rafmacalaba/data-use-mentions-extended huggingface dataset:
Two main findings are highlighted by this exercise and illustrated in tables 2 and 3 below. Firstly, most displaced routine workers end up either not employed or in non‐routine manual jobs and only a few can move up to non‐routine cognitive jobs. In addition, disproportionally more women, and younger and less educated people fall down in the distribution of occupations when de‐routinization hits. Both these shifts translate into large losses in terms of earnings, as shown in figure 11, and ultimately explain the rising inequality of the distribution of earnings.

More in details, Table 2 describes for the three countries, where each individual of a group of 100 counterfactual routine workers actually ends up in the 2013 observed data. Consider, for example, the case of Germany: 15 workers are now not employed, 8 of them are in non‐routine manual jobs, and only 6 are in non‐routine cognitive jobs; and up to 71 remain in routine jobs. For all the countries the movement into non‐routine manual intensive jobs is larger than the movement into non‐routine cognitive task intensive jobs, which is consistent with the predictions of Autor (2010) for the US.

<u>Table 2 – Flows out of routine‐task intensive occupations</u>

|||Observed o|ccupations||Counter‐<br>factual|
|---|---|---|---|---|---|
||Not employed|Non‐routine,<br>manual|Non‐routine,<br>cognitive|Routine|All in routine|
|||Movers||Stayers||
|Germany|15|8|6|71|100|
|Poland|10|15|9|66|100|
|Spain|24|11|3|62|100|

Source: Authors’ calculations. Note: This table presents the observed occupations of 100 individuals who would be,
employment could depend on the source country and its level of development: South-South FDI appears to generate more jobs than North-South FDI (Coniglio et al., 2015). Drilling down to specific countries and regions within South-South FDI, FDI from China seems to generate the largest employment increase, followed by FDI from South African firms and from the MENA region. Importantly, these differences are not driven by sectoral specialization of firms – FDI from other developing countries seems to target blue-collar, relatively less-skilled jobs, and generate relatively more of such employment.

The World Bank Enterprise Surveys confirm that growth in full-time employment in foreign multinationals occurs at a larger scale as a whole (see Fig. 8), yet not necessarily more frequently than employment contraction in several developing countries. As differences

> 3See, for example, Girma (2005) for the United Kingdom; Bandick and Karpaty (2007) for Sweden; Balsvik and Haller (2010) for Norway.
exploiting the location of migrants.

We also extend the literature concerning the connection between remittances and financial development (Aggarwal et al., 2011; Demirg ̈uc ̧-Kunt et al., 2011; Ambrosius and Cuecuecha, 2016; Fromentin, 2018; Uddin et al., 2022).<sup>5</sup> Using an event study approach, we find the elasticity between distance and transaction bank accounts became negative during the early COVID-19 period. This supports the hypothesis that a larger increase in formal remittances in areas near a border crossing consequently produced more bank accounts in those regions.

# **2 Remittances during COVID-19 and the role of distance**

As a result of the implementation of COVID-19 lockdown measures in the United States, international and financial organizations predicted a significant decline in remittances to Mexico.<sup>6</sup> In fact, household surveys conducted by phone in Latin America show that on average, 64 percent of households that typically received remittances reported a decline in inflows from May through June 2020.<sup>7</sup> In the case of Mexico, this figure was 36.5 percent; 46 percent reported no changes, and only 17 percent reported an increase. Nevertheless, formal remittances to Mexico experienced record growth in every month of our period of analysis since the start of the pandemic even though unemployment among Mexican workers in the United States rocketed during the same period, as shown in panel A of Figure 1.<sup>8</sup> Several hypotheses have attempted to explain this phenomenon, including the role of the large US fiscal stimulus package and the heightened incentives of migrants to help relatives in Mexico who were affected by the crisis. Others have proposed a sudden acceleration in the formalization of remittances during COVID-19 as a potential explanation. Mexico’s proximity to its diaspora provides a good setting to examine the last hypothesis.

Without mobility restrictions, the cost of sending remittances informally would typically increase with the cost of travel and physical
Republic of Turkey Ministry of Family and Social Policy and The World Bank. 2017. Turkey’s Integrated Social Assistance System. Ankara, Turkey.

Salemi, C., Bowman, J., Compton, J., 2018. Services for Syrian Refugee Children and Youth in Jordan: Forced Displacement, Foreign Aid, and Vulnerability. Economic Research Forum Working Paper Series No. 1188. Cairo, Egypt.

Samuel Hall, 2018. Syria’s Spontaneous Returns.

Shammout, R., Vandecasteele, O., 2019. Education: needs, rights and access in displacement. <mark>Forced Migration Review. 60. 61-64.</mark> <mark>Sävje, F., Aronow, P.M. and Hudgens, M.G., 2017. Average treatment effects in the presence of unknown interference.</mark> _<mark>arXiv preprint arXiv:1711.06399</mark>_ <mark>.</mark> TURKSTAT Labor Market Statistics, June 2020. Employment by status in social security registration, June 2019, June 2020 data.

UK House of Commons (2019) Forced Displacement in Africa: “Anchors not Walls.” UK Government, House of Commons, International Development Committee, 2019. <u>https://publications.parliament.uk/pa/cm201719/cmselect/cmintdev/1433/143302.htm.</u> <mark>United Nations Children's Fund, United Nations High Commissioner for Refugees, World Food Programme. 2018.</mark> Vulnerability Assessment of Syrian Refugees in Lebanon (VASyR 2018).

<mark>United Nations High Commissioner for Refugees</mark> (UNHCR), 2020. Global Trends in Forced Displacement 2019.

Valli, E., Peterman, A., Hidrobo, M., 2019. Economic Transfers and Social Cohesion in a Refugee-Hosting Setting. J Dev Stud. 13;55(sup1):128–46.

World Food Program,
tures are reviewed; overspending, because huge disbursements in future years are hidden; conservatism, because incremental changes do not open up large future vistas; and parochialism, because programs tend to be viewed in isolation rather than in comparison with their future costs in relation to expected revenue.” At the basis of an MTEF is a commitment by the budget actors to a medium-term, typically two to four years, ...scal trajectory. Thus, it can be seen either as an ex ante constraint on the government, similar to a balanced budget requirement (Alesina and Perotti 1996), or as a "contract approach" to centralizing the budget process through a broad-based political agreement, as opposed to a "delegation approach" where the executive receives enhanced powers (von Hagen and Harden 1995). While the theoretical underpinnings of this institution are well understood, the empirical evidence on its impact is scarce. The main obstacle has been the shortage of data on MTEF adoption. An additional impediment has been the lack of sources of exogenous variation in national budget institutions (Acemoglu 2005).<sup>3</sup> This paper reports on the ...rst large-sample empirical study of the MTEFs’impacts on ...scal performance. As part of a larger World Bank (2012) study, we collect and analyze MTEF adoption data for a panel of 181 countries over the period 1990-2008, the most comprehensive dataset to date on worldwide MTEF adoption. Following World Bank (2012) we classify MTEFs into three phases, based on their level of sectoral disaggregation, and generate testable hypotheses about the e¤ects of each phase on multiple dimensions of ...scal performance.

The rich time variation in the data allows us to model the dynamics of the ...scal adjustment process, as well as address the endogeneity of MTEF to ...scal performance. We use a Di¤erence Generalized Method of Moments (D-GMM) approach to estimate dynamic panel data models of ...scal
This paper proposes a roadmap for improving the design of in-service teacher training in Ecuador using available data from student and teacher assessments. Even though Ecuador has made substantial efforts to periodically evaluate student and teacher performance, the data resulting in these evaluations have not been used systematically to guide teacher development programs. The results of this study indicate that systematically using the data has the potential to help design more relevant programs that respond to observed skills gaps and allow prioritizing investments in teachers and students with the most substantial needs.

The study also intends to raise awareness about the potential gains of capitalizing on available information on student and teacher assessments. Many top education systems invest heavily in student and teacher evaluations. Finland, Japan, the Republic of Korea, China, the United States, and Singapore, among others, all have robust systems that rely on comprehensive student and teacher assessments that serve as inputs to develop more pertinent in-service teacher training programs. However, to date, the use of data resulting from teacher evaluations in Latin America for formative purposes is much more limited (Bruns and Luque 2014). Chile and Mexico are the education systems in Latin America with the most consolidated systems of teacher evaluation. Among other information, both systems collect information about teachers’ cognitive skills. Data resulting from these assessments are often used to make decisions about a teacher's career progression, but not necessarily to develop tailor-made in-service training programs. Thus, the framework presented by the study applies to other countries that, like Ecuador, dispose of detailed information about cognitive skills gaps of teachers, but largely underuse it.

The paper is structured as follows. Section II briefly describes the design and implementation of, and investments made in, in-service teacher training in Ecuador by the Ministry of Education (MINEDUC) under its main national teacher development program, the Comprehensive <mark>System for the Development of Education Professionals</mark> (SIPROFE) program. Section III provides a brief overview of the information collected by the national teacher’s assessment ( _Ser
Conflict Index|||1.7e-05 [7.4e-06]**|
|Community attachment index||-6.8e-04 [6.7e-04]||
|Community basics index||2.3e-04 [7.8e-04]||
|Civic engagement index||9.6e-04 [8.1e-04]||
|Satisfied with freedomto choose||-0.122[0.040]***||
|Country fixed effects|Yes|Yes|Yes|
|Locality fixed effects|Yes|Yes|Yes|
|Survey wavesfixed effects|Yes|Yes|Yes|
|Observations|37556|30192|29151|
|Log Pseudolikelihood|-102838.94|-83589.897|-81039.659|
|Wald test of indp.Eqns. (p-value)|0.063|0.046|0.038|

Notes: The reference categories for the employment status is “Out of workforce or unemployed”, for the highest education level is “Completed elementary education or less”, and for the locality fixed effects is “Rural area or on a farm”. X(#) and X(@)denote Ln(X) and Ln(X+1), respectively. We use Ln(X) for income to exclude individuals with zero income and Ln(X+1) for the number of children under 15 in the household to not exclude households with zero values on this variable. Standard errors are clustered by country/wave and robust to heteroscedasticity. Asterisks denote significance levels as follows: *** p<0.01, ** p<0.05, * p<0.1. Column 1 reports results with imputed missing values. Column 2 reports results with controls for conflict. Column 3 reports results with additional controls.

Furthermore, country-level time-varying omitted factors may also influence our results. It may be the case that support for violent extremism may be affected by terrorist incidents at
widely discussed improvements in educational mobility in India in last few decades should be interpreted with due caution because they are based on IGRC estimates from coresident samples, and thus are likely to be substantially biased downward, and the apparent improvements in IGRC may largely (or at least partly) be driven by declining coresidency rates in younger generations.

# **8. Conclusions**

We take advantage of two rich data sets from Bangladesh and India to explore the direction and magnitude of coresident sample selection bias in the two most widely used measures of intergenerational persistence: intergenerational regression coefficient (IGRC) and intergenerational correlation (IGC). The evidence reported in this paper shows that the worry about coresidency bias is well-justified when the focus is on estimating IGRC, by far the most popular measure among development economists.<sup>27</sup> The IGRC estimates, in general, suffer from substantial downward bias in coresident samples vindicating the skepticism among researchers and journal editors about the usefulness of data with coresidency restriction. The bias in IGC estimates is, however, much smaller in magnitude, less than one-third of that in the IGRC estimates on an average. We discuss theoretical explanations and intuitions behind the empirical results. The biases in both IGC and IGRC converge to zero as coresidency rate converges to 100 percent, but, even with high coresidency rates, IGC estimates are preferable.

Robust evidence on the direction of bias, i.e., that the estimates of intergenerational persistence are highly likely to be biased downward in coresident samples can be useful in understanding changes in economic mobility over time, given that coresidency rates are usually lower in the younger generations. This adds a caveat to the optimistic picture of intergenerational educational mobility found in some countries such as India based on declining IGRC estimates from coresident samples over time.

Our analysis also suggests that the IGC estimates are much less sensitive to the vari-

> 27The same negative conclusion is likely to hold for other related measures of mobility where the focus is on a the slope parameter of
So, the null hypothesis of weak identification is rejected. Employing two instruments for two endogenous variables, we are unable to test for orthogonality in the falsification test. However, based on the literature on social conformity I believe that taxpayers’ trust in public institutions and the neighborhood is the only channel through which the community level trust in public institutions and the neighborhood affect the taxpayer’s morale.

# **4.4 Set of controls**

## **Individual-level controls** .

Assessing the determinants of tax morale, I should keep in mind the drawbacks of using selfreported information which might be subject to potential biases (Bertrand and Mullainathan, 2001). One can imagine that respondents will tend to overstate their attitude towards taxes to avoid the social stigma associated with illegal activities (Kountouris and Remoundou, 2013). Moreover, attitudes towards taxes and trust might be affected by important omitted variables and potential unobservable individual characteristics. Although the identification strategy presented above minimizes issues related to omitted variables, error measurement, and reverse causality biases, the findings may still be biased due to unobservables’ characteristics correlated with trust and tax morale. In this section, I include three additional variables, which might help to minimize such biases.

The WVS contains three critical variables that can be used to minimize issues from unobservable characteristics and assess the sensitivity of the findings. The first relevant question asks: "how justifiable is avoiding a fare on public transport?" Responses are on a 10-point scale and relabeled as before so that 1 corresponds to "always justified" and 10 to "never justified". Controlling for this variable to minimize biases arising from important omitted variables and unobservable heterogeneity. As one might expect, the variable is highly correlated with tax morale and might capture some of the intrinsic characteristics of the taxpayer in term trust and tax morale. The pairwise correlation coefficient is 0.557 and statistically significant at the 1 percent level. The additional variable is included in the 30
both public and external), (ii) exchange rates, (iii) interest rates, (iv) currency composition of debt, (v) debt service, and (vi) current account balances. Standard variables for liquidity risk are: (i) redemption schedules and (ii) amount of short term debt. Commitment risks include: (i) reserves proxying as buffers to unexpected shocks and (ii) differences between observed primary balances and primary balances required to stabilize debt. Links to the external and financial market include external financing needs, short-term external debt, non-performing loans, and interest rate spreads between loans and deposits. Data are sourced from the World Bank’s WDI indicators, the Macro Poverty Outlook and International Debt Statistics, others are sourced from Bloomberg, the Bank of Canada and the IMF’s WEO. Ideally, one would want to cast a wide net and capture all of these data but they are not all available for every market-access country in this study. Thus, the set of variables will vary across countries in our sample.

A summary of data and their sources used in this study include:

- **Indicators of public debt** : Debt maturity profiles (World Development Indicators (WDI), Bloomberg) and repayment schedules (Bloomberg) to assess liquidity risks; debt service costs (MPO, WDI, WEO) and currency composition (WDI, Bloomberg) to assess exchange rate risks; fiscal space (Macro Poverty Outlook (MPO), WDI, World Economic Outlook (WEO)), gross public financing needs, and required primary balances(MPO, WDI, WEO) to assess scope and ability of policy adjustments; and actual defaults (Bank of Canada, credit ratings (Bloomberg).

- **Indicators of resilience** : Foreign exchange reserves (WDI), exchange rate regime (Ilzetzki et al. 2017), monetary policy regimes, credible institutions (Country Pol-

8
social activities and see themselves as "fallen." In most instances they are cut off from their families, even though some continue to send them remittances. Children of sex workers cannot find spouses from mainstream society and have to marry within the community - which is also indicative of a sex worker's status as an outcaste. In another survey conducted in Sonagachi in 1992, sex workers were asked the reason why they entered the profession. Table 1 summarizes the responses by income categories of sex workers showing that the majority of sex workers (49 per cent) became sex workers because of acute poverty. This includes situations where destitute parents were compelled by circumstances sell their daughters to pimps or their agents, or where widowed women are forced into it to subsist after the death of their husbands. In an ethnographic study of Sonagachi, Sleightholme and Sinha (1997) suggest that a large number of women enter the profession in order to escape violent husbands, or to support themselves after being abandoned by their husbands. This is substantiated in Table 1 that reports results from an earlier survey of Sonagachi conducted in 19923. It shows that 1.6 per cent of women entered the profession because of family disputes. Despite the highly stigmatized nature of the

> _3_ We have not been able to obtain these data and are therefore unable to further analyze them.

S
response to the illness of others in their household, we acknowledge that other explanations are possible. For instance, perhaps wage workers fill in for each other in order to make sure timesensitive work gets done. Even if one of these alternate mechanisms is behind the labor supply result for wage workers, our results nonetheless provide new insight linking illness to labor supply in low-income countries.

# **4. Household-level outcomes**

We now examine the overall effects of a member’s illness-related absence on household level income and labor supply, including both the direct effects of the missed work and the compensatory responses of other household members documented in the previous two subsections. We also look separately at households for whom these compensatory responses are likely to be strongest – those with two or more earners at baseline<sup>22</sup> and those with at least one male worker – and households in which there are no male earners or a female is the highest earner, given the minimal net response by females shown in table 2.

In each case, we conduct a back-of-the-envelope exercise that estimates the fraction of the expected income loss of the sick individual covered by the household income inclusive of compensatory behavior. Specifically, we compute the expected income loss of a sick individual in a specific class of households by taking the average usual reported income from the baseline survey of individuals in those households who are unexpectedly sick over the course of the survey. We then compute the fraction of the expected loss covered by the coefficient estimate

> 22 These are also the households providing the majority of the identifying variation in the individual-level results, since a worker in the regression would need to have another family member who was planning to work in order to have identifying variation in the _Other Worker Ill_ variable.  While we estimate equation 1 on the whole sample of workers employed at baseline (workers who are the only worker in their family over the entire survey help to identify the week fixed effects), results are almost identical if we use only the sample
we first briefly describe the weather data and household data. We then discuss the blinding of the research team and the data integration process. We conclude with a discussion of some descriptive statistics for the combined weather-household data sets.

# **2.1 Weather Data**

We use a variety of public domain sources of weather data sets representing different modeling types, input sources and spatial resolutions. Table 1 briefly describes each data sources, including the length of record, spatial and temporal resolution, and the type of data recorded. Although there are many possible weather products to consider, we sought to include the remote sensing data products most commonly used by economists. However, to ensure consistency and enable the production of common metrics across the analysis, we imposed two inclusion criteria. The source had to have (1) high temporal resolution (daily) and (2) a minimum 30-year length of record (19872017). Daily resolution was necessary for us to calculate some of the most common weather metrics used in the literature today, such as growing degree days (GDD). A consistent length of record was necessary to cover all years of the LSMS-ISA, provide sufficient historical data to measure seasonal deviations in long-term trends, and provide weather data sets all of the same length. This last point was necessary in order to maintain the blinding of the Data Analysis Group from the data sources. Unfortunately, this criteria meant that some data sources used by economists, such as the various versions of the monthly _Terrestrial Air Temperature and Precipitation_ from the Center for Climatic Research at the University of Delaware was excluded.<sup>5</sup> While all the weather product data used in this analysis are publicly available, accessing the data for analysis remains challenging due to differences in formats and platforms. See online Appendix A for more details on each remote sensing product and guidance for economists on merging these data with survey data.

## **2.1.1 Merged Gauge and Satellite Data**

In the