rafmacalaba/data-use-mentions-extended huggingface dataset:
# Seasonality

The 1993 and 1995 surveys were conducted only over a period of several months: June to November 1993 and April and May 1995. Households were interviewed once during that period, and were asked about expenditures over the past week, month, three months or year depending on the category of expenditure. If either of these surveys were conducted during periods of exceptionally low or high expenditure, then the estimates of mean expenditure and the poverty incidence calculations could be biased. To assess whether there is a bias, we need some way of determining how expenditure varies over the year and correcting for seasonal fluctuations. Fortunately the 1985-1988 CILSS surveys can be used for this purpose.

Households in the CILSS surveys were interviewed once about their expenditures, with the interviews conducted randomly throughout the year. Since the CILSS surveys had a two week recall period for both purchased food and many nonfood items, a high percentage of expenditure would potentially be subject to seasonal variation. This variation in expenditure could arise from seasonal fluctuations prices, particularly food prices, that are not offset by variations in quantity, or to seasonal variation in income, to the extent that households do not smooth their consumption over the course of a year. There could also be seasonal variation present in the estimate of expenditure on items for which the recall period was a year if the period of the year during which the interview takes place influences the size of the recall error. For example, households interviewed during a period in which they typically purchase more durable goods, send more remittances, or consume more home production might remember more of their expenditures than households interviewed during other periods of the year. Or the value households attach to the consumption of home production may vary with the market price of the home-consumed product. Or the estimate of the quantity consumed each day or the number of days per month a home-produced good is consumed may reflect the pattern during the month that household is interviewed rather than the average over the year.<sup>23</sup> Thus any
|
|g<br>HFPS|Phone|12|0.05|
|Tanzania<br>NPS 2021<br>HFPS|In-person<br>Phone|409<br>8|0.25<br>0.04|
|Uanda<br>UHIS 2021/22|In-person|383|0.19|
|g<br>HFPS|phone|9|0.03|
||**_Overall_**|||
||_Min_|_Max_|_Mean_|
|_Costper interview_||||
|In-person|230|409|314|
|HFPS(phone)|8|12|10|
|_Costperquestionper interview_||||
|In-person|0.10|0.25|0.19|
|HFPS(phone)|0.03|0.06|0.05|

_Source: Authors’ calculation based on personal communication with World Bank staff supporting the implementation of the surveys._ The above comparisons are based on surveys conducted via CATI, but phone survey costs can vary significantly by approach. Based on IPA’s review of phone surveys using 27 studies in 18 countries, on a cost per interview basis, IVR is the cheapest mode ($4.86 on average), followed by automated SMS ($7.75), and CATI ($11.97) (Glazerman et al., 2020). Henderson and Rosenbaum (2020) add to the comparison a manual SMS survey in Nepal, where interviewers instead of an automated system were used to send text messages to respondents, with a cost per interview higher than CATI at $17.41 per 11 The calculations in Table 3 are based on the number of questions in the questionnaires, not on the questions actually asked. The total number of questions actually asked to respondents will always be lower, as not all questions will be applicable to all households.

22
Some 18 percent of the global death toll is claimed by UMICs, whereas just 3.1 and 0.2 percent, respectively, by LMICs and LICs. In other words, the two poorest groups of countries collectively represent almost half of the global population but account for only 3.3 percent of global deaths that have COVID-19 as the underlying cause. These large differences almost come across as if the world is affected by ‘two pandemics’.

# **Demography Contradicts the Excess Inequality between Rich and Poor Countries**

One natural way to interpret the seeming dichotomy between rich and poor countries would be to attribute it to differences in demographic structure (Figure 8). The strong age gradient in COVID19 fatalities has been well established by now.<sup>7</sup> Hence, the demographic diversity we witness around the world will matter greatly (World Bank, 2016). The world could be split up into roughly two groups: pre- and early-dividend countries, which continue to have high fertility rates and a still-rapidly increasing working-age share in their total populations, and late- and post-dividend countries, which are aging at rapid speed or have aged already very considerably.<sup>8</sup> This typology of demographic diversity carries over in large part to the income classification given the tight twoway connections between demography and development. As Figure 8.A illustrates, younger age cohorts are much more significant in the age distributions of LICs and LMICs than in those of UMICs and HICs.

> 7 See Baud and others, 2020; Begley, 2020, Belligoni, 2020; Courage, 2020; Dylan, 2020; Huang and others, 2020; Mahase, 2020; Onder and others, 2020; Richardson and others, 2020, Soneji and others, 2020; Wu and McGoogan, 2020.

> 8 See World Bank (2016) for a detailed discussion of the demographic typology.

15
Policy Research Working Paper 9218

# **Abstract**

This paper examines the performance of globally engaged firms in Argentina in the past decade. Using highly disaggregated firm-level customs transaction data for imports and exports, the paper documents the progressive retreat of Argentine firms from global markets. Between 2007 and 2017, the number of exporters decreased by 30 percent. Benchmarking the characteristics of these exporters with similar countries reveals that Argentine exporters are disproportionally fewer and individually larger, with export value extremely concentrated in a few firms. Firm churning rates are disproportionately low and survival rates of entrants are high. These findings reflect exceptionally high entry costs of export, which are the result of anti-export bias and import substitution policies that sought unsuccessfully to develop the local industry. The paper shows that exporters that import directly intermediate and capital goods have better export outcomes than other exporters.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at danielreyes@worldbank.org and sfranco2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
effective tariff|U.S. cents per m3|60|57|3–60|

_Source:_ Demographic and Health Survey (DHS) and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data).Note: Access figures calculated by the AICD based on the 2000 and 2006 DHS figures published by the Joint Monitoring Program (WHO 2010). — = Not available.

Only 40 percent of urban dwellers rely on advanced water technologies for their water supply. Close to 40 percent of the population has access to utility water—mainly stand posts—in urban areas, and around 5 percent in rural areas. Almost 70 percent of rural Sudanese rely on wells and boreholes, 30 percent higher than in urban areas. It is very striking that there is little difference in the reliance on surface water—around 25 percent of both rural and urban Sudanese obtain their water from open sources.

There is significantly greater usage of improved sanitation technology types in urban areas (figure 14). Urban areas have twice the level of access to flush toilets or improved

**Figure 14. Limited access to advanced water technologies in urban Sudan**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Urban Rural<br>% population<br><!-- End of picture text --> _Source:_ Access figures calculated by AICD using data 2009 DHS data.

latrines compared to rural areas. In the case of flush toilets, the access rate in urban areas is 14 percent compared to 1 percent in rural areas. Sixty percent of the urban population relies on improved latrines compared to 32 percent of the population in rural areas (figure 15). Some 68 percent of the rural population defecate in the open compared to
# **C.2 Heterogeneous Effects of Livelihood Diversification**

In the main paper we address two pre-specified research questions. In this section, we explore our third pre-specified research question: does income diversification have disparate impacts on different country subgroups in the context of the COVID-19 pandemic? Specifically, we investigate heterogeneous impacts for male- and female-headed households as well as urban and rural households. To detect these potentially disparate effects, we include binary interactions term in our ANCOVA and difference-in-difference specifications indicating head-of-household gender and household sector. Because we use only the ANCOVA and difference-in-difference specification to answer this question, we restrict our analysis to just the indices that rely on baseline data (fractional indices 3 and 4 and HHI 1 and 2).

Table D2 displays coefficient estimates for the ANCOVA specification with a head-of-household gender interaction. In this context, male-headed households are the comparison group. While nearly every coefficient of interest is not significant, coefficients tend to be slightly positive coefficients, suggesting that households headed by women may experience increased food insecurity when household incomes are more specialized. As seen in Table D3, using the difference-in-difference estimator also produces null results with a similar tendency for most coefficients to be slightly positive.

We also test for heterogeneous impacts across rural and urban populations. Similar to the results for differences based on gender of the head-of-household, all of the results for differences between urban and rural households are statistically insignificant. Unlike the head-of-household gender results, the specifications that include urban/rural indicators do not point to a consistent relationship in terms of sign. For these specifications, rural households serve as the comparison group.

As seen in Table D4 and Table D5, coefficients for the interaction term do not evidence a differential impact of livelihood diversification on food security for urban versus rural populations. Coefficient estimates are never statistically significant and do not follow a discernible trend across countries.

Overall we do not find any significant differences in how livelihood diversification impacts welfare outcomes based on the gender of the head-of-household or whether the household is rural or urban.
IFLS 2000, 2007/08 and 2014/15, MxFLS 2002, 2005/06 and 2009/12, LSMS-ISA 2010/11, 2012/13 and 2015/16, KHDS 1991/94, 2004 and 2010, and PSID 2001, 2007 and 2017. We drop respondents who are not self-employed, paid workers, or not employed in the baseline wave of each survey. Refer to online appendix section B for more details on variable construction in the different surveys. Each column reports marginal effects from a probit regression of an indicator for migration on an indicator for self-employment at baseline. Each regression controls for country-wave fixed effects. Columns (4)-(6) additionally control for the inverse hyperbolic sine (arcsinh) of the respondent’s income in the baseline wave. Standard errors in parenthesis are clustered at the respondent level.

42
imply that actual aggregate disbursements on loans from official creditors in a given country-year are largely associated with past loan commitments, and not loans approved in the current year. In the median country-year observation in my full sample of 102 countries over the period 1970-2010, 89% of disbursements are associated with loans approved in previous years. For the 25th and 75th percentiles, the corresponding figures are 72% and 99%.

Nevertheless, as discussed in the previous section, even these disbursements on previouslyapproved loans might still be endogenous responses to contemporaneous shocks, to the extent that the disbursement decision in the current year reflects current shocks rather than being predetermined at

> 10 Anecdotally, another potential explanation for this pattern is that occasionally loans take the form of revolving credit lines that can be drawn upon and paid down multiple times.  In this case the maximum loan amount is recorded as the loan commitment, and subsequent multiple drawings are recorded as disbursements and can easily exceed the recorded commitment amount.  Unfortunately, however, the DRS database does not systematically identify such credit lines.

> 11 This strategy is conservative in the sense that if I exclude valid loans using these criteria, this will only weaken my first-stage relationship between changes in predicted disbursements and changes in government spending.

12
# 1 Introduction

Modern production assigns a prominent role to international task trade. The delivery of a good or service to a consumer typically requires the completion of a myriad of tasks. Increasingly, the performance of these tasks is spread across the globe, with an impressive share of o¤shore production in the value of many ...nal goods. As a result, international trade is less today a matter of countries’specialization in particular industries and more about their specialization in particular occupations and tasks.

Much has been written about the growth of o¤shoring between countries that stand at di¤erent levels of development, i.e., countries that have dissimilar factor endowments and disparate technological capabilities.<sup>1</sup> Yet, as important as this sort of o¤shoring is becoming in world trade, it pales in comparison to task trade between countries that are similar in terms of these characteristics. Although aggregate data on o¤shoring from one developed country to others are di¢ cult to come by, we do know that most trade in intermediate goods takes place between and among the advanced industrialized economies and that most value added generated by foreign subsidiaries of American multinational corporations originates in other OECD countries.<sup>2</sup> Both facts suggest that the advanced economies are engaged in a substantial amount of international production sharing with others at similar levels of development.

Not only does most trade ‡ow between and among the advanced industrialized economies, but these economies are engaging in an ever more intricate web of production-sharing arrangements. The Boeing 787 Dreamliner is a case in point.<sup>3</sup> The production of this new midsize jet involves 43 suppliers spread over 135 sites around the world. Boeing relies heavily on local expertise when making its sourcing decisions. The wings are produced in Japan, the engines in the United Kingdom and the United States, the ‡aps and ailerons in Canada and Australia, the fuselage in Japan, Italy and the United
GDP %|
|---|---|---|---|---|
|1966–86 (actual)|260,000,000|15,103,940|13|0.6|
|1991–97 (actual, NDP 7)|870,746,559|63,258,385|40|1.0|
|1997–2003 (actual, NDP|||||
|8)|661,274,384|27,119,970|16|0.4|
|2003–09 (planned, NDP<br>9)|2,467,660,000|72,944,329|39|0.6|
|2004 (actual)|315,433,705|73,784,049|41|0.7|
|2005 (actual)|604,046,771|125,494,297|68|1.2|
|2006 (actual)|791,362,633|139,454,757|75|1.2|
|2007 (actual)|1,157,844,594|188,610,310|100|1.5|

_Source:_ Botswana’s Department of Roads for spending under NDP;<sup>3</sup> AICD annual data 2004–07; authors’ calculations.

> 3 www.gov.bw/en/Ministries--Authorities/Ministries/Ministry-of-Communications-Science-Technology/Departments/Department-of-Roads/Divisions/#de53b8ebc29748f6af6959fd7b5fab33.

12
Table 4: ITT Effects on Employment Outcomes

||Empl|oyment||
|---|---|---|---|
||(1)<br>Any|(2)<br>Salaried|(3)<br>Outside<br>Wage|
|_Pan_|_el A: Midl_|_ine (6 mon_|_ths)_|
|ITT|0.08<sup>_∗∗_</sup>|-0.08<sup>_∗∗∗_</sup>|36870.754|
||(0.03)|(0.03)|(26375.090)|
||[0.020]|[0.010]|[0.149]|
|Observations|547|547|539|
|Control Mean|0.72|0.16|3.95e+05|
|Adj. R<sup>2</sup>|0.175|0.140|0.164|
|_Panel _|_B: Endlin_|_e (18–24 M_|_onths)_|
|ITT|0.03|-0.05<sup>_∗_</sup>|6154.552|
||(0.03)|(0.03)|(23810.000)|
||[0.624]|[0.178]|[0.782]|
|Observations|545|545|535|
|Control Mean|0.81|0.14|3.60e+05|
|Adj. R<sup>2</sup>|0.064|0.026|0.153|

_Note:_ We winsorize Outside Wage at the 99th percentile. Coefficients are ANCOVA estimates that control for the outcome at baseline, the respondent’s location, marital status, household size, number
**exceed cash for the median firm.** Exhibit 3 provides the main descriptive statistics of these financial vulnerability indicators and Exhibit 4 breaks them down by region and by sector.

- In 2019Q4, the median firm in our sample has an ICR of 2.57, and the 75<sup>th</sup> percentile of ICR is well above 1, suggesting that most firms in this sample are able to cover their interest expenses. At the same time, the 25<sup>th</sup> percentile is well below 1, suggesting that a sizeable portion of firms experience difficulties.

- In contrast, the median firm’s QR is only slightly above 1, and firms between the 25<sup>th</sup> and 75<sup>th</sup> percentile have similar quick ratios slightly below or above 1. This suggests that many firms may be vulnerable to rollover risks if a sudden drop in liquid assets were to occur.

- Finally, strong cash buffers are crucial for firms during the economic slowdown. We therefore calculate the difference between cash and cash equivalent assets and current liabilities, scaled by total assets. Similar to Banerjee et al. (2020), we find that this number is slightly negative for the corporate sector . Current liabilities exceed cash and cash equivalents by 0.23 percent of total assets for the median firm, suggesting the specter of financial stress if funding conditions were to tighten and inventories cannot be promptly liquidated.

**Our data on corporate insolvency are an index constructed as an average percentile rank of three indicators from Doing Business representing different aspects of the strength of corporate insolvency institutions of a country.** The three indicators are Strength of insolvency and Legal rights index, Recovery rate and Quality of judicial process index, representing legal framework, process efficiency and quality of civic and community justice, respectively. We calculate an average percentile rank of these three indicators, where a higher percentile rank indicates weaker corporate insolvency institutions/
26 ( 0.61)|3.30 ( 0.07)|
|**Cognitive skills**|1.86 ( 0.17)|0.24 ( 0.62)|3.41 ( 0.06)|
|**SEM skills**|1.67(0.20)|3.00(0.08)|1.03(0.31)|

_Test of coefficients equality – Women sample_

||**English=Mathematics**|**English=Urdu**|**Mathematics=Urdu**|
|---|---|---|---|
|**Schooling**|<br>4.06 ( 0.04)|<br>3.97 ( 0.05)|0.12 ( 0.73)|
|<br>**Cognitive skills**|<br>3.75 ( 0.05)|<br>3.97 ( 0.05)|<br>0.16 ( 0.69)|
|<br>**SEM skills**|<br>0.74(0.39)|<br>2.48(0.12)|<br>0.47(0.49)|

_Notes_ . This table reports (mean) estimates of the relationships between schooling, skills, and earnings in Pakistan. The dependent variable “monthly earnings (TC)” is top-coded at 100,000 PKR per month (961.5 USD). The cognitive skills index is computed using Item Response Theory with a two-parameter logistic (2PL) model on the English/Mathematics/Urdu items only in columns (1) and (4)/(2) and (5)/(3) and (6), respectively. The socioemotional (SEM) skills index is computed using principal-component
We are able to shed light on this issue by following the labor-market trajectories of workers leaving firms that exit the market. To follow them, we use our linked employer employee panel datasets. Workers’ trajectories can be tracked as long as they remain formally employed, and as long as the characteristics of the firms in which they gain employment can be identified. Given the features of the dataset, an exit of workers from the registry can be interpreted as evidence that the workers are either no longer employed, or that they are employed in informal work.

Table 9 reports the transitions of workers who were employed in firms that exited after the 2016 minimum-wage increase. The vast majority of workers from exiting firms had not been formally reemployed by the end of 2016 (Table 9, Panel a). Only 15 percent had found another formal job, meaning that 85 percent were either unemployed, or employed informally one year after the minimum wage rose. Workers who were employed in large, formal firms that exited are more likely to have found other formal employment, compared with workers who were employed in microenterprises.

Table 9, Panel b, restricts the transition matrix to the minority of workers who had found other formal employment by the end of 2016. The panel exhibits evidence for the upward mobility of some workers who transitioned into larger firms. Among workers who had been employed in microenterprises prior to the wage increase, and who had found formal employment by the end of 2016, more than two-thirds found employment in non-microenterprises. Meanwhile, only limited, downward mobility is evident among formal-sector workers who had been employed in large firms that exited after the minimum-wage increase. More than 80 percent of these workers had been reemployed by other large formal firms by the end of 2016. This indicates a certain level of positive reallocation among workers who had found other formal jobs.

The EIS data do not allow us to observe what happened to workers who were employed by firms that wound up existing, and who were no longer formally employed after the minimum
my interpretation of the different effects of inventor diaspora is plausible is to include tertiary educated stocks of migrants as extracted from census data. The data come from Docquier et al. (2009) and were originally built from two census rounds, 1990 and 2000, in which the regressions were used, respectively, for the years 1991–2000 and the years 2001–2010 (recall that censuses record data only every 10 years). As can be seen in table 5, column (2), the coefficient for the inventor 30