rafmacalaba/data-use-mentions-extended huggingface dataset:
. Miyakawa et al. (2021) estimated an increased firm exits in Japan of about 20% compared to the pre-pandemic year. OECD (2021) shows that among all surveyed countries, monthly bankruptcies dropped year-on-year by more than 30% on average since March 2020. Moreover, from September to November 2020, no significant rebound was observed and the total number of bankruptcies in 2020 remained lower than in the previous year.

A few developing countries are individually collecting survey data to understand the impact of the pandemic. For instance, FAO, IOM, and ITC (2021) follows 893 Iraqi firms in three rounds of surveys to find that nearly all firms register a decline in production or sales between February 2020, the pre-COVID-19 period, and the end of the year. By June 2020, firms suffered an average decline in revenue by 67%. Although revenue partially recovered between July and October 2020, it was still 23% below the pre-pandemic level. Using a panel of Chinese firms operating on Alipay platform, Kong et al. (2021) find that one year after the exposure to the COVID-19 pandemic, the operational status of micro-and small enterprises has significantly improved. However, they still face serious cash flow constraints. A higher percentage of newly established businesses in China adopted online sales and electronic information systems than those established earlier. Nevertheless, the expectations on expansion in employment remain subdued.

With entry-exit dynamics, the pandemic is bound to change the market structure and competition which in turn will also affect productivity. This will have profound consequences in shaping the recovery process in the long-run. However, the causality runs both ways. The pandemic shock 6
**<br>(0.007)|0.002***<br>(0.001)|0.002***<br>(0.001)|
|Observations|1,199,814|1,199,814|1,199,814|1,199,814|
|R-squared|0.323|0.332|0.117|0.153|

Notes: Robust standard errors in parentheses clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). The CEFs are based on Table A.2. Quadratic model is adopted following Becker et al. (2015). Unweighted estimates are same as estimates reported in Table 2 and Table 3 for years of schooling and primary completion outcomes, respectively. Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Years of schooling (Edu) in Census 2000 was calculated based on the education level completed. Years of schooling (Edu) in the IFLS was calculated based on highest grade completed in an education level. Primary completion takes the value of 1 if a child has completed 6 or more years of schooling and 0 otherwise. Family background is measured by father’s years of schooling. The Inverse Probability Weighting (IPW) estimates are calculated using 1975 district level density interacted with time trend. Data sources: Indonesia’s full count census 2000, Duflo (2001), IFLS and IFLS-East.

70
in some cases. For example, WRSI weights losses in rainfall differently based on when they occur and NDVI, by capturing vegetation should, already capture the non-linear impact of weather conditions on yields. Another reason for not observing a consistent non-linear pattern could be that we have few household survey observations at extreme values of the anomaly which is where observations would be needed to pick up non-linear effects.

In the maize livelihood zone some nonlinearity is present: there is a stronger relationship between hazard anomalies and welfare in the middle of the distribution (where anomalies and therefore welfare changes are smaller) and a smaller impact of anomalies on welfare at the tails of the distribution. A steeper relationship is also observed in the quadratic and cubic specification for the roots and lowland livelihood zone, but no tapering off for negative values (Figures 4.1 and 4.2). Despite being significant in this case, the squared and cubic terms do not indicate much nonlinearity in the relationship. Nonlinearities are much more important when using the total rainfall measure; this makes sense because this measure does not reflect any nonlinear relationship between rainfall and crop production in how it is constructed. In these cases, the nonlinearity indicates a larger impact on welfare as the anomaly becomes bigger and more negative, and a smaller impact on welfare as the anomaly becomes more positive, even turning negative for very large values of total rainfall that may reflect waterlogging and flooding.

Third, for soil moisture negative impacts are felt for both landed and landless households with no consistent pattern of who is most affected. For the highlands and NDVI measures in roots and tubers livelihood area, there is a strong and significant impact on landed households, but little impact on landless households. Overall, though given the relatively similar impact in many specifications it suggests that using weather variation in a 20km radius includes the impact of the weather on immediate labor markets.

15 The term “hazard” as used here includes the full distribution of the weather anomaly used—that is, positive and negative events
The table shows the regression results on pre-specified child outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Strengths and Difficulties Questionnaire (SDQ) is a screening questionnaire administered to a randomly selected child aged three to eight years old and covers emotional, conduct, hyperactivity, peer, and prosocial problems. SDQ is standardized by subtracting the control group’s mean and dividing by its standard deviation.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 8 in the preanalysis plan.

# Table (E13) Impacts of the Program on Household-Level Child Outcomes

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Count of Non-Adult Dependents in Household|-0.07|(0.18)|[1.00]|3.30|(2.02)|1,422
# **4. Conclusion**

COVID-19 is spreading fast across the globe. At the time of writing,<sup>6</sup> the WHO reported cases of COVID19 in 206 countries with the tragic deaths of more than 40,000 people. The primary focus is necessarily on containment, treating the ill and helping communities cope with the epidemic. Our illustrative scenarios indicate that the potential loss of income in affected countries could be significant, with global GDP declining by up to 3.9%, and developing countries hit the hardest (4% on average, but some over 6.5%). Governments will need to offer significant support to affected businesses and households.

Our analysis is likely to underestimate the potential economic costs of the epidemic. We do not fully capture several important channels, such as the uncertainty-driven contraction in demand and FDI, and other real effects of a financial shock. We capture some aspects of global value chains trade through inputoutput linkages and assumptions that mimic the durability of relationships between firms in value chains but plan to extend the analysis using a richer data set. We have examined, but not yet finalized, the effects of raising domestic trade costs as well as demand switching away from activities requiring direct contact with other people. Our analysis will evolve as we bring assumptions and scenarios in line with more recent health and economic indicators.

Early indications of the economic costs and the magnitude of estimated impacts demonstrate the need for a coordinated international response to the crisis. A global crisis requires a global response and there is a need for global collaboration not just on health, but also on trade, finance and macroeconomic policies. Fortunately, global institutions are beginning to catalyze and coordinate global efforts, as well as to provide technical and financial support to countries coping with the health and economic consequences of the outbreak.

6 March 31, 2020.

- 17 -
M. Spiegel (1994), “The Role of Human Capital in Economic Development: Evidence from Aggregate Cross-Country Data”. Journal of Monetary Economics, vol. 34(2): 143-74.

- Betcherman G. 2014. “Designing labor market regulations in developing countries.” IZA World of Labor 2014: 57.

- Biljanovska N., and D. Sandri. 2018. “Structural reform priorities for Brazil.” IMF working paper 18/224. Washington, DC.

- Blanchard O. and F. Giavazzi. 2003. “Macroeconomic effects of regulations and deregulation in goods and labor markets.” Quarterly Journal of Economics. Vol. 118, Issue 3. 879-907.

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- Bouis R., and R. Duval. 2011. “A quantitative assessment of the potential gains from various structural reforms in the OECD area and beyond.” OECD Economics Department Working Papers No. 835

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- Chen C-F
kleptocrat able to insure an honest bureaucracy or one that must contend with a corrupt one? No clear answer is possible. It depends upon the losses from systemic corruption at every x, compared with the ruler's incentives to select an inefficiently large government.I As we pointed out above, kleptocratic rulers may simply be unable to create the conditions needed for an honest bureaucracy to flourish. For example, a kleptocrat has an incentive to increase the number of bureaucratic obstacles to business activity in order to increase the bribes from business people seeking to overcome those obstacles. However, staff must be available to enforce the obstacles and collect the bribes. A principal/agent problem exists.'<sup>9</sup> Alternatively, if no cooperation is required from subordinates, the kleptocrat will try to keep his or her own peculation secret and pose as a benevolent autocrat in order to avoid having to share the gains. Since a corrupt ruler will not want the same level of state intervention as a benevolent ruler, corruption might be inferred from the rent generating options selected. The need, at least, to appear honest may constrain the venal ruler. Of course, even honest rulers may have to contend with corrupt subordinates. But the task of motivating and retaining honest and competent officials is surely more difficult for a corrupt ruler than for an honest one.

# _E. Corruption and growth_

The cross-country studies cited above suggest that high levels of corruption are associated with lower rates of investment and growth. Does our discussion of autocratic rulers shed any light on the mechanisms behind this result? To proceed we need to introduce a temporal dimension. There are three aspects to this issue. Two concern the ruler's preferences, and the last involves the calculation of outside investors.

First, the flow of net corrupt benefits may be distributed over time quite differently from 20
higher cost-reflective tariffs than the average unit price paid by all households that report paying a positive amount for piped water. This already shows that all countries operate in a way that on average subsidizes residential consumers. However, six of the ten countries show a unit price higher than the estimated CRTOpex, suggesting that they can cover at least operating expenditures.

**Table 2. Tariff data and estimated unit costs and prices**

|Country|Number of<br>providers<br>covered in|Type of tariff<br>structure|Estimated cost-<br>reflective tariff<br>(unit cost/m<sup>3</sup>)|Estimated OPEX<br>cost-reflective<br>tariff (unit|Estimated<br>effective<br>average|
|---|---|---|---|---|---|
||the||CRT|cost/m<sup>3</sup>) CRTopex|price/m<sup>3</sup>|
||analysis<sup>a</sup>||($2017)|($2017)|($2017)|

6
within the selected clusters (using a simple random sample protocol) to participate in the baseline survey.

The second component of the L2T survey was a monthly household panel conducted over the phone, with a randomly selected subset of households that participated in the baseline. Selection was stratified such that a fixed number of households were selected to participate in each cluster included in the baseline survey. This approach ensured that the geographic spread of participants remained as dispersed as in the baseline survey and minimized the intra-cluster correlation of respondent characteristics to the best extent possible. The target sample size over the relevant period in 2020 was 1,200 households per month. The sample weights for households were adjusted to reflect the subsample design conducted using the baseline interviews and updated monthly to account for (modest) rates of attrition.<sup>9</sup> The panel component of the L2T survey largely focuses on dimensions of well-being that have the potential to fluctuate over time, including (i) migration and remittances, (ii) jobs and income, (iii) deprivations and subjective well-being, and (iv) utilities and services. During the pandemic, the survey was updated with a new module about the well-being of the population, including indicators on the public’s understanding of preventive and risk-minimization measures related to COVID-19. Several questions from the COVD-19 focused module are of particular interest in this study. These include the questions fully described in Appendix C.

Table 2 provides a summary of interviews completed and risk reducing behaviors reported. A total of 10,963 interviews including the COVID-19 specific module were completed between April 2020 and January 2021. On average, households reported a total of 5.9 of the 9 responses available. As official endorsement of the module was received late in April 2020, only 219 interviews including those questions could be completed in April. Thereafter, the sample reached was generally slightly below the monthly target of 1,200 interviews per month. Nearly all respondents reported changing at least some routines to reduce the risk of
Table 6: 2SLS Estimation of Labor Supply by Skill Level

||Wage Em|ployment|Self Em|ployment|
|---|---|---|---|---|
||(1)<br>Low Skilled|(2)<br>High Skilled|(3)<br>Low Skilled|(4)<br>High Skilled|
|Panel A: Labor market par|ticipation||||
|Household with migrant|-0.205**|-0.486|0.199***|0.324|
||(0.098)|(0.305)|(0.069)|(0.346)|
|Observations|14054|2825|14054|2825|
|Wald _χ_<sup>2</sup>|3577.578|997.890|3864.717|2175.469|
|_F_-test 1stage|68.765|8.179|68.765|8.179|
|Coecient 1stage|1.925***|1.460***|1.925***|1.460***|
|Panel B: Hours supply|||||
|Household with migrant|-15.028***|1.032|-13.064***|5.048|
||(5.754)|(11.830)|(4.454)|(14.563)|
|Observations|4714|1131|10319|1199|
|Wald _χ_<sup>2</sup>|2170.635|505.787|1130.111|1815.099|
|_F_-test 1stage|33.235|7.061|63.906|6.082|
|Coecient 1stage|1.820***|1.665***
. (2012). Social role theory. _Handbook of theories of social psychology_ , 2:458–476.

Edmonds, E., Feigenberg, B., and Leight, J. (2021). Advancing the agency of adolescent girls. _Review of Economics and Statistics_ , pages 1–46.

Exley, C. L. and Kessler, J. B. (2022). The gender gap in self-promotion. _The Quarterly Journal of Economics_ , 137(3):1345–1381.

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Frese, M., Fay, D., Hilburger, T., Leng, K., and Tag, A. (1997). The concept of personal initiative: Operationalization, reliability and validity in two german samples. _Journal of occupational and organizational psychology_ , 70(2):139–161.

Ganimian, A., Barrera-Osorio, F., Biehl, M. L., and Cortelezzi, M. A.<sup> ́</sup> (2020). Hard cash and soft skills: Experimental evidence on combining scholarships and mentoring in argentina. _Journal of Research on Educational Effectiveness_ , 13(2):380–400.

Hart, C. M., Ritchie, T. D., Hepper, E. G., and Gebauer, J. E. (2015). The balanced inventory of desirable responding short form (BIDR-16). _Sage Open_ , 5(4):2158244015621113.

25
Policy Research Working Paper 8625

# **Abstract**

Is the variation in bilateral trade flows across countries primarily due to differences in the number of exporting firms (the extensive margin) or in the average size of an exporter (the intensive margin)? And how does this affect the estimation and quantitative implications of the Melitz (2003) trade model? The benchmark Melitz model with Pareto-distributed firm productivity and fixed costs of exporting, predicts that, conditional on the fixed costs of exporting, all variation in exports across trading partners should occur on the extensive margin. This paper subjects this theoretical prediction to a reality check drawing upon the World Bank’s Exporter Dynamics Database (EDD) which has firm-level exports from 50 developing countries to all destinations. Around 50 percent of the variation in exports across trading partners is shown to be along the intensive margin, contradicting the benchmark Melitz-Pareto model. The paper finds that moving from a Pareto to a lognormal distribution of firm productivity allows the Melitz model to successfully match the role of the intensive margin evident in the EDD. The paper then studies the implications of our findings for quantitative trade theory. Using likelihood methods and the EDD, a generalized Melitz model with a joint lognormal distribution for firm productivity, fixed costs and demand shifters is estimated, and exact hat algebra is used to quantify the counterfactual effects of a decline in trade costs on trade flows and welfare in the estimated model. Finally, these effects are compared to those that would be predicted by the Melitz-Pareto model, with the Pareto shape parameter chosen to match the average trade elasticity implied by the estimated Melitz-lognormal model. The paper shows that the effects on welfare turn out to be quite close to those in the standard Melitz-Pareto model even though the effects on trade flows remain different.

This paper is a product of the Development Research Group, Development Economics. 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
320*<br>(0.172)|0.425**<br>(0.170)|0.447**<br>(0.195)|
|Stove Use X Baseline Age ≥ 36 mo.||||-0.016<br>(0.155)|0.026<br>(0.153)||
|Additional controls|yes|yes|yes|yes|yes|yes|
|Household random effects||yes|||yes||
|Household fixed effects|||yes|||yes|
|Observations|1,152|1,152|1,152|1,152|1,152|1,152|

Notes: Table reports intent to treat estimates of stove offers in columns 1-3 and instrumental variable effects of stove use in columns 4-6. Columns 2 and 5 include household level random effects and columns 3 and 6 include household fixed effects. Dependent variable is child height-for-age (z-scores), standardized according to the CDC growth tables. Sample is all children ages 15 and under. All models include a cubic polynomial in age in months, sex, mother's height and age, and father's age. Household level controls are omitted from regressions with household fixed effects, and include household size and demographic composition, age, education and sex of the household head, dwelling characteristics, community fixed effects, and a composite wealth index. Standard errors clustered at the household level in parentheses. ** p<0.05, * p<0.10 Column 1 presents treatment estimates for both those children exposed while in the sensitive period and older children. The estimates suggest being in a household that was offered a _Mirt_ stove while in the sensitive period of growth is associated with a 0.277 standard deviation increase in predicted height-for-age relative to the control children of the same age. This is a sizeable effect and similar in scale to the
32 <!-- Start of picture text --> 5 __ _ _ _ _ _ _ _ _ _ _ _ _ _ _3 00<br>+ 250.<br>+ ~~~~~200.<br>C~~~~~~<br>1150<br>+ ~~~ ~~~~~50 +<br>1 +<br>0 50 100 150 200 250 300 0 50 100 150 200 260 300<br>Pro-crisis Volatiliy Pe-crisis Volatiity<br>Figure 19 Figure 20<br><!-- End of picture text --> ii) Similarly, figures 21 and 22 below show that also the second implication holds: the mean and the volatility of the risk premium have a positive relation.

<!-- Start of picture text --> 300 o 350<br>250<br>300<br>#200 24<br>200<br>~100 +<br>+~~~~~<br>50. 4 L4 150<br>isPsi Men+ssMa<br>0 o 500 1000 1500 2000 10 500 _ _ _ 1000 _ _ _ _ 1500 _ _ _ _ _ 2000 _ _ _ _ 2500<br>Pre-crisis Mean Cdlas Mean<br>Figure 21 Figure 22<br><!-- End of picture text --> _Figure 22_ iii) Finally, we try the following experiment to look for evidence supporting the third implication. For both periods - pre-crisis and crisis - we rank the countries according to the _probability of default_ (1- **i** ) - proxied by the pre-crisis mean of the risk premium - and then we plot the volatility on this rank. For the pre-crisis period the ranking is (from the most likely to the less likely to default): Nigeria, Venezuela, Bulgaria, Ecuador, Argentina, Brazil, Poland, Philippines and Mexico. For the
index is greater than or equal to 3, (which is about the median), and a value of 0 if the index takes a value less than 3.9 Second we use a combination of the size of the public sector and corruption to create a variable of transparency. Kopits and Craig (1998) argue that the transparency in government behavior is reflected mainly in the structure and functions of the public sector, such as financing operations. Thus, we create a measure of public sector size as the ratio of credits directed to the government and the public sectors relative to total domestic.'<sup>0</sup> We define country with large (small) public sector if the share is greater (smaller) than the median. We combined this measure with our measure of corruption and define three levels of transparency. Poor transparency if a country is corrupt and has large public sector; Medium transparency if a country is corrupt but has a small public sector or is not corrupt but has a large public sector; Good transparency is a country, which is not corrupt and has a small public sector.

One caveat of using corruption as a proxy for transparency is that corruption by itself may affect the probability of a crisis. Financial liberalization may offer new opportunities for corruption that may lead to banking crisis. Thus, transparency may not affect banking crisis but corruption does. In order to test and address this issue, we use another proxy for transparency that is not based on corruption. Our third measure of transparency is from the World Competitiveness Yearbook. The variable is based on a survey of executives in 46 countries who were asked to state whether they agree or disagree to the following statement "the government does not communicate its policy intentions clearly". The variable takes values from 1, agree, to 8, disagree. Unfortunately, this data is available only for 1998. Thus, 9 The results are robust to the exact definition. The data is taken from IFS. The ratio is (line