rafmacalaba/data-use-mentions-extended huggingface dataset:
statistically significant revisions of growth expectations. The magnitudes of these revisions are economically meaningful and close to the usual adjustments made by a professional forecaster in response to the release of a major news event. On average, an announcement of a tightening(loosening) of capital controls is associated with a downward(upward) revision of growth expectation of around 0.23 percent. This is supportive of the countercyclical capital control policy rule used in my theoretical model. The exception is the tightening announcement in March 2008, when market participants actually revise their growth expectations upward. To sum up, empirical evidence from high-frequency survey data suggests that expectations of fundamentals do respond to capital control measures. However, these results are based on mean expectations of 120 forecasters using different forecasting methodologies, and can be hard to interpret in greater details. Hence, in the next section, I take a stand on the forecasting methodology used to form expectations.

Table 4: Event study: capital controls and revisions to GDP growth expectations

|Event date|Type|Mean growth forecast revision(%)|T-statistic|
|---|---|---|---|
|3/12/2008|tightening|-0.05**|2.12|
|10/20/2009|tightening|0.02|-1.40|
|10/04/2010|tightening|0.70***|-72.79|
|12/01/2011|loosening|0.02|0.19|
|03/09/2012|tightening|0.03**|-1.98|
|06/04/2013|loosening|0.16***|4.77|

Coefficient is positive if it has the expected sign. T-statistic reported here are for a statistical test of the hypothesis that mean abnormal growth expectations is zero.

# **5.4 Using capital controls to improve GDP nowcast**

In this
# Electronic Payment Technology and Tax Compliance: Evidence from Uruguay’s Financial Inclusion Reform<sup>_∗_</sup>

Anne Brockmeyer & Magaly S ́aenz Somarriba Originally published in the Policy Research Working Paper Series on _February 2022._ This version is updated on _July 2022._ To obtain the originally published version, please email prwp@worldbank.org.

_JEL classification:_ H26, H32, G18, O16.

_Keywords:_ Credit/debit card payment, tax compliance, third-party reporting, VAT.

> _∗_ Brockmeyer (anne.brockmeyer@ifs.org.uk): Institute for Fiscal Studies, University College London, World Bank and CEPR; S ́aenz Somarriba (msaenzsomarriba@worldbank.org): World Bank and University of Delaware. We are deeply indebted to the Uruguayan authorities for an outstanding collaboration. We particularly thank Felipe Quintela and Fernando Pelaez at the General Directorate of Taxation, Ariel Cancio and Florencia L ́opez from the Ministry of Economy and Finance, and Martin Vallcorba. We are grateful to La ́ısa Rachter de Sousa Dias for excellent research assistance and to Deeksha Kokas for help with the Findex data. We thank Juliana Londo ̃no-V ́elez for excellent contributions in the first part of this project. Marcelo Bergolo, Naomi Feldman, Dirk Foremny, Rafaella Giacomini, Sean Higgins, Michael Keen, Leora Klapper, Dennis Kristensen, Joana Naritomi, Panayiotis Nicolaidis, Eduardo Olaberria, Daniel Prinz, Dorothee Singer, Joel Slemrod, Tavneet Suri, Alisa Tazhitdinova, Javier V ́azquez-Grenno and seminar/conference participants at the World Bank, NTA, AEA, the Gates Foundation, Chr. Michelsen Institute, IEB Barcelona, the IGC and IFS-UCL-STICERD provided helpful comments. This work was funded by the World Bank through the Research Support Budget and the Macroeconomics, Trade and Investment Global Practice, and by UK aid from the UK government through the Centre for Tax Analysis in Developing Countries (TaxDev). The findings, interpretations, and conclusions expressed in this work
estimates from the first and second steps.<sup>13</sup> The change in sugar prices will affect labor income through two channels: wages and employment. We focus on each of these in turn.

To capture the first effect, let us define the wage elasticity of individual _i_ as _ωi_ . Then using (5) But we are interested in the elasticity with respect to world prices. Using the estimates in (1) we have that _∂_ ln _p_<sup>_d_</sup> _s_<sup>_/∂_ln</sup><sup>_pw_=</sup><sup>_γ_</sup> _s_<sup>.</sup> Thus, the percent change in real hourly wages experienced by individual _i_ following a 10 percent increase in the world price of sugar is given by: Denote _<u>wi</u>_ the predicted real hourly wage of person _i_ before the sugar price change, conditional on her being employed. Then her predicted real hourly wage after the 10 percent sugar price increase is _<u>w</u>_<sup>_~~′~~_</sup> _i_<sup>= (1 + �</sup><sup>_ωi_</sup><sup><u>)</u></sup> _<u>wi</u>_ .

Thus, for those currently working, the change in the expected real monthly

> 13Note that to have the full impact on household’s real income we would need to have information on the share of sugar in the consumption basket of each household, as well as the sensitivity of sugar demand to changes in prices (see e.g., Nicita, 2004b). This information is not available from labor surveys, so we focus only on the impact on labor income. Because sugar prices are increasing this means that at least
# **3.4 Multiple Hypothesis Testing**

We estimate many different treatment effects given our use of multiple outcomes, four treatments, and two rounds of surveys. We use three different approaches for dealing with multiple hypothesis testing. First, we aggregate different outcomes in a domain into pre-specified standardized aggregate indices. In particular, we use an aggregate index of 41 different business practices, and an overall sales/profit index that aggregates our four measures of firm sales and profits. This enables us to examine whether the average impact of a given treatment on these key main outcomes is significant or not. Second, since we are also interested in the individual outcomes, for each treatment we take the set of all 26 main outcome-survey round estimates across Tables 4 and 5, and calculate sharpened q-values that hold constant the false discovery rate (Anderson 2008). These estimates are reported in Appendix Table 9.1. Similarly, we correct for the full set of 26 mechanism outcome-survey round estimates in Tables 6, 7 and 9, and provide sharpened q-values in Appendix Table 9.2. Third, following Young (2019), we conduct a Wald omnibus test of overall experimental significance. This tests the joint hypothesis that all treatment effects on business practices and firm growth are zero. The corresponding p-values are 0.006 for survey round 1, and 0.008 for survey round 2. That is, we can reject the null that all treatments are jointly irrelevant.

# **4. Impacts on Main Outcomes**

We begin by examining effects on business survival. This is not because business survival was expected to be one of the main outcomes of the program, but rather because our interpretation of impacts on all other outcomes will be affected if there are large survival differences across treatment groups. Survival rates were high, with 97.7 percent of control group firms interviewed in our first follow-up survey and 90.2 percent of those in our second follow-up surviving. The interventions do not appear to have had
characteristics that affect firms’ financial constraints (inferred from the firm’s investment Bellman equation). Beck et al (2006), using the same World Bank Enterprise Surveys as we use, follow a different approach that looks at firms’ self-reporting financial obstacles. They show that older, larger and foreignowned firms report lower financing obstacles. In addition, firms in countries with higher levels of financial intermediary development, stock market development, legal system efficiency and higher GDP per capita report lower financing obstacles. In another paper with the same dataset (Beck et al, 2008), they examine the disadvantages of small firms in the access to a broad spectrum of financing sources, including leasing, supplier, development and informal finance.

The remainder of the paper is organized as follows. Section 2 describes the data and presents summary statistics. Then, section 3 explores factors that affect the likelihood of having to put collateral when borrowing. Next, section 4 studies determinants of the required amount of collateral and finally, section 5 concludes.

# **2 Data and descriptive analysis**

We use the World Bank’s Enterprise Surveys (WBES), a rich, firm-level survey database that provides information about firms’ characteristics. These characteristics are: ownership, size, sector, region in which it is located, annual sales, capacity utilization, employment, length in operation, whether it has loans, and in cases that it has, whether collateral was required, type of assets used as collateral and how much collateral was needed. The database also contains information about lenders such as the type of lending institutions: private commercial banks, state banks or non-bank institutions which include microfinance institutions, credit cooperatives, credit unions or financial companies. The database covers many small and medium size firms, thereby allowing us to study the issue of collateralized borrowing across countries and firm characteristics.

We restrict our analyses to 22,263 firms across 131 countries between 2008 and 2017 (the list of countries is given in Table A1). These
past less of a priority for national governments and it is only recently and with the numbers of forcibly displaced people increasing that this is becoming an issue. Experiments are now being conducted with innovative technologies such as remote sampling with aerial or satellite imagery and also with non‐ sampling techniques, but there is still no blueprint for sampling refugees and IDPs and more generally very mobile populations.

**Unit of analysis** . As for other studies based on micro data, the primary unit of analysis for refugees and IDPs is the individual and/or household. The added difficulty in the context of refugees and IDPs is how to identify the household and allocate individuals to single households. Refugee households are complex by definition. Some household members may stay behind or join later, others may leave or shuttle between places. Some families travel as clans with multiple families living together, others travel as single families. Sometimes individuals or families try to register in multiple places to claim more benefits. Deciding what constitutes a household or a family is not simple. The UNHCR has its own definitions of family and household but it is not always straightforward for field workers to apply such definitions. Again, while for national household budget surveys these questions are largely resolved, for mobile populations such as refugees and IDPs they are not and this is of key importance for economists working with unit data.

**Welfare** . How to measure the welfare of refugees? Welfare is perhaps the main outcome to measure for economists. Welfare economists would typically use income, expenditure or wealth to measure welfare but refugees and IDPs have a very peculiar situation when it comes to these measures. They have probably left behind most of their wealth and assets with the exception of what they could carry with them. Income from work, if any, is informal and very low whereas other incomes are mostly represented by cash grants provided by humanitarian organizations. Expenditure is probably restricted in terms of items purchased and largely driven by what can be purchased with food vouchers. Expenditure
Understanding firm growth is at the heart of the development process, making it a much researched area in finance and economics. More recently, the field has seen resurgence in interest from policymakers and researchers, with a new focus on the broader business environment in which firms operate. Researchers have documented through surveys that firms report many features of their business environment as obstacles to their growth. Firms report being affected by inadequate security and enforcement of property rights, inefficient functioning of financial markets, poor provision of infrastructure services, inefficient regulations and taxation, and broader governance features such as corruption and macroeconomic instability. Many of these perceived obstacles are correlated with low performance.

These findings can inform government policies that shape the opportunities and incentives facing firms, by influencing their business environment. However, even if firm performance is likely to benefit from improvements in all dimensions of the business environment, addressing all of them at once would be challenging for any government. Thus, understanding how these different obstacles interact and which ones influence firm growth directly is important in prioritizing reform efforts. Further, since the relative importance of obstacles may also vary according to the level of development of the country and according to firm characteristics such as firm size, it is important to assess whether the same obstacles affect all sub-populations of firms.

In this paper we examine which features of the business environment directly affect firm growth. We use evidence from the World Business Environment Survey (WBES), a major firm level survey conducted by the World Bank in 1999 and 2000 in 80 developed and developing countries around the world. We use this data to assess (i) 2
lighting or grid connection. In addition, the study site was also home to a substantial ultra-poor population that could be the main target of solar products. Third, in this study, we randomized the intensity of solar lighting by varying the product amount. Providing multiple solar lanterns reduces the likelihood that other family members exclusively utilize the products and therefore, discourage children’s nighttime use for study.

As the empirical analysis will demonstrate, the treated children who received solar lanterns achieved no significant improvements in academic performance as measured by the school-based annual examination results, regardless of whether they received all three solar products or only one. To detect a possible channel that hindered the effectiveness of solar lanterns, we further explored whether the treated households actually used the solar lanterns, particularly for their children’s nighttime study. We found that the treated students significantly increased their number of study hours, particularly at night and before exams and that they relied more on solar lanterns than on kerosene lamps. Furthermore, their school attendance initially increased for several months after the provision of the solar products; however, this effect disappeared over time. These increased education inputs (i.e., study hours and attendance rates), however, did not translate into improved educational achievements. Based on careful field observations and empirical findings provided by several exercises performed in this study, we provided evidence suggesting that the “no academic performance effects” are not attributable to the possible misuse of the products and spillover effects resulting from the within-grade randomization.

Consequently, an attempt to improve home-study facilities by providing solar lanterns alone did not result in (at least, short-term) positive impacts on children’s educational achievement. Notably, this finding was obtained in our study setting, whereby students’ marginal educational returns to solar products might have been the greatest due to their limited access to electricity at the time of intervention. This conclusion may suggest that unless other education-related inputs are also improved, it is difficult to systematically improve children’s academic performance in the developing
(SD)|-0.37*|(0.21)|[0.30]|0.04|(1.02)|425|
|elderly go hungry more often than young (SD)|-0.02|(0.28)|[1.00]|0.09|(1.06)|135|
|Reduced coping|-1.68*|(0.86)|[0.30]|3.10|(6.91)|1,102|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: N means yesterday reports the number of meals the respondent ate yesterday. “How many meals did you eat yesterday? Tea alone is not to be considered as a meal.” Food diversity reports the number of food categories out of 9 the respondent ate from in the past 12 months. Days last week FR/adults/children went hungry reports the number of nights in the past 7 days the respondent/adults in household/children in household went to bed hungry. Women/girls/elderly go hungry more often than men/boys/young reports agreement with the statement as a standardized 5-point likert scale. Reduced coping is the reduced Coping Strategies Index, where larger numbers correspond to more food insecurity.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated
<br>0<br>0|
||L|21<br>07<br>26|
||anguage|.<br>.<br>.|
||Occupational experience|55.4<br>17.6<br>25.4|
|**Personal**|<br>Personal|17.2<br>15.7<br>31.7|
|**Condition**|Location|6.5<br>6.9<br>5|
||Salary|0<br>9.5<br>7|
||Work condition|0<br>5.6<br>1.9|
|**Others**|Others|8.6<br>25.4<br>20.8|
|||0<br>20<br>40<br>60<br>0<br>20<br>40<br>60<br>0<br>20<br>40<br>60|
|||Percentage<br>Percentage<br>Percentage|

Source: Authors’ analysis using Babajob data (Jan – Oct, 2015).

Text analysis can provide additional insight into employer preferences beyond minimum qualification requirements, and it can be especially useful in understanding the cognitive and non-cognitive skills that employers demand for each position. Employers rarely ask job seekers about their non-cognitive skills directly, since there are few objective measures for this skill type. While information on demand for these skills may be obtained from interviews with employers and surveys, an analysis of job advertisements may be more accurate, as it reflects the real-world behavior of employers.

Figure 5 illustrates the skill- and qualification-related words that appeared most frequently in the job descriptions posted to Babajob in 2014. Figure 6 shows the proportion of advertisements using the clusters of these keywords. Overall, skill- and qualification-related words appear
# 1 Introduction

An important topic in economics is understanding the contribution of agglomeration economies and competition for local growth. Even though this is an old debate, most of the empirical work on this topic is fairly recent. Since productivity growth is not directly observed, economists have tried to find proxies for it. Most of the empirical literature used employment growth to proxy for productivity growth at the local level even though this requires strong assumptions. In this paper I examine and then relax some of these assumptions to derive an alternative measure of productivity growth based on regional wage growth. I then apply it to the study of the sources of local growth using data from a matched employer-employee data set for Portugal. With the regional adjusted wage growth I find evidence of MAR externalities in some sectors and no evidence of Jacobs or Porter externalities in most of the manufacturing sectors. These results are at odds with those for employment based regressions where concentration and region size has a negative and significant effect in most of the sectors.

The importance of scale economies in explaining the agglomeration of economic activity was first recognized by Marshall (1890). The idea behind this concept is that it would be beneficial to locate where other firms in the same sector already produce, for reasons such as the availability of intermediate goods, of specialized labor force, the existence of a large product demand or the diffusion of knowledge in the local environment. Recently, the literature on endogenous growth models emphasizes the role of dynamic information externalities as a driving force for technological innovations and, hence, for economic growth. According to this literature, the accumulation of knowledge in the geographical area, is a key driving factor for current productivity growth. Marshall (1890), Arrow (1962) and Romer (1986) argue that the most important externality derives from a buildup of knowledge associated with communications among local firms in the same sector (MAR externalities). Sector concentration in a region helps knowledge spillovers between firms and, therefore, that sector’s growth in the region.
# **6 Results**

We now follow our empirical strategy to document, in three sets of empirical results, how pollution exposure shapes production activity in urban Uganda, and how high and low ability managers deal with this challenge.

## **6.1 Road Traffic Bundles Pollution Exposure with Market Access**

We first study the characteristics of available locations within the city to establish that road traffic bundles a good (market access) with a bad (pollution exposure). This result implies that managers face a trade-off in their location choice, in whether to avoid pollution or maximize firm revenues and profits.

We proceed in four steps. First, we establish that large roads are more polluted. Second, we find evidence that this pollution is generated by road traffic. Third, we show that firms located near larger roads have higher revenues and profits per worker, conditional on a rich set of controls. Finally, we explore the mechanism linking pollution to profitability using our rich survey data and argue that is primarily due to access to market demand.

**Larger roads are more polluted.** We follow the empirical specification in equation 3 to show that pollution is higher in grid cells with larger roads. The results are shown in Table 2: an increase in median road size in the cell of one unit is associated with an increase in residual pollution of about 7-8%, a result significant at the 1% level. Comparing column 1 with column 2 we note that the magnitude of the coefficient is barely affected by the introduction of sub-county fixed effects, thus showing that the relationship is driven by variation within as opposed to across sub-counties.

In columns 3 and 4, we check that these results are robust to conducting the analysis at the level of the single pollution measurement rather than the grid cell, by running the same equation 3 using variation across all the observed GPS coordinates _m_ , rather than aggregating at the grid-level.<sup>35</sup> Reassuringly, the results are remarkably