rafmacalaba/data-use-mentions-extended huggingface dataset:
term the stakes are higher, and students avoid larger amounts of debt. We also find that the program led to a lower likelihood of having loans with repayment delays in the long run, by about 0.9 percentage point, compared to 15 percent of control students who have loans with repayment delays.

To study the effects of the program on entrepreneurship, we use data on formal business ownership that is available in the BCB administrative records. The MEI (individual microentrepreneur) data set covers about 42 percent of all registered businesses in Brazil. We examine the effect on formal microenterprise ownership for two post-program periods: (i) one to seven years after graduating, when students may still have been in university; and (ii) eight to nine years after graduating, when most students were most likely in the labor market. We find that treatment group students are not more likely to be microentrepreneurs than control students one to seven years after graduating, but eight to nine years after graduating, treatment students are 10 percent more likely to own a formal microenterprise than control students (a 0.69 percentage point increase relative to 6.9 percent of control students with an MEI). We also find that treatment group students are 1.2 percentage points less likely to hold a formal job, i.e., a job with a written contract, relative to 49.5 percent of control students with a formal job, suggesting that the financial education program caused them to switch occupations from being employees to being business owners.

These effects on employment outcomes may be attributed to the fact that the program was comprehensive and included modules on work and entrepreneurship. A related study by Chioda et al. (2021) examines the labor market effects of a 3-week entrepreneurship program for high school students in Uganda. Data from a follow-up survey, conducted 3.5 years after the program ended, shows that the program increased the probability of having a business by about 6 percentage points, relative to 33.6 percent of control
Policy Research Working Paper 10579

# **Abstract**

Using three rounds of data from the Business Pulse Survey in South Asia, this paper studies the differential effects of the COVID-19 shock on informal firms. It also captures heterogeneity within informal firms based on the degree and motivation of informality. The findings suggest that the severity of the impact of the COVID-19 shock and the recovery speed are strongly associated with the degree of informality. Firms’ external attributes, such as size, sector, age, and gender of the owner, do not explain the depth of the impact. Internal characteristics such as poor management capabilities and education of the manager and owners are strong predictors of vulnerability among informal firms.

In particular, necessity firms experience a larger drop in sales relative to the parasitic type of informal firms. To add to this, the adjustment response (for example, the use of digital platforms) of informal firms is smaller, which perpetuates the gap between formal and informal firms. Within informal firms, the parasitic type typically have a smaller adjustment response. These findings have implications for policies to support the private sector in the presence of informality, including considerations pertaining to targeting, modality of support, and the instruments required for designing more impactful programs during shocks.

This paper is a product of the International Finance Corporation and the Office of the Chief Economist, South Asia 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 agrover1@ifc.org and mpereiralopez@ifc.org.

_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
percent of those in the short recall group report working at all, in an average of 1.19 activities in 3.6 months for 360 total hours. With the exception of total hours worked these measures are all higher for short recall compared to long recall. Ninety percent of subjects in the short recall group report working on the household farm or in other household agricultural businesses. Much smaller numbers report working in a non-agricultural family business (9.4%) or as wage employees (13.7%). Like the overall measures, these numbers are all lower in the long recall group. Overall, this sample is heavily involved in household agricultural activities, with limited participation in other activities. Non-farm activities, when reported, are more likely to be secondary activities.

# **3. Empirical Approach**

In the final interview, we randomized the order of the questions in the labor module so that some households used the long (annual) recall window first while others used the short (90-day) recall first. For those who received the short recall questions first, we use labor measures based exclusively on their quarterly survey responses, and for those who received the long recall first, we use measures based exclusively on the annual recall responses.<sup>14</sup> Our main analysis leverages this randomization to test for differences between these alternative recall windows using the following main empirical specification: L L L L L L 0 1L L L L L ii ii ii ii The coefficient of interest is 1, the difference in average reported labor measures between = ββ + ββ + XX + εε individuals using long and short recall. is a set of gender by age group by reporting status (proxy ββ ii or self) fixed effects. Age groups are defined as individuals under 25, 25-34, 35-49, and 50 and XX above, approximately quartiles of the study sample. Robust standard errors are clustered at the household level, the level of the treatment. In additional analyses we
Taken together, these estimates suggest that 5–54 million people in Pakistan may lack access to electricity (table 1).

Pakistan also has some of the worst power outages in the world. According to a 2013 World Bank Enterprise Survey, 81 percent of firms reported being affected by outages on average 75 times a month, with the average outage lasting 17 hours. The IFC survey suggests that Pakistani households typically face 16 hours a day of scheduled blackouts (also called load shedding) during the summer and 12 hours a day during the winter (IFC 2015). The latest _Global Competitiveness Report_ ranks Pakistan 115th among 137 economies in the reliability of electricity supply (Schwab 2018). Largely as a result of unreliable electricity supply, per capita electricity consumption in Pakistan stayed flat for almost a decade, after peaking in 2006.

The electricity deficit in Pakistan is attributable to both lack of investment and inefficiencies in every link of electricity supply. Natural gas—the most important indigenous source of energy— was priced at roughly 36 percent of the international benchmark price in fiscal year 2016. Underpricing gas contributes to gas shortages, because it encourages wasteful energy consumption and reduces suppliers’ interest in gas exploration and production. In fiscal year 2016, gas shortage reached about one‐third of already constrained demand (Ministry of Finance 2016). On top of low gas supply, Pakistan lost more than 12 percent of gas to leakages and theft in 2016—many times more than the roughly 1–2 percent in OECD countries.

With domestic gas production faltering, in 2005 the government adopted a gas allocation policy that gives lower priority to the power sector (with gas going first to residential users and the fertilizer sector). To bridge the shortfall, power generators have increasingly turned to diesel and furnace oil. Liquid fuel–based power generation is much more expensive than gas‐based generation: It accounts for 40 percent of total generation but about 70 percent of the total cost.

The high cost of generation has had a knock‐
(demeaned growth rate)|
|Renewable energy supply, annual (demeaned growth rate)|
|Fossil energy supply, annual (demeaned growth rate)<br>CO2 emissions, annual (demeaned growth rate)|
|Domestic fossil fuel producer prices, quarterly (demeaned growth rate)|
|OECD fossil fuel producer prices, quarterly (demeaned growth rate)|
|European Union Real Gross Domestic Product per capita, quarterly (demeaned growth rate)|
|European Union Gross Domestic Product defator infation, quarterly (double-sided HP fltered)|
|European Union export price defator, quarterly (demeaned growth rate)<br>Euro Area 10-year government bond interest rate, quarterly (double-sided HP fltered)|

# **B.2 Empirical model parametrization**

Tables B2 and B3 provide all relevant information regarding the calibration. They list every model parameter including a brief description. They report the calibrated values and how we came up with them: calibrated, or restricted.

A set of parameters have been restricted in order for a set of related variables to match their empirical counterparts.<sup>25</sup> Typically, parameters have been restricted which have strong effects on the steady state but not so much on the transitional dynamics. To give one example, the consumption demand scaling parameter is restricted to ensure that GDP is equal to one at the steady state. Then, the steady states of many other variables have straightforward interpretations. To give another example, we know from the data that the ratio between renewable and fossil energy is around 0 _._ 2 in T ̈urkiye. Hence, we restrict the

> 25For the estimation, a parameter restriction implies that the affected parameter is adjusted in every iteration of the sequential estimation procedure in order to uphold the restriction. As the Bayesian algorithm travels the parameter space to assess how likely certain parameter values are the restricted parameters are updated every step of the way.

59
|Rice importers|-7|24|-9|-8|154|-50|
|Rice exporters|-14|31|-22|-18|87|-33|
|Wheat importers|-7|10|-22|-19|35|-20|
|Wheat exporters|-21|8|-14|-37|45|-9|
|Maize|-9|10|na|-12|18|na|
|Soybean|-18|7|na|3|31|na|
|Sugar|-44|35|na|-60|50|na|

> <sup>a</sup> Unweighted average of national NACs each year, averaged over the number of years in each period.

Source: Authors’ calculations based on updated NRA estimates from Anderson and Valenzuela (2008).
MONGOLIA'S INFORMAL SECTOR Appendix B: Methodologies and Descriptive Statistics 42

# **Average Income of Kiosk Proprietors**

120. The estimates of income were adjusted to reflect a 200 hour month. As with taxicabs, the median adjusted income was chosen to supplement the mean, in order to reduce the effect of the most profitable kiosks, and provide a better picture of the income of the "typical" kiosk in Ulaanbaatar. Estimates of mean and median adjusted income for kiosks in Ulaanbaatar are provided in Table B3, below.

**Table B3. Monthly Income of Kiosks**

||**observations**|**mean**|**median**|
|---|---|---|---|
|Overall sample|241|71,400|44,900|
|Bayangol|31|31,900|31,400|
|Bayanzurh|69|42,200|42,000|
|Chingeltei|19|25,100|25,000|
|Han-Uul|21|57,000|57,600|
|Songino-Hairhan|101|111,400|88,400|
|Suhbaatar|0|n/a|n/a|

_Source:_ Survey of kiosks conducted by the author.
|9.0|11.0|9.4|16.0|
|Diourbel|43.9|60.6|44.7|61.4|
|Fatick|49.2|74.7|52.6|77.3|
|Kaffrine|53.0|78.0|54.5|74.0|
|Kedougou|61.9|82.5|69.0|82.0|
|Koalack|41.5|58.9|40.6|60.4|
|Kolda|56.6|83.3|57.2|82.0|
|Louga|43.4|66.1|45.0|67.0|
|Matam|47.7|88.1|44.6|89.0|
|Saint-Louis|40.1|56.0|41.5|55.3|
|Sedhiou|65.6|89.5|73.0|90.1|
|Tambacounda|61.9|84.0|60.6|79.4|
|Thies|34.1|45.7|33.2|51.0|
|Ziguinchor|51.1|83.0|55.0|72.0|
|National|37.8|55.9|38.2|55.7|

_Sources:_ Calculations using EHCVM (Enquête Harmonisée sur le Conditions de Vie des Ménages 2018–2019; Harmonized Survey on Household Living Standards 2018–2019), Living Standards Measurement Study, World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/4292; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics
<mark>intra-household heterogeneity in the response to – and the impacts of – climate change based on individual characteristics (including gender, age, and education) and endowments. In the case of panel surveys, such as many of those implemented by the LSMS team, this also provides a unique opportunity to study intergenerational relationships that may influence climate-induced migration.</mark> <mark>Another key feature of LSMS surveys is the georeferencing of community, household, and plot locations which facilitates integration with high-resolution third-party geospatial data products which have greatly increased the scope of microeconomic research on climate change adaptation and impacts. This specific feature is a central element for enabling data integration and therefore instrumental to increasing the relevance of household survey data for climate-migration analysis, given the current data landscape characterized by the spreading availability of novel data products such as big data.</mark> <mark>Lately, the LSMS team has been testing and validating new modes for collecting national household survey data with the goal of increasing the adaptability of survey systems in the context of unforeseen shocks so they are better positioned to provide rapid responses to shifting data collection needs, such as those that emerged during the COVID-19 pandemic crisis (Zezza et al., 2022; Gourlay et al., 2021).</mark><sup>27</sup> <mark>This kind of innovative solution can play a critical role in increasing the availability of timely and high-frequency data to monitor and assess the socioeconomic impacts of different types of shocks, including those triggered by slow- and rapid-onset changes in climate.</mark> <mark>Leveraging these comparative advantages (over other traditional migration data sources), LSMS has</mark> great potential for increasing its fitness for purpose in climate migration research. To achieve this objective, there are methodological shortcomings that need to be resolved. The next subsection identifies the priority areas of a potential methodological agenda for unleashing the potential of LSMS-type surveys in the climate migration research domain over the short
public expenditure only – it is an issue of overall investment in physical as well as human infrastructure. Some existing studies, such as Jimenez (1994), Devarajan et al. (1996), also point out this research gap. There might not be a generic answer to this question. The optimal mix of investments between the physical infrastructure and human capital could vary across countries, depending on their specific situations. It would, therefore, be interesting to investigate this variation and provide insights to policy makers.

# **_6.6 Long-Run Impacts of Infrastructure Investment_**

Panel data studies such as Calderón and Servén (2010) pick up the combined effects of infrastructure adjustment processes over time for countries with differing initial economic and institutional levels of development. Such studies can be used to assess statistically both the impact effects of changes in infrastructure development and longer-term effects. However, such research does not in itself provide insight on how infrastructure affects development over the longer-term. Experimental studies (e.g., Lenz et al. 2017) can provide insight on short-term impacts of infrastructure changes for certain users but have less to say about the longer-term impacts across the economy. Long-term structural models can provide that information, but such models are more complex to build and estimate, and the literature contains only a few exceptions (see, e.g., Roberts et al. 2012).

# **7. Conclusion**

Developing countries are facing a crucial question: what to prioritize when the financial resources needed for economic development are limited. Should they invest more in physical infrastructures, such as roads, rail, electricity supply systems, or should they focus on services, such as health care and education? Researchers have attempted to address this question through empirical analysis over the past three decades. This paper aims to review the literature and summarize the insights coming from the existing studies. Both types of studies -- those analyzing the impacts of public investment in general, and those examining the effects of physical infrastructure
# **B Additional figures**

**Figure B.1: Number of factories and employment in the Bangladeshi ready-made garment sector**<sup>82</sup>

<!-- Start of picture text --> 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Year<br>Number of BGMEA member factories (left axis)<br>Number of workers in BGMEA member factories in millions (right axis)<br>Data source: BGMEA Trade Statistics 2017 and 2019<br>Note that data is on a fiscal year basis, i.e. the data point for 2015 captures July 2015 to June 2016.<br>4<br>6000<br>5000<br>3<br>4000<br>2<br>3000<br>2000<br>1<br>1000<br>0 0<br><!-- End of picture text -->

> 82Note that the number of BGMEA member factories dropped by more than a quarter in 2013-2014. This coincides with the period after the collapse of the Rana Plaza factory in April 2013, during which more than a thousand workers died. In the aftermath of the collapse, the Bangladeshi government, the BGMEA and buyer organizations intensified inspections and closed down some factories, but it is unclear whether this alone explains the large drop. It is also possible that factories deregistered to avoid scrutiny or that non-operating factories were purged from lists following inspections. For more information on the response to the Rana Plaza disaster, see Boudreau (2019).

46
Google Trends data and their usage require some caveats. First, the Google Trends platform calculates comparable search indices for a maximum of five search terms and five geographic areas only. This technical restriction forces users to download search volume indices for multiple countries – 182 countries in our case – and multiple terms separately, rendering them incomparable across geographic areas and terms. Therefore, users need to renormalize the indices with a reference country to make them comparable across geographic areas. While there are different approaches to do so, we use the approach suggested by Narita and Yin (2018) to make the search index for our terms comparable across countries.<sup>5</sup> See Appendix A for a detailed description of the renormalization.

Second, potential differences in search language across countries call for a more careful approach to extracting the Google search data. In a given geographic area, some people could submit their search queries in local (non-English) language and others in English. In order to best capture search popularity for our selected terms in countries where English is not the main language, we request search terms in English and in a local language that is widely spoken in the country. We mass translate our search terms from English to the respective local languages using Google Translate API for all countries and terms.<sup>6</sup> We extract sector-specific search queries both in English and the most dominant local language. For the United States, for example, search intensity is measured in English, whereas in Japan, it is measured both in English and Japanese.

The COVID-19 data on confirmed cases and deaths are extracted from the Johns Hopkins University Center for Systems Science and Engineering for the period from January 22, 2020 to April 30, 2020. We aggregate the number of daily new cases and deaths to create weekly confirmed cases and deaths for each country. This allows us to link the weekly Google Trend data with COVID-19 cases and deaths. The number of COVID-19 cases and deaths are expected to inform consumer behavior
and education expenditure are expressed in the units of 10,000 rupiahs and are deflated by rural and urban average rice prices in each wave of the survey. In the education expenses sample (pooled 1993, 1997, 2000, 2007), the average educational expenditure by rural households is 0.64, and it is more than twice as high in the urban households at 1.51. The household average expenditures in the parental generation (using 1993, 1997, and 2000 waves) are 199.32 for rural areas, and 334.79 in the urban areas. The summary statistics thus provide a vivid picture of the rural-urban divide in Indonesia.

16
60% of all public charging stations, this implies the total cost of ¥13.2 billion for all public charging stations in the sample cities. The total induced sales by the availability of the charging infrastructure are estimated to be 501,313 during our data period based on results in Table 4. These results imply an average government cost of ¥26,350 to induce consumers to buy one extra EV through subsidizing/investing charging stations. This cost estimate is an under-estimation of the effectiveness of the charging infrastructure since these charging stations will continue to contribute to future sales of EVs that we do not include in our calculation.

The estimates above suggest that investing in charging station is nearly four times as effective as subsidizing consumer purchases in promoting EV sales from the perspective of government expenditure. The better cost-effectiveness of charging infrastructure in promoting EVs is consistent with the finding in other countries. Both Li et al. (2017) and Springel (2019) show that subsidizing charging stations is more than twice as effective as subsidizing consumer purchases on a per dollar basis in the US and Norway, respectively. Both studies also find that the effect of subsidies on charging stations tapers off as the charging network gets larger. The former uses aggregate sales data by city from 2011 to 2013 while the latter uses individual registration record from 2011 to 2015. Our finding of the relative cost-effectiveness is even more striking given that our data is from 2015 to 2018 and the fact that Chinese consumers appear to be more price sensitive than those two countries. The stronger cost-effectiveness of subsidizing charging station in China than in US and Norway could be driven by the following features of housing and urban structure in China: the vast majority of city residents in China live in apartment complexes and do not have space to build private charging; and cities are denser, which allows public charging stations to serve more drivers.

# **6 Conclusion**

This study provides to our knowledge the first empirical analysis on the underlying driving factors behind the rapid growth of the
on one of three primary identification strategies. The first are VAR‐based identification schemes, of which Blanchard and Perotti (2002) is a leading example. These studies rely on the availability of quarterly data, together with the assumption that discretionary changes in fiscal policy take sufficiently long to implement that they cannot react to contemporaneous economic activity within a quarter.<sup>1</sup>

> 1 This identification strategy is infeasible in the majority of developing countries, and especially in the poorest low‐ income countries that are the focus of this paper, as most do not report fiscal or macro data on a quarterly basis. For a sample of 27 middle‐income countries, Ilzetzki and Végh (2008) and Ilzetzki, Mendoza and Végh (2010) are able to assemble quarterly data in order to analyze the cyclical effects of fiscal policy in these countries using standard VAR‐based identification strategies that have been applied to industrial countries.  However, there is no overlap between their sample of emerging market economies with available quarterly data and my sample of low‐ income aid‐dependent countries.

2