rafmacalaba/data-use-mentions-extended huggingface dataset:
namely that incumbent exporters benefit from their insider status in existing markets-products. Tariffs show up insignificant at both margins of trade.

**Table 11**

**Firm-Level Export Growth and the Role of Export Costs along the Margins of Trade**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|gr_rupees|gr_rupees|gr_rupees|gr_rupees|gr_rupee|sgr_rupees|
|foreign demand,log-change(ldnet_imp)|0.0371***|0.0291***|0.0297***|0.0291***|-0.0514**|-0.0462*|
||(0.00703)|(0.00701)|(0.00699)|(0.00701)|(0.0240)|(0.0245)|
|# days customsprocedures,change|-0.198|-0.0372|13.23***|-0.0371|-0.0149|13.23***|
||(0.352)|(0.358)|(1.120)|(0.358)|(0.356)|(1.118)|
|mfn tariff rate|0.0317|-0.0446|-0.0489|-0.0437|-0.0454|-0.0455|
||(0.0472)|(0.0501)|(0.0501)|(0.0507)|(0.0502)|(0.0508)|
|complexitytariff regime(sd_mfn_hs4)|-0.125**|-0.0756|-0.0721|-0.0982
Tobin’s Q.<sup>17</sup> Since many of the firms in our sample are not publicly traded, we do not have sufficient information to compute firm-level Tobin’s Q ratios. We therefore rely on data from all listed firms in China to compute an industry-level average Tobin’s Q using the value-weighted formula.<sup>18</sup> When firms are financially constrained, an increase in cash flows, which is assumed to convey no additional information about firms’ investment opportunities, would be associated with a rise in investment spending. We therefore expect a positive coefficient for the cash flow variable in this regression model if firms’ investment is influenced by their availability of internal funds. In addition to the sensitivity of investment to cash flows, we also include access to bank loans, trade credit and UCA as indicators of access to external finance. We view this as an informative additional check on the relative severity of financial constraints, to the extent that investment remains relatively more sensitive to internal cash flows for firms with weaker connections to the government when these variables are included in the regression.

Since some _X_ , all F, and lagged sales growth rates are available for only one year, we use the cross sectional estimation method in our base specification. For CF, Tobin’s Q, and sales intensity, we have two years of data, and so we also present fixed effects specifications in models with fewer variables than in the base specification. In those models, we are relying on withinfirm differences to identify the effects of cash flows and Tobin’s Q on investment. Many of our variables have notable outliers, such as investment intensity (and its two variants), sales

> 17 Tobin’s Q ratio is the market value of a firm’s total assets divided the book value of those assets.

> 18 We obtain similar results when using the industry-year median from the same data set of listed firms and after experimenting with various ways to value non-tradable shares
of all firms ranked by productivity and sales, respectively. Table 12 shows the share of these informal firms, compared to formal firms, for those variables that are significantly associated with productivity and/or sales.<sup>23</sup> Importantly, the share of informal firms in the top productivity and sales deciles having good characteristics is substantial: 37 percent of top decile productivity firms (19 out of 51) and 25 percent of top decile sales firms (11 out of 44) are informal firms. The results are quite similar for the top decile of productivity and sales, reflecting the strong positive association in the Senegal micro-enterprise data between productivity and sales. Both in terms of the smallest absolute and relative (as a multiple of the lower group) differences in shares between informal and formal firms, the top 5 characteristics that are most similar include having electricity and using mobile phones to communicate with suppliers (almost all firms have these characteristics, making them a pre-requisite for being in the top decile of both productivity and sales), as well as using mobile money to pay suppliers (roughly a third of both informal and formal firms use this DT). The largest differences between informal and formal firms in the top decile are whether the firm had a loan, has supplier credit, and has a computer: the differences are in the order of 40-50 percentage points. This suggests, intriguingly, that it is possible to be in the top deciles of productivity and sales even without having access to these forms of financing: roughly 60% of formal firms have not had a loan, and 95% of informal firms have not had a loan, though they are still able to be top performers.<sup>24</sup> Using inventory control/POS software is an important attribute of formal firms in the top productivity and sales decile, with just over one-half of firms using this management tool. In contrast, only one informal firm uses this type of DT: this is both indicative of
**Figure 25. Job posting’s salaries relative to applicants’ desired salaries**

_Source_ : Authors’ calculation using Rozee.pk data.

# 4.2 Selection: Short listing

This section analyzes how employers select workers. This analysis is rare and nearly impossible with traditional labor force survey data. An exceptional example is Nomura et al. (2017), who examine what affects the probability of being shortlisted by using online job portal data in India.

## **_Q. What type of applications are more likely to draw employers’ attention and be shortlisted?_**

## **_A. Applicants whose qualifications match job postings have a higher probability of being shortlisted. Both underqualified and overqualified candidates have a lower probability of being shortlisted._**

Figure 26 illustrates the timing of applications submitted (blue solid line) and the probability of an application being viewed by employers (red dash line). As already discussed, 80% of applications were received in 14 days, and 90% were received in 25 days. The probability of being viewed decreases as an application is submitted later. If an application is submitted on the same day of job posting, the probability of being viewed is 17%, but the probability is 13% and 6% if submitted 20 and 60 days later, respectively. The timing of application matters in short listing.

29
visits into doctor‘s visits for acute and chronic illnesses, the majority of these declines are in doctor visits for acute illnesses, which also account for the bulk of visits. Individuals and households report a higher labor impacts due to sickness in the weekly recall surveys. Across the observational and experimental samples, such reports drop from .09 per person-month to .06 (observational) or from .10 to .05 (experimental). Finally, there are large reported declines in the use of self-medication, both measured as the frequency of any selfmedication and the total number of medications taken.

Table 2 also shows how recall effects alter our perception of average household morbidity and treatmentseeking patterns. In the weekly data, households (outcome variables are aggregated up to the household level by summing across individuals) report a total of 5 acute illnesses a month. On average, 2.4 individuals in every household report at least one acute illness in a month, and household members spend a total of between 23 and 26 days sick per month. There are between 2 (observational) and 3 (experimental) doctor visits every month, the bulk of which are accounted for by acute illnesses—a result very much in line with our own experiences during the initial field work. Monthly-recall data present a completely different picture. Reported acute illnesses drop and doctor visits are down to 1.28 (observational) to 1.4 (experimental) visits per household per month. With an average of 5.2 members per household, this is just above the average of 3 visits per individual per year reported in the US data (National Ambulatory Medical Survey 2000).

In Table 3, we account for time-invariant individual heterogeneity and seasonality in the observational and experimental data. Specifically, for the observational data, we estimate where yit is the outcome variable for individual or household i at time t. Tit is a dummy variable that takes the value 1 for individuals/households who were surveyed using a weekly recall format at time t;
cover thoroughly in one paper, and there is every reason to expect such research to continue for a long time to come. The Crash of 1929 and ensuing Great Depression resulted in over 50 years of great research. For example, it took about 30 years to discover the full role of the shrinkage of the money supply in causing and perpetuating the Great Depression (Friedman and Schwartz, 1963), and it required about 50 years to understand how the loss of bank credit supply resulting from bank failures during the crisis made the Depression much worse (Bernanke, 1983). The research on the GFC and the Great Recession it generated has been ongoing for more than a decade and is still filling the journals as of this writing. The COVID-19 crisis, with all its unique characteristics, is likely to create a tidal wave of research papers for many years to come, and we hope that this paper may help perpetuate and improve this research.

It is also notable that a number of valuable research datasets are now available to research the COVID-19 crisis that were created for the purpose of monitoring the banks in the wake of the GFC. As examples, the Federal Reserve’s Y14 A, Q, and M datasets provide detailed information on large banking organizations’ projections of their conditions under a variety of macroeconomic scenarios; their asset classes and capital positions; and their loan portfolios; respectively. Some of these data have already been put to work in the research we review here, and we expect much more will be forthcoming in the next years.

Our main goals are to help understand the COVID-19 crisis and the resultant economic and financial research efforts as they relate to banking. We review, to the extent feasible, the extant research from the first year or so of past research. We focus most intently on how future banking research on this crisis can be improved, broadened, and deepened. We leverage considerably on the much larger banking research agenda from the GFC, which provides some useful directions where the COVID-19 research might
Table 1: Classification of skills

|**Skill classifcation**|**Occupations**<br>**I**|**SCO-08 (2 digits)**|
|---|---|---|
|**High-Skill**|Managers|11-14|
||Professionals|21-26|
||Technicians|31-35|
|**Medium-Skill**|Clerical support<br>Sales|41-44<br>52|
||Skilled agricultural workers|61-62|
|**Low-Skill**|Agricultural workers|63 & 92|
||Protective services|54|
||Food prep., cleaning, transp.|91 & 93-96|
||Personal care/services|51 & 53|

other maintenance basic occupations are defined as low-skill occupations in the NQF classification. This distinction is essential when interpreting the results since some of the changes in the premium for the same skill category between both classifications just capture differences in how some occupations enter the low skill category.

# **3.3 of innovative**

A critical step for the empirical analysis is the definition of innovative firms. One key challenge when working with employer-employee dataset is the lack of firm-level information, which in the case of RAIS is restricted to employment, wages, occupations, and geographical location. Innovation information is often found in other surveys. In the case of Brazil, the national innovation survey PINTEC (Industrial Research on Technological Innovation) is a representative survey for more than 17,000 firms every three years that includes information on firms’ expenditure on R&D. The survey, however, only includes manufacturing, services and electricity and does not cover all states of the country. Also, given the probabilistic sampling design and the three-year gap in data collection, the longitudinal dimension is not well in the data set.

Given the existing limitations in the data, a proxy of innovative activity can be developed using the share of occupations dedicated to research and
economy, that these resources will become unavailable for use by the private sector. And this transfer should be undertaken whenever a potential project available to the government offers social benefits greater than the loss sustained by removing these resources from the private sector." In practice, Quirk and Terasawa (1991) note that this comes down to using a weighted average of the consumer rate of interest and the pre-tax corporate rate of return as the government discount rate. Those authors object to that discount rate because '-the true opportunity cost of a government project is the value of the best available opportunity foregone because of the project, not simply the value of any available opportunity foregone." Determining the value of the best opportunity foregone by the government seems difficult to operationalize, however.

While no single rate is, perhaps, an adequate composite of private rates, recent data indicate that the interest rate on peso loans is presently 10% (International Monetary Fund, _International Financial Statistics (IFS)._ May, 1997). Another school of thought holds that, since the government often borrows to finance its deficit, the appropriate rate is the one at which it can borrow. In the U.S., a reasonable proxy is the Treasury Bill rate. For Argentina, money markets rates (which in the U.S. case are typically slightly higher than the 30-day T-Bill rate), are currently 6%, which might suggest a lower discount rate than 10%. In practice, however, the World Bank uses a 10% discount on many of its long-term projects in Argentina. Given the other rates, that one seems a reasonable estimate.

7 These figures exclude data from Santiago del Estero due to measurement problems. That privatization was finalized in September, 1996. The Fondo Fiduciario data for the residual entity are dated July 3 1, 1996. and should, therefore, be an accurate reflection of assets and liabilities at the close of the sale. The asset and liability figures from the Central Bank for 1995
to the MDGs: the infant mortality rate, child mortality rate, and prevalence of malnutrition. Using data from Demographic and Health Surveys (DHS), the authors go beyond the traditional cross-country regressions of new growth theory—exploiting the variability in outcomes and explanatory variables observed between asset quintiles within countries. They show that apart from traditional variables (income, assets, education, direct health interventions), better access to basic infrastructure services plays an important role in improving child health outcomes. Their analysis of interaction effects between interventions also suggests the importance of combining interventions to achieve the MDGs.

Jayasuriya and Wodon (2003) complement those findings by demonstrating the inefficiency of policies implemented by the international community and countries themselves in trying to reach the MDGs. These concerns are increasingly being voiced by key actors in international organizations: Manning (2004) for the OECD in a speech at the World Institute for Development Economics Research, Sachs and his colleagues at Colombia University for the UN in MDG papers posted on an earmarked Website, and Bourguignon and his team at the World Bank, who are currently working on better modeling of these interactions (this is still work in progress, with no public documents yet available).

> 3 This is in close coordination with the implementation of Poverty Reduction Strategy Papers (PRSP), which drive resource allocations within many developing countries. For an interesting overview of the issues raised by PRSPs, see Booth (2003) in his introduction to a special volume of the _Development Policy Review_ on PRSPs. 4 There is clear recognition of these interactions at the sector level. See, for instance, Ballar and others (2000) and Wang (2002), who examine this issue in the context of the health sector. 5 The World Bank is generating equivalent snapshots for Africa and a number of Asian countries. But data problems have been so severe that publication of the report keeps being postponed.
having more downward mobility than the national average in the first period to having less downward mobility in the second period. Other states (Punjab, Maharashtra, and Tamil Nadu) may not achieve this impressive turnaround but also manage to significantly reduce their mobility over time.

It can be useful to compare mobility profiles for some states with different performances. We pick two states at the two ends of the range of performance, Orissa and Delhi, which respectively have below-average performance in terms of upward mobility and achieve much improvement in the two periods under consideration (Figure 4). We provide estimation results for upward mobility and downward mobility for these two states in Figures 6 and 7. Both states mostly show qualitatively similar patterns to the national trends. For example, there is a similar pattern of attenuation for downward mobility (Figure 7) for the poorly educated, the rural, and the disadvantaged social groups. But more interestingly, certain nuanced differences exist between them. In particular, having some college education is more strongly correlated with more upward mobility (Figure 6) and less downward mobility (Figure 7) for Delhi than Orissa. Furthermore, this correlation also becomes much stronger over time for Delhi, which points to education as an important factor in raising upward mobility. This pattern is qualitatively similar when we compare a pair of two other states that are closer to the middle range of the performance, Bihar and Punjab (Figures A.1 and A.2 in the Appendix).

# **VI. Conclusion**

We analyze welfare dynamics in the past 25 years using five “thick” rounds of the NSS survey data between 1987/8 and 2011/12. In the absence of actual panel data, we construct synthetic panels that allow us to investigate dynamic patterns. We find increasing general mobility as well as upward mobility over time for the country. Yet, a majority of the poor who have escaped poverty remain vulnerable. Our estimation results suggest that higher education levels, and self-employment and wage-earning activities,
for female headed households, appear to be driven by changes in financial behavior - in particular, increased financial resilience and occupational choices for the women who made the decision to move out of agriculture. Mobile money has therefore increased the efficiency of the allocation of consumption over time while allowing a more efficient allocation of labor, resulting in a reduction of poverty in Kenya.

In sum, there are notable contributions to the literature on how the advent of digital technologies affect local labor markets in Africa, and on how household welfare has been improved by the adoption of digital tools that reduce the costs of financial transactions. Yet, as mentioned above, there seems to be a blind spot in the literature when it comes to assessing the empirical relationship between unemployment and the digital economy. The following section describes the international and publicly available data we use to estimate the partial correlation between unemployment and the digital economy across countries.

# **III. Data**

We first describe the data concerning the dependent variable, namely national unemployment rates. In turn, this section discusses the data concerning the explanatory variables including the proxies of the incidence of the digital economy and other controls used in the econometric estimations.

# _A. Dependent Variable: Unemployment Rates_

The dependent variable is the average unemployment rate of a large sample of countries between the years 2000 and 2017. The time period is determined by our view of when digital technologies became commonplace; they were virtually non-existent in the twentieth century.

7
# **IV. Impact of the Taiping Rebellion on Population Levels**

We now examine the relationship between the Taiping Rebellion and the prefecture population levels. We use the panel data for the 266 prefectures and the seven specific snapshots across two centuries (i.e., 1820, 1851, 1880, 1910, 1953, 1982, and 2000). The pre-treatment years are 1820 (the default year) and 1851; the post-treatment years are the rest. The baseline regression is as follows: ln⁡popit = dt + ηi + Xi,t′ β+ ∑βt tDtZi + + ∑αt 1tDtTPi + ∑αt 2tDtTP_Oci + ei,t (1) Here dt and ηi are the year and the prefecture fixed effects. The prefecture fixed effects capture all time-invariant factors such as geography and culture. Xi,t is a vector of time-varying controls including the duration of treaty ports, concessions, and leased territories, and war frequency, which has been shown to matter for China’s development (Bai and Kung, 2015; Jia 2014). Zi is a vector of time-invariant controls including geographical and historical variables (i.e., the distance to Yangzi River, to the coastline, and to the Grand Canal; the number of neighboring provinces; the pre-war level of land taxes per mu; the total number of palace graduates per million people from 1793 to 1820; the silk and tea prefectures dummies; the four post designations of the Qing government in 1820), which are allowed to have period-specific effects so that the local endowment could have a different effect over time. e is the error term. i,t _The OLS Results_ . Column (1) in Table 2 presents the regression results using the two Taiping treatments. To allow for potential selection bias, we allow the period immediately before the Taiping Rebellion to have distinct effect for the Taiping areas — as a test of selection bias. The estimate of the Taiping coefficient before the rebellion (1851) is small (at
financial exclusion in Colombia as revealed in the Bogotá survey and banks’ risk-averse behavior following the crisis, it is unlikely that private banks will provide basic account services at their own initiative, and thus, Regulation could be catalyst. The Superintendencia Bancaria could support this by, for example, monitoring the share of basic accounts as a percentage of total accounts in defined lower income categories.

43. All of the initiatives will require the concerted efforts of the Colombian Bankers’ Association, as well as a continued dialogue within the banking sector, so that they can effectively lead to the acquisition of new profitable clients and the expansion of the banking sector.

44. Existing anti-money laundering regulation clearly is an impediment for the unbanked to access formal financial services. In particular, multiple account opening requirements could be reduced to one simple 63
quantity to quality gradient, more than tripling the contribution of human capital. We find even steeper gradients by regions. For example, when measured by schooling, human capital accounts for 31 percent of cross-country income differences in Europe and just 5 percent in sub-Saharan Africa. When we include learning, this gap widens to 67 percent in Europe but just 8 percent in sub-Saharan Africa. This substantial heterogeneity reveals the importance of the inclusion a global distribution of countries covering multiple stages of economic development to account for the role of human capital.

Finally, we compare our measure of human capital to alternatives in Table V. We find that our measure of human capital has a stronger and more statistically significant association with growth than human capital measures in prominent global databases such as the Penn World Tables (PWT) and the Human Development Index (HDI). This is the case in when comparing measures on their own in columns (1)-(4). Each variable is transformed to a log scale to compare percent changes or 13