rafmacalaba/data-use-mentions-extended huggingface dataset:
Therefore, we conduct a series of robustness checks using alternative measures of informality, including the survey-based share of self-employed in total employment, the MIMIC measure of informality, and a DGE-based measure of informality that is benchmarked to survey-based estimates of self-employment. The survey-based share of self-employment does not rely on strong assumptions but is more tenuously linked to credit than output-based measures. It is available for 160 economies over 2000-18. The MIMIC-based estimates cover a wider range of indicators associated with informality but include a measure of financial size in its construction. Estimates are taken from Medina and Schneider (2019) and are available for 155 economies over the period 2000-17. The model of Elgin and Oztunali (2012) benchmarked to survey-based estimates of selfemployment is decoupled from MIMIC-based estimates of informality and does not require ad-hoc assumptions regarding the relationship between formal and informal productivity.<sup>12</sup> It is available for up to 155 economies during 2000-18.

Table 10 shows that the baseline results are largely robust to the use of alternative informality measures. All regressions that instrument the informal economy with commercial bank branches in the region or with distance-discounted commercial bank branches continue to indicate that greater financial development reduces the share of the informal economy.

# **5. Conclusion**

It is well-established in the literature that widespread informal economic activity is associated with underdeveloped financial systems through a wide range of mechanisms. What has not been well-established is the direction of causality, at least in a multi-country setting. In this paper, we use an instrumental variable approach to establish such a causal link. Using a novel instrument— financial sector development in other (neighboring) countries— we show that greater financial sector development, here proxied by access to banking services, reduces the share of informal output to GDP. The impact is stronger in countries with greater trade openness and capital account openness. These findings are robust to the use of alternative informality measures and various indicators for financial development.

Confidence in a causal effect of financial development supplies policy makers with the information
V. **Discussion and conclusion** This paper presents empirical evidence on the magnitude of liberal grade promotion in public schools in Nepal. The pass rate in the year-end exam whose scores determine grade transition is 256 percent higher than the pass rate in internally administered trimester exams. This suggests that as many as seven out of 10 students who transition from grade eight to nine may be doing so due to the policy.

The study has a number of limitations. First, due to the unavailability of data, the estimates are not conclusive. In particular, the study cannot positively rule out the possibility that other inputs like teacher attendance, time spent on classroom instruction, and remedial classes for the end-of-theyear exams were responsible for the observed leap in pass rates. Second, the small number of schools in the study were selected purposively from semi-urban areas of Kathmandu for a randomized controlled trial of student incentives, so these schools are not representative of all the schools in the district or the country. Further research is necessary to establish the external validity of the results.

Although not a causal attribution, liberal promotion policy appears to have been successful in reducing the system’s inefficiency: between 1999 and 2014, the repetition rate at the primary level dropped from 23 percent to 10 percent, the transition rate from primary to lower secondary level climbed from 80 percent to 87 percent, and the survival rate to grade five, the last grade of the primary school cycle, rose from 59 percent to 70 percent (World Development Indicators).<sup>11</sup> There is, however, little evidence of improvement in the quality of education over the same period. The pass rate in School Leaving Certificate (SLC) exams, the terminal exam taken in grade 10, continues to remain low; in 2014, it was 34 percent in community schools compared to 89 percent in institutional schools.<sup>12</sup> According to the national assessment of grade 10 students in 2011, the average achievement level
, though this was not the case in India. Mejia-Dorantes et al. (2021) analyzed mobility trends and patterns in the metropolitan area of Barcelona before and after the COVID-19 pandemic outbreak using a combination of traditional travel surveys and smartphone data. They found that after the pandemic outbreak, highly educated population groups and those with higher income were more likely to change their mobility patterns compared to others and that, while remote work and studies may shape new mobility patterns for some population segments, not all mobility

> 2 World Bank World Development Indicators.

2
<!-- Start of picture text --> Brazil<br>0.70<br>0.50<br>0.40<br>3 0.30<br>5<br>g 0.20<br>0.10<br>-0.10<br>-0.20<br>1995-1998 1998-2001 2001-2004 2004-2007 2007-2010 2010-2013<br>——Total earnings Worker effect<br>Firm effect +ssas+ Covariance worker-firm effect<br><!-- End of picture text --> <!-- Start of picture text --> Costa Rica<br>0.50<br>0.40<br>0.30<br>22<br>s 0.20<br>><br>0.10<br>0.00<br>-0.10<br>2006-2008 2009-2011<br>——Total earnings Worker effect<br>Firm effect +seae» Covariance worker-firm effect<br><!-- End of picture text --> <!-- Start of picture text --> Ecuador<br>0.50<br>0.400.30 a<br>22<br>& 0.20<br>s$<br>0.10<br>-0.10<br>2006-2009 2009-2012<br>——Total earnings Worker effect<br>Firm effect ++eas+ Covariance worker-firm effect<br><!-- End of picture text -->
mentoring, easy access to decision tools to improve diagnosis and treatment, and the collection of more timely and relevant data. But the use of these technologies in LMICs is still in its infancy and evidence on its effectiveness and costs remains limited.<sup>33</sup> Additional interventions to improve quality of care are more of regulatory and policy nature, such as licensing of providers, accreditation of facilities and oversight of training institutions.

After a decade of evidence from SDI surveys, we identify a set of potential developments that could further strengthen its use and impact in improving quality of care, such as faster and cheaper ways to collect data, attention to a broader set of medical conditions and increased effort to understand drivers of performance differences. As countries increasingly track availability of inputs through health management information systems, SDI could progressively focus more on process indicators of quality. This is in line with the recommendations by the recently published findings from the Lancet Global Health Commission on High Quality Health Systems which identified competent care as one of the key dimensions to measure to advance the quality agenda.

# **Contributors**

JS and WW led the team developing the design of the Service Delivery Indicators instrument. WW supervised data collection in the 10 countries. All authors contributed in drafting and revising the manuscript and approved the final version for submission.

# **Declaration of interests**

We declare no competing interests.

**Funding** The World Bank, Service Delivery Indicators Trust Fund (funded in large part by the

Hewlett Foundation).

# **Acknowledgments**

We are grateful to the many researchers, survey experts and enumerators who have worked on the country surveys that made this cross-country paper possible and are especially grateful for the contributions th <mark>e African Economic Research Consortium.</mark> We are grateful to the World Bank, and in particular the Service Delivery Indicators Trust Fund (funded in large part 19
and parameters they consider while hiring candidates for different functional areas, especially when hiring female candidates.

The added richness of this data set comes from the fact that employers were asked about their attitudes to gender norms. Two questions – whether girls and boys should receive equal education and whether women should work after they are married - follow from tradition in South Asia (like elsewhere) where families historically tended not to educate daughters as highly as they did their sons and where there are strong beliefs that a woman’s place is in the home. We also analyze results from four additional questions in the survey that relate directly to the labor market. They follow questions in global surveys and ask if, when jobs are scarce, men should have more right to jobs; if men and women deserve the same wages and benefits if they do the same jobs and if men make better employees than women do. Finally, to get at attitudes regarding attitudes towards occupational sex segregation, we asked if certain jobs are better suited to men or to women.

While it does not detract from the richness of our data set, we are acutely conscious of the fact that norms and attitudes are notoriously tricky to measure. First, responses to questions on attitudes are inexorably ascribed by context – who the respondent is, where they live, how they were raised, among others (see for instance Grand, Bernier and Strohmer, 1982). These can affect both the respondents’ interpretation of the question as well as their response. According to Zaller (1992), ‘individuals do not typically possess ‘true attitudes’...rather, they construct ‘opinion statements’ on the fly...based on whatever considerations are momentarily salient’ (Zaller, 1992). This also means that a change in question order or wording may result in different responses. Second, the more specific an attitude measure, the more likely it will be related to actual behavior (Ajzen, 1988). There are several scales that measure employer
change in log night lights between two years before a disaster has taken place and the following year. The figure reveals no apparent discontinuity at the thresholds. Moreover, when we estimate the impact of Fonden it yields a small coefficient that is statistically indistinguishable from zero, 0.016 (t=0.45).

# **6 Night lights as proxies of subnational economic activity**

While our primary interest lies in determining whether log night lights can predict changes in municipal level GDP, in the absence of this type of data we begin our analysis by investigating the relationship between log night lights and proxies of economic activity at the municipal level. Specifically, we calculate by municipality, from the 2005 population conteo and the 2010 population census, the number of dwellings with the following characteristics: dwelling has non

> 11In particular, Fonden housing reconstruction efforts occur through the temporary work program. This program is designed to hire homeowners to rebuild their own houses.

15
institutions and the private sector and found that there is a high degree of collinearity across the forecasts—across international institutions and the private sector, the forecasts made at a given time period for a given country and year tend to be similar.

# III. Data and Empirical Specifications

The main data sources for GDP growth forecasts are the same-year growth forecasts made in January by the IMF’s _World Economic Outlook_ (WEO) and the World Bank’s _Global Economic Prospects_ (GEP).<sup>4</sup> Growth forecasts by these international institutions are widely used by authorities to assess economic prospects, benchmark their own projections, and are also used by private-sector forecasters (Genberg and Martinez, 2014; Genberg, Martinez, and Salemi, 2014; Beaudry and Willems, 2022).<sup>5</sup> In addition, we explore same-year growth forecasts made in April, June and October by the WEO; one-year-ahead and two-year-ahead growth forecasts made in January by the GEP. Supplementary data include same-year growth forecasts made in January by the private sector obtained from Consensus Forecasts (all countries except MENA countries) and Focus Economics (for MENA countries only).<sup>6</sup> For our baseline regression analysis that includes the main covariates, we use a common sample that covers the same set of 126 countries from 2010 to 2020 in the World Bank GEP January Forecasts sample for same-year forecasts and the IMF WEO January Forecasts sample for same-year forecasts. Private sector consensus (average) January Forecasts sample for same-year forecasts have much lower coverage resulting in 56 countries from 2015 to 2020 in the baseline regression with the main covariates.

## III.1.Forecast Errors

Data from the common sample of the IMF’s WEO and the World Bank’s GEP shows that globally, between 2010 and 2020,<sup>7</sup> the average same-year GDP growth forecast error—the difference between forecasted GDP growth and realized GDP growth—is 1.3 percentage points
regulation affects firm entry costs, workers’ unemployment benefits and firing restrictions. They use panel VAR estimates for OECD countries to demonstrate the recessionary short-run and expansionary long-run effects of reforms. The authors, thus, prove a strong effect of market deregulation on business cycle fluctuations’ efficiency.

In the context of construction permitting, another area measured by _Doing Business_ , Poel et al. (2014) find that higher administrative burdens adversely affect economic growth. Using a fixed effect regression analysis of data from 182 economies, the authors show a clear correlation between the number of days and procedures required to obtain a construction permit and economic growth. Plus, a similar relationship exists between economic growth and days and procedures to start a business as well as to pay taxes. Likewise, in the area of getting electricity, the analysis of Abotsi (2016), using the Enterprise Surveys data for Africa, demonstrates that the number of power outages experienced in a given month negatively impacts firms’ production efficiency. Several other studies emphasize the importance of effective tax regulation on firm formalization. Rocha et al. (2018) analyzing firm-level administrative and individual panel data in Brazil show that the costs of remaining formal in the case of taxes – and not entry costs – pose a serious obstacle to formalization. Specifically, reducing taxes after the registration costs have already been removed lowers firm informality rates, while registration costs reduction per se has no evident effect on informality. At the same time, lower taxes do not necessarily impact creation of new formal firms.

Analyzing regulation of property registration, Berkowitz et al. (2015) show that a property law enactment, which grants more rights to creditors over the assets that are used for loans to private firms leads to a substantial increase in firm value. And the impact is stronger on firms that are less politically connected, have lower internal cash flows and have more opportunities for 8
procedure was implemented using smartphones and an application designed on the SurveyCTO Collect platform. Length, latitude, and altitude information was also collected for monitoring enumerators' work remotely. Enumerators then created a record for each residential unit and households following the protocol described in the 2019 Manual for Enumerators.<sup>14</sup>

# III.3 Selection probabilities and sampling weights

Given the sampling design discussed above, the probability phij of selecting a household in the second-stage stratum hij of sector hi of first-stage stratum h is given by:

## where

- kh is the number of sectors selected in the first-stage stratum h (20 in a stratum of low density, 80 in medium density and 100 in high density),

- nhi is the population in sector hi , as per the 2010 Housing and Population Census;

- Nh is the population in first-stage stratum h , as per the 2010 Housing and Population Census;

- mhij is the number of households selected in the second-stage stratum hij ; and Mhij is the total number of households in the second-stage stratum hij .

Notice, two fractions on the right-hand side of (1) represent the probability of selecting the sector in the first stage and the conditional probability of choosing the household in the second stage, respectively. The first probability is correct regardless of the accuracy of Telefonica's predicted number of Venezuelan phones in each sector.

The data from each household in hij should be affected by a sampling weight (or raising factor) whij , equal to the inverse of its selection probability (i.e. whij = 1 / phij ) to produce unbiased estimates from the sample.

# III.4 Non-response adjustment and calibration

To account for non-response during the listing operation, the total number of households in each secondstage stratum hij (the denominator of the second ratio in (1)) was estimated as: where Yhij is the total number of households listed in hij and Y'hij is the total number of households registered in hij by the 2010 Housing and Population Census
is a “masculine” activity (Coffey, et al., 2016). Similarly, when a sample of latrine owners in Uttar Pradesh were asked why they defecated in the open, 27% indicated that they were simply used to it, 23% did so because it has been practiced for generations, and 20% had simply never thought of defecating in latrines (SaniFOAM‐UP, 2016). This suggests that behavior change in this context may also require changing people’s mental models about how they think about defecation practices. Latrine access requires supplementation with behavior change communication. Ending open defecation likely requires addressing the social, cultural, and normative barriers that may get in the way of improved sanitation practices vis‐à‐vis open defecation.</mark> <mark>What forms should behavior change strategies take? The messages in communication strategies regarding open defecation and toilet use can take multiple forms, including efforts to highlight class aspirations, nationalism, ethnic or caste role and pride, gender roles, or urbanism and modernization, among others. In this paper, we test the content of two more generic messages – social empirical expectations, and the rebranding of cleanliness. We also test two modalities – pamphlets and the use of individuals who sit in center of village‐level social networks, whom we call “norm entrepreneurs.” More generally, we aim to provide an empirical foundation for behavior change strategies used in the field.</mark>

# **<mark>Social Norms and Cultural Schema</mark>**

1 According to government estimates, since the commencement of the Swachh Bharat Mission in October 2014, nearly 92 million latrines have been built across rural India as of January 2019 (SBM, 2019). The 2017‐18 National Annual Rural Sanitation Survey (NARSS) reported 77% toilet penetration across India.

2 Estimates of regular toilet use in rural India vary. The NARSS 2017‐18 found that 93.4% of people who had access to toilets
Policy Research Working Paper 9775

# **Abstract**

This paper explores how different investment frictions affect the patterns of responses of labor markets to tariff cuts. To investigate these patterns, this paper formulates a multi-sector dynamic model featuring capital and labor adjustment costs that is fitted to Argentine data. Counterfactual simulations of a tariff decline in the textile sector are used to show that capital adjustment can create long-run responses of real wages that are larger than the short-run responses. This happens as textile firms disinvest during the transition. This paper also shows that the reduction of tariffs on capital inputs boosts investment and real wages across sectors. This paper assesses the nature of capital adjustment costs, including fixed, convex, and irreversibility costs in determining these patterns of labor market responses to trade reforms.

This paper is a product of the Development Research Group, Development Economics. 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 at eartuc@worldbank.org; irene.brambilla@econo.unlp.edu.ar; or guido.porto@depeco.econo.unlp.edu.ar.

_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
)|(0.1200)|(0.0242)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.5712***|2.9181***|1.3450***|3.5091***|4.9270***|2.1595***|2.9019***|0.8883***|
||(0.0961)|(0.4433)|(0.2898)|(0.3731)|(0.3201)|(0.3182)|(0.3225)|(0.0496)|
|Observations|28,920|28,920|28,920|28,920|28,920|28,920|28,920|28,920|
|R-squared|0.389|0.180|0.117|0.160|0.241|0.164|0.101|0.275|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

60
outcomes between PSNP beneficiary and non-beneficiary households. We employ the following difference-in-difference specification: h 0P P 1P P h ∗P P (1) htt tt tt htt YY = αα + ββ + ββ + εε where stands for food security and related maternal and child diet measures for household _h_ and round htt _t_ . h stands for household fixed effects, which capture all time-invariant differences YY between PSNP beneficiaries and non-beneficiaries. P P h represents households’ access to PSNP αα transfers. P P is a dummy variable, assuming a value of 1 for the phone survey conducted during the COVID-19 pandemic (henceforth post-COVID-19 round) and 0 for the pre-COVID-19 round. tt The parameter associated with this time dummy captures aggregate trends in food security or potential differences in our outcomes of interest driven by differences in survey methods (face-toface or phone survey).

face or phone survey). The key parameter of interest in equation (1) is 1. This parameter identifies potential differences in temporal evolution of food security outcomes of PSNP beneficiaries and nonββ beneficiaries. In the absence of unobservable time-varying variables with differential impact on PSNP beneficiaries, this parameter can be interpreted as the protective impact of the PSNP against income losses caused by the COVID-19 pandemic. To be credible as an estimation strategy, this requires that, in the absence of the pandemic, food security status of PSNP and non-PSNP beneficiaries would follow similar trends. To indirectly probe this assumption, we run the same specification using the two pre-COVID-19 surveys (March 2019 and August 2019) and assess changes in our outcomes of interest. As shown in Table A4, the trends in food gap and diet diversity of mothers and children across these two rounds for PSNP and non-PSNP beneficiaries were similar.

In all our specifications, we control for observable potential time-varying confounders such as the locust invasion that hit parts of the country. We include a categorical variable that measures whether households were affected by locust swarms. The use of alternative definitions of access to PSNP helps us to probe the robustness of our findings to