rafmacalaba/datause-encoder-data huggingface dataset:
Furthermore, in terms of the representativeness of the business owners in the study sample, we are reassured that phone-based surveys of small businesses sampled from national business registries conducted by the World Bank and partners across 51 countries from AprilAugust 2020 document similar impacts on business closures, sales and employment among small and medium enterprises as we find in this paper using the Future of Business survey (Apedo-Amah, 2020; Adian, 2020). Additionally, Torres et al. (2021) analyze the World Bank Business Pulse Survey data by gender and corroborate the findings in this paper that women-led businesses (micro) were disproportionately hit by the COVID-19 shock compared to businesses led by men.

While our measure of business closure is self-reported by the respondent in the survey, an additional concern might be that business owners who close their business could immediately unpublish their Facebook Business Page, and therefore be excluded from the sampling frame. The sample for the FoB survey is restricted to those who have active, published Pages, where active means they had some kind of activity in the previous 28 days. Business owners are not required to unpublish a Page if they close their business nor does Facebook require a Page to be unpublished after a business is closed. Activity and published status are assessed two weeks before fielding of the survey to minimize this concern. However, we do contend that results on business closures may only offer a lower-bound estimate on the true rate of closure. Appendix A includes further details of the Future of Business survey, as well as a detailed description of the main variables used in the analysis.

# **4 Empirical Strategy**

In this section we first outline the empirical strategy to examine the differential effect of the overall COVID-19 pandemic for male- and female-led firms. Next, we turn to the empirical strategy used to analyze the impact of school closure policies related to COVID-19 for maleand female-led businesses. We begin the analysis for all business owners and managers in the sample and go on to also restrict the sample to those who report that they live with any 15
Administration (NOAA) helps in identifying lights that are stable over time. The NGDC algorithm removes sunlight, glare and moonlight data and observations with clouds or lighting features from the aurora in the northern hemisphere are also excluded. The resulting global composites are known as DMSP-OLS stable nightlights (Elvidge et al. 1997).

Data collected by six DMSP-OLS satellites between 1992 and 2013 has been made publicly available. The design of the OLS sensor onboard DMSP has not changed significantly since 1979, and new satellite deployments aim at ensuring continuity of data collection (Elvidge et al. 2013). Due to the absence of onboard in-flight calibration, differences caused by sensitivity discrepancies between satellite instruments are addressed by inter-calibrating the data (Elvidge et al. 2009).

The release of DMSP-OLS nightlight data was discontinued in 2013. Since then, a new data product has become available. In 2011, NASA and NOAA deployed the Suomi National Polar Partnership (SNPP) satellite with the Visible Infrared Imaging Radiometer Suite (VIIRS). Data from SNPP-VIIRS has several improvements over DMSP-OLS, including a finer spatial resolution (approximately 0.5 km<sup>2</sup> ), a wider radiometric detection range (which solves oversaturation at bright core centers), and onboard calibration (Elvidge et al. 2013). However, the publicly available SNPP-VIIRS data still require processing before use, as some temporary lights and background noise remain.

Several strategies can be considered to clean the VIIRS data. The first approach consists of removing individual nightlight observations below a certain intensity threshold. This threshold is defined by sampling nightlight data from places known to lack human activity, such as natural parks and mountain ranges (Ma et al. 2014). The second approach consists of removing all observations from areas seen as a “background noise mask”. These areas are identified by first removing outlier observations from each location, and then clustering the remaining 5
-2-

international migration. Third, a relative deprivation approach to migration has important implications for development policy. For example, the effects of rural development policies on rural out-migration, as predicted by an expected income model, may be precisely opposite to those predicted by a relative deprivation model.

In Section I of the paper we outline the absolute income and relative deprivation models of migration and present an illustration of their divergent policy implications. We also consider the likely case in which the decision to migrate and the choice of migrant destination arc influenced by both absolute income and relative deprivation objectives. In this case, income remittances from household members who migrate have a dual impact on the household's well-being: first, by contributing to its absolute income; second, by improving its income position relative to that of other village households. An attempt is made to identify distinct empirical implications of these two motives for migrating. In Section II, a migration decision model is estimated and is used to explore absolute and relative income motives for internal and international migration in a sample of rural Mexican households, as well as the extent to which the degree of discontinuity in labor markets shapes the choice of migrant destination.

# I. ABSOLUTE AND RELATIVE INCOME HYPOTHESES OF MIGRATION

Empirical economic studies of migration are based on the general assumption that individuals migrate to maximize expected utility EU, which is typically defined on income Y at the end of the relevant time period:
coefficients in detailed sectors may be the best place to start in building regional IO accounts if there is no additional prior information on regional technology or cost structure available.<sup>9</sup> Comparing estimates from different test experiments, there are several interesting observations. First, when there is no additional information that could be incorporated into the estimation framework, a more detailed model may not perform better than a simpler model. Comparing results from Exp-1 and Exp-5, the more sophisticated extended model actually brings less accurate estimates overall because of increasing numbers of unknown variables without additional known data. However, as results in Experiments 2, 3, 6, and 7 show, the estimation accuracy does improve by a more detailed model when more useful data become available. Second, the marginal accuracy gained from actual individual regional IO flows is significant in estimating interregional trade flows using the extended model, but very small in the aggregate version. In contrast, the marginal value of accurate interregional shipment data is rather small in estimating individual regional IO coefficients under both versions of the model. Finally, Batten’s model performed poorly in interregional shipment estimation, but obtained similar estimates on individual regional IO flows as our model, providing further evidence that there _may_ be no high dependency between individual regional IO coefficients and interregional trade flows. However, this is not a firm conclusion because the particular data set used to test the model in this paper may be part of the problem. Since the United States, EU and Japan are all large economies, their intermediate demands are largely met by their own production. Therefore, the correlation between individual inter-industrial flow and inter-regional shipments may be particularly low.

The extended model only provides better estimates of interregional shipments when regional IO data are available, so the aggregate version of the model specified in this paper may be the best practitioner’s tool in estimating interregional trade flows because of the lack of subnational IO data in the real world. It demands less statistical information and has a smaller model dimension, which facilitates the implementation and computation process.<sup>10</sup> 19
by repeated substitution, the future value budget constraint at time _T_ can be expressed as: Through this equation, it can be seen that the smaller the chosen rate of growth of inflation θ today _,_ the higher the debt level at time _T_ .

We now examine the implication of incorporating debt management in _(12)._ This is achieved by drawing on Bohn (1995). Substituting the safe interest rate with the marginal rates of consumption substitution _(9)_ and a primary balance that depends on the state of nature at time _t_ , we obtain the inter-temporal government budget constraint for _t = 2,...,T_ of the following form: − _k_ where _P_ ( _st_ + _k_ ) = 1 * β _U_ (' _Ct_ + _k_ − 1) denotes equilibrium value of the marginal rate of 1 + _n U_ (' _C_ ( _st_ + _k_ )) substitution between time _t+k-1_ and _t+k_ consumption, or the time varying interest rate on the risky debt _._ Equation _(12)_ states that the future stock of debt at time _T_ given _θ_ is equal to the initial debt less the future value of the expected future primary surpluses and the future seigniorage, compounded by a factor that varies over time and states of nature.

Using the fact that the expectation of a product is the product of the expectations plus the covariance, and using _Et_ 1[ + _r_ ( _st_ + _k_ )] = 1 + _rt f_ , equation _(13)_ can further be re-written as: 24
When the global crisis struck in 2008, Poland undertook some fiscal expansion. Poland’s economy suffered less than many others in the region, with the more moderate dependence on the external sector softening the impact of the external crisis. The government borrowed externally from international capital markets and official donors and undertook further expenditure rationalization, while providing support to the economy. The IMF estimates that the country provided significant fiscal stimulus during the crisis, with a discretionary fiscal relaxation estimated at 1.15 percent of GDP in 2008 and 2.5 percent of GDP in 2009, in part resulting from tax cuts that were approved prior to the crisis and not compensated by budget cuts as initially intended in 2009. The increase in the fiscal deficit from 2 percent of GDP in 2007 to 7 percent of GDP in 2009 reversed the trend in place since 2000. The excessive deficit procedure under the SGP was initiated in 2008 due to the deficit overrun.<sup>18</sup> As a consequence public debt escalated from 45 percent of GDP in 2007 to an estimated 51 percent in 2009.

Overall expenditure control remains relatively weak. The 2010 Bank Public Expenditure Review<sup>19</sup> (PER) stressed the need to better align budgetary allocations within a mid-term consistent framework, a point that the 2003 PER had stressed but where apparently progress had been limited. Some MTEF elements were introduced with the new Law on Public Finance of 2009 and the first adoption of the Medium-Term Financial Plan of the State in late July 2010. Despite the national and supra-national rules and reforms in tax administration, Poland could not contain its deficit or debt to GDP ratios. Fiscal rules could not substitute for political fragmentation and were not useful in a crisis.

> 18  In 2009, despite a preparation to reduce state expenditures by 10 percent, state related expenditures, excluding EU-related spending, increased by 20 percent in current prices during the first half of the year, but the July supplementary budget changed the 2009 to cut expenditures helping contain the general budget deficit to about 6 percent of GDP.

> 19 Public Expenditure Reviews by the World Banks assess the fiscal policy and institutions, particularly as they relate to fiscal expenditures.

Page | 32
Policy ReseaRch WoRking PaPeR 4671

# **Abstract**

This paper compares the wages of workers inside the United States to the wages of observably identical workers outside the United States—controlling for country of birth, country of education, years of education, work experience, sex, and rural-urban residence. This is made possible by new and uniquely rich microdata on the wages of over two million individual formal-sector wage-earners in 43 countries. The paper then uses five independent methods to correct these estimates for unobserved differences and introduces a selection model to estimate how migrants’ wage gains depend on their position in the distribution of unobserved wage determinants. Following all adjustments for selectivity and compensating differentials, the authors estimate that the wages of a Bolivian worker of equal intrinsic productivity, willing to move, would be higher by a factor of 2.7 solely by working in the United States. While this is the median, this ratio is as high as 8.4 (for Nigeria). The paper documents that (1) for many countries, the wage gaps caused by barriers to movement across international borders are among the largest known forms of wage discrimination; (2) these gaps represent one of the largest remaining price distortions in any global market; and (3) these gaps imply that simply allowing labor mobility can reduce a given household’s poverty to a much greater degree than most known _in situ_ antipoverty interventions.

This paper—a product of the World Development Report Team, Development Economics Department; the Center for Global Development (CGDEV) and the Harvard Kennedy School—is part of a larger effort in the department to improve our understanding of the consequences of migration restrictions. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at: mclemens@cgdev.org, cmontenegro@worldbank. org, or lant_pritchett@harvard.edu.

_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
non-completion, 67 percent of participants refused to respond and one-third reported connectivity problems.

Finally, we present some descriptive statistics of the sensitive information measures (stress and perceptions about effectiveness of physical punishment) in panel B. On average, caregivers in our sample in the midline survey display low levels of mental health—about 43 percent exhibit an above-normal level of stress—and more than half of participants report that physical punishment is an effective method to discipline a child. These levels are very similar in the baseline survey (31 percent report high stress and 48 percent report that physical punishment is effective), but they are significantly lower in the endline survey (only 17 percent of participants report high levels of stress and only 37 percent agree that punishment is an effective method of discipline).

# **4 Empirical Strategy**

To identify whether response rates, composition of respondents, and measures of consistency in the reporting of sensitive issues are affected by survey mode, we exploit the random assignment of each participant to a particular survey method and estimate the following regression: where _Yi_ stands for the response rate, sensitive issue outcome, or consistency of reports measured for individual _i_ . _Phonei_ is a survey mode indicator that takes the value 1 if individual was assigned to CATI or 0 if she was assigned to the WhatsApp group.<sup>�</sup><sup>_n_</sup> _j_<sup>_Xji_is a vector of</sup><sup>_j_time-invariant participant characteris-</sup> tics, which include age and gender. We also add municipality fixed effects ( _Mi_ ) to control for time-invariant shocks at the municipal level and to improve the precision of the estimates. The coefficient associated with the survey mode indicator in Equation (1), _δ_<sup>ˆ</sup> , captures the impact of using the phone-based survey mode and represents our coefficient of interest (ITT). _εi_ is an idiosyncratic error term. Since the randomization occurred at the individual level and we do not expect clusters in our sample, we report heteroskedasticity14
# **_5. References_**

Bloom, N., and J. Van Reenen. 2010. “Why Do Management Practices Differ across Firms and Countries?” _Journal of Economic Perspectives_ 24 (1): 203–24.

Bloom, N., and J. Van Reenen. 2007. “Measuring and Explaining Management Practices Across Firms and Countries.” _Quarterly Journal of Economics_ 122 (4): 1351–1408.

Cirera, X., V. López-Bassols, and S. Muzi. 2017. “Measuring Firm Innovation: A Review of Existing Approaches.” Unpublished paper, World Bank, Washington, DC.

Cirera, X. and W. Maloney, 2017 “The Innovation Paradox: Developing-Country Capabilities and the Unrealized Promise of Technological Catch-Up” World Bank, Washington, DC Cirera, X.. 2017. “Management Practices as an Input for Innovation and Productivity in Developing Countries.” Unpublished paper, World Bank, Washington, DC.

Huettner, F. and M. Sunder. 2012. “Axiomatic arguments for decomposing goodness of fit according to Shapley and Owen values” _Electronic Journal of Statistics_ (6):1239-1250 Iacovone, L., and M. Pereira-Lopez. 2017. “Management Practices as Drivers of Innovation: New Evidence from Mexico.” Unpublished report, World Bank, Washington, DC.

Mason, A. and S. Shetty. 2018. “A Resurgent East Asia: Navigating a Changing World” World Bank, Washington, DC Mohnen, P., and B. H. Hall. 2013. “Innovation and Productivity: An Update.” _Eurasian Business Review_ 3 (1): 47–65.

Maloney, William F., and Mauricio Sarrias. 2017. “Convergence to the Managerial Frontier.” _Journal of Economic Behavior & Organization_ 134 (C): 284–306.

World Bank. 2017 “Study on the Effectiveness of the Human Resources Development Fund” Unpublished Memo 24
<!-- Start of picture text --> 25<br>20 /\/<br>toy<br>1\<br>f ue<br>i<br>15 vah\ 4 ---7i<br>| \u7<br>/<br>/<br>10 an woos TT Z Yo<br>———_— aie<br>5<br>()<br>1995 2000 2005 2010 2015<br><!-- End of picture text -->
Aspect Open (e.g. WMS) Closed (e.g. MOPs) Accuracy of responses High: Interview format gives opportunity to Medium: Harder to elicit truthful answers if probe and ask for examples. Possible to respondents have preconceptions. Greater risk implement “double blind” method to reduce that respondents might misinterpret questions or preconception bias. tush through the survey. Cost per survey High: High quality trained interviewers needed Low: Initial design and execution costs, but this to run survey. Training includes one week initial fixed cost can be spread over a very large number training and ongoing debriefing and calibration. of respondents, so cost per survey is low. Costs Interviewers’ time primarily spent recruitmg can be higher in poorer countries where managers to take part in the survey (rather than enumerators administer surveys onsite because of just running interviews). unreliable mail and e-mail networks. Response rates Medium: Interview is interactive and managers High: Co-operation with a National Statistics more engaged. We obtainedan average response Agency can enable the survey to be mandatory. rate of 40%. Given this response rates of around 80%. Without such co-operation, response rates will be low. Replicability Medium: Training needed to ensure the surveyis High: Questionnaire essentially the same across delivered in same way. Useful to have some countries and already available pre-tested fromby individuals who have worked in previous survey US Census Bureau. waves as trainers for other surveys foster comparability. Training and survey material is available on-line. International High: Multiple countries can be interviewed Medium/High: Easier to implement but there is a comparability from same location. Using bilingual interviewers risk of differential interpretation if this is not means makes it is easier to cross-check carefully translated across languages. responses. Speed of delivery High: Can complete a full survey wave in about Medium: Involves cooperation with national 10 weeks. So including recruitment and set-up statistical agencies, so more planning work in tume possible to complete a survey wave from advance. The survey period typically is around 3 scratch in about 4 months. months plus 1 to 3 months of data cleaning.
# **<u>Appendix table A6: Balance test between PSG and of pre-treatment indicators</u>**

||<br>**PSG beneficiaries**|<br>**Non PSG b**|**eneficiaries**|**Standardiz**|**ed difference**|
|---|---|---|---|---|---|
|||**Pre matching**|**Post matching**|**Pre matching**|**Post matching**|
|Agricultural land (ha)|19.92|9.05|19.91|0.414|0.00009|
|Winter crop 2019 (ha)|6.18|2.92|6.18|0.285|0.00005|
|Winter crop 2020 (ha)|4.45|2.04|4.45|0.266|0.00005|
|Winter crop 2021 (ha)|5.04|2.27|5.04|0.276|0.00006|
|Winter crop 2022 (ha)|4.58|2.05|4.58|0.268|0.00005|
|Summer crop 2019 (ha)|10.37|4.23|10.37|0.360|0.00010|
|Summer crop 2019 (ha)|10.44|4.32|10.44|0.356|0.00009|
|Summer crop 2019 (ha)|10.85|4.46|10.84|0.365|0.00009|
|Summer crop 2019 (ha)|11.03|4.60|11.03|0.369|0.00009|
|Rain 2019 sowing (mm)|118.0|124.9|118.0|-0.172|-0.00027|
|Rain 2019 growing (mm)|257.7|276.4|257.7|-0.231|-0.00032|
|Rain 2020 sowing (mm)|91.0|90.1|91.0|0.041|0.00001|
|Rain 2020 growing (mm)|295.3|310.7|295.3|-0.174|-0.00020|
|Rain 2021 sowing (mm)|103.0|104.1|103.0|-0.055|-0.00009|
|Rain 2021 growing (mm)|274.0|274.5|274.0|-0.014|-0.00013|
|Rain 2022 sowing (mm)|87.0|88.3|87.0|-0.045|-0.00005|
|Rain 2022 growing (mm)|122.8|127.0|122.8|-0.084|-0.00006|
|Summer crop GDD 2019|1493|1461|1493|0.202|0.00026|
|Summer crop GDD 2010|1290|1261|1290|0.197|0.00025|
|Summer crop GDD 2021|1317|1290|1317|0.219|0.00029|
|Summer crop GDD 2022|1343|1313|1343|0.185|0.00023|
|No. ofobs.|19,660|23,|343|||

_Source:_ Own computation from SAR records and cadastral data as described in the text Note: Pre refers to before entropy balancing and post refers to after entropy balancing that adjusts for differences in the first and second moment of all the covariates and reweighting.

31