the working of critical market-supporting institutions such as banks and other financial intermediaries, which play critical roles in maintaining economic prosperity (Levine 1997), especially at the time of crises when otherwise healthy firms face risks of bankruptcy without adequate access to finance. Firms could then utilize their resources such as firm networks or 3
Cull et al. (2009a) use an updated and expanded version of the MIX dataset to explore questions around the commercialization of microfinance, jumping into the debate highlighted by the public offering of stock by Mexico’s Banco Compartamos. Turning to the global landscape, we focus on eight questions: Who are the lenders? How widespread is profitability? Are loans in fact repaid at the high rates advertised? Who are the customers? Why are interest rates so high? Are profits high enough to attract profit-maximizing investors? How important are subsidies? And, how robust are the financial data?

For this and the other 2009 studies, we incorporate new data to bring the total number of institutions to 346, with at least one observation per institution from 2002 to 2004. For the 2009a paper on commercialization, we use the full dataset, which includes observations from institutions in 67 developing countries, looking for patterns in profitability and outreach related to institutional structure. We again use FSS as the primary measure of profitability. In addition to the variables listed in section 3, we include the number of active borrowers<sup>3</sup> , operating cost as a percent of loan value, operating cost per active borrower (in PPP$), return on equity, subsidy per borrower (in PPP$), and the noncommercial funding ratio.

We show that, despite the attention generated by commercial microfinance, NGOs continue to dominate the sample of microfinance institutions collected for the _MicroBanking Bulletin_ , accounting for 45 percent of institutions and 51 percent of borrowers. More than half of the institutions in the sample are profitable (defined as having a financial sustainability ratio above 1), including a large share of microfinance institutions with “non-profit” status. However, unsurprisingly, a larger share of banks are profitable than of NGOs. Both commercial and noncommercial lenders do quite well in terms of repayment rates, with 30-day portfolio at risk below 4% at the median for all categories.

On average, commercial microfinance banks make loans that are about four times larger than loans from NGOs, suggesting that they tend to serve a substantially better-off group of borrowers.<sup>4</sup> We also find that as a group, NGOs charge interest rates roughly double the size of

> 3 In the _MicroBanking Bulletin_ , “active borrowers” refers to “the number of borrowers with loans outstanding, adjusted for standardized write-offs” (MicroBanking Bulletin, 2005, pg. 57). 4 In these analyses, the comparisons involve simple averages, not weighted by the size of institutions.

8
macroeconomic factors. Likewise we find the decision to return amongst ever migrants is most strongly associated with preferences, with family and lifestyle reasons being stronger predictors of return than the extent of the income gains from migrating. We also find educational bonding to be an important reason for return of Papua New Guineans, with little subsequent re-migration after the two-year required period is completed. Overall the data support a limited role for income maximization in determining the migration decisions of the highly skilled, and a need to consider the other elements of the utility maximization problem.

The remainder of the paper is structured as follows. Section 2 describes the construction of the sample frame and the survey implementation. Section 3 analyzes the incidence of emigration and return migration among the top students. Section 4 models the determinants of ever migrating, and section 5 the determinants of return migration. Section 6 concludes.

# **2. Putting Together a Sample Frame and the Survey**

To examine the determinants of highly-skilled emigrants, we need comprehensive data on both the highly skilled who migrate and those who return. Standard surveys do not provide this information. Many studies of migration use nationally representative surveys in the migrant origin country. Typically households are then asked to report on absent migrant members. Such surveys suffer two drawbacks for studying the migration of the highly skilled. First, they miss individuals who migrate with their entire households, which may be more likely for the highly skilled than the less skilled.<sup>3</sup> Secondly, nationally representative surveys will contain few, if any, of the most highly skilled individuals from a country. Specialized surveys of immigrants in the destination country can help solve the first issue, but again will contain very few highly skilled migrants. Census microdata from both source and destinations may contain sufficient numbers of the highly skilled, but do not have enough detailed information on these individuals to examine the determinants of migration. As a result, a new specialized survey approach is needed.

> 3 See for example McKenzie and Rapoport (2008), who find that Mexican migrants to the U.S. are more likely to have their spouse accompany them if they have more education.

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change in the distribution of relative incomes which is independent of the mean (Datt and Ravallion, 1992).<sup>5</sup>

**Figure 3 – Decomposition of change in distribution and poverty into growth and distributional Effects**

<!-- Start of picture text --> Poverty line<br>New distribution<br>(I)<br>Initial distribution<br>Growth effect<br>on poverty<br>Distributional<br>effect on poverty<br>4 6 8 10<br>Real per capita household income (logarithmic scale)<br>Growth effect Distributional effect<br>.6<br>.4<br>Density<br>.2<br>0<br><!-- End of picture text --> Source: Bourguignon (2005).

Assuming every individual has the same increase of income that equals to the mean income growth of the entire population, the new hypothetic distribution would have the same distribution as the initial one and the same mean value as the final one. It can thus be presented as the horizontal translation of the initial density curve to curve (I) in Figure 3, which stands for the pure “growth effect” with no change taking place in the distribution of relative incomes. The “distributional effect”, which corresponds to the change in the distribution of “relative” income, is hence captured by the difference between this hypothetic intermediate distribution and the final one. In Figure 3, moving from curve (I) to the new distribution curve that occurs at constant mean income corresponds to the “distributional” effect.<sup>6</sup>

# **4.3. Decomposition of Income Growth**

Oaxaca decomposition, first developed by Oaxaca (1973) then by Smith and Welch (1989), quantifies the influence of various variables in increasing household incomes by decomposing their effects into (i) main effect that occurs because of changes in household characteristics and (ii) year effect resulted from changes in returns to the specific characteristics. This methodology allows examining the roles of different attributes – such as individual demographical characteristics, schooling attainment, and occupation – in driving the differentiated income growth over time.

> 5 See also Bourguignon (2003), Dollar and Kraay (2002), Fields (2001), Kakwani (1993) and Ravallion (2001). 6 The decomposition can be path-dependent, and results may be slightly different between a “first horizontal then vertical” translation and a “first vertical then horizontal” translation. We presume the difference is sufficiently small for our purpose of analysis.

6
|_exual be-_<br>_for sexu-_<br>_ve)_<br>(10)|-0.366|(0.324)|[0.439]<br>-1.181|(1.448)|[0.532]<br>0.724|(0.756)|680|0.126|Yes|-0.213|3.862|e 1, 5 and 10|mple includes|h the family,|uslim, speaks||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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|_towards_<br>_al behav-_<br>(8)|-0.253|(0.220)|[0.439]<br>-1.214|(1.069)|[0.532]<br>0.630|(0.800)|924|0.058|Yes|-0.107|3.512|enote signi|messages o|ingle, does|secondary e||
|_Attitudes_<br>_risky sexu_<br>_ior_<br>(7)|-0.300|(0.210)|[0.290]||||924|0.057|Yes|-0.107|3.512|*, ** and * d|ent with the|eplications.<br>sh spoken, s|higher than||
|_testing_<br>(6)|-0.520|(0.313)|[0.263]<br>1.719|(1.266)|[0.541]<br>-0.812|(0.984)|924|0.140|Yes|-0.164|4.410|clusters). **|more consist|d on 10,000 r<br>cation, engli|her obtained||
|_HIV _<br>(’5)|-0.454|(0.296)|[0.195]||||924|0.139|Yes|-0.164|4.410|level (80|outcomes|kets, base<br>ars of edu|ation, mot||
|_titudes_<br>(4)|-0.080|(0.258)|[0.749]<br>0.636|(1.028)|[0.559]<br>0.187|(0.732)|924|0.094|Yes|-0.316|3.601|ning centre|respond to|quare brac<br> school, ye|ndary educ||
|_HIV at_<br>(3)|-0.063|(0.256)|[0.772]||||924|0.093|Yes|-0.316|3.601|t the scree|ariable cor|-values in s<br> attending|r than seco||
|_owledge_<br>(2)|0.587|(0.375)|[0.263]<br>1.388|(1.221)|[0.541]<br>-0.799|(0.738)|924|0.119|Yes|-0.185|6.063|r clustering a|dependent v|R adjusted p<br>ge, currently|tained highe||
|_r. (Yt):_<br>_HIV kn_<br>(1)|f Treated<br>0.642*|(0.376)|[0.216]<br>f Treated*Opposite Sex||e Sex||tions<br>924|ed<br>0.119|(_a_)<br>Yes|ep. Var. (Control)<br>-0.185|Dep. Var. (Control)<br>6.063|ndard errors in parentheses adjusted fo|el, respectively. Increasing values of the|ends that have non-missing Y_t−_1. FWE<br>ls in each regression include: female, a|ize, wealth index, home owner, father ob|a native language.|
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xxviii
distance.<sup>11</sup> Goldstein and Udry (1999) map agricultural plots in Ghana using GPS equipment and compare the estimates to cultivators’ estimates of plot size. Figure 3 shows their striking results: the correlation between reported plot size and mapped plot size is only 0.15. Moreover, this measurement error is non-random: using their data we find that richer households report a smaller area than do poorer households for the same measured plot size<sup>12</sup> , and that women report a larger area than men do for the same measured plot size.<sup>13</sup>

**Figure 3: The low correlation between reported plot size and GPS-measured plot size in**

**rural Ghana**

<!-- Start of picture text --> 0 1 2 3<br>Measured Size (Hectares)<br>80<br>60<br>40<br>Reported Size (Ropes)<br>20<br>0<br><!-- End of picture text --> Source: Figure A1 in Goldstein and Udry (1999).

11 There are also several other surveys which have collected both self-reports and validated measures, for which formal write-ups do not yet exist. Alan de Brauw and John Hoddinott at IFPRI report that surveys in rural Ethiopia and China have generally found quite good agreement between self-reported area and measured area, whereas in Mozambique self-reports were very inaccurate. One hypothesis is that the accuracy of land area measurement is related to the scarcity of land: in places like China and Ethiopia, where land is scare and allocated by the Government, people have a better idea of the size of their land, than in places where land is relatively abundant. Roberts et al (2006) report on a survey in Bukoba, Tanzania where self-reported distance was compared with distances calculated by using pedometers and an estimate of average step length. They find that over 60 percent of self-reported distances were more than twice the calculated distances.

12 This parallels the classic psychology study of Bruner and Goodman (1947) in which poor children recall coins as bigger than rich children do.

13 These coefficients come from a median regression of reported area per measured area on gender, education, and household wealth, Both gender and household wealth are significant at the 1% level.

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per capita GNP and density of paved roads in good condition) and 9 (increase in both per capita GNP and density of paved roads in good condition).

# A Discussion of Causality

Assessing the impact of road infrastructure on economic performance is not straightforward because many other factors are involved. As we mentioned earlier, direction of causation between changes in income and changes in road infrastructure are not clear cut. One could argue that causation in the equations described in this paper could run in either direction. However, there are .*jme indications that roads should precede development, of which a few examples are: (a) In estimating the aggregate supply response of agriculture, Chhibber (17) showed that both price and non-price variables have significant effect. Although he did not explicitly consider a proxy for road stock in his analyses, it was implied that non-price variables included transport and communication facilities.

(b) Binswangex (18) found that the lack of roads is a significant constraint on the supply response of agriculture.

(c) In India, a Central Road Research Institute study by Dhir, Lal and Mfital (19) has shown that literacy, agricultural yield and health care increase with road density.

(d) Shah (20) used a restricted equilibrium framework to estimate the contribution of public investment in infrastructure to private sector profitability in Mexico. He concluded that a policy emphasis should be to upgrade the public infrastructure (including roads) so that scale economies could be exploited in the future.

(e) The linkage between roads and development has also been acknowledged by country leaders. As an example, President Bush has asserted that the interstate highway system fueled development in the U.S. for a generation, uniting the states as never before - economically, politicaily, socially (21).

(f) Hirschman (22) pointed out that highway construction can be conceived as the laying down of a "prerequisite" for further development. As such, it permits and invites, rather than compel, other activities to follow suit. This is in line with Owen's (2) assertion that comparisons of income and road infrastructure are not meant to imply that a road by itself is capable of developing a country or region, but that it is a necessary element in the development process.

(g) Using US data in the period 1949 to 1985, Aschauer (16), has shown that productivity (i.e., output per unit of private capital and labor) is positively related to government spending on infrastructure, including roads. He also observed that underinvestment in the U.S. infrastructure started in about 1968, and the effects 9
4 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity region into sub-regions to facilitate the planning and distribution of resources (Fischer, 1980). At the city level, the polycentrist view argues that institutional fragmentation of cities is equivalent to creating additional layers of decentralization, which can, in fact, enhance economic growth. This is thought to be achieved through two mechanisms. First, decentralized authorities are better informed of local needs and, therefore, can be more efficient in the provision of public goods (Ostrom, 2010). Second, increased competition between individual local governments constrains their ability to extract monopoly rents, thereby enhancing economic efficiency and, hence, economic growth (Stansel, 2005).

The second line of thought, the centrist, argues that the presence of multiple local governments within metropolitan areas may generate coordination failures that reduce efficiency in the provision of transport infrastructure and land use planning, and therefore affect economic performance (see Ahrend et al., 2014). Fragmentation may also reduce the metropolitan area’s ease of doing business because of the additional bureaucracy (Kim et al., 2014). According to Cheshire and Gordon (1996) and Feiock (2009), the presence of administrative boundaries within the functional region generates higher transaction costs and barriers to the diffusion of growth-promoting policies. As additional evidence in favor of the centrist view, Foster (1993) found a negative association between the proportions of population unincorporated to the metropolitan area and income growth.

Finally, the regionalist view can be seen as a middle way between the polycentrist and the centrist views. The regionalist view recognizes the benefits of local governments while highlighting the importance of metropolitan coordination defined as the efforts of governmental institutions to manage and solve problems in common between municipalities. According to Grassmueck and Shields (2010), more important than the existence of multiple local governments is the way in which they interact and perceive each other. Ahrend et al. (2014) found that the presence of a governance body that coordinates municipalities halved the penalty associated with fragmentation. Foster (1993) and Nelson and Foster (1999) also found empirical support for the regionalist view as a positive association between income growth and the presence of overarching decision-making mechanisms such as multi-jurisdictional, multipurpose regional governments. Also, the existence of singlepurpose districts associated with large-scale infrastructure provision (e.g. water and wastewater systems) fosters income growth.
employers and on the direct training costs at the firm level. Other variables available at the firm level include the firm’s location, ISIC 5-digit sector of activity, value added, number of workers and a measure of the capital, given by the book value of capital depreciation<sup>8</sup> , average age of the workforce and share of males in the workforce. It also collects several measures of the firm’s employment practices such as the number of hires and fires within a year (which will be important to determine average worker turnover within the firm). We use information for manufacturing firms between 1995-1999. This gives us a panel of 1, 500 firms (corresponding to 5, 501 firm-year observations). On average, 53% of the firms in the sample provide some training.

Relative to other datasets that are used in the literature, the one we use has several advantages for computing the internal rates of return of investments in training. First, information is reported by the employer. This may be better than having employee reported information about past training if the employee recalls less and more imprecisely the information about on-the-job training. Second, training is reported for all employees in the firm, not just new hires. Third, the survey is mandatory for firms with more than 100 employees (34% of the total workforce in 1995). This is an advantage since a lot of the empirical work in the literature uses small sample sizes and the response rates on employer surveys tend to be low.<sup>9</sup> Fourth, it collects longitudinal information for training hours, firm productivity and direct training costs at the firm level. More than 50% of the firms are observed at least

> 8We assume that depreciation is a linear function of the book value of the firm’s capital stock : Dept = π ∗ Kt.

> 9Bartel (1991) uses a survey conducted by the Columbia Business School with a 6% response rate. Black and Lynch (1997) use data on the Educational Quality of the Worforce National Employers survey, which is a telephone conducted survey with a 64% ”complete” response rate. Barrett and O’Connell (2001) expand an EU survey and obtain a 33% response rate.

7
phone data using a data mining approach that is agnostic about the outcome variable. Our approach is instead tailored to one outcome, repayment. We extract a set of objects that may have an intuitive relationship to repayment, and then compute features that summarize these objects. We focus on features with an intuitive relationship because implementation partners can be wary of ‘black box’ methods, and indicators that have a theoretical link are more likely to have a stable relationship to the outcome of interest. While our approach is likely to extract some features similar to Blumenstock et al. (2015), it will also measure more nuanced features that would not have been generated by a generic method.

Phone usage captures many behaviors that have some intuitive link to repayment. A phone account _is_ a financial account, and captures a slice of a person’s expenditure. Most of our indicators measure patterns in how expenses are managed, such as variation (is usage erratic?), slope (is usage growing or shrinking over time?), and periodicity (what are the temporal patterns of usage?). In particular, individuals with different income streams are likely to have different periodicities in expenditure (formal workers may be paid monthly; vendors may be paid on market days). We also capture nuances of behavior that may be indirectly linked to repayment, including usage on workdays and holidays, and patterns of geographic mobility, which can reflect information on employment. Although social network measures may be predictive (who one is connected to may reflect one’s level of responsibility or ability to access resources), we include only basic social network measures that do not rely on the other party’s identity (degree, and the distribution of transactions across contacts), as we are hesitant to suggest that a person’s lending prospects should be affected by their contacts. While many traditional credit scoring models aim to uncover a person’s fixed type (whether the person is generally a responsible borrower), the high frequency behavior we capture may also pick up features specific to the time when an individual is being evaluated for credit (a person may be likely to repay this credit, even if they are not generally responsible). Our process has three steps: First, the method identifies atomic events observed in the data, each represented as a tuple _(i, t, e, Xiet)_ , where _i_ represents an individual, _t_ represents the timestamp, _e_ represents an event type, and _Xiet_ 10