rafmacalaba/data-use-mentions-extended huggingface dataset:
[0.001]|[0.001]|[0.001]|
|Mobile account|-0.030|0.094***|0.038|0.094***|0.051|0.071***|
||(0.037)|(0.034)|(0.023)|(0.022)|(0.035)|(0.027)|
||[0.260]|[0.019]|[0.128]|[0.001]|[0.160]|[0.022]|
|Baseline controls|X|X|X|X|X|X|
|Test: payroll = account|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|1,311|1,311|1,311|1,311|1,311|1,311|
|R-squared|0.094|0.068|0.250|0.256|0.153|0.353|
|Mean control [endline]|0.402|0.252|0.081|0.049|0.613|0.120|

Notes: The table reports treatment effects on learning. Estimates in panel A are based on administrative data. The dependent variable is a dummy equal to one if at least one transaction was recorded in a given month. The omitted group consists of observations prior to random assignment to treatment, for which all outcomes are mechanically equal to zero. Robust standard errors, in parentheses, are clustered at the account level. Estimates in panel B are based on endline survey data. The omitted group consists of respondents in the control who continued to be paid in cash and did not receive an account. Controls include stratification dummies, marital status,
# **Table 4. Participation in societal institutions**

|**Dependent variable**|**Log revenue**|**Years in business**|**Pseudo R**<sup>**2**</sup>|**# obs**|
|---|---|---|---|---|
|Registered with federal treasury|.661<br>(37.7)|.006<br>(3.95)|0.200|8870|
|Registered with local treasury|.371<br>(26.9)|.006<br>(3.57)|0.083|8870|
|Pays any taxes|.556<br>(35.6)|.008<br>(5.35)|0.158|8870|
|Any paid workers registered with<br>IMSS|.634<br>(17.2)|.010<br>(2.95)|0.161|1697|
|Enumerated in 1989 census|.538<br>(26.5)|.013<br>(6.26)|0.154|5121|
|Registered with business<br>guild/organization|.383<br>(26.8)|.006<br>(3.87)|0.090|8870|
|Pays any dues|.312<br>(21.6)|.007<br>(4.02)|0.066|8870|

Probit regressions. Absolute values of z statistics in parentheses. The standard errors have been corrected for arbitrary forms of heteroskedasticity.

**Table 4.A. Participation in societal institutions** (difference in probabilities from a one unit change in the regressor)

|**Dependent variable**
manufacturing, information and communication technology, hospitality, entertainment, and construction). We targeted firms that had 2 to 15 employees (the same range as the experiment in Anderson and McKenzie, 2020), and that had been in business for at least one year. Firms were sampled using a combination of list-based interviewing and door-to-door screening. Kantar began with its own database of firms, and set of appointments to survey firms which met the sector and firm size criteria. Once the survey team were traveling to a given commercial area or industrial cluster, they then went door-to-door to find and interview other firms that met the sampling criteria. This resulted in an overall sample of 1,054 firms, of which 397 came from the original lists and 657 from door-to-door sampling. Baseline data collection took place between September 2 and October 14, 2019.

Table 2 provides some descriptive statistics on these firms. The entrepreneurs have an average age of 38, have been running their firms for a median of 8 years, and 64 percent have at least two years of university education, and 26 percent have post-graduate education. Twenty-five percent are female. The mean (median) firm has 5.9 (5) employees and USD $2291 ($833) in monthly sales. Baseline use of the market for business service providers is low, with only 5 percent having used a human resources (HR) firm to find a worker in the past year, and only 3 percent of firms currently using an outside accounting or marketing firm. We used the indirect method to assess the reasons for the lack of use of this market, asking firms why they thought SME owners in Nigeria (including themselves) did not use this market. Firms answered on a 5-point Likert scale, and we consider the percent of firms who think a factor is very likely or extremely likely to be a key factor for not using this market. Many of the answers given as key factors by firms are consistent with information frictions: 27 percent say not knowing
currency. Specifically, they show that fiscal rule adoption is associated with a reduction in foreign currency borrowing of between 1 and 1.9 percentage points.<sup>9</sup> A related strand of the literature has studied the factors influencing the adoption of fiscal rules (IMF 2009; Hallerberg and Scartascini 2015; Elbadawi, Schmidt-Hebbel, and Soto 2015, Altunbaş and Thornton 2017; Badinger and Reuter 2017). These studies have found that the political landscape can be an important factor for adopting fiscal rules. Similarly, economic conditions may play a key role: higher levels of debt or an economic crisis might affect the likelihood that a fiscal rule is adopted. However, these studies do not examine how these factors influence the subsequent impact of the rules. In this paper, we analyze how the economic and political environments prevailing at the time of fiscal rule adoption shape their medium-term effectiveness. We consider some of the conditions that can be seen as determinants influencing the adoption of fiscal rules, while others are more incidental (i.e. reflecting the specific environment at the time of adoption).

# **3. Data and methodology**

To investigate the dynamic effects of fiscal rule adoption, we focus on the response of the primary balance. The primary balance excludes interest payments from the budget, which are largely outside the control of the incumbent government and do not reflect fiscal policies implemented in the current period. This measure, therefore, effectively summarizes how fiscal policy responds to debt sustainability concerns (Bohn 1998).

We study the response of the primary balance over a ten-year period following the introduction of fiscal rules. This timeframe allows us to observe both the immediate and medium-term effects of introducing fiscal rules. Differences could arise, among other things, due to changing conditions that initially supported the adoption of the rules, potentially weakening commitment over time.

Our sample includes 108 countries, also including countries that never adopted fiscal rules. With fiscal rules data available up to 2021, we study their adoption
the employment offer.

## **5.6. Well-being**

We use two indicators to assess the program’s impacts on the well-being of the female beneficiaries and their husbands. The five-item Mental Health Inventory (MHI-5) developed by Veit and Ware (1983) is a measure of overall emotional functioning. Data on the MHI-5 were collected for part of the sample at midline for part and for the whole sample at endline (only the latter is reported here). Second, we compute Rosenberg’s (1965) self-esteem scale.

Our contemporaneous results show that both women and men do not experience any changes in their mental well-being or self-esteem due to the public works program (Table 9). As expected, given the absence of effects contemporaneous to the public works activities, there are no statistical differences several months after the program ended between women and men in households that are offered the program and those that do not receive the offer.

# **6. Treatment Effect Heterogeneity**

Our study sought to document whether there is any relevant heterogeneity in treatment effect by baseline socio-economic characteristics and explore whether any subgroups 24
Facilitating the entry of foreign firms has potentially large implications for consumer welfare and labor market dynamics. Atkin et al. (2018) analyze the effects of a period of accelerated foreign supermarket entry in Mexico. Using highly detailed microdata, such as bar code–level prices and consumption quantities, store-level profits, and worker-level incomes, they are able to disentangle the various channels through which foreign entry affected household welfare in the municipalities of entry. Relying on a flexible model, the authors estimate important welfare gains for the average household of 6 percent of initial household income. While there were no effects on average municipality-level household incomes or employment, there were substantial reductions in markups and marginal costs of local firms, suggesting spillovers from foreign entry (for example, better management practices or improved logistics). Lower prices and increased product varieties benefited all households, although richer households benefit more as their elasticity of substitution across different stores is higher: the richest households substitute over 50 percent of their retail consumption to higher-quality foreign stores, while the poorest substitute less than 15 percent.

In a recent working paper, Farrokhi and Pellegrina (2022) rely on data at the 10km<sup>2</sup> level from the Food and Agriculture Organization (FAO) to calibrate a structural model that emphasizes the links between trade, crop specialization, technology choices, and agricultural productivity worldwide. Through counterfactual exercises, the authors show that reductions in trade costs from 1980 to 2007 increased food consumption by 4 percent and welfare by 2.5 percent. The drivers of these improvements are, with roughly equal importance, gains through technology adoption and crop specialization across countries, given their natural preconditions for different crops. The paper provides important evidence on the gains of reducing trade barriers, especially for economies that still rely largely on agriculture.

Besides these aggregate results, some research examines how trade openness and FDI affects labor market outcomes at the firm level. Using firm-level data, Bloom et al. (2016) exploit the increased import competition in 12 European
.17|Oxford Coronavirus Government Response Tracker|
|Stringency index|56.29|8.90|Hale et al 2021|
|Union density|22.38|16.05|International Labour Organization|
|Democracy|7.02|4.76|Polity 5|

# **2. School Closures and Learning Losses**

The literature suggests that school closures are an efficient strategy to reduce the overall duration of a pandemic (Bin Nafisah et al 2018). The decision to close down schools was primarily aimed at mitigating the spread of COVID-19 during the beginning of the pandemic (Raffetti and Baldassarre 2022). Models from previous epidemics such as influenza suggested the role of schools as places that facilitate the spreading and the possible benefit from closure (Cauchemez et al. 2008; Jackson et al. 2014). It was estimated that since school holidays could lead to a 20-29 percent reduction in the rate at which influenza is transmitted to children, then this might work for COVID-19. School closures delay the epidemic peak if implemented earlier but do not eliminate it. Imperfect knowledge led to no guidance on how long the duration of closures should be. Democratic countries tended to implement closures more quickly from the start of the pandemic than those with a more authoritarian regime (Cronert 2020).

Researchers using national data – for example, the United States national achievement test, NAEP – have shown that there is a significant correlation between the length of school closures and learning loss, confirming earlier research (Barnum 2022; Goldhaber et al 2022; Jack et al 2022; Lehrer-Small 2022; West 2022). In the United States, public schools averaged less in-person teaching than private schools. These results are explained in large part by political preferences, vaccination rates, teacher unionization rates, and local labor conditions (Kurmann and Lalé 2021). (Teacher) unionization did not necessarily always favor school closures. For example, in Greece, teachers went on strike opposing the government’s distance education plan (Lambropoulos
Birth order FE|x|x|x|x|
|Birth order x Birth year FE|x|x|x|x|
|Birth order x State FE|x|x|x|x|
|State x Birth year FE|x|x|x|x|
|_Firstborn girl_ x State FE|x|x|x|x|
|_Firstborn girl_ x Birth order FE|x|x|x|x|

NOTES: Sample of second- and higher-order births. Each column is a separate regression. Column (1)(4) include samples with recall period within 10,12, 15, and 20 years. Specification same as column (3) of Table 3. The outcome is an indicator for death before age 5. We drop children that are less than 5 years old to allow each child in the sample full exposure to the risk of under-5 mortality. We always control for _Female_ and fixed effects (FE) for birth year and birth order and for _Firstborn girl_ and state FE. The vector _Xijt_ comprises mother’s age at birth, household wealth quintiles, caste, religion, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

68
12 and has a mean of 7.121. Official supervisory power (borrower) is an index of the power of the supervisory authorities in the borrower country to take specific actions to prevent and correct

> 9 <mark>One of the subcomponents of Capital regulation (Overall capital stringency) cannot be calculated using data from the fifth wave of the survey because of missing information. Hence, for this wave we take the values of Capital regulation in the fourth wave and adjust it using changes in the other subcomponent, Initial capital regulation, that is available.</mark>

9
|**Share of treatment households...**<br>||||
|---|---|---|---|
|(in parentheses: only compliant households)|%|**Pico‐PV lamp is mainly used by...**|**%**|
|using the kit at least once a day|86 (95)|Female adult >17 years old|49|
|...using the kit for lighting|85 (97)|Male adult >17 years old|23|
|...using the kit for listening to the radio|68 (79)|Female between 12 and 17 years old|10|
|...using the kit for charging mobile phones|10 (11)|Male between 12 and 17 years old|7|
|...use the battery pack|65 (71)|Collectively used by whole family|6|
|||Children between six and 11 years old|5|

_Source:_ Household data set 2012.

Most lamp users are satisfied with the lighting quality of the lamp. More than 70 percent of all lamp users report they are “always” or “often” satisfied with the lighting quality. Only 22 percent report to be satisfied only seldom and six percent are never satisfied. Satisfaction levels with traditional lamps are substantially lower. For wick lamps and hand‐crafted LED lamps, 94 percent and 91 percent, respectively, report to be satisfied seldom or never.

Since both treatment and control households are located within the same communities, spill‐over effects might occur. Especially children often meet and play with friends and there might be positive spill‐over effects on other households’ children. If among these ‘other’ households are households from our control group, it may even downward bias our impact estimates. Yet we did not find any evidence for spill‐overs. For instance
6|75.5|84.7|88.3|55.4|59.3|54.1|48.6|54.7|51.6|44.6|39.8|40.7|

B.sad on Rbmos (1986).

> a/ Jamnry-h43st.

b/ Vard&iatis In the CPI.

c/ Ecdhazp rate vwrlAtlims deflated by the differen betwemn eoternal and iternul W?I.
Of these six eligible proposals, three were randomly allocated to the treatment arm of the study, while the remainder served as controls. Each of the resulting 153 treatment groups then received about Br 3,000 (USD 220).<sup>6</sup> In addition, the treatment groups received technical support from the DAs while implementing their project. The members of the treatment FIF group were expected to bear 5 percent of the nonlabor costs of implementing their ideas. Control groups did not receive any funding or technical support for their proposal.

As mentioned above, FIF had mostly male participants. The Government of Ethiopia and the World Bank Gender Innovation Lab therefore later designed a second component to this agricultural intervention that specifically targeted women. The Women Innovation Fund (WIF) was created not only to increase women’s participation but also to address some of the identified drivers behind women’s low productivity in agriculture. By increasing women’s access to extension services, and easing associated financial constraints, the WIF intended to generate empirical evidence on the impact this had on agricultural productivity, as well as off-farm incomegenerating activities and household welfare.

The WIF was implemented in the FIF kebeles. Of the 51 FIF kebeles, 20 were randomly allocated to receive the WIF. The WIF randomization used the household list from the FIF baseline survey as its sampling frame. Within each of the 20 treatment kebeles, 10 to 15 women were randomly selected from FIF eligible households to form a WIF group.<sup>7</sup> As previously for the FIF, the DA helped each group develop a proposal for its own income-generating activity. The 20 treatment groups each received roughly Br 10,000 (USD 730) in addition to the technical support from DAs while implementing their projects.<sup>8</sup> The money was used to finance the group’s income-generating activity. It should be noted that the WIF funding was more than three times the amount of funding that FIF groups received, and
)|(0.54)|(0.30)|(0.56)|
|2007|8.9|9.6|9.9|8.8|10.1|
||(0.6089)|(0.27)|(0.34)|(0.28)|(0.45)|

Source: HESs (2001, 2007). Bootstrapped standard errors in parentheses (500 repetitions).

With the second test we impute into the LFS, but still only for the HES years, so that also in this case we are able to compare our estimates to the official poverty estimates. If anything, the imputation-based estimates match the “true” poverty rates even more closely. One possible explanation for this finding is that the LFS is considerably larger than the HES (although note that the “true” estimates are obtained using the HES). It is striking how accurately we are able to estimate poverty in 2007 based on a model estimated using 2001 consumption data, and vice versa.

**Table 5 – Validation Tests Results, cross-survey**

|**Year**|**Official Poverty**<br>**Estimates**|**2001**<br>**Model**|**2007**<br>**Model**|
|---|---|---|---|
|2001|15.3|15.2|15.1|
||(0.5445)|(0.27)|(0.51)|
|2007|8.9|8.4|8.6|
||(0.6089)|(0.19)|(0.28)|

Source: HESs (2001, 2007). Bootstrapped standard errors in parentheses (500 repetitions).

18
complex structural relationships between the number of people reporting in each income bin; these relationships are determined by how the true income distribution is evolving over time, how the true income distribution is mapped to the reported income distribution, as well as growth of the BF registry over time. By making Identification Assumption 2, we are essentially assuming that the differences over time between the number of people reporting in each income bin (which are determined by unknown structural relationships) are well approximated by polynomials which would have persisted in absence of the reform. Identification Assumption 2 is not fully testable in the same way that the standard differences-in-differences assumption (that parallel pre-trends persist post treatment) is not fully testable. However, as in the case of the standard “parallel trends” assumption, we can gauge whether Identification Assumption 2 seems sensible by looking at prereform data. Identification Assumption 2 implies that the difference between the log number of households in any two 7-increment bins below R$77 is governed by a stable polynomial in absence of the reform. Appendix C.4 shows how the differences log � _N_ (70 _,_ 77]� _−_ log � _N_ ( _x−_ 7 _,x_ ]� and log � _N_ (63 _,_ 70]� _−_ log � _N_ ( _x−_ 7 _,x_ ]� evolved in the pre-reform period for _x ∈{_ 7 _,_ 14 _, ...,_ 63 _}_ . These differences follow stable, low order polynomial relationships in the pre-period. While this of course does not imply that these relationships would have persisted into the post-reform period (just as parallel pre-trends do not imply that the trends would continue on a parallel path post treatment), it is at least suggestive that stable relationships exists between the various income bins.

However, because we have several control bins, we can _partially_ test the validity of our two identification assumptions. In particular, under Identification Assumption 1, our control bins should
Policy Research Working Paper 9722

# **Abstract**

Short-cycle higher education programs (SCPs) can play a central role in skill development and higher education expansion, yet their quality varies greatly within and among countries. This paper explores the relationship between programs’ practices and inputs (quality determinants) and student academic and labor market outcomes. It designs and conduct a novel survey to collect program-level information on quality determinants and average outcomes for Brazil, Colombia, Dominican Republic, Ecuador, and Peru. Categories of quality determinants include training and curriculum, infrastructure, faculty, link with productive sector, costs and funding, and practices on student admission and institutional governance. The paper also collects administrative, student-level data on higher education and formal employment for SCP students in Brazil and Ecuador and match it to survey data. Machine learning methods are used to select the quality determinants that predict outcomes at the program and student levels. Estimates indicate that some quality determinants may favor academic and labor market outcomes while others may hinder them. Two practices predict improvements in all labor market outcomes in Brazil and Ecuador—teaching numerical competencies and providing job market information—and one practice— teaching numerical competencies—additionally predicts improvements in labor market outcomes for all survey countries. Since quality determinants account for 20-40 percent of the explained variation in student-level outcomes, quality determinants might have a role shrinking program quality gaps. Findings have implications for the design and replication of high-quality SCPs, their regulation, and the development of information systems.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 mferreyra@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
respect to cases is of similar orders of magnitude and can explain most of the reduction in mobility, dwarfing the effect of NPIs.

Third, that said, there is evidence that less affluent countries were also able to implement NPIs. LMICs and UMICs appear to have been able to engineer as much or more of a fall in mobility through NPIs as some HICs.

Fourth, our global data suggest that other measures beyond closing nonessential workplaces have important impacts-school closures, restrictions on internal mobility/shut-down of public transportation. Counterintuitively, public information campaigns appear to raise mobility- information on protective measures may make individuals feel more confident moving about.

13