rafmacalaba/data-use-mentions-extended huggingface dataset:
of the young, but there is no proven evidence on long-term behavior” (Entorf and Hou 2018).

This paper addresses this evidence gap by studying the long-term effects of a comprehensive highschool financial education program in Brazil. The program was implemented during a 17-month period across the 2010 and 2011 academic years. Bruhn et al. (2016) measured the short-term effects of the program in a randomized controlled trial, spanning 892 public high schools in six Brazilian states and approximately 25,000 students, with a baseline and two follow-up surveys conducted in 2010 and 2011. Half the schools were randomly selected to receive teacher training and financial education textbooks, which they integrated into the existing curriculum during the two last years of high school, i.e., the second and third years. Control group schools did not receive training or materials but participated in the surveys. Students in the randomized controlled trial graduated from high school at the end of the program and the control group was never exposed to the financial education program.

The short-term effects measured in Bruhn et al. (2016) show increased financial knowledge, as well as positive effects on savings attitudes, self-reported saving for purchases, money management, and budgeting. By contrast, the program also led to significantly greater use of expensive financial products such as credit cards, and a higher likelihood of being behind on credit repayments, likely because the program tried to inform students about these products but did not

> 1 An exception is Frisancho (2021), who examines credit bureau data for up to three years after a financial literacy intervention started in Peruvian high schools (corresponding to one to two years after the students graduated). The intervention led to less borrowing for students in some subgroups.

2
combined with the baseline survey cover the period from January to October 2020. Hence, the study period falls squarely within the timeline of the first wave of pandemic spread and the associated stringent government response measures.

First, we use the phone surveys to document the large negative impacts of the crisis on income generation, business outcomes, and life satisfaction among the control group. This analysis shows that 78% of respondents report a negative change to their income within three months of the start of the pandemic (i.e., by June 2020). In addition, only 50% of businesses survive the early months of the pandemic, and new business startup rates drop sharply from over 50% pre-pandemic to an average of 10% in the March-October period. Among the businesses that existed before to the pandemic, sales and profits fall sharply by over 65% compared to prior performance, and business investment and employees decline correspondingly. At the personal level, our data show 40% of youth lack confidence in maintaining their living standards in the aftermath of the pandemic, and 79% are dissatisfied with life. Hence, from both business and individual well-being perspectives, the effects of the crisis were hard hitting and immediate.

Next, we turn to the causal impacts of business grants and BDS. The study design has multiple arms, with one group randomly assigned to receive business grants of Ksh. 40,000 (about US$ 360); a second group assigned to BDS in the form of classroom, digital business training, and follow-up counseling visits; a third group assigned both grants and BDS; and a final group assigned as control. The business grant is equivalent to approximately two months of baseline average sales and five months of average profits. Therefore, it represents a sizable monetary boost to entrepreneurs in our sample. While the grant disbursement was not conditioned on business outputs or outcomes, youth were subject to monitoring via a mix of phone follow-up and spot checks.<sup>2</sup> Our results
(PFCE) per capita based on national accounts and night-time lights data from 2014 to 2020 from Beyer, et al. (2021) to validate our main results. Nighttime light data are aggregated to the district level and measured in Nanowatts/cm2/steradian.

# **2.3 Differences between CPHS and NSS consumption surveys**

In this section, we systematically document differences between CPHS and NSS consumption surveys that hamper direct comparisons of consumption levels between the two surveys.

_Sampling differences._ First, the rural and urban substrata in the two surveys constitute different geographical units. The rural FSUs in the NSS’ 2011-12 survey were drawn based on 2001 population census village boundaries, whereas the rural FSUs in the CPHS are based on the 2011 round of the census. The number of statutory towns 8
# **Figure A.9. Probabilities of Female-Headed Households Escaping Poverty in Second Year Conditional on Being Poor in First Year (percentage)**

<!-- Start of picture text --> Egypt 2017-2020 Iraq 2007-2012<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Jordan 2010-2013 Mauritania 2014-2019<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Palestine 2011-2017 Tunisia 2015-2021<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>FHH Non-FHH<br>34<br>50<br>32 48<br>30 46<br>Percentage (%) Percentage (%)<br>28 44<br>46<br>56<br>54 44<br>52<br>42<br>50<br>40<br>48<br>Percentage (%) Percentage (%)<br>46 38<br>45<br>23<br>40 22<br>21<br>35<br>20<br>Percentage (%) Percentage (%)<br>30 19<br><!-- End of picture text --> Note: Estimates are obtained with synthetic panel data and weighted with population weights where the second survey round is used as the base year. The figure shows the percentage of the population that moves out of poverty in the second year. FHH characteristics are measured in second period. Dashed red lines represent the national average for each period. Household heads' ages are restricted to 25-55 for the first survey round and adjusted accordingly for the second survey round. Standard errors are estimated with 1,000 bootstraps.

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In the first three years of the 1990s earnings were reduced 36 percent. Eurthermore, benefits such as, for examnple, housing and schooling allowances enjoyed by manufacturing sector workers were cut. Earnings in the manufacturing sector were eroded, being 68 percent above average real earnings in 1979 and only 36 percent above in 1990 (Knight; 1996). A decline in real earnings of this magnitude may indicate that the Zimbabwean labor market is rather flexible.

The first government after Independence launched income policies in an attempt to obtain a more equal wage distribution. It actively intervened in the wage determination and launched a minimum wage law. The minimum wage increased rapidly in real terms in the following two years. One consequence of the minimum wage law and the requirement for permission to lay-off and dismiss workers is that employers are unable to offer lower wages, despite being obliged to give greater job security.<sup>4</sup> In recent years collective bargaining plays a more important role in setting wages in Zimbabwe. The combination of income policies and a rise in the minimum wage following Independence placed a legal boundary on potential, downward real wage adjustments (see Fallon and Lucas; 1993).

**Table 2**

|||**Average real**|**earnings by sector (**|**1979=100)**||
|---|---|---|---|---|---|
|Year|All**sectors**|**Agriculture**|**Domestic Service**|**Mining**|**Manufacturing_**|
|1989|114.7|133.3|101.8|130|93.6|
|1993|85|86.3|47|113.1|71.1|

_Source:_ CSO quarterly.

Also real manufacturing wages fell in the 1980s and 1990s. As earlier mentioned, the number of
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Since Mishra’s (2007) paper, a few other studies that focus on national wage effects have found similar results. Using data drawn from the Canadian, Mexican, and U.S. Censuses, Aydemir and Borjas (2007) conclude that a 10 percent change in labor supply is associated with a 3 to 4 percent change in wages in the opposite direction. In a study of Puerto Rican workers, Borjas (2008) finds that a 10 percent emigration-induced fall in the number of workers in a particular skill group raises the average wage by about 2 percent.

The literature on emigration and wages has thus far focused only on the North American experience with international migration—particularly in Canada, the United States and Mexico. In large part, this has been out of convenience, as close to all migrants from Mexico and Puerto Rico are in the U.S., allowing for empirical analysis drawing on U.S. data. Recent evidence shows that although United States is the largest immigrant recipient (in absolute size) of any country in the world, most of the top emigration source countries (in percent of the population) are outside North America. There is, however, no accumulated empirical evidence on the impact of migration on wages in these other countries. Thus, there is a significant knowledge gap in the emigration literature on countries outside North America.

Examining the impact of emigration empirically is challenging because source countries typically do not maintain data on emigrants. In this paper, we examine for the first time the impact of emigration on wages in Moldova using demographic and labor market information on emigrants documented by a recent, nationally representative survey. Moldova provides an ideal case study, given that Moldova’s emigrants represent 17 percent of its population and about 30 percent of its labor force, placing it at the highest end of the global distribution of emigrants as a share of (source country) labor force.<sup>4</sup> The majority of workers are in Russia, close
maintain workers depends on firms’ ability to sustain their liquidity and solvency. Using World Bank Enterprise Survey data, Bosio et al. (2020) find that firms have suffered liquidity shortages regardless of their age, size, and productivity levels. Alfaro et al. (2020) model the impact of the direct supply shock caused by lockdowns as well as demand shocks through consumer demand, supply chain disruption, and the overall aggregate demand effects on the Colombian labor market. They predicted that the crisis would result in 24 percent of jobs being lost and total wage income losses of 17 percent. They showed that 56 percent of jobs are at risk, with 67 percent at risk as the crisis deepens due to cumulative liquidity effects on firms (with losses relative to the 2019 baseline). A forthcoming paper (Buba et al., 2021) analyzes cross country differences in jobs outcomes at formal firms using firm survey data and establishes that at least in the short term, countries with more highly regulated labor markets retained more formal jobs, that countries with more stringent lockdowns had greater job losses, and that on average countries with supportive policies have been more able to preserve these jobs.

Despite this rich literature on the labor market impacts of the pandemic, there have been limited studies that seek to empirically identify the factors that matter most in determining the level and distribution of jobs lost. This paper contributes to filling this gap.

# **3. Context, Data and Methodology**

- 3.1 Data

We use linked firm-level and labor force survey data to empirically analyze job loss in formal private firms for both Jordan and Georgia. Firm data are provided by the COVID-19 Follow Up World Bank Enterprise Surveys (CFUWBES) for these two countries. These are surveys of companies included in a recently completed preCOVID World Bank Enterprise Surveys (WBES) to measure the impact of the COVID-19 pandemic on the private sector. We analyze only the loss of a “permanent” job, because a firm’s workforce level is the
_Services PTAs_ 2007; Yotov et al., 2016).<sup>17</sup> We use two types of country-pair fixed effects (FEs) in this paper: _symmetric_ pair FEs when investigating the effect of policies with no distinction on the direction of trade (or VA) flows; _asymmetric_ pair FEs when separating the effect depending on the direction of the flow (e.g. high-income to low-income country and _vice-versa_ ).

In addition, the country-year fixed effects, _μ_ **it** and _δ_ **jt** , control for any time-varying country characteristics that could affect its services trade, or sourcing of VA, such as incomes, business cycles, national policies, etc. These fixed effects also account for the multilateral resistance terms of the gravity equation, which capture the fact that changes in bilateral trade costs due to entering a PTA are not independent from changes in trade costs with third countries (Anderson and Van Wincoop, 2003).

Services PTAs are not typically stand-alone agreements; rather, services chapters are negotiated as part of agreements covering a range of trade areas. In this paper, we only use information from the ’Services’ chapter of the WB-DTA. It is possible that services provisions are correlated with provisions in other subject areas, which are unobserved here but which may also affect services trade and value added. This could give rise to an omitted variable bias. The WB-DTA 2.0 database encompasses a total of 18 subject areas, and at the level of individual agreements we are able to control for the potential impact of provisions in other subject areas outside of service chapters by including a ‘nonservices depth variable’ that contains the number of provisions in a given PTA outside services. Inclusion of this control variable does not change any of our main results, and its estimated coefficient is typically near zero. We therefore omit it in most tables but

> 17Alternative methods based control functions and instrumental variables have been proposed to account for endogenous trade policy variables (see e
these differences. In studies from Uganda and South Africa, for example, Jacobs and Kes (2015) find that around 10 to 16 percent of women in Uganda own land in their own right, and that the majority of couples disagree specifically on joint ownership. In a study from Ecuador, Twyman et al. (2015) find higher agreement on joint ownership of land (in 79 percent of parcels, couples agree on joint ownership), but also do find that where disagreement occurs, it is almost always where one spouse reports a parcel is owned jointly, while the other attributes ownership to oneself/their spouse. Similarly, where discrepancies over reported/economic ownership occur in the IHPS (Table 3), it typically involves one spouse claiming joint ownership (and in this case, the other claiming no ownership). There are, otherwise, very few cases of disagreement where each spouse claims they are the exclusive owner/have

> 26 The corresponding question in the agricultural questionnaire asks, “Who in the household makes the decisions concerning crops to be planted, input use and the timing of cropping activities on this [PLOT]?” There is one respondent for each plot, who identifies one main decision-maker and two additional decision-makers on each plot. In the IHS4, 78 percent of individuals who were identified as the main decision-maker were also the respondent for the plot. This figure stood at 95 percent while looking at whether the respondent was one of the three decision-makers. For the IHPS, these shares were 74 and 93 percent, respectively.

> 27 Documented ownership is almost negligible, with husbands and wives agreeing 98 percent of the time that they do not have an ownership document for the parcel in question.

> 28 The country pilots that had been supported by the EDGE initiative had presented substantial agreement among couples (83 percent in Georgia and Mongolia, 90 percent in the Philippines), although this was regarding dwelling as opposed to agricultural land (United Nations, 2019).

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of them often remain for long periods in a given location, live in rented houses, and have a job (often informal) (IOM, 2018b, 2019).

In our empirical analysis, we use DTM data for the period June 2017 to February 2020.<sup>15</sup> The DTM collects information on migrants located in, arriving to, and departing from the area around each FMP. For each FMP, it reports the three most common countries of origin, the three most common countries of destination, and the share of migrants by length-of-stay category (i.e., less than 2 weeks, less than 3 months, between 3 and 6 months, more than 6 months). Data are collected daily and reported weekly. In our analysis, we aggregate them at the monthly level.

The primary method of data collection at the Flow Monitoring Point (FMP) is through Key

> 13The IOM definition of migrant does not distinguish between legal and illegal migration. Yet, as discussed in Di Maio et al. (2023), migration flows recorded by the DTM are likely to be mostly made of irregular migrants.

> 14According to the Libyan immigration law, individuals entering the country without a permit are at risk of detention at any time. The Directorate to Combat Illegal Migration (DCIM) (established in 2012 to tackle irregular migration flows into the country) is in charge of arresting and organizing the deportations of irregular migrants, and managing the detention centers. While immigration law is rarely enforced, migrants arrested and detained often face severe abuses by security forces and militias, including torture, rape, extortion, forced labor, and extra-judicial execution (Mixed Migration Centre, 2019). The living conditions in the detention centers remain an issue of grave concern with serious humanitarian consequences for the migrants detained (UNSMIL, 2018). In 2019, 4,700 migrants (around 1% of the total migrant population) were in detention centers.

> 15The period of analysis begins with the
sample dummies are jointly highly significant for the former and lack significance for the latter.<sup>14</sup> Also, as table 7 illustrates, the variable indicating the overlap between the period during which diary information was collected and the period covered by the recall survey is not significant in any of the specifications, most likely because where it existed, the overlap was quite short.

This evidence might also provide the basis for substantive conclusions regarding the types of households for which recall-based estimates of production might yield more or less reliable results.<sup>15</sup> Differences are estimated to consistently increase in household size, presumably because with larger size the probability of members managing their own plots is likely to increase. Regressions also suggest that differences decline in area cropped as well as yields plausibly because larger and more productive farmers will be better able to keep track of production outcomes. Finally, results in columns 1 and 2 also suggest that differences decline with the head‟s education and ownership of cattle as a major productive asset.

# **4.3 Implications for using diaries in practice**

Taking the results from Tables 7 and 3 together, it appears that, at least in some respects, well-supervised diaries can complement more traditional types of data collection methods. To see this, recall that in Table 3, non-response was estimated to increase with education and asset ownership and that the likelihood of diary completion increased with household size. Table 7 suggests that all of these variables are associated with larger differences between recall-based estimates of production and those obtained from diaries, so if the latter are closer to the true value, the potential for improvement would be considerable.

While the above results suggests that diaries can plausibly provide more accurate information on output quantities than recall-based surveys, a key argument against their more widespread use has been the supposedly high cost of conducting them. The Uganda experience reported here suggests otherwise: local monitors were paid some US$ 10 per month to assist in keeping of diaries in
adopt an approach that favors having the same definition of a firm from the perspective of the employer across the whole economy and across countries. We then combine this information with the moments of the distribution of firms across sizes from other data sources.

# **3.1 Estimating the total number of firms**

The simplest calculation of the total number of firms is the sum of people reporting to be em. ployers<sup>7</sup> This is facilitated by the availability of data on employers, employees, and own-account workers provided by ILO (2023). The latest available data point is for 2020 and indicates that there are about 12.7 million employers and 232 million own-account businesses on the African continent. There is large variation on the number of firms across countries (see figure 1.a). These numbers are strongly associated with population size in these countries, with a correlation of 0.74. Regarding density, North and South Africa are the regions with a larger number of firms relative to the working age population (see figure 1.b). There is low association between the density of firms and the total population, with a correlation of 0.13. Yet, as discussed in the next chapter, firm density is positively and strongly associated with per capita income.

> 7Conceptually, this excludes own-account from the group of employers since an employer hires at least one worker. In our conceptual framework, the group of sole proprietors that would usually be covered in an economic census, particularly if they are formal, is within the own-account category.

8